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 |
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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 | |
Publication status | Published - 1 Jan 2020 |
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The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression : An Individual Participant Data Meta-Analysis. / He, Chen; Levis, Brooke; Riehm, Kira E.; Saadat, Nazanin; Levis, Alexander W.; Azar, Marleine; Rice, Danielle B.; Krishnan, Ankur; Wu, Yin; Sun, Ying; Imran, Mahrukh; Boruff, Jill; Cuijpers, Pim; Gilbody, Simon; Ioannidis, John P.A.; Kloda, Lorie A.; McMillan, Dean; Patten, Scott B.; Shrier, Ian; Ziegelstein, Roy C.; Akena, Dickens H.; Arroll, Bruce; Ayalon, Liat; Baradaran, Hamid R.; Baron, Murray; Beraldi, Anna; Bombardier, Charles H.; Butterworth, Peter; Carter, Gregory; Chagas, Marcos Hortes Nisihara; Chan, Juliana C.N.; Cholera, Rushina; Clover, Kerrie; Conwell, Yeates; De Man-Van Ginkel, Janneke M.; Fann, Jesse R.; Fischer, Felix H.; Fung, Daniel; Gelaye, Bizu; Goodyear-Smith, Felicity; Greeno, Catherine G.; Hall, Brian J.; Harrison, Patricia A.; Härter, Martin; Hegerl, Ulrich; Hides, Leanne; Hobfoll, Stevan E.; Hudson, Marie; Hyphantis, Thomas N.; Inagaki, Masatoshi; Ismail, Khalida; Jetté, Nathalie; Khamseh, Mohammad E.; Kiely, Kim M.; Kwan, Yunxin; Lamers, Femke; Liu, Shen Ing; Lotrakul, Manote; Loureiro, Sonia R.; Löwe, Bernd; Marsh, Laura; McGuire, Anthony; Mohd-Sidik, Sherina; Munhoz, Tiago N.; Muramatsu, Kumiko; Osório, Flávia L.; Patel, Vikram; Pence, Brian W.; Persoons, Philippe; Picardi, Angelo; Reuter, Katrin; Rooney, Alasdair G.; Da Silva Dos Santos, Iná S.; Shaaban, Juwita; Sidebottom, Abbey; Simning, Adam; Stafford, Lesley; Sung, Sharon; Tan, Pei Lin Lynnette; Turner, Alyna; Van Weert, Henk C.P.M.; White, Jennifer; Whooley, Mary A.; Winkley, Kirsty; Yamada, Mitsuhiko; Thombs, Brett D.; Benedetti, Andrea.
In: Psychotherapy and Psychosomatics, Vol. 89, No. 1, 01.01.2020, p. 25-37.Research output: Contribution to journal › Article › Academic › peer-review
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 -