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.

The transition from military service to civilian life is a high-risk period for suicide attempts (SAs). Although stressful life events (SLEs) faced by transitioning soldiers are thought to be implicated, systematic prospective evidence is lacking.

Participants in the Army Study to Assess Risk and Resilience in Servicemembers (STARRS) completed baseline self-report surveys while on active duty in 2011–2014. Two self-report follow-up Longitudinal Surveys (LS1: 2016–2018; LS2: 2018–2019) were subsequently administered to probability subsamples of these baseline respondents. As detailed in a previous report, a SA risk index based on survey, administrative, and geospatial data collected before separation/deactivation identified 15% of the LS respondents who had separated/deactivated as being high-risk for self-reported post-separation/deactivation SAs. The current report presents an investigation of the extent to which self-reported SLEs occurring in the 12 months before each LS survey might have mediated/modified the association between this SA risk index and post-separation/deactivation SAs.

The 15% of respondents identified as high-risk had a significantly elevated prevalence of some post-separation/deactivation SLEs. In addition, the associations of some SLEs with SAs were significantly stronger among predicted high-risk than lower-risk respondents. Demographic rate decomposition showed that 59.5% (s.e. = 10.2) of the overall association between the predicted high-risk index and subsequent SAs was linked to these SLEs.

It might be possible to prevent a substantial proportion of post-separation/deactivation SAs by providing high-risk soldiers with targeted preventive interventions for exposure/vulnerability to commonly occurring SLEs.

Neuropsychiatric symptoms (NPS) occur in people with dementia throughout disease course and across etiologies. NPS are associated with significant morbidities and hastened disease processes. Nevertheless, people with dementia are not systematically assessed for NPS in clinical settings. We review existing NPS measures for clinical and/or research purposes, and identify measurement gaps.

We conducted a computerized search of peer-reviewed published studies of measures (January 1, 1980–December 1, 2013) using multiple search terms. Measures selected for review were in English, had adequate psychometric properties, and were developed for or used with people with dementia. Papers describing measures were evaluated by three coders along seven characteristics: behavioral domains, number of items, method of administration, response categories, targeted population, setting, and psychometric properties.

Overall, 2,233 papers were identified through search terms, and 36 papers from manual searches of references. From 2,269 papers, 85 measures were identified of which 45 (52.9%) had adequate psychometric properties and were developed or used with dementia populations. Of these, 16 (35.6%) were general measures that included a wide range of behaviors; 29 (64.4%) targeted specific behaviors (e.g. agitation). Measures differed widely as to behaviors assessed and measurement properties.

A robust set of diverse measures exists for assessing NPS in different settings. No measures identify risk factors for behaviors or enable an evaluation of the context in which behaviors occur. To improve clinical efforts, research is needed to evaluate concordance of behavioral ratings between formal and informal caregivers, and to develop and test measures that can identify known risks for behaviors and the circumstances under which behaviors occur.