ArticlePsychological medicine2017
Using self-report surveys at the beginning of service to develop multi-outcome risk models for new soldiers in the U.S. Army.
Article in Psychological medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- SAFEGUARD: Transforming Military Suicide Prevention Through Predictive Analytics and Targeted Interventions.Current psychiatry reports · 2026Review
- A novel assessment of self-perceptions of recovery from illness and injury in U.S. Navy service members.Frontiers in rehabilitation sciences · 2026Article
- SCORE2 Outperforms Pol-SCORE in Detecting Increased Cardiovascular Risk.Pathophysiology : the official journal of the International Society for Pathophysiology · 2025Article
- Epidemiological Analysis of Cardiovascular Diseases with Consideration of Risk Factors, Health Awareness, and Preventive Behaviors in Civilian and Military Populations.Journal of clinical medicine · 2025Article
- Organizational Capacity for Sexual Assault Prevention Within a U.S. Army Installation.Military medicine · 2025Article
- Genetic, environmental, and behavioral correlates of lifetime suicide attempt: Analysis of additive and interactive effects in two cohorts of US Army soldiers.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Article
- Social network size and personality traits independently and prospectively predict distress disorders and suicidal behavior in U.S. Army soldiers.Psychological medicine · 2023Article
- Predicting correlated outcomes from molecular data.Bioinformatics (Oxford, England) · 2021Article
- The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS): progress toward understanding suicide among soldiers.Molecular psychiatry · 2019Review
- Predeployment predictors of psychiatric disorder-symptoms and interpersonal violence during combat deployment.Depression and anxiety · 2018Article
- Improving risk prediction accuracy for new soldiers in the U.S. Army by adding self-report survey data to administrative data.BMC psychiatry · 2018Article
Corrections and comments
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Authors and funding
14 authors at 6 institutions in 1 country.
Funding
Abstract
backgroundThe 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.
methods21 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.
resultsThe 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.
conclusionsData 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.
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