Did COVID Vaccines Cost More Lives Than They Saved? Public Deserves a Rigorous, Truthful Evaluation [10/12/24]

The complex medical question of the COVID-19 vaccine’s impact on all-cause mortality across society remains one of profound uncertainty. Unfortunately, fact-checking organizations often oversimplify complex medical questions and present certainty where none exists.

By Dr. Joseph Fraiman 

Introduction

“Do you think there would have been less deaths overall if we hadn’t had a vaccine?”

This question was posed to Dr. Aseem Malhotra by Steven Bartlett during an interview on Bartlett’s podcast “Diary of a CEO.” To which Malhotra responded simply “Yes.”

Full Fact, a fact-checking organization, has written a verdict on Malhotra’s answer, claiming: “False. There is clear evidence that the vaccines saved far more lives than they cost.”

Part I: The illusion of certainty — Deconstructing claims of vaccine efficacy

The assertion that “There is clear evidence” of COVID-19 vaccines’ benefits outweighing their harms” exemplifies a dangerous oversimplification of complex medical realities.

This claim, often propagated by fact-checkers and mainstream narratives, fails to acknowledge the fundamental limitations in our current understanding and the methodological flaws inherent in much of the existing research.

The missing gold standard: Randomized controlled trials (RCTs)

In evidence-based medicine, properly conducted RCTs measuring all-cause mortality are the gold standard for determining an intervention’s overall impact. For COVID-19 vaccines, no such trials have demonstrated an all-cause mortality benefit.

The original trials were not designed or powered to detect differences in all-cause mortality, and follow-up periods were too short to capture long-term effects. Without this crucial evidence, claims of clear benefit are premature at best and misleading at worst.

The pitfalls of observational studies

In the absence of robust RCT data, fact-checkers often turn to observational studies. However, these studies are fraught with potential biases that consistently overestimate benefits and underestimate harm:

Selection distortion: Healthy user bias and time-dependent effects inflate apparent vaccine benefits and mask potential harms due to inherent differences in vaccinated groups and changing study conditions.

Temporal misclassification: Survivorship bias and miscategorization of vaccination status in early post-injection periods artificially inflate efficacy estimates and underestimate potential harms.

Classification bias: Vaccine status classification errors occur in a single direction, with the vaccinated often misclassified as unvaccinated. This results in infections and harms in the vaccinated being misattributed to the unvaccinated group, overestimating benefits and underestimating harms.

Reporting bias: Systematic underreporting of adverse events following vaccination due to factors like lack of recognition, dismissal of potential vaccine-related causes, or fear of professional repercussions leads to underestimation of vaccine risks and overstates safety.

Publication bias: The preferential publication and promotion of studies showing positive vaccine effects, coupled with the suppression or non-publication of studies showing no effect or negative effects, skews the overall body of evidence and public perception.

The modeling mirage

Fact-checkers often rely on modeling studies to support dramatic claims of lives saved, compounding the issues of observational studies:

• Amplification of errors: Small inaccuracies in input data or assumptions lead to wildly inaccurate projections.
• Oversimplification: Complex real-world dynamics are reduced to equations that may not capture crucial nuances.
• Confirmation bias: Models can be inadvertently (or deliberately) tuned to produce expected or desired results.
• Lack of falsifiability: Unlike controlled experiments, many model predictions are not truly testable.
• Overconfidence: Precise-looking numbers create a false sense of certainty.

In conclusion, modeling studies often use overestimates of benefits taken from observational studies to create oversimplified models tuned to further amplify these overestimated benefits. By extrapolating across millions, they produce unrealistic estimates that can never be verified by proper scientific experimentation.

The magnitude of benefit from COVID-19 vaccines is likely much smaller than portrayed by observational and modeling studies. To determine the net effect of the vaccines, both known harms and potential yet unknown harms must be carefully considered against this uncertain benefit.

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