Cross-Sectional Association Between Life Expectancy and Unhealthy Life Years: Proof of Concept Tests of the CroHaM Hypothesis

The recently presented CroHaM hypothesis says (1) that longitudinal health domain-specific expansion and compression effects depend primarily on the health domains' mortality risk and (2) that these effects exist equivalently in the cross-sectional context, affecting differences in healthy life years (HLY) between populations and subpopulations with different levels of life expectancy (LE). We test this hypothesis by analysing the association between LE and unhealthy life years (ULY) at age 50 for a large number of subpopulations. The analyses are carried out for three health domains which are differently related to mortality: poor self- perceived health and strong activity limitation with comparatively high mortality, and chronic morbidity with comparatively low risk of dying. Data on gender- and subpopulation-specific prevalence of these health conditions are taken from the Actual German Health Study 2012 (GEDA). LEs are estimated with the "Longitudinal Survival Method", using data of the German Life Expectancy Survey. ULY are estimated with the "Sullivan Method". Differences in ULY between each subpopulation and the total population and between women and men for each subpopulation are decomposed into the effects caused by differences in health ("health effect") and mortality ("mortality effect") with the "Nusselder/Looman Method". The results confirm the CroHaM hypothesis: we find a positive relationship between LE and ULY only for chronic morbidity, whereas this relationship is negative for poor self- perceived health and strong activity limitation. However, when the mortality effect is controlled for, we find a negative relationship between LE and ULY for all three health domains. The practical relevance of these findings is discussed using the example of the so-called "gender paradox" in health and mortality. We conclude that the CroHaM hypothesis may describe an important determinant of life years spent with and without health impairment, and it may help to better understand and interpret trends and differentials in HLY or ULY based on cross-sectional data.