A I Hijas-Gómez1, A Ayala2, M P Rodríguez-García3, C Rodríguez-Blázquez4, V Rodríguez-Rodríguez5, F Rojo-Pérez6, G Fernández-Mayoralas7, A Rodríguez-Laso8, A Calderón-Larrañaga9, M J Forjaz10. 1. Health Technology Assessment Agency (AETS), Carlos III Institute of Health, Madrid, Spain. Electronic address: ahijas@isciii.es. 2. National School of Public Health, Carlos III Institute of Health and Research Network on Health Services and Chronicity (REDISSEC), Madrid, Spain. Electronic address: arwen.alba@gmail.com. 3. Hospital General Universitario de Ciudad Real and Centro de Salud de Fuencaliente, Gerencia de Atención Integrada de Puertollano, SESCAM, Ciudad Real, Spain. Electronic address: mdelrg@sescam.jccm.es. 4. National Centre of Epidemiology, Carlos III Institute of Health and Centers for Networked Biomedical Research (CIBERNED), Madrid, Spain. Electronic address: crodb@isciii.es. 5. Institute of Economic, Geography and Demography (IEGD), Centre for Human and Social Sciences (CCHS), Spanish Scientific Research Council (CSIC), Madrid, Spain. Electronic address: vicente.rodriguez@csic.es. 6. Institute of Economic, Geography and Demography (IEGD), Centre for Human and Social Sciences (CCHS), Spanish Scientific Research Council (CSIC), Madrid, Spain. Electronic address: fermina.rojo@cchs.csic.es. 7. Institute of Economic, Geography and Demography (IEGD), Centre for Human and Social Sciences (CCHS), Spanish Scientific Research Council (CSIC), Madrid, Spain. Electronic address: gloria.fernandezmayoralas@cchs.csic.es. 8. Biomedical Research Foundation of the University Hospital of Getafe (FIBHUG), Spain. Electronic address: arodriguezlaso@salud.madrid.org. 9. Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Sweden; EpiChron Research Group on Chronic Diseases, Aragón Health Sciences Institute, IIS Aragón, Miguel Servet University Hospital, Zaragoza, Spain. Electronic address: amaia.calderon.larranaga@ki.se. 10. National School of Public Health, Carlos III Institute of Health and Research Network on Health Services and Chronicity (REDISSEC), Madrid, Spain. Electronic address: jforjaz@isciii.es.
Abstract
BACKGROUND: The World Health Organization's active ageing model is based on the optimisation of four key "pillars": health, lifelong learning, participation and security. It provides older people with a policy framework to develop their potential for well-being, which in turn, may facilitate longevity. We sought to assess the effect of active ageing on longer life expectancy by: i) operationalising the WHO active ageing framework, ii) testing the validity of the factors obtained by analysing the relationships between the pillars, and iii) exploring the impact of active ageing on survival through the health pillar. METHODS: Based on data from a sample of 801 community-dwelling older adults, we operationalised the active ageing model by taking each pillar as an individual construct using principal component analysis. The interrelationship between components and their association with survival was analysed using multiple regression models. RESULTS: A three-factor structure was obtained for each pillar, except for lifelong learning with a single component. After adjustment for age, gender and marital status, survival was only significantly associated with the physical component of health (HR = 0.66; 95% CI = 0.47-0.93; p = 0.018). In turn, this component was loaded with representative variables of comorbidity and functionality, cognitive status and lifestyles, and correlated with components of lifelong learning, social activities and institutional support. CONCLUSION: According to how the variables clustered into the components and how the components intertwined, results suggest that the variables loading on the biomedical component of the health pillar (e.g. cognitive function, health conditions or pain), may play a part on survival chances.
BACKGROUND: The World Health Organization's active ageing model is based on the optimisation of four key "pillars": health, lifelong learning, participation and security. It provides older people with a policy framework to develop their potential for well-being, which in turn, may facilitate longevity. We sought to assess the effect of active ageing on longer life expectancy by: i) operationalising the WHO active ageing framework, ii) testing the validity of the factors obtained by analysing the relationships between the pillars, and iii) exploring the impact of active ageing on survival through the health pillar. METHODS: Based on data from a sample of 801 community-dwelling older adults, we operationalised the active ageing model by taking each pillar as an individual construct using principal component analysis. The interrelationship between components and their association with survival was analysed using multiple regression models. RESULTS: A three-factor structure was obtained for each pillar, except for lifelong learning with a single component. After adjustment for age, gender and marital status, survival was only significantly associated with the physical component of health (HR = 0.66; 95% CI = 0.47-0.93; p = 0.018). In turn, this component was loaded with representative variables of comorbidity and functionality, cognitive status and lifestyles, and correlated with components of lifelong learning, social activities and institutional support. CONCLUSION: According to how the variables clustered into the components and how the components intertwined, results suggest that the variables loading on the biomedical component of the health pillar (e.g. cognitive function, health conditions or pain), may play a part on survival chances.
Keywords:
Active aging; International Classification of Functioning, Disability and Health; Principal component analysis; Survival; World Health Organization
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