Namik Kirlic1, Elisabeth Akeman1, Danielle C DeVille1,2, Hung-Wen Yeh3, Kelly T Cosgrove1,2, Timothy J McDermott1,2, James Touthang1, Ashley Clausen4,5, Martin P Paulus1, Robin L Aupperle1,6. 1. Laureate Institute for Brain Research, Tulsa, OK, USA. 2. Department of Psychology, University of Tulsa, Tulsa, OK, USA. 3. Health Services & Outcomes Research, Children's Mercy Hospital, Kansas City, MO, USA. 4. Education and Clinical Center, VA Mid-Atlantic Mental Illness Research, Durham, NC, USA. 5. Duke University Brain Imaging and Analysis Center, Durham, NC, USA. 6. School of Community Medicine, University of Tulsa, Tulsa, OK, USA.
Abstract
OBJECTIVE: To identify robust and reproducible factors associated with suicidal thoughts and behaviors (STBs) in college students. METHODS: 356 first-year university students completed a large battery of demographic and clinically-relevant self-report measures during the first semester of college and end-of-year (n = 228). Suicide Behaviors Questionnaire-Revised (SBQ-R) assessed STBs. A machine learning (ML) pipeline using stacking and nested cross-validation examined correlates of SBQ-R scores. RESULTS: 9.6% of students were identified at significant STBs risk by the SBQ-R. The ML algorithm explained 28.3% of variance (95%CI: 28-28.5%) in baseline SBQ-R scores, with depression severity, social isolation, meaning and purpose in life, and positive affect among the most important factors. There was a significant reduction in STBs at end-of-year with only 1.8% of students identified at significant risk. CONCLUSION: Analyses replicated known factors associated with STBs during the first semester of college and identified novel, potentially modifiable factors including positive affect and social connectedness.
OBJECTIVE: To identify robust and reproducible factors associated with suicidal thoughts and behaviors (STBs) in college students. METHODS: 356 first-year university students completed a large battery of demographic and clinically-relevant self-report measures during the first semester of college and end-of-year (n = 228). Suicide Behaviors Questionnaire-Revised (SBQ-R) assessed STBs. A machine learning (ML) pipeline using stacking and nested cross-validation examined correlates of SBQ-R scores. RESULTS: 9.6% of students were identified at significant STBs risk by the SBQ-R. The ML algorithm explained 28.3% of variance (95%CI: 28-28.5%) in baseline SBQ-R scores, with depression severity, social isolation, meaning and purpose in life, and positive affect among the most important factors. There was a significant reduction in STBs at end-of-year with only 1.8% of students identified at significant risk. CONCLUSION: Analyses replicated known factors associated with STBs during the first semester of college and identified novel, potentially modifiable factors including positive affect and social connectedness.
Authors: P Mortier; K Demyttenaere; R P Auerbach; P Cuijpers; J G Green; G Kiekens; R C Kessler; M K Nock; A M Zaslavsky; R Bruffaerts Journal: J Affect Disord Date: 2016-09-28 Impact factor: 4.839
Authors: Randy P Auerbach; Philippe Mortier; Ronny Bruffaerts; Jordi Alonso; Corina Benjet; Pim Cuijpers; Koen Demyttenaere; David D Ebert; Jennifer Greif Green; Penelope Hasking; Sue Lee; Christine Lochner; Margaret McLafferty; Matthew K Nock; Maria V Petukhova; Stephanie Pinder-Amaker; Anthony J Rosellini; Nancy A Sampson; Gemma Vilagut; Alan M Zaslavsky; Ronald C Kessler Journal: Int J Methods Psychiatr Res Date: 2018-11-18 Impact factor: 4.035
Authors: Elisabeth Akeman; Namik Kirlic; Ashley N Clausen; Kelly T Cosgrove; Timothy J McDermott; Lisa D Cromer; Martin P Paulus; Hung-Wen Yeh; Robin L Aupperle Journal: Depress Anxiety Date: 2019-11-04 Impact factor: 6.505
Authors: Daniel Marbach; James C Costello; Robert Küffner; Nicole M Vega; Robert J Prill; Diogo M Camacho; Kyle R Allison; Manolis Kellis; James J Collins; Gustavo Stolovitzky Journal: Nat Methods Date: 2012-07-15 Impact factor: 28.547