Abstract
OBJECTIVES: Use latent class analysis (LCA) to identify patterns of multidimensional dependency in a sample of older adults and assess sociodemographic, predictors of class membership. MATERIAL AND METHODS: Longitudinal data were used from the Mexican Health and Aging Study (MHAS). 7,920 older adults, 55% women, were recruited. LCA were used to identify meaningful subgroups. LCA was conducted using MPlus version. The final class model was chosen based on the comparison of multiple fit statistics and theoretical parsimony, with models of increasing complexity analyzed sequentially until the best fitting model was identified. Covariates were incorporated to explore the association between these variables and class membership. RESULTS: Three classes groups based on the nine indicators were identified: "Active older adults" was comprised of 64% of the sample participants, "Relatively independent" and "Physically impaired" were comprised of 26% and 10% of the sample. The "Active older adults" profile comprised the majority of respondents who exhibited high endorsement rates across all criteria. The profiles of the "Active older adults" and "Relatively independent" were comparatively more uniform. Finally, respondents belonging to the "Physically impaired" profile, the smallest subgroup, encompassed the individuals most susceptible to a poor dependency profile. CONCLUSIONS: These findings highlighted the usefulness to adopt a person-centered approach rather than a variable-centered approach, suggesting directions for future research and tailored interventions approaches to older adults with particular characteristics. Based on patterns of multidimensional dependency, this study identified a typology of dependency using data from a large, nationally representative survey.