Contributions of age groups and causes of death to the sex gap in lifespan variation in Europe.

Much less is known about the sex gap in lifespan variation, which reflects inequalities in the length of life, than about the sex gap in life expectancy (average length of life). We examined the contributions of age groups and causes of death to the sex gap in lifespan variation for 28 European countries, grouped into five European regions. In 2010-15, males in Europe displayed a 6.8-year-lower life expectancy and a 2.3-year-higher standard deviation in lifespan than females, with clear regional differences. Sex differences in lifespan variation are attributable largely to higher external mortality among males aged 30-39, whereas sex differences in life expectancy are due predominantly to higher smoking-related and cardiovascular disease mortality among males aged 60-69. The distinct findings for the sex gap in lifespan variation and the sex gap in life expectancy provide additional insights into the survival differences between the sexes.

Sex-differences in excess death risk during the COVID-19 pandemic: an analysis of the first wave across Italian regions. What have we learned?

In this commentary, we bring together knowledge on sex-differences in excess death during the first wave of the COVID-19 pandemic in Italy, one of the most hit European countries. We zoom into Italian regions to account for the spatial gradient of the spread of the virus. Analyses of excess death by sex during the COVID-19 pandemic have been possible thanks to weekly mortality data released by national statistical offices, mainly in developed countries. The general finding is that males up to 75 years old have been suffering more excess death compared to females. However, the picture is less clear-cut at older ages. During previous epidemics, such as SARS, Swine Flu, and MERS, studies are limited and produce scattered, non-conclusive evidence. Knowledge of the sex-pattern of susceptibility to mortality from virulent respiratory diseases and its interplay with age could improve crisis management during future epidemics and pandemics. National statistical offices should provide weekly mortality data with spatial granularity, disaggregated by sex and age groups, to allow for such analyses.

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.

Health care system efficiency and life expectancy: A 140-country study.

Despite the evidence of links between health expenditure and health care efficiency, it is still unclear why countries with similar levels of health expenditures experience different outputs in terms of life expectancy at birth. Health care system efficiency might shed some light on the question. Using output-oriented data envelopment analysis, we compared the health systems of 140 countries in terms of attained life expectancy. Efficiency is determined by the distance from the closest country on the best practice frontier, which identifies the highest attainable life expectancy observed for any given level of health care spending. By using national data form the Human Development Data, we built the efficiency frontier and computed the potential life expectancy increase for each country. The potential improvement was, on average, 5.47 years [95%CI: 4.71-6.27 years]. The least efficient countries (10th percentile of the efficiency score) could improve by 11.78 years, while the most efficient countries (90th percentile of the efficiency score) could only improve by 0.83 years. We then analyzed, with regression analysis stratified by average education level, and by the role of health-related variables in differentiating efficient and inefficient countries from each other. The results suggest that, among countries with lower levels of education, decreasing unemployment and income inequality increases average life expectancy, without increasing health expenditure levels.

Death rates at specific life stages mold the sex gap in life expectancy.

Why do women live longer than men? Here, we mine rich lodes of demographic data to reveal that lower female mortality at particular ages is decisive-and that the important ages changed around 1950. Earlier, excess mortality among baby boys was crucial; afterward, the gap largely resulted from elevated mortality among men 60+. Young males bear modest responsibility for the sex gap in life expectancy: Depending on the country and time, their mortality accounts for less than a quarter and often less than a 10th of the gap. Understanding the impact on life expectancy of differences between male and female risks of death by age, over time, and across populations yields insights for research on how the lives of men and women differ.

Understanding Health Deterioration and the Dynamic Relationship between Physical Ability and Cognition among a Cohort of Danish Nonagenarians.

This study aims to determine how demographics, socioeconomic characteristics, and lifestyle affect physical and cognitive health transitions among nonagenarians, whether these transitions follow the same patterns, and how each dimension affects the transitions of the other. We applied a multistate model for panel data to 2262 individuals over a 2-year follow-up period from the 1905 Danish Cohort survey. Within two years from baseline, the transition probability from good to bad physical health-ability to stand up from a chair-was higher than dying directly (29% vs. 25%), while this was not observed for cognition (24% vs. 27%) evaluated with Mini-Mental State Examination-a score lower than 24 indicates poor cognitive health. Probability of dying either from bad physical or cognitive health condition was 50%. Health transitions were associated with sex, education, living alone, body mass index, and physical activity. Physical and cognitive indicators were associated with deterioration of cognitive and physical status, respectively, and with survivorship from a bad health condition. We conclude that physical and cognitive health deteriorated differently among nonagenarians, even if they were related to similar sociodemographic and lifestyle characteristics and resulted dynamically related with each other.

Matrix methods in health demography: a new approach to the stochastic analysis of healthy longevity and DALYs.

BACKGROUND: Increases in human longevity have made it critical to distinguish healthy longevity from longevity without regard to health. Current methods focus on expectations of healthy longevity, and are often limited to binary health outcomes (e.g., disabled vs. not disabled). We present a new matrix formulation for the statistics of healthy longevity, based on health prevalence data and Markov chain theory, applicable to any kind of health outcome and which provides variances and higher moments as well as expectations of healthy life. METHOD: The model is based on a Markov chain description of the life course coupled with the moments of health outcomes ("rewards") at each age or stage. As an example, we apply the method to nine European countries using the SHARE survey data on the binary outcome of disability as measured by activities of daily living, and the continuous health outcome of hand grip strength. RESULTS: We provide analytical formulas for the mean, variance, coefficient of variation, skewness and other statistical properties of healthy longevity. The analysis is applicable to binary, categorical, ordinal, or interval scale health outcomes. The results are easily evaluated in any matrix-oriented software. The SHARE results reveal familiar patterns for the expectation of life and of healthy life: women live longer than men but spend less time in a healthy condition. New results on the variance shows that the standard deviation of remaining healthy life declines with age, but the coefficient of variation is nearly constant. Remaining grip strength years decrease with age more dramatically than healthy years but their variability pattern is similar to the pattern of healthy years. Patterns are similar across nine European countries. CONCLUSIONS: The method extends, in several directions, current calculations of health expectancy (HE) and disability-adjusted life years (DALYs). It applies to both categorical and continuous health outcomes, to combinations of multiple outcomes (e.g., death and disability in the formulation of DALYs) and to age- or stage-classified models. It reveals previously unreported patterns of variation among individuals in the outcomes of healthy longevity.

Women live longer than men even during severe famines and epidemics.

Women in almost all modern populations live longer than men. Research to date provides evidence for both biological and social factors influencing this gender gap. Conditions when both men and women experience extremely high levels of mortality risk are unexplored sources of information. We investigate the survival of both sexes in seven populations under extreme conditions from famines, epidemics, and slavery. Women survived better than men: In all populations, they had lower mortality across almost all ages, and, with the exception of one slave population, they lived longer on average than men. Gender differences in infant mortality contributed the most to the gender gap in life expectancy, indicating that newborn girls were able to survive extreme mortality hazards better than newborn boys. Our results confirm the ubiquity of a female survival advantage even when mortality is extraordinarily high. The hypothesis that the survival advantage of women has fundamental biological underpinnings is supported by the fact that under very harsh conditions females survive better than males even at infant ages when behavioral and social differences may be minimal or favor males. Our findings also indicate that the female advantage differs across environments and is modulated by social factors.

The emergence of longevous populations.

The human lifespan has traversed a long evolutionary and historical path, from short-lived primate ancestors to contemporary Japan, Sweden, and other longevity frontrunners. Analyzing this trajectory is crucial for understanding biological and sociocultural processes that determine the span of life. Here we reveal a fundamental regularity. Two straight lines describe the joint rise of life expectancy and lifespan equality: one for primates and the second one over the full range of human experience from average lifespans as low as 2 y during mortality crises to more than 87 y for Japanese women today. Across the primate order and across human populations, the lives of females tend to be longer and less variable than the lives of males, suggesting deep evolutionary roots to the male disadvantage. Our findings cast fresh light on primate evolution and human history, opening directions for research on inequality, sociality, and aging.

Why did Danish women's life expectancy stagnate? The influence of interwar generations' smoking behaviour.

The general health status of a population changes over time, generally in a positive direction. Some generations experience more unfavourable conditions than others. The health of Danish women in the interwar generations is an example of such a phenomenon. The stagnation in their life expectancy between 1977 and 1995 is thought to be related to their smoking behaviour. So far, no study has measured the absolute effect of smoking on the mortality of the interwar generations of Danish women and thus the stagnation in Danish women's life expectancy. We applied a method to estimate age-specific smoking-attributable number of deaths to examine the effect of smoking on the trends in partial life expectancy of Danish women between age 50 and 85 from 1950 to 2012. We compared these trends to those for women in Sweden, where there was no similar stagnation in life expectancy. When smoking-attributable mortality was excluded, the gap in partial life expectancy at age 50 between Swedish and Danish women diminished substantially. The effect was most pronounced in the interwar generations. The major reason for the stagnation in Danish women's partial life expectancy at age 50 was found to be smoking-related mortality in the interwar generations.

Unobserved Heterogeneity of Frailty in the Analysis of Socioeconomic Differences in Health and Mortality.

The concepts of unobserved frailty and selection have been extensively analyzed with respect to phenomena like mortality deceleration at old ages and mortality convergence or cross overs between populations (for example American black and white populations, men and women). Despite the long-time observation of converging mortality risks in differential socioeconomic mortality research, the interest in the connection between frailty, selection, and health and mortality inequalities over a life course approach has increased only recently. This overview of the literature summarizes the main concepts of unobserved frailty and socioeconomic differences in mortality and how frailty and selection relate to these differences at old ages. It then reviews the evidence coming from the existing studies. Although the number of studies is still limited, the body of research on unobserved frailty and socioeconomic inequalities in health and mortality in a life course approach is growing. The results, however, are contradictory, and whether selection plays a major role in shaping the observed socioeconomic mortality patterns at old ages is still controversial.

Mortality by education level at late-adult ages in Turin: a survival analysis using frailty models with period and cohort approaches.

OBJECTIVES: Neglecting the presence of unobserved heterogeneity in survival analysis models has been showed to potentially lead to underestimating the effect of the covariates included in the analysis. This study aimed to investigate the role of unobserved heterogeneity of frailty on the estimation of mortality differentials from age 50 on by education level. DESIGN: Longitudinal mortality follow-up of the census-based Turin population linked with the city registry office. SETTING: Italian North-Western city of Turin, observation window 1971-2007. POPULATION: 391 170 men and 456 216 women followed from age 50. PRIMARY OUTCOME MEASURES: Mortality rate ratios obtained from survival analysis regression. Models were estimated with and without the component of unobserved heterogeneity of frailty and controlling for mortality improvement over time from both cohort and period perspectives. RESULTS: In the majority of cases, the models without frailty estimated a smaller educational gradient than the models with frailty. CONCLUSIONS: The results draw the attention of the potential underestimation of the mortality inequalities by socioeconomic levels in survival analysis models when not controlling for unobserved heterogeneity of frailty.