Abstract
Abstract
Background
Health, as defined by the WHO, is a multidimensional concept that includes different aspects. Interest in the health conditions of the oldest-old has increased as a consequence of the phenomenon of population aging. This study investigates whether (1) it is possible to identify health profiles among the oldest-old, taking into account physical, emotional and psychological information about health, and (2) there are demographic and socioeconomic differences among the health profiles.
Methods
Latent Class Analysis with covariates was applied to the Mugello Study data to identify health profiles among the 504 nonagenarians residing in the Mugello district (Tuscany, Italy) and to evaluate the association between socioeconomic characteristics and the health profiles resulting from the analysis.
Results
This study highlights four groups labeled according to the posterior probability of determining a certain health characteristic: “healthy”, “physically healthy with cognitive impairment”, “unhealthy”, and “severely unhealthy”. Some demographic and socioeconomic characteristics were found to be associated with the final groups: older nonagenarians are more likely to be in worse health conditions; men are in general healthier than women; more educated individuals are less likely to be in extremely poor health conditions, while the lowest-educated are more likely to be cognitively impaired; and office or intellectual workers are less likely to be in poor health conditions than are farmers.
Conclusions
Considering multiple dimensions of health to determine health profiles among the oldest-old could help to better evaluate their care needs according to their health status.
Publisher
Springer Science and Business Media LLC
Subject
Geriatrics and Gerontology
Reference58 articles.
1. World Health Organization. Global Health and aging. Geneva: U.S. National Institute of Aging; 2011.
2. Bambra C, Pope D, Swami V, Stanistreet D, Roskam A, Kunst A, et al. Gender, health inequalities and welfare state regimes: a cross-national study of 13 European countries. J Epidemiol Community Health. 2009;63:38–44. https://doi.org/10.1136/jech.2007.070292.
3. World Population Prospects - Population Division - United Nations n.d. https://esa.un.org/unpd/wpp/ (Accessed May 17, 2018).
4. Ministero dell’economia e delle finanze (RGS). Il monitoraggio della spesa sanitaria. Rapporto n° 6. Roma; 2019. http://www.rgs.mef.gov.it/_Documenti/VERSIONE-I/Attivit--i/Spesa-soci/Attivit-monitoraggio-RGS/2019/IMDSS-RS2019.pdf. Accessed 23 Mar 2020.
5. Skirbekk VF, Staudinger UM, Cohen JE. How to measure population aging? The answer is less than obvious: a review. Gerontology. 2018;1:1–9. https://doi.org/10.1159/000494025.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献