A Unique and Rigorous Data Platform for Depression Screening Research
Follow DEPRESSD Project Twitter Account!
We are happy to share that we have created a DEPRESSD Project twitter account: @DepressdP. Going forward, we will be using the platform to share updates and news about the DEPRESSD Project. Please follow us here.
Congrats to Dr. Brooke Levis and the DEPRESSD team on their JAMA publication!
We are thrilled to announce that a DEPRESSD project evaluating the screening accuracy of the PHQ-2 alone and in combination with the PHQ-9 has been published in JAMA. The study, led by Brooke Levis, synthesized data from 100 studies (>44,000 participants), and found that the combination of PHQ-2 (with a cutoff of >=2) followed by the PHQ-9 (with a cutoff of >=10) had similar accuracy to PHQ-9 alone, but reduced the number of participants needing to complete the full PHQ-9 by 57%. Access the full article here
11 presentations by team members accepted to the 2020 Cochrane Colloquium!
Congratulations to current and former team members Zelalem, Dipika, Brooke, Marleine, Yin, and Kim, who had 5 oral presentations and 6 poster presentations accepted for the 27th Cochrane Colloquium! Presentations related to the DEPRESSD Project include: individual participant data meta-analyses of PHQ-2, PHQ-9, EPDS, and HADS accuracy; selective cutoff reporting in depression screening accuracy studies; imperfect reference standards for major depression classification; and a knowledge translation tool for clinician understanding of diagnostic accuracy estimates. Presentations related to meta-research and policy include: reporting of conflicts of interest in Cochrane and non-Cochrane meta-analyses of drug trials, and factors associated with contribution of data to individual participant data meta-analyses of intervention effectiveness.
Living systematic review funded by CIHR!
This week, members of the team received $50,000 from the CIHR to supplement MI4 funding for a living systematic review on mental health during the COVID-19 pandemic. This grant supports more effective dissemination and work in processing a high volume of Chinese-language evidence. Thank you to the CIHR! Congratulations to team members Olivia Bonardi, Danielle Rice, Jill Boruff, Marleine Azar, Chen He, Dr. Sarah Markham, Sheryl Sun, Dr. Yin Wu, Ankur Krishnan, Ian Thombs-Vite, and Drs. Benedetti and Thombs.
Congratulations to Zelalem Negeri whose oral presentation was accepted at the JSM 2020 Meetings in Philadelphia, USA
Dr. Zelalem Negeri’s latent class modelling for individual participant data meta-analysis (IPDMA) titled Latent class models for individual participant data meta-analyses of diagnostic test accuracy studies with imperfect reference standards” was accepted for an oral presentation at the 2020 Joint Statistical Meetings (JSM) to be held in Philadelphia, Pennsylvania from August 1-6, 2020. Dr. Negeri’s work aimed to propose and evaluate both Frequentist and Bayesian latent class models for IPDMA in the presence of imperfect reference standards. The proposed statistical methods will be illustrated using the Patient Health Questionnaire-9 (PHQ-9) database, which constitutes 100 studies, more than 44,000 participants and over 4,500 major depression cases. The JSM is the largest gatherings of statisticians in North America, and it attracts more than 1000 participants each year.
Congratulations to Zelalem Negeri on winning 2nd place at the 2020 EBOSS Poster Presentations
Dr. Zelalem Negeri’s poster presentation entitled won the 2nd best poster presentation award worth $50.00 at the 2020 McGill University’s EBOSS Research Day. Dr. Negeri’s work showed that, among other things, the diagnostic test accuracy of the PHQ-9 was better when compared with semi-structured reference standards than with fully structured or the MINI reference standards and that sex and age of participants were significantly associated with the specificities of the PHQ-9.
DEPRESSD article was one of the top downloaded in recent publication history!
We are excited to share that our research "Comparison of major depression diagnostic classification probability using the SCID, CIDI, and MINI diagnostic interviews among women in pregnancy or postpartum: An individual participant data meta‐analysis" is among the top 10% most downloaded papers!Among work published between January 2018 and December 2019, our research received some of the most downloads in the 12 months following online publication and generated immediate impact and helped to raise the visibility of the International Journal of Methods in Psychiatric Research. Read more about the study
DEPRESSD team funded for living systematic review on mental health in COVID-19
This week, members of the team received $65,000 from the McGill Interdisciplinary Initiative in Infection and Immunity (MI4)'s Emergency COVID-19 Research Funding Program to conduct a living systematic review on mental health during the COVID-19 pandemic. The objectives of this living systematic review are to evaluate (1) changes in mental health symptoms; (2) factors associated with levels or changes in symptoms during COVID-19; and (3) the effect of interventions on mental health symptoms during COVID-19. A huge thank-you to the MI4 initiative who generously supports this important research project during these trying times. Congratulations for team members Olivia Bonardi, Danielle Rice, Jill Boruff, Marleine Azar, Chen He, Dr. Sarah Markham, Sheryl Sun, Dr. Yin Wu, Ankur Krishnan, Ian Thombs-Vite, and Drs. Benedetti and Thombs.
Want to join our team? We are hiring! APPLY NOW
The DEPRESsion Screening Data (DEPRESSD) Project is a collaborative endeavor that was set up by Drs. Brett Thombs and Andrea Benedetti, who partnered with local and international experts to evaluate the diagnostic accuracy of depression screening tools by conducting individual participant data meta-analyses (IPDMAs).
We are currently seeking a full-time Analytical Research Coordinator and full-time Postdoctoral Fellows to join the team.
Congratulations to the DEPRESSD team, whose Journal of Clinical Epidemiology article was featured in Mad in America. The study, led by Brooke Levis, found that prevalence based on the PHQ-9 screening tool (at the standard cutoff of ≥ 10) was double (12% greater) prevalence based on a validated diagnostic interview for major depression, the SCID. No alternative PHQ-9 cutoff matched SCID prevalence consistently. Read more here
Congratulations to Yin, Brooke, and Dipika had presentations accepted for the MEMTAB 2020
We are excited to announce that Drs. Yin Wu and Brooke Levis had oral presentations accepted and Dipika Neupane had a poster presentation accepted for the MEMTAB2020 (Methods for Evaluation of medical prediction Models, Tests And Biomarker 2020) symposium in Leuven, Belgium. Every year, MEMTAB attracts researchers, healthcare workers, policymakers, and manufacturers actively involved in the development, evaluation or regulation of tests, (bio)markers, models, tools, apps, devices or any other modality used for the purpose of diagnosis, prognosis, risk stratification or (disease or therapy) monitoring. Read more.
Congratulations on DEPRESSD's New Publication in the Journal of Clinical Epidemiology
Researchers commonly report the percentage of participants scoring above screening tool thresholds as the prevalence of depression, even though screening tools are designed to identify individuals who may have depression and further assessment is required to confirm whether individuals meet diagnostic criteria. In this study, led by Brooke Levis, the team combined data from 44 primary studies (9242 participants) and compared prevalence based on the PHQ-9 screening tool to prevalence based on a validated diagnostic interview for major depression, the SCID. Using the standard PHQ-9 cutoff score of ≥ 10, the prevalence was 12% greater than SCID-based prevalence. No alternative PHQ-9 cutoff matched SCID prevalence consistently. Read more about the study here.
Congratulations to the DEPRESSD Team for receiving a CIHR Operating Grant!
We are excited to announce that the DEPRESSD Team’s research proposal “Depression Trajectories in Pregnant and Postpartum Women: An Individual Participant Data Meta-Analysis” was awarded an operating grant from the Canadian Institutes of Health Research (CIHR), and we ranked 1st place in a committee of 60 applications! In this project, we will use the existing DEPRESSD-EPDS database to try to describe the trajectory of major depression and identify risk factors throughout the pregnancy and postpartum period.
We would like to thank our DEPRESSD Team members for their great work on the project.
Congratulations on DEPRESSD's New Publication in Journal of Psychosomatic Research
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
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:
Dr. Brooke Levis Successfully Defends her Ph.D. Thesis
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
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
Brooke Levis receives 2019 Trainee Paper of the Year Award from the Lady Davis Institute
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 ().
Yin Wu and Brooke Levis Receive the Fonds de Recherche Québec - Santé (FRQS) Postdoctoral Fellowships
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
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 () to read more about this study.