A network analysis of depression and anxiety symptoms among Chinese elderly living alone: based on the 2017–2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS)

Elderly individuals living alone represent a vulnerable group with limited family support, making them more susceptible to mental health issues such as depression and anxiety. This study aims to construct a network model of depression and anxiety symptoms among older adults living alone, exploring the correlations and centrality of different symptoms. The goal is to identify core and bridging symptoms to inform clinical interventions. Using data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), this study constructed a network model of depression and anxiety symptoms among elderly individuals living alone. Depression and anxiety symptoms were assessed using the Center for Epidemiologic Studies Depression Scale-10 (CESD-10) and the Generalized Anxiety Disorder Scale-7 (GAD-7), respectively. A Gaussian Graphical Model (GGM) was employed to build the symptom network, and the Fruchterman-Reingold algorithm was used for visualization, with the thickness and color of the edges representing partial correlations between symptoms. To minimize spurious correlations, the Least Absolute Shrinkage and Selection Operator (LASSO) method was applied for regularization, and the optimal regularization parameters were selected using the Extended Bayesian Information Criterion (EBIC). We further calculated Expected Influence (EI) and Bridge Expected Influence (Bridge EI) to evaluate the importance of symptoms. Non-parametric bootstrap methods were used to assess the stability and accuracy of the network. The Network centrality analysis revealed that GAD2 (Uncontrollable worry) and GAD4 (Trouble relaxing) exhibited the highest strength centrality (1.128 and 1.102, respectively), indicating their significant direct associations with other symptoms and their roles as core nodes in the anxiety symptom network. Other highly central nodes, such as GAD1 (Nervousness or anxiety) and GAD3 (Generalized worry), further underscore the dominance of anxiety symptoms in the overall network. Betweenness centrality results highlighted GAD1 (Nervousness or anxiety) and GAD2 (Uncontrollable worry) as critical bridge nodes facilitating information flow between different symptoms, while CESD3 (Feeling depressed) demonstrated a bridging role across modules. Weighted analyses further confirmed the central importance of GAD2 (Uncontrollable worry) and GAD4 (Trouble relaxing). Additionally, the analysis showed gender differences in the depression-anxiety networks of elderly individuals living alone. This study, through network analysis, uncovered the complex relationships between depression and anxiety symptoms among elderly individuals living alone, identifying GAD2 (Uncontrollable worry) and GAD4 (Trouble relaxing) as core symptoms. These findings provide essential insights for targeted interventions. Future research should explore intervention strategies for these symptoms to improve the mental health of elderly individuals living alone.