Thanks to the brilliant work of Ron Johnson and his staff, we now know that the FDA knew that a more accurate analysis method was throwing off safety signals so the FDA silenced the whistleblower.
May 02, 2026
Executive summary
As detailed in the Johnson report and hearing Unmasked: How Biden Health Officials Purposely Turned a Blind Eye Toward COVID–19 Vaccine Safety Signals, you can find above the list of safety signals that triggered highlighted in yellow.
The left column is the new method that exposes the signal. A value of 2 means “reported at twice the rate expected.”
The right column is the preferred FDA method showing there is nothing to worry about because every single adverse event was reported at a lower rate than what is expected.
The new method was invented by William DuMouchel and Rave Harpaz and used by FDA scientist Ana Szarfman.
From the report:
In short, the FDA knew about the more accurate method of detecting safety signals but determined that the method would fuel anti-vaccine rhetoric so they silenced her research and findings. They didn’t want to hear about it.
The report
Links
The press release with links to the data and report
RGPS paper (Oracle whitepaper fully describing the RGPS method)
Szarfman paper on 7 adverse events
AI on the RGPS method
Impact: The authors found that RGPS consistently produced higher signal scores than standard methods. In the case of herpes zoster, RGPS identified a signal that standard methodologies effectively failed to detect (keeping it below the signaling threshold).
rom a statistical and pharmacovigilance standpoint, the RGPS method is technically valid for its intended purpose. It is a significant improvement over earlier generation algorithms because it attempts to address the “polypharmacy confounding” problem—which is arguably the single greatest weakness of spontaneous reporting data.
However, its utility is best viewed as a signal generation tool rather than a definitive diagnostic tool. It successfully narrows the field of potential safety signals, but it does not replace the necessity for clinical investigation, pharmacological research, and independent verification of the identified associations. It effectively solves the problem of “too much noise” in the data, but it remains a tool for screening, not a tool for proving causality.
AI on the Szarfman paper
⚖️ Conclusion
This paper is a significant contribution to the field of pharmacovigilance. It provides empirical evidence that the standard tools used for vaccine surveillance are fundamentally flawed in the context of the COVID-19 vaccine rollout due to their susceptibility to masking. It confirms that the use of advanced regression-adjusted methods is not just an academic preference, but a practical necessity for identifying potential safety signals in a rapid, complex reporting environment.
Summary
The FDA wasn’t aware of any safe signals in the VAERS data because they ordered the scientist who spotted the safety signals to stop talking about them because they might feed into anti-vaccine rhetoric.
I’m baffled as to how burying safety signals is in the public interest.
Before anything criminal can be pursued, it typically goes through:
- HHS Office of Inspector General (OIG) investigations
- Congressional oversight (hearings, document production)
- Independent scientific review of the analyses and methods
- Potential civil litigation if harm and causation can be shown
If they knowingly suppressed material safety risks to mislead that could be criminal. But establishing that requires strong, specific evidence of intent, not just signals in a table or disagreements over methods.
Photo Credit: pixabay.com
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