Health disparities are defined as health differences that adversely affect socially disadvantaged populations. This new module is designed to introduce the theory and practice underlying an approach to health disparities research focused on both social determinants of health and genetic risk factors.

Theoretical background lectures will be paired with practical lab sessions, using R and various bioinformatics applications, documented and run using Jupyter Notebook, to analyze heterogenous biobank data. Students will be provided with a conceptual foundation on how health disparities are defined, quantified, and characterized, and they will use biobank demographic and electronic health record data to quantify health outcomes and disparities. The module will emphasize genetic ancestry inference as a means to decompose genetic and socioenvironmental contributions to health disparities, covering admixture regression techniques used to associate ancestry with health outcomes.

• If you're not familiar with GitHub and git, you can simply get started by downloading this repository by clicking the green button on top right which says <> Code and select the Download Zip option.
• If you are familiar with Git, then please feel free to git clone this public repo.

• Students are required to bring their own laptops for computer lab sessions.

• Quantative and statistical analyses will be conducted in R using Jupyter Notebook.

• You can get set up for the class using the prerequisite file here.

Quantitative and statistical methods that will be covered in this module include:

• Computational phenotyping with electronic health record data

• Age- and sex-adjusted disease prevalence estimation

• Multivariable disease modeling with linear and logistic regression

• Genetic diversity and population structure characterization

• Genetic ancestry inference

After attending this module, participants will be able to:

• Understand how health disparities are defined, quantified, and characterized

• Understand the conceptual relationships and differences between race, ethnicity, and ancestry

• Quantify health outcomes and disparities using biobank electronic health record data

• Model the associations of social determinants of health and genetic risk factors with health outcomes and disparities

• Infer genetic ancestry from genomic variant data

• Model the associations of genetic ancestry with health outcomes and disparities