Developing a Risk Model to Target High-risk Preventive Interventions for Sexual Assault Victimization among Female U.S. Army Soldiers.
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.
National Center for PTSD, VA Boston Healthcare System, and Department of Psychiatry, Boston University School of Medicine.
Amy E
AE
Street
Department of Health Care Policy, Harvard Medical School.
Anthony J
AJ
Rosellini
Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University School of Medicine.
Robert J
RJ
Ursano
Institute for Social Research, University of Michigan.
Steven G
SG
Heeringa
Department of Health Care Policy, Harvard Medical School.
Eric D
ED
Hill
School of Law, University of Virginia.
John
J
Monahan
Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University School of Medicine.
James A
JA
Naifeh
Department of Health Care Policy, Harvard Medical School.
Maria V
MV
Petukhova
Predictive Medicine Group, Boston Children's Hospital and Harvard Medical School.
Ben Y
BY
Reis
Department of Health Care Policy, Harvard Medical School.
Nancy A
NA
Sampson
Darla Moore School of Business, University of South Carolina.
Paul D
PD
Bliese
Departments of Psychiatry and Family Medicine & Public Health, University of California San Diego, and VA San Diego Healthcare System.
Murray B
MB
Stein
Department of Health Care Policy, Harvard Medical School.
Alan M
AM
Zaslavsky
Department of Health Care Policy, Harvard Medical School.
Ronald C
RC
Kessler
Machine learning; military sexual trauma; prediction model; rape; risk model; sexual assault