Better in their words…

JAMES LYONS-WEILER, PHD
MAR 25, 2026
 
“We need to see the same analysis of on the unhealthy people excluded from the study.”
A recent social media claim on X asserts that a newly published study demonstrates that COVID‑19 vaccination reduces the risk of sudden death in healthy adolescents and young adults. That claim is false. It does not follow from the study’s design, its data, or its own internal validation analyses. It arises from a familiar and well-characterized failure mode in observational epidemiology: conditioning on the wrong variables, then mistaking the resulting artifact for a biological effect.


The paper in question—Abdel‑Qadir et al., PLoS Medicine (2026), PMID 41855201—does not establish protection against sudden death. It cannot. Its endpoint is compromised, its cohort is selected through a collider, and its regression model adjusts for variables that encode health-seeking behavior rather than causal structure. The apparent “protective effect” collapses under a within-person design that the journal’s own reviewer required. The conclusion is not subtle: the headline interpretation is an overreach.
The Endpoint Fails Before the Model Begins
The study defines cases as deaths occurring outside hospital or within 24 hours of hospital arrival with a cardiac arrest diagnosis code. Of 4,963 cases, 4,448—89.6%—occurred in the prehospital setting. For these deaths, the authors state explicitly that they could not verify cause and could not exclude motor vehicle collisions, violence, or suicide.
This is not a minor limitation. It is a structural failure of the outcome definition.
The exclusions for trauma, mental illness, and substance use apply only to the minority of cases that reached hospital coding. The overwhelming majority of cases—the ones that determine the results—remain unverified. Opioid-related deaths could only be excluded through June 30, 2022, due to database limitations, leaving the remainder of the study period unfiltered.
The regression output confirms contamination. Mood or anxiety disorder carries an adjusted odds ratio of 3.46. That signal is incompatible with a clean sudden cardiac death phenotype. It is entirely consistent with a mixed outcome dominated by behavioral and unclassified out-of-hospital deaths.
A study that cannot determine what its cases died from cannot claim protection against a specific biological mechanism.
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Socioeconomic Bias and Healthy Vaccinee Effect
One crucial factor absent from popular interpretations of the recent PLoS Medicine study on COVID-19 vaccination and sudden death is socioeconomic disparity. Unvaccinated individuals disproportionately represent lower socioeconomic groups, who inherently face higher mortality from violence, undiagnosed fentanyl overdoses, and suicides. This disparity drives mortality rates upward among unvaccinated cohorts—not because they lack vaccination, but because of structural and socioeconomic factors unrelated to vaccination itself.
By contrast, vaccinated individuals represent a socioeconomically advantaged and health-conscious group with inherently lower baseline mortality. Their reduced mortality rate reflects who they are, not the injection they received. The study itself inadvertently provides compelling evidence of this confounding: influenza vaccination appeared to “prevent” sudden death. Clearly, influenza vaccines do not prevent cardiac arrests. This is textbook “healthy vaccinee” bias, demonstrating how lifestyle and health-seeking behavior—not vaccination—explain the protective association.
Thus, finding lower mortality among vaccinated individuals reveals nothing meaningful about vaccine risks or benefits. It merely restates well-known patterns of healthcare access and engagement.
The Forest Plot Reveals Survivorship Bias
The published forest plot exposes deeper contradictions:
Overall vaccination yields an adjusted odds ratio (aOR) of 0.57, suggesting vaccinated individuals have only 57% of the mortality rate seen among unvaccinated individuals.
Strikingly, a single vaccine dose yields an aOR of 0.88, significantly closer to no protection.
However, receiving two or more doses produces an even stronger “protective” signal, with an aOR of 0.53, lower than the overall vaccinated population.
This strange dose-dependent pattern exposes an underlying survivorship bias: those who experienced mortality or serious health events after the first dose never progressed to a second dose. Consequently, the population remaining to receive subsequent doses is selectively healthier. The reduced odds ratio at two or more doses isn’t genuine protection—it’s merely a biased artifact of survivors progressing through the vaccine schedule.
Why Self-Controlled Analyses Mislead Here
When group comparisons fail due to unbalanced confounding, researchers often turn to self-controlled study designs. Self-controlled studies typically compare a person’s risk before and after a specific event or exposure. However, this methodology fundamentally fails when measuring mortality associated with vaccination:
Dead individuals cannot subsequently receive a vaccine. Thus, there’s no pre-vaccination mortality rate available among the vaccinated cohort for a meaningful self-controlled comparison.
What the authors inadvertently performed instead was a comparison of post-vaccination mortality rates in vaccinated individuals against a baseline mortality derived from the entire population—including the socioeconomically disadvantaged and inherently higher-risk unvaccinated population. This baseline is artificially inflated and not comparable to the healthier vaccinated individuals.
That their results found similar mortality rates between vaccinated individuals post-vaccination and this artificially high baseline is not reassuring. Given that vaccinated individuals represent a healthier cohort, their mortality rates should naturally have been lower than this broad baseline. Finding it comparable instead indicates a hidden, increased mortality risk introduced by vaccination.
The claimed protective effects observed in this study are artifacts of socioeconomic bias, survivorship bias, and flawed methodological choices—not genuine signals of vaccine protection. Assertions that COVID-19 vaccines reduce sudden death among healthy individuals profoundly overinterpret the data, misrepresent the underlying bias, and mislead public understanding of vaccine safety.
Accurate interpretation of such studies demands rigorous scrutiny of underlying biases and methodological flaws. As demonstrated here, failing to do so produces dangerously misleading conclusions about vaccine benefits and risks.
The Cohort Is Conditioned on a Collider
The study population is described as “apparently healthy.” In practice, this means individuals with no documented major illness and at least one healthcare encounter in the prior decade.
That phrase—“no documented illness”—defines the problem.
Diagnosis requires healthcare contact. Vaccination uptake also tracks healthcare engagement. Conditioning on documented health status therefore conditions simultaneously on latent disease and healthcare behavior. This is a classic collider structure:
True health status → likelihood of diagnosis
Healthcare engagement → likelihood of diagnosis
Healthcare engagement → likelihood of vaccination
By selecting only individuals without documented disease, the study conditions on a variable influenced by both latent illness and healthcare engagement. That opens a noncausal path between vaccination and outcome.
This is not residual confounding. It is selection bias induced by conditioning on a collider. 
The vaccinated group becomes enriched for individuals who are both healthier and more engaged with healthcare systems. The unvaccinated group retains a higher proportion of individuals with undiagnosed conditions and lower engagement. The resulting association is structurally biased toward apparent protection. And therefore any generalizations to the entire population are unwarranted.
No amount of regression adjustment fixes this. The bias is baked into cohort construction.
The Model Adjusts for the Wrong Variables
The regression includes:
Influenza vaccination
Number of SARS-CoV-2 PCR tests
Prior positive SARS-CoV-2 tests
These are not neutral confounders. They are proxies for healthcare engagement and behavioral phenotype.
Adjusting for them introduces overadjustment bias and amplifies collider pathways. These variables sit downstream of both healthcare behavior and, in part, vaccination itself. They do not isolate a causal signal. They distort it.
The model therefore does not “control for confounding.” It conditions on outcomes and behaviors artifacts that encode the very selection process creating the bias.
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The Protective Signal Is the Warning Sign
The results imply protection against sudden death display a pattern that does not require interpretation—it requires rejection:
Any vaccination: aOR 0.57
Two doses: aOR 0.53
One dose: aOR 0.88
Vaccination within 6 weeks: aOR 0.63
Influenza vaccination: aOR 0.72
The six-week window is the biologically relevant period for myocarditis-related risk. It appears “protective.” That alone invalidates a causal interpretation.
The dose-response gradient strengthens the artifact: more doses appear more protective. That is not a plausible biological effect against sudden death. It is the signature of selection bias interacting with behavioral variables.
The influenza vaccination signal removes any remaining ambiguity. Influenza vaccination cannot plausibly prevent sudden death in this context. Its apparent protective effect demonstrates that the model is capturing healthcare engagement, not biology.
This is the textbook expression of collider bias and healthy-user bias interacting inside an overadjusted regression. The study provides an expert class in how not to do these types of studies.
We need to see the same analysis of on the unhealthy people excluded from the study.
Peer Review Identified the Failure—and Broke the Result
Reviewer #2 identified the issue immediately:
The influenza signal indicated unresolved bias
The stronger protection with more doses suggested health-seeking behavior effects
A small increased risk could be masked
The reviewer required a self-controlled case-series (SCCS) analysis.
That analysis compares individuals to themselves over time, eliminating fixed between-person differences such as baseline health and healthcare engagement.
When the SCCS was performed, the protective effect disappeared:
Dose 1: RI 0.87 (95% CI 0.54–1.40)
Dose 2: RI 0.94 (95% CI 0.57–1.57)
Dose 3: RI 0.87 (95% CI 0.37–2.05)
No protection. No signal. Wide intervals that do not exclude moderate increases in risk.
The authors were then required to modify their abstract and explicitly acknowledge that the SCCS was added after peer-review concerns. The final wording avoids any claim of protection.
The study invalidated its own headline result under the first methodologically appropriate stress test. But the updated results are ignored by those who offer overinterpretation instead of knowledge.
Generalizability Is Not Established
The dataset is administrative, region-specific (Ontario), and filtered through healthcare contact requirements. Even the narrower conclusion—that no large increase in sudden death was detected—cannot be generalized broadly.
The cohort excludes individuals without healthcare interaction. It excludes those with documented disease. It relies on incomplete cause-of-death classification.
The findings apply only to this constructed population under these constraints. They do not establish population-wide safety effects, and they do not resolve risk in subgroups outside the administrative frame.
What the Study Actually Supports
One conclusion survives:
The data do not reveal a large, obvious short-term increase in a broad category of sudden out-of-hospital death following vaccination in this selected population.
That is all. And “in this selected population” is the key. The study is not relevant for public health conclusions.
The study does not demonstrate protection. It does not establish causal reduction in risk. It does not eliminate moderate adverse effects. It does not define a clean cardiac endpoint.
The protective claim is an artifact of model structure.
On Public Overinterpretation
The claim that this study shows vaccination reduces sudden death represents a categorical error: treating a confounded associational estimate as a causal effect.
Citation Demerit: Dr. David Juurlink
The public statement linked above converts an adjusted odds ratio derived from a collider-conditioned, over-adjusted case-control model into a claim of biological protection with causality to boot. It ignores:
Endpoint misclassification
Collider bias from cohort construction
Overadjustment via behavioral proxies
Internal contradiction with SCCS results
Reviewer-identified methodological failure
This is not a minor overstatement. It is a misrepresentation of the study’s evidentiary content.
Other outlets are committing the same cardinal sin. CIDRAP reported that “healthy adolescents and young adults vaccinated against COVID-19 were 43% less likely to experience sudden death than non-vaccinated people.” CIDRAP That is a direct restatement of the case-control aOR as a causal protective effect — precisely the overclaim the author made on an X post.
The study should be retracted.
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