A thought I've been having - while we should acknowledge that #LongCovid is a real issue and impacts a large proportion of people, we should be careful with the exact numbers we are reporting
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Some of my research shows this for chronic disease - lots of people stop using apps even if they are beneficial, and this pattern is non-random https://www.jmir.org/2020/9/e20283/
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What we'd need to really understand the prevalence of
#LongCovid in populations is to enroll a large group of people - including asymptomatic cases and people who don't have COVID - and follow them over time to see what happensShow this thread -
We'd also need to try and get data from pretty much everyone at every time point, because it's likely that those who drop out are different to those who don't in ways that may be all but impossible to measure
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Unfortunately, that's really hard to do, so we are left to interpret the observational data that gives us, at best, a biased view of the situation
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Whenever you see a proportion being proposed (i.e. 1 in 20 people have Long COVID) it's important to understand the denominator Is it everyone? Only those in one study? Who did that study enroll?
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Important note - it's possible I've missed a large prospective study that does answer this question, I've tried to look but please do let me know if one has been published
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Important note #2 - this bias could easily bias the results IN BOTH DIRECTIONS, meaning that there could be less or far more
#LongCovid than reported. There are reasonable arguments both ways!Show this thread
End of conversation
New conversation -
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This data is one of the worst sources for the reasons in this thread. Since December it has been replaced by a study by the Office of National Statistics that did exactly what you suggested. The new ONS study found 10% are still ill at 12 weeks, compared to the 1-2% in the app.https://twitter.com/ahandvanish/status/1313973286364229638 …
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