As the pandemic got going, most informed observers, including the present author, expected that the poor nations of the world would face the brunt of it. As I wrote back in March 2020:
The one thing we can be sure about is that the brunt of the pandemic will be borne by the poor nations of the world. During the 1918 pandemic, half the fatalities were in India. This time around, Africa will likely fare even worse because of a major divergence within developing nations. I wager that the best predictors of the distribution of the death toll across nations will be state capacity and health status. The present distributions reflects the intensity of global networks — the tripolar core of the world economy is the most tightly integrated and therefore the earliest to be exposed. But this is merely the beginning. This thing is going to go everywhere; in multiple cycles. The ultimate death toll will obey Matthew's Law — those who are the worst off today will pay the steepest price. Why is our world so unfair??
But what actually obtained was something utterly unexpected. The toll appeared to be upside-down. The rich industrial nations of the world were the most affected; the poor nations of the global south got off relatively unscathed. The covid death toll turned out to be positively correlated to per capita income, socio-development index, mean adult height, protein intake per capita, calorie intake per capita, and absolute latitude.
The puzzle of the upside-down covid death toll led to me to the hypothesis that population genetics was playing a major role in relative exposure to the pandemic. The thesis was grounded in the idea that the populations of low-latitude nations were relatively immune to the novel coronavirus. I found evidence that phylogenetic distance and serological variation predicted the covid death toll in the international cross-section.
I was criticized for failing to adjust for age in these analyses. It was a valid critique — the most significant risk factor for severe covid is age, so the younger populations of the poor nations were less at risk than the older populations of the advanced industrial nations. Since then a lot more data has become available and the pandemic has had a chance to reach even the most isolated populations. Data is now available for twice as many countries, including data on demographics. So it’s a good time to revisit the question.
The next set of scatter plots shows covid deaths per million against two development metrics and two demographic factors. Without age-adjustment, the toll remains upside-down. However, the gradient of both median age and percent of the population older than 65 is very significant.
The share of seniors in the population is stronger predictor of the toll (t = 11.0) than median age (t = 10.0). We therefore use the former to adjust for age. Once we adjust for age, the upside-down pattern vanishes and sanity is restored. The next set of scatter plots shows Pearson residuals obtained from regressing out age effects. The upside-down pattern vanishes after we control for age.
Comparing age-adjusted response and features is equivalent to controlling for the age in a linear regression. That exercise reveals the same pattern. Life expectancy falls into insignificance after we control for demographics.
And so does the human development index.
Although sanity has been restored, a milder version of the puzzle remains. Why don’t underdeveloped nations have higher burdens than the advanced industrial nations? Does Matthew’s Law not apply to this pandemic after all?