The Times' piece on "Red Covid" obscures the reality of the pandemic and manipulates data in favor of a self-congratulatory liberalism.
A widely shared article recently appeared in The New York Times’ “The Morning” newsletter titled “Red Covid,” authored by David Leonhardt. This article, presented as news reporting and not an opinion piece, argues that deaths from COVID-19 are “showing a partisan pattern,” with the worst impacts of the disease “increasingly concentrated in red America.” Given that this narrative perfectly flatters a liberal sense of superiority, it has predictably gained substantial traction on MSNBC and on Twitter.
One particular claim in the Times' article caught my attention: that there is a clear and strong association on a county level between COVID deaths and support for Donald Trump in the 2020 election. Specifically, the article alleged that those counties which voted overwhelmingly for Donald Trump had more than a four-fold greater mortality rate than those counties which decisively voted against Trump. If true, that would indeed be a striking observation.
But, as is often the case with epidemiological observations, the question is more complicated than two variables. There are three analytic errors that can lead someone to make false conclusions from what appears to be a meaningful association between two variables: bias, confounding variables, and random statistical error. In this case, the Times’ analysis failed to discuss significant confounding variables.