Research Highlights
The research of the last years was determined in particular by the ERC Starting Grant project HEMOX (see Projects). Most importantly, it enabled us to extend our research fields from mainly mortality-related topics to the broader study of health. Albeit these topics seem closely related, the study of health and its determinants is much more complicated, and also its relationship with mortality is highly complex. We extended the state-of-the-art by the "CroHaM hypothesis" (Cross-sectional association between Health and Mortality) which states that the well-established longitudinal relationships between health and mortality (i.e., a postponement of functional limitations and disabilities with higher levels of life expectancy, but an increase in chronic diseases and conditions) exist equivalently in a cross-sectional context regarding health differences between subpopulations with different levels of life expectancy (LE) (Luy 2020). Consequently, we formulated the "longevity hypothesis", stating that women show higher morbidity rates than men for longstanding illnesses not because they are female, but because they are the sex with higher LE (Luy and Minagawa 2014). We found empirical support by testing the hypothesized relationship between health and longevity across different subpopulations which are characterized by different levels of LE and corresponding gender gaps. The conceptual basis of this analytical approach lies in our "risk group hypothesis", stating that the current extent of the gender gap in LE is predominantly caused by disproportionate high mortality levels of specific male subpopulations which are primarily related to socioeconomic status (SES) (Luy and Gast 2014). To test this hypothesis we carried out an extensive meta-analytic study of the range of mortality differentials within women and men. In line with our hypothesis we found that the range of mortality differentials is larger among men in around 80 percent of all analysed risk factors, and in more than 90 percent of the risk factors related to SES.
Besides introducing these new hypotheses and perspectives, we advanced our understanding of the major biological and non-biological drivers of gender differences in mortality. With regard to the specific risk factors of mortality, tobacco consumption is seen as the decisive driver of both the trend and the extent of the gender gap in LE. Our recent empirical findings for more than 50 industrialized populations show, however, that the impact of smoking is context-specific and differs significantly between populations. Furthermore, we found that while the impact of smoking declines in all industrialized societies, the contribution of other non-biological factors has been mostly increasing during the last decades (Luy and Wegner-Siegmundt 2015). We also extended the knowledge regarding the size of the biological basis of the differences in LE between women and men. We introduced a new approach to derive the impact of biological factors from the overall extent of the gender gap with the COMMS data of our Cloister Study, leading to an estimated impact of biological factors between 0.8 and 1.6 years (Luy 2016). These studies served finally for our projection of the gender gap in LE in Germany until 2050. In contrast to the usually conducted forecasts on the basis of purely mathematical models without consideration of causal factors, we projected the gender gap along its major causation components biological factors, smoking, and other non-biological factors (Wiedemann et al. 2015).
Finally, we developed the decisive analytical tools and data bases which enabled us to carry out the described studies. These are, in particular, the Modified Orphanhood Method (Luy 2012), the Serendipity-based Meta-Analysis (Luy and Gast 2014), and the Longitudinal Survival Method (Luy et al. 2015). The latter (LSM) is an innovative technique to construct cross-sectional life tables on the basis of cohort-specific survival experiences from longitudinal survey data. The logic of this method is adopted from the Modified Orphanhood Method with the difference that the basic survival experiences do not refer to close relatives of the survey respondents but to the respondents themselves. The LSM was the fundamental basis for our empirical tests of the CroHaM and longevity hypotheses because it enables us to construct life tables for specific subpopulations in order to estimate their LE and health expectancy (HE). Another important achievement that has been made possible by the resources of the HEMOX project was the extremely work-intensive development of a database including several information about people's health, health behaviours and socio-economic characteristics stemming from different survey data (cross-sectional as well as longitudinal, in most cases representative of the national populations). By facing an array of comparative challenges, this tool enables us to analyse the data from the individual surveys—including those listed above and the ASCOM data of our Cloister Study— in a harmonized setting. This database will also be important for the follow-up studies in our new ERC Consolidator Grant project LETHE (see Projects).
Besides introducing these new hypotheses and perspectives, we advanced our understanding of the major biological and non-biological drivers of gender differences in mortality. With regard to the specific risk factors of mortality, tobacco consumption is seen as the decisive driver of both the trend and the extent of the gender gap in LE. Our recent empirical findings for more than 50 industrialized populations show, however, that the impact of smoking is context-specific and differs significantly between populations. Furthermore, we found that while the impact of smoking declines in all industrialized societies, the contribution of other non-biological factors has been mostly increasing during the last decades (Luy and Wegner-Siegmundt 2015). We also extended the knowledge regarding the size of the biological basis of the differences in LE between women and men. We introduced a new approach to derive the impact of biological factors from the overall extent of the gender gap with the COMMS data of our Cloister Study, leading to an estimated impact of biological factors between 0.8 and 1.6 years (Luy 2016). These studies served finally for our projection of the gender gap in LE in Germany until 2050. In contrast to the usually conducted forecasts on the basis of purely mathematical models without consideration of causal factors, we projected the gender gap along its major causation components biological factors, smoking, and other non-biological factors (Wiedemann et al. 2015).
Finally, we developed the decisive analytical tools and data bases which enabled us to carry out the described studies. These are, in particular, the Modified Orphanhood Method (Luy 2012), the Serendipity-based Meta-Analysis (Luy and Gast 2014), and the Longitudinal Survival Method (Luy et al. 2015). The latter (LSM) is an innovative technique to construct cross-sectional life tables on the basis of cohort-specific survival experiences from longitudinal survey data. The logic of this method is adopted from the Modified Orphanhood Method with the difference that the basic survival experiences do not refer to close relatives of the survey respondents but to the respondents themselves. The LSM was the fundamental basis for our empirical tests of the CroHaM and longevity hypotheses because it enables us to construct life tables for specific subpopulations in order to estimate their LE and health expectancy (HE). Another important achievement that has been made possible by the resources of the HEMOX project was the extremely work-intensive development of a database including several information about people's health, health behaviours and socio-economic characteristics stemming from different survey data (cross-sectional as well as longitudinal, in most cases representative of the national populations). By facing an array of comparative challenges, this tool enables us to analyse the data from the individual surveys—including those listed above and the ASCOM data of our Cloister Study— in a harmonized setting. This database will also be important for the follow-up studies in our new ERC Consolidator Grant project LETHE (see Projects).
