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

Rosellini, Anthony J.; Monahan, John; Street, Amy E.; Heeringa, Steven G.; Hill, Eric; Petukhova, Maria; Reis, Ben Y.; Sampson, Nancy A.; Bliese, Paul D.; Schoenbaum, Michael; Stein, Murray B.; Ursano, Robert J.; Kessler, Ronald C.

doi:10.1017/s0033291715001774

Cited by 29 publications

(27 citation statements)

References 53 publications

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“…In addition, roughly three-fourths of the predictors in the optimal models for minor violent crime were similar to predictors in our previously-published models for major violent crime (Rosellini et al, 2016). Based on these results, a question could be raised whether the previously-developed models for major violent crime might be equally useful in predicting minor violent crime.…”

Section: Resultssupporting

confidence: 79%

“…Although numerous studies have examined risk factors for soldier-veteran violence (Elbogen et al, 2014a; Elbogen et al, 2013; Elbogen et al, 2012; Elbogen et al, 2014b; Elbogen et al, 2010b; Gallaway et al, 2012; Gallaway et al, 2013; Hellmuth et al, 2012; Jakupcak et al, 2007; MacManus et al, 2012a; MacManus et al, 2012b; MacManus et al, 2013; Sullivan & Elbogen, 2014), no attempts were made to develop individual-level risk scores prior to our recent work predicting major violence (Rosellini et al, 2016). The goal of the current study was to develop comparable models for minor violent crime.…”

Section: Discussionmentioning

confidence: 99%

“…A model of this sort was recently developed to identify U.S. Army soldiers at high risk of major physical violent crime perpetration (e.g., homicide-manslaughter, aggravated assault) (Rosellini et al, 2016). The 5% of soldiers with highest predicted risk in that model accounted for roughly one-third of all major physical violent crimes among soldiers.…”

Section: Introductionmentioning

confidence: 99%

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

Rosellini

Monahan

Street

et al. 2017

Journal of Psychiatric Research

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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.

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Section: Resultssupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Rosellini

Monahan

Street

et al. 2017

Journal of Psychiatric Research

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Rosellini

Monahan

Street

et al. 2017

American Journal of Preventive Medicine

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Introduction The Department of Defense uses a universal prevention framework for sexual assault prevention, with each branch implementing their own branch-wide programs. Intensive interventions exist, but would be cost-effective only if targeted at high-risk personnel. This study developed actuarial models to identify male U.S. Army soldiers at high risk of administratively-recorded sexual assault perpetration. Methods This study investigated administratively-recorded sexual assault perpetration among the 821,807 male Army soldiers serving 2004–2009. Other temporally prior administrative data were used as predictors. Penalized discrete-time (person-month) survival analysis (conducted in 2016) was used to select the smallest possible number of stable predictors to maximize number of sexual assaults among the 5% of soldiers with highest predicted risk of perpetration (top-ventile concentration of risk [COR]). Separate models were developed for assaults against non-family and intra-family adults and minors. Results 4,640 male soldiers were found to be perpetrators against non-family adults, 1,384 against non-family minors, 380 against intra-family adults, and 335 against intra-family minors. Top-ventile COR was 16.2–20.2% predicting perpetration against non-family adults and minors and 34.2–65.1% against intra-family adults and minors. Final predictors consisted largely of measures of prior crime involvement and the presence-treatment of mental disorders. Conclusions Administrative data can be used to develop actuarial models that identify a high proportion of sexual assault perpetrators. If a system is developed to routinely consolidate administrative predictors, predictions could be generated periodically to identify those in need of preventive intervention. Whether this would be cost-effective, though, would depend on intervention costs, effectiveness, and competing risks.

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“…This targeting would require valid risk prediction tools. Recent studies have shown that Army and DoD administrative data can be used to develop such tools to predict negative soldier outcomes such as suicide (Kessler et al 2015), violent crime perpetration (Rosellini et al 2016), and sexual assault victimization (Street et al 2016), but these models are limited by the fact that administrative data only become available over the course of time and are unavailable when preventive interventions might most logically be implemented at the beginning of service. An alternative would be to implement a risk factor survey at the beginning of service to target new recruits for preventive interventions..…”

Section: Introductionmentioning

confidence: 99%

Using self-report surveys at the beginning of service to develop multi-outcome risk models for new soldiers in the U.S. Army

et al. 2017

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Background The U.S. Army uses universal preventives interventions for several negative outcomes (e.g., suicide, violence, sexual assault) with especially high risks in the early years of service. More intensive interventions exist, but would be cost-effective only if targeted at high-risk soldiers. We report results of efforts to develop models for such targeting from self-report surveys administered at the beginning of Army service. Methods 21,832 new soldiers completed a self-administered questionnaire (SAQ) in 2011–2012 and consented to link administrative data to SAQ responses. Penalized regression models were developed for 12 administratively-recorded outcomes occurring by December 2013: suicide attempt, mental hospitalization, positive drug test, traumatic brain injury (TBI), other severe injury, several types of violence perpetration and victimization, demotion, and attrition. Results The best-performing models were for TBI (AUC=0.80), major physical violence perpetration (AUC=0.78), sexual assault perpetration (AUC=0.78), and suicide attempt (AUC=0.74). Although predicted risk scores were significantly correlated across outcomes, prediction was not improved by including risk scores for other outcomes in models. Of particular note: 40.5% of suicide attempts occurred among the 10% of new soldiers with highest predicted risk, 57.2% of male sexual assault perpetrations among the 15% with highest predicted risk, and 35.5% of female sexual assault victimizations among the 10% with highest predicted risk. Conclusions Data collected at the beginning of service in self-report surveys could be used to develop risk models that define small proportions of new soldiers accounting for high proportions of negative outcomes over the first few years of service.

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

Cited by 29 publications

References 53 publications

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

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

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

Using self-report surveys at the beginning of service to develop multi-outcome risk models for new soldiers in the U.S. Army

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