Suicide is a major concern among active-duty military personnel. Aggression represents a salient risk factor for suicide among civilians, yet is relatively understudied among military populations. Although several theories posit a relation between aggression and suicide with putative underlying mechanisms of social isolation, access to firearms, and alcohol use, researchers have yet to test these potential mediators. This study uses rich, longitudinal data from the Army Study to Assess Risk and Resilience (STARRS) Pre/Post Deployment Study (PPDS) to examine whether aggression longitudinally predicts suicide attempts and to identify mediators of this association.

Army soldiers (N = 8483) completed assessments 1 month prior to deployment and 1, 2–3, and 9–12 months post-deployment. Participants reported on their physical and verbal aggression, suicide attempts, social network size, firearm ownership, and frequency of alcohol use.

As expected, pre-deployment aggression was significantly associated with suicide attempts at 12-months post-deployment even after controlling for lifetime suicide attempts. Social network size and alcohol use frequency mediated this association, but firearm ownership did not.

Findings further implicate aggression as an important suicide risk factor among military personnel and suggest that social isolation and alcohol use may partially account for this association.

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.

Despite a significant need, there are currently no rigorously developed empirically based models for what personal recovery from a suicidal episode looks like.

To develop a theoretical model of personal recovery after a suicidal episode, based on a comprehensive literature review and stakeholder feedback.

A scoping review of all empirical studies on this topic was conducted, followed by a thematic analysis to create a preliminary framework. Consultation-based revisions were then made based on feedback from a stakeholder panel to develop the final theoretical model.

The final model comprised seven themes: choosing life, optimising identity, understanding oneself, rediscovering meaning, acceptance, growing connectedness and empowerment (acronym ‘COURAGE’). Although there are some similarities between COURAGE and other models of personal recovery, there are components, such as ‘choosing life’ and ‘understanding oneself’, that are specific to recovery after an acute suicidal episode.

To our knowledge, this is the first study to use a comprehensive literature review with stakeholder feedback to develop a conceptual model of personal recovery after an acute suicidal episode. This model has important implications for both researchers and clinicians to consider. Looking ahead, COURAGE can inform the reconceptualisation of assessment, research and clinical care of individuals who have experienced a suicidal episode.

Neurocognitive testing may advance the goal of predicting near-term suicide risk. The current study examined whether performance on a Go/No-go (GNG) task, and computational modeling to extract latent cognitive variables, could enhance prediction of suicide attempts within next 90 days, among individuals at high-risk for suicide.

136 Veterans at high-risk for suicide previously completed a computer-based GNG task requiring rapid responding (Go) to target stimuli, while withholding responses (No-go) to infrequent foil stimuli; behavioral variables included false alarms to foils (failure to inhibit) and missed responses to targets. We conducted a secondary analysis of these data, with outcomes defined as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as interrupted/aborted attempt or preparatory behavior, or neither (noSE), within 90-days after GNG testing, to examine whether GNG variables could improve ASA prediction over standard clinical variables. A computational model (linear ballistic accumulator, LBA) was also applied, to elucidate cognitive mechanisms underlying group differences.

On GNG, increased miss rate selectively predicted ASA, while increased false alarm rate predicted OtherSE (without ASA) within the 90-day follow-up window. In LBA modeling, ASA (but not OtherSE) was associated with decreases in decisional efficiency to targets, suggesting differences in the evidence accumulation process were specifically associated with upcoming ASA.

These findings suggest that GNG may improve prediction of near-term suicide risk, with distinct behavioral patterns in those who will attempt suicide within the next 90 days. Computational modeling suggests qualitative differences in cognition in individuals at near-term risk of suicide attempt.

This study examined the relationship of self-reported histories of childhood trauma to measures of affective instability in a sample of unmedicated outpatients with various personality disorders (n=174).

Childhood trauma was measured by the Childhood Trauma Questionnaire. Affective instability comprises at least two dimensions: affective lability, assessed using the Affective Lability Scale, and affective intensity, assessed using the Affective Intensity Measure.

A history of emotional abuse was the only trauma variable that significantly correlated with the affect measures in the total sample (r=.21–.30). More fine-grained analyses revealed that the relationship of emotional abuse and affective instability measures varied as a function of both gender and personality disorder type. In subjects with borderline personality disorder, the correlation for emotional abuse was greatly attenuated for both Affective Lability Scale (r=.10) and Affective Intensity Measure (r=.15) total scores.

This suggests that nontrauma-related factors may be more predominant in affective dyscontrol in individuals with borderline personality disorder.