3 ms·

Yes, and look at the way they corrected for this. They rebalanced for demographics, and that doubled their estimate. This is exactly the opposite of what they

Yes, and look at the way they corrected for this. They rebalanced for demographics, and that doubled their estimate. This is exactly the opposite of what they should have done. For example, zip codes farther from the testing sites were less likely to get tested, and more likely to be positive. What you see is that certain groups were less likely to go get tested, UNLESS they had a good reason to think that they were positive. This should cause them to want to underweight the data from the underrepresented groups, but instead they overweighted it. You can almost say that there were two populations that they were testing: White women who lived near a testing site who thought "what the hell, I'm bored with this quarantine so might as well get my finger pricked", and people who thought "I wonder if that thing I had a few weeks ago was COVID." The number you want is the prevalence from the bored white women. Bored white women might be somewhat skewed from other demographics, but probably not by that much. People who thought they might have had it is a massively skewed group. Effectively their correction removes the bored white women near a testing site, and only counts the people who have reason to think they had COVID.

guenthert 7y ago
With the average adult getting two bouts of common cold each year and mild COVID symptoms resembling a common cold, I'd think a large fraction of the population might think they had it. I wouldn't dear to guess in which way this shifts the estimate. I think it's understood, that with such a small sample size, results are to be taken cautiously. You have to start somewhere though.
- cameldrv 7y ago
  There was another serological survey of blood donors in Castiglione d'Adda in Italy. That one came up 70% positive. That town had 1.4% of its population die in March. That suggests an IFR of 2%, not counting people who were sick and might die from COVID in April.
  - usaar333 7y ago
    We do need to be careful talking about "IFR" as with a disease with such high age-dependence demographic skews heavily alter population IFR. Castiglione d'Adda has more people over 70 than Santa Clara County has over 65; population over 65 is about 60% higher relatively. Population under 18 conversely is about 22% lower. Hard to do the math exactly, but if you simply switch 8.1% of your population from being children to being 80+, you raise IFR by 0.6% per the Imperial College China estimates. Combined with the hospital triaging Italy was doing, I don't think sub-1% IFRs in Santa Clara county are improbable. (though I do think this survey's claims are improbably low) sources: https://www.citypopulation.de/php/italy-localities-lombardia.php?cityid=09801410001 https://www.citypopulation.de/php/italy-localities-lombardia... https://www.census.gov/quickfacts/santaclaracountycalifornia https://www.census.gov/quickfacts/santaclaracountycalifornia https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext https://www.thelancet.com/journals/laninf/article/PIIS1473-3...
    - cameldrv 7y ago
      I agree that you have to make an age adjustment for IFR, and your numbers seem reasonable. However, in the U.S. you also have a lot more diabetes, obesity, and high blood pressure, which are major risk factors. I wouldn't be surprised if that required a bigger adjustment than the age difference.
      - usaar333 7y ago
        Agreed that US as a whole can have a high IFR. Santa Clara county though is one of the healthiest places in the US. Life expectancy of 84 exceeds Italy in fact.
- cactus2093 7y ago
  Given that testing is still limited and you have to really make a case for yourself to get it, the group of people who have been tested (not for the antibodies, for infection) is at least a lower bound on the people who think they had it. And statewide we have been at around 12% positives, so it's reasonable to say at least 88% of people who think they had it didn't.
- SubiculumCode 7y ago
  3,330 tests isn't generally considered a small sample
irq11 7y ago
That is just nonsense. You don’t adjust for demographic bias in a sample by conditioning on a second parameter. There’s no universe in which underweighting the underweighted group makes any sense at all. Moreover, there isn any support for the idea that “bored white women” are more or less likely to get tested for symptomatic illness than anyone else. Your entire argument boils down to “I don’t like the implications of their adjustment, and this other one would have produced results more consistent with my theories.”
- cameldrv 7y ago
  Not in this case. It's basically Simpson's paradox. Honestly I think the study is basically useless, because instead of a random sample, it's a sample of people who, when they saw an ad on Facebook for a free Coronavirus test, decided to sign up, and then actually show up for the test. The point is that the higher your (voluntary) participation rate is within a group, the less chance you have of selection bias. This goes up to the limit of 100% participation, where you have no selection bias. What we see in this study is that groups with a lower participation rate (Men, nonwhites, people farther from a testing site) have a higher positivity rate. That is exactly the selection bias we would expect. They then proceed to "correct" the data by undercounting the group (white women that live near a testing site) that has the least selection bias.
  - SubiculumCode 7y ago
    3,300 people signed up for this study and completed this within weeks. Have you recruited for a study before? That kind of participation is hard to achieve...practically unheard of, unless you have widespread interest and time: Something which a whole lot of people have right now given layoffs, fear, and the bit of monetary compensation for participation, I bet sampling was much wider and less biased than you think.
    - cameldrv 7y ago
      Yeah but most studies aren't testing for a virus that has everyone locked in their houses causing a global pandemic.
deleted 7y ago
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guenthert 7y ago

With the average adult getting two bouts of common cold each year and mild COVID symptoms resembling a common cold, I'd think a large fraction of the population might think they had it. I wouldn't dear to guess in which way this shifts the estimate. I think it's understood, that with such a small sample size, results are to be taken cautiously. You have to start somewhere though.

cameldrv 7y ago
There was another serological survey of blood donors in Castiglione d'Adda in Italy. That one came up 70% positive. That town had 1.4% of its population die in March. That suggests an IFR of 2%, not counting people who were sick and might die from COVID in April.
- usaar333 7y ago
  We do need to be careful talking about "IFR" as with a disease with such high age-dependence demographic skews heavily alter population IFR. Castiglione d'Adda has more people over 70 than Santa Clara County has over 65; population over 65 is about 60% higher relatively. Population under 18 conversely is about 22% lower. Hard to do the math exactly, but if you simply switch 8.1% of your population from being children to being 80+, you raise IFR by 0.6% per the Imperial College China estimates. Combined with the hospital triaging Italy was doing, I don't think sub-1% IFRs in Santa Clara county are improbable. (though I do think this survey's claims are improbably low) sources: https://www.citypopulation.de/php/italy-localities-lombardia.php?cityid=09801410001 https://www.citypopulation.de/php/italy-localities-lombardia... https://www.census.gov/quickfacts/santaclaracountycalifornia https://www.census.gov/quickfacts/santaclaracountycalifornia https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext https://www.thelancet.com/journals/laninf/article/PIIS1473-3...
  - cameldrv 7y ago
    I agree that you have to make an age adjustment for IFR, and your numbers seem reasonable. However, in the U.S. you also have a lot more diabetes, obesity, and high blood pressure, which are major risk factors. I wouldn't be surprised if that required a bigger adjustment than the age difference.
    - usaar333 7y ago
      Agreed that US as a whole can have a high IFR. Santa Clara county though is one of the healthiest places in the US. Life expectancy of 84 exceeds Italy in fact.
cactus2093 7y ago
Given that testing is still limited and you have to really make a case for yourself to get it, the group of people who have been tested (not for the antibodies, for infection) is at least a lower bound on the people who think they had it. And statewide we have been at around 12% positives, so it's reasonable to say at least 88% of people who think they had it didn't.
SubiculumCode 7y ago
3,330 tests isn't generally considered a small sample

irq11 7y ago

That is just nonsense. You don’t adjust for demographic bias in a sample by conditioning on a second parameter. There’s no universe in which underweighting the underweighted group makes any sense at all. Moreover, there isn any support for the idea that “bored white women” are more or less likely to get tested for symptomatic illness than anyone else. Your entire argument boils down to “I don’t like the implications of their adjustment, and this other one would have produced results more consistent with my theories.”

cameldrv 7y ago
Not in this case. It's basically Simpson's paradox. Honestly I think the study is basically useless, because instead of a random sample, it's a sample of people who, when they saw an ad on Facebook for a free Coronavirus test, decided to sign up, and then actually show up for the test. The point is that the higher your (voluntary) participation rate is within a group, the less chance you have of selection bias. This goes up to the limit of 100% participation, where you have no selection bias. What we see in this study is that groups with a lower participation rate (Men, nonwhites, people farther from a testing site) have a higher positivity rate. That is exactly the selection bias we would expect. They then proceed to "correct" the data by undercounting the group (white women that live near a testing site) that has the least selection bias.
- SubiculumCode 7y ago
  3,300 people signed up for this study and completed this within weeks. Have you recruited for a study before? That kind of participation is hard to achieve...practically unheard of, unless you have widespread interest and time: Something which a whole lot of people have right now given layoffs, fear, and the bit of monetary compensation for participation, I bet sampling was much wider and less biased than you think.
  - cameldrv 7y ago
    Yeah but most studies aren't testing for a virus that has everyone locked in their houses causing a global pandemic.

deleted 7y ago

[deleted]