When I kept people included under dose 1 even after subsequent doses, and when I calculated excess mortality based on the age composition of the cohort without adjusting for seasonal variation in mortality, then the total excess mortality up to September 2023 was about 109% for people who received the first dose in April 2021, 29% in May, -14% in June, -12% for July, -12% for August, 18% for September, and 51% for October: https://mongol-fi.github.io/moar.html#Effect_of_missing_doses_during_the_rollout_of_the_first_dose. Among the late vaccinees who received the first dose in September 2021 or later, there continued to be elevated excess mortality even in 2023.
The month with the most first doses given was August. Other doses also seem to have a similar "late vaccinee effect" where people who received the dose during the later part of the rollout peak later had higher excess mortality than people who received the dose during the earlier part of the rollout peak.
So there seems to be a distribution where first a small number of the earliest vaccinees have high mortality, second a large number of earlier vaccinees have low mortality, and third a large number of later vaccinees have high mortality. And the proportion of doses that are missing from the NZ data gradually gets lower over time, so the underrepresentation of the first group is counteracted by the overrepresentation of the third group.
Great work. I may have asked this question previously, but how did you work out expects deaths. If it was based on either regression fitting of 2015-2019 weekly crude deaths, like the Karbinsky and Kobac model, then that will be flawed. If it is based on regression of weekly trends of death rates, it will be less flawed. You have to factor in the absence of seasonal winter deaths, a situation which was removed from the population in early 2020 and maintained through to late 2022. Net migration also went negative for most of that time, meaning the population stalled. Any useful model has to reflect this lull in population growth, which the K&K method does not. But more importantly, one must factor in the declining death rates among ALL population cohorts. I have looked at all 5-year band cohorts over 60 and fitted 9-year regression lines, 2011-2019 And even though there were bad flu years in 2015, 2017 and 2019, the death rates were on a declining trend. In other words, the "natural" increase in crud3 deaths expexted in an aging population, has to be oss-set by the an increase in life expectancy. When you remove the flu and other imported winter challenges from the vulnerable population you got the unusually low death count of 2020, lower than 2019, despite an aging demographic.
Asso, as the weekly death rate analysis of 2021 shows, 2021 was tracking to be even a lower death rate than 2020 in the first 17-weeks, but a switch flipped in week 17, causing the dsath rates to step up for the next 25-weeks. Aging demographics can't explain this: the population doesn't suddenly age from week 16, to week 17 of 2021.
Finally, I'm tracking weekly deaths to the end of 2023 and there has been a surge in the summer months. December 3rd week has 770+ deaths, a crazy figure for that time of the year. That's 130 more deaths than in the same week of 2022.
Your passion, skill and attention to detail are commendable.
I cannot support "I’ll be happy to proceed, on the premise that the one losing will go to retirement from public positions".
If mistakes have been made or perspectives and representations not fully understood or explained by either side - they can be corrected and enhanced to arrive at stronger conclusions.
After all, that is how criminal trials (used to) work.
I would hate to see either side disappear from the public forum - even Einstein made mistakes (the added "constant").
You don't have to go on a mission to reach both north and south poles - tied together!
For those the jab doesn't kill, statistics will likely bore them to death anyway. We don't need statistician wars; we need to acknowledge the genocide and look for ways to mitigate it. I feel like I'm listening to nerds arguing about which is the best Star Trek iteration. https://timothywiney.substack.com/p/a-call-to-arms
When I kept people included under dose 1 even after subsequent doses, and when I calculated excess mortality based on the age composition of the cohort without adjusting for seasonal variation in mortality, then the total excess mortality up to September 2023 was about 109% for people who received the first dose in April 2021, 29% in May, -14% in June, -12% for July, -12% for August, 18% for September, and 51% for October: https://mongol-fi.github.io/moar.html#Effect_of_missing_doses_during_the_rollout_of_the_first_dose. Among the late vaccinees who received the first dose in September 2021 or later, there continued to be elevated excess mortality even in 2023.
The month with the most first doses given was August. Other doses also seem to have a similar "late vaccinee effect" where people who received the dose during the later part of the rollout peak later had higher excess mortality than people who received the dose during the earlier part of the rollout peak.
So there seems to be a distribution where first a small number of the earliest vaccinees have high mortality, second a large number of earlier vaccinees have low mortality, and third a large number of later vaccinees have high mortality. And the proportion of doses that are missing from the NZ data gradually gets lower over time, so the underrepresentation of the first group is counteracted by the overrepresentation of the third group.
Great job. I, too believe the bulk of mortality should be occurring within days.
Great work. I may have asked this question previously, but how did you work out expects deaths. If it was based on either regression fitting of 2015-2019 weekly crude deaths, like the Karbinsky and Kobac model, then that will be flawed. If it is based on regression of weekly trends of death rates, it will be less flawed. You have to factor in the absence of seasonal winter deaths, a situation which was removed from the population in early 2020 and maintained through to late 2022. Net migration also went negative for most of that time, meaning the population stalled. Any useful model has to reflect this lull in population growth, which the K&K method does not. But more importantly, one must factor in the declining death rates among ALL population cohorts. I have looked at all 5-year band cohorts over 60 and fitted 9-year regression lines, 2011-2019 And even though there were bad flu years in 2015, 2017 and 2019, the death rates were on a declining trend. In other words, the "natural" increase in crud3 deaths expexted in an aging population, has to be oss-set by the an increase in life expectancy. When you remove the flu and other imported winter challenges from the vulnerable population you got the unusually low death count of 2020, lower than 2019, despite an aging demographic.
Asso, as the weekly death rate analysis of 2021 shows, 2021 was tracking to be even a lower death rate than 2020 in the first 17-weeks, but a switch flipped in week 17, causing the dsath rates to step up for the next 25-weeks. Aging demographics can't explain this: the population doesn't suddenly age from week 16, to week 17 of 2021.
Finally, I'm tracking weekly deaths to the end of 2023 and there has been a surge in the summer months. December 3rd week has 770+ deaths, a crazy figure for that time of the year. That's 130 more deaths than in the same week of 2022.
Your passion, skill and attention to detail are commendable.
I cannot support "I’ll be happy to proceed, on the premise that the one losing will go to retirement from public positions".
If mistakes have been made or perspectives and representations not fully understood or explained by either side - they can be corrected and enhanced to arrive at stronger conclusions.
After all, that is how criminal trials (used to) work.
I would hate to see either side disappear from the public forum - even Einstein made mistakes (the added "constant").
You don't have to go on a mission to reach both north and south poles - tied together!
For those the jab doesn't kill, statistics will likely bore them to death anyway. We don't need statistician wars; we need to acknowledge the genocide and look for ways to mitigate it. I feel like I'm listening to nerds arguing about which is the best Star Trek iteration. https://timothywiney.substack.com/p/a-call-to-arms