Frailty-free life expectancy and its association with socio-economic characteristics: an analysis of the English Longitudinal Study of Ageing cohort study.

Abstract

Background: Frailty is more prevalent in socio-economically disadvantaged groups; however, little is known about how this translates to differences in the number of years people live with and without frailty. We investigate differences in frailty-free and frail life expectancies among population groups stratified by wealth, area deprivation, education and marital status.

Methods: The English Longitudinal Study of Ageing cohort study was used to follow the frailty trajectories of 15,003 individuals over 18 years. A multi-state model assessed the risk of transitioning between frailty states and death based on socio-economic characteristics. These risks were translated into state-specific life expectancies.

Results: Wealth had the strongest association with frailty-free and frail life expectancies. Increased wealth, reduced deprivation, higher educational attainment and marriage all correlate with increased frailty-free life expectancies and reduced frail life expectancies. At age 50, the wealthiest population quintile can expect to live 11.1 [10.1-12.1] years (women) and 9.8 [8.8-10.8] years (men) longer frailty-free than the poorest population quintile. The wealthiest quintile live less than half the number of years with frailty than the poorest quintile. There is no difference in frailty-free life expectancy between the poorest men and women; however, the wealthiest women have longer frailty-free life expectancies than the wealthiest men.

Conclusions: Large inequalities in frailty-free and frail life expectancies exist across socio-economic groups, with wealth and area deprivation the most important socio-economic determinants. Narrowing these inequalities may extend frailty-free life expectancies more for women than men, suggesting strategies to reduce disparities should consider both socio-economic factors and gender. Care policies should account for the geographical clustering of socio-economically disadvantaged populations. Reducing socio-economic inequalities could increase frailty-free life expectancies and reduce health and social care costs.