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Mortality rates are useful but miss an important point: a life lost is a life lost, regardless of borders. Excess mortality counts in the absolute afford a more useful perspective if our goal is to assess the true extent of life lost during this pandemic.
Public commentary on the mortality burden of the pandemic has excessively focused on “mortality rates”. Mortality rates afford a relative perspective: the number of deaths during a specific interval relative to a defined population. They’re useful to measure the intensity of the pandemic and the performance of policy. But they do miss one important point: a life lost is a life lost, regardless of borders.
From a global impact perspective, one may be more concerned with a moderate mortality rate in Bangladesh than a high mortality rate in Belgium as the much larger population of Bangladesh will likely produce a much larger death count in the absolute. This is not to dispel the importance of relative measurements or to diminish the tragedy of death in any country – large or small. But conversely let us not lose sight of the global picture, which shines through most clearly by counting the number of deaths in the absolute.
The absolute number of excess deaths is estimated since such data are not readily available for each and every country. We use the mid-point estimates of cumulative excess mortality derived from the excess death model by The Economist.
The chart shows that the global mortality picture is totally different from what we’re accustomed to: developing countries – not high-income countries – account for the bulk of global mortality. Upper-middle, lower-middle and low income countries comprise the developing world and they make up most of the land mass adjusted by the tally of excess mortality.
For completeness, the chart below combines the absolute perspective with the relative one. Land mass continues to represent excess deaths in the absolute, but the colors now reflects the excess death rate expressed per 100K people. The breaks are quantiles (which divide the frequency distribution into equal groups).
Taking a deeper look, let’s examine the distribution by World Bank income classification. Below we show how the 20 million excess deaths so far are distributed across high-income and developing countries. The developing world accounts for 84% of estimated global excess mortality. Lower-middle-income countries (LMICs) represent most of that. They claim 48% of global excess mortality.
This discussion matters tremendously if we are to advocate for vaccine equity. The relative perspective afforded by reported COVID-19 mortality rates conveys the wrong impression that the pandemic has been mild in developing countries. This argument has been used over and over again to question why countries that have “weathered the pandemic well” thanks to their younger age structure and despite considerable transmission need vaccines in the first place.
There are at least three problems with that argument: (1) alternative measures of excess mortality that take into account deficiencies in data quality and testing regimes arrive at exactly the opposite conclusion: even on a relative basis, developing countries have suffered a more intense pandemic, (2) we cannot lump the developing world into one broad category and ignore the differences within, and (3) as argued in this post, we need to complement our relative perspectives with absolute ones.
From a global social welfare perspective, a life lost is a life lost. At a minimum, equal moral concern should apply to that life no matter where that person happened to live and irrespective of the population size of the country.
Going one step further, universally recognized ethical principles that highlight “priority for the disadvantaged” would in fact attach greater moral concern to a life lost among the disadvantaged. This provides a powerful argument in favor of vaccine equity, since most of the absolute mortality toll has indeed been claimed by countries with lower levels of per capita income.
Note: Thanks to Pierre-Andre Cornillon and Florent Demoraes for helpful coding and technical advice on the cartogramR package, which produced the cartograms shown in this post.
Disclaimer: Posts by the Center for Global Development reflect the views of the authors, drawing on prior research and experience in their areas of expertise. CGD is a nonpartisan, independent organization and does not take institutional positions. Likewise, views expressed do not necessarily reflect those of the United Nations, the United Nations Development Programme, its programmes/projects or governments. The designations employed do not imply the expression of any opinion whatsoever concerning the legal status of any country, territory or area, or its frontiers or boundaries.
