2017
DOI: 10.1016/j.rmed.2017.10.024
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The prevalence of COPD in England: An ontological approach to case detection in primary care

Abstract: We demonstrate a robust reproducible method for COPD case detection in routinely collected primary care data. Our calculated prevalence differed significantly from current estimates based upon P4P data, suggesting that the burden of COPD in England is greater than currently predicted.

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Cited by 12 publications
(8 citation statements)
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“…The prevalence of COPD in England in 2016 was estimatedusing an ontological approachto be 4.6% among adults aged ! 35 years [20]. Based on claims data from the United States (US), individuals with COPD appear to be at increased risk of pertussis infection, as the incidence of pertussis was 1.9-3.6-fold higher among adults with COPD than matched adults without COPD or asthma [21].…”
Section: What Is the Impact?mentioning
confidence: 99%
“…The prevalence of COPD in England in 2016 was estimatedusing an ontological approachto be 4.6% among adults aged ! 35 years [20]. Based on claims data from the United States (US), individuals with COPD appear to be at increased risk of pertussis infection, as the incidence of pertussis was 1.9-3.6-fold higher among adults with COPD than matched adults without COPD or asthma [21].…”
Section: What Is the Impact?mentioning
confidence: 99%
“…However, our ontological approach to detecting cases of AA and associated comorbidities will improve accuracy compared with the use of diagnostic codes alone. 69 70 Exclusion of individuals with potentially confounding conditions (ie, other causes of hair loss) will also improve the accuracy of our estimates. The algorithms used to detect common mental health conditions have been previously validated in UK primary care.…”
Section: Discussionmentioning
confidence: 99%
“…An ontological approach to AD case detection improves accuracy compared with the use of diagnostic codes alone. 23 The approach to identify AD to be used here has been validated in other similar datasets and has been shown to have an excellent positive predictive value. 25 Exclusion of individuals with potentially confounding conditions will also improve the accuracy of our estimates.…”
Section: Strengths and Limitations Of The Research Methodsmentioning
confidence: 99%
“…20 Studies in several chronic diseases such as diabetes, atrial fibrillation, chronic kidney disease, asthma and chronic obstructive pulmonary disease have been published using RCGP RSC data. [21][22][23][24] Open access Study participants' eligibility All children and adults registered with an RCGP RSC affiliated practice between 1 January 2009 and 1 January 2019 will be extracted from the RCGP RSC database and eligible for inclusion in the studies. People will be excluded if they have less than 12 months of follow-up in the database (unless aged <1 year) and if they have not had at least one consultation during the study period.…”
Section: Data Sourcementioning
confidence: 99%