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 m orbidity or health need in population subgroups or across geographic areas. This paper considers the estimation of small area variations in relative h ealth need, as measured by routinely collected hospital admissions data, af ter allowing for variation in general practice (primary care) and hospital (supply) effects. A fully Bayesian hierarchical modelling framework is adop ted, 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 hospi tals being represented by a gravity model. Variations in health outcomes ar e modelled by using a range of random-effects structures for each cross-cla ssification of strata, together with a consideration of ward, practice, hos pital 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.