There is a published survey which makes a claim on how many people may have been killed by vaccines.
I have read the method they used, and then read it again, and again. I can’t believe it, it reminds me of arguments from flat-Earthers.
Here is a direct quote. I will go through it step by step after the quote. You can skip past the block.
Comparing serious adverse events between publicly available data and the survey
Several steps are required to compare data on COVID-19 vaccine adverse events from the survey with publicly available government data.
In the first step, public data on COVID-19 fatalities from the CDC [12] is combined with COVID-19 vaccine-related adverse events from VAERS [13] to create the ratio of COVID-19 vaccine-related fatalities to fatalities from the COVID-19 illness.
The same ratio from the survey data is calculated so that a comparison can be made. To examine differences, the null hypothesis (H0) is defined such that the True Ratio, X, is equal to the CDC ratio which is in turn equal to the survey ratio: X = CDC Ratio = Survey Ratio. The alternative hypothesis, Ha, is X = CDC Ratio < Survey Ratio.
This hypothesis is tested using state-by-state VAERS data on reported COVID-19 vaccine fatalities and CDC data on COVID-19 illness fatalities.
If there is a statistically significant difference, the two ratios can be used to estimate nationwide COVID-19 vaccine fatalities under the assumption that the survey is accurate:
They did a survey asking people if they knew anyone who had died after COVID and a separate question if they knew anyone who had died after being vaccinated.
The vaccine question was
Of the people you know who experienced a health problem after being vaccinated, think about the one you know BEST. Please describe the health condition experienced by that person.
The COVID question was
Of the people you know who experienced a health problem after being sick from COVID-19 (but not from the vaccine), think about the one you know BEST. Please describe the health condition experienced by that person.
There is something odd about the COVID question.
Firstly it is worded with an exclusion for vaccines, but the vaccine question doesn’t have a similar exclusion for covid.
Secondly, it is asking about a health problem “after being sick from covid.” That implies a person who has recovered from covid and then had a health problem.
Clearly these two questions are biased, but bias is the least of the problems.
Now we move into the weird part
In the first step, public data on COVID-19 fatalities from the CDC [12] is combined with COVID-19 vaccine-related adverse events from VAERS [13] to create the ratio of COVID-19 vaccine-related fatalities to fatalities from the COVID-19 illness.
My immediate reaction was, Why? Why would you take the number of persons who died from covid and compare that to the number of reports in VAERS of possible reactions to a vaccine? Also the wording is poor:
COVID-19 vaccine-related fatalities
Neutral wording could be reports of death subsequent to Covid-19 vaccination.
What is the rationale for putting those two unrelated numbers into a ratio? It would have some relevance if you had proof of deaths from the two causes & you wanted to do a cost benefit report, but that isn’t how the paper uses this ratio.
If the vaccines were shown to be killing large numbers, it could be in some way important to compare to the number being killed by the disease, if you wanted to justify continuing to use the vaccine, but the authors seem to think this ratio helps them determine the real number of vaccine caused deaths.
How? How could it possibly do that?
Step 2 is to create the same odd ratio using the COVID deaths and the survey result based on how many people say they knew someone who died at some time after being vaccinated.
Okay, why? Who knows?
Then this bizarre part
To examine differences, the null hypothesis (H0) is defined such that the True Ratio, X, is equal to the CDC ratio which is in turn equal to the survey ratio: X = CDC Ratio = Survey Ratio. The alternative hypothesis, Ha, is X = CDC Ratio < Survey Ratio.
So, either those two ratios are the same or they are different. Well, I am betting they will be different.
It is almost certain they will be different. How much chance is there that deaths reported on VAERS will be exactly the same as a random survey finding out how many people know someone who died?
Why on earth does this matter?
(Also, being pedantic, if your null hypothesis is that A=B, how does disproving that prove A>B ?)
Step 3 is to test this hypothesis, as if you needed to test it
This hypothesis is tested using state-by-state VAERS data on reported COVID-19 vaccine fatalities and CDC data on COVID-19 illness fatalities.
What? How does this test whether those two ratios are the same? You just compare the two ratios. I would be tempted to bet $1 million that if you just compare the two ratios they will be different.
Okay, maybe I am missing something. So, we are now going to create 50 ratios?
If there is a statistically significant difference...
What? Of course there will be statistically significant differences. Do you really think that Texas & Idaho wouldn’t have different ratios of anything?
Wracking my brain to try to find something meaningful here.
the two ratios can be used to estimate nationwide COVID-19 vaccine fatalities under the assumption that the survey is accurate:
What? WHAT?
First how can this meaningless ratio enable anyone to estimate anything?
Second, what on earth was there in looking at 50 States meaningless ratios, finding that they vary and then concluding that the survey is accurate. What???
How does that say anything about the survey?
This where I gave up. Either I have lost my mind or whoever created this methodology has.
It does go on, but as nothing made any sense to me up to this point I didn’t have any desire to continue.
Is anyone capable of explaining the rationale of the first steps? Why does anyone want to calculate VAERS/COVID ? What relevance is that? Then they calculate Survey/COVID.
Then for some reason the two are compared.
As the Covid part is used in both ratios and then they compare the two ratios why not leave out COVID?
The math seems to be that they have computed V/C and then compared that to S/C which is the same as comparing V directly with S.
As these are two numbers arrived at by completely different process and not even measuring the same thing, why on earth would anyone expect them to be identical?
That seems deranged.
Then the next step is to test whether they are identical by computing the ratio VEARS/COVID 50 times over, once for each state. And then claiming that if they differ that proves the survey right.
Is someone having a laugh?
This is what stage magicians do with metal hoops and distraction. This is utterly bonkers.
And it was published.
Can you make sense of this? Please leave a comment
Sources are in the text
The paper is at
https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-023-07998-3#MOESM1
The questions are not shown in the paper’s cited file number 1, but can be found in files 2 & 3
There was an editor's note dated 26 Jan 2023 because of reading objections to the paper.