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Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.
We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.
Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.
An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.
Demand for building competencies in implementation research (IR) outstrips supply of training programs, calling for a paradigm shift. We used a bootstrap approach to leverage external resources and create IR capacity through a novel 2-day training for faculty scientists across the four Texas Clinical & Translational Science Awards (CTSAs). The Workshop combined internal and external expertise, targeted nationally established IR competencies, incorporated new National Institutes of Health/National Cancer Institute OpenAccess online resources, employed well-known adult education principles, and measured impact. CTSA leader buy-in was reflected in financial support. Evaluation showed increased self-reported IR competency; statewide initiatives expanded. The project demonstrated that, even with limited onsite expertise, it was possible to bootstrap resources and build IR capacity de novo in the CTSA community.
Although research has shown that exposure to potentially traumatic and morally injurious events is associated with psychological symptoms among veterans, knowledge regarding functioning impacts remains limited.
A population-based sample of post-9/11 veterans completed measures of intimate relationship, health, and work functioning at approximately 9, 15, 21, and 27 months after leaving service. Moral injury, posttraumatic stress, and depression were assessed at ~9 months post-separation. We used Latent Growth Mixture Models to identify discrete classes characterized by unique trajectories of change in functioning over time and to examine predictors of class membership.
Veterans were assigned to one of four functioning trajectories: high and stable, high and decreasing, moderate and increasing, and moderate and stable. Whereas posttraumatic stress, depression, and moral injury associated with perpetration and betrayal predicted worse outcomes at baseline across multiple functioning domains, moral injury associated with perpetration and depression most reliably predicted assignment to trajectories characterized by relatively poor or declining functioning.
Moral injury contributes to functional problems beyond what is explained by posttraumatic stress and depression, and moral injury due to perpetration and depression most reliably predicted assignment to trajectories characterized by functional impairment over time.
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