Using administrative data to identify U.S. Army soldiers at high-risk of perpetrating minor violent crimes

Abstract

Growing concerns exist about violent crimes perpetrated by U.S. military personnel. Although interventions exist to reduce violent crimes in high-risk populations, optimal implementation requires evidence-based targeting. The goal of the current study was to use machine learning methods (stepwise and penalized regression; random forests) to develop models to predict minor violent crime perpetration among U.S. Army soldiers. Predictors were abstracted from administrative data available for all 975,057 soldiers in the U.S. Army 2004–2009, among whom 25,966 men and 2,728 women committed a first founded minor violent crime (simple assault, blackmail-extortion-intimidation, rioting, harassment). Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build separate male and female prediction models that were then tested in an independent 2011–2013 sample. Final model predictors included young age, low education, early career stage, prior crime involvement, and outpatient treatment for diverse emotional and substance use problems. Area under the receiver operating characteristic curve was 0.79 (for men and women) in the 2004–2009 training sample and 0.74–0.82 (men-women) in the 2011–2013 test sample. 30.5–28.9% (men-women) of all administratively-recorded crimes in 2004–2009 were committed by the 5% of soldiers having highest predicted risk, with similar proportions (28.5–29.0%) when the 2004–2009 coefficients were applied to the 2011–2013 test sample. These results suggest that it may be possible to target soldiers at high-risk of violence perpetration for preventive interventions, although final decisions about such interventions would require weighing predicted effectiveness against intervention costs and competing risks.

Prediction of Sex-Specific Suicide Risk Using Machine Learning and Single-Payer Health Care Registry Data From Denmark

“…The data reduction process included elimination of rare predictors (≤10 observations) and predictors with negligible associations with suicide (unadjusted odds ratio of <0.9 or ≥1.1). We eliminated diagnoses occurring in the emergency department owing to low positive predictive value .…”

Predicting Sexual Assault Perpetration in the U.S. Army Using Administrative Data

“…In addition to identifying soldiers at high risk of sexual assault victimization, this approach has been used recently to predict non-sexual violence perpetration. 17,18 The goal of the current report is to present results of an effort to develop a comparable model for soldier sexual assault perpetration. Consistent with current research on risk factors for sexual assault perpetration, 19 separate models were developed for perpetration against: (i) non-family adults, (ii) non-family minors, (iii) intra-family adults, and (iv) intra-family minors.…”

Risk and Protective Factors for Difficulty Controlling Violent Behavior in National Guard and Reserve Service Members

“…In addition, factors associated with lower risk for violent behavior among Iraq and Afghanistan veterans include income level, psychosocial factors like social support, and physical health (Elbogen, Johnson, Wagner, et al, 2012; Elbogen, Wagner, et al, 2010; Shin et al, 2012). In the general population, as well as among military service members and veterans, demographic factors associated with an increased risk of violent behavior include male sex, young age, and lower educational attainment (Elbogen, Fuller, et al, 2010; Elbogen, Wagner, et al, 2010; Macmanus et al, 2013; MacManus et al, 2015; Rosellini et al, 2016; Rosellini et al, 2017; Shin et al, 2012).…”