In Frontiers in psychiatry
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 (SD = 8.2) years old. Four CART models were used to select influential factors using the criteria: areas under the curve (AUC) ≥ 0.8, sensitivity ≥ 0.8, and specificity ≥ 0.8. Each model included a lead predictor plus 8-10 hierarchically nested factors. Depression was the first to be selected in Model 1 as the lead predictor; After depression was excluded, quality of life (QOL) was selected in Model 2; After depression and QOL were excluded, social support was selected in Model 3. Finally, after all 3 lead factors were excluded, marital status was selected in Model 4. In addition, CART demonstrated the significance of several influential factors that would not be associated with suicide if the data were analyzed using the conventional logistic regression.
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.
Chen Xinguang, Mo Qiqing, Yu Bin, Bai Xinyu, Jia Cunxian, Zhou Liang, Ma Zhenyu
2022
depression, machine learning, quality of life, rural Chinese, social support, suicide
