This is very long and quite detailed. The work looks, at least so much as I've read to this point, to be of high quality.
A few points of skepticism to note, however:
COVID messes up the trend lines. Naturally for deaths, pneumonias, and associated causes of death, this makes sense. COVID showed up and caused these. We expect to see ~6-7 million (whatever the global death count is now) excess deaths in the data. We should see a large spike in excess deaths in 2020-21 as frail and elderly people who otherwise would have lived longer died of COVID, complications, or lack of care for other conditions during lockdowns.
COVID also messes up the trend lines for non-death conditions though. Remember that they shut down the hospitals for all elective care for a time. When things reopened, physicians' offices, clinics, and hospitals ended up swarmed with people who had put off care now trying to "catch up." But with any disease, if you leave it untreated, it progresses. So, doctors were treating higher acuity (more severely ill) patients than normal here. This isn't just because of the shutdowns either, drugs became more expensive, supply chain issues limited access, doctors and labs were closed to routine monitoring and therapy adjustments. So people who were quite stable sometimes became unstable during this time. We should expect to see a rise in most diagnoses post-reopening with the exception (maybe) of genetic diseases, which might drop due to a slower rate of diagnosis.
We should see flu/pneumonia deaths due to flu drop because flu was basically renamed as COVID during the 2021 flu season, in which we saw a (lol) 95% decrease in flu incidence.
What is most concerning though is the scale. The author here presents deviation from the norm in terms of sigma. Sigma is a standard deviation from the norm. A 1 sigma difference represents deviation from ~68% of the population, 2 sigma: ~95%, 3 sigma: ~97% of the population. A 6 sigma difference is ~1 in a million, and is commonly used as a metric by industrial manufacturers for ensuring their processes are highly optimized and produce very few errors. So when we see a 9 sigma deviation in lymphomas, that's a massive deviation from normal.
*Exhibit B is my take-away. While it's a correlation, not causation, this is a terrifyingly clear way of showing what we all suspect: that the vaccines themselves correlate strongly with excess death. "Safe and effective", my ass.
This is very long and quite detailed. The work looks, at least so much as I've read to this point, to be of high quality.
A few points of skepticism to note, however:
COVID messes up the trend lines. Naturally for deaths, pneumonias, and associated causes of death, this makes sense. COVID showed up and caused these. We expect to see ~6-7 million (whatever the global death count is now) excess deaths in the data. We should see a large spike in excess deaths in 2020-21 as frail and elderly people who otherwise would have lived longer died of COVID, complications, or lack of care for other conditions during lockdowns.
COVID also messes up the trend lines for non-death conditions though. Remember that they shut down the hospitals for all elective care for a time. When things reopened, physicians' offices, clinics, and hospitals ended up swarmed with people who had put off care now trying to "catch up." But with any disease, if you leave it untreated, it progresses. So, doctors were treating higher acuity (more severely ill) patients than normal here. This isn't just because of the shutdowns either, drugs became more expensive, supply chain issues limited access, doctors and labs were closed to routine monitoring and therapy adjustments. So people who were quite stable sometimes became unstable during this time. We should expect to see a rise in most diagnoses post-reopening with the exception (maybe) of genetic diseases, which might drop due to a slower rate of diagnosis.
We should see flu/pneumonia deaths due to flu drop because flu was basically renamed as COVID during the 2021 flu season, in which we saw a (lol) 95% decrease in flu incidence.
What is most concerning though is the scale. The author here presents deviation from the norm in terms of sigma. Sigma is a standard deviation from the norm. A 1 sigma difference represents deviation from ~68% of the population, 2 sigma: ~95%, 3 sigma: ~97% of the population. A 6 sigma difference is ~1 in a million, and is commonly used as a metric by industrial manufacturers for ensuring their processes are highly optimized and produce very few errors. So when we see a 9 sigma deviation in lymphomas, that's a massive deviation from normal.
*Exhibit B is my take-away. While it's a correlation, not causation, this is a terrifyingly clear way of showing what we all suspect: that the vaccines themselves correlate strongly with excess death. "Safe and effective", my ass.
...wonderful observations, expertly stated and framed....
My recollection is they stopped elective procedures for a time. Things of critical importance still continued, even during 'lockdown.'