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

Author:

Street Amy E.1,Rosellini Anthony J.2,Ursano Robert J.3,Heeringa Steven G.4,Hill Eric D.2,Monahan John5,Naifeh James A.3,Petukhova Maria V.2,Reis Ben Y.6,Sampson Nancy A.2,Bliese Paul D.7,Stein Murray B.8,Zaslavsky Alan M.2,Kessler Ronald C.2

Affiliation:

1. National Center for PTSD at VA Boston Healthcare System and Boston University School of Medicine

2. Harvard Medical School

3. Uniformed Services University School of Medicine

4. University of Michigan

5. University of Virginia

6. Boston Children’s Hospital and Harvard Medical School

7. University of South Carolina

8. 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. Between 33.7% and 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.

Publisher

SAGE Publications

Subject

Clinical Psychology

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