Examining the Effect of Different Factors on Self-Rated Health in Texas Counties

Audience

Course module will be deployed Winter ‘22.

Expected that some students will have little familiarity with data science. We intend to teach the basics of data science that the students will use in completing the four module assignments. 

Other applicable courses that may benefit from this module:

The layouts of the Colab and module assignments could be useful in many types of introductory courses, especially those in social sciences and psychology. 

Project Summary

Primary Objective

Give students exposure to data science to help them to better understand the course material. Our Modules each week relate to each other and encourage students to think about health disparities across the many health factors they learn about in class. 

Goals

• Establish an understanding of data visualization methods

• Present interactive maps that are easy to use and understand

• Give guiding questions and have students make predictions about the trends of the next module

Content Outline

Each Google Colab module contains a map of Texas, which overlays self-rated health with module-specific, potentially-influential factors and a linear regression graph relating the same variables. Each Colab module has an accompanying assignment in Canvas, formatted as a quiz.

Module 1: Socioeconomic

• Median Income of Texas Counties

• High School Graduation Rate (%) of Texas Counties

Module 2: Health Behaviors

• % Physically Inactive of Texas Counties

• % Population Smoking Across Texas Counties

Module 3: Physical Environment

• Air Pollution (ppm) Across Texas Counties

• OverCrowding (%) Across Texas Counties

Module 4: Access to Clinical Care

• Uninsured (%) Across Texas Counties

• Ratio of Population to Mental Health Providers Across Texas Counties

*The assignment for Module 4 also contains a correlation matrix that relates the different health factors to each other.

For more information email: difuse-pi-group@dartmouth.edu