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.”