The Impact of Delivery Hospital on the Outcomes of Premature Infants in the PostSurfactant Era: An Instrumental Variables Approach
Scott A. Lorch, MD, MSCE1,2,3
Michael Baiocchi4
Corinne Fager2
Dylan S. Small4
1
Department of Pediatrics, The Children’s Hospital of Philadelphia and The University of
Pennsylvania School of Medicine, Philadelphia, PA
2
Center for Outcomes Research, The Children’s Hospital of Philadelphia, Philadelphia, PA
3
Senior Fellow, Leonard Davis Institute of Health Economics, University of Pennsylvania,
Philadelphia, PA
4
Department of Statistics, The Wharton School, University of Pennsylvania
Word Count: 2998
Abstract Word Count: 281
Abstract
Context: Even though prior work suggests that delivery at a high-level neonatal intensive care
unit (HL NICU) is associated with improved outcomes, greater percentages of women deliver at
hospitals without HL NICUs. Prior studies may provide biased assessments of deregionalization
policies because many differences in casemix are not measured.
Objective: To determine the impact of delivering at HL NICUs on mortality and common
complications of premature birth after controlling for unmeasured differences in casemix using
an instrumental variables approach.
Design: Retrospective population-based cohort study
Setting: All hospital-based deliveries in Pennsylvania, Missouri, and California between 19932005.
Patients or Other Participants: Women delivering at a gestational age between 23 and
37weeks gestation (N=1,598,400)
Main Outcome Measure(s): Neonatal and fetal death, 7 complications of premature birth
Results: Women delivering at a HL NICU were more likely to have a preexisting comorbid
condition or a complication of pregnancy. After controlling for unmeasured and measured
factors, infants delivering at a HL NICU had 2.9 to 12.4 fewer deaths per 1000 deliveries, with
similar rates of most complications studied except for lower BPD rates at Missouri HL NICUs
and higher infection rates at HL NICUS in Pennsylvania and California. The association
between delivery hospital and neonatal outcomes differ between the three states studied.
Without accounting for ummeasured differences in casemix, HL NICUs would have significantly
higher complication rates with similar neonatal mortality rates.
Conclusions: There is continued benefit to neonatal outcomes when high-risk infants are
delivered at HL NICUs in the post-surfactant time period. To obtain accurate estimates of
policies where patients receive care based on severity of medical condition, such as perinatal
regionalization policies, either more sophisticated methodological approaches or more detailed
clinical data are needed.
Introduction
Regionalization of health care may help provide high quality and cost-efficient health care by
directing patients to facilities with optimal capabilities for any given type of illness or injury.1, 2
For perinatal care, a regionalized model of care was developed in the 1970s to centralize the care
of the very-low-birth weight (VLBW) infant at specialized hospitals with the adequate personnel
and technology. However, by the 1990s, the regional model of perinatal care began to weaken in
many areas of the United States,3-6 with fewer VLBW births at regional perinatal centers.
Although several studies from the early 1990s suggest that delivery at a high volume, high
technology hospital reduces neonatal mortality,7-10 this issue is worth further study. First, since
the 1990s, there has been increased use of antenatal corticosteroids, routine use of postnatal
surfactant and higher use of Cesarean sections, and there is little information about how this has
affected the relationship between delivery hospital and neonatal outcomes. Also, no study has
examined other outcomes, such as complication rates, or compared the effect of delivery hospital
in different states. Additionally, these prior studies suffer from a common problem associated
with observational studies: selection bias. Specialty hospitals manage sicker patients with a
higher risk of poor outcomes. Typical methods cannot adjust for unmeasured or unrecorded
factors in available data, such as the severity of an antenatal comorbid condition, lab results, or
fetal heart tracing results. Failing to account for these unmeasured factors may result in a biased
assessment of the impact of delivering at a high volume, high level NICU compared to other
delivery hospitals.
The goals of this study are to (1) obtain unbiased measurements of the impact on mortality of
delivering at a high volume, high level NICU compared to other delivery hospitals using more
recent data; (2) examine additional outcomes besides mortality; and (3) examine between states
with different systems of regionalization. To control for unmeasured differences between high
volume, high level NICU and other delivery hospitals, we will employ an instrumental variables
study design. This study design is new to the perinatal literature, but has been used in other
health policy settings.11-15
Methods
Study Design
Observational studies of perinatal regionalization need to adjust for differences in casemix
between high-level NICUs and other delivery hospitals. Past studies have used regression
analysis to control for those aspects of casemix that are recorded in the data set, such as the birth
weight and gestational age of the infant and the co-existing medical conditions of the mother.
However, the regression approach is biased if important factors are left unmeasured.
An instrumental variables approach controls for both measured and unmeasured differences in
casemix between these groups of hospitals. In this study design, a variable referred to as an
instrument encourages patients to deliver at a particular hospital, in essentially a randomized
fashion. The instrument must have three characteristics: (i) it must be independent of
unmeasured confounding variables conditional on measured confounding variables; (ii) it must
influence where a patient delivers (iii) it should only influence the observed outcome of the
patient through its effect on where a patient delivers. A strong and valid instrument varies where
a mother delivers, while equalizing other measured and unmeasured factors.
To ensure that patients with higher and lower values of the instrument are comparable, we
employ a matched pairs study. Here, we match patients on 59 measured covariates while
maximizing the difference in the instrument, a design referred to as "near-far matching."16 This
matching design parallels a matched pair randomized controlled trial of patients encouraged to
deliver at a high level NICU vs. patients not encouraged to deliver at a high level NICU. This
method reduces the influence of patients who typically deliver only at high-level NICUs in the
final analysis, such as infants with severe congenital anomalies. By including both an
instrumental variables approach and this matched pairs design, we improve the equality of the
two study groups, which improves the accuracy of the study results (Technical Appendix 1).16
Data Population and Sources
We obtained birth certificates from all deliveries occurring in Pennsylvania and California
between 1/1/1995 and 6/30/2005 and Missouri between 1/1/1995 and 12/31/2003. Each state’s
department of health linked these birth certificates to death certificates using name and date of
birth, and then de-identified the records. We then matched over 98% of birth certificates to
maternal and newborn hospital records using prior methods.8, 17, 18 Over 80% of the unmatched
birth certificate records were missing hospital, suggesting a birth at home or a birthing center.
The unmatched records had similar gestational age and racial/ethnic distributions to the matched
records. The Institutional Review Boards of The Children’s Hospital of Philadelphia and the
departments of health in California, Missouri, and Pennsylvania study approved this study.
Infants included in this study had a gestational age between 23 and 37 weeks, and a birth weight
between 400 to 8000 grams. Birth records were excluded if the birth weight was more than 5
standard deviations from the mean birth weight for the recorded gestational age in the cohort,
because of the high likelihood of a recording error in one or both variables.19 Initially, 1,362,782
birth records were identified for this project; 34,650 met the exclusion criteria, leaving 1,328,132
births in the final cohort.
Definition of Study Outcomes
The primary outcome for this study was mortality. Neonatal deaths were defined as any death
during the initial birth hospitalization. We examined fetal deaths because poor resuscitation
around the time of delivery could convert some neonatal deaths into fetal deaths. Fetal deaths
were defined in two ways. First, we included all fetal deaths in each county with either a
minimum gestational age of 23 weeks or a birth weight of 400 grams. Second, we included
those fetal deaths that met a prior definition of a potentially preventable fetal death by care
delivered at the hospital.8 We also examined common complications of premature birth as
secondary outcome measures. The complications and identifying ICD-9CM codes are listed in
Table 1.
Definition of covariate variables
We included specific covariate variables in our matching algorithm based on their association
with one or more study outcomes. The final models included gestational age; birth weight,
grouped into 250-500 gram strata; maternal sociodemographic factors, such as race, age,
education, and insurance status; maternal comorbid conditions listed in the technical appendix 2;
and 49 congenital anomalies grouped by affected organ system.8
Hospital Definitions
Based on prior work,7, 8 a specialty hospital was defined as a level III or higher facility that
delivered at least 50 VLBW infants, on average, per year. All levels of care were obtained from
the American Academy of Pediatrics perinatal survey20 and validated using procedure codes
from hospitalizations at each hospital.
Instrument
The instrument for this study is based on prior studies that suggest that women tend to deliver at
hospitals near their residential zip code. For each residential zip code in this study, we calculated
the difference in travel times between the nearest high-level NICU and the nearest other delivery
hospital. We calculated the differential travel time between the residential zip code and the
closest high level NICU vs. the residential zip code and the closest non-high level NICU delivery
hospital using ArcView software from ESRI, Inc., as in our prior work.21 Women with negative
differential travel times lived in residential zip codes that were closest to a high-level NICU,
whereas women with positive differential travel times had to bypass a nearby hospital and travel
further to deliver at a high-level NICU.
To examine validity of the instrument, we measured the distribution of measured covariates
across various quartiles of the instrument and across the near and far matched pairs. Ideally, a
measured covariate would have the same distribution across the quartiles of the instrument
before matching, which lends credibility to the assumption that the instrument is also balancing
unmeasured covariates associated with the measured covariates. However, if the measured
characteristic is unbalanced across quartiles, the matched pair design will balance the
characteristic. The equality of the measured covariates across quartiles before matching was
assessed by calculating the standardized difference of each variable, which equals the (largest
pairwise difference in means across quartiles of the instrument) ÷ (standard deviation of entire
group). The equality after matching was assessed by calculating (difference in means between
near and far patients) ÷ (standard deviation of entire group). A value less than 0.20 is considered
adequate balance.22, 23
Data Analysis
Three analyses will be presented. First, we present a naïve analysis using unadjusted differences
in each of the nine outcome measures between patients delivering a high-level NICU and other
delivery hospitals. Next we performed a more sophisticated, though still inadequate, analysis
where we controlled for measured differences in casemix with a matched-paired propensity score
analysis. Finally, we performed the appropriate analysis which controls for measured and
unmeasured differences using a matched-pairs instrumental variables analysis. Risk differences
and relative risks are presented for each analysis. Confidence intervals for risk differences were
calculated by standard inversion of a pivot-based test of the null, at an alpha error rate of 0.05,16
while confidence intervals for relative risks were calculated using bootstrap methods. All data
are presented separately by state to allow for inter-state comparisons.
Results
Overall, women who delivered at a high level NICU were more likely to have either a
preexisting comorbid condition, such as diabetes mellitus, or a complication of pregnancy, such
as preterm labor or pregnancy-induced hypertension (Table 2). As a result, infants delivered at
high level NICUs were smaller and had a younger gestational age.
Strength of Instrument and Equality of Population after use of Instrument
Results Appendix 1 shows the strength of the instrument. In Pennsylvania, 79.8% of the
pregnancies in the first quartile delivered at a high-level NICU, compared to 23.9% in the fourth
quartile. Similar strengths of the instrument were seen in California (79.6% versus 38.3%
respectively) and Missouri (55.7% versus 10.1% respectively). In all three states, women in the
middle two quartiles delivered at high-level NICUs at rates in the between the two extremes.
The instrument also balances measured covariates in each state (Appendix 2). Standardized
differences in clinically relevant factors such as birth weight, gestational age, singleton birth, and
every maternal comorbid condition and complication of pregnancy was less than 0.2 across the
four quartiles of the instrument. For some socioeconomic variables, such as race and insurance
status, the combination of the instrument and the matched pair analysis balanced all measured
variables between those patients encouraged to deliver at a high level NICU vs. patients not
encouraged to deliver at a high level NICU (Table 3).
Association of delivery hospital and mortality rates
After adjusting for both measured and unmeasured casemix differences between hospital types,
delivering at a high-level NICU was associated with lower mortality rates in all three states
(Table 4). In Pennsylvania, there was a reduction of 7.2 neonatal deaths per 1000 deliveries
(95% CI 3.7-10.7), with a relative risk (RR) of 0.27 (95% CI 0-0.59). Fetal deaths in
Pennsylvania were slightly reduced at high-level NICUs. In California, neonatal deaths were
only reduced by 0.7 deaths per 1000 deliveries, but fewer fetal deaths occurred at deliveries at
high-level NICUs (reduction of 5.4 fetal deaths/1000 deliveries, 95% CI 3.6-6.9; reduction of 2.2
preventable fetal deaths/1000 deliveries, 95% CI 1.2-3.1). The reduction of neonatal deaths in
Missouri just missed reaching statistical significance (RR 0.56, 95% CI 0.27-1.27).
Without accounting for unmeasured differences in casemix, neonatal mortality rates were
statistically similar in Missouri (RR 1.01, 95% CI 0.92-1.01), Pennsylvania (RR 0.95, 95% CI
0.85-1.05), and California (RR 0.96, 95% CI 0.93-1.01). Pregnancies ending in a preventable
fetal death were lower in both California and Missouri (Results Appendix 2).
Association of delivery hospital and rates of neonatal complications
In unadjusted and propensity score analyses, there were higher rates of all studied complications
at high-level NICUs regardless of state (Results Appendix 2). After accounting for unmeasured
casemix differences, few of these differences remained. Delivering at a high-level NICU in
Missouri was associated with lower rates of BPD. Rates of other complications such as NEC
and ROP were similar between the high-level NICU and other delivery hospital group.
Infection rates remained significantly higher in high-level NICUs compared to other delivery
hospitals, although the risk difference decreased from 5-45 extra infections/1000 deliveries to 014 cases/1000 deliveries (Table 4).
State Differences in Outcomes
For mortality, Pennsylvania and Missouri showed a 2-fold reduction in neonatal mortality rates
with delivery at a high-level NICU, while California showed such a reduction in fetal, but not
neonatal mortality rates (Table 4). The risk difference for most complications such as NEC,
ROP, and ROP surgery showed some variation between states (Table 4). However, Missouri
showed a large reduction for BPD rates when infants were delivered at high-level NICUs
(reduction 9.5 cases/1000 deliveries), whereas Pennsylvania and California showed little change
in BPD rates.
Discussion
Determining the true impact of a policy intervention such as perinatal regionalization is critical
to accurately weighing the benefits and costs of the intervention. In perinatal regionalization,
specialty hospitals treat sicker patients.4 After adjusting for all differences between delivery
hospitals, our study suggests that there is a continued mortality benefit to delivering premature
infants at high-level NICUs. This decrease was 50% larger than in previously reported studies,
such as a 4-fold reduction in neonatal mortality in Pennsylvania and a 2-fold reduction in
preventable fetal mortality in California. The sizes of these reductions are comparable to the
overall Black-White disparity in infant mortality seen in the United States [ref]. However, if we
examined this question using only factors currently measurable in most population-based
datasets, such as birth weight and maternal medical conditions, we would not have found results
similar to studies conducted with older data.4-9, 24-27 Increased access to clinical information,
such as those data potentially available in electronic health records, would improve casemix
adjustment and decrease the need for more sophisticated methods to obtain accurate estimates of
the impact of health policies.
Instrumental variables approaches have been used in studies where patients with certain
characteristics are more likely to receive a given treatment.11-15 In these studies, the use of the
instrument gave a result that was more similar to randomized studies.12 For perinatal
regionalization studies, our data found statistically significant results that were much larger than
prior studies.
The strongest effect of delivery hospital was seen in the continued improvement in mortality
rates. The almost 100% higher mortality rates at delivery hospitals without a high-level NICU
suggest that, in the current era of widespread use of antenatal corticosteroids and postnatal
surfactant, the delivery, resuscitation, and initial management of a premature infant is even more
important to that infant’s survival than in prior time periods. Our work also examined the
association between delivery hospital and neonatal complication rates. After adjusting for
unmeasured differences in casemix, rates of important complications for long-term
neurocognitive development, such as BPD, NEC, and ROP,28 were statistically similar in the two
hospitals. This similarity occurred even though high-level NICUs with lower mortality rates are
saving infants who would otherwise die if delivered at other hospitals.
One complication that remains elevated at high-level NICUs in all three states was bacterial
sepsis. Studies suggest that organizational factors of the NICU, such as increased patient/nurse
ratios and fewer sinks/staff are associated with higher infection rates.29, 30 Although increased
volume or increased hospital level were not associated with increased infections in those studies,
these units in our study may be more likely to have periods of crowding or increased occupancy.
These factors have also been associated with higher infection rates in other work.31-34
Determining other additional hospital characteristics associated with worse outcomes is
important for understanding the ideal health care system to care for premature infants.
Finally, our study suggests that the association between delivery hospital and neonatal outcomes
differs by states. While our study design may adjust for unmeasured differences between types
of hospitals, systematic differences between states may still exist. One difference may be in the
distribution of hospitals between states from regionalization legislation or financial incentives to
hospitals.2 For example, California has stronger regionalization legislation which may reduce
the number of level III hospitals. Thus, there are more level II NICUs in California than in the
other states. These level II hospitals may have different structural characteristics than in other
states. Also, there are no validated measures of the actual degree of regionalization and interhospital coordination. Each of these factors could contribute to the state differences in outcomes
seen in this study.
There are several limitations to this study. First, we used ICD-9CM codes to detect
complications of pregnancy and of premature birth. Thus, there may be some heterogeneity in
how different hospitals code for these conditions. We include two surgical conditions, which
should be coded more accurately than medical diagnoses, and found similar patterns to the 5
medical diagnoses in all three states across the different analyses. Finally, the instrumental
variables approach estimates the effect size based on patients for whom distance to the hospital
affects where they deliver. Analyses of the instrument suggest that it is a strong and valid
instrument in that it produced groups of women similar in measured medical complications and
conditions but divergent in where they chose to delivery. However, we cannot estimate what
would happen to those women who, because of pre-existing medical conditions or
sociodemographic factors, would always deliver at a high-level NICU regardless of distance.
For these subgroups, the effect on mortality is likely larger than we have been able to report.
In conclusion, our work suggests that the mortality benefit to delivering at a high-level NICU not
only persists into the post-surfactant time period, but is larger than previously reported. This
survival benefit does not result in higher rates of many neonatal complications. State differences
likely occur because of other previous unreported differences in the organization of perinatal
services between states. To improve the assessment of perinatal regionalization and individual
hospitals, improved casemix adjustment should occur with clinical variables that may be
available with wider-spread use of electronic health records.
Acknowledgments
Authors’ Contributions:
Study concept and design: Lorch, Baiocchi, Small
Acquisition of the data: Lorch
Analysis and interpretation of the data: Lorch, Fager, Baiocchi, Small
Drafting of the manuscript: Lorch, Baiocchi, Small
Critical revision of the manuscript for important intellectual content: Lorch, Fager, Baiocchi,
Small
Statistical Analysis: Lorch, Fager, Baiocchi, Small
Obtained funding: Lorch
Administrative, technical, or material support: Lorch, Fager, Baiocchi, Small
Study supervision: Lorch, Small
Author Access to Data: Scott A. Lorch, MD, MSCE, principal investigator, had full access to
all the data in the study and takes responsibility for the integrity of the data and the accuracy of
the data analysis.
Conflicts of Interests: There are no potential conflicts of interests.
Funding/Support and Role of Sponsor: This study was funded by AHRQ Grant # R01 HS
015696, “Perinatal Regionalization and Quality of Care”. AHRQ was not responsible for the
design and conduct of the study; collection, management, analysis, and interpretation of the data;
and preparation, review, or approval of the manuscript.
Presentations: This work was presented in abstract form at the Pediatric Academic Societies
meeting, Vancouver, BC, Canada, May 2, 2010 and at the AcademyHealth national meeting,
Boston, MA, June 2010.
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Figure Legend
Figure 1: Risk Differences for premature infants delivering at high-level and other delivery
hospitals in Pennsylvania, California, and Missouri for all outcomes. The “x” for each outcome
represents the risk difference unadjusted for differences in casemix. The dot represents the risk
difference after using an instrumental variables approach, which controls for both measured and
unmeasured differences in casemix between facility types. The bar surrounding the dot shows
the 95% confidence interval. Positive risk differences indicate more events at high-level NICUs
compared to other delivery hospitals, while negative risk differences indicate fewer events at
high-level NICUs.
Table 1: ICD-9CM codes used to identify complications of premature birth
Comorbid Condition
Bronchopulmonary Dysplasia
Necrotizing Enterocolitis
Fungal Sepsis
Bacterial Sepsis
Retinopathy of Prematurity
Retinopathy of Prematurity Surgery
ICD-9CM code
770.7
777.5
112.x, 771.7
038.x, 995.90-995.94, 041.x, 790.7
362.21
14.2x, 14.34, 14.4x, 14.5x
45.6x, 45.7x, 45.8, 45.9x, 46.0x, 46.1x,
Laparotomy
46.2x, 46.3x, 46.4x, 46.5x, 46.8x, 54.1
For each code, “x” represents any number at the digit location.
Table 2: Demographics of Patients delivering at high-level and other delivery hospitals, Pennsylvania, California, and Missouri 19952005
Pennsylvania
High Level Other Delivery
NICU
Hospital
6.97
21.81
2,474
2,725
34.7
35.7
Δ/SD*
-0.84
-0.34
-0.39
California
High Level
NICU
3.45
2,716
34.9
Other Delivery
Hospital
14.52
2,936
35.4
-0.29
0.35
0.02
-0.02
59.20%
9.90%
9.40%
19.80%
-0.08
0.07
0.05
-0.04
-0.04
-0.16
0.05
Differential Travel Time (min)
Birth Weight (grams)
Gestational Age (wks)
Race
White
64.50%
77.90%
Black
22.20%
9.30%
Asian
1.30%
1.10%
Other
3.00%
3.40%
Insurance Status
FFS
19.50%
22.80%
HMO
37.80%
34.60%
Public
31.80%
29.70%
Other
9.40%
10.50%
Uninsured
1.20%
1.70%
Singleton Birth
80.60%
86.50%
SGA
16.90%
15.20%
Maternal Comorbid Conditions and Complications of Pregnancy
Comorbid Conditions
Chronic HTN
2.00%
1.17%
Gestational Diabetes
5.49%
4.85%
Diabetes Mellitus
2.13%
1.40%
Renal Disease
0.33%
0.24%
Congenital Heart Disease
0.16%
0.05%
Complications of Pregnancy
Preterm Labor
48.65%
39.79%
PIH
12.14%
8.25%
PPROM
20.85%
14.86%
Oligohydraminos
4.61%
3.02%
Disorders of Placentation
6.57%
4.57%
Δ/SD*
-0.58
-0.27
-0.21
Missouri
High Level
NICU
14.99
2,650
34.7
Other Delivery
Hospital
39.5
2,853
35.3
Δ/SD*
-0.59
-0.27
-0.23
60.40%
6.30%
8.80%
23.00%
-0.02
0.13
0.02
-0.08
77.90%
18.80%
2.10%
0.70%
74.70%
23.00%
1.60%
0.60%
0.08
-0.10
0.04
0.01
4.50%
46.10%
45.50%
0.90%
3.00%
87.80%
11.40%
6.70%
36.60%
51.40%
1.20%
4.10%
91.90%
8.60%
-0.10
0.19
-0.12
-0.03
-0.06
-0.13
0.09
32.40%
23.80%
37.40%
2.70%
3.50%
86.20%
14.00%
30.60%
17.60%
42.90%
5.80%
2.90%
91.00%
11.10%
0.04
0.16
-0.11
-0.15
0.03
-0.16
0.09
0.07
0.03
0.05
0.02
0.03
1.17%
5.75%
1.54%
0.18%
0.06%
0.72%
4.42%
0.78%
0.14%
0.03%
0.04
0.06
0.07
0.01
0.02
1.71%
4.86%
1.66%
0.30%
0.08%
1.20%
3.91%
1.06%
0.19%
0.05%
0.04
0.05
0.05
0.02
0.01
0.18
0.13
0.16
0.08
0.09
34.06%
8.00%
12.37%
3.81%
5.08%
22.29%
5.46%
9.26%
2.21%
3.65%
0.26
0.1
0.1
0.09
0.07
37.01%
9.62%
16.26%
5.86%
5.83%
26.74%
8.05%
10.86%
3.29%
4.05%
0.22
0.06
0.16
0.13
0.09
* Δ/SD is the standardized difference between the high-level NICU and other delivery hospital groups for a specific variable, defined
as (difference in means between two groups of patients) ÷ (standard deviation of entire cohort). A value less than 0.20 is concerned
adequate balance between groups.
Table 3: Improved balance of measured covariates between high-level NICUs and other delivery hospitals after use of instrument and
matching, Pennsylvania, California, and Missouri 1995-2005
Pennsylvania
High Level Other Delivery
NICU
Hospital
Deliver at High Level NICU
Δ/SD*
California
High Level
NICU
Other Delivery
Hospital
Δ/SD*
Missouri
High Level
NICU
Other Delivery
Hospital
Δ/SD*
65.7%
2,598
24.7%
2,597
0.82
0.00
2,849
2,849
0.00
31.7%
2,818
13.0%
2,817
0.42
0.00
35.2
35.2
0.00
35.2
35.2
0.00
35.2
35.2
0.00
White
85.0%
85.9%
-0.02
68.4%
71.9%
-0.07
91.6%
91.7%
0.00
Black
5.1%
4.7%
0.01
5.9%
5.8%
0.01
7.3%
7.3%
0.00
Asian
1.1%
0.4%
0.06
8.0%
6.8%
0.04
0.6%
0.5%
0.01
Other
2.3%
1.3%
0.05
16.5%
13.9%
0.06
0.4%
0.4%
0.00
FFS
23.9%
25.1%
-0.03
3.3%
5.4%
-0.09
27.8%
25.6%
0.05
HMO
37.0%
30.9%
0.13
45.7%
44.2%
0.03
21.1%
19.7%
0.03
Public
28.7%
33.4%
-0.10
47.2%
45.6%
0.03
45.5%
49.1%
-0.07
Other
9.1%
9.0%
0.00
0.9%
1.5%
-0.06
3.3%
3.6%
-0.01
Uninsured
1.0%
1.2%
-0.01
3.0%
3.3%
-0.02
2.3%
2.0%
0.01
Singleton Birth
84.4%
83.9%
0.01
89.7%
88.9%
0.03
90.6%
90.1%
0.02
SGA
16.0%
16.4%
-0.01
10.7%
10.9%
-0.01
11.9%
11.7%
0.01
Birth Weight, grams
Gestational Age, weeks
Race
Insurance Status
Maternal Comorbid Conditions and Complications of Pregnancy
Comorbid Conditions
Chronic HTN
1.1%
1.2%
0.00
0.9%
1.0%
-0.01
1.0%
1.0%
0.00
Gestational Diabetes
4.7%
4.7%
0.00
5.2%
5.3%
0.00
3.8%
3.4%
0.02
Diabetes Mellitus
1.4%
1.8%
-0.03
1.0%
1.1%
0.00
1.0%
1.1%
-0.01
Renal Disease
0.2%
0.3%
-0.01
0.1%
0.2%
-0.01
0.2%
0.2%
-0.01
Congenital Heart Disease
0.1%
0.1%
0.00
0.0%
0.1%
-0.01
0.1%
0.0%
0.01
Complications of Pregnancy
Preterm Labor
45.2%
45.2%
0.00
28.7%
28.4%
0.01
30.0%
29.9%
0.00
9.7%
10.4%
-0.03
6.6%
7.6%
-0.04
8.0%
8.3%
-0.01
18.3%
17.7%
0.02
10.2%
11.4%
-0.04
11.5%
11.6%
0.00
Oligohydraminos
3.3%
3.1%
0.01
3.0%
2.9%
0.00
3.8%
3.5%
0.01
Disorders of Placentation
4.2%
5.0%
-0.03
3.7%
4.2%
-0.03
3.2%
3.7%
-0.02
PIH
PPROM
* Δ/SD is the standardized difference between the high-level NICU and other delivery hospital groups for a specific variable, defined
as (difference in means between two groups of patients) ÷ (standard deviation of entire cohort). A value less than 0.20 is concerned
adequate balance between groups.
Table 4: Difference in mortality and complications of prematurity for premature infants delivering at a high-level NICU compared to
other delivery hospitals, Pennsylvania, California, and Missouri 1995-2005*
Outcome
Neonatal Death
Fetal Death
Preventable Fetal Death
BPD
NEC
Fungal Sepsis
Bacterial Sepsis
ROP
Surgery for ROP
Lapartomy
Measure
RD per 1000 deliveries*
RR**
RD per 1000 deliveries
RR
RD per 1000 deliveries
RR
RD per 1000 deliveries
RR
RD per 1000 deliveries
RR
RD per 1000 deliveries
RR
RD per 1000 deliveries
RR
RD per 1000 deliveries
RR
RD per 1000 deliveries
RR
RD per 1000 deliveries
RR
Pennsylvania
California
Missouri
-7.2 (-10.7, -3.7)
-0.5 (-2.0, 1.0)
-8.4 (-17.5, 0.7)
0.27 (0, 0.59)
0.56 (0.27, 1.07)
-0.9 (-3.0, 1.1)
0.94 (0.79, 1.07)
-5.3 (-6.9, -3.6)
0.83 (0.51, 1.25)
0.70 (0.64, 0.78)
0.87 (0.37, 3.07)
-0.6 (-2.0, 0.8)
-2.2 (-3.1, -1.2)
-4.2 (-9.1, 0.7)
0.72 (0.22, 1.46)
0.60 (0.48, 0.74)
0.32 (0, 1.30)
0 (-3.5, 3.6)
1.02 (0, 2.53)
1.6 (-0.9, 4.1)
***
4.9 (2.3, 7.6)
3.67 (1.88, 11.6)
10.1 (4.6, 15.6)
2.37 (1.50, 4.51)
-0.7 (-3.2, 1.9)
0.38 (0, 6.34)
-1.1 (-2.3, 0)
***
-0.6 (-2.3, 1)
***
1.0 (-0.3, 2.4)
1.21 (0.96, 1.53)
1.7 (0.7, 2.6)
1.98 (1.46, 3.04)
0.7 (-0.4, 1.8)
1.28 (0.95, 1.87)
15.9 (13.4, 18.3)
1.92 (1.69, 2.05)
3.0 (1.6, 4.4)
2.52 (1.52, 3.33)
1.6 (0.9, 2.3)
***
-1.3 (-2.1, -0.4)
0.16 (0, 0.70)
-9.5 (-18.4, -0.7)
0.05 (0, 1.00)
-4.5 (-9.8, 0.7)
0.28 (0, 1.20)
3.2 (-4.3, 10.7)
1.67 (0, 17.7)
10.6 (-4.4, 25.7)
1.29 (0.92, 2.02)
3.5 (-5.6, 12.7)
1.31 (0.60, 3.52)
1.3 (-2.8, 5.3)
***
0 (-4.6, 4.6)
1.00 (0, 11.5)
-1.3 (-9.5, 6.9)
* All values in parenthesis indicate 95% confidence intervals for a given statistic. All results statistically significant at a p<0.05 level
are shown in bold.
** RD = Risk Difference between groups. A positive risk difference indicates a higher mortality rate at high-level NICUs compared
to other delivery hospitals. A negative risk difference indicates a lower mortality rate at high-level NICUs compared to other delivery
hospitals.
*** RR = Relative Risk. A relative risk > 1 indicates a higher mortality rate at high-level NICUs compared to other delivery
hospitals. A relative risk < 1 indicates a lower mortality rate at high-level NICUs compared to other delivery hospitals.