What are the effects of preventative interventions on major depressive disorder (MDD) in young adults? A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Depression is a prevalent disorder with a peak rate of onset in young adulthood from 18 to 25 years. To date, no review has systematically assessed the effectiveness of programs that aim to reduce depressive symptoms or diagnosis of depression in young adults. METHOD: A systematic search was performed in Cochrane, PubMed, PsycINFO and EMBASE. We performed a random-effects meta-analysis of the randomized controlled studies that compared an intervention for young adults (aged 18-25) without a diagnosis or history of depression and a control condition. Comparisons between intervention and control group outcomes were carried out at the post-intervention time point. We also compared intervention and control group outcomes at later follow-up time points where data were available. RESULTS: Twenty-six randomized controlled trials among 2865 young adults were included in the analysis. The pooled effect size of the interventions versus control at post-intervention was g?=?0.37 (95% CI: 0.28-0.47, NNT?=?9) and heterogeneity was moderate I2?=?36 (95% CI: 11-64). There were no significant effects in terms of the type of delivery, focus of study, type of control, or type of support within the interventions. LIMITATIONS: The authors were unable to assess the effects of interventions on the onset of depression as none of the included studies measured incidence. The risk of bias was high in most studies (81%). Only one study included a follow-up of more than a year. Demographic factors were inconsistently reported in the included articles. CONCLUSION: While it was not possible to investigate the effects of interventions on depression incidence, some evidence was found for the effectiveness of preventative interventions in reducing depressive symptoms in young adults. Future research should address limitations of the current evidence base to allow stronger conclusions to be drawn

Unleashing talent in mental health sciences: gender equality at the top

Society is undergoing a shift in gender politics. Science and medicine are part of this conversation, not least as women's representation and pay continue to drop as one progresses through more senior academic and clinical levels. Naming and redressing these inequalities needs to be a priority for us all

Individual participant data (IPD) meta-analysis of psychological relapse prevention interventions versus control for patients in remission from depression: A protocol

This is the final version. Available from the publisher via the DOI in this record.Psychological interventions and antidepressant medication can be effective interventions to prevent depressive relapse for patients currently in remission of depression. Less is known about overall factors that predict or moderate treatment response for patients receiving a psychological intervention for recurrent depression. This is a protocol for an individual participant data (IPD) meta-analysis which aims to assess predictors and moderators of relapse or recurrence for patients currently in remission from depression. Methods and analysis Searches of PubMed, PsycINFO, Embase and Cochrane Central Register of Controlled Trials were completed on 13 October 2019. Study extractions and risk of bias assessments have been completed. Study authors will be asked to contribute IPD. Standard aggregate meta-analysis and IPD analysis will be conducted, and the outcomes will be compared with assess whether results differ between studies supplying data and those that did not. IPD files of individual data will be merged and variables homogenised where possible for consistency. IPD will be analysed via Cox regression and one and two-stage analyses will be conducted. Ethics and dissemination The results will be published in peer review journals and shared in a policy briefing as well as accessible formats and shared with a range of stakeholders. The results will inform patients and clinicians and researchers about our current understanding of more personalised ways to prevent a depressive relapse. No local ethics approval was necessary following consultation with the legal department. Guidance on patient data storage and management will be adhered to. PROSPERO registration number CRD42019127844.Amsterdam Public Health Institute Collaborativ

Continuation of Antidepressants vs Sequential Psychological Interventions to Prevent Relapse in Depression An Individual Participant Data Meta-analysis

This is the author accepted manuscript. The final version is available from the American Medical Association via the DOI in this recordNOTE: the title of the author accepted manuscript is different from the final published versionImportance Depression frequently recurs. To prevent relapse, antidepressant medication is often taken in the long term. Sequentially delivering a psychological intervention while undergoing tapering of antidepressant medication might be an alternative to long-term antidepressant use. However, evidence is lacking on which patients may benefit from tapering antidepressant medication while receiving a psychological intervention and which should continue the antidepressant therapy. A meta-analysis of individual patient data with more power and precision than individual randomized clinical trials or a standard meta-analysis is warranted. Objectives To compare the associations between use of a psychological intervention during and/or after antidepressant tapering vs antidepressant use alone on the risk of relapse of depression and estimate associations of individual clinical factors with relapse. Data Sources PubMed, the Cochrane Library, Embase, and PsycInfo were last searched on January 23, 2021. Requests for individual participant data from included randomized clinical trials (RCTs) were sent. Study Selection Randomized clinical trials that compared use of a psychological intervention while tapering antidepressant medication with antidepressant monotherapy were included. Patients had to be in full or partial remission from depression. Two independent assessors conducted screening and study selection. Data Extraction and Synthesis Of 15 792 screened studies, 236 full-text articles were retrieved, and 4 RCTs that provided individual participant data were included. Main Outcomes and Measures Time to relapse and relapse status over 15 months measured via a blinded assessor using a diagnostic clinical interview. Results Individual data from 714 participants (mean [SD] age, 49.2 [11.5] years; 522 [73.1%] female) from 4 RCTs that compared preventive cognitive therapy or mindfulness-based cognitive therapy during and/or after antidepressant tapering vs antidepressant monotherapy were available. Two-stage random-effects meta-analysis found no significant difference in time to depressive relapse between use of a psychological intervention during tapering of antidepressant medication vs antidepressant therapy alone (hazard ratio [HR], 0.86; 95% CI, 0.60-1.23). Younger age at onset (HR, 0.98; 95% CI, 0.97-0.99), shorter duration of remission (HR, 0.99; 95% CI, 0.98-1.00), and higher levels of residual depressive symptoms at baseline (HR, 1.07; 95% CI, 1.04-1.10) were associated with a higher overall risk of relapse. None of the included moderators were associated with risk of relapse. Conclusions and Relevance The findings of this individual participant data meta-analysis suggest that regardless of the clinical factors included in these studies, the sequential delivery of a psychological intervention during and/or after tapering may be an effective relapse prevention strategy instead of long-term use of antidepressants. These results could be used to inform shared decision-making in clinical practice.Amsterdam Public Health Research Institut

Identifying Relapse Predictors in Individual Participant Data with Decision Trees

This is the author accepted manuscript/Availability of data and material. The datasets generated and/or analysed during the current study are not publicly available due to patient consent restrictions but are available from the corresponding author on reasonable request.Background: Depression is a highly common and recurrent condition. Predicting who is at most risk of relapse or recurrence can inform clinical practice. Applying machine-learning methods to Individual Participant Data (IPD) can be promising to improve the accuracy of risk predictions. Methods: Individual data of four Randomized Controlled Trials (RCTs) evaluating an tidepressant treatment compared to psychological interventions with tapering (N = 714) were used to identify predictors of relapse and/or recurrence. Ten baseline predictors were assessed. Decision trees with and without gradient boosting were applied. To study the robust ness of decision-tree classifications, we also performed a complementary logistic regression analysis. Results: The combination of age, age of onset of depression, and depression severity sig nificantly enhances the prediction of relapse risk when compared to classifiers solely based on depression severity. The studied decision trees can (i) identify relapse patients at intake with an accuracy, specificity, and sensitivity of about 55% (without gradient boosting) and 58% (with gradient boosting), and (ii) slightly outperform classifiers that are based on logistic regression. Conclusions: Decision tree classifiers based on multiple–rather than single–risk indicators may be useful for developing treatment stratification strategies. These classification models have the potential to contribute to the development of methods aimed at effectively prioritizing treatment for those individuals who require it the most. Our results also underline the existing gaps in understanding how to accurately predict depressive relapse.AR

An Individual Participant Data meta-analysis of psychological interventions for preventing depressive relapse

This is the author accepted manuscript.Data availability: This is individual participant data from randomised controlled trials which cannot be shared publicly due to ethical and consent restrictions that are in place. For data access, please contact the corresponding author. Data access can be provided if these conditions are met 1) there is approval from all co-authors for the data to be shared, 2) there is a data sharing agreement in place (which adheres to the requirements for data sharing by the Amsterdam University Medical Centre), 3) individual studies have participant consent and ethics approvals in place to allow for further onward sharing 4) there is an analysis plan in place that all co-authors agree with. Upon data sharing data can only be used for the specified purposes.Code availability: Analysis code can be found here: https://osf.io/fyr7hMajor depressive disorder (MDD) is a leading cause of disability worldwide, identifying effective strategies to prevent depressive relapse is crucial. This individual patient data meta-analysis (IPDMA) addresses whether and for whom psychological interventions can be recommended for relapse prevention of MDD. One and two-stage IPDMA on 14 randomised controlled trials (N = 1720) were conducted. The relapse risk over 12 months was substantially lower for those who received a psychological intervention versus treatment as usual (TAU), antidepressant or evaluation only control (Hazard Ratio (HR): 0.60 (95% CI 0.48 – 0.74). The number of previous depressive episodes moderated the treatment effect, with psychological interventions demonstrating greater efficacy for patients with three or more prior episodes. Our results suggest that adding psychological interventions to current treatment in order to prevent depressive relapse is recommended. For patients at lower risk of relapse, less intensive approaches may be indicated.Alliance for Public Health, Amsterdam University Medical Centr