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The Accuracy of the Patient Health Questionnaire-9 (PHQ-9) Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-analysis
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2020
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Source: Psychother Psychosom. 89(1):25-37
Details:
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Alternative Title:Psychother Psychosom
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Personal Author:
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Description: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 individual participant data meta-analysis (IPDMA) 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 & Other Non-Indexed Citations, PsycINFO, Web of Science (January 1, 2000 – 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 to 0.24 lower compared to fully structured interviews and 0.06 to 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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Source:
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Pubmed ID:31593971
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Pubmed Central ID:PMC6960351
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Funding:
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Volume:89
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Issue:1
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