• Anthony J Rosellini, John Monahan, Amy E Street, Maria V Petukhova, Nancy A Sampson, David M Benedek, Paul Bliese, Murray B Stein, Robert J Ursano, and Ronald C Kessler.
• Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts; Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts.
• Am J Prev Med. 2017 Nov 1; 53 (5): 661669661-669.

IntroductionThe Department of Defense uses a universal prevention framework for sexual assault prevention, with each branch implementing its 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.MethodsThis study investigated administratively recorded sexual assault perpetration among the 821,807 male Army soldiers serving 2004-2009. Administrative data were also used to operationalize the 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). Separate models were developed for assaults against non-family and intra-family adults and minors.ResultsThere were 4,640 male soldiers 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 concentration of risk 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 and treatment of mental disorders.ConclusionsAdministrative data can be used to develop actuarial models that identify a high proportion of sexual assault perpetrators. If a system is developed to consolidate administrative predictors routinely, then 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.Copyright © 2017 American Journal of Preventive Medicine. All rights reserved.

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