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 Vol. 11; no. 9; p. e0162388 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
15.09.2016
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0162388 |
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| Abstract | 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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| AbstractList | 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. 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. 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. 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. 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.OBJECTIVESUK 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.METHODSWe 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.RESULTS AND SIGNIFICANCEFor 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. ObjectivesUK 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.MethodsWe 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 significanceFor 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. |
| Audience | Academic |
| Author | Mathur, Rohini Chaturvedi, Nishi Atkinson, Mark Allen, Naomi Brophy, Sinead Eastwood, Sophie V de Lusignan, Simon Sudlow, Cathie Flaig, Robin |
| AuthorAffiliation | 7 United Kingdom, Biobank, Stockport, United Kingdom 1 Institute of Cardiovascular Sciences, University College London, London, United Kingdom 4 Centre for Clinical Brain Sciences (CCBS), University of Edinburgh, Edinburgh, United Kingdom 3 CIPHER (Centre for the Improvement of Population Health through e-Records Research) College of Medicine, Swansea University, Swansea, United Kingdom 2 Department of Non-Communicable Diseases, London School of Hygiene & Tropical Medicine, London, 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 Deutsches Diabetes-Zentrum Leibniz-Zentrum fur Diabetes-Forschung, GERMANY |
| AuthorAffiliation_xml | – name: 3 CIPHER (Centre for the Improvement of Population Health through e-Records Research) College of Medicine, Swansea University, Swansea, United Kingdom – name: 6 Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom – name: 2 Department of Non-Communicable Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom – name: 7 United Kingdom, Biobank, Stockport, United Kingdom – name: 5 Department of Clinical and Experimental Medicine, University of Surrey, Guilford, United Kingdom – name: 1 Institute of Cardiovascular Sciences, University College London, London, United Kingdom – name: Deutsches Diabetes-Zentrum Leibniz-Zentrum fur Diabetes-Forschung, GERMANY – name: 4 Centre for Clinical Brain Sciences (CCBS), University of Edinburgh, Edinburgh, United Kingdom |
| Author_xml | – sequence: 1 givenname: Sophie V surname: Eastwood fullname: Eastwood, Sophie V – sequence: 2 givenname: Rohini surname: Mathur fullname: Mathur, Rohini – sequence: 3 givenname: Mark surname: Atkinson fullname: Atkinson, Mark – sequence: 4 givenname: Sinead surname: Brophy fullname: Brophy, Sinead – sequence: 5 givenname: Cathie surname: Sudlow fullname: Sudlow, Cathie – sequence: 6 givenname: Robin surname: Flaig fullname: Flaig, Robin – sequence: 7 givenname: Simon surname: de Lusignan fullname: de Lusignan, Simon – sequence: 8 givenname: Naomi surname: Allen fullname: Allen, Naomi – sequence: 9 givenname: Nishi orcidid: 0000-0002-6211-2775 surname: Chaturvedi fullname: Chaturvedi, Nishi |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27631769$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1023/B:EJEP.0000027350.85974.47 10.1111/j.1464-5491.2009.02917.x 10.1186/1472-6947-9-3 10.2188/jea.JE20120221 10.1093/pubmed/fdl028 10.1097/GME.0000000000000189 10.1016/S0140-6736(12)60404-8 10.1111/j.1464-5491.2011.03419.x 10.1371/journal.pmed.1001779 10.1136/bmj.300.6732.1092 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 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. Competing Interests: The authors have declared that no competing interests exist |
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| References | ref12 J Chisholm (ref13) 1990; 300 ref15 A Goto (ref4) 2013; 23 RA Lyons (ref11) 2009; 9 MM Bergmann (ref7) 2004; 19 ref2 S de Lusignan (ref9) 2012; 29 ref1 LA Sigfrid (ref14) 2006; 28 S de Lusignan (ref8) 2010; 27 R Collins (ref3) 2012; 379 C Sudlow (ref10) 2015; 12 I Sluijs (ref6) 2010; 68 JM Jackson (ref5) 2014; 21 |
| References_xml | – volume: 19 start-page: 411 year: 2004 ident: ref7 article-title: Agreement of self-reported medical history: comparison of an in-person interview with a self-administered questionnaire publication-title: Eur J Epidemiol doi: 10.1023/B:EJEP.0000027350.85974.47 – ident: ref1 – volume: 27 start-page: 203 year: 2010 ident: ref8 article-title: A method of identifying and correcting miscoding, misclassification and misdiagnosis in diabetes: a pilot and validation study of routinely collected data publication-title: Diabet Med doi: 10.1111/j.1464-5491.2009.02917.x – ident: ref2 – volume: 9 start-page: 3 year: 2009 ident: ref11 article-title: The SAIL databank: linking multiple health and social care datasets publication-title: BMC Med Inform Decis Mak doi: 10.1186/1472-6947-9-3 – volume: 23 start-page: 295 year: 2013 ident: ref4 article-title: Validity of diabetes self-reports in the Saku diabetes study publication-title: J Epidemiol doi: 10.2188/jea.JE20120221 – volume: 28 start-page: 221 year: 2006 ident: ref14 article-title: Using the UK primary care Quality and Outcomes Framework to audit health care equity: preliminary data on diabetes management publication-title: J Public Health (Oxf) doi: 10.1093/pubmed/fdl028 – volume: 21 start-page: 861 year: 2014 ident: ref5 article-title: Validity of diabetes self-reports in the Women's Health Initiative publication-title: Menopause doi: 10.1097/GME.0000000000000189 – volume: 379 start-page: 1173 year: 2012 ident: ref3 article-title: What makes UK Biobank special? publication-title: Lancet doi: 10.1016/S0140-6736(12)60404-8 – volume: 29 start-page: 181 year: 2012 ident: ref9 article-title: Miscoding, misclassification and misdiagnosis of diabetes in primary care publication-title: Diabet Med doi: 10.1111/j.1464-5491.2011.03419.x – volume: 68 start-page: 333 year: 2010 ident: ref6 article-title: Ascertainment and verification of diabetes in the EPIC-NL study publication-title: Neth J Med – volume: 12 start-page: e10001779 year: 2015 ident: ref10 article-title: UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age publication-title: PLOS Medicine doi: 10.1371/journal.pmed.1001779 – ident: ref12 – volume: 300 start-page: 1092 year: 1990 ident: ref13 article-title: The Read clinical classification publication-title: BMJ doi: 10.1136/bmj.300.6732.1092 – ident: ref15 |
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| Snippet | 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.... 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... 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... ObjectivesUK 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... 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... |
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| SubjectTerms | 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 |
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| Title | Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank |
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