5/n Instead, what we appear to have here is an opaque search methodology, little information on how inclusion/exclusion criteria were applied (and no real justification for those criteria)pic.twitter.com/35EYmKMBFE
Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him
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5/n Instead, what we appear to have here is an opaque search methodology, little information on how inclusion/exclusion criteria were applied (and no real justification for those criteria)pic.twitter.com/35EYmKMBFE
6/n For example, seroprevalence studies including healthcare workers were excluded, because the samples are biased, but studies including blood donors were not, even though these are arguably even more biased That's a strange inconsistency
7/n Studies only described in the media were excluded, but this appears to have included government reports as well Again, there's no justification for this and it is REALLY WEIRD to exclude government reports (they're doing most of the testing!)
8/n Moving on, the study then calculated an inferred IFR, if the authors hadn't already done so. The calculation is crude, but not entirely wrong However, there's an issue - the estimates were then 'adjusted'
9/n Spefically, the IFR estimates were cut by 10-20% depending on whether they included different antibody tests or not I had a look at the reference here, and it definitely doesn't support such a blanket judgementpic.twitter.com/AYzDRR5suk
10/n Ok, so, on to the results This table is basically the crux of the review. 12 included studies, with "corrected" IFR ranging from 0.02-0.4% MUCH lower than most published estimatespic.twitter.com/hDyd9YQgOL
11/n A colleague and I did a systematic review and meta-analysis of published estimates of IFR and came to an aggregated estimate of 0.74% (0.51-0.97%) so this is a bit of a surprise to me https://www.medrxiv.org/content/10.1101/2020.05.03.20089854v2.article-metrics … What's happening here?
12/n Looking at this table, there are some things that immediately spring out Firstly, three of these studies are of blood donorspic.twitter.com/5wyn4wPbXD
13/n It is pretty easy to see why these studies aren't actually estimates of IFR - blood donors are by definition healthy, young etc, and so any IFR calculated from these populations is going to be MUCH lower than the true figurepic.twitter.com/EaJZlygIRc
Slightly puzzled here. Why are blood donors not representative of seroprevalence? In a very weak paper, this at least doesn’t seem to be a weakness.
Pretty simple - they're heavily selected by default. You can't donate blood if you have one of a variety of diseases, or are over/under certain ages, for example
But what effect do those criteria have on seroprevalence?
Not on seroprevalence - on extrapolating to a population IFR
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