The prevalence of misinformation coming from the most trusted form of coronavirus test may be more significant than previously thought.
Three types of tests are used to diagnose current, active infection by the COVID-19 virus: PCR tests, rapid molecular tests, and antigen tests. PCR tests are generally held to be the most accurate, and are often used as the “gold standard” against which the other types of tests are measured.
Medical and public health professionals have generally treated positive results from PCR-based tests for COVID-19 as if they are completely reliable. We’re told that if we test positive, then we are infected with the virus, and that positive results in PCR tests are rarely if ever inaccurate. In fact, however, false positives occur in PCR tests for COVID-19 often enough to be a significant problem.
What is a PCR test? PCR stands for polymerase chain reaction. This is a chemical reaction that repeatedly duplicates certain targeted segments of the virus’s RNA, until there’s enough of it to be detected. The targeted segments are carefully selected to be unique to the COVID-19 virus, and thus absent from other genetic material – human tissues or other pathogens – that might be present in human respiratory samples. This means that with a well-designed test, we can be highly certain that any reaction and duplication occurs only with genetic material from the COVID-19 virus, and that no false positives are produced as a result of the test reacting with some other biological material.
However, in practice, there are other ways that false positives can arise. Samples can get mixed up, software glitches can produce erroneous interpretations of test results, and mistakes can be made when entering or communicating data. Because of the high degree of duplication of the targeted genetic segments by PCR tests (which, depending on the number of duplication cycles run, can multiply an initially low concentration of these segments by a billion times or more), trace amounts of contamination can produce false-positive results that are indistinguishable from true positive results. Such minute levels of contamination can be extremely hard to control. False positives from contamination have been regularly documented in diagnostic PCR tests, including in the most highly regarded laboratories.
In this article, I define the false-positive rate for COVID-19 PCR tests as the percentage of samples from uninfected individuals that incorrectly yield a positive test result. We have three lines of evidence indicating that COVID-19 PCR tests have a low but significant false-positive rate. Some of this is derived from studies called external quality assessments, which assess the accuracy of diagnostic tests conducted by laboratories by providing participating labs with blind sets of positive and negative samples to analyze. From the results for the negative samples, we can calculate false-positive rates.
When we began this work in March, no external quality assessments had yet been completed for COVID-19 PCR tests. So we reviewed 43 external quality assessments of PCR tests for other viruses similar to COVID-19, and found that half of these assessments produced false-positive rates between 0.8 and 4.0 percent, with a median value of 2.3 percent.
Since then, results have become available from a few external quality assessments of COVID-19 tests. These have false-positive rates ranging from under 0.4 to 0.7 percent.
Our third line of evidence is from real-world uses of COVID-19 PCR tests wherein positive results were checked with additional tests. In most of these, the false-positive rate was between 0.2 and 0.9 percent.
Evidence for a Significant False Positive Rate (FPR) in PCR Tests for COVID-19
|Data from External Quality Assessments of PCR tests for other RNA viruses||• FPR usually between 0.8% and 4.0%
• Median = 2.3%; Pooled mean = 3.2%
|Data from External Quality Assessments of PCR tests for COVID-19||• FPR between <0.4% and 0.7%
• Pooled mean = 0.6%
|Data from actual use of PCR tests for COVID-19||• FPR usually between 0.2% and 0.9%
These rates may sound low, but when the rate of infection is low, even a small false-positive rate can greatly diminish the reliability of positive test results. For example, consider the scenario diagrammed below, in which 100,000 people are tested, with 1 percent, or 1,000 of them, being infected. If the false-negative rate is 25 percent, a typical estimate from the scientific literature on COVID-19, and the false positive rate is 0.5 percent, a reasonable estimate from our data, then a quarter of the 1,000 samples from infected individuals – that is, 250 samples – will be false negatives, and the rest will be true positives.
The false positives and true negatives are calculated in the diagram in the same way. In the lower part of the diagram, these figures are used to calculate that the percentage of negative results that are false is 0.25 percent, and the percentage of positive results that are false is nearly 40 percent.
So, with a 1-percent infection rate in the test population, a false-positive rate of only 0.5 percent leads to nearly 40 percent of the positive results being wrong. And although the false-negative rate is 50 times higher than the false-positive rate, it is nevertheless much more likely (nearly 160 times more likely) that a positive result will be wrong than a negative result will be wrong. Notice that this doesn’t align with what most health authorities have been telling us, which is that we can trust a positive PCR result as proof that we’re definitely infected, but that we can’t rely on a negative result as proof that we’re not infected. In fact, just the opposite is true – negative results are reliable and positive results are not – when the infection rate is low.
So, how often is the infection rate low enough for false positives to be a problem? The next slide shows the test positivity (what fraction of people tested received a positive result) in New York State from late March to September, and the percentage of positive results that would be wrong, assuming the same false-negative and false-positive rates of 25 and 0.5 percent. As you can see, when the disease was spiking, at the beginning of this period, there would have been very few false positives, and positive results could be trusted. However, as the infection rate and test positivity fell, in May, an increasingly large portion of the positive results would be wrong. Now that new cases in the U.S. are exceeding 100,000 daily, an unprecedented figure, the pendulum appears to be swinging the other way.
However, this data, doesn’t tell the whole story. Individuals who exhibit COVID-19 symptoms are more likely to be infected than individuals who have no symptoms. Thus, people who are tested because they have symptoms will likely have a higher test positivity, especially during an outbreak; for these people, positive results will be more reliable, more likely to be true positives, than the average data would indicate.
On the other hand, people who do not have symptoms will be less likely to be infected and will have a lower test positivity. For these people, positive results will be more likely to be wrong than the averages indicate. Thus, even during a major outbreak, there may be portions of the test population consisting of individuals that are mostly or entirely asymptomatic – individuals tested in nursing homes, homeless shelters, prisons, or other congregate living situations; patients tested automatically upon hospital admission or prior to surgeries; athletes tested as a requirement for participation in sports activities; etc. – for whom positive PCR results may likely be false.
Programming Note: Listen to Andrew Cohen report this story live today during Talk Ten Tuesdays, 10-10:30 a.m. EST.