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Suicidality disproportionately affects Veterans, and in 2020 the Military Health and Well-Being Project was conducted in part to study the link between risk and protective constructs with suicidality among Veterans. In the present study, we investigate the relative contribution of risk (i.e., military self-stigma, daily stress, combat exposure, substance use, traumatic brain injury, and moral injury) and protective constructs (i.e., social integration, social contribution, public service motivation, purpose and meaning, and help-seeking) with suicidality. Using cross-sectional Pearson correlation and linear regression models, we studied the independent and relative contribution of risk and protective correlates in a sample of 1469 Veterans (male: n = 985, 67.1 %; female: n = 476, 32.4 %; transgender, non-binary, prefer not to say: n = 8, 0.5 %). When we investigated protective constructs individually as well as simultaneously, social contribution (β = -0.39, t = -15.59, p < 0.001) was the strongest protective construct against suicidality. Social integration (β = -0.13, t = -4.88, p < 0.001) additionally accounted for significant reduction in suicidality when all protective constructs were considered together. When we investigated the contribution of risk constructs towards suicidality, moral injury was most strongly associated with suicidality (r = 0.519, p < 0.001), yet when studied simultaneously for their relative contribution none of the constructs accounted for a significant amount of the variance in suicidality (|t|s ≤ 1.98, ps ≥ 0.07). These findings suggest that among Veterans it is possible that social contribution is protective against suicidality and could be a possible treatment target for the prevention or reduction of suicidality among Veterans., Competing Interests: Declaration of competing interest There are no competing interests to disclose., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Suicide is a widespread public health concern, including among military Veterans. Traumatic brain injuries (TBIs) and lack of social integration have both been shown to increase risk for suicidality, an outcome that includes, among other suicide-related variables, suicidal ideation, suicide attempts, and suicide death. Interestingly, TBIs have also been identified as a risk factor for social integration problems. In this cross-sectional study we investigated associations between TBI, social integration, and suicidality. Additionally, mediation analysis was used to test whether social integration mediated the association between TBI and suicidality. A sample of 1469 military Veterans (male, n = 1004, 67.2 %; female, n = 457, 32.3 %; transgender/non-binary/prefer not to say, n = 8, 0.5 %) completed an online survey as part of the Military Health and Well-Being Project. TBI was negatively associated with social integration (r = -0.084, p < .001) and positively with suicidality (r = 0.205, p < .001). Social integration was negatively associated with suicidality (r = -0.161, p < .001). Finally, social integration partially mediated the relationship between TBI and social integration (B = 0.121, 95 % CI [0.031-0.23]). This work shows that in the context of TBI, lack of social integration may promote suicidality. It provides support for many theories of suicide which propose social problems as a risk factor of suicide-related outcomes. It further highlights social integration as potential fodder for novel interventions for suicidality, an approach that would have transtheoretical support., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to disclose., (Copyright © 2023. Published by Elsevier B.V.)

Suicide is the second leading cause of death in adolescents; a frequent precursor of suicide is suicidal ideation (SI). Literature indicates that Post-Traumatic Stress Disorder (PTSD) and SI are robust cross-sectional correlates of one another, with PTSD often being conceptualized as a risk factor (i.e., conferring risk) for SI. Indeed, PTSD is a well-established risk factor for SI; however, SI is an understudied risk factor for PTSD. It is possible that, yet unknown if, PTSD and SI promote each other over time in a bidirectional fashion. We investigated the bidirectional longitudinal associations between PTSD and SI in a large, diverse sample, who at baseline were adolescents. Participants were interviewed between 1995 and 1998 and again between 2004 and 2008. We hypothesized that PTSD and SI would be cross-sectionally, longitudinally, and bidirectionally related and that the number of traumas endorsed at baseline would be positively associated with PTSD and SI at baseline and follow-up. Indeed, PTSD and SI were cross-sectionally correlated at baseline, but not follow-up. PTSD predicted SI over nine years; however, SI during adolescence did not predict PTSD in adulthood. Finally, poly-trauma endorsed at baseline was associated with increased risk of SI, but not PTSD, over nine years., (Published by Elsevier Ltd.)

Theoretically-driven models of suicide have long guided suicidology; however, an approach employing machine learning models has recently emerged in the field. Some have suggested that machine learning models yield improved prediction as compared to theoretical approaches, but to date, this has not been investigated in a systematic manner. The present work directly compares widely researched theories of suicide (i.e., BioSocial, Biological, Ideation-to-Action, and Hopelessness Theories) to machine learning models, comparing the accuracy between the two differing approaches. We conducted literature searches using PubMed, PsycINFO, and Google Scholar, gathering effect sizes from theoretically-relevant constructs and machine learning models. Eligible studies were longitudinal research articles that predicted suicide ideation, attempts, or death published prior to May 1, 2020. 124 studies met inclusion criteria, corresponding to 330 effect sizes. Theoretically-driven models demonstrated suboptimal prediction of ideation (wOR = 2.87; 95% CI, 2.65-3.09; k = 87), attempts (wOR = 1.43; 95% CI, 1.34-1.51; k = 98), and death (wOR = 1.08; 95% CI, 1.01-1.15; k = 78). Generally, Ideation-to-Action (wOR = 2.41, 95% CI = 2.21-2.64, k = 60) outperformed Hopelessness (wOR = 1.83, 95% CI 1.71-1.96, k = 98), Biological (wOR = 1.04; 95% CI .97-1.11, k = 100), and BioSocial (wOR = 1.32, 95% CI 1.11-1.58, k = 6) theories. Machine learning provided superior prediction of ideation (wOR = 13.84; 95% CI, 11.95-16.03; k = 33), attempts (wOR = 99.01; 95% CI, 68.10-142.54; k = 27), and death (wOR = 17.29; 95% CI, 12.85-23.27; k = 7). Findings from our study indicated that across all theoretically-driven models, prediction of suicide-related outcomes was suboptimal. Notably, among theories of suicide, theories within the Ideation-to-Action framework provided the most accurate prediction of suicide-related outcomes. When compared to theoretically-driven models, machine learning models provided superior prediction of suicide ideation, attempts, and death., Competing Interests: The authors have declared that no competing interests exist.

For decades, our ability to predict suicide has remained at near-chance levels. Machine learning has recently emerged as a promising tool for advancing suicide science, particularly in the domain of suicide prediction. The present review provides an introduction to machine learning and its potential application to open questions in suicide research. Although only a few studies have implemented machine learning for suicide prediction, results to date indicate considerable improvement in accuracy and positive predictive value. Potential barriers to algorithm integration into clinical practice are discussed, as well as attendant ethical issues. Overall, machine learning approaches hold promise for accurate, scalable, and effective suicide risk detection; however, many critical questions and issues remain unexplored., (© 2019 John Wiley & Sons, Ltd.)

Short sleep duration is linked with suicide risk in adolescence. Sexual and gender minority (SGM) adolescents experience substantially increased risk for suicide compared to their non-SGM peers. We investigated the role of sleep duration in SGM adolescent suicide risk using population-based, cross-sectional data from the 2022 Minnesota Student Survey (MSS; N = 85,610, M age = 14.8). Adolescents reported average school-night sleep duration; those reporting <6 h were classified as having very short sleep duration. The MSS additionally assessed past-year suicidal ideation and suicide attempt. Mediation analyses assessed the role of sleep duration in explaining associations between SGM identity and suicide risk. Further, to examine intervention mechanisms, among SGM adolescents (n = 20,171, 23.6%), a logistic regression model assessed associations among demographic factors, perceived parental care, and very short sleep duration. As compared to non-SGM adolescents, SGM adolescents reported substantially higher prevalence of past-year suicidal ideation and suicide attempt and 2.6× higher prevalence of very short sleep duration (all p < 0.001). Mediation analyses demonstrated that very short sleep duration partially mediated the pathway between SGM identity and past-year suicidal ideation (15.5% mediated) and suicide attempt (17.2% mediated). Among SGM adolescents, a striking positive dose-response relationship was observed between level of perceived parental care and very short sleep duration. As perceived parental care decreased, so too did hours of sleep. Sleep duration is a crucial and understudied mechanism underlying suicide risk disparities affecting SGM adolescents. Family-based interventions may improve SGM adolescent sleep and reduce suicide risk. • SGM adolescents are at elevated risk of very short sleep duration (<6 h). • SGM adolescents also report higher risk of past-year suicide ideation and attempt. • Very short sleep duration underlies suicide disparities between SGM and non-SGM adolescents. • Perceived parental care is strongly associated with SGM adolescents' sleep duration. [ABSTRACT FROM AUTHOR]