Identification of depression types and key influencing factors among rural elderly in China: a comprehensive analysis based on machine learning and network analysis.

backgroundDepression poses a severe public health challenge for older adults, especially in rural China. In rural settings, the heavy burden of chronic diseases, inadequate medical resources, insufficient family support, and economic stress may lead to the heterogeneous manifestations of depressive symptoms. Most conventional studies adopt depression scores or cut-off standard-based classification approaches, which tend to ignore unique symptom characteristics and the interactive relationships between associated factors. This study aimed to identify latent depression types among the rural elderly in China, and further explore relevant influencing factors as well as their conditional association patterns through machine learning and network analysis.

methodsCross-sectional data were derived from the 2022 China Family Panel Studies, covering a final sample of 3,114 rural elderly participants. Based on the 8-item Center for Epidemiologic Studies Depression Scale (CES-D8), latent class analysis was conducted to identify depression types. Univariate analysis was adopted to compare the demographic and health-related baseline characteristics across distinct latent depression types. Variables with statistical significance were screened via random forest and LASSO regression models. Subsequently, multinomial logistic regression was applied to examine the associations between core influencing factors and depression type classification. Finally, network analysis was conducted to explore the conditional associations among screened factors, and the bootstrap algorithm was utilized to verify the edge-weight accuracy and centrality stability.

resultsFour latent depression types were identified in the sample, including the low-level depression type (36.0%), mild depression type (25.2%), emotional impairment type (23.5%), and high depression risk type (15.4%). Each type presented distinct CES-D8 symptom characteristics. Seven core influencing factors were screened out by random forest and LASSO models, namely chronic disease, physical discomfort, self-rated health status, medical expenditure, inpatient service utilization, smoking behavior, and daily physical exercise. The results of multinomial logistic regression indicated that chronic disease, physical discomfort, poorer self-rated health, increased medical costs, inpatient inpatient admission, and smoking were significant risk factors for high-risk depression types, while daily exercise served as a protective factor. Network analysis results revealed that physical discomfort and self-rated health occupied central positions in the network, while chronic disease and medical expenses played bridging roles. The bootstrap results indicated that the constructed network possessed favorable structural stability and estimation accuracy.

conclusionDepression among rural older adults in China exhibited prominent latent heterogeneity. Health burden, medical economic pressure, healthcare service utilization, and health behaviors are associated with the classification of depression type. Physical discomfort and self-rated health may serve as key indicators for inform targeted screening and priority intervention inrural primary healthcare. These findings highlight the necessity of shifting from single-threshold depression assessment toward stratified type identification and integrated health management strategies for rural elderly population.