Racial and Ethnic Disparities in Emergency Department Care and Health Outcomes Among Children in the United States

Abstract

Background: There is an incomplete understanding of disparities in emergency care for children across racial and ethnic groups in the United States. In this project, we sought to investigate patterns in emergency care utilization, disposition, and resource use in children by race and ethnicity after adjusting for demographic, socioeconomic, and clinical factors. Methods: In this cross-sectional study of emergency department (ED) data from the nationally representative National Hospital Ambulatory Medical Survey (NHAMCS), we examined multiple dimensions of ED care and treatment from 2005 to 2016 among children in the United States. The main outcomes include ED disposition (hospital admission, ICU admission, and in hospital death), resources utilization (medical imaging use, blood tests, and procedure use) and patient ED waiting times and total length of ED stay. The main exposure variable is race/ethnicity, categorized as non-Hispanic white (white), non-Hispanic black (Black), Hispanic, Asian, and Other. Analyses were stratified by race/ethnicity and adjusted for demographic, socioeconomic, and clinical factors. Results: There were 78,471 pediatric (≤18 years old) ED encounters, providing a weighted sample of 333,169,620 ED visits eligible for analysis. Black and Hispanic pediatric patients were 8% less likely (aOR 0.92, 95% CI 0.91–0.92) and 14% less likely (aOR 0.86, CI 0.86–0.86), respectively, than whites to have their care needs classified as immediate/emergent. Blacks and Hispanics were also 28 and 3% less likely, respectively, than whites to be admitted to the hospital following an ED visit (aOR 0.72, CI 0.72–0.72; aOR 0.97, CI 0.97–0.97). Blacks and Hispanics also experienced significantly longer wait times and overall visits as compared to whites. Conclusions: Black and Hispanic children faced disparities in emergency care across multiple dimensions of emergency care when compared to non-Hispanic white children, while Asian children did not demonstrate such patterns. Further research is needed to understand the underlying causes and long-term health consequences of these divergent patterns of racial disparities in ED care within an increasingly racially diverse cohort of younger Americans.

Full text

ORIGINAL RESEARCH
published: 19 December 2019
doi: 10.3389/fped.2019.00525
Frontiers in Pediatrics | www.frontiersin.org 1December 2019 | Volume 7 | Article 525
Edited by:
Dora Il’yasova,
Georgia State University,
United States
Reviewed by:
Lia Scott,
Georgia State University,
United States
Tammy Chung,
University of Pittsburgh Medical
Center, United States
*Correspondence:
Xingyu Zhang
zhangxyu@umich.edu
†These authors have contributed
equally to this work
Specialty section:
This article was submitted to
Children and Health,
a section of the journal
Frontiers in Pediatrics
Received: 09 September 2019
Accepted: 04 December 2019
Published: 19 December 2019
Citation:
Zhang X, Carabello M, Hill T, He K,
Friese CR and Mahajan P (2019)
Racial and Ethnic Disparities in
Emergency Department Care and
Health Outcomes Among Children in
the United States.
Front. Pediatr. 7:525.
doi: 10.3389/fped.2019.00525
Racial and Ethnic Disparities in
Emergency Department Care and
Health Outcomes Among Children in
the United States
Xingyu Zhang 1
*†, Maria Carabello 2†, Tyler Hill 1, Kevin He 3, Christopher R. Friese1and
Prashant Mahajan 4
1Department of Systems, Populations, and Leadership, University of Michigan School of Nursing, Ann Arbor, MI,
United States, 2Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor,
MI, United States, 3Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States,
4Department of Emergency Medicine, University of Michigan School of Medicine, Ann Arbor, MI, United States
Background: There is an incomplete understanding of disparities in emergency care
for children across racial and ethnic groups in the United States. In this project, we
sought to investigate patterns in emergency care utilization, disposition, and resource
use in children by race and ethnicity after adjusting for demographic, socioeconomic,
and clinical factors.
Methods: In this cross-sectional study of emergency department (ED) data from the
nationally representative National Hospital Ambulatory Medical Survey (NHAMCS), we
examined multiple dimensions of ED care and treatment from 2005 to 2016 among
children in the United States. The main outcomes include ED disposition (hospital
admission, ICU admission, and in hospital death), resources utilization (medical imaging
use, blood tests, and procedure use) and patient ED waiting times and total length of
ED stay. The main exposure variable is race/ethnicity, categorized as non-Hispanic white
(white), non-Hispanic black (Black), Hispanic, Asian, and Other. Analyses were stratified
by race/ethnicity and adjusted for demographic, socioeconomic, and clinical factors.
Results: There were 78,471 pediatric (≤18 years old) ED encounters, providing a
weighted sample of 333,169,620 ED visits eligible for analysis. Black and Hispanic
pediatric patients were 8% less likely (aOR 0.92, 95% CI 0.91–0.92) and 14% less likely
(aOR 0.86, CI 0.86–0.86), respectively, than whites to have their care needs classified as
immediate/emergent. Blacks and Hispanics were also 28 and 3% less likely, respectively,
than whites to be admitted to the hospital following an ED visit (aOR 0.72, CI 0.72–0.72;
aOR 0.97, CI 0.97–0.97). Blacks and Hispanics also experienced significantly longer wait
times and overall visits as compared to whites.
Conclusions: Black and Hispanic children faced disparities in emergency care across
multiple dimensions of emergency care when compared to non-Hispanic white children,
while Asian children did not demonstrate such patterns. Further research is needed to
understand the underlying causes and long-term health consequences of these divergent
patterns of racial disparities in ED care within an increasingly racially diverse cohort of
younger Americans.
Keywords: pediatrics, emergency department, disparity, racial/ethnic, trend

Zhang et al. Racial/Ethnic Disparities in Emergency Care
INTRODUCTION
In 2010, the American Academy of Pediatrics (AAP) released a
report reviewing the extant literature on racial/ethnic health and
health care disparities among US children, concluding that care
disparities were “extensive, pervasive, and persistent” (p. e979)
(1). The report described disparities across multiple dimensions
of health and health care, including all-cause mortality. Despite
the strong conclusions drawn from the 111 articles reviewed,
the authors noted several gaps in the broader literature. In
studies that fell short of review requirements, the most common
flaws included failing to analyze children separately from adults
and analyzing all non-white racial/ethnic demographics as one
population. Further, even among the studies included in the final
review, nearly a quarter failed to adjust for likely confounders,
such as other demographic and socioeconomic factors.
While some of these gaps have been addressed in recent years,
research on health disparities in children lags behind that focused
on adults (2,3). This is especially concerning given that the
racial and ethnic composition of American youth is changing
rapidly, with the US Census Bureau projecting that more than
half of US children will be non-white or Hispanic by the year
2020 (4). Given this major demographic shift within younger
cohorts, thoroughly understanding the nature and trends of
youth racial/ethnic health disparities represents a critical area of
concern for both clinical practice and health policy.
To fill this knowledge gap in an important sector of the
US health care system, we examined patterns in emergency
department (ED) care outcomes for Black, Hispanic, and Asian
children relative to non-Hispanic white children. We also
analyzed factors that may contribute to care disparities, including
patient demographic, socioeconomic, and clinical factors—all
identified as primary drivers of health disparities in the broader
literature. Our analysis aims to explore correlates of racial/ethnic
disparities in emergency care and treatment using a nationally
representative sample of US children.
METHODS
We conducted a cross-sectional study of ED data obtained
from a multiyear, nationally representative survey carried out
in the US. This study used pre-existing, de-identified data and
was categorized as exempt by the University of Michigan’s
Institutional Review Board.
Data Source and Study Population
The study population was derived from the National
Hospital Ambulatory Medical Survey (NHAMCS) Emergency
Department Subfile (NHAMCS-ED) between 2005 and 2016
(5). NHAMCS-ED is a multistage stratified probability sample
of ED visits in the US, administered by the National Center for
Health Statistics, Centers for Disease Control and Prevention.
The NHAMCS-ED sample is collected from ∼300 hospital-based
EDs per year, randomly selected from roughly 1,900 geographic
areas in all 50 states. The survey uses a standardized data
collection form to capture detailed information from ∼100
patients per hospital-based ED.
A total of 3,58,163 patient (weighted N=1,560,846,342)
visits from 3,764 hospital-based EDs were included in the
analytic dataset. To restrict our sample to pediatric patients
with a single documented race/ethnicity, we excluded patients
with unknown or multiple races listed (n=31,703; weighted
N=161,739,887) and those over the age of 18 (n=247,989,
weighted N=1,065,936,835). This resulted in a final sample of
78,471 (Weighted N=333,169,620) pediatric patients presenting
to US EDs.
Study Outcomes
The primary study outcome variables include the Emergency
Severity Index (ESI), a five-level ED triage algorithm providing
clinically relevant stratification of patients into five groups from
1 (most urgent) to 5 (least urgent) on the basis of visit acuity
level and resource needs; ED disposition, specifically hospital
admission and intensive care unit (ICU) admission; medical
resource utilization (blood test, imaging, and other procedures;
see Supplement Table 1 for a full list); waiting time (time
between arrival and seeing a physician); and length of visit (time
from arrival to discharge/disposition) for the ED encounter.
Death outcomes include deaths in either the ED or hospital.
The primary exposure variables for the analysis were a patient’s
racial/ethnic categorization. Race was predefined by NHAMCS
as Asian, Black, white, or Other (including those who identify
as American Indian/Alaska native and Native Hawaiian/Other
Pacific Islander), and ethnicity was categorized as either Hispanic
or non-Hispanic. From these categorizations we arrived at the
following mutually exclusive groups for analysis: non-Hispanic
Black (hereafter referred to as Black), Hispanic, Asian, Other,
and non-Hispanic white (hereafter referred to as white), as the
baseline for comparison. For simplicity, we refer to these as racial
groups throughout the remainder of the manuscript; all non-
white categories are considered racial minorities for the purposes
of our analysis. Given the small sample size and heterogeneity of
“Other,” we report data for this group in the tables but do not
focus on them in our discussion.
For adjusted analyses, we included patient demographic
variables (sex and age group); variables indicative of
socioeconomic status, including residence type (private home,
nursing home, homeless, or other) and insurance type (private
insurance, Medicaid/CHIP, Medicare, uninsured, or other);
mode of arrival (ambulance vs. not); day of the week; and time
of arrival. We also included clinical variables, such as triage vital
signs (body temperature, heart rate, diastolic blood pressure,
and pain scale). Additional patient-level covariates included the
primary reason for the ED visit (categorized by system-based
symptom clusters because this information was available in
the dataset for the entire patient population) and whether the
patient had visited the ED within the past 72 h. We also included
information on the US census region of the ED.
Statistical Analysis
Population characteristics were described and compared among
different racial groups. The proportion of each outcome
variable among different racial groups and covariate groups
were compared using χ-square tests. Multinomial logistic
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Zhang et al. Racial/Ethnic Disparities in Emergency Care
regression models were used to estimate the association between
the ESI scores (categorical) and racial groups. Models were
sequentially adjusted for demographic, socioeconomic, and
visit/clinical variables. Multivariable logistic regression models
were used to estimate the association between each binary
outcome (hospital admission, ICU admission, death, blood
test, medical imaging utilization, and procedure use) and
the racial groups. These models were sequentially adjusted
for demographic, socioeconomic, and visit/clinical variables;
specifically, we adjusted for ESI scores to test for changes in the
associations between racial group and binary outcomes.
Multivariable linear regression was used to test the association
between the waiting time and length of visit (continuous
variables) and racial groups after adjusting for other confounding
variables. Because these two variables were not normally
distributed, a log transformation was performed prior to
the regression model. Models were sequentially adjusted
for demographic, socioeconomic, visit/clinical variables, and
ESI score.
Poisson regression was used to estimate the prevalence rate of
disposition and resource utilization outcomes each year among
the racial groups, adjusting for age, gender, and insurance type,
with time modeled linearly as years since 2005. An interaction
between the racial group and time was included in each model
to test whether the trend in each outcome differed across
racial groups.
The NHAMCS-ED dataset used in this analysis relies on
imputation for missing data (5). Specifically, the survey uses
hot deck-based single, sequential regression to impute 3-digit
ICD-9-CM codes for certain items, such as age, sex, primary
diagnosis, ED volume, and geographic region. For the remaining
variables (for which missing values are not imputed in the
NHAMCS-ED dataset) we analyzed missing data patterns using
the MI procedure in SAS. All the variables were deemed Missing
Completely at Random. The missing data for these variables were
imputed with the median of the corresponding variables prior
to generating the logistic regression models, multivariable linear
regression models, and Poisson models. SAS (version 9.4) was
used for analyses with α=0.05 set as the statistical significance
threshold. All confidence intervals are 95%.
RESULTS
During the 12-years study period between 2005 and 2016,
NHAMCS collected data on 78,471 pediatric (≤18 years old)
ED encounters, providing a weighted sample of 333,169,620
for analysis. The analysis was stratified by racial group, with
the following proportions represented in the final analytic
sample: white (52.1%), Black (24.0%), Hispanic (20.6%), Asian
(2.0%), and Other (1.3%). Rates of uninsurance were highest
for Hispanics (10.6%) and Blacks (8.4%) and lowest for Asians
(6.3%) and whites (7.8%). Across racial groups, children between
1 and 6 years old accounted for the highest proportion of ED
visits. In terms of symptoms, Black patients presented with the
highest proportion of respiratory issues (20.9% of visits) and
Hispanics presented with the highest proportion of digestive
issues (15.6% of visits). The full set of ED pediatric patient
characteristics stratified by race or ethnicity are presented in
Table 1 and Supplement Table 2. All differences between groups
were significant (p<0.01).
Tables 2,3and Supplement Table 3 summarize the main
outcomes of interest across the sample and stratified by race.
After adjusting for other covariates, Black patients and Hispanic
patients were 8% less likely (aOR 0.92, CI 0.91–0.92), and 14%
less likely (aOR 0.86, CI 0.86–0.86), respectively, than whites to
receive immediate or emergent ESI score as opposed to semi- or
non-urgent scores. Asian patients were more likely than whites
to receive immediate or emergent (aOR 1.05, CI 1.04–1.0.5) or
urgent care (aOR 1.15, CI 1.15–1.16) scores as opposed to semi-
or non-urgent care needs in all models.
After adjusting for other covariates (including ESI level),
Blacks and Hispanics were also 28 and 3%, respectively, less likely
than whites to be admitted to the hospital following their ED visit
(aOR 0.72, CI 0.72–0.72; aOR 0.97, CI 0.97–0.97). Asian patients
were 1.08 times more likely than whites to be admitted to the
hospital following an ED visit (aOR 1.08, CI 1.07–1.08). Blacks
and Hispanics were 1.04 and 1.21 times, respectively, more likely
to receive ICU admission in the fully adjusted models (aOR 1.04,
CI: 1.03–1.05; OR 1.21, CI 1.20–1.21).
After adjusting for other covariates (including ESI level), Black
patients were 24% (aOR 0.76, CI 0.76–0.76) less likely to have
a blood test during the ED visit than whites. Blacks, Hispanics
and Asians were 17% (aOR 0.83, CI 0.83–0.83), 9% (aOR 0.91,
CI 0.90–0.91), and 8% (aOR 0.92, CI 0.92–0.93) less likely to
receive any imaging than whites. Specifically, Blacks, Hispanics
and Asians were 28% (aOR 0.72 CI 0.72–0.72), 4% (aOR 0.96, CI
0.96–0.96), and 42% (aOR 0.92, CI 0.57–0.58) less likely to receive
a CT scan as compared to white pediatric ED patients. However,
Blacks, Hispanics, and Asians were 1.04 (aOR 1.04, CI 1.04–1.05),
1.43 (aOR 1.43, CI 1.43–1.44), and 1.23 (aOR 1.23, CI 1.22–1.24)
times, respectively, more likely than whites to receive ultrasound.
Relative to whites, Blacks and Hispanics were modestly less likely
to receive general procedures (aOR 0.98, CI 0.98–0.98; and aOR
0.97, CI 0.97–0.97, respectively) while Asians were not (aOR
1.00, CI 1.00–1.01). After adjusting for other covariates, waiting
times in the ED were significantly greater for Black and Hispanic
children (p<0.001) than for white children (Table 4).
Figure 1 displays trends in disposition and resource
utilization outcomes over time (2005–2016) by racial group.
Table 5 includes estimated rates and changes in rates over time
for disposition and resource utilization outcomes. Rates of
hospitalization significantly decreased over time in all racial
groups. However, these rates decreased the least among whites as
compared to other racial groups (p<0.001). Of note, hospital
admission for whites decreased by 21.99%, compared to 34.63
and 35.18% for Black and Hispanic patients, respectively. Rates
of medical imaging utilization significantly decreased over time
in whites and Blacks but increased in Hispanics and Asians.
Rates of blood testing significantly decreased over time among all
racial groups, but these rates decreased the least in white patients
as compared to other racial groups (p<0.001). Procedure
utilization rates increased across all racial groups during the
period of our study.
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Zhang et al. Racial/Ethnic Disparities in Emergency Care
TABLE 1 | Baseline characteristics of patients presenting to the ED, stratified by race/ethnics, NHAMCS 2005–2016.
All n(%) White n(%) Black n(%) Hispanic n(%) Asian n(%) Other n(%)
333,169,620 173,692,657 (52.1) 80,086,839 (24.0) 68,613,863 (20.6) 6,503,054 (2.0) 4,273,207 (1.3)
Male 172,897,072 (51.9) 90,601,735 (52.2) 40,677,155 (50.8) 35,788,268 (52.2) 3,496,132 (53.8) 2,333,782 (54.6)
Age
0–<1 39,525,262 (11.9) 17,668,772 (10.2) 10,280,378 (12.8) 9,927,448 (14.5) 952,275 (14.6) 696,388 (16.3)
1–<6 114,713,519 (34.4) 55,276,342 (31.8) 28,425,637 (35.5) 26,595,323 (38.8) 2,887,704 (44.4) 1,528,512 (35.8)
6–<12 72,902,816 (21.9) 38,100,893 (21.9) 17,011,239 (21.2) 15,654,639 (22.8) 1,289,632 (19.8) 846,414 (19.8)
12–18 106,028,023 (31.8) 62,646,650 (36.1) 24,369,584 (30.4) 16,436,453 (24.0) 1,373,443 (21.1) 1,201,893 (28.1)
Residence type
Private residence 319,702,310 (99.1) 166,366,985 (99.1) 76,879,363 (99.0) 66,034,405 (99.4) 6,289,858 (99.4) 4,131,700 (98.4)
Nursing home 419,284 (0.1) 186,213 (0.1) 94,569 (0.1) 124,861 (0.2) 13,641 (0.2) 0 (0.0)
Homeless 320,886 (0.1) 143,226 (0.1) 119,622 (0.2) 38,973 (0.1) 1,799 (0.0) 17,266 (0.4)
Other 2,054,522 (0.6) 1,198,103 (0.7) 529,544 (0.7) 250,041 (0.4) 25,572 (0.4) 51,263 (1.2)
Insurance type
Private insurance 106,953,324 (33.9) 72,494,939 (44.0) 17,034,962 (22.7) 13,403,030 (20.6) 2,785,655 (45.6) 1,234,739 (31.1)
Medicare 3,066,983 (1.0) 1,625,044 (1.0) 757,276 (1.0) 636,444 (1.0) 24,913 (0.4) 23,308 (0.6)
Medicaid or CHIP 170,807,425 (54.2) 73,809,598 (44.8) 49,296,858 (65.6) 42,752,364 (65.8) 2,738,750 (44.8) 2,209,855 (55.6)
Uninsured 26,695,106 (8.5) 12,793,709 (7.8) 6,298,764 (8.4) 6,883,680 (10.6) 385,087 (6.3) 333,866 (8.4)
Other 7,616,926 (2.4) 4,181,455 (2.5) 1,764,992 (2.3) 1,323,063 (2.0) 177,780 (2.9) 169,636 (4.3)
Arrival by ambulance 19,797,958 (6.1) 9,896,740 (5.8) 5,592,751 (7.2) 3,524,421 (5.3) 473,467 (7.4) 310,580 (7.5)
Seen within last 72 h 9,948,261 (3.5) 4,975,333 (3.3) 2,299,231 (3.4) 2,234,071 (3.8) 220,758 (3.8) 218,866 (5.5)
Pain level
No pain 82,904,253 (37.9) 39,358,670 (34.0) 22,649,468 (43.1) 17,757,196 (41.4) 1,964,514 (44.6) 1,174,405 (39.7)
Mild 34,275,302 (15.7) 18,904,680 (16.3) 7,751,433 (14.8) 6,497,999 (15.1) 702,518 (16.0) 418,672 (14.2)
Moderate 63,055,670 (28.8) 36,086,738 (31.1) 13,505,438 (25.7) 11,362,699 (26.5) 1,272,600 (28.9) 828,195 (28.0)
Severe 38,450,234 (17.6) 21,564,307 (18.6) 8,588,085 (16.4) 7,298,073 (17.0) 464,206 (10.5) 535,563 (18.1)
Temperature (C◦)
36–38 264,632,865 (84.0) 140,243,512 (85.5) 63,344,163 (83.6) 52,819,823 (81.2) 4,833,426 (78.4) 3,391,941 (83.7)
≤36 11,990,313 (3.8) 7,218,093 (4.4) 2,509,484 (3.3) 1,978,962 (3.0) 161,091 (2.6) 122,684 (3.0)
>38 38,485,798 (12.2) 16,631,739 (10.1) 9,927,444 (13.1) 10,216,631 (15.7) 1,171,313 (19.0) 538,671 (13.3)
Heart rate (BPM)
≤90 115,436,813 (34.6) 63,708,888 (36.7) 27,813,844 (34.7) 20,825,030 (30.4) 1,796,717 (27.6) 1,292,333 (30.2)
90–100 43,881,682 (13.2) 24,463,373 (14.1) 10,233,172 (12.8) 8,036,223 (11.7) 679,903 (10.5) 469,010 (11.0)
100–110 37,041,723 (11.1) 20,330,558 (11.7) 8,977,997 (11.2) 6,649,624 (9.7) 713,344 (11.0) 370,199 (8.7)
110–120 34,718,454 (10.4) 17,778,029 (10.2) 8,239,891 (10.3) 7,661,983 (11.2) 542,102 (8.3) 496,448 (11.6)
>120 102,090,949 (30.6) 47,411,809 (27.3) 24,821,934 (31.0) 25,441,002 (37.1) 2,770,988 (42.6) 1,645,217 (38.5)
DBP
<60 134,026,805 (40.2) 72,642,858 (41.8) 32,263,768 (40.3) 25,319,628 (36.9) 2,368,029 (36.4) 1,432,522 (33.5)
60–80 144,151,078 (43.3) 71,315,170 (41.1) 35,072,408 (43.8) 32,722,224 (47.7) 3,084,776 (47.4) 1,956,501 (45.8)
>80 54,991,737 (16.5) 29,734,629 (17.1) 12,750,662 (15.9) 10,572,012 (15.4) 1,050,250 (16.2) 884,184 (20.7)
Census region
Northeast 52,724,321 (15.8) 28,388,204 (16.3) 10,055,425 (12.6) 12,602,174 (18.4) 1,437,669 (22.1) 240,849 (5.6)
Midwest 73,567,662 (22.1) 43,868,335 (25.3) 19,384,965 (24.2) 8,591,856 (12.5) 1,178,627 (18.1) 543,878 (12.7)
South 144,485,257 (43.4) 72,175,266 (41.6) 46,509,451 (58.1) 23,487,388 (34.2) 1,515,022 (23.3) 798,130 (18.7)
West 62,392,381 (18.7) 29,260,852 (16.8) 4,136,997 (5.2) 23,932,446 (34.9) 2,371,736 (36.5) 2,690,350 (63.0)
Reason for visit (by symptom cluster)
General 66,792,161 (20.1) 30,514,486 (17.6) 16,784,380 (21.0) 17,076,980 (25.0) 1,663,937 (25.7) 752,378 (17.8)
Psychiatric 5,950,254 (1.8) 3,693,778 (2.1) 1,330,481 (1.7) 783,822 (1.1) 82,064 (1.3) 60,110 (1.4)
Neurological 12,546,663 (3.8) 6,881,087 (4.0) 2,981,361 (3.7) 2,349,128 (3.4) 193,330 (3.0) 141,757 (3.3)
Cardiovascular and Lymphatic 1,163,082 (0.4) 661,025 (0.4) 252,045 (0.3) 223,382 (0.3) 10,889 (0.2) 15,740 (0.4)
Eyes and Ears 19,043,040 (5.7) 9,467,954 (5.5) 4,780,233 (6.0) 4,250,476 (6.2) 300,945 (4.6) 243,432 (5.8)
(Continued)
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Zhang et al. Racial/Ethnic Disparities in Emergency Care
TABLE 1 | Continued
All n(%) White n(%) Black n(%) Hispanic n(%) Asian n(%) Other n(%)
Respiratory 57,172,175 (17.2) 26,829,823 (15.5) 16,686,052 (20.9) 11,664,756 (17.1) 1,118,061 (17.3) 873,482 (20.6)
Digestive 43,745,812 (13.2) 21,521,065 (12.4) 10,126,542 (12.7) 10,674,855 (15.6) 797,588 (12.3) 625,762 (14.8)
Genitourinary 7,913,120 (2.4) 3,721,422 (2.1) 2,247,735 (2.8) 1,779,917 (2.6) 95,201 (1.5) 68,844 (1.6)
Skin, Nails, and Hair 17,686,667 (5.3) 8,655,898 (5.0) 4,758,158 (6.0) 3,741,652 (5.5) 347,128 (5.4) 183,831 (4.3)
Musculoskeletal 27,378,747 (8.2) 16,509,157 (9.5) 5,906,982 (7.4) 4,121,511 (6.0) 495,275 (7.6) 345,822 (8.2)
Other 72,553,947 (21.9) 44,642,401 (25.8) 13,936,233 (17.5) 11,676,985 (17.1) 1,376,668 (21.2) 921,660 (21.8)
The missing proportion for arrival time, residency type, insurance, reason for visit and residency type is <5%; the missing proportion for temperature, heart rate, and blood pressure is
5–10%; the missing proportion for triage level is 20%, for Seen within last 72 h is 15%, for pain level is 49%. All p-values from chi-square tests are <0.001.
TABLE 2 | Proportion of emergency severity index, hospital admission, ICU admission, medical resources utilization, stratified by race/ethnics, NHAMCS 2005–2016.
All White Black Hispanic Asian Other
ESI score
Immediate 5,020,918 (1.8) 2,741,469 (1.9) 1,200,498 (1.8) 901,281 (1.6) 112,686 (2.0) 64,984 (1.9)
Emergent 21,346,130 (7.8) 12,062,886 (8.4) 4,870,376 (7.4) 3,723,519 (6.7) 441,215 (7.9) 248,133 (7.1)
Urgent 99,842,177 (36.3) 52,495,741 (36.4) 23,862,805 (36.3) 19,894,999 (35.6) 2,253,751 (40.4) 1,334,881 (38.2)
Semi-urgent 115,276,218 (41.9) 59,887,811 (41.5) 27,380,048 (41.7) 24,107,749 (43.1) 2,365,042 (42.4) 1,535,568 (43.9)
Non-urgent 33,470,812 (12.2) 17,076,986 (11.8) 8,406,217 (12.8) 7,263,184 (13.0) 411,097 (7.4) 313,328 (9.0)
Hospital admission 15,865,082 (4.8) 9,149,439 (5.3) 3,133,698 (3.9) 2,971,794 (4.3) 342,705 (5.3) 267,446 (6.3)
ICU 914,937 (0.3) 453,609 (0.3) 202,374 (0.3) 210,375 (0.3) 45,502 (0.7) 3,077 (0.1)
In hospital death 139,666 (0.0) 63,413 (0.0) 35,237 (0.0) 38,287 (0.1) 2,729 (0.0) 0 (0.0)
Blood test 58,141,100 (17.5) 32,166,587 (18.5) 12,315,970 (15.4) 11,792,576 (17.2) 1,097,368 (16.9) 768,599 (18.0)
Any image 108,114,905 (32.5) 62,043,795 (35.7) 23,385,877 (29.2) 19,350,391 (28.2) 2,049,570 (31.5) 1,285,272 (30.1)
X-ray 89,642,862 (26.9) 51,045,276 (29.4) 20,059,185 (25.0) 15,607,215 (22.7) 1,795,095 (27.6) 1,136,091 (26.6)
CT 19,123,492 (5.7) 12,132,915 (7.0) 3,365,450 (4.2) 3,205,937 (4.7) 236,894 (3.6) 182,296 (4.3)
Ultrasound 5,184,122 (1.6) 2,643,688 (1.5) 1,055,389 (1.3) 1,327,356 (1.9) 102,929 (1.6) 54,761 (1.3)
MRI 537,589 (0.2) 258,519 (0.1) 123,646 (0.2) 155,110 (0.2) 314 (0.0) 0 (0.0)
Other Image 1,909,039 (0.6) 1,009,607 (0.6) 477,377 (0.6) 362,436 (0.5) 33,549 (0.5) 26,070 (0.6)
Procedure 122,284,391 (36.7) 67,698,028 (39.0) 28,013,125 (35.0) 22,628,628 (33.0) 2,436,860 (37.5) 1,507,750 (35.3)
Waiting time
[minutes, means (95% CI)]
46.1 (45.6–46.7) 42.0 (41.4–42.7) 50.5 (49.5–51.6) 52.0 (50.7–53.2) 44.0 (40.5–47.5) 40.9 (37.6–44.2)
Length of visit
[minutes, means (95% CI)]
152.9
(151.6–154.1)
143.9
(142.2–145.6)
159.5
(157.0–161.9)
167.7
(164.4–170.9)
167.1
(159.2–175.0)
137.2
(129.5–144.9)
Waiting time is time from arrival to seeing the physician; length of visit is time from arrival to discharge. All p-values from chi-square tests are <0.001.
DISCUSSION
We examined differences in many aspects of pediatric ED
evaluation, management, disposition, throughput, and resource
utilization among racial groups and explored trends in these
measures over time. Our analysis responds to documented
shortcomings in the pediatric racial health disparities literature
by including a meaningful comparison group (whites), analyzing
three racial minority groups rather than a combined non-white
group, adjusting for likely confounding social and demographic
factors, and focusing on multiple aspects of the high-stakes
arena of emergency care (1). Across our analyses, we observed
significant racial differences in ED encounters and treatment
for pediatric patients between 2005 and 2016. We discuss these
findings within two broad categories: characteristics of the ED
visit and care received in the ED.
Characteristics of ED Visit
Black and Hispanic children were less likely than whites
to be classified as needing immediate or emergent care as
opposed to semi- or non-urgent care, which is consistent
with prior literature (6–9). This difference could not be fully
explained by possible confounding factors available in the dataset,
such as demographic, socioeconomic, or clinical variables. An
epidemiological study of an urban ED (Baltimore, Maryland)
found that Black children were more likely than whites to be
brought to the ED for non-urgent care needs, the authors positing
that Black families’ greater proximity to the ED may be a primary
cause of the disparity (10). Similarly, a study of predominately
Black (96%) caregivers in an urban setting (New Orleans, LA)
found that one-third routinely brought their children to the
ED for non-urgent acute illness (11). The authors reported that
the ED’s shorter wait and discharge times were the foremost
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Zhang et al. Racial/Ethnic Disparities in Emergency Care
TABLE 3 | Odds ratio of emergency severity index, hospital admission, ICU admission, medical resources utilization, stratified by race/ethnics, NHAMCS 2005–2016.
Race/ethnic
group
Crude odds ratio Adjusted for
Demographics +Social economic +Visiting &
clinical
+ESI score
ESI Score:
immediate or
emergent vs. semi
or non-urgent
White Reference (1) Reference (1) Reference (1) Reference (1)
Black 0.88 (0.88–0.88) 0.89 (0.89–0.89) 0.96 (0.96–0.96) 0.92 (0.91–0.92)
Hispanic 0.77 (0.77–0.77) 0.78 (0.78–0.78) 0.84 (0.84–0.85) 0.86 (0.86–0.86)
Asian 1.04 (1.03–1.04) 1.07 (1.07–1.07) 1.05 (1.05–1.05) 1.05 (1.04–1.05)
Other 0.88 (0.88–0.88) 0.88 (0.88–0.89) 0.95 (0.95–0.95) 0.94 (0.93–0.94)
ESI score: urgent vs.
semi or non-urgent
White Reference (1) Reference (1) Reference (1) Reference (1)
Black 0.98 (0.98–0.98) 0.99 (0.99–0.99) 1.03 (1.03–1.03) 1.00 (1.00–1.00)
Hispanic 0.93 (0.93–0.93) 0.95 (0.95–0.95) 0.98 (0.98–0.98) 0.94 (0.94–0.94)
Asian 1.19 (1.19–1.19) 1.23 (1.23–1.23) 1.21 (1.21–1.21) 1.15 (1.15–1.16)
Other 1.06 (1.06–1.06) 1.07 (1.07–1.07) 1.08 (1.08–1.08) 1.06 (1.06–1.07)
Hospital admission White Reference (1) Reference (1) Reference (1) Reference (1) Reference (1)
Black 0.73 (0.73–0.73) 0.74 (0.74–0.74) 0.74 (0.74–0.74) 0.69 (0.69–0.69) 0.72 (0.72–0.72)
Hispanic 0.81 (0.81–0.82) 0.83 (0.83–0.83) 0.88 (0.88–0.88) 0.85 (0.84–0.85) 0.97 (0.97–0.97)
Asian 1.00 (1.00–1.00) 1.03 (1.03–1.03) 1.05 (1.04–1.05) 0.95 (0.94–0.95) 1.08 (1.07–1.08)
Other 1.20 (1.20–1.21) 1.18 (1.18–1.19) 1.34 (1.33–1.34) 1.21 (1.21–1.22) 1.43 (1.43–1.44)
ICU White Reference (1) Reference (1) Reference (1) Reference (1) Reference (1)
Black 0.97 (0.96–0.97) 0.95 (0.95–0.96) 0.94 (0.93–0.94) 0.82 (0.81–0.82) 1.04 (1.03–1.05)
Hispanic 1.18 (1.17–1.18) 1.15 (1.14–1.16) 1.22 (1.21–1.22) 1.17 (1.17–1.18) 1.21 (1.20–1.21)
Asian 2.69 (2.67–2.72) 2.59 (2.57–2.62) 2.65 (2.62–2.67) 2.46 (2.43–2.48) 2.83 (2.80–2.86)
Other 0.28 (0.27–0.29) 0.26 (0.25–0.27) 0.38 (0.37–0.39) 0.33 (0.31–0.34) 0.39 (0.37–0.40)
Blood test White Reference (1) Reference (1) Reference (1) Reference (1) Reference (1)
Black 0.80 (0.80–0.80) 0.83 (0.83–0.83) 0.82 (0.82–0.82) 0.74 (0.74–0.74) 0.76 (0.76–0.76)
Hispanic 0.91 (0.91–0.91) 1.00 (1.00–1.00) 1.05 (1.05–1.05) 0.90 (0.90–0.90) 0.96 (0.96–0.96)
Asian 0.89 (0.89–0.90) 1.02 (1.02–1.02) 1.05 (1.05–1.05) 0.92 (0.92–0.92) 0.93 (0.92–0.93)
Other 0.97 (0.96–0.97) 1.02 (1.02–1.03) 1.11 (1.11–1.12) 1.03 (1.03–1.04) 1.14 (1.14–1.14)
Any imaging White Reference (1) Reference (1) Reference (1) Reference (1) Reference (1)
Black 0.74 (0.74–0.74) 0.78 (0.78–0.78) 0.80 (0.80–0.80) 0.83 (0.83–0.83) 0.83 (0.83–0.83)
Hispanic 0.71 (0.71–0.71) 0.77 (0.77–0.77) 0.83 (0.83–0.83) 0.88 (0.88–0.88) 0.91 (0.90–0.91)
Asian 0.83 (0.83–0.83) 0.94 (0.94–0.94) 0.96 (0.96–0.96) 0.93 (0.93–0.93) 0.92 (0.92–0.93)
Other 0.77 (0.77–0.78) 0.82 (0.82–0.82) 0.87 (0.87–0.87) 0.83 (0.83–0.84) 0.84 (0.84–0.84)
X-ray White Reference (1) Reference (1) Reference (1) Reference (1) Reference (1)
Black 0.80 (0.80–0.80) 0.83 (0.83–0.83) 0.84 (0.84–0.84) 0.88 (0.88–0.88) 0.86 (0.86–0.86)
Hispanic 0.71 (0.71–0.71) 0.75 (0.75–0.75) 0.80 (0.80–0.80) 0.86 (0.85–0.86) 0.85 (0.85–0.85)
Asian 0.92 (0.92–0.92) 1.00 (1.00–1.00) 1.03 (1.03–1.03) 0.99 (0.99–1.00) 0.98 (0.98–0.98)
Other 0.87 (0.87–0.87) 0.91 (0.90–0.91) 0.96 (0.96–0.96) 0.94 (0.94–0.95) 0.94 (0.94–0.94)
CT White Reference (1) Reference (1) Reference (1) Reference (1) Reference (1)
Black 0.58 (0.58–0.59) 0.63 (0.63–0.63) 0.68 (0.68–0.68) 0.69 (0.69–0.69) 0.72 (0.72–0.72)
Hispanic 0.65 (0.65–0.65) 0.77 (0.77–0.77) 0.86 (0.86–0.86) 0.87 (0.87–0.87) 0.96 (0.96–0.96)
Asian 0.50 (0.50–0.51) 0.62 (0.62–0.63) 0.64 (0.63–0.64) 0.62 (0.61–0.62) 0.58 (0.57–0.58)
Other 0.59 (0.59–0.60) 0.66 (0.66–0.67) 0.72 (0.72–0.73) 0.65 (0.64–0.65) 0.76 (0.75–0.76)
Ultrasound White Reference (1) Reference (1) Reference (1) Reference (1) Reference (1)
Black 0.86 (0.86–0.87) 0.92 (0.92–0.93) 0.98 (0.98–0.98) 0.91 (0.91–0.92) 1.04 (1.04–1.05)
Hispanic 1.28 (1.27–1.28) 1.52 (1.52–1.53) 1.45 (1.45–1.46) 1.24 (1.24–1.24) 1.43 (1.43–1.44)
Asian 1.04 (1.03–1.05) 1.37 (1.36–1.38) 1.28 (1.27–1.28) 1.31 (1.31–1.32) 1.23 (1.22–1.24)
Other 0.84 (0.83–0.85) 0.96 (0.95–0.97) 0.90 (0.89–0.90) 0.86 (0.86–0.87) 1.05 (1.04–1.06)
MRI White Reference (1) Reference (1) Reference (1) Reference (1) Reference (1)
Black 1.04 (1.03–1.04) 1.10 (1.09–1.10) 1.11 (1.10–1.12) 1.16 (1.16–1.17) 1.08 (1.07–1.09)
Hispanic 1.52 (1.51–1.53) 1.71 (1.69–1.72) 1.89 (1.88–1.91) 2.04 (2.03–2.05) 2.35 (2.33–2.36)
(Continued)
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Zhang et al. Racial/Ethnic Disparities in Emergency Care
TABLE 3 | Continued
Race/ethnic
group
Crude odds ratio Adjusted for
Demographics +Social economic +Visiting &
clinical
+ESI score
Asian 0.03 (0.03–0.04) 0.04 (0.03–0.04) 0.04 (0.04–0.04) 0.04 (0.03–0.04) 0.05 (0.04–0.05)
Other – – – – –
Procedure White Reference (1) Reference (1) Reference (1) Reference (1) Reference (1)
Black 0.86 (0.86–0.86) 0.89 (0.89–0.89) 0.92 (0.92–0.92) 0.97 (0.97–0.98) 0.98 (0.98–0.98)
Hispanic 0.82 (0.82–0.82) 0.87 (0.87–0.87) 0.90 (0.90–0.90) 0.95 (0.95–0.95) 0.97 (0.97–0.97)
Asian 1.00 (1.00–1.00) 1.09 (1.09–1.09) 1.08 (1.08–1.08) 1.09 (1.09–1.10) 1.00 (1.00–1.01)
Other 0.86 (0.86–0.86) 0.90 (0.90–0.90) 0.92 (0.92–0.93) 0.89 (0.89–0.89) 0.89 (0.89–0.89)
+Demographics adjusts for gender, age group; +Socioeconomic adjusts for residence type, insurance type, census region; +Visiting & Clinical: year, week of day, arrival by ambulance,
seen within last 72 h, pain level, temperature, heart rate, dialytic blood pressure.
TABLE 4 | Linear regression between wait time or length of visit and by
race/ethnics, NHAMCS 2005–2016.
Wait time Length of visit
Beta (95% CI) p-value Beta (95% CI) p-value
White Reference (1) Reference (1)
Black 0.190 (0.157–0.224) <0.001 0.138 (0.123–0.152) <0.001
Hispanic 0.178 (0.141–0.214) <0.001 0.173 (0.157–0.189) <0.001
Asian −0.018 (−0.102–0.065) 0.665 0.010 (0.063–0.137) <0.001
Other 0.068 (−0.046–0.183) 0.242 0.003 (−0.024–0.080) 0.287
The model was adjusted for the following factors: demographics (gender, age group),
socioeconomic (residence type, insurance type, census region), visiting and clinical (year,
week of day, arrival by ambulance, seen within last 72 h, pain level, temperature, heart
rate, dialytic blood pressure), and ESI score. The betas are the coefficients between the
log (wait time or length of visit) and the racial/ethnic group.
motivation for this care-seeking pattern (11). More research
is needed that captures patient-level metrics of the distance,
time of travel, mode of transportation, and actual or perceived
advantages of ED vs. non-ED care options in the local context, in
order to properly adjust for such confounders.
Our analysis also revealed that Asian children were more
likely than whites to present to the ED for needs classified as
immediate or emergent, which marks a divergence from the other
racial minority groups in our sample that cannot be adequately
explained by other socioeconomic or clinical characteristics.
However, Asian children have been found to have better health
profiles as compared to other racial groups in the US, which
could have a significant influence on their use of and treatment
in the healthcare system (3). However, the literature on health
disparities among Asian children in the US remains sparse, and
thus more research is needed in various healthcare settings to
identify possible contributing factors.
Relative to Asians and whites, Blacks and Hispanics showed
a lower likelihood of being admitted to the hospital following
an ED visit, even after adjustment for clinical factors and vital
signs in addition to social and demographic measures. Our
plot of predicted rates of hospitalization over the 12-years span
(2005–2016) reveals that racial minority groups are experiencing
more significant declines in hospitalization rates as compared
to whites, although hospitalization is trending downward for
all groups.
The pattern of admissions to the ICU diverged slightly, with
only Black children less likely than whites to be admitted to the
ICU, while Hispanic and Asian children showed higher odds
of ICU admission than whites. In terms of 12-years trends,
we see declines in the predicted rates of ICU admission for
Asian, white, and Black children, while rates have increased
over time for Hispanic children (note that high ICU admission
rates for Asian children near the start of the 2005–2016 period
are likely spurious results, affected by small sample sizes). In
the fully adjusted linear regression models, Hispanic and Black
children were predicted to have significantly longer wait times
in the ED as compared to whites, as well as longer visit times
overall. The finding for Hispanic children is consistent with a
previous study on NHAMCS-ED data from 1997 to 2000, while
the longer wait and visit times for Black children were not
significant in the earlier dataset with a smaller sample size (12).
Compared to whites, Asian children did not have significantly
different wait times but did have slightly greater overall length of
ED stay.
Care Received in the ED
Our analysis also focused on the administration of tests, imaging,
and general procedures during children’s visits to the ED. The
patterns in the medical resource utilization aspect of ED care
varied to a greater degree across the racial minority groups. For
example, Black, Hispanic, and Asian children were significantly
less likely than whites to receive blood tests, X-rays, and CT scans,
but Blacks and Hispanics were more likely than whites to receive
MRI scans. While finer-grained information is not available, the
receipt of general procedures was slightly lower in Black and
Hispanic relative to white children. In contrast, Asians were as
likely as whites to receive general procedures.
In terms of 12-years trends, the rates of medical imaging
utilization decreased slightly but significantly for whites and
Blacks, while rates increased slightly for Hispanics and Asians.
Rates of blood tests significantly decreased over time for all racial
groups, but these rates decreased the least among white children.
Utilization rates for general procedures also increased across all
racial groups.
Frontiers in Pediatrics | www.frontiersin.org 7December 2019 | Volume 7 | Article 525

Zhang et al. Racial/Ethnic Disparities in Emergency Care
FIGURE 1 | Racial/ethnic-specific ED health outcome and medical resource utilization rate from 2005 to 2016: NHAMCS 2005–2016.
LIMITATIONS
A major limitation of our study is the potential for sampling
biases and errors in the NHAMCS-ED data. Namely,
heterogeneity in documentation (e.g., due to differences in
electronic health records practices) may involve abstraction
errors, missing responses, and inaccurate responses. However,
such systematic biases should not moderate, in any consistent
way, the statistical associations reported herein. Additionally, 6%
of the total NHAMCS-ED sample was removed for not having
a documented race/ethnicity; however, this figure is below the
acceptable non-response threshold for this type of data source
(13) and thus does not invalidate the primary exposure variable
for this analysis. Another limitation of our study is our use of
system-based reasons for ED visit (e.g., “respiratory”) as units of
analysis in our model rather specific complaints (e.g., “shortness
of breath”). Future investigations of disparities in ED outcomes
and resource utilization for more specific reasons for visit could
be revealing.
CONCLUSIONS
Our study revealed disparities across multiple dimensions
of the ED visits and care received by Black and Hispanic
pediatric patients in the US, while Asian children did not
experience similar disparities. Our analysis cannot determine
the extent to which the observed disparities owe to bias on
the part of ED staff and care providers. Understanding the
role of personnel bias remains a topic for future research and
is particularly pressing for the development of institutional
correctives. To inform public policy, further research is needed
to identify other underlying causes as well as the long-
term health consequences of the observed racial disparities
in pediatric emergency care, especially in light of the shift
Frontiers in Pediatrics | www.frontiersin.org 8December 2019 | Volume 7 | Article 525

Zhang et al. Racial/Ethnic Disparities in Emergency Care
TABLE 5 | Race/ethnicity-specific rates of disposition outcome and medical resources utilization: NHAMCS 2005–2016.
Rate* Trend p p†
Outcome Race/ethnicity 2005 2016 2005–2016 trend Race/ethnic
difference in trend
Hospital Admission White 0.0564 0.044 −21.99% <0.001
Black 0.0462 0.0302 −34.63% <0.001 <0.001
Hispanic 0.0523 0.0339 −35.18% <0.001 <0.001
Asian 0.0686 0.0364 −46.94% <0.001 <0.001
Other 0.0652 0.0523 −19.79% <0.001 <0.001
ICU Admission White 0.0043 0.0013 −69.77% <0.001
Black 0.0037 0.0014 −62.16% <0.001 <0.001
Hispanic 0.0026 0.0031 19.23% <0.001 <0.001
Asian 0.0167 0.0015 −91.02% <0.001 <0.001
Other – 0.0025 <0.001 <0.001
Blood test White 0.1836 0.1647 −10.29% <0.001
Black 0.1635 0.1378 −15.72% <0.001 <0.001
Hispanic 0.1867 0.1618 −13.34% <0.001 <0.001
Asian 0.2063 0.1431 −30.63% <0.001 <0.001
Other 0.1924 0.1633 −15.12% <0.001 <0.001
Any Imaging White 0.3387 0.3224 −4.81% <0.001
Black 0.2935 0.2844 −3.10% <0.001 <0.001
Hispanic 0.2806 0.2941 4.81% <0.001 <0.001
Asian 0.3048 0.3275 7.45% <0.001 <0.001
Other 0.3902 0.2161 −44.62% <0.001 <0.001
Procedure White 0.4298 0.4362 1.49% <0.001
Black 0.3998 0.4272 6.85% <0.001 <0.001
Hispanic 0.4099 0.4114 0.37% <0.001 <0.001
Asian 0.4432 0.4723 6.57% <0.001 <0.001
Other 0.3369 0.4988 48.06% <0.001 <0.001
*Predicted rate and trend were derived from a model using data over the time period, modeling time as a linear trend.
†From the time by Race/ethnicity interaction in the Poisson regression model.
toward greater representation of racial minorities in this
age group.
DATA AVAILABILITY STATEMENT
The NHAMCS-ED dataset can be accessed through the website
of the US Centers for Disease Control and Prevention (CDC)
(https://www.cdc.gov/nchs/ahcd/index.htm).
ETHICS STATEMENT
This study used pre-existing, de-identified data and was
categorized as exempt from ethical approval by the University of
Michigan’s Institutional Review Board.
AUTHOR CONTRIBUTIONS
XZ had full access to all the data in the study and takes
responsibility for the integrity of the data and the accuracy of the
data analysis. XZ and PM: concept and design. MC, XZ, and TH:
drafting of the manuscript. KH, CF, and PM: critical revision of
the manuscript for important intellectual content. XZ: statistical
analysis. XZ and PM: obtained funding. XZ: administrative,
technical, or material support. XZ and PM: supervision. All
authors: acquisition, analysis, or interpretation of data.
FUNDING
This work was supported by Michigan Institute for Clinical
and Health Research (MICHR) pilot grant (UL1TR002240). No
funding bodies had any role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fped.
2019.00525/full#supplementary-material
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Zhang et al. Racial/Ethnic Disparities in Emergency Care
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2019 Zhang, Carabello, Hill, He, Friese and Mahajan. This is an open-
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Frontiers in Pediatrics | www.frontiersin.org 10 December 2019 | Volume 7 | Article 525