1/ NL, AUT, SWE, NO, CH age adjusted mortality update.
I have added data up to week 47 now (28th November).
Left column: crude mortality (normalized to total population).
Right column: ASMR using NL2011 std. population.
AUT and NL go up, not NO, CH, SWE.
2/ The mortality increase in NL and AUT is not related to vaxx. The vaxx is at the same time not visible as lifesaving in all cause as C19 is not the driving parameter, neither is the vaxx.
We likely see the price to pay for permanent lockdowns, social isolation, fear, etc.
3/ Let's dig deeper than AMSR: now we plot mortality by age bin population. This is more precise in order to understand what is going on.
No surprise, the old are dying. Do people not know this? And are we now "bin counting" 90+ people to make lockdown panic? Stop this please.
4/ Let's go down by age now.
65-79 years.
Norway is a good place to be. Surprised? Equally vaxxed as the others. Why?
NO👉 The richest country in Europe if we exclude the "micky mouse" fiscal paradise countries like Luxemburg of course.
5/ Let's go down to 50-64 years. Nothing in NL and NO, some little excess in SWE, AUT, CH.
6/ So let's dig deeper hereon 50 years spike. I bet those are men.
7/ Coffee break with Herbert Grönemeyer now. @freiheit_ruft
8/ I hope point 5 makes clear why aggregated mortality and excess analysis is a misleading way of understanding causality of mortality and why we dig down on population normalized raw data by multivariate parameters like age, sex...
13/ Found a little mistake while appending (bad idea) in the new W44-47 data. The deaths changed in some cases, so I double counted some weeks (e.g. 43) where the duplicate removal consequently did not work (as deaths also changed). That gave the male spikes (not real).
14/ So here again. And gone is the spike. So the men are lucky at the end. It was just a duplicate line. Still, I hope you enjoyed Grönemeyer 😇
15/ The other age groups
15/ Also again here the first graphs without those (data duplicates) spikes.
Lesson learned: for 2021 data, start with a fresh download as the late weeks are still changing due to reporting lag.
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Note that we have one month different season definitions. Small detail. I used 1st September as it seems to be the historical death minimum over years and countries.
3/ Now let's play. More countries.
And here we start to see interesting things like the disastrous performance of AUT, CH, FR on 20/21 season. Surprised me
We learn: locking down in 19/20 season makes 20/21 explode.
2/ The std. mortality (blue) is continuously improving, although stagnating in NL as compared with top references Sweden and Norway.
Std. mortality depends on many health parameters like lifestyle and culture. Improving it is a slow process and won't stop red going up at ~1%.
3/ Both the red and blue curves are important.
Blue: monitors health. Here NL is doing fine.
Red: determines infrastructure load. It's the result of demographics and something that doesn't come unannounced. Here NL will be under pressure, for decades to come.
1/ "Age adjusted all cause mortality trends 2000-2021 in Europe"
This was quite some work, so I hope you appreciate the article. I don't think that this kind of analysis using 5 year age bin granularity over 20 year trends has been done elsewhere.
2/ The age adjustement was done on 5 year age bins.
Some groups @CebmOxfordcebm.net/covid-19/exces… report age adjusted mortality for 2020. But the method is inaccurate as too wide age bins are used.
3/ For teaching purpose, we also applied the WHO2015-2025 standard population in some graphs to demonstrate the problem if applying this to an old population.
1/ I spotted one little (manipulative?) move by @OurWorldInData using wayback machine. In July 2021 they change the colour code of the life expectancy world map.