Interesting analysis, though he mentions early on a huge issue with the analysis:
> It does come with one major caveat: Because it counts ALL deaths, it cannot on its own disentangle deaths caused by Covid itself, impacts from the lockdowns themselves, impacts from vaccines, or unrelated death trends.
Especially as time goes on, and we see the effects of missed cancer screenings, economic destruction, increased obesity, etc, excess mortality will be almost entirely a measure of side effects of the pandemic response.
That is not an issue with the analysis as a whole, and indeed he addresses several of those points. It is an issue only with the excess deaths statistic. The data we have so far does not show a correlation between lockdowns and GDP, and tends to show a correlation between lockdowns and a reduction in excess deaths.
The data is the data, but there is a huge missing story that this analysis completely misses.
Why?
It is not appropriate to mix an all-cause mortality number (which is what this is) without actually tracking the underlying separation between groups to answer the why question. E.g. Vaccinated/Unvaccinated, WhichVaccine, Obesity, Age, WhichVariant, and the related ICD codes for someone's health.
Furthermore, it is a travesty to attempt to draw huge conclusions (which many are now doing) from all cause mortality at a population level without that important separation of data elements.
Because Covid in many cases does not relate to seropositivity, cases is not an accurate count, as some people with strong immune systems will never be considered a "case" [1]
We need strong analyses around health, weight, obesity, and other comorbidities, not data mingled bungled studies that look only at outcomes with no relationship. Tragically, this study points out that perhaps we should incarcerate entire populations, because if we do that, we can drive down death rates.
Is it really a "caveat" when it entirely invalidates any attempt at causal analysis with this data? So much for "maximum truth". This is a nice data visualization exercise and descriptive analysis, but that's it.
There is plenty of other data that supports the assumption that the vast majority of deaths here is due to COVID. Not all causes of death here are equally plausible.
That might affect "deaths due to COVID-19" counts, but it does not, cannot, affect "excess deaths" counts, which don't include a cause of death. It is for this reason, among others, that people looking at global trends focus on "excess deaths."
but a vaccinated population in which a vaccine is driving up adverse events would likewise contribute to All Cause Mortality would fall into this trap.
Likewise, extreme draconian governmental responses that destroyed economies, hurt supply chains, and drove many to suicide and substance abuse would increase all cause mortality.
All Cause Mortality is best when comparing outcomes between different groups A/B, but not at entire population levels which is what this "analysis" does.
We must not conflate cause and effect by using this flawed method.
Which is why comparing countries which had strict lockdowns with countries which did not have strict lockdowns is helpful in showing that stricter lockdowns resulted in lower comparative excess deaths.
Instead of hypothetical scenarios in which suicide spikes and all-cause mortality rises, we have realities in which the opposite happened[0].
There is no collection of facts from the real world in which the vaccines are causing more harm than they prevent, and no collection of facts from the real world in which lockdowns didn't save lives where implemented. But nothing is likely to convince the true believers, so carry on.
> There is no collection of facts from the real world in which the vaccines are causing more harm than they prevent, and no collection of facts from the real world in which lockdowns didn't save lives where implemented. But nothing is likely to convince the true believers, so carry on.
Actually there is.
It is called VAERS, and the statistics on Absolute Risk Reduction.
> "The concept of risk, and our ability to assess risk, has also made the headlines in the context of the COVID-19 vaccine trials. Using data from a Nov 26 opinion piece in the British Medical Journal (BMJ), we can see that vaccine efficacy in terms of the relative reduction of the risk of getting ill is around 95%. For example, in the Pfizer trial, assuming an equal split of the 44,000 participants into the vaccine and placebo groups, 0.74% of the placebo group fell ill but only 0.04% of the vaccinated participants did. The relative risk reduction is calculated as the difference between these two incidences (0.7%) divided by the placebo value (0.74%), arriving at the conclusion that 95% of COVID-19 could be avoided if people got immunized. However, there is another way of looking that the same data: The risk reduction in absolute terms is only 0.7%, from an already very low risk of 0.74% to a minimal risk of 0.04%. Thus, risk reduction is 95%, but it also is just 0.7%." [1]
> "A critical appraisal of phase III clinical trial data for the Pfizer/BioNTech vaccine BNT162b2 and Moderna vaccine mRNA-1273 shows that absolute risk reduction measures are very much lower than the reported relative risk reduction measures. Yet, the manufacturers failed to report absolute risk reduction measures in publicly released documents. As well, the U.S FDA Advisory Committee (VRBPAC) did not follow FDA published guidelines for communicating risks and benefits to the public, and the committee failed to report absolute risk reduction measures in authorizing the BNT162b2 and mRNA-1273 vaccines for emergency use. Such examples of outcome reporting bias mislead and distort the public’s interpretation of COVID-19 mRNA vaccine efficacy and violate the ethical and legal obligations of informed consent."
Now, you may want to argue this for higher risk groups with comorbidities. What about Absolute Risk for teenage males where the risk of covid 19 is a fatality of 1 in hundreds of thousands to millions, but an MRNA vaccine carries a significantly higher chance of pericarditis/myocarditis, not to mention other AEs? What about athletes where the risk of a vaccine increases the chance the injection will not be intramusculuar, but instead, intravenous?
Some of the of the MRNA vaccines effect on the heart was not known at launch [2]
Anecdotally, I know two people that had severe adverse events to the vaccine, one almost died. The chances of that happening with a safe vaccine are miniscule. One of the aforementioned was likewise a healthy athlete with no comorbidities, in excellent health, of young age.
On the one hand, you have oddball blogs playing percentage games and arguing about semantics with "absolute" and "relative" in opposition with the history of how we've measured the value of vaccines for decades based on vaccine trials from 2021. This is not what I would consider the real world.
On the other hand we have the collection of facts from the real world I mentioned, as in this example of worldwide excess death counts by country. In this real world, the vaccines saved many lives, despite your anecdotes which definitely really happened.
Especially when you consider that countries with stricter lockdowns had considerably lower excess deaths. People just want to be contrarian about this for whatever reason and throw everything they can think of against the wall.
Seems like cancer and obesity are already quite well measured and robustly reported, though. Are there numbers to back up your hypothesis that people are dying more due to cancer and heart disease over the past two years?
Or, if you're just proposing that that "will" happen in the future, it seems like it's not really a refutation of the data in the linked article.
Does the magnitude explain excess deaths though? What this paper is saying is that rate of weight gain among already-obese kids was higher during the pandemic.
Does that rate correlate to covid death rates? Seems like a metric for childhood obesity isn't going to get there in any case, since these folks aren't dying at significant rates.
This kind of argument is exhausting. You said that excess deaths are confounded with obesity. I said that if they were, it would be comparatively easy to measure, so I suspect your hypothesis is simply wrong. Then you came back with a number that showed weight gain among kids, which I didn't try to refute.
Your hypothesis is a Big Thing. You're saying people are dying in very large numbers (millions a year), from existing syndroms that we have been tracking for decades. And... somehow epidemiologists are silent on that? It seems suspicious, you agree, right? Isn't the Occam's razor take is that you're simply wrong here, and that people are dying from a pandemic and not obesity?
> It does come with one major caveat: Because it counts ALL deaths, it cannot on its own disentangle deaths caused by Covid itself, impacts from the lockdowns themselves, impacts from vaccines, or unrelated death trends.
Especially as time goes on, and we see the effects of missed cancer screenings, economic destruction, increased obesity, etc, excess mortality will be almost entirely a measure of side effects of the pandemic response.