Had Purdue chosen to test all 50,000 students and staff every week, 10 times the number would have reported as testing positive weekly. By those increased numbers of testing, 4% of our Indiana population is now being tested for COVID-19 every week. © 2020 MedPage Today, LLC. CP Scott: "Comment is free, but facts are sacred" Manufacturers' data have not yet been corroborated by the agency. Contact traced people identified as being close to a COVID patient WITH symptoms (>10% incidence of testing positive for COVID) would also be another category and those identified by contact tracing who were near a person who tested positive WITHOUT symptoms (>1% incidence of having COVID) would be a fourth. Hence a 2 SD range of +/- 7% of the mean, which gives the right range. As the unemployment rate soared in April to its highest levels since the Great Depression, with 14.7 percent of workers without jobs, the coronavirus shutdown fell … The Prosecutor’s Fallacy can be avoided by making sure the probability answers the right question, by focusing on how the evidence applies to the ‘defendant’ and not on the ‘evidence’ alone in the absence of other relevant factors. I’m not sure if it’s 10% or 50% but it’s undoubtedly more than 5% of the positive tests that are not true positives. How can the range be so narrow and stable? A systematic review of the accuracy of covid-19 tests reported false negative rates of between 2% and 29% (equating to sensitivity of 71-98%), based on negative RT-PCR tests which were positive on repeat testing.6The use of repeat RT-PCR testing as gold standard is likely to underestimate the true rate of false negatives, as not all patients in the included studies received repeat testing and … This shows that NZ is doing around 100-200k tests a month, https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-situation/covid-19-current-cases. My guess is that most of these are likely unknown. Had this data been commingled with testing of symptomatic individuals, there certainly would have been an outcry by the casual observer to close everything down again. Since staff and students combined are 50,000 at Purdue University, 5,000 tests are done every week. Across MedPage Today and its businesses, digital accessibility is a core priority for us throughout our design and development phases. But keep in mind you can also do multiple primers (roughly checking for different viral genes) and see some but not others cross the threshold. An elaborate plan was implemented, including a signed pledge from all students to behave properly, wear masks, maintain social distancing. From this perspective, false positive pcr can occur if the person has had Covid and has residual viral RNA (which lasts for weeks) but is no longer shedding live virus. I think the timing on registration of everything, cases, deaths, tests (maybe not hospitalizations but maybe even that) is so all over the place that it’s hard to pin down leading and lagging based on daily or weekly numbers. November 7, 2020. Doing quantitative PCR testing is more difficult than doing qualitative testing, see e.g. But .00032 / .0092 is 3.5%, not .35%. Guess not everyone is prepared to believe the rate in New York is as low as it appears. Robert Hagen, MD A decision was made to perform random testing on 10% of the students and staff each week. We also rely on our community to tell us when they experience an issue with any of our sites, and we give consideration to all feedback that is provided to us. I don’t really know what to think about all this, but I’ll share with you. I expect that under these conditions people are doing better than that, but maybe they’re contaminating 0.2% of tests… that’d still be in the 10 or 20% of positives are false. So it's all very confusing. So far, 90% of the students who test positive do not develop symptoms. Base rate neglect is a form of fallacy and also cognitive bias where only part of a statistic is focused on and a conclusion is drawn from this partial premise. A PCR test can only really come up false positive because of contamination of one kind or another. 2 here gives some sense of how these Ct values vary with different machines and reagents (and also with viral load): https://www.medrxiv.org/content/10.1101/2020.08.03.20167791v1. Base rate fallacy, or base rate neglect, is a cognitive error whereby too little weight is placed on the base, or original rate, of possibility (e.g., the probability of A given B). The number of tests doesn’t seem to be changing that much, so it would still imply an oddly flat curve. Testing procedures might be different between countries too. COVID deaths in Indiana average about 23 per day, but that too is going up. Different places use different primers, equipment, and sample collection then different thresholds for what counts as a positive. First, contrary to the conventional wisdom, a thorough examination of the literature reveals that base rates are almost always used and that their degree of use depends on task structure and internal task representation. Hmm. Without knowing the specificity of the test, the number of these positives that are false positives is unknown. At the empirical level, a thorough examination of the base rate literature (including the famous lawyer—engineer problem) does not support the conventional wisdom that people routinely ignore base rates. The material on this site is for informational purposes only, and is not a substitute for medical advice, diagnosis or treatment provided by a qualified health care provider. One study analysing excess deaths for influenza over four years estimated the number for 2016-2017 “season” (the highest of the four years) to be 24,981. The article doesn’t mention international comparisons. Restaurant occupancy, sporting events and other large gatherings are again limited at a greater level than state requirements. Throw all those four groups in together if you want, but just understand you are not getting a true picture of what is going on. Maybe NY is post-pandemic, in the “endemic” phase of the disease, so it’s basically constant rather than exponentially growing/declining? Keywords: Experimental Analysis of Behavior, Heuristics, Base-Rate Neglect Suggested Citation: Suggested Citation Pico, Claudia and Gil Mateus, Edwin and Clavijo Álvarez, Álvaro, Cognición y Conducta en La Falacia de Las Tasas de Base (Cognition and Behavior in the Fallacy of Base Rates… We will make all reasonable efforts to address your concerns. I think you misplaced a decimal for the SD. Base rate fallacy/false positive paradox unfortunately becomes ignored when one does this. Our state has a population of 6.5 million. https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30424-2/fulltext. > These are not randomized tests, through a sparse, clustered set of interactions with a great deal of heterogeneity. A classic explanation for the base rate fallacy involves a scenario in which 85% of cabs in a city are blue and the rest are green. Maybe. We have learned in the past from routine PSA testing and mammograms that a positive test in a screening situation needs to be taken in context. https://www.wvdl.wisc.edu/wp-content/uploads/2013/01/WVDL.Info_.PCR_Ct_Values1.pdf. Such improvements to our sites include the addition of alt-text, navigation by keyboard and screen reader technology, closed captioning, color contrast and zoom features, as well as an accessibility statement on each site with contact information, so that users can alert us to any difficulties they have accessing our content. Description: Ignoring statistical information in favor of using irrelevant information, that one incorrectly believes to be relevant, to make a judgment. Base rate fallacy – making a probability judgment based on conditional probabilities, without taking into account the effect of prior probabilities. And in the age of COVID-19 there's plenty of fear going around (so expect a lot of it). Contact tracers are telling positive testers who have nowhere to isolate to be evaluated at their hospital emergency room. And I would imagine that the positives and thus false positives might be clustered by region. >>where whole countries like New Zealand can have no cases despite continued testing? Did the only the doctor receive the yes-no or does the lab test itself only produce a yes-no? All rights reserved. Hmmm, I get a different standard deviation but the same range. Now the cases/deaths declines are not extremely steep declines. It might be useful, if we’re speculating about contamination, to find out how often labs have to discard results because the controls show a problem. I think that would be a reasonable expectation but there’s so many inconsistencies in timing and, as you point out, even in the basic definitions. So if that were why, then would we expect the trend to change soon (IE either hospitalizations to drop, or cases to rise)? MedPage Today is committed to improving accessibility for all of its users, and has committed significant resources to making our content accessible to all. The COVID PCR test just returned a yes-no, “But similar PCR tests for other viruses do offer some sense of how contagious an infected patient may be: The results may include a rough estimate of the amount of virus in the patient’s body.”. So how many false positives has NZ had ever since the start of the pandemic? Therefore, of the positive results, only 60/ (60+97)≈38% will be correct! Sure makes sense. I thought these were standardized for commercial testing equipment and so should give standard output. I think your .35% SD was intended as a percentage of the mean of ~.92%. Repeat the PCR test multiple times and see it come up negative repeatedly. For Covid 19, we have far more accurate figures from 20 February 2020 to the time of writing: 32,330 deaths. Or is there some reason why that is plausible? We must compare apples to apples and oranges to oranges rather than just making fruit salad out of the whole thing. Unfortunately, the lack of understanding of the statistical principle of base rate fallacy/false positive paradox has led to some confusing numbers. Those 35 students who test positive daily are added to our county totals (many of those county positive tests are done on people with COVID-19 symptoms). I’m pretty sure they do something else, instead of running the same test on the same sample over and over again, without the knowledge whether the specimen is positive or negative. The Times article, which is not so old—it’s from 29 Aug—is entitled, “Your Coronavirus Test Is Positive. Purdue University made the decision in late spring to resume in-person classes for its fall session. If you … MedPage Today believes that accessibility is an ongoing effort, and we continually improve our web sites, services, and products in order to provide an optimal experience for all of our users and subscribers. New York City was the first major urban center of the COVID-19 pandemic in the USA. What do you make out of that? To first order you might say the probability of a false positive is something like k * pp, where pp is the percentage of true positives and k is a number between say 0.003 and 0.1 but If pp = 0 then doesn’t matter how big k is you won’t get any. That’s close to the range stated (.85 – /99%). In the past few months, we've seen that one of these odd behaviors is attributed to a significant number of health-news headlines recommending vitamin C to purportedly assist one's immune response to COVID-19. Medpage Today is among the federally registered trademarks of MedPage Today, LLC and may not be used by third parties without explicit permission. There’s just no common timeline upon which things can lead or lag each other in a way that shows up in the trackers. Half a million passengers travelled in the U.S. on June 11, continuing a travel rebound that would mean, one commentator says, a full return to normal by the end of summer. Conjunction fallacy – the assumption that an outcome simultaneously satisfying multiple conditions is more probable than … So the test serves as its own ‘post-measure’ or gold standard. A classic 1978 article in the New England Journal of Medicine reveals this problem. As demonstrated with the above mentioned figures, COVID-19 has still not reached a point where it surpasses other illnesses … Data were collected from 177 Zip Code Tabulation Areas (ZCTA) in New … The incidence of a disease in the population that you are testing is extremely important for accuracy. For a positive control you run the test with known fragments of RNA in it (or known to have virus grown in culture in it). I haven’t run numbers on that, but by eye it looks to have a weekly modulation. If you get a positive here in the US where we’re generating 40000 new cases a day country wide, no one is going to pay any extra attention to it. Our efforts are ongoing. September 15, 2020 at 4:27 pm. When the incidence of a disease in a population is low, unless the test used has very high specificity, more false positives will be determined than true positives. I have worked with PCR data for a long time. It is not implausible that testing is “growing / shrinking in step with the spread / decline of the virus”: the more people in my circle being diagnosed to be positive, the more likely I am undergoing a test. Tests for the coronavirus range from 90% to 99% specificity. In most diagnostic tests, one needs to have a completely different and verifiable way of assessing the presence or absence of something (e.g. In order to have the best experience on our sites, we recommend that users utilize the latest available versions of web browsers and assistive technology. Base rate fallacy/false positive paradox is derived from Bayes theorem. Robert Hagen, MD, is recently retired from Lafayette Orthopaedic Clinic in Indiana. I also wonder if it could be an issue of defining “COVID related” hospitalizations. I may have missed it, but what exactly is the gold standard (post-test) used to verify if a PCR test is indeed a FP? The temporary introduction of … For example, this happens when scholars like Kahneman and Tversky attribute to their experimental subjects the errors of the so-called conjunction fallacy and base-rate fallacy, and also when it is claimed that someone has committed the gambler's fallacy (Woods , 478–492). (2) Should it indicate virulence and the likelihood of a person’s own mortality due to Covid? When these tests return negative, significant confusion occurs. The purpose of the random testing was surveillance to encourage students and staff to maintain proper behavior. To go back to … If we are doing the same kind of test, then that’s what we’d expect to be generating EVERY DAY in the US. That’s what contact tracing does. The base rate … Yes. The Indiana State Department of Health advised against a random testing program, as it felt overall data accuracy would be difficult. Yes NY has a significant proportion of false positives, It’d have to at that low level. Do you think we are at the limits of the test and there may be a significant amount of false positives? There would also be variation in the number of tests performed each day. I’ll point out that some of these tests will be repeatedly re-testing the same people, so the sampling variation could be even smaller than that. Well, in designing the test, you run the test adding “nucleotide free water” instead of sample, and this is your negative control. As of today, that Washington Post cases and death tracker added an increase of 2,732 to their deaths count for New York state by yet again changing their definition. It doesn’t look like that variations are too much out of line, but I don’t know how they can be reconciled with false positive rates we’ve seen in the papers. Purdue has discussed using a serial testing protocol. Most of us in healthcare have a fairly good understanding of math but are not nuanced in the field of statistics. But that assumes that each daily or weekly “rate of hospitalizations” has a fixed relationship to the underlying population at risk, same with cases and deaths. These are not randomized tests, through a sparse, clustered set of interactions with a great deal of heterogeneity. Even using a test with only 90% specificity, the number of false positives will be much less significant. If the only variation of the numbers were from random sampling variation, then the standard deviation would be about 0.35%, based on 90,000 tests per day (test count data from https://coronavirus.jhu.edu/testing/individual-states/new-york). Two SDs of this would translate +/- 0.7%. . A few years ago I had the assignment to review different validation plans for a diagnostic test. The actual sensitivity and specificity of COVID-19 tests are unknown as these tests were okayed by the FDA under Emergency Use Authorization. The numbers have caused our county health department to move cautiously. Lite if the positives come from places where the base rate is higher than 0.85-0.99%. So then would the picture of the “base rate fallacy” effect be different than if there were no heterogeneity and the base rate was uniform? Hospitalizations ought to lag cases, but lead deaths. Often there are false positives in a validation but the test will still have a specificity near 100%. E.g. You definitely don’t need an entirely different kind of test as Navigator suggested. Its worse than that. It’s more than sufficient to test for contamination. A few options to consider: (1) Should a positive test only indicate presence or vestige of the virus? No wonder FP and FN rates are all over the place than. biopsy verified by open surgery to detect FP/FN). why do you state that there is a high proportion of false positives? . And cases are possibly messy because TX is reporting a lot of back-log old cases not counted in the “new daily”. (4) Should it predict the likelihood that a person can infect another person, and under what conditions? If the true infection rate of those tested is .92%, then I get a standard deviation of Sqrt(.0092 * .9908 / 90000) = .00032. Base rate neglect is a specific form of the more general extension neglect. So you can also decide if you need all, just one, or 2/3 to indicate a positive. Remember if you contaminate 1% of the tests with your positive control, then you’ll get 1% positive rate, and that’s easy to do by accident. The cut-off for a yes/no test is determined based on the validation, typically a number near but below the truncation value. Test results of a population of 2,000, with a virus prevalence of 30% (top) and 3% (bottom), for a test with a 5% false-positive rate. Yes, and this might be true in some places, but looking at the # of tests performed in NY it does not seem to be true there. The truncation value is usually 40 but I have seen 45. We have been oversold on the base rate fallacy in probabilistic judgment from an empirical, normative, and methodological standpoint. this is why the U.S. health care system is the most expensive in the world, https://www.wvdl.wisc.edu/wp-content/uploads/2013/01/WVDL.Info_.PCR_Ct_Values1.pdf, https://www.medrxiv.org/content/10.1101/2020.08.03.20167791v1, https://coronavirus.jhu.edu/testing/individual-states/new-york, https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30424-2/fulltext, Are female scientists worse mentors? Latest opinion, analysis and discussion from the Guardian. The tests being used have changed over time. Base rate fallacy/false positive paradox is derived from Bayes theorem. I know that US testing runs 40+ cycles. As happens sometimes, I receive two related emails on the same day. The base rate fallacy, also called base rate neglect or base rate bias, is a fallacy. The Prosecutor’s Fallacy is … This study pretends to know, Basbøll’s Audenesque paragraph on science writing, followed by a resurrection of a 10-year-old debate on Gladwell, Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond, “We’ve got to look at the analyses, the real granular data. The tests are "good enough" for diagnosing patients with symptoms but not nearly as effective when used for a random testing program. We might think that the rate of “hospitalizations” would drop followed by a drop in the rate of “deaths”. Also I definitely believe that false positives are related to true positives. Eight weeks ago, Indiana was performing 20,000 tests per day. Given the possibility of ‘stale’ PCR tests for weeks or even months after infection, if everyone who is admitted to hospital is tested, could that mess things up if there are relatively few currently symptomatic people but many cases in the recent past? The difference in the numbers can be quite striking and certainly not inherently understandable. The check samples are inserted into the sample stream by the people collecting the samples. If NZ decides to run only out to say 30 cycles, then they won’t detect microscopic contamination (10 extra cycles is about ~ 1000x extra amplification). Something odd going on right now in TX (and probably other states). What counts as “COVID related hospitalization” has changed over time. It’s kinda like when you find a burnt spot of ground: sure, that area may not be in flames now, but there sure was a fire, so you want to know whereit may have spread while it was burning. Diversity in an approach is fine but the problem is that how the details vary over time and location are unavailable then all the numbers get treated the same. So areas where the base positive rate is higher, the % of positives that are false positives is lower? You also do not know if a low virus concentration in the sample really means a low virus concentration, for example the swabbing may not have been done properly. Which NY State numbers are we talking about? What we really need is a test to tell us whether a symptomatic person is shedding virus and is therefore infectious. The results the lab sees will look something like this: To prove that the test is sufficiently sensitive and specific you run the test on several 96 well plates with a known pattern of synthetic positives and synthetic negatives. In the US we’re doing 700-800k tests a DAY. In effect what you’re looking for is an expected temporal sequence among what are likely non-comparable tallies. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the COVID-19 test positivity rate. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. Certainly positivity rates are going up here. The base rate of jobseeker was inadequate before the pandemic and it will remain inadequate unless the government acts to increase it permanently. Luckily, Purdue keeps their own dashboard and with some calculations their data can be extracted from the county data to give us a ballpark guess. I know there is some rushing with COVID-19, but any diagnostic test should go through a validation, a series of experiments to assess it specifications. Day after day the positive percentage stays in a tight range of about 0.85-0.99%. I do not think that assumption is valid anywhere in USA over any period longer than a few weeks.

base rate fallacy covid

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