RESUMO
PURPOSE: Accurate epidemiological surveillance of leprosy is a matter of international public health concern. It often suffers, however, from potential problems of under-registration of reported cases, particularly in poorer and more socially deprived areas. Such problems also apply in the surveillance of many other communicable or transmissible diseases. We develop a Bayesian model for small-area disease rates that allows for censoring of case detection in suspect districts and can therefore be used to estimate under-reporting of cases in a given study region. METHODS: Such methods are applied to leprosy incidence in a municipality of Pernambuco State in North Eastern Brazil, using a social deprivation indicator as the basis for considering data from certain districts to be censored. The time period we consider was immediately prior to an extension of the coverage and efficacy of the control program and model predictions concerning under reporting can therefore be compared with more reliable data subsequently collected from the same region. RESULTS: The proposed method produces informative estimates of under detection of leprosy cases in the defined study region and these estimates compare well, both in size and in geographical location, with the numbers of cases subsequently detected. CONCLUSIONS: As illustrated by the application discussed in this article, the proposed model provides a general tool that may be used in spatial epidemiological surveillance situations where the available data is suspected to contain significant under-registrations of cases in certain geographical areas.
Assuntos
Hanseníase/epidemiologia , Vigilância da População/métodos , Teorema de Bayes , Brasil/epidemiologia , Métodos Epidemiológicos , Humanos , Reprodutibilidade dos TestesRESUMO
The study of the geographical distribution of disease incidence and its relationship to potential risk factors (referred to here as "geographical epidemiology") has provided, and continues to provide, rich ground for the application and development of statistical methods and models. In recent years increasingly powerful and versatile statistical tools have been developed in this application area. This paper discusses the general classes of problem in geographical epidemiology and reviews the key statistical methods now being employed in each of the application areas identified. The paper does not attempt to exhaustively cover all possible methods and models, but extensive references are provided to further details and to additional approaches. The overall aim is to provide a picture of the "current state of the art" in the use of spatial statistical methods in epidemiological and public health research. Following the review of methods, the main software environments which are available to implement such methods are discussed. The paper concludes with some brief general reflections on the epidemiological and public health implications of the use of spatial statistical methods in health and on associated benefits and problems.