Study on depressive symptom trajectories based on heterogeneous statistical learning

Depression, a critical and widespread mood disorder, poses a major challenge to international health. It significantly disrupts personal, professional, and academic pursuits beyond ordinary sadness, affecting vast numbers globally and emerging as a primary driver of disability, per the World Health Organization. Its association with serious physical conditions such as heart disease, diabetes, and heightened suicide risk further exacerbates its impact on personal well-being, familial stability, and economic health, underscoring its extensive burden on society. This study aims to explore the relationship between educational levels and depressive symptoms among the elderly in China, particularly through the application of heterogeneous mediation effect analysis to identify potential mediating variables between the two. Utilizing the longitudinal dataset from the China Health and Retirement Longitudinal Study (CHARLS), the study employs structural equation modeling (SEM) to screen potential mediating variables and explores the heterogeneity of mediation effects among different groups through grouping methods. The results indicate that subjects can be divided into four groups, each with significantly different levels and trends of depressive symptoms. Further analysis reveals variations in the paths of mediation effects between different groups, highlighting the complexity of the relationship between education level and depressive symptoms. The significance of this study lies in its first application of heterogeneous mediation effect analysis to the CHARLS dataset, offering a new perspective on understanding how education impacts the mental health of the elderly through various mediating paths. We recommend that future research further investigate the confounding factors in the mediating relationships between education, economic status, and depressive symptoms to more comprehensively understand the interactions among these variables.