Using random forests models in this large sample of college students we found that four main baseline variables predicted STB at 12-month: suicidal thoughts at baseline, trait anxiety, depression symptoms, and self-esteem. The model including these variables showed good predictive performance (AUC = 0.8) estimated using cross-validation. In sensitivity analyses excluding baseline STB, the main predicting variables were trait anxiety, depression symptoms, self-esteem, and perceived stress. These predictors did not differ according to gender, and all models showed similar good predictive performances. Finally, childhood adversity variables did not contribute to STB prediction.
To our knowledge, only two prior studies have developed STB predictive models in students and reported comparable predictive performances to our study. One study used the random forests method to predict suicide attempts among medical students, using a cross-sectional design.19 The other study used a logistic regression model to develop a risk-screening algorithm for persistence of suicidal behaviors during college.20
STB prediction was not influenced by childhood trauma or perceived parental support, which are usually strongly associated with STB in young adults.21,22 These results are in line with previous studies.20,23 This finding highlights that association doesn’t necessarily means prediction, 11 and that proximal risk factors of STB may be better than distal one for predicting one-year STB.
We identified a small number of major predictors that ensured high accuracy in STB prediction. These predictors, derived from short and commonly used questionnaires, may help developing a large-scale screening tool for university students. For example, they could be integrated into a short online screening administered upon college entrance. An online questionnaire may prove acceptable to students, and would provide an alternative to mental health assessment by a physician for students who are often reluctant to disclose sensitive personal information in face-to-face interviews.24,25
The quantitatively most important predictor was suicidal thoughts at baseline.20,26 Likewise, anxiety and depression were often comorbid with STB in students.27 After excluding baseline STB, we found the same main three variables as in the main models, but with an increased prediction importance. Interestingly, self-esteem emerged as one of the main predictors of STB. Low self-esteem is known to be a part of social anxiety, and to overlap with depression, both of which are associated with STB.28 Self-esteem, which is an important marker of psychological vulnerability in young adults29–31 has also been found associated with suicidality.32 Our study showed that self-esteem is an independent and prominent predictive marker of STB and should therefore be used in a screening tool.
Overall, our results suggested that baseline suicidal ideation associated with four validated psychological scales (Rosenberg scale for self-esteem, STAI-YB Spielberger scale for trait anxiety, PHQ-9 for depression, and perceived stress scale PSS-4) are informative enough to identify students who will present STB at the one-year assessment.
Key strengths of this study are the large sample of students and the longitudinal design. Since there are many different paths to STB, accurate STB prediction requires the consideration of a complex combination of a large number of factors.13 The i-Share baseline questionnaire includes a large number of variables, which enabled analyses with a large number of potential STB predictors (70 in the main analyses and 87 for the secondary analyses). Our analyses were conducted following the current recommendations and best methods for prediction analysis, especially the use of different samples for creating the predictors and then for calculating the predictive performance, which prevents the performance measures from being overfitted.10,11 The variables identified as main predictors of STB were consistent across main and secondary analyses, suggesting robust and consistent findings. Some limitations should nevertheless be acknowledged when interpreting the results. First, the follow-up response rate (33.5%) was moderate, as is common in longitudinal studies with students33 and differences were observed between respondents and non-respondents in the follow-up. These differences were not major (proportions were similar) and should have a limited impact when identifying STB predictors. Nevertheless, caution is needed regarding the external validity of our results and the possibility of generalizing conclusions to all students and to all settings. Second, girls were over-represented in our sample, and our sample might not be representative of the whole student population. Third, the self-reported questionnaires could lead to information and recall bias, particularly if participants under-reported their frequency of STB due to concerns about social desirability. However, such under-reporting is likely to be reduced by the use of an online questionnaire. Additionally, and more importantly, relying on other data (e.g., clinical assessment) would defeat our aim of finding easily assessable predictors of SBT in large university student samples. Finally, we could not strictly separate analyses between suicidal ideation and suicide attempts due to the small number of one-year suicide attempts in our sample.
In conclusion, we identified a parsimonious number of predictors that can be used to accurately identify students who will present STB within one-year from the predictor assessment. Pending replication of these results in other studies, these predictors may be used to develop a screening tool to be routinely used among university students. For example, a web-based screening tool could represent a promising approach for identifying students at suicide risk and to refer them to counselling and mental health services.