Assessing Psychiatric Morbidity from Community Registers: Methods for Bayesian Adjustment

This paper considers Bayesian approaches to adjusting small area prevalence estimates derived from a community register of the seriously mentally ill, by taking account of underlying variability in latent prevalence between areas. Adjustment of individual prevalence rates to take account of the entire spatial distribution has implications both for epidemiological inference and resource rankings for localities. The more commonly adopted empirical Bayes approaches are here compared with fully Bayes approaches. Fully Bayes methods allow for uncertainty in the prevalence parameters and also permit the inclusion of information from other sources (for example, hospital admissions for mental illness) or from previous studies. The impact of socioeconomic indices on morbidity and its relevance for adjusted prevalence estimates is also considered. A case study is applied to an East London Health Authority, Barking and Havering.