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GidMK's profile
Health Nerd
Health Nerd
Health Nerd
Verified account
@GidMK

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Health NerdVerified account

@GidMK

Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him

Sydney, New South Wales
theguardian.com/profile/gideon…
Joined November 2015

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    Health Nerd‏Verified account @GidMK 20 Sep 2020

    Lots of people have asked the question: why is COVID-19 more fatal in one place than another? Our paper largely answers this question - it is mostly explained by age! https://www.medrxiv.org/content/10.1101/2020.07.23.20160895v4 …pic.twitter.com/BmlUYUEMp1

    2:59 PM - 20 Sep 2020
    • 93 Retweets
    • 198 Likes
    • Noise aka Noise Reduction Anonymous Sea Cow Gordon:Covid grapher, data hunter-gatherer onur the warm embrace of an asteroid impact fipco HP Sauce FVM Clark H
    20 replies 93 retweets 198 likes
      1. New conversation
      2. Health Nerd‏Verified account @GidMK 20 Sep 2020

        For example, in the US: Utah has the lowest IFR in the country, with our estimate putting it at exactly 0.5% Indiana has a much higher IFR, at roughly 1.1%pic.twitter.com/PuG8VpL3Tu

        2 replies 2 retweets 14 likes
        Show this thread
      3. Health Nerd‏Verified account @GidMK 20 Sep 2020

        But this is LARGELY explained by differences in the age breakdown of infections - in Utah ~50% of all infections were in people <45yo when we ran our analysis compared to 40% in Indianapic.twitter.com/luJwPXFMGh

        1 reply 1 retweet 17 likes
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      4. Health Nerd‏Verified account @GidMK 20 Sep 2020

        This might seem like a small difference, but even relatively minor changes in the age breakdown of infections can have an outsized impact

        1 reply 1 retweet 21 likes
        Show this thread
      5. Health Nerd‏Verified account @GidMK 20 Sep 2020

        This does not mean that no other explanations are possible, but correcting for age explains 90% of the difference between IFR in different areas, which is pretty significant!

        2 replies 2 retweets 31 likes
        Show this thread
      6. Health Nerd‏Verified account @GidMK 20 Sep 2020

        Also worth noting - given that there are very few longitudinal samples, we cannot exclude the possibility that other things are impacting IFR more recently (i.e. better treatments)

        1 reply 1 retweet 19 likes
        Show this thread
      7. Health Nerd‏Verified account @GidMK 20 Sep 2020

        One interesting consequence of the exponential gradient is that you can't divide people into "old" and "young" easily! There's no easy delineation between 'low' and 'high' risk, it's a spectrum

        1 reply 0 retweets 19 likes
        Show this thread
      8. End of conversation
      1. New conversation
      2. Centrist  🤝Madness‏ @CentristMadness 20 Sep 2020
        Replying to @GidMK

        Africa?

        3 replies 0 retweets 1 like
      3. Health Nerd‏Verified account @GidMK 20 Sep 2020
        Replying to @CentristMadness

        We limited our analysis to seroprevalence studies conducted in OECD countries, but as far as I've seen this does explain a lot of the low death rate in Africa (although that's also a more complex question)

        1 reply 1 retweet 4 likes
      4. Show replies
      1. daveminyo‏ @daveminyo 20 Sep 2020
        Replying to @GidMK

        So on that basis, could one say, ironically..the areas with highest COVID19 mortality are the 'healthiest' (nebulous term klaxon) areas, as they have populations that manage to live to ripe old ages in the first place? Or am I making zero sense there? (wouldn't be first time! 🤣)

        0 replies 0 retweets 2 likes
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      1. New conversation
      2. Dr Emma Beckett‏ @synapse101 20 Sep 2020
        Replying to @GidMK

        I note Japan isn't on there, it wouldn't fit that line.

        1 reply 0 retweets 1 like
      3. Health Nerd‏Verified account @GidMK 20 Sep 2020
        Replying to @synapse101

        It does, kinda! The problem with Japan is that the only good serosurvey found so few infections that the confidence interval for seroprevalence included 0 (0.1, 0-0.3%) so we couldn't include in the metaregression

        1 reply 0 retweets 16 likes
      4. Show replies

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