14 ms·

Covid-19 Antibody Seroprevalence in Santa Clara County, California

122 points by buboard 6y ago

chris_va 6y ago
With only 30 people in the negative control group, the confidence interval on the false positive rate would easily swamp out the results of the study for 1.5% positive sample ratio, no? Seems kind of useless unless your control group is a lot larger, and the false positive rate can be shown to be <<1.5%.
vikramkr 6y ago
That is certainly alarming that it is so much more widespread than expected - it really highlights the failure of our controls against the virus and the importance of early testing. It I'd good news that this implies the death rate is less severe. However, a caveat is that antibody tests might have high rates of false positives, which would be a particularly large problem when very few people are actually positive. So read this with a grain of salt. The paper isn't peer reviewed yet etc.
- oldgradstudent 6y ago
  On the contrary, this is not alarming at all. It would point out that actual Covid-19 mortality could be close to that of seasonal flu, as it tells us the number of people infected is 50X-85X than the number of confirmed cases. Assuming the data is reliable, of course.
  - buboard 6y ago
    the authors infer an IFR of 0.12 - 0.2% which is smaller than other antibody studies (like the one in germany which inferred 0.37% IIRC). While it s indeed small, it reflects the current situation, not what will happen 2 weeks down the road, plus the sensitivity of their antibody testing is not precisely known yet.
- makomk 6y ago
  Most signs so far suggest that the virus is basically uncontainable, short of everyone on the planet taking aggressive measures to stop it escaping China back in January or December.
  - zaroth 6y ago
    That was back when China was claiming it didn’t transmit between humans, and ordering labs to destroy their samples. https://www.nationalreview.com/the-morning-jolt/chinas-devastating-lies/ https://www.nationalreview.com/the-morning-jolt/chinas-devas...
    - SpicyLemonZest 6y ago
      Right, that's why people say the Chinese government's lies (and the WHO's acceptance of them) are at fault. The rest of the world certainly should have reacted sooner, ramping up PPE production and researching treatments and trying to slow it down. But the idea of complete containment, forming widespread green zones where the virus is not merely suppressed but eradicated, probably became impossible as early as February. (Some island or basically-island countries appear to be making it work, but it's not clear what their long-term plan is - are they just going to enact sakoku for the next 18 months?)
      - zaroth 6y ago
        Well and I say this not even to indite China. Maybe they really and truly believed at the time there wasn’t human transmission. Maybe a virus originating in France or the US would have played out mostly the same way. (I doubt it, but that’s irrelevant) My point was just that with the information we had in December, there was no way politically to enact the extraordinary (and extraordinarily damaging) measures that would have been required to prevent an epidemic. Not only was it politically untenable (Trump was only acquitted on February 5th!) but it was simply scientifically unsupported and unjustifiable at that time. IMO even with hindsight being 20/20 I can’t say given the information we had on January 1 that we could or should have done any differently than the travel ban on January 31st, which itself was extraordinary. Everything post January 31st however is an absolute boondoggle of the highest degree. As far as PPE and testing capacity, I believe those are both systemic issues which needed to be dealt with years ago. Good luck with the US trying to procure a billion masks from China right at the onset of their own health crisis — imagine how that would play out with 1,000s of deaths in Wuhan and 0 known cases in the US. We would have been accused of hoarding and causing the spread in China. The testing issue was mainly an easily foreseeable and preventable regulatory problem at the FDA which has existed for some time. It took too long to remove the regulatory hurdles, but that there was not already a pandemic regulatory response framework ready to activate is totally inexcusable since we already went through this in 2009.
- tshadley 6y ago
  Note coauthor name "John Ioannidis". That gives the paper a healthy boost in my view.
  - projektfu 6y ago
    Careful, though, because just being a critic doesn't mean he isn't susceptible to the same sorts of biases and issues that cause a lot of other medical papers to fail. I'm hoping he's on the paper because they ran it by him for a thumbs-up before submitting it.
    - danieltillett 6y ago
      I hope he is not on the paper for that reason as this is basically honorary authorship. If he just reviewed the paper before submission then he should be in the acknowledgements.
h2odragon 6y ago
I think the numbers will coma out to show this ripped through the USA in February; we slammed the barn doors closed well after the horse was gone. Of course that's not the narrative that fits agendas so just like with "why cant we test people?" back then, there will be many official reasons not to look for antibody data and to doubt what data gets gathered.
- pwned1 6y ago
  Based on the difference between CA and NY, I'd be willing to bet that it got here earlier, maybe in December.
  - greedo 6y ago
    You would have seen more reports in hospitals of interstitial pneumonia, as well as a higher YoY death rate during January.
    - hedora 6y ago
      The CDC is reporting an abnormally high pneumonia death rate in January, and it can’t be explained by confirmed flu cases: https://www.cdc.gov/flu/weekly/#S6 https://www.cdc.gov/flu/weekly/#S6 Look carefully at the black and red graph after the phrase “mortality surveillance data”.
      - greedo 6y ago
        The graph and text clearly state it refers to pneumonia and influenza, if you refer to the legend where it says Percent P&I. The P&I for the first 8 weeks of the year is clearly within normal ranges. It spikes from baseline in the first week of March continuing on to the present day. If you look at the first set of graphs, you'll see a spike around the same time for influenza tests. I don't know how fast pneumonia occurs in people weakened by influenza, but I would expect some delay. I think the spike in both influenza and in death due to influenza and pneumonia is probably due to testing. If you presented with influenza like symptoms in March, you were definitely given a flu test to rule out influenza.
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
DennisP 6y ago
The problem with every one of these studies I've seen is that they come up with around 3% of the population having antibodies, while a typical false-positive rate for these tests is also around 3%. In this case they claim they adjust for the specificity (basically false-positive rate), but I didn't see that they reported what the specificity was. If the mortality really is very low, then it should be easy to prove. Just go to NYC, do some random testing, and show us an infection rate that far exceeds the false-positive rate.
- CubsFan1060 6y ago
  This is certainly not a study. And certainly shouldn't be taken as good data of any kind, but it is interesting: https://www.livescience.com/coronavirus-in-pregnant-woman-high-nyc.html https://www.livescience.com/coronavirus-in-pregnant-woman-hi... Sure as hell isn't random (all pregnant women... so all women for one. All at the same hospital is another). "Between March 22 and April 4, those hospitals screened 215 pregnant women for SARS-CoV-2 (the virus that causes COVID-19), and 33 women, or 15%, tested positive. Of these who tested positive, 29 women — or nearly 14% — showed no symptoms."
  - eightysixfour 6y ago
    Anyone know if there's been a follow-up to see if the women eventually developed symptoms?
- deleted 6y ago
  [deleted]
sxp 6y ago
There are similar high infection rates from other semi-randomized samples. https://www.nejm.org/doi/full/10.1056/NEJMc2009316 https://www.nejm.org/doi/full/10.1056/NEJMc2009316 > Between March 22 and April 4, 2020, a total of 215 pregnant women delivered infants at the New York–Presbyterian Allen Hospital and Columbia University Irving Medical Center . All the women were screened on admission for symptoms of Covid-19. Four women (1.9%) had fever or other symptoms of Covid-19 on admission, and all 4 women tested positive for SARS-CoV-2 (Figure 1). Of the 211 women without symptoms, all were afebrile on admission. Nasopharyngeal swabs were obtained from 210 of the 211 women (99.5%) who did not have symptoms of Covid-19; of these women, 29 (13.7%) were positive for SARS-CoV-2. Thus, 29 of the 33 patients who were positive for SARS-CoV-2 at admission (87.9%) had no symptoms of Covid-19 at presentation.
- dmitriy_ko 6y ago
  So 15% infection rate among pregnant women. I would assume pregnant women would be more careful than average about avoiding getting infected. So overall infection rate in New York City must be even higher.
  - dragonwriter 6y ago
    > So 15% infection rate among pregnant women. I would assume pregnant women would be more careful than average about avoiding getting infected. Pregnant women would seem to be much less able to avoid certain activities that involve transmission (like visiting medical facilities and associated close contact with people who also work in hospitals with inadequate and improvised PPE), so that’s probably not a great assumption.
- usaar333 6y ago
  NYC has 30x the per capita death rate of Santa Clara county. Santa Clara county only being 15% as infected is not consistent.
  - Fjolsvith 6y ago
    What if there are other, undiscovered factors that could explain the difference? Such as NYC populace having had exposure to some other virus that predisposed them to a greater vulnerability. Or perhaps, air pollution exposure predisposing them to greater vulnerability.
fspeech 6y ago
There could be a meaningful distinction between exposure and infection. I would define the first as anyone who was exposed to the virus (or even just parts of the virus) and generated antibodies. The second would be an actual infection and presumably a sufficient antibody response to repel the virus and confer immunity to another infection. Really you can generate antibodies without even getting exposed to live virus, just like you can develop allergy to pollen or other foreign objects that enter your body. I would say only the second case is meaningful for public health. For that you need to look at how people do convalescent plasma. You need to test for specific antibodies that actually confer immunity.
mgreg 6y ago
There's also some data coming out of Wuhan around antibody testing. The methodology is not as well formed as the study in the OP but there is still value in the data. > Wuhan’s Zhongnan Hospital found that 2.4% of its employees and 2% to 3% of recent patients and other visitors, including people tested before returning to work, had developed antibodies, according to senior doctors there. Additional antibody testing is also underway in Wuhan. Source: https://www.wsj.com/articles/wuhan-starts-testing-to-determine-level-of-immunity-from-coronavirus-11587039175 https://www.wsj.com/articles/wuhan-starts-testing-to-determi...
- usaar333 6y ago
  It is far more credible that Wuhan hit 3% infection rate (that's about double the PCR testing rate of the evacuees) than Santa Clara county, with an order of magnitude lower death count, having a 2% rate.
- BryanBigs 6y ago
  Except for people bored out of their minds, wanting to get away from their kids or spouse for an hour, etc. Also, there is an altruistic motivation - it feels like you are doing SOMETHING to help by being in this study.
gryson 6y ago
"We recruited participants by placing targeted advertisements on Facebook aimed at residents of Santa Clara County..." I wonder if these ads mentioned the purpose of the study, or if that information was only given out after initial contact (I don't know enough about IRB requirements here). A concern is that this could cause a selection bias for people who suspected they had the virus. People may respond to such an ad out of curiosity ("I think I had the virus, so it would be good to know for sure") or obligation ("I'm pretty sure I had the virus, so I should help out with this study"). It wouldn't have to be a large selection bias, either. Of the 3,330 people they tested, they found only 50 who tested positive. I would like to see a bit of a better method of sample selection before drawing any conclusions.
- tshadley 6y ago
  Why would this matter as long as they got a good geographical sampling? "We used Facebook to quickly reach a large number of county residents and because it allows for granular targeting by zip code and sociodemographic characteristics. We used a combination of two targeting strategies: ads aimed at a representative population of the county by zip code, and specially targeted ads to balance our sample for under-represented zip codes. In addition, we capped registrations from overrepresented areas." Update: Okay, "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."
  - rhema 6y ago
    If part of the value for participants was to know whether they had antibodies, then those that had gone through a flu (or covid) would be more likely to respond. This means that the overall sample likely includes more people who had COVID than a true random sampling would.
    - tshadley 6y ago
      It looks like there's an argument to be made it could overcount or undercount depending on importance of each potential bias. "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 overrepresentation 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."
standardUser 6y ago
Add this to growing pile of evidence that this coronavirus is more widespread than most testing to date would suggest. How much more widespread still appears very unclear. There is a major ramping up of antibody testing happening right now, and multiple new tests being introduced, so we should have a better idea very soon. But the takeaway should be that this is a good thing. Every unknown case we uncover decreases the overall rate of hospitalization and death, and potentially decreases the effective transmission rate (assuming most people who had the virus develop immunity and that the immunity lasts, which is also still unclear).
- rumanator 6y ago
  I don't follow your argument. If the epidemic is more widespread than initially thought, why do you assume that the effective transmission rate would potentially decrease? If anything, the transmission rate was higher than assumed and, due to the higher number of infections and how they spread exponentially,it would only ramp up. And additionally, there are reports of reinfections among covid19 patients who were found to be cured.
  - not2b 6y ago
    There's good news and bad news: the good news is the more cases that are found from people who didn't know they had it, the less deadly the disease is. The bad news: if it's all over the place it will be really hard to stamp it out.
    - war1025 6y ago
      I assume it's endemic at this point and we just need to learn how to live with it. Society can brace for a storm, but at some point you need to adapt and learn to live with the weather.
  - timr 6y ago
    "If the epidemic is more widespread than initially thought, why do you assume that the effective transmission rate would potentially decrease?" Herd immunity. If testing is only detecting 1/50th of cases in NYC, for example, it means that there have been about 6M cases, or about 69% of NYC's population. That's basically the threshold where we'd expect to see the infection counts level off naturally.
tgb 6y ago
I made this comment the last time a serological testing study came out: they have 30 negative controls and claim 1.5% positives among their tests. So the number of controls is insufficient to rule out this being entirely false positives. You have to then rely on the test manufacturer's claims of false positives rates, which comes from 371 negative controls. They say: > our estimates of specificity are 99.5% (95 CI 98.1-99.9%) and 100% (95 CI 90.5-100%) Look at the those confidence intervals, even the narrower one (from the manufacturer's data)! The bottom is a four-fold increase in false positives, compared to the point-estimate, and is greater than the total positives they had in their finding.
- tom-thistime 6y ago
  This (I think?). I am not a doctor or biologist. Anyone who knows more than me, which is not a high bar, please step in and correct me. Based on quick amateur reading of the paper, the rate of crude positives (1.5%) might be entirely real cases, entirely false positives, or anywhere in between. The real rate of infection could be anywhere from 0% to 5%. The authors report around 3,500 real tests and 30 known-negative tests. We need 30,000 real data points (or 300 known-negative tests) to conclude anything. (Right?) I am not criticizing the experts who conducted the study. Thank you for doing the study. I'm only trying to understand what the study reveals. It may reveal that the group needs 10x more funding right away.
- ajross 6y ago
  To repeat you in plainer English: the serum test isn't infallible and reports "infected", incorrectly, for uninfected patients. It does this at some rate that is very near, or perhaps larger than, the fraction of "true" infected patients they report in their results. Teasing out data from all that noise requires that the false positive error rate be measured very accurately. And they didn't do that, so really this doesn't tell us much. Edit: Alternatively, borrowing jargon from a more common field around here: the measured infection rate of ~3% is very close to the noise floor of the experiment. It might be that, or it might be near zero, and we can't tell the difference. This study is very good evidence that the infection fraction is not much larger, however. We can easily rule out high infection rates like the 30% numbers that seems to get thrown around.
beamatronic 6y ago
“These prevalence estimates represent a range between 48,000 and 81,000 people infected in Santa Clara County by early April, 50-85-fold more than the number of confirmed cases. Conclusions The population prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that the infection is much more widespread than indicated by the number of confirmed cases.”
georgewfraser 6y ago
There are now multiple sources of evidence pointing to a very low true infection fatality rate, as low as 0.1% in some regions. Iceland has been testing a semi-random sample using PCR since early in the epidemic, and there have been several small antibody surveys in “hot spots” showing infection rates over 15%. You can also use CDC surveillance of flu-like illnesses to estimate the total number of infected nationwide; this morning I posted an analysis of this data estimating only 1 in 40 cases were detected in March, consistent with the Stanford results: https://fivetran.com/blog/covid-19-count https://fivetran.com/blog/covid-19-count
- projektfu 6y ago
  It may have a low overall fatality rate, yet it seems to have a high rate of hospitalization in clinical cases.
  - BearOso 6y ago
    And it’s more contagious than the flu, so even if the fatality rate is identical, more people are at risk.
  - grandmczeb 6y ago
    > it seems to have a high rate of hospitalization in clinical cases. The raw hospitalization rate is inflated because both treatment and testing are mostly limited to very severe cases in places where there's a lot of confirmed cases (e.g. NYC.)
    - projektfu 6y ago
      Still, comparing it to influenza... we dont see this many severe cases typically.
      - grandmczeb 6y ago
        Its pretty obvious this is worse than the flu. I don't think anyone is really disputing that at this point.
microdrum 6y ago
This is very strong evidence that the "realists" like Alex Berensen were correct. The shutdowns probably should end, while encouraging at-risk populations (mostly retired) to stay at home.
- cdelsolar 6y ago
  nope.
  - microdrum 6y ago
    Yep. You've got nothing. CFR is low, and this has been knowable for a long time. You're scared. It's OK. It's illogical and anti-scientific, but it's OK. But you won't be able to hurt ordinary people any longer. Here's Germany's imputed CFR: https://spectator.us/covid-antibody-test-german-town-shows-15-percent-infection-rate/ https://spectator.us/covid-antibody-test-german-town-shows-1... Here's Denmark: https://www.dr.dk/nyheder/indland/doedelighed-skal-formentlig-taelles-i-promiller-danske-blodproever-kaster-nyt-lys https://www.dr.dk/nyheder/indland/doedelighed-skal-formentli... Here's Iceland: https://reason.com/2020/04/03/what-we-should-have-learned-from-icelands-response-to-covid-19/ https://reason.com/2020/04/03/what-we-should-have-learned-fr... More studies coming next week from LA.
    - DanBC 6y ago
      Why quote Denmark but ignore Sweden? Sweden are doing what you're calling for, and they have pretty high numbers of deaths.
      - thu2111 6y ago
        It's not "pretty high", it's middle of the pack and nowhere near the apocalypse that was supposed to be the outcome of their chosen path.
        DanBC 6y ago
        Deaths per 1m population in Sweden is currently 139. For Denmark it's 58, and for Norway it's 30.
BearOso 6y ago
In our small town, I know of 3 people who came into direct contact with the first confirmed case, exhibited symptoms, but were refused testing. I can definitely believe their guess that the number of cases is an order of magnitude greater than the number confirmed.
ludwigschubert 6y ago
Great to see actual testing on a representative population sample, though I’m not sure what the consequences ought to be: > These prevalence estimates represent a range between 48,000 and 81,000 people infected in Santa Clara County by early April, 50-85-fold more than the number of confirmed cases.
- devit 6y ago
  So with 69 deaths so far and 9 deaths last week in Santa Clara County, projecting that to an extra 4 weeks to 105 deaths for those infected in early April, that means a 0.16% death rate?
  - femto113 6y ago
    Lots of reasons I don't think you can extrapolate meaningful death rates from this yet. One big one is that time from infection to death covers a very wide range: in Wuhan time from hospitalization to death had both a mean and a standard deviation of about 2 weeks, and time from infection to hospitalization isn't well known yet, but is likely at least a week. Add to that the fact that some cases are diagnosed post-mortem and you might not know about all of the fatalities for early April cases until well into May.
terramars 6y ago
my friend probably had the virus (2 inconclusive tests) and tried to get into this study. some group asked her for $125 to get tested. i'm not sure if she saw any of the facebook ads. so, grain of salt about representativeness of the sample. there's for sure a response bias towards people who think they had it.
- jhwang5 6y ago
  Just curious, did your friend have pretty severe symptoms? As in, was it worse perceivably than say a case of bad flu?
- nradov 6y ago
  The Stanford study was free. They didn't charge $125.
opportune 6y ago
I don't see anything about sampling bias here. I would assume that people who have been ill, experienced mild symptoms, or who were exposed to confirmed cases would be more likely to respond to the facebook ads recruiting testing volunteers. I'm sure we are significantly undercounting cases but I highly doubt we are off by a magnitude of 50x.
- brink 6y ago
  I know. 50-85x is hard to believe. How could we be that far off?
  - jplayer01 6y ago
    It’s not hard to believe when you consider testing criteria.
  - gfodor 6y ago
    The # of cases reported that I believe they are comparing against is the number of confirmed positive tests, not the number of assumed cases in the county based on some other model that is trying to project # infected. I don't think anyone thought that we had 100% coverage where all cases were tested. So we're not "wrong" if such a study as this sees a gap between reported confirmed cases and expected infected, it just means we have a better understanding of the testing gap, which is self evident in existence but the magnitude of which we don't know. Given all the other factors, an order of magnitude or more gap between tests and sick people doesn't seem completely out of the question. I would be curious if there are other models using a different methodology which could help us get a handle on the conditional probability chain leading to tests being done or not on an individual, to see if there is a similar set of conclusions.
  - usaar333 6y ago
    It's impossible to believe. Currently, Santa Clara's crude CFR is 3.7%. NYC has similar demographics (and as bad nursing home hits?) -- 0.14% of the population has died from covid. That puts an upper bound of 26x (and that's if the entire population was infected)
erentz 6y ago
It’s hacky and I’m happy to be told how wrong it is. But based on these recent antibody studies in Germany, Finland, and now here in CA, I’ve been assuming an actual fatality rate of about 0.4, and thus an actual infected rate of 250x our known deaths, which is a much firmer number. This is only a small comfort since it means we may have had about 8.75 million infected and presumably now immune in the USA. Or about 2.6%. There’s still a long way to go in that case.
- all_usernames 6y ago
  It's known that the vast majority infected do not experience symptoms, or have mild symptoms. I don't think you can draw the conclusion that they had prior immunity.
  - erentz 6y ago
    Not concluding prior immunity but concluding that they now have immunity after having been infected. That is in progress towards herd immunity this number of people actually infected being revealed by these antibody studies is what is interesting. And based on my hacky heuristic described above that is currently around 2.6%. So we are still a long way off herd immunity. But at the same time closer than we thought we were.
    - ignoramous 6y ago
      > So we are still a long way off herd immunity. But at the same time closer than we thought we were. Yes and no. A disease is endemic when R0 x S = 1 [0]. R0 for SARS-CoV-2 is estimated to be 2.5 to 3.5 and some models predicting a much higher 5.7 [1]. The herd immunity threshold is given by (1 - S)%, which implies when R0 = 2.5, 60% of the population would need to be immune; similarly 71.42% and 82.45% for R0s, 3.5 and 5.7, respectively. [0] https://en.wikipedia.org/wiki/Endemic_(epidemiology) https://en.wikipedia.org/wiki/Endemic_(epidemiology) [1] https://news.ycombinator.com/item?id=22817942 https://news.ycombinator.com/item?id=22817942
      - 6y ago
Medicalidiot 6y ago
Antibody tests are not as accurate as RT-PCR, but the sensitivity that was observed here was 91% and 99% specificity when compared to confirmed COVID cases. I just want to point out that these researchers are using the corrects tests to do this.
- jacquesm 6y ago
  They use the correct tests, but they use them on a group that isn't even close to a random sample of the population, which is what you'd need to do to get to their conclusion.
  - Medicalidiot 6y ago
    I'm not going to comment on that because I have a basic understanding of public health and epidemiology on the diagnostic side of things. Why do you think that this isn't a "good" random sample?
    - mlyle 6y ago
      It's not a random sample; it's a convenience sample of people who responded to Facebook ads.
      - Medicalidiot 6y ago
        What about Facebook ads doesn't mean that they're an adequate sample? Is there a confounding factor that you have in mind?
        mlyle 6y ago
        Many confounding factors. Socioeconomic factors that can't be fixed with stratified sampling (access to transportation, usage of Facebook, etc.. conversely the willingness of a $10 amazon gift card to motivate people to participate will vary). People who are concerned about possible past exposure to COVID-19 may have a different probability to respond. Of course, the bigger issue is that we can't rule out that the antibody tests have a false positive rate that can explain the whole result. We need serology tests in places with a higher positive rate to know. Basically, 50 positive results out of 3330 is a really small signal. Just a small false positive rate or sampling issue could explain all the positive results.
virusduck 6y ago
I don't see anything in the methods about potential crossreactivity with NL63, OC43, or HKU1 coronaviruses. Presumably this was done by the company, but crossreactivity is an extremely important control when it comes to this type of test. It is common, and if it's not vetted thoroughly, you may be measuring something else completely.
tomerico 6y ago
Saw this comment [1] on Reddit that outlines the limitations of the study well: This is the most poorly-designed serosurvey we've seen yet, frankly. It advertised on Facebook asking for people who wanted antibody testing. This has an enormous potential effect on the sample - I'm so much more likely to take the time to get tested if I think it will benefit me, and It's most likely to benefit me if I'm more likely to have had COVID. An opt-in design with a low response rate has huge potential to bias results. Sample bias (in the other direction) is the reason that the NIH has not yet released serosurvey results from Washington: We’re cautious because blood donors are not a representative sample. They are asymptomatic, afebrile people [without a fever]. We have a “healthy donor effect.” The donor-based incidence data could lag behind population incidence by a month or 2 because of this bias. Presumably, they rightly fear that, with such a high level of uncertainty, bias could lead to bad policy and would negatively impact public health. I'm certain that these data are informing policy decisions at the national level, but they haven't released them out of an abundance of caution. Those conducting this study would have done well to adopt that same caution. If you read closely on the validation of the test, the study did barely any independent validation to determine specificity/sensitivity - only 30! pre-covid samples tested independently of the manufacturer. Given the performance of other commercial tests and the dependence of specificity on cross-reactivity + antibody prevalence in the population, this strikes me as extremely irresponsible. This paper elides the fact that other rigorous serosurveys are neither consistent with this level of underascertainment nor the IFR this paper proposes. Many of you are familiar with the Gangelt study, which I have criticized. Nevertheless, it is an order of magnitude more trustworthy than this paper (both insofar as it sampled a larger slice of the population and had a much much higher response rate). It also inferred a much higher fatality rate of 0.37%. IFR will, of course, vary from population to population, and so will ascertainment rate. Nevertheless, the range proposed here strains credibility, considering the study's flaws. 0.13% of NYC's population has already died, and the paths of other countries suggest a slow decline in daily deaths, not a quick one. Considering that herd immunity predicts transmission to stop at 50-70% prevalence, this is baldly inconsistent with this study's findings. For all of the above reasons, I hope people making personal and public health decisions wait for rigorous results from the NIH and other organizations and understand that skepticism of this result is warranted. I also hope that the media reports responsibly on this study and its limitations and speaks with other experts before doing so. [1] https://www.reddit.com/r/COVID19/comments/g32wjh/covid19_antibody_seroprevalence_in_santa_clara/fnotu78/ https://www.reddit.com/r/COVID19/comments/g32wjh/covid19_ant...
robotcookies 6y ago
"Participants were recruited using Facebook ads targeting a representative sample of the county by demographic and geographic characteristics." How is it representative when it's only Facebook users in this sample?
- ajross 6y ago
  In the south bay? Pretty good, unless you want to argue for a demographic skew because of Facebook's poor uptake among iOS users. I mean, no. It's not perfect. It will miss demographics like the elderly with lower social media use, but that group tends to be well-sampled already due to their risk profile. It will probably miss some immigrants too, which seems like a bigger problem. But really, it's a pandemic. It doesn't care about your socioeconomic status. One of its defining qualities is the extent to which it does not cluster in particular communities like more typical epidemics.
  - jboy55 6y ago
    They also were unable to remove any bias towards people self-selecting to be part of the test because they felt they had symptoms of COVID. The bay area has been in lock down since early March, its really hard to imagine someone participating if they didn't feel they had some symptoms and wanted to 'know for sure'.
- usaar333 6y ago
  Unless you think FB users for some reason would be infected at significantly different rates, that shouldn't be much of a problem. I'm much more concerned about selection bias toward prior sick people; IIRC, Stanford offered to report positive results to the patient.
  - geoelectric 6y ago
    There would potentially be an age bias, though probably less in FB than a lot of other services you could cherrypick from. It'd also arguably bias older, since FB has been aging up in audience from what I can tell. Without reading the study, though, possible they actually controlled that in the demographic profile for the ad. Edit: Going to the other thread confirmed this. 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."
lmlsna 6y ago
I suspected the asymptomatic rate to be high, but not by a factor of 50-85! That's very great news no?
member221 6y ago
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cbare 6y ago
This thread critiques this article in a way I find convincing: https://twitter.com/DiseaseEcology/status/1251225273871134721 https://twitter.com/DiseaseEcology/status/125122527387113472...

122 points by buboard 6y ago

With only 30 people in the negative control group, the confidence interval on the false positive rate would easily swamp out the results of the study for 1.5% positive sample ratio, no? Seems kind of useless unless your control group is a lot larger, and the false positive rate can be shown to be <<1.5%.

vikramkr 6y ago

That is certainly alarming that it is so much more widespread than expected - it really highlights the failure of our controls against the virus and the importance of early testing. It I'd good news that this implies the death rate is less severe. However, a caveat is that antibody tests might have high rates of false positives, which would be a particularly large problem when very few people are actually positive. So read this with a grain of salt. The paper isn't peer reviewed yet etc.

oldgradstudent 6y ago
On the contrary, this is not alarming at all. It would point out that actual Covid-19 mortality could be close to that of seasonal flu, as it tells us the number of people infected is 50X-85X than the number of confirmed cases. Assuming the data is reliable, of course.
- buboard 6y ago
  the authors infer an IFR of 0.12 - 0.2% which is smaller than other antibody studies (like the one in germany which inferred 0.37% IIRC). While it s indeed small, it reflects the current situation, not what will happen 2 weeks down the road, plus the sensitivity of their antibody testing is not precisely known yet.
makomk 6y ago
Most signs so far suggest that the virus is basically uncontainable, short of everyone on the planet taking aggressive measures to stop it escaping China back in January or December.
- zaroth 6y ago
  That was back when China was claiming it didn’t transmit between humans, and ordering labs to destroy their samples. https://www.nationalreview.com/the-morning-jolt/chinas-devastating-lies/ https://www.nationalreview.com/the-morning-jolt/chinas-devas...
  - SpicyLemonZest 6y ago
    Right, that's why people say the Chinese government's lies (and the WHO's acceptance of them) are at fault. The rest of the world certainly should have reacted sooner, ramping up PPE production and researching treatments and trying to slow it down. But the idea of complete containment, forming widespread green zones where the virus is not merely suppressed but eradicated, probably became impossible as early as February. (Some island or basically-island countries appear to be making it work, but it's not clear what their long-term plan is - are they just going to enact sakoku for the next 18 months?)
    - zaroth 6y ago
      Well and I say this not even to indite China. Maybe they really and truly believed at the time there wasn’t human transmission. Maybe a virus originating in France or the US would have played out mostly the same way. (I doubt it, but that’s irrelevant) My point was just that with the information we had in December, there was no way politically to enact the extraordinary (and extraordinarily damaging) measures that would have been required to prevent an epidemic. Not only was it politically untenable (Trump was only acquitted on February 5th!) but it was simply scientifically unsupported and unjustifiable at that time. IMO even with hindsight being 20/20 I can’t say given the information we had on January 1 that we could or should have done any differently than the travel ban on January 31st, which itself was extraordinary. Everything post January 31st however is an absolute boondoggle of the highest degree. As far as PPE and testing capacity, I believe those are both systemic issues which needed to be dealt with years ago. Good luck with the US trying to procure a billion masks from China right at the onset of their own health crisis — imagine how that would play out with 1,000s of deaths in Wuhan and 0 known cases in the US. We would have been accused of hoarding and causing the spread in China. The testing issue was mainly an easily foreseeable and preventable regulatory problem at the FDA which has existed for some time. It took too long to remove the regulatory hurdles, but that there was not already a pandemic regulatory response framework ready to activate is totally inexcusable since we already went through this in 2009.
tshadley 6y ago
Note coauthor name "John Ioannidis". That gives the paper a healthy boost in my view.
- projektfu 6y ago
  Careful, though, because just being a critic doesn't mean he isn't susceptible to the same sorts of biases and issues that cause a lot of other medical papers to fail. I'm hoping he's on the paper because they ran it by him for a thumbs-up before submitting it.
  - danieltillett 6y ago
    I hope he is not on the paper for that reason as this is basically honorary authorship. If he just reviewed the paper before submission then he should be in the acknowledgements.

h2odragon 6y ago

I think the numbers will coma out to show this ripped through the USA in February; we slammed the barn doors closed well after the horse was gone. Of course that's not the narrative that fits agendas so just like with "why cant we test people?" back then, there will be many official reasons not to look for antibody data and to doubt what data gets gathered.

pwned1 6y ago
Based on the difference between CA and NY, I'd be willing to bet that it got here earlier, maybe in December.
- greedo 6y ago
  You would have seen more reports in hospitals of interstitial pneumonia, as well as a higher YoY death rate during January.
  - hedora 6y ago
    The CDC is reporting an abnormally high pneumonia death rate in January, and it can’t be explained by confirmed flu cases: https://www.cdc.gov/flu/weekly/#S6 https://www.cdc.gov/flu/weekly/#S6 Look carefully at the black and red graph after the phrase “mortality surveillance data”.
    - greedo 6y ago
      The graph and text clearly state it refers to pneumonia and influenza, if you refer to the legend where it says Percent P&I. The P&I for the first 8 weeks of the year is clearly within normal ranges. It spikes from baseline in the first week of March continuing on to the present day. If you look at the first set of graphs, you'll see a spike around the same time for influenza tests. I don't know how fast pneumonia occurs in people weakened by influenza, but I would expect some delay. I think the spike in both influenza and in death due to influenza and pneumonia is probably due to testing. If you presented with influenza like symptoms in March, you were definitely given a flu test to rule out influenza.

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
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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

DennisP 6y ago

The problem with every one of these studies I've seen is that they come up with around 3% of the population having antibodies, while a typical false-positive rate for these tests is also around 3%. In this case they claim they adjust for the specificity (basically false-positive rate), but I didn't see that they reported what the specificity was. If the mortality really is very low, then it should be easy to prove. Just go to NYC, do some random testing, and show us an infection rate that far exceeds the false-positive rate.

CubsFan1060 6y ago
This is certainly not a study. And certainly shouldn't be taken as good data of any kind, but it is interesting: https://www.livescience.com/coronavirus-in-pregnant-woman-high-nyc.html https://www.livescience.com/coronavirus-in-pregnant-woman-hi... Sure as hell isn't random (all pregnant women... so all women for one. All at the same hospital is another). "Between March 22 and April 4, those hospitals screened 215 pregnant women for SARS-CoV-2 (the virus that causes COVID-19), and 33 women, or 15%, tested positive. Of these who tested positive, 29 women — or nearly 14% — showed no symptoms."
- eightysixfour 6y ago
  Anyone know if there's been a follow-up to see if the women eventually developed symptoms?
deleted 6y ago
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sxp 6y ago

There are similar high infection rates from other semi-randomized samples. https://www.nejm.org/doi/full/10.1056/NEJMc2009316 https://www.nejm.org/doi/full/10.1056/NEJMc2009316 > Between March 22 and April 4, 2020, a total of 215 pregnant women delivered infants at the New York–Presbyterian Allen Hospital and Columbia University Irving Medical Center . All the women were screened on admission for symptoms of Covid-19. Four women (1.9%) had fever or other symptoms of Covid-19 on admission, and all 4 women tested positive for SARS-CoV-2 (Figure 1). Of the 211 women without symptoms, all were afebrile on admission. Nasopharyngeal swabs were obtained from 210 of the 211 women (99.5%) who did not have symptoms of Covid-19; of these women, 29 (13.7%) were positive for SARS-CoV-2. Thus, 29 of the 33 patients who were positive for SARS-CoV-2 at admission (87.9%) had no symptoms of Covid-19 at presentation.

dmitriy_ko 6y ago
So 15% infection rate among pregnant women. I would assume pregnant women would be more careful than average about avoiding getting infected. So overall infection rate in New York City must be even higher.
- dragonwriter 6y ago
  > So 15% infection rate among pregnant women. I would assume pregnant women would be more careful than average about avoiding getting infected. Pregnant women would seem to be much less able to avoid certain activities that involve transmission (like visiting medical facilities and associated close contact with people who also work in hospitals with inadequate and improvised PPE), so that’s probably not a great assumption.
usaar333 6y ago
NYC has 30x the per capita death rate of Santa Clara county. Santa Clara county only being 15% as infected is not consistent.
- Fjolsvith 6y ago
  What if there are other, undiscovered factors that could explain the difference? Such as NYC populace having had exposure to some other virus that predisposed them to a greater vulnerability. Or perhaps, air pollution exposure predisposing them to greater vulnerability.

fspeech 6y ago

There could be a meaningful distinction between exposure and infection. I would define the first as anyone who was exposed to the virus (or even just parts of the virus) and generated antibodies. The second would be an actual infection and presumably a sufficient antibody response to repel the virus and confer immunity to another infection. Really you can generate antibodies without even getting exposed to live virus, just like you can develop allergy to pollen or other foreign objects that enter your body. I would say only the second case is meaningful for public health. For that you need to look at how people do convalescent plasma. You need to test for specific antibodies that actually confer immunity.

mgreg 6y ago

There's also some data coming out of Wuhan around antibody testing. The methodology is not as well formed as the study in the OP but there is still value in the data. > Wuhan’s Zhongnan Hospital found that 2.4% of its employees and 2% to 3% of recent patients and other visitors, including people tested before returning to work, had developed antibodies, according to senior doctors there. Additional antibody testing is also underway in Wuhan. Source: https://www.wsj.com/articles/wuhan-starts-testing-to-determine-level-of-immunity-from-coronavirus-11587039175 https://www.wsj.com/articles/wuhan-starts-testing-to-determi...

usaar333 6y ago
It is far more credible that Wuhan hit 3% infection rate (that's about double the PCR testing rate of the evacuees) than Santa Clara county, with an order of magnitude lower death count, having a 2% rate.
BryanBigs 6y ago
Except for people bored out of their minds, wanting to get away from their kids or spouse for an hour, etc. Also, there is an altruistic motivation - it feels like you are doing SOMETHING to help by being in this study.

gryson 6y ago

"We recruited participants by placing targeted advertisements on Facebook aimed at residents of Santa Clara County..." I wonder if these ads mentioned the purpose of the study, or if that information was only given out after initial contact (I don't know enough about IRB requirements here). A concern is that this could cause a selection bias for people who suspected they had the virus. People may respond to such an ad out of curiosity ("I think I had the virus, so it would be good to know for sure") or obligation ("I'm pretty sure I had the virus, so I should help out with this study"). It wouldn't have to be a large selection bias, either. Of the 3,330 people they tested, they found only 50 who tested positive. I would like to see a bit of a better method of sample selection before drawing any conclusions.

tshadley 6y ago
Why would this matter as long as they got a good geographical sampling? "We used Facebook to quickly reach a large number of county residents and because it allows for granular targeting by zip code and sociodemographic characteristics. We used a combination of two targeting strategies: ads aimed at a representative population of the county by zip code, and specially targeted ads to balance our sample for under-represented zip codes. In addition, we capped registrations from overrepresented areas." Update: Okay, "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."
- rhema 6y ago
  If part of the value for participants was to know whether they had antibodies, then those that had gone through a flu (or covid) would be more likely to respond. This means that the overall sample likely includes more people who had COVID than a true random sampling would.
  - tshadley 6y ago
    It looks like there's an argument to be made it could overcount or undercount depending on importance of each potential bias. "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 overrepresentation 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."

standardUser 6y ago

Add this to growing pile of evidence that this coronavirus is more widespread than most testing to date would suggest. How much more widespread still appears very unclear. There is a major ramping up of antibody testing happening right now, and multiple new tests being introduced, so we should have a better idea very soon. But the takeaway should be that this is a good thing. Every unknown case we uncover decreases the overall rate of hospitalization and death, and potentially decreases the effective transmission rate (assuming most people who had the virus develop immunity and that the immunity lasts, which is also still unclear).

rumanator 6y ago
I don't follow your argument. If the epidemic is more widespread than initially thought, why do you assume that the effective transmission rate would potentially decrease? If anything, the transmission rate was higher than assumed and, due to the higher number of infections and how they spread exponentially,it would only ramp up. And additionally, there are reports of reinfections among covid19 patients who were found to be cured.
- not2b 6y ago
  There's good news and bad news: the good news is the more cases that are found from people who didn't know they had it, the less deadly the disease is. The bad news: if it's all over the place it will be really hard to stamp it out.
  - war1025 6y ago
    I assume it's endemic at this point and we just need to learn how to live with it. Society can brace for a storm, but at some point you need to adapt and learn to live with the weather.
- timr 6y ago
  "If the epidemic is more widespread than initially thought, why do you assume that the effective transmission rate would potentially decrease?" Herd immunity. If testing is only detecting 1/50th of cases in NYC, for example, it means that there have been about 6M cases, or about 69% of NYC's population. That's basically the threshold where we'd expect to see the infection counts level off naturally.

tgb 6y ago

I made this comment the last time a serological testing study came out: they have 30 negative controls and claim 1.5% positives among their tests. So the number of controls is insufficient to rule out this being entirely false positives. You have to then rely on the test manufacturer's claims of false positives rates, which comes from 371 negative controls. They say: > our estimates of specificity are 99.5% (95 CI 98.1-99.9%) and 100% (95 CI 90.5-100%) Look at the those confidence intervals, even the narrower one (from the manufacturer's data)! The bottom is a four-fold increase in false positives, compared to the point-estimate, and is greater than the total positives they had in their finding.

tom-thistime 6y ago
This (I think?). I am not a doctor or biologist. Anyone who knows more than me, which is not a high bar, please step in and correct me. Based on quick amateur reading of the paper, the rate of crude positives (1.5%) might be entirely real cases, entirely false positives, or anywhere in between. The real rate of infection could be anywhere from 0% to 5%. The authors report around 3,500 real tests and 30 known-negative tests. We need 30,000 real data points (or 300 known-negative tests) to conclude anything. (Right?) I am not criticizing the experts who conducted the study. Thank you for doing the study. I'm only trying to understand what the study reveals. It may reveal that the group needs 10x more funding right away.
ajross 6y ago
To repeat you in plainer English: the serum test isn't infallible and reports "infected", incorrectly, for uninfected patients. It does this at some rate that is very near, or perhaps larger than, the fraction of "true" infected patients they report in their results. Teasing out data from all that noise requires that the false positive error rate be measured very accurately. And they didn't do that, so really this doesn't tell us much. Edit: Alternatively, borrowing jargon from a more common field around here: the measured infection rate of ~3% is very close to the noise floor of the experiment. It might be that, or it might be near zero, and we can't tell the difference. This study is very good evidence that the infection fraction is not much larger, however. We can easily rule out high infection rates like the 30% numbers that seems to get thrown around.

beamatronic 6y ago

“These prevalence estimates represent a range between 48,000 and 81,000 people infected in Santa Clara County by early April, 50-85-fold more than the number of confirmed cases. Conclusions The population prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that the infection is much more widespread than indicated by the number of confirmed cases.”

georgewfraser 6y ago

There are now multiple sources of evidence pointing to a very low true infection fatality rate, as low as 0.1% in some regions. Iceland has been testing a semi-random sample using PCR since early in the epidemic, and there have been several small antibody surveys in “hot spots” showing infection rates over 15%. You can also use CDC surveillance of flu-like illnesses to estimate the total number of infected nationwide; this morning I posted an analysis of this data estimating only 1 in 40 cases were detected in March, consistent with the Stanford results: https://fivetran.com/blog/covid-19-count https://fivetran.com/blog/covid-19-count

projektfu 6y ago
It may have a low overall fatality rate, yet it seems to have a high rate of hospitalization in clinical cases.
- BearOso 6y ago
  And it’s more contagious than the flu, so even if the fatality rate is identical, more people are at risk.
- grandmczeb 6y ago
  > it seems to have a high rate of hospitalization in clinical cases. The raw hospitalization rate is inflated because both treatment and testing are mostly limited to very severe cases in places where there's a lot of confirmed cases (e.g. NYC.)
  - projektfu 6y ago
    Still, comparing it to influenza... we dont see this many severe cases typically.
    - grandmczeb 6y ago
      Its pretty obvious this is worse than the flu. I don't think anyone is really disputing that at this point.

microdrum 6y ago

This is very strong evidence that the "realists" like Alex Berensen were correct. The shutdowns probably should end, while encouraging at-risk populations (mostly retired) to stay at home.

cdelsolar 6y ago
nope.
- microdrum 6y ago
  Yep. You've got nothing. CFR is low, and this has been knowable for a long time. You're scared. It's OK. It's illogical and anti-scientific, but it's OK. But you won't be able to hurt ordinary people any longer. Here's Germany's imputed CFR: https://spectator.us/covid-antibody-test-german-town-shows-15-percent-infection-rate/ https://spectator.us/covid-antibody-test-german-town-shows-1... Here's Denmark: https://www.dr.dk/nyheder/indland/doedelighed-skal-formentlig-taelles-i-promiller-danske-blodproever-kaster-nyt-lys https://www.dr.dk/nyheder/indland/doedelighed-skal-formentli... Here's Iceland: https://reason.com/2020/04/03/what-we-should-have-learned-from-icelands-response-to-covid-19/ https://reason.com/2020/04/03/what-we-should-have-learned-fr... More studies coming next week from LA.
  - DanBC 6y ago
    Why quote Denmark but ignore Sweden? Sweden are doing what you're calling for, and they have pretty high numbers of deaths.
    - thu2111 6y ago
      It's not "pretty high", it's middle of the pack and nowhere near the apocalypse that was supposed to be the outcome of their chosen path.
      - DanBC 6y ago
        Deaths per 1m population in Sweden is currently 139. For Denmark it's 58, and for Norway it's 30.

BearOso 6y ago

In our small town, I know of 3 people who came into direct contact with the first confirmed case, exhibited symptoms, but were refused testing. I can definitely believe their guess that the number of cases is an order of magnitude greater than the number confirmed.

ludwigschubert 6y ago

Great to see actual testing on a representative population sample, though I’m not sure what the consequences ought to be: > These prevalence estimates represent a range between 48,000 and 81,000 people infected in Santa Clara County by early April, 50-85-fold more than the number of confirmed cases.

devit 6y ago
So with 69 deaths so far and 9 deaths last week in Santa Clara County, projecting that to an extra 4 weeks to 105 deaths for those infected in early April, that means a 0.16% death rate?
- femto113 6y ago
  Lots of reasons I don't think you can extrapolate meaningful death rates from this yet. One big one is that time from infection to death covers a very wide range: in Wuhan time from hospitalization to death had both a mean and a standard deviation of about 2 weeks, and time from infection to hospitalization isn't well known yet, but is likely at least a week. Add to that the fact that some cases are diagnosed post-mortem and you might not know about all of the fatalities for early April cases until well into May.

terramars 6y ago

my friend probably had the virus (2 inconclusive tests) and tried to get into this study. some group asked her for $125 to get tested. i'm not sure if she saw any of the facebook ads. so, grain of salt about representativeness of the sample. there's for sure a response bias towards people who think they had it.

jhwang5 6y ago
Just curious, did your friend have pretty severe symptoms? As in, was it worse perceivably than say a case of bad flu?
nradov 6y ago
The Stanford study was free. They didn't charge $125.

opportune 6y ago

I don't see anything about sampling bias here. I would assume that people who have been ill, experienced mild symptoms, or who were exposed to confirmed cases would be more likely to respond to the facebook ads recruiting testing volunteers. I'm sure we are significantly undercounting cases but I highly doubt we are off by a magnitude of 50x.

brink 6y ago
I know. 50-85x is hard to believe. How could we be that far off?
- jplayer01 6y ago
  It’s not hard to believe when you consider testing criteria.
- gfodor 6y ago
  The # of cases reported that I believe they are comparing against is the number of confirmed positive tests, not the number of assumed cases in the county based on some other model that is trying to project # infected. I don't think anyone thought that we had 100% coverage where all cases were tested. So we're not "wrong" if such a study as this sees a gap between reported confirmed cases and expected infected, it just means we have a better understanding of the testing gap, which is self evident in existence but the magnitude of which we don't know. Given all the other factors, an order of magnitude or more gap between tests and sick people doesn't seem completely out of the question. I would be curious if there are other models using a different methodology which could help us get a handle on the conditional probability chain leading to tests being done or not on an individual, to see if there is a similar set of conclusions.
- usaar333 6y ago
  It's impossible to believe. Currently, Santa Clara's crude CFR is 3.7%. NYC has similar demographics (and as bad nursing home hits?) -- 0.14% of the population has died from covid. That puts an upper bound of 26x (and that's if the entire population was infected)

erentz 6y ago

It’s hacky and I’m happy to be told how wrong it is. But based on these recent antibody studies in Germany, Finland, and now here in CA, I’ve been assuming an actual fatality rate of about 0.4, and thus an actual infected rate of 250x our known deaths, which is a much firmer number. This is only a small comfort since it means we may have had about 8.75 million infected and presumably now immune in the USA. Or about 2.6%. There’s still a long way to go in that case.

all_usernames 6y ago
It's known that the vast majority infected do not experience symptoms, or have mild symptoms. I don't think you can draw the conclusion that they had prior immunity.
- erentz 6y ago
  Not concluding prior immunity but concluding that they now have immunity after having been infected. That is in progress towards herd immunity this number of people actually infected being revealed by these antibody studies is what is interesting. And based on my hacky heuristic described above that is currently around 2.6%. So we are still a long way off herd immunity. But at the same time closer than we thought we were.
  - ignoramous 6y ago
    > So we are still a long way off herd immunity. But at the same time closer than we thought we were. Yes and no. A disease is endemic when R0 x S = 1 [0]. R0 for SARS-CoV-2 is estimated to be 2.5 to 3.5 and some models predicting a much higher 5.7 [1]. The herd immunity threshold is given by (1 - S)%, which implies when R0 = 2.5, 60% of the population would need to be immune; similarly 71.42% and 82.45% for R0s, 3.5 and 5.7, respectively. [0] https://en.wikipedia.org/wiki/Endemic_(epidemiology) https://en.wikipedia.org/wiki/Endemic_(epidemiology) [1] https://news.ycombinator.com/item?id=22817942 https://news.ycombinator.com/item?id=22817942
    - 6y ago

Medicalidiot 6y ago

Antibody tests are not as accurate as RT-PCR, but the sensitivity that was observed here was 91% and 99% specificity when compared to confirmed COVID cases. I just want to point out that these researchers are using the corrects tests to do this.

jacquesm 6y ago
They use the correct tests, but they use them on a group that isn't even close to a random sample of the population, which is what you'd need to do to get to their conclusion.
- Medicalidiot 6y ago
  I'm not going to comment on that because I have a basic understanding of public health and epidemiology on the diagnostic side of things. Why do you think that this isn't a "good" random sample?
  - mlyle 6y ago
    It's not a random sample; it's a convenience sample of people who responded to Facebook ads.
    - Medicalidiot 6y ago
      What about Facebook ads doesn't mean that they're an adequate sample? Is there a confounding factor that you have in mind?
      - mlyle 6y ago
        Many confounding factors. Socioeconomic factors that can't be fixed with stratified sampling (access to transportation, usage of Facebook, etc.. conversely the willingness of a $10 amazon gift card to motivate people to participate will vary). People who are concerned about possible past exposure to COVID-19 may have a different probability to respond. Of course, the bigger issue is that we can't rule out that the antibody tests have a false positive rate that can explain the whole result. We need serology tests in places with a higher positive rate to know. Basically, 50 positive results out of 3330 is a really small signal. Just a small false positive rate or sampling issue could explain all the positive results.

virusduck 6y ago

I don't see anything in the methods about potential crossreactivity with NL63, OC43, or HKU1 coronaviruses. Presumably this was done by the company, but crossreactivity is an extremely important control when it comes to this type of test. It is common, and if it's not vetted thoroughly, you may be measuring something else completely.

tomerico 6y ago

Saw this comment [1] on Reddit that outlines the limitations of the study well: This is the most poorly-designed serosurvey we've seen yet, frankly. It advertised on Facebook asking for people who wanted antibody testing. This has an enormous potential effect on the sample - I'm so much more likely to take the time to get tested if I think it will benefit me, and It's most likely to benefit me if I'm more likely to have had COVID. An opt-in design with a low response rate has huge potential to bias results. Sample bias (in the other direction) is the reason that the NIH has not yet released serosurvey results from Washington: We’re cautious because blood donors are not a representative sample. They are asymptomatic, afebrile people [without a fever]. We have a “healthy donor effect.” The donor-based incidence data could lag behind population incidence by a month or 2 because of this bias. Presumably, they rightly fear that, with such a high level of uncertainty, bias could lead to bad policy and would negatively impact public health. I'm certain that these data are informing policy decisions at the national level, but they haven't released them out of an abundance of caution. Those conducting this study would have done well to adopt that same caution. If you read closely on the validation of the test, the study did barely any independent validation to determine specificity/sensitivity - only 30! pre-covid samples tested independently of the manufacturer. Given the performance of other commercial tests and the dependence of specificity on cross-reactivity + antibody prevalence in the population, this strikes me as extremely irresponsible. This paper elides the fact that other rigorous serosurveys are neither consistent with this level of underascertainment nor the IFR this paper proposes. Many of you are familiar with the Gangelt study, which I have criticized. Nevertheless, it is an order of magnitude more trustworthy than this paper (both insofar as it sampled a larger slice of the population and had a much much higher response rate). It also inferred a much higher fatality rate of 0.37%. IFR will, of course, vary from population to population, and so will ascertainment rate. Nevertheless, the range proposed here strains credibility, considering the study's flaws. 0.13% of NYC's population has already died, and the paths of other countries suggest a slow decline in daily deaths, not a quick one. Considering that herd immunity predicts transmission to stop at 50-70% prevalence, this is baldly inconsistent with this study's findings. For all of the above reasons, I hope people making personal and public health decisions wait for rigorous results from the NIH and other organizations and understand that skepticism of this result is warranted. I also hope that the media reports responsibly on this study and its limitations and speaks with other experts before doing so. [1] https://www.reddit.com/r/COVID19/comments/g32wjh/covid19_antibody_seroprevalence_in_santa_clara/fnotu78/ https://www.reddit.com/r/COVID19/comments/g32wjh/covid19_ant...

robotcookies 6y ago

"Participants were recruited using Facebook ads targeting a representative sample of the county by demographic and geographic characteristics." How is it representative when it's only Facebook users in this sample?

ajross 6y ago
In the south bay? Pretty good, unless you want to argue for a demographic skew because of Facebook's poor uptake among iOS users. I mean, no. It's not perfect. It will miss demographics like the elderly with lower social media use, but that group tends to be well-sampled already due to their risk profile. It will probably miss some immigrants too, which seems like a bigger problem. But really, it's a pandemic. It doesn't care about your socioeconomic status. One of its defining qualities is the extent to which it does not cluster in particular communities like more typical epidemics.
- jboy55 6y ago
  They also were unable to remove any bias towards people self-selecting to be part of the test because they felt they had symptoms of COVID. The bay area has been in lock down since early March, its really hard to imagine someone participating if they didn't feel they had some symptoms and wanted to 'know for sure'.
usaar333 6y ago
Unless you think FB users for some reason would be infected at significantly different rates, that shouldn't be much of a problem. I'm much more concerned about selection bias toward prior sick people; IIRC, Stanford offered to report positive results to the patient.
- geoelectric 6y ago
  There would potentially be an age bias, though probably less in FB than a lot of other services you could cherrypick from. It'd also arguably bias older, since FB has been aging up in audience from what I can tell. Without reading the study, though, possible they actually controlled that in the demographic profile for the ad. Edit: Going to the other thread confirmed this. 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."

lmlsna 6y ago

I suspected the asymptomatic rate to be high, but not by a factor of 50-85! That's very great news no?

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cbare 6y ago

This thread critiques this article in a way I find convincing: https://twitter.com/DiseaseEcology/status/1251225273871134721 https://twitter.com/DiseaseEcology/status/125122527387113472...