Keeping count of the big picture

Media attention has focused excessively on officially reported COVID-19 mortality rates. To assess global impact accurately, we need to look beyond that.

Narrow perspectives fueled by the poor interpretation of data have marred the policy discourse over the entire duration of the COVID-19 pandemic. The use and abuse of mortality statistics in this context have distracted us from the big picture that the brunt of the death toll of the pandemic has been borne by the developing world.  

One reason for this distraction has been the excessive reliance on official COVID-19 mortality data, which have fooled us more than once when taken at face value. Early on, official data underpinned the idea that COVID-19 would leave developing countries “unscathed”. Later this was replaced by the claim that the pandemic has been “mild” for these countries; after all, they have predominantly young populations unlikely to succumb to the severe manifestations of COVID. Therefore, also, there is “no urgency to get vaccines to them”. But once we take a broader look and consider estimates of excess mortality – the gold standard for the measurement of the true and total impact of the pandemic – it becomes immediately apparent that none of the above claims hold true. 

The second reason for the confusion about the global impact of the pandemic is the excessive reliance on mortality rates as opposed to head counts. While mortality rates are useful, data analysis needs to be complemented by head counts if we are to get an accurate perspective of the main locus of the pandemic’s global impact. For it is all too easy to arrive at the conclusion that rich countries have been the greatest contributors to global mortality because their mortality rates have been so high. And indeed they have been and continue to be the highest in the world, at least when we look at officially reported COVID-19 stats. But that, as per the first reason, is not the best way to look at mortality and adds to the distortion of the big picture perspective. 

This post attempts to paint the big picture of pandemic mortality in a correct way. First, it uses estimates of excess mortality as opposed to official reports of COVID-19 deaths. Second, it complements relative perspectives with absolute ones by keeping track of mortality head counts. This combined view, reflecting both counts and rates and relying on excess rather than reported mortality, will be shown in a series of dynamic visualizations. They confirm beyond a shred of doubt that developing country populations have suffered the most during this pandemic.

The big excess mortality picture

Taking a dynamic perspective on excess mortality counts and rates, we plot how these variables have evolved over the course of the pandemic. Unlike conventional practice, counts feature on the vertical axis and rates on the horizontal one, which helps focus attention on the counts (which as argued have been underplayed). Time progresses in 14-day intervals starting in March 2020. The excess death estimates are the mid-points of the intervals produced by The Economist, where we recognize that the margin of uncertainty around them is higher for the poorer countries. 

Results by World Bank income group

Let us start with the aggregated results by the country income groups of the World Bank’s income classification. There are four of them and they have very different population size dimensions. As per the UN’s 2021 medium-variant estimate, high income countries (HICs) count 1.2 billion people, upper-middle income countries 2.6 billion, lower-middle income countries (LMICs) 3.4 billion and low-income countries (LICs) 0.7 billion. The chart scales the bubbles according to population size.

The global excess death count is totally dominated by developing countries. LMICs account for the bulk of it, followed by UMICs, then HICs and only then LICs. Interestingly, as of today, the absolute count in LICs is about 1/2 that of HICs, that of HICs about 1/2 that of UMICs and that of UMICs about 1/2 that of LMICs. (Note: For a static version of the above chart as of the latest date, click here). 

What to make out of this?

  • We would naturally expect that the developing world would account for a high share in global excess mortality since it is very populous. Even accounting for differences in age structure, we would expect larger excess mortality in developing countries. That is simply because their elderly age cohorts remain very large in the absolute even though the elderly share in their populations is lower than in the richer countries. 
  • But the elevated levels of excess mortality in developing countries cannot be explained by demography alone: excess mortality is a lot higher than what we would have expected based on demography.  Relatedly, we find that excess mortality rates in LMICs considerably exceed those of HICs, whereas the those of UMICs and LICs are surprisingly similar to those of HICs. As it turns out, epidemiological odds (risk of infection and risk of death once infected) seem to have been considerable worse in developing countries. Indirect mortality effects have also played a role. See here for a detailed discussion. 

Results by World Bank region

Let us repeat the above analysis focusing this time on the World Bank’s regional classification. This divides the world into 7 regions with the following population sizes (2021 medium variant of WPP): East Asia & Pacific (EAP) at 2.4 billion, Europe & Central Asia (ECA) at 0.9 billion, Latin America & Caribbean (LAC) at 0.7 billion, Middle East & North Africa (MNA) at 0.5 billion, North America (NAM) at 0.4 billion, South Asia (SAR)at 1.9 billion and Sub-Saharan Africa (SSA) at 1.2 billion.

The results show a huge disparity across regions in both mortality counts and rates. The complete outlier is South Asia (SAR), which saw both metrics sky-rocket around May 2021. But also other regions saw a steady increase in both metrics. Interestingly, mortality rates in Europe and Central Asia (ECA) and Latin America and Caribbean (LAC) are quite similar to those in SAR. East Asia and Pacific has low mortality rates but given the size of the continent the count is nevertheless very pronounced. (Note: For a static version of the above chart as of the latest date, click here). 

Country outliers

To complement the above big picture results, the chart below shows the progression of pandemic mortality by country. As before we scale the bubble by a country’s population size. The colors here represent the World Bank’s income classification. We highlight the absolute outliers with their ISO-3 country code. 

India stands out for its enormous increase in the estimated excess mortality count around May 2021. The rise in India’s mortality rate is also very pronounced among large countries. The other major outlier is Russia, which among large countries has not only a high head count but also a very high rate. The other countries that contribute the most to global excess mortality are Brazil, China, Indonesia, Mexico, Pakistan and the USA. The chart below provides a static picture of the latest available information.

Finally, for information, we provide more detail on the results by country focusing on each World Bank income group separately. This time we show the results statically for the latest data available. Within each income group, we label the 10 outliers that have the highest excess mortality counts.  

In conclusion

Global perspectives on pandemic mortality are easily distorted by poor data and narrow thinking. To keep count of the big picture, let us squarely focus on excess mortality estimates and let us not just rely on the relative perspective afforded by mortality rates. A life lost is a life lost, regardless of borders. Mortality rates are useful to assess intensity relative to a given population. But to get an accurate view of the global impact, we need to pay more attention to the head counts. 

As this post has shown, the dual perspective on counts and rates, both based on excess mortality, highlights the sheer tragedy of the pandemic and articulates the oversized contribution of the developing world.

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