Seminar 2024-01-19

Inferential Challenges with Spatial Data in (Air Pollution) Epidemiology

Kayleigh Keller

Assistant Professor

Department of Statistics

Colorado State University

Date: Friday, January 19, 2024

Time: 11:00 A.M. – 12:00 P.M. Eastern Time

Location: Nguyen Engineering Building, Room 1109

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 challenges in analyses of regional and national cohort studies of air pollution exposure and birth, cardiovascular, atopic, and cognitive 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.

Kayleigh Keller is an Assistant Professor in the Department of Statistics at Colorado State University (CSU). She received her PhD in Biostatistics from the University of Washington and was a postdoctoral fellow at John Hopkins Bloomberg School of Public Health before coming to CSU in 2018. Her research is primarily in environmental biostatistics, where she develops and applies statistical methods for studying the human health impacts of environmental exposures.

David Kepplinger

Nicholas Rios