ArticleJournal of epidemiology and community health2024
Frailty or frailties: exploring frailty index subdimensions in the English Longitudinal Study of Ageing.
Article in Journal of epidemiology and community health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Nutrition literacy, dietary diversity, and frailty among older adults in China.BMC geriatrics · 2026Article
- Prevalence and impact of frailty on unplanned hospitalizations among adult patients with cancer in Taiwan.The oncologist · 2025Article
- Uncovering the multivariate genetic architecture of frailty with genomic structural equation modeling.Nature genetics · 2025Article
- Multi-Polygenic prediction of Frailty and its Trajectories highlights Chronic Pain, Rheumatoid Arthritis, and Educational Attainment pathways.medRxiv : the preprint server for health sciences · 2025Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundFrailty, 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.
methodsExploratory 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.
resultsFactor 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.
conclusionsDistinct 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.
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