U.S. health agencies have failed to factor in false positive results among confirmed cases of COVID-19 and, based on statistical science, could be overstating positive cases by as much as 100 percent, say physicians.
Of 65.7 million COVID tests reported by the Centers for Disease Control and Prevention (CDC) as of August 10, 2020, 5.9 million or nine percent showed positive results. But infectious disease expert, Erwin Haas, M.D., says none of the data reported by health agencies mentions the possibility that a percentage of the positive results could be false.
“It is a well-known principle in statistics that all tests will have some biologic false positives that unfortunately label individuals as being diseased but who are not afflicted,” Haas wrote in the American Thinker, July 21. Haas stated he was only able to find one published study, one by the Foundation for Innovative New Diagnostics, which found a 100 percent sensitivity (the measure of positive accuracy) but with 96 percent specificity (the measure of negative accuracy), suggesting four percent of all results are false. Applying four percent to 65.7 million equals 2.4 million, nearly half of all confirmed cases. (See related article).
“That is being charitable. I think most tests would say a four percent false positive rate would be a very, very good test,” Haas said August 4 on The Heartland Daily Podcast.
Barbara Yaffe, M.D., Canada’s Associate Chief Medical Officer of Health, made similar remarks to The Scoop on August 1.
“If you’re testing in a population that doesn’t have very much COVID, you’ll get false positives almost half the time,” Yaffe told The Scoop.
“If a test for a disease is 98 percent accurate, and you test positive, the probability you actually have the disease is not 98 percent,” states Stan Brown, July 29, 2020 on his blog, “Brown Math.” “In fact, the more rare the disease, the lower the probability that a positive result means you actually have it, despite that 98% accuracy. The difference lies in the rules of conditional or contingent probability.” Brown gives examples with numbers to explain why the false positive rate applies to all the tests given minus the number of people who are truly sick – which is not the same as people who test positive on a test.
Too Many Tests
Haas says much of the problem is trying to test a broad population which makes the possibility of false positive rates more probable.
“Targeted testing is a well-established practice for any disease,” Haas later told Health Care News.
Bayesian inference, a statistical inference which adjusts the probability of a hypothesis as more information becomes available, is a good way to look at the problem, Haas says. Assuming the actual number of cases to be constant at any moment in time, the more you test, the higher the number of false positives. The fewer you test, the lower the number of false positives relative to people who are truly sick.
Haas offers another example. “One million Americans develop cancer every year. It’s a terrible disease and we should try to diagnose it earlier to try to get a better cure rate,” Haas said, using an example. “Let’s say there is a test which has a 10 percent false negative rate and a 10 percent false positive rate. We do the test on all 330 million Americans. Of [each] one million new cases of cancer, we pick up 900,000 and they are very grateful.”
The false positives, however, 33 million, are more problematic, Haas said.
“These 33 million Americans will be very upset and will require much more diagnostic work-up,” Haas said. “The 900,000 who have been told correctly of their cancer diagnosis will be mixed in with this group. It is fairly easy to see testing everyone is a very bad idea and will create huge numbers of false positives which is what is basically happening today.”
Haas says although COVID-19 won’t require the same confirmation work-up as cancer, the false positive results are being used to contract trace others, who will be given tests, with or without symptoms, and the false positive problem perpetuates.
“Being ‘safe’ by overcalling or tolerating a high false positive rate encourages one faction of our political blowhards to exaggerate the seriousness of the ‘pandemic,’ as to ramp up spending or borrowing and intruding into American life,” Haas said.
A False Second Surge
Haas says because of the testing problems, it is reasonable to question whether the nation is undergoing a second surge.
“For one thing, we are sampling a totally different population [now],” Haas said. “We are testing people we don’t even have a suspicion that they may even have the illness.”
Testing the general population in this way violates standard screening protocol, Haas says.
“You don’t do screening tests on people you think will have a low incidence of positives,” Haas said.
All Roads Lead Back to the CDC
Early in the pandemic, the CDC prevented other agencies from developing a COVID-19 diagnostic test and then, weeks later, as the virus gained a foothold, produced a test that was unreliable.
What evolved next was the “wild west,” Haas says. “The CDC’s next brilliant move was to allow anyone to develop his own test after ‘internal validation,’ which meant that any backwash could set up their own proprietary test and charge for doing it as many times as the advertising budget could attract paying customers,” Haas said. “I saw yet another ‘test site’ sign this morning on my daily rounds. The owners of what are licenses to print money have no incentives to challenge the validity of their early retirement modalities.”
AnneMarie Schieber (firstname.lastname@example.org) is managing editor of Health Care News.
“Is There a Second Wave?” The Heartland Daily Podcast, August 4, 2020: https://www.heartland.org/multimedia/podcasts/is-there-a-second-wave-guest-edwin-haas