Abstract: Health outcomes are linked to air pollution, demographic, or socioeconomic factors which vary across space and time. Thus, it is often found that relative risks in spatial health data have locally different patterns. In such cases, latent modeling is useful in the disaggregation of risk proﬁles. In particular, spatial-temporal mixture models can help to isolate spatial clusters each of which has a homogeneous temporal pattern in relative risks. Mixture models are assumed as they have various weight structures and considered in two situations: the number of underlying components is known or unknown. In this paper, we compare spatial-temporal mixture models with different weight structures in both situations. For comparison, we propose a set of spatial cluster detection diagnostics which are based on the posterior distribution of weights. We also develop new accuracy measures to assess the recovery of true relative risk. Based on the simulation study, we examine the performance of various spatial-temporal mixture models in terms of proposed methods and goodness-of-ﬁt measures. We examine two real data sets: low birth weight data and chronic obstructive pulmonary disease data.
Abstract: This paper reviews the current state of point event modeling in spatial epidemiology from a Bayesian perspective. Point event (or case event) data arise when geo-coded addresses of disease events are available. Often this level of spatial resolution would not be accessible due to medical confidentiality constraints. However, for the examination of small spatial scales it is important to be capable of examining point process data directly. Models for such data are usually formulated based on point process theory. In addition, special conditioning arguments can lead to simpler Bernoulli likelihoods and logistic spatial models. Goodness-of-fit diagnostics and Bayesian residuals are also considered. Applications within putative health hazard risk assessment, cluster detection, and linkage to environmental risk fields (misalignment) are considered.
Abstract: Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be
correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this paper, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher
than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the
alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application
to diseases of the respiratory system in South Carolina is finally presented. Keywords: disease surveillance; multiple diseases; Shared component model; conditional predictive ordinate
Abstract: We develop a new Bayesian two-stage space-time mixture model to investigate the effects of
air pollution on asthma. The two-stage mixture model proposed allows for the identification
of temporal latent structure as well as the estimation of the effects of covariates on health
outcomes. In the paper, we also consider spatial misalignment of exposure and health data.
A simulation study is conducted to assess the performance of the 2-stage mixture model.
We apply our statistical framework to a county-level ambulatory care asthma data set in the
US state of Georgia for the years 1999-2008.
Key words: Space-time mixture model; air pollution; covariate adjustment; asthma;