Journal
PSYCHOLOGICAL MEDICINE
Volume 52, Issue 15, Pages 3472-3483Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/S0033291721000131
Keywords
Confirmatory factor analysis; depression; Latent variable modeling; screening
Categories
Funding
- Canadian Institutes of Health Research (CIHR) [KRS-134297, PCG-155468]
- Deutsche Forschungsgemeinschaft [Fi 1999/6-1]
- German Federal Ministry of Education and Research [01GY1150]
- Fonds de recherche du Quebec -Sante (FRQS) Postdoctoral Training Fellowship
- FRQS researcher salary awards
- FRQS Postdoctoral Training Fellowship
- Research Institute of the McGill University Health Centre
- G.R. Caverhill Fellowship from the Faculty of Medicine, McGill University
- Vanier Canada Graduate Scholarship
- CIHR Frederick Banting and Charles Best Canada Graduate Scholarship master's awards
- Cumming School of Medicine, University of Calgary
- Alberta Health Services through the Calgary Health Trust
- Hotchkiss Brain Institute
- Senior Health Scholar Award from Alberta Innovates Health Solutions
- Canada Research Chair in Neurological Health Services Research
- AIHS Population Health Investigator Award
- Department of Education [H133B080025]
- National Multiple Sclerosis Society [MB 0008]
- Lundbeck International [M-288]
- Tehran University of Medical Sciences
- CIHR
- Crohn's and Colitis Canada
- Bingham Chair in Gastroenterology
- Waugh Family Chair in Multiple Sclerosis
- Research Manitoba Chair
- PRogramme for Improving Mental health carE (PRIME)
- UK Department for International Development [201446]
- Department of Education, National Institute on Disability and Rehabilitation Research, Spinal Cord Injury Model Systems: University of Washington [H133N060033]
- Baylor College of Medicine [H133N060003]
- University of Michigan [H133N060032]
- Grand Challenges Canada [0087-04]
- NIMH [R24 MH071604]
- Centers for Disease Control and Prevention [R49 CE002093]
- Spanish Ministry of Health's Health Research Fund (Fondo de Investigaciones Sanitarias) [97/1184]
- Duke Global Health Institute [453-0751]
- United States Agency for International Development Victims of Torture Fund [AID-DFD A-00-08-00308]
- Consejo Nacional de Ciencia y Tecnologia/National Council for Science and Technology [CB-2009-133923-H]
- Reitoria de Pesquisa da Universidade de Sao Paulo [09.1.01689.17.7]
- Banco Santander [10.1.01232.17.9, PQ-CNPq-2 -number 301321/2016-7]
- Pfizer, Germany
- medical faculty of the University of Heidelberg, Germany [121/2000]
- Department of Defense [W81XWH-08-2-0100/W81XWH-08-2-0102, W81XWH-12-2-0117/W81XWH-12-2-0121]
- Italian Ministry of Health [U10CA21661, U10CA180868, U10CA180822, U10CA37422]
- National Cancer Institute
- Pennsylvania Department of Health - United Kingdom National Health Service Lothian Neuro-Oncology Endowment Fund [NCI K07 CA 093512]
- Lance Armstrong Foundation - United States Department of Health and Human Services, Health Resources and Services Administration [R40MC07840]
- NIH [T32 GM07356]
- Agency for Healthcare Research and Quality [R36 HS018246]
- National Center for Research Resources [TL1 RR024135]
- junior research grant from the medical faculty, University of Leipzig - bequest from Jennie Thomas through the Hunter Medical Research Institute
- Netherlands Organization for Health Research and Development (ZonMw) Mental Health Program [100.003.005, 100.002.021]
- Academic Medical Center/University of Amsterdam
Ask authors/readers for more resources
In a comprehensive dataset of diagnostic studies, scoring using complex latent variable models do not improve screening accuracy of the PHQ-9 meaningfully as compared to the simple sum score approach.
Background Previous research on the depression scale of the Patient Health Questionnaire (PHQ-9) has found that different latent factor models have maximized empirical measures of goodness-of-fit. The clinical relevance of these differences is unclear. We aimed to investigate whether depression screening accuracy may be improved by employing latent factor model-based scoring rather than sum scores. Methods We used an individual participant data meta-analysis (IPDMA) database compiled to assess the screening accuracy of the PHQ-9. We included studies that used the Structured Clinical Interview for DSM (SCID) as a reference standard and split those into calibration and validation datasets. In the calibration dataset, we estimated unidimensional, two-dimensional (separating cognitive/affective and somatic symptoms of depression), and bi-factor models, and the respective cut-offs to maximize combined sensitivity and specificity. In the validation dataset, we assessed the differences in (combined) sensitivity and specificity between the latent variable approaches and the optimal sum score (> 10), using bootstrapping to estimate 95% confidence intervals for the differences. Results The calibration dataset included 24 studies (4378 participants, 652 major depression cases); the validation dataset 17 studies (4252 participants, 568 cases). In the validation dataset, optimal cut-offs of the unidimensional, two-dimensional, and bi-factor models had higher sensitivity (by 0.036, 0.050, 0.049 points, respectively) but lower specificity (0.017, 0.026, 0.019, respectively) compared to the sum score cut-off of > 10. Conclusions In a comprehensive dataset of diagnostic studies, scoring using complex latent variable models do not improve screening accuracy of the PHQ-9 meaningfully as compared to the simple sum score approach.
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