Statistically significant 0.09% daily rise in excess deaths among covid-vaccinated Kiwis in 2022, driven by boosters [STOP BOOSTING] —— Good news: excess deaths flattened in 2023
Try to redo your excess mortality calculation based on my buckets.gz file so that you don't adjust for seasonality but you simply calculate the expected number of deaths based on the number of person-days for each age. If you do the calculation right then your results will be completely different from your v5 spreadsheet. And it will be so much different that the difference can't be explained by the lack of adjustment for seasonality. So then either calculation has to be wrong.
The only thing you have to do is to calculate a sum of person-days for each age: `curl -Ls sars2.net/f/buckets.gz|gzip -dc>buckets;sed 1d buckets|awk '{age=$4;if(age>95)age=95;a[age]+=$5}END{for(i in a)print i,a[i]}'`.
Then take the yearly mortality rates for each age in the second sheet of your spreadsheet, divide them by 365, and calculate the sum of each mortality rate multiplied by the number of person-days for the age.
It gives you a baseline of 37896 deaths. But there's a total of 37315 deaths in the pay-per-dose data, so you get about -1.5% excess mortality.
If the PPD data includes almost half of the NZ population, how can people in the PPD data have 27% excess mortality in 2023 when the total NZ population had close to 0% excess mortality in 2023? Most deaths are in elderly age groups and the vast majority of elderly people are vaccinated, and actually the PPD data includes about 75-80% of the total NZ population in ages 75 and above. So in the scenario that the people in the PPD data are representative of vaccinated people and the average excess mortality among all vaccinated people would be 27%, then unvaccinated people would need to have below -100% excess mortality, which is not possible.
It's also a hack-ass fix that you're adding 1 year to all ages to account for the aging of the cohort. If you accounted for aging correctly then the baseline would be higher in 2023 and lower in 2021. You could do your analysis based on this file instead, which shows the number of person-days and deaths for each age and date so that the ages of people are recalculated on each day: sars2.net/f/buckets.gz. I generated the file with this R script, which is similar to buckets.py except it calculates the ages of people correctly: sars2.net/moar.html#Reimplementing_buckets_py_in_R. (In buckets.py the age of each person is the age at death for people who died, or the age on the day when the script was ran for people who didn't die.)
Try to redo your excess mortality calculation based on my buckets.gz file so that you don't adjust for seasonality but you simply calculate the expected number of deaths based on the number of person-days for each age. If you do the calculation right then your results will be completely different from your v5 spreadsheet. And it will be so much different that the difference can't be explained by the lack of adjustment for seasonality. So then either calculation has to be wrong.
The only thing you have to do is to calculate a sum of person-days for each age: `curl -Ls sars2.net/f/buckets.gz|gzip -dc>buckets;sed 1d buckets|awk '{age=$4;if(age>95)age=95;a[age]+=$5}END{for(i in a)print i,a[i]}'`.
Then take the yearly mortality rates for each age in the second sheet of your spreadsheet, divide them by 365, and calculate the sum of each mortality rate multiplied by the number of person-days for the age.
It gives you a baseline of 37896 deaths. But there's a total of 37315 deaths in the pay-per-dose data, so you get about -1.5% excess mortality.
You're still calculating the baseline wrong, because the baseline shouldn't be that low in 2023. Your results are not consistent with the results of me, canceledmouse, or Uncle John Returns: https://openvaet.substack.com/p/yet-another-dive-in-the-new-zealand, https://twitter.com/UncleJo46902375/status/1732496623123517742, https://twitter.com/UncleJo46902375/status/1741423429641711929, https://twitter.com/UncleJo46902375/status/1751277431929909653.
If the PPD data includes almost half of the NZ population, how can people in the PPD data have 27% excess mortality in 2023 when the total NZ population had close to 0% excess mortality in 2023? Most deaths are in elderly age groups and the vast majority of elderly people are vaccinated, and actually the PPD data includes about 75-80% of the total NZ population in ages 75 and above. So in the scenario that the people in the PPD data are representative of vaccinated people and the average excess mortality among all vaccinated people would be 27%, then unvaccinated people would need to have below -100% excess mortality, which is not possible.
It's also a hack-ass fix that you're adding 1 year to all ages to account for the aging of the cohort. If you accounted for aging correctly then the baseline would be higher in 2023 and lower in 2021. You could do your analysis based on this file instead, which shows the number of person-days and deaths for each age and date so that the ages of people are recalculated on each day: sars2.net/f/buckets.gz. I generated the file with this R script, which is similar to buckets.py except it calculates the ages of people correctly: sars2.net/moar.html#Reimplementing_buckets_py_in_R. (In buckets.py the age of each person is the age at death for people who died, or the age on the day when the script was ran for people who didn't die.)