On 2022-03-01 21:32:30, user Maurizio Rainisio wrote:
Computing NNT would have highlighted that to spare one hospitalization over the 4 months duration of the vaccination protection would require approximately 15,000 full treatments for the 5-11 kids and 7,000 for the 12-17. Adding an NNT column to table 1 would be of great help.
On 2022-03-02 17:00:41, user Carol Taccetta, MD, FCAP wrote:
Re the severe disease (hospitalizations), I posed a question to the corresponding author: does Table 11 from NY's Pediatric COVID-19 update (link below) reflect admitted FOR covid or all comers (admitted FOR + WITH covid)? There appears to be a similar table (Table 1) in this pre-print.
Table 11 is described within the link below as "Examining new hospital admissions with laboratory-confirmed COVID-19, per Table 11:", yet it does not specify a differentiation between admitted for and admitted with covid-19:
On 2022-03-09 21:24:14, user Les Funk wrote:
Figure 1 caption contains the false statement, "Disposition is presented for all enrolled participants..." There are 1841 participants not included in the figure after dose 2: 1258 from the BNT162b2 group and 583 from the placebo group. What happened to them?
On 2022-03-13 07:22:25, user Hanjun Lee wrote:
Dear Dr. Colson,
Thanks a lot for sharing this preprint and for your work regarding the virology of SARS-CoV-2. I would like to ask regarding the possibility of Amplicon-related artifacts in the Nanopore sequencing data. One of your key results that surprised me the most was the identification of a recombination event at p.156–179 of the SARS-CoV-2 Spike protein. However, looking into the Artic amplicon panel (ARTIC nCoV-2019 Amplicon Panel v4.1) that has been utilized during the amplification step, I have a fear that the recombination event at p.156–179 may be the consequence of primer set #73's preference toward a specific variant. The Artic amplicon panel that has been utilized has 99 different primer sets. While many regions are covered by more than two primer sets, some regions are covered by a single primer set. If a region is covered by a single primer set, this makes it harder to distinguish true hybrid "deltamicron" or "deltacron" genomes from a co-infection or co-incorporation of delta and omicron genomes. Spike protein's p.125–178, which has a very great overlap with the p.156–179 region that has been identified by the authors, is one such region; it is covered by ARTIC's 73rd primer set, but is just outside of the regions covered by the 72nd and 74th primer sets. Do the authors think it is possible that the 3' end of the recombination event (p.178 or 179) might be the starting site of the forward primer of the 74th primer set, not a bona fide recombination locus? Or do the authors think there is enough evidence that can outlaw such a possibility?
Again, thank you so much for your work, and stay safe.
On 2022-03-27 17:55:48, user Ludo Waltman wrote:
I have posted a review of this paper at https://doi.org/10.21428/7c....
On 2022-04-19 13:42:56, user Matthew MacLeod wrote:
This document was peer reviewed and published by Defence Research and Development Canada, which does not provide DOIs, so cannot be linked to by medRxiv. Permanent link to the citation record is here:
https://pubs.drdc-rddc.gc.c...
And PDF of the final version is here:
On 2022-05-18 16:21:05, user Carly Boye wrote:
Great presentation at #BoG2022! The scale of this study was very impressive. I was interested in your idea that BCRs might affect DD risk through noncoding mechanisms by disrupting lincRNA genes. I typically study smaller variants (such as SNPs) and it made me wonder if SNPs could potentially disrupt an interaction between lincRNA and DNA, and if this would affect transcription (and then if it could lead to DDs). Also, will your model be available once this work is published?
On 2022-05-23 16:01:08, user John Dough wrote:
Very nice work. I have a suggestion that is a relatively easy fix. I suggest you change from gender to sex. Gender is a social construct while sex is biological and my understanding is you studied sex here.
On 2022-05-24 18:55:47, user Ricardo Durães wrote:
Available at PLoS ONE: https://journals.plos.org/p...
On 2022-06-11 15:05:07, user Jae Kyoung Kim wrote:
On 2022-06-14 12:08:52, user Robert Clark wrote:
In Fig. 3, it is notable that for the severe cases, the ivermectin group had a factor of 1.79 advantage on the scale of faster time to recovery. The question arises in this subgroup analysis of whether this is a real effect?
A good way to check is to also look at the hospitalizations, emergency room visits, urgent care, etc. numbers specifically for the severe cases. Note that its expected that a large proportion of these should come from the severe cases. Then does IVM have such a clear advantage for the severe cases here as well?
Robert Clark
On 2022-06-17 09:43:38, user Will wrote:
A persistent link to the supplementary materials can be found here: https://doi.org/10.5281/zen...
On 2022-06-17 14:00:48, user Todd Lee wrote:
The authors are to be commended on a very important comparative effectiveness trial. Thank you! From a reporting standpoint:
Additional details worthy of reporting in Table 1 are the presence of "do not resuscitate orders" which would prevent intubation and the administration of co-interventions like remdesivir which may impact both the need for ventilation and mortality (CATCO, CMAJ 2022). Could the authors provide this information?
Additionally, the composite outcome is non-inferior. Yet, the composite contains two clinical outcomes which (a) differ in importance in that death is worse than ventilation and (b) may be related along a causal pathway in that patients who are ventilated are more likely to die. Could the authors revise the manuscript text or tables to include each component of the composite outcome separately by Day 28 or report death in the "adverse events"?
Additionally, could post-hoc subgroup analyses be performed by age, gender, vaccine status, and CRP greater than and less than 75 (recovery criteria).
I appreciate these were not pre-specified outcomes on clinicaltrials.gov, but they are essential to peer review and to contextualizing these results with the existing literature.
On 2022-06-23 08:44:59, user Andrew Fischer Lees wrote:
This is interesting work. Your 4th limitation is the biggest issue from a clinical side (I say this as a clinician). 99% of the time I order a hemoglobin it is as part of a CBC. Sometimes I order the hemoglobin because I want the Hb, but sometimes I order it because I want the WBC, or the platelets, or all of these things. There is not a great reason to order just WBC if that is all I care about, because the same reagents are used in the lab to process the sample, so the extra tests are "free information", that is, the marginal cost is zero.<br /> Your stability model should really be executed at the level of the CBC bundle. Asking clinicians to not order a Hb when the marginal cost is zero doesn't really save any money for the system or lab draws for the patient. But If the whole CBC is predictably stable, in that situation it would make sense to surface that information to the clinician in the form of Clinical Decision Support.
On 2022-07-08 05:23:36, user Adrian Campos wrote:
This manuscript was published in November 2021: https://www.nature.com/arti...
On 2022-07-08 18:08:52, user Angel perez wrote:
This paper has been published in Applications and Applied Mathematics.<br /> The published version can be found at https://digitalcommons.pvam...
On 2022-07-14 07:12:37, user Marek Widera wrote:
Finally, this work has been published in eBioMedicine Volume 82, August 2022, 104158
On 2022-07-29 04:26:04, user Miguel García Grimshaw wrote:
Hello, can you please include de link for the published version?
DOI: 10.1111/ene.15504
On 2022-08-05 06:55:12, user Robert Clark wrote:
The quite key question however remains unaddressed: how does vaccination status effect rebound?<br /> Because of the evidence overtime the vaccine reduces immune response, the correlation to number of shots and time since vaccination should also be made in regards to rebound.
Robert Clark
On 2022-08-10 10:36:43, user Leen Jabban wrote:
This paper is now published in Journal of NeuroEngineering and Rehabilitation<br /> https://jneuroengrehab.biom...
On 2020-04-02 00:27:40, user Sinai Immunol Review Project wrote:
Summary:<br /> The authors of this study provide a comprehensive phenotypic analysis of the adaptive immune cell pool in 38 COVID-19 patients with mild or no symptoms in comparison to 18 healthy donors. Using flow cytometry of circulating PBMC, the authors found that the total lymphocyte count in COVID-19 patients was slightly reduced. This is in striking contrast to severe patients in which severe lymphopenia is common and correlates with severity of disease. The relative CD19 cell count including particularly Germinal Center B cell (GCB) counts were significantly increased in COVID-19 patients. With respect to T cells, the authors describe no substantial differences in CD4 and CD8 T cell counts. However, when analyzing T cell subsets, the authors discovered significantly increased expression of CD25 and PD-1 as markers of late activation and exhaustion in CD8+ T cells, respectively. Moreover, the amount of naïve CD4 T cells as well as of T follicular helper (Tfh) cells were significantly increased in COVID-19 patients.
In an attempt to find correlations between patient characteristics and the status of the adaptive immune system, the authors applied Person’s correlation coefficient and found no major impact of age on CD8+ T cell activation as well as on Tfh and GCB-like T cell differentiation.
Summing up, the authors performed a thorough phenotypic analysis of B and T cell subsets in COVID-19 patients giving a promising insight into the status of the adaptive immune system in their patient cohort with mild symptoms.
Critical analysis:<br /> The strength of this study is the in-depth multiparameter analysis of adaptive immune cells in a reasonably sized cohort of 38 patients and 18 healthy controls. The assumption that COVID-19 patients with mild disease show an appropriate antigen-specific response due to an increase of Tfh and GCB cells is justified by the data.
Implications of the findings in the context of current epidemics:<br /> The study clearly shows that patients with mild-disease have increased Tfh and GCB and understanding the specificity of this response and how they correlate with disease course will be interesting to explore. Defining patient groups at risk for severe courses is crucial in order to be able to intervene early using experimental therapeutic strategies.
On 2022-08-13 18:37:14, user Christian Fiala, MD, PhD wrote:
Is there any information as to the requirements and/or use of face masks by pregnant women during the pandemic in the region?
On 2022-08-15 10:05:00, user james hurley wrote:
I congratulate the authors on their protocol for a ‘Systematic Review and Meta-Analysis of Selective Decontamination of the Digestive Tract in Invasively Ventilated Patients’ [1]. That there are already over 40 published articles with “Systematic Review”, “Meta-Analysis” and “Selective Digestive Decontamination” in the title indicate that this is a vexed topic and the definitive publication is yet to appear. <br /> A simple reading of recent Cochrane reviews appears to indicate that SDD lowers both infection incidence and mortality in this patient group, whereas four other interventions do not [2-7]. However, what are the substantial areas of doubt and how can these be best addressed [8]?<br /> May I make some suggestions that might increase the chance that their proposed Systematic Review might be definitive?<br /> Firstly, is the mechanism of action of how Selective Decontamination of the Digestive Tract decrease infection and mortality in invasively ventilated patients understood? Are the animal studies undertaken in mice in the early 1980’s, from which the term ‘Selective Decontamination’ originated, still regarded as valid? Is the term “Selective Digestive Decontamination” a triple misnomer? Several have proposed that the term ‘Control of Gut overgrowth’ as a more accurate term to describe the presumed mechanism [9, 10].<br /> Second, is it true to state that the “Uncertainty about the effectiveness of SDD is due to concerns about the generalisability of RCTs with limited internal and external validity.”? Why did the use of SDD fall out of favour among neutropenic patients in the 1990’s? Is there a potential for rebound infections? Will this proposed systematic review address the question of rebound? Is there a possibility that SDD is ineffective among ICU patients? Is there a possibility that SDD and the rebound effect on its withdrawal is harmful? <br /> Thirdly, the authors will need to confront data inconsistencies between various versions of the published SDD trials that appear in the two Cochrane reviews of this topic [2, 3]. The earlier review obtained ‘Intention to treat’ data from several of the authors of the primary SDD studies which differs from the ‘on treatment’ data as published. The latter often excluded patients who died before completing the four days regarded as necessary to achieve ‘Selective Decontamination’. As a consequence, there is both survivorship bias and an underestimation of infection and mortality incidences in the ‘on treatment’ data. In addition, will the authors use the original data for the study groups as randomly allocated or will they use the adjusted data as published?<br /> Fourth, the authors propose a subgroup analysis comparing the results for “Individual patient vs unit level randomisation (i.e. cluster and cluster/cluster-cross-over).” However, their hypothesis is that the effect is unidirectional, i.e. they expect a benefit to be “,…greater in individual patient randomised trials compared to unit level randomised trials.” This expectation is a restatement of the ‘Stoutenbeek’ postulate, stated in the first SDD study undertaken in the ICU setting, that there would be a contextual effect of using SDD in the ICU context and that this effect would be beneficial to any concurrent control groups patients and, as a consequence, bias downwards the estimates of the SDD intervention within individual patient randomised trials [11, 12]. Stated otherwise, this postulate implies a herd effect similar to that of herd protection from vaccination within a population. <br /> This postulate creates several difficulties for this proposed systematic review. By raising this postulate, does this invalidate the Stable Unit Treatment Value Assumption (SUTVA) that is fundamental to valid estimates of effect size from concurrent controlled trials? If the SUTVA is questioned here, will this invalidate the estimates from the proposed systematic review? Moreover, given this postulate and proposed subgroup test, will the test be one-sided, with the expectation that the effect is uni-directional [only beneficial effect possible], or two sided?<br /> There is evidence that the results of individual [i.e. concurrent control] patient randomised trials of SDD differ to those of unit level [or historical control; i.e. non-concurrent controls] randomised trials and that the SUTVA is questionable for these studies. This has only been addressed in first and second meta-analyses on this topic both published 25 years ago [13, 14]. These indicate that the effect is greater in the former, i.e. contrary to the direction postulated by Stoutenbeek. There is further and more recent evidence for this discrepancy. On the one hand, the three largest subsequently published studies of SDD versus either standard care or SOD, which were all undertaken using unit level randomization [i.e. and non-concurrent controls], showed absolute differences in bacteremia and mortality [before any statistical adjustments] of less than 5 percentage points [15-17]. On the other hand, the most recent Cochrane review of the studies of SDD in this population, which included mostly trials using individual patient randomization [i.e. and concurrent controls], showed absolute differences in pneumonia and mortality of five percentage points or greater [3]. <br /> Will the proposed protocol use the unadjusted data or the adjusted data from these trials? Does the data adjustment account for the Stoutenbeek effect?<br /> Finally, to provide a definitive review, the authors will need to explain why event rates [pneumonia, bacteremia, candidemia and mortality] are generally higher among control groups within trials using individual patient randomization [i.e. with concurrent controls] versus control groups within trials using unit level randomization [i.e. with non-concurrent controls], versus control groups from studies of interventions other that SDD, and versus groups of studies without an intervention. Moreover, why is it that the event rates in the SDD intervention groups are similar to intervention groups from studies of interventions other that SDD in this patient group? The higher event rates are apparent in closer scrutiny of the summary results of the five Cochrane reviews [3-7]. On the one hand, the median control group event rates for pneumonia and mortality [18] are highest within the control groups of studies of SDD versus control groups of studies of other interventions and yet, on the other hand, the event rates for the intervention groups are paradoxically similar to intervention groups of studies of other interventions.<br /> I wish the authors well and hope that they succeed in providing the definitive systematic review of this topic over the arc of time [19].<br /> References<br /> 1. Hammond NE, Myburgh J, Di Tanna GL, Garside T, Vlok R, Mahendran S, Adigbli D, Finfer S, Goodman F, Guyatt G, Venkatesh B. Selective Decontamination of the Digestive Tract in Invasively Ventilated Patients in an Intensive Care Unit: A protocol for a Systematic Review and Meta-Analysis. medRxiv. 2022 Jan 1.<br /> 2. Liberati A, D'Amico R, Pifferi, et al: Antibiotic prophylaxis to reduce respiratory tract infections and mortality in adults receiving intensive care. Cochrane Database Syst Rev 2009; 4: CD000022.<br /> 3. Minozzi S, Pieri S, Brazzi L, Pecoraro V, Montrucchio G, D'Amico R. Topical antibiotic prophylaxis to reduce respiratory tract infections and mortality in adults receiving mechanical ventilation. Cochrane Database of Systematic Reviews 2021, Issue 1. Art. No.: CD000022.<br /> 4. Wang L, Li X, Yang Z, Tang X, Yuan Q, Deng L, Sun X. Semi-recumbent position versus supine position for the prevention of ventilator-associated pneumonia in adults requiring mechanical ventilation. Cochrane Database Syst Rev 2016(1). DOI: 10.1002/14651858.CD009946.pub2.<br /> 5. Gillies D, Todd DA, Foster JP, Batuwitage BT. Heat and moisture exchangers versus heated humidifiers for mechanically ventilated adults and children. Cochrane Database Syst Rev. 2017(9). DOI: 10.1002/14651858.CD004711.pub3.<br /> 6. Bo L, Li J, Tao T, Bai Y, Ye X, Hotchkiss RS, Kollef MH, Crooks NH, Deng X. Probiotics for preventing ventilator-associated pneumonia. Cochrane Database Syst Rev. 2014(10). DOI: 10.1002/14651858.CD009066.pub2.<br /> 7. Zhao T, Wu X, Zhang Q, Li C, Worthington HV, Hua F. Oral hygiene care for critically ill patients to prevent ventilator-associated pneumonia. Cochrane Database Syst Rev. 2020(12).<br /> 8. Hurley JC Selective digestive decontamination, a seemingly effective regimen with individual benefit or a flawed concept with population harm? Crit Care. 2021;25(1).<br /> 9. Silvestri L, Miguel A, van Saene HK. Selective decontamination of the digestive tract: the mechanism of action is control of gut overgrowth. Intensive Care Med. 2012;38(11):1738-50.<br /> 10. Hurley JC (2020) Structural equation modeling the “control of gut overgrowth” in the prevention of ICU-acquired Gram-negative infection. Crit Care 24(1):1-2.<br /> 11. Stoutenbeek CP, Van Saene HK, Miranda DR, et al: The effect of selective decontamination of the digestive tract on colonisation and infection rate in multiple trauma patients. Intensive Care Med 1984; 10(4):185-192.<br /> 12. Hurley JC. Incidences of Pseudomonas aeruginosa-associated ventilator-associated pneumonia within studies of respiratory tract applications of polymyxin: testing the Stoutenbeek concurrency postulates. Antimicrob Agents Chemother. 2018;62(8):e00291-18.<br /> 13. Vandenbroucke-Grauls CM, Vandenbroucke JP. Effect of selective decontamination of the digestive tract on respiratory tract infections and mortality in the intensive care unit. The Lancet. 1991;338:859-62.<br /> 14. Hurley JC. Prophylaxis with enteral antibiotics in ventilated patients: selective decontamination or selective cross-infection?. Antimicrobial agents and chemotherapy. 1995;39(4):941-7.<br /> 15. de Smet AMGA, Kluytmans JAJW, Cooper BS, et al: Decontamination of the digestive tract and oropharynx in ICU patients. N Engl J Med 2009, 360:20–31.<br /> 16. Oostdijk EA, Kesecioglu J, Schultz MJ, Visser CE, De Jonge E, van Essen EH, Bernards AT, Purmer I, Brimicombe R, Bergmans D, van Tiel F. Notice of retraction and replacement: Oostdijk et al. effects of decontamination of the oropharynx and intestinal tract on antibiotic resistance in ICUs: a randomized clinical trial. JAMA 2014; 312 (14): 1429-1437. JAMA 2017; 317(15):1583-4.<br /> 17. Wittekamp BH, Plantinga NL, Cooper BS, et al: Decontamination strategies and bloodstream infections with antibiotic-resistant microorganisms in ventilated patients: a randomized clinical trial. JAMA 2018;320(20):2087-2098. <br /> 18. Hurley JC Discrepancies in Control Group Mortality Rates Within Studies Assessing Topical Antibiotic Strategies to Prevent Ventilator-Associated Pneumonia: An Umbrella Review. Critical care explorations. 2020;2(1).<br /> 19. Pizzo PA. Management of patients with fever and neutropenia through the arc of time: a narrative review. Ann Intern Med. 2019;170(6):389–97.
On 2022-08-24 17:17:22, user Mario Malicki wrote:
The references are not linked to any statements in the manuscript. Could you fix this?
On 2022-09-29 18:22:07, user Nikhil Padhye wrote:
Published version can be found here:<br /> Padhye, N. (2022). Bayesian Measurement of Diagnostic Accuracy of the RT-PCR Test for COVID-19. Metrology, 2(4), 414–426. https://doi.org/10.3390/met...
On 2020-05-27 03:20:48, user G M wrote:
Will be picked up during review no doubt but describing qPCR as an 'antigen' test is false, and personally distracting.
On 2020-04-23 15:59:44, user gmshedd wrote:
If we take the observed fatalities (by residence) in the Bronx (2258 as of 4-22) and Queens (3432), and apply the suggested infection fatality rates of 0.12% to 0.20%, we can infer that between 80% and 133% of Bronx residents have already been infected, and that between 76% and 127% of Queens residents have also been infected. Therefore, Bronx and Queens residents have achieved herd immunity, so they can re-open everything immediately. This is such great news! Oh, but you say, these populations aren't similar. OK, so I'll use Nassau County (Long Island)--median income $111k vs $116k in Santa Clara County. 1431 Nassau County residents have died, from which we would infer that between 53% and 88% of the 1,356,924 county residents have been infected. My point is that the suggested infection fatality rates don't pass the eye test, and, since they are derived from the infection rates that are at the center of the controversy, it would seem that the publication's Santa Clara County infection rates are higher than seems reasonable for the NYC area--unless California COVID-19 has a significantly lower infection fatality rate than New York COVID-19.
On 2020-04-24 05:58:27, user JM V wrote:
With 80 (1.7%) people dead in Castiglione d'Adda (Caveats: Old/Smoking/Unlucky/Collapse of Health care system/Some would have died anyway) this was already extremely unlikely. Now, with NYC 0.22% excess deaths and 21.2% of shoppers having antibodies, an IFR of 0.8% - 1.2% appears plausible.
On 2020-04-25 07:19:42, user John Smith wrote:
On 2020-04-25 21:08:15, user outdoorgirl0814 wrote:
My primary question on this study is why the IgM and IgG specific results were not presented, but rather pooled together. This seems like important information. From what I can tell, the test identifies them separately.
On 2020-05-01 18:30:42, user Dean Karlen wrote:
The findings reported in the first version suffered from serious mistakes in statistical treatment. Now two weeks later, the authors have slightly adjusted their stated confidence intervals reported in the abstract and elsewhere in the paper. Ignore the abstract and skip to the final page.
There, the authors finally admit that their 95% CL intervals would contain 0% if the analysis is done correctly:
There is one important caveat to this formula: it only holds as long as (one minus) the specificity of the test is higher than the sample prevalence. If it is lower, all the observed positives in the sample could be due to false-positive test results, and we cannot exclude zero prevalence as a possibility.
So in order to report intervals that exclude 0%, they have to assume that the prevalence is high enough to use an approximate approach that will yield intervals that exclude 0% prevalence. This is nonsense. The abstract should clearly state that the study cannot exclude 0% prevalence at 95% CL.
On 2020-05-03 01:41:44, user Danny C. wrote:
Can we get the rigor of statistical analysis for the rt-PCR tests being used currently please? So many experts here weighing in.. But what about the current tests providing the current numbers? Thanks!
On 2020-05-06 14:10:54, user David wrote:
Whitman et al. evaluated Premier Biotech Biotest test used in this study using 108 pre-COVID blood samples (collected July 2018). They found 3 false positive, giving a specificity of 97.22% (92.10-99.42% 95% C.I.). I note that the authors updated their paper with tests run on many more pre-COVID samples, so this might just be bad luck.
Whitman, J.D., Hiatt, J., Mowery, C.T., Shy, B.R., Yu, R., Yamamoto, T.N., Rathore, U., Goldgof, G.M., Whitty, C., Woo, J.M. and Gallman, A.E., 2020. Test performance evaluation of SARS-CoV-2 serological assays. medRxiv.
On 2020-04-24 15:20:42, user Lawrence Mayer wrote:
Again I suggest readers that want to see discussion of these papers and others in Clinical Epidemiology and Science consider joining or group if they have healthcare or Science credentials.
Clinical Epidemiological Discussion of COVID19 Pandemic Group<br /> https/facebook.com/groups/covidnerds
On 2020-04-24 20:11:40, user Diego B. wrote:
This article has been posted on JAMA Network: https://jamanetwork.com/jou...
On 2020-04-24 20:18:46, user Diego B. wrote:
This article has been published on JAMA Network: <br /> https://jamanetwork.com/jou...
On 2020-04-25 04:04:36, user Deevish N D wrote:
The radiometer used in this study - UV513 AB detects a peak wavelength of 365 nm as per its manual. But the actual germicidal wavelength is around 254 nm. I believe the dose needed for UV disinfection has been under-reported in this article. Authors please correct me if am wrong.
On 2020-04-25 18:43:20, user Retelska wrote:
Excuse, me, I don't know if I understand correctly. Do the 2 Elisa essays yield 5% false positives? Were these tests used to establish that 5% of general population has now been infected? You expect 5% false positives, right? How do you correct for this effect? Only the 3rd test with 0% false positives seems specific enough.
On 2020-04-25 21:34:02, user Christopher Rentsch wrote:
We believe that Magagnoli et al failed to correctly identify intubation occurring in hospitalized patients testing positive for COVID-19. They used CPT codes 31500, 94002, 94003, and E0463 and ICD-10 procedure codes indicative of assistance with respiratory ventilation, or extracorporeal membrane oxygenation (ECMO). We identified 5,906 COVID-19 patients treated in the Veterans Health Administration between March 1 and April 21, 2020. In addition to the above CPT codes, we identified intubation according to ICD-10 procedure codes for insertion of endotracheal airway, and respiratory ventilation, which were usually concordant. We cross-validated with medications typically used during intubation, such as neuromuscular blocking agents (e.g., succinylcholine, rocuronium) and short acting sedatives (e.g., propofol, midazolam). We also found these intubation codes most frequently in the context of intensive care. We did not find similar evidence of face validity for ventilation assistance codes. No instances of ECMO were found as this procedure is unlikely to be used in the Veterans Health Administration.
We classified 307/5,906 = 5.2% patients as intubated. Using the Magagnoli algorithm, only 96/5,906 = 1.6% patients were said to be intubated. Of these, 37 were classified based on ventilation assistance codes, not indicative of intubation.
List of ICD-10 Procedure codes used to identify intubation
Codes in both Magagnoli and Tate lists<br /> - Respiratory Ventilation (5A1935Z 5A1945Z 5A1955Z)
Codes in Magagnoli list, but not Tate list<br /> - Assistance With Respiratory Ventilation (5A09357 5A09358 5A09359 5A0935B 5A0935Z 5A09457 5A09458 5A09459 5A0945B 5A0945Z 5A09557 5A09558 5A09559 5A0955B 5A0955Z)<br /> - Extracorporeal Oxygenation, Membrane (5A1522F 5A1522G 5A1522H)
Codes in Tate list, but not Magagnoli list<br /> - Insertion of Endotracheal Airway Into Trachea (0BH13EZ 0BH17EZ 0BH18EZ)
Janet P. Tate (Janet.Tate2@va.gov)<br /> Christopher T. Rentsch (@DarthCTR)<br /> Joseph T. King Jr.<br /> Amy C. Justice
VA Connecticut Healthcare System<br /> West Haven, CT
On 2020-04-26 13:05:40, user Bin_Pei wrote:
Thanks for kind reminder of the reviewers, there is an unintentional editing error that we accidentally mixed the name of two cities in affliation in the original manuscript. We have submitted a revision already, there might be few days delay and we will be more careful in the future work.
On 2020-04-26 15:20:46, user Robert Clark wrote:
I was interested to read of your report on over 4,000 COVID-19 cases in New York. Collecting health histories for a large data set of patients of COVID-19 may provide a rapid means of determining which medicines could be effective in combating it:
Big data to fight COVID-19 and other diseases.<br /> https://medium.com/@rgregor...
The idea is to find if certain medications are *missing* from the patients prior health histories, suggesting those medications may be protective against the disease.
Robert Clark
On 2020-04-27 11:07:45, user Pilar Domingo Calap wrote:
We have detected a small factual error in the text. The sentence containing the error is the following:
"The first confirmed case in the Iberian Peninsula was communicated on February 24, 2020 in Burriana, a small town nearby the city of Valencia, followed by another case the following day in Valencia."
This sentence should be instead be:
"The first three confirmed cases in the Iberian Peninsula were communicated on February 25, 2020 in Madrid, Barcelona, and Villareal, a small town nearby the city of Valencia."
Pilar Domingo-Calap (co-author of the preprint)
On 2020-04-28 16:33:17, user Katri Jalava wrote:
Interesting paper, and fascinating model. I was a bit curious of your contact percentages. How do you come up with the numbers? E.g. for CS adult-adult would be reduced only by 20 % by closing the public events. I could argue that it is at least 60 %, especially if you have a look on SF1 in 10.1371/journal.pcbi.1005697. Also, if you have both CS and HO in place, you get 80 % + 20 % =100 % reduction for child-child contact(?).
Getting any data on impact of the closure measures from publications is hard. I think they have tried this in the UK from the case load data. Do you think you could do a telephone survey among Germans? Or if an app company would make a data collection tool where everyone could register their daily contacts during the outbreak, that would be cool. Good luck and thank you.
On 2020-04-29 10:51:47, user Dan wrote:
Hi! Is there any information on how much each of those underlying health conditions increases risk of severe COVID-19 disease? Thanks
On 2020-04-29 21:17:20, user Rick56 wrote:
The authors are addressing an important question. But I believe they have underestimated the length of time between exposure and testing positive.
This matters because if you look at the raw data currently available for Wisconsin at the Johns Hospkins github, you see what appears to be a flattening of the number of new cases starting April 6 -- followed by a substantial spike starting April 22.
Given this, it is especially important how one models the time between exposure (election; April 7) and testing positive. Because if that time could be 15-19 days, then there is a very plausible spike resulting from election exposures.
The time from exposure to positive test = <br /> exposure to symptoms (incubation period) plus <br /> symptoms to testing (let's call it "testing delay").
But the testing delay is also influenced by how readily testing is available.
So, two problems:
But the Li paper notes that the 95%ile for this distribution is 12.5 days.
When I use R to generate gamma distributions with a mean of 5.2 and 95%ile at 12.5, the SD is substantially greater than 2.3. Also -- that gamma gives about 18% of the incubation periods <2 days.
Based on this, it seems likely that the author's distribution has a much thinner right tail than is consistent with the Li data. And perhaps 18% of their distribution could be < 2 days. So we need the specifics of the distribution the authors created.
So, was the testing delay in Wisconsin as short as that in Beijing? Did the average person in Wisconsin get tested 4.3 days after symptoms start? Seems unlikely. Since the US has had such a terrible problem getting people tested, we need evidence that their testing delay is reasonable for Wisconsin.
Unless the authors can address these points, I think it very inadvisable to claim that the spike in positive cases starting April 22 is completely unrelated to the April 7 election.
[you'll have to look up the Wisconsin data on your own. I attempted to attach a plot multiple times without success].
~~~~~ here are the methods details from the authors' supplementary
"We assume the incubation period distribution is gamma with mean and SD of 5.2 and 2.3 days [3]. We assume that the distribution of the time between symptom onset and confirmation is gamma with mean and standard deviation (SD) of 4.3 and 3.2 days, based on 186 cases reported in Jan-Feb 2020 in Beijing [4]."
from their References<br /> 3. Li Q, Guan X, Wu P et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 2020;382:1199-1207.<br /> 4. Leung K, Wu JT, Leung GM. First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment. Lancet 2020;395:published online April 8.
On 2020-04-30 08:59:22, user Skerdi wrote:
It would be great if you could compute the RAASi/nonRAASi difference adjusted for the relevant comorbidities you cite (age, gender, ICU entry on day of admission, serum ferritin, insulin-dependent diabetes mellitus and cardiac arrhythmia history).
This would help give a sense of whether RAASi are likely to work only if taken chronically before encountering Covid-19 (in which case their effect adjusted on clinical baseline would shrink or disappear) or if they could also work when started after disease onset (if adjustment for clinical baseline does not erase their effect).
On 2020-04-30 13:35:27, user John Lambiase wrote:
This has greater implications than just covud 19. It could effect most "enveloped" respiratory pathogens. The antimicrobial processes signalled by vitamin D are absolutely fascinating. They trigger multiple facets of immunity.
On 2020-04-30 15:02:24, user Subhradip AIIMS New Delhi wrote:
Why gene expression RPKM for ACE2 is very low in lungs? Source : TiGER, Protein Atlas
On 2020-04-30 20:54:07, user Frank Conijn wrote:
I don't have any objections against retrospective cohort studies, because they are sometimes all one can do, and they can give valuable insight. But the compared groups must have equal baseline disease severity. And am I overlooking something, or is that information missing?
Furthermore, the Brief Summary on page 2 says (emphasis by me): "The use of antiviral drugs (chloroquine, oseltamivir, arbidol, and lopinavir/ritonavir) did not shorten viral RNA clearance, especially in non-serious cases." But the text and figures show that that still concerned patients with pneumonia or worse. I don't find that non-serious cases. Those are moderate cases on a scale from light - mild - moderate - severe - critical.
On 2020-05-01 05:08:22, user Adapt Research wrote:
Hi, far too early to be speculating on this. The high GHSI countries are also the high GDP ones and the high air traffic ones. The number of tests is mostly correlated with the number of cases, not the GHSI. It may yet be the case that high GHSI countries end up with less deaths per capita than low GHSI ones. We are nowhere near the end yet, and don't know what will happen in Africa where the largest concentration of low GHSI countries is. The correlations are interesting, but we're not able to draw conclusions yet.
On 2020-05-01 12:10:55, user Roman wrote:
Infection fatality rate is calculated from official number od deaths. If we calculate from oposition estimates, there was 2470 deaths as of 24. April. <br /> That would mean fatality rate 0,4%.
On 2020-05-02 06:26:29, user Jasmin Zessner wrote:
How come the authors only looked into countries most affected by SARS -COV-2 while ignoring the ones where lockdown was effective (Austria, Germany) and extrapolate that “lockdown is not effective in western Europe”
On 2020-05-03 09:08:28, user Daniel Corcos wrote:
These calculations rely on wrong estimates.<br /> 1) The delay between infection (does it include incubation time?) and death is based on a preprint from data on the Diamond Princess epidemic. There were 7 deaths at that time but the current number is 14 (1). The case fatality rate in South Korea was 1.6%, but now it is 2.32% (2). Delayed deaths should be taken into account.<br /> 2) A zero generation time is unrealistic, as the virus must multiply before spreading, and estimates of the generation time have been calculated to be between 4 and 5 days (3,4) .<br /> Changing these parameters should alter the conclusions.
1) https://en.wikipedia.org/wi...<br /> 2) https://en.wikipedia.org/wi...<br /> 3) https://www.sciencedirect.c...<br /> 4) https://www.medrxiv.org/con...
On 2020-05-04 06:30:08, user japhetk wrote:
This study has serious flaws and I will reject if I were a reviewer.
First, this study doesn't have a control data such as the blood sample of a few years ago. Although, the kit maker advocates the specificity of 100%, various test kits including the innovita's one which championed 100% specificity were already shown to show the inferior data compared with the maker's advocates.
Second, as pointed out,
Tests were done for randomly selected preserved serum from patients who visited outpatient clinics of the hospital and received blood testing for any reason. Patients who visited the emergency department or the designated fever consultation service were excluded to avoid the overestimation of SARS-Cov-2 infection.
SARS-COVID-19 is already known to cause atypical symptoms even in the "asymptomatic" (in terms of typical symptoms of infection) such as stroke, and various other thrombotic symptoms. So, this exclusion criteria is not enough apparently to avoid biased sampling and overestimation.
In Japan, this apparently seriously flawed study without review is reported widely and people even some doctors now say the real fatality rate of SARS-COVID-19 is 0.05%! based on this study (they seemed to have forgotten Japanese patients in the diamond princess ship showed the higher mortality rate compared with age-matched patients of westerners in the same ship). This is a nightmare for the public health of Japan.
On 2020-05-05 20:54:15, user japhetk wrote:
This study has serious flaws and I will reject if I were a reviewer.
First,<br /> this study doesn't have a control data such as the blood sample of a <br /> few years ago. Although, the kit maker advocates the specificity of <br /> 100%, various test kits including the innovita's one which championed <br /> 100% specificity were already shown to show the inferior data compared <br /> with the maker's advocates.
Second, as pointed out,
Tests<br /> were done for randomly selected preserved serum from patients who <br /> visited outpatient clinics of the hospital and received blood testing <br /> for any reason. Patients who visited the emergency department or the <br /> designated fever consultation service were excluded to avoid the <br /> overestimation of SARS-Cov-2 infection.
SARS-COVID-19 is already <br /> known to cause atypical symptoms even in the "asymptomatic" (in terms of<br /> typical symptoms of infection) such as stroke, and various other <br /> thrombotic symptoms. So, this exclusion criteria is not enough <br /> apparently to avoid biased sampling and overestimation.
In Japan, this apparently seriously flawed study without review is reported widely<br /> and people even some doctors now say the real fatality rate of <br /> SARS-COVID-19 is 0.05%! based on this study (they seemed to have <br /> forgotten Japanese patients in the diamond princess ship showed the <br /> higher mortality rate compared with age-matched patients of westerners <br /> in the same ship). This is a nightmare for the public health of Japan.
On 2020-05-05 14:23:59, user John Huppenthal wrote:
From January 1, 2020 to April 11th, the study period, over 40,000 fewer people died in 2020 than in the same period in 2019.
That's an amazing number.
You would expect an additional 13,000 people would die in 2020 just from the increase and aging of the population.
Adjusted for that effect, 53,000 more people died in 2019 than in 2020.
By the logic of the study, Covid-19 had 53,000 excess deaths in 2019.
A lot more than the 15,000 it had in 2020.
Every year, they do a vaccine effectiveness study. The results of that study need to be coughed up a whole lot sooner this year to unravel the true numbers.
This study did not produce the true numbers, not even close.
On 2020-05-05 20:58:20, user japhetk wrote:
A brief comment. <br /> This study's conclusion that the proportion of asymptomatic patients among the infected is 99.99% is not consistent with the fact that 9 Japanese out of 300 infected Japanese passengers among 1341 total passengers in the diamond princess ship (where all passengers went through PCR testing) have died (half the infected (which was confirmed by PCR) showed the symptoms by the way). And their fatality rate was higher than the age-matched westerners. Although, they were mostly old, so are the 30 percent of Japanese.
On 2020-05-05 21:56:48, user Un Kwon-Casado wrote:
Hi Anne- Great and exciting work! Do you know if its primarily shedded viral particles versus infected cells in the saliva samples?
On 2020-05-08 04:56:24, user Dan T.A. Eisenberg wrote:
This paper is very important. My lab is planning to implement an assay inspired by it. Can you elaborate on how long healthcare worker samples were stored at +4 for before testing?
On 2020-05-07 02:38:21, user Variant wrote:
In most cases, peak deaths and infections preceded the point at which any SAHO orders could have had impact. In fact, virus "curves" are nearly identical between states where there have been significant movement restrictions and those that haven't.
On 2020-05-07 20:33:43, user Walter Langel wrote:
The fit and extrapolation of the total infection number by a logistic function has been continued till first week of May and extended to several countries. Now the excel file containing all data and fits is available at researchgate:<br /> https://www.researchgate.ne...
On 2020-05-08 18:55:00, user hans wrote:
Please consider to cite:
Bartels, Christian (2020): Predicting the impact of a new infectious disease based on the basic reproduction number is challenging. figshare. Presentation. https://doi.org/10.6084/m9....
On 2020-05-10 14:19:22, user Minta Péter wrote:
Hi, something has happened to the pdf, can't download and doesn't show the size.
On 2020-05-11 09:18:27, user David Sbabo wrote:
Russia and Ukraine in the HCQ group?
Their third death occured in March in both countries. HCQ was autorised in mid April in both countries Unless they can go back in time, HCQ cannot have any influence here.
So the main result of this study is null and void.
On 2020-05-11 20:53:16, user Erik Hansson wrote:
Thank you for your work, it is valuable to consider that that people have different social activity levels, but I am concerned that your approach miss two important aspects which will underestimate the herd immunity threshold/make it less valid as an indicator of the risk of new severe epidemic flares:
Lower social classes may be less able to achieve a low level of social activity due to household crowding, dependence on public transportation and inability to work from home due to having manual work. This may lead to higher disease transmission in lower than higher social classes. Add to this the situation in elderly care in which the absence of PPE has probably led to quite intense transmission both from and to workers, who are strongly concentrated to lower social classes in Stockholm.
Outcome data is scarce but there seems to be empirical evidence of such a social gradient in covid-19 transmission both in hospitalized cases and very limited seroprevalence studies (contact Björn Olsen in Uppsala for more details or read media reports from last week - their study found 0% seroprevalence at Östermalm (~Knigthsbridge) in the end of April, n=?). Information from other major cities tell a similar story of a social gradient.
Under "normal" circumstances persons from lower social classes may not necessarily have higher levels of social activity than persons from the more affluent classes. I am concerned it may rather be the opposite as people from higher social classes may more likely engage in several activities less accessible to persons from lower classes, activities that do not happen in a semi-quarantine setting, such as culture and sports events, parties, eating out, bars, office work and meetings, conferences, university education, etc, but that are expected to be possible to do in a society having reached herd immunity.
Furthermore, due to prevailing segregation by class and ethnicity such post-social-distancing activities will likely primarily be done together with other persons likewise having been able to limit their activities during the first phase of the epidemic. There are thus conditions that allow rapid disease transmission within more affluent social strata if these go back to business as usual. It may even be argued that estimating a herd immunity threshold as an average percentage within a strongly segregated city is not especially meaningful. If there are large enough pools of connected susceptible individuals there is still a possibility of epidemics that overwhelm the healthcare system.
Another concern, which is partly related to the present manuscript is the use of quite uncertain and potentially inflated modeled estimates to make predictions of when Stockholm will reach the disease-induced herd immunity threshold, in June 2020, less than 3 weeks from now. This model estimated 26% had been infected by May 1. A critique of this model estimated 5-10% (https://twitter.com/AdamJKu... "https://twitter.com/AdamJKucharski/status/1254084771535376391)"), and the only (to my knowledge) somewhat representative seroprevalence study found 7.5% (Björn Olsen) at that time. Two separate methods that concur so well seems more credible than one modeled estimate.
The combination of estimating an artificially low herd immunity threshold and using potentially exaggerated cumulative infected proportion risk declaring “all-clear” in Stockholm much prematurely.
Erik Hansson, <br /> MD, MSc Epidemiology
On 2020-05-15 18:30:49, user Bruce wrote:
How do those results compare to chloroquine?
On 2020-05-24 21:33:11, user Tim Tarr wrote:
DOXY was suggested as replacement for azithromycin for those with heart issues. Seems azithromycin may compound HCQ risk to the heart. Now put Zinc in the mixture.<br /> DOXY+HCQ+Zinc sulfate <br /> The lab work should be run for vitamins D&C deficiency and Zinc. Lab work on kidney & liver status is pretty standard for admission.Also if heart function not known that should be checked, also usually a basic admission process.
On 2020-04-03 14:51:49, user Jack Debrueil wrote:
What is the biological plausibility of this association. The ABO-type is related to red blood cells. IT is important to know associaitons with leukocytes and HLA-types. Before concluding anything these reulst must be stratified by HLA-types.
On 2020-03-23 16:59:18, user Rintaro N. wrote:
?“The distribution of ABO blood groups in blood donors... (ref.7)” might not be a good control. It can be different from “true” distribution.?
?cf.?<br /> ?Blood Type and Blood Donation Behaviors: An Empirical Test of Pure Altruism Theory<br /> https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3171957
On 2020-04-02 20:21:31, user drumfucius wrote:
I am an O positive nonsecretor. Wondering if there is any info regarding susceptibility in regards to secreter status.
On 2020-08-04 12:21:27, user Mike Dammann wrote:
Here are some of the important parts from the revised version of the NYP study highlighting the important of the Rh negative blood group: https://www.rhesusnegative....
On 2020-04-04 01:25:42, user GLB wrote:
The data from Wuhan are used to characterize the influence of social distancing. From the paper "To be specific, the generalizable information from Wuhan was the impact that social distancing had on maximum death rate and time to reach the inflection point.". Many sources have raised doubts about the veracity of the Wuhan data. Does this render the characterization of the efficacy of social distancing methods in the model suspect? Can the model be tested by using a different location (say, Italy) as the training data set to see how the analysis changes?
On 2020-04-02 04:55:23, user Sola Grantham wrote:
I would like to see an explanation of why states with lower current rates of growth are projected to have later peaks. This makes sense to me only in the case of herd immunity being the cause of the peak. Then the area under the graph would remain the same. Thus, to reach the critical percentage of population with immunity, a slower rate of infection would lead to a later peak. But if the cause of the peak is the assumed perfect adherence to social distancing, then wouldn't the date of the peak be more related to the date of practical enactment of the social distancing measures?
On 2020-04-02 16:55:14, user VWFeature wrote:
What happens if instead of "assuming full social distancing through May 2020" we see what's actually happening? (Deaths go way up.)<br /> What's the assumption of death rates when hospitals and ICUs exceed capacity?
When no beds are available, a reasonable assumption would be that 80% of people needing hospital, and 100% of those needing ICU would die.
This study keeps getting cited as "best possible outcome." It's intellectually dishonest to present a "best possible" without a "most likely" and "worst case" projection.
This study is already inducing a false sense of security. This is the BEST POSSIBLE outcome. The most likely is far worse.
On 2020-04-02 22:52:25, user Qi Ying wrote:
The error function used in the study can be derived from the assumption that the daily death follows a normal distribution. Our experience in China shows that it is not the case. The tail in the daily death rate distribution is much longer. The predicted deaths are likely underestimated. Also, the error function fitting leads to significant under-predictions when the inflection point in the death rate has not arrived, which is likely the case for many US states. Thus, I believe these estimations presented in the paper as well as on their website are going to be significantly biased low. The actual situation could be much much worse.
On 2020-04-04 14:57:41, user Alexandros Heraclides wrote:
Maybe better to refer to "differing Relative Risks for dying", rather than "differing mortality impacts"? The latter points to absolute risk difference, while you are referring to relative risks. Great paper though!
On 2020-04-06 16:47:11, user smallbusinessrocks wrote:
A MORE REASONABLE DEATH RATE FOR THE C-19 FLU 4/6/2020
Food for thought.
As a young actuaries, many years ago a group of us tried to identify causes of death from older people dying with several SUD (serious underlying disease). We gave up, cannot clearly identify cause. Most doctors certifying cause of death do not know what caused it, if from SUD. Most people age 65 and over have two or more SUD. Seven thousand people with SUD die each day in the United States.
People have touted various rate of death from the C-19 flu in America, starting with 4.5% and reducing quickly to current 1.29%. There will be many more deaths from the current infected.
These death rates are grossly overstated – every pandemic, it is the same thing – death rates are wildly overstated at the beginning. A calculation, using a better basis, is 0.73% -<br /> more than the ordinary flu, but not 1.3% or 4.5%
Truer denominator: in all but Iceland and Pacific Princess, we need to multiply the total cases by four. Why? Because we are only testing a segment of symptomatic cases (coughing, etc), but the asymptomatic cases are 80% of the total. Except for Iceland - they tested a large group drawn from the general population, not just the ones showing<br /> symptoms, found 75% asymptomatic (multiply denominator by factor of four). The<br /> Pacific Princess tested all 2500 on boat – the Pacific Princess 1% death rate<br /> is highly affected by median age of cruise passengers, in general, of 60 to 69<br /> years. Diamond Princess has asymptomatic 83% - multiply by five
Truer numerator, is much less than the reported deaths, we would estimate about 0.2 of the 81% of deaths who have "SUD" - serious underlying disease; and 1.0 for all others. We estimate a weighted multiplier as (.2 deaths x 1.0 + .8 deaths x 0.2 = .36 of deaths<br /> reported). Why? Because many die from pneumonia in the USA each year, typically as the final stage of some other SUD (per NCHS). Doctors cannot prove a death from someone having the C-19 flu is CAUSED BY the C-19 flu, rather than the person with C-19 flu died WITH the C-19 flu. Needs research, but impossible to split causes. Reported deaths of<br /> person WITH C-19 flu now are 100% ascribed to C-19 flu currently.
In USA, a truer estimate of the<br /> actual death rate is therefore, at April 5, 0.73%:
Numerator: 8173 deaths x .36 = 2942<br /> deaths FROM C-19 flu – multiply this times 3 for future deaths from this<br /> cohort equals 8826 - divided by - Denominator:<br /> 301147 cases x 4 = 1204558 to include asymptomatic – yep, the current number of<br /> cases is four times the reported numbers. This is very good news, because it<br /> reduces the mortality rate.
Twelve months from now, we can look<br /> at the total deaths in the USA, and compare that with the 2.8 million deaths<br /> for 2018. 2.6 million of 2018 deaths were from about seven serious<br /> underlying diseases, many people having three or more suds.
Equals 0.73% truer death rate...more<br /> than 0.12% from ordinary flu, but well below 1.29%
The C-19 flu is just a flu. <br /> The C-19 flu is just a flu <br /> The C-19 flu is just a flu
Pete A
On 2020-04-07 14:19:14, user Sudin Bhattacharya wrote:
When will the data be available in GEO?
On 2020-04-07 23:34:47, user Snap wrote:
We are also publishing similar results for China, Italy and USA<br /> https://www.medrxiv.org/con...
On 2020-04-08 13:20:06, user Devi Dayal wrote:
Through this publication, we just added some more data to the recently published articles on a protective role of BCG vaccination against COVID-19, reassuring for countries with limited resources to fight the pandemic on their own.
On 2020-04-09 03:16:28, user Knut M. Wittkowski wrote:
You state that "the central government of the People's Republic of China imposed a lockdown and social distancing measures in this city and surrounding areas starting on January 23 2020", without reference. On that date, travel restrictions were imposed, preventing citizens of Wuhan to leave by train (starting in the morning) or car (starting in the afternoon). Do you have primary references indicating when which social distancing measures were imposed?
On 2020-04-09 06:55:47, user Cy Husain wrote:
Helpful study on "best available" (read: not very good) evidence for #hydroxychloroquine. In short, this study:<br /> - Is too small<br /> - Has no control group<br /> - Only looks at a very specific patient pool<br /> - Does not consider side-effects<br /> - It's NOT a double blind study, so allows for researcher bias!
On 2020-03-31 18:01:15, user bixiou wrote:
There is a syntax mistake in the abstract I guess: "Notably, all 4 patients progressed to severe illness that occurred in the control group." should be "Notably, all 4 patients who progressed to severe illness ~~that~~ ~~occurred~~were in the control group."
On 2020-04-02 00:26:45, user Rick wrote:
This must have been translated from Chinese, because some sentences make no sense, and probably have placed the wrong words in places of importance, ie. Besides, a larger proportion of patients with improved pneumonia in the <br /> HCQ treatment group (80.6%, 25 of 32) compared with the control group <br /> (54.8%, 17 of 32). Notably, all 4 patients progressed to severe illness <br /> that occurred in the control group." Flip the words, improved and pneumonia, and the whole meaning changes. Did patients "improved with pneumonia", or what?
Also, what the hell does absorption of pneumonia mean? Did they get better or worse? It's very hard to tell from this translation.
On 2020-04-09 10:11:57, user Andrea Zille wrote:
Thank you for your excellent work. I have a suggestion to improve the protocol. In my opinion the 4 day "rest" of the PPE especially the masks should be implemented after the disinfection step. Leave used mask for 4 days could improve the proliferation of bacteria. Especially for the low temperature (80ºC) treatment, this could lead to a substancial bacterial load that a this temperature could improve the selection of more resistant and nasty bacteria. Fort this, I will also suggest to not use low temperature alone but eventually as a further step after UV treatment that affecting directly the DNA/RNA is much more effective in degrading virus and bacteria.
Andrea Zille, PhD<br /> 2C2T - Centre for Textile Science and Technology, University of Minho<br /> Campus de Azurém<br /> 4800-058 Guimarães, Portugal<br /> Tel: +351-253510285 <br /> Fax: +351-253510293<br /> e-mail: azille@2c2t.uminho.pt
On 2020-04-14 01:27:03, user Sinai Immunol Review Project wrote:
Title: Association of BCG vaccination policy with prevalence and mortality of<br /> COVID-19
Immunology Keywords<br /> Bacillus Calmette–Guérin (BCG) Immunization, COVID-19 prevalence, COVID-19 deaths
Main findings<br /> Previously reported immunization programs using BCG vaccines have demonstrated heterologous protection against other unrelated pathogens that associated with lower mortality and morbidity risks [1]. Therefore this study investigated the possible correlation between COVID-19 death cases or prevalence with BCG vaccination. The authors used publicly available COVID-19 data from 136 countries as well as vaccination demographics from the BCG World Atlas to perform a linear regression modeling.
After correcting for life expectancy and the onset of the spread of the virus (n=40), the analyses revealed a positive effect of current BCG vaccination programs and controlling the number of COVID-19 cases and deaths.
The amount of variance explained by BCG vaccination was 20% for number of cases and significant for both groups of countries, the ones that used to have a BCG immunization program in the past (b = 0.6122, p = .0024) and the ones that never have it (b = 0.6511, p = .0326).
Only the group of countries that never vaccinated against BCG showed significance in deaths/cases ratio but explains only 3.39% of the observed variance.
The authors concluded that BCG immunization may provide protection against COVID-19 probably due to the infection spread reduction. BCG immunization doesn’t have a significant impact in the mortality induced by COVID-19.
Limitations:<br /> As acknowledged by the authors of this study, there are large number of unexplained potential confounding variables such as BCG immunization coverage, and onset of virus spread in different countries. <br /> The authors cite that BCG immunization coverage could be variable among countries, but they didn’t explore it. Further, vaccination coverage changes at different rates over time across countries for different reasons [2]. Additionally, the authors did not consider the variable immunization coverage within countries, where unequal access to healthcare is frequently observed [3, 4]. <br /> The authors do not adequately control for time of spread in infection for each country [5].
The authors discuss the importance of validating experimentally the results observed and claim that BCG vaccination could provide non-specific protection against COVID-19. A stronger discussion of the use of BCG vaccine would have included known considerations on efficacy considering route of administration (intravenous, intradermal), vaccine strains which are known to differ in the number of viable bacteria and duration of protection.
Relevance: <br /> This study presented preliminary data on possible non-specific protection by BCG immunization on COVID-19 infection.
References
Review 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.
On 2020-04-15 14:08:11, user Barry I. Levine wrote:
Waiting to see adequate data re ARBs. Losartan shows lung protective effects in many animal studies vs. ARDS, and in at least 2 human retrospective studies vs. ARDS or COPD, and may be a useful adjunctive treatment for COVID-19
On 2020-03-15 09:12:21, user fuyutao wrote:
Wow, this paper may be a historical one when the findings are verified. I would encourage the authors to refine grammar and stick with accepted virology terms. For example "<br /> HKU-1 and OC43 (the source of FCS sequence-PRRA) caused influenza" is an easy target. But, the content of the paper does fill in several important pieces of the SARS-CoV-2 puzzle. It took so long for this boot to drop, I am surprised social media hasn't jumped on this yet :)
On 2020-03-18 08:25:22, user Alberto wrote:
Althought It could survive for some period of time, its title (concentration) maybe is constantaly descending as a negative exponential function. That means that in a shorter period of time the efective probability of transmisión is lower. I have studied bacteriophages, but I suppose that dynamics of inhabilitation shows the same dinamics.
On 2020-03-21 19:57:31, user KnowItAll wrote:
I am struggling to understand the labeling of the individual sequences in the tree. For France there are sequences such as hCoV-19/France/IDF0372/2020 and hCoV-19/France/IDF0372-isl/2020. IDF refers to Isle De France and I assume 0372 refers to a patient or sample number, so what does the -isl refer to, are these two sequences from the same sample? Same with hCoV-19/France/IDF0386-islP1/2020 and hCoV-19/France/IDF0386-islP3/2020
On 2020-03-22 16:23:48, user Sinai Immunol Review Project wrote:
Main findings: Colonic enterocytes primarily express ACE2. Cellular pathways associated with ACE2 expression include innate immune signaling, HLA up regulation, energy metabolism and apoptotic signaling.
Analysis: This is a study of colonic biopsies taken from 17 children with and without IBD and analyzed using scRNAseq to look at ACE2 expression and identify gene families correlated with ACE2 expression. The authors find ACE2 expression to be primarily in colonocytes. It is not clear why both healthy and IBD patients were combined for the analysis. Biopsies were all of children so extrapolation to adults is limited. The majority of genes found to be negatively correlated with ACE2 expression include immunoglobulin genes (IGs). IG expression will almost certainly be low in colonocytes irrespective of ACE2 expression.
Importance: This study performs a retrospective analysis of ACE2 expression using an RNAseq dataset from intestinal biopsies of children with and without IBD. The implications for the CoV-19 epidemic are modest, but do provide support that ACE2 expression is specific to colonocytes in the intestines. The ontological pathway analysis provides some limited insights into gene expression associated with ACE2.
On 2020-03-24 23:29:04, user A Z wrote:
Nice paper! My team is going to test your constructs soon.<br /> Just one thing:<br /> Line 188/189: "amino acid 1-14, MFIF….TSGS". This amino acids do not match with your sequences on beiresources.org nor with MN908947.3. It seems that it is coming from an older SARS coronavirus (e.g. AY291315), this should be corrected.
On 2020-03-29 04:08:16, user DrKOS wrote:
There’s a typo on line 140. It should say “live virus” not “life virus”
On 2020-03-25 18:42:15, user Sinai Immunol Review Project wrote:
This study describes the occurrence of a cytokine release syndrome-like (CRSL) toxicity in ICU patients with COVID-19 pneumonia. The median time from first symptom to acute respiratory distress syndrome (ARDS) was 10 days. All patients had decreased CD3, CD4 and CD8 cells, and a significant increase of serum IL-6. Furthermore, 91% had decreased NK cells. The changes in IL-6 levels preceded those in CD4 and CD8 cell counts. All of these parameters correlated with the area of pulmonary inflammation in CT scan images. Mechanical ventilation increased the numbers of CD4 and CD8 cells, while decreasing the levels of IL-6, and improving the immunological parameters.
The number of patients included in this retrospective single center study is small (n=11), and the follow-up period very short (25 days). Eight of the eleven patients were described as having CRSL, and were treated by intubation (7) or ECMO (2). Nine patients were still in the intensive care unit at the time of publication of this article, so their disease outcome is unknown.
The authors define a cytokine release syndrome-like toxicity in patients with COVID-19 with clinical radiological and immunological criteria: 1) decrease of circulating CD4, CD8 and NK cells; 2) substantial increase of IL-6 in peripheral blood; 3) continuous fever; 4) organ and tissue damage. This event seems to occur very often in critically ill patients with COVID-19 pneumonia. Interestingly, the increase of IL-6 in the peripheral blood preceded other laboratory alterations, thus, IL-6 might be an early biomarker for the severity of COVID-19 pneumonia. The manuscript will require considerable editing for organization and clarity.
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
On 2020-03-25 20:57:32, user Sinai Immunol Review Project wrote:
Summary of Findings: <br /> - Study used online datasets (scRNAseq GSE131685, scRNAseq GSE107585, Human Protein Atlas, GTEx portal, CCLE) to analyze ACE2 expression in different human organs. <br /> - Study re-analyzed three clinical datasets (n=6, n=99, and n=41) to show 3~10% of 2019-nCoV patients present with abnormal renal function. <br /> - Results indicate ACE2 highly expressed in renal tubular cells, Leydig cells and seminiferous ductal cells of testis.
Limitations: <br /> - Very preliminary transcript/protein dataset analysis in healthy cohorts; does not necessarily translate to actual viral tropism and permissiveness. <br /> - Clinically, would be important to determine with larger longitudinal dataset if SARS-CoV-2 infection changes sperm quality or testicular inflammation. <br /> - Similarly, would be important to determine if simultaneous HBV or syphilis infection and orchitis impacts SARS-CoV-2 severity. <br /> - Examination and follow-up of renal function and viral orchitis/sperm quality of CoVID-19 patients not done in this preliminary study.
Importance/Relevance: <br /> - Kidney ACE2 result supports other concurrent sequencing studies (https://doi.org/10.1101/202... ) and clinical reports of abnormal renal function or even kidney damage in patients infected with 2019-nCoV (https://doi.org/10.1101/202... ). <br /> - High ACE2 expression in testis suggests potential tropism of the virus to testicular tissues and indicates potential risks for male fertility. Viral orchitis reported for SARS-CoV previously [1], but no clear evidence so far of infertility in SARS, MERS or CoVID-19 patients.
References:
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.
On 2020-03-26 19:48:29, user Dan Grey wrote:
There were references for the choices of values for "proportion of population at risk of severe disease".
On 2020-03-29 19:06:26, user MingXia Gao wrote:
Fever also can cause damage to the sperm, leading to a high LH. Most of your objects had a fever, so I don't think the increase of LH is because of COV-19. You should test the tissue or seminal fluid to make sure whether there are COV-19 exist in male reproductive organs.
On 2020-03-30 17:42:47, user Preci Genome wrote:
Digital PCR appears to be a very powerful tool to diagnose the infection of COVID-19.
On 2020-03-30 18:55:11, user Preci Genome wrote:
Another related papers was also published recently. SARS-CoV-2 detection using digital PCR for COVID-19 diagnosis, treatment monitoring and criteria for discharge<br /> https://www.medrxiv.org/con...
On 2020-03-31 18:43:53, user Thierry Grenet wrote:
This animation helps to better visualise how countries follow their "space phase" epidemic trajectory : https://epidemictracking.wo...<br /> T. Grenet
On 2020-04-01 08:31:20, user Bob O'Hara wrote:
The paper refers to Supplementary Material which gives details about the model fitting. Could this be uploaded too, please?
On 2020-04-01 13:02:24, user Carlos Affonso wrote:
Congratulation for the amazing article ! <br /> Are the data image available ?
On 2020-04-01 16:52:15, user Johannes Opsahl Ferstad wrote:
Link to tool: https://surf.stanford.edu/c...
On 2020-04-01 17:13:32, user Hikmat Ghosson wrote:
I do not understand why Lebanon is considered as a country with high rate of COVID19-related deaths. Actual data (01/04/2020) do not demonstrate this assumption:
14 deaths (2 deaths in 1 M of population), vs. 43 recoveries (0.33 of deaths-to-recoveries ratio).
Meanwhile for Italy:<br /> 13155 deaths (218 deaths in 1 M of population), vs. 16847 recoveries (0.78 of deaths-to-recoveries ratio).
For The Netherlands:<br /> 1173 deaths (68 deaths in 1 M of population), vs. 250 recoveries (4.69 of deaths-to-recoveries ratio).
For Belgium:<br /> 828 deaths (71 deaths in 1 M of population), vs. 2132 recoveries (0.39 of deaths-to-recoveries ratio).
For The U.S.:<br /> 4394 deaths (13 deaths in 1 M of population), vs. 8698 recoveries (0.50 of deaths-to-recoveries ratio).
Otherwise, how other determinant factors potentially influencing infection and death rates (e.g. age medians, healthcare systems, population concentrations, social traditions, screening test numbers, crisis management policies) can be assessed and then excluded from the correlation models?
Thanks in advance.
(data source: https://www.worldometers.in..., 01/04/2020 - 4:45 PM GMT update)
On 2020-04-03 00:55:22, user ???? wrote:
What I felt strange was, in Japan, though the number of the infected persons have been increasing, the fatality rate is apparently low in comparison with the corresponding numbers in the U.S. and in the Europe (except Portugal, in which the BCG vaccination is mandatory, while the fatality rate in Spain, where the vaccination is NOT mandatory, has become around 60 times more than in Portugal).
I think the number of the infected persons in Japan must be much higher than the one reported so far (i.e., there must be a lot of actually infected people not diagnosed with the new coronavirus); however, it cannot explain the low fatality rate in Japan.
In addition, it's notable that those who passed away due to the virus in Japan (except the foreigners, who account for as much as around 30% of the infected persons in Japan) are almost limited to elderly persons, while the BCG vaccination became mandatory in 1940s and 70-year-old or older Japanese tend not to have taken the vaccination.
On 2020-04-02 10:15:46, user Francois Alexandre wrote:
This study is interesting, but the reasoning is incomplete. Indeed, it takes about 1 month to die from the time when people get infect (about 10 days of incubation + 20 days between symptoms onset and decease). Therefore, the real number of patients infected is between 670 000 and 3.3 millions 1 month before the time where the decease number was collected, i.e. near the end of February. For an estimation of the number of cases at the end of March, we should wait for the number of deceased patients at the end of April.
On 2020-04-03 02:08:32, user Shawn wrote:
There seems to be no discussion in this paper of the fact that the exponential spread could be accounted for by close in-person contact. One could reason that a virus can spread quickly in a susceptible population regardless of weather if there is a short distance between an infected and susceptible individual. A viral particle won't need to spend much time in the environment in this particular scenario and likely can avoid any negative impacts due to ambient temperature/humidity.
The authors should have refrained from making such a definitive conclusion about SARS-CoV-2 in any respect.
On 2020-12-05 20:10:39, user Ian Tomm wrote:
Thank you for this important work to help rationalize indoor exposure. I have used your model for a number of small, confined spaces (cabin of an A-Star B2 helicopter, etc) and its been very useful. There is a bug on the online calculator that crashes the site repeatedly, happy to provide further info if interested, please DM me.
On 2020-12-06 16:52:10, user Jammi N Rao wrote:
This paper has two major flaws: <br /> 1. is the sample size. There is no mention of whether there was any prior sample size estimation and if there was, which of the 6 primary outcome measures determined it. If mortality at 30 days was the main outcome measure then there is no statement as to the minimum reduction that was considered clinically relevant and was therefore the delta that informed the sample size calculatiom for adequate power.<br /> 2. Perhaps the bigger problem is that it was NOT an intention to treat analysis. Two patients were randomised to the Itolizumab arm but withdrawn due to a reaction. One of these patients sadly died at 9 days. If these 2 patients are added into the analysis then the result of the mortality outcome becomes statistically non-significant.
I wrote this up in an op-ed piece here:
On 2020-08-04 19:58:40, user Marm Kilpatrick wrote:
Thank you for this study. Could you please report your raw results by age categories? Specifically, please indicate the sample size for each age range and the number that were seropositive. <br /> Could you also post the deaths by age of people in the study area?<br /> Thank you!<br /> marm
On 2020-08-08 18:25:58, user DFreddy wrote:
Overall, valuable study that gains some more insight into Belgian's seroprevalence. From the charts, I see clear waning of seroprevalence for those 80 and older.
I have problems with one statement in the conclusion: "... The latter (i.e. the response to future waves) is still a challenge as the low reported seroprevalences (2.9-6.9%) are far from required herd immunity levels. "
What are required herd imm. levels to avoid deaths? This is a debatable number, since most covid-19 deaths come from seriously unhealthy people who likely die not so much from covid, but from their frail health and /or old age. As far as I know, dying is a reality of living a life.
On 2021-01-17 10:18:21, user T.R. Raju wrote:
I am unable to figure out the time points- when the study was initiated and when it was closed.
On 2020-08-13 12:21:38, user ArthurVandelay wrote:
This is a fascinating study ! To the authors: any preliminary speculation on the mechanism(s) of why there would be a protective effect of using ARBs ?
On 2020-08-14 09:04:15, user Alexandre Júlio wrote:
The first female medical Doctor from "La Sapienza" lived in Barcelona during the "Spanish Flu". She knew the importance of sunlight and open air to continue the work at "Casei dei Bambini". Working with less than 60 children up to 7 years old. Not thousand(s) of passionate teenagers & young adults of most high-schools & universities.<br /> What will you do in crowded spaces to drink or eat?
On 2020-08-14 23:07:40, user Luis Carlos Gutiérrez-Negrín wrote:
But even taking into account the probable under-count of Covid 19 active cases and deaths in Kenya in April 30-Jun16, the prevalence of the IgG antibodies in 5% of the population (1 in 20) is astonishly high. These results are, however, compatible with the find of IgG antibodies in seric tests made in other country on samples taken months before the first outbreak of the virus in Wuhan. One sounding alternative explanation could be that one or more older corona viruses, sharing almost the same protein forming their respective peaks, have previously infected Kenya (or certain counties like Nairobi, Mombasa and Kisumu), as well as the country where old IgG antibodies were found.
On 2020-08-17 01:51:08, user Jon Twiss wrote:
Herd immunity occurs only when enough of the population acquires immunity to suppress the spread of a virus, yet there is no clear evidence how long immunity for SARS Cov-2 will even last. To suggest Sweden has herd immunity is not at all credible. Estimates for SARS Cov-2 herd immunity range between 60%- 70% if it exists at all, and even with unreported case estimates, Sweden is a long, long way from getting there.
On 2020-08-17 05:22:58, user Liz Halcomb wrote:
Please note that there are three errors in the preprint version of this paper. These are as follows;<br /> 1. Section 3.2.1 - The first three lines should read "Over three quarters of participants (77.2%) identified the need for access to an adequate supply of personal protective equipment to enable the provision of quality routine care during the pandemic. This accounted for some 29.3% of the overall statements."
Section 3.2.2 - The first three lines should read "Just over half of the participants (55.4%) referred to the need for high level communication supports in order to continue to provide quality nursing care."
Table 1 Key category Funding of services should read;<br /> Total 134 (11.0%)<br /> Nurse telehealth item 62<br /> Nurse billing 36<br /> Fund services 18<br /> Job security 15<br /> Nurse practitioner 3
On 2020-08-17 05:42:46, user P Donohue wrote:
Now for review & replication! Where will it publish after review?
On 2020-08-17 13:17:29, user jrzsy7 wrote:
In this study, we provided the first mapping of immune responses in paired blood and lung samples using the scRNA/scTCR-seq. In severe COVID-19 patients, there are increased functional paralyzed myeloid suppressor cells (MDSCs) in peripheral blood. In contrast, monocyte-macrophages in the lung are producing high levels of cytokines and chemokines, but no IFNs. For the lymphoid compartment, we found depletion of innate T cells and CD8+ T cells. In contrast, CD4+ T cell responses and clonal expansion dominate. Peripheral T cells (most likely non-specific bystander cells) are massively recruited to the lung.
On 2020-08-17 18:14:49, user OxImmuno Literature Initiative wrote:
On 2020-08-17 18:55:57, user OxImmuno Literature Initiative wrote:
On 2020-08-18 12:07:32, user Hagai Perets wrote:
A possible explanation for these results could be related to the following study:<br /> https://www.preprints.org/m...<br /> discussing possible immunity by prior virus infection.
Note the following quote from the paper:
"It is possible, and even likely that in a fraction of the cases the preceding low-virulence strain (LVS) only provides partial immunity, allowing for the SARS-CoV-2 high virulence strain (HVS) infection, but leading to a less virulent form of COVID-19. In such a case we might expect a higher fraction of newly identified COVID-19 patients to be, on average, more symptomatic during the early phases of the pandemic when it still spreads exponentially, before the LVS achieves her-immunity. At these early times the LVS has not yet infected the majority of the population and newly HVS-infected people are not likely to have been previously infected by the LVS, and be partially immunized. After the LVS infected a large fraction of the population, new HVS-infections are far more likely to be of previously LVS-infected people, who already acquired partial immunity.We might therefore expect a lower fraction of asymptomatic cases, and a higher morbidity rate during the early exponential growth of the HVS, in comparison with later times after sub-exponential-growth and later decay in the number of cases is observed."
On 2020-08-19 17:08:34, user diymasks wrote:
Graph 3.1 shows the impact the change in HSE mask policy had from April22d. Strong data collecting, would like to see more.
On 2020-08-19 19:37:03, user Michelle Furtado wrote:
Im curious, isnt it possible that the three fishermen who had antibodies prior to departure were in fact the ones who spread it to the rest of the crew? They had neutralizing antibodies prior to leaving, which indicates a possible mild infection that in fact made them asymptomatic spreaders and subsequently infected the rest of the crew. No one is mentioning how these people who have antibodies could be spreading the infection. Those three should have never been allowed out of port on that ship if they knew they had been infected.
On 2020-08-20 18:03:31, user robert lindsay wrote:
Website needs some editing, spacing is off
On 2020-08-24 08:15:32, user Mark Wilson wrote:
A weak surrogate end point (droplets). Speak to surgeons about masks and they tell a different story. https://fee.org/articles/eu...
On 2020-08-24 14:30:47, user Javeria wrote:
This will definitely help everyone out until we find out an ultimate treatment. Brilliant work done by the authors!!
On 2020-08-24 15:23:42, user Ivan Berlin wrote:
Very nice study, maybe the first one that includes a control group.<br /> The authors do not discuss the intriguing discrepancy in the results that is that hospitalization for COVID-19 is less likely among NRT users compared to non-users but mortality is substantially higher. Is it conceivable that NRT users (proxy of smokers) are less likely to be hospitalized than nonsmokers? A selection bias potentially originating from the fact that smokers have more respiratory (and other) symptoms than nonsmokers leading to reduced hospitalization rate of smokers by health care providers. If this is likely, then hospitalization cannot be a proxy of COVID-19 severity.
On 2020-08-27 23:02:34, user drklausner wrote:
This is an important and innovative report demonstrating the value of hospital based surveillance and how that informs our understanding of the Covid-19 epidemic. It demonstrates that there was an early and rapid introduction of cases resulting in hospitalizations in February and March. <br /> The continued monitoring of hospilazation data showed the severity of the "second wave" in the end of June and July was less severe than some thought.<br /> The report also describes the heterogeneity of the epidemic in the United States and both the time-dependent and geographical variation. Understanding that heterogeneity is critical such that the United States as a whole is not considered monolithically or with a one-size-fits-all approach. <br /> In terms of the measure of epidemic growith, the rate of change in incidence over time, that is similar to acceleration or deceleration in velocity and a useful parameter for epidemic monitoring. Epidemics may decelerate prior to declines in incidence.<br /> Congratulations to Dr. Bhatia
On 2020-08-28 09:33:01, user Dr Aniruddha Malpani, MD wrote:
Shouldn't the Conclusion be exactly the opposite ?
Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines thelow risk of acquiring COVID-19 from surface and air contamination in managing COVID-19.
Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.
On 2020-08-29 19:38:23, user Davers wrote:
So much missing from this study. Why weren't blood levels of en vivo vitamin D, magnesium, etc taken and reported per patient before, during, and after DMB? This is helpful to know when and where this protocol will be most beneficial. Significant shorter hospital stay was identified as an outcome, but no numbers are given, nor was given the criteria by which patients were released. Why does the table appear to be incomplete with regard to those needing ICU care (see LB comment). Studies like this could be transformative but little mistakes like these (even if they are just reporting errors) make them too easily dismissed as being low quality.
On 2020-08-30 04:40:35, user puzzled_one wrote:
Hi Joao - Thank you for the analysis - good effort. Question: your paper states just over 40% of patients over 60 were vaccinated against influenza in the past two years. But other papers state over 70% of Brazilians over 60 are vaccinated. Since less than half your cohort were vaccinated, does this imply vaccination has further protective effects (unhospitalised infections)?
Second question: on page 11, people over 60 show a *positive* association (1.12) between past influenza vaccination and Covid-19 mortality. Is that correct? Does this group mean people vaccinated in the last two years (prior to the current campaign)?
On 2020-09-01 21:00:35, user Brian Gardner wrote:
What is catching my eye here is the second morphological alteration: spherocytes.
Assuming that controls were in place for spherocytosis, this seems to indicate (along with other findings showing decreased hemoglobin), that there may be an elevated risk for severity for those with spherocytosis.
On 2020-09-01 22:49:40, user AlvaroFdez wrote:
I think this is a very in-depth, useful paper, and tool. Among other things, it proves the main shortcoming of the 6-foot social distancing rule regarding indoors given the chance of rapidly accumulating infectous quanta over time in this kind of environment.
It would very interesting to expand on the model described (well-mixed room ventilation, "ventilation outflow rate (Q), as distinct from air recirculation rate," as cited by the paper, as in many buildings there is (and will continue to be) a very strong influence of recirculation air vs outdoor air intake, and commonly used MERV or F filters in HVAC would not be enough to properly filter micrometer or less-sized aerosols, specially when generated continously). For many buildings, seems common a 80 (recirculated air)/20 (outdoor fresh air intake) ratio. There are some works considering this latter aspect (ie, FATIMA by NIST, https://doi.org/10.6028/NIS..., giving a calculation for recirculation airflow rate) that it would be nice to incorporate in this work.
On 2020-09-02 02:20:53, user Shekhar Mande wrote:
Please also read together with:
On 2020-09-05 17:19:15, user DFreddy wrote:
Conclusion not based on primary studied data. Potentially misleading in its current verbose.
On 2020-09-06 01:19:56, user Jack Winters wrote:
I've done a lot of modeling of physiological systems (e.g., writing a textbook on physiological modeling and control, Emeritus Professor of Biomedical Engineering), and implementation of the core SEIR model is straightforward (e.g., set up Matlab and JavaScript versions, as classic forward dynamic simulations). I'm aiming to use (and perhaps improve) this model. But I've dug around, and despite manifold data and supplementary info, I cannot find any examples of representative parameters (e.g., sigma, alphas, gamma1&2, and so on - it's not a large list). I know they're fit (e.g., by state), but there should be "typical values" available. Maybe for Oregon (where I live now)? I'm less worried about Beta, essentially the input signal (surprised AI methods (vs stat fitting) aren't being used for it, but that's a separate matter). Did I miss something? Can someone help?
On 2020-09-11 05:09:05, user Ramesh Sharma wrote:
Biomed now uses the Multiplexing QM system instead of QS which is a vast improvement to test quality and testing speed.
On 2020-09-13 07:55:39, user OxImmuno Literature Initiative wrote:
On 2020-09-17 07:41:34, user Dhriti Gupta wrote:
What are the 3 phenotypes? The article has not defined the 3 types of clinical phenotypes anywhere.
On 2020-09-17 17:32:47, user kpfleger wrote:
This is a great analysis. It is a shame it hasn't gotten more widespread attention. Causal inference is an important technique that not enough researchers have close familiarity with. The analysis here is not hard to follow, even for non-experts or non-scientists. While there were other important pieces of evidence linking vitamin D causally to health outcomes in other infectious disease and lung injury prior to 2020, this was the first paper to provide solid evidence of a causal relationship between vitamin D and COVID-19 specifically.
On 2020-09-18 14:18:31, user mark.goldberg@mcgill.ca wrote:
Similar to the ecological studies on mortality fro covid and air pollution this study is biased. You may want to read the paper by Paul Villeneuve and myself in Environmental Health Perspectives: "Methodological Considerations for Epidemiological Studies of Air Pollution and the SARS and COVID-19 Coronavirus Outbreaks", https://doi.org/10.1289/EHP...
On 2020-09-19 13:32:11, user kdrl nakle wrote:
You need to explain your tables better, you did not put any effort to that in your test.
On 2020-09-20 04:33:18, user nkeegel wrote:
I have read that blood type O protects against Covid-19. Is there evidence for or against that?
On 2020-09-21 08:43:03, user ?????? ??????????? wrote:
The simplicity of the model, together with its generalization, are the<br /> advantages over complex models. Do you know that W. O. Kermack and A. G. McKendrick model can be reduced to the Verhulst equation?
On 2020-09-30 21:20:56, user Fabrizio Illuminati wrote:
Excellent analysis, showing the crucial role of heterogeneity in pathogen transmission. However, one should be aware of the fact that heterogeneity is a metastable trait: in time, and in a dynamic situation of shifting habits and contacts, large-scale homogeneity sets in. This implies that on medium- to long-time scales the type of immunity provided by heterogeneity is lost. It is ok and an important ingredient to set up containment strategies on a short- to medium-time scale while we wait for an effective vaccine. Moreover, it clearly works well at the urban level, where one can assume constant and persistent levels of heterogeneity. It will not work so well for the interaction with suburban and rural areas.
On 2020-10-03 21:14:41, user Alberto Villena wrote:
Please, read the following Preprint: L. Ferretti et al., The timing of COVID-19 transmission.<br /> https://www.medrxiv.org/con...
On 2020-10-11 21:53:53, user Sam Wheeler wrote:
You can also use Betadine for nasal irrigation. Perhaps it also helps to stop covid.<br /> https://link.springer.com/a...<br /> 27 September 2019<br /> Concentrated solutions of 5% and 10% PVP-I were ciliotoxic, and advised caution with its use in the nose. As an upper limit of tolerability, PVP-I diluted to 1.25% has been shown not to be ciliotoxic in vitro, while a dilution to as low as 0.01% has shown to be the lower limit of active potency. A diluted PVP-I concentration of 0.08% was arbitrarily chosen as it was deemed to fall within the safe window of activity and permitted easy mixture for patients by diluting 2 mL of commercially available 10% aqueous Betadine into 240 mL of normal saline. Patients were instructed to rinse each side of the nose with 0.08% PVP-I every other day for 7 weeks.
On 2020-10-12 14:43:35, user Arturo Tozzi cns wrote:
Why at four days there is a slight increase of the slopes , instead of going straightly to decrease?
On 2020-10-17 14:19:54, user jeff209 wrote:
COVID-19 is evolving, which significantly affects reliability and purpose of the solidarity trials. Moreover, the sample size of each age group is so small. The same protocol with narrower trial time window, larger sample size in each age group, and double blind trials are more definitive than this study. Thus the results from this study is questionable, and could be quite misleading to the public.
On 2020-10-20 01:41:37, user Karime Kalil wrote:
I congratulate the authors on their paper.
Their main contribution is to reveal that a more diverse microbiome before treatment correlates with better outcomes in paatients with cervical cancer undergoing CRT and to characterize the main bacterial species with which samples from patients with better and worse outcomes were enriched.
Their results raise the hypothesis that the manipulation of the gut microbiome and enrichment with certain bacterial species before treatment may result in better outcomes for locally advanced cervical cancer patients. However, it is still necessary to demonstrate if potentially transient changes in the microbiome using microbiome modifiers would influence response to any anti-cancer treatment.
The main strengths of the paper was that their experiments were well designed and included a robust method for bacterial identification (16S rRNA analysis) and laborious data analysis Besides, because stool samples were collected in five relevant time-points, this research will allow for future analysis of the behavior of the gut microbiome during and after chemoradiation and correlation of the changes with treatment-related adverse events, as well as with response to treatment according to RECIST criteria in the MRIs performed.
On 2020-10-27 12:08:10, user BigRed wrote:
The study rests on its the definition of "cost" and the definition use in the paper is extremely questionable. The authors equate the economic costs with the estimated decline in GDP. The GDP has a handful of now widely discussed methodological weaknesses -- such as that polluting production is as much a part of the GDP as the elimination of that pollution, or that the production of advertising has a positive impact on the GDP -- that stem from the fact that everything that is sold and bought is valued (with the well-known side effects that socially useful services such as housework and volunteerism are not included).<br /> But this cost accounting then becomes blurred because the authors look at the "costs" of hospital care without making a clear distinction between the two. If the latter costs are expenditures of private persons, they could have a negative effect on their financial situation, which can be reduced or even completely neutralized by the state with support payments. If the authors refer to costs for drugs, equipment and personnel that are paid directly by the state, there are no negative financial effects for private individuals at all (at least as long as the state does not pay for these expenses in foreign currency).<br /> In both cases, however, hospital costs are NOT costs in the sense of GDP reductions - on the contrary, any payment for a drug, a ventilator, or a nurse's salary is positively reflected in GDP. (that the chronically ill are good for GDP is another absurdity of GDP calculation)
The fact that the total "costs" are so ambiguously defined also means that the per capita costs as a figure are rather worthless and not suitable as a basis for debate.
A much more interesting approach would be "quality of life" considerations, which the authors very briefly touch on: a complete lockdown without unconditional salary compensation for part-time unemployed people has serious consequences for their quality of life; the same applies to small (micro) enterprises, especially those that depend on daily customer traffic. These companies need non-repayable support to ensure their continued existence and minimize the financial damage to their owners. Psychologically, lockdown experiences in various countries have shown that depression increased, as did anxiety, the perception of being lonely and resulting drug abuse of all kinds, especially among people who were already socially isolated before.
In other words, there are many important aspects to the question of "national lockdown vs. test-trace-isolate" and especially the question of how isolation and prevention of certain economic activities should be designed, but a headline-grabbing 45M US$ per life saved is not one of them.
On 2021-05-28 07:23:17, user Roger Morrison wrote:
If masks were effective against respiratory illnesses like the flu and Covid-19 the CDC would have recommended it years ago.
From the CDC "surgical masks won't stop the wearer from inhaling small particles which can cause infection. The CDC recommends a surgical mask ONLY for people who already show symptoms of coronavirus and must go outside since wearing a mask can ONLY help prevent spreading the virus by protecting others." [1.]
A study in November 2020 out of Denmark showed that masks do not stop the spread of Covid-19. "The researchers found no statistically significant difference between mask wearers and bare-faced participants." and "The recommendation to wear surgical masks to supplement other public health measures did not reduce the SARS-CoV-2 infection rate among wearers." [2.]
On March 5, 2021 the CDC put out a report which recommends wearing masks, however, the report also says "Daily case and death growth rates before implementation of mask mandates were not statistically different from the reference period." Then why the recommendation? [3.]
Center For Disease Control and Prevention - Time magazine 2020
Annals of Internal Medicine - Effectiveness of Adding a Mask Recommendation to Other Public Health Measures to Prevent SARS-CoV-2 Infection in Danish Mask Wearers<br /> https://www.acpjournals.org...
Center For Disease Control and Prevention - Association of State-Issued Mask Mandates and Allowing On-Premises Restaurant Dining with County-Level COVID-19 Case and Death Growth Rates — United States, March 1–December 31, 2020 | MMWR<br /> https://www.cdc.gov/mmwr/vo...
On 2021-05-29 00:24:49, user Bill Kelly wrote:
This is great information. While this is only a preliminary paper that has been submitted but not reviewed and accepted, the study looks solid. I might quibble with how cases are counted, but we're not going to get perfect counting because the counting was handled poorly. This study is consistent with many others that show that the masks are not effective at stopping the transmission of viral diseases.
On 2021-06-13 01:37:36, user Fisher Wright wrote:
The study shows that there is a negligible differential effect size of masks when not-very-large, different proportions of mask use (e.g. 65% vs. 75%) between populations occur. What the study doesn't show is there is no effect size from mask use. You would have to compare no use to use (e.g. 0% vs. 75%) between populations to do that.
Secondly, I have some skepticism concerning the main result since it didn't seem like they controlled for confounders. States with greater mask use may share other differences (e.g. denser urban areas with crowded housing) compared to states with lesser mask use. Did they simply assume these confounders would cancel out?
On 2021-06-03 12:03:42, user Sahan Laboratory wrote:
I think use of HCQ with Glychrryzin will be best for recovery of COVID patient. <br /> This type of study should be combined with other form which will provide best result for upcoming trials. <br /> Suggestion - Use of HCQ with Glychrryzin in treatment of Ventilated COVID19 patient.
On 2021-06-11 13:50:33, user Jay Alan Erdman wrote:
My apologies, I didn't see the full text so some of my criticism can be refuted. Nonetheless it is a chart review with not a very large N. And again, most importantly, it says nothing about current treatment.
On 2021-06-11 18:28:35, user lee_r wrote:
I see no indication of how many patients received the high doses of hydroxychloroquine and zinc, so it is impossible to tell how meaningful the results are. If 78.2% of the 255 patients died, that leaves only 55 or 56 who survived. Is the increased survival rate a function of the small number phenomenon, the drug regimen, both, neither?
On 2021-06-13 20:13:06, user Christopher Knittel wrote:
I would like to receive the underlying data and code for this paper. The paper states a CSV is available, but there are no contact details for the authors listed. Please let me know an email address that I can use to get access to the data. Thank you.
On 2021-06-05 13:34:28, user Gowtham Kumar wrote:
We were trying to access the scans available in Oasis-1 dataset but we were unable to open the scans because the extensions of the files are .nifti.img , if you have any idea to open this please help us.
On 2021-06-06 07:02:22, user Ju-Sheng Zheng wrote:
This work is now published in the Journal of Genetics and Genomics. 2021. doi: 10.1016/j.jgg.2021.04.002
On 2021-06-07 07:55:12, user David Curtis wrote:
The confounding effects of ancestry on genetic risk prediction are well established. See for example:
Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia. <br /> https://journals.lww.com/ps...
On 2021-06-08 03:28:15, user mengmeng Wang wrote:
one standard deviation (SD) is equal to ? natural log transformed random glucose mmol/L?
On 2021-06-08 18:41:47, user Keith Flippen wrote:
Was there tracking of HIV viral load and T cell count during the infection and are numbers known prior to COVID?
On 2021-06-09 16:25:27, user Aliya Amirova wrote:
The peer-reviewed paper is here: https://openheart.bmj.com/c...
On 2021-06-10 17:58:43, user Aliya Amirova wrote:
A blog post: https://aliyaamirova.medium...
On 2021-06-09 17:03:22, user Squid Tractor wrote:
Were all participants exposed to the live virus to ensure infection would have happened?
On 2021-06-09 22:00:05, user Dr Chad wrote:
This would coincide with virtually all other RNA respiratory viruses studied. I am surprised that the initial instinct seems to be resistant to recognizing that natural immunity would be inferior to induced immunity when we have no precedent to suggest that would be the case.
On 2021-06-14 00:01:49, user Lemarque wrote:
Hello, <br /> I have a doubt, it was not clear for me when the serological test was done. Looking for AZ numbers, its clear that the antibodies start to rise at week 3 or more, >15 days after vaccination with lower numbers in the timeframe 7 to 14 days (less than 50% had antibodies in this timeframe). The serological tests was made at the end of the timeframe or sorted during the timeframe (eg: 7 to 14 it was done on day 14, and 15 to 21 it was done on day 21)?
On 2021-06-14 23:51:06, user Mark Czeisler wrote:
Note from the authors:
This paper was published in Epidemiology and Psychiatric Sciences on 14 June 2021 following peer review. Below is a link to the article, along with the PubMed citation.
https://www.cambridge.org/c...
Czeisler MÉ, Wiley JF, Czeisler CA, Rajaratnam SMW, Howard ME. Uncovering survivorship bias in longitudinal mental health surveys during the COVID-19 pandemic. Epidemiol Psychiatr Sci. 2021 May 26;30:e45. doi: 10.1017/S204579602100038X. PMID: 34036933.
On 2021-06-17 16:40:52, user S Venkata Mohan wrote:
This pre-print has been peer-reviewed and published in ‘Environmental Technology & Innovation’, doi.org/10.1016/j.eti.2021.....
On 2021-06-22 14:30:04, user ibamvidivici wrote:
2 more questions:
you count the number of SARS-Covid-19 from the danish data base. Are in this database only symptomatic cases? Because if there are also asymptomatic cases (as it is in Germany), this has a high impact on the result. A Virus who is infecting a human cannot see, if this human is vaccinated or not, so the infection happens, whatever the human vaccination status is. Then the PCR test resluts should be nearly the same, however the vaccination status of the case/control group.
Table 2 shows the numbers for unvacc. and vacc. people. The time period for the unvacc. people is 54 days (=study time period). But the time period for the vacc. People is different, depends on the date when they got their vaccination. Is the different time period included in the Adjusted-VE calculations?
On 2021-06-23 23:58:28, user ID9192 wrote:
Psychological stress is known to bring about several changes in the brain, and it is possible that people who contracted covid-19 were really stressed out and this may explain the changes in the brain.
On 2021-06-26 16:12:33, user Daisy wrote:
Why on earth wouldn’t they include all age groups? Or at least those 60 and up, who have lower immune response.
On 2021-06-30 13:49:14, user Toksyuryel wrote:
Conclusion is poorly-worded. This study only looked into the potential for re-infection and the conclusion should reflect that. There are other studied benefits to vaccinating previously infected individuals and you should not imply to invalidate those with a study that looked into none of those benefits.
On 2021-07-01 17:46:23, user mehuldpatel wrote:
The published version is available at: https://jamanetwork.com/jou...
On 2021-07-03 05:01:14, user Covid wrote:
Is this pre-print going to be published on real peer reviewed journals?
On 2021-07-06 10:59:10, user Princess Aakuti wrote:
Great Read, first vaccine reporting clinical efficacy on detla
On 2021-07-07 22:42:55, user Leo G. wrote:
That confirms that mass vaccination with spike-only antigen streamlines the coronavirus evolution.
On 2021-07-09 01:05:25, user Steven Scullion wrote:
Is this study connected in any way to the research being done by Cedars-Sinai: https://link.springer.com/a...
On 2021-07-16 09:48:28, user Šafo wrote:
Question: Do patients who have overcome the disease have the mentioned S-protein S1 in the body? Or only vaccinated people have S-protein in their body!
On 2021-07-17 15:00:07, user pfwag wrote:
The VAERS reported death toll is now almost 11,000.
In 2010 the Department of Health and Human Services (HHS) awarded a million-dollar grant to the Harvard Medical School to investigate the accuracy of the reporting. Their report concluded that “Fewer than one percent of vaccine adverse events are reported by the VAERS System.
https://digital.ahrq.gov/si...
As of July 16, according to VAERS, more people have died from the Covid-19 vaccinations than from all other vaccines in the previous 25 years combined. I wonder how many people have to die before the AEs become "serious"?
On 2021-09-11 05:04:14, user Michael Karesh wrote:
The original, uncorrected version of this study is being used by right-wing outlets to justify and encourage distrust of the vaccine. "People smart enough to have a Ph.D. are most hesitant." Is this what the researchers intended to accomplish? The correction is, in contrast, receiving no coverage.
On 2021-09-11 12:29:44, user David Freyer wrote:
Do we know what cycle threshold was used to determine a positive test; and was it the same for all groups?
On 2021-09-12 15:29:25, user Guy André Pelouze wrote:
May I raise a question: was an Anti Sars antibody test done before vaccination in all the young people of this study?<br /> I didn't find the answer in the paper. <br /> if not it could be a bias as we know that one shot is enough in patients that had a previous infection.
On 2021-09-13 17:23:34, user Damon Hill wrote:
On 2021-09-14 00:59:46, user Viola Wilkinson wrote:
Could the authors of this paper please explain why the affiliation of co-author Josh Stevenson with the COVID-contrarian group "Rational Ground" (https://rationalground.com/... "https://rationalground.com/team/)") is not being disclosed?
On 2021-09-15 21:29:23, user omar okasha wrote:
I have to say this is rather odd way to do BRA. I am not commenting on your observed rates, as others already did. So I will focus on the expected rate side. First, the OE analysis should be based on background rates of myocarditis in the general population. There is an abundance of publicly available data from massive collaborative projects such as OHDSI or ACCESS, so I don't really understand your decision not to compare background rates of myocarditis. But more troubling is the choice of COVID hospitalisations. It's almost a rule of thumb that the expected rates should never be based on a condition that may be influenced by the vaccine in question or, for that matter, any public health/mitigation policies that are contextually related. One could immediately see vaccination could have differential effect on both sides of the comparison: on one side it would drive COVID hospitalisation down, given the potential effect on transmission, and on the other side it could potentially drive myocarditis among vaccinated kids up, if the risk is real. As a result, you will overestimate the risk of myocarditis among the vaccinated. The effect of non-interventional mitigation measures will further decrease the risk of COVID-19 hospitalisation, but without having an effect on the risk of post-vaccination myocarditis, leading to further overestimation of the risk of myocarditis among the vaccinated. Though too obvious, this was apparently overlooked by the US CDC. The OE analysis should also be based on the same conditions and the same risk windows, which is far from what you did here. Thus, the risk of COVID-19 hospitalisations cannot be considered the counterfactual in this analysis. <br /> Last, I think you should have highlighted in the abstract that the risk of COVID hospitalisations among those with comorbidities was actually greater than the risk of myocarditis. Otherwise it's rather misleading.
On 2021-09-17 11:53:19, user LE wrote:
So many questions about these methods. <br /> - Was there any approach to minimize the possibility of including duplicate entries from VAERS?<br /> - Application of the case definition for “probable” myocarditis requires that no other alternative explanation is found - how did they reduce the inclusion of myocarditis which could have been caused by COVID-19 or other etiologies? <br /> - Outcomes should be reported as likelihood of having a report to VAERS for probable myocarditis as compared to hospitalization during a 120-day period.<br /> - They apply quite a lot of assumptions related to incidental covid-19 and presence of comorbidities.<br /> - Hospitalization for covid may be expected to be of longer duration, severity, and potentially with longer term outcomes or mortality. <br /> - No inclusion of MISC hospitalizations.
On 2021-09-13 13:36:20, user lbaustin wrote:
Has this article been submitted to a journal? This information needs to be disseminated.
On 2021-09-14 11:42:39, user Alex wrote:
"possible transient biases"?? You may want to explain that in detail - because there is a clearly observable initial x3 time increase in the "cases" even after the first dose eg. https://americasfrontlinedo... & far more concerning then just RT-PCR "cases", there is also a no-less clear 2-3x initial peak in **severe covid +IVL hospitalizations** after the 3rd dose in August 2021 . The data for the later are available even from the Israeli gov public site & I suggest to use "all age groups" combined to avoid Simpson paradox. As the result, afics , there was no statistically significant difference in the average over the month & all age group severe case hospitalizations between unvaccinated & vaccinated with 3 doses (but there was a comparative average ~30-40% benefit in 2 dose vaccinations.) The above certainly does not match the information in your present paper & you may want to consider clarifying the reasons for the difference - if only for yourself - to be sure that you are doing research correctly. What I observe was an indication of the overloaded immune system with both excessive agent doses & unnecessary "boosterization"
Regards
On 2021-09-16 18:50:38, user pedro alvaro Szasz wrote:
the results of the survey conclude about a very low efficiency of coronavac above 90 years<br /> and also a small decrease for vaxzevria<br /> death protection efficiency <br /> age Vaxzevria Coronavac<br /> 70-79 90% 77%<br /> 80-89 89% 67%
=90 65% 33%
these results are not consistent with real numbers<br /> a simple comparison of deaths by age and month shows that is not such decrease on efficiency:
next table shows the figures for January 2021 (where there are no vaccine) and in May and June and July, where almost all people above 70 has complete his immunization process:
Brazil- Covid19 deaths by month and age <br /> age absolute values % relative to january values <br /> -----------------------month----------------- -----------------month---------------------- <br /> <60 jan may jun jul jan may jun jul<br /> 70-79 8337 8168 6239 4375 100% 52.5% 74.8% 52.5%<br /> 80-89 6325 5057 5082 3378 100% 53.4% 80.3% 53.4%
=90 2050 1576 1472 1199 100% 58.5% 71.8% 58.5%
great majority og people above 90 years has taken coronavac in february, <br /> If efficiency against death on thiis group were just 33% (or 67% of deaths) against 67% (or 33% of deaths for youngers, these figures should apear in may, June an july with relative death index ( relative ti=o january) twice times bigger( than the young group ( 70-89)
what we see is tha the figures are more or less the same and just a small increase on the older group(>90) that seems to be small decrease on efficiency with time (the older where vacinated before.
On 2021-09-17 10:31:14, user 4qmmt wrote:
Conclusion in Abstract:
Individuals who were both previously infected with SARS-CoV-2 and given a single dose of the vaccine gained additional protection against the Delta variant.
They clarify the meaning of this vague statement at the end of the study::
Notably, individuals who were previously infected with SARS-CoV-2 and given a single dose of the BNT162b2 vaccine gained additional protection against the Delta variant.
Model 3 discussion:
Examining previously infected individuals to those who were both previously infected and received a single dose of the vaccine, we found that the latter group had a significant 0.53-fold (95% CI, 0.3 to 0.92) (Table 4a) decreased risk for reinfection
We conducted a further sub-analysis, compelling the single-dose vaccine to be administered after the positive RT-PCR test. This subset represented 81% of the previously-infected-and-vaccinated study group. When performing this analysis, we found a similar, though not significant, trend of decreased risk of reinfection, with an OR of 0.68 (95% CI, 0.38 to 1.21, P-value=0.188).
Their conclusion in the Abstract and discussion is completely misleading. It is causing people to assume that people with previous infection who are then vaccinated with a single dose are more protected against reinfection. But individuals who were previously infected with SARS-CoV-2 and given a single dose by definition means vaccinating after infection, which they admit in the last quote shown above shows no statistically meaningful benefit.
Even worse, in order to show benefit for previously infected receiving a single dose, they included data for people who were vaccinated before a positive PCR in the cohort. That means that ~ 19% of their data was actually vaccinated who later got infected.
On 2021-09-17 20:03:09, user USA Bottom Line wrote:
Would it be possible to include a fourth "No Immunity" group in this study? It would be extremely interesting to see how a group with no prior infection or vaccination would perform under the same conditions. The authors would be able to compute the efficacy of both vaccination and natural immunity during the study's time period. Perhaps they didn't do this because the population of "no immunity" people has become too small in Israel?
On 2021-09-19 18:43:06, user 4qmmt wrote:
While interesting and adding to our knowledgebase, and thus possibly important for something in the future, these quantitative titer studies don't seem to be useful for predicting outcomes or comparing protection. Many are now using these titer comparisons in vaccine studies in what appears to be the hope of predicting future immune response for both vaccine and or previous infection.
But the authors of the study Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection in Science point out the obvious and logical problem with these studies. pg 6:
While immune memory is the source of long-term protective immunity, direct conclusions about protective immunity cannot be made on the basis of quantifying SARS-CoV-2 circulating antibodies, memory B cells, CD8+ T cells, and CD4+ T cells, because mechanisms of protective immunity against SARS-CoV-2 or COVID-19 are not defined in humans.
pg 2:
A thorough understanding of immune memory to SARS-CoV-2 requires evaluation of its various components, including B cells, CD8+ T cells, and CD4+ T cells, as these different cell types may have immune memory kinetics relatively independent of each other.
On 2021-10-03 07:50:36, user kdrl nakle wrote:
Interesting, there is another paper here on medrxiv that states that, in case of Delta VOC, there is no difference in viral load.
On 2021-10-07 08:31:47, user Emmanuel André wrote:
1) Considering that the risk of myocarditis exists during natural infection and after vaccination (altough the risk after vaccination is lower)
2) Considering that it has been shown by others (https://www.nejm.org/doi/fu... ) that the risk of myocarditis was higher after the second dose of vaccine than after the first dosis, suggesting a "boosting effect" for that risk
3) Considering that immunization among younger populations can be achieved after a combination of natural infection followed by one only dose of vaccine
-> Did you observe a higher risk of post-vaccination myocarditis among previously infected individuals?
Best regards,
On 2021-10-21 23:30:23, user hiennessey wrote:
Dr. Adragao et al,
It was with great interest that I reviewed your article, and I thank you for taking the effort to report on an important subject. Atypical atrial flutter is more difficult to treat as compared to typical atrial flutter, and precise mechanism generating atypical ECG flutter patterns can only be determined by mapping and pacing EP studies. Your finding of low-density and prolonged LAT-Valley in a heterogeneous low-voltage area being an important triad to determine areas for successful ablation seems very relevant. I also appreciated the technical description of the mapping techniques as well as the diagrams which showed conduction through normal and scarred cardiac tissue, which would be helpful to other researchers who would like to duplicate your work. <br /> I share your opinions regarding the limitations of your study, the main ones being the reduced sample size, the "survival bias" and the unicentric retrospective aspect. As a clinician and a public health student, I would have liked to see more clinical information regarding the 9 patients chosen for the study: were they on any medications that may have affected their success rates, did they have prior cardiac surgeries. This information, if available, would have been helpful for me to determine whether my patients are similar to your study population. Furthermore, you also indicated that 2 of them had a previous typical AFL ablation and 1 patient had a previous mitral AFL ablation, and 3 patients had never performed a catheter ablation procedure. Therefore, it seems that your study is of a mixed patient population consisting of those who once had typical and now atypical atrial flutter, those who have had prior ablation which failed, and those who are undergoing ablation for the first time. It is interesting to see that all types of patients were successfully converted using your triad technique. However as you noted, there is no information regarding whether these patients successfully remained in sinus rhythm, or whether they converted back to atrial flutter. Furthermore, having such a heterogenous population may make stratification of results more complicated.<br /> Thank you again for a wonderful paper. I look forward to seeing future studies on this important subject.
On 2021-09-02 22:45:41, user Roger Marble wrote:
I understand the increase in hospitalization but doesn't that also mean a significant increase just in the number / % infected? CDC wants people who test as infected to be under quarantine for 2 weeks. How does that work when we soon will have a majority of the population infected even if they are not sick? I thought the vaccines prevented serious illness and some deaths but did not prevent infection.
On 2021-09-03 13:01:15, user David B wrote:
Wouldn't Figure 4 and Suppl. Table 2 suggest that for Biontech/Pfizer 19-29 days between doses should be preferred over 30-44 days for people aged 50-64 years, if longer periods between doses aren't possible? Seems to me that there is something in the data, some confounding variable that leads to lower VE in that group. If this is not due to some uncontrolled underlying factor, then 3 weeks between doses should be the preferred option over 6 weeks, if you want to stay within the range suggested by the manufacturer.