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• W3028238412 abstract "Use latent class analysis (LCA) to identify patterns of multidimensional dependency in a sample of older adults and assess sociodemographic, predictors of class membership.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.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.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.Utilizar el análisis de clase latente (ACL) para la identificación de patrones de dependencia multidimensional en una muestra de adultos mayores y evaluar factores sociodemográficos predictivos de pertenencia a una clase.Se utilizaron datos longitudinales del Estudio Nacional de Salud y Envejecimiento en México (ENASEM). Se analizaron 7,920 adultos mayores, el 55% eran mujeres. El ACL fue utilizado para la identificación de subgrupos significativos. Para el ACL se realizó con el software MPlus. El modelo de clase final se eligió con base a la comparativa de estadísticas de ajuste múltiple y parsimonia teórica, con modelos de complejidad creciente analizados secuencialmente hasta que se identificó el modelo de mejor ajuste. Se incorporaron covariables para explorar la asociación entre estas variables y la pertenencia a clases.Se identificaron tres grupos de clases basados en los nueve identificadores. “Adultos mayores activos” estaba compuesto por el 64%. “Relativamente independiente” y “Discapacidad física” comprenden el 26% y el 10% de la muestra, respectivamente. Los perfiles de “Adultos mayores activos” y “Relativamente independiente” eran comparativamente más uniformes. El grupo de “discapacidad física” comprenden a los individuos más propensos a un perfil de dependencia.Estos hallazgos resaltan la utilidad de adoptar un enfoque centrado en la persona lo que sugiere nuevos horizontes de investigación e intervención a medida de las características particulares de los adultos mayores. Basado en patrones de dependencia multidimensional, este estudio identificó una tipología de dependencia utilizando datos de una encuesta representativa a nivel nacional." @default.
• W3028238412 created "2020-05-29" @default.
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• W3028238412 date "2020-04-01" @default.
• W3028238412 modified "2023-10-02" @default.
• W3028238412 title "Multidimensional dependency subgroups in community-dwelling older adults: A latent class analysis" @default.
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• W3028238412 doi "https://doi.org/10.18273/revsal.v52n2-2020004" @default.
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