Data for ten European countries which provide detailed distribution of COVID-19 cases by sex and age show that among people of working age, women diagnosed with COVID-19 substantially outnumber infected men. This pattern reverses around retirement: infection rates among women fall at age 60-69, resulting in a cross-over with infection rates among men. The relative disadvantage of women peaks at ages 20-29, whereas the male disadvantage in infection rates peaks at ages 70-79. The elevated infection rates among women of working age are likely tied to their higher share in health- and care-related occupations. Our examination also suggests a link between women's employment profiles and infection rates in prime working ages. The same factors that determine women's higher life expectancy account for their lower fatality and higher male disadvantage at older ages.

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.

There is consistent evidence that women live longer than men at all ages, but spend a higher proportion of their total life expectancy in poorer health, a phenomenon described as the "male-female health-survival paradox" or the "gender paradox in health and mortality". However, it is difficult to explain the process because morbidity by sex differs considerably across domains of health, age groups, social contexts and severity level. In addition, women and men report differently their health in surveys, making it cumbersome to understand whether what drives the paradox is a higher female morbidity or male mortality, a different reporting behavior, or all of those aspects together. The aim of this chapter is to demonstrate the magnitude of those differences in Europe using different health domain indicators (activity limitation, chronic morbidity and self-perceived health) from the EHEMU Information System and the reporting behavior by sex from the SHARE survey vignettes.

The number of COVID-19 infections is key for accurately monitoring the pandemics. However, due to differential testing policies, asymptomatic individuals and limited large-scale testing availability, it is challenging to detect all cases. Seroprevalence studies aim to address this gap by retrospectively assessing the number of infections, but they can be expensive and time-intensive, limiting their use to specific population subgroups. In this paper, we propose a complementary approach that combines estimated (1) infection fatality rates (IFR) using a Bayesian melding SEIR model with (2) reported case-fatality rates (CFR) in order to indirectly estimate the fraction of people ever infected (from the total population) and detected (from the ever infected). We apply the technique to the U.S. due to their remarkable regional diversity and because they count with almost a quarter of all global confirmed cases and deaths. We obtain that the IFR varies from 1.25% (0.39–2.16%, 90% CI) in Florida, the most aged population, to 0.69% in Utah (0.21–1.30%, 90% CI), the youngest population. By September 8, 2020, we estimate that at least five states have already a fraction of people ever infected between 10% and 20% (New Jersey, New York, Massachussets, Connecticut, and District of Columbia). The state with the highest estimated fraction of people ever infected is New Jersey with 17.3% (10.0, 55.8, 90% CI). Moreover, our results indicate that with a probability of 90 percent the fraction of detected people among the ever infected since the beginning of the epidemic has been less than 50% in 15 out of the 20 states analyzed in this paper. Our approach can be a valuable tool that complements seroprevalence studies and indicates how efficient have testing policies been since the beginning of the outbreak.

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The COVID-19 pandemic caused an increase in mortality in 2020 with a resultant decrease in life expectancy in most countries around the world. In Germany, the reduction in life expectancy at birth between 2019 and 2020 was comparatively small, at -0.20 years. The decrease was stronger among men than among women (-0.24 vs. -0.13 years) and in eastern rather than in western Germany (-0.36 vs. -0.16 years). Men in eastern Germany experienced the biggest decline in life expectancy at birth (-0.41 years). For western German men, the decline was less pronounced (-0.19 years). Among women, the decline in life expectancy at birth was also greater in eastern (-0.25 years) than in western Germany (-0.10 years). As a result of these developments, the differences in life expectancy between the two parts of Germany, and between women and men, increased compared with the previous year. Life expectancy at age 65 decreased more strongly than life expectancy at birth for both sexes and in all regions. This reflects the fact that it was mainly older age groups that were affected by the increase in mortality in 2020. This paper provides further insights into mortality changes in 2020, based on age decomposition and an analysis of lifespan inequality. We conclude that the population in eastern Germany was hit harder by the COVID-19 pandemic in 2020 than the population in the western Germany.

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