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

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. Data come from everal waves of a prospective cohort study...

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Vydáno v:Psychological medicine Ročník 54; číslo 5; s. 971 - 979
Hlavní autoři: Hasking, Penelope A., Robinson, Kealagh, McEvoy, Peter, Melvin, Glenn, Bruffaerts, Ronny, Boyes, Mark E., Auerbach, Randy P., Hendrie, Delia, Nock, Matthew K., Preece, David A., Rees, Clare, Kessler, Ronald C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Cambridge University Press 01.04.2024
Témata:
Algorithms
At risk populations
At risk students
Behavior
Cohort analysis
College students
Colleges & universities
Female
High risk
Humans
Intervention
Male
Medical technology
Mental health
Predictions
Prevention programs
Probability
Prospective Studies
Response rates
Risk
Risk Factors
Risk groups
Risk reduction
Safety
Self destructive behavior
Sex crimes
Students
Suicidal behavior
Suicidal Ideation
Suicide
Suicide prevention
Suicides & suicide attempts
Telecommunications
Telemedicine
Telephone surveys
Universities
Volunteers
retrospective cohort trial
tertiary education
suicide prevention
predictive risk
treatment access
algorithm
ISSN:0033-2917, 1469-8978, 1469-8978
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Shrnutí: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. Data come from everal waves of a prospective cohort study (2016-2022) of college students ( = 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. 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. Predictive risk algorithms embedded into universal screening, coupled with telehealth intervention, offer significant potential as a suicide prevention approach for students.
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ISSN:0033-2917
1469-8978
1469-8978
DOI:10.1017/S0033291723002714