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.
We provide a systematic country and age group comparison of the gender gap in several generic health indicators and more specific morbidity outcomes. Using data from the Survey of Health, Ageing and Retirement (SHARE), we examined the gender gap in the prevalence of poor self-rated health, chronic health conditions, activity limitations, multimorbidity, pain, heart attacks, diabetes, and depression in three age groups (50-64, 65-79, and 80+) based on linear probability models with and without adjustment for covariates. While women were typically disadvantaged regarding poor self-rated health, chronic health conditions, activity limitations, multimorbidity, pain, and depression, men had a higher prevalence of heart attacks and diabetes. However, the gender gap's magnitude and sometimes even its direction varied considerably with some age trends apparent. Regarding some health indicators, the gender gap tended to be higher in Southern and Eastern Europe than in Western and Northern Europe. All in all, the presence of a gender health gap cannot be regarded as a universal finding as the gap tended to widen, narrow or even reverse with age depending on the indicator and country.
Comparative analyses frequently examine respondents’ self-rated health (SRH), assuming that it is a valid and comparable measure of generic health. However, given SRH’s vagueness, this assumption is questionable due to (1) manifold non-health influences, such as personal characteristics including optimism, interviewer effects on the rating, and cultural contexts, as well as (2) potential gender, age- or country-specific expectations for one’s health or frames of reference. Conceptually, two major components of SRH can be distinguished: latent health and reporting behavior. While latent health exclusively refers to objective health status, reporting behavior collectively refers to non-health characteristics (NH) affecting SRH. The present paper is primarily concerned with the latter and aims to identify whether and how NH bias SRH, including possible differences by gender, age, and country of residence. The presented analyses are based on data from 16,183 participants in five countries drawn from the fifth wave of the Survey of Health, Ageing and Retirement in Europe (SHARE). Latent health is controlled via a wide array of health indicators and the residuals are examined with a model covering NH from three different sources: the interviewer, the respondent, and the country of residence. To identify subgroup-specific response behaviors, all analyses are carried out separately by gender, three age groups (50-64, 65-79, and 80+ years), and country of residence. The analyses uncovered influences of – among others–the interviewer’s SRH, the respondent’s life satisfaction, and the country of residence on SRH, while other factors differed by subgroup. The amount of explained variance due to such reporting behavior (with a mean of seven percent) can be deemed meaningful, considering that controlling for latent health already explains around half of SRH’s variance. The greatest source of non-health influences was respondent characteristics, with the interviewer and country having smaller effects. These results illustrate the importance of taking NH into account when using SRH measures. Future research on complementing SRH with factual questions in survey design is advisable. * This article belongs to a special issue on “Levels and Trends of Health Expectancy: Understanding its Measurement and Estimation Sensitivity”.
DefinitionThe male-female health-mortality paradox results from the fact that females live longer than males, but spend a higher proportion of their total life expectancy in poorer health states. The phenomenon is depicted in the schematic Figure 1, where the grey shaded area represents the proportion of total life expectancy spent in poor health, for females and males, respectively on panels a and b. It is clear that the grey shaded areas, representative of poor life expectancy, is larger for women than for men. The sum of the white area and the grey shaded area is equal to the total life expectancy. Since health is an important predictor of death, the fact that women live longer in spite of a higher proportion of their lives spent in unhealthy state puzzles researchers. Some other terms used to describe the phenomenon are: "gender and health paradox", "morbidity paradox", "morbidity-mortality paradox", or "male-female healthsurvival paradox".
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