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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.

Background Identification of genetic risk factors may inform the prevention and treatment of posttraumatic stress disorder (PTSD). This study evaluates the associations of polygenic risk scores (PRS) with patterns of posttraumatic stress symptoms following combat deployment. Method US Army soldiers of European ancestry (n = 4900) provided genomic data and ratings of posttraumatic stress symptoms before and after deployment to Afghanistan in 2012. Latent growth mixture modeling was used to model posttraumatic stress symptom trajectories among participants who provided post-deployment data (n = 4353). Multinomial logistic regression models tested independent associations between trajectory membership and PRS for PTSD, major depressive disorder (MDD), schizophrenia, neuroticism, alcohol use disorder, and suicide attempt, controlling for age, sex, ancestry, and exposure to potentially traumatic events, and weighted to account for uncertainty in trajectory classification and missing data. Results Participants were classified into low-severity (77.2%), increasing-severity (10.5%), decreasing-severity (8.0%), and high-severity (4.3%) posttraumatic stress symptom trajectories. Standardized PTSD-PRS and MDD-PRS were associated with greater odds of membership in the high-severity v. low-severity trajectory [adjusted odds ratios and 95% confidence intervals, 1.23 (1.06–1.43) and 1.18 (1.02–1.37), respectively] and the increasing-severity v. low-severity trajectory [1.12 (1.01–1.25) and 1.16 (1.04–1.28), respectively]. Additionally, MDD-PRS was associated with greater odds of membership in the decreasing-severity v. low-severity trajectory [1.16 (1.03–1.31)]. No other associations were statistically significant. Conclusions Higher polygenic risk for PTSD or MDD is associated with more severe posttraumatic stress symptom trajectories following combat deployment. PRS may help stratify at-risk individuals, enabling more precise targeting of treatment and prevention programs.

To derive and initially validate a brief bedside clinical decision support tool that identifies emergency department patients at high risk of substantial, persistent posttraumatic stress symptoms after a motor vehicle collision.Derivation (n=1,282, 19 ED sites) and validation (n=282, 11 separate ED sites) data were obtained from adults prospectively enrolled in the Advancing Understanding of RecOvery afteR traumA study who were discharged from the ED after motor vehicle collision-related trauma. The primary outcome was substantial posttraumatic stress symptoms at 3 months (Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders-5 ≥38). Logistic regression derivation models were evaluated for discriminative ability using the area under the curve and the accuracy of predicted risk probabilities (Brier score). Candidate posttraumatic stress predictors assessed in these models (n=265) spanned a range of sociodemographic, baseline health, peritraumatic, and mechanistic domains. The final model selection was based on performance and ease of administration.Significant 3-month posttraumatic stress symptoms were common in the derivation (27%) and validation (26%) cohort. The area under the curve and Brier score of the final 8-question tool were 0.82 and 0.14 in the derivation cohort and 0.76 and 0.17 in the validation cohort.This simple 8-question tool demonstrates promise to risk-stratify individuals with substantial posttraumatic stress symptoms who are discharged to home after a motor vehicle collision. Both external validation of this instrument, and work to further develop more accurate tools, are needed. Such tools might benefit public health by enabling the conduct of preventive intervention trials and assisting the growing number of EDs that provide services to trauma survivors aimed at promoting psychological recovery.

While literature has suggested that the duration of a major depressive episode (MDE) may affect both symptomatic and functional outcomes in major depressive disorder (MDD), study designs are limited in their ability to isolate a causal relationship.A targeted literature review was conducted using the MEDLINE database to assess whether there was an association between (1) shorter duration of an MDE, or (2) increased rapidity of symptom improvement, and MDD outcomes in adult patients. Given findings from the literature, we hypothesized that rapid symptom improvement could be associated with other longer-term clinical outcomes and used a previously-developed microsimulation model to test this hypothesis. The base case of the model replicated step-therapy treatment patterns, for 10,000 simulated patients, based on lines of therapy related to standard of care, observed remission rates, and observed time to relapse from the STAR*D study. In alternative scenario analyses, the step 1 remission rate was varied by +25 % and +50 % from the base case value to simulate the potential impact of improved earlier remission on disease trajectory and patient-level clinical outcomes.The literature review (N = 35 studies) suggests a statistically significant relationship between the duration of MDE or early symptom improvement and MDD outcomes. The microsimulation model corroborated these findings and demonstrated that increasing the rate of remission in step 1 results in patients experiencing decreased number of treatment steps, faster time to remission, decreased rate of reaching treatment-resistant depression, and delayed time to relapse.Rates of relapse in STAR*D were deemed unreliable due to the high-loss of follow-up; rates of relapse for the MDD DTM were instead derived using parametric extrapolation methods (i.e., exponential, Weibull, log-logistic, Gaussian, log-normal, logistic). Adherence to treatment was assumed to be 100 %; however, non-adherence is expected to result in lower cumulative remission rates.Findings from the literature, coupled with quantification through a novel microsimulation model, demonstrate the potential impact of increased remission on disease trajectory and patient outcomes in MDD. While additional analyses with the model may be warranted to explore the impact of novel interventions on population health, including long-term outcomes (i.e., 5-year follow-up, lifetime follow-up), efforts by clinicians to increase remission early in the disease trajectory may improve long-term outcomes.

Background: Attention-deficit/hyperactivity disorder (ADHD) and posttraumatic stress disorder (PTSD) are associated, but it is unclear if this is a causal relationship or confounding. We used genetic analyses and sibling comparisons to clarify the direction of this relationship. Methods: Linkage disequilibrium score regression and 2-sample Mendelian randomization were used to test for genetic correlation (r g) and bidirectional causal effects using European ancestry genome-wide association studies of ADHD (20,183 cases and 35,191 controls) and 6 PTSD definitions (up to 320,369 individuals). Several additional variables were included in the analysis to verify the independence of the ADHD-PTSD relationship. In a population-based sibling comparison (N = 2,082,118 individuals), Cox regression models were fitted to account for time at risk, a range of sociodemographic factors, and unmeasured familial confounders (via sibling comparisons). Results: ADHD and PTSD had consistent r g (r g range, 0.43–0.52; p < .001). ADHD genetic liability was causally linked with increased risk for PTSD (β = 0.367; 95% CI, 0.186–0.552; p = 7.68 × 10 −5). This result was not affected by heterogeneity, horizontal pleiotropy (Mendelian randomization Egger intercept = 4.34 × 10 −4, p = .961), or other phenotypes and was consistent across PTSD datasets. However, we found no consistent associations between PTSD genetic liability and ADHD risk. Individuals diagnosed with ADHD were at a higher risk for developing PTSD than their undiagnosed sibling (hazard ratio = 2.37; 95% CI, 1.98–3.53). Conclusions: Our findings add novel evidence supporting the need for early and effective treatment of ADHD, as patients with this diagnosis are at significantly higher risk to develop PTSD later in life.

Background The transition from military service to civilian life is a high-risk period for suicide attempts (SAs). Although stressful life events (SLEs) faced by transitioning soldiers are thought to be implicated, systematic prospective evidence is lacking. Methods Participants in the Army Study to Assess Risk and Resilience in Servicemembers (STARRS) completed baseline self-report surveys while on active duty in 2011–2014. Two self-report follow-up Longitudinal Surveys (LS1: 2016–2018; LS2: 2018–2019) were subsequently administered to probability subsamples of these baseline respondents. As detailed in a previous report, a SA risk index based on survey, administrative, and geospatial data collected before separation/deactivation identified 15% of the LS respondents who had separated/deactivated as being high-risk for self-reported post-separation/deactivation SAs. The current report presents an investigation of the extent to which self-reported SLEs occurring in the 12 months before each LS survey might have mediated/modified the association between this SA risk index and post-separation/deactivation SAs. Results The 15% of respondents identified as high-risk had a significantly elevated prevalence of some post-separation/deactivation SLEs. In addition, the associations of some SLEs with SAs were significantly stronger among predicted high-risk than lower-risk respondents. Demographic rate decomposition showed that 59.5% (s.e. = 10.2) of the overall association between the predicted high-risk index and subsequent SAs was linked to these SLEs. Conclusions It might be possible to prevent a substantial proportion of post-separation/deactivation SAs by providing high-risk soldiers with targeted preventive interventions for exposure/vulnerability to commonly occurring SLEs.

Background Although non-suicidal self-injury (NSSI) is known typically to begin in adolescence, longitudinal information is lacking about patterns, predictors, and clinical outcomes of NSSI persistence among emerging adults. The present study was designed to (1) estimate NSSI persistence during the college period, (2) identify risk factors and high-risk students for NSSI persistence patterns, and (3) evaluate the association with future mental disorders and suicidal thoughts and behaviors (STB). Methods Using prospective cohorts from the Leuven College Surveys (n = 5915), part of the World Mental Health International College Student Initiative, web-based surveys assessed mental health and psychosocial problems at college entrance and three annual follow-up assessments. Results Approximately one in five (20.4%) students reported lifetime NSSI at college entrance. NSSI persistence was estimated at 56.4%, with 15.6% reporting a high-frequency repetitive pattern (≥five times yearly). Many hypothesized risk factors were associated with repetitive NSSI persistence, with the most potent effects observed for pre-college NSSI characteristics. Multivariate models suggest that an intervention focusing on the 10–20% at the highest predicted risk could effectively reach 34.9–56.7% of students with high-frequency repetitive NSSI persistence (PPV = 81.8–93.4, AUC = 0.88–0.91). Repetitive NSSI persistence during the first two college years predicted 12-month mental disorders, role impairment, and STB during the third college year, including suicide attempts. Conclusions Most emerging adults with a history of NSSI report persistent self-injury during their college years. Web-based screening may be a promising approach for detecting students at risk for a highly persistent NSSI pattern characterized by subsequent adverse outcomes.

Dissociation, a disruption or discontinuity in psychological functioning, is often linked with worse psychiatric symptoms; however, the prognostic value of dissociation after trauma is inconsistent. Determining whether trauma-related dissociation is uniquely predictive of later outcomes would enable early identification of at-risk trauma populations. The authors conducted the largest prospective longitudinal biomarker study of persistent dissociation to date to determine its predictive capacity for adverse psychiatric outcomes following acute trauma.All data were part of the Freeze 2 data release from the Advancing Understanding of Recovery After Trauma (AURORA) study. Study participants provided self-report data about persistent derealization (N=1,464), a severe type of dissociation, and completed a functional MRI emotion reactivity task and resting-state scan 2 weeks posttrauma (N=145). Three-month follow-up reports were collected of posttraumatic stress, depression, pain, anxiety symptoms, and functional impairment.Derealization was associated with increased ventromedial prefrontal cortex (vmPFC) activation in the emotion reactivity task and decreased resting-state vmPFC connectivity with the cerebellum and orbitofrontal cortex. In separate analyses, brain-based and self-report measures of persistent derealization at 2 weeks predicted worse 3-month posttraumatic stress symptoms, distinct from the effects of childhood maltreatment history and current posttraumatic stress symptoms.The findings suggest that persistent derealization is both an early psychological and biological marker of worse later psychiatric outcomes. The neural correlates of trauma-related dissociation may serve as potential targets for treatment engagement to prevent posttraumatic stress disorder. These results underscore dissociation assessment as crucial following trauma exposure to identify at-risk individuals, and they highlight an unmet clinical need for tailored early interventions.

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, 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 Personality traits (e.g. neuroticism) and the social environment predict risk for internalizing disorders and suicidal behavior. Studying these characteristics together and prospectively within a population confronted with high stressor exposure (e.g. U.S. Army soldiers) has not been done, yet could uncover unique and interactive predictive effects that may inform prevention and early intervention efforts. Methods Five broad personality traits and social network size were assessed via self-administered questionnaires among experienced soldiers preparing for deployment (N = 4645) and new soldiers reporting for basic training (N = 6216). Predictive models examined associations of baseline personality and social network variables with recent distress disorders or suicidal behaviors assessed 3- and 9-months post-deployment and approximately 5 years following enlistment. Results Among the personality traits, elevated neuroticism was consistently associated with increased mental health risk following deployment. Small social networks were also associated with increased mental health risk following deployment, beyond the variance accounted for by personality. Limited support was found for social network size moderating the association between personality and mental health outcomes. Small social networks also predicted distress disorders and suicidal behavior 5 years following enlistment, whereas unique effects of personality traits on these more distal outcomes were rare. Conclusions Heightened neuroticism and small social networks predict a greater risk for negative mental health sequelae, especially following deployment. Social ties may mitigate adverse impacts of personality traits on psychopathology in some contexts. Early identification and targeted intervention for these distinct, modifiable factors may decrease the risk of distress disorders and suicidal behavior.

Suicide is among the leading causes of death in children and adolescents. There are well-known risk factors of suicide, including childhood abuse, family conflicts, social adversity, and psychopathology. While suicide risk is also known to be heritable, few studies have investigated genetic risk in younger individuals.Using polygenic risk score analysis, we examined whether genetic susceptibility to major psychiatric disorders is associated with suicidal behaviors among 11,878 children enrolled in the ABCD (Adolescent Brain Cognitive Development) Study. Suicidal ideation and suicide attempt data were assessed using the youth report of the Kiddie Schedule for Affective Disorders and Schizophrenia for DSM-5. After performing robust quality control of genotype data, unrelated individuals of European descent were included in analyses (n = 4344).Among 8 psychiatric disorders we examined, depression polygenic risk scores were associated with lifetime suicide attempts both in the baseline (odds ratio = 1.55, 95% CI = 1.10-2.18, p = 1.27 × 10The largest genetic sample of suicide risk data in U.S. children suggests a significant genetic basis of suicide risk related to attention-deficit/hyperactivity disorder and depression. Further research is warranted to examine whether incorporation of genomic risk may facilitate more targeted screening and intervention efforts.

Purpose: To investigate the associations of childhood adversities (CAs) with lifetime onset and transitions across suicidal thoughts and behaviors (STB) among incoming college students. Methods: Web-based self-report surveys administered to 20,842 incoming college students from nine countries (response rate 45.6%) assessed lifetime suicidal ideation, plans and attempts along with seven CAs: parental psychopathology, three types of abuse (emotional, physical, sexual), neglect, bully victimization, and dating violence. Logistic regression estimated individual- and population-level associations using CA operationalizations for type, number, severity, and frequency. Results: Associations of CAs with lifetime ideation and the transition from ideation to plan were best explained by the exact number of CA types (OR range 1.32–52.30 for exactly two to seven CAs). Associations of CAs with a transition to attempts were best explained by the frequency of specific CA types (scaled 0–4). Attempts among ideators with a plan were significantly associated with all seven CAs (OR range 1.16–1.59) and associations remained significant in adjusted analyses with the frequency of sexual abuse (OR = 1.42), dating violence (OR = 1.29), physical abuse (OR = 1.17) and bully victimization (OR = 1.17). Attempts among ideators without plan were significantly associated with frequency of emotional abuse (OR = 1.29) and bully victimization (OR = 1.36), in both unadjusted and adjusted analyses. Population attributable risk simulations found 63% of ideation and 30–47% of STB transitions associated with CAs. Conclusion: Early-life adversities represent a potentially important driver in explaining lifetime STB among incoming college students. Comprehensive intervention strategies that prevent or reduce the negative effects of CAs may reduce subsequent onset of STB.

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.

The first year of college may carry especially high risk for onset of alcohol use disorders. We assessed the one-year incidence of alcohol use disorders (AUD) among incoming first-year students, predictors of AUD-incidence, prediction accuracy and population impact. A prospective cohort study of first-year college students (baseline: N = 5843; response rate = 51.8%; 1-year follow-up: n = 1959; conditional response rate = 41.6%) at a large university in Belgium was conducted. AUD were evaluated with the AUDIT and baseline predictors with the Composite International Diagnostic Interview Screening Scales (CIDI-SC). The one-year incidence of AUD was 3.9% (SE = 0.4). The most important individual-level baseline predictors of AUD incidence were being male (OR = 1.53; 95% CI = 1.12-2.10), a break-up with a romantic partner (OR = 1.67; 95% CI = 1.08-2.59), hazardous drinking (OR = 3.36; 95% CI = 1.31-8.63), and alcohol use characteristics at baseline (ORs between 1.29 and 1.38). Multivariate cross-validated prediction (cross-validated AUC = 0.887) shows that 55.5% of incident AUD cases occurred among the 10% of students at highest predicted risk (20.1% predicted incidence in this highest-risk subgroup). Four out of five students with incident AUD would hypothetically be preventable if baseline hazardous drinking was to be eliminated along with a reduction of one standard deviation in alcohol use characteristics scores, and another 15.0% would potentially be preventable if all 12-month stressful events were eliminated. Screening at college entrance is a promising strategy to identify students at risk of transitioning to more problematic drinking and AUD, thus improving the development and deployment of targeted preventive interventions. ispartof: European Child & Adolescent Psychiatry vol:31 issue:7 ispartof: location:Germany status: Published online

ImportanceTask sharing, the training of nonspecialist workers with no formal experience in counseling, is a promising strategy for addressing the large gap in treatment for depression in low- and middle-income countries (LMICs).ObjectiveTo examine the outcomes and moderators of task-shared psychological interventions associated with depression severity, response, and remission.Data SourcesSystematic literature searches in PubMed, Embase, PsycINFO, and Cochrane Library up to January 1, 2021.Study SelectionRandomized clinical trials (RCTs) of task-shared psychological interventions compared with control conditions for adults with depressive symptoms in LMICs were included.Data Extraction and SynthesisTwo researchers independently reviewed the titles, abstracts, and full text of articles from an existing generic meta-analytic database that includes all RCTs on psychotherapy for depression. A systematic review and individual patient data (IPD) meta-analysis was used to estimate the outcomes of task-shared psychological interventions across patient characteristics using mixed-effects models. Procedures for abstracting data and assessing data quality and validity followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline.Main Outcomes and MeasuresPrimary outcome was reduction in depression symptom severity measured by the 9-item Patient Health Questionnaire (PHQ-9). Response and remission rates were also estimated.ResultsOf 13 eligible trials, 11 (4145 participants) contributed IPD. Task-shared psychological interventions were associated with a greater decrease in depressive symptom severity than control conditions (Hedges g, 0.32; 95% CI, –0.26 to –0.38). Participants in the intervention groups had a higher chance of responding (odds ratio, 2.11; 95% CI, 1.60 to 2.80) and remitting (odds ratio, 1.87; 95% CI, 1.20 to 1.99). The presence of psychomotor symptoms was significantly associated with the outcomes of task-shared psychological interventions (β [SE], –1.21 [0.39]; P = .002). No other significant associations were identified. Heterogeneity among the trials with IPD was 74% (95% CI, 53%-86%).Conclusions and RelevanceIn this meta-analysis of IPD, task-shared psychological interventions were associated with a larger reduction in depressive symptom severity and a greater chance of response and remission than control conditions. These findings show potential for the use of task-sharing of psychological interventions across different groups of patients with depression. Further research would help identify which people are most likely to benefit and strengthen larger-scale implementation of this strategy to address the burden of depression in LMICs.

Objective: To estimate structural and attitudinal reasons for premature discontinuation of mental health treatment, socio-demographic and clinical correlates of treatment dropout due to these reasons, and to test country differences from the overall effect across the region of the Americas. Methods: World Health Organization-World Mental Health (WMH) surveys were carried out in six countries in the Americas: Argentina, Brazil, Colombia, Mexico, Peru and USA. Among the 1991 participants who met diagnostic criteria (measured with the Composite International Diagnostic Interview (WMH–CIDI)) for a mental disorder and were in treatment in the prior 12-months, the 236 (12.2%) who dropped out of treatment before the professional recommended were included. Findings: In all countries, individuals more frequently reported attitudinal (79.2%) rather than structural reasons (30.7%) for dropout. Disorder severity was associated with structural reasons; those with severe disorder (versus mild disorder) had 3.4 (95%CI=1.1–11.1) times the odds of reporting a structural reason. Regarding attitudinal reasons, those with lower income (versus higher income) were less likely to discontinue treatment because of getting better (OR=0.4; 95%CI= 0.2–0.9). Country specific variations were found. Limitations: Not all countries, or the poorest, in the region were included. Some estimations couldn´t be calculated due to cell size. Causality cannot be assumed. Conclusion: Clinicians should in the first sessions address attitudinal factors that may lead to premature termination. Public policies need to consider distribution of services to increase convenience. A more rational use of resources would be to offer brief therapies to individuals most likely to drop out of treatment prematurely.

BACKGROUND University-life usually happens during a period of transition in life during which the incidence of mental health and substance use problems and disorders peak. However, relatively few students obtain effective treatment and support. E-interventions have been proven effective in improving psychological outcomes of university students and they have the potential to provide scalable services that can be easily integrated into existing models of care. Minder is a mobile application co-developed with university students that offers users a collection of evidence-based interventions that have been tailored to help university students maintain their mental health and wellbeing, as well as manage their substance use. OBJECTIVE The aim of this protocol paper is to provide a description of the randomized controlled trial that aims to assess the effectiveness of the Minder mobile app in improving the mental health and substance use outcomes of university students. METHODS This is a 2-arm parallel assignment single blinded 30-day randomized controlled trial with 1 intervention group and 1 waitlist control group. A total of 1496 (748 per trial arm) university students at the Vancouver Campus of the University of British Columbia (N = 54000) who are 17 years of age or older, have a smartphone with Wi-Fi or cellular data, and speak English will be recruited via a variety of online and offline strategies. Participants will be randomized into either the intervention or control group after completion of a baseline survey. Those randomized into the intervention group will gain immediate access to the Minder app and be assessed at 2 weeks and 30 days. Those randomized into the control group will be given access to the app content after their follow-up assessment at 30 days. The primary outcomes are measured from baseline to follow-up at 30 days and include changes in general anxiety symptomology, changes in anxious and depressive symptomology, as well as changes in alcohol consumption risk measured by the General Anxiety Disorder 7-Item (GAD-7) scale, Patient Health Questionnaire 9-item (PHQ-9) scale, and the consumption component of the Alcohol Use Disorders Identification Test, adapted for Use in the United States (USAUDIT-C) respectively. Secondary outcomes include measures related to changes in frequency of substance use, changes in mental well-being, changes in self-efficacy in managing mental health and substance use, changes in readiness to change, and changes in self-reported use of mental health services and supports (including referral) from baseline to follow-up at 30 days. RESULTS This study was registered on ClinicalTrials.gov (NCT05606601) on November 4, 2022. Trial recruitment and data collection began in September 2022 and completion of data collection for trial is anticipated by June 2023. As of May 10, 2022, 1425 participants have been enrolled. CONCLUSIONS The randomized controlled trial described in this protocol paper will assess whether the Minder app is effective in improving the mental health (PHQ-9 and GAD-7) and substance use outcomes (USAUDIT-C) of a general population of Canadian university students. Further secondary outcome research aims to explore additional outcomes of interest for further research and to better understand how to support students' general mental wellbeing. CLINICALTRIAL ClinicalTrials.gov NCT05606601; https://clinicaltrials.gov/ct2/show/NCT05606601

Recently developed measures of genetic liability to suicide attempt may convey unique information regarding an individual’s risk of suicidal behavior. We calculated a polygenic risk score for suicide attempt (SA-PRS) for soldiers of European ancestry who participated in the Army STARRS New Soldier Study (NSS; n = 6573) or Pre/Post Deployment Study (PPDS; n = 4900). Multivariable logistic regression models were fit within each sample to estimate the association of SA-PRS with lifetime suicide attempt (LSA), and to examine whether SA-PRS displayed additive or interactive effects with environmental and behavioral risk/protective factors (lifetime trauma burden, childhood maltreatment, negative urgency impulsivity, social network size, perceived mattering, and dispositional optimism). Age, sex, and within-ancestry variation were included as covariates. Observed prevalence of LSA was 6.3% and 4.2% in the NSS and PPDS samples, respectively. In the NSS model, SA-PRS and environmental/behavioral factors displayed strictly additive effects on odds of LSA. Results indicated an estimated 21% increase in odds of LSA per 1 SD increase in SA-PRS [adjusted odds ratio (AOR; 95% CI) = 1.21 (1.09–1.35)]. In PPDS, the effect of SA-PRS varied by reports of optimism [AOR = 0.85 (0.74–0.98) for SA-PRS x optimism effect]. Individuals reporting low and average optimism had 37% and 16% increased odds of LSA per 1 SD increase in SA-PRS, respectively, whereas SA-PRS was not associated with LSA in those reporting high optimism. Overall, results suggested the SA-PRS had predictive value over and above several environmental and behavioral risk factors for LSA. Moreover, elevated SA-PRS may be more concerning in the presence of environmental and behavioral risk factors (e.g., high trauma burden; low optimism). Given the relatively small effect magnitudes, the cost and incremental benefits of utilizing SA-PRS for risk targeting must also be considered in future work.

AIMS: Likelihood of alcohol dependence (AD) is increased among people who transition to greater levels of alcohol involvement at a younger age. Indicated interventions delivered early may be effective in reducing risk, but could be costly. One way to increase cost-effectiveness would be to develop a prediction model that targeted interventions to the subset of youth with early alcohol use who are at highest risk of subsequent AD. DESIGN: A prediction model was developed for DSM-IV AD onset by age 25 years using an ensemble machine-learning algorithm known as 'Super Learner'. Shapley additive explanations (SHAP) assessed variable importance. SETTING AND PARTICIPANTS: Respondents reporting early onset of regular alcohol use (i.e. by 17 years of age) who were aged 25 years or older at interview from 14 representative community surveys conducted in 13 countries as part of WHO's World Mental Health Surveys. MEASUREMENTS: The primary outcome to be predicted was onset of life-time DSM-IV AD by age 25 as measured using the Composite International Diagnostic Interview, a fully structured diagnostic interview. FINDINGS: AD prevalence by age 25 was 5.1% among the 10 687 individuals who reported drinking alcohol regularly by age 17. The prediction model achieved an external area under the curve [0.78; 95% confidence interval (CI) = 0.74-0.81] higher than any individual candidate risk model (0.73-0.77) and an area under the precision-recall curve of 0.22. Overall calibration was good [integrated calibration index (ICI) = 1.05%]; however, miscalibration was observed at the extreme ends of the distribution of predicted probabilities. Interventions provided to the 20% of people with highest risk would identify 49% of AD cases and require treating four people without AD to reach one with AD. Important predictors of increased risk included younger onset of alcohol use, males, higher cohort alcohol use and more mental disorders. CONCLUSIONS: A risk algorithm can be created using data collected at the onset of regular alcohol use to target youth at highest risk of alcohol dependence by early adulthood. Important considerations remain for advancing the development and practical implementation of such models. ispartof: Addiction vol:118 issue:5 pages:954-966 ispartof: location:England status: published

PURPOSE: To investigate the prevalence and predictors of perceived helpfulness of treatment in persons with a history of DSM-IV social anxiety disorder (SAD), using a worldwide population-based sample.METHODS: The World Health Organization World Mental Health Surveys is a coordinated series of community epidemiological surveys of non-institutionalized adults; 27 surveys in 24 countries (16 in high-income; 11 in low/middle-income countries; N = 117,856) included people with a lifetime history of treated SAD.RESULTS: In respondents with lifetime SAD, approximately one in five ever obtained treatment. Among these (n = 1322), cumulative probability of receiving treatment they regarded as helpful after seeing up to seven professionals was 92.2%. However, only 30.2% persisted this long, resulting in 65.1% ever receiving treatment perceived as helpful. Perceiving treatment as helpful was more common in female respondents, those currently married, more highly educated, and treated in non-formal health-care settings. Persistence in seeking treatment for SAD was higher among those with shorter delays in seeking treatment, in those receiving medication from a mental health specialist, and those with more than two lifetime anxiety disorders.CONCLUSIONS: The vast majority of individuals with SAD do not receive any treatment. Among those who do, the probability that people treated for SAD obtain treatment they consider helpful increases considerably if they persisted in help-seeking after earlier unhelpful treatments.

Childhood trauma is a known risk factor for trauma and stress-related disorders in adulthood. However, limited research has investigated the impact of childhood trauma on brain structure linked to later posttraumatic dysfunction. We investigated the effect of childhood trauma on white matter microstructure after recent trauma and its relationship with future posttraumatic dysfunction among trauma-exposed adult participants (n = 202) recruited from emergency departments as part of the AURORA Study. Participants completed self-report scales assessing prior childhood maltreatment within 2-weeks in addition to assessments of PTSD, depression, anxiety, and dissociation symptoms within 6-months of their traumatic event. Fractional anisotropy (FA) obtained from diffusion tensor imaging (DTI) collected at 2-weeks and 6-months was used to index white matter microstructure. Childhood maltreatment load predicted 6-month PTSD symptoms (b = 1.75, SE = 0.78, 95% CI = [0.20, 3.29]) and inversely varied with FA in the bilateral internal capsule (IC) at 2-weeks (p = 0.0294, FDR corrected) and 6-months (p = 0.0238, FDR corrected). We observed a significant indirect effect of childhood maltreatment load on 6-month PTSD symptoms through 2-week IC microstructure (b = 0.37, Boot SE = 0.18, 95% CI = [0.05, 0.76]) that fully mediated the effect of childhood maltreatment load on PCL-5 scores (b = 1.37, SE = 0.79, 95% CI = [−0.18, 2.93]). IC microstructure did not mediate relationships between childhood maltreatment and depressive, anxiety, or dissociative symptomatology. Our findings suggest a unique role for IC microstructure as a stable neural pathway between childhood trauma and future PTSD symptoms following recent trauma. Notably, our work did not support roles of white matter tracts previously found to vary with PTSD symptoms and childhood trauma exposure, including the cingulum bundle, uncinate fasciculus, and corpus callosum. Given the IC contains sensory fibers linked to perception and motor control, childhood maltreatment might impact the neural circuits that relay and process threat-related inputs and responses to trauma.

BackgroundPatients exposed to trauma often experience high rates of adverse post-traumatic neuropsychiatric sequelae (APNS). The biologic mechanisms promoting APNS are currently unknown, but the microbiota-gut-brain axis offers an avenue to understanding mechanisms as well as possibilities for intervention. Microbiome composition at the time of trauma exposure has been poorly examined regarding neuropsychiatric outcomes. We aimed to determine whether baseline the gut microbiomes of trauma-exposed emergency department patients who later develop APNS have dysfunctional gut microbiome profiles and discover potential associated mechanisms.MethodsWe performed metagenomic analysis on stool samples (n=51) from a subset of adults enrolled in the Advancing Understanding of RecOvery afteR traumA (AURORA) study. Twelve-week post-trauma outcomes for post-traumatic stress disorder (PTSD) (PTSD checklist for DSM-5), normalized depression scores (PROMIS Depression Short Form 8b) and somatic symptom counts were collected. Generalized linear models were created for each outcome using microbial abundances and relevant demographics. Mixed-effect random forest machine learning models were used to identify associations between APNS outcomes and microbial features and metabolic pathways.ResultsMicrobial species, includingFlavonifactor plauttiandRuminococcus gnavus,which are associated with inflammation and poor health outcomes, were found to be important in predicting worse APNS outcomes. Notably, worse APNS outcomes were highly predicted by decreased L-arginine related pathway genes and increased citrulline and ornithine pathways.ConclusionsPro-inflammatory microbes that are enriched in individuals who develop APNS. More notably, we identified a biological mechanism through which the gut microbiome reduces global arginine bioavailability, which also has been demonstrated in patients with PTSD.

BackgroundThe most common treatment for major depressive disorder (MDD) is antidepressant medication (ADM). Results are reported on frequency of ADM use, reasons for use, and perceived effectiveness of use in general population surveys across 20 countries.MethodsFace-to-face interviews with community samples totaling n = 49 919 respondents in the World Health Organization (WHO) World Mental Health (WMH) Surveys asked about ADM use anytime in the prior 12 months in conjunction with validated fully structured diagnostic interviews. Treatment questions were administered independently of diagnoses and asked of all respondents.Results3.1% of respondents reported ADM use within the past 12 months. In high-income countries (HICs), depression (49.2%) and anxiety (36.4%) were the most common reasons for use. In low- and middle-income countries (LMICs), depression (38.4%) and sleep problems (31.9%) were the most common reasons for use. Prevalence of use was 2–4 times as high in HICs as LMICs across all examined diagnoses. Newer ADMs were proportionally used more often in HICs than LMICs. Across all conditions, ADMs were reported as very effective by 58.8% of users and somewhat effective by an additional 28.3% of users, with both proportions higher in LMICs than HICs. Neither ADM class nor reason for use was a significant predictor of perceived effectiveness.ConclusionADMs are in widespread use and for a variety of conditions including but going beyond depression and anxiety. In a general population sample from multiple LMICs and HICs, ADMs were widely perceived to be either very or somewhat effective by the people who use them.

To examine the prevalence of 12-month mood disorders and receipt of mental health treatment among a volunteer sample of higher education students during the 2nd and 3rd COVID-19 wave in the Flanders region. Web-based self-report surveys were obtained from 9101 students in higher education in the Flemish College Surveys (FLeCS) in Flanders, Belgium. As part of the World Health Organization's World Mental Health–International College Student Initiative, we screened for 12-month mood disorders (major depressive episode (MDE), mania/hypomania), and service use. We used poststratification weights to generate population-representative data on key socio-demographic characteristics. 50.6% of the respondents screened positive for 12-month mood disorders (46.8% MDE, of which 22.9% with very severe impact). Use of services was very low, with estimates of 35.4% for MDE, 31.7% for mania, and 25.5% for hypomania. Even among students with very severe disorders, treatment rates were never higher than 48.3%. Most common barriers for not using services were: the preference to handle the problem alone (83.4%) and not knowing where to seek professional help (79.8%). We found a high unmet need for mood problems among college students; though caution is needed in interpreting these findings given the volunteer nature of the sample. A reallocation of treatment resources for higher education students should be considered, particulary services that focus on innovative, low-threshold, and scalable interventions.

IntroductionThe health burden due to depression is ever increasing in the world. Prevention is a key to reducing this burden. Guided internet cognitive–behavioural therapies (iCBT) appear promising but there is room for improvement because we do not yet know which of various iCBT skills are more efficacious than others, and for whom. In addition, there has been no platform for iCBT that can accommodate ongoing evolution of internet technologies.Methods and analysisBased on our decade-long experiences in developing smartphone CBT apps and examining them in randomised controlled trials, we have developed the Resilience Training App Version 2. This app now covers five CBT skills: cognitive restructuring, behavioural activation, problem-solving, assertion training and behaviour therapy for insomnia. The current study is designed as a master protocol including four 2×2 factorial trials using this app (1) to elucidate specific efficacies of each CBT skill, (2) to identify participants’ characteristics that enable matching between skills and individuals, and (3) to allow future inclusion of new skills. We will recruit 3520 participants with subthreshold depression and ca 1700 participants without subthreshold depression, to examine the short-term efficacies of CBT skills to reduce depressive symptoms in the former and to explore the long-term efficacies in preventing depression in the total sample. The primary outcome for the short-term efficacies is the change in depressive symptoms as measured with the Patient Health Questionnaire-9 at week 6, and that for the long-term efficacies is the incidence of major depressive episodes as assessed by the computerised Composite International Diagnostic Interview by week 50.Ethics and disseminationThe trial has been approved by the Ethics Committee of Kyoto University Graduate School of Medicine (C1556).Trial registration numberUMIN000047124.

Considerable racial/ethnic disparities persist in exposure to life stressors and socioeconomic resources that can directly affect threat neurocircuitry, particularly the amygdala, that partially mediates susceptibility to adverse posttraumatic outcomes. Limited work to date, however, has investigated potential racial/ethnic variability in amygdala reactivity or connectivity that may in turn be related to outcomes such as post-traumatic stress disorder (PTSD). Participants from the AURORA study (n = 283), a multisite longitudinal study of trauma outcomes, completed functional magnetic resonance imaging and psychophysiology within approximately two-weeks of trauma exposure. Seed-based amygdala connectivity and amygdala reactivity during passive viewing of fearful and neutral faces were assessed during fMRI. Physiological activity was assessed during Pavlovian threat conditioning. Participants also reported the severity of posttraumatic symptoms 3 and 6 months after trauma. Black individuals showed lower baseline skin conductance levels and startle compared to White individuals, but no differences were observed in physiological reactions to threat. Further, Hispanic and Black participants showed greater amygdala connectivity to regions including the dorsolateral prefrontal cortex (PFC), dorsal anterior cingulate cortex, insula, and cerebellum compared to White participants. No differences were observed in amygdala reactivity to threat. Amygdala connectivity was associated with 3-month PTSD symptoms, but the associations differed by racial/ethnic group and were partly driven by group differences in structural inequities. The present findings suggest variability in tonic neurophysiological arousal in the early aftermath of trauma between racial/ethnic groups, driven by structural inequality, impacts neural processes that mediate susceptibility to later PTSD symptoms.