Network Analysis of Relationships and Change Patterns in Depression and Multiple Chronic Diseases Based on the China Health and Retirement Longitudinal Study.

This study aims to explore the relationships and change patterns between depression and multiple chronic diseases using network analysis techniques applied to the China Health and Retirement Longitudinal Study (CHARLS) data. Depression and chronic diseases often coexist and have a significant impact on individuals’ health and well-being. However, the complex interplay and dynamic nature of these conditions remain poorly understood. By utilizing network analysis on longitudinal data, we aim to uncover the underlying network structure and identify key variables associated with depression and multiple chronic diseases. Specifically, we employed Mixed Graphical Model (MGM) networks to estimate the relationships among selected items and investigated network interconnectedness, stability, temporal differences, community structure, and bridge nodes. Our network analyses were conducted on a large cohort sample of middle-aged participants, revealing central items and strong associations. These findings contribute to a better understanding of the complex relationships between depression and chronic diseases, offering insights for the development of targeted interventions to improve health outcomes.