Predicting the prevalence of chronic kidney disease in the English population: a cross-sectional study.
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 Nephrol, 14 (1).
Available under License : See the attached licence file.
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 ISRCTN56023731. Note that this study reports the results of a cross-sectional analysis of data from this trial.
|Divisions :||Faculty of Arts and Social Sciences > Surrey Business School|
|Date :||25 February 2013|
|Identification Number :||10.1186/1471-2369-14-49|
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|Additional Information :||BioMed Central Open Access license agreement Anyone is free: to copy, distribute, and display the work; to make derivative works; to make commercial use of the work; Under the following conditions: Attribution the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are; any of these conditions can be waived if the authors gives permission. The full license can be found at http://www.biomedcentral.com/about/license|
|Depositing User :||Symplectic Elements|
|Date Deposited :||05 Jul 2013 09:43|
|Last Modified :||26 Jul 2016 10:09|
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