Really worrying information on false positives and negatives: "if an LFIA is applied to a population with a...prevalence of 10%, for every 1000 people tested, 31...will be incorrectly told they are immune, and 34...will be incorrectly told that they were never infected."
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The review also found that the evidence used to generate sensitivity/specificity was woefully inadequate and at high risk of biaspic.twitter.com/ldugtRdp0Y
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This is really bad for serological surveys (many of which have used these point-of-care tests) Raises the question about infection-fatality estimates, particularly those based on populations with low rates of infection
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Sorry, forgot to link. Study is here:https://www.bmj.com/content/370/bmj.m2516 …
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The problem for many serosurveys is that they used ELISA tests for IgG. According to this study, the pooled estimate for specificity of these tests is 98.9% and sensitivity of 80.6%
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Now in a population with a 10% prevalence, that's pretty bad. In 1000 people, you miss 20 true positives and get 10 false positives, so you underestimate prevalence substantially
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Conversely, if you have 1% prevalence, you miss 2 true positives but get 11 false ones, so you overestimate the prevalence of COVID infections enormously This is a big problem!
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Now, some serosurveys have corrected for issues like this, but these new results suggest that commercial tests are less reliable and that their figures are more likely to be wrong That's a worry
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Also, apologies, the initial tweet was incorrect - this SR/MA was for ALL serology tests for COVID-19 not just POC ones
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Replying to @GidMK
Does this significantly change your views on the likely IFR? Or your confidence in those views?
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Views no, confidence maybe. I've thought that the IFR is likely an underestimate for some time, but this makes that more likely
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