TY - GEN
T1 - Network Analysis of Relationships and Change Patterns in Depression and Multiple Chronic Diseases Based on the China Health and Retirement Longitudinal Study
AU - Li, Xia
AU - Li, Shuo
AU - Liu, Ying
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Chronic disease
KW - Depression
KW - Multimorbidity
KW - Network analysis
UR - http://www.scopus.com/inward/record.url?scp=85175811293&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-7108-4_3
DO - 10.1007/978-981-99-7108-4_3
M3 - Conference contribution
AN - SCOPUS:85175811293
SN - 9789819971077
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 30
EP - 39
BT - Health Information Science - 12th International Conference, HIS 2023, Proceedings
A2 - Li, Yan
A2 - Huang, Zhisheng
A2 - Sharma, Manik
A2 - Chen, Lu
A2 - Zhou, Rui
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th International Conference on Health Information Science, HIS 2023
Y2 - 23 October 2023 through 24 October 2023
ER -