Day: January 29, 2023

This is the most important article I have ever written in my life.

It shows a novel method that anyone can use to prove that the COVID vaccines are leading to premature death in anyone who takes them, no matter what age. So you don’t have to believe me. You can collect the data yourself and do the same analysis I did. It’s very easy. It took me about an hour to collect the data and analyze it.

The methodology is both technically sound and objective. Anyone can collect their own data including any state in the US and many foreign governments. I predict no one will look. That tells you everything you need to know.

I asked UK Professor Norman Fenton to critique the method I used here. More about him in the text below. Bottom line: he loved the method I used (which he hadn’t seen before), he validated the calculations in the figure below, and he wasn’t aware of any way the conclusion could be legitimately challenged. There are always all sorts of hand-waving arguments such as “your study wasn’t IRB approved” or “your study is unethical because you are looking at deaths from the COVID vaccine” but they are just that: hand-waving.

To further prove my article cannot be challenged, I am pioneering a unique approach to that as well that is fair, thorough, and transparent. I’m publicly offering 10X your wager to anyone who believes that the data actually shows the opposite of what I claimed. See details of the offer in the text below. If you think I got it wrong, you can turn $25K into $250K in days!

This article describes how a simple objective analysis of objective death data (age, date died, date of last COVID vaccination) can be used to prove 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 predict that 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 numbers for the vaccinated or shows us a more comprehensive, trustable data set, the game is now over.

If the vaccines are safe, the CDC should have produced this analysis using statewide data long ago. It is trivial to do. Why didn’t they? The answer is simple: because they know it would blow the narrative and prove to the world that they are incompetent fools.

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, their age, or the 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 any longer.

We’ll see if anyone wants to challenge this article and get paid 10X their wager if they are right. Bring it on!

This article is a follow up on my article entitled, “The death records show the COVID vaccines are shortening lifespan worldwide.” That article gives John Beaudoin credit for being the first to realize that linking the death and vaccination records (a table join) is key to ending the false narrative.

In this article, I show a clever new method for analyzing the death/vax records that is simple and objective; it relies on just a simple division of two time measurements.

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.

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 if you don’t like the age categories I chose. It won’t change the result.

Here’s how the method works (credit to Clare Craig who suggested this wording):

Imagine a timeline for 2021 and 2022. For the unvaccinated we would expect an even distribution of deaths over time except for seasonal differences. For each person, we can compare how long they did live in that period with how long they could have lived. A few who died early would have lived for only a tiny fraction of their potential and a few that died late for a large fraction. However, most will be in between and the mean will be 0.5.

For the vaccinated, we start the clock on their date of their last vaccine. The timeline will therefore vary for each person but with a harmless vaccine we would still expect exactly the same distribution – a few early, a few late and most in the middle with a mean of 0.5.

If the vaccine killed people we would end up with more deaths early on. The mean ratio of life lived compared with life that could have been lived will fall below .5.

Given ratio=((time in category)/(time possible in category)) and knowing that the person died sometime in Jan 2021-Dec 2022, we have:

1. If the intervention (i.e., the vax) does nothing, ratio = .5

2. If the invention shortens life, ratio <.5

3. If the intervention increases lifespan, ratio > .5

It’s that simple. The important thing is that the ratio tells us if the intervention is helpful, neutral, or harmful.

The analysis is independent of the rates people die. The fact that older people die faster than younger people is immaterial. Pre-existing conditions, etc. do not matter.

There is an argument to be made that people who got vaccinated first were more vulnerable and were more likely to die, and thus the rate in a category changes over time, but that effect isn’t very large. I’ve run the numbers for those who died and were last vaccinated in 2022 and the numbers are all less than .5. You are welcome to prove me wrong, but you’ll need to do it with evidence, i.e., actual queries and not hand-waving arguments. Numbers talk.

To date, everyone who thinks they can debunk this has produced only handwaving arguments and no analysis.

Sorry, but that’s not very convincing.

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).

The people who answered are my followers and are most unvaccinated themselves. They are reporting deaths of the person they know the best, whether vaxxed or unvaxxed. I invite fact checkers to validate that people were true to the direction they were given. There are more vaccinated deaths reported simply because 75% of the US population is vaccinated.

The percentage of unvaccinated to total deaths was 29% (222/(222+542)).

So you might think “Ah ha! That proves that the unvaxxed are dying at a higher rate than the vaxxed because it should be only 25% of the deaths that should be vaccinated so this PROVES the vaccines are saving lives!”

No, it just proves that unvaccinated people hang around other unvaxxed people and are slightly more likely to report their deaths.

This is very helpful for our survey for two big reasons:

1. It gives us enough data in both the vaxxed and unvaxxed buckets so we can do meaningful comparisons between the two buckets

2. I can’t be accused of bias, e.g., you anti-vaxxers are just reporting vaccinated deaths to make the vax look bad. Clearly this isn’t the case… they are reporting disproportionately more unvaccinated deaths. So it looks very credible because it’s consistent with what you expect to see.

Note that the mix of vaxxed/unvaxxed deaths is immaterial to this analysis. Each cohort is examined independently. If I had 50% vaxxed and 50% unvaxxed deaths, the results would be exactly the same.

It’s important to note that my followers cannot determine the date of death of unvaccinated or vaccinated individuals (unless they have God-like powers). And I have contact info for all the records so they can be “spot checked” to validate that people followed my instructions to report the person they are most familiar with.

There is a recall bias in that people are more likely to report deaths that happened more recently. This shifts the average death time to the right. This is why unvaxxed are > .5 (more about that later).

For vaccinated people, there is also a healthy patient bias. If you are going to die in days due to a fatal cancer, most people would not get vaccinated.

There is some amount of seasonality in deaths that might skew things somewhat. It’s minimal for those <60, and small for the elderly. But we’re looking at a 2 year period so it shouldn’t be much different between vaxxed and unvaxxed.

It wasn’t possible to game the survey because nobody, including myself, knew how I was going to analyze the data until after the data was collected.

There was one person who put in a bogus entry (record #260) but that was easily spotted and removed.

The analysis cut off time was before this article was written so anyone trying to pollute the data will be unsuccessful since any new records aren’t included in the analysis.

The database has been in public view the entire time that the data has been gathered. When a record is submitted, it appears in the public view.

No submissions were deleted (other than record 260 which was clearly gamed) or modified which can be verified by the changelog of the data. The database is hosted by a third party firm.

There is an “integrity check” field indicating which records passed simply sanity check such as date vaccinated < date died. Only those records were processed.

I have the contact information for each reporter. I am looking forward to being contacted by any mainstream “fact check” organization who is willing to be recorded on video as we discuss the article. I’m happy to supply contact info for any line(s) in the survey so the fact checker can verify every record is legitimate.

People who die within 2021 to 2022 should be expected to die evenly throughout the period (there is some seasonality so it isn’t flat over the calendar months). Therefore, with no biases, we’d expect that the average days of life is 1 year in any 2 year observation period. So a ratio of .5. The seasonality cancels out.

But due to recall bias (since we are asking people to recall deaths rather than using government records), we’d expect the number to be skewed to dying more recently so maybe we’d see a ratio of .55 for the unvaccinated.

The vaccinated benefit from both recall bias and the healthy patient bias, so it might be .58 or more.

If the vaccines are safe and effective, the ratio of the vaccinated > ratio of the unvaccinated due to the healthy patient bias.

If the vaccines are killing people, the ratio of the vaccinated <= ratio of the unvaccinated (since the healthy patient bias would give the vaccinated an advantage).

If the vaccines are killing people, the ratio will be <0.5.

If the vaccines are safe, the ratio will be >0.5.

Guess what we found? 🙂

The data couldn’t be more clear: the shots are killing people.

The ratio for the vaccinated is .31 or less for every age range with > 5 records.

For the unvaccinated, the ratios are .52 or better for every age range with >5 records

The data is remarkably consistent when there are enough records for the range (generally 10 or more records per the uV# or V # columns).

The values in red are unreliable due to a lack of sufficient data points.

For the unvaccinated, my Airtable filter looked like this and I used the unVaxxed days alive/days possible columns:

For the vaccinated, my Airtable filter looked like this and I used the Vaxxed days died/days available columns.

NOTE: The “Integrity check” is NOT complete. But when coupled with the restrictions of the two filtering conditions, invalid records are all filtered out of the final result.

This is an Occam’s razor analysis. You could get fancier but it wouldn’t change the result. The signal is very very strong that the vaccines should be immediately stopped.

If I have made a mistake, I’d be grateful to see the correct analysis of the data using the same methodology. So if you object, show us the proper analysis.

The data is remarkably consistent for each age range. But there is a huge difference between the vaxxed (.3) and the unvaxxed (.58). This is exactly what I expected to see; no surprises. But it’s IMPOSSIBLE for the blue-pilled medical community to explain how this could possibly happen if the vaccine is so safe since it was supposed to be the other way around.

A simple look at the Notes field confirms the role of the vaccine in these deaths. That’s subjective proof. It shows that the vaccines are not as safe as claimed.

As far as confidence intervals, the numbers are remarkably consistent so the confidence intervals appear to be small. I’ve asked Professor Fenton for the correct way to ascertain these. He’s thinking about it. I’ll update this when I hear back.

But there’s more confirmation…

Is this analysis consistent with reliable evidence? Yes.

It turns out for the COVID vaccines, the best evidence we have is anecdotal evidence where everything is tracked since government data can be badly wrong as we learned in the UK where mistakes led them into thinking the vaccines were safe (see UK ONS admits their data is flawed; the vaccines may not be beneficial after all. Sorry about that).

As it turns out, it’s easy to find failure anecdotes for the COVID vaccines. The anecdotes we generally find show STRONG failures.

By contrast, it is nearly impossible to find a “success anecdote,” even a weak success. I always ask doctors who will talk to me and they’ve never mentioned a single success story. I do this constantly on Twitter Spaces in full public view and NONE of the DOCTORS will EVER be able to cite an example. In fact, I have not found any medical doctor who has ever been able to cite a single geriatric practice or nursing home where deaths dropped after the vaccines rolled out.

If the vaccines were saving lives, there should be THOUSANDS of “poster elderly” success stories, yet there are none. All the anecdotes are strongly negative. That’s simply impossible if the vaccines are saving “tens of millions of lives” as Neil deGrasse Tyson said on YouTube. When I called Neil to ask him for a success anecdote, he hung up the phone on me.

So we have a pretty good sense just from the failure to find a success that the vaccines are an utter disaster. We didn’t even need to do any numerical calculations!

But just to mention a few other data points that also confirm our hypothesis:

1. MIT Professor Retsef Levi calls for a halt to the COVID mRNA vaccines.

2. The vaccine isn’t as effective as the NEJM led you to believe. A key paper is deeply flawed. In fact, it shows very troubling data as people will soon see: that the vaccine makes .

   

3. Large Cleveland Clinic study shows the more you vaccinate yourself, the greater the risk of getting COVID. Whoops!

4. A New Zealand funeral director noticed 95% of his cases died within 14 days of the shot. I spoke directly with Brenton. He lives in the middle of a retirement community. This is the very age that is supposed to be protected by the shots. Average age is 70+. His records can be verified. Any takers?

5. Embalmer Anna Foster found that 93% of her cases had telltale rubbery clots. How can anyone explain that? She is hardly alone… 80% of embalmers surveyed report seeing these new style blood clots; they have never been seen before the COVID vaccines rolled out.

6. Southwest airlines: Pilot deaths have increased 5X after the vaccines rolled out and disability shot up by 10X normal. Pilots are among the healthiest people on the planet.

7. Geriatric practice: I finally found a large geriatric practice of 1,000 patients, 75% are over 65. Their normal death rate is 11 per year (the mean). In 2022, they had 39 deaths for the entire year. They attribute the 28 excess deaths to the vaccine. If it wasn’t the vaccine, someone needs to explain to us what is killing these people because whatever it is, it needs to be IMMEDIATELY stopped. They can’t go public for fear of retribution.

8. Savo Island Cooperative (Berkeley, CA): Roughly 150 people. No deaths for 5 years before COVID; 0 in 2020; 1 in 2021; 3 in 2022 and they were all vaccinated and boosted (plus 3 strokes and 4 heart attacks). Reported to me by Jane Stillwater last night at an event I spoke at. Nobody at the event could recall any success anecdotes.

9. Ed Dowd mentioned the vaccines have killed 800K Americans and disabled 4X as many as killed, 3.2M since the vaccine program began.

10. The peer-reviewed scientific literature published a paper by Mark Skidmore showing over 217,000 deaths in 2021 alone due to the COVID vaccine.

11. Josh Stirling looked at how cities in the US did in 2022 vs. 2021. So it’s a longitudinal study where you compare the city with itself one year ago. This is the best way to see what is going on… did your mortality increase or decrease. Check this out: cities with higher vaccination had larger all-cause mortality increases than cities with lower vaccination rates. In other words, the line goes the “wrong way.” This is devastating for the narrative, but of course consistent with what the death reports are saying. The R2 doesn’t need to be .9 for this to be convincing. They are correlated and it’s the slope of the line that is significant. The slope is the wrong way. That’s the point.

    

    

    

If the CDC doesn’t surface the statewide data for the public to see, then I believe it is time for the CDC to change their ads to look something like this:

I’ve had both the method and the data reviewed by UK Professor Norman Fenton. He’s the guy that proved the UK government data was fraudulent and cannot be used to prove the vaccines work. Nobody else in the world had been able to do that. Nobody wants to debate Norman on any of his work because it is so impeccable. The top medical journals also hate him because he finds serious errors in key papers published in their journals (such as The Lancet and Science). He validated it as sound and reliable.

Others, such as Clare Craig of the HART Group, called the method described here “genius.”

How about a novel fully open peer review process for this analysis that is not subject to the whims of the scientific journals and shows to the public that the article cannot be attacked by anyone in the world?

You send me your wager via Solana blockchain and I’ll give you 10X your wager if you can show we got it wrong and the data shows the opposite conclusion.

Professor Fenton will judge your entry. He has high integrity and is on the side of truth.

The way it works is that you send me your wager ($5K to $25K worth of Solana) as your entry fee to: 9BBhGEfAMSHg8Mxyb4x67VKMhyomJ3MAPa9mqXtaP8xZ
with a comment with your contact info (or the contact info of your lawyer if you want to remain anonymous). This shows up in Solscan as “Memo Program V2.”

You can use Ledger Live or Phantom which supports adding a note to the transfer.

This will create a public record for all to see that you are accepting my offer. If you win, you get 10X your wager sent back to the address you sent your funds from. If you lose, you lose your deposit. This ensures people aren’t wasting my time with non-serious challenges.

Also, the size of the wagers tells you how confident people are in their analysis. I predict no one will wager anything. If I’m wrong, I lose up to $250K (if you wagered $25K).

I’m kept honest by the public nature of this. People could legitimately discredit me if I don’t honor my offer. That would be a first.

Your burden is to show that my analysis of the data is flawed and that the data that was submitted actually shows the opposite: that the vaccines are saving lives.

People can look at Solscan to see the entries so that everything is in full public view. No gaming is possible.

Fenton’s decision will be in writing as well and subject to public scrutiny.

So if there are no challenges at my 10:1 odds, it’s de facto proof that I’m right.

It’s a novel method of public peer-review that is impossible to game or censor. It is open to anyone worldwide.

Make my day. Or as data scientist Joel Smalley might say, “Put up or shut up.”

My offer is open to the drug companies as well or any other institution.

Terms:

1. Offer terms can be changed at any time (e.g., I may want to raise or lower the min/max bet).

2. Send wager to: 9BBhGEfAMSHg8Mxyb4x67VKMhyomJ3MAPa9mqXtaP8xZ

3. Min/max wager is currently: $5/25K

4. First come, first served. The Solana blockchain is the source of truth for the entry timing.

5. Maximum of only one winner. If someone wins, any deposits received after that person will be refunded to the address of the sender.

6. Disputes will be settled by JAMS mediation.

7. Your sending me the Solana with the contact info signifies your acceptance of the offer.

8. Professor Norman Fenton will judge your submission in a live Zoom call that is recorded and publicly broadcast. You will have 1 hour to present your argument. He will put his decision in writing and post it to his Substack.

9. To win, you must show that the data shows, for all age cohorts with >10 entries, that the vaccines are beneficial (i.e., the vaccines have a ratio of >.5). You can show this using my data (preferred), or, if you can show that my data is unfairly biased, you can use it on your own dataset provided you can show that your data was fairly collected in a neutral manner (which includes complete state data joining the death and vaccination database tables). If the data is privately collected, prospective collection by a neutral party is preferred since otherwise the provenance of the data would be hard to prove.

10. You can attack the method, the data, or both to prove your point.

11. Offer expires March 30, 2023 to encourage submission.

12. You can request a written, legally enforceable definitive agreement if you don’t trust me. Use the Contact Me link and include the name and phone number of your attorney so we can work out the details.

13. Anyone can enter, individuals, corporations, etc. including, but not limited to:

    1. CDC Director Rochelle Walensky

    2. Tom Shimabukuro and his pal John Su of the CDC (these are the guys who hid the 700+ safety signals from the world; see this article on the death safety signal and this article on 700+ safety signals)

    3. The people at the CDC and FDA who put me on the email block list so that I was not able to speak at the public comment section of the FDA or CDC meetings which violates my rights to free speech… I get it ….. if you can’t argue based on the data, you censor your opposition. That’s the way science works at the FDA and CDC.

    4. Tedros from the WHO who just declared COVID to still be an emergency…what a fruitcake!

    5. UCSF, Stanford, Harvard, MIT, …

    6. Executives at YouTube, especially those involved in censoring truthful content

    7. All LinkedIn executives involved in the decision to ban me for life on LinkedIn

    8. Medium CEO Ev Williams for supporting the decision to ban me for life

    9. All Wikipedia executives for supporting the decision to ban me for life and supporting the efforts to trash my Wikipedia profile to make me look like a menace to society. And how dare you remove my National Caring Award from my Wikipedia profile. You are a truly evil and corrupt organization.

    10. Stanford Professor and ACIP Chair Grace Lee who would rather call the Palo Alto Police on me than watch the video from the Israeli Ministry of Health proving the vaccines are not safe. Watch the video; it is a classic (Pierre Kory loved it).

    11. CDC

    12. ACIP and VRBPAC committee members

    13. The comedy team of US Surgeon General Vivek Murthy and Ashish Jha

    14. ZDoggMD

    15. Dr. Susan Oliver (along with her dog Cindy Oliver)

    16. Your Local Epidemiologist (YLE)

    17. David Gorski

    18. Jonathan Jarry

    19. UPenn Professor Jeffrey Morris

    20. Paul Offit

    21. Peter Hotez

    22. Truth warrior Dr. Angela Rasmussen

    23. Eric Topol

    24. Anthony “I am the Science” Fauci

    25. “Debunk the Funk” aka Daniel Wilson

    26. Bob Wachter, Dean of Medicine, UCSF

    27. Lloyd Minor, Dean of Medicine, Stanford University

    28. Harvey Cohen, Stanford University

    29. 60 Minutes

    30. The New York Times

    31. The Wall St. Journal

    32. The Washington Post

    33. The BBC

    34. Reuters Fact Check and any other so-called “fact checker”… and even Dr. Adrian Wong

    35. All of the members of the CETF SAB… the ones who said I was wrong and that they never wanted to speak to me again

    36. Any member of US Congress

    37. Any world leader who is pushing these vaccines

    38. Any health official anywhere in the world who is pushing these vaccines

    39. Neil deGrasse Tyson… who hung up on me when I challenged him on his bullshit statement that the vaccines have saved millions of lives. Give me a break.

    40. Their champion (if I can find him/her)

What a joke. None of these people can show I got it wrong. They are all afraid of the truth, the whole lot of them.

And if they can’t show I got it wrong, they should publicly admit I got it right. That would be the right thing to do.

The method presented in this article is a simple, straightforward analysis that can be used with death records to reveal the truth about the vaccines.

The results I obtained from over 1,500 death records are self consistent and are consistent with other data we have collected. I wasn’t surprised at all to get this result.

If anything, the vaccinated should have done better than the unvaccinated in this survey because of the healthy patient bias effect. But, as we found, it did far worse. Reading the comments in the death submissions confirms that a large number of cases are vaccine related.

Also, it is remarkable that the #1 feature of a vaccine death is “died suddenly.” Does that sound familiar?

The study cannot be attacked as “biased” unless you can explain how my followers can either 1) cause the premature death of vaccinated friends OR 2) deliberately ignored instructions to report the person they knew the best and instead they all followed a different set of instructions that they magically all agreed on.

That’s far fetched because:

1. Most people who reported a death only knew one person who died. So it’s not like they can choose the “best” answer.

2. Because they are my followers, they’ll want to help me and that means following my instructions which was to choose the friend whose details they knew the best

3. At the time the survey was done, nobody knew how the data would be analyzed, not even me. They would have to ignore the instruction they were given and instead follow the “proper algorithm” in massive numbers. Why would they ignore my instructions? They are my followers and want to help me! And how would they know the “correct” instructions?

So where’s the evidence of bias?

There are only three ways to legitimately attack this study:

1. Show an error in the methodology and show us our data that is correctly analyzed and where the medical community agrees you have the CORRECT analysis.

2. Show an error in the data. I’m happy to have someone contact random people who answered the survey to ask those people if they followed the instructions to report the person they knew the best or not and if not why not.

3. Show us a larger dataset showing the opposite result where we can verify every record with the primary data source. For some reason, no government will show us the record level data where the death age and date of death is tied to the vaccination records. Critical thinkers want to know: if the vaccine is so safe, why is this data being hidden from public view?

The bottom line: the COVID vaccines increase your chance of dying for every age group (that we had sufficient data on) and should be immediately halted. They are the biggest mistake in modern medical history. This analysis is completely objective, the data is very consistent, there is no evidence of bias that could explain the outcome, and there is no escaping the truth.

The medical community needs to take sides on my article and stop sitting on the sidelines. They have an ethical obligation to immediately either:

1. call for a halt to the COVID vaccines or

2. show us how we got it wrong.

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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!

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.

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.

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.

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).

The people who answered are my followers and are most unvaccinated themselves. They are reporting deaths of the person they know the best, whether vaxxed or unvaxxed. I invite fact checkers to validate that people were true to the direction they were given. There are more vaccinated deaths reported simply because 75% of the US population is vaccinated.

The percentage of unvaccinated to total deaths was 29% (222/(222+542)).

So you might think “Ah ha! That proves that the unvaxxed are dying at a higher rate than the vaxxed because it should be only 25% of the deaths that should be vaccinated so this PROVES the vaccines are saving lives!”

No, it just proves that unvaccinated people hang around other unvaxxed people and are slightly more likely to report their deaths.

This is very helpful for our survey for two big reasons:

1. It gives us enough data in both the vaxxed and unvaxxed buckets so we can do meaningful comparisons between the two buckets

2. I can’t be accused of bias, e.g., you anti-vaxxers are just reporting vaccinated deaths to make the vax look bad. Clearly this isn’t the case… they are reporting disproportionately more unvaccinated deaths. So it looks very credible because it’s consistent with what you expect to see.

Note that the mix of vaxxed/unvaxxed deaths is immaterial to this analysis. The mix doesn’t matter at all.

It’s important to note that my followers cannot determine the date of death of unvaccinated or vaccinated individuals. And I have contact info for all the records so they can be “spot checked” to validate that people followed my instructions to report the person they are most familiar with.

There is a recall bias in that people are more likely to report deaths that happened more recently. This shifts the average death time to the right. This is why unvaxxed are > .5 (more about that later).

For vaccinated people, there is also a healthy patient bias. If you are going to die in days due to a fatal cancer, most people would not get vaccinated.

There is some amount of seasonality in deaths that might skew things somewhat. It’s minimal for those <60, and small for the elderly. But we’re looking at a 2 year period so it shouldn’t be much different between vaxxed and unvaxxed.

It wasn’t possible to game the survey because nobody, including myself, knew how I was going to analyze the data until after the data was collected.

There was one person who put in a bogus entry (record #260) but that was easily spotted and removed.

The analysis cut off time was before this article was written so anyone trying to pollute the data will be unsuccessful since any new records aren’t included in the analysis.

The database has been in public view the entire time that the data has been gathered. When a record is submitted, it appears in the public view.

No submissions were deleted (other than record 260 which was clearly gamed) or modified which can be verified by the changelog of the data. The database is hosted by a third party firm.

There is an “integrity check” field indicating which records passed simply sanity check such as date vaccinated < date died. Only those records were processed.

I have the contact information for each reporter. I am looking forward to being contacted by any mainstream “fact check” organization who is willing to be recorded on video as we discuss the article. I’m happy to supply contact info for any line(s) in the survey so the fact checker can verify every record is legitimate.

People who die within 2021 to 2022 should be expected to die evenly throughout the period (there is some seasonality so it isn’t flat over the calendar months). Therefore, with no biases, we’d expect that the average days of life is 1 year in any 2 year observation period. So a ratio of .5. The seasonality cancels out.

But due to recall bias (since we are asking people to recall deaths rather than using government records), we’d expect the number to be skewed to dying more recently so maybe we’d see a ratio of .55 for the unvaccinated.

The vaccinated benefit from both recall bias and the healthy patient bias, so it might be .58 or more.

If the vaccines are safe and effective, the ratio of the vaccinated > ratio of the unvaccinated due to the healthy patient bias.

If the vaccines are killing people, the ratio of the vaccinated <= ratio of the unvaccinated (since the healthy patient bias would give the vaccinated an advantage).

If the vaccines are killing people, the ratio will be <0.5.

If the vaccines are safe, the ratio will be >0.5.

Guess what we found? 🙂

The data couldn’t be more clear: the shots are killing people.

The ratio for the vaccinated is .31 or less for every age range with > 5 records.

For the unvaccinated, the ratios are .52 or better for every age range with >5 records

The data is remarkably consistent when there are enough records for the range (generally 10 or more records per the uV# or V # columns).

The values in red are unreliable due to a lack of sufficient data points.

For the unvaccinated, my Airtable filter looked like this and I used the unVaxxed days alive/days possible columns:

For the vaccinated, my Airtable filter looked like this and I used the Vaxxed days died/days available columns.

This is an Occam’s razor analysis. You could get fancier but it wouldn’t change the result. The signal is very very strong that the vaccines should be immediately stopped.

If I have made a mistake, I’d be grateful to see the correct analysis of the data using the same methodology. So if you object, show us the proper analysis.

The data is remarkably consistent for each age range. But there is a huge difference between the vaxxed (.3) and the unvaxxed (.58). This is exactly what I expected to see; no surprises. But it’s IMPOSSIBLE for the blue-pilled medical community to explain how this could possibly happen if the vaccine is so safe since it was supposed to be the other way around.

A simple look at the Notes field confirms the role of the vaccine in these deaths. That’s subjective proof. It shows that the vaccines are not as safe as claimed.

But there’s more confirmation…

Is this analysis consistent with reliable evidence? Yes.

It turns out for the COVID vaccines, the best evidence we have is anecdotal evidence where everything is tracked since government data can be badly wrong as we learned in the UK where mistakes led them into thinking the vaccines were safe (see UK ONS admits their data is flawed; the vaccines may not be beneficial after all. Sorry about that).

As it turns out, it’s easy to find failure anecdotes for the COVID vaccines. The anecdotes we generally find show STRONG failures.

By contrast, it is nearly impossible to find a “success anecdote,” even a weak success. I always ask doctors who will talk to me and they’ve never mentioned a single success story. I do this constantly on Twitter Spaces in full public view and NONE of the DOCTORS will EVER be able to cite an example. In fact, I have not found any medical doctor who has ever been able to cite a single geriatric practice or nursing home where deaths dropped after the vaccines rolled out.

If the vaccines were saving lives, there should be THOUSANDS of “poster elderly” success stories, yet there are none. All the anecdotes are strongly negative. That’s simply impossible if the vaccines are saving “tens of millions of lives” as Neil deGrasse Tyson said on YouTube. When I called Neil to ask him for a success anecdote, he hung up the phone on me.

Here are just a few of the failure anecdotes:

1. A New Zealand funeral director noticed 95% of his cases died within 14 days of the shot. I spoke directly with Brenton. He lives in the middle of a retirement community. This is the very age that is supposed to be protected by the shots. Average age is 70+. His records can be verified. Any takers?

2. Embalmer Anna Foster found that 93% of her cases had telltale rubbery clots. How can anyone explain that? She is hardly alone… 80% of embalmers surveyed report seeing these new style blood clots; they have never been seen before the COVID vaccines rolled out.

3. Southwest airlines: Pilot deaths have increased 5X after the vaccines rolled out and disability shot up by 10X normal. Pilots are among the healthiest people on the planet.

4. Geriatric practice: I finally found a large geriatric practice of 1,000 patients. Their normal death rate is 11 per year (the mean). In 2022, they had 39 deaths for the entire year. They attribute the 28 excess deaths to the vaccine. If it wasn’t the vaccine, someone needs to explain to us what is killing these people because whatever it is, it needs to be IMMEDIATELY stopped. They can’t go public for fear of retribution.

5. Savo Island Cooperative (Berkeley, CA): Roughly 150 people. No deaths for 5 years before COVID; 0 in 2020; 1 in 2021; 3 in 2022 and they were all vaccinated and boosted (plus 3 strokes and 4 heart attacks). Reported to me by Jane Stillwater last night at an event I spoke at. Nobody at the event could recall any success anecdotes.

6. Ed Dowd mentioned the vaccines have killed 800K Americans and disabled 4X as many as killed, 3.2M since the vaccine program began.

7. The peer-reviewed scientific literature published a paper by Mark Skidmore showing over 217,000 deaths in 2021 alone due to the COVID vaccine.

8. Josh Stirling looked at how cities in the US did in 2022 vs. 2021. So it’s a longitudinal study where you compare the city with itself one year ago. This is the best way to see what is going on… did your mortality increase or decrease. Check this out: cities with higher vaccination had larger all-cause mortality increases than cities with lower vaccination rates. In other words, the line goes the “wrong way.” This is devastating for the narrative, but of course consistent with what the death reports are saying

   

   

   

This analysis is yet another data point that the vaccines cause harm.

If the CDC doesn’t surface the statewide data, then I believe it is time for the CDC to change their ads like this:

The method presented in this article is a simple, straightforward analysis that can be used with death records to reveal the truth about the vaccines.

The result I obtained from over 1,500 death records are self consistent and are consistent with other data we have collected. I wasn’t surprised at all to get this result.

If anything, the vaccinated should have done better than the unvaccinated in this survey because of the healthy patient bias effect. But, as we found, it did far worse. Reading the comments in the death submissions confirms that a large number of cases are vaccine related.

Also, it is remarkable that the #1 feature of a vaccine death is “died suddenly.” Does that sound familiar?

The study cannot be attacked as “biased” unless you can explain how my followers can either 1) cause the premature death of vaccinated friends OR 2) deliberately ignored instructions to report the person they knew the best and instead they all followed a different set of instructions that they magically all agreed on.

That’s far fetched because:

1. Most people who reported a death only knew one person who died. So it’s not like they can choose the “best” answer.

2. Because they are my followers, they’ll want to help me and that means following my instructions which was to choose the friend whose details they knew the best

3. At the time the survey was done, nobody knew how the data would be analyzed, not even me. They would have to ignore the instruction they were given and instead follow the “proper algorithm” in massive numbers. Why would they ignore my instructions? They are my followers and want to help me! And how would they know the “correct” instructions?

So where’s the evidence of bias?

There are only three ways to legitimately attack this study:

1. Show an error in the methodology and show us our data that is correctly analyzed and where the medical community agrees you have the CORRECT analysis.

2. Show an error in the data. I’m happy to have someone contact random people who answered the survey to ask those people if they followed the instructions to report the person they knew the best or not and if not why not.

3. Show us a larger dataset showing the opposite result where we can verify every record with the primary data source. For some reason, no government will show us the record level data where the death age and date of death is tied to the vaccination records. Critical thinkers want to know: if the vaccine is so safe, why is this data being hidden from public view?

The bottom line: the vaccines increase your chance of dying for every age group and should be immediately halted. They are the biggest mistake in modern medical history. This analysis is completely objective, the data is very consistent, there is no evidence of bias that could explain the outcome, and there is no escaping the truth.

The medical community needs to take sides on my article and stop sitting on the sidelines. They have an ethical obligation to immediately either:

1. call for a halt to the COVID vaccines or

2. show us how we got it wrong.

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