The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis
In: pii: 000502294; He, C., Levis, B., Riehm, K. E., Saadat,, 2020
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Zugriff:
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.
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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
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Autor/in / Beteiligte Person: | 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 ; Harter, M ; Hegerl, U ; Hides, L ; Hobfoll, SE ; Hudson, M ; Hyphantis, TN ; Inagaki, M ; Ismail, K ; Jette, N ; Khamseh, ME ; Kiely, KM ; Kwan, Y ; Lamers, F ; Liu, S-I ; Lotrakul, M ; Loureiro, SR ; Loewe, B ; Marsh, L ; McGuire, A ; Mohd-Sidik, S ; Munhoz, TN ; Muramatsu, K ; Osorio, 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 |
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Zeitschrift: | pii: 000502294; He, C., Levis, B., Riehm, K. E., Saadat,, 2020 |
Veröffentlichung: | KARGER, 2020 |
Medientyp: | academicJournal |
ISSN: | 0033-3190 (print) ; 1423-0348 (print) |
DOI: | 10.1159/000502294. |
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