The CDC and associated regulatory agencies seem to be intentionally hiding signals of dramatic vaccine risk, then reporting numbers on two sides of risk-benefit analyses that are tallied by extremely different criteria to engineer an impression that the COVID-19 vaccines are "safe and effective". This alone should be reason enough to halt and re-evaluate the mass vaccination program using experimental vaccine technologies.

For those unfamiliar with the context, please refer to Part 1 and Part 2. I wrote the first article naive to the topic, but it is noteworthy that I have since conversed with numerous experts in and around the pharmacovigilance research community and every single one of them confirmed my conclusions. My education has been accelerated by professionals and investigators who prefer to remain anonymous out of fear for their careers or worse. However, some researchers are taking my points, adding to them, and will soon head toward publication with what sounds like damning critiques of the CDC's safety signal analysis. That leaves me free to do what I am better at, which is surveying the big picture and finding previously under-analyzed problems.

Yesterday I was asked to formulate a succinct argument regarding what appears to be an industrial system designed to mask vaccine safety signals. This post attempts to put the argument into words that can be understood by a responsible judge whose familiarity with and understanding of complex data may be good, may be bad, or may be somewhere in between. One-on-one, I suspect I could talk through what I've learned about pharmacovigilance over the past several weeks with anyone, but laying out a summarized argument is always a challenge because the needs are different with each individual.

Proportional Reporting Ratios, Similar Ratio Signals, and Inequivalent Mortality Definitions

The Vaccine Safety Datalink (VSD) is a collaborative project between the CDC and nine integrated health care organizations, most of which are part of the Kaiser Parmanente network. The stated goal of the VSD is to "detect adverse events following vaccination in near real time so the public can be informed quickly of possible risks." On face, such a project appears to be a good idea in that active involvement with patients should detect a higher proportion of adverse events (AEs) than we should expect from the VAERS database, where under-reporting rates can only be estimated.

While it would take a serious investigative effort to find out what data gets collected, but does not get reported to the public, let us note that the recent Rapid Cycle Analysis (RCA) report compiled by Nicola Klein, MD, PhD at Kaiser Permanente's Vaccine Study Center (henceforth the "VSD RCA") excludes the one serious AE for which we have no good direct way to even estimate the all important under-reporting rate (for indirect methods, see here, here, and here), which is vaccine-induced mortality. It is noteworthy that in all CDC documents referred to (inappropriately) as "risk-benefit" analyses, there is similarly no effort made to study the enormous number of death reports in VAERS to demonstrate any of them are not associated with the COVID-19 vaccines. Without reference to a single autopsy, the CDC bypasses usual vaccine requirements despite reports from pathologists in other nations (here and here) suggesting that causal vaccine link proportions are very high. Using the VAERS, V-Safe, VSD, and other AE reporting databases to examine proportionality signals does nothing to change that fact. Even the CDC admits that VAERS reports cannot determine the true magnitude of AEs, including death.

How can informed consent possibly take place without analyses of vaccine-induced mortality? Clearly, it cannot. All risk-benefit analyses (such as this one) claimed by the CDC and other reporting agencies are warrantless claims so long as they fail to mention that the summary exclusion of nearly all vaccine-associated deaths takes place in absence of any analysis, despite domestic VAERS reports and foreign experts concluding the opposite (for the very same vaccines) in their respective geographies. These deaths are the single most important element of the risk side of a risk-benefit analysis. It is noteworthy that to date, not one study or analysis that includes such reported deaths has demonstrated a net benefit for vaccinated individuals in all cause mortality, including Pfizer's own six month trial report.

Put another way, the CDC is publishing risk-benefit reports in which they count COVID-19 deaths without analysis of causality, but summarily exclude vaccine-associated deaths without analysis of causality. Such analysis is equivalent to calling all business income "profit" and proof of net productivity without measurement of the costs and resources consumed. But in particular, determining causality rates of the many thousands of vaccine-associated deaths is the CDC's very first responsibility by the Precautionary Principle. Such clear and demonstrable false equivalence in the estimation of the absolute most important possible endpoint (mortality) indicts the vaccine manufacturers, the CDC, and everyone else responsible for such reporting.

What the VSD RCA does do, however, is perform safety signal analysis ostensibly designed to detect high rates of AEs that is similar to the use of Proportional Reporting Ratio (PRR). The VSD RCA does not completely define what it calls Adjusted Rate Ratios (ARRs), but the resulting statistics appear to be PRRs with some form of adjustments for data collection sites and demographics that are somehow translated back to a single number before presentation as signal analysis. Were there signals in subgroups? Wouldn't that be important to report?

These adjustments, however they are calculated in the VSD black box, have nothing to do with the problems inherent in the use of PRR in safety signal detection, which I will now describe.

Ratios Versus Magnitudes

There are many forms of safety signals used in reporting. The pharmacovigilance research community has studied their use well. However exotic many of the algorithms might be, none of them can replace the most basic reporting of magnitudes (simple counts) of outcomes or the statistically normalized per dose or per patient rates of those magnitudes.

The use of PRR (and similarly ARR) is most appropriate when examining adverse events associated with a heterogeneous pool of drugs or other therapies. For instance, a paper by Evans et al used PRR to detect excess proportions of AEs among 15 newly-marketed drugs of different kinds and purposes while detecting 481 safety signals. So, why is it that the CDC and VSD RCA detect absolutely none, despite historically high magnitudes of VAERS reports associated with COVID-19 vaccines that showed high levels of AEs during trials that excluded most high risk demographics (0.7% severe AEs)?

In their 2017 paper, Hauben and Maigen explain the problem known as "signal masking" or simply "masking". To paraphrase:

Masking of high AE magnitude takes place in PRR analysis when drugs or therapeutics are primarily compared with similar medical interventions.

That is to say that nobody familiar with pharmacovigilance signals should expect for PRR or what seems to be a highly similar ARR analysis used in the VSD RCA to detect large numbers of adverse events as signals for concern, including those that can permanently injure or even kill. This view was confirmed by Wisniewski et al (including authors notably employed by Pfizer and Astra-Zeneca) when they concluded that such signals, "are not easily interpretable in terms of clinical impact," stating clearly that, "calculation of PRRs...should not replace nor delay the performance of epidemiological studies." (emphasis mine)

Demonstrating the Problem

I put together a spreadsheet here to compute PRRs. I plugged in some exact values from the VSD RCA for a handful of AEs, categorizing the Janssen and mRNA vaccines as Klein did in her report. I created some fictional data for total AEs and "Other Comparators" (other vaccines such as the CDC uses for comparison, but with far lower numbers of reported AEs). Not knowing the VSD RCA ARR formula, I was able to achieve PRR values that were relatively close to the VSD RCA values in most cases, but that is beside the primary point. When we scale the already high numbers of AEs by a factor of 20, there is still not a safety signal triggered as all of the PRRs remain under 2:

In other words, plainly stated, even in an empirically disastrous scenario in which this new class of vaccines is dangerously volatile across the board, this metric employed by the CDC raises no alarms.

Even when scaled to 1000-fold the number of each AE, only one of the 12 PRR signals gets triggered (assuming it meets the additional criterion of a chi-squared statistic being higher than 4, which it does not under this scaling). In fact, none of the other 11 PRR signals ever gets triggered as that scale factor approaches infinity. Nothing could further demonstrate the uselessness of such signals in this context.

It seems highly unlikely that the statisticians at the CDC and VSD are unaware of this fact, and it would have been appropriate to demonstrate some kind of sensitivity analysis showing where signals do in fact show up. Instead, the VSD RCA computes p-values, which are similarly inappropriate to the task. A p-value measures the probability of an event occurring by random chance, and any statistician cognizant of that meaning would not be using Fisher's tests to compute a p-value for a statistic such as PRR or ARR that normalizes results (not once, but twice) through clearly related observed events. Even worse, the VSD RCA seems to require even smaller p-values than the usual standard of statistical significance before indicating a safety signal, supposedly to remove false positives. This stands out as another egregious form of statistical masking that is nonsensical on its face.

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Conclusion

The safety signal analysis employed by the CDC, VSD, and associated regulatory agencies uses methods that are not simply ill-suited to the task, but mask substantial indications that large numbers of people are being injured, often seriously, or even killed by the COVID-19 vaccines currently in use. The inequivalent definitions used in mortality calculations results in inexcusably rigged risk-benefit analyses, and the lack of investigation into causes of mortality (both for the COVID-19 disease and also for the COVID-19 vaccines) stands out as historically monumental malpractice and dereliction of duty. The mass vaccination program should be halted while true risk-benefits of the vaccines are assessed, and regulatory agencies fully investigated for conflicts of interest and intention to defraud the public of its opportunity for informed consent.

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Defining Away Vaccine Safety Signals, Part 3