Design of a multicenter randomized controlled trial of a post-discharge suicide prevention intervention for high-risk psychiatric inpatients: The Veterans Coordinated Community Care Study.

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).

The case for eliminating excessive worry as a requirement for generalized anxiety disorder: a cross-national investigation.

BACKGROUND: Around the world, people living in objectively difficult circumstances who experience symptoms of generalized anxiety disorder (GAD) do not qualify for a diagnosis because their worry is not 'excessive' relative to the context. We carried out the first large-scale, cross-national study to explore the implications of removing this excessiveness requirement. METHODS: Data come from the World Health Organization World Mental Health Survey Initiative. A total of 133 614 adults from 12 surveys in Low- or Middle-Income Countries (LMICs) and 16 surveys in High-Income Countries (HICs) were assessed with the Composite International Diagnostic Interview. Non-excessive worriers meeting all other DSM-5 criteria for GAD were compared to respondents meeting all criteria for GAD, and to respondents without GAD, on clinically-relevant correlates. RESULTS: Removing the excessiveness requirement increases the global lifetime prevalence of GAD from 2.6% to 4.0%, with larger increases in LMICs than HICs. Non-excessive and excessive GAD cases worry about many of the same things, although non-excessive cases worry more about health/welfare of loved ones, and less about personal or non-specific concerns, than excessive cases. Non-excessive cases closely resemble excessive cases in socio-demographic characteristics, family history of GAD, and risk of temporally secondary comorbidity and suicidality. Although non-excessive cases are less severe on average, they report impairment comparable to excessive cases and often seek treatment for GAD symptoms. CONCLUSIONS: Individuals with non-excessive worry who meet all other DSM-5 criteria for GAD are clinically significant cases. Eliminating the excessiveness requirement would lead to a more defensible GAD diagnosis.

Undetected suicide attempts among U.S. soldiers: results from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

BACKGROUND: While previous studies have reported high rates of documented suicide attempts (SAs) in the U.S. Army, the extent to which soldiers make SAs that are not identified in the healthcare system is unknown. Understanding undetected suicidal behavior is important in broadening prevention and intervention efforts. METHODS: Representative survey of U.S. Regular Army enlisted soldiers (n = 24 475). Reported SAs during service were compared with SAs documented in administrative medical records. Logistic regression analyses examined sociodemographic characteristics differentiating soldiers with an undetected SA v. documented SA. Among those with an undetected SA, chi-square tests examined characteristics associated with receiving a mental health diagnosis (MH-Dx) prior to SA. Discrete-time survival analysis estimated risk of undetected SA by time in service. RESULTS: Prevalence of undetected SA (unweighted n = 259) was 1.3%. Annual incidence was 255.6 per 100 000 soldiers, suggesting one in three SAs are undetected. In multivariable analysis, rank ⩾E5 (OR = 3.1[95%CI 1.6-5.7]) was associated with increased odds of undetected v. documented SA. Females were more likely to have a MH-Dx prior to their undetected SA (Rao-Scott χ21 = 6.1, p = .01). Over one-fifth of undetected SAs resulted in at least moderate injury. Risk of undetected SA was greater during the first four years of service. CONCLUSIONS: Findings suggest that substantially more soldiers make SAs than indicated by estimates based on documented attempts. A sizable minority of undetected SAs result in significant injury. Soldiers reporting an undetected SA tend to be higher ranking than those with documented SAs. Undetected SAs require additional approaches to identifying individuals at risk.

Predicting Suicides Among US Army Soldiers After Leaving Active Service.

Importance: The suicide rate of military servicemembers increases sharply after returning to civilian life. Identifying high-risk servicemembers before they leave service could help target preventive interventions. Objective: To develop a model based on administrative data for regular US Army soldiers that can predict suicides 1 to 120 months after leaving active service. Design, Setting, and Participants: In this prognostic study, a consolidated administrative database was created for all regular US Army soldiers who left service from 2010 through 2019. Machine learning models were trained to predict suicides over the next 1 to 120 months in a random 70% training sample. Validation was implemented in the remaining 30%. Data were analyzed from March 2023 through March 2024. Main outcome and measures: The outcome was suicide in the National Death Index. Predictors came from administrative records available before leaving service on sociodemographics, Army career characteristics, psychopathologic risk factors, indicators of physical health, social networks and supports, and stressors. Results: Of the 800 579 soldiers in the cohort (84.9% male; median [IQR] age at discharge, 26 [23-33] years), 2084 suicides had occurred as of December 31, 2019 (51.6 per 100 000 person-years). A lasso model assuming consistent slopes over time discriminated as well over all but the shortest risk horizons as more complex stacked generalization ensemble machine learning models. Test sample area under the receiver operating characteristic curve ranged from 0.87 (SE = 0.06) for suicides in the first month after leaving service to 0.72 (SE = 0.003) for suicides over 120 months. The 10% of soldiers with highest predicted risk accounted for between 30.7% (SE = 1.8) and 46.6% (SE = 6.6) of all suicides across horizons. Calibration was for the most part better for the lasso model than the super learner model (both estimated over 120-month horizons.) Net benefit of a model-informed prevention strategy was positive compared with intervene-with-all or intervene-with-none strategies over a range of plausible intervention thresholds. Sociodemographics, Army career characteristics, and psychopathologic risk factors were the most important classes of predictors. Conclusions and relevance: These results demonstrated that a model based on administrative variables available at the time of leaving active Army service can predict suicides with meaningful accuracy over the subsequent decade. However, final determination of cost-effectiveness would require information beyond the scope of this report about intervention content, costs, and effects over relevant horizons in relation to the monetary value placed on preventing suicides.

Lifetime prevalence, risk, and treatment of mood and anxiety disorders in Qatar's national mental health study.

OBJECTIVES: To estimate lifetime prevalence, risk, and treatment for mental disorders and their correlates in Qatar's general population for the first time. METHODS: We conducted a national phone survey of 5,195 Qatari and Arab residents in Qatar (2019-2022) using the Composite International Diagnostic Interview Version 3.3 and estimated lifetime mood and anxiety defined diagnoses. Survival-based discrete time models, lifetime morbid risk, and treatment projections were estimated. RESULTS: Lifetime prevalence of any disorder was 28.0% and was associated with younger cohorts, females, and migrants, but lower formal education. Treatment contact in the year of disorder onset were 13.5%. The median delay in receiving treatment was 5 years (IQR = 2-13). Lifetime treatment among those with a lifetime disorder were 59.9% for non-healthcare and 63.5% for healthcare; it was 68.1% for any anxiety and 80.1% for any mood disorder after 50 years of onset. Younger cohorts and later age of onset were significantly predictors of treatment. CONCLUSIONS: Lifetime prevalence of mental disorders in Qatar is comparable to other countries. Treatment is significantly delayed and delivered largely in non-healthcare sectors thus the need for increased literacy of mental illness to reduce stigma and improve earlier help-seeking in healthcare settings.

Twelve-month prevalence, persistence, severity, and treatment of mood and anxiety disorders in Qatar's national mental health study.

OBJECTIVES: To estimate 12-month prevalence, persistence, severity, and treatment of mental disorders and socio-demographic correlates in Qatar. METHODS: We conducted the first national population-based telephone survey of Arab adults between 2019 and 2022 using the Composite International Diagnostic Interview and estimated 12-month DSM-5 mood and anxiety disorders and their persistence (the proportion of lifetime cases who continue to meet 12-month criteria). RESULTS: The 12-month prevalence of any disorder was 21.1% (10.4% mild, 38.7% moderate, and 50.9% severe) and was associated with: younger age, female, previously married, and with persistence of any disorder. Persistence was 74.7% (64.0% mood and 75.6% anxiety) and was significantly associated with secondary education or lower. Minimally adequate treatment received among those with any 12-month mental disorder was 10.6% (74.6% in healthcare and 64.6% non-healthcare sectors). Severity and the number of disorders significantly associated with each other and with treatment received (χ2 = 7.24, p = 0.027) including adequate treatment within the mental health specialty sector (χ2 = 21.42, p < 0.001). CONCLUSIONS: Multimorbidity and sociodemographics were associated with 12-month mental disorder. Treatment adequacy in Qatar are comparable to high-income countries. Low treatment contact indicate need for population-wide mental health literacy programes in addition to more accessible and effective mental health services.

Clinical reappraisal of the composite international diagnostic interview version 3.3 in Qatar's National Mental Health Study.

OBJECTIVES: Lifetime DSM-5 diagnoses generated by the lay-administered Composite International Diagnostic Interview for DSM-5 (CIDI) in the World Mental Health Qatar (WMHQ) study were compared to diagnoses based on blinded clinician-administered reappraisal interviews. METHODS: Telephone follow-up interviews used the non-patient edition of the Structured Clinician Interview for DSM-5 (SCID) oversampling respondents who screened positive for five diagnoses in the CIDI: major depressive episode, mania/hypomania, panic disorder, generalized anxiety disorder, and obsessive-compulsive disorder. Concordance was also examined for a diagnoses of post-traumatic stress disorder based on a short-form versus full version of the PTSD Checklist for DSM-5 (PCL-5). RESULTS: Initial CIDI prevalence estimates differed significantly from the SCID for most diagnoses ( χ 1 2 ${\chi }_{1}^{2}$  = 6.6-31.4, p = 0.010 < 0.001), but recalibration reduced most of these differences and led to consistent increases in individual-level concordance (AU-ROC) from 0.53-0.76 to 0.67-0.81. Recalibration of the short-form PCL-5 removed an initially significant difference in PTSD prevalence with the full PCL-5 (from χ 1 2 ${\chi }_{1}^{2}$  = 610.5, p < 0.001 to χ 1 2 ${\chi }_{1}^{2}$  = 2.5, p = 0.110) while also increasing AU-ROC from 0.76 to 0.81. CONCLUSIONS: Recalibration resulted in valid diagnoses of common mental disorders in the Qatar National Mental Health Survey, but with inflated prevalence estimates for some disorders that need to be considered when interpreting results.

Comparative effectiveness of remote digital gamified and group CBT skills training interventions for anxiety and depression among college students: Results of a three-arm randomised controlled trial.

Digital interventions can enhance access to healthcare in under-resourced settings. However, guided digital interventions may be costly for low- and middle-income countries, despite their effectiveness. In this randomised control trial, we evaluated the effectiveness of two digital interventions designed to address this issue: (1) a Cognitive Behavioral Therapy Skills Training (CST) intervention that increased scalability by using remote online group administration; and (2) the SuperBetter gamified self-guided CBT skills training app, which uses other participants rather than paid staff as guides. The study was implemented among anxious and/or depressed South African undergraduates (n = 371) randomised with equal allocation to Remote Group CST, SuperBetter, or a MoodFlow mood monitoring control. Symptoms were assessed with the Generalized Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9). Intention-to-treat analysis found effect sizes at the high end of prior digital intervention trials, including significantly higher adjusted risk differences (ARD; primary outcome) in joint anxiety/depression remission at 3-months and 6-months for Remote Group CST (ARD = 23.3-18.9%, p = 0.001-0.035) and SuperBetter (ARD = 12.7-22.2%, p = 0.047-0.006) than MoodFlow and mean combined PHQ-9/GAD-7 scores (secondary outcome) significantly lower for Remote Group CST and SuperBetter than MoodFlow. These results illustrate how innovative delivery methods can increase the scalability of standard one-on-one guided digital interventions. PREREGISTRATION INTERNATIONAL STANDARD RANDOMISED CONTROLLED TRIAL NUMBER (ISRTCN) SUBMISSION #: 47,089,643.

Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.

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.

Factors associated with satisfaction and perceived helpfulness of mental healthcare: a World Mental Health Surveys report.

BACKGROUND: Mental health service providers are increasingly interested in patient perspectives. We examined rates and predictors of patient-reported satisfaction and perceived helpfulness in a cross-national general population survey of adults with 12-month DSM-IV disorders who saw a provider for help with their mental health. METHODS: Data were obtained from epidemiological surveys in the World Mental Health Survey Initiative. Respondents were asked about satisfaction with treatments received from up to 11 different types of providers (very satisfied, satisfied, neither satisfied nor dissatisfied, somewhat dissatisfied, very dissatisfied) and helpfulness of the provider (a lot, some, a little, not at all). We modelled predictors of satisfaction and helpfulness using a dataset of patient-provider observations (n = 5,248). RESULTS: Most treatment was provided by general medical providers (37.4%), psychiatrists (18.4%) and psychologists (12.7%). Most patients were satisfied or very satisfied (65.9-87.5%, across provider) and helped a lot or some (64.4-90.3%). Spiritual advisors and healers were most often rated satisfactory and helpful. Social workers in human services settings were rated lowest on both dimensions. Patients also reported comparatively low satisfaction with general medical doctors and psychiatrists/psychologists and found general medical doctors less helpful than other providers. Men and students reported lower levels of satisfaction than women and nonstudents. Respondents with high education reported higher satisfaction and helpfulness than those with lower education. Type of mental disorder was unrelated to satisfaction but in some cases (depression, bipolar spectrum disorder, social phobia) was associated with low perceived helpfulness. Insurance was unrelated to either satisfaction or perceived helpfulness but in some cases was associated with elevated perceived helpfulness for a given level of satisfaction. CONCLUSIONS: Satisfaction with and perceived helpfulness of treatment varied as a function of type of provider, service setting, mental status, and socio-demographic variables. Invariably, caution is needed in combining data from multiple countries where there are cultural and service delivery variations. Even so, our findings underscore the utility of patient perspectives in treatment evaluation and may also be relevant in efforts to match patients to treatments.

Predicting Homelessness Among Transitioning U.S. Army Soldiers.

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.

Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia.

STUDY OBJECTIVES: The standard of care for military personnel with insomnia is cognitive behavioral therapy for insomnia (CBT-I). However, only a minority seeking insomnia treatment receive CBT-I, and little reliable guidance exists to identify those most likely to respond. As a step toward personalized care, we present results of a machine learning (ML) model to predict CBT-I response. METHODS: Administrative data were examined for n = 1,449 nondeployed US Army soldiers treated for insomnia with CBT-I who had moderate-severe baseline Insomnia Severity Index (ISI) scores and completed 1 or more follow-up ISIs 6-12 weeks after baseline. An ensemble ML model was developed in a 70% training sample to predict clinically significant ISI improvement (reduction of at least 2 standard deviations on the baseline ISI distribution). Predictors included a wide range of military administrative and baseline clinical variables. Model accuracy was evaluated in the remaining 30% test sample. RESULTS: 19.8% of patients had clinically significant ISI improvement. Model area under the receiver operating characteristic curve (standard error) was 0.60 (0.03). The 20% of test-sample patients with the highest probabilities of improvement were twice as likely to have clinically significant improvement compared with the remaining 80% (36.5% vs 15.7%; χ21 = 9.2, P = .002). Nearly 85% of prediction accuracy was due to 10 variables, the most important of which were baseline insomnia severity and baseline suicidal ideation. CONCLUSIONS: Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment. Parallel models will be needed for alternative treatments before such a system is of optimal value. CITATION: Gabbay FH, Wynn GH, Georg MW, et al. Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia. J Clin Sleep Med. 2024;20(6):921-931.

Prospective association of attachment style with suicide attempts among US Army soldiers.

BACKGROUND: Insecure attachment styles are associated with retrospectively reported suicide attempts (SAs). It is not known if attachment styles are prospectively associated with medically documented SAs. METHODS: A representative sample of US Army soldiers entering service (n = 21 772) was surveyed and followed via administrative records for their first 48 months of service. Attachment style (secure, preoccupied, fearful, dismissing) was assessed at baseline. Administrative medical records identified SAs. Discrete-time survival analysis examined associations of attachment style with future SA during service, adjusting for time in service, socio-demographics, service-related variables, and mental health diagnosis (MH-Dx). We examined whether associations of attachment style with SA differed based on sex and MH-Dx. RESULTS: In total, 253 respondents attempted suicide. Endorsed attachment styles included secure (46.8%), preoccupied (9.1%), fearful (15.7%), and dismissing (19.2%). Examined separately, insecure attachment styles were associated with increased odds of SA: preoccupied [OR 2.5 (95% CI 1.7-3.4)], fearful [OR 1.6 (95% CI 1.1-2.3)], dismissing [OR 1.8 (95% CI 1.3-2.6)]. Examining attachment styles simultaneously along with other covariates, preoccupied [OR 1.9 (95% CI 1.4-2.7)] and dismissing [OR 1.7 (95% CI 1.2-2.4)] remained significant. The dismissing attachment and MH-Dx interaction was significant. In stratified analyses, dismissing attachment was associated with SA only among soldiers without MH-Dx. Other interactions were non-significant. Soldiers endorsing any insecure attachment style had elevated SA risk across the first 48 months in service, particularly during the first 12 months. CONCLUSIONS: Insecure attachment styles, particularly preoccupied and dismissing, are associated with increased future SA risk among soldiers. Elevated risk is most substantial during first year of service but persists through the first 48 months. Dismissing attachment may indicate risk specifically among soldiers not identified by the mental healthcare system.

Patterns, predictors, and patient-reported reasons for antidepressant discontinuation in the WHO World Mental Health Surveys.

BACKGROUND: Despite their documented efficacy, substantial proportions of patients discontinue antidepressant medication (ADM) without a doctor's recommendation. The current report integrates data on patient-reported reasons into an investigation of patterns and predictors of ADM discontinuation. METHODS: Face-to-face interviews with community samples from 13 countries (n = 30 697) in the World Mental Health (WMH) Surveys included n = 1890 respondents who used ADMs within the past 12 months. RESULTS: 10.9% of 12-month ADM users reported discontinuation-based on recommendation of the prescriber while 15.7% discontinued in the absence of prescriber recommendation. The main patient-reported reason for discontinuation was feeling better (46.6%), which was reported by a higher proportion of patients who discontinued within the first 2 weeks of treatment than later. Perceived ineffectiveness (18.5%), predisposing factors (e.g. fear of dependence) (20.0%), and enabling factors (e.g. inability to afford treatment cost) (5.0%) were much less commonly reported reasons. Discontinuation in the absence of prescriber recommendation was associated with low country income level, being employed, and having above average personal income. Age, prior history of psychotropic medication use, and being prescribed treatment from a psychiatrist rather than from a general medical practitioner, in comparison, were associated with a lower probability of this type of discontinuation. However, these predictors varied substantially depending on patient-reported reasons for discontinuation. CONCLUSION: Dropping out early is not necessarily negative with almost half of individuals noting they felt better. The study underscores the diverse reasons given for dropping out and the need to evaluate how and whether dropping out influences short- or long-term functioning.

Estimated Average Treatment Effect of Psychiatric Hospitalization in Patients With Suicidal Behaviors: A Precision Treatment Analysis.

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.

Optimism, Sociability, and the Risk of Future Suicide Attempt among U.S. Army Soldiers.

INTRODUCTION: Most research on suicide attempts among U.S. service members has been focused on risk factors that occur during service. There is an important gap in our understanding of premilitary factors, such as personality characteristics, that may be associated with future suicide attempt risk during service. Of particular importance is identifying risk factors for the 1/3 of suicide attempters who never receive a mental health diagnosis (MH-Dx)-and therefore are not identified as having a mental health problem in the military healthcare system-prior to their suicide attempt. MATERIALS AND METHODS: Using two components of the Army Study to Assess Risk and Resilience in Servicemembers, we examined the association of personality facets from the Tailored Adaptive Personality Assessment System, a computerized instrument administered prior to entering service, with medically documented suicide attempts during service. A 2010-2016 sample of historical administrative records from U.S. Regular Army enlisted soldiers with complete data on 11 commonly administered Tailored Adaptive Personality Assessment System facets was examined using a series of logistic regression analyses to identify the facets associated with future suicide attempt. Significant facets were then applied to data from a longitudinal cohort study of 11,288 soldiers surveyed upon entering basic combat training and followed via administrative records for their first 48 months of service. This research was approved by the Institutional Review Boards at the collaborating institutions. RESULTS: Analysis of the historical administrative data (87.0% male, 61.6% White non-Hispanic), found that low Optimism (odds ratio (OR) = 1.2 [95% CI = 1.0-1.4]) and high/low (vs. moderate) Sociability (OR = 1.3 [95%CI = 1.1-1.6]) were associated with suicide attempt after adjusting for other univariable-significant facets and socio-demographic and service-related variables. When examined in the longitudinal survey cohort, low Optimism (OR = 1.7 [95% CI = 1.1-2.4]) and high/low (vs. moderate) Sociability (OR = 1.7 [95% CI = 1.1-2.5]) were still associated with increased odds of documented suicide attempt during service, even after adjusting for each other, socio-demographic and service-related variables, and medically documented MH-Dx. Mental health diagnosis had a significant two-way interaction with Optimism (F = 5.27, p = 0.0236) but not Sociability. Stratified analyses indicated that low Optimism was associated with suicide attempt among soldiers without, but not among those with, a MH-Dx. Interactions of Optimism and Sociability with gender were nonsignificant. In the full model, population attributable risk proportions for Optimism and Sociability were 15.0% and 18.9%, respectively. Optimism and Sociability were differentially associated with suicide attempt risk across time in service. CONCLUSIONS: Optimism and Sociability, assessed prior to entering U.S. Army service, are consistently associated with future suicide attempt during service, even after adjusting for other important risk factors. While Sociability is equally associated with suicide attempt among those with and without a MH-Dx, Optimism is specifically associated with suicide attempt among soldiers not identified in the mental healthcare system. Risk differences across time in service suggest that Optimism and Sociability interact with stressors and contextual factors in particular developmental and Army career phases.

Internet-delivered cognitive behavior therapy versus treatment as usual for anxiety and depression among Latin American university students: A randomized clinical trial.

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).

A practical risk calculator for suicidal behavior among transitioning U.S. Army soldiers: results from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS).

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.

Age of onset and cumulative risk of mental disorders: a cross-national analysis of population surveys from 29 countries.

BACKGROUND: Information on the frequency and timing of mental disorder onsets across the lifespan is of fundamental importance for public health planning. Broad, cross-national estimates of this information from coordinated general population surveys were last updated in 2007. We aimed to provide updated and improved estimates of age-of-onset distributions, lifetime prevalence, and morbid risk. METHODS: In this cross-national analysis, we analysed data from respondents aged 18 years or older to the World Mental Health surveys, a coordinated series of cross-sectional, face-to-face community epidemiological surveys administered between 2001 and 2022. In the surveys, the WHO Composite International Diagnostic Interview, a fully structured psychiatric diagnostic interview, was used to assess age of onset, lifetime prevalence, and morbid risk of 13 DSM-IV mental disorders until age 75 years across surveys by sex. We did not assess ethnicity. The surveys were geographically clustered and weighted to adjust for selection probability, and standard errors of incidence rates and cumulative incidence curves were calculated using the jackknife repeated replications simulation method, taking weighting and geographical clustering of data into account. FINDINGS: We included 156 331 respondents from 32 surveys in 29 countries, including 12 low-income and middle-income countries and 17 high-income countries, and including 85 308 (54·5%) female respondents and 71 023 (45·4%) male respondents. The lifetime prevalence of any mental disorder was 28·6% (95% CI 27·9-29·2) for male respondents and 29·8% (29·2-30·3) for female respondents. Morbid risk of any mental disorder by age 75 years was 46·4% (44·9-47·8) for male respondents and 53·1% (51·9-54·3) for female respondents. Conditional probabilities of first onset peaked at approximately age 15 years, with a median age of onset of 19 years (IQR 14-32) for male respondents and 20 years (12-36) for female respondents. The two most prevalent disorders were alcohol use disorder and major depressive disorder for male respondents and major depressive disorder and specific phobia for female respondents. INTERPRETATION: By age 75 years, approximately half the population can expect to develop one or more of the 13 mental disorders considered in this Article. These disorders typically first emerge in childhood, adolescence, or young adulthood. Services should have the capacity to detect and treat common mental disorders promptly and to optimise care that suits people at these crucial parts of the life course. FUNDING: None.