Official data have fooled us more than once into regarding COVID-19 as a rich-country pandemic


Official data on pandemic mortality have fooled us more than once. Early on in the pandemic, official data underpinned the claim that COVID-19 would leave the developing world unscathed. Later on, again underpinned by official data, this became: the pandemic has been mild in developing countries with young populations and therefore they do not need vaccines. But nothing could be further from the truth once we take a broader look. This post documents the discrepancies between official data and the most recent estimates on excess mortality. A wholly different picture emerges. 

“COVID-19, the rich-country disease” 

Early on in the pandemic, Cash and Patel asked whether COVID-19 had subverted global health. They declared that “for the first time in post-war epidemics, there is a reversal of which countries are most heavily affected by a disease pandemic” (The Lancet, May 2020). Rich countries have been affected the most – not the rest of the world which is “historically far more used to being depicted as the reservoir of pestilence and disease.”

The statement of course resonated with the data officially available at that time.  As the first chart below shows, the share of high-income countries in daily (but weekly averaged) COVID-19 deaths reported globally reached a high of 92% on April 1, 2020. In cumulative terms – the second chart – the high-income country share reached 87%.  


Despite considerable fluctuation, the share of high-income countries in reported COVID-19 mortality remained persistently high as the pandemic matured. In fact, the daily shares reached another peak of 60% around January 2021 and have recently exceeded again the 75% mark (even though of course the global number of deaths is a lot lower now thanks to vaccines). In cumulative terms, we can see that the high-income share seems to have stabilized at around 37%. Even that remains remarkably high, because high-income countries represent only 15% of the global population. 

But the high share of high-income countries could be easily explained: the richer countries have older societies, where the elderly are more likely to be grouped in care homes. The reasoning was appealing: developing countries have young populations and COVID-19 discriminates in favor of the young. It was used successively to explain why the pandemic may likely pass the developing world and later on, as soon as it became clear that this was false, why by and large the impact had nevertheless been mild among developing countries. 

The argument of a mild pandemic in the developing world later morphed into efforts to undermine the call for vaccine equity between rich and poor countries.  As the chart above shows, official mortality rates of especially lower-middle-income (LMIC) and low-income countries (LIC) were a lot lower than those for high-income countries (HICs). They are a lot lower even today. Why then is there such pressing need to redeploy scarce resources to this poorer half of the world? And even if vaccines were more abundant, as they are now, why should COVID-19 vaccination deserve priority relative to other pressing health needs in such countries?

Myths reliant on poor data and narrow thinking

The trouble with the above arguments is that they are based on poor data and narrow thinking. 

Poor data because data quality concerns have been pervasive during the pandemic. Such concerns have been not just the preserve of the developing world – many of the richest high-income countries have faced difficulties in correctly measuring and attributing COVID-19 as a cause of death. At the same time, we need to recognize that pre-existing inadequacies in CRVS (civil registration and vital statistics) systems before the pandemic will have amplified the challenge of capturing pandemic-related mortality correctly.  

Narrow thinking because the mortality toll of the pandemic goes well beyond direct COVID-19 mortality and includes the fatalities that are indirectly related as well as the fatalities that may have been avoided. Examples of additional fatalities include deaths due to unmanaged or undermanaged diseases as resources got redirected to COVID and existing patients became more hesitant to visit hospitals. Examples of deaths that have been avoided can be related to the reduction of other infectious diseases like the flu thanks to mask-wearing and distancing. 

Excess mortality data provide a way to partly redress the problem of poor data and narrow thinking. Excess mortality can be defined as the gap between the total number of deaths that occur for any reason and the amount that would be expected under normal circumstances. To the extent that deaths are captured as part of all-cause mortality, excess mortality estimates pick up a rise in misattributed fatalities. It also captures the broader effects on mortality that are beyond ordinary given previous patterns.

Unfortunately, data on all-cause excess mortality are sparsely available, with less than half of all countries providing regular, timely and comprehensive information. This is why exercises to capture the true death toll of the pandemic need to rely on estimates, where excess deaths tend to be estimated directly rather than derived from the difference in observed all-cause mortality and an extrapolated trend. The excess deaths model of The Economist is one such example. It constitutes a colossal attempt to fill the data gaps with estimates based on the information that IS available: over 100 indicators that associate with and help predict excess deaths. 

In what follows, we compare the officially reported COVID-19 mortality data and the estimated all-cause mortality data from The Economist. We will take the mid-point estimates. We will differentiate between absolute numbers and relative ones (the mortality rates). Finally, we will make the comparisons by World Bank income classification, World Bank region and UN subregion. 

Reported COVID-19 data vs excess mortality estimates

In the charts below, we contrast reported COVID-19 deaths with estimated excess deaths. 

Results by World Bank income group

First, let’s look at the results by World Bank income group. The left axis in the chart immediately below expresses the absolute mortality toll in millions for each income group; the right axis shows the mid-point excess mortality count. 

Note how similar the numbers are for high-income countries. Also note how the slope of the curve between the two concepts tilts rather dramatically as we progress from low to upper-middle and especially lower-middle income groups. Indeed, the lion share of absolute excess mortality has happened in middle-income countries (LMICs and UMICs).


In the chart above, we show these same results in relative terms. In other words, we compare death rates per 100,000 people based on reported COVID-19 mortality data and estimates of excess mortality capturing all causes. This yields another set of interesting results. Excess death rates in high-income countries are somewhat higher than reported COVID rates, but the huge increases are observed in the developing world. The increase for LMICs is eye-popping. But also for LICs and UMICs we see very considerable changes.

From the charts above, it is clear that global health has not been subverted. First of all, on an absolute basis (counts of the dead) and as a share in the global totals, the developing world makes up most of global mortality (see last section on interpretation for further discussion). Second, even on a per capita basis, it is not the high-income world that has suffered the most. No, lower-middle-income countries have suffered far more, whereas upper-middle-income countries slightly more. Third, low-income countries have registered mortality rates that are likely far higher than what the official data would suggest. 

Results by World Bank region

Let us now look at the differences from a geographical point of view and use the World Bank’s regional classification that divides the world into 7 regions.

The absolute comparisons in the chart immediately below suggest again that South Asia is responsible for most of excess mortality. Second is Europe & Central Asia and third is East Asia & Pacific. (Note that North America might optically look as if it sees a decline, but actually it rises marginally.) 

The relative comparisons suggest that South Asia has had so far the highest cumulative excess mortality rate and also the steepest slope with respect to the official data. South Asia is closely followed by Europe & Central Asia and Latin America & Caribbean. Note also the big rise we see for Sub-Saharan Africa to levels well above East Asia & Pacific. The second-steepest rise (after South Asia) is for Middle East & North Africa.

Results by UN subregion

The World Bank’s regional classification is rather aggregate and divides the world into regions that combine geographic areas (for internal reasons) that may exhibit very distinct patterns (e.g. Europe and Central Asia). Let us therefore also look at the subregional classification of the UN, which provides more granular detail. 

What pops out even more now in the absolute comparison is the contribution of Southern Asia. This subregion stands out completely and makes all other regions pale in comparison. Of course that is, in large part (though not exclusively), driven by the contribution made by India, which has the second-largest population in the world.

Interestingly, on a per capita basis, it is Eastern Europe that stands out both in terms of slope and level: it has by far the highest excess mortality rate and the discrepancy with the official data is also the greatest in this region. This is for the most part driven by Russia according to the estimates by The Economist. Central America (which includes Mexico as per the UN’s M-49 geo-scheme) stands second and Southern Africa third.

The other pattern that is remarkable is that Northern Europe and Western Europe are the only two subregions where the excess mortality rate is lower than the reported COVID-19 rate. All other twenty regions see higher excess mortality rates and the slopes are dramatically steeper for the regions that include many developing countries. 


What to make out of all of this? In answering that question let’s return to the perspective of the global mortality distribution by World Bank income classification – as we will argue, the proxy of development afforded by the income classification will matter more than whether or not a country happens to be located in a particular geographical region. 

The high-income country share is three times lower

The chart below recaps the result to be explained. It presents the same data that we reviewed before, but this time not as absolute totals or relative rates, but as shares in global totals (the global total for reported COVID fatalities and the global total for estimated excess deaths). We also add the “developing world” group into the income classification, which includes all countries that do not belong to the high-income group and thus comprises of the UMICs, LMICs and LICs.

The high-income share in what we think is the true death toll of the pandemic is estimated to be three times lower than the share in what is reported. Indeed, the HIC share in estimated excess mortality is 13%, whereas the share in officially reported COVID-19 deaths is 38%. These are cumulative numbers as of the latest date available (mentioned in the footnote to the chart). 

By definition, the patterns observed in the developing world are the mirror image here of what transpired in the high-income countries, since we’re dealing here with shares in global totals. Thus, the share of the developing world in excess mortality is currently 87%. This is well above its share of 62% in what has been reported so far. 

Note however the stark differences within the developing world. The numbers go down for UMICs as we move from reported COVID deaths to estimated excess deaths, but they go up much more considerably for LMICs and LICs. The LMICs stand out with a share in excess mortality that is 2.5x the share in reported mortality. The LIC share in excess mortality is lower, but its discrepancy with reported mortality is the greatest: the difference reflects a factor of 6.

Demography explains a lot 

One reason why developing countries contribute a lot to mortality – and let’s focus here just on direct COVID-19 mortality – is demography (population size and age structure).  Developing countries are on average younger relative to their own demographic pyramids, but because they are so much more populous than the “older” high-income countries their elderly populations outsize those in richer countries by a significant margin.

This is why we predicted back in April/May 2020 that the developing country share in the global COVID-19 death toll would rise to at least 70% based on demography alone. Back then the share stood at just 16%. Sadly, this prediction has come true and the increase has more than materialized (see next subsection for the excess mortality perspective). At the time, though, the prospect of such a massive shift of the mortality burden to the developing world seemed unfathomable (as this article in the Guardian discusses). 

How did we arrive at the 70% prediction? The simulation isolates the effect of demography, which captures here both population size and age structure. We simulated the hypothetical post-pandemic distribution of the global death toll under the assumption that all countries are equal in the way the pandemic affects them, except for population size and age structure. In other words, we assume that everyone gets infected at the same rate and faces the same age-adjusted infection fatality risk.

The simulations capture two opposing effects. First, richer countries are older so they should attract higher mortality rates on account of purely age structure (in other words, their share in global mortality should exceed their share in global population). Second, rich countries are less populous than poorer countries, so while we would expect they have higher mortality rates, their actual share in global mortality should be a lot smaller than the share of poorer countries given the differences in population size. 

Worse epidemiological odds and indirect effects

Let us now combine the previous two insights – that excess death shares exceed reported death shares and that demographically-inspired all-else-equal simulations of the global mortality distribution exceed reported death shares. 

The chart below shows in the first bar (in blue) the all-else-equal simulations. Again, asides from demography, all else is kept equal: everyone around the world, in rich and poor countries, face the same epidemiological odds in terms of getting infected and facing death once infected. In other words, infection prevalence rates (IPRs) are constant and identical across countries, age cohorts and over time; age-adjusted infection fatality rates (IFRs) vary across age cohorts but are the same across countries and over time. The second bar (in red) then shows the mortality distribution through the lens of excess death estimates. 

The fact that the excess death share of the developing world is well above the all-else-equal simulations of the mortality toll suggests a combination of two things: that epidemiological odds have been much worse for the developing world (i.e. higher infection prevalence rates and/or higher age-adjusted infection fatality rates) and/or the non-COVID-19, indirect effects of the pandemic as captured by excess deaths have been more pronounced. 

The above conclusion is diametrically opposite to the notion that “COVID-19 has subverted global health” in the sense that we have seen a reversal in the countries traditionally most-affected by a disease pandemic (i.e. high-income instead of lower-income developing countries). 

Not only did the absolute death toll (and hence the share in global deaths) catch up with the world’s demographic reality, we are also experiencing far higher death tolls in per capita terms in several countries and regions. These are filtering through the income classification and elevating developing country rates well above those of high-income countries. 

We can think of many structural reasons why that would be the case. Infection prevalence has likely been fueled by environmental factors such as urban density as well as poverty and informality, which complicate physical distancing. Over 1 billion people, mostly in developing countries, live in slums. Flattening the curve will therefore be more difficult in many developing countries, meaning that preexisting health capacity constraints will become binding more quickly.

Age-specific infection fatality rates are also likely more elevated than in HICs. Comorbidities are highly prevalent in the developing world. Of the 1.1 billion people with hypertension, two-thirds live in developing countries. Over the last decade, the number of cases and prevalence of diabetes has risen most quickly in the developing world. Also,  limited access to quality health care in developing countries would mean that many ailments would be left untreated or undertreated, heightening vulnerability.

In sum

Official data on COVID-19 mortality have fooled us more than once. But the sad reality is that developing countries have been far more severely affected during the pandemic than is commonly thought. They have indeed borne the brunt of the pandemic’s global mortality impact.  

In other words, global health has not been subverted: traditional bottlenecks in developing countries have still exerted a disproportionate influence, reducing and offsetting the benefits of any demographic hedges afforded by a younger population. The call for vaccine equity should therefore not be dismissed on account of “the pandemic being more mild in the developing world”. 

COVID-19 acts as a heat-seeking missile speeding toward the most vulnerable That metaphor applies not just to the rich world; the vulnerable in the rest of the world are not more immune. They have proven to actually be easier targets.