As a result of the COVID-19 pandemic the number of excess deaths in Europe reached a peak in 2020 with over 400 thousand. Excess deaths in Europe also remained relatively high in 2021 and 2022. Through week 40 in 2023, around 80 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.

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.

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.

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.

For the week ending June 20, 2025, weekly deaths in England and Wales were 374 below the number expected, compared with 228 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 Coronavirus (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.

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.

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

This collection automatically captures metadata, the source of which is the GOVERNMENT OF THE REPUBLIC OF SLOVENIA STATISTICAL USE OF THE REPUBLIC OF SLOVENIA and corresponding to the source collection entitled “Excess mortality, Slovenia, 2020M01-2023M02”.

Actual data are available in Px-Axis format (.px). With additional links, you can access the source portal page for viewing and selecting data, as well as the PX-Win program, which can be downloaded free of charge. Both allow you to select data for display, change the format of the printout, and store it in different formats, as well as view and print tables of unlimited size, as well as some basic statistical analyses and graphics.

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 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/

A measure of the increase in winter mortality, provided on an annual basis, in the form of the excess winter mortality figure. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: Excess Winter Mortality

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.

The number of deaths in Sweden in 2020 amounted to over 98,000. A high share of the deaths in 2020 were related to the coronavirus pandemic. However, in 2021, the number sank below 92,000, before increasing to over 94,000 in 2022 and 2023. The highest number of coronavirus deaths were among individuals age 70 and older. Sweden is the Nordic country that has reported the highest number of COVID-19-related deaths since the outbreak of the pandemic.

The most common causes of death

The most common cause of death in 2022 was diseases of the circulatory system (cardiovascular diseases). This cause was followed by cancerous tumors.

 Ischemic heart disease

Among the diseases in the circulatory system, the one that caused the most deaths was chronic ischemic heart disease. Chronic ischemic heart disease is when the blood flow to the heart is reduced because the arteries of the heart are blocked. In 2020, ischemic heart disease caused more than 50,000 deaths per 100,000 inhabitants.

A measure of the increase in winter mortality defined as the difference between the number of deaths in the winter months of December to March and the average number of deaths in the preceding August to November and the following April to July.

Source agency: Northern Ireland Statistics and Research Agency

Designation: National Statistics

Language: English

Alternative title: Excess Winter Deaths

Excess Winter Deaths Index (all ages single year)

This data shows premature deaths (Age under 75) from Circulatory Disease, numbers and rates by gender, as 3-year moving-averages.

Circulatory diseases include heart diseases and stroke, and others. Socio-economic and lifestyle factors are associated with circulatory disease deaths and inequalities in circulatory disease rates. Modifiable risk factors include smoking, excess weight, diet, and physical inactivity.

Directly Age-Standardised Rates (DASR) are shown in the data, where numbers are sufficient, so that death rates can be directly compared between areas. The DASR calculation applies Age-specific rates to a Standard (European) population to cancel out possible effects on crude rates due to different age structures among populations, thus enabling direct comparisons of rates.

A limitation on using mortalities as a proxy for prevalence of health conditions is that mortalities may give an incomplete view of health conditions in an area, as ill-health might not lead to premature death.

Data source: NHS Digital Compendium hub, dataset unique identifier P00395. This data is updated annually.

This repository contains the data and results from the paper Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities published in Nature Medicine (https://doi.org/10.1038/s41591-024-03452-2).

It provides projections of excess death rates and burden for the period 2015-2099 for five age groups in 854 cities across 30 countries, under three Shared Socioeconomic Pathway (SSP) scenarios, and four adaptation scenarios. The results include point estimates for five-year periods and four global warming levels, along with 95% empirical confidence intervals.

The fully reproducible analysis code using the data and producing the results included in this repository is provided in GitHub. The results can be visualised and explored in a dedicated Shiny app.

Content

This repository contains three zip files, each with an internal codebook:

• data.zip: contains the input data necessary to run the analysis. It includes historical and projected daily temperature at the city level, age-group specific projections of population and survival rates at the country level, and exposure-response functions extracted from another Zenodo repository (https://doi.org/10.5281/zenodo.10288665). This file also include a script showing how each dataset was extracted for the purpose of this projection study.
• results_csv.zip: contains the full results from the health impact projections. It includes one file for each combination of geographical level (city, country, region or European wide) and scale of reporting (five year periods or global warming levels).
• results_parquet.zip: contains the same information as the results_csv.zip but in a parquet format. This allows for more efficient storage and data reading.

It is recommended to only download results_csv.zip for a quick exploration of the results, or only results_parquet.zip when the results are to be loaded into a software for deeper analysis.

Background Studies from high-income countries reported reduced life expectancy in children with cerebral palsy (CP), while no population-based study has evaluated mortality of children with CP in sub-Saharan Africa. This study aimed to estimate the mortality rate (MR) of children with CP in a rural region of Uganda and identify risk factors and causes of death (CODs). Methods and Findings This population-based, longitudinal cohort study was based on data from Iganga-Mayuge Health and Demographic Surveillance System in eastern Uganda. We identified 97 children (aged 2–17 years) with CP in 2015, whom we followed to 2019. They were compared with an age-matched cohort from the general population (n=41 319). MRs, MR ratios (MRRs), hazard ratios (HRs), and immediate CODs were determined. MR was 3952 per 100 000 person years (95% CI 2212–6519) in children with CP and 137 per 100 000 person years (95% CI 117–159) in the general population. Standardized MRR was 25·3 in the CP cohort, compared with the general population. In children with CP, risk of death was higher in those with severe gross motor impairments than in those with milder impairments (HR 6·8; p=0·007) and in those with severe malnutrition than in those less malnourished (HR=3·7; p=0·052). MR was higher in females in the CP cohort, with a higher MRR in females (53·0; 95% CI 26·4–106·3) than in males (16·3; 95% CI 7·2–37·2). Age had no significant effect on MR in the CP cohort, but MRR was higher at 10–18 years (39·6; 95% CI 14·2–110·0) than at 2–6 years (21·0; 95% CI 10·2–43·2). Anaemia, malaria, and other infections were the most common CODs in the CP cohort. Conclusions Risk of premature death was excessively high in children with CP in rural sub-Saharan Africa, especially in those with severe motor impairments or malnutrition. While global childhood mortality has significantly decreased during recent decades, this observed excessive mortality is a hidden humanitarian demand that needs to be addressed.

The dataset contains of the following files: - CP_cohort–Children_and_youth_at_the_IM-HDSS.csv - CoD–General_population_of_children_and_youth_IM-HDSS.csv - Variable_list.pdf

Details about the variables in the tables can be found in the variable list.