The impact of population's educational composition on Healthy Life Years: An empirical illustration of 16 European countries

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

The Sensitivity of the Healthy Life Years Indicator: Approaches for Dealing with Age-Specific Prevalence Data

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”

Decrease in Life Expectancy in Germany in 2020: Men from Eastern Germany Most Affected

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

Regionale Unterschiede und Trends in gesunder Lebenserwartung in Deutschland

Hintergrund: Vor dem Hintergrund steigender Lebenserwartung stellt sich die Frage, in welchem Gesundheitszustand die hinzugewonnenen Lebensjahre verbracht werden. Ziel der vorliegenden Untersuchung ist die erstmalige Berechnung regional differenzierter Unterschiede in gesunder Lebenserwartung für Deutschland. Methoden: Das Konzept der gesunden Lebenserwartung erlaubt es, regionale Unterschiede in Gesundheitszustand und Sterblichkeit in einer Maßzahl zu vereinen. Im vorliegenden Beitrag kommt das Konzept der partiellen gesunden Lebenserwartung zum Einsatz. Mit amtlichen Daten zu Todesfällen und Bevölkerungszahlen berechnen wir verkürzte Sterbetafeln. Die Daten des Sozio-oekonomischen Panels (SOEP) werden zur Ermittlung der alters- und geschlechtsspezifischen Prävalenzen des Gesundheitszustands herangezogen. Die Analyse regionaler Unterschiede erfolgt anhand einer Einteilung Deutschlands in 4 Regionen (Norden, Süden, Osten, Westen) von 2002 bis 2019. Ergebnisse: Die regionalen Unterschiede in der gesunden Lebenserwartung in Deutschland sind größer als Unterschiede in der Lebenserwartung an sich und Trends der gesunden Lebenserwartung verlaufen teilweise anders als die entsprechenden Trends der Sterblichkeit. Diese Unterschiede im zeitlichen Verlauf variieren des Weiteren nach Alter: Während es bei der Bevölkerung zwischen 20 und 64 Jahren in der Tendenz zu einer Stagnation, teilweise zu Rückgängen in der gesunden Lebenserwartung gekommen ist, nahmen die Anzahl und der Anteil der Jahre in guter Gesundheit bei den Älteren bis Alter 79 zu. Fazit: Es gibt auffällige regionale Unterschiede und Trends in der Verteilung der erwarteten Jahre in guter Gesundheit in Deutschland. Die rechtzeitige Identifikation regional abweichender Entwicklungen kann dazu beitragen, gezielte gesundheitsfördernde Maßnahmen zu ergreifen.Background: Against the background of increasing life expectancy, the question arises in which state of health the additional years of life are spent. The aim of this study is to assess for the first time regional differences in healthy life expectancy for Germany. Methods: The concept of healthy life expectancy allows for the combination of regional differences in health status and mortality in a single measure. This article uses the concept of partial healthy life expectancy. We use official data on deaths and population numbers to calculate abridged life tables. Data from the Socio-Economic Panel (SOEP) are used to determine the age- and sex-specific prevalences of health status. Regional differences are analyzed from 2002 to 2019 by dividing Germany into four regions (North, South, East, West). Results: The regional differences in healthy life expectancy in Germany are greater than differences in life expectancy, and trends in healthy life expectancy partly differ from the corresponding trends in mortality. These differences over time also vary according to age: while healthy life expectancy has tended to stagnate and, in some cases, decline among the population aged between 20 and 64, the number and proportion of years in good health has increased among older adults up to the age of 79. Conclusion: There are striking regional differences and trends in the distribution of expected years in good health in Germany. The timely identification of regionally divergent developments could facilitate the implementation of targeted health-promoting measures

The role of education for assessing population health: An analysis of healthy life expectancy by educational attainment for 16 European countries

Healthy life expectancy (HLE) is a prominent summary indicator for evaluating and comparing the levels of population health status across Europe. Variations in HLE, however, do not necessarily reflect underlying differences in health and mortality levels among countries and are particularly sensitive when broken down by population subgroups. For instance, despite European countries showing large HLE inequalities by educational level, these countries are also highly heterogenous regarding their educational population composition, which most likely affects their HLE levels. We demonstrate how this compositional effect shapes HLE levels by providing HLE estimates of educational attainment and gender for 16 European countries using the Sullivan method. We use prevalence data about activities of daily living (ADLs) limitations from the European Union Statistics on Income and Living Conditions (EU-SILC) and mortality data from the Eurostat database. We then quantify the magnitude of educational inequalities based on the composite inequality index (CII). Finally, we express total HLE as the sum of education-specific HLEs, weighted by the educational population structure. As expected, we find large educational inequalities in HLE, with men's CII ranging from about 8.5 years in Portugal to approximately 3 years in Romania. For women, educational inequalities are slightly smaller. The decomposition reveals the population structure's strong effects on HLE, which can elicit misleading conclusions about people's health status and potentially turn HLE into an improper measure of educational differences as opposed to a measure of health gaps. For example, low-, medium-, and highly educated individuals in Portugal show more healthy life years than their counterparts in Poland. Still Poland's total HLE value slightly exceeds that of Portugal, indicating favorable health and mortality conditions in Poland. However, Poland's greater relative number of highly educated individuals in its population is responsible for producing this higher total HLE value. We conclude that education is not only paramount for assessing health inequalities across European countries, but also the population composition by educational attainment, because it drives the differences in HLE levels

MortalitySexGap

Materials for the paper "Sex Differences in Cause-Specific Mortality: Regional Trends in Seven European Countries, 1996–2019" accepted in the European Journal of Public Health

Spatial_Differences_e0_Gains

Materials for the paper: Sauerberg, M., Bonnet, F., Camarda, GC., Grigoriev, P. 2024. Mortality convergence in Europe? Spatial differences in life expectancy gains between 1995 and 2019. Population and Development Review (Forthcoming)

MortalitySexGap

Materials for the paper "Sex Differences in Cause-Specific Mortality: Regional Trends in Seven European Countries, 1996–2019" accepted in the European Journal of Public Health