Exposure Prediction Error and Spatial Confounding in Air Pollution Epidemiology

Long-term exposure to air pollution has been linked to multiple cardiovascular and respiratory morbidities. Large administrative cohorts, such as the United States Medicaid population, provide an opportunity to investigate these associations on a national scale. To conduct inference about air pollution exposures and the aggregated health data, several challenges must be addressed. Spatial misalignment between pollution monitors and area-level health data require spatial prediction of exposure. In this talk, I present a comparison of approaches for estimating area-level averages of air pollution, when the exposure prediction model is known to be mis-specified. Additionally, the limited amount of individual-level data can lead to unmeasured spatial confounding. I present a method for linking flexible spatial confounding adjustment to spatial scales. These methods are applied to an analysis of long-term particulate matter exposure and asthma morbidity among U.S. children in Medicaid.