RSPP Working Paper

RSPP
Working Paper

Socio-Demographic
Disparities in COVID-19 Case
Rates and Testing: An
exploratory spatial analysis
of ZIP code data in Chicago,IL
Kevin CREDIT

Nº 2020.002 – Special Series on Covid-19

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Disclaimer: This paper should not be reported as representing
the views of the Regional Science Policy & Practice(RSPP). The views expressed are those of the authors and do not necessarily reflect those of the RSPP.

RSPP Working Paper

Socio-Demographic Disparities in COVID-19 Case
Rates and Testing: An exploratory spatial analysis of
ZIP code data in Chicago, IL
By Kevin Credit1

ABSTRACTThe recent release of ZIP code level data on the novel coronavirus (COVID-19) provides an important
opportunity to look at neighborhood-level socio-demographic and economic correlated of case andtesting rates. This exploratory spatial analysis finds that black neighborhoods and those with older
residents and higher proportions of employment in service occupations tend to have higher case rates(even when controlling for access to testing sites), while Hispanic neighborhoods have a lower than
expected level of testing for COVID-19, even when controlling for the level of observed infection activity.

KEYWORDS
COVID-19, social determinants of health, spatial access, spatial econometrics

1. Introduction and BackgroundThe novel coronavirus SARS-CoV-2 (COVID-19) was first discovered in 2019 in the Hubei province ofChina and has since spread across the world, creating a global pandemic that has resulted in over
2,770,000 confirmed cases and over 190,000 deaths (as of April 24, 2020) (Worldometer, 2020). Whilesignificant research effort has been dedicated to tracking the dynamics of the spread of the virus at thenational and county levels (JHU, 2020; Dong, Du, & Gardner, 2020; CSDS, 2020), data at smaller spatial
scales – such as ZIP code level – has only recently been made available in many jurisdictions, allowing
for neighborhood-level analyses. These data provide the first opportunity to begin to explore the spatial
associations between socio-demographic and economic characteristics and neighborhood-level virus
activity.
Neighborhood-level analyses are particularly important in the context of social determinants of health(SDOH), an emerging body of research that recognizes that the social and economic conditions in whichpeople live shape their ultimate health outcomes in a number of critically-important ways (Kolak et al.,2020; Solar & Irwin, 2010). In the US, historic and continuing patterns of racial and ethnic segregation
often overlap with environmental degradation and socioeconomic deprivation, creating a context in which
non-white communities are often systematically faced with higher rates of chronic disease, lower levels
of infrastructure investment, lower access to healthcare and healthy food options, higher crime, andlower life expectancies (Braveman et al., 2010; Williams et al., 2010; Williams, Priest, & Anderson, 2016).
As one of the most segregated regions in the US, the Chicago, IL, metropolitan area provides animportant case in which to explore the possible socio-demographic disparities in COVID-19 case rates
and testing: according to a recent analysis of life expectancy at the Census tract scale, life expectancies
in Cook County (the central county in the region containing the City of Chicago) range from 59.9 (lower

1

Kevin Credit, Assistant Instructional Professor in GIScience and Assistant Director for Urban Informatics, Center
for Spatial Data Science, University of Chicago, 1155 E 60th St, Room 211A, Chicago, IL, USA (e-mail:kcredit@uchicago.edu).

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RSPP Working Paper

than countries such as Angola, Mali, and Zimbabwe) to 90 (which, as a country, would be the highest in
the world - higher than Monaco at 89.4) (Arias et al., 2018; NCHS, 2015; CIA Factbook, 2017).
Following this pattern, hospitalizations and deaths from COVID-19 at the individual case level have —to this point — been strongly associated with minority groups and those with underlying conditions.
According to the Chicago Department of Public Health (CDPH), as of April 23, 91.1% of Chicago
residents who have died from COVID-19 had evidence of at least one chronic underlying healthcondition, and 75.9% of deaths have been from Latinx or black residents, compared to only 17.7% ofdeaths from white residents (2020).
Given these initial indications of disparate outcomes – and the fact that those in minority-majority
neighborhoods may be more likely to work in service occupations for essential businesses that are notable to be shut down during Illinois’ ongoing state-wide stay-at-home order – this paper explores thespatial relationships between black and Hispanic neighborhoods, case rates, testing rates, and otherrelevant age-related, transportation, and economic features in order to better understand the current
impact of COVID-19 in the Chicago region and its burden on particular neighborhoods. Indeed, theexploratory results suggest that black neighborhoods and those with older residents and higher
proportions of employment in service occupations tend to have higher case rates (even when controllingfor access to testing sites), while Hispanic neighborhoods have a lower than expected level of testing for
COVID-19, even when controlling for the level of observed infection activity.

2. Data Preparation
The data for this paper come from a variety of sources: the number of COVID-19 tests and confirmed
cases by ZIP code as of April 16th, 2020, come from the website of the Illinois Department of Public
Health (IDPH, 2020). This information was joined to Census data at the ZIP code Tabulation Area (ZCTA)
from the 2014-2018 American Community Survey (ACS) for ZIP codes within the Illinois portion of the
Chicago Core-Based Statistical Area (CBSA), i.e., the greater Chicago region. A range of Census
variables were originally considered, including: percent population aged 0 – 44, 44 – 64, and 65+; percent
white (non-Hispanic), black (non-Hispanic), and Hispanic population; percent of workers commuting byvarious modes, including automobile, transit, walking/biking, and telecommuting (working from home);
and the percent of workers in various occupations, including “office” and technical occupations,healthcare service, protective service (such as fire fighters and police officers), food preparation,cleaning service, and transportation. Data on the location of COVID-19 testing sites (as of March 16th) in
the Chicago region were also obtained from an open-source workbook compiling the location of testingsites in the US, as there was at the time no official government documentation of testing sites available(COVID-19 Drive Thru Location Tracking, 2020).
Given the collinearity of several of these sets of predictor variables, Principle Components Analysis(PCA) was used in three cases to reduce the number of data dimensions and combine variation fromseveral correlated variables into a smaller number of uncorrelated components. The loadings of the three
PCA analyses using the Singular Value Decomposition (SVD) method in GeoDa v.1.14 with a Z-scoretransformation are shown in Table 1.
Components explaining a large amount of variation that matched conceptually with features of interest
were retained; specifically, of the transportation variables, PC1 (PC_TPT) was positively related to transit
and pedestrian commuting and teleworking, thus capturing the higher income urban professional
population who use transit but are also able to work from home and afford to live close enough to work
to walk. PC2 (PC_TRANS), on the other hand, related positively only to transit commuting, capturing a
different type of (likely) lower income transit-dependent population. For the age variables, PC1(PC_YOUNG) generally captures younger populations, while PC2 (PC_OLD) captures older populations;

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RSPP Working Papertransforming these variables using the PCA ensures that they are uncorrelated with one another andthus able to be used in a spatial econometric model. Finally, PC1 (PC_SRV) of the occupation variables
was retained, as it captured the most important feature of the input occupational data: a negative
association with technical occupations (most likely to be able to work remotely during the pandemic) andpositive associations with each of the other specific service occupations, chosen because they are all
related to essential services that cannot be performed at home during a pandemic.
TABLE 1. PCA Variable Loadings for Principle Components (components retained in bold)Transportation
Variables

Age Variables

Occupation
Variables

Original Variables% Auto% Transit% Pedestrian andBicycling% Working from Home
% 0-44% 45-64% 65+% Office and Technical% Healthcare Service% Protective Service% Food Preparation% Cleaning Service% Transportation

PC1-0.6370.457

PC2-0.124
0.646

PC3
0.011
-0.362

PC4
0.761
0.493

0.512

-0.241

0.733

0.379

0.351
0.656
-0.510-0.557-0.527
0.383
0.220
0.414
0.448
0.394

-0.714-0.049-0.765
0.642
0.044
0.398
0.537
-0.498-0.4130.365-0.576
0.753
0.393
0.527
-0.050-0.368
0.792
0.084
0.179
-0.442

0.187-0.119-0.7390.020-0.121
0.022
0.651

PC5

PC6

-0.1600.046-0.127-0.7350.631-0.132

0.823
0.104
0.139
0.136
0.445
0.274

Confirmed cases and tests were divided by ZIP code population in order to obtain a normalized rate per
population; due to their right-skew, the natural log of both rate variables was calculated for final use (thisalso means that ZIP codes with 0 cases or tests were removed in the final analysis, to avoid problems
with estimating models with missing values). Percent white, black, and Hispanic are by ZIP code used
to classify each ZIP code as either majority-white, majority-Hispanic, or majority-black, based on a levelof >50% population of the given racial/ethnic group in that ZIP code. Given the strong historical patternof segregation in Chicago, these are the key neighborhood indicators of interest, and themselves capturemuch correlated information on educational attainment, economic opportunity, environmental injustice,
healthcare access, etc.
Spatial access to testing locations was calculated using the “access” package in python using a simple“gravity”-based method. This approach calculates the weighted Euclidean distance from each ZCTA
centroid to each testing location based on a distance decay parameter of (in this case) -.5 and then sums
the total weighted distances from each origin to all testing locations within a given maximum thresholdof (in this case) 48,000 meters (or roughly 30 miles). The resulting summed weights were then scaled
using a max-min standardization so that the final testing access scores – representing spatial accessfrom a given ZIP code to testing sites within driving distance – ranged from 0 – 100.

3. Methods and Results
This paper takes an exploratory approach to mapping, analyzing, and modeling these data. Maps
showing the general spatial patterns of the variables of interest are used to better understand spatial
relationships. A correlation analysis also shows the quantitative association between variables of interest
without taking into account controlling factors. Finally, two sets of spatial econometric models are
estimated using the approach and diagnostics laid out in Anselin and Rey (2014) – the first examine therelationship between neighborhood type and COVID-19 testing rates while controlling for case rates tobetter understand underlying disparities in testing for COVID-19. The second examine covariates of case

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RSPP Working Paper

rates while controlling for spatial access to testing sites in order to better understand disparities in theneighborhood burden of COVID-19 in the region.
Figure 1 shows the general spatial patterns for 6 variables of interest; Pearson correlations between all
variables of interest in the dataset are shown in Table 2. Several relationships of interest stand out: first,
case rate and testing rate are highly-related (.88 correlation), but not perfectly correlated. Both are
positively related to the black majority neighborhood dummy and negatively correlated to whiteneighborhoods, but, interestingly, the case rate is more highly-correlated with Hispanic neighborhoods
than testing rate, suggesting that there may be differential testing rates for Hispanic neighborhoods.
There is also some overlap between young neighborhoods and those with higher rates of workers inservice occupations with Hispanic neighborhoods, while black neighborhoods show higher correlations
with service occupations and the proportion of commuters who are transit-dependent. Whiteneighborhoods, on the other hand, are relatively strongly negatively correlated with every variable ofinterest other than the “high income” urban professional/transit commuting principle component(PC_TPT). The measure of spatial access to testing sites also displays relatively high correlations withcase rates and testing rates, but no large relationship with any of the neighborhood types.
TABLE 2. Correlation Table for Variables of Interest
Log(Case
Rate)
Log(Test Rate)

Log(Test
Rate)

Black

White

Hispanic

PC_OLDPC_
YOUNG

PC_SRV

PC_TPT

PC_
TRANS

0.88

Black
Neighborhood
White
Neighborhood
Hispanic
Neighborhood

0.50

0.47

-0.57

-0.40

-0.52

0.15

0.01

-0.11

-0.41

PC_OLD

0.32

0.34

0.12

-0.13

0.11

PC_YOUNG

0.11

-0.01

0.02

-0.34

0.34

0.17

PC_SRV

0.44

0.26

0.44

-0.64

0.34

0.12

0.29

PC_TPT

0.24

0.31

0.14

0.00

-0.07

0.32

0.34

-0.28

PC_TRANS

0.51

0.49

0.38

-0.44

0.19

0.21

0.29

0.43

0.28

Spatial Accessto Test Sites

0.40

0.42

0.05

-0.15

0.15

0.18

-0.01

0.09

0.14

0.33

In order to better understand whether these relationships are statistically-significant in the presence ofcontrol variables – and controls for spatial dependence – two sets of spatial lag models are estimatedusing the Spatial Two-Stage Least Squares (S2SLS) approach laid out in Anselin and Rey (2014) in
GeoDaSpace v.1.2 using White Standard Errors. Spatial lags are appropriate for both sets both from atheoretical standpoint – the vírus is directly transmitted from person to person, and thus we would expect
spatial spillovers to occur directly between neighborhoods via this mechanism – and based on standard
diagostic tests, namely significant robust Lagrange Multipliers (lag) when applied to Ordinary Least
Squares (OLS) estimates of both models with associated spatial weights matrices. The generalframework of the S2SLS approach is to treat the spatial lag parameter (𝐖𝐲) from the standard spatial
lag model as an endogenous variable that is instrumented for by the first-order spatially-lagged
explanatory variables. The standard spatial lag specification is shown in Equation (1).
𝑦 = 𝜌𝐖𝐲 + 𝐗𝛽 + 𝑢,(1)

where 𝑦 is the dependent variable of interest, 𝐖 is a spatial weights matrix (a first-order queen contiguity
matrix in this case), 𝐗 is a vector of explanatory variables, and 𝑢 is the error term. To correct for theendogeneity of the spatially-lagged variable, a matrix of instrumental variables 𝐐 is specified in Equation(2).

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RSPP Working Paper

𝐐 = [𝐗, 𝐖𝐗].

(2)

The first set of models addresses the question of whether or not particular types of neighborhoods
demonstrate lower-than-expected testing rates, which is important to understand, given the early stageof the pandemic and the current lack of testing availability. If tests were being conducted without disparity,
we would expect the high correlation between testing rate and case rate to apply equally across all threeneighborhood types. To examine whether this is the case, three spatial lag models were run with
Log(Test Rate) as the dependent variable, Log(Case Rate) as an independent variable – to importantly
control for the fact that observations of large or small case rates may in fact be due to the testing
occurring within each neighborhood – and each individual neighborhood type.
As the results in Table 3 show, both white and black neighborhoods have a significant positive
relationship with testing rate, even after controlling for case rates. However, the results show that
Hispanic neighborhoods have a significant negative relationship with testing rate even after controlling
for the fact that these neighborhoods may have lower case rates than others. This suggests that there
may be some systematic disparity in access to testing for Hispanic neighborhoods, or perhaps a lack ofpropensity to be tested for residents with uncertain legal immigration status. The spatial lag of thedependent variable is also significant in each model, suggesting that observations of testing rates are
spatially-dependent.
TABLE 3. Spatial Two Stage Least Squares Model Results for COVID-19 Testing Rate
Dependent Variable =Log(Test Rate)

White Neighborhoods

Black Neighborhoods

Variables
Constant
Log(Case Rate)
Neighborhood Dummy
W_Log(Test Rate)

Coef.
0.003
0.508***
0.106***.337***

Coef.
-0.2620.448***
0.065*
0.344***

Number of Observations
Degrees of Freedom
Pseudo R-squared

285
281
0.838

Std. Error
0.149
0.060
0.033
0.092

285
281
0.833

Std. Error
0.164
0.054
0.033
0.091

Hispanic
Neighborhoods
Coef.
-0.0490.474***-0.230***0.352***

Std. Error
0.140
0.051
0.041
0.089

285
281
0.847

Notes: * denotes estimates significant at p ≤ .1, ** denotes p ≤ .05, and *** denotes p ≤ .01. Instruments for W_Log(TestRate) are W_ Log(Case Rate) and W_Neighborhood Dummy.

What are the correlates of COVID-19 case rates in the Chicago region? Table 4 shows the results of thesecond set of models, which estimate the relationship between the transportation, occupation, andracial/ethnic covariates of interest with observed case rates. The progression from OLS model results tospatial lag results uncovers some interesting information about the relationship between the variables
examined here. In the initial OLS model, all variables other than the Hispanic neighborhood dummy are
significant, suggesting that black neighborhoods and those with larger proportions of older residents,
workers in service occupations, and commuters using transit (whether transit-dependent or not) are allmore likely to demonstrate higher case rates, even when controlling for spatial access to testing sites.
However, when the spatial dependence parameter is added into the second model, both transit variables(as well as the principle component denoting higher proportions of younger residents) become
insignificant, suggesting that at least some of the positive association between transit ridership andincreased COVID-19 case rates is in fact due to the spatial spillover of case rates, i.e., the proximity ofthese neighborhoods to other neighborhoods with high case rates explains more variation in observedcase rates than the propensity of transit use in these neighborhoods. Three features remain significant
predictors of COVID-19 case rates, though, even after controlling for spatial dependence and spatial
access to testing sites: black majority neighborhoods, neighborhoods with higher proportions of older
residents, and neighborhoods with higher proportions of service workers in essential occupations.
TABLE 4. OLS and Spatial Two Stage Least Squares Model Results for COVID-19 Case Rate.

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RSPP Working Paper

Dependent Variable =Log(Case Rate)
Variables
Constant

OLS Estimation
Coef.
-6.488***Std. Error
0.098

Coef.
-2.690***Black Neighborhood Dummy

0.551***

0.108

0.366***

Hispanic Neighborhood Dummy
PC_OLD
PC_SRV
PC_TPT
PC_TRANS
PC_YOUNG
Spatial Access to Testing Sites
W_Log(Case Rate)

0.171
0.133***
0.138***
0.121***
0.122**-0.078***0.008***

0.113
0.045
0.033
0.035
0.056
0.029
0.002

0.051
0.077*
0.066**
0.011
0.053-0.0150.004***
0.597***

Number of Observations
Degrees of Freedom
Adjusted/Pseudo R-squared

284
275
0.512

Spatial Lag Estimation
Std. Error
0.590
0.081
0.090
0.041
0.033
0.039
0.073
0.028
0.001
0.093

284
275
0.709

Notes: * denotes estimates significant at p ≤ .1, ** denotes p ≤ .05, and *** denotes p ≤ .01. Robust LM value for OLS
specification = 14.1 w/ .0002 probability. Instruments for W_Log(Case Rate) are W_Black Neighborhood Dummy, W_Hispanic
Neighborhood Dummy, W_PC_OLD, W_PC_SRV, W_PC_TPT, W_PC_TRANS, W_PC_YOUNG, and W_Spatial Access to
Testing Sites.

4. Discussion and Conclusion
The results of this analysis, while exploratory, provide some useful information about the current sociodemographic disparity in COVID-19 testing and (observed) infection rates in the Chicago, IL region. First,
there appears to be a disparity in COVID-19 testing accessibility in Hispanic majority neighborhoods,
even when controlling for the observed case rates in these neighborhoods. Whether this represents asystematic disparity in physical (transportation-related) access to testing locations, a propensity to avoid
testing for those with uncertain legal immigration status, or other social, cultural, or economic reasons,remains to be seen. However, this observed disparity could have consequences in the future course of
the pandemic if Hispanic neighborhoods become more heavily-affected by the virus at a later time. Thesefindings could also suggest that actual rates of infection in Hispanic neighborhoods are higher thancurrently reported, creating an important information gap in our understanding of the virus’ spread andultimate toll.
Second, three primary features appear to significantly predict observed COVID-19 case rates, evenwhen controlling for spatial spillovers in infection activity and spatial access to testing sites: black majority
neighborhoods, neighborhoods with larger proportions of older (65+) residents, and neighborhoods withhigher proportions of workers in service occupations (including healthcare service, protective service,
food preparation, cleaning service, and transportation workers who perform essential jobs that are notable to stay at home during the pandemic) all have a significant positive relationship with COVID-19
infections. From this analysis, it does not appear that high proportions of transit use are significantly
related to infection rates beyond the spatial spillover characteristics of infections. While these findings
match the existing hypotheses about COVID-19 risk, they also underscore the important spatial andracial/ethnic disparity of COVID-19’s burden, as these neighborhoods also tend to be the most lacking
in economic and political capital and access to quality healthcare.
While these data are certainly preliminary – as we have no knowledge about the ultimate course of thepandemic – if these findings continue to hold, they suggest that 1) in the short term, additional resources
need to be deployed to minority-majority neighborhoods, both to increase access to testing and toenhance social distancing and other protective public health measures, and 2) in the long term, policies
to effect significant structural change in these neighborhoods should be pursued to help majority-minority

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neighborhoods (and thus our society as a whole) become more resilient to future pandemics and otherpublic health crises.

5. Figures

Figure 1. Spatial Patterns for Variables of Interest in Chicago, IL Region

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