title: Quality of recording of diabetes in the UK: how does the GP's method of coding clinical data affect incidence estimates? Cross-sectional study using the CPRD database.
abstract: To assess the effect of coding quality on estimates of the incidence of diabetes in the UK between 1995 and 2014.
  
  
  
  A cross-sectional analysis examining diabetes coding from 1995 to 2014 and how the choice of codes (diagnosis codes vs codes which suggest diagnosis) and quality of coding affect estimated incidence.
  
  
  
  Routine primary care data from 684 practices contributing to the UK Clinical Practice Research Datalink (data contributed from Vision (INPS) practices).
  
  
  
  Incidence rates of diabetes and how they are affected by (1) GP coding and (2) excluding 'poor' quality practices with at least 10% incident patients inaccurately coded between 2004 and 2014.
  
  
  
  Incidence rates and accuracy of coding varied widely between practices and the trends differed according to selected category of code. If diagnosis codes were used, the incidence of type 2 increased sharply until 2004 (when the UK Quality Outcomes Framework was introduced), and then flattened off, until 2009, after which they decreased. If non-diagnosis codes were included, the numbers continued to increase until 2012. Although coding quality improved over time, 15% of the 666 practices that contributed data between 2004 and 2014 were labelled 'poor' quality. When these practices were dropped from the analyses, the downward trend in the incidence of type 2 after 2009 became less marked and incidence rates were higher.
  
  
  
  In contrast to some previous reports, diabetes incidence (based on diagnostic codes) appears not to have increased since 2004 in the UK. Choice of codes can make a significant difference to incidence estimates, as can quality of recording. Codes and data quality should be checked when assessing incidence rates using GP data.
authors.affiliation: Department of Informatics, University of Sussex, Brighton, UK.
authors.forename: A Rosemary
authors.initials: AR
authors.lastname: Tate
authors.affiliation: Department of Informatics, University of Sussex, Brighton, UK.|CPRD, MHRA, London, UK.
authors.forename: Sheena
authors.initials: S
authors.lastname: Dungey
authors.affiliation: Division of Primary Care and Public Health, Brighton and Sussex Medical School, Brighton, UK.
authors.forename: Simon
authors.initials: S
authors.lastname: Glew
authors.affiliation: Department of Informatics, University of Sussex, Brighton, UK.
authors.forename: Natalia
authors.initials: N
authors.lastname: Beloff
authors.affiliation: CPRD, MHRA, London, UK.
authors.forename: Rachael
authors.initials: R
authors.lastname: Williams
authors.affiliation: CPRD, MHRA, London, UK.
authors.forename: Tim
authors.initials: T
authors.lastname: Williams
mesh_terms: D059019:Clinical Coding / Q000592:standards*; D003430:Cross-Sectional Studies; D016208:Databases, Factual; D003922:Diabetes Mellitus, Type 1 / Q000453:epidemiology*; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D004282:Documentation / Q000592:standards*; D058005:General Practitioners; D006801:Humans; D015994:Incidence; D010818:Practice Patterns, Physicians'; D011320:Primary Health Care; D006113:United Kingdom / Q000453:epidemiology
keywords: Data quality; Misclassification; PRIMARY CARE