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Inpatient Childhood Asthma Treatment
Relationship of Hospital Characteristics to Length of Stay and Cost: Analyses of New York State Discharge Data, 1995
Zhihuan J. Huang, PhD;
Bonnie J. LaFleur, PhD;
James M. Chamberlain, MD;
Mark F. Guagliardo, PhD;
Jill G. Joseph, MD, PhD
Arch Pediatr Adolesc Med. 2002;156:67-72.
ABSTRACT
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Background There is increasing pressure to optimize asthma treatment efficiency.
It is possible that hospital characteristics influence such efficiency.
Objective To examine the association of selected hospital characteristics with
cost and length of stay (LOS) among pediatric patients with asthma after adjusting
for patient characteristics.
Design Secondary analysis of 1995 hospital discharge data in the state of New
York.
Subjects Nineteen thousand six hundred eighty-eight patients aged 1 to 17 years
with asthma discharged from 206 acute care hospitals in New York in 1995.
Main Outcome Measures Length of stay and hospital cost.
Analysis Hospitals were described with respect to teaching status and ownership.
The differences in the distribution of children within each hospital type
were assessed by  2 tests. In multivariate analyses, hierarchical
models were constructed to analyze cost and LOS, adjusting for both hospital-
and patient-level covariance.
Results Asthma severity did not meaningfully differ by hospital ownership and
teaching status. Public and teaching hospitals had more minority and Medicaid
patients. After adjusting for patient- and hospital-level covariates and for
the hierarchical nature of the data, there were no statistically significant
differences between public and private hospitals in mean cost or LOS. Adjusted
mean LOS in teaching hospitals was not significantly shorter, while costs
were significantly but not meaningfully greater ($2459 vs $2271; P<.001).
Conclusion Hospitals providing medical education to pediatricians and safety net
care do so without increasing LOS or cost of care for pediatric asthma.
INTRODUCTION
ASTHMA IS the leading cause of chronic illness in children and young
adults and one of the most frequent causes of hospitalization.1-4
Furthermore, pediatric asthma hospitalization rates have increased in the
past 2 decades.2-5
Asthma alone accounts for 1% of all health care expenditures in the nation,6 with a substantial portion of this cost derived from
inpatient care.7 Therefore, it is increasingly
important to optimize inpatient treatment, and one approach to doing so is
to examine institutional variability in efficiency in inpatient treatment
of asthma in pediatric patients.
Available evidence is limited regarding the relationship between hospital
type and efficiency of care for children. Teaching hospitals perform most
of the charity care in the United States8 and
academic physicians care for a higher percentage of uninsured patients.9 Pediatric patients with asthma who are uninsured,
Medicaid-insured, or live in neighborhoods with lower socioeconomic status
(SES) are more severely ill, have longer length of stay (LOS), and higher
hospital costs.10 Studies of LOS and hospital
charges in teaching hospitals have yielded contradictory findings regarding
efficiency after adjusting for case mix or severity of illness.11-13
Nevertheless, it has been suggested that health plans have been reluctant
to contract with teaching hospitals for all but the most complex care because
they perceive teaching hospitals to be too costly and inefficient.14
Similarly, public hospitals have a unique and important role in the
health care market.15 In particular, New York
State has a large number of public hospitals providing "safety net care" for
disadvantaged populations.15 Few studies have
compared their performance with that of private hospitals. Concerning private
hospitals, many assume that the growth of for-profit hospitals is attributable
to lower costs and greater efficiency. However, administrative costs are proportionately
higher in private for-profit hospitals.16
To date, there is a single report, by Meurer et al,12
comparing hospital efficiency in treating pediatric asthma in different types
of hospitals. Although this important study found that mean charges for childhood
asthma varied significantly by hospital ownership and teaching status, the
authors analyzed data from the Healthcare Cost and Utilization Project, with
oversampling of small teaching hospitals and large nonteaching hospitals.
Individual hospital charge-to-cost conversion ratios and patient ethnicity
data were not available for adjustment. No specific statistical procedure
was used to control for the clustering of relevant patient characteristics
within hospitals.
Analyses reported here examine the association of selected hospital
characteristics (teaching status and hospital ownership) with cost and LOS
among pediatric patients admitted for asthma. We linked a unique population-based
discharge data set from the state of New York to census and American Hospital
Association data and used statistical modeling to adjust for the effect of
clustered patient characteristics in each hospital. Using these methods, we
tested the hypothesis that hospital costs and LOS for children with asthma
are associated with hospital type.
METHODS
DATA SOURCES
We analyzed the 1995 Statewide Planning and Research Cooperative System
(SPARCS) database, describing all inpatient discharges from New York State
acute care hospitals. Hospital information from the discharge data abstract
and the uniform billing form completed by billing departments are merged to
create SPARCS. Duplicate records and missing data are identified and corrected.17 The database includes demographic, diagnostic, utilization,
and financial information. Primary diagnosis, up to 14 secondary diagnoses,
procedures, patient insurance status, and the Permanent Facility Identifier
for hospitals are included. American Hospital Association data and the appropriate
census data18 were linked to 1995 SPARCS by
the Permanent Facility Identifier and patient ZIP code, respectively. This
provided information on hospital type and socioeconomic indicators in the
ZIP code of residence for each discharged patient.
The Children's National Medical Center institutional review board granted
an exemption from review based on the anonymous nature of the data and their
public availability.
INCLUSION CRITERIA
Patients were included in this study if they were aged 1 to 17 years
with a primary diagnosis of asthma (International Classification
of Diseases, Ninth Revision19 codes
493.00-493.91). To construct models that best represented most asthma discharges,
we excluded outliers with total cost or LOS greater than the 98th percentile
and those with cost less than the second percentile.
MEASURES
The outcome measures were total cost and LOS. The total 1995 dollar
cost for each discharge was calculated from the total charges by application
of the hospital-specific overall charge-to-cost ratio. This information is
provided by each hospital in the state of New York and available as part of
selected versions of the SPARCS data set. Length of stay was recorded in SPARCS
in full-day units. Hospital characteristics, such as ownership (public/private)
were abstracted from the American Hospital Association database. Teaching
hospitals were defined by (1) membership in the Council of Teaching Hospitals
of the Association of American Medical Colleges or (2) residency training
approved by the Accreditation Council for Graduate Medical Education.20
There are obvious difficulties with developing severity measures in
administrative databases.21 For this reason,
a standard method with accepted definitions but well-recognized limitations
was used: the All Patients Refined Diagnosis Related Group (APR-DRG), which
was applied by using standard software developed by 3M Information Systems
(Salt Lake City, Utah) and the National Association of Children's Hospital
and Related Institutions (Alexandria, Va). The severity of APR-DRG is based
on secondary diagnoses and categorizes patients from low to high severity
by placing them in 1 of 4 categories.22-23
Age in years, sex, and race (white, black, other) were recorded in SPARCS.
Insurance type was classified into 3 groups: Medicaid, commercial, and other.
To estimate individual socioeconomic status (not available in SPARCS),
the median household income by ZIP code was retrieved from 1990 census data.18 Zip codes were then categorized into 4 groups based
on the median household income according to the Healthcare Cost and Utilization
Project database: (1) less than $25 000; (2) $25 001 to $30 000;
(3) $30 001 to $35 000; (4) greater than $35 000. The validation
and usefulness of using census data in this way has been documented in several
studies.24 Krieger24
suggested that such census data should be used only for analyses occurring
within 5 years of the census because population growth and migration alter
a neighborhood's composition. The data we used met this criterion.
STATISTICAL ANALYSES
We first examined whether severity differed by hospital type. For each
hospital characteristic, separate tables were produced examining severity
by hospital type (teaching vs nonteaching and private vs public). The statistical
significance of the distribution of severity within each hospital type and
each of the patient characteristics was assessed by a 2 test.
Mean values of the dependent variables (cost and LOS) were also calculated
by hospital type and by patient characteristics. These variables were not
normally distributed. For this reason as well as to reduce the chance of a
type I error, we chose to use bootstrap confidence intervals for the within-variable
mean comparisons. These intervals use the bias-corrected and accelerated (BCa) method, which is preferred when normality is questionable.25
The overall effect of the hospital characteristics on LOS and cost,
while adjusting for patient characteristics, was evaluated by a series of
regressions. When examining data collected on more than 1 level (eg, hospital
and patient) there is the chance that the SEs of the parameter estimates are
biased. Hierarchical models (also called random coefficients, mixed models,
and multilevel models) are often used.26 These
models adjust the SEs and allow for correct test statistics to be calculated.
We implemented these models by using SAS PROC MIXED (general linear mixed
models) (SAS Institute, Cary, NC) for the cost outcome and the SAS GLIMMIX
macro (generalized linear mixed models) for the Poisson regressions on the
LOS outcome. These models are covariance-adjusted for the hospital-level characteristics
by using the hospital identifier as the clustering variable. Covariance adjustment
involves using models that account for within- and between-hospital variation.
We compared models that adjust for this variance with standard regression
models to assess the need for this type of analysis. All statistical analyses
were performed using SAS version 8.1.
RESULTS
The 1995 SPARCS data set contains 19 688 discharges of patients
between the ages of 1 and 17 years with a primary diagnosis of asthma, representing
discharges from 206 hospitals in New York State. Nearly half of these institutions
were teaching hospitals and only 12% were public hospitals (Table 1). As presented in Table
2, most patients were cared for in teaching hospitals (89%) and
privately owned hospitals (69%).
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Table 1. Ownership and Teaching Status for 206 New York State Hospitals*
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Table 2. Asthma Discharges for Patients Aged 1 to 17 Years in New York
State in 1995 in 206 Hospitals by Their Ownership and Teaching Status*
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Table 3 presents selected
patient characteristics in different types of hospitals. There was little
difference in APR-DRG severity by hospital type. Public and teaching hospitals
had more patients insured by Medicaid and fewer commercially insured. Age
distributions were similar in different types of hospitals but there were
uniformly more boys (60%-62%) than girls. Most patients in public hospitals
were of races listed as "other than white or black," but in private hospitals
race was distributed more uniformly. Teaching hospitals had more minority
patients than nonteaching hospitals, in which most were white (76%). Patients
from poorer neighborhoods tended to be discharged from public and teaching
hospitals.
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Table 3. Characteristics of Asthma Discharge Diagnoses for Patients
Aged 1 to 17 Years in Different Types of Hospitals in New York State in 1995*
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Unadjusted mean costs differed by hospital characteristics (Table 4). Mean cost was significantly but
trivially higher in private hospitals ($1868 vs $1771) and in nonteaching
hospitals ($1876 vs $1528). Mean LOS (2.07 days) did not differ significantly
by hospital characteristics.
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Table 4. Relationship of Hospital Characteristics to Unadjusted Mean
Cost and LOS of Childhood Asthma Treatment in New York State in 1995*
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Regression models assessed mean LOS and cost by each hospital characteristic
after adjusting for the other hospital characteristic and again after adjusting
for both the other hospital characteristic and patient characteristics. The
hospital level variance explained 17% of the total variability in cost but
only 4% of the overall variability in LOS. Using Akaike's27
information criteria, an informal method of examining the goodness of fit
between adjusted and unadjusted models, there is evidence that the adjusted
models do improve fit. However, the fact that only 4% of the overall variance
in LOS is explained by the within-hospital correlation suggests that the multilevel
model does not substantially improve explained variability in this outcome.
As presented in Table 5,
after adjusting for the patient-level data and the effect of the hierarchical
nature of the data, there are not meaningful differences in LOS or cost among
different types of hospitals. On the other hand, because of the large population,
some of these differences achieve statistical significance (eg, teaching hospitals
cost $188 more per day).
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Table 5. Relationship of Hospital Type to Cost and LOS of Asthma Inpatient
Treatment After Adjustment*
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Although analyses excluded LOS and cost outliers, other patient characteristics
could have affected the results. Therefore, additional analyses excluded patients
who were transferred or who had severe secondary diagnoses (such as congenital
heart disease or cystic fibrosis) identified by a consensus panel of acute
care pediatricians. There were only 584 patients (2.9%) with severe comorbidities
and 548 patients (2.8%) who were transferred. The main results (Table 5) did not differ in these supplementary analyses and are
therefore not reported further.
COMMENT
These findings provide little empiric evidence for a relationship between
resource utilization and hospital type. Unlike previous studies, we applied
statistical methods, such as hierarchical modeling and bootstrap confidence
intervals, to account for the clustering effect of hospitals and nonnormal
distribution of the outcomes.
Almost one third of pediatric patients with asthma were treated in public
hospitals and, not unexpectedly, were disproportionately drawn from socioeconomically
disadvantaged areas, were nonwhite, and were insured through Medicaid. Given
these facts, it becomes especially important to consider the efficiency of
public hospitals. This large, comprehensive administrative database revealed
that public hospitals performed as well as private hospitals in mean length
and cost of pediatric asthma admissions, suggesting that these safety net
hospitals provided care for large numbers of disadvantaged children with asthma
in an equivalently efficient manner.
A recent study also failed to find a significant difference in average
LOS by hospital ownership.12 Average charges
were higher in private for-profit hospitals compared with public hospitals,
but no difference was found between private nonprofit hospitals and public
hospitals. In New York State in 1995, the small number of childhood asthma
discharges from private for-profit hospitals (43 discharges [0.02%]) made
analysis of such hospitals impossible. Almost all private hospitals in our
study were nonprofits and thus, our finding is consistent with this previous
report.
Although there were approximately equal numbers of teaching hospitals
and nonteaching hospitals in New York State, teaching hospitals accounted
for nearly 90% of the pediatric discharges and these children were more frequently
Medicaid-insured. After adjustment for other covariates and the hierarchical
nature of the data, neither LOS nor cost was meaningfully different in hospitals
training pediatricians.
It is interesting that cost was lower in teaching hospitals before adjustment
for other hospital characteristics but higher when adjusted. This is probably
because more teaching hospitals were public and cost in public hospitals was
lower than in private hospitals. When adjusted for ownership, the cost became
higher in teaching hospitals.
The relationship between hospital type and efficiency of treatment has
been examined in adult populations, but far less is known regarding this important
issue in children. Many analyses of adult conditions have dealt with technologically
complex or procedure-driven treatments, such as coronary angioplasty.28-29 We focused on asthma, the most common
condition requiring hospitalization in children. Our results do not reflect
efficiencies in specific procedures or technologies but are potentially more
relevant to multiple and diverse diagnoses.
New York State data were analyzed for a variety of reasons, including
the diversity of the population and of the hospitals. It is unknown whether
these results can be generalized to other states and regions. Further, we
recognize that LOS in New York State is known to be generally longer than
many other areas.30-31 Our choice
of 1995 data reflected the need to validly link to the 1990 census data and
the desire to analyze information obtained prior to managed care penetration.
Local markets in a large and heterogeneous state may change in relatively
unpredictable and variable fashion early in managed care penetration. We believe
a more thorough analysis of this issue in its own right is required rather
than confounding analyses of hospital characteristics with the equally complex
issue of managed care effects. Nonetheless, taken together, these limitations
must be considered when interpreting the analyses.
Our analyses are incomplete in several ways. Most notably, in common
with all administrative data, they do not include detailed patient or hospital
information. For example, there is no clinical information that might provide
a more detailed severity adjustment, and the databases do not include factors
such as staff ratios and labor costs in the participating hospitals. For these
reasons, we chose a straightforward and descriptive interpretation rather
than speculating well beyond the scope of the data.
Another limitation of this administrative database is that no unique
patient identifier was available and therefore repeated admissions cannot
be traced. Using the modest data available, a proxy identifier was constructed
using race, sex, age, insurance type, and residence ZIP code for each discharge
in our study. Using this measure, we determined that 18% of discharges could
be accounted for by readmission. However, due to the obvious limitation of
the estimation, we did not use it in our hypothesis testing or account for
the "admission clustering," by which 1 admission raises the probability of
another. Furthermore, in common with other analyses on administrative data,
the inability to sort out readmissions creates theoretical difficulties concerning
the statistical assumption of independent observations.
In the face of these limitations, we nonetheless documented that the
efficiency in hospitals that provide medical education and safety net care
is equivalent to that found in nonteaching and privately owned hospitals.
Full discussion of these results requires consideration of much broader but
pressing issues. In particular, how does one value the training of future
pediatricians or caring for disadvantaged children? Is it even reasonable
to use efficiency to determine support for both training and safety net care
in our currently complex and evolving health care market? It is our hope that
such important and on-going policy discussions can be informed by the analyses
provided in this article.
Using a large administrative database for the state of New York in 1995,
we found that public hospitals delivered asthma hospital care to disadvantaged
patients. Using appropriate statistical modeling, the cost and LOS for care
in public hospitals was not different from that in private hospitals. There
were statistically significant but trivial differences in the care provided
by teaching hospitals, with mean cost being slightly higher and mean LOS,
lower. These results have potentially important implications for the organization
of health care delivery in the United States.
| What This Study Adds
Asthma is the most common admitting diagnosis in children and there
is increasing pressure to optimize treatment efficiency. It is possible that
hospital characteristics influence such efficiency. Using advanced statistical
models, we found statistically significant but trivial differences in the
care provided by teaching hospitals, with mean cost being slightly higher.
On the other hand, public hospitals provided care that did not differ in either
cost or LOS from that provided in private hospitals. Thus, hospitals providing
both training for future pediatricians and safety net care for disadvantaged
children treat pediatric asthma without increasing LOS or cost.
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AUTHOR INFORMATION
Accepted for publication September 23, 2001.
This study was presented in part at the annual meeting of the Ambulatory
Pediatric Association, San Francisco, Calif, May 1, 1999.
Corresponding author and reprints: Jill G. Joseph, MD, PhD, Center
for Health Services and Clinical Research, Children's National Medical Center,
111 Michigan Ave NW, Washington, DC 20010 (e-mail: jjoseph{at}cnmc.org).
From the Center for Health Services and Community Research, Children's
National Medical Center (Drs Huang, LaFleur, Guagliardo, and Joseph), The
George Washington University School of Medicine and Health Sciences (Drs LaFleur,
Guagliardo, and Joseph), and the Department of Emergency Medicine (Dr Chamberlain),
Washington, DC.
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