U.S. Department of Health and Human Services 
Office of Inspector General 
Targeted Provider Relief Funds 
Allocated to Hospitals Had Some 
Differences with Respect to the 
Ethnicity and Race of Populations 
Served 
 
Ann Maxwell  
Deputy Inspector General 
for Evaluation and Inspections 
July 2023, OEI-05-20-00580  U.S. Department of Health and Human Services 
O  ffice of Inspector General 
 
Report in Brief 
 
J uly 2023, OEI-05-20-00580 
 
 
Targeted Provider Relief Funds Allocated to Hospitals Had Some 
Differences with Respect to the Ethnicity and Race of Populations 
Served 
Why OIG Did This Review 
Key Results 
The COVID-19 pandemic highlighted longstanding inequities, 
Communities with greater  like disparities in funding levels for health care providers by 
concentrations of Hispanic/Latino  locations and populations served, as well as inequitable access 
residents were generally associated  to quality health care.1  From the beginning of the pandemic, 
with less 2020 COVID-19 targeted  reports indicated that people of color (e.g., Hispanic/Latino 
provider relief funding for hospitals 
and Black Americans) and people from economically 
than communities with lower 
disadvantaged communities were at greater risk of COVID-19 
concentrations.  This funding disparity 
exposure, illness, hospitalization, and death than members of 
raises concerns because 
predominantly Non-Hispanic White communities.2, 3, 4, 5, 67   In 
Hispanic/Latino Americans have been 
addition, the Centers for Disease Control and Prevention (CDC) 
more likely to be hospitalized or die 
considers Hispanic/Latino ethnicity and Black race to be 
from COVID-19 than non-Hispanic 
White Americans.    associated with social vulnerability, along with external 
stressors such as poverty and poor housing conditions.  This 
On the other hand, high concentrations 
means that communities with greater concentrations of 
of Non-Hispanic Black residents were 
sometimes associated with more  Hispanic/Latino residents, greater concentrations of Black 
funding, and funding levels were not  residents, and/or higher rates of people experiencing poverty 
found to be associated with poverty.   may be at a greater risk of experiencing long-term financial 
hardship due to disease outbreaks.8, 9   
In April 2020, the U.S. Department of Health and Human Services (HHS) began distributing Provider 
Relief Fund (PRF) payments through the Health Resources and Services Administration (HRSA) to 
support health care providers, including hospitals, on the front line of the pandemic response.  To 
respond to the urgent need for health care funding, Congress required HHS to make PRF payments 
using the most efficient payment systems practicable.  HHS had to make decisions quickly about how to 
allocate money in accordance with statutory criteria associated with the funds.  
 
HHS has placed a priority on promoting health equity and reducing health disparities during the 
COVID-19 pandemic, including in the distribution of resources.10, 11  While the PRF was not designed 
with the goal of addressing health disparities, understanding how early PRF Targeted Distributions 
(Targeted PRF) correlated with racial, ethnic, and economic characteristics of the communities providers 
serve can help to inform decisions for future public health funding and the opportunities they present 
to advance the health equity goals of HHS. 
 
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    1 
OEI-05-20-00580     
How OIG Did This Review 
To analyze hospital funding according to populations served, we took allocations to hospitals from the 
Targeted PRF in 2020 and translated them into estimated PRF per person amounts for each U.S. 
census tract (in this report, we also refer to census tracts as communities).  To do so, we used 
Medicare data about the census tracts served by each hospital, and assigned each hospitals funding 
allocations to those census tracts proportionately.  We then determined whether there were statistically 
significant correlations between PRF per person and the racial, ethnic, and economic composition of the 
census tracts.  To account for other community characteristics that could help explain differences in PRF 
per person, we analyzed rural and nonrural census tracts separately.  We conducted this analysis for the 
approximately $44 billion in Targeted PRF allocated to hospitals in 2020 through the four allotments 
designated for 1) COVID-19 High Impact Area Hospitals, 2) Safety Net Hospitals, 3) Rural Hospitals, and 
4) Indian Health Service and Tribal Hospitals.  
What OIG Found 
Our analysis identified some differences in PRF per person across census tracts with respect to the race 
and ethnicity of their residents, but not with respect to poverty rates.  Specifically, when we analyzed all 
four Targeted PRF allotments combined, we found that communities with greater concentrations of 
Hispanic/Latino residents were associated with less PRF per person than communities with smaller 
concentrations of Hispanic/Latino residents.  In nonrural areas, communities with greater concentrations 
of Non-Hispanic Black residents were associated with more PRF per person than communities with 
smaller concentrations of Non-Hispanic Black residents, but this pattern did not occur in rural areas.  
We did not find a meaningful association between PRF per person and the proportion of residents 
experiencing poverty in the community. 
 
When we analyzed the four Targeted PRF allotments individually, we found the most notable trends in 
the allotment targeted to rural hospitals (about $9.7 billion): Communities with greater concentrations 
of Hispanic/Latino residents or Non-Hispanic Black residents were associated with less PRF per person 
than communities with smaller concentrations of Hispanic/Latino Residents or Non-Hispanic Black 
residents. 
Why This Matters 
Differences in hospital funding with respect to the characteristics of the populations hospitals serve
including race and ethnicitycould potentially exacerbate pre-existing disparities in health outcomes.  
If hospitals that serve populations experiencing disparate health outcomes are under-resourced, those 
populations may be left with less access to high-quality care, which could widen gaps in health 
outcomes. 
 
Health care funding is an important tool that can help HHS contribute to goals of reducing health 
disparities, both in the context of COVID-19 and more broadly.  We hope that this analysis is useful to 
HHS in planning for future emergency funding scenarios and identifying opportunities to support these 
goals, to the extent permitted by law.   
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    2 
OEI-05-20-00580  BACKGROUND 
The Provider Relief Fund 
The Provider Relief Fund (PRF) refers to $178 billion in funds appropriated by 
Congress in 2020 to prevent, prepare for, and respond to COVID-19, and reimburse 
health care providers for expenses or lost revenues related to COVID-19.  Congress 
did not require that PRF be allocated in a way that addressed health disparities or 
build in considerations about which populations were impacted by funding 
decisions.12  Congress delegated PRF payment calculation and disbursement to HHS 
and required HHS to make PRF payments using the most efficient payment systems 
practicable.13   
HHS distributed the PRF through a series of payments (hereafter referred to as 
allotments) to eligible hospitals and other health care providers with the requirement 
that funds be used for health-care-related expenses or lost revenue attributable to 
COVID-19.  HHS designed the General Distributions to provide financial relief to 
providers, including hospitals and other facilities, during the COVID-19 pandemic 
based on provider characteristics such as Medicare reimbursements and Medicaid 
and CHIP participation.14  Targeted Distributions were designed to provide financial 
relief and address COVID-19 challenges for specific types of hospitals and other 
facilities based on characteristics such as high numbers of COVID-19 admissions and 
rural or tribal designations.15  HHS worked with The Office of the Assistant Secretary 
for Planning and Evaluation and Health Resources and Services Administration 
(HRSA), along with multiple stakeholders including third-party contractors, to 
administer the PRF.  In April 2020, HHS designated HRSA as the primary operating 
division in charge of coordinating and overseeing the creation of payment files, 
calculation of payment allocations for eligible health care providers and facilities, and 
disbursement of funds for allotments of the General Distributions and Targeted 
Distributions.  
For additional details about the PRF, see Appendix A. 
Scope and Limitations of Our Evaluation 
We analyzed the following Targeted PRF allotments that were made to hospitals in 
2020: 
  COVID-19 High Impact Area Hospitals (hereafter refered to as High Impact 
Hospitals)  
  HRSA determined eligibility for this allotment based on hospitals that had 
high numbers of confirmed COVID-19 positive inpatient admissions in 
January-June 2020. 
  Safety Net Hospitals  
  Rural Hospitals 
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    3 
OEI-05-20-00580    Indian Health Service and Tribal Hospitals (hereafter referred to as Tribal 
Hospitals) 
 
For our analysis, we excluded Targeted PRF amounts that were allocated to facilities 
other than hospitals (such as nursing homes), as well as Targeted PRF amounts that 
were allocated to childrens hospitals.  
 
As a result of focusing on these specific allotments, we analyzed $43 billion of the 
$46 billion total Targeted PRF allocations that went to hospitals in 2020.  
 
We could not assess patterns 
Exhibit 1: The allotments we reviewed 
in allocations to hospitals 
totaled over $40 billion in Targeted PRF, 
from General Distributions.  
and the allotment targeted at High Impact 
Because these distributions 
Hospitals accounted for almost half that 
were allocated to parent 
companies, data for individual  amount.    
hospital allocations do not 
exist in the case of hospitals in 
hospital systems.  The extent 
to which inclusion of General 
Distribution allocations may 
have resulted in different 
outcomes than those we 
identified is unknown.  
 
We did not review other relief 
funding that HHS is 
distributing outside the PRF, 
such as funds targeted to 
rural communities through 
the American Rescue Plan. 
The extent to which additional 
 
funding may have resulted in 
Source: OIG analysis of subset of Targeted PRF allocations after 
different outcomes than those 
exclusions.  
we identified is unknown. 
 
We were only able to analyze the amounts HRSA allocated to hospitals.  Therefore, 
for the subset of hospitals in our analysis that are owned by a parent organization, 
we are not able to see where the funds were ultimately distributed.  After HRSA 
calculated allocations for each hospital, HRSA then combined the allocations and 
distributed a single payment to parent organizations for all of their hospitals.  Parent 
organizations were given flexibility to re-distribute funding to the facilities under 
their umbrellas at their discretion.    
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    4 
OEI-05-20-00580  This report describes correlations between Targeted PRF per person and census tract 
characteristics.  Our analysis should not be read to imply that differences in census 
tract characteristics caused differences in PRF per person.  Additionally, we did not 
assess compliance with Federal requirements.  
Methodology in Brief 
To compare funding across populations, we took Targeted PRF allocations to 
hospitals and translated them into estimated PRF per person amounts for each U.S. 
census tracta based on data about the census tracts served by each hospital.  We used 
Medicare hospital claims to identify the census tracts where each hospitals patients 
live.b  We attributed each hospitals Targeted PRF allocations to census tracts 
proportionally to the number of the hospitals Medicare claims for patients living in 
each census tract.  Finally, we divided the Targeted PRF amount we attributed to each 
census tract (i.e., from allocations for all hospitals that served the tract) by the 
population of the tract.  See Exhibit 2 for a diagram illustrating the hospital service 
area methodology.    
 
_____________________________________________________________________________________________________________________________ 
a After excluding tracts in the territories and with data anomalies, we examined PRF per person for 70,342 
census tracts (96.2% of tracts in the 50 states + DC). 
b We linked hospital allocations to census tract populations based on Medicare hospital utilization 
patterns because we do not have access to comprehensive nationwide utilization data from other payers.  
For Medicaid, although we have access to hospital claims, we were not able to match these to patients 
residence census tracts.  We conducted a sensitivity test to ensure that this approach did not 
meaningfully impact our results.  See Methodology for more information.   
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    5 
OEI-05-20-00580  Exhibit 2: Model to illustrate the hospital service area methodology we used 
to calculate each census tracts PRF per person  
 
After calculating PRF per person for each census tract, we determined whether PRF 
per person correlated with these demographic and economic characteristics of each 
census tract: racial and ethnic composition and poverty rates.  We performed this 
analysis for the four Targeted PRF allotments combined, as well as for each of the 
allotments individually.  
To account for differences between rural and nonrural communities, we analyzed rural 
and nonrural census tracts separately based on HRSAs designations.  We examined 
PRF per person for 14,814 rural census tracts and 55,528 nonrural census tracts.  
Finally, we created graphs to illustrate the correlations that we found between PRF per 
person and certain demographic characteristics (e.g., race and ethnicity).  We split 
census tracts into four groups based on the shares of specific races or ethnicities and 
then compared the PRF per person for each group.  
See Methodology section for additional details.
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    6 
OEI-05-20-00580  RESULTS 
Across the Targeted PRF allocations we analyzed, both rural and 
nonrural communities with more Hispanic/Latino residents had 
less PRF per person  
We analyzed Targeted PRF amounts across all four allotments to 
We analyzed the 
identify whether there were any overall funding differences when the 
relationship between 
allotments were combined and considered together.  We identified 
community 
some notable patterns in PRF per person related to race and ethnicity, 
characteristics and OIGs 
which we describe below in detail.  Refer to Appendix C for detailed 
estimate of PRF per 
results of all community characteristics that we analyzed.  
person, which is based 
on Targeted PRF  Rural and Nonrural Communities with Greater Concentrations of 
allocations to hospitals  Hispanic/Latino Residents 
in 2020 and data about 
We found census tracts with greater concentrations of Hispanic/Latino 
the census tracts served 
residents were associated with less PRF per person than census tracts 
by each hospital.  
with smaller concentrations of Hispanic/Latino residents.  We 
identified the same trend among both rural and nonrural census tracts, but these 
differences in PRF per person were larger among rural census tracts. 
Exhibit 3: In both rural and nonrural census tracts, we found that 
communities with more Hispanic/Latino residents had less PRF per person. 
 
Notes: The bar graphs illustrate the correlations described in the main text of the Results.  Pop represents the total 
population that lives in each group of census tracts. 
Source: OIG analysis of PRF allocations, HHS Protect Data, ACS Data, Medicare hospitals claims and enrollment data, 
USA Facts COVID-19 data, 2021  
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    7 
OEI-05-20-00580  In both groups of census tracts, we identified a negative correlation between the 
proportion of Hispanic/Latino residents in a census tract and the PRF per person (rural 
correlation coefficient = -0.154, p < .0001; nonrural correlation coefficient = -0.088c, p 
< .0001d,), which means census tracts with greater concentrations of Hispanic/Latino 
residents were associated with less PRF per person than census tracts with smaller 
concentrations of Hispanic/Latino residents.  These patterns are clear when we split 
census tracts into groups by their share of Hispanic/Latino residents to represent the 
correlation results visually, as shown in Exhibit 3.  In rural census tracts, census tracts 
with greater than 15 percent Hispanic/Latino residents were allocated about 16 
percent less PRF per person than census tracts with less than 5 percent 
Hispanic/Latino populations.  We found a funding difference among nonrural census 
tracts but with the same notable pattern: nonrural census tracts with greater than 15 
percent Hispanic/Latino residents were allocated about 6 percent less PRF per person 
than nonrural census tracts with less than 5 percent Hispanic/Latino residents. 
See Appendix C for further statistical analysis.  
Across the Targeted PRF allotments we analyzed, nonrural 
communities with more Non-Hispanic Black residents had more 
PRF per person  
When we analyzed Targeted PRF amounts across all four allotments, we identified a 
meaningful association for nonrural census tracts, but we did not identify a 
meaningful association between PRF per person and the concentration of Non-
Hispanic Black residents for rural census tracts. 
Nonrural Communities with Greater Concentrations of Non-Hispanic Black 
Residents 
We found nonrural census tracts with greater concentrations of Non-Hispanic Black 
residents were associated with more PRF per person than census tracts with smaller 
concentrations of Non-Hispanic Black residents. 
_____________________________________________________________________________________________________________________________ 
c OIG identified correlations with coefficients less than -0.05 or greater than 0.05 as representing 
meaningful associations.  For example, some researchers may consider only stronger correlations, such as 
those with coefficients less than -0.10 or greater than 0.10, as representing meaningful associations.  
d Generally, a p-value less than 0.05 indicates a statistically significant correlation.  Correlation coefficients 
represent the strength of the correlation: coefficients closer to -1.0 indicate a stronger inverse 
relationship, a coefficient of 0 indicates no relationship, and coefficients closer to 1.0 indicate a stronger 
direct relationship. 
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    8 
OEI-05-20-00580  Exhibit 4: In nonrural census 
tracts, we found that 
communities with more Non-
Hispanic Black residents had 
more PRF per person.   
We identified a positive 
correlation between the 
proportion of Non-Hispanic Black 
residents in a nonrural census 
tract and PRF per person 
(correlation = 0.136, p < .0001).  
Nonrural census tracts with 
greater than 15 percent Non-
Hispanic Black residents were 
allocated about 32 percent  Notes: The bar graph illustrates the correlations described in the 
main text of the Results.  Pop represents the total population that 
more PRF per person than 
lives in each group of census tracts. 
census tracts with less than 5   
percent Non-Hispanic Black  Source: OIG analysis of PRF allocations, HHS Protect Data, ACS 
Data, Medicare hospitals claims and enrollment data, USA Facts 
populations. 
COVID-19 data, 2021  
Rural Communities and Concentra tions of Non-Hispanic Black Population 
When we analyzed the Targeted PRF amounts across all four allotments for rural 
census tracts, we did not identify a meaningful association between the PRF per 
person and concentrations of Non-Hispanic Black residents.  The correlation between 
the proportion of Non-Hispanic Black residents and PRF per person is close to zero, 
indicating no meaningful association (correlation = 0.021, p = 0.01). 
 
Across the Targeted PRF allotments we analyzed, there was no 
meaningful association between the proportion of residents 
experiencing poverty and PRF per person  
We did not find a meaningful association between PRF per person and the proportion 
of people experiencing poverty in rural or nonrural communities across the country.  
The correlation coefficients between poverty rates and PRF per person are very small 
for both rural and nonrural census tracts (nonrural census tracts: correlation = -0.030, 
p < .0001; rural census tracts: correlation = 0.040, p < .0001).  
 
   
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    9 
OEI-05-20-00580    Findings Related to Additional Race Groups 
For Non-Hispanic White residents, we found results that were opposite the 
  results for Non-Hispanic Black residents.  Specifically, census tracts with greater 
concentrations of Non-Hispanic White residents were associated with less PRF 
per person than census tracts with smaller concentrations of Non-Hispanic 
 
White residents in both nonrural and rural census tracts, though the correlation 
was stronger in nonrural census tracts.  These results logically make sense as 
proportion of Non-Hispanic White and Non-Hispanic Black populations in 
 
census tracts likely have an inverse relationship.   
 
  We found correlations between PRF per person and concentrations of 
additional race groups.  We did not focus our major takeaways on these groups 
because they comprise relatively small proportions of the population in our 
 
analysis.  For example, we found a negative correlation between the proportion 
of Non-Hispanic residents that identify as Other Race in a census tract and the 
PRF per person, and this correlation was stronger in nonrural census tracts.  
 
Additionally, in our analysis of the Tribal Hospitals allotment specifically, we 
found a positive correlation between the proportion of Non-Hispanic American 
  Indian/Alaska Native (AI/AN) residents in a census tract and the PRF per person, 
indicating that greater concentrations of Non-Hispanic AI/AN residents were 
associated with higher PRF per person from that allotment. 
 
 
See Appendix C for additional details. 
 
In the PRF allotment targeted to rural hospitals, communities 
with more Hispanic/Latino and Non-Hispanic Black Residents 
had less PRF per person  
In addition to analyzing Targeted PRF amounts across all four allotments, we analyzed 
each allotment individually.  Targeted PRF was allocated with different funding 
formulas in the different allotments, so understanding the results of specific 
allotments can help inform future funding decisions. 
We found the most notable trends in the Rural Hospitals allotment.  We found two 
similar trends in this allotment: Communities with greater concentrations of 
Hispanic/Latino residents were associated with less PRF per person than communities 
with smaller concentrations of Hispanic/Latino residents, and communities with 
greater concentrations of Non-Hispanic Black residents were associated with less PRF 
per person than communities with smaller concentrations of Non-Hispanic Black 
residents.    
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    10 
OEI-05-20-00580  In the sections below, we only present the results related to rural census tracts, and 
not nonrural census tracts, because there was only a small percentage of the Rural 
Hospitals allotment that was allocated to rural hospitals that served nonrural 
communities.  However, we did see similar trends in nonrural census tracts as well.  
See Appendix C for detailed analysis. 
The Rural Hospitals Allotment and Hispanic/Latino Residents 
When looking within each individual Targeted PRF allotment, the overall trend of less 
PRF per person to census tracts with greater concentrations of Hispanic/Latino 
residents (as described in the first result) was also evident in the Rural Hospitals 
allotment.  
Exhibit 5: Among rural census 
tracts, we found that 
communities with more 
Hispanic/Latino residents had 
less PRF per person in the Rural 
Hospitals allotment. 
The overall pattern we identified in 
the analysis across all four Targeted 
PRF allotments (i.e., less PRF per 
person allocated to hospitals that 
served communities with greater 
concentrations of Hispanic/Latino 
residents) appears to be driven 
primarily by the differences within  Notes: The bar graph illustrates the correlations described in the 
the Rural Hospitals Allotment,  main text of the Results.  Pop represents the total population 
that lives in each group of census tracts. 
specifically.  In the Rural Hospitals 
 
allotment, we identified a negative  Source: OIG analysis of PRF allocations, HHS Protect Data, ACS 
Data, Medicare hospitals claims and enrollment data, USA Facts 
correlation between the 
COVID-19 data, 2021  
concentration of Hispanic/Latino 
 
residents in a rural census tract and the PRF per person (rural correlation = -0.149, p < 
.0001).  Among the three other individual Targeted PRF allotments (High Impact 
Hospitals, Safety Net Hospitals, Tribal Hospitals), the correlations between the 
concentration of Hispanic/Latino residents and PRF per person were smaller or close 
to zero.  See Appendix C for specific correlation coefficients for each allotment.  
The Rural Hospitals Allotment and Non-Hispanic Black Residents 
In the Rural Hospitals allotment, rural communities with greater concentrations of 
Non-Hispanic Black residents were associated with less PRF per person than rural 
communities with smaller concentrations of Non-Hispanic Black residents.  For the 
Rural Hospitals allotments, we found a negative correlation between PRF per person 
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    11 
OEI-05-20-00580  and concentration of Non-Hispanic Black population in rural census tracts (correlation 
= -0.117, p < .0001).e 
Exhibit 6: Among rural census 
tracts, we found that communities 
with more Non-Hispanic Black 
residents had less PRF per person 
in the Rural Hospitals allotment, 
but this trend was offset by the 
opposite pattern in two other 
allotments.  
 
 
 
Notes: The bar graphs illustrate the correlations described in the main text of the Results.  Pop represents the total 
population that lives in each group of census tracts. 
Source: OIG analysis of PRF allocations, HHS Protect Data, ACS Data, Medicare hospitals claims and enrollment data, 
USA Facts COVID-19 data, 2021 
 
We found that this trend related to Non-Hispanic Black residents was offset by the 
opposite pattern in other individual allotments (see Exhibit 6), which may explain why, 
as noted previously, we did not find a meaningful association between PRF per person 
and concentration of Non-Hispanic Black residents in rural communities when looking 
across all four Targeted PRF allotments.  When we analyzed the High Impact Hospitals 
_____________________________________________________________________________________________________________________________ 
e The correlation in the Tribal Hospitals allotment was also negative (correlation = -0.067, p < .0001).  We 
did not include this allotment in Exhibit 6 because the dollar amounts per person across all rural census 
tracts are too small to be visible on the same scale.  The total money targeted to Tribal Hospitals was 
much smaller than the other allotments.   
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    12 
OEI-05-20-00580  allotment and the Safety Net Hospitals allotment individually, we found a positive 
correlation between PRF per person and concentration of Non-Hispanic Black 
population in rural census tracts (for rural census tracts, High Impact Hospitals 
allotment correlation = 0.286, p < .0001; Safety Net Hospitals allotment correlation = 
0.234, p < .0001).  
 
 
 
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    13 
OEI-05-20-00580  CONCLUSION 
While the PRF was not specifically intended to address health disparities, health care 
funding, in general, is an important tool that can help HHS contribute to goals of 
reducing health disparities by supporting communities and populations with greater 
risk of poor health outcomes, both in the context of COVID-19 and more broadly.  
HHS has prioritized health equity and reducing health disparities, especially during 
the COVID-19 pandemic.16, 17  Many HHS programs, including those that target 
funding and resources, are meant to reach underserved populations and address 
disparities.  Further, HHSs emergency preparedness goals include responding to 
public health emergencies in ways that meet the needs of at-risk populations, which, 
in the case of COVID-19, may include people of color and people from communities 
experiencing higher rates of poverty.18, 19  
Understanding the relationship between the PRF per person and community 
characteristics such as race, ethnicity, and income can help to inform HHS on 
decisions about allotting future emergency funding.  For example, in the PRF 
allotment targeted to rural hospitals, communities with more Hispanic/Latino and 
Non-Hispanic Black Residents had less PRF per person.  However, Hispanic/Latino 
Americans have been more likely to be hospitalized or die from COVID-19 than non-
Hispanic White Americans.  Thus, it may be especially important to consider HHSs 
goal to reduce health disparities when allocating emergency funding to rural 
communities in the future. 
We hope that this analysis is useful to HHS in planning for future emergency funding 
scenarios and identifying opportunities to support these goals, to the extent 
permitted by law.  Given that emergency funding, like PRF, is designed to respond to 
an unexpected and urgent need for health care funding and the Department will have 
to make decisions quickly about how to allocate money, effective planning could help 
ensure HHS emergency funding is allocated in ways that meet the needs of at-risk 
populations and further reduce health disparities.  Because statutory parameters can 
impact HHSs ability to direct funding, it may also be important for HHS to engage 
with Congress regarding the authority needed to most effectively consider health 
equity, alongside other priorities like efficiency and expeditiousness, in emergency 
funding scenarios.   
 
 
 
 
 
 
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    14 
OEI-05-20-00580 
  METHODOLOGY 
Data Sources 
PRF allocations  
HRSA provided OIG with data on all Targeted PRF allocations by provider CMS 
Certification Number (CCN) that were allocated during calendar year 2020.    
Hospital data 
We used data related to individual hospitals from HHS Protect.  HHS Protect is a 
COVID-19 data hub managed by HHS.20  We used this data source to identify 
hospitals and their locations.   
Data from HHS Protect rely on reports submitted by hospitals.  HHS Protect was 
rolled out for use on April 10, 2020.21  
Medicare hospital claims and enrollment data 
We used Medicare Part A and B claims data and Part C encounter data from calendar 
years 2020 and 2021 to identify the service area of each hospital.  We used hospital 
inpatient and outpatient claims of discharges to identify the Medicare patients who 
were served by each hospital, and then used Medicare enrollment files to identify the 
census tracts where those patients live.  
Census tract characteristics  
We used Census American Community Survey (ACS) 2019 5-year estimates for census 
tract social factors: percent of each self-identified race and ethnicity and percent of 
residents with income less than the poverty level (poverty rate) Census.22 
The ACS data are self-reported, based on a sample, and are subject to sampling 
variability.  A relatively small sample of each census tracts population responded to 
the ACS survey, and respondents were instructed to choose which category best fits 
with how they self-identify.   
In the ACS survey, respondents are asked to self-identify their race.  In 2019, 
respondents were presented the following options and told to check all that apply: 
White, Black or African Am., American Indian or Alaska Native, Asian Indian, Chinese, 
Filipino, Japanese, Korean, Vietnamese, Native Hawaiian, Guamanian or Chamorro, 
Samoan, Other Asian, Other Pacific Islander, Some other race.23  The ACS 5-year 
estimates data combine multiple Asian races into one category and multiple Native 
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    15 
OEI-05-20-00580 
  Hawaiian/Pacific Islander races into one category.f  For this study, we further 
combined Asian and Native Hawaiian/Pacific Islander to one category.  We considered 
respondents as Other Race by combining the count of respondents who selected 
Some other race with the count of respondents who checked more than one race.  
In the ACS survey, respondents were asked about their ethnicity separate from race.  
Respondents were presented five options: 1) not Hispanic, Latino, or Spanish origin, 2) 
Yes, Mexican, Mexican Am., Chicano, 3) Yes, Puerto Rican, 4) Yes, Cuban, 5) Yes, 
another Hispanic Latino, Spanish origin (with a text box below to Print origin).  Option 
1 categorizes individuals into not Hispanic, Latino, or Spanish origin while options 2-5 
categorize individuals into Hispanic, Latino, or Spanish origin.  Because ethnicity is 
separate from race, respondents of Hispanic, Latino, or Spanish origin could select any 
of the races presented.   
To simplify our analysis, we combined ACS respondent data about races and 
ethnicities to create the following groups: Hispanic (of any race); non-Hispanic White; 
non-Hispanic Black; non-Hispanic Asian or Native Hawaiian/Pacific Islander (Asian); 
non-Hispanic American Indian/Alaska Native (AIAN); non-Hispanic Other Race (other 
than White, Black, Asian, AIAN); or more than one Race (other Race). 
To define rural and nonrural census tracts, we used HRSAs list of rural tracts.  HRSA 
identifies rural vs. nonrural census tracts using Rural-Urban Commuting Area (RUCA) 
codes created by the U.S. Department of Agricultures Economic Research Service.24 
To determine the distance from each census tract to the nearest hospital, which we 
used as a control in our regression models, we used Medicare enrollment data for 
enrollee addresses and the HHS Protect data above for hospital locations.  For each 
Medicare enrollee, we calculated the straight-line distance to each hospital and 
determined the minimum distance.  We then assigned the census-tract minimum 
distance as the median of all the minimum distances for beneficiaries living in that 
census tract. 
COVID-19 deaths 
We used county COVID-19 death count estimates from calendar year 2020 from 
USAFacts.org.  USAFacts is a nonpartisan non-profit organization that collects and 
shares public data reported by state and local governments.25  The USAFacts data 
provide the cumulative cases and deaths by county from January 22, 2020, through 
December 31, 2020.  Because census-tract level data on COVID-19 deaths were not 
available, we assigned data from each county to the individual census tracts located in 
that county.  Data from USAFacts rely on measurement and reporting from state- and 
local-level public health agencies.26  As such, data collection and reporting 
methodologies may vary.   
_____________________________________________________________________________________________________________________________ 
f We used ACS Table B03002, Hispanic or Latino Origin By Race. 
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
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OEI-05-20-00580 
  Data Analysis 
We analyzed the relationship between PRF per person and the racial, ethnic, and 
economic composition of each census tract.  Specifically, we analyzed these variables: 
concentration of Hispanic/Latino residents, concentration of each race group (e.g., 
Non-Hispanic Black), and concentration of residents experiencing poverty.  For our 
regression modeling, we also analyzed COVID-19 deaths per capita and distance to 
the nearest hospital.  
Calculating PRF allocations per person 
We used Targeted PRF allocation data that we received directly from HRSA to 
calculate the total amount of Targeted PRF for each hospital.  We summed all 
Targeted PRF allotments that were allocated in 2020 except for the allotment targeted 
to childrens hospitals.  We excluded this allotment because we used Medicare data to 
assign PRF dollars to census tracts, as described below.  
We analyzed allocation amounts to most hospitals that were allocated Targeted PRF, 
including acute care hospitals, tribal hospitals, and critical access hospitals.  We 
excluded 20 hospitals that were allocated Targeted PRF but were not required to 
submit COVID-19 data in HHS Protect, for example, rehabilitation and psychiatric 
hospitals.27 
After calculating the Targeted PRF amounts across all four allotments for each 
hospital, we calculated PRF per person for each census tract using data about 
hospital service areas.  We used Medicare claims and enrollment data to identify the 
census tracts of the Medicare patients who had Medicare inpatient or outpatient 
claims from each hospital during calendar year 2020 or 2021.  For each hospital, we 
then assigned PRF dollars to each census tract in its service area, proportional to the 
number of claims for patients who live in each census tract.  For each census tract, we 
summed all the money assigned from all hospitals that served that census tract.  
Lastly, we divided Targeted PRF amounts across all four allotments for each census 
tract by the population of that census tract.  See Exhibit 7 (same as Exhibit 2 from 
Background) for a diagram of the hospital service area methodology using a very 
simple example to illustrate.  We repeated this same methodology for each of the 
individual allotments of funding (High Impact Hospitals, Safety Net Hospitals, Rural 
Hospitals, and Tribal Hospitals), using the same hospital service areas.    
 
 
 
 
 
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Exhibit 7: Model to illustrate the hospital service area methodology we used 
to calculate each census tracts PRF per person  
 
  
Excluded census tracts 
We calculated PRF per person for all census tracts in which Medicare beneficiaries 
served by hospitals live.  However, we then excluded a small number of census tracts 
from the analyses.  We limited our analysis to tracts in 50 States and DC, excluding 
tracts in U.S. territories.  We also excluded census tracts with missing population 
characteristics, especially low population estimates, and data anomalies.  Specifically, 
we excluded tracts with fewer than 100 residents or fewer than 10 Medicare enrolled 
residents per ACS estimates, tracts with greater than 50% of the residents living in 
institutional settings per ACS, and tracts that were outliers when we compared 
Medicare enrollment counts and the ACS estimates of the number of people with 
Medicare coverage.  We excluded 5% of tracts for final analysis of 70,342 tracts. 
Statistical analysis 
For all allotments combined, and separately for the four allotments, we performed the 
following statistical analysis to understand the relationships between PRF per person 
and census tract community characteristics.  
Correlation analysis.  To examine the relationships between PRF per person and 
census tract population characteristics, we calculated Pearson correlation coefficients.  
We did this separately for rural and nonrural census tracts.  We used log transformed 
variables in the correlation analysis to reduce the influence of outliers.  
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  We reported a correlation between variables as those with a Pearson correlation 
coefficient greater than 0.05 or less than -0.05 and a p-value less than .05.  We 
reported no meaningful association between variables when Pearson correlation 
coefficients were between -0.05 and 0.05, even if those correlations were statistically 
significant.  
Regression analysis.  To understand relationships between multiple variables, we ran 
linear regressions controlling for COVID-19 deaths in January 22-June 10, 2020, and 
June 11-December 31, 2020, distance to the nearest hospital, and the census tract 
social factors (proportion of each race and ethnicity group and proportion of 
population experiencing poverty) to understand which factors were associated with 
PRF per person while controlling for the other factors in the model.  Specifically, we 
used the following independent variables: proportion of Hispanic/Latino, Non-
Hispanic Black, Non-Hispanic Asian/PI, and Non-Hispanic AI/AN residents, residents 
who identify as Non-Hispanic Other Race, proportion of people experiencing poverty, 
2020 COVID-19 deaths per capita, and distance to the nearest hospital.  We used the 
same set of independent variables across all regression models so that it would be 
more possible to compare the estimated parameters among models.  We log 
transformed both the dependent and independent variables in all regression models 
to reduce the influence of outliers. 
Graphics.  To highlight the notable correlations that we discuss in the main report, we 
summarized the correlation analysis by creating bar graphs.  We subdivided the 
census tracts into groups based on the population characteristics (percent 
Latino/Hispanic and percent non-Hispanic Black).  For each of these groups, we 
calculated the total PRF divided by the total population and then compared the PRF 
per person across the groups.  We created the groups by using interval cutoffs.  For 
example, we compared PRF per person for tracts with 0-<5% Hispanic/Latino, 5%-
<10% Hispanic/Latino, 10%-<15% Hispanic/Latino, and 15% or higher 
Hispanic/Latino.  The group labeled 0-<5% Hispanic/Latino was created by including 
census tracts with equal to or greater than 0% of Hispanic/Latino residents and less 
than 5% of Hispanic/Latino residents.  5%-<10% Hispanic/Latino was created by 
including census tracts with equal or to greater than 5% Hispanic/Latino residents and 
less than 10% Hispanic/Latino residents.  We continued to use this method for the last 
two interval cutoff groups. 
Scope 
For this evaluation, we analyzed the Provider Relief Funds allocated in Targeted 
Distributions to hospitals during calendar year 2020.  We did not examine General 
Distributions because allocations could not be always linked to individual hospitals.  
We excluded the Targeted Distribution targeted to childrens hospitals because the 
hospital service area distribution limited to people covered by Medicare may not be 
representative of childrens hospital service areas. 
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  Our analysis was limited to Targeted PRF allocated to hospitals in calendar year 2020.  
This means our analysis does not provide information regarding any PRF allocated to 
non-hospital providers, or about any PRF that were distributed after the end of 2020.   
Our analysis should not be read to imply that differences in census tract 
characteristics caused differences in PRF per person.  Additionally, we did not assess 
compliance with Federal requirements. 
Limitations 
We could not assess the patterns in allocations from the General Distribution with 
respect to county population characteristics because data for individual hospital 
allocations do not always exist.  General Distribution payments were not always 
allocated at the facility level because, in the case of hospitals in hospitals systems, 
they were instead allocated directly to parent companies.  Further, we were only able 
to analyze Targeted Distribution amounts HRSA allocated to hospitals initially, not 
where the funds were ultimately distributed, because parent organizations, which can 
include multiple hospitals located in different areas, were given flexibility to re-
allocate and distribute funding to facilities under their umbrella at their discretion.  
Therefore, we could not assume parent organizations disbursed the money in any 
certain way.  It is unknown the extent to which General Distribution allocations may 
have changed the outcomes we identified, particularly for hospitals that did not 
receive any funding through the Targeted Distribution but did receive funding 
through later phases of the General Distribution.   
However, researchers have raised concerns about the equity of the earlier phases of 
General Distributions.  A study published in the Journal of the American Medical 
Association suggested that the formulas used to allocate money may have favored 
hospitals that primarily serve White and higher-income populations.28  Specifically, 
initial distributions were allocated proportionally to hospitals past revenues.  
Hospitals that primarily serve White populations tend to have higher revenues, on 
average, than hospitals that primarily serve people of color and whose patients are 
more likely to be covered by Medicaid or underinsured.29, 30  However, because 
General Distribution payments were often calculated and reported at the parent 
organization level, there is not comprehensive data on how much individual facilities 
and providers qualified for.  
We used Medicare data in our calculation of PRF per person, rather than information 
on all hospital patients, because we do not have access to comprehensive nationwide 
utilization data from other payers that could be matched with residence census tracts.  
Medicare claims data might not always be representative of the full population that 
receives care at any given hospital.  Although we do have hospital utilization from 
Medicaid, we could not effectively match Medicaid enrollees to their residence census 
tracts so we could not use this data.  
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  In this study, we used demographic data about characteristics of people living in 
census tracts.  Geography is a proxy that can help us understand the potential impact 
on certain individuals, but it is not a substitute for lived experiences of individuals.  
Additionally, we used county-level data for 2020 COVID-19 deaths because 2020 
COVID-19 deaths data were not available at the census tract level.   
Standards 
We conducted this study in accordance with the Quality Standards for Inspection and 
Evaluation issued by the Council of the Inspectors General on Integrity and Efficiency.  
 
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  APPENDIX A 
Detailed information about Provider Relief Fund and related 
relief funding 
Provider Relief Fund 
The PRF refers to relief funds appropriated by Congress to the Public Health and 
Social Services Emergency Fund based on the Coronavirus Aid, Relief, and Economic 
Security (CARES) Act in March 2020, the Paycheck Protection Program and Health 
Care Enhancement Act (PPHCEA) in April 2020, and the Coronavirus Response and 
Relief Supplemental Appropriations (CRRSA) Act  in December 2020.  The CARES Act 
provided $100 billion, the PPHCEA added $75 billion, and the CRRSA Act added an 
extra $3 billion.31, 32, 33   
Through these Acts, Congress delegated PRF payment calculation and disbursement 
to HHS and required HHS to make PRF payments using the most efficient payment 
systems practicable.34  Congress appropriated these funds to prevent, prepare for, 
and respond to coronavirus, domestically or internationally, for necessary expenses to 
reimburse health care providers for health care related expenses or lost revenues that 
are attributable to coronavirus.35  Additionally, some of the PRF appropriations were 
used for claims reimbursement programs and COVID-19 Coverage Assistance Fund.36, 
37 
HHS worked with The Office of the Assistant Secretary for Planning and Evaluation 
and Health Resources and Services Administration (HRSA), along with multiple 
stakeholders including third-party contractors, to administer the PRF.  In April 2020, 
HHS designated HRSA as the primary operating division in charge of coordinating 
and overseeing the creation of payment files, calculation of payment allocations for 
eligible health care providers and facilities, and disbursement of funds for allotments 
of the General Distributions and Targeted Distributions.g  
General Distributions 
General Distributions were intended to provide financial relief to providers, including 
hospitals and other facilities, during the COVID-19 pandemic based on characteristics 
such as participation in Medicare or Medicaid.38  General Distributions consisted of 
four phases of allotments.39, 40, h  Phase 1 consisted of a total of $41.9 billion to 
_____________________________________________________________________________________________________________________________ 
g According to HRSA, third-party contractors developed the methodology and determined the calculated 
payments files for hospitals and providers.  Then, HRSA and the third-party contractors identified 
ineligible hospitals and providers.  Lastly, the United Health Group allocated final calculated distributions 
to the eligible providers.  
h Dollar amounts for General Distribution Phases 1-3 allotments are current as of January 2023. 
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OEI-05-20-00580 
  providers who billed Medicare fee-for-service in calendar year 2019.41  Phase 2 
consisted of $4.5 billion to providers that participated in state Medicaid/Childrens 
Health Insurance Plan or Medicaid managed care plans, dental providers, assisted 
living facilities, and certain Medicare providers.42  Phase 3 consisted of $19.5 billion to 
providers previously eligible for earlier phases, while also expanding to additional 
provider types such as behavioral health providers.43  Phase 3 payment calculations 
included a deduction of all prior PRF payments for providers that received any PRF 
funds.44  Phase 4 has consisted of $15.4 billion to providers previously eligible for 
earlier phases.  Phase 4 payments consisted of 1) base payments based on providers 
reported changes in revenues and expenses from July 1, 2020, to March 31, 2021; and 
2) bonus payments based on providers level of participation in Medicaid, the 
Childrens Health Insurance Program, and Medicare.45, i  
According to HRSA, General Distribution payments were sometimes allocated and/or 
disbursed to parent organizations, rather than individual facilities or providers.  At the 
beginning of the pandemic, health care facilities experienced immediate financial 
hardship.46  HHS made payments quickly to provide financial support to ensure 
providers such as hospitals could sustain services.47  The CARES Act required HHS to 
use the most efficient payment systems practicable to provide PRF payments.48  
According to HHS guidance, any parent organization with more than one facility was 
allowed to allocate and distribute funding to the facilities under its umbrella at its 
discretion.49   
Targeted Distributions 
Targeted Distributions were intended to provide financial relief to address added 
COVID-19 challenges, specific types of hospitals, and other facilities based on certain 
metrics and characteristics (e.g., high numbers of COVID-19 admissions, rural or tribal 
designations).  Targeted Distributions consisted of several allotments that each had 
different formulas and methodologies to determine PRF payment amounts.50      
Targeted Distributions consisted of several allotments, some of which included 
payments to hospitals.j, 51  The High Impact allotment consisted of $20.7 billion to 
hospitals that had a high number of confirmed COVID-19 positive inpatient 
admissions.52  The Rural allotment consisted of approximately $11 billion to rural 
hospitals including rural acute care general hospitals and Critical Access Hospitals 
(CAHs), primarily based on facility operating expenses.53  The IHS and Tribal Hospitals 
allotment consisted of $494 million to IHS and tribal facilities based on facility or 
program operating expenses and estimated service population.54  The Safety Net 
Hospitals allotment consisted of $13.9 billion to Safety Net Hospitals, acute care 
hospitals, and childrens hospitals based on profit margin, proportion of Medicare 
_____________________________________________________________________________________________________________________________ 
i Dollar amount for General Distribution Phase 4 allotment is current as of January 2023. 
j Not all $178 billion of the PRF has been allocated to hospitals and providers.  
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OEI-05-20-00580 
 
 
  patients, and proportion of Medicaid patients for the childrens hospitals.55  Exhibit 8 
describes the metrics used for each PRF Targeted Distribution allotment. 
Exhibit 8: 2020 PRF Targeted Distribution allotments that included hospitals  
Targeted PRF 
Allotments  Total Amounts  Metric Used to Target Allotments 
COVID-19 High  $20.7 billion  Number of COVID-19 positive inpatient admissions  
Impact Area 
(High Impact 
Hospitals) 
Safety Net  $12.8 billion  Proportion of the individual facility score (number of facility 
Hospitals: Acute  beds multiplied by Medicare Disproportionate Patient 
Care Facilities  Percentage for an acute care facility) to the cumulative 
facility scores 
Rural Hospitals  $10.7 billion  Base payments plus operating expenses (including lost 
and Clinics  revenue) of rural acute care hospitals and CAHs, operating 
expenses of Sole Community Hospitals, Medicare 
Dependent Hospitals, & Rural Referral Center Hospitals in 
Small Metro Areas 
Safety Net  $1.1 billion  Proportion of the individual facility score (facility beds 
Hospitals:  multiplied by Medicaid only ratio) to the cumulative facility 
Childrens  scores 
Hospitals 
Indian Health  $0.49 billion  Base payments plus operating expenses of IHS and Tribal 
Services and  Hospitals and estimated service population 
Tribal Hospitals 
 
Notes: These amounts represent the total number of dollars distributed in the listed Targeted PRF allotments.  They do 
not represent the subset of Targeted PRF allotments that were in OIGs analysis. 
Source: HRSA, Provider Relief Fund Past Payments Targeted Distributions website.56  Dollar amounts are current as of 
November 2022. 
According to HRSA, Targeted Distribution allotments were allocated for each 
individual facility and were either distributed to individual facilities or rolled up and 
distributed to parent organizations. 
Related COVID-19 Relief Funding 
In addition to PRF, HHS, through HRSA, began distributing American Rescue Plan 
payments to rural health care providers in November 2021.  These funds were 
intended to address the disproportionate impact of COVID-19 on rural health care 
providers and rural communities.57  These payments were based on characteristics 
such as Medicare reimbursements for services to rural beneficiaries.58   
 
 
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  Related OIG Work 
The Office of Inspector General (OIG) is conducting strategic oversight of HHSs 
pandemic response and recovery efforts, including additional reviews related to the 
PRF.  For example, OIG found that HHS and HRSA developed controls related to 
selected PRF program requirements to ensure that providers received the correct PRF 
payments from the Phase 1 General Distribution in a fast, fair, and transparent manner 
but that some of those controls could be improved.59 
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OEI-05-20-00580 
  APPENDIX B 
Rural and Nonrural Census Tracts 
Hospitals serving rural census tracts were allocated more PRF per person than 
hospitals serving nonrural census tracts.  The PRF per person in rural census tracts was 
$218 per person, which is 81% higher than the PRF per person in nonrural census 
tracts.  
The COVID-19 pandemic has impacted urban and rural populations differently.  The 
disease initially spread most rapidly in large metropolitan areas (nonrural), which 
aligns with the fact that most of the U.S. population lives in dense, urban regions.  By 
late October 2020, the number of cumulative COVID-19 cases in rural areas exceeded 
those in urban areas.60   
In addition, the racial and ethnic makeup of rural and nonrural census tracts differs 
considerably.  Exhibit 9 summarizes many of the differences in specific racial and 
ethnic group populations between rural and nonrural census tracts. 
Because of these differences, we analyzed rural and nonrural census tracts separately 
based on HRSAs designations. 
Exhibit 9: Statistics on nonrural and rural census tracts 
  Mean in Nonrural  Mean in Rural 
Census Tracts   Census Tracts 
(n = 55528 tracts)   (n = 14814 tracts) 
2020 COVID-19  106.87  108.92 
Deaths/100,000 
residents 
% Poverty  13.96%  15.99% 
% Non-Hispanic Black  14.86%  7.80% 
% Non-Hispanic Asian/PI  6.21%  0.95% 
% Non-Hispanic AI/AN  0.39%  1.71% 
% Non-Hispanic Other  2.81%  2.08% 
Race 
% Non-Hispanic White  57.05%  78.98% 
% Hispanic/Latino  18.68%  8.48% 
Note: 2020 COVID-19 deaths represent the total deaths during the range of dates included in our study, January 22, 
2020, to December 31, 2020.  
Source: OIG Analysis of USAFacts COVID-19 data, ACS data, HRSA Rural Census Tract Designations.  
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
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OEI-05-20-00580  APPENDIX C 
Detailed Statistical Analysis for All PRF and Individual Waves 
We found that the median PRF per person across the four allocations was $106, and 
PRF per person ranged between $49 per person and $203 per person.     
Correlation Analysis Results 
In the main report, we reported on the correlation coefficients between PRF per 
person and proportion of Hispanic/Latino ethnicity, Non-Hispanic Black, and 
proportion of people experiencing poverty for all Targeted PRF allotments.  We also 
performed the same analysis with regards to the following census tract characteristics: 
proportion of Non-Hispanic Asian/PI residents, Non-Hispanic AI/AN residents, 
residents who identify as Non-Hispanic Other Race, 2020 COVID-19 deaths per capita, 
and distance to the nearest hospital.  Exhibit 10 shows the correlation coefficients for 
each of these characteristics in all Targeted PRF allotments.  We calculated two sets of 
correlation coefficients for all Targeted PRF allotments: 1) using only nonrural census 
tracts and 2) using only rural census tracts. 
The purpose of this report was to analyze correlations between PRF per person and 
community demographic characteristics, including race, ethnicity, and economic 
status.  Because of this, we did not focus on the correlations between PRF per person 
and 2020 COVID-19 deaths or PRF per person and distance to the nearest hospital, 
even though these correlations were often stronger than that of the variables we 
discussed in our main findings. 
Please note that most of the census tracts have small percentages of residents that 
identify as Non-Hispanic Other Race (a race not listed or more than one race), Non-
Hispanic Asian/PI, and Non-Hispanic AI/AN (please see Appendix B for further 
details).   
   
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  Exhibit 10: Results for correlation analysis for all Targeted PRF allotments in 
nonrural tracts and rural tracts 
 
Note: Census tract characteristics were log transformed in all regression models. 
All Targeted PRF Allotments 
Pearson Correlation Coefficients (p-values) 
Nonrural Tracts (n = 55528 tracts) 
Census   
Tract  2020  2020  % Non-  
Charact- COVID  COVID  % Non- % Non-  % Non- Hispanic  % Non- Distance 
eristics  Deaths  Deaths  Hispanic  Hispanic  Hispanic  Other  %  Hispanic  %  from 
(JanJun)  (JunDec)  AIAN  Asian  Black  Race  Hispanic  White  Poverty  hospital 
PRF per  0.637  0.015  -0.097  -0.034  0.136  -0.142  -0.088  -0.192  -0.030  -0.125 
Person 
p-value  <.0001  0.0006  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001 
Rural Tracts (n = 14814 tracts) 
Census  2020  2020  % Non-
Tract  COVID  COVID  % Non- % Non- % Non- Hispanic  % Non- Distance 
Charact- Deaths  Deaths  Hispanic  Hispanic  Hispanic  Other  %  Hispanic  %  from 
eristics  (JanJun)  (JunDec)  AIAN  Asian  Black  Race  Hispanic  White  Poverty  hospital 
PRF per  0.021  0.082  0.072  -0.101  0.021  -0.072  -0.154  -0.060  0.040  -0.251 
Person 
p-value  0.0113  <.0001  <.0001  <.0001  0.0096  <.0001  <.0001  <.0001  <.0001  <.0001 
 
We also calculated correlation coefficients between PRF per person and community 
characteristics for each individual Targeted PRF allotment.  Exhibits 11-14 show those 
correlation coefficients for High Impact Hospitals, Safety Net Hospitals, Rural 
Hospitals, and Tribal Hospitals allotments.  We calculated correlation coefficients 
separately for nonrural and rural census tracts. 
   
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  Exhibit 11: Results for correlation analysis for High Impact Hospitals 
allotments in nonrural tracts and rural tracts 
Note: Census tract characteristics were log transformed in all regression models. 
High Impact Hospitals Allotments 
Pearson Correlation Coefficients (p-values) 
Nonrural Tracts (n = 55528 tracts) 
Census   
Tract  2020  2020  % Non-  
Charact- COVID  COVID  % Non- % Non-  % Non- Hispanic  % Non- Distance 
eristics  Deaths  Deaths  Hispani Hispanic  Hispanic  Other  %  Hispanic  from 
(JanJun)  (JunDec)  c AIAN  Asian  Black  Race  Hispanic  White  % Poverty  hospital 
PRF per  0.795  -0.024  -0.159  0.060  0.156  -0.142  -0.009  -0.186  -0.104  -0.139 
Person 
p-values  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  0.0343  <.0001  <.0001  <.0001 
Rural Tracts (n = 14814 tracts) 
Census  2020  2020  % Non-
Tract  COVID  COVID  % Non- % Non- % Non- Hispanic  % Non- Distance 
Charact- Deaths  Deaths  Hispani Hispanic  Hispanic  Other  %  Hispanic  from 
eristics  (JanJun)  (JunDec)  c AIAN  Asian  Black  Race  Hispanic  White  % Poverty  hospital 
PRF per  0.514  0.096  -0.102  -0.029  0.286  -0.101  -0.029  -0.164  -0.046  0.007 
Person 
p-values  <.0001  <.0001  <.0001  0.0004  <.0001  <.0001  0.0004  <.0001  <.0001  0.3739 
 
Exhibit 12: Results for correlation analysis for Safety Net Hospitals allotments 
in nonrural tracts and rural tracts 
Note: Census tract characteristics were log transformed in all regression models. 
Safety Net Hospitals Allotments 
Pearson Correlation Coefficients (p-values) 
Nonrural Tracts (n = 55528 tracts) 
Census   
Tract  2020  2020  % Non-  
Charact- COVID  COVID  % Non- % Non-  % Non- Hispanic  % Non- Distance 
eristics  Deaths  Deaths  Hispanic  Hispanic  Hispanic  Other  %  Hispanic  %  from 
(Jan  Jun)  (Jun  Dec)  AIAN  Asian  Black  Race  Hispanic  White  Poverty  hospital 
PRF per  0.264  0.044  -0.029  -0.018  0.153  -0.068  0.017  -0.224  0.093  -0.133 
Person 
p-values  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001 
  Rural Tracts (n = 14814 tracts)   
Census  2020  2020  % Non-
Tract  COVID  COVID  % Non- % Non- % Non- Hispanic  % Non- Distance 
Characteri Deaths  Deaths  Hispanic  Hispanic  Hispanic  Other  %  Hispanic  %  from 
stics  (Jan  Jun)  (Jun - Dec  AIAN  Asian  Black  Race  Hispanic  White  Poverty  hospital) 
PRF per  0.118  0.057  -0.032  -0.078  0.234  -0.020  -0.066  -0.124  0.191  0.003 
Person 
p-values  <.0001  <.0001  <.0001  <.0001  <.0001  0.015  <.0001  <.0001  <.0001  0.7052 
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  Exhibit 13: Results for correlation analysis for Rural Hospitals allotments in 
nonrural tracts and rural tracts 
Note:  Census tract characteristics were log transformed in all regression models. 
Rural Hospitals Allotments 
Pearson Correlation Coefficients (p-values) 
Nonrural Tracts (n = 55528 tracts) 
Census 
tract  2020  2020  % Non-
Charact- COVID  COVID  % Non- % Non-  % Non- Hispanic  % Non- Distance 
eristics  Deaths  Deaths  Hispanic  Hispanic  Hispanic  Other  %  Hispanic  %  from 
(JanJun)  (Jun  Dec)  AIAN  Asian  Black  Race  Hispanic  White  Poverty  hospital
PRF per  -0.348 0.137  0.140  -0.314 -0.198 -0.056 -0.319 0.271  -0.032 0.252 
Person 
p-values <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001
Rural Tracts (n = 14814 tracts) 
Census  2020  2020  % Non-
Tract  COVID  COVID  % Non- % Non- % Non- Hispanic  % Non- Distance 
Character Deaths  Deaths  Hispanic  Hispanic  Hispanic  Other  %  Hispanic  %  from 
istics  (Jan  Jun)  (Jun  Dec)  AIAN  Asian  Black  Race  Hispanic  White  Poverty  hospital
PRF per  -0.167 0.074  0.015  -0.097 -0.117 -0.065 -0.149 0.111  0.001  -0.310
Person 
p-values <.0001  <.0001  0.0763  <.0001  <.0001  <.0001  <.0001  <.0001  0.8842  <.0001
Exhibit 14: Results for correlation analysis for Tribal Hospitals allotments in 
nonrural tracts and rural tracts 
Note: Census tract characteristics were log transformed in all regression models. 
Tribal Hospitals Allotments 
Pearson Correlation Coefficients (p-values) 
Nonrural Tracts (n = 55528 tracts) 
Census 
Tract  2020  2020  % Non-
Charact- COVID  COVID  % Non- % Non-  % Non- Hispanic  % Non- Distance 
eristics  Deaths  Deaths  Hispanic  Hispanic  Hispanic  Other  %  Hispanic  from 
(JanJun)  (JunDec)  AIAN  Asian  Black  Race  Hispanic  White  % Poverty  hospital
PRF per  -0.092 0.062  0.448  -0.035 -0.055 0.079  0.057  0.037  0.037  0.001 
Person 
p-values <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001  0.8141 
Rural Tracts (n = 14814 tracts) 
Census  2020  2020  % Non-
Tract  COVID  COVID  % Non- % Non- % Non- Hispanic  % Non- Distance 
Charact- Deaths  Deaths  Hispanic  Hispanic  Hispanic  Other  %  Hispanic  from 
eristics  (JanJun)  (JunDec)  AIAN  Asian  Black  Race  Hispanic  White  % Poverty  hospital
PRF per  -0.008 -0.008 0.641  0.015  -0.067 0.262  0.044  -0.283 0.104  0.045 
Person 
p-values 0.3218  0.3462  <.0001  0.0673  <.0001  <.0001  <.0001  <.0001  <.0001  <.0001
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    30 
OEI-05-20-00580   
Regression Analysis Results 
We used multivariable linear regressions to determine if the relationships we 
identified in the correlation analyses remain when all the census tract characteristics 
are included in a single model.  Our regressions control for COVID-19 deaths in 
January 22-June 10, 2020, and June 11-December 31, 2020, distance to the nearest 
hospital, and the census tract social factors (proportion of each race/ethnicity group, 
proportion of population experiencing poverty) to understand which factors were 
related to PRF per person while controlling for the other factors in the model.  We log 
transformed both the dependent and independent variables in all regressions to 
reduce the influence of outliers.  We report regression results for the five different 
outcomes: all Targeted PRF allotments per person and each of the four individual 
allotments per person (High Impact Hospitals, Safety Net Hospitals, Rural Hospitals, 
and Tribal Hospitals).  In addition to examining all tracts in a single model, we 
modeled the relationships by HRSA designation. 
It should be noted that the variation in PRF per person is more fully explained by the 
included community characteristics for the High Impact Hospitals, Tribal and Rural 
Hospitals allotments and less well explained in the models for Safety Net Hospitals 
allotments as indicated by the Adjusted R-Squared values.  We also tested alternative 
models for Rural, Tribal and Safety Net Hospitals that received PRF in those 
allocations.  This substantially improved the Adjusted R-Squared for the Safety Net 
Hospitals models, but the resulting set of independent variables would have been 
different from the models for High Impact Hospitals.  Results in Exhibits 15-19 should 
be interpreted with this limitation of the models in mind. 
Exhibit 15: Linear regression model results for all Targeted PRF allotments 
Note: Census tract characteristics and PRF per person were log transformed in all regression models. 
All Targeted PRF Allotments 
Linear Regression Model of Targeted PRF per Person 
  Nonrural   Rural  
(n =55528 tracts)  (n = 14814 tracts) 
Estimate  Std. Error  p-value  Estimate  Std. Error  p-value 
Intercept  3.488  0.040  <.0001  5.928  0.046  <.0001 
COVID-19 Deaths Jan-Jun  0.550  0.003  <.0001  0.009  0.004  0.0345 
COVID-19 Deaths Jun-Dec  -0.061  0.008  <.0001  0.028  0.006  <.0001 
% Non-Hispanic AI/AN  0.043  0.010  <.0001  0.140  0.008  <.0001 
% Non-Hispanic Asian/PI  -0.074  0.004  <.0001  -0.133  0.011  <.0001 
% Non-Hispanic Black  0.045  0.003  <.0001  -0.012  0.005  0.0226 
% Non-Hispanic Other Race  -0.163  0.006  <.0001  -0.111  0.010  <.0001 
% Hispanic/Latino  -0.070  0.004  <.0001  -0.125  0.005  <.0001 
% Poverty Rate  -0.057  0.006  <.0001  0.010  0.012  0.3751 
Distance  -0.053  0.006  <.0001  -0.252  0.007  <.0001 
Adjusted R-Squared   0.4273  0.1359 
 
   
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    31 
OEI-05-20-00580 
  Exhibit 16: Regression model results for High Impact Hospitals allotments  
Note: Census tract characteristics and PRF per person were log transformed. 
High Impact Hospitals Allotments 
Linear Regression Model of Targeted PRF per Person 
  Nonrural   Rural  
(n = 55528 tracts)  (n = 14814 tracts) 
Estimate  Std. Error   p-value  Estimate  Std. Error   p-value 
Intercept  1.636  0.045  <.0001  2.418  0.073  <.0001 
COVID-19 Deaths Jan-Jun  0.988  0.003  <.0001  0.420  0.007  <.0001 
COVID-19 Deaths Jun-Dec  -0.157  0.009  <.0001  0.018  0.010  0.0722 
% Non-Hispanic AI/AN  -0.077  0.011  <.0001  -0.017  0.013  0.2035 
% Non-Hispanic Asian/PI  -0.035  0.005  <.0001  -0.038  0.018  0.032 
% Non-Hispanic Black  0.124  0.004  <.0001  0.211  0.008  <.0001 
% Non-Hispanic Other Race  -0.245  0.007  <.0001  -0.117  0.016  <.0001 
% Hispanic/Latino  0.047  0.004  <.0001  -0.009  0.009  0.3203 
% Poverty Rate  -0.272  0.007  <.0001  -0.374  0.019  <.0001 
Distance  0.015  0.007  0.0391  0.044  0.010  <.0001 
Adjusted R-Squared  0.655  0.3073  
 
Exhibit 17: Linear regression model results for Safety Net Hospitals 
allotments 
Note: Census tract characteristics and PRF per person were log transformed in all regression models.  
Safety Net Hospitals Allotments 
Linear Regression Model of Targeted PRF per Person 
  Nonrural   Rural  
(n = 55528 tracts)  (n = 14814 tracts) 
Estimate  Std. Error  p-value  Estimate  Std. Error  p-value 
Intercept  2.331  0.057  <.0001  2.150  0.085  <.0001 
COVID-19 Deaths Jan-Jun  0.232  0.004  <.0001  0.052  0.008  <.0001 
COVID-19 Deaths Jun-Dec  0.030  0.011  0.0051  -0.008  0.012  0.4744 
% Non-Hispanic AI/AN  0.046  0.013  0.0005  -0.031  0.016  0.0451 
% Non-Hispanic Asian/PI  -0.016  0.006  0.0066  -0.117  0.021  <.0001 
% Non-Hispanic Black  0.090  0.005  <.0001  0.180  0.010  <.0001 
% Non-Hispanic Other Race  -0.118  0.009  <.0001  -0.011  0.019  0.5697 
% Hispanic/Latino  -0.006  0.005  0.1873  -0.085  0.010  <.0001 
% Poverty Rate  0.060  0.008  <.0001  0.326  0.022  <.0001 
Distance  -0.131  0.009  <.0001  0.042  0.012  0.0006 
Adjusted R-Squared  0.0921  0.0789 
 
   
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
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OEI-05-20-00580 
  Exhibit 18: Linear regression model results for Rural Hospitals allotments  
Note: Census tract characteristics and PRF per person were log transformed in all regression models. 
Rural Hospitals Allotments 
Linear Regression Model of Targeted PRF per Person 
  Nonrural   Rural  
(n = 55528 tracts)  (n = 14814 tracts) 
Estimate  Std. Error  p-value  Estimate  Std. Error  p-value 
Intercept  1.561  0.042  <.0001  5.918  0.059  <.0001 
COVID-19 Deaths Jan-Jun  -0.229  0.003  <.0001  -0.103  0.005  <.0001 
COVID-19 Deaths Jun-Dec  0.220  0.008  <.0001  0.065  0.008  <.0001 
% Non-Hispanic AI/AN  0.250  0.010  <.0001  0.069  0.011  <.0001 
% Non-Hispanic Asian/PI  -0.203  0.004  <.0001  -0.190  0.014  <.0001 
% Non-Hispanic Black  -0.160  0.003  <.0001  -0.110  0.007  <.0001 
% Non-Hispanic Other Race  0.016  0.007  0.0135  -0.111  0.013  <.0001 
% Hispanic/Latino  -0.277  0.004  <.0001  -0.167  0.007  <.0001 
% Poverty Rate  0.084  0.006  <.0001  0.042  0.015  0.006 
Distance  0.117  0.007  <.0001  -0.421  0.009  <.0001 
Adjusted R-Squared  0.3285  0.2021 
 
Exhibit 19: Linear regression model results for Tribal Hospitals allotments 
Note: Census tract characteristics and PRF per person were log transformed in all regression models. 
Tribal Hospitals Allotments 
Linear Regression Model of Targeted PRF per Person 
  Nonrural   Rural  
(n = 55528 tracts)  (n = 14814 tracts) 
Estimate  Std. Error  p-value  Estimate  Std. Error  p-value 
Intercept  -0.123  0.010  <.0001  -0.392  0.035  <.0001 
COVID-19 Deaths Jan-Jun  -0.003  0.001  <.0001  0.026  0.003  <.0001 
COVID-19 Deaths Jun-Dec  0.030  0.002  <.0001  0.019  0.005  <.0001 
% Non-Hispanic AI/AN  0.262  0.002  <.0001  0.578  0.006  <.0001 
% Non-Hispanic Asian/PI  -0.008  0.001  <.0001  -0.031  0.008  0.0002 
% Non-Hispanic Black  -0.008  0.001  <.0001  -0.021  0.004  <.0001 
% Non-Hispanic Other Race  0.019  0.002  <.0001  0.103  0.008  <.0001 
% Hispanic/Latino  0.009  0.001  <.0001  -0.008  0.004  0.0704 
% Poverty Rate  -0.005  0.001  0.0003  0.047  0.009  <.0001 
Distance  -0.005  0.002  0.0016  0.009  0.005  0.0625 
Adjusted R-Squared  0.2101  0.4209 
 
 
   
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    33 
OEI-05-20-00580 
  Stepwise Regression Analysis for All Targeted PRF Allotments 
To better understand the relationship between proportion of Hispanic/Latino 
residents in a census tract and PRF and proportion of Non-Hispanic Black residents 
and PRF, we performed stepwise regression analysis on all Targeted PRF allotments.  
For both nonrural and rural census tracts, we started with a model including variables 
for race, ethnicity, and poverty, and then added controls to the model in steps to 
observe how estimates in the model changed.    
In Exhibit 20, you can see that there is a positive correlation between the proportion 
of Non-Hispanic Black residents and PRF per person in nonrural census tracts when 
only race/ethnicity and poverty variables are included in the model.  However, the 
estimate associated with proportion of Non-Hispanic Black residents decreases 
dramatically when the COVID-19 death rate variables are introduced to the model.  
This indicates that although higher proportions of Non-Hispanic Black residents are 
associated with higher PRF per person in nonrural census tracts, this relationship can 
be partially explained by the positive association between COVID-19 death rates and 
PRF per person in All Targeted PRF Allotments combined.  
In both Exhibits 20 and 21 (both nonrural and rural census tracts), you can see that 
the estimate associated with proportion of Hispanic/Latino residents does not change 
meaningfully when the COVID-19 death rate variables are introduced to the model.  
This indicates that the difference in PRF per person between high-Hispanic/Latino and 
low-Hispanic/Latino communities cannot be explained by low COVID-19 impact in 
areas with greater concentrations of Hispanic/Latino residents. 
Exhibit 20: Stepwise regression model results for all Targeted PRF allotments 
in nonrural census tracts 
Note: Census tract characteristics and PRF per person were log transformed in all regression models. 
All Targeted PRF Allotments 
Stepwise Regression Model of Targeted PRF per Person  
  Nonrural Tracts (n = 55528 tracts) 
Step 1 (No COVID-19  Step 2 (Include COVID-19  Step 3 (Include COVID-19 
Deaths and Distance)  Deaths; No Distance)  Deaths and Distance) 
Estimate  Std.  p-value  Estimate  Std.  p-value  Estimate  Std.  p-value 
Error  Error  Error 
Intercept  4.817  0.020  <.0001  3.333  0.036  <.0001  3.488  0.040  <.0001 
% Non-Hispanic AI/AN  -0.158  0.012  <.0001  0.043  0.010  <.0001  0.043  0.010  <.0001 
% Non-Hispanic Asian/PI  0.027  0.005  <.0001  -0.067  0.004  <.0001  -0.074  0.004  <.0001 
% Non-Hispanic Black  0.171  0.004  <.0001  0.047  0.003  <.0001  0.045  0.003  <.0001 
% Non-Hispanic Other Race  -0.307  0.008  <.0001  -0.161  0.006  <.0001  -0.163  0.006  <.0001 
% Hispanic/Latino  -0.070  0.004  <.0001  -0.067  0.003  <.0001  -0.070  0.004  <.0001 
% Poverty Rate  -0.135  0.007  <.0001  -0.046  0.006  <.0001  -0.057  0.006  <.0001 
COVID-19 Deaths Jan-Jun        0.555  0.003  <.0001  0.550  0.003  <.0001 
COVID-19 Deaths Jun-Dec        -0.061  0.008  <.0001  -0.061  0.008  <.0001 
Distance              -0.053  0.006  <.0001 
Adjusted R-Squared  0.064  0.427  0.427 
 
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    34 
OEI-05-20-00580 
  Exhibit 21: Stepwise regression model results for all Targeted PRF 
allotments in rural census tracts 
Note: Census tract characteristics and PRF per person were log transformed in all regression models. 
All Targeted PRF Allotments 
Stepwise Regression Model of Targeted PRF per Person 
  Rural Tracts (n = 14814 tracts) 
Step 1 (No COVID-19  Step 2 (Include COVID-19  Step 3 (Include COVID-19 
Deaths and Distance)  Deaths; No Distance)  Deaths and Distance) 
Estimate  Std.  p-value  Estimate  Std.  p-value  Estimate  Std.  p-value 
Error  Error  Error 
Intercept  5.330  0.033  <.0001  5.125  0.043  <.0001  5.928  0.046  <.0001 
% Non-Hispanic AI/AN  0.112  0.009  <.0001  0.111  0.009  <.0001  0.140  0.008  <.0001 
% Non-Hispanic Asian/PI  -0.080  0.011  <.0001  -0.069  0.012  <.0001  -0.133  0.011  <.0001 
% Non-Hispanic Black  0.012  0.005  0.0214  0.003  0.006  0.5816  -0.012  0.005  0.0226 
% Non-Hispanic Other  -0.099  0.011  <.0001  -0.088  0.011  <.0001  -0.111  0.010  <.0001 
Race 
% Hispanic/Latino  -0.104  0.006  <.0001  -0.106  0.006  <.0001  -0.125  0.005  <.0001 
% Poverty Rate  0.049  0.012  <.0001  0.046  0.012  0.0002  0.010  0.012  0.3751 
COVID-19 Deaths Jan-Jun        0.008  0.004  0.0744  0.009  0.004  0.0345 
COVID-19 Deaths Jun-Dec        0.047  0.007  <.0001  0.028  0.006  <.0001 
Distance              -0.252  0.007  <.0001 
Adjusted R-Squared  0.042  0.048  0.133 
  
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    35 
OEI-05-20-00580 
  ACKNOWLEDGMENTS AND CONTACT 
Acknowledgments 
Samantha Handel-Meyer and Hilary Slover served as the team leaders for this study, 
and Thaddeus Chatto served as the lead analyst.  Others in the Office of Evaluation 
and Inspections Regional Offices who conducted the study include Rebecca Gorges 
and Brianna Weldon.  Office of Evaluation and Inspections headquarters staff who 
provided support include Althea Hosein, Robert Gibbons, Michael Novello, and Sarah 
Swisher.  
We would also like to acknowledge the contributions of other Office of Inspector 
General staff, including Elina Breton, Bianca Hernandez, Justin Koppa, and Jason Lee.  
This report was prepared under the direction of Laura Kordish, Regional Inspector 
General for Evaluation and Inspections in the Chicago Regional Office, Adam 
Freeman, Deputy Regional Inspector General, and Hilary Slover, Assistant Regional 
Inspector General.  
Contact 
To obtain additional information concerning this report, contact the Office of Public 
Affairs at Public.Affairs@oig.hhs.gov.  OIG reports and other information can be found 
on the OIG website at oig.hhs.gov. 
Office of Inspector General 
U.S. Department of Health and Human Services 
330 Independence Avenue, SW 
Washington, DC 20201 
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    36 
OEI-05-20-00580 
  ENDNOTES 
 
1 Commonwealth Fund, Achieving Racial and Ethnic Equity in U.S. Health Care.  Accessed at Achieving Racial and Ethnic Equity 
in U.S. Health Care: Scorecard | Commonwealth Fund on May 15, 2023.  
2 CDC, COVID-19 in Racial and Ethnic Minority Groups.  Accessed at COVID-19 racial and ethnic health disparities : 1. 
Introduction (cdc.gov) on November 6, 2020. 
3 CNN, Black communities account for disproportionate number of COVID-19 deaths in the US, study finds.  Accessed at Black 
communities account for disproportionate number of Covid-19 deaths in the US, study finds | CNN on November 6, 2020. 
4 Washington Post, The coronavirus is infecting and killing black Americans at an alarmingly high rate.  Accessed at 
https://www.washingtonpost.com/nation/2020/04/07/coronavirus-is-infecting-killing-black-americans-an-alarmingly-high-
rate-post-analysis-shows/ on November 6, 2020.  
5 Brookings, Racial economic inequality amid the COVID-19 crisis.  Accessed at https://www.brookings.edu/research/racial-
economic-inequality-amid-the-covid-19-crisis/ on November 6, 2020. 
6 Pew Research Center, For U.S. Latinos, COVID-19 Has Taken a Personal and Financial Toll. Accessed at For U.S. Latinos, 
COVID-19 Has Taken a Personal and Financial Toll | Pew Research Center on August 27 2021. 
7 COVID-19 has had a disproportionate impact among high-poverty populations: National Center for Biotechnology 
Information, Coronavirus infections and deaths by poverty status: The effects of social distancing.  Accessed at 
https://pubmed.ncbi.nlm.nih.gov/33362321/ on August 27, 2021. 
8 HHS Office of Minority Health, Minority Health Social Vulnerability Index Overview.  Accessed at Minority Health Social 
Vulnerability Index Overview (hhs.gov) on October 26, 2022. 
9 CDC, CDC SVI 2018 Documentation.  Accessed at SVI2018Documentation_01192022_1.pdf (cdc.gov) on October 27, 2022. 
10 Executive Order 13995, Ensuring an Equitable Pandemic Response and Recovery, Code of Federal Regulations, Vol. 86, No. 15 
(enacted January 26, 2021).  
11 HHS, HHS Equity Action Plan.  Accessed at HHS Equity Action Plan on May 16, 2021.  
12 Coronavirus Aid, Relief, and Economic Security (CARES) Act, Div. B, tit. VIII, P.L. 116-136 (enacted March 27, 2020).  
13 Ibid. 
14 HRSA, Provider Relief Fund: General Information.  Accessed at CARES Act Provider Relief Fund: General Information | 
HHS.gov on August 19, 2021. 
15 HRSA, Provider Relief Fund Past Payments Targeted Distributions.  Accessed at Past Targeted Distributions | Official web site 
of the U.S. Health Resources & Services Administration (hrsa.gov) on September 8, 2021. 
16 HHS, Office of Minority Health, About COVID-19 Health Equity Task Force.  Accessed at Health Equity Task Force - The Office 
of Minority Health (hhs.gov) on October 1, 2021. 
17 HHS, Strategic Plan FY 2018  2022, Strategic Objective 2.4: Prepare for and respond to public health emergencies.  Accessed 
at Strategic Goal 2: Protect the Health of Americans Where They Live, Learn, Work, and Play | HHS.gov on December 3, 2021. 
18 CDC, Health Equity Considerations & Racial & Ethnic Minority Groups.  Accessed at Health Equity Considerations and Racial 
and Ethnic Minority Groups | CDC on January 25, 2022. 
19 HHS, HHS Equity Action Plan.  Accessed at HHS Equity Action Plan on May 16, 2021. 
20 HHS, HHS Protect FAQ. Accessed at hhs-protect-faqs.pdf on October 25, 2021.  
21 Ibid. 
 
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    37 
OEI-05-20-00580   
22 U.S. Census Bureau, Why We Ask Each Question About / Race, Accessed at Race | American Community Survey | U.S. Census 
Bureau on June 11 2023. 
23 U.S. Census Bureau, Questionnaire Archive.  Accessed at https://www.census.gov/programs-
surveys/acs/methodology/questionnaire-archive.2019.html at on October 7, 2021. 
24 HRSA, Defining Rural Population. Accessed at Defining Rural Population | HRSA on April 8, 2022.  
25 USA Facts, About USAFacts.  Accessed at https://usafacts.org/about-usafacts/ on October 21, 2021. 
26 USA Facts, Detailed Methodology and Sources: COVID-19 Data.  Accessed at https://usafacts.org/articles/detailed-
methodology-covid-19-data/ on November 12, 2020. 
27 HHS, COVID-19 Guidance for Hospital Reporting and FAQs For Hospitals, Hospitals Laboratory, and Acute Care Facility Data 
Reporting.  Accessed at covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf< (hhs.gov) on 
October 25, 2021. 
28 Journal of American Medical Association, Allocation of COVID-19 Relief Funding to Disproportionately Black Counties.  
Accessed at Allocation of COVID-19 Relief Funding to Disproportionately Black Counties | Health Disparities | JAMA | JAMA 
Network on August 25, 2021. 
29 Journal of Health Politics, Policy and Law, Duke University Press, Unsanitized and Unfair: How COVID-19 Bailout Funds Refuel 
Inequity in the US Health Care System.  Accessed at Unsanitized and Unfair: How COVID-19 Bailout Funds Refuel Inequity in the 
US Health Care System | Journal of Health Politics, Policy and Law | Duke University Press (dukeupress.edu) on November 29, 
2021. 
30 KFF, Changes in Health Coverage by Race and Ethnicity since the ACA, 2010-2018.  Accessed at https://www.kff.org/racial-
equity-and-health-policy/issue-brief/changes-in-health-coverage-by-race-and-ethnicity-since-the-aca-2010-2018/ on March 
5, 2020. 
31 CARES Act, Div. B, tit. VIII, P.L. 116-136 (enacted March 27, 2020).  
32 Paycheck Protection Program and Health Care Enhancement Act, Div. B, tit. I, P.L. 116139 (enacted April 24, 2020). 
33 Coronavirus Response and Relief Supplemental Appropriations Act, 2021, Div. M, tit. III, P.L. 116-260 (enacted December 27, 
2020). 
34 CARES Act, Div. B, tit. VIII, P.L. 116-136 (enacted March 27, 2020). 
35 Ibid. 
36 HRSA, COVID-19 Claims Reimbursement to Health Care Providers and Facilities for Testing, Treatment, and Vaccine 
Administration for the Uninsured.  Accessed at COVID-19 Claims Reimbursement to Health Care Providers and Facilities for 
Testing, Treatment, and Vaccine Administration for the Uninsured | Official web site of the U.S. Health Resources & Services 
Administration (hrsa.gov) on January 27, 2022. 
37 HRSA, COVID-19 Coverage Assistance Fund.  Accessed at COVID-19 Coverage Assistance Fund | Official web site of the U.S. 
Health Resources & Services Administration (hrsa.gov) on January 27, 2022. 
38 HRSA, Provider Relief Fund Past Payments General Distributions.  Accessed at Past General Distributions | Official web site of 
the U.S. Health Resources & Services Administration (hrsa.gov) on September 8, 2021. 
39 Ibid. 
40 HRSA, Provider Relief Funds Future Payments.  Accessed at Future Payments | Official web site of the U.S. Health Resources 
& Services Administration (hrsa.gov) on September 15, 2021. 
41 HRSA, Provider Relief Fund Past Payments General Distributions.  Accessed at Past General Distributions | Official web site of 
the U.S. Health Resources & Services Administration (hrsa.gov) on September 8, 2021. 
42 Ibid. 
43 Ibid. 
 
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    38 
OEI-05-20-00580   
44 HRSA, Provider Relief Fund 3: Payment Calculation Methodology.  Accessed at Provider Relief Fund Phase 3: Payment 
Calculation Methodology (hrsa.gov) on February 7, 2022. 
45 HRSA, Provider Relief Funds Future Payments.  Accessed at Future Payments | Official web site of the U.S. Health Resources 
& Services Administration (hrsa.gov) on October 25, 2022. 
46 In March 2020, hospitals reported substantial challenges with mitigation strategies, and needs for assistance related to 
personal protective equipment (PPE), testing, staffing, supplies and durable equipment; maintaining or expanding facility 
capacity; and financial concerns.  OEI-06-20-00300. 
47 HHS Press Release, HHS to Begin Immediate Delivery of Initial $30 Billion of CARES Act Provider Relief Funding.  Accessed at 
2020-12-31 08:51 | Archive of HHS.gov (pagefreezer.com) on January 14, 2022. 
48 CARES Act, Div. B, tit. VIII, P.L. 116-136 (enacted March 27, 2020). 
49 HRSA, Provider Relief Fund General Information.  Accessed at Provider Relief Fund General Information | Official web site of 
the U.S. Health Resources & Services Administration (hrsa.gov) on September 17, 2021.  
50 HRSA, Provider Relief Fund Past Payments Targeted Distributions.  Accessed at Past Targeted Distributions | Official web site 
of the U.S. Health Resources & Services Administration (hrsa.gov) on September 8, 2021. 
51 Ibid. 
52 Ibid. 
53 Ibid. 
54 Ibid. 
55 Ibid. 
56 Ibid. 
57 HRSA, Current and Future Payments.  Accessed at Current and Future Payments | HRSA on December 19, 2022. 
58 Ibid. 
59 OIG, HHSs and HRSAs Controls Related to Selected Provider Relief Fund Program Requirements Could Be Improved.  Accessed 
at HHS's and HRSA's Controls Related to Selected Provider Relief Fund Program Requirements Could Be Improved A-09-21-
06001 09-26-2022 on December 7, 2022. 
60 USDA Economic Research Service, The COVID-19 Pandemic and Rural America.  Accessed at USDA ERS - The COVID-19 
Pandemic and Rural America on August 17, 2021. 
Targeted Provider Relief Funds Allocated to Hospitals Had Some Differences with Respect to the Ethnicity and Race of 
Populations Served    39 
OEI-05-20-00580