Question:
You are given the full text of a scientific research paper.

    Summarize the study’s **policy implications**—how the research findings can inform policy or decision-making. Your response should:
    - Interpret what the results suggest for governments, regulators, or institutions
    - Highlight potential actions, strategies, or decisions informed by the study
    - Use policy-appropriate language without speculation beyond the study’s conclusions
    - Be clear, practical, and informative

    Write a short paragraph for the “Policy Implications” section of a policy brief.

    Scientific Research Paper:
    https://doi.org/10.1177/00221465221074915
Journal of Health and Social Behavior
2022, Vol. 63(4) 472–490
© American Sociological Association 2022
DOI: 10.1177/00221465221074915
journals.sagepub.com/home/hsb
Original Article
The COVID-19 pandemic has been one of the most
disruptive world events in modern times in terms of
health and fundamentally reshaping the economy
and people’s social lives. Moreover, this impact has
not been equally shared. Some people, because of
existing social and economic resources, have been
better able to shield themselves from transmission of
the disease and the various negative consequences
reverberating from the pandemic. In particular,
Black, Latino, and Native American populations in
the United States have experienced higher rates of
infection and, when infected, have been more likely
to experience severe disease and death (Millett et al.
2020; Peek et al. 2021; Ramos and Zamudio 2020).
Other work has shown how the effects of the pan-
demic are also spaced and placed, with poor and
minority neighborhoods suffering disproportion-
ately from the disease and having limited access to
care sites and testing locations (McMinn et al. 2020;
Yang, Choi, and Sun 2021). In this analysis, we
focus on racial-ethnic disparities in access to the vac-
cine in the United States specifically as it relates to
racial-ethnic residential segregation.
On December 11, 2020, the U.S. Food and Drug
Administration issued the first emergency use
authorization for the Pfizer-BioNTech vaccine for
COVID-19 in individuals 16 years of age and older
in the United States. Eight days later, a second vac-
cine, Moderna, was approved for use in individuals
ages 18 and older. However, distribution of the vac-
cine has been a logistical hurdle necessitating
extremely low temperature cold storage and person-
nel needs, all while having to maintain COVID-19
1074915 HSBXXX10.1177/00221465221074915Journal of Health and Social BehaviorAnderson and Ray-Warren
research-article2022
1University of Houston, Houston, TX, USA
Corresponding Author:
Kathryn Freeman Anderson, Department of Sociology,
University of Houston, 3551 Cullen Blvd, PGH Building,
Room 450, Houston, TX 77204-3012, USA.
Email: kateanderson@uh.edu
Racial-Ethnic Residential
Clustering and Early COVID-19
Vaccine Allocations in Five
Urban Texas Counties
Kathryn Freeman Anderson1
 and Darra Ray-Warren1
Abstract
Previous research has indicated that racial-ethnic minority communities lack a wide variety of health-
related organizations. We examine how this relates to the early COVID-19 vaccine rollout. In a series of
spatial error and linear growth models, we analyze how racial-ethnic residential segregation is associated
with the distribution of vaccine sites and vaccine doses across ZIP codes in the five largest urban counties
in Texas. We find that Black and Latino clustered ZIP codes are less likely to have vaccine distribution sites
and that this disparity is partially explained by the lack of hospitals and physicians’ offices in these areas.
Moreover, Black clustering is also negatively related to the number of allocated vaccine doses, and again,
this is largely explained by the unequal distribution of health care resources. These results suggest that
extant disparities in service provision are key to understanding racial-ethnic inequality in an acute crisis
like the COVID-19 pandemic.
Keywords
COVID-19, health care, race-ethnicity, residential segregation, vaccines

Anderson and Ray-Warren
473
safety protocols. For example, unpunctured vials of
the vaccines must be kept between –90°C and
–60°C for the Pfizer-BioNTech vaccine and between
–50°C and –15°C for the Moderna vaccine, well
below the temperature of a standard freezer. An
obvious question that has emerged is how to do this
equitably, especially when vaccine supply is low.
Roughly following guidelines from the Centers for
Disease Control and Prevention (CDC; 2020a), in
the case of Texas, which is focus of this analysis,
Phase 1A of the rollout was reserved for frontline
health care workers, residents of long-term nursing
facilities for the elderly, and workers in those same
facilities. Phase 1B included people ages 65 and
above and people ages 16 and above with at least
one comorbidity defined by the state as being par-
ticularly vulnerable for the disease, such as cancer,
type 2 diabetes, obesity, chronic heart disease, and
so on. Although the goal was to equitably distribute
vaccine doses to reach the vulnerable, the question
remains whether this goal was met.
This is the overarching question that we tackle
with this analysis. We focus only on the Phase 1A
and 1B periods to examine who got early access
while vaccine supply and eligibility were still lim-
ited. Obviously, many considerations are at play for
how any individual can get the vaccine, and early
media reports indicate that access to reliable Internet
and transportation play a major role in people’s abil-
ity to find an appropriate appointment time and
travel to the location (Menchaca and Agnew 2021;
Prescott and Prescott 2021; Stone 2021). Here, we
focus on the structural element of this story to exam-
ine where these vaccine allocations went and
whether certain types of neighborhoods were advan-
taged or disadvantaged in the rollout. We ask: What
neighborhood characteristics are related to the dis-
tribution of COVID-19 vaccines? Specifically, are
racial-ethnic minority communities less likely to
have vaccine supply? Furthermore, how do these
patterns relate to extant inequalities in health care
provision? We posit that racial-ethnic minority areas
will be less likely to receive vaccine doses and that
this will be explained away or mediated by existing
disparities in service provision across neighbor-
hoods given the difficult infrastructure requirements
of distributing the two vaccines.
Background
Segregation and Access to Health Care
Research has long documented important health and
health care outcomes related to racial-ethnic residential
segregation for Black and Latino communities in the
United States. For instance, analysts have documented
a relationship between segregation and racial differ-
ences in mortality for several causes of death (Collins
1999; Collins and Williams 1999; Hart et al. 1998),
including infant mortality (Grady 2006; McFarland and
Smith 2011). In addition, other studies have examined
overall physical health, mental health, and functional
disability and found that minority residents of racially
segregated neighborhoods are more likely to report
experiencing overall poorer physical and mental health
and disability (Acevedo-Garcia 2000; Acevedo-Garcia
et al. 2003; Anderson and Fullerton 2014; Lee 2009).
Finally, a few studies on access to health care demon-
strate an association between racial-ethnic segregation
and diminished access to health care coverage, having
a personal physician, and health care utilization
(Anderson and Fullerton 2014; Gaskin et  al. 2009;
Rodriguez et al. 2007).
Although there is robust literature demonstrat-
ing the association between segregation and several
health and health care outcomes, less attention has
been given to understanding and testing the mecha-
nisms that could link these two. A notable exception
is Williams and Collins’s (2001) piece in which
they describe racial-ethnic minority segregation as
a “fundamental cause” of health and detail how this
form of segregation can be fundamentally linked to
health outcomes. They suggest that racial-ethnic
minority segregation, with its intimate ties to socio-
economic status and overall life chances, can be
thought of as a fundamental cause of poor health/
health care outcomes in these communities (Williams
and Collins 2001). Williams and Collins (2001)
describe several specific mechanisms by which
racial-ethnic minority segregation may limit life
opportunities that impact health/health care out-
comes. One such mechanism argues that racial-eth-
nic minority residential segregation may be related
to health outcomes because it can constrain access
to a variety of key community resources, including
daily necessities such as food, recreation, and pub-
lic services, and critical resources, such as health
care. This perspective focuses on the unequal distri-
bution of resources throughout urban areas and how
that inequity may lead to diminished health out-
comes for these residents. This is the approach uti-
lized in the study presented here.
Recently, there has been an explosion of interest
in this mechanism, and such work has shown how
different neighborhood characteristics, mainly dif-
ferentiated by race and class, relate to service pro-
viders and community organizations. The bulk of
this research has focused on food resources, known

474
Journal of Health and Social Behavior 63(4)
as the “food deserts” or “food swamps” literature
(Beaulac, Kristjansson, and Cummins 2009;
Cooksey-Stowers, Schwartz, and Brownell 2017).
This literature has demonstrated that disproportion-
ately poor and minority neighborhoods lack food
resources and have demonstrated important differ-
ences in quality and price across neighborhoods
(Beaulac et al. 2009; Cooksey-Stowers et al. 2017;
Moore and Diez Roux 2006; Walker, Keane, and
Burke 2010). Although food is the focus of much of
this literature, other work has shown a similar pat-
tern for physical fitness centers, park and green
space, retail establishments, nonprofit associations,
and social services (Allard 2009; Anderson 2017;
Gordon-Larsen et al. 2006; Marwell and Gullickson
2013; Small and McDermott 2006).
Whereas the work on food and retail has been
well studied, there are relatively few sociological
studies on the case of health care specifically, which
is most relevant to our present study on the COVID-
19 vaccine distribution. The studies that exist on
health care have generally shown a similar pattern
in that Black and Latino segregated areas are less
likely to have a wide variety of health care estab-
lishments in terms of density or distance to facilities
(Anderson 2017; Dai 2010; Dinwiddie et al. 2013;
Gaskin et al. 2012; Ko et al. 2014; Rodriguez et al.
2007). Specifically, this work has demonstrated that
racial-ethnic minority segregation and socioeco-
nomic variables are related to a lower incidence of
physicians’ offices, primary care providers, mental
health practitioners, urgent care facilities, auxiliary
health care practitioners, surgical centers, and dial-
ysis facilities (Anderson 2017; Dai 2010; Dinwiddie
et  al. 2013; Gaskin et  al. 2012; Ko et  al. 2014;
Rodriguez et al. 2007).
Some limited work has also linked the distribu-
tion of health care locations to health and health
care outcomes. For example, Dai (2010), in a study
of Detroit area neighborhoods, found that Black
residents of segregated neighborhoods had fewer
facilities that provided mammography services and
consequently had higher rates of late-stage breast
cancer diagnosis. Another study found that racial-
ethnic residential segregation was linked to a
greater likelihood of patients seeing nonpsychia-
trists for mental health needs (Dinwiddie et  al.
2013). A study of the Phoenix area found that the
lack of health care service provision in Latino-
segregated neighborhoods was linked to a lower
likelihood of seeing a personal physician for pediat-
ric care and greater use of clinics (Anderson 2020).
Chan et al. (2012) also found that although Black
and Latinos living in highly segregated areas had
adequate spatial access to facilities, they were less
likely to receive certain services, especially special-
ist care.
This work provides some initial evidence for a
disparity by racial-ethnic residential segregation in
health care service provision. In this analysis, we
aim to extend this literature to examine how this
lack of adequate health care provision relates to the
COVID-19 pandemic and the early rollout of the
vaccine. There may be myriad reasons that affect
where and how people choose to procure provider
services, with physical proximity/convenience
being only one of them that may be important to
people of limited means, who lack of access to
transportation or are elderly/homebound and may
find travel difficult. Distance from providers does
not create an impenetrable barrier to access (in this
case, vaccine access), but it can increase the “fric-
tion of distance,” which may be difficult to over-
come absent other kinds of individual or household
resources (Tobler 1970). Therefore, with this analy-
sis, we are not assuming that people can only or
will only seek vaccine access within their local
communities but, rather, that having access in close
proximity would facilitate their ability to get it,
especially for those who are more vulnerable.
Segregation and the COVID-19
Pandemic
At this stage, limited work has been conducted in a
systematic fashion on the COVID-19 pandemic
and, more specifically, the vaccine rollout. However,
the extant work published over the past year, includ-
ing media reporting, has demonstrated vast inequal-
ities by race-ethnicity throughout the pandemic.
This work has shown that racial-ethnic minority
populations have been more vulnerable to COVID-
19 disease in terms of rates of infection and mortal-
ity (Millett et al. 2020; Ramos and Zamudio 2020).
As it relates to segregation specifically, new work
has shown that areas with higher numbers of racial-
ethnic minorities were more likely to have higher
rates of infections and that this pattern was further
exacerbated where residential segregation was also
high (Yang et al. 2021). Various news reports have
also indicated that minority-segregated areas lack
vital resources to contend with the spread of the
virus, such as testing facilities and health care
resources (Garnam and Cai 2021; Godoy and Wood
2020; Martinez 2021; McMinn et al. 2020).
Moreover, what is happening now is clearly part
of a broader pattern of inequality in infectious
disease as it relates to racial-ethnic residential

Anderson and Ray-Warren
475
segregation. Previous work has shown important
disparities by racial-ethnic residential segregation
in terms of infectious disease, such as tuberculosis
and sexually transmitted diseases, among others
(Acevedo-Garcia 2000, 2001; Biello et  al. 2012;
Strully 2011). The limited literature that examines
past pandemics and social inequality has focused
primarily on race-ethnicity and socioeconomic sta-
tus (Økland and Mamelund 2019; Roberts and
Tehrani 2020; Strully 2011).
From this limited early work on the topic at hand
and the more extensive literature on broader inequal-
ities in health care by segregation, we expect these
inequalities by segregation to carry over to the early
COVID-19 vaccine rollout. Throughout fall 2020,
when the possibility of a vaccine was becoming a
reality, public health and medical practice journals
published numerous editorials with recommenda-
tions for how to effectively and equitably roll out
the vaccine (Persad, Peek, and Emanuel 2020).
However, states must work within the existing public
health and health care infrastructure to provide the
vaccine in a manner that fulfills more stringent
requirements than most vaccines. Given the existing
unequal infrastructure, it would be difficult to imag-
ine that the vaccine distribution could be done equi-
tably. The first weeks of vaccine distribution saw
reporting from media outlets questioning the equity
of allocation locations (Harper 2021; Oladipo 2021).
This remains an empirical question, though, because
no systematic work has been conducted on this topic
concerning the COVID-19 pandemic.
Conceptual Framework and Hypotheses
From the theory and extant work, we thus have sev-
eral hypotheses for this analysis. Given the docu-
mented inequalities in community establishments,
we expect that the gap in service provision in segre-
gated communities will extend to the case of the
COVID-19 vaccine rollout. We examine this in two
ways—the number of vaccination sites and the allo-
cation of doses of vaccine. We also examine this at
the neighborhood level by examining the geographic
clustering of groups in space (details in the follow-
ing). Thus, we have the following first hypothesis:
Hypothesis 1: A higher degree of racial-ethnic
minority clustering across urban areas will be
related to fewer vaccination sites and fewer
vaccine doses.
However, we also expect that this will be related to
the extant lack of health care provision within these
neighborhoods. Thus, we expect that the density of
existing health care organizations within urban
neighborhoods will attenuate this association. This
leads us to our second hypothesis:
Hypothesis 2: The density of health care service
providers across urban areas will attenuate
the association between the racial-ethnic
minority clustering and vaccine provision.
We test these two hypotheses in a study of early vac-
cine allocations over a 9-week period in the five
largest urban counties in Texas.
Data and Methods
Data
To examine the association between racial-ethnic
minority clustering and the vaccine rollout, we com-
bined several sources of area-level data measured at
the ZIP (Zone Improvement Plan) area unit of analy-
sis (N = 431). First, for data on vaccine allocations,
we used data from the Texas Department of State
Health Services. Since the first shipment of the
Pfizer-BioNTech vaccine on December 14, 2020,
the state of Texas has released information on a
weekly basis on the location and quantity of doses
allocated across the state by county. We collected
these data each week for the first 10 weeks of the
vaccine rollout (December 14, 2020, to February 15,
2021). However, we decided to exclude the first
week from our analysis because these allocations
went only to major hospital sites and were exclu-
sively distributed to first-tier health care workers
under Texas’s Phase 1A of the vaccine distribution.
We chose these weeks for several reasons. We
wanted to focus on the early vaccine rollout when
supplies were limited and access was difficult to
examine which areas got early access to this privi-
lege. Second, we chose this specific set of weeks due
to several circumstances in the weeks that followed.
In the subsequent week (Week 11 of the rollout),
most of the state of Texas experienced a catastrophic
winter storm that affected vaccine distributions both
in terms of the ability to make shipments to vaccine
sites and the ability to keep those doses properly
chilled because of widespread power outages.
Following this event, in March, the state began to
open up eligibility, and the federal government allo-
cated more funding to the vaccine rollout that dra-
matically increased vaccine supplies across the state.
To analyze the spatial distribution of these sites,
an address location was provided for each site, and

476
Journal of Health and Social Behavior 63(4)
we geocoded these to a point-level location in
ESRI’s ArcMap. Furthermore, because we were
primarily interested in how these inequalities relate
to urban dynamics, not differences between urban
and rural locales, we focused on the five largest
counties in the state, which are also the core coun-
ties of the five largest cities in the state. These
included Bexar County (San Antonio), Dallas
County (Dallas), Harris County (Houston), Tarrant
County (Fort Worth), and Travis County (Austin).
We used counties rather than cities or metropolitan
areas because we paired the vaccine data with data
on infection rates by ZIP code, which were pro-
vided by county health departments.
We recognize that other states have provided
similar data about their vaccine allocations.
However, we chose to focus on a single state
because each state has implemented its own criteria
for how vaccines should be allocated and who
should be eligible to receive them. Texas, in partic-
ular, allocated vaccines to sites based on two pieces
of information: an advisory panel, which made rec-
ommendations for allocations, and a registration
system for vaccine providers to determine eligibil-
ity and feasibility. Texas appointed a team of sub-
ject matter experts onto an Expert Vaccine Advisory
Panel to develop vaccine allocation strategies as
recommendations to the Texas Commissioner of
Health. Information from the CDC and an
appointed Advisory Committee on Immunization
Practices helped develop these strategies. Vaccine
equity was one of the stated goals of this panel, but
the exact algorithm used by the panel is not publicly
available. On the supply side of this question, vac-
cine provider registration data aided in determining
the physical locations and quantities for distribu-
tion. According to the CDC provider agreement,
registration data included licensure information,
patient population numbers, and other logistical
details required to ensure each facility’s ability to
store and administer the vaccines.
Although we limited our analysis to this one
state, we argue that Texas serves as a good test case.
Part of this rationale is practical—Texas provided
detailed information on the location and quantities
of vaccines in a publicly available format. It is also
the only state with that many major U.S. cities (5 in
the top 20 largest cities in the United States) and
that also has a high degree of racial-ethnic diversity
across all three of the largest racial-ethnic groups in
the United States. Although Texas cities do not
have the highest rates of residential segregation,
certain Texas cities are fairly segregated, and there
is quite a bit of variation across cities to allow for
comparison. Full descriptive statistics for variables
used in our analysis and the racial-ethnic break-
down for each county (divided by county) can be
found in Appendix A in the online version of the
journal for reference.
We combined these data with sociodemographic
data and data on establishments from two census
products. First, for sociodemographic data, includ-
ing our measures of racial-ethnic clustering, we
used the 2014 to 2018 American Community
Survey 5-year estimates at the ZIP code tabulation
area level. The Census Bureau only provides data at
this small geographic unit using 5-year aggregates
because the data are not representative for small
units of analysis like the ZIP code for a single year.
We also combined this with data on establishments
from the 2016 County Business Patterns (CBP) ZIP
Code Industry Detail File. The CPB uses IRS tax
records to provide counts of establishments by ZIP
code and by industry type using the North American
Industry Classification System (NAICS). We
included several industry codes related to health
care, which we hypothesized may be related to
higher vaccine allocations (details in the follow-
ing). Although 2016 is not the most recent year of
data publicly available (2018), starting in 2017, the
Census Bureau stopped releasing counts of estab-
lishments where the count for the ZIP code was less
than three to further deidentify the information.
Because we included several industry classifica-
tions that are relatively rare, such as hospitals, we
opted to use the older version of the data to get
better estimates of the available resources in
neighborhoods.1
Across all models, we also included a control
variable for the cumulative number of infections in
the ZIP code for all five of our counties. These
came from publicly available sources from each
county’s public health department. These were col-
lected in each of the five counties on February 9,
2021, which was the second to last week of our vac-
cine rollout time frame such that it could have
affected allocation decisions. We included this as a
covariate in the event that state health officials were
using the infection rate as the basis by which vac-
cines were being allocated by factoring in local
vulnerability.
We included two dependent variables, each
meant to capture a different facet of the vaccine
rollout across urban areas in the state. First, we
examined the number of vaccine sites per 100,000
people in a ZIP code. This came from the geocoded
locations for vaccine allocations and reflected a
simple count of the locations providing COVID-19

Anderson and Ray-Warren
477
vaccines. We also divided this number by the popu-
lation size and multiplied by 100,000 to derive a
rate per 100,000 people. Beyond the mere availabil-
ity of a vaccine site, we also examined the distribu-
tion of vaccine doses per 10,000 people to each site
for each week of the 9-week period. The state pro-
vided data on the week-by-week allocations to each
of the geocoded locations. To derive a rate, we
divided this number by the population and multi-
plied by 10,000.
Our main independent variables in this analysis
included a set of variables for racial-ethnic cluster-
ing. Although much of the literature on racial-
ethnic segregation is focused on measuring global
segregation across a large area, such as the county
or metropolitan area (Massey and Denton 1988), in
this study, we were interested in coding for which
areas within the county have disproportionately
high numbers of certain groups. Typically, studies
that examine racial-ethnic concentration at a
smaller geographic unit of analysis use composition
scores, which reflect simply the percentage of a
group over a certain area.2 However, this approach
is aspatial and ignores the role of geographic clus-
tering across space and how adjacent areas may
influence each other (Reardon and O’Sullivan
2004; Roberto 2018). Thus, for this analysis, we
used a geographic clustering score that considered
two pieces of information: the concentration of a
group in an area (ZIP codes) and the extent to which
that group is geographically clustered. To be more
precise, we refer to this as clustering rather than
segregation throughout the discussion of this analy-
sis, and we conceptualized this as a neighborhood-
level measure of segregation. We used the following
formula:
C
x
w x
i
i
j
j i
n
ij
j
=
=
≠
∑
1,
,
where xi is the variable for ZIP code i, x j is the vari-
able for ZIP code j, and wij is the spatial weight
between ZIP codes i and j (Anderson 2017). The
measure is essentially the product of the percentage
of a certain group in a ZIP code and the spatial
weight of its neighborhoods (row standardized). We
used a queen contiguity matrix to calculate the spa-
tial weight. This produces a theoretical range of 0 to
10,000. For example, a ZIP code could have a score
of 10,000 if that ZIP code contained 100% of its
residents from a certain group and all adjacent
neighborhoods also had a population composition
of 100% of the same group. In practice, no ZIP code
in Texas has this high of a score for any group, and
the scores vary considerably depending on the
county and group in question. For this reason, we
also group mean centered (to the county) each of
these scores to make them relative to the population
sizes of the county. Otherwise, these measures
might reflect differences in the relative sizes of
these groups across areas rather than differences
within a particular county in terms of how these
groups are spatially patterned. For example, San
Antonio is 64.2% Latino, whereas Austin is only
33.9% Latino. In each of these contexts, what might
be considered a disproportionately Latino commu-
nity would be different, and group mean centering
would contextualize this difference. This is an
approach used in previous work examining neigh-
borhoods (Sampson, Raudenbush, and Earls 1997).
For this study, we included three scores: clustering
measure for percentage Black (non-Latino), cluster-
ing measure for percentage Latino (of any race),
and clustering measure for percentage Asian
(non-Latino).3
To examine whether these patterns relate to
existing disparities in resource distribution, we also
included four variables for counts of organizations
that are more likely to receive vaccine allocations.
These variables came from the CBP data set, which
classifies establishments by industry code using
NAICS codes. In this analysis, we included counts
by ZIP code of general hospitals (622///), physi-
cians’ offices (6211//), pharmacies (446110), and
retirement and assisted living communities for the
elderly (6233//).4
We also included several control variables to
account for populations that are eligible to receive
the vaccine earlier on the priority list, vulnerability
to the disease, and other sociodemographic factors.
These included population density, percentage age
65 and above, percentage of people employed in
service occupations, median family income, per-
centage of people with a bachelor’s degree or
higher, percentage of households with no private
vehicle, and cumulative number of infections in a
ZIP code. Descriptive statistics for all variables can
be found in Table 1.
Methods
To model these two different dependent variables,
we present two sets of models, one for each out-
come. First, for the number of vaccine distribution
sites, we estimated a series of spatial error models.
We calculated univariate global Moran’s I statistics

478
Journal of Health and Social Behavior 63(4)
for our key independent variables that suggested
that spatial autocorrelation was a problem and
would therefore not meet the assumptions of ordine-
ary least squares (OLS) regression. Furthermore,
the LaGrange multiplier statistics indicated that the
spatial error model was the most appropriate method
to contend with this autocorrelation (Anselin,
Florax, and Rey 2004). Specifically, we used a
queen spatial weight matrix because this was found
to best maximize global Moran’s I for each of the
key variables used in the models (Anselin 1995;
Anselin, Florax, and Rey 2004). We also included
Kelejian and Prucha (2010) robust standard errors
to account for significant heteroscedasticity.
However, of note, the term for lambda is not signifi-
cant in the models presented in Table 2, suggesting
that correlated errors in omitted variables may not
be particularly a problem here. The OLS results
were also virtually identical to what is presented
here in terms of the sign, significance, and relative
effect sizes for each of our variables. Because spa-
tial autocorrelation was significant in our prelimi-
nary analyses, though, we chose to present the
results as spatial error models. These results can be
found in Table 2.
For the second set of models for the week-by-
week allocation of vaccine doses, we present a series
of hierarchical linear growth models (Raudenbush
and Bryk 2002; Singer and Willett 2003) with a cor-
rection for spatial dependency using a queen conti-
guity spatial weight matrix (Savitz and Raudenbush
2009). Given the nested structure of the data,
weekly allocations per ZIP code, we used this
approach to model the cumulative change over time
in the number of vaccine doses per ZIP code.
However, because the data were still organized by
physically adjacent spatial units of analysis at Level
2, we used Savitz and Raudenbush’s (2009) routine
to account for spatial dependency using HLM 8.1,
which is an approach used in similar health research
Table 1.  Descriptive Statistics for Variables Used in Statistical Models.
Variable Name
Mean
SD
Range
Description
Dependent variables
Vaccine sites
7.92
17.95
0 to 169.71
Number of vaccine sites per
100,000 people in a ZIP code
Vaccine doses
1,199.01
8,295.32
0 to 238,415.58
Number of vaccine doses allocated
to ZIP code per 10,000 people
Independent variables
Black clustering
0
648.99
−700.47 to 4,150.46
Clustering measure of % Black
Latino clustering
0
1,611.93
−2,986.80 to 5,851.91 Clustering measure of % Latino
Asian clustering
0
84.01
−73.08 to 812.42
Clustering measure of % Asian
Population density
3,291.17
2,359.18
6.83 to 16,811.43
Population density per square mile
Population age 65+
10.94
4.13
0 to 36.60
% of population age 65 and above
% Service work
17.01
6.43
0 to 43.40
% of employed population in
service occupations
Household income
68,094.12
31,467.35
17,798 to 240,417
Median household income
% Bachelor’s degree
32.96
21.30
2.60 to 90.00
% of population over 25 with at
least a bachelor’s degree
% No vehicle
6.10
5.38
0 to 39
% of households with no private
vehicle
Infections
2,127.59
1,652.52
0 to 9,906
Number of positive SARS-CoV-2
infections
Hospitals
.61
1.46
0 to 15
Number of general hospitals
Physicians’ offices
26.84
41.99
0 to 294
Number of physicians’ offices
Pharmacies
3.89
3.39
0 to 18
Number of pharmacies
Retirement
communities
1.41
1.88
0 to 14
Number of retirement and
assisted living facilities for the
elderly
Note: N = 431. Data come from the Texas Department of State Health Services, the 2014–2018 American
Community Survey, and the 2016 County Business Patterns.

Anderson and Ray-Warren
479
(O’Connell 2015). These results can be found in
Table 3.
For each set of models, we first present a model
with only the clustering scores and ZIP-code-level
control variables included (Model 1). Then, we add
the health care resource variables one by one in the
model (Models 2–5) to avoid the problems of mul-
ticollinearity and to isolate the effect of each orga-
nizational type as health care organizations tend to
agglomerate. To our knowledge, no formal media-
tion test exists that can account for the spatial
dependencies in the models and the multilevel
structure of the second set of models. Therefore,
we used an informal approach and examine change
in the effect sizes with the inclusion of certain
variables.
Results
From the results in Table 2, we can see that the
racial-ethnic clustering scores have a significant
relationship to the density of vaccine sites.
Specifically, for Black and Latino clustering, these
scores are significant and negative, meaning that as
the clustering of these two groups increases, the
number of vaccine sites per 100,000 people
decreases. Essentially, the higher the concentration
and clustering of these two groups, the lower the
Table 2.  Coefficients and Z-Ratios from Spatial Error Models of Vaccination Sites per 100,000 People.
Variable Name
Model 1
Model 2
Model 3
Model 4
Model 5
Black clusteringa
−.535***
(–3.299)
−.503**
(–3.044)
−.461**
(–2.819)
−.516**
(–3.091)
−.547***
(–3.326)
Latino clusteringa
−.137*
(–2.185)
−.101
(–1.530)
−.104
(–1.625)
−.120†
(–1.793)
−.149*
(–2.315)
Asian clusteringa
.474
(.383)
.198
(.226)
.100
(.097)
.230
(.187)
.565
(.446)
Population density
−.001**
(–2.729)
−.001**
(–2.687)
−.002**
(–3.033)
−.002**
(–2.845)
−.001**
(–2.680)
Population age 65 and up
.099
(.353)
.173
(.594)
.085
(.291)
.105
(.377)
.204
(.691)
% Service work
−.426†
(–1.767)
−.391†
(–1.697)
−.406†
(–1.711)
−.418†
(–1.723)
−.413†
(–1.713)
Median household incomea
−.073
(–1.380)
−.029
(–.629)
−.053
(–1.137)
−.068
(–1.318)
−.081
(–1.482)
% Bachelor’s degree
.172†
(1.826)
.102
(1.206)
.100
(1.069)
.164†
(1.757)
.187†
(1.897)
% No vehicle
2.193***
(4.175)
2.014***
(3.726)
2.040***
(3.887)
2.180***
(4.122)
2.153***
(4.132)
Infectionsa
.004
(.100)
−.015
(–.386)
−.024
(–.598)
−.030
(–.754)
.022
(.533)
Hospitals
3.290**
(2.866)

Physicians’ offices
.097*
(2.383)

Pharmacies
.419†
(1.677)

Retirement communities
−.719*
(–2.387)
Lambda
−.935
(–.765)
−1.263
(–1.000)
−.740
(–.603)
−.775
(–.640)
−.660
(–.540)
Pseudo R2
.345
.408
.384
.349
.350
Note: N = 431. Data come from the Texas Department of State Health Services, the 2014–2018 American
Community Survey, and the 2016 County Business Patterns.
aCoefficient multiplied by 100 for the ease of presentation.
†p < .1, *p < .05, **p < .01, ***p < .001 (for two-tailed test).

480
Journal of Health and Social Behavior 63(4)
number of vaccine sites. These effect sizes are also
large. We discuss these coefficients in terms of stan-
dard deviation changes because the scale of each
variable is so different, although the original coeffi-
cients are available in the tables. In the case of Black
clustering, a 1 SD (648.99) increase in Black clus-
tering relates to 3.47 decrease in the number of vac-
cine sites per 100,000 people. Given that the average
number of sites per 100,000 people is only 7.92, this
is a notable change. For Latino clustering, a 1 SD
(1,611.93) increase in Latino clustering is related to
a 1.59 decrease in the number of vaccine sites per
100,000 people. These are both sizable coefficients
and indicate that Black- and Latino-clustered areas
are less likely to have vaccine sites.
Several of the other area-level coefficients are
significant as well. Population density is significant
and negative, the percentage of households with no
car is significant and positive, and two others, per-
centage college educated and percentage in service
work, are significant at the .1 level. The largest
effect comes from the percentage of households
with no car, where a 1 SD increase (5.38) relates to
an increase of 11.8 vaccine sites. This seems to run
counter to what we would expect from the literature
because private vehicle ownership may relate to
Table 3.  Coefficients and T-Ratios from Linear Growth Models of Weekly Vaccination Allocations per
10,000 People.
Variable Name
Model 1
Model 2
Model 3
Model 4
Model 5
Fixed effects
Week
312.927***
(8.553)
312.927***
(8.553)
312.927***
(8.553)
312.927***
(8.553)
312.927***
(8.553)
Black clustering
−1.014*
(–1.984)
−.820†
(–1.785)
−.593
(–1.215)
−.907†
–1.771)
−1.036*
(–2.029)
Latino clustering
−.424†
(–1.657)
−.204
(–.886)
−.250
(–1.026)
−.345
(–1.337)
−.465†
(–1.810)
Asian clustering
4.731
(1.256)
3.212
(.949)
2.669
(.745)
3.597
(.947)
4.956
(1.317)
Population density
−.376*
(–2.355)
−.347*
(–2.415)
−.467**
(–3.072)
−.437**
(–2.687)
−.352*
(–2.190)
Population age 65 and up
−141.990†
(–1.841)
−90.095
(–1.298)
−150.614*
(–2.060)
−140.289†
(–1.825)
−109.474
(–1.364)
% Service work
−154.071†
(–1.941)
−138.248†
(–1.939)
−138.666†
(–1.842)
−148.326†
–1.873)
−147.523†
(–1.858)
Median household income
−.038*
(–2.109)
−.012
(–.752)
−.027
(–1.608)
−.035*
(–1.970)
−.040*
(–2.250)
% Bachelor’s degree
42.995
(1.348)
2.743
(.095)
6.362
(.207)
39.659
(1.245)
48.578
(1.513)
% No vehicle
485.960***
(6.382)
384.743***
(5.568)
401.661***
(5.487)
478.015***
(6.288)
471.639***
(6.149)
Infections
−.223
(–1.132)
−.323†
(–1.826)
−.354†
(–1.891)
−.371†
(–1.759)
−.156
(–.774)
Hospitals
1855.981***
(10.099)

Physicians’ offices
50.823***
(6.954)

Pharmacies
192.471†
(1.908)

Retirement communities
−241.504
(–1.440)
Random effects
Level 2 error variance
30,132,845.834
23,546,583.226
26,689,874.927
29,920,526.251
30,046,250.421
Level pseudo R2
.146
.333
.244
.152
.149
Note: Level 1 N = 3,879. Level 2 N = 431. Data come from the Texas Department of State Health Services, the
2014–2018 American Community Survey, and the 2016 County Business Patterns. The Level 2 pseudo R2 is calculated
from the proportional reduction in error variance from a model with no Level 2 variables (τ = 35,296,518.409).
†p < .1, *p < .05, **p < .01, ***p < .001 (for two-tailed test).

Anderson and Ray-Warren
481
socioeconomic status, but it may also be an indica-
tion of central city location where residents may
perceive less of a need to own a vehicle. Taken
together, from the pseudo R2 value, these sociode-
mographic characteristics of ZIP codes explain
34.5% of the variation in vaccine sites per 100,000
people, which is sizable.
In Models 2 to 5, when we add the health care
establishment variables, this pattern changes some-
what. First, when we include two such health care
establishments, the number of hospitals and the
number of physicians’ offices, these are both sig-
nificant and positive. This indicates that having
more health care establishments in a ZIP code
relates to a greater number of vaccine distribution
sites. This is a notable increase as well, where each
additional hospital is related to an increase of 3.29
vaccination sites and each additional physician’s
office is related to a .10 increase in sites. This is
expected because hospitals and clinics are where
most of the vaccine doses were allocated.
The addition of these two variables also reduces
the size of the coefficients for the clustering vari-
ables, suggesting that the lower number of vaccine
sites in these areas is a function of their lack of
health care resources. In the case of Latino cluster-
ing, the coefficients drop to nonsignificance with
the inclusion of hospitals and physicians’ offices,
and these changes relate to a 26.48% and a 23.99%
reduction, respectively, in the size of the coeffi-
cients for Latino clustering. This percentage change
in the coefficient size is somewhat smaller for
Black clustering at 9.09% and 13.77%, respec-
tively. This is reflected in their pseudo R2 values as
well, where hospitals alone explain an additional
6.33% of the variation in vaccine sites and physi-
cians’ offices explain 3.96%. This suggests that
existing health care resources in areas may explain
some part of the negative association between
minority clustering in ZIP codes and the number
of vaccine sites. However, these findings are only
significant at the .1 level for pharmacies and signif-
icant and negative for retirement communities.
Therefore, the bulk of this effect seems to be driven
by hospitals and physicians’ offices rather than
other kinds of facilities that may offer the vaccine.
We also illustrate these patterns graphically in a
series of maps for each county. These can be found
in Figures 1 to 5. In the background of each map is
a choropleth quintile map of the two clustering
scores with the location of the vaccine sites overlaid
on top. Choropleth maps use shading to map pat-
terns across a polygonal area. Here, we use a quin-
tile map, meaning that the range of mapped values
in the polygons include an equal number of ZIP
codes for each shade. Note that because these are
evenly distributed quintile maps, the scale is differ-
ent for each group and location. From these maps, it
is clear that minority clustered areas are less likely
to have vaccine sites, but these patterns differ some-
what by county, with more stark patterns in certain
counties over others and depending on the group in
question. For example, Harris and Travis counties
each have a clearer clustering pattern to them where
the vaccine sites appear to be less likely to be
located in racial-ethnic minority areas. By contrast,
Tarrant County (Fort Worth) has a more scattered
pattern to the location of the facilities.
In Table 3, we present the results for the number
of vaccine allocations over the 9-week period using
a series of linear growth models. The results with
this dependent variable reflect a similar pattern to
the one described previously with some notable dif-
ferences. Again, in Model 1, the coefficient for
Black clustering is significant and negative, indicat-
ing that the higher the degree of concentration and
clustering of Blacks across these five counties, the
smaller the vaccine allocations week to week.
Specifically, every 1 SD increase in Black cluster-
ing (648.99) relates to 658.08 fewer vaccine doses
per 10,000 people in a ZIP code. The same figure
for a 1 SD change in Latino clustering (1,611.93) is
492.66 vaccine doses per 10,000 people, which is
not an inconsequential amount, although this coef-
ficient is only significant at the .1 level for Latino
clustering. In all subsequent models, the results for
Latino clustering are not significant. Concerning
the control variables, the results are similar to pre-
vious models except that median household income
is significant and negative here.
When we add the different types of health care
establishments to the baseline model, the results are
similar in pattern to the results previously described.
Once again, the distribution of hospitals and physi-
cians’ offices is significant and positive, meaning
that their presence in an area is related to a higher
allocation of vaccine doses per 10,000 people.
Moreover, the inclusion of these variables weakens
the association between the clustering scores and
the number of vaccine doses per 10,000 people,
especially in the case of Black clustering. Black
clustering is only significant in Model 1 and Model
5, and the inclusion of hospitals and physicians’
offices rather substantially reduces the size of those
coefficients. These reductions are percentage
changes of 19.13%, 41.52%, and 10.55% for hospi-
tals, physicians’ offices, and pharmacies, respec-
tively. Hospitals and physicians’ offices appear to

482
Journal of Health and Social Behavior 63(4)
explain away a substantial portion of the differen-
tial allocations across areas related to Black cluster-
ing. We also see changes in the size of the
coefficients for Latino clustering, but this score was
only significant at the .1 level in Model 1.
Discussion
In this study, we aim to understand how neighbor-
hood sociodemographic characteristics relate to the
early COVID-19 vaccine rollout. We hypothesize
that racial-ethnic minority clustered areas will be
less likely to have vaccination sites, and we surmise
that this is primarily a function of a lack of key
health care sites prior to the pandemic. We test these
hypotheses across the five largest urban counties in
the state of Texas during the first 10 weeks of the
vaccine rollout in Texas (excluding the first week).
Moreover, we test this using two ways of capturing
access to the vaccine for neighborhoods—the num-
ber of vaccine sites and the number of doses allo-
cated to each site.
For the first outcome, examining the number of
vaccine sites across these urban counties, we find
that a higher concentration and clustering of Black
and Latino residents in ZIP codes is associated with
fewer vaccine sites per 100,000 people. These
results provide support for Hypothesis 1 for Black
Figure 1.  Vaccine Distribution Sites over Racial-Ethnic Clustering Scores in Bexar County (San
Antonio), Texas.
Note: Data come from the Texas Department of State Health Services and the 2014–2018 American Community
Survey.

Anderson and Ray-Warren
483
and Latino clustering (not Asian clustering). Moreover,
this negative association is partially explained by
the existing distribution of health care resources
across communities, especially hospitals and physi-
cians’ offices. The presence of these establishments
is associated with a higher number of vaccine sites,
so the lack of these sites in segregated communities
means fewer vaccine sites for these areas. However,
we do not observe the same association for pharma-
cies and retirement communities. Indeed, the asso-
ciation for retirement communities is actually
significant and negative and appears to have a sup-
pression effect with the racial-ethnic clustering
scores. We speculate that this may be because these
groups are less likely to use retirement homes for
elderly relatives as other work suggests (Dilworth-
Anderson, Williams, and Gibson 2002), although
we cannot directly test this assertion with the data
here. These findings provide partial support for
Hypothesis 2, with stronger evidence for the case of
Latino clustering.
For the second outcome, we find a similar pat-
tern, although with somewhat weaker effects. Black
clustering is again significant and negatively asso-
ciated with the number of vaccine doses per 10,000
people in a ZIP code over a 9-week period (although
this is only significant for Latino clustering at the .1
level), meaning that minority segregated communi-
ties were less likely to receive doses of vaccine over
this period. This provides further partial support for
Figure 2.  Vaccine Distribution Sites over Racial-Ethnic Clustering Scores in Dallas County (Dallas),
Texas.
Note: Data come from the Texas Department of State Health Services and the 2014–2018 American Community
Survey.

484
Journal of Health and Social Behavior 63(4)
Hypothesis 1 in the case of Black clustering. And,
again, these results are somewhat attenuated with
the inclusion of the number of hospitals and physi-
cians’ offices. Both these types of establishments
are strongly associated with a higher number of
vaccine doses, and they reduce the size of the coef-
ficient for Black clustering. Thus, we find partial
support for Hypothesis 2.
These findings are fitting with previous litera-
ture on segregation and the distribution of resources
more broadly that demonstrates poor access to a wide
variety of community establishments that would sup-
port well-being (Anderson 2017; Dinwiddie et al.
2013; Gaskin et al. 2012; Ko and Ponce 2013). We
find this for Black and Latino clustering but not for
Asian clustering. However, the existing work on
Asian segregation is limited and has not found the
same inequalities as Black and Latino segregations
(Anderson 2017). The findings presented here also
point to a much stronger race story than one about
socioeconomic dynamics, which is a prominent
theme in much of the previous literature on resource
allocation across neighborhoods (Beaulac et  al.
2009; Ko et  al. 2014). In our study, area-level
median income was not significant in the first anal-
ysis, and in the second analysis, it was significant
and negative, meaning that higher income areas
were less likely to receive doses. Thus, the story
Figure 3.  Vaccine Distribution Sites over Racial-Ethnic Clustering Scores in Harris County (Houston),
Texas.
Note: Data come from the Texas Department of State Health Services and the 2014–2018 American Community
Survey.

Anderson and Ray-Warren
485
seems to be more about race specifically rather than
class, which limited research has also shown
(Anderson 2017; Small and McDermott 2006).
Given the lack of detail provided by the state in
terms of how specifically the vaccine allocation
algorithm was designed, we are not necessarily sug-
gesting that this is an act of blatant and purposive
racism on the part of the advisory board responsible
for vaccine allocation decision-making. Rather,
because the infrastructure in terms of health care
resources already disadvantages racial-ethnic minor-
ity communities, this represents a case of structural
discrimination. Thus, we extend this literature by
demonstrating yet another empirical instance of a
racial disparity, especially for a hugely impactful
event like the current COVID-19 crisis. Some lim-
ited findings on the COVID-19 pandemic, including
media sources, has revealed important disparities by
race-ethnicity in infection rates and access to medi-
cal services, such as testing sites and ICU facilities
(CDC 2020b; McMinn et al. 2020; Miller, Peek, and
Parker 2020; Millett et  al. 2020; Ross 2021). The
vaccine rollout appears to fit within this pattern. This
is relevant to the current inequities we have seen
throughout the pandemic and has implications for
the next crisis.
Figure 4.  Vaccine Distribution Sites over Racial-Ethnic Clustering Scores in Tarrant County (Fort
Worth), Texas.
Note: Data come from the Texas Department of State Health Services and the 2014–2018 American Community
Survey.

486
Journal of Health and Social Behavior 63(4)
Despite these advances to the literature, the
study has several limitations. First, the study only
relates the sociodemographic characteristics of
neighborhoods with the distribution of vaccine
sites and doses and not who is being vaccinated at
these sites. Media reporting on access to the vac-
cine has highlighted how differential access to the
Internet to search for vaccine appointments, time
to spend searching for open appointments, and
adequate transportation have led to important dis-
parities in early access to the vaccine, even for eli-
gible populations (Garnam and Cai 2021; Harper
2021; Menchaca and Agnew 2021; Oladipo 2021;
Ross 2021; Stone 2021). This study only factors in
disparities in the built environment in terms of the
location of vaccine sites without accounting for
who is going to these sites. Second, the data from
the Texas Department of Health Services only
includes address locations for where vaccine doses
were shipped. Although the vast majority of ship-
ping locations were the same as where the vaccines
were administered, this approach fails to consider
mobile units that some city and county health
departments have utilized to reach special popula-
tions, like mobility-challenged patients in elder
care facilities. These types of activities were not
possible to systematically track over time. Future
work should consider these limitations.
Figure 5.  Vaccine Distribution Sites over Racial-Ethnic Clustering Scores in Travis County (Austin),
Texas.
Note: Data come from the Texas Department of State Health Services and the 2014–2018 American Community
Survey.

Anderson and Ray-Warren
487
With this study, we contribute to the literature
on the unequal distribution of resources across
neighborhoods, particularly by racial-ethnic minor-
ity clustering. Moreover, we demonstrate that these
patterns are not a neutral fact. Not having health
care infrastructure in place means that when con-
fronted with a public health catastrophe, the exist-
ing inequalities in our health care system are
deepened. The COVID-19 pandemic is, for many
people, one of the most disruptive and challenging
public health events of our lifetimes. Vaccination in
this context represents a lifeline to spare further
human suffering and loss of life and a potential
return to normalcy. However, this valuable resource
was not distributed evenly across urban areas, with
limited access to populations already at risk for
complications from the virus. Although state public
health officials implemented eligibility systems that
prioritized health care workers, the elderly, and
those with medical comorbidities, the geographic
component of the vaccine rollout and allocation of
the vaccine over time has not been equal. This high-
lights the necessity of creating more equitable
access to care broadly so that in crisis times, the
infrastructure is available to equitably meet the
needs of the affected communities.
ORCID iD
Kathryn Freeman Anderson
 https://orcid.org/0000-
0002-7425-8923
Supplemental Material
Appendix A is available in the online version of the
article.
Notes
1.
We also ran the same models using the 2018 version
of the data, which is the most recent version avail-
able but likely has undercounts of establishments in
many areas. The results generally followed the same
pattern as what is presented here but with different
effect sizes in some cases. As an additional sensitiv-
ity check, we also checked previous waves of the
data to see how correlated the counts are over time
to justify the use of an earlier date. Using the 2012
CBP file (being four years before our data, which
is the same distance from 2016 to 2020), we found
that all four of our organizational types were highly
correlated. From 2012 to 2016, hospitals, physi-
cians’ offices, pharmacies, and retirement com-
munities had a correlation of .93, .98, .86, and .86,
respectively, indicating that these organizational
resources are relatively stable over time, especially
health care provision.
2.
Our analysis focused on these clustering scores as
a more geographically informed way of examining
the problem. To relate these findings to the broader
literature that typically uses composition scores, we
ran the same models using percentage non-Latino
Black, percentage Latino, percentage non-Latino
Asian, and another version of these models’ vari-
ables that also included their spatial W lags (which
is the other term in the clustering equation). The
results were similar to what we found in the pres-
ent study, with some differences in the effect sizes
(results available on request). We chose to present
the results with the clustering score as indicated by
the formula because we think this best captures the
spatial dynamics of neighborhood-level segregation
in a manner that accounts for both the composition
of groups in an area and the extent to which they are
spatially clustered, which is one of the main ways
that segregation is theorized and conceptualized in
the literature (Massey and Denton 1988).
3.
The distribution of Whites also plays a role in the
segregation level of an area. However, due to multi-
collinearity, we could not include all four clustering
scores for each of these groups in a single model.
Instead, we focused on the distribution of racial-
ethnic minority populations across these counties.
As a check on this choice of approach, we also ran
the same models using the clustering score for per-
centage White. This score for Whites was not sig-
nificant in any of the models, suggesting that the
distribution of Whites across areas is unrelated to
the distribution of vaccine sites and allocations.
4.
We also tested several other organizational types
that might have received vaccine allocations,
including general stores (452///), supermarkets
(4451//), freestanding ambulatory care facilities
(621493), and other (nonelderly) nursing and resi-
dential care facilities (623///). We only present the
results for those that were most relevant and had
significant results.
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Author Biographies
Kathryn Freeman Anderson is an associate professor in the
Department of Sociology at the University of Houston. Her
research focuses on understanding the social sources of health
disparities in the United States. In particular, she examines the
role of race-ethnicity and urban neighborhood dynamics to
analyze how these factors may affect individual health. Her
recent work has been published in Social Problems, City &
Community, and Race and Social Problems.
Darra Ray-Warren is a graduate student in the Department
of Sociology at the University of Houston. Her research
interests are in the socioeconomic determinants affecting
mental and physical health, social status, and the public
policies that may impact human flourishing. Her current
research focuses on criminal law and punishment ratio-
nale, emerging progressive prosecutorial and indigent
defense styles, and the social factors that drive criminal
case outcomes.

    Policy Implications:

Answer: The COVID-19 pandemic is, for many people, one of the most disruptive and challenging public health events of their lifetimes. However, not having adequate health care infrastructure in place means that when confronted with this public health catastrophe, the existing inequalities in our health care system were deepened. Even though state public health officials implemented eligibility systems that prioritized health care workers, the elderly, and those with medical comorbidities, the allocation of the vaccine over time was not implemented equitably and served to further disadvantage minority communities. This suggests that we need to create more equitable access to care broadly so that in crisis times, the infrastructure is already available to equitably meet the needs of affected communities.