Executive summary
In a fixed-cohort analysis anchored to ISO week 2021-24 (baseline window 2021-24 to 2021-40), vaccinated all-cause mortality rose from 23.297 to 31.190 deaths per 100,000 person-weeks (RR 1.3388, +33.9%), while unvaccinated mortality rose from 13.017 to 17.625 (RR 1.3541, +35.4%). The unvaccinated-to-vaccinated ratio of rate ratios was 1.011 (95% interval 0.985 to 1.039), with male (1.003) and female (1.021) values also close to parity. The two groups did not have equal baseline risk: in many birth-year bands, unvaccinated baseline ACM rates were more than twice vaccinated rates. Nonetheless, the wave-period rise, expressed relative to each cohort’s own baseline, remained near parity, so a stable vaccinated all-cause mortality advantage is not observed in this fixed-cohort specification. This is an observational comparison and should not be interpreted as a causal effect estimate.
In other words, it didn’t work.
My open offer
If you think my analysis methodology of the Czech data using ACM differential cohort mortality during COVID v non-COVID is wrong, then please post the correct method and what it shows. The Czech data has been publicly available for nearly 2 years. If I’m wrong, why hasn’t anyone posted the proper analysis of the data showing a benefit?
I even applied the method used in Palinkas to the Czech data and the results were virtually identical to the method I used.
- Pálinkás used regression-adjusted epidemic-vs-nonepidemic HR ratios.
- We used fixed cohorts, person-weeks, and all-cause wave/baseline mortality ratio
So two completely different methods, same result. No discernable benefit.
Hellbound and Down 