The mapping of disease incidence and prevalence has long been a part of public health, epidemiology, and the study of disease in human populations. In this area, there have been always many challenge of obtaining reliable statistical estimates of local disease risk based on counts of observed cases within small administrative districts or regions coupled with potentially relevant background information. In this proposed session, three speakers will focus on three special aspects: how to use covariate adjustment and ranking methods to identify regions with high and low mortality rates; how to model the spatial structure of the structural zero class and provides estimates of the spatial distribution of the probability of being structural or not; and how to utilize Dirichlet Process priors in clustering of disease related data.
Often geospatial disease outcomes are characterized by sparseness when viewed as a count distribution. This is sometimes called zero-inflation. Classically the models for zero inflation are of two types: zero class modelled as ‘structural’ or ‘Poisson’ or as hurdle models where the zeroes are treated completely separately from the truncated Poisson positive counts. Often the […]
We illustrate some applications of Dirichlet Process Priors for modeling random effects in small area models. We also show some of its application in disease mapping by using a Dirichlet process prior with a baseline CAR model. One advantage of such priors is that there is automatic clustering as well as tracking a multimodal posterior […]
Identifying regions with the highest and lowest mortality rates and producing the corresponding color-coded maps help epidemiologists identify promising areas for analytic etiological studies. Based on a two-stage Poisson–Gamma model with covariates, we use information on known risk factors, such as smoking prevalence, to adjust mortality rates and reveal residual variation in relative risks that […]