ObjectiveTo assess the effect of coding quality on estimates of the incidence of diabetes in the UK between 1995 and 2014.DesignA 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.SettingRoutine primary care data from 684 practices contributing to the UK Clinical Practice Research Datalink (data contributed from Vision (INPS) practices).Main outcome measureIncidence 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.ResultsIncidence 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.ConclusionsIn 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.

Abstract-There is currently no widely recognised methodology for undertaking data quality assessment in electronic health records used for research. In an attempt to address this, we have developed a protocol for measuring and monitoring data quality in primary care research databases, whereby practice-based data quality measures are tailored to the intended use of the data. Our approach was informed by an in-depth investigation of aspects of data quality in the Clinical Practice Research Datalink Gold database and presentations of the results to data users. Although based on a primary care database, much of our proposed approach would be equally applicable to other health care databases.

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The importance of good high quality electronic healthcare records has been set forth along with the need for a practical user-considered and collaborative approach to its improvement.