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The 2013 US Veterans Administration/Department of Defense Clinical Practice Guidelines (VA/DoD CPG) require comprehensive suicide risk assessments for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessments. Given that clinician-based assessments are not known to be strong predictors of suicide, we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among outpatients. We focused on male nondeployed Regular US Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naive Bayes, random forests, support vector regression and elastic net penalized regression) were explored. Of the Army suicides in 2004-2009, 41.5% occurred among 12.0% of soldiers seen as outpatient by mental health specialists, with risk especially high within 26 weeks of visits. An elastic net classifier with 10-14 predictors optimized sensitivity (45.6% of suicide deaths occurring after the 15% of visits with highest predicted risk). Good model stability was found for a model using 2004-2007 data to predict 2008-2009 suicides, although stability decreased in a model using 2008-2009 data to predict 2010-2012 suicides. The 5% of visits with highest risk included only 0.1% of soldiers (1047.1 suicides/100 000 person-years in the 5 weeks after the visit). This is a high enough concentration of risk to have implications for targeting preventive interventions. An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk mandated by the VA/DoD CPG to be recorded.

Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Introduction: This study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive intervention., Methods: The sample included 4,790 soldiers from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in 1 of 3 Army STARRS 2011-2014 baseline surveys followed by the third wave of the STARRS-LS online panel surveys (2020-2022). Two machine learning models were trained: a Stage-1 model that used administrative predictors and geospatial data available for all TSMs at discharge to identify high-risk TSMs for initial outreach; and a Stage-2 model estimated in the high-risk subsample that used self-reported survey data to help determine highest risk based on additional information collected from high-risk TSMs once they are contacted. The outcome in both models was homelessness within 12 months after leaving active service., Results: Twelve-month prevalence of post-transition homelessness was 5.0% (SE=0.5). The Stage-1 model identified 30% of high-risk TSMs who accounted for 52% of homelessness. The Stage-2 model identified 10% of all TSMs (i.e., 33% of high-risk TSMs) who accounted for 35% of all homelessness (i.e., 63% of the homeless among high-risk TSMs)., Conclusions: Machine learning can help target outreach and assessment of TSMs for homeless prevention interventions., (Published by Elsevier Inc.)

Importance: Psychiatric hospitalization is the standard of care for patients presenting to an emergency department (ED) or urgent care (UC) with high suicide risk. However, the effect of hospitalization in reducing subsequent suicidal behaviors is poorly understood and likely heterogeneous., Objectives: To estimate the association of psychiatric hospitalization with subsequent suicidal behaviors using observational data and develop a preliminary predictive analytics individualized treatment rule accounting for heterogeneity in this association across patients., Design, Setting, and Participants: A machine learning analysis of retrospective data was conducted. All veterans presenting with suicidal ideation (SI) or suicide attempt (SA) from January 1, 2010, to December 31, 2015, were included. Data were analyzed from September 1, 2022, to March 10, 2023. Subgroups were defined by primary psychiatric diagnosis (nonaffective psychosis, bipolar disorder, major depressive disorder, and other) and suicidality (SI only, SA in past 2-7 days, and SA in past day). Models were trained in 70.0% of the training samples and tested in the remaining 30.0%., Exposures: Psychiatric hospitalization vs nonhospitalization., Main Outcomes and Measures: Fatal and nonfatal SAs within 12 months of ED/UC visits were identified in administrative records and the National Death Index. Baseline covariates were drawn from electronic health records and geospatial databases., Results: Of 196 610 visits (90.3% men; median [IQR] age, 53 [41-59] years), 71.5% resulted in hospitalization. The 12-month SA risk was 11.9% with hospitalization and 12.0% with nonhospitalization (difference, -0.1%; 95% CI, -0.4% to 0.2%). In patients with SI only or SA in the past 2 to 7 days, most hospitalization was not associated with subsequent SAs. For patients with SA in the past day, hospitalization was associated with risk reductions ranging from -6.9% to -9.6% across diagnoses. Accounting for heterogeneity, hospitalization was associated with reduced risk of subsequent SAs in 28.1% of the patients and increased risk in 24.0%. An individualized treatment rule based on these associations may reduce SAs by 16.0% and hospitalizations by 13.0% compared with current rates., Conclusions and Relevance: The findings of this study suggest that psychiatric hospitalization is associated with reduced average SA risk in the immediate aftermath of an SA but not after other recent SAs or SI only. Substantial heterogeneity exists in these associations across patients. An individualized treatment rule accounting for this heterogeneity could both reduce SAs and avert hospitalizations.

Objective: Untreated mental disorders are important among low- and middle-income country (LMIC) university students in Latin America, where barriers to treatment are high. Scalable interventions are needed. This study compared transdiagnostic self-guided and guided internet-delivered cognitive behavioral therapy (i-CBT) with treatment as usual (TAU) for clinically significant anxiety and depression among undergraduates in Colombia and Mexico., Method: 1,319 anxious, as determined by the Generalized Anxiety Disorder-7 (GAD-7) = 10+ and/or depressed, as determined by the Patient Health Questionnaire-9 (PHQ-9) = 10+, undergraduates (mean [SD] age = 21.4 [3.2]); 78.7% female; 55.9% first-generation university student) from seven universities in Colombia and Mexico were randomized to culturally adapted versions of self-guided i-CBT (n = 439), guided i-CBT (n = 445), or treatment as usual (TAU; n = 435). All randomized participants were reassessed 3 months after randomization. The primary outcome was remission of both anxiety (GAD-7 = 0-4) and depression (PHQ-9 = 0-4). We hypothesized that remission would be higher with guided i-CBT than with the other interventions., Results: Intent-to-treat analysis found significantly higher adjusted (for university and loss to follow-up) remission rates (ARD) among participants randomized to guided i-CBT than either self-guided i-CBT (ARD = 13.1%, χ12 = 10.4, p = .001) or TAU (ARD = 11.2%, χ12 = 8.4, p = .004), but no significant difference between self-guided i-CBT and TAU (ARD = -1.9%, χ12 = 0.2, p = .63). Per-protocol sensitivity analyses and analyses of dimensional outcomes yielded similar results., Conclusions: Significant reductions in anxiety and depression among LMIC university students could be achieved with guided i-CBT, although further research is needed to determine which students would most likely benefit from this intervention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Background: 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., Methods: 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., Results: 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., Conclusions: 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.

Importance: Guided internet-delivered cognitive behavioral therapy (i-CBT) is a low-cost way to address high unmet need for anxiety and depression treatment. Scalability could be increased if some patients were helped as much by self-guided i-CBT as guided i-CBT., Objective: To develop an individualized treatment rule using machine learning methods for guided i-CBT vs self-guided i-CBT based on a rich set of baseline predictors., Design, Setting, and Participants: This prespecified secondary analysis of an assessor-blinded, multisite randomized clinical trial of guided i-CBT, self-guided i-CBT, and treatment as usual included students in Colombia and Mexico who were seeking treatment for anxiety (defined as a 7-item Generalized Anxiety Disorder [GAD-7] score of ≥10) and/or depression (defined as a 9-item Patient Health Questionnaire [PHQ-9] score of ≥10). Study recruitment was from March 1 to October 26, 2021. Initial data analysis was conducted from May 23 to October 26, 2022., Interventions: Participants were randomized to a culturally adapted transdiagnostic i-CBT that was guided (n = 445), self-guided (n = 439), or treatment as usual (n = 435)., Main Outcomes and Measures: Remission of anxiety (GAD-7 scores of ≤4) and depression (PHQ-9 scores of ≤4) 3 months after baseline., Results: The study included 1319 participants (mean [SD] age, 21.4 [3.2] years; 1038 women [78.7%]; 725 participants [55.0%] came from Mexico). A total of 1210 participants (91.7%) had significantly higher mean (SE) probabilities of joint remission of anxiety and depression with guided i-CBT (51.8% [3.0%]) than with self-guided i-CBT (37.8% [3.0%]; P = .003) or treatment as usual (40.0% [2.7%]; P = .001). The remaining 109 participants (8.3%) had low mean (SE) probabilities of joint remission of anxiety and depression across all groups (guided i-CBT: 24.5% [9.1%]; P = .007; self-guided i-CBT: 25.4% [8.8%]; P = .004; treatment as usual: 31.0% [9.4%]; P = .001). All participants with baseline anxiety had nonsignificantly higher mean (SE) probabilities of anxiety remission with guided i-CBT (62.7% [5.9%]) than the other 2 groups (self-guided i-CBT: 50.2% [6.2%]; P = .14; treatment as usual: 53.0% [6.0%]; P = .25). A total of 841 of 1177 participants (71.5%) with baseline depression had significantly higher mean (SE) probabilities of depression remission with guided i-CBT (61.5% [3.6%]) than the other 2 groups (self-guided i-CBT: 44.3% [3.7%]; P = .001; treatment as usual: 41.8% [3.2%]; P < .001). The other 336 participants (28.5%) with baseline depression had nonsignificantly higher mean (SE) probabilities of depression remission with self-guided i-CBT (54.4% [6.0%]) than guided i-CBT (39.8% [5.4%]; P = .07)., Conclusions and Relevance: Guided i-CBT yielded the highest probabilities of remission of anxiety and depression for most participants; however, these differences were nonsignificant for anxiety. Some participants had the highest probabilities of remission of depression with self-guided i-CBT. Information about this variation could be used to optimize allocation of guided and self-guided i-CBT in resource-constrained settings., Trial Registration: ClinicalTrials.gov Identifier: NCT04780542.

Importance: Understanding the association of civil violence with mental disorders is important for developing effective postconflict recovery policies., Objective: To estimate the association between exposure to civil violence and the subsequent onset and persistence of common mental disorders (in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition [DSM-IV]) in representative surveys of civilians from countries that have experienced civil violence since World War II., Design, Setting, and Participants: This study used data from cross-sectional World Health Organization World Mental Health (WMH) surveys administered to households between February 5, 2001, and January 5, 2022, in 7 countries that experienced periods of civil violence after World War II (Argentina, Colombia, Lebanon, Nigeria, Northern Ireland, Peru, and South Africa). Data from respondents in other WMH surveys who immigrated from countries with civil violence in Africa and Latin America were also included. Representative samples comprised adults (aged ≥18 years) from eligible countries. Data analysis was performed from February 10 to 13, 2023., Exposures: Exposure was defined as a self-report of having been a civilian in a war zone or region of terror. Related stressors (being displaced, witnessing atrocities, or being a combatant) were also assessed. Exposures occurred a median of 21 (IQR, 12-30) years before the interview., Main Outcomes and Measures: The main outcome was the retrospectively reported lifetime prevalence and 12-month persistence (estimated by calculating 12-month prevalence among lifetime cases) of DSM-IV anxiety, mood, and externalizing (alcohol use, illicit drug use, or intermittent explosive) disorders., Results: This study included 18 212 respondents from 7 countries. Of these individuals, 2096 reported that they were exposed to civil violence (56.5% were men; median age, 40 [IQR, 30-52] years) and 16 116 were not exposed (45.2% were men; median age, 35 [IQR, 26-48] years). Respondents who reported being exposed to civil violence had a significantly elevated onset risk of anxiety (risk ratio [RR], 1.8 [95% CI, 1.5-2.1]), mood (RR, 1.5 [95% CI, 1.3-1.7]), and externalizing (RR, 1.6 [95% CI, 1.3-1.9]) disorders. Combatants additionally had a significantly elevated onset risk of anxiety disorders (RR, 2.0 [95% CI, 1.3-3.1]) and refugees had an increased onset risk of mood (RR, 1.5 [95% CI, 1.1-2.0]) and externalizing (RR, 1.6 [95% CI, 1.0-2.4]) disorders. Elevated disorder onset risks persisted for more than 2 decades if conflicts persisted but not after either termination of hostilities or emigration. Persistence (ie, 12-month prevalence among respondents with lifetime prevalence of the disorder), in comparison, was generally not associated with exposure., Conclusions: In this survey study of exposure to civil violence, exposure was associated with an elevated risk of mental disorders among civilians for many years after initial exposure. These findings suggest that policy makers should recognize these associations when projecting future mental disorder treatment needs in countries experiencing civil violence and among affected migrants.

Importance: The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information., Objective: To determine whether model prediction could be improved by adding information extracted from clinical notes and public records., Design, Setting, and Participants: Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short-term (less than 365 days) psychiatric hospitalizations between the beginning of 2010 and September 1, 2012 (299 050 hospitalizations, with 916 hospitalizations followed within 12 months by suicides) and tested in the hospitalizations from September 2, 2012, to December 31, 2013 (149 738 hospitalizations, with 393 hospitalizations followed within 12 months by suicides). Validation focused on net benefit across a range of plausible decision thresholds. Predictor importance was assessed with Shapley additive explanations (SHAP) values. Data were analyzed from January to August 2022., Main Outcomes and Measures: Suicides were defined by the National Death Index. Base model predictors included VHA electronic health records and patient residential data. The expanded predictors came from natural language processing (NLP) of clinical notes and a social determinants of health (SDOH) public records database., Results: The model included 448 788 unique hospitalizations. Net benefit over risk horizons between 3 and 12 months was generally highest for the model that included both NLP and SDOH predictors (area under the receiver operating characteristic curve range, 0.747-0.780; area under the precision recall curve relative to the suicide rate range, 3.87-5.75). NLP and SDOH predictors also had the highest predictor class-level SHAP values (proportional SHAP = 64.0% and 49.3%, respectively), although the single highest positive variable-level SHAP value was for a count of medications classified by the US Food and Drug Administration as increasing suicide risk prescribed the year before hospitalization (proportional SHAP = 15.0%)., Conclusions and Relevance: In this study, clinical notes and public records were found to improve ML model prediction of suicide after psychiatric hospitalization. The model had positive net benefit over 3-month to 12-month risk horizons for plausible decision thresholds. Although caution is needed in inferring causality based on predictor importance, several key predictors have potential intervention implications that should be investigated in future studies.

Aims: Childhood adversities (CAs) predict heightened risks of posttraumatic stress disorder (PTSD) and major depressive episode (MDE) among people exposed to adult traumatic events. Identifying which CAs put individuals at greatest risk for these adverse posttraumatic neuropsychiatric sequelae (APNS) is important for targeting prevention interventions., Methods: Data came from n = 999 patients ages 18-75 presenting to 29 U.S. emergency departments after a motor vehicle collision (MVC) and followed for 3 months, the amount of time traditionally used to define chronic PTSD, in the Advancing Understanding of Recovery After Trauma (AURORA) study. Six CA types were self-reported at baseline: physical abuse, sexual abuse, emotional abuse, physical neglect, emotional neglect and bullying. Both dichotomous measures of ever experiencing each CA type and numeric measures of exposure frequency were included in the analysis. Risk ratios (RRs) of these CA measures as well as complex interactions among these measures were examined as predictors of APNS 3 months post-MVC. APNS was defined as meeting self-reported criteria for either PTSD based on the PTSD Checklist for DSM-5 and/or MDE based on the PROMIS Depression Short-Form 8b. We controlled for pre-MVC lifetime histories of PTSD and MDE. We also examined mediating effects through peritraumatic symptoms assessed in the emergency department and PTSD and MDE assessed in 2-week and 8-week follow-up surveys. Analyses were carried out with robust Poisson regression models., Results: Most participants (90.9%) reported at least rarely having experienced some CA. Ever experiencing each CA other than emotional neglect was univariably associated with 3-month APNS (RRs = 1.31-1.60). Each CA frequency was also univariably associated with 3-month APNS (RRs = 1.65-2.45). In multivariable models, joint associations of CAs with 3-month APNS were additive, with frequency of emotional abuse (RR = 2.03; 95% CI = 1.43-2.87) and bullying (RR = 1.44; 95% CI = 0.99-2.10) being the strongest predictors. Control variable analyses found that these associations were largely explained by pre-MVC histories of PTSD and MDE., Conclusions: Although individuals who experience frequent emotional abuse and bullying in childhood have a heightened risk of experiencing APNS after an adult MVC, these associations are largely mediated by prior histories of PTSD and MDE.

Introduction: The ability to predict and prevent homelessness has been an elusive goal. The purpose of this study was to develop a prediction model that identified U.S. Army soldiers at high risk of becoming homeless after transitioning to civilian life based on information available before the time of this transition., Methods: The prospective cohort study consisted of observations from 16,589 soldiers who were separated or deactivated from service and who had previously participated in 1 of 3 baseline surveys of the Army Study to Assess Risk and Resilience in Servicemembers in 2011-2014. A machine learning model was developed in a 70% training sample and evaluated in the remaining 30% test sample to predict self-reported homelessness in 1 of 2 Longitudinal Study surveys administered in 2016-2018 and 2018-2019. Predictors included survey, administrative, and geospatial variables available before separation/deactivation. Analysis was conducted in November 2020-May 2021., Results: The 12-month prevalence of homelessness was 2.9% (SE=0.2%) in the total Longitudinal Study sample. The area under the receiver operating characteristic curve in the test sample was 0.78 (SE=0.02) for homelessness. The 4 highest ventiles (top 20%) of predicted risk included 61% of respondents with homelessness. Self-reported lifetime histories of depression, trauma of having a loved one murdered, and post-traumatic stress disorder were the 3 strongest predictors of homelessness., Conclusions: A prediction model for homelessness can accurately target soldiers for preventive intervention before transition to civilian life., (Copyright © 2022 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Importance: Claims of dramatic increases in clinically significant anxiety and depression early in the COVID-19 pandemic came from online surveys with extremely low or unreported response rates., Objective: To examine trend data in a calibrated screening for clinically significant anxiety and depression among adults in the only US government benchmark probability trend survey not disrupted by the COVID-19 pandemic., Design, Setting, and Participants: This survey study used the US Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance System (BRFSS), a monthly state-based trend survey conducted over the telephone. Participants were adult respondents in the 50 US states and District of Columbia who were surveyed March to December 2020 compared with the same months in 2017 to 2019., Exposures: Monthly state COVID-19 death rates., Main Outcomes and Measures: Estimated 30-day prevalence of clinically significant anxiety and depression based on responses to a single BRFSS item calibrated to a score of 6 or greater on the 4-item Patient Health Questionnaire (area under the receiver operating characteristic curve, 0.84). All percentages are weighted based on BRFSS calibration weights., Results: Overall, there were 1 429 354 respondents, with 1 093 663 in 2017 to 2019 (600 416 [51.1%] women; 87 153 [11.8%] non-Hispanic Black; 826 334 [61.5%] non-Hispanic White; 411 254 [27.8%] with college education; and 543 619 [56.8] employed) and 335 691 in 2020 (182 351 [51.3%] women; 25 517 [11.7%] non-Hispanic Black; 250 333 [60.5%] non-Hispanic White; 130 642 [29.3%] with college education; and 168 921 [54.9%] employed). Median within-state response rates were 45.9% to 49.4% in 2017 to 2019 and 47.9% in 2020. Estimated 30-day prevalence of clinically significant anxiety and depression was 0.4 (95% CI, 0.0 to 0.7) percentage points higher in March to December 2020 (12.4%) than March to December 2017 to 2019 (12.1%). This estimated increase was limited, however, to students (2.4 [95% CI, 0.8 to 3.9] percentage points) and the employed (0.9 [95% CI, 0.5 to 1.4] percentage points). Estimated prevalence decreased among the short-term unemployed (-1.8 [95% CI, -3.1 to -0.5] percentage points) and those unable to work (-4.2 [95% CI, -5.3 to -3.2] percentage points), but did not change significantly among the long-term unemployed (-2.1 [95% CI, -4.5 to 0.5] percentage points), homemakers (0.8 [95% CI, -0.3 to 1.9] percentage points), or the retired (0.1 [95% CI, -0.6 to 0.8] percentage points). The increase in anxiety and depression prevalence among employed people was positively associated with the state-month COVID-19 death rate (1.8 [95% CI, 1.2 to 2.5] percentage points when high and 0.0 [95% CI, -0.7 to 0.6] percentage points when low) and was elevated among women compared with men (2.0 [95% CI, 1.4 to 2.5] percentage points vs 0.2 [95% CI, -0.1 to 0.6] percentage points), Non-Hispanic White individuals compared with Hispanic and non-Hispanic Black individuals (1.3 [95% CI, 0.6 to 1.9] percentage points vs 1.1 [95% CI, -0.2 to 2.5] percentage points and 0.7 [95% CI, -0.1 to 1.5] percentage points), and those with college educations compared with less than high school educations (2.5 [95% CI, 1.9 to 3.1] percentage points vs -0.6 [95% CI, -2.7 to 1.4] percentage points)., Conclusions and Relevance: In this survey study, clinically significant US adult anxiety and depression increased less during 2020 than suggested by online surveys. However, this modest aggregate increase could mask more substantial increases in key population segments (eg, first responders) and might have become larger in 2021 and 2022.

Suicide risk is elevated among military service members who recently transitioned to civilian life. Identifying high-risk service members before this transition could facilitate provision of targeted preventive interventions. We investigated the feasibility of doing this by attempting to develop a prediction model for self-reported suicide attempts (SAs) after leaving or being released from active duty in the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS). This study included two self-report panel surveys (LS1: 2016-2018, LS2: 2018-2019) administered to respondents who previously participated while on active duty in one of three Army STARRS 2011-2014 baseline self-report surveys. We focus on respondents who left active duty >12 months before their LS survey (n = 8899). An ensemble machine learning model using predictors available prior to leaving active duty was developed in a 70% training sample and validated in a 30% test sample. The 12-month self-reported SA prevalence (SE) was 1.0% (0.1). Test sample AUC (SE) was 0.74 (0.06). The 15% of respondents with highest predicted risk included nearly two-thirds of 12-month SAs and over 80% of medically serious 12-month SAs. These results show that it is possible to identify soldiers at high post-transition self-report SA risk before the transition. Future model development is needed to examine prediction of SAs assessed by administrative data and using surveys administered closer to the time of leaving active duty., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Objectives: To illustrate the use of machine learning methods to search for heterogeneous effects of a target modifiable risk factor on suicide in observational studies. The illustration focuses on secondary analysis of a matched case-control study of vitamin D deficiency predicting subsequent suicide., Methods: We describe a variety of machine learning methods to search for prescriptive predictors; that is, predictors of significant variation in the association between a target risk factor and subsequent suicide. In each case, the purpose is to evaluate the potential value of selective intervention on the target risk factor to prevent the outcome based on the provisional assumption that the target risk factor is causal. The approaches illustrated include risk modeling based on the super learner ensemble machine learning method, Least Absolute Shrinkage and Selection Operator (Lasso) penalized regression, and the causal forest algorithm., Results: The logic of estimating heterogeneous intervention effects is exposited along with the illustration of some widely used methods for implementing this logic., Conclusions: In addition to describing best practices in using the machine learning methods considered here, we close with a discussion of broader design and analysis issues in planning an observational study to investigate heterogeneous effects of a modifiable risk factor., (© 2021 The Authors. International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd.)

Study Objectives: Many patients in Emergency Departments (EDs) after motor vehicle collisions (MVCs) develop post-traumatic stress disorder (PTSD) or major depressive episode (MDE). This report from the AURORA study focuses on associations of pre-MVC sleep problems with these outcomes 8 weeks after MVC mediated through peritraumatic distress and dissociation and 2-week outcomes., Methods: A total of 666 AURORA patients completed self-report assessments in the ED and at 2 and 8 weeks after MVC. Peritraumatic distress, peritraumatic dissociation, and pre-MVC sleep characteristics (insomnia, nightmares, daytime sleepiness, and sleep duration in the 30 days before the MVC, trait sleep stress reactivity) were assessed retrospectively in the ED. The survey assessed acute stress disorder (ASD) and MDE at 2 weeks and at 8 weeks assessed PTSD and MDE (past 30 days). Control variables included demographics, MVC characteristics, and retrospective reports about PTSD and MDE in the 30 days before the MVC., Results: Prevalence estimates were 41.0% for 2-week ASD, 42.0% for 8-week PTSD, 30.5% for 2-week MDE, and 27.2% for 8-week MDE. Pre-MVC nightmares and sleep stress reactivity predicted 8-week PTSD (mediated through 2-week ASD) and MDE (mediated through the transition between 2-week and 8-week MDE). Pre-MVC insomnia predicted 8-week PTSD (mediated through 2-week ASD). Estimates of population attributable risk suggest that blocking effects of sleep disturbance might reduce prevalence of 8-week PTSD and MDE by as much as one-third., Conclusions: Targeting disturbed sleep in the immediate aftermath of MVC might be one effective way of reducing MVC-related PTSD and MDE., (© Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.)

Objectives: Comorbidity is a common feature of mental disorders. However, needs assessment surveys focus largely on individual disorders rather than on comorbidity even though the latter is more important for predicting suicidal thoughts and behaviors. In the current report, we take a step beyond this conventional approach by presenting data on the prevalence and correlates (sociodemographic factors, college-related factors, and suicidal thoughts and behaviors) of the main multivariate profiles of common comorbid Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV disorders among students participating in the first phase of the World Health Organization World Mental Health International College Student initiative., Method: A web-based mental health survey was administered to first year students in 19 colleges across eight countries (Australia, Belgium, Germany, Mexico, Northern Ireland, South Africa, Spain, United States; 45.5% pooled response rate) to screen for seven common DSM-IV mental disorders: major depression, mania/hypomania, generalized anxiety disorder, panic disorder, attention-deficit/hyperactivity disorder, alcohol use disorder, and drug use disorder. We focus on the 14,348 respondents who provided complete data; 38.4% screened positive for at least one 12-month disorder., Results: Multivariate disorder profiles were detected using latent class analysis (LCA). The least common class (C1; 1.9% of students) was made up of students with high comorbidity (four or more disorders, the majority including mania/hypomania). The remaining 12-month cases had profiles of internalizing-externalizing comorbidity (C2; 5.8%), internalizing comorbidity (C3; 14.6%), and pure disorders (C4; 16.1%). The 1.9% of students in C1 had much higher prevalence of suicidal thoughts and behaviors than other students. Specifically, 15.4% of students in C1 made a suicide attempt in the 12 months before the survey compared with 1.3-2.6% of students with disorders in C2-4, 0.2% of students with lifetime disorders but no 12-month disorders (C5), and 0.1% of students with no lifetime disorders (C6)., Conclusions: In line with prior research, comorbid mental disorders were common; however, sociodemographic correlates of LCA profiles were modest. The high level of comorbidity underscores the need to develop and test transdiagnostic approaches for treatment in college students., (© 2018 John Wiley & Sons, Ltd.)

Although earlier trauma exposure is known to predict posttraumatic stress disorder (PTSD) after subsequent traumas, it is unclear whether this association is limited to cases where the earlier trauma led to PTSD. Resolution of this uncertainty has important implications for research on pretrauma vulnerability to PTSD. We examined this issue in the World Health Organization (WHO) World Mental Health (WMH) Surveys with 34 676 respondents who reported lifetime trauma exposure. One lifetime trauma was selected randomly for each respondent. DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th Edition) PTSD due to that trauma was assessed. We reported in a previous paper that four earlier traumas involving interpersonal violence significantly predicted PTSD after subsequent random traumas (odds ratio (OR)=1.3-2.5). We also assessed 14 lifetime DSM-IV mood, anxiety, disruptive behavior and substance disorders before random traumas. We show in the current report that only prior anxiety disorders significantly predicted PTSD in a multivariate model (OR=1.5-4.3) and that these disorders interacted significantly with three of the earlier traumas (witnessing atrocities, physical violence victimization and rape). History of witnessing atrocities significantly predicted PTSD after subsequent random traumas only among respondents with prior PTSD (OR=5.6). Histories of physical violence victimization (OR=1.5) and rape after age 17 years (OR=17.6) significantly predicted only among respondents with no history of prior anxiety disorders. Although only preliminary due to reliance on retrospective reports, these results suggest that history of anxiety disorders and history of a limited number of earlier traumas might usefully be targeted in future prospective studies as distinct foci of research on individual differences in vulnerability to PTSD after subsequent traumas.

Background: Research on post-traumatic stress disorder (PTSD) course finds a substantial proportion of cases remit within 6 months, a majority within 2 years, and a substantial minority persists for many years. Results are inconsistent about pre-trauma predictors., Methods: The WHO World Mental Health surveys assessed lifetime DSM-IV PTSD presence-course after one randomly-selected trauma, allowing retrospective estimates of PTSD duration. Prior traumas, childhood adversities (CAs), and other lifetime DSM-IV mental disorders were examined as predictors using discrete-time person-month survival analysis among the 1575 respondents with lifetime PTSD., Results: 20%, 27%, and 50% of cases recovered within 3, 6, and 24 months and 77% within 10 years (the longest duration allowing stable estimates). Time-related recall bias was found largely for recoveries after 24 months. Recovery was weakly related to most trauma types other than very low [odds-ratio (OR) 0.2-0.3] early-recovery (within 24 months) associated with purposefully injuring/torturing/killing and witnessing atrocities and very low later-recovery (25+ months) associated with being kidnapped. The significant ORs for prior traumas, CAs, and mental disorders were generally inconsistent between early- and later-recovery models. Cross-validated versions of final models nonetheless discriminated significantly between the 50% of respondents with highest and lowest predicted probabilities of both early-recovery (66-55% v. 43%) and later-recovery (75-68% v. 39%)., Conclusions: We found PTSD recovery trajectories similar to those in previous studies. The weak associations of pre-trauma factors with recovery, also consistent with previous studies, presumably are due to stronger influences of post-trauma factors.

Introduction: The Department of Defense uses a universal prevention framework for sexual assault prevention, with each branch implementing its own branch-wide programs. Intensive interventions exist, but would be cost effective only if targeted at high-risk personnel. This study developed actuarial models to identify male U.S. Army soldiers at high risk of administratively recorded sexual assault perpetration., Methods: This study investigated administratively recorded sexual assault perpetration among the 821,807 male Army soldiers serving 2004-2009. Administrative data were also used to operationalize the predictors. Penalized discrete-time (person-month) survival analysis (conducted in 2016) was used to select the smallest possible number of stable predictors to maximize number of sexual assaults among the 5% of soldiers with highest predicted risk of perpetration (top-ventile concentration of risk). Separate models were developed for assaults against non-family and intra-family adults and minors., Results: There were 4,640 male soldiers found to be perpetrators against non-family adults, 1,384 against non-family minors, 380 against intra-family adults, and 335 against intra-family minors. Top-ventile concentration of risk was 16.2%-20.2% predicting perpetration against non-family adults and minors and 34.2%-65.1% against intra-family adults and minors. Final predictors consisted largely of measures of prior crime involvement and the presence and treatment of mental disorders., Conclusions: Administrative data can be used to develop actuarial models that identify a high proportion of sexual assault perpetrators. If a system is developed to consolidate administrative predictors routinely, then predictions could be generated periodically to identify those in need of preventive intervention. Whether this would be cost effective, though, would depend on intervention costs, effectiveness, and competing risks., (Copyright © 2017 American Journal of Preventive Medicine. All rights reserved.)

Background : Although post-traumatic stress disorder (PTSD) onset-persistence is thought to vary significantly by trauma type, most epidemiological surveys are incapable of assessing this because they evaluate lifetime PTSD only for traumas nominated by respondents as their 'worst.' Objective : To review research on associations of trauma type with PTSD in the WHO World Mental Health (WMH) surveys, a series of epidemiological surveys that obtained representative data on trauma-specific PTSD. Method : WMH Surveys in 24 countries (n = 68,894) assessed 29 lifetime traumas and evaluated PTSD twice for each respondent: once for the 'worst' lifetime trauma and separately for a randomly-selected trauma with weighting to adjust for individual differences in trauma exposures. PTSD onset-persistence was evaluated with the WHO Composite International Diagnostic Interview. Results : In total, 70.4% of respondents experienced lifetime traumas, with exposure averaging 3.2 traumas per capita. Substantial between-trauma differences were found in PTSD onset but less in persistence. Traumas involving interpersonal violence had highest risk. Burden of PTSD, determined by multiplying trauma prevalence by trauma-specific PTSD risk and persistence, was 77.7 person-years/100 respondents. The trauma types with highest proportions of this burden were rape (13.1%), other sexual assault (15.1%), being stalked (9.8%), and unexpected death of a loved one (11.6%). The first three of these four represent relatively uncommon traumas with high PTSD risk and the last a very common trauma with low PTSD risk. The broad category of intimate partner sexual violence accounted for nearly 42.7% of all person-years with PTSD. Prior trauma history predicted both future trauma exposure and future PTSD risk. Conclusions : Trauma exposure is common throughout the world, unequally distributed, and differential across trauma types with respect to PTSD risk. Although a substantial minority of PTSD cases remits within months after onset, mean symptom duration is considerably longer than previously recognized.

Background: The U.S. Army uses universal preventives interventions for several negative outcomes (e.g. suicide, violence, sexual assault) with especially high risks in the early years of service. More intensive interventions exist, but would be cost-effective only if targeted at high-risk soldiers. We report results of efforts to develop models for such targeting from self-report surveys administered at the beginning of Army service., Methods: 21 832 new soldiers completed a self-administered questionnaire (SAQ) in 2011-2012 and consented to link administrative data to SAQ responses. Penalized regression models were developed for 12 administratively-recorded outcomes occurring by December 2013: suicide attempt, mental hospitalization, positive drug test, traumatic brain injury (TBI), other severe injury, several types of violence perpetration and victimization, demotion, and attrition., Results: The best-performing models were for TBI (AUC = 0.80), major physical violence perpetration (AUC = 0.78), sexual assault perpetration (AUC = 0.78), and suicide attempt (AUC = 0.74). Although predicted risk scores were significantly correlated across outcomes, prediction was not improved by including risk scores for other outcomes in models. Of particular note: 40.5% of suicide attempts occurred among the 10% of new soldiers with highest predicted risk, 57.2% of male sexual assault perpetrations among the 15% with highest predicted risk, and 35.5% of female sexual assault victimizations among the 10% with highest predicted risk., Conclusions: Data collected at the beginning of service in self-report surveys could be used to develop risk models that define small proportions of new soldiers accounting for high proportions of negative outcomes over the first few years of service.

Objectives: The US Veterans Health Administration (VHA) has begun using predictive modeling to identify Veterans at high suicide risk to target care. Initial analyses are reported here., Methods: A penalized logistic regression model was compared with an earlier proof-of-concept logistic model. Exploratory analyses then considered commonly-used machine learning algorithms. Analyses were based on electronic medical records for all 6,360 individuals classified in the National Death Index as having died by suicide in fiscal years 2009-2011 who used VHA services the year of their death or prior year and a 1% probability sample of time-matched VHA service users alive at the index date (n = 2,112,008)., Results: A penalized logistic model with 61 predictors had sensitivity comparable to the proof-of-concept model (which had 381 predictors) at target thresholds. The machine learning algorithms had relatively similar sensitivities, the highest being for Bayesian additive regression trees, with 10.7% of suicides occurred among the 1.0% of Veterans with highest predicted risk and 28.1% among the 5.0% of with highest predicted risk., Conclusions: Based on these results, VHA is using penalized logistic regression in initial intervention implementation. The paper concludes with a discussion of other practical issues that might be explored to increase model performance., (Copyright © 2017 John Wiley & Sons, Ltd.)

Objectives: To examine associations of administratively recorded sexual assault victimization during military service with subsequent mental health and negative career outcomes among US Army women controlling for nonrandom victimization exposure., Methods: We used data from the Army Study to Assess Risk and Resilience in Servicemembers to apply propensity score methods to match all 4238 female Regular Army soldiers with administratively recorded sexual assault victimization during 2004 to 2009 to 5 controls per case with similar composite victimization risk. We examined associations of this victimization measure with administratively recorded mental health treatment, suicide attempt, and Army career outcomes over the subsequent 12 months by using survival analysis for dichotomous outcomes and conditional generalized linear models for continuous outcomes., Results: Women with administratively recorded sexual assault had significantly elevated odds ratios (ORs) of subsequent mental health treatment (any, OR = 2.5; 95% confidence interval [CI] = 2.4, 2.6; specialty, OR = 3.1; 95% CI = 2.9, 3.3; inpatient, OR = 2.8; 95% CI = 2.5, 3.1), posttraumatic stress disorder treatment (any, OR = 6.3; 95% CI = 5.7, 6.9; specialty, OR = 7.7; 95% CI = 6.8, 8.6; inpatient, OR = 6.8; 95% CI = 5.4, 8.6), suicide attempt (OR = 3.0; 95% CI = 2.5, 3.6), demotion (OR = 2.1; 95% CI = 1.9, 2.3), and attrition (OR = 1.2; 95% CI = 1.1, 1.2)., Conclusions: Sexual assault victimization is associated with considerable suffering and likely decreased force readiness.

Objective: Prior research has shown that a substantial portion of suicide decedents access health care in the weeks and months before their death. We examined whether this is true among soldiers., Method: The sample included the 569 Regular Army soldiers in the U.S. Army who died by suicide on active duty between 2004 and 2009 compared to 5,690 matched controls. Analyses examined the prevalence and frequency of health care contacts and documentation of suicide risk (i.e., the presence of prior suicidal thoughts and behaviors) over the year preceding suicide death. Predictors of health care contact and suicide risk documentation were also examined., Results: Approximately 50% of suicide decedents accessed health care in the month prior to their death, and over 25% of suicide decedents accessed health care in the week prior to their death. Mental health encounters were significantly more prevalent among suicide decedents (4 weeks: 27.9% vs. 7.9%, χ2 = 96.2, p < .001; 52 weeks: 59.4% vs. 33.7%, χ2 = 120.2, p < .001). Despite this, risk documentation was rare among suicide decedents (4 weeks: 13.8%; 52 weeks: 24.5%). Suicide decedents who were male, never married, and non-Hispanic Black were less likely to access care prior to death. Number of mental health encounters was the only predictor of suicide risk documentation among decedents at 4 weeks (OR = 1.14) and 52 weeks (OR = 1.05) prior to their death., Conclusions: Many soldiers who die by suicide access health care shortly before death, presenting an opportunity for suicide prevention. However, in most cases, there was no documentation of prior suicidal thoughts or behaviors, highlighting the need for improvements in risk detection and prediction. Increasing the frequency, scope, and accuracy of risk assessments, especially in mental health care settings, may be particularly useful. (PsycINFO Database Record, ((c) 2017 APA, all rights reserved).)

Importance: Previous research has documented significant variation in the prevalence of posttraumatic stress disorder (PTSD) depending on the type of traumatic experience (TE) and history of TE exposure, but the relatively small sample sizes in these studies resulted in a number of unresolved basic questions., Objective: To examine disaggregated associations of type of TE history with PTSD in a large cross-national community epidemiologic data set., Design, Setting, and Participants: The World Health Organization World Mental Health surveys assessed 29 TE types (lifetime exposure, age at first exposure) with DSM-IV PTSD that was associated with 1 randomly selected TE exposure (the random TE) for each respondent. Surveys were administered in 20 countries (n = 34 676 respondents) from 2001 to 2012. Data were analyzed from October 1, 2015, to September 1, 2016., Main Outcomes and Measures: Prevalence of PTSD assessed with the Composite International Diagnostic Interview., Results: Among the 34 676 respondents (55.4% [SE, 0.6%] men and 44.6% [SE, 0.6%] women; mean [SE] age, 43.7 [0.2] years), lifetime TE exposure was reported by a weighted 70.3% of respondents (mean [SE] number of exposures, 4.5 [0.04] among respondents with any TE). Weighted (by TE frequency) prevalence of PTSD associated with random TEs was 4.0%. Odds ratios (ORs) of PTSD were elevated for TEs involving sexual violence (2.7; 95% CI, 2.0-3.8) and witnessing atrocities (4.2; 95% CI, 1.0-17.8). Prior exposure to some, but not all, same-type TEs was associated with increased vulnerability (eg, physical assault; OR, 3.2; 95% CI, 1.3-7.9) or resilience (eg, participation in sectarian violence; OR, 0.3; 95% CI, 0.1-0.9) to PTSD after the random TE. The finding of earlier studies that more general history of TE exposure was associated with increased vulnerability to PTSD across the full range of random TE types was replicated, but this generalized vulnerability was limited to prior TEs involving violence, including participation in organized violence (OR, 1.3; 95% CI, 1.0-1.6), experience of physical violence (OR, 1.4; 95% CI, 1.2-1.7), rape (OR, 2.5; 95% CI, 1.7-3.8), and other sexual assault (OR, 1.6; 95% CI, 1.1-2.3)., Conclusion and Relevance: The World Mental Health survey findings advance understanding of the extent to which PTSD risk varies with the type of TE and history of TE exposure. Previous findings about the elevated PTSD risk associated with TEs involving assaultive violence was refined by showing agreement only for repeated occurrences. Some types of prior TE exposures are associated with increased resilience rather than increased vulnerability, connecting the literature on TE history with the literature on resilience after adversity. These results are valuable in providing an empirical rationale for more focused investigations of these specifications in future studies.

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.

Sexual violence victimization is a significant problem among female U.S. military personnel. Preventive interventions for high-risk individuals might reduce prevalence, but would require accurate targeting. We attempted to develop a targeting model for female Regular U.S. Army soldiers based on theoretically-guided predictors abstracted from administrative data records. As administrative reports of sexual assault victimization are known to be incomplete, parallel machine learning models were developed to predict administratively-recorded (in the population) and self-reported (in a representative survey) victimization. Capture-recapture methods were used to combine predictions across models. Key predictors included low status, crime involvement, and treated mental disorders. Area under the Receiver Operating Characteristic curve was .83-.88. 33.7-63.2% of victimizations occurred among soldiers in the highest-risk ventile (5%). This high concentration of risk suggests that the models could be useful in targeting preventive interventions, although final determination would require careful weighing of intervention costs, effectiveness, and competing risks., Competing Interests: Disclosures: Dr Stein has been a consultant for Care Management Technologies, received payment for his editorial work from UpToDate, Depression and Anxiety, and Biological Psychiatry, and has been a paid consultant for Pfizer, Tonix, and Janssen. Dr Monahan is co-owner of Classification of Violence Risk (COVR), Inc. Dr Kessler has been a consultant for Johnson & Johnson Wellness and Prevention and Sonofi-Aventis Groupe and has served on an advisory board for Lake Nona Institute. Dr Kessler is a co-owner of DataStat, Inc. The other authors report no financial relationships with commercial interests.

Background: Civilian suicide rates vary by occupation in ways related to occupational stress exposure. Comparable military research finds suicide rates elevated in combat arms occupations. However, no research has evaluated variation in this pattern by deployment history, the indicator of occupation stress widely considered responsible for the recent rise in the military suicide rate., Method: The joint associations of Army occupation and deployment history in predicting suicides were analysed in an administrative dataset for the 729 337 male enlisted Regular Army soldiers in the US Army between 2004 and 2009., Results: There were 496 suicides over the study period (22.4/100 000 person-years). Only two occupational categories, both in combat arms, had significantly elevated suicide rates: infantrymen (37.2/100 000 person-years) and combat engineers (38.2/100 000 person-years). However, the suicide rates in these two categories were significantly lower when currently deployed (30.6/100 000 person-years) than never deployed or previously deployed (41.2-39.1/100 000 person-years), whereas the suicide rate of other soldiers was significantly higher when currently deployed and previously deployed (20.2-22.4/100 000 person-years) than never deployed (14.5/100 000 person-years), resulting in the adjusted suicide rate of infantrymen and combat engineers being most elevated when never deployed [odds ratio (OR) 2.9, 95% confidence interval (CI) 2.1-4.1], less so when previously deployed (OR 1.6, 95% CI 1.1-2.1), and not at all when currently deployed (OR 1.2, 95% CI 0.8-1.8). Adjustment for a differential 'healthy warrior effect' cannot explain this variation in the relative suicide rates of never-deployed infantrymen and combat engineers by deployment status., Conclusions: Efforts are needed to elucidate the causal mechanisms underlying this interaction to guide preventive interventions for soldiers at high suicide risk.

Background: Diagnostic criteria for DSM-5 posttraumatic stress disorder (PTSD) are in many ways similar to DSM-IV criteria, raising the possibility that it might be possible to closely approximate DSM-5 diagnoses using DSM-IV symptoms. If so, the resulting transformation rules could be used to pool research data based on the two criteria sets., Methods: The pre-post deployment study (PPDS) of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) administered a blended 30-day DSM-IV and DSM-5 PTSD symptom assessment based on the civilian PTSD Checklist for DSM-IV (PCL-C) and the PTSD Checklist for DSM-5 (PCL-5). This assessment was completed by 9,193 soldiers from three US Army Brigade Combat Teams approximately 3 months after returning from Afghanistan. PCL-C items were used to operationalize conservative and broad approximations of DSM-5 PTSD diagnoses. The operating characteristics of these approximations were examined compared to diagnoses based on actual DSM-5 criteria., Results: The estimated 30-day prevalence of DSM-5 PTSD based on conservative (4.3%) and broad (4.7%) approximations of DSM-5 criteria using DSM-IV symptom assessments were similar to estimates based on actual DSM-5 criteria (4.6%). Both approximations had excellent sensitivity (92.6-95.5%), specificity (99.6-99.9%), total classification accuracy (99.4-99.6%), and area under the receiver operating characteristic curve (0.96-0.98)., Conclusions: DSM-IV symptoms can be used to approximate DSM-5 diagnoses of PTSD among recently deployed soldiers, making it possible to recode symptom-level data from earlier DSM-IV studies to draw inferences about DSM-5 PTSD. However, replication is needed in broader trauma-exposed samples to evaluate the external validity of this finding., (© 2015 Wiley Periodicals, Inc.)

Analyses from the National Comorbidity Study Replication provide the first nationally representative estimates of the co-occurrence of impulsive angry behavior and possessing or carrying a gun among adults with and without certain mental disorders and demographic characteristics. The study found that a large number of individuals in the United States self-report patterns of impulsive angry behavior and also possess firearms at home (8.9%) or carry guns outside the home (1.5%). These data document associations of numerous common mental disorders and combinations of angry behavior with gun access. Because only a small proportion of persons with this risky combination have ever been involuntarily hospitalized for a mental health problem, most will not be subject to existing mental health-related legal restrictions on firearms resulting from a history of involuntary commitment. Excluding a large proportion of the general population from gun possession is also not likely to be feasible. Behavioral risk-based approaches to firearms restriction, such as expanding the definition of gun-prohibited persons to include those with violent misdemeanor convictions and multiple DUI convictions, could be a more effective public health policy to prevent gun violence in the population., Competing Interests: None of the other authors has any potential conflicts to report., (Copyright © 2015 John Wiley & Sons, Ltd.)

Importance: The US Army experienced a sharp increase in soldier suicides beginning in 2004. Administrative data reveal that among those at highest risk are soldiers in the 12 months after inpatient treatment of a psychiatric disorder., Objective: To develop an actuarial risk algorithm predicting suicide in the 12 months after US Army soldier inpatient treatment of a psychiatric disorder to target expanded posthospitalization care., Design, Setting, and Participants: There were 53,769 hospitalizations of active duty soldiers from January 1, 2004, through December 31, 2009, with International Classification of Diseases, Ninth Revision, Clinical Modification psychiatric admission diagnoses. Administrative data available before hospital discharge abstracted from a wide range of data systems (sociodemographic, US Army career, criminal justice, and medical or pharmacy) were used to predict suicides in the subsequent 12 months using machine learning methods (regression trees and penalized regressions) designed to evaluate cross-validated linear, nonlinear, and interactive predictive associations., Main Outcomes and Measures: Suicides of soldiers hospitalized with psychiatric disorders in the 12 months after hospital discharge., Results: Sixty-eight soldiers died by suicide within 12 months of hospital discharge (12.0% of all US Army suicides), equivalent to 263.9 suicides per 100,000 person-years compared with 18.5 suicides per 100,000 person-years in the total US Army. The strongest predictors included sociodemographics (male sex [odds ratio (OR), 7.9; 95% CI, 1.9-32.6] and late age of enlistment [OR, 1.9; 95% CI, 1.0-3.5]), criminal offenses (verbal violence [OR, 2.2; 95% CI, 1.2-4.0] and weapons possession [OR, 5.6; 95% CI, 1.7-18.3]), prior suicidality [OR, 2.9; 95% CI, 1.7-4.9], aspects of prior psychiatric inpatient and outpatient treatment (eg, number of antidepressant prescriptions filled in the past 12 months [OR, 1.3; 95% CI, 1.1-1.7]), and disorders diagnosed during the focal hospitalizations (eg, nonaffective psychosis [OR, 2.9; 95% CI, 1.2-7.0]). A total of 52.9% of posthospitalization suicides occurred after the 5% of hospitalizations with highest predicted suicide risk (3824.1 suicides per 100,000 person-years). These highest-risk hospitalizations also accounted for significantly elevated proportions of several other adverse posthospitalization outcomes (unintentional injury deaths, suicide attempts, and subsequent hospitalizations)., Conclusions and Relevance: The high concentration of risk of suicide and other adverse outcomes might justify targeting expanded posthospitalization interventions to soldiers classified as having highest posthospitalization suicide risk, although final determination requires careful consideration of intervention costs, comparative effectiveness, and possible adverse effects.

Background: Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question., Method: Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes., Results: Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6-72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors., Conclusions: Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.

Background: The US Army suicide rate has increased sharply in recent years. Identifying significant predictors of Army suicides in Army and Department of Defense (DoD) administrative records might help focus prevention efforts and guide intervention content. Previous studies of administrative data, although documenting significant predictors, were based on limited samples and models. A career history perspective is used here to develop more textured models., Method: The analysis was carried out as part of the Historical Administrative Data Study (HADS) of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). De-identified data were combined across numerous Army and DoD administrative data systems for all Regular Army soldiers on active duty in 2004-2009. Multivariate associations of sociodemographics and Army career variables with suicide were examined in subgroups defined by time in service, rank and deployment history., Results: Several novel results were found that could have intervention implications. The most notable of these were significantly elevated suicide rates (69.6-80.0 suicides per 100 000 person-years compared with 18.5 suicides per 100 000 person-years in the total Army) among enlisted soldiers deployed either during their first year of service or with less than expected (based on time in service) junior enlisted rank; a substantially greater rise in suicide among women than men during deployment; and a protective effect of marriage against suicide only during deployment., Conclusions: A career history approach produces several actionable insights missed in less textured analyses of administrative data predictors. Expansion of analyses to a richer set of predictors might help refine understanding of intervention implications.

Context: Knowing the collaterals is essential for a spleen-preserving distal pancreatectomy with resection of the splenic vessels., Objective: To ascertain the sources of the blood supply to the spleen after a spleen-preserving distal pancreatectomy with resection of the splenic vessels., Methods: Perfusion of the cadaveric left gastric and right gastroepiploic arteries with methylene blue after occlusion of all the arteries except the short gastric arteries (n=10). Intraoperative color Doppler ultrasound was used for the evaluation of the hilar arterial blood flow at distal pancreatectomy (n=23) after 1) clamping of the splenic artery alone, 2) clamping of the splenic and left gastroepiploic arteries and 3) clamping of the splenic and short gastric arteries. CT angiography of the gastric and splenic vessels before and after a spleen-preserving distal pancreatectomy (n=10)., Results: Perfusion of the cadaveric arteries revealed no effective direct or indirect (through the submucous gastric arterial network) communication between the left gastric and the branches of the short gastric arteries. In no case did intraoperative color Doppler ultrasound detect any hilar arterial blood flow after the clamping of the splenic and left gastroepiploic arteries. The clamping of the short gastric arteries did not change the flow parameters. In none of the cases did a post-spleen-preserving distal pancreatectomy with resection of the splenic vessels CT angiography delineate the short gastric vessels supplying the spleen. In all cases, the gastroepiploic arcade was the main arterial pathway feeding the spleen., Conclusion: Experimental, intra- and postoperative instrumental investigations did not show the short gastric arteries to be engaged in the blood supply to the spleen after a spleen-preserving distal pancreatectomy with resection of the splenic vessels. In all cases, the left gastroepiploic artery was the main collateral vessel.

The spleen preservation by distal pancreatic resection can be performed either with spleen vessels preservation or with the ligation of the least. The experiment evolved ligation of all gastric arteries but the short gastric arteries in 20 cadaveric organocomplexes, followed by ink perfusion through the left gastric and left gastro-epiploic arteries. The study was amplified with the intraoperative dopplerography of portal arteries of the spleen after crossclamping of the left gastro-epiploic and short gastric arteries. Ten patients after distal pancreatic resection with spleen preservation and splenic vessels ligation had the CT-angiography before and after the surgery. All the conducted studies demonstrated the incapability of short gastric arteries to supply the satisfactory spleen perfusion. The left gastro-epiploic artery proved to be the main source of splenic blood supply after splenic vessels ligation.

Aim: To evaluate the potential of the color duplex scanning in revealing of the portal hypertension in patients with chronic pancreatitis., Materials and Methods: 94 patients with chronic pancreatitis were investigated. In 61 patients (65%) pancreatitis was complicated by extrahepatic portal hypertension (EHPH) and 31 patients (35%) were without signs of rising of the portal pressure., Results: Investigations in B-regime have shown that in patients with EHPH chronic calculous pancreatitis was revealed in 48%, development of pancreatic hypertension in 75%, increase of the pancreas head up to more than 40 mm in 58% of patients. We did not reveal statistically significant differences in pseudo-cysts in pancreas, extension of extrahepatic and intrahepatic ducts and infiltrative changes in parapancreatic cellular tissue in patients with and without EHPH. A development of EHPH in 31 (51%) patients was preconditioned by an extravasal compression of veins of portal system, combination of extravasal compression with thrombosis was found in 11 (18%) patients, in 12 (20%) patients thrombosis of the magistral veins of portal system was revealed and in 7 (11%) patients hemodynamics was not changed. Resections were found to be preferable operations for recovery of portal circulation. Increase and normalization of portal circulation found after transversal section of pancreas (Beger operation, pancreo-duodenal resection, distal resection of pancreas). A tendency to normalization of the blood flow was observed after the Frey operation.