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A practical risk calculator for suicidal behavior among transitioning U.S. Army soldiers: results from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS)

Published online by Cambridge University Press:  09 March 2023

Jaclyn C. Kearns ,
Emily R. Edwards ,
Erin P. Finley ,
Joseph C. Geraci ,
Sarah M. Gildea ,
Marianne Goodman ,
Irving Hwang ,
Chris J. Kennedy ,
Andrew J. King  and
Alex Luedtke
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Jaclyn C. Kearns
Affiliation:
National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
Emily R. Edwards
Affiliation:
Transitioning Servicemember/Veteran And Suicide Prevention Center (TASC), VISN 2 Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center, Bronx, New York, NY, USA Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Erin P. Finley
Affiliation:
Center of Excellence for Research on Returning War Veterans, VISN 17, Doris Miller VA Medical Center, Waco, TX, USA Center for the Study of Healthcare Innovation, Implementation, and Policy (CSHIIP), VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
Joseph C. Geraci
Affiliation:
Transitioning Servicemember/Veteran And Suicide Prevention Center (TASC), VISN 2 Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center, Bronx, New York, NY, USA Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA Center of Excellence for Research on Returning War Veterans, VISN 17, Doris Miller VA Medical Center, Waco, TX, USA Resilience Center for Veterans & Families, Teachers College, Columbia University, New York, NY, USA
Sarah M. Gildea
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
Marianne Goodman
Affiliation:
Transitioning Servicemember/Veteran And Suicide Prevention Center (TASC), VISN 2 Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center, Bronx, New York, NY, USA Center of Excellence for Research on Returning War Veterans, VISN 17, Doris Miller VA Medical Center, Waco, TX, USA
Irving Hwang
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
Chris J. Kennedy
Affiliation:
Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
Andrew J. King
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
Alex Luedtke
Affiliation:
Department of Statistics, University of Washington, Seattle, WA, USA Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
Brian P. Marx
Affiliation:
National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
Maria V. Petukhova
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
Nancy A. Sampson
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
Richard W. Seim
Affiliation:
Center of Excellence for Research on Returning War Veterans, VISN 17, Doris Miller VA Medical Center, Waco, TX, USA
Ian H. Stanley
Affiliation:
Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA Department of Emergency Medicine, Center for COMBAT Research, University of Colorado School of Medicine, Aurora, CO, USA
Murray B. Stein
Affiliation:
Department of Psychiatry, University of California San Diego, La Jolla, CA, USA School of Public Health, University of California San Diego, La Jolla, CA, USA VA San Diego Healthcare System, La Jolla, CA, USA
Robert J. Ursano
Affiliation:
Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
Ronald C. Kessler*
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
*
Author for correspondence: Ronald C. Kessler, E-mail: kessler@hcp.med.harvard.edu
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Abstract

Background

Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.

Methods

We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.

Results

Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.

Conclusions

An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

Keywords

Machine learningsuicide attemptsuicide preventionveterans
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 15 , November 2023 , pp. 7096 - 7105
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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