Health Impacts of Environmental Inequalities in Redlined Areas

Abstract

Chicago has a long history of redlining, a discriminatory housing process that has led to segregation in Chicago up to the modern day. This practice marginalized people of color and created a significant and still prevalent wealth gap between redlined and non-redlined communities. It is well known that poorer communities in Chicago are victims of negative environmental factors, such as industrial corridor proximity, and higher air pollution levels. There have been studies done on these things, but there are many overlapping factors that have led to Chicago’s prevalent inequality, creating a complex problem.

Our study aimed to tackle some of this complexity and to analyze how various environmental indicators impacted the health outcomes of people based on the HOLC (Home Owners’ Loan Corporation) grade they lived in. We conducted a literature review to see which health impacts were tied to which significant environmental indicators and designed our technical portion to account for the complexity and inter-relation of the data, but we wanted to do more with the communities as well, rather than just analyzing data. A major part of our project was inspired by the work of Phillip Boda of UIC in regards to designing research centered around communities rather than around data. 

We came to the conclusion that the scope of our project was larger and more complex than any two people sufficiently cover and decided to design a coding tool alongside our analysis that will allow for communities to both interpret our findings and run their own analyses in the ways they deem most useful. We hope that this can both provide communities with a more thorough understanding of the complex relations between the environment and health. We also hope to provide a tool that can be more useful than existing resources we’ve used and found to have issues, such as the EPA’s Environmental Justice Screening tool (EJScreen).

We found that higher percentages of greenlined and bluelined areas had little significance to negative health outcomes, or lowered risk, while higher percentages of red and yellowlined areas had greater significance to the model, increasing risk of negative health outcomes with increasing area. The impact of environmental indicators on our model varied depending on the health impact being analyzed, but in the case of every health impact, there was a semi-linear positive trend when a model was created taking into account all of the independent variables and their coefficients.