13/n There are also still clear numeric errors remaining from previous versions of the study. For example, this number from a paper looking at people going to hospital in New York should read 44%, and not 47%pic.twitter.com/Q2lUREo20f
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24/n In Denmark, a robust population estimate put the figure at 1.1%, while their blood donor study estimates 1.9% have been infected previouslypic.twitter.com/BT1eqgjtuT
25/n Indeed, in every location where both a non-probabilistic, convenience sample has been taken (not just blood donors) AS WELL AS a well-done population estimate, the convenience sample overestimates the seroprevalence
26/n We have a new paper that we're working on that suggests that using such estimates will usually overstate the true seroprevalence by a factor of about 2x Which means the true IFR would be double the number computed from such studies
27/n There are also still, after many revisions, studies that have been excluded inappropriately from the estimates This study from Italy, for example, which produces an estimate of 7% (!) for IFR in the regionpic.twitter.com/41OGhRzSoN
29/n Similarly, there are numerous country-wide efforts not looked at in any way, such as the large population studies conducted in Italy (150,000 participants) and Portugal (2,300 participants)pic.twitter.com/Ur07B0QRpM
30/n And while there is a very brief discussion of the variation in IFR by region, the main component (age) - as we have demonstrated - was barely addressed, with the author instead focusing on vague speculation about healthcare systemshttps://www.medrxiv.org/content/10.1101/2020.07.23.20160895v6 …
31/n We can actually see how age of those infected impacts IFR quite neatly from some of the studies in this review Qatar (0.01%) and Spain (1.15%) look very different, right?pic.twitter.com/kwAOJ4QzCX
32/n Wrong! In fact, the difference here is entirely explained by age! In Qatar, infections have mostly been limited to the immigrant worker population (<40 years), with this group representing more than 50% of infectionshttps://twitter.com/GidMK/status/1300938689535565824?s=20 …
33/n Since this group is at a very low risk of death from COVID-19, the population IFR is MUCH lower than in Spain, where infections among the elderly have been much more common
34/n All of these errors are a shame, because to a certain extent I agree with the author IFR is NOT a fixed category. In the metaregression linked above in the thread, we demonstrated that ~90% of variation in IFR between regions was probably due to the age of those infected!pic.twitter.com/fj0k5oyW2B
35/n Unfortunately, Prof Ioannidis appears not to have read this study, but if you are interested here is the preprint version to perusehttps://www.medrxiv.org/content/10.1101/2020.07.23.20160895v6 …
36/n Anyway, there are numerous errors remaining in the text that I haven't pointed out, but if you've reached this far in the thread I'm sure you're tired of me telling them to you straight up. Have a really careful look and see if you can find them!
37/n (As a start, there is now a representative population estimate from Wuhan out that implies an IFR SUBSTANTIALLY lower than the ones inferred in this paper from samples including hospitalized patients)
38/n Regardless, the main take-home remains, unfortunately, that this paper is overtly wrong in a number of ways, it does not adhere to even the most basic guidelines for this type of research, and thus the point estimate is probably wrong
39/n Sorry, typo in tweet 37 - should read an IFR SUBSTANTIALLY *higher*, not lower. The SEROPREVALENCE is lower (at ~2%) which implies an IFR of ~1.2%
40/n Oh, on an unrelated sidenote, it's quite funny that the author spends some time arguing that using a median is more appropriate than doing a R-E meta-analysis (as @LeaMerone and I did), so I quickly calculated the median for our study and it is higher at 0.79% for IFR
pic.twitter.com/QTkJKNzMnb
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