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.

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

Proceedings of IMPRS ◽

10.18060/24769 ◽

2020 ◽

Vol 3 ◽

Author(s):

Cameron Snapp ◽

Bill Trimoski ◽

Martin Brown ◽

Amy Han ◽

Tatiana Kostrominova

Keyword(s):

Low Income ◽

Household Income ◽

Demographic Data ◽

Black Population ◽

Poverty Line ◽

Local Health ◽

Median Household Income ◽

Gary Indiana ◽

Black Populations ◽

Zip Code

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.

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The Relationship Between Estimated Median Household Income and Critical Care Length of Stay in Children With Diabetic Ketoacidosis

Global Pediatric Health ◽

10.1177/2333794x20956770 ◽

2020 ◽

Vol 7 ◽

pp. 2333794X2095677

Author(s):

Meredith C. G. Broberg ◽

Jerri A. Rose ◽

Katherine N. Slain

Keyword(s):

Length Of Stay ◽

Diabetic Ketoacidosis ◽

Low Income ◽

Household Income ◽

Severity Of Illness ◽

Pediatric Intensive Care ◽

Median Household Income ◽

Poverty Threshold ◽

The Relationship ◽

Zip Code

Diabetic ketoacidosis (DKA) is an important diagnosis in the pediatric intensive care unit (PICU) and is associated with significant morbidity. We hypothesized children with DKA living in poorer communities would have unfavorable outcomes while critically ill. This single-center retrospective study included children with DKA admitted to a PICU over a 27-month period. Patients were classified as low-income if they lived in a ZIP code where the median household income was estimated to be less than 200% of the federal poverty threshold, or $48 016 for a family of 4. In this study, living in a low-income ZIP code was not associated with increased severity of illness, longer PICU length of stay (LOS), or readmission.

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“Association between Median Household Income, State Medicaid Expansion Status, and COVID-19 Outcomes Across US Counties”

10.1101/2021.02.03.21251075 ◽

2021 ◽

Author(s):

Tsikata Apenyo ◽

Antonio Vera-Urbina ◽

Khansa Ahmad ◽

Tracey H. Taveira ◽

Wen-Chih Wu

Keyword(s):

Mortality Risk ◽

Low Income ◽

Household Income ◽

Medicaid Expansion ◽

Median Household Income ◽

Incidence And Mortality ◽

Significant Difference ◽

Us Counties ◽

The Relationship ◽

Income Quartile

AbstractObjectiveThe relationship between socioeconomic status and its interaction with State’s Medicaid-expansion policies on COVID-19 outcomes across United States (US) counties are uncertain. To determine the association between median-household-income and its interaction with State Medicaid-expansion status on COVID-19 incidence and mortality in US countiesMethodsLongitudinal, retrospective analysis of 3142 US counties (including District of Columbia) to study the relationship between County-level median-household-income (defined by US Census Bureau’s Small-Area-Income-and-Poverty-Estimates) and COVID-19 incidence and mortality per 100000 of the population in US counties from January 20, 2020 through December 6, 2020. County median-household-income was log-transformed and stratified by quartiles. Medicaid-expansion status was defined by US State’s Medicaid-expansion adoption as of first reported US COVID-19 infection, January 20, 2020. Multilevel mixed-effects generalized-linear-model with negative binomial distribution and log link function compared quartiles of median-household-income and COVID-19 incidence and mortality, reported as incidence-risk-ratio (IRR) and mortality-risk-ratio (MRR), respectively. Models adjusted for county socio-demographic and comorbidity conditions, population density, and hospitals, with a random intercept for states. Multiplicative interaction tested for Medicaid-expansion*income quartiles on COVID-19 incidence and mortality.ResultsThere was no significant difference in COVID-19 incidence across counties by income quartiles or by Medicaid expansion status. Conversely, significant differences exist between COVID-19 mortality by income quartiles and by Medicaid expansion status. The association between income quartiles and COVID-19 mortality was significant only in counties from non-Medicaid-expansion states but not significant in counties from Medicaid-expansion states (P<0.01 for interaction). For non-Medicaid-expansion states, counties in the lowest income quartile had a 41% increase in COVID-19 mortality compared to counties in the highest income quartile (MRR 1.41, 95% CI: 1.25-1.59).Conclusions and RelevanceMedian-household-income was not related to COVID-19 incidence but negatively related to COVID-19 mortality in US counties of states without Medicaid-expansion. It was unrelated to COVID-19 mortality in counties of states that adopted Medicaid-expansion. These findings suggest that expanded healthcare coverage should be investigated further to attenuate the excessive COVID-19 mortality risk associated with low-income communities.Key FindingsQuestionIs there a relationship between COVID-19 outcomes (incidence and mortality) and household income and status of Medicaid expansion of US counties?FindingsIn this longitudinal, retrospective analysis of 3142 US counties, we found no significant difference in COVID-19 incidence across US counties by quartiles of household income. However, counties with lower median household income had a higher risk of COVID-19 mortality, but only in non-Medicaid expansion states. This relationship was not significant in Medicaid expansion states.MeaningExpanded healthcare coverage through Medicaid expansion should be investigated as an avenue to attenuate the excessive COVID-19 mortality risk associated with low-income communities.

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Trends in COVID-19 Health Disparities in North Carolina: Preparing the Field for Long-Haul Patients

Healthcare ◽

10.3390/healthcare9121704 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1704

Author(s):

Thais Muratori Holanda ◽

Claudia Alberico ◽

Leslimar Rios-Colon ◽

Elena Arthur ◽

Deepak Kumar

Keyword(s):

North Carolina ◽

Health Disparities ◽

Low Income ◽

Household Income ◽

Vaccination Status ◽

Income Poverty ◽

Median Household Income ◽

Social Vulnerability Index ◽

Persistent Symptoms ◽

Populations At Risk

Long-term coronavirus disease 2019 (long-COVID) refers to persistent symptoms of SARS-CoV-2 (COVID-19) lingering beyond four weeks of initial infection. Approximately 30% of COVID-19 survivors develop prolonged symptoms. Communities of color are disproportionately affected by comorbidities, increasing the risk of severe COVID-19 and potentially leading to long-COVID. This study aims to identify trends in health disparities related to COVID-19 cases, which can help unveil potential populations at risk for long-COVID. All North Carolina (NC) counties (n = 100) were selected as a case study. Cases and vaccinations per 1000 population were calculated based on the NC Department of Health and Human Services COVID-19 dashboard with reports current as of 8 October 2021, which were stratified by age groups and race/ethnicity. Then, NC COVID-19 cases were correlated to median household income, poverty, population density, and social vulnerability index themes. We observed a negative correlation between cases (p < 0.05) and deaths (p < 0.01) with both income and vaccination status. Moreover, there was a significant positive association between vaccination status and median household income (p < 0.01). Our results highlight the prevailing trend between exacerbated COVID-19 infection and low-income/under-resourced communities. Consequently, efforts and resources should be channeled to these communities to effectively monitor, diagnose, and treat against COVID-19 and potentially prevent an overwhelming number of long-COVID cases.

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Poverty In The United States: A Continuing And Growing Problem

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v5i12.2610 ◽

2011 ◽

Vol 5 (12) ◽

Author(s):

Berch Haroian ◽

Elizabeth C. Ekmekjian ◽

Elias C. Grivoyannis

Keyword(s):

Household Income ◽

Insurance Coverage ◽

Poverty Line ◽

Health Insurance Coverage ◽

The United States ◽

Poverty Rate ◽

Median Household Income ◽

The Past ◽

Area Of Interest ◽

The Impact

In recent years, the ability to deal with the problem of poverty in the US, in light of the new “Federalism,” is an area of interest to scholars. The poverty rate over the past 50 years has fluctuated from a high of 22.4% in 1959 to a low of 11.1% in 1973. Under George Bush’s presidency, we again see an increase in the poverty rate to 12.7% in 2004. This paper provides an overview of poverty data for the 21st century, by region, race and age.  A discussion and comparison of median household income follows. Facts and figures are then provided/compared, tying in health care issues to income levels and citizenship/ethnicity. A brief introduction of the various attempts over the past years by the federal government to reduce the proportion of the American population that falls below the poverty line follows.  This section merely provides a listing of programs designed to satisfy social and equity considerations.  This paper does not provide the reader with the impact of these programs on the economy; a brief mention is provided to generate further thought and discussion.  The paper concludes with a summary of key elements of the above issues. The sole purpose is to provide an overview of historical data as concerns poverty, median household income and health insurance coverage. The ability to deal with the problem of poverty in the U S, is left for another paper.

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Neighbourhood characteristics and health outcomes: evaluating the association between socioeconomic status, tobacco store density and health outcomes in Baltimore City

Tobacco Control ◽

10.1136/tobaccocontrol-2017-053945 ◽

2017 ◽

Vol 27 (e1) ◽

pp. e19-e24 ◽

Cited By ~ 6

Author(s):

Panagis Galiatsatos ◽

Cynthia Kineza ◽

Seungyoun Hwang ◽

Juliana Pietri ◽

Emily Brigham ◽

...

Keyword(s):

Socioeconomic Status ◽

Life Expectancy ◽

Health Outcomes ◽

Low Income ◽

Household Income ◽

Spatial Regression ◽

Median Household Income ◽

Baltimore City ◽

Education Status ◽

The Relationship

IntroductionSeveral studies suggest that the health of an individual is influenced by the socioeconomic status (SES) of the community in which he or she lives. This analysis seeks to understand the relationship between SES, tobacco store density and health outcomes at the neighbourhood level in a large urban community.MethodsData from the 55 neighbourhoods of Baltimore City were reviewed and parametric tests compared demographics and health outcomes for low-income and high-income neighbourhoods, defined by the 50th percentile in median household income. Summary statistics are expressed as median. Tobacco store density was evaluated as both an outcome and a predictor. Association between tobacco store densities and health outcomes was determined using Moran’s I and spatial regression analyses to account for autocorrelation.ResultsCompared with higher-income neighbourhoods, lower-income neighbourhoods had higher tobacco store densities (30.5 vs 16.5 stores per 10 000 persons, P=0.01), lower life expectancy (68.5 vs 74.9 years, P<0.001) and higher age-adjusted mortality (130.8 vs 102.1 deaths per 10 000 persons, P<0.001), even when controlling for other store densities, median household income, race, education status and age of residents.ConclusionIn Baltimore City, median household income is inversely associated with tobacco store density, indicating poorer neighbourhoods in Baltimore City have greater accessibility to tobacco. Additionally, tobacco store density was linked to lower life expectancy, which underscores the necessity for interventions to reduce tobacco store densities.

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Abstract 16279: Impact of Race and Socioeconomic Status on Rates of Congestive Heart Failure Admissions, Readmissions, and Length of Stay

Circulation ◽

10.1161/circ.142.suppl_3.16279 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Muhammad A Shakir ◽

Meghan E Buckley ◽

William D Surkis ◽

John M Clark

Keyword(s):

Heart Failure ◽

Socioeconomic Status ◽

Length Of Stay ◽

Low Income ◽

Household Income ◽

High Income ◽

Median Household Income ◽

Admission Rates ◽

Black Patients ◽

White Patients

Introduction: Health disparities due to race and socioeconomic status persist among congestive heart failure (CHF) patients. Our hospital in the Philadelphia area is uniquely situated to study disparities as it sits on the border of a diverse inner city and suburban population. The area west of our hospital is known to have a drastically higher median income than the area to the east. We aimed to evaluate differences in rates of CHF admissions, readmissions, and length of stay (LOS) for patients based on race and socioeconomic status. Methods: From 3/1/2018 to 3/31/2020, 6,785 total patients were admitted to our hospital due to acute decompensated CHF. To compare rates of admission, readmission and a LOS > 5 days based on race and socioeconomic status, we used the SlicerDicer function of the EPIC electronic record platform. For race, we compared data for white and black patients. For socioeconomic status, we included a 10-mile radius around our hospital and used public records to collect median household income for 11 zip codes to the east and 11 to the west. The average yearly median household income for the east and west zip codes were USD $27,171 and $134,390, respectively. Outcomes are expressed as percentages and compared using a Chi-square test of independence and 95% confidence interval (CI) for differences. Significance was assessed at the 0.05 level. Results: Admission rates were significantly higher among Black patients at 67% compared to White patients at 58% (95% CI 7-11%, p<0.05). There was no significant difference between rates of readmission (60% for Black vs. 58% for White patients, 95% CI 0-4%, p=0.11) or LOS > 5 days (56% for Black vs. 55% for White patients, 95% CI 0-3%, p=0.42). Admission rates were significantly higher among patients from low income areas at 70% compared to high income areas at 56% (95% CI 11-17%, p<0.05). Readmission rates were not significantly different, 57% for low income and 56% for high income areas (95% CI 0-4%, p=0.82). Patients from low income areas were significantly more likely to have a LOS > 5 days at 57% compared to patients from high income areas at 53% (95% CI 0.8-8%, p<0.05). Conclusions: Race and socioeconomic status continue to impact CHF patients’ health outcomes including rates of admissions, readmissions, and length of stay.

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An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City

10.1101/2020.04.17.20069823 ◽

2020 ◽

Cited By ~ 6

Author(s):

Richard S. Whittle ◽

Ana Diaz-Artiles

Keyword(s):

New York ◽

New York City ◽

Low Income ◽

York City ◽

Household Income ◽

Health Management ◽

Median Household Income ◽

Female Ratio ◽

Aging Populations ◽

The Usa

AbstractBackgroundNew York City was the first major urban center of the COVID-19 pandemic in the USA. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the number of detected COVID-19 cases.MethodsData were collected from 177 Zip Code Tabulation Areas (ZCTA) in New York City (99.9% of the population). We fit multiple Bayesian Besag-York-Mollié (BYM) mixed models using positive COVID-19 tests as the outcome and a set of 10 representative economic, demographic, and health-care associated ZCTA-level parameters as potential predictors. The BYM model includes both spatial and nonspatial random effects to account for clustering and overdispersion.ResultsMultiple different regression approaches indicated a consistent, statistically significant association between detected COVID-19 cases and dependent (under 18 or 65+ years old) population, male to female ratio, and median household income. In the final model, we found that an increase of only 1% in dependent population is associated with a 2.5% increase in detected COVID-19 cases (95% confidence interval (CI): 1.6% to 3.4%, p < 0.0005). An increase of 1 male per 100 females is associated with a 1.0% (95% CI: 0.6% to 1.5%, p < 0.0005) increases in detected cases. A decrease of $10,000 median household income is associated with a 2.5% (95% CI: 1.0% to 4.1% p = 0.002) increase in detected COVID-19 cases.ConclusionsOur findings indicate associations between neighborhoods with a large dependent population, those with a high proportion of males, and low-income neighborhoods and detected COVID-19 cases. Given the elevated mortality in aging populations, the study highlights the importance of public health management during and after the current COVID-19 pandemic. Further work is warranted to fully understand the mechanisms by which these factors may have affected the number of detected cases, either in terms of the true number of cases or access to testing.

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COVID-19 and exacerbation of screening mammography inequities.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.6543 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 6543-6543

Author(s):

Gary X. Wang ◽

Jarvis Chen ◽

Leslie Lamb ◽

Christian Testa ◽

Pamela Waterman ◽

...

Keyword(s):

Low Income ◽

Screening Program ◽

Tertiary Care ◽

Screening Mammography ◽

Population Composition ◽

Chi Square ◽

P Values ◽

Increased Risk ◽

Race Ethnicity ◽

Zip Code

6543 Background: After state-mandated cessation of screening mammography (SM) in Spring 2020 due to COVID-19, centers were urged to resume screening, particularly of patients at increased risk. As our tertiary-care medical center’s screening program provides SM at four sites across our metropolitan area, we examined whether sites that historically served more patients from more disadvantaged areas returned slower to pre-COVID volumes. Methods: Patient records were linked by ZIP code of residence to ZIP Code Tabulation Area (ZCTA)-level area-based social metrics (ABSMs) from the 2014-2018 American Community Survey. We compared baseline pre-COVID (May-October, 2015-2019) SM population ABSMs between our four imaging sites for: % persons below poverty (≥ vs < 10%), % persons of color (POC) (quintiles: top 2 vs bottom 3), index of racialized economic segregation (quintiles: bottom 2 [more POC low-income households] vs top 3 [more white non-Hispanic (WNH) high-income households]); and race/ethnicity (% WNH vs POC). We modeled weekly SM volumes per screening day by site using Poisson regression and tested for weekly differences at each site, COVID-era (May-October 2020) vs pre-COVID; and tested for monthly differences in SM population composition by logistic regression modeling. Results: There were 89,082 pre-COVID and 16,220 COVID-era SM exams. At pre-COVID baselines the four sites differed in population composition by ABSMs and race/ethnicity (all chi-square P values <.001) (Table). The two sites that served more disadvantaged populations (A, B) returned slower to pre-COVID volumes (site-specific weekly screening volume no longer different [ P >.05] vs pre-COVID) (Table). As a result, compositions of the aggregate SM population across all sites showed a smaller proportion of patients from the most disadvantaged ZCTAs by ABSMs (all P values <.001) before returning to pre-COVID compositions three months after SM resumption. Conclusions: SM was slower to return to pre-COVID volumes at imaging sites that historically served lower-income communities of color. As a result, our COVID-era SM population skewed away from patients in disadvantaged ZCTAs. Our findings highlight the need to monitor for emergent disparities in the pandemic era. Future work will focus on understanding causes of inequitable SM engagement across our imaging sites to mitigate care disparities for our most vulnerable patients.[Table: see text]

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Abstract 16364: Effect of Social Determinants on Outcomes in a High-risk Cardio-obstetrics Population

Circulation ◽

10.1161/circ.142.suppl_3.16364 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Corinne Carland ◽

Danielle Panelli ◽

Christine Lee ◽

Elizabeth Sherwin ◽

Eleanor Levin ◽

...

Keyword(s):

Heart Disease ◽

High Risk ◽

Social Determinants ◽

Household Income ◽

Neonatal Death ◽

Median Income ◽

Lower Income ◽

Median Household Income ◽

Significant Difference ◽

Zip Code

Introduction: Cardiovascular disease is the leading cause of maternal mortality. The hemodynamic changes that occur during pregnancy make this a particularly vulnerable time for women with heart disease. Additionally, it is known that social determinants have an effect on certain outcomes in pregnancy, although research to quantify this effect is limited. We compared demographics and outcomes for women in upper- and lower-income brackets based on zip codes. Methods: We performed a retrospective cohort study of high-risk pregnant patients with cardiac diagnoses between November 2010 and June 2019. Patients were stratified into upper- and lower-income based on median household income in their zip code (2018 U.S. census). Results: We studied 191 pregnancies. Patients were stratified by zip code into lower (<$118,201/yr, N = 95) and upper median household income (N = 96) groups (Table 1). Women in the lower income bracket had more antepartum hospitalizations (38.3% vs 17.9%), were younger (30.6 vs 33.9 years), Hispanic (42.1% vs 10.4%), and more likely to have public insurance (46.8% vs 21.3%). There was a difference in cardiac diagnoses between the two groups; those with lower income had more structural heart disease (41.1% vs 17.7%) and fewer arrhythmias (25.3% vs 39.6%). In the lower income group, there were 2 maternal deaths and 1 neonatal death before discharge, while in the upper income there was 1 neonatal death. Conclusions: Our study examined the relationship between median income per zip code and pregnancy outcomes, and demographics in women with heart disease. Our observations demonstrate a significant difference in maternal age, race, distribution of cardiac diagnoses, and antepartum hospitalizations. Despite all women being treated at the same facility, antepartum hospitalizations differed based on income bracket. Social determinants of health are important factors that impact outcomes in the cardiac-obstetric population and require further investigation.

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