10/n Google even points this out in the explainer for mobility data. Most people already spend 12+ of their 24 hours a day at home, so the "residential" category is the LEAST LIKELY to show any increase/decreasespic.twitter.com/nTEo19B0gP
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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10/n Google even points this out in the explainer for mobility data. Most people already spend 12+ of their 24 hours a day at home, so the "residential" category is the LEAST LIKELY to show any increase/decreasespic.twitter.com/nTEo19B0gP
11/n It makes sense when you remember that Google mobility data tracks CHANGES, not absolute figures. So 50% of the population working from home 100% would reduce office mobility by 50%, but only increase residential by a fraction of that amount
12/n For example, here is the "residential" vs "workplace" mobility data for the state of Victoria in Australia during their mammoth lockdown. "Residential" never goes above a 25% increase, but "workplace" decreases FAR morepic.twitter.com/iUfR2QGHcz
13/n What this means is that by comparing "residential" mobility, you are the most likely to find no difference by default. This is called a bias towards the null, and it's not ideal
14/n Furthermore, remember my asterisk from above? Yeh, turns out that it's really hard to find out what "residential" actually means, how it's calculated, or what the raw figures are based on, presumably because this is proprietary Google analysis
15/n So the conclusions about staying at home make no sense at all. "Residential" mobility data might not have been different between places, but for all we know that's a meaningless measure anyway that has very little to do with how much people stay at homepic.twitter.com/6BZyfOXqj7
16/n On top of that, this study suffers from the same drawbacks that most ecological trials do. To their credit, the authors acknowledge this in the discussion, but it certainly hasn't filtered through to the publicpic.twitter.com/XMeDYJanin
17/n Limitations inherent in this sort of research are many and varied, but as one example it's hard to make any realistic inferences about individuals staying at home when your unit of study is Spain vs the United States of America
18/n Even within Australia, which was included, the massive Victorian outbreak/lockdown skew the figures enormously, because one state with 1/4 of the population locked down while the rest of the country opened up
19/n We might actually expect null findings from an ecological trial of this sort, because at the country level heterogeneity in local policy irons out a lot of the impact
20/n It's also worth noting that the study literally does not address the question of whether government orders influenced COVID-19 deaths. Even if you ignore all the other issues, "residential" mobility data simply can't answer that question!
21/n There are many reasons that people stay at home, and given the opacity of "residential" data it's hard to say much about the results other than that this is a hard question that we may never answer well
22/n That being said, the idea that this study disproves staying at home as a driver of COVID-19 mortality is obviously wrong - at best, it is an example of how difficult answering that question can be
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