Simple jQuery Dropdowns
Please use this identifier to cite or link to this item:
Title: The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis
Authors: Chen He
Brooke Levis
Kira E. Riehm
Nazanin Saadat
Alexander W. Levis
Marleine Azar
Danielle B. Rice
Ankur Krishnan
Yin Wu
Ying Sun
Mahrukh Imran
Jill Boruff
Pim Cuijpers
Simon Gilbody
John P.A. Ioannidis
Lorie A. Kloda
Dean McMillan
Scott B. Patten
Ian Shrier
Roy C. Ziegelstein
Dickens H. Akena
Bruce Arroll
Liat Ayalon
Hamid R. Baradaran
Murray Baron
Anna Beraldi
Charles H. Bombardier
Peter Butterworth
Gregory Carter
Marcos Hortes Nisihara Chagas
Juliana C.N. Chan
Rushina Cholera
Kerrie Clover
Yeates Conwell
Janneke M. De Man-Van Ginkel
Jesse R. Fann
Felix H. Fischer
Daniel Fung
Bizu Gelaye
Felicity Goodyear-Smith
Catherine G. Greeno
Brian J. Hall
Patricia A. Harrison
Martin Härter
Ulrich Hegerl
Leanne Hides
Stevan E. Hobfoll
Marie Hudson
Thomas N. Hyphantis
Masatoshi Inagaki
Khalida Ismail
Nathalie Jetté
Mohammad E. Khamseh
Kim M. Kiely
Yunxin Kwan
Femke Lamers
Shen Ing Liu
Manote Lotrakul
Sonia R. Loureiro
Bernd Löwe
Laura Marsh
Anthony McGuire
Sherina Mohd-Sidik
Tiago N. Munhoz
Kumiko Muramatsu
Flávia L. Osório
Vikram Patel
Brian W. Pence
Philippe Persoons
Angelo Picardi
Katrin Reuter
Alasdair G. Rooney
Iná S. Da Silva Dos Santos
Juwita Shaaban
Abbey Sidebottom
Adam Simning
Lesley Stafford
Sharon Sung
Pei Lin Lynnette Tan
Alyna Turner
Henk C.P.M. Van Weert
Jennifer White
Mary A. Whooley
Kirsty Winkley
Mitsuhiko Yamada
Brett D. Thombs
Andrea Benedetti
Melbourne Institute
Melbourne School of Psychological Sciences
San Francisco VA Health Care System
Mackay Medical College
Calvary Mater Newcastle
Duke-NUS Medical School Singapore
City of Minneapolis
Hunter Medical Research Institute, Australia
Niigata Seiryo University
Bar-Ilan University School of Social Work
Makerere University
Concordia University
University Medical Center Utrecht
Royal Women's Hospital, Carlton
Harvard T.H. Chan School of Public Health
KU Leuven– University Hospital Leuven
University of Queensland
Mackay Memorial Hospital Taiwan
University of New South Wales (UNSW) Australia
University of Edinburgh
Yong Loo Lin School of Medicine
Shimane University
Charité – Universitätsmedizin Berlin
Universiti Putra Malaysia
The University of North Carolina at Chapel Hill
KU Leuven
Iran University of Medical Sciences
Prince of Wales Hospital Hong Kong
University of Rochester Medical Center
University of California, San Francisco
Neuroscience Research Australia
Universidade de Macau
Lady Davis Institute for Medical Research
UNC School of Medicine
Technical University of Munich
Monash University
Deakin University
National Center of Neurology and Psychiatry Kodaira
University of Newcastle, Faculty of Health and Medicine
University of York
Saint Joseph's College of Maine
Faculty of Medicine, Ramathibodi Hospital, Mahidol University
University of Aberdeen
University of Pittsburgh
University of Washington, Seattle
Universidade Federal de Pelotas
Icahn School of Medicine at Mount Sinai
Stanford University
King's College London
Istituto Superiore Di Sanita
Singapore Institute of Mental Health
Australian National University
Vrije Universiteit Amsterdam
Goethe-Universität Frankfurt am Main
Centre universitaire de santé McGill
Johns Hopkins Bloomberg School of Public Health
University of Auckland
Nanyang Technological University
Universitätsklinikum Hamburg-Eppendorf und Medizinische Fakultät
Panepistimion Ioanninon
Chinese University of Hong Kong
Harvard Medical School
School of Medical Sciences - Universiti Sains Malaysia
McGill University
Tan Tock Seng Hospital
Baylor College of Medicine
University of Calgary
Amsterdam UMC - University of Amsterdam
Johns Hopkins School of Medicine
Private Practice for Psychotherapy and Psycho-oncology
National Institute of Science and Technology
Allina Health
Keywords: Medicine;Psychology
Issue Date: 1-Jan-2020
Citation: Psychotherapy and Psychosomatics. Vol.89, No.1 (2020), 25-37
Abstract: © 2019 S. Karger AG, Basel. All rights reserved. Background: Screening for major depression with the Patient Health Questionnaire-9 (PHQ-9) can be done using a cutoff or the PHQ-9 diagnostic algorithm. Many primary studies publish results for only one approach, and previous meta-analyses of the algorithm approach included only a subset of primary studies that collected data and could have published results. Objective: To use an individual participant data meta-analysis 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. Methods: Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, Web of Science (January 1, 2000, to February 7, 2015). Eligible studies that classified current major depression status using a validated diagnostic interview. Results: Data were included for 54 of 72 identified eligible studies (n participants = 16,688, n cases = 2,091). Among studies that used a semi-structured interview, pooled sensitivity and specificity (95% confidence interval) were 0.57 (0.49, 0.64) and 0.95 (0.94, 0.97) for the original algorithm and 0.61 (0.54, 0.68) and 0.95 (0.93, 0.96) for a modified algorithm. Algorithm sensitivity was 0.22-0.24 lower compared to fully structured interviews and 0.06-0.07 lower compared to the Mini International Neuropsychiatric Interview. Specificity was similar across reference standards. For PHQ-9 cutoff of ≥10 compared to semi-structured interviews, sensitivity and specificity (95% confidence interval) were 0.88 (0.82-0.92) and 0.86 (0.82-0.88). Conclusions: The cutoff score approach appears to be a better option than a PHQ-9 algorithm for detecting major depression.
ISSN: 14230348
Appears in Collections:Scopus 2020

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.