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      Indicators of mental disorders in UK Biobank—A comparison of approaches

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          Abstract

          Objectives

          For many research cohorts, it is not practical to provide a “gold‐standard” mental health diagnosis. It is therefore important for mental health research that potential alternative measures for ascertaining mental disorder status are understood.

          Methods

          Data from UK Biobank in those participants who had completed the online Mental Health Questionnaire ( n = 157,363) were used to compare the classification of mental disorder by four methods: symptom‐based outcome (self‐complete based on diagnostic interviews), self‐reported diagnosis, hospital data linkage, and self‐report medication.

          Results

          Participants self‐reporting any psychiatric diagnosis had elevated risk of any symptom‐based outcome. Cohen's κ between self‐reported diagnosis and symptom‐based outcome was 0.46 for depression, 0.28 for bipolar affective disorder, and 0.24 for anxiety. There were small numbers of participants uniquely identified by hospital data linkage and medication.

          Conclusion

          Our results confirm that ascertainment of mental disorder diagnosis in large cohorts such as UK Biobank is complex. There may not be one method of classification that is right for all circumstances, but an informed and transparent use of outcome measure(s) to suit each research question will maximise the potential of UK Biobank and other resources for mental health research.

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          Most cited references32

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          Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure.

          Questionnaires are used to estimate disease burden. Agreement between questionnaire responses and a criterion standard is important for optimal disease prevalence estimates. We measured the agreement between self-reported disease and medical record diagnosis of disease. A total of 2,037 Olmsted County, Minnesota residents > or =45 years of age were randomly selected. Questionnaires asked if subjects had ever had heart failure, diabetes, hypertension, myocardial infarction (MI), or stroke. Medical records were abstracted. Self-report of disease showed >90% specificity for all these diseases, but sensitivity was low for heart failure (69%) and diabetes (66%). Agreement between self-report and medical record was substantial (kappa 0.71-0.80) for diabetes, hypertension, MI, and stroke but not for heart failure (kappa 0.46). Factors associated with high total agreement by multivariate analysis were age 12 years, and zero Charlson Index score (P < .05). Questionnaire data are of greatest value in life-threatening, acute-onset diseases (e.g., MI and stroke) and chronic disorders requiring ongoing management (e.g.,diabetes and hypertension). They are more accurate in young women and better-educated subjects.
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            Concordance of the Composite International Diagnostic Interview Version 3.0 (CIDI 3.0) with standardized clinical assessments in the WHO World Mental Health Surveys

            The DSM‐IV diagnoses generated by the fully structured lay‐administered Composite International Diagnostic Interview Version 3.0 (CIDI 3.0) in the WHO World Mental Health (WMH) surveys were compared to diagnoses based on follow‐up interviews with the clinician‐administered non‐patient edition of the Structured Clinical Interview for DSM‐IV (SCID) in probability subsamples of the WMH surveys in France, Italy, Spain, and the US. CIDI cases were oversampled. The clinical reappraisal samples were weighted to adjust for this oversampling. Separate samples were assessed for lifetime and 12‐month prevalence. Moderate to good individual‐level CIDI‐SCID concordance was found for lifetime prevalence estimates of most disorders. The area under the ROC curve (AUC, a measure of classification accuracy that is not influenced by disorder prevalence) was 0.76 for the dichotomous classification of having any of the lifetime DSM‐IV anxiety, mood and substance disorders assessed in the surveys and in the range 0.62–0.93 for individual disorders, with an inter‐quartile range (IQR) of 0.71–0.86. Concordance increased when CIDI symptom‐level data were added to predict SCID diagnoses in logistic regression equations. AUC for individual disorders in these equations was in the range 0.74–0.99, with an IQR of 0.87–0.96. CIDI lifetime prevalence estimates were generally conservative relative to SCID estimates. CIDI‐SCID concordance for 12‐month prevalence estimates could be studied powerfully only for two disorder classes, any anxiety disorder (AUC = 0.88) and any mood disorder (AUC = 0.83). As with lifetime prevalence, 12‐month concordance improved when CIDI symptom‐level data were added to predict SCID diagnoses. CIDI 12‐month prevalence estimates were unbiased relative to SCID estimates. The validity of the CIDI is likely to be under‐estimated in these comparisons due to the fact that the reliability of the SCID diagnoses, which is presumably less than perfect, sets a ceiling on maximum CIDI‐SCID concordance. Copyright © 2006 John Wiley & Sons, Ltd.
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              Three Approaches to Understanding and Classifying Mental Disorder: ICD-11, DSM-5, and the National Institute of Mental Health’s Research Domain Criteria (RDoC)

              The diagnosis of mental disorder initially appears relatively straightforward: Patients present with symptoms or visible signs of illness; health professionals make diagnoses based primarily on these symptoms and signs; and they prescribe medication, psychotherapy, or both, accordingly. However, despite a dramatic expansion of knowledge about mental disorders during the past half century, understanding of their components and processes remains rudimentary. We provide histories and descriptions of three systems with different purposes relevant to understanding and classifying mental disorder. Two major diagnostic manuals-the International Classification of Diseases and the Diagnostic and Statistical Manual of Mental Disorders-provide classification systems relevant to public health, clinical diagnosis, service provision, and specific research applications, the former internationally and the latter primarily for the United States. In contrast, the National Institute of Mental Health's Research Domain Criteria provides a framework that emphasizes integration of basic behavioral and neuroscience research to deepen the understanding of mental disorder. We identify four key issues that present challenges to understanding and classifying mental disorder: etiology, including the multiple causality of mental disorder; whether the relevant phenomena are discrete categories or dimensions; thresholds, which set the boundaries between disorder and nondisorder; and comorbidity, the fact that individuals with mental illness often meet diagnostic requirements for multiple conditions. We discuss how the three systems' approaches to these key issues correspond or diverge as a result of their different histories, purposes, and constituencies. Although the systems have varying degrees of overlap and distinguishing features, they share the goal of reducing the burden of suffering due to mental disorder.
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                Author and article information

                Contributors
                katrina.davis@kcl.ac.uk
                Journal
                Int J Methods Psychiatr Res
                Int J Methods Psychiatr Res
                10.1002/(ISSN)1557-0657
                MPR
                International Journal of Methods in Psychiatric Research
                John Wiley and Sons Inc. (Hoboken )
                1049-8931
                1557-0657
                08 August 2019
                September 2019
                : 28
                : 3 ( doiID: 10.1002/mpr.v28.3 )
                : e1796
                Affiliations
                [ 1 ] Institute of Psychiatry Psychology and Neuroscience King's College London London UK
                [ 2 ] NIHR Biomedical Research Centre South London and Maudsley NHS Foundation Trust London UK
                [ 3 ] Mental Health and Wellbeing, The Academic Centre, Gartnavel Royal Hospital University of Glasgow Glasgow UK
                [ 4 ] Division of Psychiatry University of Edinburgh Edinburgh UK
                [ 5 ] Peninsula Schools of Medicine and Dentistry Plymouth University Plymouth UK
                [ 6 ] Devon Partnership NHS Trust Psychological Medicine, Exeter, UKUK Biobank, Office of the UKB Chief Scientist Edinburgh UK
                [ 7 ] Office of the UKB Chief Scientist UK Biobank Edinburgh UK
                Author notes
                [*] [* ] Correspondence

                Katrina A.S. Davis, Academic Department of the Division of Psychological Medicine, 3rd Floor East, KCL Institute of Psychiatry Psychology and Neuroscience, 16 De Crespigny Park, London SE5 8AF, UK.

                Email: katrina.davis@ 123456kcl.ac.uk

                Author information
                https://orcid.org/0000-0001-5945-4646
                https://orcid.org/0000-0002-7259-9505
                https://orcid.org/0000-0002-6334-7349
                https://orcid.org/0000-0002-6759-0944
                https://orcid.org/0000-0002-3980-4466
                Article
                MPR1796 IJMPR-Sep-2018-0104.R2
                10.1002/mpr.1796
                6877131
                31397039
                6d58772d-149b-44cc-8321-28f137c66518
                © 2019 The Authors International Journal of Methods in Psychiatric Research Published by John Wiley & Sons Ltd

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 04 October 2018
                : 04 April 2019
                : 20 May 2019
                Page count
                Figures: 0, Tables: 7, Pages: 12, Words: 10382
                Funding
                Funded by: National Institute for Health Research , open-funder-registry 10.13039/501100000272;
                Funded by: National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South West Peninsula
                Funded by: The Dr Mortimer and Theresa Sackler Foundation , open-funder-registry 10.13039/501100003754;
                Funded by: Scottish Executive Chief Scientist Office
                Award ID: DTF/14/03
                Funded by: The Sackler Trust
                Funded by: MRC Grant
                Award ID: MC_PC_17209
                Funded by: Wellcome Trust Strategic Award
                Award ID: 104036/Z/14/Z
                Funded by: Maudsley Charity , open-funder-registry 10.13039/100012176;
                Award ID: 980
                Funded by: Guy's and St Thomas's Charity
                Award ID: TR130505
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                September 2019
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.2 mode:remove_FC converted:19.11.2019

                Clinical Psychology & Psychiatry
                cohort study,diagnosis,epidemiology,mental disorder,online survey,uk biobank

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