Journal of Data Science

Abstract: Particulate matter smaller than 2.5 microns (PM2.5) is a com monly measured parameter in ground-based sampling networks designed to assess short and long-term air quality. The measurement techniques for ground based PM2.5 are relatively accurate and precise, but monitoring lo cations are spatially too sparse for many applications. Aerosol Optical Depth (AOD) is a satellite based air quality measurement that can be computed for more spatial locations, but measures light attenuation by particulates throughout in entire air column, not just near the ground. The goal of this paper is to better characterize the spatio-temporal relationship between the two measurements. An informative relationship will aid in imputing PM2.5 values for health studies in a way that accounts for the variability in both sets of measurements, something physics based models cannot do. We use a data set of Chicago air quality measurements taken during 2007 and 2008 to construct a weekly hierarchical model. We also demonstrate that AOD measurements and a latent spatio-temporal process aggregated weekly can be used to aid in the prediction of PM2.5measurements.

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.