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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.

Purpose: The purpose of this study was to examine the effect of depression on the association between a history of military service and life satisfaction among a nationally representative sample of US men.Methods: Data from 57,905 men were obtained from the 2006 Behavioral Risk Factor Surveillance survey that assessed depression, history of military service, and life satisfaction. Multivariable logistic regression was conducted, controlling for demographics and physical health characteristics.Results: In non-depressed men, a history of military service was associated with higher odds of life satisfaction, OR (95% CI) = 1.39 (1.07, 1.81). However, the interaction between depression and a history of military service was significant, OR (95% CI) = 0.56 (0.38–0.84), such that a history of military service was associated with equivalent odds of satisfaction in depressed men, OR (95% CI) = 0.78 (0.56–1.09).Conclusions: Intervention efforts targeting depression in men with a history of military service may have a significant impact on their well-being. Future research should replicate these findings, examine potential mechanisms of the effects, and study the utility of life satisfaction measures in this population. [ABSTRACT FROM AUTHOR]

Gaps in understanding of how area-based differences in exposure to violence are associated with asthma prevalence may limit the development of effective prevention programs and the identification of risk for asthma episodes. The current investigation examines the associations between sexual violence victimization and asthma episodes among US adult women across three different metropolitan settings. The association between sexual assault victimizations and asthma attacks in the past year was examined using data from the 2005, 2006, and 2007 Behavioral Risk Factor Surveillance System surveys. Cross-sectional analyses were based on adult women with current asthma (n = 4,099). Multivariate logistic regression models were used to identify associations between four categories of sexual violence victimization and asthma episodes across three categories of metropolitan and non-metropolitan settings. Our findings show that unwanted touching, attempted unwanted intercourse, forced unwanted intercourse, and any sexual violence victimization (touching, attempted intercourse, or forced intercourse) were significantly associated with asthma episodes (OR(adj.) = 3.67, 95% CI, 1.76-7.69; OR(adj.) = 1.77, 95% CI, 1.32-2.37; OR(adj.) = 2.24, 95% CI, 1.64-3.05, and OR(adj.) = 1.93, 95% CI, 1.47-2.53, respectively). While no significant differences in the associations between asthma episodes and metropolitan status were found, a significant interaction between non-metropolitan areas and attempted sexual intercourse was identified (OR(adj) = 0.53, 95% CI, 0.29-0.96). Sexual victimization appears to be an important, but understudied, correlate of asthma morbidity among adult women in the USA, suggesting that additional research is needed to better understand the associations between sexual violence, psychological distress, and asthma. [ABSTRACT FROM AUTHOR]

Objective. This study examined whether youth who live in an urban, disadvantaged community are significantly more likely than youth representing the nation to engage in a range of health-compromising behaviors.Methods. Analyses were based on the Youth Violence Survey conducted in 2004 and administered to students (n=4, 131) in a high-risk school district. Students in ninth grade (n= 1,114) were compared with ninth-grade students in the 2003 national Youth Risk Behavior Survey (n=3,674) and the National Longitudinal Study of Adolescent Health conducted in 1995/1996 (n=3,523). Analyses assessed the differences in prevalence of risk and protective factors among ninth-grade students from the three studies using Chi-square tests.Results. The results showed that youth in this urban, disadvantaged community were significantly more likely than their peers across the country to report vandalism, theft, violence, and selling drugs. Youth in this community also reported significantly less support from their homes and schools, and less monitoring by their parents. Moreover, youth in this community were significantly less likely to binge drink or initiate alcohol use prior to age 13 than youth across the U.S.Conclusions. Youth who live in this urban, disadvantaged community reported significantly higher prevalence of some, but not all, risky behaviors than nationally representative U.S. youth. These findings highlight that some caution is justified when defining what might constitute high risk and that demographic and other characteristics need to be carefully considered when targeting certain high-risk behaviors. [ABSTRACT FROM AUTHOR]

Objective: This study sought to compare patterns of alcohol use between male veterans and nonveterans.Methods: Data came from the 2004 Behavioral Risk Factor Surveillance System, a U.S. national telephone survey using stratified random sampling. Outcomes were 30-day alcohol use, binge drinking (5+ drinks on one occasion), and heavy drinking (2+ drinks per day). Age-stratified weighted regression analyses compared men who were veterans (n = 36,874) to those who were not (n = 77,056), and veterans who used Veterans Health Administration (VHA) services in the past year (n = 7,606) to veterans who did not, adjusting for potential confounders.Results: Veterans aged 41 to 60 were less likely to binge drink (adjusted odds ratio [AOR] = 0.8) and veterans aged 61 to 70 were more likely to drink heavily compared to same-age men without military experience (AOR = 1.7). There were no significant differences in binge or heavy drinking for other age groups. Among veterans aged 51 to 60, those who used VHA services were more likely to report binge drinking than those who did not (AOR = 1.4).Conclusions: Male veterans generally have similar patterns of alcohol use as men without a history of military service, but the elevated alcohol use of specific groups of veterans merits concern. [ABSTRACT FROM AUTHOR]

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.)

Background: The period after psychiatric hospital discharge is one of elevated risk for suicide-related behaviors (SRBs). Post-discharge clinical outreach, although potentially effective in preventing SRBs, would be more cost-effective if targeted at high-risk patients. To this end, a machine learning model was developed to predict post-discharge suicides among Veterans Health Administration (VHA) psychiatric inpatients and target a high-risk preventive intervention., Methods: The Veterans Coordinated Community Care (3C) Study is a multicenter randomized controlled trial using this model to identify high-risk VHA psychiatric inpatients (n = 850) randomized with equal allocation to either the Coping Long Term with Active Suicide Program (CLASP) post-discharge clinical outreach intervention or treatment-as-usual (TAU). The primary outcome is SRBs over a 6-month follow-up. We will estimate average treatment effects adjusted for loss to follow-up and investigate the possibility of heterogeneity of treatment effects., Results: Recruitment is underway and will end September 2024. Six-month follow-up will end and analysis will begin in Summer 2025., Conclusion: Results will provide information about the effectiveness of CLASP versus TAU in reducing post-discharge SRBs and provide guidance to VHA clinicians and policymakers about the implications of targeted use of CLASP among high-risk psychiatric inpatients in the months after hospital discharge., Clinical Trials Registration: ClinicalTrials.Gov identifier: NCT05272176 (https://www., Clinicaltrials: gov/ct2/show/NCT05272176)., (© 2024 The Author(s). International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd.)

Background: Public stigma is a significant deterrent to mental health service use for U.S. veterans. Media campaigns are often used to dispel stigmatizing beliefs and actions. Segmentation is an evidence-based practice for their effective use; however, little data has been published on veteran segments to target with anti-stigma messages., Aims: This article aims to identify and describe initial typologies of stigmatizing attitudes within a group of U.S. military veterans., Methods: Telephone-based cross-sectional surveys were conducted with a national random sample of veterans from 2014 to 2016 ( N  = 2142). Stigma outcomes were measured using a brief, validated instrument used in population-based surveys of public perceptions toward people with mental illness. Cluster analysis was conducted to identify specific groupings along multiple dimensions., Results: A final four-cluster solution was identified among veterans with distinct patterns of attitudes toward mental illness and include: 1) the undecided, 2) the influencer, 3) the ambivalent, and 4) the potential ally. Several strategies were also identified for designing anti-stigma messaging toward these segments., Conclusions: This research demonstrates veterans can be segmented by attitudes to target with anti-stigma campaign messages.

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.

Background: Insomnia and depression frequently co-occur. Significant barriers preclude a majority of patients from receiving first line treatments for both disorders in a sequential treatment episode. Although digital versions of cognitive behavioral therapy for insomnia (CBTI) and for depression (CBTD) hold some promise to meet demand, especially when paired with human support, it is unknown whether heterogeneity of treatment effects exist, such that some patients would be optimally treated with single or sequential interventions., Objective: Describe the protocol for a two-phase, prescriptive comparative effectiveness study to develop and evaluate an individualized intervention rule (IIR) for prescribing the optimal digital treament of co-occurring insomnia and depression., Methods: The proposed sample size is 2300 U.S. military veterans with insomnia and depression recruited nationally (Phase 1 = 1500; Phase 2 = 800). In each phase, the primary endpoint will be remission of both depression and insomnia 3 months following a 12-week intervention period. Phase 1 is a 5-arm randomized trial: two single digital interventions (CBT-I or CBT-D); two sequenced interventions (CBT-I + D or CBT-D + I); and a mood monitoring control condition. A cutting-edge ensemble machine learning method will be used to develop the IIR. Phase 2 will evaluate the IIR by randomizing participants with equal allocation to either the IIR predicted optimal intervention for that individual or by randomization to one the four CBT interventions., Results: Study procedures are ongoing. Results will be reported in future manuscripts., Conclusion: The study will generate evidence on the optimal scalable approach to treat co-occurring insomnia and depression., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. WRP received consulting payments from Abbvie for work that is unrelated to the manuscript. SMS served as a paid serves on the Scientific Advisory Board for Headspace for which he receives compensation and has received consulting payments from Boehringer Ingelheim and Otsuka Pharmaceuticals for unrelated work. TMB, RMB & RCK each report no competing financial interests., (Published by Elsevier Inc.)

Background: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA)., Methods: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample., Results: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors., Conclusions: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Background: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan., Methods: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample., Results: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables., Conclusions: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Background: Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA)., Methods: A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample., Results: 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics., Limitations: Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results., Conclusions: A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection., Competing Interests: Conflict of interest In the past 3 years, Dr. Kessler was a consultant for Cambridge Health Alliance, Canandaigua VA Medical Center, Holmusk, Partners Healthcare, Inc., RallyPoint Networks, Inc., and Sage Therapeutics. He has stock options in Cerebral Inc., Mirah, PYM, and Roga Sciences. Dr. Pigeon consulted for CurAegis Technologies and received clinical trial support from Pfizer, Inc. and Abbvie, Inc. Dr. Zubizarreta consulted for Johnson & Johnson Real World Data Analytics. Dr. Cipriani is supported by the National Institute for Health Research (NIHR) Oxford Cognitive Health Clinical Research Facility, by an NIHR Research Professorship (grant RP-2017-08-ST2-006), by the NIHR Oxford and Thames Valley Applied Research Collaboration and by the NIHR Oxford Health Biomedical Research Centre (grant BRC-1215-20005); he has also received research, educational and consultancy fees from INCiPiT (Italian Network for Paediatric Trials), CARIPLO Foundation and Angelini Pharma. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, or the UK Department of Health. The remaining authors report no conflicts of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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.

Background: Physician responsiveness to patient preferences for depression treatment may improve treatment adherence and clinical outcomes., Objective: To examine associations of patient treatment preferences with types of depression treatment received and treatment adherence among Veterans initiating depression treatment., Design: Patient self-report surveys at treatment initiation linked to medical records., Setting: Veterans Health Administration (VA) clinics nationally, 2018-2020., Participants: A total of 2582 patients (76.7% male, mean age 48.7 years, 62.3% Non-Hispanic White) MAIN MEASURES: Patient self-reported preferences for medication and psychotherapy on 0-10 self-anchoring visual analog scales (0="completely unwilling"; 10="completely willing"). Treatment receipt and adherence (refilling medications; attending 3+ psychotherapy sessions) over 3 months. Logistic regression models controlled for socio-demographics and geographic variables., Key Results: More patients reported strong preferences (10/10) for psychotherapy than medication (51.2% versus 36.7%, McNemar χ 2 1 =175.3, p<0.001). A total of 32.1% of patients who preferred (7-10/10) medication and 21.8% who preferred psychotherapy did not receive these treatments. Patients who strongly preferred medication were substantially more likely to receive medication than those who had strong negative preferences (odds ratios [OR]=17.5; 95% confidence interval [CI]=12.5-24.5). Compared with patients who had strong negative psychotherapy preferences, those with strong psychotherapy preferences were about twice as likely to receive psychotherapy (OR=1.9; 95% CI=1.0-3.5). Patients who strongly preferred psychotherapy were more likely to adhere to psychotherapy than those with strong negative preferences (OR=3.3; 95% CI=1.4-7.4). Treatment preferences were not associated with medication or combined treatment adherence. Patients in primary care settings had lower odds of receiving (but not adhering to) psychotherapy than patients in specialty mental health settings. Depression severity was not associated with treatment receipt or adherence., Conclusions: Mismatches between treatment preferences and treatment type received were common and associated with worse treatment adherence for psychotherapy. Future research could examine ways to decrease mismatch between patient preferences and treatments received and potential effects on patient outcomes., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Background: Major depressive disorder (MDD) is a leading cause of disease morbidity. Combined treatment with antidepressant medication (ADM) plus psychotherapy yields a much higher MDD remission rate than ADM only. But 77% of US MDD patients are nonetheless treated with ADM only despite strong patient preferences for psychotherapy. This mismatch is due at least in part to a combination of cost considerations and limited availability of psychotherapists, although stigma and reluctance of PCPs to refer patients for psychotherapy are also involved. Internet-based cognitive behaviorial therapy (i-CBT) addresses all of these problems., Methods: Enrolled patients (n = 3360) will be those who are beginning ADM-only treatment of MDD in primary care facilities throughout West Virginia, one of the poorest and most rural states in the country. Participating treatment providers and study staff at West Virginia University School of Medicine (WVU) will recruit patients and, after obtaining informed consent, administer a baseline self-report questionnaire (SRQ) and then randomize patients to 1 of 3 treatment arms with equal allocation: ADM only, ADM + self-guided i-CBT, and ADM + guided i-CBT. Follow-up SRQs will be administered 2, 4, 8, 13, 16, 26, 39, and 52 weeks after randomization. The trial has two primary objectives: to evaluate aggregate comparative treatment effects across the 3 arms and to estimate heterogeneity of treatment effects (HTE). The primary outcome will be episode remission based on a modified version of the patient-centered Remission from Depression Questionnaire (RDQ). The sample was powered to detect predictors of HTE that would increase the proportional remission rate by 20% by optimally assigning individuals as opposed to randomly assigning them into three treatment groups of equal size. Aggregate comparative treatment effects will be estimated using intent-to-treat analysis methods. Cumulative inverse probability weights will be used to deal with loss to follow-up. A wide range of self-report predictors of MDD heterogeneity of treatment effects based on previous studies will be included in the baseline SRQ. A state-of-the-art ensemble machine learning method will be used to estimate HTE., Discussion: The study is innovative in using a rich baseline assessment and in having a sample large enough to carry out a well-powered analysis of heterogeneity of treatment effects. We anticipate finding that self-guided and guided i-CBT will both improve outcomes compared to ADM only. We also anticipate finding that the comparative advantages of adding i-CBT to ADM will vary significantly across patients. We hope to develop a stable individualized treatment rule that will allow patients and treatment providers to improve aggregate treatment outcomes by deciding collaboratively when ADM treatment should be augmented with i-CBT., Trial Registration: ClinicalTrials.gov NCT04120285 . Registered on October 19, 2019., (© 2022. The Author(s).)

Background: Identifying solutions to the continued rise in overdose deaths is a public health priority. However, there is evidence of change in recent substance type associated with morbidity and mortality. To better understand the continued rise in overdose deaths, in particular those attributed to opioid and stimulant use disorders, increased knowledge of patterns of use is needed., Methods: Retrospective cohort study of Veterans diagnosed with an opioid or stimulant use disorder between 2005 and 2019. The outcome of interest was diagnosis of substance use disorders, specifically examining combinations of opioid and stimulant use disorders among this population., Results: A total of 1932,188 Veterans were diagnosed with at least one substance use disorder (SUD) during the study period, 2005 through 2019. While the annual prevalence of opioid use disorder (OUD) diagnoses increased more than 155%, OUD diagnoses absent of any other SUD diagnosis increased by an average of 6.9% (95% CI, 6.4, 7.5) per year between 2005 and 2019. Between 2011 and 2019, diagnoses of co-morbid methamphetamine use disorder (MUD) and OUD increased at a higher rate than other SUD combinations., Conclusions: The prevalence of comorbid SUD, in particular co-occurring opioid and methamphetamine use disorder, increased at a higher rate than other combinations between 2005 and 2019. These findings underscore the urgent need to offer patients a combination of evidence-based treatments for each co-morbid SUD, such MOUD and contingency management for persons with comorbid opioid and methamphetamine use disorders., (Published by Elsevier B.V.)

This issue contains a thoughtful report by Gradus et al. (Am J Epidemiol. 2021;190(12):2517-2527) on a machine learning analysis of administrative variables to predict suicide attempts over 2 decades throughout Denmark. This is one of numerous recent studies that document strong concentration of risk of suicide-related behaviors among patients with high scores on machine learning models. The clear exposition of Gradus et al. provides an opportunity to review major challenges in developing, interpreting, and using such models: defining appropriate controls and time horizons, selecting comprehensive predictors, dealing with imbalanced outcomes, choosing classifiers, tuning hyperparameters, evaluating predictor variable importance, and evaluating operating characteristics. We close by calling for machine-learning research into suicide-related behaviors to move beyond merely demonstrating significant prediction-this is by now well-established-and to focus instead on using such models to target specific preventive interventions and to develop individualized treatment rules that can be used to help guide clinical decisions to address the growing problems of suicide attempts, suicide deaths, and other injuries and deaths in the same spectrum., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Aims: Gulf War Illness (GWI) remains a significant health concern for many veterans. The relation of pre-war health conditions and symptoms to GWI could aid in developing a more accurate case definition of GWI. The objective of this study was to investigate pre-war predictors of GWI in a population-based sample of Gulf War veterans using two definitions of GWI., Main Methods: Data come from the 1995-1997 National Health Survey of Persian Gulf War Era Veterans, a survey of a representative sample of deployed and non-deployed US veterans. Using two definitions of GWI (CDC/Kansas and a newly developed 3-domain definition), we conducted a series of multivariable logistic regression analyses to assess the associations of demographic, lifestyle factors, and pre-war medical conditions and symptoms to subsequent GWI., Key Findings: All pre-war symptom predictor domains were significantly and positively associated with GWI using a new 3-domain definition with aORs for individual domains ranging from 2.17 (95% CI = 1.99-2.38) for dermatologic conditions to 3.06 (95% CI = 2.78-3.37) for neurological conditions. All symptom predictor domains were associated with significantly increased likelihood of GWI using the CDC/Kansas definition, with aORs ranging from 2.54 (95% CI = 2.31-2.81) for inflammatory conditions to 3.22 (95% CI = 2.94-3.55) for neurological conditions. These estimates were attenuated but remained significant after inclusion of all significant symptom predictor domains., Significance: Results from this study suggest that demographic/lifestyle factors and pre-war medical conditions are strong predictors of GWI. Additional research is needed to confirm these findings, and to clarify the unique characteristics of this common, but still poorly understood illness., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Objectives. To examine associations of current mental and substance use disorders with self-reported gun ownership and carrying among recently separated US Army soldiers. Veterans have high rates of both gun ownership and mental disorders, the conjunction of which might contribute to the high suicide rate in this group. Methods. Cross-sectional survey data were collected in 2018-2019 from 5682 recently separated personnel who took part in the Army Study to Assess Risk and Resilience in Servicemembers. Validated measures assessed recent mood, anxiety, substance use, and externalizing disorders. Logistic regression models examined associations of sociodemographic characteristics, service characteristics, and mental disorders with gun ownership and carrying. Results. Of the participants, 50% reported gun ownership. About half of owners reported carrying some or most of the time. Mental disorders were not associated significantly with gun ownership. However, among gun owners, major depressive disorder, panic disorder, posttraumatic stress disorder, and intermittent explosive disorder were associated with significantly elevated odds of carrying at least some of the time. Conclusions. Mental disorders are not associated with gun ownership among recently separated Army personnel, but some mental disorders are associated with carrying among gun owners. ( Am J Public Health . 2021;111(10):1855-1864. https://doi.org/10.2105/AJPH.2021.306420).

Introduction: Understanding how soldiers die after separation from military service, particularly those who die shortly after separating from service, may help to identify opportunities to ease transitions to civilian life., Materials and Methods: Mortality data were analyzed for male U.S. Army soldiers who died within 18 months of their separation from service (from 1999 to 2011). Descriptive statistics were calculated for natural, accidental, suicidal, homicidal, undetermined, and legal/operation of war deaths and were stratified by age, component, time in service, and type of discharge. Crude and age-adjusted mortality rates, standardized for all years using the 2004 Regular Army population, were also calculated. The Public Health Review Board of the U.S. Army Public Health Center approved this study as Public Health Practice., Results: Of the 1,884,653 male soldiers who separated from service during the study period, 3,819 died within 18 months of separation. A majority of all separations were Reserve or National Guard (58%), and 62% of decedents were in the Reserve or National Guard. Deaths from natural causes (38%) were the most common, followed by accident deaths (34%), suicides (20%), homicides (6%), undetermined deaths (2%), and legal/operation of war deaths (<1%). For overall mortality, age-adjusted rates were higher among the male U.S. population when compared to male soldiers who had separated from the Army., Conclusions: The time period immediately following separation from the Army presents a unique challenge for many soldiers. Developing more effective pre-separation prevention programs that target specific risks requires knowing the causes of death for natural deaths, suicides, and accidents. Over half of all deaths occurring shortly after separation from service are preventable. Continued surveillance of specific causes of preventable deaths following separation can improve pre-separation prevention programs and transitions to post-service care., (Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2020. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Background: Psychiatric comorbidities may complicate depression treatment by being associated with increased role impairments. However, depression symptom severity might account for these associations. Understanding the independent associations of depression severity and comorbidity with impairments could help in treatment planning. This is especially true for depressed Veterans, who have high psychiatric comorbidity rates., Methods: 2,610 Veterans beginning major depression treatment at the Veterans Health Administration (VHA) were administered a baseline self-report survey that screened for diverse psychiatric comorbidities and assessed depression severity and role impairments. Logistic and generalized linear regression models estimated univariable and multivariable associations of depression severity and comorbidities with impairments. Population attributable risk proportions (PARPs) estimated the relative importance of depression severity and comorbidities in accounting for role impairments., Results: Nearly all patients (97.8%) screened positive for at least one comorbidity and half (49.8%) for 4+ comorbidities. The most common positive screens were for generalized anxiety disorder (80.2%), posttraumatic stress disorder (77.9%), and panic/phobia (77.4%). Depression severity and comorbidities were significantly and additively associated with impairments in multivariable models. Associations were attenuated much less for depression severity than for comorbidities in multivariable versus univariable models. PARPs indicated that 15-60% of role impairments were attributable to depression severity and 5-32% to comorbidities., Limitations: The screening scales could have over-estimated comorbidity prevalence. The cross-sectional observational design cannot determine either temporal or causal priorities., Conclusions: Although positive screens for psychiatric comorbidity are pervasive among depressed VHA patients, depression severity accounts for most of the associations of these comorbidities with role impairments., (Copyright © 2021. Published by Elsevier B.V.)

Introduction: The Veterans Health Administration (VHA) supports the nation's largest primary care-mental health integration (PC-MHI) collaborative care model to increase treatment of mild to moderate common mental disorders in primary care (PC) and refer more severe-complex cases to specialty mental health (SMH) settings. It is unclear how this treatment assignment works in practice., Methods: Patients (n = 2610) who sought incident episode VHA treatment for depression completed a baseline self-report questionnaire about depression severity-complexity. Administrative data were used to determine settings and types of treatment during the next 30 days., Results: Thirty-four percent (34.2%) of depressed patients received treatment in PC settings, 65.8% in SMH settings. PC patients had less severe and fewer comorbid depressive episodes. Patients with lowest severity and/or complexity were most likely to receive PC antidepressant medication treatment; those with highest severity and/or complexity were most likely to receive combined treatment in SMH settings. Assignment of patients across settings and types of treatment was stronger than found in previous civilian studies but less pronounced than expected (cross-validated AUC = 0.50-0.68)., Discussion: By expanding access to evidence-based treatments, VHA's PC-MHI increases consistency of treatment assignment. Reasons for assignment being less pronounced than expected and implications for treatment response will require continued study., Competing Interests: Conflict of interest: In the past 3 years, RCK was a consultant for Datastat, Inc., RallyPoint Networks, Inc., Sage Pharmaceuticals, and Takeda. The remaining authors report no conflict of interest., (© Copyright 2021 by the American Board of Family Medicine.)

The rapid spread of the coronavirus disease (COVID-19) has impacted the lives of millions around the globe. The COVID-19 pandemic has caused increasing concern among treatment professionals about mental health and risky substance use, especially among those who are struggling with a substance use disorder (SUD). The pandemic's impact on those with an SUD may be heightened in vulnerable communities, such as those living in under-resourced and rural areas. Despite policies loosening restrictions on treatment requirements, unintended mental health consequences may arise among this population. We discuss challenges that under-resourced areas face and propose strategies that may improve outcomes for those seeking treatment for SUDs in these areas., (Copyright © 2020. Published by Elsevier Inc.)

Background: For over a decade, there has been a surge in opioid-related morbidity and mortality among Veterans. To better understand the impact of the growing epidemic, it is important to identify the cause-specific mortality rates among Veterans with a prior nonfatal opioid overdose., Methods: We followed 8370 Veterans who received medical care for a nonfatal opioid overdose between 2011 through 2015.Mortality records were linked to clinical records from the Veterans Health Administration (VHA). We compared the mortality rates among those with a nonfatal opioid overdose to a 5 % stratified random sample of patients accessing services during the same time period. SMRs were calculated using age-adjusted cause-specific mortality rates for the l U.S. population obtained from the Centers for Disease Control and Prevention's Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER)., Results: The crude mortality for Veterans with a history of a nonfatal overdose was 370.6 per 10,000 person years. Those with a prior nonfatal overdose had a higher risk of substance-related mortality (aHR [adjusted Hazard Ratio] 5.0), including a higher risk of death from drugs (aHR 6.9) and alcohol (aHR 2.7). Similarly, cause-specific mortalities assessed between Veterans and the U.S. population, SMRs were also highest for deaths associated with substances (114.0)., Conclusion: Veterans with a prior nonfatal overdose experienced substantially higher mortality rates compared to other Veterans or the general U.S., Population: Causes of death related to substance use and mental health were significantly higher than other causes of death, highlighting the importance of integrated treatment and substance use services., (Published by Elsevier B.V.)

Purpose: The aim of this investigation was to document the prevalence and correlates of refusing to answer a US federal health survey item about firearms in the household., Design: The cross-sectional analysis was conducted with 2004 and 2017 Behavioral Risk Factor Surveillance System (BRFSS) survey data from Texas, Oregon, Idaho, California, Kansas, and Utah states whose surveys included items about firearms in the household., Participants: Probability-based samples of adults over the age of 18 (n = 34 488 in 2017 BRFSS; n = 33 136 in 2004 BRFSS)., Measures: Dichotomized measure of whether respondents answered versus refused to answer "Are any firearms now kept in or around your home?", Analysis: Weighted multiple logistic regression was used to assess how sociodemographic and health-related characteristics were associated with item refusal., Results: Approximately 1.8% (95% CI: 1.6-2.1) of respondents in 2004 and 3.9% (95% CI: 3.4-4.5) of respondents in 2017 sample refused the firearms item ( P < .01). Men were more likely than women (2004: adjusted odds ratio [aOR] = 1.81, 95% CI: 1.24-2.62; 2017: aOR = 1.60, 95% CI = 1.17-2.18) and Latino/a respondents were less likely than white respondents (2004: aOR = 0.24, 95% CI: 0.10-0.60; 2017: aOR = 0.21, 95% CI: 0.13-0.34) to refuse the firearms question. In 2004, refusal was more likely among older than younger respondents, but in 2017, age was not associated with refusal., Conclusions: Refusal to firearm-related survey items along sociodemographic characteristics warrants further research. Community-informed strategies (eg, focus groups, cognitive testing, in-depth interviews) could improve the context and wording of firearm-related items to maximize response to these items in public health surveys.

Background: Health care systems struggle to identify risk factors for suicide. Adverse social determinants of health (SDH) are strong predictors of suicide risk, but most electronic health records (EHR) do not include SDH data., Objective: To determine the prevalence of SDH documentation in the EHR and how SDH are associated with suicide ideation and attempt., Design: This cross-sectional analysis included EHR data spanning October 1, 2015-September 30, 2016, from the Veterans Integrated Service Network Region 4., Participants: The study included all patients with at least one inpatient or outpatient visit (n = 293,872)., Main Measurements: Adverse SDH, operationalized using Veterans Health Administration (VHA) coding for services and International Statistical Classification of Diseases and Related Health Problems (ICD)-10 codes, encompassed seven types (violence, housing instability, financial/employment problems, legal problems, familial/social problems, lack of access to care/transportation, and nonspecific psychosocial needs). We defined suicide morbidity by ICD-10 codes and data from the VHA's Suicide Prevention Applications Network. Logistic regression assessed associations of SDH with suicide morbidity, adjusting for socio-demographics and mental health diagnoses (e.g., major depression). Statistical significance was assessed with p < .01., Key Results: Overall, 16.4% of patients had at least one adverse SDH indicator. Adverse SDH exhibited dose-response-like associations with suicidal ideation and suicide attempt: each additional adverse SDH increased odds of suicidal ideation by 67% (AOR = 1.67, 99%CI = 1.60-1.75; p < .01) and suicide attempt by 49% (AOR = 1.49, 99%CI = 1.33-1.68; p < .01). Independently, each adverse SDH had strong effect sizes, ranging from 1.86 (99%CI = 1.58-2.19; p < .01) for legal issues to 3.10 (99%CI = 2.74-3.50; p < .01) for non-specific psychosocial needs in models assessing suicidal ideation and from 1.58 (99%CI = 1.10-2.27; p < .01) for employment/financial problems to 2.90 (99%CI = 2.30-4.16; p < .01) for violence in models assessing suicide attempt., Conclusions: SDH were strongly associated with suicidal ideation and suicide attempt even after adjusting for mental health diagnoses. Integration of SDH data in EHR could improve suicide prevention.

Objective: The Department of Veterans Affairs and Department of Defense Mortality Data Repository (MDR) compiles National Death Index records for all veterans and military service members. This study aimed to compare MDR findings with those from a preexisting data source., Methods: Veteran suicide rates estimated from death certificates were replicated with the MDR. Annual suicide rates were computed for veterans overall, by gender, and by Veterans Health Administration service utilization and compared with rates for adult nonveterans by using standardized mortality ratios (SMRs)., Results: Suicide rates and SMRs differed between the data sources. The 2010 MDR-derived veteran suicide rate was 27.4 deaths per 100,000 veterans, compared with an earlier estimate of 35.9. Differences were greater for females. Divergence was attributed to improved accuracy identifying veteran suicide decedents in the MDR., Conclusions: Conditions with low base rates can be major public health problems, and minor misclassification can substantially affect surveillance accuracy, prevention efforts, and the validity of study findings.

There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the remaining 30% holdout sample. Predictors included VHA administrative variables and small area geocode data linked to patient home addresses. The models had AUC=.79-.82 for time horizons between 1 week and 6 months and AUC=.74 for 12 months. An analysis of operating characteristics showed that 22.4%-32.2% of patients who died by suicide would have been reached if intensive case management was provided to the 5% of patients with highest predicted suicide risk. Positive predictive value (PPV) at this higher threshold ranged from 1.2% over 12 months to 3.8% per case manager year over 1 week. Focusing on the low end of the risk spectrum, the 40% of patients classified as having lowest risk account for 0%-9.7% of suicides across time horizons. Variable importance analysis shows that 51.1% of model performance is due to psychopathological risk factors accounted, 26.2% to social determinants of health, 14.8% to prior history of suicidal behaviors, and 6.6% to physical disorders. The paper closes with a discussion of next steps in refining the model and prospects for developing a parallel precision treatment model., (Copyright © 2020 Kessler, Bauer, Bishop, Demler, Dobscha, Gildea, Goulet, Karras, Kreyenbuhl, Landes, Liu, Luedtke, Mair, McAuliffe, Nock, Petukhova, Pigeon, Sampson, Smoller, Weinstock and Bossarte.)

The aim of this study was to investigate sexual orientation differences in gun ownership and gun safety beliefs among U.S. adults. We used information from the General Social Survey (2010-2016) to assess presence of guns in the household, personal gun ownership, and endorsement of a gun safety law among sexual minority ( n  = 195) versus heterosexual ( n  = 4359) respondents. Methods employed multivariate logistic regression analyses adjusted for confounding including gender, age, race/ethnicity, education level, size of household, urbanicity, military veteran status, and political views. Gay/bisexual men were more likely to endorse a gun safety law (adjusted odds ratio [aOR] = 3.24, 95% confidence interval [CI] = 1.79-5.88) and less likely to report guns in the household (aOR = 0.34, 95% CI = 0.18-0.65) than heterosexual men. In contrast, lesbian/bisexual and heterosexual women similarly endorsed a gun safety law and reported household guns. However, among women reporting a household gun, lesbian/bisexual women were more likely to be the personal gun owner (aOR = 3.97, 95% CI = 1.43-11.03). Attitudes toward a gun safety law and gun ownership differ by sexual orientation, but patterns vary by gender. We recommend that clinicians inquire about gun ownership and gun storage practices with both heterosexual and sexual minority patients., Competing Interests: No competing financial interests exist for any authors., (Copyright 2020, Mary Ann Liebert, Inc., publishers.)

Purpose: Intimate partner violence (IPV) is a pervasive public health problem. Existing research has focused on reports from victims and few studies have considered pre-marital factors. The main objective of this study was to identify pre-marital predictors of IPV in the current marriage using information obtained from husbands and wives., Methods: Data from were obtained from married heterosexual couples in six countries. Potential predictors included demographic and relationship characteristics, adverse childhood experiences, dating violence, and psychiatric disorders. Reports of IPV and other characteristics from husbands and wives were considered independently and in relation to spousal reports., Results: Overall, 14.4% of women were victims of IPV in the current marriage. Analyses identified ten significant variables including age at first marriage (husband), education, relative number of previous marriages (wife), history of one or more categories of childhood adversity (husband or wife), history of dating violence (husband or wife), early initiation of sexual intercourse (husband or wife), and four combinations of internalizing and externalizing disorders. The final model was moderately predictive of marital violence, with the 5% of women accounting for 18.6% of all cases of marital IPV., Conclusions: Results from this study advance understanding of pre-marital predictors of IPV within current marriages, including the importance of considering differences in the experiences of partners prior to marriage and may provide a foundation for more targeted primary prevention efforts.

Objectives: To assess possession of household firearms among veterans receiving mental health care and the frequency of their discussions with clinicians about firearms. Methods: We surveyed random samples of veterans receiving mental health care in each of five purposively chosen, geographically diverse VA facilities; 677 (50% of recipients) responded. Results: 45.3% (95% CI 41.2, 49.3) of veteran respondents reported household firearms; 46.9% of those with suicidal thoughts and 55.6% with a suicide plan had household firearms. Only 27.5% of all veteran respondents and 44% of those with recent suicidal ideation and household firearms had had a firearm-related discussion with a clinician. Discussion: Many veterans receiving mental health care can readily access firearms, a highly lethal means for suicide. Increasing clinician-patient discussions and health system efforts to reduce firearm access might reduce suicide in this clinical population.

Suicide is a leading cause of death. A substantial proportion of the people who die by suicide come into contact with the health care system in the year before their death. This observation has resulted in the development of numerous suicide prediction tools to help target patients for preventive interventions. However, low sensitivity and low positive predictive value have led critics to argue that these tools have no clinical value. We review these tools and critiques here. We conclude that existing tools are suboptimal and that improvements, if they can be made, will require developers to work with more comprehensive predictor sets, staged screening designs, and advanced statistical analysis methods. We also conclude that although existing suicide prediction tools currently have little clinical value, and in some cases might do more harm than good, an even-handed assessment of the potential value of refined tools of this sort cannot currently be made because such an assessment would depend on evidence that currently does not exist about the effectiveness of preventive interventions. We argue that the only way to resolve this uncertainty is to link future efforts to develop or evaluate suicide prediction tools with concrete questions about specific clinical decisions aimed at reducing suicides and to evaluate the clinical value of these tools in terms of net benefit rather than sensitivity or positive predictive value. We also argue for a focus on the development of individualized treatment rules to help select the right suicide-focused treatments for the right patients at the right times. Challenges will exist in doing this because of the rarity of suicide even among patients considered high-risk, but we offer practical suggestions for how these challenges can be addressed.

Background: The objectives of this study were to determine whether short-term exposure to firearm safety messaging significantly improved (1) firearm storage practices, and (2) attitudes of safe firearm storage behaviors among U.S. veterans, a group at elevated risk for firearm suicide., Design: A three-arm, parallel-group RCT was conducted online in the U.S. nationwide from December 2015 to January 2016., Setting: A national random sample of U.S. veterans (N = 358) was recruited from the GfK KnowledgePanel, a probability-based internet panel representative of U.S. adults. All study activities were administered online over a three-week study period., Intervention: Participants were randomized and exposed three times (once per week) to either (a) firearm safety message only (n = 115); (b) firearm safety and mental health promotion messages (n = 133); or (c) active control group exposed to mental health promotion message only (n = 110). Each message was less than two minutes long., Measures: Assessments were completed at baseline (pre-randomization) and at end-of-trial. Changes in awareness of risk for injuries, attitudes/beliefs related to safe storage practices, behavioral intentions, and storage practices were measured using self-reported surveys. Linear mixed effect models with weighted generalized estimating equations were used to test for exposure effects. Analyses were conducted February 2018., Results: Analyses restricted to those with baseline firearm access (n = 195) identified no significant changes for intentions or safe storage practices across exposure groups. At baseline, participants' attitudes and beliefs were generally supportive of safe firearm storage. The Firearm Safety message yielded small increases in agreement with the concept that secure storage is "important during emotional or stressful times" (0.36; 95% CI = 0.08, 0.64). Other significant changes in awareness and beliefs were found, but across all study conditions., Conclusion: Results reinforce the critical need for considerable research and testing prior to the widespread implementation of public messages to increase the likelihood for desired exposure effects., (Published by Elsevier Ltd.)

Clinical trials have identified a variety of predictor variables for use in precision treatment protocols, ranging from clinical biomarkers and symptom profiles to self-report measures of various sorts. Although such variables are informative collectively, none has proven sufficiently powerful to guide optimal treatment selection individually. This has prompted growing interest in the development of composite precision treatment rules (PTRs) that are constructed by combining information across a range of predictors. But this work has been hampered by the generally small samples in randomized clinical trials and the use of suboptimal analysis methods to analyze the resulting data. In this paper, we propose to address the sample size problem by: working with large observational electronic medical record databases rather than controlled clinical trials to develop preliminary PTRs; validating these preliminary PTRs in subsequent pragmatic trials; and using ensemble machine learning methods rather than individual algorithms to carry out statistical analyses to develop the PTRs. The major challenges in this proposed approach are that treatment are not randomly assigned in observational databases and that these databases often lack measures of key prescriptive predictors and mental disorder treatment outcomes. We proposed a tiered case-cohort design approach that uses innovative methods for measuring and balancing baseline covariates and estimating PTRs to address these challenges., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Large-scale public health prevention initiatives and interventions are a very important component to current public health strategies. But evaluating effects of such large-scale prevention/intervention faces a lot of challenges due to confounding effects and heterogeneity of study population. In this paper, we will develop metrics to assess the risk for suicide events based on causal inference framework when the study population is heterogeneous. The proposed metrics deal with the confounding effect by first estimating the risk of suicide events within each of the risk levels, number of prior attempts, and then taking a weighted sum of the conditional probabilities. The metrics provide unbiased estimates of the risk of suicide events. Simulation studies and a real data example will be used to demonstrate the proposed metrics.

Background: Communication campaigns offer a viable mechanism to promote suicide prevention and reinforce mental health for U.S. veterans in midlife, a group with a high suicide burden. However, little empirical investigation of this type of messaging has been conducted, with formative campaign research conspicuously missing from the limited literature., Aims: Using the theory of planned behavior as a guide, formative research was conducted to inform the design of suicide prevention messaging by (a) describing and measuring several theoretical constructs among our audience and (b) modeling associations between constructs and intentions to seek help for suicidal behaviors and mental health more broadly., Methods: Telephone-based cross-sectional surveys were collected between 2014 and 2016 from a nationally representative sample of veterans with analyses restricted to those aged between 44 and 65 years ( n = 809). Multiple logistic regression was used to identify significant factors associated with intentions to seek help for either suicidal behaviors or mental health., Results: Perceived behavioral control was one of the largest predictors of intentions to seek help for both suicidal behaviors and mental health concerns. Descriptive norms were also significantly associated with suicide-related intentions. Data further suggest several types of attitudes (i.e., discordant beliefs, stereotypes) to consider when designing messages for this group., Discussion: This study represents one of the first efforts to document and describe theoretical constructs and their influence on intentions among veterans in midlife to contribute to the development of evidence-based messaging for veterans informed by a conceptual framework., Conclusion: Findings have important implications as the use of communication strategies for suicide prevention grows increasingly popular.

Mefloquine (Lariam ® ; Roche Holding AG, Basel, Switzerland) has been linked to acute neuropsychiatric side effects. This is a concern for U.S. veterans who may have used mefloquine during recent Southwest Asia deployments. Using data from the National Health Study for a New Generation of U.S. Veterans , a population-based study of U.S. veterans who served between 2001 and 2008, we investigated associations between self-reported use of antimalarial medications and overall physical and mental health (MH) using the twelve-item short form, and with other MH outcomes using the post-traumatic stress disorder Checklist-17 and the Patient Health Questionnaire (anxiety, major depression, and self-harm). Multivariable logistic regression was performed to examine associations between health measures and seven antimalarial drug categories: any antimalarial, mefloquine, chloroquine, doxycycline, primaquine, mefloquine plus any other antimalarial, and any other antimalarial or antimalarial combination while adjusting for the effects of deployment and combat exposure. Data from 19,487 veterans showed that although antimalarial use was generally associated with higher odds of negative health outcomes, once deployment and combat exposure were added to the multivariable models, the associations with each of the MH outcomes became attenuated. A positive trend was observed between combat exposure intensity and prevalence of the five MH outcomes. No significant associations were found between mefloquine and MH measures. These data suggest that the poor physical and MH outcomes reported in this study population are largely because of combat deployment exposure.

Objective: Previous mortality studies of U.S. Gulf War veterans through 2000 and 2004 have shown an increased risk of brain cancer mortality among some deployed individuals. When veterans possibly exposed to environmental contaminants associated with demolition of the Khamisiyah Ammunition Storage Facility at Khamisiyah, Iraq, have been compared to contemporaneously deployed unexposed veterans, the results have suggested increased risk for mortality from brain cancer among the exposed. Brain cancer mortality risk in this cohort has not been updated since 2004., Methods: This study analyzes the risk for brain cancer mortality between 1991-2011 through two series of comparisons: U.S. Gulf War deployed and non-deployed veterans from the same era; and veterans possibly exposed to environmental contaminants at Khamisiyah compared to contemporaneously deployed but unexposed U.S. Gulf War veterans. Risk of brain cancer mortality was determined using logistic regression. Life test hazard models were created to plot comparisons of annual hazard rates. Joinpoint regression models were applied to assess trends in hazard rates for brain cancer mortality., Results: U.S. Army veterans possibly exposed at Khamisiyah had similar rates of brain cancer mortality compared to those not possibly exposed; however, veterans possibly exposed had a higher risk of brain cancer in the time period immediately following the Gulf War., Conclusion: Results from these analyses suggest that veterans possibly exposed at Khamisiyah experienced different patterns of brain cancer mortality risk compared to the other groups., (Published by Elsevier Ltd.)

Objectives: In this analysis of a cohort of older homeless veterans, we examined psychosocial, health, housing, and employment characteristics to identify predictors of mortality., Method: Our sample of 3,620 older veterans entered Veteran Affairs homeless programs in years 2000-2003. Fifteen variables from a structured interview described this sample and served as predictors. National Death Index data for years 2000-2011 were used to ascertain death. Survival table analyses were conducted to estimate and plot cumulative survival functions. To determine predictors and estimate hazard functions, Cox proportional hazards regression analysis was conducted., Results: Five variables (presence of a serious health issue, hospitalization for alcohol abuse, alcohol dependency, unemployment for 3 years, and age 60+) were associated with increased risk of death; three (non-White, drug dependency, and dental problems) were associated with reduced risk. A risk score, based on total unit-weighted risk for all eight predictors, was used to identify three groups that were found to differ significantly in mortality., Conclusions: These analyses underline the jeopardy faced by older homeless veterans in terms of early death. We were able to identify several variables associated with mortality; more importantly, we were able to show that a risk score based on status for these variables was significantly related to survival., (Published by Oxford University Press on behalf of The Gerontological Society of America 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Background: Early diagnosis and treatment of chronic obstructive pulmonary disease (COPD) can slow disease progression. The Department of Veterans Affairs (VA)/Department of Defense Clinical Practice Guidelines (CPG), established to improve patient outcomes, recommend the use of spirometry in the COPD diagnostic process. The aims of this study were to assess VA health care providers' performance related to CPG-recommended spirometry administration in the evaluation of newly diagnosed COPD among veterans, determine the patient characteristics that may influence the adherence rate, and compare VA concordance rates to those of other health plans., Methods: Administrative health care data related to Operations Enduring Freedom/Iraqi Freedom/New Dawn (OEF/OIF/OND) veterans was used to identify newly diagnosed COPD cases and the proportion of cases receiving spirometry. Cases were defined as veterans who had their first medical encounter with a coded diagnosis of COPD ≥ 6 months after their initial VA health care evaluation. The relationship between prediagnostic and comorbid conditions and the administration of CPG-concordant spirometry was examined using regression analyses., Findings: Among the 923,646 OEF/OIF/OND veterans receiving VA health care between January 2002 and December 2014, 32,076 (3%) had a coded diagnosis of COPD. Among those, 22,156 (69%) were identified as newly diagnosed COPD cases; only 6,827 (31%) had CPG-concordant spirometry. Concordant spirometry was more likely to occur in veterans aged ≥40. A pre-existing tobacco use disorder marginally changed the concordance rate., Discussion: VA provider adherence to CPG-concordant spirometry would decrease the prevalence of false-positive COPD cases and lead to more targeted disease treatment. Future research should focus on such cases by assessing the association between COPD diagnosis and bronchodilator responsiveness., (Reprint & Copyright © 2017 Association of Military Surgeons of the U.S.)

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.)

Backgrounds: Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful., Method: We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments., Results: Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials., Conclusions: Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.