Description: This course provides a survey of spatial problems in epidemiology with a
specific focus on public health applications of spatial analysis.
Topics covered will include the different types of spatial data, causal inference with spatial data, and specific examples of applications of spatial analysis to epidemiological problems.
Course Goals: This course is meant to introduce graduate students to the logic of spatial
analysis in epidemiology and public health. By the end of the course, students
will understand when spatial analysis is necessary, and common issues of causal inference with spatial data (e.g. ecological fallacies). Students will become familiar with the different ways spatial analysis is employed in different sub-fields of epidemiology and public health, ranging from chronic and infectious disease to mental and cognitive health and in the assessment of environmental exposure.
Competencies: 1. Apply systems thinking to a public health issue.
2. Discuss the means by which structural bias, social inequities and racism undermine health and create challenges to achieving health equity at organizational, community and societal levels.
Learning Objectives: 1. Describe the circumstances when spatial analysis is necessary and useful for
different types of epidemiological problems and contexts.
2. Understand and describe key issues of causal inference in spatial analysis (e.g. ecological and atomistic fallacies).
3. Become familiar with statistical concepts underlying spatial epidemiological analysis.