1 Department of Health and Human Performance, College of Ch

1 Department of Health and Human Performance, College of
Charleston,
Charleston, SC, USA
2 Department of Health Sciences, James Madison University,
Harrisonburg,
VA, USA
3 Department of Health Behavior and Health Systems,
University of North
Texas Health Science Center, Fort Worth, TX, USA
4 Department of Anthropology, Rollins College, Winter Park,
FL, USA
5 Department of Social and Behavioral Sciences, College of
Public Health,
Temple University, Philadelphia, PA, USA
Corresponding Author:
Sarah B. Maness, PhD, College of Charleston, Department of
Health and
Human Performance, 24 George St, Charleston, SC 29401, USA.
Email: [email protected] cofc. edu
Commentary
Public Health Reports
2021, Vol. 136(1) 18-22
© 2020, Association of Schools and
Programs of Public Health

All rights reserved.
Article reuse guidelines:
sagepub. com/ journals- permissions
DOI: 10. 1177/ 0033 3549 20969169
journals. sagepub. com/ home/ phr
Social Determinants of Health and
Health Disparities: COVID-19 Exposures
and Mortality Among African American
People in the United States
Sarah B. Maness, PhD1 ; Laura Merrell, PhD2; Erika L.
Thompson, PhD3 ;
Stacey B. Griner, PhD3 ; Nolan Kline, PhD4; and Christopher
Wheldon, PhD5
The coronavirus disease 2019 (COVID-19) pandemic in the
United States provides yet another example of the enduring
and pernicious effect of social determinants of health (SDH)
on African American communities. SDH, as defined by the
Healthy People 2020 SDH framework, include domains of
economic stability, education, social and community con-
text, health and health care, and neighborhood and built
environment.1 Within each domain, key areas represent ele-
ments of focus for the decade (Box). Compared with non-
Hispanic White people, African American people have
higher rates of COVID-19 cases (2.6 times higher), hospital-
ization (4.7 times higher), and death (2.1 times higher).2-4
Although the pandemic is ongoing, it is not premature to call
attention to the root causes of health inequity in the United
States that have persisted for decades and are being high-
lighted in the current crisis.

The disparities in COVID-19 case fatality rates between
African American and White people have been referred to
as a “perfect storm.”5 Such a comparison obfuscates the
larger social and political circumstances that structure poor
health. Unlike a storm, which is a natural phenomenon that
cannot be prevented, the higher rate of COVID-19 deaths
among African American people was predictable and pre-
ventable because of racial injustice. These deaths were pre-
dictable because of the long history of health inequities in
the United States and preventable through systemic changes
to eliminate systemic racism and improve SDH. The social
and political will needed to correct these injustices histori -
cally has been, and continues to be, lacking. SDH underlie
health disparities that increase the potential for exposure to,
and higher death rates from, COVID-19 among African
American people across the United States.2-4 We provide a
framework- based explanation on how systemic racism
gives rise to differences in SDH that affect differences in
health outcomes, including COVID-19, and make a call for
change.
Systemic Racism
We begin by outlining how systemic racism influences SDH
using the Healthy People 2020 Social Determinants of
Health Framework.1 SDH have been shown to contribute to
a wide range of health disparities in the United States and are
interrelated with systemic racism.1 We define systemic rac-
ism as the exploitative and discriminatory practices, unjustly
gained resources and power, and maintenance of major
resource inequalities by ideological and institutional mecha-
nisms that are controlled by White people.6 Systemic racism
underlies many aspects of SDH.
Education

Although the racist practice of educational segregation for -
mally ended in public schools in 1954, the residual effects
remain in our current educational system.7-9 Race/ethnicity,
class, and neighborhood are highly interrelated in the United
States, from where children attend school to the quality of
schools.10 African American children, on average, attend
schools where they are of the majority race, yet they also
disproportionately attend schools with the highest poverty
concentrations and lower- than- average test scores.11 Data
mailto:[email protected]
https://journals.sagepub.com/home/phr
https://orcid.org/0000-0003-0757-7972
https://orcid.org/0000-0002-7115-0001
https://orcid.org/0000-0002-2774-5841
http://crossmark.crossref.org/dialog/?doi=10.1177%2F00333549
20969169&domain=pdf&date_stamp=2020-11-11
Maness et al 19
from fall 2015 indicate that 58% of African American stu-
dents (vs 5% of White students) enrolled in public schools
attended a school in which the combined enrollment of
racial/ethnic minority students was at least 75% of enroll -
ment.12 Disparities among African American people in edu-
cation persist into adulthood: fewer African American people
than White people enroll in college and complete a bache-
lor’s degree (26.1% vs 40.1%), which leads to income
inequalities across the lifecourse.13
Economic Status
African American people have been disproportionately affected
economically through practices of systemic racism that have
made it difficult for them to accumulate wealth over genera-
tions.14 Wealth is the total market value of all assets available

to
an individual or family.15 It is created over time and has inter -
generational effects that perpetuate, provide opportunities, and
allow for the pursuit of education and increased choice in
employment. Creating wealth is particularly challenging for
African American people for multiple reasons, including sys-
temic racism that exists in employment, hiring practices, pay,
housing discrimination, and the justice system.16 African
American adults are more likely to be unemployed (11.8% men,
10.1% women) than non- Hispanic White adults (5.1% men,
4.6% women), even when controlling for differences in educa-
tion, age, and experience (data averaged from 1994 to 2016).16
Housing
Quality and stability of housing are important for human health.
Systemic racism historically has manifested in segregation and
housing discrimination in the form of “redlining.” Redlining is
the systematic denial of services (banking, insurance, health
care, retail) by the government and/or private sector to residents
of specific neighborhoods (typically based on racial/ethnic
composition), either directly or through selectively raising
prices for certain neighborhoods. A result of redlining is the de
facto racial segregation of neighborhoods, which shapes social
conditions for individuals and communities and underlies the
health disparities between African American people and White
people.17 Despite federal and state legislation to combat these
racially motivated practices, redlining is perpetuated through
the weakening of federal protections for fair financial lending,
the reduction of federal funding for community investment, and
current zoning practices, all of which disproportionately affect
African American people.18,19 The effects of these practices
are
seen in the intersection of place, race, and health disparities in
chronic conditions.

Former and current redlining practices continue to shape the
built environment of predominantly African American neigh-
borhoods. African American neighborhoods are more likely
than neighborhoods of other racial/ethnic composition to be
exposed to poisonous toxins and chemicals such as lead.20 One
example is the water crisis in Flint, Michigan, where 54% of the
population is African American and 40% of the total population
lives below the federal poverty level.21,22
Community
Injustices rooted in systemic racism have been noted at every
level of the US criminal justice system, including policing, pre-
trial detention, sentencing, parole, and post- parole.23 As a
result
of inequitable processes across all levels of the criminal justice
system, African American people are incarcerated at more than
5 times the rate of White people and receive longer sentences.23
In addition to injustices concomitant with the broader criminal
justice system, African American people are also more likely to
encounter lethal force from law enforcement officers than their
non- Hispanic White or Hispanic counterparts.24 Furthermore,
some police practices, such as “stop and frisk,” target African
American people. Such practices constitute a public health
problem because they perpetuate stress and trauma by
Box. Healthy People 2020 Social Determinants of Health
Framework1
Social determinants of health domains and key areas
Economic stability
Poverty
Employment

Food security
Housing stability
Education
High school graduation
Enrollment in higher education
Language and literacy
Early childhood education and development
Social and community context
Social cohesion
Discrimination
Civic participation
Incarceration
Health and health care
Access to health care
Access to primary care
Health literacy
Neighborhood and built environment
Access to healthy foods

Crime and violence
Environmental conditions
Quality of housing
Public Health Reports 136(1)20
translating Blackness into deviance.25 Mass incarceration not
only affects the people in the criminal justice system, it also
affects the families and communities left behind by causing
family disruptions, financial strain, and emotional
difficulties.26
Access to Health Care
The experience of the health care system may further exacer-
bate risks for mortality among African American people as a
result of systemic racism. Implicit bias on the part of health
care providers may affect clinical decision making in diagno-
sis, treatment, pain management, and referral.27 As a result,
the prevention and management of chronic morbidities are
affected. Persistent and well- documented inequities exist in
access to health care among African American people.
Compared with non- Hispanic White people, African
American people are less likely to be insured28 and, even
with access to health care, are less likely to use health care
services because of a distrust in health care providers rooted
in a history of systemic racism in health care.29
Health Disparities Among African
American People
We now focus on how differences in SDH that are rooted in

systemic racism are responsible for persistent health dispari -
ties. When we think about limitations in access to housing,
education, economic status, health care, and equity in the
criminal justice system, one outcome is poor health. African
American people are significantly more likely than non-
Hispanic White people to receive a diabetes diagnosis and
die as a result of diabetes, 40% more likely to have high
blood pressure, and 8.4 times more likely to be diagnosed
with HIV/AIDS.30 African American women have higher
obesity rates than women of any other racial/ethnic group,
and they have a 20% higher chance of having asthma, a 40%
higher chance of dying from liver cancer, and nearly 4 times
the death rate from breast cancer than non- Hispanic White
women, despite similar rates of diagnosis.30 Survival rates
among African American men are, on average, 5 years lower
for many common cancers, and the death rate from liver can-
cer is 60% higher, than among non- Hispanic White men.30
Overall, the lifespan for African American men is 4.5 years
lower than for non- Hispanic White men.31
Increased Exposure to COVID-19
Among African American People
Now we focus on how systemic racism and social determinants
of health are affecting African American people during the
COVID-19 pandemic. Social distancing, the measure that the
United States has taken as the largest effort to prevent the
spread
of COVID-19, is an SDH. The ability to social distance is a
privilege linked to key areas of housing, community, and eco-
nomic status. Lower- wage jobs are often jobs that cannot be
translated to work from home, have been deemed essential, and
may involve increased interaction with the public (eg, cashiers,
sanitation workers, home health aides, food service workers).

Although African American people account for just 13.4% of
the US population,32 they account for a larger percentage
(17.1%) of the service sector, including cashiers (19.9%), bus
drivers (27.0%), taxi drivers (29.5%), housekeeping (14.4%),
janitorial staff (18.2%), and sanitation workers (18.2%).33 Such
jobs are less likely than office- based jobs to be able to be per-
formed from home via teleworking strategies, thereby increas-
ing exposure to community- acquired COVID-19. African
American people are also more likely than people of any other
racial/ethnic group to use public transit,34 which may provide
increased exposure to community- acquired infection.
In addition to social distancing, a recent Centers for Disease
Control and Prevention (CDC) guideline has been to wear
masks when going out in public. Wearing a mask is problematic
for African American people, who have expressed fear of being
mistaken for criminals; it is compounded by a longstanding
conflation of race and criminality.35 Incarceration is linked to
health disparities among African American people, through
both the disproportionate number of African American people
who are imprisoned and, during the COVID-19 pandemic, the
inability to social distance in a prison or jail setting.
Inconsistent
policies have been placed across the country in terms of protec-
tions for incarcerated people during COVID-19. In one exam-
ple, it led to an ACLU class- action lawsuit against the Dallas
County Jail for its management of inmate exposure to the
virus.36
COVID-19 Mortality Among African
American People
Preliminary data also indicate higher COVID-19 mortality rates
among African American people than among White people in
the United States.2-4 These deaths are likely linked to underly-

ing conditions such as type 2 diabetes, hypertension, and
asthma, from which African American people have dispropor-
tionately higher rates than non- Hispanic White people.30 CDC
has reported that risk factors for serious illness when contract-
ing COVID-19 include older age and underlying medical con-
ditions, including chronic lung disease, asthma, heart
conditions,
immunocompromised states (ie, a common result of treatment
for cancer or HIV/AIDS), severe obesity, diabetes, chronic kid-
ney disease, and liver disease.37 These disparities are often a
result of race- based inequities among SDH in areas of educa-
tion, economic status, housing, community context, and access
to health care.1 When the risk of death from COVID-19 is
higher among people with underlying health conditions, it is
Maness et al 21
clear that African American people will be more at risk than
populations without higher rates of chronic disease.
Moving Toward Health Equity During the
COVID-19 Pandemic and Beyond
Systemic racism is an aspect of public health that underlies
health inequities and results in unequal health outcomes in soci -
ety. Whether past or present, overt or covert, intentional or sub-
conscious, racism must be rooted out in our society in all its
forms. By examining the relationship between systemic racism
and SDH, we call for the implementation of widespread, socie-
tal change that extends beyond the interpersonal to permeate the
systems in which racism operates. In terms of COVID-19, an
impetus for societal change will involve robust research that
collects representative data as the pandemic continues. This
information will inform government, employers, providers of

social services, and society as a whole in the ways that current
policies negatively influence SDH and outcomes of COVID-
19. This work will not only inform the current COVID-19 pan-
demic, but can also inform planning for future emerging
infectious diseases. In addition, it will highlight the ongoing
need to address SDH to reduce a multitude of health disparities
in the United States that affect the quality of life and lifespan of
African American people.
As the Healthy People 2020 goals draw to a close, SDH
should be a continued priority for the United States, as ineq-
uities in socioeconomic status and links to health outcomes
persist. This pandemic underscores the systemic racism and
disparities that have persisted for decades. Now is the time to
rework our government, our public health and medical sys-
tems, our workplaces, our criminal justice systems, and our
communities with a centering foundation of health equity for
African American people.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with
respect
to the research, authorship, and/or publication of this article.
Funding
The authors received no financial support with respect to the
research, authorship, and/or publication of this article.
ORCID iDs
Sarah B. Maness, PhD https:// orcid. org/ 0000- 0003- 0757-
7972
Erika L. Thompson, PhD https:// orcid. org/ 0000- 0002- 7115-
0001

Stacey B. Griner, PhD https:// orcid. org/ 0000- 0002- 2774-
5841
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precautions/groups-at-higher-risk.htmlSocial Determinants of
Health and Health Disparities: COVID-19 Exposures and
Mortality Among African American People in the
United StatesSocial Determinants of Health and Systemic
RacismEducationEconomic StatusHousingCommunityAccess to
Health CareSocial Determinants of Health and
Health Disparities Among African American PeopleSocial
Determinants of Health and Increased Exposure to COVID-19
Among African American PeopleSocial Determinants of Health
and COVID-19 Mortality Among African American
PeopleMoving Toward Health Equity During the COVID-19
Pandemic and BeyondDeclaration of Conflicting
InterestsFundingORCID iDsReferences
Lohia et al. Respir Res (2021) 22:37
https://doi.org/10.1186/s12931-021-01647-6
R E S E A R C H
Preexisting respiratory diseases and clinical
outcomes in COVID-19: a multihospital cohort
study on predominantly African American
population
Prateek Lohia1* , Kalyan Sreeram1, Paul Nguyen1, Anita
Choudhary1, Suman Khicher1, Hossein Yarandi2,
Shweta Kapur2 and M. Safwan Badr1

Abstract
Background: Comorbidities play a key role in severe disease
outcomes in COVID-19 patients. However, the literature
on preexisting respiratory diseases and COVID-19, accounting
for other possible confounders, is limited. The primary
objective of this study was to determine the association between
preexisting respiratory diseases and severe disease
outcomes among COVID-19 patients. Secondary aim was to
investigate any correlation between smoking and clinical
outcomes in COVID-19 patients.
Methods: This is a multihospital retrospective cohort study on
1871 adult patients between March 10, 2020, and
June 30, 2020, with laboratory confirmed COVID-19 diagnosis.
The main outcomes of the study were severe disease
outcomes i.e. mortality, need for mechanical ventilation, and
intensive care unit (ICU) admission. During statistical
analysis, possible confounders such as age, sex, race, BMI, and
comorbidities including, hypertension, coronary artery
disease, congestive heart failure, diabetes, any history of cancer
and prior liver disease, chronic kidney disease, end-
stage renal disease on dialysis, hyperlipidemia and history of
prior stroke, were accounted for.
Results: A total of 1871 patients (mean (SD) age, 64.11 (16)
years; 965(51.6%) males; 1494 (79.9%) African Americans;
809 (43.2%) with ≥ 3 comorbidities) were included in the study.
During their stay at the hospital, 613 patients (32.8%)
died, 489 (26.1%) needed mechanical ventilation, and 592
(31.6%) required ICU admission. In fully adjusted models,
patients with preexisting respiratory diseases had significantly
higher mortality (adjusted Odds ratio (aOR), 1.36; 95%
CI, 1.08–1.72; p = 0.01), higher rate of ICU admission (aOR,
1.34; 95% CI, 1.07–1.68; p = 0.009) and increased need for
mechanical ventilation (aOR, 1.36; 95% CI, 1.07–1.72; p =
0.01). Additionally, patients with a history of smoking had

significantly higher need for ICU admission (aOR, 1.25; 95%
CI, 1.01–1.55; p = 0.03) in fully adjusted models.
Conclusion: Preexisting respiratory diseases are an important
predictor for mortality and severe disease outcomes,
in COVID-19 patients. These results can help facilitate efficient
resource allocation for critical care services.
Keywords: COVID-19, Mechanical ventilation, Intensive care,
Smoking, tobacco, Mortality
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Introduction

Coronavirus disease-2019 (COVID-19) has infected
close to 55.6 million people worldwide and resulted
in more than 1.34 million deaths as of late-Novem-
ber 2020. In the United States (US) alone, more than
11.6 million people have been infected and 250,000
Open Access
*Correspondence: [email protected]
1 Department of Internal Medicine, Wayne State University,
4201 St
Antoine, Detroit, MI UHC 5C, USA
Full list of author information is available at the end of the
article
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Page 2 of 9Lohia et al. Respir Res (2021) 22:37
people have died. Early reports from the US sug-
gest that patients with preexisting comorbid diseases
including chronic lung diseases are at a higher risk of
severe COVID-19 disease [1–3]. Similar studies from
China [4–7] and Italy [8] have noted that patients with
preexisting respiratory diseases have higher mortal-
ity. According to the global burden of disease, Chronic
Obstructive Pulmonary Disease (COPD) is the third
leading cause of death worldwide [9] and chronic lower
respiratory diseases have been identified as the fourth
leading cause of death in the US accounting for 5.7%

of total deaths [10]. Obstructive sleep apnea (OSA) is
another common preexisting respiratory condition
affecting close to 1 billion people worldwide, with high
prevalence in the US [11]. However limited informa-
tion is available describing mortality and the need for
mechanical ventilation in patients with preexisting res-
piratory diseases and COVID-19.
A study by the Chinese Center for Disease Control
and Prevention had reported an average case-fatality
rate of around 2.3%, however, significantly higher mor-
tality was noted in critically ill patients in intensive care
[4]. Other studies have reported that 2–3% of patients
infected with COVID-19 require mechanical ventila-
tion [12–14] and reported a case fatality rate of 1.2% in
the US [15].
Literature is abundant on the negative impact of
smoking on lung health and its association with a
plethora of respiratory conditions. Smoking is also det-
rimental to the immune system [16] and its response to
various infections. Studies have delineated the implica-
tions of increased risk of infection among smokers [17].
Recently an association of smoking with negative pro-
gression and adverse outcomes in COVID-19 patients
has been reported [18].
The increased number of COVID-19 patients pre-
senting with critical illness has resulted in limited
availability of intensive care beds and strained hospi -
tal resources [19]. It is important to identify patients
who are at risk for critical illness, need intensive care
and mechanical ventilation to optimize the use of criti -
cal care resources, especially in inner-city and pre-
dominantly underserved areas. It can aid in efficient
resource allocation, planning for critical care surge, and

appropriate deployment of health care workers.
The main objective of this study is to determine the
correlation between preexisting respiratory diseases
and severe disease outcomes i.e. mortality, need for
mechanical ventilation, and intensive care unit (ICU)
admission among COVID-19 patients. Our study
also explores if the history of smoking in COVID-19
patients is associated with the severe disease outcomes
mentioned above.
Methods
Study design
We conducted a retrospective cohort study on 1871
adult patients with confirmed COVID-19 diagnosis. This
study was deemed exempt by the Detroit Medical Center
(DMC) and Wayne State University institutional review
board. (IRB application #20-07-2528). No external fund-
ing was received for conducting the study.
Study site and patient population
Adult patients (≥ 18 years of age) with a confirmed
COVID-19 diagnosis (either via nasopharyngeal or oro-
pharyngeal swab) were included. Testing for COVID-19
was done at the DMC, one of the largest academic medi-
cal centers and healthcare providers in Southeast Michi -
gan. DMC comprises four distinct hospitals in Michigan
and data from all four hospitals have been included in
this study. These hospitals primarily serve the Detroit
metropolitan area catering to an underserved population
majority of which is African American.
Data collection
A list of patients was collected in collaboration with insti-
tutional information technology services. Patients who
visited DMC between March 10, 2020, and June 30, 2020,

with a laboratory confirmed COVID-19 PCR diagnosis
were included. Patients under the age of 18, any readmis-
sion during the time frame, ambulatory surgery patients,
and pregnant patients were excluded from the study.
Patients who were transferred to an outside facility for
extracorporeal membrane oxygenation (ECMO) therapy
were also excluded.
To determine preexisting respiratory diseases and
smoking status, along with other variables, we manu-
ally searched through clinical notes, emergency depart-
ment (ED) notes, and prior history tab in the electronic
medical records (EMR). Preexisting respiratory diseases
included in the study were COPD, asthma, pulmonary
hypertension, OSA, pulmonary embolism, sarcoidosis,
lung cancer, prior tuberculosis, and interstitial lung dis-
ease. Data points were manually collected and coded for
each patient. Data regarding radiographic imaging dur-
ing hospitalization, initial chest X-ray and chest com-
puterized tomography (CT) scan were also collected for
all the patients, whenever available. The severity of the
preexisting respiratory diseases was also noted, if the
information was available. Disease severity for each con-
dition was determined as follows: (a) COPD severity was
based on the GOLD grade using the pulmonary function
tests (PFTs), (b) OSA severity was classified based on the
apnea–hypopnea index (AHI) from the sleep studies,
(c) asthma severity was determined based upon symp-
toms, nocturnal awakening and PFT’s (d) pulmonary
hypertension, based on mean pulmonary arterial pres-
sure on right heart catheterization, and (e) sarcoido-

sis, based on the baseline chest X-ray findings. Positive
smoking status was established based on the documented
smoking history on the review of EMR. Quantification
of the amount of smoking and categorization of smokers
into current and former smokers could not be done due
to the lack of consistent documentation in EMR. Also,
the nature and clinical course of the patient’s hospitaliza-
tion and their disposition from the ED visit were noted.
Outcomes
The main outcomes for this study were mortality, need
for mechanical ventilation, and ICU admission. Together,
they have been referred to as severe disease outcomes in
COVID-19. All of the patients included in the study had
a documented acute care endpoint (mortality/discharged
status) at the time of data collection. Additionally, the
number and type of prior comorbidities, BMI, disposi-
tion upon ED visit (discharge home, inpatient admission,
and direct ICU admission) were collected. Data regard-
ing whether or not the patient received corticosteroid
treatment during the course of their hospitalization were
also obtained. Charts were screened to determine if the
patient required up-gradation of care to the ICU from
inpatient floors. Demographic data collected included
age, sex, and race.
Statistical analysis
Categorical variables have been described as frequency
and percentages, and continuous variables have been
described as mean and standard deviation. A crude rela-
tive association measure (Odds ratio, OR) was calculated
for each correlation using the Pearson chi-square and
Fisher test. An adjusted odds ratio was calculated using
binary logistic regression. In the fully adjusted models,
adjustments were made for age, sex, race, BMI, and prior
comorbidities including, hypertension, coronary artery

disease (CAD), congestive heart failure (CHF), diabe-
tes, any history of cancer and prior liver disease, chronic
kidney disease (CKD), end-stage renal disease (ESRD)
on dialysis, hyperlipidemia and history of prior stroke.
Age and BMI were taken as continuous variables while
the remaining were categorical variables. A p-value of
less than 0.05 was determined to be significant. Stepwise
regression using forward selection (Wald) method was
also performed to obtain an optimal model and further
validate the findings. Subgroup analyses were done based
on the type of preexisting respiratory disease. Analysis
based on the severity of preexisting respiratory disease
could not be conducted due to the non-availability of this
data for a large number of patients. Statistical analyses
were completed using IBM SPSS Statistics software (ver-
sion 26).
Results
Baseline characteristics
There were 2001 adult patient records with positive
COVID-19 test at the 4 DMC hospitals with a naso-
pharyngeal/oropharyngeal PCR swab between March 10,
2020, and June 30, 2020. A total of 130 patient records
were excluded based on the exclusion criteria, and 1871
patients were included in the study. In the cohort anal-
ysis, there was an almost equal distribution of males
(n = 965, 51.6%) and females (n = 906, 48.4%). The mean
age of patients was 64.11 years (Standard deviation SD
16). More than half the patients (n = 997, 53.3%) were
65 years or older, with African Americans being the
predominant race (n = 1494, 79.9%). About 43% of the
patients had three or more comorbid diseases (n = 809).
The mean BMI of the patient cohort was 31.14 kg/m2 (SD
8.82) and 47% (n = 897) patients were in the obese cat-
egory, 23 patients were missing BMI information in the

chart. About 30.7% of all the patients (n = 575) had a doc-
umented preexisting respiratory disease as part of their
medical history. Additionally, 37.6% (n = 704) of patients
had a history of smoking identified as a part of their
social history. The baseline characteristics of the popula-
tion included are detailed in Table 1.
Clinical course
The total mortality in the cohort was 32.8% (n = 613).
About 17.5% (n = 327) patients were admitted directly
to ICU from the ED. An additional 265 were later trans-
ferred to ICU from the inpatient service. Approximately
one in every three patients (31.6%) who presented to ED
ended up requiring ICU services. Around 8.8% of the
total patients were sent home from ED (n = 165), while
73.7% (n = 1379) were admitted to the inpatient ser-
vice. During the course of hospitalization, 26.1% of the
patients (n = 489) required mechanical ventilation. Uni-
lateral/bilateral infiltrates on chest X-ray at admission
was the most common radiographical finding. Further
details on the clinical course of the patients and radio-
graphical findings are summarized in Table 2.
Preexisting respiratory disease and severe disease
outcomes
Patients with preexisting respiratory diseases had signifi-
cantly higher mortality, higher need for ICU admission,
and a greater need for mechanical ventilation, compared
to the patients without preexisting respiratory diseases.
In unadjusted analysis, patients with preexisting res-
piratory disease were associated with higher mortality,
compared to those without any preexisting respiratory

disease (OR = 1.29; 95% CI, 1.05–1.58; p = 0.02). Having
a preexisting respiratory disease was also associated with
a higher rate of ICU admission (OR, 1.33; 95% CI, 1.08–
1.64; p = 0.007) as well as increased need for mechanical
ventilation (OR, 1.40; 95% CI, 1.13–1.74; p = 0.002).
Even after adjusting for age, sex, race, BMI, and prior
comorbidities including, hypertension, CAD, CHF,
Table 1 Baseline characteristics of patients
Characteristics Cohort (n = 1871)
Age, n (%)
Mean (SD) 64.11 (16)
< 65 874 (46.7)
≥ 65 997 (53.3)
Sex, n (%)
Male 965 (51.6)
Female 906 (48.4)
Race/ethnicity, n (%)
African American 1494 (79.9)
White 340 (18.2)
Asian 21 (1.1)
Middle Eastern 14 (0.7)
Latino/Hispanic 2 (0.1)

BMI, mean (SD) 31.14 (8.82)
< 18.5 (underweight) 46 (2.5)
18.5–24.9 (normal) 411 (22)
25–29.9 (overweight) 512 (27.4)
≥ 30 (obese) 897 (47)
Preexisting respiratory disease, n (%) 575 (30.7)
COPD 317 (16.9)
Asthma 134 (7.2)
Obstructive sleep apnea 63 (3.4)
Pulmonary embolism 27 (1.4)
Pulmonary hypertension 10 (0.5)
Sarcoidosis 8 (0.4)
Lung cancer 9 (0.5)
Prior TB/ILD 5 (0.3)
Number of comorbidities, n (%)
0 257 (13.7)
1 362 (19.3)
2 443 (23.7)
≥ 3 809 (43.2)
Current or former smoker, n (%) 704 (37.6)
Individual preexisting respiratory disease severity
COPD, n (%) 317

GOLD grade I 40 (12.6)
GOLD grade II 18 (5.7)
GOLD grade III 16 (5)
GOLD grade IV 5 (1.6)
Cannot be determined 238 (75.1)
Asthma, n (%) 134
Intermittent 9 (6.7)
Mild 13 (9.7)
Moderate 5 (3.7)
Severe 2 (1.5)
Obstructive sleep apnea, n (%) 63
Mild (5 ≤ AHI < 15) 7 (11.1)
Moderate (15 ≤ AHI < 30) 6 (9.5)
SD Standard deviation, AHI Apnea–hypopnea index, mPAP
Mean pulmonary
arterial pressure
mPAP Mean pulmonary arterial pressure
Table 1 (continued)
Characteristics Cohort (n = 1871)
Severe (AHI ≥ 30) 18 (28.6)

Pulmonary Hypertension (based on mPAP), n (%) 10
Mid 4 (40)
Moderate 1 (10)
Severe 2 (20)
Cannot be determined 3 (30)
Sarcoidosis, n (%) 8
Stage 0 3 (37.5)
Stage 1 2 (25)
Table 2 Clinical course of patients (cohort n = 1871)
Mortality 613 (32.8)
Mechanical ventilation 489 (26.1)
ICU admission 592 (31.6)
Admission disposition
ER Visit Only (Discharged from ER) 165 (8.8)
Inpatient Admission 1379 (73.7)
Direct ER to ICU admission 327 (17.5)
Chest x-ray at admission 1821

Infiltrates (unilateral/bilateral) 1242 (68.2)
Atelectasis 208 (11.4)
Pleural effusion 31 (1.7)
Pulmonary vascular congestion/edema 106 (5.8)
Normal 234 (12.9)
CT scan findings during admission 93
Consolidation 15 (16.1)
Ground glass opacities 59 (63.4)
Pulmonary infiltrates (unilateral/bilateral) 11 (11.8)
Interstitial abnormalities (reticular, fibrous stripes, inter -
lobular septal thickening)
7 (7.5)
Normal 1 (1.1)
Corticosteroids during admission 571
Preexisting respiratory disease 230 (40.3)
No preexisting respiratory disease 341 (59.7)
diabetes, any history of cancer and prior liver disease,

CKD, ESRD on dialysis, hyperlipidemia, and history
of prior stroke, patients with preexisting respiratory
diseases had higher mortality (adjusted(a)OR = 1.36;
95% CI, 1.08–1.72; p = 0.01), increased need for ICU
admission (aOR = 1.34; 95% CI, 1.07–1.68; p = 0.009),
and higher rates of requiring mechanical ventilation
(aOR = 1.36; 95% CI, 1.07–1.72; p = 0.01). Further details
on the results of unadjusted models and fully adjusted
models for the association between preexisting respira-
tory disease and the three severe disease outcomes are
outlined in Table 3. The results for stepwise regression
models exploring the association between preexisting
respiratory disease and the clinical outcomes have been
summarized in Table 4.
Type of preexisting respiratory disease and severe disease
outcomes
Among patients with preexisting respiratory diseases, the
most prevalent condition was COPD, present in more
than half of the patients (n = 317). In the unadjusted
models, COPD (OR, 1.47; 95% CI, 1.14–1.88; p = 0.002),
Asthma (OR, 0.57; 95% CI, 0.38–0.87; p = 0.008) and
OSA (OR, 2.04; 95% CI, 1.23–3.37; p = 0.005) dem-
onstrated significant association with mortality. The
need for mechanical ventilation was also significantly
higher for patients with COPD (OR, 1.35; 95% CI, 1.04–
1.76; p = 0.02) and OSA (OR, 2.85; 95% CI, 1.72–4.73;
p < 0.001). In fully adjusted models, however, only the
association of OSA with the three severe disease out-
comes was found to be statistically significant, mortality
(aOR, 2.59; 95% CI, 1.46–4.58; p = 0.001), ICU admis-
sion (aOR, 1.95; 95% CI, 1.14–3.32; p = 0.01) and need
for mechanical ventilation (aOR, 2.20; 95% CI, 1.28–3.78;
p = 0.004). Table 5 summarizes the association of dif-
ferent preexisting respiratory diseases with the severe

disease outcomes in the unadjusted as well as the fully
adjusted models.
Smoking and severe disease outcomes
Smoking was associated with higher mortality (OR,
1.26; 95% CI, 1.03–1.53; p = 0.02) and increased need for
ICU admission (OR, 1.33; 95% CI, 1.09–1.62; p = 0.005).
The association between smoking and the need for
Table 3 Association between preexisting respiratory
disease/smoking and severe disease outcomes- Mortality,
Mechanical ventilation and ICU admission unadjusted
and adjusted for age, sex, race, BMI and comorbidities
*Fully adjusted for age, sex, race, BMI and comorbidities which
include hypertension, coronary artery disease, diabetes, chronic
kidney disease, ESRD on dialysis,
congestive heart failure, any cancer, any liver disease,
hyperlipidemia and history of previous stroke
OR odds ratio, CI Confidence Interval
Characteristic Mortality ICU Admission Mechanical ventilation
OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-
value
Preexisting respiratory disease
Unadjusted 1.29 (1.05–1.58) 0.02 1.33 (1.08–1.64) 0.007 1.40
(1.13–1.74) 0.002
Fully adjusted* 1.36 (1.08–1.72) 0.01 1.34 (1.07–1.68) 0.009
1.36 (1.07–1.72) 0.01
Smoking

Unadjusted 1.26 (1.03–1.53) 0.02 1.33 (1.09–1.62) 0.005 1.23
(0.99–1.52) 0.05
Fully adjusted* 1.14 (0.91–1.42) 0.25 1.25 (1.01–1.55) 0.03
1.15 (0.92–1.44) 0.21
Table 4 Association between preexisting respiratory
disease/smoking and severe disease outcomes- Mortality,
Mechanical ventilation and ICU admission (using stepwise
regression, forward selection Wald approach)
*Variables in the optimal model- age, sex, BMI, diabetes,
chronic kidney disease and preexisting respiratory diseases
**Variables in the optimal model- age, sex, BMI, diabetes,
chronic kidney disease and preexisting respiratory diseases
***Variables in the optimal model- age, sex, BMI, diabetes,
hypertension, and preexisting respiratory diseases
^Variables in the optimal model- age, sex, BMI, diabetes,
chronic kidney disease and smoking
OR odds ratio, CI confidence interval, NS nonsignificant
Mortality ICU Admission Mechanical ventilation
OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-
value
Preexisting respiratory diseases
1.38 (1.10–1.74)* 0.005 1.34 (1.08–1.66)** 0.009 1.34 (1.07–
1.69)*** 0.01

Smoking
NS 1.28(1.04–1.57)^ 0.02 NS
mechanical ventilation was not statistically significant
(OR, 1.23; 95% CI, 0.99–1.52; p = 0.05). After adjusting
for age, sex, race, BMI, and comorbidities, a significant
association was only noted between smoking and ICU
requirement (aOR, 1.25; 95% CI, 1.01–1.55; p = 0.03).
Table 3 outlines the association of smoking with severe
disease outcomes.
Discussion
This retrospective cohort study provides novel findings
indicating the role of preexisting respiratory diseases as
an important predictor of severe disease outcomes in
patients hospitalized with COVID-19. The study dem-
onstrated a significant association between the pres-
ence of preexisting respiratory diseases and mortality,
ICU admission, and need for mechanical ventilation.
Even when adjusted for possible confounders such as
age, sex, race, BMI and ten prevalent comorbidities,
patients with preexisting respiratory disease had sig-
nificantly higher mortality, greater need for ICU admis-
sion, and increased need for mechanical ventilation.
Hence, the study demonstrates that preexisting respira-
tory diseases are an important predictor for severe dis-
ease outcomes in COVID 19 patients.
Hypertension, coronary artery disease, and diabetes
are the most common reported comorbidities among

COVID-19 patients [6, 20–24], and they have been
found to be associated with severe disease outcomes.
Studies from China [5, 12, 13, 25–27] and Italy [28]
have reported that patients with chronic lung diseases
have worse clinical outcomes, however, they evaluated
a much smaller cohort. Obesity also has been reported
by some, to be a risk factor for mortality in COVID-19
[20, 29, 30]. To date, the literature on the role of pre-
existing respiratory conditions in the clinical course
of COVID-19 positive patients has been limited, and
our study highlights that the presence of preexisting
Table 5 Association between individual preexisting respiratory
disease and severe disease outcomes- Mortality,
Mechanical ventilation and ICU admission
*Fully adjusted for age, sex, race, BMI and comorbidities which
include hypertension, coronary artery disease, diabetes, chronic
kidney disease, ESRD on dialysis,
congestive heart failure, any cancer, any liver disease,
hyperlipidemia and history of previous stroke
OR odds ratio, CI Confidence Interval
Characteristic Number of events
n (%)
Unadjusted Fully adjusted*
OR (95% CI) p-value OR (95% CI) p-value
Mortality
COPD 127 (40.1) 1.47 (1.14–1.88) 0.002 1.20 (0.91–1.58) 0.2
Asthma 30 (22.4) 0.57 (0.38–0.87) 0.008 0.98 (0.61–1.58) 0.94

Obstructive sleep apnea 31 (49.2) 2.04 (1.23–3.37) 0.005 2.59
(1.46–4.58) 0.001
Pulmonary embolism 13 (48.1) 1.92 (0.90–4.12) 0.09 1.86
(0.82–4.23) 0.14
Pulmonary hypertension 4 (40) 1.37 (0.38–4.87) 0.62 1.09
(0.28–4.23) 0.9
Sarcoidosis 1 (12.5) 0.29 (0.04–2.38) 0.28 0.39 (0.04–3.46) 0.4
Lung cancer 4 (44.4) 1.65 (0.44–6.15) 0.45 1.24 (0.30–5.10)
0.76
ICU admission
COPD 114 (36) 1.26 (0.98–1.63) 0.07 1.20 (0.92–1.58) 0.18
Asthma 41 (30.6) 0.95 (0.65–1.39) 0.79 1.16 (0.77–1.74) 0.47
Obstructive sleep apnea 32 (50.8) 2.3 (1.39–3.80) 0.001 1.95
(1.14–3.32) 0.01
Pulmonary embolism 13 (48.1) 2.30 (0.95–4.34) 0.06 2.03
(0.93–4.44) 0.08
(0.34–4.62) 0.74
Sarcoidosis 1 (12.5) 0.31 (0.04–2.50) 0.49 0.39 (0.05–3.21)
0.38
Lung cancer 2 (22.2) 0.62 (0.13–2.97) 0.54 0.63 (0.12–3.28)
0.58

Mechanical ventilation
COPD 99 (31.2) 1.35 (1.04–1.76) 0.02 1.28 (0.96–1.69) 0.09
Asthma 33 (24.6) 0.92 (0.61–1.38) 0.68 1.08 (0.69–1.67) 0.74
Obstructive sleep apnea 31 (49.2) 2.85 (1.72–4.73) < 0.001
2.20 (1.28–3.78) 0.004
Pulmonary embolism 9 (33.3) 1.42 (0.63–3.18) 0.39 1.45(0.63–
3.34) 0.39
(0.23–3.92) 0.95
Sarcoidosis 1 (12.5) 0.40 (0.0–3.28) 0.69 0.52 (0.06–4.30) 0.54
Lung cancer 1 (11.1) 0.35 (0.04–2.82) 0.46 0.35 (0.04–3.00)
0.34
respiratory diseases has a significant impact on clinical
outcomes.
To our knowledge, this is the first study that has
looked at the association of all the prominent respira-
tory diseases with severe disease outcomes in COVID-
19 patients. Patients with OSA had significantly higher
mortality, a higher need for mechanical ventilation, and
a greater need for ICU admission in our study. A recent
study by Cade et al. [31] also noted a significant crude
association between sleep apnea and mortality. However,
in their study, the associations were somewhat attenuated

after adjusting for BMI and other comorbidities. Another
study by Maas et al. [32] reported that OSA was associ-
ated with an increased risk of hospitalization and approx-
imately double the risk of developing respiratory failure.
The patients with OSA in our study were also more than
twice as likely to require mechanical ventilation, com-
pared to the patients without OSA. Prior diagnosis of
OSA in COVID-19 patients has also been reported to be
associated with increased risk of death at day 7 [33]. A
review by Miller et al. [34] provides a plausible explana-
tion linking OSA and COVID-19. It hypothesizes that
periods of hypercapnia and hypoxemia, surges of sympa-
thetic activation, and increased inflammatory markers in
OSA, may contribute to worse outcomes in COVID-19
patients. Further research is warranted to better under-
stand the mechanism by which OSA might be contribut-
ing to worse clinical outcomes in COVID-19 patients.
In our study, patients with COPD also had increased
mortality and a higher need for mechanical ventilation.
However, upon adjusting for age, sex, race, BMI, and
comorbidities, associations were attenuated and failed to
reach the level of traditional significance. In the study by
Grasselli et al. [28] COPD was noted to be significantly
associated with mortality in multivariable analysis, how -
ever, this study did not adjust for BMI which could be a
possible confounder and was accounted for in our study.
Also, in their cohort of 3988 ICU patients, only 0.02%
of the patients had COPD, thereby one can surmise that
COPD does not have a significant association with the
higher need for ICU admission, as seen in our study.
We were unable to demonstrate any statistically signifi-
cant correlation between other respiratory conditions,
apart from COPD and OSA, and the severity outcomes
explored by this study. This may be, in part, due to the
far smaller sample sizes for these other respiratory condi -

tions in our cohort.
This study also demonstrated a crude association
between smoking and severe disease outcomes, par-
ticularly with mortality and the need for intensive care
services. Similar studies looking at the association of
smoking have also demonstrated worse clinical out-
comes in patients with COVID-19[5, 13], increased
rate of hospitalizations [35] and increased incidence
of COVID-19 among young adults [36]. Recent litera-
ture shows an association of smoking and expression of
angiotensin converting enzyme-2 (ACE-2) in small air-
way epithelia [37, 38], which has been identified as the
cell entry receptor for the SARS-CoV 2 virus [39–41].
A recent meta-analysis done by Karanasos et. al. [42]
showed smoking modestly increased disease severity in
COVID-19 patients, similar to what has been reported by
our study. However, vast majority of the studies included
in this meta-analysis did not adjust for confounders. In
our study, when we controlled for age, sex, race, comor-
bidities, and BMI, we still noted a significant association
between smoking and the need for ICU admission.
Our study has several limitations that must be
acknowledged. The data collected relied on clinical
notes to gather the history of preexisting respiratory
disease and smoking. It is subject to both selection and
information bias. Although we had a large database of
2000 + patients, the number of patients with certain pre-
existing respiratory diseases such as OSA, Pulmonary
Hypertension, Sarcoidosis, lung cancer was relatively
small. Also, this is a retrospective study on the data from
4 hospitals in a single geographic location, predomi-
nantly serving the underserved population with a major-
ity of patients being African American, having multiple

comorbidities. This may limit the generalization of these
results. We could not explore further if the severity of
respiratory disease had any impact on COVID-19 dis-
ease progression or clinical outcomes since the data used
to determine the severity of preexisting respiratory dis-
eases were not available for a large number of patients in
this cohort. Another limitation of our study is the lack
of detailed smoking history, including the duration and
amount of smoking. Due to the lack of detailed informa-
tion in the EMR, we could not differentiate between cur-
rent and former smokers. Therefore, any history of past
or current smoking was counted as the smoking status to
be positive. Despite these limitations, the findings of this
study can help to fill some of the vital voids that currently
exist in the understanding of COVID-19.
Conclusion
Preexisting respiratory diseases are an important comor -
bid condition associated with worse clinical outcomes,
higher mortality, greater need for ICU admission, and
increased need for mechanical ventilation, in COVID-19
patients. These results can be useful in planning treat-
ment and allocation of critical care resources, espe-
cially during surges, in regions where such resources are
limited.
Abbreviations
COVID-19: Coronavirus disease; US: United States; COPD:
Chronic obstructive
pulmonary disease; OSA: Obstructive Sleep Apnea; ICU:
Intensive care unit;
DMC: Detroit Medical Center; ED: Emergency department;

ECMO: Extracorpor-
eal membrane oxygenation; EMR: Electronic medical records;
PFT: Pulmonary
function test; CAD: Coronary artery disease; CHF: Congestive
heart failure;
CKD: Chronic kidney disease; ESRD: End stage renal disease;
OR: Odds ratio;
CI: Confidence interval; SD: Standard Deviation; AHI: Apnea–
hypopnea index;
mPAP: Mean pulmonary arterial pressure.
Acknowledgements
We extend our gratitude to the Research Design and Analysis
Unit at Wayne
State University for their assistance with the analyses of the
project.
Authors’ contributions
PL conceptualized the study and performed the lead role in data
acquisition,
data analysis, data interpretation, along with supervising the
project, drafting
the manuscript, and reviewing it for critical intellectual content.
KS, PN, AC,
and SKhicher conceptualized the study, collected the data, and
made sup-
porting contribution editing the manuscript. HY contributed to
data analysis,
data interpretation and made supporting contribution editing the
manu-
script. SKapur was the equal contributor in data analysis, data
interpretation,
drafting the manuscript, and reviewing the manuscript. SB
conceptualized
the study along with supervising the project, data interpretation,
editing the

manuscript, and reviewing it for critical intellectual content. All
authors read
and approved the final manuscript; agree to be accountable for
all aspects of
the work.
Funding
None.
Availability of data and materials
The deidentified data that support the findings of this study can
be available
from the corresponding author upon reasonable request and
appropriate
permission from the institutional IRB.
Ethics approval and consent to participate
The study was exempt by the Detroit Medical Center (DMC)
and Wayne State
University Institutional Review Board. (IRB application #20-
07-2528).
Consent for publication
Not applicable.
Competing interests
All authors declare that they have no competing interests.
Author details
1 Department of Internal Medicine, Wayne State University,
4201 St Antoine,
Detroit, MI UHC 5C, USA. 2 Wayne State University, Detroit,
MI, USA.
Received: 23 November 2020 Accepted: 31 January 2021

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Preexisting respiratory diseases and clinical outcomes
in COVID-19: a multihospital cohort study on predominantly
African American populationAbstract Background: Methods:
Results: Conclusion: IntroductionMethodsStudy designStudy

site and patient populationData collectionOutcomesStatistical
analysisResultsBaseline characteristicsClinical
coursePreexisting respiratory disease and severe disease
outcomesType of preexisting respiratory disease and severe
disease outcomesSmoking and severe disease
outcomesDiscussionConclusionAcknowledgementsReferences
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Racial Disparities in Healthcare: How COVID-19
Ravaged One of the Wealthiest African American
Counties in the United States
Darius D.Reed
To cite this article: Darius D.Reed (2021) Racial Disparities in
Healthcare: How COVID-19
Ravaged One of the Wealthiest African American Counties in
the United States, Social Work in
Public Health, 36:2, 118-127, DOI:
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Racial Disparities in Healthcare: How COVID-19 Ravaged One
of the
Wealthiest African American Counties in the United States
Darius D.Reed a,b
aDepartment of Social Work, Indiana Wesleyan University,
Marion; bSchool of Social Work, Walden University

ABSTRACT
The COVID-19 pandemic swept the globe in January of 2020
causing mass
panic and extreme hysteria. While pandemics are not new,
COVID-19 is
emerging as a public health crisis in nearly every household in
America. In
this paper, I discuss how COVID-19 has ravaged one of the
wealthiest African
American counties in the United States. Using Public Health
Critical Race
Praxis (PHCR) I seek to examine how disparities exist in health
care and public
funding is not equally distributed regardless of wealth and
status for minor-
itized communities. Using PCHR’s framework I highlight many
of the dispa-
rities that exist in health care for people of color during this
global health
crisis and provide implications for improvement in federal,
state, and local
funding in communities of color. This article advances
scholarship on the
intersection between public health and social work particularly
alluding to
the need for increased advocacy for marginalized communities.
KEYWORDS
Anxiety; COVID-19; public
health critical race praxis
(PHCR); social work; African
Americans; marginalized
communities
Introduction

First detected in Wuhan, China, a virus known as severe acute
respiratory syndrome coronavirus (i.e.,
SARS-CoV-2) has presented not only an environmental-based
risk but also a global response (The
Center for Systems Science and Engineering (CSSE) at Johns
Hopkins University, 2020). Since the
proliferation of this virus, public health officials have termed
the subsequent disease as ”COVID-19”
(Centers for Disease Control and Prevention [CDC], 2020).
Since sparking international recognition,
the field of social work practice and education has begun
exploring its impact on different systems
(e.g., education, financial, health, population). As a result,
under the Trump Administration, the
White House Coronavirus Task Force has commissioned key
leaders within public health to combat
its upward progression within U.S. borders. Thus, this sparked
social work to respond to the COVID-
19 pandemic with challenges faced across all levels, especially
a public health perspective.
The mass hysteria presented by the COVID-19 pandemic
impacted every sector of life across the
world. In the beginning stages of the virus many in the African
American community felt that they
were immune from the virus, because media reports primarily
showed White Americans contracting
the Coronavirus. The first publicized case of an African
American testing positive was Donovan
Mitchell, guard for the Utah Jazz (Ellentuck, 2020). This
dispelled the myth that African Americans
could not catch the virus. Since that time CDC data shows that
African Americans have been
disproportionally affected by the virus at much higher levels
than all other races in the United
States (Bouie, 2020). Undoubtedly, this swift change caused

undue anxieties for many African
Americans related as well as health and safety concerns.
Recognizing the anxiety-induced trauma
this presented for African Americans I explored how COVID-19
has affected the wealthiest African
American county in the United States.
CONTACT Darius D.Reed [email protected] 9205 Rice Avenue,
Glenarden, MD 20706 .
SOCIAL WORK IN PUBLIC HEALTH
2021, VOL. 36, NO. 2, 118–127
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https://crossmark.crossref.org/dialog/?doi=10.1080/19371918.2
The article will address how COVID-19 has ravaged one of the
wealthiest African American
County in the United States and the mental health implications
that may result from the fallout. It
will also address the taken for granted perspective of public
health social workers and the potential
fallout that may arise due to the fluid and ever evolving public
health crisis and its subsequent impact
on the mental health of African Americans. Moreover, as an
African American social worker and
educator residing in Prince Georges County Maryland, I give
voice to the unrealized repercussion that
this pandemic has imposed on frontline workers such as myself.
In the section that follows, I will give

a brief literature review on the evolution of COVID-19 not only
locally but also globally. In that same
vein, situate the racial disparities narratives within the
theoretical framework of Public Health Critical
Race Praxis (PHCR) to further elaborate on gravity this
pandemic imposes an already inequitable and
under-resourced healthcare system. Finally, I hope that by
nuancing this virus’s impact; particularly,
among public health social workers will inform how to further
interventions and policies in the event
of another global crisis, whether it be from a social work
education or practice stance.
COVID-19
As stated earlier, in the article, this virus originated within the
borders of mainland China. Since its
global appearance medical and social scientists have engaged in
international deliberations to pinpoint
the exact evolution of SARS-CoV-2 since December 2019
(Holshue et al., 2020). Scientists have
hypothesized that the virus may be airborne thus allowing it to
spread mainly from person to person,
through respiratory droplets (e.g., sneezing, coughing, bodily
fluids) produced by an infectious
person(s). Other discussion involved that due to the
configuration of the virus (e.g., spike proteins)
droplets can land in the mouths or noses of people who are
nearby or possibly be inhaled into the lungs
(CDC, 2020). Therefore, the Trump Administration, and the
guidance of the U.S. Surgeon General,
Jerome M. Adams, they issued a list of recommendations to
combat the spread of SARS-CoV-2 in the
U.S (CDC, 2020).
For context, the first confirmed case of SARS-CoV-2 in the

U.S. was reported on January 31, 2020,
in Washington State (Holshue et al., 2020). Based on current
data, there are now 1,602,148 confirmed
cases as of May 23, 2020; which exceeds cases reported in all
other countries in the world (CSSE, 2020).
As a result of the ever-increasing numbers local and state
governments instituted “shelter-in-place” or
“stay-at-home orders” in order to decrease the number of
COVID-19 cases plaguing the continental
U.S. Understandably, such orders placed an undue economic
and social burdens on the United States;
however, enacting such orders was for the safety and protection
of all citizens. President Trump and
his cabinet encouraged individuals to wear face masks and
engage in “social distancing” where people
practice at least a 6ʹ feet distancing from one another in order to
reduce the surge in COVID-19 cases
(CDC, 2020).
Having given a thorough review of this virus’s origin, it would
now be fair to take into considera-
tion The White House’s response toward treating the confirmed
SARS-CoV-2 cases. Through the
regular and sometimes disorganized White House briefing,
Trump’s White House COVID-19
response team presented the American population with
conflicting health messages in regards to
the severity of its impact as well as potential “treatments.” In
one breadth, Dr. Facui delivered sound
empirical knowledge speaking to the fluidity of the virus global
progression; however, in the same not
being allowed to fully desegregate myth from the fact due to
socio-political constraints. President
Trump initially down-played the severity of the virus, followed
by reversing course and insisting that
Americans take the virus seriously, while in the same breath

expressing that it would “blow over” soon
(Milbank, 2020). As a seasoned social worker this messaged
presented numerous inconsistencies and
undoubtedly resulted in the high level of coronavirus cases.
SOCIAL WORK IN PUBLIC HEALTH 119
The county
Prince George’s County is located in the U.S. state of Maryland,
bordering the eastern portion of
Washington, D.C. As of the 2010 U.S. Census, the population
was 863,420, making it the second-most
populous county in Maryland, behind Montgomery County
(United States Census Bureau, 2010).
Current estimates for the 2020 census place the county at a
population of 909,327 Americans (US
Census Bureau, 2019). Long regarded as a symbol of Black
wealth and excellence with a high
population of highly educated Black professionals,
entrepreneurs and government officials, where
African Americans make up 65% of households and the median
household income is 81,969 USD (US
Census Bureau, 2019). In many affluent African American
communities outside of the Beltway (I-495
highway that splits Prince Georges County’s inner suburban
communities from outer suburban
communities), median household incomes exceed 150,000 USD
(Black Entertainment Television
(BET), 2017). In comparison communities inside the beltway
closer to Washington DC boast
a median income of 55,000. USD Poverty in the county sits at
just under 9% (US Census Bureau,
2019).

Theoretical approaches
Critical race theory (CRT) can be used to explore what it means
to center race/racism throughout
our public healthcare system. Critical race theory brings from
the margins the experiences of racial
and ethnic minorities and how these groups perceive acts of
institutional and structural racism
(Delgado & Stefancic, 2012) to the center in terms of social
work practice. For example, a central
theme of CRT is that race is permanently present in our
everyday lives (Delgado & Stefancic, 2012).
Critical race theory allows for an intersectional critique of the
various ways in which minority
groups can be oppressed (Delgado & Stefancic, 2012) in this
instance inequalities in healthcare stand
out. Additionally, CRT challenges the current multicultural
color-blind approach in social work
education as it relates to educating future public health social
work practitioners about issues of
diversity, inclusion, oppression, discrimination, power, and
privilege (Gutiérrez, 1990; Ortiz & Jani,
2010). Therefore, I argue that social work educators and
practitioners must consider their own
positionality within the larger scheme of societal injustices and
how racism manifests itself in social
work education, practice, and healthcare systems within the
United States (Abrams & Moio, 2009;
Randolph, 2010).
Encompassed within this CRT methodological analysis are the
four focal theoretical tenets of Public
Health Critical Race Praxis (PHCR) which are as follows: 1)
contemporary racial relations, 2) knowl-
edge production, 3) conceptualization and measurement, and 4)

action (Ford & Airhihenbuwa, 2010a,
2010b, 2018c, Gilbert & Ray, 2016). Each tenet supports the
mode of translating the findings not only
qualitatively but also culturally while situating the experiences
of African Americans in Prince Georges
County at the intersection of race, gender, class, and health, and
politics within the current American
landscape. As pointed out by Carbado and Roithmayr (2014),
“Existing literature shows a small
number of critical race theorists working at the intersection of
CRT and the social sciences” (p. 150).
Critical race methodology (CRM)
The broader approach from which this paper emerges focused on
the following three questions: 1)
How does death transcend wealth in the wake of a public health
crisis? 2) What healthcare disparities
are present in predominately African American communities? 3)
What are the implications of
continued healthcare disparities in minority communities? CRT
proceeds from an understanding
that while structural racism is less visible than individual
racism, it is just as, if not more, influential.
Unlike individual racism, structural racism is a systemic,
historically rooted form of oppression that
cannot be eradicated simply at the level of individual attitudes
or behavior. Indeed, the individuals
120 D. D. REED
operating within institutions may be, in practice,
nondiscriminatory, but still operate within a larger
structurally racist context (Freeman, Gwadz, & Silverman et al.,

2017).
Critical race methodology (CRM) operationalizes CRT and
offers a way to understand the experi-
ences of people of color (Solorzano & Yosso, 2002). As a
methodology, CRM uses counter-storytelling
as an analytical tool for understanding discourses on race and
the intersections of other forms of
oppression. Counter-storytelling is a type of storytelling that
acts as a form of resistance to standard or
majoritarian-stories. In this instance, I dispel the myth that
healthcare is distributed equitably across
the continental United States. Grounded in CRT, which argues
that the voices and experiential
knowledge of people of color must be recognized, counter -
storytelling is a “tool for exposing,
analyzing, and challenging the majoritarian stories of racial
privilege” (Solorzano & Yosso, 2002,
p. 32). Therefore, the next section which follows is a
representation of the post-oppositional theorizing
(Bhattacharya, 2016) of the COVID-19 pandemic within the
realm of social work and public health.
Analysis of data
According to the Johns Hopkins Center for Systems Science and
Engineering (2020), there are 13,077
cases of Coronavirus in Prince Georges County (see Table 1),
the most located in the Capital Beltway
area, which consists of the District, and nearby counties in
Virginia and Maryland where, thus far, 477
people have died. When compared with the rest of the state
(44,424 case, 2,207 deaths) Prince Georges
County represents 33% of all cases (CSSE, 2020). One may ask
how does a county with high wealth
suffer from high cases of COVID-19 and death. The reality lies

in the fact that many residents are
front-line workers exposed daily to the virus, and Prince
Georgians disproportionately suffer from
underlying health conditions that make the virus deadlier
(Chason, Wiggins, & Harden, 2020). Nearly
14% of adults in Prince George’s have diabetes, according to
county health statistics, 36% are obese,
and 64% of the county’s Medicare beneficiaries suffer from
hypertension rates above national and
statewide averages (PGC Healthzone, 2017). There are fewer
hospital beds and primary care doctors
than in neighboring jurisdictions, which means residents are
less likely to treat medical problems early.
The county also spends less on public health efforts than its
wealthier neighbors (Chason et al., 2020).
Maryland’s first coronavirus death, announced March 18, was a
Prince Georges County man in his
60s with underlying health conditions. The deaths that fol lowed
have been people from poor
neighborhoods inside the Capital Beltway and wealthy
subdivisions outside of it, representing that
the virus transcends all income brackets and has no specific
group that it will attach to. While it is true
that the majority of deaths from COVID-19 have been African
Americans, one may ask why, when the
access to healthcare is readily available in 2020. The reality is
that healthcare disparities remain in high
African American and minority communities. Despite high per
capita incomes, Prince George’s
County spends less on health and human services than its
neighbors. With 38.94 USD per capita in
general fund investment (see table 2), it falls behind others like
Baltimore County, which spends 45.13
Table 1. Washington region COVID-19 cases.

Variable N %
Maryland
Prince Georges County
Montgomery County
13,077
9,432
27.98
20.18
Anne Arundel County 3,207 6.86
Charles County 956 2.04
Washington DC
DC
7,893 16.88
Virginia
Fairfax 8,734 18.68
Arlington 1,795 3.83
Alexandria 1,657 3.55
SOCIAL WORK IN PUBLIC HEALTH 121
USD; Anne Arundel, at 90.54 USD; Howard County with 109.37
USD; and Montgomery County with
224.25 USD (Maryland, 2019).
The disparities in COVID-19 cases speak to the broader health
care disparities that are often seen in

minority communities, whether in the presence of absence of
Coronavirus. Healthcare can be less
available and accessible in minority areas and also some
mistrust of the health care system because of
past lived experiences. These disparities transcend all economic
levels and platforms throughout the
county. Despite the concentration of wealth and education in the
county, there remain pockets of
poverty, and grave inconsistency in the types of fresh food
options that the county attracts, which plays
a role in the healthcare of African Americans. Lower quality
foods equal higher health problems over
time. Moreover, despite its wealth 11% of residents do not have
insurance, higher than state and local
averages. There are 477 primary care physicians in Prince
George’s, fewer than half the 1,420 in
neighboring, more affluent and whiter Montgomery County
(County Health Rankings, 2020), which
has about 20% more residents. To understand this disparity, you
must first understand Tax Reform
Initiative by Marylanders (TRIM) which limited county tax
revenue by capping property taxes in 1978.
Followed by the recession in the 1990’s which slashed funding
for health and social services. The
trickle-down effect of such resulted in years of lower funding
for services that are greatly needed in
a predominately African American and minority county.
Communities of color share common social and economic
factors, already in place before the
pandemic, that increase their risk for COVID-19. While
disparities in healthcare remain one of the top
reasons for Coronavirus cases in Prince Georges County, I
would be remiss to not mention some of the
other factors that play a role in the high number of cases. One
might be the housing conditions that

many African Americans in major cities reside in. Crowded
living conditions represent a difficult
challenge that is the result of longstanding racial residential
segregation and prior redlining policies for
African Americans and minorities in general. It becomes
difficult to put social distancing practices in
place when multiple people reside in one residence, while
potentially being exposed to the virus as
a result of essential jobs that may not provide protective
equipment (PPE) to their employees. Some of
these essential positions could be environmental services, food
services, transportation, and healthcare
services. These services represent positions that cannot be done
remotely, therefore put many African
Americans and minorities in close contact with others who may
have the virus. Lastly, stress is one of
the most pressing factors that play a role in the virus
manifesting itself. Studies have proved that stress
has a physiological effect on the body’s ability to defend itself
against disease. Income inequality,
discrimination, violence and institutional racism contribute to
chronic stress in people of color that
can wear down their immunity, making them more vulnerable to
infectious disease.
I would be remiss to not mention risk factors within
communities of color that contribute to poor
health outcomes such as: poor nutrition, physical inactivity,
obesity, high blood pressure, and
substance abuse. Noonan, Velasco-Mondragon, and Wagner
(2016) state that access to healthy
foods is a frequent problem in poor African American
communities. Many African American
communities are considered “food deserts” which, describe
neighborhoods without easy access to
supermarkets that sell fresh produce and other healthy foods.

Black neighborhoods have significantly
fewer supermarkets than white ones (Noonan et al., 2016) and
Prince Georges County is no different
despite its wealth status. This in turn results in poor nutrition
which leads to other health problems
Table 2. Health and human services spending
per capita.
General Fund Spending Per Capita
County
Prince Georges County
Baltimore County
$38.94
$45.13
Anne Arundel County $90.54
Howard County $109.37
Montgomery County $224.25
122 D. D. REED
such as obesity and high blood pressure, which could be deemed
an underlying health condition
related to COVID-19. Substance abuse is also included as a risk
factor due to its ability to decrease an
individual’s overall quality of life and lead to severe health
problems. While these risk factors are
standard across the board in all communities, White individuals
have the means and access to better
healthcare and services than many communities of color,
thereby improving their overall quality of

life.
Given the role that public health social workers play in
maintaining continuity of care for those
existing on the margins (e.g., African Americans, Asians,
Hispanics, etc.). It is indictive of policy
makers and those in charge of governance understand the depth
of healthcare disparities for people of
color. The lack of PPE, inconsistent access to healthcare due to
lack of insurance or underinsurance,
chronic health conditions in communities of color, and crowded
living conditions is not only
troubling, but indictive of the lack of governmental investment
and oversight for communities of
color. As I now begin to discuss implications for social work
research, policy, and education. It is
important to put into context just how broken the United States’
healthcare truly is. Regardless of the
socio-political climate, the author’s forthcoming discussion will
support the depth of how present
systems monetize “life” within the United States.
Implications
The aim of this article is to establish the relevance of
application in social work practice for addressing
social justice and healthcare disparities within the social
ecologies of African-Americans at risk for
COVID-19 the following theoretical frameworks: Critical Race
Theory, Critical Race Methodology,
and Public Health Critical Race Praxis. The data presented in
this article elucidate the multiplicity of
ways in which healthcare disparities are present for African
Americans in Prince Georges County. As
highlighted above, if genuine change is to occur within the field
of public health social work, we must

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