The Autoregressive Structural Model for analyzing longitudinal health data of an aging population in China

TL;DR

This paper introduces an Autoregressive Structural Model to analyze how social activity, physical activity, and health status influence depression over time in China's aging population, accounting for temporal dependencies.

Contribution

The paper develops a novel Autoregressive Structural Model that integrates autoregressive dependence into structural equation modeling for longitudinal health data analysis.

Findings

Social and physical activities reduce depressive symptoms over five years

Functional health mediates the effects of social and physical activities

Model effectively captures temporal dynamics in longitudinal data

Abstract

We seek to elucidate the impact of social activity, physical activity and functional health status (factors) on depressive symptoms (outcome) in the China Health and Retirement Longitudinal Study (CHARLS), a multi-year study of aging involving 20,000 participants 45 years of age and older. Although a variety of statistical methods are available for analyzing longitudinal data, modeling the dynamics within a complex system remains a difficult methodological challenge. We develop an Autoregressive Structural Model (ASM) to examine these factors on depressive symptoms while accounting for temporal dependence. The ASM builds on the structural equation model and also consists of two components: a measurement model that connects observations to latent factors, and a structural model that delineates the mechanism among latent factors. Our ASM further incorporates autoregressive dependence into both components for repeated measurements. The results from applying the ASM to the CHARLS data indicate that social and physical activity independently and consistently mitigated depressive symptoms over the course of five years, by mediating through functional health status.