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Meta-Analysis
. 2020;89(1):25-37.
doi: 10.1159/000502294. Epub 2019 Oct 8.

The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis

Chen He  1   2 Brooke Levis  1   2 Kira E Riehm  1 Nazanin Saadat  1 Alexander W Levis  1   2 Marleine Azar  1   2 Danielle B Rice  1   3 Ankur Krishnan  1 Yin Wu  1   2   4 Ying Sun  1 Mahrukh Imran  1 Jill Boruff  5 Pim Cuijpers  6 Simon Gilbody  7 John P A Ioannidis  8 Lorie A Kloda  9 Dean McMillan  7 Scott B Patten  10   11 Ian Shrier  1   2 Roy C Ziegelstein  12 Dickens H Akena  13 Bruce Arroll  14 Liat Ayalon  15 Hamid R Baradaran  16   17 Murray Baron  1   18 Anna Beraldi  19 Charles H Bombardier  20 Peter Butterworth  21   22 Gregory Carter  23 Marcos Hortes Nisihara Chagas  24 Juliana C N Chan  24   25   26 Rushina Cholera  27 Kerrie Clover  23   28 Yeates Conwell  29 Janneke M de Man-van Ginkel  30 Jesse R Fann  31 Felix H Fischer  32 Daniel Fung  33   34   35   36 Bizu Gelaye  37 Felicity Goodyear-Smith  14 Catherine G Greeno  38 Brian J Hall  39   40 Patricia A Harrison  41 Martin Härter  42 Ulrich Hegerl  43 Leanne Hides  44 Stevan E Hobfoll  45 Marie Hudson  1   18 Thomas N Hyphantis  46 Masatoshi Inagaki  47 Khalida Ismail  48 Nathalie Jetté  10   11   49 Mohammad E Khamseh  16 Kim M Kiely  50   51 Yunxin Kwan  52 Femke Lamers  53 Shen-Ing Liu  36   54   55   56 Manote Lotrakul  57 Sonia R Loureiro  49 Bernd Löwe  58 Laura Marsh  59 Anthony McGuire  60 Sherina Mohd-Sidik  61 Tiago N Munhoz  62 Kumiko Muramatsu  63 Flávia L Osório  49   64 Vikram Patel  65   66 Brian W Pence  67 Philippe Persoons  68   69 Angelo Picardi  70 Katrin Reuter  71 Alasdair G Rooney  72 Iná S da Silva Dos Santos  62 Juwita Shaaban  73 Abbey Sidebottom  74 Adam Simning  29 Lesley Stafford  75   76 Sharon Sung  33   36 Pei Lin Lynnette Tan  52 Alyna Turner  77   78 Henk C P M van Weert  79 Jennifer White  80 Mary A Whooley  81   82   83 Kirsty Winkley  84 Mitsuhiko Yamada  85 Brett D Thombs  86   87   88   89   90   91 Andrea Benedetti  2   18   92
Affiliations
Meta-Analysis

The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis

Chen He et al. Psychother Psychosom. 2020.

Abstract

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.

Keywords: Depression; Diagnostic accuracy; Meta-analysis; Patient Health Questionnaire-9; Screening.

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Conflict of interest statement

Statement of Ethics: The authors have no ethical conflicts to disclose

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