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Aim: To assess the incremental cost-effectiveness ratio (ICER) of group transdiagnostic cognitive-behavioural therapy (tCBT) added to treatment as usual (TAU) for anxiety disorders compared to TAU only from the healthcare system perspective over a 1-year time horizon. Methods: Data from a pragmatic multisite randomized controlled trial where adults (18–65 years) with an anxiety disorder were randomized to tCBT + TAU (n = 117) or TAU (n = 114). Group tCBT is a 12-week (2h weekly sessions) community-based intervention. Health service utilization and related costs were captured from medico-administrative data and included those for the intervention, ambulatory visits, hospitalizations and medications. Effectiveness was based on quality-adjusted life years (QALYs). The study included measures at baseline, 4, 8, and 12 months. Intention-to-treat and complete case analyses were carried out. Missing data were imputed using multiple imputation analyses. Seemingly unrelated regression analyses were used to assess the effect of the intervention on total costs and QALYs while also adjusting for baseline confounders. The probability of cost-effectiveness of the intervention was assessed according to different willingness-to-pay (WTP) thresholds using the net benefit regression method. Results: The ICER of tCBT + TAU as compared to TAU in the intention-to-treat analysis was $6,581/QALY. Complete case analyses showed a similar ICER of $6,642/QALY. The probability at a WTP threshold of $20,000 and $40,000 that tCBT + TAU as compared to TAU is cost-effective is 93.0% and 99.9%. Conclusion: tCBT added to TAU appears to be cost-effective from the healthcare system perspective for treating adult patients with anxiety disorders. Larger trials including young and older adults as well as a range of anxiety disorders are needed to further investigate the cost-effectiveness of tCBT in different patient populations. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to provide a general overview of mental health randomized controlled trials (RCTs) and summarize the temporal trends in terms of the number of studies, median sample sizes, and median effect sizes using data collected from the Cochrane Database of Systematic Reviews (CDSR). Methods: Using data collected from the CDSR, the temporal trends are compared in terms of the number of studies, median sample sizes, and median effect sizes between two broad categories of interventions: pharmacological RCT (ph-RCT) and non-pharmacological RCT (nph-RCT), and in conjunction with major mental disorder categories. Results: Chronologically, the number of mental health RCTs reported in publications has increased exponentially from 1955 to 2020. While ph-RCT comprised a majority of mental health RCTs in the earlier years, the proportion of nph-RCTs increased more quickly over time and markedly exceeded ph-RCT after 2010. The median sample size for all 6,652 mental health RCTs was 61, with 61 for ph-RCT and 60 for nph-RCT. Over time, the median fluctuated but an increasing trend was observed over the past 60+ years. The median of the effect size, measured by Pearson's r, for overall RCTs was 0.18, and nph-RCT (0.19) had a larger median effect size compared to ph-RCT (0.16). Over the years, the nph-RCT had a larger median effect size than the ph-RCT. Differences in the median effect sizes among the categories of mental disorders were also noted. Schizophrenia had the most RCTs, with a median Pearson's r value of 0.17. Mood disorder had the second largest number of RCTs and a median Pearson's r value of 0.15. Neurotic/stress-related mental disorder had the third largest number of RCTs with the highest median Pearson's r being 0.23. Conclusions: This study provides meaningful information and filled the knowledge gap in mental health RCTs. [ABSTRACT FROM AUTHOR]

Background: Major depressive disorder (MDD) is a common and burdensome condition that has low rates of treatment success for each individual treatment. This means that many patients require several medication switches to achieve remission; selecting an effective antidepressant is typically a sequential trial-and-error process. Machine learning techniques may be able to learn models that can predict whether a specific patient will respond to a given treatment, before it is administered. This study uses baseline clinical data to create a machine-learned model that accurately predicts remission status for a patient after desvenlafaxine (DVS) treatment. Methods: We applied machine learning algorithms to data from 3,399 MDD patients (90% of the 3,776 subjects in 11 phase-III/IV clinical trials, each described using 92 features), to produce a model that uses 26 of these features to predict symptom remission, defined as an 8-week Hamilton Depression Rating Scale score of 7 or below. We evaluated that learned model on the remaining held-out 10% of the data (n = 377). Results: Our resulting classifier, a trained linear support vector machine, had a holdout set accuracy of 69.0%, significantly greater than the probability of classifying a patient correctly by chance. We demonstrate that this learning process is stable by repeatedly sampling part of the training dataset and running the learner on this sample, then evaluating the learned model on the held-out instances of the training set; these runs had an average accuracy of 67.0% ± 1.8%. Conclusions: Our model, based on 26 clinical features, proved sufficient to predict DVS remission significantly better than chance. This may allow more accurate use of DVS without waiting 8 weeks to determine treatment outcome, and may serve as a first step toward changing psychiatric care by incorporating clinical assistive technologies using machine-learned models. [ABSTRACT FROM AUTHOR]

Objective: The increasing focus on adolescent personality disorder has tended to ignore evidence of the developmental continuity of the period from puberty to young adulthood. This study aims to: (1) describe the characteristics of a sample of young people with borderline personality disorder (BPD) who had no previous history of evidence-based treatment for the disorder and (2) compare their characteristics by participant age group. Methods: One hundred and thirty-nine young people (15 to 25 years) with BPD, newly enrolled in the Monitoring Outcomes of BPD in Youth randomized controlled trial, completed semi-structured interview and self-report measures assessing demographic, clinical, and functional characteristics. Younger (aged 15 to 17 years; n = 64) and older (aged 18 to 25 years; n = 75) participants were compared on these same variables using t -tests, chi-square tests, and logistic regression. Results: Young outpatients with BPD had extensive and severe psychopathology and were functioning poorly. Adolescents and young adults with BPD showed substantial similarities on 20 key aspects of their presentation. Significant between-groups differences were observed in household makeup, treatment history, antisocial personality disorder, emotion dysregulation, substance use, age of commencement and extent of self-harm, and achievement of age-appropriate educational milestones. Adolescent BPD group membership was predicted by family composition and self-harm, whereas young adult BPD group membership was predicted by not achieving age-appropriate milestones, vocational disengagement, and emotion dysregulation. The final model explained 54% of the variance and correctly classified 80.2% of the sample by age. Conclusions: Both adolescents and young adults with early stage BPD present with severe and often similar problems to one another, supporting developmental continuity across this age range. However, there are also meaningful differences in presentation, suggesting that pathways to care might differ by age and/or developmental stage. Detection and intervention for personality disorder should not be delayed until individuals reach 18 years of age. [ABSTRACT FROM AUTHOR]

Objective: Addiction comorbidity is an important clinical challenge in mood disorders, but the best way of pharmacologically treating people with mood disorders and addictions remains unclear. The aim of this study was to assess the efficacy of pharmacological treatments for mood and addiction symptoms in people with mood disorders and addiction comorbidity.Methods: A systematic search of placebo-controlled randomized controlled trials investigating the effects of pharmacological treatments in people with bipolar disorder (BD) or major depressive disorder (MDD), and comorbid addictions was performed. Treatment-related effects on mood and addiction measures were assessed in a meta-analysis, which also estimated risks of participant dropout and adverse effects.Results: A total of 32 studies met systematic review inclusion criteria. Pharmacological therapy was more effective than placebo for improving manic symptoms (standardized mean difference [SMD] = -0.15; 95% confidence interval [95% CI], -0.29 to -0.02; P = 0.03) but not BD depressive symptoms (SMD = -0.09; 95% CI, -0.22 to 0.03; P = 0.15). Quetiapine significantly improved manic symptoms (SMD = -0.23; 95% CI, -0.39 to -0.06; P = 0.008) but not BD depressive symptoms (SMD = -0.07; 95% CI, -0.23 to 0.10; P = 0.42). Pharmacological therapy was more effective than placebo for improving depressive symptoms in MDD (SMD = -0.16; 95% CI, -0.30 to -0.03; P = 0.02). Imipramine improved MDD depressive symptoms (SMD = -0.58; 95% CI, -1.03 to -0.13; P = 0.01) but Selective serotonin reuptake Inhibitors (SSRI)-based treatments had no effect (SMD = -0.06; 95% CI, -0.30 to 0.17; P = 0.60). Pharmacological treatment improved the odds of alcohol abstinence in MDD but had no effects on opiate abstinence.Conclusions: Pharmacological treatments were significantly better than placebo in improving manic symptoms, MDD depressive symptoms, and alcohol abstinence but were not better for bipolar depression symptoms. Importantly, quetiapine was not more effective than placebo in improving bipolar depression symptoms nor were SSRI's for the treatment of MDD depression. Our findings highlight the need for further high-quality clinical trials of treatments for mood disorders and comorbid addictions. [ABSTRACT FROM AUTHOR]

Objective: Housing First (HF) has been linked to increased tenure in housing for homeless people with mental illness, but the effect of HF on housing stability for people with borderline or lower intellectual functioning has not been examined. This study of homeless adults with mental illness in Toronto, Ontario assessed whether the association between housing stability and HF differed for adults with borderline or lower intellectual functioning, compared to adults with above borderline intellectual functioning.Method: This study included 172 homeless adults with mental illness from the Toronto site of the At Home-Chez Soi randomized trial that compared receiving HF relative to treatment as usual. This sample was divided into two intellectual functioning groups: 1) adults with borderline or lower intellectual functioning (IQ < 85, 16%), and 2) adults with above borderline intellectual functioning (IQ ≥ 85, 84%). We compared these groups by modelling the percentage of days stably housed using a linear multivariable generalized estimating equation and included interaction between treatment and intellectual functioning. An interaction between treatment and time was also included.Results: There were no overall differences in housing stability for individuals with borderline or lower intellectual functioning compared to people with higher than borderline intellectual functioning in either the HF or the treatment as usual groups.Conclusion: This study is the first to demonstrate that for homeless adults with mental illness, borderline or lower intellectual functioning did not significantly affect housing stability. This accentuates the need for more research and potentially wider consideration of their inclusion in housing interventions, such as HF. [ABSTRACT FROM AUTHOR]

Background: The Canadian Network for Mood and Anxiety Treatments (CANMAT) conducted a revision of the 2009 guidelines by updating the evidence and recommendations. The scope of the 2016 guidelines remains the management of major depressive disorder (MDD) in adults, with a target audience of psychiatrists and other mental health professionals.Methods: Using the question-answer format, we conducted a systematic literature search focusing on systematic reviews and meta-analyses. Evidence was graded using CANMAT-defined criteria for level of evidence. Recommendations for lines of treatment were based on the quality of evidence and clinical expert consensus. "Pharmacological Treatments" is the third of six sections of the 2016 guidelines. With little new information on older medications, treatment recommendations focus on second-generation antidepressants.Results: Evidence-informed responses are given for 21 questions under 4 broad categories: 1) principles of pharmacological management, including individualized assessment of patient and medication factors for antidepressant selection, regular and frequent monitoring, and assessing clinical and functional outcomes with measurement-based care; 2) comparative aspects of antidepressant medications based on efficacy, tolerability, and safety, including summaries of newly approved drugs since 2009; 3) practical approaches to pharmacological management, including drug-drug interactions and maintenance recommendations; and 4) managing inadequate response and treatment resistance, with a focus on switching antidepressants, applying adjunctive treatments, and new and emerging agents.Conclusions: Evidence-based pharmacological treatments are available for first-line treatment of MDD and for management of inadequate response. However, given the limitations of the evidence base, pharmacological management of MDD still depends on tailoring treatments to the patient. [ABSTRACT FROM AUTHOR]