Statistical Methods for Complex Environmental Health Data Project Summary Ambient particulate matter (PM) air pollution is a major threat to public health, but current approaches to setting air quality standards do not reflect the complex multi-pollutant nature of the PM chemical mixture. Recent work indicates that opportunities may exist to reduce the public health burden of ambient PM by targeting the sources of PM that produce the most harmful chemical constituents. Currently, the scientific basis for developing new multi-pollutant air quality intervention strategies is insufficient and available statistical methods do not adequately address the challenges presented by the data. The investigators have developed widely-used statistical methodology for conducting national epidemiological studies of ambient air pollution and health and have identified the critical need for a new set of statistical methods for assessing the health effects of complex air pollutant mixtures.
The first aim will develop a spatial-temporal Bayesian hierarchical multivariate receptor model for identifying sources of air pollution chemical mixtures and estimating their effect on population health outcomes. Innovation focuses on (a) conducting an integrated national assessment of the health effects of pollution sources; (b) the use of spatial-temporal models for source apportionment; and (c) the introduction of national databases on source profiles and emissions to inform model development and parameter estimation.
The second aim will develop novel multivariate spatial-temporal models for estimating community-level health effects of ambient environmental exposures, accounting for spatial misalignment and measurement error.
The third aim will apply the newly developed statistical methods to data from a national study of air pollution and health outcomes, the Medicare Cohort Air Pollution Study, to (a) estimate short-term population health effects of PM sources on a national, regional, and local scale; (b) estimate short- and long-term health effects of PM constituents and identify the sources of toxic constituents.
The fourth aim will develop modular and extensible open source software implementing new statistical methods. By providing critical evidence about the relative toxicities of PM constituents and sources in a national study and by developing novel statistical approaches to overcome current methodological challenges, the aims of this application will lay the foundation for targeted interventions and air quality control strategies that will have a substantial public health impact across broad populations.
Ambient particle air pollution is a major public health problem and current approaches to regulating pollutant levels are sub-optimal. This project will develop novel statistical methods to be applied to national databases for estimating the health effects of ambient particle air pollution chemical constituents and sources. The evidence generated by this work will serve as the foundation for more targeted air quality control strategies.
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