Identifying air pollution mixtures and investigating their associations with pediatric asthma in a time-series framework Open Access
Gass, Katherine (2014)
Published
Abstract
Every time we take a breath outside we inhale a mixture of different pollutants; however, examining the associations between these pollutant mixtures and health endpoints is challenging. This dissertation uses different methodological approaches to understand the associations between air pollution mixtures and emergency department visits for pediatric asthma. Time series with daily counts of emergency department visits for any diagnosis of asthma or wheeze among pediatric patients were obtained from hospitals in metropolitan Atlanta (1999-2010), Dallas (2006-09) and St. Louis (2001-07). Daily measurements of ambient concentrations of ozone, carbon monoxide, nitrogen dioxide, and particulate matter <2.5 microns in diameter (PM2.5) were obtained from monitors in all three metropolitan areas. In addition, daily estimates of PM2.5 source concentrations were made available for Atlanta using a Bayesian-based ensemble source apportionment technique.
A modified classification and regression tree algorithm was developed to enable the identification of multipollutant joint effects. This algorithm was then used to determine the multipollutant joint effects associated with pediatric asthma in Atlanta, as well as the common multipollutant joint effects identified in Atlanta, Dallas and St. Louis. These analyses found certain types of days, characterized by their multipollutant profiles, to be associated with a statistically significant increase in asthma emergency department visits.PM2.5 appeared to be one of the pollutants driving the formation of these harmful day types and thus further analyses were conducted to determine the associations between pediatric asthma and PM2.5 sources. A positive association was observed for the cumulative, seven-day effect a 1 microgram increase in biomass burnings (rate ratio: 1.02, 95% confidence interval: 1.01, 1.03), diesel vehicle emissions (rate ratio: 1.05, 95% confidence interval: 1.01, 1.08), and gasoline vehicle emissions (rate ratio: 1.07, 95% confidence interval: 1.03, 1.11). These confidence intervals account for uncertainties in the source apportionment estimates using multiple imputation methods.
This dissertation makes methodological contributions to the field of epidemiology with the development of a classification and regression algorithm that is well-suited for identifying joint effects of exposure mixtures. It also adds to the growing body of literature which suggests a harmful effect of multipollutant exposures on pediatric asthma.
Table of Contents
Chapter 1: Introduction 1
Chapter 2: Ambient Air Pollutants and Associated Health Effects 7
Chapter 3: Asthma and Air Pollution 25
Chapter 4: Epidemiologic Methods for Multipollutants 33
Chapter 5: Classification and Regression Trees for Epidemiologic Research (Study 1) 53
Chapter 6: A Three-City Analysis of Multipollutant Joint Effects: a comparison of classification and regression trees with conventional multipollutant models (Study 2) 79
Chapter 7: Ensemble-Based Source Apportionment of Fine Particulate Matter and Emergency Department Visits for Pediatric Asthma (Study 3) 112
Chapter 8: Conclusion 140
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