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Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank
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 algorith...
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Published in: | PloS one 2016-09, Vol.11 (9), p.e0162388-e0162388 |
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description | 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.
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.
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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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.
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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0162388</identifier><identifier>PMID: 27631769</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adjudication ; Aged ; Algorithms ; Biological Specimen Banks ; Biology and Life Sciences ; Blood tests ; Clinical medicine ; Design ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 2 - epidemiology ; Diabetes therapy ; Family medical history ; Female ; Gestational diabetes ; Health care ; Health care information services ; Health screening ; Health services ; Hospitals ; Humans ; Incidence ; Male ; Medical diagnosis ; Medical research ; Medicine ; Medicine and Health Sciences ; Middle Aged ; Population ; Prevalence ; Primary care ; Questionnaires ; Researchers ; Studies ; Type 2 diabetes ; United Kingdom - epidemiology ; Validity ; Womens health</subject><ispartof>PloS one, 2016-09, Vol.11 (9), p.e0162388-e0162388</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Eastwood et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2016 Eastwood et al 2016 Eastwood et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c725t-1b2e23e455f08ded2f186c786f0abf45203471cefbc1d05a245e405f870b17133</citedby><cites>FETCH-LOGICAL-c725t-1b2e23e455f08ded2f186c786f0abf45203471cefbc1d05a245e405f870b17133</cites><orcidid>0000-0002-6211-2775</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1819907479/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1819907479?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27631769$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Herder, Christian</contributor><creatorcontrib>Eastwood, Sophie V</creatorcontrib><creatorcontrib>Mathur, Rohini</creatorcontrib><creatorcontrib>Atkinson, Mark</creatorcontrib><creatorcontrib>Brophy, Sinead</creatorcontrib><creatorcontrib>Sudlow, Cathie</creatorcontrib><creatorcontrib>Flaig, Robin</creatorcontrib><creatorcontrib>de Lusignan, Simon</creatorcontrib><creatorcontrib>Allen, Naomi</creatorcontrib><creatorcontrib>Chaturvedi, Nishi</creatorcontrib><title>Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.
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.
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.</description><subject>Adjudication</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Biological Specimen Banks</subject><subject>Biology and Life Sciences</subject><subject>Blood tests</subject><subject>Clinical medicine</subject><subject>Design</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus, Type 2 - epidemiology</subject><subject>Diabetes therapy</subject><subject>Family medical history</subject><subject>Female</subject><subject>Gestational diabetes</subject><subject>Health care</subject><subject>Health care information services</subject><subject>Health screening</subject><subject>Health services</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Incidence</subject><subject>Male</subject><subject>Medical diagnosis</subject><subject>Medical research</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Middle Aged</subject><subject>Population</subject><subject>Prevalence</subject><subject>Primary care</subject><subject>Questionnaires</subject><subject>Researchers</subject><subject>Studies</subject><subject>Type 2 diabetes</subject><subject>United Kingdom - 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epidemiology</topic><topic>Diabetes therapy</topic><topic>Family medical history</topic><topic>Female</topic><topic>Gestational diabetes</topic><topic>Health care</topic><topic>Health care information services</topic><topic>Health screening</topic><topic>Health services</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Incidence</topic><topic>Male</topic><topic>Medical diagnosis</topic><topic>Medical research</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Middle Aged</topic><topic>Population</topic><topic>Prevalence</topic><topic>Primary care</topic><topic>Questionnaires</topic><topic>Researchers</topic><topic>Studies</topic><topic>Type 2 diabetes</topic><topic>United Kingdom - epidemiology</topic><topic>Validity</topic><topic>Womens health</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Eastwood, Sophie V</creatorcontrib><creatorcontrib>Mathur, Rohini</creatorcontrib><creatorcontrib>Atkinson, Mark</creatorcontrib><creatorcontrib>Brophy, Sinead</creatorcontrib><creatorcontrib>Sudlow, Cathie</creatorcontrib><creatorcontrib>Flaig, Robin</creatorcontrib><creatorcontrib>de Lusignan, Simon</creatorcontrib><creatorcontrib>Allen, Naomi</creatorcontrib><creatorcontrib>Chaturvedi, Nishi</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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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.
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.
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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27631769</pmid><doi>10.1371/journal.pone.0162388</doi><tpages>e0162388</tpages><orcidid>https://orcid.org/0000-0002-6211-2775</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adjudication Aged Algorithms Biological Specimen Banks Biology and Life Sciences Blood tests Clinical medicine Design Diabetes Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - epidemiology Diabetes therapy Family medical history Female Gestational diabetes Health care Health care information services Health screening Health services Hospitals Humans Incidence Male Medical diagnosis Medical research Medicine Medicine and Health Sciences Middle Aged Population Prevalence Primary care Questionnaires Researchers Studies Type 2 diabetes United Kingdom - epidemiology Validity Womens health |
title | Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank |
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