Small area variation in hospital admission rates: Bayesian adjustment for primary care and hospital factors

Hospital admission rates are often used as a proxy to reflect patterns of morbidity or health need in population subgroups or across geographic areas. This paper considers the estimation of small area variations in relative health need, as measured by routinely collected hospital admissions data, after allowing for variation in general practice (primary care) and hospital (supply) effects. A fully Bayesian hierarchical modelling framework is adopted, using combinations of electoral ward populations and general practice patients' lists to define catchment groups for analysis. Hospitals create a further stratum, with flows of patients between catchment groups and hospitals being represented by a gravity model. Variations in health outcomes are modelled by using a range of random-effects structures for each cross-classification of strata, together with a consideration of ward, practice, hospital and crossed level covariates. The approach is applied to case-studies of child respiratory and total emergency hospital admissions for residents in a London health authority.