key: cord-0721254-919v85xl authors: Bhayani, Siddharth; Sengupta, Ranit; Markossian, Talar; Tootooni, Samie; Luke, Amy; Shoham, David; Cooper, Richard; Kramer, Holly title: Dialysis, COVID-19, Poverty, and Race in Greater Chicago: An Ecological Analysis date: 2020-07-30 journal: Kidney Med DOI: 10.1016/j.xkme.2020.06.005 sha: bdd25a75a6865b33405c6ca583a7aba5852f14a7 doc_id: 721254 cord_uid: 919v85xl RATIONALE AND OBJECTIVE: Persons with end-stage renal disease (ESRD) receiving in-center maintenance hemodialysis may be at high risk for SARS-CoV-2 exposure and severe outcomes with Coronavirus disease 2019 (COVID-19). The objective of this study was to examine the correlation of SARS-CoV-2 positivity rate per capita and COVID-19 associated deaths with number of dialysis stations and demographics of residents within ZIP codes in Cook County, Illinois. STUDY DESIGN: Ecological analysis SETTING AND PARTICIPANTS: Data on SARS-CoV-2 tests and COVID-19 associated deaths during January 21- June 15, 2020 among the 5,232,412 residents living within the 163 ZIP codes in Cook County, Illinois were merged with demographic and income data from U.S. Census Bureau. The total number of positive tests in this population was 84,353 and the total number of deaths was 4007. ASSESSMENTS: Number of dialysis stations and stations per capita within a ZIP code was calculated. The SARS-CoV-2 positive tests per capita was calculated as number of positive tests divided by the ZIP code population. COVID-19 deaths per capita were calculated as the COVID-19 deaths among residents for a given ZIP code divided by the ZIP code population. ANALYTIC APPROACH: Spearman’s rank correlation coefficients were calculated to examine the correlation of SARS-CoV-2 positive tests per capita and COVID-19 deaths per capita with dialysis stations, demographics and household poverty. To account for multiple testing, statistical significance was considered as p<0.005. RESULTS: Among the 163 Cook County ZIP codes, there were 2501 dialysis stations. Positive tests per capita were significantly associated with number of dialysis stations (r = 0.25; 95% CI 0.19, 0.29; P < 0.005) but not with dialysis stations per capita (r=0.02; 95% CI -0.03, 0.08; P = 0.7). Positive tests per capita also correlated significantly with number of households living in poverty (r= 0.57; 95% CI 0.53, 0.6; P < 0.005), and percentage of residents reporting Black race (r = 0.28 p < 0.005, CI = 0.23, 0.33) and Hispanic ethnicity (r = 0.68 p < 0.001, CI: 0.65 — 0.7). COVID-19 deaths per capita correlated significantly with the percentage of residents reporting Black race (r=0.24; 95% CI 0.19, 0.29; P < 0.005) and with percentage of households living in poverty (r=0.34; 95% CI 0.29, 0.38; P < 0.005). The association between the number of COVID-19 deaths per capita and total number of dialysis stations (r=0.20; 95% CI 0.14, 0.25; P = 0.01) did not achieve a priori significance, while the association with dialysis stations per capita (r=0.12; 95% CI 0.07, 0.17; P = 0.01) was not significant. LIMITATIONS: Analysis is at the ZIP code level and not at the person level. CONCLUSION: The number of dialysis stations within a ZIP code correlates with SARS-CoV-2 positivity rate per capita in Cook County, Illinois and this correlation may be driven by population density and the demographics of the residents. These findings highlight the high risk of SARS-CoV-2 exposure for patients with ESRD living in poor urban areas. dialysis stations, demographics and household poverty. To account for multiple testing, statistical significance was considered as p<0.005. Results: Among the 163 Cook County ZIP codes, there were 2501 dialysis stations. Positive tests per capita were significantly associated with number of dialysis stations (r = 0.25; 95% CI 0.19, 0.29; P < 0.005) but not with dialysis stations per capita (r=0.02; 95% CI -0.03, 0.08; P = 0.7). Positive tests per capita also correlated significantly with number of households living in poverty (r= 0.57; 95% CI 0.53, 0.6; P < 0.005), and percentage of residents reporting Black race (r = 0.28 p < 0.005, CI = 0.23, 0.33) and Hispanic ethnicity (r = 0.68 p < 0.001, CI: 0.65 -0.7). COVID-19 deaths per capita correlated significantly with the percentage of residents reporting Black race (r=0.24; 95% CI 0.19, 0.29; P < 0.005) and with percentage of households living in poverty (r=0.34; 95% CI 0.29, 0.38; P < 0.005). The association between the number of COVID-19 deaths per capita and total number of dialysis stations (r=0.20; 95% CI 0.14, 0.25; P = 0.01) did not achieve a priori significance, while the association with dialysis stations per capita (r=0.12; 95% CI 0.07, 0.17; P = 0.01) was not significant. Limitations: Analysis is at the ZIP code level and not at the person level. The number of dialysis stations within a ZIP code correlates with SARS-CoV-2 positivity rate per capita in Cook County, Illinois and this correlation may be driven by population density and the demographics of the residents. These findings highlight the high risk of SARS-CoV-2 exposure for patients with ESRD living in poor urban areas. The coronavirus disease 2019 is caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and can lead to severe acute respiratory distress requiring hospitalization and even death. On January 21st, the first case of COVID-19 was identified in Washington State followed just three days later by a COVID- 19 The COVID-19 epidemic has generated intense focus on the long-standing socioeconomic disparities in public health surveillance and disease prevention that also influence the development of chronic diseases, including end-stage renal disease (ESRD). 1-4 ESRD is a chronic disease occurring at a high prevalence in communities with a high minority population and a high percentage of households living in poverty. 4, 5 Individuals with ESRD are particularly susceptible to SARS-CoV-2 exposure and are at risk for severe outcomes with COVID-19 6-10 in part due to the increased frequency at which dialysis patients must leave their home and enter the congregant setting of a dialysis facility. In addition to this, transportation to the dialysis center may require interactions with transportation personnel. Patients receiving maintenance dialysis are therefore one of the most vulnerable populations for the duration of the COVID-19 epidemic. 3, 11 The first death reported associated with COVID-19 in the U.S. was a patient receiving maintenance hemodialysis at the Northwest Kidney Center in the State of Washington. Several additional patients from that dialysis unit subsequently died of COVID- 19. 2 In this ecological analysis, we used data from the Illinois Department of Public Health, which reports SARS-CoV-2 tests by residence ZIP code, to examine the correlation of SARS-CoV-2 positive cases and COVID-19 associated deaths with number of dialysis stations, a proxy of ZIP code ESRD prevalence, and the poverty status and demographics of the populations within a ZIP code. We hypothesized that the SARS-CoV-2 positive rate and positive tests per capita correlates with number of dialysis stations and with the demographics and poverty status of that ZIP code. Illinois SARS-CoV-2 testing data were obtained from the Illinois Department of Public Health website through the Illinois' National Electronic Disease Surveillance System (I-NEDSS) data system. 1 Data used for this analysis were last updated June 15, 2020 but I-NEDSS is regularly being updated as the number of total tests performed and positive tests accumulate. Our ecological analysis was limited to Cook County which includes 163 ZIP codes with a total population of 5,232,412 residents. All of the data used in this study are publicly available and stripped of any identifiers; therefore, Institutional Review Board approval was not required and informed consent was not obtained. We calculated the SARS-CoV-2 positive test rate and the total SARS-CoV-2 tests per capita within a ZIP code to address population density. The SARS-CoV-2 positive test rate was defined as the total number of positive tests divided by the total number of SARS-CoV-2 tests within a ZIP Code; likewise the SARS-CoV-2 positive tests per capita measure was defined as the total number of SARS-CoV-2 positive tests divided by the total population for that ZIP code. The Cook County Medical Examiner's office reports deaths that fall under its jurisdiction and occurred in Cook County through June 15, 2020. Death rate was defined as total number of COVID-19 deaths reported divided by the total number of SARS-CoV-2 positive tests within a ZIP code. Deaths per capita was defined as total number of reported COVID-19 deaths within a ZIP code divided by the total population of that ZIP code. Data on income, race and ethnicity for the Cook County ZIP codes were accessed from the United States Census Bureau's online data tool and based on the 2018 American Community Survey (ACS) Demographic and Housing Estimates. 12 Poverty was defined by the ACS and based on a graded scale of household size and total household income. Poverty thresholds do not depend on geographic area within the U.S. but are updated for inflation based on the Consumer Price Index. The median household income is defined by total income reported for all individuals age 15 years and older for a given household. Distribution of race/ethnicity within a ZIP Code was calculated as the percent of total population identifying as one or more of the following: White, Black or African American, American Indian and Alaska Native, Asian, Native Hawaiian and other Pacific Islander, and other races. Ethnicity was calculated as a percent of the total population identifying as Hispanic/Latino. Location and number of dialysis centers and stations within a center for a given ZIP code were obtained from the Center for Medicare and Medicaid Services (CMS) Medicare Database tool, 13 which provides a list of all dialysis facilities and the number of dialysis stations within a facility by ZIP code. We used the total number of dialysis stations within a ZIP code as a proxy for ESRD prevalence within a ZIP code. The latitude and longitude of each ZIP code was obtained from the United States Census Bureau's TigerWeb tool. 14 This site provided ZIP code tabulation data from 2010 containing the center latitude and center longitude of each ZIP code, which was used for plotting data on a geomap. The distribution of demographic variables, income and poverty status was examined within each ZIP code. Kolmogorov-Smirnov test for goodness of fit was applied to test for normality of data. Correlations of total number of SARS-CoV-2 tests, positive rates, and positive tests per capita, COVID-19 deaths per capita and death rate with distribution of demographics, income, poverty status and number of dialysis stations within a ZIP code were computed with Spearman's rank correlation. Only median household income and total tests per capita were found to be normally distributed, while the rest of data violated the normality assumption. Therefore, we performed our correlation analysis with non-parametric Spearman's correlation, which is a rank based correlation measure and does not rest upon an assumption of normality, and 95% confidence intervals were calculated using methods described by Dr. Fisher in 1925. 15 All statistical analyses were performed in Python with the scipy and statsmodels modules. Due to the large number of correlations examined, we used a P value < 0.005 as the level of statistical significance. Geomapping focused on Cook County by only using ZIP codes with a Cook County census designation. We mapped total SARS-CoV-2 positive tests per capita within a ZIP code with total dialysis stations within a ZIP code and with percent of households living below the Federal poverty threshold within a ZIP code. Geomapping was performed using Mathworks MATLAB software and the native "geobubble" tool. ZIP codes were mapped using latitude and longitude data from the US Census Bureau website. The "sizedata" function was used to plot total positive SARS-CoV-2 tests per capita and total SARS-CoV-2 tests conducted in their respective maps. The "sizedata" function plotted these data with relative size of bubbles. The "colordata" function was used to plot total dialysis stations and percent below poverty line in their respective maps. The "colordata" function plotted these data with a grayscale color map after binning the data into 6 and 4 groups for total dialysis stations and percent households living below Federal poverty threshold, respectively. Among In an ecological analysis of a large metropolitan area, the number of SARS-CoV-2 positive tests per capita was correlated with the number of dialysis stations (a proxy for kidney failure prevalence) and with the percentage of households living below the Federal poverty threshold. This ecological analysis was not undertaken to determine cause and effect relationships but rather as a surveillance tool. The findings reinforce clinical observations that patients with ESRD living in poor urban areas face high risk of exposure to SARS-CoV-2. 3, 7, 11, 16 Our findings show that populations living in highly populated areas with high poverty have high risk of COVID-19 exposure; given that this is a similar risk population for kidney failure, many persons receiving maintenance dialysis are highly vulnerable to SARS-CoV-2 exposure. Multiple modes of transmission could be involved. Maintenance hemodialysis for ESRD treatment requires leaving home three days per week and interacting with numerous healthcare personnel within a dialysis unit. Traveling to and from the dialysis unit may also require interaction with travel personnel. Due to the co-morbidities and reduced immunity associated with ESRD, COVID-19 in this population is often associated with hospitalization and mortality. 8, 9 The vulnerability of patients receiving maintenance dialysis to develop COVID-19 combined with high rates of acute kidney injury 4-6 requiring dialysis in hospitalized patients with COVID-19 has led to substantial strains on hospital dialysis staff, equipment and supplies in order to provide adequate dialysis for hospitalized patients. While on-going transmission will occur for many months to come, there is an urgent need to continue to refine our understanding of very high risk sub-populations and initiate mitigation efforts. Interventions to reduce transmission of SARS-CoV-2 in high risk areas, such as increased testing and contact tracing, 17 could potentially reduce transmission. The percentage of racial/ethnic minorities within a ZIP code was also correlated positively with the number of SARS-CoV-2 positive tests per capita. These findings are supported by the COVID-19 pandemic disproportionately affecting minority communities. 16, 18 However, race should not be the sole focus regarding disparities in COVID-19. Our results show a strong correlation of SARS-CoV-2 positive tests per capita with median income and with poverty status within a ZIP Code. A disproportionate number of all positive SARS-CoV-2 tests (21%) occurred within ZIP Codes with greater than 20% of households living below the Federal poverty line yet these ZIP Codes account for only 13% of all ZIP Codes analyzed. There is a strong body of research demonstrating how poverty disproportionally impacts minorities due to structural racism and bias, and the inherent challenge of examining the independent effects of race/ethnicity and poverty on health disparities because these two factors are interwoven. 19, 20 Poverty status may be associated with decreased ability to shelter at home and to implement social distancing. Poverty status is also associated with higher burden of co-morbidities that increases risk of hospitalization and mortality associated with COVID-19. 7, 16, 21 This study has several strengths. All correlations significant at P < 0.005 except * P<0.05; + P>0.05 Values for continuous variables are given as median (IQR). Three months in: A timeline of how COViD-19 has unfolded in the U.S. so far Mitigating risk of COVID-19 in dialysis facilities Time trends in the association of ESRD incidence with area-level poverty in the US population Neighborhood poverty and racial differences in ESRD incidence Clinical characteristics of coronavirus disease 2019 in China Kidney disease is associated with in-hospital death of patients with COVID-19 Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China Preliminary estimates of the prevalence of selected underlying health conditions among patients with coronavirus disease 2019 -United States Hemodialysis and COVID-19: An Achilles' heel in the pandemic health care response in the United States Accessed May 6, 2020. 13. Center for Medicare and Medicaid services census bureau TIGER/line geo databases Statistical methods for research workers COVID-19 data snapshot fact sheet A national plan to enable comprehensive COVID-19 case finding and contact tracing in the US COVID-19 and African Americans Race and hypertension: Science and nescience Socioeconomic status and health in blacks and whites: The problem of residual confounding and the resiliency of race Neighborhood poverty and racial differences in ESRD incidence End-stage renal disease (ESRD) in the United States