The identified spectacular rise van plotselinge en onverwachte Duitse overlijdens in 2021 zou voortkomen uit fouten in de data. Het waren op het oog zeker onwaarschijnlijk sensationele cijfers en tegelijkertijd: als iets zo weinig voorkomt als 'sudden death', dan zie je al snel verdubbelingen of meer als er ook maar iets beweegt op dat vlak. Daarbij: de data waren afkomstig van een excellent overheidsinstituut en de betrokken analist Tom Lausen is uitstekend thuis in de materie. Diverse onafhankelijke reviews bevestigden zijn bevindingen in grote lijnen.
Natuurlijk werd er ook hevig gesputterd: diverse factchecks komen niet verder dan dat de analyse van Tom Lausen niet klopte en dat de cijfers niet bedoeld waren voor zo'n analyse. Ook wordt beweerd dat de data opzettelijk op een manier zijn opgevraagd die alleen maar tot dit resultaat kon leiden. Kortom: paniek bij veel factcheckers, exceptions apart from that.
But: there does seem to be something going on. Where did we fall for it? Have we really stepped into something? Then let's see for yourself.
What exactly has been requested?
Three data packages were requested. The first two data packages were only insured in 2021. This could therefore not include mortality figures from previous years, which would explain the lack of mortality codes until 2021. But the third package was really about all insured persons of 2016-2021.
U draagt zorg voor toezending van een uitsplitsing van de frequentie van alle ICD-codes van alle verzekerden - exclusief de verzekerden uit pakket 1 - voor de periode 2016 tot en met 2021, indien beschikbaar ook voor 2022, naar kwartalen. De gegevens moeten worden opgevraagd met V en G.
Confirmation data request
Paket 3. Sie beantragen die Übermittlung einer Auf,istung der Häufigkeit aller ICD-Codes aller Versicherten - ohne die Versichtertenmenge aus Paket 1 - für de Zeitraum 2016 bis 2021, falls anteilig vorliegend auch für 2022, nach Quartalen. Die Datenabfrage soll mit V und G erfolgen.
Er wordt nu beweerd dat pakket 3 onvolledig is geleverd: De verzekerden die al voor 2021 waren overleden, zaten er niet bij, het zou alleen personen bevatten die in 2021 nog als verzekerde waren geregistreerd. Die konden dus geen sterftecodes hebben in 2016-2020... Vandaar de absurde verschillen met 2021. Moedwil of misverstand, dat laten we in het midden.
Yet that is not true either
We assume for a moment that the data did indeed concern people who were still alive in 2021. Apparently, 1 January 2021 has been taken as the benchmark date. If December 31 had been chosen, no death would have been visible, at least according to this reasoning. After all, you can assume that someone, once deceased, is no longer considered an insured person. On 31 December, all deceased persons who died that year were deregistered as insured. Then you have nothing left to report on if you think straightforwardly enough.
If someone who had to collect the data has realized that, it is at least striking that they did not take 2016-2020 with them or else asked what the intention was.
With this in mind, the overall graph shows something strange:
Among the people who were still alive in 2021, one and a half times as many died in 2016-2020 as in 2021 itself.
Read that again.
Dit wordt door experts gemakshalve verklaard als 'invoerfout'. Daarvoor hebben we begrip; de vaccins kunnen het immers niet zijn. Maar dan betreft het wel een invoerfout die steeds makkelijker werd gemaakt, getuige de licht stijgende tendens van 2016-2019. We mogen ervan uitgaan dat die trend in 2021 doorzette. Dan is dus ca. 40% van de door het Paul Ehrlich Instituut geleverde data van 2021 gewoon fake want er is geen reden om aan te nemen dat die invoerfouten daar plotseling niet meer voorkwamen...
En als data van de jaren vóór 2021 overledenen überhaupt niet meegeleverd zijn, waar komen die 'invoerfouten' dan eigenlijk vandaan!?
40% 'invoerfouten', is dat zelfs voor de medische industrie niet erg veel?
If it is entered incorrectly at the source, it is not easy to find out. That source is the doctors: it concerns claims (cost reports) that doctors send to insurers. That is why we also think of fraud instead of errors. Doctors would have deliberately given up more actions than they actually performed. This also raises questions and objections:
- Het gaat hier niet om handelingen, het zijn overlijdensomstandigheden, zoals "overleden zonder andere aanwezigen"
- Krijgen artsen extra geld uitgekeerd als iemand overlijdt zonder andere aanwezigen...?
- If you, as a doctor, want to increase something, do you do so with the sudden death of your own patients (your customer base, you are reducing your practice) or do you tick extra ingrown toenails, stitches or ear and eye infections? There are undoubtedly less drastic, more common, more time-consuming actions available than the observation of a sudden death that can no longer be done.
Is it sometimes with the health insurers or with the federal umbrella?
Another point of pollution can be the health insurers themselves. They are not allowed to make mega profits, so they could come up with costs to keep the premiums up to standard. Even then, deaths do not seem to be the appropriate cost item, at least if you do not want to make it stand out.
Back to the question: How does this enormous pollution get into the data? Is the fault perhaps with the research institute for vaccines and biomedicines, the Paul Ehrluch Institute, which collected all the data? Cardinal mistakes are also made at institutes, which we have seen repeatedly at our own institutes.
Het is goed om te beseffen dat de situatie in Duitsland anders is dan in NL: Een 'federaal' instituut verzamelt daar de data van de bondsstaten. Elke bondsstaat heeft een eigen zorgverzekeraar ofwel 'Kassenärtzlichen Vereinigung' (NRW heeft er twee). Er zijn dus 17 KV's, verenigd in een Bundesvereinigung.
Factor 17
If the federal institute has incorrectly requested data from all but one of the institutes, that could explain this graph.
Then there is one institute that has also passed on 2016-2019, that's where that handful of data comes from. The remaining sixteen have only submitted 2021.
Indication
Can that ever be reconstructed? The institute may have passed on the question directly and only 1 of the seventeen understood the question as it was intended, the rest only on autopilot (after Package 1 and Package 2) also turned out Package three for 2021 only.
When merging the data, there is again no attention. A data-responsible government institute should see that datasets are not compiled consistently. Gene input control? No output control?
That factor of 17 should then more or less also apply to other Codes. We saw a factor of 16.7, which is in line with that. For the other codes, this should more or less be the case. With some margin, they should be on the order of between 15 and 20.
That is not the case. The factors there are approx. 10, 6 and 3 (only factor 3 graph shown, 293% to be exact). By no means a factor of 17 and the Bundesländer will not be that different, will they?
Factor 0.2
So much for the absurd differences. Let's just look at Code I46.1: Death due to sudden heart failure, dan is het verschil ineens slechts zo'n 20% met 2020. Welke systeemfout of 'invoerfout' kan er nu weer zijn gemaakt? Plotseling hartfalen is een echte doodsoorzaak, de andere Codes zijn omstandigheden, misschien is dat anders behandeld? Deze code is ook later toegevoegd.
Would this be the only graph in which the years 2016-2020 are correctly included? In any case, an increase of 15-20% looks more credible than one of 1700%. The message does not get any less urgent: Investigate this! (and in the meantime stop that vaccination on nothing).
How do other I.46 codes work?
Cardiac positions with successful resuscitation (I46.0) are in line with the normal trend. Data really seems OK.
Daarnaast is er een categorie "Cardiac arrest, not specified." Daarin zien we bijna dezelfde jump als in Death due to sudden heart failure.
To what extent the various categories are duplications or overlap, I do not know, not every database design is equally conclusive.
These graphs are generated on https://corih.de/KBV-Daten/index.php?ohneuberhang=1&uberproz=0&mind=0&icd=I46
The quarters of 2021 are correct
The data for 2021 also seem to be correct. So only look at the red bars in the graphs below: each bar is a quarter 2021, the dark red one is Q1 2022.
In all categories, the biggest blow falls immediately in the first quarter of 2021, after which the numbers fall back in Q2 and from there rise again in Q3 and Q4.
If there are any ideas on how delayed care, lockdowns or long covid can cause this, I'd love to hear from you. With vaccinations, I can think of something: the most vulnerable were pricked first. Germany started this at the end of December 2020.
from other persons
How does this unfold?
I am very curious about the whole story with which all the above questions are answered:
- How does the pollution end up in the dataset?
- How can the susceptibility to error (degree of contamination) differ so much per code?
- How can some pollutants show a stable/descending trend and others an increasing trend?
- If the data were not complete, are the data from the I46.1 (sudden cardiac death) correct?
Before we start speculating further: first transparency and open data so that we can find out with targeted independent research whether the vaccinations have anything to do with this at all. There is sufficient circumstantial evidence.
"... the biggest blow immediately in the first quarter of 2021, after which the numbers drop back in Q2 and from there rise again in Q3 and Q4 .... If there are any ideas on how delayed care, lockdowns or long covid can cause this, I'd love to hear from you."
I think there are a lot of variables. The Alfa variant became dominant especially in Q1. Contagiousness rate increased rapidly to 50% and at the same time, especially B.1.1.7, turned out to be a lot more sickening than the previous variant.
After that Delta came as dominant but by that time you have possible influences of vaccinations and lockdown, built-up resistance and so on. For me, the rise in Q3 is more puzzling.
Another problem with Q1s are sometimes after-effects from the end of Q4 of the year before. People get sick more often in a Q1 precisely because a lot of resistance disappears in Q4, winter, food, sitting at home, etc.
Those are not really typical variables for heart failure or "Less than 24 hours after the onset of symptoms, not otherwise specified". Looking at previous Q1's, this one looks pretty bright.
I'm afraid that the data pollution/incorrect input is a plausible explanation.
You're dealing with government agencies. I also work for it and if there is a bad deal with data somewhere, it is with governments. Why don't they do anything about it? For 20 years I have been providing solutions for simple registrations that have been going wrong for 20 years due to wrong processes and none! data management, I'm right and people love it, but then they don't get a foot on the ground and they don't do anything.
Another problem may be that due to the spontaneous focus on data and figures, suddenly everyone has started to enter more serious figures. For example, I made up figures for CBS statistics for years because the requested was nowhere in our files. They demanded an answer and got one, for the source the data is worthless so sowhat. And there was never a fact check by CBS by, for example, requesting the figures elsewhere or otherwise.
And why does the government never do anything about this or even don't want to do anything about it? Firstly, it is very useful for the government not to have good data. (saves black ink for bars) You can then (apparently) continue to waste millions without any problems and no one does anything about it or understands anything about it. Secondly, this is the easiest way to cover up the truth. Suppose the vaccination causes only 5% more deaths, bad enough in itself to stop of course. But if the figures start to deviate as they do now, they become implausible and the "real" 5% disappears due to the collapse of, for example, the 17% when Tom Lausen's report can be undermined as far as that 17% is concerned. Then when the data problem becomes known, can be solved, he does not have to come back with the "real" 5%. Credibility has been lost.
The government also deliberately keeps itself stupid. In the land of the blind, one-eyed is king. For example, by appointing a management that knows nothing about the content and by systematically promoting the real content experts in dead-end positions. This has been happening structurally at IenM since 2004, hence one scandal after another or blunders and budget overruns of millions in a year. Also look at the nitrogen approach, misplaced sniffers and bad data and then a model that you can set exactly so that the farmers you want to get rid of are shown as peaks in the map. Fishing is also being pilloried again by wrong data, etc. etc. Data is a weapon against the population and they use it everywhere. And you just have to be stupid for it, the most important competence to be a civil servant alongside a university education in lacemaking.
You confirm my worst suspicions...
Typically, it is now claimed that these data are not suitable for drawing conclusions (in this case about trends in causes of death), while during the entire pandemic the number of positive corona tests was used without any criticism as if it were a representative sample of the population. If the data are complete (and if not, they simply need to be supplemented) it seems to me that the chance of bias in this case is many times smaller than in the case of corona tests.
By the way, I can well imagine that the data was not accidentally delivered incorrectly. By giving the data the appearance of an unrealistically strong increase, the whole story immediately becomes suspicious. Although in Germany alone the association with AfD actually ausreicht for that.
Kerryn Phelps top doctor and politician in Australia, seriously and permanently (?) ill after 2nd Pfizer vaccination. Revealed that doctors in her country are being censored under penalty of high fines and loss of registration.
https://www.smh.com.au/politics/federal/not-anti-vaxxers-dr-kerryn-phelps-says-she-suffered-covid-vaccine-injury-calls-for-more-research-20221220-p5c7ry.html
The 'top doctor' Kerryn Phelps is also a big proponent of lockdowns, vaccination and mouth mask requirements, also indoors. In September 2022, she called for all strict covid measures to be reintroduced!
With such hard-learned medics, no pity fits. Karma is...
Ik heb het artikel uit de Herald vertaald https://virusvaria.nl/oproep-van-australische-hardliner-met-vaccinschade-wij-zijn-geen-anti-vaxxers/
Was the reason for anonymizing Dutch excess mortality data perhaps to prevent the Dutch people from finding out through BSN numbers that only a small part of Dutch politicians have actually been injected with Spike protein mRNA, just as was noticed in Japan:
https://twitter.com/riseupandresist/status/1599160471046983680
In addition, we need to look beyond just (excess) mortality; Also increase in the disease itself (cardiovascular, pulmonary, neuro, etc.).