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Background <p>There are fewer studies on prospective predictors of first-time suicidal thoughts and behaviors (STB) among first-year university students and fewer studies prospectively identifying and screening for those at high risk of suicide among college students. This study assessed the impact of prospective baseline variables on the risk of new STB onset among first-year university students over two years and developed a multivariate risk prediction model.</p> Methods <p>4,560 first-year university students (38.4% males, mean age:18.34) from China participated and completed this prospective cohort study over a three-year period from 2018 to 2020. LASSO regression, and logistic regression models under resilient networks, were used for risk predictor variable screening and final prediction model building. Independent validation sets were used for external validation of the models. Independent validation sets were used for external validation of the models. Area Under the Curve (AUC), accuracy, F1 scores, and Hosmer-Lemeshow test metrics were used to evaluate the model performance.</p> Results <p>The incidence rates of suicidal thoughts, suicidal behaviors, and STB within two years were 4.89%,1.03%, and 4.96%, respectively. Predictors in the final model included females, always solo activity, bigotry under pressure, socially oriented perfectionism, drinking to relieve stress, autonomy attitude, poorer parental marriage satisfaction, maternal emotional warmth, perceived others social support, and number of lifetime severe traumatic events. The predictive model had an AUC of 0.738 (95% CI: 0.697–0.780) for predictive accuracy in the training dataset as well as 0.710 (95% CI: 0.657–0.763) for predictive accuracy in the validation dataset, which represents a high degree of model discrimination.</p> Conclusion <p>Based on this predictive model of suicidal thoughts and behaviors, this study may help to assess and screen college students at risk for STB and develop suicide prevention strategies for at-risk populations.</p>

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Development and validation of a predictive model for suicidal thoughts and behaviors among freshmen

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月22日

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  • Yan Qin,
  • Sifang Niu,
  • Xingmeng Niu,
  • Yangziye Guo,
  • Yu Sun,
  • Shuzhang Hu,
  • Fuqin Mu,
  • Ying Zhang,
  • Min Liu,
  • Jianli Wang,
  • Yan Liu

摘要

Background

There are fewer studies on prospective predictors of first-time suicidal thoughts and behaviors (STB) among first-year university students and fewer studies prospectively identifying and screening for those at high risk of suicide among college students. This study assessed the impact of prospective baseline variables on the risk of new STB onset among first-year university students over two years and developed a multivariate risk prediction model.

Methods

4,560 first-year university students (38.4% males, mean age:18.34) from China participated and completed this prospective cohort study over a three-year period from 2018 to 2020. LASSO regression, and logistic regression models under resilient networks, were used for risk predictor variable screening and final prediction model building. Independent validation sets were used for external validation of the models. Independent validation sets were used for external validation of the models. Area Under the Curve (AUC), accuracy, F1 scores, and Hosmer-Lemeshow test metrics were used to evaluate the model performance.

Results

The incidence rates of suicidal thoughts, suicidal behaviors, and STB within two years were 4.89%,1.03%, and 4.96%, respectively. Predictors in the final model included females, always solo activity, bigotry under pressure, socially oriented perfectionism, drinking to relieve stress, autonomy attitude, poorer parental marriage satisfaction, maternal emotional warmth, perceived others social support, and number of lifetime severe traumatic events. The predictive model had an AUC of 0.738 (95% CI: 0.697–0.780) for predictive accuracy in the training dataset as well as 0.710 (95% CI: 0.657–0.763) for predictive accuracy in the validation dataset, which represents a high degree of model discrimination.

Conclusion

Based on this predictive model of suicidal thoughts and behaviors, this study may help to assess and screen college students at risk for STB and develop suicide prevention strategies for at-risk populations.