Abstract Number:
2812
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Mandy Yao (1), Meredith Franklin (1)
Institutions:
(1) University of Toronto, Toronto, Ontario, Canada
Co-Author:
First Author:
Presenting Author:
Abstract Text:
Wildfire events have been increasing in frequency, duration, and severity. Coupled with these events is poor air quality, which is known to be detrimental to health. Air pollution is a complex mixture, and traditional approaches to understanding exposure-health associations, which typically examine one pollutant at a time, do not capture the multidimensional dynamics and correlational structure of its multiple co-existing components. While some multi-pollutant modelling strategies have been developed, there remain shortcomings. We adapt a principal stratification approach so that its application can be generalized to a broad range of environmental and health data. We demonstrate these developments using chemically speciated particulate matter air pollution concentrations coupled with satellite-derived wildfire smoke plumes in California, which have been matched to participants in the California Teachers Study (CTS) cohort. We examine the association between different biomarkers of inflammation with the multi-pollutant mixture under wildfire and non-wildfire conditions, taking into account uncertainties present in the estimation of the risk of adverse health effects.
Keywords:
Principal Stratification|Multi-Pollutant Models|Propensity Score Matching|Sliced Inverse Regression| |
Sponsors:
Section on Statistics and the Environment
Tracks:
Climate and Meteorology
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