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Trends of Racial/Ethnic Differences in Emergency Department Care Outcomes Among Adults in the United States From 2005 to 2016

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Importance: While the literature documenting health disparities has advanced in recent decades, less is known about the pattern of racial/ethnic disparities in emergency care in the United States. Objective: To describe the trends and differences of health outcomes and resource utilization among racial/ethnic groups in US emergency care for adult patients over a 12-year period. Design, Setting, and Participants: This cross-sectional study of emergency department (ED) data from the nationally representative National Hospital Ambulatory Medical Survey (NHAMCS) examined multiple dimensions of ED care and treatment from 2005 to 2016 among adults in the US. Main Outcomes and Measures: The main outcomes include ED care outcomes (hospital admission, ICU admission, and death in the ED/hospital), resource utilization outcomes (medical imaging use, blood test, and procedure use), and patients' waiting time in the ED. The main exposure variable is race/ethnicity including white patients (non-Hispanic), black patients (non-Hispanic), Hispanic patients, Asian patients, and Other. Results: During the 12-year study period, NHAMCS collected data on 247,989 adult (> 18 years old) ED encounters, providing a weighted sample of 1,065,936,835 ED visits for analysis. Asian patients were 1.21 times more likely than white patients to be admitted to the hospital following an ED visit (aOR 1.21, 95% CI 1.12–1.31). Hispanic patients presented no significant difference in hospital admission following an ED visit (aOR 1.01, 95% CI 0.97–1.06) with white patients. Black patients were 7% less likely to receive an urgent ESI score than white patients less likely to receive immediate or emergent scores, as opposed to semi- or non-urgent scores. Black patients were also 10% less likely than white patients to be admitted to the hospital and were 1.26 times more likely than white patients to die in the ED or hospital. Conclusions and Relevance: Race is associated with significant differences in ED treatment and admission rates, which may represent disparities in emergency care. Hispanic and Asian Americans were equal or more likely to be admitted to the hospital compared to white patients. Black patients received lower triage scores and higher mortality rates. Further research is needed to understand the underlying causes and long-term health consequences of these disparities.
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ORIGINAL RESEARCH
published: 25 June 2020
doi: 10.3389/fmed.2020.00300
Frontiers in Medicine | www.frontiersin.org 1June 2020 | Volume 7 | Article 300
Edited by:
Zheng Feei Ma,
Universiti Sains Malaysia Health
Campus, Malaysia
Reviewed by:
Deborah Levine,
Cornell University, United States
Robert Drury,
ReThink Health, United States
*Correspondence:
Xingyu Zhang
zhangxyu@umich.edu
These authors have contributed
equally to this work
Specialty section:
This article was submitted to
Family Medicine and Primary Care,
a section of the journal
Frontiers in Medicine
Received: 24 November 2019
Accepted: 26 May 2020
Published: 25 June 2020
Citation:
Zhang X, Carabello M, Hill T, Bell SA,
Stephenson R and Mahajan P (2020)
Trends of Racial/Ethnic Differences in
Emergency Department Care
Outcomes Among Adults in the
United States From 2005 to 2016.
Front. Med. 7:300.
doi: 10.3389/fmed.2020.00300
Trends of Racial/Ethnic Differences
in Emergency Department Care
Outcomes Among Adults in the
United States From 2005 to 2016
Xingyu Zhang 1
*, Maria Carabello 2†, Tyler Hill 3, Sue Anne Bell 1, Rob Stephenson 1and
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 Psychology, College of Literature, Science, and the Arts, University of Michigan, Ann
Arbor, MI, United States, 4Department of Emergency Medicine, University of Michigan School of Medicine, Ann Arbor, MI,
United States
Importance: While the literature documenting health disparities has advanced in recent
decades, less is known about the pattern of racial/ethnic disparities in emergency care
in the United States.
Objective: To describe the trends and differences of health outcomes and resource
utilization among racial/ethnic groups in US emergency care for adult patients over a
12-year period.
Design, Setting, and Participants: This cross-sectional study of emergency
department (ED) data from the nationally representative National Hospital Ambulatory
Medical Survey (NHAMCS) examined multiple dimensions of ED care and treatment from
2005 to 2016 among adults in the US.
Main Outcomes and Measures: The main outcomes include ED care outcomes
(hospital admission, ICU admission, and death in the ED/hospital), resource utilization
outcomes (medical imaging use, blood test, and procedure use), and patients’
waiting time in the ED. The main exposure variable is race/ethnicity including white
patients (non-Hispanic), black patients (non-Hispanic), Hispanic patients, Asian patients,
and Other.
Results: During the 12-year study period, NHAMCS collected data on 247,989
adult (>18 years old) ED encounters, providing a weighted sample of 1,065,936,835
ED visits for analysis. Asian patients were 1.21 times more likely than white patients
to be admitted to the hospital following an ED visit (aOR 1.21, 95% CI 1.12–1.31).
Hispanic patients presented no significant difference in hospital admission following
an ED visit (aOR 1.01, 95% CI 0.97–1.06) with white patients. Black patients were
7% less likely to receive an urgent ESI score than white patients less likely to receive
immediate or emergent scores, as opposed to semi- or non-urgent scores. Black
patients were also 10% less likely than white patients to be admitted to the hospital
and were 1.26 times more likely than white patients to die in the ED or hospital.
Zhang et al. Racial/Ethnic Differences in Ed Care
Conclusions and Relevance: Race is associated with significant differences in ED
treatment and admission rates, which may represent disparities in emergency care.
Hispanic and Asian Americans were equal or more likely to be admitted to the hospital
compared to white patients. Black patients received lower triage scores and higher
mortality rates. Further research is needed to understand the underlying causes and
long-term health consequences of these disparities.
Keywords: health disparities, African-American, emergency care, health outcomes, resource utilization, trend
INTRODUCTION
Nearly two decades ago, the Institute of Medicine (IOM) released
a historic report documenting significant racial and ethnic
disparities in the US healthcare delivery system, along with
policy recommendations to address their findings (1). The IOM
report noted that minority groups, including black and Hispanic
populations, face critical differences in healthcare access owing
to higher rates of uninsurance, reduced choice in where to
receive care, and a variety of structural, cultural, and linguistic
barriers as compared to white people (2). Further, black and
Hispanic individuals are less likely than whites to have a primary
care provider for routine and preventive health needs and are
more likely to seek care in a hospital emergency department
(ED) (24).
Consistent with these findings, the ED is more likely to serve
as the entry point into the healthcare system for racial and ethnic
minorities than it is for white populations in the US (5,6).
Upon arrival to the ED, minority populations have also been
found to receive disparate treatment for a number of common
symptoms, including chest pain and acute coronary events; (7
11) trauma; (1214) stroke symptoms and brain injuries; (15,16)
and pain management for bone fractures, migraines, and back
pain as compared to white patients (1719). Such disparities are
alarming in light of the strong association between emergency
care quality and mortality risk and the heightened threat of racial
biases affecting providers’ decision-making in the fast-paced,
information-poor ED context (20).
In seeking to explore and document racial and ethnic
disparities in ED care and outcomes, we and prior researchers
recognize the nature of race and ethnicity as socially-constructed
categories that have real consequences for the experiences and
life chances of people of color within US society writ large
(21), and also within the US healthcare system. In such studies,
race and ethnicity are used as proxies for the true underlying
determinant of the observed disparities, which is racism (22). As
an entrenched and hierarchical system of stratification, racism
structures how minority groups currently and historically have
faced differential access to resources, opportunities, and risks,
making it a fundamental cause of health and health disparities
(21,23). Documenting and seeking to eliminate racial and ethnic
disparities in emergency care represents a crucial step in ongoing
efforts to create a more equitable healthcare system for all patients
in the United States (24), and will require further research
and intervention into the broader systems of stratification that
seed sources of inequity within our health and social systems.
To help inform these efforts, we examined patterns in ED
care outcomes and utilization rates for Asian, black, Hispanic,
and non-Hispanic white adults and investigated factors that
may contribute to observed care disparities, using a nationally
representative dataset for US ED visits.
METHODS
This is a cross-sectional study of ED data obtained from a
multiyear, nationally representative survey carried out in the
US. This study used preexisting, de-identified data and thus
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
(25). NHAMCS-ED is a multistage, stratified probability sample
of ED visits in the US, administered by the National Center for
Health Statistics (a branch of the Centers for Disease Control
and Prevention) (26). The NHAMCS-ED sample is collected
from 300 hospital-based EDs per year, randomly selected
from 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 358,163 patient (Weighted N=1,560,846,342) visits from
3,764 hospital-based EDs were included in the survey datasets
from 2005 to 2016. To restrict our sample to adult patients with
one documented race/ethnicity, we excluded all pediatric visits
(age <18 years, n=81,452, Weighted N=354,288,756) and
patients with unknown or multiple races (n=28,722; Weighted
N=140,620,751), resulting in a total of 247,989 (Weighted N=
1,065,936,835) patients visits for analysis.
Study Outcomes
The primary study outcome variables include the triage level
[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], hospital admission,
intensive care unit (ICU) admission, death in the ED/hospital,
medical resources utilization (blood test, imaging, and other
procedures; see Supplement Table 1 for a full), waiting time
(time between arrival and seeing a physician), and length of visit
(time from arrival to discharge) for the ED encounter. Death
Frontiers in Medicine | www.frontiersin.org 2June 2020 | Volume 7 | Article 300
Zhang et al. Racial/Ethnic Differences in Ed Care
TABLE 1 | Baseline characteristics of patients presenting to the ED, stratified by race/ethnicity, NHAMCS 2005–2016 (weighted sample).
All White patients Black patients Hispanic patients Asian patients Other
1,065,936,835 684,948,994 (64.3) 235,356,879 (22.1) 116,893,764 (11.0) 18,123,016 (1.7) 10,614,182 (1.0)
Male 458,688,091 (43.0) 300,429,341 (43.9) 95,120,971 (40.4) 50,676,958 (43.4) 7,945,064 (43.8) 4,515,757 (42.5)
Age
18–39 454,826,892 (42.7) 267,506,162 (39.1) 114,288,373 (48.6) 60,799,082 (52.0) 7,398,866 (40.8) 4,834,409 (45.5)
40–49 180,241,636 (16.9) 111,092,537 (16.2) 44,013,302 (18.7) 20,476,127 (17.5) 2,513,301 (13.9) 2,146,369 (20.2)
50–59 157,552,708 (14.8) 100,746,731 (14.7) 37,331,253 (15.9) 15,295,944 (13.1) 2,594,595 (14.3) 1,584,185 (14.9)
60–74 149,354,632 (14.0) 105,915,810 (15.5) 26,418,286 (11.2) 12,708,516 (10.9) 2,933,089 (16.2) 1,378,930 (13.0)
75 123,960,967 (11.6) 99,687,753 (14.6) 13,305,663 (5.7) 7,614,095 (6.5) 2,683,166 (14.8) 670,289 (6.3)
Residence type
Private residence 975,224,202 (95.1) 622,404,310 (94.6) 218,576,189 (95.9) 107,770,949 (96.2) 16,659,256 (96.3) 9,813,496 (94.8)
Nursing home 25,715,854 (2.5) 20,136,772 (3.1) 3,770,466 (1.7) 1,342,768 (1.2) 368,971 (2.1) 96,877 (0.9)
Homeless 7,801,609 (0.8) 4,396,947 (0.7) 1,940,447 (0.9) 1,147,133 (1.0) 84,241 (0.5) 232,841 (2.2)
Other 16,683,842 (1.6) 10,798,624 (1.6) 3,717,765 (1.6) 1,771,526 (1.6) 186,037 (1.1) 209,890 (2.0)
Insurance type
Private insurance 321,335,956 (32.0) 226,685,656 (34.8) 57,394,147 (26.2) 27,602,519 (25.7) 6,798,262 (40.0) 2,855,372 (28.0)
Medicare 243,754,059 (24.3) 184,553,685 (28.4) 39,850,264 (18.2) 14,215,845 (13.2) 3,418,729 (20.1) 1,715,536 (16.8)
Medicaid or CHIP 218,162,456 (21.7) 113,269,715 (17.4) 64,314,663 (29.4) 33,678,741 (31.3) 3,714,171 (21.8) 3,185,167 (31.3)
Uninsured 178,164,973 (17.7) 98,987,765 (15.2) 49,073,069 (22.4) 26,032,882 (24.2) 2,337,085 (13.7) 1,734,173 (17.0)
Other 43,012,416 (4.3) 27,293,947 (4.2) 8,288,832 (3.8) 5,997,610 (5.6) 734,364 (4.3) 697,663 (6.8)
Year
2005 82,947,472 (7.8) 54,395,817 (7.9) 16,112,439 (6.8) 10,066,446 (8.6) 1,606,437 (8.9) 766,333 (7.2)
2006 90,593,915 (8.5) 56,734,743 (8.3) 21,008,698 (8.9) 10,121,898 (8.7) 1,748,174 (9.6) 980,402 (9.2)
2007 78,675,398 (7.4) 51,657,544 (7.5) 17,658,282 (7.5) 7,564,031 (6.5) 1,156,816 (6.4) 638,725 (6.0)
2008 81,178,690 (7.6) 53,715,245 (7.8) 18,223,694 (7.7) 7,254,941 (6.2) 1,225,025 (6.8) 759,785 (7.2)
2009 93,571,540 (8.8) 60,455,172 (8.8) 21,576,760 (9.2) 8,547,722 (7.3) 1,929,975 (10.6) 1,061,911 (10.0)
2010 92,276,613 (8.7) 60,909,002 (8.9) 19,058,853 (8.1) 9,953,062 (8.5) 1,512,008 (8.3) 843,688 (7.9)
2011 93,739,810 (8.8) 59,512,636 (8.7) 21,647,273 (9.2) 9,922,551 (8.5) 1,640,597 (9.1) 1,016,753 (9.6)
2012 87,134,529 (8.2) 54,874,117 (8.0) 18,995,723 (8.1) 10,664,317 (9.1) 1,605,220 (8.9) 995,152 (9.4)
2013 87,119,811 (8.2) 56,919,172 (8.3) 18,411,529 (7.8) 9,407,125 (8.0) 1,306,551 (7.2) 1,075,434 (10.1)
2014 90,554,699 (8.5) 54,674,408 (8.0) 22,597,298 (9.6) 10,798,049 (9.2) 1,620,620 (8.9) 864,324 (8.1)
2015 89,005,064 (8.3) 56,828,676 (8.3) 19,634,219 (8.3) 10,709,406 (9.2) 1,243,528 (6.9) 589,235 (5.6)
2016 99,139,294 (9.3) 64,272,462 (9.4) 20,432,111 (8.7) 11,884,216 (10.2) 1,528,065 (8.4) 1,022,440 (9.6)
Day of Week
Sunday 146,504,562 (13.7) 96,204,439 (14.0) 30,683,285 (13.0) 15,512,945 (13.3) 2,536,509 (14.0) 1,567,383 (14.8)
Monday 168,098,140 (15.8) 106,063,337 (15.5) 38,260,640 (16.3) 19,080,935 (16.3) 2,805,636 (15.5) 1,887,592 (17.8)
Tuesday 155,565,333 (14.6) 98,022,792 (14.3) 36,189,001 (15.4) 17,381,428 (14.9) 2,593,571 (14.3) 1,378,541 (13.0)
Wednesday 152,566,716 (14.3) 98,207,296 (14.3) 33,715,222 (14.3) 16,745,407 (14.3) 2,444,785 (13.5) 1,454,007 (13.7)
Thursday 146,894,239 (13.8) 94,402,962 (13.8) 32,608,278 (13.9) 15,824,170 (13.5) 2,595,204 (14.3) 1,463,625 (13.8)
Friday 148,615,588 (13.9) 95,474,110 (13.9) 32,379,118 (13.8) 16,819,533 (14.4) 2,563,946 (14.1) 1,378,881 (13.0)
Saturday 147,692,258 (13.9) 96,574,059 (14.1) 31,521,334 (13.4) 15,529,347 (13.3) 2,583,366 (14.3) 1,484,153 (14.0)
Arrive by ambulance 196,390,765 (18.8) 131,522,933 (19.6) 41,232,649 (17.9) 17,981,146 (15.8) 3,507,756 (19.8) 2,146,282 (20.7)
Seen within last 72 h 44,700,888 (4.8) 29,205,272 (4.8) 9,031,747 (4.5) 5,039,648 (4.9) 914,018 (5.5) 510,202 (5.2)
Pain level
No pain
191,922,708 (23.1) 126,398,959 (23.4) 40,018,910 (22.1) 19,382,697 (21.7) 4,284,336 (30.1) 1,837,805 (21.6)
Mild 90,787,924 (10.9) 63,142,483 (11.7) 15,962,562 (8.8) 9,413,458 (10.6) 1,737,260 (12.2) 532,160 (6.3)
Moderate 255,116,768 (30.6) 165,947,253 (30.8) 53,107,523 (29.3) 28,833,991 (32.3) 4,724,159 (33.2) 2,503,842 (29.4)
Severe 294,597,482 (35.4) 183,760,861 (34.1) 72,162,722 (39.8) 31,540,073 (35.4) 3,503,778 (24.6) 3,630,048 (42.7)
Temperature
36–38C 923,981,783 (92.0) 590,014,678 (91.6) 207,627,018 (93.4) 101,661,293 (92.1) 15,433,830 (90.9) 9,244,962 (92.3)
36C 58,681,432 (5.8) 41,248,048 (6.4) 10,024,208 (4.5) 5,920,677 (5.4) 977,390 (5.8) 511,109 (5.1)
38C 21,147,842 (2.1) 12,837,322 (2.0) 4,696,475 (2.1) 2,778,455 (2.5) 570,379 (3.4) 265,210 (2.6)
(Continued)
Frontiers in Medicine | www.frontiersin.org 3June 2020 | Volume 7 | Article 300
Zhang et al. Racial/Ethnic Differences in Ed Care
TABLE 1 | Continued
All White patients Black patients Hispanic patients Asian patients Other
Heart rate
90 692,407,280 (65.0) 437,892,700 (63.9) 155,914,279 (66.2) 79,870,849 (68.3) 12,105,387 (66.8) 6,624,066 (62.4)
90–100 177,024,146 (16.6) 114,148,145 (16.7) 40,367,221 (17.2) 17,916,420 (15.3) 2,787,718 (15.4) 1,804,643 (17.0)
100–110 100,961,893 (9.5) 68,101,115 (9.9) 20,940,376 (8.9) 9,327,277 (8.0) 1,618,249 (8.9) 974,876 (9.2)
110–120 51,685,009 (4.8) 35,362,040 (5.2) 10,098,936 (4.3) 4,775,100 (4.1) 753,308 (4.2) 695,625 (6.6)
>120 43,858,506 (4.1) 29,444,993 (4.3) 8,036,068 (3.4) 5,004,119 (4.3) 858,354 (4.7) 514,971 (4.9)
DBP
<60 490,634,962 (46.0) 315,052,788 (46.0) 104,018,788 (44.2) 57,632,101 (49.3) 9,111,084 (50.3) 4,820,201 (45.4)
60–80 103,631,700 (9.7) 68,712,622 (10.0) 20,434,534 (8.7) 11,619,767 (9.9) 1,818,276 (10.0) 1,046,501 (9.9)
>80 471,670,172 (44.2) 301,183,584 (44.0) 110,903,557 (47.1) 47,641,896 (40.8) 7,193,657 (39.7) 4,747,479 (44.7)
Census Region
Northeast 191,192,054 (17.9) 129,999,216 (19.0) 29,802,911 (12.7) 27,427,725 (23.5) 3,310,281 (18.3) 651,921 (6.1)
Midwest 250,844,979 (23.5) 178,113,941 (26.0) 58,620,522 (24.9) 10,411,755 (8.9) 2,115,228 (11.7) 1,583,533 (14.9)
South 421,782,783 (39.6) 251,090,677 (36.7) 130,502,027 (55.4) 35,832,957 (30.7) 2,816,720 (15.5) 1,540,402 (14.5)
West 202,117,020 (19.0) 125,745,159 (18.4) 16,431,418 (7.0) 43,221,328 (37.0) 9,880,788 (54.5) 6,838,327 (64.4)
Reason for visit
General symptoms 203,213,691 (19.1) 131,873,758 (19.3) 44,378,354 (18.9) 21,275,654 (18.3) 3,922,060 (21.7) 1,763,865 (16.7)
Symptoms referable to
psychological and mental
disorders
32,995,307 (3.1) 22,552,772 (3.3) 6,386,028 (2.7) 3,255,770 (2.8) 504,223 (2.8) 296,513 (2.8)
Symptoms referable to the
nervous system
83,266,087 (7.8) 53,334,813 (7.8) 18,462,744 (7.9) 9,206,729 (7.9) 1,485,478 (8.2) 776,323 (7.3)
Symptoms referable to the
cardiovascular and
lymphatic systems
21,182,499 (2.0) 13,986,368 (2.0) 4,764,015 (2.0) 1,887,719 (1.6) 435,322 (2.4) 109,075 (1.0)
Symptoms referable to the
eyes and ears
23,544,404 (2.2) 14,145,130 (2.1) 5,806,456 (2.5) 2,924,836 (2.5) 471,989 (2.6) 195,993 (1.9)
Symptoms referable to the
respiratory system
108,894,097 (10.2) 68,165,421 (10.0) 27,305,739 (11.6) 10,599,465 (9.1) 1,704,670 (9.4) 1,118,803 (10.6)
Symptoms referable to the
digestive system
166,981,035 (15.7) 103,946,767 (15.2) 36,765,578 (15.7) 21,423,846 (18.4) 2,833,010 (15.7) 2,011,834 (19.0)
Symptoms referable to the
genitourinary system
54,443,113 (5.1) 29,878,987 (4.4) 15,254,564 (6.5) 7,710,106 (6.6) 1,140,625 (6.3) 458,830 (4.3)
Symptoms referable to the
skin, nails, and hair
34,639,996 (3.3) 21,236,239 (3.1) 8,272,821 (3.5) 4,098,009 (3.5) 710,716 (3.9) 322,212 (3.0)
Symptoms referable to the
musculoskeletal system
169,150,947 (15.9) 109,127,670 (16.0) 38,665,017 (16.5) 17,543,747 (15.1) 2,089,632 (11.6) 1,724,881 (16.3)
Other 164,108,043 (15.4) 114,520,152 (16.8) 28,509,476 (12.2) 16,496,810 (14.2) 2,771,491 (15.3) 1,810,115 (17.1)
The missing proportion for arrival time, residency type, insurance, 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 16%, for Seen within last 72 h is 13%, for pain level is 21%.
outcomes include deaths in the ED and deaths in the hospital.
The primary exposure variables for the analysis was 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
those racially-classifed as “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) and variables indicative of
socioeconomic status, including residence type (private home,
nursing home, homeless, or other), insurance type (private
insurance, Medicare, Medicaid/CHIP, uninsured, or other),
arrival mode, arrival day of the week, and time category. 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 whether or not the
patient had visited the ED within the past 72 h. We also included
Frontiers in Medicine | www.frontiersin.org 4June 2020 | Volume 7 | Article 300
Zhang et al. Racial/Ethnic Differences in Ed Care
TABLE 2 | Proportion of emergency severity index, hospital admission, ICU admission, medical resources utilization, stratified by race/ethnicities, NHAMCS 2005–2016
(weighted sample).
All White patients Black patients Hispanic patients Asian patients Other
ESI score
1–Immediate 24,176,650 (2.7) 16,835,752 (2.9) 4,760,815 (2.5) 2,004,310 (2.1) 362,564 (2.4) 213,209 (2.5)
2–Emergent 111,093,774 (12.6) 75,525,674 (13.2) 22,039,245 (11.4) 10,341,490 (10.8) 2,232,668 (14.5) 954,698 (11.3)
3–Urgent 417,130,120 (47.1) 271,814,776 (47.5) 87,648,287 (45.4) 45,935,756 (47.9) 7,768,313 (50.5) 3,962,988 (46.8)
4–Semi-urgent 262,314,439 (29.6) 165,371,860 (28.9) 61,097,051 (31.7) 28,975,364 (30.2) 4,182,829 (27.2) 2,687,335 (31.7)
5–Non-urgent 70,396,103 (8.0) 42,986,980 (7.5) 17,331,203 (9.0) 8,580,884 (9.0) 844,076 (5.5) 652,960 (7.7)
Hospital Admission 171,492,659 (16.1) 121,926,052 (17.8) 30,310,443 (12.9) 14,290,598 (12.2) 3,460,811 (19.1) 1,504,755 (14.2)
ICU 20,678,842 (1.9) 14,678,517 (2.1) 3,830,975 (1.6) 1,578,750 (1.4) 392,470 (2.2) 198,131 (1.9)
ED/In-hospital death 5,797,774 (0.5) 4,160,464 (0.6) 1,067,849 (0.5) 365,729 (0.3) 175,796 (1.0) 27,936 (0.3)
Blood test 512,300,921 (48.1) 336,577,728 (49.1) 105,873,762 (45.0) 55,383,422 (47.4) 9,732,635 (53.7) 4,733,374 (44.6)
Any image 538,613,213 (50.5) 361,948,731 (52.8) 106,748,828 (45.4) 55,835,981 (47.8) 9,284,380 (51.2) 4,795,295 (45.2)
Procedure 527,191,203 (49.5) 344,838,866 (50.3) 109,011,311 (46.3) 58,116,519 (49.7) 10,160,146 (56.1) 5,064,363 (47.7)
Waiting time (min,
MEANS (95% CI))
49.7 (49.4–50.0) 45.2 (44.8–45.6) 60.3 (59.5–61.1) 55.3 (54.2–56.4) 49.9 (47.5–52.2) 45.8 (42.8–48.9)
Length of visit (min,
MEANS (95% CI))
222.0 (221.0–223.0) 211.8 (210.5–213.0) 237.7 (235.4–239.9) 249.5 (245.8–253.2) 243.3 (235.9–250.7) 201.1 (192.4–209.8)
Waiting time, time from arrival to seeing the physician; Length of visit, time from arrival to discharge.
information on the US census region of the ED and the primary
reason for the ED visit based on system-based symptom clusters
(e.g., “symptoms referable to the respiratory system”). We used
these symptom clusters as they were present in the dataset for the
entire patient population.
Statistical Analysis
Population characteristics were described and compared
among different racial/ethnic groups. The proportion of
each outcome variable among different racial/ethnic groups
and covariate groups were compared using chi-square
tests. 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 racial/ethnic
groups. Multinomial logistic regression models were used to
estimate the association between the ESI scores (categorical)
and racial/ethnic groups. 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/ethnic 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/ethnic groups after adjusting
for other confounding variables. Because these two variables are
not normally distributed, a log transformation was performed
prior the regression model. Poisson regression was used
to model trends in the outcome rates among racial/ethnic
groups, adjusting for age, gender, and insurance type, with
time modeled linearly as years since 2005. An interaction
between the racial/ethnic group and time was included in each
model to test whether the trend in each outcome differed by
racial/ethnic group.
The NHAMCS-ED dataset used in this analysis relies on
imputation for missing data. Specifically, the survey uses a
hot deck-based single, sequential regression methodology to
impute 3-digit ICD-9-CM codes for items such as age, sex,
primary diagnosis, ED volume, and geographic region. The other
variables were imputed with the median of the corresponding
variables prior to generating the logistic regression models and
multivariable linear regression models. SAS (version 9.4) was
used for analyses, and alpha =0.05 was set as the statistical
significance threshold.
RESULTS
During the 12-year study period between 2005 and 2016,
NHAMCS collected data on 247,989 adult (>18 years old)
ED encounters with a discrete race categorization, providing
a weighted sample of 1,065,936,835 for analysis (Table 1 and
Supplement Table 2). The analysis was stratified by racial/ethnic
groups in the following proportions: white patients, 64.3%;
black patients, 22.1%; Hispanic patients, 11.0%; Asian patients,
1.7%; and other, 0.01%. Rates of uninsurance were highest for
Hispanic patients (24.2%) and black patients (22.4%) and lowest
for white (15.2%) and Asian patients (13.7%). Compared to
Asian and white patients, a greater proportion of black patients,
Hispanic patients, and other racial/ethnic minority ED patients
belonged to the 18–39 age group. In terms of symptoms, black
patients presented with the highest proportion of respiratory
issues (11.6% of visits), and Hispanic patients presented with the
highest proportion of digestive issues (18.4% of visits).
Tables 2,3, and Supplement Table 3 summarized the main
outcomes of interest across the sample as a whole and stratified by
race/ethnicity. After adjusting for other covariates, black patients
were 10% less likely (aOR 0.93, 95% CI 0.90–0.97) to receive
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Zhang et al. Racial/Ethnic Differences in Ed Care
TABLE 3 | Odds ratio of emergency severity index, hospital admission, ICU admission, medical resources utilization, stratified by race/ethnicity, NHAMCS 2005–2016.
Racial/ethnic
group
Crude odds
ratio
Adjusted for*
Demographics +Socioeconomic +Visiting & clinical +ESI scores
ESI score: Immediate or
emergent vs. semi- or
non-urgent
White (1) (1) (1) (1)
Black 0.82 (0.79–0.84) 0.95 (0.92–0.98) 0.98 (0.95–1.01) 0.93 (0.90–0.97)
Hispanic 0.88 (0.85–0.92) 1.05 (1.01–1.10) 1.07 (1.02–1.12) 1.08 (1.03–1.13)
Asian 1.23 (1.13–1.34) 1.24 (1.13–1.35) 1.23 (1.13–1.34) 1.19 (1.08–1.30)
Other 0.76 (0.66–0.87) 0.85 (0.74–0.97) 0.85 (0.74–0.97) 0.80 (0.69–0.93)
ESI score: Urgent vs.
semi- or non-urgent
White (1) (1) (1) (1)
Black 0.90 (0.88–0.92) 0.98 (0.95–1.00) 1.01 (0.98–1.03) 0.98 (0.95–1.00)
Hispanic 1.00 (0.97–1.03) 1.11 (1.07–1.14) 1.11 (1.08–1.15) 1.08 (1.05–1.12)
Asian 1.22 (1.14–1.30) 1.22 (1.15–1.31) 1.17 (1.10–1.25) 1.13 (1.05–1.21)
Other 0.98 (0.89–1.07) 1.04 (0.95–1.14) 1.00 (0.91–1.10) 0.95 (0.86–1.04)
Hospital Admission White (1) (1) (1) (1)
Black 0.73 (0.71–0.75) 0.94 (0.91–0.97) 0.93 (0.91–0.96) 0.90 (0.87–0.93) 0.90 (0.87–0.93)
Hispanic 0.72 (0.69–0.74) 0.94 (0.91–0.98) 0.97 (0.94–1.01) 0.97 (0.93–1.01) 1.01 (0.97–1.06)
Asian 1.17 (1.10–1.25) 1.17 (1.10–1.25) 1.28 (1.20–1.38) 1.23 (1.14–1.32) 1.21 (1.12–1.31)
Other 0.82 (0.74–0.92) 1.00 (0.89–1.12) 1.09 (0.97–1.22) 1.00 (0.89–1.13) 0.98 (0.86–1.13)
ICU White (1) (1) (1) (1)
Black 0.81 (0.75–0.87) 1.11 (1.03–1.19) 1.10 (1.02–1.19) 1.09 (1.00–1.18) 1.14 (1.05–1.24)
Hispanic 0.65 (0.59–0.72) 0.90 (0.81–1.00) 0.91 (0.82–1.01) 0.96 (0.86–1.07) 0.97 (0.86–1.09)
Asian 0.96 (0.80–1.15) 0.94 (0.78–1.13) 0.96 (0.80–1.16) 0.95 (0.78–1.15) 0.92 (0.75–1.14)
Other 0.83 (0.62–1.11) 1.03 (0.77–1.39) 1.07 (0.79–1.44) 0.96 (0.70–1.30) 0.93 (0.66–1.31)
Death White (1) (1) (1) (1)
Black 0.74 (0.65–0.86) 1.22 (1.05–1.40) 1.18 (1.02–1.37) 1.23 (1.05–1.44) 1.26 (1.06–1.49)
Hispanic 0.58 (0.47–0.71) 0.93 (0.76–1.15) 0.96 (0.78–1.18) 1.05 (0.84–1.30) 1.12 (0.88–1.42)
Asian 1.49 (1.13–1.96) 1.45 (1.10–1.91) 1.53 (1.15–2.05) 1.64 (1.20–2.22) 1.64 (1.17–2.31)
Other 0.63 (0.34–1.17) 0.88 (0.47–1.65) 0.92 (0.49–1.73) 0.71 (0.36–1.41) 0.82 (0.40–1.66)
Blood test White (1) (1) (1) (1)
Black 0.87 (0.85–0.89) 1.01 (0.99–1.03) 1.00 (0.98–1.02) 0.94 (0.92–0.96) 0.96 (0.94–0.99)
Hispanic 0.96 (0.93–0.98) 1.14 (1.11–1.17) 1.17 (1.14–1.20) 1.13 (1.10–1.16) 1.15 (1.11–1.19)
Asian 1.24 (1.17–1.30) 1.24 (1.18–1.31) 1.28 (1.22–1.36) 1.20 (1.13–1.28) 1.22 (1.14–1.31)
Other 0.95 (0.88–1.03) 1.07 (0.99–1.16) 1.11 (1.02–1.20) 1.02 (0.94–1.12) 0.96 (0.86–1.06)
Any Imaging White (1) (1) (1) (1)
Black 0.73 (0.72–0.75) 0.83 (0.82–0.85) 0.84 (0.83–0.86) 0.83 (0.81–0.85) 0.84 (0.82–0.86)
Hispanic 0.79 (0.77–0.81) 0.91 (0.89–0.94) 0.98 (0.96–1.01) 1.02 (0.99–1.05) 1.04 (1.01–1.07)
Asian 0.97 (0.92–1.02) 0.96 (0.91–1.02) 1.05 (0.99–1.10) 1.15 (1.08–1.21) 1.16 (1.09–1.23)
Other 0.81 (0.75–0.88) 0.90 (0.83–0.97) 0.99 (0.92–1.08) 0.96 (0.88–1.04) 0.98 (0.89–1.07)
Procedure White (1) (1) (1) (1)
Black 0.85 (0.83–0.86) 0.91 (0.89–0.93) 0.94 (0.92–0.96) 0.95 (0.93–0.97) 0.96 (0.94–0.98)
Hispanic 1.00 (0.98–1.03) 1.08 (1.05–1.11) 1.10 (1.07–1.13) 1.10 (1.07–1.13) 1.10 (1.07–1.13)
Asian 1.21 (1.15–1.27) 1.20 (1.14–1.27) 1.16 (1.10–1.22) 1.16 (1.10–1.22) 1.15 (1.08–1.21)
Other 0.95 (0.88–1.02) 1.00 (0.93–1.08) 0.98 (0.90–1.05) 0.95 (0.87–1.02) 0.93 (0.86–1.02)
*+Demographics adjusts for gender, age group; +Socioeconomic adjusts for residence type, insurance type, census region; +Visiting & clinical: year, week of day, arrive by ambulance,
seen within last 72 h, pain level, temperature, heart rate, dialytic blood pressure.
immediate or emergent as opposed to semi- or non-urgent
scores. Similarly, Hispanic patients were 8% more likely (aOR
1.08, 95% CI 1.03–1.13) than white patients to receive immediate
or emergent scores as opposed to semi- or non-urgent scores.
Asian ED patients were more likely than white patients to receive
immediate and emergent (aOR 1.19, 95% CI 1.08–1.30) or urgent
care (aOR 1.13, 95% CI 1.05–1.21) scores as opposed to semi- or
non-urgent care needs in all models.
After adjusting for other covariates (including ESI level), black
patients and Hispanic patients were also 10% less likely than
white patients to be admitted to the hospital following their ED
visit (aOR 0.90, CI 0.87–0.93). Asian patients were 1.21 times
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Zhang et al. Racial/Ethnic Differences in Ed Care
TABLE 4 | Linear regression between log transform of wait time or length of visit
and by race/ethnicity, NHAMCS 2005–2016.
Wait time Length of visit
Beta (95% CI) p-value Beta (95% CI) p-value
White (1) (1)
Black 0.27 (0.25–0.28) <0.0001 0.18 (0.17–0.19) <0.0001
Hispanic 0.13 (0.11–0.15) <0.0001 0.21 (0.20–0.22) <0.0001
Asian 0.06 (0.02–0.09) 0.001 0.13 (0.11–0.16) <0.0001
Other 0.08 (0.03–0.13) 0.002 0.01 (0.04–0.02) 0.45
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, arrive by ambulance, seen within last 72 h, pain level, temperature, heart
rate, dialytic blood pressure), and ESI score.
more likely than white patients to be admitted to the hospital
following an ED visit (aOR 1.21, 95% CI 1.12–1.31). Black
patients were 1.14 times more likely to receive ICU admission in
the fully adjusted models (aOR 1.14, 95% CI: 1.05–1.24). Odds of
dying in the hospital/ED differed among the groups. Relative to
white patients, Black patients were 1.26 times more likely to die
in hospital (aOR 1.26, 95% CI 1.06–1.49); Hispanic patients, 1.12
times more likely (aOR 1.10, 95% CI 0.88–1.42); Asian patients,
1.64 (aOR 1.64, 95% CI 1.17–2.31).
After adjusting for other covariates (including ESI level),
black patients were 16% (aOR 0.84, 95% CI 0.82–0.86) less
likely to receive any imaging and 4% (aOR 0.96, 95% CI
0.94–0.99) less likely to have a blood test during ED visit
than white patients. In contrast, Asian patients were 1.22
(aOR 1.22, 95% CI 1.14 1.31) times more likely than white
patients to receive a blood test. Hispanic and Asian patients
were 1.10 times and 1.15 times (aOR 1.12, 95% CI 1.07–
1.13; aOR 1.15, 95% CI 1.08–1.21) more likely to receive a
procedure in the ED than white patients. After adjusting for
other covariates, waiting times in the ED were significantly
longer for all minorities (p<0.001) as compared to white
patients (Table 4).
Figure 1 displays trends of different health outcome and
resource utilization variables over time (2006–2016) by
racial/ethnic group. Table 5 includes the estimated health
outcome and resource utilization rates and changes over time.
Rates of hospital and ICU admission significantly decreased over
time in all racial/ethnic groups. However, these rates decreased
the least in white patients as compared to other groups (p<
0.01). Of particular note, hospital and ICU admission for white
patients decreased by 30.01 and 30.57%, respectively, compared
to 42.48 and 57.79% for black patients. Death rates in the ED
for black and white patients decreased over the 12 years by
37.84 and 33.33%, respectively, whereas the Asian and Hispanic
patients’ death rates increased by 250.00 and 10.53%, respectively
(see Discussion for analysis of the large increase for Asian
patients). Blood test rates and medical imaging utilization rates
in the ED increased (though with different 2006–2016 percent
changes) while the procedure utilization rates dropped across all
racial/ethnic groups.
DISCUSSION
We observed significant racial/ethnic differences in the
evaluation and management of adult patients in the ED based on
the nationally representative NHAMCS, between 2005 and 2016.
In the ED, black patients and those in the other racial/ethnic
group were less likely than white patients to receive immediate or
urgent ESI scores as opposed to semi- or non-urgent care needs.
Our study indicates that this racial/ethnic disparity could not
be explained by demographic, socioeconomic, or factors related
to the patients’ clinical presentation or the context of their visit.
While we cannot infer the cause of these ESI disparities based
on the current study, we point to two potential explanations.
First, as past research has found, racial/ethnic minorities are
less likely than white patients to have a primary care provider
and are more likely to rely on ED services for routine care
needs (24). This differential use of the healthcare system
by racial groups may partially account for the disparate ESI
scores. Alternatively, racial bias on the part of nurses and other
healthcare providers may contribute to the lower ESI scores
assigned to racial and ethnic minorities. The influence of racial
bias in ED decision-making has been noted in prior research
(20), including one study of a large, urban-based university
hospital in the US (27), but further observational research is
needed to better understand the complex interplay of healthcare
utilization patterns and the patient experience of different
racial/ethnic groups.
Unlike the other racial/ethnic minority groups, Asian patients
were more likely than white patients to present to the ED with
immediate or urgent care needs. While the higher socioeconomic
indicators ascribed to the Asian patients in our sample (e.g.,
higher rates of private insurance and inhabiting a private
residence) may contribute, it is worth noting that the pattern
remained in the fully adjusted models accounting for these
factors. Unfortunately, due to a lack of granularity in the
NHAMCS-ED, we were unable to further stratify the Asian
sample by region or country of origin, nor could we test for
the effects of patient-level factors such as income, education,
primary language, or duration of US residence on the outcomes
analyzed in this study. Such information would be valuable in
attempting to explain the divergent patterns observed between
Asian patients and other racial/ethnic minorities included in
our sample (28).
Black groups were also less likely than white patients to
be admitted to the hospital following an ED encounter after
controlling for model covariates. Compared to white patients,
Asian patients were either more or equally likely to be admitted
to the hospital following an ED visit, which again diverges from
the pattern of the other racial/ethnic groups and cannot be fully
explained by the factors available in the NHAMCS-ED dataset.
Although mortality rates were <1% for all groups, Asian and
black patients were more likely than white patients to experience
in-hospital death following an ED visit, after we adjusted for the
patients’ ESI scores. While the data indicate that Asian patients
have remarkably high odds (1.93), compared to white patients,
of dying in the ED/hospital, we suspect that this result may owe
in part to the small sample size of Asian patients relative to the
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Zhang et al. Racial/Ethnic Differences in Ed Care
FIGURE 1 | Racial/Ethnic-Specific ED health outcome and medical resource utilization rate from 2005 to 2016: NHAMCS 2005–2016. *Predicted rate were derived
from a model using data over the time period, modeling time as a linear trend. Age, gender, and health insurance type were adjusted.
other racial/ethnic groups and the infrequency of deaths in the
ED/hospital (Supplement Table 3, unweighted N=54).
In the adjusted models, we also found that black and other
racial/ethnic patients were less likely than white patients to
receive blood tests or other procedures in their ED visit. In
contrast, Asian and Hispanic patients were more likely than white
patients to receive blood tests or other procedures in the ED.
The persistence of these disparities after stringent adjustment in
our models suggests a need for further observational research
within EDs. For example, research is needed to understand
why Hispanic patients, who fared worse than white patients
on most outcomes in our analysis, have higher utilization
rates for blood tests and other procedures relative to white
patients. It is particularly noteworthy that adjusting for ESI
score did not eliminate racial/ethnic disparities in hospital/ICU
admission, death in the ED/hospital, or medical resource
utilization. This finding indicates that, even if racial/ethnic bias
does not influence ESI score assignment, such biases may still
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Zhang et al. Racial/Ethnic Differences in Ed Care
TABLE 5 | Race/ethnicity-specific rates and trends of health outcome and medical resources utilization: NHAMCS 2005–2016.
Rate* Trend p-value p
Outcome Race/ethnicity 2005 2016 2005–2016 trend Racial/ethnic difference in trend
Hospital Admission White 0.159 0.111 30.01%<0.001 Reference
Black 0.157 0.090 42.48%<0.001 <0.001
Hispanic 0.149 0.092 38.10%<0.001 <0.001
Asian 0.181 0.113 37.41%<0.001 <0.001
Other 0.158 0.102 35.09%<0.001 <0.001
ICU Admission White 0.016 0.011 30.57%<0.001 Reference
Black 0.020 0.008 57.79%<0.001 <0.001
Hispanic 0.015 0.009 43.71%<0.001 <0.001
Asian 0.014 0.013 6.47%<0.001 <0.001
Other 0.018 0.012 32.39%<0.001 0.0035
Death White 0.003 0.002 33.33%<0.001 Reference
Black 0.004 0.002 37.84%<0.001 <0.001
Hispanic 0.002 0.002 10.53%<0.001 <0.001
Asian 0.002 0.007 250.00%<0.001 <0.001
Other 0.002 0.001 62.50%<0.001 <0.001
Blood test White 0.444 0.482 8.61%<0.001 Reference
Black 0.451 0.465 3.11%<0.001 <0.001
Hispanic 0.470 0.508 8.02%<0.001 <0.001
Asian 0.489 0.526 7.60%<0.001 <0.001
Other 0.461 0.440 4.53%<0.001 <0.001
Any imaging White 0.486 0.522 7.36%<0.001 Reference
Black 0.456 0.477 4.54%<0.001 <0.001
Hispanic 0.458 0.531 16.11%<0.001 <0.001
Asian 0.472 0.507 7.41%<0.001 <0.001
Other 0.421 0.495 17.59%<0.001 <0.001
Procedure White 0.603 0.519 13.87%<0.001 Reference
Black 0.588 0.503 14.41%<0.001 <0.001
Hispanic 0.646 0.546 15.59%<0.001 <0.001
Asian 0.694 0.543 21.71%<0.001 <0.001
Other 0.566 0.524 7.53%<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.
influence healthcare providers’ decision-making throughout the
emergency care process.
We further found that black patients and Hispanic patients
endured significantly longer wait times as compared to white
patients, even after adjusting for ESI scores. This finding is
consistent with prior research pointing to the demographic
case mix and volume of visits within individual emergency
departments as primary contributing factors to differential wait
times among racial/ethnic groups (29,30). However, all three
of the main racial/ethnic minority groups in our analysis were
found to spend more time in the ED overall, with the longest
visits experienced by Hispanic patients. It is possible that a need
for language translation may partially contribute to this outcome
(31,32), but we were not able to control for primary language or
use of translation services in our analysis.
Finally, our modeling of trends from 2005 to 2016 suggests
that certain outcomes in ED care were improving across all
racial/ethnic groups at a consistent rate. However, racial/ethnic
minorities’ rate of improvement lagged behind that of white
patients in terms of ICU admission rates, risk of death in the
ED/hospital, and levels of general procedures and blood tests
performed during ED visits. The largest disparity existed between
black and white patients over this 12-year period. Future clinical
interventions and health policies should focus on targeting
improvements toward racial and ethnic minorities, especially
black and Hispanic patients.
LIMITATIONS
The major limitation of our study relates to potential sampling
biases and errors inherent in the NHAMCS-ED data. Namely,
heterogeneity in documentation (e.g., due to differences in
electronic health records practices) may involve abstraction
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Zhang et al. Racial/Ethnic Differences in Ed Care
errors, missing responses, and inaccurate responses. However, we
do not suspect that such systematic biases would moderate the
associations with race/ethnicity noted herein in any consistent
way. 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 and thus does not invalidate the primary
exposure variable for this analysis (33). Another limitation of our
study is our use of broader system-based reasons for ED visit
(e.g., “symptoms referable to the respiratory system”) as a unit of
analysis in our model, rather than performing the more limited
analysis that would result from examining each chief complaint
(e.g., “shortness of breath”). Future investigations into disparities
in ED outcomes and resource utilization for more specific reasons
for visit could be revealing.
CONCLUSIONS
The emergency care of black patients was characterized by
disparities in multiple dimensions of care. Namely, black patients
received lower ESI scores, were less likely to receive tests in the
ED, were less likely to be admitted to the hospital and/or ICU,
and had a higher death rate in the ED and hospital. Some of these
findings were in contrast to Hispanic and Asian patients, who,
in general, received equivalent or greater ED resources compared
to white patients. Further research is needed to understand the
underlying causes and long-term health consequences of these
racial/ethnic disparities in ED care in order to inform clinical
guidelines and policies for eliminating racial differences in this
critical area of US healthcare.
DATA AVAILABILITY STATEMENT
Publicly available datasets were analyzed in this study. The data
can be accessed from the CDC website: https://www.cdc.gov/
nchs/ahcd/index.htm.
AUTHOR CONTRIBUTIONS
XZ: full access to all the data in the study and takes responsibility
for the integrity of the data and accuracy of the data analysis.
XZ and PM: concept and design. MC, XZ, and TH: drafting
of the manuscript. RS, SB, and PM: critical revision of the
manuscript for important intellectual content. XZ: statistical
analysis. XZ and PM: obtained funding. XZ, MC, and TH:
administrative, technical, or material support. XZ and PM:
supervision. All authors: acquisition, analysis, or interpretation
of data.
FUNDING
This study was supported by Michigan Institute for Clinical
and Health Research (MICHR No. UL1TR002240). The
funders/sponsors had no role in the design and conduct
of the study; collection, management, analysis, and
interpretation of the data; preparation, review, or approval
of the manuscript; and decision to submit the manuscript
for publication.
ACKNOWLEDGMENTS
We thank Dr. M. Fernanda Bellolio of the Department of
Emergency Medicine, Mayo Clinic, for her suggestions in the
drafting of this manuscript.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmed.
2020.00300/full#supplementary-material
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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 © 2020 Zhang, Carabello, Hill, Bell, Stephenson 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
original publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not comply
with these terms.
Frontiers in Medicine | www.frontiersin.org 11 June 2020 | Volume 7 | Article 300
... There were no statistically significant differences between Black and White PEH in regard to achieving post-program outcomes (Table 5). (Zhang et al., 2020), less likely to be admitted to the hospital (Zhang et al., 2020), less likely to have diagnostic imaging ordered in EDs (Ross et al., 2020), and more likely to die in the ED or hospital (Ross et al., 2020 (Spector et al., 2020). Additionally, residence in permanent housing may reduce the perception of being discriminated against due to being homeless and poor (Wenzel et al., 2019) in addition to being a Black person. ...
... There were no statistically significant differences between Black and White PEH in regard to achieving post-program outcomes (Table 5). (Zhang et al., 2020), less likely to be admitted to the hospital (Zhang et al., 2020), less likely to have diagnostic imaging ordered in EDs (Ross et al., 2020), and more likely to die in the ED or hospital (Ross et al., 2020 (Spector et al., 2020). Additionally, residence in permanent housing may reduce the perception of being discriminated against due to being homeless and poor (Wenzel et al., 2019) in addition to being a Black person. ...
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