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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • 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, S-I; 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

Published Date

  • 2020

Published In

Volume / Issue

  • 89 / 1

Start / End Page

  • 25 - 37

PubMed ID

  • 31593971

Pubmed Central ID

  • PMC6960351

Electronic International Standard Serial Number (EISSN)

  • 1423-0348

Digital Object Identifier (DOI)

  • 10.1159/000502294

Language

  • eng

Conference Location

  • Switzerland