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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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
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This would help convince me that long COVID it not just hypochondria or fatigue cause by chronic stress and isolation.
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Why do you think that hypochondria is the most likely reason for a decent percentage of covid patients reporting issues months on?
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@ONS is actually following positively tested. Which has many asymptomatics. Maybe there is a bias as a proportion of asymptomatics won't get tested.
https://www.gov.uk/government/publications/ons-update-on-long-covid-19-prevalence-estimate-1-february-2021 …Thanks. Twitter will use this to make your timeline better. UndoUndo
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