It was always predictable that COVID-19 denialism would morph into anti-vaccine advocacy because it was never about public health, it was always about attacking government measures
The Great Barrington Declaration was sponsored by an organisation that promotes tobacco smoking, denies global warming, and lies about asbestos. There's a reason no serious public health scientists signed it!
If your entire philosophy is predicated on reactionary outrage over any government intervention it's pretty much a given that you'd move on to being anti-vaccine when safe+effective vaccines came out for COVID
I understand having philosophical disagreements with government interventions, but the point is that none of this was ever actually about how many people died or suffered, and that is being thrown into stark relief now that vaccines are out
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@VPrasadMDMPH@CT_Bergstrom My favorite part of this is that we can actually do a fairly basic empirical test of whether the idea that twitter royalty is required to be a FB fact-checker is true, or whether it's simply a correlation due to pandemic expertise by looking at pre-pandemic follower counts
@VPrasadMDMPH@CT_Bergstrom Of the people quoted for the healthfeedback piece, the median number of twitter followers was 4,514, with two people having well below 1,000 prior to COVID-19. The mean is skewed up to 35k by Topol
2/n Paper is here, it's a pretty simple ecological study comparing countries on their deaths/million from COVID-19 and Google mobility data nature.com/articles/s4159…
3/n The authors modelled the impact of time spent in "residential" areas as shown by Google against number of COVID-19 deaths in different areas, and in most cases found that there was no significant explanatory power for this model
My first degree was a double major in psychology and the philosophy of science, and I love this stuff. The idea that science is some fixed system breaks down remarkably easy
One brilliant exercise - take a list of fields and classify them into science, pseudoscience, and not science
This is usually fairly easy!
Now, try and describe ~why~ things fall into the categories they do
In patients where a clinical cause was able to be identified, the vast majority of cases were clearly deaths caused by COVID-19, which means that this is likely a true undercount of the deaths
Worryingly, only 1 in 7 of the children who died of COVID-19 had been tested for it beforehand
Also, this was a beautiful thing to see while reading the study. "We made a mistake at the start so we fixed it but here's all the data so you can tell for yourself" is absolutely the right thing to do when reporting on your trial outcomes!
I suspect when I do a formal risk of bias score for the study it will come out looking fantastic simply from this one thing. Researchers who are entirely open about their methods are the ones who publish the best studies!