Kearns, B., Gallagher, H. and de Lusignan, S. (2013) Predicting the prevalence of chronic kidney disease in the English population: a cross-sectional study. BMC Nephrology, 14. 49. ISSN 1471-2369
Abstract
Background There is concern that not all cases of chronic kidney disease (CKD) are known to general practitioners, leading to an underestimate of its true prevalence. We carried out this study to develop a model to predict the prevalence of CKD using a large English primary care dataset which includes previously undiagnosed cases of CKD. Methods Cross-sectional analysis of data from the Quality Improvement in CKD trial, a representative sample of 743 935 adults in England aged 18 and over. We created multivariable logistic regression models to identify important predictive factors. Results A prevalence of 6.76% was recorded in our sample, compared to a national prevalence of 4.3%. Increasing age, female gender and cardiovascular disease were associated with a significantly increased prevalence of CKD (p < 0.001 for all). Age had a complex association with CKD. Cardiovascular disease was a stronger predictive factor in younger than in older patients. For example, hypertension has an odds ratio of 2.02 amongst patients above average and an odds ratio of 3.91 amongst patients below average age. Conclusion In England many cases of CKD remain undiagnosed. It is possible to use the results of this study to identify areas with high levels of undiagnosed CKD and groups at particular risk of having CKD. Trial registration Current Controlled Trials ISRCTN: ISRCTN56023731. Note that this study reports the results of a cross-sectional analysis of data from this trial.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: |
© 2013 Kearns et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Chronic Kidney Disease; Renal Disease; Nephrology; Statistical modelling; Prevalence estimate |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Jul 2014 10:12 |
Last Modified: | 23 Jun 2023 21:40 |
Published Version: | http://dx.doi.org/10.1186/1471-2369-14-49 |
Status: | Published |
Publisher: | BioMed Central |
Refereed: | Yes |
Identification Number: | 10.1186/1471-2369-14-49 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:79712 |