This post contains quick links to news stories and articles on false positive tests examples and issues from mainstream newspapers and reputable peer-reviewed journals; understanding the severity of the false positives issue is central to understanding how badly pandemic policy backfired and caused more problems than it solved
I’ve written extensively on how false positives have from the very beginning of the Covid-19 pandemic given the false impression of large numbers of Covid-19 cases, and as a result led to massive backfires in pandemic policy around the world. The false positives issue applies equally to cases, hospitalizations and Covid-19 deaths figures.
Understanding the degree to which “the false positive catastrophe” has led to an exaggeration of the pandemic is essential for ending this pandemic and avoiding the next one too.
The false positives issue is particularly important at times of lower prevalence because lower prevalence of the disease in the population renders almost all test positives false positives when testing is done without regard to symptoms (“asymptomatic testing” or “screening”). However, even at times of higher prevalence of the disease (“spikes”) the same issue renders most test positives false positives.
“Diagnostic testing,” which is testing after symptoms are present, gives rise to significantly less false positives than the screening or asymptomatic testing I focus on in this article.
This article presents a number of mainstream news articles and peer-reviewed scientific papers as references for understanding the false positive catastrophe. It also can be shared with people who are seeking to understand what’s happened the last couple of years, or those who question whether the false positives issue has been a serious issue, since we hear frequently that “false positives are rare.” This is a dangerously inaccurate statement and these resources below demonstrate why. False positives are extremely common, not rare.
First, a few examples of how badly false positives for both PCR and antigen tests have impacted policy choices in specific areas:
A remote region in Australia in early 2022 saw two small villages with 55 false positive PCR tests forced into lockdowns, only to find out later that all positive test results were false positives probably due to contamination. Many of these people also displayed symptoms that were thought to be Covid-19 but it turned out they were all either common flu or cold symptoms (since none of them tested positive for Covid-19 upon verification testing): “additional testing saw the cases reclassified as false positives, with a number of residents suffering other respiratory infections like the flu and rhinovirus at the time of testing.”
Similarly, A Hong Kong lab reported, in June 2022, 136 positive PCR test results in what was thought to be a new spike in cases, only to report soon after that only 3 of these tested positive upon preliminary verification testing — for a total of 133 out of 136 false positives or “indeterminate.” Again these false positives were thought to be the result of contamination. And upon final verification testing, zero of the 136 positive tests were found to be positive, for a total of 136 out of 136 false positives or “indeterminate” results.
77 positive PCR test results from a number of NFL (American football) teams in August 2020 were all found to be false positives upon re-testing, again probably because of contamination.
In Hawaii where I live, all people arriving on the Big Island had to take an antigen test upon arrival. 93% of these test positives were found, upon re-testing, to be false positives.
Las Vegas nursing homes found a very high rate of false positives with antigen tests in 2020, resulting in a suspension of their use — until the federal government stepped in an forced them to continue using these highly flawed tests.
Next, I’ll provide some more general warnings about false positive issues for both PCR and antigen tests:
The FDA warned in November 2020 how antigen tests, even if they are 98% accurate in lab testing, can produce 96% false positives with low disease prevalence (the 96% false positives occur when actual disease prevalence in the population is 0.1%, which is about where it has been during much of the pandemic). This is due to low “positive predictive value” when testing people who don’t have symptoms. This issue has been absolutely central to the false positive catastrophe I’ve been writing about for the last two years. The bottomline, as FDA describes in this article, is that even tests that are very accurate in the lab can badly backfire in the field when testing people who don’t have symptoms.
This New York Times article, “Your Coronavirus Test Is Positive. Maybe It Shouldn’t Be,” from August 2020, quotes a number of experts about finding up to 90% false positive PCR test results due to sky-high “cycle thresholds,” which is the number of times the RNA is amplified in the lab. I wrote a follow-up article on this issue and interviewed former Harvard professor Michael Mina about his statements to the New York Times. Anything over about 30 cycle threshold is very often just a false positive, or at best a “weak positive,” due to excessive amplification.
Another New York Times article warned in January 2022 about very high false positives with respect to birth defects testing, with good examples that apply equally to Covid-19 testing of asymptomatics, and resulting false positives. The article, “When They Warn of Rare Disorders, These Prenatal Tests Are Usually Wrong,” states in its first graphic:
Harvard Medical School professor Westyn Branch-Elliman and two other academics described in a US News & World Report article from July 2021 how Covid-19 testing in schools could, with 0.1% actual disease prevalence and testing of asymptomatics, lead to 71 out of 72 test positives being false positives. They argue that schools should not engage in such testing because of this extremely high rate of false positives. This very high false positive rate arises due to widespread testing of asymptomatic people and highlights why we shouldn’t be testing people without Covid-19 symptoms.
A Guardian article from April 2021 explained why low background prevalence of the disease at issue will result in huge percentages of false positives, based on Bayesian logic. This issue arises with any kind of test for any disease, and is a widely known issue in epidemiology. But for some unknown reason this extremely important problem was mostly ignored in the Covid-19 pandemic.
Next, I’ll offer some peer-reviewed papers that highlight the false positives issues:
A May 2022 paper in JAMA (Connor et al. 2022) found a 62% false positive rate for antigen test screening for Covid-19 in workplace testing. The actual false positive rate was very likely far higher because these researchers simply assumed that the PCR test used to verify the initial antigen tests, used in the workplace testing, is 100% accurate, but we’ve just seen how inaccurate this assumption can be. This high rate of false positives again arises due to widespread testing of asymptomatic people.
Skittrall et al. 2020 (Nov. 2020) calculated that screening 100,000 people chosen randomly from the general UK population in June 2020 would result in 25 times more false positives than true positives (50 false positives and 2 true positives), even using a test with a very high 99.95% specificity in the lab. In actuality, none of the antigen tests or PCR tests are 99.5% specificity even in the lab, so the resulting false positives in the field are even higher than this 50:2 ratio. Again, this highlights the problems from testing people without symptoms.
Healy et al. 2021, a British study looking at 5,110 PCR tests at a hospital in Swansea, Wales, from June 2020 to June 2021. There were 31 positive test results but upon verification testing only 5 of these were found to be likely positives and 26 were found to be “likely false positives” (defined as only a single gene probe positive at over 35 cycle threshold). This is an 84% false positive rate (26/31)! Furthermore, nine of the 26 false positives were thought to be “symptomatic” for Covid, but clearly had other illnesses rather than Covid — much like occurred in the Kimberly, Australia, example above. The authors also warn about the potentially serious consequences of false positives:
— unnecessary treatment and investigation
— missing or delayed surgery
— unnecessary isolation and contact tracing with subsequent negative impact on workforce and resources
— a risk of subsequent increased exposure if the individual changes their behaviour as a result of believing that they have been infected
— the individual being placed with other inpatients with COVID-19 and consequently exposed to the virus.
A Cochrane meta-analysis (Dinnes et al. 2020), thought to be the “gold standard” of scholarship on these issues, makes similar conclusions about the potential for false positives with respect to antigen tests, assuming a test specificity of (99.6%) in their calculations: “At 0.5% [background disease] prevalence applying the same tests in asymptomatic people would result in [a Positive Predictive Value] of 11% to 28% meaning that between 7 in 10 and 9 in 10 positive results will be false positives, and between 1 in 2 and 1 in 3 cases will be missed.” As we illustrate in our preprint below the background prevalence has indeed been about half a percent even during “spikes” in cases, as demonstrated by the large vaccine clinical trials.
Last, I’ll offer a number of essays and papers written by me and my collaborators on the false positives issue:
An article written by me, which is a shorter version of an academic paper (preprint) by me, Dr. Blaine Williams (an ER doctor in Honolulu), and Dr. Daniel Howard (a Ph.D with training in epidemiology), explain the details of the “false positive catastrophe” in the Covid-19 context.
We also explained these issues in a “rapid response” essay, which is reviewed by the editors, for the British Medical Journal (BMJ) from June 2021.
I did the math on public data from China’s Covid-19 testing programs in Shanghai in early 2022, and found that it was very likely that fully 99.9% of Shanghai’s positive cases were false positives, again due to widespread testing of asymptomatic people. And yet China continues to pursue its insanely damaging “Covid-zero” policies, apparently for purely political reasons.
I and my coauthors Dr. Williams and Dr. Howard, also explained in this article how extensively the false positives issue led to large-scale exaggeration of cases, hospitalizations and Covid-19 deaths data. We estimate that all public Covid-19 stats should probably be reduced by 90% in order to yield a more accurate figure for cases, hospitalizations and deaths.