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Abstract Since its emergence in December 2019, the COVID-19 pandemic has resulted in a significant increase in deaths worldwide. This article presents a detailed analysis of the mortality burden of the COVID-19 pandemic across 569 regions in 25 European countries. We produce age and sex-specific excess mortality and present our results using Age-Standardised Years of Life Lost in 2020 and 2021, as well as the cumulative impact over the two pandemic years. Employing a forecasting approach based on CP-splines that considers regional diversity and provides confidence intervals, we find notable losses in 362 regions in 2020 (440 regions in 2021). Conversely, only seven regions experienced gains in 2020 (four regions in 2021). We also estimate that eight regions suffered losses exceeding 20 years of life per 1000 population in 2020, whereas this number increased to 75 regions in 2021. The contiguity of the regions investigated in our study also reveals the changing geographical patterns of the pandemic. While the highest excess mortality values were concentrated in the early COVID-19 outbreak areas during the initial pandemic year, a clear East-West gradient appeared in 2021, with regions of Slovakia, Hungary, and Latvia experiencing the highest losses. This research underscores the importance of regional analyses for a nuanced comprehension of the pandemic’s impact.

Abstract Objective To measure the burden of the COVID-19 pandemic in 2020 at the subnational level by estimating excess mortality, defined as the increase in all-cause mortality relative to an expected baseline mortality level. Methods Statistical and demographic analyses of regional all-cause mortality data provided by the vital statistics systems of 21 European countries for 561 regions in Central and Western Europe. Life expectancy losses at ages 0 and 60 for males and females were estimated. Results We found evidence of a loss in life expectancy in 391 regions, whilst only three regions exhibit notable gains in life expectancy in 2020. For 12 regions, losses of life expectancy amounted to more than 2 years and three regions showed losses greater than 3 years. We highlight geographical clusters of high mortality in Northern Italy, Spain and Poland, whilst clusters of low mortality were found in Western France, Germany/Denmark and Norway/Sweden. Conclusions Regional differences of loss of life expectancy are impressive, ranging from a loss of more than 4 years to a gain of 8 months. These findings provide a strong rationale for regional analysis, as national estimates hide significant regional disparities.

Abstract Background Evaluating mortality effects of the COVID-19 pandemic using all-cause mortality data for national populations is inevitably associated with the risk of masking important subnational differentials and hampering targeted health policies. This study aims at assessing simultaneously cause-specific, spatial and seasonal mortality effects attributable to the pandemic in Germany in 2020. Methods Our analyses rely on official cause-of-death statistics consisting of 5.65 million individual death records reported for the German population during 2015–2020. We conduct differential mortality analyses by age, sex, cause, month and district (N = 400), using decomposition and standardisation methods, comparing each strata of the mortality level observed in 2020 with its expected value, as well as spatial regression to explore the association of excess mortality with pre-pandemic indicators. Results The spatial analyses of excess mortality reveal a very heterogenous pattern, even within federal states. The coastal areas in the north were least affected, while the south of eastern Germany experienced the highest levels. Excess mortality in the most affected districts, with standardised mortality ratios reaching up to 20%, is driven widely by older ages and deaths reported in December, particularly from COVID-19 but also from cardiovascular and mental/nervous diseases. Conclusions Our results suggest that increased psychosocial stress influenced the outcome of excess mortality in the most affected areas during the second lockdown, thus hinting at possible adverse effects of strict policy measures. It is essential to accelerate the collection of detailed mortality data to provide policymakers earlier with relevant information in times of crisis.

The Healthy Life Years (HLY) indicator is the official European Union indicator and a cornerstone of many health policies used in over 15 countries in the EU region to set national health plans and monitor targets. It is also used to investigate trends over time in the proportion of total life years spent in good or poor health, socioeconomic inequalities in health and mortality and the male-female health survival paradox. Based on the Global Activity Limitation Indicator (GALI) included in the European Union Statistics on Income and Living Conditions (EU-SILC), a great amount of effort has been directed at harmonising and making HLY comparable across countries. Nonetheless, the characteristics of the age-specific prevalence distribution are still rarely accounted for, regardless of the fact that patterns of prevalence often fluctuate considerably by age. In addition, the impact of assumptions used at very young ages on HLY estimates are seldom discussed, despite the fact that the majority of policies and initiatives at the EU level use HLY at birth, while data on health is only available after age 16. In this paper, we assess whether smoothing the age-specific prevalence distributions by different methods, extrapolating to older ages and changing assumptions at younger ages affect HLY estimates. Overall, assumptions made before age 15 are the most important and affect women and men differently, thus affecting HLY at birth for some countries. Estimates at age 65 are very slightly impacted. Generalised linear models (GAMs) seem promising for harmonising and extrapolating to older ages, while using polynomials or aggregating into 5-year age groups seem best for younger ages. As most EU policies use HLY at birth and by sex for developing and monitoring health policies, caution is needed when estimating HLY at birth. * This article belongs to a special issue on “Levels and Trends of Health Expectancy: Understanding its Measurement and Estimation Sensitivity”.

Measurement(s) mortality Technology Type(s) digital curation Factor Type(s) Death counts Sample Characteristic - Organism Homo sapiens • Covid-19 deaths Sample Characteristic - Environment Country Sample Characteristic - Location Austria, Belgium, Canada, England and Wales, Denmark, France, Germany, Italy, Japan, the Netherlands, Norway, Portugal, the Republic of Korea, the Republic of Moldova, Romania, Scotland, Spain, Sweden, Switzerland, Ukraine, and the United States of America

Abstract Background Healthy life years have superseded life expectancy (LE) as the most important indicator for population health. The most common approach to separate the total number of life years into those spent in good and poor health is the Sullivan method which incorporates the health dimension to the classic period life table, thus transforming the LE indicator into the health expectancy (HE) indicator. However, life years derived from a period life table and health prevalence derived from survey data are based on different conceptual frameworks. Method We modify the Sullivan method by combining the health prevalence data with the conceptually better fitting cross-sectional average length of life (CAL). We refer to this alternative HE indicator as the “cross-sectional average length of healthy life” (HCAL). We compare results from this alternative indicator with the conventional Sullivan approach for nine European countries. The analyses are based on EU-SILC data in three empirical applications, including the absolute and relative level of healthy life years, changes between 2008 and 2014, and the extent of the gender gap. Results HCAL and conventional HE differ in each of these empirical applications. In general, HCAL provides larger gains in healthy life years in recent years, but at the same time greater declines in the proportion of healthy life years. Regarding the gender gap, HCAL provides a more favourable picture for women compared to conventional HE. Nonetheless, the extent of these differences between the indicators is only of minor extent. Conclusions Albeit the differences between HE and HCAL are small, we found some empirical examples in which the two indicators led to different conclusions. It is important to note, however, that the measurement of health and the data quality are much more important for the healthy life years indicator than the choice of the variant of the Sullivan method. Nonetheless, we suggest to use HCAL in addition to HE whenever possible because it widens the spectrum of empirical analyses and serves for verification of results based on the highly sensitive HE indicator.

The COVID-19 pandemic caused an increase in mortality in 2020 with a resultant decrease in life expectancy in most countries around the world. In Germany, the reduction in life expectancy at birth between 2019 and 2020 was comparatively small, at -0.20 years. The decrease was stronger among men than among women (-0.24 vs. -0.13 years) and in eastern rather than in western Germany (-0.36 vs. -0.16 years). Men in eastern Germany experienced the biggest decline in life expectancy at birth (-0.41 years). For western German men, the decline was less pronounced (-0.19 years). Among women, the decline in life expectancy at birth was also greater in eastern (-0.25 years) than in western Germany (-0.10 years). As a result of these developments, the differences in life expectancy between the two parts of Germany, and between women and men, increased compared with the previous year. Life expectancy at age 65 decreased more strongly than life expectancy at birth for both sexes and in all regions. This reflects the fact that it was mainly older age groups that were affected by the increase in mortality in 2020. This paper provides further insights into mortality changes in 2020, based on age decomposition and an analysis of lifespan inequality. We conclude that the population in eastern Germany was hit harder by the COVID-19 pandemic in 2020 than the population in the western Germany.

Healthy Life Years (HLY) is a prominent summary indicator for evaluating and comparing the levels of population health status across Europe. Variations in HLY, however, do not necessarily reflect underlying differences in health and mortality levels among countries and the indicator is particularly sensitive when broken down by subpopulations. For instance, despite European countries showing large HLY inequalities by educational level, these countries are also heterogenous regarding their population composition by educational attainment, which most likely affects their HLY levels. We demonstrate how this compositional effect shapes HLY levels by providing estimates for HLY by educational attainment and gender for 16 European countries using the Sullivan method. We use prevalence data about limitations in daily activities from the European Union Statistics on Income and Living Conditions (EU-SILC) and mortality data from the Eurostat database. Finally, we adjust for compositional effects by means of standardization. The education-adjusted HLY estimates do not differ much from conventional HLY. Yet, we find that in some countries HLY levels are indeed affected by the population composition by educational attainment. For example, low-, medium-, and high educated individuals in Portugal show more HLY than their counterparts in Poland. Still, Poland's total HLY value slightly exceeds that of Portugal, indicating favorable health and mortality conditions in Poland. It is Poland's lower relative number of low educated individuals in its population that is responsible for producing this higher total HLY value. We conclude that differentials in HLY due to differences in the relative size of educational subpopulations are generally small in HLY across Europe but they can play an important role for countries that experienced large differences in their educational expansion.

An amendment to this paper has been published and can be accessed via the original article.