One can over-estimate seroprevalence (+ thus under-estimate IFR) by measuring seroprevalence in a sample that does not represent the general population, and then extrapolating that sample to the general population.
- he could just wait for representative sampling in less hard hit areas
- areas often looked less hard hit because they under-estimated COVID-19 deaths, so including them under-estimates IFR
It's 0.31% IFR is unreliable anyway since, for example, the studies for Santa Clara, New York (both), + Chelsea used non-representative sampling. Miami-Dade was wrong.
There are at least three approaches to dealing with areas lacking representative samples:
1) exclude those areas + wait for data 2) use regions with representative samples to extrapolate over 3) include non-representative samples from those areas
#3 is worst because it extrapolates from inaccurate samples, under-estimating IFR. Yet that's what Ioannidis chooses to do + uses Bobrovitz for.
#1 makes sense; that's what "Meyerowitz-Katz" (@GidMK) did. But if you must have data for policy or planning, #2 can work.
24/J
And now in his discussion section, Ioannidis turns to the core point.
I'll spend a few tweets on this because this is *the* central pillar of his position, and is how he's been misleading millions of people for over a year.
Suppose you want to know what proportion of people in a city like dogs.
You could survey people in 1 building.
By luck the percentage you get might match the percentage you would get for the city overall. But you didn't design the survey to make that more likely.
26/J
The same point applies to seroprevalence studies.
Non-representative sampling might *luckily* get results that match the overall population. But representative sampling is *designed* to be more likely to match the population.
Scientists know methods that get representative samples that are more likely to match the general population; they applied them to diseases before COVID-19.
Ioannidis discards those methods, + relies on non-representative sampling luckily matching.
So his "[n]o consensus" claim is misleading. There's an evidence-based consensus (outside of Ioannidis) that those samples could *luckily* match, but are not designed to + are thus less likely to.
And "non-participating invitees" are less likely to be infected, so Ioannidis was wrong. We don't the response rate for his Santa Clara study, since he has no targeted sample.
Most infected people increase antibody levels. In the general population that antibody increase persists for ≥6 months in most people, besides with some assays like Abbott.
Levin et al. focused on studies where death numbers were not accelerating after the seroprevalence study. That helped mitigate over-estimation of deaths link.springer.com/article/10.100…
Now, some people might wonder why I spent so much time explaining factual errors in this paper.
Well, it's wrong and needs correction. But there's another reason:
It's the most unprofessional + disingenuous peer-reviewed paper I've ever read.
40/J
Re: "It's the most unprofessional + disingenuous peer-reviewed paper I've ever read."
If you don't believe me on that, then read Appendix 1 on page 38, and ask yourself if you've *ever* seen this in a scientific paper from a competent scientist:
And unsurprisingly, Ioannidis extends his evidence-free ideologically-motivated smears to Ferguson et al.'s team at Imperial College, without ever admitting they did not over-estimate IFR.
So for Ioannidis' defenders, that's what you're left with:
An ideologue who makes mathematically impossible claims that suit his ideology, and names Twitter accounts in his paper since he can't rebut their critiques.
Found the PubPeer thread on Ioannidis' paper, for those who want to defend it or critique it (I don't post on PubPeer, nor do I have an account there):
@luckytran In which Bhattacharya does the intellectual equivalent of claiming vaccine denialists are being unfairly persecuted because Andrew Wakefield's blog told him so
"What they're doing is focused protection, and you can see the result. The infection rates are going up in Sweden, but the death rates are not." edhub.ama-assn.org/jn-learning/vi…