Use excess death counts to better appreciate the true extent of the loss of life during the COVID-19 pandemic. Far too much attention has been paid to officially reported COVID-19 mortality rates, which are based on unreliable data and obfuscate the big picture by diluting impact in large-population settings. This posts reviews the global mortality impact through the lens of excess mortality counts. Contrasting these with rates, it illustrates just how much the developing world has suffered during this pandemic.
Much of the policy discourse during this pandemic has relied on officially published data on COVID-19 fatalities, which are presented as mortality rates normalized by the total population. This approach, as will be argued below, has led to the false impression that the death toll in much of the developing world has been minimal.
To better appreciate the true and total impact of the pandemic at the global level, we need a change of focus. We need to shift from just looking at the reported COVID-19 mortality stats to also considering estimates of excess mortality. The official data have fooled us more than once when taken at face value. It is important to take a broader look and consider estimates of excess mortality, which are the gold standard for the measurement of the true and total impact of the pandemic.
In addition, and this applies to both the official stats of COVID mortality and the estimates of excess deaths, more attention would be welcome on the absolute measurements in the form of mortality counts. Pandemic commentary has excessively focused on mortality rates. These are useful to measure intensity and performance, but do a poor job in assessing global impact correctly. In fact, they discriminate against the much more populous developing world which because of its large populations dilutes the per capita numbers.
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 3/4 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?
But the high excess death counts 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 UMICs and LMICs are surprisingly similar to those of HICs whereas those of LICs are surprisingly elevated given their demographic profile. 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.
The results show a huge disparity across regions in both mortality counts and rates. South Asia (SAR) saw a big boost in both metrics around May 2021. But also other regions saw a steady increase in both metrics. Mortality rates in ECA, LAC and NAM are all higher than in SAR, but there are large differences in absolute terms. EAP and SSA have similar rate, with the former outnumber the latter by a large amount. MNA middles on rates and has a relatively small absolute count given also its population size. (Note: For a static version of the above chart as of the latest date, click here).
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 death 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 the global excess death count 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 death counts.
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 death counts. After all, a life lost is a life lost, regardless of borders, so let us measure pandemic mortality more broadly and more accurately. And let us also focus on the absolute counts as opposed to mortality rates that obfuscate the tragedy within developing countries simply because they have a large population size.
Once we do that, the true extent of the loss of life in the developing world emerges. And as this post has shown developing countries have carried the brunt of the total mortality impact of this pandemic. Indeed, estimates of excess death counts support the conclusion that this is – and in fact has always been – mainly a developing country pandemic.
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