On 2021-05-12 13:15:26, user John Smith wrote:
Hi, I see the IHME have just published their excess death figures:
http://www.healthdata.org/s...
I was wondering how they came to their figures on Japan and Kazakhstan which differ from yours substantially.<br /> They have Japan as 108,320 excess deaths and Kazakhstan as 81,696. They also differ with many others also. Interesting reading.
On 2021-05-14 01:56:12, user J.A. wrote:
In reviewing Tom Argoaic comment, I looked at the public dataset. In the dataset, the days from exposure to starting study drug or placebo are listed as 1 to 6 days. In the preprint tables 1 and 2, there are none listed for 1 day and 28 people listed for 7 days. It seems very clear that the authors of the preprint have altered the data. There is nothing in the methods explains that the data were altered. It looks like the authors chose to inflate the delay from exposure to starting medicine by +1 days for everyone. As this time from exposure to starting the study medicine is the primary focus of the preprint, this should be clear to readers and should be correct. Furthermore, not altering the data would seem to yield the same statistical analysis, yet have the benefit of being correct. This should be corrected.
Second, the authors should consider making a figure to visually show what the authors are trying to present. While there are many tables, visually showing the percentage with COVID-19 by day 1-6 would be a better way to present the data, with the mean +/- 95 confidence interval for the estimate.
Third, the authors should discuss why the placebo event rate varies over time. The placebo event rate is 10%, 15%, 19%, 12%, 13%, and 0% over the day 1-6, is there a biological reason for this variation or this random variation? The day 3 group has the highest event rate (18.9%), which then makes the statistical difference. Is this an artifact or is there biological plausibility for why taking placebo on day 2 or 4 is much better than day 3. Perhaps add this to the discussion to explain why this is not all just a post-hoc artifact of small subgroups.
On 2021-09-17 21:12:27, user Jeff wrote:
The denominator for the number of vaccinations seems wrong. The paper says "we recorded all vaccinations given in the Ottawa area between 1st June and 31st July 2021," and "there were 15,997 doses of Moderna vaccine, and 16,382 doses of Pfizer vaccine administered over the study period, for a total of 32,379 doses". But this seems like a gross undercount. On the Ottawa public health vaccine dashboard (https://www.ottawapublichea... "https://www.ottawapublichealth.ca/en/reports-research-and-statistics/COVID-19_Vaccination_Dashboard.aspx)"), the chart showing doses administered per week <br /> suggests that over 800,000 mRNA vaccine doses were administered in this time period. Were other criteria applied to reduce the denominator, or is this an error?
On 2021-09-25 16:30:29, user TeeJay2000 wrote:
I left a comment reflecting on the reputation hit that the Ottawa Heart Institute will take on this, but my comment was removed. Thank you medRxiv for encouraging discussion. I have now written to the Ottawa Heart Foundation, to indicate my withdrawal of support, until the Institute makes a formal statement how this paper made it even to a 'preprint', given the colossal size of the error.
On 2021-09-19 11:26:29, user Day Evenson wrote:
Is there any study that separates the unvaccinated into those who have had Covid and those who have not? I can't find any research on vaccinated vs previously infected. What is the lasting immunity between these groups?
On 2021-08-04 16:08:46, user Sam Chico wrote:
They compare raw Ct values which are meaningless in qPCR testing and must be correlated with something, e.g. a dilutions series of positive control COVID RNA at the very least. Should really be related back to viral counts using plates and plaques.
This is very lazy research that only shows that vaccinated people can harbor detectable amounts of viral RNA, its quite a reach to say that implications of this data is to say that vaccinated people are infectious. The only way to do that without clearly and reproducibly demonstrating it is looking at the population level data.
Its irresponsible and borderline unethical to publish data like this, the MIQE guidelines made clear what publishable qPCR data should look like. These sorts of publications undermine the impact of properly performed studies with truly actionable data. The authors should know better.
On 2021-08-05 17:53:16, user Ultrafiltered wrote:
Not even peered reviewed and yet the authors and local news stations in Dane County are promoting and reporting on this paper. That sends a dangerous message to the public that anyone can write anything and its believable/credible. Seeing the other comments here on the fallibility of the PCR test, as CDC has called out and published authoritatively along with other FDA withdrawals of rapid assay test procedures, indicates mine and their comments are just as good as this paper. The authors should remember that information is like a match, it can burn a whole lot of people if left unchecked/ unqualified / uncontrolled.
On 2021-09-20 13:55:05, user mike marchywka wrote:
There is a lot tto be learned from the aging issues,
On 2021-09-21 04:34:22, user Shawn Mclean wrote:
Wheres the raw data for this survey? It would be great if it's made public so we can dig into it ourselves.
On 2021-10-20 01:13:14, user rferrisx wrote:
The PHD hesitant numbers were adjusted for v2?:<br /> https://www.medrxiv.org/con...<br /> Page 24 table PHD:<br /> 23.9 (22.7, 25.1)v1<br /> 14.6 (13.5, 15.6)v2
On 2021-08-19 02:48:38, user Sheila Halpenny wrote:
I assume everyone responding here will use the same “logic” to apply to every other poll taken.
On 2021-08-09 21:26:26, user CdSS wrote:
Where can I get the reasons why PhDs answered "probaly not/definitely not"?
On 2021-09-21 09:06:34, user Muhammad Yousuf wrote:
Notwithstanding the comments this preprint is generating, it would also be interesting to compare three groups in this study with another group who were COVID-19-naive but received 3 doses of Pfizer's vaccine including a booster dose. If the immunity and protection against SARS-CoV-2 is still higher in infected plus vaccinated compared with those having three doses of COVID-19 vaccine, this will indicate that there is immune memory (1) through bone marrow plasma cells at play.
On 2021-10-15 12:41:54, user Mithat Temizer wrote:
Here is the question. Age is being treated in all models as a potential confounder. For a confounder age needs to be associated with COVID-19 outcome regardless of vaccination status AND age should be associated with vaccinations status regardless of COVID-19 infection. Both assumptions can be considered fulfilled in the model. Yet, the third assumption that age is not on the pathway between vaccination status and the COVID-19 infection/complication (such as hospitalization/death). In this case, we cannot confirm that age is not in the pathway. That is age is not a confounder. Could be an intermediate variable or more likely an effect modifier for the vaccine-induced/native infection-induced immunity against COVID-19 infection. In this case, why is age not considered in analyses as an effect modifier? I wonder whether the authors have checked for the effect size of vaccination (compared to natural immunity) in any of the 3 models, stratifying on age (such as those below 60 years versus those aged 60 years or more). Any comment on this?
On 2021-10-21 02:21:14, user JWrenn wrote:
A few odd things with this study.<br /> 1. why wasn't a control group of infection rates /hospitalizations of unvaccinated and never had covid included? <br /> 2. why wasn't a control made for behavior difference?
Seems like the numbers rates they put forward are not a great number to base this all on. Instead we should be looking at difference between totally unimmunized and immunized via vaccination and unimmunized and immunized via infection. Otherwise the numbers come out so tiny that it gets very weird...ie 1 vs 8 out 32k really is almost so small that it becomes random.
However if say 1000 would have been sick with no intervention then you get better numbers. like 1000-1=999 less vs 1000-8=992 less and you can see that both are very effective, but one is more so.
Also, the 2nd point really kind of breaks the whole thing. In my experience people who had Covid (and were not asymptomatic) are far more careful than people who have not gone through that hell.
The info is good just seems incomplete, and that behavior aspect I think is fare more important that we are wanting to admit as well as hard to account for in database studies.
On 2021-08-26 11:54:58, user Gubbedefekt wrote:
Can someone confirm: what difference did it make if the natural immunity was acquired from a delta variant infection compared with one from another variant? Did the study look at this?
In any case the comparison looks devastating for Pfizer and other vaccine producers.
On 2021-08-26 18:20:25, user thestreetlawyer1 wrote:
Love to see this peer reviewed. Been really trying to determine what is the best move for me. Hard to put all the dogma and peer pressure aside (from both sides). I'm curious how this data reckons with u.s data on hospitalizations, seems most of our hosp cases are unvaccinated.
On 2021-08-28 19:52:31, user Catriona wrote:
“ individuals who were previously infected with SARS-CoV-2 and given a single dose of the BNT162b2 vaccine gained additional protection against the Delta variant.”
I’m confused. Didn’t your study show no significant difference for symptomatic infections in previously infected individuals with or without a single vaccination dose? Or did I get that wrong? The table says the previously infected and vaccinated odds ratio for symptomatic infection is 0.65 but confidence intervals were 0.34-1.25 and P value was 0.194.
In which case, exactly what were they protected against? Is there some sort of clinical significance regarding a positive PCR in the absence of any symptoms? Or are you implying that individuals can have atypical symptoms that can cause them health problems but aren’t recorded as symptoms? The study didn’t look at infectivity.
Are you claiming the vaccinated individuals were healthier in some way because they had fewer positive PCRs? Or are you claiming they did not pass on the Delta variant as often? Or was there something else that you meant by “additional protection against the Delta variant?”
Would love some clarification on these issues.
On 2021-09-22 19:56:45, user Steve E wrote:
Unfortunately, even your high-vaccination-hesitancy-level scenario, which leads to national vaccination coverage saturation at 70%, now seems too optimistic. Today's CDC vaccination data shows we may not even reach 60% by year's end (unless the Biden mandates change the situation dramatically). How do your projections change in a 60% scenario?
On 2021-09-29 06:49:42, user Amador Goodridge wrote:
Excellent article. Looking at mask use behavior remains key to acknowledge the human being response for this and any future repiratory pandemic. Fernandez-Marin and his co-authors highligth the variations of mask use behavior. I agree that special attention should be directed to suburban areas, where social determinants for public health are sustaining the transmision of COVID-19 and many other infectious diseases. Congratulations to the authors!
On 2021-10-01 11:24:21, user Mahalul Azam wrote:
This work now published in the Int. J. Environ. Res. Public Health with the DOI https://doi.org/10.3390/ije...
On 2021-10-01 21:43:06, user Frank Jones wrote:
This study is deeply flawed as it relies on PCR. The PCR tests do not perform melting curve analysis to identify false positives due to primer dimers or other unspecific products. This is especially a problem if the target template concentration is low or if over 30 cycles are performed. I did thousands of quantitative PCRs and yet have to come across a primer pair that does never produce unspecific signal at high cycle numbers. This process is stochastic due to the nature of primer annealing, so a sample can be false positive or negative when running it multiple times under identical conditions which explains why some patients test positive and the next day they are negative. Also, there is no appropriate control to identify false positives. The no-template negative control is not sufficient since it obviously cannot prove the primers or probes do not amplify off target templates. Only the sample of a confirmed Covid negative person would be acceptable, yet this is not done.
On 2021-10-03 05:25:47, user kdrl nakle wrote:
Put the dates in abstract or in title. June 2020 for this serosurvey. Quite irrelevant now, more than a year after, good only for historical reference.
On 2021-10-03 11:19:07, user kdrl nakle wrote:
You could call this "Large University in a Large Town". These titles are really ridiculous. Can't you just write USC? <br /> Another thing, faculty over the age 52 are 3.4 times more likely to be unvaccinated than those in age group 20-32? That does not make sense to me.
On 2021-10-06 08:55:05, user Ken wrote:
at the time being, working on an update, we found that the spirals work even better if you sobsitute the number of infected witrh the number of infected per 100.000 inhabitants.<br /> Using the incidence could help with the calculations by simplifying the 0 pahse
On 2021-10-10 05:35:31, user kdrl nakle wrote:
I am sorry but this is example of a poor research. "We suspect Delta variant"? Couldn't you find that out? "The infection does not spread (much) thoughout body"? Really? What does "much" mean here?
On 2021-10-11 18:46:13, user Andrew T Levin wrote:
Given the stated purpose of this study, it is remarkable that the manuscript never specifically defines the term “community-dwelling population.” In practice, the study analyzes the incidence of COVID-19 fatalities that have occurred outside of nursing homes, but even that distinction is not very precise. For example, the spectrum of U.S. nursing homes encompasses board & care homes, assisted care facilities, and skilled nursing facilities. About two-thirds of U.S. nursing home residents rely on Medicaid to cover that cost. By contrast, higher-income individuals can afford to receive home health care or choose to live in “retirement communities” with on-site medical staff. In effect, the distinction of whether someone is “community-dwelling” or a “nursing home resident” is linked to a complex set of socioeconomic characteristics as well as to various aspects of their individual health. Making international comparisons along these lines is even more fraught with difficulty, because the size and composition of the nursing home population inevitably reflects differences in social norms as well as socioeconomic factors, access to healthcare, and the extent of public assistance. Indeed, such comparisons may be practically meaningless when considering developing countries such as the Dominican Republic and India, where nursing home care may only be an option for a very small fraction of the population.
Search Procedure. This manuscript uses an arbitrary search cutoff date of 31 March 2021, which excludes some landmark seroprevalence studies that have been disseminated since then. For example, Sullivan et al. (2021) analyzed seroprevalence of the U.S. population over the second half of 2020 using a large representative sample that included 1154 adults ages 65+, and hence that study would clearly satisfy the stated eligibilitry criteria for this meta-analysis.[11] Moreover, the study carefully adjusts for assay characteristics and seroreversion and estimates that as of 31 October 2020, the IFR for U.S. adults ages 65+ was 7·1% (CI: 5·0¬-10·4%). Those results can be also be used in conjunction with data on nursing home deaths to obtain the corresponding IFR estimate of 4·7% for community-dwelling adults ages 65+.
Minimum Size Threshold. This analysis excludes seroprevalence results from any studies involving fewer than 1000 adults ages 70+, and hence it is remarkable that the manuscript neither provides any rationale for imposing such a constraint nor provides citations to any existing works that might motivate it. Indeed, this approach is inconsistent with basic principles of statistical analysis, e.g., making inferences based on all available information and avoiding arbitrary selection criteria that could induce bias in the results. Consequently, meta-analysis should downweight studies with relatively lower precision rather than simply discarding those studies. Moreover, it is incoherent to specify an eligibility criterion based solely on sample size, because the precision of seroprevalence estimates also hinges on the level of prevalence. A small sample may be adequate in a context of relatively high prevalence, whereas a much larger sample may be needed to obtain precise inferences in a context of very low prevalence. The national study of Hungary was included in this meta-analysis because that study included 1454 adults ages 70+. However, only nine of those individuals were seropositive. Consequently, the test-adjusted seroprevalence for this cohort of older adults is not statistically distinguishable from zero, and hence the confidence interval of the age-specific IFR is not even well-defined.[12] By contrast, the regional study of Geneva was excluded from this meta-analysis because it only included 369 individuals ages 65+. But that sample was large enough to facilitate inferences about seroprevalence (6·8%; CI: 3·8¬¬ 10·5%) and corresponding inferences regarding IFR for that age cohort (5·6%: CI: 4·3 7·4%).[13, 14] Finally, setting the sample size threshold at 1000 is clearly an arbitrary choice. Since seroprevalence studies can be readily identified using the SeroTracker tool, this meta-analysis should be extended using a lower threshold of 250 adults ages 65+ that would encompass the national studies of Netherlands and Sweden as well as a substantial number of regional studies.
Sample Selection. In characterizing which seroprevalence studies have been included in <br /> the meta-analysis, this manuscript specifies the key criterion as “aimed to generate samples reflecting the general population.” However, this criterion is extraordinarily vague and judgmental (as evident from subjective words like “aimed” and “reflecting”). <br /> (a) United Kingdom. The inadequacy of this approach to sample selection is evident from the fact that the meta-analysis places equal weight on four U.K. seroprevalence studies, even though only two of those studies (UK BioBank and REACT-2) utilized samples designed to be representative of the general population.[15, 16] By contrast, the other two studies used convenience samples that were not designed or even re-weighted to be broadly representative, and hence those two studies should have been excluded from this meta-analysis. First, Hughes et al. (2020) studied a panel of primary and secondary patients at a large Scottish health board, with the stated objective of assessing viral transmission patterns.[17] The paper never suggested that this panel could be interpreted as representative of the wider population; indeed, some of these patients may have been receiving care related to COVID-19. Second, in one of its weekly surveillance reports, Public Health England (2020) reported seroprevalence results for a panel of patients ages 65+ who had a routine blood test at the Royal College of General Practioners Research and Surveillance Centre.[18] Evidently, this panel was not aimed to reflect the general population and may well have included patients recovering from COVID or experiencing COVID-like symptoms. <br /> (b) United States. One of the two U.S. seroprevalence studies used a sampling design that is intended to be broadly representative, whereas the other U.S. study used a convenience sample of patients at kidney dialysis centers. Unfortunately, as a consequence of gross disparities in healthcare access, higher-income individuals typically utilize in-home dialysis machines, whereas low-income individuals must travel multiple times per week to a dialysis center, often using public transit. Consequently, the prevalence of COVID-19 infections among such patients has crucial public health implications but should not be interpreted as representative of the general population.<br /> (c) Canada. Among the three Canadian seroprevalence studies, two use representative sampling designs (Ontario and Canada-ABC), whereas the third study conducted by Canadian Blood Services (CBS) uses a convenience sample of blood donors. In its public announcement of those results, CBS specifically warned that “caution should be exercised in extrapolating findings to all healthy adult Canadians, because blood donors self-select to be blood donors, in some areas access to a donation clinic may be limited, and there are fewer elderly donors who donate blood compared to the general population.” [19] That caution was specifically cited as the reason for excluding this study from a previous meta-analysis.[5] Indeed, given the scarcity of elderly blood donors, there is an even stronger rationale for excluding that study from the analysis here. Indeed, this meta-analysis should have specifically excluded all convenience samples, whether from blood donors, commercial lab tests, or medical patients. Dodd et al. (2020) analyzed a large panel of U.S. blood donors and found that the proportion of first-time donors jumped in June 2020 following the introduction of COVID-19 antibody testing, consistent with the hypothesis of “donors with higher rates of prior exposure donating to obtain antibody test results,” and concluded that “blood donors are not representative of the general population.”[20] Bajema et al. (2021) found seroprevalence of 4·94% using commercial lab residual sera from residents of Atlanta (USA), compared to seroprevalence of 3·2% using a representative sample of the same location.[21, 22] These findings highlight the extent to which convenience samples may be associated with upward bias in seroprevalence and hence downward bias in IFR. It should also be noted that the incidence of COVID-19 infections has a strong association with race and ethnicity, reflecting disparities in employment, residential arrrangements, and various other factors. Such patterns have been evident in numerous countries (not just the USA), and hence the manuscript should follow a consistent approach in addressing this issue.
Open-Ended Age Brackets. This manuscript proceeds on the assumption that open-ended age brackets for older adults are essentially equivalent regardless of whether the bracket is 60+, 65+, or 70+. But this assumption is inconsistent with the consistent findings of preceding studies, namely, the IFR for COVID-19 increases continuously with age rather than jumping discretely at any specific age threshold. Indeed, the measured IFR for any particular age bracket is a convolution of the age distribution of the population, the age-specific pattern of prevalence, and the fact that IFR increases exponentially with age. The complexity of this convolution underscores the pitfalls of comparing IFRs for open-ended age brackets of older adults. Ontario serves as a useful case study for illustrating these issues. The Ontario Public Health seroprevalence study reported results for three broad age brackets: 0-19, 20-59, and 60+ years. However, this manuscript assesses IFR for ages 70+ using results obtained via private correspondence. However, that assessment may be very imprecise, because COVID-19 prevalence was very low in the general population and may well have been even lower among the oldest community-dwelling adults. By contrast, the Ontario study is very informative for characterizing the cohort of individuals ages 60-69 years. In particular, there were 9 positives among 804 specimens for that cohort; the test-adjusted prevalence of about 1% indicates that about 17000 Ontario residents ages 60-69 had been infected by mid-June 2020. As of 30 June 2020, that age group had 240 COVID-19 deaths—none of which occurred in nursing homes. Consequently, the IFR for community-dwelling Ontario adults ages 60-69 was 1·4% -- identical to the predicted IFR t the midpoint of this age interval from the metaregression of Levin et al. (2020).[5]
Adjusting for Assay Characteristics. Seroprevalence studies have generally been conducted using antibody assays with imperfect specificity and sensitivity, and these characteristics exhibit substantial variation across assays. Moreover, the implications of these characteristics depend on the actual level of prevalence, e.g., adjusting for specificity is crucial in a context of relatively low prevalence.[23] Consequently, all three of the preceding meta-analyses consistently used seroprevalence estimates and confidence intervals that had been adjusted for test sensitivity and specificity using the Gladen-Rogan formula and/or Bayesian methods.[5, 8, 9] By contrast, this meta-analysis simply uses raw seropositive data from those studies that did not report test-adjusted seroprevalence.
Low Prevalence. The shortcomings of this manuscript’s approach are particularly evident in assessing IFRs for locations with relatively low prevalence. For example, as shown in manuscript Table 1, the seroprevalence study of Hungary used the Abbott Architect IgG assay to analyze 1454 specimens and obtained 9 positive results, i.e., raw seropositivity of 0·6%. According to the manufacturer’s data submitted to the U.S. Food and Drug Administration, this assay has sensitivity of 100% and specificity of 99·6%.[24] Consequently, the Gladen-Rogan formula indicates that the test-adjusted prevalence is only 0·2%, i.e., only one-third of the observed seropositive results were likely to be true positives. Moreover, this test-adjusted estimate has a 95% confidence interval of 0 to 0·4%, i.e., the prevalence is not statistically distinguishable from zero, and hence its IFR does not have a well-defined confidence interval. Indeed, that was precisely the reason why this cohort was not included in the meta-analysis of Levin et al. (2020).
Unmeasured Antibodies. This manuscript follows a completely unorthodox approach in adjusting seroprevalence for unmeasured antibodies: “When only one or two types of antibodies (among IgG, IgM, IgA) were used in the seroprevalence study, seroprevalence was corrected upwards (and inferred IFR downwards) by 10% for each non-measured antibody.” (p.8) This approach is particularly objectionable when applied to test-adjusted seroprevalence results, since those estimates have already been adjusted to reflect sensitivity and specificity. Moreover, such an approach has never been used by any other epidemiologist or statistician, in the context of the COVID-19 pandemic or for any other purpose, and hence should not be applied in a meta-analysis without providing any compelling rationale for doing so.
Seroreversion. The manucript “explores” the issue of seroreversion using proportionality factors based on the timing of each seroprevalence study relative to the preceding peak of COVID-19 deaths. However, the manuscript provides no rationale for following this approach instead of the rigorous Bayesian methodology utilized in a preceding meta-analysis.[9] Moreover, the manuscript makes no reference to the findings of longitudinal studies of the evolution of antibodies in confirmed positive individuals, which have concluded that the degree of seroreversion is substantial for some assays and negligible for others.[25, 26]
Measurement of Fatalities. Data on COVID-19 fatalities should be obtained directly from official government sources, not from media reports, web aggregators, or Wikipedia. For example, the European Center for Disease Control has an online COVID-19 database with daily data on reported cases and fatalities for nearly every country in the world. Moreover, whenever possible, fatalities should be measured using official tabulations of case data (based on actual date of death) rather than real-time reports that may be relatively incomplete and subject to substantial revision over time. These issues are particularly relevant for assessing fatalities in nursing homes: If a patient tested positive for COVID-19 and died soon thereafter, investigation would be needed to determine whether the death resulted from COVID-19 or unrelated causes. To illustrate these issues, consider the manuscript’s estimate of IFR based on the U.S. national seroprevalence study of Kalish et al. (2021). As shown in table 1 and appendix table 2 of this manuscript, the U.S. CDC case database (accessed in February 2021) indicates a total of 103862 deaths for adults ages 70+ as of 04 July 2020. To determine the corresponding fatalities in U.S. nursing homes, however, the manuscript relies on a news summary dated 26 June 2020 that reported a total of 52428 nursing home deaths in 41 U.S. states.[27] Using that real-time report, manuscript infers a somewhat higher national total of 57291 nursing home deaths and hence 46571 deaths outside of nursing homes. By contrast, the U.S. CMS case database (accessed in August 2021) indicates 38239 deaths in U.S. nursing homes as of 05 July 2020.[28] Evidently, there were 65623 fatalities outside of nursing homes, implying a correspondingly higher IFR of 3·6% for U.S. community-dwelling adults ages 70+.
Developing Countries. The use of confirmed COVID-19 fatalities can be highly misleading in assessing IFRs of developing countries, where testing capacity has been much more limited than in Europe or North America. Consequently, in developing country locations, the measure of fatalities should include both confirmed and suspected COVID-19 cases, or alternatively, a measure of excess deaths relative to preceding years. Indeed, several recent studies of India have concluded that confirmed COVID-19 fatalities understate the true death toll by an order of magnitude.[29-31]
Younger Age Groups. The manuscript states that “the studies considered here offered a <br /> prime opportunity to assess IFR also in younger age strata” (p.9) even though such analysis <br /> had not been proposed in the protocol. Nevertheless, this secondary analysis is at odds with the key eligibility criterion of this meta-analysis, namely, seroprevalence studies with at least 1000 participants ages 70+. Indeed, imposing that eligibility criterion leads to the exclusion of numerous other seroprevalence studies that would be highly informative for analyzing IFRs of younger adults, with an unknown degree of bias associated with that exclusion.
Self-Citations. A meta-analysis is intended to serve as an objective synthesis of information extracted from existing studies. Consequently, methodological decisions and substantive claims should not be based solely on citations of the authors’ own prior work. For example, in discussing the preceding meta-analysis of Levin et al. (2020), the manuscript asserts that “almost all included studies came from hard-hit locations, where IFR may be substantially higher”, with a sole citation to Ioannidis (2021a). However, that assertion is clearly false: The meta-analysis of Levin et al. (2020) included locations such as Australia, New Zealand, Ontario, and Salt Lake City that experienced very few infections during the first wave of the pandemic. Similarly, the manuscript asserts that “selection bias for studies with higher seroprevalence and/or higher death counts may explain why their estimates for middle-aged and elderly are substantially higher than ours” (p.14), with a sole citation to Ioannidis (2021b).
References Cited Here:<br /> 1. Ferguson N, Laydon D, Nedjati-Gilani G, et al. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand2020.<br /> 2. Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. eurosurveillance. 2020;25(10). doi:10.2807/1560-7917.ES.2020.25.10.2000180<br /> 3. Salje H, Tran Kiem C, Lefrancq N, et al. Estimating the burden of SARS-CoV-2 in France. Science. 2020;369(6500):208-11. doi:10.1126/science.abc3517<br /> 4. Verity R, Okell LC, Dorigatti I, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. lancet infectious diseases. 2020;20(6):669-77. doi:10.1016/S1473-3099(20)30243-7<br /> 5. Levin AT, Hanage WP, Owusu-Boaitey N, Cochran KB, Walsh SP, Meyerowitz-Katz G. Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications. European Journal of Epidemiology. 2020;35(12):1123-38. doi:10.1007/s10654-020-00698-1<br /> 6. Williamson EJ, Walker AJ, Bhaskaran K, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020. doi:10.1038/s41586-020-2521-4<br /> 7. Mak JKL, Kuja-Halkola R, Wang Y, Hägg S, Jylhävä J. Frailty and comorbidity in predicting community COVID-19 mortality in the U.K. Biobank: The effect of sampling. Journal of the American Geriatrics Society. 2021;69(5):1128-39. doi:https://doi.org/10.1111/jgs...<br /> 8. O’Driscoll M, Ribeiro Dos Santos G, Wang L, et al. Age-specific mortality and immunity patterns of SARS-CoV-2. Nature. 2021;590(7844):140-5. doi:10.1038/s41586-020-2918-0<br /> 9. Brazeau N, Verity R, Jenks S, al. e. COVID-19 Infection Fatality Ratio: Estimates from Seroprevalence. 2020. doi:https://doi.org/10.25561/83545.<br /> 10. Arora RK, Joseph A, Van Wyk J, et al. SeroTracker: a global SARS-CoV-2 seroprevalence dashboard. The Lancet Infectious Diseases. 2020. doi:10.1016/s1473-3099(20)30631-9<br /> 11. Sullivan PS, Siegler AJ, Shioda K, et al. Severe Acute Respiratory Syndrome Coronavirus 2 Cumulative Incidence, United States, August 2020–December 2020. Clinical Infectious Diseases. 2021. doi:10.1093/cid/ciab626<br /> 12. Merkely B, Szabo AJ, Kosztin A, et al. Novel coronavirus epidemic in the Hungarian population, a cross-sectional nationwide survey to support the exit policy in Hungary. Geroscience. 2020;42(4):1063-74. doi:10.1007/s11357-020-00226-9<br /> 13. Perez-Saez J, Lauer SA, Kaiser L, et al. Serology-informed estimates of SARS-CoV-2 infection fatality risk in Geneva, Switzerland. The Lancet Infectious Diseases. doi:10.1016/S1473-3099(20)30584-3<br /> 14. Stringhini S, Wisniak A, Piumatti G, et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study. The Lancet. 2020;396(10247):313-9. doi:10.1016/s0140-6736(20)31304-0<br /> 15. United Kingdom BioBank. UK Biobank SARS-CoV-2 Serology Study Weekly Report - 21 July 2020. 2020.<br /> 16. Ward H, Atchison CJ, Whitaker M, et al. Antibody prevalence for SARS-CoV-2 in England following first peak of the pandemic: REACT2 study in 100,000 adults. medRxiv. 2020:2020.08.12.20173690. doi:10.1101/2020.08.12.20173690<br /> 17. Hughes EC, Amat JAR, Haney J, et al. Severe Acute Respiratory Syndrome Coronavirus 2 Serosurveillance in a Patient Population Reveals Differences in Virus Exposure and Antibody-Mediated Immunity According to Host Demography and Healthcare Setting. The Journal of Infectious Diseases. 2020;223(6):971-80. doi:10.1093/infdis/jiaa788<br /> 18. U.K. Public Health England. Weekly Coronavirus Disease 2019 (COVID-19) Surveillance Report, Week 32. 2020. <br /> 19. Canadian Blood Services and COVID-19 Immunity Task Force. Final Results of Initial Canadian SARS-Cov-2 Seroprevalence Study Announced. 2020. <br /> 20. Dodd RY, Xu M, Stramer SL. Change in Donor Characteristics and Antibodies to SARS-CoV-2 in Donated Blood in the US, June-August 2020. JAMA. 2020;324(16):1677-9. doi:10.1001/jama.2020.18598<br /> 21. Bajema KL, Dahlgren FS, Lim TW, et al. Comparison of Estimated Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence Through Commercial Laboratory Residual Sera Testing and a Community Survey. Clinical Infectious Diseases. 2020. doi:10.1093/cid/ciaa1804<br /> 22. Boyce RM, Shook-Sa BE, Aiello AE. A Tale of 2 Studies: Study Design and Our Understanding of Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence. Clinical Infectious Diseases. 2020. doi:10.1093/cid/ciaa1868<br /> 23. Gelman A, Carpenter B. Bayesian analysis of tests with unknown specificity and sensitivity. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2020;n/a(n/a). doi:10.1111/rssc.12435<br /> 24. U.S. Food and Drug Administration. EUA authorized serology test performance. 2020.<br /> 25. Dan JM, Mateus J, Kato Y, et al. Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection. Science. 2021;371(6529):eabf4063. doi:10.1126/science.abf4063<br /> 26. Muecksch F, Wise H, Batchelor B, et al. Longitudinal Serological Analysis and Neutralizing Antibody Levels in Coronavirus Disease 2019 Convalescent Patients. The Journal of Infectious Diseases. 2020;223(3):389-98. doi:10.1093/infdis/jiaa659<br /> 27. Kaiser Family Foundation. This Week in Coronavirus: June 18 to June 25. 2020. <br /> 28. U.S. Center for Medicare & Medicaid Services (CMS). COVID-19 Nursing Home Data. 2021. <br /> 29. Anand A, Sandefur J, Subramanian A. Three New Estimates of India’s All-Cause Excess Mortality during the COVID-19 Pandemic. Center for Global Development. 2021. <br /> 30. Deshmukh Y, Suraweera W, Tumbe C, et al. Excess mortality in India from June 2020 to June 2021 during the COVID pandemic: death registration, health facility deaths, and survey data. medRxiv. 2021:2021.07.20.21260872. doi:10.1101/2021.07.20.21260872<br /> 31. Shewade HD, Parameswaran GG, Mazumder A, Gupta M. Adjusting Reported COVID-19 Deaths for the Prevailing Routine Death Surveillance in India. Frontiers in Public Health. 2021;9(1045). doi:10.3389/fpubh.2021.641991
On 2021-10-12 14:55:07, user Ho Hum wrote:
The study was based on vaccination's done over a 2 month period. I could see that 32K might be too low but 800K seems too high considering that the entire population of Ottawa is just under 1 million. Did they vaccinate 80% of the population of Ottawa in just two months?
Something very wrong here.
The 1 in 1000 could be valid for the age 16-25 group. I could see a total of 32K of this demo being vaccinated in two months.
Too bad the researchers lost all their credibility over bad math
On 2021-10-13 14:52:47, user Stephen B. Strum wrote:
Everyone has their unique response to an antigen, be it natural or a vaccine. The proof of the pudding is the end response relating to protection--from severe illness, to chronic COVID-19, to hospitalization, to needing an ICU, and to death. For certainty--being vaccinated is better than not. For breakthrough infections the data "appears" that Moderna is superior to Pfizer--but how about an analysis of those who had breakthrough infections? Age, Sex, BMI, Diabetes, Immune status, Medications, etc? I have not read the full paper but going through the publication I do not see that mentioned. How about a probably surrogate or correlate of protection in the form of total immunoglobulin (Ig) G against the S1 protein as measured by the LabCorp or Quest Roche Elecsys test? In my case (age 79, light chain amyloidosis in complete remission (CR) & off chemo or immunotherapy x 1 year) my SARS-CoV-2 Ab (antibody) level at 1 month post two doses of Pfizer was > 250 U/ml, only to drop to 59 at 4 months. Then, I received a Moderna booster on 9/1/21 & on 10/5/21 my Ab level was > 2,500 U/ml. These are tests that are commercially available. The results are back in 24 hrs; the test is not expensive. There's a huge difference in individuals, especially by age and by comorbidity. <br /> LabCorp test code 164090: SARS-CoV-2 Semi-Quantitative Total Antibody, Spike using Roche Elecsys. <br /> Quest Test Code 39820 SARS-CoV-2 Total Antibody, spike, semi-quantitative using Roche Elecsys. <br /> With the huge # of publications on COVID-19, there should be articles correlating the level of IgG vs. breakthrough infections. Where is that article(s)??<br /> Stephen B. Strum, MD, FACP
On 2021-08-12 06:11:36, user Gabriele Pizzino wrote:
The study brings up relevant insights.<br /> I agree with Richard, uniformity of testing frequency is a potential confounding factor that should be taken into account.
Also, you may have a selection bias generated by vaccination policies, and timeline.<br /> I’m gonna use Italy to give you the idea: Pfizer was rolled out earlier, and in a much more massive way, than Moderna. The same was true for the AstraZeneca one.<br /> The Italian government gave priority to high-risk workers (especially health workers, and whoever was working into medical facilities or nursing homes), and to high-risk individuals (so elderly people, and/or people with severe underlying conditions).<br /> At the time, the choice was between Pfizer and AstraZeneca; considering Pfizer showed a better efficacy, and it was better perceived in terms of safety profile by the public, the very very large majority of those high-risk categories received the Pfizer shots.
That is a textbook-level selection bias, which could potentially affect the results in a significant way.<br /> I live in Italy, so I followed the vaccination process here much more closely; I don’t know if the same dynamics I described stand true also for the US, but I think it is worth to take a look and check it.
On 2021-08-12 19:49:26, user Steven Ramirez wrote:
From the preprint:
"Date of vaccination (bucketed). For a given individual in the mRNA-1273 cohort who<br /> received their first vaccine dose on a given date, only individuals in the BNT162b2<br /> cohort who were vaccinated on the same date or within two weeks after that date were<br /> considered for matching. This match helps to ensure that matched individuals reach their<br /> date of full vaccination (14 days after the second dose) on approximately the same date."
This seemingly addresses my point and effectively controls for diminution over time.
Perhaps an explicit sentence in the preprint would help clarify this point.
On 2021-10-15 22:17:00, user baruch1014 wrote:
so the gist of what i read here is that people who developed encephalopathy due to the severity of infection were more at risk for neurologic and psychiatric issues six months post-infection... but, i mean, you could contextually make the same determination with regard to auto accident survivors who develop encephalopathy in relation to the severity of the auto accident, or mma fighters, or football players, or people who have almost drowned or otherwise were deprived of oxygen to the brain... am i incorrect? basically, any trauma to the brain, if severe enough, can cause later psychiatric or neurologic affects.
On 2021-10-16 11:50:49, user Markus Falk wrote:
Very interesting read. Does the unvaccinated group contain persons with a past infection?
On 2021-10-25 17:08:33, user Arron190 wrote:
It would be interesting to see how the data changes if those with naturally acquired immunity (ie been infected) are removed.<br /> Around 13% of US citizens have been infected so far.<br /> Many of the uninfected may be interested to know what level of protection the vaccine provides.
On 2021-10-16 23:35:24, user Mike New wrote:
Here is the pertinent question that I would like a straight answer on:
Does the Singapore study suggest that a vaccinated person is more likely to be "asymptomatic" with the delta strain than an unvaccinated person ? yes or no ?
On 2021-10-18 00:04:56, user Geoffrey Graham wrote:
An encouraging study! Mobile HEPA filters may do a great deal of good.
Cigarette filters can also remove aerosols of biologically relevant sizes from an air stream. Seventy-five half-length filters in parallel will transmit enough air for a facemask wearer to breathe comfortably. Cigarette filters are very common around the world and so are other materials from which facemasks could be made. Building a 75-filter facemask from these materials is straightforward. If cigarette filters can also remove SARS-CoV-2 from an air stream (this needs to be tested), we could save a lot of lives this winter.
Here is a brief account of where things stand:<br /> See “The Saga of the Universal Anti-COVID Facemask: Where Things Stand”<br /> at:<br /> https://geoffreyjgraham.sub...
And here is a comprehensive (read “gargantuan”) account of all significant results.<br /> http://distributiveeconomic...
Clearly, the cigarette filters must be tested against actual virus. I am soliciting advice on the best way to do this. Beyond this, I welcome advice on what to do (and what not to do) next.
Geoff Graham<br /> gjgraham4health@protonmail.com
On 2021-10-19 16:26:22, user Andy Bloch wrote:
There was a significant (and substantial) statistical difference that they brushed off, and they included the first 2 weeks after injection in the comparison. Pfizer is clearly more effective than AZ after 2 weeks against positive tests, as the Hazard Ratio chart in their Figure 2 shows.<br /> https://twitter.com/Andy_Bloch/status/1450397100341035021
On 2021-10-21 13:28:45, user CDSL JHSPH wrote:
Dear Authors,
This Study was extremely consequential and extremely well constructed. Particularly in the advancements in identifying previously unknown areas responsible for atrial flutters via utilization of electroanatmocal mapping systems. The triad of identification from density based maps, definition of criteria in voltage density and tachycardic cycle length are great strategies in looking at these complex cases. A few critiques I would however like to levy though is that due to the large amounts of technical jargon within this paper especially displayed within the raw data output by the EAM systems. Further explanation of the data in the figures and results would improve the overall readability of the study and contextualize further on the crucial outcomes. Another point I believe already brought up is due to the low number of patients in the cohort and the survivorship bias in all cases, the true possibilities of the CARTO EAM based mapping systems have yet to be evaluated. The last critique I would like to present is I was extremely curious regarding the radiofrequency doses administered between numbers of VALLEYS when treated I would assume longer times of treatment as well as larger dosages as more areas were responsible for these arrhythmias and would greatly clarify some of the data presented in the second Table. However this paper was extremely enjoyable to sift through and thank you for your work!
On 2021-10-24 02:36:03, user randy tangang wrote:
The great work in the analysis of how gut microbiome BA dysregulation can increase the risk of immune or metabolic disorders. As it was pointed out in the paper, lifestyle was one of the factors that were left out in this analysis of gut microbes. One factor that I thought was very important was how the environment had an influence on these microbes. The experiment used just 3 countries and all in one continent and as I know, people in different continents are exposed to different bacteria and turn to harbor different bacteria in their guts depending on the environment they live in. someone in the western world (Europe) who has access to clean water and different antibiotics in their food will have a different gut microbiome BA from someone who lives in Africa or South America where people are exposed to many bacteria on daily basis. The paper talks about using this analysis for therapeutic targets in immune or metabolic disorders. And so if the end goal is to use this universally, I recommend studying and analyzing how different environments affect gut microbes BA in different continents.
On 2021-10-25 21:40:02, user John Hulleman wrote:
This preprint was subsequently published in Molecular Vision on April 2nd, 2021. Please see link below for the accepted article:
On 2021-10-26 09:42:04, user Stephen Hinkle wrote:
I think this study calls for an important discussion about how we approach COVID-19 in the future. I think that it is clear that people can get this more than once. Other studies have shown that vaccine immunity is not lifelong either. I think we need to INFORM THE GENERAL PUBLIC OF THIS TRUTH and have a public policy discussion where the public is invited to participate on how the public wants to confront COVID-19 longer term going forward. It is likely that this will be an endemic virus (this is the conclusion of many top public health universities including Brown, Harvard, Stanford, Johns Hopkins, University of Minnesota, Imperial College London, University of Alabama at Birmingham, University of Arizona, University of Sydney, University of Queensland, Oxford, and others). Many countries have abandoned their "Zero Covid" strategies as well realizing this including Australia, New Zealand, Vietnam, Thailand, Singapore, and others. This study covering Iran shows that people got infected many times.
Do we stay in lock down and abandon some activities and pleasures in life forever possibly leaving businesses permanently closed or forcing everyone to say their last goodbye to our friends, abandon all group activities, sports, performing arts, dating, and our pleasures in life forever in an attempt to stay alive or stop the virus? Do we open up and accept the risk of ongoing community spread of COVID-19 and keep getting booster shots for individual immunity and new variants? Should getting vaccines be mandatory or an individual level decision? How do we protect the immunocompromised and those who are more vulnerable or who the vaccines do not work well on? Do we do a massive COVID-19 testing operation and try to eliminate the virus through daily tests and quarantine people if they are infected an allow the others to go on with normal life activities? What level of death and disability should society choose to accept to have the levels of freedom of movement and/or non-household member social interaction we want in the future if the COVID-19 virus will be endemic? Should shuttered sectors of the economy be allowed to reopen or not? Should in-person schooling continue or not? Should masks be required indefinitely or should it be optional or not required?
I think it is time to start a policy conversation with the GENERAL PUBLIC to determine what they want the un-perfect pandemic endgame to be in terms of living with the virus and going on with life as safe as we can but it is likely the day-to-day risk will not be zero. It is clear to me based on all the recent evidence from this study and all the current data trending in other recent studies is showing that COVID-19 will become ENDEMIC and that this pandemic is going to have a social ending as opposed to a eradication or herd immunity outcome most likely. But the real question now is what will a divided public tolerate in terms of COVID-19 policy longer term and what is the public health end goal now? Maybe it is time to ASK THE GENERAL PUBLIC FOR IDEAS here.
Personally, I think that the COVID-19 pandemic is another case of humans showing a poor record of eradicating diseases.
On 2021-10-26 17:04:29, user Robert wrote:
In the history of Vaccines I have yet to see where a drug company is not working on a new or altered vaccine within 6 months of the original. Given the speed these vaccines were released you would think that alternate or new and improved mRNA would be released or spoken of. I have seen nothing or read nothing. <br /> Additionally. This is the only vaccine I ever seen pushed that does not have the listed side affects.
On 2021-10-26 21:25:43, user Eugene Peskin wrote:
The article doesn't provide much clarity how the number of cases among the non-immune was actually calculated.<br /> If accomodation for immune layer of 46% has been done to re-calculate attack rate for control group, it should also be accomodated for the main group calculation, as 46% one-time vaccinated already had immunity before vaccination (actually less, you should deduct those who got their immunity from previous vaccination).
On 2021-11-07 15:28:01, user DinCville wrote:
What can a study of 60+ year olds who had breakthrough infections tell us about the risk for all 60+ who are vaccinated? How representative are those 60+ with breakthrough infections? Could they be more likely to have pre-existing conditions that affected the effectiveness of their vax response? Concerned that these results be interpreted to mean all 60+ with vax are unprotected from long covid.
On 2022-01-07 19:58:49, user Clive wrote:
Hi, please correct me if I'm wrong, but reading this study I'm not seeing any comparison to the baseline population that has not contracted COVID. If, as I'm sure you're aware of, a broader worsening of health conditions has increased across the population during the time frame of this study, could the data be misrepresenting a change to the mean in both the vaccinated and unvaccinated groups. For a specific example, I've heard anxiety rates have tripled since the start of the pandemic across the population, and if that pattern was ongoing at the time of this study could the study be overestimating the increase in anxiety among both vaccinated and unvaccinated groups, as everyone, not just those who contracted COVID, experienced an increase in anxiety?
On 2020-04-22 20:38:01, user David Swiff wrote:
Macrolides can prolong the QT and QTc interval and cause cardiac arrhythmias, including TdP, ventricular tachycardia, and ventricular fibrillation, via their effect on the IKr potassium channel.
On 2020-04-23 05:17:25, user B Yabut wrote:
The authors forgot the known main mechanism by which hydroxychloroquine works. Late administration at the point needing intubation means the cytokine storm has alreadybeen set in motion. Biomolecular and cellular studies showed that hydroxychloroquine works at the point of viral cellular entry and early inside the cell. Granted it also has a still unelaborated effect on the inflammatory process the study from France specifically included the pre-condition "Early administration."
On 2020-04-24 00:57:17, user Philip Davies wrote:
Well, well well ...
This pre-print would make a good script for an episode of Columbo.
The retrospective analysis, as presented, leads the reader to just one conclusion in a bazaar of many possible conclusions.
I am even starting to have sympathy with D. Raoult and his team. I note his hot tempered response to this paper, where he lists two enormous factors that should be considered when wrestling with the data: the fact that the HCQ and HCQ & AZ cohorts were a sicker crowd (he lists lymphopenia) and that the sickest of the non-HCQ ventilated patients were then given HCQ (plus AZ in most cases) in a desperate last bid only for most to die.
Raoult's point is certainly valid.
We must remember that for most of the study period the use of HCQ was "ex-license" on a compassionate basis only. This means only the sickest patients got it. Remember also that this is a retrospective analysis, therefore observational. It was not run as a therapeutic trial. On the other hand, the use of AZ was already accepted (hence 30% of the non-HCQ cohort got it anyway).... although do be aware that by this time there had been quite a lot of focus on potentially dangerous QT lengthening when HCQ and AZ were used together in very sick patients.
The HCQ cohort was, across all key determinants, the weakest and sickest group (it had the poorest prospects looking at age, ethnicity, smoking status, congestive heart failure, peripheral vascular disease, cerebrovascular disease (strokes),dementia, COPD, Diabetes (with and without complications)! ... and indeed, the HCQ and HCQ & AZ cohorts did have 100% more lymphopenia than the non-HCQ group.
BUT, the big asymmetric issues become obvious when we look at the pre- and post- ventilator numbers.
In terms of patients discharged without needing ventilation, the "victorious" non-HCQ group performs poorer than the 2 treated groups. This despite having a better prognostic baseline. But the results for this group change dramatically (for the better) when we look at the outcomes of ventilation. 25 ventilated patients came from this group.... but 19 of these 25 patients were then started on HCQ or HCQ & AZ after ventilation was started. It is screamingly obvious that these would be the sickest patients in that group: they were given such compassionate drugs in extremis. So having ejected 19 of 25 ventilated patients into the other cohorts, the non-HCQ group only had 3 deaths from its remaining 6 ventilated patients.
The numbers of ventilated patients in the other cohorts (HCQ and HCQ & AZ) were thus substantially inflated with these new super-sick patients, who mostly died.
There really can be no conclusion at all when looking at a study of this nature without knowing much more about individual clinical conditions and guiding principles behind clinician's decision making. It's still possible to make some reasonable assumptions:
If I were Columbo?... I would say the non-HCQ cohort contained patients of extremes, with the best and worst potential. The worst would have been the very frail (malignancy and or congestive heart failure maybe ... see the stats), who probably were earmarked for 'supplemental oxygen' only from the very start. Such patients would not have been suitable for compassionate use of non proven drugs (remember, most of this came before the "emergency use" edict by FDA). This would explain the number of non-ventilated patients who died in this group (they may have been given AZ only, not being a controversial drug, but otherwise they did not get any significant interventional therapy). These patients would have had significant chronic disease and very poor obs/indices (including lymphopenia). But given that this cohort had, overall, a better starting prognosis than the other two groups, it means that the remaining patients in the group were promising candidates for survival (with better obs/indices). Such patients, not being part of a clinical trial, would not have been offered HCQ on a compassionate basis unless they got dramatically worse .... and of course, the ones who did get worse on the ventilator were started on HCQ (& often AZ as well) and thus swapped into the HCQ / HCQ & AZ cohorts.
If we can understand that, then we might start to think that in fact HCQ & AZ is the best performing cohort with the other 2 vaguely distant. But this is being unfair to the HCQ cohort:
The reason that a sick patient would be given one experimental drug on a compassionate basis (HCQ) but not have a rather less experimental drug further added (AZ), can really only be explained by considering risk versus benefit. A clinician would choose to use HCQ because the patient was particularly sick. The clinician would only add AZ if it was felt that this was worth the risk.... but a particularly sick patient with significant cardiovascular disease (the HCQ contained the most CVD risk) might then die of a more abrupt arrhythmia through adding yet another QT lengthening drug. I dare say the clinicians were tempted to make some "Hail Mary" plays, but we must remember, these patients were not part of an ongoing trial, these drugs were "ex-license" for compassionate use only and clinicians were still accountable for responsible actions. So for those particularly sick frail patients, it wasn't worth the risk.
I am pretty sure that the HCQ cohort (which had pretty good pre-ventilator stats) crashed badly because it was loaded with the sickest patients .... patients that were too sick to risk adding AZ.
So, the findings of this retrospective analysis are, in my opinion, likely to be incorrect.
I believe I can confidently state that:
It is almost impossible to reach a conclusion from all this. BUT, the most likely finding is NOT that adding HCQ delivers a worse outcome than standard treatment. In fact, if we look at the pre-ventilator stats, the addition of HCQ might actually have provided considerable benefit to a particularly sick group of patients. Whether or not the addition of AZ to HCQ adds benefit is also unclear ... although my 'swingometer' is pointing slightly more to benefit than harm.
Once again. I suggest that a robust study into prophylaxis and early treatment (using sensible safer doses adjusted for pulmonary sequestration) will deliver the most interesting results for CQ/HCQ.
Dr Phil Davies<br /> Aldershot Centre For Health<br /> http://thevirus.uk
On 2020-04-24 04:44:30, user joe2.5 wrote:
I don't know if I'm the only one to totally miss, in this paper, the main point I should be paying attention to. Anecdotal data that started the idea that OH-chloroquine could be of value in treating Covid-19 indicated quick decrease of the viral load hen administered just at the start of symptoms or even before. I read the paper twice without being able to see any mention of the time from first symptoms to treatment. So the impression is that the study was not trying to answer the initial question.
On 2020-04-21 20:47:16, user Savio wrote:
Cytokine bath or flooding is causing co-morbidity. HCQ can apparently reduce the inflammation in response to the virus but not counter the virus itself.
Here is a review paper describing the “Mechanisms of action of hydroxychloroquine and chloroquine: implications for rheumatology”.
https://www.nature.com/arti...
“Hydroxychloroquine and chloroquine are weak bases and have a characteristic ‘deep’ volume of distribution and a half-life of around 50 days. These drugs interfere with lysosomal activity and autophagy, interact with membrane stability and alter signalling pathways and transcriptional activity, which can result in inhibition of cytokine production and modulation of certain co-stimulatory molecules.”
On 2020-04-21 22:10:28, user Marv Goosen wrote:
The problem with this study is that it is retrospective and as the authors state in their discussion, patients in worse shape may have been put in the HC group which would also account for higher mortality. Unfortunately until a prospective study with severity matched controls is done, no conclusions can be made.
On 2020-04-22 12:57:03, user Jorgen Schultz wrote:
Interesting - and chocking, I must say. I hope the report I have read on medRxiv is not final, because I am missing the following:<br /> 1) As I understand it, the combo-treatment is effective in treating patients with Coronavirus BEFORE it is "to late". Giving a treatment with known side effects during late stage infection is recommended by?<br /> 2) The screening done (on patients) prior to treatment - I must have missed it or? Just to compare: as I understand it, in IHU's treatment (besides patient-groups being not comparable) screening is done prior to any medication, and patients in risk form a kind of control group as best as can be in this effort to safe lives. <br /> 3) Dosage and duration of treatment?<br /> 4) Did patients with cardiovascular symptoms receive hydroxychloroquine? Likewise with patients showing symptoms of "Cytokine Storms"?<br /> 5) According to "Cytokine storm and immunomodulatory therapy in COVID-19: role of chloroquine and anti-IL-6 monoclonal antibodies" by Ming Zhao, Hydroxychloroquine is mentioned for its effect to inhibit viral replication. Is that not very much prior to the case of the patients in this study? <br /> Did other - and more relevant drugs - replace the use of Hydroxychloroquine if the later stages of the infection?<br /> 6) Why study a subject you have already discarded and emotionally distanced yourself from (in "Background" it describes the use of Hydroxychloroquine as "anecdotal")?
As the French President said: We are at war. <br /> But here - another war seems to be fought!<br /> With human sacrifice and casualties as a result.<br /> Colatteral damage?
I am chocked and saddened by the loss of life during this study.
On 2020-04-22 15:03:20, user Eric Hall wrote:
But not a prospective study with a randomized control group. How do we know the HCQ groups weren't just sicker and it was used more like maximum medical therapy. Correlation doesn't equal causation.
On 2020-04-23 00:19:12, user Michael S. Y. Lee (biologist) wrote:
Hello,
Did you infect Vero-E6 cells from each patient just once (and harvest the cells in quadruplicates), or did you infect the Vero-E6 cells from each patient four times (and harvest the cells once per infection).
This is very important for statistics.
Mike
On 2020-04-21 22:42:10, user Dan Johnson wrote:
Great work, quite an undertaking. This is a very minor question: why "Pair-wise p-values were calculated between isolates using the t-test" rather than use Tukeys or one of the many post-hoc tests designed to get around the Type I error problems of multiple t-tests. Is that what you mean by "adjusted p"?
On 2020-04-24 07:31:44, user Dennis Menace wrote:
The children they tested were brought in by their parents. These are also not independent, is this a problem ?
On 2020-04-21 03:58:41, user David Feist wrote:
The USC study had a slightly different methodology, and has given the same result as the LA County test ie 4.1% of 863 residents had the virus. The lead investigator at USC was Neeraj Sood, a professor of health policy and vice dean for research.
Two tests with the same result. Evidence is mounting...
On 2020-04-22 05:08:10, user G.O.B. wrote:
Well, this changes everything...
“County of Santa Clara Identifies Three Additional Early COVID-19 Deaths”
First US death was in California over 3 weeks earlier than the previously reported first US death in Seattle on Feb 29.
On 2020-04-17 20:43:16, user Drew Middlesworth wrote:
I was estimating the true infected numbers were 10-20x higher than the reported numbers based off the hospitalization rates, I wasn't expecting it to be this high. Although folding these numbers into NY infected counts would mean that over 100% were infected which can't be right. My estimate would show about 25-50% infected currently.
Although the other thing that would skew this, by using Facebook ads and not doing a random population pick, you would skew the results higher as people who were sick in Jan-Feb would be more likely to respond to the ad to get tested. They could easly be 2-3x too high which would be more inline with the numbers I was estimating based off hospitalization rates.
On 2020-04-18 00:16:22, user Rob Kuchta wrote:
I am very suspicious of the 50- to 85-fold difference in confirmed cases based on the NY (and NYC) infection rates. NY has a 1.2% infection rate as a whole, and for NYC it is 1,5%. Using these values, that would indicate that from 60-100% of the population have been infected in NY state, and infections should be dropping rapidly (also, I have never heard of a virus having this sort of infectivity). In NYC, the infection rate would be 75-100%. This suggests that either there is an unknown issue with the test or that the Ab generated by being infected by this virus do not prevent subsequent infection. This latter situation would be rather worrisome in terms of a vaccination strategy to prevent infection.
On 2020-04-18 16:15:43, user spacecat56 wrote:
In reading the draft report of the study (pre-print, dated April 11, 2020) my predominant thought was, in choosing to respond to the invitation to the study participants are overwhelmingly likely to have self-selected based on their recent prior experience of symptoms that they suspect may have been due to COVID-19 infection.
The draft acknowledges the possibility of this bias but tosses it off as "hard to ascertain". But the draft also says that data on prior symptoms were collected; data which are entirely omitted both from the published analysis and from the published tables.
Because the analysis ignores this factor and because of the potential for this bias to totally dominate the analysis, in my opinion after reading the study draft, we still know effectively nothing at all about the prevalence of infection in the studied population. Accordingly I would expect to vigorously object to any attempt to incorporate the reported results into public policy and planning.
I would urge the study team to bend their efforts to addressing this deficiency. At a minimum, I suggest, the report should include the withheld prior-symptoms data. Preferably, some efforts should be made to deal with the difficulty of estimating the bias. Perhaps it would be helpful to subdivide the sample data based on yes/no prior symptoms and analyze each subset?
On 2020-04-18 16:27:11, user Zev Waldman MD wrote:
I agree with other commenters that people who suspected prior Covid infection (or exposure) are more likely to seek antibody testing than those who did not. While participants were asked about prior symptoms, it is not clear what was done with this information. The rate of symptoms/exposure could be reported, compared to the community rates, used as a risk factor for positive antibody testing, etc.
My other concern that has gone less discussed is their calculation of the case fatality rate. While they recognize that reported case numbers as of April 1 are an underestimate, it seems that they forget this skepticism when looking at reported deaths. They seem to take it as a given that 50 people died of Covid in the county as of April 10 as reported, and used this to project to deaths by April 22; however, like case counts, there are multiple reasons to suspect this number of deaths might be higher:
Reporting of deaths is well-known to be delayed - i.e., date of reporting does not equal date of death
People who actually died of Covid may never have been tested, and thus may not be included as cases or deaths
The doubling time of deaths used to project to April 22 is also based on reported deaths; if reporting of deaths is delayed, the doubling time may appear slower than it actually was.
If the death estimate due to illness before April 1 is too low, their corresponding CFR would be an underestimate as well. (This would be exacerbated if their case estimate is too high due to self-selection into the study, as seems possible.) At the very least, some sort of uncertainty around the death estimate should be provider, which in turn would increase the uncertainty around the final CFR.
I know CFR wasn't the main focus on the article, but worry that, because these results support their prior beliefs, some readers may take the results at face value and push them to policymakers before they have been more widely vetted by the scientific community.
On 2021-12-12 01:32:39, user Wild Bill, Jr. ????:????:?? wrote:
When I first read this paper, a year ago, I was skeptical about it. The alarmists presented what seemed to be some strong arguments against it
However, with the passing of time, infection-fatality rate statistics and deaths from all causes statistics support the Stanford team's hypothesis: The dreaded virus has turned out to be a lot less lethal than the alarmists claimed it was.
On 2020-04-24 14:35:40, user VirusWar wrote:
Interesting study. some comments :<br /> 1. The increase of QTc can be due as well to renal diseases due to COVID19, Such renal diseases were pointed in this study "The QT Interval in Patients with SARS-CoV-2 Infection Treated with Hydroxychloroquine/Azithromycin" https://www.medrxiv.org/con...<br /> Renal diseases cause big levels of Potassium in the blood and increase QTc, so the level of Potassium should be checked as well, especially when QTc>=460 ms. If level of Potassium is high, action can taken (like treat renal disease, eat less Potassium, extra magnesium given). In some cases (QTc >460 ms and QTc<500ms), risk seems manageable. <br /> 2. There is no point to use hydroxychloroquine for severe patients. It takes 3 days to have effect on early stage, in combination with azithromycine. For severe patients, there are usually not much virus left but big damages, so it is too late to give hydroxychloroquine.
On 2020-04-24 14:59:19, user Tomas Hull wrote:
"New York antibody study estimates 13.9% of residents have had the coronavirus" Gov. Cuomo says<br /> https://www.cnbc.com/2020/0...
Congrats, Dr. Witkowski. You stand vindicated...
On 2020-04-25 01:25:07, user Brent Tharp wrote:
Not even close to the real numbers, which are 50 to 100 times greater.
On 2020-06-08 19:23:59, user Animesh Ray wrote:
This is an interesting study, but the conclusions should be considered with caution. The causal modeling used here "suggest" that the chosen data are consistent with the hypothesis that Vitamin D deficiency might be correlated with increased morbidity of COVID-19. There are several caveats, however. (1) In meta-analysis of this sort, it is very difficult to be quantitative unless the observed data of the same data-type are shown to be at least comparable in variance. I did not see an effort to establish that. (2) Even though statistical analysis by multiple regression is precluded because the data were obtained from different sources, at least an effort to center the various data around means and doing a multiple regression to ascertain the magnitude of the variables' effects and their interactions would have been interesting. The problem here is that there are so many explicit variables and so many hidden ones in each experimental datasets, it is rather difficult to pinpoint any one--in this case vitamin D status--as causal. As von Neumann once stated, 'Give me four variables and I will make an elephant out of them; give me five, I will make it wave its trunk'. (3) Finally, the authors' molecular explanation--that Vitamin D inhibits rennin-angiotensin axis--is as easily explained in favor of the model as against the model (e.g., a lowered expression of ACE2 receptor due to inhibition by vitamin D might enable SARS-CoV-2 viruses to saturate these receptors far more easily than if the receptors are normally expressed, thus precipitate the loss of blood pressure control and cardiac output more readily than otherwise. In other words, the effect of normal vitamin D could enhance, not prevent, SARS-CoV-2 virus's clinical impact.) Thus the value of the molecular causality, as claimed by the authors, as a critically falsifiable test is doubtful. Nonetheless, many epidemiological success stories are built upon causal inferences based on precisely this type of analysis: one can cite examples of cholera on shallow wells in London in 18th century, scurvy and vitamin C, and now well established role of vitamin A and general resistance to childhood infections. On that basis, the idea that vitamin D might indeed be protective against COVID-19 complications merits further study.
On 2020-04-25 15:20:13, user Alex Backer wrote:
Very nice analysis. Here is a paper from 3 weeks ago that is very relevant to this: https://ssrn.com/abstract=3... (The Impact of Solar Irradiance on COVID-19). <br /> These two are relevant as well:<br /> https://ssrn.com/abstract=3... (Why African Americans are dying more)<br /> https://ssrn.com/abstract=3... (Why Latinos are dying less)
On 2025-02-26 18:14:55, user Benjamin Isaac wrote:
Reference 12, referring to the article here https://pmc.ncbi.nlm.nih.gov/articles/PMC8784688/ doesn't list the journal or date. The APA citation would be: Patterson, B. K., Francisco, E. B., Yogendra, R., Long, E., Pise, A., Rodrigues, H., Hall, E., Herrera, M., Parikh, P., Guevara-Coto, J., Triche, T. J., Scott, P., Hekmati, S., Maglinte, D., Chang, X., Mora-Rodríguez, R. A., & Mora, J. (2022). Persistence of SARS CoV-2 S1 Protein in CD16+ Monocytes in Post-Acute Sequelae of COVID-19 (PASC) up to 15 Months Post-Infection. Frontiers in immunology, 12, 746021. https://doi.org/10.3389/fimmu.2021.746021
On 2020-05-04 18:24:02, user Dan Kammen wrote:
The full data sets and code can also be accessed here:
On 2025-09-24 08:57:19, user Sophie PILLERON wrote:
This paper states that it uses the Globocan dataset; however, Globocan does not provide cancer incidence trends data. I suspect that the authors actually used CI5 data instead, which are available up to 2017.
In addition, this paper is very similar to another one ( https://pubmed.ncbi.nlm.nih.gov/34866023/ <br /> ), which the authors did not cite. The main differences between the two are the age groups analysed and the fact that the cited paper used data only up to 2012.
I would also recommend specifying the data source in the abstract, as this information is useful for interpreting the findings.
A justification for grouping all individuals aged 50+ together is needed, as this is a very heterogeneous age group. While I understand that the main focus of the paper is on the younger age group, the comparison would be more meaningful if the age categories used were more relevant.
I also suggest authors to reconsider the use of statistical testing. The study aim being descriptive, the use of statistical test is not needed as no a priori hypothesis are tested.
On 2025-09-25 01:05:32, user Florian Hladik wrote:
The abstract states, "we treated eight recipients with material from a single donor". However, it seems you treated four recipients with VMT and the other four with the placebo. Correct? It's confusing as written. In the Results too. Otherwise, great work! The other paper reporting the L. crispatus RCT is cool as well!
On 2025-10-14 23:19:52, user T. De La Cruz wrote:
On behalf of the authors, we would like to update to the medRxiv community that the present preprint is now published in a journal and available at https://doi.org/10.1016/j.ijidoh.2025.100078 .
On 2025-10-26 09:31:29, user Hannah Maude wrote:
A wonderful study and very interesting results! A comment on the discussion, noting on page 22 "Brain-expressed genes contribute to ME/CFS risk". Given the involvement of the peripheral nervous system in ME, might it be valid to say "Genes expressed in brain and neural tissue"? Peripheral nervous tissue is not so well represented in GTEx, although the Nerve_Tibial column shows high PPH4 for several genes in fig 4. Also perhaps of interest are sex-specific patterns of gene expression in peripheral nervous tissue https://pmc.ncbi.nlm.nih.gov/articles/PMC6412153/
On 2025-11-24 13:20:05, user SkepticalScientist wrote:
Nice paper. I would be interested in knowing whether the pattern of results implied anything for recovery or had any other cognitive consequences. Also, it was unclear to me whether you corrected for TIV in the volumetric analyses - especially important given you didn’t correct for sex.
On 2020-04-22 01:23:46, user michael triplett wrote:
Thank you again for your review, @Bio. I’ll follow your format in my reply.
To explain this another way, consider the kinematic equations for velocity. With the time variable included, v(t)=v(0)+a*t. As a function of position, v(s)^2=v(0)^2+2a*s. I’ve essentially presented this model as a function of position, rather than time.
You are correct that many other factors were omitted from analysis. It was stated in the introduction that no speculation was made outside of included data. The confounding factors you described are important, but including arbitrary adjustments can be equally confounding. “Expert opinion” is rarely better than “arbitrary” when applying quantitative correction factors. Binning of such a large sample size is generally more reliable than applying such correction factors to low-level data. Furthermore, to address the specific concern about lockdown measures, March 27 was inside the first two weeks for almost every country included. So, given the two week incubation period, lockdown measures could not have had a significant effect on case rates by that time.
I understand your point about low-level location, point-wise population mapping, etc., but I will just point to the binned data again. The sliding window approach has the effect spreading those point-wise populations and case rates so that such biases are minimized. It is not at all practical to map every case cluster, and adequate predictive data is unavailable at such a low-level. As for outliers, they were all in “cold” northern regions so omitting them actually made the analysis more conservative.
To address your point about temporal sequencing, which was my main concern with the data, it can be seen in Fig. 3 that case rates below -30 degrees also began to spike above tropical/equatorial regions. Travel patterns may have accelerated growth in that region, but the question then would be, “are travel patterns more/as significantly different by latitude than temperature?” And, given that the virus was in existence before humans began collecting data, it is likely safe to assume that travel-related factors are not terribly significant. Perhaps further study will tell. But, If this were a only a matter of origin and spread, the shared climates between northern and southern sub-tropical regions, as the seasons move through spring and fall, would also reduce significance of the temperature variable relative to latitude. I also struggle to imagine a real physical impedance to the virus’ movement through equatorial regions in the modern world. It originated in the northern hemisphere, but equatorial regions are skirted by large relative case rates on both sides. Maybe it isn’t effected by temperature directly, but it appears to be affected by a set of variables that are also correlated with temperature. That is really the point.
Finally, thank you again for your feedback. As stated in the conclusion, this analysis was intended to spur further research... not provide anything causal. Rather than rework what amounts to an out-of-date data set, I have taken your feedback and constructed a predictive model that accounts for the practically applicable variables you mentioned. I will be publishing the write up shortly.
In the meantime, we’ll see how things pan out.
On 2020-04-22 01:51:11, user fourierTF wrote:
On p. 14, the rate alpha of asymptomatic virus carriers is estimated quite low, and variated in simulations between 0.01 and 0.16. But why should manifestation indexes between 51% and 81% be irrelavant for this number? Because, "in a setting where contacts have been traced properly, thereby effectively isolating the exposed population in an early stage before they become asymptomatic carriers, and when extensive tests are performed, this fraction would be minimal"? But at present and in the near future, we don't have this setting of extensive testing.
An even more important problem of parameter estimation seems to me: By varying the bevahiour influenced parameter R1, data for Germany were fitted to the cumulative number of reported cases. But these strongly depend on the number of tests. Most importantly, from week 11 to 12 (march 7 to 20), both numbers increased about 3-fold, compare Table 4 of the situation report by Robert Koch institute (RKI), as of april 15. Since the observed reproductive number directly depends on the number of registered infected persons, the peak in this number at march 10 appears to be largely artificial. Even more, since certainly the number of tests also jumped up from week 10 to 11 - but data are not published.
Finally, a personal-political remark: In the appendix of page 14, the authors give the usual scientific disclaimer: "Currently, there is no reliable data available about the asymptomatic cases." This is only read by specialists. The apodictic abstract of this highly influencing paper, however, is noticed by the media, by a larger public and presumably by German chancellor Angela Merkel, according to her related public statements. "We strongly recommend to keep all NPIs in place and suggest to even strengthen the measures in order to accelerate reaching the state of full control, thus, also limiting collateral damage of the NPIs in time." Otherwise, the authors threaten with their horror scenario A (p. 8): "the health care system will in expectation need a peak capacity of 500,000 ICUs or more" (comparable to the falsified prediction of far more than 200.000 ICUs at march 21, by Deutsche Gesellschaft für Epidemiologie). At this place, no carefulness about the "collateral damages" of continuing the lockdown at all, for families, children, culture, (small) enterprises and many others. These countermeasures nead strong reasons - but no word about strong scientific counter-arguments!
Johannes Wollbold, Weimar / Germany
On 2020-04-01 11:46:11, user Philip Shaw wrote:
Is there any mathematical description of the model available?
On 2020-04-22 08:21:01, user Stinsen wrote:
David,
As clearly decribed in the paper the, start parameters are uncertain and might be different from the ones used. However the only parameter that really changes the scene dramatically is d, the doubling time. d depends on the reduced Ro. The initial doubling time can be estimated from old data and other measurements, and here is a true problem since a erroneous d can change the curves, but the new dE is more difficult since that is the only parameter that is changed by protective measurements by reducing Ro, I.e. social habits, lockdown, etc.<br /> To sum up the difference in other parameters only shifts the timeline with days.
On 2020-04-06 12:25:59, user Sinai Immunol Review Project wrote:
Clinical Characteristics of 2019 Novel Infected Coronavirus Pneumonia:A Systemic Review and Meta-analysis
The authors performed a meta analysis of literature on clinical, laboratory and radiologic characteristics of patients presenting with pneumonia related to SARSCoV2 infection, published up to Feb 6 2020. They found that symptoms that were mostly consistent among studies were sore throat, headache, diarrhea and rhinorrhea. Fever, cough, malaise and muscle pain were highly variable across studies. Leukopenia (mostly lymphocytopenia) and increased white blood cells were highly variable across studies. They identified three most common patterns seen on CT scan, but there was high variability across studies. Consistently across the studies examined, the authors found that about 75% of patients need supplemental oxygen therapy, about 23% mechanical ventilation and about 5% extracorporeal membrane oxygenation (ECMO). The authors calculated a staggering pooled mortality incidence of 78% for these patients.
Critical analysis:<br /> The authors mention that the total number of studies included in this meta analysis is nine, however they also mentioned that only three studies reported individual patient data. It is overall unclear how many patients in total were included in their analysis. This is mostly relevant as they reported an incredibly high mortality (78%) and mention an absolute number of deaths of 26 cases overall. It is not clear from their report how the mortality rate was calculated. The data is based on reports from China and mostly from the Wuhan area, which somewhat limits the overall generalizability and applicability of these results.
Importance and relevance: This meta analysis offers some important data for clinicians to refer to when dealing with patients with COVID-19 and specifically with pneumonia. It is very helpful to set expectations about the course of the disease.
Francesca Cossarini
On 2020-04-06 19:19:57, user Maxim Sheinin wrote:
Given that the majority of people dying from Covid-19 are elderly (60+) and BCG vaccine is given only in childhood, it would likely make more sense to look at the BCG vaccination status at the time when these elderly people were supposed to receive the vaccine, instead of the BCG status today. This will likely complicate the story, since many European countries that don't use BCG on a routine basis today used to do that in the past, and, conversely, some of the LICs introduced BCG relatively recently (http://www.bcgatlas.org/) "http://www.bcgatlas.org/)")
On 2020-04-12 16:58:54, user Dragana Stojkovic wrote:
The Mycobacterium tuberculosis membrane protein Rv0899 (rv0899 gene) are important for vaccines and defence against COVID-19.<br /> For those interested I can offer an explanation.<br /> Kind regards,<br /> Dr Slobodan Stojkovic
On 2020-04-07 13:31:01, user Jaco Brand wrote:
I see clinical trials being initiated based on a paper that have not been peer-reviewed or published. The trend with income can be interpreted in a myriad of different ways, like lifestyle choices and diet. This is exactly why Fig. 3 show a different death rate between low and medium-high income countries, despite both groups having a universal BCG vaccination policy. This is a highly unscientific speculative statistical correlation study. I have highlighted further comments to the paper as a download
On 2020-04-07 16:32:46, user Roberta Caruso wrote:
Using the Diamond Princess (DP) as a case study, the authors estimate an IFR 'slightly less than 1%, although statistically affected by a rather large uncertainty due to the small number of deceased'. It should be noted that when analyzing data on such a reduced 'statistical' sample, it is not appropriate to refer to statistical uncertainties - the sample is too small to actually compute statistical errors that have any sense for the analysis. One should instead focus on the analysis of the systematic errors that affect the estimation of IFR in order to obtain the actual relevance of the estimation obtained by simply dividing the number of died passengers for the total number of infected people on board. In other words, since these errors cannot be computed for IFR on the DP, this number should not be used as a benchmark for further analyses. <br /> The final estimation of the total number of infected cases is so vague (line 332-333 and line 355-356: between 660 000 and 3 300 000 - a difference of 500%!) that there is no practical use for it. The lower boundary of the estimation is questionable in itself, given the criticalities of the estimations performed using the DP case study, thus implying a possibly larger error bar on the estimation of the total infected. <br /> Such a large uncertainty poses serious questions on the scientific soundness of the study.
On 2020-04-07 18:16:27, user xahdum16x wrote:
This study at this time is useless to me. What is the comorbid breakdown of the patients, they only say sex and age are homogenous. What is the CI of the results, I don't care about a low pvalue. What were the "moderate adverse reactions" and how did they judge pneumonia improvement on imaging, what category since all these patients were mild to moderate where there baseline imaging similar or not. Lastly, since it does not say blinded, maybe the physicians were more apt to hold off on aggressive therapy in the "treatment" arm as opposed to the "placebo arm" due to flase security or hoping that it would help create significant results. There is a reason we blind studies to prevent bias.
On 2020-04-14 21:52:36, user Dr Eric Grossi Neurocirurgia wrote:
Is missing the intervals of age - 29.4 to 60 years only - Enormous Bias
On 2020-04-15 11:04:57, user Dr Eric Grossi Neurocirurgia wrote:
I would like to highlight a serious methodological error in this study. What we want for a drug treatment of COVID-19, only two objectives, to avoid and / or treat SARS and reduce contagion, therefore pragmatism in the selection of patients must be as close as possible to the clinical reality, which did NOT occur, since only patients between the ages of 29.4 to 60 years were analyzed. This alone invalidates any useful result, since the vast majority of human losses are over the age of 64.
On 2020-04-08 14:37:18, user alexishmatov wrote:
Problem of high or low AH is not a problem
The recent study has shown that problem of high or low AH in timing respiratory infections may be resolved by using the physical effect in the airways (supersaturation and enhanced condensational growth in the airways).
The main sense of the supersaturation in the airways is that this effect depends simultaneously on both temperature and RH of inhaled air. Thus, temperature and RH are the parameters of one simple function — it is the effect of supersaturation.
Ishmatov A. Influence of weather and seasonal variations in temperature and humidity on supersaturation and enhanced deposition of submicron aerosols in the human respiratory tract, Atmospheric Environment, V. 223, 2020, 117226, https://doi.org/10.1016/j.a...
On 2020-04-08 17:16:11, user buongustaio1964 wrote:
This study appears to fail control for scores of additional obvious, potential confounds. These include but are not limited to population density, dwelling density, household sizes, educational level, employment profiles...I could go on. The conclusion could reasonably be a call for more research. But that the "study results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis" is neither convincing nor warranted.
On 2020-04-09 01:55:01, user Emma McBryde wrote:
Thanks for the comment Robert. I am updating my data on imported versus local cases on a daily basis. When this preprint was made, the data were very sparse, and I had to assume undetermined cases were local. I will revise this for any peer-reviewed print. Meanwhile, I would recommend this website for the best publically available data www.covid19data.com.au
On 2020-04-10 11:48:59, user Srinivasa Kakkilaya wrote:
It's a very interesting analysis which should show the way forward in this crisis. If I'm allowed, I am posting a brief analysis that I did the day before, with data collected from various official sources and publications. It's here below:
Corona Virus Disease (COVID) 2019: Comparison of Cases in India and Abroad
Summary:
The trends of COVID 19 infections, complications and mortality are similar in almost all the countries, including India.
Risk of developing severe disease and death is higher in those aged 60+ years, and particularly in those with modern diseases such as hypertension, diabetes and coronary artery disease.
In India, 8.5% of the population is aged 60+ years, and 4-11% of the population aged less than 40 years is afflicted with hypertension and diabetes, and these are vulnerable to severe COVID 19.
The common factor for increased risk of severe COVID 19 is the presence of the so called metabolic syndrome at any age, old or young. These disorders are related to consumption of sugars and sweets, fruit juices, sweetened beverages, processed and fast foods, fried foods etc., and also alcohol consumption, and smoking. Avoiding these will be helpful in combating COVID 19.
COVID 19 remains a mild illness in almost 80-90% of those infected, and many patients lesser than 30 years of age are likely to have very mild or no symptoms.
Details:
India has already recorded about 5500 cases and more than 160 deaths due to COVID 19. The following analysis is based on the scientific and media reports published so far from India and elsewhere.
Corona Virus Infections - Age Distribution:
India:
47% of infections in age <40 years<br /> 34% in age 40-60 years<br /> 19% in age >60 years.
Wuhan, China:
27.2% in the age 0-39 years<br /> 41.6% in 40-59 years<br /> 31.2% in >60 years
It's almost identical in India and China and it correlates with the age distribution of population.
China:
<60 years - 82% of the population, 69% of infections
60 years - 18% of the population, 31% of the infections.
India:
<60 years - 91.5% of the population, 83% of the infections
60 years - 8.5% of the population, 19% of the infections
The higher percentage of infections in the elderly is likely due to more prominent symptoms than the younger population and hence presentation to the hospitals in more numbers.
COVID 19 Deaths: Age Distribution and Risk Factors
India
63% of deaths in those 60+ years of age 30% in those aged 40-60 <br /> 7% in those below 40 years
Average age of victims - 60 years
Average Case Fatality Rate -2.7%<br /> 0.4% for those below 40 years<br /> 2.4% for 40-60 years<br /> 8.9% for those above 60 years
86% had pre-existing conditions<br /> 17% had more than three diseases<br /> 40% had two<br /> 35% had one<br /> 56% had diabetes<br /> 47% had hypertension<br /> 20% had lung disease<br /> 16% had heart disease with diabetes and/or hypertension.
This pattern is also comparable with other countries.
China
81% deaths in age 60+ years<br /> 16.4% in 40-60 years<br /> 2.6% in 10-40 years<br /> 0 in <10 years
The average case fatality rate 2.3%;<br /> 0.2% for those below 40<br /> 0.85% for 40-60<br /> 8.8% for those above 60 years<br /> (14.8% in patients above 80 years)
Italy
95% deaths in age 60+ years<br /> 4.7% in 40-60 years<br /> 0.27% in 0-40 years
99.2% had one or more pre-existing diseases (75% had high blood pressure, 35% had diabetes and 33% had coronary heart disease)
United States (of the first 1150 deaths)
89.9% in 55 years and above<br /> 9.4% in 35-54 years<br /> 0.7% in 0-34 years
UK (of 750 deaths)
69% aged above 75+ years<br /> 96% had pre-existing conditions
These details clearly show that in all the countries, the case fatality of COVID 19 has shown direct correlation with age of the patients and with age-related diseases such as hypertension, diabetes and coronary artery disease and that the mortality was higher in men compared to women.
In India, 63% of deaths occurred in those above 60 years of age, and 30% deaths occurred in those aged 40-60. Considering the fact that 86-90% of the deaths occurred in those who had pre-existing diseases, the higher number of deaths in the 40-60 years age group seen in India is attributable to younger onset of these diseases in Indians. In India, the overall prevalence of hypertension is about 30%, and about 11% in the age group of 40 years or lesser. Type 2 Diabetes has an overall prevalence of 16-19%, whereas in the young, it is about 4-8%. These diseases, coupled with consumption of alcohol and tobacco, increase the risk for COVID 19 complications in those aged above 60 and also in those who are younger. Otherwise, COVID remains a mild illness in almost 80-90% of those infected, and many patients lesser than 30 years of age are very likely to have very mild or no symptoms.
If I may add, it appears that the deaths are directly related to metabolic syndrome linked disorders and the 33 cases that apparently had no identifiable cause in NY in your series might have had other problems of metabolic syndrome such as hypertriglyceridemia or premature balding etc., all of which are linked to hyperinflammatory state.<br /> Thank you again for the interesting and path breaking effort!
On 2020-04-12 00:51:03, user Art Shaposhnikov wrote:
What is the point in computing the absolute risk and comparing it to the miles driven? It could be very misleading to people who don't understand what the absolute risk means. The absolute risk of dying from covid-19 last year in the US was zero - zero miles driven was riskier. Based on the zero absolute risk number, we should not have spent any resources to prepare for it last year, right? Applying the same logic, since the absolute risk is very low now, we should stop the quarantine immediately, stop the vaccine developments and observe the final absolute risk based on excess mortality data in 2022, which could very well be greater by a factor of 10 to 10,000 than now.
On 2020-04-12 02:47:16, user Petard Stamo wrote:
I don't understand his twist in his analysis. Initially he insisted that testing is crucial to determine an aproximate value of Infection Fatality Rate. And that in his opinion was the measure which determines how dangerous was the virus. Now he has completely disregarded the number of infections in his analysis. The analysis is based only on deaths partitioned on age and sex and total number of population partitioned on age and sex. What is the difference between P(dying from Covid19 / <65) and P(dying from Covid19 / infected, <65)? How can you say that all people have been infected if we still don't have reliable data about the total number of infections? What proportion of the population has been infected?
On 2020-04-10 14:54:38, user Neil Lancastle wrote:
For clarity: Figure 5 is countries with BIGGEST falls in mean growth... from the text on p7...'growth rates have fallen most compared to earlier period' and, excluding China, these countries are France, Spain, Switzerland, Italy, UK and Norway.
On 2020-04-11 02:02:32, user SFHarry wrote:
It is important to note that the words "higher" and "lower" risk were used. If you look at the numbers it doesn't show the risk being that much higher (or lower). People should not be making decisions regarding how much risk they should allow themselves when interacting with the public without understanding these facts..
On 2020-04-11 08:17:28, user Xavier de Roquemaurel wrote:
Great work. Thanks.<br /> Can i suggest to please run a similar study concept, yet this time identifying countries according to the different BCG strains:<br /> BCG Japan (Tokyo)<br /> BCG Brazil / Moreau<br /> BCG Denmark<br /> ...<br /> This is also an hypothesis to test.<br /> Thanks<br /> Xavier
On 2020-04-11 12:51:33, user ybysk wrote:
In my understanding, what the authors (and many readers) want to know is whether or not BCG vaccine effectively protects individuals from infection (i.e., the effect on infection-per-exposure) and also death (i.e., the effect on death-per-infection). I have not understood how the authors justify to use the number of total cases and deaths per one million population as measures of the effectiveness of BCG. Are they supposed to be equivalent to infection-per-exposure and death-per-infection?
On 2020-04-11 18:07:45, user Aaron Gasaway wrote:
Scientists and medical researchers: please look into whether it's dust that is sometimes allowing the virus to become "aerosolized." I've read a little about dust particles carrying influenza, so it seems plausible. Also, the recent Chinese study showed higher concentrations on the floor (where dust would fall). Dust as the vehicle would also explain it being found in AC vents. Central Air units suck up a huge amount of dust and some of it makes it through the filters and back out into the air.
Of course none of this means the moisture in exhalations or coughs couldn't also be the vehicle. On the whole it would seem not to be spreading enough for normal exhalations to be the primary vehicle, although it seems from the Washington choir episode that with enough force behind the exhalations, it could be.
I am sorry if this question about dust seems amateurish or crackpot. I just don't know if anyone qualified is looking into this possibility, so thought I should post it here.
On 2020-04-13 11:38:17, user Sanjiv Vij wrote:
Thank you. My concern is that 205 patients is too small a number and 7 days is too early to be reliable. Will it be possible to get more data from the registry, and, have data that spans admission to death or discharge for as many patients as possible? With regards to how many were on ACE / ARB / Immuno-modulating drugs / Neither[none]. That will help in risk stratification in a more reliable way. Regards Sanjiv
On 2020-04-13 13:32:07, user Rosemary TATE wrote:
Hi, I dont see the STROBE guidelines checklist uploaded, although you ticked yes to this<br /> "I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. " <br /> A lot of people seem to ignore these but they are important and any good journal will require them.<br /> Can you please upload? Many thanks.
On 2020-04-13 19:18:24, user Charles R. Twardy wrote:
Very similar to Benvenuto et al from 26 Feb, whom they cite as [4] and [9], but applying only to Italy. The earlier paper fit an ARIMA to worldwide Hopkins data through 10-Feb (then 43K cases) and, like this paper, found that we had just passed the peak. The previous forecast was absurdly optimistic. The current paper benefits from another month of data, and a single country.
Perhaps it does better. Eventually it's bound to converge, but it would seem the main value in the limited 4-day forecast is recognizing when the data has violated your model so you can put more weight on another one.
Benevenuto et al: https://www.ncbi.nlm.nih.go...
On 2020-04-14 09:12:21, user Lisa Kane wrote:
'Hoax' seems a rather strong comment, and to dismiss the whole paper is not helpful. The authors all appear to be legitimate scholars. While causality is not indicated, possible associations are useful to identify at this stage of exploration of the pandemic and can be further tested by other scholars.
On 2020-04-14 11:02:19, user Philip Davies wrote:
This is a very interesting pre-print. BUT, I think the data in table 3 has been mixed up (deaths for low v high dose are incorrect). The authors need to correct this and then ensure the tables are correct everywhere else. I have asked the authors to look at this and re-issue a corrected version (I also question whether the qSOFA results (table 1) were meant to be for values >2 rather than <2.
This is important. It could mean that lower dose chloroquine is not only safe but could prove to be statistically better than placebo (will need the full 28 days analysis to know that).
Dr Phil Davies
On 2020-04-15 11:35:12, user GP MD wrote:
Ceftriaxone and azithromycin have their own toxicities....including mitochondrial dysfunction and ROS over-production in mammalian cells. The dosing of all three agents means a much higher risk of oxidative damage to mammalian DNA, proteins and membrane lipids. This would be worsened in those with impaired production of glutathione or reduced glutathione levels due to acetaminophen dosing.
On 2020-04-14 17:58:53, user Badly Shaved Monkey wrote:
From a U.K. perspective:
My common sense reservation is that if Coronavirus was going to hit, say 60% of the U.K. population and 0.1% of those would die as suggested by Silverman and Washburne, that’d be about 40,000 deaths in total in the U.K. We’ve already hit 12,000 under the influence of a significant degree of social restriction over several weeks. While it is hard to predict the logistic asymptote from the exponential-like phase, it stretches credulity to suggest that the unmoderated U.K. epidemic would have burnt itself out with 40,000 deaths.
On 2020-04-14 19:47:35, user Sinai Immunol Review Project wrote:
Main findings:
The aim of this study was to assess an association between reduced blood lymphocyte counts at hospital admission and prognosis of COVID-19 patients (n=192). The authors found:<br /> - Patients with lymphopenia are more likely to progress to severe disease or succumb to COVID-19 (32.1% of COVID-19 patients with lymphocyte reduction died). <br /> - Reduction of lymphocytes mainly affects the elderly (> 70 years old). <br /> - Lymphocyte reduction is more prevalent in COVID-19 patients with cardiac disease and pulmonary disease, patients with increase in the chest CT score (key marker of lung injury) and a decrease in several respiratory function markers (PaCO2, SpO2, oxygenation index).
Limitations of the study:
Reduced blood lymphocyte counts with aging have been known (https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.03.08.20031229v2)") https://onlinelibrary.wiley... "https://onlinelibrary.wiley.com/doi/epdf/10.1111/sji.12413)"). Therefore, it is not unexpected that a larger fraction of COV ID-19 patients above 70 years old have lower lymphocytes counts. Since age has been reported to be a major factor that determines outcome for COVID-19, lymphocyte counts and prognosis should have been adjusted by age. Multivariate analysis to identify independent risk factors is lacking.
Relevance:
Previous studies demonstrated that SARS-CoV-2 infection leads to a decrease of the T cell count. This study confirms these results and shows that lymphocyte reduction mainly affects the elderly. Lymphopenia was associated with disease severity as well as worse prognosis. Future studies need to address if lymphopenia is a negative predictive factor independent from age.
Review by Meriem Belabed as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai
On 2020-04-15 15:04:27, user Mehee f wrote:
this is completely unscientific the US has a population of 335M on this basis it will have 4M cases and 100,000 fatalities by now. Also you did not compare figures with other countries there is a big variation of mortality rates between 0.5% in South Korea to 12% in Italy. It is very biased report based on all estimates and no data pure speculation.
On 2020-04-15 19:07:06, user Gregory Armstong wrote:
Thanks for contributing this. It's a very important topic. There are few well controlled studies of risk factors for COVID-19.
My one comment--and I didn't read the entire manuscript: I'd strongly recommend removing vital signs and laboratory findings from the regression. Those are manifestations of severe disease, not risk factors for it. They're some of the main considerations in deciding whether to hospitalize and whether to admit to the ICU. They should correlate strongly with both, and including them in the model will severely dilute the impact of true risk factors and could completely hide them. They shouldn't be considered independent variables.
On 2020-04-15 20:48:45, user empiricist2 wrote:
According to the French study, the addition of Azithromycin cleared out the virus in 4 days, versus at least twice that without it. And others use zinc, considered a very important factor. So why just test HCQ alone? And what were the antivirals that hampered results? Certainly there are other studies to report on that include zinc etc. Even in China they had reported that IV Vit C helped significantly. One doctor takes 10g daily as preventative. Vit C has a long history of antiviral benefits in large doses.
On 2020-04-18 21:54:04, user Jim Trader wrote:
Kind of a useless study. With an average delay of 16 days from symptom onset to enrollment and treatment in this trial, those patients are pretty much past the viral phase of the disease, where an antiviral treatment would have the most value, and are well on their way to pneumonia and a cytokine storm problem, which is ultimately what kills. Even the subset analysis of patients with a 7 day delay from symptoms to enrollment is still too long. As the authors state, it is very difficult to do these kinds of trials when patients average 12 days from symptoms before they come into the hospital, and can be explored as trial participants. So unfortunately there is no signal here, and from a molecular biology viewpoint, that is exactly what I would expect with this trial design. HCQ therapy needs to start within 48 hrs of symptom onset, ideally 24.
On 2020-04-16 14:51:37, user agoraks wrote:
Quote: "The assays are sensitive and specific, allowing for screening and identification of COVID19 "
Question: what is the actual sensitivity and specificity of the assay for detecting active disease by IgM or convalescence IgG in Sars-CoV-2 infected patients ?
On 2020-04-16 19:48:56, user Stef Verlinden wrote:
It is troublesome that the article does not give the materials and the method used to derive/calculate the QTc. Formally a QTc can only be calculated from a signal derived from Lead II of V5 registered with a 12 lead ECG machine. QTc's calculated by a computer algorithm (unless it is specifically validated to do that job) are not to be trusted. This needs to be done by hand by a well-trained person.
Most importantly, the QTc calculation is not linear. A heart rate of 90 gives an overestimation of the QTc of 50 ms (when using the Bazett method). Did the authors correct for this? Based on this paper, it is not possible to establish whether the QTc's are truly prolonged or that these are false positive outcomes.
For reference check; QTc: how long is too long? https://www.ncbi.nlm.nih.go...
On 2020-04-17 07:35:29, user Mansour Tobaiqy wrote:
Thank you, the paper has bow been published at IPIP:
On 2020-11-19 18:22:04, user Ivan Ivanov wrote:
Impressive work. It is probably misunderstanding but the authors use 10mM KCl/10mM NH4SO4 in the buffer for Bst 2.0 and they call it 1x Isothermal Buffer which infact is the commonly known ThermoPol buffer (optimal for Bst LF, not for Bst 2.0). NEB sells Bst 2.0 with Isothermal buffer containng 50mM KCl/10mM NH4SO4.
On 2020-11-23 23:12:54, user Louis Rossouw wrote:
Why does this paper compare mortality July 2019 to June 2020 to try and determine the impact "after" COVID-19 when the pandemic started in Feb-March 2020? So the "after COVID-19" of the title of the paper includes more time before COVID-19 than after the epidemic started?
The authors also do not allow for any changes in age distribution / popualtion mix that may be affecting observed mortality trends.
On 2020-11-25 07:42:20, user Carol Shadford wrote:
When you say 'continual sensation of having had a “nasal douche”' are you referring to a feeling that the sinuses have been cleared out and are now empty or is that referring to the rushing feeling you get when chlorinated water accidentally goes up your nose -- a wasabi-like feeling?
On 2020-11-26 05:48:46, user Community Medicine with velz wrote:
Re-infection can be defined only by viral genome sequencing. The implication of these results can be mis-leading as re-infection has been defined by RT-PCR in this study.
On 2020-11-27 19:21:54, user Jackie A wrote:
This work is useful because it is the first modeling paper reckoning IFN role in the disease - and an early administration of it could be beneficial, which is consistent with recent RTC for IFN-beta. However, it is built on rodents and the extrapolation to humans is not clear and not validated by tissue data extracted from humans. The authors assume that tissue viral loads correlate with organ failure which has not been demonstrated.
On 2020-11-27 21:02:26, user Robert Brown wrote:
Vitamin D, Magnesium, Steroids, PPI and COVID-19; Interactions and Outcomes - Response to ‘Effect of Vitamin D3 Supplementation vs Placebo on Hospital Length of Stay in Patients with Severe COVID-19: A Multicenter, Double-blind, Randomized Controlled Trial’ [Preprint] [1]
Thank you and congratulations on your important and significant paper. This is only the fourth[2] [3] [4] reported RCT examining vitamin D supplementation as a therapeutic intervention for COVID-19. Biology provides multiple pathways by which vitamin D hydroxylated-derivatives[5] may impact Covid-19 risks [including via; ACE2 receptors; airway-epithelial-cell tight-junction-function, immune responses [affecting lymphocytes, macrophages T cells, T helper cells, Th1, -17; Tregs; cytokine secretion IL-1, -2, -4, -5, -6 -10,-12; IFN-beta, TNF-alpha; defensins and cathelicidin, and receptors HLA-DR, CD4, CD8, CD14, CD38. Vitamin D also regulates; mitochondrial respiratory, inflammatory, oxidative and other functions; RXR and other receptor links between steroids, retinoids, hormonal vitamin D, thyroid hormone, oxidised lipids and peroxisomal pathway immune responses.][6]
Significant evidence [40+ patient-papers[7]] suggests higher Vitamin D status [serum/plasma 25(OH)D concentration] is associated with diminished COVID-19 infection rates,and reduced severity [including ICU admission and mortality].[2 3 4]
Thus, it is crucial, to consider if the preprint’s broad-based conclusion “Vitamin D3 supplementation does not confer therapeutic benefits among hospitalized patients with severe COVID-19”, [time to discharge as well as lack of observed ICU and mortality rate benefits], stands scrutiny when any one, or combination of, the following factors are considered: -
Delay in vitamin D administration after severe symptoms onset
Patients presented “10.2 days after symptoms”, thus were already verging on serious outcomes at admission; “89.6% required supplemental oxygen at baseline [183 oxygen therapy; 32 non-invasive ventilation] and 59.6% had computed tomography<br /> scan findings suggestive of COVID-19.” [Days to dyspnoea from overt infection average 7-8, and acute-respiratory-distress-syndrome [ARDS] develops after median 2.5 days.[8]]
Further, the timing of vitamin D supplementation, at or after <br /> hospitalisation, was not specified, despite timing clearly being an important factor, given the advanced stage of illness at admission.
Baseline vitamin D status [serum 25(OH)D concentrations] were relatively ‘good’
Baseline 25(OH)D values averaged 21.0ng/ml and 20.6ng/ml in the treatment and control groups respectively, i.e. they were relatively ‘good’, and above levels reported as being associated with the greatest COVID-19 risks.[9] [10]Sub-analysis of patients < 10ngml +/-Dexamethasone would be instructive. Further, deficiencies such as magnesium (an essential ‘D’ enzyme co-factor) might factor more in the lack of observed benefits for Covid-19 severity, than vitamin D status itself.
Corticosteroids
COVID-19 related corticosteroid vitamin ‘D’ interactions require<br /> investigation. 64.2%(Treatment) and 60.8%(Control) group patients respectively, were treated with Corticosteroids (Dexamethasone?), and mortality was somewhat higher in the Treatment than Control arm. Interactions between vitamin D and steroids including dexamethasone are observed[11], including “decreased synthesis of active vitamin D, and impairment of biological action at tissue level.”[12] However these potential effects have not been investigated in COVID-19 patients treated with both vitamin D and dexamethasone.
It would be most useful to know therefore, at what stage corticosteroid treatment began, and at what dosages, what other treatments were given [and at what dosage], and when such treatments were stopped, so that potential interactions between vitamin D, corticosteroids and other treatments for COVID-19<br /> patients could be elucidated.
In particular, any negative or neutralising effect of corticosteroids on<br /> ‘D’-derivatives and pathways, could account for the lack of reduction in risks of ICU and mortality outcomes, including slightly higher mortality, in those given vitamin D, a matter of importance, since dexamethasone, given before onset of serious ARDS, was reported in Oxford[13] to increase, not reduce, mortality.
Proton pump inhibitors.
PPI are known to lower serum magnesium,[14] an essential ‘D’ hydroxylase-enzyme co-factor. 47/120-(39%)[Treatment] and 49/120-40%[Control] used PPI, compared to 9.2% population usage in USA.[15] PPI-induced related serum magnesium reduction, +/- dietary insufficiency, is a reported COVID-19 risk factor,[16] thus possibly helping account, for D3 treatment, failing to reduce Brazilian Covid-19 mortality. Thought-provokingly a Brazilian paper reported “There is chronic latent magnesium deficiency in apparently healthy university students”, which deficiency is potentially more widespread.[17]
Conversely, RCT administration of magnesium with vitamin D reduced COVID-19 in-patient mortality.2
Rate of increase of Serum 25(OH)D
It is unclear when blood was sampled for determination of serum 25(OH)D concentrations, or if this was standardised for all patients.
A large bolus will increase 25(OH)D values in the healthy, “Oral D2 and D3 (100,000 to 600,000 IU) significantly increased serum 25(OH)D from baseline in all reviewed studies” . . . “peak levels were measured at 3 days (34) and 7 days following dosing,”[18]
However, timing matters, because hepatic hydroxylation5 to form 25(OH)D (Calcifediol) is likely reduced by; severe illness, as well as by obesity diabetes, and possibly hypertension,[19] conditions already recognised as risk factors for covid-19 severity.[20]
The Cordoba study[3] suggests that 25(OH)D [Calcifediol, that could be given together with vitamin D3, cholecalciferol], may be key to effective treatment of severe COVID-19 illness. There is no suggestion Cordoba patients were treated with corticosteroids. Cordoba patients were administered calcifediol on admission-day, but the period between overt infection and hospital admission <br /> was not reported.
Risk-factor Differentials in Patient Groups
A skew in risk factors favouring the control?
Control-Placebo to Treatment-D3:
Increased risk factors - Overweight (31/37, 0,84); Obesity (58/63, 0,92); Hypertension (58/68, 0,74); Diabetes II 35/49, 0,71); COPD (5/7,0,71); Asthma (7/8, 0,88); Chronic Kidney Disease (0/2, 0,0); Rheumatic Disease (10/13, 0,77)[21]; Black (14/20) Male 965/70).
Decreased factors - White (79/62) Female (55-50)
Improved oxygen parameters are not reflected in conclusion
“Despite the D3 group being at a greater risk, including due to hypertension, COPD and diabetes, known risk factors, significant differences in oxygen supplementation favour the D3 treatment group“.21
Oxygen supplementation (%) Placebo No. (%) D3 <br /> No oxygen therapy 9 (7.5) 16 (13.3)<br /> Oxygen therapy 97 (80.8) 86 (71.7)<br /> Non-invasive ventilation 14 (11.7) 18 (15.0)
Conclusion requires Caveats?
Thus, the un-caveated conclusion “Vitamin D3 supplementation does not confer therapeutic benefits among hospitalized patients with severe COVID-19”, likely requires caveats about possible effects of the several factors discussed above.
Further, the reported finding cannot be extrapolated to care of all Covid-19 patients, since the above- mentioned-potential interactions require further investigation, including; as to effects of; magnesium
status; treatment with PPI inhibitors, impact of corticosteroids in severe Covid-19 illness on vitamin D biology and outcomes, and consideration of pre-existing vitamin D status.
Further public health policy directed at reducing vitamin D, and other nutrient deficiencies for mitigation of COVID-19 risks at population levels, should not be conflated with clinical optimisation of vitamin D and metabolites for treatment of severe COVID-19 illness.
[1] Murai,I., Fernandes, A., Sales, L., Pinto, A., Goessler, K., et. al. 17th November 2020). Effect of Vitamin D3 Supplementation vs placebo on Hospital Length of Stay in Patients with Severe COVID-19 A Multicenter, Double-blind, Randomized Controlled Trial. medRxiv 2020.11.16.20232397; doi: https://doi.org/10.1101 /2020.11.16.20232397 Available at: https://www.medrxiv.org/content/10.1101/2020.11.16.20232397v1<br />
[2] Tan, C., Ho, L., Kalimuddin, S., Cherng, B., Teh, Y., et.al. (10th June 2020). A cohort study to evaluate the effect of combination Vitamin D, Magnesium and Vitamin B12 (DMB) on progression to severe outcome in older COVID-19 patients. doi: https://doi.org/10.1101/202... Available at: https://www.medrxiv.org/content/10.1101/2020.06.01.20112334v2<br />
Now published in Nutrition doi:10.1016/j.nut.2020.111017 <br />
[3] Entrenas Castillo, M., Entrenas Costa, L., Vaquero Barrios, J., Alcalá Díaz, J., López Miranda, J., Bouillon, R., & Quesada Gomez, J. (29th August 2020). Effect of calcifediol treatment and best available therapy versus best available therapy on intensive care unit admission and mortality among patients hospitalized for COVID-19: A pilot randomized clinical study. The Journal of steroid biochemistry and molecular biology, 203, 105751. https://doi.org/10.1016/j.j... Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7456194/<br />
[4] Rastogi, A., Bhansali, A., Khare, N., et. Al. (12th November 2020).<br />
Short term, high-dose vitamin D supplementation for COVID-19 disease: a randomised, placebo-controlled, study (SHADE study). Postgraduate Medical Journal Published Online First:. doi: 10.1136/postgradmedj-2020-139065 Available at: https://pmj.bmj.com/content/early/2020/11/12/postgradmedj-2020-139065<br />
[5] Bouillon, R., & Bikle, D. (2019). Vitamin D Metabolism Revised: Fall of Dogmas. J Bone Miner Res. 2019 Nov;34(11):1985-1992. doi:<br />
10.1002/jbmr.3884. Epub 2019 Oct 29. PMID: 31589774. Available at: https://asbmr.onlinelibrary.wiley.com/doi/full/10.1002/jbmr.3884<br />
[6] Brown, R., Rhein, H., Alipio, M., Annweiler, C., Gnaiger, E., Holick M., Boucher, B., Duque, G., Feron, F., Kenny, R., Montero-Odasso, M., Minisola, M., Rhodes, J.,Haq., A, Bejerot, S., Reiss, L., Zgaga, L., Crawford, M., Fricker, R., Cobbold, P., Lahore, H., Humble, M., Sarkar, A., Karras, S., Iglesias-Gonzalez, J.,Gezen-Ak, D., Dursun E., Cooper, I., Grimes, D. & de Voil C. (April 20, 2020). COVID-19 ’ICU’ risk – 20-fold greater in the Vitamin D Deficient. BAME, African Americans, the Older, Institutionalised and Obese, are at greatest<br />
risk. Sun and ‘D’-supplementation – Game-changers? Research urgently required’: ‘Rapid response re: Is ethnicity linked to incidence or outcomes of COVID-19?’: BMJ, 369(m1548). DOI: 10.1136/bmj.m1548. Available at: https://www.bmj.com/content... (Accessed: 24 November2020. - Alipio study<br />
now in question – rest stands)<br />
[7] Brown R. (15 Oct 2020). Vitamin D Mitigates COVID-19, Say 40+ Patient Studies (listed below) – Yet BAME, Elderly, Care-homers, and Obese are still ‘D’ deficient, thus at greater COVID-19 risk - WHY? BMJ 2020;371:m3872 Available at https://www.bmj.com/content/371/bmj.m3872/rr-5 (Retrieved 24 Nov 2020) <br />
[8] Cohen, P., Blau, J., Eds: Elmore, J., Kunins, L., & Bloom, A. (2020). MD disease 2019 (COVID-19): Outpatient evaluation and management in adults. Literature review. Wolters Kluwner. Available at: https://www.uptodate.com/contents/coronavirus-disease-2019-covid-19-outpatient-evaluation-and-management-in-adults/print<br />
(retrieved 25th November 2020)<br />
[9] Jain, A., Chaurasia, R., Sengar, N., Singh, M., Mahor, S., & Narain, S. (19th Nov 2020). Analysis of vitamin D level among asymptomatic and critically ill COVID-19 patients and its correlation with inflammatory markers. Sci Rep. 2020 Nov 19;10(1):20191. doi: 10.1038/s41598-020-77093-z. PMID: 33214648; PMCID: PMC7677378. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677378/<br />
[10] Radujkovic, A., Hippchen, T., Tiwari-Heckler, S., Dreher, S., Boxberger, M., & Merle, U. Vitamin D Deficiency and Outcome of COVID-19 Patients. Nutrients 2020, 12, 2757. Available at https://www.mdpi.com/2072-6643/12/9/2757 <br />
[11] Hidalgo, A. A., Trump, D. L., & Johnson, C. S. (2010). Glucocorticoid regulation of the vitamin D receptor. The Journal of steroid biochemistry and molecular biology, 121(1-2), 372–375. https://doi.org/10.1016/j.j... Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2907065/<br />
[12] Giustina, A., Bilezikian, J. (eds) (2018). Vitamin D and Glucocorticoid-Induced Osteoporosis. Vitamin D in Clinical Medicine. Front Horm Res. Basel, Karger, 2018, vol 50, pp 149-160 (DOI:10.1159/000486078) Available at https://www.karger.com/Article/Pdf/486078<br />
[13] The RECOVERY Collaborative Group. (17th July 2020). Dexamethasone in Hospitalized Patients with Covid-19 — Preliminary Report. J New England Journal of Medicine R10.1056/NEJMoa2021436 https://www.nejm.org/doi/fu... Available at https://www.nejm.org/doi/full/10.1056/NEJMoa2021436<br />
[13] FDA. (8th Apr 2017). FDA Drug Safety Communication: Low magnesium levels can be associated with long-term use of Proton Pump Inhibitor drugs (PPIs) https://www.fda.gov/drugs/drug-safety-and-availability/fda-drug-safety-communication-low-magnesium-levels-can-be-associated-long-term-use-proton-pump (Accessed 25th November 2020)<br />
[14] Hughes, J., Chiu, D., Kalra, P., & Green, D. (2018). Prevalence and outcomes of proton pump inhibitor associated hypomagnesemia in chronic kidney disease. PLoS ONE 13(5): e0197400. https://doi.org/10.1371/jou... Available at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197400<br />
[15] Lee, S., Ha, E., Yeniova, A., et. al. (30th July 2020). Severe clinical outcomes of COVID-19 associated with proton pump inhibitors: a nationwide cohort study with propensity score matching. Gut Published Online First: 30 July 2020. doi: <br />
10.1136/gutjnl-2020-322248 Available at: <br />
https://gut.bmj.com/content/early/2020/07/30/gutjnl-2020-322248<br />
[17] Hermes Sales, C., Azevedo Nascimento, D., Queiroz Medeiros, A., Costa Lima, K., Campos Pedrosa, L., & Colli, C. (2014). There is chronic latent magnesium deficiency in apparently healthy university students. Nutr Hosp. 2014 Jul 1;30(1):200-4. doi: 10.3305/nh.2014.30.1.7510. PMID: 25137281. Available at: http://www.aulamedica.es/nh/pdf/7510.pdf<br />
[18] Kearns, M., Alvarez, J., & Tangpricha, V. (2014). Large, single-dose, oral vitamin D supplementation in adult populations: a systematic review. Endocrine practice: official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists, 20(4), 341–351. https://doi.org/10.4158/EP1... Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4128480/<br />
[19] Kheiri,B., Abdalla, A., Osman, M. et al. (2018) Vitamin D deficiency and risk of cardiovascular diseases: a narrative review. Clin Hypertens 24, 9 (2018). https://doi.org/10.1186 /s40885-018-0094-4 Available at https://clinicalhypertension.biomedcentral.com/articles/10.1186/s40885-018-0094-4 <br />
[20] Kruglikov, L,. Shah, M., Scherer, E. (Sept 2020). Obesity and diabetes as comorbidities for COVID-19: Underlying mechanisms and the role of viral-bacterial interactions. Elife. 2020 Sep 5;9:e61330. doi: 10.7554/eLife.61330. PMID: 32930095; PMCID: PMC7492082.<br />
[21] Borsche L. Private email 19.11.20
On 2020-12-01 21:23:17, user NubOfTheMatter wrote:
This trial appears to have been a wasted opportunity.
It is unclear why Vitamin D3 was orally administered to severely ill C-19 patients. It can take a fortnight to be metabolised into Calcifediol, the active, metabolised form required to trigger an immune response. A most unhelpful delay to the effective treatment of severely ill COVID-19 patients.
The RCT conducted at the teaching hospital in Cordoba used Calcifediol, with dramatic results. Compared to the untreated ‘control group’ there was a 96% reduction in the need for Intensive Care Unit admission, and a commensurate reduction in deaths.
One has to ask, therefore, why, in Sao Paulo, it was decided to administer un-metabolised Vitamin D3 rather than Calcifediol to severely ill C-19 patients for whom every day of non-treatment reduces the chance of a good outcome if not survival? Indeed, an experienced physician could have forecast the results obtained without the need for a trial.
On 2020-12-07 13:02:33, user Fernando wrote:
Good morning, I have some comments: I couldn’t find the final D level at 7 and 10 days (hospital discharge), this information is crucial to establish the effectiveness of the ministered dose. <br /> At a first glance, considering the patients were mostly deficient in vitamin D and obese (slow D absorption) the dose provided seamed too low to produce results in such a short time (7-10 days), specially as it was vitamin D in its over the counter form (not calcifediol).<br /> Also not clear how many days after testing positive did the patients take vitamin D. It seems that in Brazil people are only hospitalized after aggravation.<br /> Thank You!
On 2020-12-01 14:30:16, user Peter Griffiths wrote:
Published as: Griffiths, P., Saville, C., Ball, J., Culliford, D., Pattison, N., Monks, T., 2020. Performance of the Safer Nursing Care Tool to measure nurse staffing requirements in acute hospitals: a multicentre observational study. BMJ Open 10 (5), e035828.10.1136/bmjopen-2019-035828
On 2020-12-03 15:34:00, user joetanic wrote:
Quite interesting data. I'm wondering whether anyone knows why France does not perform these tests?
Or the US? It seems a broad study, especially in France which seems to be bucking the trend, so to speak, would make clear what the future holds in many places.
On 2020-12-09 15:46:15, user Laura wrote:
This preprint has been published in The Journal of Immunology. You can read the latest version here: https://doi.org/10.4049/jim...
On 2020-12-11 16:21:41, user Fred wrote:
Disappointing study. I would not expect that antivirals are of any use if started when patients are already hospitalized. I would recommend to start with antivirals as soon as possible regardless wheter the patient has symptoms or not. But in this case we need studies comprising many more patients than in this small study
On 2020-12-11 18:07:58, user Thierry Grenet wrote:
Another discussion of the trajectories is given here : https://bmcmedresmethodol.b....
On 2020-12-12 17:48:59, user Patrick Karas wrote:
Congratulations on this excellent work. Molecular subtyping for meningioma is much needed to help develop future therapies, and your work pushes this forward. How do you think your subtypes A, B and C compare to the meningioma molecular types similarly labeled type A, B, and C published last year in PNAS by Patel et al (doi: 10.1073/pnas.1912858116)? It seems like there is a lot of overlap (group A with intact NF2; group B and C with NF2 loss; group C with increased FOXM1 expression and high copy number variation). This is a great step forward validating these subtypes through a different approach.
On 2020-12-12 23:20:04, user Omid Jahanian wrote:
This is an original manuscript of an article published by Taylor & Francis in the Journal of Spinal Cord Medicine on 09 Nov 2020, available online: https://www.tandfonline.com....
On 2020-12-13 04:57:43, user Haitham kussaibi wrote:
Dear valuable readers,
The current manuscript has been peer reviewed and published by <br /> Karger, Acta cytologica<br /> DOI:10.1159/000509669<br /> Thank you for your interest.<br /> The Authors
On 2020-12-16 20:05:03, user Wolfgang Lins wrote:
the authors discuss "anterior nasal (AN)" swabs, and on page 4 refer to this as:
Participants first underwent collection of the AN-sample, using the specific nasal swab provided in the test kit of the manufacturer, according to the instructions for use, which also correspond to the U.S. CDC instructions [4]. Briefly, while tilting the patient’s head back 70 degrees, the swab was inserted about 2cm into each nostril, parallel to the palate until resistance was met at turbinates, then rotated 3-4 times against the nasal walls on each side
First, the collection procedure described here in act matches to one in that CDC manual [4], however not to the AN collection but to the collection of Nasal mid-turbinate (NMT) specimen. That is a floppy usage of the terms AN versus NMT, and should at least been detailled when defining AN in this paper.
Second, the paper refers to a "specific nasal swab provided in the test kit of the manufacturer" for AN collection. Three lines later they write "a separate NP-swab (provided in the manufacturer test kit) for the Ag-RDT". <br /> As of my knowledge, the "STANDARD Q COVID-19 Ag Test" of SD Biosensor/Roche comes with a single kit - a NFS-1 from Noble Biosciences Inc., which is a swab for NP.
I think it would help this paper to identify the particular "specific nasal swab" used here to obtain the AN/NMT swab - since the conclusions of this paper make a claim that "using a professional AN-sampling kit is at least equal to....". Reference 5 with a link to a sdbiosensor IFU that refers to NP swab only does not put any more light into this.
Such a finding without properly identifying the particular kit unnecessarily reduces the value of this work.
On 2020-12-25 14:25:32, user muthu venkat wrote:
The Systematic Review and Meta-Analysis is interesting to read and need of the time to compile such an evidence particularly for middle and low income country. The authors have made sure that there is no bias in selection and reporting the evidences through use of appropriate software and methods.
On 2020-12-26 19:56:50, user Dr S K Maheshwari wrote:
This systematic review is focusing on measuring effectiveness of mHealth interventions on antenatal and postnatal care utilization in low and middle-income countries. A strong methodology was used along with wide inclusion of relevant studies from low and middle-income countries. The search strategy criteria were used very specific. I appreciate this work and recommend.
On 2020-12-25 16:08:55, user Deepak K. Gupta wrote:
Full paper now published in the Tremor and Other Hyperkinetic Movements journal here: https://tremorjournal.org/a...
On 2020-12-26 04:49:01, user Peter Tomasi wrote:
Correction: If we assume a latency of 28 days, a substantial amount of samples REPRESENTING A POINT IN TIME WHEN THE level of the exposure in the environment was not yet as high as the one the authors draw their conclusions for, could have been collected.
On 2021-01-01 12:19:30, user Ed wrote:
This article is a preprint and has not been peer-reviewed. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.
..and yet it is being used for exactly that, ref. Department of Health and Social Care Joint Committee on Vaccination and Immunisation: advice on priority groups for COVID-19 vaccination, 30 December 2020 6th bullet-point of the introduction.
On 2021-01-05 07:56:40, user Rita Pizzi wrote:
previous researches
Ghate VS, Ng KS, Zhou W, Yang H, Khoo GH, Yoon WB, Yuk HG.
“Antibacterial effect of light emitting diodes of visible wavelengths on
selected foodborne pathogens at different illumination temperatures.”
International Journal of Food Microbiology. 166 (2013) 399.
Ghate VS, Leong AL, Kumar A, Bang WS, Zhou W, Yuk HG. “Enhancing the
antibacterial effect of 461 and 521 nm light emitting diodes on selected
foodborne pathogens in trypticase soy broth by acidic and alkaline pH
conditions” Food Microbiology. 48 (2015) 49.
Ghate, V, A Kumar, W Zhou and HG Yuk. 2015. Effect of organic acids
on the photodynamic inactivation of selected foodborne pathogens using
461 nm LEDs. Food Control 57:333–340.
Vaitonis and Ž. Lukšiene – Institute of Applied Research, Vilnius
University, Saul?etekio 10, LT-10223 Vilnius, Lithuania “Led-based light
sources for decontamination of food: modelling photosensitization-based
inactivation of pathogenic bacteria” Lithuanian Journal of Physics,
Vol. 50, No. 1, pp. 141–145 (2010)
http://www.lmaleidykla.lt/p...
Nicolai Ondrusch, Jürgen Kreft “Blue and Red Light Modulates
SigB-Dependent Gene Transcription, Swimming Motility and Invasiveness in
Listeria monocytogenes” Published: January 11, 2011DOI:
10.1371/journal.pone.0016151
On 2021-01-06 22:52:53, user Ana Laura Teodoro De Paula wrote:
Where's the funnel plot? The result can't be considered if you cite a funnel plot and don't show it.
On 2021-01-07 03:20:52, user Robin Whittle wrote:
The Mark Alipio article (43) is fake and was withdrawn in August. Please see https://researchveracity.in... for information on this article and two more by "Raharusun" et al. and "Glicio" et al.
On 2021-01-07 08:06:09, user Giuseppe novelli wrote:
Congratulations, the work confirms and extends the considerations reported by us in two published papers, which happy if the authors could quote in an updated version of the manuscript Thanks
Giuseppe Novelli
Novelli A, et al., Analysis of ACE2 genetic variants in 131 Italian SARS-CoV-2-positive patients. Hum Genomics. 2020 Sep 11; 14 (1): 29. doi: 10.1186 / s40246-020-00279-z. PMID: 32917283
Latini A, et al., COVID-19 and Genetic Variants of Protein Involved in the SARS-CoV-2 Entry into the Host Cells. Genes (Basel). 2020 Aug 27; 11 (9): E1010. doi: 10.3390 / genes11091010. PMID: 32867305
On 2021-01-07 14:34:51, user Meerwind7 wrote:
Households with single parents are also considered as socially deprived in other contexts. For infection rates, it might be of benefit if there is no second adult that could bring infections to the family. It was a pity if the effect was not taken into account. I also do not know if the social deprivation was evaluated for each individual child and its infection risks, or just for the schools and the aggregat of their pupils overall.
On 2021-01-08 21:53:07, user Marek J wrote:
Your study says that methylglyoxal contained in honey cause immunostimulatory or pro-inflammatory action via stimulating the production of immunological mediators, also IL-1?. This study https://www.nature.com/arti...<br /> Shows that low levels of IL-1 are linked to lower mortality.<br /> Is it then safe to recommend using honey rich for MGO, e.g. Manuka honey?
On 2021-01-09 18:28:12, user Alex Backer wrote:
This study appears to have been used to Curative to justify their methodology, yet the false positive and false negative rates in the FDA report are incredibly high. Look at the numbers: https://www.linkedin.com/pu...
On 2021-01-10 12:16:07, user Hans Binder wrote:
The paper is published under<br /> https://www.mdpi.com/1999-4...
On 2021-01-10 19:47:40, user Wayne Griff wrote:
21 days after the 1st dose of the Pfizer Vaccine, patients have 1/5th the viral neutralizing power of Convalescent Plasma. In contrast, 7 days after the 2nd dose, patients have 2-4 times the neutralizing power of Convalescent Plasma. NEJM Also, the 47% effectiveness rate is only up to 3 weeks. It's definitely going to be less at 6, 9, or 12 weeks.<br /> Giving only 1 vaccination is a waste of a vaccination. It provides little, if any immunity.
On 2021-01-12 20:36:18, user Michael Meyer wrote:
Can someone tell me if this study has completed the peer-review process prior to publication in the Journal of Infectious Diseases? If not, is it currently in that process?
On 2021-01-14 00:01:25, user Emmanuel Aluko wrote:
Will such a study be done on older cohorts to show the age-dependent efficacy of Ivermectin on reducing mortality, for mortality is seen mainly in older patients with associated co-morbidities? Days to negative tests on such cohorts would also provide a better basis for accepting Ivermectin as a worthy COVID-19 therapeutic.
On 2021-01-15 12:19:29, user Kit Byatt wrote:
Given that:<br /> 1. Excess mortality for a year can't be known for several weeks into the following year <br /> 2. Especially in a year with Christmas & New Year's Day on Fridays, and a pandemic impeding the bureaucracy of collecting & collating mortality data - particularly in cases where the Coroner [or equivalent] is involved)<br /> 3. Different countries have exhibited different patterns (or none) of winter excess mortality peaks (as shown in Figure 1c)
a) Is it not premature to undertake a comparison before all the data are in (or alternatively, at least title as 'Preliminary observations on...'?
b) Might not the association end up significantly higher, the same, or lower, then?
On 2021-01-27 10:12:04, user Elias Hasle wrote:
some countries being hardly hit while others to date are almost unaffected
Readers will tend to read this as "barely hit", the opposite of the intended meaning. "hit hardly" would be less ambiguous.
On 2021-01-17 06:42:44, user Seri Ratu Putri Binti Baharom wrote:
Hi, where can I see the survey form?
On 2021-01-18 11:40:13, user Kit Byatt wrote:
Was there a sex difference in LOS?
We know there are age differences in LOS (median & variance increasing with age). What were the LOSs for age brackets (e.g. each decade)?
Given the very skew LOS distributions in hospital in-patients usually, and especially here, mightn't it be helpful to put the inter-quartile range for the median LOS in 'Outcomes' para 2?
Could you calculate the 'decay' in in-patient numbers (i.e. the equation for the 'current number of inpatients' curve from time zero, for 1000 pts with the LOS distribution you know)? This could be invaluable for modelling bed occupancy.
It is difficult to know the percentage figure for each bar in the symptom combinations and ICU admission matrix. Either putting the number over/in each column (as in Figure 15 Top), or at least showing minor tick marks at 0.01 intervals would greatly improve the clarity of this chart.
On 2021-01-21 13:24:54, user Miroslava Stancíková wrote:
Testing was not voluntary, it was in conflict with the Constitution of the Slovak Republic and the Charter of Fundamental Human Rights
On 2021-01-25 15:10:24, user Chip Hughes wrote:
Thank you all for this great research. It will really help OSHA target enforcement to the highest risk sectors and job classifications so that we can hope to reduce deaths among frontline workers with the highest level of COVID19 exposures on the job. Chip Hughes, USDOL-OSHA DAS
On 2021-02-02 18:17:42, user Robert Enger wrote:
The #1 candidate is "cook". Frequently that person stands in proximity to the grill, which is a high air flow location, due to the high power exhaust fan system in the range-hood over the grill. This acts as a funnel, sucking air from the kitchen (and surrounding areas back to the various points of makeup-air ingress). The cook is thus "downstream" from numerous co-workers in the kitchen, and from persons in other locations between the makeup-air ingress points and the range hood (potentially all the restaurant patrons, if interior dining resumes).
This should sound familiar. Recall the studies of remote distance Covid transmission studied in Korea and China. In those studies, high air flow from air conditioner output ports carried virus from infected individuals to victims many feet removed from the infected party. In this case, the cook near the range-hood is "downstream" from potentially significant numbers of individuals (depending on the location of makeup-air ingress points, etc).
If this line of reasoning is found to be sound, then providing a direct ingress path for outside fresh air to enter into the kitchen "may" reduce the exposure of the cook (and nearby kitchen staff).
On 2021-01-30 20:33:49, user Michael Höhle wrote:
Dear authors,
thanks for posting this very relevant manuscript! Please consider having a look at the statistical methodology of the two following papers, which are very closely related to your work, as they also use an imputation + nowcast + backprojection approach:
Nowcasting the COVID-19 pandemic in Bavaria:<br /> https://onlinelibrary.wiley...
Analysis of the early Covid-19 epidemic curve in Germany by<br /> regression models with change points:<br /> https://www.medrxiv.org/con...
Best regards,<br /> Michael Höhle
On 2021-02-04 19:21:10, user Eli Rosenberg wrote:
We note that the final form of this article was published in JAMA Network Open on February 3, 2021:
https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2775827
We thank the medRxiv community for your interest in our work.
Eli Rosenberg<br /> Associate Professor<br /> Department of Epidemiology and Biostatistics<br /> University at Albany School of Public Health, State University of New York
On 2021-02-22 19:53:12, user Alexander Porter wrote:
What does: '8,041 individuals received two doses of a COVID-19 vaccine and were at risk for infection at least 36 days after their first dose.' mean? Were these individuals exposed to SARS-CoV-2 directly every day?
Were all PCR methods run with the same cycle threshold before and after administration?
On 2021-04-15 21:01:27, user CY Liu wrote:
Did you control batch effect, position effect, covariates in the data analysis? I could not see it in the paper.
On 2020-10-30 18:25:19, user gatwood wrote:
I suspect there could be a strong corellation between vaccination status<br /> and following a strict adherence to all COVID anti-infection <br /> guidelines, PPE etc... Experienced and medically trained Drs and nurses<br /> more likely have been vaccinated and also are more likely to follow PPE<br /> wearing and careful anti-infection routines. Support staff (food <br /> service, assistants and claening staff) with less formal medical <br /> training and understanding of infection are probably less likely to be <br /> vacinnated and also may be less likely to carefully employ all technical<br /> anti-infection measures. Would this account for the vaccinated folks <br /> having less COVID infection?
On 2020-11-06 05:07:01, user Schneide Fu wrote:
Not sure they got the dosage correctly, as Nitazoxanide has a half life of 2.9 hrs. Should have been on divided doses, several times a day.
On 2020-11-06 14:33:43, user Dr. Thiagarajan wrote:
Timely need article and very interesting. We need to understand the challenges faced by dialysis professionals during this COVID 19. I congratulate Dr. Ravi Kumar for this timely article. Looking for this complete manuscript.
On 2021-08-10 22:41:16, user Ashley Derrick wrote:
I had covid in May of 2020. I had many long covid issues for months after - but the biggest issue was chest (heart and lung) pain. The symptoms included shortness of breath, burning sensation in the chest, difficulty getting a deep breath and feeling as if I was having a heart attack when more active (exercising). I went to both a heart specialist and a pulmonary specialist and nothing ever was found to be "wrong". The heart doctor was convinced it was inflammation in and around my lungs and heart that caused this. After 9 months, it went away (shortly before my first vaccine). I felt "normal" for about 4.5 months, but two weeks ago, the chest and heart issues came back. Same feelings. I am a fit 49 year old woman. Wondering if there are any studies that see symptoms going away and then months later coming back. Thanks -
On 2021-08-12 17:18:46, user Iñigo Ximeno wrote:
Although the observation is very interesting, it does not lead to the conclusion that it is the most prudent thing to continue with indiscriminate vaccination in the middle of a pandemic when the vaccine does not prevent the transmission of the pathogen.
It is not relevant how many mutations are detected but the importance of them. It is obvious that among people with some immunity the mutations for which the antibodies can neutralize the virus will not be spreaded and therefore those will not be detected. However the mutations that can evade the immunity, even if they are few, will be more virulent and letal.
On 2021-08-14 05:50:28, user Brad Mellen wrote:
I assume the 167 infected people tracked in the study were previously vaccinated. Is it possible that antibody mitigated viral enhancement played a role in the increased viral loads? A study done by Wen Shi Lee et al in the Nature Microbiology volume 5, pages 1185–1191 (2020) noted the potential dangers of Covid 19 vaccinations could increase viral loads and ultimately increase the spread of Covid 19.
On 2021-08-15 14:53:19, user Johannes Hambura wrote:
The study authors found that the prevalence of mutations is higher in B cell epitopes than in T cell epitopes. They infer that vaccines that rely primarily on T cell immunity should confer protection more durable against the worrisome variants of SARS-CoV-2.<br /> It would be logical and consistent to assess this T cell immunity in unvaccinated and recovered Covid-19 patients, in order to better coordinate the strategy towards collective immunity.
The authors report, based only on 47 cases studied, that unvaccinated patients share significantly more genomic mutational similarities with the variants of concern than patients with a breakthrough infection.<br /> This finding is interesting, but it requires statistically significant evaluation and verification.<br /> Especially since<br /> - in vitro, the selection pressure imposed by the antibodies, induced by the vaccines, has led to the emergence of new variants of SARS-CovV-2 (1);<br /> - in vivo, the case of a severe and prolonged form of infection by SARS-CoV-2, treated with antibodies taken from convalescents, favored the appearance of a variant. The variants decreased when the treatment with the injected antibodies was stopped and reappeared with new doses of the injected antibodies. In the absence of the antibodies, the wild strains again became the majority (2).
In addition, the following findings by the authors tend to favor selective pressure exerted by vaccinations:<br /> - the diversity of SARS-CoV-2 lines decreases at country level with an increased rate of mass vaccination (negatively correlated with the increase in the rate of mass vaccination in the countries analyzed);<br /> - The decline in lineage diversity is coupled with the increased dominance of variants of concern;<br /> - vaccine breakthrough patients harbor viruses with significantly lower diversity compared to unvaccinated COVID-19 patients.
The question still remains open, whether vaccination should not be limited to only vulnerable people in the world population, in analogy with a risk calculator for the population proposed by American scientists (3).
On 2021-08-18 17:39:03, user John Baron wrote:
The first sentence states that this is a study of individuals who underwent SARS-CoV-2 testing, presumably (though not stated) during the study period. Unless there was population testing surveillance in place, there must have been selection factors (e.g. COVID exposure or symptoms) prompting testing. This would bias estimates of infection rates upward, though the bias is unlikely to differ between vaccine groups and may not differ much between vaccinated and unvaccinated groups. Tthe relative infection measures might be OK.
In the "final" paper, it would be important to account for the usual cohort issues: censoring, movement out of the health care system, etc. Also, the first paragraph of the Methods section should include unvaccinated individuals.
On 2021-08-21 17:14:03, user Alan wrote:
How will increased vaccination make a difference if the efficacy is as is being reported less than 50%? Once one is infected it does not appear to reduce one's odds of being hospitalized. So if the efficacy approaches zero, there would be zero benefit to being vaccinated, with the currently available vaccines.
On 2021-08-23 13:04:39, user Eyal Oren wrote:
I don't see any mention of vaccination status. Were these patients vaccinated or unvaccinated? Also the study would be stronger if comparison done to other patients in your catchment area vs CDC data (I believe that dataset is across US and not limited to Georgia?).
On 2021-08-24 09:17:07, user Martin Steppan wrote:
A fundamental methodological caveat of this article is the date / season of sample collection. Breakthrough infection samples were collected in the sprjng-summer period of 2021 only (apr-jul), whereas unvaccinated samples seem to be predominantly from fall / winter 2020 (apr-dec). It has been shown in many studies that sars-cov-2 virions are temperature-sensitive and less active / infectious in warm environments. Hence, the results of this manuscript may reflect a seasonal pattern in infectivity. The authors may want to control for this statistically by either (1) using a matched design of samples from similar dates; (2) include historical temperature data for the Netherlands as a covariate / proxy for this likely bias. Due to this reason, the analyses in their current form do not rule out this bias, which casts doubt on the authors' implicit hypothesis that vaccination status moderates the link between viral load and infectiousness.
On 2021-08-27 11:08:39, user Lakesha Scout wrote:
Most people do not understand how their immune system works, and fall back upon the inept press as their inept narratives. Antibodies do not continue long term, as such a condition would in fact prove disastrous.
Long term immunity relies upon the creation of memory .. specifically memory B and T cells. These are the cells which identify later reemergence of a pathogen (such as SARS-CoV-2) in the body and mount a successful and rapid response to its demise.
There are two pathways to "Active" immunity (infection, and inoculation) <br /> Also there is "Passive" immunity which occurs through such things as a blood plasma infusion from a prior infected person who has existing antibodies. <br /> Any of these three paths, can provide immunity to a pathogen.
The false narrative being pumped by the media is that the only way to overcome a pathogen is through inoculation (vaccination).... that quite simply is not true, neither in medical sense nor in scientific sense. As a mater of medical fact, some inoculations (specifically mRNA) have been proven to enhance a pathogens capability to infect. This condition is known as Antibody Dependent Enhancement or (ADE).
On 2021-08-27 17:26:33, user Mohammad Ali wrote:
Please find the published article at BMJ Open here: https://bmjopen.bmj.com/con...
On 2021-08-03 22:35:32, user Gemma Hollie wrote:
Can you advise how many pregnant women / live births / still births there was during this period. Also, for moderate to severe disease, you report 45% for the delta variant. Can you advise, is this of the total 3371 women or the 1137 admitted due to symptoms of covid? <br /> Vaccine safety for pregnant women has not been well filtered down to health care professionals, therefore the uptake of women getting the vaccine has not high. Now more women are having the vaccine, i wonder if future results of women getting moderate to severe disease will continue to support recommending the vaccine. Will you be doing another study in the near futute to update the findings? Thank you
On 2021-08-09 18:06:43, user MJ wrote:
".....assuming that baseline mitigation measures of simple ventilation and handwashing reduce the second+ary attack rate by 40%".
Congress gave billions of dollars to upgrade schools, to properly fit them with the necessary equipment for proper ventilation/air purification. Schools have had a year to do it, yet it hasn't been done. Schools are going to be reopened in the next few weeks without proper mitigation strategies for airflow.. Schools will be a breeding ground for this virus, even scarier now that the transmission rate is triple what it was a year ago. Speaking as someone who was infected via classroom exposure, and still suffering effects from the virus, more needs to be done to protect and reduce the risk to the children and staff
On 2021-12-08 00:42:55, user Sam Smith wrote:
Summary:<br /> These data demonstrate that both heterologous Ad26.COV2.S and homologous BNT162b2 increased antibody responses in individuals who were vaccinated at least 6 months previously with BNT162b2. <br /> Ad26.COV2.S and BNT162b2 led to Similar antibody titers by week 4 following the boost immunization but exhibited different immune kinetics5. <br /> Ad26.COV2.S led to greater increases in CD8+ T cell responses than BNT162b2!!<br /> However, the durability of these immune responses remain to be determined.<br /> These data suggest different immune phenotypes following heterologous (“mix-and-match”) compared with homologous boost strategies for COVID-19.
On 2021-12-08 18:41:55, user Brian Mowrey wrote:
~18k rapid antigen tests from before Nov 8 were added to the NICD data on November 23, and are included in the results in this update per the text.
How many positives in the Nov 23 dump were missing sample dates? Sample dates are implied not to be "complete within the data set" per the text. Hence why receipt dates were used - but "problems have been identified with accuracy of specimen receipt dates for tests associated with substantially delayed reporting from some laboratories," again per the text.
How many with missing sample dates were scored as "reinfections" in November? 1? 100? 1000? Delayed, non-sample-dated rapid antigen tests for summer Delta infections dumped on Nov 23 could account for the entirety of the "Omicron reinfections" presented in the update, or certainly a significant portion of them.
On 2021-12-13 18:57:26, user joetanic wrote:
breakthrough infections in previously infected
Is this some assumption of the equality of natural immunity to vaccinations? It seems there are more antibodies generated in natural immunity, some that are external to the spike, and furthermore natural immunity seems far longer lasting than does vaccination.
So from whence does this belief come?
On 2021-12-17 16:06:10, user PasserBy wrote:
This article makes several claims of significant differences, yet does not report the statistical tests used. Given the very small sample size, it would be beneficial to know the tests used, the actual data, and the probability levels associated with the statistical test values.
On 2021-12-21 11:23:13, user Shallee Page wrote:
These descriptive data provide really useful data for thinking about LC and the visualizations are good.
It seems like there is a lot of room for more caveats for this self-selected groups.
The speculation about why so many tested negative for COVid seems shoddy. Primer sets are not the main problem here. There is a subset of respondents where there is little evidence that their symptoms are caused by SARS-CoV2. The number of negatives is higher than the false negative rates reported by the molecular biologists. Consulting some would be wise because I think timing of virus load is much more likely to be the problem than some primer pair problems that tests suffered from in early 2020.
On 2021-12-22 18:19:23, user Kurt the Turk wrote:
I hope the post boost samples can be made available to the Emory lab to show neutralization of live viruses. That would really complete the picture.
On 2021-12-29 20:08:54, user Greatwiz wrote:
Did they change anything about the booster shot. Any update sequencing?
On 2021-12-23 12:09:50, user Matthew Nelson wrote:
This is a superb paper, especially the careful approach to CNV calling and the Bayesian methods used throughout. Searching through the supplementary material, there is one important piece of the story that appears to be missing. Where are the counts of de novo missense, PTV, and CNV mutations per gene? These were nicely captured in the Satterstrom and Kaplanis NDD publications. These are really important for understanding potential population sizes for prioritizing therapeutic discovery and development. Have I missed them?
On 2021-12-29 00:23:23, user madmathemagician wrote:
How do they deal with a zero in the data set, there are no zeros in a Benfords' distribution.
On 2021-12-30 08:12:32, user J Sato wrote:
Could you breakdown the risk of hospitalization/death by vaccination status/time?
On 2021-12-30 16:39:32, user Igor Chudov wrote:
Very interesting article!
Could you please clarify something for me.
You say "Moreover, the magnitude of Omicron cross-reactive T cells was similar to that of the Beta and Delta variants, despite Omicron harbouring considerably more mutations. Additionally, in Omicron-infected hospitalized patients (n = 19), there were comparable T cell responses to ancestral spike, nucleocapsid and membrane proteins to those found in patients hospitalized in previous waves dominated by the ancestral, Beta or Delta variants (n = 49).".
This is great, but no Covid vaccine generates or in any way is related to "nucleocapsids" or "membrane proteins". If so, is the mentioned T-Cell reaction to nucleocapsids somehow related to vaccination? Covid-naive vaccinees never come in contact with "nucleocapsids".
Were the nucleocapsid T cell reactions only in the covid-recovered?
A clarification would be greatly appreciated. Thank you very much.
On 2021-12-31 07:47:11, user eriugena wrote:
The current stats in countries like Ireland show 1/3 to 1/2 people testing positive every day. Ok, the PCR test - putting it mildly - is imperfect. However, we can take it using very simple math that 100% of the population is infected within a week. End of "pandemic".
On 2022-01-10 20:48:26, user Siguna Mueller, PhD, PhD wrote:
Dear authors,
Thank you for providing these important data. I am afraid I cannot see how you actually arrive at your conclusion. Can you please help me understand:
How did you actually know people got infected - with the respective variant? You state that cases were identified by “by whole-genome sequencing or a novel variant specific PCR test targeting the 452L mutation.” How many were verified by the former? As for the PCR test, can you please comment how your modifications overcome the limitations as recently announced by the CDC when withdrawing its EUA (https://www.cdc.gov/csels/d...? "https://www.cdc.gov/csels/dls/locs/2021/07-21-2021-lab-alert-Changes_CDC_RT-PCR_SARS-CoV-2_Testing_1.html)?")
Can you say more how VE relates to individual people rather than the numbers that your results and conclusions are based on? You seem to draw your conclusion on statistical means alone. Yet, the average person just does not exist. Moreover, your analysis involves huge standard deviation values.
You say that in the first month already, for some, VE is only 23.5% or even -69%, for vaccination with the Moderna or Pfizer vaccine respectively. How is this supporting your conclusion, please? Are you suggesting more repeated boosters than every month? What would that do to the already minimal or even negative protection? How could a repeated and ongoing “negative protection” – meaning that the vaccine causes an increased risk of infection - as experienced by some (Moderna), suddenly become positive and even truly protective?
You also say that VE is re-established upon revaccination. I am afraid I do not see the results. All I see is numbers – which are less than encouraging: the for the BNT162b2 vaccine: 54.6%, 95% CI: 30.4 to 70.4%. Even if for many the VE may be 54%, this does not mean that VE is re-established. But perhaps the data supporting the VE are still missing? I cannot find details supporting your assertion. The legend to the Table states that there was “[i]insufficient data to estimate mRNA-1273 booster VE against Omicron.” Yet, I am having difficulty finding the data that would support your conclusion that VE is reestablished for revaccination with the Pfizer vaccine. Actually, both the Figure and the Table show the same details for both vaccines under consideration. I am afraid I don’t see anything in your manuscript as to why there are more data for Pfizer, and why these would support your assertion.
Your methods section describes that unvaccinated individuals were followed up from November 20th. Unfortunately I do not see any specific outcomes for the unvaccinated. Your data in the Table and Figure list vaccinated persons only.
Can you please further explain how VE is calculated? Is zero efficacy relative to those who were unvaccinated? Yet, in the Results section you say that, relative to the booster you used “those with only primary vaccination as comparison.”
Further, when assessing the effect of boosters, you state that your “analysis [is] restricted to 60+ year-olds.” Yet, as stated in the beginning of the Results section, the median age of those infected with Omicron by December was 28 years. Why are you suddenly shifting to a different population group when analyzing boosters?
You suggest that the “negative estimates in the final period arguably suggest different behaviour and/or exposure patterns in the vaccinated and unvaccinated cohorts causing underestimation of the VE.” While behavioral issues may indeed impact the outcome of your analysis, your data are not merely negative in the final period. Moreover, for Moderna, there were obviously always some individuals for whom the VE was indeed negative. By contrast, for Pfizer, during the first month at least, VE was in the non-negative range (larger than 23.5, that is). So, it cannot be behavior alone. Else, how could there be such a drastic difference between Pfizer and Moderna (23.5 versus -69.9)?
Cases of reinfections are exploding for both vaccines, for Omicron but also Delta, in an exponential way (as seen in the Table). Moreover, the negative entries in most of your points of analysis, makes me wonder how VE relates to individual patients? Other than in the 1-30 day group for Pfizer and Omicron, each of the other points contain individuals that obviously are more susceptible to infection once they have been vaccinated. Can you say more about those, please? You say that “VE was calculated as 1-HR with HR (hazard ratio) estimated in a Cox regression model adjusted for age, sex and geographical region.” From your data it is apparent that there are many in your study population who experienced negative VE. They are obviously a large proportion, and according to your VE calculation, apparently not isolated cases only. It instead seems as if those with negative VE comprise entire groups of individuals, stratified according to your analysis. Who are they? Are these the elderly, the immune-compromised, or who else?
On 2022-01-02 11:26:15, user Lu Ko wrote:
the difference between midturbinate and nasophyryngeal swab is around 15-20%. https://pubmed.ncbi.nlm.nih...<br /> https://pubmed.ncbi.nlm.nih...<br /> The saliva swab is more error prone in real world setting I guess . Does it make sense to switch from NP to throat swab with antigen testing?
On 2022-01-03 14:25:00, user sad wrote:
The disrespect of these French researchers for international efforts of genomic surveillance is astounding. GISAID explicitly requests that all providers of sequences analyzed be credited, and a collaboration is encouraged. None of this happened here. <br /> And they dare name a variant based on a hospital acronym despite the Pango designation. Just mediocre and sad… Luckily other countries are more respectful.<br /> A proper analysis would have also estimated the emergence date with confidence intervals (not as done here) and the growth dynamics of the lineage.
On 2022-01-04 18:02:40, user Barbara A. Zambrano wrote:
What do the 21 “uncomplicated pregnancies” mean with respect to the babies, and how does this differ from the 29 who delivered healthy babies? Were the 21 babies born from uncomplicated pregnancies also healthy???
On 2022-01-05 17:26:08, user Terry Baines wrote:
I greatly appreciate this effort of trying to analyze the unique challenges which Tribes and Tribal members in managing COVID-19 when confounding social, medical, and community issues are involved. With the complex challenges Tribes face, unique and robust solutions need to be devised. Thank you for those efforts; I would love to see more like it.
On 2022-01-06 12:47:12, user Anonymous wrote:
As far as I know that delta variant doesnt have the Deletion at 69 and 70. Yet the Taqpath kit gave sgtf s gene target failure for 8 delta variant samples. This shows that the kit is not a foolproof kit and rather has many issues which is why it was taken out from the market when it started giving false negative results during the alpha variant breakout. Also the authors have conveniently kept mum and skipped over the fact of these questionable 8 delta variant positive cases.
On 2022-01-08 19:16:50, user madza wrote:
Excuse my ignorance but when SAR % is lower in unvaxxed than in vaxxed, how come the Conclusion is that vax reduces the transmissibility?
On 2022-01-09 17:21:03, user Kevin McKernan wrote:
You should test if the primers light up on SARs-CoV-2.<br /> With the high break through rate, People want to differentiate vax from C19. Would also be interested to know if SARs-CoV-2 also ends up in the lipid fraction. I suspect not. The heavy methylation of these RNAs changes their lipophilicity.
Longer testing window would help answer some questions.
Thank you for putting the primers public.
On 2022-01-11 20:23:50, user Sam Smith wrote:
What is the optimal RH = relative humidity for health? Too dry air increases covid risk because it is not healthy for your airways.
On 2022-01-12 17:54:37, user Martha Metevelis wrote:
I am blown away with the similarity to PCS and toxic B6 symptoms I have endured for over three years. I and many others are often dismissed as just suffering anxiety and told thatour symptoms are not real. Many of us rely on a face book site called exploring b6 toxicity for support and share our experiences and what helps. Being ignored and unfairly labeled is the norm. Suicide has occurred with those unable to cope with what becomes a debilitating issue. We experience insomnia, intense pain, sensory and mobility issues, tinnitus that is unbearable, facial pain, anxiety off the chart,vision changes,peripheral neuropathy,swallowing issues, palpitations, internal and extremity tremors, brain fog, memory loss, hair loss to name a few. <br /> I am anxious to know if the subjects involved in your research have vitamin n6 levels checked and if they are taking more than rda of b6 in any form.<br /> I pray that we all get answers soon. The vitamin I took contained 75 mg of b6! And was suggested by my physician. I had been 100% healthy! This upended my life. <br /> I know exactly how long haul suffers feel and hope my input leads to a solution. Thanks for listening.
On 2022-01-17 11:18:12, user Free Man in London wrote:
This is another Coke vs. Pepsi paper. There are only 2 conditions in this paper: vaccinated vs. unvaccinated. There are multiple conditions: vaccinated with supplements, unvaccinated with supplements Vitamin D, Quercetin, Melatonin, unvaccinated with supplements quercetin and zinc, etc. The paper prima facie is interesting but devoid of an answer to the real underlying question: are supplements more effective than the vaccines? Moderna and pfizer are more interested in keeping the question framed around simply 2 conditions. Until we force a re-framing to the way this effectiveness question is answered, we are not really solving the problem of stopping the virus. That said, omicron seems to be doing that quite well on its own.
On 2022-01-18 16:19:10, user NoSafeSpaces wrote:
Perfect information on who received a vaccine versus imperfect information on who got COVID. Add in a side of everyone gets the vaccine and not everyone gets COVID with a dash of some of the people getting the vaccine have natural immunity from COVID, and you get a weak justification for putting your children at risk because you don't understand bad assumptions.
On 2022-01-31 18:32:19, user Jared Roach wrote:
These are very minor comments about the wording of the Abstract that I would make if I was reviewing the paper:
"strikingly different"<br /> I would delete the vague adjective "strikingly" as it could be interpreted many different ways. Rather insert the exact number of mutations or some similar quantitative metric.
"rapidly replaced"<br /> maybe OK to leave "rapidly", but also add in a sense of how long (e.g., over a period of 3.5 weeks) - or whatever the number of weeks was.<br /> "rapidly" makes sense only in context of other strain replacements such as Alpha-> Delta or Delta-> Omicron. If it isn't much faster than these, I would use an exact time rather than the comparative and vague adjective "rapidly"
On 2022-02-04 18:46:24, user asciguy wrote:
Thank you for the new vocabulary word. I should have noticed ‘inflammasome’ by now in “Laboratory Medicine” but must have missed it somehow.
On 2022-03-22 03:17:02, user ST wrote:
The scientific article was very intriguing, and the abstract sparked my interest when the link between detecting changes in the gut microbiome and concussion diagnosis was made. The introduction was informative and included different helpful statistics on football players and the number of concussions they may receive in a singular football season. <br /> The PCR target region was the full 16S gene (V1-V9). The reason why the authors sequenced the full gene rather than the more common use of one region (i.e. V3-V4) should be included. A strength of the paper is that the scientists included the number of PCR cycles, however, 35 cycles is quite high. The higher the cycle number, the more errors that can be produced (Sze & Schloss, The impact of DNA polymerase and number of rounds of amplification in PCR on 16S rRNA gene sequence data 2019). This should be listed as a potential study limitation. PCR primers were not specifically listed and should be included in the methods section to ensure replicability. IF part of the Barcoding Kit, that should be stated as well. The scientists did state that there were problems with the ONT primer but there should be follow-up discussion of Bifidobacteriaceae taxa found in this study, if any. No quality controls/filtering steps for the sequences (such as removing low quality sequences and chimeras) were included. The scientists do not analyze specifically dominant and rare taxa. Given that microbial communities are highly dominated and uneven, analysis of the dominant taxa alone can be revealing, since metrics using all taxa can be strongly affected by the very numerous but low abundant rare taxa. The rare and dominant taxa of environmental pathogens are mentioned but what does this mean for the microbes found in the oral cavity and the gut? I suggest that bioinformatics should be performed on dominant as well as all taxa. The scientists do include rare taxa abundance in three of their figures to show the abundance of the different microbes found in fecal samples and salivary samples. I thought that the description of the sampling was good but there could be more of a description of what was in the tubes for collection? Were buffers used? What specific tubes were used? Were the samples taken straight to the lab? It would also be helpful to say what kit the tubes were from before stating that a collection tube and funnel were used. The fecal sample and saliva sample collection were described well but I think that scientists should make saliva sample collection have its own paragraph. One flaw of the article is that the scientists never discussed variance in 16S copy number and genome size in bacteria, which affects abundance data of 16S profiles. Were sequences binned or clustered before identification with Kraken? A rarefaction curve would be beneficial to include, and also sequencing coverage measures, such as Good’s coverage and Chao1 compared to observed OTUs. The article should include internal standards for PCR, DNA extraction, and sequencing. These internal standards may include utilizing a negative control, a strain grown in the lab, running a gel, and utilizing a known DNA sequence. I would like to comment that DNA extraction and amplification should be in the same section as I was a tiny bit confused when I read amplification in the sequencing section. The best option for readers would be to list the methods performed in chronological order. The sampling strategy is very well described and figure 1 demonstrates the samples collected as well as a visual representation of the collection strategy. The scientists discuss the number of classified reads for the samples but the number of reads per sample is not stated. Additionally, the study does not include any methods that quantify total bacterial numbers (16S community sequencing is normalized and only contains relative abundance data, not absolute abundance). <br /> Authors should include the specific parameters that were utilized when the samples were vortexed for replicability. Additionally, the stool and the saliva samples were stored at room temperature, and this needs clarification as storing the samples at room temperature for a long period of time may invalidate the experiment. The amount of time that the samples were stored at room temperature needs to be added for clarification. The specific statistical test that is done in figure 2e should be clearly stated. Data that was collected in this experiment could be compared to The Human Microbiome Project.<br /> The optic nerve sheath should be better explained, and it would be beneficial to know what is normal without a concussion and with a concussion. The relationship between the optic nerve and concussions should be better explained so the reader understands its significance.
Overall an interesting study! Thank you!
-Sydney T.<br /> #SHSU5394
On 2023-01-13 11:35:49, user SB wrote:
Hi, thanks for the preprint.<br /> I do not understand the last sentence of the conclusion "Individuals with hybrid immunity had the highest magnitude and durability of protection against all outcomes". That's because the results section pointed out: "Against reinfection at 6 months, there was similar protection from HE with first booster vaccination (effectiveness 46·5% [36·0-57·3%]), HE with primary series (60·4% [49·6-70·3%]), and prior infection alone (51·2% [38·6-63·7%]), with all three types of immunity conferring significantly greater protection than primary series vaccination alone (15·1% [11·3-19·8%]) or first booster vaccination alone (24·8% [18·5-32·5%])"<br /> So the protection against reinfections seems to be for hybrid immunity NOT OVER then for other types of immunity.