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OBJECTIVE: To develop methods to produce small-area estimates of need for hip and knee replacement surgery to inform local health service planning. METHODS: Multilevel Poisson regression modeling was used to estimate rates of need for hip/knee replacement by age, sex, deprivation, rurality, and ethnic mix using a nationally representative population-based survey (the English Longitudinal Study of Ageing, n = 11,392 people age > or =50 years). Estimates of need from the regression model were then combined with stratified census population counts to produce small-area predictions of need. Uncertainty in the predictions was obtained by taking a Bayesian simulation-based approach using WinBUGS software. This allows correlations in parameter estimates to be appropriately incorporated in the credible intervals for the small-area predictions. RESULTS: Small-area estimates of need for hip/knee replacement have been produced for wards and districts in England. Rates of need are adjusted for the sociodemographic characteristics of an area and include 95% credible intervals. Need for hip/knee replacement varies geographically, dependant on the sociodemographic characteristics of an area. CONCLUSION: For the first time, small-area estimates of need for hip/knee replacement surgery have been produced together with estimates of uncertainty to inform local health planning. The methodologic approach described here could be reproduced in other countries and for other disease indicators. Further research is required to combine small-area estimates of need with provision to determine whether there is equitable access to care.

Original publication




Journal article


Arthritis rheum

Publication Date





1667 - 1673


Aged, Aged, 80 and over, Arthroplasty, Replacement, Hip, Arthroplasty, Replacement, Knee, Bayes Theorem, Cohort Studies, Data Interpretation, Statistical, Databases, Factual, England, Female, Health Services Needs and Demand, Humans, Joint Diseases, Male, Middle Aged, Models, Statistical, Needs Assessment, Poisson Distribution, Predictive Value of Tests, Uncertainty