Safe and Effective? Part 4
Dismantling the Dogma
Safe and Effective? Part IV
Dismantling the Dogma
In the last three essays, Safe and Effective? 1, 2 and 3, I summarized what I believe is the most solid evidence that the Covid-19 vaccines are not nearly as safe or effective as publicized. This evidence comes from what should be mutually agreed upon sources: peer-reviewed and published trial data, memoranda between Pfizer and the FDA, CDC reports of adverse events from their own surveillance systems.
Here are the basic points:
The trial data demonstrated that the risk/benefit ratio of the primary series should not have resulted in Emergency Use Authorization of the product
There was evidence of investigator unblinding in the trial results themselves
This was corroborated by allegations from a whistleblower, a clinical trial coordinator at one of Pfizer’s test sites
Unblinding of the investigators allowed them to hide serious adverse events that were occurring at a greater rate in the vaccine wing of the trial
These numbers approximate what we have been seeing in official Vaccine Adverse Event surveillance system data
Most of the trial participants who were expressing Covid-19 symptoms were not confirmed by PCR
This introduces significant uncertainty regarding the efficacy of the vaccine
Real world data confirmed that the stellar performance of the vaccine in preventing SARS-COV2 infection during the trial was dubious
If the explanations in the aforementioned posts do not raise a few questions about the “Safe and Effective” narrative for you, I would venture to say that it is due to one or more of these three reasons:
You still need to confirm that I have fairly assessed the information coming from the material I have cited
You simply cannot believe that so many smart people could be so wrong for so long
There is too much other data that proves that they are, in fact, “Safe and Effective”
If it is 1), good. Always confirm for yourself. Let’s take a closer look at the other two possibilities…
The Presumptive Legitimacy of the Majority
If it is 2), you are not alone. This is a very common source of bias, the presumptive legitimacy of the majority. What I mean is that we believe that the majority of people will be right most of the time.
Most of the time.
How do we know this is one of those times? Before getting to that, let’s take a harder look at why we believe the majority will get it right most of the time to begin with.
For the purposes of illustration, let us take the topic of vaccine safety. It is reasonable to assume that if more people believe they are “safe”, then they are most likely safe. But how did the majority come to their conclusion?
If every person in the majority independently arrived at the conclusion that “vaccines are safe”, this would constitute a true majority opinion, one that carries a greater potential of being proven correct. Let’s call this a “true” majority.
This is much different than the far more common situation, one where the “majority” is made up of only a small number of people who have done their due diligence before coming to their conclusion and a much larger number of people who simply go along because they either trust in those who researched the topic.
In fact, most majorities are composed of three groups. The ones who do the research (let’s call them the experts), those who trust the experts and those who go along because they believe that the majority is probably going to be right. It is this last subgroup that introduces the most amount of distortion around the idea of a majority.
Why? Because this subgroup is going along simply because more people are going along. We can see that this way of forming a majority gathers momentum as more “followers” get on board and the majority gets bigger. If the majority gets bigger, this attracts more “followers”.
It should be evident that if everyone independently researched the topic of vaccine safety and 70% of people concluded that they were safe, there is a greater possibility that this type of majority will eventually be proven correct than if the 70% were made up of “trusters” and “followers” with a much smaller number of “experts”.
Why? It’s because a lot more “experts” would have to be wrong for the majority to be wrong in a “true” majority.
With regard to these vaccines, we find ourselves in a different kind of situation. The average person cannot be expected to “do their own research” properly. Most people have no idea what “gain of function” research is. Neither are they familiar with such things as “furin cleavage sites”, antibody-dependent enhancement, immune imprinting, cellular vs. humoral responses or even “vaccine efficacy”. Doing one's own research means consulting your doctor or going to the CDC website.
I have asked dozens of my physician colleagues about what they know about the vaccine trials. Not one had ever read the published data on this landmark study. Neither could they tell me what the absolute risk reduction the vaccine would provide those to whom they were recommending it to.
Their excuse: “I don’t have the time to read all the studies. I have to go with CDC guidelines and recommendations.”
The point here is that there are very, very few “experts" that are actually investigating these things. How do we know they have gotten it right? There are other “experts” that are skeptical of the narrative. How do we know which ones to believe?
Before discussing more data, it’s important to be aware of how we come to conclusions as individuals and as a group. The reason why we should be aware of this process of forming majorities is that the more people who vote with the majority because they believe the majority will be right, the fewer people there are that actually do the required research before coming to a conclusion. Making a decision prior to availing yourself of the available information is the definition of bias, something that abounds in society and in scientific research too.
It is not easy for a mind to realize it’s biased. It would be akin to trying to see a blindspot. You would need to deduce it from other information available to you. In this situation, it is helpful to simply notice why you may not be openly considering an opposing opinion. How certain can you be that you are right if you refuse to listen to the counter argument?
Now let’s consider the last point. Is there really a lot of proof of safety and efficacy? It depends on the lens through which we peer.
The Four “Ignoble” Truths of Covidianism
The Covid-19 Vaccines are Safe
The Covid-19 Vaccines are Effective
There is misinformation about the Covid-19 Vaccines
If information contradicts the first two truths, it must be misinformation
Though much of the population believes they are being objective by “trusting the science”, there are a lot of people, both scientists and laypersons, who subscribe to the four “ignoble” truths above.
How often does an opinion get discarded without consideration simply because it opposes the safe and effective narrative? How often do we check to make sure that an article’s origin is not a “known source of misinformation” before we read it? Who do we rely upon to make that determination? Are they objective? Or do they subscribe to the Four “ignoble” truths too?
The point here is that taken together, these “truths” form the basis of dogmatic thinking. Practically speaking, if someone subscribes to these principles there is no way for them to ever break free of their way of looking at things. What evidentiary argument will ever suffice if it is immediately labeled as misinformation? There isn’t any.
It is not easy to help someone break free of dogmatism. What is particularly unhelpful is to bombard them with endless information that they will discard out of hand. If anything, it reinforces their belief in truth #3. Not only does misinformation exist, it’s rampant and we need to be ever more vigilant!
So, what can be done about this?
Because evidence of Covid-19 vaccine harm and inefficacy is systematically discarded by those who subscribe to the Covidian dogma, the best (and only?) way to breakthrough is to point out the hypocrisy in their position. Do they have double standards in the way they are regarding evidence? They do.
Double Standard #1: Classification of deaths
A Covid-19 death, as defined by the CDC, is not a fatality from Covid; it’s a fatality with Covid. Here’s the CDC’s guidance:
The ICD-10 codes are used by the CDC to tally the Covid deaths. Note that laboratory confirmation is not required. All that is required is a presumption.
If those are the standards for labeling a death a “Covid-19” death, why are they so different from Covid-19 vaccine deaths? The CDC dismisses the 30,000+ deaths in VAERS as “unverified”. What would it take to verify them? An autopsy, with specific immunohistochemical studies to rule out every other possible cause. Of course they are not being done, but why the immense double standard?
On one hand, all we need is the presumption of Covid infection with no laboratory confirmation while on the other enormous standards of proof are required to prove vaccine harm. Why?
Double Standard #2: The use of VAERS (Vaccine Adverse Event Reporting System) data
The CDC has dismissed tens of thousands of potential vaccine mediated deaths and hundreds of thousands of serious adverse events because they consider that system to be unreliable. Reports can be fabricated and thus overreported.
Okay, fine. But look at this:
This was part of a presentation given to the advisory committee to the FDA in the summer of 2021 after it was recognized that myocarditis was a known complication of mRNA vaccines, especially in young males. Where did the CDC get their numbers? VAERS!
Question: why is VAERS a legitimate source of information with regard to myocarditis but not to be relied upon when it comes to vaccine deaths and other adverse events?
Double Standard #3: Myocarditis is taken seriously but Vaccine Deaths are not
Another reason why the CDC acknowledges the real risk of vaccine-induced myocarditis is the temporal relationship between inoculation and the onset of myocarditis symptoms. Here is another slide from the CDC:
This is a very telling graph as it demonstrates that people who succumbed to myocarditis after vaccination began having symptoms soon after the injection. Very few of the people who suffered myocarditis were asymptomatic for a week before feeling ill. This temporal association does not prove causation, but it was enough for the CDC to take it seriously.
Okay, but what about reports of deaths after vaccination?
Here’s a graph from a paper published in the Lancet, written by CDC authors. They queried the VAERS database and summarized the death reports for the six months and found this:
Once again, we see the same temporal relationship between vaccination and day of death. Why doesn’t this prompt a deep dive for them?
Instead, this kind of association is being dismissed by the mantra “Correlation does not equal Causation!!”. True. But why are we looking at these two very serious adverse events differently?
Here’s another way to look at this graph: the high number of deaths within just a few days of the shot doesn’t prove causation. On the other hand, if the shot did cause the deaths, that is precisely what the data would look like.
Double Standard #4: What’s more important? Absolute or Relative Risk?
The benefit of Covid-19 vaccines is widely promoted by citing their efficacy. Vaccine efficacy is a function of relative risk. For example, the Pfizer/BioNTech formulation was found to have an efficacy of 90%. This is because nine times more placebo recipients suffered Severe Covid-19 than those who got the primary series. In fact there was only one vaccine recipient that got Severe disease. This meant that out of approximately 40,000 participants, only 10 people contracted severe Covid.
That means that the vaccine reduces absolute risk of severe disease by approximately 0.04%. Which number do we hear about? 0.04%? Or 90%?
Here I am not arguing about which number is more salient, rather I am pointing out something about how vaccine risk is discussed by our health authorities.
Look at the CDC table showing observed vs expected risk of myocarditis. The risk of developing myocarditis, by the CDC’s own data, is about 50-60 per million in the highest risk groups. In fact that is what the public is told, that it is an exceedingly rare complication. This is the absolute risk of myocarditis from the vaccine.
What is the relative risk of getting myocarditis if you are male between the ages of 12 and 24? You have to look at the “expected” column, in other words, what is the background rate of this serious condition in the population. It’s anywhere between 25 to 200 times lower than what was “observed” after vaccination. The relative risk of getting myocarditis after vaccination is 25 to 200.
Why is the public told about vaccine efficacy in relative terms but about vaccine risk in absolute terms?
It is perfectly accurate to say that the vaccine reduces your chances of getting severe Covid by 0.04% but increases your rate of getting myocarditis by 200 if you are a young male.
Double Standard #5: Causation vs. Correlation
Those of us who have concerns around the stunning number of reported vaccine injuries are being continually reminded that “Causation does not equal Correlation”.
True. But what do you think the trials and observational data that demonstrate vaccine efficacy tell us? Causation? No. Only correlation. There isn’t any proof of causation. There’s only an “associated” benefit.
To be fair, that is the best we can do in a trial, especially with a preventive measure. That’s how medicine works. The point here is that if there is an associated harm, it needs to be investigated too.
We were quick to applaud our vaccine manufacturers when they conducted their vaccine trials in the summer and fall of 2020 and reported a stunningly high vaccine efficacy of their product. However it is widely reported that excess deaths are up in many parts of the world.
Excess deaths are deaths in excess of what is to be expected given historical levels. For example, an excellent analysis of actuarial data from Germany was published in 8/2022 by Kunbander and Reitzner.
Here’s an important graphic from their paper:
What we see here is striking. The excess mortality in 2020 across all ages is shown in the gray bar in the left graph. It’s less than 0.5% over what was expected. This was occurring during the pandemic when there was no vaccine.
On the right we see what happened in Germany in 2021. Excess deaths are over 3%. At least six times higher than in 2020. Moreover, nearly all age groups are demonstrating excess mortality at significantly higher rates than in 2020 when a virus ripping through the world unabated.
What was common to both years? Was there a virus infecting and killing people? Yes. Were there lockdowns and restrictions imposed which significantly affected people’s livelihood and overall health? Yes. Was it harder to receive medical attention and cancer screenings? Yes.
What was present in 2021 that was absent in 2020?
Of course this doesn’t prove that Covid-19 vaccination has caused these excess deaths. It’s merely an association, just like in the vaccine trials that showed efficacy through association too. Why do we grant vaccine efficacy by association in the trials but ignore the association with excess deaths over the next two years?
Double Standard #6: Immunogenicity vs Efficacy
Immunogenicity is the vaccine’s ability to provoke a person’s immune system to synthesize antibodies. Efficacy is the vaccine’s ability to protect a person from disease, or in this case symptoms.
From the beginning of the pandemic, the FDA has steadfastly insisted that antibody levels are not a surrogate for protection. This is why they insisted that everyone get jabbed, even those who had survived a bout of Covid and had antibodies to prove it.
Yet pediatric trials for the original formulation did not demonstrate any significant vaccine efficacy. There weren’t enough outcomes. Authorization for pediatric use came from antibody levels, or immunogenicity.
In fact, none of the on-going Phase III trials involving the current bivalent booster seek to demonstrate efficacy. They are only measuring immunogenicity.
How can the FDA use antibody levels to measure vaccine efficacy while at the same time maintain that they “should not be used to evaluate protection from Covid-19”? Is it possible to be any more hypocritical?
Double Standard #6: Benefits are touted while harm is ignored from the same data set
The six month trial data from Pfizer demonstrated a single Covid-19 death was prevented through vaccinating approximately 20,000 people.
This gives us a VE in Covid-19 deaths of 50%. On the other hand, the risk of death from cardiac arrest went up by a factor of 4 if vaccinated. There is no logical or scientific basis to assert that the vaccine prevented the single Covid-19 death while denying the possibility that it caused the four deaths from cardiac arrest.
Moreover, mortality from all causes after six months is higher in the vaccinated wing of the trial (15 vs 14 deaths). To my knowledge, there is no other therapy that has ever been approved or authorized by the FDA where Phase III trials demonstrated an increased risk of dying if given. The Pfizer Covid vaccine stands alone in this regard.
Double Standard #7: Why are Vaccine Manufacturers not liable if their product causes harm?
Is there another product given to humans where the manufacturer is protected if harm ensues from its use? Please let me know in the comments.
The reality is, there is a lot of signs out there that are suggesting harm; we just don’t seem to see them…