Why we shouldn’t test asymptomatics for COVID-19
Common sense would suggest that a test with 99% specificity would return only about 1 in a 100 false positive results. But this is not how it works. The false positive rate is far higher when disease prevalence is as low as the studies just cited have found. In other words: the Positive Predictive Value of screening testing is very low when background prevalence is low (Bokhorst et al. 2012; Skittrall et al. 2020; Dinnes et al. 2021).
Here’s why: If we test 1,000 people randomly in a population where 1% have the illness at issue, and our test is 99% specific to that illness, we will have one true positive and one false positive for each 100 tests. So testing 1,000 people results in 10 true positives and 10 false positives.
Figure 1 below examines COVID-19 antigen testing, a type of test commonly used for screening, including primarily asymptomatics, since antigen tests became available in the summer of 2020. The figure is based on the British Medical Journal (BMJ)’s COVID-19 test accuracy interactive calculator (Watson 2020; see link for the calculator in the references; it’s educational to play with the calculator to see how different inputs affect false results, also shown in Figure 3).
In populating the three cells in the calculator (at the top of the image) we’ve conservatively assumed 1% pre-test probability of active infection, which is, based on the data reviewed above, a higher level of active infection than was found in the large vaccine clinical trials.
We also assumed 58% sensitivity and 99% specificity, which are the findings of a recent Cochrane meta-analysis combining 64 published studies of antigen test accuracy, when used to test asymptomatics (Dinnes, J. et al. 2021).
The result in this scenario is 50% false positives (1 true positive and 1 false positive) — even with a 99% specificity test. There would theoretically be zero false negatives, so the risk of missing actual infections is not at issue.
This result is not surprising because the numbers are the same as the hypothetical scenario just discussed. The Dinnes meta-analysis concludes similarly, but for a lower background prevalence and a slightly higher test specificity (99.6%): “At 0.5% prevalence applying the same tests in asymptomatic people would result in [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.”