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

Objective: To assess the effect of coding quality on estimates of the incidence of diabetes in the UK between 1995 and 2014.

Design: 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.

Setting: Routine primary care data from 684 practices contributing to the UK Clinical Practice Research Datalink (data contributed from Vision (INPS) practices).

Main outcome measure: 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.

Results: 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.

Conclusions: 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.

Keywords: Data quality; Misclassification; PRIMARY CARE.

Figure 1

Distribution of the practice incidence of diabetes per 100 000 according to the different code categories between 1995 and 2014. Some very extreme values ( >1000) have been removed for clarity of presentation.

Figure 2

Incidence of diabetes per 100 000 between 1990 and 2013 for (A) all codes and (B) all codes excluding codes with the word ‘seen’ in the code description.

Figure 3

Distribution of patients per practice with incorrect diabetes coding according to de Lusignan algorithm. (Some very extreme values have been removed for clarity of presentation.) Categories: (1) misclassification of type 1, (2) misclassification 2, (3) misclassification of type 2 (false positives) and (4) non-diagnosis of type 2 (false negatives). (Category 5 is not shown as it is the same as Read codes category 3).