College students' screening early warning factors in identification of suicide risk

Ke Han; Lei Ji; Changfeng Chen; Binyin Hou; Decheng Ren; Fan Yuan; Liangjie Liu; Yan Bi; Zhenming Guo; Na Wu; Mofan Feng; Kai Su; Chenliu Wang; Fengping Yang; Xi Wu; Xingwang Li; Chuanxin Liu; Zhen Zuo; Rong Zhang; Zhenghui Yi; Yifeng Xu; Lin He; Yi Shi; Tao Yu; Guang He

doi:10.3389/fgene.2022.977007

College students' screening early warning factors in identification of suicide risk

Front Genet. 2022 Nov 10:13:977007. doi: 10.3389/fgene.2022.977007. eCollection 2022.

Authors

Ke Han^{1

2}, Lei Ji^{1

2}, Changfeng Chen³, Binyin Hou^{1

2}, Decheng Ren^{1

2}, Fan Yuan^{1

2}, Liangjie Liu^{1

2}, Yan Bi^{1

2}, Zhenming Guo^{1

2}, Na Wu^{1

2}, Mofan Feng^{1

2}, Kai Su^{1

2}, Chenliu Wang^{1

2}, Fengping Yang^{1

2}, Xi Wu^{1

2}, Xingwang Li^{1

2}, Chuanxin Liu³, Zhen Zuo³, Rong Zhang⁴, Zhenghui Yi², Yifeng Xu², Lin He^{1

2}, Yi Shi^{1

2}, Tao Yu^{1

4}, Guang He^{1

2}

Affiliations

¹ Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai, China.
² Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.
³ School of Mental Health, Jining Medical University, Jining, Shandong, China.
⁴ Shanghai Center for Women and Children's Health, Shanghai, China.

Abstract

This study aimed to explore the main influencing factors of suicide risk among Chinese students and establish an early warning model to provide interventions for high-risk students. We conducted surveys of students in their first and third years from a cohort study at Jining Medical College. Logistic regression models were used to screen the early warning factors, and four machine learning models were used to establish early warning models. There were 8 factors related to suicide risk that were eventually obtained through screening, including age, having a rough father, and CES-D, OHQ, ASLEC-4, BFI-Neuroticism, BFI-Openness, and MMC-AF-C scores. A random forest model with SMOTE was adopted, and it verified that these 8 early warning signs, for suicide risk can effectively predict suicide risk within 2 years with an AUC score of 0.947. Among the factors, we constructed a model that indicated that different personality traits affected suicide risk by different paths. Moreover, the factors obtained by screening can be used to identify college students in the same year with a high risk of suicide, with an AUC score that reached 0.953. Based on this study, we suggested some interventions to prevent students going high suicide risk.

Keywords: affiliation attribution; college students; early warning; masking effect; suicide risk.