by Steve Kirsch, Steve Kirsch’s newsletter:
There is no longer any doubt that the vaccines are SHORTENING lifespans for everyone who takes them. They should be immediately stopped.
NOTE
I’m publishing this WITHOUT pushing it out to all subscribers to get any feedback before it is mass distributed. So if you are reading this, you are one of my earlier peer reviewers and I’ll look at the comments carefully. Thanks!
TRUTH LIVES on at https://sgtreport.tv/
Executive summary
This is the most important article I have ever written. It shows a method that anyone can use to prove that the vaccines are leading to premature death in anyone who takes them, no matter what age.
A simple objective analysis of objective death data (age, date died, date of last COVID vaccination) proves beyond a reasonable doubt that the COVID vaccines are shortening lifespans and should be immediately halted.
This explains why all the world’s health authorities are keeping their data secret; their data would reveal that all world governments have been killing millions of people worldwide. No government wants that disclosed. They won’t debate me on this. They will try to censor this article because they can’t hide from the truth. Or they will try to create FUD by arguing the survey is biased without describing the bias.
I am putting this out now for others to find a flaw in my analysis. I spoke with UK Professor Norman Fenton before I wrote this article. He didn’t find any flaws in the methodology. Neither did Edward Dowd. I discussed the bias issue with Fenton and he agreed that the biases would help the vaccinated so the vaccinated should do better than the unvaxxed. But the reverse is true so the result is impossible to explain.
This article will be ignored by the mainstream press and the medical community. The longer they ignore me, the worse it will look for them. The first rule of holes is that when you find yourself in a hole, stop digging.
Unless there is a serious error in my methodology or someone can explain precisely how surveying “my followers” creates a biased sample that shifts the sample for the vaccinated, the game is now over.
If the vaccines are safe, the CDC should have produced this analysis using statewide data. It is trivial to do. Why didn’t they? The answer is simple: because they know it would blow the narrative.
If you want to prove me wrong, let’s get the statewide data from all states and make it public. All we need is Age, date of death, date of last COVID vaccine. That does not violate HIPAA or a dead person’s privacy because there is no PII.
But states will refuse to release that data because they know if they did, they are finished.
So in the meantime, they will say, “Your survey is biased.” But nobody can explain the “bias” that explains the result because my readers DO NOT CONTROL THE DATE THAT THEIR FRIENDS WERE VACCINATED or they DATE they died.
My readers may be more affluent than the average American so that’s a bias. But if the vaccine is killing affluent people, we have a problem. My readers might be more intelligent than the average American, so that’s a bias. They may have more intelligent friends. So this survey, it could be argued, just shows that intelligent people are being killed by the vaccine. That SHOULD be a stopping condition.
Or you could argue that my readers are less intelligent than the average person. And once again, unless you are trying to cull a society, that should be a stopping condition as unethical.
ANYONE CAN REPLICATE MY SURVEY if you think it is “biased.” The New York Times could replicate my survey and prove I’m wrong.
But they won’t.
And that tells you everything you need to know, doesn’t it?
If they want to argue with this article, THEY need to show us THEIR data and not engage in hand-waving arguments to create FUD that have no evidentiary basis.
The game is over. We have won. You cannot hide from the truth.
The survey
A month ago, on December 25, 2022, I announced the survey below. As of January 29, 2023, I received 1,634 responses. The analysis here looks at the responses.
We only consider OBJECTIVE data and our analysis is OBJECTIVE. It’s all math.
If the vaccines are causing death, the analysis will pick it up.
Methodology
The analysis is done by looking at “days in category before death” divided by “days possible in category if you had lived to the end of the observation period.”
We do this for both vaxxed and unvaxxed people… across all ages, and also in various age ranges which I arbitrarily chose. You can choose your own.
So let’s take a simple example. We look over a 2 year period from Jan 2021 to Dec 2022.
For the unvaxxed, people die evenly through the period so that the time spent alive averages out to be half of the total time. In short, since the survey was only of people who died, people on average will die halfway through the period if everything is normal. It’s a Poisson distribution.
For the vaxxed, suppose we have a vaccine which kills people 1 day after we give it to them. Let’s say we have a total of 100 people who were vaxxed. Say that 40 were last vaxxed on Jan 2021 and the other 60 were last vaxxed on Jan 2022. They will have just 100 days alive in the vaxxed state (since each person lasts just one day before they die per our assumption), but they had 365*2*40 + 365*60 days that they could possibly be alive in the observation period. We’d compute a ratio of 100/(365*2*40 + 365*60) = .001 which is a VERY deadly vaccine! We’d want our vaccine to be around .5 if it’s safe and isn’t disturbing our Poisson statistics at all.
But if our vaccine killed these 100 people and they were vaccinated at random times throughout the 2 year period and they lived for exactly halfway to the end of the period, then the ratio would be .5 and it would be a safe vaccine with nothing going on.
Limitations
My survey includes reporters from all over the world, but all the readers speak English and 70% are in the US. The data can be analyzed just for the US and for specific vaccines as well, but below I include all the records to show that I’m not cherry picking and also to get more stability in the numbers (fewer data points creates more noise).
Read More @ stevekirsch.substack.com
