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Predicting non-familial major physical violent crime perpetration in the US Army from administrative data

Published online by Cambridge University Press:  06 October 2015


A. J. Rosellini
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
J. Monahan
Affiliation:
School of Law, University of Virginia, Charlottesville, VA, USA
A. E. Street
Affiliation:
National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
S. G. Heeringa
Affiliation:
Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
E. D. Hill
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
M. Petukhova
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
B. Y. Reis
Affiliation:
Predictive Medicine Group, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
N. A. Sampson
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
P. Bliese
Affiliation:
Darla Moore School of Business, University of South Carolina, Columbia, South Carolina, USA
M. Schoenbaum
Affiliation:
Office of Science Policy, Planning and Communications, National Institute of Mental Health, Bethesda, MD, USA
M. B. Stein
Affiliation:
Departments of Psychiatry and Family Medicine & Public Health, University of California San Diego, La Jolla, CA, USA VA San Diego Healthcare System, San Diego, CA, USA
R. J. Ursano
Affiliation:
Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University School of Medicine, Bethesda, MD, USA
R. C. Kessler
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
Corresponding

Abstract

Background.

Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among US Army soldiers.

Method.

A consolidated administrative database for all 975 057 soldiers in the US Army in 2004–2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Of these soldiers, 5771 committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression, random forests, penalized regressions). The model was then validated in an independent 2011–2013 sample.

Results.

Key predictors were indicators of disadvantaged social/socioeconomic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver-operating characteristic curve was 0.80–0.82 in 2004–2009 and 0.77 in the 2011–2013 validation sample. Of all administratively recorded crimes, 36.2–33.1% (male-female) were committed by the 5% of soldiers having the highest predicted risk in 2004–2009 and an even higher proportion (50.5%) in the 2011–2013 validation sample.

Conclusions.

Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks.


Type
Original Articles
Copyright
Copyright © Cambridge University Press 2015 

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