Using recursive partitioning to predict presence and severity of suicidal ideation amongst college students

Abstract

Objective Predicting the presence and severity of suicidal ideation in college students is important, as deaths by suicide amongst young adults have increased in the past 20 years. Participants We recruited college students (N = 5494) from ten universities across eight states. Method Participants answered three questionnaires related to lifetime and past month suicidal ideation, and an indicator of suicidal ideation in a DSM-5 symptom measure. We used recursive partitioning to predict the presence, absence, and severity, of suicidal ideation. Results Recursive partitioning models varied in their accuracy and performance. The best-performing model consisted of predictors and outcomes measured by the DSM-5 Level 1 Cross-Cutting Symptom Measure. Sexual orientation was also an important predictor in most models. Conclusions A single measure of DSM-5 symptom severity may help universities understand suicide severity to promote targeted interventions. Though further work is needed, as similar scaling amongst predictors could have influenced the model.