A multimillion dollar debate puts COVID vaccine safety data under the microscope

• A new analysis of Czech Republic health data using a novel method (KCOR) suggests a dose-dependent increase in all-cause mortality following COVID-19 mRNA vaccination.
• The analysis shows that individuals who received a booster dose had a significantly higher mortality rate over two years compared to those who only received two doses.
• A $3.3 million debate hinges on this data, with one side arguing the methodology is flawed and the other asserting it reveals undeniable net harm from the vaccines.
• The findings challenge the established narrative of clear vaccine benefit and highlight a lack of mainstream scientific engagement with the dataset.
• The controversy underscores ongoing global debates about vaccine safety, data transparency and the influence of financial and professional incentives in science.

In an unprecedented scientific and legal showdown, a $3.3 million debate is centering on a single, critical question: Did COVID-19 mRNA vaccines cause more deaths than they prevented? The controversy hinges on a novel analysis of comprehensive health data from the Czech Republic, which its creator, Silicon Valley entrepreneur Steve Kirsch, claims provides undeniable evidence of net harm. His methodological opponent, backed by significant financial incentive, has failed to dismantle the core findings, setting the stage for a pivotal moment in the ongoing assessment of pandemic-era health policies. The outcome of this clash has profound implications for public trust, regulatory accountability and the future of mass vaccination campaigns.

The KCOR method: A new lens on mortality data

At the heart of the debate is a new analytical tool called KCOR (Kirsch’s CORrector), developed by Kirsch to evaluate the impact of an intervention—in this case, vaccination—on all-cause mortality. Applied to the Czech Republic’s record-level public health data, which tracks vaccination status and deaths for millions of individuals, KCOR aims to account for biases like the “healthy vaccinee effect,” where healthier people are more likely to get vaccinated. The results, according to Kirsch, are stark. The analysis indicates that while two doses showed a relatively flat mortality risk compared to the unvaccinated, a third booster dose was associated with a significant and sustained increase in mortality—by approximately 25% to 35%—that persisted for nearly two years.

The most compelling visual evidence, Kirsch argues, comes from comparing different vaccinated groups. The data shows that individuals who received two doses but declined a booster maintained a stable mortality rate over time. In contrast, the mortality rate for the boosted group began to climb steadily weeks after the shot and remained elevated. This “vaccinated versus vaccinated” comparison is presented as a clean test, theoretically eliminating many external confounders like behavior or healthcare access, and pointing directly to the booster as the differentiating factor.

A fortress of objections and a foundation of data

Facing Kirsch in the high-stakes debate is Saar Wilf, who was highly incentivized to find fatal flaws in the KCOR methodology. Wilf raised a series of technical objections, including the initial inclusion of non-mRNA vaccines, coding errors that overstated early dose harm, and arguments that the model’s confidence intervals were too narrow and its assumptions about mortality trends were oversimplified. He also contended that the scale of harm suggested by KCOR would imply an implausibly large number of excess deaths at a national level.

However, after Kirsch’s team addressed the legitimate coding issues and restricted the analysis to mRNA vaccines only, the central finding remained intact. The mortality increase for the boosted cohort persisted. Critically, the flat mortality line for the two-dose group versus the unvaccinated served as a powerful internal validation; if KCOR were fundamentally unstable or biased, this null result would not be so clean. Kirsch and his AI-assisted analysis conclude that Wilf’s surviving objections are largely theoretical or serve only to create confusion, as they cannot explain away the consistent, dose-dependent harm signal that also aligns with a separate, standard mortality analysis method.

The silence of the scientific establishment

The debate raises a perplexing ancillary question: Why has such a rich, public dataset been largely ignored by the mainstream epidemiological community? The Czech data, which includes individual-level records on vaccination, demographics and outcomes, represents a potential goldmine for answering fundamental questions about vaccine safety and efficacy. Yet, no major study from this data has been published in a leading journal like The Lancet or The New England Journal of Medicine that performs a straightforward all-cause mortality analysis.

Analysts suggest this silence may not be due to a lack of answers, but a fear of them. Potential reasons include professional ostracism, loss of funding, journal rejection for contradicting the established public health narrative, and a deep-seated confirmation bias where the science is considered “settled.” This avoidance is particularly striking given that other studies using the Czech data have focused narrowly on COVID-specific outcomes, avoiding the broader—and more telling—question of net mortality benefit.

A global pattern of concern

The Czech findings resonate with concerning signals from other highly vaccinated nations. A recent study from Japan, which has the world’s highest per-capita rate of mRNA vaccination, noted a massive spike in non-COVID excess deaths in 2022 and 2023, coinciding with the booster rollout. The Japanese government’s vaccine injury relief system has already paid out for more deaths following COVID-19 vaccination than for all other vaccines combined over the past 47 years. Similarly, analyses of data from South Korea and the EuroMOMO network have continued to fuel debates about the role of vaccines in observed excess mortality patterns, particularly among younger age groups.

An unsettled conclusion with high stakes

The $3.3 million debate over the Czech data is more than a technical dispute; it is a microcosm of the larger struggle over the narrative of the pandemic response. Proponents of the KCOR analysis see it as an unbiased estimator revealing an uncomfortable truth that powerful institutions are unwilling to confront. Skeptics view it as a flawed model generating alarming results from noise. What remains clear is that the demand for transparent data and rigorous, apolitical analysis has never been greater. As the world prepares for the possibility of future pandemics, the lessons learned—or ignored—from this chapter will be critical. The Czech data, and the fierce debate it has ignited, stands as a testament to the enduring need for scientific accountability and the courage to follow evidence, wherever it may lead.

Sources for this article include:

Substack.com

Substack.com

PubMed.com