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

Hasking PA; Robinson K; McEvoy P; Melvin G; Bruffaerts R; Boyes ME; Auerbach RP; Hendrie D; Nock MK; Preece DA; Rees C; Kessler RC

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

dc.citation.issue	5
dc.citation.volume	54
dc.contributor.author	Hasking PA
dc.contributor.author	Robinson K
dc.contributor.author	McEvoy P
dc.contributor.author	Melvin G
dc.contributor.author	Bruffaerts R
dc.contributor.author	Boyes ME
dc.contributor.author	Auerbach RP
dc.contributor.author	Hendrie D
dc.contributor.author	Nock MK
dc.contributor.author	Preece DA
dc.contributor.author	Rees C
dc.contributor.author	Kessler RC
dc.coverage.spatial	England
dc.date.accessioned	2024-10-04T01:01:51Z
dc.date.available	2024-10-04T01:01:51Z
dc.date.issued	2024-04-01
dc.description.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.
dc.description.confidential	false
dc.edition.edition	April 2024
dc.format.pagination	971-979
dc.identifier.author-url	https://www.ncbi.nlm.nih.gov/pubmed/37732419
dc.identifier.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).
dc.identifier.doi	10.1017/S0033291723002714
dc.identifier.eissn	1469-8978
dc.identifier.elements-type	journal-article
dc.identifier.issn	0033-2917
dc.identifier.pii	S0033291723002714
dc.identifier.uri	https://mro.massey.ac.nz/handle/10179/71604
dc.language	eng
dc.publisher	Cambridge University Press
dc.publisher.uri	https://www.cambridge.org/core/journals/psychological-medicine/article/development-and-evaluation-of-a-predictive-algorithm-and-telehealth-intervention-to-reduce-suicidal-behavior-among-university-students/BCDACC91A454665076D05FA2323B9C38#
dc.relation.isPartOf	Psychol Med
dc.rights	(c) 2023 The Author/s
dc.rights	CC BY 4.0
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	algorithm
dc.subject	predictive risk
dc.subject	retrospective cohort trial
dc.subject	suicide prevention
dc.subject	tertiary education
dc.subject	treatment access
dc.subject	Humans
dc.subject	Female
dc.subject	Male
dc.subject	Suicidal Ideation
dc.subject	Prospective Studies
dc.subject	Universities
dc.subject	Students
dc.subject	Algorithms
dc.subject	Telemedicine
dc.subject	Risk Factors
dc.title	Development and evaluation of a predictive algorithm and telehealth intervention to reduce suicidal behavior among university students.
dc.type	Journal article
pubs.elements-id	487185
pubs.organisational-group	Other