Hierarchical and nested associations of suicide with marriage, social support, quality of life, and depression among the elderly in rural China: Machine learning of psychological autopsy data.

Objectives: To identify mechanisms underpinning the complex relationships between influential factors and suicide risk with psychological autopsy data and machine learning method. Design: A case-control study with suicide deaths selected using two-stage stratified cluster sampling method; and 1:1 age-and-gender matched live controls in the same geographic area. Setting: Disproportionately high risk of suicide among rural elderly in China. Participants: A total of 242 subjects died from suicide and 242 matched live controls, 60 years of age and older. Measurements: Suicide death was determined based on the ICD-10 codes. Influential factors were measured using validated instruments and commonly accepted variables. Results: Of the total sample, 270 (55.8%) were male with mean age = 74.2 ( Conclusion: Associations between the key factors and suicide death for Chinese rural elderly are not linear and parallel but hierarchically nested that could not be effectively detected using conventional statistical methods. Findings of this study provide new and compelling evidence supporting tailored suicide prevention interventions at the familial, clinical and community levels.