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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 C 1 , Levis B 1 , Riehm KE 1 , Saadat N 1 , Levis AW 1 , Azar M 1 Show all authors , Rice DB 1 , Krishnan A 1 , Wu Y 1 , Sun Y 1 , Imran M 1 , Boruff J 2 , Cuijpers P 3 , Gilbody S 4 , Ioannidis JPA 5 , Kloda LA 6 , McMillan D 4 , Patten SB 7 , Shrier I 1 , Ziegelstein RC 8 , Akena DH 9 , Arroll B 10 , Ayalon L 11 , Baradaran HR 12 , Baron M 1 , Beraldi A 13 , Bombardier CH 14 , Butterworth P 15 , Carter G 16 , Chagas MHN 17 , Chan JCN 17 , Cholera R 18 , Clover K 16 , Conwell Y 19 , de Man-van Ginkel JM 20 , Fann JR 21 , Fischer FH 22 , Fung D 23 , Gelaye B 24 , Goodyear-Smith F 10 , Greeno CG 25 , Hall BJ 26 , Harrison PA 27 , Härter M 28 , Hegerl U 29 , Hides L 30 , Hobfoll SE 31 , Hudson M 1 , Hyphantis TN 32 , Inagaki M 33 , Ismail K 34 , Jetté N 7 , Khamseh ME 12 , Kiely KM 35 , Kwan Y 36 , Lamers F 37 , Liu SI 38 , Lotrakul M 39 , Loureiro SR 40 , Löwe B 41 , Marsh L 42 , McGuire A 43 , Mohd-Sidik S 44 , Munhoz TN 45 , Muramatsu K 46 , Osório FL 40 , Patel V 47 , Pence BW 48 , Persoons P 49 , Picardi A 50 , Reuter K 51 , Rooney AG 52 , da Silva Dos Santos IS 45 , Shaaban J 53 , Sidebottom A 54 , Simning A 19 , Stafford L 55 , Sung S 23 , Tan PLL 36 , Turner A 56 , van Weert HCPM 57 , White J 58 , Whooley MA 59 , Winkley K 60 , Yamada M 61 , Thombs BD 62 , Benedetti A 63

Affiliations 

  • 1 Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada
  • 2 Schulich Library of Physical Sciences, Life Sciences and Engineering, McGill University, Montreal, Québec, Canada
  • 3 Department of Clinical, Neuro- and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
  • 4 Hull York Medical School and the Department of Health Sciences, University of York, Heslington, United Kingdom
  • 5 Department of Medicine, Department of Health Research and Policy, Department of Biomedical Data Science, Department of Statistics, Stanford University, Stanford, California, USA
  • 6 Library, Concordia University, Montréal, Québec, Canada
  • 7 Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
  • 8 Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
  • 9 Department of Psychiatry, Makerere University College of Health Sciences, Kampala, Uganda
  • 10 Department of General Practice and Primary Health Care, University of Auckland, Auckland, New Zealand
  • 11 Louis and Gabi Weisfeld School of Social Work, Bar Ilan University, Ramat Gan, Israel
  • 12 Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran
  • 13 Kbo-Lech-Mangfall-Klinik Garmisch-Partenkirchen, Klinik für Psychiatrie, Psychotherapie und Psychosomatik, Lehrkrankenhaus der Technischen Universität München, Munich, Germany
  • 14 Department of Rehabilitation Medicine, University of Washington, Seattle, Washington, USA
  • 15 Centre for Research on Ageing, Health and Wellbeing, Research School of Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia
  • 16 Centre for Brain and Mental Health Research, University of Newcastle, Newcastle, New South Wales, Australia
  • 17 Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
  • 18 Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
  • 19 Department of Psychiatry, University of Rochester Medical Center, Rochester, New York, USA
  • 20 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
  • 21 Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA
  • 22 Department of Psychosomatic Medicine, Center for Internal Medicine and Dermatology, Charité - Universitätsmedizin Berlin, Berlin, Germany
  • 23 Department of Child and Adolescent Psychiatry, Institute of Mental Health, Singapore, Singapore
  • 24 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
  • 25 School of Social Work, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
  • 26 Global and Community Mental Health Research Group, Department of Psychology, Faculty of Social Sciences, University of Macau, Macau SAR, China
  • 27 City of Minneapolis Health Department, Minneapolis, Minnesota, USA
  • 28 Department of Medical Psychology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
  • 29 Department of Psychiatry, Psychosomatics and Psychotherapy, Goethe-Universität Frankfurt, German Depression Foundation, Frankfurt, Germany
  • 30 School of Psychology, University of Queensland, Brisbane, Queensland, Australia
  • 31 STAR-Stress, Anxiety and Resilience Consultants, Chicago, Illinois, USA
  • 32 Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
  • 33 Department of Psychiatry, Faculty of Medicine, Shimane University, Shimane, Japan
  • 34 Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neurosciences, King's College London Weston Education Centre, London, United Kingdom
  • 35 School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
  • 36 Department of Psychological Medicine, Tan Tock Seng Hospital, Singapore, Singapore
  • 37 Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
  • 38 Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
  • 39 Department of Psychiatry, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
  • 40 Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
  • 41 Department of Psychosomatic Medicine and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
  • 42 Baylor College of Medicine Houston and Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
  • 43 Department of Nursing, St. Joseph's College, Standish, Maine, USA
  • 44 Cancer Resource and Education Centre, and Department of Psychiatry, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia
  • 45 Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
  • 46 Department of Clinical Psychology, Graduate School of Niigata Seiryo University, Niigata, Japan
  • 47 Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA
  • 48 Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
  • 49 Department of Adult Psychiatry, University Hospitals Leuven, Leuven, Belgium
  • 50 Centre for Behavioural Sciences and Mental Health, Italian National Institute of Health, Rome, Italy
  • 51 Practice for Psychotherapy and Psycho-Oncology, Freiburg, Germany
  • 52 Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburg, Edinburgh, United Kingdom
  • 53 Department of Family Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia
  • 54 Allina Health, Minneapolis, Minnesota, USA
  • 55 Centre for Women's Mental Health, Royal Women's Hospital, Parkville, Victoria, Australia
  • 56 School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia
  • 57 Department of General Practice, Amsterdam Institute for General Practice and Public Health, Amsterdam University Medical Centers, Amsterdam, The Netherlands
  • 58 Monash University, Melbourne, Victoria, Australia
  • 59 Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
  • 60 Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, London, United Kingdom
  • 61 Department of Neuropsychopharmacology, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan
  • 62 Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada, brett.thombs@mcgill.ca
  • 63 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada
Psychother Psychosom, 2020;89(1):25-37.
PMID: 31593971 DOI: 10.1159/000502294

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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