2/n You can find the study here https://www.who.int/bulletin/online_first/BLT.20.265892.pdf … And my previous threads on it here https://twitter.com/GidMK/status/1283232023402868737?s=20 …https://twitter.com/GidMK/status/1262956011872280577?s=20 …
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2/n You can find the study here https://www.who.int/bulletin/online_first/BLT.20.265892.pdf … And my previous threads on it here https://twitter.com/GidMK/status/1283232023402868737?s=20 …https://twitter.com/GidMK/status/1262956011872280577?s=20 …
3/n I should say at the outset here - the only personal comment I would like to make about Professor Ioannidis is that he is a very smart man who I respect tremendously I will, however, examine the paper, because I think that is what science is all about
4/n At first glance, and indeed on deeper reading, it is clear that very little has changed from my previous looks into the paper
5/n The methodology is still the same, and the eventual conclusion remains that the median IFR of COVID-19 is 0.27% (originally he estimated 0.26%)pic.twitter.com/kBhIWCEUAL
6/n The author then concludes that the IFR "tended to be much lower than estimates made earlier in the pandemic", which is odd because his own estimates made earlier in the pandemic (in May) were...lowerpic.twitter.com/7awIA4gEoW
7/n Indeed, as we can easily see, the resulting low IFR is simply a consequence of the low quality of the review itself and has very little to do with when the estimates were made
8/n For example, the review does not adhere to PRISMA guidelines (the most basic recommendations for reviews of this kind) which is very strange given that Prof Ioannidis himself is a co-author on the original PRISMA statementpic.twitter.com/fgwRh67bkL
9/n This has lead to a problematic situation, where there is no rating for study quality, publication bias, and indeed little consideration in the manuscript for how the quality of the published evidence might impact the review
10/n As we pointed out in our systematic review and meta-analysis of COVID-19 IFR, this is an issue because higher-quality studies tend to show a lower seroprevalence and thus a higher IFR https://www.sciencedirect.com/science/article/pii/S1201971220321809 …pic.twitter.com/Jf2TJH3GVP
11/n (Interestingly, Ioannidis cites our study but gets the numbers wrong, in what is distressingly something of a trend in the paper generally - we actually estimated 0.68% in the published paper which came out recently)pic.twitter.com/63z3Mw61uS
12/n We can see the issue with non-adherence to PRISMA in the methods section. These are clearly not the search terms used, as entering them into these databases results in 100,000s of resultspic.twitter.com/hElvwBf2rj
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
14/n And there are new errors as well. In this study of blood donors in Rhode Island, the authors estimate a seropositivity of 0.6%, while the review paper has 3.9% insteadpic.twitter.com/MvF6e9Psgd
15/n But by far and away, the biggest error in the text is simply to do with using clearly inappropriate samples to estimate population prevalence This is a fundamental flaw in the paper, and really something of a basic epidemiological mistake
16/n Some of these studies are just so clearly inappropriate to infer a population estimate that it doesn't really require explaining. Samples of a single business in a city, or inpatient dialysis unitspic.twitter.com/CTWnw0Fbjf
18/n Then we have blood donors, who again may give an erroneous result. These are people who, DURING A PANDEMIC are happy to go out and about and give blood. It is quite possible that they are MORE likely to have been infected than the general population!pic.twitter.com/0ME5x1br1B
18.5/n There are also a lot of included studies from places in which there is almost certainly an enormous undercount of deaths For example, India, where the official death counts may represent a substantial underestimatehttps://www.bmj.com/content/370/bmj.m2859 …
19/n A very basic, reasonable thing to do would be to conduct a sensitivity analysis excluding these biased estimates, to see what happens when you only use representative population estimates Which we can do
20/n If we take the median of only these somewhat good-quality studies (some of them still aren't great, but at least they're not clearly inappropriate), we get a value of 0.5% Double the estimate of 0.27%pic.twitter.com/tQSICfngT9
21/n I thought at this point I'd briefly look at blood donor studies, because they are an interesting case study The author argues that these should be included because, due to "healthy volunteer bias", at worse any estimate should bias the IFR results upwardspic.twitter.com/zfMeDdKKgn
22/n Well, we can now actually test this theory and see if it is true. Enough studies have been done that we have COVID-19 seroprevalence estimates from BOTH blood donor studies AND representative samples and compare them
23/n For example, in England an ongoing study on blood donors by PHE estimates that 8.5% of the population has developed antibodies to COVID-19 However, the ONS with their massive randomized study puts the figure at 6% insteadpic.twitter.com/ocnYbQoazk
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 …
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