Inferential Challenges with Spatial Data in (Air Pollution) Epidemiology

Many large-scale epidemiological studies investigate relationships between spatial and spatiotemporal exposures and adverse health outcomes. However, the spatiotemporal nature of these exposures can lead to inferential challenges including measurement error and unmeasured spatial confounding. Spatiotemporal prediction of exposures induces errors that can be correlated across space and lead to bias in point estimates and standard errors of estimated health effects. Unmeasured factors that vary spatially and impact health can further cause confounding bias that is difficult to diagnose. In this talk, I will present methods for addressing both of these challenges in analyses of regional and national cohort studies of air pollution exposure and birth, cardiovascular, and atopic health outcomes. The limitations of these correction approaches highlight important aspects of study design that can mitigate the effects of measurement error and unmeasured spatial confounding on inference.