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Socioeconomic Status and In-Hospital Pediatric
Mortality
WHAT’S KNOWN ON THIS SUBJECT: Socioeconomic status (SES) is
inversely related to mortality and health in children; the higher an
individual’s SES, the less likely illness and death. It is unknown
whether the association of SES and pediatric mortality exists in
the inpatient setting.
WHAT THIS STUDY ADDS: Within children’s hospitals, in-hospital
mortality is inversely associated with SES, but is lower than
expected for even the lowest SES quartile. The association
between SES and mortality varies by clinical service line.
abstract
OBJECTIVE: Socioeconomic status (SES) is inversely related to pediat-
ric mortality in the community. However, it is unknown if this associ-
ation exists for in-hospital pediatric mortality. Our objective was to
determine the association of SES with in-hospital pediatric
mortality among children’s hospitals and to compare observed
mortality with expected mortality generated from national all-
hospital inpatient data.
METHODS: This is a retrospective cohort study from 2009 to 2010 of all
1 053 101 hospitalizations at 42 tertiary care, freestanding children’s
hospitals. The main exposure was SES, determined by the median
annual household income for the patient’s ZIP code. The main out-
come measure was death during the admission. Primary outcomes of
interest were stratified by income and diagnosis-based service lines.
Observed-to-expected mortality ratios were created, and trends
across quartiles of SES were examined.
RESULTS: Death occurred in 8950 (0.84%) of the hospitalizations. Over-
all, mortality rates were associated with SES (P,.0001) and followed
an inverse linear association (P,.0001). Similarly, observed-to-
expected mortality was associated with SES in an inverse
association (P= .014). However, mortality overall was less than
expected for all income quartiles (P,.05). The association of SES
and mortality varied by service line; only 3 service lines (cardiac,
gastrointestinal, and neonatal) demonstrated an inverse association
between SES and observed-to-expected mortality.
CONCLUSIONS: Within children’s hospitals, SES is inversely associated
with in-hospital mortality, but is lower than expected for even the
lowest SES quartile. The association between SES and mortality varies
by service line. Multifaceted interventions initiated in the inpatient
setting could potentially ameliorate SES disparities in in-hospital
pediatric mortality. Pediatrics 2013;131:e182–e190
AUTHORS: Jeffrey D. Colvin, MD, JD,
a
Isabella Zaniletti,
PhD,
b
Evan S. Fieldston, MD, MBA, MSHP,
c
Laura M. Gottlieb,
MD, MPH,
d
Jean L. Raphael, MD, MPH,
e
Matthew Hall, PhD,
b
John D. Cowden, MD, MPH,
f
and Samir S. Shah, MD, MSCE
g
Sections of
a
Pediatric Hospital Medicine and
f
General Pediatrics,
Department of Pediatrics, Children’s Mercy Hospitals and Clinics,
University of Missouri-Kansas City School of Medicine, Kansas
City, Missouri;
b
Children’s Hospital Association, Overland Park,
Kansas;
c
Department of Pediatrics, Perelman School of Medicine
at the University of Pennsylvania, and The Children’s Hospital of
Philadelphia, Philadelphia, Pennsylvania;
d
Department of Family
and Community Medicine, School of Medicine, University of
California-San Francisco, San Francisco, California;
e
Department
of Pediatrics, Texas Children’s Hospital, Baylor College of
Medicine, Houston, Texas; and
g
Division of Hospital Medicine,
Cincinnati Children’s Hospital Medical Center and Department of
Pediatrics, University of Cincinnati College of Medicine,
Cincinnati, Ohio
KEY WORDS
socioeconomic status, income, poverty, health status disparities,
child mortality, hospital mortality
ABBREVIATIONS
APR-DRG—All Patient-Refined, Diagnosis-Related Groups v.24
CCC—complex chronic condition
ICD-9-CM—International Classification of Diseases, Ninth Revi-
sion, Clinical Modification
PHIS—Pediatric Health Information System
Q-AHI—quartile of annual median household income
SES—socioeconomic status
Dr Colvin has participated in the study design, analysis and
interpretation of the manuscript, provided critical intellectual
content in the revision of the manuscript, was the primary
author of the manuscript, and has approved the final version of
the manuscript being submitted; and Drs Zaniletti, Fieldston,
Gottlieb, Raphael, Hall, Cowden, and Shah have participated in
the study design, analysis and interpretation of the manuscript,
provided critical intellectual content in the revision of the
manuscript, and have approved the final version of the
manuscript being submitted.
www.pediatrics.org/cgi/doi/10.1542/peds.2012-1215
doi:10.1542/peds.2012-1215
Accepted for publication Sep 17, 2012
Address correspondence to Jeffrey Colvin, MD, JD, Department of
Pediatrics, Children’s Mercy Hospitals and Clinics, 2401 Gillham
Rd, Kansas City, MO 64108. E-mail: jdcolvin@cmh.edu
PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).
Copyright © 2013 by the American Academy of Pediatrics
FINANCIAL DISCLOSURE: The authors have indicated they have
no financial relationships relevant to this article to disclose.
FUNDING: No external funding.
e182 COLVIN et al by guest on April 21, 2017Downloaded from
Socioeconomic status (SES) is inversely
related to mortality and health status in
both children and adults; the higher an
individual’s SES, the less likely illness
and death.
1–15
This association is based
in part on direct income-related rea-
sons, such as health insurance sta-
tus,
10,16
access to health care,
4,16–18
material deprivation,
19–23
and educa-
tion.
24
It is also based on differences in
psychological factors (eg, sense of
control),
25–29
social factors (eg, social
capital),
30–32
and the physical environ-
ment (eg, housing and neighborhood
conditions).
33–39
These factors vary by
SES and cause differential effects on
health care access, health behaviors,
stress, and exposures to pathogens.
8,40
Death is a rare outcome in pediatrics,
and only half of pediatric deaths occur
in hospitals.
41
However, because pre-
hospital health is associated with SES,
including the prevalence of chronic
conditions (more prevalent among
those with lower SES), health-related
quality of life, and access to care and
specialists,
10
it would be expected that
in-hospital mortality might reflect the
broader epidemiological association of
SES and pediatric mortality. Previous
studies of SES and in-hospital death
have focused on a limited number of
diagnoses or institutions, making it
difficult to determine whether SES–
mortality associations were specific
only to the examined diagnosis or in-
stitution/region.
42–45
It remains un-
known whether the association of SES
and mortality exists generally in the
inpatient pediatric setting and how
that association might differ across
diagnoses.
The objective of this study was to
determine the association of SESwith in-
hospital pediatric mortality. We hypoth-
esized that inpatient mortality, being
dependent on prehospital health, would
be associated with SES in an inverse
gradient relationship for all diagnoses.
Similarly, we also hypothesized that
patients with lower SES would experi-
ence higher mortality rates than ex-
pected mortality rates generated from
national all-hospital inpatient data.
METHODS
Data Source
Data for this retrospective cohort study
were obtained from the Pediatric Health
Information System (PHIS), which con-
tains administrative data from 43 free-
standing tertiary care children’s
hospitals across the United States. For
each hospital discharge, the PHIS da-
tabase includes disposition (eg, death,
home), patient demographics, up to 41
International Classification of Dis-
eases, Ninth Revision, Clinical Modifi-
cation (ICD-9-CM) diagnoses, and up to
41 ICD-9-CM procedures. The Children’s
Hospital Association (Overland Park,
KS), participating hospitals, and Thom-
son Reuters Healthcare (New York, NY)
jointly ensure data quality and re-
liability as described elsewhere.
46–48
This study was approved with informed
consent waiver by the Institutional Re-
view Board at Children’s Mercy Hospital.
Study Participants
All inpatient hospitalizations in calen-
dar years 2009 and 2010 at 42 hospitals
were included in this analysis; 1 hos-
pital contributing data to PHIS was ex-
cluded because patient ZIP code–linked
census data were not available. No age
range limitations were made because
patients .18 years have been shown
to be at high risk of mortality at child-
ren’s hospitals.
49
Study Definitions
Patient demographic variables included
age, gender, race/ethnicity, and primary
payer. Race/ethnicity categories in-
cluded white, black or African American,
Hispanic or Latino, American Indian or
Alaska Native, Asian, Native Hawaiian or
other Pacific Islander, and other. The
“other”category included unreported
or missing data or any category not
previously mentioned. The primary
payer variable of “public”included
Medicaid (including Medicaid managed
care) and Title V. “Commercial”payer
included privately purchased health in-
surance and TRICARE. “Uninsured”in-
cluded “self-pay”and “charity.”“Other”
indicated Medicare, worker’scompen-
sation, other governmental insurance,
missing payer information, and those
patients who were not charged for the
services provided.
PHIS uses the All Patient-Refined,
Diagnosis-Related Groups v.24 (APR-
DRGs) (3M Health Information Sys-
tems, St. Paul, MN) that are based on
ICD-9-CM diagnosis and procedure
codes assigned during each patient’s
episode of care. These APR-DRGs are
further categorized into 11 clinical
service lines based on the primary
organ system or procedure of the APR-
DRG. The service lines were developed
by PHIS participating hospitals and the
Children’s Hospital Association. The
defined service lines are neonatal,
cancer/hematology, cardiac, respira-
tory, orthopedics/joint, transplantation,
gastrointestinal, neurologic, and infec-
tious disease; the service lines “other
medical condition”and “other surgical
condition”are those APR-DRGs not
principally described as one of the
previously mentioned service lines.
Other clinical variables include use of
intensive care and mechanical ventila-
tion. ICD-9-CM codes were used to de-
tect the presence of complex chronic
conditions (CCCs) by using a previously
reported classification scheme.
50,51
A
CCC is defined as “any medical condi-
tion that can be reasonably expected to
last at least 12 months (unless death
intervenes) and to involve either several
different organ systems or one system
severely enough to require specialty pe-
diatric care and probably some period
of hospitalization in a tertiary care
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center.”
50,51
ICD-9-CM codes were also
used to detect the presence of a noso-
comial infection by using a previously
reported classification scheme.
49,52,53
The APR-DRG system also assigns each
hospitalization a severity-of-illness level
based on the degree of organ dysfunction
andarisk-of-mortalitylevelbasedonthe
likelihood of death.
54,55
The severity and
risk-of-mortality scores account for
specific patient factors, including pri-
mary and secondary diagnoses, the
combination of diagnoses, procedures
performed, and patient age.
49,56,57
Thomson Reuters then assigns an ex-
pected mortality rate to each dis-
charge based on the assigned APR-DRG
and risk-of-mortality.
57
The expected
mortality rates are based on Thomson
Reuters’database of .20 million an-
nual pediatric discharges from .2700
US acute, nonfederal, general hospi-
tals.
56
Therefore, the expected mortal-
ity rates reflect national all-hospital
data and not data solely from PHIS
hospitals or other children’s hospitals.
Main Exposure
The main exposure of interest was SES,
represented in this study by the median
annual household income for the ZIP
code of the patient’s residence. Quar-
tiles of annual median household in-
come (Q-AHI) were generated from
2010 US Census data as quartile 1,
$33 311 or less; quartile 2, $33 332 to
$41 386; quartile 3, $41 387 to $54 013;
and quartile 4, $54 014 or more. Patients
were assigned to a quartile based on
the median annual household income
of their home ZIP code. ZIP code–based
median household income has been
previously demonstrated to be a useful
proxy for patient SES when individual-
level data are unavailable.
58–60
Main Outcome Measure
The main outcome measure of interest
was death occurring during hospitali-
zation.
Analytic Sequence
All statistical analyses were performed
by using SAS v.9.3 (SAS Institute, Cary,
NC), and Pvalues ,.05 were consid-
ered statistically significant. First, the
percentage of mortality in each Q-AHI
was calculated. Bivariate analyses with
the use of the x
2
test were performed
to compare survivors with nonsurvivors
for demographic and clinical charac-
teristics. The x
2
test was also used to
determine the association between the
percentage of mortality by Q-AHI and
each service line. The Cochran-Mantel-
Haenszel test was used to assess linear
trends across Q-AHI.
Observed-to-expected mortality ratio
was also considered. An observed-to-
expected mortality ,1 indicated that
in-hospital mortality was less than
expected. The observed-to-expected
mortality ratio was analyzed by using
the Flora Zscore to identify service
lines and quartiles of income in which
the observed mortality was signifi-
cantly lower (or higher) than the
expected mortality.
61
A Poisson re-
gression model for each service line
was used to verify whether there was
asignificant trend in observed-to-
expected mortality across Q-AHI.
RESULTS
Demographic and Clinical
Characteristics
Of the 1 053 101 hospitalizations in
years 2009 and 2010, death occurred
in 8950 (0.85%). Demographic and
clinical characteristics are shown in
Table 1. Nonsurvivors were younger
than survivors with 41.2% of non-
survivors being neonates, compared
with 11.2% of survivors (P,.001).
Nonsurvivors had a significantly higher
percentage of governmental insurance
compared with survivors (51.5% vs
47.5%, P,.001). In comparison with
survivors, nonsurvivors had more CCCs
(73.6% vs 37.6%, P,.001), and required
more ICU services (55.1% vs 14.1%, P,
.001), NICU services (35.8% vs 5.9%, P,
.001), and mechanical ventilation (87.1%
vs 8.2%, P,.001). Nonsurvivors had
more admissions associated with the
neonatal service line than survivors
(36.0% vs 7.9%, P,.001). Nonsurvivors
also had more nosocomial infections
(5.4%vs0.3%,P,.001).
Demographic and clinical character-
istics are stratified by income quartile
and survival in Table 2. Within each in-
come quartile, differences between
survivors and nonsurvivors were statis-
tically significant (P,.001) for all de-
mographic and clinical characteristics,
with the exception of the presence of
a hematologic/immunologic CCC in the
lowest income quartile (P= .264). In
comparison with survivor patients, the
distribution of nonsurvivor patients was
shifted toward the neonatal age category
and neonatal service line for all income
quartiles. Nonsurvivors also had higher
percentages of patients in the cardiac,
transplantation, and other surgical con-
dition service lines in all income quartiles.
With the exception of the first income
quartile of the hematology/immunology
CCC, nonsurvivors had higher percen-
tages of all CCCs for all income quartiles.
Nonsurvivors also had .87% of patients
in the major/extreme severity level for all
income quartiles. Across all income
quartiles, nonsurvivors also had 3 times
higher usage of intensive care, neonatal
intensive care, or mechanical ventilation
in comparison with survivors.
Association of Mortality and
Income
The percentage of mortality by income
quartile and service line is shown in
Table 3. For all hospitalizations, mor-
tality was associated with income (P,
.0001) in an inverse linear association
(P,.0001), with mortality rates de-
creasing as income increased. The re-
lationship between income and
mortality, however, varied by service
e184 COLVIN et al by guest on April 21, 2017Downloaded from
line. Five of the 11 service lines dem-
onstrated a statistically significant, in-
verse linear association between
mortality and income. One additional
service line (other medical condition)
demonstrated an inverse linear asso-
ciation (P= .012) between income and
mortality across income quartiles; that
service line, however, did not have
statistically significant differences (P=
.072) in mortality between income
quartiles.
To assess for differences in baseline
expected mortality between income
quartiles, the ratio of observed-to-
expected mortality by income quartile
is displayed in Fig 1. For all hospital-
izations, a linear inverse association
between observed-to-expected mortal-
ity and income existed (P= .014). The
observed mortality, however, was sig-
nificantly less than expected for all in-
come quartiles. The association of
observed-to-expected mortality to in-
come varied by service line. Only 3 of 11
service lines demonstrated an inverse
linear association between observed-
to-expected mortality and income; the
other 8 service lines had no associa-
tion. Even for the 3 service lines with an
inverse linear association between
observed-to-expected mortality and
income (neonatal, cardiac, gastroen-
terology), observed mortality was sig-
nificantly lower than expected for most
income quartiles. Observed mortality
was significantly lower than expected
for all income quartiles in the other 8
service lines, with the exception of or-
thopedics and transplantation.
DISCUSSION
This study describes associations be-
tween SES and inpatient mortality in
children hospitalized at freestanding
children’s hospitals in the United
States. We identified an inverse re-
lationship between pediatric inpatient
mortality and household income. This
relationship is similar to that seen
epidemiologically,
7,9,11,12
with death
highest in the lowest income quartile
and decreasing as income increases.
However, we found variations across
clinical service lines. When examined
individually, most service lines dem-
onstrated no association between in-
come and mortality. In addition, we find
that observed mortality was less than
expected. Even for the 3 of 11 service
lines with associations between
observed-to-expected mortality and
income, observed mortality was lower
than expected for all income quartiles.
TABLE 1 Demographic and Clinical Characteristics for All Hospitalizations at 43 Children’s
Hospitals, 2009–2010
Characteristics n%
Total 1 053 101
Age
Neonate, 0–30 d 120 609 11.5
Infant, 31–365 d 171 673 16.3
1–4 y 207 955 19.8
5–12 y 270 500 25.7
13–18 y 217 767 20.7
.18 y 64 591 6.1
Gender
Male 562 824 53.4
Race
a
White 506 393 48.1
Black or African American 211 868 20.1
Hispanic or Latino 203 986 19.4
Asian 27 552 2.6
Other 103 261 9.8
Payer
Public 500 634 47.5
Commercial/private/employer-based 403 199 38.3
Uninsured 20 688 2.0
Other 128 580 12.2
Nosocomial infection 3515 0.3
CCCs 399 516 37.9
Gastrointestinal 24 877 2.4
Hematologic or immunologic 41 238 3.9
Malignancy 92 981 8.8
Metabolic 23 285 2.2
Neuromuscular 108 083 10.3
Other congenital or genetic 52 815 5.0
Renal 18 555 1.8
Respiratory 43 882 4.2
Service line: primary
Neonatal 85 499 8.1
Cancer/hematology 84 423 8.0
Cardiac 45 618 4.3
Respiratory 184 085 17.5
Ortho/joint 62 085 5.9
Transplantation 4344 0.4
Gastrointestinal 145 604 13.8
Neurologic 98 771 9.4
Infectious disease 80 378 7.6
Other medical condition 179 066 17.0
Other surgical condition 83 228 7.9
Severity level
Minor 819 687 77.8
Moderate 154 805 14.7
Major/extreme 78 141 7.4
ICU 152 140 14.5
Mechanical ventilation 93 516 8.9
NICU 64 588 6.1
a
The race/ethnicity categories of American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander were 0.0%.
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Previous diagnosis- and age-specific
studies have found differing relation-
ships between inpatient pediatric mor-
tality and SES. In a Canadian sample, Wang
et al
42
found that inpatient mortality for
infants with CCCs was highest in the
bottom income quartile. Similarly, in
patients with congenital diaphragmatic
hernia, Sola et al
43
found inpatient
mortality differences between children
from the highest and lowest income
quartiles. In contrast, Chang et al
44
found
a nonstatistically significant SES gra-
dient in pediatric inpatient mortality
for cardiac surgery. McCavit et al
45
found
no association between SES and in-
patient mortality in patients with sickle
cell disease. The current study offers an
TABLE 2 Percentage of Survivors and Nonsurvivors by Demographic and Clinical Characteristics Across Income Quartiles
Characteristics Income Quartile 1 Income Quartile 2 Income Quartile 3 Income Quartile 4
Survivor Nonsurvivor Survivor Nonsurvivor Survivor Nonsurvivor Survivor Nonsurvivor
n281 089 2666 245 222 2280 256 529 2127 261 311 1877
Age
Neonate, 0–30 d 11.5 44.6 11.3 42.1 10.5 39.8 11.5 36.9
Infant, 31–365 d 18.1 18.9 17.3 16.5 16.4 16.8 13.2 16.0
1–4 y 20.8 11.0 20.6 11.1 20.1 12.2 17.8 11.6
5–12 y 24.2 10.8 25.7 12.3 26.5 12.0 27.0 14.3
13–18 y 19.4 9.9 20.1 11.5 21.0 13.4 22.6 14.2
.18 y 6.1 4.8 5.0 6.5 5.5 5.9 7.8 7.0
Gender
Male 53.6 55.3 54.3 54.8 53.8 55.1 52.1 56.0
Race
a
White 32.5 33.5 47.1 46.1 53.3 47.8 60.8 53.8
Black or African American 33.9 28.4 20.4 16.5 14.9 14.3 10.3 10.3
Hispanic or Latino 22.8 19.5 21.8 19.0 20.1 18.8 12.8 12.0
Asian 1.1 1.0 1.6 1.3 2.1 2.2 5.6 5.0
Other 9.7 17.6 9.1 17.2 9.6 17.0 10.5 18.9
Payer
Public 65.5 67.3 54.6 56.1 43.8 47.3 25.1 28.2
Commercial/private/employer-based 24.0 22.4 32.7 30.1 40.5 37.0 56.9 50.5
Uninsured 1.9 3.2 2.2 3.9 2.1 3.3 1.6 2.7
Other 8.6 7.2 10.5 9.9 13.6 12.5 16.4 18.7
Nosocomial infection 0.3 5.4 0.3 5.2 0.3 6.1 0.2 4.9
CCCs 35.4 71.2 38.0 72.2 38.5 76.2 38.8 75.8
Gastrointestinal 2.0 4.7 2.3 5.7 2.4 5.5 2.6 5.2
Hematologic or immunologic 4.8
b
4.4
b
3.9 4.8 3.6 5.5 3.3 5.9
Malignancy 7.1 10.8 8.7 12.4 9.2 15.0 10.3 17.1
Metabolic 2.0 6.9 2.2 7.8 2.2 8.4 2.3 9.4
Neuromuscular 9.5 18.8 10.4 20.8 10.6 21.6 10.3 22.3
Other congenital or genetic 4.3 14.9 4.9 15.8 5.3 15.7 5.2 13.9
Renal 1.6 5.7 1.8 6.4 1.9 6.2 1.7 5.1
Respiratory 4.1 15.2 4.3 14.3 4.3 13.2 3.7 12.2
Service line: primary
Neonatal 7.9 38.0 7.7 36.8 7.1 35.6 8.8 32.6
Cancer/hematology 7.6 4.3 8.2 5.4 8.0 6.3 8.4 7.8
Cardiac 4.0 7.7 4.2 7.9 4.4 7.5 4.5 8.3
Respiratory 19.6 10.8 18.4 10.9 17.2 11.8 14.9 11.8
Ortho/joint 5.5 0.4 5.6 0.3 6.0 0.4 6.6 0.7
Transplantation 0.3 2.2 0.4 2.3 0.4 2.7 0.5 2.9
Gastrointestinal 13.0 5.3 13.9 4.4 14.5 4.2 14.2 3.7
Neurologic 8.7 7.8 9.4 8.2 9.7 8.5 9.8 8.4
Infectious disease 8.3 5.6 8.0 6.1 7.6 6.1 6.8 5.7
Other medical condition 17.1 7.7 16.7 7.7 17.2 7.6 17.3 8.1
Other surgical condition 7.8 10.3 7.6 10.0 7.8 9.3 8.3 10.1
Severity level
Minor 79.5 2.3 78.0 2.4 78.0 2.9 78.3 3.2
Moderate 13.6 9.0 15.0 9.7 15.1 9.5 15.4 9.4
Major/extreme 6.8 88.4 7.0 87.8 6.9 87.4 6.3 87.3
ICU 13.7 52.1 14.6 56.0 14.4 55.4 13.8 58.2
Mechanical ventilation 8.8 88.6 8.7 88.3 8.0 85.8 7.3 84.8
NICU 6.4 39.8 6.1 36.4 5.5 34.2 5.5 31.3
a
The race/ethnicity categories of American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander were 0.0% for all income quar tiles.
b
All differences between survivors and nonsurvivors within each income quar tile significant (P,.001) except where indicated (CCC: Hematologic or immunologic).
e186 COLVIN et al by guest on April 21, 2017Downloaded from
opportunity to look across multiple
diagnostic groups (service lines) and
at a larger number of institutions, in
part, overcoming the limitations of pre-
vious analyses. The strong relationships
seen in some, but not all, service lines
confirm that, although inconsistent, the
relationship between SES and inpatient
mortality can be important.
It remains unclear whether this in-
consistent relationship between SES
and pediatric mortality is due to dif-
ferences in the prehospitalization SES
gradient across services. In other
words, it is possible that SES variably
affects certain diagnoses (and associ-
ated service lines), shaping children’s
prehospital risk of mortality. This
would produce the observed incon-
sistent relationships between SES and
inpatient mortality across service lines.
Alternatively, in-hospital processes may
be able to decrease SES disparities in
some service lines more than others.
According to this explanation, high-
quality care, provided equitably to all
children within some service lines,
would be able to overcome prehospital
mortality risk factors. Possible ways
that hospital service lines might reduce
SES disparities include the structure of
providing care (eg, standardization of
care) or the provision of additional
services designed to assist vulnerable
patients (eg, social work, medical–legal
partnerships, standardized screening
for social problems).
62–66
However, the
provision of those additional services
could only be expected to reduce mor-
tality in future hospitalizations. No data
were available in this study to deter-
mine how the care structure or pro-
cesses might affect the hospital care
provided to vulnerable populations.
These explanations are not mutually
exclusive. Additional studies could ex-
plore whether the absence of an SES
gradient in inpatient mortality for some
service lines is at least in part due to in-
hospital processes or to an absence of
an SES-based risk gradient at the time of
admission. If in-hospital care attenu-
ates prehospital SES differences for
certain diagnoses, this should be ex-
amined, and ideally replicated so that
low-SES patients might benefit. Addi-
tional training or inpatient service
programs could reduce SES disparities
further. For instance, previous studies
have indicated that physicians recog-
nize the importance of social factors in
determining patient outcomes,
67
but they
are either unable to identify critical so-
cial factors
68
or lack the capacity
67,69
TABLE 3 Mortality by Quartiles of Median Household Income, by Service Line
Service Line Quartile of Household Income, % x
2
P
a
Cochran-Mantel-
Haenszel P
b
1234
All service lines 0.9 0.9 0.8 0.7 ,.001 ,.001
Neonatal 4.3 4.3 4.0 2.6 ,.001 ,.001
Cancer/hematology 0.5 0.6 0.6 0.7 .322 .073
Cardiac 1.8 1.7 1.4 1.3 .004 .001
Respiratory 0.5 0.6 0.6 0.6 .731 .285
Orthopedic/joint 0.1 0.1 0.1 0.1 .824 .782
Transplantation 5.8 5.2 5.2 4.4 .531 .160
Gastrointestinal 0.4 0.3 0.2 0.2 ,.001 ,.001
Neurologic 0.9 0.8 0.7 0.6 .011 .001
Infectious disease 0.6 0.7 0.7 0.6 .589 .662
Other medical condition 0.4 0.4 0.4 0.3 .072 .012
Other surgical condition 1.2 1.2 1.0 0.9 ,.001 ,.001
a
The x
2
assesses general differences in the mortality rate across quartiles of household income.
b
The Cochran-Mantel-Haenszel assesses a linear trend in the mortality rate across quar tiles of household income.
FIGURE 1
Observed-to-expected inpatient mortality by service line and trend across quartile of median household
income. Each cell demonstrates the trend across Q-AHI for the service line, starting with all service lines.
The horizontal line indicates the observed-to-expected mor tality ratio of 1. Solid black squares indicate
statistically significant differences in observed compared with expected mortality for that quartile. The
Pvalue in each box indicates trends (slopes) in observed-to-expected mortality across quartiles. For
example, for the cardiac service line, all income quartiles have an observed-to-expected mortality of
,1, but only the third and fourth quartiles are statistically significant; the trend across quartiles is
statistically significant (P= .006).
ARTICLE
PEDIATRICS Volume 131, Number 1, January 2013 e187
by guest on April 21, 2017Downloaded from
to connect patients to resources to
ameliorate those factors. Further phy-
sician training and an increase in
available resources (eg, intensive so-
cial work interventions, medical–legal
partnerships) could lessen those defi-
ciencies. These strategies may not im-
pact the risk of mortality in the
immediate hospitalization but would
have a global impact on SES’s associa-
tion with child health, thereby affect-
ing the risk of mortality in subsequent
hospitalizations.
This study also finds that overall mortality,
regardless of income quartile, was lower
in freestanding children’s hospitals than
expected mortality rates generated from
national all-hospital inpatient data. One
plausible explanation for lower-than-
expected mortality rates may be that
children in this study are essentially all
covered by insurance; only a small per-
centage (2%) of the study population was
not covered by some form of either pri-
vate or public health insurance. This
seems to reaffirm the importance of
efforts to preserve and expand access to
coverage for children.
70
Because of co-
linearity, however, our study was not
able to separate the independent ef-
fects of SES and insurance status.
Although patients may be admitted
without insurance, children’s hospitals
often assist patients in obtaining cov-
erage. Our administrative data, which
reflect insurance status at the time of
discharge, do not permit us to know pre-
hospital insurance status, and, there-
fore, findings may underestimate the
number of uninsured patients at the
time of admission. It is also possible
that the children’s hospitals in this study
were able to provide high-quality care
that overcame expected survival rates.
Limitations to this study are important to
consider. We were unable to account for
SES differences in out-of-hospital mor-
tality, such as the use of home palliative
care. We also were unable to assess for
any SES differences in referral patterns or
family preferences for treatment at
children’s hospitals. Therefore, differ-
ences based on SES may exist because of
differences in the types of patients
seeking care at children’shospitalsand
whether out-of-hospital palliative care is
used. Because the studied hospitals
represent tertiary care, academic medi-
cal centers dedicated to pediatric care, it
will be important in future studies to
determine if the associations found here
exist in other settings, such as non-
children’s hospitals. In addition, because
of colinearity, we were unable to study
the independent effects of race/ethnicity.
As a result, we were unable to determine
if SES was a proxy for race/ethnicity in
this study. Furthermore, we used ZIP
code–level income data to approximate
patient-level income. Although this ap-
proximation has been used previously, it
may result in biases of unknown di-
rection or significance, and individual
experiences within a ZIP code may also
differ from the ZIP code–wide experience
(eg, an individual household may have an
income higher or lower than the me-
dian).
71
In addition, grouping related di-
agnoses into service lines may obscure
important SES differences between di-
agnoses within a service line.
Finally, as described above, we were
unable to separate prehospital differ-
ences owing to SES from in-hospital
factors. One possible way to help dif-
ferentiate prehospital differences
from in-hospital factors would be to
exclude deaths occurring within the
first 24 hours of hospitalization. This
would assume that deaths occurring in
the first 24 hours of hospitalization are
more likely to be unpreventable and
more related to prehospital factors, an
assumption that has not been directly
studied to our knowledge. However, in
the current study, we were unable to
exclude deaths occurring in the first 24
hours of admission because the
expected mortality calculations were
based on APR-DRG and Thomson Reu-
ters calculations that use all hospi-
talizations without excluding deaths
based on when they occur in the hos-
pitalization. To exclude deaths occur-
ring in the first 24 hours in the
observed data but not the expected
data would result in misleading
observed-to-expected mortality ratios.
Future studies should attempt to sep-
arate prehospital and in-hospital fac-
tors relating to SES differences in
hospital pediatric mortality.
CONCLUSIONS
Lower income is associated with all-
cause inpatient mortality at free-
standing children’s hospitals, but this
association varies by service line, and
mortality is lower than expected for
all income quartiles. Further research
is needed to determine the potential
effects of in-hospital processes and
interventions on the relationship be-
tween SES and pediatric mortality.
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Raphael, Matthew Hall, John D. Cowden and Samir S. Shah
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