Engineering Study Estimates COVID Injections Have Killed 150,00 People in the US. The Injections Kill More People Than They Save. Thus, 2 Separate Conditions to Stop the injections Have Been Satisfied

From The Brownwatch:

Estimating the number of COVID vaccine deaths in America” From [HERE] By Steve Kirsch, Jessica Rose, Mathew Crawford

Last update: December 24, 2021: Added excess death study so there are 9 ways to get to >150K Americans killed by the COVID vaccines

Abstract: Analysis of the Vaccine Adverse Event Reporting System (VAERS) database can be used to estimate the number of excess deaths caused by the COVID vaccines. A simple analysis shows that it is likely that over 150,000 Americans have been killed by the current COVID vaccines as of Aug 28, 2021.

At this point, two separate stopping conditions have been satisfied:

  1. The vaccines kill more people than they save
  2. The vaccines have killed over 150,000 Americans so far.

This is an engineering estimate This is an engineering analysis, not a strict scientific analysis.

What I mean by this is that our objective is to use all the available data and our own expert judgement in interpreting that data in a reasonable way in an attempt to get an accurate estimate.

For example, one analysis we reference said that up to 86% of VAERS deaths could be caused by the vaccine and 14% could not be. However, we know more about the causes of death after vaccination than someone who doesn’t understand the mechanisms of action of the vaccine and common side effects reported by victims. So we took the high end of the estimate as being closer to the truth.

Similarly, critics delight in saying that the English translation of the Schirmacher article says he estimated that between 30% to 40% of the bodies he examined died from the vaccine. However, we know from personal contacts that the 30% to 40% is a floor.

Similarly, using anaphylaxis as a proxy for the URF was chosen because in our judgement, anaphylaxis should always be reported at a higher rate than deaths. It’s the best-case adverse event. So calculating a URF from anaphylaxis yields a value that should always underestimate the number of actual events when applied to any event (such as death). Nobody who has disputed this choice has produced any data at all that supports their hypothesis that our assumption wasn’t correct; they just use hand-waving arguments.

So all this extra knowledge is included in interpreting the data.

Because we validated our death estimates against the analysis of different datasets done by different people, we have high confidence our estimates are reasonable.

It is easy to criticize every single method and to tell us “you can’t do that” or “you have to use DB-RCT data” or other objections.

More constructive would be for our critics to come up with their estimate and provide the 7 independent ways they validated that their estimates were valid. And then show that all 8 of our methods are flawed. Then we can simply compare which analysis better fits the observed data.

Nobody seems to want to do that for some odd reason. We can’t fathom why…

Our research is supported by the peer reviewed

literature
Our estimate is supported by multiple papers in the peer-reviewed scientific literature including:

Why are we vaccinating children against COVID-19? by Ron Kostoff
“Compared with the 28,000 deaths the CDC stated were due to COVID-19 and not associated morbidities for the 65+ age range, the inoculation-based deaths are an order-of-magnitude greater than the COVID-19 deaths!

The Walach paper found the same thing: that the vaccines harm more people than they save. It has now been re-published in Science, Public Health Policy and the Law which is a peer-reviewed medical journal. The Walach paper appears in this issue along with a scathing editorial by the journal editor talking about how the paper authors were mistreated by the scientific community.

Critical Appraisal of VAERS Pharmacovigilance: Is the U.S. Vaccine Adverse Events Reporting System (VAERS) a Functioning Pharmacovigilance System? By Jessica Rose. “Using this URF for all VAERS-classified SAEs, estimates to date are as follows: 205,809 dead, 818,462 hospitalizations, 1,830,891 ER visits, 230,113 life-threatening events, 212,691 disabled and 7,998 birth defects to date [39].”

Note that in this paper, the 205,809 deaths were not categorized into background deaths and excess deaths. We do that calculation in this paper. The point of this paper is she determined a URF of 31 using a very conservative method which determines a lower bound on the URF. Even with a URF of 31, the death toll is horrendous, and as we show in Risk benefit by age of the COVID vaccines, virtually all these deaths are “excess” deaths.

And other independent studies such as:

Vaccine death report

The VAERS database is the only pharmacovigilance database used by FDA and CDC that is accessible to the public. It is the only database to which the public can voluntarily report injuries or deaths following vaccinations. Medical professionals and pharmaceutical manufacturers are mandated to report serious injuries or deaths to VAERS following vaccinations when they are made aware of them. It is a “passive” system with uncertain reporting rates. VAERS is called the “early warning system” because it is intended to reveal early signals of problems, which can then be evaluated carefully by using an “active” surveillance system.

Those who believe the FDA mantra that you cannot use VAERS to determine causality, should start by reading this editorial: If Vaccine Adverse Events Tracking Systems Do Not Support Causal Inference, then “Pharmacovigilance” Does Not Exist.

There are effectively two separate determinations:

  1. What is the number of “excess deaths” which is the total # of deaths from this vax – # of deaths normally expected from the typical vaccine. Causality plays no role whatsoever in determining this number.
  2. Ascribing a cause to the excess deaths. Were these excess deaths caused by the vaccine or by something else?

The detailed steps are:

  1. Determine the under-reporting factor (URF) by using a known significant adverse event rate
  2. Determine the number of US deaths reported into VAERS
  3. Determine the propensity to report (PTR) significant adverse events this year
  4. Estimate the number of excess deaths using these numbers
  5. Validate the result using independent methods

Determining the VAERS under-reporting factor

click to read more.

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