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 Self-rated health (SRH) is arguably the most widely used generic health measurement in survey research. However, SRH remains a black box for researchers. In our paper, we want to gain a better understanding of SRH by identifying its determinants, quantifying the contribution of different health domains to explain SRH, and by exploring the moderating role of gender, age groups, and the country of residence. Method Using data from 61,365 participants of the fifth wave (2013) of the Survey of Health, Ageing and Retirement in Europe (SHARE) living in fifteen European countries, we explain SRH via linear regression models. The independent variables are grouped into five health domains: functioning, diseases, pain, mental health, and behavior. Via dominance analysis, we focus on their individual contribution to explaining SRH and compare these contributions across gender, three age groups, and fifteen European countries. Results Our model explains SRH rather well (R 2 = .51 for females/.48 for males) with functioning contributing most to the appraisal (.20/.18). Diseases were the second most relevant health dimension (.14/.16) followed by pain (.08/.07) and mental health (.07/.06). Health behavior (.02/.01) was less relevant for health ratings. This ranking held true for almost all countries with only little variance overall. A comparison of age groups indicated that the contribution of diseases and behavior to SRH decreased over the life-course while the contribution of functioning to R 2 increased. Conclusion Our paper demonstrates that SRH is largely based on diverse health information with functioning and diseases being most important. However, there is still room for idiosyncrasies or even bias.