Different places use different primers, equipment, and sample collection then different thresholds for what counts as a positive. Let's take a closer look. https://www.wvdl.wisc.edu/wp-content/uploads/2013/01/WVDL.Info_.PCR_Ct_Values1.pdf. Yes NY has a significant proportion of false positives, It’d have to at that low level. The cut-off for a yes/no test is determined based on the validation, typically a number near but below the truncation value. less than 0.6% by Indiana University/Fairbanks data. 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. If the true infection rate of those tested is .92%, then I get a standard deviation of Sqrt(.0092 * .9908 / 90000) = .00032. The temporary introduction of … Purdue is a major research university with a strong emphasis on STEM education. Eight weeks ago, Indiana was performing 20,000 tests per day. By base rate fallacy/false positive paradox, if the specificity of a test is 95%, when used in a population with a 2% incidence of disease -- such as healthy college students and staff -- there will be 5 false positives for every 2 true positives. Could this be the reason for increased hospitalizations? Restaurant occupancy, sporting events and other large gatherings are again limited at a greater level than state requirements. 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. You can’t contaminate a well with a positive sample if you don’t have any positive samples. An elaborate plan was implemented, including a signed pledge from all students to behave properly, wear masks, maintain social distancing. 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… The Washington Post has reported Covid-19 death rates as high as 5% in the United States. The tests being used have changed over time. How the swab is performed shouldn’t really matter, as those who are shedding will have viral RNA throughout the whole airway: mouth, throat, nose and nasopharynx. And having high test numbers means lab technicians put trays of 96 or 144 samples in a machine, run a preset procedure, and determine a result. What I was referring to was when you get a positive result that you think might be from contamination of the test, you then rerun the test going back to the original swab sample on a different machine with a different lab tech at a different time in duplicate or triplicate etc… if you get all negatives, you can conclude contamination was the issue. Students who test positive have to isolate in an old dormitory or go home. Therefore, of the positive results, only 60/ (60+97)≈38% will be correct! 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. In the United States, that appears to be between 5 percent and 15 percent. Maybe. 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. Abstract. Commingling of data in our county from the people tested WITH symptoms together with the randomly tested Purdue students WITHOUT symptoms has occurred. I know that US testing runs 40+ cycles. Now the cases/deaths declines are not extremely steep declines. What counts as a “case” has changed over time. The check samples are inserted into the sample stream by the people collecting the samples. The usual diagnostic tests may simply be too sensitive and too slow to contain the spread of the virus.”. The samples are prepped and analyzed in the order specified by the collectors, and lab prepping the samples also splits every sample so it can be tested later. Hmm. The NFL contamination case in August is an example of how a high false positive rate tied into a situation in a lab. This is actually a thing I’ve heard advocated here in the US, reducing the maximum cycle count so as to avoid this issue. What we really need is a test to tell us whether a symptomatic person is shedding virus and is therefore infectious. There's certainly no denying the severity of COVID-19 in the U.S., but the numbers of positive tests reported can lead to confusion – especially for those of us in university towns. If we are doing the same kind of test, then that’s what we’d expect to be generating EVERY DAY in the US. 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. 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. Well, in designing the test, you run the test adding “nucleotide free water” instead of sample, and this is your negative control. For Covid 19, we have far more accurate figures from 20 February 2020 to the time of writing: 32,330 deaths. We will make all reasonable efforts to address your concerns. 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. E.g. Had Purdue chosen to test all 50,000 students and staff every week, 10 times the number would have reported as testing positive weekly. 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. 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. False positives might also occur due to cross-reactivity with other corona viruses. 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. Base rate fallacy/false positive paradox unfortunately becomes ignored when one does this. And cases are possibly messy because TX is reporting a lot of back-log old cases not counted in the “new daily”. There are both known positive and negative controls on those trays. 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 … Guess not everyone is prepared to believe the rate in New York is as low as it appears. It would be a welcome advance to be able to discern, separate, and quantify concerns here. Also because of additional testing being available, Indiana is now performing at times 40,000 COVID tests per day. Is it that they do a serology (antibodies) one or something else? 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. If you … Also I definitely believe that false positives are related to true positives. A few years ago I had the assignment to review different validation plans for a diagnostic test. I think the “positive tests” mean different things to different people. We strive to make all of our content accessible to all users and continually work to improve various features of our sites. False negatives should not really occur in those with recent onset symptoms as viral shedding occurs prior to and for the first week or so of the clinical course. Two SDs of this would translate +/- 0.7%. 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. A test with 95% specificity has a 5% false-positive rate. He's an adjunct professor at Indiana University, a past president and board member of the Indiana Orthopaedic Society, and a past member of the Board of Councilors for the American Academy of Orthopaedic Surgeons. Robert Hagen, MD, is recently retired from Lafayette Orthopaedic Clinic in Indiana. 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. 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? But .00032 / .0092 is 3.5%, not .35%. Modelers at Imperial College London estimated something closer to 1% in early February. https://www.washingtonpost.com/graphics/2020/national/coronavirus-us-cases-deaths/?utm_campaign=wp_to_your_health&utm_medium=email&utm_source=newsletter&wpisrc=nl_tyh&wpmk=1. When these tests return negative, significant confusion occurs. I was just thinking that back in June cases and % positive rose while deaths were falling, and the people who were predicting that it was just a leading/lagging indicator issue (rather than for example an extremely dramatic drop in IFR*) turned out to be right. Maybe It Shouldn’t Be. Testing procedures might be different between countries too. Another wrinkle for the measurement problem; both of contagious individuals and viral load sufficient to be related to death. If the lab has the more detailed results then the information is out there somewhere. (The actual incidence of active COVID-19 in college age students is not known but estimated to be less than 0.6% by Indiana University/Fairbanks data. 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). You definitely don’t need an entirely different kind of test as Navigator suggested. 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. Typically specificity, 1- the false positive rate, is reported as 99.9%, not 100%, when there are no false positives. Data were collected from 177 Zip Code Tabulation Areas (ZCTA) in New … Now I’m commenting on things I understand poorly, but wouldn’t you expect that the contamination rate would be fairly variable, depending on whether some lab tech got a bad night’s sleep or was fighting with their partner, etc.? The last example brings me to what is perhaps the most pervasive reason behind the conjunction fallacy: we tend to ignore base rates. So far, 90% of the students who test positive do not develop symptoms. No wonder FP and FN rates are all over the place than. Certainly positivity rates are going up here. I was wondering if you have any comment on the NY State Covid numbers. Ideally, testing those WITH symptoms would be reported separately from those randomly being tested WITHOUT symptoms. The incidence of a disease in the population that you are testing is extremely important for accuracy. Medpage Today is among the federally registered trademarks of MedPage Today, LLC and may not be used by third parties without explicit permission. The base rate … In mining and metal exploration all assays are done using the same chemical process, but checked using duplicates, certified blanks and certified standards. But still, something seems weird. Or is there some reason why that is plausible? 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. *I’m sure IFR has dropped somewhat, but deaths did rise significantly in July…, Statistical Modeling, Causal Inference, and Social Science, In case you’re wondering . Panic happens because the media industry tends to engage in what can be described as a base rate fallacy (Hardman, 2015) which is the idea that people tend attribute a higher level of risk to a situation when they are not aware of the actual base rates of such phenomena. How can the range be so narrow and stable? Our efforts are ongoing. 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. If negative do nothing. To go back to … So it's all very confusing. 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. Did the only the doctor receive the yes-no or does the lab test itself only produce a yes-no? As demonstrated with the above mentioned figures, COVID-19 has still not reached a point where it surpasses other illnesses … So areas where the base positive rate is higher, the % of positives that are false positives is lower? Of the 1,940 without the disease, 97 (5%) will receive a false positive. The Prosecutor’s Fallacy is … A few options to consider: (1) Should a positive test only indicate presence or vestige of the virus? Yet those numbers would be only representative of the positivity of mass testing, not the prevalence of infective patients. The base rate fallacy, also called base rate neglect or base rate bias, is a fallacy. If positive the person is quarantined and contacts are traced and tested. Often there are false positives in a validation but the test will still have a specificity near 100%. So two SDs is .00064, which gives the range .856% – .984%, as you said. Even using a test with 99% specificity with a 1% population incidence generates 10 false positives for every 9 true positives. 6 The 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 … Well, as Daniel Lakeland mentions above, if the cause of false positives is cross-contamination from genuine positives, then no false positives among PCR tests in an area without virus isn’t incompatible with a meaningful number of false positives in an area with virus. © 2020 MedPage Today, LLC. 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. Of course it’s possible to contaminate with the synthetic positive control, but again, if everyone jumps on the positive result and does a re-test, re-testing will reveal it was spurious. 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. Stopping an outbreak is always time-sensitive, so you don’t really have time to double-check results before you initiate tracing contacts and isolating them. Purdue has discussed using a serial testing protocol. Haven’t read all responses, but assume it is PCR tests and their false positives that are discussed. So you can also decide if you need all, just one, or 2/3 to indicate a positive. So what is going on? Cases down / tests up (leading indicator). But hospitalizations almost perfectly flat. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the COVID-19 test positivity rate. Or actually the true positives would be clustered by region but the false positives not so – so (they’d remain a constant as a % of the number of tests)? 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The confidence that we should have in antibody tests depends on a key factor that is often ignored: the base rate of the coronavirus. There’s just no common timeline upon which things can lead or lag each other in a way that shows up in the trackers. 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). 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. Doing quantitative PCR testing is more difficult than doing qualitative testing, see e.g. 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. Base rate fallacy/false positive paradox is derived from Bayes theorem. The difference in the numbers can be quite striking and certainly not inherently understandable. My guess is that most of these are likely unknown. Hmmm, I get a different standard deviation but the same range. I haven’t run numbers on that, but by eye it looks to have a weekly modulation. The researchers asked 60 Harvard physicians and medical students a seemingly simple question: If a test to detect a disease with a prevalence of 1/1,000 has a false positive rate of 5%, what is the chance that a person found to have a positive result actually has the disease? Thus, it makes it look like our county's number of positive tests has doubled since Purdue started in-person classes in August. Using the same test on patients with COVID-19 symptoms, because their incidence of disease is 50% or greater, the test does not have to be perfect. We’re doing in the US as many tests every day as NZ has done EVER. Germany had an effective R near 1 during late May and early June: public health measures/reopening is adapted to the rate of spread and has “R near or below 1” as a target, so steady numbers may simply be a result of politics and behaviour. 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). One night, a cab is involved in a hit and run accident. I think you misplaced a decimal for the SD. 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. >>where whole countries like New Zealand can have no cases despite continued testing? It’s more than sufficient to test for contamination. NZ went a long time with no positive samples, during that period I’d expect very low false positive rates. Only 14% gave the correct answer of 2% with most answering 95%. 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. Abstract: We have been oversold on the base rate fallacy in probabilistic judgment from an empirical, normative, and methodological standpoint. And the questionably “false” positives where the sample is really positive in the PCR sense (there is actual COVID RNA) but the person is not sick or infectious (the viral RNA is old fragments of virus, not “live” infectious virus) will only occur if some of the population tested has had COVID in the past. And in the age of COVID-19 there's plenty of fear going around (so expect a lot of it). The base rate is the actual amount of infection in a known population. New York City was the first major urban center of the COVID-19 pandemic in the USA. In most diagnostic tests, one needs to have a completely different and verifiable way of assessing the presence or absence of something (e.g. Most of us in healthcare have a fairly good understanding of math but are not nuanced in the field of statistics. Many of these classes include practicums, laboratory sessions, and group projects that require some in-person attendance. There's nothing like fear to generate abnormal behavior. I have worked with PCR data for a long time. Maybe NY is post-pandemic, in the “endemic” phase of the disease, so it’s basically constant rather than exponentially growing/declining? Hmmm. We have been oversold on the base rate fallacy in probabilistic judgment from an empirical, normative, and methodological standpoint. CP Scott: "Comment is free, but facts are sacred" But I think in early summer cases rose, then hospitalizations, then deaths. Manufacturers' data have not yet been corroborated by the agency. 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). by The base rate fallacy, also called base rate neglect or base rate bias, is a fallacy. That’s close to the range stated (.85 – /99%). 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. The original question is why the % positive is so consistent. Base Rate Fallacy Defined Over half of car accidents occur within five miles of home, according to a report by Progressive Insurance in 2002. Lite if the positives come from places where the base rate is higher than 0.85-0.99%. Two SDs of this would translate +/- 0.7%. That’s what contact tracing does. Yes. Deaths down (lagging indicator). 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