Download Patterns of Emergency Department Utilization in New York City, 2008

Patterns of Emergency Department Utilization
in New York City, 2008
Prepared by:
United Hospital Fund
David Gould, Senior Vice President
Ewa Wojas, Senior Programmer/Analyst
Consultant:
Maria Raven, MD
Contact:
David A. Gould
United Hospital Fund
1411 Broadway, 12Floor
New York, New York 10018
212-494-0740
[email protected]
I.
Introduction
In the past two decades, emergency department (ED) use has increased while the number
of EDs has declined.1 Evidence shows that EDs are increasingly being used by patients for nonemergent care.2 Nationally, it is clear that ED use is on the rise, but exactly what is driving this
increase remains unclear. Frequent ED use may be a marker for unsuccessfully treated health
and social issues, as well as a consequence of fragmented care.3-5 It is highly likely that the
rising rates of ED use nationwide result from a convergence of multiple issues, including
declining availability of primary care, especially among specific populations including Medicaid
beneficiaries, and rising rates of chronic disease.
Use of EDs to manage problems suited for an ambulatory care environment may be
suboptimal, as ED care is episodic and administered by varying providers which promotes lack
of care continuity. On the other hand, it is available 24-7 and offers the opportunity to
immediately address what could be a severe health issue, with a waiting time limited normally
by minutes to hours rather than days, weeks, or months as can be the case for outpatient care.
Until we can better understand patterns and causes of ED use, including whether ED use varies
by geographic area, it will be difficult to optimize care delivery within and outside of EDs and
improve access to needed health and social services.
Recent work by Wennberg et.al., published in the Dartmouth Atlas6 and elsewhere, has
highlighted the importance of evaluating small area variation, or large differences in the rates
of use of medical services between geographic regions when conducting health services
research. Variation in practice and outcomes exists even among locations that are
geographically juxtaposed. New York City is a unique environment and often, its inhabitants
define themselves by their neighborhood: East Harlem, Upper West Side, Soho, Greenwich
Village, Astoria, the people make the neighborhoods. Does the neighborhood, with its unique
population mix and distribution of resources (or lack thereof) influence health services use?
This report is the result of a HEAL-9 grant to allow the United Hospital Fund (UHF) to examine a
critical issue for policy makers, providers, and planners: the use of hospital EDs in New York
City.7 This issue is especially timely given the current state and federal focus on health care and
Medicaid reform, much of which is centered on improving care and reducing costs for heavy
users of health care services. We looked at ED utilization in UHF neighborhoods to capture
potential small area variations in care seeking and delivery that would be important for
Page | 2
stakeholders, from hospital administrators to policy makers at New York State invested in this
issue. In building this report, we took advantage of our ability to examine variations in ED
utilization based on UHF neighborhood, enabling readers to view differences in ED utilization
patterns among the population of ED users at a local level. We also are able to distinguish
patterns of use by individuals who had more than one ED visit in the year a first look at
frequent users. By conducting the analyses herein, we hope to create a tool that can be used
by individual communities and hospitals to better understand how consumers access the
emergency care system and the role EDs play in the health of the communities they serve.
II.
Data Sources and Methodology
A. Data Sources
This report uses four sources of data: 1) the SPARCS ED visit dataset, 2) hospital cost
reports, 3) “Community Health Profiles” prepared by the New York City Department of Health
and Mental Hygiene (NYCDOHMH), and 4) the SPARCS hospital inpatient dataset.
1. SPARCS ED Visit Dataset
This report examines all-payer ED utilization in 2008 for treat and release visits (ED visits
that did not result in hospitalization) within the Statewide Planning and Research Cooperative
System (SPARCS) dataset. SPARCS is a comprehensive data reporting system mandatory for all
hospitals within New York State. SPARCS collects patient level data on specific characteristics
related to each hospital discharge, ambulatory surgery, and most recently, starting in 2003,
emergency department (ED) visits in New York State.
The dataset includes only ED visits that did not result in hospital admission. ED visits
that resulted in admission can be identified in SPARCS hospital discharge datasets, but we did
not analyze these visits in our study because they are unlikely to be preventable. For the same
reason we decided to leave out ED visits that took place in specialty hospitals (Manhattan Eye,
Ear, & Throat Hospital; Memorial Hospital for Cancer and Allied Diseases; and New York Eye and
Ear Infirmary).
Our study explores only ED visits by New York City residents to New York State hospitals,
not all ED visits occurring at hospitals located in NYC. We deleted the “overflow records”
(records with sequence number greater than one). They have been used in SPARCS since 1994 if
Page | 3
more than five UB-92 Accommodation Codes or more than 20 Ancillary Services Codes were
reported for a patient stay. According to a SPARCS representative, if there are multiple records
for a particular patient the only difference between the records is in the Accommodation
and/or Ancillary Services codes. All other data elements for the same patient are repeated, that
is all diagnosis, procedures codes, etc. are the same for each record. “Overflow records”
account for about 1% of all records and were deleted.
In addition, we excluded patients with Emergency Department Indicator "A" for
Ambulatory Surgery from Emergency Department or blank for Ambulatory Surgery only. We
also decided to delete patients transferred to a Short-Term General Hospital for Inpatient Care,
Designated Cancer Center or Children's Hospital.
Most importantly, the dataset we received includes patient identifiers, permitting the
analysis of not only visits, but also individual patients.
2. Hospital Cost Reports
Prior to the release of SPARCS ED data, the only available data on ED visits in New York
City were reported in hospitals’ Institutional Cost Reports (ICRs) that are filed annually with the
New York State Department of Health (NYSDOH). ED visit data in ICRs include only total counts
of ED visits and counts of ED visits not resulting in admission by hospital or hospital system, by
payer. Because patient origin data are not available in ICRs, ED visits can be identified only by
hospital location and not by patient residence. For each category of ED visit (admit and treat
and release), payer class information is also available in ICRs.
We used ICR data in this report to assess the completeness and reliability of SPARCS ED
visit data by comparing visit counts and payer mix by hospital from both sources.
3. Community Health Profiles
The Community Health Profiles prepared by the NYCDOHMH provide a comprehensive
set of population demographic/SES and health status indicators for each of the 42 UHF
neighborhoods. Indicators of health status in the profiles are obtained from a community
survey that was most recently conducted in 2009. We also used 2008 New York City
Department of Health Population Estimates for UHF neighborhoods.
Page | 4
4. SPARCS inpatient dataset
Patient-level SPARCS dataset for 2008 describes all inpatient services provided within
New York State. This dataset includes patient characteristics, diagnoses, treatments, services,
and payer classes. Most importantly, UHF had access to patient identifiers that linked inpatient
data with SPARCS ED which gave us a unique opportunity to compare inpatient and ED
utilization of individual patients.
B. Methodology
1. Upweighting Process
We first evaluated the SPARCS ED visit dataset to determine if it was sufficiently
complete and reliable to conduct the study. To assess the completeness of the data, we
compared counts of ED visits without admission by hospital in SPARCS to those reported in
hospitals’ ICRs. Citywide, visits were underreported in SPARCS by 13%. 70% of hospitals had
variances under 20%.
Many researchers who work with SPARCS discharge data correct for underreporting by
“grossing up” discharges by hospital to counts reported in ICRs. This method assumes that
unreported discharges have the same characteristics (demographics, diagnoses, procedures) as
reported discharges.
If reported data are not representative, “grossing up” may magnify the
misrepresentation of hospitals that have atypical patient populations (e.g., safety net hospitals
with large concentrations of low income, HIV, mentally ill, and substance abuse patients have
more underreporting than other types of hospitals). A second consideration, especially
relevant to study, was that ED visits in some key low income neighborhoods would be
significantly underreported if we did not “gross up” visits.
To evaluate this possibility, we examined the patient characteristics (age, sex, clinical
mix) in 2007 for hospitals with underreporting in 2008 of 20% or more. We found that patient
characteristics in both years were similar for all of these hospitals. Thus, we decided to “gross
up” or “upweight” ED visits for all hospitals to ICR counts. For two hospitals (Parkway and
Caritas) that had much better reporting in other years, we substituted their earlier data (2005
in the case of Parkway and 2006 data in the case of Caritas) in our 2008 dataset. Since 2008 ICR
numbers were not available for Our Lady of Mercy and Peninsula we used 2007 figures for the
Page | 5
upweights. We also decided to delete specialty hospitals (Manhattan Eye, Ear, & Throat
Hospital; Memorial Hospital for Cancer and Allied Diseases; and New York Eye and Ear
Infirmary) and that minimum weight will be 1 (ignoring cases where ICR < SPARCS).
We evaluated the reliability of data elements to be used in our study (patient zip code,
patient demographics, payer class, and clinical diagnoses) through comparison with alternative
data sources where they were available (e.g., payer class data in ICRs) or through tests of
reasonableness where alternative data sources were not available.
Our findings from this evaluation are summarized below:
2. Duplicate Records
When discovered, 9.6% of the records (294,094) were deleted based on: unique
personal identifier (UPIDE), encrypted date of birth (DOBE), sex, date of admission, time of
admission and primary diagnosis code.
3. Linking Patients
The dataset we received includes patient identifiers. We were able to identify individual
patients using the ED by linking patients using UPIDE, DOBE and sex.
4. Race/Ethnicity
Some values were missing or coded as “other.” Health and Hospitals Center (HHC) data
was less complete than nonprofit hospitals. We obtained improved data from HHC, which we
used to adjust for the race/ethnicity analysis.
5. UHF Neighborhoods
The 42 UHF neighborhoods consist of adjoining zip code areas with similar
characteristics, designated to approximate New York City Community Planning Districts, and
based on the demographic, economic, and social diversity found there.
The assignment of zip code areas to neighborhoods, the decisions about which
community planning districts were most appropriate to combine, and the delineation of
neighborhoods were made by UHF staff in consultation with staff of the New York City Planning
Commission and the New York City Health Systems Agency. Originally developed in 1982, this
neighborhood listing was updated in 2002 to reflect socio-demographic changes.
Page | 6
6. UHF Neighborhood 999
A small subset of records within the SPARCS dataset contain a NYC county code but do
not link to a specific UHF neighborhood because they have an incomplete or missing 5-digit zip
code. These data were pooled into a single category 999, and account for 0.3% of people and
0.2% of all ED visits in the 2008 SPARCS dataset.
7. ED Use Definitions
As no uniform definition of frequent ED users exists, for the purposes of this analysis,
we categorized use in multiple ways. An ED user was any person with at least one ED visit in
2008.
We also examined those with 2, 3, 4, and 5 or more visits in 2008. Super users were
those with serial use, who had 5 or more visits in each of three consecutive years (2006, 7, 8).
8. Movers
To assign ED users with at least two visits residing in more than one UHF neighborhood
to one neighborhood we used the following decision rules:

Patients with two visits who resided in two neighborhoods: one of the two
neighborhoods was randomly selected.

Patients with 3 or more visits: the most frequent neighborhood was used (mode).
Where there was no mode, one of the neighborhoods was chosen at random.
9. Clinical Data
We used H-CUP Clinical Classifications Software (CCS) level two (about 140 categories)
to analyze data on diagnoses and procedures.
10. Payer Mix
To determine the accuracy of the payer reported in the SPARCS ED dataset, we
compared each hospital’s payer mix as reported in SPARCS to its ICR for years 2005 through
2008. We believe the ICR is more accurate because it is submitted at the end of the year
reviewed by an independent auditor.
Page | 7
The SPARCS ED dataset has two data fields that identify payer: Source of Payment and
Expected Principal Reimbursement. We compared hospital’s payer mix using both SPARCS data
fields to the ICR and determined that the Expected Principal Reimbursement field is more
accurate (Figure i). The percent of total admissions reported as Medicaid in the SPARCS ED
Source of Payment field varied from the ICR by more than 50% at 28 of 44 hospitals. The
Expected Principal Payment field for Medicaid varied from ICR data by more than 50% at only
one hospital. The Expected Source of Payment field also proved more accurate with other
payer classes.
Figure i: Hospital payer mix reported in SPARCS and ICR, 2008.
Medicare
Medicaid
Self-Pay
All Other
Number of hospitals 25% over/under
Source of Payment
28
34
13
36
Expected Principal
6
Payment
Number of hospitals 50% over/under
9
18
14
5
28
6
34
2
1
4
10
Source of Payment
Expected Principal
Payment
Total number of hospitals = 44
We found that in SPARCS data, hospitals tend to over-count Medicaid visits and undercount self-pay visits (Figure ii). Data accuracy improved by combining these two payer classes
into one category which we labeled, Safety Net, though this action came at the expense of
losing some detail of the data.
Page | 8
Figure ii: Payer Data: SPARCS EXPECTED and ICR
Medicaid
Self-Pay
Medicaid + Self-Pay
Number of hospitals 25% over/under
25% over
6
8
2
25% under
3
10
0
50% over
1
0
0
50% under
0
4
0
Number of hospitals 50% over/under
Total number of hospitals =44
11. Hour of Discharge
Six percent of our sample had negative average length of stay (LOS) in the ED. After
closely examining those records we decided that the negatives reflect times that should have
been recorded for the next day and we corrected those cases accordingly. We also decided to
delete records with zero LOS (3% of our sample) since they were randomly distributed between
age and hospitals.
Twenty four hospitals (including HHC), which represent 35% of ED visits had the hour of
discharge coded as 99 (unknown). Three hospitals: Lutheran, LI Jewish, and North General had
extremely high average LOS (10 hours or more), and two: Cabrini and LIJ Schneider’s Children
reported inexplicably low visit volumes. We decided to exclude them from the analysis as these
data are likely inaccurate.
Our analysis of the average length of stay in the ER was limited to 36 voluntary hospitals
in the city for which data was complete.
12. Neighborhood Quartile Analysis
We divided UHF neighborhoods by quartiles based on adjusted ED rates per 100
population. There are 42 UHF neighborhoods so we assigned 11 neighborhoods in the quartiles
with the highest and lowest ED rates and 10 neighborhoods to the middle quartiles.
Page | 9
13. Analysis of Variance (ANOVA) Analysis
Analysis of Variance (ANOVA) is a powerful statistical test used to determine whether
the means between two or more groups are equal (the null hypothesis) or different. We
performed one-way ANOVA analyses to determine the association of multiple population and
health system factors with ED use in neighborhood groupings characterized by low, medium,
and high ED use rates. Statistically significant results indicate more difference between groups
of neighborhoods than within ED user groups.
III. Study Strengths and Limitations
To our knowledge, this is one of few in-depth analysis of ED use employing mandated data
reported to New York State’s SPARCS system. Specifically, ED data reporting was mandated
starting in 2003 and, as a result, hospitals have had the opportunity to address inaccuracies or
incompleteness of reporting. However, as mentioned, some of these data were not accurately
reported in SPARCS and as a result, we used appropriate statistical techniques to account for
these inaccuracies.
Our ability to examine small area variations in health services use by UHF neighborhood
provides a unique opportunity to draw attention to potential gaps in care. In addition, our
ability to link UHF neighborhood ED use with income and education level provides additional
valuable context for this analysis.
We were able to link SPARCS ED data to inpatient data, providing unique insight into the
relationship between ED use and hospitalizations. In addition, we present some novel findings
that show a possible link between ED use and unstable housing, which to date has been difficult
to demonstrate using large administrative datasets.
ED visits and other person-level characteristics were assigned to a neighborhood based on
the person’s reported zip code of residence. However, we cannot be certain that the health
services use of those within specific UHF neighborhoods actually occurred within those
neighborhoods. While portions of the study examined data over multiple years, the majority
was based on a single year of data, 2008.
Page | 10
IV. Citywide ED Use and Neighborhood Variation
In 2008, New York City residents made approximately three million visits to the ED at acute
care general hospitals that did not result in an admission (treat and release, or T&R), which
accounted for 79.6% of all ED visits. This translates into 36 T&R visits per 100 residents, similar
to rates found in prior UHF work analyzing 2006 SPARCS data.* When accounting for multiple
visits made by individuals in 2008, just over one in five (22%) New York City residents visited an
ED. The following analyses deal only with T&R ED visits.
1. ED Use by UHF Neighborhood
Table 1 (see Attachment I for all tables) shows the percent of the NYC population with at
least one ED visit in 2008 divided into UHF neighborhoods, which are grouped by borough.
Throughout this report, distinct borough patterns emerge. In the Bronx, six of seven UHF
neighborhoods had use rates that were above the city average, with the exception being
Kingsbridge/Riverdale. Of Brooklyn’s 11 neighborhoods, eight had lower than average use, but
three (Central Brooklyn, East New York/New Lots, and Bushwick/Williamsburg) had use rates
quite a bit above the citywide average of 22%. Manhattan showed a mixed picture among its
10 neighborhoods, with some of the most dramatic variation encompassing not only UHF
neighborhoods with the highest use (Central Harlem and East Harlem) but also the lowest
(Upper East Side). These neighborhoods are in close proximity, but are comprised of people
from differing socio-economic backgrounds who have access to different health care resources,
which may account for such stark contrasts. Queens (10 neighborhoods) and Staten Island
(four neighborhoods) had mostly below-average ED use, with a few exceptions that did not
depart too greatly from average.
The percentage of the neighborhood population visiting an ED varied from a low of 8% in
the Upper East Side to a high of 41% in East Harlem. There was an almost seven-fold difference
in age/sex-adjusted use rates among neighborhoods in the city from a high of 83 ED visits per
100 residents in East Harlem to a low of 13 ED visits per resident in Greenwich Village/Soho, the
Upper East Side, and Northeast Queens (Table 2). The 10 neighborhoods with the highest ED
*
A previous UHF study calculated 37 visits per 100 residents using 2006 SPARCS data. The 2008 rates, which we
believe to be more accurate, were calculated using 2008 population estimates from DOHMH. The previous UHF
estimate relied on 2000 census data for the population denominator. In the current analysis, we also deleted
duplicate records.
Page | 11
visit rates (less than 25% of all neighborhoods and comprising 34% of the total population)
accounted for nearly 46% of citywide ED visits.
It should be noted that neighborhood use rates were calculated at the visit level rather
than the person level. As a result, we cannot determine to what extent visit rates are
attributable to multiple users, which is explored below. However, we did find, and show later,
that patients with frequent ED visits in 2008 were more concentrated in neighborhoods with
higher overall rates of ED use.
V.
Race/Ethnicity
Without controlling for income, Black and Hispanic residents use a disproportionate
amount of ED services. Blacks constitute 23% of the NYC population, yet account for 34% of all
ED visits. Hispanics constitute 28% of the total population, and account for 37% of all visits. In
contrast, Whites are less likely to use the ED, constituting 35% of the population but only 16%
of all ED users (Table 3).
These differential use rates by race/ethnicity are consistent with the variation we see
among communities in Table 2. Recent studies8 have shown disproportionate and growing ED
use among non-Hispanic Blacks. Our analysis supports this finding.
VI. Gender
Females made 55% of all ED visits in 2008, slightly greater than their 52% share of the NYC
population (Table 4).
VII. Payer-Mix
Citywide, 65% of ED visits are categorized as safety-net (combined Medicaid and self-pay)
(Table 5). In fact, this proportion is a minimum estimate because SPARCS groups patients
enrolled in Medicaid Managed Care plans with commercial insurance. Therefore, the safety-net
category undercounts persons with Medicaid and overcounts the “commercial/other” category.
The difference between neighborhoods with high and low proportion of safety-net is
striking (80% in Hunts Point and Mott Haven as compared to 28% in the South Shore or 29% in
Upper East Side). These rates closely reflect the variation of socio-economic characteristics
between NYC neighborhoods.
Page | 12
VIII. ANOVA Analysis
We used ANOVA techniques to examine the association between neighborhood level ED
use and the population and health system characteristics of neighborhoods where patients
resided. We divided the 42 UHF neighborhoods into three groups according to ED use based on
our finding that the citywide neighborhood average is 36 visits per 100 population. High use
neighborhoods had over 40 visits per 100 population, middle use neighborhoods 26-39 visits
per 100 population, and low use neighborhoods had less than 23 visits per 100 population. We
then used one-way ANOVA to test the significance of the association between the factors
below and our neighborhood groupings. Below is a listing of the factors examined and data
sources, followed by a graph highlighting differences between high, middle, and low use
neighborhoods.
Measures of Population & Health Systems Characteristics
by UHF Neighborhood Selected for the Study
Demographics
Source
% Below poverty level
U.S. Census (2000)
% Black
DOHMH Population estimates (2008)
% Hispanic
DOHMH Population estimates (2008)
Health Status
% Reporting poor/fair health status
Community Health Survey (Avg. of 2007, 2008 and 2009)
Hospitalization rates/100
Internal analysis of 2008 SPARCS
Access to Care
% Uninsured
Community Health Survey (Avg. of 2007, 2008 and 2009)
% Foreign born
U.S. Census (2000)
% Reporting no regular doctor
Community Health Survey (2008)
% Reporting no doctor visit in the last 12 m
Community Health Survey (2008)
% Reporting that did not get needed med care
Community Health Survey (2009)
Page | 13
Percent of Neighborhood Residents with Selected Characteristics by ED
use category
In this analysis, the level of ED use in 2008 was strongly associated with neighborhood
poverty; proportion of uninsured residents, Blacks, and Hispanics; and residents reporting
poor/fair health status, no regular doctor, and problems with getting medical care when
needed. Significant differences between neighborhood groupings are indicated at the bottom
of the graph.
IX.
Age
Young children age 0-4 use a disproportionate amount of ED services, accounting for 7%
of the population and 14% of all visits (Table 7). Looking at neighborhood variation, we find that
the spread between high and low use rates decreases in the 18-39 and 40-64 age groups, which
account for almost two-thirds (65%) of all ED visits.
To examine differences in neighborhood ED use rates between children and adults, whose
health needs vary, we examined sex adjusted ED use rates per 100 population in children ages
0-17 and adults aged 18 and above. Children had higher adjusted rates of ED visits (46 per 100)
than adults (33 per 100) (Tables 6a and 6b). Given their smaller share of the population,
however, children aged 0-17 accounted for just 29% of total ED visits (Table 7).
Page | 14
When examining neighborhood use rates by age group, we find several consistent
patterns. High use neighborhoods shown in Table 2 also show high rates of use among children
aged 0-17. Notably, for every UHF neighborhood, with the exception of Greenwich Village/Soho
and Greenpoint, ED use rates for the 0-17 population were higher than adult use rates. The
average citywide ratio of ED visits in the 0-17 population to ED visits in the population 18 and
older was 1.4, and only a few neighborhoods departed significantly from the norm (Tables 6a
and 6b).
To more closely examine ED use by children, we separated those aged 0-4 from those aged
5-17. 44% of 0-4 year olds had at least one ED visit, the highest visit rate of any age group
under study. One neighborhood, West Queens, deserves further discussion. While West
Queens had lower-than-average ED use in its over-18 population, its under-18 population saw
higher than average ED use (Table 6a) driven largely by the 0-4 year old population, as seen in
Table 7. At 23%, West Queens was the UHF neighborhood with the highest proportion of ED
use among 0-4 year olds, suggesting the value of further study of its current pediatric
ambulatory capacity.
In contrast, 22% of 5-17 year olds had an ED visit. When examining the proportion of
children with at least one ED visit by neighborhood, we found a concentration in the same 17
neighborhoods (see box in Table 8a and Table 8b). These neighborhoods were home to
children in both age groups who experienced more than the citywide visit rate of 44%, with
neighborhood rates ranging from 72% in Central Harlem to 45% in Inwood/Washington Heights
for children 0-4 and from 72% in Central Harlem to 47% in the Rockaways for children 5-17.
X.
ED Diagnoses
To examine the clinical reasons persons visited the ED, we grouped UHF neighborhoods
into four quartiles (as described in the Methodology section). Looking at patterns of use among
these four quartiles we can observe whether variations existed among high and low ED use
neighborhood groupings (Table 9).
Neighborhoods in the highest use quartile had the lowest proportion of ED visits
categorized as injury, and this proportion increased with decreasing neighborhood quartile ED
use. If injury is not considered a preventable reason for an ED visit, it may be that in
neighborhoods with lower overall ED use rates, larger proportions of visits are categorized as
Page | 15
unpreventable visits, so that both overall use rates are lower as is ED use for preventable
causes. Conversely, neighborhoods in the highest use quartile had the highest proportion of ED
visits categorized as general medicine, and these numbers decreased with decreasing ED use. It
is likely that at least some of these medicine-related ED visits serve as substitutes for
ambulatory care.
Proportions of ED visits categorized as psychiatric or alcohol/drug related did not vary
notably by quartile of neighborhood ED use, and were relatively uncommon diagnoses
compared to injury and general medicine categorizations. We shall observe later in this report
that while diagnoses related to mental health and substance use were less common in the
general population of ED users, they were highly prevalent among the heaviest users of NYC
EDs. Diagnoses related to mental health and substance use may be under-reported in SPARCS.
However, a NYCDOHMH report from November 2010 stated that alcohol-related ED visits have
steadily increased since 2003: in 2009, 2.75% of ED visits had an alcohol-related chief
complaint, which is within the range of our findings in this analysis.*
XI. Frequent ED Users
As there is no commonly accepted and uniform definition of frequent ED users, for the
purposes of this analysis we categorized frequency of use in multiple ways. We show use by
one through five or more visits in 2008; we examine persons with three or more visits in 2008
and five or more visits in 2008; and we take a closer examination of persons who display what
we call “serial use” or “super-users” — those who had 5 or more visits in each of three
consecutive years.
The majority of ED users are not frequent users. Of all ED users in NYC in 2008, 74.3% had
only one visit, and 16% had only two visits. However, those with more frequent use have a
disproportionate impact on the emergency care system in NYC: persons with three visits
accounted for 5.3% of all ED users and 10.7% of all ED visits; persons with four visits accounted
for 2.1% of all ED users and 5.8% of all ED visits; persons with five or more visits accounted for
2.3% of all ED users and 11.4% of all ED visits (Tables 10a and 10b).
*
http://www.nyc.gov/html/doh/downloads/pdf/survey/survey-2010alcohol.pdf) NYC Vital Signs A data report
from the NYC health Dept: consequences of alcohol use in NYC. Nov 2010 vol. 9 no. 5
Page | 16
2.1% of NYC residents (176,000) had three or more ED visits in 2008 (which accounted for
27.9% of all visits), ranging from a low of 0.4% of residents in Northeast Queens to a high of
6.4% in East Harlem (Table 10c). Here too we find a common pattern of higher use among a
familiar set of neighborhoods including East Harlem, Central Harlem, and the cluster of central
Brooklyn and Bronx neighborhoods.
We also noted variation in the frequent user population when broken down by age group.
On average, 5% of all 0-4 year olds had three or more ED visits in 2008, the highest percentage
of frequent ED users among any age group. In addition, this 0-4 age group had the most
neighborhood variation: in East Harlem, 14% of 0-4 year olds were frequent ED users, while 1%
of 0-4 year olds were frequent users in Southwest Brooklyn, the Upper East Side, Gramercy
Park/Murray Hill, Greenwich Village/Soho, Northeast Queens, and the South Shore. In contrast,
there were lower rates of frequent ED use among all other age groups (1%-2% on average) and
among neighborhoods. This may represent more variability of resources available to the
pediatric population by neighborhood, and likely reflects a lower threshold among parents of
young children to turn to the emergency department for evaluation. A lower than NYC average
rate of frequent ED use amongst the older (65+) population likely reflects the fact that these
data are restricted to ED visits that do not result in hospitalization. It may be that providers
have lower thresholds for admission to the hospital among those who are aged 65 and older.
The percentage of residents in each neighborhood who were frequent ED users was highly
correlated with overall neighborhood use rates in Table 2 (correlation coefficient 0.98),
demonstrating that frequent ED users are concentrated in areas with high overall ED use rates.
XII. ED Use Variation by Day and Time
Understanding patterns of ED use by time may shed light on the interplay between
resource availability and health seeking behaviors, and the following analyses, which examine
patterns at the neighborhood level, are among the first efforts to explore this issue.
1. Day of Week
Citywide, most ED visits were made on Monday, and visit volumes slowly declined steadily
for the rest of the week. To determine if this pattern was sustained when evaluating all UHF
neighborhoods, we divided the neighborhoods into quartiles based on level of ED use, as
Page | 17
described in the Methodology section. The general pattern was sustained in three of the four
quartiles: in all but the lowest use quartile, the majority of visits occurred on a Monday with
much lower use through the week and the least use on weekends. In the lowest use quartile,
we found a lower peak occurred on Monday with higher use levels on the weekend. (Figure A)
When we isolated individual UHF neighborhoods, the variation was more striking. On the
Upper East Side, a wealthier neighborhood with the lowest ED use rates, the majority of visits
occurred on the weekend. In contrast, Central Harlem, a neighborhood affected by higher
poverty and ED use rates, followed use patterns present throughout most of the city.
(Figure B)
People who reside in neighborhoods with higher weekday use and also higher poverty
levels, such as Central Harlem, may have less access to ambulatory care, fewer linkages to
primary care physicians, or may be unaware of community resources for acute care outside of
the ED independent of day of the week. Alternatively, on the Upper East Side and in other
higher income neighborhoods, weekday options for care outside of the ED may be more
adequate, but deteriorate over the weekend (fewer primary care weekend hours, etc).
Page | 18
Figure A
Figure B
Page | 19
2. Hour of Visit
Citywide, the volume of admissions* varied quite a bit throughout the day, with the peak at
11 a.m. and the trough at approximately 5 a.m. (Tables 11 and 12). We distinguished weekend
use from weekday use to determine if peak hours differed between the two. The general
pattern of peak use at approximately 11 a.m. with a trough around 5 a.m. did not differ from
weekdays (Figure C and Table 11) to weekend (Figure D and Table 12). Similar patterns also
held across the four use quartiles.
However, on weekdays and weekends (Figures C and D), there was notable variation when
we examined the ratio of peak-to-trough use in each quartile. On weekdays, Group 1, the
quartile of UHF neighborhoods with the highest ED use, follows a similar curve to the other 3
groups, but the peak hour volume jumps disproportionately when compared to Groups 2, 3,
and 4. Indeed, the ratio of peak-to-trough visit volume jumps in a linear fashion from Group 4,
the lowest use quartile, to Group 1 the highest use quartile: the ratio is 5.0 in Group 4; 5.2 in
Group 3; 5.4 in Group 2; and 6.5in Group 1, indicating a disproportionate increase in peak hours
ED use for neighborhoods with the highest population-level ED use (Table 11). When
examining weekend patterns (Figure D), we find that the ratio of peak-to-trough is relatively flat
for the three higher use quartiles (3.6 to 3.7) but increases to 4.0 for the lowest use quartile
(Table 12). (Please note the scale of the Y-axis differs between Figures C and D: the hourly
changes in ED volume are not as extreme on weekends.)
*
The SPARCS data dictionary defines “hour of admission” as “the hour during which the patient was admitted for
*ED+ services.”
Page | 20
Figure C
Figure D
Page | 21
3. Off- vs. On-Hours ED Use
A majority of visits (57%) occurred “off-hours,” which we defined as weekdays between 5
p.m. and 8 a.m. or anytime on the weekend (Table 13) and comprise 73% of the hours in a
week. Most physician offices and clinics are not open during these times, though the Medicaid
program now offers enhanced reimbursement for expanded hours.
Frequent ED users had essentially the same share of off-hour visits as other ED users. Onetime users had 58% of their visits off-hours while those with three or more visits had 57% of
their visits off-hours (Table 13).
There was not a large variation in the share of off-hour visits by neighborhood (maximum
62%; minimum 54%). However, there was a strong negative correlation between off-hour ED
use and overall neighborhood ED use (-0.83): the neighborhoods with the highest ED use rates
had the lowest percentage of off-hour ED use, a pattern consistent with weekend use rates.
(Figure E and Table 14)
Figure E
This makes intuitive sense. It may be that residents of areas with lower poverty rates,
higher self-reported health status, and lower overall ED use rates are more likely to go to the
ED off-hours when they have decreased access to other more regular sources of outpatient
Page | 22
primary care. In contrast, if residents of lower income neighborhoods with higher ED use have
less access to alternative ambulatory health services and primary care regardless of the time of
day or day of week, the off- /on-hours distinction is not important.
We found very little variation in off- /on-hour usage between visits categorized as general
medicine ED visits, ED visits for injury or behavioral health issues, and ED visits that were
unclassifiable due to vague diagnostic data (Table 15). However, the percentage of off-hour
visits in all categories was highest for the youngest patients, ages 0-4. This relatively high offhours use amongst the youngest group of children may be due to factors such as working
parents less able to take acutely ill children for medical attention during normal pediatric office
business hours, or parents worried overnight about sick children who are not improving with
supportive care at home. In addition, since many children in that age group are unable to fully
verbalize what might be bothering them, it is more difficult for on-call pediatricians to diagnose
issues off-hours or overnight via telephone. Many pediatrician offices do offer off-hours care
including the ability to make sick visits on weekends to accommodate such needs, and the
population served by such offices may not be aware of these services.
XIII. Length of Stay
We examined reported length of stay (LOS) based on hour of admission and hour of
discharge variables as reported to SPARCS by 36 voluntary hospitals, which had an average
overall length of ED stay of 4 hours and 4 minutes. Table 16 shows the mean LOS for each of
the 36 hospitals compared to the average for this analysis. The mean LOS was slightly higher for
adults (4.27 hours) than for children aged 0-17 (3.10 hours) (Table 17). As expected, the ED
stay for patients reported to have injuries was shorter on average than those who sought care
for general medicine reasons (Table 18). Interestingly, LOS was not correlated with ED volume
(correlation coefficient 0.01) (Figure F).
Page | 23
Figure F
XIV. Residential Mobility and ED Use
One characteristic of frequent ED users is that they appear to have unstable housing
situations. On average, 11% of all NYC ED users with two or more visits had at least one move
between UHF neighborhoods (Table 19). The number of movers increases dramatically with
increasing numbers of ED visits.
Table 19 shows that as the number of ED visits increase, the likelihood that an ED user
resided in more than one UHF neighborhood in the same year also increases. While 7% of all
ED users with two ED visits reported living in more than one UHF neighborhood, almost onequarter (24%) of ED users with five to ten visits lived in more than one neighborhood in the
year. There was a near linear relationship between number of annual ED visits and the
likelihood of at least one move between UHF neighborhoods. A full 67% of those with very high
numbers of annual visits (30 or more in 2008) had at least one move that year.
XIV. Super-users
As policy makers, planners, and health care providers and payers attempt to control health
care spending, “super-users” of health services have recently been a hot topic of coverage in
the lay press.9-10 Despite this coverage, much remains to be answered about this population of
very heavy health services users. To determine how many frequent ED users remained
frequent users from year to year, we isolated the subset of people with five or more visits in
Page | 24
2006, and tracked their ED utilization in the following two years. There were 37,460 people
with five or more visits in 2006 (0.5% of the population, 2% of all ED users). Of this population,
only 4,147 (11%) remained frequent users in the subsequent two years, 2007 and 2008. This
high degree of regression to the mean in the frequent user population points to the importance
of using predictive modeling or other tools to best target the small but significant segment of
the population whose frequent use will be sustained, and benefit from intervention. Limited
health care dollars must be directed carefully, as most frequent users do not remain frequent
users over time.
1. Demographics
We also examined demographics and payer mix for “super-users.” 47.4% were nonHispanic Blacks, 26.6% were Hispanic, 14.4% were white non-Hispanic, and 16.7% were
categorized as other. Most were aged 18-39 (35.6%) or 40-64 (47.4%) with smaller percentages
of the population distributed among the 0-4 and 5-17 age groups (5.8% and 4.5%) and those 65
and older (6.8%). 51% were female and 49% male, which is reflective of the general NYC
population (Table 20). These numbers reflect lower numbers of “super-users” at the extremes
of age, yet as mentioned previously, applies only to treat and release visits and not to ED use
that leads to inpatient hospitalizations. The majority of “super-users” (72.2%) were categorized
as safety-net patients (uninsured and Medicaid beneficiaries).
2. Diagnoses
To examine this small subpopulation in greater detail, we evaluated the reasons for their
visits using Clinical Classifications Software (CCS) categorizations developed by the Agency for
Healthcare Research and Quality (AHRQ). (Table 21) This system aggregates codes from
the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) into
clinically related groups that can be employed in many types of projects analyzing data on
diagnoses and procedures.11 Not surprisingly, ED visit categorizations among the “super-users”
varied notably from those with just one ED visit in 2008. The top three categorizations among
those with a single visit in 2008 were 1) symptoms, signs, and ill-defined conditions (9.2%), 2)
respiratory infections (7.5%), and 3) superficial injury; contusion (5.7%).
Page | 25
Among “super-users” the top three categorizations were alcohol related disorders (13%),
asthma (11%), and, tied for third, symptoms, signs, and ill-defined conditions and psychiatric
disorders (6% each). Diagnoses related to mental health accounted for only 2% of one-time
users, ranked 16th of all categorizations, and asthma accounted for 2.3% of all visits in one-time
users, ranked 15th. While alcohol-related disorders were the most common ED diagnosis
among “super-users”, these disorders accounted for only 1.6% of visits among one-time users
in 2008 Table 21).
The implications of these findings are that the subgroup of “super-users” is a population
with very different needs than those with fewer visits, and are affected by high rates of mental
illness and substance use for which they are seeking frequent ED care. The predominant
diagnoses in this small but important subset of ED users stand in stark contrast to the rest of
the population whose use of the ED to address behavioral health conditions is much lower. In
addition, asthma, a disease for which timely and effective outpatient care should prevent
hospital admissions, is a common reason for seeking ED care in the “super-user” population.
Our findings highlight the need to improve connections to appropriate ambulatory care settings
and other health and social services via care management and other interventions.
3. Use of Inpatient Services
ED “super-users” also utilized inpatient hospital services. In each year from 2006-2008, 60%
of “super-users” also had at least one hospital admission. The majority of “super-users” had
between one and six hospital admissions in a given year. However, a small subset (from 348370 ED “super-users” each year) had 10 or more hospital admissions in a single year. Each year,
the maximum number of annual hospital admissions among ED “super users” ranged from 67
to 91. (Table 22) This indicates that for a small subset of ED “super-users”, the amount of time
spent in the hospital in a given year may have been greater than the amount of time spent in
the community. The financial implications of this extent of acute health services use are
enormous for both payers and hospitals. While a financial analysis is beyond the scope of this
report, interventions aimed at addressing the health and social factors related to such heavy
health services use could pay for themselves through use of alternative, less costly sites of care
and, for the homeless, stable supportive housing.
Page | 26
“Super-users” were not evenly distributed among NYC hospitals. Among the top ten
hospitals serving those with five or more visits each year from 2006-2008, half were HHC
facilities. (Table 23) It may be that EMS providers direct “super-user” patients
disproportionately to safety-net hospitals for various reasons including payment, ability to care
for this specific population, or patient preference. Improved coordination between EMS
providers and hospitals that care disproportionately for “super-users” could improve care for
this population.
XV. NYU ED Study
The NYU ED algorithm was developed by John Billings and colleagues from the NYU Center
for Health and Public Service Research as a mechanism to help classify ED utilization. 12 The
algorithm was developed with the advice of a panel of ED and primary care physicians, and it is
based on an examination of a sample of almost 6,000 full ED records.
The algorithm first excludes several categories of visits that it does not classify (injuries,
mental illness, alcohol/drug abuse, and medical conditions for which samples of medical charts
were too small to include–about a third of visits in New York City) and then classifies the
remaining two-thirds of visits, which fall under the broad category of “general medicine.” The
algorithm classifies these general medicine ED visits into one of the following categories:

Non-emergent - The patient's initial complaint, presenting symptoms, vital signs,
medical history, and age indicated that immediate medical care was not required within
12 hours;

Emergent/Primary Care Treatable - Based on information in the record, treatment was
required within 12 hours, but care could have been provided effectively and safely in a
primary care setting. The complaint did not require continuous observation and no
procedures were performed or resources used that are not available in a primary care
setting (e.g., CAT scan or certain lab tests);

Emergent, ED Care Needed-Preventable/Avoidable - ED care was required based on
the complaint or procedures performed/resources used, but the emergent nature of the
condition was potentially preventable/avoidable if timely and effective ambulatory care
had been received during the episode of illness (e.g., the flare-ups of asthma, diabetes,
congestive heart failure, etc.); and
Page | 27

Emergent, ED Care Needed-Not Preventable/Avoidable - ED care was required and
ambulatory care treatment could not have prevented the condition (e.g., trauma,
appendicitis, myocardial infarction, etc.).
We applied the NYU ED algorithm to our 2008 SPARCS ED data to determine whether the
above visit categorizations differed by neighborhood or were correlated with overall ED use
rates (Table 24). Bearing in mind that injury and behavioral health diagnoses are excluded,
non-emergent was the most frequent visit classification (39%). On average, 35% of all visits
were categorized as emergent, primary care treatable; 11% emergent, ED care neededpreventable/avoidable; and 15% as emergent, not preventable/avoidable.
Notably, we found that UHF neighborhoods with the lowest ED use rates had the most visits
categorized by the algorithm as being “Emergent,ED Care Needed.” Adjusted ED use rates were
highly negatively correlated with this categorization (correlation coefficient 0.81). This
indicates that not only the amount of utilization, but also the type and urgency of utilization,
varies by neighborhood. These results are consistent with our findings in the “ED Diagnoses”
section, which found that rates of non-preventable visits (diagnoses categorized as injuries)
were highest in neighborhoods with the lowest rates of population-level ED use. In addition,
adjusted ED rates were moderately negatively correlated with the EmergentPreventable/Avoidable categorization (correlation coefficient 0.63), so that in neighborhoods
with higher levels of ED use, visits occurred significantly more often for conditions that could
have been effectively treated in the ambulatory setting. Again, in a familiar subset of
neighborhoods, ED use may be serving as a substitute for timely ambulatory care as evidenced
by higher levels of visits for ambulatory sensitive conditions, and should be studied in more
depth (Table 24). We noted much less variation in visits for Primary Care Treatable and NonEmergent conditions, which did not vary significantly across neighborhoods.
Whether a visit is truly preventable is necessarily influenced by the fact that off-hours
generally indicates less access to ambulatory care. As a result, we ran the NYU ED algorithm
separately for visits made during “off- and on-hours.” On average, there was little difference in
citywide NYU ED algorithm classifications for off- vs. on-hours (Tables 24a and 24b). When
broken down by neighborhood, the variation in off- and on-hours use was similar to that in
Section XII, subheading 3.
Page | 28
XVI. Prevention Quality Indicators Analysis
Because of increasing emphasis being placed on preventable and avoidable hospital
admissions, and because for many hospitals the ED is a main channel for inpatient stays, we
examined admission rates for Prevention Quality Indicators (PQIs), developed by the federal
AHRQ, in each UHF neighborhood to determine if ED use was correlated with other potential
markers for suboptimal outpatient care. We evaluated PQIs in the adult population aged 18
and over, as PQIs for the pediatric population differ from those in adults (Table 25). PQIs are
measures based on inpatient discharge data developed by AHRQ to allow policy makers,
investigators, and care providers to track hospital admissions for conditions that should be
amenable to outpatient treatment, thereby identifying potential problem areas in the
outpatient care delivery system. PQIs include conditions such as bacterial pneumonia,
congestive heart failure, and adult asthma. Previous research has shown that frequent ED
users have high rates of behavioral health issues such as mental health and substance use
diagnoses, and these data are not captured with PQIs, which focus on non-behavioral health
medical diagnoses.
PQIs can be collapsed into three composite measures: Overall PQI, Acute PQI, and Chronic
PQI.13 Acute disease indicators comprise the Acute PQI score and include perforated
appendicitis, dehydration, bacterial pneumonia, and urinary infections. The Chronic PQI score
is composed of chronic disease indicators including chronic obstructive pulmonary disease,
angina without procedure, and adult asthma.
In this analysis, neighborhood level Acute and Chronic PQIs were correlated with adult
neighborhood ED use rate, so that neighborhoods with higher adjusted population level ED use
rates also had higher PQI admissions rates, markers for higher rates of potentially preventable
admissions to the hospital. Acute PQIs were moderately correlated with adjusted
neighborhood ED use rates (0.68) and chronic PQIs were highly correlated (0.95). The
composite of measure of all PQIs, Overall PQI, was also highly correlated with neighborhood ED
use rates (0.92). The chronic PQI categorization comprises ongoing health conditions that
require consistent and effective outpatient care for proper control: this significant correlation
with ED use rates is yet another marker for specific neighborhoods in New York City deserving
of special attention due to deficiencies in the outpatient care system.
Page | 29
Beyond their relationship with ED use, PQIs can serve as important markers for potential
problems within the health care delivery system such as inadequate patient education or
discharge planning. PQIs may also help identify issues within communities related to poverty
and access to care that directly affect health services use, including limited access to outpatient
care, suboptimal living environments, and difficulty affording necessary prescription
medications.
XVII. ED Use and Payer Status
The influence of poverty on ED use can be seen, without the desired precision, in payer
data reported to SPARCS. We conducted a city level analysis of ED use by payer, and divided
payers into Safety-Net (Medicaid and Self-Pay), Medicare, and All Other (commercial
insurance). The data are problematic because a considerable but indefinable portion of the
“Commercial and Other” category are in fact persons enrolled in Medicaid Managed Care plans,
thus leading to an undercount of Medicaid and in turn the Safety-Net category. Another
problem arises from the belief that a greater proportion of Medicare beneficiaries are admitted
to inpatient services and therefore deleted from the treat and release data base.14 Previous
work by the United Hospital Fund showed that rates of admission to the hospital from the ED,
or “conversion rates” in the 65 and over population are high (27 ED admissions per 100
population) compared to two admissions per 100 for the 5-17 age group and 11 admissions per
100 for the 40-64 age group. As a result, this analysis underreports overall ED utilization for
both Medicaid and Medicare.
With these caveats, Safety Net populations constituted 44% of the general population but
had the majority (65%) of all treat and release ED visits in NYC in 2008 (Table 5). This high rate
is certainly an undercount, due to the fact that Medicaid beneficiaries enrolled in managed care
plans are reported in the “Commercial and Other” category.
At the neighborhood level, payer mix for ED visits was unsurprising, with safety net payers
the major contributor in low-income neighborhoods, and commercial insurance (other)
contributing larger percentages in wealthier neighborhoods.
Page | 30
XVIII. Frequency of ED Use and Hospitalization
Neighborhood ED use rates were highly correlated with neighborhood inpatient use rates
(correlation coefficient 0.94). Both UHF neighborhood ED and inpatient use rates were also
correlated with neighborhood self-reported poor health status.
234,634 people (2.8% of the total population) in NYC in the SPARCS dataset had at least
one ED visit and hospitalization in 2008. Of those individuals, 85% were adults (18 and over).
Among this population with both ED and inpatient use, the average number of hospitalizations
increased with increasing numbers of ED visits. For example, those with one ED visit in 2008
had an average of 1.53 hospitalizations that year. Those with two ED visits had an average of
1.66 hospitalizations, and those with three or more ED visits had an average of 2.27
hospitalizations. Most of the average increase in hospitalizations could be accounted for by the
adult population: average hospitalizations among children 0-17 remained more constant
(Tables 26-29).
When isolating this population of people with both ED and inpatient use, average numbers
of hospital admissions among the population did not vary as much among UHF neighborhoods:
for example, in East Harlem, those with 3 or more ED visits had an average of 2.82 hospital
admissions, and those on the Upper East Side had an average of 2.6. However, a
disproportionate share of this population resided in East Harlem (n=1,961) compared to the
Upper East Side (n=330). This indicates that UHF neighborhoods with high ED use and higher
poverty levels also have higher numbers of people with heavy health services use more
generally. Future work that examines in-depth characteristics of this population with
concomitant ED and inpatient use will be important, as this is likely an especially high need,
high cost population.
XIX. Discussion
We found significant neighborhood variation in ED use across New York City’s
neighborhoods. Our analysis showed that neighborhood level ED use variation is highly
correlated (0.73) with safety net payer status (0.73); poverty (0.81); education/not graduating
high school (0.73); and fair or poor health status (0.64). The fact that several measures of socioeconomic status were correlated with ED use indicates an important connection between
resource availability, access to services, and health services use. Such connections have been
Page | 31
documented previously15,16 but this analysis presents a slightly different look by examining
these links at the smaller, neighborhood level.
Neighborhood use rates were moderately correlated with reported neighborhood health
status defined as the percentage of residents reporting poor or fair health in the New York City
Community Health Survey (correlation coefficient 0.63). We used 2000 census data to
determine whether UHF neighborhood poverty rates and education level were correlated with
neighborhood ED use rates. The percent of neighborhood residents at or below poverty level
was highly correlated (correlation coefficient 0.810) as was the percentage not graduating high
school (0.731). Our ANOVA analysis took this one step further, and determined that the level of
ED use in 2008 was strongly associated with neighborhood poverty, proportion of uninsured,
fraction of Blacks and Hispanics, and residents reporting poor/fair health status, no regular
doctor and problems with getting medical care when needed.
These findings underscore the fact that neighborhood characteristics, in addition to
factors such as access to care, are likely interrelated and important issues that affect ED use. In
neighborhoods with high overall ED use rates, community leaders, and policy makers can
evaluate existing non-ED resources and determine the need to invest in additional resources,
community education and outreach, or both.
The issue of payer is quite important. Our analysis is in line with recent literature
showing that Medicaid beneficiaries4, 8 are disproportionately driving the nationwide increase
in ED utilization. Interventions and demonstrations aimed at curbing ED use would be best
focused on this population.
Relatively heavy ED use by children ages 0-17 in comparison to the adult population was
a significant finding. Adjusting for their smaller share of the total population, children were
40% more likely than adults to use an ED. Furthermore, ED use rates were higher amongst
children compared with adults in nearly all UHF neighborhoods, and varied more by
neighborhood than in the adult population. The fact that the heaviest ED use was concentrated
within the same 17 neighborhoods among 0-4 and 5-17 year-olds suggests that within those
communities, interventions specific to the pediatric population and their caregivers regarding
ED use may be warranted. In addition, a thorough evaluation of existing and needed health
and social resources for children in these communities may uncover neighborhood-specific
factors contributing to higher rates of ED use.
Page | 32
We report interesting and novel data regarding patterns of ED use by date and time that
demonstrates a relationship between resource availability and health seeking behaviors. The
neighborhood variations in daily ED volume have important implications for EDs and healthcare
systems that aim to staff appropriately and efficiently based on patient volume. As it is likely
that such patterns vary by facility, individual EDs could examine similar data in order to
establish the most efficient staffing patterns to correlate with periods of high and low patient
volume. In groups of neighborhoods with higher income and presumably more resources, we
noted that the visit volume at peak hours is less extreme than that in neighborhoods with lower
income and fewer resources or less access to care. Among the populations residing in lower
income neighborhoods, ED use at peak hours is disproportionately higher. There is a clear need
to further evaluate, at the neighborhood level, which communities would benefit from
potential solutions, such as extended clinic and office hours, and those that could benefit from
strengthening the primary care and outpatient safety net system more generally.
The relationship between residential mobility and ED visits demonstrated that the
number of moves between UHF neighborhoods increased with increasing numbers of ED visits.
This mobility, which may be an important proxy for unstable housing or homelessness, must be
taken into account when designing interventions targeted at high users, and could complicate
patient outreach and engagement. However, the fact that such patients are likely to “touch”
the system frequently could be used as an opportunity for outreach and engagement within the
healthcare system and through partnerships with community organizations.
The NYU ED algorithm re-emphasized that the majority of ED visits are not emergent and
many may be primary care treatable. A familiar grouping of UHF neighborhoods—those with
lower baseline level ED use and also lower poverty levels-- had a significantly higher percentage
of “Emergent, ED care needed” visits and lower percentages of visits for conditions categorized
as “Emergent, Preventable/Avoidable,” a proxy for ambulatory care sensitive conditions. These
results are consistent with our findings from the PQI analysis, discussed below. There was less
neighborhood variation among ED use for primary care treatable conditions. Results may
indicate that populations in neighborhoods with lower baseline ED use-the same
neighborhoods with lower poverty levels-have more access to ambulatory care services that
can manage chronic disease and avoid ED visits for preventable issues (e.g. asthma flares,
congestive heart failure exacerbations) during both off- and on-hours, as we noticed little
Page | 33
difference between the two analyses.
As we did not control for the general health of the population, it may also be that in
certain neighborhoods, prevalence of chronic disease is simply higher and results in more ED
visits related to the progression or exacerbation of chronic disease. If this is the case, increased
access to primary care services during both off- and on-hours will be even more important. It is
important to remember that the NYU ED algorithm only classifies general medicine visits, and
thus our results do not include visits for injury or behavioral health conditions. However, in
Section X-ED Diagnoses, we explain that the overall proportion of visits for behavioral health
conditions was quite significant in the frequent ED user population; visits for these conditions
did not vary much by neighborhood in overall population of NYC ED users. Below, we include a
more detailed discussion of our analysis of ED by time of day and day of week, which took all
visit types into account.
We found important connections between ED use and use of inpatient services: those
with higher numbers of annual ED visits also had increasing numbers of hospitalizations. In
contrast to ED visits not resulting in hospitalization, which were more prevalent in the 0-17
population, the adult population (18 and older) accounted for the majority of the
hospitalizations that increased with increasing annual ED visits. This is of key importance for
hospitals, policy makers, and planners, as hospitalizations are currently a main source of
revenue or expenditures, depending on one’s perspective.
The fact that neighborhood level PQI admission rates, or preventable medical
admissions, were moderately to highly correlated with neighborhood ED use rates indicates
that improvements in access to appropriate outpatient care varies at the neighborhood level as
evidenced not only by high levels of treat and release ED visits, but also by potentially
preventable hospital admissions. Further study is needed to determine whether these
variations are due to health care system gaps or failures, challenges encountered by the people
living in these neighborhoods with navigating an incomplete and complex delivery system, or a
combination. It should be emphasized again that PQIs do not encompass behavioral health
diagnoses, and may thus underestimate or fail to capture the issue of preventable hospital
admissions in their entirety.
We felt it important to delineate subsets of frequent users within the ED user
population more generally, and thus had a particular focus on what we named “super-users,”
Page | 34
those with 5 or more annual ED visits for three consecutive years. This group was in fact
distinct from other ED user groups, with much higher prevalence of psychiatric and substance
use diagnoses, as well as residential moves and use of inpatient services. Future studies should
link “super-users” to other areas of the health care system, including pharmacy data, to
determine if prescriptions are being provided and filled and if appropriate outpatient care is
accessed by this population. In addition, to best reach and engage this population,
interventions should tailor services to address these particular needs and focus on the safety
net population.
Currently, there is intense focus on the highest users of health care services as they are
thought to exert significant pressure on an overtaxed health care system and can generate high
costs to the health and social care system. The New York State Medicaid Redesign Team, which
is developing plans to implement health homes and other proposals designed to better
integrate health care with housing stability, is appropriately attempting to target these
frequent users of ED services, especially those who remain frequent users over time and
generate concomitant high expenditures.
In anticipation of health reform implementation in 2014 there is an increasing emphasis
on Patient Centered Medical Homes, Health Homes (to provide care coordination services), and
a movement towards Accountable Care Organizations at both the state and federal levels.
These new mechanisms for care delivery present real opportunities to improve care and will
reward providers for delivering the “right” care via enhanced reimbursements. Each will
require an emphasis on ambulatory service delivery: community-level evaluations of ED use for
primary care treatable and ambulatory care sensitive conditions such as this, in addition to local
evaluation of primary care capacity will be fundamental if true improvements in the health
delivery system are to be made.
Page | 35
References Cited
1.
System CotFoECitUSHC. The Future of Emergency Care in the United States Health Care
System2006.
2.
Pitts SR, Carrier ER, Rich EC, Kellermann AL. Where Americans Get Acute Care:
Increasingly, It’s Not At Their Doctor’s Office. Health Affairs. September 1, 2010
2010;29(9):1620-1629.
3.
Fuda KK, Immekus R. Frequent Users of Massachusetts Emergency Departments: A
Statewide Analysis. Ann Emerg Med. July 2006 2006;48(1):9-16.
4.
Hunt KA, Weber EJ, Showstack JA, Colby DC, Callaham ML. Characteristics of Frequent
Users of Emergency Departments. Annals of Emergency Medicine. 2006;48(1):1-8.
5.
Kennedy D, Ardagh M. Frequent attenders at Christchurch Hospital's Emergency
Department: a 4-year study of attendance patterns. New Zealand Med J.
2004;117(1193):U871.
6.
Fisher E, Wennberg J. Health Care Quality, Geographic Variations, and the Challenge of
Supply-Sensitive Care. Perspectives in Biology and Medicine. 2003;46(1):69-79.
7.
UHF. “Excessive use of emergency rooms for ambulatory care sensitive conditions can
also be studied through local planning.”. New York, NY: Heal NY Phase 9: Local Health
Planning Initiatives; 2009.
8.
Tang N, Stein J, Hsia RY, Maselli JH, Gonzales R. Trends and Characteristics of US
Emergency Department Visits, 1997-2007. JAMA: The Journal of the American Medical
Association. August 11, 2010 2010;304(6):664-670.
9.
Gladwell M. Million-Dollar Murray: Why problems like homelessness may be easier to
solve than to manage. New Yorker New York, New York: Conde Nast; 2006.
10.
Gawande A. The Hot Spotters: Can we lower medical costs by giving the neediest
patients better care? The New Yorker. New York: Conde Nast; 2011.
11.
AHRQ. AHRQ Health Care Innovations Exchange:Innovations and Tools to Improve
Quality and Reduce Disparities. 2008;
http://www.innovations.ahrq.gov/content.aspx?id=516.
12.
Billings J, Parikh N, Mijanovich T. Emergency department use: the New York story: The
Commonwealth Fund;2000.
Page | 36
13.
AHRQ. Expanding the Uses of AHRQ's Prevention Quality Indicators: Validity from the
Clinician Perspective. 2010;
http://www.ahrq.gov/about/annualconf10/davies_steiner/davies.HTM. Accessed March
9, 2011.
14.
Salit S, Wojas E, Cavanaugh S. Use of Hospital Emergency Departments in New York City:
What Does It Tell Us About Access to Care? A Report to the New York City Council:
United Hospital Fund;2006.
15.
Blackwell D. Family structure and children's health in the United States: findings from
the National Health Interview Survey, 2001-2007.Vital Health Stat 10. Vol 246.
Hyattsville, Maryland: Division of Health Interview Statistics. U.S. Department of Health
and Human Services, Centers for Disease Control and Prevention, National Center for
Health Statistics; 2010:1-166.
16.
Akinbami L, Moorman J, Liu W. Asthma prevalence, health care use, and mortality:
United States, 2005-2009. Natl Health Stat Report. Vol 32. Hyattsville, MD Office of
Analysis and Epidemiology, National Center for Health Statistics, U.S. Department of
Health and Human Services, Centers for Disease Control and Prevention; 2011:1-44.
Page | 37