Evidence map›Paper›PMID 28122831›Full record

ArticleBMJ open2017

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.

A Rosemary Tate, Sheena Dungey, Simon Glew, Natalia Beloff, Rachael Williams, Tim Williams

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Article in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  20. Validation of Diagnostic Coding for Diabetes Mellitus in Hospitalized Patients.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2022
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

A Rosemary TateDepartment of Informatics, University of Sussex, Brighton, UK.
Sheena DungeyDepartment of Informatics, University of Sussex, Brighton, UK.
Simon GlewDivision of Primary Care and Public Health, Brighton and Sussex Medical School, Brighton, UK.
Natalia BeloffDepartment of Informatics, University of Sussex, Brighton, UK.
Rachael WilliamsCPRD, MHRA, London, UK.
Tim WilliamsCPRD, MHRA, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

General PractitionersPrimary Health CareClinical CodingCross-Sectional StudiesDatabases, FactualDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2DocumentationHumansIncidencePractice Patterns, Physicians'United KingdomData qualityMisclassificationPRIMARY CARE

Identifiers

PMID28122831
PMCPMC5278252

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.