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


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.

SUBMITTER: Street AE 

PROVIDER: S-EPMC5279920 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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

Street Amy E AE   Rosellini Anthony J AJ   Ursano Robert J RJ   Heeringa Steven G SG   Hill Eric D ED   Monahan John J   Naifeh James A JA   Petukhova Maria V MV   Reis Ben Y BY   Sampson Nancy A NA   Bliese Paul D PD   Stein Murray B MB   Zaslavsky Alan M AM   Kessler Ronald C RC  

Clinical psychological science : a journal of the Association for Psychological Science 20160729 6


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 dev  ...[more]

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