Tuesday, November 23, 2021

"How Covid-19 Jab Benefits Are Exaggerated", by Dr. Joseph Mercola

 Source:

"In a November 12, 2021, blog post,(1) Maryanne Demasi, Ph.D., reviews how the benefits of the COVID-19 shots have been exaggerated by the drug companies and misrepresented to the public by an uncritical media.

She has previously given many lectures on how the drug companies conflated absolute and relative risks for statin drugs.(2)

Demasi was a respected Australian science presenter at ABC television until she produced a Catalyst report on the dangers of Wi-Fi and cellphones. In the wake of the controversy it raised, she and 11 of her staff members were axed and the episode retracted.(3)

That was 2016.

Today, Demasi is one of the few professional journalists seeking and publishing the truth about COVID-19.

Absolute Versus Relative Risk Reduction

In her post, Demasi highlights one of the most commonly used tricks in the book — conflating absolute and relative risk reduction.

As noted by Demasi, AstraZeneca and Australia’s health minister, Greg Hunt, claimed the AstraZeneca injection offered “100% protection” against COVID-19 death.

How did they get this number?

Demasi explains:(4)
    “In the trial(5) of 23,848 subjects … there was one death in the placebo group and no deaths in the vaccinated group.

One less death out of a total of one, indeed was a relative reduction of 100%, but the absolute reduction was 0.01%.”

Similarly, Pfizer’s COVID shot was said to be 95% effective against the infection, but this too is the relative risk reduction, not the absolute reduction.

The absolute risk reduction for Pfizer’s shot was a meager 0.84%.

It’s worth noting that an incredibly low number of people were infected in the first place.

Only 8 out of 18,198 vaccine recipients developed COVID symptoms (0.04%), and 162 of the 18,325 in the placebo group (0.88%).

Since your risk of COVID was minuscule to begin with, even if the shot was able to reduce your absolute risk by 100%, it would still be trivial in real-world terms.

According to Gerd Gigerenzer, director of the Harding Centre for Risk Literacy at the Max Planck Institute, only quoting the relative risk reduction is a “sin” against transparent communication, as it can be used as a “deliberate tactic to manipulate or persuade people.”

Demasi also quotes John Ioannidis, professor at Stanford University, who told her:(6)

“This is not happening just for vaccines. 

Over many decades, RRR [relative risk reduction] has been the dominant way of communicating results of clinical trials.

Almost always, RRR looks nicer than absolute risk reductions.”

Demasi continues:(7)

“When asked if there was any justification for misleading the public about the vaccine’s benefits to encourage uptake, Prof Ioannidis rejected the notion.

‘I don’t see how one can increase uptake by using misleading information.

I am all in favor of increasing uptake, but this needs to use complete information, otherwise sooner or later incomplete information will lead to misunderstandings and will backfire,’ says Ioannidis.

The way authorities have communicated risk to the public, is likely to have misled and distorted the public’s perception of the vaccine’s benefit and underplayed the harms.

This, in essence, is a violation of the ethical and legal obligations of informed consent.”

US Health Authorities Have Misrepresented the Data

U.S. health authorities, like Australia’s, are guilty of misrepresenting the data to the public.

In February 2021, Centers for Disease Control and Prevention director Rochelle Walensky co-wrote a JAMA paper(8) which stated that “Clinical trials have shown that the vaccines authorized for use in the U.S. are highly effective against COVID-19 infection, severe illness and death.”

Alas, “there were too few deaths recorded in the controlled trials at the time to arrive at such a conclusion,” Demasi writes.(9)

This observation was made by professor Peter Doshi, associate editor of The BMJ, during Sen. Ron Johnson’s Expert Panel on Federal Vaccine Mandates, November 1, 2021.(10)

During that roundtable discussion, Doshi stated that:

“The trials did not show a reduction in deaths, even for COVID deaths …

Those who claimed the trials showed that the vaccines were highly effective in saving lives were wrong.

The trials did not demonstrate this.”

Indeed, the six-month follow-up of Pfizer’s trial showed 15 deaths in the vaccine group and 14 deaths in the placebo group.

Then, during the open label phase, after Pfizer decided to eliminate the placebo group by offering the actual shot to everyone who wanted it, another five deaths occurred in the vaccine group.

Two of those five had originally been in the placebo group, and had taken the shot in the open label phase.

So, in the end, what we have are 20 deaths in the vaccine group, compared to 14 in the placebo group.

We also have the suspicious fact that two of the placebo participants suddenly died after getting the real deal.

How You Express Effect Size Matters

As noted in a July 2021 Lancet paper,(11) “fully understanding the efficacy and effectiveness of vaccines is less straightforward than it might seem.

Depending on how the effect size is expressed, a quite different picture might emerge.”

The authors point out that the relative risk reduction really needs to “be seen against the background risk of being infected and becoming ill with COVID-19, which varies between populations and over time.”

This is why the absolute risk reduction figure is so important:(12)

“Although the RRR considers only participants who could benefit from the vaccine, the absolute risk reduction (ARR), which is the difference between attack rates with and without a vaccine, considers the whole population …

ARR is also used to derive an estimate of vaccine effectiveness, which is the number needed to vaccinate (NNV) to prevent one more case of COVID-19 as 1/ARR.

NNVs bring a different perspective:
 81 for the Moderna–NIH,
78 for the AstraZeneca–Oxford …
84 for the J&J, and
119 for the Pfizer–BioNTech vaccines.

The explanation lies in the combination of vaccine efficacy and different background risks of COVID-19 across studies:
0.9% for the Pfizer–BioNTech …
1.4% for the Moderna–NIH,
1.8% for the J&J, and
1.9% for the AstraZeneca–Oxford vaccines.

ARR (and NNV) are sensitive to background risk

— the higher the risk, the higher the effectiveness

— as exemplified by the analyses of the J&J’s vaccine on centrally confirmed cases compared with all cases:

both the numerator and denominator change, RRR does not change (66–67%), but the one-third increase in attack rates in the unvaccinated group (from 1.8% to 2.4%) translates in a one-fourth decrease in NNV (from 84 to 64) …

With the use of only RRRs, and omitting ARRs, reporting bias is introduced, which affects the interpretation of vaccine efficacy.

When communicating about vaccine efficacy, especially for public health decisions such as choosing the type of vaccines to purchase and deploy, having a full picture of what the data actually show is important,

and ensuring comparisons are based on the combined evidence that puts vaccine trial results in context and not just looking at one summary measure, is also important.”

The authors go on to stress that comparing the effectiveness of the COVID shots is further hampered by the fact that they use a variety of different study protocols, including different placebos.

They even differ in their primary endpoint, i.e., what they consider a COVID case, and how and when diagnosis is made, and more.

“We are left with the unanswered question as to whether a vaccine with a given efficacy in the study population will have the same efficacy in another population with different levels of background risk of COVID-19,” the authors note.

One of the best real-world examples of this is Israel, where the relative risk reduction was 94% at the outset and an absolute risk reduction of 0.46%, which translates into an NNV of 217.

In the Phase 3 Pfizer trial, the absolute risk reduction was 0.84% and the NNV 119.13

As noted by the authors:(14)
    “This means in a real-life setting, 1.8 times more subjects might need to be vaccinated to prevent one more case of COVID-19 than predicted in the corresponding clinical trial.”

SARS-CoV-2 Specific Antibodies Pose Danger for the Obese

In related news, a recent study(15) published in the International Journal of Obesity warns that “the majority of SARS-CoV-2-specific antibodies in COVID-19 patients with obesity are autoimmune and not neutralizing.”

In plain English, if you’re obese, you’re at risk of developing autoimmune problems if you get the natural infection.

You’re also at higher risk of a serious infection, as the antibodies your body produces are not the neutralizing kind that kill the virus.

As explained by the authors:(16)
   “SARS-CoV-2 infection induces neutralizing antibodies in all lean but only in few obese COVID-19 patients. SARS-CoV-2 infection also induces anti-MDA [malondialdehyde, a marker of oxidative stress and lipid peroxidation] and anti-AD [adipocyte-derived protein antigens] autoimmune antibodies more in lean than in obese patients as compared to uninfected controls.

Serum levels of these autoimmune antibodies, however, are always higher in obese versus lean COVID-19 patients.

Moreover … we also evaluated the association of anti-MDA and anti-AD antibodies with serum CRP and found a positive association between CRP and autoimmune antibodies.

Our results highlight the importance of evaluating the quality of the antibody response in COVID-19 patients with obesity, particularly the presence of autoimmune antibodies, and identify biomarkers of self-tolerance breakdown.

This is crucial to protect this vulnerable population at higher risk of responding poorly to infection with SARS-CoV-2 than lean controls.”

Now, these findings apply to obese people who develop the natural infection, but it makes one wonder whether the same holds true for the COVID jab.

If the antibodies produced in response to the actual virus are primarily autoantibodies, will obese people develop autoantibodies instead of neutralizing antibodies in response to the COVID shot as well?

For clarity, an auto-antibody is an antibody that is directed against one or more of your own body’s proteins.

Many autoimmune diseases are caused by autoantibodies that target and attack your own tissues or organs.

So, this is no small concern, seeing how the mRNA in the COVID shots (and subsequent SARS-CoV-2 spike protein, which is what your body produces antibodies against) gets distributed throughout your body and accumulates in various organs.(17),(18)

Vermont’s COVID Cases Despite Highest Vaccination Rate

At this point, there’s an overwhelming amount of evidence showing the COVID shots are not working.

What little protection you do get clearly wanes within a handful of months, and may leave you worse off than you were before.

We’re seeing data to this effect from a number of different places.

In the U.S., we can now look at Vermont.(19)

At nearly 72% vaccinated, it has the highest rate of “fully vaccinated” residents in the country, according to ABC News,(20) yet COVID cases are now suddenly surging to new heights.

U.S. Centers for Disease Control and Prevention data show Vermont had the 12th highest COVID case rate in the nation as of November 9, 2021.

Over the previous seven days, cases had increased by 42%.

It couldn’t have been due to a surge in testing, though, as the weekly average of tests administered had only increased by 9% in that time.

What’s more, during that first week of November, the hospital admission rate for patients who were fully vaccinated increased by 8%, while the admission rate for those who were not fully vaccinated actually decreased by 15%.

Data from physician assistant Deborah Conrad shows vaccinated people are nine times more likely to be hospitalized than the unvaccinated.

Keep in mind that you’re not considered “fully vaccinated” until two weeks after your second injection.

If you got your second dose a week ago and end up in the hospital with COVID symptoms, you’re counted as unvaccinated.

This gross manipulation of reality makes it very difficult to interpret the data, but even with this manipulation it is beyond obvious that the vaccines are failing.

Overall, the case rate in Vermont is FAR higher now than it as in the fall of 2020, when no one had gotten the “vaccine.”

According to Vermont health commissioner Dr. Mark Levine, the surge is occurring primarily among unvaccinated people in their 20s and children aged 5 through 11 — a curious coincidence, seeing how the shots are just now being rolled out for 5- to 11-year-olds.

Levine blames the surge on the highly infectious delta variant, but delta has been around for months already.

The first case of delta in Vermont was identified in mid-May 2021.(21)

Surely, it wouldn’t have taken six months for this most-infectious of variants to make the rounds and cause an unprecedented spike?

Two clues are given by Levine, however, when he admits that
a) Vermont has one of the lowest rates of natural immunity in the U.S. and 


b) protection is waning among those who got the COVID shot early to mid-year.

Breakthrough cases among the fully vaccinated shot up 31% during the first week of November.(22)

Fully Vaxxed Are Nine Times More Likely To Be Hospitalized

Coincidentally, data from physician assistant Deborah Conrad, presented by attorney Aaron Siri(23) October 17, 2021, shows vaccinated people are nine times more likely to be hospitalized than the unvaccinated.

The key, however, was in what they counted as vaccinated.

Rather than only including those who had gotten the shot two weeks or more before being hospitalized, they simply counted those who had one or more shots, regardless of when, as vaccinated.

This gives us an honest accounting, finally!

As explained by Siri:(24)

“A concerned Physician Assistant, Deborah Conrad, convinced her hospital to carefully track the COVID-19 vaccination status of every patient admitted to her hospital.

The result is shocking.

As Ms. Conrad has detailed, her hospital serves a community in which less than 50% of the individuals were vaccinated for COVID-19

but yet, during the same time period, approximately 90% of the individuals admitted to her hospital were documented to have received this vaccine.

These patients were admitted for a variety of reasons, including but not limited to COVID-19 infections.

Even more troubling is that there were many individuals who were young, many who presented with unusual or unexpected health events, and many who were admitted months after vaccination.”

Despite these troubling findings, health authorities ignored Conrad when she reached out.

In mid-July 2021, Siri’s law firm also sent formal letters to the CDC, the Health and Human Services Department and the U.S. Food and Drug Administration on Conrad’s behalf,(25) and those were ignored as well.

“This again highlights the importance of never permitting government coercion and mandates when it comes to medical procedures,” Siri writes.(26)

Now, one of the most shocking details gleaned from Conrad’s data collection, which Siri failed to make clear but Steve Kirsch highlights in a recent substack post is that:(27)

“The only way you can get those numbers is if vaccinated people are 9 times more likely to be hospitalized than unvaccinated.

It is mathematically impossible to get to those numbers any other way.

... This is known as an ‘inconvenient truth.’”

Indeed, the more data we gain access to, the worse it looks for these COVID shots.

Unfortunately, those who push them seem hell-bent on ignoring any and all data that don’t support their stance.

Worse, it seems data and statistics are being intentionally manipulated by our health authorities to present a false picture of safety and effectiveness.

All such tactics are indefensible at this point, and people who believe the official narrative without doing their own research do so at their own risk."

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