On 2023-03-29 14:07:37, user YUE BAI wrote:

Hi,

Could you please link the preprint to the published article (https://ieeexplore.ieee.org...? "https://ieeexplore.ieee.org/document/9670745)?") Thank you!

On 2023-04-16 07:02:33, user Basheer wrote:

The paper is published at https://www.eurekaselect.co...

On 2023-11-21 02:18:06, user Marco Confalonieri wrote:

The finding that high glucose levels can predict glucorticoids (GCs) benefit surprised most of us. All we who performed the included RCTs thincked to hyperglycemia as an adverse effect of GCs, not paying attention to glucose blood level at admission. Nevertheless, there are several reports pointing out hyperglycemia but not diabetes alone associated with increased in-hospital mortality in community-acquired pneumonia (BMJ Open Diab Res Care 2022;10:e002880). It should be noted that AI doesn't have the same prejudices than human researchers.

On 2024-02-22 14:38:06, user Albee Messing wrote:

Please provide more clinical information about the proband.

On 2020-04-16 00:27:24, user Adam Danischewski wrote:

China has a BCG Vaccination policy and there may be other aspects that may cause Chinese results to differ from the United States.

On 2020-04-15 14:12:18, user Ed Fisher wrote:

This regimen looks to be substantially different from those being used in the US. E.g., approximately double the HCQ dosage, no Azithromycin and in some US uses, no zinc. How does that impact the comparison?

On 2024-10-16 16:27:20, user CDSL JHSPH wrote:

I quite enjoyed this article. I found it very interesting as it proposed significant thoughts to how we can improve antibiotic treatment. I wanted to comment about some of the thoughts I had while reading this article. I first wanted to see if the results that were found in TB could be translated into other bacteria infections, such as staph. or strep. species. I also wanted to see if the results found for antibiotics in this article could be translated to other pathogenic treatments including antivirals or antifungals. Finally, in terms of future approaches could we see a systemic or ordered approach when it came to treatment duration whether bacterial, viral, or fungal in nature, or is it mostly going to be drug/ species specific?

On 2024-10-23 00:04:57, user Mohammad Shah wrote:

Hello!

Thank you for sharing this preprint. I really enjoyed reading it. Your application of techniques like MCP-Mod and FP for duration-ranging trials provides valuable insights into detecting duration-response relationships much more effectively than traditional approaches. I also appreciate how you highlight the risks of underestimating the MED in smaller sample sizes and suggest using conservative thresholds to mitigate those risks—this is such a critical point.

One thing that really stood out to me was how you clearly lay out the limitations of traditional duration-response methods, while proposing model-based techniques, like MCP-Mod, as a better alternative. Your comparison of different models and how they behave with varying sample sizes and regimen responses is especially insightful for optimizing TB treatment duration.

Like others have mentioned, it’d be fascinating to see how this approach could be applied to other chronic diseases, such as HIV or hepatitis. Is that something you’re considering or perhaps already working on? Additionally, applying these model-based techniques to real-world patient data, where comorbidities and adherence issues add more complexity, seems like a natural next step. It would be interesting to see how that plays out in practice.

I also found your discussion on model selection particularly thought-provoking. Your suggestion of using MCP-Mod alongside Fractional Polynomials under different assumptions opens up an exciting possibility for integrating multi-model approaches in early-phase trials. I wonder if combining these models, maybe in a hybrid MCP-Mod/FP approach, could improve adaptability, especially in trials with more heterogeneous patient populations—those with comorbidities or fluctuating adherence, for example.

Lastly, your use of simulations to predict treatment efficacy in the face of sample size imbalances touches on a key challenge in trial design. Have you thought about how this framework might be extended to adaptive trial designs? It seems like interim analyses could help adjust treatment durations dynamically based on early patient responses, which could make trials even more efficient.

Overall, this was a great article, very informative and forward-thinking!

On 2024-10-18 20:47:08, user CDSL JHSPH wrote:

First of all, thank you very much for writing this preprinted paper. This article has provided me with a number of ways to assess the duration of treatment with tuberculosis therapy. This paper provides a robust framework for comparing the effectiveness of different approaches through rigorous simulation studies. By carefully modeling a variety of regimen-response relationships and testing various methods under a variety of sample sizes and conditions, the authors provide a comprehensive assessment of the relative strengths and weaknesses of each method, making a valuable contribution to the field of clinical trial design. These findings have the potential to improve the efficiency and accuracy of trials for infectious diseases (especially tuberculosis) and can be generalized to other therapeutic areas. This paper opens up promising new research directions and provides a solid foundation for future work in the field. From my personal perspective, one limitation that needs to be mentioned is the risk of underestimating the Minimum Effective Duration (MED), especially when the sample size is small. If the duration of treatment is underestimated, patients may receive suboptimal treatment, which may lead to higher recurrence rates or incomplete cure.

On 2020-05-01 10:56:16, user Ivan Berlin wrote:

Rentsch CT et al. Covid-19 Testing, Hospital Admission, and Intensive Care Among 2,026,227 United States Veterans Aged 54-75 Years. <br /> medRxiv preprint doi: https://doi.org/10.1101/202... version posted April 14, 2020<br /> Comment of the results concerning smoking related issues. Corrected Version. Please ignore the previous one.<br /> Ivan Berlin, Paris, France<br /> The title is somewhat misleading. Only 3789 persons were tested for SARS-CoV-2, no data on the 2,022,438 are reported.<br /> Data are extracted from the Veteran Administration (USA) Birth Cohort born between 1945 and 1965 electronic database. Between February 8 and March 30, 2020, 3789 persons were tested for SARS-CoV-2. Among them 585 were tested SARS-CoV-2 positive (15.4%) and 3204 SARS-CoV-2 negative. (Remark: the authors frequently confound testing for SARS-CoV-2 and having the disease: COVID-19 +.)<br /> Testing used nasopharyngeal swabs, 1% of the testing samples was from other unspecified sources. Testing was performed “in VA state public health and commercial reference laboratoires”, page 7. No further specification about the testing method is provided. Data are analyzed as if no between test-sources variability existed. However, it is unlikely that between test-source variability would influence the findings.<br /> It seems that only individuals with symptoms were tested, however this is not clearly stated.<br /> Data extraction included diagnostics by diagnostic codes of comorbidities, non-steroid inflammatory drug (NSAID), angiotensin converting enzyme inhibitor (ACE) and angiotensin II receptor blocker (ARB) use, vital signs, laboratory results, hepatic fibrosis score, presence or absence of alcohol use disorder and smoking status.<br /> Smoking status data, never, former, current smokers were extracted using the algorithm described in McGinnis et al. Validating Smoking Data From the Veteran’s Affairs Health Factors Dataset, an Electronic Data Source. Nicotine & Tobacco Research, Volume 13, Issue 12, December 2011, Pages 1233–1239, https://doi.org/10.1093/ntr... used for HIV patients. According to this paper, the algorithm correctly classified 84% of never-smokers 95% of current smokers but only 43% of former smokers. The reported overall kappa statistic was 0.66. When categories were collapsed into ever/never, the kappa statistic was somewhat better: 0.72 (sensitivity = 91%; specificity = 84%), and for current/not current, 0.75 (sensitivity = 95%; specificity = 79%). Thus, classification error cannot be excluded in particular in classifying former smokers. <br /> In unadjusted analyses (Table 1) factors associated significantly with SARS-CoV-2 positivity were: male sex, black race, urban residence, chronic kidney disease, diabetes, hypertension, higher body mass index, vital signs but not NSAID or ACE/ARB exposure. It is to note, that among the laboratory findings, severity of hepatic fibrosis was associated with positive SARS-CoV-2 tests. <br /> Among those with positive SARS-CoV2 alcohol use disorder was reported by 48/585 (8.2%), versus 480/3204 (15%) among those with negative SARS-CoV-2 test. Among those with alcohol use disorder, 9.1 tested positive. <br /> Among SARS-CoV-2 positives there were 216/585 (36.9%) never smokers vs 826/3204 (25.8%) among SARS-CoV-2 negatives. 20.7% tested positive among never smokers. Among SARS-CoV-2 positive persons 179 (30.6%) were former smokers vs 704 (22%) among SARS-CoV-2 negatives. 20.3 % tested positive among former smokers. Among SARS-CoV-2 positive individuals 159 (27.7%) were current smokers vs 1444 (45.1%) among SARS-CoV-2 negative individuals. 9.9% tested positive among current smokers. Expressed otherwise, among SARS-CoV-2 negative individuals, there were less never smokers, less former smokers and more current smokers. Among individuals with SARS-CoV-2 positivity there were 338/585 (61%) persons with smoking history (former + current smokers=ever smokers) and among those with SARS-CoV-2 negativity 2149/3204 (72%) were ever smokers. <br /> COPD, known to be strongly related to former or current smoking, was more frequent among SARS-CoV-2 negative (28.2%) than among SARS-CoV-2 positive (15.4%) individuals.<br /> In multivariable analyses (Table 2), male sex, black ethnicity, urban residence, lower systolic blood pressure, prior use of NSAID but not ACE/ARB use and obesity were associated with SARS-CoV-2 positive test; current smoking (OR: 0.45, 91% CI: 0.35-057), alcohol use disorder (OR 0.58, 95% CI: 0.41-0.83) and COPD (OR: 0.67, 95%CI: 0.50-0.88) were associated with decreased likelihood of SARS-CoV-2 positive test. No association with age and SARS-CoV-2 positive test was observed. The association with hepatic fibrosis with SARS-CoV-2 positive tests remained significant in the multivariable analysis and the authors point out (page 15) that the “pronounced independent association with FIB-4 (fibrosis) and albumin suggest that virally induced haptic inflammation may be a harbinger of the cytokine storm.”, page 15. <br /> The main risk factors for hospitalization or ICU among SARS-CoV-2 positive persons are those that associated with worse clinical signs (status). This is expected: clinical decision about severity is based on current clinical signs and not on previous history. <br /> Neither co-morbidities, nor smoking status or alcohol use disorder were associated with hospitalization/ICU. Surprisingly, age was inversely associated with hospitalization (Table 4) among SARS-CoV-2 positive individuals.<br /> Conclusion

To the best of our knowledge, this is the first report showing that there are less current smokers among SARS-CoV-2 positive persons. However, looking at smoking history (former + current smoking=ever smokers), less subject of classification bias, the difference seems to be less. It is not known what is the percent of former smokers who were recent quitters; duration of previous abstinence from smoking is a crucial variable in assessing associations with smoking status. There is no report of biochemical verification of smoking status. <br /> It is not known when smoking status is reported with respect of the SARS-CoV-2 testing. It is likely that individuals with clinical symptoms stopped smoking some days before testing and considered themselves as former smokers.

The fact that alcohol use disorder, which is frequently associated with tobacco use disorder, is also less frequent among SARS-CoV-2 positive individuals raises the question of the specificity of the smoking finding and raises the contribution of substance use disorders overall i.e. the finding about current smoking is part of a cluster of various previous or current substance use disorders e.g. cannabis use, potentially associated with SARS-CoV-2 negative test directly or through associated health disorders e.g. hepatic disorders as a consequence of alcohol use. <br /> COPD as well as current smoking are being reported to be more frequent among SARS-CoV-2 negative individuals raising the possibility that reduced respiratory function (entry of SARS-CoV-2 is by the respiratory tract) is associated with lower likelihood of SARS-CoV-2 positive tests. <br /> It seems that all individuals included were tested because they had symptoms suggestive of COVID-19. It is surprising that only 585/3789 (15.4%) tested positive. This should be discussed.<br /> The paper does not report on analyses of smoking by clinical signs/co-morbidities interactions. It is likely that former smokers or those with alcohol use disorders are more frequent among individuals with comorbidities. Based on previous knowledge about smoking associated health disorders, one can assume that more severe clinical signs were associated with current smoking or among recent quitters; the smoking x clinical signs interaction is not tested. <br /> The authors conclude on page 14 “To wit, we found that current smoking, COPD, and alcohol use disorder, factors that generally increase risk of pneumonia, were associated with decreased probability of testing positive. While they were not associated with hospitalization or intensive care, it is too early to tell if these factors are associated with subsequent outcomes such as respiratory failure or mortality.”<br /> The reduced current smoking rate among SARS-CoV-2 positive individuals is an interesting but preliminary finding. It is likely that it is part of a more complex symptomatology and not specific to current smoking. Smoking status should have been assessed on a more detailed manner. The current findings, from a retrospective, cross sectional analysis, are insufficient to support the hypothesis that current smoking protects against SARS-CoV-2 positivity.

On 2020-04-16 21:17:49, user Sinai Immunol Review Project wrote:

Key findings:

The authors wanted to better understand the dynamics of production SARS-CoV-2-specific IgM and IgG in COVID-19 pneumonia and the correlation of virus-specific antibody levels to disease outcome in a case-control study paired by age. The retrospective study included 116 hospitalized patients with COVID-19 pneumonia and with SAR-CoV-2 specific serum IgM and IgG detected. From the study cohort, 15 cases died. SARS-CoV-2 specific IgG levels increased over 8 weeks after onset of COVID-19 pneumonia, while SARS-CoV-2 specific IgM levels peaked at 4 weeks. SARS-CoV-2 specific IgM levels were higher in the deceased group, and correlated positively with the IgG levels and increased leucocyte count in this group, a indication of severe inflammation. IgM levels correlated negatively with clinical outcome and with albumin levels. The authors suggest that IgM levels could be assessed to predict clinical outcome.

Potential limitations:

There are limitations that should be taken into account. First, the sample: small size, patients from a single-center and already critically ill when they were admitted. Second, the authors compared serum IgM levels in deceased patients and mild-moderate patients and found that the levels were higher in deceased group, however even if the difference is statistically significant the number of patients in the two groups was very different. Moreover, receiving operating characteritics (ROC) curves were used to evaluate IgM and IgG as potential predictors for clinical outcome. Given the low number of cases, specially in the deceased group, it remains to be confirmed if IgM levels could be predictive of worst outcome in patients with COVID-19 pneumonia. The study did not explore the role of SARS-CoV-2-specific IgM and IgG in COVID-19 pneumonia.

Overall relevance for the field:

Some results of this study have been supported by subsequent studies that show that older age and patients who have comorbidities are more likely to develop a more severe clinical course with COVID-19, and severe SARS-CoV-2 may trigger an exaggerated immune response. The study seems to demonstrate that the increase of SARS-CoV-2-specific IgM could indicate poor outcome in patients with COVID-19 pneumonia, however given the very small sample size, the results are not yet conclusive.

Review by Meriem Belabed as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-04-16 22:12:24, user Amy E. Herr wrote:

During the COVID-19 pandemic, we are grateful for the authors’ urgency in assessing N95 respirator decontamination methods. It is in this spirit of collegiality that we draw attention to an aspect that could (unintentionally) cause confusion: the PS19Q thermopile sensor mentioned in the Methods section does not appear to be suited to detect the virus-killing UV-C light emitted from the source. The authors are aware of the possible confusion and are working diligently to check into and, if needed, address the concern.

As background: from the manufacturer’s specifications, the PS19Q thermopile sensor mentioned in the preprint appears to only detect wavelengths as low as 300 nm, which is above the UV-C germicidal wavelength range (<280 nm). Low-pressure mercury UVGI bulbs emit a 253.7 nm peak [EPA]. 260 nm is the peak UV-C germicidal wavelength for inactivating virus via DNA and RNA damage [Kowalski et al., 2009, Ito and Ito, 1986]. The germicidal efficacy arises primarily from the UV-C dose, with the UV-B dose (280-320 nm) providing significantly lower germicidal efficacy. At 300 nm, UV light is ~10x less effective at killing pathogens than at 254 nm [Lytle and Sagripanti 2005]. UV-A dose (320-400 nm) is considered minimally germicidal [Kowalski et al., 2009; Lytle and Sagripanti 2005; EPA]. We are concerned about the potential adverse health outcomes that might stem from use of the PS19Q thermopile sensor not matched to the UVGI wavelengths for N95 FFR decontamination.

As best practices, all researchers working on UV-C methods are encouraged to use a calibrated, NIST-traceable, UV-C-specific radiometer to report not just UV-C irradiance, but also UV-C specific dose, as a minimally acceptable UV-C dose of 1.0 J/cm^2 is sought on all N95 FFR surfaces. For additional detail from the peer-reviewed literature, please see the 2020 scientific consensus summaries on N95 FFR decontamination at: n95decon.org

Again, we thank the authors for their timely research and quick action to confirm suitability of their experimental design, all of which aim to better inform decision makers working to protect the health of heroic front-line healthcare professionals during the COVID-19 pandemic.

References cited: <br /> • Manufacturer’s specifications, the PS19Q thermopile sensor: https://www.coherent.com/me...<br /> • EPA: ULTRAVIOLET DISINFECTION GUIDANCE MANUAL FOR THE FINAL LONG TERM 2 ENHANCED SURFACE WATER TREATMENT RULE: https://nepis.epa.gov/Exe/Z...<br /> • Kowalski et al., 2009: https://link.springer.com/c...<br /> • Ito and Ito, 1986: https://onlinelibrary.wiley...<br /> • Lytle and Sagripanti 2005: https://www.ncbi.nlm.nih.go...

On 2025-06-15 21:35:28, user CP wrote:

Great paper! The text makes reference to a "Supplementary Notes" section that doesn't seem to be in the PDF - is this part of the material that will be made available after peer reviewed publication? Sorry if this is a naive question; I'm new to preprints.

On 2022-01-26 22:15:44, user Siguna Mueller, PhD, PhD wrote:

Does the "fully vaccinated" group ALWAYS include those with (partial) natural immunity (i.e., those previously infected? This is at least what Table 1 says: these belong into the same group. Yet, throughout, this group is referred to as the "fully vaccinated." This does not seem to affect the conclusion that vaccination is in large part responsible for driving O's increased transmissibility (because the incr. OR is seen for the booster group as well). Apart from this, I am struggling to see how the other results are obtained. I seem to be missing how the factor of previously infection gets incorporated in the study. It would be helpful if this could be made explicit, please. Thanks!

On 2022-01-28 20:33:22, user Mohamad Kabbani wrote:

Fantastic article! Very informative and the ideas are easy to understand. This is a good baseline to get a better understanding on how different things have become during and after covid-19. We can learn what a pandemic can do to a population and compare it to this data as a reference point.

On 2022-01-29 19:43:45, user A Thumb wrote:

Where is the VE data for 2 doses against severe outcomes with omicron in the abstract? Annoying omission.

On 2022-02-03 17:13:18, user Brian R Wood wrote:

Has the study accounted for the fact that if Omicron has less severe symptoms than Delta or COVID-19 Classic, the number of reported infections is likely to be significantly lower? Additionally, I would speculate that those who got vaccinated and boosted are also more likely to be tested than those who did not, but just speculation, no data to back it up.

On 2021-12-25 21:47:17, user Daniel Song wrote:

Um the results don't seem to back the conclusions the authors come up with<br /> Back to the drawing board I guess

On 2022-02-08 21:10:34, user Sara wrote:

Thank you for your comment, unfortunately, I did not receive your comment once you replied. 1- we are in the era in the big data, more projects are aimed at generation of large cohort that we can depend upon to derive our clinical decision. <br /> The analysis used the data from US, the model will be deployed and can be used after that to predict the survival time of small cohorts. <br /> 2- We investigated the hazards assumption, we agree with you, we should add the results in the manuscript<br /> 3- SEER database identify the surgery as the surgical removal of the tumour.<br /> 4- I agree with you on the grade, it was on the old grading system for glioblastoma which is mentioned on SEER guidelines. Updated version will be posted and will update the analysis removing this one<br /> 5- we agree with you, we will change it in the updated comments<br /> 6- It is not insane! Developing models that consider these cases is a challenge. These models will be deployed for survival prediction of different cases of glioblastoma with different survival times.

7- we are developing a model that can be used for the routine data "we use", in this case US cancer data. We have a model that performed well so it can be deployed in the future for the clinical use for our routine data. the model is trained on large sample size that we believe it will achieve accurate prediction results for any routine data. The deployment of the model and its use in clinical practice is the goal. I hope you see the full picture.

Thank you for your comments.

On 2022-02-09 01:07:23, user Avi Bitterman wrote:

This paper dichotomizes a continuous variable to get a barely statistically significant result (P=0.044). But this is just dichotomania. Time to treatment is a continuous variable, not a binary variable. The appropriate test for this continuous variable is a regression along the continuous variable. Not a dichotomized sub-group analysis.

Using the same numbers this author uses from Table 1, we ran a regression which failed to show a significant effect of treatment delay on outcome P=0.13

Aside from being the appropriate test, another advantage of a regression here is it avoids the possibility selective dichotomization along the proposed moderator variable to get the desired result (a barely significant P value the authors just so happen to have found).

I would also be happy to have a discussion with the authors to elaborate on the above as well as discuss numerous other critical errors with this analysis as well.

On 2021-06-23 21:55:50, user David Wiseman PhD wrote:

Summary:<br /> Regarding the continued and unnecessary confusion related to the Argoaic and Artuli comments.<br /> 1. These are in reality distractions from the central issue that the original NEJM paper remains uncorrected in NEJM as to shipping times. Although a secondary issue, also uncorrected is the "days" nomenclature that is the reason for confusion in the Argoaic and Artuli comments on this forum. Also uncorrected in the original paper is the exposure risk definition which were informed were also incorrect. Together, these issues controvert the conclusions of the original study.<br /> 2. The incorrect nomenclature for "days" in the NEJM paper as well as in a follow up work (Clin Infect Dis, Nicol et al.) inflates the number of "elapsed time" days. This has not been corrected by the original authors. We on the other hand have corrected this by providing the correct information in our preprint.<br /> 3. Dr. Argoaic seems to have been given a wrong and earlier version (10/26) of the data which, although contains a variable that is supposed to correct the above problem, does not. In fact one cannot come to any conclusion that there is a discrepancy based on this incorrect 10/26 version, unless you have some preconceived notion.<br /> 4. Other post hoc analyses reported in follow up works (including social media) by the original authors looking at time from last exposure, or using a pooled placebo group, although flawed for a several reasons, when examined closely, nonetheless support our conclusions that early PEP prophylaxis with HCQ is associated with a reduction of C19.

Detail:<br /> Any confusion about "days" would disappear once the original authors correct the NEJM June 2020 paper as well as a follow up letter in Dec 2020 Clin Infect Dis (see upper red graph in Nicol et al. pubmed.ncbi.nlm.nih.gov/332... "pubmed.ncbi.nlm.nih.gov/33274360/)"). These errors inflate the "DAYS" by 1 day because the nomenclature for describing "days" was incorrect. As far as we know those corrections have not been made in the journals where these errors appear and in a way that can be retrieved in pubmed etc..

As far as we can tell, anyone who has cited the NEJM paper (NIH guidelines, NEJM editorial, many meta-anlayses etc., our protocol in preprint version) also misunderstood the "days" to mean the inflated figure. So the authors need to correct this. As far as we know we are the only ones to do this. After we were informed of this error by the PI (who was unaware of the problem himself) we described this problem very clearly in our preprint, distinguishing between elapsed time and the day on which a study event occurred. For the benefit of those who remain confused, we will endeavor to make it even clearer in a future version. You can read our correspondence log referenced in the preprint to verify that the incorrect "days" nomenclature was unknown to the PI, at least until 10/27 when he informed us about it.

You are confusing "DAY ON which an event occurred" with "DAYS FROM when an event occurred." For example the original NEJM Table 1 says "1 day, 2 days etc." for "Time from exposure to enrollment". This falsely inflates the number of elapsed time days by 1, and as the authors informed us (documented in our preprint), this really means DAY ON which enrollment occurred, with Day 1 = day of exposure, so you need to subtract 1 from the days to get elapsed time FROM exposure. The same error is repeated in Nicol et al. (note: we discuss other unrelated issues relating to time estimates in our preprint).

To confuse matters further, the problem is not even corrected in the dataset linked (datestamp 10/26/20) in the Argoaic comment. In column FS there is a variable "exposure_days_to_drugstart." This appears to indicate elapsed time (ie DAYS FROM) when it actually means the "DAY ON" nomenclature. We were only informed of the nomenclature error on 10/27/20 and later provided with a new version of the dataset on 10/30 where an additional variable "Exposure_to_DrugStart" (column GR) was provided that corrects this error by subtracting 1 from all the values.

Why the Argoaic comment does not link to the correct 10/30 version is unclear, but in this incorrect 10/26 version, the values for the new variable "Exposure_to_DrugStart" (column GR) are IDENTICAL to those in the "exposure_days_to_drugstart" (column FS) variable (they should be smaller by 1). Accordingly, unless Drs. Argoaic and Artuli had a preconceived notion (without checking the data) that some alteration had occurred, it is impossible to draw such a conclusion (albeit one that is incorrect for other reasons) from this incorrect 10/26 dataset. A number of colleagues have downloaded the 10/26 dataset from the link provided in the Agoraic comment, and have verified this problem.

So in addition to the original data set released in August 2020, as well as the three revisions (9/9, 10/6 and 10/30) we describe in our preprint there is this incorrect 10/26 version. I don't know how many people this affects but it would be appropriate for them to be notified that the version they have may be an incorrect one. An announcement on the dataset signup page covidpep.umn.edu/data would also be in order (nothing there today).

Regarding the possibly higher placebo rate of C19 on numbered day 4 (18.9%). This is matched by a commensurate change in its respective treatment arm, yielding RR=0.624 similar to that for numbered days 2 (0.578) and 3 (0.624), justifying pooling. We don't know if the 18.9% represents normal variation or has biological meaning.

Although they used enrollment time data (completely irrelevant to considering whether or not early prophylaxis is beneficial), the original authors (Nicol et al.) in a post hoc analysis, used a pooled placebo cohort to compare daily event rates (red bar graph). This would mitigate possible effects of an outlying value in the placebo cohort. We applied this same pooled placebo method to the data that correctly takes into account shipping times. This method is still limited because it may obscure a poorly understood relationship between time and development of Covid-19. Although at best this would be considered a sensitivity analysis, we did it to answer the Artuli question. This approach yields the same trends as our primary analysis. Using 1-3 days elapsed time of intervention lag (numbered days 2-4) for Early prophylaxis, there is a 33% reduction trend in Covid-19 associated with HCQ (RR 0.67 p=0.12). Taking only 1-2 days elapsed time intervention lag, we obtain a 43% reduction trend (RR 0.57 p=0.09). This analysis appears to reveal a strong regression line (p=0.033) of Covid-19 reduction and intervention lag.

We also looked at the post hoc analysis provided by the original authors (Nicol et al.) that used “Days from Last Exposure to Study Drug Start,” a variable not previously described in the publication, protocol or dataset, so we have no way of verifying it from the raw data. As in a similar PEP study (Barnabas et al. Ann Int Med) this variable has limited (or no) value, as we are trying to treat as quickly as possible from highest risk exposure, not an event (ie Last Exposure) that occurs at an undefined time later. (even the use of highest risk exposure has some limitation, which the authors pointed out to us and which we discuss in our preprint). Further the Nicol analysis used a modified ITT cohort, rather than the originally reported ITT cohort. with these limitations, pooling data for days 1-3 and comparing with the pooled placebo cohort (yields a trend reduction in C19 associated with HCQ (it is unclear which "days" nomenclature is used) after last exposure from 15.2% to 11.2% (RR 0.74, p=0.179).

Taken together with these "sensitivity" analyses inspired by the original authors' methodology, suggests that this is not an artifact of subgroup analysis. It could be said that any conclusions made by the sort of analyses conducted by Nicol are equally prone to the "subgroup artifact" problem. (also note that in our paper, the demographics for placebo and treatment arms in the early cohort match well).

Mention has been made elsewhere of two other PEP studies (Mitja, Barnabas) which concluded no effect of HCQ. It is important to note that the doses used in these studies were much lower than those used in the Boulware et al. NEJM study. Further, according to the PK modelling of the Boulware group (Al-Kofahi et al.) these doses would not have been expected to be efficacious (the Barnabas study used no substantial loading dose). So citing the Mitja and Barnabas studies to support claims of HCQ inefficacy in the Boulware et al paper is unjustified. On the contrary, taken together three studies suggest a dose-response effect. We discuss this in detail in our preprint.

Lastly it is important to note the since the original NEJM study was terminated early, the entire original analysis can be thought of as a subgroup analysis, with all of the attendant problems referenced by the original authors (and us). There is certainly a great deal of under powering and propensity to Type 2 errors, among the issues inherent in a pragmatic study design. The study was not powered as an equivalence study and so no definitive statement can be made that the HCQ is not efficacious. Along with the still uncorrected (in the original journal) issues of shipping times, "days" nomenclature and exposure risk definitions, there are are certainly many efficacy signals that oppugn the original study conclusions,and controvert the statement made in a UMN press release (covidpep.umn.edu/updates) "covidpep.umn.edu/updates)") that the study provided a "conclusive" answer as to the efficacy of HCQ.

_________________<br /> Please note that despite our offer to Dr. Argoaic to contact us directly to walk though the data to try to identify any issues, we have not been contacted.That offer is still extended to anyone who remains confused. We have also attempted to locate both Drs. Argoaic and Artuli to try to clear up their confusion, but these names do not exist in the mainstream literature (i.e pubmed, medrxiv), nor do they appear to have any kind of internet footprint.

With regard to Table 1 of our preprint, the reason why there are no patients for “Day 1” is that there were no patients who received drug the same day as their high-risk exposure. This is consistent with the PIs comment on 8/25/20 (p10 of email log) (at a time when he thought that there was a “Day zero”) “Exposure time was a calculated variable based date of screening survey vs. data of high risk exposure. Same day would be zero. (Based on test turnaround time, I don’t think anyone was zero days).”

We notice an obvious typo in the heading for the second column of our Table 1, which says “To”. But it should say “nPos”, to match the 5th column (and other tables). It is patently absurd that there should be a category of “1 to 0” days or “7 to 5” days etc. “From” makes no sense either and these typos have absolutely no effect on the analysis, interpretation or conclusions. This will be corrected in a later version.

On 2022-02-09 11:30:32, user Felix Schlichter wrote:

The authors explain that the data was gathered from community testing. They further note that mass testing has been available to "Dutch citizens experiencing COVID-19 like symptoms or who have been in contact with someone testing positive for SARS-CoV-2".

If one assumes that the inmune status affects the intensity and probability of exhibiting symptoms, wouldn't the sample be biased? Even if the real odds of being positive for individuals with primary vaccionation and booster were equal, the ones with booster would be underrepresented as they would not test as often if they tend to exhibit less symptoms. Is this not a limitation of the study?

Could the authors not show the results separated by the reason for testing (contact vs symptomatic) to account for this limitation? if the reason for testing was having been a contact, this limitation would not be there.

On 2025-11-11 03:32:18, user Evolutionary Health Group wrote:

We at the Evolutionary Health Group ( https://evoheal.github.io/) "https://evoheal.github.io/)") really enjoyed this paper.

Here are our highlights:

In the week after the Jan 7 ignitions, virtual (clinic) respiratory visits jumped 41% in highly exposed areas and 34% in moderately exposed areas, totaling 3,221 excess visits, a clear, short-term signal health systems can act on.

Virtual cardiovascular visits rose by ~35% across exposure groups in that first week (~2,424 excess visits), pointing directly to surge planning for virtual care during wildfire weeks.

On the day of ignition (Jan 7) in highly exposed areas, outpatient neuropsychiatric and injury visits were about 18% higher than expected, evidence that mental-health demand starts immediately, not just respiratory care.<br /> The exposure framing is reproducible: simple proximity bands (<20 km vs >=20 km within LA County) applied to a 3.7-million-member health system and a five-category visit dashboard (all-cause, cardiovascular, injury, neuropsychiatric, respiratory) that others can copy.

Scaled to all LA County residents, the estimates imply ~16,171 excess cardiovascular and ~21,541 excess respiratory virtual visits in the week after ignition, strong justification to expand virtual capacity during major fires.

On 2022-02-17 20:55:31, user RT1C wrote:

Table 3 (bottom) contains HR for boosted vs. non-boosted at various times (<6, 6-9, >=9 months). Aside from the minor labeling issue (hopefully not actual analysis issue!) that 6-9 months and >=9 months are not distinct subsets, overlapping at 9 months, I don't see how you could have made this analysis in the first place unless you have incorrectly defined POIC. You wrote, "we defined the proximate overt immunologic challenge (POIC) as the most recent exposure to SARS-CoV-2 by infection or vaccination." That means POIC for boosted subjects would be time since the booster dose as that is the most recent vaccination. Yet, considering how recently boosting began, how could you have boosted subjects with 6-9 or >=9 months POIC? (In your text you wrote, "For those boosted, the median time to being boosted was 16 days prior to the study start date (IQR -38 to 6 days).")

On 2022-02-17 21:29:51, user RT1C wrote:

You state, "For those boosted, the median time to being boosted was 16 days prior to the study start date (IQR -38 to 6 days)." Is that a typo or did you truly mean a positive 6? i.e., did you mean -38 to -6 days, or -38 to 6 days? If the latter, you actually included subjects who were vaccinated with boosters after the study period began? If that's the IQR, then I assume the full range extends much further into the study range. Those are VERY recently boosted. In your discussion, you should not say, "boosting with a vaccine designed for an<br /> earlier variant of COVID-19 still provides significant protection against infection with the Omicron variant." without also providing a time associated with that. For example, you might add to that sentence "for a period of at least 1 month" or whatever. It seems important to stress the limitation of the study in this manner, to avoid giving the impression that the booster provides long-lasting protection against infection when that is not shown by your study.

Finally, on a related matter, how did you treat individuals who tested positive before 7 days after their booster? If, as some research suggests, vaccination temporarily increases susceptibility to infection (for about 2 weeks), by including subjects who were vaccinated within the study period, you may have biased findings against those without boosters.

On 2025-11-26 01:10:16, user Alda wrote:

Congratulations for all the colaborators!! <br /> When will be published ? (perhaps 2026)

On 2022-03-03 12:42:31, user Dan Bolser wrote:

This paper seems to be published here:<br /> https://pubmed.ncbi.nlm.nih...

but the preprint doesn't link it.

On 2022-03-04 16:06:11, user Tracy Beth Høeg, MD, PhD wrote:

The peer reviewed version including numerous international datasets estimating rates of post vaccination myocarditis is now available. We have included risk-benefit calculations for children with a history of infection and used overall infection hospitalization risks (rather than just 120 days risks) both pre and during omicron. http://doi.org/10.1111/eci....

On 2022-03-28 18:14:47, user August Blond wrote:

Dear colleagues,<br /> I am having difficulty understanding figure 3, the two graphs that are plotted with GFP/EGFR.<br /> Zooming in on the four ovals - red, blue, black, green - I see that the scattered-plots are themselves contained in a smaller perfect ovoid.<br /> Can you explain how you manage the computer processing of your samples?<br /> In reference 13, the method for doing multiplex FACS, these close to perfect ovals do not appear. There are still points that are not perfectly integrated into the "virtual" geometrical structure.<br /> As is the case with all FACS using gating.<br /> Would it be possible to generate point clouds that have not been "artificially" modified after gating?<br /> Best regards,<br /> August Blond

On 2022-06-08 17:08:32, user Ted Gunderson wrote:

Should this be considered a scientific study or an advertisement?

What evidence is there that what the authors refer to as "(non-variola orthopoxvirus and monkeypoxvirus specific)" actually causes the disease that is currently being diagnosed all over the world as "monkeypox".

This is a paper funded by Roche that says "Our tests work!"

"ML and DN received speaker honoraria and related travel expenses from Roche Diagnostics."

Roche has gotten lots of press recently about their monkeypox tests.

https://medicalxpress.com/n...

On 2022-06-09 20:11:19, user John Doe wrote:

Interesting paper that confirms and complements prior molecular findings on this devastating malignancy. A strength of this study is the inclusion of a relatively large series of patients (n = 47) considering the rareness of the disease. The results suggesting a diverse origin of BPDCN are of special interest, and the figure on potential therapies against the disease is visually appealing. However, data analysis and data interpretation have certainly problems and inconsistencies. In particular, the results on CNV pathogenicity produced by X-CNV are highly questionable and dubious, and I would strongly advise against using those results to guide data interpretation. Among deleted regions (suppl. data) classified as non-pathogenic by X-CNV are: 1p36.11 (ARID1A), 5q33.1 (NR3C1), 7p12.2 (IKZF1) and 9p21.3 (CDKN2A–B). All these are well-known tumor suppressors with demonstrated pathogenicity in numerous human cancers. Besides, prior studies back up the recurrent deletion and pathogenicity of these cancer genes in BPDCN [refer to papers by Lucioni M et al. Blood. 2011;118(17), Emadali et al. Blood. 2016;127(24), Bastidas AN et al. Genes Chromosomes Cancer. 2020;59(5), Renosi F et al. Blood Adv. 2021 9;5(5)].

Puzzling enough, despite claiming the use of the X-CNV results to determine pathogenicity of CNVs, it appears that the authors chose to highlight anyway some deleted and gained regions classified as non-pathogenic by X-CNV (ARID1A, CDKN2A) as well as other regions not even formally called by GISTIC (e.g. TET2). This is even harder to comprehend considering that 7p12.2 (IKZF1) is clearly one of the most conspicuous peaks in the analysed cohort (Figure 3A); yet, completely ignored in the text and figure!? Quite baffling. In short, the paper would greatly benefit and improve from re-interpreting and discussing the data considering the existing literature on BPDCN genetics.

On 2020-04-21 21:10:27, user Bruno Vuan wrote:

Article says, page 7,

"This study had several limitations. First, our sampling strategy selected for members of Santa Clara County with access to Facebook and a car to attend drive-through testing sites. This resulted in an overrepresentation of white women between the ages of 19 and 64, and an under-representation of Hispanic and Asian populations, relative to our community. Those imbalances were partly addressed by weighting our sample population by zip code, race, and sex to match the county. We did not account for age imbalance in our sample, and could not ascertain representativeness of SARS-CoV-2 antibodies in homeless populations. Other biases, such as bias favoring individuals in good health capable of attending our testing sites, or bias favoring those with prior COVID-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain."

In summary sample has

Overrepresentation white woman 19-64<br /> Age imbalance not accounted <br /> Partial weighting by zip code, race and sex<br /> Biased favoring good health individuals and those seeking antibody confirmation

Conclusion: "overall effect of such biases is hard to ascertain"

1. Not balanced by age is a signal of impossibility of weighting by age without significative umbalance in the other dimmensions, as mentioned "result in small-N bins". Ignoring age balancing in a phenomena which is strongly age related is something that may bring a strong source of additional errors.
2. If authors recognize that these biases are hard to ascertain, and no further discussion appears, is that this uncertainty is not included in error range. So, error range of this experiment appears to be totally unknown for the authors.

Additionally

There is no discusion on sampling effect by facebook ads, as answering rates, impact of facebook ads algorithm which is optimized to get maximum amount of answers. It is well known that this convenience samples are non probabiistical, so this has to be included in error range evaluation, (1)

1. Baker R. et al, Non-Probability Sampling, AAPOR, June 2013 https://www.aapor.org/Educa...

On 2020-04-23 04:46:38, user Dennis Maeder wrote:

Although flawed, this emphasizes the need for good representative sampling and antibody testing and the strong possibility that current case counts are wildly underestimated.

On 2020-04-20 08:50:19, user dixon pinfold wrote:

A great many commenters assert that people worried that they'd been exposed to the virus would be over-represented in the study. This seems speculative up to a point, but certainly plausible, and I do not wish to debate it.

Some then go on to assert that anyone else would be too afraid to leave the house. Here I think they are on much less solid ground.

For one thing, not knowing what was in the recruitment ad, we don't know if they were aware that they would remain in their cars, windows rolled up till finger-prick time.

For another, it places a decisively higher estimation on fear (of infection resulting from the single excursion to the testing site) than on curiosity motivated by the desire to be rid of such fear once and for all. This must be true for some people, but for some, surely, it was the other way around.

Then there is the matter of age. Here I freely admit to the speculative element, but it has been for me very easy when out in public in recent weeks to tell by their behaviour how much less fearful younger people are, if they're fearful at all. Who could fail to notice?

On 2020-04-17 21:03:35, user John Ryan wrote:

The researchers identified that 50 out of 3,300 participants tested positive for CV-19 antibodies. Spinning this as 50 to 80 times greater than current prevalence rates as determined through testing is disingenuous. The posted study does not account for the significant upward adjustment from 1.5% of participants to a 2.4% to 4.2% in the general population given the study participants were a skewed convenience sample drawn from Facebook participants, many who believed they had been exposed to CV-19 and had had previously experienced symptoms consistent with CV-19.

On 2020-04-17 21:28:07, user Daniel Shanklin wrote:

This study abstract should be rewritten as follow: "A study of Facebook users who thought they might have COVID-19 resulted in a roughly 2.49% to 4.16% positive-test rate"

The fact that you've extrapolated this to an entire population is confounding.

On 2022-06-24 22:03:50, user Charles Warden wrote:

Hi,

Thank you very much for posting this preprint. This certainly represents a large amount of work and careful consideration!

I have some questions / comments:

1) Is there a way for me to calculate enhanced scores for myself?

For example, I would like to learn more, but I was not very satisfied with the PRS that I listed for my own genomic sequence in this blog post:

https://cdwscience.blogspot...

2) In the blog post link above, there seemed to be a noticeable disadvantage to the PRS without taking the BMI into consideration for Type 2 Diabetes.

In this paper, age is an important factor in Figure 1 for the PRS.

If other non-genetic factors are known, do you have a comparison for non-PRS models? <br /> For example, I wonder how performance of age + BMI (+ other established factors) compares to the plot for Type 2 diabetes in Figure 1.

3a) I see that the percent variance explained is sometimes provided (such as Supplemental Figure 5), but sometimes it is not.

For example, in Figure 3, the effect per 1 SD of PRS is higher for LDL cholesterol than height. However, how does the ability to predict an individual's height from genetics alone compare to the ability to predict an individual's LDL from genetics alone?

After a certain age (as an adult), the exact value for my own LDL has varied more than my height. However, I was not sure how that variation by year compared to others and/or the variation over decades.

In general, I would like to have a better sense of how absolute predictability compares for height versus disease scores. I also understand that there are complications with binary versus continuous assignments, but it is something that I thought might be helpful.

3b) I see AUC statistics in Supplemental Figure 2, described as for AUROC. However, am I correct that some of the cases are not well balanced with controls?

If so, should something like AUPRC be provided (possibly as a complementary supplemental figure)? I believe the idea is described in Saito and Rehmsmeier 2015; the application is very different, but you can see the inflated AUROC values in Figure 1A of Xi and Yi 2021. I expect that there are other good ways to illustrate the differences with PRS in cases and controls of varying proportions, but that was one thought.

In the context of genomic risk, I might expect that high predictability in a small number of individuals may be preferable over a small difference in low predictability in a large number of individuals. There is emphasis on thresholds like top/bottom 3% (in many but not all figures), which I thought might be consistent with that opinion.

So, I think something like Figure 1 was helpful. In order to try and capture how false positives change when sensitivity increases, I am not sure if something similar for positive predictive value might help? I would consider that very important if the PRS might be used for screening purposes.

4) In the Supplemental Methods, I believe that you have a minor typo:

Current: 100,000 Genomes Project (100KGP). The 100,00 Genomes Project, run by Genomics England,<br /> Corrected: 100,000 Genomes Project (100KGP). The 100,000 Genomes Project, run by Genomics England,

Thank you very much!

Sincerely,<br /> Charles

On 2022-08-01 22:06:02, user Saiful Islam wrote:

The authors also need to clarify masks vs respirators as they provide different level of protection.

On 2022-08-06 11:55:02, user Dieter Mergel wrote:

I have a question concerning the following passage:

"Previous work demonstrated that vaccination reduces severe COVID-19 and hospitalisation 46 and also the risk of Long COVID 7, 47. However, we did not observe evidence of qualitatively different symptom clustering in vaccinated vs. unvaccinated individuals, with either alpha or delta variants."

Does it mean: <br /> (a) Vaccination does not reduce the risk of Long Covid.<br /> or<br /> (b) Vaccination reduces the risk of Long Covid, but if (!) vaccinated people get Long Covid, then (!) the symptoms are similar to those of unvaccinated people.

On 2022-08-14 15:08:34, user Peter J. Yim wrote:

The trial registration at ClinicalTrials.gov listed three primary endpoints:<br /> 1. Number of hospitalizations as measured by patient reports. [ Time Frame: Up to 14 days ]<br /> 2. Number of deaths as measured by patient reports [ Time Frame: Up to 14 days ]<br /> 3. Number of symptoms as measured by patient reports [ Time Frame: Up to 14 days ]

The publication reports the outcomes for none of those endpoints. (the endpoints were changed after publication on ClinicalTrials.gov)

1. The rate of hospitalization was reported at 28 days. That was registered as a secondary outcome.
2. Mortality was reported at 28 days. That was registered as a secondary outcome.
3. The number of symptoms was only reported at baseline.

This article is close to irrelevance on the question of the efficacy of ivermectin in COVID-19.

On 2021-08-11 10:34:14, user Apriyano Oscar wrote:

I am sorry, I am just a layman. I want to ask about the 1.8% tested positive (608 people). Does it mean that the effectiveness of the Pfizer vaccine in this study is 98.2% ? And is this also the same as what is called as 'efficacy' ?

On 2021-05-24 16:53:32, user Gustavo Bellini wrote:

Congratulations on the work! It would be interesting to analyze the action of vitamin D in the MHC complex, MICA / MICB.

• A subgroup of lupus patients with nephritis, innate T cell activation and low vitamin D is identified by the enhancement of circulating MHC class I-related chain A<br /> https://doi.org/10.1111/cei...

"Indeed, immune cells significantly up-regulate vitamin D receptor (VDR) transcription upon activation and proliferation (reviewed in [28]). In turn, through the binding of VDR, vitamin D induces the expression of anti-proliferative/pro-apoptotic molecules, thereby evoking immune tolerance 29, 30. Interestingly, recent data showed that MICA stands as a VDR-sensitive molecule, through which vitamin D renders tumour cells susceptible to NK cytotoxicity 31. According to this view, in our patients the gene expression of MICA in T cells was not associated with the up-regulation of TLR or ISG, as could have been expected, but paralleled levels of vitamin D instead. All these observations suggest that vitamin D could help to restore homeostasis of the immune system during flares, and that its deprivation may jeopardize MICA-dependent cell growth control."

In addition, the inverse relationship between circulating sMICA and vitamin D found in our cohort suggests that the vitamin could prevent MICA shedding. Alternatively, sMICA impairment of NK functions could promote the uncontrolled proliferation of immune cells which, in turn, would facilitate the depletion of vitamin D.

In summary, we propose a particular disease pheno-type characterized by the disruption of MICA-dependent cytotoxicity in patients with innate activation of T cells and possibly facilitated by low vitamin D levels."

• In vivo response of the human epigenome to vitamin D: A Proof-of-principle study<br /> https://doi.org/10.1016/j.j...

"Basically all cellular components of PBMCs belong to the innate and adaptive immune system. Therefore, it is not surprising that the immunologically most important region of the human genome, the HLA cluster, also highlights as a “hotspot” in the epigenome of PBMCs.<br /> However, it is remarkable that the HLA cluster is also a focused region of the vitamin D responsiveness of the epigenome. This observation provides a strong link to the impact of vitamin D on the control of theimmune system.<br /> In conclusion, in this proof-of-principle study we demonstrated that under in vivo conditions a rather minor rise in 25(OH)D3 serum levels results in significant changes at hundreds of sites within the epigenome of human leukocytes."

The study below has shown evidence that the vitamin D endocrine system is dysregulated in sars-cov-2 infection.

• Evidence of a dysregulated vitamin D endocrine system in SARS-CoV-2 infected patient’s lung cells<br /> https://doi.org/10.1038/s41...

On 2020-12-07 04:30:15, user Murilo Perrone wrote:

Because the spreading of this information is so important, I suggest changing the license. See:

Why Sharing Academic Publications Under “No Derivatives” Licenses is Misguided

PS: Because of CC BY-ND I don't think I can comment on your maps in my youtube videos.

On 2021-12-13 11:53:14, user Undertow of Discourse wrote:

The summary of findings in the abstract is defective in relation to PIMS-TS. It says “ The overall PIMS-TS rate was 1 per 4,000 SARS-CoV-2 infections”. Rate of what? Occurrence of PIMS-TS? Hospitalization with PIMS-TS? Death from PIMS-TS?

On 2023-05-09 17:56:41, user Dr. Gerald Zincke wrote:

I am missing indication at which point in time after the vaccination an infected patient was counted to the vaccinated group.

(For the importance of this, please refer to Prof. Norman Fenton's description of the statistical illusion that can occur when vaccinated people are counted as unvaccinated for a period of time after the shot. https://youtu.be/Gkh6N-ZL3_k )

On 2021-08-14 17:37:30, user Uwe Schmidt wrote:

The study states a hospitalisation rate of 6% for children.

This rate needs to be strongly questioned as it is internationally significantly higher than any other rate observed. In fact, it is higher by roughly factor 10-12. E.g. in Germany, at the peak of the pandemic in week 51/2020, less than 100 children were hospitalised nationwide, 1/3 of them newborn, who just stayed in hospital a little longer. The number of positive tested children in that week was ~20,000. For July 2021, the number of hospitalised children is less than 10, no ICU.<br /> In England, one out of 200 (0.5%) children are hospitalised.<br /> In Israel, no patient below the age of 30 is in critical condition.

Questions for the authors:<br /> 1. Does the total number of children tested positive really consist of ALL PCR-positive or only a subgroup reported by certain institutions?<br /> 2. Of those 5,213 hospitalised, how many were hospitalised because of COVID-19 and how many because of other conditions?

On 2020-07-25 23:24:04, user BannedbyN4stickingup4Marjolein wrote:

I'm not a bio-mathematician but I've had a similar idea in my head for some time. I'm not comfortable with all of the maths so to an extent I have to take some of this on trust.

But the basics of it, as I understand it, is that transmission takes place when some yet to be defined criteria are satisfied (through air, via a surface, without a mask, indoors, whilst singing, who knows?) through a temporal network. It would certainly help to understand this mechanism better, but that's not the focus of the paper.

Early infection removes the easiest nodes from this network - those people most easily susceptible overlapping with those peole with the most contacts. The mechanism of node removal is death in a few cases and post infection immunity in the majority.

Just a couple of notes of caution then:

One obvious one is how long does immunity last? Suppose some kind of herd immunity is achieved at 20% infection of the population, but that a typical population (not a densely populated city like New York) is not infected to this level until infection acquired immunity starts to wane?

The second - and I am disappointed not to see more mention of this in the paper - what if a significant element of node removal is down not to post infection immunity but to changes in social behaviour in response to the epidemic?

R is a function not just of the pathogen but of the population it infects - its density is relevant, but so is its behaviour. This applies whether one models the population as a simple homogenous mass (SIR type models) or as a set of discrete interconnected agents.

Then no sooner does everyone revert gung ho to their previous pattern of behaviour (we're at herd immunity, we're safe!) then infection takes off again.

On 2020-12-28 18:05:42, user Rogerio Atem wrote:

The 3 preprints of this series on COVID-19 epidemic cycles were <br /> condensed into a single article that summarizes our findings using the <br /> analytical framework we developed. The framework provides cycle pattern <br /> analysis, associated to the prediction of the number of cases, and <br /> calculation of the Rt (Effective Reproduction Number). In addition, it <br /> provides an analysis of the sub-notification impact estimates, a method <br /> for calculating the most likely Incubation Period, and a method for <br /> estimating the actual onset of the epidemic cycles.

We also offer an innovative model for estimating the "inventory" of infective people.

Check it at:

(Revised, not yet copy-edited)<br /> https://doi.org/10.2196/22617

On 2021-12-28 00:53:06, user Drew wrote:

Two issues need to be corrected for in the data before any real conclusions can be drawn. First, is there a relationship between age stratification, higher vaccination status and higher symptomatic disease - i.e., Simpson's Paradox. Second, was there a behavioral reason that impacted the results? For example, if vaccinations were required for admittance to crowded venue during the initial spike in Omicron cases, it would have skewed the results toward negative effectiveness.

On 2021-01-17 21:18:23, user Arturo Sanchez-Lorenzo wrote:

The paper has been published:

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

On 2020-08-12 11:44:27, user My Opinion wrote:

In my opinion...this supports the explanation why certain facilities (e.g. nursing homes, prisons, cruise ships, church gatherings) experience large numbers of individuals who become infected....I have never believed that the primary mode of transmission was a cough or sneeze....in some prison facilities....we have seen 80% of the population inside the facility become infected, including prison guards....the virus spreads too efficiently to blame it on a cough or sneeze....for example, we know that small pox can be spread through exhaled respiration...this research appears to be the first published study to definitively prove COVID-10 can float in the air and infect people quite distant from the infectious source (17-feet)....this explains how large numbers of people can become infected quickly...it is in the air...Thomas Pliura, M.D., Le Roy, IL

On 2021-06-13 21:16:52, user thomas wrote:

I am not in the health field (that may be obvious from the questions I have) but I am very interested in this study because my parents (in their 70's) both had and recoverd from covid. They have not received a vax yet.

1. Why wouldn't having the infection give immunity? Is there something about this specific virus, or this type of virus in general, that it wouldn't be expected to give immunity?

2. If infection doesn't give immunity, how will the vaccines work? I realize some vaccines are mRNA or viral vector, but at least the two Chinese ones, the Indian one, and a new one the French are working on are all based on using a dead/weakened virus. Shouldn't recovering from an actual infection work just as good as the simulated infection of a vaccine?

3. Is 1,359 subjects really considered small? How big where the sample sizes for the initial vaccine studies? What would be an acceptable size? My background is more in the social sciences, and we often see samples in the hundreds.

4. Is it really correct to assume that people who had COVID would be more careful afterwards? I know with my parents, they were almost consumed with fear about catching the disease, but once they did and recovered, much of that went away. I wasn't around to see their behavior, but just based on conversations, I find it hard to believe they were more careful.

When my parents saw the doctor after recovering, he told them they could not get the vaccine for at least 3 months and that they didn't need to get it until after 6 months. So this study seems in line with what the medical establishment was already saying (they had COVID back in March).

On 2020-07-08 11:38:25, user peter kilmarx wrote:

Congrats on your bibliometric analysis. Here's a reference for you: Grubbs JC, Glass RI, Kilmarx PH. Coauthor Country Affiliations in International Collaborative Research Funded by the US National Institutes of Health, 2009 to 2017. JAMA Netw Open. 2019 Nov 1;2(11):e1915989. doi: 10.1001/jamanetworkopen.2019.15989.

We found that publications coauthored by US-affiliated and non-US-affiliated investigators had a higher mean citation index (1.99) than those whose authors were only US affiliated (1.54) or non-US affiliated (1.35).

On 2024-07-24 16:07:33, user Jim Woodgett wrote:

A sobering study! I have a couple of questions about the population evaluated and timing of the study. In Methods the "Pandemic" group (G1) included subjects with scans before and after pandemic onset (N =404; 247 female), further split into "Pandemic–COVID-19" (G3, N = 121; 75 female) and "Pandemic–No-COVID-19" (G4, N = 283; 172 female). So there were 121 who had (at least one?) Covid-19 infection and 283 who had no infection. This seems an unusual sampling ratio given known serological analysis and overall penetrance of infection. How long after infection were the MRIs performed and at what point were subjects classified as Covid infected or not (presumably, the majority became infected during the study)? Were there sufficient subjects and data to assess degree of brain aging vs multiplicity of infection? Is there data on subjects self-reporting long Covid effects?

On 2021-01-27 06:59:12, user Peter Hessellund Sørensen wrote:

In the graph showing mortality vs COVID19 cases as a function of T cell imunity. In Singapore 95% of the cases were in migrant workers in their 20s and 30s. Similar problems are probably present in the other countries in the sense that the way of counting cases and deaths is not the same and different population groups are infected in different countries. <br /> Allready with Singapore removed the statistical significance of the graph has vanished.

On 2021-09-26 18:18:34, user wolvverinepld wrote:

How long was the period from the second dose to the study?

Pfizer study (Qatar)

Asymptomatic infection protection<br /> Highest 63.7% at 0-4 weeks<br /> 0% by 15 weeks

Symptomatic<br /> 49.6% at 15 weeks<br /> 0% by 20 weeks

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

On 2020-04-30 19:12:43, user Sinai Immunol Review Project wrote:

Main findings<br /> This report describes the use of systemic tissue plasminogen activator (tPA) to treat venous thromboembolism (VTE) seen in four critically ill COVID-19 patients with respiratory failure. These patients all exhibited gas exchange abnormalities, including shunt and dead-space ventilation, despite well-preserved lung mechanics. A pulmonary vascular etiology was suspected.

All four patients had elevated D-dimers and significant dead-space ventilation. All patients were also obese, and 3/4 patients were diabetic.

Not all patients exhibited an improvement in gas exchange or hemodynamics during the infusion, but some did demonstrate improvements in oxygenation after treatment. Two patients no longer required vasopressors or could be weaned off them, while one patient became hypoxemic and hypotensive and subsequently expired due to a cardiac arrest. Echocardiogram showed large biventricular thrombi.

Limitations<br /> In addition to the small sample size, all patients presented with chronic conditions that are conducive to an inflammatory state. It is unclear how this would have impacted the tPA therapy, but it is likely not representative of all patients who present with COVID-19-induced pneumonia. Moreover, each patient had received a different course of therapy prior to receiving the tPA infusion. One patient received hydroxychloroquine and ceftriaxone prior to tPA infusion, two patients required external ventilator support, and another patient received concurrent convalescent plasma therapy as part of a clinical trial. Each patient received an infusion of tPA at 2 mg/hour but for variable durations of time. One patient received an initial 50 mg infusion of tPA over two hours. 3/4 patients were also given norepinephrine to manage persistent, hypotensive shock. Of note, each patient was at a different stage of the disease; One patient showed cardiac abnormalities and no clots in transit on an echocardiogram, prior to tPA infusion.

Significance<br /> The study describes emphasizes the importance of coagulopathies in COVID-19 and describes clinical outcomes for four severe, COVID-19 patients, who received tPA infusions to manage poor gas exchange. While the sample size is very limited and mixed benefits were observed, thrombolysis seems to warrant further investigation as a therapeutic for COVID-19-associated pneumonia that is characterized by D-dimer elevation and dead-space ventilation. All four patients had normal platelet levels, which may suggest that extrinsic triggers of the coagulation cascade are involved.

The authors suspect that endothelial dysfunction and injury contribute to the formation of pulmonary microthrombi, and these impair gas exchange. Pulmonary thrombus formation has also been reported by other groups; post-mortem analyses of 38 COVID-19 patients' lungs showed diffuse alveolar disease and platelet-fibrin thrombi (Carsana et al., 2020). Inflammatory infiltrates were macrophages in the alveolar lumen and lymphocytes in the interstitial space (Carsana et al., 2020). Endothelial damage in COVID-19 patients has also been directly described, noting the presence of viral elements in the endothelium and inflammatory infiltrates within the intima (Varga et al., 2020). One hypothesis may be that the combination of circulating inflammatory monocytes (previously described to be enriched among PBMCs derived from COVID-19 patients) that express tissue factor, damaged endothelium, and complement elements that are also chemotactic for inflammatory cells may contribute to the overall pro-coagulative state described in COVID-19 patients.

References<br /> Carsana, L., Sonzogni, A., Nasr, A., Rossi, R.S., Pellegrinelli, A., Zerbi, P., Rech, R., Colombo, R., Antinori, S., Corbellino, M., et al. (2020) Pulmonary post-mortem findings in a large series of COVID-19 cases from Northern Itality. medRxiv. 2020.04.19.20054262.

Varga, Z., Flammer, A.J., Steiger, P., Haberecker, M., Andermatt, R., Zinkernagal, A.S., Mehra, M.R., Schuepbach, R.A., Ruschitzka, F., Moch, H. (2020) Endothelial cell infection and endotheliitis in COVID-19. Lancet. 10.1016/S0140-6736(20)30937-5.

The study described in this review was conducted by physicians of the Divisions of Pulmonary, Critical Care, and Sleep Medicine, Cardiology, Nephrology, Surgery, and Neurosurgery and Neurology at the Icahn School of Medicine at Mount Sinai.

Reviewed by Matthew D. Park as part of a project by students, postdocs, and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2021-02-04 09:44:55, user Sepp271 wrote:

Taking into account the 7-day incidence of that region (Munich) and the number of tests taken, about 1 or 2 positive cases would have been expected when similar testing would have been done in general population. Taking dark number of incidence into concern this figure goes up to roughly 2 or 3.

Therefor within this study one can not state that the observed number of positive cases of 2 found in primary schools, kindergartens and nurseriesis is significantly different from the infection numbers in the general population.

It would have helped if the authors had made a strict comparison of both groups including statements about the confidence interval.

On 2021-10-02 06:16:24, user Not Ready to Panic Dog wrote:

Since low Vitamin D levels are associated with increased incidence of cancer, heart disease, diabetes, and various auto-immune, neurological and inflammatory disorders, how did you account for the patients’ comorbidity influence on disease progression? https://pubmed.ncbi.nlm.nih...

On 2020-09-01 09:40:12, user Roland Salmon wrote:

This is a thorough piece of field epidemiology, although like much field epidemiology today, the data substantially comes from existing information sources. As a former director of the Communicable Disease Surveillance Centre Wales (CDSC), I am pleased that Public Health Wales, via CDSC staff, past and present, produces work of this quality.

The study demonstrates, persuasively, that much of the problem with infection in care homes, resulted from the care home's size, rather than from receiving infected patients, discharged from hospital. Nevertheless, I do not think that it should be stated ("Research in context"p.3) that "Our analysis found no effect of hospital discharges on care home outbreaks once care home size had been adjusted for" (my underline). In fact, as the discussion section makes clearer (p11), the observed hazard ratio is 1.15 and the effect could be as high as 1,47 (Table 2), although the result is not statistically significant at the 5% level. (It would be interesting inter alia to know the actual probability of this, the most probable estimate of hazard of 1.15.) Table 3, looking at the risk of outbreaks, by care home capacity, further, implies that the effect of discharges might be particularly marked in the smaller homes (<10 beds) where I calculate that the crude relative risk of an outbreak in the post hospital discharge risk period is 3.2. compared with around 1.2 for larger homes. Anyway, an intervention that reduced the risk of outbreaks, in this vulnerable population, by some 15% would be considered by most people as well worth having.

It's thus important to reflect whether the failure to demonstrate an effect of this size merely reflects a lack of statistical power, some of which could be due to misclassification of the outcome. The study authors recommend, in "Conclusions and recommendations" (p12), that, "further analyses should investigate the risk where discharges were confirmed or probable cases of Covid-19, and also consider additional evidence on likely chains of transmission that may become available from sources such as.....viral genetic sequence data". This is an important supplementary piece of work. In addition, the risk from hospital discharges, unlike that from home size, does not extend over the whole period of the study. I note that 16 outbreaks that occurred before certain homes received any discharges are included in the dataset so homes, therefore, enter the study before they are at risk of any infection introduced by receiving patients discharged from hospital. Secondly, homes remain in the study after 2nd May, when universal testing of hospital patients for SARS CoV2, prior to discharge to care homes, is introduced. Thus, from, a few days after this until the 27th June, the study's end date, effectively, risk from hospital discharges is eliminated whereas the risk from home-size remains. The authors consider this and report that they fitted their model, with a factor for the two time periods (before and after 2nd May). They tell us that, "this factor was found not to be significant, and did not significantly alter the hazard ratios". Whilst I understand that any alteration of the hazard ratios was not significant at alpha =5%, I would like to actually see the change in the observed hazard ratios. It might be expected that the hazard of receiving hospital discharges was higher in the period up to 2nd May, than in the period from 2nd May to the study's end.

I was curious as to why Cox's Proportional Hazard was the test used. I don't altogether see that the risk of outbreaks following introduction, by hospital discharge is particularly time dependent, given how readily and for how long SARS CoV 2 can spread in institutional settings. Thus, I don't really see why that risk factor could not be expressed as a categorical variable (outbreak, no-outbreak) which would allow a much simpler analytical approach. I, frankly, also, don't understand the detail of the sensitivity analyses, presented, for choosing different at-risk time periods which, I feel, for a general readership, certainly, merits being explained more fully.

Finally, I think that the discussion section could be more robust. If home size is the issue, then shouldn't the authors be saying that larger homes need to consider having dedicated areas, facilities and staff for smaller subsets of their residents. Maybe larger homes should have more stringent planning requirements. I also think that rather more should be made of the contribution of hospital discharge (notwithstanding it's failure to achieve conventional 5% levels of statistical significance) than the rather anodyne paragraph at the foot of page 11 which bears all the hallmarks of the dead hand of the corporate public relations department.

Nonetheless, overall, this is an accomplished piece of epidemiology with important practical implications.

Dr Roland Salmon

On 2021-07-04 05:23:23, user PriyankaPulla wrote:

Major protocol violations occurred at the largest site of the Covaxin phase 3 trial, a private hospital called People's Hospital, which recruited 1700 participants. These violations are documented extensively by multiple media outlets. And these violations raise questions about the integrity of the Phase 3 trial data. They also raise questions about the sponsors' attitude to due process, and the independence/training of the DSMB: both sponsors (the Indian Council of Medical Research and Bharat Biotech) responded to the allegations with cursory dismissals, while the DSMB remained mum.

Further details here: https://www.thequint.com/co...

I am listing a few of the documented irregularities:

1. Participants told media outlets that they didn't give their informed consent, an Indian legal requirement. Many participants belonged to disadvantaged tribal communities/were illiterate, which necessitates special consent protocols under Indian law, which investigators didn't follow.

Investigators admitted in a video-recorded press conference that they didn't give participants a copy of their informed-consent form during their first visit, unless participants explicitly asked for it. This strongly suggests that the investigators weren't trained in Indian legal requirements or Good Clinical Practices.

1. Investigators allegedly advertised the trial as a vaccination drive in communities of poor and illiterate people.

2. Dozens of participants say the trial team did not contact them to record solicited adverse events. These participants often didn't have their own mobile phones (mobile phones are the mode through which solicited adverse events were to be collected, as per trial protocol). Even though these participants came from poor communities, investigators didn't foresee the fact that they may not have their own mobile phones, and may be hard to contact. Nor did they attempt to contact them in their homes in the days following the doses.

3. People's Hospital recruited a record 1700 participants in 1.5 months (no other Covaxin trial site in India managed such numbers). In contrast, another government-run Covaxin site in Bhopal struggled to even recruit a few hundred participants, and was, therefore, excluded from the trial. This supports the allegation that People's Hospital misadvertised the trial as a vaccination drive.

4. Many participants told media outlets that they suffered Covid-like symptoms post jab, but the investigators never called them to collect this information. Nor did the participants know where to report their symptoms. This raises questions about how well Covid cases were recorded.

5. Participants say they were denied medical treatment at People's Hospital when they fell sick. This, again, raises questions about how well the investigators captured adverse-events.

6. When one participant at the Bhopal site died, investigators ignored his family's version of the participant's symptoms in their causality analysis. In the family's version, the participant suffered from very severe symptoms (vomiting, dizziness, weakness) for 7-8 days before death, while the investigators claimed he was fine during solicited-adverse event monitoring, and died suddenly.

The dismissal of the family's version of events, when the family was present during the participant's death (but the investigators weren't), raises serious questions about how Serious Adverse Events are investigated. No post-mortem report or causality analysis was shared with the family despite multiple requests. Further, the family alleges that the deceased participant received no phone calls from the investigators to record solicited adverse events in the days leading up to his death.

The investigators could easily have shared proof of their claims by sharing a record of the phone calls with the family. They haven't.

Despite the above serious concerns (which are supported by video testimony from participants broadcast on multiple media outlets, specifically NDTV), the trial's government sponsor, ICMR, and Bharat Biotech, denied all allegations in a cursory manner. Further, the preprint makes no mention of them, or explain how these irregularities were handled.

This raises questions about overall data integrity in Bharat Biotech's phase 3 trial. Bharat Biotech has been under substantial pressure from the government to roll out Covaxin fast, which may explain why the company is overlooking such data integrity issues. More details here: https://www.livemint.com/sc...

Reviewers of this paper, and licensing authorities, including the World Health Organisation, must investigate these allegations thoroughly.

On 2021-08-28 18:17:03, user Squid Pro Crow wrote:

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

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

On 2021-09-05 04:24:28, user Adriana Perez wrote:

Regrettable the matching of the groups requires to use conditional logistic regression for the analysis which the authors did not do otherwise they would have written it. The lack of control in the matching indicates that the results can not be trusted.

On 2020-09-08 12:00:16, user Wendy Olsen wrote:

I noted that the assumptions going into this model are a consistent proportion of Overseas and Home students, and a similar size student body, as last year. In addition the cases arriving at UK campuses would be over half from UK Home Students. So even if the assumption of consistent proportion from Overseas turns out untrue, there is still the problem that having more UK Home students will bring more cases into the campuses. I also noted the summary, written by the authors:

Their core estimate is that "81% of the 163 UK Higher Educational Institutes (HEIs) have more than a 50% chance of having at least one COVID-19 case arriving on campus when considering all staff and students. Across all HEIs it is estimated that there will be a total of approximately 700 COVID-19 cases (95% CI: 640 - 750) arriving on campus of which 380 are associated from UK students, 230 from international and 90 from staff. This assumes all students will return to campus and that student numbers and where they come from are similar to previous years. According to the current UK government guidance approximately 237,370 students arriving on campus will be required to quarantine because they come from countries outwith designated travel corridors. Assuming quarantining is 100% efficient this will potentially reduce the overall number of cases by approximately 20% to 540 (95% CI: 500 - 590). Universities must plan for COVID-19 cases ... and ... reduce the spread of disease. It is likely that the first two weeks will be crucial to stop spread of introduced cases. Following that, the risk of introduction of new cases onto campus will be from interactions between students, staff and the local community as well as students travelling off campus for personal, educational or recreational reasons.

"COVID-19 has resulted in the on-campus closure of HEIs across the UK in March 2020 (1). Since that point universities have been working predominantly as virtual establishments with most staff working from home. Autumn sees the start of the new academic term with the potential return of more than 1.5 million UK and almost half a million international students (2).

"The COVID-19 pandemic continues ... approximately 1000 new cases reported each day in the UK, 25,000 across Europe and 250,000 worldwide ((3) accessed 28/03/20). There have been a number of outbreaks of COVID-19 reported in universities in the USA (The University of North Carolina, Notre Dame in Indiana, Colorado College, Oklahoma State and University of Alabama (4)) where the national infection rate is approximately 10 times higher than the UK (3). advice ...(5, 6). However, it is currently unknown to what extent COVID-19 will be brought to campus by staff and students whether from the UK or abroad."

On 2021-10-23 16:32:56, user CDSL JHSPH wrote:

I really enjoyed reading about this topic and what the implications drawn by your results could mean to the medical field in regards to the development of clinical traits associated with height. Although you do draw many parallels between specific clinical traits and height, I was left confused about which height range you were drawing your associations from. I see that you do provide the average height of the individuals in the sample (individuals of approximate 176 cm height); however, are the associations being measured effective on all heights above this number or is there a specific height range in which we begin to see the development of these traits? I would suggest to clearly define this in your Introduction section in order to provide better context of which height range are significantly showing associations with each of the clinical traits detected. Further, just as my colleague below, I was wondering if you plan on publishing this study in a journal of genomics or statistical science? Your paper contains advanced vocabulary on both of these topics, and although the findings are incredibly interesting to any science-oriented reader, I do feel that it is perhaps a paper that is better aimed towards an audience with a background in genomics or statistical science. But other than this, congratulations on this paper, it is incredibly thought-provoking!

On 2021-10-27 15:17:33, user Edward Jones wrote:

I find this study very biased considering they use the 16.7% with such a small sample size, usually you'd discount that number. Also no consideration was given to the type of virus being investigated, the paper is regarding SARS COV and yet you quote 16.7% inaccuracy in Ebola virus. Furthermore, the statement saying that uninfected individuals will be in risk of exposure is nonsense. A false positive would mean they may have to isolate, having the opposite effect.

On 2020-07-17 15:36:12, user Mathieu Perrin wrote:

Dear authors,

thank you for raising awareness that a low FPR does not per se translate into a high significance of a positive result. Hopefully, this will encourage labs to make an EQA for SARS-CoV-2.

1) Is it possible that the FPR actually depends on the prevalence rate? For instance, if the prevalence is high, there is a greater risk to contaminate a negative sample.<br /> 2) Should the FPR be lower if two tests are conducted on the same person? I expect this to be the case unless a negative patient always give positive results for some reason. If so, the practice of double testing should be generalized. I understand you have trouble determining precisely how each country defines a positive result, whether it is on a sample basis or if samples are pooled or if an individual gets tested several times.

Regards,<br /> Mathieu

On 2020-10-25 19:08:24, user Daniel Haake wrote:

Dear study team,

Thank you for your study, which shows that the risk of COVID-19 death increases significantly with age. To improve the quality of the study I have some comments regarding the statistical analysis of the study. In the following I would like to go into it.

The time of the determination of the death figures

You write that antibodies are formed in 95% of people after 17-19 days. In contrast, 95% of deaths are reported after 41 days. That is a difference of 22-24 days. Nevertheless, you take the number of deaths 28 days after the midpoint of the study. Why do you take a later point in time than you yourselve have determined? Even with this approach, you are 4 - 6 days too late and overestimate the number of deaths. Why even this would be too late, I will explain in more detail below.

The 41 days were given for the USA. But what is the situation in other countries? In Germany, for example, there is a legal requirement that the death must be reported after 3 working days at the latest. Of course there can also be unrecognized deaths in Germany, where it takes longer to report. But this should be the minority. If we transfer however this fact of the USA to other countries, in which the risk of the long reporting time does not exist in such a way, you take up too many deaths into the counter of the quotient with. This leads to a too high IFR.

Counting the deaths 28 days after the study midpoint is also problematic because in the meantime, further deaths may appear in the statistics that were not infected until after the infected persons identified in the study became infected. This is because not all deaths take as long to report. These are then deaths that are not related to the study. You yourself write that the average value of the report of a dead person lasts 7 days with an IQR of 2 - 19 days. These figures speak in the statistical sense for a right-skewed distribution in the reporting of death figures. This in turn means that the majority of the deceased have a rather shorter reporting time. The procedure leads to a too high number of deaths. This is a problem especially with still existing infection waves, even with already declining infection waves.

You write: “The mean time interval from symptom onset to death is 15 days for ages 18–64 and 12 days for ages 65+, with interquartile ranges of 9–24 days and 7–19 days.”<br /> If we assume the 3 days reporting time for Germany, we receive 18 days for the age 18-64 and 15 days for 65+. In contrast, 95% of the antibodies are formed after 17-19 days, which is about the same or later than the time when the dead appear in the statistics. For other countries this may be different and would therefore need to be investigated. In any case, a blanket assumption from the USA is not possible for studies outside the USA.

Since the mean time interval from onset of symptoms to death is 15 days for the age 18-64 with the interquartile range of 9-24 days, but the midpoint of the range would be 16.5 days, this suggests a right-skewed distribution in the values. The same applies to the mean time interval from the onset of symptoms of 12 days with interquartile range of 7-19 days for the age 65+, where the midpoint of this range is 13 days. This also speaks for a right-skewed distribution of the values. This would mean that the majority of the values would be below the mean value in each case, making shorter times more likely. This also shifts the time too far back. Therefore it would be better to assume the median value, because it is less prone to outliers.

Your example infection wave from figure 1 also shows the problem with this procedure. As you say, antibodies are formed in 95% of people after 17 - 19 days. Now you have an example study with the median 14 days after the start of infection. At that time, only a few of the infected persons have formed antibodies at all, since just 14 days before the infection wave starts with low numbers and then increases. Only 4 days before is the peak of the infection wave. This means that the time period, which is very strongly represented, cannot have developed any antibodies at all. This leads to the fact that only very few infected persons are recognized as infected. In your example, 95% of the deceased are now infected, but only very few of the infected. This leads to a clear overinterpretation of the IFR.

Due to the problems mentioned, the number of deaths should therefore be taken at the median time of the study. Of course, it would be best if the studies took place immediately after the end of a wave of infection, where the death rates are stable and the expression of antibodies is complete.

Antibody Studies

You write: "A potential concern about measuring IFR based on seroprevalence is that antibody titers may diminish over time, leading to underestimation of true prevalence and corresponding overestimation of IFR, especially for locations where the seroprevalence study was conducted several months after the outbreak had been contained.“

You have made many assumptions about the death figures and adjusted the death figures (upwards) accordingly. Here you find that the antibodies disappear over time and that this can lead to an underestimation of the number of infected persons. However, you do not adjust the number of infected persons upwards, unlike your approach to adjusting the death figures. For example, a study by the RKI found that 39.9% of those who tested positive for PCR before did not develop antibodies (https://www.rki.de/DE/Conte... "https://www.rki.de/DE/Content/Gesundheitsmonitoring/Studien/cml-studie/Factsheet_Bad_Feilnbach.html)"). From this, we could conclude that the antibody study only detected around 60% of those previously infected and that the number of infected persons would have to be adjusted accordingly. But you have not done that. I can understand that you did not do that. I wouldn't have done it either, because we don't know how this is transferable to other studies. But in adapting the dead, you have transferred such assumptions to other studies. This should therefore also be avoided. There, too, we do not know how transferable it is. If you only make an adjustment in the dead, but not justifiably in the infected, this leads to an overestimated IFR.

PCR tests from countries with tracing programs

You write in your appendix D: "By contrast, a seroprevalence study of Iceland indicates that its tracing program was effective in identifying a high proportion of SARS-CoV-2 infections“.

In my opinion this is a wrong conclusion. In my opinion, it is not the success of the tracing program, but the number of tests and thus fewer unreported cases. To date, Iceland has performed almost as many tests as there are inhabitants in Iceland. Therefore they could keep the number of unreported cases lower. Other countries did not test as much. Therefore the results are not easily transferable to other countries. The PCR tests only show the present, but not the past and not the untested.<br /> You write it yourself: „(…) hence we make corresponding adjustments for other countries with comprehensive tracing programs, and we identify these estimates as subject to an elevated risk of bias.“<br /> Nevertheless, you leave these studies in meta-analysis, although for the reasons mentioned above this leads to severe problems. The figures for countries with tracing programs should therefore not have been included. The estimated number of unreported cases is not known and cannot be taken over by Iceland.

Study selection

You sort out some seroprevelence studies. These include Australia [63], Blaine County, Idaho, USA [67], Caldari Ortona, Italy [72], Chelsea, Massachusetts, USA [73], Czech Republic [75], Gangelt, Germany [79], Ischgl, Austria [81], Riverside County, California, USA [98] , Slovenia [101] and Santa Clara, California, USA [116]. For the most part, these studies are sorted out because there is no age specification for seroprevelence. Since this is the study's investigation, this is of course understandable. However, these studies in particular have shown calculated IFR values between 0.1% and 0.5%. At the same time, you leave the numbers of PCR tests from countries with tracing programs in the meta-analysis. As already mentioned, this is not correct due to the unknown dark figure and the transfer from Iceland is also not possible, as described before. This leads to the fact that studies with low values are sorted out, but at the same time uncertain numbers with high values are left in the study. This shifts the calculated IFR value upwards in purely mathematical terms.

It is precisely the outliers upwards that cause problems in the calculation. Since the numbers are rather small (in a mathematical sense), there can be no deviation as strong downwards as upwards. This means that there may be studies that deviate perhaps 0.2 percentage points downwards, but other studies that deviate upwards by 1.2 percentage points. This is a problem for the regression, because the regression then leads to too high values. Therefore, outlier detection should be performed upstream and the outliers should be excluded. You can also make it easier by taking the median value, since it is less susceptible to outliers. But then you would have only one value.

You write: “The validity of that assumption is evident in Figure 3: Nearly all of the observations fall within the 95% prediction interval of the metaregression, and the remainder are moderate outliers.”<br /> You can see it in figure 3, but due to the logarithmic scale it is difficult to estimate the ratios. Better suited is Figure 4, which would be desirable for the different age groups to be able to make a better estimation there. Figure 4 shows that many studies are outside the confidence interval, often to a considerable extent and to a greater extent also towards the high IFR values. Looking at the values and the confidence interval, these studies must have significant z-scores, which would show that these are clearly outliers that should not be considered. This leads to the fact that the regression will be brought further in the direction of high values, which results in too high IFR values.

Adjustment of death rates for Europe due to excess mortality

In Appendix Q you write: "In the absence of accurate COVID-19 death counts, excess mortality can be computed by comparing the number of deaths for a given time period in 2020 to the average number of deaths over the comparable time period in prior calendar years, e.g., 2015 to 2019. This approach has been used to conduct systematic analysis of excess mortality in European countries.[159] For example, the Belgian study used in our metaregression computed age-specific IFRs using seroprevalence findings in conjunction with data on excess mortality in Belgium“

I understand why you want to do this. But there are some dangers involved. The above statement may be true for Belgium, but it cannot be transferred to other countries in a general way. Especially since you cannot say in general terms that every dead person above average is a COVID 19 dead person. Mathematically, this would mean that there have been COVID-19 deaths in some of the last few years, because there have been periods with more deaths than the average. This makes the average straight. Especially since, as I said, you can't simply say that every death above the average is a COVID-19 death. The majority will be it, but not necessarily everyone. Thus, even cancer operations that did not take place or untreated heart attacks due to the circumstances and unnoticed visits to the doctor may have contributed a share. Whether this is the case, we do not know without a study. A blanket assumption that every death above the mean value is a COVID-19 death is not correct. From the statement "For example, the Belgian study used in our metaregression computed age-specific IFRs using seroprevalence findings in conjunction with data on excess mortality in Belgium", we could also conclude that the number of reported COVID-19 deaths is correct and can therefore be used as the numerator of the quotient for calculating the IFR. <br /> If you take this as a blanket assumption, how do you deal with those countries that do not have excess mortality but have several thousand COVID-19 deaths in the official statistics? Would you then correct the number of COVID-19 deaths downwards, perhaps even to 0? Certainly not.

Variation in the IFR

You write: "We specifically consider the hypothesis that the observed variation in IFR across locations may primarily reflect the age specificity of COVID-19 infections and fatalities.“

It is also possible that the variation in the calculated IFRs occurs due to still different dark figures. If, for example, the PCR tests are taken in countries with a tracing app, but an IFR based on Iceland is calculated there, this can lead to incorrect and too high IFR values. Also the adjustments of the death rates themselves or the late time of the death rate determination 4 weeks after the study center can lead to this high variance.

Conspicuous features regarding the correct determination of the death figures

In Table 1 you write that on July 15 there were 8 million inhabitants with a projected 1.6 million infections. According to my research there are 8.4 million inhabitants. You calculate the 1.6 million infected on the basis of the 22.7% infected in the study. However, the blood samples were taken between April 19 and 28, so the infections occurred before or until the beginning/middle of April. So you now take the number of infected persons from the beginning/mid-April or from April 24 (study midpoint) and insert them for July 15, i.e. just under 3 months later! In the meantime, however, not only people have died, but have also become infected and formed antibodies. They thus increase the numerator of the quotient, but leave the denominator unchanged, although the denominator would also be higher. So you shift the IFR upwards here as well.

The study on Gangelt, which was not taken into account, shows a similar picture. You write that at the end of June there were 12 deaths and therefore the IFR rises to 0.6%. That is 8 weeks (!) after the study center. This does not take into account that in Germany the deaths must be reported after 3 days. If you have proceeded in this way when calculating the other IFRs from other studies, this suggests that the IFR values are too high.

Calculation of the IFR of Influenza

You calculate the IFR of influenza based on the CDC figures for the 2018/2019 influenza season and indicate the IFR as 0.05%. Firstly, it should be said that statistically it is never good to look at just one value. The average of a time series should be considered. You calculate the value by looking at the estimated deaths and looking at how many were estimated to be symptomatically infected with influenza. You use a study according to which about 43.4% of cases are asymptomatic or subclinical (95% CI 25.4%-61.8%). You then take the mean value from the confidence interval with the value 43.6% and use this figure to calculate how many people were probably infected with influenza. Statistically it is not correct to take the average value of 43.6%. The value of 43.4% must be taken. Due to the small difference, this does not make much difference, but it shows the statistically imprecise consideration that runs through the study and generally leads to an IFR that is too high or, in the case of influenza, too low.

Now a statement on the selection of the 2018/2019 flu season, the CDC writes: "These estimates are subject to several limitations. (...) Second, national rates of influenza-associated hospitalizations and in-hospital death were adjusted for the frequency of influenza testing and the sensitivity of influenza diagnostic assays, using a multiplier approach3. However, data on testing practices during the 2018-2019 season were not available at the time of estimation. We adjusted rates using the most conservative multiplier from any season between 2010-2011 and 2016-2017, Burden estimates from the 2018-2019 season will be updated at a later date when data on contemporary testing practices become available. (...) Fourth, our estimate of influenza-associated deaths relies on information about location of death from death certificates. However, death certificate data during the 2018-2019 season were not available at the time of estimation. We have used death certification data from all influenza seasons between 2010-2011 and 2016-2017 where these data were available from the National Center for Health Statistics. (…)

The CDC writes the same for the 2017/2018 season, so the values, which were always only estimated anyway, were estimated even more due to missing data. Therefore we should have considered the figures for the seasons 2010/2011 to 2017/2017. If we calculate the IFR of influenza in this way and also use the confidence interval to calculate the number of people potentially infected per season, we get an IFR of influenza of 0.077%, ranging from 0.036% to 0.164%. Every single year prior to the 2018/2019 season was above the 0.05% and the average of 0.077% is also 54% above your reported value. This means that influenza is still not as lethal as COVID-19 has been so far, but the factor is not as high as suggested by your study.

It should also be noted that it is not possible to compare an IFR calculation that is equally distributed over age with an IFR of influenza that is not equally distributed over age. You do not do it directly, but by naming these numerical values, this has been taken up by the media. The IFR just indicates the mortality per actually infected person. Therefore the IFR of the actually infected persons of COVID-19 must be compared with the IFR of influenza. You can of course calculate a hypothetical IFR assuming that every age is equally likely to be infected. In this case, however, the calculation must be performed not only for COVID-19, but also for influenza.

I hope I can help you to improve the study in terms of statistical issues. I remain with kind regards.

On 2020-10-29 21:32:27, user Dan Dan wrote:

I believe high dose angiotensin 2 type 1 receptor blockade would alleviate this phenotype as, for example, olmesartan dose dependeny blunts tgfb as well as inhibits the fibrotic response and cardiac remodelling.

On 2021-11-14 09:47:38, user Justbeenschooled wrote:

There are a few incorrect spellings in this document. But the study is excellent and should be peer-reviewed.

On 2020-05-24 21:18:10, user helgarhein wrote:

Thank you for your impressive study. I would like to ask, would you be able to check retrospectively serum 25-hydroxyvitamin-D levels (25(OH)D) in blood samples of the hospitalised covid-19 patients? The findings are probably interesting and might explain some of the excess mortality in people with dark skin types and those who are overweight. I suspect the lower the 25(OH)D level was, the worse the outcome will have been, as found in many observational studies https://www.bmj.com/content....

The crucial point is to understand that the full beneficial functioning of vitamin D will only appear after a blood 25(OH)D level of around 100 nmol/l (40 ng/ml), unlike the erroneous definition of sufficiency of 25 nmol/l (10 ng/ml) by NICE and SACN. https://www.grassrootshealt...

Vitamin D is the substrate for a pleiotropic seco-steroid hormone with multiple gene regulating functions in the immune system and sufficiency will most likely have beneficial influence on the covid-19 illness progression, suggested by 30 experts recently: https://www.bmj.com/content...

A sufficient 25(OH)D level is mainly derived from UVB rays on our skin, or vitamin D supplementation. However, lighter skin types have adapted to be more efficient in using the scarcer sun light of Northern areas, and dark skin types will need much longer sun exposure to produce the same amount of 25-hydroxyvitamin-D https://pubmed.ncbi.nlm.nih... <br /> as do overweight individuals because fat tissue accumulates it. A large number of human diseases are linked to deficient 25(OH)D levels (osteomalacia, depression, diabetes, autism, cancers, infections, inflammatory bowel diseases and many others) and vitamin D deficiency is a worldwide problem.

I have recently retired from over 30 years working as GP in deprived areas of Edinburgh. I have seen many clinical improvements in my patients after rectifying their deficient vitamin D levels, as well as clear differences in 25(OH)D levels in different ethnic groups https://www.ncbi.nlm.nih.go...<br /> 24/5/20 Helga Rhein https://scotsneedvitamind.com

On 2020-04-04 09:25:59, user Oleg Gasul wrote:

The main proof will be if you found statistic of dead by reason of virus with BCG and without.

On 2021-12-04 13:02:08, user Kiwinka74 wrote:

Hi, I think at the very end of the paper there is a typo with the term primary infection, as it reads instead as 'primary reinfection'.

On 2020-12-24 06:54:17, user altizar wrote:

I know a nursing home where at least 6 nurses have been re-infected after around 30 days of having Covid the 1st time. So based on empirical evidence, your study is not accurate.

On 2020-06-05 08:30:20, user Mohamed Abu-farha wrote:

The choice of the control population shows a serious limitation to this study. It technically assumes that people who were not infected are genetically different than those who were infected with the virus.

On 2021-01-15 14:58:17, user Lane Dedrick wrote:

What was the standard care treatment regimen? Did those in the experiment arms receive steroids? Did those in the standard care control arm receive steroids?<br /> What were the outcomes of the ivermectin only arm?

On 2020-04-14 21:58:30, user Rod wrote:

Completly irresponsible work. The treatment must use hidroxycloroquine not cloroquine, and with reduce doses.

On 2020-03-03 14:31:32, user palazmarkazi wrote:

So if the assumption is correct, then we should see those who suffer from it decline the following week

On 2020-09-28 20:41:58, user Paul McKeigue wrote:

The correct link for the code is

https://github.com/mcol/cov...

On 2020-05-13 18:10:56, user João Conrado Blum Júnior wrote:

The finding is reliable. Great text. Congratulations!

On 2020-03-25 21:03:54, user Sinai Immunol Review Project wrote:

Summary of Findings: <br /> - Clinical data from 116 hospitalized CoVID-19 patients analyzed over 4 weeks for correlation with renal injury. Comorbidities included chronic renal failure (CRF) in 5 patients (4.3%). <br /> - Approx 10.8% of patients with no prior kidney disease showed elevations in blood urea or creatinine, and 7.2% of patients with no prior kidney disease showed albuminuria. <br /> - Patients with pre-existing CRF underwent continuous renal replacement therapy (CRRT) alongside CoVID-19 treatment. Renal functions remained stable in these patients. <br /> - All 5 patients with CRF survived CoVID-19 therapy without progression to ARDS or worsening of CRF.

Limitations: <br /> - Renal injury biomarkers in patients with incipient kidney abnormalities not tabulated separately, making overall data hard to interpret. It will be critical to separately examine kidney function (BUN, urine creatinine and eGFR) in patients that developed any kidney abnormalities (7.2~10.8% of cohort). <br /> - No information on type of CoVID-19 therapy used across cohort; will be useful to correlate how treatment modality influences kidney function (and other parameters). <br /> - Invokes previous clinical-correlation studies that indicate low instances of kidney damage [1,2], but those studies did not track longitudinal urine samples for acute renal injury markers and viral shedding. <br /> - CRRT in patients with CRF is standard therapy irrespective of CoVID-19 status; it will be important to compare clinical parameters of these patients (n=5) with virus-naïve CRF patients (none in this study) to make any meaningful conclusions.

Importance/Relevance: <br /> - This study argues that renal impairment is uncommon in CoVID-19 and not associated with high mortaility, in stark contrast to a concurrent study (https://doi.org/10.1101/202... ). If supported by further studies, it suggests kidney impairment is secondary to cytokine storm/inflammation-induced organ failure, and not due to direct viral replication. <br /> - Will be important to comprehensively characterize larger datasets of CoVID-19 patients across hospitals (meta-analyses) to conclude if kidney function is actively disrupted due to viral infection, and if renal disease is a major risk factor for worse CoVID-19 outcomes.

References: <br /> 1. Wang D, Hu B, Hu C, et al. JAMA 2020; published online Feb 7. <br /> doi: 10.1001/jama.2020.1585

1. Guan WJ, Ni ZY, Hu Y, et al. MedRvix 2020; <br /> doi: https://doi.org/10.1101/202....

Review by Samarth Hegde as part of a project by students, postdocs and faculty at the <br /> Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-03-28 16:06:33, user Mark Dane wrote:

Thank you for this important study. Did you collect samples in patient rooms who were either known or expected to not have the virus so we can see the null data?

On 2020-04-21 07:32:38, user disqus_WCLRBohCOX wrote:

It would have been helpful to have Figures 1, 4, and 5 on a log-scale -- especially the visual comparison with actual data in Figure 4 would be much more meaningful.

On 2020-11-05 08:59:04, user Vishal Barot wrote:

Can I have the data of the patient as I would like to perform some prediction based on the historical data?<br /> I have trained certain model using deep learning and would like to contribute for the same.

Thank you

On 2022-09-20 10:24:38, user Okechukwu Onianwa wrote:

Excellent work tracking levels of MPXV environmental contamination during the course of infection. Do you have any thoughts on the impact of daily cleaning on the study data?

On 2020-04-23 21:28:37, user David Scott wrote:

This is interesting, using text mining to estimate how many people are sick. Perhaps people may be more inclined to post that they are sick than actually go to the doctor? I think the article is worth reading.

On 2023-08-18 10:43:55, user Riinu wrote:

Thank you everyone for this amazing piece of work!<br /> The link to download data is broken, this is the correct one I believe: https://mover.ics.uci.edu/

On 2020-04-27 15:15:35, user Teresita Clark wrote:

Do we know the infection-mortality rate? At this point, I thought we only knew the case-fatality rate.

On 2020-07-14 15:20:37, user Robert Kernodle wrote:

How do the authors sustain their claims in light of this:

https://vixra.org/pdf/2006....

On 2020-05-02 20:41:46, user wubba lubba dub dub wrote:

1. The CDC numbers may not be the actual ones. 2. Does not seem to count the fact that the number of testing has increased.

On 2025-07-24 23:50:17, user Rong Liu wrote:

Update on the association between influenza vaccination and cardiovascular outcomes

Dear readers,<br /> As the authors of a living systematic review on the association between influenza vaccination, cardiovascular mortality and hospitalization, (1) we want to update readers on the findings from our most recent search. The review protocol specifies updates every six months for a minimum of three years, commencing April 2022. The baseline search was conducted on 31 May 2022, with subsequent updates on 25 January 2023 and 1 September 2023. Results from these initial searches were published in Vaccine in January 2024. (1)

The latest search, completed on 31 March 2025, identified two studies that meet the eligibility criteria for review inclusion. Both are multi-center trials conducted within a single country, with a follow-up duration of at least 12 months. A third study was excluded due to its shorter follow-up period of only six months. (2,3) The eligible studies include a China-based trial (PANDA II) enrolling patients hospitalized for heart failure, (4) and an India-based trial (FLUENTI-MI) enrolling patients with recent myocardial infarction. (5,6) In both studies, the intervention is influenza vaccination. The comparator in FLUENTI-MI is saline placebo, and standard care in PANDA II. The primary outcome in both trials is a composite of all-cause mortality and all-cause hospitalization during the locally defined influenza season.

As of 6 June 2025, neither study has publicly available results, and therefore we are unable to update the meta-analysis at this time. Table 1 summarizes the study characteristics and expected timelines. PANDA II completed recruitment in February 2024 and is expected to report results within the next year. (4) FLUENTI-MI is projected to complete recruitment in October 2028. Given the current pace of research in this area, we believe that biannual updates are no longer necessary, and we will transition to annual updates for the next five years, starting from the date of this latest search.

Reference <br /> 1. Liu R, Fan Y, Patel A, et al. The association between influenza vaccination, cardiovascular mortality and hospitalization: A living systematic review and prospective meta-analysis. Vaccine. 2024/02/15/ 2024;42(5):1034-1041. doi: https://doi.org/10.1016/j.vaccine.2024.01.040 <br /> 2. Liu R, Patel A, Du X, et al. Association between influenza vaccination, all-cause mortality and cardiovascular mortality: a protocol for a living systematic review and prospective meta-analysis. BMJ Open. 2022;12(3):e054171. doi:10.1136/bmjopen-2021-054171<br /> 3. Tkaczyszyn M. Vaccination Against Influenza Pre-discharge in Heart Failure. https://clinicaltrials.gov/study/NCT06725927 <br /> 4. Zhang Y, Liu R, Zhao Y, et al. Influenza vaccination in patients with acute heart failure (PANDA II): study protocol for a hospital-based, parallel-group, cluster randomized controlled trial in China. Trials. 2024/11/25 2024;25(1):792. doi:10.1186/s13063-024-08452-8<br /> 5. Roy A. Influenza Vaccine to reduce cardiovascular events in patients with recent myocardial infarction: a multicentric randomized, double-blind palcebo-controlled trial. https://trialsearch.who.int/Trial2.aspx?TrialID=CTRI/2024/05/067056 <br /> 6. Roy A, Yadav S. Influenza vaccine in cardiovascular disease: Current evidence and practice in India. Indian Heart Journal. 2024/11/01/ 2024;76(6):365-369. doi: https://doi.org/10.1016/j.ihj.2024.11.247 <br /> https://uploads.disquscdn.c...

On 2025-07-30 13:21:44, user Abebe Almu Fola wrote:

the preprint has been published PMID: 40681807 PMCID: PMC12274420 DOI: 10.1038/s43856-025-01008-0. Can you help me to do connection between the preprint and the published version

On 2020-03-23 18:11:51, user Butch Davis wrote:

Seems to me that without the rH factor it doesn't have much meaning

On 2022-07-25 08:55:06, user Dr. D. Miyazawa MD wrote:

Explained for the general public<br /> https://note.com/miyazawane...

On 2022-10-24 16:26:51, user KaAcWh wrote:

Dear Drs. Pretorius and Bell. Thank you for this interesting study. I am concerned, however, of your experimental design and statistical approach used. We know now, from various peer-reviewed publications, that covid vaccines can cause microclotting, platelet hyperactivation, immune dysregulation and can also lead to autoantibodies being produced. By having two study groups: i.e. controls and long-COVID sufferers, each including a fair amount of vaccinated individuals (33 vs 24%) and then comparing mean values of each response variable between these groups, it is not possible to examine the contribution of COVID vaccination to the values. This is a serious flaw in your study as it currently stands. I would be very interested in seeing the new results with two-way ANOVAs, GLMs or mixed effects models to take into account the effect of vaccination to your observed results. Without this approach, it is simply not possible to conclude what you and your coauthors have concluded.

On 2022-11-18 07:18:20, user Ehrenfried Schindler wrote:

Dear authors,<br /> first, I would like to congratulate to this fantastic initiative. The study is of great importance for pediatric anesthesia. In order to avoid limitations of the apricot study together with unsharp data I want to suggest a (online) meeting with the apricot steering committee to discuss your protocoll. This may help your study to hopefully much better data quality as in the apricot <br /> Once again, congrats and good luck to you all!<br /> Ehrenfried Schindler, Bonn, Germany

On 2022-12-01 02:06:01, user Kenneth Alfano wrote:

NOTE: A revised version of this article has been published in the Journal of Clinical Trials (open access), after peer review and edits/corrections. Citation: Tarasev M, Chakraborty S, Alfano K, Muchnik M, Gao X, Davenport R. (2022) Use of Packed Red Blood Cell Mechanical Fragility to Indicate Transfusion Outcomes. J Clin Trials. S19:001. DOI: 10.35248/2167-0870.22.S19.001

On 2022-12-09 15:39:45, user Christine wrote:

An updated count of US PICU beds from 2018 by the AAP shows n= 5,388. Still a disproportionate amount compared to Canada!

https://doi.org/10.1542/ped...

On 2022-12-22 00:47:40, user Charles Warden wrote:

Hi,

Thank you very much for posting this preprint.

I apologize if I am overlooking something, but I was trying to learn more about Supplemental Table S1. Is this missing a reference in the main text?

Also, separate from Table S1 in the PDF, there is an uploaded Excel file that says "vip_gene_count" in the sheet name. Is this meant to be referenced in the first paragraph of the results for the 'VIP' database? If so, should there be a main text reference and an identifying number such that the total tables would then become Table S1-S5 (instead of the current Table S1-S4 in the PDF)?

Thank you very much!

Sincerely,<br /> Charles

On 2024-01-17 20:36:43, user Karen Eddleman wrote:

In the acknowledgements section, there is a sentence that appears in both paragraphs: "The All of Us Research Program would not be possible without the partnership of contributions made by its participants."

Also, the correct spelling should be "principal" in this sentence: "See the supplementary information for a roster of past and present All of Us principle investigators."

On 2022-12-29 21:25:40, user Tugba Onal-Suzek wrote:

This article is published at https://dergipark.org.tr/en... The title of this article changed with one additional author

On 2023-01-21 12:41:08, user Joel Pessa wrote:

There appear to be 3 paths for CSF to drain (recirculate) from the brain:

1. direct drainage through CSF channels in the dura (hence the name canaliculi) directly along the falx to the thoracic duct

2. thru arachnoid granulations into the sagittal sinus veins

3. thru extra-dural lymphatic communications to the head and neck (scalp, nasal sinus)

If the first path is blocked, CSF backs up in the optic nerve (pia and epidural CSF channels), leading to optic disc edema.

This is a proposed etiology for the spaceflight-associated neuro-ocular syndrome.

On 2023-02-08 09:14:39, user Jacopo wrote:

This manuscript has been published on Communications Medicine with the following DOI https://doi.org/10.1038/s43856-023-00245-5

On 2023-02-10 20:50:43, user Onuralp wrote:

This preprint shows a very useful application of NLP for annotating clinical trial outcomes and makes the derived labels publicly available on their platform. We have previously used these annotations to assess the relative importance of human genetics evidence, and would appreciate if authors can cite our work where relevant.

https://arxiv.org/abs/2207....

On 2023-03-15 10:27:28, user Chinh Quoc Luong wrote:

Dear The medRxiv Team and Readers,

Our article has been published in BMJ Open.[1]

Thank you very much!

Best regards,<br /> Chinh Quoc Luong

[1] Do SN, Dao CX, Nguyen TA, et al. Sequential Organ Failure Assessment (SOFA) Score for predicting mortality in patients with sepsis in Vietnamese intensive care units: a multicentre, cross-sectional study. BMJ Open 2023;13:e064870. doi: 10.1136/bmjopen-2022-064870.

Online publication is available at: https://bmjopen.bmj.com/con...

On 2023-04-17 21:38:28, user Dean F. Sittig wrote:

Did all of the studies you reviewed use the same order-retract-reorder measure to determine potential wrong-patient errors? If so, did any of them consider the implications of using this type of estimate in situations in which the likelihood of a user noticing that they made a wrong patient decreases as the number of charts open at the same time increases? If this hypothesis re: the limitations of the measurement were true, what would that do to the conclusions drawn from the studies that used the measure?

On 2023-04-25 17:12:24, user Héctor Ramos wrote:

The paper has been accepted and published at Georgian Medical News.

Pubmed: https://pubmed.ncbi.nlm.nih...<br /> GMN: https://www.geomednews.com/...

On 2023-04-26 15:28:46, user Matthew A Stults-Kolehmainen wrote:

There is a correction needed in the original preprint.

On lines 346-347, it should read, "Model B had a better fit index than Model A, as quantified by the cophenetic correlation 346 coefficient (c = 0.85 and 0.50, respectively)".

On 2023-05-05 14:25:04, user David Curtis wrote:

A number of other studies have examined the effects on common phenotypes of rare coding variants identified in exome-sequenced UK Biobank samples.

This paper examines the effect of using different annotation schemes to quantify missense variant pathogenicity, including in GCK, LDLR and PCSK9:

Curtis D. Exploration of weighting schemes based on allele frequency and annotation for weighted burden association analysis of complex phenotypes. Gene 2022 809 146039.

These papers report that rare variants can have large effects on phenotype, again including in GCK, LDLR and PCSK9:

Curtis D. Analysis of rare coding variants in 200,000 exome-sequenced subjects reveals novel genetic risk factors for type 2 diabetes. Diabetes Metab Res Rev 2021.

Curtis D. Analysis of 200,000 exome-sequenced UK Biobank subjects implicates genes involved in increased and decreased risk of hypertension. Pulse 2021 9 17-29.

Curtis D. Weighted burden analysis in 200,000 exome-sequenced subjects characterises rare variant effects on BMI. Int J Obes 2022 46 782-792.

Curtis D. Analysis of 200,000 exome-sequenced UK Biobank subjects illustrates the contribution of rare genetic variants to hyperlipidaemia. J Med Genet 2021.

All these papers seem very relevant and I would have thought that they ought to be cited.

On 2023-05-28 06:54:51, user Stuart Gilmour wrote:

Dear authors, I really want to believe this study (I am vulnerable to Ramsay Hunt Syndrome and have got this vaccine, and I would love to believe it also reduces my risk of dementia!) but I think you have massively under-estimated the effectiveness of the vaccine, which is a real missed opportunity. I want to explain why and I hope you'll take my comments into account. I think there are three sources of error in your study which I list in order of severity: 1) failure to take into account period at risk, 2) the change in slope term and 3) confounding due to education/wealth in sub-analyses.

[Obviously, the comments that follow assume I have correctly understood your methods, so please forgive me if I have missed something your explanation]

1) is the reason I think the study under-estimates the effect. I wondered why it is that you found a vaccine efficacy (after adjusting for take-up of the vaccine) against shingles of 41%, while the 2005 NEJM study you reference finds it to be 55%, and I think this is because you have not properly accounted for follow-up time. Judging by how you report probabilities, you seem to have calculated the proportion of people over seven years who got shingles (Fig 2) or dementia (FIg 3). This is also clear from your equation (1), which is a linear probability model. But since shingles incidence, dementia incidence and death risk increase by age and your primary study cohort is 80 years old, follow-up time is a very important variable. Judging from your figure 2, the youngest people were 78 and the oldest 82 in this study. It's very likely therefore that the youngest people had to be followed for considerably longer before a diagnosis of shingles/dementia, and were also less likely to die of other causes. A person who dies of other causes before getting shingles/dementia should not be considered in the calculation, since we didn't find out whether they got it - they should be censored. Then, if we calculate incidence densities, we will find the youngest people (with the lowest proportion of cases) have a considerably longer follow-up time to diagnosis, and were less likely to drop out of follow-up early due to death. If you properly account for this in the model, I think you'll find that the rate in younger people is much lower than in older people and the discontinuity is greater.

I don't have UK data to hand, but I do have a life table by single year of age for the USA, which implies that there would probably be about 40% more follow-up time in the 78 year olds than the 82 year olds over the entire 7 years of the study, simply because of drop out due to death from all causes - a 78 year old american has a 4% chance of dying in one year, while an 82 year old has a 5.7% chance. Those differences add up over 7 years of follow-up!

This study is a classic survival study, and your decision not to use the follow-up time means that you have over-estimated the incidence density in young people and under-estimated it in older people. This also explains why your sex-stratified analysis finds no effect in men. How could the vaccine not work in men but work in women? Because at this age (~80 years old) men are dying much faster than women, with death rates increasing more rapidly over the study period, which attenuates the effectiveness more in men than in women.

If you use an incidence density (Poisson regression) or survival approach, it's easy to reproduce the approach described in equation (1) but you'll be properly accounting for follow-up time, avoiding the known problems associated with a linear probability model, and properly able to compare your results with those of the previous shingles vaccine studies.

[I'm sorry all my comments here hinge on my interpretation from your methods that you have assumed a 7 year follow-up for everyone, and simply calculated the proportion of events as the number who got shingles/dementia divided by the number at risk at the start of the 7 years. If I'm wrong about this, please ignore everything I wrote!]

For problem 2), the change of slope term, it seems obvious to me that the slope after week 0 in figure 3A is poorly fitted. If there was no change of slope term in this model, the change in level would be smaller and your study would show no effect. Was the beta3 term in your model for figure 3 statistically significant? I think it wasn't - there is no visible change of slope in the data shown there. Given how borderline your estimate of the change in level (Beta1) is, I think the conclusion of this analysis depends heavily on whether you choose to include the non-significant change of slope. Of course, this isn't very important because a) we should always report studies of this kind separately by sex and b) once you properly adjust for follow-up time the effect of the vaccine will be so huge that we'll immediately have a statistically significant effect with or without the change of slope term.

For problem 3), you estimate the CACE based on the assumption that there is "no other difference in characteristics that affects the probability of our outcomes occurring", and date of birth eligibility threshold "is a valid instrumental variable to identify the causal effect of receipt of the zoster vaccine on our outcomes". I'm not sure why you would believe this. People who receive any voluntary preventive health care in the UK are much more likely to be wealthy, to be better educated, and to be from certain occupations and backgrounds, and I would suggest it's highly likely that these factors are strongly associated with reduced risk of dementia. The method here is nice, but the assumption is completely unreasonable in the NHS context, and it's likely that these confounding factors would lead to a reduction in the CACE estimate. Again, if you properly account for follow-up time I doubt this will matter because the raw impact of the vaccine eligibility itself will be so much larger than your estimate that you will find a much bigger impact without needing to do any calculation of CACE (but anyway a simple caveat about this, or a calculation separately in each wealth stratum, might solve the issue).

I can't see any way that the lack of proper calculation of follow-up time would reduce the effectiveness of the intervention you have tested, so I'm going to continue to believe that this vaccine prevents dementia, but I worry that you have massively under-estimated the size of the effect and I guess there is a tiny chance the impact of this mis-calculation could go the other way.

I guess you could argue it doesn't matter if you've under-estimated the effect but I would say it does. I'm sure you're aware that in the UK the chickenpox vaccine is not part of the routine childhood immunization schedule. If your study finds a huge effect of shingles on dementia risk, this is a strong argument for preventing it at childhood, through inclusion of the vaccine in the routine schedule. But currently your study finds no benefit for men, a 20% overall reduction in relative risk, and about a 40% reduction in relative risk for women. I think if you properly account for follow-up time the effects will be much larger and consistent across men and women. Even a cursory consideration of such large numbers would surely be sufficient to tip even the UK's relatively anti-vaccination institutions into recommending both a) routine chickenpox vaccination of children b) routine shingles vaccination of adults and c) earlier implementation of adult vax. Currently for example in Japan the vaccination for shingles is recommended at age 50 but not covered under insurance, costs about 40,000 yen (350 pounds) and is not widely taken. If it has a huge impact on dementia risk the policy implications are enormous. So please don't undersell your work by using this linear probability model!!!

Thank you!<br /> Stuart Gilmour<br /> Professor, Biostatistics and Bioinformatics<br /> St. Luke's International University<br /> Tokyo<br /> Japan

On 2023-07-03 11:11:46, user David Escors wrote:

The peer-reviewed published version is in here: https://www.embopress.org/d...

On 2023-08-13 10:24:43, user Cath Miller wrote:

Why are the "never vaccinated" grouped with those with 1 or 2 doses?

Is there a likelihood that people who reacted badly to 1st or 2nd jab didn't take a third?

On 2023-08-13 18:29:56, user Zrinka Starcevic wrote:

The authors present a well-designed study with few weaknesses that should be adressed before publishing or discussed in the manuscript. The strength of this study is inclusion of patients from a younger age group, as opposed to studies that have been published about the subject so far. The English in the manuscript is written in a professional manner and is in need of minor changes. A more detalied introduction would be something I would suggest as well as more information on the choice of inclusion criteria. Furthermore, limitations in the study are in need of further investigation and elaboration. Lastly, I commend the authors on this study and encourage further work on the subject.

On 2023-09-19 08:13:05, user Robert Eibl wrote:

To my understanding both bivalent vaccines seem to work very well. I wonder if the slight differences could be attributed to the different group composition - although very comparable (but one group had about 10% more diabetics and also more heart diseases in the group). The technology of the mRNA vaccines seems to be comparable, but if I remember correctly, Moderna uses 100 microgramm per dose, BioNTech only 30, right? Could this be an explanation for the supposed better outcome with the higher dose?

On 2023-10-03 13:28:53, user Will wrote:

The full published version of this article is available here: https://doi.org/10.1186/s12...

On 2023-10-09 09:09:13, user Susie Huntington wrote:

The article has now been accepted for publication by the European Journal of Cancer Prevention without edits and will be published shortly.

On 2023-10-25 00:51:38, user Samina Sultana wrote:

Wonderful research structure! It's very admirable how much detail was provided to illustrate the process by which the literature to support the study purpose was selected. Guiding the readers through step by step process in which eligibility of each paper was determined through meticulous and fully blinded process not only instills trust from the audience but it also validates the credential of the information that has been analyzed and dissected to be included in this paper. I understand that the actual transition practices were vaguely described, but were there any information provided that would help synthesize the outcome of these 10 selected transition strategies already in practice? It would be a useful piece of information to support the purpose of this paper, which is to establish what already exists as a basis for future work on relative effectiveness. Examining the efficacy of these initial topology to highlight the importance of work done in this paper.

On 2023-11-05 07:39:12, user Anikó Lovik wrote:

This preprint has now been accepted for publication in The Lancet Regional Health - Europe. The final version can be accessed here: https://www.thelancet.com/j... or through DOI: 10.1016/j.lanepe.2023.100756 or

On 2023-11-26 10:25:15, user Chris Dye wrote:

Just for fun, you might also be interested in the long-forgotten... Epidemiology Infect. (1995), 115, 603-621 Measles vaccination policy

On 2023-11-29 10:44:02, user Theo Sanderson wrote:

Thank you for posting this preprint.

Sequencing data can be found via Sequence Read Archive (SRA) accession PRJNA1043164.

I have not been able to locate these data on the SRA - could you provide a link? Many thanks.

On 2023-12-12 14:56:43, user Tanmoy Sarkar Pias wrote:

This paper has been accepted to an IEEE conference. A link (& DOI) to the IEEE Xplore will be added when this article is published. Please see the following copy right details of IEEE..

2023 26th International Conference on Computer and Information Technology (ICCIT), 13-15 December, Cox’s Bazar, Bangladesh

979-8-3503-5901-5/23/$31.00 ©2023 IEEE

On 2023-12-28 06:16:03, user William Bond wrote:

Thank you for the excellent research.

I would like to help in two ways. I think these changes will make a difference in the peer review process.

First there are two misplaced numbers in Table 1. The median age numbers were obviously put in the wrong columns. This is a very serious error that does not impact the rest of the paper. The total median age of 54.1 is correct. The median age for vaccinated is actually 55.7 (not 45.3 as erroneously printed) and the median age of the unvaccinated is 45.3 (not 55.7 as erroneously printed). The numbers as printed are impossible.

Second, because of this significant disparity in median ages, I would like to see your analysis for each age category. Of course, the younger group is statistically healthier. We want to know how people who are 20-29 and vaccinated compare to people 20-29 who are unvaccinated, and so on for each age category (30-39, 40-49, 50-59, 60-69, 70-79, 80+). Median ages and comorbidities should be disclosed for both groups in each age category.

You have gathered some of the best data in the world. These additional reports could be done in a later study; the p value will likely increase, which can make publication more difficult.

On 2023-12-29 17:07:05, user Brinda Gour wrote:

Hi, greetings for the day!<br /> This paper mentions that the supplementary data will be available upon request. Could you please grant access to supplementary material for me? This will be helpful for my review. Thanking you.

On 2024-01-17 12:03:28, user Leonardo Martins wrote:

This ancestral-reconstruction based phylogeographic approach has been used before by us in SARS-CoV-2 analyses: <br /> 1. for finding the number of transmission events into or outside Lebanon https://www.ncbi.nlm.nih.go... <br /> 2. For estimating migration patterns between regions of England https://www.nature.com/arti...<br /> 3. To count the number of exports and importations into Pakistan https://www.ncbi.nlm.nih.go...

In our case we used the mugration model as implemented in TreeTime or ASR models implemented in Castor for R (https://cran.r-project.org/... "https://cran.r-project.org/web/packages/castor/index.html)")

On 2024-04-27 21:04:36, user Christopher Ho wrote:

I'm so glad that more research is being conducted on Tsw. As a TSW sufferer myself, it gives me hope to know that there may be better ways of diagnosing and treating the condition. I hope more studies come out of this and thank the authors very much for taking a big brave step forward towards publishing this for our community. It means a lot to us that this condition is acknowledged and medical guidance for use of TCS is properly updated by pharmaceutical companies and dermatologists.

On 2024-05-13 23:39:06, user Srinidhi wrote:

So happy to see research being done in this space. Please continue- this helps TSW warriors and everyone in the skin community!

On 2024-05-19 16:23:05, user Millie Le wrote:

Five dermatologists and a referral to Johns Hopkins culminated into an informal diagnosis of “funky eczema”, launching my 12-year relationship with TCS and triamcinolone injections. No one could make sense of my eight biopsies. They all noted the same result - the possibility of a drug reaction. Because no one in the medical community equated TCS to this possibility, I continued to manage my misdiagnosis with the medication that started it all. Until it was unmanageable. From unknown hives to TSW, my journey is a familiar story. “Although no formal diagnostic criteria for TSW exist, reports of patients experiencing TSW are very common online.” Yes. Sadly, this. I am forever thankful to the Internet which led me to ITSAN and a global community who understood what I was experiencing. My entire world changed with this knowledge and four years later, I no longer have “funky eczema”. I am grateful to researchers who did not cast a whole community aside as overzealous medical sleuths quarterbacking the medical community from their desks. With continued research such as this, we are equipped with the tools to educate and prevent others from making the same unnecessary TSW journey where, “The only way out is through.” Thank you for your dedicated work which further validates and acknowledges the existence of TSW. More research to understand TSW, diagnose it, and treat it, please.

On 2024-05-06 14:16:22, user DocLee wrote:

How about you break it down by infection versus reinfection? I would think you have the relevant data as you seem to have the history of those previously infected and not infected. But, for some reason, you don't seem to be putting it in these papers of yours.

If you're using the entire population which includes all persons previously infected and those not previously infected, claiming that the number of doses correlates to an increase in infection doesn't make any sense as a good portion of your population has already been infected. Without breaking it down into infection versus reinfection, you really can't make that claim as you're actually looking at initial infection versus reinfection. If the rate of initial infection increases with each dose among those that have not been previously infected, you'd have an argument.

On 2024-07-09 12:41:39, user Peter A McCullough wrote:

Binkhorst and Goldstein have used a small sample size which is insufficient to find the signal of COVID-19 vaccination, subclinical myocarditis, and sudden death in athletes triggered by catecholamines during exertion. In June 2021 the US FDA and CDC issued a warning on mRNA COVID-19 vaccines and myocarditis as a serious adverse event. Binkhorst and Daniels are advised to be cautious and conservative on new genetic biotechnology that has clear cardiac safety concerns in the published literature and by regulatory authorities.

We have found it takes a vaccinated population of ~2 million to readily observe the vaccine effect of increasing cardiac arrests. See Hulscher, N.; Cook, M.; Stricker, R.; McCullough, P. A. Excess Cardiopulmonary Arrest and Mortality after COVID-19 Vaccination in King County, Washington. Preprints 2024, 2024051665. https://doi.org/10.20944/pr...

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

On 2024-07-10 21:16:54, user Nicolas Hulscher wrote:

This study inadequately addresses the role of vaccination by failing to account for the documented adverse events associated with mRNA vaccines. According to Polykretis et al ( https://doi.org/10.1111/sji.13242 ), there is substantial evidence of serious adverse events following mRNA vaccination, including myocarditis, which is known to increase the risk of SCD. Their analysis highlights that the incidence of myocarditis post-vaccination is significantly higher than post-SARS-CoV-2 infection, especially in young males. Additionally, their findings show a concerning rise in athlete deaths due to cardiovascular complications since the rollout of mRNA vaccines, far exceeding pre-pandemic averages. Thus, Binkhorst and Goldstein's conclusion that there is no evidence of a link between mRNA COVID-19 vaccination and SCD in athletes is unsubstantiated and overlooks critical data suggesting otherwise .

On 2024-08-14 07:26:35, user Christina Dahm wrote:

Hi there!<br /> Since posting here, our paper has been published. Could you add this information to the preprint listing? Thanks!<br /> 10.1007/s00394-023-03090-3<br /> bw Christina

On 2024-08-23 00:32:16, user julioplazadaz wrote:

The preprint is now published in Nutrients. 2022 Dec 28;15(1):147. doi: 10.3390/nu15010147.

On 2024-10-14 17:14:18, user muschellij2 wrote:

Is the code for the fitted model actually released anywhere or is the link just to the general nnUNET repo?