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

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

doi:10.1159/000502294

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

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. ZiegelsteinDickens 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

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Pages (from-to)	25-37
Number of pages	13
Journal	Psychotherapy and Psychosomatics
Volume	89
Issue number	1
Early online date	8 Oct 2019
DOIs	https://doi.org/10.1159/000502294
Publication status	Published - 1 Jan 2020

Access to Document

10.1159/000502294

Cite this

He, C., Levis, B., Riehm, K. E., Saadat, N., Levis, A. W., Azar, M., Rice, D. B., Krishnan, A., Wu, Y., Sun, Y., Imran, M., Boruff, J., Cuijpers, P., Gilbody, S., Ioannidis, J. P. A., Kloda, L. A., McMillan, D., Patten, S. B., Shrier, I., ... Benedetti, A. (2020). The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis. Psychotherapy and Psychosomatics, 89(1), 25-37. https://doi.org/10.1159/000502294

@article{0fe5e83ccd9542e2958c7257c3dc82fb,

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

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",

author = "Chen He and Brooke Levis and Riehm, {Kira E.} and Nazanin Saadat and Levis, {Alexander W.} and Marleine Azar and Rice, {Danielle B.} and Ankur Krishnan and Yin Wu and Ying Sun and Mahrukh Imran and Jill Boruff and Pim Cuijpers and Simon Gilbody and Ioannidis, {John P.A.} and Kloda, {Lorie A.} and Dean McMillan and Patten, {Scott B.} and Ian Shrier and Ziegelstein, {Roy C.} and Akena, {Dickens H.} and Bruce Arroll and Liat Ayalon and Baradaran, {Hamid R.} and Murray Baron and Anna Beraldi and Bombardier, {Charles H.} and Peter Butterworth and Gregory Carter and Chagas, {Marcos Hortes Nisihara} and Chan, {Juliana C.N.} and Rushina Cholera and Kerrie Clover and Yeates Conwell and {De Man-Van Ginkel}, {Janneke M.} and Fann, {Jesse R.} and Fischer, {Felix H.} and Daniel Fung and Bizu Gelaye and Felicity Goodyear-Smith and Greeno, {Catherine G.} and Hall, {Brian J.} and Harrison, {Patricia A.} and Martin H{\"a}rter and Ulrich Hegerl and Leanne Hides and Hobfoll, {Stevan E.} and Marie Hudson and Hyphantis, {Thomas N.} and Masatoshi Inagaki and Khalida Ismail and Nathalie Jett{\'e} and Khamseh, {Mohammad E.} and Kiely, {Kim M.} and Yunxin Kwan and Femke Lamers and Liu, {Shen Ing} and Manote Lotrakul and Loureiro, {Sonia R.} and Bernd L{\"o}we and Laura Marsh and Anthony McGuire and Sherina Mohd-Sidik and Munhoz, {Tiago N.} and Kumiko Muramatsu and Os{\'o}rio, {Fl{\'a}via L.} and Vikram Patel and Pence, {Brian W.} and Philippe Persoons and Angelo Picardi and Katrin Reuter and Rooney, {Alasdair G.} and {Da Silva Dos Santos}, {In{\'a} S.} and Juwita Shaaban and Abbey Sidebottom and Adam Simning and Lesley Stafford and Sharon Sung and Tan, {Pei Lin Lynnette} and Alyna Turner and {Van Weert}, {Henk C.P.M.} and Jennifer White and Whooley, {Mary A.} and Kirsty Winkley and Mitsuhiko Yamada and Thombs, {Brett D.} and Andrea Benedetti",

year = "2020",

month = jan,

day = "1",

doi = "10.1159/000502294",

language = "English",

volume = "89",

pages = "25--37",

journal = "Psychotherapy and Psychosomatics",

issn = "0033-3190",

publisher = "S. Karger AG",

number = "1",

}

He, C, Levis, B, Riehm, KE, Saadat, N, Levis, AW, Azar, M, Rice, DB, Krishnan, A, Wu, Y, Sun, Y, Imran, M, Boruff, J, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Akena, DH, Arroll, B, Ayalon, L, Baradaran, HR, Baron, M, Beraldi, A, Bombardier, CH, Butterworth, P, Carter, G, Chagas, MHN, Chan, JCN, Cholera, R, Clover, K, Conwell, Y, De Man-Van Ginkel, JM, Fann, JR, Fischer, FH, Fung, D, Gelaye, B, Goodyear-Smith, F, Greeno, CG, Hall, BJ, Harrison, PA, Härter, M, Hegerl, U, Hides, L, Hobfoll, SE, Hudson, M, Hyphantis, TN, Inagaki, M, Ismail, K, Jetté, N, Khamseh, ME, Kiely, KM, Kwan, Y, Lamers, F, Liu, SI, Lotrakul, M, Loureiro, SR, Löwe, B, Marsh, L, McGuire, A, Mohd-Sidik, S, Munhoz, TN, Muramatsu, K, Osório, FL, Patel, V, Pence, BW, Persoons, P, Picardi, A, Reuter, K, Rooney, AG, Da Silva Dos Santos, IS, Shaaban, J, Sidebottom, A, Simning, A, Stafford, L, Sung, S, Tan, PLL, Turner, A, Van Weert, HCPM, White, J, Whooley, MA, Winkley, K, Yamada, M, Thombs, BD & Benedetti, A 2020, 'The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis', Psychotherapy and Psychosomatics, vol. 89, no. 1, pp. 25-37. https://doi.org/10.1159/000502294

TY - JOUR

T1 - The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression

T2 - 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

PY - 2020/1/1

Y1 - 2020/1/1

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.

KW - Depression

KW - Diagnostic accuracy

KW - Meta-analysis

KW - Patient Health Questionnaire-9

KW - Screening

UR - http://www.scopus.com/inward/record.url?scp=85073678831&partnerID=8YFLogxK

U2 - 10.1159/000502294

DO - 10.1159/000502294

M3 - Article

C2 - 31593971

AN - SCOPUS:85073678831

VL - 89

SP - 25

EP - 37

JO - Psychotherapy and Psychosomatics

JF - Psychotherapy and Psychosomatics

SN - 0033-3190

IS - 1

ER -

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

Abstract

Access to Document

Other files and links

Cite this