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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).

Risk of U.S. Army soldier suicide-related behaviors increases substantially after separation from service. As universal prevention programs have been unable to resolve this problem, a previously reported machine learning model was developed using pre-separation predictors to target high-risk transitioning service members (TSMs) for more intensive interventions. This model is currently being used in a demonstration project. The model is limited, though, in two ways. First, the model was developed and trained in a relatively small cross-validation sample ( n  = 4044) and would likely be improved if a larger sample was available. Second, the model provides no guidance on subtyping high-risk TSMs. This report presents results of an attempt to refine the model to address these limitations by re-estimating the model in a larger sample ( n  = 5909) and attempting to develop embedded models for differential risk of post-separation stressful life events (SLEs) known to mediate the association of model predictions with post-separation nonfatal suicide attempts (SAs; n  = 4957). Analysis used data from the Army STARRS Longitudinal Surveys. The revised model improved prediction of post-separation SAs in the first year (AUC = 0.85) and second-third years (AUC = 0.77) after separation, but embedded models could not predict post-separation SLEs with enough accuracy to support intervention targeting.

Background Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent among university students and predict impaired college performance and later life role functioning. Yet most students do not receive treatment, especially in low-middle-income countries (LMICs). We aim to evaluate the effects of expanding treatment using scalable and inexpensive Internet-delivered transdiagnostic cognitive behavioral therapy (iCBT) among college students with symptoms of MDD and/or GAD in two LMICs in Latin America (Colombia and Mexico) and to investigate the feasibility of creating a precision treatment rule (PTR) to predict for whom iCBT is most effective. Methods We will first carry out a multi-site randomized pragmatic clinical trial ( N = 1500) of students seeking treatment at student mental health clinics in participating universities or responding to an email offering services. Students on wait lists for clinic services will be randomized to unguided iCBT (33%), guided iCBT (33%), and treatment as usual (TAU) (33%). iCBT will be provided immediately whereas TAU will be whenever a clinic appointment is available. Short-term aggregate effects will be assessed at 90 days and longer-term effects 12 months after randomization. We will use ensemble machine learning to predict heterogeneity of treatment effects of unguided versus guided iCBT versus TAU and develop a precision treatment rule (PTR) to optimize individual student outcome. We will then conduct a second and third trial with separate samples ( n = 500 per arm), but with unequal allocation across two arms: 25% will be assigned to the treatment determined to yield optimal outcomes based on the PTR developed in the first trial (PTR for optimal short-term outcomes for Trial 2 and 12-month outcomes for Trial 3), whereas the remaining 75% will be assigned with equal allocation across all three treatment arms. Discussion By collecting comprehensive baseline characteristics to evaluate heterogeneity of treatment effects, we will provide valuable and innovative information to optimize treatment effects and guide university mental health treatment planning. Such an effort could have enormous public-health implications for the region by increasing the reach of treatment, decreasing unmet need and clinic wait times, and serving as a model of evidence-based intervention planning and implementation. Trial status IRB Approval of Protocol Version 1.0; June 3, 2020. Recruitment began on March 1, 2021. Recruitment is tentatively scheduled to be completed on May 30, 2024. Trial registration ClinicalTrials.gov NCT04780542 . First submission date: February 28, 2021.

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.

ABSTRACT Transitioning service members (TSMs) leaving military service have high risks of unemployment, homelessness, nonfatal suicide attempt (SA), and suicide death. Data from n  = 7188 recently separated TSMs from the U.S. Army were used to update previously developed models for post-separation homelessness and SA based on data at the time of separation and to develop a new unemployment model. Predicted probabilities of suicide from a model developed elsewhere were imputed for comparison purposes. Cross-validated predictions were significant for the homelessness (AU-ROC = 0.68) and SA (AU-ROC = 0.78) models but not the unemployment model (AU-ROC = 0.60). Elevated cross-validated risk was found for the 10% of TSMs at the highest predicted risk of homelessness (SN = 26.6%), 20% for SA (SN = 60.9%), and 10% for suicide death (SN = 34.1%). 28% of TSMs were in the highest risk categories for at least one and 10% for more than one outcome. Findings regarding incomplete overlap highlight the complexities of risk targeting when multiple outcomes are of interest.

Web-based cognitive behavioral therapy (wb-CBT) is a scalable way to reach distressed university students. Guided wb-CBT is typically superior to self-guided wb-CBT over short follow-up periods, but evidence is less clear over longer periods. This study aimed to compare short-term (3 months) and longer-term (12 months) aggregate effects of guided and self-guided wb-CBT versus treatment as usual (TAU) in a randomized controlled trial of Colombian and Mexican university students and carry out an initially unplanned secondary analysis of the role of differential predicted compliance in explaining these differences. The 1319 participants, recruited either through email and social media outreach invitations or from waiting lists of campus mental health clinics, were undergraduates (1038/1319, 78.7% female) with clinically significant baseline anxiety (Generalized Anxiety Disorder-7 score≥10) or depression (Patient Health Questionnaire-9 score≥10). The intervention arms comprised guided wb-CBT with weekly asynchronous written human feedback, self-guided wb-CBT with the same content as the guided modality, and TAU as provided at each university. The prespecified primary outcome was joint remission (Generalized Anxiety Disorder-7 score=0-4 and Patient Health Questionnaire-9 score=0-4). The secondary outcome was joint symptom reduction (mean scores on the Patient Health Questionnaire Anxiety and Depression Scale) at 3 and 12 months after randomization. As reported previously, 3-month outcomes were significantly better with guided wb-CBT than self-guided wb-CBT (P=.02) or TAU (P=.02). However, subsequent follow-up showed that 12-month joint remission (adjusted risk differences=6.0-6.5, SE 0.4-0.5, and P<.001 to P=.007; adjusted mean differences=2.70-2.69, SE 0.7-0.8, and P<.001 to P=.001) was significantly better with self-guided wb-CBT than with the other interventions. Participants randomly assigned to the guided wb-CBT arm spent twice as many minutes logged on as those in the self-guided wb-CBT arm in the first 12 weeks (mean 12.5, SD 36.9 vs 5.9, SD 27.7; χ =107.1, P<.001), whereas participants in the self-guided wb-CBT arm spent twice as many minutes logged on as those in the guided wb-CBT arm in weeks 13 to 52 (mean 0.4, SD 7.5 vs 0.2, SD 4.4; χ =10.5, P=.001). Subgroup analysis showed that this longer-term superiority of self-guided wb-CBT was confined to the 40% (528/1319) of participants with high predicted self-guided wb-CBT compliance beyond 3 months based on a counterfactual nested cross-validated machine learning model. The 12-month outcome differences were nonsignificant across arms among other participants (all P>.05). The results have important practical implications for precision intervention targeting to maximize longer-term wb-CBT benefits. Future research needs to investigate strategies to increase sustained guided wb-CBT use once guidance ends. ClinicalTrials.gov NCT04780542; https://www.clinicaltrials.gov/study/NCT04780542. RR2-10.1186/s13063-022-06255-3.

The World Health Organization (WHO) World Mental Health-International College Student (WMH-ICS) initiative aims to screen for mental health and substance use problems among post-secondary students on a global scale as well as to develop and evaluate evidence-based preventive and ameliorative interventions for this population. This protocol paper presents the Canadian version of the WMH-ICS survey, detailing the adapted survey instrument, the unique weekly cross-sectional administration, the multi-tiered recruitment strategy, and the associated risk mitigation protocols. This paper aims to provide a methodological resource for researchers conducting cross-national comparisons of WMH-ICS data, as well as to serve as a useful guide for those interested in replicating the outlined cross-sectional methodology to better understand how mental health and substance use vary over time among university students. The online survey is based on the WMH-ICS survey instrument, modified to the Canadian context by the addition of questions pertaining to Canadian-based guidelines and the translation of the survey to Canadian French. The survey is administered through the Qualtrics survey platform and is sent to an independent stratified random sample of 350 students per site weekly, followed by two reminder emails. Upon survey closure every week, a random subsample of 70 non-responders are followed up with via phone or through a personal email in an effort to decrease non-responder bias. The survey is accompanied by an extensive risk mitigation protocol that stratifies respondents by level of need and provides tailored service recommendations, including a facilitated expedited appointment to student counselling services for those at increased risk of suicide. Anticipated sample size is approximately 5,500 students per site per year. In February 2020, the Canadian survey was deployed at the University of British Columbia. This was followed by deployment at Simon Fraser University (November 2020), McMaster University (January 2021), and University of Toronto (January 2022). Data collection at all four sites is ongoing. As of May 6th 2022, 29,503 responses have been collected. Based on an international collaboration, the Canadian version of the WMH-ICS survey incorporates a novel methodological approach centered on the weekly administration of a comprehensive cross-sectional survey to independent stratified random samples of university students. After 27 months of consecutive survey administration, we have developed and refined a survey protocol that has proven effective in engaging students at four Canadian institutions, allowing us to track how mental health and substance use vary over time using an internationally developed university student survey based on DSM-5 criteria.

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. 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. 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. 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.

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