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Characteristics of Emergency Department Patient Visits Referred for Follow-Up Medical Care After Discharge, National Hospital Ambulatory Medicare Care Survey—United States, 2018

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Objective To describe characteristics of a nationally representative sample of patient visits that ended with a referral for follow-up medical care after discharge from hospital emergency department (ED) visits. Methods We used 2018 National Hospital Ambulatory Medical Care Survey data to identify patient characteristics associated with higher rates of visits with referrals for follow-up medical care after ED discharge from nonfederal short-stay and general hospitals throughout the United States. Referral included categories of all disposition variables that indicated referral to a source of care consistent with the patient’s clinical condition at ED discharge. Results Approximately 97 million of 130 million visits (29 700/100 000 US resident population) were referred for follow-up medical care during 2018. Visit referral rates were higher among females (33 100) than among males (26 300/100 000 population); higher among Black patients (61 700) than among White patients (25 600/100 000 population); highest in the South (33 200/100 000 population); and similar rates in Nonmetropolitan (29 900/100 000 population) and Metropolitan Statistical Areas (30 200/100 000 population). Visit referral rates were higher for patients with Medicaid/Children's Health Insurance Program (CHIP) (66 900) than those with Medicare (31 500) or private insurance (14 000/100 000 population). Abnormal clinical findings and injuries were the discharge diagnoses most often referred for follow-up medical care. Conclusion Higher visit referral rates were observed among female sex, non-Hispanic Black race, Medicaid/CHIP, abnormal clinical findings, and injuries. Future studies might reveal reasons that prompted higher referral rates among various patients’ characteristics.
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Characteristics of Emergency Department
Patient Visits Referred for Follow-Up
Medical Care After Discharge, National
Hospital Ambulatory Medicare Care
SurveyUnited States, 2018
Nelson Adekoya
1
, Henry Roberts
1
, and Benedict I. Truman
1
Abstract
Objective: To describe characteristics of a nationally representative sample of patient visits that ended with a referral for fol-
low-up medical care after discharge from hospital emergency department (ED) visits.
Methods: We used 2018 National Hospital Ambulatory Medical Care Survey data to identify patient characteristics associated
with higher rates of visits with referrals for follow-up medical care after ED discharge from nonfederal short-stay and general
hospitals throughout the United States. Referral included categories of all disposition variables that indicated referral to a source
of care consistent with the patients clinical condition at ED discharge.
Results: Approximately 97 million of 130 million visits (29 700/100 000 US resident population) were referred for follow-up
medical care during 2018. Visit referral rates were higher among females (33 100) than among males (26 300/100 000 popula-
tion); higher among Black patients (61 700) than among White patients (25 600/100 000 population); highest in the South
(33 200/100 000 population); and similar rates in Nonmetropolitan (29 900/100 000 population) and Metropolitan Statistical
Areas (30 200/100 000 population). Visit referral rates were higher for patients with Medicaid/Childrens Health Insurance
Program (CHIP) (66 900) than those with Medicare (31 500) or private insurance (14 000/100 000 population). Abnormal clinical
ndings and injuries were the discharge diagnoses most often referred for follow-up medical care.
Conclusion: Higher visit referral rates were observed among female sex, non-Hispanic Black race, Medicaid/CHIP, abnormal clinical
ndings, and injuries. Future studies might reveal reasons that prompted higher referral rates among various patientscharacteristics.
Keywords
emergency service, hospital, prognosis, case management, quality assurance, health care, wounds and injuries
During 2018, approximately 130 million visits were made to
hospital emergency departments (EDs),
1
representing an
increase of 11% from 2007 ED visits.
2
ED visits have increased
consistently, and the characteristics of patients with high rates
of ED use for primary health care services have not changed
(eg, Medicaid recipients, Medicare recipients, older persons,
and non-Hispanic Black patients).
1,2
Although concerns with
ED overcrowding and associated excess costs continue to dom-
inate health care delivery and policy debates,
3,4
the Affordable
Care Act has begun to alleviate certain problems.
5,6
Published
reports demonstrate that unnecessary ED visits can be
reduced drastically among frequent users and pediatric patients
when such visits are followed by receipt of social services
outside the ED,
3,7,8
thus alleviating unnecessary ED visits.
Although US ED visits focus on alleviating emergency
medical concerns, such visits also present opportunities for
addressing vital social care needs of the served community.
9
1
National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention,
Centers for Disease Control and Prevention, Atlanta, GA, USA
Submitted April 4, 2022. Revised May 3, 2022. Accepted June 15, 2022.
Corresponding Author:
Nelson Adekoya, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB
Prevention, Centers for Disease Control and Prevention, MS-E07, Atlanta, GA
30341, USA. Email: NBA7@CDC.GOV
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Original Research
Health Services Research and
Managerial Epidemiology
Volume 9: 1-8
© The Author(s) 2022
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/23333928221111269
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Because the majority of patients who receive primary care ser-
vices from EDs have insurance coverage, researchers have rec-
ommended that shifting those services out of EDs can reduce
the volume of unnecessary ED visits by providing better and
more accessible primary care to which ED patients are
referred.
10
Medical providers working with community-based organi-
zations have successfully delivered medical and social services
to populations who experience risk for disease acquisition and
transmission.
11
By addressing nonmedical needs of these
patients, unnecessary ED visits might be decreased.
12
Published research results indicate economic benets when
social service referrals are incorporated into ED care
13
because persons who repeatedly use EDs often have social, sub-
stance abuse, or mental health problems that worsen their
chronic illness.
14
Although certain factors (eg, patient age,
race/ethnicity, diagnosis, health condition, availability of
primary care provider, health insurance, income, homelessness,
and resources) inuence patient disposition and referral at the
end of an ED visit,
15-17
one public health goal is achieving
the best possible outcome for each patient and the patients
community. ED visits will continue to be vital in our health
care delivery system. Optimal disposition of ED visits and
referral for follow-up medical care are shared interests of
patients, government ofcials, and health insurance companies
because of population growth, hospital readmissions, maximiz-
ing clinical care, racial/ethnic disparities in care and outcomes,
and ED overcrowding.
16,18-21
We describe characteristics of
patients and ED visits that result in referral for follow-up
medical care in a nationally representative sample of ED
visits by age group, sex, race/ethnicity, US geographic region,
insurance payment type, and diagnosis, and we highlight
inequalities by population group.
Methods
Study Sample
The National Hospital Ambulatory Medical Care Survey
(NHAMCS) uses a multistage probability design with
samples of primary sampling units, hospitals within primary
sampling units, and patient visits within emergency service
areas of each selected hospital. For the ED sample of the
2018 NHAMCS, eld representatives of the US Census
Bureau completed electronic ED patient record forms for a
nationally representative probability sample of 20 291 patient
visits to US nonfederal short-stay and general hospital EDs
during January-December 2018. This is accomplished through
a review of the patient medical records by the US Census
Bureau agent. Data processors and medical coders at RTI
International prepared the public use dataset and data diction-
ary. We restricted our analytic sample to ED visits with any
of 8 visit dispositions that required medical follow-up, and
we excluded visits in which the disposition was no answer to
item,left without being seen,left before treatment complete,
left against medical advice,dead on arrival,ordied in ED.
In the 2018 survey, 490 hospitals were selected for the
survey; 112 of those hospitals were determined to be ineligible;
378 hospitals were eligible; and 323 of the eligible hospitals
eventually participated, for a response rate of 85.5%
(unweighted) and 88.5% (weighted) (see ftp://ftp.cdc.gov/pub/
Health_Statistics/NCHS/Dataset_Documentation/NHAMCS).
At each sampled ED, staff were asked to complete encounter
forms for a systematic random sample of patient visits occurring
during a randomly assigned 4-week reporting period. Forms
might also be abstracted by the eld representative. Five or
fewer reasons for an ED visit could be reported (ie, complaints
or symptoms), preferably by using the patients own words.
Measured Patient and Visit Attributes
On the patient record form, the attending physicians primary
diagnosis is usually the most important item that reects the
providers best judgment at the time of visit. NHAMCS-ED
has 5 diagnosis elds. Referral for follow-up visits are forms
of visit disposition and reects where the patient went after
leaving the ED. For example, a patient might (a) require no
additional follow-up, (b) be asked to return to the ED, (c) left
before the completion of a treatment, (d) left without being
seen, (e) be admitted to the hospital or intensive care unit, (f)
be transferred to another facility, (g) be dead on arrival or die
in the ED, (h) be admitted to the observation unit then dis-
charged, (i) be admitted to the observation unit then hospital-
ized, (j) be transferred to a psychiatric hospital, or (k) be
transferred to a nursing home. Sixteen disposition options are
available on the survey form, 8 of which support follow-up
referral visits. We selected all visits for which follow-up refer-
rals were indicated by 1=Yes for that disposition code. The
primary diagnosis codes for visits with these referral follow-up
codes were indicated by major International Classication of
Diseases, 10th Revision (ICD-10) categories (eg, certain infec-
tious diseases, blood disorders, neoplasm, eye and adnexa dis-
eases, musculoskeletal system, or respiratory diseases).
22
The
expected source of payment (PAYTYPER) is based on hierar-
chical recode by using the hierarchy payment of Medicare,
Medicaid/Childrens Health Insurance Program (CHIP) (or
other state-based program), private insurance, workers com-
pensation, self-pay, no charge/charity, other, and unknown.
(A special request was made to obtain relevant denominators
for calculating referral rates for payment type from the
National Center for Health Statistics.)
Statistical Analysis
We analyzed the data by using SAS version 9.4 procedures
designed for NHAMCSs complex sample design. We
restricted our analysis to visits paid for by private insurance,
Medicare, or Medicaid. All other payment options were
grouped as Other (eg, self-pay, no charge, or workers compen-
sation). We calculated visit referral rates per 100 000 among the
2018 US resident population and 95% CI by age group, sex,
race/ethnicity, Metropolitan Statistical Area (MSA), and
2Health Services Research and Managerial Epidemiology
census region. We examined the diagnosis type by age group
and race/ethnicity. Estimates of the 2018 US population denom-
inators for calculating visit rates were obtained from the US
Census Bureau. Regarding hospital EDs, a visit is dened as
a direct contact with a physician or a staff member acting
under the direction of a physician for the purpose of seeking
care and rendering health services. Data are weighted to gener-
ate US national estimates from the complex survey design.
Results
The sample of 20 291 completed patient record forms was from
378 hospital EDs, nested within 279 emergency service areas for
response rates of 60.2% (weighted) and 57.3% (unweighted).
The analytic sample of 15 518 ED visits represented an estimated
130 million ED visits
1
during 2018, of which 80% (approxi-
mately 97 million) were referred for medical follow-up after dis-
charge. The rate of referral for follow-up medical care was
29 700 visits/100 000 US resident population during 2018
(Table 1). The highest referral rate was among those aged 75
years (36 400 visits/100 000 population), followed by those
aged 0-14 years (33 000 visits/100 000 population). In 2018,
female patients (33 100 visits/100 000 population) were referred
more often for follow-up medical care after an ED visit than male
patients (26 300 visits/100 000 populations). Approximately,
half of the visits with referrals were made by non-Hispanic White
patients, and the rate of referral for this population (25 600
visits/100 000 population) was less than half the referral rate
for non-Hispanic Black persons (61 700 visits/100 000 popula-
tion). Although referral rates by census region were similar, the
highest referral rate was in the South (33 200 visits/100 000
population), followed by the Midwest (29 800 visits/100 000
population). The majority of those referred were from a MSA
(87%), possibly reecting the distribution and location of the
sampled EDs. However, referral rates were similar for both
MSA and non-MSA (30 200 vs 29, 900 visits/100 000 persons).
Table 1. Number and Rates of Emergency Department Visits per 100 000 US Resident Population Among Patients who Were Referred for
Follow-up Medical Care, by age, sex, Race/Ethnicity, US Census Region, MSA or Non-MSA and Insurance Payment Type, National Hospital
Ambulatory Medical Care SurveyUnited States, 2018.
Characteristic
Unweighted no. of visits referred for
follow-up
Weighted no. of visits referred for
follow-up
Referrals per 100 000 US
resident population (95% CI)
Age group (years)
0-14 3067 20 117 253 33 000 (24 000-43 000)
15-24 2088 12 742 044 29 700 (24 700-34 700)
25-44 4310 26 444 993 30 400 (25 500-35 300)
45-64 3545 22 466 241 26 800 (22 700-30 800)
65-74 1130 7 501 478 24 600 (20 100-29 200)
75 1254 7 983 709 36 400 (29 800-43 000)
Sex
Female 8547 54 905 325 33 100 (27 900-38 200)
Male 6847 42 350 394 26 300 (22 400-30 200)
Race/ethnicity
Whites NH 8196 51 395 215 25 600 (21 100-30 000)
Blacks NH 3995 26 483 373 61 700 (47 900-75 500)
Hispanic 2580 15 741 347 26 300 (20 400-32 200)
Other NH 623 3 634 784 15 600 (11 700-19 600)
US Census Bureau region
Midwest 3553 20 352 939 29 800 (18 800-40 800)
South 5455 41 356 445 33 200 (24 800-41 500)
West 3148 20 254 666 26 000 (18 900-33 000)
Northeast 3238 15 291 669 27 300 (19 100-35 400)
MSA
MSA 13 399 84 816 885 30 200 (24 500-36 000)
Non-MSA 1995 12 438 834 29 900 (12 900-46 800)
Payment type
Private
insurance
3903 24 367 492 14 000 (11 700-16 400)
Medicaid 5287 33 208 903 66 900 (52 100-81 600)
Medicare 2711 17 490 849 31 500 (25 900-37 100)
Other 3493 22 188 474 51 200 (40 700-61 600)
Total 15 394 97 255 718 29 700 (25 200-34 200)
Abbreviations: CI, condence interval; NH, non-Hispanic; MSA: Metropolitan Statistical Area.
Adekoya et al 3
Medicaid was the single most likely payment type for
follow-up referrals (66 000/100 000 population), followed by
Medicare and private insurance. Approximately one-quarter
of referral visits were from abnormal clinical ndings (esti-
mated 25 233 579 visits), followed by injury, poisoning, and
other external causes (approximately 18 802 244 visits) and
respiratory system diseases (approximately 10 684 485 visits)
(Table 2). Abnormal clinical ndings and injuries were also
the leading diagnoses by age group and race/ethnicity. Injury
was the leading diagnosis for those aged 15 years. For
White patients, Black patients, and all age groups (except
15 years), mental and behavioral disorders were among the
10 leading diagnoses. Infectious diseases were the fourth
leading diagnosis among those aged 15 years (Appendixes
A and B). The type of disposition recorded supports abnormal
clinical ndings where an estimated 90% of referrals were cat-
egorized as return or refer to a physician or clinic for follow-up
services (Table 3).
Abnormal clinical ndings represented a considerable pro-
portion of referral visits. These were a conglomeration of ill-
dened conditions for which no diagnosis could be assigned
by the attending physician. Typically, these diagnoses are
used when etiology is unknown, when a provisional diagnosis
is made but the patient fails to return for further investigation
or care, when a precise diagnosis is unavailable for any
reason, when a case is referred elsewhere before the diagnosis
is made, or when the patient presents with a transient condition
at the initial encounter.
20
Because practical solutions for these
problems are unknown, we examined injury visits for which
practical approaches to prevention exist (Table 4). Referrals
visit rates for injury were highest among persons aged 0-14
years (7887/100 000 population); among males (5867/
100 000 population); among Black patients (9339/100 000 pop-
ulation); and in the South (6181/100 000 population). Nine of
10 follow-up referral visits for injury were from a MSA
(approximately 16 million visits).
Discussion
Previous research examining ED visits demonstrated that
unnecessary ED visits might be drastically reduced if primary
care practices (eg, extended times for facility operation) are
addressed apart from problems related to patient characteristics
associated with unnecessary ED visits.
11
Increasing percentages
of the US resident population among older age groups might
explain why ED visits by persons aged 75 years accounted
for the majority of visits with referrals. The concentration of
chronic conditions (eg, diabetes or hypertension) that lead to
recurring hospital ED visits in the South might explain higher
rates of referral in this geographic region.
23-25
A higher
prevalence of chronic conditions, eg, diabetes, hypertension,
and other conditions
26
may explain the higher referral rates
Table 2. Number and Percentage of Emergency Department Visits That Received a Referral for Follow-up Care, by Condition Diagnosis
Category, National Hospital Ambulatory Medical Care SurveyUnited States, 2018.
Diagnosis type
Unweighted no. of visits referred for
follow-up
Weighted no. of visits referred for
follow-up
Weighted % (95%
CI)
Abnormal clinical nding 3750 25 233 579 26.0 (24.7-27.3)
Injury, poisoning, or other external
cause
2865 18 802 244 19.4 (18.3-20.4)
Respiratory system disease 1743 10 684 485 11.0 (9.5-12.5)
Musculoskeletal system 1283 8 134 914 8.4 (7.6-9.2)
Digestive system 845 5 440 643 5.6 (5.2-6.1)
Genitourinary system disease 820 5 503 134 5.7 (5.1-6.2)
Mental or behavioral disorder 759 3 360 014 3.5 (3.0-3.9)
Skin or subcutaneous disease 562 3 434 903 3.5 (3.2-3.9)
Certain infectious disease 439 2 548 170 2.6 (2.2-3.0)
Circulatory infectious disease 428 2 710 846 2.8 (2.4-3.1)
Health status factor 421 2 351 526 2.4 (2.0-2.8)
Pregnancy or childbirth 334 1 870 758 1.9 (1.6-2.2)
Ear disease 337 1 774 007 1.8 (1.6-2.1)
Nervous system disease 285 1 903 286 2.0 (1.6-2.3)
Endocrine or metabolic disease 208 1 345 564 1.4 (1.2-1.6)
Eye or adnexa disease 161 1 070 450 1.1 (0.9-1.3)
Blood disorder 70 462 713 0.5 (0.3-0.6)
Neoplasm 29 212 757 0.2 (0.1-0.3)
Congenital malformation,
deformations
14 128 895 0.1 (0-0.2)
Perinatal period conditions 13 107 663 0.1 (0-0.2)
Total 15 366 97 080 550 100.0
Abbreviation: CI, condence interval.
4Health Services Research and Managerial Epidemiology
among Black patients. Moreover, similar referral rates for MSA
and non-MSA are reassuring. A closer examination through a
chart review of abnormal clinical ndings and their associated
diagnostic codes is essential for understanding the causes of
different referral rates by age, race/ethnicity, insurance
payment type, and sex, including within-group differences.
Although millions of ED visits for infectious diseases occur
annually, we do not know with certainty why so many visits are
Table 3. Number and Percentage of Emergency Department Visits Referred for Follow-up Medical Care by Type of Disposition, National
Hospital Ambulatory Medical Care SurveyUnited States, 2018.
Disposition type
Unweighted no. of visits referred for
follow-up
Weighted no. of visits referred for
follow-up
Weighted %
(95% CI)
Admit to hospital 316 2 330 540 2.4 (1.7-3.1)
Admit to observation unit then
discharged
385 1 893 256 2.0 (1.4-2.5)
Admit to hospital unit then hospitalized 146 871 527 0.9 (0.6-1.2)
Return/refer to physician/clinic for
follow-up
13 617 86 631 390 89.1 (87.6-90.5)
Return to emergency department 2436 15 666 108 16.1 (11.0-21.2)
Return/transfer to nursing home 74 513 663 0.5 (0.4-0.7)
Transfer to other hospital 367 1 963 401 2.0 (1.4-2.6)
Transfer to psychiatric hospital 199 1 090 774 1.1 (0.8-1.4)
Abbreviation: CI, condence interval.
Table 4. Number and Rates of Emergency Department Visits for Injuries That Received a Referral for Follow-up Medical Care by age, sex, Race/
Ethnicity, US Census Region, and MSA or Non-MSA, National Hospital Ambulatory Medical Care SurveyUnited States, 2018.
Characteristic
Unweighted no. of visits referred for
follow-up
Weighted no. of visits referred for
follow-up
Referrals per 100 000 US resident
population
(95% CI)
Age group (years)
0-14 673 4 802 244 7887 (5730-10 045)
15-24 435 2 618 390 6093 (4737-7450)
25-44 755 4 669 729 5369 (4350-6388)
45-64 590 4 003 560 4772 (3860-5683)
65-74 193 1 278 904 4194 (2724-5665)
75 219 1 429 416 6515 (5167-7864)
Sex
Female 1388 9 348 927 5631 (4641-6620)
Male 1477 9 453 316 5867 (4871-6863)
Race
Whites NH 1782 11 451 459 5695 (4660-6731)
Blacks NH 561 4 009 506 9339 (7031-11 646)
Hispanic 392 2 558 598 4273 (3120-5427)
Other NH 130 782 680 3360 (2176-4545)
US Census Bureau region
Midwest 676 3 765 429 5512 (3465-7560)
South 985 7 710 715 6181 (4484-7878)
West 611 4 264 233 5467 (3664-7271)
Northeast 593 3 061 867 5457 (3497-7417)
MSA
MSA 2453 16 386 605 5843 (4665-7021)
Non-MSA 412 2 415 639 5799 (2484-9113)
Total 2865 18 802 244 5747 (4799-6695)
Abbreviations: CI, condence interval; NH, non-Hispanic; MSA: Metropolitan Statistical Area.
Adekoya et al 5
not referred for follow-up medical care; the following explana-
tions are plausible, however. First, many infectious diseases
cannot be denitively diagnosed during a short hospital ED
visit. Second, vaccination, pre- and postexposure prophylaxis
for certain infections (eg, HIV infection, hepatitis A and hepa-
titis B infection, or typhoid disease) are the focus of ED atten-
tion when persons who need these services are identied.
27
Tuberculosis (TB), a major infectious disease frequently rst
diagnosed in hospital EDs, has decreased substantially in the
United States
28
because of concerted efforts at the national,
state, and local levels. As a supplement to local health depart-
ment efforts, screening patients for latent or active TB infection
in hospital EDs and referring them for follow-up medical care
can be effective in preventing reactivation of TB disease and
infection transmission, thus further reducing TB incidence in
the United States.
13
Menzies et al
29
also have identied
higher risks for active TB and latent TB infection among
non-US born US residents who immigrated from countries
with a high incidence of active TB. Such patients can be
screened for latent or active TB infection during hospital ED
visits and referred for follow-up medical care to reduce TB inci-
dence among US residents.
29
One advantage of the
NHAMCS-ED is that its reliable and uniformly collected esti-
mates can be used with condence for addressing and monitor-
ing such public health challenges as TB or HIV infection.
This analysis demonstrates that visits for injuries have higher
referral rates for follow-up medical care, reecting relative cer-
tainty of the diagnosis and need for follow-up medical care.
Injuries remain a major public health concern in the United
States. Modiable health behaviors (eg, not wearing seat belts
in cars or helmets when riding bicycles or motorcycles) and
other behaviors that lead to motor vehicle or violence-related
injuries (eg, texting while driving, driving while under the inu-
ence of alcohol or drugs, and gunshot or knife wounds) account
for a substantial proportion of hospital ED visits.
30
ED physi-
cians have opportunities for screening these patients for
injury risk factors and for referring them to sources of preven-
tive interventions.
31
A recent intervention linked young victims
of interpersonal violence to sources of health care and social
services for meeting their psychosocial needs. The associated
study concluded that referral of young victims of violence
from the ED for psychosocial services can be successful by
using a case-management model and an alliance between a
health care system and a social service agency.
32
Ensuring
that ED practitioners identify patients who can benet from
follow-up counseling, behavioral interventions, and medical
care after ED discharge is crucial.
Our study ndings have at least 5 limitations. First, US
national and regional estimates are not state-specic.
However, states can use those estimates (eg, ndings regarding
injuries listed in Table 4) as proxy measures of racial/ethnic dis-
parities within states with large numbers of minority popula-
tions. Second, NHAMCS does not contain information
regarding the quality of hospital ED care, which is indicated
in part by the patients satisfaction with ED and follow-up
care and the care outcome that in turn depends on follow-up
medical care received after ED discharge. This study is strictly
descriptive and was not designed to include inferential statistics
because follow-up medical care is a conglomeration of multiple
referral options. As such, independent risk factors associated
with all (combined) medical care follow-up referrals would be
less informative and counterproductive in addressing this
concern. Additionally, we did not examine comorbidities
which could be important in referrals for certain population
groups (eg, older adults, patients living in the South who may
have higher chronic conditions, etc). Third, because we do
not know the location of the particular EDs included in the
sample analyzed, Black populations who had higher referral
rates might have been disproportionately included; weighting
should have reduced incompletely the salience of this limita-
tion. Because non-Hispanic Black populations have higher
ED visit rates than other population groups,
33
assuming that
the same might be true for referrals is reasonable. Fourth, infor-
mation abstracted must be identied in the record, but the
quality of documentation cannot be determined by this study.
Lastly, because visits with a second-, third-, fourth-, or
fth-listed diagnosis were not included in the sample analyzed,
the estimates might be low.
Future studies should examine the reasons why visits in the
South were referred for follow-up at higher rates overall and for
injury-specicrelated visits, compared with other regions. The
majority of referrals were from MSA; therefore, because the
economic benet of social services for large hospitals is well-
documented,
13
greater attention to strategies for reducing and
preventing injuries is likely to provide positive results. Given
the increasing importance of health information systems,
linking referrals to enhance follow-up services is vital, includ-
ing system evaluation. The majority of referrals are paid
through Medicaid insurance or Medicare, which means that
substantial populations are still relying solely on the govern-
ment for their health care delivery. Nationally, Medicaid/
CHIP and Medicare patients represent 52% of ED visits,
1
and
the same percentage was referred for medical follow-up
during 2018. However, the number of adults aged 65 years
has increased, and a considerable percentage of those persons
are still working. Traditionally, Medicare would be paying for
the majority of those referrals, but apparently, older adults are
prepared with nancial tools and resources that are needed as
the population ages. Many older persons continue to work,
thus earning a living, including employer-supported health
insurance, and are creating a new way of looking at the future
(eg, nancial freedom). In our study, private insurance repre-
sents approximately one-fourth of all referrals (approximately
24 367 492 during 2018), compared with Medicare (17 490
849 referrals). This major nding might also indicate that
those who have private insurance are getting better care than
the others who do not (ie, a disparity of health care among
persons having private vs government care) have private insur-
ance. Future research should examine any differences in patient
referrals by health insurance type and the same discharge diag-
nosis. Lastly, because access to primary care providers is
important in reducing ED visits, implications of the recent
6Health Services Research and Managerial Epidemiology
health care reform on ED visits should be examined. For
example, expansion of the Community Health Centers might
potentially reduce both social service referrals and ED visits
because social services are incorporated in those centers
operation.
34
Conclusion
Results of this study provide health care insurance companies,
federal and state governments, and health policy ofcials with
important statistics on referrals for follow-up medical care
after ED discharge. Results indicate disparities in several
patient characteristics. Because disposition of ED visits and
referral for follow-up visits are shared public health interests,
understanding the reasons for these disparities is crucial to max-
imize health outcomes. Partners, collaborators, and allies may
use these estimates to design programs for the population
who are disproportionately affected (eg, injury prevention).
Authors Note
The ndings and conclusions in this report are those of the
author and do not necessarily represent the ofcial position of
the Centers for Disease Control and Prevention.
Acknowledgments
The authors acknowledge the editorial support of C. Kay Smith. In
addition, Christopher Cairns (CDC/NCHS) provided appropriate
denominators and reviewed the manuscript.
Declaration of Conicting Interests
The authors declared no potential conicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The authors received no nancial support for the research, authorship,
and/or publication of this article.
ORCID iD
Nelson Adekoya https://orcid.org/0000-0003-4780-3997
Supplemental material
Supplemental material for this article is available online.
References
1. Cairns C, Ashman JJ, Kang K. Emergency department visit rates
by selected characteristics: United States, 2018. National Center
for Health Statistics; 2021. NCHS Data Brief, No. 401. doi: 10.
15620/cdc:102278.
2. Niska RW, Bhuiya F, Xu J. National hospital ambulatory medical
care survey: 2007 emergency department summary. US Department
of Health and Human Services; 2010. Advance Data from Vital
and Health Statistics No. 26.
3. Adams JG. Emergency department overuse: perceptions and solu-
tions. JAMA. 2013;309(11):1173-1174. doi: 10.1001/jama.2013.
2476.
4. Abir M, Goldstick JE, Malsberger R, et al. Evaluating the impact
of emergency department crowding on disposition patterns and
outcomes of discharged patients. Int J Emerg Med.
2019;12(1):4. doi: 10.1186/s12245-019-0223-1.
5. Kominski GF, Nonzee NJ, Sorensen A. The Affordable Care Acts
impacts on access to insurance and health care for low-income
populations. Annu Rev Public Health. 2017;38:489-505. doi: 10.
1146/annurev-publhealth-031816-044555.
6. Sommers BD, Gunja MZ, Finegold K, Musco T. Changes in self-
reported insurance coverage, access to care, and health under the
affordable care act. JAMA. 2015;314(4):366-374. doi: 10.1001/
jama.2015.8421.
7. Ross JW, Roberts D, Campbell J, Solomon KS, Brouchard BH.
Effects of social work intervention on nonemergent pediatric
emergency department utilization. Health Soc Work.
2004;29(4):263-273.
8. Sandoval E, Smith S, Walter J, et al. A comparison of frequent and
infrequent visitors to an urban emergency department. J Emerg
Med. 2010;38(2):115-121. doi: 10.1016/j.jemermed.2007.09.042.
9. Gordon JA. The hospital emergency department as a social
welfare institution. Ann Emerg Med. 1999;33(3):321-325. doi:
10.1016/s0196-0644(99)70369-0.
10. Lowe RA, Localio AR, Schwarz DF, et al. Association between
primary care practice characteristics and emergency department
use in a medicaid managed care organization. Med Care.
2005;43(8):792-800. doi: 10.1097/01.mlr.0000170413.60054.54.
11. Schluger NW, Huberman R, Wolinsky N, Dooley R, Rom WN,
Holzman RS. Tuberculosis infection and disease among persons
seeking social services in New York city. Int J Tuberc Lung
Dis. 1997;1(1):31-37.
12. LaCalle E, Rabin E. Frequent users of emergency departments: the
myths, the data, and the policy implications. Ann Emerg Med.
2010;56(1):42-48. doi: 10.1016/j.annemergmed.2010.01.032.
13. Gordon JA. Cost-benet analysis of social work services in the
emergency department: a conceptual model. Acad Emerg Med.
2001;8(1):54-60. doi: 10.1111/j.1553-2712.2001.tb00552.x.
14. Genell Andren K. A study of the relationship between social
network, perceived ill health and utilization of emergency care.
A case-control study. Scand J Soc Med. 1988;16(2):87-93. doi:
10.1177/140349488801600205.
15. Figueroa-Sierra M, Young J, Goodbar B, Taylor SP. Patterns of
discharge disposition among patients diagnosed with venous
thromboembolism in the emergency department: a multicenter ret-
rospective cohort evaluation. Clin Hematol and Res. 2017;1(1):
7-9. doi: 10.36959/831/376.
16. Singh JA, Kallan MJ, Chen Y, Parks ML, Ibrahim SA. Association
of race/ethnicity with hospital discharge disposition after elective
total knee arthroplasty. JAMA Netw Open. 2019;1(10):e1914259.
doi: 10.100/jamanetworkopen.2019.14259.
17. Tarity TD, Swall MM. Current trends in discharge disposition and
post-discharge care after total joint arthroplasty. Curr Rev
Musculoskelet Med. 2017;10(3):397-403. doi: 10.1007/
s12178-107-9422-7.
Adekoya et al 7
18. Khera R, Wang Y, Bernheim SM, Lin Z, Kruholz HA.
Post-discharge acute care and outcome following readmission
reduction initiatives: national retrospective cohort study of medi-
care beneciaries in the United States. Br Med J.
2020;368:16831. doi: 10.1136/bmj.l6831.
19. Zhang X, Carabello M, Hill T, Friese CR, Mahajan P. Racial and
ethnic disparities in emergency department care and health out-
comes among children in the United States. Front Pediatr.
2019;7:525. doi: 10.3389/fped.2019.00525.
20. Schrader CD, Robinson RD, Blair S, et al. Identifying diverse con-
cepts of discharge failure patients at emergency department in the
USA: a large-scale retrospective observational study. BMJ Open.
2019;9(6):e028051. doi: 10.1136/bmjopen-2018-028051.
21. Sheikh H, Brezar A, Dzwonek A, Yau L, Calder LA. Patient
understanding of discharge instructions in the emergency depart-
ment: do different patients need different approaches? Int J
Emerg Med. 2018;11(1):5. doi: 10.1186/s12245-018-0164-0.
22. Centers for Disease Control and Prevention (CDC). International
Classication of Diseases, Tenth Revision (ICD-10).US
Department of Health and Human Services, CDC; 2004 Update.
23. Conway BN, Han X, Munro HM, et al. The obesity epidemic and
rising diabetes incidence in a low-income racially diverse southern
US cohort. PLoS One. 2018;13(1):e0190993. doi: 10.1371/
journal.pone.0190993.
24. Voeks JH, McClure LA, Go RC, et al. Regional differences in dia-
betes as a possible contributor to the geographic disparity in stroke
mortality: the reasons for geographic and racial differences in
stroke study. Stroke. 2019;39(6):1675-1680. doi: 10.1161/
STROKEAHA.107.507053.
25. Centers for Disease Control and Prevention. National Diabetes
Statistics Report, 2020. Centers for Disease Control and Prevention,
U.S. Department of Health and Human Services; 2020.
26. US Department of Health and Human Services (DHHS). Healthy
people 2020. DHHS, Ofce of Disease Prevention and Health
Promotion; 2020. https://www.healthpeople.gov/2020
27. Adamson R, Reddy V, Jones L, et al. Epidemiology and burden
of hepatitis A, malaria, and typhoid in New York city associated
with travel: implications for public health policy. Am J Public
Health. 2010;100(7):1249-1252. doi: 10.2105/AJPH.2009.
178335.
28. Centers for Disease Control and Prevention. Summary of noti-
able diseasesUnited States, 2008. MMWR Morb Mortal Wkly
Rep. 2010;57(53):1-94.
29. Menzies HJ, Winston CA, Holtz TH, Cain KP, MacKenzie WR.
Epidemiology of tuberculosis among US- and foreign-born chil-
dren and adolescents in the United States, 1994-2007. Am J
Public Health. 2010;100(9):1724-1729. doi: 10.2105/AJPH.
2009.181289.
30. Centers for Disease Control and Prevention. Web-based injury sta-
tistics query and reporting system (WISQARS). US Department of
Health and Human Services, CDC; 2010. http://www.cdc.gov/
ncipc/wisqars/default.htm
31. Bernstein SL. The clinical impact of health behaviors on emergency
department visits. Acad Emerg Med. 2009;16(11):1054-1059. doi:
10.1111/j.1553-2712.2009.00564.x.
32. Zun LS, Downey L, Rosen J. Violence prevention in the ED:
linkage of the ED to a social service agency. Am J Emerg Med.
2003;21(6):454-457. doi: 10.1016/s0735-6757(03)00102-5.
33. Adekoya N. Infectious diseases treated in emergency departments:
united States, 2001. J Health Care Poor Underserved.
2005;16(3):487-496. doi: 10.1353/hpu.2005.0044.
34. Adashi EY, Geiger HJ, Fine MD. Health care reform and primary
care: the growing importance of the community health center. N
Engl J Med. 2010;362(22):2047-2050. doi: 10.1056/NEJMp1003729.
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