Early in Taffy Brodesser-Akner’s 2019 novel Fleishman Is in Trouble, there’s a scene in which a man recently separated from his wife is interrogated by another parent in his child’s class.
It was a familiar pattern: “Had things been hard for long?” “Had you tried therapy? “Did you guys have a regular date night?”
Brodesser-Akner’s character observes, “these questions weren’t really about him…people pretended to care for him while they were really asking after themselves.”
So it is with the COVID-19 crisis.
As David Lat, a legal blogger-turned-recruiter who was on a ventilator at NYU Langone Medical Center, put it, “Suddenly getting lots of queries about my health (from folks not familiar with my story). I’m 44, not overweight, no smoking, no drugs, hardly any drinking. No health conditions except exercise-induced asthma, which I manage with an inhaler.”
We want to know these things, not because we are particularly curious about Lat, but because we want to reassure ourselves by distinguishing our own medical history and demographic profile from his and others afflicted with severe or deadly cases of COVID-19.
In reporting COVID-19 fatalities, state health authorities seem to be trying to strike a balance between protecting patient privacy and feeding the public’s insatiable desire for details.
Different states are trying different approaches. Massachusetts, where I live, is reporting deaths by age (“100s,” “90s”), sex, county, preexisting conditions (yes/no/unknown), and whether the person had been hospitalized.
There have been medical journal articles and journalistic speculation linking COVID-19 risk to everything from blood type (A is higher risk, O is lower) to blood-pressure medication (it might hurt, or it might help). There may be genetic predispositions. Merely describing someone as having a “preexisting condition” isn’t much help, as by some expansive definitions (high blood pressure, obesity) the federal government has claimed that as many of 50 percent of Americans have some sort of pre-existing condition.
It’d be useful for the states or federal authorities to publish this information. Were the coronavirus fatalities smokers or vapers? If they had pre-existing conditions, what precisely were they? What prescription or over-the-counter medications were they on?
Modern “big data” analytic computer science is quite sophisticated at finding patterns in data like this, especially if there is a way to compare the data for the COVID-19 deaths with recovered severe cases, with milder cases, or with the general population.
The point isn’t only to provide reassurance to the public, but also to guide policymakers who have to make decisions on things such as opening or closing public schools, libraries, or playgrounds. Individuals make risk assessments all the time. Do I get on this airplane, get in this Uber, walk down this city sidewalk, go for this swim in the ocean, take this downhill ski trail, eat this steak with French fries? In most cases those choices are informed by life experience and, in some cases, by rigorous long-term observational studies like the Framingham Heart Study and the Nurses’ Health Study.
What’s baffling about COVID-19 is that it is so new—literally, the novel coronavirus—that people don’t have a decent sense of what their risk is. Testing is so limited that no one understands well what the chances are of dying of it are. The chances could be less than one in a thousand, or they could be more.
Anyway, government officials have taken aggressive action in response to the disease, from infringing on freedoms of assembly and religion to ordering General Motors to make ventilators and imposing stay-at-home orders on large populations.
So long as the government is ordering people and businesses around, let it order up some transparency. It may help find a cure for the disease, or, at the very least, allow preventive measures that are more narrowly tailored than shutting down much of the American economy. Maybe insurers can use the data to devise actuarial tables, allowing people to eventual insure some of their COVID-19 risk.
Best of all, such transparency can help individuals make better informed, less panicked decisions about their own risks. Without it, instead of making our own free choices, we’re all prisoners of politicians following rapidly changing epidemiological models.
To return to the Fleishman Is in Trouble example, it’d be as if rather than letting individuals decide for themselves whether to get married, the politicians decided to pause all dating based on some demographer’s prediction about the future divorce rate. People prefer to know the details of the individual cases, and for good reason.
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