Depressive symptoms and healthcare utilization among older adults in China: A cross-sectional examination of the national CHARLS data guided by Andersen behavioral model
Abstract
Background With global population ageing, the mental health of older adults is a significant public health concern. Depression and depressive symptoms are increasingly prevalent and have been shown to influence healthcare utilization, but the mechanisms underlying these effects operate remain underexplored, particularly among older adults in China. Building on this context, this study draws on the Andersen behavioral model to explore the associations between depressive symptoms and healthcare utilization among older adults in China. Methods Using data from the 2018 China Health and Retirement Longitudinal Study, a nationally representative sample of 7,777 individuals aged 60 and above was analyzed. A Bayesian Generalized Structural Equation Model was constructed within the framework of the Andersen behavioral model to estimate both the direct and indirect associations between depressive symptoms and inpatient healthcare utilization. Results Results showed a direct association between depressive symptoms and inpatient healthcare utilization (0.16, 95% HDI: 0.02, 0.29), accounting for a small portion of the total association. The remaining 93.55% operated through indirect pathways, with health status, chronic diseases, satisfaction with health, activities of daily living limitations, disability, and alcohol use identified as key mediators. The model showed strong convergence and estimation stability. Conclusions These findings highlight the importance of distinguishing between direct and indirect associations when evaluating the relationship between depressive symptoms and healthcare utilization. Such differentiation helps to clarify the underlying mechanisms and provides an empirical basis for developing more targeted interventions to improve healthcare service use among Chinese older adults with depressive symptoms.
Article Details
Authors (8)
Yue Wu
Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA.
Teresa Wills
Aine O’Donovan
Caroline Kilty
Jin Liu
Jian Huang
Qingyue Wang
Nicola Cornally