  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 (ELSA), 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 wellbeing, 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
potential to provide a more nuanced understanding of an individual's state of
health across multiple deficits.
