4 ms·

This page only has death rates, not number of cases for each group, so it only tells half of the story. See Table 1 of the actual paper for complete stats (sour

• dredmorbius 7y ago
  And screencapped image: https://imgur.com/a/Ve5gT3O https://imgur.com/a/Ve5gT3O (posted by me)
  • citilife 7y ago
    Seems like the larger correlation in deaths is related to societal status - farmers, laborers, and retirees have much higher death rates.
    • oefrha 7y ago
      Farmers — likely crappy rural healthcare. Retirees — obviously age-skewed.
      • dredmorbius 7y ago
        Quite possibly also: arriving sicker. Hypothesis: If you're a rural / non-urban farmer, under quarantine and travel restrictions, you don't go to a hospital unless you're already very sick. One of the real challenges here is in separating data artefacts from ground truth. Testing, surveillance, self-presentation, doctor-lobbying (by the patient or family) can all influence that. This makes all data already somewhat suspect, before any overt manipulations are considered (and makes those manipulations all the more toxic to a full understanding). And of course, there are numerous other factors. Farmers may well have poorer overall health (nutrition, behavioural risk factors (smoking, drink), preexisting conditions, latent or opportunistic infection, etc., etc.). All of which I'm very openly speculating on -- these are hypotheses which would have to be demonstrated through clinical assessment or systematic observation. Note that "systematic" and "complete" aren't the same -- if you are following a defensible sampling method, then 100% coverage isn't necessary. But you'd want to capture samples from other populations. The overall prevalence of COVID-19 within even Wuhan City and Hubei Province is still low, based on assessed cases (clinical symptoms or the more accurate PCR genetic screen for the SARS-nCoV-2 virus), still under 1%. Which means that population sampling would be unlikely to detect many cases without something on the order of many hundreds, possibly thousands, of samples. You're looking for an accurate central-tendency descriptor of a ~1:100 event. Knowing what the actual exposure rate has been would be useful. Whether it's sufficiently useful to come up with a comprehensive large-scale sampling methodology is another question. Note that it's not merely the sampling size, but the selection and successful measurement from each of the selected individuals for sampling which is necessary. (I've some statistics background, and some healthcare background. Enough to know basics of sampling, error, and bias. Epidemiological public health monitoring is beyond my pay grade however.)
  • AndrewThrowaway 7y ago
    What would explain kids being so immune? ~1000 confined cases under 19 year old while you have 10 000 50-59 year olds?
    • bart_spoon 7y ago
      This is the aspect of the disease that is so interesting to me. With many diseases, like the flu, the most at risk are the elderly and the very young. But barely 1% of infections in China are children younger than 10, and 0 deaths.
      • AndrewThrowaway 7y ago
        Can it be that smoking plays big part in this? As this also explains gender statistics.
        • oefrha 7y ago
          An early preprint from the team of China’s leading expert Zhong Nanshan tagged smokers and non-smokers among the 1099 cases they analyzed. 85.4% supposedly never smoked, with 14.5% of those cases severe. So it’s not like non-smokers are very much immune. https://www.medrxiv.org/content/10.1101/2020.02.06.20020974v1 https://www.medrxiv.org/content/10.1101/2020.02.06.20020974v... https://www.medrxiv.org/content/10.1101/2020.02.06.20020974v1.full.pdf https://www.medrxiv.org/content/10.1101/2020.02.06.20020974v...
          • AndrewThrowaway 7y ago
            I would say that it really dismisses smoking as a big factor then.
            • oefrha 7y ago
              The percentage of severe cases among smokers is definitely higher, although the sample size is small.
            • 7y ago