EXPLAINER

This chart shows the evolution over time of the ‘excess severity ratio’ across World Bank income groups.  

The excess severity ratio relates excess mortality accrued over the course of the COVID-19 pandemic to the level and profile of pre-pandemic mortality. The ratio itself is defined simply as the ratio between (1) the total number of estimated daily excess deaths (averaged over the course of a week) and (2) the total number of reported daily all-cause deaths in 2019 (averaged over the course of a year). Note that all-cause mortality from 2019 is averaged yearly in the interest of having a globally comprehensive sample.

The excess death estimates are derived from the excess death model by The Economist. We use the mid-point estimates that their model generates. Note that there may be a wide band of uncertainty around these estimates.

The excess severity ratio thus makes a comparison with the level of 2019 mortality. It compares excess mortality during the pandemic with all-cause mortality in 2019 over the same interval of time. If the ratio is, say, 10% then we can say that excess mortality corresponds in magnitude to about a 10th of the dying that happened in 2019 over a similar interval of time.

In the above chart, we focus purely on the evolution of the excess severity across income groups and don’t make any comparisons with the top causes of death in 2019. See the related charts below for such comparisons. 

Note that the expression of mortality in relative terms is a useful way to communicate the severity of the pandemic. Countries will have adapted to their specific patterns of mortality. Deviations from this pattern may create pressure points, such as on the health system. Comparisons with previous patterns give a country-specific and intuitive flavor of the severity of the COVID-19 pandemic. A statement such as “the excess mortality toll currently amounts to more lives lost than the loss of life due to the top cause of death n 2019” may convey a better feel for the severity of the pandemic than a reference to a crude mortality rate (total deaths per 100k people).

BACKGROUND

Pandem-ic uses the World Bank income classification as a major building block in the analysis of the impact of the pandemic.

 

The income classification groups countries in four buckets by per capita income levels: high-income countries (HICs), upper-middle-income countries (UMICs), lower-middle-income countries (LMICs) and low-income countries (LICs). We use the current FY2022 classification, which determines the thresholds of the buckets as follows:  

 

  • LICs are defined as those with a GNI per capita, calculated using the World Bank Atlas method, of $1,045 or less in 2020;
  • LMICs are those with a GNI per capita between $1,046 and $4,095; 
  • UMICS are those with a GNI per capita between $4,096 and $12,695;
  • HICs are those with a GNI per capita of $12,696 or more.

 

See here for a dynamic visualization of how the income classification of countries has changed over time through the current FY2022 classification

 

A good part of this site also analyzes the pandemic by region (where we use the World Bank regional classification and the UN geo-scheme of subregions). In both cases (i.e. across income groups and regions), the universe of countries is based on the World Bank income classification. More on that in the next note.

The universe of countries on this website is determined as follows.

 

  • We start with the FY2022 World Bank income classification, which comprises of World Bank member countries as well as other economies with populations over 30,000 people (see World Development Indicators database). This means that we exclude the Holy See, Cook Islands and Niue since they are are not included in the WB income classification.  
  • We then narrow down this list to 196 countries/economies by retaining only the member states of the UN, one non-member state with observer status (the State of Palestine listed as West Bank and Gaza in the income classification) and two members/observers in UN Specialized Agencies  (Kosovo and Chinese Taipei listed as Taiwan, China in the income classification). This leads to the exclusion of a further 22 territories and dependencies (21 HIC and 1 UMIC) from the World Bank income classification. 

 

Note that the vaccination data is pulled from Our World in Data, which utilizes a slightly different universe of locations. In sticking with the above 196 countries and economies, we have made the following adjustments relative to the OWID universe.

  • Given their UN membership status, we extract the following UN members from US totals and list them separately:
    • Federated States of Micronesia;
    • Marshall Islands;
    • Republic of Palau.
  • Conversely, given that they are not identified as separate members of the UN or UN specialized agencies, we do not separately mention the following entities but instead include their data into the totals of the country they are a territory or dependency of:
    • Hong Kong SAR and Macao SAR are added to China totals;
    • Faroe Islands and Greenland are added to Denmark totals;
    • Aruba and Curaçao are added to the totals of The Netherlands;
    • Data for Anguilla, Bermuda, Cayman Islands, Falkland Islands, Gibraltar, Guernsey, Isle of Man, Jersey, Montserrat, Saint Helena and Turks and Caicos are add to the totals of the United Kingdom.

 

For each of the above adjustments to the vaccination data, we make adjustments to the demographic data that vaccine information is related to (including population size, age structure and priority group size).

 

Finally, note that no adjustments are required to the totals for France as its overseas territories and dependencies are already included.

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.  Given the massive undercounting of the mortality toll both directly and indirectly attributed to COVID-19, excess mortality provides a useful way to get a glimpse of the true mortality toll.

 

Unfortunately, however, data on excess mortality are not universally available. Only 84 countries release some sort of data (national or subnational; regular or one-off) on excess deaths.  This is where the excess deaths model of The Economist comes in as it tries to fill the gaps on the basis of a well-calibrated model that takes advantage of various types of data that CAN be observed.

 

At its core, the model relies on a machine-learning algorithm (a gradient booster) that learns from official excess-mortality data and over 100 other statistical indicators. Where data on excess deaths is available, they are used.  Where such data are not available, the model fills the gaps in the form of single-point estimates.

 

Given the vast degree of uncertainty surrounding any point estimate, the model then uses a bootstrapping method to calculate standard errors. This amounts to using subsets of the full dataset (in terms of country-week pairs) and training different gradient-boosting models on each of these data subsets. The central estimate is derived then from the trained model on the full set of data, whereas the middle 95 of the predications generated by the 100 other models produce the 95% confidence intervals. 

 

Further details on the methodology can be found here and the full model can be viewed here.

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