5 ms·

Caveat from the paper: "This study had several limitations. First, our sampling strategy selected for members of Santa Clara County with access to Facebook and

by cuchoi 6y ago

Caveat from the paper: "This study had several limitations. First, our sampling strategy selected for members of Santa Clara County with access to Facebook and a car to attend drive-through testing sites. This resulted in an over-representation of white women between the ages of 19 and 64, and an under-representation of Hispanic and Asian populations, relative to our community. Those imbalances were partly addressed by weighting our sample population by zip code, race, and sex to match the county. We did not account for age imbalance in our sample, and could not ascertain representativeness of SARS-CoV-2 antibodies in homeless populations. Other biases, such as bias favoring individuals in good health capable of attending our testing sites, or bias favoring those with prior COVID-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain."

deleted 6y ago
[deleted]
cameldrv 6y ago
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 6y 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 6y 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 6y 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 6y 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.

by cuchoi 6y ago

deleted 6y ago

[deleted]

cameldrv 6y ago

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 6y 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 6y 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 6y 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 6y 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.