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Clin Psychol Sci. 2016;4(6):939-956. doi: 10.1177/2167702616639532. Epub 2016 Jul 29.

Developing a Risk Model to Target High-risk Preventive Interventions for Sexual Assault Victimization among Female U.S. Army Soldiers.

Author information

1
National Center for PTSD, VA Boston Healthcare System, and Department of Psychiatry, Boston University School of Medicine.
2
Department of Health Care Policy, Harvard Medical School.
3
Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University School of Medicine.
4
Institute for Social Research, University of Michigan.
5
School of Law, University of Virginia.
6
Predictive Medicine Group, Boston Children's Hospital and Harvard Medical School.
7
Darla Moore School of Business, University of South Carolina.
8
Departments of Psychiatry and Family Medicine & Public Health, University of California San Diego, and VA San Diego Healthcare System.

Abstract

Sexual violence victimization is a significant problem among female U.S. military personnel. Preventive interventions for high-risk individuals might reduce prevalence, but would require accurate targeting. We attempted to develop a targeting model for female Regular U.S. Army soldiers based on theoretically-guided predictors abstracted from administrative data records. As administrative reports of sexual assault victimization are known to be incomplete, parallel machine learning models were developed to predict administratively-recorded (in the population) and self-reported (in a representative survey) victimization. Capture-recapture methods were used to combine predictions across models. Key predictors included low status, crime involvement, and treated mental disorders. Area under the Receiver Operating Characteristic curve was .83-.88. 33.7-63.2% of victimizations occurred among soldiers in the highest-risk ventile (5%). This high concentration of risk suggests that the models could be useful in targeting preventive interventions, although final determination would require careful weighing of intervention costs, effectiveness, and competing risks.

KEYWORDS:

Machine learning; military sexual trauma; prediction model; rape; risk model; sexual assault

Conflict of interest statement

Disclosures: Dr Stein has been a consultant for Care Management Technologies, received payment for his editorial work from UpToDate, Depression and Anxiety, and Biological Psychiatry, and has been a paid consultant for Pfizer, Tonix, and Janssen. Dr Monahan is co-owner of Classification of Violence Risk (COVR), Inc. Dr Kessler has been a consultant for Johnson & Johnson Wellness and Prevention and Sonofi-Aventis Groupe and has served on an advisory board for Lake Nona Institute. Dr Kessler is a co-owner of DataStat, Inc. The other authors report no financial relationships with commercial interests.

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