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

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.

Fig 1. Data sources used in the development of UK Biobank diabetes prevalence and incidence algorithms.

Solid arrows indicate established linkages, dotted arrows indicate anticipated linkages. ¹data used to derive prevalence algorithms, ²data used to test prevalence algorithms, ³data used to derive incidence algorithms, ⁴data used to test incidence algorithms. HES = Hospital episode statistics, SMR01 = Scottish morbidity record, SAIL = Secure anonymised information linkage databank, PEDW = patient episode database for Wales, CPRD = clinical practice research datalink.

Fig 2

(a) Prevalence algorithm 1: Distinction between diabetes presence or absence, and initial sorting of diabetes type using baseline UK Biobank assessment data. See S1 appendix for rationale and further data for each step. (b) Prevalence algorithm 2: Finalising type 1 diabetes diagnosis and classification into probable and possible categories. See S1 appendix for rationale and further data for each step.(c) Prevalence algorithm 3: Finalising type 2 diabetes diagnosis and classification into probable and possible categories. See S1 appendix for rationale and further data for each step.(d) Final diabetes diagnostic status in UKB.

Fig 3

a. Diabetes incidence algorithms for primary care data, run in CPRD. b. Diabetes incidence algorithm for secondary care data, run in UK Biobank-held in-patient data. *Includes categories:probable type 1 diabetes, probable type 2 diabetes,. **ICD-10: E10, E11, E13, E14. Includes main or secondary diagnostic codes for in-patient data.

Fig 4. Flow of participants identified with diabetes in UK Biobank.

*or mid-point of last consultation/episode without diabetes diagnosis (UK Biobank inception if not available) and 1^st diabetes diagnosis dates.