Young scholar starts at the intersection of residential segregation and health

When our communications team was asked to review and possibly publish an article by Julia Perrino, we were immediately impressed with her writing ability. When we realized she was in high school, we knew we had to do a complemental story just on her. 

As a 16 year old Gen Z scholar from Long Island, Perrino chose to study the health impacts of government sanctioned segregation for a project in her science research course. When asked why, she said she always thought if you worked hard, you could make your living just as the people who lived next to you, down the street or even a couple (or more) states over. However, she quickly came to realize through her research that zip codes often mattered across America because of residential segregation. The more halting bit was that those disparities were actively created and enforced by the federal government throughout the 20th century by using the now defunct Home Owners Loan Corporation’s (HOLC) lending maps. By dividing neighborhoods into one of four lending categories — “hazardous,” “definitely declining,” “still desirable” and “best” — the HOLC was able to keep financial resources from reaching low- and moderate-income communities and communities of color. 

While her original research for the project did not meet her hypothetical expectations, she pushed forward. Taking it upon herself, she conducted additional statistical analysis on the same data, and uncovered significant differences between best and hazardous neighborhoods and their rates of diabetes, obesity and hypertension. Neighborhood quintiles were established based on the value of the health variable. The highest rates of diabetes, obesity and hypertension are found in quintile five, the lowest in quintile one.

Sifting through data is often an evolving story, and Perrino found an important relationship:

For all health conditions, HOLC’s hazardous neighborhoods had significantly higher mean rates in the fifth quintile as compared to the first. For both obesity and hypertension, best areas had significantly higher mean rates in the first quintile than the fourth or fifth. Furthermore, comparisons between best and hazardous neighborhoods revealed significant differences between these mean rates for the first and fifth quintiles. This indicates that best neighborhoods tend to have lower overall health rates than hazardous neighborhoods. Based on these conclusions, it can be reasonably inferred that there is a relationship between hazardous grades and higher rates of diabetes, obesity and hypertension. Furthermore, there appears to be a correlation between lower rates of obesity and hypertension in best graded areas.

Perrino has plans to collect data on different diseases and use different locations for a more representative study of cases. Her tenacity to investigate such a complex yet important subject is inspiring. Even as smart as she is, it’s shocking how quickly she picked up on the incapacity of our political and economic systems to remedy their mistakes. However, as segregation persistently retrenches itself into the American landscape, it’s important we have young people like Perrino engaging in the conversation.

Maxim Applegate is a communications intern with NCRC.

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Redlining and Neighborhood Health

Before the pandemic devastated minority communities, banks and government officials starved them of capital.

Lower-income and minority neighborhoods that were intentionally cut off from lending and investment decades ago today suffer not only from reduced wealth and greater poverty, but from lower life expectancy and higher prevalence of chronic diseases that are risk factors for poor outcomes from COVID-19, a new study shows.

The new study, from the National Community Reinvestment Coalition (NCRC) with researchers from the University of Wisconsin–Milwaukee Joseph J. Zilber School of Public Health and the University of Richmond’s Digital Scholarship Lab, compared 1930’s maps of government-sanctioned lending discrimination zones with current census and public health data.

Table of Content

  • Executive Summary
  • Introduction
  • Redlining, the HOLC Maps and Segregation
  • Segregation, Public Health and COVID-19
  • Methods
  • Results
  • Discussion
  • Conclusion and Policy Recommendations
  • Citations
  • Appendix

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