A Longitudinal Network Analysis of Depressive Symptoms Among Older Adults: Findings From an 8‐Year Prospective China National Survey

Background: Late-life depression (LLD) is a significant global public health challenge among older adults. Exploring central/influential symptoms with longitudinal study designs can enhance the efficacy of detection, early prevention, and interventions for LLD. This study aimed to identify key symptoms of LLD using a panel graphical vector autoregression (panel-GVAR) model based on longitudinal national survey data. Methods: Data from the China Health and Retirement Longitudinal Study (CHARLS) between 2013 and 2020, encompassing four waves, were utilized to construct a longitudinal depressive symptom network. Depressive symptoms were assessed using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). In expected influence (in-EI) and out expected influence (out-EI) were identified to characterize the interaction of symptoms within the temporal network, while expected influence (EI) was used to examine the interaction of symptoms in both the contemporaneous network and the between-subjects network. Results: A total of 1393 older adults were assessed. A persistently significant increase in the prevalence of depression was observed over time. In the temporal network, "restless sleep" (CESD7) and "could not get going" (CESD10) were the most influential symptom and most influenced symptom, respectively. In both the contemporaneous network and the between-subjects network, "felt depressed" (CESD3) emerged as the most central symptom within the community of depressive symptoms. Conclusions: Given the challenges associated with treating LLD and its adverse effects on daily life for older adults, timely interventions targeting identified key symptoms may help prevent and mitigate depression in this population.