Editorial

Two articles in Volume 2 address the question of detecting signals in the US Vaccine Adverse Events Reporting System (VAERS). Co-managed by the US CDC and the US FDA, VAERS is represented as having the strengths of containing national data, being able to “rapidly detect ‘safety’ signals” and “detect rare adverse events”, and that the VAERS system brings “data to the public”. The following limitations are also listed: reporting bias, inconsistent data quality and completeness of information, lack of unvaccinated comparison group, and, importantly, that it is “not designed to assess causality” (Shimabukuro, 2021). The deep and irreconcilable incongruence between “can detect rare adverse events” and “not designed to assess causality” by Dr. Shimabukuro is noteworthy. The data in VAERS are collected passively, leading to underreporting.

The study by Tomljenovic et al. (2021) assesses directly the issue of case definition in adverse events reported following the administration of Human Papillomavirus Virus (HPV) vaccines, revealing interobserver variation in vaccine adverse event rating that is not addressed by the VAERS system and the under-reporting of serious adverse events. Their findings warrant reconsideration of the reliability of past analyses of HPV-vaccine-related adverse events in VAERS. Further, there is no reason to assume that the significance of their findings is limited to adverse events reported in relation to HPV vaccines.

The second article addressing data in VAERS is by Rose (2021), who independently followed up on a challenge finding published to social media which showed that the number of deaths following COVID-19 vaccination were not evenly distributed across the days in the reporting period following receipt of the COVID-19 vaccines. Should events attributed to vaccination not be causal, it is expected that they would be evenly distributed in the days following vaccine administration.

Dr. Rose’s analyses reveal that the major increase in autoimmune-related conditions in VAERS reports following the introduction of COVID-19 vaccines, as well as the undeniable explosion of reports of deaths compared to the pre-COVID-19 vaccine era, both violate the expectation of random reporting. Under a null random reporting model, the number of reports for any adverse event, including deaths, would fall on any given day following the vaccination exposure event. Dr. Rose’s analysis is the first to note a remarkable and clear increase in autoimmune- related reports following COVID-19 vaccination, providing evidence in support of a potential indication of pathogenic priming at work. Dr. Rose’s exquisite and thorough analyses provide a landmark study which highlights patterns in data that are not consistent with expectations of non-causal, spurious reporting. They present a serious and noteworthy challenge to results reported by Dr. Shimabukuro of “no associations” between any adverse event reported in VAERS following COVID-19 vaccination. This report was given to the Advisory Committee on Immunization Practices on March 1, 2021.

Dr. Shimabukuro’s results are beyond surprising, because counter-evidence that makes the Shimabukuro results unlikely include acknowledgement of increased cases of Bell’s palsy in the medical literature (Ozonoff et al., 2012) and plausible mechanisms of causality (Soeiro et al., 2021). Other patterns in the data that are inconsistent with a random, non-causal relationship between vaccines and adverse events include gender biases and age clustering which show a different distribution of variance that expected in the absence of causality, given the distribution of vaccine exposure.

Both of these studies should lead to changes in practices of vaccine adverse events tracking. The American public was promised that the National Childhood Vaccine Injury Act of 1986 would provide scientifically rigorous defenses against vaccine injury. The options available to the future include (a) status quo, (b) revoking the NCVIA, (c) reforming the NCVIA. Reform can start now — with stiff penalties to physicians for failing to report potential vaccine adverse events (instead of mild “encouragement”), ending the passive reporting. Current techniques for “official” VAERS reports include the use of Bayesian methods, introducing subjective prior probabilities that can be readily manipulated. This is likely the cause of the failure of the reports cited by Shimabukuro (2021) to detect known associations.

This failure is consistent with practices and policies that led to codified biases in the capture and rendering of public data that skew public perception of vaccine risk toward universal safety. CDC’s decisions to count all positive PCR results as COVID-19 cases and the use of PCR cycle thresholds as high as 40 or 45 lead to biased estimates of the number of COVID-19 cases, whereas the recent decision by CDC to count vaccine failure cases (so-called “breakthrough” cases) using Ct thresholds of >26, and then only if patients are hospitalized or die (CDC, 2021), will bias data on COVID-19 vaccine failure. We should not have two criteria for the diagnosis of COVID-19 based on vaccination status, and yet this is precisely what these policies and practices represent. This discrepancy, as well as those revealed by Tomljenovic et al. and Rose (2021), demand explanation.

The long history of abuses of science to stand up a bulwark of defenses against vaccine risk awareness, including the lies vaccinologists tell themselves, is an affront to objective scientists and ethical physicians around the world.

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