On 2021-12-24 07:45:25, user Jeff H wrote:

So assume the results you like (high VE for recent vaccination) are causal, but hand wave confounders at results you don't like (negative VE for distant vaccination)? Science?

On 2021-12-24 21:34:35, user Robert Parker wrote:

So, these vaccines are, essentially, not effective against Omicron. The upside is that Omicron seems, at the moment, to be like getting a really bad cold. Very little hospitalization, and no deaths as far as I can find. This may be a Godsend. It is highly transmissible, with few bad effects. It may actually serve as a means to herd immunity, with few deaths. Hope springs eternal.

On 2021-12-28 14:49:56, user Bob Horvath wrote:

There is a typo in the confidence interval reported here: "36.7% (95% CI: 69.9 to 76.4%)", since the confidence interval needs to incorporate the value of 36.7%.

Also, this paper defines vaccine effectiveness (VE) as protection against infection. As can be seen from some of the tweets on this study, that is confusing readers who don't realize the EUA granted in the U.S., for example, is based on definitions of effectiveness related to hospitalization and/or death. It would be very helpful to many to have this even very briefly clarified in your paper, that, for example, even if the VE was 0% (or even negative, as some of the threads here claim is being shown after 90 days) according to the definition used in this study, as long as it had more than 50% effectiveness against hospitalization and death, that it would still be used in the U.S.

On 2022-01-13 13:10:50, user David Knight wrote:

Scotland's latest official public health real world data tallies up with the negative effectiveness found by the scientists that carried out this study.

https://publichealthscotlan...

See Table 15.

People who had only 2 jabs were almost 3 times more likely to catch Covid in the week 25th Dec-31st Dec than the unvaccinated (who were similar to the 'boosted')

Unvaccinated 1,555,449, cases 20,276, 1.3%<br /> 2 Doses 1,522,961, cases 54,727, 3.59%<br /> Boosted 2,429,498, cases 30,222, 1.24%

But if you are boosted you appear to be at least 4 times less likely to be hospitalised or worse from Covid, than the 2 jabbed/unvaccinated. See tables 16 and 17. So there still is a case for the vaccines

On 2021-12-25 04:08:33, user Nick Bauer wrote:

Figure 1 legend should explain the colors better because it's hard to interpret otherwise.

On 2021-12-25 09:26:15, user ReviewNinja wrote:

Interesting samples.<br /> One important flaw: you cannot compare Ct values from PCRs performed with different laboratory workflows as is the case here. The Abbot RealTime test for example tests 2 targets in the same channel, which might give you an earlier Ct. Also pre-PCR worklfow matters.

On 2021-12-27 02:04:58, user vepe wrote:

I could be missing but after reading the study it looks like you have included both vaccinated and unvaccinated in the post-positive test(i.e. infection) cardiac adverse events.<br /> have you considered stratifying the post-positive test group by vaccination status?

That way, we may assess the actual risk associated with the vaccines when it comes to cardiac issues

thanks for your work btw

edit, to clarify, the risk associated with vaccines is: <br /> risk of getting an adverse event after getting jabbed + risk of getting an adverse event after breakthrough infection. Without stratifying the post-infection results based on vaccination status, then we can't estimate the second part of the equation

On 2022-01-08 03:47:33, user Robyn Chuter wrote:

Also, deaths occurring in people who developed myocarditis due to a breakthrough infection should be distinguished from deaths of people whose myocarditis was not related to breakthrough infection.

This would help identify whether ADE or related phenomena are contributing to myocarditis.

On 2021-12-27 23:17:44, user Ralph Bertram ?????????????/ ???? /???? wrote:

Dear readers, the supplemental html files are available here

https://c.gmx.net/@63828482... <br /> (all nodes)

https://c.gmx.net/@63828482... <br /> (only pos)

On 2021-12-28 09:32:57, user Joe Random User wrote:

Table B clearly says that some / many of the participants had only just received their booster jab on December 2nd. The date of the private gathering was "early December" and the genetic sampling results were received by December 8th. So most likely the private gathering took place between December 2nd and December 7th.

Everbody knows that it takes 2 weeks for the antibodies to develop to their full potential after the third booster jab.

Since this article does not specify how many of the participants had not been fully vaccinated with the 3rd booser jab the data in the article is insufficient to learn anything about the omicron variant's resistance to the booster jab.

I recommend that the authors produce a revised paper where they more carefully describe the vaccination dates for the 3rd jab for all participants.

On 2021-12-28 14:23:53, user Zacharias Fögen wrote:

Dear Authors,

Thank you for this study, which clearly demonstrates that there is no IgA response to vaccination, thus not causing immunity to infection. Yet, irritatingly, you claim the opposite.

Figure 2F shows that there is no significant RBD-IgA after 2 vaccinations.

As for Spike-IgA, there is a wrong labeling in Figure 2E, as the "ns" should belong to the comparison "neg-ctrl vs. mrna 2 doses" and not to "covid-19 vs.mrna 2 doses" the latter being clearly significant, the former showing that the median of "mrna 2 Doses" is below the positive cutoff.

Furthermore, your "Baseline" mean in figure 2K is much higher (about 2,5%) than "negative control" in figure 2E (about 0%). Since both "baseline" and "negative control" are not vaccinated, this points to a selection bias for your negative control.<br /> Figure 2K also shows that there is no significant difference concerning "Baseline" and "2-4 weeks post dose 2". Yet, there is a significant difference between doses 1 and 2, as well as 1 and baseline.<br /> When comparing "baseline" and "mrna 2 Doses", "mrna 2 doses" is as high as "2-4 weeks post dose 2", which is not significantly different from "baseline" (Figure 2K).

So, there is no significant IgA (both Spike-IgA and RBD-IgA) after 2 doses of vaccination.

As far as the increase after 1st dose, but not after the second dose, this either points to an unknown bias, or it shows that multiple vaccinations do not increase IgA production, hinting at a lack of booster efficiency.

In Version 2 you had 6 month follow-up values in figure 1, yet in figure 3 the 6-month follow up (now figure 2) was removed. Why is that?

I kindly ask for the underlying data.

Greetings, Zacharias Fögen

On 2021-12-30 19:39:03, user Jesusswept wrote:

Study Funded by the Bill & Melinda Gates Foundation.

On 2021-12-30 20:48:47, user rick wrote:

Donating vaccines is a completely uninformed idea. There are plenty of vaccines. Latin America is more vaccinated than the U.S. Nigeria, on the other hand, is plowing expired vaccines into landfill, because nobody wants the stuff. Pfizer says they can make a lot more vaccine right away; they just need orders. If you want to make sure no poor nation is deprived you send money, not vaccines. If you are afraid that they will go hungry, you sent money for that too. You don 't mail them french fries.

On 2021-12-31 16:37:47, user Chris Holm wrote:

I think this part is very important. "We observed no difference in the LoS for patients not admitted to ICU,nor odds of in-hospital death between vaccinated and unvaccinated <br /> patients."

So, the unvaccinated doesn't spend more time in <br /> the hospital (except for those admitted to the ICU). And in any case, <br /> the unvaccinated are not more likely to die from covid. Good to know.

On 2022-01-02 19:41:20, user James Gator wrote:

Great preprint, I think clarifying what "early vaccinees" vs "late vaccinees" is a valuable addition. It's not immediately clear which time point they are early or late from

On 2022-01-04 08:01:45, user Cathy wrote:

It seems the only valid conclusion from this study is that immunocompromised patients who SURVIVE having covid have similar antibody levels. Not surprising, those that did not likely are the ones who died. You cannot make such a conclusion without measuring the antibody levels in both categories who died. Come on now, don't lead immunocompromised people to believe something you have NOT proven. I sure hope this gets revised before it gets released. It is dangerous.

On 2022-01-06 17:05:32, user SE Justus wrote:

Where/how do those who have recovered from the disease (naturally immune) fit into this study?<br /> Thank you.

On 2022-01-06 22:05:45, user Faithkills wrote:

Failure to include those with acquired immunity from recovery makes this of little use and exposes a likely bias of the researchers.

On 2022-01-06 18:42:10, user sd wrote:

Anyone interested in validating the NPRP criteria in their clinical setting please do and post your results here. Also see the published version in Prim Care Diab

On 2022-01-06 21:58:54, user zlmark wrote:

The interpretation the authors give to what they have actually calculated is highly misleading.

What they compute is the probability of a single transmission event in a *specific place*, whereas in order to estimate the costs of the policy, one needs to compute the probability of a transmission in *any one of the places* of the given type, which is several orders of magnitude larger.

Moreover, they completely ignore the compounding effect, though which even minor differences in R can lead to exponentially growing difference in the number of cases.

So no - 1000 people do NOT need to be excluded to prevent one COVID case - not even close.

On 2022-01-07 20:51:11, user Sam Miller wrote:

By now, we know that the transmission rate of omicron is high, regardless of vaccination status. Reducing transmission is a marginal, secondary goal of vaccine passport/mandates. Whether we think it is an ethical policy or not, the primary goal is to significantly increase the vaccination rate through carrot/stick motivators to prevent hospitalizations and health care system failure. Data from several countries have shown proportionally higher hospitalizations rates for unvaccinated. Although it may be difficult to quantize, a more pertinent question on policy efficacy would be "How much have these mandates/passports increased vaccination rates and reduced hospitalizations, and at what social capital cost?"

I think Aaron Prosser said in his Youtube interview with Vinay Prasad, MD, that the vaccination rate was 85% in his area. I wonder if he thinks that rate could have been reached without some type of vaccine passport/mandate policy? A recent Lancet study, "The effect of mandatory COVID-19 certificates on vaccine uptake," states that COVID-19 certification led to increased vaccinations 20 days before implementation in anticipation, with a lasting effect up to 40 days after. It concludes that "mandatory COVID-19 certification could increase vaccine uptake, but interpretation and transferability of findings need to be considered in the context of pre-existing levels of vaccine uptake and hesitancy, eligibility changes, and the pandemic trajectory."

On 2022-01-07 00:54:00, user Mark wrote:

The vaccination rate documented in TriNetX is only about 2%, whereas the reported vaccination rates during this time period12 indicate that most patients in the study population were likely to have been vaccinated.

Although partially mitigated by propensity matching, this is a huge limitation that makes it hard to separate the protective effect of vaccination (since it was essentially unreported) from that of Omicron -- which is the whole point...

On 2022-01-09 12:17:20, user Christopher Hickie wrote:

What? No comments allowed? I am a pediatrician with a PhD in neuroscience. My comment earlier was valid.

On 2022-01-07 10:51:52, user Zacharias Fögen wrote:

Dear Authors, <br /> your cohort is not well matched. You have +4.8% unvaccinated in Delta which are essentially replaced by 2x vaccinated. Considering the huge protection the vaccinated have for severe outcomes, this is clearly a bias. please use 1:1 matching.<br /> Also, since age is a very strong predictor, (about risk x2 per 6-7 years), please use 5 years age groups and also use it for people aged 80+ for matching purposes. <br /> if possible, also take a closer look at the risks of age groups 60+ by relinquishing region and onset date to increase the cohort.<br /> best,<br /> Zacharias Fögen

On 2022-01-07 12:28:26, user Alex Frost wrote:

So...robust evidence of strong protection via prior infection (lower for Omicron but still c. 60%).<br /> Separately, clear evidence of protection against symptomatic infection against Omicron for 3 doses of vaccine (2 shots + booster). <br /> Has anyone studied the effect of hybrid immunity = prior infection + 3 doses/3 doses + breakthrough infection? Surely that is endgame for global populations against Covid19.

On 2022-01-10 09:28:33, user RBNZ wrote:

How can there be 83 covid related events in the unvaccinated population (n=11)? 3 of the unvaccinated had "No chronic disease", does that mean that 8 had chronic disease? This would be a significant confounder due the small size of the unvaccinated.

On 2022-01-10 14:20:30, user Siguna Mueller, PhD, PhD wrote:

Dear authors,

thank you for your detailed results. Regarding the stats: an average patient just does not exist. Your SDs are rather big. Can you possibly say anything about characteristics of those individuals that exhibited negative efficacy? Is there any overlap to those groups that were excluded during the initial trials?

Thank you.

On 2022-01-18 15:16:11, user Dena Schanzer wrote:

Dear Authors:

I suggest looking at the historic trends in the rate ratio, or the relative risk (RR) of testing positive for COVID-19 for vaccinated compared to unvaccinated Ontario populations. The crude rate ratio can be calculated daily for cases, hospital and ICU occupancy from population level data provided by Ontario Public Health (https://data.ontario.ca/en/dataset/covid-19-vaccine-data-in-ontario ). The crude RR dropped below 1 by the end of December 2021 and has since steadied around 0.8 since Ontario closed high risk venues such as bars and restaurants. Hence, as this study suggests, it seems quite clear that the vaccinated population as a whole is currently at higher risk of infection than those who are unvaccinated. And, it is not surprising that a VE calculated as 1-OR, even in the test-negative control design would eventually become negative as well.

The steady decline in the crude RR can likely be explained by the lack of mixing between the vaccinated and unvaccinated populations (accentuated by the vaccine passport) and the higher transmission rate in the vaccinated group. If the effective reproductive number (Re) is higher in the vaccinated group, the RR should continue to decline even if the VE is held constant. It would be very helpful get your infectious disease colleagues (from the Ontario Science Table) to run a few infectious disease model simulations. I suspect that differences in Re were responsible for some of the downward drift in the RR in November when delta dominated. I’d suggest including the control group in the modelling exercise as well. I doubt that the Re gap is the same in the ‘other respiratory virus’ group. If it is (for example if you use the double vaccinated as the control for triple vaccinated), I would expect your test-negative control design would effectively control for biases introduced by the drift in exposure risks.

This study raises interesting questions. In the end, we will have a better understanding of how to monitor epidemics in near real-time. Perhaps monitoring the difference in the week-over-week percentage change in the vaccinated and unvaccinated groups could have provided an early warning indicator that either VE has dropped or contact rates have increased in the vaccinated group to a level where the vaccinated start driving the epidemic growth. Simulation studies should provide valuable insight on how to interpret this data!

Dena Schanzer

On 2022-01-13 14:05:52, user Zsofi Igloi wrote:

now published in: https://pubmed.ncbi.nlm.nih...

On 2022-01-13 14:39:26, user Peter wrote:

I thought this was a fascinating article. I tweeted.

I thought that the conclusion went further than the evidence.

You state that "…have<br /> been training dogs to detect Sars-CoV-2 virus in human sweat, by detecting volatile organic<br /> compounds (VOCs) in infected patients [1]. The VOCs exact nature is still under identification<br /> [2]."

In other words, you do not suggest that the dogs detect the virus per se; just that whatever they smell allows them to distinguish people with Covid-19 from people without the infection.

This study shows that they can detect the same smell in at least some people with Long Covid.

But you then conclude that "This study suggests the persistence of a viral infection in some Long COVID patients".

Given that there is nothing to suggest that the dogs can smell the virus, per se, the fact that they can detect the same smell in people with Long Covid certainly does not suggest the persistence of a viral infection.

There may be many hypotheses - probably better hypotheses - to explain the finding; but the conclusion is clearly unwarranted.

I do not know if you saw the tweet https://twitter.com/Evidenc... from @EvidenceMatters in reply to my tweets. It reads: "Beyond the information that none of the LC people had been admitted to ICU, it would have been helpful to know how many had been hospitalised and some info. about their vaccine history/plausible variant for infection etc.<br /> I was unclear on how many sniff sessions there had been."

I note that the paper has not yet been peer reviewed. Perhaps you will address some of these points before it is published.

On 2022-01-14 00:43:08, user disqus_mV149tuM7g wrote:

I am not a medical professional, but a common sense confounding variable immediately popped up in my mind, for which this (and most other studies) did not control for (though I understand it may not have been possible to control for it in this study given the data collection method, but more so I am baffled that from what I see 0 scientists and humans on earth apparently have thought of this common sense confounding variable and 0 studies that I know for attempted to control for it):

A) Do we not know that omicron is more similar to the common cold compare to delta? B) Do we not know that there is at least some common T cell protection across different coronaviruses, such that even T cells produced from a common cold give at least some protection against covid?

So then, without any further medical knowledge, the immediate common sense confounding variable that pops up in my mind using basic inferential logic is that if A and B are true, could it be that given the timing of omicron (came in early winter) compared to delta (came in summer), much more people had a common cold before omicron as opposed to delta? Also, less people abided by restrictions in Fall 2021 compared to Spring 2021. So couldn't this partially be the reason for why "omicron" is more mild than delta? Of course, that would mean that "omicron in those who had a common cold recently" is more mild than delta, NOT that "omicron" is more mild than delta. Do you see how dangerous it is (for people who did not have a common cold in a long time, especially if unvaccinated) to claim that "omicron" is more mild than delta? Again, I don't know if all of this is true or not, but I certainly think it warrants a more closer look.

Another confounding variable I can think of (though this one I am less certain of, but I don't think it hurts to put it out there): I remember early studies in 2020 showed viral load was associated with illness severity, and that those who wore masks tended to have less severe illness. Assuming those studies were correct, could it be that because omicron is more transmissible, more people are getting infected with omicron with low viral load compared to delta? For example, maybe more people are getting delta through droplet spread resulting in higher viral load, and more people who wear surgical masks but get omicron due to being in a small store with enough aerosols going through the mask and giving them omicron get omicron, resulting in less viral loads overall for omicron infections. Has this been controlled for? I have yet to see any studies that controlled for it.

On 2022-01-17 23:34:14, user Saar Wilf wrote:

Thank you for sharing this very interesting data! <br /> Unfortunately, I don't think it supports the suggested conclusion.

A few things don't match the hypothesis:<br /> 1. Hospitalizations don't show the pattern you'd expect under the hypothesis. There are more unvaccinated hospitalized on the day of PCR+, but after that there is no difference. <br /> 2. There seems to be no effect below age 55.

It's unlikely for a treatment to have an ongoing effect on deaths but not on hospitalizations, and only at certain age groups.

So what could it be?

I believe the hospitalizations on the day of PCR+ are a sign of either:<br /> 1. Hospitalization for another reason and PCR+ upon admission.<br /> 2. Hospitalization immediately upon PCR+ due to the patient being high risk.

I couldn't understand whether date of PCR means date of swabbing or date of result, That would determine which of the two is the correct interpretation (if any).

If it is 1, then I believe the entire finding is an artifact of unvaccinated older people being less likely to use (or have easy access to) health services. They are therefore likely to seek hospital care only in life threatening situations, and therefore more likely to die following hospitalization.

If it is 2, then I believe the entire finding is an artifact of very frail people not being vaccinated due to their state, hospitalized immediately upon PCR+, and then having a higher probability of death unrelated to vaccination status.

You are welcome to discuss further on twitter @saarwilf

On 2022-02-04 14:45:12, user Mukhtar wrote:

Hey, we have three affected individuals and found a very convincing homozygous missense mutation in KCNC2. The variant co-segregates with disease phenotype. Parents are heterozygous carriers. The phenotype of patients is vision impairment. I am just wondering if your patient also has some vision defects.

On 2022-02-15 08:41:08, user Sylvia van der Woude wrote:

What a poor study with a far too small sample size!!! Was it cherry-picked from a much larger pool of patients? Also, symptoms were not taken into account, even though they are most important! ps: Isn't the funding by the B&M Gates foundation a conflict of interest?

On 2022-02-16 21:04:27, user Showme wrote:

How long is the peer review still going to take ... ?

On 2022-02-20 16:13:15, user pwlg wrote:

Reassuring evidence from South Africa indeed and hopefully similar results are seen in Denmark as BA.2 becomes dominant there.

On 2020-07-16 10:16:25, user Hagai Perets wrote:

See https://arxiv.org/abs/2007.... for a possible explanation of the observed differences and origin of herd immunity, from a preceding low-virulence strain.

On 2020-07-18 18:45:51, user James Truscott wrote:

Hi, I think there is an error in the model as laid out in Supplementary Text File 1. The variable z represents all non-susceptibles, which includes the infectious-infected , y. The rate of loss of immunity term in equation 1 is gamma*z, but infectious individuals presumably don't have immunity to lose. They first recover (at rate sigma) and then can lose immunity. The term in equation 1 should therefore be gamma*(z-y). This change will affect the algebraic result, probably, and may change the dynamics significantly at some time points and/or parameter values.

On 2020-07-21 19:08:09, user Jeremy Rolls wrote:

Fascinating paper. Looking at the antibody data (such as there is any published here in the UK) about 18% of people in London have antibodies compared to about 8% nationally. On that basis alone 82% of Londoners may still get infected compared to 92% nationally - i.e. you would expect the mortality rate in London still to be pretty close to the national rate. Yet the hospital death stats for covid-19 in recent weeks shows London's rate consistently to be less than 40% of the national rate. Something else must, therefore, be going on - a) London is locking down better (unlikely), b) antibody immunity does not give the complete picture (possible given the data coming out of Sweden showing that for every person having antibodies two others have T-cell immunity) or c) there is a % of the population who have pre-existing resistance (from exposure to other corona-viruses) or are biologically incapable of getting infected. Ruling out a), a quick bit of maths shows about 75% of the population must fall into b) or c). So, on that basis, in London well over 90% have either been exposed to the virus or have pre-existing immunity and maybe 80-85% nationally. I suggest herd immunity has probably been achieved in London and is close in many other parts of the UK.

On 2020-07-16 13:02:24, user Dimy Fluyau wrote:

We humbly request your comments and constructive suggestions on this paper.

On 2020-07-16 18:06:03, user Marcos Woelz wrote:

What about recovered people´s blood serum? Any good news from that already? Untill that, let´s keep on helping people stay at home

On 2020-07-17 01:54:28, user Born in Akron wrote:

Is LD-RT a widely known specific therapy? This paper does not indicate the type of radiation. X-rays, Gamma rays, proton accelerator, sun lamp? The dose is 1.5 Grays = 1.5 Joules/kg = 150 rad. But the biological effect in rem or Sieverts depends on the type of radiation and duration of the exposure. Even if LD-RT is always, say, X-rays, shouldn't the effects depend on the energy of the X-rays? Unless LD-RT has a unique definition this preprint is deliberately irreproducible, perhaps to gain advantage for patent protection during a worldwide pandemic.

On 2020-07-17 12:36:05, user Hassaan wrote:

Very informative and well researched article.

On 2020-07-20 21:52:28, user Deborah Barr wrote:

It might be useful to correlate by medications taken. Depletion of magnesium and zinc affect clotting.

"drug-induced nutrient depletions are well known by pharmacists, many are underdiscussed and subsequently underdiagnosed and undertreated."<br /> 33 citations.<br /> https://www.uspharmacist.co...

Uwe Gröber's Magnesium and Drugs, https://www.ncbi.nlm.nih.go... with an excellent image of ways that drug interfere with nutrient levels in the body, and a table specific to Magnesium.

On 2020-07-21 15:50:10, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-07-21 16:14:03, user Kamran Kadkhoda wrote:

Baes on the current estimates, the sero-prevalence in Idaho is around 4% at most; such high percentages are most likely false positives; I refer authors to the study just posted here on medrxiv from China showing sero-prevalence of 2% or less in Wuhan! They used PRNT to confirm the results. That's the right way. <br /> Abbott is clear in their IFU by saying they did NOT use samples from cases with confirmed infection with common CoVs…<br /> Despite publications using "convenience samples" specificity shows its shortcoming while used large scale in the field...here's one example!

On 2020-07-22 17:00:56, user Robin Whittle wrote:

As Karl Pfleger suggested, I hope there will be more detailed information on 25OHD levels, symptoms at admission and as treatment progresses.

In light of a recent review (Charoenngam & Holick for a recent review https://doi.org/10.3390/nu1... "https://doi.org/10.3390/nu12072097)") which states that 40 to 60ng/ml 25OHD is required for proper immune system function, the 25OHD thresholds and D3 doses seem inadequate. This article also recommends an initial 12.5mg D3 (50,000IU) for all COVID-19 patients.

According to the present article, patients with 30ng/ml or more are given no D3 at all. Daily doses for those with lower levels are only 0.02mg (800IU) per day, which is a 20% or less of what most people would require to maintain 40ng/ml - assuming the supplement was taken with a fatty meal and well absorbed. https://journals.plos.org/p... indicates that average weight people need about 0.125mg (5000IU) a day to reach the middle of the 40 to 60ng/ml target range.

Surely all these low 25OHD levels (and the researchers report 21.6% of patients with initial levels below 6ng/ml and some below the 3.2ng/ml detection limit) warrant urgent action. What objection would there be to bringing all patients up to at least 40ng/ml with oral or IV 25OHD cholecalciferol (Rayaldee)? This would go into circulation immediately without relying on potentially hepatic conversion of D3 to 25OHD, which takes days or a week or so - even if the liver is functioning properly.<br /> The present article cites, as prior observations of low vitamin D levels correlating with COVID-19 symptom severity, an Indonesian article (26), an Indian article (27) and one from the Philippines (28). The first two have been withdrawn. Please see my page https://researchveracity.in... for the reasons which lead me believe that none of these three articles report actual research.

I think that the present article and a recent one An autocrine Vitamin D-driven Th1 shutdown program can be exploited for COVID-19 Reuben McGregor et al. 2020-07-19 https://www.biorxiv.org/con... are important steps in elucidating the role of vitamin D deficiency in COVID-19 severe symptoms. I have cites both articles at my page on vitamin D and COVID-19: http://aminotheory.com/cv19/ .

More research is urgently needed, but since vitamin D is a safe, inexpensive, nutrient which most people are deficient in (by the 40+ ng/ml standards we now know are important for immune system health) robust supplementation programs for all in need (most humans) need not await further research or clinical trials.

On 2020-06-24 05:32:27, user Gavin Donaldson wrote:

Were patients ineligible to the dexamethasone arm excluded completely from the recovery trial or allocated to the other arms including the standard care arm?<br /> Could the reasons for the exclusions be included in a consort diagram of the participant flow.<br /> There was an imbalance between the two arms in age. Is there any data on obesity or hypertension since these are important risk factors for covid-19 mortality.

On 2020-06-29 17:14:09, user Aiman Tulaimat wrote:

The study reports a mortality ~40% in patients on vent on the control arm. This is much lower than what is reported by the critical care audit from the UK, which reports mortality > 60% in such patients. The study reports ~25% mortality and ~60% discharge alive. Are we missing 15% of patients? If the analysis is a Cox hazard, why is the report using relative risk? How did 13% of patients with no oxygen therapy die? This is very high? Did their covid deteriorate or did they die from other reason? why were these patients hospitalized if they were not hypoxic? did they decline life support when it became needed? where they not on oxygen because they were in hospice like setting? how did the other patients die? Were patients on the ventilator made DNR early? Was prone position used? Was it used more in the dexa arm? Was there imbalance in the ramdomization by center?

On 2020-07-10 18:25:07, user Joanna Spencer-Segal wrote:

The authors speculate in the discussion that "It is also possible there is an effect via mineralocorticoid receptor binding in the context of SARS-CoV-2 induced dysregulation of the renin-angiotensin system." It is not clear what this means, but dexamethasone has minimal activity at the mineralocorticoid receptor, which distinguishes it from the other corticosteroids often used in critically ill patients (methylprednisolone, hydrocortisone). More clarification of what they mean regarding "mineralocorticoid effect" and rationale about why dexamethasone was chosen for this study would be welcome.

On 2020-06-24 18:56:17, user André GILLIBERT wrote:

Title : Proposal for improved reporting of the Recovery trial<br /> André GILLIBERT (M.D.)1, Florian NAUDET (M.D., P.H.D.)2<br /> 1 Department of Biostatistics, CHU Rouen, F 76000, Rouen, France<br /> 2 Univ Rennes, CHU Rennes, Inserm, CIC 1414 (Centre d’Investigation Clinique de Rennes), F- 35000 Rennes, France

**Introduction**

Dear authors,<br /> We read with interest the pre-print of the article entitled “Effect of Dexamethasone in Hospitalized Patients with COVID-19: Preliminary Report”. This reports the preliminary results of a large scale randomized clinical trial (RCT) conducted in 176 hospitals in the United Kingdom. To our knowledge it is the largest scale pragmatic RCT comparing treatments of the COVID-19 in curative intent. The 28-days survival endpoint is objective, clinically relevant and should not be influenced by the measurement bias that may be caused by the open-label design. While 2,315 study protocols have been registered on ClinicalTrials.gov about COVID-19, as of June 24th 2020, Recovery is, to our knowledge, the only randomized clinical trial on COVID-19 that succeeded to include more than ten thousands patients. The open-label design and simple electronic case report form (e-CRF) may have helped to include a non-negligible proportion of all COVID-19 patients hospitalized in the United Kingdom (UK). Indeed, as of June 24th 2020, approximatively 43,000 patients died of COVID-19 in hospital in the UK, of whom approximatively 0.24 × 11,500 = 2,760, that is more than 6% of all hospital deaths of COVID-19, where included in the Recovery study.<br /> Having read with interest version 6.0 of the publicly available study protocol (https://www.recoverytrial.n... "https://www.recoverytrial.net/files/recovery-protocol-v6-0-2020-05-14.pdf)") we had hoped for more details in the reporting of methods and results of this trial and take advantage of the open-peer review process offered by pre-prints servers to suggest improving some aspects of the reporting before the final peer-reviewed publication. Please, find below some easy to answer comments that may help to improve the article overall.

**Interim analyses and multiple treatment arms**

The first information would be about interim analyses. The protocol (version 6.0) specifies that it is adaptive and that randomization arms may be added removed or paused according to decisions of the Trial Steering Committee (TSC) basing its decision on interim analyses performed by the Data Monitoring Committee (DMC) and communicated when “the randomised comparisons in the study have provided evidence on mortality that is strong enough […] to affect national and global treatment strategies” (protocol, page 16, section 4.4, 2nd paragraph). The Supplementary Materials of the manuscript specifies that “the independent Data Monitoring Committee reviews unblinded analyses of the study data and any other information considered relevant at intervals of around 2 weeks”. This suggests that many interim analyses may have been performed from the start (March 9th) to the end (June 8th) of the study.<br /> Statistically, interim analyses not properly taken in account generate an inflation of the type I error rate which may be increased again by the multiple treatment arms. Methods such as triangular tests make it possible to control the type I error rate. Most methods of control of type I error rate in interim analyses require that the maximal sample size be defined a priori and that the timing and number of interim analyses be pre-planned. This protocol being adaptive, new arms were added, implying new statistical tests in interim analyses, and no pre-defined sample size as seen in page 2 of the protocol: “[...] it may be possible to randomise several thousand with mild disease [...], but realistic, appropriate sample sizes could not be estimated at the start of the trial.” This make control of the type I error rate difficult. The fact that the study has been stopped on the final analysis as we understand from the current draft rather than interim analysis does not remove the type I error rate inflation. The multiple treatment arms lead to another inflation of the type I error rate.<br /> The current manuscript does not specify any procedure to fix these problems. The Statistical Analysis Plans (SAP) V1.0 (in section 5.5) and V1.1 (in section 5.6) specify that “Evaluation of the primary trial (main randomisation) and secondary randomisation will be conducted independently and no adjustment be made for these. Formal adjustment will not be made for multiple treatment comparisons, the testing of secondary and subsidiary outcomes, or subgroup analyses.” and nothing is specified about interim analysis. Therefore, we conclude that no P-value adjustment for multiple testing has been performed, neither for multiple treatment arms nor for interim analysis. If an interim analysis assessing 4 to 6 treatment arms at the 5% significance level has been performed every 2 weeks from march to June, up to 50 tests may have been performed, leading to major inflation of type I error rate. In our opinion, the best way to assess and maybe fix the type I error rate inflation, is to report with maximal transparency every interim analysis that has been performed, with the following information:<br /> 1. Date of the interim analysis and number of patients included at that stage<br /> 2. Was the interim analysis planned (e.g. every 2 weeks as planned according to supplementary material) or unplanned (e.g. due to an external event, for instance the article of Mehra et al about hydroxychloroquine published in The Lancet, doi:10.1016/S0140-6736(20)31180-6), and if exceptional, why?<br /> 3. Which statistical analyzes, on which randomization arms, have been performed at each stage <br /> 4. If predefined, what criteria (statistical or not) would have conducted to early arrest of a randomization arm for inefficiency and what criteria would have conducted to arrest for proved efficacy?<br /> 5. If statistical criteria were not predefined, did the DMC provide a rationale for his choice to communicate or not the results to the TSC? If yes, could the rationale be provided?<br /> 6. The results of statistical analyzes performed at each step<br /> 7. The decision of the DMC to communicate or not the results to the TSC and which results have been reported as the case may be<br /> The information about interim analyses and multiple randomization arms will help to assess whether the inflation of type I error rate is severe or not. A post hoc multiple testing adjustment, taking in account the many randomized treatments and interim analyses, should be attempted, and discussed, even though there may be technical issues due to the adaptative nature of the protocol.

**Adjustment for age**

An adjustment for age (in three categories <70 years, 70-79, >= 80 years, see legend of table S2) in a Cox model was performed for the comparison of dexamethasone to standard of care in the article. This adjustment was not specified in the version 6.0 of the protocol but was, according to the manuscript “added once the imbalance in age (a key prognostic factor) became apparent”. This is confirmed by the addition of a words ““However, in the event that there are any important imbalances between the randomised groups in key baseline subgroups (see section 5.4), emphasis will be placed on analyses that are adjusted for the relevant baseline characteristic(s).” in section 5.5 page 16 of the SAP V1.1 of June 20th compared to the SAP V1.0 of June 9th which specified a log-rank test. The SAP V1.0 of the 9th June may have been written before the database has been analyzed (data cut June 10th) but the SAP of the 20th has probably been written after preliminary analysis have been performed. This is consistent with the words “became apparent” of the manuscript. Therefore, in our opinion, this adjustment must be considered as a post hoc analysis rather than as the main analysis. Moreover, even though the SAP V1.1 specifies that an “important imbalance” will lead to an “emphasis” on adjusted analyses, it does not change the primary analysis (see section 5.1.1 page 14). It is not clear what “important imbalance” means. To interpret that, we will perform statistical tests to assess balance of key baseline subgroups specified in SAP V1.1 (see section 5.4):<br /> 1. Risk group (three risk groups with approximately equal number of deaths based on factors recorded at randomisation). Its distribution is shown in figure S2. A chi-square tests on the distribution of risk groups in Dexamethasone 1255/500/349 and Usual care 2680/926/715 groups, lead to a P-value=0.092. A chi-square test for trend yields a P-value equal to 0.23.<br /> 2. Requirement for respiratory support at randomisation (None; Oxygen only; Ventilation or ECMO). P-value=0.89 for chi-square test and P-value=0.86 for chi-square for trend.<br /> 3. Time since illness onset (<=7 days; >7 days). P-value=0.17<br /> 4. Age (<70; 70-79; 80+ years). P-value=0.016 for chi-square test, p=0.019 for chi-square test for trend<br /> 5. Sex (Male; Female). P-value=0.97 for chi-square test<br /> 6. Ethnicity (White; Black, Asian or Minority Ethnic). No data found.<br /> The criteria to define “important imbalance” seems to be statistical significance at the 0.05 threshold, however that should have been stated and tests for all other variables should have been provided too.<br /> First, this adjustment, from a theoretical point-of-view, was not necessary since the study was randomized; if the exact condition of imbalance triggering the adjustment was pre-specified in the protocol or SAP before the imbalance was known, it could induce a very slight reduction of the type I error rate and power. However, as it was performed when the imbalance was known, there is a risk that the sign of the imbalance (i.e. higher age in the dexamethasone group) have influenced the choice of adjustment. Indeed, an adjustment conditional to a higher age in the dexamethasone group will increase the estimated effect of dexamethasone in these conditions, and so, provide an inflation of the type I error rate. If the same conditional adjustment were further considered for other prognostic variables, the inflation could even be higher. <br /> Unless there is strong evidence that the amendment to the SAP was performed without knowledge of the sign of the imbalance (higher age in the dexamethasone group), we suggest that the primary analysis be kept as originally planned, without adjustment, and that the age adjustment be performed in a sensitivity analysis only. The knowledge of the sign of the unbalance is unclear in the last version of the SAP (V1.1, June 20th) and in the manuscript. In addition, in an open label trial, it is always better to stick to the protocol.

**Results in other treatment arms**

The manuscript specifies that “the Steering Committee closed recruitment to the dexamethasone arm since enrolment exceeded 2000 patients.” It is not stated whether any other treatment arm has exceeded 2000 patients or not and whether the study is still ongoing. Results of treatment arms that have been stopped should be provided (all arms having enrolled more than 2000 patients?). If not, the number of patients randomized in other treatment arms should, at least, be reported. If the study is completely stopped, all treatments should be analyzed and reported, unless there is a specific reason not to do so; that reason should be stated as the case may be. This data would be useful to provide evidence on other molecules. It would also clarify the number of statistical tests that have been performed or not, providing more information about the overall inflation of alpha risk.

**Sample size**

The paragraph about the sample size suggests that inclusions were planned, at some time, to stop when 2000 patients were included in the dexamethasone arm. The amended protocol (May 14th), the SAP V1.0 (June 9th) and the SAP V1.1 (June 20th, 4 days after the results have been officially announced) all have a paragraph about the sample size but all specify that the sample size is not fixed and none specify any criteria of arrest of the research based on sample size. There are 2104 patients included in this arm, which is substantially larger than the target of 2000 patients. The exact chronology and methodology should be clarified: when was the sample size computed and what was the exact criteria to arrest the research? Could the document (internal report?) related to this sample size calculation and statistical or non-statistical decision of arrest of the research be published in supplementary material?<br /> Indeed, assessment of the type I error rate requires knowing exactly when and why the research has been arrested: arrest for low inclusion rate of new patients or for reaching target sample size cannot be interpreted the same as arrest for high efficacy observed on an interim analysis.

**Future of the protocol**

With the new evidence about dexamethasone, the protocol will probably be stopped or evolve. The future recruitment may slow as the peak of the epidemic curve in United Kingdom is passed. The past, present and future of the protocol needs also to be known to assess the actual type I error rate. Indeed, future analyses, that have not yet been performed influence the overall type I error rate. That is why we suggest that author’s provide the daily or weekly inclusion rate from March to June and discuss the future of the study.

**Loss to follow-up**

Table S1 shows that the follow-up forms have been received for 1940/2104 (92.2%) patients of the dexamethasone group and 3973/4321 patients of the usual care group (91.9%). The patients without follow-up forms (8.5% overall) may either be lost to follow-up or have been included in the 28 last days before June 10th 2020 (data cut). The manuscript mentions that 4.8% of patients “had not been followed for 28 days by the time of the data cut”, suggesting that 8.5%-4.8% = 3.7% of patients are lost to follow-up, but that is our own interpretation. We suggest that authors report the actual number of loss to follow-up and how their data have been imputed or analyzed. The number of loss to follow-up may differ for different outcomes. For instance, if the Office of National Statistics (ONS) data has been used for vital status assessment, there should be no loss to follow-up on that outcome.

**Vital status**

The current manuscript only specifies the data of the web-based case report (e-CRF) form, filled by hospital staff, as source of information, suggesting that it is the only source of information about the vital status. The document entitled “Definition and Derivation of Baseline Characteristics and Outcomes” provided at https://www.recoverytrial.n... specifies many other sources. For instance, the vital status had to be assessed from the Office of National Statistics (ONS). Other sources, including Secondary Use Service Admitted Patient Care (SUSAPC) and e-CRF could be used for interim analysis. The ONS was considered as the defining source (most reliable). Whether the ONS data has been used or not should be clarified. If the ONS data have been used, statistics of agreement of the two data sources (e-CRF and ONS) may be provided to help assessing the quality of data. If the ONS data have not been used, this deviation from the planned protocol should be documented.<br /> The manuscript as well as the recovery-outcomes-definitions-v1-0.pdf file specifies that the follow-up form of the e-CRF is completed at “the earliest of (i) discharge from acute care (ii) death, or (iii) 28 days after the main randomisation”. If the follow-up form is not updated further, patients discharged alive before day 28 (e.g. day 14) may have incomplete vital status information at day 28. The following information should be specified:<br /> 1. Whether the follow-up form of the e-CRF had to be updated by hospital staff at day 28 for these patients<br /> 2. If response to (1) is yes, whether there was a means to distinguish between a lost to follow-up at day 28 (form not updated) and a patient discharged and alive at day 28 (form updated to “alive at day 28”)<br /> 3. If response to (2) is yes, how many patients discharged before day 28 were lost to follow-up at day 28<br /> 4. If response to (2) is yes, how has their vital status at day 28 been imputed or managed in models with censorships (log-rank, Kaplan-Meier, Cox)<br /> Of course, this information is really needed if the ONS and SUSAPC data have not been used.<br /> The quality of the vital status information is critical in such a large scale open-label multi-centric trial, because there is a risk that one or more center selectively report death, biasing the primary analysis.

**Inclusion distribution by center**

A multicentric study provides stronger evidence than a single-center study but sometimes, few centers include most patients, with a risk of low-quality data or selection bias. The very high number of included patients in the Recovery trial suggests that many centers included many patients but the distribution of inclusions per center could be reported.

**Randomization**

The protocol specifies that “in some hospitals, not all treatment arms will be available (e.g. due to manufacturing and supply shortages); and at some times, not all treatment arms will be active (e.g. due to lack of relevant approvals and contractual agreements).” This is further clarified in the SAP V1 (section 2.4.2 Exclusion criteria, page 8) by the sentence “If one or more of the active drug treatments is not available at the hospital or is believed, by the attending clinician, to be contraindicated (or definitely indicated) for the specific patient, then this fact will be recorded via the web-based form prior to randomisation; random allocation will then be between the remaining (or indicated) arms.” Showing that randomization arms may be closed on an individual basis, when the patient is included, with the argument of contraindication or definitive indication. It seems that the “standard of care” group could not be removed and that at least another randomization arm had to be kept as suggested by the words “random allocation will then be between the remaining arms (in a 2:1:1:1, 2:1:1 or 2:1 ratio)” in section 2.9.1 page 11 of the SAP V1.0. Even exclusion of a single randomization arm can lead to imbalance between groups. For instance, if physicians believed that a treatment was contraindicated for the most severe patients, only non-severe patients could be randomized to the treatment’s arm, while most severe patients would be randomized to other arms. Several things can be done to assess and fix this bias. First, report how many times this feature has been used and which randomization arms have been most excluded. If it has been used many times, provide the pattern of use that help to assess whether this is a collective measure (e.g. 2-weeks period of shortage of a treatment in a center ? no major selection bias) or individual measure. If its use has been rare, a sensitivity analysis could simply exclude these patients. If it has been frequent, we suggest a statistical method to analyze this data without bias, based on the following principles: patients randomized between 3 randomization arms A, B and C (population X) are comparable for the comparisons of A to B. Patients randomized between A, B and D (population Y), are comparable for the comparisons of A to B. Population X and population Y may differ but, inside each population, A can be compared to B. Therefore, the within-X comparison of A to B and within-Y comparison of A to B are both valid and can be meta-analyzed to assess a global difference between A and B. This can be simply done with an adjustment on the population (X or Y) in a fixed effects multivariate model. Pooling of X and Y populations should not be performed without adjustment.<br /> A second problem with randomization exists although the dexamethasone arm is the least affected. Randomization arms have been added in this adaptative trial. When a new randomization arm is added, new patients may be randomized to this arm and fewer patients are randomized to other arms. Consequently, the distribution of dates of inclusion may differ between groups. This may have some impact on the mortality at two levels: (1) the medical prescription of hospitalization may have evolved as the epidemic evolved, with hospitalization reserved to most severe patients at the peak of epidemic and maybe wider hospitalization criteria at the start of epidemic and (2) evolution of patients included in the Recovery trial. Indeed, even if centers should have included as many patients as possible as soon as their inclusion criteria were met, it is possible that they have only included part of eligible patients and that this part evolved with time. This bias can be easily assessed and fixed: the curves of inclusions in the different arms and mortality rate in the Recovery trial can be drawn as a function of date (from March to June) and an adjustment on date of inclusion may be performed in a sensitivity analysis.

**Conclusion**

Recovery is the study with the best methodology that we have seen on COVID-19 treatments in curative intent and we salute the initiative of publishing transparently the protocol, its amendments, the statistical analysis plan and the first draft of the report. We hope that our reporting suggestions will be taken in account in the final version of the paper. We think that discussing these points will qualify the interpretation of results, further improve the transparent approach adopted by designers of the study and improve the reliability of the conclusions. We expect a high-quality reporting of these final results, with full transparency on interim analyses, statistical analysis plans and statistical analysis reports. We hope that these comments are helpful and again we acknowledge that this study is not solely outstanding in terms of importance of the results but is also a stellar example for the whole field of therapeutic research. We invite other researchers to provide comments to this article to engage in Open Science.

On 2020-12-19 17:11:21, user Gary Bayer wrote:

As an actuary whose required training includes construction of mortality tables, life tables and life expectancies, I attempted to verify the results. Unfortunately the details of the methods are too vague to be easily followed, so instead I attempted a standard approach to creating life expectancies. Starting with the 2017 US life tables, I explored modifying the "qx's" (probabilities of death in the next year for an individual aged x) but assuming a one time nature of Covid-19, only the specific current age (and perhaps the following age) should be adjusted for any age cohort. Therefore, for an individual age 10, only the qx for age 10, and perhaps age 11, should be adjusted to reflect the impact of Covid-19 on life expectancies. The age adjustment should be reflective of mortality risk at that age. At this point on time, based on the CDC's reporting of excess mortality, there is no evidence of increased mortality for idividuals under the age of 15. In other words, Covid-19 has not changed this cohort of individuals at all.<br /> The best guess that I can make as to what the authors were trying to express is that Covid-19 has, or is expected to reduce the average age at death this year by a year. I do not know if this is true or not but can see some merit in estimating that result.<br /> One final note, I visit the IHME Covid-19 website almost daily. It is a great tool for seeing the current state of Covid-19 in the United States, and a great tool for policy makers to get insights on what they may need to be planning for in the next couple of weeks. However, a simple look at it's various projections for daily deaths clearly shows the naivety of the estimates of what might happen in the beyond a couple of weeks. An adage that I always rely on as an actuary is the results can only be as good as the assumptions--even if the model being used is good.

On 2020-07-25 12:17:53, user John H Abeles wrote:

Hydroxychloroquine ( HCQ ) and Covid19

The negative observational and controlled clinical studies to date refer mainly to using hydroxychloroquine (HCQ) in serious, later stage, hospitalised Covid19 patients

In both the Solidarity/WHO study and the Recovery/UK study extremely high, even massive doses ( up to 6 times that recommended for early CoVid19 patients!) were used for unknown reasons - since the half-life of HCQ is around 21-30 days these daily massive doses could have caused very high blood levels and likely were fatal in some instances - so HCQ group deaths could have been caused by such high dose regimes, so probably skewed the results ..

Also this is likely the wrong group of patients to treat with maximum effect, in the first place — early Covid19 is the best arena for HCQ treatment in combination with zinc and either azithromycin or doxycycline...

It must be stated that no known oral antiviral for outpatients works maximally unless given quite early in disease eg oseltamivir/Tamiflu influenza; valacyclovir/Valtrex in herpes

Even iV remdesivir - a potent SARS-CoV-2 antiviral - didn’t achieve hoped for results in hospitalised patients

Later stage Covid19 patients are mostly suffering from the effects of hyperinflammation ( cytokine storm) and when viral titres are well beyond their peaks. Hyperinflammation can cause myocarditis which can certainly predispose to further cardiac toxicity.

[There are interesting thoughts that the hospitalised patients with cytokine storm / hyperinflammation in reality have a form of ADE ( antibody dependent enhancement of disease ) ie a hyperimmune reaction to a second SARS-CoV-2infection or as a result of a SARS-CoV-2 infection after a previous infection with a closely related virus]

HCQ was also used in the negative studies without added zinc which could be a design for failure, as one of the main, but certainly not only, antiviral actions of HCQ is as a zinc ionophore ie it gets zinc to enter cells much more easily where it can exert its added and established antiviral actions

HCQ is a known antiinflammatory and this action may be of some use in the hyperinflammation stage in hospitalised patients, but other more potent immunosuppressive ( and a few candidates that are nonimmusuppresive immunotherapies) could be more demonstrative in this regard.

Despite this there are some data to suggest benefit of HCQ even in hospitalised patients

For early Covid19 the usually prescribed course is for 5 to 7 days of around 400 mg daily HCQ with 100-200 mg zinc which would not invoke the long term side effects mentioned so often - and very few toxicities are reported even in long term therapy for autoimmune disorders. Any short-term arrhythmia concerns can be allayed by making sure of normal potassium blood levels

In the several thousands of outpatient Covid19 case reports published up to now , when used in early disease, there have been few if any major side effects noted.

(But in later stage, serious hospitalised patients many other drugs are also used, bringing into question the possibility of toxic interactions with HCQ. Also organ damage including myocarditis -heart inflammation-could be a particular predisposing factor in hospitalised patient toxicity predisposition to HCQ )

HCQ is a cheap, easily made generically available drug - and main manufacturers, like Novartis and Teva have donated billions of doses worldwide since the event of Covid19, so shortages, as some fear, for those taking it for malaria ( preventions or treatment) or for autoimmune diseases, like lupus or rheumatoid arthritis etc are highly unlikely

Here below are some pertinent positive references for further reading on the question of HCQ plus zinc plus either doxycycline ( my preferred choice because it isn’t associated with further small cardiac risk) or azithromycin

Note : Most of the successful reports of the use of HCQ plus zinc etc are in early stage, outpatients and not in late stage, hospitalised patients

The first link is a large data base (more than 50 studies ) on HCQ in Covid19 treatment

The second reference is an important review from a Yale University professor ...

The third and fourth are on a recent, large, well conducted observational study from Henry Ford Hospital ...

The fifth is an important outpatient study ...

https://c19study.com/

https://academic.oup.com/aj...

https://www.ijidonline.com/...

https://www.henryford.com/n...

https://www.preprints.org/m...

https://www.ijidonline.com/...

https://www.preprints.org/m...

https://aapsonline.org/hcq-...

https://www.medrxiv.org/con...

https://www.preprints.org/m...

https://www.evms.edu/media/...

https://link.springer.com/a...

https://pjmedia.com/news-an...

https://www.medrxiv.org/con...

https://www.medrxiv.org/con...

https://www.middleeasteye.n...

http://www.ijmr.org.in/prep...

https://aapsonline.org/hydr...<br /> decide/

https://www.indiatoday.in/i...

https://www.medrxiv.org/con...

On 2020-06-25 11:12:33, user Dude Dujmovic wrote:

Faulty study. The BCG cohort is older than non-BCG cohort, likely by 2-3 years on average. That is not a small difference when the samples are so big. Amazing how they did not notice that. There is always a good reason why randomized samples are used. Your samples are biased based on age. You need to have samples with about the same average age and about the same standard deviation. And more.

On 2020-07-25 19:31:23, user ???? ??? wrote:

It reflect the PK/PD pharmacological predication of efficacy <br /> The problem of LPV is complicated PK . Strong protein binding 98 % , extensive metabolism , long list of drug interaction. Therapeutic drug monitoring is mandatory to adjust dose in clinical setting. Moreover extrapolation of in EC50 to the current dose is not prefect. It was suggested to use PBA EC90 . the base line protein binding adjusted 90 % effective concentration. There is a debate about ability of current regimen to achieve Cmax > PBA EC 90 at lung tissues in severe cases

On 2020-07-26 14:06:30, user Gordon Erlebacher wrote:

I started to read the paper, but all the equations are missing. <br /> Here is an additional question. The contact matrix Mij measures to the average number of contacts between one person in group I and all members of group j. But are these different contacts or contacts with repetition? The different possible choices affects the spread of the virus. Any insight is appreciated.

On 2020-07-28 17:02:07, user Liam Golding wrote:

Nice research on an important matter. I really appreciate your work.

It would be nice to know the sample sizes you experimented on to obtain the statistical differences. Can you provide these on request?

Cheers,

Liam G

On 2020-07-28 19:39:18, user Dude Dujmovic wrote:

People that are vaccinated are usually more careful about their health so direct causality here is non-existent.

On 2020-06-30 21:10:32, user Stephen Cherniske wrote:

This is rather paradoxical, in that IL-13 is generally considered to be an anti-inflammatory cytokine, as in IBS. Even more surprising: the observed beneficial effect of DHEA treatment of murine IBS appears to result in part from increased IL-13 expression in colonic epithelial cells. REF: Immunobiology. 2016 Sep;221(9):934-43. doi: 10.1016/j.imbio.2016.05.013. <br /> Dehydroepiandrosterone (DHEA) Restrains Intestinal Inflammation by Rendering Leukocytes Hyporesponsive and Balancing Colitogenic Inflammatory Responses<br /> Vanessa Beatriz Freitas Alves , Paulo José Basso et al.

On 2020-08-03 13:54:21, user Charles R. Twardy wrote:

Forgive me if this is covered in the paper - today I am just skimming abstracts. But another preprint out today shows a mortality risk reduction of 0.7 per 100 kJ/m^2 of ultraviolet (UVA) exposure, in three countries measured at the county level. Is US altitude a proxy for UVA? Vice versa? Could you two combine models to look for residual effects?

On 2020-07-02 15:39:58, user Kamran Kadkhoda wrote:

The reported prevalence is very high suggesting high false positivity rate. The actual sero-prevalence for that county is estimated to be around 6% as of today (if we assume only 20% of cases are tested by for RNA). It would have definitely been much lower back in April. Another reason serology should not be used given it's high rate of false positivity mostly due to common CoVs like OC43 and HKU1.

On 2020-08-04 08:17:28, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-08-04 08:27:16, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-07-02 18:46:51, user Julio C. Spinelli wrote:

Having personally arquitected several clinical trials, the phase II results in young volunteers forces me to provide a word of caution to our collective desire to quickly develop a vaccine for COVID-19. <br /> The frequency and severity of many of the AE's described in this preprint for the young (18-55) and healthy population described here doesn't bode well for the results of a phase III clinical trial. Not until the phase II results of the older cohort are known. Furthermore, extrapolating these data to the Latin and Black populations would be pure hubris on our part. Further phase II data is required before we move into phase III trials<br /> Dr. Julio C. Spinelli

On 2020-07-02 20:46:36, user C'est la même wrote:

The claim of 99.3% specificity seems very high compared to other antibody tests when tested with large population samples.

But that aside, some readers seem to be inappropriately concluding that undersampling in specific regions during that period can be generalised to conclude that the true cumulative incidence is ten times the total number of confirmed cases.

This is unfounded for two reasons. The first is that regions with very high case numbers (Such as NYC) were temporarily overwhelmed in terms of testing capacity and correspondingly very high test-positivity rates. However over time, the testing caught up with demand and with expanded testing, the test-positivity rates dropped by the expected order of magnitude and likely "caught up" for at least some of the participants who were missed.<br /> The second reason is the sample in the study is not a true random population based sample, but a convenience sample which is also biased towards higher test-positivity rates.

Thus while I don't disagree with the conclusion of the authors, I urge strong caution among readers who are tempted to conclude that true case numbers are a magnitude of order higher than officially reported.

On 2020-07-03 20:21:43, user Marm Kilpatrick wrote:

Interesting paper. <br /> Could you clarify if the incidence values in Fig 1,2 and throughout are:<br /> Incidence = Cases in age group X/total population<br /> OR<br /> Incidence = Cases in age group X/population of age group X<br /> Since the age groups represent different fractions of the total populations this would change the intercept of the different incidence values/curves.<br /> Thanks!<br /> marm

On 2020-08-07 20:38:52, user Adam Garland wrote:

We are developing a test for SARS-CoV-2 in saliva. Is there any chance you have saliva samples leftover from this study that you'd be willing/able to share with us?

On 2020-08-10 08:17:44, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-07-06 16:17:27, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-07-06 16:20:42, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-07-06 18:41:52, user Fatnot wrote:

Unlikely that just zinc supplementation would work,,,a zinc ionophore is also required.. We also have the report from Dr Zelensky, in Rockland County, NY, who treated hundreds with<br /> a combo including zinc and HCQ, resulting that few required hospitalization. The report is anecdotal...but another term for a set of anecdotes is of course DATA And with data and analysis, one can draw conclusions and confidence intervals.

On 2020-08-12 08:06:03, user Marek Kochanczyk wrote:

The source code to analyze data and generate figures as well as updated figures are available at GitHub: https://github.com/kochancz...

On 2020-08-12 12:37:37, user Marc Imbert wrote:

It is worth to not that this study has more cormobity and symptomes for the group treated with HCQ and AZT. All patient not treated has a mild desease while about only 63% in the group treated. Further one should use a healthy scientific scepticims regarding hasting conlusions based on studies at the late stage of the desease. In particular with the description of the evolution of the disease which is now known,

.

On 2020-07-13 11:08:47, user Andrew D'Silva wrote:

In any infection IgM responses converting to IgG responses fall over time and rise when there is a secondary antigen exposure. Why do these findings suggest that there is loss of immunity with declining neutralising antibody levels? Surely the questions are: what happens after secondary antigen exposure? Do the neutralising antibody levels rise again? Do they protect from developing the same clinical disease again? Do they affect severity of disease after second exposure?

On 2020-08-17 22:15:16, user Roy Verdery wrote:

Neutralizing activity assessed using Vero cells may not reflect clinical<br /> activity. Nature (2020). https://doi.org/10.1038/s41....

On 2020-07-15 13:15:39, user E Y wrote:

Something is wrong here, the IHME projected 2020 total US death is about 250000, that's 0.08% of the US population, how can that cause 1% of reduction of life expectancy of US population?

On 2020-08-20 08:37:09, user Mitchell X Kriegman wrote:

does second hand smoke carry covid shedding?

On 2020-08-21 19:06:14, user Heather Gray wrote:

I'm a layman but I want to express my appreciation for exploring the issue from this angle

On 2020-08-25 09:01:02, user Tjabbe wrote:

Evidence for what, that it doesn't work for late stage covid in hospitalised patients? Is that even news? How come at this stage in the pandemic we are still publishing reports that claim medication be ineffective "for treating covid19" when in fact it was only tested for patients with severe covid19 already in the hospital. We all know patients will not be sent to a hospital in the Netherlands for covid unless they have progressed pretty far. <br /> The report describes hcq being used on patients when deteriorating in several of the hospitals, affecting mortality, and media outlets conveniently leave out this part of the puzzle.

If you want to curb covid, or if you want to write off medication as being useless "for covid" , start doing trials on early outpatient treatment.

On 2020-08-30 11:33:10, user Martijn Weterings wrote:

One problem with those S(E)IR compartmental models is that they always assume/pretend that a virus is spread homogeneously among a well mixed population. According to such models, the chance that someone in a small village in the South infects somebody else, is the same chance for anyone. The same for somebody in the North as somebody in their immediate family or other people in close neighborhood.

Such compartments are obviously not realistic for modeling an entire country. More suitable are networked S(E)IR' or spatial S(E)IR models. In such models, the virus spreads more like an ink blot.

Due to the local saturation, growth rates are already decreasing early on. Models that do not incorporate local saturation will 'compensate' (in order to get the same early deflection) by either reducing R0, or the (effective) population, or the reporting factor (upscaling the number of infected). If you try to fit a simple compartment SIR model to real data, then you will get unrealistic epidemiological parameters.

What they are doing in this article, dividing the population into layers with different rates of infection, is effectively shrinking the population that is 'reached' by the virus.

So this effectively makes the population smaller, but the question is whether it is the right way to shrink the population? Instead of a parameter in a mechanistic model, it might better be regarded as a parameter in an empirical model. It is an extra variable to ensure that the unsuitable simple SEIR model corresponds somewhat better with the measurements.

In reality, there are several effects that cause the observed epidemiological curves to deviate from the simple models (Besides heterogeneity, the use of local distribution in spatial or networked S(E)IR models, instead of global homogeneous compartments, is another important one).

By only including only a single effect in fitting, you get that all other effects are absorbed by that one effect. The result is an unrealistic estimate of the epidemiological parameters, which will not be suitable for extrapolation (for example calculating the 'herd immunity' percentage).

It is to be expected that this model, with only the heterogeneity incorporated, will likely underestimate the percentage to reach herd immunity. This is because it is overestimating the effect to compensate for the lack of other non-incorporated effects (and spatial models will be able to model the same deflection of the curves, but with less reduction of the herd immunity).

The above is a severe systematical problem, which will result in a bias towards smaller herd immunity percentages.

In addition: The fit with the curve is strongly determined by an interaction of the population size and the factor between the reported infections and actual infections (in a simple S(E)IR model, the two have the same effect). Such correlation between the two parameters will cause great inaccuracy.

And these are considerations that do not yet mention the problems with measurements of the epidemiological curve. For instance, the inaccuracies in reporting are not easily solved with a single (constant) reporting fraction. In order to estimate epidemiological parameters we need more direct experimental data (e.g. detailed information about contact tracing). From those we can deduce more directly the variations in infection rates and estimate the potential impact on herd immunity. Just fitting a model to the curve is a bad idea.

On 2020-09-02 15:42:59, user Abhaya Indrayan wrote:

We are waiting for further doubling of cases to 4 million in India so that we can test our model.

On 2020-09-23 07:52:50, user Subhajit Biswas wrote:

Pleased to see other scientists are supporting with further evidences, the trend we had observed and reported as early as April 2020.

Based on non-overlap of dengue and COVID-19 global severity maps and evidences of SARS-CoV-2 serological cross-reactions with dengue, we proposed that immunization of susceptible populations in Europe, North America and Asia (China, Iran) with available live-attenuated dengue vaccines, may cue the anti-viral immune response to thwart COVID-19.

https://www.preprints.org/m...

Our publications in this area to support our proposition:<br /> 1) COVID-19 Virus Infection and Transmission are Observably Less in Highly Dengue-Endemic Countries: Is Pre-Exposure to Dengue Virus Protective Against COVID-19 Severity and Mortality? Will the Reverse Scenario Be True?

Clinical and Experimental Investigations, Volume 1(2): 2-5.<br /> https://www.sciencereposito...

1. Nath, H., Mallick, A., Roy, S., Sukla, S., & Biswas, S. (2020, June 19). Computational modelling predicts that Dengue virus antibodies can bind to SARS-CoV-2 receptor binding sites: Is pre-exposure to dengue virus protective against COVID-19 severity?. https://doi.org/10.31219/os...

2. This one in medRxiv!

Now, other scientists are observing the same trend in Brazil! Exciting!

See recent publication below and news coverage

1.https://www.medrxiv.org/content/10....

1. Study suggests dengue may provide some immunity against COVID-19.<br /> https://timesofindia.indiat...

Amazing! Nature has its own ways of controlling parasite aggression! Antigenic correlation between a flavivirus and a coronavirus was unprecedented.

Existing and licensed dengue vaccines could be tested in SARS CoV2 animal models and tried in dengue non-endemic countries.

Use in dengue-endemic countries may be problematic as such vaccination can elicit antibody-dependent enhancement of subsequent dengue infections.

On 2020-11-19 15:54:00, user Lorenzu Borsche wrote:

Hello, this sentence:

Subsequently, we calculated sample size assuming a 50% between-group difference in hospital length of stay (considering 7 days as a median time of stay, with an expected variability of 9 days).

to me is not quite clear: do you mean, that you preset a desired length of stay to 7 days and the grouped the data so that both groups fit these 7 days? Thus did you mean a 50:50 distribution wrt the 7 days? If so, this cannot be done without distorting the data. If not, please explain, TIA Lorenz Borsche

On 2020-11-28 13:16:44, user Angie wrote:

The description of the amount of vitamin D used doesn't account for the mistake made in calculating vitamin D needs, nor is that mistake discussed in the article. In addition, making active forms of VitD from what is ingested is not an instant magic process. A body under attack may lack the energy to carry it out. Maybe it's just giving something by a pill is ineffective right now. What if you did transdermal? That would avoid the stomach/gut which is a place we know the virus attacks. Also vitamin D doesn't act alone. A person in ICU may not get a lot of vitK and may even be on anti-K blood thinners if they are a stroke risk. How many patients were on Lovenox vs something that thins blood via the vitamin K route? A daily exposure to a UV lamp may be more efficient for providing Vitamin D.

Anyway, the point is, I am not convinced that this test was properly done with reference to vitamin D. It takes weeks to normalize vitamin D in tissues where it is needed. Just testing the blood level after you gave a bolus pill is lying to yourself. It's like adding dye to water and saying, look, the sand at the bottom of the river turned all blue, we can assume it goes deep. What's the vitamin D status of hepatocytes after the one pill you gave? How much enzyme activity was there in the kidney to activate the D you gave?

Giving someone a vitamin is not like giving them a drug. The vitamin has to go to the tissues and do its work. You're thinking far too simplistically. VitaD affects thousands of reactions in the body and is not actively excreted as if it were an invader. That's nothing like a drug. Vitamins aren't drugs, that goes double for the fat soluble ones.

On 2020-12-24 23:19:01, user Matthias Fax wrote:

By design, this study could only fail to meet its hypothesis. It only proves what was to be expected. They used inappropriate dosage in oral form. They accepted an inappropriate delay after onset of symptoms. They didn't mention the significance of 25OHD sufficiency for patient outcome, indicating that the oral dosage was given too late to be of any immunological use.

On 2020-11-18 12:16:47, user Robin Whittle wrote:

I did not see any mention about how long after supplementation the 25OHD levels were tested. D3 takes some days to be converted in the liver to circulating 25OHD.

Since the intervention was with already-hospitalised patients, on average 10 days after their symptoms began - and with 25OHD levels rising over a period days, with the average length of stay about 7 days, this intervention may have been too late, and perhaps too little.

In the Cordoba trial (Castillo et al. https://doi.org/10.1016/j.j... "https://doi.org/10.1016/j.jsbmb.2020.105751)") 0.532 mg 25OHD calcifediol would have raised 25OHD levels within a few hours, probably above 100ng/ml on average - if one extrapolates from the curve shown for 0.266mg (a single Hidroferol capsule, of which two were used in Cordoba) in this patent: https://patents.google.com/... This greatly reduced the need for intensive care and eliminated deaths.

Since hospitalised COVID-19 patients have an extremely urgent need for raised 25OHD levels, so the autocrine signaling systems of their immune cells and many other cell types can function properly (McGregor et al. https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/2020.07.18.210161v1)"), a combination of 25OHD calcifediol with bolus D3 may prove more effective than either treatment alone. The bolus D3 would sustain 25OHD levels for weeks, and the D3 itself may protect the endothelium (Gibson et al. https://doi.org/10.1371/jou... "https://doi.org/10.1371/journal.pone.0140370)").

On 2020-11-20 16:49:15, user Jean Kaweskars wrote:

Hello,

Your figures are not consistent.<br /> If you have 11% IFR for 80-90-year-old subjects who represent 6,3% of France population (> 80 years old), it does translate into a minimum of 0,69% overall IFR. If you apply your IFR rates by age, you would actually get an overall 0,91% IFR at minimum.

Regards

On 2020-11-25 15:49:17, user James Wyatt wrote:

In the discussion of weaknesses, you failed to mention that you eliminated approximately 1/3 of the cases for lack of complete data. Did you study these cases to see if their exclusion could possibly have biased your results? What was the crude death rate among these cases?

In Table 1, the disparity between mortality rates per 100k population is solely a function of the difference in incidence rates. That's significant, for sure, but the fact that CFR is the same for whites and blacks is also significant. In its rawest state, that indicates that, once someone is sick, race seems not to matter in the outcome. Doesn't that bear some discussion?

How did you determine cause of death? Covid-19 is rarely the sole cause when death certificates are completed competently and there is some judgment required to clearly identify a covid death. As follow-ups, were there non-Covid-19 deaths in your data? How were they identified? If there were none, can you justify that?

In mortality studies, the key question often is: How did you calculate the exposure? That is, how did you determine the denominators for your ratios? You reference some models, but you give no details.

The paper needs a lot more work, don't you agree?

On 2020-11-25 19:54:26, user Puya Dehgani-Mobaraki wrote:

Interesting data, which are also seen on our study were the persistency of the antibodies were detected and persisted during 8 months.

https://www.medrxiv.org/con...

I do would like to have more informations in regards of the patients selection for the 8 months analysis.<br /> Our cohort was based in patients resulted positive for Sara-Cov-2 early days of March, Italy. As far as my knowledge, very few cases were reported in Australia at that time.<br /> Puya Dehgani-Mobaraki

On 2020-12-04 13:43:42, user Ben Finn wrote:

Why does the paper make no mention at all of the large risk differences between sexes & races? (Men, Black and most Asian people have 2+ times COVID mortality of others.) Straightforward to model. Without considering this it can’t claim to be an ‘optimal vaccination strategy’.

On 2020-12-04 20:45:12, user Sam Wheeler wrote:

Is the full text possible to read as HTML somewhere? I did find the PDF.

On 2020-12-08 09:28:26, user Neville Calleja wrote:

Obviously the people receiving the influenza vaccine are (a) the most at risk of dying with COVID-19, either as an underlying cause of death, or as a contributory cause, and (b) more likely to be coming from affluent countries wherein identification of deaths as a COVID-related death is much more likely due to enough resources permitting testing of all patients. The latter could have been corrected by using excess mortality figures rather than reported COVID-19 deaths, which is highly dependent on the countries' capacity to test. Nonetheless, the first clearly explains the findings completely wherein countries with high life expectancy and therefore high proportion of elderly population with co-morbidities who are typically protected in winter using the influenza vaccines, could not be protected from COVID-19. This could be considered a correlational study at best.

On 2020-12-08 11:30:27, user Abdur Rahman wrote:

This preprint is accepted for publication, and the new link is: <br /> https://onlinelibrary.wiley...

On 2020-12-09 12:20:53, user Vladimir Gusiatnikov wrote:

The authors appear to treat viral loads quantified in transfer media as viral concentrations in mucus, however there typically is a 1.5 to 2 order-of-magnitude dilution as material is eluted from swab into media. As a result, the authors' estimates for the copy generation rate and copies per infectious quantum may be 1.5 to 2 orders of magnitude too low.

On 2020-12-09 16:34:09, user Livia Dovigo wrote:

The elegebility criteria (that lead to only 5 studies to be included) has not be clearly described... Searches returned more than 90 entries, the authors needed to inform the methodology for studies selection. Otherwise, results may not be reliable.

On 2020-12-11 00:07:54, user Peter Novák wrote:

PARTICIPANT CONSENT?

Authors claim, cite: "All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived."

I find this proclamation highly dubious.

I'm not sure how many of the the mass tested people have signed a form of informed consent, and I'd like to see how would the authors prove that. I personally have asked several participants and they insist they did not sign anything. But even in the case some, or even the majority signed something, what weight would that have under circumstances?

The people subjected to the mass testing (that technically means biological material extraction) with consideration, that those who do not subject, will be quarantined for week or two - that means, forced to stay home under threat of penalty as high as 1650 EUR (average monthly income in Slovakia is 1100 EUR brut), with few exceptions (e.q. nearest grocery store, drugstore and necessary health care), but certainly denied the access to workplace with no lost worktime salary compensation at all, neither from state nor employer.[1] A proposition effectively resembling a home prison in my opinion, and what's even worse in situation of economical crisis - with published threats from some employers that untested employees may lose their job eventually, thus undermining the public confidence in freedom of choice furthermore.[2]

It is known that criminal complaints on the accounts of possible coercion, health care law violations, human rights violations etc have been filled to the public prosecutor office. These are yet to be resolved.<br /> I acknowledge that some of the people have attended the testing voluntarily indeed, probably a minority though, as indicated by low compliance (15%) to the third round of testing where quarantine threat was removed.

Nevertheless, I doubt anyone could assume the actions under such circumstances constitute a "participant consent" by standards of any possibly existing ethical guidelines.

Or maybe I just read the citation wrong and the authors did not mean the 3,6 million people undergoing the biologic material extraction to be the subject of the "necessary patient/participant consent"...?

[1] Public Health Office Edict No. 16 from 30.10.2020. Government Bulletin vol. 30 no. 12. http://www.minv.sk/swift_da...

[2] Dobrovolne nasilu? Niektorým ludom bez testovania hrozia výpovedou. Pravda, 26.10.2020. https://ekonomika.pravda.sk...

On 2021-02-01 21:30:19, user Igi Dano wrote:

As a Slovak citizen, I agree with most comments/notes presented here. I could as well add my own experience with "following testing procedure recommended by manufacturer..", where this testing procedure was conducted outside (of any premise, just an open tent) with temperature well below recommended range.

But that is not the point of my post here. The point is that Slovakia is currently (1.2.2021) ending the second round of another population-wide screening.<br /> I am desperately waiting for another study from the authors, confronting the newest results with original ones. <br /> Without that I would recommend potential readers of this study to use extreme carefulness with interpretations of it..

On 2021-02-02 12:13:10, user Miriam wrote:

Nobody in Slovakia was informed about this research. And it was not voluntary as they signed. There was and there is still strictly prohibited to go at work and to the nature if we are not tested. The final result of this mass testing is, that numbers of covid positive strongly increase. That is all. I am really afraid about my human rights in future.

On 2021-02-19 01:42:51, user Oliver Cudziš wrote:

Voluntary? "All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived - Yes" What is this, areu all blind or what. Slovak nation was like experimental rabbit without knowing, congratulations you just made stage for Slovak national uprising 2, good luck.

On 2020-12-13 13:26:48, user Sam Smith wrote:

A concern about the Córdoba study is that 25OHD serum levels were not measured, so we do not know if the treatment was associated with a benefit only in patients who were deficient. A randomized controlled trial will be needed to determine whether calcifediol will benefit hospitalized COVID-19 patients who are not deficient.

Sadly, calcifediol is sold in some European countries, including Finland?

On 2020-12-15 10:43:49, user NK wrote:

Re: article pre-published at https://www.medrxiv.org/con...

There are several methodological problems in this study.

1. Findings that suggest increased ORs among primary school teachers, child care workers and secondary education teachers are not properly presented and discussed

The summary states: "Teachers had no or only moderately increased odds of COVID-19". This finding is mentioned several places in the text of the article. Teachers are repeatedly referred to as having a low risk, even when the results for teachers show a significant increase in admissions and borderline significant increase in infection rates. Quotes: «First, our findings give no reason to believe that teachers are at higher risk of infection», and in the conclusion: “Teachers had no increased risk to only a moderate increased risk of COVID-19”. We wonder why the authors find it important to repeatedly mention this the result for teachers when the results for the last period does not exclude a substantial increased risk for teachers, whereas occupational groups with lower risk than teachers are not mentioned in the summary.

The part of “Supplementary table 1” shown below does not provide a basis for such a conclusion that teachers are a low risk group.

The OR (95% CI) for 1) primary school teachers 2), child care workers and 3) secondary education teachers were 1.142 (0.99-1.32), 1.145 (1.02-1.29) and 1.095 (0.82-1.47) respectively. The upper confidence limits are does not exclude 29 % to 47 % increased ORs, which represent substantial increases.

Concerning the results on the risk of admission, it is stated: «None of the included occupations had any particularly increased risk of severe COVID-19, indicated by hospitalization, when compared with all infected in their working age (Figure 3, S-table 2), apart from dentists, who had 7 ( 2-18) times increased odds ratio, and pre-school teachers, child care workers and taxi, bus and tram drivers who had 1-2 times increased odds ratio”.

This finding is not discussed or mentioned in the summary, even if the findings were statistically significant for pre-school teachers as well as for child care workers.

1. The study periods include periods when the schools were closed and include no period with high infection rate among children and youths.

It is not to be expected that teachers have higher infection rates than the average working population in periods when school are closed and when the infection rates are low in the age groups 0 - 9 and 10 -19 years. This problem is not discussed in the paper. Schools were closed from 12 March to 27 April. For a majority of the schools, holiday started from Friday 19 June.

The first study period lasted from February 27 to July 17. Thus, schools were closed for over 70 days of the first study period of 139 days. The infection rates in children at school age in the first study period were rather low (3.6 per 100 000 children per week between in the age group 10 -19 in week 19, 1.1 per 100 0000 children per week in week 25). In the last study period, the infection rates varied between 7 to 17 per 100 000 per week in the age group 10 - 19. Even if these rates are much lower than later weeks that were not studied (after week 42), the results from this second part of the study suggest an increased risk for teachers.

Thus, the infection rates among children started to increase from week 43, after the end of the study period. By not including this period, the study design excludes the possibility to detect if these high rates among pupils could be related to increase infection rates among teachers.

It is a problem that the results from this pre-published study has been quoted in the media and referred to as if teachers have no excess risk, or even possibly a reduced risk at the time that several municipalities were to decide what type of restrictions at schools should be introduced to reduce the risk of transmission among school children, see https://www.barnehage.no/korona/ny-forskning-nei-barnehagelaerere-har-ikke-okt-risiko-for-smitte/211143

On 2020-12-20 20:25:16, user Pablo Pablo wrote:

I can't explain why this result is in contradiction with this other one.eurosurveillance<br /> Could it be because of the Simpson paradox?

On 2021-01-05 09:55:32, user Disqus wrote:

In addition to the previuos comments I read, page 7 "SARS-CoV-2 positive incidence rates were calculated for staff and students attending an educational setting, irrespective of whether the infection was acquired within or outside the educational setting."

thus it is evident that if the incidence is higher among teachers than the general population, <br /> schools are not the safest places, with a perhaps low transmission rate among students but a <br /> greater transmission rate from students to teachers

On 2020-12-22 08:44:55, user Maxime Baud wrote:

Study published in Lancet Neurology, now available at https://www.thelancet.com/j...

On 2020-12-28 23:51:01, user ErikCarter wrote:

You really ought to test for infectious virus, rather than just RNA. Otherwise you can't truthfully claim that the variant results in higher "viral load"...you've not measure viral load, you've measured RNA load. These are not the same thing.

On 2021-01-08 15:09:29, user Kevin McKernan wrote:

Can the authors explain the mess in Table 2? This dilution series is non-linear and any student delivering such data would be told to repeat it. If it is in 6 replica's, you should share the dispersion in that data. There should a clear 3.3 Ct shift in each 10X dilution. If you dont have linearity in your dilution series how can you make a Ct cutoff? The non-linearity is non-concordant across different amplicons? It is frightening this is being used as a diagnostic test. Are there any internal controls in the test to ascertain the sample prep variance? Dahdouh et al demonstrates 10-16 Ct variance in RNaseP signals (human gene) suggesting tests that lack internal controls to normalize for swab and sample prep variance are random number generators. Table 2 also looks like a random number generator.

On 2020-12-29 01:55:20, user F Alexander Diaz-Quijano wrote:

This article is now published in the Journal of Clinical Epidemiology.

Share Link:<br /> https://authors.elsevier.co...

On 2020-12-31 14:23:25, user Don Wheeler wrote:

Interesting. Additional research with a larger sample size featuring a broader cross section of the population will be most beneficial. Let's see where this leads.. Great work! @ComaRecoveryLab #covid19

On 2021-01-05 03:55:19, user mahejibin khan wrote:

Though air transmission of the virus has been suspected , swab samples collected in unsterile conditions for RT-PCR screening of human subjects continues to be a practice in many regions/countries. <br /> Two mass scale nasopharyngeal swabs of employees of an establishment in Mysore, Karnataka, India, collected under unsterile conditions in their premises, by seating them in an open ground and screened for SARS-CoV-2 infection by RT-PCR, identified a large number of asymptomatic SARS-CoV-2 positive cases. Thus the establishment forced a two-day campus lockdown, on both the occasions, in order to sanitize and break the virus transmission chain. Since most of the infected subjects remained asymptomatic through their home quarantined period, they were certified for fitness to resume work. Since reports have shown patients fighting SARS-CoV2 infection developing IgM and IgG antibodies between 6–15 days after disease onset. Blood samples of two RT-PCR positive asymptomatic subjects after 17 day home quarantine were analysed for the presence of IgM and IgG antibodies. Absence of detectable titres of antibodies to SARS-CoV-2 virus in the two blood samples suggested lack of acquired immunity due to asymptomatic patients unexposed to the virus. Nasopharyngeal swabs positive for the virus by RT-PCR inferences establishment of the Covid-19 pathogen infection in the host. Absence of prodromal symptoms for the disease in these subjects and some of them testing negative in a second Rapid Antigen Detection Test (RAD) opinion, when swabs were sampled in designated hospital rooms, suggested occurrence of air borne virus and swab contamination during sample collection under unsterile conditions. <br /> Droplets that are sneezed or coughed behave differently in the open air, according to environmental conditions like temperature, humidity, ventilation, and the amount of virus deposited.<br /> My observations on plausibility of air borne SARS-CoV2, RT-PCR determining their fairly high numbers and prevalence of asymptomatic subjects living in that environment provides leads for studies with reference to herd immunity from the purview of viral attenuation due to environment and/or innate immunity initiation through pattern recognition receptors

On 2021-01-05 15:56:38, user Ti wrote:

You write that "the best performing method is XRAI (AUPRC = 0.224 ± 0.240)". Meaning that the AUPRC ranges from -0.016 to 0.464. Surely, you cannot have a negative area under the curve.

On 2021-01-05 16:49:15, user Ania Lorenc wrote:

Hi, I was wondering - have you excluded known factors contributing to severity - sex + age?

On 2021-01-09 15:11:56, user Derek Enlander, MD, MRCS, LRCP wrote:

The "long Haul Post Viral" SARS 2 Covid19 effects, Fatigue, Myalgia, Cognative defect, insomnia etc are reminiscent of the symptoms reported historically by Melvin Ramsay in 1955 when he reported these symptoms in a cohort of young doctors and nurses in the Royal Free Hospital in London. He termed the outbreak as Post Viral Fatigue, renamed Myalgic Encephalomyelitis (ME) and later Chronic Fatigue Syndrome (CFS).

On 2021-01-10 22:17:22, user Wayne Griff wrote:

Single dose vaccine efficacy is not 90%. It's less than 50%, and that's after only 3 weeks. It would be even less effective at 6, 9, 12 weeks or more. More importantly, at 3 weeks the neutralizing ability of 1 dose is only 1/5th as much as Convalescent Plasma (NEJM)

On 2021-01-11 23:14:43, user Chaitanya wrote:

Excited to read the paper since its amazing that the authors have both in vitro and patient sample data. I a curious to read more with regard to false positive/negative and the role of NASBA amplification

On 2021-01-13 11:39:34, user carina brehony wrote:

hopefully a full published paper will acknowledge the laboratories, public health departments and the Health Protection Surveillance Centre that collected, validated and provided the data which was then made available publicly

On 2021-01-15 16:00:23, user Martin Reijns wrote:

Congratulations on this work. One comment though: I know it's difficult (if not impossible) to keep up with all the literature on SARS-CoV-2, but I just wanted to say that the statement "Currently, no test combines detection of widely used SARS-CoV-2 E- and N-gene targets and a sample control in a single, multiplexed reaction" is incorrect. Our paper on this has been on medRxiv since June:

https://www.medrxiv.org/con...

and was recently published in PLoS Biology

https://journals.plos.org/p...

All the best, Martin

On 2021-01-18 05:38:05, user Benjamin Seng wrote:

This paper has been published in General Hospital Psychiatry (journal) and is available at the following link: https://doi.org/10.1016/j.g...

On 2021-01-21 09:05:28, user Dominik wrote:

The conclusion drawn here is simply wrong: "suggesting that current SARS-CoV-2 vaccines will protect against the 20B/501Y.V1 strain" when in fact they didn't check for all 17 epitope changes of mentioned strain but only N501Y which was never thought to be immune evasive. The same erroneous conclusion was drawn in the paper of Uni Texas which also only tested against N501Y but not all mutations.

On 2021-01-24 20:42:28, user Thomas Arend wrote:

Just some short remarks to table 5:

(35-5)/35 is 85.71% and not 82.86%

If you round 32/23 = 91.4285.. you get 91.43 not 91.42

Typo? 34/35 = 97.1428 ~ 97.14 ... not 97.12

And I agree with Michel Schrader comments to the presicion of 4 digits.

The hypothesis H0: specifity = 94% / Ha: specifity <> 94% you can only be rejected for AgPOCT V. p=0,0168.

H0: Specifiy = 92% cant be rejected for any AgPOCT.

You should calculate a CI for the specifity.

On 2021-01-26 23:19:50, user Janet Aisbett wrote:

The analysis as presented does not appear to support the conclusion that “individuals discharged from hospital following COVID-19 face elevated rates of multi-organ dysfunction…..”. We can conclude that these individuals have elevated rates of multi-organ dysfunction, but we have no way of knowing whether these were ‘new-onset’ events after discharge or were factors contributing to the severity of the individual’s COVID episode. This is because ‘new-onset’ events are defined with respect to HES APC and GDPPR extracts over the ten years prior to 2020. It would help if counts of ‘new-onset events’ were provided, broken into those which first appear in 2020 but before discharge (e.g., as secondary diagnostic codes alongside the COVID primary) and those which first appear post-discharge.

Forgive me if I have missed something, but I also have concerns about the 1:1 matching of the COVID cases to controls. Supplementary Table 1 suggests very coarse matching criteria. The use of an age category 70+ is particularly striking, given the comparative age distributions of COVID versus all hospitalisations. The matching of clinical characteristics also deserves further explanation. As presented, it appears that an episode of skin cancer eight years ago could allow a match to an individual with metastasised tumours requiring palliative care. Since more serious comorbidities may be a factor in COVID hospitalisation, matching on coarse clinical characteristics may tend to select a healthier control group. Presenting frequencies of selected sets of ICD-10 codes by age for the COVID cohort versus the control group would help resolve this question. Also worthy of explanation are the decisions not to include dementia in the matched histories, and not to consider previous hospitalisation.

Finally, the Supplementary Table 3 shows quite different outcomes for controls matched to ICU COVID cases compared with controls matched to non-ICU cases. These differences are not reflected in the COVID cohort. Although numbers are small for the ICU control group, the discrepancy is worthy of comment.

On 2021-01-27 14:15:18, user Antonio Beltrão Schütz wrote:

I think that this article is important, considering that in spite of does not proof by mean RT-PCR test that ivermectin can turn negative viral load in patients with increased viral load of Covid-19, it decreased the mortality (4/112) patients. This data extrapolated to 100.000 or 1,000.000 cases is significant.

On 2021-04-12 15:56:55, user Philip Machanick wrote:

I am also curious how in a double-blind trial it comes about that someone in the control is given the test drug.

On 2021-02-04 14:21:22, user Peter Ray wrote:

The suggested reason for the increased case rates for the first 10 days or so after injection is a possible change in behaviours to being less cautious.

Another possible reason is the dramatic increase in Covid prevalence occurring in Israel generally at the start of the vaccination program (late December). Given that the positive case data is available in public it might be worthwhile including a comparison of general population case rate on the daily incidence chart.

On 2021-02-04 21:24:38, user Charles wrote:

I am a bit unsettled by the days they decide to pick to get their best (around 90%) estimate.

They use the daily rate from day 1 to 12. Day 1 it's .028% and average until day 12 is around .041% (there is a spike in infection from day 1 to 12). Day 21 is .004%, day 22 is .011%, day 24 .006%, i.e. there is some standard-error. <br /> Now, it's all about which days one picks. <br /> - if one calculates Expected as being day 1, the best effectiveness rate is 86% on day 21, but on day 22 it dwindles at 61%...<br /> - if you use day 1 to 12 as Expected, effectiveness rate is around 85% on day 24 and 73% on day 22. <br /> - to get the 90% in the paper, you need to pick day 21 (the lowest incidence, that went up again the next days) and the Expected as days 1 to 12 (the highest incidence).

It seems that efficiency estimate does improve over time, but reaching 90% depends on which days one picks, both in term of "actual" and "expected". This choice might very well explain fluctuations between 60% and 90%, i.e. the estimate is very sensitive to small numbers and differences. Differences with previous estimate might be methodological (no proper control group).

On 2021-02-05 13:36:40, user Marie wrote:

Could you, as required by law, please declare your conflict of interest?

On 2021-02-06 02:50:56, user Crystal Sonia wrote:

How accurate and reliable is this SIR model? With the mco arent the cases still increasing? How are the beta and gamma estimated? What's the sensitivity and specificity of this model?

On 2021-02-15 15:10:33, user Paul Wolf wrote:

Near the end of the abstract, you say the data is "suggesting parallel evolution of a trait that may confer an advantage in spread or transmission." Why would this mutation be occuring in different parts of the US and nowhere else in the world? That doesn't suggest convergent evolution, but a common origin.

On 2021-02-16 20:47:03, user jiver wrote:

NHS are obsessed with over simplifying race. Why ask people to self report in only 3 categories? And two are skin colour but the other is geographical. Why on earth? What did you hope to achieve by only going this far? Already people are using this to discredit non whites. And NHSP have done it before, exactly the same, 3 categories allowed only.

On 2021-02-19 20:04:10, user Jacqueline J Clarke wrote:

Have you confused the symbols lesser than and greater than ? The occupying sentences don't make sense ?

On 2021-03-09 09:36:30, user Raph Ohuru wrote:

Published here https://doi.org/10.1093/cli...

On 2021-03-20 14:23:19, user Alexander Mathioudakis wrote:

This study has now been published here:

https://www.mdpi.com/2075-1...

On 2020-10-31 07:52:03, user Robert Eibl wrote:

This looks interesting, although there are a few caveats mentioned in the paper. Nevertheless, it should be possible almost everywhere, and even restrospectively, to check the vaccination status of Covid-19 patients, not only for influenza - and compare this with the average vaccination status of a whole country. Then it should be immediately clear, if there is a major benefit.

On 2020-11-16 06:38:23, user whitecat31 wrote:

At the 39th replicant when the exponential phase is basically over? Am I understanding that correctly? Seriously? Did you guys run a comparison standard curve with 39 points? Something like the 39th replicant would be considered below the limit of detection and LOQ. So yeah.. your sample was contaminated.

On 2020-06-26 16:11:55, user Kevin McKernan wrote:

Very interesting work. There are many data points suggestion earlier introduction.<br /> It would strengthen the manuscript if you could provide the CQs for your NTC for the late PCRs.

I would also suggest sequencing the amplicons to see if these are ancestral or SARS-CoV-2.

https://youtu.be/guMqyrmni4I

https://youtu.be/2SfXZa7-JKM

On 2021-05-19 18:42:43, user Fred Bass wrote:

Were the patients randomized into those getting usual and those getting 12 week delay? Having 99 in one group and 73 in the other does not seem like a random split of 172 people! A bias toward giving healthier seniors the longer interval might account for some or even all of the results.

On 2021-01-21 16:36:21, user Michael wrote:

DL+DiReCT including the trained model is also available on github: https://github.com/SCAN-NRA...

This method is mentioned in the manuscript and refers to https://doi.org/10.1002/hbm...

On 2021-01-27 15:14:20, user Florence Paré wrote:

How do you account for the possibility of COVID infections disproportionately occurring later in the period under study (due to rapidly rising numbers of infections), whereas influenza and respiratory tract infections may tend to slightly go down over the period due to distancing measures? This seems to risk introducing a confounding variable - mental health deterioration due to social distancing and pandemic-related anxiety. Did you or do you intend to make adjustments to the control cohorts to match the distribution of events over the period under study?

On 2021-01-28 19:01:30, user lbaustin wrote:

This leaves out two simple blood tests that are more predictive than any of the parameters on the list: initial blood sugar of >140 and 25(OH)D of less than 20ng/ml. Please add these to the model prior to publication.

On 2021-01-29 12:29:23, user stephan walrand wrote:

Nice correlation with the cloudiness and sun light insolation, but which is also compatible with vitamin D production!!! However, it is obvious that when comparing deaths from March to July, it is impossible to see any latitude correlation, because sun elevation averaged between March-July is almost equal for all countries.

On 2021-01-31 18:31:27, user Graeme Ackland wrote:

The statement

"we showed approximately 51% effectiveness of BNT162b2 COVID-19 vaccine against PCR-confirmed SARS-CoV-2 infection 13-24 days"

Is highly misleading. The data suggests more like "15% effectiveness 13-18 days, 85% effectiveness 19-24 days.". The most relevant day is day 21, when the second dose is meant to be given.

So their conclusion is that someone else should be deprived of 85% protective first dose, in order to give an 10% uplift with a second dose.<br /> I find that logic debatable

On 2021-01-31 21:31:27, user Ilya Zakharevich wrote:

The last two columns in the tables do not match each other (as they probably “should” for all developed countries, if one wants to get “meaningful comparison”; look for Lithuania vs Liechtenstein). I think that this is due to very different strategies to count child mortality.

Is it possible to replace the last column, dividing by the mortality (say) after age 1 year? As I said, it may be a “more interesting” number. (Less dependent on arbitrary accounting policies…)