Frailty or frailties: exploring frailty index subdimensions in the English Longitudinal Study of Ageing.

Autor:	Johnson L; The University of Edinburgh School of Engineering, Edinburgh, UK.; Advanced Care Research Centre, University of Edinburgh, Edinburgh, UK., Guthrie B; Advanced Care Research Centre, University of Edinburgh, Edinburgh, UK.; The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK., Kelly PAT; Advanced Care Research Centre, University of Edinburgh, Edinburgh, UK., Anand A; Advanced Care Research Centre, University of Edinburgh, Edinburgh, UK.; The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK., Marshall A; Advanced Care Research Centre, University of Edinburgh, Edinburgh, UK.; The University of Edinburgh School of Social and Political Science, Edinburgh, UK., Seth S; Advanced Care Research Centre, University of Edinburgh, Edinburgh, UK Sohan.Seth@ed.ac.uk.; The University of Edinburgh School of Informatics, Edinburgh, UK.
Jazyk:	angličtina
Zdroj:	Journal of epidemiology and community health [J Epidemiol Community Health] 2024 Aug 25; Vol. 78 (10), pp. 609-615. Date of Electronic Publication: 2024 Aug 25.
DOI:	10.1136/jech-2023-221829
Abstrakt:	Background: Frailty, a state of increased vulnerability to adverse health outcomes, has garnered significant attention in research and clinical practice. Existing constructs aggregate clinical features or health deficits into a single score. While simple and interpretable, this approach may overlook the complexity of frailty and not capture the full range of variation between individuals. Methods: Exploratory factor analysis was used to infer latent dimensions of a frailty index constructed using survey data from the English Longitudinal Study of Ageing, wave 9. The dataset included 58 self-reported health deficits in a representative sample of community-dwelling adults aged 65+ (N=4971). Deficits encompassed chronic disease, general health status, mobility, independence with activities of daily living, psychological well-being, memory and cognition. Multiple linear regression examined associations with CASP-19 quality of life scores. Results: Factor analysis revealed four frailty subdimensions. Based on the component deficits with the highest loading values, these factors were labelled 'mobility impairment and physical morbidity', 'difficulties in daily activities', 'mental health' and 'disorientation in time'. The four subdimensions were a better predictor of quality of life than frailty index scores. Conclusions: Distinct subdimensions of frailty can be identified from standard index scores. A decomposed approach to understanding frailty has a potential to provide a more nuanced understanding of an individual's state of health across multiple deficits. Competing Interests: Competing interests: None declared. (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)
Databáze:	MEDLINE
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