Risk factors and their association network for young adults’ suicidality: a cross-sectional study

Abstract

Abstract Background Understanding the intricate influences of risk factors contributing to suicide among young individuals remains a challenge. The current study employed interpretable machine learning and network analysis to unravel critical suicide-associated factors in Chinese university students. Methods A total of 68,071 students were recruited between Sep 2016 and Sep 2020 in China. Students reported their lifetime experiences with suicidal thoughts and behaviors, categorized as suicide ideation (SI), suicide plan (SP), and suicide attempt (SA). We assessed 36 suicide-associated factors including psychopathology, family environment, life events, and stigma. Local interpretations were provided using Shapley additive explanation (SHAP) interaction values, while a mixed graphical model facilitated a global understanding of their interplay. Results Local explanations based on SHAP interaction values suggested that psychoticism and depression severity emerged as pivotal factors for SI, while paranoid ideation strongly correlated with SP and SA. In addition, childhood neglect significantly predicted SA. Regarding the mixed graphical model, a hierarchical structure emerged, suggesting that family factors preceded proximal psychopathological factors, with abuse and neglect retaining unique effects. Centrality indices derived from the network highlighted the importance of subjective socioeconomic status and education in connecting various risk factors. Conclusions The proximity of psychopathological factors to suicidality underscores their significance. The global structures of the network suggested that co-occurring factors influence suicidal behavior in a hierarchical manner. Therefore, prospective prevention strategies should take into account the hierarchical structure and unique trajectories of factors.

Keywords

• Suicide
• Machine learning
• Network analysis
• Risk factor
• Prediction