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. Methods 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. Results 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. Conclusion 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.

Retrospective Analysis of COVID-19 Pandemic Impact on Breast Cancer Screening Rates in Northwest Indiana

Background: Few changes to healthcare delivery during the COVID-19 pandemic altered pre-pandemic diagnostic testing as much as those made to cancer screening. Several studies show that screening volumes decreased by as much as 80% across multiple modalities and cancer types in the spring of 2020. These studies examined large hospital systems in the American East and West, but communities with predominantly Black populations like Gary, Indiana, have been absent from this research. Methods: Our study captures how the COVID-19 pandemic affected access to diagnostic screening for cancerous and precancerous breast lesions through mammography using patient-level data. “Hospital A” provided data from 17,973 mammography encounters that occurred between March 2019 and June 2021. Screening volumes from the eight-week period from March 23rd and May 17th in 2020, the period elective procedures were suspended, was compared to three other distinct periods: the previous 8-week period, the next 8-week period, and the same 8-week period from 2019. Results: From the 17,973 encounters, the average patient age was 61.7 (SD 11.4) years, 61.0% of patients paid with Medicare or Medicaid, and 66.0% of patients identified as Black. Despite performing a weekly average of 190 (12.3) mammograms during the 2019 baseline period and 158 (16.1) mammograms in the eight weeks preceding the COVID-19 pandemic, the weekly average fell to 13 (22.4) mammograms during the study period with zero occurring in a four-week stretch. Fortunately, volume returned sharply to near pre-pandemic levels in the eight weeks following the study period with 139 (18.9) average weekly mammograms. Conclusion: Despite a 93% year-over-year decrease in mammography during the height of the pandemic, volume returned in the summer of 2020. Concerning, however, is that average monthly volume (582 (88.5) mammograms) in the first six months of 2021 remains 22.1% lower than 2019 numbers (747 (66.7) mammograms).

Comparing the Impact of Youth Violence upon Mental Health Before and During COVID-19 Pandemic vs the Effects of a Violence Prevention Program in a Cohort of Students in Gary, IN.

Background/Objective: Violence (arguments, fights, sometimes gunshots) and bullying (interpersonal, online, social media) are prevalent in schools all across the country. This exposure may cause damaging effects on youth mental health. Project Outreach and Prevention (POP) on Youth Violence, promotes a message of violence prevention through workshops and events that promote conflict resolution, unity, and positive behaviors while inspiring healthy lifestyles and accessible career opportunities. We hypothesize that exposure to violence and bullying may lead to increased self-reports of anxiety, difficulty coping, or depression among adolescents. Methods: In a prospective study, 7th and 8th grade students at Bailly Preparatory Academy and 9th and 10th graders at Westside Leadership Academy will be digitally surveyed. This survey will determine their level of exposure to violence and bullying and if they report experiencing symptoms of anxiety, difficulty coping, or depression as a result. We will also determine if the lifestyle changes caused by the COVID-19 pandemic has increased youth violence, as well as, the location of where students experience bullying and violence. Lastly, we hope to examine if students who are adversely affected have access to mental health care. Results: Surveys will be distributed during the fall. Survey responses will be analyzed to examine correlations between exposure to violence and self-reported anxiety, difficulty coping, or depression, as well as, access to care. Conclusion and Potential Impact: Violence and bullying are prevalent at specific schools in Gary, Indiana. We will learn how much an adolescent’s mental health is affected by this before and during the COVID Pandemia. This information will raise awareness about the psychological effects of violence and bullying. It will also help school administrators to implement programs and resources that can be used to help with their student’s mental health.

Exploratory Analysis of COVID-19 Case Demographics in Gary, Indiana

Background and Hypothesis: Health disparities are prevalent in Black populations, and COVID-19 is not an exception. COVID-19 is a pandemic that has been confirmed in 3.8 million Americans and has caused 133,283 deaths in the US (4/20/2020). Recent literature suggests that minoritized and impoverished populations are more severely impacted by COVID-19. 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 income (median household income measured by zip code) is negatively correlated with COVID-19 deaths. Experimental Design and Project Methods:In collaboration with the Gary Health Department, we analyzed demographic data on all positive cases in the city from 4/16/2020 through 6/19/2020. Case data was de-identified with 16 dimensions including age, race, sex, ethnicity, hospitalization, death, and zip code. Data was analyzed using Pearson's chi-square test and regression analysis. Results: Positive cases and hospitalizations are 2-fold and 3-fold more frequent in the Black population compared to the non-Black population in Gary (p<0.0001, P<0.01, age and population-adjusted), respectively. Median household income of a zip code is exponentially and negatively correlated with COVID-19 related deaths in that zip code (R2=0.7450, p=0.0123). Conclusion and Potential Impact: In Gary, there is a clear health disparity of both income and race, specifically in the context of COVID-19. Health officials can utilize this data to reallocate resources to highly populated, low income, and predominantly Black neighborhoods. In addition, future predictive analysis could be beneficial in developing a model to predict COVID-19 prevalence and severity. Such a model would help local health departments prepare for a second Covid-19 wave, providing for better outcomes for at risk populations through resource allocation.