A large Korean study just published in Biomarker Research analyzed cancer diagnosis by COVID-19 vaccination status in 1yr following primary or booster doses of vaccines.

They report more diagnoses after vaccination after primary doses, but a closer look shows the patterns are inconsistent, biologically implausible, and best explained by surveillance bias/reverse causality, not vaccine causality. 🧵 The study used Korean National Health Insurance data (2021–23), assessing whether cancer diagnoses were greater in the year after vaccination vs. unvaccinated controls.

The study compared 2.38M primary vaccinated vs. 595k unvaccinated, propensity score matched on age (<65yrs, 65-74yrs, 75yrs+), sex, SES, comorbidities (0, 1, 2+), and infection history.

It also compared 711k boosted vs. 356k non-boosted, also matched.

Outcomes were stratified by age, sex, and vaccine type (mRNA vaccines and cDNA vaccines), and also presented for 1m, 3m, 6m, 9m and 12m after vaccination.

The study looked at both overall cancer diagnoses and incidence of many individual cancer types.

During the recent Senate hearings, participants highlighted a Henry Ford Health System study, billed in a forthcoming documentary as ‘An Inconvenient Study,’ and portrayed it as conclusive proof that vaccines elevate childhood chronic-disease risk, claiming it was concealed by researchers fearing retaliation.

Drawing on the Senate hearings, public reports, and other online materials, I reconstructed the study details and in this post summarize some of my main findings and concerns. These overlap with Dr. Scott’s observations, with several additional points of my own.

Study at a glance. The cohort includes ~18,500 children from the Henry Ford Health System (Michigan), born 2000–2016, comparing ~16,500 vaccinated to ~2,000 unvaccinated on diagnoses of multiple chronic conditions, as documented in the Ford electronic medical records.

Reported results. The overall rate of “any selected chronic disease” was about 2.5× higher in vaccinated children. For various individual or grouped outcomes, many estimates were statistically significant and fell in the 3.0–6.0×range, leading the authors to conclude that “vaccine exposure in children was associated with increased risk of developing a chronic health disorder.”

Why causal claims aren’t warranted. The dataset is valuable, but the study has major limitations that compromise the ability to make strong conclusions.
1. Baseline imbalance/confounding: large differences between groups, with key potential confounders not taken into account.
2. Ascertainment bias from health-care utilization differences: Major differences in encounter rates likely inflate detection in one group.
3. Age-related ascertainment bias from unequal follow-up: Vaccinated and unvaccinated have very different follow-up distributions, affecting who ages into typical diagnosis windows.

Follow-up is the critical limitation of this study. Median follow-up is ~1.2 years (unvaccinated) versus ~2.7 years (vaccinated): far too short, especially for many of these conditions often first identified around ages 5–10 years once children are in school.

This severely limits the study’s ability to assess whether vaccine exposure increases risk of developing chronic disorders over childhood, regardless of statistical significance in the reported comparisons.

I’ll delve into these points in more detail below. First a brief summary of the study and results:

Design and cohorts. The study follows ~18,500 children in the Henry Ford Health System (Michigan), born 2000–2016. About 2,000 were completely unvaccinated and ~16,500 received at least one vaccine(ranging from 2 to 22 doses).

Analysis. The study compares vaccinated and unvaccinated children using two approaches:
1. Incidence Rate Ratios (IRR): Poisson models of raw diagnosis counts per person-time, without confounder adjustment.
2. Hazard Ratios (HR): Cox time-to-event models, reported both unadjusted and adjusted for sex, race, gestational age, birth weight, respiratory trauma at birth, and birth trauma.

I appreciate that @joshg99 has unblocked me, allowing me to respond to his thoughtful comments on my previous post discussing his recent preprint. The paper reports a potential safety signal for pregnancy loss among women vaccinated between 8–13 weeks of gestation.

Before responding to specific points, I want to better summarize and explain the points I made in my analysis, and then will refer back to these in my responses.

First, I want to emphasize that there is much to appreciate about this paper:

1. It uses a large, high-quality healthcare dataset that is far superior to passive surveillance systems like VAERS, which lack control groups and suffer from heterogeneous reporting biases.
2. The modeling is careful and the writing is clear.
3. The authors responsibly acknowledge that their findings are not causal and require further validation.

That said, the paper’s central conclusion, a potential safety signal, is driven almost entirely by a small, highly selected subgroup: women receiving their first COVID vaccine dose between gestational weeks 8 and 13. There is no indication this window was pre-specified in a protocol or analysis plan.

The authors also suggest a safety signal for third doses in the 8–13 week window. However, their own observed-minus-expected plot shows this appears to be concentrated in a single gestational week (likely week 10, see below), raising questions about the robustness of that result.

To fully evaluate the strength and validity of the conclusions, several additional analyses are essential.

These fall into four main areas, which I will next summarize. 1. Calendar Time Confounding

The pivotal 1st dose 8–13 wk cohort includes just 1.9% of pregnancies and 1.8% of pregnancy losses, and >90% of this group had last menstrual periods (LMPs) between Oct 2020 and Jan 2021: i.e., immediately after vaccine rollout.

To assess potential confounding by calendar time:

1. The LMP distribution is only shown for the pivotal COVID 1st dose 8–13 wk group. It should also be shown for all of the other vaccination cohorts, especially the flu 8–13 wk group used as a control.

2. The authors should present observed-minus-expected pregnancy loss results for unvaccinated women, both overall (for completeness and transparency) and for subcohorts with LMP distributions matched to each vaccinated group (e.g., 1st dose 8–13 wk, 3rd dose 8–13 wk, 14–27 wk groups, flu 8–13 wk, etc.)

3. It would also be informative to present observed-minus-expected pregnancy loss for unvaccinated women as a function of LMP date (monthly?) throughout the pandemic, which would be a nice descriptive analysis of potential calendar time confounding.

Differences in timing could influence results through factors like lockdowns, pandemic wave exposures, and healthcare access and utilization.

Showing raw and observed-minus-expected outcomes for matched unvaccinated cohorts would help clarify whether calendar time is a source of bias. Differences would warrant caution in interpretation; consistency would increase confidence.

A new preprint has posted online assessing potential safety signals for pregnancy loss after mRNA vaccination during pregnancy using Israeli electronic health records (EHR data), with first author @joshg99 and senior author @RetsefL (newly appointed member of CDC's ACIP).

Their primary conclusions were that pregnant women vaccinated for COVID in the second half of the first trimester (wk8-13) had greater observed pregnancy loss rates than expected from the pre-pandemic regression model computable from EHR.

They also found that pregnant women vaccinated for COVID in the second trimester (wk14-27) and pregnant women vaccinated for flu from wk8-13 or wk14-27 had significantly lower-than-expected observed pregnancy loss rates during the pandemic,
and that women vaccinated for COVID or flu before pregnancy had slightly lower-than-expected pregnancy loss rates. They attribute these results to residual confounding (i.e. healthy vaccinee effect HVE)

The paper is exceptionally well written and introduces a rigorous approach for identifying potential safety signals from EHR data, an active reporting approach that avoids the key limitations of passive reporting systems (like VAERs in USA, AEFI in Israel): (1) reporting bias and (2) the lack of control group.(poorly understood limitations of these systems that I have harped upon ad nauseum on social media)

However, the paper has some key omissions that limit the ability to carefully interpret the results, including:
1. Failure to investigate and fully account for pandemic-related calendar time varying confounders.
2. Lack of assessment of whether women remaining unvaccinated throughout pregnancy during the pandemic had higher- or lower-than expected pregnancy loss.
3. No assessment of whether COVID-19 infections later in pregnancy were factors in post-vaccination pregnancy losses, especially for the primary cohorts.
4. Incomplete summary of results of vaccination before pregnancy
5. Lack of assessment of whether women vaccinated before week 8 of their pregnancy had higher- or lower-than-expected pregnancy loss.
6. Lack of summary of which type of pregnancy loss outcomes (spontaneous abortion, induced abortion, stillbirth) dominated the events for each modeled cohort.

The authors’ observed-expected analysis approach could be readily applied to perform each of these suggested analyses.

Given that the primary vaccinated subgroups driving their conclusions are very small (e.g. 1st dose wk8-13 cohort being 1.9% of pregnancies and 1.8% of pregnancy losses) and with pregnancies during specific times during the pandemic (e.g. ~90% of 1st dose wk8-13 having last menstrual period (LMP) between 10/2020 and 1/2021), there is concern for remaining residual confounding in these cohorts, from pandemic-related or medically-related confounders, and these analyses could shed more light on whether this concern is significant or not.

The inclusion of these results would provide a more complete and transparent picture of potential COVID vaccine effects on pregnancy loss.

It is not valid to dismiss any results showing lower-than-expected pregnancy loss in vaccinated subgroups as driven by residual confounding (by claiming HVE) without acknowledging that the higher-than-expected pregnancy loss in one small vaccinated subgroup (covid vaccinated wk8-13) could similarly be driven by residual confounding.

This thread will walk through the key details of their study and results, and elaborate some on these concerns. This paper (see link below) sets out to investigate whether there is a potential safety signal for pregnancy loss when vaccinated pregnant women for COVID-19 during the pandemic.

Their study is based on electronic health record (EHR) data from Maccabbi, one of four HMO’s in Israel, accounting for 26% of the Israeli population.

As mentioned above, this qualifies as an active reporting system study of vaccine safety that is much more rigorous than passive reporting systems (e.g. VAERs in USA or AEFI in Israel), since the EHR contains events for control groups and does not have the same highly variable reporting bias.

medrxiv.org/content/10.110…

Cleveland Clinic researchers have posted a preprint on medRxiv assessing the flu infection rate among employees who received the flu vaccine relative to those who did not receive the flu vaccine.

Using a Cox regression model while adjusting for age, sex, clinical nursing job, and location (but not propensity to test for flu), they found the vaccinated had a hazard ratio of 1.27, concluding the vaccinated had a 27% higher risk of infection and thus concluding -27% negative vaccine effectiveness.

As in their previous studies on COVID-19 vaccines (cited by many as evidence of negative vaccine effectiveness), this study suffers the same fatal flaw of testing bias, with vaccinated significantly more likely to test for flu than unvaccinated, 27% more likely in fact.

They try to dismiss this concern by claiming the test positivity is equivalent between vaccinated and unvaccinated based on a strange and poorly justified linear regression analysis.

But when looking at the data in their figures 1-2 and summarizing on an appropriate scale, it is clear that the test positivity is substantially lower in vaccinated than unvaccinated (20% lower), so the testing bias cannot be dismissed, and suggesting the increased testing in vaccination is a bias that can explain their HR=1.27 effect, and not indication of higher infection rates.

Thus, their conclusion of -27% negative vaccine effectiveness is not supported by their data.

medrxiv.org/content/10.110… They compute the daily testing rate in vaccinated and unvaccinated and plotted the ratio of vaccinated/unvaccinated in Table 1a, from which they acknowledge the vaccinated test at a significantly higher rate than unvaccinated.

It is to their credit they acknowledge this.

David Geier has apparently been commissioned by HHS secretary RFK, Jr. to study potential links between vaccination and autism.

He has papers in the past using vaccine safety datalink (VSD) data to perform case-control studies assessing potential links between exposure to Thimerisol-containing hepatitis B vaccines as infants and later diagnosis of atypical autism, or of obesity.

He found highly significant associations in both studies -- linked below -- and conclude this proves that Thimerisol in Hepatitis-B vaccines significantly increases risk of atypical autism, and of obesity.

However, MAJOR PROBLEM:
In both studies his cases and controls were during different time periods,

In the autism study, the dates were:
Autism Cases: 1991-1998
Controls: 1991-1992.

In the obesity study, the dates were:
Obesity Cases 1991-2000
Controls: 1991-1994.

Besides the arbitrary nature of these choices of dates raising suspicion, the use of different dates for cases and controls raises major concerns of time-confounding, especially if Hepatitis-B vaccination rates differed sustantially over the 1991-2000 time period.

See link below to the Hepatitis-B vaccination rates over time -- indeed the vaccination rate increased from <10% in 1991 to ~30% in 1994 to 85-90% in 1998-2000.

Thus, the case-control status is almost COMPLETELY CONFOUNDED with vaccination status, since very few would be vaccinated in 1991-1994 and vast majority vaccinated by 1996-2000.

Thus, the vaccinated would be SEVERELY OVERREPRESENTED in the cases and SEVERELY UNDERREPRESENTED in the controls, producing a spurious association between the exposure and case/control status no matter what outcome is being studied.

In other words, the methodology used in the studies was completely invalid, and the studies fatally flawed.

I hope they do better in the upcoming studies -- ideally they should include experienced biostatisticians and epidemiologists to help use a valid study design and analysis, and ensure that their interpretation of the study is supported by the empirical evidence in the data.

autism study: sciencedirect.com/science/articl…

obesity study: pmc.ncbi.nlm.nih.gov/articles/PMC49…

Information on Hepatitis B vaccination uptake over time: cdc.gov/mmwr/preview/m…

Let's do an exercise to show how their study flaw can generate false associations and invalid conclusions.

Here is a plot of live births and cell phone usage in Sri Lanka from 2001-2009.

Note that cell phone usage increased from <10% in 2001 to 80% by 2009.

We will ask, "Is a woman's cell phone use associated with their likelihood of having a live birth in Sri Lanka?"

We will make the same case/control design mistake made by Geier to introduce the exact same type of time bias and show how it leads to completely spurious results and invalid conclusions.

There is talk from HHS that fluoride in water mau lead to reduced cognitive levels in children.

There is a new published paper on a Bangladeshi study assessing links between urinary fluoride levels and cognitive performance of children at 5yrs and 10yrs that is being touted as evidence of this link.

The authors conclude links between prenatal maternal urinary fluoride levels and children's cognitive levels at 5yrs and 10yrs, between children's urinary fluoride levels at 10yrs and cognitive levels at 10yrs, and also say there is a negative association between urinary fluoride levels at 5 years and cognitive levels at 5yrs and 10yrs that is not statistically significant.

However, when looking at the data and analyses in the paper in detail, the evidence for these links are at best very weak, and the paper really shows very little evidence.

In this thread, I will explain some of the data and statistical details that lead me to that conclusion.
The primary question of this study is whether fluoride exposure from water is linked with lower cognition, which in this study involves analyzing potential associations between urinary fluoride levels at 5yrs and and cognitive scores at 5yrs and 10yrs, urinary fluoride levels at 10yrs with cognitive scores at 10yrs, and prenatal urinary fluoride levels at 8wks gestation with cognitive scores at 5yrs and 10yrs.

They propose that given fluoride's low half-life, the urinary fluoride levels should be a reasonable surrogate for fluoride levels in the water, and they measure water fluoride levels at the 10yr time point to show evidence of some correlation with the urinary levels.

Many people misunderstand "indirect costs" (i.e. F&A) from NIH grants, thinking these are superfluous or unnecessary costs or a "gift" to universities.

Indirect costs are actually "Facility and Administrative" (F&A) expenses that are not allowed to be included in the direct cost budget of an NIH grant, but are essential for research.

Below is a video that explains how the system works, what F&A expenses cover, how they are determined, and why they are essential, and how the proposed severe reduction of 65-80% of these expenses endangers the entire USA research enterprise that leads the world and provides great societal benefit, technologically, medically, and economically.

youtube.com/watch?v=NtqK8S… The indirect costs, or Facilities and Administrative (F&A) expenses, are not a "tip" or "fluff" for the universities, but cover essential research costs that are not allowed to be included in the direct cost budget for an NIH grant, including:

* Laboratory and other research-specific facilities
* Utilities including electricity, gas, water, HVAC
* Shared research instruments
* Maintenance and security for research facilities
* IT and cybersecurity infrastructure
* Administrative staff needed to meet federal requirements including safety, compliance, and research operations.

Research could not be done without these expenses, and without F&A it is not clear how they can be covered.

Journal of Infection and Public Health just published a paper summarizing case fatality rate (CFR) and infection mortality rate (IFR) for all of Austria from February 2020 through May 2023.

Critically, they also split out results over time, by variant, age group, vaccination and previous infection status, sex, and nursing home residency status.

Key results were:
1. CFR was much higher in older age groups and nursing home residents
2. CFR decreased greatly with later variants, coincident with increases in population immunity levels from vaccination and previous infection.
3. Vaccinated individuals had substantially lower CFR for each variant/age group
4. Those surviving previous infection had substantially lower CFR for each variant/age group.
5. The immune protection evident in reduced CFR was similar in vaccinated and those surviving previous infection for each variant/age group.

In this thread, I will summarize and try to make sense of its key results.

Link to paper:
sciencedirect.com/science/articl… There are other papers looking at CFR/IFR, but none as expansive or rigorous as this one.

Austria had very restrictive early mitigation measures and mass vaccination like most higher income countries, which makes its results relevant to many richer Western countries.

Also, Austria had the highest SARS-CoV-2 testing rate among non-island nations in the world, making it ideal for this study since they had fewer undocumented infections than other countries.

My colleagues and I just published a paper in eClinicalMedicine evaluating effects of vaccination on long COVID risks in children and adolescents during the Delta and early Omicron periods.

These data were from the RECOVER network including 21 pediatric hospital networks from all over the USA, including 112,590 adolescents during the Delta period, and 84,735 adolescents and 188,894 children during the early Omicron period.

Long COVID-19 (post-acute sequelae of SARS-CoV-2, PASC, or multi-system inflammatory syndrom, MIS) was defined using a symptom-based computable phenotype definition based on five body systems.

Our analyses utilized propensity score weighting to adjust for confounding from age, demographics, medical co-morbidities as well as healthcare utilization including past COVID-19 testing practices, and we used proximal analyses with negative control exposures and outcomes to investigate and adjust for potential residual bias from unmeasured confounders.

In adolescents 12-20yrs, we found vaccination resulted in 95.4% reduced risk of long COVID-19 during the Delta period, and 75.1% during the Omicron period.

In children 5-11yrs, we found vaccination resulted in 60.2% reduced risk of long COVID-19 during he Omicron period.

To evaluate how much of this vaccine protection was from reduced risk of infection and how much was reduced risk of long COVID-19 independent of any effect in reducing infection, we performed a causal mediation analysis to split the total vaccine effect into indirect effects, mediated through reducing risk of infection, and direct effects, independent of any reduced risk of infection.

Again, propensity score weighting was used to carefully adjust for potential confounders.

We found that the protective effect of vaccines on long COVID-19 was almost wholly mediated through its reduced risk of infection.

Various sensitivity analyses were done and included in the online supplement along with a detailed description and explanation of all methods and modeling decisions.

@chenyong1203

sciencedirect.com/science/articl…

These data were from the RECOVER network including 21 pediatric hospital networks from all over the USA, including 112,590 adolescents during the Delta period, and 84,735 adolescents and 188,894 children during the early Omicron period

Earlier this month, a research group from University of Southern California published a paper studying long term (>1000 days) risk of major adverse cardiac events (MACE, including myocardial infarction, stroke, and all cause mortality) after documented COVID-19 infections from population level data from the UK Biobank.

Confirming many other studies, they found increased risk of MACE after COVID-19, 2.09x higher for all levels of severity and 3.65x higher in hospitalized COVID-19.

Notably, they found that hospitalization for COVID-19 was a coronary artery disease risk equivalent, such that a person with no history of cardiovascular disease who got hospitalized COVID-19 was higher risk than a person with history of cardiovascular disease without hospitalized infection.

This, of course, validates what has been found in many other studies -- that COVID-19 infected have increased risk of severe cardiovascular events even after recovery, especially if they had severe/hospitalized COVID-19, and explains the increased cardiac risk in the past few years.

BTW, this study covers infections in 2020, before vaccines were available.

ahajournals.org/doi/10.1161/AT… The study was quite well designed, using n=10,005 COVID-19 cases between February 1, 2020 and December 31, 2020 in the UK Biobank, compared with population controls (n=217,730) and matched controls (n=-38,660) based on propensity scores including age, sex, ethnicity, education, diabetes, asthma, smoking status, and use of lipid-lowering or antihypertension medication.

The comparison with the propensity score-matched controls is more valid for estimating COVID-19 effects given that it adjusts for confounders.

They found that with >1000 days of followup, the risk of MACE was 3.65x higher in hospitalized COVID-19 group than matched controls.

A group of French researchers published a paper in JAMA today assessing long term cardiac outcome with 18 months after vaccine-caused myocarditis after mRNA vaccines and compared with post-covid-19 mycarditis and conventional myocarditis using a large whole-country cohort covering the entire 12-49 year old population of France.

They found 558 cases of post-vaccine myocarditis out of ~64 million doses of vaccine in this age group (376 after 2nd dose), for an incidence of 1 in 115k doses (1 in 64k after 2nd dose), and 298 with post-COVID myocarditis and 3779 with conventional myocarditis.

A total of 18/15/1 of the 558 post-vaccine myocarditis patients were rehospitalized for myocarditis, had other cardiovascular events, or all cause death, versus 12/22/4 of 298 post-covid myocarditis patients and 277/49/17 of 3779 conventional myocarditis patients.

After rigorously adjusting for confounders including age, sex, region, SES, lifestyle habits, comorbidities, vaccination history, and medications using propensity score weighting to calibrate all populatons to the conventional myocarditis group (the comparison group), they statistically compared the post-vaccine and post-covid groups to the conventional myocarditis group with respect to
(1) Rehospitalization with myocarditis
(2) Cardiac events, including heart failure, heart rhythm and conduction disorders, and cardiomyopathy
(3) All cause death
(4) Rehospitalization for any reason
(5) Composite outcomes (1)-(3)
(6) Composite outcomes (1)-(4)

From this comparison, they found the risk of all of these 6 events were all lower after post-vaccine myocarditis than conventional myocarditis by 25%/46%/47%/31%/45%/36%, respectively, while risk after post-covid myocarditis was similar to conventional myocarditis, at the higher levels.

Myocarditis is the key minority harm risk for mRNA vaccines, especially for young men and especially after 2nd dose.

Before this study, there were very little looking at moderate to long term cardiac outcomes of myocarditis, so this paper is an important addition to the literature.

More study of this important question is needed.

In this thread, I discuss a few of the details and strengths and limitations of this study. SARS-CoV-2 vaccines have been shown by many studies to greatly reduce the morbidity and mortality from COVID-19 during the pandemic, especially during 2021-2022. mRNA vaccines have been the predominate type used in many places around the world, and has consistently shown the highest effectiveness in most studies.

However, within a few months of rollout, a key minority harm risk of myo/pericarditis was discovered to occur in a subset of individuals soon after vaccination, with rates highest in teen boys and young men, especially after 2nd dose given shortly after 1st dose, and with Moderna having the highest rate. It is important to weight this risk against the benefits, especially in young men who have lower risks of severe/fatal COVID-19.

While most cases appeared to be mild and quickly resolved, myocarditis is serious and there is always a risk of cardiovascular sequelae after myocarditis, so long term follow up studies are critical to assess potential long term cardiac damage in those experiencing post-vaccine myocarditis.

How can excess deaths be higher in 2021 in 2020 if vaccines had any benefit?

This is a good question I see many ask.

Some conclude from this question that vaccines must have been completely ineffective, or perhaps even have made things worse.

In this thread, I will show that this is not the contradiction that it appears to be.

If you look at the available data and studies and think through the various relevant factors, it is clear that the following are simultaneously true:
1. Excess deaths in the world were higher in 2021 than 2020
2. Vaccines were highly effective in reducing risk of covid death, the primary driver of excess deaths.
3. The excess deaths in 2021 would have been MUCH WORSE had there not been covid vaccines.

The key factors I will highlight in this thread include:
1. Far more people were exposed to COVID-19 in 2021 than 2020
2. In most places, the strict containment measures of 2020 were lifted in 2021
3. The variants emerging in 2021 were demonstrably more transmissible than those in 2020
4. The vast majority of people in the world were unvaccinated for most of 2021
5. Vaccines were highly effective in 2021, but not perfect. 1. Far more people were exposed to COVID-19 in 2021 than 2020

While the pandemic first emerged and was most disruptive in 2020, relatively few people were actually exposed to SARS-CoV-2 in 2020.

Below are plots of confirmed COVID-19 cases over time in the world from OWID

We see that only 1% (10k per 1m) of the world had confirmed COVID-19 cases in 2020, while another 2.5% (25k per 1m) had confirmed COVID-19 cases in 2021.

So we see 2.5x more confirmed COVID-19 cases in 2021 than 2020, with 2021 a full pandemic year and 2020 only a partial pandemic year.

Of course, most SARS-CoV-2 infections are not formally documented as confirmed cases, so more than 1% were exposed to SARS-CoV-2 in 2020.

Serology studies from around the world provide more information about what % were exposed to SARS-CoV-2.

Mostert et al. published a paper in BMJ Public Health “Excess Mortality across countries in the Western World since the COVID-19 pandemic: ‘Our World in Data’ estimates of January 2020 to December 2022”
()

The paper presents excess death estimates for 47 Western countries from the publicly available “World Mortality Dataset” (Karlinsky and Kobak, 2021) and reports excess deaths from 2020-2022, with 13 countries peaking in 2020, 21 in 2021, and 12 in 2022.

Their conclusion expresses surprise that excess deaths continued from 2020 into 2021 and 2022 “despite the implementation of containment measures and COVID-19 vaccines” claiming “this raises serious concerns.”

They discuss several potential causes of excess deaths, but do not delve into any detail about the evidence or lack thereof for any of them based on existing literature and data.

Their conclusion is that “government leaders and policymakers need to thoroughly investigate the underlying causes of persistent excess mortality”, implying there is not already substantial evidence for what the primary driving factor is, and that studies investigating potential causes of excess deaths are not being done.

Overall, I welcome any call for more studies characterizing how the pandemic affected mortality rates and investigating potential primary and secondary contributing factors. I’m always in favor of more data and studies (and that they are appropriately analyzed using valid statistical approaches).

However, this study does not accurately represent the existing understanding about sources of excess deaths, downplaying the COVID-19 deaths that are clearly the driving factor throughout 2020-2022, as I will show, and implicitly magnifying the potential role of vaccines beyond what is supported by the data.

Also, many popular media articles about this study, including the Telegraph article () entitled “Covid vaccines may have helped fuel rise in excess deaths”, blatantly misrepresent the content of the paper.

They make it sound as if the paper was primarily about vaccines, which it is not, or provides evidence for vaccines being a potential driving factor, which it does not.

This misrepresentation is so egregious that BMJ felt the need to post a statement about it() and, who knows, may be considering retraction of the article.

In this thread, I will discuss some of these points.bmjpublichealth.bmj.com/content/2/1/e0…
telegraph.co.uk/news/2024/06/0…

https://x.com/bmj_company/status/1798751750046019666

First, I am not sure why this article is classified as “original research” and not a “narrative review” or “commentary”.

There is no primary data collection or original data analysis in this paper.

The study design and text and equations for the methods are almost verbatim from the public repository for the World Mortality Data set (WMD, Karlinsky and Kobak 2021 ) which is also where they got their data. The authors cite the paper, but it is highly unusual in an “original research” article to have such large sections match so closely with an existing publication and with nothing new added.

Their results are simply the publicly available excess deaths from Karlinsky and Kobak’s WMD for 47 countries, aggregating by month and year to get total excess counts in 2020, 2021, and 2022, with the results simply showing that there were excess deaths in 2020, 2021, and 2022, which has been well known for a long time.

I have no idea how BMJ Public Health considered this manuscript sufficiently novel for publication as an “original article”.

The remainder of the paper consists of a narrative-driven review of various aspects of the pandemic and a commentary suggesting further investigation of causes of excess deaths, following the format of a “narrative review” or “commentary”, not an original research article.elifesciences.org/articles/69336

In this thread I will make some comments on the recently published paper in Cureus entitled "Increased age-adjusted cancer mortality after the third mRNA lipid nanoparticle vaccine dose during the covid-19 pandemic"

The title is extremely misleading, given their data do not show an increase in age adjusted cancer mortality, and the paper provides no evidence relating the cancer mortality trends to the 3rd doses of mRNA vaccines given in the country outside of simple post hoc arguments based on the fact they were given in 2022.

Their paper shows that the age-adjusted cancer mortality remains stable and in fact slightly decreases, but it decreases at a slower rate than predicted by linearly extrapolating pre-pandemic decreases in cancer mortality from 2010-2019.

They don't provide any evidence that the vaccinated or those receiving 3rd dose boosters are at any higher risk of cancer death than those of similar age/co-morbidity status who didn't, or otherwise provide any evidence that vaccination is responsible for the arrest of the pre-pandemic decreasing trend and not any one of the many other factors greatly impacting life in Japan during the pandemic.

While the paper summarizes a large and important data set and does some nice things (like compute age-adjusted rates), the general conclusions they make, and certainly the conclusions that people are representing about the paper on social media, are not supported by the data.

cureus.com/articles/19627… First, some positives.

This paper analyzes a large data set of population-level cancer death rates in a large country, Japan, and this type of large data set is valuable for assessing trends during the pandemic.

The authors duly recognize that changing age distributions over time impact the cancer incidence and death rates, so compute age-adjusted cancer mortality estimates which is the right approach to assess changes over time.

They also looked at subgroup analyses by cancer type and age, which provide useful breakdowns in smaller groups that can shed light on the trends.

New paper in Heart compares vaccinated to unvaccinated with respect to cardiovascular events including myocardial infarction, heart failure, arrhythmia/cardiac arrest, myocarditis and clotting events including strokes, thrombosis deep vein thrombosis, pulmonary embolism, and venous thromboembolism through 1, 2, 6, and 12 months in electronic medical record cohorts of 20 million from UK, Spain and Estonian.

They adjust for measured cofounders using propensity scores weighting, adjust for unmeasured confounders using negative controls, and federated learning to combine data across countries.

All events had significantly lower risk through 1m including myocarditis, and most through 12 months, providing strong evidence vaccination rollouts substantially reduced risk of cardiovascular events.

heart.bmj.com/content/early/… I should have clarified these cardiovascular events being compared are post SARS-CoV-2 infection

Also should have specified it covers the time period starting early 2021 when rollouts started and the various data sources ending December 2021, February 2022, June 2022, and December 2022, so is predominately in pre-Omicron era, with a lot of early Omicron era and a small part later in 2022 Omicron era.

Recall that from many dozens of studies vaccines strongly reduced risk of infection throughout 2021, and this effect was much weaker in 2022 with Omicron variants, but looking across the dozens of studies during the Omicron era in 2022 reduced risk of infection was still evident.

Here I will briefly discuss this paper you linked talking about negative effectiveness observed in some subgroups for a small subset of studies

I will first summarize their points, and then go through every study they mention.

It is not a reasonable viewpoint that vaccines have negative effectiveness and make covid risks worse based on the many published studies and data Here is their overall conclusions: that these rare cases where negative VE is observed is likely causedThey discuss some of these ,biases by confounding bias in settings where true VE is very small such as Omicron vs infection long after vaccination

An Epoch Times article discusses a recent paper by FDA researchers assessing safety of Pfizer and Moderna bivalent boosters given to 8,638,661 and 5,240,178 individuals, respectively, comparing incidence rate of 18 different serious adverse events of special interest (including various cardiovascular events) in data bases from Carelon Research, CVS Health, and Optum.

Out of all 18 events, they only identified safety signals for:
1. anaphylaxis for both vaccines 18-64yr in 1 out of the 3 data bases (but not the other 2), and
2. myocarditis/pericarditis for 18-35yr for Pfizer vaccine in 1 out of the 3 data bases (but not the other 2)
They concluded results were consistent with previous studies and supporting the safety profile for these vaccines

However, the Epoch Times article highlights the following numbers, suggesting they are from this paper:
1. anaphylaxis rate was 74.5 cases per 100k person-years following Pfizer vaccination
2. anaphylaxis rate was 109.4 cases per 100k person-years following Moderna vaccination
3. myo/pericarditis rate was 131.4 cases per 100k person-years
These figures to a novice reader might make one think that the rate of anaphylaxis per person is
1 per 1342 (100,000/74.5) after Pfizer,
1 per 914 (100,000/109.4) after Moderna,
and the rate of myo/pericarditis is
1 per 761 (100,000/131.4) after Pfizer,
which would seem alarmingly high, seemingly contradicting the conclusions of the paper. However, this is completely false.

As I will show in this thread, these figures:
1. were not even reported in the paper, but computed for the Epoch Times article from Table 3.
2. were actually mis-computed from Table 3 in the paper
3. based on rate per person-year is not the most useful summary for incidence after vaccination given the time frame for these events varies from 2 days (anaphylaxis) to 28 or 42 days after vaccination, and easily misconstrued/misinterpreted.

Looking at the paper, what they actually found for incidence was:
1. anaphylaxis from d0-d1 after bivalent vaccine booster occurred at rate between 1/4.3m and 1/430k for Pfizer, and between 1/2.6m and 1/260k for Moderna
2. myo/peri-carditis from d0-d21 after bivalent vaccine booster occurred at rate of 1/27k for Pfizer and 1/29k for Moderna
3. For 5-17yr olds, the myo/peri-carditis rate from d0-d21 after bivalent vaccine booster occured at rate between 1/535k and 1/54k for Pfizer, and <1/50k for Moderna
4. For 18-64yr olds, the myo/peri-carditis rates from d0-d21 after bivalent boosters was 1/53k for Pfizer and 1/55k for Moderna

These are in line with previous literature, and not alarming -- the calculation and inclusion of the rates per person-year in the Epoch article appear to be trying to exaggerate the risk

I'll transparently explain where I get these numbers from in this thread...

theepochtimes.com/health/fda-fin… First, let's consider the anaphylaxis data. Here is the part of Table 3 containing the number of cases and person-years of follow up for two vaccines for the different age groups.

Privacy rules forbid them from publishing any raw counts <11 in the table, so "<11" means some unknown number between 1 and 10.

Based on that I compute the event rate per 100,000 person years.

For Pfizer, we see that combining age groups, the event rate is between 4.3 and 42.7 anaphlaxis events per 100k person-years

This is significantly lower than the 74.5 per 100k person-years that Epoch put in their article -- not sure where they got that from.

However, this is not a very meaningful measure since this event is only defined at d0-d1 after vaccination, meaning a person is only at risk for 2 days for this event.

It would be more meaningful for us to consider the proportion of individuals who experience anaphylaxis in d0-d1 after the vaccine, i.e. at the individual level, not person-years.

1/n
Annals of Internal Medicine just published our epidemiological study of vaccine effectiveness in children and teens during the delta and omicron waves based on large USA pediatric cohorts accounting for nearly 4% of the USA pediatric population.

The study found strong vaccine effectiveness vs. infection, severe disease, and ICU during the delta wave for adolescents and the omicron wave for children and adolescents, with no evidence of increased risk of cardiac outcomes.

Senior authors are Yong Chen, Christopher Forrest, and Jeffrey S Morris, and Lead authors are Qiong Wu and Jiayi (Jesse) Tong.

#PEDSNet #COVID_19 #COVIDVaccine @ChildrensPhila @UPennDBEI @PennMedicine

acpjournals.org/doi/10.7326/M2…
2/n
Study Data
This study was done using electronic medical records data from PEDSnet, a network of 8 USA pediatric health systems across 23 states and the District of Columbia, capturing nearly 4% of all children and adolescents in the USA, including large primary care systems, including the Children’s Hospital of Philadelphia, Nationwide Children’s Hospital, and Nemours Children Health.

This study included a total of 77,392 adolescents (45,007 vaccinated) during the Delta wave, a total of 111,539 children (50,398 vaccinated) and 56,080 adolescents (21,180 vaccinated) during the Omicron wave, with extensive follow up of these cohorts providing the longest follow up among studies of pediatric vaccine effectiveness.

@TracyBethHoeg, Duresetti, and @VPrasadMDMPH published a commentary in @NEJM yesterday pointing out unmeasured confounding due to the healthy vaccinee effect (HVE) in a December 2021 @NEJM paper published by a group led by Clalit researcher @ArbelRonen.

They pointed out a 94.8%… https://t.co/KAUrLUtaL4twitter.com/i/web/status/1…
The healthy vaccinee effect, or healthy user effect, is a well-known phenomenon that people who follow public health recommendations tend to be healthier in general, and this can manifest in vaccine effectiveness studies as unmeasured confounders beyond those included in the… https://t.co/EJ9J23oqRitwitter.com/i/web/status/1…

I have seen many people forwarding this graphic around, claiming that the UK ONS data show that the death rate for vaccinated individuals was much higher than unvaccinated, suggesting vaccines were dangerous and killing people.

This plot is inaccurate and misleading.

In this 🧵, I will demonstrate that the UK ONS data show consistently lower death rates for the ever vaccinated than the unvaccinated throughout the pandemic, for COVID-19 deaths, non-COVID-19 deaths, as well as all cause deaths.

The only way people can spin these data to suggest vaccinated have higher death rates is if they magnify the very small vaccine subgroups and minimize the majority of the vaccinated, as I will show. @Tony48781320 @DuttyMonkey_ Here is the data for 40-49

https://twitter.com/jsm2334/status/1679994146277453824?s=20