Citation

Bernecker SL, Zuromski KL, Gutierrez PM, Joiner TE, King AJ, Liu H, Nock MK, Sampson NA, Zaslavsky AM, Stein MB, Ursano RJ, Kessler RC. Behav. Res. Ther. 2018; ePub(ePub): 103350.

Affiliation

Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA, USA. Electronic address: kessler@hcp.med.harvard.edu.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.brat.2018.11.018

PMID

30598236

Abstract

Most nonfatal suicide attempts and suicide deaths occur among patients who deny suicidal ideation (SI) during suicide risk screenings. Little is known about risk factors for suicidal behaviors among such patients. We investigated this in a representative sample of U.S. Army soldiers who denied lifetime SI in a survey and were then followed through administrative records for up to 45 months to learn of administratively-recorded suicide attempts (SA). A novel two-stage risk assessment approach was used that combined first-stage prediction from administrative records to find the subsample of SI deniers with highest subsequent SA risk and then used survey reports to estimate a second-stage model identifying the subset of individuals in the high-risk subsample at highest SA risk. 70% of survey respondents denied lifetime SI. Administrative data identified 30% of this 70% who accounted for 81.2% of subsequent administratively-recorded SAs. A relatively small number of self-report survey variables were then used to create a prediction model that identified 10% of the first-stage high-risk sample (i.e., 3% of all soldiers) at highest SA risk (accounting for 45% of SAs in the total sample). We close by discussing potential applications of this approach for identifying future SI deniers at highest SA risk.

Copyright © 2018 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Army; Machine learning; Military; Risk assessment; Suicidal ideation; Suicide attempt