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Trajectories of Functional Decline in Older Adults: A Latent Class Growth Curve Analysis

There have been few studies examining trajectories of functional decline among older adults in the United States using large representative databases. The purpose of this study was to describe the mean trajectory of functional decline for a representative sample of US older adults, to determine the...

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Detalles Bibliográficos
Autor principal: Dombrowsky, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359954/
https://www.ncbi.nlm.nih.gov/pubmed/37288523
http://dx.doi.org/10.1177/01939459231180365
Descripción
Sumario:There have been few studies examining trajectories of functional decline among older adults in the United States using large representative databases. The purpose of this study was to describe the mean trajectory of functional decline for a representative sample of US older adults, to determine the optimal number of latent classes within that sample, and to identify key differences between the classes on select variables. Through the use of link functions, non-linear trajectories can be modeled. Three classes were identified and were named Rapid Decline, Late Decline, and High Baseline. The Late Decline Group was the most numerous and was characterized by low initial functional disability with a steep rise starting around age 85. The Rapid Decline Group also had low initial functional disability, but decline started around age 80. The High Baseline Group had high initial functional disability and less steep trajectory. Age and comorbidity were the most influential factors in functional decline. Race was statistically significant but the difference disappeared when controlling for other covariates. Sex did not significantly influence the trajectory. There were significant differences among the classes for mortality during study, initial age, initial functional status, and for several specific comorbidities: arthritis, diabetes, lung disease, and stroke.