Spoiler: A major study claimed the Covid vaccines are over 90% effective. But when you look at the details of the study you find that a whopping 37.2% of all vaccinated participants who were tested within 14 days of the first dose were confirmed as Covid cases. None of these ‘cases’ were counted in the efficacy calculation. Also, out of the subset of 1,482 participants with confirmed symptomatic Covid, that were part of the study, not a single one died, despite 812 of these being unvaccinated.

Deception is now endemic, and this article describes the ‘how to guide’ on manufacturing high efficacy illusions.

Here we outline an easy to follow five step foolproof method to ensure a vaccine will be accepted as highly effective and look at a specific research paper to illustrate how this is done.

Step 1: Suppress legitimate criticism

Step 2: Publish in a (bought and sold?) ‘reputable’ journal

The study looked at both Pfizer’s BNT162b2 and Moderna’s mRNA-1273 and concluded the following:

In fact, for ‘any’ of the vaccines the effectiveness of ‘complete vaccination’ was stated to be 90.4% (with Moderna coming in at a whopping 96.3% and Pfizer at 88.8%). This information is all provided in their Table 3:

But before we look at the revealing information provided in the table, it is important to understand what kind of study this was. Most people assume that if you want to evaluate the efficacy of a vaccine in an observational study you simply compare the outcomes (e.g. infections, hospitalisations, deaths) of those in a large cohort of vaccinated people with those in a large cohort of unvaccinated people and then adjust for any known confounders. But that is not what was done here. This was a so-called “test-negative case–control study”. What they did was look at a set of people who tested positive for Covid at some time during the 19 weeks of the study – they selected 1,472 out of the 8,365 testing positive (so this would contain a mixture of vaccinated and unvaccinated). These are the ‘case participants’ in the table above. Then they try to find a set of ‘matching’ people who only ever tested negative during the 19 weeks. Again, this would contain a mixture of vaccinated and unvaccinated. These are the ‘control participants’ in the table above.

This method is similar to this study described in a recent post and (as explained there) we feel there are problems with this method. But any inherent problems with the method are not relevant to the major flaws we now consider.

Step 3: Ignore infected cases that are vaccinated

So, imagine the most extreme case in which every vaccinated person gets Covid within the first two weeks of their first dose. Then, assuming (as is likely) that none get infected a second time within the 19 weeks, according to the study definition no vaccinated people ever got Covid over the whole period of the study.

If only one person in the the unvaccinated comparative cohort had got Covid, over the same period, the vaccine efficacy (defined as one minus the proportion of vaccinated infected divided by the proportion of unvaccinated infected times 100) will be reported as 100%.

Now, while it is certainly not the case that all the vaccinated here got Covid within the first 14 days it seems that quite a lot of them did. If you look at the first two data rows of the table you can see a total 353 out of 948 (37.2%) who were tested within 13 days of their first dose were positive (the percentage is even higher, 40%, for those tested within the first 10 days). Does that sound like a vaccine that is effective in anything other than giving people Covid?

Step 4: Don’t look for Covid if you don’t want to find it

Unfortunately the study report provides no information whatsoever on how frequently people were tested. In theory if only people with Covid symptoms got tested then having a high proportion of those being confirmed positive would not be surprising (if the test was accurate). But, as these were all healthcare workers, it’s likely they were tested very frequently and we know that during the first two weeks after vaccination it was fairly routine to get tested. If people were tested every two weeks then we could reasonably conclude the vaccinated were getting infected – within two weeks of their first jab – at a rate that was almost 50 times greater than the general rate for this population.

So if you don’t look for Covid, by not testing for it, or by ignoring the test results you wont find it.

Step 5: Ignore outcomes that make your vaccine look ineffective

Conclusion: Why all observational studies (and many RCTs) of vaccine effectiveness and safety exaggerate vaccine effectiveness claims

All studies on vaccine effectiveness suffer from one or more (often most or even all) of the following systematic biases:

• Misclassification: Participants who got a PCR positive test within 14 days of first vaccination (resp. second, third etc.) are either classified as unvaccinated (resp. 1-dosed, 2-dosed etc) or simply not counted. Yet in many studies there are especially high infection rates for the vaccinated during this period.  As explained here this would result in high efficacy rates even for a placebo vaccine.
• Delayed reporting If reporting of Covid cases is delayed (e.g. by a week or two) during vaccine rollout then this results in exactly the same illusion/exaggeration of efficacy as misclassification.
• Illegitimate comparisons: Efficacy assessed by comparing the never vaccinated only with the ‘fully vaccinated’ (based on whatever the definition of fully vaccinated was at time of study, e.g. at least two weeks after second dose, third dose etc).
• Different testing protocols between vaccinated and unvaccinated: People who were unvaccinated were generally required to get tested far more frequently than the vaccinated even if they were asymptomatic. Hence, Covid positives were more likely to be found in the unvaccinated irrespective of the true difference in susceptibility between groups.
• Survivor/selection bias: People who were symptomatic or PCR positive when called for vaccination were recommended to wait until they were PCR negative before being vaccinated; this means that all such people had natural immunity when they did get vaccinated and hence were less likely to subsequently get Covid.
• Study took place during period of naturally falling infection rate: Timing of study coincided with period when infection rates were decreasing: This would be certain to create a statistical illusion of efficacy.
• Vaguely defined outcomes: By not being explicit about the outcomes and end dates for the study, many studies can simply choose which outcome makes the ‘best case’ for the vaccine. So, in the above example, only in the detailed results do we find there were no deaths (in either the unvaccinated or unvaccinated) and almost no hospitalizations); hence the impact of the vaccine on these key outcomes were conveniently ignored.