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

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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.
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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
Frontiers in Pediatrics | www.frontiersin.org 2December 2019 | Volume 7 | Article 525
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.
Frontiers in Pediatrics | www.frontiersin.org 3December 2019 | Volume 7 | Article 525
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 (69). 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
Frontiers in Pediatrics | www.frontiersin.org 5December 2019 | Volume 7 | Article 525
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)
Frontiers in Pediatrics | www.frontiersin.org 6December 2019 | Volume 7 | Article 525
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
Frontiers in Pediatrics | www.frontiersin.org 9December 2019 | Volume 7 | Article 525
Zhang et al. Racial/Ethnic Disparities in Emergency Care
REFERENCES
1. Flores G. Racial and ethnic disparities in the health and health care of children.
Pediatrics. (2010) 125:e979–1020. doi: 10.1542/peds.2010-0188
2. Flores G, Tomany-Korman SC. The language spoken at home and disparities
in medical and dental health, access to care, and use of services in US children.
Pediatrics. (2008) 121:e1703–14. doi: 10.1542/peds.2007-2906
3. Mehta NK, Lee H, Ylitalo KR. Child health in the United States:
recent trends in racial/ethnic disparities. Soc Sci Med. (2013) 95:6–15.
doi: 10.1016/j.socscimed.2012.09.011
4. Vespa J, Medina L, Armstrong D. Demographic Turning Points for the
United States: Population Projections for 2020 to 2060. Current Population
Reports. Washington, DC: US Census Bureau (2018). p. 25–1144.
5. National Center for Health Statistics, Centers for Disease Control and
Prevention. National Hospital Ambulatory Medical Care Survey: 2016
NHAMCS Micro-Data File Documentation. Available online at: https://www.
cdc.gov (accessed August 4, 2019).
6. Kubicek K, Liu D, Beaudin C, Supan J, Weiss G, Lu Y, et al. A profile of
nonurgent emergency department use in an Urban pediatric hospital. Pediatr
Emerg Care. (2012) 28:977–84. doi: 10.1097/PEC.0b013e31826c9aab
7. Brousseau DC, Gorelick MH, Hoffmann RG, Flores G, Nattinger AB.
Primary care quality and subsequent emergency department utilization
for children in wisconsin medicaid. Acad Pediatr. (2009) 9:33–9.
doi: 10.1016/j.acap.2008.11.004
8. Halfon N, Wood DL, Newacheck PW, St Peter RF. Routine emergency
department use for sick care by children in the United States. Pediatrics.
(1996) 98:28–34.
9. Huang C, Poirier M, Cantwell J, Ermis P, Isaacman D. Prudent layperson
definition of an emergent pediatric medical condition. Clin Pediatr (Phila).
(2006) 45:149–55. doi: 10.1177/000992280604500206
10. Zimmer KP, Walker A, Minkovitz CS. Epidemiology of pediatric emergency
department use at an urban medical center. Pediatr Emerg Care. (2005)
21:84–9. doi: 10.1097/01.pec.0000159050.19188.23
11. Moon TD, Laurens MB, Weimer SM, Levy JA. Nonemergent emergency room
utilization for an inner-city pediatric population. Pediatr Emerg Care. (2005)
21:363–6. doi: 10.1097/01.pec.0000166725.76685.4a
12. James CA, Bourgeois FT, Shannon MW. Association of race/ethnicity
with emergency department wait times. Pediatrics. (2005) 115:e310–5.
doi: 10.1542/peds.2004-1541
13. Johnson TP, Wislar JS. Response rates and nonresponse errors
in surveys. JAMA. (2012) 307:1805–6. doi: 10.1001/jama.20
12.3532
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-
access article distributed under the terms of the Creative Commons Attribution
License (CC BY). The use, distribution or reproduction in other forums is permitted,
provided the original author(s) and the copyright owner(s) are credited and that the
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Frontiers in Pediatrics | www.frontiersin.org 10 December 2019 | Volume 7 | Article 525
... These percentages are predicted to change by 2050 to reflect an increasing percentage of children who are Hispanic, Black, Asian, and other non-Hispanic races [24]. Investigating and addressing racial and ethnic health disparities is crucial to continue providing the highest quality of pediatric care to all patients [25]. This analysis of the association between race and outcomes following open treatment of femoral shaft fractures found that pediatric patients from the URM cohort had an increased length of hospital stay but did not have any significant differences in 30-day postoperative complications when compared to patients who were White. ...
Article
Introduction Femoral shaft fractures are a common pediatric injury that can require non-operative or operative management. Several studies have shown that race impacts pain management and a number of emergency department visits in the pediatric femur fracture population. This study aimed to investigate any association between pediatric patient race and number of comorbidities, 30-day postoperative outcomes, and length of stay following open surgical treatment of femoral shaft fractures. Methods Pediatric patients who underwent open treatment of femoral shaft fracture were identified in the National Surgical Quality Improvement Program-Pediatric database from 2012-2019. Patients were categorized into two cohorts: White and underrepresented minority (URM). URM groups included Black or African American, Hispanic, Native American or Alaskan, and Native Hawaiian or Pacific Islander. Demographics, comorbidities, and postoperative complications were compared using bivariate and multivariable regression analyses. Results Of the 5,284 pediatric patients who underwent open treatment of femoral shaft fracture, 3,650 (69.1%) were White, and 1,634 (30.9%) were URM. Compared to White patients, URM patients were more likely to have a higher American Society of Anesthesiologists score (p=0.012), more likely to have pulmonary comorbidities (p=0.005), require preoperative blood transfusion (p=0.006), and have an increased risk of prolonged hospital stay (OR 2.36; p=0.007). Conclusion Pediatric URM patients undergoing open treatment of femoral shaft fractures have an increased risk of extended hospital stay postoperatively compared to White patients. As the racial and ethnic constitution of the pediatric population changes, understanding racial and ethnic health disparities will be crucial to providing equitable care to all patients.
Article
Background Concussions affect millions of youths in the United States each year, and there is concern about long-term health effects from this injury. Purpose To examine the association between sports- or physical activity–related concussion and health risk behaviors among middle and high school students in 9 states. Study Design Cross-sectional study; Level of evidence, 3. Methods Data from the 2019 middle school and high school Youth Risk Behavior Survey were used for this analysis. Nine states were identified that included the same question on concussion and similar questions on health risk behaviors in their 2019 Youth Risk Behavior Survey. Students were asked to self-report whether they had ≥1 sports- or physical activity–related concussions during the 12 months preceding the survey. Self-reported concussion was the primary outcome of interest. Other variables included sex, race/ethnicity, played on a sports team, were physically active 5 or more days/week, ever tried cigarette smoking, ever used an electronic vapor product, academic grades, drank alcohol, were in a physical fight, seriously considered attempting suicide, made a suicide plan, and attempted suicide. Results Among the 9 states, 18.2% of middle school students and 14.3% of high school students self-reported ≥1 sports- or physical activity–related concussions. Among both middle school and high school students, the prevalence of ≥1 sports- or physical activity–related concussions was higher among students who played on a sports team, were physically active 5 or more days per week, had ever tried cigarette smoking, had ever used an electronic vapor product, had seriously considered attempting suicide, had made a suicide plan, and had attempted suicide compared with those who had not engaged in those behaviors. The prevalence of sports- or physical activity–related concussion was consistently higher among middle school students than high school students across sex, race/ethnicity, and adverse health behaviors. Conclusion Middle school students with a history of concussion warrant attention as an at-risk population for concussions and adverse health behaviors. Health care providers may consider screening students for adverse health behaviors during preparticipation examinations and concussion evaluations.
Article
Introduction: The purpose of this study was to obtain a broad view of the knowledge, attitudes, beliefs, and lived experiences of emergency nurses regarding implicit and explicit bias. Methods: An exploratory, descriptive, sequential mixed-methods approach using online surveys and focus groups to generate study data. Two validated instruments were incorporated into the survey to evaluate experiences of microaggression in the workplace and ethnocultural empathy. Focus group data were collected using Zoom meetings. Results: The final sample comprised 1140 participants in the survey arm and 23 focus group participants. Significant differences were found in reported experiences of institutional, structural, and personal microaggressions for non-White vs White participants. Respondents who identified Christianity as their religious group had lower mean scores on items representing empathetic awareness. Respondents who identified as nonheterosexual had significantly higher mean total Scale of Ethnocultural Empathy scores, empathetic awareness subscale scores, and empathetic feeling and expression subscale scores. Thematic categories that arose from the focus group data included witnessed bias, experienced bias, responses to bias, impact of bias on care, and solutions. Discussion: In both our survey and focus group data, we see evidence that racism and other forms of bias are threats to safe patient care. We challenge all emergency nurses and institutions to reflect on the implicit and explicit biases they hold and to engage in purposeful learning about the effects of individual and structural bias on patients and colleagues. We suggest an approach that favors structural analysis, intervention, and accountability.
Article
OBJECTIVES To describe the characteristics and outcomes of children discharged from the hospital with new nasoenteral tube (NET) use after acute hospitalization. METHODS Retrospective cohort study using multistate Medicaid data of children <18 years old with a claim for tube feeding supplies within 30 days after discharge from a nonbirth hospitalization between 2016 and 2019. Children with a gastrostomy tube (GT) or requiring home NET use in the 90 days before admission were excluded. Outcomes included patient characteristics and associated diagnoses, 30-day emergency department (ED-only) return visits and readmissions, and subsequent GT placement. RESULTS We identified 1815 index hospitalizations; 77.8% were patients ≤5 years of age and 81.7% had a complex chronic condition. The most common primary diagnoses associated with index hospitalization were failure to thrive (11%), malnutrition (6.8%), and acute bronchiolitis (5.9%). Thirty-day revisits were common (49%), with 26.4% experiencing an ED-only return and 30.9% hospital readmission. Revisits with a primary diagnosis code for tube displacement/dysfunction (10.7%) or pneumonia/pneumonitis (0.3%) occurred less frequently. A minority (16.9%) of patients progressed to GT placement within 6 months, 22.3% by 1 year. CONCLUSIONS Children with a variety of acute and chronic conditions are discharged from the hospital with NET feeding. All-cause 30-day revisits are common, though revisits coded for specific tube-related complications occurred less frequently. A majority of patients do not progress to GT within a year. Home NET feeding may be useful for facilitating discharge among patients unable to meet their oral nutrition goals but should be weighed against the high revisit rate.
Article
To determine if racial disparities exist in the management of febrile seizures in a large pediatric emergency department (ED), We performed a retrospective cross-sectional analysis of children 6 months to 6 years-old who presented to the ED with a febrile seizure over a 4-year period. Multivariate logistic regression models were built to examine the association between race and the primary outcome of neuroimaging, and secondary outcomes of hospital admission and abortive anticonvulsant prescription at ED discharge. There were 980 ED visits during the study period. Overall, 4.0% of children underwent neuroimaging and 11.1% were admitted. Of the 871 children discharged from the ED, 9.4% were prescribed an abortive anticonvulsant. There were no differences by race in neuroimaging or hospital admission. However, black children were less likely to be prescribed abortive anticonvulsants (adjusted odds ratio [aOR] 0.47; 95% confidence interval [CI]: 0.23–0.96) compared to non-black peers, when adjusting for demographic and clinical confounders. Stratification by insurance revealed that this disparity existed in Medicaid-insured patients (aOR 0.33, 95% CI: 0.14–0.78) but not in privately-insured patients. We found no racial disparities in neuroimaging or hospital admission among ED patients with febrile seizures. We did find racial disparities in our secondary outcome of abortive anticonvulsant prescription, driven primarily by individuals on Medicaid insurance. This pattern of findings may reflect the lack of standardized recommendations regarding anticonvulsant prescription, in contrast to the guidelines issued for other ED management decisions. Further investigation into the potential for treatment guidelines to reduce racial disparities is needed.
Article
Introduction Triage requires rapid determination of acuity and resources. Current modalities allow for individual judgment, with varied application of algorithmic rules. Although artificial intelligence can improve triage accuracy, gaps remain in understanding implementation facilitators and barriers, especially those related to the cultural understandings by nurses of emergency department presentations. The purpose of this study was to explore the cultural and technological elements of the implementation of an artificial intelligence clinical decision support aid (i.e., KATE) in an emergency nursing triage process in an urban community hospital on the West Coast of the United States. Method An exploratory qualitative study using semi-structured small group and individual interviews and constant comparison analysis strategies. The sample comprised 13 emergency department triage nurses at one site. Campinha-Bacote’s theory of cultural competence framed the study. Results Responses yielded the overall theme of We know these people and we know these things. Supporting categories included the problem of aire; just another checkbox; gut trumps data; higher acuity with no resources; and technology as a safety net. Participants reported reliance on clinical experience and cultural knowledge to assign acuity. Discussion The implementation of an artificial intelligence program was initially received skeptically due to the acontextual nature of AI, but grew to be perceived as a safety net for triage decision making among emergency nurses.
Article
BACKGROUND AND OBJECTIVES The American Academy of Pediatrics recommends preterm newborns undergo car seat tolerance screening (CSTS) before discharge despite limited evidence supporting the practice. We examined subsequent health care utilization in screened and unscreened late preterm and low birth weight newborns. METHODS This observational study included late preterm (34–36 weeks) and term low birth weight (<2268 g) newborns born between 2014 and 2018 at 4 hospitals with policies recommending CSTS for these infants. Birth hospitalization length of stay (LOS) in addition to 30-day hospital revisits and brief resolving unexplained events were examined. Unadjusted and adjusted rates were compared among 3 groups: not screened, pass, and fail. RESULTS Of 5222 newborns, 3163 (61%) were discharged from the nursery and 2059 (39%) from the NICU or floor. Screening adherence was 91%, and 379 of 4728 (8%) screened newborns failed the initial screen. Compared with unscreened newborns, adjusted LOS was similar for newborns who passed the CSTS (+5.1 hours; -2.2–12.3) but significantly longer for those who failed (+16.1; 5.6–26.7). This differed by screening location: nursery = +12.6 (9.1–16.2) versus NICU/floor = +71.2 (28.3–114.1) hours. Hospital revisits did not significantly differ by group: not screened = 7.3% (reference), pass = 5.2% (aOR 0.79; 0.44–1.42), fail = 4.4% (aOR 0.65; 0.28–1.51). CONCLUSIONS Hospital adherence to CSTS recommendations was high, and failed screens were relatively common. Routine CSTS was not associated with reduced health care utilization and may prolong hospital LOS, particularly in the NICU/floor. Prospective trials are needed to evaluate this routine practice for otherwise low-risk infants.
Article
Introduction: The purpose of this study is to compare the prevalence of hospitalization after an emergency department (ED) visit at an urban safety net hospital for youth with and without a substance use disorder. Methods: This study used a retrospective cohort design of adolescents (aged 15-21 y; n = 14,852) treated in the ED and compared the risk of hospitalization within 90 days. Results: A substance use disorder diagnosis in the ED more than doubled the risk of 90-day hospitalization (5.4% vs 2.38%; P < 0.0001). Conclusions: Compared with youth without a substance use disorder, youth with substance use disorders are likely to require additional services after an ED visit.
Article
Background and objectives: The Dysfunctional Voiding and Incontinence Scoring System (DVISS) is a validated tool to evaluate lower urinary tract dysfunction (LUTD) severity in children. DVISS provides a quantitative score (0-35) including a quality-of-life measure, with higher values indicating more/worse symptoms. Clinically, variability exists in symptom severity when patients present to pediatric urology with LUTD. We hypothesized that symptom severity at consultation varied based on race, gender, and/or socioeconomic status. Methods: All urology encounters at a single institution with completed modified DVISS scores 6/2015-3/2018 were reviewed. Initial visits for patients 5-21 years old with non-neurogenic LUTD were included. Patients with neurologic disorders or genitourinary tract anomalies were excluded. Wilcoxon rank sum tests compared scores between White and Black patients and between male and female patients. Multiple regression models examined relationships among race, gender, estimated median household income, and insurance payor type. All statistics were performed using Stata 15. Results: In total, 4086 initial patient visits for non-neurogenic LUTD were identified. Median DVISS scores were higher in Black (10) versus White (8) patients (p < 0.001). Symptom severity was higher in females (9) versus males (8) (p < 0.001). When estimated median income and insurance payer types were introduced into a multiple regression model, race, gender, and insurance payer type were significantly associated with symptom severity at presentation. Conclusions: Race, gender, and socioeconomic status significantly impact LUTS severity at the time of urologic consultation. Future studies are needed to clarify the etiologies of these disparities and to determine their clinical significance.
Article
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Fifty-five million Americans speak a non-English primary language at home, but little is known about health disparities for children in non-English-primary-language households. Our study objective was to examine whether disparities in medical and dental health, access to care, and use of services exist for children in non-English-primary-language households. The National Survey of Childhood Health was a telephone survey in 2003-2004 of a nationwide sample of parents of 102 353 children 0 to 17 years old. Disparities in medical and oral health and health care were examined for children in a non-English-primary-language household compared with children in English- primary-language households, both in bivariate analyses and in multivariable analyses that adjusted for 8 covariates (child's age, race/ethnicity, and medical or dental insurance coverage, caregiver's highest educational attainment and employment status, number of children and adults in the household, and poverty status). Children in non-English-primary-language households were significantly more likely than children in English-primary-language households to be poor (42% vs 13%) and Latino or Asian/Pacific Islander. Significantly higher proportions of children in non-English-primary-language households were not in excellent/very good health (43% vs 12%), were overweight/at risk for overweight (48% vs 39%), had teeth in fair/poor condition (27% vs 7%), and were uninsured (27% vs 6%), sporadically insured (20% vs 10%), and lacked dental insurance (39% vs 20%). Children in non-English-primary-language households more often had no usual source of medical care (38% vs 13%), made no medical (27% vs 12%) or preventive dental (14% vs 6%) visits in the previous year, and had problems obtaining specialty care (40% vs 23%). Latino and Asian children in non-English-primary-language households had several unique disparities compared with white children in non-English-primary-language households. Almost all disparities persisted in multivariable analyses. Compared with children in English-primary-language households, children in non-English-primary-language households experienced multiple disparities in medical and oral health, access to care, and use of services.
Article
Full-text available
This technical report reviews and synthesizes the published literature on racial/ethnic disparities in children's health and health care. A systematic review of the literature was conducted for articles published between 1950 and March 2007. Inclusion criteria were peer-reviewed, original research articles in English on racial/ethnic disparities in the health and health care of US children. Search terms used included "child," "disparities," and the Index Medicus terms for each racial/ethnic minority group. Of 781 articles initially reviewed, 111 met inclusion criteria and constituted the final database. Review of the literature revealed that racial/ethnic disparities in children's health and health care are quite extensive, pervasive, and persistent. Disparities were noted across the spectrum of health and health care, including in mortality rates, access to care and use of services, prevention and population health, health status, adolescent health, chronic diseases, special health care needs, quality of care, and organ transplantation. Mortality-rate disparities were noted for children in all 4 major US racial/ethnic minority groups, including substantially greater risks than white children of all-cause mortality; death from drowning, from acute lymphoblastic leukemia, and after congenital heart defect surgery; and an earlier median age at death for those with Down syndrome and congenital heart defects. Certain methodologic flaws were commonly observed among excluded studies, including failure to evaluate children separately from adults (22%), combining all nonwhite children into 1 group (9%), and failure to provide a white comparison group (8%). Among studies in the final database, 22% did not perform multivariable or stratified analyses to ensure that disparities persisted after adjustment for potential confounders. Racial/ethnic disparities in children's health and health care are extensive, pervasive, and persistent, and occur across the spectrum of health and health care. Methodologic flaws were identified in how such disparities are sometimes documented and analyzed. Optimal health and health care for all children will require recognition of disparities as pervasive problems, methodologically sound disparities studies, and rigorous evaluation of disparities interventions.
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Children enrolled in Medicaid have disproportionately high emergency department (ED) visit rates. Despite the growing importance of patient reported quality-of-care assessments, little is known about the association between parent-reported quality of primary care and ED utilization for these high-risk children. Our goal was to determine the association between parent-reported primary care quality and subsequent ED utilization for children in Medicaid. We studied a retrospective cohort of children enrolled in Wisconsin Medicaid. Parents of children sampled during fall 2002 and fall 2004 completed Consumer Assessment of Healthcare Providers and Systems surveys assessing their child's primary care quality in 3 domains: family centeredness, timeliness, and realized access. Primary outcomes were the rates of subsequent nonurgent and urgent ED visits, extracted from claims data for the year after survey completion. Negative binomial regression was used to determine the association between the domains of care and ED utilization. A total of 5468 children were included. High-quality family centeredness was associated with a 27% (95% confidence interval [95% CI] 11%-40%) lower nonurgent ED visit rate, but no lowering of the urgent visit rate. High-quality timeliness was associated with 18% (95% CI, 3%-31%) lower nonurgent and 18% (95% CI, 1%-33%) lower urgent visit rates. High-quality realized access was associated with a 27% (95% CI, 8%-43%) lower nonurgent visit rate and a 33% (95% CI, 14%-48%) lower urgent visit rate. Parent-reported high-quality timeliness, family centeredness, and realized access for a publicly insured child are associated with lower nonurgent ED, with high-quality timeliness and realized access associated with lower urgent ED utilization.
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Full-text available
The use of the emergency departments as a regular source of sick care has been increasing, despite the fact that it is costly and is often an inappropriate source of care. This study examines factors associated with routine use of emergency departments by using a national sample of US children. Data from the 1988 National Health Interview Survey on Child Health, a nationally representative sample of 17710 children younger than 18 years, was linked to country-level health resource data from the Area Resource File. Bivariate and multivariate analyses were used to assess the association between children's use of emergency departments as their usual sources of sick care and predisposing need and enabling characteristics of the families, as well as availability of health resources in their communities. In 1988 3.4% or approximately 2 million US children younger than 18 years were reported to use emergency departments as their usual sources of sick care. Significant demographic risk factors for reporting an emergency department as a usual source of sick care included black versus white race (odds ratio [OR], 2.08), single-parent versus two-parent families (OR, 1.53), mothers with less than a high school education versus those with high school or more (OR, 1.76), poor versus nonpoor families (OR, 1.76), and living in an urban versus suburban setting (OR, 1.38). Specific indicators of need, such as recurrent health conditions (asthma, tonsillitis, headaches, and febrile seizures), were not associated with routine use of emergency departments for sick care. Furthermore, health insurance status and specifically Medicaid coverage had no association with use of the emergency department as a usual source of sick care. Compared with children who receive well child care in private physicians' offices or health maintenance organizations, children whose sources of well child care were neighborhood health centers were more likely to report emergency departments for sick care (OR, 2.01). Children residing in counties where the supply of primary care physicians was in the top quintile had half the odds (OR, 0.50) of reporting emergency departments as usual sources of sick care. Reliance on hospital emergency departments for routine sick care is strongly associated with demographic and social characteristics of the child and family, the type and source of available well child care, and the supply of primary care physicians. Because health insurance status was not a significant predictor of use, public policies aimed at reducing the use of emergency departments by children will need to address other factors. These include the organizational characteristics and responsiveness of the health care system and the motivation of families for routine use of hospital emergency departments.
Article
In the United States, race and ethnicity are considered key social determinants of health because of their enduring association with social and economic opportunities and resources. An important policy and research concern is whether the U.S. is making progress toward reducing racial/ethnic inequalities in health. While race/ethnic disparities in infant and adult outcomes are well documented, less is known about patterns and trends by race/ethnicity among children. Our objective was to determine the patterns of and progress toward reducing racial/ethnic disparities in child health. Using nationally representative data from 1998 to 2009, we assessed 17 indicators of child health, including overall health status, disability, measures of specific illnesses, and indicators of the social and economic consequences of illnesses. We examined disparities across five race/ethnic groups (non-Hispanic white, non-Hispanic black, Hispanic, non-Hispanic Asian, and non-Hispanic other). We found important racial/ethnic disparities across nearly all of the indicators of health we examined, adjusting for socioeconomic status, nativity, and access to health care. Importantly, we found little evidence that racial/ethnic disparities in child health have changed over time. In fact, for certain illnesses such as asthma, black-white disparities grew significantly larger over time. In general, black children had the highest reported prevalence across the health indicators and Asian children had the lowest reported prevalence. Hispanic children tended to be more similar to whites compared to the other race/ethnic groups, but there was considerable variability in their relative standing.
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This study was designed to develop a descriptive profile of parents and caregivers who bring their children to the emergency department (ED) for nonurgent issues and to explore the reasons for presenting to an urban hospital pediatric ED for nonurgent conditions. Such work is necessary to develop effective interventions. A total of 106 parents/caregivers whose child was triaged with a nonemergent/urgent condition completed a 15- to 20-minute computerized survey (English and Spanish) in an urban pediatric ED. Most respondents described themselves as Latino (76%) and foreign born (62%). About one half (49%) reported having an annual income of less than $20,000, and 43% of respondents did not have health insurance for themselves. Almost all (95%) of the index children had a primary care physician and health insurance. Despite being triaged as nonurgent, more than one half (63%) described their child's condition as "very" or "extremely" urgent. About one half of the respondents reported not receiving basic information on childhood illnesses from their child's physician. Reasons for nonurgent visits seemed to revolve around issues of convenience and perception of quality of care. Interventions should focus on health literacy and ensure that parents are provided relevant and accurate education on pediatric illnesses and common safety concerns; by increasing parental education on pediatric health, parents may be better able to assess acuity of their child's health issues.
Article
As perhaps the most well known of all social science methodologies, survey research findings are commonly reported in the professional literature. Indeed, many biomedical journals routinely publish articles that report results from high-quality survey studies. Discerning unbiased survey findings can be challenging, however, to both editors and readers, necessitating some reflection as to the multiple sources of error in surveys and how to assess them. In this Viewpoint, we provide an overview of one of these sources of survey error—nonresponse bias—and briefly consider approaches for estimating, evaluating, and reporting it.
Article
Dramatic increases in emergency department (ED) use contribute to rising healthcare costs and decrease continuity of care in the United States. Yet little is known about the acuity, frequency of visits, and demographic characteristics of children using the ED. This study examines general demographic trends over a 3-year period and examines whether there are factors associated with varying acuity at an urban academic pediatric ED. Analysis of administrative ED records from fiscal years (FY) 1999 to 2001 for children 0 to 18 years was performed to assess demographic characteristics, periodicity of ED use, and acuity level. Patient demographic characteristics, periodicity, and acuity were comparable for ED visits across each study year with approximately 25,000 annual visits. Among ED users in FY 2001, 42% sought urgent care exclusively, 12% received both urgent and nonurgent care, and 46% used the ED solely for nonurgent care. Of those with only nonurgent visits, 80% had 1 visit. In FY 2001, ED use was predominantly among patients who were black (77.3%) and were 1 to 4 years of age (35.4%). Relative to all patients, a greater percentage of those who used the ED exclusively for nonurgent care were black (87.2% vs. 76.0%, P < 0.05) and lived within 2 miles of the hospital (45.2% vs. 37.4%, P < 0.05). Nearly half of pediatric emergency visits are for nonurgent care. Racial disparities in use of the ED for nonurgent care may be related to patient's proximity to the hospital. Patterns of use are stable across the 3 years. Further study is needed to identify mutable factors in emergency care use.
Article
To determine whether wait times for children treated in emergency departments (EDs) nationally are associated with patient race/ethnicity. Data were obtained from the National Hospital Ambulatory Medical Care Survey, which collects information on patient visits to EDs throughout the United States. We examined data for patients < or =15 years of age who presented to EDs during the 4-year period of 1997-2000. Sample weights were applied to the identified patient records to yield national estimates. For the purposes of this study, race/ethnicity was analyzed for 3 major groups, ie, non-Hispanic white (NHW), non-Hispanic black (NHB), and Hispanic white (HW). During the 4-year study period, 20633 patient visits were surveyed, representing a national sample of 92.9 million children < or =15 years of age. The race/ethnicity distribution included 9019 NHW children (59.5%), 3910 NHB children (23.9%), and 2991 HW children (16.6%). The wait time for all groups was 43.6 +/- 1.7 minutes (mean +/- SEM). There were significant unadjusted intergroup differences in wait times (38.5 +/- 1.6 minutes, 48.7 +/- 0.5 minutes, and 54.5 +/- 0.1 minutes for NHW, NHB, and HW children, respectively). Visit immediacy (triage status), when reported, was categorized as <15 minutes for 2203 children (17.1%), 15 to 60 minutes for 5324 (41.4%), 1 to 2 hours for 3010 (25.1%), and >2 to 24 hours for 1910 (16.4%). There were significant unadjusted differences in triage status according to race, with 14.6% of NHW patients being placed in the >2-hour immediacy range, compared with 18.8% of NHB patients and 20.0% of HW patients. In a linear regression analysis with logarithmically transformed wait time as a dependent variable and with adjustment for potential confounders, including hospital location, geographic region, and payer status, both NHB and HW patients waited longer than NHW patients, although the results were statistically significant only for HW patients. These nationally representative data suggest that children who come to EDs have wait times that vary according to race/ethnicity. There are several potential explanations for this observation, including discrimination, cultural incompetence, language barriers, and other social factors. These data and similar data from the National Hospital Ambulatory Medical Care Survey are useful in identifying nonclinical influences on the delivery of pediatric emergency care.
Article
To identify patient reasons for accessing an urban Pediatric Emergency Room (PER) for primary care and to explore attitudes and practice regarding alternative sources for their medical home. A total of 210 questionnaires, consisting of 24 questions each, were completed in a face-to-face interview performed by trained interviewers. Questions asked included sources of medical care, frequency of use, and factors that went into caregiver decisions for using different sources of care. Caregivers choose the PER because of the short amount of time it takes for their child to be seen and discharged by a physician. Nearly 60% ranked wait time to see a doctor more important than seeing the same doctor every time (37.6%). About one-third of caregivers routinely brought their children to the PER for illness that is not serious. Only 77% of caregivers claimed that their children have a regular doctor. Many caregivers cited that they are seen more by their regular doctor for shots (well visits) than for ill visits and are seen in the PER for illness. In this study, 56% of children did not see the same regular doctor as their siblings. Efficiency and speed of health care delivery is of prime importance to this primarily Medicaid urban population. If strategies are to be implemented to attract these patients to a medical home that will strengthen their ties to their regular doctor, then the needs prioritized by the caregiver must be taken into consideration.