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Open Access Highly Accessed Research article

Predicting the prevalence of chronic kidney disease in the English population: a cross-sectional study

Benjamin Kearns1*, Hugh Gallagher2 and Simon de Lusignan3

Author Affiliations

1 School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK

2 Division of Public Health Sciences and Education, St George’s-University of London, London, SW17 0RE, UK

3 Department of Health Care Management and Policy, University of Surrey, Guildford, GU2 7XH, UK

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BMC Nephrology 2013, 14:49  doi:10.1186/1471-2369-14-49

Published: 25 February 2013

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.

Keywords:
Chronic kidney disease; Renal disease; Prevalence; Statistical modelling; Association; Cardiovascular disease