Citation

Lu J, Jin Y, Liang S, Wang Q, Li X, Li T. BMC Public Health 2024; 24(1): e1378.

Copyright

(Copyright © 2024, Holtzbrinck Springer Nature Publishing Group - BMC)

DOI

10.1186/s12889-024-18860-9

PMID

38778312

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.

Language: en

Keywords

Humans; Cross-Sectional Studies; Risk Factors; Adult; Female; Male; Universities; Adolescent; Suicide; Young Adult; Risk factor; Network analysis; Prediction; Suicide/psychology/statistics & numerical data; Machine learning; Machine Learning; *Students/psychology/statistics & numerical data; *Suicidal Ideation; China/epidemiology; Suicide, Attempted/statistics & numerical data/psychology