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.
4/ For 2020, this is the ESP2013 age adjusted full year mortality country ranking obtained.
5/ For reference, the below shows the full pandemic 96 weeks (2020 W1-2021 W44) ESP2013 age adjusted mortality country ranking. Many countries had to be excluded due to limited availability of mortality data for 2021.
6/ An in depth look on the current year 2021 was done as there seems to be some concern about higher excess. To my view, this is mainly a random seasonal effect and maybe the price for lockdowns
7/ We further looked in depth to the NL situation in 2021. Nothing particular is happening in the age group <65. The excess is dominated by the elderly group (like in 2020). Covid is not visible <65.
8/ We further noticed, that using symmetric standard populations for sex is “old men friendly”. It will be less sensitive towards elderly female deaths. EC and @who standardisation groups are advised to adapt and remove this artefact.
9/ We further see that the open approach of Sweden lead to outperforming their lockdown neighbours on the 96 weeks 2020-2021 timeframe.
This is likely an anti-correlation with the cumulative stringency of lockdowns.
Sweden was right.
DNK and FI wrong.
NO is simply rich.
10/ 2021: Neither a positive nor negative impact of the vaccine can be seen. At least it’s not of any relevant dominance. Other causes dominate. Probably lockdown, or random seasonality as also lockdown hardliners like France is doing ok for now.
11/ The observed excess mortality in AT and NL, is dominated by mortality in the elderly age bins. But those are 95 % vaccinated like in SWE and FR. The vaccine doesn’t reduce nor increase all cause mortality. QR passports and the one dimensional C19 health focus has to stop.
12/ I'm adding 2021 interim result for the first 44 weeks. That will certainly change once we look to this in some month or two. The raw mortality data even for the available weeks may still change due to delayed reporting.
France.😀 Didn't have them on the radar. Bah oui 👋
13/ The French paradox. Is it BMI? Anyone who has lived there and in other countries knows their outperformance on food culture although I do prefer the high fat/carb Italian / Spanish kitchen.
🇫🇷 France wins on food category, like it or not. We know their secret now.
For comparison, 2020-2021 96 weeks and the vaccination map in the 60+ age group.
We can also do from SE raw. And we can also show how rural stations look. Frederik does like them. Climate agenda is measured in downtowns of the capitals?
Not sure if it’s normal that amateurs now have to lecture academics…?
The downtown station logs hourly=no need for even Ekholm, no need for re-sampling. Does Frederik even know what we mean? Nothing is adjusted. Also PHA leaves it as is as it only detects breakpoints (not UHI).
Yes. Hausfather & Berkeley Earth are pushing it.
But it’s not a measurement. Not one station shows that.
It’s what you get when you aggregate rot over time.
On the left: 8 pristine USCRN sites. Same y-scale.
Now look what they did.👇
2/ Was wir hier sehen: Die Datenreihe ist ein Komposit (sehr beliebt, wenig seroes, in der Klima-„Wissenschaft“).
Die Messmethode (und mehr) hat sich verändert – von analogen zu digitalen Sensoren. Die Entropie der Nachkommastellen zeigt das – deutlich.
1/ The result is simply wrong.
There are 2 stations there — we can compare.
🟥Red: Carlwood
🟩Green: Gatewick
We clearly see the overshoot.
Moreover: They’re using subhourly spikes (error) from a single, low-inertia sensor.
Total incompetence.
2/ Using TMAX from a low-quality single urban sensor is already peak incompetence.
But they go further — they take the spikes.
Even top-tier stations like USCRN show 2–3°C error at peak forcing.
USCRN uses triple sensors — worst spikes get voted out.
3/ The UK has nothing like the USCRN triple-sensor setup.
So when two nearby stations disagree, the right move is simple:
Discard the implausible one — in this case, Charlwood.
What does the agenda-captured @metoffice do?
They run with the error.
They hoax the public.
ISO9001🤡
Not a high-quality reference site like
Valentia Observatory (Ireland) or h-USCRN sites.
But: Lower urban bias than cities like Kyoto or Tokyo. It starts to show the well known flatliner we see at stable sites.
3/ To see it better, here’s 4 months side by side:
🟥 Kyoto
⬛️ Tokyo
🟦 Suttsu
This is man-made. The T trend is just unrelated to climate. It measures the site and environment change. Suttsu as expected least impacted. But it still is.
The red areas are fully man-made—built or cultivated.
You cannot measure climate anywhere near them.
And MODIS still misses a lot.
In reality, it’s worse.
When we inspected what @BerkeleyEA calls “rural”?
Almost all those stations are worthless
Imagine a field looks like it does on the left…alive.
And later, like the right. Dead and brown.
Still think you'll measure the same 2m temperature?
Or might that just—possibly—have a major impact as the surroundings changed? GPT estimates 3C. It's not wrong.
We now combine MODIS 🟥 and P2023A 🟪 (10m resolution).
Look: MODIS misses entire urban zones— Ireland. Or Liverpool.
And yet @hausfath and @BerkeleyEarth built their “rural” claims on MODIS junk.
Shameful deception.
The paper needs a retraction.