Development and evaluation of a predictive algorithm and telehealth intervention to reduce suicidal behavior among university students.

Abstract
BACKGROUND: Suicidal behaviors are prevalent among college students; however, students remain reluctant to seek support. We developed a predictive algorithm to identify students at risk of suicidal behavior and used telehealth to reduce subsequent risk. METHODS: Data come from several waves of a prospective cohort study (2016-2022) of college students (n = 5454). All first-year students were invited to participate as volunteers. (Response rates range: 16.00-19.93%). A stepped-care approach was implemented: (i) all students received a comprehensive list of services; (ii) those reporting past 12-month suicidal ideation were directed to a safety planning application; (iii) those identified as high risk of suicidal behavior by the algorithm or reporting 12-month suicide attempt were contacted via telephone within 24-h of survey completion. Intervention focused on support/safety-planning, and referral to services for this high-risk group. RESULTS: 5454 students ranging in age from 17-36 (s.d. = 5.346) participated; 65% female. The algorithm identified 77% of students reporting subsequent suicidal behavior in the top 15% of predicted probabilities (Sensitivity = 26.26 [95% CI 17.93-36.07]; Specificity = 97.46 [95% CI 96.21-98.38], PPV = 53.06 [95% CI 40.16-65.56]; AUC range: 0.895 [95% CIs 0.872-0.917] to 0.966 [95% CIs 0.939-0.994]). High-risk students in the Intervention Cohort showed a 41.7% reduction in probability of suicidal behavior at 12-month follow-up compared to high-risk students in the Control Cohort. CONCLUSIONS: Predictive risk algorithms embedded into universal screening, coupled with telehealth intervention, offer significant potential as a suicide prevention approach for students.
Description
Keywords
algorithm, predictive risk, retrospective cohort trial, suicide prevention, tertiary education, treatment access, Humans, Female, Male, Suicidal Ideation, Prospective Studies, Universities, Students, Algorithms, Telemedicine, Risk Factors
Citation
Hasking PA, Robinson K, McEvoy P, Melvin G, Bruffaerts R, Boyes ME, Auerbach RP, Hendrie D, Nock MK, Preece DA, Rees C, Kessler RC. (2024). Development and evaluation of a predictive algorithm and telehealth intervention to reduce suicidal behavior among university students.. Psychol Med. 54. 5. (pp. 971-979).
Collections