Understanding Frailty in India: A State-Level Bayesian Spatial Model Study.
Abstract
This study investigates state-specific frailty risks and common predictors among older adults in India, using data from the Longitudinal Ageing Study in India (LASI) wave 1, comprising 27,540 individuals aged 60 and above. By integrating ecological systems theory framework with innovative Bayesian spatial modeling, the methodology incorporates a holistic approach. The findings identify Telangana (RR1.382, 95%CI1.246-1.524), West Bengal (RR1.369, 95%CI1.249-1.482), Sikkim (RR1.286, 95%CI1.075-1.516), and Kerala (RR1.236, 95%CI1.109-1.356) as states with significantly higher frailty risks. Key predictors include living alone (RR5.76, 95%CI5.472-5.969), a history of falls (RR2.55, 95%CI2.501-2.592), and experiences of everyday discrimination (RR2.12, 95%CI 2.048-2.141). These results emphasize the critical need for state-specific interventions that account for the complex interactions of micro, meso, and macro-level determinants. The study advocates for the development of tailored strategies, highlighting the limitations of a one-size-fits-all approach in addressing frailty within India's heterogeneous aging population.
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Research summary
The paper identifies state-specific frailty risks and predictors among older adults in India. The study addresses frailty, a significant aspect of aging, but focuses more on symptom management rather than root causes of aging.
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