A simple all-cause mortality analysis of the Czech data shows COVID vaccines killed > saved
Executive summary
To date, nobody has done a proper analysis of the data from any country in the world to determine whether there was a COVID mortality benefit.
All the studies that have been done to date are very seriously flawed. They nearly always depend upon identification of COVID cases and COVID deaths in the vaccinated and unvaccinated cohorts. Most all assume they can account for the unvaccinated mortality and healthy vaccinee effect (HVE) using mathematical models rather than measurements.
Nobody does it the correct way, which is by comparing the all-cause mortality (ACM) of the vaccinated vs. unvaccinated over a 1 year (or more) period of time relative to their baseline mortality during non-COVID.
There are no such studies. Zero. Zip. Nada.
It’s actually very straightforward to do such a study. All you need is just 3 pieces of information for each person: birth year (5 year range sufficient), COVID vaccination week for the first dose (if any), and the week of death. No privacy issues there. No reason every state can’t publish this information.
Lucky for us, this information from the Czech Republic has been in public view since March 2024.
Yet nobody has analyzed it in the manner outlined above.
So I’m going to show just how trivial it is to do it in this article.
I show, for example, if you were 65-70 years old when you got the shot, you would be (somewhat less than) 32% more likely to die than the unvaccinated as shown in the relative cumulative mortality curve below:
We’ve seen similar numbers before: a 36% minimum all-cause mortality increase after 1 year in the Levi Florida study.
Nearly every health department has the data to do this analysis. Yet not a single one lifted a finger to do the analysis. One health authority, Te Whatu Ora (Health New Zealand), spent millions of dollars paying lawyers to criminally prosecute their own database administrator (Barry Young) who was simply trying to alert them to a safety problem, and not a penny on doing the actual data analysis that would have shown them he was right.
Introduction
For a number of reasons, including differential testing requirements for vaccinated and unvaccinated, short of a double-blind randomized trial honestly performed, the only reliable way to assess whether or not the COVID shots had a mortality benefit is to do an observational study that compares the ACM of the unvaccinated vs. vaccinated of 5-year subgroups of an entire population over at least a 12 month period.
Such an observational study is trivial to do. I know that because I spent all of about 3 hours doing the analysis using the Czech data.
All you need is a record for each person of:
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their date of birth,
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date of first vaccination, and
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date of death.
The effect size is so huge, no more data than that is needed if you are dealing with full population data like we are here.
Every country should publish those 3 pieces of information. There should be a law in the US to require states to publish this info. But only one country in the world has such a law: the Czech Republic. AFAIK, they have the only record-level data for a population that has been made publicly available in the history of the world for a vaccine. As a misinformation superspreader, it was something that I could only dream about.
Once you have the data, it takes less than 30 minutes to write the code, and then a few hours to look at the data and see what it says. If you have guidance for where to look, you’re basically talking about less than 30 minutes because you can copy my code.
NOBODY IN THE WORLD HAS EVER DONE SUCH A STUDY FOR THE COVID VACCINE.
Why not? Probably because it would reveal the truth.
They all do it “the wrong way” that relies on COVID cases and COVID death assessments and most always they refuse to measure the non-COVID all-cause mortality of the unvaccinated group.
The Arbel study in Israel is exhibit #1. They made all sorts of assumptions in their models and never double checked with the data to see whether their models matched the non-COVID ACM of the unboosted. After they were caught (in the Hoeg letter), they doubled-down on their models and REFUSED to reveal the NCACM of the unboosted. When MIT Professor Retsef Levi asked for the data, they turned him down too. He had to sue them and they still didn’t turn over the data. Is this the way science works?
So I guess it’s up to the misinformation superspreaders like me and my friends to analyze the publicly available record-level data seeing how nobody else will do it the right way.
The method
Pick a start date where a majority of the elderly people have been vaccinated and there is the start of a non-COVID period, divide each 5 year cohort into vaccinated/unvaccinated at that moment, and then see how their mortality compares over time relative to their own mortality during the non-COVID baseline mortality measurement period right after they were vaccinated. For the most accurate results, then apply the appropriate HVE correction factor to each ratio.
So for example, suppose you find there are equal number of deaths in both cohorts during the baseline period. Then you just count up the number of deaths in each group over the period from the time of vaccination to the end of say 2022, and take the ratio. Once you have that, you need to apply the HVE correction factor (determined from the baseline mortality rates) to the answer.
Grok analysis
The Grok analysis shows the method is robust and makes perfect sense and is the way the day should be analyzed.
The code and analysis
Code and analysis can be found in my github.
Results
The analysis shows the vaccine increases your risk of death compared to the unvaccinated group as shown in the images below. The differential decreases over time.
Amazingly, all the curves are nearly identical which was absolutely stunning to see this replication in real-world data.
Adjusting the results to account for the long-term HVE effect
What I did was a simplistic calculation to estimate the magnitude of the mortality difference caused by the vaccines.
In a subsequent article, I’ll refine this estimate and account for the long-term HVE effect (healthy vaccinee effect).
The adjustment is because the baseline mortality of the unvaccinated cohort is much larger than the vaccinated cohort. That difference in mortality causes the deaths per month to change at a different rate in a fixed sized cohort. It has to do with picking two points on the curve below. If the slopes are the same, no problem. If the slopes are different (e.g., you pick a point to the left of the peak and the right of the peak), it creates a difference in deaths computed over a period.
For example, for those born in 1935, the vaccinated cohort is around a 9% annual mortality and the unvaccinated cohort is double that.
So if you had a perfectly safe vaccine, this kind of mortality difference would create a difference in the cumulative mortality.
A 20% annual mortality means deaths for the unvaccinated will go down every month since we are squarely on the right of the hump. A 10% annual mortality means deaths will fall only slightly every month (it’s just over the top of the hump). So it makes a neutral vaccine look bad for older age groups.
It turns out it’s hard to make the vaccine look good, and much easier for the HVE effect to make the vaccine look worse than it really is because the unvaccinated is always further right on the curve.
For younger age groups, the HVE effect is very small. For those around 85 years old, it’s significant.
This doesn’t change the risk benefit for anyone. It’s still a disaster.
For those born in 1935, it’s a 12% impact, for example reducing the 23% difference to just 11%. For other ages, it’s less of a correction so the mortality increase is more. The worst it gets is a 14% correction (when you have your two mortality points on each side of the hump).
So why isn’t anyone else doing it the right way?
Covered fully in the Grok discussion, but here is the answer:
Is there a better way to analyze the data?
I asked Grok and it couldn’t think of a better way to use publicly available raw data to answer the question, “Did the COVID vaccines save lives?”
Summary
Let me know what you think of this approach.
It seems a simple way to show, using ACM and vaccination status only, that the COVID vaccines weren’t a benefit for anyone.