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      Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank

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          Abstract

          Objectives

          UK Biobank is a UK-wide cohort of 502,655 people aged 40–69, recruited from National Health Service registrants between 2006–10, with healthcare data linkage. Type 2 diabetes is a key exposure and outcome. We developed algorithms to define prevalent and incident diabetes for UK Biobank. The algorithms will be implemented by UK Biobank and their results made available to researchers on request.

          Methods

          We used UK Biobank self-reported medical history and medication to assign prevalent diabetes and type, and tested this against linked primary and secondary care data in Welsh UK Biobank participants. Additionally, we derived and tested algorithms for incident diabetes using linked primary and secondary care data in the English Clinical Practice Research Datalink, and ran these on secondary care data in UK Biobank.

          Results and Significance

          For prevalent diabetes, 0.001% and 0.002% of people classified as “diabetes unlikely” in UK Biobank had evidence of diabetes in their primary or secondary care record respectively. Of those classified as “probable” type 2 diabetes, 75% and 96% had specific type 2 diabetes codes in their primary and secondary care records. For incidence, 95% of people with the type 2 diabetes-specific C10F Read code in primary care had corroborative evidence of diabetes from medications, blood testing or diabetes specific process of care codes. Only 41% of people identified with type 2 diabetes in primary care had secondary care evidence of type 2 diabetes. In contrast, of incident cases using ICD-10 type 2 diabetes specific codes in secondary care, 77% had corroborative evidence of diabetes in primary care. We suggest our definition of prevalent diabetes from UK Biobank baseline data has external validity, and recommend that specific primary care Read codes should be used for incident diabetes to ensure precision. Secondary care data should be used for incident diabetes with caution, as around half of all cases are missed, and a quarter have no corroborative evidence of diabetes in primary care.

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          Most cited references12

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          UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

          Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.
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            What makes UK Biobank special?

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              The SAIL databank: linking multiple health and social care datasets

              Background Vast amounts of data are collected about patients and service users in the course of health and social care service delivery. Electronic data systems for patient records have the potential to revolutionise service delivery and research. But in order to achieve this, it is essential that the ability to link the data at the individual record level be retained whilst adhering to the principles of information governance. The SAIL (Secure Anonymised Information Linkage) databank has been established using disparate datasets, and over 500 million records from multiple health and social care service providers have been loaded to date, with further growth in progress. Methods Having established the infrastructure of the databank, the aim of this work was to develop and implement an accurate matching process to enable the assignment of a unique Anonymous Linking Field (ALF) to person-based records to make the databank ready for record-linkage research studies. An SQL-based matching algorithm (MACRAL, Matching Algorithm for Consistent Results in Anonymised Linkage) was developed for this purpose. Firstly the suitability of using a valid NHS number as the basis of a unique identifier was assessed using MACRAL. Secondly, MACRAL was applied in turn to match primary care, secondary care and social services datasets to the NHS Administrative Register (NHSAR), to assess the efficacy of this process, and the optimum matching technique. Results The validation of using the NHS number yielded specificity values > 99.8% and sensitivity values > 94.6% using probabilistic record linkage (PRL) at the 50% threshold, and error rates were < 0.2%. A range of techniques for matching datasets to the NHSAR were applied and the optimum technique resulted in sensitivity values of: 99.9% for a GP dataset from primary care, 99.3% for a PEDW dataset from secondary care and 95.2% for the PARIS database from social care. Conclusion With the infrastructure that has been put in place, the reliable matching process that has been developed enables an ALF to be consistently allocated to records in the databank. The SAIL databank represents a research-ready platform for record-linkage studies.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                15 September 2016
                2016
                : 11
                : 9
                : e0162388
                Affiliations
                [1 ]Institute of Cardiovascular Sciences, University College London, London, United Kingdom
                [2 ]Department of Non-Communicable Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
                [3 ]CIPHER (Centre for the Improvement of Population Health through e-Records Research) College of Medicine, Swansea University, Swansea, United Kingdom
                [4 ]Centre for Clinical Brain Sciences (CCBS), University of Edinburgh, Edinburgh, United Kingdom
                [5 ]Department of Clinical and Experimental Medicine, University of Surrey, Guilford, United Kingdom
                [6 ]Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
                [7 ]United Kingdom, Biobank, Stockport, United Kingdom
                Deutsches Diabetes-Zentrum Leibniz-Zentrum fur Diabetes-Forschung, GERMANY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist

                • Conceived and designed the experiments: NC SVE MA SB SdL.

                • Analyzed the data: SVE RM MA.

                • Contributed reagents/materials/analysis tools: NA RM MA SB.

                • Wrote the paper: NC SVE RM CS.

                • Facilitated access to UK Biobank data, commented on manuscript: RF.

                Author information
                http://orcid.org/0000-0002-6211-2775
                Article
                PONE-D-16-04284
                10.1371/journal.pone.0162388
                5025160
                27631769
                2529ed16-4b97-462b-aa45-de18d649e672
                © 2016 Eastwood et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 January 2016
                : 22 August 2016
                Page count
                Figures: 4, Tables: 5, Pages: 18
                Funding
                Funded by: UK Biobank
                Award Recipient :
                This work was supported by a grant from UK Biobank. Funders inputted into discussion around the design of the work, and contributed to manuscript writing. They did not however have any role in data analysis.
                Categories
                Research Article
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Health Care
                Primary Care
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Medicine and Health Sciences
                Diagnostic Medicine
                Medicine and Health Sciences
                Diagnostic Medicine
                Diabetes Diagnosis and Management
                Medicine and Health Sciences
                Health Care
                Health Care Facilities
                Hospitals
                Medicine and Health Sciences
                Endocrinology
                Diabetic Endocrinology
                Insulin
                Biology and Life Sciences
                Biochemistry
                Hormones
                Insulin
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Gestational Diabetes
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Gestational Diabetes
                Custom metadata
                UK Biobank data can be obtained by submitting an application form to their website: http://www.ukbiobank.ac.uk/using-the-resource/. CPRD data can be obtained via application to their website http://www.cprd.com/dataAccess/. The independent scientific advisory committee makes a decision as to whether access is granted based on public benefit and to researchers who meet criteria for access to confidential data. SAIL data access is determined by the SAIL data management committee, contact details to initiate the access protocol are: SAILDatabank@ 123456Swansea.ac.uk .

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