Journal of Data Science

Abstract: This paper discusses a comprehensive statistical approach that will be useful in answering health-related questions concerning mortality and incidence rates of chronic diseases such as cancer and hypertension. The developed spatio-temporal models will be useful to explain the patterns of mortality rates of chronic disease in terms of environmental changes and social-economic conditions. In addition to age and time effects, models include two components of normally distributed residual effects and spatial effects, one to represent average regional effects and another to represent changes of subgroups within region over time. Numerical analysis is based on male lung cancer mortality data from the state of Missouri. Gibbs sampling is used to obtain the posterior quantities. As a result, all models discussed in this article fit well in stabilizing the mortality rates, especially in the less populated areas. Due to the richness of hierarchical settings, easy interpretation of parameters and ease of implementation, any models proposed in this paper can be applied generally to other sets of data.

Abstract: In this article, we present a joint modeling approach that com bines information from multiple diseases. Our model can be used to obtain more reliable estimates in rare diseases by incorporating information from more common diseases for which there exists a shared set of important risk factors. Information is shared through both a latent spatial process and a latent temporal process. We develop a fully Bayesian hierarchical imple mentation of our spatio-temporal model in order to estimate relative risk, adjusted for age and gender, at the county level in Iowa in five-year intervals for the period 1973–2002. Our analysis includes lung, oral, and esophageal cancers which are related to excessive tobacco and alcohol use risk factors. Lung cancer risk estimates tend to be stable due to the large number of occurrences in small regions, i.e. counties. The lower risk cancers (oral and esophageal) have fewer occurrences in small regions and thus have estimates that are highly variable and unreliable. Estimates from individual and joint modeling of these diseases are examined and compared. The joint modeling approach has a profound impact on estimates regarding the low risk oral and esophageal cancers while the higher risk lung cancer is minutely impacted. Clearer spatial and temporal patterns are obtained and the standard errors of the estimates are reduced leading to more reliable estimates.