Environmental Epidemiologic Methods: Demystifying the Hocus-Pocus

Catalog description

This course provides a comprehensive overview of epidemiological approaches and modern data science methodologies tailored for environmental health sciences. As the field of environmental health science increasingly relies on innovative designs and complex, highdimensional data, this course presents students with knowledge of study designs relevant to the field and equips them with advanced statistical tools and computational skills necessary to analyze environmental exposures and their impacts on human health. The curriculum bridges the gap between epidemiological/statistical theory and practical data application. The course will cover the following: (1) study design in environmental epidemiology, (2) advanced modeling for non-linear associations and longitudinal multi-level data analysis, (3) environmental mixtures approaches and their application to evaluate multi-pollutant exposures, (4) managing complex datasets using R software, with examples from large-scale public datasets, and (5) specific topics relevant to environmental epidemiology, such as missingness due to limits of detection, causality and heterogeneity of effect in epidemiological studies of environmental factors.