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Abstract Later life disability poses a significant public health challenge, and while research often concentrates on physical limitations, understanding the full spectrum of disability requires a comprehensive approach. This study employs latent profile analysis (LPA), a statistical technique that identifies underlying subgroups or profiles within a larger population based on patterns of responses to multiple indicators, to classify disability. We examined one wave of existing panel data from 414 participants aged 72–106 living in retirement communities. We focus on the following disability indicators: basic and instrumental activities of daily living; physical, cognitive, and sensory impairments; and participation restrictions. Three distinct profiles emerged. The largest group (58%), “Low Disability,” exhibited minimal disability across all domains and superior psychological well‐being. The second group (38%) experienced “High Physical and Functional Disability,” with a higher prevalence among older, unmarried women. The smallest group (4%) displayed “High Cognitive‐Sensory‐Functional Disability,” marked by extensive cognitive impairment and the highest functional and sensory limitations, prevalent in advanced age with poorer psychological outcomes of all profiles. Notably, participation restrictions showed minimal variation across disability profiles. The findings emphasize the need for a data‐driven policy framework addressing disability profiles. Efficient resource allocation and targeted support are crucial, and prevention strategies should proactively address individuals at risk of transitioning into higher‐multidomain disability profile membership. Given the connection between disability and mental health, addressing these concerns can enhance overall health and well‐being. Future research should explore changes in disability group membership over time in diversified samples.
Nemmers et al. (Mon,) studied this question.