COMPETITION, VOLUME, NICU LEVEL AND OUTCOMES IN CALIFORNIA (HRSA R40MC07678)

January 21, 2010

Final Report

Submitted to the Health Resources and Services Administration Maternal and Child Health Bureau

PI: Min-Woong Sohn, Ph.D.

Institute for Healthcare Studies Northwestern University

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I. Introduction

A. Nature of the research problem Reducing neonatal mortality is one of the nation’s priorities for health policy. Although

only 7.6% of births in 1998 had low birthweight (LBW, birthweight < 2,500 grams), LBW babies accounted for 80% of all neonatal deaths and 65% of all infant deaths. This suggests that one way of reducing neonatal mortality is to improve quality of care in hospitals that care for the babies born prematurely or with LBW. The objective of this study was to examine determinants of quality of neonatal care and to understand factors associated with better neonatal and infant survival.

B. Purpose, scope, and methods of the investigation The purpose of this study is to examine volume-outcome association for neonatal

intensive care among California hospitals under low and high competitive pressure and to test whether the effects of volume on neonatal intensive care outcomes are different between hospitals under high and low competitive pressure. The main hypothesis is that the neonatal intensive care volume is associated with better quality for hospitals under low competitive pressure, but that it is either not associated, or negatively associated, with quality for hospitals under high competitive pressure.

The second aim is to examine how HMO penetration is associated with the effect of volume on outcome. HMO penetration is the most commonly used measure of how mature a managed care market is. We tested two hypotheses under this aim: The effect of competition on the volume-outcome association is stronger after 1995 as the managed care market matured over time in California; and, the effect of competition on the volume-outcome association is stronger in markets where HMO penetration rates are higher at a given point in time.

The third aim is to investigate how organizational factors such as experience, staffing, size, technology, occupancy rate, teaching status and others are associated with outcomes and if they affect the volume-outcome association. Organizational factors have been largely ignored in the volume-outcome literature and this limits our understanding of what the “volume effect” really means.

Birth certificate data linked to maternal and infant hospitalization records and death certificates from California were used to identify all live births with birth weight 500 – 1,499 grams in California.

C. Nature of the findings We conducted hypothesis-testing using multivariable statistical methods. We found that

our main hypothesis was partially supported by California data. Controlling for NICU levels, high-volume hospitals tended to have lower mortality rates consistent with the volume-outcome literature for both high- and low-competition hospitals. Volume had stronger effects on mortality for hospital under low competition than it had on mortality for hospitals under intense competition. II. Review of the Literature

Several studies have demonstrated an inverse association between NICU level and neonatal mortality.1 A recent study by Warner et al. analyzed data for all live VLBW births in the greater Cincinnati region between 1995 and 1997 and found that those who were born in

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nonsubspecialty hospitals (Level II) were twice more likely to die than those who were born in subspecialty (Level III) hospitals.2 These studies all demonstrate beneficial effects of delivering high-risk births in hospitals with subspecialty (or Level III) NICUs and suggest that their transfers between hospitals should be avoided as much as possible.

NICU volume was also observed to be significantly associated with better mortality outcomes. Studies by Phibbs et al.,3 by Cifuentes et al.4 and Rogowski et al.5 reported association between volume and outcomes in neonatal care. In 2007, based on data for infants born in the 1990s, Phibbs et al. examined the neonatal mortality among infants with various levels of care and different volumes and reported findings consistent with his earlier study.6 The mortality among very-low-birth-weight infants was lowest for deliveries that occurred in hospitals with NICUs that had both a high level of care and a high volume of such patients. It suggests that increased use of such facilities might reduce mortality among very-low-birth-weight infants. Another factor thought to affect neonatal intensive care outcomes is deregionalization. Previous studies indicate that the regionalized NICU networks may be disintegrating due to intensified competition in hospital markets. While regionalization, based on a system of referral, transport, centralization of education resources, demands collaboration between hospitals at all levels of the NICU hierarchy, financial incentives for collaboration may be lacking. As competition increases, there may be an increasing tendency for lower level NICUs to add services and to retain infants who would previously have been transferred to a higher NICU level. Indeed, there is evidence that, in California in 1990, only a minority of LBW infants were delivered at hospitals with Level III NICUs.7 In another study, Howell et al. examined 15-year (1980 – 1995) data on the supply of NICU care in the metropolitan statistical areas in the U.S. and found that during this time the supply greatly outpaced the demand and that the number of beds in small NICUs continued to grow.8 More recently, a study by Haberland et al. found significant shifts in births from both high-level and low-level NICU hospitals to midlevel hospitals due to the introduction of new midlevel NICUs in California.9 This suggests a generalized trend toward deregionalization.

III. Study Design and Methods

A. Study design This study is a retrospective observational (correlational) study using patient discharge

data from hospitals providing neonatal intensive care. Patient discharge abstracts for very low birth weight (VLBW) babies born in California in 1992 – 2004 were obtained from the Office of Statewide Health Planning and Development (OSHPD).

B. Population studied The study population includes all VLBW babies admitted to NICU care facilities in

California between 1992 and 2004. NICU patients were identified by age at admission and birthweight. We included in our study sample all babies who were admitted to hospitals within 28 days of birth with birthweight between 500 and 1499 grams. Babies with birthweight less than 500 grams were excluded from the study, because their survival or death may not be attributable to quality of NICU care.

C. Sample selection The study sample after exclusion criteria were applied includes 69,089 babies with 9,939

neonatal deaths during the 13-year study period. The following exclusion criteria were used:

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1. Babies who were born with “lethal anomalies.” These are congenital anomalies that are incompatible with life and include anencephaly (ICD-9-CM codes 740.0, 740.1, 740.2, 741.19), polycystic kidney (753.13), trisomy 13 (Patau’s syndrome, 758.1), trisomy 18 (Edwards’ syndrome, 758.2).

2. Babies who were delivered alive in hospital but died soon after delivery and satisfy the conditions of “inhospital fetal deaths” discussed in Phibbs et al. (2007).

D. Instruments used

In this study, we used neonatal mortality and neonatal-related mortality rates as our outcome measures. Neonatal mortality is defined as deaths within 28 days of life and neonatal-related mortality as deaths within 28 days of life or, if continuously hospitalized, within one year.6 Infant characteristics including birthweight in grams, sex, race/ethnicity, parity, multiple birth status, insurance coverage for the infant’s neonatal care, and birthweight were obtained from birth certificates. Congenital anomalies were detected by infant medical records. We used the diagnostic risk groups in Phibbs et al (2007)6 to identify infants with any of the seven categories of congenital anomalies. Infant race/ethnicity was obtained from hospital records. When missing, we inferred their race/ethnicity from their mothers’ race/ethnicity. We also identified babies who were transferred to higher-level neonatal intensive care units (NICUs) after birth. Maternal characteristics were obtained from birth certificates or mothers’ hospital records. They include age at delivery, whether the mother received prenatal care during pregnancy, educational attainment at time of delivery, and indicators of whether there were any complications during pregnancy and/or delivery, and method of delivery. We collected a large array of data on the hospital of delivery including NICU level, NICU volume, competition, whether a hospital was an academic medical center or a system-affiliated hospital, and ownership status. NICU level for all years were coded consistently according to the classification system developed by the American Academy of Pediatrics (AAP) Committee on the Fetus and Newborn (AAP 2004). NICU level for 1992 - 2000 was identical to that used in Phibbs et al. (2007). For years 2001 – 2004, we followed the same procedure to identify NICU levels of all hospitals as discussed in Phibbs et al. (2007). Hospital volume was the total number of unique babies delivered at each hospital in a year and, for each year, an equal number of hospitals were divided into three groups according to volume. Hospital-level competition was obtained from patient origin-destination data for all neonates based on the algorithm used previously.10 Competition in this study indicates the market competitiveness a hospital experiences from other hospitals for all patients, not just for patients for neonatal intensive care. Hospitals are divided into high and low competition hospitals using the median value of hospital competitiveness determined in each year separately.

E. Statistical techniques employed We used logistic regressions to model the effect of NICU level, volume, and competition

on neonatal mortality of VLBW babies. Along with individual level (babies and mothers) risk factors, we tested whether hospital characteristics were significantly associated with mortality. Hospital characteristics included occupancy rate, ownership type (for-profit, not-for-profit, and public ownership), and the indicators that showed whether the hospital was an academic medical center or a system affiliated hospital. Data analysis was performed using SAS version 9.1 and STATA version 10.0 for Windows.

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IV. Detailed Findings

A. Crude Neonatal and Neonatal-Related Mortality Trends Over 5,000 very low birthweight (VLBW) births were delivered in California each year for a total of 69,089 VLBW babies during the 13-year study period (1992- 2004). Each year, ~770 babies on average died within 28 days of life. The crude neonatal mortality rates (NMR) were 18.2% in 1992 and 12.8% in 2001 (see Table 1 in the Appendix), which represents an impressive 29% reduction in the NMR over a period of 8 years. However, the trend since then was reversed and the NMR has increased back to 13.4% in 2004.

This upturn has primarily been driven by the increase in NMRs for babies with birthweight less than 1,000 grams. NMRs for those with birthweight 500 – 749 grams was 56% in 1992, declined rapidly to 40.4% in 2000 and then went back up to 45% by 2004. The next higher birthweight group also experienced a similar trend. Babies with birthweight 750 – 999 grams reached the lowest NMR at 10.5% in 1999 and ended back up at 11.7% in 2004. In sharp contrast, the next two higher groups (1,000 – 1,499 grams) showed a steadily declining trend throughout the entire 13-year period. Table 1 also shows neonatal-related mortality rates (NRMRs) during the same period. In addition to those who died within 28 days of life, there were ~170 babies who eventually died within a year of life after having been continuously hospitalized since birth. The NRMR was 22% in 1992 and was reduced to the low of 15.6% in 2000. Similarly to the NMR, the NRMR also showed a reversal in trends afterwards and increased back to 16.1% in 2004.

B. Changes in Newborn and Maternal Characteristics Table 2 shows detailed data on infant and maternal characteristics by year. During the 13-

year period, 42% of babies were born with extremely low birthweight (500 – 999 grams). The number of newborn deliveries by 250 gram birthweight categories in Table2 shows that the birthweight distribution has been highly stable over the years, except for the fact that the proportion of babies born with birthweight 500 – 749 grams has decreased slightly since 1992.

On the other hand, there was a substantial change in the racial and ethnic composition of babies born in California during the study period. Non-Hispanic (NH) whites accounted for the largest number of births in 1992 (36%), while babies born to Hispanic parents comprised the second largest group (35.1%). The proportion of NH white babies declined by 3% over 13 years, while that of Hispanic babies continuously increased to 43% in 2004. This came largely at the expense of the NH black babies who accounted for 19% of all VLBW births in 1992 but only 12% in 2004. Since black babies are known to have better survivability at low birthweight, this change in demographic composition of newborns might have affected the NMR and NRMR trends during the study period.

About 13% of all babies had congenital conditions during the study period. There are year-to-year fluctuations in the proportion of babies born with congenital conditions (e.g., 12.5% to 14% to 13.1% from 1994 to 1995) but there does not appear to be any consistent trend over time.

Other noticeable trends include a decrease in the proportion of firstborns (89% to 85%) and the proportion of singleton births (80% to 74%), an increase in the proportion of babies with complications during delivery (10% to 12.6%), and a very rapid increase in the proportion of babies delivered by c-section (51% to 68%) between 1992 and 2004.

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The source of payment for the newborns’ hospitalization represents a sea change in the financing of neonatal intensive care in California. In 1992, the care for one half of all newborns with VLBW was covered by Medi-Cal (Medicaid in California). This proportion rapidly decreased to 39% in 1998. This trend reflects a large number of babies born to poor mothers who transitioned from welfare to work after the legislation in 1996 of the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA), commonly known as welfare reform. Since then, the proportion of newborns by Medi-Cal was relatively stable. The 11% drop in Medi-Cal coverage of VLBW babies was largely picked up by private insurance, which covered only 26% of hospitalizations by all VLBW babies in 1992 but 40% in 1996, 51% in 2002, and 45% in 2004. The percentages of VLBW babies with uncompensated, indigent, or self-pay care was 24% in 1992 and steadily decreased to 10% in 2000 and 15% in 2004. Among maternal factors, notable changes included a decrease in teenage births from 14.9% to 10.3% and a concomitant increase in that of births to mothers with 35 years of age or older from 11.6% to 19.2%. Births by cesarean section steadily increased during the entire period from 51% to 68% for all VLBW babies. Mothers who received any prenatal care and adequate or adequate-plus prenatal care according to the Kotelchuk’s index (not shown) increased from 92% to 95% and 71% to 84%, respectively. Finally, the percent of mothers of VLBW babies who completed college education increased from 14% to 26%, while the percent of mothers with less than high school education decreased from 36% to 26% in this period.

C. Trends in Risk Factors for Neonatal Mortality The characteristics of hospitals also underwent several noticeable changes. First of all, this was a period when the for-profit hospitals almost doubled its market share for the care of VLBW babies from 7.5% in 1992 to 13.8% in 2004. This increase came at the expense of public hospitals (e.g., hospitals operated by counties and districts). More VLBW babies were increasingly admitted to medium-sized community hospitals over this period as evidenced by the rapid decrease in the share of academic medical centers from 22% to 14%, in the share of large-volume Level 3B, 3C, and 3D NICUs from 24% to 12%, and in the share of VLBW babies transferred to higher-level NICUs from 27% to 25% over this period.

D. Adjusted Trends in Mortality The odds ratios from the fully adjusted models for all VLBW babies (data not shown) show that the neonatal mortality risk was 15% higher (OR = 1.153; 95% Confidence Interval [CI], 0.0.962 – 1.382) and the neonatal-related mortality risk was 8% higher (OR = 1.085; 95% CI, 0.912 – 1.289) in 2004 than in 1998, respectively.

In a stratified analysis, the extremely low birthweight babies (500 – 999 grams) showed 32% higher risk of neonatal mortality (OR = 1.318; 95% CI, 1.013 – 1.716) and 18% higher risk of neonatal-related mortality (OR = 1.181; 95% CI, 0.909 – 1.534) in 2004 than in 1998.

Figure 1 shows adjusted mortality trends for all VLBW babies. The blue line (with diamond markers) represents trends adjusted for newborn and maternal characteristics only and the red line (with asterisk markers) represents trends adjusted for newborn, maternal, and hospital/market characteristics. Even though hospital and market characteristics as a group are significantly associated with outcomes, they do not appear to have significantly affected the trends as the two lines virtually overlap each other.

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E. Competition, Volume, and Outcomes Table 3 shows crude neonatal and neonatal-related mortality rates by competition and

volume groups. During the 13-year study period, neonatal mortality rates for low-competition hospitals were 24.3%, 17.3%, and 13.3% for low, medium, and high volume, respectively; those for high-competition hospitals were 19.0%, 16.0%, and 14.1%. Figure 2 graphically shows that the effect of volume for low-competition hospitals was much larger than that for high-competition hospitals. Neonatal-related mortality rates show a similar pattern of association between competition and volume on the one hand and mortality on the other.

Overall, there was a large difference in crude mortality rates (neonatal, 21.9% vs. 14.1%; neonatal-related, 24.4% vs. 16.9%) between low- and high-volume hospitals, consistent with previous literature on volume-outcome association for NICU care. On the other hand, the crude rates were very similar between high- and low-competition hospitals overall (neonatal, 14.2% vs. 14.7%; neonatal-related, 17.3% vs. 18.0%). But when they were broken down by volume categories, a significant pattern emerges. Considerable differences existed between these two groups in mortality rates (both neonatal and neonatal-related) for low-volume hospitals but the gap narrows and almost disappears in the other two higher volume groups.

Table 4 shows unadjusted and adjusted odds ratios of neonatal and neonatal-related deaths associated with hospital competition and volume. The adjusted model shows that, among low-competition hospitals, newborns were 33% less likely (OR = 0.774; 95% CI, 0.638 – 0.940) if they were delivered in medium-volume hospitals and almost half as likely to experience a neonatal death (OR = 0.543; 95% CI, 0.438 – 0.637) if they were delivered in high-volume than in low-volume hospitals. Among high-competition hospitals, newborns were 16% less likely (OR = 0.844; 95% CI, 0.673 – 1.059; not shown) and 32% less likely (OR = 0.682; 95% CI, 0.535 – 0.870; not shown) to experience a neonatal death if they were delivered in medium- and high-volume than in low-volume hospitals. The results from a model with neonatal-related mortality as the outcome show a very similar pattern of associations between competition, volume, and mortality.

The graded association between volume and outcomes was much steeper for low-competition than for high-competition hospitals in the direction that is consistent with the current volume-outcome literature. In general, adjusted neonatal mortality risks for babies delivered in high-competition/low-volume hospitals were not statistically different for those delivered in low-competition/low-volume, low-competition/medium-volume, and high-competition/medium-volume hospitals.

Other outcomes of interest in the study included infant mortality (death within 1 year of life) and readmission after surviving from the initial NICU care. We found that the results from a logistic model predicting infant mortality risk were almost identical to the results for a model predicting neonatal-related mortality risk as shown in Tables 4 and 5. Bivariate analyses show that 82% of all infant deaths were also neonatal deaths and 99% were also neonatal-related deaths.

Readmissions within one year of life for babies who survived the initial NICU hospitalization were not significantly associated with either volume or competition. Twenty-one percent of all VLBW newborns did not survive the initial NICU admission and there were 54,518 newborns who survived it. Of these babies, 11,923 (22%) were readmitted and 32% of these babies experienced a readmission due to transfers from the initial NICU admission. These transfers (readmissions) may not be attributable to the quality of care at the transferring hospital.

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Excluding newborns with any transfers after the initial admission, 39,488 newborns were kept in the analytic sample, of whom 20.4% were readmitted. Both unadjusted and adjusted logistic regression models suggest that competition and volume are not associated with one-year readmission rates.

F. HMO Penetration HMO penetration or the percentage of the population in county enrolled in HMOs each

year was not generally associated with either neonatal or neonatal-related mortality rates. Subgroup analyses stratified by two time periods (1992 – 1995 and 1996 – 2004) showed that HMO penetration was not significantly associated with outcomes either before or after 1995. Market maturation measured by HMO penetration does not appear to affect mortality outcomes, nor is there evidence that it affects the nature, direction, and magnitude of competition-volume-outcome association during the study period.

G. Organizational Characteristics Several organizational characteristics that are thought to mediate volume-outcome

associations in NICU care were included in the models shown in Table 5. These characteristics included the NICU Level, HMO Penetration in county where the hospital is located, occupancy rate measured as the percentage of staffed beds filled throughout a given year, academic medical center status, affiliation with multihospital systems, and hospital ownership type. We also considered the number of years a hospital provided NICU care since 1992 and other teaching indicators (Council of Teaching Hospitals membership, or whether a hospital has a residency program). None of these market- or hospital-level variables was not statistically associated with outcomes.

One exception was the public hospital ownership. Public hospitals (state, county, or district) have had significant presence in NICU care markets in California. They treated over 20% of all VLBW babies in 1992. However, the share of patients treated in public hospitals decreased steadily to a low of 11% in 2003 over the years. As Table 5 shows, newborns delivered in public hospitals have 18% (OR = 1.18; 95% CI, 1.03 – 1.35) and 13% (OR = 1.128; 95% CI, 1.00 – 1.27) higher risk of neonatal and neonatal-related mortality rates compared to those treated in non-for-profit hospitals.

Our results regarding NICU Level show that higher level hospitals have better outcomes. These results are largely consistent with previous literature and hardly surprising. We further found that babies who were transfer to a higher level hospital (first uptransfer) and those whose mothers were transferred prior to delivery (prenatal transfer) had lower risk of neonatal and neonatal-related deaths. V. Discussion and Interpretation of Findings

Our results show that volume was negatively associated with both neonatal and neonatal-

related mortality. This finding is consistent with previous studies of the volume-outcome association in NICU care and in other clinical services. Competition overall was not associated with outcomes.

Importantly, however, when hospitals were placed into three volume groups and two competition groups each year, the graded association between competition, volume and outcomes emerged. Low-competition/low-volume hospitals had the highest neonatal and

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neonatal-related mortality rates of all competition-volume groups, followed by high-competition/low-volume hospitals (Table 3).

Both unadjusted and adjusted analyses show that increased volume for both low- and high-competition hospitals were associated with lower mortality rates. However, volume appears to have stronger effect on low-competition hospitals than for high-competition hospitals. This may suggest that regionalization (e.g., delivering more newborns in high-volume hospitals) in low-competition areas may be the most beneficial in terms of saving lives. However, low-competition hospitals may be those in rural areas or sole providers in a community and alternate destinations for NICU care or delivery of potentially high-risk babies may be hard to come by.

We do not understand the mechanism by which competition and volume interact to affect outcomes the way they did in this study. One possible explanation is that the association among competition, volume, and mortality are affected by financial incentives under which NICUs are operating. Perinatal health care in the U.S. has long been organized on a regional basis, so that sick infants or expectant mothers of high-risk infants are transferred to a hospital where the infants can obtain adequate care. Several studies have demonstrated an inverse association between NICU level and neonatal mortality. However, researchers recently noted that the organized regional hierarchy of neonatal intensive care may be breaking down. While NICUs are highly profitable, there are few financial incentives for collaboration among NICUs in the same regional network. Thus, in the last couple of decades, lower-level NICUs acquired additional services (e.g., mechanical ventilation or surgery capabilities) and retained babies who would previously have been transferred to a higher-level NICU.

As a result, only the sickest of infants and those who are not financially attractive may be transferred out. Bronstein et al. (1995) found some evidence that Medicaid coverage increased the likelihood of the prenatal transfer of white women to hospitals with higher level NICUs, compared with white women without Medicaid coverage.11 Shaffer and Phibbs further showed that only a minority (31.5%) of low birthweight infants were delivered at hospitals with the tertiary NICUs (Level III in the old designation used by the California Childrens’ Services) and the proportion of low birthweight babies born in hospitals with the Level III NICUs was lower in regions where competitive pressure was stronger, indicating that hospitals in high-competition markets are less likely to transfer expectant mothers of high-risk babies to hospitals with higher-level NICUs.12

The differential effect of competition on mortality at different levels of volume may indicate that there is systematic variation in the way hospitals use selective retention and transfer strategies, depending on their volume level. For example, if low-volume hospitals under strong competition retained proportionately as many sicker infants as high-volume hospitals did, competition might have negative effects on NICU outcomes uniformly throughout volume levels. But, if high-volume hospitals retained sicker infants more than low-volume ones did, deregionalization might strengthen the association between competition and outcomes for high-volume hospitals. The findings in this study suggest that this might have been happening in California: high-volume hospitals had overall poorer outcomes under stronger competition, although the difference was not statistically significant.

From a policy standpoint, this study raises concerns about using selective referral to high-volume, high-level NICUs as a strategy for lowering mortality. Phibbs et al. showed that Level III NICUs with high volume (defined as average daily census ≥ 15) had lower mortality than other NICUs using 1990 California data for infants with birthweight between 500 and 1,800 grams.3, 6 Based on the Phibbs et al. study, the Leapfrog group estimated that selective referral to

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high-volume Level III NICUs would save 1,369 babies with birthweight between 500 and 1,500 grams and 494 babies with congenital anomalies in the U.S. per year. If the current trend toward deregionalization continues, and the cost structure of neonatal intensive care does not change, it is highly questionable whether such selective referral can achieve the intended effect of lowering mortality of low birthweight babies. VI. Conclusions to be drawn from findings

Volume-Outcome Association. Study findings suggest that high volume is associated with better outcomes in both neonatal and neonatal-related mortality rates for babies born in California in 1992 – 2004.

Competition Mediates Volume-Outcome Association. Our study also confirmed that

competition mediates the volume-outcome association. The volume-outcome association was much stronger for hospitals under low competitive pressure than those in high competitive pressure.

Competition does not have an independent effect on outcomes but in each volume

group competition has a significant effect on outcomes. Competition is not associated with neonatal outcomes, independent of volume. However, when hospitals were grouped into volume tertiles, competition was associated with significantly better outcomes (both neonatal and neonatal-related mortality rates) for low and medium volume hospitals. However, there was no association in outcomes for high-volume hospitals. VII. Explanation of study limitations

(1) This study uses pooled cross-sectional design and therefore study findings cannot be used to infer causal relationships between competition, volume, and outcomes.

(2) The study findings also need to be carefully generalized. California may be different from other states in terms of the managed care penetration and the extent of deregionalization. The study findings may not necessarily apply to NICU care in other states and thus have to be carefully generalized. VIII. Comparison with findings of other studies

(1) The results for the volume-outcome association are consistent with previous

literature in NICU volume-outcome studies. When hospitals were stratified into high and low competition groups, stronger effect of volume was found for low competition hospitals than for high competition hospitals.

(2) The results for the neonatal mortality rates are consistent with two previous

studies but the trends since 2000 have not been observed elsewhere.

IX. Possible application of findings to actual MCH health care delivery situations / Policy Implications

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(1) The volume-outcome association suggests that a stronger regionalization can potentially reduce neonatal and neonatal-related mortality rates. However, its benefits would be greater if more high-risk babies were delivered in high-volume hospitals regardless of competition.

(2) Increasing volume for hospitals in low competition areas can potentially reduce

mortality rates faster than increasing volume for hospitals in high competition areas. This is our unique contribution to the healthcare policy of MCH population. In practical terms, regionalization in NICU care needs to be strengthened in low-competition areas first.

(3) For low-volume and medium-volume centers, increased competition appears to be associated with better outcomes. This may suggest that deregionalization may not necessarily be harmful if it increases competition for neighboring hospitals with NICU. However, this needs further research. X. Suggestions for further research

One of the surprising findings from this study was the reversal in neonatal mortality trends since 2000 or 2001. A future study is needed to examine whether this trend continues in more recent years.

The competition, volume, and outcome association for other states may be different from that for California, due to differences in the nature of neonatal intensive care markets between states. A future study of NICU care in other states is needed to better understand if and how competition, volume, and outcomes are associated.

XI. List of products

No published papers yet. Two manuscripts are under development. Their tentative tiles are:

Trends in neonatal mortality of very low birthweight babies in California, 1992 – 2004

Competition, volume, and outcomes in neonatal intensive care in California

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Appendix

Table 1. Very Low Birthweight (VLBW) Births and Crude Neonatal and Neonatal Related Mortality Rates by Year and Birthweight Groups

Year VLBW Births

Neonatal Mortality Rates Neonatal-Related Mortality Rates All 500-749g 750-999g 1000-1249g 1250-1499g All 500-749g 750-999g 1000-1249g 1250-1499g

1992 5,444 18.2% 55.5% 16.6% 8.7% 4.1% 21.9% 61.3% 21.7% 12.0% 5.8% 1993 5,285 16.4% 51.3% 14.5% 6.3% 3.9% 20.3% 56.8% 20.3% 9.4% 5.9% 1994 5,478 16.1% 47.9% 14.9% 7.8% 3.5% 19.7% 55.2% 18.8% 11.1% 4.9% 1995 5,178 14.6% 44.5% 13.3% 5.7% 4.2% 18.3% 52.4% 17.9% 8.0% 5.8% 1996 5,091 14.7% 47.4% 13.0% 5.9% 4.1% 17.5% 53.1% 16.6% 8.1% 5.6% 1997 5,175 15.0% 45.1% 11.8% 5.6% 4.9% 18.2% 51.5% 15.9% 7.8% 6.1% 1998 5,244 13.7% 41.9% 12.4% 5.7% 3.5% 17.1% 50.8% 15.6% 7.3% 4.7% 1999 5,137 13.4% 42.6% 10.5% 4.8% 4.0% 16.4% 48.5% 14.5% 6.4% 5.8% 2000 5,283 13.2% 40.4% 12.4% 5.4% 3.6% 15.6% 45.3% 15.1% 7.8% 4.5% 2001 5,294 12.8% 40.5% 10.6% 4.9% 3.3% 16.0% 48.1% 14.6% 6.6% 4.5% 2002 5,316 12.9% 42.3% 11.1% 4.4% 3.7% 16.1% 49.8% 15.9% 6.0% 4.7% 2003 5,545 12.8% 41.3% 11.9% 5.2% 3.2% 15.7% 47.0% 16.5% 6.6% 4.3% 2004 5,619 13.3% 44.9% 11.7% 4.0% 3.3% 16.1% 51.7% 15.4% 5.5% 4.0% Total 69,089 14.4% 45.1% 12.7% 5.7% 3.8% 17.6% 51.7% 16.8% 7.9% 5.1%

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Table 2. Characteristics of Neonates, their Mothers, and Delivery Hospitals, 1992- 2004

Characteristics 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 All Years

All VLBW (500 - 1499 g) Births, N 5,444 5,285 5,478 5,178 5,091 5,175 5,244 5,137 5,283 5,294 5,316 5,545 5,619 69,089 Newborn Characteristics Birthweight, %

500 - 749 g 19.7 20.0 20.0 20.0 18.8 20.8 20.0 19.8 19.3 20.0 19.0 18.9 19.2 19.7% 750 - 999 g 22.3 22.9 22.5 21.3 21.3 22.2 22.7 23.2 22.8 22.0 22.4 22.1 22.4 22.3% 1000 - 1249 g 25.2 25.7 26.0 26.5 27.1 25.9 25.9 25.5 25.4 26.1 26.3 25.8 25.3 25.9% 1250 - 1499 g 32.8 31.4 31.5 32.3 32.8 31.1 31.4 31.5 32.4 31.9 32.4 33.2 33.1 32.1%

Sex, % Male 51.3 50.2 51.6 52.2 52.3 50.8 52.2 51.1 51.1 50.8 51.4 52.1 52.0 51.5% Female 48.7 49.8 48.4 47.8 47.7 49.2 47.8 48.9 49.0 49.2 48.6 48.0 48.0 48.5%

Race/Ethnicity, % Non-Hispanic White 36.0 35.0 38.7 37.4 35.1 36.0 36.0 35.0 35.2 34.1 34.7 34.5 33.3 35.5% Non-Hispanic Black 19.1 18.5 17.2 16.0 15.0 15.4 14.6 14.7 13.7 13.4 12.5 11.6 11.7 14.9% Hispanic 35.1 38.2 33.3 37.9 40.8 38.6 39.4 39.2 39.7 41.1 40.7 42.1 42.8 39.1% Asian 8.2 6.5 7.8 6.5 6.7 7.7 7.3 7.1 7.9 7.4 8.0 8.1 8.5 7.5% Other Races 1.5 1.9 3.0 2.3 2.4 2.3 2.8 3.9 3.5 4.1 4.2 3.7 3.7 3.0%

Congenital Anomalies (Any), % 13.2 12.3 12.5 14.0 13.1 14.0 12.6 13.3 13.0 13.7 14.1 12.8 12.9 13.2% Gastrointestinal 2.4 2.2 2.4 2.9 3.1 3.2 3.2 3.0 2.9 3.2 3.2 2.8 3.0 2.9% Genitourinary 0.9 0.9 0.9 0.9 1.0 1.4 1.2 1.2 1.0 1.1 1.2 1.2 1.1 1.1% Central Nervous System 4.0 3.7 3.5 3.5 3.7 3.6 3.4 3.7 3.5 3.3 3.7 3.3 3.5 3.6% Pulmonary 2.0 2.1 2.1 3.1 2.1 2.5 2.8 3.1 3.5 4.0 4.0 3.5 3.1 2.9% Cardiovascular 1.7 1.6 1.3 1.7 1.5 1.6 1.6 1.5 1.7 1.4 1.7 1.6 1.6 1.6% Chromosomal Syndromes 1.1 0.9 1.0 1.1 1.0 1.1 1.1 1.0 1.1 1.0 1.1 1.0 0.8 1.0% Other 2.9 2.7 2.9 3.2 2.9 3.0 1.9 2.0 2.0 2.2 1.6 1.8 1.9 2.4% Firstborn, % 89.0 88.5 88.5 89.0 88.4 86.3 86.0 86.6 86.4 85.8 85.8 85.5 85.4 87.0% Singleton, % 79.9 78.4 78.7 79.1 78.1 75.4 74.9 75.6 75.2 74.9 74.5 73.5 74.2 76.3% Complication during pregnancy, % 20.6 20.4 20.7 19.2 20.6 21.3 21.7 21.9 22.1 19.4 20.3 17.1 21.2 20.5% Complication during delivery, % 10.1 10.2 11.1 11.4 12.7 11.9 11.6 11.8 13.0 12.2 11.4 11.6 12.6 11.6% Gestational age, days 210 209 209 208 210 210 209 208 209 211 211 211 209 210 Delivery by C-Section, % 51.1 53.1 53.1 54.6 57.1 58.3 60.7 61.5 62.8 64.5 65.8 67.2 68.1 59.9% Payment source, %

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Private Insurance (HMO/PPO/indem) 25.8 27.7 29.9 37.9 39.9 43.4 47.2 49.5 50.5 48.0 48.4 45.4 45.0 41.4% Medi-Cal 50.0 50.9 50.9 49.0 46.8 43.6 39.1 39.1 39.1 39.5 40.0 39.9 39.8 43.7% Other 24.2 21.5 19.3 13.1 13.3 13.0 13.7 11.4 10.4 12.5 11.6 14.7 15.2 15.0%

Maternal characteristics Age at delivery

< 20 y 14.8 14.8 14.4 15.6 13.8 13.2 12.0 12.6 12.6 11.6 10.5 10.8 10.3 12.8% 20 - 34 y 70.0 69.3 68.4 66.9 65.9 66.8 66.4 66.3 66.0 67.0 67.9 66.9 66.0 67.2% >= 35 y 15.2 15.9 17.2 17.4 20.4 20.0 21.6 21.1 21.4 21.4 21.7 22.3 23.8 20.0%

Prenatal care receipt 91.8 92.3 92.4 93.3 93.8 93.5 94.1 94.2 94.7 94.3 95.0 94.7 95.3 93.8% Mother's educational attainment

< High school 35.6 35.6 35.0 35.6 32.2 30.7 28.5 29.0 26.8 28.3 26.1 26.2 26.3 30.4% High school 31.0 31.3 30.7 28.7 30.3 30.5 29.6 29.3 30.7 29.5 29.0 29.1 27.9 29.8% Some college 19.1 19.9 19.7 19.6 18.5 20.6 20.8 20.6 20.9 19.6 21.2 20.0 19.8 20.0% College grad or higher 12.5 11.7 13.7 14.7 16.9 15.7 18.3 18.5 19.1 20.0 19.8 21.3 22.3 17.3% Unknown or Not Available 1.7 1.5 1.0 1.4 2.1 2.5 2.9 2.7 2.5 2.6 3.9 3.4 3.6 2.5%

Hospital/Market characteristics Transfer to Higher Level NICU, % 19.1 19.1 18.5 18.5 20.0 18.9 19.7 19.5 19.3 20.8 20.3 19.5 18.9 19.4% Hospital Volume Tertile

Low 3.6 3.2 3.4 3.7 3.5 3.4 3.9 3.8 3.6 3.6 3.3 3.5 3.3 3.5% Medium 12.7 13.7 13.4 14.5 16.0 15.1 15.5 16.3 16.9 18.1 18.4 17.0 17.3 15.8% High 83.7 83.1 83.2 81.8 80.4 81.5 80.6 79.9 79.5 78.3 78.3 79.6 79.4 80.7%

NICU Level (5 categories) NICU Level 1 and 2 17.3 17.5 17.6 16.6 17.7 15.9 17.0 14.3 14.0 14.0 13.6 13.6 15.5 15.7% NICU Level 3A 10.2 10.3 10.0 11.4 11.5 10.9 10.6 12.8 12.1 11.3 11.4 10.2 10.4 11.0% NICU Level 3B 27.3 30.7 31.8 31.8 32.8 33.2 31.4 30.2 28.6 28.2 29.6 31.1 30.9 30.6% NICU Level 3C 26.0 23.2 22.4 21.8 19.7 21.0 22.8 24.8 28.8 29.8 29.1 29.5 27.2 25.1% NICU Level 3D 19.2 18.3 18.3 18.4 18.3 18.9 18.3 18.0 16.6 16.8 16.3 15.6 16.0 17.6%

Births at Competition level, % Low Competition 55.9 55.4 55.2 52.7 53.8 53.0 56.5 51.8 51.7 50.0 52.5 51.0 51.2 53.1% High Competition 44.1 44.6 44.8 47.4 46.2 47.0 43.5 48.2 48.3 50.0 47.5 49.0 48.8 46.9%

Births at academic medical centers, % 21.9 17.7 17.7 17.5 14.9 16.3 14.7 15.3 14.1 13.9 13.6 12.7 13.6 15.7% Births at System-Affiliated Hospitals, % 58.6 56.2 59.4 63.5 70.2 73.8 79.7 79.0 78.5 78.6 77.4 80.6 76.0 71.6% Births by Hospital Ownership, %

Not-for-profit 70.0 72.5 72.5 75.5 73.7 76.4 75.4 74.6 74.4 74.7 74.4 73.7 73.6 73.9% For-profit 7.5 7.9 7.5 7.8 11.2 10.8 12.8 13.9 14.1 13.2 13.4 15.1 13.8 11.5% Public 22.5 19.6 20.0 16.8 15.1 12.8 11.8 11.5 11.6 12.1 12.2 11.1 12.6 14.6%

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Table 3: Crude Neonatal and Neonatal-Related Mortality Rates by Competition and Volume Groups

Competition Neonatal Mortality Neonatal-Related Mortality Volume

Total Volume

Total Low Medium High Low Medium High

Low 24.3% 17.3% 13.3% 14.2% 26.5% 20.2% 16.4% 17.3% High 19.0% 16.0% 14.1% 14.7% 21.7% 18.8% 17.7% 18.0% Total 21.9% 16.6% 14.1% 14.4% 24.4% 19.4% 16.9% 17.6%

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Table 4: Unadjusted and Adjusted Odds Ratios and 95% Confidence Intervals of Neonatal and Neonatal-Related Mortality for Competition and Volume Categories

(A) Unadjusted

Competition Volume

Neonatal Mortality Neonatal-Related Mortality Low Medium High Low Medium High

Low 1.000

0.653 (0.543 – 0.786)

0.478 (0.406 – 0.563)

1.00

0.702 (0.589 – 0.836)

0.545 (0.468 – 0.636)

High 0.734 (0.591 – 0.913)

0.595 (0.498 – 0.710)

0.513 (0.438 – 0.601)

0.768 (0.628 – 0.940)

0.641 (0.545 – 0.755)

0.595 (0.513 – 0.690)

(B)Adjusted*

Competition Volume

Neonatal Mortality Neonatal-Related Mortality Low Medium High Low Medium High

Low 1.000

0.774 (0.638 – 0.940)

0.543 (0.438 – 0.673)

1.00

0.819 (0.686 – 0.976)

0.631 (0.518 – 0.769)

High 0.856 (0.660 – 1.110)

0.723 (0.590 – 0.885)

0.584 (0.471 – 0.724)

0.844 (0.671 – 1.063)

0.766 (0.641 – 0.915)

0.705 (0.577 – 0.862)

* Adjusted for infant (birthweight, congenital anomalies, race/ethnicity, sex, delivery type, singleton birth status, parity, prenatal care status, and payment source), maternal (age at delivery and educational attainment), hospital (NICU level, volume, competition, occupancy rate, academic medical school status, system affiliation, and ownership type), and market characteristics (HMO penetration). Full models are shown in Table 5 (below).

Table 5. Adjusted Odds Ratios and 95% Confidence Intervals for Factors Associated with Neonatal and Neonatal Related Mortality

Neonatal Mortality Neonatal-Related Mortality

OR 95% CI† OR 95% CI Volume & Competition [Low Vol/Low Comp]*

Medium Volume/Low Competition 0.774 (0.64 - 0.94) 0.819 (0.69 - 0.98) High Volume/Low Competition 0.543 (0.44 - 0.67) 0.631 (0.52 - 0.77) Low Volume/High Competition 0.856 (0.66 - 1.11) 0.844 (0.67 - 1.06) Medium Volume/High Competition 0.723 (0.59 - 0.88) 0.766 (0.64 - 0.91) High Volume/High Competition 0.584 (0.47 - 0.72) 0.705 (0.58 - 0.86)

Year [2000] 1992 1.345 (1.08 - 1.68) 1.463 (1.18 - 1.81) 1993 1.151 (0.92 - 1.44) 1.285 (1.04 - 1.59) 1994 1.101 (0.89 - 1.37) 1.219 (1.00 - 1.49) 1995 0.959 (0.78 - 1.18) 1.079 (0.90 - 1.30) 1996 1.006 (0.83 - 1.22) 1.052 (0.88 - 1.26) 1997 0.971 (0.80 - 1.18) 1.033 (0.86 - 1.24) 1998 0.906 (0.75 - 1.09) 0.997 (0.84 - 1.18) 1999 0.944 (0.82 - 1.08) 1.004 (0.88 - 1.14) 2001 0.988 (0.87 - 1.13) 1.059 (0.94 - 1.20) 2002 1.010 (0.87 - 1.18) 1.090 (0.96 - 1.24) 2003 1.015 (0.87 - 1.19) 1.062 (0.92 - 1.23) 2004 1.044 (0.90 - 1.21) 1.081 (0.95 - 1.23)

Birthweight [500-749g] 750-999g 0.174 (0.16 - 0.19) 0.184 (0.17 - 0.20) 1000-1249g 0.064 (0.058 - 0.070) 0.071 (0.065 - 0.078) 1250-1499g 0.036 (0.031 - 0.040) 0.040 (0.036 - 0.044)

Male [Female] 1.481 (1.41 - 1.56) 1.502 (1.43 - 1.58)

Race/ethnicity [Non-Hispanic White] Non-Hispanic Black 0.723 (0.66 - 0.79) 0.856 (0.79 - 0.93) Hispanic 1.034 (0.97 - 1.11) 1.070 (1.00 - 1.14) Non-Hispanic Asian 0.942 (0.85 - 1.04) 0.996 (0.91 - 1.09) Non-Hispanic Other 0.817 (0.68 - 0.98) 0.822 (0.69 - 0.98)

Congenital Anomalies Gastrointestinal 0.970 (0.77 - 1.22) 1.496 (1.23 - 1.82) Genitourinary 6.532 (5.29 - 8.07) 6.540 (5.52 - 7.75) Central Nervous System 0.966 (0.82 - 1.14) 1.347 (1.16 - 1.56) Pulmonary 0.342 (0.24 - 0.48) 0.519 (0.38 - 0.70) Cardiovascular 2.338 (1.84 - 2.97) 4.070 (3.35 - 4.95) Chromosomal Syndromes 11.320 (9.17 - 13.99) 11.280 (8.99 - 14.16) Other 2.045 (1.67 - 2.50) 2.174 (1.79 - 2.64)

Mother's Education [< High school] High school 0.933 (0.87 - 1.00) 0.895 (0.84 - 0.96) Some college 0.887 (0.81 - 0.97) 0.864 (0.79 - 0.95) College or higher 0.962 (0.87 - 1.06) 0.882 (0.81 - 0.96) Unknown or Not Available 2.397 (2.00 - 2.88) 2.088 (1.76 - 2.48)

Mother's age [20 - 34]

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< 20 1.142 (1.07 - 1.22) 1.148 (1.08 - 1.22) >=35 0.942 (0.88 - 1.00) 0.949 (0.89 - 1.01)

C-Section 0.506 (0.47 - 0.55) 0.558 (0.52 - 0.60) Singleton 0.853 (0.79 - 0.93) 0.879 (0.81 - 0.95) Firstborn 1.135 (1.04 - 1.24) 1.082 (0.99 - 1.18) Any prenatal care visit 0.684 (0.62 - 0.76) 0.702 (0.64 - 0.77)

Pay source [Private Insurance] Others 0.881 (0.79 - 0.98) 0.951 (0.86 - 1.05) Medi-Cal 0.895 (0.82 - 0.97) 0.911 (0.84 - 0.98)

First Transfer Up 0.338 (0.30 - 0.38) 0.573 (0.52 - 0.64) Prenatal Transfer prior to birth 0.618 (0.55 - 0.70) 0.669 (0.60 - 0.74)

NICU Level [NICU level 1&2] NICU Level 3A 0.684 (0.59 - 0.79) 0.774 (0.68 - 0.88) NICU Level 3B 0.534 (0.46 - 0.63) 0.671 (0.57 - 0.78) NICU Level 3C 0.469 (0.40 - 0.55) 0.591 (0.50 - 0.70) NICU Level 3D 0.431 (0.34 - 0.55) 0.544 (0.43 - 0.68)

HMO Penetration 0.715 (0.47 - 1.08) 0.786 (0.53 - 1.17) Occupancy Rate 0.829 (0.57 - 1.20) 0.799 (0.56 - 1.14) Academic Medical Centers 1.019 (0.87 - 1.19) 1.033 (0.90 - 1.19) System-affiliated Hospitals 1.095 (0.99 - 1.21) 1.073 (0.98 - 1.17)

Hospital Ownership [Non-Profit] For profit 0.962 (0.82 - 1.12) 0.954 (0.83 - 1.10) Public 1.180 (1.03 - 1.35) 1.128 (1.00 - 1.27)

* Reference category is in brackets. † CI refers to confidence interval.

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Figure 1. Neonatal Mortality rates by Year*

* The blue line (marked with diamonds) indicates neonatal mortality rates (NMR) adjusted for newborn and maternal characteristics. The red line (marked with asterisks) indicates NMRs adjusted for newborn, maternal, and market/hospital characteristics.

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Figure 2. Neonatal-Related Mortality rates by Year*

* The blue line (marked with diamonds) indicates neonatal-related mortality rates (NRMR) adjusted for newborn and maternal characteristics. The red line (marked with asterisks) indicates NRMRs adjusted for newborn, maternal, and market/hospital characteristics.

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Figure 2: Unadjusted Association between Competition, Volume, and Outcomes, 1992 – 2004*

(A) Neonatal Mortality

22

(B)Neonatal-Related Mortality

* Dotted line indicates mortality rates for newborns delivered in low-competition hospitals; solid line, high-competition hospitals.

23

Figure 3: Adjusted Odds Ratios of Neonatal and Neonatal-Related Deaths for Competition and Volume Groups*

(A) Neonatal Deaths

24

(B)Neonatal-Related Deaths

* Dotted line indicates mortality rates for newborns delivered in low-competition hospitals; solid line, high-competition hospitals. Odds ratios were obtained from the logistic regression model shown in Table 5.

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