The degree to which biological factors contribute to the existence and the widening of mortality differences by sex remains unclear. To address this question, a mortality analysis for the years 1890 to 1995 was performed comparing mortality data on more than 11,000 Catholic nuns and monks in Bavarian communities living in very nearly identical behavioral and environmental conditions with life table data for the general German population. While the mortality differences between women and men in the general German population increased considerably after World War II, they remained almost constant among the members of Bavarian religious orders during the entire observation period, with slight advantages for nuns. Thus, the higher differences observable in the general population cannot be attributed to biological factors. The different trends in sex-specific mortality between the general and the cloistered populations are caused exclusively by men in the general population who were unable to follow the trend in mortality reduction of women, nuns, and especially monks. Under the special environmental conditions of nuns and monks, biological factors appear to confer a maximum survival advantage for women of no more than one year in remaining life expectancy at young adult ages. Copyright 2003 by The Population Council, Inc..

Background: Although many different factors have been identified to contribute to excess male mortality, it is still unclear which path of the complex cause-effect chain is the decisive driver of the life expectancy gap between women and men. Objective: The question behind this study is whether these sex differences are caused primarily by factors leading to low female mortality or rather by factors causing high male mortality. We hypothesise that they are to a large extent caused by specific subpopulations of men with particularly high mortality levels that decrease the average life expectancy of men. Methods: To test this hypothesis, we investigate in a meta-analysis the variability in mortality (VM) in women and men - defined as the range of death rates prevailing among subpopulations - in empirical studies analysing specific phenomena of mortality differentials. We used the data of 72 empirical studies, including 146 total effects (TE) and 1,718 single effects (SE) for 21 different risk factors. Results: In 85% of TE and three quarters of SE the VM was higher in men than in women, taking into account men's higher overall mortality. The corresponding figures for the direct differences in the VM between women and men are 92 and 82%, respectively. Cases with higher female VM are rare exceptions and appear in particular in the highest age groups. Conclusions: We find support for our hypothesis that the disproportionate high mortality levels of specific male subpopulations are the central cause of the current extent of sex differences in life expectancy. Thus, public health programmes should be targeted toward these disadvantaged subpopulations among men which seem to be related primarily to socioeconomic characteristics.

Significant reductions in mortality are reflected in strong increases in life expectancy particularly in industrialized countries. Previous analyses relate these improvements primarily to medical innovations and advances in health-related behaviors. Mostly ignored, however, is the question to what extent the gains in life expectancy are related to structural changes in the populations due to increasing education levels. We decompose changes of the total populations' life expectancy at age 30 in Italy, Denmark, and the USA, over the 20-year period between 1990 and 2010 into the effects of education-specific mortality changes ("M effect") and changes in the populations' educational structure ("P effect"). We use the "replacement decomposition technique" to further subdivide the M effect into the contributions by the individual education groups. While most of the increases in life expectancy are due to the effect of changing mortality, a large proportion of improvements in longevity can indeed be attributed to the changing structure of the population by level of education in all three countries. The estimated contribution of the P effect ranges from around 15% for men in the USA to approximately 40% for women in Denmark. This study demonstrates strong associations between education and overall population health, suggesting that education policies can also be seen as indirect health policies.

Period life expectancy is one of the most used summary indicators for the overall health of a population. Its levels and trends direct health policies, and researchers try to identify the determining risk factors to assess and forecast future developments. The use of period life expectancy is often based on the assumption that it directly reflects the mortality conditions of a certain year. Accordingly, the explanation for changes in life expectancy are typically sought in factors that have an immediate impact on current mortality conditions. It is frequently overlooked, however, that this indicator can also be affected by at least three kinds of effects, in particular in the situation of short-term fluctuations: cohort effects, heterogeneity effects, and tempo effects. We demonstrate their possible impact with the example of the almost Europe-wide decrease in life expectancy in 2015, which caused a series of reports about an upsurge of a health crisis, and we show that the consideration of these effects can lead to different conclusions. Therefore, we want to raise an awareness concerning the sensitivity of life expectancy to sudden changes and the menaces a misled interpretation of this indicator can cause.

Background: Throughout industrialized countries, tobacco consumption is seen as the predominant driver of both the trend and the extent of gender differences in life expectancy. However, several factors raise doubts to this generalization. We hypothesize that the impact of smoking on the gender gap is context-specific and differs between populations. Methods: We decompose the gender differences in life expectancy into fractions caused by smoking and other non-biological factors for 53 industrialized countries and the period 1955–2009 to assess the significance of smoking among the causes that can be influenced by direct or indirect interference. Results: The trend of the gender gap can indeed be attributed to smoking in most populations of the western world. However, with regard to the overall extent of male excess mortality, smoking is the main driver only in the minority of the studied populations. While the impact of smoking to gender differences in life expectancy declines in all populations, the contribution of other non-biological factors is in most cases higher at the end than at the beginning of the observation period. Conclusions: Over-generalized statements suggesting that smoking is the main driver of the gender gap in all populations can be misleading. The results of this study demonstrate that—regardless of the prevailing effect of smoking—many populations have still remarkable potentials to further narrow their gender gaps in life expectancy. Although measures to further reduce the prevalence of tobacco consumption must be continued, more attention should be directed to the growing importance of other non-biological factors.

In the present study, we use the modified orphanhood method to analyse mortality differences by socio-economic status in Italy. This technique permits the indirect estimation of adult mortality from survey-based information on parents' survival in developed populations and helps to overcome several limitations of conventional studies on mortality differences by social class. We estimate a time series of life tables by education and occupation and analyse the differences in life expectancy by socio-economic status along with their changes between 1980-84, 1985-89, and 1990-94. Whereas mortality differences between the highest social class and the other socio-economic status groups increased among men, they decreased among women. We speculate about the reasons for these sex-specific trends and evaluate the application of indirect estimation techniques to the populations of developed countries.