Changes in life expectancy and lifespan variability by income quartiles in four Nordic countries: a study based on nationwide register data

Early-Life Disease Exposure and Its Heterogeneous Effects on Mortality Throughout Life: Sweden, 1905-2016

Exposure to infectious diseases in early life has been linked to increased mortality risk in later life in high-disease settings, such as eighteenth- and nineteenth-century Europe. Less is known about the long-term effects of early-life disease exposure in milder disease environments. This study estimates heterogeneous effects from disease exposure in infancy on later-life mortality in twentieth-century Sweden, by socioeconomic status at birth and sex. Using historical population data for southern Sweden, we study 11,515 individuals who were born in 1905-1929 from age 1 until age 85. We measure exposure to disease using the local post-early neonatal mortality rate in the first 12 months after birth and apply flexible parametric survival models. For females, we find a negative effect on life expectancy (scarring) at ages 1-85 following high disease exposure in infancy, particularly for those born to unskilled workers. For males, we find no negative effect on later-life survival, likely because stronger mortality selection in infancy outweighs scarring. Thus, even as the incidence of infectious diseases declined at the start of the twentieth century, early-life disease exposure generated long-lasting negative but heterogeneous population health effects.

Trends in socioeconomic inequalities in life expectancy and lifespan variation in Chile

Background

Socioeconomic disparities in life expectancy are well-documented in various contexts, including Chile. However, there is a lack of research examining trends in life expectancy inequalities and lifespan variation over time. Addressing these gaps can provide crucial insights into the dynamics of health inequalities.

Methods

This study utilizes data from census records, population surveys, and death certificates to compare the life expectancy and the lifespan variation at age 26 of individuals according to their rank in the distribution of years of education within their own birth cohort. The analysis spans three periods (1991, 2002, and 2017) and focuses on two educational groups: individuals in the first (lowest) quintile and tenth (highest) decile of educational attainment. Changes in life expectancy are disaggregated by major causes of death to elucidate their contributions to overall trends.

Results

Consistent with existing literature, our findings confirm that individuals with lower education levels experience lower life expectancy and higher lifespan variation compared to their more educated counterparts. Notably, by 2017, life expectancy for individuals in the lowest quintile of education has caught up with that of the top decile in 1991, albeit with contrasting trends between genders. Among women, the gap has reduced, while it has increased for males. Moreover, lifespan variation decreased (increased) over time for individuals in the tenth decile (first quintile). The leading causes of death that explain the increase in life expectancy in women and men in the tenth decile as well as women in the first quintile are cardiovascular, cancer, respiratory and digestive diseases. In the case of males in the first quintile, few gains have been made in life expectancy resulting from cancer and a negative contribution is associated with digestive conditions.

Conclusions

This study underscores persistent socioeconomic disparities in life expectancy in Chile, emphasizing the importance of ongoing monitoring of health inequalities across different demographic segments. The gender-specific and educational gradient trends highlight areas for targeted interventions aimed at reducing health disparities and improving overall population health outcomes. Further research is warranted to delve into specific causes of death driving life expectancy differentials and to inform evidence-based policy interventions.

Forecasting Inequalities in Survival to Retirement Age by Socioeconomic Status in Denmark and Sweden

In Denmark and Sweden, statutory retirement age is indexed to life expectancy to account for mortality improvements in their populations. However, mortality improvements have not been uniform across different sub-populations. Notably, in both countries, individuals of lower socioeconomic status (SES) have experienced slower mortality improvements. As a result, a uniform rise in the statutory retirement age could disproportionally affect these low-SES groups and may unintentionally lead to a reverse redistribution effect, shifting benefits from short-lived low-SES individuals to long-lived high-SES individuals. The aim of this study is twofold: to quantify and contextualise mortality inequalities by SES in Denmark and Sweden, and to assess how indexing retirement age will affect future survival to retirement age by SES in these countries. We used Danish and Swedish registry data (1988-2019), to aggregate individuals aged 50 + based on their demographic characteristics and SES. We computed period life tables by year, sex, and SES to estimate the difference in survival across different SES groups. We then forecast mortality across SES groups to assess how indexing retirement age will affect survival inequalities to retirement age, using two forecasting models-the Mode model and the Li-Lee model. Mortality inequalities are comparable in Denmark and Sweden, even though the latter generally has higher survival. We also find that indexing retirement age to life expectancy will have two main consequences: it will reduce the probability of reaching retirement for all SES groups, particularly those of low SES, and time spent in retirement will be reduced, particularly for those of high SES.

Age-specific and cause-specific mortality contributions to the socioeconomic gap in life expectancy in Germany, 2003-21: an ecological study

BACKGROUND

Earlier death among people in socioeconomically deprived circumstances has been found internationally and for various causes of death, resulting in a considerable life-expectancy gap between socioeconomic groups. We examined how age-specific and cause-specific mortality contributions to the socioeconomic gap in life expectancy have changed at the area level in Germany over time.

METHODS

In this ecological study, official German population and cause-of-death statistics provided by the Federal Statistical Office of Germany for the period Jan 1, 2003, to Dec 31, 2021, were linked to district-level data of the German Index of Socioeconomic Deprivation. Life-table and decomposition methods were applied to calculate life expectancy by area-level deprivation quintile and decompose the life-expectancy gap between the most and least deprived quintiles into age-specific and cause-specific mortality contributions.

FINDINGS

Over the study period, population numbers varied between 80 million and 83 million people per year, with the number of deaths ranging from 818 000 to 1 024 000, covering the entire German population. Between Jan 1, 2003, and Dec 31, 2019, the gap in life expectancy between the most and least deprived quintiles of districts increased by 0·7 years among females (from 1·1 to 1·8 years) and by 0·1 years among males (from 3·0 to 3·1 years). Thereafter, during the COVID-19 pandemic, the gap increased more rapidly to 2·2 years in females and 3·5 years in males in 2021. Between 2003 and 2021, the causes of death that contributed the most to the life-expectancy gap were cardiovascular diseases and cancer, with declining contributions of cardiovascular disease deaths among those aged 70 years and older and increasing contributions of cancer deaths among those aged 40-74 years over this period. COVID-19 mortality among individuals aged 45 years and older was the strongest contributor to the increase in life-expectancy gap after 2019.

INTERPRETATION

To reduce the socioeconomic gap in life expectancy, effective efforts are needed to prevent early deaths from cardiovascular disease and cancer in socioeconomically deprived populations, with cancer prevention and control becoming an increasingly important field of action in this respect.

FUNDING

German Cancer Aid and European Research Council.

Global health inequality: analyses of life disparity and healthy life disparity

BACKGROUND

Alongside average health measures, namely, life expectancy (LE) and healthy life expectancy (HLE), we sought to investigate the inequality in lifespan and healthy lifespan at the worldwide level with an alternative indicator.

METHODS

Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, we evaluated the global distribution of life disparity (LD) and healthy life disparity (HLD) for 204 countries and territories in 2019 by sex and socio-demographic index (SDI), and also explored the relationships between average and variation health indicators.

RESULTS

Substantial gaps in all observed health indicators were found across SDI quintiles. For instance, in 2019, for low SDI, female LE and HLE were 67.3 years (95% confidence interval 66.8, 67.6) and 57.4 years (56.6, 57.9), and their LD and HLD were 16.7 years (16.5, 17.0) and 14.4 years (14.1, 14.7). For high SDI, female LE and HLE were greater [83.7 years (83.6, 83.7) and 70.2 years (69.3, 70.7)], but their LD and HLD were smaller [10.4 years (10.3, 10.4) and 7.9 years (7.7, 8.0)]. Besides, all estimates varied across populations within each SDI quintile. There were also gaps in LD and HLD between males and females, as those found in LE and HLE.

CONCLUSION

In addition to the disadvantaged LE and HLE, greater LD and HLD were also found in low SDI countries and territories. This reveals the serious challenge in achieving global health equality. Targeted policies are thus necessary for improving health performance among these populations.

Average lifespan variation among people with mental disorders in Denmark: a nationwide, register-based cohort study

AIMS

Mortality associated with mental disorders has been estimated using metrics such as mortality rate ratios and life expectancy. However, the variation around the average life expectancy has never been quantified. The main aim of this study was to measure life disparity for people with mental disorders as a measure of inequality at the time of death.

METHODS

Using data from Danish registries, average life disparity was introduced and calculated to measure the lifespan variation associated with major types of mental disorders. Average life expectancy is also reported for completeness.

RESULTS

Compared with the general population, people with mental disorders not only had shorter average life expectancy, but experienced larger average life disparity. For those diagnosed with a mental disorder, average life expectancy increased between 1995 and 2021; however, average life disparity declined in women only, and did not change for men. In addition, the differences in both metrics between those with mental disorders and the general population were largest for substance use disorders and schizophrenia spectrum disorders. For these disorders, the differences even increased during the study period.

CONCLUSIONS

Mortality rates for individuals with mental disorders have been declining in recent decades in Denmark; however, the increase in the average life disparity emphasizes the increasing heterogeneity and inequality in lifespans within this group, which requires measures to promote a longer and more equal life for those with mental disorders.

Contribution of causes of death to changing inequalities in life expectancy by income in Finland, 1997-2020

BACKGROUND

Socioeconomic inequalities in mortality originate from different causes of death. Alcohol-related and smoking-related deaths are major drivers of mortality inequalities across Europe. In Finland, the turn from widening to narrowing mortality disparities by income in the early 2010s was largely attributable to these causes of death. However, little is known about recent inequalities in life expectancy (LE) and lifespan variation.

METHODS

We used individual-level total population register-based data with annual information on disposable household income and cause-specific mortality for ages 30-95+, and assessed the contribution of smoking on mortality using the Preston-Glei-Wilmoth method. We calculated trends in LE at age 30 and SD in lifespan by income quintile in 1997-2020 and conducted age and cause-of-death decompositions of changes in LE.

RESULTS

Disparity in LE and lifespan variation by income increased in 2015-2020, largely attributable to the stagnation of both measures in the lowest income quintile. The LE gap between the extreme quintiles in 2018-2020 was 11.2 (men) and 5.9 (women) years, of which roughly 40% was attributable to alcohol and smoking. However, the recent widening of the gap and the stagnation in LE in the lowest quintile over time were not driven by any specific cause-of-death group.

CONCLUSIONS

After a decade of narrowing inequalities in LE and lifespan variation in Finland, the gaps between income groups are growing again. Increasing LE disparity and stagnating mortality on the lowest income levels are no longer attributable to smoking and alcohol-related deaths but are more comprehensive, originating from most cause-of-death groups.

The impact of COVID-19 on life expectancy across socioeconomic groups in Denmark

BACKGROUND

Denmark was one of the few countries that experienced an increase in life expectancy in 2020, and one of the few to see a decrease in 2021. Because COVID-19 mortality is associated with socioeconomic status (SES), we hypothesize that certain subgroups of the Danish population experienced changes in life expectancy in 2020 and 2021 that differed from the country overall. We aim to quantify life expectancy in Denmark in 2020 and 2021 by SES and compare this to recent trends in life expectancy (2014-2019).

METHODS

We used Danish registry data from 2014 to 2021 for all individuals aged 30+. We classified the study population into SES groups using income quartiles and calculated life expectancy at age 30 by year, sex, and SES, and the differences in life expectancy from 2019 to 2020 and 2020 to 2021. We compared these changes to the average 1-year changes from 2014 to 2019 with 95% confidence intervals. Lastly, we decomposed these changes by age and cause of death distinguishing seven causes, including COVID-19, and a residual category.

RESULTS

We observed a mortality gradient in life expectancy changes across SES groups in both pandemic years. Among women, those of higher SES experienced a larger increase in life expectancy in 2020 and a smaller decrease in 2021 compared to those of lower SES. Among men, those of higher SES experienced an increase in life expectancy in both 2020 and 2021, while those of lower SES experienced a decrease in 2021. The impact of COVID-19 mortality on changes in life expectancy in 2020 was counterbalanced by improvements in non-COVID-19 mortality, especially driven by cancer and cardiovascular mortality. However, in 2021, non-COVID-19 mortality contributed negatively even for causes as cardiovascular mortality that has generally a positive impact on life expectancy changes, resulting in declines for most SES groups.

CONCLUSIONS

COVID-19 mortality disproportionally affected those of lower SES and exacerbated existing social inequalities in Denmark. We conclude that in health emergencies, particular attention should be paid to those who are least socially advantaged to avoid widening the already existing mortality gap with those of higher SES. This research contributes to the discussion on social inequalities in mortality in high-income countries.

Trends in the use of home care services among Norwegians 70+ and projections towards 2050: The HUNT study 1995-2017

BACKGROUND

Life expectancy (LE) is increasing worldwide, while there is lack of information on how this affects older individuals' use of formal home care services.

AIM

We aimed to decompose LE into years with and without home care services and estimate projected number of users towards 2050 in Norway for people 70 years or older.

METHODS

This study is based on a sample of 25,536 participants aged 70 years and older in the Trøndelag Health Study (HUNT) survey 2 (1995-1997), 3 (2006-2008), or 4 (2017-2019) linked with national data on mortality. Prevalence of home care services was standardised to the Norwegian population by age and sex. The Sullivan method was used to estimate expected years with and without home help services and nursing services for the years 1995, 2006 and 2016. Data from HUNT4 and Statistics Norway were used to estimate projected use of these services between 2020 and 2050.

RESULTS

During 1995-2017, the use of home help services decreased from 22.6% to 6.2% (p < 0.001), and from 6.4% to 5.5% (p = 0.004) for home nursing services. Adjusted for age and sex, the use of home help services decreased significantly over time (p < 0.001), while home nursing services were stable (p = 0.69). LE at age 70 increased from 11.9 to 15.3 years in men (p < 0.05) during 1995-2017, and from 14.7 to 17.1 in women (p < 0.05). In the same period, the expected years receiving home help decreased from 2.6 to 1.1 in men (p < 0.05), and from 4.4 to 2.1 in women (p < 0.05). The expected years receiving home nursing increased from 0.6 to 0.9 in men (p < 0.05), and from 1.3 to 1.7 in women (p < 0.05). Projected numbers of people 70+ in Norway in need of either of these services were estimated to rise from 64,000 in 2020 to 160,000 in 2050.

CONCLUSION

While overall life expectancy increased, the expected years receiving home help have decreased and home nursing slightly increased among the Norwegian population aged 70 years and older during 1995-2017. However, the substantial increase in the projected number of older adults using home care services in the future is an alert for the current health care planners.

Reversals in past long-term trends in educational inequalities in life expectancy for selected European countries

BACKGROUND

Across Europe, socioeconomic inequalities in mortality are large and persistent. To better understand the drivers of past trends in socioeconomic mortality inequalities, we identified phases and potential reversals in long-term trends in educational inequalities in remaining life expectancy at age 30 (e30), and assessed the contributions of mortality changes among the low-educated and the high-educated at different ages.

METHODS

We used individually linked annual mortality data by educational level (low, middle and high), sex and single age (30+) from 1971/1972 onwards for England and Wales, Finland and Italy (Turin). We applied segmented regression to trends in educational inequalities in e30 (e30 high-educated minus e30 low-educated) and employed a novel demographic decomposition technique.

RESULTS

We identified several phases and breakpoints in the trends in educational inequalities in e30. The long-term increases (Finnish men, 1982-2008; Finnish women, 1985-2017; and Italian men, 1976-1999) were driven by faster mortality declines among the high-educated aged 65-84, and by mortality increases among the low-educated aged 30-59. The long-term decreases (British men, 1976-2008, and Italian women, 1972-2003) were driven by faster mortality improvements among the low-educated than among the high-educated at age 65+. The recent stagnation of increasing inequality (Italian men, 1999) and reversals from increasing to decreasing inequality (Finnish men, 2008) and from decreasing to increasing inequality (British men, 2008) were driven by mortality trend changes among the low-educated aged 30-54.

CONCLUSION

Educational inequalities are plastic. Mortality improvements among the low-educated at young ages are imperative for achieving long-term decreases in educational inequalities in e30.

Trends in mortality patterns in two countries with different welfare models: comparisons between CUBA and Denmark 1955–2020

Cuba and Denmark represent states with different welfare models that have reached the same level of life expectancy. The purpose was to investigate and compare mortality changes in the two countries. Systematically collected information on population numbers and deaths for the entire Cuban and Danish populations was the basis of life table data used to quantify differences in the change in age-at-death distributions since 1955, age-specific contributions to differences in life expectancy, lifespan variation, and other changes in mortality patterns in Cuba and Denmark. Life expectancy in Cuba and Denmark converged until 2000, when the increase in life expectancy for Cuba slowed down. Since 1955, infant mortality has fallen in both countries but mostly in Cuba. Both populations experienced compression of mortality as lifespan variation decreased markedly, primarily due to postponement of early deaths. Given the different starting point in the mid-1900s and living conditions for Cubans and Danes, health status achieved among Cubans is striking. A rapidly ageing population is challenging both countries, but Cuban health and welfare are further burdened by a deteriorating economy in recent decades.

Dynamic Changes in the Association Between Education and Health in the United States

Policy Points We reviewed some of the recent advances in education and health, arguing that attention to social contextual factors and the dynamics of social and institutional change provide critical insights into the ways in which the association is embedded in institutional contexts. Based on our findings, we believe incorporating this perspective is fundamentally important to ameliorate current negative trends and inequality in Americans' health and longevity.

Why we should monitor disparities in old-age mortality with the modal age at death

Indicators based a fixed “old” age threshold have been widely used for assessing socioeconomic disparities in mortality at older ages. Interpretation of long-term trends and determinants of these indicators is challenging because mortality above a fixed age that in the past would have reflected old age deaths is today mixing premature and old-age mortality. We propose the modal (i.e., most frequent) age at death, M, an indicator increasingly recognized in aging research, but which has been infrequently used for monitoring mortality disparities at older ages. We use mortality and population exposure data by occupational class over the 1971-2017 period from Finnish register data. The modal age and life expectancy indicators are estimated from mortality rates smoothed with penalized B-splines. Over the 1971-2017 period, occupational class disparities in life expectancy at 65 and 75 widened while disparities in M remained relatively stable. The proportion of the group surviving to the modal age was constant across time and occupational class. In contrast, the proportion surviving to age 65 and 75 has roughly doubled since 1971 and showed strong occupational class differences. Increasing socioeconomic disparities in mortality based on fixed old age thresholds may be a feature of changing selection dynamics in a context of overall declining mortality. Unlike life expectancy at a selected fixed old age, M compares individuals with similar survival chances over time and across occupational classes. This property makes trends and differentials in M easier to interpret in countries where old-age survival has improved significantly.

The impact of income definitions on mortality inequalities

Income is a strong predictor of adult mortality. Measuring income is not as simple as it may sound. It can be conceptualized at the individual or the household level, with the former better reflecting an individual's earning ability, and the latter better capturing living standards. Furthermore, respondents are often grouped into income categories based on their positions in the income distribution, and this operationalization can be done on the basis of age-specific or total population income distributions. In this study, we look at how four combinations of different conceptualizations (individual vs. household) and operationalizations (age-specific vs. total population) of income can affect mortality inequality estimates. Using Finnish registry data, we constructed period life tables for ages 25+ from 1996 to 2017 by gender and for four income definitions. The results indicated that the slope index of inequality for life expectancy varied by 1.1-5.7 years between income definitions, with larger differences observed for women than for men. The overall age patterns of relative index of inequality for mortality rates yielded by the four definitions were similar, but the levels differed. The period trends across income definitions were consistent for men, but not for women. We conclude that researchers should pay particular attention to the choice of the income definitions when analyzing the association between income and mortality, and when comparing the magnitude of inequality across studies and over time.