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Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: a systematic review and individual participant data meta-analysis – ERRATUM
- Yin Wu, Brooke Levis, Kira E. Riehm, Nazanin Saadat, Alexander W. Levis, Marleine Azar, Danielle B. Rice, Jill Boruff, Pim Cuijpers, Simon Gilbody, John P.A. Ioannidis, Lorie A. Kloda, Dean McMillan, Scott B. Patten, Ian Shrier, Roy C. Ziegelstein, Dickens H. Akena, Bruce Arroll, Liat Ayalon, Hamid R. Baradaran, Murray Baron, Charles H. Bombardier, Peter Butterworth, Gregory Carter, Marcos H. Chagas, Juliana C. N. Chan, Rushina Cholera, Yeates Conwell, Janneke M. de Manvan Ginkel, Jesse R. Fann, Felix H. Fischer, Daniel Fung, Bizu Gelaye, Felicity Goodyear-Smith, Catherine G. Greeno, Brian J. Hall, Patricia A. Harrison, Martin Härter, Ulrich Hegerl, Leanne Hides, Stevan E. Hobfoll, Marie Hudson, Thomas Hyphantis, Masatoshi Inagaki, Nathalie Jetté, Mohammad E. Khamseh, Kim M. Kiely, Yunxin Kwan, Femke Lamers, Shen-Ing Liu, Manote Lotrakul, Sonia R. Loureiro, Bernd Löwe, Anthony McGuire, Sherina Mohd-Sidik, Tiago N. Munhoz, Kumiko Muramatsu, Flávia L. Osório, Vikram Patel, Brian W. Pence, Philippe Persoons, Angelo Picardi, Katrin Reuter, Alasdair G. Rooney, Iná S. Santos, Juwita Shaaban, Abbey Sidebottom, Adam Simning, Lesley Stafford, Sharon Sung, Pei Lin Lynnette Tan, Alyna Turner, Henk C. van Weert, Jennifer White, Mary A. Whooley, Kirsty Winkley, Mitsuhiko Yamada, Andrea Benedetti, Brett D. Thombs
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- Journal:
- Psychological Medicine / Volume 50 / Issue 16 / December 2020
- Published online by Cambridge University Press:
- 19 August 2019, p. 2816
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Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: a systematic review and individual participant data meta-analysis
- Yin Wu, Brooke Levis, Kira E. Riehm, Nazanin Saadat, Alexander W. Levis, Marleine Azar, Danielle B. Rice, Jill Boruff, Pim Cuijpers, Simon Gilbody, John P.A. Ioannidis, Lorie A. Kloda, Dean McMillan, Scott B. Patten, Ian Shrier, Roy C. Ziegelstein, Dickens H. Akena, Bruce Arroll, Liat Ayalon, Hamid R. Baradaran, Murray Baron, Charles H. Bombardier, Peter Butterworth, Gregory Carter, Marcos H. Chagas, Juliana C. N. Chan, Rushina Cholera, Yeates Conwell, Janneke M. de Man-van Ginkel, Jesse R. Fann, Felix H. Fischer, Daniel Fung, Bizu Gelaye, Felicity Goodyear-Smith, Catherine G. Greeno, Brian J. Hall, Patricia A. Harrison, Martin Härter, Ulrich Hegerl, Leanne Hides, Stevan E. Hobfoll, Marie Hudson, Thomas Hyphantis, Masatoshi Inagaki, Nathalie Jetté, Mohammad E. Khamseh, Kim M. Kiely, Yunxin Kwan, Femke Lamers, Shen-Ing Liu, Manote Lotrakul, Sonia R. Loureiro, Bernd Löwe, Anthony McGuire, Sherina Mohd-Sidik, Tiago N. Munhoz, Kumiko Muramatsu, Flávia L. Osório, Vikram Patel, Brian W. Pence, Philippe Persoons, Angelo Picardi, Katrin Reuter, Alasdair G. Rooney, Iná S. Santos, Juwita Shaaban, Abbey Sidebottom, Adam Simning, Lesley Stafford, Sharon Sung, Pei Lin Lynnette Tan, Alyna Turner, Henk C. van Weert, Jennifer White, Mary A. Whooley, Kirsty Winkley, Mitsuhiko Yamada, Andrea Benedetti, Brett D. Thombs
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- Journal:
- Psychological Medicine / Volume 50 / Issue 8 / June 2020
- Published online by Cambridge University Press:
- 12 July 2019, pp. 1368-1380
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Background
Item 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9.
MethodsWe conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy.
Results16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (−0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01).
ConclusionsPHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.
Probability of major depression diagnostic classification using semi-structured versus fully structured diagnostic interviews
- Brooke Levis, Andrea Benedetti, Kira E. Riehm, Nazanin Saadat, Alexander W. Levis, Marleine Azar, Danielle B. Rice, Matthew J. Chiovitti, Tatiana A. Sanchez, Pim Cuijpers, Simon Gilbody, John P. A. Ioannidis, Lorie A. Kloda, Dean McMillan, Scott B. Patten, Ian Shrier, Russell J. Steele, Roy C. Ziegelstein, Dickens H. Akena, Bruce Arroll, Liat Ayalon, Hamid R. Baradaran, Murray Baron, Anna Beraldi, Charles H. Bombardier, Peter Butterworth, Gregory Carter, Marcos H. Chagas, Juliana C. N. Chan, Rushina Cholera, Neerja Chowdhary, Kerrie Clover, Yeates Conwell, Janneke M. de Man-van Ginkel, Jaime Delgadillo, Jesse R. Fann, Felix H. Fischer, Benjamin Fischler, Daniel Fung, Bizu Gelaye, Felicity Goodyear-Smith, Catherine G. Greeno, Brian J. Hall, John Hambridge, Patricia A. Harrison, Ulrich Hegerl, Leanne Hides, Stevan E. Hobfoll, Marie Hudson, Thomas Hyphantis, Masatoshi Inagaki, Khalida Ismail, Nathalie Jetté, Mohammad E. Khamseh, Kim M. Kiely, Femke Lamers, Shen-Ing Liu, Manote Lotrakul, Sonia R. Loureiro, Bernd Löwe, Laura Marsh, Anthony McGuire, Sherina Mohd Sidik, Tiago N. Munhoz, Kumiko Muramatsu, Flávia L. Osório, Vikram Patel, Brian W. Pence, Philippe Persoons, Angelo Picardi, Alasdair G. Rooney, Iná S. Santos, Juwita Shaaban, Abbey Sidebottom, Adam Simning, Lesley Stafford, Sharon Sung, Pei Lin Lynnette Tan, Alyna Turner, Christina M. van der Feltz-Cornelis, Henk C. van Weert, Paul A. Vöhringer, Jennifer White, Mary A. Whooley, Kirsty Winkley, Mitsuhiko Yamada, Yuying Zhang, Brett D. Thombs
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- Journal:
- The British Journal of Psychiatry / Volume 212 / Issue 6 / June 2018
- Published online by Cambridge University Press:
- 02 May 2018, pp. 377-385
- Print publication:
- June 2018
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Background
Different diagnostic interviews are used as reference standards for major depression classification in research. Semi-structured interviews involve clinical judgement, whereas fully structured interviews are completely scripted. The Mini International Neuropsychiatric Interview (MINI), a brief fully structured interview, is also sometimes used. It is not known whether interview method is associated with probability of major depression classification.
AimsTo evaluate the association between interview method and odds of major depression classification, controlling for depressive symptom scores and participant characteristics.
MethodData collected for an individual participant data meta-analysis of Patient Health Questionnaire-9 (PHQ-9) diagnostic accuracy were analysed and binomial generalised linear mixed models were fit.
ResultsA total of 17 158 participants (2287 with major depression) from 57 primary studies were analysed. Among fully structured interviews, odds of major depression were higher for the MINI compared with the Composite International Diagnostic Interview (CIDI) (odds ratio (OR) = 2.10; 95% CI = 1.15–3.87). Compared with semi-structured interviews, fully structured interviews (MINI excluded) were non-significantly more likely to classify participants with low-level depressive symptoms (PHQ-9 scores ≤6) as having major depression (OR = 3.13; 95% CI = 0.98–10.00), similarly likely for moderate-level symptoms (PHQ-9 scores 7–15) (OR = 0.96; 95% CI = 0.56–1.66) and significantly less likely for high-level symptoms (PHQ-9 scores ≥16) (OR = 0.50; 95% CI = 0.26–0.97).
ConclusionsThe MINI may identify more people as depressed than the CIDI, and semi-structured and fully structured interviews may not be interchangeable methods, but these results should be replicated.
Declaration of interestDrs Jetté and Patten declare that they received a grant, outside the submitted work, from the Hotchkiss Brain Institute, which was jointly funded by the Institute and Pfizer. Pfizer was the original sponsor of the development of the PHQ-9, which is now in the public domain. Dr Chan is a steering committee member or consultant of Astra Zeneca, Bayer, Lilly, MSD and Pfizer. She has received sponsorships and honorarium for giving lectures and providing consultancy and her affiliated institution has received research grants from these companies. Dr Hegerl declares that within the past 3 years, he was an advisory board member for Lundbeck, Servier and Otsuka Pharma; a consultant for Bayer Pharma; and a speaker for Medice Arzneimittel, Novartis, and Roche Pharma, all outside the submitted work. Dr Inagaki declares that he has received grants from Novartis Pharma, lecture fees from Pfizer, Mochida, Shionogi, Sumitomo Dainippon Pharma, Daiichi-Sankyo, Meiji Seika and Takeda, and royalties from Nippon Hyoron Sha, Nanzando, Seiwa Shoten, Igaku-shoin and Technomics, all outside of the submitted work. Dr Yamada reports personal fees from Meiji Seika Pharma Co., Ltd., MSD K.K., Asahi Kasei Pharma Corporation, Seishin Shobo, Seiwa Shoten Co., Ltd., Igaku-shoin Ltd., Chugai Igakusha and Sentan Igakusha, all outside the submitted work. All other authors declare no competing interests. No funder had any role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.
Contributors
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- By Venkataraman Anantharaman, Philip D. Anderson, Christopher W. Baugh, J. Stephen Bohan, Kirsten Boyd, Matthias Brachmann, Peter R. Brown, Shelley Calder, David Callaway, Peter Cameron, Jody Crane, Meaghan Cussen, Christina Dempsey, Jonathan A. Edlow, Thomas Fleischmann, Robert L. Freitas, John D. Halamka, Manuel Hernandez, Cherri Hobgood, Jock Hoffman, Steven Horng, Kirk B. Jensen, Jennifer R. Johnson, Stephanie Kayden, Tasnim Khan, Daniel G. Kirkpatrick, James Lennon, Mary Leupold, Thom Mayer, J. Lawrence Mottley, Scott B. Murray, Deirdre Mylod, Larry A. Nathanson, Michael P. Pietrzak, Elke Platz, Nadeem Qureshi, Matthew M. Rice, Andrew Schenkel, Chet Schrader, Puneet Seth, Richard B. Siegrist, David Smith, Robert E. Suter, Carrie Tibbles, Sebastian N. Walker, Lee A. Wallis, Julie Welch, Leana S. Wen
- Edited by Stephanie Kayden, Philip D. Anderson, Robert Freitas, Elke Platz
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- Book:
- Emergency Department Leadership and Management
- Published online:
- 05 December 2014
- Print publication:
- 27 November 2014, pp ix-xii
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Contributors
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- By Joanne R. Adler, David A. Alexander, Laurence Alison, Catherine C. Ayoub, Peter Banister, Anthony R. Beech, Amanda Biggs, Julian Boon, Adrian Bowers, Neil Brewer, Eric Broekaert, Paula Brough, Jennifer M. Brown, Kevin Browne, Elizabeth A. Campbell, David Canter, Michael Carlin, Shihning Chou, Martin A. Conway, Claire Cooke, David Cooke, Ilse Derluyn, Robert J. Edelmann, Vincent Egan, Tom Ellis, Marie Eyre, David P. Farrington, Seena Fazel, Daniel B. Fishman, Victoria Follette, Katarina Fritzon, Elizabeth Gilchrist, Nathan D. Gillard, Renée Gobeil, Agnieszka Golec de Zavala, Jane Goodman-Delahunty, Lynsey Gozna, Don Grubin, Gisli H. Gudjonsson, Helinä Häkkänen-Nyholm, Guy Hall, Nathan Hall, Roisin Hall, Sean Hammond, Leigh Harkins, Grant T. Harris, Camilla Herbert, Robert D. Hoge, Todd E. Hogue, Clive R. Hollin, Lorraine Hope, Miranda A. H. Horvath, Kevin Howells, Carol A. Ireland, Jane L. Ireland, Mark Kebbell, Michael King, Bruce D. Kirkcaldy, Heidi La Bash, Cara Laney, William R. Lindsay, Elizabeth F. Loftus, L. E. Marshall, W. L. Marshall, James McGuire, Neil McKeganey, T. M. McMillan, Mary McMurran, Joav Merrick, Becky Milne, Joanne M. Nadkarni, Claire Nee, M. D. O’Brien, William O’Donohue, Darragh O’Neill, Jane Palmer, Adria Pearson, Derek Perkins, Devon L. L. Polaschek, Louise E. Porter, Charlotte C. Powell, Graham E. Powell, Martine Powell, Christine Puckering, Ethel Quayle, Vernon L. Quinsey, Marnie E. Rice, Randall Richardson-Vejlgaard, Richard Rogers, Louis B Schlesinger, Carolyn Semmler, G. A. Serran, Ralph C. Serin, John L. Taylor, Max Taylor, Brian Thomas-Peter, Paul A. Tiffin, Graham Towl, Rosie Travers, Arlene Vetere, Graham Wagstaff, Helen Wakeling, Fiona Warren, Brandon C. Welsh, David Wexler, Margaret Wilson, Dan Yarmey, Susan Young
- Edited by Jennifer M. Brown, London School of Economics and Political Science, Elizabeth A. Campbell, University of Glasgow
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- Book:
- The Cambridge Handbook of Forensic Psychology
- Published online:
- 06 July 2010
- Print publication:
- 29 April 2010, pp xix-xxiii
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Databases for assessing the outcomes of the treatment of patients with congenital and paediatric cardiac disease – the perspective of critical care
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- Joan M. LaRovere, Howard E. Jeffries, Ramesh C. Sachdeva, Thomas B. Rice, Randall C. Wetzel, David S. Cooper, Geoffrey L. Bird, Nancy S. Ghanayem, Paul A. Checchia, Anthony C. Chang, David L. Wessel
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- Journal:
- Cardiology in the Young / Volume 18 / Issue S2 / December 2008
- Published online by Cambridge University Press:
- 01 December 2008, pp. 130-136
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The development of databases to track the outcomes of children with cardiovascular disease has been ongoing for much of the last two decades, paralleled by the rise of databases in the intensive care unit. While the breadth of data available in national, regional and local databases has grown exponentially, the ability to identify meaningful measurements of outcomes for patients with cardiovascular disease is still in its early stages.
In the United States of America, the Virtual Pediatric Intensive Care Unit Performance System (VPS) is a clinically based database system for the paediatric intensive care unit that provides standardized high quality, comparative data to its participants [https://portal.myvps.org/]. All participants collect information on multiple parameters: (1) patients and their stay in the hospital, (2) diagnoses, (3) interventions, (4) discharge, (5) various measures of outcome, (6) organ donation, and (7) paediatric severity of illness scores. Because of the standards of quality within the database, through customizable interfaces, the database can also be used for several applications: (1) administrative purposes, such as assessing the utilization of resources and strategic planning, (2) multi-institutional research studies, and (3) additional internal projects of quality improvement or research.
In the United Kingdom, The Paediatric Intensive Care Audit Network is a database established in 2002 to record details of the treatment of all critically ill children in paediatric intensive care units of the National Health Service in England, Wales and Scotland. The Paediatric Intensive Care Audit Network was designed to develop and maintain a secure and confidential high quality clinical database of pediatric intensive care activity in order to meet the following objectives: (1) identify best clinical practice, (2) monitor supply and demand, (3) monitor and review outcomes of treatment episodes, (4) facilitate strategic healthcare planning, (5) quantify resource requirements, and (6) study the epidemiology of critical illness in children.
Two distinct physiologic risk adjustment methodologies are the Pediatric Risk of Mortality Scoring System (PRISM), and the Paediatric Index of Mortality Scoring System 2 (PIM 2). Both Pediatric Risk of Mortality (PRISM 2) and Pediatric Risk of Mortality (PRISM 3) are comprised of clinical variables that include physiological and laboratory measurements that are weighted on a logistic scale. The raw Pediatric Risk of Mortality (PRISM) score provides quantitative measures of severity of illness. The Pediatric Risk of Mortality (PRISM) score when used in a logistic regression model provides a probability of the predicted risk of mortality. This predicted risk of mortality can then be used along with the rates of observed mortality to provide a quantitative measurement of the Standardized Mortality Ratio (SMR). Similar to the Pediatric Risk of Mortality (PRISM) scoring system, the Paediatric Index of Mortality (PIM) score is comprised of physiological and laboratory values and provides a quantitative measurement to estimate the probability of death using a logistic regression model.
The primary use of national and international databases of patients with congenital cardiac disease should be to improve the quality of care for these patients. The utilization of common nomenclature and datasets by the various regional subspecialty databases will facilitate the eventual linking of these databases and the creation of a comprehensive database that spans conventional geographic and subspecialty boundaries.
Observing Hot Stars in all Four Stokes Parameters
- Thomas Eversberg, Anthony F.J. Moffat, Michael Debruyne, John B. Rice, Nikolai Piskunov, Pierre Bastien, William H. Wehlau, Olivier Chesneau
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- Journal:
- International Astronomical Union Colloquium / Volume 169 / 1999
- Published online by Cambridge University Press:
- 12 April 2016, pp. 107-110
- Print publication:
- 1999
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We introduce a new polarimeter unit which, mounted at the Cassegrain focus of any telescope and fiber-connected to a fixed CCD spectrograph, is able to measure all Stokes parameters I, Q, U and V photon-noise limited across spectral lines of bright stellar targets and other point sources in a quasi-simultaneous manner. We briefly outline the technical design of the polarimeter unit and the linear algebraic Mueller calculus for obtaining polarization parameters of any point source. In addition, practical limitations of the optical elements are discussed. We present first results obtained with our spectropolarimeter for three prototype hot-star.