KCOR results on the Japan record level data shows the same harm pattern as the Czech data

The shots increased mortality. No doubt about it anymore. There is no way to explain away this data but my critics are welcome to try.

Executive summary

Existing epidemiological methods are incapable of getting accurate results on vaccine retrospective studies as was finally admitted in the abstract of Obel:

This is why I invented KCOR, a new approach to data analysis. KCOR circumvents the healthy vaccinee effect (HVE) problem because it moves the “cohort adjustment” (so they are comparable) into mortality space instead of trying to do 1:1 matching on observable features of living people (which simply does not work as the Obel paper pointed out).

This article shows that KCOR, when applied to the Japanese record level data (2M records), shows the exact same pattern as the Czech record-level data (12M records).

I’ve reached out to Paul Offit numerous times and he refuses to look at the data. Not even for $1M. What does THAT tell you? It tell me that they don’t want to know the truth.

The KCOR Czech results

KCOR applied to the Czech Republic data graphs.

Japan KCOR curves (all ages)

KCOR Values >1 —> Vaccine group is dying more than unvaccinated.

KCOR uses FIXED (at enrollment time) cohorts: no transitions allowed. So a safe vaccine would produce a FLAT curve, not one that rises (then plateaus) shortly after the shot.

It isn’t dynamic HVE either because all the other doses track each other (dynamic HVE would produce a mirror image in the earlier dose).

You can see a telltale rise in all cause mortality is always happening only for the most recently vaccinated group which ALWAYS deviates up. Other groups (those NOT recently vaccinated) are slowly declining from their earlier vaccine mortality elevation.

The only exception is the last chart because people got 1 and 2 in rapid succession and you can see the harms were DOSE DEPENDENT with 2 shots having higher mortality than 1 shot. Dose dependency is a big deal: it’s a hallmark of causality.

If doubling the dose causes a doubling of the mortality with all other parameters kept the same, it’s hard to argue that there is no causality.

Graphs compare the various doses relative to the unvaccinated. Different enrollment dates are used to capture each shot. The most recent enrollment date is listed first.

The spreadsheet is available in my github. It took me literally 90 seconds to make (it’s just a pivot table and a graph).

Interpretation

There is only one plausible explanation for this pattern that I’m aware of: the COVID vaccines increase your risk of death.

ChatGPT, after many objections about identifiability, reluctantly agreed I was correct: there is no plausible explanation for this other than the COVID vaccines increased your net risk of death, i.e., mortality risks were much greater than any “benefits.”

Summary

No one supporting the shots (including mainstream media) wants to read the KCOR methods paper.

Nor do they want to look at the Japan or Czech data.

They simply want to believe they were right.

They don’t want to be put in a position where they would lose:

1. their job,

2. their license to practice medicine,

3. their board certifications,

4. their friends and family,

5. the respect of their colleagues,

6. or have to deal with the cognitive dissonance.

They all want to put their heads in the sand and hope this all goes away. All of them. No exceptions.

It won’t go away.

The only thing that will make this go away is open debate on the methods and the data.

If I’m wrong, face me in an open public debate.

Or simply explain to the world what causes the healthiest people to suddenly increase their mortality right after every COVID shot if it wasn’t the shots. That should be easy, shouldn’t it?

But they are all afraid of public debate and none of them can explain the data.

So it won’t go away. Ever.