Covid-19: false god of test positivity

The Covid-19 pandemic has brought a lot of problems for all of us. But on a personal level it has solved one problem for me — I don’t have to painstakingly explain what I do for a living. I still remember that on a flight in the US when someone came to know that I am studying epidemiology, he was happy to know that he was sitting next to a ‘skin doctor’. People invariably linked epidemiology to ‘epidermis’ (skin). Even for my kids it was very confusing to explain to others what their father does. Now everyone knows who epidemiologists are and what they do.

But here is another problem. As soon as the pandemic started, I started seeing what we now call ‘hobbyist’, ‘armchair’ and ‘part-time’ epidemiologists spewing different terms of epidemiology in high-level meetings without understanding the full complexities of these terms. R0 (R naught), herd immunity, ‘Big N’ and case positivity to just name a few. Where these terms are critical for epidemiologists to understand the full complexities of an epidemic, for most laymen, they become absolute indicators of good and bad work. I have discussed the ‘false god of herd immunity’ earlier so now let’s talk about the false god of test positivity.

In epidemics, if we do not have a good disease reporting (surveillance) system, then we try to validate whatever information we are getting through different lenses. One is case positivity rate meaning if 100 samples were sent to a laboratory, how many were positive. The lower this number is, the lower the prevalence of disease will be in the community. Epidemiologists look at the population demographics and risk factors where this testing is being done and only then they generalise this number. Goodhart's law in economy tells us: “when a measure becomes a target, it ceases to be a good measure.” Unfortunately, in the absence of health intelligence systems, all the push from the top was for a low test positivity rate. No one looked in-depth at how this number is being achieved. Recently, there was an investigation in a major city where non-human blood samples were being tested (to fulfil assigned daily test quotas and still keeping test positivity numbers low). In another city, health staff decided to keep testing a healthy group again and again for the same reasons. International travellers, who needed these diagnostic tests and are normally healthy, made those numbers come down by their diluting effect.

Do not feel bad as this happens all over the world. Recently it was reported that managers of behemoth Amazon hire people who are not good candidates so they could later be fired. Confused? Managers at Amazon were given an attrition rate, so that they hire the best available people, see their work for a year and let the lowest performing person go, so that only the best talent stays on the company payroll. So now managers keep their required attrition rate by just hiring expandable candidates without the need of making heart wrenching decisions of who stays and who goes. Buying in on any indicator without fully understanding how we are getting there is getting into a dangerous illusion.

For the test positivity indicator, even though it has been a “target” for more than a year now, we still don’t know how many of these test results are repeated while a person is diseased or has recovered. Do these numbers include focused testing in an organisation or an area? What is the percentage of these people who had any symptoms? How many of these tests are of contacts of a positive case? Any epidemiologist will ask these and many more similar questions to fully understand the implications of a number before making any judgements. Our stakes are still high as the variant in our neighbourhood is fast spreading around the world and is being blamed for recent surges. After more than a year if we still can’t figure out how test positivity is being calculated then let’s bury this false god and set up a real time disease surveillance system.

Published in The Express Tribune, June 5^th, 2021.

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