I have a wish list of data. Change, as all good wish lists must. The current wish list contains a lot of information related to rapid antigen tests.
I’ve been writing for months about the context in which it makes sense for these tests to be used, and the context in which it doesn’t. I have also suggested a trick (two rapid antigen tests administered in parallel) if these tests need to be administered even in the context where it doesn’t make sense. There is a reason for this. Many rapid antigen tests are inaccurate when it comes to so-called false negatives. In layman’s terms, this means that they show infected people as uninfected, potentially disastrous in the case of Covid-19 because it means that infected people will be free and infect others, including those in the most vulnerable population segments.
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But let’s go back to the wish list.
I would like all states to separately mention the number of reverse transcription polymerase chain reaction (or RT-PCR) tests, a mouthful, but these molecular tests are very accurate, and the number of rapid antigen tests that performed every day, don’t just provide a consolidated number. I’d also like you to mention how many RT-PCR tests resulted in a positive identification and how many antigen tests they gave. This may require some changes in the way the data is reported: there is no direct correspondence between the daily test and the new case numbers (the latter correspond to the previous day’s numbers when it comes to RT-PCR tests , sometimes to tests performed two, even three days before), although everyone assumes there is one. But it can be done.
This information is important because antigen testing can reduce positivity rates.
This is how math works. According to Harvard Medical School, for example, the false negative rate in antigen tests could be as high as 50%. Consider a population in which the prevalence of coronavirus disease is 20%, a proportion that is lower than that obtained in serological surveys (antibody tests) in some parts of the country. This means that in a population of 100 people, 20 are infected. Suppose that this population is analyzed, half with RT-PCR tests and the rest with antigen tests, and that the number of infected is providentially shared equally between these two groups. Of the 50 who undergo RT-PCR tests, 10 are identified as positive (there are certainly times when these tests are also incorrect, but such cases are rare). Of the 50 who undergo antigen testing, assuming a 50% false negative, only five are identified as positive. The overall positivity rate is 15%, when in fact it should be 20%. As the proportion of antigen tests in general tests increases, it is clear that the positivity rate will decrease.
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This is what states that use a disproportionate amount of antigen testing should guard against. At one level, the inaccuracy of the test means that a significant number of infected people are still at large (as opposed to quarantined). In another, it means that the actual positivity rate in the state is greater than the reported number. Because many of the infections are mild or asymptomatic, infected people are unlikely to require hospitalization, although there is always the possibility that they will infect someone from a vulnerable segment.
During the past three days (September 8-10), between 85% and 87% of all tests performed in Bihar (the state conducted more than 100,000 tests each day) were rapid antigen tests. Bihar’s average positivity rate during these three days was 1.2%.
In the same period, between 62% and 67% of all tests conducted in Uttar Pradesh (the state conducted at least 140,000 tests on each of the three days) were rapid antigen tests. The average positivity rate for Uttar Pradesh during these three days was 4.6%.
And, in Delhi, between 78% and 84% of all tests performed over the past three days – the capital performed at least 45,000 tests each day – were rapid antigen tests. Delhi’s average positivity rate for these three days was 7.6%.
Of the three, Delhi is the only state that has seen the trajectory of positivity rates in regions that perform adequate testing (an increase to a peak and then a decline; Delhi’s maximum positivity rate was 36.9 % on June 13).
I have chosen these three, but there are also other states that use a significant amount of antigen testing.
There is a context in which antigen tests can and should be used, but their indiscriminate use can present an inaccurate picture.
The good news: Accurate antigen tests have been developed (an earlier installment in this column wrote about one) and may soon be available in India.
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