On this page we provide an overview of excess mortality along with charts to explore the data. You can learn in more depth about different measures of excess mortality, their strengths and limitations, and their comparability across countries in our work with John Muellbauer and Janine Aron.
Excess mortality is a term used in epidemiology and public health that refers to the number of deaths from all causes during a crisis above and beyond what we would have expected to see under ‘normal’ conditions.1 In this case, we’re interested in how deaths during the COVID-19 pandemic compare to the average number of deaths over the same period in previous years.
Excess mortality is a more comprehensive measure of the total impact of the pandemic on deaths than the confirmed COVID-19 death count alone. In addition to confirmed deaths, excess mortality captures COVID-19 deaths that were not correctly diagnosed and reported2 as well as deaths from other causes that are attributable to the overall crisis conditions.3
Excess mortality can be measured in several ways. The simplest way is to take the raw number of deaths observed in a given period during the COVID-19 pandemic – say Week 10 of 2020, which ended on 8 March4 – and subtract the average number of deaths in that week over the previous years, for example the last five.
While the raw number of deaths helps give us a rough sense of scale, this measure has its limitations, including being less comparable across countries due to large differences in populations.
A measure that is more comparable across countries is the P-score, which calculates excess mortality as the percentage difference between the number of weekly deaths in 2020–2021 and the average number of deaths in the same week over the years 2015–2019.5
For example, if a country had a P-score of 100% in a given week in 2020, that would mean the death count for that week was 100% higher than – that is, double – the average death count in the same week over the previous five years.
While the P-score is a useful measure, it too has limitations. For example, the five-year average death count might be a relatively crude measure of ‘normal’ deaths because it does not account for trends in population size or mortality. For a more in-depth discussion of the limitations and strengths of different measures of excess mortality, see our article with John Muellbauer and Janine Aron.
Mortality data is incomplete in the weeks, and even months, after a death occurs because of delays in reporting. For example, the chart here shows US data from 20166 on the completeness of death reporting by week after a death occurs. After four weeks, only 54% of deaths have been fully recorded; by eight weeks this figure is 75%, and it doesn’t reach 100% until almost a year after the date of death.7 Similar delays in reporting exist for all countries to varying extents.
To avoid showing data that is incomplete and therefore inaccurate, we do not show the most recent weeks of countries’ data series. The decision about how many weeks to exclude is made individually for each country based on when the reported number of deaths in a given week changes by less than ~3% relative to the number previously reported for that week, implying that the reports have reached a high level of completeness.8 The exclusion of data based on this threshold varies from zero weeks (for countries that quickly reach a high level of reporting completeness) to four weeks.9
The chart here shows excess mortality during the pandemic for all ages using the P-score.10 You can see that some countries – such as England & Wales11 and Spain – suffered high levels of excess mortality, while others – such as Germany and Norway – experienced much more modest increases in mortality. To see the P-scores for other countries click Add country on the chart.
It is important to note that because the P-scores in this chart combine all ages, they are impacted by differences in mortality risk by age and countries’ age distributions. For example, countries with older populations – which have a higher mortality risk, including from COVID-19 – will tend to have higher all-age P-scores by default. When comparing countries it is informative to look at the P-scores for different age groups.
The chart here shows P-scores broken down by two broad age groups: ages 15–64, which contains most of the working age population, and ages 85+, which has the highest mortality risk.12 Two more age groups can also be selected by clicking Add country : ages 65–74 and ages 75–84.
You can see that Spain suffered high levels of excess mortality even for its younger, working population aged 15–64, while Germany experienced relatively low levels of mortality even for its most vulnerable population aged 85+.
Besides visualizing excess mortality as a percentage difference, we can also look at the raw death counts as shown here in this chart.13 The raw death counts help give us a rough sense of scale: for example, the US suffered some 275,000 more deaths than the five-year average between 1 March and 16 August 2020, compared to 169,000 confirmed COVID-19 deaths during that period.
However, this measure is less comparable across countries due to large differences in populations. You can still see the death counts for other countries by clicking “Change country” on the chart.
In our work on the Coronavirus pandemic we visualize the data on the confirmed number of deaths from COVID-19 for all countries. We update this data daily based on figures published by Johns Hopkins University (JHU).
But these figures – as reported by governments and national health ministries – are the number of confirmed deaths due to COVID-19, which may differ from the total death toll from the pandemic for several reasons:
- Some (but not all) countries only report COVID-19 deaths that occur in hospitals – people that die from the disease at home may not be recorded;
- Some countries only report deaths for which a COVID-19 test has confirmed that a patient was infected with the virus – untested individuals may not be included;
- Death reporting systems may be insufficient to accurately measure mortality – this is particularly true in poorer countries;
- The pandemic may result in increased deaths from other causes for a number of reasons including weakened healthcare systems; fewer people seeking treatment for other health risks; or less available funding and treatment for other diseases (e.g. HIV/AIDS, malaria, tuberculosis);
- The pandemic may result in fewer deaths from other causes – for example, the mobility restrictions during the pandemic might lead to fewer deaths from road accidents.
This list makes clear that the two statistics – confirmed deaths due to COVID-19 and excess mortality – are giving a perspective on different questions. The confirmed deaths often undercount the total death toll, but in contrast to excess mortality they contain information about the cause of death. The excess mortality includes not only those who have died from COVID-19, but also those from all other causes. This means both metrics are needed to understand the total death toll of the pandemic.
Excess mortality data is unfortunately not available for many countries, and because the required data from previous years is lacking this will continue to be the case. When the goal is to monitor a global pandemic, this is a major limitation of this metric.
Excess mortality can only be calculated on the basis of accurate, high-frequency data on mortality from previous years. But few countries have statistical agencies with the capacity and infrastructure to report the number of people that died in a given month, week or even day-to-day. For most low- and middle-income countries, such data is not available for previous years.
As we see from the available excess mortality estimates – all listed below – this data is most often only available for richer countries that can afford high-quality data reporting systems.
Researchers can draw on some other sources to estimate excess mortality – such as funeral or burial records – or on data from subnational regions of poorer countries (often the capital). But in many cases no information at all can be obtained.
International organizations are not publishing an international database on excess mortality
Unlike statistics on confirmed COVID-19 deaths – for which several organizations such as the WHO, ECDC, and Johns Hopkins University are collating data for all countries – there is no single source of data on excess mortality.
This is a major problem for policymakers, researchers, and the general public that have a need to understand the ongoing pandemic.
Several media publications and regional data sources are publishing public databases
Several media publications and regional data sources have been publishing excess death data for some countries.
- Human Mortality Database publishes downloadable data for a number of countries on its website. This is the source of the data in our charts.
- The Economist published the first database on excess mortality on GitHub. Its reporting on the topic can be found here.
- The New York Times publishes its dataset on excess mortality on GitHub. Its reporting on the topic can be found here.
- The Financial Times publishes its dataset on excess mortality on GitHub. Its reporting on the topic can be found here.
- The Washington Post publishes its dataset on excess mortality in the US on GitHub. The GitHub page also contains links to the Post’s reporting on the topic.
- Eurostat publishes downloadable data for European countries on its website.