On 2021-05-27 14:01:51, user unscientific science wrote:
Dr.Niaee posted this on the previous version of this article: Hi, I'm Dr.Niaee and I was surprised that even basic data from our RCT is completely mispresented and is WRONG. We had 60 indivisuals in control groups and 120 in intervention groups and even this simple thing is mispresrntated.
You can read all the comments to this article if you click on "View comments on earlier versions of this paper".
On 2021-05-27 18:54:42, user alejandro wrote:
Maybe this is interesting for you https://www.biorxiv.org/con...
On 2021-05-27 20:05:21, user NC91 wrote:
No one mentions the caution at the top of the article.
On 2021-05-28 12:42:16, user Barbara Elizabeth wrote:
For those who have commented here that wearing the mask (mandate or not) is what lowers numbers... another study on NIH site disagrees (and you've surely heard of this) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7680614/. The conclusion states: <br /> "The existing scientific evidences challenge the safety and efficacy of wearing facemask as preventive intervention for COVID-19. The data suggest that both medical and non-medical facemasks are ineffective to block human-to-human transmission of viral and infectious disease such SARS-CoV-2 and COVID-19, supporting against the usage of facemasks. Wearing facemasks has been demonstrated to have substantial adverse physiological and psychological effects. These include hypoxia, hypercapnia, shortness of breath, increased acidity and toxicity, activation of fear and stress response, rise in stress hormones, immunosuppression, fatigue, headaches, decline in cognitive performance, predisposition for viral and infectious illnesses, chronic stress, anxiety and depression. Long-term consequences of wearing facemask can cause health deterioration, developing and progression of chronic diseases and premature death. Governments, policy makers and health organizations should utilize prosper and scientific evidence-based approach with respect to wearing facemasks, when the latter is considered as preventive intervention for public health."
On 2021-05-28 12:53:12, user rusbowden wrote:
Earlier, I pointed out that the study does not show that mask use fails to protect the individual, mostly looking at more meta data about mask mandates versus virus spread. It's simply not what the study is about. It is about the effectiveness of mandates. The data the researchers used for mask use comes from Washington IMHE, which urges mask use. And here I will point that the researchers seem to be outside their areas of expertise. This study also supports the idea that masks cause delays in upswings, which may mean that contracting the virus took place more while unmasked. We cannot make the leap to say that wearing a mask does not help the individual. It is irresponsible to do so.
There's a further problem with the robustness of this study, which goes beyond simple timing and groupings. It has to do with not holding other factors constant. For instance, did mask use cause people to breach social distancing guidelines, and how much enforcement was used in different states. There's a tale of 2 cities here in Massachusetts, Lowell, which was lax in enforcement and attitudes, and Cambridge, which levied fines. When you entered Cambridge, you see most people wearing masks -- especially during the day -- not so in Lowell. Surreptitious unmasking did take place at night in Cambridge, especially among men (who should be wearing masks if for no other reason then to make others feel safe around them -- another psych/social, which were not allowed discussed). But, Cambridge, a more congested city of the same population size, had less cases per population than Lowell -- which as I mentioned earlier only got off the high-risk level a couple weeks ago.
On 2021-05-28 00:56:34, user Jian Zhang wrote:
According to Figure 2B, there is clearly a two- to three-fold increase of anti-Syncytin-1 after vaccination (especially 1-4d and 6-7w) compared to Day 0.
On 2021-05-31 06:51:28, user Luca Prosperini wrote:
Where is the TABLE 1?<br /> Thank you
On 2021-06-02 08:09:56, user Le Bon wrote:
The idea of looking at patients who had at least a substantial cumulated dose is great, but there is an important immortal time bias, since the patients can't die the first 10 days in the HCQ group, as seen on the Kaplan-Meïer
I suggest to calculate again the results with excluding the first 10 days death in the control group.
Due to the small group size, you will probably loose significance, but it still will be usefull for pooled analysis.
On 2021-06-09 16:25:49, user livefreeinTX wrote:
It's heartbreaking to me that all those hundreds, perhaps thousands, of NYC patients in the spring of 2020 were left to sit around at home until they could barely breath and were then hospitalized and put on ventilators, and were denied HCQ, which may have saved hundreds of lives. Truly sickening, imo.
On 2021-06-10 04:44:14, user John Jay wrote:
Sorry if I missed it, but was there a discussion of how the demographics (ie age, co-morbidities, status before treatment, etc.) differed or didn’t differ between the 3,000mg HCQ + 1,000 AZM group and the rest of the patients?
On 2021-06-02 17:51:00, user Jørgen K. Kanters wrote:
The paper has now been accepted and published in Scientific Reports 11(1) 1-11 (2021)
On 2021-06-03 18:02:57, user Peter Ellis wrote:
"We conclude that it is almost certain that there is increased transmissibility that will rapidly lead to B.1.617.2 becoming the prevailing variant in the UK."
This was posted on the day Public Health England released a report showing that the Delta variant (B.1.617.2) is currently 73% of sequenced cases - i.e. it was 73% of new cases a couple of weeks ago. S gene proxy data is only a week out of date and has the prevalence of Delta at 85.4%
Forget journals not being able to keep up, even preprints can't keep up with this.
On 2021-06-08 03:53:14, user Emma Tan wrote:
What is the PPA and NPA if a PRNT cut -off of 1:20 is used instead (1:20 is recommended as positive result)?
On 2021-06-09 16:22:28, user Vojtech Huser wrote:
Great paper about a tool that is used by OHDSI researchers.<br /> Relationship to Achilles Heel prior tool would be a good added discussion. Contribution from the community to the tool (besides core authors) can also be described.
On 2021-06-11 15:37:05, user Bob Leon wrote:
Below is an excerpt from the full text stating that the purpose of the study was to prove that it was beneficial for the previously infected to still receive the vaccine. Thankfully the researchers had the ethics to report that they found the opposite of what they purposed to find.
"A strong case for vaccinating previously infected persons can be made if it can be shown that previously infected persons who are vaccinated have a lower incidence of COVID-19 than previously infected persons who did not receive the vaccine.
The purpose of this study was to attempt to do just that,"
On 2021-12-30 11:33:49, user Suruchi wrote:
CORRECTION: The crude seroprevalence was 89.5% (95% CI 89.1, 89.8).
Dr. Suruchi Mishra
On 2021-06-14 03:42:27, user Jones Onigbinde wrote:
Hi. Great work. I do however have an issue with the way you injected political dichotomy into the COVID-19 outlook. In your DAG diagram you inferred that having a right wing populist idea somehow contribute to the spread of the disease but you do not mention the radical left wing rioting that occurred all around the world in the summer of 2020. You forgot that people who are right leaning are more likely to believe that the virus actually came out of a Wuhan lab which left leaning media, scientists, and politicians termed to be a conspiracy theory for the past one year. Now the theory is becoming more and more plausible. My advise is that you should keep politics out of science. Follow the evidence wherever it may leads, that's science. It appears to me that 2020 was the year science came to die because of the propensity of people like you to tailor science to politics.
On 2021-06-17 16:13:50, user Hao Yin wrote:
Note: Contents in this draft are our preliminary results. The final results of this study are published on the journal, Lancet Planetary Health, please check out our online peer reviewed paper: https://doi.org/10.1016/S25...
On 2021-06-18 23:08:13, user Number Six wrote:
A question on the definition of "infectious" people in your model.
Are you using positive test results and defining those as infectious people?
On 2021-06-22 15:51:27, user Amit Kumar wrote:
The paper has been published in a peer-reviewed journal Aerosol and Air Quality Research with the following DOI number.<br /> https://aaqr.org/articles/a...
On 2021-03-22 13:21:54, user Stephen B. Strum wrote:
The Gorial et al. study in my opinion is a weakly positive study re ivermectin. The IVM group had 25% of patients with co-morbidities vs 45% in the non-IVM group. Gorial's reference 14 is a retracted paper (Patel). Positive findings re IVM were shorter times ot – PCR with 7 days for IVM vs 12 for non-IVM. Mean hospital days 42% less in IVM arm. I wish Gorial would have detailed the 2 fatalities in the non-IVM arm. Someone should have proofed this paper; it is very sloppily written. In contrast, the paper by Krolewiecki et al. is very impressive re IVM & the importance of pharmacokinetics.
On 2021-03-24 09:22:16, user Sarwah Al-Khalidi wrote:
This paper fills an essential gap by surveying the hesitancy rates of being vaccinated against the COVID-19 virus among Arab-speaking individuals, and investigating associated hesitancy factors. It is the first study of this scale in the Arab world, with over 36 thousand individuals from all 23 Arab countries and beyond.
The multidimensionality and the well-thought out plan of both the survey and the analysis are truly impressive. The use of 29 objective points to measure the level of Hesitancy gives this paper great power. The importance of this study is evident form results that indicate that 60% of the Arab population are hesitant to take the vaccine. This is a striking percentage to anyone fighting against this pandemic. Using multivariate analysis to deconvolute key factors effecting hesitancy makes results more comprehendible. Interestingly, results of the multivariate analysis show that people typically classified as high-risk (above 60 or have a chronic illness) are the least hesitant to take the vaccine, which could be reflective of the media and government’s influence on people’s decision.
Among the tested factors that could be affecting a person’s attitude, the frequency of taking the flu vaccine seems most convincing, and could be indicative of a person’s confidence or knowledge about vaccines. It is surprising that the hesitancy among health workers is not significantly less than that of those who don’t work in the health system.
By revealing the main barriers to taking the vaccine against COVID-19, results published in this paper are an essential step forward towards tackling the pandemic in the Arab world.
On 2021-03-24 12:15:54, user Rogerblack wrote:
This studies depression and anxiety measure ASSUMES A HEALTHY PATIENT. 'little energy', 'trouble concentrating' 'moving slowly' = a minimum score of 3 due to physical symptoms of longcovid/fatigue. If very exhausted, this can easily rise into the 'severely depressed' range.
It is not unreasonable to use the PHQ-9 or similar as a screening measure of disease severity.
To use it in a patient population suffering from fatigue, concentration problems, ... is guaranteed to cross-read between those symptoms and anxiety - it is useless without a careful assessment of each question to find if you are measuring MH, or physical symptoms.
It absolutely cannot justify sentances such as "The physical, cognitive and mental health burden experienced by COVID-19 survivors was considerable. This included symptoms of anxiety and depression in a quarter" without much more work, as it will lead to the conclusion that treating depression may benefit the patient when there is no depression, and it's a scale artifact.
PHQ9 and similar scales are designed for patients without significant physical comorbidities to the mental state they are trying to measure. The normal scale cuts are only valid for this purpose.
I note similar concerns to those raised with the C-MORE paper. (https://www.medrxiv.org/con...
Edit: response to the promoter account on twitter in July raising this issue.
https://twitter.com/SithEle...<br /> '@PHOSP_COVID<br /> What is the current analysis plan and instruments (BDI,SF36) planned to be used to measure health? I am concerned that instruments can be misinterpreted and cross react between physical and mental health.'
On 2021-03-24 15:30:41, user Stephen B. Strum wrote:
I am puzzled. The full article I found with the exact same title is different from the article found on this website. The abstracts are not the same. Here's the conclusion from the pdf I found before finding this site with the exact same title and authors:
"Conclusion: The results of our target trial emulation match with previous findings of randomized clinical trials and observational studies, which showed no beneficial effects of hydroxychloroquine, ivermectin, azithromycin, or their combinations."
Compare the above with the conclusions found on this site:
"Conclusions Our study reported no beneficial effects of hydroxychloroquine, ivermectin, azithromycin. The HCQ+AZIT treatment seems to increase risk for all-cause death."
Why is there this dicrepancy?
On 2021-03-29 19:49:55, user killshot wrote:
This paper needs major review. Statins do not "improve endothelial function". If anything they are anti-inflammatory. Also there is very little discussion of randomization. If the group is not randomized minimally with vitamin D levels, the whole study is meaningless.
On 2021-04-09 01:46:20, user Yq Zhang wrote:
Is it possible to make the data public in addition to the code?
On 2021-04-10 11:14:32, user John Smith wrote:
I have been following your studies and find it fascinating, you've done an excellent job. Would be interesting to see Turkey's figures due to their rapidly rising infection rate. I see on the Economist/NYT websites they only have Istanbul figures with an undercount ration of 1.55. Is it possible to obtain the whole country's figures do you think?
It's a shame that a lot of people look to the Worldometer site for their figures when so much of the information is incorrect/incomplete. It would be a great idea if they had a separate column showing 'total deaths including excess mortality' next to the 'total deaths' column to show a truer picture.
On 2021-05-12 13:15:26, user John Smith wrote:
Hi, I see the IHME have just published their excess death figures:
http://www.healthdata.org/s...
I was wondering how they came to their figures on Japan and Kazakhstan which differ from yours substantially.<br /> They have Japan as 108,320 excess deaths and Kazakhstan as 81,696. They also differ with many others also. Interesting reading.
On 2021-04-12 14:22:11, user Okan Bulut wrote:
Our study has been accepted for publication in the Journal of Mixed Methods Research. Please see the full citation, including the title change for our study below:
Poth, C., Bulut, O., Aquilina, A., & Otto, S. (In press). Using data mining for rapid complex case study descriptions: Example of public health briefings during the onset of the COVID-19 pandemic. Journal of Mixed Methods Research.
On 2021-04-13 07:54:42, user helene banoun wrote:
Infections within 14 days of vaccination are not taken into account: experts have warned that ADE can occur in the first few days when vaccine antibodies are at low levels and low affinity
Why were people tested in PCR? were they control PCRs, were they sick, hospitalized?
The maximum Ct to consider a positive PCR is set at 33, it has been published that from 28-30 no live virus can be cultured: why not choose this threshold?
The matching may have led to the elimination of people carrying virus fragments because their Ct was higher than 33.
On 2021-04-16 12:52:13, user bluenoser2 wrote:
Peer reviews of the study are now available at Rapid Reviews: COVID-19 https://rapidreviewscovid19...
On 2021-04-14 08:04:37, user Muhammad Yousuf wrote:
Implications of SIREN Study regarding immunity and reinfection after documented SARS-CoV-2 infection
According to this study (1) done in the UK in Health Care Workers (HCWs), the cohort having evidence of previous documented SARS-CoV-2 infection had the following observations:<br /> 1. The immunity was noted up to 7 months after the incident COVID-19 infection<br /> 2. 155 infections were detected in the baseline positive cohort of 8278 participants (1.87%).<br /> 3. The cohort with past COVID-19 after reinfection were mainly asymptomatic or had milder symptoms with no mortality.<br /> This augurs well regarding immunity in most people who have recovered from COVID-19. Immunity may last for over 7 months (more follow up of this cohort will be more informative to assess the long-term immunity. However, most of such HCWs were female and younger. The immunity duration after SARS-CoV-2 infection will need more such studies in people >65 years particularly in males who are mainly being affected by COVID-19 pandemic.
On 2021-04-15 11:01:12, user Ian Viney wrote:
Interesting paper, and although an approximation I think it makes a good point. Having conducted similar studies to reconstruct research income, I share the methodological frustration that funders do not always provide basic financial details for awards (exceptions include UKRI, Wellcome, NIH and many others), institutions do not always provide details of the research projects they secure (exceptions include Kings College London, Edinburgh University and many others), publishers don't capture structured authoritative grant information in their articles (despite the efforts of FundRef etc.), and authenticated information cannot be easily re-used/compiled (despite the efforts of ORCID). As a result there are clearly a lot of investments that your study is missing. One element is the support that Oxford and other UK and international institutions will have provided to the work. The financial details for the grants you have identified will in the main be the amounts awarded by the funder, not the full economic costs of the work. Your FOI might have been an opportunity for you to collect the full cost of each project, and I'm surprised that Oxford didn't comment on this. As most government funded grants are awarded on the basis of 80% of full-economic costs, the institutional contribution may have been ranked third in your analysis. Of course the institutional contribution to the work will be supported from a variety of sources with one major element being the UK funding council grant, so also substantively publicly funded. This would therefore have not have changed your overall conclusion. Some nice context to the public and charity funding for UK health research, with an overall estimate of the contribution from various sectors can be found in the recent UKCRC report at www.hrcsonline.net.
On 2021-04-19 11:59:37, user lotrus28 wrote:
Has this study been submitted to a journal?
On 2021-04-20 16:21:52, user Laurie B wrote:
Thank you for conducting this important research work and making your results available online. This information must be widely communicated. My dad received his final Retuxin infusion January 2021. Shortly after he received Covid vaccination 1 and then February 4th the second. While still following most covid precautions he had certainly let his guard down as a "fully vaccinated" person. Turns out, he was not. He is in the ICU with covid. Please pursue a press release. Please let me know how I can help disseminate your findings. Thank you for your work!
This is how our family found out: https://www.nytimes.com/202...
And from an ICU Doc who shared: "Rituximab specifically target cancerous B cells and helps our immune system destroy them. But B cells are the very cells that make our antibodies so his response to the vaccine is going to be muted with or without the Rituximab."
On 2021-04-21 15:08:18, user Rafael wrote:
Interesting work. We also found Prevotella sp. associated with COVID-19 severity in https://www.frontiersin.org...
On 2021-04-26 00:57:11, user jgas wrote:
Intrigued by the 1-21 days pre-vaccination data.<br /> If the 5% of total positive PCR in the 21 days pre-vaccination (compared to 85% occurring in those more than 21 days pre-) is just down to people increasing behavioural shielding -extra NPI caution because "daft if I get Covid19 now when I'm so close to getting protection", what explains the reduction in self-reported 'typical Covid19 symptoms' i.e cough/fever/anosmia in the positives from this same period?<br /> If extra caution -> lower rate of positives and also less symptomatic positives is this mediated by scant innoculum?<br /> How does the timing of the big post-Christmas wave and ensuing lockdown interact with these data?
On 2021-04-26 13:56:45, user Zorak wrote:
2 deaths in 200 cases. As far as we know, it is the same ratio of "untreated" cases. There's no improvement with this treatment.
On 2021-04-27 05:35:58, user SSS wrote:
Ok so there are at least 100 articles showing vaccines cause thrombosis. you can start by looking at the references in this article. youre welcome. https://www.ncbi.nlm.nih.go...
On 2021-04-27 19:49:26, user louiea wrote:
The assumption: "we assume constant mask filtration pm over the entire range of aerosol drop sizes." is a bad assumption and supported by the referenced papers. Optimal mask efficiency can only be achieved with N95 masks that are properly worn (no gaps), All other masks types have filtration efficiencies around 50% or less. (ref 69).
On 2021-04-30 11:11:21, user Kontrolletti wrote:
I am surprised by some of the numbers in the Introduction section, specifically "...the cumulative hospitalization rate has exceeded 1300 persons per 100,000 since early 2020 (2). Hospitalized patients account for 1% of COVID-19 patients...".
Today the source given for the cumulative hospitalization rate gives a number of 531.5 persons per 100.000 for the week ending April 24. Also today, the CDC report a number of roughly 32 million total cases reported and a number of 2.1 million total new hospitalizations, which would mean that hospitalized patients would account for 6.5% of COVID-19 patients (https://www.cdc.gov/coronav... "https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html)").
On 2021-05-04 16:08:58, user Don Lotter wrote:
Could much of COVID vaccination resistance be due to the person having had the virus & belief that they are immune?<br /> What portion of that vaccine hesitant 30% are people who have had the coronavirus and, quite justifiably ( https://www.nature.com/arti... "https://www.nature.com/articles/s41590-021-00923-3)"), feel that they are already immune and don't need the shot? Why isn't this question being asked? Because it is an important one in the herd immunity calculation.<br /> And yes, I realize that there are no hard stats on previous infections, but an estimation could at least be made based on studies. It might pull the 30% down to, say, 20%, that 20% being those who were<br /> likely never infected and yet are hesitant. It would push the total immune percentage up in the herd immunity quest.
On 2021-05-07 16:36:27, user Gustavo wrote:
It would be very interesting if the researchers compared the levels of vitamin D (25OHD) in the two sisters. Vitamin D is actually a hormone with an immunomodulatory effect. A 2010 survey identified that sufficient levels of vitamin D are crucial for the activation of CD8 + killer T lymphocytes:
https://www.sciencedaily.co...<br /> von Essen et al. Vitamin D controls T cell antigen receptor signaling and activation of human T cells. Nature Immunology, 2010; DOI: http://dx.doi.org/10.1038/n...
This finding was confirmed by these two recent studies:
-Circulating Vitamin D levels status and clinical prognostic indices in COVID-19 patients<br /> https://t.co/FpTJ0vwWkc
-The association between vitamin D levels and the clinical severity and inflammation markers in pediatric COVID-19 patients: single-center experience from a pandemic hospital
On 2021-05-08 08:06:03, user Dennis Kleid wrote:
It seems to me that the model needs to take into account what folks are doing in that room. In a restaurant or a nice dinner party, people are eating, talking, laughing, and having fun over their meals. No masks, lots of aerosols; let's say just one person is sick.
The issue is: "In the presence of a quiescent ambient, they (e.g. the particles with virus), then settle to the floor". In this room, much of the exposed "floor" is interrupted by everyone's plate or food. The virus will enjoy the landing and stay for a while.
"Hey, Uncle Joe [Namath/Montana, at the other end of the table], please pass the spaghetti". Infection via the mouth needs to be considered. Airborne transmission?
On 2021-05-08 20:45:10, user greenorange041 wrote:
This is a very thorough analysis indeed. But I think it is extremely dangerous just to assume that all excess mortality is due to the COVID infection (and you do exactly this if I got it right). It may well be indirectly caused by COVID, but in fact be a more direct consequence of various restrictions imposed by governments and reduced economic activity.
When people lose jobs or their business becomes effectively banned, they do not necessarily expect to die from hunger immediately. But when usual sources of income become unavailable, it puts people under strong pressure and can quickly cause psychological problems that may make them vulnerable to other diseases and in the worst case can lead to suicides. This is especially true when the situation of high uncertainty persists over months without much hope of returning to normality quickly. And this is the moment when the risk of dying from hunger gets higher.
This is also why poorer nations are in general much more affected because their population has less savings on average and is often dependent on richer nations because this is where a considerable amount of their citizens work, including the industries affected by the restrictions (and richer countries could afford especially strict lockdowns). Even if they work in other sectors, travelling between countries became more cumbersome, which is why some of those people had to stay in their home countries having no clear prospects of finding work.
You account for none of these factors. Arguably, it is difficult, but to separate the negative effect that COVID itself has caused from the negative effect all government measures have caused is absolutely necessary for a meaningful analysis.
On 2021-05-10 09:54:41, user Auroskanda Vepari wrote:
Do the findings suggest that patients who suffered a natural infection resulting in detectable anti-spike antibodies do not necessarily require a single or double does of a vaccine?
On 2021-05-12 14:25:32, user Remi Goupil wrote:
This preprint is now published with updated data in CMAJ at https://www.cmaj.ca/content...
On 2021-05-14 01:56:12, user J.A. wrote:
In reviewing Tom Argoaic comment, I looked at the public dataset. In the dataset, the days from exposure to starting study drug or placebo are listed as 1 to 6 days. In the preprint tables 1 and 2, there are none listed for 1 day and 28 people listed for 7 days. It seems very clear that the authors of the preprint have altered the data. There is nothing in the methods explains that the data were altered. It looks like the authors chose to inflate the delay from exposure to starting medicine by +1 days for everyone. As this time from exposure to starting the study medicine is the primary focus of the preprint, this should be clear to readers and should be correct. Furthermore, not altering the data would seem to yield the same statistical analysis, yet have the benefit of being correct. This should be corrected.
Second, the authors should consider making a figure to visually show what the authors are trying to present. While there are many tables, visually showing the percentage with COVID-19 by day 1-6 would be a better way to present the data, with the mean +/- 95 confidence interval for the estimate.
Third, the authors should discuss why the placebo event rate varies over time. The placebo event rate is 10%, 15%, 19%, 12%, 13%, and 0% over the day 1-6, is there a biological reason for this variation or this random variation? The day 3 group has the highest event rate (18.9%), which then makes the statistical difference. Is this an artifact or is there biological plausibility for why taking placebo on day 2 or 4 is much better than day 3. Perhaps add this to the discussion to explain why this is not all just a post-hoc artifact of small subgroups.
On 2021-09-15 14:55:00, user Donald Milton, MD, DrPH wrote:
This manuscript was accepted by Clinical Infectious Diseases and published online on 15 September 2021. https://academic.oup.com/ci...
On 2021-09-17 21:12:27, user Jeff wrote:
The denominator for the number of vaccinations seems wrong. The paper says "we recorded all vaccinations given in the Ottawa area between 1st June and 31st July 2021," and "there were 15,997 doses of Moderna vaccine, and 16,382 doses of Pfizer vaccine administered over the study period, for a total of 32,379 doses". But this seems like a gross undercount. On the Ottawa public health vaccine dashboard (https://www.ottawapublichea... "https://www.ottawapublichealth.ca/en/reports-research-and-statistics/COVID-19_Vaccination_Dashboard.aspx)"), the chart showing doses administered per week <br /> suggests that over 800,000 mRNA vaccine doses were administered in this time period. Were other criteria applied to reduce the denominator, or is this an error?
On 2021-09-25 16:30:29, user TeeJay2000 wrote:
I left a comment reflecting on the reputation hit that the Ottawa Heart Institute will take on this, but my comment was removed. Thank you medRxiv for encouraging discussion. I have now written to the Ottawa Heart Foundation, to indicate my withdrawal of support, until the Institute makes a formal statement how this paper made it even to a 'preprint', given the colossal size of the error.
On 2021-09-19 11:26:29, user Day Evenson wrote:
Is there any study that separates the unvaccinated into those who have had Covid and those who have not? I can't find any research on vaccinated vs previously infected. What is the lasting immunity between these groups?
On 2021-09-20 13:55:05, user mike marchywka wrote:
There is a lot tto be learned from the aging issues,
On 2021-09-21 04:34:22, user Shawn Mclean wrote:
Wheres the raw data for this survey? It would be great if it's made public so we can dig into it ourselves.
On 2021-10-20 01:13:14, user rferrisx wrote:
The PHD hesitant numbers were adjusted for v2?:<br /> https://www.medrxiv.org/con...<br /> Page 24 table PHD:<br /> 23.9 (22.7, 25.1)v1<br /> 14.6 (13.5, 15.6)v2
On 2021-09-21 09:06:34, user Muhammad Yousuf wrote:
Notwithstanding the comments this preprint is generating, it would also be interesting to compare three groups in this study with another group who were COVID-19-naive but received 3 doses of Pfizer's vaccine including a booster dose. If the immunity and protection against SARS-CoV-2 is still higher in infected plus vaccinated compared with those having three doses of COVID-19 vaccine, this will indicate that there is immune memory (1) through bone marrow plasma cells at play.
On 2021-10-15 12:41:54, user Mithat Temizer wrote:
Here is the question. Age is being treated in all models as a potential confounder. For a confounder age needs to be associated with COVID-19 outcome regardless of vaccination status AND age should be associated with vaccinations status regardless of COVID-19 infection. Both assumptions can be considered fulfilled in the model. Yet, the third assumption that age is not on the pathway between vaccination status and the COVID-19 infection/complication (such as hospitalization/death). In this case, we cannot confirm that age is not in the pathway. That is age is not a confounder. Could be an intermediate variable or more likely an effect modifier for the vaccine-induced/native infection-induced immunity against COVID-19 infection. In this case, why is age not considered in analyses as an effect modifier? I wonder whether the authors have checked for the effect size of vaccination (compared to natural immunity) in any of the 3 models, stratifying on age (such as those below 60 years versus those aged 60 years or more). Any comment on this?
On 2021-10-21 02:21:14, user JWrenn wrote:
A few odd things with this study.<br /> 1. why wasn't a control group of infection rates /hospitalizations of unvaccinated and never had covid included? <br /> 2. why wasn't a control made for behavior difference?
Seems like the numbers rates they put forward are not a great number to base this all on. Instead we should be looking at difference between totally unimmunized and immunized via vaccination and unimmunized and immunized via infection. Otherwise the numbers come out so tiny that it gets very weird...ie 1 vs 8 out 32k really is almost so small that it becomes random.
However if say 1000 would have been sick with no intervention then you get better numbers. like 1000-1=999 less vs 1000-8=992 less and you can see that both are very effective, but one is more so.
Also, the 2nd point really kind of breaks the whole thing. In my experience people who had Covid (and were not asymptomatic) are far more careful than people who have not gone through that hell.
The info is good just seems incomplete, and that behavior aspect I think is fare more important that we are wanting to admit as well as hard to account for in database studies.
On 2021-09-22 19:56:45, user Steve E wrote:
Unfortunately, even your high-vaccination-hesitancy-level scenario, which leads to national vaccination coverage saturation at 70%, now seems too optimistic. Today's CDC vaccination data shows we may not even reach 60% by year's end (unless the Biden mandates change the situation dramatically). How do your projections change in a 60% scenario?
On 2021-09-24 20:53:59, user Kenny wrote:
Very surprised that there is no evidence of lowering total mortality. Essentially there is absolutely no direct evidence that vaccine saves life.
On 2021-09-28 12:29:12, user Azmeraw Ambachew Kebede wrote:
This paper has been published at PLOS ONE (https://doi.org/10.1371/jou... "https://doi.org/10.1371/journal.pone.0255021)")
On 2021-09-29 06:49:42, user Amador Goodridge wrote:
Excellent article. Looking at mask use behavior remains key to acknowledge the human being response for this and any future repiratory pandemic. Fernandez-Marin and his co-authors highligth the variations of mask use behavior. I agree that special attention should be directed to suburban areas, where social determinants for public health are sustaining the transmision of COVID-19 and many other infectious diseases. Congratulations to the authors!
On 2021-09-29 11:57:21, user kdrl nakle wrote:
Awesome, confirms what I have always suspected and it relates well to earlier research into European intro of SARS-CoV-2 (5-6 R0).
On 2021-09-30 10:22:03, user V Deepak Bamola wrote:
This preprint is under review at 'Experimental Biology and Medicine'<br /> @medrxivpreprint:Effect of Bacillus coagulans Unique IS-2 in Inflammatory Bowel Disease (IBD): A Randomized Controlled Trial https://t.co/hH32BrOdYT #medRxiv
On 2021-10-01 11:24:21, user Mahalul Azam wrote:
This work now published in the Int. J. Environ. Res. Public Health with the DOI https://doi.org/10.3390/ije...
On 2021-10-01 21:43:06, user Frank Jones wrote:
This study is deeply flawed as it relies on PCR. The PCR tests do not perform melting curve analysis to identify false positives due to primer dimers or other unspecific products. This is especially a problem if the target template concentration is low or if over 30 cycles are performed. I did thousands of quantitative PCRs and yet have to come across a primer pair that does never produce unspecific signal at high cycle numbers. This process is stochastic due to the nature of primer annealing, so a sample can be false positive or negative when running it multiple times under identical conditions which explains why some patients test positive and the next day they are negative. Also, there is no appropriate control to identify false positives. The no-template negative control is not sufficient since it obviously cannot prove the primers or probes do not amplify off target templates. Only the sample of a confirmed Covid negative person would be acceptable, yet this is not done.
On 2021-10-16 11:51:18, user vAsisTha wrote:
Explains why highly vaccinated countries still have high covid+ve rates
On 2021-10-03 05:25:47, user kdrl nakle wrote:
Put the dates in abstract or in title. June 2020 for this serosurvey. Quite irrelevant now, more than a year after, good only for historical reference.
On 2021-10-03 09:35:28, user kdrl nakle wrote:
Nothing new in this paper.
On 2021-10-03 11:19:07, user kdrl nakle wrote:
You could call this "Large University in a Large Town". These titles are really ridiculous. Can't you just write USC? <br /> Another thing, faculty over the age 52 are 3.4 times more likely to be unvaccinated than those in age group 20-32? That does not make sense to me.
On 2021-10-04 06:59:17, user kdrl nakle wrote:
EMT, does not surprise me. Too bad this is before Delta.
On 2021-10-06 08:55:05, user Ken wrote:
at the time being, working on an update, we found that the spirals work even better if you sobsitute the number of infected witrh the number of infected per 100.000 inhabitants.<br /> Using the incidence could help with the calculations by simplifying the 0 pahse
On 2021-10-10 05:22:31, user kdrl nakle wrote:
I'll keep this paper in mind, it really looks realistic but so far most of COVID projections turned out completely wrong.
On 2021-10-10 05:35:31, user kdrl nakle wrote:
I am sorry but this is example of a poor research. "We suspect Delta variant"? Couldn't you find that out? "The infection does not spread (much) thoughout body"? Really? What does "much" mean here?
On 2021-10-11 18:46:13, user Andrew T Levin wrote:
Given the stated purpose of this study, it is remarkable that the manuscript never specifically defines the term “community-dwelling population.” In practice, the study analyzes the incidence of COVID-19 fatalities that have occurred outside of nursing homes, but even that distinction is not very precise. For example, the spectrum of U.S. nursing homes encompasses board & care homes, assisted care facilities, and skilled nursing facilities. About two-thirds of U.S. nursing home residents rely on Medicaid to cover that cost. By contrast, higher-income individuals can afford to receive home health care or choose to live in “retirement communities” with on-site medical staff. In effect, the distinction of whether someone is “community-dwelling” or a “nursing home resident” is linked to a complex set of socioeconomic characteristics as well as to various aspects of their individual health. Making international comparisons along these lines is even more fraught with difficulty, because the size and composition of the nursing home population inevitably reflects differences in social norms as well as socioeconomic factors, access to healthcare, and the extent of public assistance. Indeed, such comparisons may be practically meaningless when considering developing countries such as the Dominican Republic and India, where nursing home care may only be an option for a very small fraction of the population.
Search Procedure. This manuscript uses an arbitrary search cutoff date of 31 March 2021, which excludes some landmark seroprevalence studies that have been disseminated since then. For example, Sullivan et al. (2021) analyzed seroprevalence of the U.S. population over the second half of 2020 using a large representative sample that included 1154 adults ages 65+, and hence that study would clearly satisfy the stated eligibilitry criteria for this meta-analysis.[11] Moreover, the study carefully adjusts for assay characteristics and seroreversion and estimates that as of 31 October 2020, the IFR for U.S. adults ages 65+ was 7·1% (CI: 5·0¬-10·4%). Those results can be also be used in conjunction with data on nursing home deaths to obtain the corresponding IFR estimate of 4·7% for community-dwelling adults ages 65+.
Minimum Size Threshold. This analysis excludes seroprevalence results from any studies involving fewer than 1000 adults ages 70+, and hence it is remarkable that the manuscript neither provides any rationale for imposing such a constraint nor provides citations to any existing works that might motivate it. Indeed, this approach is inconsistent with basic principles of statistical analysis, e.g., making inferences based on all available information and avoiding arbitrary selection criteria that could induce bias in the results. Consequently, meta-analysis should downweight studies with relatively lower precision rather than simply discarding those studies. Moreover, it is incoherent to specify an eligibility criterion based solely on sample size, because the precision of seroprevalence estimates also hinges on the level of prevalence. A small sample may be adequate in a context of relatively high prevalence, whereas a much larger sample may be needed to obtain precise inferences in a context of very low prevalence. The national study of Hungary was included in this meta-analysis because that study included 1454 adults ages 70+. However, only nine of those individuals were seropositive. Consequently, the test-adjusted seroprevalence for this cohort of older adults is not statistically distinguishable from zero, and hence the confidence interval of the age-specific IFR is not even well-defined.[12] By contrast, the regional study of Geneva was excluded from this meta-analysis because it only included 369 individuals ages 65+. But that sample was large enough to facilitate inferences about seroprevalence (6·8%; CI: 3·8¬¬ 10·5%) and corresponding inferences regarding IFR for that age cohort (5·6%: CI: 4·3 7·4%).[13, 14] Finally, setting the sample size threshold at 1000 is clearly an arbitrary choice. Since seroprevalence studies can be readily identified using the SeroTracker tool, this meta-analysis should be extended using a lower threshold of 250 adults ages 65+ that would encompass the national studies of Netherlands and Sweden as well as a substantial number of regional studies.
Sample Selection. In characterizing which seroprevalence studies have been included in <br /> the meta-analysis, this manuscript specifies the key criterion as “aimed to generate samples reflecting the general population.” However, this criterion is extraordinarily vague and judgmental (as evident from subjective words like “aimed” and “reflecting”). <br /> (a) United Kingdom. The inadequacy of this approach to sample selection is evident from the fact that the meta-analysis places equal weight on four U.K. seroprevalence studies, even though only two of those studies (UK BioBank and REACT-2) utilized samples designed to be representative of the general population.[15, 16] By contrast, the other two studies used convenience samples that were not designed or even re-weighted to be broadly representative, and hence those two studies should have been excluded from this meta-analysis. First, Hughes et al. (2020) studied a panel of primary and secondary patients at a large Scottish health board, with the stated objective of assessing viral transmission patterns.[17] The paper never suggested that this panel could be interpreted as representative of the wider population; indeed, some of these patients may have been receiving care related to COVID-19. Second, in one of its weekly surveillance reports, Public Health England (2020) reported seroprevalence results for a panel of patients ages 65+ who had a routine blood test at the Royal College of General Practioners Research and Surveillance Centre.[18] Evidently, this panel was not aimed to reflect the general population and may well have included patients recovering from COVID or experiencing COVID-like symptoms. <br /> (b) United States. One of the two U.S. seroprevalence studies used a sampling design that is intended to be broadly representative, whereas the other U.S. study used a convenience sample of patients at kidney dialysis centers. Unfortunately, as a consequence of gross disparities in healthcare access, higher-income individuals typically utilize in-home dialysis machines, whereas low-income individuals must travel multiple times per week to a dialysis center, often using public transit. Consequently, the prevalence of COVID-19 infections among such patients has crucial public health implications but should not be interpreted as representative of the general population.<br /> (c) Canada. Among the three Canadian seroprevalence studies, two use representative sampling designs (Ontario and Canada-ABC), whereas the third study conducted by Canadian Blood Services (CBS) uses a convenience sample of blood donors. In its public announcement of those results, CBS specifically warned that “caution should be exercised in extrapolating findings to all healthy adult Canadians, because blood donors self-select to be blood donors, in some areas access to a donation clinic may be limited, and there are fewer elderly donors who donate blood compared to the general population.” [19] That caution was specifically cited as the reason for excluding this study from a previous meta-analysis.[5] Indeed, given the scarcity of elderly blood donors, there is an even stronger rationale for excluding that study from the analysis here. Indeed, this meta-analysis should have specifically excluded all convenience samples, whether from blood donors, commercial lab tests, or medical patients. Dodd et al. (2020) analyzed a large panel of U.S. blood donors and found that the proportion of first-time donors jumped in June 2020 following the introduction of COVID-19 antibody testing, consistent with the hypothesis of “donors with higher rates of prior exposure donating to obtain antibody test results,” and concluded that “blood donors are not representative of the general population.”[20] Bajema et al. (2021) found seroprevalence of 4·94% using commercial lab residual sera from residents of Atlanta (USA), compared to seroprevalence of 3·2% using a representative sample of the same location.[21, 22] These findings highlight the extent to which convenience samples may be associated with upward bias in seroprevalence and hence downward bias in IFR. It should also be noted that the incidence of COVID-19 infections has a strong association with race and ethnicity, reflecting disparities in employment, residential arrrangements, and various other factors. Such patterns have been evident in numerous countries (not just the USA), and hence the manuscript should follow a consistent approach in addressing this issue.
Open-Ended Age Brackets. This manuscript proceeds on the assumption that open-ended age brackets for older adults are essentially equivalent regardless of whether the bracket is 60+, 65+, or 70+. But this assumption is inconsistent with the consistent findings of preceding studies, namely, the IFR for COVID-19 increases continuously with age rather than jumping discretely at any specific age threshold. Indeed, the measured IFR for any particular age bracket is a convolution of the age distribution of the population, the age-specific pattern of prevalence, and the fact that IFR increases exponentially with age. The complexity of this convolution underscores the pitfalls of comparing IFRs for open-ended age brackets of older adults. Ontario serves as a useful case study for illustrating these issues. The Ontario Public Health seroprevalence study reported results for three broad age brackets: 0-19, 20-59, and 60+ years. However, this manuscript assesses IFR for ages 70+ using results obtained via private correspondence. However, that assessment may be very imprecise, because COVID-19 prevalence was very low in the general population and may well have been even lower among the oldest community-dwelling adults. By contrast, the Ontario study is very informative for characterizing the cohort of individuals ages 60-69 years. In particular, there were 9 positives among 804 specimens for that cohort; the test-adjusted prevalence of about 1% indicates that about 17000 Ontario residents ages 60-69 had been infected by mid-June 2020. As of 30 June 2020, that age group had 240 COVID-19 deaths—none of which occurred in nursing homes. Consequently, the IFR for community-dwelling Ontario adults ages 60-69 was 1·4% -- identical to the predicted IFR t the midpoint of this age interval from the metaregression of Levin et al. (2020).[5]
Adjusting for Assay Characteristics. Seroprevalence studies have generally been conducted using antibody assays with imperfect specificity and sensitivity, and these characteristics exhibit substantial variation across assays. Moreover, the implications of these characteristics depend on the actual level of prevalence, e.g., adjusting for specificity is crucial in a context of relatively low prevalence.[23] Consequently, all three of the preceding meta-analyses consistently used seroprevalence estimates and confidence intervals that had been adjusted for test sensitivity and specificity using the Gladen-Rogan formula and/or Bayesian methods.[5, 8, 9] By contrast, this meta-analysis simply uses raw seropositive data from those studies that did not report test-adjusted seroprevalence.
Low Prevalence. The shortcomings of this manuscript’s approach are particularly evident in assessing IFRs for locations with relatively low prevalence. For example, as shown in manuscript Table 1, the seroprevalence study of Hungary used the Abbott Architect IgG assay to analyze 1454 specimens and obtained 9 positive results, i.e., raw seropositivity of 0·6%. According to the manufacturer’s data submitted to the U.S. Food and Drug Administration, this assay has sensitivity of 100% and specificity of 99·6%.[24] Consequently, the Gladen-Rogan formula indicates that the test-adjusted prevalence is only 0·2%, i.e., only one-third of the observed seropositive results were likely to be true positives. Moreover, this test-adjusted estimate has a 95% confidence interval of 0 to 0·4%, i.e., the prevalence is not statistically distinguishable from zero, and hence its IFR does not have a well-defined confidence interval. Indeed, that was precisely the reason why this cohort was not included in the meta-analysis of Levin et al. (2020).
Unmeasured Antibodies. This manuscript follows a completely unorthodox approach in adjusting seroprevalence for unmeasured antibodies: “When only one or two types of antibodies (among IgG, IgM, IgA) were used in the seroprevalence study, seroprevalence was corrected upwards (and inferred IFR downwards) by 10% for each non-measured antibody.” (p.8) This approach is particularly objectionable when applied to test-adjusted seroprevalence results, since those estimates have already been adjusted to reflect sensitivity and specificity. Moreover, such an approach has never been used by any other epidemiologist or statistician, in the context of the COVID-19 pandemic or for any other purpose, and hence should not be applied in a meta-analysis without providing any compelling rationale for doing so.
Seroreversion. The manucript “explores” the issue of seroreversion using proportionality factors based on the timing of each seroprevalence study relative to the preceding peak of COVID-19 deaths. However, the manuscript provides no rationale for following this approach instead of the rigorous Bayesian methodology utilized in a preceding meta-analysis.[9] Moreover, the manuscript makes no reference to the findings of longitudinal studies of the evolution of antibodies in confirmed positive individuals, which have concluded that the degree of seroreversion is substantial for some assays and negligible for others.[25, 26]
Measurement of Fatalities. Data on COVID-19 fatalities should be obtained directly from official government sources, not from media reports, web aggregators, or Wikipedia. For example, the European Center for Disease Control has an online COVID-19 database with daily data on reported cases and fatalities for nearly every country in the world. Moreover, whenever possible, fatalities should be measured using official tabulations of case data (based on actual date of death) rather than real-time reports that may be relatively incomplete and subject to substantial revision over time. These issues are particularly relevant for assessing fatalities in nursing homes: If a patient tested positive for COVID-19 and died soon thereafter, investigation would be needed to determine whether the death resulted from COVID-19 or unrelated causes. To illustrate these issues, consider the manuscript’s estimate of IFR based on the U.S. national seroprevalence study of Kalish et al. (2021). As shown in table 1 and appendix table 2 of this manuscript, the U.S. CDC case database (accessed in February 2021) indicates a total of 103862 deaths for adults ages 70+ as of 04 July 2020. To determine the corresponding fatalities in U.S. nursing homes, however, the manuscript relies on a news summary dated 26 June 2020 that reported a total of 52428 nursing home deaths in 41 U.S. states.[27] Using that real-time report, manuscript infers a somewhat higher national total of 57291 nursing home deaths and hence 46571 deaths outside of nursing homes. By contrast, the U.S. CMS case database (accessed in August 2021) indicates 38239 deaths in U.S. nursing homes as of 05 July 2020.[28] Evidently, there were 65623 fatalities outside of nursing homes, implying a correspondingly higher IFR of 3·6% for U.S. community-dwelling adults ages 70+.
Developing Countries. The use of confirmed COVID-19 fatalities can be highly misleading in assessing IFRs of developing countries, where testing capacity has been much more limited than in Europe or North America. Consequently, in developing country locations, the measure of fatalities should include both confirmed and suspected COVID-19 cases, or alternatively, a measure of excess deaths relative to preceding years. Indeed, several recent studies of India have concluded that confirmed COVID-19 fatalities understate the true death toll by an order of magnitude.[29-31]
Younger Age Groups. The manuscript states that “the studies considered here offered a <br /> prime opportunity to assess IFR also in younger age strata” (p.9) even though such analysis <br /> had not been proposed in the protocol. Nevertheless, this secondary analysis is at odds with the key eligibility criterion of this meta-analysis, namely, seroprevalence studies with at least 1000 participants ages 70+. Indeed, imposing that eligibility criterion leads to the exclusion of numerous other seroprevalence studies that would be highly informative for analyzing IFRs of younger adults, with an unknown degree of bias associated with that exclusion.
Self-Citations. A meta-analysis is intended to serve as an objective synthesis of information extracted from existing studies. Consequently, methodological decisions and substantive claims should not be based solely on citations of the authors’ own prior work. For example, in discussing the preceding meta-analysis of Levin et al. (2020), the manuscript asserts that “almost all included studies came from hard-hit locations, where IFR may be substantially higher”, with a sole citation to Ioannidis (2021a). However, that assertion is clearly false: The meta-analysis of Levin et al. (2020) included locations such as Australia, New Zealand, Ontario, and Salt Lake City that experienced very few infections during the first wave of the pandemic. Similarly, the manuscript asserts that “selection bias for studies with higher seroprevalence and/or higher death counts may explain why their estimates for middle-aged and elderly are substantially higher than ours” (p.14), with a sole citation to Ioannidis (2021b).
References Cited Here:<br /> 1. Ferguson N, Laydon D, Nedjati-Gilani G, et al. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand2020.<br /> 2. Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. eurosurveillance. 2020;25(10). doi:10.2807/1560-7917.ES.2020.25.10.2000180<br /> 3. Salje H, Tran Kiem C, Lefrancq N, et al. Estimating the burden of SARS-CoV-2 in France. Science. 2020;369(6500):208-11. doi:10.1126/science.abc3517<br /> 4. Verity R, Okell LC, Dorigatti I, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. lancet infectious diseases. 2020;20(6):669-77. doi:10.1016/S1473-3099(20)30243-7<br /> 5. Levin AT, Hanage WP, Owusu-Boaitey N, Cochran KB, Walsh SP, Meyerowitz-Katz G. Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications. European Journal of Epidemiology. 2020;35(12):1123-38. doi:10.1007/s10654-020-00698-1<br /> 6. Williamson EJ, Walker AJ, Bhaskaran K, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020. doi:10.1038/s41586-020-2521-4<br /> 7. Mak JKL, Kuja-Halkola R, Wang Y, Hägg S, Jylhävä J. Frailty and comorbidity in predicting community COVID-19 mortality in the U.K. Biobank: The effect of sampling. Journal of the American Geriatrics Society. 2021;69(5):1128-39. doi:https://doi.org/10.1111/jgs...<br /> 8. O’Driscoll M, Ribeiro Dos Santos G, Wang L, et al. Age-specific mortality and immunity patterns of SARS-CoV-2. Nature. 2021;590(7844):140-5. doi:10.1038/s41586-020-2918-0<br /> 9. Brazeau N, Verity R, Jenks S, al. e. COVID-19 Infection Fatality Ratio: Estimates from Seroprevalence. 2020. doi:https://doi.org/10.25561/83545.<br /> 10. Arora RK, Joseph A, Van Wyk J, et al. SeroTracker: a global SARS-CoV-2 seroprevalence dashboard. The Lancet Infectious Diseases. 2020. doi:10.1016/s1473-3099(20)30631-9<br /> 11. Sullivan PS, Siegler AJ, Shioda K, et al. Severe Acute Respiratory Syndrome Coronavirus 2 Cumulative Incidence, United States, August 2020–December 2020. Clinical Infectious Diseases. 2021. doi:10.1093/cid/ciab626<br /> 12. Merkely B, Szabo AJ, Kosztin A, et al. Novel coronavirus epidemic in the Hungarian population, a cross-sectional nationwide survey to support the exit policy in Hungary. Geroscience. 2020;42(4):1063-74. doi:10.1007/s11357-020-00226-9<br /> 13. Perez-Saez J, Lauer SA, Kaiser L, et al. Serology-informed estimates of SARS-CoV-2 infection fatality risk in Geneva, Switzerland. The Lancet Infectious Diseases. doi:10.1016/S1473-3099(20)30584-3<br /> 14. Stringhini S, Wisniak A, Piumatti G, et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study. The Lancet. 2020;396(10247):313-9. doi:10.1016/s0140-6736(20)31304-0<br /> 15. United Kingdom BioBank. UK Biobank SARS-CoV-2 Serology Study Weekly Report - 21 July 2020. 2020.<br /> 16. Ward H, Atchison CJ, Whitaker M, et al. Antibody prevalence for SARS-CoV-2 in England following first peak of the pandemic: REACT2 study in 100,000 adults. medRxiv. 2020:2020.08.12.20173690. doi:10.1101/2020.08.12.20173690<br /> 17. Hughes EC, Amat JAR, Haney J, et al. Severe Acute Respiratory Syndrome Coronavirus 2 Serosurveillance in a Patient Population Reveals Differences in Virus Exposure and Antibody-Mediated Immunity According to Host Demography and Healthcare Setting. The Journal of Infectious Diseases. 2020;223(6):971-80. doi:10.1093/infdis/jiaa788<br /> 18. U.K. Public Health England. Weekly Coronavirus Disease 2019 (COVID-19) Surveillance Report, Week 32. 2020. <br /> 19. Canadian Blood Services and COVID-19 Immunity Task Force. Final Results of Initial Canadian SARS-Cov-2 Seroprevalence Study Announced. 2020. <br /> 20. Dodd RY, Xu M, Stramer SL. Change in Donor Characteristics and Antibodies to SARS-CoV-2 in Donated Blood in the US, June-August 2020. JAMA. 2020;324(16):1677-9. doi:10.1001/jama.2020.18598<br /> 21. Bajema KL, Dahlgren FS, Lim TW, et al. Comparison of Estimated Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence Through Commercial Laboratory Residual Sera Testing and a Community Survey. Clinical Infectious Diseases. 2020. doi:10.1093/cid/ciaa1804<br /> 22. Boyce RM, Shook-Sa BE, Aiello AE. A Tale of 2 Studies: Study Design and Our Understanding of Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence. Clinical Infectious Diseases. 2020. doi:10.1093/cid/ciaa1868<br /> 23. Gelman A, Carpenter B. Bayesian analysis of tests with unknown specificity and sensitivity. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2020;n/a(n/a). doi:10.1111/rssc.12435<br /> 24. U.S. Food and Drug Administration. EUA authorized serology test performance. 2020.<br /> 25. Dan JM, Mateus J, Kato Y, et al. Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection. Science. 2021;371(6529):eabf4063. doi:10.1126/science.abf4063<br /> 26. Muecksch F, Wise H, Batchelor B, et al. Longitudinal Serological Analysis and Neutralizing Antibody Levels in Coronavirus Disease 2019 Convalescent Patients. The Journal of Infectious Diseases. 2020;223(3):389-98. doi:10.1093/infdis/jiaa659<br /> 27. Kaiser Family Foundation. This Week in Coronavirus: June 18 to June 25. 2020. <br /> 28. U.S. Center for Medicare & Medicaid Services (CMS). COVID-19 Nursing Home Data. 2021. <br /> 29. Anand A, Sandefur J, Subramanian A. Three New Estimates of India’s All-Cause Excess Mortality during the COVID-19 Pandemic. Center for Global Development. 2021. <br /> 30. Deshmukh Y, Suraweera W, Tumbe C, et al. Excess mortality in India from June 2020 to June 2021 during the COVID pandemic: death registration, health facility deaths, and survey data. medRxiv. 2021:2021.07.20.21260872. doi:10.1101/2021.07.20.21260872<br /> 31. Shewade HD, Parameswaran GG, Mazumder A, Gupta M. Adjusting Reported COVID-19 Deaths for the Prevailing Routine Death Surveillance in India. Frontiers in Public Health. 2021;9(1045). doi:10.3389/fpubh.2021.641991
On 2021-10-12 14:55:07, user Ho Hum wrote:
The study was based on vaccination's done over a 2 month period. I could see that 32K might be too low but 800K seems too high considering that the entire population of Ottawa is just under 1 million. Did they vaccinate 80% of the population of Ottawa in just two months?
Something very wrong here.
The 1 in 1000 could be valid for the age 16-25 group. I could see a total of 32K of this demo being vaccinated in two months.
Too bad the researchers lost all their credibility over bad math
On 2021-10-12 23:46:26, user M Koltai wrote:
This preprint is now published at: https://wellcomeopenresearc...
On 2021-10-13 14:52:47, user Stephen B. Strum wrote:
Everyone has their unique response to an antigen, be it natural or a vaccine. The proof of the pudding is the end response relating to protection--from severe illness, to chronic COVID-19, to hospitalization, to needing an ICU, and to death. For certainty--being vaccinated is better than not. For breakthrough infections the data "appears" that Moderna is superior to Pfizer--but how about an analysis of those who had breakthrough infections? Age, Sex, BMI, Diabetes, Immune status, Medications, etc? I have not read the full paper but going through the publication I do not see that mentioned. How about a probably surrogate or correlate of protection in the form of total immunoglobulin (Ig) G against the S1 protein as measured by the LabCorp or Quest Roche Elecsys test? In my case (age 79, light chain amyloidosis in complete remission (CR) & off chemo or immunotherapy x 1 year) my SARS-CoV-2 Ab (antibody) level at 1 month post two doses of Pfizer was > 250 U/ml, only to drop to 59 at 4 months. Then, I received a Moderna booster on 9/1/21 & on 10/5/21 my Ab level was > 2,500 U/ml. These are tests that are commercially available. The results are back in 24 hrs; the test is not expensive. There's a huge difference in individuals, especially by age and by comorbidity. <br /> LabCorp test code 164090: SARS-CoV-2 Semi-Quantitative Total Antibody, Spike using Roche Elecsys. <br /> Quest Test Code 39820 SARS-CoV-2 Total Antibody, spike, semi-quantitative using Roche Elecsys. <br /> With the huge # of publications on COVID-19, there should be articles correlating the level of IgG vs. breakthrough infections. Where is that article(s)??<br /> Stephen B. Strum, MD, FACP
On 2021-10-15 22:17:00, user baruch1014 wrote:
so the gist of what i read here is that people who developed encephalopathy due to the severity of infection were more at risk for neurologic and psychiatric issues six months post-infection... but, i mean, you could contextually make the same determination with regard to auto accident survivors who develop encephalopathy in relation to the severity of the auto accident, or mma fighters, or football players, or people who have almost drowned or otherwise were deprived of oxygen to the brain... am i incorrect? basically, any trauma to the brain, if severe enough, can cause later psychiatric or neurologic affects.
On 2021-10-16 11:50:49, user Markus Falk wrote:
Very interesting read. Does the unvaccinated group contain persons with a past infection?
On 2021-10-25 17:08:33, user Arron190 wrote:
It would be interesting to see how the data changes if those with naturally acquired immunity (ie been infected) are removed.<br /> Around 13% of US citizens have been infected so far.<br /> Many of the uninfected may be interested to know what level of protection the vaccine provides.
On 2021-10-16 23:35:24, user Mike New wrote:
Here is the pertinent question that I would like a straight answer on:
Does the Singapore study suggest that a vaccinated person is more likely to be "asymptomatic" with the delta strain than an unvaccinated person ? yes or no ?
On 2021-10-18 00:04:56, user Geoffrey Graham wrote:
An encouraging study! Mobile HEPA filters may do a great deal of good.
Cigarette filters can also remove aerosols of biologically relevant sizes from an air stream. Seventy-five half-length filters in parallel will transmit enough air for a facemask wearer to breathe comfortably. Cigarette filters are very common around the world and so are other materials from which facemasks could be made. Building a 75-filter facemask from these materials is straightforward. If cigarette filters can also remove SARS-CoV-2 from an air stream (this needs to be tested), we could save a lot of lives this winter.
Here is a brief account of where things stand:<br /> See “The Saga of the Universal Anti-COVID Facemask: Where Things Stand”<br /> at:<br /> https://geoffreyjgraham.sub...
And here is a comprehensive (read “gargantuan”) account of all significant results.<br /> http://distributiveeconomic...
Clearly, the cigarette filters must be tested against actual virus. I am soliciting advice on the best way to do this. Beyond this, I welcome advice on what to do (and what not to do) next.
Geoff Graham<br /> gjgraham4health@protonmail.com
On 2021-10-18 14:09:02, user S Wood wrote:
peer reviewed version now at
On 2021-10-19 16:26:22, user Andy Bloch wrote:
There was a significant (and substantial) statistical difference that they brushed off, and they included the first 2 weeks after injection in the comparison. Pfizer is clearly more effective than AZ after 2 weeks against positive tests, as the Hazard Ratio chart in their Figure 2 shows.<br /> https://twitter.com/Andy_Bloch/status/1450397100341035021
On 2021-10-20 03:04:28, user Noriaki Kurita (????), MD, PhD wrote:
This preprint has been published by BMC Health Services Research. <br /> https://bmchealthservres.bi...
On 2021-10-21 13:28:45, user CDSL JHSPH wrote:
Dear Authors,
This Study was extremely consequential and extremely well constructed. Particularly in the advancements in identifying previously unknown areas responsible for atrial flutters via utilization of electroanatmocal mapping systems. The triad of identification from density based maps, definition of criteria in voltage density and tachycardic cycle length are great strategies in looking at these complex cases. A few critiques I would however like to levy though is that due to the large amounts of technical jargon within this paper especially displayed within the raw data output by the EAM systems. Further explanation of the data in the figures and results would improve the overall readability of the study and contextualize further on the crucial outcomes. Another point I believe already brought up is due to the low number of patients in the cohort and the survivorship bias in all cases, the true possibilities of the CARTO EAM based mapping systems have yet to be evaluated. The last critique I would like to present is I was extremely curious regarding the radiofrequency doses administered between numbers of VALLEYS when treated I would assume longer times of treatment as well as larger dosages as more areas were responsible for these arrhythmias and would greatly clarify some of the data presented in the second Table. However this paper was extremely enjoyable to sift through and thank you for your work!
On 2021-10-24 02:36:03, user randy tangang wrote:
The great work in the analysis of how gut microbiome BA dysregulation can increase the risk of immune or metabolic disorders. As it was pointed out in the paper, lifestyle was one of the factors that were left out in this analysis of gut microbes. One factor that I thought was very important was how the environment had an influence on these microbes. The experiment used just 3 countries and all in one continent and as I know, people in different continents are exposed to different bacteria and turn to harbor different bacteria in their guts depending on the environment they live in. someone in the western world (Europe) who has access to clean water and different antibiotics in their food will have a different gut microbiome BA from someone who lives in Africa or South America where people are exposed to many bacteria on daily basis. The paper talks about using this analysis for therapeutic targets in immune or metabolic disorders. And so if the end goal is to use this universally, I recommend studying and analyzing how different environments affect gut microbes BA in different continents.
On 2021-10-25 21:40:02, user John Hulleman wrote:
This preprint was subsequently published in Molecular Vision on April 2nd, 2021. Please see link below for the accepted article:
On 2021-10-26 09:42:04, user Stephen Hinkle wrote:
I think this study calls for an important discussion about how we approach COVID-19 in the future. I think that it is clear that people can get this more than once. Other studies have shown that vaccine immunity is not lifelong either. I think we need to INFORM THE GENERAL PUBLIC OF THIS TRUTH and have a public policy discussion where the public is invited to participate on how the public wants to confront COVID-19 longer term going forward. It is likely that this will be an endemic virus (this is the conclusion of many top public health universities including Brown, Harvard, Stanford, Johns Hopkins, University of Minnesota, Imperial College London, University of Alabama at Birmingham, University of Arizona, University of Sydney, University of Queensland, Oxford, and others). Many countries have abandoned their "Zero Covid" strategies as well realizing this including Australia, New Zealand, Vietnam, Thailand, Singapore, and others. This study covering Iran shows that people got infected many times.
Do we stay in lock down and abandon some activities and pleasures in life forever possibly leaving businesses permanently closed or forcing everyone to say their last goodbye to our friends, abandon all group activities, sports, performing arts, dating, and our pleasures in life forever in an attempt to stay alive or stop the virus? Do we open up and accept the risk of ongoing community spread of COVID-19 and keep getting booster shots for individual immunity and new variants? Should getting vaccines be mandatory or an individual level decision? How do we protect the immunocompromised and those who are more vulnerable or who the vaccines do not work well on? Do we do a massive COVID-19 testing operation and try to eliminate the virus through daily tests and quarantine people if they are infected an allow the others to go on with normal life activities? What level of death and disability should society choose to accept to have the levels of freedom of movement and/or non-household member social interaction we want in the future if the COVID-19 virus will be endemic? Should shuttered sectors of the economy be allowed to reopen or not? Should in-person schooling continue or not? Should masks be required indefinitely or should it be optional or not required?
I think it is time to start a policy conversation with the GENERAL PUBLIC to determine what they want the un-perfect pandemic endgame to be in terms of living with the virus and going on with life as safe as we can but it is likely the day-to-day risk will not be zero. It is clear to me based on all the recent evidence from this study and all the current data trending in other recent studies is showing that COVID-19 will become ENDEMIC and that this pandemic is going to have a social ending as opposed to a eradication or herd immunity outcome most likely. But the real question now is what will a divided public tolerate in terms of COVID-19 policy longer term and what is the public health end goal now? Maybe it is time to ASK THE GENERAL PUBLIC FOR IDEAS here.
Personally, I think that the COVID-19 pandemic is another case of humans showing a poor record of eradicating diseases.
On 2021-10-26 17:04:29, user Robert wrote:
In the history of Vaccines I have yet to see where a drug company is not working on a new or altered vaccine within 6 months of the original. Given the speed these vaccines were released you would think that alternate or new and improved mRNA would be released or spoken of. I have seen nothing or read nothing. <br /> Additionally. This is the only vaccine I ever seen pushed that does not have the listed side affects.
On 2021-10-26 21:25:43, user Eugene Peskin wrote:
The article doesn't provide much clarity how the number of cases among the non-immune was actually calculated.<br /> If accomodation for immune layer of 46% has been done to re-calculate attack rate for control group, it should also be accomodated for the main group calculation, as 46% one-time vaccinated already had immunity before vaccination (actually less, you should deduct those who got their immunity from previous vaccination).
On 2021-11-07 15:28:01, user DinCville wrote:
What can a study of 60+ year olds who had breakthrough infections tell us about the risk for all 60+ who are vaccinated? How representative are those 60+ with breakthrough infections? Could they be more likely to have pre-existing conditions that affected the effectiveness of their vax response? Concerned that these results be interpreted to mean all 60+ with vax are unprotected from long covid.
On 2020-04-22 20:38:01, user David Swiff wrote:
Macrolides can prolong the QT and QTc interval and cause cardiac arrhythmias, including TdP, ventricular tachycardia, and ventricular fibrillation, via their effect on the IKr potassium channel.
On 2020-04-23 05:17:25, user B Yabut wrote:
The authors forgot the known main mechanism by which hydroxychloroquine works. Late administration at the point needing intubation means the cytokine storm has alreadybeen set in motion. Biomolecular and cellular studies showed that hydroxychloroquine works at the point of viral cellular entry and early inside the cell. Granted it also has a still unelaborated effect on the inflammatory process the study from France specifically included the pre-condition "Early administration."
On 2020-04-24 00:57:17, user Philip Davies wrote:
Well, well well ...
This pre-print would make a good script for an episode of Columbo.
The retrospective analysis, as presented, leads the reader to just one conclusion in a bazaar of many possible conclusions.
I am even starting to have sympathy with D. Raoult and his team. I note his hot tempered response to this paper, where he lists two enormous factors that should be considered when wrestling with the data: the fact that the HCQ and HCQ & AZ cohorts were a sicker crowd (he lists lymphopenia) and that the sickest of the non-HCQ ventilated patients were then given HCQ (plus AZ in most cases) in a desperate last bid only for most to die.
Raoult's point is certainly valid.
We must remember that for most of the study period the use of HCQ was "ex-license" on a compassionate basis only. This means only the sickest patients got it. Remember also that this is a retrospective analysis, therefore observational. It was not run as a therapeutic trial. On the other hand, the use of AZ was already accepted (hence 30% of the non-HCQ cohort got it anyway).... although do be aware that by this time there had been quite a lot of focus on potentially dangerous QT lengthening when HCQ and AZ were used together in very sick patients.
The HCQ cohort was, across all key determinants, the weakest and sickest group (it had the poorest prospects looking at age, ethnicity, smoking status, congestive heart failure, peripheral vascular disease, cerebrovascular disease (strokes),dementia, COPD, Diabetes (with and without complications)! ... and indeed, the HCQ and HCQ & AZ cohorts did have 100% more lymphopenia than the non-HCQ group.
BUT, the big asymmetric issues become obvious when we look at the pre- and post- ventilator numbers.
In terms of patients discharged without needing ventilation, the "victorious" non-HCQ group performs poorer than the 2 treated groups. This despite having a better prognostic baseline. But the results for this group change dramatically (for the better) when we look at the outcomes of ventilation. 25 ventilated patients came from this group.... but 19 of these 25 patients were then started on HCQ or HCQ & AZ after ventilation was started. It is screamingly obvious that these would be the sickest patients in that group: they were given such compassionate drugs in extremis. So having ejected 19 of 25 ventilated patients into the other cohorts, the non-HCQ group only had 3 deaths from its remaining 6 ventilated patients.
The numbers of ventilated patients in the other cohorts (HCQ and HCQ & AZ) were thus substantially inflated with these new super-sick patients, who mostly died.
There really can be no conclusion at all when looking at a study of this nature without knowing much more about individual clinical conditions and guiding principles behind clinician's decision making. It's still possible to make some reasonable assumptions:
If I were Columbo?... I would say the non-HCQ cohort contained patients of extremes, with the best and worst potential. The worst would have been the very frail (malignancy and or congestive heart failure maybe ... see the stats), who probably were earmarked for 'supplemental oxygen' only from the very start. Such patients would not have been suitable for compassionate use of non proven drugs (remember, most of this came before the "emergency use" edict by FDA). This would explain the number of non-ventilated patients who died in this group (they may have been given AZ only, not being a controversial drug, but otherwise they did not get any significant interventional therapy). These patients would have had significant chronic disease and very poor obs/indices (including lymphopenia). But given that this cohort had, overall, a better starting prognosis than the other two groups, it means that the remaining patients in the group were promising candidates for survival (with better obs/indices). Such patients, not being part of a clinical trial, would not have been offered HCQ on a compassionate basis unless they got dramatically worse .... and of course, the ones who did get worse on the ventilator were started on HCQ (& often AZ as well) and thus swapped into the HCQ / HCQ & AZ cohorts.
If we can understand that, then we might start to think that in fact HCQ & AZ is the best performing cohort with the other 2 vaguely distant. But this is being unfair to the HCQ cohort:
The reason that a sick patient would be given one experimental drug on a compassionate basis (HCQ) but not have a rather less experimental drug further added (AZ), can really only be explained by considering risk versus benefit. A clinician would choose to use HCQ because the patient was particularly sick. The clinician would only add AZ if it was felt that this was worth the risk.... but a particularly sick patient with significant cardiovascular disease (the HCQ contained the most CVD risk) might then die of a more abrupt arrhythmia through adding yet another QT lengthening drug. I dare say the clinicians were tempted to make some "Hail Mary" plays, but we must remember, these patients were not part of an ongoing trial, these drugs were "ex-license" for compassionate use only and clinicians were still accountable for responsible actions. So for those particularly sick frail patients, it wasn't worth the risk.
I am pretty sure that the HCQ cohort (which had pretty good pre-ventilator stats) crashed badly because it was loaded with the sickest patients .... patients that were too sick to risk adding AZ.
So, the findings of this retrospective analysis are, in my opinion, likely to be incorrect.
I believe I can confidently state that:
It is almost impossible to reach a conclusion from all this. BUT, the most likely finding is NOT that adding HCQ delivers a worse outcome than standard treatment. In fact, if we look at the pre-ventilator stats, the addition of HCQ might actually have provided considerable benefit to a particularly sick group of patients. Whether or not the addition of AZ to HCQ adds benefit is also unclear ... although my 'swingometer' is pointing slightly more to benefit than harm.
Once again. I suggest that a robust study into prophylaxis and early treatment (using sensible safer doses adjusted for pulmonary sequestration) will deliver the most interesting results for CQ/HCQ.
Dr Phil Davies<br /> Aldershot Centre For Health<br /> http://thevirus.uk
On 2020-04-21 20:47:16, user Savio wrote:
Cytokine bath or flooding is causing co-morbidity. HCQ can apparently reduce the inflammation in response to the virus but not counter the virus itself.
Here is a review paper describing the “Mechanisms of action of hydroxychloroquine and chloroquine: implications for rheumatology”.
https://www.nature.com/arti...
“Hydroxychloroquine and chloroquine are weak bases and have a characteristic ‘deep’ volume of distribution and a half-life of around 50 days. These drugs interfere with lysosomal activity and autophagy, interact with membrane stability and alter signalling pathways and transcriptional activity, which can result in inhibition of cytokine production and modulation of certain co-stimulatory molecules.”
On 2020-04-21 22:10:28, user Marv Goosen wrote:
The problem with this study is that it is retrospective and as the authors state in their discussion, patients in worse shape may have been put in the HC group which would also account for higher mortality. Unfortunately until a prospective study with severity matched controls is done, no conclusions can be made.
On 2020-04-22 12:57:03, user Jorgen Schultz wrote:
Interesting - and chocking, I must say. I hope the report I have read on medRxiv is not final, because I am missing the following:<br /> 1) As I understand it, the combo-treatment is effective in treating patients with Coronavirus BEFORE it is "to late". Giving a treatment with known side effects during late stage infection is recommended by?<br /> 2) The screening done (on patients) prior to treatment - I must have missed it or? Just to compare: as I understand it, in IHU's treatment (besides patient-groups being not comparable) screening is done prior to any medication, and patients in risk form a kind of control group as best as can be in this effort to safe lives. <br /> 3) Dosage and duration of treatment?<br /> 4) Did patients with cardiovascular symptoms receive hydroxychloroquine? Likewise with patients showing symptoms of "Cytokine Storms"?<br /> 5) According to "Cytokine storm and immunomodulatory therapy in COVID-19: role of chloroquine and anti-IL-6 monoclonal antibodies" by Ming Zhao, Hydroxychloroquine is mentioned for its effect to inhibit viral replication. Is that not very much prior to the case of the patients in this study? <br /> Did other - and more relevant drugs - replace the use of Hydroxychloroquine if the later stages of the infection?<br /> 6) Why study a subject you have already discarded and emotionally distanced yourself from (in "Background" it describes the use of Hydroxychloroquine as "anecdotal")?
As the French President said: We are at war. <br /> But here - another war seems to be fought!<br /> With human sacrifice and casualties as a result.<br /> Colatteral damage?
I am chocked and saddened by the loss of life during this study.
On 2020-04-22 15:03:20, user Eric Hall wrote:
But not a prospective study with a randomized control group. How do we know the HCQ groups weren't just sicker and it was used more like maximum medical therapy. Correlation doesn't equal causation.
On 2020-04-23 00:19:12, user Michael S. Y. Lee (biologist) wrote:
Hello,
Did you infect Vero-E6 cells from each patient just once (and harvest the cells in quadruplicates), or did you infect the Vero-E6 cells from each patient four times (and harvest the cells once per infection).
This is very important for statistics.
Mike
On 2020-04-21 22:42:10, user Dan Johnson wrote:
Great work, quite an undertaking. This is a very minor question: why "Pair-wise p-values were calculated between isolates using the t-test" rather than use Tukeys or one of the many post-hoc tests designed to get around the Type I error problems of multiple t-tests. Is that what you mean by "adjusted p"?
On 2020-04-24 04:44:30, user joe2.5 wrote:
I don't know if I'm the only one to totally miss, in this paper, the main point I should be paying attention to. Anecdotal data that started the idea that OH-chloroquine could be of value in treating Covid-19 indicated quick decrease of the viral load hen administered just at the start of symptoms or even before. I read the paper twice without being able to see any mention of the time from first symptoms to treatment. So the impression is that the study was not trying to answer the initial question.
On 2020-04-24 07:31:44, user Dennis Menace wrote:
The children they tested were brought in by their parents. These are also not independent, is this a problem ?
On 2020-04-21 03:58:41, user David Feist wrote:
The USC study had a slightly different methodology, and has given the same result as the LA County test ie 4.1% of 863 residents had the virus. The lead investigator at USC was Neeraj Sood, a professor of health policy and vice dean for research.
Two tests with the same result. Evidence is mounting...
On 2020-04-22 05:08:10, user G.O.B. wrote:
Well, this changes everything...
“County of Santa Clara Identifies Three Additional Early COVID-19 Deaths”
First US death was in California over 3 weeks earlier than the previously reported first US death in Seattle on Feb 29.
On 2020-04-17 20:43:16, user Drew Middlesworth wrote:
I was estimating the true infected numbers were 10-20x higher than the reported numbers based off the hospitalization rates, I wasn't expecting it to be this high. Although folding these numbers into NY infected counts would mean that over 100% were infected which can't be right. My estimate would show about 25-50% infected currently.
Although the other thing that would skew this, by using Facebook ads and not doing a random population pick, you would skew the results higher as people who were sick in Jan-Feb would be more likely to respond to the ad to get tested. They could easly be 2-3x too high which would be more inline with the numbers I was estimating based off hospitalization rates.
On 2020-04-18 00:16:22, user Rob Kuchta wrote:
I am very suspicious of the 50- to 85-fold difference in confirmed cases based on the NY (and NYC) infection rates. NY has a 1.2% infection rate as a whole, and for NYC it is 1,5%. Using these values, that would indicate that from 60-100% of the population have been infected in NY state, and infections should be dropping rapidly (also, I have never heard of a virus having this sort of infectivity). In NYC, the infection rate would be 75-100%. This suggests that either there is an unknown issue with the test or that the Ab generated by being infected by this virus do not prevent subsequent infection. This latter situation would be rather worrisome in terms of a vaccination strategy to prevent infection.
On 2020-04-18 16:15:43, user spacecat56 wrote:
In reading the draft report of the study (pre-print, dated April 11, 2020) my predominant thought was, in choosing to respond to the invitation to the study participants are overwhelmingly likely to have self-selected based on their recent prior experience of symptoms that they suspect may have been due to COVID-19 infection.
The draft acknowledges the possibility of this bias but tosses it off as "hard to ascertain". But the draft also says that data on prior symptoms were collected; data which are entirely omitted both from the published analysis and from the published tables.
Because the analysis ignores this factor and because of the potential for this bias to totally dominate the analysis, in my opinion after reading the study draft, we still know effectively nothing at all about the prevalence of infection in the studied population. Accordingly I would expect to vigorously object to any attempt to incorporate the reported results into public policy and planning.
I would urge the study team to bend their efforts to addressing this deficiency. At a minimum, I suggest, the report should include the withheld prior-symptoms data. Preferably, some efforts should be made to deal with the difficulty of estimating the bias. Perhaps it would be helpful to subdivide the sample data based on yes/no prior symptoms and analyze each subset?
On 2020-04-18 16:27:11, user Zev Waldman MD wrote:
I agree with other commenters that people who suspected prior Covid infection (or exposure) are more likely to seek antibody testing than those who did not. While participants were asked about prior symptoms, it is not clear what was done with this information. The rate of symptoms/exposure could be reported, compared to the community rates, used as a risk factor for positive antibody testing, etc.
My other concern that has gone less discussed is their calculation of the case fatality rate. While they recognize that reported case numbers as of April 1 are an underestimate, it seems that they forget this skepticism when looking at reported deaths. They seem to take it as a given that 50 people died of Covid in the county as of April 10 as reported, and used this to project to deaths by April 22; however, like case counts, there are multiple reasons to suspect this number of deaths might be higher:
Reporting of deaths is well-known to be delayed - i.e., date of reporting does not equal date of death
People who actually died of Covid may never have been tested, and thus may not be included as cases or deaths
The doubling time of deaths used to project to April 22 is also based on reported deaths; if reporting of deaths is delayed, the doubling time may appear slower than it actually was.
If the death estimate due to illness before April 1 is too low, their corresponding CFR would be an underestimate as well. (This would be exacerbated if their case estimate is too high due to self-selection into the study, as seems possible.) At the very least, some sort of uncertainty around the death estimate should be provider, which in turn would increase the uncertainty around the final CFR.
I know CFR wasn't the main focus on the article, but worry that, because these results support their prior beliefs, some readers may take the results at face value and push them to policymakers before they have been more widely vetted by the scientific community.
On 2020-04-24 14:35:40, user VirusWar wrote:
Interesting study. some comments :<br /> 1. The increase of QTc can be due as well to renal diseases due to COVID19, Such renal diseases were pointed in this study "The QT Interval in Patients with SARS-CoV-2 Infection Treated with Hydroxychloroquine/Azithromycin" https://www.medrxiv.org/con...<br /> Renal diseases cause big levels of Potassium in the blood and increase QTc, so the level of Potassium should be checked as well, especially when QTc>=460 ms. If level of Potassium is high, action can taken (like treat renal disease, eat less Potassium, extra magnesium given). In some cases (QTc >460 ms and QTc<500ms), risk seems manageable. <br /> 2. There is no point to use hydroxychloroquine for severe patients. It takes 3 days to have effect on early stage, in combination with azithromycine. For severe patients, there are usually not much virus left but big damages, so it is too late to give hydroxychloroquine.
On 2020-04-24 14:59:19, user Tomas Hull wrote:
"New York antibody study estimates 13.9% of residents have had the coronavirus" Gov. Cuomo says<br /> https://www.cnbc.com/2020/0...
Congrats, Dr. Witkowski. You stand vindicated...
On 2020-04-25 01:25:07, user Brent Tharp wrote:
Not even close to the real numbers, which are 50 to 100 times greater.
On 2020-06-08 19:23:59, user Animesh Ray wrote:
This is an interesting study, but the conclusions should be considered with caution. The causal modeling used here "suggest" that the chosen data are consistent with the hypothesis that Vitamin D deficiency might be correlated with increased morbidity of COVID-19. There are several caveats, however. (1) In meta-analysis of this sort, it is very difficult to be quantitative unless the observed data of the same data-type are shown to be at least comparable in variance. I did not see an effort to establish that. (2) Even though statistical analysis by multiple regression is precluded because the data were obtained from different sources, at least an effort to center the various data around means and doing a multiple regression to ascertain the magnitude of the variables' effects and their interactions would have been interesting. The problem here is that there are so many explicit variables and so many hidden ones in each experimental datasets, it is rather difficult to pinpoint any one--in this case vitamin D status--as causal. As von Neumann once stated, 'Give me four variables and I will make an elephant out of them; give me five, I will make it wave its trunk'. (3) Finally, the authors' molecular explanation--that Vitamin D inhibits rennin-angiotensin axis--is as easily explained in favor of the model as against the model (e.g., a lowered expression of ACE2 receptor due to inhibition by vitamin D might enable SARS-CoV-2 viruses to saturate these receptors far more easily than if the receptors are normally expressed, thus precipitate the loss of blood pressure control and cardiac output more readily than otherwise. In other words, the effect of normal vitamin D could enhance, not prevent, SARS-CoV-2 virus's clinical impact.) Thus the value of the molecular causality, as claimed by the authors, as a critically falsifiable test is doubtful. Nonetheless, many epidemiological success stories are built upon causal inferences based on precisely this type of analysis: one can cite examples of cholera on shallow wells in London in 18th century, scurvy and vitamin C, and now well established role of vitamin A and general resistance to childhood infections. On that basis, the idea that vitamin D might indeed be protective against COVID-19 complications merits further study.
On 2020-04-25 15:20:13, user Alex Backer wrote:
Very nice analysis. Here is a paper from 3 weeks ago that is very relevant to this: https://ssrn.com/abstract=3... (The Impact of Solar Irradiance on COVID-19). <br /> These two are relevant as well:<br /> https://ssrn.com/abstract=3... (Why African Americans are dying more)<br /> https://ssrn.com/abstract=3... (Why Latinos are dying less)
On 2020-04-25 18:13:47, user Pavel Valerjevich Voronov wrote:
What I do afraid - delays with vaccine because not taking that study in to account. Imagine, if they inject vaccine to mostly O- subjects, having promising results, move forward, then it "accidentally" won't work with others. Vaccines must be evaluated with A+ recipients at first, I suspect. Or at least blood type should be taken in consideration while results evaluation - A+ MUST be present. Even if this study is not finished - such testing approach shouldn't be harmful.
On 2020-04-07 09:11:10, user Pavel Valerjevich Voronov wrote:
Could anybody send me a link to a study that confirms widely supported claim that elderly people or ones with pre-existing health conditions more at risk? How come that it was widely accepted (is it also accepted by WHO?) without any links even to pre prints (maybe I missing this)? When in Iran 100 year old recover and those w/o pre-conditions in USA suddenly die? This study look like saying otherwise (at least it was so in v1). Please give me a link.
On 2020-04-07 17:56:44, user Pavel Valerjevich Voronov wrote:
https://academic.oup.com/gl... - this article from 2008 year confirms assumptions in regard to blood type, but with SARS-Cov. Please evaluate it too.
On 2020-04-11 02:02:32, user SFHarry wrote:
It is important to note that the words "higher" and "lower" risk were used. If you look at the numbers it doesn't show the risk being that much higher (or lower). People should not be making decisions regarding how much risk they should allow themselves when interacting with the public without understanding these facts..
On 2025-02-26 18:14:55, user Benjamin Isaac wrote:
Reference 12, referring to the article here https://pmc.ncbi.nlm.nih.gov/articles/PMC8784688/ doesn't list the journal or date. The APA citation would be: Patterson, B. K., Francisco, E. B., Yogendra, R., Long, E., Pise, A., Rodrigues, H., Hall, E., Herrera, M., Parikh, P., Guevara-Coto, J., Triche, T. J., Scott, P., Hekmati, S., Maglinte, D., Chang, X., Mora-Rodríguez, R. A., & Mora, J. (2022). Persistence of SARS CoV-2 S1 Protein in CD16+ Monocytes in Post-Acute Sequelae of COVID-19 (PASC) up to 15 Months Post-Infection. Frontiers in immunology, 12, 746021. https://doi.org/10.3389/fimmu.2021.746021
On 2020-05-04 18:24:02, user Dan Kammen wrote:
The full data sets and code can also be accessed here:
On 2025-09-02 10:12:20, user Audouze wrote:
Now published in Toxicology ( https://doi.org/10.1016/j.tox.2025.154225) "https://doi.org/10.1016/j.tox.2025.154225)")
On 2025-09-24 08:57:19, user Sophie PILLERON wrote:
This paper states that it uses the Globocan dataset; however, Globocan does not provide cancer incidence trends data. I suspect that the authors actually used CI5 data instead, which are available up to 2017.
In addition, this paper is very similar to another one ( https://pubmed.ncbi.nlm.nih.gov/34866023/ <br /> ), which the authors did not cite. The main differences between the two are the age groups analysed and the fact that the cited paper used data only up to 2012.
I would also recommend specifying the data source in the abstract, as this information is useful for interpreting the findings.
A justification for grouping all individuals aged 50+ together is needed, as this is a very heterogeneous age group. While I understand that the main focus of the paper is on the younger age group, the comparison would be more meaningful if the age categories used were more relevant.
I also suggest authors to reconsider the use of statistical testing. The study aim being descriptive, the use of statistical test is not needed as no a priori hypothesis are tested.
On 2025-09-25 01:05:32, user Florian Hladik wrote:
The abstract states, "we treated eight recipients with material from a single donor". However, it seems you treated four recipients with VMT and the other four with the placebo. Correct? It's confusing as written. In the Results too. Otherwise, great work! The other paper reporting the L. crispatus RCT is cool as well!
On 2025-09-30 03:53:09, user Eero Raittio wrote:
Published in Community Dentistry and Oral Epidemiology <br /> https://onlinelibrary.wiley.com/doi/10.1111/cdoe.70026
On 2025-10-14 23:19:52, user T. De La Cruz wrote:
On behalf of the authors, we would like to update to the medRxiv community that the present preprint is now published in a journal and available at https://doi.org/10.1016/j.ijidoh.2025.100078 .
On 2025-10-15 17:57:20, user Pui-Yan Kwok wrote:
This paper is now published in Nature:<br /> “Population-specific polygenic risk scores for people of Han Chinese ancestry”<br /> DOI: 10.1038/s41586-025-09350-y<br /> URL: https://www.nature.com/articles/s41586-025-09350-y
On 2025-10-23 17:28:47, user Victor Ezeugwu wrote:
Hi medRxiv team, could you please provide an update to indicate that this paper is published in PloS One: https://doi.org/10.1371/journal.pone.0293382 .<br /> Thanks.
On 2025-10-26 09:31:29, user Hannah Maude wrote:
A wonderful study and very interesting results! A comment on the discussion, noting on page 22 "Brain-expressed genes contribute to ME/CFS risk". Given the involvement of the peripheral nervous system in ME, might it be valid to say "Genes expressed in brain and neural tissue"? Peripheral nervous tissue is not so well represented in GTEx, although the Nerve_Tibial column shows high PPH4 for several genes in fig 4. Also perhaps of interest are sex-specific patterns of gene expression in peripheral nervous tissue https://pmc.ncbi.nlm.nih.gov/articles/PMC6412153/
On 2025-11-11 14:07:07, user Evolutionary Health Group wrote:
We at the Evolutionary Health Group ( https://evoheal.github.io/) "https://evoheal.github.io/)") really enjoyed this paper.
Here are our highlights:
The authors applied metagenomic sequencing to samples from multiple wastewater treatment plants to characterize the diversity and abundance of antibiotic resistance genes. Using a standardized bioinformatics pipeline, they quantified ARG classes relative to total microbial DNA, and compared treatment efficiency across plants.
They observed that water leaving the treatment plant still harbored a broad spectrum of ARGs, including multidrug-resistance genes.
The authors describe wastewater treatment plants as both sources and potential intervention points for antibiotic resistance by emphasizing that improved engineering and coordinated antibiotic-management strategies could limit the spread of resistance genes in urban systems.
These findings indicate that monitoring of municipal wastewater may serve as a real-time surveillance tool for community-level antibiotic resistance burden and inform outbreak preparedness.
On 2025-11-17 02:35:18, user Charin Modchang wrote:
Now published in PLOS Neglected Tropical Diseases. DOI: https://doi.org/10.1371/journal.pntd.0013618
On 2025-11-17 12:57:52, user Gregor wrote:
On 2025-11-24 13:20:05, user SkepticalScientist wrote:
Nice paper. I would be interested in knowing whether the pattern of results implied anything for recovery or had any other cognitive consequences. Also, it was unclear to me whether you corrected for TIV in the volumetric analyses - especially important given you didn’t correct for sex.
On 2020-04-22 01:23:46, user michael triplett wrote:
Thank you again for your review, @Bio. I’ll follow your format in my reply.
To explain this another way, consider the kinematic equations for velocity. With the time variable included, v(t)=v(0)+a*t. As a function of position, v(s)^2=v(0)^2+2a*s. I’ve essentially presented this model as a function of position, rather than time.
You are correct that many other factors were omitted from analysis. It was stated in the introduction that no speculation was made outside of included data. The confounding factors you described are important, but including arbitrary adjustments can be equally confounding. “Expert opinion” is rarely better than “arbitrary” when applying quantitative correction factors. Binning of such a large sample size is generally more reliable than applying such correction factors to low-level data. Furthermore, to address the specific concern about lockdown measures, March 27 was inside the first two weeks for almost every country included. So, given the two week incubation period, lockdown measures could not have had a significant effect on case rates by that time.
I understand your point about low-level location, point-wise population mapping, etc., but I will just point to the binned data again. The sliding window approach has the effect spreading those point-wise populations and case rates so that such biases are minimized. It is not at all practical to map every case cluster, and adequate predictive data is unavailable at such a low-level. As for outliers, they were all in “cold” northern regions so omitting them actually made the analysis more conservative.
To address your point about temporal sequencing, which was my main concern with the data, it can be seen in Fig. 3 that case rates below -30 degrees also began to spike above tropical/equatorial regions. Travel patterns may have accelerated growth in that region, but the question then would be, “are travel patterns more/as significantly different by latitude than temperature?” And, given that the virus was in existence before humans began collecting data, it is likely safe to assume that travel-related factors are not terribly significant. Perhaps further study will tell. But, If this were a only a matter of origin and spread, the shared climates between northern and southern sub-tropical regions, as the seasons move through spring and fall, would also reduce significance of the temperature variable relative to latitude. I also struggle to imagine a real physical impedance to the virus’ movement through equatorial regions in the modern world. It originated in the northern hemisphere, but equatorial regions are skirted by large relative case rates on both sides. Maybe it isn’t effected by temperature directly, but it appears to be affected by a set of variables that are also correlated with temperature. That is really the point.
Finally, thank you again for your feedback. As stated in the conclusion, this analysis was intended to spur further research... not provide anything causal. Rather than rework what amounts to an out-of-date data set, I have taken your feedback and constructed a predictive model that accounts for the practically applicable variables you mentioned. I will be publishing the write up shortly.
In the meantime, we’ll see how things pan out.
On 2020-04-22 01:51:11, user fourierTF wrote:
On p. 14, the rate alpha of asymptomatic virus carriers is estimated quite low, and variated in simulations between 0.01 and 0.16. But why should manifestation indexes between 51% and 81% be irrelavant for this number? Because, "in a setting where contacts have been traced properly, thereby effectively isolating the exposed population in an early stage before they become asymptomatic carriers, and when extensive tests are performed, this fraction would be minimal"? But at present and in the near future, we don't have this setting of extensive testing.
An even more important problem of parameter estimation seems to me: By varying the bevahiour influenced parameter R1, data for Germany were fitted to the cumulative number of reported cases. But these strongly depend on the number of tests. Most importantly, from week 11 to 12 (march 7 to 20), both numbers increased about 3-fold, compare Table 4 of the situation report by Robert Koch institute (RKI), as of april 15. Since the observed reproductive number directly depends on the number of registered infected persons, the peak in this number at march 10 appears to be largely artificial. Even more, since certainly the number of tests also jumped up from week 10 to 11 - but data are not published.
Finally, a personal-political remark: In the appendix of page 14, the authors give the usual scientific disclaimer: "Currently, there is no reliable data available about the asymptomatic cases." This is only read by specialists. The apodictic abstract of this highly influencing paper, however, is noticed by the media, by a larger public and presumably by German chancellor Angela Merkel, according to her related public statements. "We strongly recommend to keep all NPIs in place and suggest to even strengthen the measures in order to accelerate reaching the state of full control, thus, also limiting collateral damage of the NPIs in time." Otherwise, the authors threaten with their horror scenario A (p. 8): "the health care system will in expectation need a peak capacity of 500,000 ICUs or more" (comparable to the falsified prediction of far more than 200.000 ICUs at march 21, by Deutsche Gesellschaft für Epidemiologie). At this place, no carefulness about the "collateral damages" of continuing the lockdown at all, for families, children, culture, (small) enterprises and many others. These countermeasures nead strong reasons - but no word about strong scientific counter-arguments!
Johannes Wollbold, Weimar / Germany
On 2020-04-01 11:46:11, user Philip Shaw wrote:
Is there any mathematical description of the model available?
On 2020-04-22 08:21:01, user Stinsen wrote:
David,
As clearly decribed in the paper the, start parameters are uncertain and might be different from the ones used. However the only parameter that really changes the scene dramatically is d, the doubling time. d depends on the reduced Ro. The initial doubling time can be estimated from old data and other measurements, and here is a true problem since a erroneous d can change the curves, but the new dE is more difficult since that is the only parameter that is changed by protective measurements by reducing Ro, I.e. social habits, lockdown, etc.<br /> To sum up the difference in other parameters only shifts the timeline with days.
On 2020-04-06 12:25:59, user Sinai Immunol Review Project wrote:
Clinical Characteristics of 2019 Novel Infected Coronavirus Pneumonia:A Systemic Review and Meta-analysis
The authors performed a meta analysis of literature on clinical, laboratory and radiologic characteristics of patients presenting with pneumonia related to SARSCoV2 infection, published up to Feb 6 2020. They found that symptoms that were mostly consistent among studies were sore throat, headache, diarrhea and rhinorrhea. Fever, cough, malaise and muscle pain were highly variable across studies. Leukopenia (mostly lymphocytopenia) and increased white blood cells were highly variable across studies. They identified three most common patterns seen on CT scan, but there was high variability across studies. Consistently across the studies examined, the authors found that about 75% of patients need supplemental oxygen therapy, about 23% mechanical ventilation and about 5% extracorporeal membrane oxygenation (ECMO). The authors calculated a staggering pooled mortality incidence of 78% for these patients.
Critical analysis:<br /> The authors mention that the total number of studies included in this meta analysis is nine, however they also mentioned that only three studies reported individual patient data. It is overall unclear how many patients in total were included in their analysis. This is mostly relevant as they reported an incredibly high mortality (78%) and mention an absolute number of deaths of 26 cases overall. It is not clear from their report how the mortality rate was calculated. The data is based on reports from China and mostly from the Wuhan area, which somewhat limits the overall generalizability and applicability of these results.
Importance and relevance: This meta analysis offers some important data for clinicians to refer to when dealing with patients with COVID-19 and specifically with pneumonia. It is very helpful to set expectations about the course of the disease.
Francesca Cossarini
On 2020-04-06 19:19:57, user Maxim Sheinin wrote:
Given that the majority of people dying from Covid-19 are elderly (60+) and BCG vaccine is given only in childhood, it would likely make more sense to look at the BCG vaccination status at the time when these elderly people were supposed to receive the vaccine, instead of the BCG status today. This will likely complicate the story, since many European countries that don't use BCG on a routine basis today used to do that in the past, and, conversely, some of the LICs introduced BCG relatively recently (http://www.bcgatlas.org/) "http://www.bcgatlas.org/)")
On 2020-04-12 16:58:54, user Dragana Stojkovic wrote:
The Mycobacterium tuberculosis membrane protein Rv0899 (rv0899 gene) are important for vaccines and defence against COVID-19.<br /> For those interested I can offer an explanation.<br /> Kind regards,<br /> Dr Slobodan Stojkovic
On 2020-04-07 13:31:01, user Jaco Brand wrote:
I see clinical trials being initiated based on a paper that have not been peer-reviewed or published. The trend with income can be interpreted in a myriad of different ways, like lifestyle choices and diet. This is exactly why Fig. 3 show a different death rate between low and medium-high income countries, despite both groups having a universal BCG vaccination policy. This is a highly unscientific speculative statistical correlation study. I have highlighted further comments to the paper as a download
On 2020-04-07 16:32:46, user Roberta Caruso wrote:
Using the Diamond Princess (DP) as a case study, the authors estimate an IFR 'slightly less than 1%, although statistically affected by a rather large uncertainty due to the small number of deceased'. It should be noted that when analyzing data on such a reduced 'statistical' sample, it is not appropriate to refer to statistical uncertainties - the sample is too small to actually compute statistical errors that have any sense for the analysis. One should instead focus on the analysis of the systematic errors that affect the estimation of IFR in order to obtain the actual relevance of the estimation obtained by simply dividing the number of died passengers for the total number of infected people on board. In other words, since these errors cannot be computed for IFR on the DP, this number should not be used as a benchmark for further analyses. <br /> The final estimation of the total number of infected cases is so vague (line 332-333 and line 355-356: between 660 000 and 3 300 000 - a difference of 500%!) that there is no practical use for it. The lower boundary of the estimation is questionable in itself, given the criticalities of the estimations performed using the DP case study, thus implying a possibly larger error bar on the estimation of the total infected. <br /> Such a large uncertainty poses serious questions on the scientific soundness of the study.
On 2020-04-07 18:16:27, user xahdum16x wrote:
This study at this time is useless to me. What is the comorbid breakdown of the patients, they only say sex and age are homogenous. What is the CI of the results, I don't care about a low pvalue. What were the "moderate adverse reactions" and how did they judge pneumonia improvement on imaging, what category since all these patients were mild to moderate where there baseline imaging similar or not. Lastly, since it does not say blinded, maybe the physicians were more apt to hold off on aggressive therapy in the "treatment" arm as opposed to the "placebo arm" due to flase security or hoping that it would help create significant results. There is a reason we blind studies to prevent bias.
On 2020-04-08 14:37:18, user alexishmatov wrote:
Problem of high or low AH is not a problem
The recent study has shown that problem of high or low AH in timing respiratory infections may be resolved by using the physical effect in the airways (supersaturation and enhanced condensational growth in the airways).
The main sense of the supersaturation in the airways is that this effect depends simultaneously on both temperature and RH of inhaled air. Thus, temperature and RH are the parameters of one simple function — it is the effect of supersaturation.
Ishmatov A. Influence of weather and seasonal variations in temperature and humidity on supersaturation and enhanced deposition of submicron aerosols in the human respiratory tract, Atmospheric Environment, V. 223, 2020, 117226, https://doi.org/10.1016/j.a...
On 2020-04-08 17:16:11, user buongustaio1964 wrote:
This study appears to fail control for scores of additional obvious, potential confounds. These include but are not limited to population density, dwelling density, household sizes, educational level, employment profiles...I could go on. The conclusion could reasonably be a call for more research. But that the "study results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis" is neither convincing nor warranted.
On 2020-04-09 01:55:01, user Emma McBryde wrote:
Thanks for the comment Robert. I am updating my data on imported versus local cases on a daily basis. When this preprint was made, the data were very sparse, and I had to assume undetermined cases were local. I will revise this for any peer-reviewed print. Meanwhile, I would recommend this website for the best publically available data www.covid19data.com.au
On 2020-04-10 10:53:43, user supervilin wrote:
My understanding is that 30% of people placed in low nAb category reflect inability of their plasma to neutralize those few antigens (RBD, S1, and S2 proteins) expressed on pseudo SARS-CoV-2 virus. However, this work does not rule out possibility of other neutralising Abs present in this 30% category. Using real SARS-CoV-2 virus would be one way to check for this but much harder to do.
On 2020-04-10 11:48:59, user Srinivasa Kakkilaya wrote:
It's a very interesting analysis which should show the way forward in this crisis. If I'm allowed, I am posting a brief analysis that I did the day before, with data collected from various official sources and publications. It's here below:
Corona Virus Disease (COVID) 2019: Comparison of Cases in India and Abroad
Summary:
The trends of COVID 19 infections, complications and mortality are similar in almost all the countries, including India.
Risk of developing severe disease and death is higher in those aged 60+ years, and particularly in those with modern diseases such as hypertension, diabetes and coronary artery disease.
In India, 8.5% of the population is aged 60+ years, and 4-11% of the population aged less than 40 years is afflicted with hypertension and diabetes, and these are vulnerable to severe COVID 19.
The common factor for increased risk of severe COVID 19 is the presence of the so called metabolic syndrome at any age, old or young. These disorders are related to consumption of sugars and sweets, fruit juices, sweetened beverages, processed and fast foods, fried foods etc., and also alcohol consumption, and smoking. Avoiding these will be helpful in combating COVID 19.
COVID 19 remains a mild illness in almost 80-90% of those infected, and many patients lesser than 30 years of age are likely to have very mild or no symptoms.
Details:
India has already recorded about 5500 cases and more than 160 deaths due to COVID 19. The following analysis is based on the scientific and media reports published so far from India and elsewhere.
Corona Virus Infections - Age Distribution:
India:
47% of infections in age <40 years<br /> 34% in age 40-60 years<br /> 19% in age >60 years.
Wuhan, China:
27.2% in the age 0-39 years<br /> 41.6% in 40-59 years<br /> 31.2% in >60 years
It's almost identical in India and China and it correlates with the age distribution of population.
China:
<60 years - 82% of the population, 69% of infections
60 years - 18% of the population, 31% of the infections.
India:
<60 years - 91.5% of the population, 83% of the infections
60 years - 8.5% of the population, 19% of the infections
The higher percentage of infections in the elderly is likely due to more prominent symptoms than the younger population and hence presentation to the hospitals in more numbers.
COVID 19 Deaths: Age Distribution and Risk Factors
India
63% of deaths in those 60+ years of age 30% in those aged 40-60 <br /> 7% in those below 40 years
Average age of victims - 60 years
Average Case Fatality Rate -2.7%<br /> 0.4% for those below 40 years<br /> 2.4% for 40-60 years<br /> 8.9% for those above 60 years
86% had pre-existing conditions<br /> 17% had more than three diseases<br /> 40% had two<br /> 35% had one<br /> 56% had diabetes<br /> 47% had hypertension<br /> 20% had lung disease<br /> 16% had heart disease with diabetes and/or hypertension.
This pattern is also comparable with other countries.
China
81% deaths in age 60+ years<br /> 16.4% in 40-60 years<br /> 2.6% in 10-40 years<br /> 0 in <10 years
The average case fatality rate 2.3%;<br /> 0.2% for those below 40<br /> 0.85% for 40-60<br /> 8.8% for those above 60 years<br /> (14.8% in patients above 80 years)
Italy
95% deaths in age 60+ years<br /> 4.7% in 40-60 years<br /> 0.27% in 0-40 years
99.2% had one or more pre-existing diseases (75% had high blood pressure, 35% had diabetes and 33% had coronary heart disease)
United States (of the first 1150 deaths)
89.9% in 55 years and above<br /> 9.4% in 35-54 years<br /> 0.7% in 0-34 years
UK (of 750 deaths)
69% aged above 75+ years<br /> 96% had pre-existing conditions
These details clearly show that in all the countries, the case fatality of COVID 19 has shown direct correlation with age of the patients and with age-related diseases such as hypertension, diabetes and coronary artery disease and that the mortality was higher in men compared to women.
In India, 63% of deaths occurred in those above 60 years of age, and 30% deaths occurred in those aged 40-60. Considering the fact that 86-90% of the deaths occurred in those who had pre-existing diseases, the higher number of deaths in the 40-60 years age group seen in India is attributable to younger onset of these diseases in Indians. In India, the overall prevalence of hypertension is about 30%, and about 11% in the age group of 40 years or lesser. Type 2 Diabetes has an overall prevalence of 16-19%, whereas in the young, it is about 4-8%. These diseases, coupled with consumption of alcohol and tobacco, increase the risk for COVID 19 complications in those aged above 60 and also in those who are younger. Otherwise, COVID remains a mild illness in almost 80-90% of those infected, and many patients lesser than 30 years of age are very likely to have very mild or no symptoms.
If I may add, it appears that the deaths are directly related to metabolic syndrome linked disorders and the 33 cases that apparently had no identifiable cause in NY in your series might have had other problems of metabolic syndrome such as hypertriglyceridemia or premature balding etc., all of which are linked to hyperinflammatory state.<br /> Thank you again for the interesting and path breaking effort!
On 2020-04-12 00:51:03, user Art Shaposhnikov wrote:
What is the point in computing the absolute risk and comparing it to the miles driven? It could be very misleading to people who don't understand what the absolute risk means. The absolute risk of dying from covid-19 last year in the US was zero - zero miles driven was riskier. Based on the zero absolute risk number, we should not have spent any resources to prepare for it last year, right? Applying the same logic, since the absolute risk is very low now, we should stop the quarantine immediately, stop the vaccine developments and observe the final absolute risk based on excess mortality data in 2022, which could very well be greater by a factor of 10 to 10,000 than now.
On 2020-04-12 02:47:16, user Petard Stamo wrote:
I don't understand his twist in his analysis. Initially he insisted that testing is crucial to determine an aproximate value of Infection Fatality Rate. And that in his opinion was the measure which determines how dangerous was the virus. Now he has completely disregarded the number of infections in his analysis. The analysis is based only on deaths partitioned on age and sex and total number of population partitioned on age and sex. What is the difference between P(dying from Covid19 / <65) and P(dying from Covid19 / infected, <65)? How can you say that all people have been infected if we still don't have reliable data about the total number of infections? What proportion of the population has been infected?
On 2020-04-10 14:54:38, user Neil Lancastle wrote:
For clarity: Figure 5 is countries with BIGGEST falls in mean growth... from the text on p7...'growth rates have fallen most compared to earlier period' and, excluding China, these countries are France, Spain, Switzerland, Italy, UK and Norway.
On 2020-04-11 08:17:28, user Xavier de Roquemaurel wrote:
Great work. Thanks.<br /> Can i suggest to please run a similar study concept, yet this time identifying countries according to the different BCG strains:<br /> BCG Japan (Tokyo)<br /> BCG Brazil / Moreau<br /> BCG Denmark<br /> ...<br /> This is also an hypothesis to test.<br /> Thanks<br /> Xavier
On 2020-04-11 12:51:33, user ybysk wrote:
In my understanding, what the authors (and many readers) want to know is whether or not BCG vaccine effectively protects individuals from infection (i.e., the effect on infection-per-exposure) and also death (i.e., the effect on death-per-infection). I have not understood how the authors justify to use the number of total cases and deaths per one million population as measures of the effectiveness of BCG. Are they supposed to be equivalent to infection-per-exposure and death-per-infection?
On 2020-04-11 18:07:45, user Aaron Gasaway wrote:
Scientists and medical researchers: please look into whether it's dust that is sometimes allowing the virus to become "aerosolized." I've read a little about dust particles carrying influenza, so it seems plausible. Also, the recent Chinese study showed higher concentrations on the floor (where dust would fall). Dust as the vehicle would also explain it being found in AC vents. Central Air units suck up a huge amount of dust and some of it makes it through the filters and back out into the air.
Of course none of this means the moisture in exhalations or coughs couldn't also be the vehicle. On the whole it would seem not to be spreading enough for normal exhalations to be the primary vehicle, although it seems from the Washington choir episode that with enough force behind the exhalations, it could be.
I am sorry if this question about dust seems amateurish or crackpot. I just don't know if anyone qualified is looking into this possibility, so thought I should post it here.
On 2020-04-13 11:38:17, user Sanjiv Vij wrote:
Thank you. My concern is that 205 patients is too small a number and 7 days is too early to be reliable. Will it be possible to get more data from the registry, and, have data that spans admission to death or discharge for as many patients as possible? With regards to how many were on ACE / ARB / Immuno-modulating drugs / Neither[none]. That will help in risk stratification in a more reliable way. Regards Sanjiv
On 2020-04-13 13:32:07, user Rosemary TATE wrote:
Hi, I dont see the STROBE guidelines checklist uploaded, although you ticked yes to this<br /> "I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. " <br /> A lot of people seem to ignore these but they are important and any good journal will require them.<br /> Can you please upload? Many thanks.
On 2020-04-13 19:18:24, user Charles R. Twardy wrote:
Very similar to Benvenuto et al from 26 Feb, whom they cite as [4] and [9], but applying only to Italy. The earlier paper fit an ARIMA to worldwide Hopkins data through 10-Feb (then 43K cases) and, like this paper, found that we had just passed the peak. The previous forecast was absurdly optimistic. The current paper benefits from another month of data, and a single country.
Perhaps it does better. Eventually it's bound to converge, but it would seem the main value in the limited 4-day forecast is recognizing when the data has violated your model so you can put more weight on another one.
Benevenuto et al: https://www.ncbi.nlm.nih.go...
On 2020-04-14 09:12:21, user Lisa Kane wrote:
'Hoax' seems a rather strong comment, and to dismiss the whole paper is not helpful. The authors all appear to be legitimate scholars. While causality is not indicated, possible associations are useful to identify at this stage of exploration of the pandemic and can be further tested by other scholars.
On 2020-04-14 10:56:53, user Prof Page wrote:
It would be good if there were parenthetical sample sizes noted for these studies. Some are exceedingly small.
On 2020-04-14 11:02:19, user Philip Davies wrote:
This is a very interesting pre-print. BUT, I think the data in table 3 has been mixed up (deaths for low v high dose are incorrect). The authors need to correct this and then ensure the tables are correct everywhere else. I have asked the authors to look at this and re-issue a corrected version (I also question whether the qSOFA results (table 1) were meant to be for values >2 rather than <2.
This is important. It could mean that lower dose chloroquine is not only safe but could prove to be statistically better than placebo (will need the full 28 days analysis to know that).
Dr Phil Davies
On 2020-04-15 11:35:12, user GP MD wrote:
Ceftriaxone and azithromycin have their own toxicities....including mitochondrial dysfunction and ROS over-production in mammalian cells. The dosing of all three agents means a much higher risk of oxidative damage to mammalian DNA, proteins and membrane lipids. This would be worsened in those with impaired production of glutathione or reduced glutathione levels due to acetaminophen dosing.
On 2020-04-15 18:47:50, user Y H wrote:
Over thirty years ago, we found that chloroquine and related chemicals block muscarinic agonist binding. Similar findings were reported afterwards. This effect may link to a cause for cardiac arrhythmia induced by chloroquine. YH
Antimuscarinic effects of chloroquine in rat pancreatic acini<br /> Yoshiaki Habara, John A Williams, Seth R Hootman<br /> Biochemical and biophysical research communications 137 (2), 664-669, 1986
On 2020-04-14 17:58:53, user Badly Shaved Monkey wrote:
From a U.K. perspective:
My common sense reservation is that if Coronavirus was going to hit, say 60% of the U.K. population and 0.1% of those would die as suggested by Silverman and Washburne, that’d be about 40,000 deaths in total in the U.K. We’ve already hit 12,000 under the influence of a significant degree of social restriction over several weeks. While it is hard to predict the logistic asymptote from the exponential-like phase, it stretches credulity to suggest that the unmoderated U.K. epidemic would have burnt itself out with 40,000 deaths.
On 2020-04-14 19:47:35, user Sinai Immunol Review Project wrote:
Main findings:
The aim of this study was to assess an association between reduced blood lymphocyte counts at hospital admission and prognosis of COVID-19 patients (n=192). The authors found:<br /> - Patients with lymphopenia are more likely to progress to severe disease or succumb to COVID-19 (32.1% of COVID-19 patients with lymphocyte reduction died). <br /> - Reduction of lymphocytes mainly affects the elderly (> 70 years old). <br /> - Lymphocyte reduction is more prevalent in COVID-19 patients with cardiac disease and pulmonary disease, patients with increase in the chest CT score (key marker of lung injury) and a decrease in several respiratory function markers (PaCO2, SpO2, oxygenation index).
Limitations of the study:
Reduced blood lymphocyte counts with aging have been known (https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.03.08.20031229v2)") https://onlinelibrary.wiley... "https://onlinelibrary.wiley.com/doi/epdf/10.1111/sji.12413)"). Therefore, it is not unexpected that a larger fraction of COV ID-19 patients above 70 years old have lower lymphocytes counts. Since age has been reported to be a major factor that determines outcome for COVID-19, lymphocyte counts and prognosis should have been adjusted by age. Multivariate analysis to identify independent risk factors is lacking.
Relevance:
Previous studies demonstrated that SARS-CoV-2 infection leads to a decrease of the T cell count. This study confirms these results and shows that lymphocyte reduction mainly affects the elderly. Lymphopenia was associated with disease severity as well as worse prognosis. Future studies need to address if lymphopenia is a negative predictive factor independent from age.
Review by Meriem Belabed as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai
On 2020-04-14 21:52:36, user Dr Eric Grossi Neurocirurgia wrote:
Is missing the intervals of age - 29.4 to 60 years only - Enormous Bias
On 2020-04-15 11:04:57, user Dr Eric Grossi Neurocirurgia wrote:
I would like to highlight a serious methodological error in this study. What we want for a drug treatment of COVID-19, only two objectives, to avoid and / or treat SARS and reduce contagion, therefore pragmatism in the selection of patients must be as close as possible to the clinical reality, which did NOT occur, since only patients between the ages of 29.4 to 60 years were analyzed. This alone invalidates any useful result, since the vast majority of human losses are over the age of 64.
On 2020-04-15 15:04:27, user Mehee f wrote:
this is completely unscientific the US has a population of 335M on this basis it will have 4M cases and 100,000 fatalities by now. Also you did not compare figures with other countries there is a big variation of mortality rates between 0.5% in South Korea to 12% in Italy. It is very biased report based on all estimates and no data pure speculation.
On 2020-04-15 19:05:05, user disqus_NrkWMn1KOM wrote:
"collected data to emulate a target trial" - So, not a real study?
On 2020-04-15 19:07:06, user Gregory Armstong wrote:
Thanks for contributing this. It's a very important topic. There are few well controlled studies of risk factors for COVID-19.
My one comment--and I didn't read the entire manuscript: I'd strongly recommend removing vital signs and laboratory findings from the regression. Those are manifestations of severe disease, not risk factors for it. They're some of the main considerations in deciding whether to hospitalize and whether to admit to the ICU. They should correlate strongly with both, and including them in the model will severely dilute the impact of true risk factors and could completely hide them. They shouldn't be considered independent variables.
On 2020-04-15 20:48:45, user empiricist2 wrote:
According to the French study, the addition of Azithromycin cleared out the virus in 4 days, versus at least twice that without it. And others use zinc, considered a very important factor. So why just test HCQ alone? And what were the antivirals that hampered results? Certainly there are other studies to report on that include zinc etc. Even in China they had reported that IV Vit C helped significantly. One doctor takes 10g daily as preventative. Vit C has a long history of antiviral benefits in large doses.
On 2020-04-18 21:54:04, user Jim Trader wrote:
Kind of a useless study. With an average delay of 16 days from symptom onset to enrollment and treatment in this trial, those patients are pretty much past the viral phase of the disease, where an antiviral treatment would have the most value, and are well on their way to pneumonia and a cytokine storm problem, which is ultimately what kills. Even the subset analysis of patients with a 7 day delay from symptoms to enrollment is still too long. As the authors state, it is very difficult to do these kinds of trials when patients average 12 days from symptoms before they come into the hospital, and can be explored as trial participants. So unfortunately there is no signal here, and from a molecular biology viewpoint, that is exactly what I would expect with this trial design. HCQ therapy needs to start within 48 hrs of symptom onset, ideally 24.
On 2020-04-16 14:51:37, user agoraks wrote:
Quote: "The assays are sensitive and specific, allowing for screening and identification of COVID19 "
Question: what is the actual sensitivity and specificity of the assay for detecting active disease by IgM or convalescence IgG in Sars-CoV-2 infected patients ?
On 2020-04-16 19:48:56, user Stef Verlinden wrote:
It is troublesome that the article does not give the materials and the method used to derive/calculate the QTc. Formally a QTc can only be calculated from a signal derived from Lead II of V5 registered with a 12 lead ECG machine. QTc's calculated by a computer algorithm (unless it is specifically validated to do that job) are not to be trusted. This needs to be done by hand by a well-trained person.
Most importantly, the QTc calculation is not linear. A heart rate of 90 gives an overestimation of the QTc of 50 ms (when using the Bazett method). Did the authors correct for this? Based on this paper, it is not possible to establish whether the QTc's are truly prolonged or that these are false positive outcomes.
For reference check; QTc: how long is too long? https://www.ncbi.nlm.nih.go...
On 2020-04-17 07:35:29, user Mansour Tobaiqy wrote:
Thank you, the paper has bow been published at IPIP:
On 2020-11-19 18:22:04, user Ivan Ivanov wrote:
Impressive work. It is probably misunderstanding but the authors use 10mM KCl/10mM NH4SO4 in the buffer for Bst 2.0 and they call it 1x Isothermal Buffer which infact is the commonly known ThermoPol buffer (optimal for Bst LF, not for Bst 2.0). NEB sells Bst 2.0 with Isothermal buffer containng 50mM KCl/10mM NH4SO4.
On 2020-11-20 02:26:46, user Prasad Kasibhatla wrote:
Interesting.. scaling reported median risk (2e-6) for individual touches by average touches per day (336), gives a transmission risk by fomites of 10-12% over a 6 month period (roughly length of pandemic ). Seems high .. so my scaling must not be appropriate. Any thoughts?
On 2020-11-23 23:12:54, user Louis Rossouw wrote:
Why does this paper compare mortality July 2019 to June 2020 to try and determine the impact "after" COVID-19 when the pandemic started in Feb-March 2020? So the "after COVID-19" of the title of the paper includes more time before COVID-19 than after the epidemic started?
The authors also do not allow for any changes in age distribution / popualtion mix that may be affecting observed mortality trends.
On 2020-11-25 07:42:20, user Carol Shadford wrote:
When you say 'continual sensation of having had a “nasal douche”' are you referring to a feeling that the sinuses have been cleared out and are now empty or is that referring to the rushing feeling you get when chlorinated water accidentally goes up your nose -- a wasabi-like feeling?
On 2020-11-26 05:48:46, user Community Medicine with velz wrote:
Re-infection can be defined only by viral genome sequencing. The implication of these results can be mis-leading as re-infection has been defined by RT-PCR in this study.
On 2020-11-27 19:21:54, user Jackie A wrote:
This work is useful because it is the first modeling paper reckoning IFN role in the disease - and an early administration of it could be beneficial, which is consistent with recent RTC for IFN-beta. However, it is built on rodents and the extrapolation to humans is not clear and not validated by tissue data extracted from humans. The authors assume that tissue viral loads correlate with organ failure which has not been demonstrated.
On 2020-11-27 21:02:26, user Robert Brown wrote:
Vitamin D, Magnesium, Steroids, PPI and COVID-19; Interactions and Outcomes - Response to ‘Effect of Vitamin D3 Supplementation vs Placebo on Hospital Length of Stay in Patients with Severe COVID-19: A Multicenter, Double-blind, Randomized Controlled Trial’ [Preprint] [1]
Thank you and congratulations on your important and significant paper. This is only the fourth[2] [3] [4] reported RCT examining vitamin D supplementation as a therapeutic intervention for COVID-19. Biology provides multiple pathways by which vitamin D hydroxylated-derivatives[5] may impact Covid-19 risks [including via; ACE2 receptors; airway-epithelial-cell tight-junction-function, immune responses [affecting lymphocytes, macrophages T cells, T helper cells, Th1, -17; Tregs; cytokine secretion IL-1, -2, -4, -5, -6 -10,-12; IFN-beta, TNF-alpha; defensins and cathelicidin, and receptors HLA-DR, CD4, CD8, CD14, CD38. Vitamin D also regulates; mitochondrial respiratory, inflammatory, oxidative and other functions; RXR and other receptor links between steroids, retinoids, hormonal vitamin D, thyroid hormone, oxidised lipids and peroxisomal pathway immune responses.][6]
Significant evidence [40+ patient-papers[7]] suggests higher Vitamin D status [serum/plasma 25(OH)D concentration] is associated with diminished COVID-19 infection rates,and reduced severity [including ICU admission and mortality].[2 3 4]
Thus, it is crucial, to consider if the preprint’s broad-based conclusion “Vitamin D3 supplementation does not confer therapeutic benefits among hospitalized patients with severe COVID-19”, [time to discharge as well as lack of observed ICU and mortality rate benefits], stands scrutiny when any one, or combination of, the following factors are considered: -
Delay in vitamin D administration after severe symptoms onset
Patients presented “10.2 days after symptoms”, thus were already verging on serious outcomes at admission; “89.6% required supplemental oxygen at baseline [183 oxygen therapy; 32 non-invasive ventilation] and 59.6% had computed tomography<br /> scan findings suggestive of COVID-19.” [Days to dyspnoea from overt infection average 7-8, and acute-respiratory-distress-syndrome [ARDS] develops after median 2.5 days.[8]]
Further, the timing of vitamin D supplementation, at or after <br /> hospitalisation, was not specified, despite timing clearly being an important factor, given the advanced stage of illness at admission.
Baseline vitamin D status [serum 25(OH)D concentrations] were relatively ‘good’
Baseline 25(OH)D values averaged 21.0ng/ml and 20.6ng/ml in the treatment and control groups respectively, i.e. they were relatively ‘good’, and above levels reported as being associated with the greatest COVID-19 risks.[9] [10]Sub-analysis of patients < 10ngml +/-Dexamethasone would be instructive. Further, deficiencies such as magnesium (an essential ‘D’ enzyme co-factor) might factor more in the lack of observed benefits for Covid-19 severity, than vitamin D status itself.
Corticosteroids
COVID-19 related corticosteroid vitamin ‘D’ interactions require<br /> investigation. 64.2%(Treatment) and 60.8%(Control) group patients respectively, were treated with Corticosteroids (Dexamethasone?), and mortality was somewhat higher in the Treatment than Control arm. Interactions between vitamin D and steroids including dexamethasone are observed[11], including “decreased synthesis of active vitamin D, and impairment of biological action at tissue level.”[12] However these potential effects have not been investigated in COVID-19 patients treated with both vitamin D and dexamethasone.
It would be most useful to know therefore, at what stage corticosteroid treatment began, and at what dosages, what other treatments were given [and at what dosage], and when such treatments were stopped, so that potential interactions between vitamin D, corticosteroids and other treatments for COVID-19<br /> patients could be elucidated.
In particular, any negative or neutralising effect of corticosteroids on<br /> ‘D’-derivatives and pathways, could account for the lack of reduction in risks of ICU and mortality outcomes, including slightly higher mortality, in those given vitamin D, a matter of importance, since dexamethasone, given before onset of serious ARDS, was reported in Oxford[13] to increase, not reduce, mortality.
Proton pump inhibitors.
PPI are known to lower serum magnesium,[14] an essential ‘D’ hydroxylase-enzyme co-factor. 47/120-(39%)[Treatment] and 49/120-40%[Control] used PPI, compared to 9.2% population usage in USA.[15] PPI-induced related serum magnesium reduction, +/- dietary insufficiency, is a reported COVID-19 risk factor,[16] thus possibly helping account, for D3 treatment, failing to reduce Brazilian Covid-19 mortality. Thought-provokingly a Brazilian paper reported “There is chronic latent magnesium deficiency in apparently healthy university students”, which deficiency is potentially more widespread.[17]
Conversely, RCT administration of magnesium with vitamin D reduced COVID-19 in-patient mortality.2
Rate of increase of Serum 25(OH)D
It is unclear when blood was sampled for determination of serum 25(OH)D concentrations, or if this was standardised for all patients.
A large bolus will increase 25(OH)D values in the healthy, “Oral D2 and D3 (100,000 to 600,000 IU) significantly increased serum 25(OH)D from baseline in all reviewed studies” . . . “peak levels were measured at 3 days (34) and 7 days following dosing,”[18]
However, timing matters, because hepatic hydroxylation5 to form 25(OH)D (Calcifediol) is likely reduced by; severe illness, as well as by obesity diabetes, and possibly hypertension,[19] conditions already recognised as risk factors for covid-19 severity.[20]
The Cordoba study[3] suggests that 25(OH)D [Calcifediol, that could be given together with vitamin D3, cholecalciferol], may be key to effective treatment of severe COVID-19 illness. There is no suggestion Cordoba patients were treated with corticosteroids. Cordoba patients were administered calcifediol on admission-day, but the period between overt infection and hospital admission <br /> was not reported.
Risk-factor Differentials in Patient Groups
A skew in risk factors favouring the control?
Control-Placebo to Treatment-D3:
Increased risk factors - Overweight (31/37, 0,84); Obesity (58/63, 0,92); Hypertension (58/68, 0,74); Diabetes II 35/49, 0,71); COPD (5/7,0,71); Asthma (7/8, 0,88); Chronic Kidney Disease (0/2, 0,0); Rheumatic Disease (10/13, 0,77)[21]; Black (14/20) Male 965/70).
Decreased factors - White (79/62) Female (55-50)
Improved oxygen parameters are not reflected in conclusion
“Despite the D3 group being at a greater risk, including due to hypertension, COPD and diabetes, known risk factors, significant differences in oxygen supplementation favour the D3 treatment group“.21
Oxygen supplementation (%) Placebo No. (%) D3 <br /> No oxygen therapy 9 (7.5) 16 (13.3)<br /> Oxygen therapy 97 (80.8) 86 (71.7)<br /> Non-invasive ventilation 14 (11.7) 18 (15.0)
Conclusion requires Caveats?
Thus, the un-caveated conclusion “Vitamin D3 supplementation does not confer therapeutic benefits among hospitalized patients with severe COVID-19”, likely requires caveats about possible effects of the several factors discussed above.
Further, the reported finding cannot be extrapolated to care of all Covid-19 patients, since the above- mentioned-potential interactions require further investigation, including; as to effects of; magnesium
status; treatment with PPI inhibitors, impact of corticosteroids in severe Covid-19 illness on vitamin D biology and outcomes, and consideration of pre-existing vitamin D status.
Further public health policy directed at reducing vitamin D, and other nutrient deficiencies for mitigation of COVID-19 risks at population levels, should not be conflated with clinical optimisation of vitamin D and metabolites for treatment of severe COVID-19 illness.
[1] Murai,I., Fernandes, A., Sales, L., Pinto, A., Goessler, K., et. al. 17th November 2020). Effect of Vitamin D3 Supplementation vs placebo on Hospital Length of Stay in Patients with Severe COVID-19 A Multicenter, Double-blind, Randomized Controlled Trial. medRxiv 2020.11.16.20232397; doi: https://doi.org/10.1101 /2020.11.16.20232397 Available at: https://www.medrxiv.org/content/10.1101/2020.11.16.20232397v1<br />
[2] Tan, C., Ho, L., Kalimuddin, S., Cherng, B., Teh, Y., et.al. (10th June 2020). A cohort study to evaluate the effect of combination Vitamin D, Magnesium and Vitamin B12 (DMB) on progression to severe outcome in older COVID-19 patients. doi: https://doi.org/10.1101/202... Available at: https://www.medrxiv.org/content/10.1101/2020.06.01.20112334v2<br />
Now published in Nutrition doi:10.1016/j.nut.2020.111017 <br />
[3] Entrenas Castillo, M., Entrenas Costa, L., Vaquero Barrios, J., Alcalá Díaz, J., López Miranda, J., Bouillon, R., & Quesada Gomez, J. (29th August 2020). Effect of calcifediol treatment and best available therapy versus best available therapy on intensive care unit admission and mortality among patients hospitalized for COVID-19: A pilot randomized clinical study. The Journal of steroid biochemistry and molecular biology, 203, 105751. https://doi.org/10.1016/j.j... Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7456194/<br />
[4] Rastogi, A., Bhansali, A., Khare, N., et. Al. (12th November 2020).<br />
Short term, high-dose vitamin D supplementation for COVID-19 disease: a randomised, placebo-controlled, study (SHADE study). Postgraduate Medical Journal Published Online First:. doi: 10.1136/postgradmedj-2020-139065 Available at: https://pmj.bmj.com/content/early/2020/11/12/postgradmedj-2020-139065<br />
[5] Bouillon, R., & Bikle, D. (2019). Vitamin D Metabolism Revised: Fall of Dogmas. J Bone Miner Res. 2019 Nov;34(11):1985-1992. doi:<br />
10.1002/jbmr.3884. Epub 2019 Oct 29. PMID: 31589774. Available at: https://asbmr.onlinelibrary.wiley.com/doi/full/10.1002/jbmr.3884<br />
[6] Brown, R., Rhein, H., Alipio, M., Annweiler, C., Gnaiger, E., Holick M., Boucher, B., Duque, G., Feron, F., Kenny, R., Montero-Odasso, M., Minisola, M., Rhodes, J.,Haq., A, Bejerot, S., Reiss, L., Zgaga, L., Crawford, M., Fricker, R., Cobbold, P., Lahore, H., Humble, M., Sarkar, A., Karras, S., Iglesias-Gonzalez, J.,Gezen-Ak, D., Dursun E., Cooper, I., Grimes, D. & de Voil C. (April 20, 2020). COVID-19 ’ICU’ risk – 20-fold greater in the Vitamin D Deficient. BAME, African Americans, the Older, Institutionalised and Obese, are at greatest<br />
risk. Sun and ‘D’-supplementation – Game-changers? Research urgently required’: ‘Rapid response re: Is ethnicity linked to incidence or outcomes of COVID-19?’: BMJ, 369(m1548). DOI: 10.1136/bmj.m1548. Available at: https://www.bmj.com/content... (Accessed: 24 November2020. - Alipio study<br />
now in question – rest stands)<br />
[7] Brown R. (15 Oct 2020). Vitamin D Mitigates COVID-19, Say 40+ Patient Studies (listed below) – Yet BAME, Elderly, Care-homers, and Obese are still ‘D’ deficient, thus at greater COVID-19 risk - WHY? BMJ 2020;371:m3872 Available at https://www.bmj.com/content/371/bmj.m3872/rr-5 (Retrieved 24 Nov 2020) <br />
[8] Cohen, P., Blau, J., Eds: Elmore, J., Kunins, L., & Bloom, A. (2020). MD disease 2019 (COVID-19): Outpatient evaluation and management in adults. Literature review. Wolters Kluwner. Available at: https://www.uptodate.com/contents/coronavirus-disease-2019-covid-19-outpatient-evaluation-and-management-in-adults/print<br />
(retrieved 25th November 2020)<br />
[9] Jain, A., Chaurasia, R., Sengar, N., Singh, M., Mahor, S., & Narain, S. (19th Nov 2020). Analysis of vitamin D level among asymptomatic and critically ill COVID-19 patients and its correlation with inflammatory markers. Sci Rep. 2020 Nov 19;10(1):20191. doi: 10.1038/s41598-020-77093-z. PMID: 33214648; PMCID: PMC7677378. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677378/<br />
[10] Radujkovic, A., Hippchen, T., Tiwari-Heckler, S., Dreher, S., Boxberger, M., & Merle, U. Vitamin D Deficiency and Outcome of COVID-19 Patients. Nutrients 2020, 12, 2757. Available at https://www.mdpi.com/2072-6643/12/9/2757 <br />
[11] Hidalgo, A. A., Trump, D. L., & Johnson, C. S. (2010). Glucocorticoid regulation of the vitamin D receptor. The Journal of steroid biochemistry and molecular biology, 121(1-2), 372–375. https://doi.org/10.1016/j.j... Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2907065/<br />
[12] Giustina, A., Bilezikian, J. (eds) (2018). Vitamin D and Glucocorticoid-Induced Osteoporosis. Vitamin D in Clinical Medicine. Front Horm Res. Basel, Karger, 2018, vol 50, pp 149-160 (DOI:10.1159/000486078) Available at https://www.karger.com/Article/Pdf/486078<br />
[13] The RECOVERY Collaborative Group. (17th July 2020). Dexamethasone in Hospitalized Patients with Covid-19 — Preliminary Report. J New England Journal of Medicine R10.1056/NEJMoa2021436 https://www.nejm.org/doi/fu... Available at https://www.nejm.org/doi/full/10.1056/NEJMoa2021436<br />
[13] FDA. (8th Apr 2017). FDA Drug Safety Communication: Low magnesium levels can be associated with long-term use of Proton Pump Inhibitor drugs (PPIs) https://www.fda.gov/drugs/drug-safety-and-availability/fda-drug-safety-communication-low-magnesium-levels-can-be-associated-long-term-use-proton-pump (Accessed 25th November 2020)<br />
[14] Hughes, J., Chiu, D., Kalra, P., & Green, D. (2018). Prevalence and outcomes of proton pump inhibitor associated hypomagnesemia in chronic kidney disease. PLoS ONE 13(5): e0197400. https://doi.org/10.1371/jou... Available at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197400<br />
[15] Lee, S., Ha, E., Yeniova, A., et. al. (30th July 2020). Severe clinical outcomes of COVID-19 associated with proton pump inhibitors: a nationwide cohort study with propensity score matching. Gut Published Online First: 30 July 2020. doi: <br />
10.1136/gutjnl-2020-322248 Available at: <br />
https://gut.bmj.com/content/early/2020/07/30/gutjnl-2020-322248<br />
[17] Hermes Sales, C., Azevedo Nascimento, D., Queiroz Medeiros, A., Costa Lima, K., Campos Pedrosa, L., & Colli, C. (2014). There is chronic latent magnesium deficiency in apparently healthy university students. Nutr Hosp. 2014 Jul 1;30(1):200-4. doi: 10.3305/nh.2014.30.1.7510. PMID: 25137281. Available at: http://www.aulamedica.es/nh/pdf/7510.pdf<br />
[18] Kearns, M., Alvarez, J., & Tangpricha, V. (2014). Large, single-dose, oral vitamin D supplementation in adult populations: a systematic review. Endocrine practice: official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists, 20(4), 341–351. https://doi.org/10.4158/EP1... Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4128480/<br />
[19] Kheiri,B., Abdalla, A., Osman, M. et al. (2018) Vitamin D deficiency and risk of cardiovascular diseases: a narrative review. Clin Hypertens 24, 9 (2018). https://doi.org/10.1186 /s40885-018-0094-4 Available at https://clinicalhypertension.biomedcentral.com/articles/10.1186/s40885-018-0094-4 <br />
[20] Kruglikov, L,. Shah, M., Scherer, E. (Sept 2020). Obesity and diabetes as comorbidities for COVID-19: Underlying mechanisms and the role of viral-bacterial interactions. Elife. 2020 Sep 5;9:e61330. doi: 10.7554/eLife.61330. PMID: 32930095; PMCID: PMC7492082.<br />
[21] Borsche L. Private email 19.11.20
On 2020-12-01 21:23:17, user NubOfTheMatter wrote:
This trial appears to have been a wasted opportunity.
It is unclear why Vitamin D3 was orally administered to severely ill C-19 patients. It can take a fortnight to be metabolised into Calcifediol, the active, metabolised form required to trigger an immune response. A most unhelpful delay to the effective treatment of severely ill COVID-19 patients.
The RCT conducted at the teaching hospital in Cordoba used Calcifediol, with dramatic results. Compared to the untreated ‘control group’ there was a 96% reduction in the need for Intensive Care Unit admission, and a commensurate reduction in deaths.
One has to ask, therefore, why, in Sao Paulo, it was decided to administer un-metabolised Vitamin D3 rather than Calcifediol to severely ill C-19 patients for whom every day of non-treatment reduces the chance of a good outcome if not survival? Indeed, an experienced physician could have forecast the results obtained without the need for a trial.
On 2020-12-07 13:02:33, user Fernando wrote:
Good morning, I have some comments: I couldn’t find the final D level at 7 and 10 days (hospital discharge), this information is crucial to establish the effectiveness of the ministered dose. <br /> At a first glance, considering the patients were mostly deficient in vitamin D and obese (slow D absorption) the dose provided seamed too low to produce results in such a short time (7-10 days), specially as it was vitamin D in its over the counter form (not calcifediol).<br /> Also not clear how many days after testing positive did the patients take vitamin D. It seems that in Brazil people are only hospitalized after aggravation.<br /> Thank You!
On 2020-12-01 14:30:16, user Peter Griffiths wrote:
Published as: Griffiths, P., Saville, C., Ball, J., Culliford, D., Pattison, N., Monks, T., 2020. Performance of the Safer Nursing Care Tool to measure nurse staffing requirements in acute hospitals: a multicentre observational study. BMJ Open 10 (5), e035828.10.1136/bmjopen-2019-035828
On 2020-12-01 17:48:06, user Pandora wrote:
Interesting. Did you take into account regular medication taken for each group. Studies have been done on Ace2 inhibitors and blockers. There's also studies on proton-pump inhibitors and Sarscov2. They seem inconclusive too, but it may skew your results. Interestingly, a lot of these drugs and a diabetes drug are under recall for NDMA contamination at present.
On 2020-12-03 15:34:00, user joetanic wrote:
Quite interesting data. I'm wondering whether anyone knows why France does not perform these tests?
Or the US? It seems a broad study, especially in France which seems to be bucking the trend, so to speak, would make clear what the future holds in many places.
On 2020-12-09 15:46:15, user Laura wrote:
This preprint has been published in The Journal of Immunology. You can read the latest version here: https://doi.org/10.4049/jim...
On 2020-12-11 16:21:41, user Fred wrote:
Disappointing study. I would not expect that antivirals are of any use if started when patients are already hospitalized. I would recommend to start with antivirals as soon as possible regardless wheter the patient has symptoms or not. But in this case we need studies comprising many more patients than in this small study
On 2020-12-11 18:07:58, user Thierry Grenet wrote:
Another discussion of the trajectories is given here : https://bmcmedresmethodol.b....
On 2020-12-12 17:48:59, user Patrick Karas wrote:
Congratulations on this excellent work. Molecular subtyping for meningioma is much needed to help develop future therapies, and your work pushes this forward. How do you think your subtypes A, B and C compare to the meningioma molecular types similarly labeled type A, B, and C published last year in PNAS by Patel et al (doi: 10.1073/pnas.1912858116)? It seems like there is a lot of overlap (group A with intact NF2; group B and C with NF2 loss; group C with increased FOXM1 expression and high copy number variation). This is a great step forward validating these subtypes through a different approach.
On 2020-12-12 23:20:04, user Omid Jahanian wrote:
This is an original manuscript of an article published by Taylor & Francis in the Journal of Spinal Cord Medicine on 09 Nov 2020, available online: https://www.tandfonline.com....
On 2020-12-13 04:57:43, user Haitham kussaibi wrote:
Dear valuable readers,
The current manuscript has been peer reviewed and published by <br /> Karger, Acta cytologica<br /> DOI:10.1159/000509669<br /> Thank you for your interest.<br /> The Authors
On 2020-12-16 20:05:03, user Wolfgang Lins wrote:
the authors discuss "anterior nasal (AN)" swabs, and on page 4 refer to this as:
Participants first underwent collection of the AN-sample, using the specific nasal swab provided in the test kit of the manufacturer, according to the instructions for use, which also correspond to the U.S. CDC instructions [4]. Briefly, while tilting the patient’s head back 70 degrees, the swab was inserted about 2cm into each nostril, parallel to the palate until resistance was met at turbinates, then rotated 3-4 times against the nasal walls on each side
First, the collection procedure described here in act matches to one in that CDC manual [4], however not to the AN collection but to the collection of Nasal mid-turbinate (NMT) specimen. That is a floppy usage of the terms AN versus NMT, and should at least been detailled when defining AN in this paper.
Second, the paper refers to a "specific nasal swab provided in the test kit of the manufacturer" for AN collection. Three lines later they write "a separate NP-swab (provided in the manufacturer test kit) for the Ag-RDT". <br /> As of my knowledge, the "STANDARD Q COVID-19 Ag Test" of SD Biosensor/Roche comes with a single kit - a NFS-1 from Noble Biosciences Inc., which is a swab for NP.
I think it would help this paper to identify the particular "specific nasal swab" used here to obtain the AN/NMT swab - since the conclusions of this paper make a claim that "using a professional AN-sampling kit is at least equal to....". Reference 5 with a link to a sdbiosensor IFU that refers to NP swab only does not put any more light into this.
Such a finding without properly identifying the particular kit unnecessarily reduces the value of this work.
On 2020-12-25 14:25:32, user muthu venkat wrote:
The Systematic Review and Meta-Analysis is interesting to read and need of the time to compile such an evidence particularly for middle and low income country. The authors have made sure that there is no bias in selection and reporting the evidences through use of appropriate software and methods.
On 2020-12-26 19:56:50, user Dr S K Maheshwari wrote:
This systematic review is focusing on measuring effectiveness of mHealth interventions on antenatal and postnatal care utilization in low and middle-income countries. A strong methodology was used along with wide inclusion of relevant studies from low and middle-income countries. The search strategy criteria were used very specific. I appreciate this work and recommend.
On 2020-12-25 16:08:55, user Deepak K. Gupta wrote:
Full paper now published in the Tremor and Other Hyperkinetic Movements journal here: https://tremorjournal.org/a...
On 2020-12-26 04:49:01, user Peter Tomasi wrote:
Correction: If we assume a latency of 28 days, a substantial amount of samples REPRESENTING A POINT IN TIME WHEN THE level of the exposure in the environment was not yet as high as the one the authors draw their conclusions for, could have been collected.
On 2020-12-27 21:03:59, user CO2lover wrote:
I wonder if those samples are ever tested for influenza?
On 2021-01-01 12:19:30, user Ed wrote:
This article is a preprint and has not been peer-reviewed. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.
..and yet it is being used for exactly that, ref. Department of Health and Social Care Joint Committee on Vaccination and Immunisation: advice on priority groups for COVID-19 vaccination, 30 December 2020 6th bullet-point of the introduction.
On 2021-01-05 07:56:40, user Rita Pizzi wrote:
previous researches
Ghate VS, Ng KS, Zhou W, Yang H, Khoo GH, Yoon WB, Yuk HG.
“Antibacterial effect of light emitting diodes of visible wavelengths on
selected foodborne pathogens at different illumination temperatures.”
International Journal of Food Microbiology. 166 (2013) 399.
Ghate VS, Leong AL, Kumar A, Bang WS, Zhou W, Yuk HG. “Enhancing the
antibacterial effect of 461 and 521 nm light emitting diodes on selected
foodborne pathogens in trypticase soy broth by acidic and alkaline pH
conditions” Food Microbiology. 48 (2015) 49.
Ghate, V, A Kumar, W Zhou and HG Yuk. 2015. Effect of organic acids
on the photodynamic inactivation of selected foodborne pathogens using
461 nm LEDs. Food Control 57:333–340.
Vaitonis and Ž. Lukšiene – Institute of Applied Research, Vilnius
University, Saul?etekio 10, LT-10223 Vilnius, Lithuania “Led-based light
sources for decontamination of food: modelling photosensitization-based
inactivation of pathogenic bacteria” Lithuanian Journal of Physics,
Vol. 50, No. 1, pp. 141–145 (2010)
http://www.lmaleidykla.lt/p...
Nicolai Ondrusch, Jürgen Kreft “Blue and Red Light Modulates
SigB-Dependent Gene Transcription, Swimming Motility and Invasiveness in
Listeria monocytogenes” Published: January 11, 2011DOI:
10.1371/journal.pone.0016151