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Depress Anxiety. 2018 Nov;35(11):1073-1080. doi: 10.1002/da.22807. Epub 2018 Aug 13.

Predeployment predictors of psychiatric disorder-symptoms and interpersonal violence during combat deployment.

Author information

1
Center for Anxiety and Related Disorders, Boston University, Boston, Massachusetts.
2
Departments of Psychiatry and Family Medicine and Public Health, University of California San Diego, La Jolla, California.
3
VA San Diego Healthcare System, San Diego, California.
4
Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University School of Medicine, Bethesda, Maryland.
5
Darla Moore School of Business, University of South Carolina, Columbia, South Carolina.
6
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
7
School of Law, University of Virginia, Charlottesville, Virginia.
8
Department of Psychology, Harvard University, Cambridge, Massachusetts.
9
National Center for PTSD, VA Boston Healthcare System, Boston, Massachusetts.
10
Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts.

Abstract

BACKGROUND:

Preventing suicides, mental disorders, and noncombat-related interpersonal violence during deployment are priorities of the US Army. We used predeployment survey and administrative data to develop actuarial models to identify soldiers at high risk of these outcomes during combat deployment.

METHODS:

The models were developed in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) Pre-Post Deployment Study, a panel study of soldiers deployed to Afghanistan in 2012-2013. Soldiers completed self-administered questionnaires before deployment and one (T1), three (T2), and nine months (T3) after deployment, and consented to administrative data linkage. Seven during-deployment outcomes were operationalized using the postdeployment surveys. Two overlapping samples were used because some outcomes were assessed at T1 (n = 7,048) and others at T2-T3 (n = 7,081). Ensemble machine learning was used to develop a model for each outcome from 273 predeployment predictors, which were compared to simple logistic regression models.

RESULTS:

The relative improvement in area under the receiver operating characteristic curve (AUC) obtained by machine learning compared to the logistic models ranged from 1.11 (major depression) to 1.83 (suicidality).The best-performing machine learning models were for major depression (AUC = 0.88), suicidality (0.86), and generalized anxiety disorder (0.85). Roughly 40% of these outcomes occurred among the 5% of soldiers with highest predicted risk.

CONCLUSIONS:

Actuarial models could be used to identify high risk soldiers either for exclusion from deployment or preventive interventions. However, the ultimate value of this approach depends on the associated costs, competing risks (e.g. stigma), and the effectiveness to-be-determined interventions.

KEYWORDS:

army; deployment; mental disorder; military; predictive modeling; risk assessment; violence

PMID:
30102442
PMCID:
PMC6212319
[Available on 2019-11-01]
DOI:
10.1002/da.22807

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