Using self-report surveys at the beginning of service to develop multi-outcome risk models for new soldiers in the U.S. Army

Abstract

Background The U.S. Army uses universal preventives interventions for several negative outcomes (e.g., suicide, violence, sexual assault) with especially high risks in the early years of service. More intensive interventions exist, but would be cost-effective only if targeted at high-risk soldiers. We report results of efforts to develop models for such targeting from self-report surveys administered at the beginning of Army service. Methods 21,832 new soldiers completed a self-administered questionnaire (SAQ) in 2011–2012 and consented to link administrative data to SAQ responses. Penalized regression models were developed for 12 administratively-recorded outcomes occurring by December 2013: suicide attempt, mental hospitalization, positive drug test, traumatic brain injury (TBI), other severe injury, several types of violence perpetration and victimization, demotion, and attrition. Results The best-performing models were for TBI (AUC=0.80), major physical violence perpetration (AUC=0.78), sexual assault perpetration (AUC=0.78), and suicide attempt (AUC=0.74). Although predicted risk scores were significantly correlated across outcomes, prediction was not improved by including risk scores for other outcomes in models. Of particular note: 40.5% of suicide attempts occurred among the 10% of new soldiers with highest predicted risk, 57.2% of male sexual assault perpetrations among the 15% with highest predicted risk, and 35.5% of female sexual assault victimizations among the 10% with highest predicted risk. Conclusions Data collected at the beginning of service in self-report surveys could be used to develop risk models that define small proportions of new soldiers accounting for high proportions of negative outcomes over the first few years of service.

Studying the Effectiveness of Prevention Training Program of Drug Abuse on Reducing Risk Factors in Soldiers and Staffs

“…In study of Fisun, Shamreĭ, Marchenko, Sinenchenko, and Pastushenkov (2013) is discussed the importance of a healthy lifestyle to reduce the anxiety and fear of others and considers it necessary to training of healthy lifestyle to prevention of drug abuse in army forces. Also in study of Rosellini et al (2017) that conducted on American army soldiers' selfregulation, depression has been highlighted as one of drug abuse risk factors.…”

Predeployment predictors of psychiatric disorder‐symptoms and interpersonal violence during combat deployment

“…An extension of this approach would be to use administrative records and comprehensive risk factor surveys prior to deployment to develop actuarial models to predict risk of mental disorders, suicidality, and interpersonal violence during deployment. Models of this sort have been developed successfully to define small groups of female soldiers at high risk of sexual assault victimization (Street et al, 2016), male soldiers at high risk of physical violence perpetration (Rosellini et al, 2017), and soldiers in treatment who are at high risk of suicide (Kessler et al, 2017; Kessler et al, 2015). If similar models based on pre-deployment data could be developed to predict negative outcomes during deployment, results could be used to target soldiers judged to be high risk for various outcomes for diverse preventive interventions either prior to deployment (e.g., a multi-session cognitive-behavioral program for depression/anxiety, Buntrock et al, 2016; Topper, Emmelkamp, Watkins, & Ehring, 2017; anger management, Shea, Lambert, & Reddy, 2013) or during deployment (e.g., assigning a battle buddy).…”

“…Models of this sort have been developed Depress Anxiety. 2018;35:1073-1080. c 2018 Wiley Periodicals, Inc. 1073 wileyonlinelibrary.com/journal/da successfully to define small groups of female soldiers at high risk of sexual assault victimization (Street et al, 2016), male soldiers at high risk of physical violence perpetration (Rosellini et al, 2017), and soldiers in treatment who are at high risk of suicide (Kessler et al, , 2017. If similar models based on predeployment data could be developed to predict negative outcomes during deployment, results could be used to target soldiers judged to be high risk for various outcomes for diverse preventive interventions either prior to deployment (e.g.…”

Improving risk prediction accuracy for new soldiers in the U.S. Army by adding self-report survey data to administrative data

“…Although intensive preventive interventions have been developed in the civilian population and shown to reduce risk of some of these outcomes, including those involving physical and sexual violence (e.g., [ 5 , 6 ]), cost-effective implementation of these interventions would require that they be delivered only to soldiers judged to be high-risk. It has been shown that useful risk targeting systems can be developed for these outcomes based on administrative data available for all U.S. Army soldiers using machine learning methods, with the small proportions of soldiers predicted to be at high risk by these systems accounting for substantial proportions of subsequently observed instances of the outcomes [ 7 – 13 ]. However, many known risk factors for these outcomes are not assessed in Army administrative records, raising the possibility that risk targeting could be improved by expanding the predictor sets to include information from such additional data sources as self-report surveys [ 13 ] and social media postings [ 14 ].…”

“…It has been shown that useful risk targeting systems can be developed for these outcomes based on administrative data available for all U.S. Army soldiers using machine learning methods, with the small proportions of soldiers predicted to be at high risk by these systems accounting for substantial proportions of subsequently observed instances of the outcomes [ 7 – 13 ]. However, many known risk factors for these outcomes are not assessed in Army administrative records, raising the possibility that risk targeting could be improved by expanding the predictor sets to include information from such additional data sources as self-report surveys [ 13 ] and social media postings [ 14 ].…”