SignificanceWomen live longer than men in nearly all populations today. Some research focuses on the biological origins of the female advantage; other research stresses the significance of social factors. We studied male–female survival differences in populations of slaves and populations exposed to severe famines and epidemics. We find that even when mortality was very high, women lived longer on average than men. Most of the female advantage was due to differences in mortality among infants: baby girls were able to survive harsh conditions better than baby boys. These results support the view that the female survival advantage is modulated by a complex interaction of biological environmental and social factors.

Summary Aim To examine the magnitude of sex differences in survival from the coronavirus disease 2019 (COVID-19) in Europe across age groups and regions. We hypothesized that men have a higher mortality than women at any given age but that sex differences will decrease with age as only the healthiest men survive to older ages. Methods We used population data from the Institut National D’Études Démographiques on cumulative deaths due to COVID-19 from February to June 2020 in 10 European regions: Denmark, Norway, Sweden, The Netherlands, England and Wales, France, Germany, Italy, Spain and Portugal. For each region, we calculated cumulative mortality rates stratified by age and sex and corresponding relative risks for men vs. women. Results The relative risk of dying from COVID-19 was higher for men than for women in almost all age groups in all regions. The overall relative risk ranged from 1.11 (95% confidence interval, CI 1.01–1.23) in Portugal to 1.54 (95% CI 1.49–1.58) in France. In most regions, sex differences increased until the ages of 60–69 years, but decreased thereafter with the smallest sex difference at age 80+ years. Conclusion Despite variability in data collection and time coverage among regions, the study showed an overall similar pattern of sex differences in COVID-19 mortality in Europe. Supplementary Information The online version of this article (10.1007/s00508-020-01793-9) contains supplementary material, which is available to authorized users.

Semen quality has been suggested to be a biological marker of long-term morbidity and mortality; however, few studies have been conducted on this subject. We identified 5,370 men seen for infertility at Frederiksberg Hospital, Denmark, during 1977-2010, and 4,712 of these men were followed in the Danish National Patient Registry until first hospitalization, death, or the end of the study. We classified patients according to hospitalizations and the presence of cardiovascular disease, diabetes, testicular cancer, or prostate cancer. We found a clear association between sperm concentration below 15 million/mL and all-cause hospitalizations (hazard ratio = 1.5, 95% confidence interval: 1.4, 1.6) and cardiovascular disease (hazard ratio = 1.4, 95% confidence interval: 1.2, 1.6), compared with men with a concentration above 40 million/mL. The probabilities for hospitalizations were also higher with a low total sperm count and low motility. Men with a sperm concentration of 195-200 million/mL were, on average, hospitalized for the first time 7 years later than were men with a sperm concentration of 0-5 million/mL. Semen quality was associated with long-term morbidity, and a significantly higher risk of hospitalization was found, in particular for cardiovascular diseases and diabetes mellitus. Our study supports the suggestion that semen quality is a strong biomarker of general health.

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Health conditions change from year to year, with a general tendency in many countries for improvement. These conditions also change from one birth cohort to another: some generations suffer more adverse events in childhood, smoke more heavily, eat poorer diets, etc., than generations born earlier or later. Because it is difficult to disentangle period effects from cohort effects, demographers, epidemiologists, actuaries, and other population scientists often disagree about cohort effects' relative importance. In particular, some advocate forecasts of life expectancy based on period trends; others favor forecasts that hinge on cohort differences. We use a combination of age decomposition and exchange of survival probabilities between countries to study the remarkable recent history of female life expectancy in Denmark, a saga of rising, stagnating, and now again rising lifespans. The gap between female life expectancy in Denmark vs. Sweden grew to 3.5 y in the period 1975-2000. When we assumed that Danish women born 1915-1945 had the same survival probabilities as Swedish women, the gap remained small and roughly constant. Hence, the lower Danish life expectancy is caused by these cohorts and is not attributable to period effects. actors influencing human mortality and health may act at different ages, on specific generations, or at different points in time. A major challenge in analyzing particular mortality patterns is to disentangle the relative importance of the factors (1). A methodological problem arises from the interdeterminacy of linear effects attributable to period (points in time) or cohort (generations), which derives from the perfect correlation among cohort, period and age (age = period − cohort), making only deviations from the combined linearity of cohort and period comparable (1-4). As a result, debates have raged about whether period or cohort effects led to the rapid rise in life expectancy since 1900 in most western countries (1,(5)(6)(7)(8).During the latter half of the 20th century, emphasis was given to temporal effects because most population specialists thought that cohort mortality effects were small and need not be incorporated into models of mortality reductions (1, 9). Since the mid-1990s, however, the increased interest in life course effects on health and mortality has given new life to studies of cohort effects (1).A few birth cohorts have been identified with clear-cut cohort patterns: those of Britain in the late nineteenth and early twentieth centuries (10, 11); those of Japan in the early twentieth century (12); and cohorts born in Britain in the 1930s, often referred to as the "golden generations" (1, 13). Here, we present another example of cohorts influencing mortality patterns, namely the case of the interwar generations of Danish women. We illustrate how to disentangle period and cohort effects using an approach based on age decomposition, exclusion of age-period effects, and replacement of survival probabilities. Interwar Generations of Danish WomenEven though life expe...