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.
This is how "saving lives" with lockdowns in 2020 (NL and AUT) looks one season later in 20/21. Bad.
5/Summary: the countries with the hardest lockdown approach in 19/20 season like Slovenia (the role model in May 2020 for "eliminating" Covid), had the worst season in 20/21.
Data shows: You cannot escape seasonal death with lockdowns, just postpone with a worse outcome later.
6/ Adding a demonstration of calendar year versus flu year importance:
Example Romania deaths.
Left (calendar year): As we can see, the calendar year artificially splits the 2020/2021 peak in two.
Right (flu year): A much better way to define the year is by flu season.
7/ Doing the analysis by flu season (26 weeks offset), the real tragedy of prolonged lockdowns become very clear on the July 2020 - July 2021 time frame analysis. The excess was explosive in lockdowned countries.
Sweden being right in one picture 👇.
8/ Romania versus Sweden in a picture:
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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.
1/ Europe infant mortality trend. Let's have a look how NL compares with other peers.
Not so good.
Note that this is dominated by the 0-1 year group. But unfortunately, the population is only available by 0-5 years. So by averaging death_Y_LT5 / pop_LT5 we dilute a bit.
2/ The problem is that the vaccination will be diluted over many weeks.
I assumed that e.g. 10% of the population in an age group will vaccinate per week. Then this gives the expected weekly vaxx death background (red for 1:10k vaxx CFR-->1:100k, and blue 1:50k CFR-->1:500k).
3/ So we could maybe see it for the below 30y. But here, also the vaxx CFR is rather >>1:100k. So difficult. Maybe in cases when a lot of people vaccinated in the same week.
That's what @OS51388957 is hunting. He knows what he is doing. 😎🤙💪