As a result of the COVID-19 pandemic, the number of excess deaths in Europe reached a peak in 2020 with almost 400 thousand. Excess deaths in Europe also remained relatively high in 2021 and 2022. Through week 27 in 2025, around 49 thousand excess deaths were recorded. Excess death is a metric in epidemiology of the increase in the number of deaths over a time period and/or in a specific group when compared to the predicted value or statistical trend over a reference period or in a reference population.

For the week ending August 1, 2025, weekly deaths in England and Wales were 902 below the number expected, compared with 983 below what was expected in the previous week. In late 2022 and through early 2023, excess deaths were elevated for a number of weeks, with the excess deaths figure for the week ending January 13, 2023, the highest since February 2021. In the middle of April 2020, at the height of the COVID-19 pandemic, there were almost 12,000 excess deaths a week recorded in England and Wales. It was not until two months later, in the week ending June 19, 2020, that the number of deaths began to be lower than the five-year average for the corresponding week. Most deaths since 1918 in 2020 In 2020, there were 689,629 deaths in the United Kingdom, making that year the deadliest since 1918, at the height of the Spanish influenza pandemic. As seen in the excess death figures, April 2020 was by far the worst month in terms of deaths during the pandemic. The weekly number of deaths for weeks 16 and 17 of that year were 22,351, and 21,997 respectively. Although the number of deaths fell to more usual levels for the rest of that year, a winter wave of the disease led to a high number of deaths in January 2021, with 18,676 deaths recorded in the fourth week of that year. For the whole of 2021, there were 667,479 deaths in the UK, 22,150 fewer than in 2020. Life expectancy in the UK goes into reverse In 2022, life expectancy at birth for women in the UK was 82.6 years, while for men it was 78.6 years. This was the lowest life expectancy in the country for ten years, and came after life expectancy improvements stalled throughout the 2010s, and then declined from 2020 onwards. There is also quite a significant regional difference in life expectancy in the UK. In the London borough of Kensington and Chelsea, for example, the life expectancy for men was 81.5 years, and 86.5 years for women. By contrast, in Blackpool, in North West England, male life expectancy was just 73.1 years, while for women, life expectancy was lowest in Glasgow, at 78 years.

The actions of Public Health France

Public Health France’s mission is to improve and protect the health of populations. During the health crisis linked to the COVID-19 epidemic, Public Health France is responsible for monitoring and understanding the dynamics of the epidemic, anticipating the various scenarios and implementing actions to prevent and limit the transmission of this virus on the national territory.

Description of the dataset

This dataset describes the level of standardised excess mortality during the COVID-19 outbreak, at the departmental and regional level.

The level of excess mortality is described for two age categories: — for all ages; — for persons over 65 years of age.

Method of calculating levels

The data are derived from the administrative part of the death certificate, collected by the civil registry offices of the municipalities having a dematerialised transmission with INSEE. The observed number of deaths is compared to an expected number, estimated from a statistical model established by the EuroMomo consortium and used by 24 countries or regions in Europe.

The estimation of excess deaths is based on the calculation of a standardised indicator (Z-score), which makes it possible to compare excesses between different geographical levels or age groups.

The Z-score is calculated by the formula: (observed number — expected number)/standard deviation of expected number.

The five categories of excess are defined as follows: — No excess: standardised Death Indicator (Z-score) < 2 — Moderate excess of death: standardised Death Indicator (Z-score) between 2 and 4.99 — High excess of death: standardised Death Indicator (Z-score) between 5 and 6.99: — Very high excess of death: standardised Death Indicator (Z-score) between 7 and 11.99: Exceptional excess of standardised death indicator of death (Z-score) greater than 12

Limits of the calculation method

The estimated excesses are established on a set of 3000 municipalities for which Santé publique France has a long history of data. These 3000 municipalities account for 77 % of national mortality, varying from 63 % to 96 % depending on the regions and from 42 % to 98 % depending on the departments.

Taking into account the legal deadlines for declaring a death to civil status and the time taken by the civil registry office to enter the information, a period between the occurrence of the death and the arrival of the information at Santé publique France is observed. This period can be extended punctually (public holidays, extended weekends, bridges, school holidays, very strong epidemic period, confinement). Mortality data are considered consolidated within 30 days.

Background and ObjectivesThe official number of daily cases and deaths are the most prominent indicators used to plan actions against the COVID-19 pandemic but are insufficient to see the real impact. Official numbers vary due to testing policy, reporting methods, etc. Therefore, critical interventions are likely to lose their effectiveness and better-standardized indicators like excess deaths/mortality are needed. In this study, excess deaths in Istanbul were examined and a web-based monitor was developed.MethodsDaily all-cause deaths data between January 1, 2015- November 11, 2021 in Istanbul is used to estimate the excess deaths. Compared to the pre-pandemic period, the % increase in the number of deaths was calculated as the ratio of excess deaths to expected deaths (P-Scores). The ratio of excess deaths to official figures (T) was also examined.ResultsThe total number of official and excess deaths in Istanbul are 24.218 and 37.514, respectively. The ratio of excess deaths to official deaths is 1.55. During the first three death waves, maximum P-Scores were 71.8, 129.0, and 116.3% respectively.ConclusionExcess mortality in Istanbul is close to the peak scores in Europe. 38.47% of total excess deaths could be considered as underreported or indirect deaths. To re-optimize the non-pharmaceutical interventions there is a need to monitor the real impact beyond the official figures. In this study, such a monitoring tool was created for Istanbul. The excess deaths are more reliable than official figures and it can be used as a gold standard to estimate the impact more precisely.

As of January 13, 2023, Bulgaria had the highest rate of COVID-19 deaths among its population in Europe at 548.6 deaths per 100,000 population. Hungary had recorded 496.4 deaths from COVID-19 per 100,000. Furthermore, Russia had the highest number of confirmed COVID-19 deaths in Europe, at over 394 thousand.

Number of cases in Europe During the same period, across the whole of Europe, there have been over 270 million confirmed cases of COVID-19. France has been Europe's worst affected country with around 38.3 million cases, this translates to an incidence rate of approximately 58,945 cases per 100,000 population. Germany and Italy had approximately 37.6 million and 25.3 million cases respectively.

Current situation In March 2023, the rate of cases in Austria over the last seven days was 224 per 100,000 which was the highest in Europe. Luxembourg and Slovenia both followed with seven day rates of infections at 122 and 108 respectively.

Objective: This study examined cumulative excess mortality in European countries in the year of the Covid-19 pandemic and characterized the dynamics of the pandemic in different countries, focusing on Hungary and the Central and Eastern European region.Methods: Age-standardized cumulative excess mortality was calculated based on weekly mortality data from the EUROSTAT database, and was compared between 2020 and the 2016–2019 reference period in European countries.Results: Cumulate weekly excess mortality in Hungary was in the negative range until week 44. By week 52, it reached 9,998 excess deaths, corresponding to 7.73% cumulative excess mortality vs. 2016–2019 (p-value = 0.030 vs. 2016–2019). In Q1, only Spain and Italy reported excess mortality compared to the reference period. Significant increases in excess mortality were detected between weeks 13 and 26 in Spain, United Kingdom, Belgium, Netherland and Sweden. Romania and Portugal showed the largest increases in age-standardized cumulative excess mortality in the Q3. The majority of Central and Eastern European countries experienced an outstandingly high impact of the pandemic in Q4 in terms of excess deaths. Hungary ranked 11th in cumulative excess mortality based on the latest available data of from the EUROSTAT database.Conclusion: Hungary experienced a mortality deficit in the first half of 2020 compared to previous years, which was followed by an increase in mortality during the second wave of the COVID-19 pandemic, reaching 7.7% cumulative excess mortality by the end of 2020. The excess was lower than in neighboring countries with similar dynamics of the pandemic.

Number of excess deaths, including deaths due to coronavirus (COVID-19) and due to other causes. Including breakdowns by age, sex and geography.

Annual estimates of excess mortality in the United States (deaths from all causes, deaths involving Covid-19 and all other deaths), 2017 to 2021.

This dataset results from DG REGIO calculations based on Eurostat data (demo_r_mwk3_t). It presents excess mortality comparisons of the number of deaths that occurred in 2020 and 2021 with the average number of deaths that occurred in the corresponding weeks of 2015 to 2019. The age structure of the population and the deaths is not taken into account. The figures shown are rolling three week averages centred around the week in question. Access the EUROSTAT data on their webpage - deaths by week and NUTS region - https://ec.europa.eu/eurostat/databrowser/view/demo_r_mwk3_t/default/table?lang=en - and see the EUROSTAT webpage on national and regional weekly death statistics - https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Weekly_death_statistics Data is not available for Ireland. For Italy no data is available for the last weeks of 2021. This dataset presents a vertical / narrow view of the longitudinal timeseries data for 2020-2021. This dataset - https://cohesiondata.ec.europa.eu/Other/2020-2021-NUTS-Excess-mortality-3-week-average-hor/kzsy-bycf - provides the same values in a horizontal / wide format.

This dataset results from DG REGIO calculations based on Eurostat data (demo_r_mwk3_t). It presents excess mortality comparisons of the number of deaths that occurred in 2020 and 2021 with the average number of deaths that occurred in the corresponding weeks of 2015 to 2019. The age structure of the population and the deaths is not taken into account. The figures shown are rolling three week averages centred around the week in question. Access the EUROSTAT data on their webpage - deaths by week and NUTS region - https://ec.europa.eu/eurostat/databrowser/view/demo_r_mwk3_t/default/table?lang=en - and see the EUROSTAT webpage on national and regional weekly death statistics - https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Weekly_death_statistics Data is not available for Ireland. For Italy no data is available for the last weeks of 2021. This dataset presents a wide view of the longitudinal timeseries data for 2020-2021. This dataset - https://cohesiondata.ec.europa.eu/dataset/2020-2021-EU-regional-excess-mortality-3-week-aver/2kk2-t5sf - provides the same values in a vertical format.

This repository contains data to reconstruct the exposure-response functions (ERF) of temperature-related mortality by five 5 age groups in 854 cities in Europe. These ERFs have been derived in the study by Masselot et al. 2023, Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe, The Lancet Planetary Health (https://doi.org/10.1016/S2542-5196(23)00023-2). An associated semi-replicable GitHub repository is available at https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM to reproduce part of the analysis and the full results, as well as to provide technical details on the derivation of these ERFs. Note: This updated version contains revised data after the correction of an error in the code related to the computation of the age-specific baseline mortality rates. Details about the error can be found in the GitHub repository linked above. This correction only affects the figures of excess mortality (found in the results.zip archive) while the ERFs are negligibly affected. The originally published results can be found in V1.0.0 of this repository. Extraction of the ERFs The ERFs are provided as coefficients of B-spline functions that can be used to reconstruct the ERFs, along with variance-covariance matrices and quantiles from location-specific temperature distributions. The parametrisation associated with these coefficients is a quadratic B-spline (degree 2), with knots located at the 10th, 75th and 90th percentiles of the temperature distribution. In R, the associated basis can be constructed using the dlnm package, with a temperature series x, as follows:

library(dlnm) basis <- onebasis(x, fun = "bs", degree = 2, knots = quantile(x, c(.1, .75, .9))) The main files associated with ERFs are the following: coefs.csv: The B-spline coefficients for each age group and city. vcov.csv: The variance-covariance matrix of the coefficients in each city and age group. It is provided here as the lower triangular part of the matrix with names indicating the position of each value (v[row][column]). In R, assuming x is a row of this file, the matrix can be reconstructed using xpndMat(x) after loading the mixmeta package. coef_simu.csv: 1000 simulations from the distribution of each city and age-specific coefficients. Useful to derive empirical confidence intervals for derived measures such as excess deaths or attributable fractions. tmean_distribution.csv: The city-specific temperature percentiles representing the distribution of the data derived from the ERA5-Land dataset. Health impact assessment results results.zip: A summary of the results from the health impact assessment reported in the analysis. The dataset includes several impact measures provided in files representing different geographical levels, including city, country and regional level. Different files are also provided for age-group specific or all age results. Additional data We provide additional data that are useful to reproduce or extend the analysis. Please note that due to restrictive data-sharing agreements for the mortality series, only a part of the code is reproducible. See the associated GitHub repository for more details. metadata.csv: City-specific metadata used to create the ERFs and perform the health impact assessment. additional_data.zip: contains further data used to replicate the second stage of the analysis and the final health impact assessment. It includes the full city-level daily temperature series (era5series.csv), the detail of extracted metadata for available years (metacityyear.csv), a description of the city-level characteristics (metadesc.csv), and the first-stage ERF coefficients for all available city and age-groups (stage1res.csv). Additionally, the file meta-model.RData contains R object defining the second-stage model that can be used to predict new ERFs.

IntroductionBetween 2021 and 2023, a project was funded in order to explore the mortality burden (YLL–Years of Life Lost, excess mortality) of COVID-19 in Southern and Eastern Europe, and Central Asia.MethodsFor each national or sub-national region, data on COVID-19 deaths and population data were collected for the period March 2020 to December 2021. Unstandardized and age-standardised YLL rates were calculated according to standard burden of disease methodology. In addition, all-cause mortality data for the period 2015–2019 were collected and used as a baseline to estimate excess mortality in each national or sub-national region in the years 2020 and 2021.ResultsOn average, 15–30 years of life were lost per death in the various countries and regions. Generally, YLL rates per 100,000 were higher in countries and regions in Southern and Eastern Europe compared to Central Asia. However, there were differences in how countries and regions defined and counted COVID-19 deaths. In most countries and sub-national regions, YLL rates per 100,000 (both age-standardised and unstandardized) were higher in 2021 compared to 2020, and higher amongst men compared to women. Some countries showed high excess mortality rates, suggesting under-diagnosis or under-reporting of COVID-19 deaths, and/or relatively large numbers of deaths due to indirect effects of the pandemic.ConclusionOur results suggest that the COVID-19 mortality burden was greater in many countries and regions in Southern and Eastern Europe compared to Central Asia. However, heterogeneity in the data (differences in the definitions and counting of COVID-19 deaths) may have influenced our results. Understanding possible reasons for the differences was difficult, as many factors are likely to play a role (e.g., differences in the extent of public health and social measures to control the spread of COVID-19, differences in testing strategies and/or vaccination rates). Future cross-country analyses should try to develop structured approaches in an attempt to understand the relative importance of such factors. Furthermore, in order to improve the robustness and comparability of burden of disease indicators, efforts should be made to harmonise case definitions and reporting for COVID-19 deaths across countries.

As climate change causes average temperatures to rise across the European continent, this will inevitably lead to an increasing number of heat-related deaths, or deaths as a result of excess exposure to high temperatures. This is particularly an issue for southern European countries, such as Italy, Greece, Spain, and Portugal, who are already experiencing a massive uptick in the number of extreme heat days per year, with temperatures often exceeding 40 degrees Celsius in these countries during the Summer. In the Summer of 2022, Italy recorded 295 heat deaths per million inhabitants, resulting in a total of over 18,000 people dying due to excess heat exposure. Europe-wide, this rate was 114 heat deaths per million inhabitants.

Excess mortality by month

Excess Winter Deaths (EWD) by age and conditions (underlying cause of death) expressed as average per year based on 7 years pooled data, 2004-2011. EWD trend expressed as average per year based on 3 years data.

The Excess Winter Mortality Index (EWM Index was calculated based on the 'ONS Method' which defines the winter period as December to March, and the non-winter period as August to November of that same year and April to July of the following year.

This winter period was selected as they are the months which over the last 50 years have displayed above average monthly mortality. However, if mortality starts to increase prior to this, for example in November, the number of deaths in the non-winter period will increase, which in turn will decrease the estimate of excess winter mortality.

The EWM Index will be partly dependent on the proportion of older people in the population as most excess winter deaths effect older people (there is no standardisation in this calculation by age or any other factor).

Excess winter mortality is calculated as winter deaths (deaths occurring in December to March) minus the average of non-winter deaths (April to July of the current year and August to November of the previous year). The Excess winter mortality index is calculated as excess winter deaths divided by the average non-winter deaths, expressed as a percentage.

Relevant link: http://www.wmpho.org.uk/excesswinterdeathsinEnglandatlas/

These data contain estimates of temperature-related human mortality, as well as the associated economic assessments, related to urban heat islands for 85 European cities over the years 2015-2017. They are based on temperature-mortality relationships from Masselot et al. 2023 and 100m resolution UrbClim urban climate model simulations of near-surface air temperature (De Ridder et al. 2015, Hooyberghs et al. 2019), re-gridded to 500m resolution.

Details of the methodology are provided in the associated paper:

Huang, W.T.K. et al. Economic valuation of temperature-related mortality attributed to urban heat islands in European cities. Nat Commun 14, 7438 (2023). https://doi.org/10.1038/s41467-023-43135-z

And associated core analysis code is available on GitHub at https://github.com/hkatty/Paper_UHI_mortality_Europe (doi:10.5281/zenodo.8429209).

The content of the files are as follows:

spatial_timeseries zip files: These contain the most unprocessed attributable fraction estimates, with the exposure-response relationships applied to the modelled temperature, prior to any further processing.

uhi csv files: These are tables of the average mortality and years of life lost, as well as associated economic assessment, related to urban heat islands for each city. They are identical to Tables S4-S11 in the supplementary materials of the above paper.

spatial_maps_time_averaged_diff_from_rural.zip: Spatial maps showing the difference from the rural average for each day and grid box, then averaged over time.

data_urbanruralavg_timeseries.nc: Time series of urban and rural averages, as well as the difference between the two (i.e. the urban heat island effect).

avg_diff_from_rural_urbanrural.nc: The above timeseries file temporally aggregated.

simulated_urbanruraldiff_timeseries.zip: Time series of urban-rural difference in attributable fraction for 1000-member ensembles representing uncertainties in the exposure-response relationships as captured by Monte Carlo simulations.

simulated_urbanruraldiff_averaged.zip: The above simulated timeseries temporally aggregated.

Some variables explained:

fAF = forward attributable fraction (i.e. fraction of total mortality associated with a single day's temperature, cumulative over lag time)

fAD = forward attributable deaths (i.e. equivalent to fAF but for number of deaths)

tas = temperature

heat_ex = average over heat extreme days (i.e. the warmest 2% days in 2015-2017 for the city)

cold_ex = average over cold extreme days (i.e. as heat_ex but for the coldest 2% days)

heat = average over days warmer than the age-dependent optimal temperature

cold = average over days colder than the age-dependent optimal temperature

heat_count = number of days warmer than the optimal for the age group, note that for combined 2085.1 and 2085.5 age groups, days are counted if it is considered warm for at least one age group (therefore heat_count + cold_count ≠ total days over period)

cold_count = number of days colder than the optimal for the age group

rural = rural average

imd = land imperviousness

popden = population density

age groups:

20 = 20 to 44
45 = 45 to 64
65 = 65 to 74
75 = 75 to 84
85 = 85 and over
2085.1 = all above age groups combined, weighted by the local population age structure
2085.5 = all above age groups combined, weighted by the 2013 European standard population age structure

References:

De Ridder, K., Lauwaet, D., and Maiheu, B., (2015): UrbClim – A fast urban boundary layer climate model. Urban Climate, 12, 21–48. https://doi.org/10.1016/J.UCLIM.2015.01.001.

Hooyberghs, H., Berckmans, J., Lauwaet, D., Lefebre, F., and De Ridder, K., (2019): Climate variables for cities in Europe from 2008 to 2017. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.c6459d3a.

Masselot et al. (2023): Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe, The Lancet Planetary Health, https://doi.org/10.1016/S2542-5196(23)00023-2.

Mortality indicators standardized by sex and cause of death. Year: 2010. Territory: Province of Lucca. Indicators: number of deaths observed, number of deaths expected, number of excess deaths, standardised mortality ratio. Standardisation method applied: indirect. Reference population (standard population): Residents in the geographical area of the Centre (Tuscany, Umbria, Marche, Lazio). Primary data source (number of deaths observed, population resident in the province of Lucca, sex- and age-specific mortality rates of the population resident in the Centro district): ISTAT. Calculation of indicators: Statistical Office of the Province of Lucca.

Regression results, percent vaccinated, percent excess deaths and estimates of lives saved if Ukrainian vaccination levels reached that of the European average, July 2021-November 2021.

The dataset is from ISTAT, Italy's Istituto Nazionale di Statistica.