Were COVID-19 Vaccines EVER Effective at All?
Deceitful Statistical Manipulation or Accidental, Uncontrollable Bias?
When talking with people that are finally starting to realize that the COVID-19 vaccines aren’t effective at reducing infection or transmission, they will undoubtedly say, well at least they reduced disease severity (hospitalizations & deaths).
Most of the time there is no need to even argue that point because my concern is that without preventing/reducing transmission, none of the mandates and coercion tactics were supported by any fact, science, or data. Yet they were forced on us by authoritarian governments, agencies, and the mainstream media, violating our supposed inalienable rights and freedoms.
It was a disgusting and egregious overreach and use of power and authority and that is the most important point to make out of this whole pandemic train wreck.
But I digress. Today I want to talk about a couple of peer-reviewed scientific papers that discuss whether the COVID-19 vaccines were effective at all, right from the first injection, without even talking about waning effectiveness and new variants. I’m talking about bias and methodological concerns.
In December 2022, Dr. John Ioannidis published a paper in the peer reviewed journal, BMJ Evidence-Based Medicine.
In this paper, he discuses possible biases that can be present in non-randomized, real-world, observational studies on vaccine effectiveness that make the results dubious at best (emphasis wording mine). He outlined many biases that can undermine the confidence in the results of observational studies including pre-existing immunity, vaccination misclassification, exposure differences, testing, disease risk factor confounding, hospital admission decision, treatment use differences, and death attribution.
Most of these factors are not attempted to be controlled for in most COVID-19 vaccine effectiveness observational studies, but Dr. Ioannidis did not attempt to calculate just how big of an impact properly controlling for these factors would have on effectiveness results. He simply said they are necessary to control for if study results are to be considered reliable.
Segway to a more recent peer-reviewed paper by Kaiser Fung, Mark Jones, and Peter Doshi in the Journal of Evaluation in Clinical Practice, where the authors take 3 types of bias for which data is available and show how not properly controlling for them can make a vaccine that is 0% effective look like it is 50%-70% effective.
The 3 factors they looked at were:
Case Counting Window Bias AKA You’re still considered unvaccinated until 14 days after your shot!
For vaccinated groups in studies, any cases, hospitalizations, or deaths up to the 2 weeks post jab are not counted as cases in the vaccinated group. However, in the unvaccinated group there is no such offset, so all cases are always counted. The researchers suggest this could make a completely ineffective vaccine look to be 48% effective.
This is a really interesting factor because we know that in the 2 weeks post vaccination, the risk of infection and associated severe outcomes is elevated from the pre-vaccination period.
The province of Alberta, in my home country of Canada, helped us all out when they published (probably accidentally because they pulled it down as soon as it got attention) timing of COVID-19 hospitalization and death numbers from date of initial vaccination. The numbers were shocking.
There was a 5x increase for hospitalizations and 3-4x increase for deaths in the first 2 weeks post jab. Extrapolating that to cases would be huge, maybe a 10x increase? This equated to 47% of hospitalizations and 55% of deaths occurring within the 14 days post injection. So, ½ of all hospitalizations and deaths in the vaccinated population occurred during the 14 days post injection when these people were still being counted as unvaccinated!
Without the case counting window bias, the numbers in the vaccinated population would have doubled! That means all those hospitalizations and deaths were lumped into the unvaccinated numbers!
Age Bias AKA everybody has the same risk no matter what age…right?
Studies must control for age when assessing vaccine effectiveness, but this does not seem to be happening much. Why is this an issue? Because the risk of severe outcomes is much higher in the over 65–70-year-old range. In fact, age is the single most important factor related to COVID-19 severity.
Additionally, infection rates appear to be higher in younger populations when no one is vaccinated. So, if this isn’t taken into consideration when assessing vaccine effectiveness, it can make a completely ineffective vaccine look to be 51% effective. Getting contradictory results when stratifying by age and not stratifying by age is called a Simpson’s paradox. When stratifying vaccine effectiveness by age in this hypothetical scenario, vaccine effectiveness results return to accurate at 0%.
Background Infection Rate Bias AKA COVID-19 is Seasonal!
As we all should know and consider when critically thinking about anything, Correlation Doesn’t Equal Causation.
In the case of COVID-19 vaccines, the expected seasonal decrease in COVID-19 infection from winter to summer doesn’t magically equate to vaccine effectiveness, it means the opposite, but most COVID-19 studies don’t control for this. The initial vaccine roll-out took place from the end of winter through the summer. Gee, what a perfect time to inject everyone that could make even a completely useless vaccine appear up to 67% effective (researchers based this on COVID-19 infection decline data).
Dr. McCullough wrote a short article on this study and added a few major points not addressed by it, and which no COVID-19 effectiveness study properly controlled for.
The use of early treatment options
Researchers don’t have access to the CDCs vaccine record database, and this is the only supposedly accurate database around. For instance, hospitals automatically default to unvaccinated if vaccine status is not known.
PCR tests are unreliable, and this is the only metric used in these studies. They don’t use a combination of PCR and clinical assessment to confirm diagnosis.
Then there is the hospitalized “for” vs “with” and died “from” vs “with” COVID-19 issue that can also impact results.
Without properly controlling for all these variables, the results of COVID-19 vaccine effectiveness studies simply don’t provide a reliable picture of reality.
And one final thought from Dr. McCullogh on most vaccine effectiveness studies.
“Finally, the issue of investigator funding bias cannot be understated. Since the CDC and FDA are the named cosponsors of the COVID-19 vaccine campaign, they cannot be trusted sources of data or analysis on vaccine efficacy. They have been given marching orders to get a needle in every arm as a national security operation—so propaganda is widely used by the agencies. Additionally, the NIH co-owns an mRNA vaccine patient, thus they cannot be unbiased. Investigators, company employees, or scientists at institutions that have received funding from CDC, NIH, vaccine suppliers, or HHS COVID-19 counter-measure programs also have to be held in suspicion of being biased to promote vaccination even it is has failed.”
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