US health officials relied on flawed vaccine safety algorithm

An investigation published by The BMJ reveals that US health officials knowingly relied on a flawed data mining algorithm to monitor the safety of mRNA COVID-19 vaccines. The report, based on internal government emails and interviews, indicates that the Food and Drug Administration (FDA) and the Centers for Disease Control and Prevention (CDC) used a compromised methodology within the Vaccine Adverse Event Reporting System (VAERS).

The flaw stemmed from the fact that over 90% of initial VAERS reports were for the Pfizer and Moderna mRNA vaccines. Because the vaccines accounted for such a massive proportion of reports, the algorithm's mathematical model failed to trigger automated alerts for adverse events that occurred at similar rates across both products. This resulted in a near total loss of signal detection sensitivity.

Consequently, the algorithm failed to signal potential links between the vaccines and conditions such as myocarditis, pericarditis, Bell’s palsy, and tinnitus. While the CDC had planned to use multiple data mining techniques, including proportional reporting ratios (PRRs), internal records show the agencies chose to rely almost exclusively on the FDA’s Bayesian method, despite warnings regarding its limitations.