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

TY - JOUR

T1 - The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis

AU - He, Chen

AU - Levis, Brooke

AU - Riehm, Kira E

AU - Saadat, Nazanin

AU - Levis, Alexander W

AU - Azar, Marleine

AU - Rice, Danielle B

AU - Krishnan, Ankur

AU - Wu, Yin

AU - Sun, Ying

AU - Imran, Mahrukh

AU - Boruff, Jill

AU - Cuijpers, Pim

AU - Gilbody, Simon

AU - Ioannidis, John P A

AU - Kloda, Lorie A

AU - McMillan, Dean

AU - Patten, Scott B

AU - Shrier, Ian

AU - Ziegelstein, Roy C

AU - Akena, Dickens H

AU - Arroll, Bruce

AU - Ayalon, Liat

AU - Baradaran, Hamid R

AU - Baron, Murray

AU - Beraldi, Anna

AU - Bombardier, Charles H

AU - Butterworth, Peter

AU - Carter, Gregory

AU - Chagas, Marcos Hortes Nisihara

AU - Chan, Juliana C N

AU - Cholera, Rushina

AU - Clover, Kerrie

AU - Conwell, Yeates

AU - de Man-van Ginkel, Janneke M

AU - Fann, Jesse R

AU - Fischer, Felix H

AU - Fung, Daniel

AU - Gelaye, Bizu

AU - Goodyear-Smith, Felicity

AU - Greeno, Catherine G

AU - Hall, Brian J

AU - Harrison, Patricia A

AU - Härter, Martin

AU - Hegerl, Ulrich

AU - Hides, Leanne

AU - Hobfoll, Stevan E

AU - Hudson, Marie

AU - Hyphantis, Thomas N

AU - Inagaki, Masatoshi

AU - Ismail, Khalida

AU - Jetté, Nathalie

AU - Khamseh, Mohammad E

AU - Kiely, Kim M

AU - Kwan, Yunxin

AU - Lamers, Femke

AU - Liu, Shen-Ing

AU - Lotrakul, Manote

AU - Loureiro, Sonia R

AU - Löwe, Bernd

AU - Marsh, Laura

AU - McGuire, Anthony

AU - Mohd-Sidik, Sherina

AU - Munhoz, Tiago N

AU - Muramatsu, Kumiko

AU - Osório, Flávia L

AU - Patel, Vikram

AU - Pence, Brian W

AU - Persoons, Philippe

AU - Picardi, Angelo

AU - Reuter, Katrin

AU - Rooney, Alasdair G

AU - da Silva Dos Santos, Iná S

AU - Shaaban, Juwita

AU - Sidebottom, Abbey

AU - Simning, Adam

AU - Stafford, Lesley

AU - Sung, Sharon

AU - Tan, Pei Lin Lynnette

AU - Turner, Alyna

AU - van Weert, Henk C P M

AU - White, Jennifer

AU - Whooley, Mary A

AU - Winkley, Kirsty

AU - Yamada, Mitsuhiko

AU - Thombs, Brett D

AU - Benedetti, Andrea

N1 - © 2019 S. Karger AG, Basel.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

U2 - 10.1159/000502294

DO - 10.1159/000502294

M3 - SCORING: Journal article

C2 - 31593971

VL - 89

SP - 25

EP - 37

JO - PSYCHOTHER PSYCHOSOM

JF - PSYCHOTHER PSYCHOSOM

SN - 0033-3190

IS - 1

ER -