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Lookup NU author(s): Dr David SinclairORCiD, Dr Andrew KingstonORCiD, Professor Fiona MatthewsORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2025.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.
Author(s): Sinclair DR, Maharani A, Kingston A, O'Neill TW, Matthews FE
Publication type: Article
Publication status: Published
Journal: BMC Medicine
Year: 2025
Volume: 23
Issue: 1
Online publication date: 09/05/2025
Acceptance date: 29/04/2025
Date deposited: 27/05/2025
ISSN (electronic): 1741-7015
Publisher: BioMed Central Ltd
URL: https://doi.org/10.1186/s12916-025-04112-z
DOI: 10.1186/s12916-025-04112-z
Data Access Statement: The datasets analysed during the current study are available from the UK Data Service, (https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id= 200011). Restrictions apply to the availability of the mortality data for deaths after ELSA wave 6, which were used under license for the current study, and so are not publicly available.
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