The study is really interesting. They used an existing representative sample of people aged >30 to estimate the population prevalence of antibodies to SARS-CoV-2pic.twitter.com/lKl4qrSkdO
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The study is really interesting. They used an existing representative sample of people aged >30 to estimate the population prevalence of antibodies to SARS-CoV-2pic.twitter.com/lKl4qrSkdO
They then added a second group. These were people who had not previously signed up to the existing cohort, but were eligiblepic.twitter.com/1YvLwHtX5H
In Group 1, 0.97% of people had antibodies to the virus In Group 2, this doubled to 1.94%pic.twitter.com/ov81bnbRkG
Even more interesting, this difference did not disappear even when adjusting for age, sex, or reported past symptoms of COVID-19 The only major difference between the groups? Thinking you'd been exposed to COVID-19 in the pastpic.twitter.com/vChuKScITB
Two take-homes: 1. Selection bias is a big problem 2. Adjusting for demographics and symptoms may not be adequate to correct for this bias
What this means is that if you recruit people to a seroprevalence study in a biased way (say, by telling them that they can go back to normal life if they get a positive result), you might end up with a massively inflated estimate
This is important in IFR calculations If we used the representative sample, we get an IFR of ~0.8% Using the biased sample, it's halved to ~0.4% Big difference!
Thanks @MikeDeeeeeee for pointing out the research
Re: "In Group 1, 0.97% of people had antibodies to the virus
In Group 2, this doubled to 1.94%"
Should I flex?
Am I going to flex?...
...Yes. Yes, I am.
https://twitter.com/AtomsksSanakan/status/1351992222313574401 …
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