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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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 …
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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
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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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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
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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
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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 …
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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!
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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)
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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
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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%
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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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