Analysis of COVID-19 Case Demographics in Gary, Indiana
Background and Hypothesis
It has been reported in several recent studies that health disparities associated with COVID-19 infection r are prevalent in Black and impoverished populations. The contribution of multiple causes to these disparities is still not completely elucidated. Gary, Indiana has a large Black population (80%), high number of residents living below the poverty line (34%), and high unemployment rate (20%). We hypothesized that Black individuals in Gary have a higher rate of positive cases, hospitalizations, and deaths than non-Black individuals. Also, we hypothesized that (median household income measured by the zip code) is negatively correlated with COVID-19 positive cases, hospitalizations, and deaths.
In collaboration with the Gary Health Department, we analyzed data on all positive cases in the city from 06/16/2020 through 06/07/2021(totally 5149 cases). We compared this data to the data from 03/16/2020 through 06/16/2020 (totally 724 cases) that we analyzed previously. Data was de-identified and included age, race, ethnicity, and zip code. The data was analyzed using Pearson's chi-square test and regression analysis.
When compared to the non-Black population in Gary age and population-adjusted rates of hospitalizations and deaths in the Black population are 3-fold (p<9.385E-11) and 2-fold (p<0.0171) higher, respectively. Surprisingly, the non-Black population had a higher infection rate than the Black population (p<2.69E-09). Median household income of a zip code is negatively correlated with COVID-19 hospitalizations in that zip code (R2=0.6345, p=0.03), but is does not affect the .rates of infections and deaths.
Our data show that in Gary, there is a clear health disparity of both income and race, specifically in the context of COVID-19. IUSMNW and Gary health officials can collaborate and utilize this data to reallocate resources to the highly populated, low income, and predominantly Black neighborhoods.
Copyright (c) 2021 Yazan Al-Tarshan, Maryam Sabir, Cameron Snapp, Martin Brown, Roland Walker, Amy Han, PhD, Tatiana Kostrominova
This work is licensed under a Creative Commons Attribution 4.0 International License.