Mental health in UK Biobank – development, implementation and results from an online questionnaire completed by 157 366 participants: a reanalysis

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Abstract

Background

UK Biobank is a well-characterised cohort of over 500 000 participants including genetics, environmental data and imaging. An online mental health questionnaire was designed for UK Biobank participants to expand its potential.

Aims

Describe the development, implementation and results of this questionnaire.

Method

An expert working group designed the questionnaire, using established measures where possible, and consulting a patient group. Operational criteria were agreed for defining likely disorder and risk states, including lifetime depression, mania/hypomania, generalised anxiety disorder, unusual experiences and self-harm, and current post-traumatic stress and hazardous/harmful alcohol use.

Results

A total of 157 366 completed online questionnaires were available by August 2017. Participants were aged 45–82 (53% were ≥65 years) and 57% women. Comparison of self-reported diagnosed mental disorder with a contemporary study shows a similar prevalence, despite respondents being of higher average socioeconomic status. Lifetime depression was a common finding, with 24% (37 434) of participants meeting criteria and current hazardous/harmful alcohol use criteria were met by 21% (32 602), whereas other criteria were met by less than 8% of the participants. There was extensive comorbidity among the syndromes. Mental disorders were associated with a high neuroticism score, adverse life events and long-term illness; addiction and bipolar affective disorder in particular were associated with measures of deprivation.

Conclusions

The UK Biobank questionnaire represents a very large mental health survey in itself, and the results presented here show high face validity, although caution is needed because of selection bias. Built into UK Biobank, these data intersect with other health data to offer unparalleled potential for crosscutting biomedical research involving mental health.

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Most cited references 22

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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.

Yuji Okura, Lynn H. Urban, Douglas Mahoney … (2004)

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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The notion that psychological states can influence physical health is hardly new, and perhaps nowhere has the mind-body connection been better studied than in cardiovascular disease (CVD). Recently, large prospective epidemiologic studies and smaller basic science studies have firmly established a connection between CVD and several psychological conditions, including depression, chronic psychological stress, posttraumatic stress disorder (PTSD), and anxiety. In addition, numerous clinical trials have been conducted to attempt to prevent or lessen the impact of these conditions on cardiovascular health. In this article, we review studies connecting depression, stress/PTSD, and anxiety to CVD, focusing on findings from the last 5 years. For each mental health condition, we first examine the epidemiologic evidence establishing a link with CVD. We then describe studies of potential underlying mechanisms and finally discuss treatment trials and directions for future research.

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The term "selection bias" encompasses various biases in epidemiology. We describe examples of selection bias in case-control studies (eg, inappropriate selection of controls) and cohort studies (eg, informative censoring). We argue that the causal structure underlying the bias in each example is essentially the same: conditioning on a common effect of 2 variables, one of which is either exposure or a cause of exposure and the other is either the outcome or a cause of the outcome. This structure is shared by other biases (eg, adjustment for variables affected by prior exposure). A structural classification of bias distinguishes between biases resulting from conditioning on common effects ("selection bias") and those resulting from the existence of common causes of exposure and outcome ("confounding"). This classification also leads to a unified approach to adjust for selection bias.

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Author and article information

Journal

Journal ID (nlm-ta): BJPsych Open

Journal ID (iso-abbrev): BJPsych Open

Journal ID (publisher-id): BJO

Title: BJPsych Open

Publisher: Cambridge University Press (Cambridge, UK )

ISSN (Electronic): 2056-4724

Publication date (Electronic, collection): March 2020

Publication date (Electronic): 06 February 2020

Volume: 6

Issue: 2

Electronic Location Identifier: e18

Affiliations

[1]Researcher, Institute of Psychiatry, Psychology and Neuroscience, King's College London ; and South London and Maudsley NHS Foundation Trust, NIHR Biomedical Research Centre , UK

[2]Lecturer in Statistical Genetics, Institute of Psychiatry, Psychology and Neuroscience, King's College London ; and South London and Maudsley NHS Foundation Trust, NIHR Biomedical Research Centre , UK

[3]Data Scientist, Division of Psychiatry, University of Edinburgh , UK

[4]Professor, University of Oxford ; and Chief Scientist, UK Biobank, Nuffield Department of Population Health, University of Oxford Big Data Institute , UK

[5]Professor of Psychiatric Genetics, Institute of Psychiatry, Psychology and Neuroscience, King's College London ; and South London and Maudsley NHS Foundation Trust, NIHR Biomedical Research Centre , UK

[6]Senior Lecturer, Institute of Health and Wellbeing, University of Glasgow , UK

[7]Professor of Psychological Medicine, Institute of Health Research, University of Exeter Medical School, University of Exeter , UK

[8]Professor of Psychology and Affective Neuroscience, Department of Experimental Psychology, University of Oxford , UK

[9]Clinical Lecturer in General Psychiatry, Institute of Health and Wellbeing, University of Glasgow , UK

[10]Senior Research Facilitator, University of Oxford ; and UK Biobank: UK Biobank, Nuffield Department of Population Health, University of Oxford Big Data Institute , UK

[11]NIHR Research Professor in Women's Mental Health and NIHR Senior Investigator, Section of Women's Mental Health, Institute of Psychiatry, Psychology and Neuroscience, King's College London , UK

[12]Professor of Public Health and Psychiatry and Consultant Public Health Medicine, Population Data Science, Farr Institute of Health Informatics Research, Swansea University Medical School, Swansea University ; and Public Health Wales NHS Trust , UK

[13]Consultant Liaison Psychiatrist and Honorary Clinical Senior Lecturer, Devon Partnership NHS Trust; and University of Exeter Medical School, University of Exeter , UK

[14]Professor of Clinical Communication, School of Health Sciences, City, University of London , UK

[15]Professor of Biological Psychiatry, Division of Psychiatry, University of Edinburgh , UK

[16]Consultant Psychiatrist and Honorary Clinical Senior Lecturer in Psychiatry, Institute of Health and Wellbeing, University of Glasgow , UK

[17]Lecturer in Psychiatry, Institute of Health and Wellbeing, University of Glasgow , UK

[18]Director of the British Heart Foundation Data Science Centre, BHF Data Science Centre; Former Chief Scientist , UK Biobank; and Chair of Neurology and Clinical Epidemiology, Centre for Medical Informatics, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh , UK

[19]Researcher, Institute of Health and Wellbeing, University of Glasgow , UK

[20]Professor of Psychiatric Epidemiology, Centre for Academic Mental Health, University of Bristol ; and Institute of Psychological Medicine and Clinical Neurosciences, University of Cardiff, Cardiff University School of Medicine, UK

[21]Director, National Institute of Health Research Biomedical Research Centre at the Maudsley; Institute of Psychiatry, Psychology and Neuroscience, King's College London; and South London and Maudsley NHS Foundation Trust, NIHR Biomedical Research Centre , UK

Author notes

Declaration of interest: G.B. reports grants from the National Institute for Health Research during the conduct of the study; support from Illumina Ltd and the European Commission outside the submitted work. B.C. reports grants from the Scottish Executive Chief Scientist Office during the conduct of the study. C.S. reports grants from the Medical Research Council and Wellcome Trust, during the conduct of the study; and is the former Chief Scientist for UK Biobank. M.H. reports grants for IMI RADAR-CNS and personal fees as an expert witness outside the submitted work. N.A. is Chief Scientist for UK Biobank. Other authors have nothing to declare.

Correspondence: Matthew Hotopf. Email: matthew.hotopf@ 123456kcl.ac.uk

This paper is a reanalysis of data previously published in a paper – Davis KAS, Coleman JRI, et al. Mental health in UK Biobank, 2018 ( https://doi.org/10.1192/bjo.2018.12; corrections: https://doi.org/10.1192/bjo.2018.19 and https://doi.org/10.1192/bjo.2018.47) that was retracted on 17 June 2019 ( https://doi.org/10.1192/bjo.2019.46) for reasons of accuracy in the reporting of alcohol use disorders, as discussed in an editorial – Kaufman KR, Malhi GS, Bhui KS. When a corrigendum is not sufficient, 2019 ( https://doi.org/10.1192/bjo.2019.41).

Author information

Katrina A. S. Davis https://orcid.org/0000-0001-5945-4646

Louise M. Howard https://orcid.org/0000-0001-9942-744X

Andrew McIntosh https://orcid.org/0000-0002-0198-4588

Robert Pearsall https://orcid.org/0000-0002-6697-9157

Daniel J. Smith https://orcid.org/0000-0002-2267-1951

Article

Publisher Item ID: S2056472419001005

DOI: 10.1192/bjo.2019.100

PMC ID: 7176892

PubMed ID: 32026800

SO-VID: 244fc4b4-1940-4505-9520-7a3f37127be2

License:

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 28 June 2019

Date revision received : 19 November 2019

Date accepted : 17 December 2019

Page count

Figures: 1, Tables: 3, References: 44, Pages: 8

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