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Since the U.S. Environmental Protection Agency began widespread monitoring of PM2.5 (particulate matter <2.5 p in diameter) concentration levels in the late 1990s, the epidemiological community has performed several observational studies directly relating PM2.5 concentration to various health endpoints including mortality and morbidity. However, recent research suggests that human exposure to the constituents of PM2.5 may differ significantly from ambient (or outdoor) PM2.5 concentration measured by monitors because people spend a great deal of time in environments, such as various indoor environments, where they are partially shielded from ambient sources of PM and are exposed to nonambient sources of PM. Recent research has provided some ways to include exposure information, but little has been done to determine the impact of including such information in a statistical model. To address this concern, we develop a three-stage Bayesian hierarchical model based on the Poisson regression model that is traditionally used to characterize the relationship between PM2.5 concentration and health endpoints. Our approach includes a spatial model relating monitor readings to average county PM2.5 concentration and an exposure simulator that links average ambient PM2.5 concentration to average personal exposure using activity pattern data. We apply our model to a study population in North Carolina and explore the impact of various exposure modeling assumptions on the conclusions that can be drawn about the link between PM2.5 exposure and cardiovascular mortality. [ABSTRACT FROM AUTHOR]

In this article, accumulated sudden infant death syndrome (SIDS) data, from 1974-1978 and 1979-1984 for the counties of North Carolina, are analyzed. After a spatial exploratory data analysis, Markov random-field models are fit to the data. The (spatial) trend is meant to capture the large-scale variation in the data, and the variance and spatial dependence are meant to capture the small-scale variation. The trend could be a function of other explanatory variables or could simply be modeled as a function of spatial location. Both models are fit and compared. The results give an excellent illustration of a phenomenon already well-known in time series, that autocorrelation in data can be due to an undiscovered explanatory variable. Indeed, for 1974-1978 we confirm a dependence of SIDS rate on proportion of nonwhite babies born, along with insignificant spatial correlation. Without this regressor variable, however, the spatial correlation is significant. In 1979-1984, perhaps due to reporting bias or the effect of public-education programs in infant health, the proportion of nonwhite babies born is no longer an important explanatory variable. SIDS is currently a leading category of postneonatal death, yet its cause is still a mystery. It accounts for about 7,000 deaths a year in the United States, taking the lives of about two infants per 1,000 live births. In contrast to the usual pathologic and physiologic studies of SIDS, this article takes an epidemiologic approach, using data available at the county level. The SIDS data analyzed are in a form that is representative of many problems encountered in the health and social sciences. Counts of individuals from a known or estimated base occur m epidemiologic studies (e.g., consider cancer incidence in a particular year, from the base of population years at risk, for U.S. counties), census surveys (e.g., for assessing undercount consider the... [ABSTRACT FROM AUTHOR]