Moreover the best estimate from WHO is 10% infected WW. at 1.1M deaths plus a little lag, / 770M ... = ~0.2% IFR Much closer to Ioannidis meta than yours I suspect your aversion to blood donor & low prev, biases you towards the worse performing regions that are most studied
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Replying to @sangfroyd
That is incorrect. The WHO said the UPPER ESTIMATE for those infected is 10%, a more plausible reading is less than that I have no aversion to low prevalence studies, and indeed included many of them in my own meta-analysis
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Replying to @GidMK @sangfroyd
In our AGE STRATIFIED analysis, we excluded studies in which the confidence interval included 0% for age bands, because this produces a meaningless result (essentially, you get an upper bound of 100% IFR which is problematic), but that's not the same as disliking them
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Replying to @GidMK @sangfroyd
As for blood donors, I have laid out in detail why they are inappropriate to use as an estimate of population seroprevalence. This is not some kind of crazy, out-there point - we would not use blood donors to estimate the population prevalence of ANY disease precisely
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Replying to @GidMK @sangfroyd
Important to note - this does not make blood donor studies USELESS. They are great for sentinel surveillance to monitor and track trends. But we can actually demonstrate numerically that they are inadequate to estimate population prevalence of COVID-19
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Replying to @GidMK
Didn’t say crazy. Just somewhat subjective & biases / weights you towards larger studies that tend to gravitate toward heavier hit / high IFR regions Several of your peers disagree, including Ioannidis. You may be right, you may be wrong. But its extreme to call it an error
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Replying to @sangfroyd
Not at all. I think it's quite uncontroversial to say inferring directly from a biased sample to population prevalence is an error. Ioannidis justifies this by arguing that the bias will favor a higher IFR, but as I've demonstrated that is an incorrect assumption
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Replying to @GidMK
I don’t think you’ve demonstrated that at all. Two examples is not conclusive proof. Perhaps look to past pandemics for larger samples of data. It passed peer review and the WHO — which doesn’t mean much anymore. But certainly means “uncontroversial“ is a stretch.
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Replying to @sangfroyd
Not at all. This is fairly basic epidemiology, of the sort you get in a first-year course. Those two samples were elucidative - I have a dozen or so more, but the thread was already quite long. Some reading if you're interested on the question of biashttps://pubmed.ncbi.nlm.nih.gov/22742910/
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Replying to @GidMK
It would seem Ioannidis, the peer review team, and the WHO disagree with you. Perhaps you should explain this to them.
Make sure to reiterate to the chair of Stanford Epidemiology
that this is a basic mistake.2 replies 0 retweets 0 likes
It is rather odd to see such basic mistakes published in a journal, and I am indeed very confused that a researcher of his calibre was the one who made them. Bizarre indeed
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Replying to @GidMK
As basic as selecting a 4-5 wk post measurement window to capture 95% of fatalities related to the sero sample while also pulling in 80% of fatalities from infections that occurred in the 4-5 wks post measurement? Thereby inflating fatalities from inf not captured in the sample?
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Replying to @sangfroyd
Not at all, that was extremely carefully done and not at all basic. We explain why it was necessary in the paper
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End of conversation
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