> For everybody or for some people? What is the causal chain?

The study it is based on is from Al Aly [1], who is using a dataset of (mostly elderly, unhealthy) veterans who regularly seek hospital care from the US government hospital system (VA).

It is an older, sicker population, and no, the results aren't generalizable. Moreover, his methods are dubious. I've dug into one of Al Aly's other paper's downthread [2], and the comments cross-apply. He has a bunch of papers using the same dataset and core methods, and they're all pretty bad, but the press loves them. Eric Topol is either ignorant of their problems, doesn't care, or a little of both.

As an editorial aside, Eric Topol really isn't a reliable source of information. He's pretty much on par with Eric Feigl-Ding at this point. He's someone who has surrendered all former scientific credibility to sensationalism. Even if you 100% believe every word in these papers, unless you acknowledge that they're a study of older, sick people, you're sensationalizing the results and misleading the listener.

[1] https://www.nature.com/articles/s41591-022-01689-3

[2] https://news.ycombinator.com/item?id=32671383

I only have a MSc, but with a more biomedical focus. I disagree with your overall impression and contextualization (even if you may be right on some of the finer points). I don’t agree with your assessment of Topol’s credibility, or on that of the relevance and quality of the Nature article.

You're obviously entitled to your opinion, but "I don't agree" is not a rebuttal to the specific claims I'm making about Al Aly's work. I'm not asking anyone to believe me because I have a certain degree; I'm pointing out real limitations and problems with the research.

For example, there's absolutely nothing stopping Al Aly from using a real, consistent control (e.g. influenza, or even just the same negative control) instead of a grab-bag of different illnesses. The reason he switches them with every paper is probably because his synthetic weighting scheme produces inconvenient results from experiment to experiment when the same control is used. But we can only speculate, because we don't have the data. Remember: he's re-weighting his covariates using these controls! This is a real, objective problem.

My opinions about Topol are my own, which is why I said they're editorial, but I'm tired of the guy's constant doom-and-gloom messaging and inability/unwillingness to critically appraise data. Even in the most charitable, rational take on Al Aly's work, you can't casually extrapolate it to the entire population the way that Topol does.

I know that, I just: 1) didn't have the energy to expend explaining in great detail why I thought you were wrong, and; 2) believed you were sufficiently credible and also incorrect that it was important enough for other people to voice their disagreement.

Lazy? Guilty as charged.

Every significant disease can have longstanding impacts. Damage is often cumulative. Actual causation can come via many routes. One person might have diminished physical fitness due to weeks/months laid up with the disease. Another might have impact from various drugs/treatments administered during the disease. Others might come away with diminished organ functionality (heart/kidneys etc) that in turn stresses other systems. The "what doesn't kill you" mantra is just that. It isn't based in science. It is almost always better to avoid getting sick in the first place.

> What is the causal chain?

More data is needed, but it's looking like a key component of it is micro-clots: https://www.nature.com/articles/d41586-022-02286-7

Off the cuff, it sounds like a reference to an assessment of a risk-adjusted population as a whole relative to pre-COVID baseline.

COVID-19 does massive damage generally.