Longitudinal Study of Public Health Interventions for Aging Populations using Causal Inference Methods

Abstract

The global demographic shift towards an aging population presents unprecedented challenges for public health systems, necessitating evidence-based interventions that effectively mitigate age-related decline and chronic disease burden. However, evaluating the true efficacy of such interventions is often complicated by time-varying confounding and feedback loops inherent in longitudinal observational data. This paper presents a comprehensive longitudinal study utilizing advanced causal inference methods, specifically Marginal Structural Models and Inverse Probability of Treatment Weighting, to assess the impact of community-based health programs and telemedicine integration on functional independence in adults over the age of sixty-five. By analyzing a ten-year dataset comprising 15,000 participants, we move beyond traditional correlational analyses which often yield biased estimates due to the healthy-user effect or reverse causality. Our findings indicate that when properly adjusted for time-dependent confounders, sustained engagement in community health initiatives significantly reduces the rate of decline in activities of daily living. Furthermore, the causal efficacy of telemedicine interventions varies significantly by baseline mobility status, a nuance often obscured in standard regression models. The results underscore the critical importance of employing robust causal methodologies in epidemiologic research to inform policy decisions that maximize healthspan and resource allocation.