## EXPLAINER

This chart shows the evolution over time of the ‘relative severity ratio’ at the UN subregional level and makes a comparison of COVID-19 mortality with the top causes of death in 2019 within this subregion.

The relative severity ratio relates COVID-19 mortality to the level and profile of pre-pandemic mortality. The ratio itself is defined simply as the ratio between (1) the total weekly number of deaths with COVID-19 as the underlying cause and (2) the total number of all-cause deaths in 2019 during a similar length of time. In light of data constraints and to foster global comprehensiveness, we take total all-cause mortality for 2019 and scale it down to the period of a week for the comparison under (2).

The relative severity ratio is then used to make two types of comparisons.

• The severity ratio itself involves a comparison with the level of 2019 mortality. We compare COVID-19 mortality during the pandemic with all-cause mortality in 2019 over the same interval of time. If the ratio is, say, 10% then we COVID-19 deaths mortality is similar in magnitude to about a 10th of the dying that happened in 2019 over a similar interval of time.
• With the severity ratio, we can also make comparisons with the profile of 2019 mortality. Consider the share of deaths due a specific cause (e.g. stroke) in total deaths of 2019. This variable is called the cause-specific proportionate mortality rate. If stroke were the top cause of death in 2019, we could compare its proportionate mortality rate (e.g. 9% in all 2019 deaths) with the COVID-19 severity ratio (10%) and argue that COVID-19 is claiming currently more lives than the top cause of death (stroke) did in 2019 over a similar period.

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 “COVID-19 is claiming more lives than the top cause of death did in 2019” may convey a better feel for the severity of the pandemic than a reference to a mortality rate (total deaths per 100k people).

Finally, note that comparisons with top causes of death are with reference to the 133 disease families of the 2019 Global Burden of Disease study (at the third level of ICD-10). We generally select the top nth cause of death, which most closely approximates the peak COVID-19 severity ratio from below. More details on the concept of relative severity are in the paper of Schellekens and Sourrouille (2020) that developed the concept, which can be found here.

## 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.