Abstract. Background: Suicide is a complex public health problem. Suicide rates are higher in rural areas than in urban areas and among older adults than any other age group in China. Aims: This study aimed to acquire a comprehensive understanding of rural older adults’ reasons for suicide through qualitative exploration and to provide additional insights for suicide prevention work. Method: In-depth interviews were conducted with people familiar with older adults who had died by suicide (“informants”). The data were analyzed using a thematic approach. Results: Data were available for 242 people who died by suicide. Informants identified two or more reasons for suicide among most people who had died. Physical illness, psychological distress, and interpersonal conflicts were the most common reasons. The rate of mental disorders as diagnosed by psychiatrists was higher than that reported by the informants. Limitations: Information bias is inevitable because of the use of proxy informants. Conclusion: The reasons for suicide are complex and diverse, and research on suicide should focus more on the mechanisms of interaction between reasons. Greater attention should be paid to physical and mental health as well as to improving interpersonal and problem-solving skills for older adults in China.

Background To evaluate the reliability and validity of the short version six-item Quality of Life Scale (QOLS-6) and the consistency of subject-proxy data in a case-control psychological autopsy study on elderly suicide in rural China. Methods A two-stage stratified cluster sampling method was used to select research sites. We used self-administered questionnaires to collect proxy-based information from informants and subject-based information from living comparisons. Results A total of 242 pairs of suicide cases and living comparisons were selected in our research. Subject-proxy consistency for QOLS-6 was good (Intraclass correlation coefficient (ICC) was 0.688) in living controls. Good internal consistency of QOLS-6 was validated by Cronbach’s α being greater than 0.6 among suicide cases and living comparisons. The mean scores of quality of life were lower among suicide cases than living controls. Quality of life was negatively correlated with depression, loneliness, hopelessness, impulsiveness and stressful life events, while it was positively correlated with activities of daily living and family function. Conclusions QOLS-6 has good reliability and validity, which can be used for assessing quality of life among Chinese rural older adults. It is shorter and easier than any other scale for measuring quality of life and can be used as a screening tool in future studies.

ObjectivesTo identify mechanisms underpinning the complex relationships between influential factors and suicide risk with psychological autopsy data and machine learning method.DesignA 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.SettingDisproportionately high risk of suicide among rural elderly in China.ParticipantsA total of 242 subjects died from suicide and 242 matched live controls, 60 years of age and older.MeasurementsSuicide death was determined based on the ICD-10 codes. Influential factors were measured using validated instruments and commonly accepted variables.ResultsOf 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.ConclusionAssociations 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.