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Journal of Public Health Advance Access originally published online on January 25, 2006
Journal of Public Health 2006 28(1):71-81; doi:10.1093/pubmed/fdi068
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© The Author 2006, Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved.

Estimating diabetes prevalence by small area in England



Peter Congdon
Peter Congdon, Research Professor of Quantitative Geography & Health Statistics, Department of Geography, Queen Mary University of London, Mile End Road, London E1 4NS, UK

Address correspondence to Peter Congdon. Email: p.congdon{at}qmul.ac.uk

Background Diabetes risk is linked to both deprivation and ethnicity, and so prevalence will vary considerably between areas. Prevalence differences may partly account for geographic variation in health performance indicators for diabetes, which are based on age standardized hospitalization or operation rates. A positive correlation between prevalence and health outcomes indicates that the latter are not measuring only performance.

Methods A regression analysis of prevalence rates according to age, sex and ethnicity from the Health Survey for England (HSE) is undertaken and used (together with census data) to estimate diabetes prevalence for 354 English local authorities and 8000 smaller areas (electoral wards). An adjustment for social factors is based on a prevalence gradient over area-deprivation quintiles. A Bayesian estimation approach is used allowing simple inclusion of evidence on prevalence from other or historical sources.

Results The estimated prevalent population in England is 1.5 million (188 000 type 1 and 1.341 million type 2). At strategic health authority (StHA) level, prevalence varies from 2.4 (Thames Valley) to 4 per cent (North East London). The prevalence estimates are used to assess variations between local authorities in adverse hospitalization indicators for diabetics and to assess the relationship between diabetes-related mortality and prevalence. In particular, rates of diabetic ketoacidosis (DKA) and coma are positively correlated with prevalence, while diabetic amputation rates are not.

Conclusions The methodology developed is applicable to developing small-area-prevalence estimates for a range of chronic diseases, when health surveys assess prevalence by demographic categories. In the application to diabetes prevalence, there is evidence that performance indicators as currently calculated are not corrected for prevalence.

Keywords: diabetes, ethnic risks, mortality, performance indicators, prevalence, small area, social gradient


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