Demographic perspectives on the rise of longevity.

This article reviews some key strands of demographic research on past trends in human longevity and explores possible future trends in life expectancy at birth. Demographic data on age-specific mortality are used to estimate life expectancy, and validated data on exceptional life spans are used to study the maximum length of life. In the countries doing best each year, life expectancy started to increase around 1840 at a pace of almost 2.5 y per decade. This trend has continued until the present. Contrary to classical evolutionary theories of senescence and contrary to the predictions of many experts, the frontier of survival is advancing to higher ages. Furthermore, individual life spans are becoming more equal, reducing inequalities, with octogenarians and nonagenarians accounting for most deaths in countries with the highest life expectancy. If the current pace of progress in life expectancy continues, most children born this millennium will celebrate their 100th birthday. Considerable uncertainty, however, clouds forecasts: Life expectancy and maximum life span might increase very little if at all, or longevity might rise much faster than in the past. Substantial progress has been made over the past three decades in deepening understanding of how long humans have lived and how long they might live. The social, economic, health, cultural, and political consequences of further increases in longevity are so significant that the development of more powerful methods of forecasting is a priority.

A modal age at death approach to forecasting adult mortality.

Recent studies have shown that there are some advantages to forecasting mortality with indicators other than age-specific death rates. The mean, median, and modal ages at death can be directly estimated from the age-at-death distribution, as can information on lifespan variation. The modal age at death has been increasing linearly since the second half of the twentieth century, providing a strong basis from which to extrapolate past trends. The aim of this paper is to develop a forecasting model that is based on the regularity of the modal age at death and that can also account for changes in lifespan variation. We forecast mortality at ages 40 and above in 10 West European countries. The model we introduce increases forecast accuracy compared with other forecasting models and provides consistent trends in life expectancy and lifespan variation at age 40 over time.

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.

Inequalities in lifespan and mortality risk in the US, 2015-2019: a cross-sectional analysis of subpopulations by social determinants of health.

OBJECTIVE: To quantify inequalities in lifespan across multiple social determinants of health, how they act in tandem with one another, and to create a scoring system that can accurately identify subgroups of the population at high risk of mortality. DESIGN: Comparison of life tables across 54 subpopulations defined by combinations of four social determinants of health: sex, marital status, education and race, using data from the Multiple Cause of Death dataset and the American Community Survey. SETTING: United States, 2015-2019. MAIN OUTCOME MEASURES: We compared the partial life expectancies (PLEs) between age 30 and 90 years of all subpopulations. We also developed a scoring system to identify subgroups at high risk of mortality. RESULTS: There is an 18.0-year difference between the subpopulations with the lowest and highest PLE. Differences in PLE between subpopulations are not significant in most pairwise comparisons. We visually illustrate how the PLE changes across social determinants of health. There is a complex interaction among social determinants of health, with no single determinant fully explaining the observed variation in lifespan. The proposed scoring system adds clarification to this interaction by yielding a single score that can be used to identify subgroups that might be at high risk of mortality. A similar scoring system by cause of death was also created to identify which subgroups could be considered at high risk of mortality from specific causes. Even if subgroups have similar mortality levels, they are often subject to different cause-specific mortality risks. CONCLUSIONS: Having one characteristic associated with higher mortality is often not sufficient to be considered at high risk of mortality, but the risk increases with the number of such characteristics. Reducing inequalities is vital for societies, and better identifying individuals and subgroups at high risk of mortality is necessary for public health policy.

Probability of males to outlive females: an international comparison from 1751 to 2020.

OBJECTIVE: To measure sex differences in lifespan based on the probability of males to outlive females. DESIGN: International comparison of national and regional sex-specific life tables from the Human Mortality Database and the World Population Prospects. SETTING: 199 populations spanning all continents, between 1751 and 2020. PRIMARY OUTCOME MEASURE: We used the outsurvival statistic ( φ ) to measure inequality in lifespan between sexes, which is interpreted here as the probability of males to outlive females. RESULTS: In random pairs of one male and one female at age 0, the probability of the male outliving the female varies between 25% and 50% for life tables in almost all years since 1751 and across almost all populations. We show that φ is negatively correlated with sex differences in life expectancy and positively correlated with the level of lifespan variation. The important reduction of lifespan inequality observed in recent years has made it less likely for a male to outlive a female. CONCLUSIONS: Although male life expectancy is generally lower than female life expectancy, and male death rates are usually higher at all ages, males have a substantial chance of outliving females. These findings challenge the general impression that 'men do not live as long as women' and reveal a more nuanced inequality in lifespans between females and males.

Diversification in causes of death in low-mortality countries: emerging patterns and implications.

INTRODUCTION: An important role of public health organisations is to monitor indicators of variation, so as to disclose underlying inequality in health improvement. In industrialised societies, more individuals than ever are reaching older ages and have become more homogeneous in their age at death. This has led to a decrease in lifespan variation, with substantial implications for the reduction of health inequalities. We focus on a new form of variation to shed further light on our understanding of population health and ageing: variation in causes of death. METHODS: Data from the WHO Mortality Database and the Human Mortality Database are used to estimate cause-of-death distributions and life tables in 15 low-mortality countries. Cause-of-death variation, using 19 groups of causes, is quantified using entropy measures and analysed from 1994 to 2017. RESULTS: The last two decades have seen increasing diversity in causes of death in low-mortality countries. There have been important reductions in the share of deaths from diseases of the circulatory system, while the share of a range of other causes, such as diseases of the genitourinary system, mental and behavioural disorders, and diseases of the nervous system, has been increasing, leading to a more complex cause-of-death distribution. CONCLUSIONS: The diversification in causes of death witnessed in recent decades is most likely a result of the increase in life expectancy, together with better diagnoses and awareness of certain diseases. Such emerging patterns bring additional challenges to healthcare systems, such as the need to research, monitor and treat a wider range of diseases. It also raises new questions concerning the distribution of health resources.

Understanding Differences in Cancer Survival between Populations: A New Approach and Application to Breast Cancer Survival Differentials between Danish Regions.

Large variations in cancer survival have been recorded between populations, e.g., between countries or between regions in a country. To understand the determinants of cancer survival differentials between populations, researchers have often applied regression analysis. We here propose the use of a non-parametric decomposition method to quantify the exact contribution of specific components to the absolute difference in cancer survival between two populations. Survival differences are here decomposed into the contributions of differences in stage at diagnosis, population age structure, and stage-and-age-specific survival. We demonstrate the method with the example of differences in one-year and five-year breast cancer survival between Denmark's five regions. Differences in stage at diagnosis explained 45% and 27%, respectively, of the one- and five-year survival differences between Zealand and Central Denmark for patients diagnosed between 2008 and 2010. We find that the introduced decomposition method provides a powerful complementary analysis and has several advantages compared with regression models: No structural or distributional assumptions are required; aggregated data can be used; and the use of absolute differences allows quantification of the survival that could be gained by improving, for example, stage at diagnosis relative to a reference population, thus feeding directly into health policy evaluation.

Modeling and forecasting sex differences in mortality: a sex-ratio approach.

Female and male life expectancies have converged in most industrialized societies in recent decades. To achieve coherent forecasts between females and males, this convergence needs to be considered when forecasting sex-specific mortality. We introduce a model forecasting a matrix of the age-specific death rates of sex ratio, decomposed into two age profiles and time indices-before and after age 45-using principal component analysis. Our model allows visualization of both age structure and general level over time of sex differences in mortality for these two age groups. Based on a prior forecast for females, we successfully forecast male mortality convergence with female mortality. The usefulness of the developed model is illustrated by its comparison with other coherent and independent models in an out-of-sample forecast evaluation for 18 countries. The results show that the new proposal outperformed the other models for most countries.