Objectives: Recent evidence shows that more complex clusters of chronic conditions are associated with poorer health outcomes. Less clear is the extent to which these clusters are associated with different types of disability (activities of daily living (ADL) and functional mobility (FM)) over time; the aim of this study was to investigate this relationship.
Methods: This was a longitudinal analysis using the National Health and Aging Trends Study (NHATS) (n = 6179). Using latent class analysis (LCA), we determined the optimal clusters of chronic conditions, then assigned each person to a best-fit class. Next, we used mixed-effects models with repeated measures to examine the effects of group (best-fit class), time (years from baseline), and the group by time interaction on each of the outcomes in separate models over 4 years.
Results: We identified six chronic condition clusters: Minimal Disease, Cognitive/Affective, Multiple Morbidity, Osteoporosis, Vascular, and Cancer. Chronic condition cluster was related to ADL and FM outcomes, indicating that groups experienced differential disability over time. At time point 4, all chronic condition groups had worse FM than Minimal Disease.
Discussion: The clusters of conditions identified here are plausible when considered clinically and in the context of previous research. All groups with chronic conditions carry risk for disability in FM and ADL; increased screening for disability in primary care could identify early disability and prevent decline.
Keywords: activities of daily living; disability; latent class analysis; multimorbidity.
© The Author(s) 2022.