The Impact of Delivery Hospital on the Outcomes of Premature Infants in the Post-

Surfactant 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 1993-

2005.

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 0-

14 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 inter-hospital

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 ICD-9CM codeBronchopulmonary Dysplasia 770.7Necrotizing Enterocolitis 777.5Fungal Sepsis 112.x, 771.7Bacterial Sepsis 038.x, 995.90-995.94, 041.x, 790.7Retinopathy of Prematurity 362.21Retinopathy of Prematurity Surgery 14.2x, 14.34, 14.4x, 14.5x

Laparotomy 45.6x, 45.7x, 45.8, 45.9x, 46.0x, 46.1x, 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 1995-2005

Pennsylvania California Missouri High Level NICU

Other Delivery Hospital Δ/SD*

High Level NICU

Other Delivery Hospital Δ/SD*

High Level NICU

Other Delivery Hospital Δ/SD*

Differential Travel Time (min) 6.97 21.81 -0.84 3.45 14.52 -0.58 14.99 39.5 -0.59

Birth Weight (grams) 2,474 2,725 -0.34 2,716 2,936 -0.27 2,6502,85

3 -0.27Gestational Age (wks) 34.7 35.7 -0.39 34.9 35.4 -0.21 34.7 35.3 -0.23Race White 64.50% 77.90% -0.29 59.20% 60.40% -0.02 77.90% 74.70% 0.08 Black 22.20% 9.30% 0.35 9.90% 6.30% 0.13 18.80% 23.00% -0.10 Asian 1.30% 1.10% 0.02 9.40% 8.80% 0.02 2.10% 1.60% 0.04 Other 3.00% 3.40% -0.02 19.80% 23.00% -0.08 0.70% 0.60% 0.01Insurance Status FFS 19.50% 22.80% -0.08 4.50% 6.70% -0.10 32.40% 30.60% 0.04 HMO 37.80% 34.60% 0.07 46.10% 36.60% 0.19 23.80% 17.60% 0.16 Public 31.80% 29.70% 0.05 45.50% 51.40% -0.12 37.40% 42.90% -0.11 Other 9.40% 10.50% -0.04 0.90% 1.20% -0.03 2.70% 5.80% -0.15 Uninsured 1.20% 1.70% -0.04 3.00% 4.10% -0.06 3.50% 2.90% 0.03Singleton Birth 80.60% 86.50% -0.16 87.80% 91.90% -0.13 86.20% 91.00% -0.16SGA 16.90% 15.20% 0.05 11.40% 8.60% 0.09 14.00% 11.10% 0.09Maternal Comorbid Conditions and Complications of PregnancyComorbid Conditions Chronic HTN 2.00% 1.17% 0.07 1.17% 0.72% 0.04 1.71% 1.20% 0.04 Gestational Diabetes 5.49% 4.85% 0.03 5.75% 4.42% 0.06 4.86% 3.91% 0.05 Diabetes Mellitus 2.13% 1.40% 0.05 1.54% 0.78% 0.07 1.66% 1.06% 0.05 Renal Disease 0.33% 0.24% 0.02 0.18% 0.14% 0.01 0.30% 0.19% 0.02 Congenital Heart Disease 0.16% 0.05% 0.03 0.06% 0.03% 0.02 0.08% 0.05% 0.01Complications of Pregnancy Preterm Labor 48.65% 39.79% 0.18 34.06% 22.29% 0.26 37.01% 26.74% 0.22 PIH 12.14% 8.25% 0.13 8.00% 5.46% 0.1 9.62% 8.05% 0.06 PPROM 20.85% 14.86% 0.16 12.37% 9.26% 0.1 16.26% 10.86% 0.16 Oligohydraminos 4.61% 3.02% 0.08 3.81% 2.21% 0.09 5.86% 3.29% 0.13 Disorders of Placentation 6.57% 4.57% 0.09 5.08% 3.65% 0.07 5.83% 4.05% 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 California Missouri High Level NICU

Other Delivery Hospital Δ/SD*

High Level NICU

Other Delivery Hospital Δ/SD*

High Level NICU

Other Delivery Hospital Δ/SD*

Deliver at High Level NICU 65.7% 24.7% 0.82 31.7% 13.0% 0.42Birth Weight, grams 2,598 2,597 0.00 2,849 2,849 0.00 2,818 2,817 0.00

Gestational Age, weeks 35.2 35.2 0.00 35.2 35.2 0.00 35.2 35.2 0.00

Race 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% 0.00Insurance Status 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% 0.01Singleton Birth 84.4% 83.9% 0.01 89.7% 88.9% 0.03 90.6% 90.1% 0.02SGA 16.0% 16.4% -0.01 10.7% 10.9% -0.01 11.9% 11.7% 0.01Maternal Comorbid Conditions and Complications of PregnancyComorbid 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% 0.01Complications of Pregnancy

Preterm Labor 45.2% 45.2% 0.00 28.7% 28.4% 0.01 30.0% 29.9% 0.00 PIH 9.7% 10.4% -0.03 6.6% 7.6% -0.04 8.0% 8.3% -0.01 PPROM 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

* 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 Measure Pennsylvania California Missouri

Neonatal Death RD per 1000 deliveries* -7.2 (-10.7, -3.7) -0.5 (-2.0, 1.0) -8.4 (-17.5, 0.7)RR** 0.27 (0, 0.59) 0.94 (0.79, 1.07) 0.56 (0.27, 1.07)

Fetal Death RD per 1000 deliveries -0.9 (-3.0, 1.1) -5.3 (-6.9, -3.6) -1.3 (-9.5, 6.9)RR 0.83 (0.51, 1.25) 0.70 (0.64, 0.78) 0.87 (0.37, 3.07)

Preventable Fetal Death RD per 1000 deliveries -0.6 (-2.0, 0.8) -2.2 (-3.1, -1.2) -4.2 (-9.1, 0.7)RR 0.72 (0.22, 1.46) 0.60 (0.48, 0.74) 0.32 (0, 1.30)

BPD RD per 1000 deliveries 0 (-3.5, 3.6) 1.0 (-0.3, 2.4) -9.5 (-18.4, -0.7)RR 1.02 (0, 2.53) 1.21 (0.96, 1.53) 0.05 (0, 1.00)

NEC RD per 1000 deliveries 1.6 (-0.9, 4.1) 1.7 (0.7, 2.6) -4.5 (-9.8, 0.7)RR *** 1.98 (1.46, 3.04) 0.28 (0, 1.20)

Fungal Sepsis RD per 1000 deliveries 4.9 (2.3, 7.6) 0.7 (-0.4, 1.8) 3.2 (-4.3, 10.7)RR 3.67 (1.88, 11.6) 1.28 (0.95, 1.87) 1.67 (0, 17.7)

Bacterial Sepsis RD per 1000 deliveries 10.1 (4.6, 15.6) 15.9 (13.4, 18.3) 10.6 (-4.4, 25.7)RR 2.37 (1.50, 4.51) 1.92 (1.69, 2.05) 1.29 (0.92, 2.02)

ROP RD per 1000 deliveries -0.7 (-3.2, 1.9) 3.0 (1.6, 4.4) 3.5 (-5.6, 12.7)RR 0.38 (0, 6.34) 2.52 (1.52, 3.33) 1.31 (0.60, 3.52)

Surgery for ROP RD per 1000 deliveries -1.1 (-2.3, 0) 1.6 (0.9, 2.3) 1.3 (-2.8, 5.3)RR *** *** ***

Lapartomy RD per 1000 deliveries -0.6 (-2.3, 1) -1.3 (-2.1, -0.4) 0 (-4.6, 4.6)RR *** 0.16 (0, 0.70) 1.00 (0, 11.5)

* 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.