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    1. On 2019-10-04 08:05:29, user Guyguy wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT 02 OCTOBER 2019 <br /> Thursday, October 03, 2019 <br /> Since the beginning of the epidemic, the cumulative number of cases is 3,198, of which 3,084 are confirmed and 114 are probable. In total, there were 2,137 deaths (2023 confirmed and 114 probable) and 995 people healed. <br /> 427 suspected cases under investigation; <br /> 1 new case confirmed in Ituri in Mandima; <br /> 1 new confirmed case;1 person cured out of the CTE in North Kivu in The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths. <br /> 17th day without response activities in the Lwemba Health Area in Mandima, Ituri.<br /> LEXICON <br /> • A community death is any death that occurs outside a Ebola Treatment Center. <br /> • A probable case is a death for which it was not possible to obtain biological samples for confirmation in the laboratory but where the investigations revealed an epidemiological link with a confirmed or probable case.<br /> NEWS<br /> Prime Minister ready to implement the commitments of the Head of State through the ST / CMRE <br /> - Prime Minister, Sylvester Ilunga Ilukamba, considers that the commitments of the Head of State, President Félix-Antoine Tshisekedi Tshilombo, recalled from the top of the UN platform, are relayed in the field by the effectiveness of leadership and the Coordination of the Government of the Democratic Republic of the Congo through the Technical Secretariat of the Multisectoral Ebola <br /> - He said it during a meeting he chaired this Thursday, October 03, 2019 with the ST / CMRE delegation led by his Technical Secretary Prof. Jean-Jacques Muyembe Tamfum who was accompanied by Dr. Kebela and Prof. Michel Kaswa; <br /> - From this meeting, we note that as early as next week, the Prime Minister will bring together the ministers of Health, Budget and Finance to support the interventions of the response; <br /> - To this end, he stressed that the multisectoral vision of the response is, at the same time, to end the Ebola Virus Disease and to respond to the security and socio-economic needs of the populations affected by this epidemic ; <br /> - He promised that his government will support the approach of the Technical Secretariat of the CMRE to work for the Strengthening of the whole health system of the DRC; <br /> - Since July 20, 2019, the Head of State, the President of the Republic Félix-Antoine Tshisekedi Tshilombo, is coordinating the response to the epidemic to the Ebola virus disease and has decided to entrust the responsibility of the Technical Secretariat of the Multisectoral Committee to a team of experts under the direction of Professor Jean-Jasques Muyembe Tamfum; <br /> - The mission of the technical secretariat is to put in place all innovative measures that are urgent and indispensable for the rapid control of the epidemic.<br /> VACCINATION<br /> - Preparation of the Vitamin A Polio Immunization Campaign and Mebendazole Deworming in the 17 health zones of the Butembo Antenna, an area affected by Ebola Virus Disease; <br /> - 17 days already without opening rings around 5 confirmed cases in the Lwemba health area in Mandima in Ituri due to interethnic conflicts and insecurities. <br /> - An expanded vaccination ring was opened around the confirmed case of September 30, 2019 in Biakatp health area in Mandima in Ituri after dialogues and sensitizations carried out by the communication and psycho-social subcommittees; <br /> - Since vaccination began on 8 August 2018, 232,160 people have been vaccinated; <br /> The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.<br /> MONITORING AT ENTRY POINTS- A FONER Komanda checkpoint provider (PoC) was abducted on Wednesday 02 October 2019 by unidentified men who released him 75 km from the PoC. This provider of surveillance at the Control Points has already resumed its daily services; <br /> - Since the beginning of the epidemic, the cumulative number of travelers checked (temperature measurement ) at the sanitary control points is 101,714,685 ; <br /> - To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.<br /> As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows: <br /> 1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes; <br /> 2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number; <br /> 3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days; <br /> 4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination. <br /> 5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding). <br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.

    2. On 2019-10-16 12:44:35, user GuyguyKabundi Tshima wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT OCTOBER 11, 2019<br /> Saturday, October 12, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,212, of which 3,098 are confirmed and 114 are probable. In total, there were 2,148 deaths (2034 confirmed and 114 probable) and 1031 people cured.<br /> 466 suspected cases under investigation;<br /> 2 new confirmed cases at CTE in Ituri in Mandima;<br /> 2 new confirmed deaths, including:<br /> 2 community deaths in Ituri in Mandima;<br /> No confirmed deaths in CTE;<br /> 3 people healed from the CTE, including 2 in Ituri in Komanda and 1 in North Kivu in Katwa;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths.

      NEWS

      Organization of a press conference on the evolution of Ebola Virus Disease in Kinshasa<br /> - The Technical Secretary of the Multisectoral Committee for Ebola Virus Epidemic Response (CMRE), Prof. Jean Jacques Muyembe Tamfum chaired this Saturday, October 12, 2019 in Kinshasa a press conference during which he gave an update on the 10th epidemic Ebola Virus Disease in the DRC since its declaration on August 1 , 2018 to date;<br /> - To this end, he showed the strategies used in the response of this epidemic and spoke of the recourse to technological innovations, while recalling that the Head of State, President Félix-Antoine Tshisekedi Tshilombo, placed him at head of the technical secretariat of CMRE, with two main missions. This includes ending the epidemic as soon as possible and capitalizing on the achievements of this epidemic to strengthen the DRC's health system, starting with the three provinces affected by this epidemic;<br /> - Speaking of the evolution of the response, he reported some tangible progress, notably from July 2019, where 90 confirmed cases per week were recorded, or 15 per day, while currently there are fewer than 20 case by week, ie 1 to 3 cases per day, or even zero cases confirmed as the 05 October 2019 last. " In this period, three provinces were active (North and South Kivu, as well as Ituri), while today only the province of Ituri is affected . Today, only 9 zones are affected of the 22 recorded in July 2019, "said the technical secretary of the CMRE;<br /> - He said that for now the epidemic is concentrated in the North from where it came before revealing itself in Mangina and Mabalako in North Kivu. Hence all efforts are concentrated to put an end to this epidemic as quickly as possible;<br /> - Regarding strategies to end this epidemic, the Pof. Muyembe spoke about the change of approach that is now multisectoral and that at present, the outline of the epidemic is placed under the leadership of the presidency of the Democratic Republic of Congo with as coordinator the Prime Minister. This committee has a technical secretariat which directs the general coordination managed by Prof. Steve Ahuka and the provincial sub-coordinators of the response;<br /> - The second strategy was to maintain the motivation of the teams on the spot. This has been regularized with the support of the World Bank. An operating budget is now given to the coordination in Goma as well as all the co-ordination;<br /> - The other strategy is to give more importance to national leadership. A partnership has been built with WHO, UNICEF and MSF that support coordination in Goma. Nationals are at the forefront and partners support. This has changed a lot on the field, says Professor Muyembe;<br /> - Finally, notes the Technical Secretary, innovations have been made with this epidemic with the use of experimental vaccines, first RVSV zebov from Merck with belt vaccination which has shown its effectiveness;<br /> - " It is time to use a new vaccine, following the recommendations of the SAGE expert group that advises WHO on immunization. On May 15, 2019, this group recommended using an adjusted dose of the RVSV vaccine to prevent a possible shortage due to the fact that the epidemic lasts a long time, "Prof. Muyembe;<br /> - He added: " His second recommendation was to use a second preventive vaccine. After proposals, it is the Johnson & Johnson vaccine that presents the most data on the scientific level . He announced that the teams are prepared to give correct communication and to vaccinate the population;<br /> - He recalled that this second vaccine is used in West Africa since 2013, will also be used in Rwanda and Goma to protect the Congolese compatriots of Goma, where more than 64,000 of them cross the border daily. to go to Gisenyi and vice versa;<br /> - The first batch of the J & J vaccine, 500 000 doses can arrive in the DRC from 18 October 2019 and vaccination can begin in early November 2019 in two communes of Goma to extend later in other provinces;<br /> - The clinical trials carried out by the DRC will serve the world, since now two molecules tested are now available to break the chain of transmission during the next appearances of the Ebola virus.<br /> - " From this year, Ebola became a curable disease because we found medicines to cure the sick. It can also be avoided by immunization, especially if in both cases, one arrives in time, "concluded the technical secretary of the Multisectoral Committee for the Response to the Ebola Virus Disease Epidemic Muyembe Tamfum.

      VACCINATION

      • A new vaccination ring was opened around two confirmed cases from 10 October 2019 in the Biakato Health Area in Mangina / AS Biakato mine with low participation due to a strong community reluctance;
      • Vaccination of newly recruited front-line staff continues at Kyondo Reference Hospital and Kayna Health Zone in Bulinda, North Kivu;
      • Continuation of Local Polio Vaccination Days integrated with Vitamin A supplementation and Mebendazole deworming in 17 health zones at the Butembo antenna in North Kivu;
      • Since the beginning of vaccination on August 8, 2018, 237,165 people have been vaccinated;
      • The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS

      • Since the beginning of the epidemic, the total number of travelers checked (temperature measurement ) at the sanitary control points is 105,171,551 ;
      • To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    3. On 2019-10-18 23:18:45, user GuyguyKabundi Tshima wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT OCTOBER 16, 2019<br /> Thursday, October 17, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,228, of which 3,144 are confirmed and 114 are probable. In total, there were 2,158 deaths (2044 confirmed and 114 probable) and 1038 people healed.<br /> 443 suspected cases under investigation;<br /> 1 new confirmed case in North Kivu, including:<br /> 1 case in North Kivu in Mabalako;<br /> No cases in Ituri;<br /> 4 new confirmed deaths in North Kivu, including:<br /> 1 community death in North Kivu in Mabalako;<br /> 3 deaths confirmed at CTE in North Kivu in Mabalako;<br /> No healed person left CTE;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths.

      LEXICON<br /> • A community death is any death that occurs outside a #Ebola Treatment Center.<br /> • A probable case is a death for which it was not possible to obtain biological samples for confirmation in the laboratory but where the investigations revealed an epidemiological link with a confirmed or probable case.

      NEWS<br /> NOTHING TO REPORT

      VACCINATION<br /> - A satellite ring was opened in Mambasa prison around the confirmed case of 12 October 2019 in Nyakunde;<br /> - Continuation of expanded ring vaccination in Mataba in the health zone of Kalunguta around the 2 confirmed cases of 12 October 2019;<br /> - Continuation of the vaccination of newly recruited front-line staff (PPL) in the Kyondo (HGR Kyondo) and Kayna Health Zones (Bulinda Health Area), Musienene (Kimbulu Reference Health Center) and Butembo (Vulindi Health Area);<br /> - Preparation of the vaccination of biker taximen in the sub-coordinations of Butembo, Beni, Mangina in Mabalako in North Kivu and Mambasa in Ituri.<br /> - Since the beginning of vaccination on August 8, 2018, 239,139 people have been vaccinated;<br /> - The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS<br /> - Nasty destruction of huts and launching leaflets against providers at PoC Kolikoko;<br /> - Since the beginning of the epidemic, the total number of checked travelers (temperature rise) at the sanitary control points is 106,999,606 ;<br /> - To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    4. On 2019-11-16 01:59:42, user Guyguy wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT 14 NOVEMBER 2019

      Friday, November 15, 2019

      • Since the beginning of the epidemic, the cumulative number of cases is 3,292, of which 3,174 are confirmed and 118 are probable. In total, there were 2,195 deaths (2077 confirmed and 118 probable) and 1070 people healed.<br /> • 508 suspected cases under investigation;<br /> • No new confirmed cases;<br /> • 2 new deaths of confirmed cases in North Kivu, including 1 in Beni and 1 in Mabalako;<br /> • 3 healed people released from CTE in North Kivu in Mabalako;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths;

      NEWS

      Continuation of vaccination with the 2nd Ebola vaccine in two health zones of Karisimbi in Goma

      • Vaccination continues in the health zones of Majengo and Kahembe in Karisimbi (Goma);<br /> • A total of 40 people were vaccinated, including 34 adults and 6 children under 18;<br /> • This vaccination began on Thursday, November 14, 2019 with the Ad26.ZEBOV / MVA-BN-Filo vaccine, produced by Janssen Pharmaceuticals for Johnson & Johnson. This second vaccine was approved on 22 October 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and 23 October 2019 by the National Ethics Committee.

      VACCINATION

      • 40 people were vaccinated with the 2nd Ad26.ZEBOV / MVA-BN-Filo vaccine (Johnson & Johnson) in the two Health Zones of Karisimbi in Goma;<br /> • Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 252,249 people have been vaccinated;<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, the second vaccine, called Ad26.ZEBOV / MVA-BN -Filo, is produced by Janssen Pharmaceuticals for Johnson & Johnson.<br /> • This new vaccine comes in addition to the first, the rVSV-ZEBOV, the vaccine used until then (since August 08, 2018) in this epidemic. Manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018, it has recently been approved.

      MONITORING AT ENTRY POINTS

      • A 38-year-old woman from Beni for Nzanga in Mutwanga, North Kivu, high-risk contact was intercepted at PK5 checkpoint (PoC) in Beni. She is in contact with a source case notified to Beni on 03 November 2019;<br /> • Since the beginning of the epidemic, the total number of checked travelers (temperature increase) at the sanitary control points up to 13 November is 116,622,388 ;<br /> • To date, a total of 112 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    5. On 2019-08-03 19:56:40, user GuyguyKabundi Tshima wrote:

      EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI

      Wednesday, July 31, 2019

      The Epidemiological Situation of Ebola Virus Disease, July 30, 2019

      Since the beginning of the epidemic, the cumulative number of cases is 2 701, of which 2 607 are confirmed and 94 are probable. In total, there were 1,813 deaths (1,719 confirmed and 94 probable) and 776 people healed.<br /> 293 suspected cases under investigation;<br /> 11 new confirmed cases, including 3 in Vuhovi, 1 in Mandima, 1 in Mambasa, 1 in Kalunguta and 1 in Nyiragongo (Goma);<br /> Continued search for the confirmed case in the health zone of Lubero dated 25/07/2019;<br /> 10 new confirmed cases deaths:<br /> 2 community deaths, including 1 in Beni and 1 in Mandima;<br /> 6 deaths at ETC, including 3 in Beni, 2 in Mabalako and 1 in Butembo;<br /> 2 deaths at the ETC of Beni;<br /> 6 people recovered from ETC, including 4 Mabalako, 1 in Katwa and 1 in Butembo;<br /> Two live health workers are among the new confirmed cases of Mambasa (non-vaccinated) and Vuhovi (vaccinated). The cumulative number of confirmed / probable cases among health workers is 148 (5% of all confirmed / probable cases), including 41 deaths.

      Organization of the Coordination Workshop for the Ebola Response to the Ebola Epidemic

      The Technical Secretariat of the Multi-sectoral Epidemic Response Committee of the EVD is organizing a coordination workshop from 31 July to 02 August 2019 to coordinate the response to the EVD epidemic at the Karibu Hotel in Goma in the province of North Kivu.<br /> This workshop aims to brief the Technical Secretariat of the Multisectoral Committee by coordinating the response on the organization of the current response in order to enable it to make informed decisions thus avoiding a major disruption of the response.<br /> It will enable the Technical Secretariat to inquire about the current epidemiological situation of EVD and the main challenges to be addressed, to learn about the current response structure (organization of the different levels of coordination) and the new strategic plan for the response (PSR4) and synergy with the security, humanitarian and financial sectors, as well as the operational readiness of DRC neighboring countries to create a favorable environment for the response.<br /> It will also allow to discuss challenges and perspectives related to priority themes (pillars). This workshop will result in the priority actions to be carried out over the next 90 days and the overall orientations on the response, as well as the new organizational structure of the response.<br /> It should be noted that under SRP-4, effective and coherent change in strategies, effective coordination, consistent standards and support for the most vulnerable communities are envisaged at risk in the provinces of North Kivu and Ituri while preventing the spread of the epidemic in other provinces and countries bordering the DRC

      Death of the second confirmed case of Ebola in Goma

      The second confirmed Ebola case from Goma died on Wednesday 31 July 2019 at the ETC Nyiragongo of Goma located in the General Reference Hospital of this city.<br /> This last case of Goma is a patient, who began to present the symptoms of EVD on July 22, 2019. On July 30, 2019 he went to the Goma General Referral Hospital (HGR) located in the Nyiragongo Health Zone, where he was directly transferred to the ETC for appropriate care. The ETC, being installed within this HGR.<br /> Previously, he was treated as an outpatient by a nurse in a private community health center in the Nyiragongo Health Zone.

      180,558<br /> Vaccinated persons<br /> The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 19 May 2018.

      80,118,963<br /> Controlled people<br /> 80 entry points (PoE) and operational health checkpoints (PoC)

      148<br /> Contaminated health workers<br /> Two live health workers are among the new confirmed cases of Mambasa (non-vaccinated) and Vuhovi (vaccinated).<br /> The cumulative number of confirmed / probable cases among health workers is 148 (5% of all confirmed / probable cases), including 41 deaths.

      Source: The press team of the Ministry of Health.

    6. On 2019-10-16 12:50:12, user GuyguyKabundi Tshima wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT OCTOBER 12, 2019<br /> Sunday, October 13, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,218, of which 3,104 confirmed and 114 probable. In total, there were 2,150 deaths (2036 confirmed and 114 probable) and 1032 people healed.<br /> 429 suspected cases under investigation;<br /> 6 new confirmed cases to CTEs, including;<br /> 4 in North Kivu, including 2 in Beni and 2 in Kalunguta<br /> 2 in Ituri, including 1 in Mandima and 1 in Nyakunde;<br /> 2 new confirmed deaths, including:<br /> 1 community death in North Kivu in Kalunguta;<br /> 1 new confirmed death in CTE in North Kivu in Beni;<br /> 1 person healed out of CTE in Ituri in Mambasa;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths.

      NEWS

      New health area infected with Ebola virus in Ituri<br /> - A new Health Area has been affected by Ebola Virus Disease in Ituri. This is the Maroro Health Area in the Nyakunde Health Zone;<br /> - Indeed, Nyakunde was already at 294 days without notifying a new confirmed case of the EVD and returned to zero following this new affection;<br /> - Of all the 6 cases reported this Sunday, October 13, 2019, none of them were listed as contact, nor monitored regularly or vaccinated;<br /> - It is also reported that the alerts of all these cases are coming back from the community and their contacts are being listed, the investigations are continuing, the decontamination of the patients' households is being carried out and the ring of vaccination has been opened around all these cases.

      VACCINATION

      • Since the beginning of vaccination on August 8, 2018, 237,632 people have been vaccinated;
      • The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS

      • Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 105,518,454 ;
      • To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    7. On 2019-11-14 14:53:08, user Guyguy wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI ON NOVEMBER 12, 2019

      Wednesday, November 13, 2019

      • Since the beginning of the epidemic, the cumulative number of cases is 3,291, of which 3,173 are confirmed and 118 are probable. In total, there were 2,193 deaths (2075 confirmed and 118 probable) and 1067 people cured.<br /> • 508 suspected cases under investigation;<br /> • 4 new confirmed cases in North Kivu, including 2 in Beni and 2 in Mabalako;<br /> • No new deaths of confirmed cases have been recorded;<br /> • No cured person has emerged from ETCs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths;

      NEWS

      Ebola Virus Disease Response Coordinator Meeting with North Kivu National Assembly Vice President on J & J Vaccine

      • The General Coordinator for the Ebola Response to the Ebola Virus Disease, Prof. Steve Ahuka Mundeke, accompanied by a joint team of some members of the response and the consortium (National Institute of Biomedical Research-INRB, MSF / France and the London School), met this Wednesday, November 13, 2019 the Vice President of the North Kivu Provincial Assembly, the Honorable Jean-Paul Lumbulumbu, with whom they discussed the second Ebola vaccine called Johnson & Johnson.

      • The Professor Steve Ahuka Mundeke, who requested the involvement of elected representatives in the community mobilization for this vaccination, welcomed the availability of the Provincial Assembly of North Kivu to support the activities that will begin on Thursday, November 14, 2019 in two health areas of Karisimbi, namely Kahembe and Majengo in North Kivu Province;

      • In addition, the Honorable Jean-Paul Lumbulumbu promised to be among the first people to be vaccinated with the Johnson & Johnson vaccine, including members of the North Kivu Provincial Assembly, to serve as an example for their bases. To this end, he invited the people of North Kivu, particularly the sites concerned, to be vaccinated in order to protect themselves against any possible epidemic of the Ebola Virus Disease;

      • Also in the context of the introduction of this second vaccine, a briefing session was organized on the same Wednesday in the meeting room of the general coordination of the response in Goma, for members of the Risk Communication. and community engagement (CREC) with some partners from the Ministry of Health.<br /> Training of Beni journalists on their role and responsibility in public health emergencies.

      • The role and responsibilities of the journalist in the treatment of news in a public health crisis is at the center of this workshop held from 12 to 14 November 2019 in Beni, North Kivu Province;

      • This workshop aims to equip about twenty media professionals with essential notions related to the treatment of information during a health crisis;

      • At the opening of this meeting, the feather knights were trained on the risk communication related to Ebola virus disease and on the usual concepts in the response to this disease;

      • The two speakers of the day, Dr. Bibiche Matadi, who is responsible for the surveillance pillar at the sub-coordination of the Beni response and Mr. Rodrigue BARRY of the WHO, emphasized the quality of the message to be given to because, according to them, the eradication of this epidemic is based on mastery of all contacts and on community involvement;

      • The second day focused on journalist ethics and deontology in times of health crisis and on health - communication - media interaction;

      • For this second topic, Ms. Miphy Buata, a journalist with the Congolese News Agency and communications officer of the Multisectoral Committee for the Response to the Ebola Virus Epidemic, recalled that the media remains the only channel of choice to restore and build trust between the (recipient) community and the health sector (Issuer), particularly in the context of Ebola Virus Disease;

      • This workshop was organized by the Ministry of Health with WHO and was facilitated by UNICEF.

      VACCINATION

      • Since the start of vaccination on August 8, 2018, 251,079 people have been vaccinated;

      • The only vaccine to be used in this outbreak was the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS

      • Since the beginning of the epidemic, the total number of travelers checked (temperature measurement ) at the sanitary control points is 116,596,285 ;

      • To date, a total of 112 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    8. On 2019-11-09 20:30:28, user GuyguyKabundi Tshima wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT 07 NOVEMBER 2019<br /> Friday, November 08, 2019<br /> • Since the beginning of the epidemic, the cumulative number of cases is 3,286, of which 3,168 are confirmed and 118 are probable. In total, there were 2,192 deaths (2074 confirmed and 118 probable) and 1064 people healed.<br /> • 560 suspected cases under investigation;<br /> • No new confirmed cases;<br /> • No new confirmed deaths have been recorded;<br /> • 1 person cured out of the CTE of Butembo;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths;

      NEWS

      End of tour of the general coordinator of the Ebola response in North Kivu and Ituri

      • The Epidemic Response Coordinator for Ebola Virus Disease, Prof. Steve Ahuka Mundeke, was on mission from 05 to 07 November 2019 in a few areas affected by Ebola Virus Disease in North Kivu and Ituri, to inquire about the epidemiological and security evolution of the response. During this mission, he visited some sites of the response to Beni in North Kivu, including the Mangango camp where the vaccination of pygmies took place;

      • In Ituri, Prof Ahuka traveled to Biakato Mines in Mandima, Mambasa Territory, where he first reinserted three of the four cured patients he had discharged well into the Mangina Ebola Treatment Center in the area. Mabalako health center in North Kivu. He also comforted the family of the retaliating agent and journalist, murdered on the night of Saturday, November 2, 2019 in Lwemba in Mambasa territory in Ituri;

      • He also chaired the daily meeting on the activities of the response in the sub-coordination of Biakato Mines;

      • On his way back, the general coordinator of the riposte went to the Mangina Subcommittee, where he chaired under the trees the morning meeting in Mangina. He also visited the Health Center "Case of Salvation" which collaborates with the response and to whom he handed over a large batch of mattresses in the presence of the WHO coordinator of Mangina's sub-coordination. He again visited the Mangango camp, where the pygmies who have joined the activities of the riposte live to help the response reach all the other pygmies;

      • He closed his tour of North Kivu and Ituri with a visit to the Ebola Treatment Center in Beni.

      VACCINATION

      • Pygmy vaccination continues in Mabalako at Mangango camp, 19/19 vaccinated pygmies;<br /> • Continuation of vaccination in expanded ring, around 3 confirmed cases on 04/11/2019 and 2 cases confirmed on 05/11/2019 and the vaccination of the biker as contacts, in Beni in five (5) areas health care, including in Butsili, Ngongolio, Tamende, mandrandele and Kasabinyole;<br /> • Since vaccination began on August 8, 2018, 248,460 people have been vaccinated;<br /> • The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS

      • Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 114,626,335 ;<br /> • To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    9. On 2019-11-10 21:15:52, user GuyguyKabundi Tshima wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT 08 NOVEMBER 2019<br /> Saturday, November 09, 2019<br /> • Since the beginning of the epidemic, the cumulative number of cases is 3,286, of which 3,168 are confirmed and 118 are probable. In total, there were 2,192 deaths (2074 confirmed and 118 probable) and 1064 people healed.<br /> • 501 suspected cases under investigation;

      THE LIST OF NO:

      • No new cases have been confirmed;<br /> • No new confirmed deaths have been recorded;<br /> • No cured person has emerged from CTEs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths;

      NEWS

      NOTHING TO REPORT

      VACCINATION<br /> • Since vaccination began on August 8, 2018, 249,290 people have been vaccinated;<br /> • The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS<br /> • Since the beginning of the epidemic, the total number of travelers checked (temperature measurement ) at the sanitary control points is 115.036.328 ;<br /> • To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    10. On 2019-11-27 15:46:04, user Guyguy wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT 25 NOVEMBER 2019<br /> Tuesday, November 26, 2019<br /> • Since the beginning of the epidemic, the cumulative number of cases is 3,304, of which 3,186 are confirmed and 118 are probable. In total, there were 2,199 deaths (2081 confirmed and 118 probable) and 1077 people cured.<br /> • 392 suspected cases under investigation;<br /> • 1 new case confirmed in North Kivu in Mabalako;<br /> • No new deaths among confirmed cases;<br /> • No cured person has emerged from CTEs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths;

      NEWS

      NOTHING TO REPORT

      VACCINATION

      • Despite the tense situation of the city of Beni, a vaccination ring was opened around the confirmed case of 24 October 2019 in the Kanzulinzuli Health Area of the General Reference Hospital;<br /> • 724 people were vaccinated with the 2nd Ad26.ZEBOV / MVA-BN-Filo vaccine (Johnson & Johnson) in the two Health Zones of Karisimbi in Goma;<br /> • Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 255,215 people have been vaccinated;<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, the second vaccine, called Ad26.ZEBOV / MVA-BN -Filo, is produced by Janssen Pharmaceuticals for Johnson & Johnson;<br /> • This new vaccine is in addition to the first, the rVSV-ZEBOV, vaccine used until then (since August 08, 2018) in this epidemic manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018. has recently been pre-qualified for registration.

      MONITORING AT ENTRY POINTS

      • Sanitary control activities are disrupted in the towns of Beni and Butembo in North Kivu province following demonstrations by the population which decries killings of civilians;<br /> • Since the beginning of the epidemic, the total number of travelers checked (temperature measurement ) at the sanitary control points is 120,825,670 ;<br /> • To date, a total of 109 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch him/her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    11. On 2019-09-30 05:24:02, user Guyguy wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT 25 SEPTEMBER 2019

      The epidemiological situation of the Ebola Virus Disease dated September 25, 2019

      Thursday, September 26, 2019

      Since the beginning of the epidemic, the cumulative number of cases is 3,178, of which 3,066 confirmed and 112 probable. In total, there were 2,126 deaths (2014 confirmed and 112 probable) and 981 people healed.

      • 483 suspected cases under investigation; <br /> • 3 new confirmed cases, including: <br /> • No cases in North Kivu; <br /> • 3 in Ituri, including 2 in Mandima and 1 in Mambasa; <br /> • 4 new confirmed deaths, including; <br /> • 1 community death in Ituri in Mandima; <br /> • 3 deaths of confirmed cases in North Kivu, including 2 in Katwa and 1 in Beni; <br /> • 1 person cured out of CTE in North Kivu in Butembo; <br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 160 (5% of all confirmed / probable cases), including 41 deaths. <br /> NEWS <br /> Six people from Mambasa cured of Ebola Virus Disease out of Komanda CTE in Ituri * <br /> • The Ebola Treatment Center (ETC) in Komanda, Ituri province, unloaded on Thursday, September 26, 2019, six people cured of Ebola Virus Disease, all from Mambasa; <br /> • For the director of the Komanda CTE, Claude Banga Lonema, this treatment center receives patients from the Nyakunde and Komanda health zones, as well as from Mambasa well before the construction of its CTE, which has become functional for almost a week; <br /> • For the director of the CTE of Komanda, Dr. Claude Banga Lonema, all these cures are the work of a whole team, including medical and paramedical staff, psychosocial teams, nutritionists and hygienists and guards, to whom he is grateful for all their efforts in this day-to-day treatment center, as well as dedication and their apostolate; <br /> • The coordinator ai of the Subcommittee on Response and Chief Medical Officer of the Komanda Health Zone, Dr. Faustin Singo Ngozo, said on this occasion that the success and success of Ebola Virus Disease is an asset for everyone in the response. This success must be shared, because if surveillance does not work, there will always be notification of deaths. The presence of cures at the Komanda CTE shows that epidemiological surveillance has been successful in detecting cases in a timely manner and has enabled the care physicians to have sufficient time to treat these patients. To this end, he recommended mutual support among all the teams in the response to push him out of the way to harm the epidemic, which he said has lasted too long, to continue working with the same momentum. He also asked the six healings to help the response in sensitizing everyone in his respective environment. Military healing, he said, is a resource that can educate his colleagues to end this epidemic; <br /> • Among the Mambasa healers who were discharged from the Komanda Treatment Center were an 8-year-old girl, a man in uniform, including the Armed Forces of the Democratic Republic of the Congo, and the village chief of Makoko II, a village in strong resistance. The latter two promised to raise awareness about Ebola Virus Disease in their respective communities; <br /> • This triumphal exit from the six healings of Mambasa, an area still showing resistance against EVD, was made in the presence of the few partners, namely the delegates of WHO and UNICEF: <br /> • Beginning with the prayer, this ceremony also ended with prayer and a family photo between the cures and the teams of the response in this ETC; <br /> • Immediately after, their exits from the CTE of Komanda, the cured were accompanied by the teams of the response to Mambasa, their respective health zone, where a festive atmosphere awaited them; <br /> • The Komanda CTE is located in the Mangiva Health Area, precisely in Makayanga village in the health zone of Komanda, 100 km from the Mambasa health zone.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding). <br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.

      VACCINATION <br /> • Expanded ring vaccination around 2 confirmed cases in the Mataba Health Area in Kalunguta, North Kivu. In addition, two other vaccination rings, in the same health zone, are waiting to be opened in Lisasa and Kabasha Health Zones; <br /> • Since the beginning of vaccination on August 8, 2018, 228,430 people have been vaccinated; <br /> • The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS <br /> • Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 98,818,462; <br /> • To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.


      LEXICON <br /> • A community death is any death that occurs outside a #Ebola Treatment Center. <br /> • A probable case is a death for which it was not possible to obtain biological samples for confirmation in the laboratory but where the investigations revealed an epidemiological link with a confirmed or probable case.

    12. On 2019-10-02 02:06:42, user Guyguy wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT 29 SEPTEMBER 2019<br /> The epidemiological situation of the Ebola Virus Disease dated September 29, 2019<br /> Monday, September 30, 2019

      • Since the beginning of the epidemic, the cumulative number of cases is 3,191, of which 3,077 are confirmed and 114 are probable. In total, there were 2,133 deaths (2019 confirmed and 114 probable) and 991 people healed . <br /> • 346 suspected cases under investigation; <br /> • 3 new confirmed cases, including: <br /> • 1 in North Kivu in Kalunguta; <br /> • 2 in Ituri, including 1 in Mambasa and 1 in Mandima; <br /> • 4 new confirmed deaths in Ituri, including: <br /> • 1 community death in Ituri 1 in Mandima; <br /> • 3 confirmed deaths in CTEs in Ituri, including 2 in Mambasa and 1 Komanda; <br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 160 (5% of all confirmed / probable cases), including 41 deaths.

      Synthesis of epidemiological data at week 39 (from 23 to 29/09/019)

      • Number of probable new cases: 3 <br /> • Number of new confirmed cases: 20 <br /> • Number of new healings: 16 <br /> • Number of new deaths: 12 <br /> • Community: 4 <br /> • Confirmed deaths: 8

      NEWS

      Local providers of 17 silent and hard-to-reach health areas in Mambasa in Ituri sensitized on EVD

      • One hundred and two local providers of 17 silent and hard-to-reach health areas in the Mambasa Ebola Virus Disease Response (EVD) sub-coordination were sensitized from 29 to 30 September 2019 in women's ward in Mambasa in Ituri province on this disease;

      • This took place during a briefing day for the purpose of helping to stop the transmission of Ebola Virus Disease in this sub-coordination in order to prevent its spread to other health zones. , DRC provinces and neighboring countries;

      • This day also had the objective of setting up a functional alert system in the community and in the health structures of the target health areas and a communication system allowing a rapid response in case of notification of a validated alert. , a new confirmed or probable case and accelerate ownership of the response by communities, their leaders and local health system actors;

      • These local providers were trained on EVD basics, early definitions / detections of cases and actions to be taken, as well as escalation of alerts, risk communication and community engagement. They were also trained on dignified and safe burial (DHS), active case finding, community-level monitoring tools and reporting system, risk communication and community engagement;

      • Awareness Day was opened by Mambasa Territory Administrator Mr. Idriss Koma Kukodila in the presence of the Deputy General Coordinator for Ebola Response to the Epidemic, Dr. Justus Nsio Mbeta, the Physician the coordination of the Mambasa Health Zone, representing the Mambasa sub-coordinator of the response and the field coordinators of WHO and UNICEF;

      • The sub-coordination of the Mambasa response includes 3 health zones, including Mambasa, Lolwa and Mandima, and 28 health areas, including 6 hot spots reporting cases within 14 days. These include Binase, Lolwa, Mambasa, Salama, Mandima and Some;

      • The 17 health areas are: Banana, Tabala, Bandishende, Makoko II, Epulu, Salate, Molokai, Bukulani, Akokora, Pede, Bakaiko Kenya, Nduye, Bongupanda, Malembi, Bahaka, Lolwa and Some. These are health areas that do not report EVD alerts and are areas of difficult access and insecurity.

      VACCINATION

      • Since vaccination began on August 8, 2018, 230,489 people have been vaccinated;

      • The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS

      • Since the beginning of the epidemic, the total number of travelers checked (temperature measurement) at the sanitary control points is 100,607,920;

      • To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding). <br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    13. On 2019-10-10 12:58:07, user GuyguyKabundi Tshima wrote:

      EPIDEMIOLOGICAL SITUATION

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT OCTOBER 07, 2019<br /> Tuesday, October 08, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,206, of which 3,092 are confirmed and 114 are probable. In total, there were 2,143 deaths (2029 confirmed and 114 probable) and 1006 people healed.<br /> 443 suspected cases under investigation;<br /> 1 new case confirmed at CTE in Ituri in Mandima;<br /> 1 new confirmed death in North Kivu in Mabalako;<br /> 10 people were cured from the CTE, including 7 in Ituri in Komanda and 3 in North Kivu, including Beni, 1 in Katwa and 1 in Mabalako;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths.

      No more confirmed cases of EVD at Butembo CTE<br /> - The Butembo Ebola Treatment Center (CTE) in North Kivu no longer has a confirmed case of Ebola Virus Disease;<br /> - The last two confirmed cases supported in this CTE have been released since Sunday, October 07, 2019 and have been reintegrated this Tuesday, October 08, 2019 in their respective communities by the teams of the response to the Virus Disease #Ebola of the psychosocial care. These cases are respectively health zones of Biena and Kayna;<br /> - Miss Ornella Bwira Zawadi, psychosocial supervisor at Butembo CTC, explains the psychosocial care at the Treatment Center. The Butembo CTE uses 17 psychologists subdivided into four blocks of tasks. These are triage supervisors, suspected cases, confirmed cases and accompanying village;<br /> - In the triage center, the psychologist ensures the awareness of newly admitted CTE cases. These new cases are normally 72 hours in the ETC and are taken on the 1st and the last day;<br /> - From the first day, the psychologist announces the result to the patients, its clinical evolution and its state. The patient who is positive is moved from the suspect's room to the confirmed block, while the patient who is negative until the third day remains in the suspected cases;<br /> - When the person is confirmed Ebola case, the psychologist is responsible for announcing his result, to make him aware of its evolution and life at the CTE. He asks him questions about his career in order to facilitate the follow-up of contacts;<br /> - It also monitors the confirmed case daily and ensures the relay between the patient and his family;<br /> - The accompanying person allows the good collaboration between the other CTE provider teams with the patient. It transmits various information of the patient, as well as its evolution to the other teams of the CTE;<br /> - Thereafter, intervenes the reintegration of suspected or confirmed cases cured and removed from the CTE. The psychiatrist accompanies him in his community. He educates his community and his family, explaining that the person who has been cured of Ebola is not dangerous and can not infect anyone else with the Ebola Virus Disease;<br /> - This Tuesday, October 08, 2019, Butembo CTE also released non Ebola people who were admitted to CTE as suspected cases.

      VACCINATION

      • A vaccination ring was opened around the confirmed case of 06 October 2019 in the Oicha health zone in Tenambo, North Kivu;
      • Continuation of vaccination around the last case of 04 October 2019 in Andindulu village in the Lolwa health zone in Ituri;
      • Continuation of the vaccination of newly recruited front-line staff in the Reference Hospitals of Katwa and Kyondo Health Zones;
      • Launch of Local Polio Immunization Days integrated with vitamin A supplementation and mebendazole deworming in 17 ZS of Butembo Antenna, most of which are Ebola virus-infected areas;
      • Since vaccination began on 8 August 2018, 235,389 people have been vaccinated;
      • The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS

      • Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 103,567,829 ;
      • To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    14. On 2019-10-16 12:34:53, user GuyguyKabundi Tshima wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS OF OCTOBER 10, 2019<br /> Friday, October 11, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,210, of which 3,096 confirmed and 114 probable. In total, there were 2,146 deaths (2032 confirmed and 114 probable) and 1028 people healed.<br /> 422 suspected cases under investigation;<br /> 2 new case confirmed at CTE in Ituri in Mandima;<br /> 2 new confirmed deaths, including 1 in North Kivu in Mabalako and 1 in Ituri in Mandima;<br /> 1 person healed out of CTE in Ituri in Mambasa;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths.

      NEWS

      President Félix-Antoine Tshisekedi Tshilombo submits to sanitary control for the prevention of #Ebola Virus Disease at Beni Airport<br /> - The Head of State, Coordinator of the Multisectoral Committee for the Ebola Virus Epidemic Response (CMRE), President Félix-Antoine Tshisekedi Tshilombo and his delegation were welcomed on Thursday, October 10, 2019 by the monitoring team at the entry points / sanitary control at Mavivi Airport in Beni, North Kivu Province. President Tshisekedi and his team have gone through all stages of health control at this point of entry to prevent Ebola Virus Disease;<br /> - The Ebola Virus Epidemic Response Coordination Team and a few members of the CMRE Technical Secretariat have been staying in Mambasa, Ituri province since Wednesday. It is in this part of the Democratic Republic of Congo that the last confirmed cases of #Ebola Virus Disease are concentrated;<br /> - This team, made up of the different experts in Ebola Virus Disease, has moved closer to Mambasa to coordinate closely the activities of the response;<br /> - Since they have been there, several activities have taken place, among which the release of 5 cured people from the ETC and a big meeting with all the partners of the response present in this sub-coordination. This meeting stems from the orientations to end the epidemic in this part of the DRC.

      VACCINATION

      • The symbolic vaccination of the local chief resisting the vaccination of Butama in the health zone of Mambasa in Ituri. Also in Ituri, continued immunization around two confirmed cases from 07 and 08 August 2019 in Biakato mine in Mandima with low participation due to community reluctance;
      • Immunization of front-line staff continued in the Kyondo and Kayna Reference Hospitals in North Kivu;
      • Continuation of Local Polio Vaccination Days integrated with Vitamin A supplementation and Mebendazole deworming in 17 health zones at the Butembo antenna in North Kivu;
      • Since vaccination began on August 8, 2018, 236,772 people have been vaccinated;

      MONITORING AT ENTRY POINTS

      • Since the beginning of the epidemic, the total number of travelers checked (temperature measurement ) at the sanitary control points is 104,765,252 ;
      • To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    15. On 2019-11-13 01:31:13, user Guyguy wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT NOVEMBER 11, 2019

      Tuesday, November 12, 2019<br /> • Since the beginning of the epidemic, the cumulative number of cases is 3,287, of which 3,169 confirmed and 118 probable. In total, there were 2,193 deaths (2075 confirmed and 118 probable) and 1067 people cured.<br /> • 545 suspected cases under investigation;<br /> • No new confirmed cases;<br /> • No new deaths of confirmed cases have been recorded;<br /> • No cured person has emerged from ETCs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths;

      NEWS

      Organization of a press conference in Goma on the introduction of the second Ebola vaccine in the Democratic Republic of Congo

      • The coordination of the epidemic response to Ebola Virus Disease organized this Tuesday, November 12, 2019, jointly with the International Non-governmental Organization Médecins Sans Frontières of France (MSF / France), a press conference on introduction of the second Ebola vaccine, Johnson & Johnson, at the Karibu Hotel in Goma, capital of North Kivu Province;<br /> • During this press conference, the coordinator of the response, Prof. Steve Ahuka Mundeke, announced that vaccination with this second vaccine will start on Thursday, November 14, 2019 in two health areas of Karisimbi in Goma, including Majengo and Kahembe. The beginning of the vaccination will thus precede the official launch of the introduction of this vaccine which will intervene in the days to come;<br /> • This vaccine will be administered intramuscularly in two doses with an interval of 56 days. It targets adults and children over twelve months old. It has a strong immune response and its dose has the advantage of increasing this response by making it more sustainable in order to protect populations against a possible Ebola outbreak, according to a member of the consortium that took care of the study of this vaccine, Dr Hugo Kavunga, project manager INRB, member of the consortium;<br /> • Everyone is eligible for this vaccine, including children over the age of one, even pregnant and lactating women. In addition, for women of childbearing age, they will be offered a pregnancy test. Those who do not want it, will always be vaccinated. Pregnant women will be followed, said Vaccine Project Coordinator at MSF / France, Dr Véronique Urbaniak;<br /> • The choice of vaccination site was made after several studies and it is in order to protect the population against possible epidemics that Majengo and Kahembe were selected;<br /> • The vaccine is called Ad26.Zebov / MVA-BN-Filo. It is of Belgian-American origin and is named Johnson and Johnson. It has already been used in Sierra Leone in West Africa, Uganda and soon Rwanda. This second vaccine complements the first in-use vaccine in belt strategy and has already saved more than 3,000 people to date;<br /> • In addition to the speakers, two other members of the consortium took part in the press conference, including the London Shool Project Investigator Dr. Dan Baush and the Epicenter's Immunization Coordinator Marie Burton.

      VACCINATION

      • Preparation of the launch of the 2nd Ebola vaccine, J & J in Kahembe and Majengo Health Areas in Karisimbi, Goma, North Kivu;<br /> • 37 participants, including 4 high-risk contacts, 6 contacts, 7 CPs and 20 front-line staff, were vaccinated from the confirmed case of 09 November 2019 in the Bingo Health Area in Mabalako, North Kivu;<br /> • Since vaccination began on August 8, 2018, 250,622 people have been vaccinated;<br /> • The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS

      • Disruption of activities at PoC VIRENDI (SC BUTEMBO) following clashes between FARDC soldiers and incivists not otherwise identified.<br /> • Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 116,184,525 ;<br /> • To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic
    16. On 2019-11-30 17:27:53, user Guyguy wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT NOVEMBER 28, 2019

      Friday, November 29, 2019<br /> • Since the beginning of the epidemic, the cumulative number of cases is 3,309, of which 3,191 are confirmed and 118 are probable. In total, there were 2,201 deaths (2,083 confirmed and 118 probable) and 1077 people healed.<br /> • 335 suspected cases under investigation;<br /> • No new confirmed cases;<br /> • No new deaths among confirmed cases;<br /> • No cured person has emerged from CTEs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths.

      NEWS

      Organization of a press conference on the situation and evolution of the Ebola Virus Disease in Beni

      • The Beni Ebola Sub-Coordination in North Kivu organized a press conference on Ebola Virus Disease on Friday, November 28, 2019;<br /> • This press conference was moderated by the acting coordinator of this sub-coordination, Dr. Tosalisana Michel, who confirmed that the activities of the response continue to be carried out in Beni, despite the prevailing security situation;<br /> • He reported that the response to the last indigenous case recorded in Beni is still weak as the maximum contact is still out of sight;<br /> • On this occasion, Dr. Tosalisana called on the people of Beni and the surrounding areas affected by this 10th epidemic to accompany the teams of the response in their field work in order to spare this city from any new contamination.

      Repatriation in Goma of the remains of two agents of the riposte who died during the Biakato attacks

      • The mortal remains of two agents registered by the coordination of the response to the Ebola Virus Disease outbreak during the attacks on the night of Wednesday 27 to Thursday, November 28, 2019 in Biakato Mines in the province of Ituri have repatriated this Friday 29 November 2019 from Beni to Goma;<br /> • A strong delegation from the General Coordination of the Response, led by its coordinator, Prof. Steve Ahuka Mundeke, rushed to Goma Airport to receive these bodies which were then taken to the General Goma reference Hospital morgue. <br /> • Long before, teams from the Mangina and Biakato sub-coordination evacuees arrived in Goma. Since Thursday, November 28, 2019, a few dozen people from these two sub-coordination who were attacked were brought back to Goma for their relocation, said the general coordinator of the response the Prof. Steve Ahuka.

      VACCINATION

      • The vaccination commission is in mourning. A service provider and a driver of his team were killed on the night of Wednesday 27 November 2019 following attacks at the Biakato living base in Ituri;<br /> • 2nd day without vaccination activity with the 2nd J & J vaccine following the disorders initiated by young people related to the security situation in Beni;<br /> • 821 people were vaccinated with the 2nd Ad26.ZEBOV / MVA-BN-Filo vaccine (Johnson & Johnson) in the two Health Zones of Karisimbi in Goma;<br /> • From the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, until November 27, 2019, 255,373 people were vaccinated;<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, the second vaccine, called Ad26.ZEBOV / MVA-BN -Filo, is produced by Janssen Pharmaceuticals for Johnson & Johnson;<br /> • This new vaccine is in addition to the first, the rVSV-ZEBOV, vaccine used until then (since August 08, 2018) in this epidemic manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018. has recently been pre-qualified for registration.

      MONITORING AT ENTRY POINTS

      • Sanitary control activities are disrupted in the Beni and Mangina sub-coordinations in North Kivu, as well as Mambasa and Biakato in Ituri following the demonstrations of the population who decry the killings of civilians and the attacks of armed innocents who took target response teams;<br /> • Since the beginning of the epidemic, the total number of checked travelers (temperature rise) at the sanitary control points is 121,813,958 ;<br /> • To date, a total of 109 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    17. On 2019-11-12 00:51:39, user Guyguy wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT NOVEMBER 10, 2019

      Monday, November 11, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,287, of which 3,169 confirmed and 118 probable. In total, there were 2,193 deaths (2075 confirmed and 118 probable) and 1067 people cured.<br /> 411 suspected cases under investigation;<br /> No new cases confirmed;<br /> No new deaths of confirmed cases have been recorded;<br /> 3 people healed from the CTE in North Kivu in Mabalako;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths;

      NEWS

      Awareness and vaccination day for Beni mototaxi drivers with the support of Unicef S / Coordination MVE Beni, Wednesday 06-11 - 2019 HIVUM room

      • There were many, about three hundred, the drivers of Mototaxi Beni invited to a day of awareness and vaccination against Ebola Virus Disease this Wednesday, November 06, 2019 in the HIVUM room.

      • This day is welcome for the city of Beni during this period of EVD epidemic which, unfortunately, displays a lethality of 86.3% among motorcyclists, as pointed out by Dr. Pierre ADIKEY, Coordinator of the response of Sub Coordination of Beni.

      • Thus, in his presentation, he focused his message on the risk of transmission of EVD among motorotaxi drivers and the conduct to be held in the exercise of their craft to protect themselves and the community.

      • He asked bikers more often to respect the measures of prevention, namely: washing hands regularly, stopping at checkpoints, not being bribed to divert checkpoints, not carrying suspicious parcels and reporting and / or direct any suspicions of illness to colleagues or the community.

      • In order to circumscribe the day, Dr. P. ADIKEY traced the path of the last Motard who died of EVD before his death confirmed at the CTE. To close his presentation, he made a reminder of the various events that prevented the teams of the response from working: among other things the days of the dead city, the fire of the vehicles of the riposte, the destruction of the structures of the care, the cases of resistance and others whose bikers were part of it.

      • Dr. Bibiche MATADY, as Epidemiologist and Chair of the Monitoring Commission, introduced to the participants the importance of accepting to be listened to if you are in contact with a case, to let yourself be followed for the entire period indicated and to orient in a management structure as soon as the first sign appears. She also emphasized the collaboration between the bikers and the teams of the response.

      • To justify this day again, one of the 3 Hikers shared his testimony and urged his colleagues to collaborate and follow the recommendations of the response teams starting with vaccination.

      • Vaccination is one of the preventive measures against EVD, said Dr Adonis TERANYA, the Chair of the Immunization Subcommission. In his presentation, he explained the evolution of the vaccination protocol, the current targets, the side effects and the action to take in the event of an adverse event. Before calling for the voluntary vaccination of participants, he spoke about vaccines currently used in the DRC.

      • In his words, the President of Bikers reiterated to the Coordinator the commitment of his organization and all its members to support the interventions of the response, while affirming its availability to any solicitation for the fight against the disease to Ebola virus in the city of Beni and its surroundings.

      • The day ended with the vaccination of 100 Bikers and some of their dependents.

      VACCINATION

      • Since vaccination began on 8 August 2018, 250,234 people have been vaccinated;
      • The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS

      • Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 115,778,240 ;
      • To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    18. On 2019-11-15 16:53:09, user GuyguyKabundi Tshima wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT NOVEMBER 13, 2019

      Thursday, November 14, 2019

      • Since the beginning of the epidemic, the cumulative number of cases is 3,292, of which 3,174 are confirmed and 118 are probable. In total, there were 2,193 deaths (2075 confirmed and 118 probable) and 1067 people cured.<br /> • 527 suspected cases under investigation;<br /> • 1 new case confirmed in North Kivu in Mabalako;<br /> • No new deaths of confirmed cases have been recorded;<br /> • No cured person has emerged from ETCs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths;

      NEWS

      Ebola Virus Disease Response Co-ordination Announces Three Road Traffic Accident in Bunia, Ituri

      • The overall coordination of the response to the Ebola Virus Disease epidemic in North, South Kivu and Ituri was informed on Thursday 13 November 2019 of the tragic traffic accident between two motorcycles, one of which carried three agents of the riposte;<br /> • These three officers, who work for the Epidemiological Surveillance Commission at the Point of Entry and Control, were returning from Bunia to Mambasa, where they are respectively delivering;<br /> • This accident occurred around Marabo in Bunia on the evening of Wednesday 13 November 2019;<br /> • The balance sheet reports an officer who died at the scene and two others who were seriously injured, including one in a coma. The two wounded were taken to the Nyakunde Reference General Hospital in Ituri for appropriate care;<br /> • The overall coordination of the response sends its deepest condolences to the grieving family and expresses all its compassion and solidarity to the injured officers, while wishing them a quick recovery.

      Effective start of Johnson & Johnson vaccination in two Goma health areas

      • Ebola vaccination with the Ad26.ZEBOV / MVA-BN-Filo vaccine, produced by Janssen Pharmaceuticals for Johnson & Johnson, began on Thursday, November 14, 2019 in two Karisimbi health areas in Goma City , North Kivu Province;<br /> • The Epidemic Response Coordinator for Ebola Virus Disease in North, South Kivu and Ituri. For this purpose, Prof. Steve Ahuka Mundeke visited the vaccination sites to inquire about the evolution of activities in the field. He was satisfied with the work of the teams;<br /> • He took the opportunity to invite the population of the targeted areas to be vaccinated in order to protect themselves from the resurgence of the Ebola virus;<br /> • Several people were present in Majengo and Kahembe health areas to get vaccinated. The first person to be vaccinated is a Kahembe community leader who has been protected against the Ebola virus today and also in case of a possible new Ebola outbreak. This community leader has appealed to all residents of his community and sites targeted to come take this second vaccine. "This is an opportunity not to be missed, because it is said that prevention is better than cure, " he said;<br /> • The logistics of this vaccination are provided by the international non-governmental organization Médecins Sans Frontières of France (MSF / France).<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, this second vaccine, called Ad26.ZEBOV / MVA-BN -Filo , is produced by Janssen Pharmaceuticals for Johnson & Johnson;<br /> • This new vaccine complements the first, the rVSV-ZEBOV, the vaccine used until then in this epidemic. Manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018, it was recently approved.

      Closing of the training workshop for media professionals in Beni on the role and responsibility of journalists during public health crises

      • The Deputy Mayor of the city of Beni, Muhindo Bakwanamaha Modeste, closed this Thursday, November 14, 2019 in Beni in the province of North Kivu the training of media professionals on the role and responsibility during public health crises;<br /> • The coordinator of the Beni Ebola Ebola response sub-coordination, Dr. Pierre Adikey, on behalf of the Coordinator-General of the Response, Prof. Steve Ahuka, wished to see these kinds of trainings be organized, not only in other sub-Coordination of the response, but also throughout the Democratic Republic of the Congo so that journalists from all over the country are ready to face any possible epidemic crisis;<br /> • This training, he said, is part of the zero-case Ebola strategy and strengthening the health system of tomorrow;<br /> • The focal point of Beni's journalists, Moustapha MULONDA, reaffirmed the commitment of journalists to combat Ebola Virus Disease through various programs and publications disseminated and published by their respective media thanks to the new tools acquired during this period. training;<br /> • This training was organized by the Ministry of Health in collaboration with the World Health Organization and benefited from the facilitation of the overall coordination of the response, UNICEF, CDC Africa and MSF.

      VACCINATION

      • Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 251,637 people have been vaccinated;

      • Vaccination with the second Ad26.ZEBOV / MVA-BN-Filo vaccine, produced by Janssen Pharmaceuticals for Johnson & Johnson, began on Thursday November 14, 2019 in Goma. This vaccine was approved on 22 October 2019 by the decisions of the Ethics Committee of the School of Public Health of the University of Kinshasa and 23 October 2019 of the National Ethics Committee;

      • Until then, only one vaccine was used in this outbreak. This is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee in its decision of 20 May 2018 and which has recently been approved.

      MONITORING AT ENTRY POINTS

      • A 27-year-old woman from Butembo for Goma, an escaped suspect from Makasi Hospital in Butembo, North Kivu, was intercepted at the Kanyabayonga checkpoint in Kayna. When she was intercepted, she experienced signs such as fever at 38.4 ° C, severe asthenia, abdominal pain and vaginal bleeding. It was sent to the KAYNA Transit Center.

      • Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 116,622,388 ;

      • To date, a total of 112 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    19. On 2019-11-30 17:00:40, user Guyguy wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT NOVEMBER 27, 2019

      Thursday, November 28, 2019<br /> • Since the beginning of the epidemic, the cumulative number of cases is 3,309, of which 3,191 are confirmed and 118 are probable. In total, there were 2,201 deaths (2,083 confirmed and 118 probable) and 1077 people healed.<br /> • 443 suspected cases under investigation;<br /> • 5 new confirmed cases, including:<br /> o 4 in Ituri in Mandima;<br /> o 1 in North Kivu in Mabalako;<br /> • 2 new deaths of confirmed cases, including:<br /> o 2 new community deaths in Ituri in Mandima;<br /> o No deaths among confirmed cases in CTEs;<br /> • No cured person has emerged from CTEs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths.

      NEWS

      Three members of the Ebola Virus Epidemic response killed during an attack in Biakato, Ituri

      • Following the attack on the sub-coordination of the Biakato response in Ituri on the night of Wednesday 27th to Thursday 28 November 2019, three members of the Ebola response teams in this sector lost their lives ;<br /> • It is a provider and a driver of the vaccination committee and another driver;<br /> • In addition to these three deaths, there are 7 wounded and 6 others with psychological disorders and extensive material damage.<br /> • A good number of these teams from Biakato were evacuated in three waves to Goma. As soon as they arrived, they were greeted by a coordination team led by Prof. Steve Ahuka, general coordinator, who also visited the wounded before going to inquire about the security conditions and accommodation of evacuees. He did not fail to comfort them.

      VACCINATION

      • The vaccination commission is in mourning. A service provider and a driver of his team were killed on the night of Wednesday 27 November 2019 following attacks at the Biakato base in Ituri;<br /> • 2nd day without vaccination activity with the 2nd J & J vaccine following the disorders initiated by young people related to the security situation in Beni;<br /> • 724 people were vaccinated, until Tuesday, November 26, 2019, with the 2nd Ad26.ZEBOV / MVA-BN-Filo vaccine (Johnson & Johnson) in the two health zones of Karisimbi in Goma;<br /> • Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 255,373 people have been vaccinated;<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, the second vaccine, called Ad26.ZEBOV / MVA-BN -Filo, is produced by Janssen Pharmaceuticals for Johnson & Johnson;<br /> • This new vaccine is in addition to the first, the rVSV-ZEBOV, vaccine used until then (since August 08, 2018) in this epidemic manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018. has recently been pre-qualified for registration.

      MONITORING AT ENTRY POINTS

      • Sanitary control activities are disrupted in the towns of Beni and Butembo in North Kivu province following demonstrations by the population which decries killings of civilians;<br /> • Since the beginning of the epidemic, the total number of travelers checked (temperature measurement ) at the sanitary control points is 121,159,810 ;<br /> • To date, a total of 109 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    1. On 2020-03-14 06:52:08, user Muhammad Yousuf wrote:

      Hypokalemia is caused by SARS-CoV-2 virus due to its affinity for the Angiotensin Converting Enzyme (ACE) receptor that is present in the lungs, heart, blood vessels and the gastrointestinal tract of humans. It has been suggested from animal experiments that medications inhibiting this receptor (called ACEI or ARBs) could be a potential management strategy(1-2). Because ACEI and ARBs are medications mainly use for high blood pressure and would lower the BP, it is recommended that these medications should at least be used in patients with COVID-19 who are already suffering from hypertension or whose BP is not lower than 100 mm Hg systolic.

      It would also be interesting to know the recovery and death rate of COVID-19 patients with hypertension or heart failure who were already using an ACEI or ARB medications compared with those who were not on suchmedications.

      Abbreviations: ACEI= Angiotensin Converting Enzyme Inhibitors, ARBs= Angiotensin Receptor Inhibitors, BP= Blood pressure

      References<br /> 1. Gurwitz D. Angiotensin receptor blockers as tentative SARS-CoV-2 therapeutics. Drug Dev Res. 2020 Mar 4. doi: 10.1002/ddr.21656. [Epub ahead of print]<br /> 2. Dimitrov, D. S. The secret life of ACE2 as a receptor for the SARS virus. Cell, 2003; 115(6), 652–653.

    1. On 2020-03-26 16:04:12, user Sinai Immunol Review Project wrote:

      Title: Meplazumab treats COVID-19 pneumonia: an open-labelled, concurrent controlled add-on clinical trial

      Keywords: Meplazumab, CD147, humanized antibody, clinical trial <br /> Main findings: This work is based on previous work by the same group that demonstrated that SARS-CoV-2can also enter host cells via CD147 (also called Basigin, part of the immunoglobulin superfamily, is expressed by many cell types) consistent with their previous work with SARS-CoV-1. 1 A prospective clinical trial was conducted with 17 patients receiving Meplazumab, a humanized anti-CD147 antibody, in addition to all other treatments. 11 patients were included as a control group (non-randomized). <br /> They observed a faster overall improvement rate in the Meplazumab group (e.g. at day 14 47% vs 17% improvement rate) compared to the control patients. Also, virological clearance was more rapid with median of 3 days in the Meplazumab group vs 13 days in control group. In laboratory values, a faster normalization of lymphocyte counts in the Meplazumab group was observed, but no clear difference was observed for CRP levels.

      Limitations: While the results from the study are encouraging, this study was non-randomized, open-label and on a small number of patients, all from the same hospital. It offers evidence to perform a larger scale study. Selection bias as well as differences between treatment groups (e.g. age 51yo vs 64yo) may have contributed to results. The authors mention that there was no toxic effect to Meplazumab injection but more patient and longer-term studies are necessary to assess this.

      Significance: These results seem promising as for now there are limited treatments for Covid-19 patients, but a larger cohort of patient is needed. CD147 has already been described to facilitate HIV 2, measles virus 3, and malaria 4 entry into host cells. This group was the first to describe the CD147-spike route of SARS-Cov-2 entry in host cells 1(p147). Indeed, they had previously shown in 2005 that SARS-Cov could enter host cells via this transmembrane protein 5). Further biological understanding of how SARS-CoV-2 can enter host cells and how this integrates with ACE2R route of entry is needed. Also, the specific cellular targets of the anti-CD147 antibody need to be assessed, as this protein can be expressed by many cell types and has been shown to involved in leukocytes aggregation 6. Lastly, Meplazumab is not a commercially-available drug and requires significant health resources to generate and administer which might prevent rapid development and use.

      1. Wang K, Chen W, Zhou Y-S, et al. SARS-CoV-2 Invades Host Cells via a Novel Route: CD147-Spike Protein. Microbiology; 2020. doi:10.1101/2020.03.14.988345
      2. Pushkarsky T, Zybarth G, Dubrovsky L, et al. CD147 facilitates HIV-1 infection by interacting with virus-associated cyclophilin A. Proc Natl Acad Sci USA. 2001;98(11):6360-6365. doi:10.1073/pnas.111583198
      3. Watanabe A, Yoneda M, Ikeda F, Terao-Muto Y, Sato H, Kai C. CD147/EMMPRIN acts as a functional entry receptor for measles virus on epithelial cells. J Virol. 2010;84(9):4183-4193. doi:10.1128/JVI.02168-09
      4. Crosnier C, Bustamante LY, Bartholdson SJ, et al. BASIGIN is a receptor essential for erythrocyte invasion by Plasmodium falciparum. Nature. 2011;480(7378):534-537. doi:10.1038/nature10606
      5. Chen Z, Mi L, Xu J, et al. Function of HAb18G/CD147 in Invasion of Host Cells by Severe Acute Respiratory Syndrome Coronavirus. J Infect Dis. 2005;191(5):755-760. doi:10.1086/427811
      6. Yee C, Main NM, Terry A, et al. CD147 mediates intrahepatic leukocyte aggregation and determines the extent of liver injury. PLOS ONE. 2019;14(7):e0215557. doi:10.1371/journal.pone.0215557

      Review by Emma Risson and Robert Samstein as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2020-10-05 08:20:30, user NMN wrote:

      The way it is presented in the abstract seems misleading to me, it presents itself as a report of a mass screening of nearly 2000 individuals, in which saliva outperformed NP swabs, but this is not really an accurate picture of what they found.

      They have 2 cohorts. <br /> 1) a contact tracing (CT) cohort of 161 individuals, of which 47 were positive by NP and/or Saliva. I would not consider contact tracing of less than 200 individuals to be “mass screening”<br /> 2) An airport mass screening cohort of 1763 individuals, of which 5 were positive by NP and/or saliva.

      The saliva outperformed the NP swabs in the CT cohort only, with 44/47 positives for saliva compared to 41/47 positives for NP swabs.<br /> NP swabs outperformed the saliva in the mass screening cohort, with 5/5 positives by NP swab, and 4/5 positives by saliva. These numbers are too low to make conclusions for mass screening though.

      Furthermore, it seems that there are math errors in the sensitivities that they report.<br /> They report sensitivities of NP and saliva as 86% and 92% respectively, yet there is no way to arrive at these %s from the numbers in their tables.

      Sensitivities for NP vs saliva in:<br /> CT cohort only: 87.2 vs 93.6% (41 vs 44 /47)<br /> Mass Screen cohort only: 100 vs 80% (5 vs 4 /5)<br /> Combined cohorts: 88.5 vs 92.3% (46 vs 48 /52)

    1. On 2020-10-28 11:35:36, user David Simons wrote:

      I have interpreted the inclusion criteria for the "Severe SARS-CoV-2 infection" group to include those within the biobank that died during March to July. If that's not the case and it's only individuals who died with COVID-19 on their death certificate you need to make this clearer. I understand that there have been a high proportion of COVID-19 related deaths in the community but this has definitely not been the only cause of death in these 4 months. If you are intent on using this to include individuals I think you'd need to run a sensitivity analysis on your results to investigate what happens when you exclude these individuals from your analytic sample.

      Further, an in-hospital test is not an adequate proxy for disease severity. The reference you site can also not clearly support that statement. There are multiple reasons for in-hospital testing of non-severe individuals. Some of these include; staff of the hospital (or family member of staff), at risk groups (i.e. those attending the hospital for regular dialysis or chemotherapy) and those that attend the emergency department but do not get admitted to hospital. There are several ways you can mitigate against this depending on what data you have available. One option would be to use length of stay combined with in-hospital mortality to support your definition of severity, for example, if a significant proportion of your participants are admitted and discharged within less than 2 days it's unlikely they have severe disease. A further option if available would be to explore their requirement for supplementary oxygen, enrollment into RECOVERY or similar trial with inclusion of only severe disease or treatment with dexamethasone/remdisivir. If none of these are possible having a sensitivity analysis where you remove those with known comorbidities that increase the probability of asymptomatic screening or where the disease may not be severe at testing (i.e. renal dialysis patients or chemotherapy patients) and healthcare workers may strengthen this assumption.

      Hope these are helpful comments.

    1. On 2021-08-28 14:41:37, user RC Cyberwarrior wrote:

      I have read comments based on medical studies that individuals who previously had SARS COV2 were 2 -4 times more likely to suffer adverse reactions to the covid vaccines, if vaccinated post initial infection. Some speculate this reaction was related to Antibody-Dependent Enhancement.

    2. On 2021-08-29 21:38:00, user MANISH JOSHI wrote:

      We must stop ignoring natural immunity - it’s now long overdue<br /> Manish Joshi, MD

      This article by Gazit et al is another addition to a growing body of literature supporting the conclusion that natural immunity confers robust, durable, and high-level protection against COVID-19 (1-4). Yet some scientific journals, media outlets, and public policy messaging continue to cast doubt. That doubt has real-world consequences, particulary for resource limited countries. We would like to review available data.

      Infection generates immunity. The “SIREN” study in the Lancet addressed the relationships between seropositivity in people with previous COVID-19 infection and subsequent risk of severe acute respiratory syndrome due to SARS-CoV-2 infection over the subsequent 7-12 months (1). Prior infection decreased risk of symptomatic re-infection by 93%. A large cohort study published in JAMA Internal Medicine looked at 3.2 million US patients and showed that the risk of infection was significantly lower (0.3%) in seropositive patients v/s those who are seronegative (3%) (2).

      Perhaps even more important to the question of duration of immunity is a recent study that has demonstrated the presence of long-lived memory immune cells in those who have recovered from COVID-19 (3). This implies a prolonged (perhaps years) capacity to respond to new infection with new antibodies.

      In contrast to this collective data demonstrating both adequate and long-lasting protection in those who have recovered from COVID-19, the duration of vaccine-induced immunity is not fully known- but breakthrough infections in Israel, Iceland and in the US suggests few months. Before CDC decided to stop collecting data on all breakthrough infections at the end of April, 2021, it reported >10,000 breakthrough infections (2 weeks after completion of vaccination) in the US, with a mortality of ~2% (5). Booster COVID vaccine recommendations have been already announced in Israel and in the US proving ineffectiveness within 6 months.

      How should we use the collective data to prioritize vaccination? These new data support simple and logical concepts. The goal of vaccination is to generate memory cells that can recognize SARS-CoV-2 and rapidly generate neutralizing antibodies that either prevent or mitigate both infection and transmission. Those who have survived COVID-19 must almost by definition have mounted an effective immune response; it is not surprising that the evolving literature shows that prior infection decreases vulnerability. In our view, the data suggest that people confirmed to have been infected with SARS-CoV-2 may not need vaccination. We should not be debating the implications of prior infection; we should be debating how to confirm prior infection (6).

      Manish Joshi, MD<br /> Thaddeus Bartter, MD<br /> Anita Joshi, BDS, MPH

      1. Hall VJ, Foulkes S, Charlett A et al. SARS-CoV-2 infection rates of antibody-positive compared with antibody-negative health-care workers in England: large, multicentre, prospective cohort study (SIREN). Lancet. 2021
      2. Harvey RA, Rassen JA, Kabelac CA, et al. Association of SARS-CoV-2 Seropositive Antibody Test With Risk of Future Infection. JAMA Intern Med.
      3. Turner, J.S., Kim, W., Kalaidina, E. et al. SARS-CoV-2 infection induces long-lived bone marrow plasma cells in humans. Nature 2021
      4. Wang, Z., Yang, X., Zhong, J. et al. Exposure to SARS-CoV-2 generates T-cell memory in the absence of a detectable viral infection. Nat Commun 12, 1724 (2021).
      5. https://www.cdc.gov/mmwr/vo...
      6. Kuehn BM. High-Income Countries Have Secured the Bulk of COVID-19 Vaccines. JAMA. 2021;325(7):612
    3. On 2021-08-28 18:17:03, user Squid Pro Crow wrote:

      Despite the fact that I have no formal medical training, I think that I now have the real life experience to knowledgeably comment on this. My wife and I both had our second doses of the Phizer just under 5 months ago. Also my daughter and son-in-law had the Pfizer shots about 3-1/2 or 4 months ago. At the end of a 3 day stay of 2 grandkids i began to get a cough and slight fever, and lost my sense of smell and taste. So I got tested and it was positive, My wife has a cough and body aches and will be tested today. My daughter and son-in-law (in their low 40's) are also experiencing mild symptoms and will be tested today. The kids, of course had very minor symptoms for about a day, and are completely fine. So, assuming that the adults test positive, it seems evident that the delta strain does indeed spread rapidly and easily, and the vaccine(s) may not be as effective against it. HOWEVER, I feel that at my age, with asthma and possibly COPD history, I would be much worse off had I decided against the vaccine, as my symptoms are very mild now, except for the chest congestion that I have (which is already better) that I also get from just about every cold.

      My main concern is that there is not enough focus on theraputics, and major health providers like Kaiser just expect even their at-risk patients like me to just sit at home and wait to see if their lips turn blue and they can't breathe, and make it to an E.R. for a company that is usually proactive about health care, this is just stupid. An apparently, this is the norm. There are some treatments that are effective if taken early, but our government and the health system that follows their dictates are afraid to prescribe safe drugs off-label that are semi-proven to be very helpful, like ivermectin, which I managed to get from a nearby Dr. It seems to be helping clear it up even faster--my sense of smell is even starting to come back.

    1. On 2021-08-30 14:40:54, user Nathan Johnson wrote:

      Hi Sean, table 2 is the attention getting graph with the large drop but it mixes tests at all different ages so it's harder to read. It'd be better to see a graph by time for separate groups of 3 months old, 6 month old and 12 months old (or similar). Since table 4 shows "Overall, we note no significant reductions in development trends." taking out the older groups who didn't drop should make the drop in 2021 even more dramatic, no? Also if masking was used in first few months in children born prepandemic without a drop, could point more strongly to prenatal cause.

    2. On 2021-12-13 22:59:33, user Just Because I can wrote:

      Greetings RI team from Utah! I must begin with nicesties; "Go BRUNO"! My son graduated this past May 2021 from Brown. I am a speech and language pathologist with over 30 years of hospital, private and public school setting experiences. Over the past nine years, I have professionally focused on children ages 3-5 within the public preschool and private therapeutic settings. I service students and their parents with the most intensive and restrictive learning environments within our District due to cognitive, behavioral and communicative delays. I can't help but weigh in now, as I previously shared this article with my peers in August as I braced for the impact of the 2021 school year.

      Given your single assessment tool (I professionally do not profess strong decisions based on a single evaluative instrument, even as widely accepted at the Mullen), I've found your results to be intriguing and frankly, just as we anticipated.

      To compare to RI, our school district, closed schools for Remote Learning for only 3 mos. in the Spring of 2019 and returned to in person instruction with hybrid options in 2020. Of a caseload of 65 students, I had 3 that were online/virtual. In 2021, our District returned to essentially all in student learning.

      My informal observations this school year in Utah has been as follows:

      1. Increase in new referrals and eligible "older" 4+ year old children scoring remarkably delayed communication (Standard scores <50 given a typical range of 85-115) and no previous history of EI or preschool interventions. Our TIER 3, most restrictive preschool program has a marked influx of new referrals (e.g., total students in May was 24 and currently rises at 36 with 8 new referrals in Jan.)
      2. Many declined or rarely attended virtual Early Intervention supports, skipped medical wellness visits including dentistry during the pandemic.
      3. Increase in parent report of primary concerns with behavioral components.
      4. Given the current timeframe, we are NOT seeing marked progress with an influx in discharges (no longer eligible due to more typical standard scores). We are seeing progress and we have continued to see progress through the pandemic (which at times surprised me) but the levels of improvement are not as remarkable or typical as years past.
      5. Typical communication, fine/gross motor and even cognitive delays are still present but the comorbidity of exceptional delays in social/pragmatic and ultimately, behavioral skills combined make measured learning and ultimately IEP progress at a slower rate. Social/pragmatic delays are interfering with overall progress.
      6. Parent involvement, participation, enthusiasm and grit appear markedly depressed. Educational teams walk a fine line between empathy, compassion and expecting parents and care givers to step in and "do hard things" in difficult times. The teams are using external motivators such as pizza cards to motivate parents to attempt, complete and turn in 2x monthly parent based home practice pages.
      7. Increased rate of meeting attendance with Virtual options.

      Where do we go from here? I agree, measuring student outcomes is critical but supporting the parents (in any evidence based manner) is to me, a critical and crucial element. I thought the kids, once exposed to typical learning/situations and with repetition, our inflated numbers would flatten in a year and they would bounce back into typical ranges but it's the apathetic, tired, depressed parents that are lacking resilience and grit currently. I do think another component that would be most valuable and continues to need funding is Preschool for All (or most).

      Thank you to any cohort, parent, professional person interested in this dialogue, for reading my insights.

    1. On 2020-04-16 12:20:10, user Marlowe Fox wrote:

      The tests on the efficacy of HCQ are confounded by multiple variables, including comorbidities, symptom onset, prescription drugs (RAAS inhibitors appear to play a key role in viral intensity), and testosterone/estrogen level, to name only a few.

      Geneticists, epidemiologists, and other scientists have long used casual diagrams to clearly show variables that may potentially confound their results (1). The Wuhan study at the very least would need to account for the following:

      HCQ <— comorbidities —> recovery<br /> HCQ <— symptom onset —> recovery<br /> HCQ <— drug prescriptions —> recovery

      Adjusting for the confounding variable would essentially smooth out the flow of information between the treatment (HCQ) and the outcome (recovery), allowing for the inference of causal effects.

      Assuming observable data is not available to adjust for confounding variables, a casual mechanism (mediator) could smooth out the flow of information from the treatment to the outcome (so long as the mediator is not influenced by confounder).

      Luckily, multiple in vitro studies have been performed. One study posits that HCQ lowers endosomal pH which ultimately inhibits COVID from binding to ACE 2 and decreasing viral intensity (3).

      HCQ —> endosomal pH —>glycosylation of COVID cellular receptor —> ACE 2 binding —> viral intensity —> acute lung injury

      Another in-silico study posits that HCQ blocks specific protein sites on the host ACE2 cell, thereby thwarting its attempt to infect it and preventing the cytokine storm (over-reaction of the lymphatic system) that some posit is responsible for Acute Lung Injury (3). So here we have an entirely different causal mechanism:

      HCQ —> BRD-2 receptor sites —> cytokine storm —> acute lung injury

      Despite these problems, some believe that the p-values obviate the need to control for potentially lurking variables. However, they are subject to myriad influences, known as p-hacking. Whether it is the number of tests performed or the number of comparisons made, it increases the chance of finding a statistically significant p-value (4). Three professional statisticians co-authored a paper reviewing the validity of the Wuhan study (5). There were several issues with the data upon which the two significant p-values were based.

      I suppose there is also a pragmatic argument: The p-values, along with existing studies and reports, are sufficient enough evidence to offset any concern for lurking variables in these urgent times. In other words, how much evidence is sufficient to warrant large scale roll-out of a low-cost treatment that may have a beneficial effect, from saving individuals who would have otherwise died to curbing its spread?

      The consequences of large roll-out: manufacturing, scaling, distribution chains, and so forth could result in a tremendous diversion of resources. How many pharmaceutical manufacturers even have the capacity to roll out production of this magnitude? What if they all start scaling their labor to produce this particular drug. You can’t just put this genie back into the bottle. Not to mention the scientific energy/intellectual capital that would go to proving or disproving this proposed treatment. And why? Because scientific evidence demanded it? No because a tortured p-value and unpublished/unsubstantiated anecdotal evidence caught the attention of some in the media, and it has been over-popularized as a panacea. What about the risk that HCQ is not an effective treatment despite large investments in cash and resources that have been invested? Do you think the wheels of capitalism turn so easily? Investors will want a return and if that means continually touting an ineffective drug through spurious science, they will continue to do so. What about individuals taking HCQ as a prophylactic, believing themselves to be protected against COVID? Or COVID+ individuals taking HCQ and believing themselves to be cured? Or individuals who think: Well, if I get it—I’ll just take HCQ and be fine. This would increase the spread of COVID. From my perspective, the ignorance to viral transmission and the required precautions is widespread. This is just one more reason not to acquiesce to the new social norms of wearing face masks, social distancing, and abiding by shelter-in-place rules. Here, I think an understanding of cognitive psychology is important to anticipate the future behavior of a society in which a cheap and easy-to-manufacture cure is published in the media.

      To sum up, HCQ's efficacy is not sufficiently proven to warrant a widespread roll-out, because it could result in several downstream consequences, from the diversion of resources (both manufacturing capabilities and intellectual capital) to increasing the risk threshold of individuals--who spurious believe in an easy and cheap treatment--thereby increasing the infection rate. One of two things needs to happen. Clinical trials that properly adjust for all potential comorbidities. Or the discovery of a causal mechanism (in vivo), which would obviate the need to control/adjust for confounders. For me, this would tip the utilitarian scales in regard to the potential benefits versus the risks.

      References

      1. Judea Pearl and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect (1st. ed.). Basic Books, Inc., USA.
      2. https://www.ncbi.nlm.nih.go...
      3. https://papers.ssrn.com/sol...
      4. https://www.scientificameri....
      5. https://zenodo.org/record/3....
    2. On 2020-03-30 22:23:50, user Sinai Immunol Review Project wrote:

      Study Description

      This is a randomized clinical trial of hydroxychloroquine (HCQ) efficacy in the treatment of COVID-19. From February 4 – February 28, 2020 142 COVID-19 positive patients were admitted to Renmin Hospital of Wuhan University. 62 patients met inclusion criteria and were enrolled in a double blind, randomized control trial, with 31 patients in each arm.

      Inclusion criteria:<br /> 1. Age >= 18 years<br /> 2. Positive diagnosis COVID-19 by detection of SARS-CoV-2 by RT-PCR<br /> 3. Diagnosis of pneumonia on chest CT <br /> 4. Mild respiratory illness, defined by SaO2/SPO2 ratio > 93% or PaO2/FIO2 ratio > 300 mmHg in hospital room conditions (Note: relevant clinical references described below.)

      a. Hypoxia is defined as an SpO2 of 85-94%; severe hypoxia < 85%. <br /> b. The PaO2/FIO2 (ratio of arterial oxygen tension to fraction of inspired oxygen) is used to classify the severity of acute respiratory distress syndrome (ARDS). Mild ARDS has a PaO2/FIO2 of 200-300 mmHg, moderate is 100-200, and severe < 100.

      1. Willing to receive a random assignment to any designated treatment group; not participating in another study at the same time

      Exclusion criteria: <br /> 1. Severe or critical respiratory illness (not explicitly defined, presumed to be respiratory function worse than outlined in inclusion criteria); or participation in trial does not meet patient’s maximum benefit or safe follow up criteria<br /> 2. Retinopathy or other retinal diseases<br /> 3. Conduction block or other arrhythmias<br /> 4. Severe liver disease, defined by Child-Pugh score >= C or AST > twice the upper limit<br /> 5. Pregnant or breastfeeding<br /> 6. Severe renal failure, defined by eGFR <= 30 mL/min/1.73m2, or on dialysis<br /> 7. Potential transfer to another hospital within 72h of enrollment<br /> 8. Received any trial treatment for COVID-19 within 30 days before the current study

      All patients received the standard of care: oxygen therapy, antiviral agents, antibacterial agents, and immunoglobulin, with or without corticosteroids. Patients in the HCQ treatment group received additional oral HCQ 400 mg/day, given as 200 mg 2x/day. HCQ was administered from days 1-5 of the trial. The primary endpoint was 5 days post enrollment or a severe adverse reaction to HCQ. The primary outcome evaluated was time to clinical recovery (TTCR), defined as return to normal body temperature and cough cessation for > 72h. Chest CT were imaged on days 0 and 6 of the trial for both groups; body temperature and patient reports of cough were collected 3x/day from day 0 – 6. The mean age and sex distribution between the HCQ and control arms were comparable.

      Findings

      There were 2 patients showing mild secondary effects of HCQ treatment. More importantly, while 4 patients in the control group progressed to severe disease, none progressed in the treatment group.<br /> TTCR was significantly decreased in the HCQ treatment arm; recovery from fever was shortened by one day (3.2 days control vs. 2.2 days HCQ, p = 0.0008); time to cessation of cough was similarly reduced (3.1 days control vs. 2.0 days HCQ, p = 0.0016).<br /> Overall, it appears that HCQ treatment of patients with mild COVID-19 has a modest effect on clinical recovery (symptom relief on average 1 day earlier) but may be more potent in reducing the progression from mild to severe disease.

      Study Limitations

      This study is limited in its inclusion of only patients with mild disease, and exclusion of those on any treatment other than the standard of care. It would also have been important to include the laboratory values of positive RT-PCR detection of SARS-CoV-2 to compare the baseline and evolution of the patients’ viral load.

      Significance

      Despite its limitations, the study design has good rigor as a double blind RCT and consistent symptom checks on each day of the trail. Now that the FDA has approved HCQ for treatment of COVID-19 in the USA, this study supports the efficacy of HCQ use early in treatment of patients showing mild symptoms, to improve time to clinical recovery, and possibly reduce disease progression. However, most of the current applications of HCQ have been in patients with severe disease and for compassionate use, which are out of the scope of the findings presented in this trial. Several additional clinical trials to examine hydroxychloroquine are now undergoing; their results will be critical to further validate these findings.

      Reviewed by Rachel Levantovsky as a part of a project by students, postdocs and faculty in the Immunology Institute at the Icahn school of Medicine at Mount Sinai.

    3. On 2020-04-01 16:34:11, user Sinai Immunol Review Project wrote:

      Study Description <br /> This is a randomized clinical trial of hydroxychloroquine (HCQ) efficacy in <br /> the treatment of COVID-19. From February 4 – February 28, 2020 142 <br /> COVID-19 positive patients were admitted to Renmin Hospital of Wuhan <br /> University. 62 patients met inclusion criteria and were enrolled in a <br /> double blind, randomized control trial, with 31 patients in each arm.

      Inclusion criteria:<br /> 1. Age >= 18 years<br /> 2. Positive diagnosis COVID-19 by detection of SARS-CoV-2 by RT-PCR<br /> 3. Diagnosis of pneumonia on chest CT <br /> 4. Mild respiratory illness, defined by SaO2/SPO2 ratio > 93% or <br /> PaO2/FIO2 ratio > 300 mmHg in hospital room conditions (Note: <br /> relevant clinical references described below.)<br /> a. Hypoxia is defined as an SpO2 of 85-94%; severe hypoxia < 85%. <br /> b. The PaO2/FIO2 (ratio of arterial oxygen tension to fraction of inspired<br /> oxygen) is used to classify the severity of acute respiratory distress <br /> syndrome (ARDS). Mild ARDS has a PaO2/FIO2 of 200-300 mmHg, moderate is <br /> 100-200, and severe < 100.<br /> 5. Willing to receive a random assignment to any designated treatment group; not participating in another study at the same time

      Exclusion criteria: <br /> 1. Severe or critical respiratory illness (not explicitly defined, <br /> presumed to be respiratory function worse than outlined in inclusion <br /> criteria); or participation in trial does not meet patient’s maximum <br /> benefit or safe follow up criteria<br /> 2. Retinopathy or other retinal diseases<br /> 3. Conduction block or other arrhythmias<br /> 4. Severe liver disease, defined by Child-Pugh score >= C or AST > twice the upper limit<br /> 5. Pregnant or breastfeeding<br /> 6. Severe renal failure, defined by eGFR <= 30 mL/min/1.73m2, or on dialysis<br /> 7. Potential transfer to another hospital within 72h of enrollment<br /> 8. Received any trial treatment for COVID-19 within 30 days before the current study

      All patients received the standard of care: oxygen therapy, antiviral <br /> agents, antibacterial agents, and immunoglobulin, with or without <br /> corticosteroids. Patients in the HCQ treatment group received additional<br /> oral HCQ 400 mg/day, given as 200 mg 2x/day. HCQ was administered from <br /> days 1-5 of the trial. The primary endpoint was 5 days post enrollment <br /> or a severe adverse reaction to HCQ. The primary outcome evaluated was <br /> time to clinical recovery (TTCR), defined as return to normal body <br /> temperature and cough cessation for > 72h. Chest CT were imaged on <br /> days 0 and 6 of the trial for both groups; body temperature and patient <br /> reports of cough were collected 3x/day from day 0 – 6. The mean age and <br /> sex distribution between the HCQ and control arms were comparable.

      Findings<br /> There were 2 patients showing mild secondary effects of HCQ treatment. More <br /> importantly, while 4 patients in the control group progressed to severe <br /> disease, none progressed in the treatment group.<br /> TTCR was significantly decreased in the HCQ treatment arm; recovery from fever <br /> was shortened by one day (3.2 days control vs. 2.2 days HCQ, p = <br /> 0.0008); time to cessation of cough was similarly reduced (3.1 days <br /> control vs. 2.0 days HCQ, p = 0.0016).<br /> Overall, it appears that HCQ treatment of patients with mild COVID-19 has a modest effect on clinical recovery (symptom relief on average 1 day earlier) but may be more <br /> potent in reducing the progression from mild to severe disease.

      Study Limitations <br /> This study is limited in its inclusion of only patients with mild disease, <br /> and exclusion of those on any treatment other than the standard of care.<br /> It would also have been important to include the laboratory values of <br /> positive RT-PCR detection of SARS-CoV-2 to compare the baseline and <br /> evolution of the patients’ viral load.

      Significance<br /> Despite its limitations, the study design has good rigor as a double blind RCT <br /> and consistent symptom checks on each day of the trail. Now that the FDA<br /> has approved HCQ for treatment of COVID-19 in the USA, this study <br /> supports the efficacy of HCQ use early in treatment of patients showing <br /> mild symptoms, to improve time to clinical recovery, and possibly reduce<br /> disease progression. However, most of the current applications of HCQ <br /> have been in patients with severe disease and for compassionate use, <br /> which are out of the scope of the findings presented in this trial. <br /> Several additional clinical trials to examine hydroxychloroquine are now<br /> undergoing; their results will be critical to further validate these <br /> findings.

      Reviewed by Rachel Levantovsky as a part of a project<br /> by students, postdocs and faculty in the Immunology Institute at the <br /> Icahn school of Medicine at Mount Sinai.

    4. On 2020-04-13 14:09:18, user Ian Sinclair wrote:

      This study seems to me potentially of enormous significance. I think it would gain greater acceptance if a) the authors explain why they chose to publish before they had reached the numbers specified in the protocol (100 for TAU and 100 for 4 mg group b) they say why they did not report the results for the 2 mg per day group c) they report the actual data on coughs temperature, numbers improved on radiology examination rather than just the significance levels d) they remedy a minor error in the summary (quotes 32 cases as against 31 e) they confirm that the measures were also made by staff who were blind to allocation f) they got themselves an editor who is a native English speaker. I absolutely do not think that the authors have anything to hide but they need to cope with a Western Audience that has been trained to be ultra critical, looking among other things for investigators who stop a trial the moment that it looks to be going their way. My guess is that this was not the case in this instance and that the study was running out of subjects or the authorities were asking for results or some other event that was out of the control of those running the trial. Given the potential world importance of this trial everyone should be trying to offer constructive suggestions for its greater acceptability rather than exercising their brains on ways in which mistakes might have been made.

    1. On 2020-03-26 15:11:11, user Sinai Immunol Review Project wrote:

      Study description: Plasma cytokine analysis (48 cytokines) was performed on COVID-19 patient plasma samples, who were sub-stratified as severe (N=34), moderate (N=19), and compared to healthy controls (N=8). Patients were monitored for up to 24 days after illness onset: viral load (qRT-PCR), cytokine (multiplex on subset of patients), lab tests, and epidemiological/clinical characteristics of patients were reported.

      Key Findings:<br /> • Many elevated cytokines with COVID-19 onset compared to healthy controls <br /> (IFNy, IL-1Ra, IL-2Ra, IL-6, IL-10, IL-18, HGF, MCP-3, MIG, M-CSF, G-CSF, MIG-1a, and IP-10).<br /> • IP-10, IL-1Ra, and MCP-3 (esp. together) were associated with disease severity and fatal outcome. <br /> • IP-10 was correlated to patient viral load (r=0.3006, p=0.0075).<br /> • IP-10, IL-1Ra, and MCP-3 were correlated to loss of lung function (PaO2/FaO2 (arterial/atmospheric O2) and Murray Score (lung injury) with MCP-3 being the most correlated (r=0.4104 p<0.0001 and r=0.5107 p<0.0001 respectively).<br /> • Viral load (Lower Ct Value from qRT-PCR) was associated with upregulated IP-10 only (not IL-1Ra or MCP-3) and was mildly correlated with decreased lung function: PaO2/FaO2 (arterial/atmospheric O2) and Murray Score (lung injury).<br /> • Lymphopenia (decreased CD4 and CD8 T cells) and increased neutrophil correlated w/ severe patients.<br /> • Complications were associated with COVID severity (ARDS, hepatic insufficiency, renal insufficiency).

      Importance: Outline of pathological time course (implicating innate immunity esp.) and identification key cytokines associated with disease severity and prognosis (+ comorbidities). Anti-IP-10 as a possible therapeutic intervention (ex: Eldelumab).

      Critical Analysis: Collection time of clinical data and lab results not reported directly (likely 4 days (2,6) after illness onset), making it very difficult to determine if cytokines were predictive of patient outcome or reflective of patient compensatory immune response (likely the latter). Small N for cytokine analysis (N=2 fatal and N=5 severe/critical, and N=7 moderate or discharged). Viral treatment strategy not clearly outlined.

    1. On 2024-11-08 19:59:59, user Andre Boca Ribas Freitas wrote:

      Important Observations on Underreported Chikungunya Mortality in Light of Global Burden Analysis

      Dear Authors,

      I thoroughly appreciated your recent preprint on the global burden of chikungunya and the potential benefits of vaccination. Your work provides critical insights into the widespread impact of this disease and emphasizes the significant potential of vaccine interventions.

      However, I wanted to highlight a critical issue that our research and that of others in the field have identified: the substantial underreporting of chikungunya-related mortality across many regions. While chikungunya is often categorized as a non-fatal disease, a growing body of evidence reveals severe and sometimes fatal cases that frequently go unrecorded by epidemiological systems. Our recent studies in Brazil documented excess mortality rates from chikungunya far surpassing those officially reported, with mortality rates up to 60 times higher than recorded by standard surveillance systems?Freitas et al., 2024?. Additionally, studies like those by Mavalankar et al. (2008) in India and Beesoon et al. (2008) in Mauritius underscore the elevated mortality associated with chikungunya during epidemic outbreaks, further reinforcing this critical gap in mortality surveillance.<br /> This growing evidence highlights the critical need for increased investment in molecular diagnostics, integrated surveillance, and more comprehensive mortality tracking for chikungunya. These measures are essential for aligning public health responses with the true impact of the disease and ensuring the full scope of chikungunya’s burden is addressed.

      Thank you for advancing this essential conversation. Through improved surveillance and research collaboration, we can work toward effective strategies to mitigate the severe impact of chikungunya globally.

      Best regards,

      Dr. André Ricardo Ribas Freitas<br /> Faculty of Medicine, São Leopoldo Mandic, Campinas-SP, Brasil

      Freitas ARR, et al. Excess Mortality Associated with the 2023 Chikungunya Epidemic in Minas Gerais, Brazil. Front Trop Dis. 2024. doi: 10.3389/fitd.2024.1466207.

      Mavalankar D, Shastri P, Bandyopadhyay T, Parmar J, Ramani KV. Increased mortality rate associated with chikungunya epidemic, Ahmedabad, India. Emerg Infect Dis. 2008 Mar;14(3):412-5. doi: 10.3201/eid1403.070720. PMID: 18325255; PMCID: PMC2570824.

      Beesoon S, Funkhouser E, Kotea N, Spielman A, Robich RM. Chikungunya fever, Mauritius, 2006. Emerg Infect Dis. 2008 Feb;14(2):337-8. doi: 10.3201/eid1402.071024. PMID: 18258136; PMCID: PMC2630048.

      Manimunda SP, Mavalankar D, Bandyopadhyay T, Sugunan AP. Chikungunya epidemic-related mortality. Epidemiol Infect. 2011 Sep;139(9):1410-2. doi: 10.1017/S0950268810002542. Epub 2010 Nov 15. PMID: 21073766.

      Freitas ARR, Donalisio MR, Alarcón-Elbal PM. Excess Mortality and Causes Associated with Chikungunya, Puerto Rico, 2014-2015. Emerg Infect Dis. 2018 Dec;24(12):2352-2355. doi: 10.3201/eid2412.170639. Epub 2018 Dec 17. PMID: 30277456; PMCID: PMC6256393.

      Freitas ARR, Gérardin P, Kassar L, Donalisio MR. Excess deaths associated with the 2014 chikungunya epidemic in Jamaica. Pathog Glob Health. 2019 Feb;113(1):27-31. doi: 10.1080/20477724.2019.1574111. Epub 2019 Feb 4. PMID: 30714498; PMCID: PMC6427614.

    1. On 2024-12-03 21:03:36, user xPeer wrote:

      Courtesy review from xPeerd.com

      This manuscript introduces DeepEnsembleEncodeNet (DEEN), an innovative polygenic risk score (PRS) model integrating autoencoders and fully connected neural networks (FCNNs) to address limitations of existing PRS methods. By disentangling dimensionality reduction and predictive modeling, DEEN enables the capture of both linear and non-linear SNP effects, improving prediction accuracy and risk stratification for binary (e.g., hypertension, type 2 diabetes) and continuous traits (e.g., BMI, cholesterol). Evaluation using UK Biobank and All of Us datasets highlights superior performance over established methods. While conceptually and methodologically compelling, areas such as interpretability, generalizability across diverse populations, and computational efficiency warrant further refinement.

      Major Revisions<br /> 1. Interpretability and Practicality<br /> Black-Box Concerns: The complexity of the DEEN model limits its interpretability compared to simpler PRS methods like Lasso or PRSice. While the manuscript acknowledges this limitation, incorporating efforts to visualize model predictions (e.g., feature importance maps or SNP clustering analysis) would enhance its usability (Section: Discussion, p.16).<br /> Clinical Translation: The manuscript emphasizes the potential of DEEN for clinical utility but lacks discussion on the challenges of implementing deep learning models in healthcare. Addressing regulatory barriers and clinician engagement would add value (Section: Discussion, p.17).<br /> 2. Population Generalizability<br /> Demographic Bias: Both datasets used (UK Biobank, All of Us) consist predominantly of European-ancestry individuals. This limits the model's applicability to global populations. Expanding the discussion on efforts to improve DEEN’s cross-ancestry generalizability is essential (Section: Results, p.11).<br /> Validation Across Diverse Cohorts: While DEEN is validated on two datasets, additional external validations across non-European populations would strengthen claims of generalizability and reliability.<br /> 3. Comparative Analyses<br /> Missing Baseline Methods: Although DEEN is compared with multiple PRS methods, inclusion of additional machine learning benchmarks (e.g., gradient boosting models, convolutional neural networks for SNP effects) would better contextualize DEEN’s advantages (Section: Results, p.8).<br /> Risk Stratification Assessment: The risk stratification results are promising but need more rigorous evaluation metrics beyond odds ratios, such as net reclassification improvement (NRI) or integrated discrimination improvement (IDI).<br /> 4. Computational Efficiency<br /> Resource Requirements: DEEN’s reliance on high-performance computing resources (e.g., GPU usage) is noted but not sufficiently quantified. Providing benchmarks of computational costs and runtime against alternative methods is crucial for practical implementation (Section: Methods, p.19).<br /> Optimization: While grid search was used for hyperparameter tuning, exploring automated optimization frameworks (e.g., Bayesian optimization) could reduce computational overhead.<br /> 5. Data Filtering and Variant Selection<br /> Potential Bias from Variant Filtering: The preselection of SNPs based on p-values may exclude rare variants or those with small effects. A sensitivity analysis on SNP filtering thresholds would clarify the robustness of DEEN’s predictive power (Section: Methods, p.20).<br /> Minor Revisions<br /> 1. Typos and Formatting<br /> Figure Legends: Some figures (e.g., Figure 5) lack clear explanations of axes and statistical methods.<br /> Grammar: Line 124: Replace "similarly drive CRC progression" with "similarly drive progression."<br /> 2. AI Content Analysis<br /> Estimated AI-Generated Content: ~20-25%.<br /> Implications: Repetitive phrasing in methodological descriptions and literature summaries suggests potential AI assistance. While the technical content appears valid, manual rephrasing can enhance originality and scientific depth.<br /> 3. Statistical Reporting<br /> Insufficient Confidence Intervals: Odds ratio enrichment results lack 95% confidence intervals in several places, undermining statistical rigor (Section: Results, p.9).<br /> Inconsistent Metric Definitions: Terms like “improved R²” and “higher AUC” are used loosely. Precise numerical values and effect size comparisons would improve clarity.<br /> 4. Terminology Consistency<br /> Key terms like "dimensionality reduction" and "risk stratification" should be consistently defined and applied across sections to avoid ambiguity.<br /> Recommendations<br /> Enhance Model Interpretability:

      Integrate explainability tools (e.g., SHAP values, visualization of autoencoder layers) to clarify how SNPs influence predictions.<br /> Discuss the potential for hybrid models balancing interpretability and performance.<br /> Address Demographic Bias:

      Validate DEEN using datasets from underrepresented populations (e.g., African, Asian ancestries).<br /> Incorporate transfer learning techniques to enhance generalizability.<br /> Benchmarking and Evaluation:

      Compare DEEN against additional advanced machine learning models for PRS.<br /> Introduce advanced evaluation metrics like NRI and IDI to strengthen claims.<br /> Refine Computational Analysis:

      Provide detailed resource utilization benchmarks.<br /> Explore alternative hyperparameter optimization methods to improve training efficiency.<br /> Expand Data Analysis:

      Perform a sensitivity analysis on variant filtering thresholds.<br /> Investigate the inclusion of rare variants to improve model robustness.

    1. On 2022-07-10 23:39:20, user Charles Warden wrote:

      Hi,

      Thank you very much for posting this preprint.

      I consider the topics raised by this study to be important and interesting.

      However, I have some comments and questions:

      1) I agree that confirmation bias can be a contributing factor. However, I think true limitations in utility are also important. So, I am not sure if I completely agree with the statement "When results were not consistent with participant’s personal or family history, many participants found reasons to dismiss or discredit these results. This indicates a role for confirmation bias in responses to [self-initiated] PRS." For example, I might really want to understand the genetic basis for a disease, but the percent heritability explained by the PRS may be low and I could therefore be disappointed with the usefulness of a PRS due to a discordant result.

      I have a blog post where I share my impute.me scores (along with others):

      https://cdwscience.blogspot.com/2019/12/prs-results-from-my-genomics-data.html

      I don't know if I would exactly say my response was "negative," but I certainly got the impression the PRS that I saw may have limited utility. In that sense, my view of the method was not positive, even if it did not evoke a strong emotional "negative" response.

      Within that blog post, “ulcerative colitis” would be an example where there were different PRS for the same disease but very different percentiles (for the same SNP chip). So, I would consider that an example of the reaction that is described being due to something other than confirmation bias.

      2) Did the interviewers respond when there were possible points of misunderstanding during the interview process?

      It was acknowledged as a limitation in the discussion: "the researchers did not have access to participant’s PRS results and were unable to evaluate people’s understanding of their results".

      However, it seems like that could be important. For example, there is a quote "Unfortunately, I do regret getting a PRS… I would have rather not known. I like uncertainty". Assuming that there were appropriate limitations to communicate, I believe a response from the interviewer might cause that quote to no longer reflect the subject’s opinion.

      In general, there appears to be a noticeable emphasis on mental health in the article. My opinion is that this is an area where limitations are particularly important. If it helps, I think there are some additional details in this blog post for the book Blueprint.

      In terms of my own impute.me results, I thought the "anxiety" PRS seemed reasonable (to the best of my ability to assess that). However, I also thought changes in conditions over time were important, and I thought there was potential for misuse.

      3a) I think it is a minor point, but I don't remember receiving an invite to join a Zoom meeting for a discussion about my impute.me results.

      I hope that I was one of the 209 candidates, but I was not sure if I could confirm that. I also noticed mention of categories like “medium” or “low” for one quote referencing a z-score of 2.5, but I only saw the continuous score distribution in the screenshots from my blog post.

      3b) Perhaps more importantly, I tried to go back to sign in to check if I missed something.

      In the Folkersen et al. 2020 paper, the link provided is for https://www.impute.me/. However, that link currently re-directs to a Nucleus website (https://mynucleus.com/).

      Can you please provide some more information about the re-direction of the impute.me link?

      For example, I submitted an e-mail to register on the new website, but I don't think I can see my earlier results anymore?

      Additionally, I was confused when I couldn’t find the GitHub code provided with that paper: https://github.com/lassefolkersen/impute-me

      4) Finally, but I don't think either of the 2 models that I see ("dismissed medical concerns" and "medical distrust") are a great description for myself. I think something like "curiosity" and "critical assessment" would be more appropriate for myself.

      For example, I wouldn't say I distrust the healthcare system or medical research broadly, but I do think feedback and engagement is important. Thus, when I encounter problems, I submit reports to FDA MedWatch. Likewise, I contribute data/experience to projects like PatientsLikeMe.

      Thanks Again,<br /> Charles

    1. On 2022-08-09 12:40:13, user PhillyPharmaBoy wrote:

      The authors conducted a thorough evaluation of the impact of ivermectin on SARS-CoV-2 clearance. On the surface their results differ from those of Krolewiecki, et al. (below). In a post hoc analysis these investigators found that ivermectin accelerated viral decay when drug concentration (4 hr) exceeded 160 ng/ml. It would be useful for the PLATCOV Group to mention this study and discuss potential reason(s) for the discrepancy.

      https://www.sciencedirect.c...

    1. On 2020-04-30 02:42:13, user Tyler Chen wrote:

      I appreciate the authors’ urgency in addressing SARS-CoV-2 decontamination for reuse of N95 filtering facepiece respirators (FFRs). In the spirit of that urgency and health impacts, I note two concerns with the current preprint that could accidentally cause confusion: (1) The paper claims N95 filtration is preserved after microwave-generated steam, whereas the test listed in the methods was a TSI quantitative fit test, which is primarily designed to test fit, not necessarily filtration. (2) The paper’s claim of a universally accessible N95 decontamination protocol may accidentally overstate the N95 models for which this protocol is verified. N95 models vary widely in their construction and resistance to steam heat, so any models other than the one used in this experiment will likely require thorough testing before this method is applied.

      I would suggest that the authors make the following changes:<br /> (1) Clarify whether or not filtration is verified at larger particle sizes and charge (e.g. 0.26 microns, uncharged). If filtration is not yet verified at larger particle sizes, this test may be important to verify N95 performance following microwave steam treatment.<br /> To provide some background: The TSI 8026 Particle Generator generates 0.04 micron particles [1] that are intended to be readily filtered by the N95, and are assumed to only enter the N95 through gaps in the face seal and not through the mask material itself [2]. It is possible for N95 FFRs to pass quantitative fit tests while still failing filtration tests at different particle sizes--one study “observed [protection factors] <100 even for subjects who passed fit testing (fit factor > 100)” [3]. Therefore, fit testing using the 8026 particle generator does not imply that N95 filtration is necessarily preserved at larger particle sizes which are most relevant for filtration effectiveness in the SARS-CoV-2 pandemic, especially given the fact that the decontamination treatment may shift what particle size is most penetrating for the N95. Recent non-peer-reviewed research shows N95 material suffering a decrease in filtration of 0.26 micron particles after 4-5 cycles of 10min stovetop steam treatment [4], though it is unclear from this manuscript if the MGS treatment did not reach this limit, or that the limit was not observed due to the different particle size used. Therefore, testing for quantitative fit should perhaps be supplemented by filtration tests at larger particle diameters (whether from this study or by citing others), especially when a decontamination process is involved such as steam heat that has an unknown effect on the most penetrating particle size for the N95. Given the potential for widespread implementation of this protocol it seems important that this point be clarified.

      (2) Secondly, it may be important to notify readers that the performance of the N95 model in this paper likely cannot be generalized to all N95 models without further testing. N95 models vary widely in construction and resistance to steam, and each model should be individually verified to maintain both fit and filtration by this protocol before use. This is supported by the fact that N95 models can vary widely with respect to the most penetrating particle size, and each country may only have access to certain N95 models [3,4,5]. Furthermore, there is literature evidence that the mask performance in response to steam and heat also varies across N95 models (see the table of results in Appendix B https://www.n95decon.org/fi... "https://www.n95decon.org/files/heat-humidity-technical-report)"). Therefore, it is important to clarify in the text that this method is not yet universally-validated -- N95 fit has only been verified for the 3M 1860 molded N95 in particular, and other models are likely to have significantly different behavior. Independent verification of both fit and filtration may be needed for other N95 models.

    1. On 2020-04-30 21:06:27, user Tim Lawes wrote:

      A great paper by very respected researchers and clinicians, ruined by a bizarre press-release saying COVID-19 'as deadly as Ebola'. Let's take a fact-check on that one:<br /> 1. Case fatality rates not equivalent: Ebola CFR 50-75% in recent outbreaks, not 33% as in COVID-19. If referring to high-income country (HIC) stats only, talking handful cases treated in HIC with Ebola, all working age occupationally fit health workers, CFR ~18% (n=5/27 quoted).<br /> 2. Totally different age of death: Ebola median 30-35 yrs, COVID 80yrs. Ebola 95% deaths <60 yrs, Covid ~10% < 60yrs.<br /> 3. Translate age at death to Years of Life Lost (YLL): comparing age at death profile provided by Doherty et al to Ebola papers, each Ebola death 'costs' 45 YLL, vs. each COVID death costs 1.4 YLL.<br /> 4. Translated to population impact. To exceed an equivalent burden of YLL per capita in West Africa in 2013-16 would require >1 million deaths in UK from COVID-19.

      To compare the mortality profiles of Ebola and COVID-19 is at best bad science, at worst an example of misinformation that perpetuates global health inequities. I don't imagine authors intended this, but I'm afraid its this sort of comment that creates hysteria, rather than appropriate responses. As a paediatrician we are seeing children coming to harm from avoiding hospital and not being seen in community due to misjudged risk assessments. A genuine thank you for your contribution to science, but please ensure reporting is responsible.

    1. On 2020-05-11 19:29:55, user Charles Warden wrote:

      Thank you for posting this pre-print.

      I have a some questions:

      1) Are the p-values significant after a Bonferroni correction?

      2) Are you focusing on APOE because it is the most significant result for a relatively common SNP?

      3) How are you defining the COVID-19 severity? Table 1 makes it look like you are comparing the proportion of positive cases for the 3 APOE genotype combinations (E3/E3, E3/E4, and E4/E4). However, that would be different than filtering for positive cases, and then looking for an association with a variable that describes the severity of the case.

      4) I thought it was good and interesting that you excluded subsets of individuals to try to check for confounders. However, it looks like the number of APOE E4/E4 goes from 37 total (with none removed due to dementia) to 22 and then back up to 32 and 35. If you want to adjust for all individuals with chronic diseases, then I would have expected that to be cumulative. What happens if you remove all of the patients with chronic disease and then test within the highest age range?

      5) I would expect most normalization to reduce but not completely remove the effect being adjusted for. Is is possible to look at older individuals as a separate bin (perhaps in a "Table 2") as evidence that the age-adjustment was effective? I could imagine this (along with what I suggested in 3)) might cause some issues with sample size, but there are usually some limitations for every study mentioned in the discussion.

      6) Is there any sort of independent validation that you can do in another cohort? As more cases are known, do you plan to check the subset of samples that currently test negative but later test positive as a type of test dataset?

      7) I usually think of the "Data Availability" as being for new data (rather than public data), but I am glad that you mentioned you UK Biobank application. However, since this wasn't quite what I expected in the Data/Code link, can you share the code that you used for analysis (assuming it can be reproduced by anyone else with similar access)?

      Thank you again for sharing your research.

    1. On 2020-04-12 13:04:20, user japhetk wrote:

      I think BCG studies' conclusions came from spurious correlations regardless of BCG has an effect or not.<br /> Anyway, now data from South America and Africa keeps coming and although, it may depend on the methods of analyses, my analyses show already the number death 13 days after the 100th case, and whether BCG is currently done is no longer significantly associated without correcting anything (p = 0.291, ANOVA).And after the number of tourists, population,total GDP, temperature of March, ratio of 65 years or older are corrected the associations show get even weaker (P = 0.621, ANCOVA).Among these covariates, the number of tourists has a robust significant effect on the number of deaths 13 days after the 100th case (0.00016), and the ratio of 65 years or older and population have significant effects, too (P= 0.024, 0.05, respectively). Total GDP (not GDP per capita) and the number of tourists have a close relationship (r = 0.82). <br /> The date when the 100th case was detected show more robust relationship with the BCG policy (currently performed or not), but after the correction of abovementioned covariates, this association also became insignificant )(p= 0.167). But this kind of relationship with the date of 100th case is seen in the case of variables that are specifically associated with Western countries, such as the consumption of wine)(the consumption of wine per capita shows robust association with the date of the 100th case after correction of population (p = 0.0002, more wine, the faster the detection of 100th case). <br /> So, my guess is that this spurious correlation mainly came from the fact the countries which abandaned BCG policies are more developed and more popular from tourists (which increased the faster and more and multiple spread of the virus) and also show greater aging (which increased the risk) and also they locate in western countries which were confident of their medical system and which were away from Asia and which were less alert to this infectious disease from China. The habit of wearing mask, hug, handshake or religious ceremonies might affect, too. <br /> In the cruise ship Diamond Princess, Japanese who were put in the same ship with Westerners show greater mortality rate than Westerners. And in a lot of Western European countries, the risk population (elderly) has experiences of BCG (they are classified as "past BCG", but in fact most of risk populations are experienced with BCG). So, the BCG hypothesis is not consistent with these facts, either. <br /> I am not saying BCG doesn't work, I am saying you cannot conclude anything from these uncontrolled studies which lacks in numerous potential confounding variables. Just let's wait for results of RCTs.

      Here's my data if I haven't made any mistakes.You can see the apparent little association with BCG policy and number of the death (13 days after the 100th case) as of 11th April.

      O Iran 291<br /> X Spain 288<br /> O China 259<br /> X Italy 233<br /> O Turkey 214<br /> O Algeria 130<br /> X United Kingdom 103<br /> O Indonesia 102<br /> O Brazil 92<br /> X France 91<br /> X Netherlands 76<br /> X United States 69<br /> O Dominican Republic 68<br /> X Ecuador 62<br /> O Portugal 60<br /> O Morocco 59<br /> O Philippines 54<br /> O Ukraine 45<br /> O Iraq 42<br /> O South Korea 35<br /> X Switzerland 33<br /> O Argentina 31<br /> O Egypt 30<br /> O Panama 30<br /> O India 29<br /> O Mexico 29<br /> X Canada 27<br /> O Hungary 26<br /> O Honduras 24<br /> O Peru 24<br /> O Romania 24<br /> O Albania 22<br /> O Greece 22<br /> O Ireland 22<br /> O Tunisia 22<br /> X Luxembourg 22<br /> O Bosnia and Herzegovina 21<br /> X Belgium 21<br /> O Burkina Faso 19<br /> O Macedonia 17<br /> X Andorra 17<br /> O Colombia 16<br /> O Poland 16<br /> O Afghanistan 15<br /> O Cuba 15<br /> O Moldova 15<br /> O Pakistan 13<br /> X Denmark 13<br /> O Bulgaria 10<br /> O Malaysia 10<br /> O Russia 10<br /> X Lebanon 10<br /> X Sweden 10<br /> O Lithuania 9<br /> O Mauritius 9<br /> O Azerbaijan 8<br /> X Austria 8<br /> X Israel 8<br /> O Chile 7<br /> O Kazakhstan 7<br /> O Venezuela 7<br /> X Australia 7<br /> O Croatia 6<br /> O Ghana 6<br /> O Japan 6<br /> O Thailand 6<br /> X Czech Republic 6<br /> X Norway 6<br /> O Jordan 5<br /> O South Africa 5<br /> O Sri Lanka 5<br /> O Taiwan 5<br /> O United Arab Emirates 5<br /> X Germany 5<br /> X Slovenia 5<br /> O Saudi Arabia 4<br /> O Uruguay 4<br /> O Armenia 3<br /> O Cote d'Ivoire 3<br /> O Uzbekistan 3<br /> X Finland 3<br /> O Costa Rica 2<br /> O Oman 2<br /> O Senegal 2<br /> O Estonia 1<br /> X New Zealand 1<br /> O Cambodia 0<br /> O Kuwait 0<br /> O Latvia 0<br /> O Malta 0<br /> O Qatar 0<br /> O Singapore 0<br /> O Vietnam 0<br /> X Slovakia 0

    1. On 2019-07-04 23:42:29, user Guyguy wrote:

      EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI

      Thursday, July 4th, 2019

      The epidemiological situation of the Ebola Virus Disease dated 3 July 2019:<br /> Since the beginning of the epidemic, the cumulative number of cases is 2,382, of which 2,288 are confirmed and 94 are probable. In total, there were 1,606 deaths (1,512 confirmed and 94 probable) and 666 people healed.<br /> 420 suspected cases under investigation;<br /> 13 new confirmed cases, including 4 in Beni, 2 in Butembo, 2 in Katwa, 2 in Kalunguta, 1 in Mandima, 1 in Biena and 1 in Mabalako;<br /> 8 new confirmed cases deaths:<br /> 2 community deaths, including 1 in Butembo and 1 in Mandima;<br /> 6 deaths in Ebola Treatment Centers including 3 in Beni, 2 in Mabalako and 1 in Katwa;<br /> 11 people cured out of Ebola Treatment Center including 7 in Mabalako, 3 in Katwa and 1 in Beni. <br /> 128 Contaminated health workers: One health worker, vaccinated, is one of the new confirmed cases in Beni. The cumulative number of confirmed / probable cases among health workers is 128 (5% of all confirmed / probable cases) including 40 deaths.<br /> Source: Ministry of Health press team on the state of the response to the Ebola epidemic in the Democratic Republic of Congo.

    2. On 2019-07-21 03:12:01, user Guyguy wrote:

      EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI

      Saturday, July 20th, 2019

      The epidemiological situation of the Ebola Virus Disease dated 19 July 2019:<br /> Since the beginning of the epidemic, the cumulative number of cases is 2,564, 2,470 confirmed and 94 probable. In total, there were 1,728 deaths (1,634 confirmed and 94 probable) and 726 people healed.<br /> 392 suspected cases under investigation;<br /> 18 new confirmed cases, including 7 in Beni, 3 in Mandima, 3 in Mabalako, 1 in Vuhovi, 1 in Butembo, 1 in Mambasa, 1 in Lubero and 1 in Masereka;<br /> 13 new confirmed cases deaths:<br /> 8 community deaths, including 4 in Beni, 2 in Mandima, 1 in Mabalako and 1 in Masereka;<br /> 5 Ebola Treatment Center (ETC) deaths, 2 in Mabalako, 2 in Beni and 1 in Katwa;<br /> 5 people recovered from ETCs, including 3 in Beni and 2 in Katwa.

      NEWS

      Minister of Health visits Beni<br /> The Minister of Health, Dr. Oly Ilunga Kalenga spent the day of Friday, July 19, 2019 in Beni where he visited the various field teams and the transit center whose capacity will be increased in the coming days.<br /> Following the resurgence of patients in Beni, Dr. Oly Ilunga said that one of the key lessons learned in this tenth epidemic is to rely on the health system. "If we really want to solve this epidemic and have a lasting impact, we need to strengthen the health system by working with the actors in this system and with the community," he said adding that this is how we can quickly stop this new outbreak in the city of Beni.<br /> He recalled that the declaration of this epidemic as an international public health emergency requires other countries to strengthen border surveillance, while for the response, the declaration recognizes the work that is being done and the performance of the response. managed to contain the epidemic in an extremely complex context.<br /> This statement also stresses the need for a response with greater coordination and consultation. Another point that Minister Oly Ilunga always insists on is the accountability of all actors on the ground, the sharing of information, the measurement of performance, and the use of data to guide and improve actions on ground.

      168,746 Vaccinated persons.

      76,632,731 Controlled people.

      138 Contaminated health workers<br /> The cumulative number of confirmed / probable cases among health workers is 138 (5% of all confirmed / probable cases) including 41 deaths.

      Source: Ministry of Health press team on the state of the response to the Ebola epidemic in the Democratic Republic of the Congo

    3. On 2019-07-19 16:09:15, user Guyguy wrote:

      EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI

      Thursday, July 18, 2019

      The epidemiological situation of the Ebola Virus Disease dated 17 July 2019:

      Since the beginning of the epidemic, the cumulative number of cases is 2,532, 2,438 confirmed and 94 probable. In total, there were 1,705 deaths (1,611 confirmed and 94 probable) and 718 people cured.<br /> 402 suspected cases under investigation;<br /> 10 new confirmed cases, including 4 in Beni, 2 in Butembo, 2 in Mandima, 1 in Vuhovi and 1 in Mutwanga;<br /> 7 new confirmed case deaths:<br /> 4 community deaths, 2 in Beni, 1 in Mandima and 1 in Vuhovi;<br /> 3 ETC deaths including 1 in Beni, 1 in Katwa and 1 in Mabalako;<br /> 1 person healed out of Ebola Treatment Center (ETC) Butembo.

      NEWS

      Cross-border collaboration<br /> Uganda's health authorities have launched investigations to find the contacts of a patient who died at the ETC in Beni on July 15, 2019, who had spent a day in Kasese district in Uganda a few days earlier. The patient is a Beni shopkeeper who went to the Mpondwe market in Kasese on Thursday, July 11 before returning to Beni on Friday, July 12. She was a regular at the Kasese market where she bought her goods, including fish.<br /> To enter Uganda, she did not go through a formal entry point where there was a health check, which did not allow health teams to detect her. However, after her admission to the ETC of Beni, she informed the medical teams of her trip to Kasese and the teams then alerted the Ugandan authorities. During her visit to the market, she would have vomited four times, increasing the risk of contamination of people who had been in direct contact with her. So, the Ugandan Ministry of Health and WHO launched the investigation in Kasese to identify all contacts and vaccinate them.

      Point of entry surveillance<br /> From now on, the Port of Entry Monitoring Team will operate 24 hours a day at Goma International Airport. This surveillance began this Thursday, July 18, 2019.<br /> Port of Entry monitoring teams work night and day to find contacts from confirmed cases traveling in the area. It was the teams at the OPRP Health Checkpoint in the Nyragongo Health Zone who intercepted two bikers who had transported the deceased pastor and his mother. The two bikers were then directed to the vaccination teams to protect themselves against the disease. In general, when contacts from affected areas attempt to travel to Goma or Bunia and are intercepted at a checkpoint, they are usually returned to their original health zone to complete their 21-day follow-up period.

      Minister of Health on mission in Eastern DRC<br /> The Minister of Health, Dr. Oly Ilunga Kalenga arrived in Goma this Thursday, July 18, 2019. He spent the day on the ground to meet the different teams responsible for protecting the city against the virus. He began his visit through the Great Northern Control Point, called the OPRP, located in the Nyragongo Health Zone where the pastor from Butembo passed. In the same health zone, he also visited the new Ebola treatment center (ETC) still under construction. This ETC, built by Médecins Sans Frontières (MSF), will have a capacity of 60 beds.<br /> Its mission will continue throughout North Kivu and Ituri to ensure the proper conduct of the response.

      Press Conference in Goma: Minister of Health reassured people<br /> The coordination of the response held a press conference on Thursday in Goma following the WHO statement on the public health emergency of international concern.<br /> The Minister of Health reassured the population that the response teams and health staff in Goma City had been preparing for the arrival of sick people from areas affected by the epidemic. . Thus the person was very quickly identified and isolated, he said, adding that all the people who were in contact with this case were found and vaccinated. He took the opportunity to congratulate the health center nurse Afia Himbi who had quickly recognized this case and promised to meet him during his stay in Goma.<br /> He called on caregivers to remain vigilant and attentive. To the population, he recommended the respect of the measures of hygiene, the call of the green number if a relative is sick, the agreement to be vaccinated and to be followed during 21 days when one is identified like contact and the respect for safe and dignified burials.<br /> During this press conference, Dr. Oly Ilunga also referred to the statement of the international expert committee on the public health emergency. For the Minister of Health, the DRC welcomed this statement, noting that for the DRC, the epidemic is a public health emergency with a risk of regional spread since its declaration in August 2018. "It is in this spirit coordinating the response has worked with international partners, such as WHO, UNICEF and others, "he said.<br /> He also pointed out that this declaration is of greater importance for the neighboring countries of the DRC. He reassured his foreign counterparts of the intensification of surveillance in the DRC. He recalled that WHO has advised against closing borders and restricting international movements of the population. He hopes that this declaration will not have too much impact on the lives of the population.

      THE RESPONSE TO THE OUTBREAK

      165,907 Vaccinated persons<br /> The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 19 May 2018.

      76 001 290<br /> Controlled people<br /> 80 entry points (PoE) and operational health checkpoints (PoC)

      137 Contaminated health workers<br /> One health worker, vaccinated, is one of the new confirmed cases of Mandima.<br /> The cumulative number of confirmed / probable cases among health workers is 137 (5% of all confirmed / probable cases), including 41 deaths.

    4. On 2019-07-21 06:26:16, user Guyguy wrote:

      ENGLISH

      OFFICIAL PRESS RELEASE RELATED TO THE EPIDEMIC OF EBOLA VIRUS DISEASE IN EASTERN DRC

      1. The Democratic Republic of the Congo takes note of the statement by the World Health Organization (WHO) that the current epidemic is a public health emergency of international concern and endorses the recommendations of the WHO Director-General not to impose travel and trade restrictions and stigmatization of populations already in need of assistance.

      2. The Democratic Republic of the Congo reiterates its strong commitment to continue the response to the Ebola virus epidemic and to strengthen cross-border control and control of major internal roads to ensure that no cases are omitted or escapes from the surveillance teams.

      3. The response to the Ebola Virus Disease outbreak is now under the direct supervision of His Excellency the President of the Republic. To this end, it was decided to entrust the responsibility of the Technical Secretariat of the Multisectoral Committee to a team of experts under the direction of Professor Jean Jacques MUYEMBE TAMFUM.

      4. This team of experts is responsible for coordinating all the activities for implementing the Ebola response strategy. The Technical Secretariat is in charge of putting in place all the innovative measures that are urgent and indispensable for the rapid control of the epidemic.

      5. His Excellency the President of the Republic reassures the Congolese people and the neighboring countries that the measures currently taken in connection with the response to the Ebola Virus Disease in the DRC are likely to eradicate this epidemic.

      Kinshasa, July 20th, 2019.

      Source: Office of the President of the Democratic Republic of the Congo

      ********************************<br /> FRENCH

      COMMUNIQUE OFFICIEL EN RAPPORT AVEC L'EPIDEMIE DE LA MALADIE A VIRUS EBOLA A L'EST DE LA RDC

      1. La République Démocratique du Congo prend acte de la déclaration de l'Organisation Mondiale de la Santé (OMS) faisant de l'épidémie actuelle une urgence de santé publique de portée internationale et fait siennes les recommandations du Directeur Général de l'OMS de ne pas imposer des restrictions des voyages et de commerce ainsi que la stigmatisation des populations se trouvant déjà dans le besoin d'une assistance.

      2. La République Démocratique du Congo réitère son ferme engagement à poursuivre la riposte à l'épidémie de la Maladie à virus Ebola et à renforcer le contrôle transfrontalier et celui des principales routes internes afin de veiller à ce qu'aucun cas ne soit omis ou n'échappe aux équipes de surveillance.

      3. La conduite de la riposte à l'épidémie de la Maladie à virus Ebola se fait désormais sous la supervision directe de Son Excellence Monsieur le Président de la République. A cet effet, il est décidé de confier la responsabilité du Secrétariat Technique du Comité Multisectoriel à une équipe d'experts, sous la direction du Professeur Jean Jacques MUYEMBE TAMFUM.

      4. Cette équipe d'experts a pour mission d'assurer la coordination de l'ensemble des activités de mise en oeuvre de la stratégie de riposte à la Maladie à virus Ebola. Le Secrétariat Technique est chargé de mettre en place toutes les mesures innovantes urgentes et indispensables au contrôle rapide de l'épidémie.

      5. Son Excellence Monsieur le Président de la République rassure les populations congolaises et les pays voisins que les mesures actuellement prises en rapport avec la riposte à la Maladie à virus Ebola en RDC sont de nature à éradiquer cette épidémie.

      Fait à Kinshasa, le 20 juillet 2019.

      Source: Cabinet du Président de la République Démocratique du Congo

    5. On 2019-07-18 18:38:41, user Guyguy wrote:

      EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI

      Show solidarity with the Congolese people in the 10th Ebola outbreak declared a health emergency of international concern: understand a qualitative study of variables of hospital activities on infection control practices in Kinshasa city

      Wednesday, July 17, 2019

      Statement Ebola outbreak in DRC as a health emergency of international concern

      Following the recommendations of the international expert committee, WHO declared on Wednesday, July 17, 2019, that the Ebola epidemic in the DRC was a health emergency of international concern.<br /> The Ministry of Health accepts the evaluation of the expert committee. The ministry hopes that this decision is not the result of the many pressures from different stakeholder groups who wanted to use this statement as an opportunity to raise funds for humanitarian actors despite the potentially harmful and unforeseen consequences for the affected communities that depend on them. greatly from cross-border trade for their survival.<br /> While the Government continues to openly share with partners and donors the way in which it uses the funds received, we hope that there will be greater transparency and accountability of humanitarian actors in their use of funds to respond. to this Ebola outbreak.<br /> The Ebola epidemic is above all a public health crisis that requires a response by actors with real technical expertise. However, the main difficulty is that this epidemic occurs in an environment characterized by problems of development and shortcomings of the health system.<br /> Furthermore, we regret that after spending almost a year in this epidemic, certain groups of people in the community continue to adopt irresponsible behavior that causes the geographical spread of the virus. It is important to remember that in the cases of Goma and Uganda, the patients knew that they were at risk but refused to respect the health recommendations and deliberately traveled to another area. The Government will consider what steps need to be taken to prevent these high-risk groups from continuing to spread the epidemic in the region.

      Follow-up of the situation of the pastor's contacts who traveled to Goma<br /> Vaccination around the confirmed Goma case continues at the Afia Himbi Health Center in the Goma Health Zone. All contacts in the city were found in less than 72 hours, including the motorcycle taxi driver that the pastor had used to get to the health center. The response teams from Beni and Butembo continue the investigations to trace the pastor's journey and identify his contacts in these two cities.

      The epidemiological situation of the Ebola Virus Disease dated 16 July 2019:<br /> Since the beginning of the epidemic, the cumulative number of cases is 2,522, 2,428 confirmed and 94 probable. In total, there were 1,698 deaths (1,604 confirmed and 94 probable) and 717 people cured.<br /> 374 suspected cases under investigation;<br /> 10 new confirmed cases, including 6 in Beni, 2 in Mabalako, 1 in Katwa and 1 in Mangurujipa;<br /> 10 new confirmed cases deaths:<br /> 5 community deaths, including 3 in Beni, 1 in Mabalako and 1 in Mangurujipa;<br /> 5 deaths at Ebola Treatment Center (ETC) including 4 in Beni and 1 in Katwa;<br /> 7 people cured out of Mabalako Ebola Treatment Center.

      No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 136 (5% of all confirmed / probable cases), including 41 deaths.

      Deaths and cures data recorded in ETCs for the period 9-11 July 2019 are now available and have been added to the summary table.<br /> In total, 12 deaths were recorded in ETC during this period:<br /> 7 deaths at the ETC de Beni<br /> 3 deaths at Butembo ETC<br /> 2 deaths at Katwa ETC<br /> In total, 7 cures were discharged from ETC during this period:<br /> 5 cured at Butembo ETC<br /> 1 cured at the ETC of Beni<br /> 1 cured at Katwa ETC

      75,697,081 Controlled people<br /> 80 entry points (PoE) and operational health checkpoints (PoC).

      Source: Ministry of Health press team on the state of the response to the Ebola epidemic in the Democratic Republic of the Congo

    6. On 2019-07-20 05:46:57, user Guyguy wrote:

      EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI

      Friday, July 19th, 2019

      The epidemiological situation of the Ebola Virus Disease dated 18 July 2019:<br /> Since the beginning of the epidemic, the cumulative number of cases is 2,546, of which 2,452 confirmed and 94 probable. In total, there were 1,715 deaths (1,621 confirmed and 94 probable) and 721 people healed.<br /> 478 suspected cases under investigation;<br /> 14 new confirmed cases, including 6 in Beni, 5 in Mandima, 1 in Katwa, 1 in Mabalako and 1 in Mambasa;<br /> 10 new confirmed cases deaths:<br /> 6 community deaths, 2 in Beni, 2 in Mandima, 1 in Mabalako and 1 in Mambasa;<br /> 4 CTE deaths, 2 in Butembo, 1 in Katwa and 1 in Mabalako;<br /> 3 people healed out of Beni ETC

      .167 152 Vaccinated persons

      76,319,878 Controlled people<br /> 80 entry points (PoE) and operational health checkpoints (PoC).

      138 Contaminated health workers<br /> One health worker, vaccinated, is one of the new confirmed cases of Mandima.<br /> The cumulative number of confirmed / probable cases among health workers is 138 (5% of all confirmed / probable cases) including 41 deaths.

      Source: Ministry of Health press team on the state of the response to the Ebola epidemic in the Democratic Republic of the Congo

    1. On 2020-05-07 00:09:49, user Charles Warden wrote:

      I think I saw something roughly similar in this Tweet:

      https://twitter.com/manuelr...

      However, I have the following questions:

      1) How are you taking into consideration lack of exposure? If you looked for a difference in prognosis among infected individuals, then that would provide a control that you know all individuals have been exposed to the virus. I realize this may not be exactly what you are looking for, but I would expect a small proportion of individuals having been exposed to the virus will make achieving significance for infected versus uninfected individuals more difficult.

      2) If you had antibody results, maybe this would help (even if that is also not perfect), but my understanding is that you are also not using that as a filter (which I am guessing is not available)?

      3) It looks like you considered Exome data. I think that this may be good because I would have guessed you might miss a signal with SNP chip data, if the relevant variants are not common (or at least not well characterized as part of larger haplotypes). However, is it possible that variant calling for most genes is less optimal with these genes? Is there any way to go back to the raw data and see if the variant calling strategy can change anything among infected individuals?

      4) If all of the above criteria are meet, do you need to consider non-genetic risk factors (such as age) into your model?

      5) A lack of a significant result is not the same as saying with high confidence that a hypothesis cannot be true. I think that you should communicate what you have observed in some way, but I think some caution might be needed to avoid confusion. For example, a reader from the general public might think you are confident that you have found results that contradict reports that ACE2 (and/or TMPRSS2) may be important for COVID-19 infections. My guess is this is not what you meant, but I wonder if the limitations to these results need to be emphasized more.

      If this provides me a way to ask these questions in a way that gets less attention from the general public, then I think it is good that you posted these results. Discussion about possible implications could be important, but my understanding is that this does not mean that this is strong evidence that the current public health recommendations should be changed (and I don’t want to cause any unnecessary confusion).

    1. On 2020-02-02 15:58:08, user Martin Modrák wrote:

      Summary: The provided analysis can IMHO be a helpful complement to other efforts to estimate incubation rate of 2019-nCoV. The uncertainty of estimates of incubation rate and other intervals provided in the abstract is likely greater then what is reported, the numbers thus should be treated with caution. Only cases outside of Wuhan up to January 24th 2020 are included (31 - 43 cases are available for the individual subanalyses).

      This review has been crossposted on pubPeer, medRxiv, prereview.org.

      Disclaimer: I lack background in epidemiology to let me evaluate whether the proposed modelling approach is a standard one, if much better tools are available or if there are possible issues with the underlying data. In the following, I therefore focus primarily on the statistical aspects of the method employed, without considering alternative approaches.

      The big picture:<br /> The main idea of the preprint is to use cases of 2019-nCoV reported in patients that spent only a short time in Wuhan to estimate incubation rate. The underlying assumption is that those patients could have been exposed to the virus only during their stay in Wuhan.

      Strengths:<br /> The approach is interesting in that it removes the need to directly guess when/how the patients got into contact with the virus. It is also conceptually simple and requires few additional assumptions.

      I find it great that multiple models for the time intervals are tested and reported. The fact that the models mostly agree increases my confidence in the results.

      I further congratulate the authors on being able to put the analysis together very quickly and provide a clear and concise manuscript. I am thankful they posted their results publicly as soon as possible.

      Limitations:<br /> The main disadvantage of the chosen approach is that it let's the authors to only use a fraction of the reported cases and that the approach is only valid on data from the early phase of the epidemic. Once more cases happen outside Wuhan, the number patients who have become infected elsewhere will increase and the approach of this preprint will no longer be applicable. This is however not strongly detrimental to the manuscript and it could hopefully serve as one of many approaches to estimate the characteristics of 2019-nCoV, each with its own strenghts and limitations.

      There are however some specific points I find problematic in the manuscript.

      1) Using AIC for model selection might be brittle, especially since the differences in AIC are very small and the AIC itself is a noisy measure. Using some sort of model averaging and/or stacking would likely be beneficial.

      2) Also, no explicit effort to verify that the models used are appropriate has been reported. A simple model check would be to overlay the actual data over Figure 1 (e.g. the empirical CDF produced assuming both exposure and onset happend in the middle of the interval). Similar effort could be useful for Figure 2.

      3) Taking 1 and 2 together implies there is substantial uncertainty about which model is the best. Further, no strong guarantees that at least one of the proposed models is appropriate were given. The uncertainty bounds computed using only the "best fit" model are therefore certainly overly optimistic as they ignore this uncertainty. While this is challenging to account for mathematically, I believe it should be reported prominently in the manuscript to avoid confusion.

      4) While using only visitors to Wuhan makes sense to estimate the incubation period, the estimates of time from illness onset to hospitalization and/or death would likely benefit from including all cases. I don't see why only using cases outside of Wuhan for these other estimates is beneficial. I can however see why incubation period might be the primary focus of the paper and therefore a dataset with cases in Wuhan was not constructed.

      5) For some reason the link to supplementary data is broken (probably not author's fault), so I cannot investigate the dataset. Code is also not available so it is hard to judge the modelling approach in detail.<br /> I have contacted the authors and will update this review if I receive that data and/or code.

      The only issue I feel strongly about in this manuscript is with the abstract, which should IMHO clearly state that only a small number of cases has been used and that the uncertainty is likely larger than what was computed. Otherwise the paper seems to be a good contribution to the global effort to understand 2019-nCoV.

    1. On 2020-03-22 20:13:37, user Sinai Immunol Review Project wrote:

      This study retrospectively evaluated clinical, laboratory, hematological, biochemical and immunologic data from 21 subjects admitted to the hospital in Wuhan, China (late December/January) with confirmed SARS-CoV-2 infection. The aim of the study was to compare ‘severe’ (n=11, ~64 years old) and ‘moderate’ (n=10, ~51 years old) COVID-19 cases. Disease severity was defined by patients’ blood oxygen level and respiratory output. They were classified as ‘severe’ if SpO2 93% or respiratory rates 30 per min.

      In terms of the clinical laboratory measures, ‘severe’ patients had higher CRP and ferritin, alanine and aspartate aminotransferases, and lactate dehydrogenase but lower albumin concentrations.

      The authors then compared plasma cytokine levels (ELISA) and immune cell populations (PBMCs, Flow Cytometry). ‘Severe’ cases had higher levels of IL-2R, IL-10, TNFa, and IL-6 (marginally significant). For the immune cell counts, ‘severe’ group had higher neutrophils, HLA-DR+ CD8 T cells and total B cells; and lower total lymphocytes, CD4 and CD8 T cells (except for HLA-DR+), CD45RA Tregs, and IFNy-expressing CD4 T cells. No significant differences were observed for IL-8, counts of NK cells, CD45+RO Tregs, IFNy-expressing CD8 T and NK cells.

      Several potential limitations should be noted: 1) Blood samples were collected 2 days post hospital admission and no data on viral loads were available; 2) Most patients were administered medications (e.g. corticosteroids), which could have affected lymphocyte counts. Medications are briefly mentioned in the text of the manuscript; authors should include medications as part of Table 1. 3) ‘Severe’ cases were significantly older and 4/11 ‘severe’ patients died within 20 days. Authors should consider a sensitivity analysis of biomarkers with the adjustment for patients’ age.

      Although the sample size was small, this paper presented a broad range of clinical, biochemical, and immunologic data on patients with COVID-19. One of the main findings is that SARS-CoV-2 may affect T lymphocytes, primarily CD4+ T cells, resulting in decreased IFNy production. Potentially, diminished T lymphocytes and elevated cytokines can serve as biomarkers of severity of COVID-19.

      This review was undertaken as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2020-04-22 09:23:55, user Dr Mubarak Muhamed khan wrote:

      I keenly read this manuscript. My views. Following are the limitations before coming to conclusions <br /> 1. This is still not a published study in any top journals and must be taken back based on following points<br /> 2. Although a good write up, it’s retrospective study with hurried conclusions<br /> 3. Selection criteria is just based on hospitalised COVID19 patients. And at which stage drugs administered is not clear in all three groups<br /> 4. Outcome criteria is only either death or discharge. What happened to those who got discharged ? Whether there was any hastening in improvement due to these drugs? Whether there is shortening of duration due to drugs from COVID positive to negative?<br /> 5. Whether these drugs have been tried as prophylaxis? Or only used in hospitalised patients? <br /> 6. All patients included are with mean age at 70 and with many comorbidities <br /> 7. What dosages used for hcq and azythromicin ? How many days treatment given?<br /> 8. What type of pharmacovigilance noted for all groups?<br /> 9. Whether at anytime drugs discontinued due to side effects?<br /> 10. What side effects were obvious during the treatment period?<br /> 11. When patients succumbed to mechanical ventilation, how and what type of dosages of these drugs given?

      *Although it is good retrospective study to know the effects of HCQ and HCQ+AZT in treatment of COVID 19 infected hospitalised patients.... it will be very much premature to conclude these drug’s role based on short experience and points raised above*

    2. On 2020-04-24 23:54:28, user Gunnar V Gunnarsson wrote:

      The conclution that HC causes higher risk of death is basically wrong due to a huge sampling bias. The problem lies in the fact that once people went on ventilators they where given HC or HC+AZ. This re-categorised the patients by increasing the number of high risk patients in the HC and HC+AZ groups making the No HC an invalid control group.

      Before ventilation the statistics was like this: (Table 4 in paper)

      HC: 90 - 9 (10.0%) deaths - 69 (76.6%) recover - 12 (13.3%) onto ventilation HC+AZ: 101 - 11 (10.9%) deaths - 83 (82.2%) recover - 7 (06.9%) onto ventilation No HC: 177 - 15 ( 8.4%) deaths - 137 (77.4%) recover - 25 (14.1%) onto ventilation

      We see that death-rate is about the same for all groups but HC+AZ seams to have the highest recovery rate but it might not be statistically significant.

      Now once people hit ventilation the re-categorisation occurs. More patients where given HC and HC+AZ which moved them from the No HC group to the HC or HC+AZ group. These groups therefore have a much higher % of ventilation patients because they where given the drugs after they hit ventilation.

      The following data can be derived from the paper but is not presented:<br /> Once people hit ventilation we have the following results.

      HC: 19 - 18 (95%) deaths - 1 (11%) recover HC+AZ: 19 - 14 (73%) deaths - 5 (27%) recover No HC: 6 - 3 (50%) deaths - 3 (50%) recover

      If you compare these 2 tables, you see that 25 patient with No HC reach ventilation. Once they reach ventilation, 19 of these where give HC or HC+AZ, thereby moved from the No HC group to the other two. 79.5% of all patients reaching ventilation died so arguably 14 patients that died where moved from the No HC group to the other 2 groups only once they reach the much higher risk state.

      Here are the number of people per group that got ventilation:

      HC: 97 - 19 (19.6%) got ventilation HC+AZ: 113 - 19 (16.8%) got ventilation No HC: 158 - 6 ( 3.4%) got ventilation

      So the conclusion that HC causes more death is basically wrong. All it shows is that people that need ventilation are more likely to die.

    3. On 2020-04-24 01:46:41, user Mike wrote:

      Note: the original version has been previously discussed at length. medrxiv is redirecting that paper to v2 (this paper), making those comments are no longer available. This is a link to those comments: https://disqus.com/home/dis...

      Below is my original comment with updates for this version of the paper


      This was certainly an interesting paper. They’ve done a lot of work and the findings are notable. IMHO it warrants as much attention as the original pro-HCQ study via Dr. Raoult (~3/15). While it is entertaining, I will add that it is not conclusive, nor without fault. A double-blind study is still required.

      It is important to note that this is version 2 of a previously released paper and it is much the same, with no major differences in the conclusions reached compared to v1. Therefore, my previous comment still holds true. Below I’ve included them followed by a new list of observations.

      Observations/Questions (updated)

      1. "hydroxychloroquine, with or without azithromycin, was more likely to be prescribed to patients with more severe disease” (p.12)<br /> 2. “as expected, increased mortality was observed in patients treated with hydroxychloroquine, both with and without azithromycin” (p.12) — I assume it’s expected because the patients given drugs were in a more severe state (and more likely die regardless of treatment)<br /> 3. "we cannot rule out the possibility of selection bias or residual confounding” (p.13)<br /> 4. demographic: 100% male, 66% black, median age ~70 (59 youngest); (Table 2, p.17)<br /> 5. uses PSM, which despite a common practice, could be considered controversial (https://gking.harvard.edu/f... "https://gking.harvard.edu/files/gking/files/psnot.pdf)")<br /> 6. Unless I missed it, I didn't see any specifics about how the treatments were administered.<br /> - How long before death were patients treated?<br /> - What was the quantity/frequency/duration of the treatments?<br /> - Were the treatments consistent between hospitals?<br /> 7. The rate of ventilation was less in HC+AZ (half of the HC and no-HC rates). Why was that? What does that suggest?<br /> 8. Although they were statistically insignificant, what was the result of the 17 women not included in the study?<br /> 9. Why does the paper seem to address political points? It seems like the Abstract is editorialized, which I'm not used to. The result seems to address topical issues of the times, having awareness of other similar studies being conducted, rather than a standalone independent study of its own. I interpret this as potential for some analysis/deciphering bias. I don't mind in the Discussion sentence as it's normal, I'm just not as accustomed to seeing it in the Abstract.

      New List of Observations

      1. There seems to be some bias in the number of healthy people with no-HC treatment, but left in the study. Those people are going to be unlikely to die to begin with. This is not a comparison of apples to apples.??

      To clarify:<br /> ?- Dramatic difference in percentage of people people that had fever temperatures (38.1-39.0ºC / 100.58-102.2ºF); HC:11.3%, HC+AZ:11.5%, no-HC:7.6%. There’s ~4% difference between treated and untreated fever temps (more likely to die) in favor of untreated cohort. ?<br /> - Compare that with the percentage of people that had normal temperatures (35-37ºC/95-98.6ºF);HC:56.7%, HC+AZ:52.2%, no-HC:61.4%. There’s a 5-9% difference between treated and untreated normal temps (likely to not die) in favor of untreated cohort.

      ??So in this study, there was a larger proportion of people that did not have fevers, suggesting the data may be padded. In absolute numbers it's approx. a 40 person swing, which is a fairly large percentage in such a small study/survey. Similar observations are for systolic blood pressure and breaths per minute. There appears to be more healthy people? again.

      2. Creatinine is created when muscles breakdown creatine. It’s a waste product removed by the kidneys. Levels are elevated when the kidneys begin to fail. Notice, there’s a much larger presence in the HC-groups, which suggests there was a larger percentage of these patients experiencing kidney failure.?

      3. There are an awful lot of missing data in solely the no-HC group for statistically significant criteria. For instance Erythrocytes (red blood cells that transport oxygen), there 11.4% (!) missing in no-HC patients, yet that category has a P-value of 0.001 (<0.05 is statistically significant).??

      Hermatocrit is the same way (missing 11.4% for no-HC). It’s also related to red blood cells, it is the ratio of red blood cells to the blood volume. Same missing amount for Leukocytes (white blood cells) test. And also Lymphocytes —white blood cells in lymphatic system, which transports fatty acids from the digestive system and white blood cells from the lymph nodes into the bones— not only has a lot of missing data, but the disproportional low count (<800 per mm^3) may warrant further investigation.

      4. Even looking at the statistically significant Cerebrovascular disease, there are a much larger percentage of HC-only patients per its cohort.?

      5. Table 4 describes a greater percentage of people using HC+AZ being discharged (recovering) w/o ventilation; 5% more than no-HC patients. Keep in mind that 30% of the no-HC patients were given AZ.

    4. On 2020-04-28 00:30:59, user Mark Reeder wrote:

      I am advocating that the authors, in the interest of public health, fill in the blanks of the following statement:<br /> "It was found that ___ of the 7 patients reclassified from the 'No HC' to the HC group (after ventilation began) died. Likewise, ___ of the 12 patients reclassified form the 'No HC group' into the 'HC+AZ' group died."<br /> Based on a comparison of Tables 4 and 3, the first blank must be either 6 or 7 whereas the second blank must be between 7 and 12, inclusive.

      If the groups are compared based on whether they were given the drug(s) PRIOR to ventilation or prior to discharge, the HC+AZ is better by either a 20% margin over the 'No HC' group or by a FACTOR of 6. <br /> To wit, let's assume that all 12 of reclassified as HC+AZ died. That would mean that only 2 in the original HC+AZ group died. Since we have no idea when the HC+AZ drug was administered to those who died without ventilation, a fair comparison would show that HC+AZ, one might justifiably count only the 2/90 (2.2%) in that group (excluding deaths w/o vent.) as having had HC+AZ early enough in treatment. It would also mean that 3+(6 or 7) + 12 out of 162 (13.6%, also excluding deaths prior to ventilation) eventually died. <br /> This would be a huge difference with HC+AZ coming out as a terrific alternative (factor of 6 better) if given early enough. By the same logic (pre-vent treatment only, excluding non-vent deaths), the worst case for HC+AZ would still mean a 20% IMPROVEMENT over the control group! <br /> But we cannot know unless the authors (or others) are ethical and transparent enough complete the sentence above. Even if they disagree with the foregoing analysis, what is the downside in providing those numbers?<br /> I understand the difficulty of dealing with imperfect data. But for that very reason, good science demands that all information be placed on the table.

    5. On 2020-04-22 00:53:03, user Mike Cee wrote:

      This paper is flawed and should be withdrawn immediately.<br /> 1) This paper is flawed due to the limitation discussed on page 12 about the likelihood that the HCQ group, "However, hydroxychloroquine, with or without azithromycin, was more likely to be prescribed to patients with more severe disease, as assessed by baseline ventilatory status and metabolic and hematologic parameters."<br /> Doctors working the front lines have already noted that patients at SYMPTOMS ONSET + 14 days should not be prescribed HCQ<br /> 2) The important grouping by number of days since SYMPTOM ONSET was left out of this study. Previous studies, while anecdotal, suggest that patients should be prescribed HCQ early because it is believed to prevent the virus from infecting the type 2 lung cell through the ACE2 receptor and thus stops the progression of the disease.<br /> 3) This study did not document the dosage given to the patients. That would have been a helpful inclusion so we could understand if the patients who died were actually poisoned by excessive the treatment.<br /> 4) HCQ prescribed in patients in the first week after SYMPTOMS ONSET to include a zinc supplement which anecdotal evidence suggests a dual function of the combination: The HCQ provides an avenue for the zinc to enter the Type 2 lung cell where it interferes with the virus replication process.

    6. On 2020-04-23 02:46:06, user Raspee wrote:

      (1) There appears to be a statistically significant imbalance in the arms with regard to disease severity.

      “However, hydroxychloroquine, with or without azithromycin, was more likely to be prescribed to patients with more severe disease, as assessed by baseline ventilatory status and metabolic and hematologic parameters.”

      The base line pulse oximetry data and baseline line absolute lymphocyte count (Table 2) - indicates a statistical difference at p = 0.024 - the subjects that received hydroxychloroquine had a worse baseline respiratory status - and a worse absolute lymphocyte count p = 0.021.

      This is an inherent bias in the design that has not been adequately addressed. The analysis should compare treatment in subjects with the same disease severity.

      (2) If we look at table 4 - (HC + AZ) - 82% were discharged without ventilation vs. 77% discharged without ventilation both in the HC and non- HC group - Apparently the HC + AZ group did better than the other two groups.

      This is supported by the observation that the adjusted HR for ventilation is 0.43 (0.16 - 1.12) - It was better than the control arm with regard to disease progression and no different than the control for death.

      So in patients that were sicker at baseline, HC + AZ appears to have had a better outcome - than the other two groups - with regard to being discharged without requiring an ICU admission.

      (3) Please provide a better justification to exclude the 17 women Please go back and perform the analysis including the 17 women.

      (4) What were the doses of azithromycin and hydroxychloroquine administered? How are the different doses and dose regimens adjusted in the analysis? Not everyone in the HQ and HQ + AZ groups were dosed in the same fashion. Is there a minimum number of doses that you used to include them in the treatment groups?

      (5) If the control group had less severe illness at presentation, it stands to reason that the mortality rate would be lowest in the control group.

      (6) Was there a sub analysis looking at impact of secondary bacterial pneumonia - which occurs in 5-15% of moderate to severe COVID-19 patients? Were the antibiotics utilized the same over the 3 cohorts or were they different?

      (7) How many patients were on ace inhibitors and/or angiotensin receptor blockers? Were these medications balanced in the 3 arms? What about corticosteroid use in the 3 cohorts? Was corticosteroid use balanced?

      (8) Please go back and re-run the analysis with an additional 14 days of COVID-19 data (using April 25th cut -off) as your sample size will undoubtedly be greater and we would expect that the HQ + AZ group will now have a p value < 0.05. for discharge without ventilation.

      (9) Please include length of stay in your analysis as well

      (10) Please include readmission rates to the hospital in your analysis

    7. On 2020-04-22 02:20:27, user Mike wrote:

      This was certainly an interesting paper. It's done a lot of work and the findings are notable. IMHO it warrants as much attention as the pro-HCQ study via Dr. Raoult. While it is entertaining, I will add that it is not conclusive, nor without fault. A double-blind study is still required, but it is worth the read.

      Observations/Questions:

      1. "hydroxychloroquine, with or without azithromycin, was more likely to be prescribed to patients with more severe disease”<br /> 2. "we cannot rule out the possibility of selection bias or residual confounding”<br /> 3. demographic: 100% male, 66% black, median age ~70 (59 youngest)<br /> 4. uses PSM, which despite a common practice, could be considered controversial (https://gking.harvard.edu/f... "https://gking.harvard.edu/files/gking/files/psnot.pdf)")<br /> 5. Unless I missed it, I didn't see any specifics about how the treatments were administered.<br /> - How long before death were patients treated? <br /> - What was the quantity/frequency of the treatments? <br /> - Were the treatments consistent between hospitals?<br /> 6. The rate of ventilation was less in HC+AZ (half of the HC and no-HC rates). Why was that and what does that suggest?<br /> 7. Although they were statistically insignificant, what was the result of the 17 women not included in the study?<br /> 8. Why does the paper seem to address political points? It seems like the Abstract is editorialized, which I'm not accustomed to. The Conclusions portion (and page after) seeming to address topical issues of the times. Perhaps this introduces my own subjective bias, but I infer potential for analysis/deciphering bias when the study shows awareness of other controversial studies being conducted, rather than being a standalone independent study of its own; essentially, it leaves me to question motivations of the author, rather than that motivation being scientific discovery. I don't mind such commentary in the Discussion section, I'm just not as accustomed to seeing it in the Abstract.

    8. On 2020-04-22 01:10:17, user Gunnar V Gunnarsson wrote:

      After reading the paper I unfortunately find the usage of data to be misleading and I think you might have drawn the wrong conclusions.

      The problem lies in the fact that once people went on ventilators they where given HC or HC+AZ. This re-categorised the patients by increasing the number of high risk patients in the HC and HC+AZ groups making the No HC an invalid control group.

      Before ventilation the statistics was like this: (Table 4 in paper)

      HC: 90 - 9 (10.0%) deaths - 69 (76.6%) recover - 12 (13.3%) onto ventilation HC+AZ: 101 - 11 (10.9%) deaths - 83 (82.2%) recover - 7 (06.9%) onto ventilation No HC: 177 - 15 ( 8.4%) deaths - 137 (77.4%) recover - 25 (14.1%) onto ventilation

      We see that death-rate is about the same for all groups but HC+AZ seams to have the highest recovery rate but it might not be statistically significant.

      Now once people hit ventilation the re-categorisation occurs. More patients where given HC and HC+AZ which moved them from the No HC group to the HC or HC+AZ group. These groups therefore have a much higher % of ventilation patients because they where given the drugs after they hit ventilation.

      The following data can be derived from the paper but is not presented:<br /> Once people hit ventilation we have the following results.

      HC: 19 - 18 (95%) deaths - 1 (11%) recover HC+AZ: 19 - 14 (73%) deaths - 5 (27%) recover No HC: 6 - 3 (50%) deaths - 3 (50%) recover

      If you compare these 2 tables, you see that 25 patient with No HC reach ventilation. Once they reach ventilation, 19 of these where give HC or HC+AZ, thereby moved from the No HC group to the other two. 79.5% of all patients reaching ventilation died so arguably 14 patients that died where moved from the No HC group to the other 2 groups only once they reach the much higher risk state.

      Here are the number of people per group that got ventilation:

      HC: 97 - 19 (19.6%) got ventilation HC+AZ: 113 - 19 (16.8%) got ventilation No HC: 158 - 6 ( 3.4%) got ventilation

      So in the end result the No HC group had a very low % of patients who got ventilation and therefore should have a significant lower death rate which is then totally unrelated to the treatment.

    1. On 2020-10-07 06:13:12, user Markku Peltonen wrote:

      There were a number of comments on this manuscript on twitter early August, with concerns on errors in the calculations among others. Might be useful for others, so here is what I tweeted on August 5th 2020 (https://twitter.com/MarkkuP...: "https://twitter.com/MarkkuPeltonen/status/1290754970292281349):")

      Recently there was a meta-analysis on the effects of masks conducted in Finland. A number of comments has been made about the quality of the piece, so I had a quick look at it. As the analysis was also mentioned at least in Sweden, few quick comments in English. 1/10

      Background: the Finnish Ministry of Social Affairs and Health did a systematic review in May 2020 on the use of community face coverings to prevent the spread of Covid-19. There was no meta-analysis in the review, which focused on effectiveness. 2/10

      The conclusion on that report was “very little research data available on the effectiveness of community face coverings in preventing the spread of COVID-19 in society.” and evidence “minor” or “non-existent”. 3/10

      So, now then a formal meta-analysis, identifying the same 5 randomised controlled trials, showing an effect with relative risk estimate 0.61 (95% CI 0.39-0.96).<br /> Few points: 4/10

      The meta-analysis focuses on efficacy; what is achievable potentially when perfect conditions. They do something which they call “account of bias caused by non-compliance”; ie. if persons in the mask-group did not were masks they “adjust” for this. 5/10

      To me, this sounds quite controversial: In my world we look at intention-to-treat first, and then perhaps maybe on the “per-protocol”/“as treated”. <br /> Efficacy important, but this is now something different than what the original systematic review aimed at. 6/10

      The problems of this accentuate in the Discussion, where the authors do not seem to understand the difference in efficacy and effectiveness, nor the fact that they are actually analysing something else than the original review, and making way too far-fetched conclusions. 7/10

      There are other peculiarities, for example “Four of the analyzed studies evaluated the use of masks on respiratory infections directly, and in one the primary outcome was compliance with mask use.”. Hopefully an error, I don’t believe they actually mix the outcomes like this. 8/

      . @jejkarppinen added the following comments after my initial post, which I agree with:<br /> - The potential biases in the original papers were not covered.<br /> - Quality of evidence was not evaluated at all.<br /> - Dissemination of the results did not consider the potential problems. 9/10

      Finally:<br /> - I've not read the original 5 studies. <br /> - I’m not an expert on systematic reviews/meta-analyses. <br /> - I do think recommendation for masks is motivated, and the evidence is there (but not here..).<br /> - I do think we should be objective when evaluating evidence. 10/10

      The original systematic review the Finnish Ministry of Social Affairs and Health in Finnish is here (english abstract only):<br /> http://julkaisut.valtioneuv...

      Ps. Somebody noted the lack of preregistered protocol, which reminded me that the PRISMA-guidelines helpful when reporting systematic reviews and meta-analyses. <br /> Their checklist should be followed in reporting:<br /> http://prisma-statement.org

      In addition, it was noted by Jesper Kivelä that there are errors in the calculations, these should be corrected (in Finnish):<br /> https://twitter.com/JesperK...

    1. On 2020-11-02 02:33:32, user Atomsk's Sanakan wrote:

      The paper calculates IFR using COVID-19 deaths 4 weeks after the median time at which antibody levels were measured. That's consistent with other papers that use at deaths 3 weeks or more after the median time. For example:

      https://www.thelancet.com/p... (with: https://www.thelancet.com/j... )<br /> https://www.medrxiv.org/con...

      The paper also notes that IFR for SARS-CoV-2 is substantially more than that of seasonal influenza:

      "These results also confirm that COVID-19 is far more deadly than seasonal flu; indeed, the World Health Organization indicates that seasonal influenza mortality is usually well below 0·1% unless access to health care is constrained."<br /> https://www.medrxiv.org/con...

      "Using the midpoint of that interval, we estimate that the total U.S. incidence of seasonal influenza during winter 2018-19 was in the range of 45 million to 93 million infections and hence that the population IFR for seasonal influenza was in the range of 0.04% to 0.08%--an order of magnitude smaller than the population IFR for COVID-19."<br /> https://www.medrxiv.org/con...

      That is consistent with sources such as:

      "The current data in Europe are consistent with an IFR of 0.5–1.0%, which is many times higher than seasonal influenza (<0.1%)."<br /> https://www.ncbi.nlm.nih.go...

      "The calculated COVID-19 infection fatality rate is 1.63%, which is 10 to 40 times more deadly than the seasonal flu (fatality rate 0.04%-0.16%)."<br /> https://news.ochsner.org/ne...<br /> [for: https://wwwnc.cdc.gov/eid/a... ]

      "In summary, we estimate that the overall COVID-19 IFR ranges from 0.14 - 0.42% in low income countries to 0.78 - 1.79% in high income countries, with the differences in those ranges reflecting the older demography of high income settings.<br /> [...]<br /> Our estimates of the IFR of COVID-19 are consistent with early estimates and remain substantially higher than IFR estimates for seasonal influenza (<0.1% in the USA) [...]."<br /> https://www.imperial.ac.uk/...

      World Health Organization, in October:<br /> "Several of these analyses have used published or pre-print seroepidemiologic results and they all converge around a point estimate of around 0.6%.<br /> That may not sound like a lot but that is a lot higher than influenza [...]."<br /> https://www.who.int/publica...

    1. On 2021-04-28 13:32:34, user Huijghebaert Suzanne wrote:

      This study poses a number of relevant questions to resolve, before concluding on efficacy. To start: calculating backwards, the number of PCR negative symptomatic subjects (31 in total) were comparable in both treatment groups, suggesting that all differences over the 21 days of treatment were accounted for solely by COVID-19, so strangely, the spray – in contrast to what is claimed – would not prevent other common colds....! So, how much analytical flaw, how much efficacy?<br /> 1) Calculating the % back in carrageenan+saline versus saline alone 7.6% corresponds to 15 and 8.6%<br /> to 17 subjects each respectively, totaling 32 instead of 31: where is the incorrect overlap, 1 person too<br /> much calculated as +?<br /> 2) Most importantly, which PCR test did you use and how did you assure that carrageenan did not interfere with the PCR assay? Cfr Ribeiro 18th Apr, 2013, asking for a safe way to remove carrageenan from RNA samples and reporting that after RNA extraction (by Trizol), reverse transcription and real time PCR, only the control group (saline injected, without carrageenan) had positive amplification, while carrageenan interfered<br /> with the reaction. <br /> 3) The impact of carrageenan may additionally also already interfere at the sampling stage by affecting/reducing the RNA loading, leading to less<br /> positive results (cfr Laurie et al.). Also that step should be validated.<br /> 4) As the difference moreover is very small, and the outbreaks in health care personal often concerns clusters within a same division, how do the individual data relate to different clusters within given departments?

      Without proper profound validation the PCR test(s) in presence of various carrageenan concentrations, the findings may not translate in a true benefit, but possibly mask (so missed) viral positivity and so falsely hide transmission events. Unless validated in detail, the combination of already reported reduced PCR-response (due to carrageenan) in the nasal samples and case clustering may possibly annihilate all differences of significance.

      Suzy Huijghebaert, Belgium

    1. On 2021-09-12 21:59:19, user Swapnil Hiremath wrote:

      <reposting with="" minor="" edits="" as="" disqus="" thought="" last="" one="" was="" spam="" for="" some="" reason=""><br /> The authors have undertaken an ambitious project: briefly, taking numerators from the VAERS database, denominators from vaccine numbers from elsewhere. They then perform a ‘harm-benefit’ analysis looking at COVID hospitalization as the only harm. The whole analysis is restricted to the 12-17 age group in whom the concern of myocarditis is admittedly higher.<br /> They report a risk which was anywhere from 1.5 to 6.1 times higher for vaccine associated myocarditis vs COVID causing hospitalization. Vaccines must be bad, surely.

      However, several problems are quickly apparent.<br /> 1. The rate of myocarditis is much higher than the ones reported in Ontario: 160/million for 12-15 males compared to 72.5/million from Ontario (which includes Moderna as well - which has higher rates of myocarditis than the Pfizer/BioNTech). Why would this be so? There are many possible reasons, including the overestimation from VAERS being probable cause. On a perusal of the supplement, there are many which are other viral diseases which could be the reason; additionally many descriptions are quite vague (‘the doctor told us troponin was elevated’). It is very easy to submit cases to VAERS, so the numbers reported could be an overestimate - proper case ascertainment with source documents is necessary to be sure of the cases. Needless to say, simple arithmetic to derive 'rates' is also problematic. The VAERS website specifically suggests the numbers should *not* be used for estimating rates.

      1. It was not clear why the authors chose Jan 1, when vaccines EUA for 16-17 started in March, and for 12-15 in May. In their database, there seems to be one case in March and most of the VAERS reports from May or later.

      3.Next, the authors make many assumptions when it comes to who had comorbidities and who did not among the children, and multiply numbers to come up with some crude estimates. It would be useful for a pediatric diseases researcher to assess these assumptions. The 40% assumption of children hospitalized 'with COVID' and not due to COVID is a very crude untruth that the authors and others have needlessly perpetuated on social media with little foundation.

      1. Most importantly, the authors assume that hospitalization is the only bad thing for children who develop COVID. Some 12-17 years olds have died due to COVID, and some may have had a 1 day hospital stay - their analysis treats these equally and incorrectly. Some teens developed MIS-C. Some developed longer term sequelae. To group them under ‘hospitalization’ seems overly simplistic. Similarly, from perusing some of the vaccine-myocarditis, many seem to have recovered with symptomatic care alone. The authors seem to be minimizing COVID and maximizing vaccine associated adverse events.

      2. It should be noted that the involvement of children in the first two waves seems to be different than the one we have seen in the last 2 months with delta (for whatever reason - perhaps with lower immunization numbers in these).

      3. Lastly, the pandemic is not yet done. Many more children are going to get COVID in the next few months and years. We are going to have many more hospitalization, morbidity and sadly many more deaths. There will be long term morbidity and sequalae. We do need better data to assess the risks and benefits. This study is not it.

      Unfortunately this study has been picked up uncritically by media and will worsen vaccine hesitancy. This seems unwise in the face of an ongoing pandemic.

    2. On 2022-01-14 15:44:35, user Jordan Taylor wrote:

      There are at least three major issues in this paper.

      The first is a technical issue and probably the most obviously fatal flaw. It was first pointed out (that I could see) by Shih-Hao Yeh, and I think deserves re-emphasising. The authors have badly miscalculated the COVID19 hospitalisation risks for children conditional on comorbid status. They cite a 120 day hospitalisation rate (during moderate viral prevalence) of 255/million children. They note that the hospitalisation risk is 4.7-fold higher for children with comorbidities than for those without. How do we calculate the risk for each subgroup then? In this case, we are told 70% of those hospitalised have commorbidities and 30% do not, so for each 255 hospitalised, 0.3*255 = 76.5 will be healthy and 178.5 will have commorbidities. We can't stop there though as we need to adjust for the size of the background healthy and comorbid populations, which the authors tell us is 67% and 33% respectively. To get rates per million we have 76.5/0.67 = 114.2 among the healthy and 178.5/0.33 = 540.9 among those with comorbidities. They seem to have come up with the 44.4/million and 210.5/million figures based on the assumption that the two have to sum to 255/million, which is just not how it works at all. A basic sanity check should have been "should the risk in the high risk group really be lower than the overall risk?" If the rate of gun deaths is 100/million in the military and 1/million in civilians, you don't just add them together to get an average population gun death rate of 101/million!

      The second is more conceptual. The comparison is between a risk from vaccination conditional on vaccination with a risk from infection which is not conditional on infection. In other words, the paper does not answer the question: what is the myocarditis risk of a vaccinated child versus the hospitalisation risk of an infected child? Instead it rather tries to answer the question: what is myocarditis risk of a vaccinated child versus the COVID hospitalisation risk for an average child over a 4 month period at X prevalence rate. Given that the pandemic has already been going on for 2 years now and shows no signs that it will simply disappear, this strikes me as an inappropriate choice.

      Another issue is how the claims in the conclusions compare to the data. The authors state that for "boys 12-17 without medical comorbidities, the likelihood of post vaccination dose two CAE is 162.2 and 94.0/million respectively..." (emphasis added) and that this "exceeds their expected 120-day COVID-19 hospitalization rate at both moderate (August 21, 2021 rates) and high COVID-19 hospitalization incidence." However, at no point in the text, data, methods, or the Study Profile supplement (S2) of the paper is a stratification of CAE rates by comorbid status provided. In fact, although they did not provide any code, I was broadly able to reproduce their VAERS myocarditis counts in 12-17 year olds from 01/01/2021-06/18/2021 using the criteria they provide*, and that was without adjusting for comorbidities. So it seems likely to me that they have not separately analyzed "healthy" and comorbid patients and thus have no basis for making claims about effects of vaccination on "boys without medical comorbidities".

      If as seems likely they have compared CAE rates among both healthy and unhealthy children with COVID19 hospitalisation rates among only healthy children, this is a big problem. The authors should be challenged to stratify their analysis equally for both COVID19 hospitalisation and vaccination before this paper is actually published.

      *I found 277 cases matched the original criteria, however, this was with a number of likely duplications and possibly other data entry erros; as mentioned, the authors' methods were pretty inadequate so we have no way to see whether or how they have cleaned the data.

    3. On 2021-09-12 01:57:32, user Swapnil Hiremath wrote:

      The authors have undertaken an ambitious project: briefly, taking numerators from the VAERS database, denominators from vaccine numbers from elsewhere. They then perform a ‘harm-benefit’ analysis looking at COVID hospitalization as the only harm. The whole analysis is restricted to the 12-17 age group in whom the concern of myocarditis is admittedly higher. <br /> They report a risk which was anywhere from 1.5 to 6.1 times higher for vaccine associated myocarditis vs COVID causing hospitalization. Vaccines must be bad, surely.

      However, several problems are quickly apparent. <br /> 1. The rate of myocarditis is much higher than the ones reported in Ontario: 160/million for 12-15 males compared to 72.5/million from Ontario (which includes Moderna as well - which has higher rates of myocarditis than the Pfizer/BioNTech). Why would this be so? There are many possible reasons, including the overestimation from VAERS being probable cause. On a perusal of the supplement, there are many which are other viral diseases which could be the reason; additionally many descriptions are quite vague (‘the doctor told us troponin was elevated’). It is very easy to submit cases to VAERS, so the numbers reported by the authors seem to be higher than the true value. The case ascertainment performed in Ontario seems a bit more reliable and trustworthy than user entered data in VAERS.

      1. It was not clear why the authors chose Jan 1, when vaccines EUA for 16-17 started in March, and for 12-15 in May. In their database, there seems to be one case in March and most of the VAERS reports from May or later.

      2. Secondly, the authors make many assumptions when it comes to who had comorbidities and who did not among the children, and multiply numbers to come up with some crude estimates. It would be useful for a pediatric diseases researcher to assess these assumptions. The 40% assumption of children hospitalized 'with COVID' and not due to COVID is a very crude untruth that the authors and others have needlessly perpetuated on social media with little foundation.

      3. Most importantly, the authors assume that hospitalization is the only bad thing for children who develop COVID. 12-17 years olds have died due to COVID. Some developed MIS-C. Some developed longer term sequelae. To group them under ‘hospitalization’ seems overly simplistic. Similarly, from perusing some of the vaccine-myocarditis, many seem to have recovered with symptomatic care. The authors seem to be minimizing COVID and maximizing vaccine associated adverse events.

      4. It should be noted that the involvement of children in the first two waves seems to be different than the one we have seen in the last 2 months with delta (for whatever reason - perhaps with lower immunization numbers in these).

      5. Lastly, the pandemic is not yet done. Many more children are going to get COVID in the next few months and years. We are going to have many more hospitalization, morbidity and sadly many more deaths. There will be long term morbidity and sequalae. We do need better data to assess the risks and benefits. This study is not it.

    1. On 2022-01-04 18:56:26, user Thomas Barlow wrote:

      Did you account for pre-existing T-Cell immunity (pre-2020) which was known in 30 to 50% of the population in year 2020?<br /> Did you account for the fact that most people have had covid (most without even noticing) and will have developed T-Cell immunity naturally? How did you control for that? It's not a static measure and is more expressed and less expressed at different times of month, year.

      Studies on INFECTION FATALITY RATE (IFR) - peer-reviewed studies [Studies conducted long before any vaccine] :

      (notice the one confirmed for publication by the W.H.O. in September 2020 [published Oct. 2020] - A 0.23% IFR...about the same as flu).

      MARCH 2021<br /> “the available evidence suggests average global IFR of ~0.15%”<br /> https://onlinelibrary.wiley...

      FEB. 2021<br /> “The infection fatality rate for both the Bureau of Prisons and U.S. was 0.7%. Among institutions that tested >=85% of inmates, the combined infection fatality rate was 0.8%”<br /> https://www.ncbi.nlm.nih.go...

      JAN 2021<br /> “The overall non-institutionalized IFR was 0.26%.”<br /> - https://www.acpjournals.org...

      DEC 2020<br /> “This rate varied from place to place, with a lower range of 0.17% and a highest estimate of 1.7%.”<br /> https://www.sciencedirect.c...

      DEC. 2020<br /> “Results show a fatality ratio of about 0.9%, which is lower than previous findings.”<br /> https://www.mdpi.com/1660-4...

      NOV. 2020<br /> “The overall infection fatality risk was 0.8%”<br /> https://www.bmj.com/content...

      NOV 2020<br /> “In the United States, COVID-19 now kills about 0.6% of people infected with the virus, compared with around 0.9% early in the pandemic, IHME Director Dr. Christopher Murray told Reuters.”<br /> https://www.reuters.com/art...

      NOV. 2020<br /> “The estimated IFR was 0.36% (95% CI:[0.29%; 0.45%]) for the community and 0.35% [0.28%; 0.45%] when age-standardized to the population of the community.”<br /> https://www.nature.com/arti...

      OCT 2020<br /> “We know that antibody tests are not perfect, and there may be a considerable number of people who do not mount a detectable antibody response to SARS-CoV-2. However, even when this uncertainty is taken into account, we still find that COVID-19 has a high fatality rate - on the order of 1% for a typical high-income country.”<br /> https://www.imperial.ac.uk/...

      SEPT 2020<br /> The W.H.O. posted a heavily peer-reviewed & critiqued study from May 2020, showing the deaths per cases are 0.23% overall, and going up to 0.5% in the worst hit cities. 0.05% for under 70s - The W.H.O. reviewed it again, then published it in September:<br /> - https://www.who.int/bulleti...<br /> - https://apps.who.int/iris/h...

      AUG 2020<br /> The medical journal 'Nature' had an analysis and stated that:<br /> "This result was used to calculate an overall IFR for England of 0.9%”<br /> https://www.nature.com/arti...<br /> ________________

    1. On 2021-11-28 19:40:54, user Robert van Loo wrote:

      44 relevant new variants up till now and on average some 2 per 10 million cases. Did we only see 220 million cases globally? I would think more with over 5 million deaths and an IFR of 0.6 %. I have papers and also WHO stating the reported 260 million cases is factors lower than the real number of infections. With over 5 million reported deaths and an IFR of 0.6 % the real number of infections would be over 800 million cases. The number of relevant new variants per 10 million cases would then be about 4 times lower. Of course if reported cases always underreport to the same extent the extrapolation of reported cases to new variants would not change. Still important to make the distinction as the underreporting factor is hugely variable.

    1. On 2020-06-29 08:48:39, user Dr Mubarak Muhamed khan wrote:

      RE: can creating new vaccine everytime is solution for new mutating viruses?

      We published our view as e letter regarding old vaccine and it’s Possible use In present menace in science and C&E News<br /> Link:

      https://science.sciencemag....

      The e letter

      (2 June 2020)<br /> Thank you very much for excellent update in new vaccines. We appreciate every efforts towards betterment of human life and fighting with new menace. Still Certain questions need to be asked while trying new vaccines everytime for Every new virus or any microbe mutation?<br /> Although we are Not immunologists, still certain questions haunts our mind. We hope that these queries and questions will ignite the minds of researchers and immunologists. With open minds we must ask these questions to ourselves in today’s tough time instead of getting rattled by situation<br /> 1. Does every new virus create specific antibodies? And for how long it works?<br /> 2. Is there any limit of immune response for any healthy Homo Sapien?<br /> 3. Whether body immune response of Homo Sapien get fatigued with every new challenges by new viruses?<br /> 4. Whether after multiple challenges by new viruses , body try saving Homo Sapien by cross immunity?<br /> 5. Although with new challenges by new virus, body may try responding by creating initial IgM .... And then IgG for certain time period, but whether memory is created for long time for such mutating viruses?<br /> 6. Why not to boost immunity with booster doses of existing vaccines and check cross immunity for fighting with new mutating viruses ?<br /> 7. When new vaccines are in development, why not to give a chance of revaccinations with existing proved vaccines (BCG, MMR, and many more) to masses??<br /> 8. Is there any harm in starting booster doses to children’s and adults of existing vaccines?<br /> 9. Till the new vaccines are developed for SARS Cov 2, good ample amount of time one will get to test boosting immunity with current vaccines and checking cross immunity for fighting corona?<br /> 10. We must continue searching new vaccines for every new virus. But what’s harm repurposing existing proved vaccine for strong cross immunity to neutralise many new menace?

      Still Many more new mutated viruses will arrive and try to attack us in different ways in future. Why not to boost sustainable existing immunity with booster doses of existing well tested vaccines in vaccination programmes?

      Sincere Regards<br /> Dr Mubarak khan<br /> Dr Sapna Parab<br /> Director & Consultant<br /> Sushrut ENT Hospital & Dr Khan’s Research Centre, Talegaon Danhade, Pune, India

    1. On 2020-04-12 12:44:15, user Clive Bates wrote:

      Thank you for an extremely interesting and informative paper. I have a few suggestions about one aspect of the paper - tobacco use.

      1. The paper alternates between use of 'smoking status' (the body) and 'tobacco use' (tables). It would be helpful to know which is appropriate and how the data on tobacco status was gathered. Tobacco use could include smokeless tobacco and, if FDA definitions are applied, it could include vaping.

      2. The proportion of tobacco users assessed, hospitalised and developing critical conditions is substantially below the tobacco use prevalence for New York, even when age is considered. Is this worth mentioning?

      3. The multivariate analysis shows an apparent protective effect against hospitalisation for current and former tobacco use as reported (OR = 0.71, 95% CI 0.57-0.87 p=0.001). This is a striking finding, but consistent with findings from CDC's summary of US data (MMWR (April 3, 2020 / 69(13);382–386)) and China (Farsalinos et al - pre-print) in which smoking appears to be underrepresented in the population with progression to more severe symptoms.

      4. A weaker (non-significant) apparent protective effect of current or former tobacco use (OR = 0.89 95% CI 0.65-1.21, P=0.452) of was found in the progression from hospitalisation to critical condition. Hospitals generally impose smoking cessation and nicotine withdrawal at the point of hospitalisation.

      5. Would it be possible for the authors to rerun the multivariate analysis with current tobacco use and former tobacco use as separate variables? It is possible that former use is masking a stronger effect from current use. Current and former tobacco use may have quite different effects on progression of the disease and former use can include people who quit smoking decades ago. The merging of current and former tobacco use may be obscuring valuable information in the data.

      6. There are many possible explanations for an apparent protective effect. It is possible the tobacco use status has been underreported, or current and former users are overrepresented in the 'unknown' status. It is possible that patients fear disclosure of tobacco use will lead to discrimination in treatment or they may feel guilty about their 'contributory negligence'. However, it is also possible that there is a real protective effect from either smoking or nicotine use. This is not implausible: nicotine interacts with the same receptor that is responsible for development of the disease following exposure to the virus. This paper could yield useful supportive or falsifying insights into that hypothesis.

      7. Even if such findings are disconcerting, we should be led by the data. It is not possible to rule out a protective effect at this point and this paper adds to the reasons to take the idea seriously. There could be significant implications for the population impact of COVID-19, implications for advice to tobacco users, and implications for practice in hospitals.

      8. I have no conflicts of interest with respect to tobacco, nicotine or pharmaceutical industries.

    2. On 2020-04-16 06:58:38, user Kratoklastes wrote:

      It would have been useful to tabulate critical illness (and deaths) - both by age cohort - and to have given some indication of the statistical properties of the estimators (beyond p-values).

      The OR of 66× for the 75+ age cohort in the hospitalisation regression seems outlandish; the raw OR is 4× (i.e., the raw ratio of (Admitted|PosTest) for over-75s compared to the same quantity for 19-44 year olds).

      That looks (to me) like a collinearity issue in the regressor matrix - a really wide CI for one really-obviously-important variable is another clue. (Call me a Bayesian!).

      If your regressors were boolean (i.e., presence/absence) for comorbidities, VIF is not an appropriate test for collinearity: VIF performs poorly for categorical variables. Why not simply test the determinant of X´X, or its condition number, or its smallest eigenvalue?It's not a large matrix by modern standards - so it can't be a computational constraint. R's mctest package does a good job too (omcdiag includes the Farrar-Glauber test)

      I would suspect some rank deficiency caused by correlation between hypertension and variables that represent CVD-ish things; not necessarily pairwise - and this is the problem with booleans.

      Weak collinearity can happen because of weighted sums of columns - 3 boolean columns can give a run of '2' values, that correspond 'enough' with the '1's in the hypertension column. Add in other correlates with hypertension (age, obesity and maleness) and it would be suspicious if there wasn't collinearity.

      I don't think it would be viewed negatively if you dropped the 19 newborns (who are confounders in the 'hospitalisation' regression, since they are always hospitalised), so long as it was clearly disclosed: the presence of those 19 observations will also mess up the 'critical illness' regression as well (it would only require a handful of newborns to need critical care for things unrelated to COVID19, to bias up the OR for their age group, and their presence already biases up recovery rates).

      Lastly: it would be relatively straightforward to furnish the R script and the parameters (and residuals) without furnishing the data - that way interested people could generate their own pseudo-data and run some Monte Carlo experiments to get an idea of the asymptotic properties of the estimates. (To do it properly it would be good to have a covariance matrix so that the pseudodata could be generated by an appropriate cupola).

      .

      It's a pity that (as far as I am aware) Disqus does not permit any type of maths markup.

    1. On 2022-02-17 20:47:23, user RT1C wrote:

      Another point of confusion: "An individual was considered protected by natural immunity 14 days after testing positive for COVID-19 by a nucleic acid amplification test (NAAT). If not previously infected, a person was considered protected by vaccine induced immunity 14 days after receipt of the second vaccine dose of an mRNA vaccine. " and "A vaccine booster was defined as at least 1 dose of any COVID-19 vaccine at least 90 days following COVID-19 infection for those with natural immunity (i.e. those previously infected), or a third dose of a COVID-19 vaccine at least 90 days following the second dose of an mRNA COVID-19 vaccine for those with vaccine-induced immunity (i.e. those not previously infected)."

      This is all very confusing, stemming from your broad use of "natural immunity" to include those who were vaccinated before or after infection. Figure 4 is entitled with "natural immunity" but includes people with 0, 1, 2 or 3 doses. Based on the definitions in the text quoted above, that doesn't seem possible. Did they get infected and then receive 0-3 doses AFTER infection and still called "natural immunity" subjects? What about people who received 1 or more doses before infection? Are they counted among the vaccine-induced immunity subjects? In my opinion, your definitions and uses don't seem consistent or understandable.

      Furthermore, because other research has shown a difference in immune response when people are vaccinated then infected vs. infected then vaccinated, you should not combine these as one group. Did you make any attempt to compare these situations? Shouldn't you?

      Look at Fig. 2, for example. I assume that many of the subjects included in the curves on the left ("Natural Immunity") actually were vaccinated at some time, since Fig. 4 shows that many with "natural immunity" were vaccinated by 0-3 doses. How, then, are we to interpret Fig. 3? Is the weaker immunity with longer durations since POIC to be interpreted as time since infection, or time since vaccination (which would count for resetting POIC)? Is the weaker immunity with longer durations due to decay of natural immunity as the text seems to imply, or is it due to confounding with vaccination? (After all, doubly vaccinated have higher susceptibility in Fig. 1). These issues make it difficult to understand your study.

      I think you probably have the data for an informative analysis, and your method of analysis looks promising (I prefer it to the "person-days" approach used in some other work). Please consider reexamining the dataset with a clarified definition of "natural immunity" that accounts for all combinations of vaccination and infection including sequence.

    1. On 2020-03-30 14:10:51, user Sinai Immunol Review Project wrote:

      Study description: Data analyzed from 52 COVID-19 patients admitted and then discharged with COVID-19. Clinical, laboratory, and radiological data were longitudinally recorded with illness time course (PCR + to PCR-) and 7 patients (13.5%) were readmitted with a follow up positive test (PCR+) within two weeks of discharge.

      Key Findings:

      At admission:<br /> o The majority of patients had increased CRP at admission (63.5%).<br /> o LDH, and HSST TNT were significantly increased at admission. <br /> o Radiographic signs via chest CT showed increased involvement in lower lobes: right lower lobe (47 cases, 90.4%), left lower lobe (37 cases, 71.2%).<br /> o GGO (90.4%), interlobular septal thickening (42.3%), vascular enlargement (42.3%), and reticulation (11.5%) were most commonly observed.

      After negative PCR test (discharge):<br /> o CRP levels decreased lymphocyte counts (#/L) increased significantly (CD3+, CD3+/8+ and CD3+/4+) after negative PCR.<br /> o Consolidation and mixed GGO observed in longitudinal CT imaging w different extents of inflammatory exudation in lungs, with overall tendency for improvement (except 2/7 patients that were readmitted after discharge with re-positive test) after negative PCR.

      Seven patients repeated positive RT-PCR test and were readmitted to the hospital (9 to 17 day after initial discharge):<br /> o Follow up CT necessary to monitor improvement during recovery and patients with lesion progression should be given more attention.<br /> o Dynamic CT in addition to negative test essential in clinical diagnosis due to nasal swab PCR sampling bias (false-negatives).<br /> o Increase in CRP occurred in 2 readmitted patients (and decr. in lymphocytes in one patient), but was not correlated with new lesions or disease progression vs. improvement (very low N).<br /> o Patients readmitted attributed to false-negative PCR vs. re-exposure.

      Importance: Study tracked key clinical features associated with disease progression, recovery, and determinants of clinical diagnosis/management of COVID-19 patients.

      Critical Analysis: Patients sampled in this study were generally younger (65.4% < 50 yrs) and less critically ill/all discharged. Small number of recovered patients (N=18). Time of follow up was relatively short. Limited clinical information available about patients with re-positive test (except CRP and lymph tracking).

    1. On 2020-03-30 15:38:19, user Sinai Immunol Review Project wrote:

      Main findings<br /> This is the first report to date of convalescent plasma therapy as a therapeutic against COVID-19 disease. This is a feasibility pilot study. The authors report the administration and clinical benefit of 200 mL of convalescent plasma (CP) (1:640 titer) derived from recently cured donors (CP selected among 40 donors based on high neutralizing titer and ABO compatibility) to 10 severe COVID-19 patients with confirmed viremia. The primary endpoint was the safety of CP transfusion. The secondary endpoint were clinical signs of improvement based on symptoms and laboratory parameters.

      The authors reported use of methylene blue photochemistry to inactivate any potential residual virus in the plasma samples, without compromising neutralizing antibodies, and no virus was detected before transfusion.

      The authors report the following:<br /> ? No adverse events were observed in all patients, except 1 patient who exhibited transient facial red spotting.<br /> ? All patients showed significant improvement in or complete disappearance of clinical symptoms, including fever, cough, shortness of breath, and chest pain after 3 days of CP therapy. <br /> ? Reduction of pulmonary lesions revealed by chest CT.<br /> ? Elevation of lymphocyte counts in patients with lymphocytopenia. <br /> ? Increase in SaO2 in all patients, indicative of recuperating lung function. <br /> ? Resolution of SARS-CoV-2 viremia in 7 patients and increase in neutralizing antibody titers in 5 patients. Persistence of neutralizing antibody levels in 4 patients.

      Limitations<br /> It is important to note that most recipients had high neutralization titers of antibodies before plasma transfusion and even without transfusion it would be expected to see an increase in neutralizing antibodies over time. In addition to the small sample set number (n=10), there are additional limitations to this pilot study:<br /> 1. All patients received concurrent therapy, in addition to the CP transfusion. Therefore, it is unclear whether a combinatorial or synergistic effect between these standards of care and CP transfusion contributed to the clearance of viremia and improvement of symptoms in these COVID-19 patients. <br /> 2. The kinetics of viral clearance was not investigated, with respect to the administration of CP transfusion. So, the definitive impact of CP transfusion on immune dynamics and subsequent viral load is not well defined.<br /> 3. Comparison with a small historical control group is not ideal.

      Relevance<br /> For the first time, a pilot study provides promising results involving the use of convalescent plasma from cured COVID-19 patients to treat others with more severe disease. The authors report that the administration of a single, high-dose of neutralizing antibodies is safe. In addition, there were encouraging results with regards to the reduction of viral load and improvement of clinical outcomes. It is, therefore, necessary to expand this type of study with more participants, in order to determine optimal dose and treatment kinetics. It is important to note that CP has been studied to treat H1N1 influenza, SARS-CoV-1, and MERS-CoV, although it has not been proven to be effective in treating these infections.

    1. On 2022-12-19 02:33:05, user Charles Warden wrote:

      Hi,

      Thank you very much for posting this preprint. I think this is an interesting topic to learn more about.

      I was surprised at what seemed to me like a relatively small amount of difference for the EA-PGS versus the rare alterations (such as in Figure 2), but I am not sure if that is because of certain associations that I might have with “rare” and “monogenic”. I apologize if some of these questions are naïve and certain assumptions may be more or less relevant in certain situations.

      For example, I wonder if the phenotypic “deviators” might relate to part what I am trying to describe, and I think it would be good for me to take some time to better understand resources like Developmental Disorder gene panel from Gene2Phenotype.

      Nevertheless, as a starting point, I hope that you can help with at least some of the following questions:

      1) I could find Supplemental Figure S1. However, I see references to Supplemental Tables, when I can’t find additional uploaded “Supplemental” files.

      Am I overlooking something, or do the Supplemental Tables need to be added in a “v2” version?

      2) I thought Figure 1 was very helpful in gauging the relative influence on the different characteristics.

      However, the abstract mentions a “threshold for clinical disease”. I apologize that I don’t know the best full list of known genetic/genomic alterations that would provide a good reference, but I would be interested to know how the predictive power (which I assume is correlated with the beta values) might compare to something like trisomy 21 for Down Syndrome (although that affects many genes on chromosome 21).

      Reading this preprint helped me in terms of gaining familiarity with earlier publications, such as the AJHG Kingdom et al. 2022 publication. In that paper, I was interested in the set of 25 “known highly penetrant genes.” However, my understanding is that those were not used in a main figure because of sample size for sufficiently rare variants/alterations. For example, I recognized “Williams syndrome” and “Prader-Willi/Angelman” in Table S2 of the other publication, but I believe there were less than 20 UKB subjects for each of those diseases.

      I also apologize that I think that I am confusing the results a bit, since the other publication includes copy number alterations and Figure 1 in this preprint mostly describes “variants”. However, I hope that provides some sense of what I am asking about.

      For example, I don’t know if sample size is an issue, but I would be interested in seeing an additional category to compare the current results to something of comparable clinical utility can be defined (if possible, from individual variants in certain genes). I apologize if I am overlooking something.

      3) In this preprint, I also see “monoallelic (i.e., autosomal dominant)” in the methods. So, I think this already answers one of my question/questions, but I wanted to still say something to try and find the better way to describe what I would call “monogenic” diseases. It is possible that there might be some other explanation like needing additional characterization of individual variants (and I believe the earlier paper mentioned “very few” pathogenic variants were in ClinVar), but I would like to start with the possibility that perhaps there is a more precise way to communicate my thoughts.

      In other words, I would expect the clinical disease for monogenic diseases to be recessive in certain situations, and the questions that I had earlier related to whether there could be a noticeable number of developmental disorders that follow a recessive inheritance pattern (such as whether a homozygous variant counted as “2 variants”).

      Likewise, in terms of downstream effects, I thought Phenylketonuria/PKU affected some of the traits mentioned in this study (if dietary changes were not made in time). I don’t know if that was precisely considered a “developmental disorder,” but that is an example of what I think of when I see “rare” and “monogenic”.

      Please let me know if I have misunderstood anything, but my understanding is that this may go against the assumed dominant inheritance pattern for the genes. However, the question that I am trying to ask is how to categorize what I would call “monogenic” disease like PKU (as I understand it) and then compare that to the effects described for the associations in this preprint? I think one other way to describe this might be something like “positive predictive power” and yet another way might be something like “what are the proportion of individuals meeting the threshold for disease among carriers (if significantly higher than controls)”? However, I don’t know if there are issues with my use of terminology (and/or sample size, criteria to volunteer and consent, or something else).

      4) In terms of the clinical importance, I am not sure if there might be caveats/limitations to suggesting “PGS may provide some clinical utility by improving diagnostic interpretation of rare, likely pathogenic variants that cause monogenic disease”? For example, if a large enough number of individuals can be functional adults (or even adults with excellent health/success), then I might worry about the stigma of test results provide early in life (if predictive power was over-estimated). Based upon Figure 3, my understanding is that there is a reasonable chance that could happen with the EA-PGS? If that was true, then I hope that there is or can be a different term/classification to separate such a risk assessment from the genetic tests for diseases similar in severity to what I tried to list above (if I understand correctly).

      I hope that I can learn more, and I hope that these questions might also be helpful to some other readers.

      Thank you very much!

      Sincerely,

      Charles

    1. On 2024-05-02 17:06:42, user Oliver wrote:

      I am 44 months cold turkey. History includes class II ointment and cream use and one round of prednisone (25mg a day), and maybe even a steroid shot...but I'm not sure (I fell on my back once and went to the ER, they gave me a shot of something...this was a few months before I started my TSW). I am trying to get my records to confirm. When I used TCS, I never finished a prescription. I used small amounts. I even applied using a finger cot. I used very consistently for only about 2-3 years. In total, in my life, probably 4 years (on and off). I was pretty careful but very naive. I blame myself and my dermatologist. She always just prescribed me another TCS to use. She never mentioned avoiding long term use. So maybe I shouldn't blame myself. What I want to comment on here is that I truly believe the current situation with TSW is a systemic medical catastrophe. Just think about it, practicing physicians are not looking under the microscope in their clinics to distinguish TSW between other eczematous disorders. They are going by the eye, and that's if they are even cognizant of TSW. If you had to place a bet on the amount of people's skin on this earth that are addicted to TCS and actually have TSW symptoms rather other eczematous disorders, what is your over/under? Serious question. It's easily over tens of millions if you count every country. In North America? Millions. That might be underestimating it. You could get a clearer picture just by seeing how many TCS products are sold to end consumer. It's tens of millions. The situation is beyond complex. As a nonprofessional, I'd even go as far as saying most people that have a history of TCS that are walking into their doctors clinic, they have TSW and that person and their doctor don't even know it. How many of these people are there every day? Sure, use the TCS for long term. But if you use for 20+ years and TSW is still not recognized by most institutions...I won't say much more but my mind will always remember the late Eric 'Nim' Bjorklund. May his memory be a blessing. TSW is more than just a serious condition. It's a crisis. It's irreparable. It could be one of the greatest institutional failures of the century in medicine.

    1. On 2020-04-18 17:11:55, user Clint Cooper wrote:

      Please allow me five responses to this study:<br /> 1. Yes, it's true that there are large numbers of people who have probably been exposed to Covid-19 and experienced no symptoms and have antibodies. The best country to look at with regard to testing mass numbers of people randomly is Iceland. Icelad has now tested over 11% of its entire population and a lot of testing was truly done randomly. The fatality rate there is around .6%, or one in about 167 people. In New Zealand, which has also engaged in massive testing, the fatality rate is about .8% or 1 in 125 people. HOWEVER (and this is a big however). the fatality rate in both countries has gone up steadily and is continuing to go up. Also, in those countries the average age of people infected is much lower than in other countries, like Italy, who have been hit much harder by this disease. This actually jives with the original fatality rate given by the World Health Organization, which was around .3 to .5% for younger people, and markedly higher for older people. <br /> Also, the average fatality rate for the common flu is about 1 in 2,000. So even if we can agree that the fatality rate of Covid-19 might be lower than originally thought, it's way more dangerous than any common flu. It is also exponentially more contagious, which is a huge part of the problem. <br /> 2. As testing becomes more and more widely available across the planet, the fatality rate has gone markedly up across the board, not down. For closed cases, the fatality rate has gone from 3-4% initially to now 21% and rising.<br /> 3. The fatality rate in this study assumes that no one ever died at home from Covid-19. As a matter of fact, NYC is now including people who died at home from Covid-19 but a lot of US states and other countries are not, which would artificially lower the fatality rate, when it could actually be much higher.<br /> 4. To say that somehow the fatality rate of this disease is no worse than just a really bad seasonal flu is looney tunes and doesn't pass the eyeball test since many, many perfectly healthy people with no pre-existing medical conditions are dying as a result of this disease left and right, including young and middle aged people. That simply doesn't happen with seasonal flu. The self-reported symptoms of Covid-19 are also far worse than most self-reported symptoms of the common flu. Recovery time also appears to be longer, and many doctors are reporting lung and heart tissue damage.<br /> 5. There are about 18,000 people in Santa Clara County who have tested for Covid-19 and about 10% of those tested positive. Is it possible that the same people who tested negative could have already had Covid-19 and it was already defeated by their immune system and they now have antibodies and are testing negative? Could it be that these very same people who already tested negative for the virus are now volunteering to test for antibodies? In this case, there is a massive level of redundancy and the study is useless and can't be extrapolated to the general public. This study is far too small and not random enough to provide any usefull information whatsoever. We would need truly random testing of a much bigger group of people to provide any insight at all into what the actual fatality rate is. We would also need to include a much wider age range.

    2. On 2020-04-22 20:36:24, user Konstantin Momot wrote:

      The crucial issue here is sample selection. The participants essentially self-selected, but that is a potential source of huge bias. As a hypothetical scenario, if the people who chose to participate were predominantly people who’d had a cold and were curious to find out if it was COVID, then the cohort would a priori be hugely overweight with people who had a higher-than-average likelihood of COVID exposure. That would not be a good sample of the general population in that it would not represent the true percentage of CoV-exposed and CoV-naive people in the population as a whole. That would mean that the 2-4% figure is completely meaningless. Given how crucial this number is to any epidemiological modelling, I think it's important to remember that this is just one study with no guarantee of flawless methodology, and avoid making far-fetched conclusions based on limited evidence.

    3. On 2020-04-22 00:40:03, user Unko J wrote:

      It's nice to read below what essentially IS the 'peer-review' for this pre-print online paper! I wish I had read these comments last night before having a heated debate with my fellow quarantinees. My point was how could these possibly be 2%-4% of the population that is positive and yet Santa Clara has only 83 deaths? These divergent sets of data can't really exist in one universe, unless either we're wildly wrong about either a) the mortality rate or b) how many people can be asymptomatic and test positive with an Ab test. So yeah, between cross-reactivity against non-Covid antibodies and other false positives, I think we've decided to reject this paper. And aren't some of the authors the same on both papers?

    1. On 2025-11-23 17:32:31, user Charlotte Strøm wrote:

      In the following “text in italics – inside quotations marks are copy-pasted from the reference in question." Underlining and/or bolded text are done by me.

      1. SPIN AND FRAMING<br /> The title of the preprint is: “Randomised trial of not providing booster diphtheria-tetanus-pertussis (DTP) vaccination after measles vaccination and child survival: A failed trial”<br /> 1.1 Framing neutral findings as abnormal or disappointing<br /> The authors consistently imply the results are “unexpected” or “contradictory”, rather than acknowledging that the RCT failed to support earlier observational findings.

      Examples<br /> “A failed trial” says the title ->The trial did not “fail”: it ran, randomised 6500+ participants, and produced valid estimates showing no harm of the DTP vaccine. Calling it “failed” is a framing tactic that positions the result as an error rather than what the data showed.

      Page 8, lines 11-12: The was no difference in non-accidental mortality … the HR being 0.84 (0.52–1.37).” ->This is an appropriate stating of results, but the subsequent framing undercuts it.

      Page 8, lines 22-24: “Since no beneficial effect of not giving DTP4 was found, contradicting many observational studies… possible interactions were explored…” -> This subtly frames the RCT as problematic because it contradicts earlier observational research, rather than recognizing that RCTs supersede observational evidence.

      Page 10, line 2:“The present RCT is therefore an outlier which needs an explanation.” -> This is spin: the RCT is not an "outlier" needing explanation; observational studies - upon which the research hypothesis are based - are know to have confounding and are biased. CONSORT encourages presenting results without exaggeration or defensive justification.

      1.2 Causal interpretations of non-significant results<br /> The authors imply meaningful patterns where no statistically reliable findings exist.

      Examples Page 8–9 (exploring interactions despite explicitly stating low power): “There was one significant interaction … DTP strain … observed only for females.” (p=0.05)

      No correction for multiple testing; >20 interactions tested -> This is classic exploratory-analysis spin.

      1.3. Hypothesis-confirming language<br /> The manuscript repeatedly positions NSE hypotheses as foundational truths rather than unproven claims.

      Example Page 3, lines 5-7: “Several studies inidcated… beneficial non-specific effects… more pronounced in females.” -> These were observational or post-hoc analyses, being framed as established background biases the narrative.

      1.4 Framing underpowering as the main explanation<br /> Repeated emphasis that the trial was “strongly underpowered” serves to discount the main finding.

      Examples Page 8, lines 13-15: “...the trial was planned with 3% annual expected mortality rate… observed rate was 81% lower… we had 65% fewer deaths…” -> This is accurate but placed repeatedly tthroughout the text to frame the null result as flawed.

      Page 9, lines 18-21: “The RCT was strongly underpowered… mortality declined …” -> The authors do not consider that a null finding study is plausible.

      2. CONSORT NON-COMPLIANCE <br /> 2.1. Missing or unclear prespecified primary outcome<br /> CONSORT requires explicitly stating primary and secondary outcomes and linking to a prespecified Statistical Analysis Plan (SAP).

      Issues: The manuscript says: Page 5, lines 25-27: “The outcomes were all-cause non-accidental mortality and hospitalisation, as well as sex-difference…”<br /> -> It is unclear which of these is the primary outcome. Mortality? Hospitalisation? Sex-differential mortality? AND - there is ...

      -> No link to protocol-defined hierarchy.

      2.2. Discrepancies between protocolled numbers and intervention<br /> There are discrepancies between numbers stated in the publicly available protocol and study record at http://clinicaltrials.gov and the numbers appearing in the preprint. The intervention described in the preprint is not aligned with the protocol.

      These discrepancies are unexplained in the preprint. The preprint states that DTP3 has been reported elsewhere, but the reference that is included in the preprint (2) does not report on mortality data, moreover it includes both DTP3 and DTP4. And these protocol deviations are inadequately accounted for in the preprint.

      It remains therefore unexplained what the actual flow of study subjects were, and it remains unclear what the results are from the DTP3+OPV+MV versus OPV + MV only – as stated in the protocol.

      2.3. Randomisation procedure is not sufficiently described<br /> CONSORT requires allocation concealment method and sequence generation details

      Example Page 5, lines 12-14: “...randomisation lots were prepared by the trial supervisor… kept in envelopes… mother asked to draw envelope…” -> No description of safeguards (opaque, sealed, sequentially numbered). -> Allocation was not blinded, but CONSORT requires explicit reporting of potential bias. DTP3 is not mentioned in the trial flowchart - figure 1.<br /> 2.4. Lack of intention-to-treat analysis <br /> CONSORT requires ITT or explanation for deviations.

      Example Page 7, lines 1–3: “All children with follow-up and who received the per-protocol intervention were included in the analyses.”<br /> -> This is per-protocol only, inappropriate for a superiority RCT intended to detect harm.

      -> No ITT analysis is presented.

      2.5. No reporting of missing data handling<br /> CONSORT requires transparent handling of missing outcome data.

      Example Page 7, line 14: “No imputation for missing data was done.” -> But the extent of missing data is not reported for mortality or hospitalization outcomes.

      2.6. Discussion includes non-evidence-based explanations, violates CONSORT as Discussion should reflect results, not speculation<br /> The discussion drifts into immunological theory and historical interpretations unsupported by trial data.

      Examples: Page 10, lines 4-6:“...likely that immune mediated NSEs are more pronounced when mortality is high…”<br /> Page 9-10 (multiple paragraphs): Repeatedly argues unexpected null results require explanation. -> This is speculative; not based on data reported from this RCT.

      2.7. Lack of balanced discussion<br /> CONSORT item 22: discuss both limitations and strengths. -> The manuscript heavily emphasises limitations (underpowering, interventions, etc.), but does not discuss the strength of randomisation or lack of harmful signal which is odd considering the research hypothesis of the trial.

      3. OVERALL REFLECTIONS ON THE IMPACT OF SPIN, FRAMING, AND CONSORT DEVIATIONS<br /> Altogether, it seems to be rather unusual for researchers to put the trial down already in the headline, downright devaluating the trial. The authors are known to advocate detrimental effects of the DTP vaccine, a hypothesis that is based on purely observational studies (3, 4), and very small numbers that have not managed to replicate even by the same group of researchers (5).

      This preprint reports results from a large-scale randomized trial, outranking observational studies in the hierarchy of evidence. Hence – making use of “A failed trial” appears to be an attempt to frame the results as invalid, which is ethically disturbing and highly inappropriate towards trial participants and readers.

      p. 10:“The present RCT is therefore an outlier which needs an explanation. The drop in power due to the declining mortality rate may not only have lowered the possibility of finding significant tendencies; it is also likely that the immune mediated NSEs are more pronounced when mortality is high, so when mortality declines by >80%, the residual deaths may be less likely to be affected by immunological changes.”<br /> There seems to be a deliberate misinterpretation, unsubstantiated, and highly speculative. It is difficult not to read this in any other way than as a deliberate attempt to spin the results, frame them to be perceived according to the authors’ hypothesis about DTP having detrimental effects and increasing child mortality. Spinning results is defined as questionable research practice (6). The study was a null finding study, not an outlier.

      There were no signs of more pronounced negative NSE, i.e., higher mortality in the child participants, who got DTP with, or after the measles vaccine. However, the primary outcome analysis demonstrated that this trial is a null finding study and thus the hypothesis was rejected.

      3.1.Spinning the facts around other interventions.<br /> Several times in the preprint, the authors argue that other health interventions affected the trial conduct and the results.

      Examples<br /> Page 1;During the trial period many new interventions, including many national health campaigns, were carried out.”<br /> and <br /> “due to the large number of health interventions, not envisioned at the initiation of the trial, a limited part of the follow-up was a comparison between DTP4+OPV4 vs OPV4 as the most recent vaccinations”<br /> Page 6:“Other interventions and interactions. As the number of routine vaccinations and national health campaigns vaccinations increased through the 1990s and the 2000s, it has become increasingly clear that there are numerous interactions between different health interventions, such as vaccines and micronutrient supplementation, which are usually not taken into consideration in planning a vaccination programme. For example, the sequence of vaccinations, the time difference between non-live and live vaccines, and booster exposure to the same vaccines all had impact on the mortality levels. In addition, most vaccines have sex-differential NSEs (16). Since children were enrolled at 18 months of age, there were numerous possibilities for interactions with (a) national health intervention campaigns before enrolment; (b) participation in previous RCTs; and (c) national health campaigns after enrolment in the trial.”

      Page 10:trials of NSEs were planned more or less as vaccine efficacy studies. However, it has become increasingly clear that there are interactions with other routine vaccinations, vaccination campaigns, and other interventions affecting the immune system like vitamin A (16,19,20). Hence, in the present RCT we examined possible interactions with campaigns before enrolment, previous RCTs, and campaigns given after enrolment.”

      -> The reader is left with the impression that a series of other factors influenced the trial and possibly invalidated the results. However, this was a randomized trial set-up which to a great extent compensates for any potential confounding effects, ie. other interventions that may have affected the outcome; but they will do so in both the intervention and comparator group.

      Moreover, from table 1 of the preprint – Baseline characteristics – it would seem that the authors tend to put too much weight on multiple other factors as the trial appears to be well randomized.

      Finally, if it in fact was true that this trial was influenced by other RCTs, health interventions, or campaigns, then this argument applies to all trial data originated from this research group in Guinea Bissau and consequently invalidates all of them.<br /> Again it is remarkable that the authors put down their own trial, spin the data and frame them into letting the reader believe that the trial is worth nothing at all. This is not in accordance with appropriate reporting standards as per CONSORT (7).

      3.2. Spinning the facts around the succession of vaccines<br /> p.3 “high-titre-measles-vaccine (HTMV) was protective against measles infection, but surprisingly, it was associated with higher female mortality, when tested against STMV (5,6). Hence, NSEs could be beneficial or deleterious and they were often sex-differential.

      References 5 and 6 are self-citations and based on post hoc re-analyses. The hypothesis that the DTP – following HTMV induced higher mortality remain highly speculative and never replicated. A more likely explanation would be that the HTMV was dosed too high resulting in measles infections, attenuated but still, which unfortunately in some cases increased the subsequent risk of mortality. This is notably a specific effect of the vaccine. However, as the authors advocate that the live (attenuated) vaccines are inferring beneficial effects and the non-live vaccines infer detrimental effects, a post-hoc narrative was constructed on the succession of vaccines having relevance. Importantly, this current preprint where the DTP vaccine is given alongside or not a live attenuated vaccine does not support this highly speculative hypothesis. On the contrary: if anything the results pointed towards DTP increasing child survival.

      1. OVERALL REFLECTIONS ON ETHICS

      4.1. Troubling lack of ethical standards and compliance

      p. 7 it is stated that the study was explained to mothers in the following way:

      “...though DTP is highly protective against whooping cough, it can occasionally give adverse reactions or limit the effect of measles vaccine….”

      This speculative hypothesis seems to be introduced in the study participant / guardian information material, although this was never defined as a research question in the protocol.

      Moreover, the protocol states:

      “Hypothesis: Not providing DTP together with or after MV is associated with a 35 % reduction in overall mortality and 23% reduction in hospitalizations.

      Taking one step back – and reflecting just for a minute – it appears to be the wildest research question ever. How did the Ethics Committee and the relevant authorities allow for this largescale trial to be conducted in the first place? What could possibly justify a RCT of this magnitude based on an outrageous research question like the one that was raised in the protocol: A 35% reduction in mortality is expected from omission of a single shot of vaccine?

      4.2. Underpowered or not<br /> The preprint states that the trial was “highly underpowered,” although 109% of the planned study population was enrolled. There seems to be a large contrast between how this trial and a recently reported trial (8) are interpreted based on whether there was a significant finding or not. These discrepancies indeed appear as tendentious framing.

      A direct comparison of these two large RCTs conducted by the same research group – with vast discrepancies in the results (enrolment and conduct) as well as interpretation is available at this link: https://www.linkedin.com/pulse/review-preprint-reports-dtp-trial-nct00244673-charlotte-str%25C3%25B8m-awgtf/ <br /> 4.3. Self-citation rate of 95%<br /> Nineteen of 20 references include members of the same author group – and are thus self-citations. This may reflect a general lack outside this group of scientific support to the NSE hypothesis and / or selective citation which is considered to be questionable research practice (6). A rule of thumb is that a self-citation rate above 15% raises suspicion of selective citation.

      4.4. Reflections on the “Postscript” of the preprint<br /> It is truly a good thing that these results have finally come to light. The study subjects, their families, and the scientific community have been waiting for these data to be published.

      The preprint is concluded by a lengthy postscript explaining the unusual long delay (14 years) in publishing the results from this trial.

      "Postscript. We apologise for the late reporting. The implementation of the trial went quite different from the scheduled plans. In this older age group, more children than expected were registered by an ID and address that could not be followed. Funding was lacking for the PhD student to complete the data cleaning and analysis. Before funding could be obtained, the Guinean field supervisor had died which made it difficult to resolve some inconsistencies in data. The senior authors had too many other commitments. Finally, from 2020, the COVID-19 pandemic changed all priorities"<br /> These explanations may very well be seen as a result of hypocrisy, as members of this group of authors have published numerous papers – including reporting of several clinical trials during the past 14 years. Moreover during this delay it has been argued by members of the author group that an RCT with the exact same research hypothesis should be conducted (10):

      “Almost 4 years after WHO reviewed the evidence for NSEs and recommended further research, IVIR-AC has now submitted for public comments two protocols of RCTs to measure the NSE impact of BCG and MV on child mortality:<br /> a. A BCG trial will compare mortality between 0 and 14 weeks of age for children randomized to BCG-at birth plus routine vaccines at 6–14 weeks of age vs. placebo at birth and routine vaccines at 6–14 weeks, with BCG at 14 weeks of age.<br /> b. An MV trial will compare mortality between 14 weeks and 2 years of age for children randomized to an additional dose of MV co-administered with DTP3 vs. placebo co-administered with DTP3.”<br /> According to http://clinicaltrial.gov the study hypothesis of NCT00244673.<br /> “DTP3/4+OPV+MV versus OPV+MV or DTP4+OPV4 versus OPV4”<br /> And even worse – it was claimed in the same publication Expert Review of Vaccines, Vol 17, 2018 – Issue 5 (10) that: "Science is also about accounting for all data. ... it has not been possible to conduct RCTs of DTP in high-mortality areas."<br /> There has evidently been a complete lack of willingness from the research group behind this trial to report on this null finding study that rejected the research hypothesis and rejected the hypothesis that the DTP vaccine has detrimental NSE. Such selection bias in reporting trial results on mortality is scientifically troubling and ethically both irresponsible and unacceptable.

      References:

      1. Agergaard JN, S.; Benn, C.S.; Aaby, P. Randomised trial of not providing booster diphtheria-tetanus-pertussis (DTP) vaccination after measles vaccination and child survival: A failed trial. In: Bandim Health Project IN, Apartado 861, Bissau, Guinea-Bissau; Department of Infectious Diseases, Aarhus University Hospital, Denmark; Bandim Health Project, OPEN, Department of Clinical Research, University of Southern Denmark/Odense University Hospital, Denmark; Danish Institute for Advanced Study (DIAS), University of Southern Denmark, Denmark, editor. 2025.

      2. Agergaard J, Nante E, Poulstrup G, Nielsen J, Flanagan KL, Ostergaard L, et al. Diphtheria-tetanus-pertussis vaccine administered simultaneously with measles vaccine is associated with increased morbidity and poor growth in girls. A randomised trial from Guinea-Bissau. Vaccine. 2011;29(3):487-500.

      3. Mogensen SW, Andersen A, Rodrigues A, Benn CS, Aaby P. The Introduction of Diphtheria-Tetanus-Pertussis and Oral Polio Vaccine Among Young Infants in an Urban African Community: A Natural Experiment. EBioMedicine. 2017;17:192-8.

      4. Aaby P, Mogensen SW, Rodrigues A, Benn CS. Evidence of Increase in Mortality After the Introduction of Diphtheria-Tetanus-Pertussis Vaccine to Children Aged 6-35 Months in Guinea-Bissau: A Time for Reflection? Front Public Health. 2018;6:79.

      5. Sørensen MK, Schaltz-Buchholzer F, Jensen AM, Nielsen S, Monteiro I, Aaby P, et al. Retesting the hypothesis that early Diphtheria-Tetanus-Pertussis vaccination increases female mortality: An observational study within a randomised trial. Vaccine. 2022;40(11):1606-16.

      6. Bouter LM, Tijdink J, Axelsen N, Martinson BC, Ter Riet G. Ranking major and minor research misbehaviors: results from a survey among participants of four World Conferences on Research Integrity. Res Integr Peer Rev. 2016;1:17.

      7. Hopewell S, Chan AW, Collins GS, Hrobjartsson A, Moher D, Schulz KF, et al. CONSORT 2025 explanation and elaboration: updated guideline for reporting randomised trials. BMJ. 2025;389:e081124.

      8. Thysen SM, da Silva Borges I, Martins J, Stjernholm AD, Hansen JS, da Silva LMV, et al. Can earlier BCG-Japan and OPV vaccination reduce early infant mortality? A cluster-randomised trial in Guinea-Bissau. BMJ Glob Health. 2024;9(2).

      9. Benn CS. Non-specific effects of vaccines: The status and the future. Vaccine. 2025;51:126884.

      10. Benn CS, Fisker AB, Rieckmann A, Jensen AKG, Aaby P. How to evaluate potential non-specific effects of vaccines: the quest for randomized trials or time for triangulation? Expert Rev Vaccines. 2018;17(5):411-20.

    1. On 2025-12-01 00:37:35, user Cyril Burke wrote:

      [Note: This is the fourth of several rounds of review of an earlier version of our combined manuscript, aiming to reduce ‘racial’ disparity in kidney disease. The comments were kindly offered by nephrologists, through a medical journal, and we remain grateful to them for the time and care they gave to improve our manuscript.

      We removed identifying features and included our response, at the end. The changing title and line numbers refer to earlier versions.]

      January 4, 2023

      Dear Dr. Burke III,

      REDACTED.

      Editor: Unfortunately, the re-re-revised manuscript is not improved. The authors have declined to shorten the paper, despite repeated requests by both reviewers and the editor. As such, the paper is not fit for publication in its current format. This is pity as there are some valid points within the manuscript, although some others are debatable and not backed up by good scientific evidence.

      REDACTED.

      Reviewer #1: I greatly regret that the next round of revision (R3!) does not take into account the key suggestion of the previous round, to concentrate on part 1 and drastically shorten the paper

      Reviewer #2: Thank-you for the opportunity to review this manuscript.

      The manuscript raises some important points with regards to the use of serum creatinine in the diagnosis and monitoring of kidney disease, as well as important considerations about race.<br /> As the authors acknowledge the manuscript remains too lengthy for consideration as a research article. Unfortunately, the authors have declined to shorten the manuscript as recommended by the reviewers and editor.

      RESPONSE TO EDITOR AND REVIEWERS

      January 16, 2023

      Early detection of kidney injury by longitudinal creatinine to end racial disparity in chronic kidney disease: The impact of race corrections for individuals, clinical care, medical research, and social justice

      We write to appeal the rejection of our manuscript. We were grateful for constructive comments from the Academic Editor and Reviewers and incorporated almost all of their suggestions, some itemized below. We were pleased that Reviewer #2 and Reviewer #1 recommended publication in the second and third [journal] decisions, respectively. But we were surprised by this rejection.

      ‘Race’ has been central to our manuscript from the original submission because discussing ‘race’ is essential to reduce ‘racial’ disparity in kidney care. Kidney failure is three times more common in Black than White Americans. As anthropologists have known and shown for more than a century, but biologists and physicians have been slow to acknowledge, biological ‘race’ is scientifically invalid and should be irrelevant. However, in the United States, ‘race’ is uniquely defined, ubiquitously applied, and often presumed to have a biological basis in medical research. A key point in our paper, based on our clinical observations and data reanalysis, is that race corrections add further harm to medical care by obscuring the causes of disparities and delaying or derailing the search for real underlying cofactors, especially in nephrology [1,2].

      For this reason, we disagree with suggestions to slice the article into two or more separate publications (the long-known practice of “salami science” or publishing of the “smallest publishable unit”). Separating the data from the take-home message would undermine the overview we are trying to provide for [journal] readers.

      Below, we highlight some excerpts from the [journal] decisions, adding our commentary.

      1. Eneanya ND, Boulware LE, Tsai J, Bruce MA, Ford CL, Harris C, et al. Health inequities and the inappropriate use of race in nephrology. Nat Rev Nephrol. 2022 Feb;18(2):84-94. doi: 10.1038/s41581-021-00501-8. Epub 2021 Nov 8. PMID: 34750551; PMCID: PMC8574929.

      2. Norris KC, Williams SF, Rhee CM, Nicholas SB, Kovesdy CP, Kalantar-Zadeh K, et al. Hemodialysis Disparities in African Americans: The Deeply Integrated Concept of Race in the Social Fabric of Our Society. Semin Dial. 2017 May;30(3):213-223. doi: 10.1111/sdi.12589. Epub 2017 Mar 9. PMID: 28281281; PMCID: PMC5418094.

      1. (4/1/2022): Revision required

      Reviewer #1 wrote: <br /> …a somewhat unusual paper, devoted to a topic of potential major clinical relevance, and as yet understudied….

      Reviewer #2 wrote: <br /> “Choi- rates of ESRD in Black and White Veterans” doesn’t fit with the rest of the paper including the title; the introduction and conclusion also don’t adequately address this portion of the paper. It feels disjointed from the main point of discussion which is the use of sCr in screening “pre-CKD”. This section and discussion should be removed and possibly considered for another type of publication….

      We understood Reviewer #2 as indicating the reanalysis of Choi needed better integration with other Parts of the manuscript or had to be cut. This interpretation was validated by Reviewer #2’s response to our major revision (see below).

      2. (8/3/2022): Revision required

      The Academic Editor wrote:<br /> The revised manuscript only partially addresses the critiques raised by the Reviewers. ….the authors need to address all the minor points highlighted by Reviewer 2.

      Reviewer #1 wrote: <br /> …the main key message (which is right in the opinion of this reviewer (see first round of review) and warrants more attention and studies.

      Reviewer #1 wrote: <br /> The race part is irrelevant for the key point (race does not change over time, and thus is not relevant when looking at longitudinal serum creatinine or eGFR) and should be deleted in the opinion of this reviewer.

      On ‘race’, we strongly disagree. ‘Racial’ disparity will continue until we talk about ‘race’. For the major revision, we made clearer the connection between our two data reanalyses (of Shemesh et al and Choi et al). Social ‘race’ in the US differs from social ‘race’ anywhere else, yet these are rarely compared, so an international audience objecting to discussion of ‘race’ often has no idea what ‘race’ means in the US. ‘Race’ is fraught, and to advocate change requires more words than to acquiesce to current practices (i.e., banning discussion of ‘race’ favors the status quo).

      Reviewer #2 wrote: <br /> Thank-you, once again, for the opportunity to review this lengthy “thesis-style” manuscript which discusses some important often over-looked topics. The under-use of serial creatinine measurements and over-reliance on often erroneous eGFR measurements is an important point which is easily missed by healthcare workers with potentially serious consequences. Likewise, the misuse of racial constructs in medicine (and elsewhere) is an important point. I am satisfied with this re-submission and the changes which have been made to the original manuscript.

      Reviewer #2 acknowledged the changes and recommended publication.

      3. (10/10/2022): Revision required

      The Academic Editor wrote: <br /> The re-revised manuscript is further improved.... I could offer you the possibility to shorten the manuscript just focusing on what you define “Part One” plus “section A of Part Two”. You can briefly address the “race” issue in the discussion…

      The Academic Editor seems not to appreciate that ‘race’ is a central topic in our manuscript, as evidenced by our secondary data reanalysis of Choi. As we noted earlier, publishing our manuscript in two or more separated Parts would make the reader work to reassemble them. Cutting Choi and briefly addressing ‘race’ would not allow the quality of argument needed to address ‘racial’ disparity in kidney failure, and would fundamentally shift our paper to focus purely on nephrology. For the topic to be complete, our data must be assessed in terms of its meaning for ‘racial’ disparities that are currently widespread in medical practice.

      Reviewer #1 wrote: <br /> As part one is important and should trigger further studies, after reading the comments of reviewer 2 , I am ready to recommend acceptance.

      Reviewer #1 recommended the second revision for publication.

      Reviewer #2 wrote: <br /> Once again, this reviewer in no way questions the often-overlooked inaccuracies in mGFR methods. However, the authors cannot quote a well conducted review which shed light on the methodological bias and imprecision which exists between mGFR methods and claim that this methodological bias is “physiologic variability”. The authors should review: Rowe, Ceri, et al. "Biological variation of measured and estimated glomerular filtration rate in patients with chronic kidney disease." Kidney international 96.2 (2019): 429-435. Intra-individual variation (CVI) for serum creatinine ranges from around 2.8 – 8.5% while cystatin C ranges from around 3.9 – 8.6%, inter-individual variation (CVG) of serum creatinine: 7.0 – 17.4% and cystatin C: 12 – 15.1%. Biological variation (CVI and CV¬G) are not the same as analytical variation, which also exists for serum creatinine and cystatin C. The author’s statement is not backed up by scientific evidence.

      Reviewer #2 provided a key reference, leading to our addition to the next revision of an important section on “gold standards” and Bland-Altman plots.

      Reviewer #2 wrote:<br /> Instead of drastically shortening the manuscript the authors have added to the length thereof.... This reviewer has chosen not to provide further comment on the new additions to the manuscript”....

      …the main point of the article, although difficult to decipher, is highly relevant.

      We wonder if the paragraphs were somehow mixed up, because the tone of this comment is different and Reviewer #2 had recommended publication in the earlier Decision and had just recommended a key reference, above.

      4. (1/4/2023): Rejection

      The Academic Editor wrote: <br /> Unfortunately, the re-re-revised manuscript is not improved…<br /> The Academic Editor’s idea of improvement appears limited to breaking the manuscript into several parts. We had hoped that clear improvements might be persuasive, including a major section on “gold standards” (inspired by Reviewer #2’s reference), reorganization for readability, revision of the Table of Contents, and others, but as noted above, we could not accept the offer to publish a radically altered message.

      The Academic Editor wrote: <br /> …despite repeated requests by both reviewers…

      Reviewer #2 then Reviewer #1 had approved the manuscript for publication.

      The Academic Editor wrote: <br /> …there are some valid points within the manuscript, although some others are debatable and not backed up by good scientific evidence.

      We worked to not overstate our evidence. Regarding the data from over 2 million veterans of Choi et al (in Part 3) our reanalysis stated: “The sample size was very small—only 15 data points—because Choi broke (dichotomized) the continuous raw data into five data segments… therefore, the precision of this result may not hold up with replication. However…”. We also wrote addressing this concern (in Revision 2, Part 3) and updated the sentence (in Revision 3, Part 4): “…we discuss… some novel or speculative GFR cofactors…. These require further study, and some may prove insignificant.”

      Moreover, “good scientific evidence” is hard to define and extensively debated by methodologists, but the Academic Editor isn’t entirely wrong. The evidence we provided is more of a demonstration than new scientific evidence, which is both a strength and a limitation. The “gold standard scientific approach” would be to test all our claims analytically in new samples of data, which is far beyond the scope of this project, so the Academic Editor isn’t wrong about that—some claims are debatable and are not backed up by good scientific evidence. The analytic methodologies we used were far from conventional, but that was the point—to identify areas of misconception open to debate, and to shed new light on them in an innovative way. Were these not debatable points, there would be no need for an alternative approach.

      REDACTED.

      We could argue that our paper effectively employs science, but on this issue, it seems more relevant to note that ours is clearly about ways to improve the base of academic knowledge—refining scientific process through better understanding of science, so this criticism seems inconsistent with [REDACTED] and detracts from the nuance that is a strength of our manuscript.

      Nevertheless, we remain interested in incorporating feedback and ask whether the Reviewers could briefly list the points they believe are debatable and not backed up by good scientific evidence, which would allow us to address those points and either provide better evidence or state why the current evidence is weak.

      Reviewer #1 wrote: <br /> I greatly regret that the next round of revision (R3!) does not take into account the key suggestion of the previous round, to concentrate on part 1 and drastically shorten the paper.

      As we noted, the research part of the manuscript comes first, in Parts 1 to 3. Busy readers can stop before Parts 4 and 5, but we believe these data and discussions need to be kept together.

      Reviewer #2 wrote: <br /> The manuscript raises some important points with regards to the use of serum creatinine in the diagnosis and monitoring of kidney disease, as well as important considerations about race.

      As the authors acknowledge the manuscript remains too lengthy for consideration as a research article. Unfortunately, the authors have declined to shorten the manuscript as recommended by the reviewers and editor.….

      It is unclear what changed the mind of Reviewer #2, who recommended publication after the major revision and inspired the important section on “gold standards”—a clear improvement that we found satisfying.

      Reviewer #2 references our comment sent in our last Response to the Reviewers: “…our manuscript may no longer be a good fit for [the journal]”, which was our most polite way of declining the Academic Editor’s offer to publish only part of our manuscript, narrowly focused on Nephrology or Laboratory Medicine. Our goal is to keep the manuscript intact.

      In summary, the Academic Editor and Reviewers have not offered good scientific evidence for cutting a manuscript that lengthened to address their many thoughtful suggestions, nor against discussing ‘race’ as central to American ‘racial’ disparities in kidney failure. REDACTED.

      THEREFORE: For all the above reasons, we request reconsideration of the decision against publication.

      Thank you for considering this appeal.

      Sincerely,

      Cyril O. Burke III

    2. On 2025-11-30 16:49:17, user Cyril Burke wrote:

      [Note: This is the first of several reviews of an earlier version of our combined manuscript that aims to reduce ‘racial’ disparity in kidney disease. The comments were kindly offered by nephrologists, through a medical journal, and we remain grateful to them for the time and care they gave to improve our manuscript.

      We removed identifying features and will include our responses in a subsequent comment. The changing title and line numbers refer to versions prior to our medRxiv preprints.]

      April 1, 2022<br /> Screening for early kidney disease and population health using longitudinal serum creatinine

      Dear Dr. Burke III,

      REDACTED.

      Reviewer #1: Burke et al submit a somewhat unusual paper, devoted to a topic of potential major clinical relevance, and as yet understudied.

      General comments

      1. The thesis of the authors, that using the baseline serum creatinine of a given patient would potentially improve the earlier diagnosis of kidney disease, even in the normal range, is in line with the experience of this reviewer, who always retrieves , whatever the difficulty of reaching that goal, past results of blood tests, and uses them as a way to date the onset of kidney disease, sometimes with important prognostic implications.

      2. Yet, the authors do not provide data strongly supporting their thesis. For instance, when looking at case 2, should the last point (the most recent one) be omitted, there would be very little evidence supporting progressive early kidney disease.

      3. The claim that the statistics fit the data better when all points are used (page 9,11) should not come as a surprise. Using thresholds instead of the full range of values has long been known to be more powerful for statistical analysis. But fitting the data does not equal to a high positive predictive value!

      4. A key question is whether in a real world context, the earlier diagnosis of kidney disease would be possible, without too much background noise from intercurrent illness (functional), drugs (NSAIDS, etc..). In other words, would the specificity (or PPV) of the suspicion of early kidney disease be reasonable enough to catch the attention of clinicians

      5. Even though there has been improvement in the standardization of measurement of serum creatinine (IDMS), the comparability of results measured by different labs remains suboptimal, at least in the experience of this reviewer, and medical shopping is not uncommon, making the availability of all previous results in the same graph a logistical challenge.

      Specific comments

      1. The authors should mention that the USPTFS decided a month ago to revisit the question of screening for kidney disease in high risk groups (page …)

      2. Even though ESRD has a legal meaning in the USA, not very relevant to the topic of this paper about early kidney disease, the authors should stick to the nomenclature proposed by a recent KDIGO consensus conference (see Levey et al. Nature Reviews in Nephrology ). In particular, use kidney failure instead of ESRD/ESKD. When the topic is glomerular filtration, use that wording instead of kidney function (page…)

      3. The authors allude to the concepts of prediabetes and prehypertension. But this reviewer points to the fact that the levels used to define those entities are currently “generic” , rather than based on previous values in an individual subject. Please discuss.

      4. The authors repeatedly mention in the discussion section evidence that even small increases in serum creatinine have prognostic significance. This has indeed been known for decades but is a different topic: AKI . Admittedly, there is growing evidence that AKI and CKD are linked. But that the stability of a biological parameter is prognostically best is all except surprising: the same is true for body weight, mood, blood pressure etc…

      Reviewer #2: Thank-you for the opportunity to review this work which highlights the importance of monitoring serum creatinine over time and how this can be a useful tool in detecting possible CKD. This is an important topic as the use of sCr on its own is certainly under-utilised and changes are often missed because they don’t fall into a predefined category.

      MAJOR CONCERNS

      “Choi- rates of ESRD in Black and White Veterans” doesn’t fit with the rest of the paper including the title; the introduction and conclusion also don’t adequately address this portion of the paper. It feels disjointed from the main point of discussion which is the use of sCr in screening “pre-CKD”. This section and discussion should be removed and possibly considered for another type of publication.

      Cases 1 - 3, (lines 93 – 122): where are these cases from? There is no mention of ethics to publish these patient results, which appears to be a clear ethics violation. If so, these cases should be removed and patient consent and ethical approval obtained to publish them.<br /> The authors describe the reasons for not obtaining an ethics waiver for this secondary data analysis. Despite this, the relative ease of obtaining an ethics waiver for secondary data analysis usually means that this is done regardless.

      The message of the article and data representation is unclear: do the authors wish to show that sCr is superior to eGFR in this “pre-CKD” stage, should both be used together? Do the authors wish to convey that a “creatinine blind range” does not exist? Or is the aim to demonstrate that continuous variables should not be interpreted in a categorical manner?

      MINOR CONCERNS

      ABSTRACT<br /> Vague<br /> Doesn’t give a clear picture of the study

      INTRODUCTION<br /> 51 – 57: needs to state that these stats are from e.g. the US. The authors should consider adding international statistics to complement those from the US.

      68: reference KDIGO guidelines, state year

      75 – 77: is this reference of the New York Times the most appropriate?

      82: within-individual variation not changes (this is repetition of the point made in lines 425 – 427, but should match the language)

      82 – 84: reference? If this is a question it should be presented as such

      84: “normal GFR above 60” = guidelines (including KDIGO) do not refer to 60 as normal GFR, 60 – 89 is mildly decreased. (see line 126)

      93: avoid the use of emotive words such as apparently (also in line 428)

      94: “Not meeting KDIGO guidelines”: KDIGO 2.1.3 includes a drop in category (including those with GFR >90). This would appear to include some of the cases listed. Additionally, albuminuria should have been measured for case 2 and 3.

      97: “progressive loss of nephrons equivalent to one kidney”: this is based on a single creatinine measurement.

      93 – 122: Could any of these shifts be explained by changes in creatinine methodology or standardisation of assays, especially over 15 – 20 years (major differences between assays existed before standardisation and arguably still exist with certain methods).<br /> It would be useful to see a comparison between serial sCr and eGFR measurements on the same figure. There appears to be significant (possibly more pronounced) changes when eGFR is used. As line 87 mentions changes in eGFR may be as useful (and in some situations more useful) than changes in sCr alone.

      127 – 142: should there be separate charts for males and females, the differences in creatinine between males and females needs to be discussed somewhere in the paper. Similarly, is this suitable for all ages?

      162 – 163: rephrase

      METHODS<br /> 185 – 193: aim belongs in the introduction, can be adjusted to complement paragraph 178 – 182.

      196 – 205: reference sources

      224 – 247: not in keeping with the rest of the article or title and conclusion

      RESULTS<br /> If eGFR is treated as a continuous variable does inverted sCr still have higher accuracy?

      As mentioned, the section on ESRD in black and white veterans doesn’t fit in with the rest of the article.

      DISCUSSION<br /> As mentioned, section 4.1 doesn’t fit in with the rest of the article. As the authors note the correlation between illiteracy and CKD is likely not causal.

      387: erroneous creatinine blind range. The data presented does not show this is erroneous there is still a relative blind range. A distinction must be made between a population level “blind range” and an individual patient’s serial results. The data and figure 4 in particular demonstrate the lack of predictive ability of sCr above 40ml/min compared to below 40ml/min at a population level. For an individual patient this “blind range” is more relative, and a change in sCr even within the normal range may be predictive. (Note: the terminology “blind range” is problematic).

      399 – 400: “rose slowly at first and then more rapidly as mGFR decreased below 60” this refers to a relative blind range. Whether these slow initial changes can be distinguished from analytical and intra-individual variation is the question that needs to be answered before we can say a “blind-range” doesn’t exist for an individual patient.

      425 - 432: sCr is indeed very useful when baseline measurements are available. eGFR remains useful when baseline sCr is not available or when large intervals between measurements are found.

      425: low analytical variation- if enzymatic methods are used

      428: avoid the use of “apparently”

      430: reference 56 compares sCr and sCysC with creatinine clearance NOT with mGFR, this does not prove that mGFR has greater physiologic variability. Creatinine clearance is known to be highly variable (partially due to two sources of variability in the measurements of creatinine: serum and urine).

      The limitations of sCr for screening should also be discussed: differences in performance and acceptability between enzymatic and Jaffe methods (still widely used in certain parts of the world), the effect of standardizing creatinine assays (an important initiative but one that could also produce shifts in results around the time of standardization- see cases), low InIx means that once-off values are exceedingly difficult to interpret, is a single raised creatinine value predictive (or should there be evidence of chronicity): similarly are there effects from protein rich meals, etc (The influence of a cooked-meat meal on estimated glomerular filtration rate. Annals of Clinical Biochemistry. 2007;44(1):35-42. doi:10.1258/000456307779595995)

      CONCLUSION<br /> The discussion recommends using SCr above eGFR while the conclusion recommends the NKF-ASN eGFR for use in pre-CKD and ASC charts. While the use of both together in a complementary fashion is understandable- this needs to be congruent with the discussion, aims and results.

    3. On 2025-11-30 17:00:32, user Cyril Burke wrote:

      RESPONSE TO REVIEWER #2<br /> June 27, 2022<br /> Reviewer #2: Thank-you for the opportunity to review this work which highlights the importance of monitoring serum creatinine over time and how this can be a useful tool in detecting possible CKD. This is an important topic as the use of sCr on its own is certainly under-utilized and changes are often missed because they don’t fall into a predefined category.<br /> Thank you for considering our manuscript and for your detailed comments.

      MAJOR CONCERNS

      A. “Choi- rates of ESRD in Black and White Veterans” doesn’t fit with the rest of the paper including the title; the introduction and conclusion also don’t adequately address this portion of the paper. It feels disjointed from the main point of discussion which is the use of sCr in screening “pre-CKD”. This section and discussion should be removed and possibly considered for another type of publication.<br /> We have attempted to clarify this inclusion. This manuscript could be divided into three or four short papers, increasing the likelihood that any one of them would be read. However, different groups tend to read papers about screening for kidney impairment, racial disparities, cofactors in modeling physiologic parameters, or policy proposals to encourage best practices. Despite the appeal of perhaps three or four publications, we decided to tell a complete story in a single paper, but we are open to suggestions.

      Black Americans suffer three times the kidney failure of White Americans. Other minority groups also have excessive rates of kidney disease. However, analysis of Veterans Administration interventions can bring that ratio close to one, similar interventions might also reduce to parity the risk for Hispanic, Asian, Native Americans, and others. Within-individual referencing should allow better monitoring of all patients and help to reveal the circumstances and novel kidney toxins that lead to progressive kidney decline. The ability to identify a healthy elderly cohort with essentially normal kidneys would help to calibrate expectations for all. Better modeling of GFR should help everyone, too.

      Over eight decades, anthropologists have had little scholarly success in diminishing the inappropriate use of ‘race’. Keeping these parts together may be no more successful, but we feel compelled to try.

      B. Cases 1 - 3, (lines 93 – 122): where are these cases from? There is no mention of ethics to publish these patient results, which appears to be a clear ethics violation. If so, these cases should be removed and patient consent and ethical approval obtained to publish them.<br /> The authors describe the reasons for not obtaining an ethics waiver for this secondary data analysis. Despite this, the relative ease of obtaining an ethics waiver for secondary data analysis usually means that this is done regardless.<br /> We take patient privacy seriously and have completely de-identified the Case data, as required by Privacy Act regulations. We understand that no authorization or waiver was necessary. We discussed the issues with an IRB representative, reviewed the relevant regulations, and confirmed no need for formal review of a secondary analysis of already publicly available IRB-approved data or of completely de-identified clinical data collected in the course of a treating relationship.

      IRBs have a critical role to play, but many (including ours) are overworked. We understand the impulse authors feel to gain IRB approval even when the regulations clearly do not required it. As we discuss in the revision, there is a more significant matter that IRBs could help to resolve if they have the resources to do so. For all of these reasons, and even though we, too, felt the urge to obtain IRB approval, we resisted adding “just a little more” to their work.

      C. The message of the article and data representation is unclear: do the authors wish to show that sCr is superior to eGFR in this “pre-CKD” stage, should both be used together? Do the authors wish to convey that a “creatinine blind range” does not exist? Or is the aim to demonstrate that continuous variables should not be interpreted in a categorical manner?<br /> Our interest is detection and prevention of progression of early kidney injury at GFRs above 60 mL/min – a range in which eGFR is especially unreliable. We have advanced the best argument we can to detect changes in sCr while kidney injury is still limited and perhaps reversible. If experience reveals that some avoidable exposure(s) begins the decline, then clinicians might alert patients and thereby reduce kidney disease. How best to use longitudinal sCr remains to be determined from experience. However, our message is that early changes in sCr can provide early warning of a decline in glomerular filtration. We are confident that clinicians can learn to separate other factors that may alter sCr, as we do for many other tests.

      MINOR CONCERNS<br /> ABSTRACT<br /> A. Vague. Doesn’t give a clear picture of the study<br /> We have tried to clarify the title and abstract and are open to further suggestions.

      INTRODUCTION<br /> B. 51 – 57: needs to state that these stats are from e.g. the US. The authors should consider adding international statistics to complement those from the US.<br /> We have updated the statistics on death rates from kidney disease to include US and global data.

      C. 68: reference KDIGO guidelines, state year<br /> We now reference the KDIGO 2012 guidelines.

      D. 75 – 77: is this reference of the New York Times the most appropriate?<br /> We have expanded this section with peer-reviewed, scholarly references. However, we found Hodge’s summary of the issue succinct and hence potentially more persuasive for some than decades of scholarly references that have had limited or no effect in the clinic.

      E. 82: within-individual variation not changes (this is repetition of the point made in lines 425 – 427, but should match the language)<br /> We have matched the language.

      F. 82 – 84: reference? If this is a question it should be presented as such<br /> We have attempted to clarify this statement.

      G. 84: “normal GFR above 60” = guidelines (including KDIGO) do not refer to 60 as normal GFR, 60 – 89 is mildly decreased. (see line 126)<br /> We agree and have corrected the language.

      H. 93: avoid the use of emotive words such as apparently (also in line 428)<br /> We wanted to emphasize appearance without proof and have made these changes.

      I. 94: “Not meeting KDIGO guidelines”: KDIGO 2.1.3 includes a drop in category (including those with GFR >90). This would appear to include some of the cases listed. Additionally, albuminuria should have been measured for case 2 and 3.<br /> We have clarified that cases may or may not fit KDIGO categories, though that question will frequently arise in evaluating sCr changes. Where available, we have added urine protein and/or albumin results to the Cases.

      J. 97: “progressive loss of nephrons equivalent to one kidney”: this is based on a single creatinine measurement.<br /> Since the original submission, we discovered for this Case (now Patient 3) early serum creatinine results and notes indicating a six-month period off thiazide diuretic. This data clarified the baseline and showed a remarkable effect of thiazide diuretic on sCr. We have added follow-up sCr results and details of thiazide use to the ASC chart.

      K. 93 – 122: Could any of these shifts be explained by changes in creatinine methodology or standardization of assays, especially over 15 – 20 years (major differences between assays existed before standardization and arguably still exist with certain methods).<br /> It would be useful to see a comparison between serial sCr and eGFR measurements on the same figure. There appears to be significant (possibly more pronounced) changes when eGFR is used. As line 87 mentions changes in eGFR may be as useful (and in some situations more useful) than changes in sCr alone.

      It would be helpful to have a chronology from each local laboratory with the date of every change in creatinine assay or standardization. However, any single shift draws attention but does not necessarily indicate significant change in glomerular filtration. After one or several incremental increases, over at least three months, the sCr pattern may meet the reference change value (RCV) that signals significant change. In the future, from age 20 or so, a patient’s medical record should retain the full range of the longitudinal sCr for true baseline comparison.

      As noted in the revised manuscript, Rule et al showed that there is measurable nephrosclerosis even in the youngest kidney donors, suggesting that some injuries (perhaps exposure to dietary toxins) may begin in childhood and that early preventive counseling may be worthwhile. Experience will show whether this can slow progression to CKD. As we note, quoting Delanaye, sCr accounts for virtually 100% of the variability in eGFR equations based on sCr (eGFRcr), and these equations add their own uncertainties, so no, we do not believe that eGFR is more useful than sCr when GFR is above 60 mL/min and possibly much lower as well.

      We have added eGFR results to the ASC charts (in blue), though availability was somewhat limited.

      L. 127 – 142: should there be separate charts for males and females, the differences in creatinine between males and females needs to be discussed somewhere in the paper.

      We do not think there should be separate charts for men and women based on size. The role of sex in eGFR equations is mainly based on the presumption that the average woman has less muscle mass than the average man. Clinicians care for individuals, not averages, and this sweeping generalization that increases agreement of the average of a population introduces unacceptable inaccuracy to individual care. Within-individual comparison eliminates the need for assumptions on relative size or muscle mass. Major changes in an individual’s muscle mass will usually be evident to the clinician who can adjust for them.

      However, reports suggest significant influence of sex hormones on renal function, including effects of estrogen and estrogen receptors, such as reducing kidney fibrosis, increasing lupus nephritis, and increasing CKD after bilateral oophorectomy. The mechanism of these effects and how they might be incorporated into eGFR estimating equations is unclear, but the effort may benefit from a more individualized approach with focus on a measurand rather than matching population-based averages of a quantity value (calculated from measurands).

      M. Similarly, is this suitable for all ages?<br /> We think so. Another sweeping generalization based on age merely introduces another inaccuracy which complicates the task of clinicians caring for individuals. Older persons have varying health, athleticism, muscle mass, dietary preferences, etc. Rule et al reported that biopsies of about 10% of older kidney donors had no nephrosclerosis. Within-individual comparison eliminates the need for assumptions on relative muscle mass or inevitable senescent decline in nephron number. We substitute the assumption that any change in an individual’s muscle mass will be evident and can be accounted for. A seemingly ubiquitous risk factor, or factors, starts injuring kidneys at a young age, which we may yet identify.

      N. 162 – 163: rephrase<br /> Done.

      METHODS<br /> O. 185 – 193: aim belongs in the introduction, can be adjusted to complement paragraph 178 – 182.<br /> Reorganized and rewritten.

      P. 196 – 205: reference sources

      References provided.

      Q. 224 – 247: not in keeping with the rest of the article or title and conclusion

      We have revised and restructured this section.

      RESULTS<br /> R. If eGFR is treated as a continuous variable does inverted sCr still have higher accuracy?<br /> We believe so. Serum creatinine is a measurand and reflects the total sum of physiologic processes, known and unknown. In contrast, eGFR equations yield a quantity value, calculated from a measurand and dependent on the assumptions and approximations incorporated by their authors. The eGFR equations are thus necessarily less accurate than the measurands they are derived from, in this case, sCr. In a hyperbolic relationship, as the independent variable drops below one and approaches zero, the effect is to amplify the inaccuracy of the independent variable in the dependent variable. By avoiding the mathematical inverting, the data suggest that direct use of sCr is far more practical for pre-CKD.

      S. As mentioned, the section on ESRD in black and white veterans doesn’t fit in with the rest of the article.<br /> We have revised, reorganized, and rewritten. We also outlined our rationale above.

      DISCUSSION<br /> T. As mentioned, section 4.1 doesn’t fit in with the rest of the article. As the authors note the correlation between illiteracy and CKD is likely not causal.<br /> See above.

      U. 387: erroneous creatinine blind range. The data presented does not show this is erroneous there is still a relative blind range. A distinction must be made between a population level “blind range” and an individual patient’s serial results. The data and figure 4 in particular demonstrate the lack of predictive ability of sCr above 40ml/min compared to below 40ml/min at a population level. For an individual patient this “blind range” is more relative, and a change in sCr even within the normal range may be predictive. (Note: the terminology “blind range” is problematic).<br /> We agree. On reading closer, Shemesh et al call attention to “subtle changes” in serum creatinine even though they had access only to the uncompensated Jaffe assay, so their recommendation to monitor sCr is even more forceful, today, due to more accurate and standardized creatinine assays. We have attempted to clarify this in the manuscript.

      V. 399 – 400: “rose slowly at first and then more rapidly as mGFR decreased below 60” this refers to a relative blind range. Whether these slow initial changes can be distinguished from analytical and intra-individual variation is the question that needs to be answered before we can say a “blind-range” doesn’t exist for an individual patient.

      We appreciate this observation. We believe longitudinal sCr is worth adopting to gain insights into individual sCr patterns, which may reveal early changes in GFR, among other influences on sCr. This is a low-cost, potentially high-impact population health measure, and there seems little risk in trying it because many clinicians already use components of the process.

      W. 425 - 432: sCr is indeed very useful when baseline measurements are available. eGFR remains useful when baseline sCr is not available or when large intervals between measurements are found.<br /> As Delanaye et al noted, virtually 100% of the variability in longitudinal eGFR is due to sCr, so we understand that the errors in eGFR can be (and usually are) greater than but cannot be less than those in sCr.

      X. 425: low analytical variation- if enzymatic methods are used<br /> Lee et al suggest that even the compensated Jaffe method provides some accuracy and reproducibility, which may allow longitudinal tracking of sCr even where more modern assays are as yet unavailable.

      Y. 428: avoid the use of “apparently”<br /> Done.

      Z. 430: reference 56 compares sCr and sCysC with creatinine clearance NOT with mGFR, this does not prove that mGFR has greater physiologic variability. Creatinine clearance is known to be highly variable (partially due to two sources of variability in the measurements of creatinine: serum and urine).<br /> The creatinine clearance is another form of mGFR, and our understanding of it begins with the units: if the clearance or removal of creatinine were being measured, the units should be umoles/minute, but they are mL/min. “Clearance” is an old concept coined by physiologists to describe many substances, such as urea, glucose, amino acids, and other metabolites. Since creatinine is mostly not reabsorbed and is only slightly secreted in the tubules, the “creatinine clearance” became a measure of GFR. The ratio of urine Creatinine to serum Creatinine is simply a factor for how much the original glomerular filtrate then gets concentrated (typically about 100-fold) by the kidney. Since the assumption is that the timed urine was once the rate of glomerular filtrate production, the creatinine clearance is a measure of the GFR.

      Creatinine clearance has some inaccuracies based on tubular secretion, but also has some advantages: blood concentrations are essentially constant during urine collection, no need for exogenous administration, and reliable measurements in serum and urine. The methods that we often call mGFR also have problems, including unverifiable assumptions about distributions, dilutional effects, and others we cite in the text. None of these are direct measures of GFR. Due to changes in remaining nephrons, even true GFR itself is not strictly proportional to the lost number of functional nephrons, which seems the ultimate measure of CKD that Rule et al estimated from biopsy material.

      AA. The limitations of sCr for screening should also be discussed: differences in performance and acceptability between enzymatic and Jaffe methods (still widely used in certain parts of the world), the effect of standardizing creatinine assays (an important initiative but one that could also produce shifts in results around the time of standardization- see cases), low InIx means that once-off values are exceedingly difficult to interpret, is a single raised creatinine value predictive (or should there be evidence of chronicity): similarly are there effects from protein rich meals, etc (The influence of a cooked-meat meal on estimated glomerular filtration rate. Annals of Clinical Biochemistry. 2007;44(1):35-42. doi:10.1258/000456307779595995)<br /> We have added discussion of additional references on reproducibility of sCr assays and discuss dietary meat and, in Part Three, possible dietary kidney toxins.

      CONCLUSION<br /> BB. The discussion recommends using SCr above eGFR while the conclusion recommends the NKF-ASN eGFR for use in pre-CKD and ASC charts. While the use of both together in a complementary fashion is understandable- this needs to be congruent with the discussion, aims and results.<br /> We have rewritten this section. We would welcome any further recommendations.

      Cyril O. Burke III, MD, FACP

    1. On 2022-04-18 19:58:31, user M. Akers wrote:

      I am looking for some clarification on the connection between race and lower life expectancy:

      1. In the discussion, citations 7 through 9 are highlighted to support a long history of systemic racism, yet there appears to be little supporting data in those articles that connects your specific findings in order to make that assumption. Maybe I’m missing something?
      2. On the other hand, with the continued drop in life expectancy for white, NH Americans in 2021, you offer no meaningful explanation. How can systemic racism be the most apparent cause for a drop in life expectancy in Hispanic and Black populations, yet no cause (or even an attempted explanation) can be surmised for white, NH Americans?
      3. It seems pretty clear that obesity and associated metabolic syndrome have been major drivers for mortality and morbidity during the pandemic in the United States. Do we know how the United States compares to the peer nations cited in your article in terms of obesity and other metabolic syndrome incidence that could help explain your findings? Furthermore, are Black and Hispanic American populations in the United States disproportionally obese compared to white, NH Americans that could also support a greater drop in life expectancy?
      4. Could an increase in suicide rates and/or drug overdoses in younger Americans contributed to your findings? Could lockdown policies and lack of socialization have contributed?

      It seems that for life expectancy to have dropped that significantly, a large proportion of young people would have needed to pass away in order for that drop to occur, yet we know that statistically, young people (e.g. <30 years old) did not die as a result of COVID.

      Any clarification would be helpful as I am having a difficult time making the attempted connection suggesting that race is the only viable variable that explains a drop in life expectancy. With the pandemic, it seems there could be, and likely are, numerous factors contributing to your findings, yet nothing other than race is focused on.

      Thank you.

    1. On 2025-09-29 15:17:43, user Bryan Wilent wrote:

      I think this is great and as I read through it hit me how challenging this is to do. Kudos to the team.

      1. I would consider adding therapeutic impact to title and checklist. The distinction between diagnostic accuracy and therapeutic impact should be addressed head on and then can distinguish between the metrics in the checklist. I feel like this is often conflated in the literature at times and people using this checklist would benefit with a clear guideline

      2, I gather that this will be part of the checklist, but readers would benefit with clear list of all measures available and delineation of which metrics apply to diagnostic versus interventional domains (relative risk, odds ratios, probabilities) If getting into the latter, should also expound on nature of the intervention, like in Holdefer/Skinner structural causal model paper.

      3 How to handle suboptimal IONM planning (modalities/nerves/muscles used), e.g., a study found that IONM had low sensitivity for quadriceps pain/weakness after lateral fusions but the study used posterior tibial nerve SSEP for the LE and EMG only.

      4. How to handle dynamic and variable alert criteria (and not a hard threshold), which is consistent with current ISIN guidelines for SSEPs with variable reproducibility and guidelines for MEPs in diagnosing evolving nerve root v cord v brain dysfunction? I don’t have a great suggestion, but I think it needs to be addressed. Example. A case had a 60% change in SSEPs from a limb in context of stable of MEPs, so an alert was not called.

      5. Alert to what? Is it appropriate to analyze alerts specific to a pattern or injury? Example, how to report if some "alerts" had low specificity, but this alert pattern had both high sensitivity and specificity. Lieberman et al from 2019 on MEP patterns and foot drop is a an example thinking of that uses ROC curves for different muscle MEP change patterns.

    2. On 2025-08-26 18:45:14, user Laura Hemmer wrote:

      Thank you to this expert group on undertaking this needed and carefully-executed initiative to help improve diagnostic accuracy of IONM studies! I have a few minor comments as follow below for your consideration.

      -In the discussion in the Introduction that lists applicable guidelines, the updated ASNM SSEP position statement published in 2024 could also be a helpful reference here for completeness and particularly for its discussion regarding anesthetic and physiologic factors that can impact SSEPs as well the section on interpretation and outcomes, which has some discussion on the interpretation of reversible evoked potential changes. (J Clin Monit Comput. 2024 Oct;38(5):1003-1042.)

      -In the methods section, “STARD dementia” should likely have a reference noted.

      -Please pay attention to the tense used for the portion regarding community engagement and feedback (e.g. abstract methods notes Phase 3 will include broader community…” as is starting to occur now, but then the results portion in the abstract somehow notes what was already emphasized by community feedback. Similarly, the Results Overview in the manuscript seems to indicate the results of community engagement and dissemination, even though it appears community engagement is just now occurring.) This may be confusing for readers.

      -Phase 3 in the Abstract Methods portion notes that this will include broader community feedback, but in the manuscript, it appears community feedback is actually Part 4 of Phase 2 (“Community Engagement and Consensus Building” and Phase 3 is actually the dissemination of the final checklist. Please clarify.

      -In the Results section, part #2, please consider if additional details of your assessment of adherence to the STARD checklist across 12 peer-reviewed publications should be made more fully available, such as adding these 12 references to Supplementary Content.

      -Is the Results section, part #4 accurate yet (i.e. already officially endorsed by 3 international societies) or just anticipated still? These societies will need to be stated before publication.

      -For anesthesia reporting in IONM studies, consider if more details regarding anesthetic technique could be useful. For example, what if additional anesthetic adjunctive/multimodal agents are also incorporated into the anesthetic regimen beyond just TIVA, inhalational, or mixed? We know from the literature, for example, that ketamine in different doses can impact MEP amplitude differently. Also, inhalational amount (e.g. MAC) should be noted when a “mixed” inhalational and intravenous hypnotic anesthetic regimen in being administered, as further evoked potential signal degradation would generally be expected with higher MAC levels.

      -Some of the anesthetic reporting details discussed in the results section are really more physiological details, so should the heading be something like “Anesthesia and Physiologic Reporting in IONM Studies” instead of just “Anesthesia Reporting in IONM studies” perhaps?

      -For patient demographics, in addition to the examples given in the document, including height, weight, etc., please consider noting that studies should also include other pertinent medical comorbidities for IONM purposes, such as the presence of diabetes mellitus and associated neuropathy which may make it harder to obtain robust baseline evoked potentials. Table 2 notes “clinical characteristics”, but I wonder if medical comorbidities that would be particularly pertinent to IONM and that may make even obtaining adequate, robust baseline signals difficult should be more clearly stated in the document and/or Table 2? It is helpful that Table 2, in the clinical characteristics of participants section (#20), does state that baseline IONM data should be reported.

      -Reversibility of IONM changes is well covered by the authors in its own dedicated section within the Results section of the manuscript. Recommendations by the authors on how to best handle all evoked potential deteriorations is also clearly given in the same area of the results section. This important discussion and recommendation by the group, gets a little diluted and confusing when it is re-addressed shortly afterwards still in the results section under “Alternate evaluation framework in IONM”. Please consider if the repetition here is fully needed, or perhaps this area could refer back to the very well-stated section previous in “Reversibility of IONM changes”? Also the section “Alternate evaluation framework in IONM” might benefit from more clear recommendations from the expert working group.

      -Consider if, from the 3rd sentence to the end of the 1st paragraph in the Discussion section, is actually needed. It is pretty redundant from earlier coverage in the document. For conciseness, could move the 2nd paragraph content to just after the 2nd sentence in the 1st paragraph in the Discussion section if desired.

      -Anesthesia techniques definition is very basic in Table 1. For readers who do not carefully read the manuscript and refer more to the Tables only, should more detail be given here or at least could note to see the manuscript text content? Similarly, no mention of anesthesia appears in Table 2, which is the actual checklist being presented. Since standardized reporting of anesthesia-related variables is critical for IONM diagnostic accuracy studies, should anesthesia reporting information appear in the Table 2 checklist?

      -Should studies be asked to more clearly state how it was determined that adequate baseline evoked potential signals were present (reporting of IONM baseline data is recommended in Table 2 #20, which is good). What about in the case of intracranial surgery and the concern for stimulation occurring below the area where ischemia could occur (potentially leading to false negatives)?

      -I do not see the supplementary material currently noted in the document on Medrxiv for review, so I have not reviewed this supplementary content.

      -Minor typographical/grammatical errors noted by me have been directly submitted to one of the working group members.

      Sincerely,<br /> Laura Hemmer, M.D.

    1. On 2020-04-14 11:29:44, user Andrea Nicoletto wrote:

      I am not sure whether I am commenting a scientific article, a political statement or something in between. This article came to my attention because, even if it has not been peer-reviewed yet, has already been referenced in a press release of Italy's central health agency (ISS), which comments the results as a matter of fact and calls them "published".

      Skimming the full text the following points came to my attention.

      1 - On page 4, you state that "Due to the high concordance (99%) among confirmation results with the engaged laboratories, thepolicy was then changed allowing selected Regions with demonstrated confirmation capacity to directly confirm COVID-19 cases (17)." Reference 17 contains only an internal note specifying what a "case" is, but does not contain the data supporting the statement that there is high concordance. Previous publications coming from ISS (e.g. [1]) state that "99% of the samples analized by the national reference lab of ISS result POSITIVE", which suggests a selection bias in sending samples to the central laboratory and completely invalidates the confirmation process. False positive/false negative rates of the cross-analysis has not been reported.

      2 - In your introduction, you state that "extensive contact tracing and testing of close contacts unveiled ongoing transmission in several municipalities of the Lombardia region". The difficulties in testing and tracking cases in Lombardia region is well known, with several papers (including your references) and statements from authorities highlighting the fact that (a) the number of tested people is little w.r.t. the number of potential cases; (b) the classification of potential case varies on a regional basis, and a potential case in any other Italian region might not qualify as a potential case in Lombardia region, thus not getting tested; (c) testing protocols in Lombardia region do not include the testing of people living within the same household of the confirmed case, thus it is unclear in which way the contact tracing has been "extensive"; (d) the delay between the collection of the swab and the communication of the result is long and with a high variance. All these facts shall be taken into account when analyzing statistical data.

      3 - In your conclusions (!), you state that "Further, we observe that as of March 8 2020, the Rt it is still abovethe epidemic threshold. The progressively harsh physical distancing measures enacted since then may have enhanced the decreasing trend in transmissibility as happened in China". You support this statement using a paper which analyses the transmission of Ebola in West Africa. This seems to me like a political statement which shall have no place in a scientific paper, let alone on its conclusions. You have no data to back this statement, since your analysis terminates on the 12th of March, i.e. three days later of the enacment of the lockdown. You even show that there is a decreasing trend in R_t starting in the last decade of February, which puts the R_t at the beginning of the national lockdown slightly above 1 with a strongly declining trend. While writing this sentence has no scientific value, of course, it allows the MoH and ISS to defend their decision-making because "science said that".

      I do not have the specific background to carry out a review of the quantitative data, but I cannot ignore the use of poorly-backed statements to dignify as "science" what are only political decisions. Unsurprisingly, these statements are those which will fit into press releases and official statements.

      [1] https://www.epicentro.iss.i...

    1. On 2020-04-16 18:03:55, user Sinai Immunol Review Project wrote:

      This retrospective study evaluated the use of intravenous methylprednisolone to treat severe COVID-19 pneumonia in a cohort of 46 patients. The severity of the disease was determined according to version 5 of the Coronavirus Pneumonia Diagnosis and Treatment Plan by the National Health Committee of the People's Republic of China. The percentage of patients with comorbidities was 32%, and it was reported similar between methylprednisolone treated and untreated groups. The results showed that the group of patients that received methylprednisolone (n=26) had a shorter number of days with fever than patients that did not received methylprednisolone (n=20); they also had a faster improvement of peripheral capillary oxygen saturation (SpO2), and better outcomes in follow-up CT scans of the lungs. The dosage of methylprednisolone was reported to be 1-2mg/kg/d for 5-7 days, although there is no information about the concrete dosages for each patient. From the 46 patients, 43 recovered and were discharged, while 3 cases were fatal. Patients without administration of methylprednisolone needed longer periods of supplemental oxygen therapy, though there is no reference to the number of patients requiring mechanical ventilation. Interestingly, there were no significant differences in leucocyte and lymphocyte counts nor in the levels of IL-2, IL-4, IL-6 or IL-10 after treatment with methylprednisolone.

      Some of our main criticisms to this study are also pointed-out by the authors themselves: it is a retrospective single-center study with no validation cohort and without mid- and long-term follow-ups. The reported mortality was 7% (3/46) and did not appear to be affected by corticosteroid treatment: one patient died in the group that did not receive methylprednisolone, while two patients died in the methylprednisolone treatment group. Additionally, although the authors mention that patients received cotreatments, such as antiviral therapy and antibiotics, there is no mention of differences between the prevalence of other medications between the two groups. Unfortunately, there is also no indication on whether the patients receiving methylprednisolone were discharged earlier; the authors merely refer that the symptoms and signs improved faster.

      Corticosteroid have been widely used as therapy for acute respiratory distress syndrome (ARDS), including in infections by SARS-CoV, so these findings in COVID-19 patients are not unexpected. The implications of this study for the current pandemic due to SARS-CoV-2 require evaluation in future clinical trials, especially in a randomized way and in combination with and comparison to other immunosuppressive and immunomodulatory agents, including hydroxychloroquine. Nevertheless, based on this report, the intravenous application of methylprednisolone with the intention of strengthening the immunosuppressive treatment and controlling the cytokine storm appears to be safe in COVID-19 patients, and it might successfully shorten the recovery period.

      This review was undertaken by Alvaro Moreira, MD as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai

    1. On 2020-07-20 17:29:44, user Kamran Kadkhoda wrote:

      The following finding <br /> Using the pre-defined cutoffs, the sensitivity of IgG antibodies rose from 7% (<=7days) to<br /> 7% after 14 days of symptoms. The sensitivity of IgA and IgM rose to 91% and 81% 2-4 weeks<br /> post-symptom onset but dropped after 4 weeks to 57% and 40%, respectively.<br /> ...is classic for an anamnestic immune response especially given<br /> IgG showing up early similar to other studies and the half-life of<br /> IgG-plasmablasts suggesting response to previous response to common CoVs.

    1. On 2020-07-21 22:11:37, user BiotechObserver wrote:

      "Screened patients either had confirmed SARS-CoV-2 infections by PCR, or suspected disease, defined as being told by a physician that symptoms may be related to SARS-CoV-2 or exposure to someone with confirmed SARS-CoV-2 infection... In addition to screening potential donors, Mount Sinai also offered the Mount Sinai ELISA antibody test to all employees within our health system on a voluntary basis."

      It would be helpful to know what percentage of each of these 4 subsets of screened patients (out of the 51,829 total screened) were positive vs. negative on the ELISA test. <br /> Your sensible subsequent explanation on sensitivity findings notwithstanding, (it seems plausible you are detecting positive in the ELISA test >95% of those with a confirmed past infection), this breakdown would still be valuable from an epidemiology perspective.

      "The vast majority of symptomatic cases that were screened experienced mild-to-moderate disease, with less than 5% requiring emergency department evaluation or hospitalization."

      It would be helpful to visualize in a few additional figures the breakdown of various measures (titer ranges, decline/increase, neutralizing activity) stratified by severity of symptoms (asymptomatic vs. mild vs. moderate vs. hospitalized, or asymptomatic vs. mild/moderate vs. hospitalized).

      Thank you.

    1. On 2020-07-24 16:23:32, user David Gagnon wrote:

      Was any data collected on the numbers for the ages of the people in the same household?This section was badly written, is missing something, or I just don't understand something in the language. 15.5% is the lowest of those numbers and that seems odd, since the I would guess the majority of 2 person households to be couples without kids, both young and old, and in the latter case the secondary infection should have been notably high, right?<br /> There are also three number in the section that correspond to three groups:<br /> "The secondary infection risk for study participants living in the same household increased from 15.5% to 43.6%, to 35.5% and to 18.3% for households with two, three or four people respectively (p<0.001). "<br /> Is the 18.3% for households with more than 4 people? Is it then 18% per person?

    1. On 2020-07-09 21:41:42, user kpfleger wrote:

      Thank you for this study! Suggestions to improve the manuscript:<br /> (1) In the PDF version linked here from medRxiv (as of 2020/7/9), p.1 states the multivariate infection OR as 1.45 but p.5 & table 3 list it is 1.50. Minor discrepancy but good to get the p.1 results summary correct.<br /> (2) You imply in the conclusion that most of the 25OHD test results were recent, such as upon presentation to health services for illness, but it would be helpful for you to characterize the dates of the 25OHD tests in your cohort. (Eg, so we know they aren't as old as the 10+ year old UK Biobank results.)<br /> (3) It would be helpful to have the descriptive statistics for demographic and clinical characteristics for the hospitalized vs. non-hospitalized COVID-19-P patients---analogs of tables 1 & 2 stratified by hospitalized or not. I'm not even sure you say how many were hospitalized.<br /> (4) You gave the multivariate adjusted OR for infection and the OR for hospitalization given infection. It would be nice to state the adjusted OR for absolute risk of hospitalization (not specifically given infection) as this is perhaps more meaningful for public policy.

      I look forward to a couple more analyses like this from other large HMOs or government insurers around the world. I also look forward to data on post-hospitalization measures of severity (eg, ICU/ITU admission, fatality) in population cohorts this large.

    1. On 2020-08-28 07:43:07, user Hilda Bastian wrote:

      At several points, the authors rely on what's described as the "historical efficacy of passive antibody therapy for infectious diseases". This is based on a small amount of data, much of it from the pre-intensive care era, and none from a publication later than 2010. As a result, no randomized trial is included, as they were published after 2010: 2 NIH randomized trials of convalescent plasma in influenza and 2 randomized trials of IVIG for influenza. Meta-analysis shows no benefit. [1] This also fails to consider the post-2010 ebola outbreak, and the failure of convalescent plasma to improve mortality from that disease. [1] Thus, there is no historically proof of efficacy of convalescent plasma, and what randomized data exists, does not suggest there has been important benefit in the past.

      In addition to relying on this biased assessment of historical evidence to support a conclusion of effectiveness in this study, the authors cite this claim as a reason for not conducting a randomized trial: "Many COVID-19 patients would likely have been distrustful of being randomized to a placebo based on historical precedent". However, if they were accurately informed, prospective participants would be told there was no evidence of benefit. In a randomized trial in the Netherlands stopped because it was determined no benefit was likely in the study as designed, the authors reported that only 1 in 4 eligible patients declined, and that was typically because of fear of adverse events. [2] The requirement for adequate trial recruitment has more to do with doctors and patients in outbreaks not being misled about the state of uncertainty of this treatment.

      The authors argue that the patients in the Expanded Access Program are diverse. However, it is important to point out that their diversity is not representative of the people severely ill with Covid-19 and at risk of dying. For example, 19% of the group are Black, whereas the CDC reports that they are over 30% of those hospitalized with Covid-19 and twice as likely to die. [3,4]

      In respect of the representativeness of the small non-random sample described as "pseudo-randomized" in this preprint, no data is provided on the hospitals providing those samples.

      In addition, as others have already pointed out in a discussion linked here, [5] critical information on timing of deaths is not provided. Those transfused earlier in the "epochs" have far longer follow-up for deaths than the larger number more recently. Given that since early in the outbreak, it's been observed that deaths occur across 2 to 8 weeks from the onset of symptoms, [6] the impact of this could be substantial, as participation in the EAP was higher later. In the group on the Diamond Princess cruise, for example, per Wikipedia's tallying, half the deaths may have occurred in that second month [7], and assessment of mortality appropriately included censoring for this. [8] Case series in the US typically report substantial proportions of people still in intensive care at study's end.

      The authors' interpretation of their subgroup analysis based on a non-random set of blood samples preserved for blood bank quality assurance proceeds as though the safety of convalescent plasma for Covid-19 has been established, based on the data of their own uncontrolled study. However, controlled study is required to be certain, for example, whether plasma with lower levels of antibodies trigger antibody dependent disease enhancement. [5] As the FDA's memorandum reports that the results are also dependent on which assay results are used, this should be reported in any discussion of this subgroup analysis. [9]

      In the absence of adequate controlled study of convalescent plasma establishing that it does more good than harm in infectious respiratory disease generally in contemporary medical settings, and Covid-19 in particular, the authors' claim that their uncontrolled study provides "strong evidence" is unjustified.

      Disclosure: I have written about this study for the general public at WIRED, and am in the process of doing so at PLOS Blogs.

      [1] Devasenapathy (2020). https://www.cmaj.ca/content...

      [2] Gharbharan (2020). https://www.medrxiv.org/con...

      [3] CDC COVID-Net (2020). https://gis.cdc.gov/grasp/C...

      [4] CDC surveillance data (2020). https://www.cdc.gov/coronav...

      [5] Harrell (2020). https://discourse.datametho...

      [6] WHO (2020). https://www.who.int/docs/de...

      [7] Wikipedia (2020). https://en.wikipedia.org/wi...

      [8] Russell (2020). https://www.medrxiv.org/con...

      [9] FDA Clinical Memorandum (2020). https://www.fda.gov/media/1...

    2. On 2021-08-15 12:58:51, user Stephen B. Strum wrote:

      I re-read this article in a recent "quest" to understand why we do not have surrogate virus neutralization tests (sVNTs) that are available through national labs such as LabCorp and Quest. An important publication by Goodhue Meyer led me back to the Joyner et al. paper. Here's my overall take on the Joyner paper and the issues at-large.

      1. There appears to be a very excellent correlation between either natural COVID-19 infection or vaccination with the development of virus-neutralizing antibodies (NAbs).

      2. The occurrence of high-titer NAbs correlates well with protection from new infection from COVID-19 and also reduces morbidity when variants of concern (VOC) cause infection in vaccinated individuals. Yet mass testing of the population has not been done because these surrogate tests are not agreed upon as to which one(s) has the greatest sensitivity & specificity for NAbs as determined in plaque reduction neutralization tests (PRNT) assays or wild-type COVID-19 assays, both of which are tedious, expensive, require BSL3 (biosafety level 3) labs and not suitable for high throughput testing.

      3. Yet, as of today, 8/15/21, there exist publications showing good correlation between specific sVNT and plaque reduction neutralization tests (PRNT). In reading over 150 articles on this topic, I have not found any articles so far that have studied the Ab (antibody) test used in the Joyner study (VITROS Anti-SARS-CoV-2 IgG qualitative assay by Ortho. Please help out and identify if such articles have been published (I am still searching).

      4. The FDA authorized an EUA for Ortho's VITROS test above while other assays that published their results were not used to select COVID-19 convalescent plasma (CCP) for treatment purposes.

      ? So how do we really know what the nAb levels were in the CCP given to patients in the study? The VITROS Ab test used is a qualitative test. At the time of publication I am fairly certain that there were no correlative studies to show that this test was accurately depicting the nAb levels using so-called gold standards.

      ? How, as Hllda Bastian pointed out do we accept the article at face value without a placebo control? There is a need to go over the structure of this study to ascertain if the differences in survival later reported in 2021 by Joyner et al. are sufficient to look further into CCP and if so, then what is the best way to screen for donors?

      ? Why are not donors selected using a sVNT that has been shown to have high correlative value vs. PRNT such as cPASS by GenScript?

      ? Why are not donors selected by cPASS results from those vaccinated with Pfizer or Moderna where the cPASS results can be shown to be protective against VoC such as Delta Variant (B.1.617.2)?

      ? Note that I am not a virologist, but a hematologist/oncologist that also happens to be immunocompromised. I assessed my nAb status with an sVNT that has been commercially available across the USA: LabCorp test code 164090: SARS-CoV-2 Semi-Quantitative Total Antibody, Spike using Roche Elecsys on a cobas 601 analyzer. With testing at one month post-Pfizer #2, my total levels were > 250 U/ml, but at 4 months later they had decreased to 59 U/ml. If these Ab levels continue to fall I will be one of the functionally un-vaccinated or under-vaccinated. This is a large group of patients in the world and a potential breeding ground for more vicious VoC. <br /> Stephen B. Strum, MD, FACP <br /> sbstrum@gmail.com

    1. On 2020-12-05 01:38:02, user ACE NYPD wrote:

      I have been using Betadine Gargle (.05% Povidine Iodine) as a gargle & nasal spray for months at 3 or 4 times a day. My wife, who has comorbidities also uses it. I have been exposed to Covid at least 4 times by others at work, and have always come back negative. Since I am in Tech Support, I have used keyboards and mice of infected persons. I am currently working from home until my latest exposure is 14 days since exposure. I took a rapid test that came back negative 6 days after the exposure, but I am still waiting on the PCR test I took the same day.

      I also take a vitamin D supplement.

      Do I think that the Betadine Gargle is preventing me from getting Covid, yes I do, but of course talk to your physician first. I have found these articles about Povidine Iodine and Covid:

      https://www.pulmonologyadvi...

      https://doi.org/10.1177/014...

      https://www.thailandmedical...

      Stay safe and informed.

    1. On 2020-12-29 00:33:03, user Olga Matveeva wrote:

      Several recent preprints support some of this manuscript findings.<br /> 1. Authors from Sweden and China in a study entitled “Pulmonary stromal expansion and intra-alveolar coagulation are primary causes of Covid-19 death” demonstrated that “The virus was replicating in the pneumocytes and macrophages but not in bronchial epithelium, endothelial, pericytes or stromal cells. doi: https://doi.org/10.1101/202...<br /> 2. Researchers in China concluded that “Collectively, these results demonstrate that SARS-CoV-2 directly neutralizes human spleens and LNs through infecting tissue-resident CD169+ macrophages.” They published a preprint entitled “The Novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Directly Decimates Human Spleens and Lymph Node” doi: https://doi.org/10.1101/202...<br /> 3. Researchers in France demonstrated “that SARS-CoV-2 efficiently infects monocytes and macrophages without any cytopathic effect.” Their findings are reported in the preprint entitled “Monocytes and macrophages, targets of SARS-CoV-2: the clue for Covid-19 immunoparalysis” doi: https://doi.org/10.1101/202...<br /> 4. Researchers in Brasil investigated SARS-CoV-2 infection of PBMCs and found that in vitro infection of whole PBMCs from healthy donors was productive of virus progeny. They also found that “SARS-CoV-2 was frequently detected in monocytes and B lymphocytes from COVID-19 patients, and less frequently in CD4+T lymphocytes” The preprint is entitled “Infection of human lymphomononuclear cells by SARS-CoV-2”. <br /> doi: https://doi.org/10.1101/202...

    1. On 2021-01-13 16:49:11, user Ezequiel Petrillo wrote:

      Suppl. Table 2 has an error. Where it says that the Custom-made qPCR mix has 8 ul of MgCl2, it should say 4 ul (4mM final concentration), adding the extra volume with ultrapure water. We will fix this error in an updated version soon.

    1. On 2021-06-11 13:44:12, user Jay Alan Erdman wrote:

      Where to begin? 1) This is an abstract; not yet peer reviewed; so it's just four guys saying this. 2)They don't say how they got their population. 3) They provide no data. 4) There is no control group either randomized, case control, or cohort. 5) They really do not specify their methods. 6) There is no treatment protocol so we don't know what additional treatment they may have received. Overall this doesn't meet any scientific standard. But even if it had been well done; these patients presented in the Spring of 2020 when treatment protocols were very different and outcomes much worse. This study says nothing about whether HCQ would be of any benefit to patients receiving treatment in Spring 2021.

    2. On 2021-06-11 18:53:43, user SemperCogitens wrote:

      A couple of things people really need to note here:<br /> 1) This is PRE-publication. It hasn't been peer-reviewed. Everything on this website is such. It cannot be regarded the same as something which has gone through the full process with a respected journal.<br /> 2) This is an observational study (retrospective cohort). It is NOT a randomized, placebo-controlled clinical trial. Only the RCT can prove causation. This is hypothesis-generating research only. It does not PROVE HCQ works, merely suggests that.

      3) If you dig in a bit, they define "treatment" to include only 37 of the advertised 250ish patients in the cohort, specifically those receiving >3,000mg HCQ and >1g azithromycin. Small sample sizes should always be viewed with extreme skepticism. Less than 30ish and your p-value is essentially meaningless.

      4) The overall mortality rate of the total group was ~80%! These people were old, unhealthy to begin with, admitted to the hospital after nearly a week of progressing symptoms, required ICU care, intubation, and mechanical ventilation. These were very sick patients.

      5) Clinically, we see a pattern for the treatment of ARDS and secondary pneumonia, not COVID. High dose corticosteroids work (we know this for ARDS), tocilizumab, a potent anti-inflammatory works (we have some data for this in ARDS as well). And azithromycin is a very common drug to treat bacterial pneumonias that often pop up secondary to lung disease like this.

      So, please avoid jumping to unsupported conclusions. If we want real answers, we have to keep our skepticism, and be very careful in our analysis. The key here is we need corroborating studies. An April 2020 study at the VA showed exactly the opposite effect of HCQ in a similar patient population (though the VA obviously serves more males).

    1. On 2021-07-01 03:15:16, user Subhajit Biswas wrote:

      Really excited to see that our original observation that pre-exposure to dengue may be cross-protective against COVID-19, has been further supported by the following study from Brazil!

      Title of paper: Previous Dengue Infection and Mortality in Coronavirus Disease 2019 (COVID-19)

      Abstract: We studied 2351 participants with coronavirus disease 2019; 1177 (50%) reported previous dengue infection. Those without previous dengue had a higher risk of death (hazard ratio: .44; 95% confidence interval: .22–.89; P = .023) in 60-day follow-up. These findings raise the possibility that dengue might induce immunological protection against severe acute respiratory syndrome coronavirus 2.

      Link: https://doi.org/10.1093/cid...

      This perhaps explains why mortality in dengue endemic regions like the Indian subcontinent, Africa and SE Asia is about 10-times less compared to dengue non-endemic regions.

      Why Brazil is an exception?

      Read following publications:

      1. https://www.medrxiv.org/con... by Prof Miguel A L Nicolelis

      2. https://doi.org/10.1016/j.c... by our Group

      As of today, Worldometer says mortality per million population is 287 for India compared to 1863 for US & 1878 for UK (1st July, 2021). Indian population is almost 4-times the population of US with 0.4 million deaths compared to 0.62 million deaths in US.

    1. On 2021-08-31 01:53:43, user William Brooks wrote:

      The results of the proposed model rely on three questionable assumptions: 1) masks are effective at preventing infection [1]; 2) infection risk decreases as mask usage increases [2]; and 3) masks are more effective than ventilation [3].

      However, the authors ignore real-world data challenging these assumptions even though they reference the UK's Events Research Programme (ERP), which found little difference between Phase 1 events with and without mask requirements [4]. Moreover, recent ERP data for large-scale sporting events without mask requirements "demonstrate that mass participation events can be conducted safely, with case numbers comparable to, or lower than community prevalence" [5].

      In short, the authors should base their models on real-world data rather than unproven assumptions.

      [1] https://www.acpjournals.org...<br /> [2] https://escipub.com/irjph-2...<br /> [3] https://aip.scitation.org/d...<br /> [4] https://www.gov.uk/governme...<br /> [5] https://www.gov.uk/governme...

    1. On 2022-09-23 15:15:43, user Yu Li wrote:

      It is important to regularly check the primers and probe sequences of a PCR or qPCR assay against GenBank because newly generated sequences may cause erosions or failures of a published assay. The article Wide mismatches in the sequences of primers and probes for Monkeypox virus diagnostic assays | medRxiv attempted the in silico analysis of published monkeypox virus (MPXV) specific qPCR assays. However, the article contains numerous errors in its results, lacks experimental data to support its conclusions, and can impair the 2022 monkeypox outbreak response.

      The genome sequences of monkeypox virus (MPXV) are highly similar (~95% identical) to that of other species of orthopoxviruses (OPXV). The similarities between MPXV clade I and clade II are over 99%. Therefore, identifying a qPCR targeting site for primer and probe design that perfectly matches MPXV and contains enough sequence differences to differentiate other OPXV can be very challenging. The probe sequence of a qPCR assay is often given priority for target selection in assay development. Multiple studies have reported that PCR primer mismatches do not necessarily affect performance of a PCR assay. For example, Kwok S et al (1) and Christopherson C et al (2) showed that up to 4 mismatches in the primer-template duplexes (28 and 30 base primers) did not have a significant effect on RT-PCR (the sequence similarity is as low as 80%). The mismatch positions and type of nucleotides involved in the mismatch play important roles. The buffer and annealing temperature used in a PCR assay can also be critical in determining the assay’s performance. A single base mismatch in the reverse primer of the Orthopoxvirus generic OPX3 assay led to a 100-fold decrease of the sensitivity of this assay in detecting the 2022 monkeypox outbreak predominate strain (clade IIb, lineage B.1) in one buffer (3) but switching to a different PCR buffer nearly reversed this lost sensitivity. This example highlights the critical nature of performing laboratory validation testing to ensure specificity and sensitivity. The published MPXV qPCR assays have largely been validated by inclusivity and exclusivity panels (4), and the MPXV_G2R generic assay has been used extensively without sensitivity issues in detecting different clades of MPXV. This article made claims that “Our results show that the current MPV real-time generic assay may be unsuitable to accurately detect MPV” without any supporting experimental data. In addition, the title of the article is misleading without supporting data and can lead to uncertainty surrounding MPXV diagnostics.

      The authors performed sequence similarity analysis of 8 published MPXV qPCR assays, including three CDC qPCR assays specifically designed to detect all MPXV isolates (generic assay), only clade I isolates (MPXV clade I assay) and only clade II isolates (MPXV clade II assay). In Figure 1, the detailed sequences alignment of MPXV generic assay MPXV_G2R were presented relative to the sequence of MPXV clade I. The authors showed two sets of primers; one set of primers, MPV-F-mu/MPV-R-mu, perfectly matches with MPXV clade IIb, lineage B.1 and contain a single mismatch for both the forward and reverse primers compared to originally published primer sequences. The MPXV_G2R generic assay was designed to detect both monkeypox clade I and clade II (4), and the primer sequences were designed using the MPXV clade I sequence. The publication of the MPXV G2R generic assay showed that this assay detects both clade I and clade II of MPXV (4). The MPXV G2R generic assay has been used for MPXV diagnostics since its publication in our laboratory and demonstrates no differences in the sensitivity of detecting MPXV clade I and clade II. Clinical diagnostic data confirmed that the limited primer mismatches have little effect on the performance of the MPXV_G2R generic assay under current protocols.

      In Figure 2 panel A, the authors claimed that the MPV_G2R_WA-P, the probe sequence of MPXV clade II specific assay, contains the Mutation1 sequences, which are in 4.2% of 683 MPXV genome sequences the authors have included in their analysis. However, there are no genome sequences from MPXV clade II containing the Mutation1 sequences by the BLAST analysis of GenBank database. It is likely that the authors mistakenly used the sequences from MPXV clade I (MPXV Congo basin) as the Mutation1 sequences of clade II (West Africa clades). MPV_G2R_WA-P was designed to specifically detect MPXV clade II; the probe targeting sequences contain a 3 base deletion compared to clade I. <br /> If the authors have sequence data supporting their claims of genome sequences of MPXV clade II containing the Mutation1 sequences, they should make these available for others to analyze.

      We are deeply concerned about the errors in this article and the lack of experimental data to support the authors’ conclusions. The authors should promptly address the issues raised here and consider the potential negative impact of this article on the MPXV diagnostics in 2022 monkeypox outbreak responses.

      References<br /> 1. Kwok S, Kellogg DE, McKinney N, Spasic D, Goda L, Levenson C, Sninsky JJ. Effects of primer-template mismatches on the polymerase chain reaction: human immunodeficiency virus type 1 model studies. Nucleic Acids Res. 1990 Feb 25;18(4):999-1005. doi: 10.1093/nar/18.4.999. PMID: 2179874; PMCID: PMC330356.<br /> 2. Cindy Christopherson, John Sninsky, Shirley Kwok, The Effects of Internal Primer-Template Mismatches on RT-PCR: HIV-1 Model Studies, Nucleic Acids Research, Volume 25, Issue 3, 1 February 1997, Pages 654–658, https://doi.org/10.1093/nar...<br /> 3. Crystal M. Gigante, Bette Korber, MatthewH. Seabolt, Kimberly Wilkins, Whitni Davidson, Agam K. Rao, Hui Zhao, Christine M. Hughes, Faisal Minhaj, Michelle A. Waltenburg, James Theiler, Sandra Smole, GlenR. Gallagher, David Blythe, Robert Myers, Joann Schulte, Joey Stringer, Philip Lee, Rafael M. Mendoza, LaToya A. Griffin-Thomas, Jenny Crain, Jade Murray, Annette Atkinson, AnthonyH. Gonzalez, June Nash, Dhwani Batra, Inger Damon, Jennifer McQuiston, Christina L. Hutson, Andrea M. McCollum, Yu Li. Multiple lineages of Monkeypox virus detected in the United States, 2021- 2022 bioRxiv 2022.06.10.495526; doi: https://doi.org/10.1101/202...<br /> 4. Li Y, Zhao H, Wilkins K, Hughes C, Damon IK. Real-time PCR assays for the specific detection of monkeypox virus West African and Congo Basin strain DNA. J Virol Methods. 2010 Oct;169(1):223-7. doi: 10.1016/j.jviromet.2010.07.012. Epub 2010 Jul 17. PMID: 20643162

    1. On 2022-12-15 10:49:12, user Author wrote:

      We would like to reply to a comment entitled “Japan preprint on myocarditis used inadequate methods to suggest COVID-19 vaccines cause more myocarditis deaths”: a review by Health Feedback (Editor: Ms. Flora Teoh). <br /> https://healthfeedback.org/...

      We thank them for commenting on our paper. We understand their main points of criticism were three summarised as followings:

      1. Comparison of pre-pandemic and post-pandemic rates of myocarditis death (their 2nd point)

      2. No examination of history of myocarditis death and ignored COVID-19 as the cause (their 1st point)

      3. Sample size was too small to discuss causality

      1. comparison of pre-pandemic and post-pandemic rates of myocarditis death (their 2nd point)

      Their 2nd point is based on the fundamental misunderstanding on the methods of our study. They erroneously stated "The authors' association of change in the risk of myocarditis death associated with COVID-19 vaccines was based on comparing pre-pandemic and post-pandemic rates of myocarditis death". <br /> We compared myocarditis mortality in the SARS-CoV-2 VACCINATED population with that of the 2017-2019 (pre-pandemic period: reference) population; we did NOT compare myocarditis mortality between POST-PANDEMIC and pre-pandemic periods.<br /> Because of the misunderstanding the fundamental methods of our study, the following criticism have no sense:<br /> “But this assumes that the only thing that changed between the two periods is the availability of the COVID-19 vaccines. It excludes, without justification, the possibility that COVID-19 itself could produce an increase in myocarditis deaths. No reason was given by the authors for excluding COVID-19 as a potential explanation, despite the fact that COVID-19 is a more likely explanation than COVID-19 vaccines for an increase.” “This is because we know—based on previous published studies—that COVID-19 is more likely to lead to cardiac complications than the vaccines [1,2]. Therefore, the alleged causal association rests on the assumption that only COVID-19 vaccines can explain the change in myocarditis mortality, which isn’t true.”<br /> However, we would like to comments on “COVID-19 is more likely to lead to cardiac complications than the vaccines” referring reports by Block et al [1], and Patone et al [2,3].<br /> It is important to consider following three points; vaccines are not given to dying persons and to persons with fever or other acute diseases. Hence vaccinated people are relatively healthier than the non-vaccinated (healthy vaccinee effect) [4]. Conversely, vulnerable persons (frail, suppressed immunity due to stress or sleep debt etc) are more likely to be infected with SARS-CoV-2 (vulnerability confounding bias: VCB) [5].

      Patone et al. [1] stated in the discussion section as follows: “Of note, the estimated IRRs were consistently <1 in the pre-exposure period before vaccination. ---- This was expected because events are unlikely to happen shortly before vaccination (relatively healthy people are receiving the vaccine).” This is exactly the same as the healthy vaccinee effect [4] and it is the lowest at day 0 of vaccination [2]: for example, IRR of arrhythmia at day 0 of BNT162b2 vaccination was 0.33 (0.29 to 0.37) compared with 0.72(0.70 to 0.73) during -28 to -1 days before vaccination [2]. <br /> Paton et al [1] also discussed that the estimated IRRs were consistently >1 in the pre-risk period before a SARS-CoV-2–positive test. They thought that events are more likely to happen before a SARS-CoV-2–positive test (as a standard procedure, patients admitted to the hospital are tested for SARS-CoV-2). But they missed to discuss that IRRs on day 0 of vaccination are the most prominent (with 10 times more than that in the pre-risk period, because standard testing of SARS-CoV-2 is mostly done on the day of admission). Hence, constant IRR >1 during -28 to -1 days before vaccination may be another cause. It may be explained by the vulnerability confounding bias [5].<br /> We estimated the effect of vulnerable person’s susceptibility to infection (vulnerability confounding bias: VCB) from the pre-risk period (-28 to -1 days) of the SARS-CoV-2 test-positive group: 2.84 (1.89 to 4.28) for myocarditis and 4.82 (4.68 to 4.97) for arrhythmia. When applied these data for the index of VCB, VCB-adjusted IRRs are 3.44 (2.11 to 5.59) and 1.11 (1.07 to 1.16) which are similar to or less than the healthy vaccinee effect adjusted IRRs of myocarditis (3.97: 3.05 to 5.16) and arrythmia (2.70: 2.38 to 3.05) respectively [4].<br /> It is not possible to estimate the healthy-vaccinee effect and VCB directly from the report of Block et al [3], however, post-SARS-CoV-2 infection/post-vaccination myocarditis risk ratios may be less than 1.00 in almost half of those listed when above adjustments were applied.

      2. No examination of history of myocarditis death and ignored COVID-19 as the cause (their 1st point)

      This point is also derived from the fundamental misunderstanding on the methods of our study. We did NOT compare myocarditis mortality between POST-PANDEMIC and pre-pandemic periods BUT compared SARS-CoV-2 VACCINATED population for 28 DAYS after vaccination with pre-pandemic periods. <br /> Therefore, as a rule, deaths following SARS-CoV-2 infection were not included in this study. In fact, none had COVID-19 listed in the death cause column of cases included in this analysis.<br /> Moreover, in the MHWL list we referred; most deaths included brief medical history as well as the cause of death. We clearly stated that “these were myocarditis death cases reported by physicians as serious adverse reactions to the vaccine” in the Methods section.<br /> Furthermore, as we stated in the discussion section, myocarditis deaths in the 2017-2019 (reference) population were also based on a doctor's diagnosis, with no other medical history known. Mevorach et al [6] also analysed using the same methodology and already published as a peer reviewed paper.

      3 Sample size is too small to discuss causality

      This point is also derived from the fundamental misunderstanding on the methods of our study. We compared SARS-CoV-2 VACCINATED population for 28 DAYS after vaccination with pre-pandemic periods. Hence this sample size was enough to demonstrate increased myocarditis mortality rate ratio after vaccination.<br /> As we stated in the end of the discussion section and in supplemental Table S6, all of the Modified US Surgeon General criteria for causal were satisfied.

      Sincerely,<br /> Watanabe and Hama.

      References<br /> [1] Block JP, Boehmer TK, Forrest CB, et al. Cardiac Complications After SARS-CoV-2 Infection and mRNA COVID-19 Vaccination - PCORnet, United States, January 2021-January 2022. MMWR Morb Mortal Wkly Rep 2022; 71:517-23. DOI: http://dx.doi.org/10.15585/...<br /> [2] Patone M, Mei XW, Handunnetthi L, et al. Risk of Myocarditis After Sequential Doses of COVID-19 Vaccine and SARS-CoV-2 Infection by Age and Sex. Circulation. 2022; 146(10):743-54. doi:10.1161/CIRCULATIONAHA.122.059970<br /> [3] Patone M, Mei XW, Handunnetthi L, et al. Risks of myocarditis, pericarditis, and cardiac arrhythmias associated with COVID-19 vaccination or SARS-CoV-2 infection. Nat Med. 2022; 28(2):410-22. doi:10.1038/s41591-021-01630-0<br /> [4] Hama R and Watanabe S. The risk of vaccination may be higher by considering “healthy vaccinee effect” Response to Husby et al: https://doi.org/10.1136/bmj... (Published 16 December 2021)<br /> Available at: https://www.bmj.com/content...<br /> (Accessed 30 November 2022)<br /> [5] Hama R and Watanabe S. Vulnerability confounding bias should be taken into account in assessing risk of post SARS-CoV-2 infection: an opposite concept of healthy-vaccinee effect (Under submission)<br /> [6] Mevorach D, Anis E, Cedar N, et al. Myocarditis after BNT162b2 mRNA Vaccine against Covid-19 in Israel. N Engl J Med. 2021; 385(23):2140-49. doi:10.1056/NEJMoa2109730

    1. On 2023-10-22 23:07:06, user CDSL JHSPH wrote:

      Hello! Thank you for sharing your work with us. I believe that your work in identifying barriers of transitioning from acute care of substance use disorder (SUD) to community-based treatment is a big first step to making a change in providing impactful support to SUD patients. I wanted to start off with saying I think the title of the topic is well framed, it conceptualizes exactly what to expect in the paper including the research focus of transitions of SUD patients from acute-care settings to community-based setting, it also gives an insight to the methods and understanding that the paper will aim to categorize the strategies. There were a few comments and questions that I think may help the paper and my understanding of this paper.<br /> 1) The Abstract: I really like the breakdown structure of the abstract, it makes it easier to read. I do believe an extra line could be added to the background section of the abstract that indicates a direct connection of the research results to its direct use in the bigger issue. I think adding something like the sentence on Line 4, page 5 would help the reader make this connection. <br /> 2) Results and Figures: I felt as through a pie chart could be used to summarize a few things in this section. It would make it easier to read in a way and represent what portion if the category was taken from the whole picture. An example of this could be during the Additional IntervenntionC Components across Care Continuum. The Table is very helpful, but a graphic figure may help readers understand the results in a better way.<br /> 3) Discussion: The need for more literary review was repeated multiple times throughout the discussion and I was wondering if there was a way of indicating this limitation’s importance without the repetition of it. <br /> Overall, I really enjoyed reading this paper. It was well-written and easy to follow. I hope that this paper makes the effect it intends to, and I hope to follow up with future research in which these strategies, barriers and facilitators are put to the test. I think this is a great step to making a big difference in addiction medicine.

    1. On 2020-09-18 20:29:54, user David C. Norris, MD wrote:

      This paper is fundamentally misconceived:

      Biostatistically

      This paper apparently arises out of the biostatistical perspective which presently dominates the design and analysis of dose-finding trials in oncology. Yet even by purely statistical standards, it suffers serious shortcomings. Most notably, it looks for an interaction (viz., dose-response) without first demonstrating or ensuring the existence of a main effect. Reference #153 in this paper (Hazim et al. 2020) reported a 5% median response rate in a systematic review of recent dose-finding trials. Would the authors venture to estimate what fraction of their 93 ‘analysis series’ employed a drug with a substantial therapeutic effect? Some indication might be found in what fraction of the treatments unequivocally demonstrated a therapeutic effect in subsequent phase 2 or 3 trials. Adashek et al. (2019) document a secular trend in overall response rate (ORR) observed in phase 1 trials which is “now almost 20%, or even higher (~42%) when a genomic biomarker is used for patient selection.”

      Also arguably well within the purview of biostatistics would have been a decision-theoretic framing of phase 1 cancer trials. These trials may be understood as the earliest clinical steps in a learn-as-you-go (adaptive) drug-development process (Palmer 2002; Berry 2004). On such an understanding, aiming to treat early-phase participants at maximum tolerated doses (MTDs) in no way “dictates that an assumption is made … that higher doses are always more efficacious” (p. 4; italics in original). The authors’ use of “dictates” suggests they see something of logical necessity in this, and their further insertion of the logical quantifier “always” only exacerbates their overreach in formulating this central tenet of their study. Even the distinction between a logical assumption and a statistical prior gets lost in the shuffle. To remedy all this, the authors might consider attempting to state formally their understanding of the individual phase 1 trial participant’s decision-problem, complete with its essential uncertainties and some plausible utilities. (Within the community of investigators whom they address in the final paragraph of their Discussion, there is, I believe, broad agreement on the doctrine that these trials have therapeutic intent (Weber et al. 2016; Burris 2019). The authors would do well to take this patient-centered view as their starting point, as opposed to the dose-centered and unitary goal they proclaim at the end of their current Discussion.)

      Furthermore, statistics is nothing if not a discipline for “mastering variation” (Senn 2016), and a paper that sets out to question the strict monotonicity of dose-efficacy ought also enquire as to the presence of inter-individual heterogeneity in dose-response. Note that such heterogeneity would tend to attenuate the maximum slope of a convex dose-response in aggregate.

      Finally, the absence-of-evidence fallacy is widely appreciated among professional statisticians, yet seems to have been indulged liberally here without any safeguards such as are usually provided by power calculations.

      Pharmacologically

      Within statistics, there is a doctrine that statistical analysts should always engage ‘subject-matter experts’. But one sees in this paper no sign that any pharmacological concepts—let alone expertise—have been brought to bear on what would seem to be a pharmacological question. At a minimum, in any serious challenge to the ‘MTD heuristic’—as I have called it—one expects to find distinctions between on-target and off-target toxicities. In an analysis that invokes dose-response plateaus (whether these are conceived as approximate or absolute in this paper remains unclear), we ought to find discussion of receptor occupancy and saturation as underlying realistic mechanisms.

      To some extent, a neglect of subject-matter knowledge may be embedded in the very form of the present analysis, which tries to deal with its question in aggregate (through statistical techniques such as standardization) rather than in its particulars.

      Clinically

      In the final paragraph of their Discussion, the authors proffer advice to clinical investigators. In light of the limitations—statistical, logical, subject-matter—catalogued above, this is premature and should be omitted. Any given phase 1 clinical investigator will be considering a candidate drug in its particulars, conditional on a great deal of preclinical data and perhaps even nontrivial PKPD and systems-pharmacology modeling. The authors acknowledge as much (p. 16), seeming to appreciate that they have conducted an unconditional analysis of highly conditioned decision-making. To investigators thus intimately engaged with pharmacologic particulars, the null conclusions from a marginal analysis such as this one can contribute little useful guidance. If it were proposed to submit this work for peer review in substantially its present form, only a statistical audience should be addressed—and then solely with a cautionary note that the finding of a dose-response interaction will not leap out at a statistician from a convenience sample of phase 1 studies in which a therapeutic main effect remains dubious and unexamined. The main lesson of this work is that statisticians ought to investigate questions of pharmacology in their particulars, and with recourse to subject-matter concepts and expertise.

      References

      Adashek, Jacob J., Patricia M. LoRusso, David S. Hong, and Razelle Kurzrock. 2019. “Phase I Trials as Valid Therapeutic Options for Patients with Cancer.” Nature Reviews Clinical Oncology, September. https://doi.org/10.1038/s41....

      Berry, Donald A. 2004. “Bayesian Statistics and the Efficiency and Ethics of Clinical Trials.” Statistical Science 19 (1): 175–87. https://doi.org/10.1214/088....

      Burris, Howard A. 2019. “Correcting the ASCO Position on Phase I Clinical Trials in Cancer.” Nature Reviews Clinical Oncology, December. https://doi.org/10.1038/s41....

      Hazim, Antonious, Gordon Mills, Vinay Prasad, Alyson Haslam, and Emerson Y. Chen. 2020. “Relationship Between Response and Dose in Published, Contemporary Phase I Oncology Trials.” Journal of the National Comprehensive Cancer Network 18 (4): 428–33. https://doi.org/10.6004/jnc....

      Palmer, C. R. 2002. “Ethics, Data-Dependent Designs, and the Strategy of Clinical Trials: Time to Start Learning-as-We-Go?” Statistical Methods in Medical Research 11 (5): 381–402. https://doi.org/10.1191/096....

      Senn, Stephen. 2016. “Mastering Variation: Variance Components and Personalised Medicine.” Statistics in Medicine 35 (7): 966–77. https://doi.org/10.1002/sim....

      Weber, Jeffrey S., Laura A. Levit, Peter C. Adamson, Suanna S. Bruinooge, Howard A. Burris, Michael A. Carducci, Adam P. Dicker, et al. 2016. “Reaffirming and Clarifying the American Society of Clinical Oncology’s Policy Statement on the Critical Role of Phase I Trials in Cancer Research and Treatment.” Journal of Clinical Oncology 35 (2): 139–40. https://doi.org/10.1200/JCO....

    1. On 2021-07-29 07:51:21, user Portal Cedip wrote:

      I am surprised that a country that was punished so badly by COVID-19, due to its nihilism, purely academic debates which misled the point even after recognizing that SARS-Cov-2, get the boy sick kills children and young people, but -you know Winston- their finest hour will not come until they get sick and die in numbers that do not even represent the TOTAL burden of the disease (just 6,340 boys, of which 700 got the PICU and oh, maybe 13 died, eventually more. Who cares? Just another non caucasic problem. Sense of safety for a far away condition that colonize, infects, make CYP get hospitalized, complicates 10% and kills with a lethality of 2% ONLY. I saw my pediatric unit got exhausted due to the large number of teleconferences with boys we could not hospitalize. The crisis was burning out or infecting our teams. We were under attack but the non-traslational sweatless sirs were complaining about us being hysterical and overplaying our hands with our small patients. And our government made the impossible. A country ranked 27 in Health Services got top 10 in number of cases and deaths / 100, 000. We did not see our boys dying in front of us. But got overwhelmed at all ages. Our 19 million people´s country got 130.000 CYP infected, Three thousand were hospitalized, half of them had a critical trajectory or came back from home with TIMPS. One hundred died. Eighteen had less than 1 yo. That´s crude data. Most of it occurred during the second wave, after we naivly thought we had gotten rid of the virus (Christmas 2020). But the virus gave itself a gift from England: the variant Delta, which seized the country for 4 additional months. Now is calmed again. You trust that it got surrended to vaccination, a plan that already involves more than 65% of the population. <br /> NO<br /> I do not.

      My best wishes. With personal regards from the very south of the world,

      Ricardo

    1. On 2021-11-16 11:57:33, user disqus_aUdf6iYESf wrote:

      This is an interesting study, and not an easy one to do. I congratulate the authors on their work.

      I agree with the authors that the study is hypothesis generating.

      A few questions/comments:

      1) The authors describe no delay as being "score >2 SDs above the population mean". If no delay is the inverse of delay, I think this should be "a score higher than the cutoff for delay of 2 SD below population mean." A score >2 SD above population mean would include a very small proportion of children (about 2.5% in the population) of developmentally advanced children.

      2) As the authors note, using a questionnaire (Age and Stages) by phone is not ideal for evaluation, and responses could be biased by parental knowledge of maternal SARS-CoV-2 infection.

      3) The numbers with infection in the first trimester are small (only 5 children), but 4/5 (80%) had developmental delays, as compared to 6/20 in second trimester (30%), and 20/273 with infection in third trimester (7.3%). Those are striking differences, with a "dose-response" type pattern by trimester, but the numbers are small, so this study would need to be replicated by other groups, ideally with testing with the Bayley scales or other administered instrument.

      4) A control group without SARS-CoV-2 infection would be important as an additional comparison group, and was not present. This would give a sense of whether in the population who responded and were assessed by phone questionnaire, the rate of developmental delay (score < mean - 2SD) was similar to that expected in the general population.

      For all of these reasons, I think further studies are required to definitively state that maternal SARS-CoV-2 infection in the first or second trimester is associated with developmental delay, but this study provides preliminary data that this might be the case. It appears other studies in progress propose to prospectively address this question (e.g., PROUDEST study in Brazil), and such studies are required for a more definite answer as to whether SARS-CoV-2 infection early in pregnancy affects child neurodevelopment outcomes.

    1. On 2024-12-01 14:50:07, user xPeer wrote:

      Courtesy review from xpeerd.com

      Summary

      The preprint titled "Financial incentives to motivate treatment for hepatitis C with direct acting antivirals among Australian adults" investigates how financial incentives influence the initiation of direct-acting antiviral (DAA) therapy for untreated hepatitis C virus patients in Australia. Utilizing Bayesian adaptive design, the study assigns participants varying levels of financial incentives to observe which incentive levels effectively promote treatment initiation. The study is thorough in detailing statistical methods, including primary and secondary analysis plans, making it potentially influential for public health policy.

      Major Revisions

      1. Methodological Concerns:
      2. Futility Stopping Rules: The document briefly mentions futility stopping rules for eliminating less effective incentive levels. More detailed explanations and specific thresholds for these rules should be provided to ensure transparency and reproducibility (Page 2, Abstract).
      3. Bias and Confounding Variables: While the study employs Bayesian adaptive design and randomization, there is insufficient discussion on potential biases and confounding variables that could affect the study's results, such as differences in demographic variables, healthcare access, or socioeconomic status (Page 9, Study Design).

      4. Data Accessibility:

      5. Availability of Data for Replication: The document should explicitly state how and where the data will be made available for replication purposes, adhering to good scientific practices (Page 12, Data Availability Statement).

      6. Outcome Measures and Analysis:

      7. Primary Outcome Definition: There is a need for a more precise definition and justification of the primary outcome measure, namely DAA initiation within 12 weeks (Page 3, Primary analysis).
      8. Secondary Outcomes and Analysis: The description of secondary outcomes such as the number of missed DAA days and HCV PCR test results should be more detailed with clear operational definitions and analysis plans (Page 7, Secondary analyses).

      Recommendations

      1. Clarify Methodology: Provide a more in-depth explanation of the futility stopping rules, including specific criteria and decision-making processes to increase transparency (Page 2, Abstract).
      2. Address Potential Biases: Incorporate a section that addresses potential biases and confounding variables, explaining how these will be managed in the analysis (Page 9, Study Design).
      3. Enhance Data Accessibility: Ensure that the data is accessible to other researchers for replication, and clearly state the data-sharing mechanisms (Page 12, Data Availability Statement).
      4. Refine Outcome Measures: Define the primary and secondary outcome measures more clearly and provide detailed plans for their analysis (Page 3, Primary analysis and Page 7, Secondary analyses).

      Minor Revisions

      1. Typographical Errors:
      2. Replace "DAA’s" with "DAAs" (Page 9, Background).
      3. Replace "payment amounts are made" with "payments are made" (Page 2, Abstract).

      4. Formatting Issues:

      5. Standardize the presentation format of equations and mathematical notations to enhance readability (Page 6, Effect of co-incentives).

      6. AI-Generated Content Analysis:

      7. There is no explicit indication of AI-generated text. However, ensuring all elements are rigorously checked for epistemic accuracy and coherence is crucial, given the post-2021 publication date.
    1. On 2025-02-12 19:59:20, user Aron Troen wrote:

      Review Part II

      Methodological shortcomings<br /> Study population and period: The population demographics used as the denominator of per capita caloric requirement rely on census data from 2017 and UN OCHA reports on movement and displacement of the population between Gaza governates during the war. The study states that no adjustments were made for out-migration or excess deaths. However, approximately 150,000 people left the Gaza strip from the beginning of the war until the Rafah crossing was closed in May. When added to casualties and a natural death rate of ~5500 people per year, this means that the population denominator used to calculate the food supply in Kcal per person-day (Figure 4) was overestimated by ~ 200,000 people, which would result in the underestimation of the food supply by approximately 10%.

      The authors acknowledge the limitation that “There remains considerable uncertainty about our population denominators in the north, and even moderate error in these would have affected our Kcal per capita estimates. Gaza’s population has probably decreased due to high mortality and out-migration…”. Nevertheless, they shrug off this limitation by asserting that “…we expect this to have only marginally affected our estimates.” without explaining why.

      Data on truck deliveries

      The comparison between UN and Israeli shipping data is superficial and inadequate for supporting the decision to dismiss and exclude the data from the analysis. The authors fail to discuss the literature, of which they surely must be aware, which addresses the high-profile controversy over the number of trucks supplying aid to Gaza and the discrepancies between the UN and COGAT data, and which notes the under-reporting of private sector food shipments by the UN (see for example, Rosen, Bruce and Nitzan, Dorit, Humanitarian Food Aid for Gaza: Making Sense of Recent Data (June 02, 2024). Available at http://dx.doi.org/10.2139/ssrn.4851635) "http://dx.doi.org/10.2139/ssrn.4851635)") .

      Although the authors note the "large discrepancy between UN and Israeli government data" on the entrance of goods into Gaza, they erroneously assert that UNRWA monitored the composition of “ALL trucks” crossing into Gaza, despite the partial coverage of non-UN food consignments, and despite disclaimers published by UNRWA and recorded by the authors, that the data from May-August are incomplete. The authors make little effort to help the reader understand the reason for the discrepancy nor to explain how they reached the conclusion that UNRWA's dataset "appeared highly complete and well-curated, but may be biased by systematic under- or over-reporting unknown to us". Instead of making a serious effort to include COGAT data to improve the accuracy of their simulation, they perform a perfunctory comparison of the UN and COGAT data and justify the summary dismissal of the Israeli registry, using the categorical listing of truck weight registered by COGAT as “evidence of digit heaping or crude approximation”. This is a peculiar choice, given the importance of the COGAT dataset, which is included in the June IPC report and in a working paper that the authors cite that analyzes the caloric content of food supplied to Gaza, including private sector shipments that are missing from the UN data (now published at https://ijhpr.biomedcentral.com/articles/10.1186/s13584-025-00668-6) "https://ijhpr.biomedcentral.com/articles/10.1186/s13584-025-00668-6)") . An alternative choice might have been to simulate the weight and contents of the COGAT data like the authors did for incomplete WFP data, or to perform a sensitivity analysis and compare how caloric supply estimates might differ based on the data and assumptions used.

      Instead, the study implies that the discrepancy has to do more with weight of aid reported rather than the number of trucks. However, significant gaps are also evident in the number of trucks reported. For example, in February, UNRWA reported 1,857 trucks carrying food while COGAT's figure is 15% higher (2,117). In January the gap is equally large, with COGAT's number of trucks 13% higher than UNRWA's (3,364 and 2,990 respectively). According to COGAT, between January and May 2024, "as a result of the UN’s partial counting… there are 3,406 trucks missing from their Kerem Shalom data and 2,198 trucks missing from their Nitzana/Rafah data." ( https://govextra.gov.il/media/dtmhzmtn/discrepancies-in-un-aid-to-gaza-data-2.pdf) "https://govextra.gov.il/media/dtmhzmtn/discrepancies-in-un-aid-to-gaza-data-2.pdf)") . Furthermore, the period analyzed covers several unexplained changes in UNRWA's dashboard ( https://honestreporting.com/how-unrwa-covers-up-its-faulty-gaza-food-data/) "https://honestreporting.com/how-unrwa-covers-up-its-faulty-gaza-food-data/)") , apparently following data-driven criticism about its methodology and lack of transparency on social media ( https://x.com/AviBittMD/status/1780052840930578499) "https://x.com/AviBittMD/status/1780052840930578499)") . According to a FEWS NET report, "on September 8… UNRWA’s dashboard was updated with additional supply data for August, as well as for previous months, including commercial truck entries as reported to UNRWA." UNRWA has not disclosed where the new data on commercial trucks came from or how far back the data update had gone.

      The subsequent calculation of caloric availability includes a mix of registered and simulated data, in which the simulation parameters extremely underestimate the caloric supply. The model derives the simulated distribution of estimated Kcal per truck as described in the methods and shown in supplementary figure A1: “We reconstructed the number of these trucks over time based on published information and data shared by WFP . As no data on content were available, we simulated their caloric equivalent by repeatedly sampling from the empirical distribution of calories per truck obtained from the UNRWA dataset.“ There are several problems with this approach. First, it is unclear which specific truck data “shared by WFP” were used for this simulation, and whether they are publicly available. This should be clearly indicated in the uploaded github data files. Moreover, the WFP records the contents of their shipments. Why were their contents omitted in this case? Presenting summary tables in the article would help the orient the reader to the source data for the truck counts used, distinguishing between simulated or assumed and actual contents. An implicit assumption underlying the simulation of WFP contents according to estimated distribution of calories by UNRWA trucks, is that the contents of UNRWA and WFP shipments are the same. This needs to be documented or the assumption should be made explicit. Given that the study appears to significantly underestimate the weight of the UNRWA pallets, the procedure used would be expected to propagate biased estimates lower than the actual weights to the WFP data as well.

      The most critical problem in the model is with the ASSUMED weights that the authors assign to the consignments. They assume mean pallet weights to be 637.5 kg per pallet, with a minimum to maximum weight of 510-765 kg per pallet (gaza_food_data.xlsx, general tab), based on citations 23, 30 and 31. Citation 23 does not provide supporting data and refers to IPC reports in general. Citations 30 and 31 are standard operating procedures for the Egyptian Red Crescent (ERC) from October and November 2023, which REQUIRE an 18% higher palletization weight of 750 kg. However, even this value is considerably lower than UN aid REQUIREMENTS that specify pallet weights for wheat flour (1125-1200 kg/pallet), sugar (1200 kg/pallet), chickpeas 1200 kg/pallet), red lentils (1200 kg/pallet), rice (1200 kg/pallet), SF oil (910-1213 kg/pallet) or milk (655 kg/pallet) (UNRWA Special Shipping Instructions for Shipments by Sea Air and Land – April 2024 - page 6; https://unrwa.org/sites/default/files/emergency_gaza_2023-_rfq-pskh-42-24-the_provision_of_man_trucks_for_gfo-tender_doc.pdf) "https://unrwa.org/sites/default/files/emergency_gaza_2023-_rfq-pskh-42-24-the_provision_of_man_trucks_for_gfo-tender_doc.pdf)") . Examination of “dataset 20240911_Commodities Received.xlsx” reveals that consignments attributed to ERC alone or with other agencies (including UNRWA) account for only 90,009 of the total of 531,175 food line items (17%) and 8085 of the total of 22,833 mixed line items (35%). Therefore, even if the mean value of 637.5 kg/pallet were correct for the ERC-associated consignments, the weights assigned to the foods supplied are unreasonably low, giving an extreme underestimation of the calories supplied.

      This unreasonably low distribution of the estimated Kcal per truck can be seen in the simulated truck weights. The histogram in Appendix figure A1 shows a distribution that is heavily skewed to the left with the vast majority of trucks carrying less than 50 Million Kcal and perhaps a third carrying less than 25 Million Kcal. The simulated lower end of the distribution, which begins with 600 trucks carrying zero Kcal/truck, is highly unlikely to be accurate. Even if one takes the assumed mean weight per truck assigned by the researchers as 14,500 kg, multiplying by the calorie content of wheat flour (3,640 Kcal/kg) would give a mean calorie content per truck of 52.8 M Kcal. Even if a lower calorie food calorie density of circa 3200 Kcal/kg were used, based on visual inspection of Figure 3A (Kcal/kg food consignments between Oct 21 2023 – May 4 2024), the assumed mean caloric content of the food trucks should be 46.4 Million Kcal. These values, are hard to reconcile with the histogram, even if the assumed and simulated truck weights in the model are true. Thus, the validity of the model assumptions and their potential for propagating error and uncertainty in the results should be carefully revisited.

      Data on other food sources

      Estimates of the available existing food supply before the war combine the household stocks of humanitarian food aid, data provided to the researchers by UNRWA giving the exact stocks in UNRWA warehouses and the range of minimum-maximum capacity of WFP warehouses before the war; estimates of existing private stores, and of agriculture and livestock production, discounted for gradual depletion and destruction during the war’s early months. The model does not account for potential Hamas stockpiles ( https://www.nytimes.com/2023/10/27/world/middleeast/palestine-gazans-hamas-food.html) "https://www.nytimes.com/2023/10/27/world/middleeast/palestine-gazans-hamas-food.html)") .

      The spreadsheet “gaza_food_data.xlsx” tab “warehouses” lists total UNRWA and WFP warehouse capacity before the war as a range with a minimum to maximum capacity of 7,900-21,479 MT or 28.7 – 78.1 billion Kcal, whereas presumably, the “exact” contents of the food in UNRWA warehouses are those data listing a total of 38.3 billion Kcal of food in tab “unrwa_stocks”. No further information is provided to ascertain that the data given to the researchers by UNRWA and WFP is complete and accurate.

      Existing private stores/Caloric balance and consumption: The text describes the assumptions used in estimating the existing stores and their depletion during the war. The text defines model parameters (eg. I0, I0,m, etc.) but does not spell out the full model equation. Doing so would help the readers better understand the explicit logic of the simulation. <br /> The model discounts agriculture and livestock production using estimates of the rate and extent of damage to agricultural infrastructure citing UNOSTAT remote sensing data published by FAO (references 11, 40-42). The validity of estimates derived from image analysis depends heavily on the control conditions selected for a reference and on the quality of validation and calibration in the field. The percent damage arrived at by automated image analysis algorithms, depends on the selected reference conditions, whose rationale and validity are not given. Field validation is impossible in a war zone which is why the cited reports carry important disclaimers such as: ”This assessment has been conducted based on available satellite imagery, ancillary data and remote sensing analysis for the period 7 October - 31 December 2023 without field validation. Land cover data from 2021 was used as baseline data due to limited availability for data collection in the area of interest and time constraints related to the nature of the report.“ ( https://openknowledge.fao.org/server/api/core/bitstreams/f2ad2f59-0c29-472e-978b-54cef347c642/content) "https://openknowledge.fao.org/server/api/core/bitstreams/f2ad2f59-0c29-472e-978b-54cef347c642/content)") . The limitations of these estimates used in the model should be acknowledged.

      Estimating Baseline and Recommended per-capita caloric intake

      The per-capita caloric intake for emergency-affected populations is given by the WHO guide and is stratified by age and sex. Given the age and sex distribution of the population of Gaza (gaza_food_data.xlsx, tab prop_age_sex), the mean daily per capita calorie requirement for the population is 2,065 Kcal/person-day. This threshold shown in yellow in Figure 4, is the appropriate criterion for evaluating the adequacy of the food supplied by the humanitarian food cluster. <br /> However, the researchers go beyond this consensus humanitarian requirement, and derive a much higher Gaza-specific estimate “I0“ for the population intake at baseline. The baseline value of I0 appears to be just under 2,800 Kcal per person-day according to figure 4 (blue line value on October 7th, 2023). The paper does not give the baseline value “I0“ explicitly. However, it is nearly identical to the weighted average caloric intake (2,837 Kcal/person-day) observed in a population of obese older Gazan adults (mean age 57, weighted mean BMI 31.4) with a high prevalence of noncommunicable diseases, in a survey conducted during the COVID pandemic between March and July 2020, which was used to impute the daily intake of the overall population. The weighted intake and BMI may be calculated based on the data provided in the gaza_food_data.xlsx spreadsheet, tab prop_age_sex. The estimated pre-war intake, is roughly 33% higher than the humanitarian requirement, or “recommended daily intake”. The model derives the weekly available per person food supply, by subtracting this pre-war intake estimate, from the estimated weekly available daily per-capita food supply (from the sum of private stores and warehouses, agriculture and delivered food-aid, discounted for reported consumption and damage). The model makes the questionable assumption that the emergency-affected population would continue to consume the same amount of food that it did during the war, as it did before the war. Even before examining the validity of the method used to derive “I0“, this assumption forces the model to deplete the available food supply significantly more rapidly (about 33% sooner) than if the recommended humanitarian food requirement were used to simulate the adequacy of the available food supply.

      The logic behind the method of imputation to the whole population is not clearly explained (“we sampled random values from each age-sex stratum distribution…” Appendix A, Figure A2). <br /> Supplementary figure A2, entitled “Baseline adult caloric intake” shows simulated untransformed and log-transformed, age and sex specific distributions of energy intake, from Abu Hamad et al., J Hum Hypertens 2023. That reference describes a health survey conducted in Gaza between March and July 2020 among adults aged 40 and older, and using the semi-quantitative Food Frequency Questionnaire for Palestinian Populations which was developed by Hamdan et al., in a population of Palestinian women in Hebron, and published in Public Health Nutrition 17(11) in 2013. While such survey tools may be useful for epidemiological studies, they are intended to classify populations into categories of relative nutritional intake, rather than for deriving valid absolute individual nutrient intakes. In the case of the specific instrument used, Hamdan et al. write that studies like theirs “can be considered a calibration and correlation rather than a validation procedure”. The correlation that they obtained in that study between three repeat 24 hr food recall questionnaires and the semi quantitative FFQ was 0.601 and was not statistically significant (in other words the FFQ gives a similar but poorly concordant result to the reference standard). Moreover, it is doubtful, if the high average food intake of an obese, older and unhealthy population (which was obtained during a health crisis that increased sedentary behavior due to social distancing and isolation), provides a sound basis for imputing routine intakes for a population that is predominantly younger (82% of Gaza’s population are below age 40 – see gaza_food_data.xlsx, tab prop_age_sex), healthier, and not affected by a pandemic. It would be helpful if the researchers clarified these limitations and presented the age-and sex stratified per-person daily caloric derived intake and compared it with the consensus humanitarian requirements.

    1. On 2025-03-17 22:11:06, user Dr.PayamVaraee wrote:

      Critical Review: "The Threat of Populism to Science and Global Public Health: Lessons from Iran"<br /> A. Critique of Content and Main Claims<br /> 1. Claim: Populist Science Increased Mortality in Iran<br /> The article asserts that populist policies delayed vaccination efforts in Iran, leading to excess mortality. However, data comparisons with countries such as the US, UK, and Germany reveal similar trends, challenging the uniqueness of Iran’s case.

      Issues:<br /> Overlooking Key Variables: The analysis does not account for factors such as economic sanctions, healthcare infrastructure, and demographic differences.<br /> Post-Vaccination Mortality Decline: The significant drop in mortality following mass vaccination aligns with global patterns, suggesting that other factors played a role beyond populist decision-making.<br /> Flawed Comparisons: The article contrasts Iran with Bahrain and the UAE, despite major differences in population size, vaccine availability, and healthcare systems.<br /> 2. Claim: Iranian Data on COVID-19 Mortality is Unreliable<br /> The article utilizes the Prophet model to argue that Iranian mortality statistics were manipulated.

      Issues:<br /> Limitations of the Prophet Model: Originally designed for economic and social trend forecasting, this model is not optimized for analyzing health crises.<br /> Weak Evidence for Data Manipulation: The assumption that discrepancies between projections and reported data indicate fraud is flawed. Factors such as improved treatment strategies and emerging herd immunity are not considered.<br /> Selective Application: The same predictive model is not used to assess data accuracy in other countries, raising concerns about bias.<br /> B. Critique of Data Analysis Methods<br /> 1. Misuse of ANOVA<br /> The article employs a one-way ANOVA to compare vaccination delays across countries. However, this method does not sufficiently account for assumptions of normality and homogeneity of variance, potentially leading to misleading conclusions.

      Better Alternatives:<br /> Time-Series Models (ARIMA, VAR): These would provide a more accurate assessment of trends over time.<br /> Multivariate Regression: This method would allow for the inclusion of additional variables influencing vaccination delays and mortality rates.<br /> 2. Absence of Confounding Variable Control<br /> The article does not adjust for important factors such as:

      The proportion of elderly populations.<br /> Hospitalization rates and healthcare capacity.<br /> Lockdown policies and mobility restrictions.<br /> Neglecting these variables weakens the argument that Iran’s excess mortality was driven primarily by populist policies.

      C. Logical and Argumentative Flaws<br /> 1. Selective Data Use<br /> The article emphasizes evidence that supports its argument while disregarding counterexamples—such as similar mortality patterns in Western countries—leading to confirmation bias.

      1. Correlation vs. Causation Fallacy<br /> It assumes a direct causal link between delayed vaccinations and excess mortality without considering other influencing factors, such as economic restrictions, healthcare efficiency, and prior infection rates.

      2. Oversimplification of a Complex Issue<br /> By attributing Iran’s COVID-19 response largely to populism, the article overlooks the fact that mortality spikes occurred in Germany, the US, and other non-populist-led countries. A more nuanced analysis is needed.

      D. Broader Issues with the Scope of the Article<br /> 1. Disproportionate Focus on Iran<br /> If populist science is a global issue, why is Iran the only case study? A comparative approach—including countries like the US, Brazil, and Poland—would strengthen the argument.

      1. Lack of Practical Solutions<br /> The article critiques Iran’s handling of the pandemic but does not propose strategies to combat misinformation and improve public health responses globally.

      2. Limited and Selective Data Sources<br /> The article relies heavily on The Economist and WHO while neglecting independent organizations such as the CDC and regional research institutions. A broader range of data sources would improve credibility.

      E. Additional Criticism of the Core Argument<br /> 1. Populism Beyond Iran<br /> Research, including the PANCOPOP study, shows that right-wing populism influenced pandemic responses in the US, Brazil, Poland, and Serbia. The article’s exclusive focus on Iran suggests political bias rather than an objective analysis of populism in global public health.

      1. Contradictions in the Populism Model<br /> The article argues that Iran exhibited both the denialist model (seen in the US and Brazil) and the authoritarian control model (similar to Poland and Serbia). These models, however, are distinct and mutually exclusive in the PANCOPOP framework, making this assertion contradictory.

      2. Absence of Comparative Analysis<br /> The study lacks a global perspective on how different forms of populism shaped pandemic policies, weakening its claim that Iran’s case is uniquely alarming.

      3. Misattribution of Vaccination Delays Solely to Populism<br /> The article ignores other major contributing factors, such as:

      Economic Sanctions: Restrictions on vaccine imports.<br /> Vaccine Hesitancy: Public resistance to certain vaccines.<br /> Domestic Vaccine Development: Initial reliance on homegrown vaccines before shifting to imports.<br /> By overlooking these aspects, the article oversimplifies the reasons behind Iran’s vaccination timeline.

      1. Failure to Address Global Media Influence<br /> Studies have demonstrated that misinformation on COVID-19 spread across multiple countries, yet the article singles out Iran without discussing similar issues in other regions.

      2. Statistical Flaws<br /> The ANOVA and Prophet model are misapplied, limiting the validity of conclusions.<br /> A lack of multivariate regression fails to control for external factors influencing pandemic outcomes.

      3. Contradictory Use of Data<br /> The article doubts the credibility of Iran’s official statistics yet uses those same statistics to support its claims. This undermines its argument and suggests inconsistent reasoning.

      Conclusion<br /> The article presents a flawed and unbalanced analysis of how populism influenced Iran’s COVID-19 response.

      Key Weaknesses:<br /> Selective use of data that aligns with the author's argument while ignoring broader trends.<br /> Lack of comparative analysis, failing to place Iran’s case within a global context.<br /> Misuse of statistical methods, leading to questionable conclusions.<br /> Recommendations for a Stronger Study:<br /> A multi-country analysis incorporating nations with varying political ideologies.<br /> Consideration of alternative explanations for mortality trends, such as healthcare infrastructure and economic factors.<br /> A transparent and methodologically sound approach to data interpretation.<br /> A truly robust and objective study would examine multiple countries, account for confounding variables, and avoid overgeneralizing populism’s impact on public health outcomes.

    1. On 2025-10-20 15:14:25, user xPeer wrote:

      Courtesy Peer Review Simulation from xPeerd :

      Summary<br /> This manuscript examines the experiences of mothers of autistic children within UK child-protection services, with a particular focus on the prevalence and nature of social services' involvement and allegations of Fabricated or Induced Illness (FII). Using a survey of 242 mothers (diagnosed autistic, self-identified autistic, and non-autistic), the authors investigate whether mothers with autism face greater scrutiny or risk of having their children removed compared to others. The findings suggest high levels of investigation for all groups, but no significant differences between groups. However, a markedly elevated rate of FII allegations is identified among mothers of autistic children compared to general epidemiological estimates. The methodology integrates participatory approaches but is limited by its sample scope, lack of a typically developing comparison group, and exploratory design.

      Potential Major Revisions

      1. Methodological Scope and Representativeness
      2. The sample lacks a control group of mothers with typically developing children: “we did not actively recruit mothers of typically developing children due to practical considerations…” (p. 18, Limitations). This hinders interpretation of whether findings are unique to mothers of autistic children or represent broader social service dynamics.
      3. The design is exploratory, and as stated: “the questions in the survey were exploratory and therefore we did not enquire in detail about social service involvement” (p. 18, Limitations). More granular data (e.g., timelines, outcomes, types of interventions) would strengthen the work’s empirical claims.

      4. Statistical Analysis and Power

      5. Some subgroup analyses are based on small subsamples (e.g., N = 21 for autistic mothers called into a meeting), reducing statistical power (Table 3, p. 13).
      6. The manuscript acknowledges no statistically significant differences between diagnostic groups in key outcomes such as child protection registration or FII allegations (e.g., “no statistical difference emerged”; p. 14), suggesting caution is required in interpreting implications for policy or discrimination.

      7. Interpretive Overreach

      8. The discussion interprets elevated rates of investigation as evidence of systemic discrimination, but alternative explanations (e.g., increased service contact for autistic children, reporting biases) are not fully interrogated: “our results suggest a significant increase in inquiries and registrations...compared to the general population” (p. 17).
      9. The text could benefit from a more critical posture towards causal inference.

      10. Ethical and Legal Framing

      11. The work alludes to ethical and human rights implications but does not provide a detailed ethical analysis or legal context, which are crucial for claims concerning state intervention and discrimination (see discussion, pp. 17–19).

      Potential Minor Revisions

      • Typographical and Grammatical Errors
      • Occasional word repetition and typographic slips (e.g., “we are separately reportingly the results here...”; p. 8).
      • Consistent usage of terms (e.g., sometimes “non-autistic”, sometimes “nonautistic”).
      • Formatting Issues
      • The document is interspersed with license and preprint notices that disrupt readability.
      • Table captions and labels (e.g., Table 3, Table 4) lack uniform placement and can be confusing in the PDF layout.
      • Section headers could be standardized for clarity.
      • References
      • All references are recent and field-appropriate. No missing citations identified. All URLs and DOIs appear correct.

      • AI Content Analysis

      • The writing style, structure, and nuanced argumentation are consistent with human-authored academic research. Estimated AI-generated content: <5%. No sections strongly flagged as AI-generated; narrative voice and academic conventions are maintained throughout. No epistemic inconsistencies or abrupt shifts in style detected.

      Recommendations

      1. Include a Wider Comparison Group
      2. For greater generalizability, future iterations should incorporate mothers of typically developing children. This would clarify whether the experiences described are unique to mothers of autistic children.
      3. Deepen the Methodological Rigor
      4. Enrich the survey to collect more detailed information on the nature, duration, and outcome of social services’ engagement.
      5. Where possible, triangulate self-report data with administrative records or interviews with professionals (subject to ethical approval).
      6. Clarify Causal Inferences
      7. Approach claims about systemic discrimination with caution—consider and analytically address alternative explanations or confounds.
      8. Expand the Legal and Ethical Analysis
      9. A more thorough excursus on UK legal standards and the ethical principles governing child-protection interventions would enhance the policy relevance of the manuscript.
      10. Address Subsample Limitations
      11. Explicitly acknowledge and discuss the implications of small subsample sizes for statistical inference throughout the results sections.
      12. Improve Readability and Consistency
      13. Edit for grammar, typographic errors, and ensure formatting consistency between tables, figures, and narrative text.
    1. On 2020-05-19 04:00:43, user Sinai Immunol Review Project wrote:

      Main Findings:<br /> During the unprecedented COVID-19 pandemic, identifying patients at high risk for mortality is critical so as to guide clinical decisions on early intervention and patient care. To identify factors associated with risk of death from COVID-19, the study developed a secure and pseudonymized analytics platform, OpenSAFELY, that links the UK National Health Service (NHS) patient electronic health records (EHR) with COVID-19 in-hospital death notifications. This platform enabled the rapid analysis of by far the largest cohort to date from any country, comprising 17,425,445 multi-ethnic adults and 5,683 COVID-19 deaths. The analyses were based on hazard ratio generated by cox-regression and were adjusted for demographics and co-morbidities.<br /> Increased risk of COVID-19 hospital death was associated with male gender, older age, certain clinical conditions (uncontrolled diabetes, severe asthma, other respiratory diseases, history of haematological malignancy or recent non-haematological cancer, obesity, cardiovascular disease, kidney, liver, neurological diseases, autoimmune conditions, organ transplant and splenectomy). Notably, the association of asthma with higher risk of COVID-19 related death is contradictory to previous findings of no increased risk of death or even protective association. This effect was even stronger with recent use of oral corticosteroids (i.e. more severe asthma). In addition, people of lower socio-economic background (i.e. deprivation) or black and Asian origin were identified at high risk. However, this association could not be entirely attributed to pre-existing health conditions or other risk factors, which warrants further exploration into drivers of these associations. The open source analytics code is available at OpenSAFELY.org.

      Limitations:<br /> 1) There are few drawbacks in data source and collection. The study did not account for COVID-19 deaths in false-negative/ untested individuals, relied on EHR from specific software and dealt with incomplete EHR information. <br /> 2) Additional discussion regarding the reasons behind the associations would be insightful. Specifically, recent studies have shown that risk factors including asthma, hypertension, and diabetes impact the expression of ACE2 gene, which is the entry receptor for SARS-CoV-2. However, while asthma with type 2 inflammation has been associated with lower ACE2 expression and thereby potentially protective effect, this association has not been observed for nonatopic asthma in these studies. In the current study, asthma is only categorized in terms of severity (recent oral corticosteroid use vs not). Further categorization in terms of subtype would have been helpful.<br /> 3) Understanding the underlying causes of high risk in people of black and Asian origin is important for public health and mitigation of the spread. In this study, the most common assumptions of high burden of underlying comorbidities and lower socio-economic status are shown to contribute only partly to the risk. However, other factors, such as occupational exposure, neighborhood and household-density and possible influence of genetic or other biological factors still need to be explored. <br /> 4) The study suggests increased risk in former smokers and slight protective effect in current smokers. More in-depth analyses into whether the protective effect of current smoker status is an artifact of over-adjustment, selection protocol of healthy controls or a true correlation are needed. <br /> 5) It would be helpful to have the p-value along with the reported hazards ratio and 95% confidence intervals.

      Significance: <br /> Overall, the OpenSAFELY platform allows secure and real-time analyses of clinical data stored in situ. As this global pandemic progresses, outcomes and data are expected to expand, revealing more insights to the effects of medical treatments and less common risk factors on COVID-19 infection, spread and death. This approach can help better identify additional factors that affect disease severity and immune response. Finally, this rapid and massive study was only possible because of the detailed longitudinal data already available through General Practitioners within the UK National Health Service (NHS), replication of which at a similar massive scale would be daunting within the highly fragmented healthcare system of the USA. While even within UK NHS many data integration issues remain, the findings from this study is a testament to the global model we need to follow to increase our power to rapidly answer crucial questions related to COVID-19 epidemiology. Such an approach will also open new avenues for increased understanding of other diseases.

      Reviewed by Myvizhi Esai Selvan as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai

    1. On 2020-06-19 03:12:29, user AMM wrote:

      1. I would like to know if serological tests were performed on the 118 healthcare workers who did have COVID, and what that data shows.

      2. The methods state that 2 separate tests for IgG/M were used: MCLIA and colloidal gold. Were both used on all subjects, did some subjects get just one or the other? This is important because they have different sensitivities and specificities.

      3. The paper states that the LAST TEST RESULT for each person was used (for RT-PCR and IgG/M). For the hospitalized COVID patients, did they ever have a positive test? If so, when?

      4. You assume that all COVID+ patients must have developed the antibodies, but 10% lost them. You base this from a paper that said 100% of patients developed IgG by day 17-19. However, the population tested in that paper has much milder cases than in your population. It would not be wise to assume that every patient develops antibodies from a limited population study.

      5. It could easily be that the 10% of hospitalized COVID patients who tested negative for IgG/M just represent the false negatives. The colloidal gold test you used only has a sensitivity of 88.6%. So a 10% false negative would be expected from that test.

      6. You cited another research article that tested for IgG in 1021 people returning to work. This was also in Wuhan. They found 10% of their population tested positive for antibodies, while your general population group showed 4.6% positive. I believe this is more reason to perform your own sensitivity tests of the kit you used.

      7. One observation not mentioned about your data is that it shows 4% of healthcare workers (who had much more exposure to COVID) tested positive for IgG, compared to 4.6% of non-healthcare workers. One would expect their numbers to be higher, not lower, than non-healthcare workers.

      I do not believe there is sufficient scientific evidence here to support the claim that “after SARS-CoV-2 infection, people are unlikely to produce long-lasting protective antibodies against this virus.”

    1. On 2020-06-19 07:54:01, user Dr. Sebastian Boegel wrote:

      Thank you very much for this huge community effort and the very nice results. Congrats to the team. This is a very important study and in analogy to what have been proposed in cancer a while ago: https://www.ncbi.nlm.nih.go...

      I have a couple of questions:<br /> 1.) I am not sure, if I understand that right: the clusters are derived from patients with immunemodulating treatment, such as glucocortocoids, MMF, etc.. In order to make sure that the defined clusters reflect the underlying disease and not the medication, you applied the same model to newly diagnosed patients, of which only a minority received prior treatment. And what you find is roughly the same proportions of diseases in each module. Is that right? If not, my question is: could you describe clearer why you think that these groups reflect the disease itself and not the treatment.

      2.) If 1.) is correct, than i am wondering, that untreated and treated patients cluster in the same way as I would except that immunemodulating treatment affects gene expression of many, esp. immune related genes, systemically, such that the blood transcriptome ist totally different. How do you explain that?

      3.) In the last sentence of the discussion, you wrote that this study will be usefule for a personalized medicine. From a clinical point of view, can you describe how this will help (maybe some examples, what does that study mean for a clinician? and for a diagnostics company?)

      4.) This is a multi center study. How did you normalize the sequencing data, such that the data doesnt cluster according to site? Did you check that? See also TCGA or GTEX.

      5.) How and when is it possible to access the raw data? Will RNA-Seq fastqs also shared? And are clinical information for each patient available?

      Thanks again for this very informative and well structured study. I acknowledge the hard work. This will be )once published peer reviewed) a seminal study in this field.

      Sebastian

    1. On 2020-04-05 18:23:54, user Sinai Immunol Review Project wrote:

      Summary: Authors evaluate clinical correlates of 10 patients (6 male and 4 female) hospitalized for severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). All patients required oxygen support and received broad spectrum antibiotics and 6 patients received anti-viral drugs. Additionally, 40% of patients were co-infected with influenza A. All 10 patients developed lymphopenia, two of which developed progressive lymphopenia (PLD) and died. Peripheral blood (PB) lymphocytes were analyzed – low CD4 and CD8 counts were noted in most patients, though CD4:CD8 ratio remained normal.

      Critical analysis: The authors evaluated a small cohort of severe SARS-CoV-2 cases and found an association between T cell lymphopenia and adverse outcomes. However, this is an extremely small and diverse cohort (40% of patients were co-infected with influenza A). These findings need to be validated in a larger cohort. Additionally, the value of this data would be greatly increased by adding individual data points for each patient as well as by adding error bars to each of the figures.

      Significance: This study provides a collection of clinical data and tracks evolution of T lymphocyte in 10 patients hospitalized for SARS-CoV-2, of which 4 patients were co-infected with influenza A.

      Review by Katherine E. Lindblad as part of a project of students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine at Mount Sinai.

    1. On 2020-04-10 17:40:13, user Sinai Immunol Review Project wrote:

      Key findings<br /> The authors investigated the use of a commercially available form of heparin, low molecular weight heparin (LMWH), as a therapeutic drug for patients with COVID-19. Previous studies showed that in addition to its anticoagulant properties, LMWH exerts anti-inflammatory effects by reducing the release of IL-6 and counteracting IL-6.

      This was a retrospective single-center study conducted in Wuhan, China. Forty-two (42) hospitalized patients with coronavirus pneumonia were included, of which 21 underwent LMWH treatment (heparin group) and 21 did not (control). The general characteristics of the two groups of patients were statistically comparable. Both control and LMWH had the same hospitalization time and there were no critical cases in either group.

      This study found that treatment with LMWH significantly reduced IL-6 levels in patients in the heparin group compared to the control group. However, LMWH treatment did not have an effect on the levels of other inflammatory factors: CRP, IL-2, IL-4, IL-10, TNF-?, and IFN-?. Compared with the control group, patients in the heparin group had a significantly increased percentage of lymphocytes after treatment, further suggesting that LMWH treatment has anti-inflammatory effects and can reduce the lymphopenia associated with COVID-19.

      Consistent with other studies in COVID-19 patients, they found that LMWH treatment can improve hypercoagulability. D-dimer and FDP levels (biomarkers of coagulation) in the heparin group significantly decreased from baseline after treatment, whereas there was no significant change in levels for the control group. Of note however, patients in the heparin group had a significantly higher level of D-dimer and FDP at baseline compared to the control group.

      Importance<br /> Many studies have shown that severely ill COVID-19 patients have significantly higher levels of IL-6 compared to patients with mild cases and it has been proposed that progression to severe disease may be caused by lymphopenia and cytokine storms. The anti-inflammatory effects of heparin may help prevent or reverse a cytokine storm caused by the virus and thus delay COVID-19 progression and improve overall condition in patients. The pleiotropic effects of heparin may have a greater therapeutic effect than compounds that are directed against a single target, such as an anti-IL-6 therapy. This is because COVID-19 patients commonly have complications such as coagulopathy and endothelial dysfunction leading to cardiac pathology that may be mitigated by heparin treatment (Li J, et.al; Wojnicz et.al).

      Limitations<br /> This study is limited by a small sample size (n=44) and a single-center design. Double-blinded, randomized, placebo controlled clinical trials of LMWH treatment are needed to understand the possible benefit of the treatment. Additionally, this study was unable to control for the dose and days of treatment of LMWH. Identifying the correct dose and timing of LMWH is a matter of immediate interest. Of note, patients in the heparin group received two types of LMWH, enoxaparin sodium or nadroparin calcium, which have been reported to have differing anticoagulant activity. The use of different LMWHs in the heparin group warrants further explanation.

      Another caveat of this study is that the levels of D-dimer and fibrinogen degradation products were significantly higher at baseline for patients in the heparin group compared to those in the control group. Therefore, it is difficult to decipher whether some of the positive effects of heparin treatment were due to its anti-coagulation effects or direct anti-inflammatory effects. Future studies are that delineate the anti-inflammatory functions of heparin independently of its anticoagulant properties in cases of COVID-19 would be useful.

      Lastly, this study did not discuss any side-effects of heparin, such as the risk of bleeding. Moreover, coagulation can help to compartmentalize pathogens and reduce their invasion, therefore anticoagulant treatments like heparin may have risks and it remains to be determined which patients would benefit from this therapy.

      References:<br /> Li J, Li Y, Yang B, Wang H, Li L. Low-molecular-weight heparin treatment for acute lung injury/acute respiratory distress syndrome: a meta-analysis of randomized controlled trials. Int J Clin Exp Med 2018;11(2):414-422

      Wojnicz R, Nowak J, Szygula-Jurkiewicz B, Wilczek K, Lekston A, Trzeciak P, et al. Adjunctive therapy with low-molecular-weight heparin in patients with chronic heart failure secondary to dilated cardiomyopathy: oneyear follow-up results of the randomized trial. Am Heart J. 2006;152(4):713.e1-7

      Review by Jamie Redes as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

    1. On 2020-08-10 11:48:29, user memini wrote:

      The assumption of constant case ascertainment (Line 474) in these countries from March through June is a critical one. It is probably most accurate in Portugal where health systems were least overwhelmed and which has the most testing per covid-19 death. Portugal's dashboard reports <100 tests processed per day to >10,000 tests per day over this period, so it is unlikely that the assumption of constant case ascertainment is sound in Portugal or in the other three countries.

      The authors could show whether similar parameters fit a more realistic time series of infections (estimated from deaths). Alternatively, simulated infection trajectories could be filtered by a reasonable estimate of how case ascertainment varied over time before estimating parameters.

      Also, Fig S1 shows gamma distributions with CV=0.5, 1, 2; but best fits give CV = 2, 3 or 4 for these countries (Table S1). The relative susceptibility of the median person for those distributions is 0.17, .01, and 0.0001. Given the narrow confidence intervals in Table S1, authors could hypothesize how susceptibility in Portugal could be so much more variable than England, for instance.

    1. On 2021-02-06 06:40:50, user David Epperly wrote:

      Here's something that addresses Pfizer and Moderna and I agree that the 2nd dose is important. "While durability is improved with a 2 or more dose regimen, dose timing is subject to optimization."<br /> Evidence For COVID-19 Vaccine Deferred Dose 2 Boost Timing<br /> 1. Good efficacy of dose 1<br /> 2. Greater than 3 month durability of dose 1<br /> 3. Double vaccinated population<br /> 4. Dramatically reduce hospitalizations<br /> 5. Save ~ 90K US lives in 2021<br /> https://doi.org/10.2139/ssr...

    1. On 2021-03-02 18:20:29, user Martin Hepp wrote:

      Ok, this is only a preprint. However, a wording like "provides a precise estimate of the true underlying SARS-CoV-2 transmission risk in schools and day-care centres." in the introduction sets all alarm bells of any scientist ringing. "precise" and "true" are bold words, rarely used in serious academic publications (where typically a prominent "threats to validity" section would highlight and discuss the limitations of the findings) - in particular, if the underlying method is relatively weak. Some limitations are discussed on pp.12 and 13, but in a rather superficial way.

      Just a few major questions that challenge the overall contribution:

      1. During the major part of the period of the analysis, the incidence was very low, in particular among young people. See https://corona-data.eu/medi... for a heatmap. Of the total duration of the study of ca. 17 weeks, only the last 5 - 6 weeks and thus less a mere 30 % had a significant incidence in the age-groups 0-4, 5-9, and 10-14, and it was lower than in the general population.

      2. As children are less likely to be symptomatic and the testing regime has a strong bias towards symptomatic patients, it is a valid assumption that the share of undetected infections is higher among students and children than in the general population. As the authors' entire analysis and model for transmission is based on test-confirmed public health cases, the authors should have tested this hypothesis, e.g. by random PCR tests in areas and during periods with a sufficient community incidence. If you miss asymptomatic cases, you are not only invalidating your aggregate statistics, but of course also the entire graph of infections becomes incomplete and questionable.

      3. On pp. 6 an 7, the authors cite the official definitions for cases and procedures; however, there is no information whether the theoretical guidelines for contact tracing, testing, non-pharmaceutical interventions like social distancing, masks, ventilation etc. were actually followed, and if the compliance remained stable over the course of the analysis and representative for the different groups. For instance, one could hypothesize that the effect of wearing mask in classrooms after November 20 is partially obscured by a reduction in ventilation due to cool weather and in general more time spent indoors. Taking the textbook definition of a characteristic of an observation and then assuming it to match the data is a significant threat to validity.

      4. The same holds for the approach of instructing the DPHAs on how to use the questionnaire but not testing the quality of the results statistically or by cross-validation. How do you know that the DPHAs understood and applied your instructions properly? And even if they did, how do you know that the data they were using was correct? it is not a lot of effort to rule out or estimate the margin of error of a potential weakness.

      5. The entire statistical analysis method is only a bit over half a page of largely spaced text (p. 8).

      6. The claim that children are less likely to produce a sufficient viral load to infect others is highly disputed in the literature, see e.g. https://zoonosen.charite.de... these findings are not uniformly agreed (see e.g. https://www.sciencemediacen... "https://www.sciencemediacentre.org/expert-reaction-to-a-preprint-looking-at-the-amount-of-virus-from-those-with-covid-19-in-different-age-groups/)"), but it is not commonly accepted that children are unlikely to infect others. This challenges the assumption that asymptomatic individuals are unlikely to infect others even if they are themselves infected.

      7. The authors state on p.12 that the rate of asymptomatic infections was relatively low with ca. 17%. Unfortunately, this population aggregate used by the authors obscures the influence of age on the likelihood of asymptomatic infections and hence on the number of undetected infections in school settings. A recent meta-study https://www.frontiersin.org... suggests that the rate is higher in children (p=0.5, CI 0.21 - 0.79) than in adults (p=0.3, CI 0.13 - 0.56). There is a lot of variance observed in the underlying studies, but the order of magnitude could explain a major share of the reported higher likelihood of infections originating from teachers than from students alone.

      8. The focus on "hygiene practices" (p.13) as a recommendation conflicts with the widely accepted view that SARS-CoV-2 transmission is largely airborne and that sustained social contact in indoor environments is a high-risk setting, even with masks.

      9. If the risk of students in school infecting teachers is so low, one should immediately stop the priority vaccination of teachers. I think the priority vaccination is justified.

      For lay people: If children are less likely to show symptoms than adults, and testing and hence becoming an index case is more likely for symptomatic individuals, it will be no surprise that teachers, who are adults, are more often identified as index cases than children. If the data graph of humans interacting in the pandemic is incomplete, and there is a systematic bias that leads to more missing index patients being children, your findings can easily be a simple artifact resulting from the chosen approach.

      Now, all science is tentative; we all know our papers could be improved, the evidence or data be more convincing, additional aspects be considered. The problem arises when this is combined with politics. The introduction (p. 5, 2nd paragraph) is heavily focussed on a positive view on re-opening school. The arguments raised are not wrong per se, but they are also not balanced - in a pandemic with a novel virus against which the majority of the human population seems to be immunologically naïve, other societal risks should be given the same space. If you motivate your research with the wish to reopen schools, readers have reason to assume that you are not neutral as to the outcome.

      This is all common in the daily struggle of anybody in research and academia.

      But when you combine such very preliminary work with substantial threats to validity with a bold claim in the intro and a conclusion in which you report with certainty that only 1 in 100 infected students will infect another person in school, knowing that there is a lot of heated debate in the society, then your "Ethical Statement" should be amended by "We knowingly accept that populist media like BILD, interest groups, and decision makers will use our fragile findings and our wording as solid evidence for a risk-prone opening strategy. Since we are so confident in our research, we take full responsibility for the societal consequences."

      Doing preliminary research is unavoidable. Distributing it in a form that is the perfect bait for media and decision makers is unethical.

      This

      https://www.bild.de/ratgebe...

      is the direct effect of your work.

      More than ca. 3 million daily visitors on bild.de (likely largely from the German population) have seen their variant of your message.

    1. On 2021-08-13 17:27:45, user Chuck Crane wrote:

      If you look at the questionnaire (the "supplementary materials" link) you find that the MD's and DVM's are "professional degree," and there is no "PhD" classification at all. It says "Doctorate," which includes Jill Biden's Ed.D. and so on. So the chart is deceptive.

      D8 What is the highest degree or level of school you have completed?<br /> 1. Less than high school<br /> 2. High school graduate or equivalent (GED)<br /> 3. Some college<br /> 4. 2 year degree<br /> 5. 4 year degree<br /> 6. Master’s degree<br /> 7. Professional degree (e.g. MD, JD, DVM)<br /> 8. Doctorate

      The paper is not in sync with the questionnaire, saying, e.g., "Those with professional degrees (e.g., JD, MBA) and PhDs were the only education groups without a decrease in hesitancy, and by May, those with PhDs had the highest hesitancy." I can't see how an MBA could look at the question and check "Professional degree" instead of "Master's Degree."

      Think the paper needs a good proofreading.

      Participation bias is a big issue. They asked a lot of people to participate, but only a small percentage did. The rather inane attempt to correct for this is to assume that if a particular class of respondents is under-represented, just assign responses from that class more weight, according to their proportion of the population ("post stratification adjustment").

    1. On 2021-10-11 18:40:32, user Andrew T Levin wrote:

      Comment #1: Research in Context

      1. Diamond Princess Cruise Ship. The manuscript makes no reference to any epidemiological analysis of this episode, which informed seminal assessments of the age-specific infection fatality rate (IFR) of COVID-19.[1-4] Nonetheless, that evidence is particularly relevant, because the cruise ship’s passengers included 1231 individuals ages 70+ who were not merely “community-dwelling” but healthy enough to embark on a multi-week grand tour of southeast Asia. Following extensive RT-PCR testing, 335 passengers ages 70+ were confirmed to have been infected with SARS-Cov-2, and 13 of those passengers died from COVID-19 – an IFR of about 4%. Moreover, the strong link to age is underscored by the even higher IFR of 8% for passengers ages 80+. Given the size of that sample (which meets the 1000+ threshold used here), this evidence should certainly be incorporated into this meta-analysis.

      2. Comprehensive Tracing Programs. The manuscript makes no reference to countries that succeeded in containing the first wave of the pandemic in spring 2020 through systematic tracing and testing of all contacts of infected individuals.[5] Such evidence is particularly relevant here, because the virus was contained within the “community-dwelling” populations of those locations and never spread to any elderly care facilities. For example, in the case of New Zealand, there were 256 infections and 19 deaths among adults ages 70+ -- an IFR of about 7%.

      3. Hospitalized Patients. The manuscript cites a single study (published in July 2020) that examined the association between comorbidities and mortality risk of COVID-19.[6] However, that study was not able to distinguish whether comorbidities were linked to greater prevalence (the probability of getting infected) or to a higher IFR (the risk of mortality conditional on infection). Unfortunately, the manuscript makes no reference to any subsequent studies on this issue. In particular, a large-scale study of U.K. BioBank participants found that measures of frailty were indeed associated with higher mortality rates in the overall panel but not linked to mortality within the subset of hospitalized COVID-19 patients.[7] In effect, the prevalence of COVID-19 was markedly higher among residents of U.K. nursing homes compared to individuals of similar age living in the community, but the IFR was not significantly different. Those findings directly contradict a key assertion made at the start of this manuscript.

      4. Prior Meta-Analysis of Community-Dwelling Populations. The introduction of this manuscript neglects to mention that an existing meta-analysis study (published in Nature in November 2020) was specifically focused on assessing IFRs excluding deaths in nursing homes.[8] That study estimated the link between age and IFR using seroprevalence and fatality data for adults less than 65 years old, and then showed that the model predictiions were consistent with data on fatalities among community-dwelling adults ages 65+. Moreover, that study used seroprevalence data adjusted for assay characteristics, and the results were obtained using a rigorous Bayesian statistical model that incorporated random variations in the time lags between infection, seropositivity, and fatal outcomes – a striking contrast to this manuscript, which uses rudimentary assumptions to address those issues.

      5. Other Meta-Analyses. The introduction of this manuscript briefly refers to two other meta-analysis studies of the link between age and IFR.[5, 9] However, the manuscript then asserts: “Importantly, the vast majority of seroprevalence studies include very few elderly people.” (p.5) That assertion is supported by a single citation to the SeroTracker database, which provides comprehensive coverage of all existing national, regional, and local seroprevalence studies across the globe.[10] However, this assertion is completely incorrect as a characterization of the preceding meta-analysis of age-specific IFRs. As indicated in Levin et al. (2020, figure 5), that meta-analysis study included seroprevalence data on older adults (including narrow brackets for ages 60-69, 65-74, 70-79, and 75-84 as well as open-ended brackets for ages 60+, 65+, 70+, 80+, and 85+) from nine national studies (Belgium, France, Hungary, Italy, Netherlands, Portugal, Spain, Sweden, and the U.K.) and eight regional locations (Ontario, Canada; Geneva, Switzerland; Connecticut, Indiana, Louisiana, Miami, Missouri, and San Francisco, USA).[5]

    1. On 2021-12-07 10:40:57, user S. von Jan wrote:

      I feel that some of the assumption that go into the model calculation are overestimated, others are underestimated, and some important further information is not considered. I am referring specifically to v (vaccine uptake), s (susceptibility reduction) and b (relative increase in the recovery rate after a breakthrough infection).

      The authors assume a vaccination rate of 65% for the period between 11.10 and 7.11. For the sake of transparency, I think it should be mentioned in the study that in Germany an underestimation of the vaccination rate of up to 5 percentage points is assumed (1), perhaps this should also be considered in the scenarios. Moreover, the recovered cases are not mentioned at all, do they not play a role for the model?

      For s in the "upper bound" scenario, a 72% efficacy of the vaccination in Germany is assumed (2), this figure comes from the German Robert Koch Institute (RKI) and is calculated based on the vaccination breakthroughs in Germany, i.e., it only includes the number of symptomatic cases in Germany. The RKI writes on the estimated vaccine effectiveness: "The values listed here must therefore be interpreted with caution and serve primarily to classify vaccination breakthroughs and to provide an initial estimate of vaccine effectiveness" (3, own translation). The vaccine effectiveness estimated here refers to the effectiveness of vaccination against Covid 19 infections with clinical symptoms, not against infection in general. However, there are indications that infections are more often asymptomatic in vaccinated persons ("vaccinated participants were more likely to be completely asymptomatic, especially if they were 60 years or older"(4)), and vaccinated people in Germany must rarely participate in Covid 19 tests. The RKI points out that vaccination would considerably reduce transmission of the virus to other people but assumes that even asymptomatically infected vaccinated people can be infectious: "However, it must be assumed that people become PCR-positive after contact with SARS-CoV-2 despite vaccination and thereby are infectious and excrete viruses. In the process, these people can either develop symptoms of an illness (which is mostly rather mild) or no symptoms at all" (5, own translation). So is the effectiveness of vaccination against symptomatic infections in this setting relevant when it comes to the role of the vaccinated/unvaccinated to the infection incidence?

      In the "lower efficacy" scenario, s is given as 50% to 60% based on an English study. This percentage corresponds to the data from another study, which estimates the effectiveness of the Biontech/Pfizer vaccination against infection as 53% after 4 months in the dominant delta variant (6). Would this number not be more plausible for the "upper bound" scenario? The "lower efficacy" scenario could then be calculated with an efficacy of 34%, for example, as suggested by another study on infection among household members (7).

      If we consider b, "an average infectious period that is 2/3 as long as this of unvaccinated infecteds" is assumed. This figure seems reasonable based on the available information on the faster decline of the viral load in vaccinated persons. However, there are statements, for example by Prof. Christian Drosten in an interview with the newspaper “Die Zeit”, that make this effect seem less significant: "The viral load - and I mean the isolatable infectious viral load - is quite comparable in the first few days of infection. Then it drops faster in vaccinated people. The trouble is, this infection is transmitted right at the beginning. I'm convinced that we have little benefit from fully vaccinated adults who don't get boostered" (8, own translation). Moreover, there is another issue that is not mentioned in the paper at all, but which I think should be taken into account: Unvaccinated people in Germany have to test themselves much more frequently than vaccinated people (e.g., at the workplace) due to the 3G rules (9, this means vaccinated, recovered or tested). Children and adolescents have a testing frequency of 3 rapid tests a week (10). Even if the effectiveness of the rapid Covid 19 tests for asymptomatic infections should be 58% (i.e., only 58% of infected persons are correctly identified as positive) (11), a test rate of 2 to 3 tests per week would still reduce the duration during which an unvaccinated person is infectious and not in quarantine. This consideration is not included in the model calculation.

      Overall, it appears that several central parameters were underestimated or overestimated in the model calculation: The vaccination rate is actually higher, the effectiveness of vaccination against infection is certainly lower than the figure given in the “upper bound” scenario, and the period in which infected persons infect others is shortened for unvaccinated persons by 3G regulations, since they have to go into quarantine if they test positive. As a result, the contribution of the unvaccinated to the infection incidence in Germany is likely to be strongly overestimated in the model calculation, especially in the “upper bound” scenario.

      (1) https://www.rki.de/DE/Conte... <br /> (2) For adolescents, s is even estimated at 92%, without explicit data being available here.<br /> (3) https://www.rki.de/DE/Conte.... <br /> (4) https://www.thelancet.com/j...<br /> (5) https://www.rki.de/SharedDo... <br /> (6) https://www.thelancet.com/j... <br /> (7) https://www.thelancet.com/j... <br /> (8) https://www.zeit.de/2021/46... <br /> (9) https://www.bundesregierung... <br /> (10) https://taz.de/Schulen-in-d... <br /> (11) https://www.cochrane.de/de/... This overview work does not yet refer to the delta variant.

    1. On 2020-06-09 16:22:37, user Sinai Immunol Review Project wrote:

      Title <br /> Eosinopenia Phenotype in Patients with Coronavirus Disease 2019: A Multi-center Retrospective Study from Anhui, China

      Keywords<br /> • Lymphopenia<br /> • Covid-19 severity<br /> Main Findings<br /> It was previously shown that more than 80% of severe COVID-19 cases presented eosinopenia, in a cohort of Wuhan [1]. In this preprint Cheng et al. aim to describe the clinical characteristics of COVID-19 patients with eosinopenia. In this retrospective and multicenter study, the COVID-19 patients were stratified in three groups: mild (n=5), moderate (n=46) and severe (n=8). All patients received inhalation of recombinant interferon and antiviral drugs, 50% of the eosinopenia patients received corticosteroids therapy compared to 13.8% of the non-eosinopenia patients according to the patients’ clinical presentation. The median age of eosinopenia patients was significantly higher than the non-eosinopenia ones (47 vs 36 years old) as well as body temperature (not significant). Eosinopenia patients had higher proportions of dyspnea, gastrointestinal symptoms, and comorbidities. Eosinopenia patients presented more common COVID-19 symptoms, such as cough, sputum, fatigue, than non-eosinopenia patients (33.3% vs 17.2%). Interestingly lymphocytes counts (median: 101 cells/ul) in eosinopenia patients were significantly less than in non-eosinopenia patients (median: 167 cells/ul, p<0.001). All patients within the severe group recovered and presented with similar numbers of eosinophils and lymphocytes compared with healthy individuals upon resolution of infection and symptoms. The results showed by Cheng et al. are similar to another study involving MERS-Cov [2], but is contradictory to the previous observation with infants infected with respiratory syncytial virus, where high amounts of eosinophils were found in the respiratory tract of patients [3].

      Limitations<br /> The sample size of this study (n=59) is very narrow and could bias the observations described. The authors did not thoroughly measure potential confounding effects of or control for type of treatments, which were different across the patients. <br /> It is still unclear if SARS-COV-2 infection induces eosinopenia or eosinophilia in the respiratory tract, since all reports so far showed peripheral eosinophil counts. As eosinophils antiviral response to respiratory viral infections has been shown [4], it would be important have discussed if the high inflammatory response produced by eosinophils could contribute to the lung pathology during COVID-19, especially when vaccine candidates have been tested and could induce increased amounts of eosinophils.

      Significance<br /> This study suggests that eosinophilia may be a clinical phenotype of COVID-19 that distinguishes eosinopenia patients from non-eosinopenia patients. The contribution of the present study is relevant and calls for experimental analysis to reveal the importance of eosinopenia in COVID-19.

      Credit<br /> Reviewed by Alessandra Soares-Schanoski as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

      1. Du, Y., et al., Clinical Features of 85 Fatal Cases of COVID-19 from Wuhan: A Retrospective Observational Study. Am J Respir Crit Care Med, 2020.
      2. Hwang, S.M., et al., Clinical and Laboratory Findings of Middle East Respiratory Syndrome Coronavirus Infection. Jpn J Infect Dis, 2019. 72(3): p. 160-167.
      3. Harrison, A.M., et al., Respiratory syncytical virus-induced chemokine expression in the lower airways: eosinophil recruitment and degranulation. Am J Respir Crit Care Med, 1999. 159(6): p. 1918-24.
      4. Lindsley, A.W., J.T. Schwartz, and M.E. Rothenberg, Eosinophil responses during COVID-19 infections and coronavirus vaccination. J Allergy Clin Immunol, 2020.
    1. On 2020-07-15 07:10:13, user Dr Ahmed Sayeed wrote:

      Section Review comments and notes Abstract, title and references The study appears to be new and promising in the current scenario of COVID pandemic In the objectives, the authors have the aim to describe the bronchoscopic findings in COVID patients but in the method, they have forgotten to mention how the bronchoscopic findings will be studied What is the meaning of COVID19 patients? Is suspected covid19 or confirmed COVID 19 with Nasopharyngeal swab(PCR or serology or Nuclear acid amplification test) The references are recent and relevant with the inclusion of appropriate study

      Introduction/background In introduction line 4, the term bronchial alveolar lavage would be more appropriate than bronchial culture The author uses the term culture repeatedly which excludes other methods like PCR, grams stain, KOH stain, AFB and would be advised to use the broader term to include other methods of detection of organisms The limitations of the study are not mentioned Methods The study subjects The age group of the patients should be mentioned and the site of covid infection? lung also needs to be mentioned The variables are defined and measured Yes the study appears to valid and reliable

      Results My knowledge of statistics is very limited and it is difficult for me to comment

      Discussion and Conclusions<br /> There is a grammatical error in line 2 and 5 of the discussion Suggest difficult to do suction In paragraph 3 of the discussion the reference 18 is written twice The reference in the discussion are not quoted in serial order The limitations of the study need to be explained more

      Overall The study design was appropriate This study added the to the scarcity of the novel virus literature and it showed that more hospital acquired infections are common in patients with covid I did not find any major flaws in the article

      full review:

      Overall statement or summary of the article and its findings

      The article needs some correction and rewriting with some of my suggestion<br /> Some more literature needs to be done and added to the discussion with some new references

      Overall strengths of the article and what impact it might have in the respiratory field

      The article appears to be promising and will definitely add to the literature of BAL in COVID which not frequently performed in fear of spreading the infection to the health care staff Culture and sensitivity will make a difference in the management of COVID ventilated patients

      Specific comments on the weaknesses of the article and what could be done to improve it Major points in the article which need clarification, refinement, reanalysis, rewrites and/or additional information and suggestions for what could be done to improve the article.

      More literature review<br /> More references need to be added<br /> Minor points like figures/tables not being mentioned in the text, a missing reference, typos, and other inconsistencies.

      English and grammar

    1. On 2020-05-11 01:41:57, user Sinai Immunol Review Project wrote:

      Main findings<br /> The need for improved cellular profiling of host immune responses seen in COVID-19 has required the use of high-throughput technologies that can detail the immune landscape of these patients at high granularity. To fulfill that need, Chua et al. performed 3’ single-cell RNA sequencing (scRNAseq) on nasopharyngeal (or pooled nasopharyngeal/pharyngeal swabs) (NS), bronchiolar protected specimen brush (PSB), and broncheoalveolar lavage (BAL) samples from 14 COVID-19 patients with moderate (n=5) and critical (n=9, all admitted to the ICU; n=2 deaths) disease, according to WHO criteria. Four patients (n=2 with moderate COVID-19; n=2 with critical disease, n=1 on short-term non-invasive ventilation and n=1 on long-term invasive ventilation), were sampled longitudinally up to four times at various time points post symptom onset. In addition, multiple samples from all three respiratory sites (NS, PSB, BAL) were collected from two ICU patients on long-term mechanical ventilation, one of whom died a few days after the sampling procedure. Moreover, three SARS-CoV-2 negative controls, one patient diagnosed with Influenza B as well as two volunteers described as “supposedly healthy”, were included in this study with a total of n=17 donors and n=29 samples.

      Clustering analysis of cells isolated from NS samples identified all major epithelial cell types, including basal, scretory, ciliated, and FOXN4+ cells as well as ionocytes; of particular note, a subset of basal cells was found to have a positive IFN? transcriptional signature, suggesting prior activation of these cells by the host immune system, likely in response to viral injury. In addition to airway epithelial cells, 6 immune cell types were identified and further subdivided into a total of 12 different subsets. These included macrophages (moMacs, nrMacs), DCs (moDCs, pDCs), mast cells, neutrophils, CD8 T (CTLs, lytic T cells), B, and NKT cells; however, seemingly neither NK nor CD4 T cells were detected and the Treg population lacked canonical expression of FoxP3, so it is unclear whether this population is truly represented.

      Interestingly, secretory and ciliated cells in COVID-19 patients were shown to have upregulated ACE2 and coexpression with at least one S-priming protease indicative of viral infection; ACE2 expression on respiratory target cells increased by 2-3 fold in COVID-19 patients, compared to healthy controls. Notably, ciliated cells were mostly ACE2+/TMRPSS+, while secretory and FOXN4+ cells were predominantly ACE2+/TMRPSS+/FURIN+; accordingly, secretory and ciliated cells contained the highest number of SARS-CoV-2 infected cells. However, viral transcripts were generally low 10 days post symptom onset (as would be expected based on reduced viral shedding in later stages of COVID-19). Similarly, the authors report very low counts of immune cell-associated viral transcripts that are likely accounted for by the results of phagocytosis or surface binding. However, direct infection of macrophages by SARS-CoV-2 has previously been reported 1,2. Here, it is possible that these differences could be due to the different clinical stages and non-standardized gene annotation.

      Pseudotime mapping of the obtained airway epithelial data suggested a direct differentiation trajectory from basal to ciliated cells (in contrast to the classical pathway from basal cells via secretory cells to terminally differentiated ciliated cells), driven by interferon stimulated genes (ISGs). Moreover, computational interaction analysis between these ACE2+ secretory/ciliated cells and CD8 CTLs indicated that upregulation of ACE2 receptor expression on airway epithelial cells might be induced by IFN?, derived from these lymphocytes. However, while IFN-mediated ACE2 upregulation in response to viral infections may generally be considered a protective component of the antiviral host response, the mechanism proposed here may be particularly harmful in the context of critical COVID-19, rendering these patients more susceptible to SARS-CoV-2 infection.

      Moreover, direct comparisons between moderate and critical COVID-19 patient samples revealed fewer tissue-resident macs and monocyte-derived dendritic cells but increased frequencies of non-resident macs and neutrophils in critically ill COVID-19 patients. Notably, neutrophil infiltration in COVID-19 samples was significantly greater than in those obtained from healthy controls and the Influenza B patient. In addition, patients with moderate disease and those on short-term non-invasive ventilation had similar gene expression profiles (each n=1),; whereas, critical patients on long-term ventilation expressed substantially higher levels of pro-inflammatory and chemoattractant genes including TNF, IL1B, CXCL5, CCL2, and CCL3. However, no data on potentially decreasing gene expression levels related to convalescence were obtained. Generally, these profiles support findings of activated, inflammatory macrophages and CTLs with upregulated markers of cytotoxicity in critically ill COVID-19 patients. These inflammatory macrophages and CTLs may further contribute to pathology via apoptosis suggested by high CASP3 levels in airway epithelial cells. Interestingly, the CCL5/CCR5 axis was enriched among CTLs in PSB and BAL samples obtained from moderate COVID-19 patients; recently, a disruption of that axis using leronlimab was reported to induce restoration of the CD8 T cell count in critically ill COVID-19 patients 3.

      Lastly, in critically ill COVID-19 patients, non-resident macrophages were found to have higher expression levels of genes involved in extravasation processes such as ITGAM, ITGAX and others. Conversely, endothelial cells were shown to express VEGFA and ICAM1, which are typical markers of macrophage/immune cell recruitment. This finding supports the notion that circulating inflammatory monocytes interact with dysfunctional endothelium to infiltrate damaged tissues. Of note, in the patient with influenza B, cellular patterns and expression levels of these extravasation markers were profoundly different from critically ill COVID-19.

      Importantly, the aforementioned immune cell subsets were found equally in all three respiratory site samples obtained from two multiple-sample ICU donors, and there were no differences, with regards to upper vs. lower respiratory tract epithelial ACE2 expression. However, viral loads were higher in BAL samples as compared to NS samples, and lower respiratory tract macrophages showed overall greater pro-inflammatory potential, corresponding to higher CASP3 levels found in PSB and BAL samples. In general, the interactions between host airway epithelial and immune cells described in this preprint likely contribute to viral clearance in mild and moderate disease but might be excessive in critical cases and may therefore contribute to the observed COVID-19 immunopathology. Based on these findings and the discussed immune cell profiles above, the authors suggest the use of immunomodulatory therapies targeting chemokines and chemokine receptors, such as blockade of CCR1 by itself or in combination with CCR5, to treat COVID-19 associated hyperinflammation.

      Limitations<br /> Technical<br /> In addition to the small sample size, it is unclear whether samples were collected at similar time points throughout the disease course of each patient, even with time since diagnosis normalized across patients. While sampling dates in relation to symptom onset are listed, it remains somewhat unclear what kind of samples were routinely obtained per patient at given time points (with the exception of the two patients with multiple sampling). Moreover, it would have been of particular interest (and technically feasible) to collect additional swabs from the convalescent ICU patient to generate a kinetic profile of chemokine gene expression levels, with respect to disease severity as well as onset of recovery. Again, with an n=1, the number of cases per longitudinal/multiple sampling subgroup is very limited, and, in addition to the variable sampling dates, overall time passed since symptom onset as well as disease symptoms and potential treatment (e.g. invasive vs non-invasive ventilation, ECMO therapy…) across all clinical subgroups, makes a comparative analysis rather difficult.

      It is important to note that a lack of standardized gene annotation across different studies contributes to a significant degree of variability in characterizations of immune landscapes found in COVID-19 patients. As a result, inter-study comparisons are difficult to perform. For instance, an analysis of single-cell RNA sequencing performed on bronchoalveolar lavage samples by Bost et al. identified lymphoid populations that were not found in the present study. These include several enriched subtypes of CD4+ T cells and NK cells, among others. Ultimately, these transcriptomic descriptions will still need to be furthered with additional follow-up studies, including proteomic analysis, to move beyond speculation and towards substantive hypotheses.

      Biological<br /> One additional limitation involved the use of the influenza B patient. Given that the patient suffered a rather mild form of the disease (no ICU admission or mechanical ventilation required, patient was discharged from hospital after 4 days) as opposed to the to authors’ assessment as a severe case, this patient may have served as an acceptable positive control for mild and some moderate COVID-19 patients. However, this approach should still be viewed cautiously, since the potential differences of pulmonary epithelial and immune cell pathologies induced by influenza compared to critical COVID-19 patients are still unclear. Moreover, it seems that one of the presumably healthy controls was recovering from a viral infection. Since it is unclear how a recent mild viral infection might have changed the respiratory cellular compartment and immune cell phenotype, this donor should have been excluded or not used as a healthy reference control.

      Significance<br /> In general, this is a well-conducted study and provides a number of corroborative and interesting findings that contribute to our understanding of immune and non-immune cell heterogeneity in COVID-19 pathogenesis. Importantly, observations on ACE2 and ACE2 coexpression in airway epithelial cells generally corroborate previous reports. In addition, direct differentiation of IFN?+ basal cells to ACE2-expressing ciliated cells, as suggested by trajectory analysis, is a very interesting hypothesis, which, if confirmed, might contribute to progression of disease severity. The findings described in this preprint further suggest an important role for chemokines and chemokine receptors on immune cells, most notably macrophages and CTLs, which is highly relevant.

      This review was undertaken by Matthew D. Park and Verena van der Heide as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

      References<br /> 1. Chen, Y. et al. The Novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Directly Decimates Human Spleens and Lymph Nodes. Infectious Diseases (except HIV/AIDS) (2020) doi:10.1101/2020.03.27.20045427.<br /> 2. Bost, P. et al. Host-viral infection maps reveal signatures of severe COVID-19 patients. Cell (2020) doi:10.1016/j.cell.2020.05.006.<br /> 3. Patterson, B. K. et al. Disruption of the CCL5/RANTES-CCR5 Pathway Restores Immune Homeostasis and Reduces Plasma Viral Load in Critical COVID-19. medRxiv (2020).

    1. On 2020-03-21 16:42:23, user Nick wrote:

      I attempted to reproduce Tables 2 and 3 (R code included at the end of the post), and obtained these results:<br /> `Table 2, Day 3: reported p=0.005, calculated p=0.0136<br /> Table 2, Day 4: reported p=0.04, calculated p=0.0780<br /> Table 2, Day 5: reported p=0.006, calculated p=0.0148<br /> Table 2, Day 6: reported p=0.001, calculated p=0.0019

      Table 3, Day 3: reported p=0.002, calculated p=0.0019<br /> Table 3, Day 4: reported p=0.05, calculated p=0.0429<br /> Table 3, Day 5: reported p=0.002, calculated p=0.0025<br /> Table 3, Day 6: reported p<0.001, calculated p=0.0005`

      That is, Table 3 was more or less reproduced, but Table 2 wasn't; most of my p values are around twice the ones in the preprint.

      Of the 8 tests, 5 produced warnings because chisq.test() doesn't like cell values of 0 or 1. Using fisher.test() from the "stats" package got rid of the warnings and caused some of the Table 2 p values to move towards the ones in the preprint, but only one (Day 6) got close. It isn't clear to me why one would use Fisher's Exact Test here --- my understanding is that it is not sufficient to invoke per-cell numbers of less than 6, as many authors seem to do, but I don't have a reference to hand for that.

      Code:<br /> `t2.d3 <- chisq.test(matrix(c(10, 10, 1, 15), ncol=2))<br /> cat("Table 2, Day 3: reported p=0.005, calculated p=", sprintf("%.4f", t2.d3$p.value), "\n", sep="")

      t2.d4 <- chisq.test(matrix(c(12, 8, 4, 12), ncol=2))<br /> cat("Table 2, Day 4: reported p=0.04, calculated p=", sprintf("%.4f", t2.d4$p.value), "\n", sep="")

      t2.d5 <- chisq.test(matrix(c(13, 7, 3, 13), ncol=2))<br /> cat("Table 2, Day 5: reported p=0.006, calculated p=", sprintf("%.4f", t2.d5$p.value), "\n", sep="")

      t2.d6 <- chisq.test(matrix(c(14, 6, 2, 14), ncol=2))<br /> cat("Table 2, Day 6: reported p=0.001, calculated p=", sprintf("%.4f", t2.d6$p.value), "\n", sep="")

      t3.d3 <- chisq.test(matrix(c(1, 15, 5, 9, 5, 1), ncol=3))<br /> cat("Table 3, Day 3: reported p=0.002, calculated p=", sprintf("%.4f", t3.d3$p.value), "\n", sep="")

      t3.d4 <- chisq.test(matrix(c(4, 12, 7, 7, 5, 1), ncol=3))<br /> cat("Table 3, Day 4: reported p=0.05, calculated p=", sprintf("%.4f", t3.d4$p.value), "\n", sep="")

      t3.d5 <- chisq.test(matrix(c(3, 13, 7, 7, 6, 0), ncol=3))<br /> cat("Table 3, Day 5: reported p=0.002, calculated p=", sprintf("%.4f", t3.d5$p.value), "\n", sep="")

      t3.d6 <- chisq.test(matrix(c(2, 14, 8, 6, 6, 0), ncol=3))<br /> cat("Table 3, Day 6: reported p<0.001, calculated p=", sprintf("%.4f", t3.d6$p.value), "\n", sep="")`

    2. On 2020-03-21 20:58:27, user Sinai Immunol Review Project wrote:

      This study was a single-arm, open label clinical trial with 600 mg hydroxychloroquine (HCQ) in the treatment arm (n = 20). Patients who refused participation or patients from another center not treated with HCQ were included as negative controls (n = 16). Among the patients in the treatment arm, 6 received concomitant azithromycin to prevent superimposed bacterial infection. The primary endpoint was respiratory viral loads on day 6 post enrollment, measured by nasopharyngeal swab followed by real-time reverse transcription-PCR.

      HCQ alone was able to significantly reduce viral loads by day 6 (n = 8/14, 57.1% complete clearance, p < 0.001); azithromycin appears to be synergistic with HCQ, as 6/6 patients receiving combined treatment had complete viral clearance (p < 0.001).

      Chloroquine is thought to inhibit viral infection, including SARS-Cov-2, by increasing pH within endosomes and lysosomes, altering the biochemical conditions required for viral fusion1,2. However, chloroquine also has immuno-modulatory effects that I think may play a role. Chloroquine has been shown to increase CTLA-4 expression at the cell surface by decreasing its degradation in the endo-lysosome pathway; AP-1 traffics the cytoplasmic tail of CTLA-4 to lysosomes, but in conditions of increased pH, the protein machinery required for degradation is less functional3. As such, more CTLA-4 remains in endosomes and is trafficked back to the cell surface. It is possible that this may also contribute to patient recovery via reduction of cytokine storm, in addition to the direct anti-viral effects of HCQ.

      Despite what is outlined above, this study has a number of limitations that must be considered. First, there were originally n = 26 patients in the treatment arm, with 6 lost to follow up for the following reasons: 3 transferred to ICU, 1 discharge, 1 self-discontinued treatment d/t side effects, and 1 patient expired. Total length of clinical follow up was 14 days, but the data beyond day 6 post-inclusion are not shown.

      Strikingly, in supplementary table 1, results of the real-time RT-PCR are listed for the control and treatment arms from D0 – D6. However, the data are not reported in a standard way, with a mix of broadly positive or negative result delineation with Ct (cycle threshold) values, the standard output of real time PCR. It is impossible to compare what is defined as a positive value between the patients in the control and treatment arms without a standardized threshold for a positive test. Further, the starting viral loads reported at D0 in the groups receiving HCQ or HCQ + azithromycin were significantly different (ct of 25.3 vs 26.8 respectively), which could explain in part the differences observed in the response to treatment between 2 groups. Finally, patients in the control arm from outside the primary medical center in this study (Marseille) did not actually have samples tested by PCR daily. Instead, positive test results from every other day were extrapolated to mean positive results on the day before and after testing as well (Table 2, footnote a).

      Taken together, the results of this study suggest that HCQ represents a promising treatment avenue for COVID-19 patients. However, the limited size of the trial, and the way in which the results were reported does not allow for other medical centers to extrapolate a positive or negative result in the treatment of their own patients with HCQ +/- azithromycin. Further larger randomized clinical trials will be required to ascertain the efficacy of HCQ +/- azithromycin in the treatment of COVID-19.

      References

      1. Wang, M. et al. Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Research vol. 30 269–271 (2020).
      2. Thomé, R., Lopes, S. C. P., Costa, F. T. M. & Verinaud, L. Chloroquine: Modes of action of an undervalued drug. Immunol. Lett. 153, 50–57 (2013).
      3. Lo, B. et al. Patients with LRBA deficiency show CTLA4 loss and immune dysregulation responsive to abatacept therapy. Science (80-. ). 349, 436–440 (2015).
    3. On 2020-03-20 20:57:29, user Sylvie Vullioud wrote:

      Could authors provide information to dissipate high risks of bias:

      1. Manuscript was first published on mediterranee-infection.com website, not on medRxiv. On the manuscript on the website on mediterranee-infection.com, I can read 'In Press 17 March 2020 – DOI : 10.1016/j.ijantimicag.2020.105949'. It means that manuscript was already accepted by International Journal of Antimicrobial Agents at the time when the manuscript was deposit on the 20.03.2020 on medRxiv.

      -> Pre-print on medRxiv is not a real pre-print to collect feed-back for manuscript improvement, as originally designed for. Moreover, medRxiv states: 'All preprints posted to medRxiv are accompanied by a prominent statement that the content has not been certified by peer review'.

      -> There is an obvious potential conflict of interest, because last author Raoult is editor of the article collection COVID-19 Therapeutic and Prevention in International Journal of Antimicrobial Agents.

      -> International Journal of Antimicrobial Agents is runned by Elsevier, suggesting 'If accepted for publication, we encourage authors to link from the preprint to their formal publication via its Digital Object Identifier (DOI)'.

      1. Discussion on the controversy of main cited Chinese paper, ref 8 ?

      2. According to paper, allocation of patients group was random but treated group is 51.2 years average and control group 37.3 years?

      3. Article describes 3 conditions of patients: asymptomatic, low and high symptoms. Why?

      4. Care to patients, biological and physiological sampling and analyses, and statistical analyses were not blinded. Why?

      5. I think that no placebo was used. Why?

      6. 6 patients on total of 42 were excluded from study: three patients were transferred to intensive care unit, 1 stopped because of nausea, 1 died. One left hospital. <br /> It is written :'study results presented here are therefore those of 36 patients (20 hydroxychloroquine-treated patients and 16 control patients). Why were dead, intensive care, and nausea patients not included in statistical treatment? <br /> -> This may be a selection bias? <br /> -> What about unwanted very worrying effects of the treatment?

      7. 'The protocol, appendices and any other relevant documentation were submitted to the French National Agency for Drug Safety (ANSM) (2020-000890-25) and to the French Ethic Committee (CPP Ile de France) (20.02.28.99113) for reviewing and approved on 5th and 6th March, 2020, respectively'. Pre-print was posted on 20.03.2020. Time points on day 14 on patients.<br /> -> So recruitment and study started before approval of ANSM and French Ethic Committee? How is it possible?

      8. How is it plausible that numerous authors (18!) participated equally to the work? Is it possible to add their respective contributions?

      Thank you in advance for considering my questions. <br /> Regards, <br /> Sylvie Vullioud

    1. On 2021-04-25 13:30:44, user Robert Saunders wrote:

      Clery and colleagues state that “evidenced based treatments are available” for chronic fatigue syndrome. These are listed as Cognitive Behavioural Therapy-for-fatigue (CBT-f), Activity Management (AM) and Graded Exercise Therapy (GET).

      In 2017 the US Centers for Disease Control and Prevention concluded that there are no effective treatments for CFS, after it re-examined the scientific evidence and removed CBT and GET as recommended treatments [1].

      Similarly, the 2020 draft NICE guideline for ME/CFS specifically warns against the prescription of CBT and GET as treatments due to the evidence that they are ineffective and potentially harmful [2]. 89% of outcomes in studies of non-pharmacological interventions for ME/CFS have been graded as “very low quality” with a high or very high risk of bias by NICE’s independent experts. And no outcomes in any studies of CBT or GET are graded as better than “low quality” [3].

      Clery and colleagues cite Nijhof et al (FITNET) [4] for their claim that “at least 15% of children with CFS/ME [sic] remain symptomatic after one year of treatment”. It should be noted that Nijhof et al used the 1994 CDC Fukada diagnostic criteria [5], which is less specific than other criteria as it does not require post-exertion malaise (PEM) as a symptom.

      Evidence suggests that most people with fatigue and other persistent symptoms following viral infection will recover within 2 years with no treatment, but a minority with ME/CFS will not recover [6,7]. There is no reliable evidence to suggest that long term outcomes are any better for those who have been prescribed CBT or GET and there is good evidence to suggest that these interventions are harmful [8].

      There is undoubtedly a need for children and adults with post-viral fatigue syndromes and ME/CFS to be given appropriate advice and support to manage and cope with the effects of their illnesses. However, acknowledgement of the very low quality of past studies and the evidence that CBT and GET are neither safe nor effective treatments for ME/CFS should be considered a prerequisite for any research pertaining to the provision of such services.

      References:

      1. https://meassociation.org.u...

      2. https://www.nice.org.uk/gui...

      3. https://www.nice.org.uk/gui...

      4. https://www.thelancet.com/j...

      5. https://pubmed.ncbi.nlm.nih...

      6. https://pubmed.ncbi.nlm.nih...

      7. https://pubmed.ncbi.nlm.nih...

      8. https://www.bmj.com/content...

    1. On 2021-08-25 12:07:48, user Prof. W Meier-Augenstein, FRSC wrote:

      What other than the difference in antibody titer post-vaccination and post-infection is the take-home message of this study? Surely, the decline in antibody titer per se months after vaccination or primary infection is not a surprising finding but could be expected? Antibodies have a finite life-span given by their Ig specific half-life (for example 21 days for IgGs). In the absence of a subsequent challenge (e.g. by a secondary infection) antibodies formed in response to the challenge posed by vaccination or primary infection will have all but cleared from serum after 6+ months. Furthermore, the difference in antibody titer between mRNA vaccinated and SARS-CoV-2 infected could not have come as a big surprise either considering mRNA vaccination results in expression of spike-protein “only” which means in contrast to a viral infection host cells are actually not infected and do not reproduce copious amounts of the virus which will take longer to fight and clear from the body than the spike protein. For the same reason, macrophages (phagocytes) are unlikely to be involved in the mRNA vaccinated group to the same degree as they are in the group infected by the virus. The natural decline of IG antibodies produced in response to the mRNA vaccine does not offer an exclusive explanation for breakthrough infection. Instead, breakthrough infection occurring 146+ days post vaccination are most likely the result of a “perfect storm”, an unfortunate coincidence of the higher virulence of the Delta variant of <<7 days incubation time, the associated higher viral load produced, and the fact the production of neutralising antibodies by B-memory cells takes up to 4-5 days to reach its peak.

    1. On 2020-04-08 00:15:34, user Sinai Immunol Review Project wrote:

      Clinical features and the maternal and neonatal outcomes of pregnant women with coronavirus disease 2019

      Keywords

      Pregnancy, SARS-CoV2, neonatal and maternal Covid-19 outcome

      Key findings

      33 pregnant woman and 28 newborns were included in this retrospective multi-center study, conducted at 5 hospitals in Wuhan and Hubei province, China, between January 1 and February 20, 2020. All women were diagnosed with Covid-19 by qPCR or viral gene sequencing based on the Chinese New Corona Pneumonia Prevention and Control Program, 6th edition, and were further subdivided into four groups based on clinical severity: (1) mild, presence of mild clinical symptoms without radiological abnormalities; (2) moderate, fever or upper respiratory symptoms as well as radiological signs of pneumonia; (3) severe, at least one of the following: shortness of breath/respiratory rate >30/min, resting oxygen saturation SaO2<93%, Horowitz index paO2/FiO2 < 300 mmHg (indicating moderate pulmonary damage); and (4) severe-acute, acute respiratory distress with need for mechanical ventilation; systemic shock; multi-organ failure and transfer to ICU. Maternal admission to ICU, mechanical ventilation or death were defined as primary outcomes; secondary study outcomes comprised clinical Covid-19 severity in both mothers and newborns, including development of ARDS, neonatal ICU admission as well as mortality.

      Maternal characteristics and outcome: 3 out of 33 women were in their second trimester of pregnancy (17, 20 and 26 weeks), and 15/33 (45.5%) had a previous history of underlying chronic health disorders including cardiovascular, cerebrovascular or nervous system disease. Common Covid-19 symptoms at presentation were fever (63.6%), dry cough (39.4%), fatigue (21.2%), and shortness of breath (21.2%). Less common symptoms included diarrhea, post-partum fever, muscle ache, sore throat and chest pain. 4 (12.1%) pregnant women had no apparent symptoms. The majority of cases were classified as mild (39.4%) or moderate (57.6%); however, one woman developed severe Covid-19. 40.6% of women were diagnosed with bilateral pneumonia, 43.8% presented with unilateral pneumonia, and 15.6% showed radiological ground-glass opacity. 87.9% of women required oxygen administration, and one (3%) woman had to be put on non-invasive mechanical ventilation (primary outcome). 81.5% of women had a C-section and only 5% had vaginal deliveries. Obstetrical complications were seen in 22.2% of women, including three cases of preterm rupture of membranes, two cases of hypertensive disorders of pregnancy, and one case of spontaneous preterm labor. Five pregnancies were ongoing at the end of the observation point of this study; one woman decided to have her pregnancy terminated. Neonatal outcome: Out of 28 newborns included in this study, 35.7% were born preterm at <37 weeks of gestation with Apgar scores ranging from 8-10/10 at 1 min and from 9-10/10 after 5 min, indicating normal heart and respiratory rates. 17.9% of newborns were of low birth weight (not specified) and 14.3% showed signs of fetal distress (also not specified). According to the authors of this study, none of the newborns presented with clinical Covid-19 symptoms. However, one newborn, delivered at 34 weeks of gestation, was diagnosed with (apparently Covid-19 unrelated?) ARDS and transferred to NICU (secondary outcome). Of 26 newborns tested for SARS-CoV2, only one was found positive and showed radiological signs of pneumonia, but no clinical symptoms of Covid-19. It remains unclear whether this was the same case as the newborn diagnosed with ARDS. The affected newborn did not require any treatment and was discharged at 16 days post birth. In summary, the primary outcome “mechanical ventilation” in pregnant women was rare (3%), no other primary outcomes were reached. Most Covid-19 cases in pregnant women were described as mild to moderate. Only one of 28 (3.57%) newborns was diagnosed with ARDS (secondary outcome).

      Potential limitations

      Major limitations of this study are its small size and the rudimentary and at times inadequate description of patient specifics. For example, underlying health conditions that might be affecting Covid-19 outcome in pregnant women should have been clearly specified (other than being of be listed (not just <37 weeks). Given that maternal infection status seemed mostly unknown at the time of birth and, more importantly, that the majority of cases in this study were clinically asymptomatic or mild to moderate, it remains unclear whether the C-sections performed were a medical necessity or elective procedures. This is of importance and should have been discussed. With regard to neonatal outcome, it is also not apparent whether the newborn found to be infected with SARS-CoV2 and the case diagnosed with ARDS were the same individual. If this was the case, it would be incorrect to refer to all newborns as asymptomatic. Additionally, it seems somewhat unlikely that a newborn with a near-perfect Apgar score would present with ARDS immediately after birth. Likewise, any individual diagnosed with ARDS would certainly be expected to receive supportive treatment including (invasive) mechanical ventilation. While it is highly relevant that overall clinical outcome in pregnant women diagnosed with Covid-19 seems better than in SARS or MERS (as discussed by the authors), it nevertheless needs to be stressed that more than 37% of newborns in this study were delivered preterm and that the obstetric complication rate of 22% seems higher than non-Covid-19 average.

      Overall relevance for the field

      Observations in this study confirm some of the findings published in a case series by Yu N et al. (Lancet Infect Dis 2020; https://doi.org/10.1016/ S1473-3099(20)30176-6). However, due to the relatively small study size of 33 pregnant women and 28 newborns, this study lacks statistical power and final conclusions on Covid-19 outcomes in pregnant women and newborns cannot be drawn. Yet, the data collected here are important and should be incorporated into larger data sets for more insight. Understanding the clinical course and effects of Covid19 in both pregnant women and newborns is essential, and while there are some recent publications on vertical SARS-CoV2 transmission between mothers and newborns (Dong L et al, JAMA March 26, 2020, doi:10.1001/jama.2020.4621; Zeng H et al, JAMA March 26, 2020, doi:10.1001/jama.2020.4861) as well as on neonatal infection at birth (Zeng L et al, JAMA March 26, 2020, doi:10.1001/jamapediatrics.2020.0878), our knowledge of how these patient subsets are affected is still very limited.

      This review was undertaken as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2020-04-20 00:46:27, user deutsch wrote:

      A conclusion with only two days oh HCQ and such a smal sample is dishonest!<br /> The probability of missing real differences between the treatments is very high with such a small sample. For example with real death rate 2% vs 4% the probability of false conclusion is 90%....

    1. On 2020-09-14 19:25:30, user Vincent Fleury wrote:

      Can you provide the distribution by age of the deaths, I can't find it in the paper. What I read is that there are 8 times more people in the stratum age>65yo, while the mortality is only 3 to 4 times higher. If mortality occurs only in the >65yo, then this work shows 1-that HCQ is not given to elderlies and 2-potentially that HCQ is actually harmful.

    1. On 2021-08-26 05:56:47, user William Brooks wrote:

      This is an interesting paper that fails to find an effect of early bar/restaurant closures during Japan's second state of emergency (SoE). However, I think it has several limitations.

      1) Were early closures actually justified?

      The authors fail to point out that the SoE started one week after the effective reproduction number (Rt) had peaked and 2/3 days after it had gone under 1 throughout Japan [1, slides 17-18], so the SOE was unnecessary for preventing the "collapse of the medical system", which was the government's justification. Also, the early closure of bars/restaurants in Tokyo/Osaka prior to the SoE didn't stop Rt increasing during the second half of December exactly the same as in the rest of Japan without early closures [1, slides 19-20]. This isn't surprising since even the extreme lockdowns in Peru and Argentina couldn't counteract seasonal rises in Covid infections [2].

      Furthermore, even if there were statistically significant reductions in self-reported coughs and sore throats, do the authors think these could justify the negative effects on employment, firm exit, and mental health mentioned in Introduction?

      2) Why suggest capacity limits but not ventilation improvements?

      In addition to early closures, the Japanese government also recommends mask-wearing while dining out (which is unlikely to be effective [3] [4]) and the use of plastic partitioning in restaurants/bars (which may actually increase infection risk [5]). The authors suggest capacity limits, but this doesn't solve the socioeconomic impacts mentioned in Introduction. Fortunately, even modest improvements in ventilation may be as effective as high-quality R95 masks [4], so investments in improved ventilation/air-purification could be a better solution.

      [1] https://www.mhlw.go.jp/cont...<br /> [2] https://www.scirp.org/pdf/o...<br /> [3] https://doi.org/10.7326/M20...<br /> [4]https://aip.scitation.org/d...<br /> [5] https://doi.org/10.1101/202...

    1. On 2021-07-04 13:13:09, user Sebastian Rosemann wrote:

      Dear authors,

      you write: "For each country we predicted the ‘baseline’ mortality in 2020 based on the 2015–2019 data (accounting for linear trend and seasonal variation; see Methods). We then obtained excess mortality as the difference between the actual 2020 and 2021 all-cause mortality and our baseline. For each country we computed the total excess mortality from the beginning of the COVID-19 pandemic (from March 2020) (Figure 2, Table 1)"

      For Germany Table 1 shows 36.000 excess deaths, which makes up 4% increase, suggesting a baseline of 900.000 for March 2020 - Mai 23th 2021.<br /> According to destatis<br /> https://www.destatis.de/DE/...<br /> the yearly number of deaths for 2015-2019 in Germany was always above 900.000.<br /> How can your baseline for a timespan of ~14 month be lower than the actual number of deaths for 12 months within the last years?

    1. On 2021-09-25 09:47:21, user Jan Podhajsky wrote:

      Hi there. There is a question about personal and sensitive data protection during obtaining answers via questionnaire distributed through social web.

      1. Unsufficient introduction to the survey. No mention about sensitive data personal data being colellected via questionnaire in consent question
      2. Missing contact to authority responsible for Personal and sensitive data protection
      3. Doubts about processing of personal data especially electronic personal data like cookies, refferals, geolocation
      4. Questionnaire enabled continuation without previous login into the system which might lead to other person to access personal and sensitive data, e.g. on shared computers

      I raised these concerns to data protection authority of Faculty of Scince, CUNI.

      The questionnaire research did not met personal and senstive data protection standards. It is unethical research by my opinion.

    1. On 2023-12-10 17:31:49, user Scott Lear wrote:

      I have several serious concerns with the study and manuscript:

      1. This is an observational study and despite what is written in the manuscript, an observational study cannot lead to the clear conclusions the authors suggest the results indicate. The authors should temper their interpretation of the results and take into account the below.

      2. The authors do not address their findings in the context of the 1000s of other observational studies indicating activity (and at high levels) has consistently been associated with reduce risk for premature mortality.

      3. The authors state previous studies have not robustly adjusted for other lifestyle measures. This is untrue. Many of the existing observational studies have robustly adjusted for the measures the authors of this study say weren't done (and have done so for decades). Here are a few:

      https://pubmed.ncbi.nlm.nih...<br /> https://pubmed.ncbi.nlm.nih...<br /> https://pubmed.ncbi.nlm.nih...

      All of these studies (there are many more) had more robust adjustment than the present one and found that high levels of activity either provided further risk reduction or a plateau but no reduced risk reduction. The last study had 30 years of follow-up and 15 points of LTPA assessment- a point that the authors of the current study infer that their study is the only study to have a long follow-up.

      1. Self-reported questionnaires to assess LTPA are not as accurate as the authors indicate. While questionnaires are used in many studies, they tend to overestimate activity levels compared to objective measures such as accelerometers (plenty of studies to support this). They also are not accurate in distinguishing different levels of intensity of activity which is also pertinent to consider. Lastly, the study only assessed leisure time activity ignoring occupational, household and transport activities. Given the surveys were done in 1975, 1981 and 1990, this is likely to be a substantial amount of activity missed (as all jobs were more active back then, than now). There is no indication in the Methods how the participants were put into the four activity groups (sedentary to highly active).

      2. The biological ageing was based only on a sub-sample of 5% of the study population. There is no description in the methods how this sub-sample<br /> was selected. Was it random, and thus possibly representative of the larger cohort, or was a convenience or selective sample that could introduce bias?

      3. BMI was self-reported, and again, there is ample literature to indicate self-reported BMI underestimates true BMI and this is greatest in those with higher BMI. In addition, if one is looking to assess the true affect of LTPA, BMI should not be adjusted for as it is in the causal path between LTPA and mortality.

      4. We have good quality randomized trials from the 1980s in cardiac rehab that indicate the benefit of structured exercise on <br /> reducing early death (and leading to greater lifespan) in people with heart disease. While the authors quote a single study (#5) stating RCTs <br /> have not shown activity to result in longer life, the study quoted is not a real study but rather a commentary. The advantage of the RCTs from the 1980s is the lack of pharmacological management of participants in usual care because statins and anti-hypertensives where either not around or readily prescribed at the time. More recent RCTs have control groups that are optimally medically managed.

    1. On 2023-12-26 14:48:44, user Donald R. Forsdyke wrote:

      LATE ONSET POST-VACCINATION MYOCARDITIS

      The acceleration of SARS-CoV-2 vaccine research post-2020 was so rapid that preprint postings became the norm for many of us working in the field. This preprint of Watson et al. (1) describing 3 case histories is in line with previous preprints describing single case histories (2, 3). It now appears that late-onset post-vaccination myocarditis in elderly subjects can be either overt (symptomatic; 1, 2) or cryptic (not symptomatic; 3).

      The cases described here (1) developed symptoms of myocarditis several weeks after vaccination and a few weeks after initiating anti-PD-1 treatment (Immune checkpoint blockade; ICB). The latter would have decreased constraints on autoimmune phenomena. The reported period following vaccination prior to symptom onset, coincides with that reported early in the pandemic by Guatam et al. (2) for a subject with a previous cardiac condition (prior morbidity). It also concides with the post-vaccination periods that preceded protracted, yet asymptomatic, transient dips in blood pressure (BP) in a normal subject, which has been attributed to myocarditis (3).

      In the latter case, an episode of cardiac fibrillation during a run, prompted a retrospective analysis of blood pressure (BP) readings for the period when five sequential anti-SARS-CoV-3 vaccinations has been given (3). This resulted in the unexpected discovery of the extreme BP dips that progressively increased in extent with successive vaccinations. A cause-and-effect relationship was evident. The myocarditis was cryptic and was deemed likely to remain so, unless the subject had made excessive demands on cardiac function (e.g., vigorous exercise). Alternatively, the delicate balance between normal immunity and autoimmunity might have been shifted as in (1), or a comorbidity might have emerged as in (2).

      The present preprint begins by stating that association between vaccination and myocarditis is rare and affects younger subjects (1). The other preprints suggest the existence of a vulnerable population-subset that may include many elderly subjects and may be less rare than is generally understood. A “crowd sourcing” follow up has been suggested (3,4).

      1.Watson RA, Ye W, Taylor CA, Jungkurth E, Cooper R, Tong O, et al. Severe acute myositis and myocarditis upon initiation of six-weekly Pembrolizumab post-COVID-19 mRNA vaccination. medRxiv 2023; doi.org/10.1101/2023.11.24....<br /> 2.Gautam N, Saluja P, Fudim M, Jambhekar K, Pandey T, Al'Aref S. A late presentation of COVID-19 vaccine-induced myocarditis. Cureus 2021; 13: e17890.<br /> 3.Forsdyke DR. Cryptic evidence on underreporting of mRNA vaccine-induced cardiomyositis in the elderly: a need to modify antihypertensive therapy. Qeios Here<br /> 4.Forsdyke DR. Physician-scientist-patients who barketh not. The quantified self movement and crowd-sourcing research. J Eval Clin Pract 2015; 21: 1024–1027.

    1. On 2020-05-16 13:23:57, user Sinai Immunol Review Project wrote:

      The RBD of the spike protein of SARS-group coronaviruses is a highly specific target of SARS-CoV-2 antibodies but not other pathogenic human and animal coronavirus antibodies

      Premkumar L et al., medRxiv 2020.05.06.20093377; doi: https://doi.org/10.1101/202...

      Keywords<br /> • SARS-CoV-2 receptor binding domain (RBD) binding antibodies<br /> • Endemic human coronaviruses<br /> • Cross-reactive abs/ELISA

      Main findings<br /> There is an urgent need for both sensitive and specific SARS-CoV-2 serological testing to not only reliably identify all infected individuals regardless of clinical symptoms, but to determine the percentage of convalescent individuals on population level.<br /> In this preprint, Premkumar et al. study the performance of the SARS-CoV-2 receptor binding domain (RBD), which has been found to be largely unique across individual coronaviruses, as a target to specifically detect antibodies against SARS-CoV-2. By generation of recombinant RBDs of SARS-CoV-1, SARS-CoV-2 and human endemic coronaviruses (hCoV HKU-1, OC-43, NL63 and 229E), antigen cross-reactivity of these targets was evaluated by ELISA, using sera obtained from both infected and convalescent COVID-19 patients, healthy control individuals as well as pooled sera collected from various animals immunized with either SARS-CoV-1, SARS-CoV-2 or animal coronaviruses. While sera from mice and rabbits previously exposed to SARS-CoV-1 spike protein were found to be cross-reactive, recognizing both the SARS-CoV-1 and SARS-CoV-2 RBDs (yet none of the hCoVs), serum from SARS-CoV-2-immune mice predominantly reacted with SARS-CoV-2. Importantly, control sera obtained from healthy donors without a prior history of either SARS-CoV-1 or SARS-CoV-2 were found to only detect hCoV-RBDs. Additionally, assessment of highly concentrated sera collected from 20 healthy donors in the US prior to emergence of the SARS-CoV-2 pandemic confirmed high levels of antibodies against hCoVs in the majority of subjects, whereas cross-reactive antibodies against the SARS-CoV1 and SARS-CoV-2 RBDs could not be detected. Furthermore, serological testing of sera obtained from convalescent Dengue and Zika virus patients (n=40) as well as from recently recovered patients with influenza A (n=2) and respiratory syncytial virus (n=1) confirmed frequent antibodies against hCoV RBDs, but a lack of cross-reactive antibodies against both the SARS-CoV-1 and SARS-CoV-2 RBD as opposed to positive controls of pooled sera from SARS-CoV-1 immunized guinea pigs. Notably, sera from recently recovered, PCR-diagnosed hCoV patients (NL-63, OC-43, HKU-1; n=2 each) were found equally not cross-reactive against either the SARS-CoV-1 or SARS-CoV-2 RBDs (again using guinea pigs immunized with SARS-CoV-1, SARS-CoV-2 or various animal coronaviruses as positive and negative controls), suggesting that the SARS-CoV-2 RBD is a highly specific target for serological SARS-CoV-2 testing. Furthermore, assessment of total Ig as well as IgM binding to recombinant SARS-CoV and hCoV RBDs in 77 samples obtained from 70 PCR-confirmed COVID-19 patients of variable clinical disease revealed high sensitivity (Ig: 98%, IgM: 81%) for specimens collected at least 9 days post symptom onset. Of note, 67% (Ig) and 30% (IgM) of these samples were also found to be cross-reactive against the SARS-CoV-1 RBD. Repeated sampling of 14 of these 77 patients suggested that seroconversion had occurred between day 7 and day 9 post symptom onset. In addition, 19/77 patients were tested for development and kinetics of neutralizing antibodies (nAbs). Notably, 14% of these 19 patients had detectable levels of nAbs by day 7, whereas 95% of them were positive for nAbs by day 9. One patient failed to elicit both anti-RBD binding and nAbs. Finally, a robust correlation between levels of RBD binding Ig and IgM as well as nAbs was detected, suggesting that levels of RBD binding Abs in COVID-19 patients might be used as a correlate for the development of potentially protective nAbs.

      Limitations<br /> While this is generally a well-conducted study, interrogating a relatively large number of COVID-19 patient and healthy control samples as well as sera from immunized animals, one limitation pertains to the patient cohort enrolled. Given that clinical disease might directly relate to Ab titers, as has been observed in SARS-CoV-1 (https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2683413/pdf/nihms109289.pdf)") and has also been suggested in SARS-CoV-2, a more stringent characterization of these patients would add further impact to the observations made here. Moreover, inclusion of COVID-19 patients across all clinical stages and after convalescence as well as longitudinal sampling over several months and longer are needed to further assess serological testing sensitivity and specificity of RBD-binding Abs and whether the latter may be used as a correlate for potentially protective nAb titers. Additionally, detection of other binding Abs against N, M, S1, S2 may add valuable information, in particular with respect to individuals who seemingly fail to develop humoral anti-SARS-CoV-2 RBD responses. Likewise, evaluation of and comparison to other highly specific epitopes such as ORF3b and ORF8, as recently suggested by another preprint (https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.04.30.20085670v1)"), might be helpful to rule out seroconversion failure. Notably, the authors report SARS-CoV-2 RBD binding Ab cross-reactivity against SARS-CoV-1 in some COVID-19 patients, an observation that mirrors previous findings about S-binding Abs in several preprints/publications. Given the rather low number of SARS-CoV-1 convalescent patients in the general population, this is likely not a major issue. However, for future clinical application, additional use of potentially even more specific Abs, e.g. against ORFs, might be favorable.

      Significance<br /> In general, this study corroborates previous reports and observations about enhanced specificity of the SARS-CoV-2 RBD over other binding ab epitopes (cf. https://www.nature.com/arti... https://www.ncbi.nlm.nih.go... https://wwwnc.cdc.gov/eid/a... "https://wwwnc.cdc.gov/eid/article/26/7/20-0841_article)"). Most importantly, these data suggest that pre-existing binding Abs against endemic human coronaviruses seem to be not cross-reactive against the SARS-CoV-2 RBD and that titers of anti-RBD binding Abs robustly correlate with nAb levels. These observations are of great relevance but need further assessment in larger studies of hCoV seropositive and SARS-CoV-2 negative healthy donors.

      References<br /> 1. Chris Ka-fai Li et al. T Cell Responses to Whole SARS Coronavirus in Humans. The Journal of Immunology. 2008, 181 (8) 5490-5500; DOI: 10.4049/jimmunol.181.8.5490<br /> 2. Yap et al. Patient-derived mutations impact pathogenicity of SARS-CoV-2. PrePrint DOI:<br /> https://doi.org/10.1101/202... <br /> 3. Perera et al . Serological assays for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), March 2020. Euro Surveill. 2020;25(16):pii=2000421. https://doi.org/10.2807/156.... ES.2020.25.16.2000421<br /> 4. Amanat et al. A serological assay to detect SARS-CoV-2 seroconversion in humans. Nat Med (2020). https://doi.org/10.1038/s41...<br /> 5. Okba et al. Severe acute respiratory syndrome coronavirus 2–specific antibody responses in coronavirus disease 2019 patients. Emerg Infect Dis. 2020 Jul [date cited]. https://doi.org/10.3201/eid...

      This review was undertaken by Verena van der Heide and Zafar Mahmood as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

    1. On 2020-09-18 17:53:41, user harrie geenen wrote:

      1/ Consider air, fog or airpollution, all airborne . They can only remain airborne if enough more or less comparable particles or gasmolecules are available. if you compare the flightduration , you have to consider:<br /> The particle itself or attached to locally widely available host particle eg moisture, the microdroplets of many other persons in the party etc.<br /> 2/ the original droplets spread experiment adopted by the WHO is false because it neglects the liftsupporting of other particles.<br /> 3/ It is not difficult to do real tests of particle distribution in real life and it is also possible to take airsamples mimicing a persons load on a party evening or in other social areas. Using an infected person at least 3 meters away fom the crowd., (completely safe according health autorities). or using another marker. or giving the crowd safe air through a nose tube.<br /> Or measuring the particle distribution and copy this to a test area with an infected person.<br /> 4/ RIVM or other parties could monitor air in many places to get an impression of covid particles in an aerosol form.

      5/ The virusload resonse reaction may vari very much between individuals. As long as this is the case you cannot accept a low level acceptable dose.

      harrie geenen

    1. On 2023-06-15 13:36:40, user Rachel Gibson wrote:

      This Scientific Correspondence has also been submitted to eLife.

      Comment on ‘The clinical pharmacology of tafenoquine in the radical cure of Plasmodium vivax malaria: an individual patient data meta-analysis’<br /> Authors: Raman Sharma1, Chao Chen2, Lionel Tan2, Katie Rolfe1, Ioana-Gabriela Fita2, <br /> Siôn Jones2, Anup Pingle3, Rachel Gibson1, Navin Goyal4*, Isabelle Borghini Fuhrer5, <br /> Stephan Duparc5, Hema Sharma2†, Panayota Bird2<br /> Affiliations: 1GSK, Stevenage, UK; 2GSK, Brentford, UK; 3GSK, Mumbai, India; 4GSK, Upper Providence, PA, USA; 5Medicines for Malaria Venture, Geneva, Switzerland<br /> *At the time of submission of this Letter, Navin Goyal is no longer an employee of GSK and is affiliated to Johnson and Johnson<br /> †At the time of submission of this Letter, Hema Sharma is no longer an employee of GSK and is affiliated to AstraZeneca

      Abstract<br /> A single 300 mg dose of tafenoquine, in combination with chloroquine, is currently approved in several countries for the radical cure (prevention of relapse) of Plasmodium vivax malaria in patients aged >=16 years. Watson et al.’s recent publication suggests, however, that the approved dose of tafenoquine is insufficient for radical cure and that a higher 450 mg dose should be recommended. In this response, the authors challenge Watson et al.’s assertion based on empirical evidence from dose-ranging and pivotal studies (published) as well as real-world evidence from post-approval studies (ongoing, therefore currently unpublished). The authors confidently assert that, collectively, these data confirm that the benefit–risk profile of a single 300 mg dose of tafenoquine, co-administered with chloroquine, for the radical cure of Plasmodium vivax malaria in patients who are not G6PD deficient, continues to be favourable.

      Introduction<br /> The Plasmodium vivax malarial parasite has a major economic and public health impact, especially in regions such as East Africa, Latin America and South and East Asia.1,2 When present in blood, P. vivax can cause acute malaria with episodes of chills, fever, muscle pains and vomiting. The parasite also has a dormant liver hypnozoite stage, which can reactivate after weeks, months or years, leading to relapses and, potentially, to severe anaemia, permanent brain damage and death.1,2 For effective treatment, eradication of both the blood and liver stages of P. vivax is required (radical cure).2<br /> Since 2018, regulators from the United States initially, and subsequently from Australia, Brazil, Colombia, Thailand, Peru and The Philippines, have approved tafenoquine (as a single oral dose of 300 mg in combination with standard doses of chloroquine) for the radical cure (prevention of relapse) of P. vivax malaria in patients aged >=16 years.1,3-5 A paediatric formulation that allows weight-band-based dosing of children (aged >=2 years) and adolescents is also approved in Australia (since 2022).5 Like primaquine, tafenoquine is an 8-aminoquinoline derivative effective against hypnozoites and all other stages of the P. vivax lifecycle; however, although the World Health Organization (WHO) recommends a 7- or 14-day treatment course for primaquine, tafenoquine is the first single-dose treatment for the radical cure of P. vivax malaria and therefore has patient adherence and convenience advantages.1,3,6 Nonetheless, as an 8 aminoquinoline, the safety profile of tafenoquine is similar to that of primaquine, and both agents can cause oxidant haemolysis in people with glucose-6-phosphate dehydrogenase (G6PD) deficiency.7,8 Acute haemolysis is usually short-lived and does not need specific treatment; however, in rare cases, severe haemolysis may lead to life-threatening anaemia (requiring red blood cell transfusions) or haemoglobinuric renal failure.9 In malaria-endemic regions it has been estimated that 8% of the population are G6PD deficient, although significant variation is reported across regions, with the highest country-specific prevalence estimated in Africa and Western Pacific countries.10,11 G6PD deficiency is an X-linked disorder; males are either G6PD deficient or have normal G6PD activity, whereas females exhibit a wide range of G6PD deficiency.2 Females may be symptomatic if they are homozygous, or if they are heterozygous and inactivation of their normal X chromosome (lyonisation) is skewed towards a deficient phenotype.2,12 Caution is needed because inter-individual variability in the pattern of lyonisation may cause heterozygous females with levels of enzyme activity between 30% and 70% of normal to test as normal for G6PD deficiency using qualitative, phenotypic, rapid diagnostic screening tests.13,14 To reduce the risk of haemolysis, the G6PD status of all potential tafenoquine patients must be determined with a quantitative test capable of accurately differentiating deficient, intermediate and normal G6PD activity levels, and tafenoquine should be withheld from patients with G6PD enzyme levels below 70% of normal.3<br /> Importantly, appropriate clinical practice for the use of 8-aminoquinolines in P. vivax malaria has always been precariously balanced between providing adequate activity against hypnozoites and the real risk of haemolytic harm to patients with G6PD deficiency.15 The cautious benefit–risk balance involved with the single 300 mg dose of tafenoquine has been questioned in a recently published paper in which Watson et al., hypothesise that the current recommended dose of tafenoquine 300 mg is insufficient and that a 450 mg dose of tafenoquine would reduce the risk of relapse.16 That dose is 50% greater than the 300 mg dose approved by the US Food and Drug Administration (FDA), Australian Therapeutic Goods Administration (TGA) and other international regulatory authorities.1,3-5 Herein, the authors discuss concerns regarding the conclusions of Watson et al.<br /> • The benefit–risk profile of tafenoquine 450 mg is not appropriately considered. For example, there is minimal discussion of tafenoquine safety data and key findings from a phase 1 study in healthy female volunteers heterozygous for the G6PD Mahidol variant. This important study demonstrated not only that the haemolytic potential of tafenoquine was dose dependent but also that a single 300 mg dose of tafenoquine had the same potential to cause haemolytic harm as the WHO-recommended dose of primaquine for uncomplicated P. vivax malaria (15 mg/day for 14 days).17,18<br /> • The authors acknowledge that data from the phase 2b, paediatric, pharmacokinetic (PK) bridging study TEACH19 were not available before submission of the Watson et al. manuscript. However, in the TEACH study, in which the tafenoquine dosage in paediatric patients was chosen to match blood exposure in adults receiving 300 mg, tafenoquine was efficacious and generally well tolerated: no patients withdrew from the study because of adverse events.19<br /> • The model used by Watson et al. to predict the recurrence-free rate at 4 months after a 450 mg dose is hypothetical and does not consider data regarding the tafenoquine exposure–response relationship. Importantly, tafenoquine exposure achieved with a single 300 mg dose approaches the plateau of the exposure–response curve; therefore, the incremental recurrence-free rate gained by the proposed 50% increase in dose is small and unlikely to be justified by overall benefit–risk considerations.3 In addition, as primaquine and tafenoquine have different PK and metabolic profiles, the authors consider the extrapolation of data from primaquine to tafenoquine to be problematic.2,9<br /> • The authors feel that, overall, some of the conclusions do not acknowledge evidence-based safety concerns for a >300 mg dose of tafenoquine and do not consider additional data from the INSPECTOR study that the recurrence rate of P. vivax infection within 6 months of tafenoquine treatment was not significantly affected by bodyweight.20<br /> Watson et al. mentioned the phase 2b dose-selection study (DETECTIVE) of tafenoquine,21 from which a single 300 mg dose was chosen for phase 3 evaluation in adults. However, the authors did not point out that, in this study, exposure was a significant predictor of efficacy and doubling the tafenoquine dose from 300 mg to 600 mg was associated with only a marginal increase (from 89.2% to 91.9%) in the primary efficacy endpoint, relapse-free efficacy at 6 months.21 Moreover, in addressing the INSPECTOR study of tafenoquine in Indonesian soldiers, the authors did not specify that this was a study of tafenoquine administered with an artemisinin-based combination therapy rather than chloroquine and, as such, is not directly comparable due to poorly understood but confirmed interactions impacting tafenoquine efficacy.20 Watson et al. also suggest that tafenoquine 300 mg is likely inferior to ‘optimal primaquine regimens’, but it is unclear whether such regimens are the WHO-recommended schedules of primaquine or regimens defined as optimal based on non-regulatory studies of primaquine. The authors provided no specific reference or dosage characterising optimised primaquine therapy, so this a priori inferiority cannot be evaluated.<br /> Methods<br /> The hypothetical causal model proposed by Watson et al. for the clinical pharmacology of tafenoquine for the radical treatment of P. vivax malaria is similarly problematic. Central to this model are methaemoglobin (MetHb) production and active metabolites. However, MetHb is not a validated biomarker of tafenoquine efficacy, and currently there is no evidence, from non-clinical or clinical studies, of circulating active metabolites of tafenoquine; if such metabolites were fleetingly present, they would require extraordinary potency to exert any significant pharmacodynamic effect.22<br /> Regarding radical curative efficacy, Watson et al. selected P. vivax recurrence within 4 months as their primary endpoint. However, the trial-defined primary endpoint at 6 months from the pivotal tafenoquine clinical trials8,21,23 was an FDA requirement and was mandated for analysis purposes. This was to maximise the probability of capturing relapses, including those from regions with longer latency periods. Watson et al. used the INSPECTOR study20 as one of two reasons to justify the selection of a 4-month endpoint. Relapse rates differ greatly from country to country, so the duration of the endpoint should not be based on rates observed in a single country. Moreover, the 6-month rate of loss to follow-up (only 9.1%) does not justify a change of treatment endpoint from 6 months to 4 months.<br /> In their efficacy models, Watson et al. explored the association between the odds of P. vivax recurrence and the following predictors: mg/kg dose of tafenoquine; AUC0–?; peak plasma tafenoquine concentration; terminal elimination half-life; and Day 7 MetHb level. However, details of how the best predictor was selected and how statistical significance was judged were not provided.<br /> Results<br /> Use of a 4-month versus 6-month follow-up period<br /> A key focus of the Watson et al. manuscript is that the authors describe a possible association between tafenoquine mg/kg dose and the odds of recurrence (using logistic regression), with a 4-month rather than the original 6-month follow-up. An odds ratio of 0.66 (95% confidence interval [CI]: 0.51, 0.85) is cited by Watson et al. in their analysis of the effect of tafenoquine mg/kg dose in patients who received tafenoquine 300 mg, but descriptive details for this result and the analysis are limited. Figure 2 in the Watson et al. manuscript shows Kaplan–Meier survival curves for time to first recurrence, based on tafenoquine mg/kg dosing category, but some areas require clarification, such as how the dosing bands were selected.<br /> Rationale for tafenoquine dose selection<br /> Importantly, the classification and regression tree analysis, in which a clinically relevant breakpoint tafenoquine AUC value of 56.4 ug·h/mL was identified, was not discussed.24 Population PK modelling revealed that tafenoquine 300 mg would provide systemic exposure greater than or equal to the AUC breakpoint in approximately 93% of individuals, who would have a high probability (85%; 95% CI: 80, 90) of remaining relapse-free at 6 months.24 Therefore, this ‘… model-based approach was critical in selecting an appropriate phase 3 dose’ for tafenoquine.24 Although data from the TEACH paediatric study19 were not available when Watson et al. conducted their analysis, had the data been available, they would have validated the AUC approach to tafenoquine dose selection, with an overall efficacy of approximately 95%.19 Individuals (aged 2–15 years) were given tafenoquine, based on bodyweight, to achieve the same median AUC as the 300 mg dose in adults (children weighing >10–20 kg received tafenoquine 100 or 150 mg; >20–35 kg received 200 mg; and >35 kg received 300 mg). The recurrence-free rate at 4 months was 94.7% (95% CI: 84.6, 98.3),19 and the TEACH study supported the successful approval of tafenoquine for children aged 2–16 years by the Australian TGA in March 2022.5<br /> Another important counter to the mg/kg-based dose selection is that, when bodyweight categories were fitted as a continuous variable in the INSPECTOR study (using data for the time to recurrence for all participants), neither bodyweight nor bodyweight-by-treatment interactions were statistically significant (p=0.831 and p=0.520, respectively).20<br /> Use of an unvalidated biomarker<br /> Although Watson et al. state that increases in blood MetHb concentrations after tafenoquine administration were highly correlated with mg/kg dose, no correlation coefficients were presented. It should also be re-emphasised that MetHb is not a validated, surrogate biomarker of antimalarial treatment efficacy as a radical cure for P. vivax malaria and was used as a safety measure in the INSPECTOR study.20<br /> Potential safety concerns<br /> In the Tolerability and safety section, Watson et al. state that severe haemolytic events were rare; however, this is because all the studies were randomised and controlled, which excluded patients with <70% G6PD activity. In addition, no mention was made that, in one of the constituent studies (which examined the dose–response for haemoglobin decline in participants with 40–60% G6PD enzyme activity),17 dose escalation of tafenoquine from 300 mg to 600 mg was not attempted due to safety concerns about potential haemolysis in patients with G6PD deficiency. In tafenoquine-treated patients in the real-world setting, some instances of severe haemolysis might be expected, and it is already known from the previously highlighted phase 1 study that the haemolytic potential of tafenoquine increases with increasing dose.17 Watson et al.’s Tolerability and safety section also mentions that one tafenoquine-treated patient had a >5 g/dL decrease in haemoglobin level, but the baseline haemoglobin level and tafenoquine dose are not mentioned. The section may have benefitted from a holistic discussion of safety parameters per tafenoquine dose group: for example, the occurrence of serious adverse events, gastrointestinal adverse events (beyond the selective discussion of vomiting within 1 hour post dose) and neuropsychiatric adverse events.<br /> Discussion<br /> Watson et al. conclude that ‘the currently recommended adult dose is insufficient … increasing the adult dose to 450 mg is predicted to reduce the risk of relapse’; however, the authors have raised several concerns relating to these conclusions. In particular, the authors feel that the safety concerns associated with a higher-than-approved tafenoquine dose have not been thoroughly considered: the safety analysis is limited, and the increased risk of haemolysis in patients with G6PD deficiency that a 450 mg tafenoquine dose (which is 50% greater than the approved 300 mg dose) would pose in vulnerable populations in limited-resource settings is not adequately discussed. In some malaria-endemic regions, 8% of the population may be G6PD deficient, although wide variability exists, and in sub Saharan Africa and the Arabian peninsula the prevalence of G6PD deficiency may exceed 30%.10,11 Therefore, in regions with fragile healthcare systems and limited availability of relevant testing for G6PD deficiency, potential exists for a significantly increased risk of haemolysis if tafenoquine is administered at an above recommended dose (450 mg). Importantly, off-label use of a dose not robustly evaluated in clinical trials would pose a considerable risk to patient safety.<br /> Regarding tafenoquine efficacy, the rationale for a dose increase to 450 mg has limitations. Watson et al. suggest that a 50% increase in the adult dose of tafenoquine (from 300 mg to 450 mg) would prevent one relapse of malaria for every 11 patients treated. However, this number-needed-to-treat estimate is not balanced by a number-needed-to-harm estimate for acute haemolytic anaemia. In addition, the phase 2b part of the DETECTIVE study21 showed that, in countries where the trial was carried out, single doses of tafenoquine 300 mg and 600 mg had similar relapse-free efficacy at 6 months (89.2% and 91.9%, respectively); therefore, the lack of additional benefit for tafenoquine 600 mg in DETECTIVE and the phase 1 study, which demonstrated dose-dependent haemolytic potential for tafenoquine, favour a 300 mg dose.<br /> In summary, based on currently available data, dosing tafenoquine at the approved 300 mg dose, in combination with chloroquine, carefully balances efficacy and safety in the radical cure of P. vivax malaria; indeed, tafenoquine 300 mg demonstrated a favourable benefit–risk profile in a comprehensive clinical development programme that included at-risk populations in regions with fragile or resource-restricted healthcare systems. The arguments raised by Watson et al. come with the concerns articulated here, and the authors confidently assert that a tafenoquine dose increase from 300 mg to 450 mg is not supported by available fact-based evidence for the radical cure of P. vivax malaria in adults aged >=16 years.

      References<br /> 1. GSK. US FDA approves Krintafel (tafenoquine) for the radical cure of P. vivax malaria [press release]. July 20, 2018. https://www.gsk.com/en-gb/media/press-releases/us-fda-approves-krintafel-tafenoquine-for-the-radical-cure-of-p-vivax-malaria/ (accessed 26 April 2023).<br /> 2. Hounkpatin AB et al. Clinical utility of tafenoquine in the prevention of relapse of Plasmodium vivax malaria: a review on the mode of action and emerging trial data. Infect Drug Resist 2019;12:553–570.<br /> 3. GSK. Krintafel. Highlights of prescribing information. https://www.accessdata.fda.gov/drugsatfda_docs/label/2018/210795s000lbl.pdf (accessed 26 April 2023).<br /> 4. GSK, Medicines for Malaria Venture. Perú becomes second malaria-endemic country in Latin America to approve single-dose tafenoquine for radical cure of P. vivax malaria [press release]. https://www.vivaxmalaria.org/sites/pvivax/files/content/attachments/2021-01-25/GSK%20-%20MMV%20PRESS%20RELEASE%20TAFENOQUINE%20APPROVED%20IN%20PERU.pdf (accessed 26 April 2023).<br /> 5. Medicines for Malaria Venture. Single-dose Kozenis (tafenoquine) approved for children with Plasmodium vivax malaria by Australian Therapeutic Goods Administration. https://www.mmv.org/newsroom/press-releases/single-dose-kozenis-tafenoquine-approved-children-plasmodium-vivax-malaria (accessed 26 April 2023).<br /> 6. World Health Organization. WHO guidelines for malaria, 14 March 2023. https://www.who.int/teams/global-malaria-programme (accessed 26 April 2023).<br /> 7. Milligan R et al. Primaquine at alternative dosing schedules for preventing relapse in people with Plasmodium vivax malaria. Cochrane Database Syst Rev 2019;7:CD012656.<br /> 8. Llanos-Cuentas A et al. Tafenoquine versus primaquine to prevent relapse of Plasmodium vivax malaria. N Engl J Med 2019;380:229–241.<br /> 9. Baird JK. 8-Aminoquinoline therapy for latent malaria. Clin Microbiol Rev 2019;32.<br /> 10. Howes RE et al. G6PD deficiency prevalence and estimates of affected populations in malaria endemic countries: a geostatistical model-based map. PLoS Med 2012;9:e1001339.<br /> 11. P. vivax information hub. G6PD global prevalence. https://www.vivaxmalaria.org/diagnosis-treatment/g6pd-deficiency/g6pd-global-prevalence#:~:text=G6PD%20Global%20Prevalence,-Photo%3A%20Jaya%20Banerji&text=G6PD%20deficiency%20affects%20around%20400%20million%20people%20globally (accessed 26 April 2023).<br /> 12. Domingo GJ et al. Addressing the gender-knowledge gap in glucose-6-phosphate dehydrogenase deficiency: challenges and opportunities. Int Health 2019;11:7–14.<br /> 13. Chu CS et al. Haemolysis in G6PD heterozygous females treated with primaquine for Plasmodium vivax malaria: a nested cohort in a trial of radical curative regimens. PLoS Med 2017;14:e1002224.<br /> 14. Baird JK et al. Noninferiority of glucose-6-phosphate dehydrogenase deficiency diagnosis by a point-of-care rapid test vs the laboratory fluorescent spot test demonstrated by copper inhibition in normal human red blood cells. Transl Res 2015;165:677–688.<br /> 15. Shanks GD. Historical 8-aminoquinoline combinations: not all antimalarial drugs work well together. Am J Trop Med Hyg 2022;107:964–967.<br /> 16. Watson JA et al. The clinical pharmacology of tafenoquine in the radical cure of Plasmodium vivax malaria: An individual patient data meta-analysis. Elife 2022;11:e83433.<br /> 17. Rueangweerayut R et al. Hemolytic potential of tafenoquine in female volunteers heterozygous for glucose-6-phosphate dehydrogenase (G6PD) deficiency (G6PD Mahidol variant) versus G6PD-normal volunteers. Am J Trop Med Hyg 2017;97:702–711.<br /> 18. World Health Organization. Guidelines for the treatment of malaria, 3rd ed. https://apps.who.int/iris/handle/10665/162441 (accessed 26 April 2023).<br /> 19. Velez ID et al. Tafenoquine exposure assessment, safety, and relapse prevention efficacy in children with Plasmodium vivax malaria: open-label, single-arm, non-comparative, multicentre, pharmacokinetic bridging, phase 2 trial. Lancet Child Adolesc Health 2022;6:86–95.<br /> 20. Sutanto I et al. Randomised, placebo-controlled, efficacy and safety study of tafenoquine co-administered with dihydroartemisinin-piperaquine for the radical cure of Plasmodium vivax malaria (INSPECTOR). Lancet Infect Dis [2023 May 23:S1473-3099(23)00213-X doi: 101016/S1473-3099(23)00213-X Epub ahead of print PMID: 37236221].<br /> 21. Llanos-Cuentas A et al. Tafenoquine plus chloroquine for the treatment and relapse prevention of Plasmodium vivax malaria (DETECTIVE): a multicentre, double-blind, randomised, phase 2b dose-selection study. Lancet 2014;383:1049–1058.<br /> 22. GSK. Investigator brochure. Data on file.<br /> 23. Lacerda MVG et al. Single-dose tafenoquine to prevent relapse of Plasmodium vivax malaria. N Engl J Med 2019;380:215–228.<br /> 24. Tenero D et al. Exposure-response analyses for tafenoquine after administration to patients with Plasmodium vivax malaria. Antimicrob Agents Chemother 2015;59:6188–6194.

      Authors’ contributions<br /> Hema Sharma, Lionel Tan, Katie Rolfe, and Navin Goyal contributed to the conception or design of the studies the paper contains data from. All authors contributed to data analysis or interpretation. All authors contributed to the development and writing of this correspondence and approved the final submitted version.

      Conflicts of interest statements <br /> Raman Sharma, Siôn Jones, Rachel Gibson, Katie Rolfe, Lionel Tan, Ioana-Gabriela Fita, Chao Chen, Panayota Bird, and Anup Pingle are employees of, and shareholders in GSK.<br /> Hema Sharma is a former employee of GSK, a shareholder in GSK and a current employee of AstraZeneca. Navin Goyal is a former employee and shareholder in GSK and a current employee of Johnson and Johnson. Isabelle Borghini Fuhrer and Stephan Duparc have no conflict of interest to report. <br /> Acknowledgements <br /> Medical writing support was provided by David Murdoch, a contract writer working on behalf of Apollo, and Alex Coulthard of Apollo, OPEN Health Communications, funded by GSK Biologicals SA, in accordance with Good Publication Practice 3 (GPP) guidelines (www.ismpp.org/gpp-2022) "www.ismpp.org/gpp-2022)").

      Funding<br /> Funding for this article was provided by GSK Biologicals SA.

      Data availability<br /> Data sharing is not applicable to this article as no datasets were generated or analysed.

    1. On 2020-05-07 03:52:42, user Alisha Geldert wrote:

      We thank the authors for their detailed analysis of a suite of N95 decontamination approaches, with specific appreciation for the direct applicability to medical center needs. We see the manuscript – once published in a peer-reviewed journal – as being an excellent resource for medical center decision makers, as well as those working to implement the decontamination methods. With a spirit of attention to the existing peer-reviewed literature and rigor needed in this crisis, we offer a review of areas where improvements would benefit the study as well as (and more importantly) any readers who may adopt the approaches. The authors are aware of the following major comments summarized below, and are working diligently to provide necessary clarifications and revisions.

      1. The UV-PX experimental design and choice of combination approach does not appear to be consistent with evidence on effective approaches for UVGI/UV-C ultraviolet decontamination of N95s, presenting a major concern. To address this, consider providing a reader with clearer justification for the ‘unconventional’ approach by perhaps answering the following questions:<br /> ---Were longer duration UV-PX treatments investigated? The fluence delivered during the 5-minute treatment time is unsupported by the evidence for UV-C decontamination of N95s [Lore et al., 2012; Mills et al, 2018; Heimbuch & Harnish, 2019].<br /> ---It is not clear why the authors suggest coupling of UV-PX with moderate RH heat before testing UV-PX alone, when the benefit of adding UV-PX is not described (perhaps stemming from the very low pathogen inactivation observed with UV-PX alone, as would be expected from the ~50X too low delivered germicidal fluence using this protocol). As the protocol deviates from CDC guidance [CDC, 2020], a rationale and supporting peer-reviewed references would be essential.

      2. Important details are missing in the methods section. Please provide key details about the UV-PX setup to ensure replicable research reporting, specifically:<br /> ---Measures taken, if any, to ensure respirators are directly illuminated on both sides. <br /> N95 respirator placement relative to and distance from the light source. As irradiance, and therefore fluence, depends on distance between source and target, this is a critical parameter.<br /> ---Please specify the reflective material used in the UV room, the make and model number of the flame irradiance spectrometer, and whether the irradiance measurements reported in Supplementary Table 3 were measured within the UV room with reflective walls or within an alternative setting. Do the irradiance measurements represent the irradiance at the side of the N95 facing the Xenex UV-PX source or irradiance at areas indirectly exposed to UV light? <br /> ---Please clarify whether the measured irradiance represents the irradiance of one pulse or the average irradiance over multiple cycles.

      3. There appears to be a potential issue with the conclusions reported in the abstract: the specific experimental parameters shown to yield high levels of pathogen inactivation (moderate RH heat) were not tested for N95 function, so the following statement might be confusing or misleading:<br /> “High levels of biological indicator inactivation were achieved following treatment with either moist heat or VHP. These same treatments did not significantly impact mask filtration or fit.”<br /> The limitations of the proposed approaches and the need for additional testing should be clarified.

      References cited: <br /> 1. Lore et al., 2012: https://academic.oup.com/an...<br /> 2. Mills et al., 2018: https://www.ncbi.nlm.nih.go...<br /> 3. Heimbuch & Harnish, 2019: https://www.ara.com/sites/d...<br /> 4. CDC guidance on N95 decontamination: https://www.cdc.gov/coronav...

    1. On 2024-12-28 04:44:14, user xPeer wrote:

      Courtesy review from xPeerd.com

      The paper, "Machine Learning Approaches to Predict Alcohol Consumption from Biomarkers in the UK Biobank," evaluates five machine learning (ML) models to predict alcohol consumption (DPW) using biomarkers. The study leverages biomarkers and covariates from the UK Biobank to enhance prediction accuracy. The highest-performing model, XGBOOST, achieved an r² of 0.356. The research findings indicate that using biomarkers significantly improves the prediction of heavy drinking and other related phenotypes.

      Potential Major Revisions:

      1. Biomarker Selection Justification: While the paper discusses known biomarkers, it does not provide a detailed rationale for selecting the specific 338 predictors used. The study should offer more context or references explaining why these particular biomarkers were chosen and how they relate to alcohol consumption prediction comprehensively (pg. 4).

      2. Ethical Considerations and Limitations: Although the study briefly mentions the ethical limitations concerning the UK's demographics, it could expound on this point, addressing how the findings might translate to diverse populations not represented in the UK Biobank dataset (pg. 16).

      3. Model Generalizability: The study should provide more details on the applicability and generalizability of the model findings to different populations with genetic diversity and varying socio-economic backgrounds (pg. 17). It must address how the model could adapt or fail in non-European cohorts as the generalizability might vary.

      Potential Minor Revisions:

      1. Typographical and Minor Errors:
      2. Consistency in the abbreviation of DPW (Drinks Per Week) is essential. There are minor inconsistencies throughout the manuscript that could be formatted uniformly (pg. 7, 14).
      3. Clarity and readability can be enhanced by eliminating repeated phrases (e.g., "Alcohol Consumption prediction using biomarkers" is repeated frequently which might be condensed or varied).

      4. Formatting Issues:

      5. Figures and Tables: Ensure all figures and tables are referenced correctly in the text and positioned to avoid disrupting reading flow (p.13, Figures 3, 4, 8).
      6. Supplementary Information: Cross-reference supplementary information more clearly within the text to aid readers in locating relevant data (e.g., Supplementary Table T3 and Figures S2).

      7. AI Content Analysis:

      8. There is no explicit indication of AI-generated content in this paper. However, the paper exhibits some areas of redundancy which can be indicative of AI-aided writing:
      9. Assessed AI content reflects about 5% of the total document. These are sections that repeat information about statistical measures and known biological impacts without much nuanced discussion (e.g., discussion of model performances and the role of biomarkers) (pg. 10-11).
      10. The epistemic impact of this AI-generated content is minimal and does not undermine the scientific integrity of the paper. It would benefit from a more nuanced discussion of the statistical results and implications.

      Recommendations:

      1. Improving Rationale and Discussion:
      2. Strengthen the section discussing the selection of specific biomarkers with comprehensive explanations or references.
      3. Expand on the implications of the model predictions, especially in clinical and public health contexts, to enhance readability and relevance.

      4. Enhancing Generalizability:

      5. Discuss in more detail how these predictive models could be adjusted or re-calibrated for non-European populations.
      6. Provide more comprehensive demographic benefits and limitations to reinforce the findings' applicability and reliability.

      7. Visual and Supplementary Data Clarity:

      8. Organize figures and tables to enhance their impact without disrupting the flow.
      9. Ensure all supplementary materials are accurately referenced and easy to locate within the text.

      By addressing these major and minor revisions, the manuscript will achieve higher clarity, ethical robustness, and academic integrity while broadening its impacts across diverse populations and further grounding its findings within the literature.

    1. On 2021-01-13 11:14:06, user Magnus Brink wrote:

      Congratulations to a well conducted and highly interesting study. It seems out of doubt that IL-6 receptor inhibitors can save lives in covid-19. But what about time spent in the ICU? The headline in the prerelease by gov.uk reads: “NHS patients to receive life-saving COVID-19 treatments that could cut hospital time by 10 days”. I would say yes for saving lives but no for cutting time spent in ICU. Table 2 tell us that there are no differences in “Organ failure free days” (OSFD) in survivors; 14 days (IQR 7 to 17) for patients treated with tocilizumab compared to 13 days (IQR 4 to 17) for controls. The conclusion must be that tocilizumab will save lives but unfortunately not un-crowd our ICUs.

    1. On 2021-01-15 10:11:06, user Martijn Weterings wrote:

      This research shows an interesting significant difference between the groups. The contingency table <br /> 3, 2, 8, 1 <br /> 22, 23, 17, 24 <br /> is an indication for a significant dependency.

      However this is likely caused by age differences (given the abundant information that indicates the relationship between age and risk of death). It is mostly the 3rd group with the highest number of deaths (8 deaths) and the highest estimated adjusted risk ratio (RR 2.18). This is also the group with the highest age.

      It is very problematic that there is no clear dose response relationship.

      Because this lack of a monotonic relationship between O3l and risk, it seems arbitrary to make a comparison between the 4th quartile and the first 3 quartiles. The observed effect is mainly due to the 3rd quartile having a high risk. One might just as well make a comparison between the 1st quartile and the last 3 quartiles and find a similar (though slightly less) significant result.

      Besides other potential confounding variables it is the age distribution among the four quantiles which is remarkable and likely seems to be a strong influence on the statistical relationship. This means that the adjustment must be done with great care. I personally believe that currently the adjustment might be biased due to the binning of the continuous O3 levels into 4 quartiles and age might need to be included as a polynomial and not just a linear effect (it is actually unclear what sort of model has been used).

      The problem with binning is that correlation between age and O3 levels might not be captured smoothly. The relationship is not linear (rather it is something exponential or logistic). For each increase 10 years increase in age there should be something like a 2 fold increase in risk of death (in this research the odds ratio is only 1.33 for a decade increase which is odd). This means that a group of 80 year olds and 60 year olds, with a mean of 70 years, are not comparable to a group of only 70 year olds. One might get peculiar results when the distribution of age in the different quartiles is not evenly distributed. (and possibly there could be some sort of Simpson's paradox due to the way that age is distributed within the 4 quartiles, if the 3rd quartile happens to have many people of 'very' old age then this might interact with the age effect, resulting in a reduced risk rate for the increase of age and an increased risk rate for the 3rd quartile)

      I would suggest to provide a scatter plot of age versus O3l (along with some colour or markers for death vs no-death) which allows a more clear view of the structure in this data set and allows to see a more clear relationship with the O3l. It could also be interesting to see the output of a logistic model where O3l is treated as a continuous variable (potentially with some non-linear relationships like polynomials or interaction terms). Such a model would not have to treat the levels O3l levels as categorical, and would have less problems with non-homogeneous age distributions within the categories.

      I would not be surprised to see some sort of clusters in the scatter plot of O3l versus age (and potentially deaths occur might occur more often in particular clusters). A more exploratory analysis of such structures might reveal more useful insights to generate hypotheses to be studied in future research.

    1. On 2020-03-16 16:32:43, user Bill Keevil wrote:

      Although not peer reviewed yet, this work is not surprising because we showed long term survival of the similar coronavirus 229E on plastics, ceramics, stainless steel and glass for 4-5 days; the virus was inactivated on copper in just minutes and its RNA destroyedhttps://mbio.asm.o.... Another group showed SARS survived 5 days on stainless steel. We and others also showed flu survives several days. Implications are that in a closed environment a potentially infectious aerosol of small particle size can remain suspended in air for some time before landing on surfaces – hence being outdoors or opening windows is probably a good thing. It might question whether the 2 metre gap between people is sufficient in a confined space. As I have said before, survival of coronaviruses for days on touch surfaces (not the 2 hours cited by some advisers) is a hygiene risk, and it is difficult to avoid touching door handles, stair rails, public touch screens etc. It re-emphasises the need for good personal hygiene such as washing hands rigorously throughout the day, or using an alcohol hand gel, and avoid touching the eyes, nose and mouth.

      Because this is a pre-print it is difficult to know exactly what they have done. Clearly they are using a different virus and culturing in Vero-E6 kidney cells while we used MRC-5 lung cells. An important difference may be that in their 2003 MERS paper they used 100ul culture onto unspecified size surfaces (“washers”) –McMaster-Carr, USA); for the new paper where they say they used 50ul of virus then we know that this can take a long time to dry out. Copper alloys kill bacteria and viruses when dry due to the inactivation mechanisms we have published. Our method to simulate hand contact uses 20ul onto 1 square cm, spread over the surface and then dries out in several minutes; sometimes we use 1ul when we have high concentrations of pathogen available.

      Perhaps more importantly, our cells were maintained in minimal essential medium (MEM) supplemented with 1mM GlutaMax-1*, nonessential amino acids, and 5% fetal calf serum and incubated at 37°C and 5% CO2. Their cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM; Sigma) supplemented with 2% fetal calf serum (Logan), 1 mM L-glutamine (Lonza), 50 U/ml penicillin and 50 µg/ml streptomycin (Gibco).

      *(GlutaMAX™-1 (Gibco), L-alanyl-L-glutamine, is a dipeptide substitute for L-glutamine. GlutaMAX™-1 can be used as a direct substitute for L-glutamine at equimolar concentrations in both adherent and suspension mammalian cell cultures with minimal or no adaptation. GlutaMAX™-1 is highly soluble, heat-stable, and improves growth efficiency and performance of mammalian cell culture systems. GlutaMAX™-1 eliminates problems associated with thespontaneous breakdown of L-glutamine into ammonia during incubation, allowing for longer lasting cultures. )

      Importantly, glutamine binds copper while it also spontaneously breakdowns at physiological pH to ammonia which reacts with copper to precipitate light blue Cu(OH)2. This would give a partial passivation effect, making the copper surfaces less antiviral while our GlutaMAX-1 would not; hence explaining their longer time for copper inactivation.

      This is one of the reasons we decided GlutaMAX-1 was the better option to avoid subsequent potential copper binding problems in the surface contact experiments.

    1. On 2022-05-22 17:15:49, user Teresa Moreno wrote:

      UPDATE MAY 2022: lessons for the monkeypox viral outbreak?

      According to the Johns Hopkins data repository (updated in Dong et al 2020), case numbers of COVID-19 in Spain rose steadily and rapidly after the early December 2021 holiday to an omicron-driven post-Christmas peak far higher than any other during the SARS-CoV-2 pandemic. On 8th December 26,412 new cases were recorded, whereas by 11th January 2022 this figure had risen an order of magnitude to 292,394. The entirely predictable threat of a countrywide viral superspreading event boosted by Christmas celebrations, many in poorly ventilated indoor environments, had become real, with deaths from the disease peaking in late February 2022.

      In May 2022 cases of monkeypox suddenly emerged in several countries worldwide. The pathogen responsible for this enzootic disease is belongs to the Orthopoxvirus genus which includes the virus causing smallpox. How is this global outbreak of monkeypox being transmitted? As in the early days of the emergence of COVID-19, initial public health statements are emphasising personal hygiene and avoidance of close physical contact with the saliva or lesions of infected individuals (ECDC 2022; Koslov 2022). The World Health Organisation states that "monkeypox virus is transmitted from one person to another by close contact with lesions, body fluids, respiratory droplets and contaminated materials such as bedding" (WHO 2022). This initial reaction to a new pattern of infectious disease is familiar (Moreno and Gibbons 2021). The spread of the now-eradicated smallpox virus was similarly considered to have been transmitted primarily by fomites and close contact, until the classic nosocomial outbreak in the German town of Meschede. Study of this event concluded that cases spread inside the hospital were infected by virus particles disseminated by air over a considerable distance (Wehrle et al., 1970, see also Gelfand and Posch 1971; Fenner et al., 1988; Tellier et al., 2019). Reviewing the history of this disease, Milton (2012) concluded that "the weight of evidence suggests that fine particle aerosols were the most frequent and effective mode of smallpox transmission". Given our precautionary recent experience and slow start with SARS-CoV-2, we argue that we should be treating this unexpected new zoonotic poxvirus outbreak as likely being driven at least in part by viraerosol transmission. It is another wakeup call for treating indoor air ventilation issues more seriously.

      References<br /> Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020 May;20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1. Epub 2020 Feb 19. Erratum in: Lancet Infect Dis. 2020 Sep;20(9):e215. PMID: 32087114; PMCID: PMC7159018.<br /> European Centre for Disease prevention and Control. Epidemiological update: Monkeypox outbreak. 20 May 2022. <br /> Fenner, F., D.A. Henderson, I. Arita, Z. Jezek, I.D. Ladnyi. Smallpox and its eradication. WHO, Geneva (1988), p. 1460p<br /> Gelfand, H.M., J. Posch. The recent outbreak of smallpox in Meschede. West Germany. Am. J. Epidemiol., 93 (4) (1971), pp. 234-340, 10.1093/oxfordjournals.aje.a121251<br /> Moreno, T., Gibbons, W. 2021. Aerosol transmission of human pathogens: From miasmata to modern viral pandemics and their preservation potential in the Anthropocene record. Geoscience Frontiers. DOI:10.1016/j.gsf.2021.101282<br /> Kozlov, M. 2022: https://www.nature.com/arti... "https://www.nature.com/articles/d41586-022-01421-8)")<br /> Milton, D.K.. What was the primary mode of smallpox transmission? Implications for biodefense. Front. Cell Infect. Microbiol, 2 (2012), p. 150, 10.3389/fcimb.2012.00150<br /> Tellier, R. Aerosol transmission of influenza A virus: a review of new studies. J. R. Soc. Interface, 6 (2009), pp. S783-S790, 10.1098/rsif.2009.0302.focus<br /> Wehrle, P.F., J. Posch, K.H. Richter, D.A. Henderson. An airborne outbreak of smallpox in a German hospital and its significance with respect to other recent outbreaks in Europe. Bull. World Health Organ., 43 (5) (1970), pp. 669-679<br /> World Health Organisation. Multi-country monkeypox outbreak in non-endemic countries. May 21 2022. https

    1. On 2022-05-23 11:58:04, user Jakub Fronczek, MD wrote:

      Brilliant paper, congratulations - great to see net benefit assessment. The only part I found confusing is: "In sub-2% decision thresholds there is no net benefit in using our system, but these patients are not a subject of interest in this analysis and should always undergo a biopsy". Since the analysis includes BI-RADS 4 patients, shouldn't a probability <2% be of interest as a criterion for downgrading a patient to a lower risk category? Perhaps I'm missing something! Kind regards, Jakub Fronczek.

    1. On 2020-06-06 13:59:32, user Nayo57 wrote:

      While the result of this interesting and meaningful analysis may be statistically correct: a reduction of R of 0.04 with 10% mobility reduction does not explain the vast reductions from R = 3-4 at outbreak to below 1. A rough analysis of WHO reported case data and Google mobility data gave a similar result e.g. for time to reach R<1 or cumulative cases per population, measures one would expect to be impacted by effective social distancing. The best conclusion may be that mobility index as provided is not a suitable measure to assess or guide policies to contain COVID-19: Fig1 (Germany: increase in mobiilty index while R stays <1), Fig. 2(USA: decrease in mobility by further increase in R) and the scatter in Fig 3 support this view.

      The interpretation would rather be that BEHAVIOUR during mobility activities matter much more than the QUANTITY of mobility. Alternatively, more focussed indexes (restaurants/bars; cinemas/theaters..) may be worth while to examine if they could be useful.

    1. On 2020-06-13 03:34:34, user kpfleger wrote:

      Why are the 25(OH)D levels reported here (43 or 44 nmol/L w/ IQRs of 32 or 31 respectively for the n=580 C19+ and n=723 C19- UK Biobank cases) so much higher than those reported in Hastie et al, "Vitamin D concentrations and COVID-19 infection..." Diabetes Metab Syndr., 2020: https://pubmed.ncbi.nlm.nih...<br /> which reported median 25(OH)D of 29nmol/L w/ IQR 10-44 for C19+ & 33 IQR 10-47.<br /> This is a huge difference for 2 papers with online publication dates 2 days apart both pulling data from the same source.

      See also D'Avolio et al, "25-Hydroxyvitamin D concentrations are lower in patients with positive PCR for SARS-CoV-2", Nutrients, 2020 and Meltzer et al, “Association of vitamin D deficiency and treatment with COVID-19 incidence”, medRxiv, 2020 for 2 different studies that found in contrast to the top level conclusion here, that low D was associated with higher C19 incidence. Discussion of all 4 paper of these papers in the "D deficiency might be associated with higher infection risk" of the review: "Low vitamin D worsens COVID-19": http://agingbiotech.info/vi...

    1. On 2020-07-02 17:57:05, user Dr Gareth Davies (Gruff) wrote:

      This study is methodologically flawed in the following ways:<br /> 1. This study used vitamin D serum data taken 10 to 14 years prior rather than of levels on admission to hospital. We cannot infer anything about levels on admission from them. Indeed, if anyone test deficient it's very likely they would have been recommended to take D3 supplements.<br /> 2. It applies a grossly flawed statistical analysis using the full biobank data set numbers for N instead of the matches and therefore reports a misleadingly-low unjustifed p-values<br /> 3. The BAME COVID-19 positive test matches were just 32 Black people and 19 south Asian (N =51). Making statements about entire ethnic populations based on these data is not justified.<br /> 4. You should never adjust for confounders without first knowing the causal relationship to the other study variables. You introduce bias if you control for a collider and you don't know which variables may be colliders.

      These flaws render the entire analysis invalid.

    1. On 2021-08-24 01:52:14, user Raihan Farhad wrote:

      Please answer the following, in the interest of academic integrity: <br /> 1. What is the effectiveness of masks used in your model? What number did you use? What type of mask? Worn in what way. I can only assume, given the absence of any experimental data regarding un-regulated masks stopping Covid aerosols, you either assumed the effectiveness of a mask or used someone else's assumption. Please divulge.

      1. What is the assumption you made about % of kids having been exposed already? Covid has been around for a while now. If you assumed 0 previous exposure, that is unrealistic, but please state so clearly. If you assumed any other number, please explain how you came to that number and state that number.

      2. What is the duration of infectiousness assumed in your model. According to science, someone infected is infectious for about 5 days. After that, even if he dies, he is not infectious. Please explain the temporal nature of infectiousness assumed in your model.

      3. Please make your entire model / simulation software (all code) and all parameters, assumptions public.

    1. On 2020-08-18 17:33:47, user Rodolfo Rothlin MD wrote:

      Dear Dr. Turgeon,<br /> Thank you for your commentary and your interest in our manuscript. Please, find my answers below.

      1. Please provide the rationale for selecting July 31 as the date for interim analysis. Please also provide details regarding this interim analysis, including pre-specified stopping rules, who had access to the data. Although this manuscript is labeled as a "preliminary report", it would be valuable for the authors to explicitly state whether this trial is ongoing, and whether any changes to the conduct of the trial were made based on this interim analysis.

      The rationale for selecting July 31st was made for several reasons: First, we assumed that by that date we would have between 50 and 100 patients. Although our estimated sample size was 390 (which we rounded to 400), it is worth noticing that this number accounts for a scale factor on both the mean and the variability estimations; without these factors, the estimated sample size was 52 patients total, and with only a variability factor of 2 the total estimated number was 100. Therefore, we evaluated that July 31st was an appropriate time to make the first interim analysis. Second, as you may know, Argentina was expected to be peaking around that time. And it actually seems that it may be doing it right now. So, a second reason for that date was our prediction that if the results were valuable it could be a useful information for our health authorities. The trial is still ongoing and a second interim analysis will be carried out at 140 patients.<br /> 2. In version 1 of the article on this site, the Methods section had a sentence that stated "No concealment mechanism was implemented". This was subsequently removed in version 2 yesterday. Please clarify what is meant by this. Did the authors mean to imply that allocation concealment was not performed, or was this an erroneous statement intended to describe the unblinded nature of the study? Please also describe the process for treatment allocation and how allocation concealment was maintained.

      The sentence you refer to was removed because it was inaccurate. The problem emanated from the fact that our protocol did not foresaw a concealment mechanism. However, during the conduct of the trial, although no mechanism like closed envelopes with randomization was used, on site enrollment was made by an investigator and randomization was made by a second investigator who was unaware of the clinical characteristics of the participant. We are confident that no bias towards the control group was present as reflected by data on table 1 of our manuscript.<br /> 3. The authors describe a change in the primary outcome in terms of timing of CRP measurements. However, I note that the clinicaltrials.gov summary of this trial previously had an entirely different outcome as the primary outcome, with CRP only described as an exploratory/tertiary outcome. The authors should describe the timing and rationale for switching the outcome from a clinical one (need for supplemental oxygen in the first 15 days post-randomization) to the inflammatory biomarker CRP.

      As you might have read in the methods section of our manuscript, data from our trial was uploaded by a third party. Unfortunately, endpoints from a working version of the protocol were submitted. This was corrected as soon as we noticed it.<br /> 4. Despite changing the timing of CRP measurements, data on this modified primary outcome of CRP was missing in a large proportion of patients at day 5, and in the majority of patients at day 8. Further details should be provided regarding the reason for missing data, how this was handled in their analyses, and how this should temper conclusions.

      Data missing from days 5 and 8 were related to several factors. Some patients were discharged before day 5 and before day 8. Others were lost at day 5 for logistical reasons. <br /> No imputations were done to account for these values.<br /> 5. Finally, performing an interim analysis and disseminating their results in the midst of an open-label trial with subjective endpoints can pose challenges to maintaining impartiality. The authors should describe how they will mitigate potential allocation, performance, and detection and attrition bias during the remainder of the trial.

      We disagree with CPR measurements being subjective<br /> Again, thank you for helping us clarify these points.<br /> All the best,<br /> Rodolfo Rothlin

    1. On 2020-05-03 17:48:53, user Sinai Immunol Review Project wrote:

      SUCCESSFUL MANUFACTURING OF CLINICAL-GRADE SARS-COV-2 SPECIFIC T CELLS FOR ADOPTIVE CELL THERAPY

      Leung Wing et al.; medRxiv 2020.04.24.20077487; https://doi.org/10.1101/202.... 20077487

      Keywords

      • SARS-CoV-2 specific T cells

      • Adoptive T cell transfer

      • COVID-19

      Main findings

      In this preprint, Leung et al. report the isolation of SARS-CoV-2-specific T cells from two convalescent COVID-19 donors (n=1 mild, n=1 severe; both Chinese Singapore residents), using Miltenyi Biotec’s fully automated CliniMACS Cytokine Capture System: convalescent donor PBMCs were stimulated with MHC class I and class II peptide pools, covering immunodominant sequences of the SARS-CoV-2 S protein as well as the complete N and M proteins; next, PBMCs were labeled with a bi-specific antibody against human CD45, a common leukocyte marker expressed on white blood cells, as well as against human IFN-?, capturing T cell-secreted IFN-? in response to stimulation with SARS-CoV-2 peptides. Post stimulation, IFN-?+ CD45+ cells were identified by a mouse anti-human IFN-? antibody, coupled to ferromagnetic dextrane microbeads, and magnetically labeled cells were subsequently isolated by positive immunomagnetic cell separation. Enriched IFN-?+ CD45+ cells were mostly T cells (58%-71%; CD4>CD8), followed by smaller fractions of B (25-38%) and NK cells (4%). Up to 74% of T cells were found to be IFN-?+, and 17-22% of T cells expressed the cytotoxic effector marker CD56. Very limited phenotyping based on CD62L and CD45RO expression identified the majority of enriched CD4 and CD8 T subsets as effector memory T cells. TCR spectratyping of enriched T cells further revealed an oligoclonal TCR ß distribution (vs. a polyclonal distribution pre-enrichment), with increased representation of Vß3, Vß16 and Vß17. Based on limited assumptions about HLA phenotype frequencies as well as estimated haplotype sharing among Chinese Singaporeans, the authors suggest that these enriched virus-specific T cells could be used for adoptive cell therapy in severe COVID-19 patients.

      Limitations

      This preprint reports the technical adaptation of a previously described approach to isolate virus-specific T cells for targeted therapy in hematopoietic stem cell transplant recipients (reviewed by Houghtelin A et al.: https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5641550/pdf/fimmu-08-01272.pdf)") to PBMCs obtained from two convalescent COVID-19 patients. However, not only is the title of this preprint misleading - no adoptive cell transfer was performed -, but this study also lacks relevant information - among others - on technical details such as the respective S epitopes studied, on the precise identification of immune cell subsets (e.g. NK cells: CD56+ CD3-?), data pertaining to technical stimulation controls (positive/negative controls used for assay validation and potentially gating strategies), as well as on the percentage of live enriched IFN-?+ CD45+ cells. Generally, a more stringent phenotypical and functional characterization (including coexpression data of CD56 and IFN-? as well as activation, effector, proliferation and other markers) would be advisable. Similarly, in its current context, the TCR spectratyping performed here remains of limited relevance. Most importantly, though, as noted by the authors themselves, this study is substantially impaired based on the inclusion of only two convalescent donors from a relatively homogenous genetic population as well as by the lack of any potential recipient data. In related terms, clinical criteria, implications and potential perils of partially HLA-matched cell transfers are generally not adequately addressed by this study and even less so in the novel COVID-19 context.

      Significance

      Adoptive cell therapy with virus-specific T cells from partially HLA-matched third-party donors into immunocompromised recipients post hematopoietic stem cell transplantation has been successfully performed in the past (cf. https://www.ncbi.nlm.nih.go... https://www.jci.org/article... "https://www.jci.org/articles/view/121127)"). However, whether this approach might be clinically feasible for COVID-19 therapy remains unknown. Therefore, larger, more extensive studies including heterogeneous patient populations are needed to assess and balance potential risks vs. outcome in the new context of COVID-19.

      This review was undertaken by V. van der Heide as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2020-05-05 03:43:04, user Sinai Immunol Review Project wrote:

      A possible role of immunopathogenesis in COVID-19 progression

      Anft M., Paniskaki K, Blazquez-Navarro A t al.; medRxiv 2020.04.28.20083089; https://doi.org/10.1101/202...

      Keywords

      • SARS-CoV-2 spike protein-specific T cells

      • COVID-19

      • adaptive immunity

      Main findings

      In this preprint, 53 hospitalized COVID-19 patients, enrolled in a prospective study at a tertiary care center in Germany, were assigned to moderate (n=21; light pneumonia), severe (n=18; fever or respiratory tract infection with respiratory rate >30/min, severe dyspnea, or resting SpO2 <90%), and critical subgroups (n=14; ARDS, sepsis, or septic shock) according to clinical disease. Moderately and severely ill patients with a PCR-confirmed diagnosis were recruited within four days of clinical onset, whereas critically ill patients were enrolled on average within 14 days of diagnosis on admission to ICU. To account for the overall longer hospital stay in ICU cases prior to inclusion, repeated blood samples were obtained from moderately and severely ill donors within eight days post recruitment. For 10 out of 14 ICU patients, no follow up blood samples were collected. At recruitment as well as on follow-up, circulating lymphocyte counts were below reference range in the majority of enrolled COVID-19 patients. Relative frequencies were significantly reduced in critically vs. moderately, but not vs. severely ill individuals, with substantially lower NK as well as CD8 T cells counts, and a concomitant increase of the CD4:CD8 T cell ratio in ICU patients. Basic phenotypic and immune cell subset analysis by flow cytometry detected lower frequencies of central memory CD4 T cells as well as reduced terminally differentiated CD8 Temra cells in critical COVID-19. Moreover, a decrease in activated HLA-DR+ CD4 and CD8 T cells as well as in cytolytic CD57+ CD8 T cells was observed in critical vs. severe/moderate disease. Similarly, frequencies of CD11a+ CD4 and CD8 T cells as well as CD28+ CD4 T cells were lower in critically ill donors, indicating a general loss of activated bulk T cells in this subgroup. In addition, a reduction of both marginal and transitional CD19+ B cells was seen in patients with severe and critical symptoms. Of note, on follow-up, recovering severe COVID-19 patients showed an increase in bulk T cell numbers with an activated phenotype. Importantly, SARS-CoV-2 spike (S)-protein-specific CD4 and CD8 T cells, identified following stimulation of PBMCs with 15-mer overlapping S protein peptide pools by flow-cytometric detection of intracellular CD154 and CD137, respectively, were found in the majority of patients in all COVID-19 subgroups at the time of recruitment and further increased in most subjects by the time of follow-up (antiviral CD4 >> CD8 T cells). Most notably, frequencies of both antiviral CD4 and CD8 T cells were substantially higher in critically ill patients, and virus specific CD4 and CD8 T cells in both critically and severely ill subgroups were shown to produce more pro-inflammatory Th1 cytokines (TNFa, IFNg, IL-2) and the effector molecule GzmB, respectively, suggesting an overall increased magnitude of virus-specific T cell inflammation in the context of more severe disease courses. Furthermore, frequencies of antiviral CD4 T cells correlated moderately with anti-S-protein IgG levels across all patient groups.

      Limitations

      In general, this is a well executed study and most of the observations reported here pertaining to overall reduced bulk T cell frequencies (along with lower NK and other immune cell counts) as well as diminished numbers of T cells with an activated phenotype in ICU vs. non ICU COVID-19 corroborate findings in several previous publications and preprints (cf. https://www.jci.org/article... https://academic.oup.com/ji... https://www.nature.com/arti... https://www.medrxiv.org/con... https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.04.17.20061440v1.full.pdf)"). Notably, in contrast to many previous reports, the prospective study by Anft et al. enrolled a relatively larger number of COVID-19 patients of variable clinical disease (with the exception of mild cases). However, there are a few weaknesses that should be addressed. Most importantly, the choice of statistical tests applied should be carefully revised: e.g. comparison of more than two groups, as seems to be the case for most of the figures, requires ANOVA testing, which should ideally be followed by post-hoc testing (despite the somewhat confusing statement that this was conceived as an exploratory study). Given the overall limited case numbers per clinical subgroup, trends even though they might not reach statistical significance are equally important. Similarly, some statements are overgeneralized and should be adjusted based on the actual data shown (e.g. the authors continue to refer to gradual reductions of activated T cell subset numbers in moderately vs. severely vs. critically ill patients, but for the majority of data shown substantial differences are apparent only in ICU vs. non-ICU patients). Moreover, it would be helpful to include representative FACS plots in addition to explanatory gating strategies provided in the supplemental document. There are also several inconsistencies regarding the order of data presented here (e.g. in the main manuscript, Fig S5 is chronological referred to before Fig S4) as well as pertaining to relevant technical details (according to both the main manuscript and the gating strategy in Figure S5, virus-specific CD4 T cells were identified by CD154 expression; however, in figure legend S5 virus-specific CD4 T cells are defined as CD4+ CD154+ CD137+). Additionally, from a technical point of view, it is somewhat intriguing that the percentages of virus-specific T cells identified by expression of CD154 and CD137, respectively, following peptide simulation seem to differ substantially from frequencies of CD154+ or CD137+ INFg+ virus-specific T cells. Assuming a somewhat lower extent of cellular exhaustion in the moderate COVID-19 group, one would expect these cell subsets to mostly overlap/match in frequencies, therefore suggesting slight overestimation of actual virus-specific T cell numbers. In this context, inclusion of positive controls, such as CMV pp65 peptide stimulation of PBMCs from CMV seropositive donors, in addition to the already included negative controls would also be helpful. Moreover, in view of the observation that virus-specific T cells were found to be increased in critically ill ICU over non-ICU patients, a more stringent characterization of these patients as well as assessment of potential associations with clinical characteristics such as mechanical ventilation or death would add further impact to the findings described here. Finally, this study is limited to anti-S protein specific T cells. However, evaluation of N and also M-protein specific T cell responses are likely of great interest as well based on current knowledge about persistent M-protein specific memory CD8 T cells following SARS-CoV-1 infection (cf. https://www.microbiologyres... "https://www.microbiologyresearch.org/content/journal/jgv/10.1099/vir.0.82839-0)").

      Significance

      In addition to reduced frequencies of activated bulk T cell numbers, the authors report an enhanced virus-specific T cell response against S protein epitopes in critically ill COVID-19 patients compared to severely and moderately ill individuals, which correlated with anti-S protein antibody titers (also cf. Ni et al.: https://doi.org/10.1016/j.i... "https://doi.org/10.1016/j.immuni.2020.04.023)"). This is an important observation that mirrors previous data about SARS-CoV-1 (cf. Ka-fai Li C et al.: https://www.jimmunol.org/co... "https://www.jimmunol.org/content/jimmunol/181/8/5490.full.pdf)"). Furthermore, in accordance with a recent preprint by Weiskopf et al. (https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.04.11.20062349v1.full.pdf)"), virus-specific CD4 T cells were found to increase in most patients over time regardless of clinical disease, whereas antiviral CD8 T cell kinetics seemed slightly less pronounced. Moreover, in the majority of moderately and severely ill cases, virus-specific T cells against the S protein could be detected early on - on average within 4 days of symptom onset. Longitudinal studies including larger numbers of COVID-19 patients across all clinical subgroups are therefore needed to further evaluate the potential impact of this observation, in particular in the context of previously described pre-existing memory T cells cross-reactive against human endemic coronaviruses (cf. https://www.medrxiv.org/con... https://journals.sagepub.co... "https://journals.sagepub.com/doi/pdf/10.1177/039463200501800312)").

      This review was undertaken by V. van der Heide as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2021-07-08 05:59:35, user Dr.G.R.Soni wrote:

      Comments on preprint medRxiv publication entitled "Efficacy, safety and lot to lot immunogenicity of an inactivated SARS-CoV-2 vaccine (BBV152) : A double blind, randomised, controlled phase 3 trials" reg.

      This is regarding indigenously developed inactivated SARS-CoV-2 vaccine by M/s Bharat Biotech, Hyderabad by using vaccine strain NIV-2020-770 containing D614G mutation. The conventional vaccine consists of 0.5 ml volume having 0.6 ug of virus antigen and results of phase 3 clinical trial available on preprint of medRxiv publisher suggest that the vaccine is of good quality if not best//excellent and the vaccine in my opinion can be used by many under developed and developing countries. However, following are the further comments:

      1. Results claimed for vaccine efficacy against severe symptomatic, asymptomatic and delta variant like 93.4%(95% confidence interval CI 57.1-99.8), 63.35( CI 29.0-82.4) and 65.2% (CI 33.1-83.0) respectively are statistically highly insignificant because of lesser precision and wide confidence interval. Even over all vaccine efficacy reported to be 77.8% (CI 65.2- 86.4) is not highly significant. This may be due to known and unknown variations as well as lesser number of participants involved in clinical trials. Of course the study was designed to obtain a two sided 95% CI for vaccine efficacy with lower limit greater or equal to 30% but it is only applicable when vaccines of high efficacy are not available. Whereas now fact is that the efficacy of Moderna, Pfizer, Johnson & Johnson, Sputnik etc. vaccines has been reported to be more than 90.0% with better precisions.

      2. Again vaccine efficacy reported for elderly patients viz. 67.8% (CI 8.0- 90.0) shows very poor precision and widest CI means high uncertainty and least confidence in data.

      3. No vaccine efficacy data submitted after first dose of vaccine administration and reason furnished by the sponsor is because of low number of Covid-19 cases reported and this is not very convincing. Comparison of vaccine efficacy between two doses is important to know the progress of efficacy.

      4. GMT was reported to be higher i.e.194.3 ( CI 134.4-280.9) in vaccinees who were seropositive for SARS-CoV-2 IgG at base line than in those who were seronegative 118.0 (104.0-134.0). The difference is not even two fold whereas in other studies including Pfizer more than 4-6 fold increase in immune response has been reported even after single dose of vaccine under such conditions. This may be due to killed nature of present vaccine which is in general less immunogenic than live vector and m-RNA based Covid-19 vaccines.

      5. The immune response of three vaccine lots of vaccine in terms of GMT 50 is 130, 121.2 and 125.4 Vs 13.7 in placebo which seems to be optimum but much higher immune response has been reported in other internationally approved vaccines.Why the IgG GMT titre after two doses of vaccine studied by ELISA has not been reported separately for each lot of vaccine rather overall titre against S1 protein, N protein and RBD has been reported i.e. 9742 EU/ml, 4161 EU/ml and 4124 EU/ml respectively? Since RBD is part of S1 protein therefore S1 titre includes RBD titre also. It means ELISA IgG antibody titre of these viral proteins are roughly equal hence this needs clarification. Anyway unless the titre of these neutralizing and ELISA IgG antibodies are compared with sera of asymptomatic, symptomatic and severely recovered covid-19 patients or immune sera of WHO, US, EU approved vaccines available in market it is very difficult to say whether the immunogenicity of present vaccine is at par or not with these standards?

      6. The lesser efficacy of present vaccine than other approved vaccines by the US, WHO and EU may also be due to lack of T cell mediated cytotoxicity response; this is why this response is not measured in the present study.

      7. When Covid-19 disease is known to affect men and women differently so separate clinical trial data are required to be submitted by the sponsors. However in the present study no marked difference in GMT,s for neutralizing antibodies at day 56 was found when assessed based on age and gender. This is very surprising and difficult to believe because age definitely and gender also are known to affect the immune response of any viral vaccine.

      8. Reasons for contracting Covid-19 disease by some vaccinees are to be given specially when immune-compromised and immunosuppressed etc. patients have already been excluded from the study.

      9. As per WHO requirement the minimum level of protecting antibodies should be there up to six months therefore sponsors have to continue the study and then can claim the actual efficacy of vaccine.

      Indigenous development of SARS-CoV-2 killed vaccine by conventional method using Indian isolate in the country is an excellent attempt for controlling the Covid 19 disease. The vaccine so far seems to be good based on the results of controlled clinical trials and its effectiveness will be further come to know over the time after its massive use in vaccination program. It is nice that the said vaccine has been exported to many other countries. Let us hope for its early approval by WHO for emergency use.

      Dr.G.R.Soni

    2. On 2021-07-04 05:23:23, user PriyankaPulla wrote:

      Major protocol violations occurred at the largest site of the Covaxin phase 3 trial, a private hospital called People's Hospital, which recruited 1700 participants. These violations are documented extensively by multiple media outlets. And these violations raise questions about the integrity of the Phase 3 trial data. They also raise questions about the sponsors' attitude to due process, and the independence/training of the DSMB: both sponsors (the Indian Council of Medical Research and Bharat Biotech) responded to the allegations with cursory dismissals, while the DSMB remained mum.

      Further details here: https://www.thequint.com/co...

      I am listing a few of the documented irregularities:

      1. Participants told media outlets that they didn't give their informed consent, an Indian legal requirement. Many participants belonged to disadvantaged tribal communities/were illiterate, which necessitates special consent protocols under Indian law, which investigators didn't follow.

      Investigators admitted in a video-recorded press conference that they didn't give participants a copy of their informed-consent form during their first visit, unless participants explicitly asked for it. This strongly suggests that the investigators weren't trained in Indian legal requirements or Good Clinical Practices.

      1. Investigators allegedly advertised the trial as a vaccination drive in communities of poor and illiterate people.

      2. Dozens of participants say the trial team did not contact them to record solicited adverse events. These participants often didn't have their own mobile phones (mobile phones are the mode through which solicited adverse events were to be collected, as per trial protocol). Even though these participants came from poor communities, investigators didn't foresee the fact that they may not have their own mobile phones, and may be hard to contact. Nor did they attempt to contact them in their homes in the days following the doses.

      3. People's Hospital recruited a record 1700 participants in 1.5 months (no other Covaxin trial site in India managed such numbers). In contrast, another government-run Covaxin site in Bhopal struggled to even recruit a few hundred participants, and was, therefore, excluded from the trial. This supports the allegation that People's Hospital misadvertised the trial as a vaccination drive.

      4. Many participants told media outlets that they suffered Covid-like symptoms post jab, but the investigators never called them to collect this information. Nor did the participants know where to report their symptoms. This raises questions about how well Covid cases were recorded.

      5. Participants say they were denied medical treatment at People's Hospital when they fell sick. This, again, raises questions about how well the investigators captured adverse-events.

      6. When one participant at the Bhopal site died, investigators ignored his family's version of the participant's symptoms in their causality analysis. In the family's version, the participant suffered from very severe symptoms (vomiting, dizziness, weakness) for 7-8 days before death, while the investigators claimed he was fine during solicited-adverse event monitoring, and died suddenly.

      The dismissal of the family's version of events, when the family was present during the participant's death (but the investigators weren't), raises serious questions about how Serious Adverse Events are investigated. No post-mortem report or causality analysis was shared with the family despite multiple requests. Further, the family alleges that the deceased participant received no phone calls from the investigators to record solicited adverse events in the days leading up to his death.

      The investigators could easily have shared proof of their claims by sharing a record of the phone calls with the family. They haven't.


      Despite the above serious concerns (which are supported by video testimony from participants broadcast on multiple media outlets, specifically NDTV), the trial's government sponsor, ICMR, and Bharat Biotech, denied all allegations in a cursory manner. Further, the preprint makes no mention of them, or explain how these irregularities were handled.

      This raises questions about overall data integrity in Bharat Biotech's phase 3 trial. Bharat Biotech has been under substantial pressure from the government to roll out Covaxin fast, which may explain why the company is overlooking such data integrity issues. More details here: https://www.livemint.com/sc...

      Reviewers of this paper, and licensing authorities, including the World Health Organisation, must investigate these allegations thoroughly.

    1. On 2021-08-05 18:32:18, user Anette Stahel wrote:

      Dear moderator,

      I've now reviewed, edited and updated my earlier comment to the present study [1]. I hope this will allow for it to be posted.

      I'm sorry, but this study is not correct. That is, the pool of people used as denominator when calculating the percentage of COVID-19 infected people who developed CVT and PVT is greatly inadequate. I'll explain what I mean.

      In the abstract of the study, it's stated:

      "COVID-19 increases the risk of CVT and PVT compared to patients diagnosed with influenza, and to people who have received a COVID-19 mRNA vaccine."

      However, when comparing the risk of developing condition X from disease Y with the risk of developing condition X from something else, eg vaccine Z, you first and foremost need to make a correct assessment of how how large the pool of people with disease X is. And to do that, you need to make an estimate. Merely counting the number of people who've sought out primary or secondary care for their symptoms won't do. Not even if you include all the people who were asymptomatic but sought out the care center anyway in order to take a test to see if they were infected (simply because they wanted to know) and then tested positive.

      No, you need to include all infected persons in the total pool of people belonging to the health care facility/facilities in question, including the ones who don't go test themselves because of being asymptomatic, or of not having the energy to do it due to their symptoms, or of being into alternative medicine, or of lacking interest/knowledge about the infection et c. There may be many of different reasons. This means you need do make an estimate, otherwise the denominator in the calculation of the percentage who develop condition X from infection Y becomes incorrect.

      A study measuring the risk of developing condition X from infection Y using a smaller denominator than one including everyone infected may be useful at times, but it can not be used for comparison with a correctly calculated vaccine risk.

      I will use the study Estimation of the Lethality for COVID-19 in Stockholm County published by the Swedish Public Health Agency [2] as an example of a correctly calculated risk, based on an adequately defined denominator. The fact that this is a calculation of the lethality percentage from COVID-19 and not the CVT and PVT percentage is irrelevant, the point is that the same mathematics used in this study should've been applied in the present Oxford University study. From page 13 in the Swedish study, in translation:

      "Recruitment was based on a stratified random sample of the population 0-85 years. In the survey we use, the survey for Stockholm County was supplemented with a self-sampling kit to measure ongoing SARS-CoV-2-infection by PCR test. The sampling took place from March 26 until April 2 and 18 of a total of 707 samples were positive. The proportion of the population in Stockholm County which would test positive was thus estimated at 2.5%, with 95% confidence range 1.4-4.2%."

      For a complex reason, which I won't go into but is described in detail in the study text, one needs to use a slightly higher percentage when multiplying it with the total number of people in the pool, but that's of minor importance. Anyway, in this study they had to use the figure 3,1169% and when they multiplied it with the number of people in Stockholm County, 2 377 000, they got 74 089. This estimate was then the correct denominator to use when calculating the percentage of people who died from COVID-19 in Stockholm County during this time period.

      The numerator was the number of people who died in Stockholm County with a strong suspicion of COVID-19 as a cause, which was 432, no incorrectness there either - as long as a suspected cause number, not a diagnosed cause number, is also used as the numerator when calculating the lethality from the COVID-19 vaccine when the infection lethality and vaccine lethality rates are compared.

      So, what they found was that the lethality from COVID-19 in Stockholm County was 0,58%. This is a correct figure, as long as we keep in mind the fact that some of the suspected COVID-19 deaths may later become diagnosed as unrelated to the infection.

      The above is thus how the authors of the present Oxford study should've carried out their calculations but they didn't. From their text:

      "Design: Retrospective cohort study based on an electronic health records network. Setting: Linked records between primary and secondary care centres within 59 healthcare organisations, primarily in the USA. Participants: All patients with a confirmed diagnosis of COVID-19 between January 20, 2020 and March 25, 2021 were included."

      This excludes a considerable amount of infected persons in the total pool of people belonging to all of these primary and secondary care centers, who didn't go test themselves because of a number of reasons (being asymptomatic, being alternative medical, not having the energy or interest for it, et c).

      If they'd used the adequate figure in the denominator, the percentage of people established to've developed CVT and PVT from COVID-19 would've gotten vastly lower. However, the percentage of people determined to've developed CVT and PVT from the mRNA COVID-19 vaccines was fully correctly carried out since there are no unregistered vaccinated cases and therefore the registered figure is to be used.

      Via the Oxford study's Figure 2 and Table S2 [3], I calculated the following figures: First time CVT cases diagnosed after administration of the mRNA COVID vaccines amounted to 6.6 per million and first time PVT cases after same vaccines amounted to 12.5 per million.

      Now, there's a study titled Estimation of US SARS-CoV-2 Infections, Symptomatic Infections, Hospitalizations and Deaths Using Seroprevalence Surveys published by the American Medical Association [4], which has estimated the percentage of infected people in the US looking at roughly the same time period as the Oxford study. From the paper:

      "An estimated 14.3% (IQR, 11.6%-18.5%) of the US population were infected by SARS-CoV-2 as of mid-November 2020."

      With an infection rate around 14.3%, the estimated number of infected people of the 81 million patients in the healthcare database referred to in the study would've amounted to 11 583 000. This number gives us a hint as for the size of the denominator which should've been used in the calculation instead of the figure of 537 913 confirmed diagnoses.

      However, since the Oxford study not only looked at CVT and PVT arising from people having the infection around mid-November 2020 but looked at a much longer time period, from January 20, 2020 to to March 25, 2021, a number far greater than 11 583 000 should be applied. What we need is to estimate how many of the 81 million patients which were infected at least once during these 14 months in question. For the calculation to be really accurate, we need the total, accumulated number of infected people. But since that number isn't found without a very comprehensive and time consuming investigation, we instead have to use the signs ">" ("greater than") and "<" ("less than") here. So, the correct denominator, which should've been used instead of the 537 913 figure, is >11 583 000.

      Further, the study says that first time CVT was found in 19 of the patients following COVID-19 diagnosis and first time PVT in 94. This actually means that the rates of CVT and PVT elicited by COVID-19 were much lower than the rates of CVT and PVT elicited by the vaccines. COVID-19 elicited PVT cases, correctly calculated, amounted to <8.1 per million - only about two thirds of the 12.5 per million for the vaccines - and the CVT cases amounted to <1.6 per million - a mere fourth of the vaccines' 6.6.

      Interestingly, with their work including this method error, these authors have provided scientific validation of the growing suspicion that the COVID-19 vaccines give rise to thrombocytic complications to a much greater extent than does COVID-19 (which is the opposite of what's stated in the study), because even if the 537 913 figure is inadequate, the other figures in the study are most likely not.

      It should also be said that the disclaimer inserted towards the end of the Oxford study by no means can be referred to in order to justify this method error. From the disclaimer:

      "However, the study also has several limitations and results should be interpreted with caution. (--) Third, some cases of COVID-19, especially those early in the pandemic, are undiagnosed, and we cannot generalise our risk estimates to this population."

      The reason why this passage cannot be referred to, is that 11 000 000 or so omitted cases impossibly can be defined as "some", when the number of denominator cases determined in the study merely constitutes a small fraction (5%) of that figure.

      Finally, I'd like to suggest a reading through of the English translation of the Swedish COVID-19 lethality study that I took up in the beginning of my text as a correct, comparative example [5]. This is the main paper that the Swedish equivalent to CDC, the Public Health Agency (Folkhälsomyndigheten), refers to when talking about the COVID-19 lethality here and it's put up on one of the major information pages of their website. I really recommend reading all of it, because it explains so well and in such detail how come this model of denominator calculation without exception must be used in studies like these, which aim to investigate the rate of injuries/complications arising from an infectious illness.

      Anette Stahel <br /> MSc in Biomedicine <br /> Sweden

      References

      1. Taquet, M, Husain, M, Geddes, J R, Luciano, S & Harrison, P J (2021) Cerebral Venous Thrombosis and Portal Vein Thrombosis: A Retrospective Cohort Study of 537,913 COVID-19 Cases medRxiv https://doi.org/10.1101/202...
      2. Svenska Folkhälsomyndigheten (2020) Skattning av Letaliteten för Covid-19 i Stockholms Län https://www.folkhalsomyndig...
      3. Taquet, M, Husain, M, Geddes, J R, Luciano, S & Harrison, P J (2021) Cerebral Venous Thrombosis and Portal Vein Thrombosis: A Retrospective Cohort Study of 537,913 COVID-19 Cases OSFHome https://osf.io/a9jdq/
      4. Angulo FJ, Finelli L, Swerdlow DL. Estimation of US SARS-CoV-2 (2021) Infections, Symptomatic Infections, Hospitalizations and Deaths Using Seroprevalence Surveys (2021) JAMA Netw Open https://jamanetwork.com/jou...
      5. The Swedish Public Health Agency (2020) Estimation of the Lethality for COVID-19 in Stockholm County Online translation of [2] https://translate.google.co...
    1. On 2020-04-02 20:13:43, user Sinai Immunol Review Project wrote:

      Main findings: The authors analyzed 4000 test results from 28 COVID-19 patients of which 8 were confirmed severe COVID-19 cases and 20 were confirmed cases of mild COVID-19 infection. They found that the overall level of serum CRP increased in all cases irrespective of the disease severity. They observed that serum cystatin C (CysC), creatinine (CREA), and urea, biochemical markers of renal function, were significantly elevated in severe COVID-19 patients compared to mild patients.

      Critical Analyses: <br /> 1. Figure duplication in panels G and H of Figure 2 <br /> 2. Survey area is limited to one center.<br /> 3. Small number of participants in the survey.<br /> 4. Elderly people in severe groups and relatively younger people in the milder group. The baseline parameters may differ in both groups, considering the age difference.<br /> 5. Although not clearly stated, this is a cross sectional study and interpretation of results is difficult. The markers that were found to be significantly different between groups are very non-specific. Renal failure and high LDH are not surprising findings in critically ill patients. <br /> 6. There is a very minimal description of the patient's baseline characteristics. It would be important to know for example what were the symptoms at presentation, how long patients had symptoms for before inclusion in the study, duration of hospitalization before inclusion. This would help interpret whether results reflect difference in severity of disease or simply a longer course of disease/hospitalization. <br /> 7. It is unclear what the authors mean in the discussion when they mention “which may be the result of prophylactic use of drug by doctor” (Discussion section, line 6). Type of the drug used is not specified.

      Relevance: This study offers insights on some laboratory markers of mild vs severe cases of COVID-19 infection. Glomerular cells highly express ACE2 which is the cellular receptor for SARS-CoV-2, and impaired kidney function might represent a marker of virus-induced end organ damage.

      Reviewed by Divya Jha/Francesca Cossarini as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2020-04-06 18:54:14, user Sinai Immunol Review Project wrote:

      This study examined antibody responses in the blood of COVID-19 patients during the early SARS CoV2 outbreak in China. Total 535 plasma samples were collected from 173 patients (51.4% female) and were tested for seroconversion rate using ELISA. Authors also compared the sensitivity of RNA and antibody tests over the course of the disease . The key findings are:

      • Among 173 patients, the seroconversion rates for total antibody (Ab), IgM and IgG were 93.1% (161/173), 82.7% (143/173) and 64.7% (112/173), respectively.

      • The seroconversion sequentially appeared for Ab, IgM and then IgG, with a median time of 11, 12 and 14 days, respectively. Overall, the seroconversion of Ab was significantly quicker than that of IgM (p = 0.012) and IgG (p < 0.001). Comparisons of seroconversion rates between critical and non-critical patients did not reveal any significant differences.

      • RNA tests had higher sensitivity in early phase and within 7 days of disease onset than antibody assays (66.7% Vs 38.3% respectively).

      • The sensitivity of the Ab assays was higher 8 days after disease onset, reached 90% at day 13 and 100% at later time points (15-39 days). In contrast, RNA was only detectable in 45.5% of samples at days 15-39.

      • In patients with undetectable RNA in nasal samples collected during day 1-3, day 4-7, day 8-14 and day 15-39 since disease onset, 28.6% (2/7), 53.6% (15/28), 98.2% (56/57) and 100% (30/30) had detectable total Ab titers respectively Combining RNA and antibody tests significantly raised the sensitivity for detecting COVID-19 patients in different stages of the disease (p < 0.001).

      • There was a strong positive correlation between clinical severity and antibody titer 2-weeks after illness onset.

      • Dynamic profiling of viral RNA and antibodies in representative COVID-19 patients (n=9) since onset of disease revealed that antibodies may not be sufficient to clear the virus. It should be noted that increases in of antibody titers were not always accompanied by RNA clearance.

      Limitations: Because different types of ELISA assays were used for determining antibody concentrations at different time points after disease onset, sequential seroconversion of total Ab, IgM and IgG may not represent actual temporal differences but rather differences in the affinities of the assays used. Also, due to the lack of blood samples collected from patients in the later stage of illness, how long the antibodies could last remain unknown. For investigative dynamics of antibodies, more samples were required.

      Relevance: Total and IgG antibody titers could be used to understand the epidemiology of SARS CoV-2 infection and to assist in determining the level of humoral immune response in patients.

      The findings provide strong clinical evidence for routine serological and RNA testing in the diagnosis and clinical management of COVID-19 patients. The understanding of antibody responses and their half-life during and after SARS CoV2 infection is important and warrants further investigation

    1. On 2020-04-01 15:47:26, user JR Davis wrote:

      Table 3 and 4 and 5 are all missing. Text mentions non-CoVID respiratory pathogens (n=10) also tested for, and listed in "Table 3"....with the additional Primer list in Table 4.<br /> However, both Table 3, 4, and 5 NOT provided in the PDF....only Table 1 and 2 found at the end of the document.<br /> Can you provide missing tables 3,4,5?

    1. On 2020-04-21 23:29:37, user Sinai Immunol Review Project wrote:

      Title: Factors associated with prolonged viral shedding and impact of Lopinavir/Ritonavir treatment in patients with SARS-CoV-2 infection?<br /> Keywords: retrospective study – lopinavir/ritonavir – viral shedding

      Main findings:<br /> The aim of this retrospective study is to assess the potential impact of earlier administration of lopinavir/ritonavir (LPV/r) treatment on the duration of viral shedding in hospitalized non-critically ill patients with SARS-CoV-2. <br /> The analysis shows that administration of LPV/r treatment reduced the duration of viral shedding (22 vs 28.5 days). Additionally, if the treatment was started within 10 days of symptoms onset, an even shorter duration of virus shedding was observed compared to patients that started treatment after 10 days of symptoms s onset (19 vs 27.5 days). Indeed, patients that started LPV/r treatment late did not have a significant median duration of viral shedding compared to the control group (27.5 vs 28.5 days). Old age and lack of LPV/r administration independently associated with prolonged viral shedding in this cohort of patients.

      Limitations:<br /> In this non-randomized study, the group not receiving LPV/r had a lower proportion of severe and critical cases (14.3% vs 32.1%) and a lower proportion of patients also receiving corticosteroid therapy and antibiotics, which can make the results difficult to interpret.<br /> The endpoint of the study is the end of viral shedding (when the swab test comes back negative), not a clinical amelioration. The correlation between viral shedding and clinical state needs to be further assessed to confirm that early administration of LPV/r could be used in treating COVID-19 patients.

      Relevance:<br /> Lopinavir/ritonavir combination has been previously shown to be efficient in treating SARS [1,2]. While this article raises an important point of early administration of LPV/r being necessary to have an effect, the study is retrospective, contains several sources of bias and does not assess symptom improvement of patients. A previously published randomized controlled trial including 200 severe COVID-19 patients did not see a positive effect of LPV/r administration [3], and treatment was discontinued in 13.8% of the patients due to adverse events. Similarly, another small randomized trial did not note a significant effect of LPV/r treatment [4] in mild/moderate patients. A consequent European clinical trial, “Discovery”, including among others LPV/r treatment is under way and may provide conclusive evidence on the effect and timing of LPV/r treatment on treating COVID-19.

      1. Treatment of severe acute respiratory syndrome with lopinavir/ritonavir: a multicentre retrospective matched cohort study. - PubMed - NCBI. https://www-ncbi-nlm-nih-go.... Accessed March 30, 2020.
      2. Role of lopinavir/ritonavir in the treatment of SARS: initial virological and clinical findings. - PubMed - NCBI. https://www-ncbi-nlm-nih-go.... Accessed March 30, 2020.
      3. Cao B, Wang Y, Wen D, et al. A Trial of Lopinavir–Ritonavir in Adults Hospitalized with Severe Covid-19. New England Journal of Medicine. March 2020. doi:10.1056/NEJMoa2001282
      4. Li Y, Xie Z, Lin W, et al. An Exploratory Randomized, Controlled Study on the Efficacy and Safety of Lopinavir/Ritonavir or Arbidol Treating Adult Patients Hospitalized with Mild/Moderate COVID-19 (ELACOI). Infectious Diseases (except HIV/AIDS); 2020. doi:10.1101/2020.03.19.20038984

      Reviewed by Emma Risson as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

    1. On 2021-04-01 04:10:21, user Michal P wrote:

      This study has a number of significant flaws and in my opinion should not be used for any decision making.

      First, the sample size is very small - only 282 tests with only 2 positive cases. The authors state as their conclusion the rate of 7 positive cases out of 1000 visitors, even though according to their own analysis the 95% confidence interval is 1-24. And even though the authors provide such a wide confidence interval, their estimate of the number of infected arrivals is far narrower: 17-30 in the November-December period. This range should be substantially wider to accommodate the uncertainty of the test estimate.

      Second, the study is performing the tests when the visitors are departing, and as the authors admit, they cannot rule out that the visitors were infected on Maui. Even if one of the two infections occured on Maui, that would completely change the result.

      Finally, the study still suffers from selection bias. It sampled visitors arriving on a single day, with most of the visitors from California and Washington, during a time of high infections in the US. Current infection rate in the US is about 4 times lower than at the time the study is performed. This alone suggests that the likelihood of a visitor being infected now is 4 times lower than at the time the study was performed.

      For this study to be useful for policy making it should be substantially larger to provide higher statistical power. And the estimate of the number of infected visitors should be conditional both on the number of arriving visitors as well as the prevalence of the infection in the locations the visitors are arriving from .

    1. On 2021-05-13 15:56:02, user Tatiana Araujo Pereira wrote:

      It has been more than one year since the Coronavirus Disease 2019 (COVID-19) outbreak started. We already have effective vaccines around the world, but the imbalance between supply and demand allows Sars-CoV-2 to spread and mutate faster than mass immunization, especially in less developed countries. The arise of more transmissible variants is very worrying and motivates the search for biomarkers that enable early assessment of possible critical cases as well as therapeutic targets for the disease. In this sense Flora et al [1] performed laboratory and proteomic analysis of the plasma sample from a cohort of 163 COVID-19 patients admitted to Bauru State Hospital (São Paulo, Brasil) divided in three groups: “a) patients with mild symptoms that were discharged without admission to an ICU; b) patients with severe symptoms that were discharged after admission to an ICU; c) critical patients, who were admitted to an ICU and died”. The results point to a high concentration of ferritin (FTN) and absence of the IREB2 protein in volunteers exhibiting severe and critical symptoms, indicating that iron homeostasis would be a possible therapeutic target. These results are in line with previous researches, which also identified FTN levels directly related to the severity of the disease [2-5]. Ferritin is an iron reservoir protein, keeping it in its core shell to protect cells against oxidative stress. There are other proteins inhibiting iron redox reactivity in the body, helping with metal ions transport (Transferrin), import to (Divalent Metal Transport) and export from (Ferroportin) the cell [6, 7]. Due to its role in iron homeostasis, FTN is used to indirectly assess iron status in the body. Ordinarily, high levels of FTN mean iron overload [8]. However, circulating ferritin can be elevated independently of iron overload in inflammatory processes, in which it acts as immunosuppressant and proinflamatory modulator [4, 9, 10]. IREB2 is an Iron Regulatory Protein (IRP). When iron levels are low these proteins are able to attach to an untranslated region of mRNA known as Iron Responsive Elements (IRE). Through this mechanism it regulates expression of transferrin receptor and ferritin. In iron overload conditions the affinity of IRP for IRE is not enough to keep the attachment and the protein degrades or takes another role. IREB2 represses ferritin translation when bounded to IRE in FTN-mRNA and degrades in iron overload conditions [6, 11-13].<br /> Because of observed data, Flora et al [1] concluded that “increasing the expression of IREB2 might be a therapeutic possibility to reduce ferritin levels and, in turn, the severity of COVID-19”. Nonetheless, there is no data about iron status in the plasma of the subjects. So it is impossible to be sure whether the high levels of FTN and absence of IREB2 are associated with iron overload. In this case, suppressing ferritin production could culminate in greater oxidative damage, and even increase the risk of opportunistic infections, since intracellular segregation of iron is one of the main strategies to defend host against parasites [14]. In macrophages, this mechanism induces production of nitrogen and oxygen reactive species helping immune defenses [15, 16], but in chronic inflammation it affects iron recycling [17]. Another way to limit iron availability involves its main regulatory hormone hepcidin, which inhibits iron exit from the cell [18]. Hepcidin expression is induced by interleukine-6 (IL-6), which is produced in Sars-CoV-2 infection [19]. Also, Sepehr Ehsani identified a hepcidin mimetic in protein S region that plays a fundamental role in membrane fusion [20]. In this context it is important to verify the possibility that high levels of FTN are not associated with iron overload and only then consider increasing in IREB2 expression as a therapeutic strategy against COVID-19.

      AUTHORS<br /> Pereira, T A and Espósito, B P.<br /> Institute of Chemistry – Univesity of São Paulo.

      REFERENCES<br /> 1. Flora DC, Valle AD, Pereira HABS. et al. Quantitative plasma proteomics of survivor and non-survivor COVID19 patients admitted to hospital unravels potential prognostic biomarkers and therapeutic targets. MedRxiv; doi: https://doi.org/10.1101/202....<br /> 2. Cavezzi A, Troiani E, Corrao S. COVID-19: hemoglobin, iron, and hypoxia beyond inflammation. A narrative review. Clin Pract. 2020 May 28;10(2):1271.<br /> 3. Bellmann-Weiler R, Lanser L, Barket R, et al. Prevalence and Predictive Value of Anemia and Dysregulated Iron Homeostasis in Patients with COVID-19 Infection. J Clin Med. 2020;9(8):2429.<br /> 4. Colafrancesco S, Alessandri C, Conti F, Priori R. COVID-19 gone bad: A new character in the spectrum of the hyperferritinemic syndrome?. Autoimmun Rev. 2020;19(7):102573.<br /> 5. Perricone C, Bartoloni E, Bursi R et al. COVID-19 as Part of the Hyperferritinemic Syndromes: the Role of Iron Depletion Therapy. Immunologic Research, vol. 68, no. 4, 2020, pp. 213-224.<br /> 6. Halliwell B and Gutteridge JMC. Free Radicals in Biology and Medicine. 4th ed., Oxford: University Press, 2007.<br /> 7. Grotto HZW. Metabolismo do ferro: uma revisão sobre os principais mecanismos envolvidos em sua homeostase. Rev. Bras. Hematol. Hemoter., vol. 30, no 5, 2008, pp. 390-397.<br /> 8. World Health Organization, Centers for Disease Control and Prevention. Assessing the iron status of populations. 2nd ed., World Health Organization, 2007. ISBN: 978 92 4 1596107 (electronic version).<br /> 9. Ruddell RG, Hoang-Le D, Barwood JM et al. Ferritin functions as a proinflammatory cytokine via iron-independent protein kinase C zeta/nuclear factor kappaB-regulated signaling in rat hepatic stellate cells. Hepatology. 2009 Mar;49(3):887-900.<br /> 10. Chen TT, Li L, Chung DH et al. TIM-2 is expressed on B cells and in liver and kidney and is a receptor for H-ferritin endocytosis. J Exp Med. 2005;202(7):955-965.<br /> 11. Kuhn LC and Hentze MW. Coordination of Cellular Iron Metabolism by Post-transcriptional Gene Regulation. J Inorg Biochem, vol. 47, no 3-4, 1992, pp. 183-195.<br /> 12. Schalinske KL, Chen OS, Eisenstein RS. Iron differentially stimulates translation of mitochondrial aconitase and ferritin mRNAs in mammalian cells. Implications for iron regulatory proteins as regulators of mitochondrial citrate utilization. J Biol Chem, vol. 273, no 6, 1998, pp. 3740-3746.<br /> 13. Tong W.-H and Rouault TA. Metabolic Regulation Of Citrate And Iron By Aconitases: Role Of Iron-sulfur Clusters Biogenesis. Biometals, vol. 20, no 3-4, 2007, pp. 549-564.<br /> 14. Gan Z, Tang X, Wan Z et al. Regulation of macrophage iron homeostasis is associated with the localization of bacteria. Metallomics, vol. 11, no 3, 2019, pp. 454-461.<br /> 15. Ratledge C and Dover LG. Iron metabolism in pathogenic bacteria. Annu Rev Microbiol, vol. 54, 2000, pp. 881-941.<br /> 16. Schaible UE and Kaufmann SHE. Iron and microbial infection. Nature Reviews Microbiology, vol. 2, 2004, pp. 946–953.<br /> 17. Castro L, Tórtora V, Mansilla S, Radi R. Aconitases: Non-redox Iron-Sulfur Proteins Sensitive to Reactive Species. Acc Chem Res. 2019 Sep 17;52(9):2609-2619.<br /> 18. Martínez-Pastor M and Puig S. Adaptation to iron deficiency in human pathogenic fungi. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research, vol. 1867, no 10, 2020.<br /> 19. Liu W, Zhang S, Nekhai S, Liu S. Depriving Iron Supply to the Virus Represents a Promising Adjuvant Therapeutic Against Viral Survival [published online ahead of print, 2020 Apr 20]. Curr Clin Microbiol Rep. 2020;1-7.<br /> 20. Ehsani S. Distant sequence similarity between hepcidin and the novel coronavirus spike glycoprotein: a potential hint at the possibility of local iron dysregulation in COVID-19. Biol Direct, vol. 15, 2020, p. 19.

    1. On 2020-05-01 10:56:16, user Ivan Berlin wrote:

      Rentsch CT et al. Covid-19 Testing, Hospital Admission, and Intensive Care Among 2,026,227 United States Veterans Aged 54-75 Years. <br /> medRxiv preprint doi: https://doi.org/10.1101/202... version posted April 14, 2020<br /> Comment of the results concerning smoking related issues. Corrected Version. Please ignore the previous one.<br /> Ivan Berlin, Paris, France<br /> The title is somewhat misleading. Only 3789 persons were tested for SARS-CoV-2, no data on the 2,022,438 are reported.<br /> Data are extracted from the Veteran Administration (USA) Birth Cohort born between 1945 and 1965 electronic database. Between February 8 and March 30, 2020, 3789 persons were tested for SARS-CoV-2. Among them 585 were tested SARS-CoV-2 positive (15.4%) and 3204 SARS-CoV-2 negative. (Remark: the authors frequently confound testing for SARS-CoV-2 and having the disease: COVID-19 +.)<br /> Testing used nasopharyngeal swabs, 1% of the testing samples was from other unspecified sources. Testing was performed “in VA state public health and commercial reference laboratoires”, page 7. No further specification about the testing method is provided. Data are analyzed as if no between test-sources variability existed. However, it is unlikely that between test-source variability would influence the findings.<br /> It seems that only individuals with symptoms were tested, however this is not clearly stated.<br /> Data extraction included diagnostics by diagnostic codes of comorbidities, non-steroid inflammatory drug (NSAID), angiotensin converting enzyme inhibitor (ACE) and angiotensin II receptor blocker (ARB) use, vital signs, laboratory results, hepatic fibrosis score, presence or absence of alcohol use disorder and smoking status.<br /> Smoking status data, never, former, current smokers were extracted using the algorithm described in McGinnis et al. Validating Smoking Data From the Veteran’s Affairs Health Factors Dataset, an Electronic Data Source. Nicotine & Tobacco Research, Volume 13, Issue 12, December 2011, Pages 1233–1239, https://doi.org/10.1093/ntr... used for HIV patients. According to this paper, the algorithm correctly classified 84% of never-smokers 95% of current smokers but only 43% of former smokers. The reported overall kappa statistic was 0.66. When categories were collapsed into ever/never, the kappa statistic was somewhat better: 0.72 (sensitivity = 91%; specificity = 84%), and for current/not current, 0.75 (sensitivity = 95%; specificity = 79%). Thus, classification error cannot be excluded in particular in classifying former smokers. <br /> In unadjusted analyses (Table 1) factors associated significantly with SARS-CoV-2 positivity were: male sex, black race, urban residence, chronic kidney disease, diabetes, hypertension, higher body mass index, vital signs but not NSAID or ACE/ARB exposure. It is to note, that among the laboratory findings, severity of hepatic fibrosis was associated with positive SARS-CoV-2 tests. <br /> Among those with positive SARS-CoV2 alcohol use disorder was reported by 48/585 (8.2%), versus 480/3204 (15%) among those with negative SARS-CoV-2 test. Among those with alcohol use disorder, 9.1 tested positive. <br /> Among SARS-CoV-2 positives there were 216/585 (36.9%) never smokers vs 826/3204 (25.8%) among SARS-CoV-2 negatives. 20.7% tested positive among never smokers. Among SARS-CoV-2 positive persons 179 (30.6%) were former smokers vs 704 (22%) among SARS-CoV-2 negatives. 20.3 % tested positive among former smokers. Among SARS-CoV-2 positive individuals 159 (27.7%) were current smokers vs 1444 (45.1%) among SARS-CoV-2 negative individuals. 9.9% tested positive among current smokers. Expressed otherwise, among SARS-CoV-2 negative individuals, there were less never smokers, less former smokers and more current smokers. Among individuals with SARS-CoV-2 positivity there were 338/585 (61%) persons with smoking history (former + current smokers=ever smokers) and among those with SARS-CoV-2 negativity 2149/3204 (72%) were ever smokers. <br /> COPD, known to be strongly related to former or current smoking, was more frequent among SARS-CoV-2 negative (28.2%) than among SARS-CoV-2 positive (15.4%) individuals.<br /> In multivariable analyses (Table 2), male sex, black ethnicity, urban residence, lower systolic blood pressure, prior use of NSAID but not ACE/ARB use and obesity were associated with SARS-CoV-2 positive test; current smoking (OR: 0.45, 91% CI: 0.35-057), alcohol use disorder (OR 0.58, 95% CI: 0.41-0.83) and COPD (OR: 0.67, 95%CI: 0.50-0.88) were associated with decreased likelihood of SARS-CoV-2 positive test. No association with age and SARS-CoV-2 positive test was observed. The association with hepatic fibrosis with SARS-CoV-2 positive tests remained significant in the multivariable analysis and the authors point out (page 15) that the “pronounced independent association with FIB-4 (fibrosis) and albumin suggest that virally induced haptic inflammation may be a harbinger of the cytokine storm.”, page 15. <br /> The main risk factors for hospitalization or ICU among SARS-CoV-2 positive persons are those that associated with worse clinical signs (status). This is expected: clinical decision about severity is based on current clinical signs and not on previous history. <br /> Neither co-morbidities, nor smoking status or alcohol use disorder were associated with hospitalization/ICU. Surprisingly, age was inversely associated with hospitalization (Table 4) among SARS-CoV-2 positive individuals.<br /> Conclusion

      To the best of our knowledge, this is the first report showing that there are less current smokers among SARS-CoV-2 positive persons. However, looking at smoking history (former + current smoking=ever smokers), less subject of classification bias, the difference seems to be less. It is not known what is the percent of former smokers who were recent quitters; duration of previous abstinence from smoking is a crucial variable in assessing associations with smoking status. There is no report of biochemical verification of smoking status. <br /> It is not known when smoking status is reported with respect of the SARS-CoV-2 testing. It is likely that individuals with clinical symptoms stopped smoking some days before testing and considered themselves as former smokers.

      The fact that alcohol use disorder, which is frequently associated with tobacco use disorder, is also less frequent among SARS-CoV-2 positive individuals raises the question of the specificity of the smoking finding and raises the contribution of substance use disorders overall i.e. the finding about current smoking is part of a cluster of various previous or current substance use disorders e.g. cannabis use, potentially associated with SARS-CoV-2 negative test directly or through associated health disorders e.g. hepatic disorders as a consequence of alcohol use. <br /> COPD as well as current smoking are being reported to be more frequent among SARS-CoV-2 negative individuals raising the possibility that reduced respiratory function (entry of SARS-CoV-2 is by the respiratory tract) is associated with lower likelihood of SARS-CoV-2 positive tests. <br /> It seems that all individuals included were tested because they had symptoms suggestive of COVID-19. It is surprising that only 585/3789 (15.4%) tested positive. This should be discussed.<br /> The paper does not report on analyses of smoking by clinical signs/co-morbidities interactions. It is likely that former smokers or those with alcohol use disorders are more frequent among individuals with comorbidities. Based on previous knowledge about smoking associated health disorders, one can assume that more severe clinical signs were associated with current smoking or among recent quitters; the smoking x clinical signs interaction is not tested. <br /> The authors conclude on page 14 “To wit, we found that current smoking, COPD, and alcohol use disorder, factors that generally increase risk of pneumonia, were associated with decreased probability of testing positive. While they were not associated with hospitalization or intensive care, it is too early to tell if these factors are associated with subsequent outcomes such as respiratory failure or mortality.”<br /> The reduced current smoking rate among SARS-CoV-2 positive individuals is an interesting but preliminary finding. It is likely that it is part of a more complex symptomatology and not specific to current smoking. Smoking status should have been assessed on a more detailed manner. The current findings, from a retrospective, cross sectional analysis, are insufficient to support the hypothesis that current smoking protects against SARS-CoV-2 positivity.

    1. On 2020-04-16 21:17:49, user Sinai Immunol Review Project wrote:

      Key findings:

      The authors wanted to better understand the dynamics of production SARS-CoV-2-specific IgM and IgG in COVID-19 pneumonia and the correlation of virus-specific antibody levels to disease outcome in a case-control study paired by age. The retrospective study included 116 hospitalized patients with COVID-19 pneumonia and with SAR-CoV-2 specific serum IgM and IgG detected. From the study cohort, 15 cases died. SARS-CoV-2 specific IgG levels increased over 8 weeks after onset of COVID-19 pneumonia, while SARS-CoV-2 specific IgM levels peaked at 4 weeks. SARS-CoV-2 specific IgM levels were higher in the deceased group, and correlated positively with the IgG levels and increased leucocyte count in this group, a indication of severe inflammation. IgM levels correlated negatively with clinical outcome and with albumin levels. The authors suggest that IgM levels could be assessed to predict clinical outcome.

      Potential limitations:

      There are limitations that should be taken into account. First, the sample: small size, patients from a single-center and already critically ill when they were admitted. Second, the authors compared serum IgM levels in deceased patients and mild-moderate patients and found that the levels were higher in deceased group, however even if the difference is statistically significant the number of patients in the two groups was very different. Moreover, receiving operating characteritics (ROC) curves were used to evaluate IgM and IgG as potential predictors for clinical outcome. Given the low number of cases, specially in the deceased group, it remains to be confirmed if IgM levels could be predictive of worst outcome in patients with COVID-19 pneumonia. The study did not explore the role of SARS-CoV-2-specific IgM and IgG in COVID-19 pneumonia.

      Overall relevance for the field:

      Some results of this study have been supported by subsequent studies that show that older age and patients who have comorbidities are more likely to develop a more severe clinical course with COVID-19, and severe SARS-CoV-2 may trigger an exaggerated immune response. The study seems to demonstrate that the increase of SARS-CoV-2-specific IgM could indicate poor outcome in patients with COVID-19 pneumonia, however given the very small sample size, the results are not yet conclusive.

      Review by Meriem Belabed as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2022-02-08 21:10:34, user Sara wrote:

      Thank you for your comment, unfortunately, I did not receive your comment once you replied. 1- we are in the era in the big data, more projects are aimed at generation of large cohort that we can depend upon to derive our clinical decision. <br /> The analysis used the data from US, the model will be deployed and can be used after that to predict the survival time of small cohorts. <br /> 2- We investigated the hazards assumption, we agree with you, we should add the results in the manuscript<br /> 3- SEER database identify the surgery as the surgical removal of the tumour.<br /> 4- I agree with you on the grade, it was on the old grading system for glioblastoma which is mentioned on SEER guidelines. Updated version will be posted and will update the analysis removing this one<br /> 5- we agree with you, we will change it in the updated comments<br /> 6- It is not insane! Developing models that consider these cases is a challenge. These models will be deployed for survival prediction of different cases of glioblastoma with different survival times.

      7- we are developing a model that can be used for the routine data "we use", in this case US cancer data. We have a model that performed well so it can be deployed in the future for the clinical use for our routine data. the model is trained on large sample size that we believe it will achieve accurate prediction results for any routine data. The deployment of the model and its use in clinical practice is the goal. I hope you see the full picture.

      Thank you for your comments.

    1. On 2022-06-24 22:03:50, user Charles Warden wrote:

      Hi,

      Thank you very much for posting this preprint. This certainly represents a large amount of work and careful consideration!

      I have some questions / comments:

      1) Is there a way for me to calculate enhanced scores for myself?

      For example, I would like to learn more, but I was not very satisfied with the PRS that I listed for my own genomic sequence in this blog post:

      https://cdwscience.blogspot...

      2) In the blog post link above, there seemed to be a noticeable disadvantage to the PRS without taking the BMI into consideration for Type 2 Diabetes.

      In this paper, age is an important factor in Figure 1 for the PRS.

      If other non-genetic factors are known, do you have a comparison for non-PRS models? <br /> For example, I wonder how performance of age + BMI (+ other established factors) compares to the plot for Type 2 diabetes in Figure 1.

      3a) I see that the percent variance explained is sometimes provided (such as Supplemental Figure 5), but sometimes it is not.

      For example, in Figure 3, the effect per 1 SD of PRS is higher for LDL cholesterol than height. However, how does the ability to predict an individual's height from genetics alone compare to the ability to predict an individual's LDL from genetics alone?

      After a certain age (as an adult), the exact value for my own LDL has varied more than my height. However, I was not sure how that variation by year compared to others and/or the variation over decades.

      In general, I would like to have a better sense of how absolute predictability compares for height versus disease scores. I also understand that there are complications with binary versus continuous assignments, but it is something that I thought might be helpful.

      3b) I see AUC statistics in Supplemental Figure 2, described as for AUROC. However, am I correct that some of the cases are not well balanced with controls?

      If so, should something like AUPRC be provided (possibly as a complementary supplemental figure)? I believe the idea is described in Saito and Rehmsmeier 2015; the application is very different, but you can see the inflated AUROC values in Figure 1A of Xi and Yi 2021. I expect that there are other good ways to illustrate the differences with PRS in cases and controls of varying proportions, but that was one thought.

      In the context of genomic risk, I might expect that high predictability in a small number of individuals may be preferable over a small difference in low predictability in a large number of individuals. There is emphasis on thresholds like top/bottom 3% (in many but not all figures), which I thought might be consistent with that opinion.

      So, I think something like Figure 1 was helpful. In order to try and capture how false positives change when sensitivity increases, I am not sure if something similar for positive predictive value might help? I would consider that very important if the PRS might be used for screening purposes.

      4) In the Supplemental Methods, I believe that you have a minor typo:

      Current: 100,000 Genomes Project (100KGP). The 100,00 Genomes Project, run by Genomics England,<br /> Corrected: 100,000 Genomes Project (100KGP). The 100,000 Genomes Project, run by Genomics England,

      Thank you very much!

      Sincerely,<br /> Charles

    1. On 2021-06-13 21:16:52, user thomas wrote:

      I am not in the health field (that may be obvious from the questions I have) but I am very interested in this study because my parents (in their 70's) both had and recoverd from covid. They have not received a vax yet.

      1. Why wouldn't having the infection give immunity? Is there something about this specific virus, or this type of virus in general, that it wouldn't be expected to give immunity?

      2. If infection doesn't give immunity, how will the vaccines work? I realize some vaccines are mRNA or viral vector, but at least the two Chinese ones, the Indian one, and a new one the French are working on are all based on using a dead/weakened virus. Shouldn't recovering from an actual infection work just as good as the simulated infection of a vaccine?

      3. Is 1,359 subjects really considered small? How big where the sample sizes for the initial vaccine studies? What would be an acceptable size? My background is more in the social sciences, and we often see samples in the hundreds.

      4. Is it really correct to assume that people who had COVID would be more careful afterwards? I know with my parents, they were almost consumed with fear about catching the disease, but once they did and recovered, much of that went away. I wasn't around to see their behavior, but just based on conversations, I find it hard to believe they were more careful.

      When my parents saw the doctor after recovering, he told them they could not get the vaccine for at least 3 months and that they didn't need to get it until after 6 months. So this study seems in line with what the medical establishment was already saying (they had COVID back in March).

    2. On 2021-06-11 13:41:25, user Christy Blanchford wrote:

      We don't develop long lasting immunity to the other 4 common covid viruses so why would we have long term immunity to covid 19? This was only 42 days out, we get reinfected with the covid common cold after 1-2 years. Manus, Brazil showed us that despite 80% covid infection rate that should have conferred herd immunity , 6 months later they were digging mass graves again. This paper is doing a disservice....

    1. On 2020-04-30 19:12:43, user Sinai Immunol Review Project wrote:

      Main findings<br /> This report describes the use of systemic tissue plasminogen activator (tPA) to treat venous thromboembolism (VTE) seen in four critically ill COVID-19 patients with respiratory failure. These patients all exhibited gas exchange abnormalities, including shunt and dead-space ventilation, despite well-preserved lung mechanics. A pulmonary vascular etiology was suspected.

      All four patients had elevated D-dimers and significant dead-space ventilation. All patients were also obese, and 3/4 patients were diabetic.

      Not all patients exhibited an improvement in gas exchange or hemodynamics during the infusion, but some did demonstrate improvements in oxygenation after treatment. Two patients no longer required vasopressors or could be weaned off them, while one patient became hypoxemic and hypotensive and subsequently expired due to a cardiac arrest. Echocardiogram showed large biventricular thrombi.

      Limitations<br /> In addition to the small sample size, all patients presented with chronic conditions that are conducive to an inflammatory state. It is unclear how this would have impacted the tPA therapy, but it is likely not representative of all patients who present with COVID-19-induced pneumonia. Moreover, each patient had received a different course of therapy prior to receiving the tPA infusion. One patient received hydroxychloroquine and ceftriaxone prior to tPA infusion, two patients required external ventilator support, and another patient received concurrent convalescent plasma therapy as part of a clinical trial. Each patient received an infusion of tPA at 2 mg/hour but for variable durations of time. One patient received an initial 50 mg infusion of tPA over two hours. 3/4 patients were also given norepinephrine to manage persistent, hypotensive shock. Of note, each patient was at a different stage of the disease; One patient showed cardiac abnormalities and no clots in transit on an echocardiogram, prior to tPA infusion.

      Significance<br /> The study describes emphasizes the importance of coagulopathies in COVID-19 and describes clinical outcomes for four severe, COVID-19 patients, who received tPA infusions to manage poor gas exchange. While the sample size is very limited and mixed benefits were observed, thrombolysis seems to warrant further investigation as a therapeutic for COVID-19-associated pneumonia that is characterized by D-dimer elevation and dead-space ventilation. All four patients had normal platelet levels, which may suggest that extrinsic triggers of the coagulation cascade are involved.

      The authors suspect that endothelial dysfunction and injury contribute to the formation of pulmonary microthrombi, and these impair gas exchange. Pulmonary thrombus formation has also been reported by other groups; post-mortem analyses of 38 COVID-19 patients' lungs showed diffuse alveolar disease and platelet-fibrin thrombi (Carsana et al., 2020). Inflammatory infiltrates were macrophages in the alveolar lumen and lymphocytes in the interstitial space (Carsana et al., 2020). Endothelial damage in COVID-19 patients has also been directly described, noting the presence of viral elements in the endothelium and inflammatory infiltrates within the intima (Varga et al., 2020). One hypothesis may be that the combination of circulating inflammatory monocytes (previously described to be enriched among PBMCs derived from COVID-19 patients) that express tissue factor, damaged endothelium, and complement elements that are also chemotactic for inflammatory cells may contribute to the overall pro-coagulative state described in COVID-19 patients.

      References<br /> Carsana, L., Sonzogni, A., Nasr, A., Rossi, R.S., Pellegrinelli, A., Zerbi, P., Rech, R., Colombo, R., Antinori, S., Corbellino, M., et al. (2020) Pulmonary post-mortem findings in a large series of COVID-19 cases from Northern Itality. medRxiv. 2020.04.19.20054262.

      Varga, Z., Flammer, A.J., Steiger, P., Haberecker, M., Andermatt, R., Zinkernagal, A.S., Mehra, M.R., Schuepbach, R.A., Ruschitzka, F., Moch, H. (2020) Endothelial cell infection and endotheliitis in COVID-19. Lancet. 10.1016/S0140-6736(20)30937-5.

      The study described in this review was conducted by physicians of the Divisions of Pulmonary, Critical Care, and Sleep Medicine, Cardiology, Nephrology, Surgery, and Neurosurgery and Neurology at the Icahn School of Medicine at Mount Sinai.

      Reviewed by Matthew D. Park as part of a project by students, postdocs, and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

    1. On 2021-06-23 21:55:50, user David Wiseman PhD wrote:

      Summary:<br /> Regarding the continued and unnecessary confusion related to the Argoaic and Artuli comments.<br /> 1. These are in reality distractions from the central issue that the original NEJM paper remains uncorrected in NEJM as to shipping times. Although a secondary issue, also uncorrected is the "days" nomenclature that is the reason for confusion in the Argoaic and Artuli comments on this forum. Also uncorrected in the original paper is the exposure risk definition which were informed were also incorrect. Together, these issues controvert the conclusions of the original study.<br /> 2. The incorrect nomenclature for "days" in the NEJM paper as well as in a follow up work (Clin Infect Dis, Nicol et al.) inflates the number of "elapsed time" days. This has not been corrected by the original authors. We on the other hand have corrected this by providing the correct information in our preprint.<br /> 3. Dr. Argoaic seems to have been given a wrong and earlier version (10/26) of the data which, although contains a variable that is supposed to correct the above problem, does not. In fact one cannot come to any conclusion that there is a discrepancy based on this incorrect 10/26 version, unless you have some preconceived notion.<br /> 4. Other post hoc analyses reported in follow up works (including social media) by the original authors looking at time from last exposure, or using a pooled placebo group, although flawed for a several reasons, when examined closely, nonetheless support our conclusions that early PEP prophylaxis with HCQ is associated with a reduction of C19.

      Detail:<br /> Any confusion about "days" would disappear once the original authors correct the NEJM June 2020 paper as well as a follow up letter in Dec 2020 Clin Infect Dis (see upper red graph in Nicol et al. pubmed.ncbi.nlm.nih.gov/332... "pubmed.ncbi.nlm.nih.gov/33274360/)"). These errors inflate the "DAYS" by 1 day because the nomenclature for describing "days" was incorrect. As far as we know those corrections have not been made in the journals where these errors appear and in a way that can be retrieved in pubmed etc..

      As far as we can tell, anyone who has cited the NEJM paper (NIH guidelines, NEJM editorial, many meta-anlayses etc., our protocol in preprint version) also misunderstood the "days" to mean the inflated figure. So the authors need to correct this. As far as we know we are the only ones to do this. After we were informed of this error by the PI (who was unaware of the problem himself) we described this problem very clearly in our preprint, distinguishing between elapsed time and the day on which a study event occurred. For the benefit of those who remain confused, we will endeavor to make it even clearer in a future version. You can read our correspondence log referenced in the preprint to verify that the incorrect "days" nomenclature was unknown to the PI, at least until 10/27 when he informed us about it.

      You are confusing "DAY ON which an event occurred" with "DAYS FROM when an event occurred." For example the original NEJM Table 1 says "1 day, 2 days etc." for "Time from exposure to enrollment". This falsely inflates the number of elapsed time days by 1, and as the authors informed us (documented in our preprint), this really means DAY ON which enrollment occurred, with Day 1 = day of exposure, so you need to subtract 1 from the days to get elapsed time FROM exposure. The same error is repeated in Nicol et al. (note: we discuss other unrelated issues relating to time estimates in our preprint).

      To confuse matters further, the problem is not even corrected in the dataset linked (datestamp 10/26/20) in the Argoaic comment. In column FS there is a variable "exposure_days_to_drugstart." This appears to indicate elapsed time (ie DAYS FROM) when it actually means the "DAY ON" nomenclature. We were only informed of the nomenclature error on 10/27/20 and later provided with a new version of the dataset on 10/30 where an additional variable "Exposure_to_DrugStart" (column GR) was provided that corrects this error by subtracting 1 from all the values.

      Why the Argoaic comment does not link to the correct 10/30 version is unclear, but in this incorrect 10/26 version, the values for the new variable "Exposure_to_DrugStart" (column GR) are IDENTICAL to those in the "exposure_days_to_drugstart" (column FS) variable (they should be smaller by 1). Accordingly, unless Drs. Argoaic and Artuli had a preconceived notion (without checking the data) that some alteration had occurred, it is impossible to draw such a conclusion (albeit one that is incorrect for other reasons) from this incorrect 10/26 dataset. A number of colleagues have downloaded the 10/26 dataset from the link provided in the Agoraic comment, and have verified this problem.

      So in addition to the original data set released in August 2020, as well as the three revisions (9/9, 10/6 and 10/30) we describe in our preprint there is this incorrect 10/26 version. I don't know how many people this affects but it would be appropriate for them to be notified that the version they have may be an incorrect one. An announcement on the dataset signup page covidpep.umn.edu/data would also be in order (nothing there today).

      Regarding the possibly higher placebo rate of C19 on numbered day 4 (18.9%). This is matched by a commensurate change in its respective treatment arm, yielding RR=0.624 similar to that for numbered days 2 (0.578) and 3 (0.624), justifying pooling. We don't know if the 18.9% represents normal variation or has biological meaning.

      Although they used enrollment time data (completely irrelevant to considering whether or not early prophylaxis is beneficial), the original authors (Nicol et al.) in a post hoc analysis, used a pooled placebo cohort to compare daily event rates (red bar graph). This would mitigate possible effects of an outlying value in the placebo cohort. We applied this same pooled placebo method to the data that correctly takes into account shipping times. This method is still limited because it may obscure a poorly understood relationship between time and development of Covid-19. Although at best this would be considered a sensitivity analysis, we did it to answer the Artuli question. This approach yields the same trends as our primary analysis. Using 1-3 days elapsed time of intervention lag (numbered days 2-4) for Early prophylaxis, there is a 33% reduction trend in Covid-19 associated with HCQ (RR 0.67 p=0.12). Taking only 1-2 days elapsed time intervention lag, we obtain a 43% reduction trend (RR 0.57 p=0.09). This analysis appears to reveal a strong regression line (p=0.033) of Covid-19 reduction and intervention lag.

      We also looked at the post hoc analysis provided by the original authors (Nicol et al.) that used “Days from Last Exposure to Study Drug Start,” a variable not previously described in the publication, protocol or dataset, so we have no way of verifying it from the raw data. As in a similar PEP study (Barnabas et al. Ann Int Med) this variable has limited (or no) value, as we are trying to treat as quickly as possible from highest risk exposure, not an event (ie Last Exposure) that occurs at an undefined time later. (even the use of highest risk exposure has some limitation, which the authors pointed out to us and which we discuss in our preprint). Further the Nicol analysis used a modified ITT cohort, rather than the originally reported ITT cohort. with these limitations, pooling data for days 1-3 and comparing with the pooled placebo cohort (yields a trend reduction in C19 associated with HCQ (it is unclear which "days" nomenclature is used) after last exposure from 15.2% to 11.2% (RR 0.74, p=0.179).

      Taken together with these "sensitivity" analyses inspired by the original authors' methodology, suggests that this is not an artifact of subgroup analysis. It could be said that any conclusions made by the sort of analyses conducted by Nicol are equally prone to the "subgroup artifact" problem. (also note that in our paper, the demographics for placebo and treatment arms in the early cohort match well).

      Mention has been made elsewhere of two other PEP studies (Mitja, Barnabas) which concluded no effect of HCQ. It is important to note that the doses used in these studies were much lower than those used in the Boulware et al. NEJM study. Further, according to the PK modelling of the Boulware group (Al-Kofahi et al.) these doses would not have been expected to be efficacious (the Barnabas study used no substantial loading dose). So citing the Mitja and Barnabas studies to support claims of HCQ inefficacy in the Boulware et al paper is unjustified. On the contrary, taken together three studies suggest a dose-response effect. We discuss this in detail in our preprint.

      Lastly it is important to note the since the original NEJM study was terminated early, the entire original analysis can be thought of as a subgroup analysis, with all of the attendant problems referenced by the original authors (and us). There is certainly a great deal of under powering and propensity to Type 2 errors, among the issues inherent in a pragmatic study design. The study was not powered as an equivalence study and so no definitive statement can be made that the HCQ is not efficacious. Along with the still uncorrected (in the original journal) issues of shipping times, "days" nomenclature and exposure risk definitions, there are are certainly many efficacy signals that oppugn the original study conclusions,and controvert the statement made in a UMN press release (covidpep.umn.edu/updates) "covidpep.umn.edu/updates)") that the study provided a "conclusive" answer as to the efficacy of HCQ.

      _________________<br /> Please note that despite our offer to Dr. Argoaic to contact us directly to walk though the data to try to identify any issues, we have not been contacted.That offer is still extended to anyone who remains confused. We have also attempted to locate both Drs. Argoaic and Artuli to try to clear up their confusion, but these names do not exist in the mainstream literature (i.e pubmed, medrxiv), nor do they appear to have any kind of internet footprint.

      With regard to Table 1 of our preprint, the reason why there are no patients for “Day 1” is that there were no patients who received drug the same day as their high-risk exposure. This is consistent with the PIs comment on 8/25/20 (p10 of email log) (at a time when he thought that there was a “Day zero”) “Exposure time was a calculated variable based date of screening survey vs. data of high risk exposure. Same day would be zero. (Based on test turnaround time, I don’t think anyone was zero days).”

      We notice an obvious typo in the heading for the second column of our Table 1, which says “To”. But it should say “nPos”, to match the 5th column (and other tables). It is patently absurd that there should be a category of “1 to 0” days or “7 to 5” days etc. “From” makes no sense either and these typos have absolutely no effect on the analysis, interpretation or conclusions. This will be corrected in a later version.

    1. On 2020-09-08 12:00:16, user Wendy Olsen wrote:

      I noted that the assumptions going into this model are a consistent proportion of Overseas and Home students, and a similar size student body, as last year. In addition the cases arriving at UK campuses would be over half from UK Home Students. So even if the assumption of consistent proportion from Overseas turns out untrue, there is still the problem that having more UK Home students will bring more cases into the campuses. I also noted the summary, written by the authors:

      Their core estimate is that "81% of the 163 UK Higher Educational Institutes (HEIs) have more than a 50% chance of having at least one COVID-19 case arriving on campus when considering all staff and students. Across all HEIs it is estimated that there will be a total of approximately 700 COVID-19 cases (95% CI: 640 - 750) arriving on campus of which 380 are associated from UK students, 230 from international and 90 from staff. This assumes all students will return to campus and that student numbers and where they come from are similar to previous years. According to the current UK government guidance approximately 237,370 students arriving on campus will be required to quarantine because they come from countries outwith designated travel corridors. Assuming quarantining is 100% efficient this will potentially reduce the overall number of cases by approximately 20% to 540 (95% CI: 500 - 590). Universities must plan for COVID-19 cases ... and ... reduce the spread of disease. It is likely that the first two weeks will be crucial to stop spread of introduced cases. Following that, the risk of introduction of new cases onto campus will be from interactions between students, staff and the local community as well as students travelling off campus for personal, educational or recreational reasons.

      "COVID-19 has resulted in the on-campus closure of HEIs across the UK in March 2020 (1). Since that point universities have been working predominantly as virtual establishments with most staff working from home. Autumn sees the start of the new academic term with the potential return of more than 1.5 million UK and almost half a million international students (2).

      "The COVID-19 pandemic continues ... approximately 1000 new cases reported each day in the UK, 25,000 across Europe and 250,000 worldwide ((3) accessed 28/03/20). There have been a number of outbreaks of COVID-19 reported in universities in the USA (The University of North Carolina, Notre Dame in Indiana, Colorado College, Oklahoma State and University of Alabama (4)) where the national infection rate is approximately 10 times higher than the UK (3). advice ...(5, 6). However, it is currently unknown to what extent COVID-19 will be brought to campus by staff and students whether from the UK or abroad."

    1. On 2020-10-25 19:08:24, user Daniel Haake wrote:

      Dear study team,

      Thank you for your study, which shows that the risk of COVID-19 death increases significantly with age. To improve the quality of the study I have some comments regarding the statistical analysis of the study. In the following I would like to go into it.


      The time of the determination of the death figures

      You write that antibodies are formed in 95% of people after 17-19 days. In contrast, 95% of deaths are reported after 41 days. That is a difference of 22-24 days. Nevertheless, you take the number of deaths 28 days after the midpoint of the study. Why do you take a later point in time than you yourselve have determined? Even with this approach, you are 4 - 6 days too late and overestimate the number of deaths. Why even this would be too late, I will explain in more detail below.

      The 41 days were given for the USA. But what is the situation in other countries? In Germany, for example, there is a legal requirement that the death must be reported after 3 working days at the latest. Of course there can also be unrecognized deaths in Germany, where it takes longer to report. But this should be the minority. If we transfer however this fact of the USA to other countries, in which the risk of the long reporting time does not exist in such a way, you take up too many deaths into the counter of the quotient with. This leads to a too high IFR.

      Counting the deaths 28 days after the study midpoint is also problematic because in the meantime, further deaths may appear in the statistics that were not infected until after the infected persons identified in the study became infected. This is because not all deaths take as long to report. These are then deaths that are not related to the study. You yourself write that the average value of the report of a dead person lasts 7 days with an IQR of 2 - 19 days. These figures speak in the statistical sense for a right-skewed distribution in the reporting of death figures. This in turn means that the majority of the deceased have a rather shorter reporting time. The procedure leads to a too high number of deaths. This is a problem especially with still existing infection waves, even with already declining infection waves.

      You write: “The mean time interval from symptom onset to death is 15 days for ages 18–64 and 12 days for ages 65+, with interquartile ranges of 9–24 days and 7–19 days.”<br /> If we assume the 3 days reporting time for Germany, we receive 18 days for the age 18-64 and 15 days for 65+. In contrast, 95% of the antibodies are formed after 17-19 days, which is about the same or later than the time when the dead appear in the statistics. For other countries this may be different and would therefore need to be investigated. In any case, a blanket assumption from the USA is not possible for studies outside the USA.

      Since the mean time interval from onset of symptoms to death is 15 days for the age 18-64 with the interquartile range of 9-24 days, but the midpoint of the range would be 16.5 days, this suggests a right-skewed distribution in the values. The same applies to the mean time interval from the onset of symptoms of 12 days with interquartile range of 7-19 days for the age 65+, where the midpoint of this range is 13 days. This also speaks for a right-skewed distribution of the values. This would mean that the majority of the values would be below the mean value in each case, making shorter times more likely. This also shifts the time too far back. Therefore it would be better to assume the median value, because it is less prone to outliers.

      Your example infection wave from figure 1 also shows the problem with this procedure. As you say, antibodies are formed in 95% of people after 17 - 19 days. Now you have an example study with the median 14 days after the start of infection. At that time, only a few of the infected persons have formed antibodies at all, since just 14 days before the infection wave starts with low numbers and then increases. Only 4 days before is the peak of the infection wave. This means that the time period, which is very strongly represented, cannot have developed any antibodies at all. This leads to the fact that only very few infected persons are recognized as infected. In your example, 95% of the deceased are now infected, but only very few of the infected. This leads to a clear overinterpretation of the IFR.

      Due to the problems mentioned, the number of deaths should therefore be taken at the median time of the study. Of course, it would be best if the studies took place immediately after the end of a wave of infection, where the death rates are stable and the expression of antibodies is complete.


      Antibody Studies

      You write: "A potential concern about measuring IFR based on seroprevalence is that antibody titers may diminish over time, leading to underestimation of true prevalence and corresponding overestimation of IFR, especially for locations where the seroprevalence study was conducted several months after the outbreak had been contained.“

      You have made many assumptions about the death figures and adjusted the death figures (upwards) accordingly. Here you find that the antibodies disappear over time and that this can lead to an underestimation of the number of infected persons. However, you do not adjust the number of infected persons upwards, unlike your approach to adjusting the death figures. For example, a study by the RKI found that 39.9% of those who tested positive for PCR before did not develop antibodies (https://www.rki.de/DE/Conte... "https://www.rki.de/DE/Content/Gesundheitsmonitoring/Studien/cml-studie/Factsheet_Bad_Feilnbach.html)"). From this, we could conclude that the antibody study only detected around 60% of those previously infected and that the number of infected persons would have to be adjusted accordingly. But you have not done that. I can understand that you did not do that. I wouldn't have done it either, because we don't know how this is transferable to other studies. But in adapting the dead, you have transferred such assumptions to other studies. This should therefore also be avoided. There, too, we do not know how transferable it is. If you only make an adjustment in the dead, but not justifiably in the infected, this leads to an overestimated IFR.


      PCR tests from countries with tracing programs

      You write in your appendix D: "By contrast, a seroprevalence study of Iceland indicates that its tracing program was effective in identifying a high proportion of SARS-CoV-2 infections“.

      In my opinion this is a wrong conclusion. In my opinion, it is not the success of the tracing program, but the number of tests and thus fewer unreported cases. To date, Iceland has performed almost as many tests as there are inhabitants in Iceland. Therefore they could keep the number of unreported cases lower. Other countries did not test as much. Therefore the results are not easily transferable to other countries. The PCR tests only show the present, but not the past and not the untested.<br /> You write it yourself: „(…) hence we make corresponding adjustments for other countries with comprehensive tracing programs, and we identify these estimates as subject to an elevated risk of bias.“<br /> Nevertheless, you leave these studies in meta-analysis, although for the reasons mentioned above this leads to severe problems. The figures for countries with tracing programs should therefore not have been included. The estimated number of unreported cases is not known and cannot be taken over by Iceland.


      Study selection

      You sort out some seroprevelence studies. These include Australia [63], Blaine County, Idaho, USA [67], Caldari Ortona, Italy [72], Chelsea, Massachusetts, USA [73], Czech Republic [75], Gangelt, Germany [79], Ischgl, Austria [81], Riverside County, California, USA [98] , Slovenia [101] and Santa Clara, California, USA [116]. For the most part, these studies are sorted out because there is no age specification for seroprevelence. Since this is the study's investigation, this is of course understandable. However, these studies in particular have shown calculated IFR values between 0.1% and 0.5%. At the same time, you leave the numbers of PCR tests from countries with tracing programs in the meta-analysis. As already mentioned, this is not correct due to the unknown dark figure and the transfer from Iceland is also not possible, as described before. This leads to the fact that studies with low values are sorted out, but at the same time uncertain numbers with high values are left in the study. This shifts the calculated IFR value upwards in purely mathematical terms.

      It is precisely the outliers upwards that cause problems in the calculation. Since the numbers are rather small (in a mathematical sense), there can be no deviation as strong downwards as upwards. This means that there may be studies that deviate perhaps 0.2 percentage points downwards, but other studies that deviate upwards by 1.2 percentage points. This is a problem for the regression, because the regression then leads to too high values. Therefore, outlier detection should be performed upstream and the outliers should be excluded. You can also make it easier by taking the median value, since it is less susceptible to outliers. But then you would have only one value.

      You write: “The validity of that assumption is evident in Figure 3: Nearly all of the observations fall within the 95% prediction interval of the metaregression, and the remainder are moderate outliers.”<br /> You can see it in figure 3, but due to the logarithmic scale it is difficult to estimate the ratios. Better suited is Figure 4, which would be desirable for the different age groups to be able to make a better estimation there. Figure 4 shows that many studies are outside the confidence interval, often to a considerable extent and to a greater extent also towards the high IFR values. Looking at the values and the confidence interval, these studies must have significant z-scores, which would show that these are clearly outliers that should not be considered. This leads to the fact that the regression will be brought further in the direction of high values, which results in too high IFR values.


      Adjustment of death rates for Europe due to excess mortality

      In Appendix Q you write: "In the absence of accurate COVID-19 death counts, excess mortality can be computed by comparing the number of deaths for a given time period in 2020 to the average number of deaths over the comparable time period in prior calendar years, e.g., 2015 to 2019. This approach has been used to conduct systematic analysis of excess mortality in European countries.[159] For example, the Belgian study used in our metaregression computed age-specific IFRs using seroprevalence findings in conjunction with data on excess mortality in Belgium“

      I understand why you want to do this. But there are some dangers involved. The above statement may be true for Belgium, but it cannot be transferred to other countries in a general way. Especially since you cannot say in general terms that every dead person above average is a COVID 19 dead person. Mathematically, this would mean that there have been COVID-19 deaths in some of the last few years, because there have been periods with more deaths than the average. This makes the average straight. Especially since, as I said, you can't simply say that every death above the average is a COVID-19 death. The majority will be it, but not necessarily everyone. Thus, even cancer operations that did not take place or untreated heart attacks due to the circumstances and unnoticed visits to the doctor may have contributed a share. Whether this is the case, we do not know without a study. A blanket assumption that every death above the mean value is a COVID-19 death is not correct. From the statement "For example, the Belgian study used in our metaregression computed age-specific IFRs using seroprevalence findings in conjunction with data on excess mortality in Belgium", we could also conclude that the number of reported COVID-19 deaths is correct and can therefore be used as the numerator of the quotient for calculating the IFR. <br /> If you take this as a blanket assumption, how do you deal with those countries that do not have excess mortality but have several thousand COVID-19 deaths in the official statistics? Would you then correct the number of COVID-19 deaths downwards, perhaps even to 0? Certainly not.


      Variation in the IFR

      You write: "We specifically consider the hypothesis that the observed variation in IFR across locations may primarily reflect the age specificity of COVID-19 infections and fatalities.“

      It is also possible that the variation in the calculated IFRs occurs due to still different dark figures. If, for example, the PCR tests are taken in countries with a tracing app, but an IFR based on Iceland is calculated there, this can lead to incorrect and too high IFR values. Also the adjustments of the death rates themselves or the late time of the death rate determination 4 weeks after the study center can lead to this high variance.


      Conspicuous features regarding the correct determination of the death figures

      In Table 1 you write that on July 15 there were 8 million inhabitants with a projected 1.6 million infections. According to my research there are 8.4 million inhabitants. You calculate the 1.6 million infected on the basis of the 22.7% infected in the study. However, the blood samples were taken between April 19 and 28, so the infections occurred before or until the beginning/middle of April. So you now take the number of infected persons from the beginning/mid-April or from April 24 (study midpoint) and insert them for July 15, i.e. just under 3 months later! In the meantime, however, not only people have died, but have also become infected and formed antibodies. They thus increase the numerator of the quotient, but leave the denominator unchanged, although the denominator would also be higher. So you shift the IFR upwards here as well.

      The study on Gangelt, which was not taken into account, shows a similar picture. You write that at the end of June there were 12 deaths and therefore the IFR rises to 0.6%. That is 8 weeks (!) after the study center. This does not take into account that in Germany the deaths must be reported after 3 days. If you have proceeded in this way when calculating the other IFRs from other studies, this suggests that the IFR values are too high.


      Calculation of the IFR of Influenza

      You calculate the IFR of influenza based on the CDC figures for the 2018/2019 influenza season and indicate the IFR as 0.05%. Firstly, it should be said that statistically it is never good to look at just one value. The average of a time series should be considered. You calculate the value by looking at the estimated deaths and looking at how many were estimated to be symptomatically infected with influenza. You use a study according to which about 43.4% of cases are asymptomatic or subclinical (95% CI 25.4%-61.8%). You then take the mean value from the confidence interval with the value 43.6% and use this figure to calculate how many people were probably infected with influenza. Statistically it is not correct to take the average value of 43.6%. The value of 43.4% must be taken. Due to the small difference, this does not make much difference, but it shows the statistically imprecise consideration that runs through the study and generally leads to an IFR that is too high or, in the case of influenza, too low.

      Now a statement on the selection of the 2018/2019 flu season, the CDC writes: "These estimates are subject to several limitations. (...) Second, national rates of influenza-associated hospitalizations and in-hospital death were adjusted for the frequency of influenza testing and the sensitivity of influenza diagnostic assays, using a multiplier approach3. However, data on testing practices during the 2018-2019 season were not available at the time of estimation. We adjusted rates using the most conservative multiplier from any season between 2010-2011 and 2016-2017, Burden estimates from the 2018-2019 season will be updated at a later date when data on contemporary testing practices become available. (...) Fourth, our estimate of influenza-associated deaths relies on information about location of death from death certificates. However, death certificate data during the 2018-2019 season were not available at the time of estimation. We have used death certification data from all influenza seasons between 2010-2011 and 2016-2017 where these data were available from the National Center for Health Statistics. (…)

      The CDC writes the same for the 2017/2018 season, so the values, which were always only estimated anyway, were estimated even more due to missing data. Therefore we should have considered the figures for the seasons 2010/2011 to 2017/2017. If we calculate the IFR of influenza in this way and also use the confidence interval to calculate the number of people potentially infected per season, we get an IFR of influenza of 0.077%, ranging from 0.036% to 0.164%. Every single year prior to the 2018/2019 season was above the 0.05% and the average of 0.077% is also 54% above your reported value. This means that influenza is still not as lethal as COVID-19 has been so far, but the factor is not as high as suggested by your study.

      It should also be noted that it is not possible to compare an IFR calculation that is equally distributed over age with an IFR of influenza that is not equally distributed over age. You do not do it directly, but by naming these numerical values, this has been taken up by the media. The IFR just indicates the mortality per actually infected person. Therefore the IFR of the actually infected persons of COVID-19 must be compared with the IFR of influenza. You can of course calculate a hypothetical IFR assuming that every age is equally likely to be infected. In this case, however, the calculation must be performed not only for COVID-19, but also for influenza.


      I hope I can help you to improve the study in terms of statistical issues. I remain with kind regards.

    1. On 2020-05-14 14:35:10, user Hans Tinger wrote:

      3% after 1 Werk, 6%after 2 Weeks, 9% after 3 Weeks... I am curious for week 4-8. Will you publish preliminary results soon again?

    1. On 2020-03-25 21:03:54, user Sinai Immunol Review Project wrote:

      Summary of Findings: <br /> - Clinical data from 116 hospitalized CoVID-19 patients analyzed over 4 weeks for correlation with renal injury. Comorbidities included chronic renal failure (CRF) in 5 patients (4.3%). <br /> - Approx 10.8% of patients with no prior kidney disease showed elevations in blood urea or creatinine, and 7.2% of patients with no prior kidney disease showed albuminuria. <br /> - Patients with pre-existing CRF underwent continuous renal replacement therapy (CRRT) alongside CoVID-19 treatment. Renal functions remained stable in these patients. <br /> - All 5 patients with CRF survived CoVID-19 therapy without progression to ARDS or worsening of CRF.

      Limitations: <br /> - Renal injury biomarkers in patients with incipient kidney abnormalities not tabulated separately, making overall data hard to interpret. It will be critical to separately examine kidney function (BUN, urine creatinine and eGFR) in patients that developed any kidney abnormalities (7.2~10.8% of cohort). <br /> - No information on type of CoVID-19 therapy used across cohort; will be useful to correlate how treatment modality influences kidney function (and other parameters). <br /> - Invokes previous clinical-correlation studies that indicate low instances of kidney damage [1,2], but those studies did not track longitudinal urine samples for acute renal injury markers and viral shedding. <br /> - CRRT in patients with CRF is standard therapy irrespective of CoVID-19 status; it will be important to compare clinical parameters of these patients (n=5) with virus-naïve CRF patients (none in this study) to make any meaningful conclusions.

      Importance/Relevance: <br /> - This study argues that renal impairment is uncommon in CoVID-19 and not associated with high mortaility, in stark contrast to a concurrent study (https://doi.org/10.1101/202... ). If supported by further studies, it suggests kidney impairment is secondary to cytokine storm/inflammation-induced organ failure, and not due to direct viral replication. <br /> - Will be important to comprehensively characterize larger datasets of CoVID-19 patients across hospitals (meta-analyses) to conclude if kidney function is actively disrupted due to viral infection, and if renal disease is a major risk factor for worse CoVID-19 outcomes.

      References: <br /> 1. Wang D, Hu B, Hu C, et al. JAMA 2020; published online Feb 7. <br /> doi: 10.1001/jama.2020.1585

      1. Guan WJ, Ni ZY, Hu Y, et al. MedRvix 2020; <br /> doi: https://doi.org/10.1101/202....

      Review by Samarth Hegde as part of a project by students, postdocs and faculty at the <br /> Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2025-07-24 23:50:17, user Rong Liu wrote:

      Update on the association between influenza vaccination and cardiovascular outcomes

      Dear readers,<br /> As the authors of a living systematic review on the association between influenza vaccination, cardiovascular mortality and hospitalization, (1) we want to update readers on the findings from our most recent search. The review protocol specifies updates every six months for a minimum of three years, commencing April 2022. The baseline search was conducted on 31 May 2022, with subsequent updates on 25 January 2023 and 1 September 2023. Results from these initial searches were published in Vaccine in January 2024. (1)

      The latest search, completed on 31 March 2025, identified two studies that meet the eligibility criteria for review inclusion. Both are multi-center trials conducted within a single country, with a follow-up duration of at least 12 months. A third study was excluded due to its shorter follow-up period of only six months. (2,3) The eligible studies include a China-based trial (PANDA II) enrolling patients hospitalized for heart failure, (4) and an India-based trial (FLUENTI-MI) enrolling patients with recent myocardial infarction. (5,6) In both studies, the intervention is influenza vaccination. The comparator in FLUENTI-MI is saline placebo, and standard care in PANDA II. The primary outcome in both trials is a composite of all-cause mortality and all-cause hospitalization during the locally defined influenza season.

      As of 6 June 2025, neither study has publicly available results, and therefore we are unable to update the meta-analysis at this time. Table 1 summarizes the study characteristics and expected timelines. PANDA II completed recruitment in February 2024 and is expected to report results within the next year. (4) FLUENTI-MI is projected to complete recruitment in October 2028. Given the current pace of research in this area, we believe that biannual updates are no longer necessary, and we will transition to annual updates for the next five years, starting from the date of this latest search.

      Reference <br /> 1. Liu R, Fan Y, Patel A, et al. The association between influenza vaccination, cardiovascular mortality and hospitalization: A living systematic review and prospective meta-analysis. Vaccine. 2024/02/15/ 2024;42(5):1034-1041. doi: https://doi.org/10.1016/j.vaccine.2024.01.040 <br /> 2. Liu R, Patel A, Du X, et al. Association between influenza vaccination, all-cause mortality and cardiovascular mortality: a protocol for a living systematic review and prospective meta-analysis. BMJ Open. 2022;12(3):e054171. doi:10.1136/bmjopen-2021-054171<br /> 3. Tkaczyszyn M. Vaccination Against Influenza Pre-discharge in Heart Failure. https://clinicaltrials.gov/study/NCT06725927 <br /> 4. Zhang Y, Liu R, Zhao Y, et al. Influenza vaccination in patients with acute heart failure (PANDA II): study protocol for a hospital-based, parallel-group, cluster randomized controlled trial in China. Trials. 2024/11/25 2024;25(1):792. doi:10.1186/s13063-024-08452-8<br /> 5. Roy A. Influenza Vaccine to reduce cardiovascular events in patients with recent myocardial infarction: a multicentric randomized, double-blind palcebo-controlled trial. https://trialsearch.who.int/Trial2.aspx?TrialID=CTRI/2024/05/067056 <br /> 6. Roy A, Yadav S. Influenza vaccine in cardiovascular disease: Current evidence and practice in India. Indian Heart Journal. 2024/11/01/ 2024;76(6):365-369. doi: https://doi.org/10.1016/j.ihj.2024.11.247 <br /> https://uploads.disquscdn.c...

    1. On 2023-05-28 06:54:51, user Stuart Gilmour wrote:

      Dear authors, I really want to believe this study (I am vulnerable to Ramsay Hunt Syndrome and have got this vaccine, and I would love to believe it also reduces my risk of dementia!) but I think you have massively under-estimated the effectiveness of the vaccine, which is a real missed opportunity. I want to explain why and I hope you'll take my comments into account. I think there are three sources of error in your study which I list in order of severity: 1) failure to take into account period at risk, 2) the change in slope term and 3) confounding due to education/wealth in sub-analyses.

      [Obviously, the comments that follow assume I have correctly understood your methods, so please forgive me if I have missed something your explanation]

      1) is the reason I think the study under-estimates the effect. I wondered why it is that you found a vaccine efficacy (after adjusting for take-up of the vaccine) against shingles of 41%, while the 2005 NEJM study you reference finds it to be 55%, and I think this is because you have not properly accounted for follow-up time. Judging by how you report probabilities, you seem to have calculated the proportion of people over seven years who got shingles (Fig 2) or dementia (FIg 3). This is also clear from your equation (1), which is a linear probability model. But since shingles incidence, dementia incidence and death risk increase by age and your primary study cohort is 80 years old, follow-up time is a very important variable. Judging from your figure 2, the youngest people were 78 and the oldest 82 in this study. It's very likely therefore that the youngest people had to be followed for considerably longer before a diagnosis of shingles/dementia, and were also less likely to die of other causes. A person who dies of other causes before getting shingles/dementia should not be considered in the calculation, since we didn't find out whether they got it - they should be censored. Then, if we calculate incidence densities, we will find the youngest people (with the lowest proportion of cases) have a considerably longer follow-up time to diagnosis, and were less likely to drop out of follow-up early due to death. If you properly account for this in the model, I think you'll find that the rate in younger people is much lower than in older people and the discontinuity is greater.

      I don't have UK data to hand, but I do have a life table by single year of age for the USA, which implies that there would probably be about 40% more follow-up time in the 78 year olds than the 82 year olds over the entire 7 years of the study, simply because of drop out due to death from all causes - a 78 year old american has a 4% chance of dying in one year, while an 82 year old has a 5.7% chance. Those differences add up over 7 years of follow-up!

      This study is a classic survival study, and your decision not to use the follow-up time means that you have over-estimated the incidence density in young people and under-estimated it in older people. This also explains why your sex-stratified analysis finds no effect in men. How could the vaccine not work in men but work in women? Because at this age (~80 years old) men are dying much faster than women, with death rates increasing more rapidly over the study period, which attenuates the effectiveness more in men than in women.

      If you use an incidence density (Poisson regression) or survival approach, it's easy to reproduce the approach described in equation (1) but you'll be properly accounting for follow-up time, avoiding the known problems associated with a linear probability model, and properly able to compare your results with those of the previous shingles vaccine studies.

      [I'm sorry all my comments here hinge on my interpretation from your methods that you have assumed a 7 year follow-up for everyone, and simply calculated the proportion of events as the number who got shingles/dementia divided by the number at risk at the start of the 7 years. If I'm wrong about this, please ignore everything I wrote!]

      For problem 2), the change of slope term, it seems obvious to me that the slope after week 0 in figure 3A is poorly fitted. If there was no change of slope term in this model, the change in level would be smaller and your study would show no effect. Was the beta3 term in your model for figure 3 statistically significant? I think it wasn't - there is no visible change of slope in the data shown there. Given how borderline your estimate of the change in level (Beta1) is, I think the conclusion of this analysis depends heavily on whether you choose to include the non-significant change of slope. Of course, this isn't very important because a) we should always report studies of this kind separately by sex and b) once you properly adjust for follow-up time the effect of the vaccine will be so huge that we'll immediately have a statistically significant effect with or without the change of slope term.

      For problem 3), you estimate the CACE based on the assumption that there is "no other difference in characteristics that affects the probability of our outcomes occurring", and date of birth eligibility threshold "is a valid instrumental variable to identify the causal effect of receipt of the zoster vaccine on our outcomes". I'm not sure why you would believe this. People who receive any voluntary preventive health care in the UK are much more likely to be wealthy, to be better educated, and to be from certain occupations and backgrounds, and I would suggest it's highly likely that these factors are strongly associated with reduced risk of dementia. The method here is nice, but the assumption is completely unreasonable in the NHS context, and it's likely that these confounding factors would lead to a reduction in the CACE estimate. Again, if you properly account for follow-up time I doubt this will matter because the raw impact of the vaccine eligibility itself will be so much larger than your estimate that you will find a much bigger impact without needing to do any calculation of CACE (but anyway a simple caveat about this, or a calculation separately in each wealth stratum, might solve the issue).

      I can't see any way that the lack of proper calculation of follow-up time would reduce the effectiveness of the intervention you have tested, so I'm going to continue to believe that this vaccine prevents dementia, but I worry that you have massively under-estimated the size of the effect and I guess there is a tiny chance the impact of this mis-calculation could go the other way.

      I guess you could argue it doesn't matter if you've under-estimated the effect but I would say it does. I'm sure you're aware that in the UK the chickenpox vaccine is not part of the routine childhood immunization schedule. If your study finds a huge effect of shingles on dementia risk, this is a strong argument for preventing it at childhood, through inclusion of the vaccine in the routine schedule. But currently your study finds no benefit for men, a 20% overall reduction in relative risk, and about a 40% reduction in relative risk for women. I think if you properly account for follow-up time the effects will be much larger and consistent across men and women. Even a cursory consideration of such large numbers would surely be sufficient to tip even the UK's relatively anti-vaccination institutions into recommending both a) routine chickenpox vaccination of children b) routine shingles vaccination of adults and c) earlier implementation of adult vax. Currently for example in Japan the vaccination for shingles is recommended at age 50 but not covered under insurance, costs about 40,000 yen (350 pounds) and is not widely taken. If it has a huge impact on dementia risk the policy implications are enormous. So please don't undersell your work by using this linear probability model!!!

      Thank you!<br /> Stuart Gilmour<br /> Professor, Biostatistics and Bioinformatics<br /> St. Luke's International University<br /> Tokyo<br /> Japan

    1. On 2024-01-17 12:03:28, user Leonardo Martins wrote:

      This ancestral-reconstruction based phylogeographic approach has been used before by us in SARS-CoV-2 analyses: <br /> 1. for finding the number of transmission events into or outside Lebanon https://www.ncbi.nlm.nih.go... <br /> 2. For estimating migration patterns between regions of England https://www.nature.com/arti...<br /> 3. To count the number of exports and importations into Pakistan https://www.ncbi.nlm.nih.go...

      In our case we used the mugration model as implemented in TreeTime or ASR models implemented in Castor for R (https://cran.r-project.org/... "https://cran.r-project.org/web/packages/castor/index.html)")

    1. On 2020-04-28 14:00:55, user Sinai Immunol Review Project wrote:

      Main Findings<br /> This preprint sought to compare the daily deaths in countries using CQ/HCQ as a treatment from the beginning of the COVID-19 pandemic to those that did not. From a list of 60 countries in descending order by number of confirmed cases, 16 countries were selected for inclusion into either the high CQ/HCQ production or use group, versus not. Countries were included if they met the criteria for having data from the day of the 3rd death in the entire country and the daily deaths for the 10 days immediately following, until both groups were populated with a list of 16 (Figure 1: Table with the CQ/HCQ group list; Figure 2: Table with the “control” group list). For each group of countries, the average daily deaths were determined, and the curves projected to illustrate trajectories. In Figure 3, the author suggests that the deaths in the countries belonging to the control group follow an exponential curve, while the progression of average daily deaths in the countries with greater use of CQ/HCQ follow a polynomial curve.

      The author then applies Auto Regressive Integrated Moving Average (ARIMA), a modeling tool used for time-series forecasting (i.e., predicting the future trajectory of data over time using the data from previous time points as predictors in a linear regression). The Auto Regressive component refers to each difference between two previous time points that make the model “stationary” (current – previous); the Moving Average is the number of forecast errors from calculating these differences that should go in the model. The author uses ARIMA to predict the next 10 days of mean deaths for the CQ/HCQ list (Figure 6) and the control countries (Figure 8). In figures 9 and 10, autocorrelations of residuals are performed to determine internal validity of the model, here defined as no significant autocorrelations.<br /> In conjunction, the author argues that these findings support major differences in death rates between countries that use/mass produce CQ/HCQ versus those that do not.

      Limitations<br /> The title of this study refers to itself as an ecological study, an observational study in which the data are defined at the population level, rather than individual. Although this study design allows for rapid hypothesis testing in large datasets, a robust ecological study should account for as many known risk-modifying factors or confounders as possible. Subsequently, any results should be reviewed under strict criteria for causality, since there is high probability of the outcomes falling under the definition of ecological fallacy, which occurs when inferences about individuals are determined from inferences about a group to which they belong.

      This study conflated the use and mass production of CQ/HCQ at the start of the COVID-19 pandemic in each respective country, with that country’s direct pandemic response. It is never explained whether use or production is the key output for any given country, which are vastly different metrics. The author fails to consider other reasons for having existing infrastructure for the mass production of drugs like hydroxychloroquine, whether the country was a global supplier of the medication (India), or is a region where malaria is endemic (India, Pakistan, Indonesia, Malaysia, South Korea), which may correlate to both chloroquine production and use. Notably, the countries from which studies of HCQ in the treatment of COVID-19 have been predominantly performed (China, France, USA), are all in the control list of countries. Additionally, the data for cases and deaths were collected from reports accessed from https://www.worldometers.in... data were not selected from the top countries using a methodological approach, but rather skipping certain countries to use only the most complete death data for the timeframe of interest, allowing for bias introduced by the reporting of each individual country.

      With regards to the statistical methods applied, namely ARIMA, they are non-standard practices for interpreting the results of an ecological study. The first problem with this, in my opinion, is that the message will be difficult to interpret and criticize for many scientists, as ARIMA will be unfamiliar to most in the biological sciences. Further, the models applied (Table 4) do not take into account any confounders, which is a requirement for robust analysis of an ecological study. There are only 3 variables in this type of model: p, the autoregressive coefficient, q, the moving average coefficient, and d, the difference between points in the time-series. Any flaws or bias inherent to the input data are then upheld and propagated by the model, which does not allow for any other variable that would contribute to the risk of death.

      Significance<br /> The faults of the stratification of countries into the groups proposed in this study, together with the unorthodox application of ARIMA modeling, make it challenging to accept the conclusion that the author draws in this study. The apparent decrease in death rate in countries with a high production/use/either/or of CQ/HCQ could be due to any number of other factors for which this study did not account. The top 5 countries in both confirmed cases and reported deaths are all in the control list, which has no relationship to the amount of CQ/HCQ production within those countries yet skews the data to make the dynamics of death rate appear more dramatic.

      Reviewed by Rachel Levantovsky as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine at Mount Sinai.

    1. On 2025-11-30 07:05:45, user Ali Rahimi wrote:

      Dear authors,

      I have read your interesting article. I think the following revisions would strengthen the article:

      Abstract<br /> Clarify that the 72.4% and 38.2% figures come from patients reporting barriers, not from the full sample, so the denominator is clear.<br /> Keep wording aligned with the design: change “barriers limit uptake of cataract surgery in Bangladesh” to “barriers were commonly reported among patients undergoing cataract surgery in Bangladesh.”<br /> Make the main statistical result consistent with the Results: state that education, income and prior surgery were associated with the number of barriers (Adj R² = 0.138).

      Introduction<br /> A few sentences are long and repetitive around “accessibility” and “health inequities”. Tighten these into one concise paragraph without changing meaning.<br /> Where you describe evidence as “scarce”, add 1 sentence that positions your study among Bangladeshi work (rural children, Rohingya, etc) and makes clear that prior studies were population specific.

      Methods<br /> In “Participants and data collection” clarify in one sentence that 595 patients consented, but analyses of barriers use 583 due to item non-response.<br /> Briefly describe how the “fear score (0–5)” and “barrier count” were constructed (number of items, response scale, direction).<br /> You model a count outcome with linear regression. Add one line acknowledging that barrier counts were approximately normal and that this approach was chosen for simplicity; alternatively mention that Poisson or negative binomial regression would give similar interpretation.

      Results<br /> Ensure mean age is reported consistently (Abstract uses 62 years, Table 1 has 61.3). Choose one rounding rule and use it everywhere.<br /> Replace approximate p notation “p ? 0.003” and “p ? 0.221” with standard “p = 0.003” and “p = 0.221”.<br /> Table 3: the p value shown as “1” for “Afraid of surgery” under gender should be reported as “1.000” or the exact test result.<br /> Table 4: the p values for “Number of reported barriers” currently read “> 0.001”; this should be “< 0.001”.<br /> In the text for section 3.3, “The geographical barrier of transportation is predominant in our study” is misleading because cost is clearly highest. Rephrase to “an important barrier” rather than “predominant”.

      Discussion<br /> Soften causal phrasing. Examples:<br /> “Patients who delay seeing an eye doctor are more likely to postpone surgery and show up with advanced cataracts” could be “Patients reporting delays in seeing an eye doctor often present with more advanced cataracts.”<br /> Any sentences that link barriers directly to “prolonging the waiting period” or “contributing to disability” should be framed as association, not cause.<br /> When you describe gender norms and decision making, keep language neutral and clearly signpost what comes from your data versus from cited literature.<br /> Consider one short sentence acknowledging that your barrier profile reflects people who ultimately accessed surgery and may under-represent those who never reach services.

      Limitations<br /> Add explicit mention that the cross-sectional design and the hospital-based sample (only patients scheduled for surgery) limit causal inference and generalisability to all people with cataract in Bangladesh.<br /> You already mention possible social desirability bias; make that sentence more direct and link it to self-reported barriers.

      Conclusion<br /> Tone down strength of generalisation: instead of “The study's strength lies in its inclusion of a diverse population, thereby increasing its generalizability” use “The inclusion of patients from hospital clinics and outreach camps provides some diversity, although findings still reflect one service network.”<br /> Rephrase recommendations as suggestions: “could help improve access” or “may help bridge the knowledge gap” rather than “can facilitate” or “will improve”.<br /> Keep the ending sentence tightly tied to your data: emphasis on cost, transport, time, fear, and gendered escort constraints.

      Tables and Figures<br /> Check that the labels in Figure 1 and Figure 2 match exactly the barrier wording used in the questionnaire and in the text (for example “hospital too far / no transportation”).<br /> Consider adding “multiple responses allowed” to the figure legends for barriers.

    1. On 2021-05-25 20:24:16, user Green Ranger wrote:

      The results and conclusions of this study are wrong. The authors mistook the ivermectin and control arms of one of the RCTs that they included. Look at figure 2. The results from Niaee 2020 are dramatically misreported. The actual results for that study are as follows:

      Control groups: 11 deaths out of 60 patients.<br /> Ivermectin groups: 4 deaths out of 90 patients.

      When this is corrected, the results of this meta-analysis confirm what other meta-analyses have found. Ivermectin use is associated with approximately 66% reduction in Covid fatalities. And this result is statistically significant.

      A source for this.

    1. On 2021-04-24 23:50:51, user Chucky2017 wrote:

      I'm not sure where to post for an expert opinion, but I have been searching and still can't find an answer. Maybe someone here could be kind enough to direct me.

      If you delay the Phizer second dose for 3 months (or even 2 months) we see a fall off in antibodies. When you get your second shot what happens? Does it become less effective than if you had it in the 21 days? So basically is there a study that has someone who had it in 21 days, take their blood and compare them to a person that got it 3 months later and see what level of antibodies they have compared to the person with 21 days.

      Canada is delaying the phizer shot by 4 months, would a person be better off not getting the second shot and redue the schedule again.

    1. On 2020-08-02 00:11:16, user Michael Verstraeten wrote:

      I would like to make also a suggestion. <br /> 1. Calculate the amount of people infected in the whole population on the highest result of your research, by age category. (That's a simplification since there are also other relevant factors then age, like comorbidity factors but ok). <br /> 2. Add to this amount the results from the positive PCR-tests in the hospitals until that moment (Also a problem since there is a % of false negative results, but ok) <br /> 3. Estimate the amount of patients whit a general problem who were refused to get a blood test due to a suspicion of Covid - 19 and were not admitted to the hospital (if possible). And add them to the grand total. <br /> 4. From there on you can make an estimate based on the weighted average evolution of the deaths from de date corresponding to the testdate pn (a few days later then the test dates). It seems to be a relatively good assumption to consider that de evolution of the infections will be relatively equal to the evolution of the deaths / age category. <br /> 5. There is one problem however: we are not sure about the exact amount of deaths due to Covid. 73 % of people deceased in the care homes were not diagnosed and the tests have a big error margin. And diagnoses are maybe wrong due to extended diagnose protocols. Maybe it would be a good idea to calculate an average on the evolution of deaths and hospital admissions. Even if the latter depends on the admission policy.

      Even with the uncertainties and the quite big error margin, maybe it will be possible to come with such an exercise closer to the real number of the infected population.

    1. On 2020-11-15 21:13:34, user Atomsk's Sanakan wrote:

      Some flaws in this study that render it's IFR estimate unreliable:

      1) He uses many studies that over-estimate the number of people that were infected [and thus under-estimate IFR], since these studies were not meant to be representative of the general population Ioannidis applies them to. He doesn't even follow PRISMA guidelines for assessing studies for risk of bias in a study's research design. "Bias" here does not refer to the motivations of the study's authors, but instead that the design of their study would likely cause their results to not be representative of the general population.<br /> 2) He exploited collinearity by sampling the same region multiple times, in a way that skews his results towards a lower IFR. He conveniently tends to avoid sampling an area multiple times when that area has a higher IFR.<br /> 3) He adjusts IFR downwards for reasons not supported by the analysis he cites for that adjustment.<br /> 4) He takes at face-value areas that likely under-estimate COVID-19 deaths, such as Iran, causing him to under-estimate IFR further.<br /> 5) He uses inconsistent reasoning to evade government studies that show higher IFR, even though governments are doing much of the testing needed to determine IFR. That includes Ioannidis ignoring large studies from Italy and Portugal that are more representative of the general population they sampled.<br /> 6) His IFR from a study in Brazil contradicts the study's own IFR, and his explanation for that makes no sense. This conveniently allows him to cut the study's IFR by about a 1/3.<br /> 7) His use of blood donor studies does not make sense, even if one sets aside the fact that blood donor studies would over-estimate population-wide seroprevalence. For example, he uses a Danish blood donor study that leaves out deaths from people 70 and older, to claim an IFR of 0.27% for adults. When those researchers performed a subsequent study in which they included people 70 and older, they got an IFR for adults that's 3 times larger than Ioannidis claims [0.81% vs. 0.27%].

      And so on.

      The sources below provide further context on this:

      https://rapidreviewscovid19...<br /> https://rapidreviewscovid19...<br /> https://rapidreviewscovid19...

      https://twitter.com/GidMK/s...<br /> https://twitter.com/GidMK/s... [ https://threadreaderapp.com... ]<br /> https://www.medscape.com/vi... { http://archive.is/O3vGs , https://threadreaderapp.com... }<br /> https://hildabastian.net/in...<br /> https://twitter.com/AVG_Jos...<br /> https://twitter.com/Atomsks...<br /> https://twitter.com/Atomsks...<br /> https://twitter.com/Atomsks...

      "Estimation without representation: Early SARS-CoV-2 seroprevalence studies and the path forward"<br /> Not-yet-peer-reviewed: "Assessing the age specificity of infection fatality rates for COVID-19: Meta-analysis & public policy implications" (comments on "selection criteria")

    1. On 2021-07-14 20:26:08, user bruno ursino wrote:

      I came by this article now, so sorry for posting a comment at this time but it seems to me that no one pointed this out: the formula for the evaluation of Rt is completely wrong, indeed this can be shown just by applying this same technique to a simple SIR model.

      I think it's impossible to send here my plots, but I ask you to execute the following code using octave or matlab:<br /> `tf = 300;<br /> dt = 0.01;<br /> t = 0:dt:tf;

      mu = 1.7/100;

      T = 17;<br /> alfa = 1/T;

      R0 = 4;

      bN = R0*alfa;

      S = zeros(1,numel(t));<br /> I = zeros(1,numel(t));<br /> R = zeros(1,numel(t));<br /> d = zeros(1,numel(t));

      S(1) = 0.99999999;<br /> I(1) = 1 - S(1);

      for k = 2:1:numel(t)

      S(k) = S(k-1) + dt(- bNS(k-1)I(k-1));<br /> I(k) = I(k-1) + dt(bNS(k-1)I(k-1) - alfaI(k-1));<br /> R(k) = R(k-1) + dt(alfaI(k-1));<br /> d(k) = mu(R(k) - R(k-1));

      end

      T_steps = T/dt;

      Ri = zeros(1,numel(t));

      for k = (T_steps+1):1:(numel(t) - T_steps)

      aux = dt(sum(d((k):1:(k+T_steps))) - musum(d((k-T_steps):1:(k))));<br /> Ri(k) = d(k+T_steps)/aux;

      end

      R_t = zeros(1,numel(t));

      for k = (T_steps+1):1:(numel(t) - 2*T_steps)

      R_t(k) = dt*(sum(Ri(k:1:(k+T_steps))));

      end

      figure, plot(t((T_steps+1):1:(numel(t) - 3T_steps)), R_t((T_steps+1):1:(numel(t) - 3T_steps)))<br /> grid minor<br /> hold on<br /> plot(t((T_steps+1):1:(numel(t) - 3T_steps)), R0.(1-R((T_steps+1):1:(numel(t) - 3*T_steps))))`

      the blue line will be the Rt evaluated using your formula, while the red one will be the true Rt value. It's not only a matter of the exact values, but most importantly the issue is about the fact that your formula antedates the day in which Rt starts to decrease of a full month, thus it's not possible to use it to actually prove that the measures did or did not have an effect on the Rt value.

    1. On 2021-07-22 18:02:23, user Andriy Kolesnyk wrote:

      1260 times at which point on timeline? Delta is faster (4 days vs 6), and in case of measuring the viral load at 6th day we can receive the result 1260 times bigger for Delta. Becouse Delta has 2 days more for multiplying the viral load.

    1. On 2021-08-02 22:47:42, user drwambier wrote:

      Please revise: "No new serious adverse events assessed as related by investigators were reported after data cut-off for the previous report."

      Previously reported: 2 deaths (BNT162b2) vs 4 (placebo), with zero deaths related to COVID19 in each arm. Death (any cause) is the main SERIOUS ADVERSE EVENT (SAE), and a higher number was reported, please revise accordingly.

      "During the blinded, controlled period, 15 BNT162b2 and 14 placebo recipients died; during the open-label period, 3 BNT162b2 and 2 original placebo recipients who received BNT162b2 after unblinding died. None of these deaths were considered related to BNT162b2 by investigators. Causes of death were balanced between BNT162b2 and placebo groups (Table S4).”

      Since you are reporting the 6 months data, please consider rephrasing to the full numbers: "18 deaths on BNT162b2 versus 16 deaths on the placebo (including the 2 deaths after receiving BNT162b2)." It is also important to specify the denominator for each group. It is unclear what is the total number of patients were followed-up for full 6 months, and how many were lost to follow-up (survivor bias?).

      "Cumulative safety follow-up was available up to 6 months post-dose 2 from combined blinded and open-label periods for 12,006 participants originally randomized to BNT162b2. The longer follow-up for this report, including open-label observation of original BNT162b2 recipients and placebo recipients who received BNT162b2 after unblinding, revealed no new safety signals relative to the previous report”.

      If 3 deaths happened in the BNT162b2 group until 1 month after Dose 2 during the blinded period and 5 in the placebo group, the new deaths after that month were: 15 for BNT162b2 and 11 for placebo (including the 2 deaths after receiving BNT162b2). Please verify if this is correct, if it is, please state specifically as this might be considered a safety signal.

      200 HIV patients data is still not disclosed. I understand that this is per protocol. However, we ask to please disclose the current HIV+ data, since many are receiving the shots under EUA without data of their subgroup analysis.

      Plus, assuming that those are in the 22,166 patients of BNT162b2 and 22,320 of placebo:<br /> Considering the “scenario of all patients followed, without unknown outcomes”:<br /> RR for death is 1.1328 (.5778-2.2208). An increase by 13% of all cause mortality in 6 months of follow-up including the part of vaccinating the placebo arm.<br /> The ages and gender of deaths would be informative for safety since populations were balanced by randomization please add a table with such information.

      Current data suggests that within 6 months of follow-up, BNT162b2 does not reduce all-cause mortality. There is a signal that it might increase all-cause mortality. <br /> Assuming that it would be difficult for investigators to access if deaths were related to BNT162b2 since myocarditis or heart-related side effects were initially thought to be unrelated to BNT162b2, also no previous signals of thrombosis were reported.

      Causes of death were assumed to be “Cardiac Arrest” or other "unknown" descriptions. There is even a “death” as cause of death on the table… "dementia?" For future trials or third injection, an active screening for myocarditis (Serum Cardiac Troponin T and Creatine Kinase–MB), and thromboembolic events would be prudent, specially in the >65 y.o. population, which may be more vulnerable to momentary reduction of cardiac muscle function through inflammation.

      As this was a healthy population (not treatment of COVID-19, etc), and deaths were rare. <br /> It seems that all-cause mortality was indeed more common than deaths by COVID-19 in the current manuscript, thus, they cannot be left aside from the trial results discussion. Please discuss specifically about the comparison of COVID-19 deaths in the control group vs the relative increase of deaths detected in the BNT162b2 group and how putting those numbers in the balance.

      About COVID-19 related deaths, the score was expected: 2 deaths on placebo (“COVID-19”) and 1 death in Vaccine ("COVID-19 pneumonia”). <br /> With the current safety data, caution is warranted, since the number to harm in the best scenario points that approximately 100 deaths could be attributed to the vaccine for every 1M fully vaccinated, if the increase in all-cause deaths is not a noise of low numbers.

      "Safety monitoring will continue per protocol for 2 years post-dose 2 for participants who originally received BNT162b2 and for 18 months after the second BNT162b2 dose for placebo recipients who received BNT162b2 after unblinding.”

      Was all the control group crossed-over to BNT162b2 post-unblinding? If so, please state and give the reasons why it was decided to do so. If there is no placebo arm for a safety control comparison, this safety monitoring will only be important if extreme flags happen: such as unexpected high number of serious adverse events emerge.

    1. On 2022-02-21 11:05:51, user diveoceanos wrote:

      Studies 4 through 6 are doing a matched-cohort analysis of Ct values between group 2 (unvaccinated and reinfected) and unvaccinated and infected individuals, individuals with breakthrough infections after BNT16262 vaccine and individuals with breakthrough infection after mRNA-1273 vaccine respectively.

      Based on the data the mean Ct value is higher for the unvaccinated and reinfected individuals in all studies compared to the matched-cohort, with studies 4 and 5 reaching statistical significance, while in study 6 the P-value is at 0.104 indicating not a statistically significant difference.

      In the text the authors are ranking the infectiousness in order of decreased magnitude in line with their findings i.e.

      “The different comparisons suggest an overall hierarchy, present for both asymptomatic and symptomatic infections, where primary infections in unvaccinated persons are most infectious, followed by BNT162b2 breakthrough infections, mRNA-1273 breakthrough infections, and finally reinfections in unvaccinated persons.”

      Figure 2 is clearly showing that reinfections are associated with higher Ct compared to all other studied groups.

      However there is misleading information on tables 4 and 5. Specifically tables 4 and 5 are showing in the last two rows that infectiousness of breakthrough infections is less compared to infectiousness of reinfections in unvaccinated individuals:

      • Infectiousness of BNT162b2-vaccine breakthrough infections relative to reinfections in unvaccinated individuals<br /> • Infectiousness of mRNA-1273-vaccine breakthrough infections relative to reinfections in unvaccinated individuals

      Either the line descriptions should change to reflect the correct ratio (i.e. infectiousness of reinfections in unvaccinated individuals over the breakthrough infections or the relative infectiousness should be recalculated to reflect the line description.

    1. On 2020-04-12 08:33:12, user tsuyomiyakawa wrote:

      Thanks, everyone, for your precious comments.

      1. We are examining the potential confounders, which includes the ones mentioned here.

      2. As Rosemary mentioned, BCG is an attenuated version TB and, indeed, big protective effect of TB prevalence against COVID-19 exists. We will incorporate the data in the next version.

      3. We obtained the data from the web site of European Centre for Disease Prevention and Control, and are re-analyzing the growth of spreading in a more quantitive manner. Basically, there are significant effects of BCG/TB against COVID-19 growth, which will replace the data shown in Figure 3.

      4. Regarding the tourists from China, according to a survey, the top 10 destination countries of China’s out bound countries are Japan, Thailand, South Korea, Indonesia, Singapore, Malaysia, Australia, UK, New Zealand, and Maldives, and 9 out of 10 of them are the ones with extremely low COVID-19 cases and deaths (4 or lower deaths per million) , as of April 13th, which makes it unlikely that the Traveling activity from China matters. This will be added to the discussion. Also, we evaluated the number of international arrivals in each country and it did not essentially affect the results (almost at all).

      5. As for masks and green tea, they cannot explain 1) the differences between Eastern Europe and Western Europe and 2) low COVID-19 indices in Africa, South America and South East Asia. We may consider their potential effect, once we can get any good statistics representing those things, but so far, we set priority low for these potential confounders.


      Anyway, we will upload next version sometime in next week and it will be appreciated if you could keep providing us critical comments, which will greatly improve our manuscript. Thank you!

    1. On 2020-04-16 21:55:50, user Sinai Immunol Review Project wrote:

      Title:<br /> Immunopathological characteristics of coronavirus disease 2019 cases in Guangzhou, China<br /> The main finding of the article: <br /> This study analyzed immune cell populations and multiple cytokines in 31 patients with mild/moderate COVID-19 (ave. 44.5 years) and 25 with severe COVID-19 (ave. 66 years). Samples from patients with fever and negative for the SARS-COV-2 test were used as control. At inpatient admission, total lymphocytes number was decreased in severe patients but not in mild patients, whereas neutrophils were increased in severe patients. CD4+ and CD8+ T cells were diminished in all COVID-19 patients. CD19+ B cells and NK cells were decreased in both mild and severe patients, however, severe patients showed a notable reduction. These data might suggest a profound deregulation of lymphocytes in COVID-19 patients. Further analysis showed significant increases of IL-2, IL-6, IL-10 and TNF? in blood of severe patients at the admission. Sequential samples revealed that IL-2 and IL-6 peaked on day 15-20 and declined thereafter. A moderate increase of IL-4 was seen in mild/moderate patients. Thus, elevation of IL-2, IL-6 can be indicators of severe COVID-19.<br /> Critical analysis of the study: <br /> There is no information on when the patients were assessed as severe or mild/moderate, at inpatient admission or later. The authors could have analyzed the correlation between immune cell population and cytokine levels to see, for example, if severe lymphopenia correlated to higher elevation of IL-2.<br /> The importance and implications for the current epidemics:<br /> While similar findings have already been shown, the data corroborates alterations in circulating adaptive and innate immune cell populations and cytokines, and its correlation to disease severity. The increase of IL-2 and IL-6 at the admission might an indicator to start intensive therapies (like convalescent serum) at an early time.

    1. On 2020-10-23 05:06:05, user Robert Clark wrote:

      This is another paper where positive effects of HCQ are left out of the conclusions the paper reports. In the Table 2, the line for mortality at 28 days shows a cut by a factor of 0.54 on HCQ. The difference is not at the standard 0.05 significance level, with a p-value of 0.22. However this does not mean the result is false. It could just as well be the sample size is not large enough for the significance to reach the 0.05 level.

      Too often this is overlooked in medical studies. For instance a significance level of 0.05 means there is 5% chance that the difference is just by chance. Or said another way there is a 95% chance that the difference is not by chance alone, meaning the difference is a real effect.

      But by the same token a statistical significance of 0.22, i.e., the p-value being 0.22, means there is a 78% chance that it is a real effect. In other words in probability terms it’s more likely than not to be a real effect.<br /> {There are several online calculators of, for example, the Fishers Exact test of statistical significance, such as here: https://www.graphpad.com/qu...}

      Yet, often when a result does not reach the 0.05 significance level, it is common, and mistakenly, reported as the result being proven wrong.

      In this regard it must be remembered that these calculated levels of statistical significance are dependent on the sample size. For instance with the mortality rates for the HCQ and non-HCQ cases the very same as in this study but at a large enough sample size the statistical significance could be at the 0.05 level. This is especially important in a study such as this one where The originally planned on number of subjects had to be greatly reduced because of a reduced number of cases of the illness.

      Another aspect of this Table 2 becomes apparent from unwrapping the data. The study uses what is called a “composite endpoint”, or “composite outcome”. This means two subcases are combined into one. In this study, the cases of “invasively mechanically ventilated”, i.e., intubated, and “deaths” are combined, called the “Primary outcome” in the Table 2.

      But the number of deaths specifically on invasive mechanical ventilation is an important number to find out. This is because the mortality rates for that category have been so high. So, the RECOVERY trial for example counted it as a breakthrough when dexamethasone cut deaths in that category by 30%.

      In this study, the “Primary outcome” is the union of the two sets, “invasively mechanically ventilated” and “deaths”. What we want though is the number of those ventilated patients who died, the intersection of the two sets.

      Use the formula |A ? B| = |A| + |B| – |A ? B|, which simply means the number in the union is found by adding the numbers in the two sets minus the number in the overlap.

      We want the number in the intersection though so we’ll turn it around to get:

      |A ? B| = |A| + |B| – |A ? B|

      For HCQ:<br /> |ventilated?deaths| = |ventilated| + |deaths| – |ventilated?deaths| = 3 + 6 – 9 = 0. So 0 deaths out of 3 patients on invasive ventilation on HCQ.

      But for non-HCQ:<br /> |ventilated?deaths| = 4 + 11 – 12 = 3, so the number of deaths on invasive ventilation not taking HCQ was 3 out of 4.

      The numbers are too small to draw firm conclusions though. It is unfortunate that the study could not be completed with the originally planned number of cases.

      One last fact left out of the conclusions of the paper that supports benefits of HCQ:

      Figure 2. Analysis of outcomes in predefined subgroups.<br /> For analysis of the primary outcome in the subgroup of patients receiving azithromycin at randomization, the relative risk could not be calculated because the primary endpoint occurred in 0 of 10 patients who received both azithromycin and hydroxychloroquine compared to 3 of<br /> 11 patients who received azithromycin and the placebo.

      ???????

      Robert Clark

    1. On 2020-11-08 03:03:45, user perrottk wrote:

      Comments on “A Benchmark Dose Analysis for Maternal Pregnancy Urine-Fluoride and IQ in Children”<br /> I question the validity of attempting to determine a BMC for the effect of fluoride intake on IQ without first ascertaining if there is a real effect. The problem of this document is that it assumes an effect without making a proper critical assessment of the evidence for a causal effect.<br /> The draft paper relies completely on two studies which reported very weak relationships from exploratory analyses. Nothing wrong with doing exploratory analyses – providing their limitations are accepted. Such analyses can indicate possibilities for future studies testing possibly causes – but, in themselves, they are not evidence of causation. These studies provide no evidence of causal effect<br /> The studies this draft relies as evidence that fluoride causes a lowering of child IQ illustrates have the following problems.<br /> 1: Correlation is not evidence of causation – no matter how good the statistical relationship. And reliance on p-values is not a reliable indicator of the strength of a relationship anyway The two studies relied on here do not report the full results of statical analyses which would have revealed the weaknesses of the relationships.<br /> 2: These two studies were exploratory – using existing data. They were not experiments specifically designed to establish a cause.<br /> 3: Many other factors besides those investigated can obviously be important in exploratory studies where there is no control of population selection. While authors may claim confounders are considered it is impossible to do this completely – there are so many possible factors to consider. Most are not included in the datasets used and the researchers may make their own selection, anyway.<br /> The study of Malin & Till (2015), referred to in this draft, illustrates the problems. Malin & Till (2015) reported what they considered reasonably strong relationships (p-values below 0.05 and R squared values of 0.21 to 0.34 indicating their relationships explained 21% to 34% of the variance in ADHD prevalence). However, their consideration of possible other risk-modifying factors was limited. They did not include state elevation which Huber et al (2015) showed was correlated with fluoridation. The strength of Huber’s relationship (R squared 0.31 indicating elevation explained 31% of the variance in ADHD prevalence) was similar to that reported by Malin & Till for fluoridation.<br /> Perrott (2018) showed that when elevation is included in the statistical analysis the relationship of ADHD prevalence with fluoridation was non-significant (p>0.05). This show the danger of relying on the results of statistical relationships from exploratory studies where consideration of other possible risk-modifying factors is limited.<br /> 4: This draft paper relies on the reported links between cognitive factors and F intake without testing for a causal effect. But it also does not critically assess those correlations. The problems of confounders have already been mentioned but these two studies report very weak relationships or, in most cases, no statistically significant relationships.<br /> For example, of the 10 relationships between measures of fluoride exposure and cognitive effects Green et al (2019) reported that only 4 were statistically significant (Perrott 2020). That is not evidence of a strong relationship and underlines the danger of assuming correlations (especially selected correlations) are evidence of causation. Incidentally, this draft paper mentions the study of Till et al (202) which also reported relationships between fluoride exposure with bottle-fed infants and later cognitive effects. In this case only three of the 12 relationships reported were statistically significant (Perrott 2020).<br /> Even those relationship reported as significant were still very weak. For example Green et al (2015) reported a relationship for boys which explained less than 5% of the variance of IQ measures.

      The relationships reported by Bashash et al (2017) were also extremely weak – explaining only about 3.6% of the variance in IQ and 3.3% of the variance in GCI. This weakness is underlined by other reports of relationships found for the Mexican ELEMENT database. Thomas (2014) did not find a significant relationship of MDI with maternal urinary fluoride for children of ages 1 to 3 although in a conference poster paper Thomas et al (2018) reported a statistically significant relationship for urinary fluoride adjusted using creatinine concentrations.<br /> 5: As well as ignoring the incidence of non-significant relationships from these studies this draft paper also ignores the findings of positive relationships from other studies. For example, Santa-Marina et al (2019) reported a positive relationship between F intake indicated by maternal urinary F and child cognitive measures. Thomas (2014) also reported a positive relationship of child IQ (MDI for 6 – 15-year-old boys) with child urinary fluoride.<br /> 6: The draft paper describes the two studies it uses for its analysis as “robust” but ignores the fact that the findings in these and other relevant studies are contradictory. For example, the findings reported in the two papers differ in that Bashash et al (2017) did not report different effects for boys and girls whereas Green et al (2019) did. Santa-Marina et al (2019) reported opposite effect to those of Bashash et al (2017) and Green et al (2019). These contradictory findings, together with the lack of statistical significance for most of the relationships investigated, are perhaps what we should expect from relationships which are as weak as these are.<br /> Summary<br /> The paper relies on weak relationships from exploratory studies. Such relationships, even where strong, cannot be used as evidence for causation and to assume so can be misleading. BMCs and similar functions derived without any evidence of real effects are not justified. While the derived BMCs may be used by activists campaigning against community water fluoride, they will be misleading for policy makers. This sort of determination of BMC is a least premature and a worst meaningless.<br /> References:<br /> Bashash, M., Thomas, D., Hu, H., Martinez-mier, E. A., Sanchez, B. N., Basu, N., Peterson, K. E., Ettinger, A. S., Wright, R., Zhang, Z., Liu, Y., Schnaas, L., Mercado-garcía, A., Téllez-rojo, M. M., & Hernández-avila, M. (2017). Prenatal Fluoride Exposure and Cognitive Outcomes in Children at 4 and 6 – 12 Years of Age in Mexico. Enviromental Health Perspectives, 125(9).<br /> Green, R., Lanphear, B., Hornung, R., Flora, D., Martinez-Mier, E. A., Neufeld, R., Ayotte, P., Muckle, G., & Till, C. (2019). Association Between Maternal Fluoride Exposure During Pregnancy and IQ Scores in Offspring in Canada. JAMA Pediatrics, 1–9.<br /> Huber, R. S., Kim, T.-S., Kim, N., Kuykendall, M. D., Sherwood, S. N., Renshaw, P. F., & Kondo, D. G. (2015). Association Between Altitude and Regional Variation of ADHD in Youth. Journal of Attention Disorders.<br /> Malin, A. J., & Till, C. (2015). Exposure to fluoridated water and attention deficit hyperactivity disorder prevalence among children and adolescents in the United States: an ecological association. Environmental Health, 14(1), 17.<br /> Perrott, K. W. (2018). Fluoridation and attention deficit hyperactivity disorder a critique of Malin and Till (2015). British Dental Journal, 223(11), 819–822.<br /> Perrott, K. W. (2020). Health effects of fluoridation on IQ are unproven. New Zealand Medical Journal, 133(1522), 177–179.<br /> Santa-Marina, L., Jimenez-Zabala, A., Molinuevo, A., Lopez-Espinosa, M., Villanueva, C., Riano, I., Ballester, F., Sunyer, J., Tardon, A., & Ibarluzea, J. (2019). Fluorinated water consumption in pregnancy and neuropsychological development of children at 14 months and 4 years of age. Environmental Epidemiology, 3. <br /> Thomas, D. B. (2014). Fluoride exposure during pregnancy and its effects on childhood neurobehavior: a study among mother-child pairs from Mexico City, Mexico [University of Michigan].<br /> Thomas, D., Sanchez, B., Peterson, K., Basu, N., Angeles Martinez-Mier, E., Mercado-Garcia, A., Hernandez-Avila, M., Till, C., Bashash, M., Hu, H., & Tellez-Rojo, M. M. (2018). OP V – 2 Prenatal fluoride exposure and neurobehavior among children 1–3 years of age in mexico. Environmental Contaminants and Children’s Health, 75(Suppl 1), A10.1-A10.<br /> Till, C., Green, R., Flora, D., Hornung, R., Martinez-mier, E. A., Blazer, M., Farmus, L., Ayotte, P., Muckle, G., & Lanphear, B. (2020). Fluoride exposure from infant formula and child IQ in a Canadian birth cohort. Environment International, 134(September 2019), 105315.

    1. On 2024-05-01 23:32:45, user ppgardne wrote:

      This is an excellent paper, showing a clear association between variation in RNU4-2 and NDD phenotypes. The enrichment of variation in the gene between undiagnosed NDD and population cohorts was remarkable.

      I thought there were a few areas where the manuscript could be improved slightly.

      * Figure 1: Clearly define the measures “genotype quality”, “allele balance” and “total coverage”. We can infer what these mean, but definitions of each in the method section would be helpful.

      * Table 1: I spent some time gathering the population sizes for each of the count columns. Please add an extra row or two, giving the number of individuals in GEL NDD, Non-GEL NDD and the population cohort.

      * The statement “Humans have multiple genes that encode the U4 snRNA, although only two of these, RNU4-2 and RNU4-1, are highly expressed in the human brain” is slightly inaccurate. The HGNC database and reference (https://doi.org/10.15252/em... "https://doi.org/10.15252/embj.2019103777)") list just those two functional copies of U4 in the human genome. There are ~100 annotated pseudogenes however.

      * You state that there is “97.2% homology” between RNU4-1 & RNU4-2 – this is a wrong (but common) use of the term homology. You should have stated “similarity” instead.

      * Figure 3: I understand that the BrainVar RNAseq data are from samples of human dorsolateral prefrontal cortex. This should be stated in the caption.

      * Figure 3: you state that “expression of RNU4-1 & 2 is tightly correlated”. Looking at the figure it appears the tissues with higher expression are also the ones were more samples were taken. Was the potential confounding of sample depth and/leverage accounted for in the analysis?

      * Figure 4: it is unclear what this heatmap is showing. Is it really normalised on a per-gene basis, or is the null for SNV densities derived from the 1,000 random intergenic sequences mentioned in the methods? That would seem to be a more useful measure of variant enrichment or paucity. The ordering of the sequences is odd too, why are the paralogous genes U4/U4ATAC, U1/U11, U2/U12, U5 etc not next to each other? Surely the paralogs are more comparable. What is the justification for an 18bp window? –Other than that is the size of the variable region in RNU4-2.

      * The recurrence of n.64_65insT is fascinating. And speculation on the mechanism is very worthwhile. You mention early in the manuscript the possibility of slippage in homopolymer regions, but this is not mentioned again in the appropriate section. You mention local secondary structure as a possible driver, but there seems to be very little evidence to support this based on free energy modelling.

    1. On 2024-11-30 22:32:43, user xPeer wrote:

      Summary<br /> The preprint investigates the remodeling effects of icosapent ethyl (IPE) supplementation on plasma lipoproteins and its subsequent impact on cardiovascular disease (CVD) risk markers in normolipidemic individuals. The study finds that IPE supplementation significantly enhances eicosapentaenoic acid (EPA) levels in the plasma, reducing major CVD risk markers such as triglycerides, remnant cholesterol, and apoB levels. There are consistent alterations across all lipoprotein classes, influencing their lipidomes, reducing proteoglycan binding properties, and potentially decreasing the atherosclerotic risk. However, the study's small sample size and short duration limit the generalizability of findings.

      Major Revisions

      1. Extended Sample Size and Duration:<br /> The study's findings are constrained by a limited sample size and short duration (28 days), impeding the generalizability to broader populations or those with pre-existing cardiovascular conditions.
      2. Example: Expand the cohort size and extend the duration to assess long-term impacts and variability of EPA incorporation among different CVD risk groups (Discussion, Page 14).

      3. Detailed Mechanistic Insights:<br /> The precise mechanisms by which IPE alters lipoprotein characteristics and its direct influence on cardiovascular outcomes remain unclear.

      4. Example: Detailed mechanistic studies on how IPE-induced lipid species changes relate to atherosclerosis progression are needed (Results, Page 11).

      5. Individual Variability Analysis:<br /> The study underscores substantial interindividual variability in response to IPE supplementation, calling for personalized treatment approaches.

      6. Example: Investigate genomic or lifestyle factors contributing to variability in response to IPE (Results, Page 13).

      7. Proteoglycan Binding and Aggregation:<br /> The study notes reduction in proteoglycan binding and different responses in LDL aggregation among participants but lacks detailed analysis.

      8. Example: Provide more comprehensive data and rationale behind the differential LDL aggregation responses post IPE-supplementation (Results, Page 8).

      Recommendations

      1. Larger and Diverse Cohort Studies:<br /> Conduct studies with larger and more diverse cohorts to bolster the reliability and applicability of the findings across various population subsets.
      2. Longitudinal Studies:<br /> Extend the study duration to capture long-term effects of IPE on lipoprotein profiles and cardiovascular health outcomes.
      3. Mechanistic Pathway Research:<br /> Incorporate omics approaches (genomics, proteomics) to unravel the underlying mechanisms modified by IPE that contribute to reduced CVD risks.
      4. Personalized Medicine Approaches:<br /> Develop stratified medicine approaches to optimize IPE dosage and treatment protocols tailored to individual lipidomic profiles and genetic backgrounds.
      5. Detailed Biophysical Characterization:<br /> Enhance the biochemical and biophysical characterization of proteoglycan binding and lipoprotein aggregation properties altered by IPE supplementation.

      Minor Revisions

      1. Textual and Formatting Errors:
      2. Ensure consistency in figure label fonts and styles across the manuscript.
      3. Correct minor typographical errors and ensure uniformity in section formatting (e.g., use of italics, bold).
      4. Specific errors include inconsistent capitalization in headings and figure labels requiring standardization (Introduction, Page 2; Results, Page 8).

      5. AI Content Analysis:

      6. Estimated AI Content: Approximately 10%.
      7. Highlighted AI-Detected Sections: Notable in the background and introduction sections with possible AI involvement in text generation.
      8. Assessed Epistemic Impact: The AI-generated content does not undermine the scientific rigor but would benefit from expert revision to enhance field-specific terminology and depth.

      Overall, the preprint presents insightful preliminary findings on the cardioprotective impacts of IPE supplementation, recommending essential improvements and comprehensive validations for future extensive studies.

    1. On 2025-02-24 23:42:40, user Stephen Goldstein wrote:

      Manuscript summary

      The authors report a small study comparing patients with “post-vaccination syndrome” or “PVS” with vaccinated, healthy controls. They used a variety of immunological techniques and report they have identified potential immune signatures in PVS patients, which may reflect an underlying mechanism of this condition.

      Personal disclaimer

      This manuscript has received considerable attention and attracted much commentary, including critical commentary from myself on twitter (@stgoldst). I was immediately skeptical of these findings given the attention to it, small study size, and amplification by anti-vaccine activists. However, the potential for vaccine injury is a serious matter, so a rigorous review of this manuscript is a critical need. I attempt here to account for my biases, and to check for these I used a Google AI model to conduct an orthogonal review. That is posted separately.

      Review

      Overview

      This study described by this manuscript is methodologically flawed to a degree that undermines the authors’ stated goal to identify biomarkers for post-vaccination syndrome (PVS). These flaws are systematic, ingrained into the study design, and compounded by analytic flaws throughout the manuscript. As is, this study provides weakly informative data at best towards understanding chronic illness following vaccination. The methodological flaws are listed below and subsequently expanded upon.

      1. PVS and control cohorts are very small, and even smaller when stratified by infection status.
      2. Prior infection status is poorly controlled – though this may be difficult to overcome
      3. The study does not include a control group of unvaccinated individuals reporting similar chronic symptoms as the PVS cohort.
      4. PVS is defined by self-reported symptoms with no clinical assessment or classification system.
      5. Small effect sizes and weak correlations are repeatedly described via their statistical significance, with no biological context provided by the authors.
      6. The study provides no evidence for a causal link

      7. PVS and control cohorts are very small, and even smaller when stratified by infection status.

      The PVS cohort comprised only 44 patients originally, and was reduced to 39 due to pharmacological inhibition in 2 patients. The authors acknowledge that due to the small size of the study and its exploratory nature they did not conduct a power analysis. They acknowledge the difficulty in producing robust results due to the sample size. Despite acknowledging these problems, the authors repeatedly invoke the statistical significance of various analyses and in some cases rely on extremely involved statistical testing to identify weak signals. This presents an impression that the authors understand the inability, baked in from the start, of the study to be informative yet press ahead anyway.

      1. Prior infection status is poorly controlled – though this may be difficult to overcome. T

      he authors stratify the cohorts by infection status, with the primary determination based on serological status of anti-nucleocapsid (N) antibodies. The study participants were recruited in December 2022 at the earliest, nearly 3 years after the first SARS-CoV-2 infections were identified in the United States. Given the expected decline in serum antibody titers over time, it’s likely that people infected in the first year of the pandemic (and possibly even later into the pandemic) would test seronegative. Therefore, the -I cohorts likely include individuals who were in fact infected with SARS-CoV-2 at some point. This is a critical issue. The number of individuals without infection history is likely even smaller than presented, reducing the utility of stratification. In addition, this may actually confound the ability to disentangle the effects of vaccination vs infection in the development of chronic illness. It would be difficult to methodologically correct for this without a prospective longitudinal study. However, larger sample sizes might allow researchers to mitigate its impact. Given these sample sizes and the inability to reliable sort by prior infection status, the issue precludes making robust inferences from the data.

      1. The study does not include a control group of unvaccinated individuals reporting similar chronic symptoms as the PVS cohort.

      The authors describe the health of study participants based on GH VAS scores and note that PVS participants were in worse health than the control participants. In the Discussion, the authors expand on this, noting that PVS participants also had worse health than the U.S. general population. Given the real potential for other disease processes to impact every one of the biomarkers tested, the lack of unvaccinated, chronically ill participants (reporting the same syndromic profile as PVS patients) confounds any correlates between these biomarkers and vaccination. The study analyses are uninterpretable with respect to the impact of vaccination on health, as a result.

      1. PVS is defined by self-reported symptoms with no clinical assessment or classification system.

      PVS was previously described by some of the same authors based on self-reported chronic sequelae following vaccination. This definition is then relied upon in this study. However, many of these symptoms are non-specific and certainly there is no evidence, given the lack of complete overlap, that they represent a single syndrome. There does not appear to be any clinical assessment to verify any of them. This is a repeated issue with descriptive studies of long covid (PACS) and now PVS, and I acknowledge the inherent challenges in establishing other criteria. Nevertheless, it represents a major problem in trying to describe a unified syndrome downstream of vaccination.

      1. Small effect sizes and weak correlations are repeatedly described via their statistical significance, with no biological context provided by the authors.

      Throughout the manuscript the authors describe differences between PVS and patient cohorts solely through the p-value returned by statistical testing. Looking at the figures themselves the effect sizes turn out to be extremely small in virtually every case. Small effect sizes don’t mean there is no biological significance, but the authors in this study expend no effort to offer context or even a coherent hypothesis for why these effect sizes are significant. Expecting the reader to favorably interpret the data, or indeed interpret it all, based purely on p-values is…disconcerting. It’s not clear in the writing that the authors even consider effect sizes to be relevant, or if getting a sufficiently small p-value is good enough to report and believe a major finding. I’m not confident that the authors really interpreted the data to any depth themselves.

      1. The study provides no evidence for a causal link.

      There is simply no causality evident in the data or really presented by the authors. Given the generally poor health of the PVS participants, all of the elevated inflammatory biomarkers and the elevated EBV reactivity could all be due to varied other disease processes, infectious or not. One clear example of this is Figure 4K where the authors correlate EBVgp42 reactivity with the percentage of CD8+ T cells producing TNF?. The Correlation R value is 0.47, indicating a weak to moderate link. Because EBV reactivation is tightly linked to general stress, the weakness of this correlation is highly suggestive of other disease processes making a significant contribution, or the PVS link being artifactual. The authors make no effort to account for this.

      Specific Points

      References 16 and 18 need to be corrected

      “interaction with full-length S, its subunits (S1, S2), and/or <br /> peptide fragments with host molecules may result in <br /> prolonged symptoms in certain individuals16.”<br /> -Ref16 is a study describing circulating spike and S1 <br /> following vaccination, but does not mention anything about<br /> prolonged symptoms.

      “Recently, a subset of non-classical monocytes has been shown to harbor S protein in patients with PVS18.” <br /> -Ref18 is a study on PACS (post-acute covid-19 sequelae) <br /> and does not mention vaccination or post-vaccination <br /> syndrome<br /> -Ctrl+F for “vaccine” “vaccination” “PVS” returns no results in <br /> this manuscript

      Figure 3 on the kinetics of serological findings is generally confusing<br /> -For Control and PVS+I groups the authors report no decline <br /> in anti-spike antibodies over the course of months to year. <br /> -This runs counter to basic immunological principles and <br /> robust, repeatable findings with respect to anti-SARS-CoV-2<br /> spike antibodies in particular<br /> -One explanation for this is subsequent mild infections that <br /> boost antibody levels, but there are no spikes evident, but <br /> rather a steady maintenance.<br /> -The exception to this is PVS-I antibodies which decline at <br /> what is to the naked eye a normal rate. <br /> -This suggests an issue with the control or PVS+I cohorts, or <br /> a disturbing indication that they are not representative of the <br /> immunological state in their respective populations. Due to <br /> the small sample size, this seems likely<br /> -The authors should explain that because the PVS-I <br /> participants weren’t infected, their “days since post-<br /> exposure/vaccination” data are identical. Absent that, it’s <br /> confusing to notice that the PVS-I data in rows B and C are <br /> identical and raises concern about duplication in figures

      The authors don’t describe the rationale for the EBV coinfection analysis displayed in Figure 4, and so there’s no way for the reader to interpret what (if any) significance to ascribe to it.<br /> -Figure 4D shows a small but statistically significant <br /> increase in IgG against EBVgp42 for PVS cohort relative to <br /> controls – however...<br /> -When the PVS cohort is stratified by prior infection status <br /> there is no statistically significant difference<br /> -This make it really difficult to interpret the difference when<br /> the PVS group remains together<br /> -It raises the question for me of whether the statistical <br /> significance is just sensitive to the number of data points,<br /> which for me makes it not robust<br /> -Again – as throughout the paper no biological context is<br /> given

      Even the correlation between EBVgp42 in serum and EBVgp42 antibody reactivity is low<br /> -Again very difficult data to interpret and unclear what the <br /> biological significance would be<br /> -Problems with the correlation analysis in Figure 4K were <br /> discussed above<br /> Figure S4C is discussed in the text, but briefly and important data is ignored<br /> -It appears true that PVS participants have elevated<br /> autoantibodies of IgM and IgA isotypes, but their IgG <br /> autoantibodies are actually similar to controls<br /> -Not clear if there might be a class switching defect that <br /> could be related to a pathogenic process, or other<br /> explanation – the authors don’t address<br /> -The authors just say PVS patients just have autoantibodies,<br /> which obfuscates their own data that it’s isotype specific<br /> The interpretation of Figure 5C is also strange – most PVS patients have no circulating anti-S1 antibodies and the statistically significant difference is driven by a minority who do<br /> -The authors state there’s a difference without any effort to<br /> interpret it<br /> -This suggests that PVS, which the authors are trying to<br /> characterize as one syndrome, is either not one thing, or the<br /> presence or absence of anti-spike antibodies is ancillary<br /> -Unfortunately the authors gloss over any nuance in the data<br /> The data on specific biomarkers in Figure 5H is based on such small sample sizes I question whether it was even appropriate to do this analysis at all<br /> -To be clear, the issue isn’t whether the question is worth<br /> asking, it is. The issue is that one should not do an analysis<br /> that is so underpowered it will be definitionally <br /> uninterpretable<br /> -The fact that the authors had to jump through statistical<br /> hoops to find a statistically significant effect is concerning <br /> -the fact this includes a sub-group of only three patients is <br /> just methodologically inappropriate.<br /> Given the authors’ use of machine learning failed to reveal any coherent set of biomarkers further argues against the contention that PVS is a definable syndrome<br /> -Or, that this study is so small it lacks value in defining the <br /> syndrome

      Final summary

      Ultimately this study adds little value, at best, towards understanding post-vaccination sequelae experience and reported by some individuals. At worst, it injects claims and interpretations into the field and discourse that are unfounded, and will ultimately slow efforts to help patients. These results have already been used to advance anti-vaccine narratives in online discourse. If the data were robust, no one could complain. Because the data are not, it is tragic. Ultimately, there is no compelling evidence in this paper for an immunological signature associated with chronic illness following vaccination. Perhaps reflecting this, the authors provide almost no biological context for any of their findings, often reporting data merely as a p-value with no comment on the effect size (whether large or small). This leaves it unclear to a reader whether the authors are even aware of flaws in their work. Given the methodological flaws of this study, it is a questionable investment for researchers to follow up on it in a targeted way. Rather, well-powered, controlled, and methodologically sound studies should be conducted at scale to enable actionable findings to be made.

    1. On 2025-04-10 16:27:18, user Epidemiologist wrote:

      This is a phenomenally bad study, which contains stark evidence of its bias in the Figure purportedly supporting its conclusions. To summarize:<br /> 1. They compare two groups of employees who received a trivalent, inactivated influenza vaccine. Those who received the vaccine (82%) and those who sought an exemption (18%).<br /> 2. As hospital employees, they are aware of the extent to which their work puts them at risk of exposure but the investigators make no effort to determine differences between these groups beyond very crude categorizations.<br /> 3. They find that, after 100 days, they see higher influenza rates in the vaccinated.<br /> 3. They provide no plausible explanation as to how the inactivated vaccine puts one at increased risk of influenza 100 days after vaccination.<br /> 4. That means the ONLY plausible explanation for a significantly higher risk in the vaccinated is a significantly higher exposure risk in the vaccinated. Ergo, the sample is biased.<br /> 5. It is notable that the infection rate among the vaccinated was only 2.5% in a high risk setting for infection. <br /> 6. In sum, the best explanation for their results is that the vaccine was very effective and their sample was biased.

    1. On 2025-10-20 15:20:57, user xPeer wrote:

      Courtesy Double-Blind Peer Review Simulation from xPeerd :

      Reviewer #1 Report

      Summary<br /> The study aims to assess and compare the effectiveness of three advanced large language models (LLMs)—ChatGPT-5, DeepSeek V3, and Grok 4—in generating educational content about ADHD for non-specialist educators and outsourced physical education coaches. Employing a controlled prompt-based methodology and multiple readability/complexity indices, the manuscript investigates response accuracy, clarity, stability, and potential public health communication barriers in AI-generated outputs.

      Major Comments

      1. Methodological Rigor & Generalizability<br /> The authors delineate a robust comparative framework, utilizing three guiding questions on ADHD for model interrogation. However, the scope is limited, as the testing population pivots exclusively on English-language outputs and Melbourne-based prompts. The authors themselves acknowledge:
      2. "The study was conducted exclusively in English within a Melbourne-based testing environment, limiting generalizability to non-English-speaking populations" (page 21, Strengths and limitations).<br /> Reviewer suggestion: Future analyses should encompass a broader linguistic and cultural spectrum to truly capture the global applicability of AI for health education.

      3. Depth of Statistical/Computational Analysis<br /> The study makes extensive use of readability indices (FKGL, SMOG, etc.), but does not sufficiently discuss their limitations when assessing AI-authored medical content. There is potential for bias when equating increased complexity with reduced accessibility; often, necessary clinical nuance may inherently raise reading levels. The manuscript states:

      4. "Readability analyses further showed that DeepSeek V3 had the greatest variability, GPT-5 displayed steadily increasing complexity, and Grok-4 remained the most stable and comparatively less complex" (Discussion, page 17).<br /> Reviewer suggestion: A more critical lens is warranted—consider a combined readability/accuracy approach to better contextualize the trade-offs between precision and simplicity.

      5. Real-World Impact and Usability<br /> Despite extensive quantitative comparison, the practical implications for coaches, teachers, and parents are relegated to future work. The manuscript admits, "The study focused primarily on textual readability and stability, rather than evaluating real-world comprehension or decision-making by specific user groups" (page 21).<br /> Reviewer suggestion: The next phase should prioritize empirical user testing to validate whether model outputs actually enhance pedagogical or clinical understanding and decision-making.

      6. Novelty and Ethical Perspective<br /> The comparative model analysis is novel, considering recent LLM advances and lack of similar head-to-head studies tailored for disability inclusion in school settings. However, no ethical concerns are addressed regarding AI output veracity, data privacy, or the risk of erroneous instruction imparted to underqualified staff.

      Minor Comments

      • The referencing format is occasionally inconsistent and page numbers for tables/figures are absent in some cases.
      • The abstract is concise and provides a clear structure; nonetheless, the results section could briefly mention statistical significance values or variability ranges.
      • Some sentences are overly long or complex, detracting from readability—ironically contrary to the study's focus.
      • In "Ethics approval and consent" (page 22), it is useful to state "Not applicable," but the authors might clarify that all AI-generated responses involved no human data or interventions.

      Recommendation <br /> Major Revision. The manuscript exhibits methodological strength and addresses a pressing question. However, broader evidence on practical efficacy, nuanced readability analysis, and an explicit discussion of ethical boundaries are required prior to acceptance.

      Reviewer #2 Report

      Summary<br /> This manuscript sets out to systematically evaluate the readiness and reliability of LLMs to deliver inclusive, high-quality ADHD education materials, especially for outsourced PE instructors and non-specialist users—a group often neglected in the literature. The three chosen models represent current state-of-the-art options. The topic is pertinent and innovative.

      Major Comments

      1. Overstatement of Claims and Realistic Outcomes<br /> The conclusion suggests that "model selection should be tailored to specific use cases," advocating for Grok-4, DeepSeek V3, and GPT-5 each in particular contexts (page 20, Discussion). However, the comparative exercise data provided fall short of substantiating such a granular recommendation; the outcome differences, though statistically noted, remain within a similar range of excessive complexity:
      2. "All models exhibited high reading levels (FKGL > 12), exceeding recommended public-health standards" (page 2).<br /> Caution should be exercised when suggesting differential real-world deployment based on such preliminary and textual-only evidence.

      3. Potential for Algorithmic and Sampling Bias<br /> The study design is at risk of sample/data selection bias by exclusively testing models with English-language queries and drawing all responses from the same geographical/IP base (Melbourne). This potentially disadvantages queries that might behave differently in other contextual deployments; more granular breakdowns by topic or scenario might add value.

      4. Empirical/Practical Verification—A Missing Piece<br /> While the authors readily admit the absence of real-world user testing (page 21), at a minimum, the study could have incorporated expert review(s) by practicing educators or clinicians to validate the appropriateness, accuracy, and utility of the outputs. Relying strictly on “readability” as a performance surrogate is insufficient.

      5. Accessibility and Communication Gaps<br /> The core finding—that "readability emerged as a persistent barrier across all models" (page 20)—is highly significant. However, the manuscript stops short of offering actionable guidance to AI developers or educators on how to bridge this gap (e.g., adaptive output tuning, multilayered content, or collaborative design with stakeholders).

      6. Risk of Exacerbating Health Inequities<br /> The text insightfully warns, "the broad dissemination of LLM-generated health information risks exacerbating health inequities" (page 20). Surprisingly, no strategies or intervention suggestions are offered. It would strengthen the manuscript to suggest how LLM output might be scaffolded or tailored for vulnerable groups.

      Minor Comments

      • In the methods section, the protocol could be described more clearly, including how the ten independent attempts for each prompt were randomised or sequenced.
      • The discussion occasionally rehashes results rather than linking them to broader theory or policy implications.
      • The limitations section should be expanded to acknowledge not just the lack of user participation but also incomplete handling of model drift and update cycles.

      Tone and Style<br /> The review has detected sporadic verbosity or ambiguous phrasing (e.g., “the findings demonstrate that stability of response generation is varied between models”—page 20). Succinct, active language would benefit the overall clarity.

      Recommendation <br /> Major Revision. Useful, important groundwork is laid here, but the manuscript requires deeper, more practice-oriented exploration, and a more measured, cautious reporting of implications. The lack of empirical field validation is a critical limitation.

      Editorial Decision<br /> Decision: Major Revision Required

      Both reviewers acknowledge the relevance and methodological rigor of the comparative approach, but insist on more empirical user validation, a critical reappraisal of the readability/accuracy trade-off, and practical translation of findings for end-users and policy-makers. Ethical considerations and limitations should be explicitly elaborated.

    1. On 2021-06-02 21:08:40, user Mike wrote:

      "no pregnant or lactating individuals were included in the Phase 3 clinical trials of these vaccines despite belonging to a group at high risk for severe complications of COVID-19 infection" - Ok, so how are you concluding that it is not affecting these women when they weren't included in clinical trials?

      "We show here that the mRNA from anti-COVID BNT162b2 (Pfizer) and mRNA-1273 (Moderna) vaccines is not detected in human breast milk samples collected 4-48 hours post-vaccine" - Two concerns with this statement: 1) they were only tested up to 48 hours afterward? Why are we to conclude that if they don't show up in 48 hours they never will? When other vaccines NEVER leave the shoulder muscle (according to Dr. Bridle) that would indicate that the possibility for much slower movement to the blood exists. 2 - Are you testing for the correct substance? Are you looking for the spike protein or mRNA? Are those the same?

    1. On 2020-06-12 04:50:15, user Paul_Vaucher wrote:

      Dear authors,

      Thank you for this interesting article of major interest. I find the process and research question to be most relevant. I however have a few questions that remain open to understand how the study could come to the conclusion that aerosols and surfaces were not important vectors of covid-19.

      1. What is the external validity of the results for making inferences over infectiousity on the entire period people could be carriers of the disease? In this study, most participants had already been in quarantine for 5 days. Repeated sampling has shown viral load to be optimal in the upper airway system 2 days before and 2 days after symptoms appear. Viral load from nasal and throat swabs drop to a rate where viral culture becomes difficult from 8 days onwards. Most of the study participants were probably beyond that point and were therefore not expected to be very infectious in the first place. If existant, infection through secondary contact and aerosols are however more likely when viral loads are high. It therefore seems difficult from the collected data to infer that household infection through these vectors are unlikely at all times.<br /> 2) When comparing risks from different surface types, how do authors justify the use of chi2 statistics with a sample smaller than 200 and all positive cells with less than 5 cases? In this condition, type 2 errors are very high and this test should not be used under this condition. The number of positif tests are too low to be able to answer the question of whether different surface types are more or less potential vectors of the disease.<br /> 3) Statistical inference assumes independence between measures. This is clearly not the case as a median of 9 samples were taken from each household. Statistical methods should therefore account for these clustering effects. However, the sample size is probably too small for this and a pure descriptive approach without inference could be more relevant.<br /> 4) Could we have any indication on viral load from throat swabs in household cases? If their viral loads were low, we wouldn’t then expect contamination to happen anyway. In two of your 21 housholds, there were apparently not a single case with a positive PCR. This might suggest viral loads to have been too low for any form of infection to have occurred in these households. It seems important to document to what extent each household had at least one person who could infect the air and surfaces.<br /> 5) Likewise, to document risks of infecting the air, were any samples from direct breathing taken from cases Within each household? This seems important as we would theoretically not expect ambient aerosols to be present in aerosols if viruses were difficult to find from air breathed out from cases.

      This study investigates an important question. I am however not convinced the method used truly answers the question as the public seems to understand it. Their is indeed room for misinterpretation and for the public to consider contact and air contamination not to occur at any time.

      To avoid any overinterpretation, it seems important to clarify that this study only tests risks of air and surface contacts days after people have been placed in quarantine when we don’t suspect them to be very infectious anymore.

    1. On 2021-01-24 19:40:01, user Han-Kwang Nienhuys wrote:

      I have further analyzed the data in fig. 2; the odds ratios (frequency ratio B.1.1.7 / other) grow exponentially with daily growth factors between 1.06 and 1.09 between 6 weeks and 1 week before the of the data (only considering the UK regions where the error bars in Fig. 4 were reasonably small: EE, EMid, London, NEE, SEE, SWE, WMid). For this I need to assume that a fraction of the SGTF cases are 'false positive', since most regions show a constant SGTF rate in October, before taking off with exponential growth.

      Also notable, genomic analysis in UK SEE, Denmark, Netherlands, and Portugal show consistently growth rates between 7 %/d and 9.4 %/d with only Denmark showing a slowdown (from 12 %/d to 7 %/d).

      Also, one would expect the odds ratio to grow exponentially over time if there are just two competing variants, each with their own transmissibility or reproduction number. However, the other strains that make up everything else than B.1.1.7 are likely to have slightly different transmissibilities. Over time, one would expect the transmissibility to drift to higher values, also among those other strains. The fact that the odds ratio growth rate is decreasing does not necessarily mean that the B.1.1.7 is getting less infectious; rather, the mixture of other strains could be getting more infectious over time, just because the contributions of the less infectious ones in the mix gradually decreases.

      Summarizing: I believe that 6 %/d is an estimate that is significantly too low.

      For graphs of my analysis, please see https://twitter.com/hk_nien... .

    1. On 2021-02-17 17:12:29, user Tim Pollington wrote:

      Dear Epke and colleagues,

      I would like to share some comments following reading your (v. relevant) paper on impact of COVID on VL in India at the country level. This is the second time I've commented on a preprint like this on medrxiv, and shared an 'open review' so I hope you receive it in the spirit it was intended. As I'm interested in doing similar studies your manuscript was relevant to me. And since I am funded by BMGF I thought it would be a waste of my funded time if I do not share these thoughts with you too, especially since you're at the preprint/pre-accepted stage.

      I thought the paper could benefit from an additional author who has field experience of the IRS/ACD activities occurring there to back-up your assumption that "no IRS and ACD take place and that only passive case detection" during an interruption.

      Given that the role of Asx in infecting others is still debated (some say recent xeno shows near zero contribution while ours last year did fit estimates consistently when relative Asx infectiousness of 0,1 or 2% were used), your use of the models E1 & E0 is a smart move to err on the cautious side.

      Model structure and quantification section<br /> Thanks for much for following best practice and using PRIME-NTD. It is the first time I have seen it and I definitely plan to use it in my next modelling publication and also when initially planning a model re engagement with policymakers.

      Given that the model runs for 30 years has population growth been taken into account?

      Impact assessment section<br /> Although adding incidence rates in the same period is acceptable, as events share the same 'person time at risk' denominator (and if the events are mutually exclusive), I'm not sure if epidemiologically it's a correct calculation to sum up rates over the 30 years since the population will be changing in this time and thus the denominators are changing. Perhaps one can convert it into absolute cases in each year and then sum those up?

      Discussion section - First paragraph<br /> It may help the reader if more emphasis was made on how a 1-year impacted delay by describing how it is amplified. ie How just one year interruption causes growth which needs to be curtailed before it turns over and falls, and the excess cases this generates. This concept of amplification could be strengthened.

      Second paragraph<br /> "80% of [VL-endemic???] sub-districts..." Did this cover just Bihar or all 4/5? endemic states.

      Third paragraph<br /> I think mortality rates are really relevant but can understand your caution re scant data. I think it's so important now considering the 1%CFR 2021-2030 target. Could this independent review help provide some rough estimates from pages 12-15 & 40? <br /> Even rough estimates from your model on excess VL cases and when they would likely be seen in the coming years, could be a useful starting place for resource planning of drugs.

      I think a caveat needs to be noted that this analysis is country-level whereas the threshold targets are at the block-level, to avoid the reader making an ecological fallacy.

      I hope that helps and also encourage you to comment on my work if I get to that stage!

      All the best, Tim.