Levels of disability in the older population of England: Comparing binary and ordinal classifications.

BACKGROUND: Recent studies suggest the importance of distinguishing severity levels of disability. Nevertheless, there is not yet a consensus with regards to an optimal classification.

OBJECTIVE: Our study seeks to advance the existing binary definitions towards categorical/ordinal manifestations of disability.

METHODS: We define disability according to the WHO's International Classification of Functioning, Disability and Health (ICF) using data collected at the baseline wave of the English Longitudinal Study of Aging, a longitudinal study of the non-institutionalized population, living in England. First, we identify cut-off points in the continuous disability score derived from ICF to distinguish disabled from no-disabled participants. Then, we fit latent class models to the same data to find the optimal number of disability classes according to: (i) model fit indicators; (ii) estimated probabilities of each disability item; (iii) association of the predicted disability classes with observed health and mortality.

RESULTS: According to the binary classification criteria, about 32% of both men and women are classified disabled. No optimal number of classes emerged from the latent class models according to model fit indicators. However, the other two criteria suggest that the best-fitting model of disability severity has four classes.

CONCLUSIONS: Our findings contribute to the debate on the usefulness and relevance of adopting a finer categorization of disability, by showing that binary indicators of disability averaged the burden of disability and masked the very strong effect experienced by individuals having severe disability, and were not informative for low levels of disability.