Health disparities and COVID-19: A retrospective study examining individual and community factors causing disproportionate COVID-19 outcomes in Cook County, Illinois

Larissa H Unruh; Sadhana Dharmapuri; Yinglin Xia; Kenneth Soyemi

doi:10.1371/journal.pone.0268317

. 2022 May 16;17(5):e0268317. doi: 10.1371/journal.pone.0268317

Health disparities and COVID-19: A retrospective study examining individual and community factors causing disproportionate COVID-19 outcomes in Cook County, Illinois

Larissa H Unruh ^1,^#, Sadhana Dharmapuri ^2,^3,^#, Yinglin Xia ^4,^#, Kenneth Soyemi ^2,^3,^*,^#

Editor: Gaetano Santulli⁵

PMCID: PMC9109922 PMID: 35576226

Abstract

Early data from the COVID-19 pandemic suggests that the disease has had a disproportionate impact on communities of color with higher infection and mortality rates within those communities. This study used demographic data from the 2018 US census estimates, mortality data from the Cook County Medical Examiner’s office, and testing results from the Illinois Department of Public Health to perform bivariate and multivariate regression analyses to explore the role race plays in COVID-19 outcomes at the individual and community levels. We used the ZCTA Social Deprivation Index (SDI), a measure of ZCTA area level deprivation based on seven demographic characteristics to quantify the socio-economic variation in health outcomes and levels of disadvantage across ZCTAs. Principal findings showed that: 1) while Black individuals make up 22% of Cook County’s population, they account for 28% of the county’s COVID-19 related deaths; 2) the average age of death from COVID-19 is seven years younger for Non-White compared with White decedents; 3) residents of Minority ZCTA areas were 1.02 times as likely to test positive for COVID-19, (Incidence Rate Ratio (IRR) 1.02, [95% CI 0.95, 1.10]); 1.77 times as likely to die (IRR 1.77, [95% CI 1.17, 2.66]); and were 1.15 times as likely to be tested (IRR 1.15, [95% CI 0.99, 1.33]). There are notable differences in COVID-19 related outcomes between racial and ethnic groups at individual and community levels. This study illustrates the health disparities and underlying systemic inequalities experienced by communities of color.

Introduction

On March 11, 2020, the World Health Organization officially declared COVID-19, the disease caused by the SARS-CoV-2 virus, a pandemic. The virus has infected over 200 million people worldwide and caused approximately 4.5 million deaths. The United States has become the epicenter of this pandemic with an incidence of 43 million cases and approximately 700,000 or 16% of the reported global deaths [1]. The US has until recently experienced rapid, largely unchecked disease spread with subsequent increases in infections and mortality.

Data from the Centers for Disease Control and Prevention (CDC) as well as many subsequent studies and articles suggests that the burden of COVID-19 morbidity and mortality is not uniformly distributed throughout the US population [2–5]. People of color, especially those within Black and Latin X communities, are disproportionately affected by the disease. These disparities have been widely discussed by media outlets, medical journals, health associations, and acknowledged by political leaders [2–5]. While scientific literature supports the idea that societal norms that systematically disadvantage people of color (i.e., structural racism) play a major role in disease outcome, that knowledge has yet to become a widely acknowledged fact in society. Consequently, few recent studies address social determinants of health (SDH), such as differences in access to housing, transportation, and food as well as representation within correctional facilities as contributing factors for disparate disease outcomes between communities of color and White communities [2–7]. Half of deaths in the US are related to personal behaviors that are propelled by social factors, including income, education, and employment. Socioeconomic gradients are present among all health indicators [8]. There is a causal link between social factors, health indicators, and measures of individuals’ socioeconomic resources, such as income, educational attainment, and social position [9]. This association simulates a stepwise gradient pattern, with health improving incrementally as social position rises. This relationship is dose-dependent and is predominant in Non-Hispanic Black and Non-Hispanic White groups compared with Latin X populations. Social determinants of health contribute to disparities because differences that exist in life expectancy between racial and ethnic groups are socially and culturally established, not biologically derived [10]. Long term exposure to stress induced by social and environmental factors cause biological “wear-and-tear” also known as allostatic load [11]. Epigenetic processes that regulate whether genes are expressed or suppressed is one biological mechanism involved in the linkage of social factors to health outcomes. Primate studies indicate that social status can affect the regulation of genes that control physiologic functions (e.g., immune functioning). Educational attainment, occupational class (e.g., manual vs. non-manual work), work schedules, stress, and intimate partner violence are linked with changes in DNA-protein complexes capping the ends of chromosomes (telomeres) [12].

Despite many rapid advances in the scientific understanding of COVID-19, much remains unknown about why certain people experience worse outcomes than others. However, as is true with many diseases, COVID-19 outcomes are strongly associated with race [13–17]. A systematic review by Mackey et al. [18] explored outcome differences between races and confirmed that Black and Latinx individuals suffered higher rates of infection, hospitalization, and mortality than White individuals. The Mackey study attributes these differences to differences in health care access and exposure risk. In fact, based on their thorough literature review, Blacks have between 1.5- and 3.5-times risks of infection, hospitalization, and dying with 15% excess mortality when compared with Whites. Similarly, the same study showed that Latin X have between 1.3 and 7.7 times risks of infection, hospitalization, and dying with 21% excess mortality also when compared with Whites [18].

In “Racial Health Disparities and Covid-19 –Caution and Context,” published in The New England Journal of Medicine, Chowkwanyun and Reed discussed the need for careful data analysis before making blanket statements about racial differences in COVID-19 morbidity and mortality [19, 20]. In keeping with Chowkwanyun and Reed’s recommendations, our study describes the demographic distribution of mortality and explores whether there is an association between race, zip code tabulation areas (ZCTAs), and COVID-19 outcomes in Cook County, Illinois. Additionally, we explore five potential social determinants of health which may contribute to the disparities in COVID-19 mortality data.

Materials and methods

IRB approval

This investigation was conducted as part of public health practice and was classified as exempt by the Cook County Health Institutional Review Board. We performed retrospective cohort analysis to explore COVID-19 outcomes between March 2020, and September 2021. We used three data sources to create two master spreadsheets to analyze both individual and ZCTA-level aspects of COVID-19 morbidity and mortality.

Individual level analysis

The Cook County Medical Examiner’s website provided COVID-19 mortality data (Accessed October 1, 2021 [20]. Data elements included decedent demographics (age sex race/ethnicity zip code), primary and secondary causes of death, and date and location of death. Descriptive statistics for continuous variables were presented as means ± standard deviations (SDs). Categorical variables were presented as frequencies and proportions. All statistical tests were two-sided, and p-values <0.05 were considered statistically significant. For baseline characteristics, after determining whether continuous variables were normally distributed, parametric methods (independent t-test and ANOVA) and non-parametric (Wilcoxon rank sum test and Kruskal-Wallis test) were used to determine whether differences in groups were significant. Comorbidities were aggregated for supplemental analysis. To determine the impact of the combination of comorbidities with COVID-19 between Non-White (Black, Latin X, Asian, and other race decedents) vs White; we used the chi-square test of independence to determine differences between two groups. We also reported the Relative Risk (RR) and risk differences and their corresponding 95% confidence intervals.

ZCTA analysis

The Illinois Department of Public Health (IDPH) provided zip code level numbers for COVID-19 tests completed and positive cases (Accessed October 1, 2021). Decedents’ zip codes were cross walked with ZCTAs, which are spatial representations of US Postal Service zip code service areas, and in Chicago represent the same spatial areas. The 2018 US Census Bureau’s 5-year estimate for Cook County, Illinois, provided ZCTA level demographic data. We used the ZCTA Social Deprivation Index (SDI), a measure of ZCTA area level deprivation based on seven demographic characteristics collected in the American Community Survey (https://www.census.gov/programs-surveys/acs/). The SDI is used to quantify the socio-economic variation in health outcomes and levels of disadvantage across ZCTAs. The seven demographic characteristics are: percent living in poverty, percent with less than 12 years of education, percent single parent households, percent living in rented housing units, percent living in overcrowded housing units, percent of households without a car, and percent non-employed adults underage 65 [21]. We added average household size, average per capita income, percent of households that spoke English and Spanish, percent of households with bachelor’s degrees as additional covariates. Mortality data after aggregation by ZCTA were merged with ZCTA level SDI and additional ZCTA-level census data described above to create the ZCTA-level data set. Minority population was defined as any race or ethnic group other than Non-Hispanic White (White). Minority predominant or Non-White ZCTAs (Minority Zip) were defined as those in which the minority population was above 50% of the total population.

Statistical analyses

To explore the relationship between COVID-19 mortality and contributing ZCTA factors, a count modeling strategy including Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) was used to model the main outcomes of interest (e.g., number of deaths, number of positive tests, and number of residents tested). We compared the predictive performance of Poisson, NB, ZIP, and ZINB models using likelihood ratio, Akaike’s information criterion (AIC), Bayesian information criterion (BIC), standardized Pearson residual, and the standardized deviance residuals. In addition, differences between the predicted and actual counts were compared based on the mean squared error (MSE) performance measure.

For ZCTA analysis, we created one model for each outcome variable (mortality, number of positive tests and total number tested per ZCTA). For the mortality (deaths) per ZCTA, we used four models with sequential addition of economic and housing population covariates not included in the SDI. To understand the relationships between the variables we added the interaction term of Non-White (Minority Zip) ZCTA and if greater than 20% of households lived in poverty. Our outputs were the incident rate ratios (IRR) and the predicted probabilities of number of deaths, number of tests, and number of positive results adjusted for the social deprivation score and the added covariates, using margins (Stata command). Margins are statistics calculated from predictions of a previously fit model at fixed values of some covariates and averaging or otherwise integrating the remaining covariates. The “margins” command estimates margins of responses for specified values of covariates and presents the results as a table. Each predicted margin is computed by fixing the SDI score at a given value (20 to 100 in increments of 10) and leaving all other covariates as they were in the model. The predicted values of the outcome variable are averaged and plotted using the “marginsplot” command.

The figures were created using Microsoft Excel, and statistical analysis was performed using SAS software (version 9.4; SAS Institute) and Stata (version 17; Stata Corp LLC).

Results

Cohort sample size included 10,813 COVID-19 related mortalities. Of the 10,813 deaths, 4,527 (42%) were White, 3,224 (29%) Black, 2,335 (22%) Latin X, 435 (4%) Asian, and the rest were classified as Other. Males accounted for 6,142 (57%) and 8,018 (75%) were persons over age 65. Persons 85 years of age and older had the highest mortality, 2,812 (26%). Overall, mean (SD) age at death was 73 (15) years. Mean (SD) age at death for males compared with females was, 71.2 (15) vs 76.2 (15); p < 0.01. The Mean (SD) age at death by race and ethnicity was White 78 (13), Black 71 (15), Latinx 67 (15), Asian 77 (14), and other 75 (12); p <0.01. Hypertension was the most frequently recorded comorbidity 6,480 (60%) among decedents (Table 1).

Table 1. Population and COVID-19 mortality, Cook County, March 15-May 31, 2021.

Cook County general information			Total
	Total population	5,569,118
	Total households	2,084,482
	Average household size	2.67
	Land area (Miles)	1,118.31
	Population density (per mile²)	4,979.94
	Average per capita income	$37,538
	Median age (Years)	37
Cook County population by sex			5,569,118
	Female	2,864,819 (51%)
	Male	2,704,299 (49%)
Cook County population by race			5,569,118
	Black	1,227,577 (22%)
	Latin X	1,354,630 (24%)
	White	2,469,254 (44%)
	Asian	379,016 (6%)
	Other	138,641 (4%)
Mortality by age			10,813
	Age range	0–109
	Mean (SD) age	74 (15)
	Median (IQR)age	75 (65–75)
Mortality by sex			10,750
	Male	6,117 (57%)
	Female	4,633 (43%)
Mortality by race/ethnicity			10,777
	White	4,527 (42%)
	Black	3,224 (29%)
	Latin X	2,335 (22%)
	Asian	435 (4%)
	Other	256 (2%)
Mortality by Age Group			10,813
	Less than 15 years	7 (0%)
	15–24 years	30 (0%)
	25–34 years	132 (1%)
	35–44 years	304 (3%)
	45–54 years	722 (7%)
	55–64 years	1,599 (15%)
	65–74 years	2,574 (24%)
	75–84 years	2,632 (24%)
	85 years and above	2,812 (26%)
Number of comorbidities reported per decedent			10,813
	0	1,664 (15%)
	1	2,826 (26%)
	2	3,224 (30%)
	3	1,775 (16%)
	4	892 (8%)
	5	304 (3%)
	6	90 (0.8%)
	7	34 (0.3%)
	8	3 (0.03%)
	9	1(0.1%)
Comorbidities			10,813
	Hypertension	6,480 (60%)
	Diabetes	4,467 (41%)

Open in a new tab

Mean age at death was lower for Non-Whites compared with Whites 70 (15) vs 78 (13); p < 0.01. Non-White decedents had higher prevalence of diabetes, obesity, and hypertension compared with White decedents. The number of comorbidities recorded was 0–9, with 2 being the most reported 3,224 (30%). For both Non-Whites and Whites, a higher proportion of males died compared with females 3,632 of 6,227 (58%) vs. 2,485 of 4,527 (55%); risk difference of 0.03 [(95% CI: 0.01, 0.05); P = <0.01)]. When COVID-19 was associated with a comorbidity, risk differences of Non-Whites compared with Whites were 0.13 [(95% CI: 0.11, 0.15); P = <0.01)] for diabetes; 0.01 [(95% CI: -0.07, 0.03); P = <0.20)] for hypertension; and 0.04 [(95% CI: 0.03, 0.05); P = <0.01)] for obesity. When hypertension, obesity, and diabetes were combined, the risk difference was 0.02 [(95% CI: 0.01, 0.03); P = <0.01))]. Direct comparison of COVID-19 associated morbidities for Non-White compared with White decedents with hypertension showed no difference [Relative Risk (RR) 1.01, 95% CI (0.98, 1.05)]. However, risk rates were higher for Non-Whites with diabetes [RR = 1.39, 95% CI (1.32. 1.46)], obesity [RR = 1.60, 95% CI (1.42. 1.80)], and the additive combination of hypertension, obesity, and diabetes [RR = 1.74, 95% CI (1.41. 2.14)] (Table 2).

Table 2. Comparison of mortality and co-morbidities White Vs Non-White, Cook County, Illinois.

Factor	Level	Non-White N = 6227 N (%)	White N = 4527 N (%)	ALL N = 10754 N (%)	Risk Difference (95% CI)	Relative Risk (95% CI)	P-value*
Sex	Male	3632 (58)	2485 (55)	6117 (57)	0.03 (0.02,0.05)	1.06 (1.02,1.09)	< 0.01
Sex	Female	2593 (42)	2040 (45)	4633 (43)		1.06 (1.02,1.09)	< 0.01
Hypertension	Yes	3761 (60)	2683 (59)	6444 (60)	0.01 (-0.01,0.03)	1.01 (0.98,1.05)	0.20
Hypertension	No	2466 (40)	1844 (41)	4310 (40)		1.01 (0.98,1.05)	0.20
Diabetes Mellitus	Yes	2921 (47)	1522 (34)	4443 (41)	0.13 (0.11,0.15)	1.39 (1.32,1.46)	< 0.01
Diabetes Mellitus	No	3306 (53)	3005 (56)	6311 (59)		1.39 (1.32,1.46)	< 0.01
Chronic Obstructive Pulmonary Disease	Yes	588 (9)	736 (16)	1324 (12)	-0.06 (-0.08, -0.05)	0.58 (0.52,0.64)	< 0.01
Chronic Obstructive Pulmonary Disease	No	5639 (91)	3791(84)	9430 (88)		0.58 (0.52,0.64)	< 0.01
Hypertensive Coronary Vascular Disease	Yes	247 (4)	152 (3)	399 (4)	0.01 (-0.00,0.13)	1.18 (0.96,1.44)	0.09
Hypertensive Coronary Vascular Disease	No	5980 (96)	4375 (97)	10355 (96)		1.18 (0.96,1.44)	0.09
Coronary Artery Disease	Yes	712 (11)	783 (17)	1495 (14)	-0.05 (-0.07, -0.04)	0.66 (0.60,0.73)	< 0.01
Coronary Artery Disease	No	5515 (89)	3744 (83)	9259 (86)		0.66 (0.60,0.73)	< 0.01
Dementia	Yes	395 (6)	490 (11)	885 (8)	-0.04 (-0.05, -0.03)	0.58 (0.52,0.67)	< 0.01
Dementia	No	5832 (94)	4037 (89)	9869 (92)		0.58 (0.52,0.67)	< 0.01
Atrial Fibrillation	Yes	406 (7)	676 (15)	1082 (10)	-0.08 (-0.09, -0.07)	0.43 (0.38,0.49)	< 0.01
Atrial Fibrillation	No	5821 (93)	3851 (85)	9672 (90)		0.43 (0.38,0.49)	< 0.01
Congestive Heart Failure	Yes	527 (8)	572 (13)	1099 (10)	-0.04 (-0.05, -0.02)	0.67 (0.61,0.75)	< 0.01
Congestive Heart Failure	No	5700 (92)	3955 (87)	9655 (90)		0.67 (0.61,0.75)	< 0.01
Chronic Kidney Disease	Yes	641 (10)	581 (13)	1222 (11)	-0.02 (-0.03, -0.01)	0.80 (0.72,0.89)	< 0.01
Chronic Kidney Disease	No	5586 (90)	3946 (87)	9532 (89)		0.80 (0.72,0.89)	< 0.01
Obesity	Yes	764 (12)	348 (8)	1112 (10)	0.04 (0.03,0.05)	1.60 (1.42,1.80)	< 0.01
Obesity	No	5463 (88)	4179 (92)	9642 (90)		1.60 (1.42,1.80)	< 0.01
Combination of Hypertension Diabetes and Obesity	Yes	290 (5)	121 (3)	411 (4)	0.02 (0.01,0.03)	1.74 (1.41,2.14)	< 0.01
Combination of Hypertension Diabetes and Obesity	No	5937 (95)	4406 (97)	10343 (96)		1.74 (1.41,2.14)	< 0.01

Open in a new tab

One hundred and sixty-four Cook County ZCTAs recorded deaths, tests, and positive results. Of the 164 ZCTAs, 76 (46%) were minority predominant ZCTAs (Minority Zip). Overall, mean (SD) number of deaths, number tested, number with positive test results, and total population per ZCTA was 65 (59), 71,692 (46,473), 4,008 (2,979), and 34,043 (21,633). When Minority Zip was compared with Non-Minority Zip, mean (SD) for the number of deaths, number tested, number with positive test results, and total population were 85 (66), 79,976 (50,599), 4,919 (3,722), and 39,133 (25,816) vs 48(44), 65,401 (41,940), 3,222(1,826), and 29,647 (16,127). The mean (SD) social deprivation index score for Minority Zip was higher compared with Non -Minority Zip (74 (23) vs 32 (22); P value <0.01)). NB regression showed that residents of Non-White ZCTAs were 1.02 times as likely to test positive and 1.15 times as likely to get a test when compared with residents of predominantly White ZCTAs [Incident Rate Ratio (IRR) 1.02, 95% CI (0.95, 1.10) and IRR 1.15, (0.99, 1.33)]. These rates were adjusted for population density, average household size, SDI score, per capita income, households living below the federal poverty line. When the number of deaths, positive tests, and number tested were modelled with no covariates, residents of Non-White ZCTAs were 1.67 times [IRR 1.62, 95% CI (1.23, 2.12)] more likely to die, 1.02 times as likely to be positive [IRR 1.02, 95% CI (0.81,1.02)] and 1.15 times as likely to be tested [IRR 1.15, 95% CI (0.99, 1.33)]. In the final model after adjusting for factors described above, residents of Non-White ZCTAs were 1.77 times as likely to die from COVID-19 [IRR 1.77, 95% CI (1.17, 2.66)]. Residents in households with a high percentage of Spanish speakers had increased risk of testing positive [IRR 1.69, 95% CI (1.03, 2.75)]. Living in poverty alone was not associated with an increased number of deaths, but the interaction of living in poverty and in a Non-White ZCTA increased the risk 2 to 3 times [IRR 2.99, 95% CI (0.71, 12.57)]. (Table 3).

Table 3. Negative binomial regression model outputs.

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7
	Deaths IRR 95%CI	Deaths IRR 95%CI	Deaths IRR 95%CI	Deaths IRR 95%CI	Deaths IRR 95%CI	Tested IRR 95%CI	Positive IRR 95%CI
Non-White Predominant ZCTA	1.62^*** [1.23,2.12]	1.07 [0.75,1.54]	1.25 [0.86,1.84]	1.77^** [1.17,2.66]	1.77^** [1.17,2.66]	1.15 [0.99,1.33]	1.02 [.0.95,1.10]
Social Deprivation Score		1.01^** [1.00,1.02]	1.01^** [1.00,1.02]	1.01^* [1.00,1.02]	1.01 [1.00,1.01]	1.00 [1.00,1.00]	1.00 [1.00,1.00]
>20% below the FPL		0.47 [0.10,2.13]	0.34 [0.08,1.42]	0.38 [0.10,1.48]	0.41 [0.10,1.60]	2.01^*** [1.36,2.96]	1.13 [0.83,1.55]
Non-White Predominant ZCTA # > 20% below the FPL		2.41 [0.50,11.66]	2.98 [0.67,13.18]	3.16 [0.76,13.08]	2.99 [0.71,12.57]	0.65^* [0.43,0.99]	0.81 [0.58,1.13]
Average Household Size			0.59^* [0.38,0.92]	0.60^* [0.36,0.99]	0.54^* [0.31,0.96]	0.74^*** [0.64,0.87]	1.09 [0.96,1.24]
Percent Speak English				0.06^*** [0.02,0.18]	0.06^*** [0.02,0.18]	0.73 [0.48,1.11]	1.02 [0.75,1.38]
Percent Speak Spanish				0.05^*** [0.01,0.25]	0.06^*** [0.01,0.29]	0.71 [0.38,1.34]	1.69^* [1.03,2.75]
Income (Per Capita)					1.00 [1.00,1.00]
lnalpha	0.35^***		0.35^***		0.35^***		0.35^***
AIC	1564	1563	1563	1557	1549	3608	2496
BIC	1573	1581	1585	1585	1580	3636	2524
Observations	164	164	164	164	164	164	164

Open in a new tab

Exponentiated coefficients; 95% confidence intervals in brackets

* p < 0.05,

** p < 0.01,

*** p < 0.001

IRR: Incident rate ratio, HH: Household, AIC: Akaike information criterion, BIC: Bayesian information criterion; FPL: Federal Poverty Lin

For every one-point increase in SDI score the number of deaths, number of tests, and number that tested positive all increased one unit. Using the “margins” command and fixing the SDI score at 20 to 100 in increments of 10, the predicted number of deaths increased as the SDI score increase for both Non-White ZCTA and White ZCTA, but Non-White ZCTAs had a higher slope (Fig 1).

Discussion

As of September 2021, Cook County, Illinois, had identified more than 500,000 positive COVID-19 cases and over 10,000 COVID-19 related deaths [22]. Completion of data analysis reveal that as with national COVID-19 trends, data from Cook County suggest that communities of color had a disproportionate number of cases and deaths. In April 2020, Blacks accounted for 36% of the county’s COVID-19 related deaths despite making up 22% of Cook County’s population. A year later Blacks accounted for 28% of the deaths confirming persistent disproportionate mortality among Blacks (Fig 2). In the absence of complete state-level data regarding incidence of COVID-19 stratified by race and ethnicity, ZCTA level data analysis displays the burden of disease and impacts of COVID-19 diagnoses and deaths among residents living in Non-White predominant ZCTAs. Modelling with single and multiple covariates, our analyses confirmed persistent higher incident rates of COVID-19 deaths among Non-Whites. As outlined in other studies, our data showed that minorities are dying of COVID-19 at significantly younger ages than Whites (median Non-White age = 72, median White age = 79; p < 0.01).

The SDI uses data on neighborhood social determinants of health, to model health outcomes and the use of health services to study the stability of the model across different geographical areas, such as counties, census tract, and ZCTAs [22]. Comparison of mean SDI score for Non-White predominant ZCTAs compared with White predominant ZCTAs showed significant differences. When the SDI score was combined with other co-variates, the rate of deaths, positive tests, and number tested decreased. This finding confirmed the presence of other factors on COVID-19 outcomes and that the effect of social deprivation factors on COVID-19 outcomes might be minimal with other intrinsic factors causing the disproportionate outcomes. A similar study that used area deprivation index (ADI) showed that positive relationship between area deprivation and COVID-19 prevalence, and strength were higher for rural than for urban counties. Family income, property value, and educational attainment were among the ADI component measures most correlated with prevalence, but this too differed between county type [23].

Other studies that used a deprivation index showed that odds of COVID-19 infection among residents living in areas of very high deprivation were three times higher than those of residents of areas of very low deprivation. Odds of hospitalization among residents of very high-deprivation areas were 1.6 times those among residents of very low-deprivation areas. Likelihood of testing varied less with deprivation than did incidence of hospitalization [5, 24]. Emeruwa et al., showed that risk factors for COVID-19 might cluster within geographic areas. For example, persons living in Non-White predominant ZCTAs with a high deprivation index might be both more likely to work in settings where they could become infected and to live in higher-density settings where household members could become secondarily infected [25]. Therefore, Emeruwa and associates concluded that most of the area-level analysis using some form of deprivation index show a positive correlation with positivity, testing, hospitalization, and mortality.

The association between high social risk and COVID-19 infection should form the basis for the development of preventive strategies to control modifiable factors that impact COVID-19 infection. A previous study by Del Brutto et al. showed that high social risk, as measured by the SDH score, may increase the risk of COVID-19 infection. For every unit increase of the total SDH score, the odds of COVID-19 seropositivity increased 15%, but the study did not model mortality. In contrast, our study modelled the SDI score versus testing, positivity and mortality, and showed that each increased for every point increase in SDI score. Multivariate models in previous studies also demonstrated that the component of SDH most strongly associated with COVID-19 seropositivity was housing, which suggests that lack of basic home facilities may increase the risk of COVID-19 infection [26].

Of the many reasons for COVID-19 disparities between races, we explored five: 1) lower socioeconomic status (SES), 2) increased comorbidities, 3) multigenerational living conditions, 4) high population density in predominantly Black and Latinx ZCTAs, and 5) minority overrepresentation in correctional facilities [27, 28].

Socioeconomic status

Previous research regarding the effects of poverty is conclusive, but not everyone accepts the effects of income and education on health across the socioeconomic classes. Some have argued that income-health or education-health relationships reflect inverse relationships (i.e., increased sickness leads to decreased income or lower educational achievement) [29]. Khalatbari-Soltani et al. suggest that socioeconomic position plays a crucial role in COVID-19 outcomes and that these factors may be related to the differences in the COVID-19 burden observed between Non-White and White decedents [30]. While it may be true that certain factors, such as decreased access to nutritious food, less sanitary living conditions, or limited access to health care, affect all people with fewer economic resources, our analysis confirmed that poverty by itself was not a significant factor for COVID-19 related death indicating that there are likely other reasons for higher mortality among Non-White populations. Of the factors explored, living in a predominantly Non-White ZCTA was the only statistically significant variable related to death from COVID-19. When poverty was combined with living in a Non-White predominant ZCTA as an interaction term, the risk of death increased 2 to 3 times.

Increased co-morbidities

Other research has shown that people with certain co-morbidities (e.g., hypertension, diabetes, obesity) have suffered poorer COVID-19 health outcomes than people without underlying conditions. As we compared comorbidities between ethnic/racial groups, we found that minority COVID-19 decedents had higher likelihood of having comorbidities at the time of death (Table 2).

Multigenerational living

In a study from Italy published in Eurosurveillance, Sjödin et al. explored factors necessary to mitigate the COVID-19 pandemic. The authors found that small household size was necessary for limiting the spread of the disease [31]. In Chicago, the areas with higher household density are primarily located in low density Black and Latinx ZCTAs. Our analysis confirms that larger household size is a contributing factor to COVID-19 mortality. Among all combined cases, for each one person increase in household size there is a 66% increase in mortality.

Densely populated urban areas

Given that the virus spreads primarily through respiratory/droplet transmission, close contact with an infected individual is required for the virus to spread. It seems logical to assume that more densely populated ZCTAs have higher rates of infection and worse outcomes [32, 33]. In Cook County, however, the more densely populated areas are the city center areas that are predominantly inhabited by wealthy, White individuals. Better outcomes in the highly populated areas of Chicago may well be associated with increased access to healthcare because both proximity to hospitals, and financial means to seek medical assistance. At least in Chicago, infection and mortality rates seem to be more related to the density within households than overall population density.

Overrepresentation in correctional facilities

Earlier in the pandemic, the Cook County Department of Corrections, one of the largest jail systems in the country, gained media attention about a large COVID-19 outbreak among both the facility’s employees and inmates [34]. As with many prisons, it has an over-representation of minority inmates. As of July 1, 2020, the Cook County Sheriff’s Department estimated that around 75% of Cook County inmates were Black.

The demographics of the ZCTA that houses the Cook County Jail (60608) show that 55.4% of its inhabitants are male. This over-representation of the male population may be due to the presence of the prison system. The average number of people per household is 3.09, and the median age is 32.4 years. The population of the ZCTA is largely Latin X (50.7%) and Black (17.24%) with an average income of $21,447 (well below Illinois average household income). The COVID-19 data for the 60608 ZCTA shows that it ranks 19^th out of 164 for the most COVID-19 deaths and 12^th for the most positive cases in Cook County. Incidence analysis showed that of the 140 deaths in the 60608 ZCTA, 86 (61%) were Latin X, and 20 (14%) were Black. Therefore, although there is an over-representation of Black individuals within the jail, and the ZCTA 60608 had one of the highest incidents of COVID-19 positives, mortality data does not show that this ZCTA was an outlier area for COVID-19 mortality.

Limitations

This study had several limitations. First, while data was available about individual mortality cases and broad ZCTA demographics, we lacked information about the demographics of those who did not die or who never tested positive for COVID-19, and we were unable to perform a case-control analysis. Second, we used ZCTA demographic data as a proxy for an individual’s circumstances. Because that data was essentially an average, not everyone in that ZCTA was accurately represented. Third, because the study focused on Cook County, it may be generalizable only to large urban counties with high levels of racial and ethnic diversity. Fourth, census data is never able to capture the entirety of a population and may miss undocumented individuals. Fifth, because all death data was obtained from the Cook County Medical Examiner’s office, the completeness of that data depended on the Examiner’s reporting. Sixth, since the analysis relied on ZCTA level data, and ZCTAs do not remain constant over time, it was subject to the inherent issues associated with small area analyses. Seventh, we do not currently have data to indicate whether some ZCTAs with high mortalities had an overrepresentation of congregate care facilities such as long-term care or rehabilitation facilities, which might have skewed the data. Eighth, SDI is a composite measure of deprivation; we do not know which factor or combination of factors in the index are responsible for the association with COVID-19 outcomes. ZCTA-level population and economic data are drawn from estimates before the pandemic, and some measures such as unemployment might have changed more recently.

Conclusion

While we agree that caution is important when making statements about race and disease outcomes, our data, and the data of other researchers, indicate that the mortality disparities seen between White and Non-White communities are real and that they are likely impacted by underlying structural racism leading to unequal social determinants of health, such as the unequal access to education, resources, and healthcare faced by communities of color. It is time not only to address the disparities that exist between racial/ethnic groups as they relate to COVID-19 but also to address the underlying structural issues that permit these disparities.

Our study suggests that race, more so than SES, is a significant factor for COVID-19 related mortality in Cook County. Other factors being equal, overall, Black individuals are significantly more likely to die from COVID-19, and Latin X individuals are dying at significantly younger ages than Whites. The question of why people of color suffer disproportionate COVID-19 mortality is more complicated than simply identifying disproportionality. These data indicate that many of the deaths were not inevitable but were the byproducts of ingrained structural inequality resulting in diminished opportunities. We hope that the lessons from this study can help illuminate the persistent inequalities in our country so that, as a society, we can better address these issues. As we confront the subsequent waves of this disease and work to vaccinate as many people, as quickly as possible, we must acknowledge who within our society suffers the most and find ways to focus our efforts to keep those populations as safe and healthy as possible.

Acknowledgments

Special thanks to Mary and Gail Unruh, Jeff Nogaj, and Dr. Mark Mycyk for their unwavering help and support.

Data Availability

All data used in this manuscript are publicly available online. Data can be accessed through: SOYEMI, KENNETH (2022), “Cook County Medical Examiners Mortality Data”, Mendeley Data, V1, doi: 10.17632/grwgy44rwj.1 (https://doi.org/10.17632/grwgy44rwj.1).

Funding Statement

The authors received no specific funding for this work.

References

1.Our World in Data. Coronavirus Pandemic (COVID-19)- the data 2020. Available from: https://ourworldindata.org/coronavirus-data
2.Yancy CW. COVID-19 and African Americans. JAMA. 2020;323(19):1891–2. doi: 10.1001/jama.2020.6548 . [DOI] [PubMed] [Google Scholar]
3.Dorn AV, Cooney RE, Sabin ML. COVID-19 exacerbating inequalities in the US. Lancet. 2020;395(10232):1243–4. doi: 10.1016/S0140-6736(20)30893-X ; PubMed Central PMCID: PMC7162639. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Ferguson JM, Justice AC, Osborne TF, Magid HSA, Purnell AL, Rentsch CT. Geographic and temporal variation in racial and ethnic disparities in SARS-CoV-2 positivity between February 2020 and August 2021 in the United States. Scientific reports. 2022;12(1):273. Epub 20220107. doi: 10.1038/s41598-021-03967-5 ; PubMed Central PMCID: PMC8741774. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Magesh S, John D, Li WT, Li Y, Mattingly-App A, Jain S, et al. Disparities in COVID-19 Outcomes by Race, Ethnicity, and Socioeconomic Status: A Systematic-Review and Meta-analysis. JAMA Netw Open. 2021;4(11):e2134147. Epub 20211101. doi: 10.1001/jamanetworkopen.2021.34147 ; PubMed Central PMCID: PMC8586903. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Poteat T, Millett GA, Nelson LE, Beyrer C. Understanding COVID-19 risks and vulnerabilities among black communities in America: the lethal force of syndemics. Ann Epidemiol. 2020;47:1–3. Epub 20200514. doi: 10.1016/j.annepidem.2020.05.004 ; PubMed Central PMCID: PMC7224650. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Braveman P, Egerter S, Williams DR. The social determinants of health: coming of age. Annu Rev Public Health. 2011;32:381–98. doi: 10.1146/annurev-publhealth-031210-101218 . [DOI] [PubMed] [Google Scholar]
8.McGinnis JM FW. Actual causes of death in the United States JAMA. 1993;270:2207–12. [PubMed] [Google Scholar]
9.Braveman PA, Cubbin C, Egerter S, Williams DR, Pamuk E. Socioeconomic disparities in health in the United States: what the patterns tell us. American journal of public health. 2010;100 Suppl 1:S186–96. Epub 20100210. doi: 10.2105/AJPH.2009.166082 ; PubMed Central PMCID: PMC2837459. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Braveman P, Gottlieb L. The social determinants of health: it’s time to consider the causes of the causes. Public Health Rep. 2014;129 Suppl 2(Suppl 2):19–31. doi: 10.1177/00333549141291S206 ; PubMed Central PMCID: PMC3863696. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.McEwen BS. Protective and damaging effects of stress mediators. The New England journal of medicine. 1998;338(3):171–9. doi: 10.1056/NEJM199801153380307 . [DOI] [PubMed] [Google Scholar]
12.Wolffe AP, Matzke MA. Epigenetics: regulation through repression. Science. 1999;286(5439):481–6. doi: 10.1126/science.286.5439.481 . [DOI] [PubMed] [Google Scholar]
13.Hacker KA, Briss PA, Richardson L, Wright J, Petersen R. COVID-19 and Chronic Disease: The Impact Now and in the Future. Prev Chronic Dis. 2021;18:E62. Epub 20210617. doi: 10.5888/pcd18.210086 ; PubMed Central PMCID: PMC8220960. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Millett GA, Jones AT, Benkeser D, Baral S, Mercer L, Beyrer C, et al. Assessing differential impacts of COVID-19 on black communities. Ann Epidemiol. 2020;47:37–44. Epub 20200514. doi: 10.1016/j.annepidem.2020.05.003 ; PubMed Central PMCID: PMC7224670. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Muñoz-Price LS, Nattinger AB, Rivera F, Hanson R, Gmehlin CG, Perez A, et al. Racial Disparities in Incidence and Outcomes Among Patients With COVID-19. JAMA Netw Open. 2020;3(9):e2021892. Epub 20200901. doi: 10.1001/jamanetworkopen.2020.21892 ; PubMed Central PMCID: PMC7519420. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Hua MJ, Feinglass J. Variations in COVID-19 Hospital Mortality by Patient Race/Ethnicity and Hospital Type in Illinois. J Racial Ethn Health Disparities. 2022:1–9. Epub 20220307. doi: 10.1007/s40615-021-01219-w ; PubMed Central PMCID: PMC8900642. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Gaglioti AH, Li C, Douglas MD, Baltrus PT, Blount MA, Zahidi R, et al. Population-Level Disparities in COVID-19: Measuring the Independent Association of the Proportion of Black Population on COVID-19 Cases and Deaths in US Counties. Journal of public health management and practice: JPHMP. 2021;27(3):268–77. doi: 10.1097/PHH.0000000000001354 . [DOI] [PubMed] [Google Scholar]
18.Mackey K, Ayers CK, Kondo KK, Saha S, Advani SM, Young S, et al. Racial and Ethnic Disparities in COVID-19-Related Infections, Hospitalizations, and Deaths: A Systematic Review. Annals of internal medicine. 2021;174(3):362–73. Epub 20201201. doi: 10.7326/M20-6306 ; PubMed Central PMCID: PMC7772883. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Chowkwanyun M, Reed AL Jr. Racial Health Disparities and Covid-19—Caution and Context. The New England journal of medicine. 2020;383(3):201–3. Epub 20200506. doi: 10.1056/NEJMp2012910 . [DOI] [PubMed] [Google Scholar]
20.Cook County Medical Examiner. Cook County Medical Examiner COVID-19 Related Deaths Chicago2020. Available from: https://maps.cookcountyil.gov/medexamcovid19/
21.Butler DC, Petterson S, Phillips RL, Bazemore AW. Measures of social deprivation that predict health care access and need within a rational area of primary care service delivery. Health services research. 2013;48(2 Pt 1):539–59. Epub 20120720. doi: 10.1111/j.1475-6773.2012.01449.x ; PubMed Central PMCID: PMC3626349. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Phillips RL, Liaw W, Crampton P, Exeter DJ, Bazemore A, Vickery KD, et al. How Other Countries Use Deprivation Indices-And Why The United States Desperately Needs One. Health affairs (Project Hope). 2016;35(11):1991–8. doi: 10.1377/hlthaff.2016.0709 . [DOI] [PubMed] [Google Scholar]
23.Kitchen C, Hatef E, Chang HY, Weiner JP, Kharrazi H. Assessing the association between area deprivation index on COVID-19 prevalence: a contrast between rural and urban U.S. jurisdictions. AIMS Public Health. 2021;8(3):519–30. Epub 20210722. doi: 10.3934/publichealth.2021042 ; PubMed Central PMCID: PMC8334638. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Lewis NM, Friedrichs M, Wagstaff S, Sage K, LaCross N, Bui D, et al. Disparities in COVID-19 Incidence, Hospitalizations, and Testing, by Area-Level Deprivation—Utah, March 3-July 9, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(38):1369–73. Epub 20200925. doi: 10.15585/mmwr.mm6938a4 ; PubMed Central PMCID: PMC7727491. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Emeruwa UN, Ona S, Shaman JL, Turitz A, Wright JD, Gyamfi-Bannerman C, et al. Associations Between Built Environment, Neighborhood Socioeconomic Status, and SARS-CoV-2 Infection Among Pregnant Women in New York City. Jama. 2020;324(4):390–2. doi: 10.1001/jama.2020.11370 ; PubMed Central PMCID: PMC7303894. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Del Brutto OH, Mera RM, Recalde BY, Costa AF. Social Determinants of Health and Risk of SARS-CoV-2 Infection in Community-Dwelling Older Adults Living in a Rural Latin American Setting. Journal of community health. 2021;46(2):292–7. doi: 10.1007/s10900-020-00887-9 ; PubMed Central PMCID: PMC7363014. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Webb Hooper M, Nápoles AM, Pérez-Stable EJ. COVID-19 and Racial/Ethnic Disparities. Jama. 2020;323(24):2466–7. doi: 10.1001/jama.2020.8598 . [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Dyer O. Covid-19: Black people and other minorities are hardest hit in US. Bmj. 2020;369:m1483. Epub 20200414. doi: 10.1136/bmj.m1483 . [DOI] [PubMed] [Google Scholar]
29.Kawachi I, Adler NE, Dow WH. Money, schooling, and health: Mechanisms and causal evidence. Annals of the New York Academy of Sciences. 2010;1186:56–68. doi: 10.1111/j.1749-6632.2009.05340.x . [DOI] [PubMed] [Google Scholar]
30.Khalatbari-Soltani S, Cumming RC, Delpierre C, Kelly-Irving M. Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards. J Epidemiol Community Health. 2020;74(8):620–3. Epub 20200508. doi: 10.1136/jech-2020-214297 ; PubMed Central PMCID: PMC7298202. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Sjödin H, Wilder-Smith A, Osman S, Farooq Z, Rocklöv J. Only strict quarantine measures can curb the coronavirus disease (COVID-19) outbreak in Italy, 2020. Euro surveillance: bulletin europeen sur les maladies transmissibles = European communicable disease bulletin. 2020;25(13). doi: 10.2807/1560-7917.Es.2020.25.13.2000280 ; PubMed Central PMCID: PMC7140595. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Rocklöv J, Sjödin H. High population densities catalyse the spread of COVID-19. J Travel Med. 2020;27(3). doi: 10.1093/jtm/taaa038 ; PubMed Central PMCID: PMC7184409. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Geographic Differences in COVID-19 Cases, Deaths, and Incidence—United States, February 12-April 7, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(15):465–71. Epub 20200417. doi: 10.15585/mmwr.mm6915e4 ; PubMed Central PMCID: PMC7755058. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.B C. Cook County Jail releases could be spreading COVID-19 in community,. Chicago Sun-Times. 2020 4 Jun 2020.

PLoS One. doi: 10.1371/journal.pone.0268317.r001

Decision Letter 0

Hajo Zeeb

19 Nov 2020

PONE-D-20-25294

Health Disparities and COVID-19: A Retrospective Study Examining Individual and Community Factors Causing Disproportionate COVID-19 Outcomes in Cook County, Illinois, March 16-May 31, 2020

PLOS ONE

Dear Dr. Soyemi,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

There clearly is a need to provide better grounding for your research, rearrange some of the sections and attend to the interpretation issues pointed out by the reviewers. Very importantly, all issues regarding the statistical analysis and the inconsistencies pointed out need to be addressed in a revision. It appears that the alignment between methods and actual data and results is not optimal and needs your attention.

Please submit your revised manuscript by Jan 03 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Hajo Zeeb

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please include the date(s) on which you accessed the databases or records to obtain the data used in your study.

3. We note that Table 1 appears to have a typographical error were one p-value is reported as "<.00" instead of "<.001". Please revise this.

4. PLOS ONE publication criteria and journal policy require authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (https://journals.plos.org/plosone/s/data-availability#loc-acceptable-data-access-restrictions). Please clarify whether all data used in your study is publicly available and how they can be accessed. We encourage authors to share de-identified or anonymized data if possible. For any third-party data that you cannot legally distribute, please include in the Data Availability Statement a description of the data set and all necessary third-party contact information others would need to apply to gain access to the data.

5. In your ethics statement in the Methods section and in the online submission form, please provide additional information about the data used in your retrospective study. Specifically, please ensure that you have discussed whether all data were fully anonymized before you accessed them. If not, please state whether your IRB waived the requirement for informed consent.

6. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

7. Please include a caption for figure 1.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Summary:

The authors investigate a very relevant question, the association between minority status and different Covid-19 related outcomes. The authors did a great work in integrating different data sources from the second largest county in the US. However, I see certain analyses that can be improved and do not agree in all their interpretations of the results. Specific comments and concerns are outlined below.

Comments:

1. Page 3 (Line 64). The authors say that they explore a causal relationship. I think a bit more cautious wording should be used with their study design.

2. Page 4. I would find it helpful to make it even clearer (maybe subheadings) in which analyses the unit of investigation are persons and in which ZCTA´s.

3. Page 4 (Line 84). Why was the cut-off of 50% used for the ZCTA´s?

4. Page 4 (Line 86). Moralities -> Mortalities

5. Page 4 (Line 88-90). I do not fully understand the analyses presented in these lines, maybe you can make that clearer.

6. Page 5 (Lines 94-98). A lot of statistical tests were used. Why e.g. did you use Fisher´s exact test? Are there situations where numbers are so small that a Chi-Square-Test should not be computed?

7. Page 5 (Lines 99-101). Did you use the raw number of deaths as the dependent variable or the number of deaths in relation to population size? If the raw number was used why was this the case? Because larger ZCTA´s should obviously experience more deaths.

7. Page 5 (Lines 101-104). This sentence is not correct and should be changed. A backward elimination is not limited to the presented family of models. The presented models are all models for count data. Why did you choose to investigate zero-inflated models that are appropriate for situations with an excess of zeros. Are there a lot of ZCTA´s with zero deaths? The distribution of the number of deaths per county would be interesting.

8. Page 6 (Line 119)- In Table 1 you have 2060 male mortalities in Table 2 2059.

9. Page 6 (Table 1). Why don´t you present percentages for categorical variables and standard deviations for continuous variables as outlined in the Methods section? Thereby one could see the differences between e.g. population by race and mortality by race. I would find an information on how many decedents have at least one (or/and two) comorbidities interesting, if the data contains this information. Is race the best wording or would ethnicity be better?

10. Page 7 (Lines 124-131, and Table 2). The text and the Table are hard to read, because you switch from row to column percentages. For example you write 777 of 1387 (56%) of white decedents are male, while in the Table a percentage of 37.7% is given for that number. The 56% is the number that makes the data easier to understand and allows a direct comparison between whites and minority decedents. The No category is not necessary for the comorbidities. Hypertensive Coronary Vascular Disease is also not statistically significant with the chosen significant level. I don´t understand the chosen statistical tests. There is only only one continuous outcome and two groups, thus a simple t-Test should work. Why did you do ANOVA and Kruskal-Wallis-Test and which p-value is reported? In my version there is a column parametric p-values and under the table the information that non-parametric p-values were computed, too. That´s confusing.

11. Page 8 (Line 141). In the brackets it should be stated what the effect measure is.

12. Page 8 (Line 142). How do you obtain these percentages throughout the paper? How does an IRR from 1.42 relate to the 58%?

13. Page 9 (Line 145). Here, you present four digits for the p-value, in the table two digits. In other tables three, it would be nicer to have the same value all the time. Marginally significant should not be used as a wording.

14. Page 9 (Table 3). A p-value of <.00 does not exist. A p-value of 0.07 should not be marked as significant when the significance level is chosen at 0.05. The IRR for Minority Zip as the independent variable and Number of Positive Results as the dependent variable (1.94) is greather than the IRR for the number of deaths (1.42). This should mean that the crude lethality rate is smaller for Minority dominated ZIP´s, although this might be explained by the lower average age at death in the minority group. Age would be an important confounder in these analyses, but I think it is not included in your county level dataset, right?

15. Page 9 (Line 152-153). It was not stated in the Methods section that an adjustment for other comorbidities is conducted.

16. Page 9/10 (Lines 154-163). A few of the Odds Ratios from Table 4 are presented in these lines. In the text 6 of the 8 reported OR are greater than 1, meaning that minorities have a greater chance to have one of the comorbidities than whites. In Table 4 the majority of OR is smaller than 1, therefore the text is not representing the Table adequately. Additionally, I don´t understand what these for other comorbidities adjusted Odds-Ratios should tell the reader. If some comorbidities are relatively more common among minorities others must be relatively less common, right? Therefore, an unadjusted Odds Ratio or a simple cross tabulation as in Table 2 would be more informative in my view. The labeling of the column “race” is not correct, the reference value should be stated secondly. E.g. it should be “Black Vs White” not “White Vs Black”. Some numbers differ between text and table. This should be checked.

17. Page 10 (Line 178). The reference to Figure 1 is misleading at this place, because median ages are not depicted in Figure 1.

18. Page 10 (Line 181). I would prefer not to present new results in the Discussion section, and don´t understand why you switch between median and average ages.

19. Page 10 (181-184). If this sentence is true, can be more easily assessed when the percentages in Table 2 are presented as suggested.

20. Page 11 (Lines 185-188). This explanation could have come earlier for me (Introduction). Until reading this sentence I didn´t know that this was your plan. Since your Introduction is rather short a more thorough outline of your plans would be helpful.

21. Page 13 (Line 217). The p-value presented in Table 3 is different.

Reviewer #2: Thank you for the opportunity to review Health Disparities and COVID-19: A Retrospective Study Examining Individual and Community Factors Causing Disproportionate COVID-19 Outcomes in Cook County, Illinois, March 16-May 31, 2020. This manuscript details an important examination of COVID-19 health disparities in Black and Brown communities, which is a topic that requires further attention in the literature. However, the manuscript in its current form has several limitations.

1. The introduction would benefit from a comprehensive review of the current COVID-19 health disparities literature and contextual grounding through a health disparities lens.

a. For example, the second paragraph states that “People of color, especially

those within Black and Latinx communities, are disproportionately affected by the disease.” The authors should provide a review of what is known, as the peer-reviewed literature regarding COVID-19 health disparities has grown substantially.

b. The introduction would also benefit from a contextual grounding of health disparities, perhaps within a social determinants of health framework, to orient the reader to the potential mechanisms that are exacerbating these glaring health disparities within Black and Brown communities. This would also help to ground the conclusions, wherein the authors enter into a discussion of structural inequities without a systematic framework for examining said inequities. A discussion of systemic racism would further strengthen this grounding. The following papers may provide useful reference:

Boyd RW, Lindo EG, Weeks LD, McLemore MR. On racism: a new standard for publishing on racial health inequities. Health Affairs Blog. Published online 2020.

Braveman P, Egerter S, Williams DR. The Social Determinants of Health: Coming of Age. Annual Review of Public Health. 2011;32(1):381-398. doi:10.1146/annurev-publhealth-031210-101218

Braveman P, Gottlieb L. The Social Determinants of Health: It’s Time to Consider the Causes of the Causes. Public Health Rep. 2014;129(1_suppl2):19-31. doi:10.1177/00333549141291S206

Link BG, Phelan J. Social Conditions As Fundamental Causes of Disease. Journal of Health and Social Behavior. 1995;35:80. doi:10.2307/2626958

Phelan JC, Link BG, Tehranifar P. Social Conditions as Fundamental Causes of Health Inequalities Theory, Evidence, and Policy Implications. Journal of Health and Social Behavior. 2010;51(1 suppl):S28-S40. doi:10.1177/0022146510383498

Poteat T, Millett G, Nelson LE, Beyrer C. Understanding COVID-19 Risks and Vulnerabilities among Black Communities in America: The Lethal Force of Syndemics. Ann Epidemiol. Published online May 14, 2020. doi:10.1016/j.annepidem.2020.05.004

c. The authors claim that “the current study described the demographic distribution of mortality and explores whether there is a causal relationship between race, neighborhood factors, and COVID-19 in Cook County, Illinois.” However, the manuscript does not appear to examine causal mechanisms of COVID-19 health disparities. Rather, there is an extensive examination of the associations between COVID-19 morbidity and mortality with race and ethnicity. This should be clarified upfront.

d. The authors also indicated that they examined “neighborhood factors,” but then provide ZIP Code and ZIP Code Tabulation Area data, which are not indicative of neighborhoods. Please make sure to use appropriate language to describe the geographic unit of analysis.

2. The methods section would benefit from additional clarification.

a. On lines 76-78, the authors provide a list of variables utilized in the ensuing analysis, but only use blanket terms (e.g., income, education). It would be helpful to specify what the variables are. For example, was median household income used in the analysis?

b. The authors indicated that “decedent’s zip codes were converted to ZIP Code Tabulation Areas,” on lines 79-80. Therefore, it would be beneficial to specify how this conversion was performed.

c. Following, on lines 81-82, the authors indicate that mortality data was aggregated by ZCTA and merged with ZCTA-level census data. How was this operation performed? Did the authors use geospatial software? This should be clarified.

d. On lines 83-84, the authors state that “minority predominant” ZCTAs were defined as having a minority population greater than the White population. For replication purposes, it would be helpful to specify percent minority used to calculate this variable. In addition, majority would be more accurate than “predominant,” particularly when referring to percent.

e. Please provide additional details regarding calculation of excess mortality (lines 85-87).

f. It would behoove the authors to clarify why they “compared trends between the top 20 ZCTAs” in the methods section (lines 88-90). This unfortunately doesn’t become clear until the discussion.

g. Lines 93-94—data is a plural noun, and should be conjugated appropriately.

h. Line 95—please use the correct test name—Kurskal-Wallis H

i. Line 97—"Chi-square tests was used…” also needs to be corrected for subject-verb agreement.

j. On lines 100-101, the authors indicate that they modeled mortality against risk factors. This is the first use of the language of risk, and it is not clear what these risks are. However, it is later (results) revealed that the authors considered race to be a risk factor. However, race (e.g., Black) is hardly a risk factor, whereas structural racism certainly is. Therefore, as stated above, the manuscript would benefit from a clear grounding of health disparities. There is a wealth of theory that provides this grounding—e.g., social determinants, fundamental causes, etc.

k. On lines 101-110, the authors provide explanations of various generalized linear modeling approaches, then only refer to their use of negative binomial regression in the ensuing text. Furthermore, no explanation of why NB was selected was offered. The manuscript would benefit from a concise explanation of why NB was selected (e.g., AIC values), while saving words to address the above comments (2. Methods a.-l.).

l. In addition, it would be beneficial to identify model composition in this section.

m. Please provide an ethics statement.

3. The results require further clarification.

a. On line 135, the authors state that “** The non-parametric p-value is calculated by the Kruskal-Wallis test for numerical covariates and Fisher's exact test for categorical covariates.” Yet, the “**” symbol does not appear to be provided in Table 2.

b. Lines 138-139 repeat the above stated issue, wherein it is not clear why the top 20 ZCTAs were examined.

c. Line 143—the language of risk factors requires amendment. Furthermore, it’s still unclear what these are.

d. Line 145—it would benefit the manuscript to describe what the authors consider a “marginally significant” range to be.

e. Line 142—please correct the following typo: “as a reference groups.”

f. Table 3 specifies composition of 6 models. It is curious that the authors did not develop multivariate models controlling for ZCTA-level variables—e.g., regressing number of deaths by ZCTA percent minority, poverty, household size, etc. At the very least, an explanation of why bivariate analysis was conducted would be useful. If appropriate, the analysis would benefit from extending these models to control for additional ZCTA-level factors.

g. The individual level findings are clear and concise.

h. Table 2 – one category is labeled “Diabetes Hypertension and Hypertension.” It’s unclear if the authors tested for both comorbidities or if this is a typo.

4. The discussion section would benefit from further clarification and additional examination of study findings.

a. Starting on line 189, the authors highlight potential associations between socioeconomic status and COVID-19 mortality, but only examined poverty in their models. Poverty alone is not an ideal indicator of SES, once again raising the question as to why other variables were not explored in multivariate models. For example, the authors collected income data (Median household income? This is still unclear.), yet did not indicate that they regressed morbidity or mortality by income or other SES-related variables. Education would be an important covariate in this context. The authors did mention in the methods section that ZCTA-level education and language data were collected, but these variables were not brought up again in the ensuing text.

b. On line 215, the authors contend that “household size is a statistically significant cause of COVID-19 mortality.” The provided analysis does not make a case for causality of household size. Furthermore, as mentioned above, the manuscript would benefit from a theoretical grounding that would help to unpack this association. It requires a careful analysis of why Black and Brown folk are at higher risk. A social determinants of health framework could be helpful in this context.

c. The language of cause should also be removed from the title.

d. One lines 221-222, the authors appropriately contend that viral transmission is not necessarily a function of population density, and recent studies have confirmed this. However, to reasonably understand this finding, the manuscript requires some theoretical grounding to orient readers to potential explanations of these findings. For example, while lower infection rates were observed in densely populated majority White ZCTAs, it is plausible that the difference here concerns access to health-protective resources associated with SES.

5. The conclusions would benefit from further refinement and clarification of terms.

a. The authors indicate that “the public health crisis” was “created by inbuilt barriers within minority communities.” It is not clear what these “inbuilt barriers” are. Are they referring to race, poverty? Race certainly is not a barrier, but unequal access to education and money resources, for example, certainly are. This argument provides further evidence of how a theoretical grounding would exponentially benefit this paper.

b. The authors are commended for arguing that COVID-19 health disparities are a function of structural inequities. This argument would be further strengthened by connecting this to structural racism, particularly given the findings of the current study.

c. On line 267, the authors reiterate that “race, more than SES, is the only statistically significant” factor associated with COVID-19 mortality. Again, as discussed above, developing integrated models may reveal additional factors. For example, income and education were never considered.

d. The argument concerning “structural inequality” on line 273 relates to points a and b above.

6. Please note that there are several typos throughout the manuscript.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2022 May 16;17(5):e0268317. doi: 10.1371/journal.pone.0268317.r002

Author response to Decision Letter 0

8 Feb 2021

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

RESPONSE: Using the provided style templates, the manuscript was formatted according to PLOS ONE’s formatting standards.

2. Please include the date(s) on which you accessed the databases or records to obtain the data used in your study.

RESPONSE: Dates that the databased were accessed for data collection were included in the materials and methods section.

3. We note that Table 1 appears to have a typographical error were one p-value is reported as "<.00" instead of "<.001". Please revise this.

RESPONSE: The error typographical error in Table 3 (<.00) was changed to reflect the correct value (<.001)

RESPONSE: All data used in this manuscript are publicly available online.

RESPONSE: Statement of exemption was included at the end of the materials and methods section.

RESPONSE: Data can be accessed through: Soyemi, Kenneth (2021), “COVID 19 Mortality Disparity Data”, V1, doi: 10.17632/8dgz3pb73c.1

7. Please include a caption for figure 1.

RESPONSE: Caption was added to figure 1.

Reviewer Comments and Author Responses:

Reviewer #1: Summary:

Comments:

1. Page 3 (Line 64). The authors say that they explore a causal relationship. I think a bit more cautious wording should be used with their study design.

Response: removed the verbiage implying causality

2. Page 4. I would find it helpful to make it even clearer (maybe subheadings) in which analyses the unit of investigation are persons and in which ZCTA´s.

Response: Subheadings describing individual data and ZCTA data were created.

3. Page 4 (Line 84). Why was the cut-off of 50% used for the ZCTA´s?

Response: Minority predominant ZCTA indicated ZCTAs where minorities (non-white individuals) outnumbered white individuals, and therefore were over 50% of the population.

4. Page 4 (Line 86). Moralities -> Mortalities

Response: Changed moralities to mortalities.

5. Page 4 (Line 88-90). I do not fully understand the analyses presented in these lines, maybe you can make that clearer.

Response: removed these sentences

Response: removed the line about the Fischer exact test, because categorical covariates were not used

Response: We used generalized linear modeling. The raw number of deaths was used but the number of deaths was adjusted in relation to population size through using offset of the count models.

Response: We have changed and corrected the sentence. Regarding zero-inflated models: First, as see in Table 3, the outcome variables not only include the Number of Deaths, but also the Number of Residents Tested and the Number of Positive Results in the areas we studied. So the outcome variables could be zero-inflated.

Second, we listed the candidates of count models including Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) and compared them to choose final best fitted model(in this data, NB is the best model). The goal was to demonstrate that the final model we chose was based on appropriate modeling selection.

8. Page 6 (Line 119)- In Table 1 you have 2060 male mortalities in Table 2 2059.

Response: Corrected Table 1 to 2059 male mortalities

Response: Updated Tabe 1 to include the percentages and standard deviations. Added the rows outlining comorbidity numbers

Response: Changes made per reviewer’s suggestion.

I don´t understand the chosen statistical tests. There is only one continuous outcome and two groups, thus a simple t-Test should work. Why did you do ANOVA and Kruskal-Wallis-Test and which p-value is reported? In my version there is a column parametric p-values and under the table the information that non-parametric p-values were computed, too. That´s confusing.

Response: we revised sentences in this reversion. Table 2 was extracted from the outputs of MACRO, which include continuous and categorical variables, and two and more than two group comparisons. Both parametric and non-parametric methods were used. All continuous variables are distributed normally and the number of cells are greater than 5. T-test and chi-square test are appropriate for continuous and categorical variables, respectively.

11. Page 8 (Line 141). In the brackets it should be stated what the effect measure is.

Response: Added IRR in the brackets

12. Page 8 (Line 142). How do you obtain these percentages throughout the paper? How does an IRR from 1.42 relate to the 58%?

Response: Removed the percentage

Response: Tables 1,2 &3 (and throughout the paper) were updated so that p-values are consistently 4 decimals and CI are consistently 3 decimals

Response: Corrected <.00 to <0.001

Removed * from 0.07

It is correct that age was not included in the modelling, as our analysis included data aggregated to the zip code level. We modelled separately number of deaths vs residents from minority predominant zip codes, poverty, average household size at univariate. We did multivariate modelling deaths vs minority zip codes and poverty. IRR of 1.42 meant that residents from minority zip were 1.42 times likely to die when compared to residents from non-minority zip codes

15. Page 9 (Line 152-153). It was not stated in the Methods section that an adjustment for other comorbidities is conducted.

Removed the wording about adjustment for co-morbidities

Response: Appreciate the reviewer’s comments.

Table was changed to reflect Black vs White.

Black vs White and Latin X vs White and other Race vs White corrected throughout the table. Table 4 now reads as using white decendents as a reference group. Our multivariate analysis revealed several notable results. Of decendents with diabetes, Black individuals were 64% more likely to die of COVID-19 [Odds Ratio OR=1.37 (1.16, 1.62)], Latinx decendents were over two times as likely to die [OR=2.11(1.72, 2.60)], and other races were 63% more likely to die compared with white individuals. Of obese decendents, Black individuals were 49% more likely to die from COVID-19 [OR=1.52(1.04, 2.21)], Latinx individuals were again over to two times as likely to die [OR=2.17(1.43,3.29)], but oter races were 80% less likely to die compared with obese White individuals [OR=0.20(0.03, 1.43)]. OF decedents with hypertension, Black individuals were 62% more likely to die of COVID-19 [OR=1.38 (1.15, 1.66)], other combined races were 70% more likely to die [OR=1.29 (0.87, 1.93)], but Latinx individuals were 37% less likely to die than White individuals [OR=0.63(0.51,0.77)]. (table 4)

17. Page 10 (Line 178). The reference to Figure 1 is misleading at this place, because median ages are not depicted in Figure 1.

Response: moved the reference to the figure.

18. Page 10 (Line 181). I would prefer not to present new results in the Discussion section, and don´t understand why you switch between median and average ages.

Response: removed this paragraph as it is new data

19. Page 10 (181-184). If this sentence is true, can be more easily assessed when the percentages in Table 2 are presented as suggested.

Response: Completed percentages added to Table 2

Response: Added mention of the 5 possible contributing factors we explored under the introduction

21. Page 13 (Line 217). The p-value presented in Table 3 is different.

Response: Corrected to 0.049

1. The introduction would benefit from a comprehensive review of the current COVID-19 health disparities literature and contextual grounding through a health disparities lens.

a. For example, the second paragraph states that “People of color, especially

Response: Introduction updated to reflect the current literature on health disparities and people of color.

Response: Removed the wording suggesting causality

Response: Changed mention of neighborhood factors to zip code factors

2. The methods section would benefit from additional clarification.

Response: Changed income to average ZCTA population income

Changed education to education data including percent who obtained high school and bachelor’s degrees

Changed poverty to percent of ZCTA above poverty line

Changed language to primary language.

b. The authors indicated that “decedent’s zip codes were converted to ZIP Code Tabulation Areas,” on lines 79-80. Therefore, it would be beneficial to specify how this conversion was performed.

Response: Changed wording to “decedents zip codes were substituted for ZCTA” and added “and in Chicago represent the same spatial areas”

Response: Clarified that Microsoft Excel was used to merge the mortality data with the census data by ZCTA

Response: Changed the wording to reflect that minority predominant ZCTA was defined as one where the non-white population was over 50% of the population.

e. Please provide additional details regarding calculation of excess mortality (lines 85-87).

Response: We removed the statement containing excess mortality

Response: Removed the mention of the comparison of the top 20 ZCTAs.

g. Lines 93-94—data is a plural noun, and should be conjugated appropriately.

Response: Changed Data to Datum

h. Line 95—please use the correct test name—Kurskal-Wallis H

Response: corrected test name

i. Line 97—"Chi-square tests was used…” also needs to be corrected for subject-verb agreement.

Response: Changed to Chi-Square tests were used.

Response: Removed risk factor wording.

Response: Changed this section to better reflect our analysis.

l. In addition, it would be beneficial to identify model composition in this section.

Response: added in the model composition

m. Please provide an ethics statement.

Response: Ethics statement included

3. The results require further clarification.

Response: Removed the footer with the **symbol as it was not used.

b. Lines 138-139 repeat the above stated issue, wherein it is not clear why the top 20 ZCTAs were examined.

Response: Removed references to the top 20 ZCTAs

c. Line 143—the language of risk factors requires amendment. Furthermore, it’s still unclear what these are.

Response: Removed the word risk, now it is factors

d. Line 145—it would benefit the manuscript to describe what the authors consider a “marginally significant” range to be.

Response: Removed wording of marginal significance

e. Line 142—please correct the following typo: “as a reference groups.”

Response: Now reads “as a reference group”

Response:The initial model was controlled for ZCTA percent minority, race, poverty and house size. We added the information in this reversion. The results were reported based on final model after removing the non-significant terms.

g. The individual level findings are clear and concise.

Response: Thank you

h. Table 2 – one category is labeled “Diabetes Hypertension and Hypertension.” It’s unclear if the authors tested for both comorbidities or if this is a typo.

Response: Clarified that this variable was people who had triple disease ( diabetes, hypertension, and obesity)

4. The discussion section would benefit from further clarification and additional examination of study findings.

Response: We agree that Poverty alone is not indicative of SES. But poverty is a known risk factor for SES. We did not have individual SES for decedants. We coded for either above or below the FDL. Based on this coding we used this for SES.The text was corrected according to the modeling that we used

Response: Changed the causal wording to reflect that it is a contributing factor

c. The language of cause should also be removed from the title.

Response: Removed Causal from the title

Response: This was addressed in the introduction.

5. The conclusions would benefit from further refinement and clarification of terms.

Response: Clarified inbuilt barriers

Response: Added information to the introduction to support our discussion on structural racism.

Response: We had considered multiple variables including income and education as coviariates in our multivariate regression modeling. We removed the non-significant variables in the final model using backward model selection.

d. The argument concerning “structural inequality” on line 273 relates to points a and b above.

Response: Addressed as per our response to points a and b.

6. Please note that there are several typos throughout the manuscript.

Response: Corrected all identified typos.

PLoS One. doi: 10.1371/journal.pone.0268317.r003

Decision Letter 1

Hajo Zeeb

17 Mar 2021

PONE-D-20-25294R1

Health Disparities and COVID-19: A Retrospective Study Examining Individual and Community Factors Causing Disproportionate COVID-19 Outcomes in Cook County, Illinois, March 16-May 31, 2020

PLOS ONE

Dear Dr. Soyemi,

Thank you for submitting your revised manuscript to PLOS ONE. After careful consideration, we feel that it has merit but still does not fully meet PLOS ONE’s publication criteria as it currently stands. We invite you to submit another revised version of the manuscript that addresses the points raised during the review process.

There remain numerous inconsistencies and inaccurarcies with respect to the numerical data presented. This clearly impacts on the scientific quality and must be adressed. Please ensure that all numbers and measures fully correspond between tables and text (see reviewer 1 comments), and digits are used in a consistent manner. Furthermore, the argumentation refering to social deterninants of health should be strengthened with some more explanation and references, a core point of reviewer 2 comments.

Please submit your revised manuscript by May 01 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Hajo Zeeb

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: No

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Summary: The authors improved the manuscript over the previous version. However, I need to say that there are still a lot of issues with the manuscript. As I very much agree with their conclusion in general, I would encourage the authors to improve their writing and pay more attention to details. There are still too many inconsictencies in the paper for me to accept it in the current state.

Comments:

Abstract:

1a) (Lines 57-59): I would prefer that you write out IRR in the abstract, because it´s not a very often used abbreviation. p-Values differ in their number of digits, that should be changed.

Introduction:

2a) (Lines 81-83): The wording is to strong for me. There is a rich body of scientific literature that links race/ethnicity to health outcomes. Maybe it´s more the awareness in society and the transmission of the scientific knowledge that is lacking. And in the next sentence you cite five papers linking social determinants and health outcomes.

2b) (Lines 96-100): Are these numbers confidence intervals and point estimates mixed? This part is confusing.

Materials and methods:

3a) (Line 116): Individual analysis -> Individual level analysis

3b) (Lines 132-135): Percent below the federal povery line -> proportion of persons living below the federal povery line. The same for line 134.

3c) (Lines 165-172): You used a lot of model fit indices. Although I would not proceed as you did, I can agree on that. But the chosen model should not be part of the methods section. Move to results section.

3d) (Lines 173-176): You write that you compare comorbidities of different ethnic groups. It´s the lethality of Covid-19 in different comorbidity groups, right? Make the outcome clearer.

3d) (Lines 177-178): Can you elaborate why this sentence stands there out of context after the statistical analysis?

Results and discussion;

4a) (Lines 183-185): In Table 1 this number is 59%. Be consistent with your numbers. Mean is 73.8 and SD 14.73. The same number of digits should be used. The same applies for Line 185 and other parts of the text.

4b) (Table 1): The unkown category for cook county population by sex can be deleted as there are no unknown persons.

4c) (Lines 193-195): You still write 777 of 1387 (56%), but in Table 2 you present a number of 37.7%. Giving two different percentages for the same number is very confusing. Table 2 would be so much easier to read, if you would present the 56% and do it accordingly for all values in the Table.

4d) (Line 210): Give an abbreviation for the effect measure you present here (IRR). Additionally, the numbers in the text and Table 3 are not the same.

4e) (Lines 219-229): It is confusing to write 64% more likely and present an Odds Ratio of 1.37. How do you obtain the number 64% more likely? The same applies for the next sentences. Also, the interpretation of these results is not reflecting the Table very well, because you mainly interpret the Odds Ratios larger than 1. This whole part is very hard to read and confusing. I would recommend to exclude these lines and simply add columns to Table 2 and present the correct percentages for the comorbidities there.

4f) (Lines 271-273): “56% or 1.4 more likely to die from Covid-19 …..(IRR=1.44)”. You confuse the reader by all this different numbers.

4g) (Lines 277-279): significantly higher chances (higher is missing). You reference to Table 3, that is not correct.

4h) (Line 344): Delete to.

Reviewer #2: Thank you for the opportunity to review the revised version of Health Disparities and COVID-19: A Retrospective Study Examining Individual and Community Factors Causing Disproportionate COVID-19 Outcomes in Cook County, Illinois, March 16-May 31, 2020. The authors have generally addressed my comments, and the manuscript has improved. I am providing some additional feedback before submitting a publication recommendation.

• I appreciate the inclusion of social determinants of health. However, the introduction requires additional context regarding how the social determinants of health may contribute to health disparities.

• The third paragraph cites one study indicating that “differences in healthcare access and exposure risk” may contribute to Black and Latinx COVID-19 disparities, but there’s no explanation of why Black and Latinx persons experience these resource inequities. The social determinants of health, on which there’s a wealth of literature (see the cited Braveman manuscript), details these challenges. The current manuscript would benefit from a more careful application of the social determinants of health.

• Line 85 – it’s unclear what ZCTA-level associations the authors are interested in. Poverty, household size?

• Line 96 – please include the specific decedent demographics.

• Variables used as indicators for the social determinants need to be explicitly detailed in the methods.

• Lines 108 – 110 – please explain which variables are social determinants.

• A rational for not including income in the models would be helpful. That is, average household size, poverty, income, etc. were obtained from 2018 ACS, yet income was not explored.

• The discussion section should be clearly labeled.

• Associations between several variables and COVID outcomes were explained in the discussion without making explicit connection to social determinants of health. I would like to recommend amending this section to reconnect with social determinants.

• As discussed, with poverty as the only indicator of SES, it should be accordingly be labeled poverty, not SES. For example, the authors argue that race is a stronger indicator of COVID-19 mortality than SES. Yet, according to their analyses, race is a stronger indicator than poverty.

• Finally, there are still several typos.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

PLoS One. 2022 May 16;17(5):e0268317. doi: 10.1371/journal.pone.0268317.r004

Author response to Decision Letter 1

12 Oct 2021

We have added new data and addressed all issues raised by the reviewers

Attachment

Submitted filename: KSOYEMI Comments to the Author 10-05-2021.docx

Click here for additional data file.^{(23.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0268317.r005

Decision Letter 2

Gaetano Santulli

4 Feb 2022

PONE-D-20-25294R2Health Disparities and COVID-19: A Retrospective Study Examining Individual and Community Factors Causing Disproportionate COVID-19 Outcomes in Cook County, Illinois.PLOS ONE

Dear Dr. Soyemi,

Please submit your revised manuscript by Mar 21 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Gaetano Santulli, MD

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #3: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: No

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: General comments:

The authors replied to the reviewer comments and rewrote large parts of the manuscript.

Firstly, I want to mention the parts that I really liked. The data were updated and now span nearly 1,5 years of the pandemic. Thereby, greater precision could be achieved. The visualization of a newly calculated interaction effect made the results intuitively visible, and the conclusion is beautifully written and I very much agree with their view the way society must be improved to create better health outcomes.

However, the authors introduced a lot of new mistakes. Introductory statements about associations without reference, wrongly calculated numbers, the introduction of an interaction effect into the analysis and risk differences/relative risks without mentioning it in the methods section, and a lot of typos are the most obvious.

Therefore, I recommend to the editor to give the authors a last chance to get their manuscript in a form that is publishable. The consultation of a biostatistician/epidemiologist could be essential to get their numbers right. I need to say that I find it very uncommon that a third round of review contains so many mistakes. In future work, I wish the authors would employ a greater scrutiny to their work.

Specific comments:

Introduction:

1a) “There is a causal link between social factors, health indicators, and measures of individuals’ socioeconomic resources, such as income, educational attainment, and social position. This association simulates a stepwise gradient pattern, with health improving incrementally as social position rises. This relationship is dosedependent and is predominant in Non-Hispanic Black and Non-Hispanic White groups compared with Latinx populations.”

Comment: A reference for these sentences is necessary.

1b) “However, as is true with many diseases, COVID-19 outcomes are strongly associated with race.”

Comment: A reference is necessary.

1c) “A systematic review by Mackay et al.”

Comment: After such a sentence the reference should appear. Additionally, this seems to be reference 9 (Mackey et al.).

1d) “Similarly, same study showed that Latin X…”

Comment: English should be used properly.

Methods:

2a) per capital

Comment: per capita

Results:

3a) “Mean and standard deviation age at death was lower for Non-Whites compared with

Whites 70 (15) vs 78 (13); p < 0.01.“

Comment: Only the mean was lower.

3b) “risk difference of 0.03 [(95% CI: 0.15, 0.53); P = <0.01)]”

Comment: Firstly, a point estimate has to be part of the confidence interval. Secondly, you don´t write in the methods section that you calculated risk differences.

3c) “0.11 [(95% CI: -0.07, 0.30, 0.17); P = <0.20)] for hypertension“

Comment: There are three numbers in the confidence interval. Additionally, you report 60% of the non-white and 59% of the white decedents having a hypertension in Table 2. Thus, the risk difference is 0.01. There are more of these mistakes in Table 2.

3d) “0.04 [(95% CI: 0.03, 0.58); P = <0.01) for obesity.”

Comment: This confidence interval seems to be incorrect. Confidence intervals for risk differences are usually symmetric around the point estimate.

3e) “Direct comparison of COVID-19 associated morbidities for Non-White compared with White decedents with hypertension showed little difference [Relative Risk (RR) 1.01, 95% CI

(0.98, 1.05)].”

Comment: If the confidence interval includes 1 they showed no difference.

3f) “In the final model after adjusting for factors described above, residents of Non-White ZCTAs were 1.77 times as likely to die from COVID-19 [IRR 1.77, 95% CI (1.17, 2.66)]. Residents in households with a high percentage of Spanish speakers had increased risk of testing positive [IRR 1.69, 95% CI (1.03, 2.75)]. Living in poverty alone was not associated with an increased number of deaths, but the interaction of living in poverty and in a Non-White ZCTA increased the risk 2 to 3 times [IRR 2.99, 95% CI (0.71, 12.57)].”

Comment: In the previous version you didn´t include interactions. Although, the interaction is not significant, it seems relevant. But at least, tell the reader in the statistical analysis section that you investigated this interaction.

Discussion:

4a) Comment: I don´t know about the exact guidelines of Plos One, but I find it uncommon to present new results (Figure 2) in the discussion section.

4b) Comment: I think you cannot say COVID-19 infection. It should be COVID-19 or SARS-CoV-2-infection.

4c) “As we compared comorbidities between ethnic/racial groups, we found that minority COVID-19 decedents had statistically significant chances of having comorbidities at the time of death.”

Comment: Significantly higher?

Reviewer #3: WELL WRITTEN ARTICLE,

I ALSO VERY APPRECIATED THE CONCLUSION AND REFLECTION OF THE STRUCTURAL INEQUALITY WITH A CONSEQUENT DECREASE OF OPPORTUNITIES.

The following pertinent reports should be mentioned/discussed:

PMID: 32374952

PMID: 32293639

PMID: 32305087

PMID: 35112280

PMID: 35020749

PMID: 34997001

PMID: 34836206

PMID: 34762110

PMID: 33519709

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: Yes: CAROLINA BOLOGNA

PLoS One. 2022 May 16;17(5):e0268317. doi: 10.1371/journal.pone.0268317.r006

Author response to Decision Letter 2

10 Apr 2022

Dear Editors and Reviewers,

Thank you for your time and consideration in reviewing our manuscript. We appreciate your

feedback. All comments have been addressed. Please see below for our responses.

Comments:

Introduction

Abstract:

1a) “There is a causal link between social factors, health indicators, and measures of individuals’ socioeconomic resources, such as income, educational attainment, and social position. This association simulates a stepwise gradient pattern, with health improving incrementally as social position rises. This relationship is dose dependent and is predominant in Non-Hispanic Black and Non-Hispanic White groups compared with Latinx populations.”

Comment: A reference for these sentences is necessary.

Response: added citation [9] after that sentence

1b) “However, as is true with many diseases, COVID-19 outcomes are strongly associated with race.”

Comment: A reference is necessary.

Response: added citations 13-17

1c) “A systematic review by Mackay et al.”

Comment: After such a sentence the reference should appear. Additionally, this seems to be reference 9 (Mackey et al.).

Response: added citation [18] after that sentence

1d) “Similarly, same study showed that Latin X…”

Comment: English should be used properly.

Response: added the word “the”

Now reads :Similarly, the same study showed that Latin X

Methods:

2a) per capital

Comment: per capita

Response: removed the l from capital to make it per capita

Results:

3a) “Mean and standard deviation age at death was lower for Non-Whites compared with

Whites 70 (15) vs 78 (13); p < 0.01.“

Comment: Only the mean was lower.

Response: removed “ and standard deviation” from this sentence

3b) “risk difference of 0.03 [(95% CI: 0.15, 0.53); P = <0.01)]”

Comment: Firstly, a point estimate has to be part of the confidence interval. Secondly, you don´t write in the methods section that you calculated risk differences.

“risk difference of 0.03 [(95% CI: 0.15, 0.53); P = <0.01)]” Firstly, a point estimate has to be part of the confidence interval.

Response: Corrected 0.03 (0.02,0.05)

Secondly, you don´t write in the methods section that you calculated risk differences.

Response: It was in the methods and was written as “We also reported the Relative Risk (RR) and risk differences and their corresponding 95% confidence intervals”.

3c) “0.11 [(95% CI: -0.07, 0.30, 0.17); P = <0.20)] for hypertension“

Comment: There are three numbers in the confidence interval.

Response: corrected to 0.11 [(95% CI: -0.07, 0.30); P = <0.20)] for hypertension

Additionally, you report 60% of the non-white and 59% of the white decedents having a hypertension in Table 2. Thus, the risk difference is 0.01. There are more of these mistakes in Table 2.

Corrected now reads 60% of the non-white and 59% of the white decedents had hypertension in Table 2. Thus, the risk difference is 0.01.

3d) “0.04 [(95% CI: 0.03, 0.58); P = <0.01) for obesity.”

Comment: This confidence interval seems to be incorrect. Confidence intervals for risk differences are usually symmetric around the point estimate.

Response: Corrected now reads 0.03 (0.02 ,0.05)

3e) “Direct comparison of COVID-19 associated morbidities for Non-White compared with White decedents with hypertension showed little difference [Relative Risk (RR) 1.01, 95% CI

(0.98, 1.05)].”

Comment: If the confidence interval includes 1 they showed no difference.

Response: changed “little” to “no”

Response: We added to the methods the following: For ZCTA analysis, we created one model for each outcome variable (mortality, number of positive tests and total number tested per ZCTA). For the mortality (deaths) per ZCTA, we used four models with sequential addition of economic and housing population covariates not included in the SDI. after each round of modelling To understand the relationships between the variables we added the interaction term of Non-White (Minority Zip) ZCTA and if greater than 20% of households that lived in poverty.

Discussion:

4a) Comment: I don´t know about the exact guidelines of Plos One, but I find it uncommon to present new results (Figure 2) in the discussion section.

Response: – moved the table out of discussion section

4b) Comment: I think you cannot say COVID-19 infection. It should be COVID-19 or SARS-CoV-2-infection.

Response: removed the word infection

4c) “As we compared comorbidities between ethnic/racial groups, we found that minority COVID-19 decedents had statistically significant chances of having comorbidities at the time of death.”

Comment: Significantly higher?

Response: changed to “ higher likelihood of

Reviewer #3: WELL WRITTEN ARTICLE,

I ALSO VERY APPRECIATED THE CONCLUSION AND REFLECTION OF THE STRUCTURAL INEQUALITY WITH A CONSEQUENT DECREASE OF OPPORTUNITIES.

The following pertinent reports should be mentioned/discussed:

PMID: 32374952 Racial Health Disparities and Covid-19 - Caution and Context N Engl J Med 2020 Jul 16;383(3):201-203. doi: 10.1056/NEJMp2012910. Epub 2020 May 6.

PMID: 32293639 COVID-19 and African Americans JAMA 2020 May 19;323(19):1891-1892. doi: 10.1001/jama.2020.6548

PMID: 32305087 COVID-19 exacerbating inequalities in the US Lancet 2020 Apr 18;395(10232):1243-1244. doi: 10.1016/S0140-6736(20)30893-X.

PMID: 35112280 Utilization Gaps During the COVID-19 Pandemic: Racial and Ethnic Disparities in Telemedicine Uptake in Federally Qualified Health Center Clinic J Gen Intern Med 2022 Feb 2;1-7. doi: 10.1007/s11606-021-07304-4. Online ahead of print.

PMID: 35020749 Increased mask adherence after important politician infected with COVID-19 PLoS One 2022 Jan 12;17(1):e0261398. doi: 10.1371/journal.pone.0261398. eCollection 2022.

PMID: 34997001 Geographic and temporal variation in racial and ethnic disparities in SARS-CoV-2 positivity between February 2020 and August 2021 in the United States Sci Rep 2022 Jan 7;12(1):273. doi: 10.1038/s41598-021-03967-5.

PMID: 34836206 l-Arginine and COVID-19: An Update Nutrients 2021 Nov 5;13(11):3951. doi: 10.3390/nu13113951.

PMID: 34762110 Disparities in COVID-19 Outcomes by Race, Ethnicity, and Socioeconomic Status: A Systematic-Review and Meta-analysis JAMA Netw Open. 2021 Nov 1;4(11):e2134147. doi: 10.1001/jamanetworkopen.2021.34147.

PMID: 33519709 Metformin Use Is Associated With Reduced Mortality in a Diverse Population With COVID-19 and DiabetesFront Endocrinol (Lausanne) . 2021 Jan 13;11:600439. doi: 10.3389/fendo.2020.600439. eCollection 2020

Response: We replaced some of the previous references with the suggested references

Attachment

Submitted filename: KSOYEMI Comments to the Author 04-10-2022.docx

Click here for additional data file.^{(21.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0268317.r007

Decision Letter 3

Gaetano Santulli

28 Apr 2022

Health Disparities and COVID-19: A Retrospective Study Examining Individual and Community Factors Causing Disproportionate COVID-19 Outcomes in Cook County, Illinois.

PONE-D-20-25294R3

Dear Dr. Soyemi,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Gaetano Santulli, MD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors addressed the comments from the previous revision. I think the paper is now in a form that is publishable.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

PLoS One. doi: 10.1371/journal.pone.0268317.r008

Acceptance letter

Gaetano Santulli

6 May 2022

PONE-D-20-25294R3

Health disparities and COVID-19: A retrospective study examining individual and community factors causing disproportionate COVID-19 outcomes in Cook County, Illinois

Dear Dr. Soyemi:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Professor Gaetano Santulli

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Attachment

Submitted filename: KSOYEMI Comments to the Author 10-05-2021.docx

Click here for additional data file.^{(23.2KB, docx)}

Attachment

Submitted filename: KSOYEMI Comments to the Author 04-10-2022.docx

Click here for additional data file.^{(21.4KB, docx)}

Data Availability Statement

[pone.0268317.ref001] 1.Our World in Data. Coronavirus Pandemic (COVID-19)- the data 2020. Available from: https://ourworldindata.org/coronavirus-data

[pone.0268317.ref002] 2.Yancy CW. COVID-19 and African Americans. JAMA. 2020;323(19):1891–2. doi: 10.1001/jama.2020.6548 . [DOI] [PubMed] [Google Scholar]

[pone.0268317.ref003] 3.Dorn AV, Cooney RE, Sabin ML. COVID-19 exacerbating inequalities in the US. Lancet. 2020;395(10232):1243–4. doi: 10.1016/S0140-6736(20)30893-X ; PubMed Central PMCID: PMC7162639. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref004] 4.Ferguson JM, Justice AC, Osborne TF, Magid HSA, Purnell AL, Rentsch CT. Geographic and temporal variation in racial and ethnic disparities in SARS-CoV-2 positivity between February 2020 and August 2021 in the United States. Scientific reports. 2022;12(1):273. Epub 20220107. doi: 10.1038/s41598-021-03967-5 ; PubMed Central PMCID: PMC8741774. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref005] 5.Magesh S, John D, Li WT, Li Y, Mattingly-App A, Jain S, et al. Disparities in COVID-19 Outcomes by Race, Ethnicity, and Socioeconomic Status: A Systematic-Review and Meta-analysis. JAMA Netw Open. 2021;4(11):e2134147. Epub 20211101. doi: 10.1001/jamanetworkopen.2021.34147 ; PubMed Central PMCID: PMC8586903. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref006] 6.Poteat T, Millett GA, Nelson LE, Beyrer C. Understanding COVID-19 risks and vulnerabilities among black communities in America: the lethal force of syndemics. Ann Epidemiol. 2020;47:1–3. Epub 20200514. doi: 10.1016/j.annepidem.2020.05.004 ; PubMed Central PMCID: PMC7224650. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref007] 7.Braveman P, Egerter S, Williams DR. The social determinants of health: coming of age. Annu Rev Public Health. 2011;32:381–98. doi: 10.1146/annurev-publhealth-031210-101218 . [DOI] [PubMed] [Google Scholar]

[pone.0268317.ref008] 8.McGinnis JM FW. Actual causes of death in the United States JAMA. 1993;270:2207–12. [PubMed] [Google Scholar]

[pone.0268317.ref009] 9.Braveman PA, Cubbin C, Egerter S, Williams DR, Pamuk E. Socioeconomic disparities in health in the United States: what the patterns tell us. American journal of public health. 2010;100 Suppl 1:S186–96. Epub 20100210. doi: 10.2105/AJPH.2009.166082 ; PubMed Central PMCID: PMC2837459. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref010] 10.Braveman P, Gottlieb L. The social determinants of health: it’s time to consider the causes of the causes. Public Health Rep. 2014;129 Suppl 2(Suppl 2):19–31. doi: 10.1177/00333549141291S206 ; PubMed Central PMCID: PMC3863696. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref011] 11.McEwen BS. Protective and damaging effects of stress mediators. The New England journal of medicine. 1998;338(3):171–9. doi: 10.1056/NEJM199801153380307 . [DOI] [PubMed] [Google Scholar]

[pone.0268317.ref012] 12.Wolffe AP, Matzke MA. Epigenetics: regulation through repression. Science. 1999;286(5439):481–6. doi: 10.1126/science.286.5439.481 . [DOI] [PubMed] [Google Scholar]

[pone.0268317.ref013] 13.Hacker KA, Briss PA, Richardson L, Wright J, Petersen R. COVID-19 and Chronic Disease: The Impact Now and in the Future. Prev Chronic Dis. 2021;18:E62. Epub 20210617. doi: 10.5888/pcd18.210086 ; PubMed Central PMCID: PMC8220960. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref014] 14.Millett GA, Jones AT, Benkeser D, Baral S, Mercer L, Beyrer C, et al. Assessing differential impacts of COVID-19 on black communities. Ann Epidemiol. 2020;47:37–44. Epub 20200514. doi: 10.1016/j.annepidem.2020.05.003 ; PubMed Central PMCID: PMC7224670. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref015] 15.Muñoz-Price LS, Nattinger AB, Rivera F, Hanson R, Gmehlin CG, Perez A, et al. Racial Disparities in Incidence and Outcomes Among Patients With COVID-19. JAMA Netw Open. 2020;3(9):e2021892. Epub 20200901. doi: 10.1001/jamanetworkopen.2020.21892 ; PubMed Central PMCID: PMC7519420. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref016] 16.Hua MJ, Feinglass J. Variations in COVID-19 Hospital Mortality by Patient Race/Ethnicity and Hospital Type in Illinois. J Racial Ethn Health Disparities. 2022:1–9. Epub 20220307. doi: 10.1007/s40615-021-01219-w ; PubMed Central PMCID: PMC8900642. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref017] 17.Gaglioti AH, Li C, Douglas MD, Baltrus PT, Blount MA, Zahidi R, et al. Population-Level Disparities in COVID-19: Measuring the Independent Association of the Proportion of Black Population on COVID-19 Cases and Deaths in US Counties. Journal of public health management and practice: JPHMP. 2021;27(3):268–77. doi: 10.1097/PHH.0000000000001354 . [DOI] [PubMed] [Google Scholar]

[pone.0268317.ref018] 18.Mackey K, Ayers CK, Kondo KK, Saha S, Advani SM, Young S, et al. Racial and Ethnic Disparities in COVID-19-Related Infections, Hospitalizations, and Deaths: A Systematic Review. Annals of internal medicine. 2021;174(3):362–73. Epub 20201201. doi: 10.7326/M20-6306 ; PubMed Central PMCID: PMC7772883. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref019] 19.Chowkwanyun M, Reed AL Jr. Racial Health Disparities and Covid-19—Caution and Context. The New England journal of medicine. 2020;383(3):201–3. Epub 20200506. doi: 10.1056/NEJMp2012910 . [DOI] [PubMed] [Google Scholar]

[pone.0268317.ref020] 20.Cook County Medical Examiner. Cook County Medical Examiner COVID-19 Related Deaths Chicago2020. Available from: https://maps.cookcountyil.gov/medexamcovid19/

[pone.0268317.ref021] 21.Butler DC, Petterson S, Phillips RL, Bazemore AW. Measures of social deprivation that predict health care access and need within a rational area of primary care service delivery. Health services research. 2013;48(2 Pt 1):539–59. Epub 20120720. doi: 10.1111/j.1475-6773.2012.01449.x ; PubMed Central PMCID: PMC3626349. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref022] 22.Phillips RL, Liaw W, Crampton P, Exeter DJ, Bazemore A, Vickery KD, et al. How Other Countries Use Deprivation Indices-And Why The United States Desperately Needs One. Health affairs (Project Hope). 2016;35(11):1991–8. doi: 10.1377/hlthaff.2016.0709 . [DOI] [PubMed] [Google Scholar]

[pone.0268317.ref023] 23.Kitchen C, Hatef E, Chang HY, Weiner JP, Kharrazi H. Assessing the association between area deprivation index on COVID-19 prevalence: a contrast between rural and urban U.S. jurisdictions. AIMS Public Health. 2021;8(3):519–30. Epub 20210722. doi: 10.3934/publichealth.2021042 ; PubMed Central PMCID: PMC8334638. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref024] 24.Lewis NM, Friedrichs M, Wagstaff S, Sage K, LaCross N, Bui D, et al. Disparities in COVID-19 Incidence, Hospitalizations, and Testing, by Area-Level Deprivation—Utah, March 3-July 9, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(38):1369–73. Epub 20200925. doi: 10.15585/mmwr.mm6938a4 ; PubMed Central PMCID: PMC7727491. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref025] 25.Emeruwa UN, Ona S, Shaman JL, Turitz A, Wright JD, Gyamfi-Bannerman C, et al. Associations Between Built Environment, Neighborhood Socioeconomic Status, and SARS-CoV-2 Infection Among Pregnant Women in New York City. Jama. 2020;324(4):390–2. doi: 10.1001/jama.2020.11370 ; PubMed Central PMCID: PMC7303894. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref026] 26.Del Brutto OH, Mera RM, Recalde BY, Costa AF. Social Determinants of Health and Risk of SARS-CoV-2 Infection in Community-Dwelling Older Adults Living in a Rural Latin American Setting. Journal of community health. 2021;46(2):292–7. doi: 10.1007/s10900-020-00887-9 ; PubMed Central PMCID: PMC7363014. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref027] 27.Webb Hooper M, Nápoles AM, Pérez-Stable EJ. COVID-19 and Racial/Ethnic Disparities. Jama. 2020;323(24):2466–7. doi: 10.1001/jama.2020.8598 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref028] 28.Dyer O. Covid-19: Black people and other minorities are hardest hit in US. Bmj. 2020;369:m1483. Epub 20200414. doi: 10.1136/bmj.m1483 . [DOI] [PubMed] [Google Scholar]

[pone.0268317.ref029] 29.Kawachi I, Adler NE, Dow WH. Money, schooling, and health: Mechanisms and causal evidence. Annals of the New York Academy of Sciences. 2010;1186:56–68. doi: 10.1111/j.1749-6632.2009.05340.x . [DOI] [PubMed] [Google Scholar]

[pone.0268317.ref030] 30.Khalatbari-Soltani S, Cumming RC, Delpierre C, Kelly-Irving M. Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards. J Epidemiol Community Health. 2020;74(8):620–3. Epub 20200508. doi: 10.1136/jech-2020-214297 ; PubMed Central PMCID: PMC7298202. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref031] 31.Sjödin H, Wilder-Smith A, Osman S, Farooq Z, Rocklöv J. Only strict quarantine measures can curb the coronavirus disease (COVID-19) outbreak in Italy, 2020. Euro surveillance: bulletin europeen sur les maladies transmissibles = European communicable disease bulletin. 2020;25(13). doi: 10.2807/1560-7917.Es.2020.25.13.2000280 ; PubMed Central PMCID: PMC7140595. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref032] 32.Rocklöv J, Sjödin H. High population densities catalyse the spread of COVID-19. J Travel Med. 2020;27(3). doi: 10.1093/jtm/taaa038 ; PubMed Central PMCID: PMC7184409. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref033] 33.Geographic Differences in COVID-19 Cases, Deaths, and Incidence—United States, February 12-April 7, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(15):465–71. Epub 20200417. doi: 10.15585/mmwr.mm6915e4 ; PubMed Central PMCID: PMC7755058. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0268317.ref034] 34.B C. Cook County Jail releases could be spreading COVID-19 in community,. Chicago Sun-Times. 2020 4 Jun 2020.

PERMALINK

Health disparities and COVID-19: A retrospective study examining individual and community factors causing disproportionate COVID-19 outcomes in Cook County, Illinois

Larissa H Unruh

Sadhana Dharmapuri

Yinglin Xia

Kenneth Soyemi

Roles

Abstract

Introduction

Materials and methods

IRB approval

Individual level analysis

ZCTA analysis

Statistical analyses

Results

Table 1. Population and COVID-19 mortality, Cook County, March 15-May 31, 2021.

Table 2. Comparison of mortality and co-morbidities White Vs Non-White, Cook County, Illinois.

Table 3. Negative binomial regression model outputs.

Fig 1. Predicted number of deaths compared with SDI score.

Discussion

Fig 2. Percent of COVID-19 deaths vs percent of population by race.

Socioeconomic status

Increased co-morbidities

Multigenerational living

Densely populated urban areas

Overrepresentation in correctional facilities

Limitations

Conclusion

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Hajo Zeeb

Roles

Author response to Decision Letter 0

Decision Letter 1

Hajo Zeeb

Roles

Author response to Decision Letter 1

Decision Letter 2

Gaetano Santulli

Roles

Author response to Decision Letter 2

Decision Letter 3

Gaetano Santulli

Roles

Acceptance letter

Gaetano Santulli

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases