About Us

DEPRESSD is an international collaborative project involving experts in health policy, psychiatry and statistics as well as investigators who have collected depression screening data. Our mission is to synthesize the global depression screening data in order to develop and apply rigourous methods on assessing depression screening tool accuracy that minimize bias and provide evidence to inform research and policy to improve mental health care. The project builds a database for shared usage and also provides a unique platform for trainee development, including skills in evidence synthesis and statistical modelling.

 

Latest News

Congratulations on DEPRESSD's New Publication in Journal of Psychosomatic Research

2020/02/01

Recently, the DEPRESSD Team published their first IPD meta-analysis using the DEPRESSD-HADS database. The study, led by Yin Wu (DEPRESSD’s postdoctoral research fellow), included 15,856 participants from 73 studies (15,335 non-psychiatric medical patients, 164 partners of medical patients, and 357 healthy adults) and compared odds of major depression classification for different diagnostic interviews. It was found that controlling for HADS-D depressive symptom scores and other study and participant characteristics, there were important differences in classification probability across interviews. Among fully structured interviews designed for lay administration, the MINI classifies significantly more participants as having major depression than the CIDI. Compared with the semi-structured SCID designed for administration by a trained evaluator, CIDI is less sensitive to increases in symptom levels, and the odds of diagnosis do not increase as much as symptoms increase. Read more about the study here.

Dr.Benedetti and Dr.Thombs gave a  seminar at McGill

2020/01/27

Dr. Benedetti and Dr. Thombs introduced the DEPRESSD Project: Using Individual Participant Data Meta-Analysis to Overcome Barriers to Evaluating Diagnostic Test in the department of Epidemiology, Biostatistics and Occupational Health (EBOH) seminar at McGill. The objectives of the speech are: to describe IPDMA approaches and advantages compared to conventional aggregate-data meta-analysis; to discuss how an IPDMA approach has been used in DEPRESSD to address shortcomings in conventional meta-analyses on the accuracy of depression screening tools; and to provide examples of how an IPDMA approach has facilitated studies that illustrate and provide possible solutions to methodological challenges. More information: https://www.mcgill.ca/epi-biostat-occh/seminars-events/seminars/epidemiology

Dr. Brooke Levis Successfully Defends her Ph.D. Thesis

2019/11/01

Congratulations to Dr. Brooke Levis! Dr. Levis successfully defended her Ph.D. thesis entitled "Using individual participant data meta-analysis (IPDMA) to evaluate the diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9)" on Wednesday, October 30, 2019, at McGill University. Her thesis focused on comparing diagnostic interviews for major depression classification, evaluating the accuracy of the PHQ-9 depression screening tool, and examining bias in accuracy estimates due to using data-driven procedures in small samples. Dr. Levis is currently conducting a postdoctoral fellowship in IPDMA and prediction modelling at Keele University and continues to collaborate with the DEPRESSD Project team.

DEPRESSD Team Publishes IPD Meta-analysis of PHQ-9 Algorithm

2019/07/19

A study authored by Chen He (DEPRESSD’s research assistant) on the accuracy of the Patient Health Questionnaire-9 algorithm was just published online in the Journal of Psychotherapy and Psychosomatics. The aim of the study was to use IPDMA to evaluate the accuracy of two PHQ-9 diagnostic algorithms for detecting major depression and compare accuracy between the algorithms and the standard PHQ-9 cutoff score of ≥10. Results show that the PHQ-9 score threshold approach provides more desirable combinations of sensitivity and specificity across different cutoffs than the algorithm approach for screening and provides the flexibility to select a cutoff that would provide the preferred combination of sensitivity and specificity. Therefore, The cutoff score approach appears to be a better option than a PHQ-9 algorithm for detecting major depression. To view the full-text article, please click https://www.karger.com/Article/FullText/502294

Brooke Levis receives 2019 Trainee Paper of the Year Award from the Lady Davis Institute

2019/05/27

Brooke Levis, a doctoral trainee with the DEPRESSD team, received the LDI's 2019 Trainee Paper of the Year Award for her IPD meta-analysis on the diagnostic accuracy of the PHQ-9 for screening to detect major depression, which was recently published in the BMJ (https://www.bmj.com/content/365/bmj.l1476). Congratulations, Brooke!

Yin Wu and Brooke Levis Receive the Fonds de Recherche Québec - Santé (FRQS) Postdoctoral Fellowships

2019/05/01

Yin and Brooke were awarded two-year postdoctoral fellowships from the Fonds de Recherche Québec – Santé (FRQS). They both were ranked 1st on their respective panels of about 20 applicants. Yin will use hers with the DEPRESSD team, working on the project, “Improving Depression Screening in Geriatric Patients by Reducing Bias and Generating Individualized Accuracy Estimates: An Individual Patient Data Meta-Analysis of the Geriatric Depression Scale (GDS)”. Brooke will take hers to the UK, to the Centre for Prognosis Research at Keele University, where she will work with Prof. Richard Riley, who is the world’s foremost expert in IPD meta-analysis. Brooke will work on a project developing and applying statistical methods to overcome missing data in IPD meta-analysis and will also be involved in other projects related to IPD meta-analysis, prediction, and prognosis. 

DEPRESSD Team Publishes IPD Meta-analysis of PHQ-9

2019/04/10

Yesterday, the DEPRESSD team's IPD meta-analysis on the diagnostic accuracy of the PHQ-9 for screening to detect major depression, led by Brooke Levis, was published online in The BMJ. The team synthesized original patient data from 58 studies (17,357 participants; 2,312 major depression cases) and analyzed PHQ-9 accuracy across reference standards and across patient subgroups. PHQ-9 accuracy was higher when compared to diagnoses from semi-structured diagnostic interviews, which are designed for administration by clinicians, than when compared to diagnoses from fully structured diagnostic interviews, which are designed for lay administration. The standard cutoff score of 10 or greater maximized combined sensitivity and specificity overall and for subgroups. Please click here (https://www.bmj.com/content/365/bmj.l1476.full) to read more about this study.

 

Contact Us

The DEPRESSD Project

4333 Chemin de la Côte-Sainte-Catherine, Montréal, H3T 1E2 Canada

Tel: (514) 340-8222

Email: depressdproject@gmail.com

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