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Excess mortality during the Coronavirus pandemic (COVID-19)

We update this page with the latest available data on excess mortality around once a week, usually on Friday. Last update: 22 October 2020 (23:00 London time). Note: The date this page was updated does not necessarily match the date of the most recent data for a given country.

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.

What is ‘excess mortality’?

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

How is excess mortality measured? How does this allow us to compare countries?

Excess mortality can be measured in several ways. The simplest way is to take the raw number of deaths observed in a given period in 2020 – say Week 10, 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 and the average number of deaths in the same week over the previous five years.

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.

We exclude the most recent weeks of data because it is incomplete

Mortality data is incomplete in recent weeks because of delays in death reporting. For example, based on a 2016 study the CDC estimates that death reporting in the US is “approximately 27% complete within 2 weeks, 54% complete within 4 weeks, and at least 75% complete within 8 weeks of when the death occurred.”5 Similar delays in death 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 1–4 weeks of each country’s data series. The decision about how many weeks to exclude is made separately for each country to only show data that is at least 80% complete, based on empirical studies of delays in death reporting.6

How do levels of excess mortality compare across countries?

Excess mortality for all ages

The chart here shows excess mortality during the pandemic for all ages using the P-score.7 You can see that some countries – such as England & Wales8 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.

Excess mortality by age group

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.9 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+.

Excess mortality using raw death counts

Besides visualizing excess mortality as a percentage difference, we can also look at the raw death counts as shown here in this chart. 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, 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. 

Why is it important to look at excess mortality?

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 the European Centre for Disease Prevention and Control (ECDC).

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 other causes. When during the studied period fewer people have died from other causes (such as road accidents), the excess mortality statistics might suggest a death toll from COVID-19 that is lower than the actual total. This means both metrics are needed to understand the total death toll of the pandemic.

Excess mortality statistics will only be available for a small number of countries

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.

Publicly available data on excess mortality

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.