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. Tate, A. R., Dungey, S., Glew, S., Beloff, N., Williams, R., & Williams, T. BMJ open, 7(1):e012905, January, 2017. doi abstract bibtex 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.
@article{tate_quality_2017,
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},
volume = {7},
issn = {2044-6055},
shorttitle = {Quality of recording of diabetes in the {UK}},
doi = {10.1136/bmjopen-2016-012905},
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.},
language = {eng},
number = {1},
journal = {BMJ open},
author = {Tate, A. Rosemary and Dungey, Sheena and Glew, Simon and Beloff, Natalia and Williams, Rachael and Williams, Tim},
month = jan,
year = {2017},
pmid = {28122831},
pmcid = {PMC5278252},
keywords = {Data quality, Misclassification, primary care},
pages = {e012905},
}
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{"_id":"EpTcKiMJPbcEqjZio","bibbaseid":"tate-dungey-glew-beloff-williams-williams-qualityofrecordingofdiabetesintheukhowdoesthegpsmethodofcodingclinicaldataaffectincidenceestimatescrosssectionalstudyusingthecprddatabase-2017","downloads":0,"creationDate":"2017-08-14T06:07:44.873Z","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","author_short":["Tate, A. R.","Dungey, S.","Glew, S.","Beloff, N.","Williams, R.","Williams, T."],"year":2017,"bibtype":"article","biburl":"https://api.zotero.org/groups/1627584/items?key=9iCgi6nGZOE8CBVlzk4IrSpj&format=bibtex&limit=100","bibdata":{"bibtype":"article","type":"article","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","volume":"7","issn":"2044-6055","shorttitle":"Quality of recording of diabetes in the UK","doi":"10.1136/bmjopen-2016-012905","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.","language":"eng","number":"1","journal":"BMJ open","author":[{"propositions":[],"lastnames":["Tate"],"firstnames":["A.","Rosemary"],"suffixes":[]},{"propositions":[],"lastnames":["Dungey"],"firstnames":["Sheena"],"suffixes":[]},{"propositions":[],"lastnames":["Glew"],"firstnames":["Simon"],"suffixes":[]},{"propositions":[],"lastnames":["Beloff"],"firstnames":["Natalia"],"suffixes":[]},{"propositions":[],"lastnames":["Williams"],"firstnames":["Rachael"],"suffixes":[]},{"propositions":[],"lastnames":["Williams"],"firstnames":["Tim"],"suffixes":[]}],"month":"January","year":"2017","pmid":"28122831","pmcid":"PMC5278252","keywords":"Data quality, Misclassification, primary care","pages":"e012905","bibtex":"@article{tate_quality_2017,\n\ttitle = {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},\n\tvolume = {7},\n\tissn = {2044-6055},\n\tshorttitle = {Quality of recording of diabetes in the {UK}},\n\tdoi = {10.1136/bmjopen-2016-012905},\n\tabstract = {OBJECTIVE: To assess the effect of coding quality on estimates of the incidence of diabetes in the UK between 1995 and 2014.\nDESIGN: 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.\nSETTING: Routine primary care data from 684 practices contributing to the UK Clinical Practice Research Datalink (data contributed from Vision (INPS) practices).\nMAIN 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.\nRESULTS: 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.\nCONCLUSIONS: 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.},\n\tlanguage = {eng},\n\tnumber = {1},\n\tjournal = {BMJ open},\n\tauthor = {Tate, A. Rosemary and Dungey, Sheena and Glew, Simon and Beloff, Natalia and Williams, Rachael and Williams, Tim},\n\tmonth = jan,\n\tyear = {2017},\n\tpmid = {28122831},\n\tpmcid = {PMC5278252},\n\tkeywords = {Data quality, Misclassification, primary care},\n\tpages = {e012905},\n}\n\n","author_short":["Tate, A. 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