A brief thought by a 'biostatistician' from Leuven, Belgium. Read the screenshot, the article adds little, although there is probably more to be gained from it. You wouldn't expect a (bio)statistician to mean that that 45% shouldn't have been there... But what does he mean by "almost half"? We did the math for you.
Erratum: Calculation A was incorrect. It took me a while to understand the mistake so I left it as a puzzle challenge and to show how easy it is to cut corners. The adjusted -and hopefully definitively correct- calculation is Calculation B.
Who calculates
Calculation A
Let's see: 74% of Belgians have been vaccinated.
For ease of reading, we assume 100 patients.
If the vaccine is completely not 74 of those 100 patients would have been vaccinated.
However, there are only 55, thanks to the vaccines.
55 instead of 74: that is an effectiveness of 27%.
So if the 45 unvaccinated people had been vaccinated, there would have been 27% less: 12. So they didn't have to be there. We leave age differences between the unvaccinated and vaccinated out of the equation for a moment, because that would make it even worse.
The biostatistician calls those 12 out of 100: 'almost half of the patients'.
So, the answer according to calculation A is: According to biostatistics, 12% is equal to "almost half".
Calculation B
Calculation A therefore turned out to be too short-sighted. Various calculators came to the rescue, which brings me to the following sum:
- Given: population of 1000, of which 73% vaccinated
- Given: 100 hospitalizations, of which 55% vaccinated
- 730 vaccinated people lead to 55 zkho = 8%
- 270 unvaccinated people lead to 45 zkho = 17%
If those 270 vaccinated people were to be vaccinated, they would only yield 8% admissions instead of 17%, which is 20 instead of the current 45.
So you save 45 – 20 = 25 shots out of 100. That is 25% fewer admissions.
So, the correct answer according to calculation B is: According to biostatistics, 25% equals "almost half".
What does this mean?
First of all, it says something about the fact that math is sometimes more difficult than you think and that you have to adjust your model when feedback comes.
It says something about the 'journalist' (I would really choose a different profession if I were called that), he writes it down and the editors are happy with it.
It says something about the biostatistician, who will probably get pats on the back from the healthcare capos. The unvaccinated are blamed for a near-doubling of ICU admissions.
Fact-checkers are also often 'journalists'. Think about that when you come across a fact check again.
Doctors and biostatisticians will do 'plausibility checks' on the data from CBS. Where else should CBS get its expertise from other than at RIVM? Surely not with straightforward data analysts...?
P.S.: In the meantime, shocking calculations are emerging from various sides about excess mortality and possible relationship to vaccinations, also in relation to what they have prevented. It is being worked on. As soon as there is a solid and plausibly substantiated story, a post about it follows. In any case, the reverse-engineered data are in, in the absence of CBS activity.
Stay tuned!
