TitlePart 3. Modeling of Multipollutant Profiles and Spatially Varying Health Effects with Applications to Indicators of Adverse Birth Outcomes.
Publication TypeJournal Article
Year of Publication2016
AuthorsMolitor, J, Coker, E, Jerrett, M, Ritz, B, Li, A
Corporate AuthorsHealth Review Committee
JournalRes Rep Health Eff Inst
Issue183 Pt 3
Date Published2016 Apr
KeywordsAir Pollutants, Air Pollution, Bayes Theorem, Birth Weight, Cluster Analysis, Complex Mixtures, Environmental Exposure, Environmental Monitoring, Female, Health Status, Humans, Los Angeles, Models, Theoretical, Nitrous Oxide, Particulate Matter, Poverty, Pregnancy, Pregnancy Outcome, Regression Analysis, Socioeconomic Factors, Spatial Analysis, Time Factors, United States

The highly intercorrelated nature of air pollutants makes it difficult to examine their combined effects on health. As such, epidemiological studies have traditionally focused on single-pollutant models that use regression-based techniques to examine the marginal association between a pollutant and a health outcome. These relatively simple, additive models are useful for discerning the effect of a single pollutant on a health outcome with all other pollutants held to fixed values. However, pollutants occur in complex mixtures consisting of highly correlated combinations of individual exposures. For example, evidence for synergy among pollutants in causing health effects has been recently reviewed by Mauderly and Samet (2009). Also, studies cited in the Ozone Criteria Document (U.S. Environmental Protection Agency [U.S. EPA*] 2006) confirmed that synergisms between ozone and other pollutants have been demonstrated in laboratory studies involving humans and animals. Thus, the highly correlated nature of air pollution exposures makes marginal, single-pollutant models inadequate. This issue was raised in a report by the National Research Council (NRC 2004), which called for a multipollutant approach to air quality management. Here we present and apply a series of statistical approaches that treat patterns of covariates as a whole unit, stochastically grouping pollutant patterns into clusters and then using these cluster assignments as random effects in a regression model. Using this approach, the effect of a multipollutant pattern, or profile, is determined in a manner that takes into account the uncertainty in the clustering process. The models are set in a Bayesian framework, and in general, Markov chain Monte Carlo (MCMC) techniques (Gilks et al. 1998). For interpretation purposes, a best clustering is derived, and the uncertainty related to this best clustering is determined by utilizing model averaging techniques, in a manner such that consistent clustering obtained by the estimation process generally yields smaller standard errors while inconsistent clustering is generally associated with larger errors. These multivariate methods are applied to a range of different problems related to air pollution exposures, namely an association of multipollutant profiles with indicators of poverty and to an assessment of the association between measures of various air pollutants, patterns of socioeconomic status (SES), and birth outcomes. All of these studies involve an examination of regional-level exposures, at the census tract (CT) and census block group (CBG) levels, and individual-level outcomes throughout Los Angeles (LA) County. Results indicate that effects of pollutants vary spatially and vary in a complex interconnected manner that cannot be discerned using standard additive line ar models. Results obtaine d from these studies can be used to efficiently use limited resources to inform policies in targeting are as where air pollution reductions result in maximum health benefits.

Alternate JournalRes Rep Health Eff Inst
PubMed ID27459845