Moral injury and peri- and post-military suicide attempts among post-9/11 veterans

Background Our goal was to examine the association between moral injury, mental health, and suicide attempts during military service and after separation by gender in post-9/11 veterans. Methods A nationally representative sample of 14057 veterans completed a cross-sectional survey. To examine associations of exposure to potentially morally injurious events (PMIEs; witnessing, perpetrating, and betrayal) and suicidal self-directed violence, we estimated two series of multivariable logistic regressions stratified by gender, with peri- and post-military suicide attempt as the dependent variables. Results PMIE exposure accounted for additional risk of suicide attempt during and after military service after controlling for demographic and military characteristics, current mental health status, and pre-military history of suicidal ideation and attempt. Men who endorsed PMIE exposure by perpetration were 50% more likely to attempt suicide during service and twice as likely to attempt suicide after separating from service. Men who endorsed betrayal were nearly twice as likely to attempt suicide during service; however, this association attenuated to non-significance after separation in the fully adjusted models. In contrast, women who endorsed betrayal were over 50% more likely to attempt suicide during service and after separation; PMIE exposure by perpetration did not significantly predict suicide attempts before or after service among women in the fully adjusted models. Conclusions Our findings indicate that suicide assessment and prevention programs should consider the impact of moral injury and attend to gender differences in this risk factor in order to provide the most comprehensive care.

On the Merits of Productive Stupidity

This chapter discusses how the collective discomfort with the many things that we do not know about suicide constrains innovative thinking about suicide. It begins with a critique of contemporary suicide prevention efforts, looking at military suicide. The inherently complex nature of suicide renders it beyond the reach of causal explanations such as mental illness. Nonetheless, many in the suicide prevention community fell victim to confirmation bias, interpreting these symptoms and problems in a manner consistent with what they already believed: suicide is caused by or results from mental illness. Based on this conclusion, the message was also communicated that mental health treatment can prevent suicide. Ultimately, suicide is a complex and wicked problem with no “right” way to define or conceptualize the problem, and no “right” solutions.

The Airman’s Edge Project: A Peer-Based, Injury Prevention Approach to Preventing Military Suicide

In light of data indicating military personnel are more likely to reach out to peers during times of need, peer-to-peer (P2P) support programs have been implemented for military suicide prevention. Often designed to reduce suicidal thoughts and behaviors by reducing mental health symptom severity, existing data suggest that P2P programs have little to no effect on mental health symptoms. Conceptualizing suicide prevention from an occupational safety and injury prevention perspective to promote positive health-related behavior change at both the group and individual level may enhance the effectiveness of P2P programs and military suicide prevention efforts more broadly. To illustrate these concepts, the present article provides an overview of the Airman’s Edge project, a P2P program design based upon the occupational safety and injury prevention model of suicide prevention, and describes a program evaluation effort designed to test the effectiveness of this approach.

Machine Learning to Classify Suicidal Thoughts and Behaviors: Implementation Within the Common Data Elements Used by the Military Suicide Research Consortium

Suicide rates among military-connected populations have increased over the past 15 years. Meta-analytic studies indicate prediction of suicide outcomes is lacking. Machine-learning approaches have been promoted to enhance classification models for suicide-related outcomes. In the present study, we compared the performance of three primary machine-learning approaches (i.e., elastic net, random forests, stacked ensembles) and a traditional statistical approach, generalized linear modeling (i.e., logistic regression), to classify suicide thoughts and behaviors using data from the Military Suicide Research Consortium’s Common Data Elements (CDE; n = 5,977–6,058 across outcomes). Models were informed by (a) selected items from the CDE or (b) factor scores based on exploratory and confirmatory factor analyses on the selected CDE items. Results indicated similar classification performance across models and sets of features. In this study, we suggest the need for robust evidence before adopting more complex classification models and identify measures that are particularly relevant in classifying suicide-related outcomes.