Month of birth influences adult life expectancy at ages 50+. Why? In two countries of the Northern Hemisphere–Austria and Denmark–people born in autumn (October–December) live longer than those born in spring (April–June). Data for Australia show that, in the Southern Hemisphere, the pattern is shifted by half a year. The lifespan pattern of British immigrants to Australia is similar to that of Austrians and Danes and significantly different from that of Australians. These findings are based on population data with more than a million observations and little or no selectivity. The differences in lifespan are independent of the seasonal distribution of deaths and the social differences in the seasonal distribution of births. In the Northern Hemisphere, the excess mortality in the first year of life of infants born in spring does not support the explanation of selective infant survival. Instead, remaining life expectancy at age 50 appears to depend on factors that arise in utero or early in infancy and that increase susceptibility to diseases later in life. This result is consistent with the finding that, at the turn of the last century, infants born in autumn had higher birth weights than those born in other seasons. Furthermore, differences in adult lifespan by month of birth decrease over time and are significantly smaller in more recent cohorts, which benefited from substantial improvements in maternal and infant health.

Whether a cost of reproduction exists among humans is still questionable. A major study of aristocratic British families finds a significant positive correlation between parity and late–life mortality, which indicates a trade–off between reproduction and longevity. This result is supported by four other studies, while earlier studies have not found a relationship or came to the opposite conclusion. We show that in natural fertility populations the relationship between fertility and late–life mortality cannot be studied correctly without considering the effects of differences in health and of mortality selection during childbearing ages because these two effects lead to a dampening of the true relationship. If these effects are controlled in Hollingsworth's genealogy of the British peerage a significant trade–off between reproduction and longevity exists for females but not for males.

This keynote chapter provides an overview of research into the relationship between health and gender/sex. Since health has multiple dimensions, several measures are needed to capture variations by gender and the use of multiple measures can help us better understand the underlying explanations for gender differences. Although life expectancy and mortality are the most extreme measures of health, relative to most other health metrics they are more amenable to accurate measurement, and are more comparable across countries and over time. We start with a review of the existing literature on sex differences in survival followed by a review of the empirical evidence on gender differences in objective and subjective health. Data on objective health are collected through physical performance tests or cognitive tests, or are based on medical diagnoses that are either recoded in administrative databases or collected through self-reports of diagnoses made by physicians. Subjective health measures strongly depend on individual perceptions that can be modified by contextual factors (socioeconomic, cultural, gender roles, etc.) and awareness of objective health status. We focus on three main subjective health indicators: self-perceived health, functional status, and health-related quality of life (HRQOL). These indicators have become core components of epidemiological, social, and economic research. Later in this chapter, we review the most frequently cited explanations for gender differences in health and mortality, grouping them into the following categories: biological (genetic and hormonal), lifestyle factors (tobacco and alcohol consumption, diet, and physical activity), and social profiles (education and employment). Finally, we conclude by providing possible directions for future research in this area.

Studies state profound cross-country differences in healthy life years and its time trends, suggesting either the health scenario of expansion or compression of morbidity. A much-discussed question in public health research is whether the health scenarios are heterogeneous or homogeneous on the subnational level as well. Furthermore, the question arises whether the morbidity trends or the mortality trends are the decisive drivers of the care need-free life years (CFLY), the life years with care need (CLY), and, ultimately, the health scenarios. This study uses administrative census data of all beneficiaries in Germany from the Statutory Long-Term Care Insurance 2001–2009. We compute the CFLY and CLY at age 65+ for 412 counties. The CFLY and CLY gains are decomposed into the effects of survival and of the prevalence of care need, and we investigate their linkages with the health scenarios by applying multinomial regression models. We show an overall increase in CFLY, which is higher for men than for women and higher for severe than for any care need. However, spatial variation in CFLY and in CLY has increased. In terms of the health scenarios, a majority of counties show an expansion of any care need but a compression of severe care need. There is high spatial heterogeneity, with expansion-counties surrounding compression-counties and vice versa, which is mainly caused by divergent trends in the prevalence of care need. We show that mortality is responsible for the absolute changes in CFLY and CLY, while morbidity is the decisive driver that determines the health scenario of a county. Combining regionalized administrative data and advanced statistical methods permits a deeper insight into the complex relationship between health and mortality. Our findings demonstrate a compression of life years with severe care need, which however, depends on the region of residence. To attenuate regional inequalities, more efforts are needed that improve health by medical and infrastructural interventions and by the exchange of insights in the efficiency of small- and large-area policy measures between the vanguard and the rearguard counties. In future research, the underlying latent mechanisms should be investigated in more detail.