Excess Mortality across Countries in 2020

March 3, 2021

Ufuk Parildar, Rafael Perara, Jason Oke

The Coronavirus (SARS-nCOV2) has caused a marked increase in deaths across the world but with significant variation between countries. Some of this variation can be accounted for by differences in the way countries attribute the cause of death. This bias can be overcome by comparing excess all-cause deaths, which is a more objective measure. In addition, estimates of excess deaths can help us understand not only deaths that are directly attributed to COVID-19 but also those that result indirectly (collateral loss).

The total amount of excess mortality will also depend on the age structure of a population. Countries with age structures weighted towards an older population will experience higher mortality than a country with an age structure weighted towards a younger population. By standardising age structures we can make more appropriate comparisons.

Many early reports comparing excess deaths resulting from the COVID-19 pandemic did not take account of population size, age distribution and focussed mainly on the first phase of the pandemic. Here, we provide updated estimates of excess mortality rates overall of 2020, standardised to a reference population


Weekly mortality data from 37 countries were acquired from the Short-Term Mortality Mortality Fluctuations (STMF) data series in the Human Mortality Database (HMD). We calculated the expected mortality for each country by taking the average of the past 5 years (2015-2019). We calculated sex-specific age-adjusted excess mortality rates by standardising to the European Standard Population (2013) using age-groups of (0-14, 15-64, 65-74,75-84,85+).  Data are presented as age-standardised total mortality per 100,000, age-standardised total excess mortality per 100,000 and percentage increase in mortality per age-adjusted 100,000. Relative increases are useful when comparing countries with marked differences in annual mortality rates.

We built a web-based application that allows the user to graph and tabulates excess mortality statistics for each country using an average of the previous 5-year data. In the table, below we summarise the data for age-standardised mortality for 2020 calculating expected age-standardised mortality using an average of the previous 5-year’s data where possible and the average of 4 years where that data were not available (Chile, Germany, and Greece), using an alternative web-based application that we’ve built.

Access the Weekly Excess Mortality in 2020 app 

Expected Age-standardised Mortality 2020 (per 100,000)
Age-standardised total mortality per  100,000
Absolute excess age-standardised mortality per 100,000
Percentage increase in Mortality per Age-Adjusted 100,000
Austria 938 1009 71 7.6%
Belgium 956 1072 116 12.2%
Bulgaria 1597 1788 191 12.0%
Canada 709 751 42 6.0%
Chile* 1041 1184 143 13.8%
Czechia 1147 1258 111 9.7%
Denmark 1016 972 -44 -4.3%
England & Wales 960 1060 100 10.5%
Estonia 1178 1178 0 0%
Finland 948 919 -29 -3.1%
France 839 895 56 6.7%
Germany* 1016 1049 33 3.3%
Greece* 912 957 45 4.9%
Hungary 1420 1473 53 3.7%
Iceland 755 724 -31 -4.1%
Israel 864 920 56 6.5%
Italy 728 792 63 8.7
Latvia 1446 1414 -32 -2.2%
Lithuania 1393 1468 75 5.4%
Luxembourg 842 852 9.5 1.1%
Netherlands 971 1040 70 7.2%
Norway 893 861 -32 -3.6%
Poland 1216 1391 175 14.4%
Portugal 977 1043 66 6.8%
Scotland 1134 1219 85 7.5%
Slovakia 1219 1236 17 1.4%
Slovenia 996 1116 120 12.0%
South Korea 779 757 -22 -2.9%
Spain 838 946 108 12.9%
Sweden 883 896 13 1.5%
Switzerland 783 817 34 4.3%
USA 1020 1152 132 12.9%

Absolute excess is the difference between the Expected and total age-standardised mortality (column 2 – column 1), percentage or relative excess is (column 2/column 1) -1 *100%

Canada only has data up to week 42, and Iceland and Italy up to week 44. Slovakia only has data up to week 48, and Greece & South Korea up to week 49. Switzerland & Czechia are based on data up to week 50, and Hungary, Slovenia, and the USA based on data up to week 51.


Relative excess mortality in the countries we have examined ranges from -4.3%  to 14.4% and is strongly positively correlated with the recorded number of COVID-19 deaths (r = 0.8). Denmark, Finland, Iceland, Latvia and Norway experienced fewer deaths in 2020 according to our analysis. As we would expect these countries have recorded a lower number of COVID-19 deaths than other countries. For example, Iceland, Norway and Finland have all recorded fewer than 12 per 100k COVID-19 deaths. Denmark and Latvia are perhaps exceptions to this having recorded 32 COVID-19 deaths per 100k and Latvia 54 per 100k.

A number of eastern European countries saw little or no excess deaths in the first half of the year but have experienced significant excess mortality in the second half of 2020. Bulgaria, Czechia, Croatia, Hungary, Lithuania, Luxembourg, Poland, Slovakia, and Slovenia with Poland and Bulgaria exhibiting levels of excess mortality of the same order of magnitude as the countries in the centre of the first wave (e.g.  Spain, France, England and Wales, Italy).

The USA which has often been cited as the worse affected country (often using the total number of COVID-19 deaths) has relative excess of 12.9% which although one of the highest, is below some with even higher relative excess mortality such as Poland and Chile.

Relative standardised excess mortality is one method of measuring the impact of the SARS-nCOV2 pandemic. It is superior to comparing the total numbers  of COVID-19 deaths and arguably more useful than comparing the COVID-19 death rate per 100k as it overcomes the recording bias and measures both direct and indirect consequences of the pandemic. But it has limitations. We have noted that defining the expected number of deaths and thus the excess can vary according to whether a four or five year average is used. In addition, using simple averages of historical mortality data could underestimate if there is a significant downward trend in mortality or overestimated if there are upward trends.

Ufuk Parildar is a third year medical student in the Final Honours School of the University of Oxford.

Rafael Perera is Professor of Medical Statistics and Director of Medical Statistics at the Nuffield Department of Primary Care Health Sciences

Jason Oke is a Senior Statistician at the Nuffield Department of Primary Care Health Sciences and Module Coordinator for Statistical Computing with R and Stata (EBHC Med Stats), and Introduction to Statistics for Health Care Research (EBHC), as part of the Evidence-Based Health Care Programme.

Disclaimer: the article has not been peer-reviewed; it should not replace individual clinical judgement, and the sources cited should be checked. The views expressed in this commentary represent the views of the authors and not necessarily those of the host institution, the NHS, the NIHR, or the Department of Health and Social Care.