12/n Limitations are a bit more numerous, but two in particular I wanted to highlight: - Hard to dissociate some interventions - Some interventions not includedpic.twitter.com/eov5fAzfpc
Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him
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12/n Limitations are a bit more numerous, but two in particular I wanted to highlight: - Hard to dissociate some interventions - Some interventions not includedpic.twitter.com/eov5fAzfpc
13/n In particular, they found that while mask regulations appeared to have minimal benefit, it was hard to dissociate mask mandates from other interventions, so this might not be indicative of a lack of efficacy
14/n Indeed, many places only implemented these mandates after other regulations, so it could be that the MARGINAL (i.e. additional) benefit of masks on top of other social distancing regulations was small, but that by themselves the benefits could be largerpic.twitter.com/fMMJ5Bwize
15/n Now, as ever it is hard to infer causal conclusions from studies like this (correlation=/=causation) BUT This is a careful, well-thought-out attempt to define the benefits associated with each intervention
16/n For example, I'd say a reasonable conclusion is that the marginal benefit of stay-at-home orders on top of other interventions is probably pretty small Conversely, the benefit associated with closing universities is probably pretty big
17/n I should also note that I am not an expert in Bayesian statistical methods, so I might've missed something important in terms of limitations of the models used
Ignore the Bayesian aspect for a moment and consider whether they've addressed the limitations of other studies, e.g. outcome classification and timing of interventions, which are nonrandom I.e. masks usual came later? Maybe so, but doesn't seem like it from your review.
I think they've made a very good effort, and for everything except masks they report some ability to analyse these things separately. Masks, because often adopted late, appear to be the biggest outlier
I think in general, the outcome classification for this study is probably the strongest I've seen. Using cases and deaths to infer Reff at a timepoint gives you a pretty good, standardized outcome that is not as impacted by variations in testing and similar
Can you explain why covid-cases and deaths as used here avoids the differential testing issue? Not doubting, curious about the reasoning. Shouldn't all cause be less prone to such biases? Shouldn't we also expect diff efficacy depending on demos affected at the time of adoption?
To me, it's mostly about consistency. Testing changes over time, but less quickly than case numbers. Incorporating deaths into this - which usually don't change substantially over time - should give a much more stable estimate of Reff imo
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