Description
An infant (from the Latin word infans, meaning "unable to speak" or "speechless") is the very young offspring of a human.
ABSTRACT
Title of dissertation:
EMPIRICAL ESSAYS ON THE ECONOMICS OF NEONATAL INTENSIVE CARE Seth M. Freedman Doctor of Philosophy, 2010
Dissertation directed by:
Judith K. Hellerstein Department of Economics
The number of neonatal intensive care units (NICUs) in smaller community hospitals increased greatly during the 1980s and 1990s, attracting deliveries away from hospitals with the most sophisticated NICUs. This pattern of “deregionalization” has caused concern because previous studies ?nd higher mortality rates for high-risk infants born in hospitals with less sophisticated NICUs relative to those born in hospitals with the highest care level. In this dissertation, I provide causal estimates of the e?ect of deregionalization on infant health outcomes and treatment intensity. In Chapter 2, I argue that previous estimates of the relationship between the level of care at a high-risk infant’s birth hospital and mortality may be biased by unobserved selection. To estimate a causal relationship, I use an instrumental variable strategy that exploits exogenous variation in distance from a mother’s residence to hospitals o?ering each level of care. My instrumental variable estimates are bounded well below ordinary least squares estimates and are not statistically di?erent from
zero. These results suggest that relocating patients to hospitals with the highest level of care prior to delivery may not lead to improved mortality outcomes, because infants currently born in lower level facilities have higher unobserved mortality risk. I also provide suggestive evidence that inter-hospital transfer after birth is one mechanism by which infants born at the lowest levels of care achieve similar outcomes to those born at higher level hospitals. In Chapter 3, I test whether additional neonatal intensive care supply leads to excess neonatal intensive care utilization. I exploit within hospital-month variation in the number of vacant NICU beds in an infant’s birth hospital the day prior to birth as a source of exogenous variation in supply. I ?nd that the e?ect of empty beds on NICU admission is positive but very small for the highest risk infants as measured by very low birth weight. However, it is larger for infants with birth weights above this threshold. These results suggest that additional supply of neonatal intensive care resources can lead to increased utilization of intensive care for infants above the very low birth weight threshold.
EMPIRICAL ESSAYS ON THE ECONOMICS OF NEONATAL INTENSIVE CARE
by Seth Michael Freedman
Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial ful?llment of the requirements for the degree of Doctor of Philosophy 2010
Advisory Committee: Professor Judith Hellerstein, Chair Professor John Chao Professor Darrell Gaskin Professor Ginger Jin Professor Melissa Kearney
c Copyright by Seth Michael Freedman 2010
Dedication
To my wife Krista for her never ending love, support, and encouragement throughout this entire process.
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Acknowledgments
I am particularly indebted to Judy Hellerstein for her advice, support, and insight throughout this dissertation and my graduate studies. I am also grateful to Melissa Kearney and Ginger Jin for their many helpful suggestions and generous advice and support. I also want to thank John Cawley, Mark Duggan, Bill Evans, Craig Garthwaite, John Ham, Mara Lederman, Soohyung Lee, Tim Moore, John Shea, and participants at various seminars, particularly at the University of Maryland, for helpful comments and suggestions. Thank you to John Chao and Darrell Gaskin for serving on my dissertation committee. I would like to gratefully acknowledge Bill Evans, Mark Duggan, Judy Hellerstein, and the University of Maryland Department of Economics for ?nancial support in purchasing data. I would also like to acknowledge ?nancial support from AHRQ Dissertation Grant 1R36HS018266-01, which funded much of the work in this dissertation. I am also grateful to Ciaran Phibbs for sharing data on levels of neonatal intensive care at California hospitals and to OSHPD for assistance with the inpatient data. The content of this work does not represent the views of AHRQ or OSHPD. All errors are my own. Finally, I would like to thank my family and friends for all of their support and encouragement over the past ?ve years.
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Table of Contents
List of Tables List of Figures 1 Introduction 2 The E?ect of Deregionalization on Health Outcomes: Evidence from Neonatal Intensive Care 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Previous Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Previous Estimates of Mortality Di?erences by Level of Care . 2.2.2 Natural Experiments in Health Research . . . . . . . . . . . . 2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Linked Birth Data . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Hospital Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Empirical Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Baseline Model . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Estimating Causal E?ects . . . . . . . . . . . . . . . . . . . . 2.4.3 The Instruments . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 OLS Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 First Stage Estimates . . . . . . . . . . . . . . . . . . . . . . . 2.5.3 2SLS Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Robustness Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.1 Additional Tests of Instrument Validity . . . . . . . . . . . . . 2.6.2 Alternative Speci?cations . . . . . . . . . . . . . . . . . . . . 2.6.2.1 Zip Code of Residence Controls . . . . . . . . . . . . 2.6.2.2 Zip Code of Residence Fixed E?ects . . . . . . . . . 2.6.2.3 Pooling No NICUs and Intermediate NICUs . . . . . 2.6.2.4 Alternative Control Variables and Clustering . . . . 2.6.2.5 Alternative Mortality Measures . . . . . . . . . . . . 2.6.3 Heterogeneity and Local Average Treatment E?ects . . . . . . 2.6.4 Sample Selection . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 The 3.1 3.2 3.3 E?ect of Neonatal Intensive Care Availability Introduction . . . . . . . . . . . . . . . . . . Previous Literature . . . . . . . . . . . . . . Data . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Data Sources . . . . . . . . . . . . . 3.3.2 Imputing NICU Admission . . . . . . 3.3.3 Analysis Sample . . . . . . . . . . . . 3.4 Empirical Framework . . . . . . . . . . . . . iv on Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi viii 1
13 13 17 17 18 20 20 23 24 25 28 33 37 37 39 40 43 43 45 45 47 49 50 51 52 54 55 81 81 87 91 91 92 94 96
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Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Summary Statistics . . . . . . . . . . . . . . . . . 3.5.2 The E?ect of Empty Beds on NICU Admission . 3.5.3 The Mitigating E?ects of Inter-Hospital Transfer 3.5.4 Hospital Level Heterogeneity . . . . . . . . . . . . 3.5.5 Individual Level Heterogeneity . . . . . . . . . . . 3.5.6 Robustness . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . .
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101 101 105 109 110 114 117 119
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List of Tables
2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 Detailed Level of Care De?nitions . . . . . . . . . . . . . . . . . . . . 65 California Obstetric Hospitals by Year and Level of Care . . . . . . . 66 Sample Means by Level of Care at Birth Hospital . . . . . . . . . . . 67 Summary Statistics of Distance Variables . . . . . . . . . . . . . . . . 68 Sample Means by Distance . . . . . . . . . . . . . . . . . . . . . . . . 69 Neonatal Mortality by Level of Care, OLS Estimates . . . . . . . . . 70 Level of Care by Distance, First Stage Estimates . . . . . . . . . . . . 71 Neonatal Mortality by Level of Care, 2SLS Estimates . . . . . . . . . 72 Level of Care by Distance for Heavier Infants . . . . . . . . . . . . . . 73
2.10 Reduced Form Estimates . . . . . . . . . . . . . . . . . . . . . . . . . 74 2.11 Alternative Speci?cations: First Stage Estimates . . . . . . . . . . . . 75 2.12 Alternative Speci?cations: OLS & 2SLS Estimates . . . . . . . . . . . 76 2.13 Alternative Control Variables and Clustering . . . . . . . . . . . . . . 77 2.14 Alternative Mortality Measures . . . . . . . . . . . . . . . . . . . . . 78 2.15 Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 2.16 Robustness to Sample Restrictions . . . . . . . . . . . . . . . . . . . 80 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 Constructing Analysis Sample . . . . . . . . . . . . . . . . . . . . . . 126 Sample Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Summary Statistics of Empty Beds . . . . . . . . . . . . . . . . . . . 129 Sample Means by Residual Empty Beds . . . . . . . . . . . . . . . . 130
E?ect of Empty Beds on NICU Admission . . . . . . . . . . . . . . . 132 Mitigating E?ects of Inter-Hospital Transfers . . . . . . . . . . . . . . 133 Heterogeneous E?ects by Hospital Characteristics – NICU Admission 134 Heterogeneous E?ects by Hospital Characteristics – NICU Admission or Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 vi
3.9
Heterogeneous E?ects by Individual Characteristics – NICU Admission136
3.10 Heterogeneous E?ects by Individual Characteristics – NICU Admission or Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 3.11 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
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List of Figures
2.1 2.2 2.3 2.4 2.5 2.6 2.7 3.1 3.2 3.3 3.4 NICU Location by Level of Care in 1991 . . . . . . . . . . . . . . . . 58 Miles Saved to Nearest Community NICU or Higher, 1991 . . . . . . 59 Miles Saved to Nearest Intermediate NICU or Higher, 1991 . . . . . . 60 Coe?cient Estimate Magnitudes . . . . . . . . . . . . . . . . . . . . . 61 Changes in Community Distance, 1991 to 2001 . . . . . . . . . . . . . 62 Changes in Intermediate Distance, 1991 to 2001 . . . . . . . . . . . . 63 Demographic and Health Trends by Changes in Distance . . . . . . . 64 Hospital Level NICU Admission Density . . . . . . . . . . . . . . . . 122 Very Low Birth Weight, Mortality, and NICU Admission Over Time . 123 E?ect of Empty Beds on NICU Admission by Birth Weight . . . . . . 124 E?ect of Empty Beds on NICU Admission by Gestation . . . . . . . . 125
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Chapter 1 Introduction
Rising health care costs are a fundamental problem facing the United States economy. Health care currently accounts for about 16% of GDP and is projected to grow to about 19% percent by 2019.1 This rapid cost growth was one of the primary motivations behind the health reform passed in 2010. One of the major factors behind these rising costs are new medical technologies and service o?erings. On average, most of these new technologies have been worthwhile due to the overwhelming improvements in health that they are able to provide (Cutler and McClellan, 2001; Hall and Jones, 2007; Luce et al., 2006; Murphy and Topel, 2003). However, there is often concern that these services are not allocated optimally. The Dartmouth Atlas Project has documented large geographic variation in health expenditures which does not appear to be correlated with health outcomes (Baicker et al., 2006; Baicker and Chandra, 2004b; Fisher et al., 2003a,b; Fuchs, 2004), providing some evidence of “?at-of-the-curve” medicine, in which treatment is provided to the point where the marginal return is below the marginal cost (or even zero). This dissertation examines the organization of one particular medical service that displays these characteristics: Neonatal Intensive Care Units (NICUs). A NICU is a unit of the hospital that is separate from the traditional newborn nursery and is specially equipped to care for sick, preterm, and underweight infants. The original NICUs of the late 1960s and early 1970s provided incubation and sometimes mechanical ventilation. Since this time, technological innovations have greatly changed medical care for sick infants, and the most sophisticated NICUs are now able to
1 According to the Centers for Medicare & Medicaid Services:http://www.cms.gov/ NationalHealthExpendData/downloads/proj2009.pdf, last accessed on May 16, 2010.
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provide extensive monitoring, proper nutrition, arti?cial surfactant, extracorporeal membrane oxygenation (ECMO), and various diagnostic tests and surgical procedures.2 These innovations have clearly led to improved outcomes for high-risk infants. For example, the 28-day mortality rate for infants weighing 1,000 to 1,499 grams (2.2 and 3.3 pounds) dropped from 52.2% to 6.7% between 1960 and 1990 (Cutler and Meara, 2000).3 Recent decades have seen a trend towards “deregionalization” of neonatal intensive care in which many smaller hospitals have adopted NICUs. Despite the large average gains in infant health that have been attributed to NICUs, this trend has worried organizations such as the March of Dimes and the American Academy of Pediatrics because previous studies have found higher mortality rates for high-risk infants born in hospitals with these smaller, less sophisticated NICUs compared to those born in hospitals with “Regional” NICUs (e.g., Cifuentes et al., 2002; Phibbs et al., 2007, 1996). However, the many potential e?ects of this deregionalization are not well understood. First, in terms of the ?rst-order question of the e?ects on the health of the high-risk infants NICUs are intended to treat, the previous estimates may in fact be biased by unobserved patient selection into hospitals. Depending on the mechanisms behind and the direction of this selection, the e?ect of the level of neonatal intensive care at an infant’s birth hospital on mortality could be biased in either direction; deregionalization could be more or less detrimental to infant mortality than previously thought. Second, there may be other e?ects of deregionalization beyond the quality of care delivered to high-risk infants. These e?ects could include changes in the quality of care of lower risk infants, di?erences in
Mechanical ventilation assists infants whose lungs have not fully developed to breath. Arti?cial surfactant treats respiratory distress syndrome by helping the lungs to develop. ECMO machines pump blood out of the infant, oxygenate it, and pump it back into the infant if the infant’s heart and lungs are too weak to oxygenate the blood on its own. 3 Accounting for the costs of these innovations and the value of both lives saved and quality of life for surviving infants, Cutler and Meara (2000) calculate a 510% rate of return to spending on infant health care between 1960 and 1990.
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the intensity and cost of care, composition changes in who receives care, and utility gains for mothers who can choose more convenient hospitals o?ering NICUs. Given all of these potential e?ects, understanding the full welfare consequences of deregionalization would be a very di?cult undertaking. In this dissertation, I tackle two pieces of this puzzle. In Chapter 2, I revisit the question of how deregionalization has impacted mortality for very low birth weight infants. By exploiting exogenous variation in the distance from where mothers live to the nearest hospital o?ering each level of neonatal intensive care, I account for potential unobserved selection and estimate the causal e?ect of the level of care at the birth hospital on very low birth weight infant mortality. Chapter 3 considers the e?ect of the supply of neonatal intensive care on the level of utilization of these resources. I provide preliminary estimates of the e?ect of the number of empty NICU beds just prior to birth on the probability an infant is admitted to the NICU. I then examine how this e?ect varies across the birth weight distribution to di?erentiate how available supply a?ects utilization di?erently for high-risk and low-risk newborns. The remainder of this chapter provides further background information about neonatal intensive care and summarizes the results of Chapters 2 and 3. As neonatal intensive care developed in the 1970s, few doctors and nurses were trained in neonatology. As a result, specialists were located in regional care centers, typically associated with large teaching hospitals. In 1976 a March of Dimes report recommended that hospitals o?ering delivery services be classi?ed into three categories with the lowest providing no intensive care, and the highest providing the most complex care and acting as regional referral centers for high-risk mothers and infants (Committee on Perinatal Health, 1976).4
In general, Level I nurseries describe hospitals that provide basic birthing service and care for healthy infants. They have the facilities and sta? required for neonatal resuscitation, but must stabilize and transfer ill newborns to other facilities for further treatment. Level II nurseries treat moderately ill infants, and Level III units treat infants who are extremely premature, critically ill, or in need of surgery. In many cases, Level II and Level III units are further subdivided based on their abilities to provide mechanical ventilation, surgery, or ECMO. Additionally, units are often
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Also in the late 1970s, the Robert Wood Johnson Foundation began the Regional Perinatal Care Program. This program was intended to explore the e?ects and feasibility of encouraging regional perinatal care encompassing pre- and postbirth care of mothers and infants, including neonatal intensive care. The program consisted of grants to eight sites across the country. The grants provided funds to improve record keeping, create a referral and transportation system, and conduct education and outreach. Anecdotally, these networks functioned well. Unfortunately, this program was di?cult to evaluate because many forces were leading to nationwide reductions in infant mortality rates and regionalization was occurring outside the study sites (Holloway, 2000). Over time the technologies and trained specialists necessary to operate NICUs became more prevalent, and NICU adoption became feasible for a wider array of hospitals (McCormick and Richardson, 1995). Despite the Regional Perinatal Care Program and the March of Dimes’ recommendations of a regionalized system, exactly the opposite began to occur over the 1980s and 1990s: there was a drastic increase in the number of NICUs, and many of the new entrants were smaller units in community hospitals (e.g., McCormick and Richardson, 1995; Schwartz, 1996; Schwartz et al., 2000). Moreover, while births increased by 17.6% between 1980 and 1995 in Metropolitan Statistical Areas (MSA), the number of hospitals with NICU beds doubled, the number of NICU beds more than doubled, and the number of neonatologists more than tripled (Howell et al., 2002).5 Additionally, American Hospital Association data reveal that 89% of the new NICUs that opened between 1980 and 1996 were lower level NICUs, as opposed to only 46% of the units established before 1980 (Baker and Phibbs, 2002).
labeled as Intermediate, Community, or Regional units. In Section 2.3 I describe how I classify level of care for my study. 5 Improving quality of care over time did lead to more infants surviving and spending longer periods of time in the NICU; however, Howell et al. (2002) calculate that by 1995 the number of available NICU bed-days exceeded medically necessary bed-days by a factor of 2.5.
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Haberland et al. (2006) document that new lower level NICUs have in fact shifted deliveries of high-risk infants from Regional hospitals to the lower level hospitals in California. In a di?erence-in-di?erences framework, they show that becoming closer to a mid-level NICU, as a result of a new unit opening near a mother’s zip code of residence, increases the probability that a very low birth weight infant is born in a hospital with a mid-level NICU by 17 percentage points and decreases the probability of being born in a hospital with a Regional NICU by 15 percentage points.6 Based on evidence that mortality rates are higher for infants born in hospitals with lower level NICUs, discussed in detail in Chapter 2, and the course of deregionlization, the March of Dimes rea?rmed its recommendations in 1993 (Committee on Perinatal Health, 1993). The American Academy of Pediatrics provided similar recommendations for more regionalized care in 2004 including recommendations for consistent de?nitions of care levels and the need for high-risk infants to be born in higher level facilities. (Committee on Fetus and Newborn, 2004). It has been hypothesized that so many community hospitals adopted NICUs in order to compete for pro?table obstetric patients (McCormick and Richardson, 1995). Neonatal intensive care is typically generously reimbursed, and even managed care organizations have been hesitant to limit infant care, so NICUs can be pro?t centers for hospitals (Horwitz, 2005, see online appendix). Beyond NICUs themselves, hospitals are particularly interested in attracting obstetric patients, since mothers are typically young, healthy, and likely to return to the hospital for the later care of their families if they have a positive birth experience (Friedman et al., 2002). Almost all births in the United States are covered by some form of public or private insurance (Russell et al., 2007), limiting hospitals’ ability to compete through prices. Therefore, hospitals may compete by trying to attract this desirable
I con?rm these results in Chapter 2 by showing that mothers living closer to hospitals with lower level NICUs are more likely to choose such hospitals and less likely to choose hospitals with higher level NICUs.
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patient pool through signals of quality, such as the availability of a NICU. This type of competition is not unique to neonatal intensive care. Theoretically, the e?ects of non-price competition on hospital behavior and patient welfare are ambiguous but can potentially lead to over-provision of services known as a “medical arms race” (Gaynor, 2006). Dranove et al. (1992) ?nd that decreases in market concentration lead to increases in the number of hospitals o?ering various high tech services in that market. Others have shown that hospitals expand their capacity to perform certain procedures in order to deter other hospitals from adopting that procedure (Dafny, 2005a), and hospitals adopt particular technologies in order to steal business from their competitors (Schmidt-Dengler, 2006). In contrast, comparing the e?ect of competition on costs and mortality for heart attack patients, Kessler and McClellan (2000) ?nd that competition led to improvements in patient welfare during the 1990s. My work sheds light on how the organization of neonatal intensive care markets a?ects the quantity and quality of care provided. In Chapter 2 I revisit the question of how mortality outcomes for high-risk infants, as measured by being very low birth weight, di?er by the level of neonatal intensive care available at the hospital of birth. As brie?y discussed above and in more detail in Chapter 2, previous studies have found that very low birth weight infants born in hospitals with lower level NICUs experience higher mortality rates than those born in hospitals with the most sophisticated, Regional NICUs. Most of these previous studies utilize high-quality linked hospital inpatient, birth certi?cate, and death certi?cate data allowing them to control for many important clinical and demographic characteristics associated with infant mortality. However, there may be important unobserved di?erences between mothers who choose hospitals with varying levels of neonatal intensive care. On the one hand, it may be the case that those very low birth weight infants born in higher level hospitals are unobservably less healthy than those born in lower
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level hospitals. For example, mothers who deliver in higher level hospitals may be referred there by their physicians because of predetermined risk factors that are not perfectly measured in the data. On the other hand, it may be the case that those very low birth weight infants born in lower level hospitals are unobservably less healthy. One could imagine that mothers of very low birth weight infants who choose lower level hospitals are less well informed, less likely to plan ahead, or less risk averse than those who choose to deliver in the higher level hospitals, and these characteristics may be correlated with worse infant health outcomes. By examining the observable characteristics of my sample, I show evidence consistent with the predictions of both of these selection mechanisms. Depending on which mechanism dominates, previous estimates of the mortality gradient could be biased upwards or downwards, suggesting that deregionalization may be more or less detrimental to very low birth weight mortality than previously thought. I assess this concern by using an instrumental variable strategy to isolate exogenous variation in the level of neonatal intensive care available at the hospital in which the mother of a very low birth weight infant chooses to deliver her newborn. In the spirit of McClellan et al. (1994), I use the distances from the center of the mother’s zip code of residence to the nearest hospital o?ering each level of neonatal intensive care as instruments for the level of care at the hospital in which she delivers her newborn. The validity of these instruments is motivated three factors: the hypothesis that NICUs have been adopted in order to compete for patients instead of to address local health needs; previous evidence showing that NICU location is not correlated with infant health measures; and evidence in my sample that distance is not correlated with observable demographic and health characteristics. I also show that distance is an important predictor of the level of care chosen by mothers of very low birth weight infants. Additionally, consistent with hospitals adopting NICUs to compete for patients across the risk distribution, I show that mothers of infants
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with higher birth weights are more likely to choose a hospital with a NICU when they live closer to such a hospital as well My instrumental variables estimates indicate that very low birth weight infants born in hospitals with lower levels of neonatal intensive care do not have statistically signi?cantly di?erent mortality rates from those born in hospitals with the highest level of care. Furthermore, these instrumental variable estimates are bounded away from my ordinary least squares estimates, suggesting that even if the true e?ects are not zero, these more traditional ordinary least squares estimates exaggerate the mortality di?erences. The interesting implication of this result is that very low birth weight infants born in hospitals with lower level NICUs have higher unobserved mortality risk than those born in hospitals with higher level NICUs. This ?nding suggests that relocating deliveries to higher level hospitals prior to birth would not improve mortality outcomes because it would be relocating the deliveries of infants from the higher risk portion of the health distribution. However, these results do not imply that the higher level NICUs are of no value. In fact, very low birth weight infants born in hospitals with lower level NICUs are very likely to be transferred to higher level hospitals after birth, and I show that the probability of being transferred is not a?ected by my measures of distance. This ?nding suggests that, while the location of NICUs impacts where very low birth weight infants are delivered, it does not impact where they ultimately receive care. Post-birth inter-hospital transfers appear to be an e?ective tool to equalize mortality outcomes for infants born in hospitals with varying levels of neonatal intensive care. My ?ndings suggest that limiting the trend of deregionalization is not necessary to minimize very low birth weight infant mortality. However, networks between hospitals to facilitate post-birth transfers are instrumental in ensuring that infants eventually receive appropriate care. If hospitals coordinate su?ciently post-birth, market competition that leads to NICU adoption is not detrimental to mortality.
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That being said, it is important to recognize that mortality is not the only contributor to social welfare. Even if competition between hospitals in this market does not lead to lower quality of care, it may or may not lead to less e?cient allocation of neonatal intensive care resources. Chapter 3 of this dissertation considers one way in which neonatal intensive care resources may not be allocated e?ciently. An important issue in the provision of health care is whether the mere presence of the supply of medical services leads to excessive utilization of these resources, and I examine this question in the context of neonatal intensive care. Such a relationship could occur through two main mechanisms related to two important information asymmetries prevalent in health care markets. First, the physician often has more information about the patient’s health than the patient himself. Given this information gap, physicians may take advantage of their agency over patients to increase income by prescribing additional treatment beyond what is necessary. Because the physician is able to in?uence the patient’s demand for medical care, this behavior is called “supplier-induced demand” (Evans, 1974; Fuchs, 1978; McGuire, 2000; Pauly, 1981). The second mechanism that may cause excessive utilization when more supply is available is moral hazard in insurance, which acts through the patient’s information advantage over the insurer. Because insurance lowers the price of consuming health care, and the insurer cannot fully know the patient’s true health status, insurance can lead to the patient consuming more than the optimal amount of health care (Arrow, 1963; Cutler and Zeckhauser, 2000; Pauly, 1968). Of course, moral hazard cannot increase the amount of health care utilization if supply is not available; thus, additional supply can lead to excessive utilization of services by opening the door for latent moral hazard to be realized. Cross sectional comparisons between available supply and utilization are not su?cient to identify if this relationship exists, because there are many factors that
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may be correlated with the availability of health resources that could lead to additional utilization. Methodologically, the innovation of Chapter 3 of this dissertation is to ?nd an exogenous source of variation in available supply. I conduct a ?rst examination of the e?ect of the number of empty NICU beds available in the birth hospital on the day prior to birth on the probability that an infant is admitted to the NICU. The key to the identi?cation strategy is the use of hospital-speci?c month ?xed e?ects. With these ?xed e?ects I identify the relationship between NICU supply and utilization from within hospital-month variation in the number of empty NICU beds. The ?xed e?ects allow me to ?exibly control for characteristics of patients who choose a particular hospital, long run trends and short run seasonality of infant health, and any hospital-speci?c trends or seasonality. I argue in the chapter that conditional on observable characteristics and these ?xed e?ects, a particular infant’s unobserved health characteristics are unlikely to be correlated with the unobserved health characteristics of infants born just prior to the infant, which is what determines the number of available empty NICU beds. While this identi?cation strategy accounts for unobserved correlates between NICU supply and utilization, NICU admission is measured with error in the data that I utilize. Therefore, results in Chapter 3 are best viewed as preliminary, and I intend to verify these results using other data sources in future research. I ?nd that on average an additional empty NICU beds increases the probability of being admitted to the NICU by 1.11%. Not surprisingly, the e?ect of empty beds on NICU admission varies across the birth weight distribution. When I estimate regressions separately for subsamples strati?ed by birth weight, I ?nd that the e?ect is very small for very low birth weight infants.7 However, the e?ect size jumps discretely for infants above the very low birth weight threshhold and is largest for
The e?ect of empty beds on NICU admission is especially small for this group when I account for the fact that very low birth weight infants are likely to be transferred if NICU beds are not available for them at the birth hospital.
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infants close to the top of the low birth weight range and infants with high birth weights. These two groups are likely to be on the margin of needing neonatal intensive care. These results imply that the availability of empty NICU beds increases the utilziation of neonatal intensive care resources, particularly in the birth weight ranges where hospitals would have the most discretion over admission decisions. This analysis is quite relevant in the context of deregionalization. With the di?usion of neonatal intensive care resources, the potential for excess supply grows. This chapter estimates the e?ects of short term variation in empty NICU beds, but this variation is likely to be related to the long term trends in availability associated with deregionalization. Interestingly, I also ?nd that the e?ect of empty beds on NICU admission is the largest in hospitals with lower level NICUs as compared to hospitals with the most sophisticated NICUs. As these lower level NICUs are those units most associated with deregionalization, this ?nding suggests that deregionalization may have the scope to lead to additional intensive care utilization for lower risk infants.8 This chapter also provides an important contribution to the literature on neonatal intensive care markets by considering infants throughout the birth weight distribution. Much of the previous literature focuses on the e?ect of deregionalization on mortality outcomes for high-risk infants. It is also important to consider the implications of neonatal intensive care markets for healthier infants, and my ?ndings suggest that excess supply contributes to lower risk infants receiving additional treatment. Because care in the NICU is more expensive than care in the traditional nursery, additional supply has likely increased the cost of care for low-risk infants.9
It is also not surprising that the e?ects are smaller in higher level NICU hospitals since many high-risk infants are transferred from hospitals with lower level NICUs to these higher level hospitals. Therefore, there is likely to be less discretion and less incentive for responding to excess capacity in these higher level hospitals. 9 There may be other costs associated with excessive NICU utilization including psychic costs to the parents of seeing their infant in intensive care and the potential for hospital borne infections that are prevalent in NICUs.
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Overall, this dissertation ?nds that deregionalization has likely not been as detrimental to very low birth weight infant mortality as previously thought, but additional NICU supply contributes to increased utilization of care for lower risk infants. These two ?ndings represent two important contributions to understanding the welfare e?ects of deregionalization and open the door for further research about other aspects of the welfare calculation. Some important avenues of future research include the e?ect on broader health measures than the blunt consideration of mortality, the utility implications for mothers who are able to choose more convenient hospitals with some level of neonatal intensive care, a more speci?c understanding of costs including the ?xed costs of adopting a NICU and the costs of maintaining and operating a NICU, and the determinants of NICU adoption from the hospital point of view.
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Chapter 2 The E?ect of Deregionalization on Health Outcomes: Evidence from Neonatal Intensive Care 2.1 Introduction
Technological innovations over the past half century have greatly changed medical care for sick infants. Over this time Neonatal Intensive Care Units (NICU) have been developed to administer treatments such as mechanical ventilation, arti?cial surfactant, and extracorporeal membrane oxygenation (ECMO)1 to sick, preterm, and underweight infants, and they have clearly lead to improved outcomes for these groups. For example, the 28-day mortality rate for infants weighing 1,000 to 1,499 grams (2.2 and 3.3 pounds) dropped from 52.2% to 6.7% between 1960 and 1990 (Cutler and Meara, 2000).2 Despite these long run gains, there is concern that NICUs have not di?used optimally. The 1980s and 1990s saw a large increase in the number of NICUs in smaller, community hospitals that provide less sophisticated care compared to the original NICUs in large, regional hospitals (e.g., McCormick and Richardson, 1995; Schwartz, 1996; Schwartz et al., 2000). This trend of “deregionalization” has
Mechanical ventilation assists infants whose lungs have not fully developed to breath. Arti?cial surfactant treats respiratory distress syndrome by helping the lungs to develop. ECMO machines pump blood out of the infant, oxygenate it, and pump it back into the infant if the infant’s heart and lungs are too weak to oxygenate the blood on its own. 2 I do not focus on costs in this chapter, but anecdotally, opening a new NICU can cost between $125,000 and $200,000 per bed (Baker and Phibbs, 2002). Hospital costs for very low birth weight (VLBW) infants, those weighing less than 1,500 grams or 3.3 pounds, averaged $136,000 in California in 2000 (Schmitt et al., 2006). Nationwide, it is estimated that medical care services for high-risk infants cost $16.9 billion in 2005 (http://www.marchofdimes.com/peristats/ slidesets/slideset_6_99.ppt, last accessed on October 6, 2009). In the long run Cutler and Meara (2000) calculate a 510% rate of return to spending on infant health care between 1960 and 1990, accounting for the value of both lives saved and quality of life for surviving infants.
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worried policy makers because previous studies have found higher mortality rates for infants born in hospitals with these Community NICUs compared to those born in hospitals with Regional NICUs, conditional on observable demographic and health characteristics (e.g., Cifuentes et al., 2002; Phibbs et al., 2007, 1996). Based on this evidence, organizations such as the March of Dimes and the American Academy of Pediatrics have advocated for a stronger regional system where high-risk mothers are referred to hospitals with Regional NICUs prior to delivery in order to minimize mortality. This chapter seeks to estimate the causal e?ect on mortality of the level of care available at the hospital in which a very low birth weight (VLBW) infant – under 1,500 grams or 3.3 pounds – is born. As an empirical matter, it is not clear that the worse outcomes experienced by infants born in hospitals with lower level NICUs are attributable to the hospital type per se. Even conditional on observable characteristics, infants born in di?erent hospitals may have di?erent underlying risk factors. Depending on the mechanisms behind any unobserved selection, conventional estimates of mortality di?erences by level of care could be biased in either direction. If infants born in hospitals with lower level NICUs have lower underlying mortality risk than those born in Regional NICUs, previous estimates will have understated the mortality penalty associated with being born in lower level hospitals. Alternatively, if infants born in hospitals with lower level NICUs have higher underlying risk factors, previous estimates will have overstated the mortality di?erences. Any bias implies the system of deregionalization might actually be more harmful or less harmful than currently believed. While deregionalization may a?ect many factors other than mortality, understanding the causal e?ect of level of care on mortality of high-risk infants is of ?rst-order importance to making policy decisions about the organization of neonatal care. I propose an instrumental variables strategy to overcome selection issues asso-
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ciated with a mother’s choice of hospital. I exploit the distance a mother must travel to the nearest hospital of each level of care as a source of quasi-experimental variation in the type of hospital chosen. Distance is an important determinant of hospital choice for many medical treatments such as cardiac and cancer surgery (e.g., Cutler, 2007; Kessler and McClellan, 2000; McClellan and Newhouse, 1997; Tay, 2003) and for expectant mothers as well (Phibbs et al., 1993). I also provide evidence that distance is likely to be exogenous to unobserved health outcomes in my data set, which is not surprising given evidence that NICU location is not correlated with the geographic variation in underlying infant health conditions (Goodman et al., 2001). Using detailed data on all California VLBW births between 1991 and 2001, I estimate how the birth hospital’s level of care causally e?ects VLBW mortality. My ordinary least squares (OLS) analysis yields estimates of 7.6%, 13.4%, and 31.8% higher risk-adjusted mortality rates for infants born at hospitals o?ering three lower levels of care relative to those born in hospitals o?ering the highest level of care. These results are consistent with the previous literature, but my instrumental variable estimates provide evidence that these OLS estimates are biased upward. The instrumental variables estimates are bounded well below the OLS estimates and are not statistically di?erent from zero. My results are robust to including zip code level controls, such as population density and racial characteristics, or zip code ?xed e?ects. Comparing the OLS and the instrumental variable estimates reveals that infants born in hospitals with lower levels of care are negatively selected. This selection could occur if, for example, more uninformed mothers choose lower levels of care and have unobservably less healthy infants. This ?nding implies that relocating births to Regional NICU hospitals prior to delivery would not lead to lower mortality rates because the relocated infants would have higher unobserved mortality risk. In terms of mortality, deregionalization does not appear to have caused worse outcomes for
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high-risk infants. It is also possible that the instrumental variable estimates represent a local average treatment e?ect. I ?nd that my estimates are not heterogeneous across demographic sub-samples, but there still may be heterogeneous e?ects along unobservable dimensions. If this is the case, instrumental variables would estimate the e?ect of level of care on mortality for an unobserved subgroup of infants whose mothers’ choices of level of care are a?ected by the distance instruments. However, because variation in the instruments is directly related to deregionalization, any local e?ect is precisely the policy relevant e?ect. My estimates would imply that infants of mothers who choose to give birth in hospitals with lower level NICUs because these NICUs are available – the marginal group of infants whose delivery hospitals are impacted by deregionalization – do not experience higher mortality rates. While my results indicate that mortality does not di?er by level of care at the hospital in which an infant is born, they do not imply that Regional NICUs are of no value. In fact, I show evidence that infants born in hospitals with the lowest levels of care are likely to be transferred to Regional NICU hospitals after birth, and the geographic distribution of hospitals does not impact the probability of transfer. It is di?cult to compare outcomes to the counterfactual world that experiences deregionalization but does not allow for post-birth transfer, but my ?ndings suggest that mortality is not causally a?ected by the level of care at the birth hospital because high-risk infants eventually receive care in higher level hospitals if necessary. The remainder of this chapter is structured as follows. Section 2.2 reviews the previous literature. Section 2.3 describes the data and summary statistics. Section 2.4 provides the empirical framework. Section 2.5 presents the results, followed by robustness checks in Section 2.6. Section 2.7 concludes.
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2.2 Previous Literature 2.2.1 Previous Estimates of Mortality Di?erences by Level of Care
Multiple authors have estimated how risk-adjusted mortality varies by level of neonatal intensive care at the hospital of birth, and many of these studies use the same California inpatient data set as this chapter. The typical methodology includes a logistic regression of mortality on level of care indicators, controlling for demographic characteristics and health status. The speci?c results depend on the precise categorization of hospitals, but in general these studies ?nd higher mortality as level of care decreases for groups of high-risk infants that NICUs are intended to care for. Phibbs et al. (1996) ?nd that VLBW infants born in hospitals with the largest Regional NICUs have statistically lower mortality rates than the lower categories, but the lower categories, including hospitals with no NICU, do not di?er from each other. Cifuentes et al. (2002) use a population of infants below 2,000 grams (4.4 pounds) and ?nd that all levels except for the largest Community NICUs have higher mortality rates than Regional NICUs. As they restrict their sample to smaller and smaller birth weight groups, the gradient becomes steeper. Similarly, Gould et al. (2002) ?nd higher mortality rates at all levels relative to Regional NICUs except for those Community NICUs that are licensed under the California Children’s Services Program. Finally, in the most recent study on the relationship between level of care and mortality, Phibbs et al. (2007) distinguish mortality rates by very narrow level and volume interactions. While not necessarily statistically signi?cant within each level, they ?nd decreasing mortality across levels and by volume within levels. Based on their estimates, they conclude that if 90% of VLBW deliveries in California urban areas had been relocated to hospitals with the largest Regional NICUs, 21% of
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VLBW deaths in 2000 could have been avoided.3 However, while high-quality hospital inpatient data sets allow the ability to control for many important covariates, mothers may select into di?erent delivery hospitals based on characteristics not observed in the data. Such unobserved selection would lead to biased estimates of the mortality di?erences by level of care, and the direction of the bias would depend on the direction of the selection. One typical form of selection that biases estimates of the e?ect of health treatments on outcomes is selective referral. If mothers and physicians have additional information about the mother’s health status, and higher risk mothers are referred to hospitals with Regional NICUs, mothers would be positively selected into lower levels of care. Therefore, the mortality di?erences relative to Regional NICUs would be underestimated. On the other hand, if mothers negatively select into lower levels of care over hospitals with Regional NICUs, the mortality di?erences would be overestimated. This case might arise if more uninformed mothers are more likely to choose hospitals with lower levels of care over hospitals with Regional NICUs and infants of these uninformed mothers have higher unobserved mortality risk.
2.2.2 Natural Experiments in Health Research
This chapter is also related to the health economics literature that uses natural experiments to determine the marginal e?ects of medical treatments and technology. As with neonatal care, time series evidence suggests that most new technologies have led to vast improvements in health outcomes over time and the monetized bene?ts have outweighed the costs (Cutler and McClellan, 2001; Hall and Jones, 2007; Luce et al., 2006; Murphy and Topel, 2003). However, comparisons of health care expenditures and outcomes across geographic regions have found that higher spending
They calculate this number only considering the sample of infants for whom they deem relocation geographically feasible and note that such relocation would require new large NICUs and the closure of some smaller NICUs.
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areas do not achieve better outcomes (Baicker et al., 2006; Baicker and Chandra, 2004b; Fisher et al., 2003a,b; Fuchs, 2004). Given this contradiction, researchers have taken advantage of quasi-experimental variation to better compare individuals who di?er only in their treatment and not in other unobserved dimensions to estimate causal e?ects of treatment. Here I highlight two portions of this literature that are most related to this chapter: research on the e?ects of infant health care and research using a similar identi?cation strategy to that used in this chapter. Studies that use natural experiments to estimate the returns to incremental units of infant health care ?nd mixed results. Almond and Doyle (2008) exploit a California policy extending minimum length of hospital stays following delivery and the discontinuity in stay length for infants born just before and just after midnight. They ?nd no e?ect of increased stay length on health outcomes for uncomplicated infants. Evans et al. (2008) exploit the same policy and ?nd similar results for uncomplicated infants, but they do ?nd that longer length of stay leads to reduced hospital readmission rates for more complicated cases. Using a regression discontinuity design, Almond et al. (2008) ?nd that infants just below the VLBW cuto? receive more treatment and experience lower mortality rates than those just above the VLBW cuto?. Taken together, these studies imply that, at least for high-risk infants, increased treatment can be bene?cial. My research adds to this literature by estimating whether the facilities available at the hospital in which a high-risk infant is born a?ect mortality. McClellan et al. (1994) and Cutler (2007) use a similar identi?cation strategy to this chapter’s strategy in order to estimate the e?ect of catheterization and revascularization, respectively, following a heart attack on mortality. As with infant care, there are two selection concerns in this context, although the mechanisms are slightly di?erent. First, the healthiest patients may have less need for these intensive surgeries. Second, the sickest patients may forego surgery due to a higher risk of dy-
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ing during the procedure. To account for selection, both papers use distance to the nearest hospital providing surgery as an instrument for whether a patient receives surgery.4 Both studies ?nd that instrumental variable estimates of the bene?t of intensive surgery are substantially lower than the ordinary least squares estimates, although Cutler (2007) ?nds that the monetized bene?ts still outweigh the costs.
2.3 Data 2.3.1 Linked Birth Data
My empirical analysis requires detailed data describing infants’ hospitalizations and outcomes. The primary data set I utilize is the Linked Patient Discharge Data/Birth Cohort File (LPDD/BCF) created by the California O?ce of Statewide Health Planning and Development (OSHPD). This data set includes records of all births in non-Federal hospitals in the state of California. I have obtained data ?les for the years 1991 to 2001, comprising approximately six million births. In addition to including observations of all births from a large state, the main advantage of this data set is that it links additional data to an infant’s hospital discharge record. First, it links an infant’s delivery hospital discharge record to the mother’s discharge record and all subsequent records resulting from transfers or readmissions to California hospitals within the ?rst year of life. For each hospitalization, the data set includes detailed diagnosis and treatment variables, summary variables such as length of stay and hospital charges, and patient information including zip code of residence. Second, the hospital discharge data are linked to vital statistics data on births and infant deaths within the ?rst year of life, which include gestation, birth weight, number of prenatal care visits, month prenatal care began, and demographOther authors have also used distance as a source of exogenous variation to predict patient ?ows in order to estimate the e?ect of volume (Gowrisankaran et al., 2006) and competition (Gowrisankaran and Town, 2003; Kessler and McClellan, 2000; Tay, 2003) on health outcomes.
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ics, such as the mother and father’s race, ethnicity, and education. Additionally, these records provide information on infant mortality within the ?rst year of life, even if death occurred outside of the hospital. The main analysis sample that I consider includes VLBW infants, de?ned as weighing between 500 and 1,500 grams (1.1 and 3.3 pounds) at birth. Of the initial 6.1 million birth observations with non-missing birth weight, 72,275 fall in this birth weight range.5 To obtain my analysis sample, I ?rst exclude observations with a missing zip code of residence, a zip code of residence outside the state of California, a missing hospital identi?cation number, or that are delivered in a hospital without a delivery unit. The remaining sample contains 65,567 birth observations. I then make three restrictions to maintain a sample that is as broad as possible but that excludes observations with an unusual hospital choice set. I ?rst drop 2,704 observations where the mother’s county of residence is “non-metro” according to the O?ce of Management and Budget.6 This restriction excludes a small group of infants from the most rural areas for whom access to neonatal care is quite di?erent from other residents of the state. Additionally, the previous literature has focused on deregionalization and the e?ect of level of care on outcomes in metropolitan areas (Howell et al., 2002; Phibbs et al., 2007) where policy recommendations about delivery relocation would be most feasible. Second, I drop 7,627 infants delivered in Kaiser owned hospitals. Mothers who choose a Kaiser hospital for delivery must be covered by Kaiser insurance, and mothers covered by Kaiser insurance must deliver in a Kaiser owned hospital; therefore, choice of hospital is restricted for this group.7 Third, I exclude 4,113 observations diagnosed with a congenital anomaly.
The full data set includes 6,221,001 births of which 1.54% of the observations have a missing birth weight. 6 Based on 1993 USDA Rural-Urban Continuum Codes that are calculated from the 1990 Census. Source:http://www.ers.usda.gov/briefing/rurality/ruralurbcon/ priordescription.htm. 7 In my analysis sample, 88% of mothers with Kaiser coverage deliver in a Kaiser hospital, and 97% of mothers who deliver in a Kaiser hospital have Kaiser coverage. In results not shown here, regressions similar to the ?rst stage regressions discussed below for the sample of Kaiser insured
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This restriction is consistent with the previous literature (Phibbs et al., 2007), and it also excludes observations most likely to be selectively referred to higher levels of care due to a diagnosis during the prenatal period. I also exclude 8,115 observations of fetal deaths, which are infants who die prior to delivery and, therefore, never receive neonatal care (Phibbs et al., 2007). Finally, because I cluster standard errors at the zip code level and estimate models with zip code ?xed e?ects, I exclude 96 observations for which the mother’s zip code of residence has no other observations remaining in the data. In Section 2.6, I show that my results are robust to each of these sample restrictions. I choose my sample of high-risk infants using birth weight as the health proxy in order to be comparable to previous literature, and because it is the best measure of an infant’s health stock at birth (Almond et al., 2005; Cutler and Meara, 2000). Relative to gestation, another summary of health status at birth, Almond et al. (2008) note that birth weight is more accurately recorded, less likely to be missing in the data, and less likely to be manipulated by delaying birth because it is not possible to know birth weight ex ante.8 VLBW infants are the population most of interest because they contribute disproportionately to costs and mortality. Schmitt et al. (2006) document that VLBW infants make up 0.9% of births but account for 36% of newborn hospital costs, and tabulations of hospital charges for my sample lead to similar ?gures. Mean charges for my VLBW sample are $209,000, compared to $21,000 for low birth weight infants (1500 to 2500 grams or 3.3 to 5.5 pounds) and $2,630 for normal birth weight infants (above 2500 grams or 5.5 pounds). Likewise, length of stay after birth averages 50.6 days for VLBW infants, 9.2 days for low birth weight infants,
mothers show that distance has very little power in predicting the level of care chosen for delivery. This is in contrast to the strong predictive power of distance for the analysis sample discussed in Section 2.5. 8 Additionally, Almond et al. (2008) ?nd empirical evidence that the recording of birth weight is not manipulated by physicians.
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3.0 days for normal birth weight infants. Additionally, VLBW infants make up the vast majority of infant mortality. The main outcome I focus on in this chapter is neonatal mortality, de?ned as mortality within twenty-eight days of birth or within one year if an infant is continuously hospitalized since birth. VLBW infants have a neonatal mortality rate of 15.7%, compared to 0.7% for low birth weight infants and 0.1% for normal birth weight infants. Therefore, changes in how infant care is delivered has the most scope to a?ect outcomes for VLBW infants.
2.3.2 Hospital Data
My empirical analysis also requires data describing the level of neonatal care o?ered by each hospital that delivers infants. I obtain data from the authors of Phibbs et al. (2007) that di?erentiate hospitals into six levels of neonatal care based on the treatments each hospital provides in a given year. First, they use OSHPD hospital ?nancial data to determine which hospitals have neonatal intensive care beds. Second, they use ICD-9 CM procedure codes in the hospital inpatient data to identify which hospitals perform particular procedures. As a guide, they de?ne levels of care consistent with the six levels outlined in the American Academy of Pediatrics 2004 report.9 Table 2.1 lists the six levels and their corresponding procedures. Third, the authors con?rmed level of care designations through conversations with hospital personnel. I collapse these detailed categories into four levels of care, which I refer to as No NICU, Intermediate NICU, Community NICU, and Regional NICU hospitals. No NICU hospitals provide birthing services and well-baby care, but no neonatal intensive care (Level I in Table 2.1). Intermediate NICUs care for mildly ill infants but do not provide mechanical ventilation (Level II). Community NICUs include
The authors utilize the draft version of the American Academy of Pediatrics report because the ?nal version does not include a category that provides unrestricted ventilation but no surgery, a level of service many CA units provide.
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any unit that provides mechanical ventilation and either does not provide major surgery or provides surgery but treated less than 50 VLBW infants in 1991 (IIIA, small IIIB, and small IIIC).10 Finally, Regional NICUs include those that provide major surgeries and treated greater than 50 VLBW infants in 1991, or any unit that provides cardiac bypass and/or ECMO, the two most specialized surgical procedures, regardless of size (large IIIB, large IIIC, and all IIID). This categorization results in 161 No NICU, 58 Intermediate, 41 Community, and 36 Regional NICU hospitals at the beginning of my sample in 1991. These numbers change during my sample period as deregionalization progressed through the decade. Table 2.2 shows the number of hospitals by level and year between 1991 and 2001. The total number of hospitals providing any birthing services falls from 296 in 1991 to 269 in 2001. In contrast, the number of Community NICUs increases from 35 to a peak of 57 in 1999. 10 hospitals open new NICUs at the Community level and 21 hospitals upgrade an Intermediate NICU to the Community level. As a result of these upgrades, the aggregate number of Intermediate NICUs actually decreases from 58 to 45 over the sample period; however, there are also 15 hospitals that open new NICUs at the Intermediate level. Not surprisingly, the number of Regional NICUs, the largest, most well established, and most expensive units, remains relatively constant over the sample period.
2.4 Empirical Framework
This section describes my empirical approach to estimating the e?ect of level of neonatal care at the birth hospital on mortality. I ?rst discuss an ordinary least squares regression that estimates average mortality di?erences between infants born in No NICU, Intermediate NICU, or Community NICU hospitals and those born
10 I use the number of VLBW infants treated in 1991 to identify this classi?cation to prevent hospitals from changing levels due to changes in demand during my sample period.
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in Regional NICU hospitals, conditional on a rich set of control variables. This estimation strategy is comparable to the methodology of the previous literature and provides “risk-adjusted” mortality di?erences. I then discuss how these estimates could be upwards or downwards if mothers choose hospitals based on unobserved characteristics not included in the risk adjustment. Lastly, I discuss my instrumental variables strategy to account for unobserved selection and estimate the causal e?ect of level of care.
2.4.1 Baseline Model
I begin by estimating the average di?erence in mortality rates by level of care at the delivery hospital, controlling for observable characteristics of the mother and infant. The regression equation is as follows:
yizt = ? + Nizt ? N + Iizt ? I + Cizt ? C + Xizt ? + ?izt
(2.1)
The unit of observation is infant i, whose mother resides in zip code z , born in year t. The dependent variable, yizt , is a neonatal mortality indicator that is equal to one if an infant dies within 28 days of birth or within one year if continually hospitalized since birth, and zero otherwise.11 Xizt is a vector of observable determinants of infant izt’s health. These controls include time (year, month, and day of week indicators); mother’s demographics such as age, race, ethnicity, and insurance coverage;12 and health related controls such as the infant’s sex, birth weight, and diagnoses.13
In Section 2.6 I show that results are robust to measuring mortality across di?erent time frames. 12 Speci?c demographic controls are age, age squared, and indicators for black, other race, Hispanic, Medicaid, HMO, and self-pay. 13 Speci?c health controls are parity, sex, multiple birth status, an indicator for having a clinical condition, indicators for small and large for gestational age, birth weight dummies at 100 gram increments, the number of prenatal care visits, and the month in which prenatal care began. The clinical condition indicator is equal to one for infants having at least one of the following conditions identi?ed in Phibbs et al. (2007): hydrops due to isoimmunization, hemolytic disorders, fetal distress, fetus a?ected by maternal condition, oligohydramnios, other high-risk maternal
11
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The three explanatory variables of interest, Nizt , Iizt , Cizt , are indicators equal to one if infant izt is born in a hospital with No NICU, an Intermediate NICU, or a Community NICU, respectively. Being born in a hospital with a Regional NICU is the excluded group, so the ? j coe?cients are interpreted as the di?erence in mortality when born in a hospital with level of care j relative to being born in a Regional NICU hospital.14 For this speci?cation to estimate the causal e?ect of level of care on mortality, hospital choice must be uncorrelated with unobserved determinants of mortality captured by the error term, ?izt , conditional on the observable characteristics, Xizt (E [Hizt ?izt |Xizt ] = 0, where Hizt = [Nizt , Iizt , Cizt ]). If this condition is not met, and unobserved mortality, conditional on observables, is di?erent for infants born in hospitals with di?erent levels of care the OLS estimates of the ? j s will be biased. If infants born in lower level hospitals are unobservably healthier (lower unobserved mortality), consistent with physicians referring the highest risk mothers to Regional NICU hospitals, OLS estimates will understate the true mortality di?erence between being born in lower level hospitals and Regional NICU hospitals. On the other hand, if infants born in lower level hospitals are unobservably less healthy (higher unobserved mortality), consistent with more uninformed mothers choosing lower levels of care and having higher risk infants, OLS estimates will overstate these mortality di?erence. Sample means by level of care in Table 2.3 show that there are clear unconditional di?erences in mortality rates by level of care at the hospital in which an infant is born. Neonatal mortality rates fall from 21.9% for VLBW infants born in No NICU hospitals, to 16.9% in Intermediate NICU hospitals, 15.5% in Comconditions, placenta hemorrhage, premature rupture of membrane, and prolapsed cord. 14 It is important to point out that I am estimating mortality di?erences based on the hospital in which the infant is born. This framework does not take into account whether or not the infant was actually treated in the NICU or whether they were transferred to and treated in another hospital. In this context, my estimates can be thought of as intent-to-treat e?ects.
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munity NICU hospitals, and 14.7% in Regional NICU hospitals. However, there are also di?erences in important observable characteristics. OLS regressions control for these observable characteristics, but these di?erences motivate the concern that there may be di?erences in unobservable dimensions as well. Mothers’ demographic characteristics di?er by level of care, but not monotonically. For example, 9.8% of mothers giving birth in No NICU hospitals, 20.5% in Intermediate NICUs, 12.8% in Community NICUs, and 18.6% in Regional NICUs are black. The percentage of mothers covered by Medicaid and the percentage without any college education decreases substantially from No NICU, to Intermediate NICU, and to Community NICU hospitals, but the percentage in Regional NICU hospitals is higher than the percentage in Community NICU hospitals. These large di?erences indicate selection into level of care by mothers’ demographics, but the direction of the selection is ambiguous. Furthermore, these demographic characteristics are likely to be correlated with mortality risks. For example, Singh and Kogan (2007) show persistent infant mortality disparities by mothers’ education and socioeconomic status. There are also clear patterns of selection on infant health characteristics. Consistent with selection of healthier infants into lower levels of care, infants born at lower levels are less likely to be multiple births, have slightly higher birth weight and longer gestation, are less likely to have a clinical diagnosis, are less likely to be small or large for their gestational age, and experience lower hospital charges and shorter lengths of stay. Given the di?erences in observed characteristics by level of care, there are likely di?erences in unobserved characteristics as well (Altonji et al., 2005). Therefore, accounting for non-random selection is important, though the direction of the bias is again unclear ex ante.
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2.4.2 Estimating Causal E?ects
To understand the e?ects of deregionalization on VLBW infant outcomes, it is necessary to estimate the causal e?ect of level of care on neonatal mortality. Because OLS estimates may not be able to control for all determinants of mortality, I utilize instrumental variables to overcome unobserved selection. With three endogenous explanatory variables, at least three instruments are necessary to identify the empirical model. I construct three instruments based on the distance from a mother’s residence to each level of care, which I de?ne in more detail below. For a 3 × 1 vector of instruments, Dzt , instrumental variables estimates of ? N , ? I , and ? C will be consistent if the instruments are uncorrelated with the error term in Equation (2.1) (E [Dzt ?izt |Xizt ] = 0) and are strong determinants of the type of hospital a mother chooses, conditional on the other observable characteristics. This second condition is similar to saying that the coe?cients on the instruments are non-zero in the following set of ?rst stage regression equations of each level of care indicator on the vector of instruments and all other covariates:15 Nizt = ? N + Dzt ?N + Xizt ?N + µN izt Iizt = ? I + Dzt ?I + Xizt ?I + µI izt Cizt = ? C + Dzt ?C + Xizt ?C + µC izt Notation is as above with each ?j representing a vector of three ?rst stage coe?cients and each µj izt representing a ?rst stage error term. The parameter estimates of Equation (2.2) are used to obtain the predicted probability of choosing each level of care for each observation, and two stage least squares (2SLS) estimates are computed by estimating Equation (2.1) with these
More formally, it must be the case that the instruments are su?ciently linearly related to Hizt that E [Zizt Hizt ] is of full column rank, where Zzt = [Dzt , Xizt ]. It is also necessary for the instruments to be su?ciently linearly independent so that E [Zizt Zzt ] has full column rank (Wooldridge, 2001).
15
(2.2)
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predicted probabilities in place of the level of care indicators.16 Therefore, identi?cation of ? N , ? I , and ? C in Equation (2.1) comes from comparing mortality for otherwise identical infants who are born at di?erent levels of care because they live at di?erent distances from each level of care. For example, ? C is identi?ed from di?erences in mortality outcomes between infants who are and are not born in hospitals with Community NICUs because their mothers live within close or far proximity to a hospital o?ering a Community NICU. Intuitively, this comparison emphasizes the importance of the assumption that E [Dzt ?izt |Xizt ] = 0. In order for instrumental variables to provide causal estimates, it is crucial that mothers living at di?erent distances from each level of care not have infants that di?er in unobserved determinants of mortality. Since this strategy requires the location of NICUs to be exogenous to VLBW infant health, it is worth brie?y re-emphasizing the process by which NICUs have di?used and discussing how mothers choose hospitals. Most importantly, di?usion has been driven by many factors unrelated to the health of VLBW infants. Over time the technologies and trained specialists necessary to operate NICUs became more prevalent, and therefore, NICU adoption became feasible for community hospitals. It has been hypothesized that so many hospitals adopted lower level NICUs in order to compete for pro?table obstetric patients (McCormick and Richardson, 1995). Ninety-seven percent of births are covered by private or public insurance (Russell
Both the dependent variable and the endogenous explanatory variables in this model are binary. Bhattacharya et al. (2006) point out that two stage least squares can lead to inconsistent estimates when the mean probability of the binary dependent variable is close to zero or one, or when there is more than one endogenous binary treatment variable. They advocate a multivariate probit model which assumes that the error terms from Equations (2.1) and (2.2) follow a multivariate normal distribution. On the other hand, Angrist (2001) argues that linear models still provide good approximations of average causal e?ects, parameter estimates directly correspond to the relevant average treatment e?ects, and nonlinear models depend on the distributional assumptions and are inconsistent if these assumptions are incorrect. Wooldridge (2001) points out that some of the assumptions behind average treatment e?ects are not precisely true with binary outcomes, but linear methods may still produce reasonable average treatment e?ect estimates. I have estimated my OLS speci?cations with both probit and logit models and ?nd marginal e?ects that are almost identical to the OLS coe?cient estimates presented in Section 2.5. Future work will verify that the 2SLS estimates are not biased by the linear functional form.
16
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et al., 2007), so most families are shielded from the full cost of infant care. One way for hospitals to compete for these patients is to invest in signals of quality, which might attract risk-averse mothers. Hospitals are particularly motivated to attract obstetric patients, since mothers are typically young, healthy, and likely to return to the hospital for the later care of their families if they have a positive birth experience (Friedman et al., 2002), and NICUs themselves can be pro?table (Horwitz, 2005, see online appendix). Most preterm labor is spontaneous, and in 50% of cases, doctors are not even able to determine the cause ex post. Forty to ?fty percent of cases with an identi?ed cause are traced to an infection, but often mothers show no signs of these infections prior to labor.17 As detailed by an Institute of Medicine report, there are a variety of documented correlates of preterm delivery. These correlates range from behavioral factors such as tobacco use and nutrition, to psychosocial factors such as stress, personal resources, and social support, to medical conditions of the mother or pregnancy such as obesity or multiple births, to other factors such as exposure to environmental toxins, genetics, etc. Interrelated with many of these characteristics, there are demographic di?erences in preterm birth rates as well. Mothers at either extreme of the age distribution, unmarried mothers, black mothers, and mothers with low income or low educational attainment are all known to have higher rates of preterm delivery. Despite these correlates, this report emphasizes that there is in fact little understanding of what conditions and events can be used to predict and diagnose preterm labor before it occurs (Behrman and Butler, 2007). As a result of this unpredictability, a NICU is likely an e?ective tool for attracting patients of all risk levels. Expectant mothers usually deliver in the hospital where their obstetrician has delivery privileges, so they in e?ect choose their delivery hospital when they choose their obstetrician early in their pregnancy. If risk-averse
17
Source: www.marchofdimes.com/peristats, last accessed on September 29, 2009.
30
mothers plan ahead when choosing their obstetrician and delivery hospital, the presence of a NICU is likely to factor into their decision. A mother likely considers travel time, convenience for family members, perceived quality of care, and the possibility of transfer if higher quality care is needed. If utility is increasing in perceived quality of care and decreasing in travel time, a community hospital with a NICU can attract nearby mothers willing to trade additional perceived quality at a further Regional NICU in favor of the increased convenience of choosing the nearby hospital. Furthermore, if mothers tend to choose local obstetricians who are likely to have priveldges in local hospitals, mothers will be more likely to choose nearby hospitals. Of course, location relative to hospitals with NICUs is not the only determinant of hospital choice. Phibbs et al. (1993) estimate hospital choice models separately for high- and low-risk mothers and for publicly and privately insured mothers within each risk category. Not surprisingly, overall, mothers prefer closer hospitals, hospitals with higher quality, and hospitals with neonatal intensive care units. Despite the fact that many high-risk deliveries are unexpected, the authors do ?nd some di?erences in hospital choices among high- and low-risk mothers. For example, high-risk mothers prefer hospitals with higher measures of quality, including higher level neonatal intensive care units. This ?nding is consistent with my sample means above that ?nd higher-risk infants born in hospitals with higher levels of care. The authors also ?nd some important di?erences in hospital choice between publicly and privately insured mothers. While distance has a similar e?ect on hospital choice for both groups, publicly insured mothers deliver in hospitals with worse health outcomes and are less likely to deliver in hospitals with NICUs. These ?ndings suggest possible restrictions on access to care for publicly insured mothers.18 As discussed above, I restrict the sample to exclude Kaiser insured patients
Additionally, during my sample period California began adopting Medicaid managed care plans on a county by county basis. These plans potentially provide further restrictions on the hospitals in which some Medicaid mothers can deliver.
18
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who have little choice of delivery hospital, but the ?ndings of Phibbs et al. (1993) suggest there are likely to be other groups with varying degrees of choice restrictions including publicly insured patients. Patients with other managed care insurance are likely to be restricted somewhat as well, though to varying degrees as compared to Kaiser. That being said, the motives to compete for healthy, risk-averse mothers, evidence that growth of neonatal resources has outpaced medical need, and ?ndings that the location of neonatal intensive care resources are uncorrelated with markers of need such as occurrences of VLBW or preterm births (Goodman et al., 2001), support the exogeneity of NICU location to VLBW infant health. In the next subsection, I provide further evidence from my data supporting this claim. To the extent that some patients have restricted choice, the only e?ect would be to weaken the power of the instrument as long as these factors are not correlated with distance, which appears to be the case. It is important to point out that under the assumptions of the empirical model, the instrumental variables estimates of ? N , ? I , and ? C in Equation (2.1) are structural parameters and provide causal estimates of the e?ect of level of care at the hospital of birth on infant mortality rates. In contrast, the ?rst stage relationships in Equation (2.2) are reduced form equations where the endogenous level of care indicators are regressed on all of the model’s exogenous variables. These equations do not necessarily provide structural parameters of the neonatal intensive care level demand function.19
As discussed above, one previous study has attempted to estimate hospital demand parameters for delivery hospitals. Phibbs et al. (1993) estimate McFadden conditional logit models of hospital choice, and their model includes features such as distance from a mother’s residence and presence of a neonatal intensive care unit. Additional work in this area is left to future research, as estimating such demand functions is important for understanding how mothers choose hospitals and why hospitals choose to provide various levels of care. Additionally, many hospitals now advertise heavily about not only the quality of care, but also amenities available for expectant mothers, such as private rooms, jacuzzis, etc. Goldman and Romley (2008) ?nd that Medicare pneumonia patients in Los Angeles place a high value on non-medical amenities when choosing a hospital for treatment. Such amenities may also be an important tool for hospitals competing for maternity patients.
19
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2.4.3 The Instruments
In this section, I describe how I calculate the three distance instruments and discuss why they are likely to be exogenous to unobserved VLBW mortality. I ?rst calculate the straight line distance from the center of each patient’s zip code of residence to each hospital using GIS software. Hospital location is obtained from OSHPD’s publicly available geocoded data of hospital latitude and longitude.20 I then construct three instruments that represent the di?erential distance between the nearest hospital of a given level of care or higher and the nearest hospital with a Regional NICU, as follows:
N oDistzt = D(Regzt ) ? min[D(N ozt ), D(Intzt ), D(Comzt ), D(Regzt )] IntDistzt = D(Regzt ) ? min[D(Intzt ), D(Comzt ), D(Regzt )] ComDistzt = D(Regzt ) ? min[D(Comzt ), D(Regzt )]
(2.3a) (2.3b) (2.3c)
The D(·) operator indicates the distance from zip code z at time t to the nearest hospital o?ering a particular level of care. These measures can be thought of as the number of miles saved by choosing the nearest hospital with at least a particular level of care over the nearest hospital with the highest level of care, and therefore get larger as an individual lives closer to a hospital o?ering the particular level of care. When using di?erential distance, the hospital choice decision is modeled as a function of distance to each lower level of care relative to distance to Regional NICU hospitals.21 It emphasizes the fact that mothers make a trade o? when choosing a lower level of care at a closer hospital – they forego potentially higher quality care
OSHPD only provides this data for currently existing facilities. For those facilities for which I do not have exact location, I use the center of the hospital’s zip code obtained in the OSHPD State Utilization File of Hospitals. 21 Cutler (2007) and McClellan et al. (1994) also use di?erential distance as their instruments by subtracting distance to the nearest hospital from distance to the nearest hospital o?ering heart surgery.
20
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in exchange for a shorter travel time.22 Also, these three measures will always take on values greater than or equal to zero due to the min[·] operator in (2.3), and they equal zero if an individual lives closer to a Regional NICU than one of the lower levels of care. This speci?cation captures the fact that if a hospital nearby o?ers a particular level of care, a mother can also receive lower level care by traveling to the same hospital. These distances are not the only way one could specify exposure to NICUs. I utilize this method to best proxy for the cost of obtaining each level of care; although, one could also specify distance based on the distance to the nearest hospital of a speci?c level of care (instead of the nearest hospital with a particular level or higher). Other potential measures of exposure include hospital market shares or the number of hospitals of each level within a given radius. I choose distance so as not to impose potentially endogenous market de?nitions. As mentioned above, the goal is not to estimate structural parameters of hospital choice, but instead to exploit the exogenous variation in distance that directs patients to di?erent levels of care. Table 2.4 provides summary statistics of the four distance measures used to construct the instruments and of the three instruments themselves. On average, mothers of VLBW infants in my sample live 3.7, 5.7, 8.1, and 14.8 miles from the nearest hospital o?ering any birthing services, at least Intermediate care, at least Community care, and Regional care, respectively. The average number of miles saved by traveling to the nearest hospital with no NICU or higher relative to the nearest Regional NICU is 11.2. The average number of miles saved traveling to the nearest hospital with at least an Intermediate NICU or at least a Community NICU is 9.1 and 6.8 miles, respectively. These measures have wide variation, each with
A model with four instruments based on distance to each of the four levels of care would achieve the same goal, as it would condition on distance to the nearest Regional NICU in each ?rst stage regression. Using di?erential distance is equivalent to including all four distance measures separately, but restricting the coe?cient on the Regional distance variable. 2SLS results, not shown here, without this functional form assumption are almost identical to those presented in Section 2.5.
22
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standard deviations around 20 miles, or two to three times their means. I now provide a set of summary statistics supporting the assumption that di?erential distance is uncorrelated with the error term in Equation (2.1) and is therefore independent of unobservable determinants of VLBW mortality. Table 2.5 lists sample means of observable characteristics by the three instruments. If a detailed list of observable characteristics are independent of di?erential distance, it is likely to be the case that unobservable characteristics are as well (Altonji et al., 2005). For each instrument the table shows sample means for three groups: those observations with zero di?erential distance and those with di?erential distance below and above the median, conditional on non-zero di?erential distance. The ?rst three rows show that those individuals living in zip codes below the median typically save between one and ?ve miles by traveling to each of the three lower levels of care, and those with values above the median save between 16 and 32 miles. Other than the proportion of mothers who are black, which is about twice as large for observations at zero di?erential distance compared to individuals above the median for all three instruments, mothers’ demographics show little variation by distance. For example, the percent of mothers covered by Medicaid ranges from 48.3% to 52.1% for the Community distance groups. In contrast, this ?gure had a gap of 13.6 percentage points between No NICU and Community NICU hospitals in Table 2.3. Most importantly, infant health characteristics do not di?er much across distance groups. While the number of prenatal visits is slightly lower for those with zero miles saved, the month prenatal care began is similar across groups and there are no large di?erences in parity, multiple births, birth weight, or gestation. Most individual observable characteristics do not appear to di?er by distance, but there may be other important characteristics that do. The bottom portion of the table presents means of zip code level characteristics. These variables are collected from the 2000 census and merged to the mother’s zip code of residence, and I
35
present means treating each birth as an observation. Here, there are some potentially important di?erences by di?erential distance as median household income increases, percent urban decreases, and population density decreases across columns for each distance variable.23 Despite these di?erences, the variation in di?erential distance is not driven by population density alone. Figure 2.1 displays a map of California and plots the location of Intermediate, Community, and Regional NICUs in 1991. The light gray lines outline counties in the San Francisco Bay, Los Angeles Metro, and San Diego Metro areas. NICUs are clearly clustered around these metropolitan areas, but there does not appear to be any systematic di?erence in where each level of care is located. The geographic distribution of the community distance variable at its 1991 baseline is displayed in Figure 2.2, with Panel A showing the whole state and Panel B zooming in on the ?ve counties comprising the Los Angeles Metro area. The lightest colored zip codes have no births in the VLBW sample and the other zip codes are shaded by the three groups discussed above: those saving zero miles, and those above and below the median conditional on non-zero di?erential distance. The darker zip codes that have the largest di?erential distances, and are therefore closer to Community NICU hospitals, are more likely to be in outlying areas, but there is variation both within the major metropolitan areas and in the suburban areas with many neighboring zip codes of varying distances. Figure 2.3 shows similar maps plotting Intermediate distance. Overall, summary statistics indicate that di?erential distance is uncorrelated with most major observable demographic and infant health characteristics. To the extent that any di?erences in urban concentration are not captured by the individual controls, I examine the robustness of my results to the inclusion of zip code level controls and estimate models with zip code ?xed e?ects in Section 2.6.
23 Cutler (2007) and McClellan et al. (1994) also ?nd that areas with higher di?erential distance to hospitals o?ering heart surgery are less urban.
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2.5 Results
Comparisons of sample means in Section 2.4 revealed unconditionally higher neonatal mortality for VLBW infants born in hospitals with lower levels of care compared to those born in Regional NICU hospitals. However, there are also important di?erences in observable characteristics by level of care. This section estimates OLS speci?cations of the e?ect of level of care on mortality controlling for these observable characteristics and 2SLS estimates that account for any other unobservable determinants of mortality that may be correlated with hospital choice.
2.5.1 OLS Estimates
Table 2.6 presents OLS coe?cient estimates of ? N , ? I , and ? C . Moving across the columns, I progressively add control variables. To account for likely similarities in health conditions and hospital choices at local levels, and because the instruments vary at the zip code level when I estimate 2SLS models, standard errors of all regression estimates in this chapter are clustered by zip code. This clustering allows for arbitrary correlation of the error term within zip codes. The estimates in Column 1 re?ect the unadjusted mortality di?erences by level of care with no additional covariates and replicate the di?erences in sample means from Table 2.3. VLBW infants born in No NICU, Intermediate NICU, and Community NICU hospitals are 7.2, 2.2, and 0.8 percentage points more likely to die, respectively, than those born in Regional NICU hospitals. The Community NICU coe?cient is statistically signi?cant at the 10% level and the other two coe?cients are statistically signi?cant at the 5% level. Column 2 adds controls for long term mortality trends in the form of year dummies and within year mortality cycles in the form of eleven month-of-year dummies and six day-of-week dummies. The estimated e?ect of being born in a hospital with a Community NICU increases to
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1.3 percentage points and is now statistically signi?cant at the 5% level; the other two estimates are similar to the previous column. Column 3 adds controls for mother’s demographic characteristics. The coe?cient estimates decrease from Column 2 but are still positive and precisely estimated. Column 4 adds controls for the infant’s baseline health characteristics and prenatal care. These covariates control for underlying health risk and are similar to controls used in previous studies. This speci?cation estimates “risk-adjusted” mortality differences by level of care and will be treated as the baseline OLS estimates for the remainder of the paper. Except for the No NICU coe?cient, the estimates in Column 4 are slightly larger than those in the previous column, and the coe?cients imply that on average, infants born in hospitals with Community NICUs, Intermediate NICUs, or No NICUs are 1.2, 2.1, or 5.0 percentage points more likely to die than those born in hospitals with Regional NICUs, respectively. Relative to the sample mean mortality rate of 15.7%, these coe?cients imply e?ects of 7.6%, 13.4%, and 31.8%, respectively. OLS estimates lead to the conclusion that infants born in lower level hospitals experience higher risk-adjusted mortality rates, con?rming the previous literature. Infants born in No NICU hospitals have the highest risk-adjusted mortality rate, and most relevant to the trend towards deregionalization, infants born in Intermediate and Community NICU hospitals experience statistically and qualitatively higher mortality rates than those born in Regional NICU hospitals. However, the ?nding that the coe?cient estimates are sensitive to controls implies that observed determinants of mortality are correlated with level of care. The fact that the coe?cient estimates increase or decrease depending on which controls are added reinforces that the direction of any selection is ambiguous. Evidence of selection on the observables emphasizes the importance of accounting for any potential unobserved selection as well.
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2.5.2 First Stage Estimates
This section presents the ?rst stage estimates of the e?ect of distance on level of care chosen speci?ed in Equation (2.2). I provide evidence that the three distance measures are strong instruments and further evidence that they satisfy the exclusion restriction. Table 2.7 presents the results, building up to the baseline speci?cation by progressively adding controls across the columns for each outcome. The coe?cient estimates and standard errors show little to no change across columns. This ?nding implies little correlation between distance and observable characteristics and further supports the hypotheses that the instruments are uncorrelated with unobserved characteristics as well. Columns 4, 8, and 12, present the main ?rst stage speci?cations with all controls included. All of the ?rst stage coe?cient estimates are strongly statistically signi?cant and show the expected substitution patterns. Individuals living closer to a particular level of care are more likely to choose that level of care and less likely to choose the other levels of care. For example, a ten mile increase in ComDist, associated with living ten miles closer to a Community NICU or higher, decreases the probability of choosing a No NICU hospital and an Intermediate NICU hospital by 2.5 and 2.7 percentage points, respectively, and increases the probability of choosing a Community NICU hospital by 7.4 percentage points.24 These coe?cient estimates are equivalent to 33%, 24%, and 31% changes relative to their respective level of care indicator sample means. These are large e?ects given the standard deviations of the distance instruments are around twenty. Qualitatively, distance is an important determinant of the level of care a mother chooses. Below the estimates in each panel I report F-Statistics testing the null that
24 Though not a part of the estimation, there is implicitly a fourth relationship between the probability of choosing a hospital with a Regional NICU and distance. While not shown in the table, this same change decreases the probability of choosing a hospital with a Regional NICU by 2.2 percentage points, con?rming the ?ndings of Haberland et al. (2006) that lower level NICUs divert high-risk births from Regional NICUs.
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the three distance coe?cients are jointly equal to zero. The F-Statistics for the main speci?cations with the full set of controls are 32.46, 44.56, and 38.35, all well above the rule-of-thumb cuto? of 10 typically used to assess ?nite sample bias from weak instruments. Additionally, the fact that each instrument is signi?cant in all three equations and has a particularly large coe?cient estimate in the equation corresponding to its respective level of care, suggests that each of the three instruments provide independent variation to identify the model.
2.5.3 2SLS Estimates
Table 2.8 reports the 2SLS results. Column 1 repeats the baseline OLS results with all controls from Table 2.6. All three 2SLS coe?cient estimates in Column 2 are substantially lower than their counterparts in Column 1. The coe?cient of the No NICU indicator decreases from 0.050 to -0.030, the coe?cient of the Intermediate NICU indicator decreases from 0.021 to 0.009, and the coe?cient of the Community NICU indicator decreases from 0.012 to -0.063. The No NICU and Community NICU coe?cient estimates actually change signs and the Intermediate NICU coe?cient estimate falls by half, but the standard errors increase by a factor of between three and nine. The Community NICU coe?cient estimate is marginally statistically signi?cant (at the 10% level), but neither of the other two estimates in Column 2 are statistically signi?cant.25 Despite the large standard errors, the 2SLS estimates are clearly di?erent from and bounded below the OLS estimates. First, I conduct a Hausman test of the null hypothesis that both the OLS and 2SLS estimates are consistent against the
One might be concerned that some of the infant health and prenatal care controls are endogenous. This would be a concern if, for example, mothers who live close to Regional NICUs also have access to higher quality prenatal care, or if hospitals with di?ering levels of care have di?erent propensities to diagnose various health conditions. To account for this, I also estimate 2SLS regressions excluding this set of controls. The results are similar to those reported in Column 2 of Table 2.8 with coe?cient (standard error) estimates of -0.021 (0.037), 0.014 (0.018), and -0.041 (0.042) for No NICUs, Intermediate NICUs, and Community NICUs, respectively.
25
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alternative that only the 2SLS estimates are consistent.26 The p-value of this test is 0.031, so the null is rejected at the 5% signi?cance level. This test implies that the 2SLS coe?cient estimates are statistically di?erent from the OLS estimates and provide more consistent estimates. Second, even the upper bounds of the 2SLS estimates imply much lower quantitative and qualitative e?ects on mortality than the OLS estimates, at least for the No NICU and Community NICU coe?cients. Figure 2.4 plots the OLS and 2SLS coe?cient estimates scaled by mean neonatal mortality. It also plots one and two standard deviation intervals above the 2SLS coe?cient estimates. The OLS coe?cient estimate of the No NICU coe?cient implies 31.8% higher mortality relative to being born in a Regional NICU hospital. The 2SLS coe?cient estimate is large and negative, one standard deviation above the 2SLS coe?cient estimate is still below zero, and even two standard deviations above implies an e?ect of 17.9% – 44% lower than the OLS estimate. Similarly, one standard deviation above the Community NICU coe?cient estimate is still far below zero, and two standard deviations above implies an e?ect of 4% – 46% lower than the OLS e?ect of 7.4%. One standard deviation above the Intermediate NICU 2SLS coe?cient estimate is above the OLS estimate, but the point estimate is still 55% lower than the OLS point estimate. The 2SLS estimates are not statistically di?erent from zero and are small in magnitude compared to OLS estimates. This ?nding provides evidence that the OLS estimates of higher mortality at the three lower levels of care relative to Regional NICU hospitals are overstated. The dominant form of selection is unobservably higher risk births occurring in lower level hospitals. These results imply that policy
The usual Hausman test also assumes that the OLS estimates are e?cient under the null hypothesis. However, clustered standard errors result in a covariance matrix that is not asymptotically e?cient. Therefore, I construct the Hausman test statistic using a paired bootstrap strategy that samples at the zip code level. My sample has 1,144 zip codes, so I construct 5,000 random samples of my data that each draw 1,144 zip codes with replacement. For each bootstrap sample, I run my OLS and 2SLS regressions and save the coe?cient estimates. I then construct the estimated variance-covariance matrix of the di?erence between the OLS and 2SLS coe?cients based on the distribution of these 5,000 estimates. See Cameron and Trivedi (2005, p. 378) for details.
26
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measures aimed at reversing the e?ects of deregionalization are likely to have a limited impact on mortality. Relocating mothers who would have chosen to give birth in lower level hospitals to Regional NICU hospitals prior to birth would not improve mortality rates because the relocated deliveries would be from the less healthy portion of the distribution. It is important to emphasize that I am estimating how the level of care at the hospital in which an infant is born impacts mortality. My results do not imply that being treated in a hospital with a higher level NICU has no e?ect on outcomes. In fact, a likely mechanism behind my results is that infants born in hospitals with lower levels of care achieve similar outcomes to those born in hospitals with higher levels of care because the former group will be transferred to a higher level hospital after birth if necessary. In my sample 66% of VLBW infants born in hospitals with No NICUs or Intermediate NICUs are transferred to a higher level hospital after birth, and 85% of those that are transferred are sent to a Regional NICU hospital. In order to explore whether the probability of transfer is systematically impacted by distance, I regress an indicator for whether or not an infant is transferred to a Regional NICU hospital on the three distance instruments for the sample of VLBW infants born in No NICU or Intermediate NICU hospitals. To run this regression, I select the sample based on an endogenous variable, but statistically insigni?cant coe?cients on the three distance instruments would suggest that hospitals do not selectively transfer infants based on distance. In other words, this kind of ?nding would imply that transfers occur when medically necessary and are not impacted by where a mother lives in relation to where NICUs are located. Results of this regression do reveal a positive and statistically signi?cant coe?cient of 0.029 on the No NICU distance variable, but the coe?cient estimates on the other two distance instruments are very small and statistically insigni?cant (-0.00008 and 0.008, respectively). The positive coe?cient on No NICU di?eren-
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tial distance implies that as a mother lives closer to a hospital with No NICU or higher or farther from the nearest Regional NICU, her infant is more likely to be transferred to a Regional NICU. When I instead regress the transfer indicator on all four distance measures instead of the three di?erential distance measures, I ?nd that this coe?cient is being driven by the distance to the nearest Regional NICU hospital. This ?nding is likely a result of using the selected sample of infants born in No NICU or Intermediate NICU hospitals. Infants of mothers who live close to Regional NICU hospitals, but choose not to deliver in the Regional NICU hospital are likely to have healthier infants and less medical need for transfer. Overall, these results suggest that I ?nd no gradient between level of care at the birth hospital and mortality because VLBW infants are transferred to hospitals with higher levels of care when medically necessary, and the location of lower level NICUs does not change the probability of eventually being treated in a hospital with a higher level NICU.
2.6 Robustness Tests
In this section I further test the assumptions that lead to my conclusions and explore the robustness of my ?ndings to various alternative speci?cations. I also examine whether the e?ect of level of care on mortality di?ers among di?erent subsamples of the VLBW infant population and discuss implications of local average treatment e?ects.
2.6.1 Additional Tests of Instrument Validity
The distance instruments are motivated by the supposition that NICUs are not located according to medical need and are likely adopted to attract low-risk obstetric patients. Table 2.9 provides further evidence of this hypothesis by presenting “?rst
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stage” estimates of the e?ect of distance on mothers’ hospital choice for infants above the VLBW threshold. I display estimates for low birth weight infants (1,500 to 2,500 grams or 3.3 to 5.5 pounds), those just above the low birth weight cuto? (2,500 to 3,000 grams or 5.5 to 6.6 pounds), and the remaining normal birth weight group (3,000 to 4,500 grams 6.6 to 9.9 pounds).27 Distances are strong predictors of level of care for these samples, and the coe?cient estimates and F-Statistics actually increase in absolute value as birth weight increases. This evidence supports the anecdotes that NICUs attract all mothers and the assumption that NICU location is exogenous to the unobserved determinants of VLBW mortality. As a ?nal test of the validity of the instruments, I estimate reduced form regressions of the e?ect of the distance instruments on neonatal mortality, and examine their sensitivity to the addition of controls. The stability of the ?rst stage estimates in Table 2.7 provides evidence in favor of the exclusion restriction. A similar exercise for the reduced form estimates provides a sharper test because it provides evidence on how observable characteristics are correlated with the portion of distance that predicts neonatal mortality. If selection on the unobservables is similar to selection on the observables, and the reduced form estimates are insensitive to controls, the exclusion restriction that E [Dzt ?izt |X] = 0 is likely to hold. Table 2.10 presents the results. The ?rst column includes no controls, the second column adds time dummies, and the ?nal two columns add demographic and health characteristics. The estimates are quite stable across speci?cations. The N oDist and IntDist coe?cient estimates change slightly as controls are added, but they are quite small and statistically insigni?cant across all four columns. The ComDist coe?cient estimate is very stable across speci?cations and in the ?nal column is estimated as a statistically signi?cant -0.004. These reduced form point estimate are also small in magnitude. For example,
27
All samples are subject to the same restrictions described in Section 2.3.
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a one standard deviation change in miles saved to the nearest Community NICU or higher (18.4 miles) only leads to a 0.8 percentage point decrease in mortality. As a comparison, a one standard deviation increase in mother’s age (6.9 years) reduces mortality by 4.5 percentage points, and a one standard deviation increase in number of prenatal care visits (6.1 visits) reduces mortality by 1.8 percentage points. An increase in birth weight from the category just below the mean (900 to 999 grams) to the category just above the mean (1,000 to 1,099 grams) decreases mortality by 3.9 percentage points. All of these e?ects are much larger in magnitude than the e?ects of distance on neonatal mortality. These quantitatively and qualitatively small reduced form estimates are consistent with the small 2SLS estimates of the e?ect of level of care on mortality. 2SLS estimates scale the reduced form estimates by the size of the e?ect of distance on hospital choice. If distance a?ects the level of care chosen but not mortality, level of care cannot a?ect mortality for the population that chooses level of care as a result of distance.
2.6.2 Alternative Speci?cations 2.6.2.1 Zip Code of Residence Controls
Sample means by di?erential distance in Table 2.5 showed that individuals living closer to each of the three lower levels of care relative to Regional NICU hospitals live in zip codes with lower population density and higher income. Though I control for many individual level covariates in my main results, if these zip code level characteristics are conditionally correlated with distance and infant health, 2SLS estimates would be biased. Therefore, I test the robustness of my estimates to controlling for zip code level population density; percent black; percent Hispanic; percent of the population over 25 with no college, some college, a college degree,
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and more than a college degree; and median household income.28 Additionally, distances may factor into the hospital choice decision di?erently in urban and suburban areas. For example, ?ve miles in downtown Los Angeles may have a much di?erent travel time than ?ve miles in a suburban area. Furthermore, hospitals are located closer to each other in more urban areas than less urban areas. Using di?erential distance and controlling for all three distance variables captures some of these features, but the ?st stage regression may have more predictive power if the e?ect of distance is allowed to vary with population density. I therefore estimate models with interactions of the distance measures and zip code population density added to the instrument set. Table 2.11 presents ?rst stage estimates with the baseline speci?cation repeated in Panel A. In this table each row lists coe?cient estimates from one ?rst stage regression. When zip code level controls are added in the ?rst three rows of Panel B, the magnitudes of the ?rst stage coe?cient estimates change slightly, but they are very similar, highly statistically signi?cant, and the F-Statistics are of similar magnitudes to Panel A. The second portion of Panel B interacts the distance instruments with population density. The coe?cient estimates of the three distance measures decrease a bit in magnitude, but are still highly statistically signi?cant. The density interactions are almost all statistically signi?cant with positive diagonal elements and negative o? diagonal elements, matching the pattern of signs of the distance main e?ects. Thus, the e?ect of distance becomes stronger as population density increases, as would be expected if travel times are longer or travel is more expensive in more densely populated areas. The three added instruments result in similar F-Statistics for the No NICU and Intermediate NICU regressions, but a lower F-Statistic in the Community NICU regression that is still well above 10. The corresponding panels of Table 2.12 present the OLS and 2SLS squares
28 All zip code level variables are calculated from the 2000 Census. Unfortunately, the 1990 census does not provide comparable data at the zip code level.
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results, with each row listing coe?cient estimates from one regression. The OLS and 2SLS coe?cient and standard error estimates in speci?cations controlling for zip code level characteristics are very similar to the baseline estimates. Controlling for di?erences between urban and suburban zip codes does not impact the results. The last row of Panel B presents results when the instrument set includes interactions with population density. The standard errors of these estimates are very similar to the speci?cation with zip code level controls and the baseline speci?cation; however, the coe?cients all move towards zero and none are statistically signi?cant. If anything, allowing the e?ect of distance to di?er with population density results in point estimates that are even closer to zero.
2.6.2.2 Zip Code of Residence Fixed E?ects
Next, I estimate models with zip code of residence ?xed e?ects to control for any other characteristics that are constant within a zip code, but not accounted for by the census data controls. Identi?cation with these ?xed e?ects comes from changes over time in a zip code’s distances to each level of care caused by new, upgraded, or closed NICUs nearby during the sample period. Thus, the variation in distance is directly driven by deregionalization during the sample period. 25% of the VLBW sample lives in a zip code that at some point between 1991 and 2001 experiences a change in Intermediate Distance, and the average change is 4.5 miles. 32% lives in a zip code that experiences a change in Community Distance, and the average change is 3.9 miles. Figure 2.5 maps zip codes that become no closer, slightly closer (changes below the median), and much closer (changes above the median) to Community NICUs, with Panel A showing the whole state and Panel B focusing on the Los Angeles metro area. Zip codes with large changes in distance are more likely to be in outlying areas, but there are many neighboring zip codes experiencing di?erent changes in both urban and suburban areas. Figure 2.6 shows similar maps 47
for Intermediate distance. With ?xed e?ects the instruments are valid if zip code level changes in distance are uncorrelated with zip code level changes in unobserved mortality.29 Even if zip codes at di?erent distances di?er systematically, identi?cation will only be threatened if unobserved mortality trends are conditionally correlated with changes in distance. Figure 2.7 shows that at least trends in mean observable demographic and underlying health variables do not systematically di?er between zip codes experiencing di?erent changes in distance. This ?nding of parallel trends is not surprising given the evidence that deregionalization has not been driven by the health needs of high-risk infants. Panel C of Tables 2.11 and 2.12 show ?rst stage and second stage results with zip code ?xed e?ects, respectively. The instruments are still strong predictors of level of care chosen with large, positive, and statistically signi?cant coe?cients along the diagonal. The F-Statistics are lower than in the cross sectional speci?cations, but they are all above 16 without population density interactions and above 11 with the interactions. OLS results in Table 2.12 are similar to the cross sectional results. The 2SLS estimates are again not statistically signi?cant. The ?xed e?ects lead to much larger standard errors and more negative point estimates of the No NICU and Community NICU coe?cients, but the qualitative results are similar: negative or small point estimates, indicating no di?erence in mortality outcomes by level of care at the birth hospital. When the instruments are allowed to vary with population density, the negative point estimates of the No NICU and Community NICU coef?cients are cut by about two thirds and move towards zero as in the speci?cations without ?xed e?ects. These speci?cations con?rm that the main results are robust to the most complete possible controls for local characteristics. They also show that the cross sectional 2SLS speci?cations estimate similar e?ects to speci?cations
¨ izt ] = 0, where the dots indicate variables in deviation¨ zt ? Formally, the assumption is E [D ¨izt |X from-zip-code-mean-form.
29
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identi?ed directly from changes in distance related to deregionalization.
2.6.2.3 Pooling No NICUs and Intermediate NICUs
I also estimate models where I pool No NICU and Intermediate NICU hospitals into one category. Only 7.6% and 11.1% of the VLBW sample are born in these two types of hospitals, respectively. Thus combining them into one group may provide more precision. Additionally, some of the ?rst stage predictions of these indicators are outside the unit interval. Pooling these two groups reduces the percentage of observations with at least one of their ?rst stage predictions outside the unit interval from 12.8% to 2.7%. It is also likely medically reasonable to pool these two groups. Neither of these types of hospitals is designed to care for VLBW infants, and neither provides mechanical ventilation. Additionally, infants born at both levels of care have very similar transfer patterns. About 60% of VLBW infants born at these two levels of care are transferred to Regional NICUs. In contrast, only 20% of infants born in Community NICU hospitals are transferred to Regional NICUs. Given the likely similarity of care, it is not surprising that a ?2 test that the 2SLS No NICU and Intermediate NICU coe?cient estimates from the main speci?cation are the same does not reject the null hypothesis (p-value=0.24). Panel D of Tables 2.11 indicates that distance is still a strong predictor of level of care with even larger F-statistics than in the original estimation. In Panel D of Table 2.12 OLS estimates are as expected, with a similar Community NICU coef?cient estimate to the baseline speci?cation and coe?cient estimates of the pooled No/Intermediate NICU coe?cient between the original No NICU and Intermediate NICU coe?cient estimates. The precision gains in the 2SLS estimates are not large, but the point estimates are closer to zero, and none of them are statistically signi?cant negative estimates. 49
2.6.2.4 Alternative Control Variables and Clustering
Table 2.13 presents estimate from six other alternative speci?cations, with the baseline speci?cation repeated in Column 1. Columns 2 through 5 test whether the results change of I include various di?erent health related controls. Column 2 adds an indicator for whether the infant was delivered by cesarean section or not. I do not include this control in the main speci?cation because, as a treatment decision, it may be endogenous to the level of neonatal intensive care at the birth hospital. Despite this concern, adding it as a control variable does not appreciably change the OLS or 2SLS estimates. Column 3 and 4 provide evidence that my results are not sensitive to how I control for birth weight. In these two columns, I interact the birth weight indicators with the male dummy and re-specify the birth weight indicators in 50-gram increments instead of 100-gram increments, respectively. Both alternative speci?cations lead to OLS and 2SLS estimates that are similar to the baseline estimates. Column 5 replaces the dummy indicating whether an infant has any of the de?ned clinical conditions with a full set of indicators for each of the nine di?erent conditions.30 Again, the results are quite similar to the baseline estimates. The last two columns of Table 2.13 explore whether the standard error estimates change if the level of clustering is changed. To this point, standard error estimates have been clustered at the zip code level to allow unobserved mortality to be correlated within zip codes. Column 6 allows for more conservative geographic correlation by clustering at the HSA (Hospital Service Area) level. These HSAs are collections of zip codes for which most of their Medicare patients receive care from the same hospital.31 While these areas are calculated only with Medicare patients, they are likely good proxies for general health care markets. My sample includes
30 The nine conditions include hydrops due to isoimmunization, hemolytic disorders, fetal distress, fetus a?ected by maternal condition, oligohydramnios, other high-risk maternal conditions, placenta hemorrhage, premature rupture of membrane, and prolapsed cord (Phibbs et al., 2007). 31 Source:http://gonzo.dartmouth.edu/faq/data.shtm, last accessed May 17, 2010.
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1,144 zip codes which are grouped into 192 HSAs. The standard error estimates remain virtually unchanged when clustering at this larger geographic level. If anything the 2SLS estimate of the community NICU coe?cient becomes a bit more precise.32 Column 7 clusters standard errors by hospital instead of by geography. Allowing unobserved mortality to be correlated within hospitals does slightly in?ate the standard errors beyond those allowing unobserved mortality to be correlated within geographic areas.
2.6.2.5 Alternative Mortality Measures
Results to this point indicate that OLS estimates overstate di?erences in neonatal mortality by level of care. This de?nition of mortality includes all deaths within 28 days of birth or within one year if an infant is continuously hospitalized since birth. It may be the case that results di?er for shorter or longer term measures of mortality. In Table 2.14 I present OLS and 2SLS estimates of the e?ect of level of care on 1-day, 28-day, and 1-year mortality, regardless of hospitalization time. In general results are similar to the baseline speci?cation, repeated in Column 1. OLS estimates reveal higher mortality in lower level hospitals. The point estimates increase as the mortality window increases, but increases in the mean mortality rate as the window lengthens imply the relative magnitudes are similar for each outcome. For all three additional mortality outcomes 2SLS estimates are well below the OLS estimates and statistically insigni?cant. The ?nding that OLS estimates overstate di?erences in mortality is robust to these alternative outcome measures.
Unreported estimates reveal very similar standard error estimates when clustering at the county level. As a caveat, the asymptotics for clustered standard errors require the number of clusters to approach in?nity while the cluster size is ?xed. There are only 39 counties in the data, so this speci?cation has a small number of large clusters.
32
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2.6.3 Heterogeneity and Local Average Treatment E?ects
Throughout the paper I have assumed a homogeneous e?ect of level of care on mortality for all VLBW infants. However, it is possible that the e?ect may vary by the infant’s characteristics. This is particularly important with instrumental variable estimates because they only estimate the impact of level of care on mortality for the sub-group of infants whose mothers choose level of care based on the instruments. If these “compliers,” who choose their level of care because of distance, are di?erent from the rest of the sample, these estimates will represent a local average treatment e?ect (LATE) (Angrist et al., 1996; Imbens and Angrist, 1994). I cannot directly observe the compliers in my data, but one might be concerned that the 2SLS estimates are driven by a particular group of observations if these compliers di?er from the general population. I therefore estimate my OLS and 2SLS regression equations on various sub-samples based on observable characteristics to ensure the estimates are not being driven by any particular groups. Understanding any heterogeneity in this e?ect is also important for policy implications. If there are sub-groups for whom there is a gradient between level of care and mortality, interventions may be warranted to target these speci?c groups and ensure they are able to deliver in higher level hospitals. Table 2.15 presents results for various subsamples with the baseline estimation from Table 2.8 repeated in Column 1. Overall, the OLS and 2SLS coe?cient estimates are similar across all reported sub-groups. OLS estimates are positive and statistically signi?cant, and 2SLS estimates are small and mostly statistically insigni?cant. Column 2 shows the results for infants of Hispanic mothers. The 2SLS estimate of the e?ect of being born in an Intermediate NICU hospital (0.025) is close to the OLS coe?cient estimate (0.028) for this group, but still statistically insigni?cant. The other two 2SLS coe?cient estimates are negative, statistically insigni?cant, and similar to the baseline sample. 52
Column 3 excludes infants of black mothers from the estimation. Infants with black mothers make up a small subset of the sample, so I do not estimate the regressions for them alone, but excluding them does not have much e?ect on the estimates. This sample also provides a useful robustness check because of the di?erence in percent black by di?erential distance reported in Table 2.5. The estimates for the population of infants with mothers covered by Medicaid in Column 4 are similar to the baseline speci?cation, indicating the results are similar by insurance coverage. The sample of infants whose mothers have no college education in Column 5 has a 2SLS Intermediate NICU coe?cient that is the same as the OLS estimate (0.027), but again it is not statistically signi?cant and the other 2SLS coe?cient estimates are negative. In the previous section I show the results are robust to controlling for population density and allowing the e?ect of distance to di?er by population density. One might also be concerned that the results are driven by either urban or suburban areas, which I address in Column 6. This column presents estimates for the subsample whose zip code population density is below the median. Again, the 2SLS coe?cient on being born in an Intermediate NICU hospital (0.022) is similar to the OLS coe?cient (0.028), but the other estimates are similar to the baseline speci?cation, indicating the results are similar for individuals in urban and suburban areas. Finally, the e?ect of level of care may have changed over time. Mortality rates for VLBW infants decreased during the early 1990s, but leveled o? during the latter part of the decade (Horbar et al., 2002).33 Also, Table 2.2 shows that the di?usion of NICUs leveled o? during the second half of the decade. It is possible that the gradient between level of care and mortality changed during this time period if technology improved, if new NICUs improved over time due to learning, or if the
33 In my sample, mean neonatal mortality fell from 20.08% to 14.80% between 1991 and 1995, but only fell to 13.62% by 2001.
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propensity for lower level units to transfer infants to higher levels changed over time. Column 7 presents results for births occurring during the ?rst half of the sample from 1991 to 1995. The OLS gradient between level of care is greater during this time period as compared to results for the full time period, but because mean mortality was higher during the earlier period, the relative e?ects are similar. The 2SLS estimates are similarly small and statistically insigni?cant as compared to the baseline estimation. There is no evidence of a di?erential e?ect of level of care on mortality over time. Despite evidence that the e?ect of level of care on mortality does not vary by observable characteristics, there still may be unobserved heterogeneity. If there are heterogeneous treatment e?ects that vary by unobservables, 2SLS would estimate a LATE for a group of compliers that are not identi?able in the data. That being said, because the compliers are the infants whose mothers choose their delivery hospital based on distance, the LATE would in fact be the policy relevant e?ect. Even if the 2SLS estimates do not represent the e?ect of level of care on mortality for the entire population, they still imply that the population that would be impacted by policy measures regarding the geographic distribution of NICUs does not experience di?erent mortality rates by level of care.
2.6.4 Sample Selection
In this section I ensure that my estimates are not sensitive to the sample restrictions discussed in Section 2.3. The ?rst column of Table 2.16 repeats the estimates from the main speci?cation in Table 2.8. Columns 2 through 5 report results including various groups that were excluded from the main analysis sample. Column 2 includes infants in the most rural counties, Column 3 includes infants born in Kaiser hospitals, Column 4 includes infants diagnosed with a congenital anomaly, and Column 5 includes fetal deaths. 54
These estimates reveal that the OLS and 2SLS estimates are not appreciably a?ected by these sample restrictions. If anything, including rural residents results in 2SLS estimates that are closer to zero, although excluding these observations is still probably best, since they are likely to live furthest from all hospitals and may be unobservably di?erent from those living close to all hospitals. Including deliveries in Kaiser hospitals has little e?ect on the estimation as well. Results of ?rst stage regressions for this sample alone, not shown here, reveal that these added observations do not choose hospitals based on distance; therefore, they do not contribute to the 2SLS estimates, so it is not surprising that the results are not a?ected by including them. Including infants with congenital anomalies leads to higher coe?cient estimates in the OLS speci?cation, but similar 2SLS estimates to the baseline speci?cation. Finally, including observations of infants who die before delivery approximately doubles the magnitude of both the OLS and 2SLS coe?cient estimates. The mean mortality rate for this sample is almost twice that of the main analysis sample, so the relative e?ects are very similar. This ?nding indicates that di?erences in level of care do not di?erentially impact the probability of death prior to delivery.
2.7 Conclusion
This chapter estimates the causal e?ects of level of neonatal intensive care at the birth hospital on VLBW mortality. The issue of deregionalization – the increasing number of smaller, community hospitals opening NICUs – has gained attention in the health policy community. Evidence of higher risk-adjusted mortality rates for VLBW infants born in hospitals with lower level NICUs has led advocates to suggest high-risk mothers be referred to more sophisticated hospitals prior to delivery. However, these estimates could be biased in either direction by unobserved selection. To overcome selection concerns, I utilize an instrumental variables strat55
egy that exploits exogenous variation in distance from a mother’s residence to the nearest hospital of each level of care. NICU location has been driven by factors unrelated to the health of VLBW infants, and I provide evidence in my data that distance is uncorrelated with health conditions. My OLS estimates con?rm the previous literature and imply 7.6%, 13.4%, and 31.8% higher risk-adjusted mortality rates for VLBW infants born in Community, Intermediate, and No NICU hospitals, respectively, relative those born in Regional NICUs hospitals. However, my instrumental variables estimates imply that these mortality di?erences are overstated. 2SLS estimates are not statistically di?erent from zero and are small in magnitude. The No NICU and Community NICU 2SLS coe?cient estimates are bounded well below their OLS counterparts, with even two standard deviations above the 2SLS estimates lying about 50% below the OLS estimates. The Intermediate NICU 2SLS coe?cient estimate is not clearly bounded below the OLS estimate, but the point estimate is half the magnitude. My results are robust to controlling for zip code level characteristics and zip code level ?xed e?ects. I also ?nd no evidence that the e?ect of level of care on mortality is heterogeneous by demographics. Even if the e?ect varies on other unobservable dimensions, any unobserved heterogeneity would lead to a local average treatment e?ect directly identi?ed from infants impacted by deregionlization. The fact that the 2SLS estimates are below the OLS estimates, reveals that mothers with higher unobserved risk select into hospitals with lower levels of care. In terms of mortality, these results imply that relocating high-risk deliveries to Regional NICU hospitals prior to birth will not result in improved health outcomes. Instead, Regional hospitals would be treating new patients with higher unobserved acuity. I also show evidence that level of care at the birth hospital does not impact mortality because infants born in No NICU and Intermediate NICU hospitals are often transferred to Regional NICU hospitals, and these transfers are independent
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of how close mothers live to lower level facilities. Deregionalization does not appear to prevent infants born in No NICU or Intermediate NICU hospitals from eventually receiving care in Regional NICUs. This analysis has addressed the ?rst-order question of how deregionalization has impacted VLBW mortality. Future research is needed to understand the full welfare impacts of this trend. First, while mortality may not vary by level of care at the birth hospital, there may be important di?erences in cost of care. If larger hospitals achieve economies of scale, they may be more e?cient in treating sick infants. Inter-hospital transfers may also be costly, both monetarily and emotionally. Alternatively, more sophisticated facilities may provide more costly procedures with little marginal return. Second, there may be important e?ects of deregionalization on quality and cost of care for healthier infants. Chapter 3 examines one aspect of this question and ?nds that additional short term NICU supply leads to a higher probability of NICU admission for infants above the very low birth weight threshold. Third, if mothers value shorter travel time and more convenient visitation of family members, access to at least some level of intensive care at nearby hospitals may increase utility. Also, more competition in the neonatal intensive care market may lead to lower prices. Ho et al. (2007) study the market for Whipple surgery, a treatment for pancreatic cancer, and ?nd that regionalizing this treatment by consolidating it to the hospitals with the highest volume leads to substantial price increases.34 Finally, further research is warranted to understand the determinants of NICU adoption by hospitals and whether hospitals are able to recoup their ?xed costs by attracting pro?table patients.
These authors do ?nd that regionalization of Whipple surgery can reduce mortality, but price increases cancel out over half of the increased consumer surplus.
34
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Figure 2.1: NICU Location by Level of Care in 1991
Notes: The light gray lines outline counties in the San Fransisco Bay, Los Angeles Metro, and San Diego Metro areas. See text for de?nitions of the levels of care.
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Figure 2.2: Miles Saved to Nearest Community NICU or Higher, 1991
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(a) Full State (b) LA Metro Area Notes: These ?gures shade zip codes based on the number of miles a mother living at the center of the zip code saves by choosing the nearest Community NICU or higher over the nearest Regional NICU. Zip codes shaded in white indicate no very low birth weight births in my analysis sample. Remaining zip codes are divided into three groups: those saving zero miles, and those above and below the median conditional on non-zero di?erential distance. The dark lines in Panel A outline counties in the San Fransisco Bay, Los Angeles Metro, and San Diego Metro areas.
Figure 2.3: Miles Saved to Nearest Intermediate NICU or Higher, 1991
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(a) Full State (b) LA Metro Area Notes: These ?gures shade zip codes based on the number of miles a mother living at the center of the zip code saves by choosing the nearest Intermediate NICU or higher over the nearest Regional NICU. Zip codes shaded in white indicate no very low birth weight births in my analysis sample. Remaining zip codes are divided into three groups: those saving zero miles, and those above and below the median conditional on non-zero di?erential distance. The dark lines in Panel A outline counties in the San Fransisco Bay, Los Angeles Metro, and San Diego Metro areas.
Figure 2.4: Coe?cient Estimate Magnitudes
Notes: This ?gure plots the OLS and 2SLS coe?cient estimates from Table 2.8 divided by mean neonatal mortality (15.7%). The dashed points indicate one and two standard deviation intervals above the 2SLS coe?cient estimates.
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Figure 2.5: Changes in Community Distance, 1991 to 2001
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(a) Full State (b) LA Metro Area Notes: These ?gures shade zip codes based on changes from 1991 to 2001 in the number of miles a mother living at the center of the zip code saves by choosing the nearest Community NICU or higher over the nearest Regional NICU. Zip codes shaded in white indicate no very low birth weight births in my analysis sample. Remaining zip codes are divided into three groups: those that become no closer, slightly closer (changes below the median), and much closer (changes above the median). The dark lines in Panel A outline counties in the San Fransisco Bay, Los Angeles Metro, and San Diego Metro areas.
Figure 2.6: Changes in Intermediate Distance, 1991 to 2001
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(a) Full State (b) LA Metro Area Notes: These ?gures shade zip codes based on changes from 1991 to 2001 in the number of miles a mother living at the center of the zip code saves by choosing the nearest Intermediate NICU or higher over the nearest Regional NICU. Zip codes shaded in white indicate no very low birth weight births in my analysis sample. Remaining zip codes are divided into three groups: those that become no closer, slightly closer (changes below the median), and much closer (changes above the median). The dark lines in Panel A outline counties in the San Fransisco Bay, Los Angeles Metro, and San Diego Metro areas.
Figure 2.7: Demographic and Health Trends by Changes in Distance
(a) Demographics by ? IntDist
(b) Demographics by ? ComDist
(c) Health Characteristics by ? IntDist
(d) Health Characteristics by ? ComDist
Notes: These ?gures plot means of mothers’ demographic and infants’ health characteristics by changes in di?erential distance to Intermediate and Community NICUs. Observations are divided into three groups based on whether the zip code of residence becomes no closer, slightly closer (changes below the median), or much closer (changes above the median) to the respective level of care between 1991 and 2001. N=42,912.
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Table 2.1: Detailed Level of Care De?nitions Level I Care Provided Basic neonatal care for healthy infants No Intensive Care Unit Have an intensive care unit Care for midly ill infants Do not provide mechanical ventilation Provide mechanical ventilation with restrictions (e.g., only for less than 96 hours, or only for infants weighing above 1,000 grams) Provide mechanical ventilation without restrictions Provide major neonatal surgery excluding cardiac surgery requiring bypass and/or extracorporeal membrane oxygenation (ECMO) Provide cardiac surgery requiring bypass and/or ECMO
II
IIIA
IIIB IIIC
IIID
Notes: Level of neonatal care de?nitions from Phibbs et al. (2007). There are three ICD-9 CM codes indicating mechanical ventilation: 96 hours, and duration unknown. Hospitals with NICU beds that do not have occurrences of any of these codes are labeled as Level II. In distinguishing between Level IIIA and IIIB, Phibbs et al. (2007) count units that never provide ventilation for more than 96 hours as IIIA. For units that provide both types but do not provide any surgery, they examine the patterns of ventilation by duration and birth weight to distinguish which appear to have restrictions.
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Table 2.2: California Obstetric Hospitals by Year and Level of Care No Intermediate NICU NICU 161 153 149 147 148 140 141 139 135 130 122 58 52 53 56 49 48 47 45 44 45 45 Community NICU 35 43 45 45 51 54 55 58 60 57 57 Regional NICU 42 44 45 45 46 46 46 46 46 45 45
Year 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Total 296 292 292 293 294 288 289 288 285 277 269
Notes: Author’s tabulations based on data from Phibbs et al. (2007) and OSHPD Annual Utilization Files. See level of care de?nitions in text.
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Table 2.3: Sample Means by Level of Care at Birth Hospital
No NICU Mother’s Demographics Age Black Hispanic Medicaid HMO Self Pay No College Some College College Infant Characteristics Month Prenatal Care Began # of Prenatal Visits Parity Male Multiple Birth Birth Weight (Grams) Gestation (Weeks) Clinical Condition Small for Gest. Large for Gest. Treatment Total Length of Stay Total Charges ($1,000s) Charges/Day ($1,000s) Ventilation Transfer Outcomes 28 Day Mortality 1 Year Mortality Neonatal Mortality 28 Day Readmission 1 Yr Readmission Observations # of Hospitals 25.781 0.098 0.567 0.591 0.148 0.095 0.788 0.151 0.061 2.323 6.692 2.349 0.542 0.167 1067.017 30.079 0.153 0.034 0.008 39.179 156.595 1.656 0.136 0.706 0.202 0.235 0.219 0.043 0.223 3,268 142 Intermediate NICU 26.861 0.205 0.374 0.546 0.213 0.045 0.679 0.199 0.122 2.321 8.209 2.358 0.521 0.210 1063.166 30.083 0.192 0.049 0.009 44.197 158.987 2.894 0.235 0.638 0.150 0.185 0.169 0.036 0.240 4,788 49 Community NICU 27.939 0.128 0.454 0.455 0.276 0.031 0.643 0.195 0.162 2.190 8.718 2.209 0.512 0.218 1064.203 29.836 0.237 0.065 0.012 50.828 204.456 4.059 0.571 0.209 0.139 0.167 0.155 0.011 0.204 10,136 51 Regional NICU 28.084 0.186 0.434 0.508 0.212 0.022 0.654 0.183 0.163 2.202 8.873 2.289 0.511 0.244 1055.371 29.928 0.306 0.055 0.021 53.319 228.216 4.136 0.556 0.114 0.131 0.160 0.147 0.007 0.198 24,720 45
Notes: Columns display sample means for infants delivered in hospitals at four levels of care. Total Length of Stay and Total Charges sum length of stay and hospital charges over all contiguous hospitalizations prior to ?rst being discharged home or dying. Neonatal mortality is mortality within twenty-eight days of birth or within one year if an infant is continuously hospitalized since birth. Number of hospitals indicates the average number of hospitals providing each level of care over the 11-year sample. See Table 2.2 for number of hospitals by year.
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Table 2.4: Summary Statistics of Distance Variables Mean D(No+) D(Int+) D(Com+) D(Reg) NoDist IntDist ComDist N SD
3.673 4.206 5.709 8.065 8.064 11.983 14.830 22.991 11.156 21.723 9.120 20.249 6.766 18.446 42,912
Notes: The ?rst four rows show the mean and standard deviation of distance to the nearest hospital o?ering each level of care or higher. The next three rows show the mean and standard deviation of di?erential distance to the nearest hospital o?ering each level of care or higher relative to the nearest Regional NICU.
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Table 2.5: Sample Means by Distance
By Miles Saved to Nearest No + 0 Distance Miles Saved No+ Miles Saved Int+ Miles Saved Com+ Mother’s Demographics Age Black Hispanic Medicaid HMO Self Pay No College Some College College Infant Characteristics Mth Prenatal Care Began # of Prenatal Visits Parity Male Multiple Birth Birth Weight (Grams) Gestation (Weeks) Clinical Condition Small for Gest. Large for Gest. Zip Code Characteristics Med HH Income ($1,000) Percent Urban Population Density Observations 0.000 0.000 0.000 27.826 0.227 0.434 0.519 0.213 0.031 0.684 0.170 0.146 2.240 8.196 2.390 0.502 0.223 1056.087 30.033 0.281 0.061 0.018 40.511 0.986 8683.053 9,247 Median 26.378 21.695 16.277 27.517 0.107 0.431 0.488 0.227 0.035 0.650 0.198 0.152 2.220 8.679 2.259 0.522 0.236 1060.439 29.905 0.242 0.051 0.013 46.599 0.924 3162.460 16,889 By Miles Saved to Nearest Int + 0 1.605 0.000 0.000 27.787 0.208 0.475 0.524 0.205 0.034 0.694 0.171 0.135 2.241 8.277 2.370 0.506 0.218 1056.993 29.980 0.273 0.058 0.018 40.221 0.978 9185.203 14,585 Median 28.981 25.604 19.219 27.490 0.102 0.436 0.493 0.224 0.033 0.650 0.197 0.153 2.210 8.728 2.246 0.522 0.236 1059.441 29.859 0.236 0.050 0.013 46.690 0.925 3312.801 14,180 By Miles Saved to Nearest Com + 0 4.010 2.177 0.000 27.752 0.207 0.438 0.521 0.211 0.033 0.676 0.178 0.147 2.244 8.537 2.316 0.512 0.227 1060.140 29.975 0.287 0.057 0.018 43.265 0.973 8142.096 21,440 Median 32.040 28.958 25.420 27.411 0.095 0.462 0.499 0.224 0.030 0.661 0.197 0.142 2.194 8.683 2.270 0.523 0.226 1056.107 29.860 0.212 0.050 0.013 44.694 0.923 3456.633 10,753
69
Notes: The ?rst three columns display sample means by di?erential distance to the nearest hospital with any obstetric services, the second three columns by di?erential distance to the nearest Intermediate NICU or higher, and the ?nal three columns by di?erential distance to the nearest Community NICU or higher. For each set of columns, the sample is divided into three groups: those with zero di?erential distance, and those above and below the median conditional on non-zero di?erential distance.
Table 2.6: Neonatal Mortality by Level of Care, OLS Estimates Dependent Variable: Neonatal Mortality (1) I(No NICU) 0.072** (0.009) 0.022** (0.007) 0.008* (0.004) (2) 0.072** (0.008) 0.021** (0.006) 0.013** (0.004) X (3) 0.054** (0.009) 0.017** (0.006) 0.010** (0.004) X X (4) 0.050** (0.007) 0.021** (0.005) 0.012** (0.004) X X X
I(Intermediate NICU)
I(Community NICU)
Time FE Demographics Health Controls
Notes: Each column lists estimates with standard errors in parentheses (clustered at the zip code level) from separate regressions of neonatal mortality on indicators for delivery in a hospital with No NICU, an Intermediate NICU, and a Community NICU. Regional NICU is the excluded group. The columns successively add controls. Time ?xed e?ects include year dummies, month-of-year dummies, and day-of-week dummies. Demographics include age, age squared, race, ethnicity, and insurance coverage. Health controls include number of prenatal care visits, month in which prenatal care began, parity, sex, multiple birth status, an indicator for having a clinical condition, indicators for small and large for gestational age, and birth weight dummies at 100 gram increments. N = 42,912; * p
An infant (from the Latin word infans, meaning "unable to speak" or "speechless") is the very young offspring of a human.
ABSTRACT
Title of dissertation:
EMPIRICAL ESSAYS ON THE ECONOMICS OF NEONATAL INTENSIVE CARE Seth M. Freedman Doctor of Philosophy, 2010
Dissertation directed by:
Judith K. Hellerstein Department of Economics
The number of neonatal intensive care units (NICUs) in smaller community hospitals increased greatly during the 1980s and 1990s, attracting deliveries away from hospitals with the most sophisticated NICUs. This pattern of “deregionalization” has caused concern because previous studies ?nd higher mortality rates for high-risk infants born in hospitals with less sophisticated NICUs relative to those born in hospitals with the highest care level. In this dissertation, I provide causal estimates of the e?ect of deregionalization on infant health outcomes and treatment intensity. In Chapter 2, I argue that previous estimates of the relationship between the level of care at a high-risk infant’s birth hospital and mortality may be biased by unobserved selection. To estimate a causal relationship, I use an instrumental variable strategy that exploits exogenous variation in distance from a mother’s residence to hospitals o?ering each level of care. My instrumental variable estimates are bounded well below ordinary least squares estimates and are not statistically di?erent from
zero. These results suggest that relocating patients to hospitals with the highest level of care prior to delivery may not lead to improved mortality outcomes, because infants currently born in lower level facilities have higher unobserved mortality risk. I also provide suggestive evidence that inter-hospital transfer after birth is one mechanism by which infants born at the lowest levels of care achieve similar outcomes to those born at higher level hospitals. In Chapter 3, I test whether additional neonatal intensive care supply leads to excess neonatal intensive care utilization. I exploit within hospital-month variation in the number of vacant NICU beds in an infant’s birth hospital the day prior to birth as a source of exogenous variation in supply. I ?nd that the e?ect of empty beds on NICU admission is positive but very small for the highest risk infants as measured by very low birth weight. However, it is larger for infants with birth weights above this threshold. These results suggest that additional supply of neonatal intensive care resources can lead to increased utilization of intensive care for infants above the very low birth weight threshold.
EMPIRICAL ESSAYS ON THE ECONOMICS OF NEONATAL INTENSIVE CARE
by Seth Michael Freedman
Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial ful?llment of the requirements for the degree of Doctor of Philosophy 2010
Advisory Committee: Professor Judith Hellerstein, Chair Professor John Chao Professor Darrell Gaskin Professor Ginger Jin Professor Melissa Kearney
c Copyright by Seth Michael Freedman 2010
Dedication
To my wife Krista for her never ending love, support, and encouragement throughout this entire process.
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Acknowledgments
I am particularly indebted to Judy Hellerstein for her advice, support, and insight throughout this dissertation and my graduate studies. I am also grateful to Melissa Kearney and Ginger Jin for their many helpful suggestions and generous advice and support. I also want to thank John Cawley, Mark Duggan, Bill Evans, Craig Garthwaite, John Ham, Mara Lederman, Soohyung Lee, Tim Moore, John Shea, and participants at various seminars, particularly at the University of Maryland, for helpful comments and suggestions. Thank you to John Chao and Darrell Gaskin for serving on my dissertation committee. I would like to gratefully acknowledge Bill Evans, Mark Duggan, Judy Hellerstein, and the University of Maryland Department of Economics for ?nancial support in purchasing data. I would also like to acknowledge ?nancial support from AHRQ Dissertation Grant 1R36HS018266-01, which funded much of the work in this dissertation. I am also grateful to Ciaran Phibbs for sharing data on levels of neonatal intensive care at California hospitals and to OSHPD for assistance with the inpatient data. The content of this work does not represent the views of AHRQ or OSHPD. All errors are my own. Finally, I would like to thank my family and friends for all of their support and encouragement over the past ?ve years.
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Table of Contents
List of Tables List of Figures 1 Introduction 2 The E?ect of Deregionalization on Health Outcomes: Evidence from Neonatal Intensive Care 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Previous Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Previous Estimates of Mortality Di?erences by Level of Care . 2.2.2 Natural Experiments in Health Research . . . . . . . . . . . . 2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Linked Birth Data . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Hospital Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Empirical Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Baseline Model . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Estimating Causal E?ects . . . . . . . . . . . . . . . . . . . . 2.4.3 The Instruments . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 OLS Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 First Stage Estimates . . . . . . . . . . . . . . . . . . . . . . . 2.5.3 2SLS Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Robustness Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.1 Additional Tests of Instrument Validity . . . . . . . . . . . . . 2.6.2 Alternative Speci?cations . . . . . . . . . . . . . . . . . . . . 2.6.2.1 Zip Code of Residence Controls . . . . . . . . . . . . 2.6.2.2 Zip Code of Residence Fixed E?ects . . . . . . . . . 2.6.2.3 Pooling No NICUs and Intermediate NICUs . . . . . 2.6.2.4 Alternative Control Variables and Clustering . . . . 2.6.2.5 Alternative Mortality Measures . . . . . . . . . . . . 2.6.3 Heterogeneity and Local Average Treatment E?ects . . . . . . 2.6.4 Sample Selection . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 The 3.1 3.2 3.3 E?ect of Neonatal Intensive Care Availability Introduction . . . . . . . . . . . . . . . . . . Previous Literature . . . . . . . . . . . . . . Data . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Data Sources . . . . . . . . . . . . . 3.3.2 Imputing NICU Admission . . . . . . 3.3.3 Analysis Sample . . . . . . . . . . . . 3.4 Empirical Framework . . . . . . . . . . . . . iv on Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi viii 1
13 13 17 17 18 20 20 23 24 25 28 33 37 37 39 40 43 43 45 45 47 49 50 51 52 54 55 81 81 87 91 91 92 94 96
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3.5
3.6
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Summary Statistics . . . . . . . . . . . . . . . . . 3.5.2 The E?ect of Empty Beds on NICU Admission . 3.5.3 The Mitigating E?ects of Inter-Hospital Transfer 3.5.4 Hospital Level Heterogeneity . . . . . . . . . . . . 3.5.5 Individual Level Heterogeneity . . . . . . . . . . . 3.5.6 Robustness . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . .
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101 101 105 109 110 114 117 119
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List of Tables
2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 Detailed Level of Care De?nitions . . . . . . . . . . . . . . . . . . . . 65 California Obstetric Hospitals by Year and Level of Care . . . . . . . 66 Sample Means by Level of Care at Birth Hospital . . . . . . . . . . . 67 Summary Statistics of Distance Variables . . . . . . . . . . . . . . . . 68 Sample Means by Distance . . . . . . . . . . . . . . . . . . . . . . . . 69 Neonatal Mortality by Level of Care, OLS Estimates . . . . . . . . . 70 Level of Care by Distance, First Stage Estimates . . . . . . . . . . . . 71 Neonatal Mortality by Level of Care, 2SLS Estimates . . . . . . . . . 72 Level of Care by Distance for Heavier Infants . . . . . . . . . . . . . . 73
2.10 Reduced Form Estimates . . . . . . . . . . . . . . . . . . . . . . . . . 74 2.11 Alternative Speci?cations: First Stage Estimates . . . . . . . . . . . . 75 2.12 Alternative Speci?cations: OLS & 2SLS Estimates . . . . . . . . . . . 76 2.13 Alternative Control Variables and Clustering . . . . . . . . . . . . . . 77 2.14 Alternative Mortality Measures . . . . . . . . . . . . . . . . . . . . . 78 2.15 Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 2.16 Robustness to Sample Restrictions . . . . . . . . . . . . . . . . . . . 80 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 Constructing Analysis Sample . . . . . . . . . . . . . . . . . . . . . . 126 Sample Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Summary Statistics of Empty Beds . . . . . . . . . . . . . . . . . . . 129 Sample Means by Residual Empty Beds . . . . . . . . . . . . . . . . 130
E?ect of Empty Beds on NICU Admission . . . . . . . . . . . . . . . 132 Mitigating E?ects of Inter-Hospital Transfers . . . . . . . . . . . . . . 133 Heterogeneous E?ects by Hospital Characteristics – NICU Admission 134 Heterogeneous E?ects by Hospital Characteristics – NICU Admission or Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 vi
3.9
Heterogeneous E?ects by Individual Characteristics – NICU Admission136
3.10 Heterogeneous E?ects by Individual Characteristics – NICU Admission or Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 3.11 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
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List of Figures
2.1 2.2 2.3 2.4 2.5 2.6 2.7 3.1 3.2 3.3 3.4 NICU Location by Level of Care in 1991 . . . . . . . . . . . . . . . . 58 Miles Saved to Nearest Community NICU or Higher, 1991 . . . . . . 59 Miles Saved to Nearest Intermediate NICU or Higher, 1991 . . . . . . 60 Coe?cient Estimate Magnitudes . . . . . . . . . . . . . . . . . . . . . 61 Changes in Community Distance, 1991 to 2001 . . . . . . . . . . . . . 62 Changes in Intermediate Distance, 1991 to 2001 . . . . . . . . . . . . 63 Demographic and Health Trends by Changes in Distance . . . . . . . 64 Hospital Level NICU Admission Density . . . . . . . . . . . . . . . . 122 Very Low Birth Weight, Mortality, and NICU Admission Over Time . 123 E?ect of Empty Beds on NICU Admission by Birth Weight . . . . . . 124 E?ect of Empty Beds on NICU Admission by Gestation . . . . . . . . 125
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Chapter 1 Introduction
Rising health care costs are a fundamental problem facing the United States economy. Health care currently accounts for about 16% of GDP and is projected to grow to about 19% percent by 2019.1 This rapid cost growth was one of the primary motivations behind the health reform passed in 2010. One of the major factors behind these rising costs are new medical technologies and service o?erings. On average, most of these new technologies have been worthwhile due to the overwhelming improvements in health that they are able to provide (Cutler and McClellan, 2001; Hall and Jones, 2007; Luce et al., 2006; Murphy and Topel, 2003). However, there is often concern that these services are not allocated optimally. The Dartmouth Atlas Project has documented large geographic variation in health expenditures which does not appear to be correlated with health outcomes (Baicker et al., 2006; Baicker and Chandra, 2004b; Fisher et al., 2003a,b; Fuchs, 2004), providing some evidence of “?at-of-the-curve” medicine, in which treatment is provided to the point where the marginal return is below the marginal cost (or even zero). This dissertation examines the organization of one particular medical service that displays these characteristics: Neonatal Intensive Care Units (NICUs). A NICU is a unit of the hospital that is separate from the traditional newborn nursery and is specially equipped to care for sick, preterm, and underweight infants. The original NICUs of the late 1960s and early 1970s provided incubation and sometimes mechanical ventilation. Since this time, technological innovations have greatly changed medical care for sick infants, and the most sophisticated NICUs are now able to
1 According to the Centers for Medicare & Medicaid Services:http://www.cms.gov/ NationalHealthExpendData/downloads/proj2009.pdf, last accessed on May 16, 2010.
1
provide extensive monitoring, proper nutrition, arti?cial surfactant, extracorporeal membrane oxygenation (ECMO), and various diagnostic tests and surgical procedures.2 These innovations have clearly led to improved outcomes for high-risk infants. For example, the 28-day mortality rate for infants weighing 1,000 to 1,499 grams (2.2 and 3.3 pounds) dropped from 52.2% to 6.7% between 1960 and 1990 (Cutler and Meara, 2000).3 Recent decades have seen a trend towards “deregionalization” of neonatal intensive care in which many smaller hospitals have adopted NICUs. Despite the large average gains in infant health that have been attributed to NICUs, this trend has worried organizations such as the March of Dimes and the American Academy of Pediatrics because previous studies have found higher mortality rates for high-risk infants born in hospitals with these smaller, less sophisticated NICUs compared to those born in hospitals with “Regional” NICUs (e.g., Cifuentes et al., 2002; Phibbs et al., 2007, 1996). However, the many potential e?ects of this deregionalization are not well understood. First, in terms of the ?rst-order question of the e?ects on the health of the high-risk infants NICUs are intended to treat, the previous estimates may in fact be biased by unobserved patient selection into hospitals. Depending on the mechanisms behind and the direction of this selection, the e?ect of the level of neonatal intensive care at an infant’s birth hospital on mortality could be biased in either direction; deregionalization could be more or less detrimental to infant mortality than previously thought. Second, there may be other e?ects of deregionalization beyond the quality of care delivered to high-risk infants. These e?ects could include changes in the quality of care of lower risk infants, di?erences in
Mechanical ventilation assists infants whose lungs have not fully developed to breath. Arti?cial surfactant treats respiratory distress syndrome by helping the lungs to develop. ECMO machines pump blood out of the infant, oxygenate it, and pump it back into the infant if the infant’s heart and lungs are too weak to oxygenate the blood on its own. 3 Accounting for the costs of these innovations and the value of both lives saved and quality of life for surviving infants, Cutler and Meara (2000) calculate a 510% rate of return to spending on infant health care between 1960 and 1990.
2
2
the intensity and cost of care, composition changes in who receives care, and utility gains for mothers who can choose more convenient hospitals o?ering NICUs. Given all of these potential e?ects, understanding the full welfare consequences of deregionalization would be a very di?cult undertaking. In this dissertation, I tackle two pieces of this puzzle. In Chapter 2, I revisit the question of how deregionalization has impacted mortality for very low birth weight infants. By exploiting exogenous variation in the distance from where mothers live to the nearest hospital o?ering each level of neonatal intensive care, I account for potential unobserved selection and estimate the causal e?ect of the level of care at the birth hospital on very low birth weight infant mortality. Chapter 3 considers the e?ect of the supply of neonatal intensive care on the level of utilization of these resources. I provide preliminary estimates of the e?ect of the number of empty NICU beds just prior to birth on the probability an infant is admitted to the NICU. I then examine how this e?ect varies across the birth weight distribution to di?erentiate how available supply a?ects utilization di?erently for high-risk and low-risk newborns. The remainder of this chapter provides further background information about neonatal intensive care and summarizes the results of Chapters 2 and 3. As neonatal intensive care developed in the 1970s, few doctors and nurses were trained in neonatology. As a result, specialists were located in regional care centers, typically associated with large teaching hospitals. In 1976 a March of Dimes report recommended that hospitals o?ering delivery services be classi?ed into three categories with the lowest providing no intensive care, and the highest providing the most complex care and acting as regional referral centers for high-risk mothers and infants (Committee on Perinatal Health, 1976).4
In general, Level I nurseries describe hospitals that provide basic birthing service and care for healthy infants. They have the facilities and sta? required for neonatal resuscitation, but must stabilize and transfer ill newborns to other facilities for further treatment. Level II nurseries treat moderately ill infants, and Level III units treat infants who are extremely premature, critically ill, or in need of surgery. In many cases, Level II and Level III units are further subdivided based on their abilities to provide mechanical ventilation, surgery, or ECMO. Additionally, units are often
4
3
Also in the late 1970s, the Robert Wood Johnson Foundation began the Regional Perinatal Care Program. This program was intended to explore the e?ects and feasibility of encouraging regional perinatal care encompassing pre- and postbirth care of mothers and infants, including neonatal intensive care. The program consisted of grants to eight sites across the country. The grants provided funds to improve record keeping, create a referral and transportation system, and conduct education and outreach. Anecdotally, these networks functioned well. Unfortunately, this program was di?cult to evaluate because many forces were leading to nationwide reductions in infant mortality rates and regionalization was occurring outside the study sites (Holloway, 2000). Over time the technologies and trained specialists necessary to operate NICUs became more prevalent, and NICU adoption became feasible for a wider array of hospitals (McCormick and Richardson, 1995). Despite the Regional Perinatal Care Program and the March of Dimes’ recommendations of a regionalized system, exactly the opposite began to occur over the 1980s and 1990s: there was a drastic increase in the number of NICUs, and many of the new entrants were smaller units in community hospitals (e.g., McCormick and Richardson, 1995; Schwartz, 1996; Schwartz et al., 2000). Moreover, while births increased by 17.6% between 1980 and 1995 in Metropolitan Statistical Areas (MSA), the number of hospitals with NICU beds doubled, the number of NICU beds more than doubled, and the number of neonatologists more than tripled (Howell et al., 2002).5 Additionally, American Hospital Association data reveal that 89% of the new NICUs that opened between 1980 and 1996 were lower level NICUs, as opposed to only 46% of the units established before 1980 (Baker and Phibbs, 2002).
labeled as Intermediate, Community, or Regional units. In Section 2.3 I describe how I classify level of care for my study. 5 Improving quality of care over time did lead to more infants surviving and spending longer periods of time in the NICU; however, Howell et al. (2002) calculate that by 1995 the number of available NICU bed-days exceeded medically necessary bed-days by a factor of 2.5.
4
Haberland et al. (2006) document that new lower level NICUs have in fact shifted deliveries of high-risk infants from Regional hospitals to the lower level hospitals in California. In a di?erence-in-di?erences framework, they show that becoming closer to a mid-level NICU, as a result of a new unit opening near a mother’s zip code of residence, increases the probability that a very low birth weight infant is born in a hospital with a mid-level NICU by 17 percentage points and decreases the probability of being born in a hospital with a Regional NICU by 15 percentage points.6 Based on evidence that mortality rates are higher for infants born in hospitals with lower level NICUs, discussed in detail in Chapter 2, and the course of deregionlization, the March of Dimes rea?rmed its recommendations in 1993 (Committee on Perinatal Health, 1993). The American Academy of Pediatrics provided similar recommendations for more regionalized care in 2004 including recommendations for consistent de?nitions of care levels and the need for high-risk infants to be born in higher level facilities. (Committee on Fetus and Newborn, 2004). It has been hypothesized that so many community hospitals adopted NICUs in order to compete for pro?table obstetric patients (McCormick and Richardson, 1995). Neonatal intensive care is typically generously reimbursed, and even managed care organizations have been hesitant to limit infant care, so NICUs can be pro?t centers for hospitals (Horwitz, 2005, see online appendix). Beyond NICUs themselves, hospitals are particularly interested in attracting obstetric patients, since mothers are typically young, healthy, and likely to return to the hospital for the later care of their families if they have a positive birth experience (Friedman et al., 2002). Almost all births in the United States are covered by some form of public or private insurance (Russell et al., 2007), limiting hospitals’ ability to compete through prices. Therefore, hospitals may compete by trying to attract this desirable
I con?rm these results in Chapter 2 by showing that mothers living closer to hospitals with lower level NICUs are more likely to choose such hospitals and less likely to choose hospitals with higher level NICUs.
6
5
patient pool through signals of quality, such as the availability of a NICU. This type of competition is not unique to neonatal intensive care. Theoretically, the e?ects of non-price competition on hospital behavior and patient welfare are ambiguous but can potentially lead to over-provision of services known as a “medical arms race” (Gaynor, 2006). Dranove et al. (1992) ?nd that decreases in market concentration lead to increases in the number of hospitals o?ering various high tech services in that market. Others have shown that hospitals expand their capacity to perform certain procedures in order to deter other hospitals from adopting that procedure (Dafny, 2005a), and hospitals adopt particular technologies in order to steal business from their competitors (Schmidt-Dengler, 2006). In contrast, comparing the e?ect of competition on costs and mortality for heart attack patients, Kessler and McClellan (2000) ?nd that competition led to improvements in patient welfare during the 1990s. My work sheds light on how the organization of neonatal intensive care markets a?ects the quantity and quality of care provided. In Chapter 2 I revisit the question of how mortality outcomes for high-risk infants, as measured by being very low birth weight, di?er by the level of neonatal intensive care available at the hospital of birth. As brie?y discussed above and in more detail in Chapter 2, previous studies have found that very low birth weight infants born in hospitals with lower level NICUs experience higher mortality rates than those born in hospitals with the most sophisticated, Regional NICUs. Most of these previous studies utilize high-quality linked hospital inpatient, birth certi?cate, and death certi?cate data allowing them to control for many important clinical and demographic characteristics associated with infant mortality. However, there may be important unobserved di?erences between mothers who choose hospitals with varying levels of neonatal intensive care. On the one hand, it may be the case that those very low birth weight infants born in higher level hospitals are unobservably less healthy than those born in lower
6
level hospitals. For example, mothers who deliver in higher level hospitals may be referred there by their physicians because of predetermined risk factors that are not perfectly measured in the data. On the other hand, it may be the case that those very low birth weight infants born in lower level hospitals are unobservably less healthy. One could imagine that mothers of very low birth weight infants who choose lower level hospitals are less well informed, less likely to plan ahead, or less risk averse than those who choose to deliver in the higher level hospitals, and these characteristics may be correlated with worse infant health outcomes. By examining the observable characteristics of my sample, I show evidence consistent with the predictions of both of these selection mechanisms. Depending on which mechanism dominates, previous estimates of the mortality gradient could be biased upwards or downwards, suggesting that deregionalization may be more or less detrimental to very low birth weight mortality than previously thought. I assess this concern by using an instrumental variable strategy to isolate exogenous variation in the level of neonatal intensive care available at the hospital in which the mother of a very low birth weight infant chooses to deliver her newborn. In the spirit of McClellan et al. (1994), I use the distances from the center of the mother’s zip code of residence to the nearest hospital o?ering each level of neonatal intensive care as instruments for the level of care at the hospital in which she delivers her newborn. The validity of these instruments is motivated three factors: the hypothesis that NICUs have been adopted in order to compete for patients instead of to address local health needs; previous evidence showing that NICU location is not correlated with infant health measures; and evidence in my sample that distance is not correlated with observable demographic and health characteristics. I also show that distance is an important predictor of the level of care chosen by mothers of very low birth weight infants. Additionally, consistent with hospitals adopting NICUs to compete for patients across the risk distribution, I show that mothers of infants
7
with higher birth weights are more likely to choose a hospital with a NICU when they live closer to such a hospital as well My instrumental variables estimates indicate that very low birth weight infants born in hospitals with lower levels of neonatal intensive care do not have statistically signi?cantly di?erent mortality rates from those born in hospitals with the highest level of care. Furthermore, these instrumental variable estimates are bounded away from my ordinary least squares estimates, suggesting that even if the true e?ects are not zero, these more traditional ordinary least squares estimates exaggerate the mortality di?erences. The interesting implication of this result is that very low birth weight infants born in hospitals with lower level NICUs have higher unobserved mortality risk than those born in hospitals with higher level NICUs. This ?nding suggests that relocating deliveries to higher level hospitals prior to birth would not improve mortality outcomes because it would be relocating the deliveries of infants from the higher risk portion of the health distribution. However, these results do not imply that the higher level NICUs are of no value. In fact, very low birth weight infants born in hospitals with lower level NICUs are very likely to be transferred to higher level hospitals after birth, and I show that the probability of being transferred is not a?ected by my measures of distance. This ?nding suggests that, while the location of NICUs impacts where very low birth weight infants are delivered, it does not impact where they ultimately receive care. Post-birth inter-hospital transfers appear to be an e?ective tool to equalize mortality outcomes for infants born in hospitals with varying levels of neonatal intensive care. My ?ndings suggest that limiting the trend of deregionalization is not necessary to minimize very low birth weight infant mortality. However, networks between hospitals to facilitate post-birth transfers are instrumental in ensuring that infants eventually receive appropriate care. If hospitals coordinate su?ciently post-birth, market competition that leads to NICU adoption is not detrimental to mortality.
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That being said, it is important to recognize that mortality is not the only contributor to social welfare. Even if competition between hospitals in this market does not lead to lower quality of care, it may or may not lead to less e?cient allocation of neonatal intensive care resources. Chapter 3 of this dissertation considers one way in which neonatal intensive care resources may not be allocated e?ciently. An important issue in the provision of health care is whether the mere presence of the supply of medical services leads to excessive utilization of these resources, and I examine this question in the context of neonatal intensive care. Such a relationship could occur through two main mechanisms related to two important information asymmetries prevalent in health care markets. First, the physician often has more information about the patient’s health than the patient himself. Given this information gap, physicians may take advantage of their agency over patients to increase income by prescribing additional treatment beyond what is necessary. Because the physician is able to in?uence the patient’s demand for medical care, this behavior is called “supplier-induced demand” (Evans, 1974; Fuchs, 1978; McGuire, 2000; Pauly, 1981). The second mechanism that may cause excessive utilization when more supply is available is moral hazard in insurance, which acts through the patient’s information advantage over the insurer. Because insurance lowers the price of consuming health care, and the insurer cannot fully know the patient’s true health status, insurance can lead to the patient consuming more than the optimal amount of health care (Arrow, 1963; Cutler and Zeckhauser, 2000; Pauly, 1968). Of course, moral hazard cannot increase the amount of health care utilization if supply is not available; thus, additional supply can lead to excessive utilization of services by opening the door for latent moral hazard to be realized. Cross sectional comparisons between available supply and utilization are not su?cient to identify if this relationship exists, because there are many factors that
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may be correlated with the availability of health resources that could lead to additional utilization. Methodologically, the innovation of Chapter 3 of this dissertation is to ?nd an exogenous source of variation in available supply. I conduct a ?rst examination of the e?ect of the number of empty NICU beds available in the birth hospital on the day prior to birth on the probability that an infant is admitted to the NICU. The key to the identi?cation strategy is the use of hospital-speci?c month ?xed e?ects. With these ?xed e?ects I identify the relationship between NICU supply and utilization from within hospital-month variation in the number of empty NICU beds. The ?xed e?ects allow me to ?exibly control for characteristics of patients who choose a particular hospital, long run trends and short run seasonality of infant health, and any hospital-speci?c trends or seasonality. I argue in the chapter that conditional on observable characteristics and these ?xed e?ects, a particular infant’s unobserved health characteristics are unlikely to be correlated with the unobserved health characteristics of infants born just prior to the infant, which is what determines the number of available empty NICU beds. While this identi?cation strategy accounts for unobserved correlates between NICU supply and utilization, NICU admission is measured with error in the data that I utilize. Therefore, results in Chapter 3 are best viewed as preliminary, and I intend to verify these results using other data sources in future research. I ?nd that on average an additional empty NICU beds increases the probability of being admitted to the NICU by 1.11%. Not surprisingly, the e?ect of empty beds on NICU admission varies across the birth weight distribution. When I estimate regressions separately for subsamples strati?ed by birth weight, I ?nd that the e?ect is very small for very low birth weight infants.7 However, the e?ect size jumps discretely for infants above the very low birth weight threshhold and is largest for
The e?ect of empty beds on NICU admission is especially small for this group when I account for the fact that very low birth weight infants are likely to be transferred if NICU beds are not available for them at the birth hospital.
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infants close to the top of the low birth weight range and infants with high birth weights. These two groups are likely to be on the margin of needing neonatal intensive care. These results imply that the availability of empty NICU beds increases the utilziation of neonatal intensive care resources, particularly in the birth weight ranges where hospitals would have the most discretion over admission decisions. This analysis is quite relevant in the context of deregionalization. With the di?usion of neonatal intensive care resources, the potential for excess supply grows. This chapter estimates the e?ects of short term variation in empty NICU beds, but this variation is likely to be related to the long term trends in availability associated with deregionalization. Interestingly, I also ?nd that the e?ect of empty beds on NICU admission is the largest in hospitals with lower level NICUs as compared to hospitals with the most sophisticated NICUs. As these lower level NICUs are those units most associated with deregionalization, this ?nding suggests that deregionalization may have the scope to lead to additional intensive care utilization for lower risk infants.8 This chapter also provides an important contribution to the literature on neonatal intensive care markets by considering infants throughout the birth weight distribution. Much of the previous literature focuses on the e?ect of deregionalization on mortality outcomes for high-risk infants. It is also important to consider the implications of neonatal intensive care markets for healthier infants, and my ?ndings suggest that excess supply contributes to lower risk infants receiving additional treatment. Because care in the NICU is more expensive than care in the traditional nursery, additional supply has likely increased the cost of care for low-risk infants.9
It is also not surprising that the e?ects are smaller in higher level NICU hospitals since many high-risk infants are transferred from hospitals with lower level NICUs to these higher level hospitals. Therefore, there is likely to be less discretion and less incentive for responding to excess capacity in these higher level hospitals. 9 There may be other costs associated with excessive NICU utilization including psychic costs to the parents of seeing their infant in intensive care and the potential for hospital borne infections that are prevalent in NICUs.
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Overall, this dissertation ?nds that deregionalization has likely not been as detrimental to very low birth weight infant mortality as previously thought, but additional NICU supply contributes to increased utilization of care for lower risk infants. These two ?ndings represent two important contributions to understanding the welfare e?ects of deregionalization and open the door for further research about other aspects of the welfare calculation. Some important avenues of future research include the e?ect on broader health measures than the blunt consideration of mortality, the utility implications for mothers who are able to choose more convenient hospitals with some level of neonatal intensive care, a more speci?c understanding of costs including the ?xed costs of adopting a NICU and the costs of maintaining and operating a NICU, and the determinants of NICU adoption from the hospital point of view.
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Chapter 2 The E?ect of Deregionalization on Health Outcomes: Evidence from Neonatal Intensive Care 2.1 Introduction
Technological innovations over the past half century have greatly changed medical care for sick infants. Over this time Neonatal Intensive Care Units (NICU) have been developed to administer treatments such as mechanical ventilation, arti?cial surfactant, and extracorporeal membrane oxygenation (ECMO)1 to sick, preterm, and underweight infants, and they have clearly lead to improved outcomes for these groups. For example, the 28-day mortality rate for infants weighing 1,000 to 1,499 grams (2.2 and 3.3 pounds) dropped from 52.2% to 6.7% between 1960 and 1990 (Cutler and Meara, 2000).2 Despite these long run gains, there is concern that NICUs have not di?used optimally. The 1980s and 1990s saw a large increase in the number of NICUs in smaller, community hospitals that provide less sophisticated care compared to the original NICUs in large, regional hospitals (e.g., McCormick and Richardson, 1995; Schwartz, 1996; Schwartz et al., 2000). This trend of “deregionalization” has
Mechanical ventilation assists infants whose lungs have not fully developed to breath. Arti?cial surfactant treats respiratory distress syndrome by helping the lungs to develop. ECMO machines pump blood out of the infant, oxygenate it, and pump it back into the infant if the infant’s heart and lungs are too weak to oxygenate the blood on its own. 2 I do not focus on costs in this chapter, but anecdotally, opening a new NICU can cost between $125,000 and $200,000 per bed (Baker and Phibbs, 2002). Hospital costs for very low birth weight (VLBW) infants, those weighing less than 1,500 grams or 3.3 pounds, averaged $136,000 in California in 2000 (Schmitt et al., 2006). Nationwide, it is estimated that medical care services for high-risk infants cost $16.9 billion in 2005 (http://www.marchofdimes.com/peristats/ slidesets/slideset_6_99.ppt, last accessed on October 6, 2009). In the long run Cutler and Meara (2000) calculate a 510% rate of return to spending on infant health care between 1960 and 1990, accounting for the value of both lives saved and quality of life for surviving infants.
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worried policy makers because previous studies have found higher mortality rates for infants born in hospitals with these Community NICUs compared to those born in hospitals with Regional NICUs, conditional on observable demographic and health characteristics (e.g., Cifuentes et al., 2002; Phibbs et al., 2007, 1996). Based on this evidence, organizations such as the March of Dimes and the American Academy of Pediatrics have advocated for a stronger regional system where high-risk mothers are referred to hospitals with Regional NICUs prior to delivery in order to minimize mortality. This chapter seeks to estimate the causal e?ect on mortality of the level of care available at the hospital in which a very low birth weight (VLBW) infant – under 1,500 grams or 3.3 pounds – is born. As an empirical matter, it is not clear that the worse outcomes experienced by infants born in hospitals with lower level NICUs are attributable to the hospital type per se. Even conditional on observable characteristics, infants born in di?erent hospitals may have di?erent underlying risk factors. Depending on the mechanisms behind any unobserved selection, conventional estimates of mortality di?erences by level of care could be biased in either direction. If infants born in hospitals with lower level NICUs have lower underlying mortality risk than those born in Regional NICUs, previous estimates will have understated the mortality penalty associated with being born in lower level hospitals. Alternatively, if infants born in hospitals with lower level NICUs have higher underlying risk factors, previous estimates will have overstated the mortality di?erences. Any bias implies the system of deregionalization might actually be more harmful or less harmful than currently believed. While deregionalization may a?ect many factors other than mortality, understanding the causal e?ect of level of care on mortality of high-risk infants is of ?rst-order importance to making policy decisions about the organization of neonatal care. I propose an instrumental variables strategy to overcome selection issues asso-
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ciated with a mother’s choice of hospital. I exploit the distance a mother must travel to the nearest hospital of each level of care as a source of quasi-experimental variation in the type of hospital chosen. Distance is an important determinant of hospital choice for many medical treatments such as cardiac and cancer surgery (e.g., Cutler, 2007; Kessler and McClellan, 2000; McClellan and Newhouse, 1997; Tay, 2003) and for expectant mothers as well (Phibbs et al., 1993). I also provide evidence that distance is likely to be exogenous to unobserved health outcomes in my data set, which is not surprising given evidence that NICU location is not correlated with the geographic variation in underlying infant health conditions (Goodman et al., 2001). Using detailed data on all California VLBW births between 1991 and 2001, I estimate how the birth hospital’s level of care causally e?ects VLBW mortality. My ordinary least squares (OLS) analysis yields estimates of 7.6%, 13.4%, and 31.8% higher risk-adjusted mortality rates for infants born at hospitals o?ering three lower levels of care relative to those born in hospitals o?ering the highest level of care. These results are consistent with the previous literature, but my instrumental variable estimates provide evidence that these OLS estimates are biased upward. The instrumental variables estimates are bounded well below the OLS estimates and are not statistically di?erent from zero. My results are robust to including zip code level controls, such as population density and racial characteristics, or zip code ?xed e?ects. Comparing the OLS and the instrumental variable estimates reveals that infants born in hospitals with lower levels of care are negatively selected. This selection could occur if, for example, more uninformed mothers choose lower levels of care and have unobservably less healthy infants. This ?nding implies that relocating births to Regional NICU hospitals prior to delivery would not lead to lower mortality rates because the relocated infants would have higher unobserved mortality risk. In terms of mortality, deregionalization does not appear to have caused worse outcomes for
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high-risk infants. It is also possible that the instrumental variable estimates represent a local average treatment e?ect. I ?nd that my estimates are not heterogeneous across demographic sub-samples, but there still may be heterogeneous e?ects along unobservable dimensions. If this is the case, instrumental variables would estimate the e?ect of level of care on mortality for an unobserved subgroup of infants whose mothers’ choices of level of care are a?ected by the distance instruments. However, because variation in the instruments is directly related to deregionalization, any local e?ect is precisely the policy relevant e?ect. My estimates would imply that infants of mothers who choose to give birth in hospitals with lower level NICUs because these NICUs are available – the marginal group of infants whose delivery hospitals are impacted by deregionalization – do not experience higher mortality rates. While my results indicate that mortality does not di?er by level of care at the hospital in which an infant is born, they do not imply that Regional NICUs are of no value. In fact, I show evidence that infants born in hospitals with the lowest levels of care are likely to be transferred to Regional NICU hospitals after birth, and the geographic distribution of hospitals does not impact the probability of transfer. It is di?cult to compare outcomes to the counterfactual world that experiences deregionalization but does not allow for post-birth transfer, but my ?ndings suggest that mortality is not causally a?ected by the level of care at the birth hospital because high-risk infants eventually receive care in higher level hospitals if necessary. The remainder of this chapter is structured as follows. Section 2.2 reviews the previous literature. Section 2.3 describes the data and summary statistics. Section 2.4 provides the empirical framework. Section 2.5 presents the results, followed by robustness checks in Section 2.6. Section 2.7 concludes.
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2.2 Previous Literature 2.2.1 Previous Estimates of Mortality Di?erences by Level of Care
Multiple authors have estimated how risk-adjusted mortality varies by level of neonatal intensive care at the hospital of birth, and many of these studies use the same California inpatient data set as this chapter. The typical methodology includes a logistic regression of mortality on level of care indicators, controlling for demographic characteristics and health status. The speci?c results depend on the precise categorization of hospitals, but in general these studies ?nd higher mortality as level of care decreases for groups of high-risk infants that NICUs are intended to care for. Phibbs et al. (1996) ?nd that VLBW infants born in hospitals with the largest Regional NICUs have statistically lower mortality rates than the lower categories, but the lower categories, including hospitals with no NICU, do not di?er from each other. Cifuentes et al. (2002) use a population of infants below 2,000 grams (4.4 pounds) and ?nd that all levels except for the largest Community NICUs have higher mortality rates than Regional NICUs. As they restrict their sample to smaller and smaller birth weight groups, the gradient becomes steeper. Similarly, Gould et al. (2002) ?nd higher mortality rates at all levels relative to Regional NICUs except for those Community NICUs that are licensed under the California Children’s Services Program. Finally, in the most recent study on the relationship between level of care and mortality, Phibbs et al. (2007) distinguish mortality rates by very narrow level and volume interactions. While not necessarily statistically signi?cant within each level, they ?nd decreasing mortality across levels and by volume within levels. Based on their estimates, they conclude that if 90% of VLBW deliveries in California urban areas had been relocated to hospitals with the largest Regional NICUs, 21% of
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VLBW deaths in 2000 could have been avoided.3 However, while high-quality hospital inpatient data sets allow the ability to control for many important covariates, mothers may select into di?erent delivery hospitals based on characteristics not observed in the data. Such unobserved selection would lead to biased estimates of the mortality di?erences by level of care, and the direction of the bias would depend on the direction of the selection. One typical form of selection that biases estimates of the e?ect of health treatments on outcomes is selective referral. If mothers and physicians have additional information about the mother’s health status, and higher risk mothers are referred to hospitals with Regional NICUs, mothers would be positively selected into lower levels of care. Therefore, the mortality di?erences relative to Regional NICUs would be underestimated. On the other hand, if mothers negatively select into lower levels of care over hospitals with Regional NICUs, the mortality di?erences would be overestimated. This case might arise if more uninformed mothers are more likely to choose hospitals with lower levels of care over hospitals with Regional NICUs and infants of these uninformed mothers have higher unobserved mortality risk.
2.2.2 Natural Experiments in Health Research
This chapter is also related to the health economics literature that uses natural experiments to determine the marginal e?ects of medical treatments and technology. As with neonatal care, time series evidence suggests that most new technologies have led to vast improvements in health outcomes over time and the monetized bene?ts have outweighed the costs (Cutler and McClellan, 2001; Hall and Jones, 2007; Luce et al., 2006; Murphy and Topel, 2003). However, comparisons of health care expenditures and outcomes across geographic regions have found that higher spending
They calculate this number only considering the sample of infants for whom they deem relocation geographically feasible and note that such relocation would require new large NICUs and the closure of some smaller NICUs.
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areas do not achieve better outcomes (Baicker et al., 2006; Baicker and Chandra, 2004b; Fisher et al., 2003a,b; Fuchs, 2004). Given this contradiction, researchers have taken advantage of quasi-experimental variation to better compare individuals who di?er only in their treatment and not in other unobserved dimensions to estimate causal e?ects of treatment. Here I highlight two portions of this literature that are most related to this chapter: research on the e?ects of infant health care and research using a similar identi?cation strategy to that used in this chapter. Studies that use natural experiments to estimate the returns to incremental units of infant health care ?nd mixed results. Almond and Doyle (2008) exploit a California policy extending minimum length of hospital stays following delivery and the discontinuity in stay length for infants born just before and just after midnight. They ?nd no e?ect of increased stay length on health outcomes for uncomplicated infants. Evans et al. (2008) exploit the same policy and ?nd similar results for uncomplicated infants, but they do ?nd that longer length of stay leads to reduced hospital readmission rates for more complicated cases. Using a regression discontinuity design, Almond et al. (2008) ?nd that infants just below the VLBW cuto? receive more treatment and experience lower mortality rates than those just above the VLBW cuto?. Taken together, these studies imply that, at least for high-risk infants, increased treatment can be bene?cial. My research adds to this literature by estimating whether the facilities available at the hospital in which a high-risk infant is born a?ect mortality. McClellan et al. (1994) and Cutler (2007) use a similar identi?cation strategy to this chapter’s strategy in order to estimate the e?ect of catheterization and revascularization, respectively, following a heart attack on mortality. As with infant care, there are two selection concerns in this context, although the mechanisms are slightly di?erent. First, the healthiest patients may have less need for these intensive surgeries. Second, the sickest patients may forego surgery due to a higher risk of dy-
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ing during the procedure. To account for selection, both papers use distance to the nearest hospital providing surgery as an instrument for whether a patient receives surgery.4 Both studies ?nd that instrumental variable estimates of the bene?t of intensive surgery are substantially lower than the ordinary least squares estimates, although Cutler (2007) ?nds that the monetized bene?ts still outweigh the costs.
2.3 Data 2.3.1 Linked Birth Data
My empirical analysis requires detailed data describing infants’ hospitalizations and outcomes. The primary data set I utilize is the Linked Patient Discharge Data/Birth Cohort File (LPDD/BCF) created by the California O?ce of Statewide Health Planning and Development (OSHPD). This data set includes records of all births in non-Federal hospitals in the state of California. I have obtained data ?les for the years 1991 to 2001, comprising approximately six million births. In addition to including observations of all births from a large state, the main advantage of this data set is that it links additional data to an infant’s hospital discharge record. First, it links an infant’s delivery hospital discharge record to the mother’s discharge record and all subsequent records resulting from transfers or readmissions to California hospitals within the ?rst year of life. For each hospitalization, the data set includes detailed diagnosis and treatment variables, summary variables such as length of stay and hospital charges, and patient information including zip code of residence. Second, the hospital discharge data are linked to vital statistics data on births and infant deaths within the ?rst year of life, which include gestation, birth weight, number of prenatal care visits, month prenatal care began, and demographOther authors have also used distance as a source of exogenous variation to predict patient ?ows in order to estimate the e?ect of volume (Gowrisankaran et al., 2006) and competition (Gowrisankaran and Town, 2003; Kessler and McClellan, 2000; Tay, 2003) on health outcomes.
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ics, such as the mother and father’s race, ethnicity, and education. Additionally, these records provide information on infant mortality within the ?rst year of life, even if death occurred outside of the hospital. The main analysis sample that I consider includes VLBW infants, de?ned as weighing between 500 and 1,500 grams (1.1 and 3.3 pounds) at birth. Of the initial 6.1 million birth observations with non-missing birth weight, 72,275 fall in this birth weight range.5 To obtain my analysis sample, I ?rst exclude observations with a missing zip code of residence, a zip code of residence outside the state of California, a missing hospital identi?cation number, or that are delivered in a hospital without a delivery unit. The remaining sample contains 65,567 birth observations. I then make three restrictions to maintain a sample that is as broad as possible but that excludes observations with an unusual hospital choice set. I ?rst drop 2,704 observations where the mother’s county of residence is “non-metro” according to the O?ce of Management and Budget.6 This restriction excludes a small group of infants from the most rural areas for whom access to neonatal care is quite di?erent from other residents of the state. Additionally, the previous literature has focused on deregionalization and the e?ect of level of care on outcomes in metropolitan areas (Howell et al., 2002; Phibbs et al., 2007) where policy recommendations about delivery relocation would be most feasible. Second, I drop 7,627 infants delivered in Kaiser owned hospitals. Mothers who choose a Kaiser hospital for delivery must be covered by Kaiser insurance, and mothers covered by Kaiser insurance must deliver in a Kaiser owned hospital; therefore, choice of hospital is restricted for this group.7 Third, I exclude 4,113 observations diagnosed with a congenital anomaly.
The full data set includes 6,221,001 births of which 1.54% of the observations have a missing birth weight. 6 Based on 1993 USDA Rural-Urban Continuum Codes that are calculated from the 1990 Census. Source:http://www.ers.usda.gov/briefing/rurality/ruralurbcon/ priordescription.htm. 7 In my analysis sample, 88% of mothers with Kaiser coverage deliver in a Kaiser hospital, and 97% of mothers who deliver in a Kaiser hospital have Kaiser coverage. In results not shown here, regressions similar to the ?rst stage regressions discussed below for the sample of Kaiser insured
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This restriction is consistent with the previous literature (Phibbs et al., 2007), and it also excludes observations most likely to be selectively referred to higher levels of care due to a diagnosis during the prenatal period. I also exclude 8,115 observations of fetal deaths, which are infants who die prior to delivery and, therefore, never receive neonatal care (Phibbs et al., 2007). Finally, because I cluster standard errors at the zip code level and estimate models with zip code ?xed e?ects, I exclude 96 observations for which the mother’s zip code of residence has no other observations remaining in the data. In Section 2.6, I show that my results are robust to each of these sample restrictions. I choose my sample of high-risk infants using birth weight as the health proxy in order to be comparable to previous literature, and because it is the best measure of an infant’s health stock at birth (Almond et al., 2005; Cutler and Meara, 2000). Relative to gestation, another summary of health status at birth, Almond et al. (2008) note that birth weight is more accurately recorded, less likely to be missing in the data, and less likely to be manipulated by delaying birth because it is not possible to know birth weight ex ante.8 VLBW infants are the population most of interest because they contribute disproportionately to costs and mortality. Schmitt et al. (2006) document that VLBW infants make up 0.9% of births but account for 36% of newborn hospital costs, and tabulations of hospital charges for my sample lead to similar ?gures. Mean charges for my VLBW sample are $209,000, compared to $21,000 for low birth weight infants (1500 to 2500 grams or 3.3 to 5.5 pounds) and $2,630 for normal birth weight infants (above 2500 grams or 5.5 pounds). Likewise, length of stay after birth averages 50.6 days for VLBW infants, 9.2 days for low birth weight infants,
mothers show that distance has very little power in predicting the level of care chosen for delivery. This is in contrast to the strong predictive power of distance for the analysis sample discussed in Section 2.5. 8 Additionally, Almond et al. (2008) ?nd empirical evidence that the recording of birth weight is not manipulated by physicians.
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3.0 days for normal birth weight infants. Additionally, VLBW infants make up the vast majority of infant mortality. The main outcome I focus on in this chapter is neonatal mortality, de?ned as mortality within twenty-eight days of birth or within one year if an infant is continuously hospitalized since birth. VLBW infants have a neonatal mortality rate of 15.7%, compared to 0.7% for low birth weight infants and 0.1% for normal birth weight infants. Therefore, changes in how infant care is delivered has the most scope to a?ect outcomes for VLBW infants.
2.3.2 Hospital Data
My empirical analysis also requires data describing the level of neonatal care o?ered by each hospital that delivers infants. I obtain data from the authors of Phibbs et al. (2007) that di?erentiate hospitals into six levels of neonatal care based on the treatments each hospital provides in a given year. First, they use OSHPD hospital ?nancial data to determine which hospitals have neonatal intensive care beds. Second, they use ICD-9 CM procedure codes in the hospital inpatient data to identify which hospitals perform particular procedures. As a guide, they de?ne levels of care consistent with the six levels outlined in the American Academy of Pediatrics 2004 report.9 Table 2.1 lists the six levels and their corresponding procedures. Third, the authors con?rmed level of care designations through conversations with hospital personnel. I collapse these detailed categories into four levels of care, which I refer to as No NICU, Intermediate NICU, Community NICU, and Regional NICU hospitals. No NICU hospitals provide birthing services and well-baby care, but no neonatal intensive care (Level I in Table 2.1). Intermediate NICUs care for mildly ill infants but do not provide mechanical ventilation (Level II). Community NICUs include
The authors utilize the draft version of the American Academy of Pediatrics report because the ?nal version does not include a category that provides unrestricted ventilation but no surgery, a level of service many CA units provide.
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any unit that provides mechanical ventilation and either does not provide major surgery or provides surgery but treated less than 50 VLBW infants in 1991 (IIIA, small IIIB, and small IIIC).10 Finally, Regional NICUs include those that provide major surgeries and treated greater than 50 VLBW infants in 1991, or any unit that provides cardiac bypass and/or ECMO, the two most specialized surgical procedures, regardless of size (large IIIB, large IIIC, and all IIID). This categorization results in 161 No NICU, 58 Intermediate, 41 Community, and 36 Regional NICU hospitals at the beginning of my sample in 1991. These numbers change during my sample period as deregionalization progressed through the decade. Table 2.2 shows the number of hospitals by level and year between 1991 and 2001. The total number of hospitals providing any birthing services falls from 296 in 1991 to 269 in 2001. In contrast, the number of Community NICUs increases from 35 to a peak of 57 in 1999. 10 hospitals open new NICUs at the Community level and 21 hospitals upgrade an Intermediate NICU to the Community level. As a result of these upgrades, the aggregate number of Intermediate NICUs actually decreases from 58 to 45 over the sample period; however, there are also 15 hospitals that open new NICUs at the Intermediate level. Not surprisingly, the number of Regional NICUs, the largest, most well established, and most expensive units, remains relatively constant over the sample period.
2.4 Empirical Framework
This section describes my empirical approach to estimating the e?ect of level of neonatal care at the birth hospital on mortality. I ?rst discuss an ordinary least squares regression that estimates average mortality di?erences between infants born in No NICU, Intermediate NICU, or Community NICU hospitals and those born
10 I use the number of VLBW infants treated in 1991 to identify this classi?cation to prevent hospitals from changing levels due to changes in demand during my sample period.
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in Regional NICU hospitals, conditional on a rich set of control variables. This estimation strategy is comparable to the methodology of the previous literature and provides “risk-adjusted” mortality di?erences. I then discuss how these estimates could be upwards or downwards if mothers choose hospitals based on unobserved characteristics not included in the risk adjustment. Lastly, I discuss my instrumental variables strategy to account for unobserved selection and estimate the causal e?ect of level of care.
2.4.1 Baseline Model
I begin by estimating the average di?erence in mortality rates by level of care at the delivery hospital, controlling for observable characteristics of the mother and infant. The regression equation is as follows:
yizt = ? + Nizt ? N + Iizt ? I + Cizt ? C + Xizt ? + ?izt
(2.1)
The unit of observation is infant i, whose mother resides in zip code z , born in year t. The dependent variable, yizt , is a neonatal mortality indicator that is equal to one if an infant dies within 28 days of birth or within one year if continually hospitalized since birth, and zero otherwise.11 Xizt is a vector of observable determinants of infant izt’s health. These controls include time (year, month, and day of week indicators); mother’s demographics such as age, race, ethnicity, and insurance coverage;12 and health related controls such as the infant’s sex, birth weight, and diagnoses.13
In Section 2.6 I show that results are robust to measuring mortality across di?erent time frames. 12 Speci?c demographic controls are age, age squared, and indicators for black, other race, Hispanic, Medicaid, HMO, and self-pay. 13 Speci?c health controls are parity, sex, multiple birth status, an indicator for having a clinical condition, indicators for small and large for gestational age, birth weight dummies at 100 gram increments, the number of prenatal care visits, and the month in which prenatal care began. The clinical condition indicator is equal to one for infants having at least one of the following conditions identi?ed in Phibbs et al. (2007): hydrops due to isoimmunization, hemolytic disorders, fetal distress, fetus a?ected by maternal condition, oligohydramnios, other high-risk maternal
11
25
The three explanatory variables of interest, Nizt , Iizt , Cizt , are indicators equal to one if infant izt is born in a hospital with No NICU, an Intermediate NICU, or a Community NICU, respectively. Being born in a hospital with a Regional NICU is the excluded group, so the ? j coe?cients are interpreted as the di?erence in mortality when born in a hospital with level of care j relative to being born in a Regional NICU hospital.14 For this speci?cation to estimate the causal e?ect of level of care on mortality, hospital choice must be uncorrelated with unobserved determinants of mortality captured by the error term, ?izt , conditional on the observable characteristics, Xizt (E [Hizt ?izt |Xizt ] = 0, where Hizt = [Nizt , Iizt , Cizt ]). If this condition is not met, and unobserved mortality, conditional on observables, is di?erent for infants born in hospitals with di?erent levels of care the OLS estimates of the ? j s will be biased. If infants born in lower level hospitals are unobservably healthier (lower unobserved mortality), consistent with physicians referring the highest risk mothers to Regional NICU hospitals, OLS estimates will understate the true mortality di?erence between being born in lower level hospitals and Regional NICU hospitals. On the other hand, if infants born in lower level hospitals are unobservably less healthy (higher unobserved mortality), consistent with more uninformed mothers choosing lower levels of care and having higher risk infants, OLS estimates will overstate these mortality di?erence. Sample means by level of care in Table 2.3 show that there are clear unconditional di?erences in mortality rates by level of care at the hospital in which an infant is born. Neonatal mortality rates fall from 21.9% for VLBW infants born in No NICU hospitals, to 16.9% in Intermediate NICU hospitals, 15.5% in Comconditions, placenta hemorrhage, premature rupture of membrane, and prolapsed cord. 14 It is important to point out that I am estimating mortality di?erences based on the hospital in which the infant is born. This framework does not take into account whether or not the infant was actually treated in the NICU or whether they were transferred to and treated in another hospital. In this context, my estimates can be thought of as intent-to-treat e?ects.
26
munity NICU hospitals, and 14.7% in Regional NICU hospitals. However, there are also di?erences in important observable characteristics. OLS regressions control for these observable characteristics, but these di?erences motivate the concern that there may be di?erences in unobservable dimensions as well. Mothers’ demographic characteristics di?er by level of care, but not monotonically. For example, 9.8% of mothers giving birth in No NICU hospitals, 20.5% in Intermediate NICUs, 12.8% in Community NICUs, and 18.6% in Regional NICUs are black. The percentage of mothers covered by Medicaid and the percentage without any college education decreases substantially from No NICU, to Intermediate NICU, and to Community NICU hospitals, but the percentage in Regional NICU hospitals is higher than the percentage in Community NICU hospitals. These large di?erences indicate selection into level of care by mothers’ demographics, but the direction of the selection is ambiguous. Furthermore, these demographic characteristics are likely to be correlated with mortality risks. For example, Singh and Kogan (2007) show persistent infant mortality disparities by mothers’ education and socioeconomic status. There are also clear patterns of selection on infant health characteristics. Consistent with selection of healthier infants into lower levels of care, infants born at lower levels are less likely to be multiple births, have slightly higher birth weight and longer gestation, are less likely to have a clinical diagnosis, are less likely to be small or large for their gestational age, and experience lower hospital charges and shorter lengths of stay. Given the di?erences in observed characteristics by level of care, there are likely di?erences in unobserved characteristics as well (Altonji et al., 2005). Therefore, accounting for non-random selection is important, though the direction of the bias is again unclear ex ante.
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2.4.2 Estimating Causal E?ects
To understand the e?ects of deregionalization on VLBW infant outcomes, it is necessary to estimate the causal e?ect of level of care on neonatal mortality. Because OLS estimates may not be able to control for all determinants of mortality, I utilize instrumental variables to overcome unobserved selection. With three endogenous explanatory variables, at least three instruments are necessary to identify the empirical model. I construct three instruments based on the distance from a mother’s residence to each level of care, which I de?ne in more detail below. For a 3 × 1 vector of instruments, Dzt , instrumental variables estimates of ? N , ? I , and ? C will be consistent if the instruments are uncorrelated with the error term in Equation (2.1) (E [Dzt ?izt |Xizt ] = 0) and are strong determinants of the type of hospital a mother chooses, conditional on the other observable characteristics. This second condition is similar to saying that the coe?cients on the instruments are non-zero in the following set of ?rst stage regression equations of each level of care indicator on the vector of instruments and all other covariates:15 Nizt = ? N + Dzt ?N + Xizt ?N + µN izt Iizt = ? I + Dzt ?I + Xizt ?I + µI izt Cizt = ? C + Dzt ?C + Xizt ?C + µC izt Notation is as above with each ?j representing a vector of three ?rst stage coe?cients and each µj izt representing a ?rst stage error term. The parameter estimates of Equation (2.2) are used to obtain the predicted probability of choosing each level of care for each observation, and two stage least squares (2SLS) estimates are computed by estimating Equation (2.1) with these
More formally, it must be the case that the instruments are su?ciently linearly related to Hizt that E [Zizt Hizt ] is of full column rank, where Zzt = [Dzt , Xizt ]. It is also necessary for the instruments to be su?ciently linearly independent so that E [Zizt Zzt ] has full column rank (Wooldridge, 2001).
15
(2.2)
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predicted probabilities in place of the level of care indicators.16 Therefore, identi?cation of ? N , ? I , and ? C in Equation (2.1) comes from comparing mortality for otherwise identical infants who are born at di?erent levels of care because they live at di?erent distances from each level of care. For example, ? C is identi?ed from di?erences in mortality outcomes between infants who are and are not born in hospitals with Community NICUs because their mothers live within close or far proximity to a hospital o?ering a Community NICU. Intuitively, this comparison emphasizes the importance of the assumption that E [Dzt ?izt |Xizt ] = 0. In order for instrumental variables to provide causal estimates, it is crucial that mothers living at di?erent distances from each level of care not have infants that di?er in unobserved determinants of mortality. Since this strategy requires the location of NICUs to be exogenous to VLBW infant health, it is worth brie?y re-emphasizing the process by which NICUs have di?used and discussing how mothers choose hospitals. Most importantly, di?usion has been driven by many factors unrelated to the health of VLBW infants. Over time the technologies and trained specialists necessary to operate NICUs became more prevalent, and therefore, NICU adoption became feasible for community hospitals. It has been hypothesized that so many hospitals adopted lower level NICUs in order to compete for pro?table obstetric patients (McCormick and Richardson, 1995). Ninety-seven percent of births are covered by private or public insurance (Russell
Both the dependent variable and the endogenous explanatory variables in this model are binary. Bhattacharya et al. (2006) point out that two stage least squares can lead to inconsistent estimates when the mean probability of the binary dependent variable is close to zero or one, or when there is more than one endogenous binary treatment variable. They advocate a multivariate probit model which assumes that the error terms from Equations (2.1) and (2.2) follow a multivariate normal distribution. On the other hand, Angrist (2001) argues that linear models still provide good approximations of average causal e?ects, parameter estimates directly correspond to the relevant average treatment e?ects, and nonlinear models depend on the distributional assumptions and are inconsistent if these assumptions are incorrect. Wooldridge (2001) points out that some of the assumptions behind average treatment e?ects are not precisely true with binary outcomes, but linear methods may still produce reasonable average treatment e?ect estimates. I have estimated my OLS speci?cations with both probit and logit models and ?nd marginal e?ects that are almost identical to the OLS coe?cient estimates presented in Section 2.5. Future work will verify that the 2SLS estimates are not biased by the linear functional form.
16
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et al., 2007), so most families are shielded from the full cost of infant care. One way for hospitals to compete for these patients is to invest in signals of quality, which might attract risk-averse mothers. Hospitals are particularly motivated to attract obstetric patients, since mothers are typically young, healthy, and likely to return to the hospital for the later care of their families if they have a positive birth experience (Friedman et al., 2002), and NICUs themselves can be pro?table (Horwitz, 2005, see online appendix). Most preterm labor is spontaneous, and in 50% of cases, doctors are not even able to determine the cause ex post. Forty to ?fty percent of cases with an identi?ed cause are traced to an infection, but often mothers show no signs of these infections prior to labor.17 As detailed by an Institute of Medicine report, there are a variety of documented correlates of preterm delivery. These correlates range from behavioral factors such as tobacco use and nutrition, to psychosocial factors such as stress, personal resources, and social support, to medical conditions of the mother or pregnancy such as obesity or multiple births, to other factors such as exposure to environmental toxins, genetics, etc. Interrelated with many of these characteristics, there are demographic di?erences in preterm birth rates as well. Mothers at either extreme of the age distribution, unmarried mothers, black mothers, and mothers with low income or low educational attainment are all known to have higher rates of preterm delivery. Despite these correlates, this report emphasizes that there is in fact little understanding of what conditions and events can be used to predict and diagnose preterm labor before it occurs (Behrman and Butler, 2007). As a result of this unpredictability, a NICU is likely an e?ective tool for attracting patients of all risk levels. Expectant mothers usually deliver in the hospital where their obstetrician has delivery privileges, so they in e?ect choose their delivery hospital when they choose their obstetrician early in their pregnancy. If risk-averse
17
Source: www.marchofdimes.com/peristats, last accessed on September 29, 2009.
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mothers plan ahead when choosing their obstetrician and delivery hospital, the presence of a NICU is likely to factor into their decision. A mother likely considers travel time, convenience for family members, perceived quality of care, and the possibility of transfer if higher quality care is needed. If utility is increasing in perceived quality of care and decreasing in travel time, a community hospital with a NICU can attract nearby mothers willing to trade additional perceived quality at a further Regional NICU in favor of the increased convenience of choosing the nearby hospital. Furthermore, if mothers tend to choose local obstetricians who are likely to have priveldges in local hospitals, mothers will be more likely to choose nearby hospitals. Of course, location relative to hospitals with NICUs is not the only determinant of hospital choice. Phibbs et al. (1993) estimate hospital choice models separately for high- and low-risk mothers and for publicly and privately insured mothers within each risk category. Not surprisingly, overall, mothers prefer closer hospitals, hospitals with higher quality, and hospitals with neonatal intensive care units. Despite the fact that many high-risk deliveries are unexpected, the authors do ?nd some di?erences in hospital choices among high- and low-risk mothers. For example, high-risk mothers prefer hospitals with higher measures of quality, including higher level neonatal intensive care units. This ?nding is consistent with my sample means above that ?nd higher-risk infants born in hospitals with higher levels of care. The authors also ?nd some important di?erences in hospital choice between publicly and privately insured mothers. While distance has a similar e?ect on hospital choice for both groups, publicly insured mothers deliver in hospitals with worse health outcomes and are less likely to deliver in hospitals with NICUs. These ?ndings suggest possible restrictions on access to care for publicly insured mothers.18 As discussed above, I restrict the sample to exclude Kaiser insured patients
Additionally, during my sample period California began adopting Medicaid managed care plans on a county by county basis. These plans potentially provide further restrictions on the hospitals in which some Medicaid mothers can deliver.
18
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who have little choice of delivery hospital, but the ?ndings of Phibbs et al. (1993) suggest there are likely to be other groups with varying degrees of choice restrictions including publicly insured patients. Patients with other managed care insurance are likely to be restricted somewhat as well, though to varying degrees as compared to Kaiser. That being said, the motives to compete for healthy, risk-averse mothers, evidence that growth of neonatal resources has outpaced medical need, and ?ndings that the location of neonatal intensive care resources are uncorrelated with markers of need such as occurrences of VLBW or preterm births (Goodman et al., 2001), support the exogeneity of NICU location to VLBW infant health. In the next subsection, I provide further evidence from my data supporting this claim. To the extent that some patients have restricted choice, the only e?ect would be to weaken the power of the instrument as long as these factors are not correlated with distance, which appears to be the case. It is important to point out that under the assumptions of the empirical model, the instrumental variables estimates of ? N , ? I , and ? C in Equation (2.1) are structural parameters and provide causal estimates of the e?ect of level of care at the hospital of birth on infant mortality rates. In contrast, the ?rst stage relationships in Equation (2.2) are reduced form equations where the endogenous level of care indicators are regressed on all of the model’s exogenous variables. These equations do not necessarily provide structural parameters of the neonatal intensive care level demand function.19
As discussed above, one previous study has attempted to estimate hospital demand parameters for delivery hospitals. Phibbs et al. (1993) estimate McFadden conditional logit models of hospital choice, and their model includes features such as distance from a mother’s residence and presence of a neonatal intensive care unit. Additional work in this area is left to future research, as estimating such demand functions is important for understanding how mothers choose hospitals and why hospitals choose to provide various levels of care. Additionally, many hospitals now advertise heavily about not only the quality of care, but also amenities available for expectant mothers, such as private rooms, jacuzzis, etc. Goldman and Romley (2008) ?nd that Medicare pneumonia patients in Los Angeles place a high value on non-medical amenities when choosing a hospital for treatment. Such amenities may also be an important tool for hospitals competing for maternity patients.
19
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2.4.3 The Instruments
In this section, I describe how I calculate the three distance instruments and discuss why they are likely to be exogenous to unobserved VLBW mortality. I ?rst calculate the straight line distance from the center of each patient’s zip code of residence to each hospital using GIS software. Hospital location is obtained from OSHPD’s publicly available geocoded data of hospital latitude and longitude.20 I then construct three instruments that represent the di?erential distance between the nearest hospital of a given level of care or higher and the nearest hospital with a Regional NICU, as follows:
N oDistzt = D(Regzt ) ? min[D(N ozt ), D(Intzt ), D(Comzt ), D(Regzt )] IntDistzt = D(Regzt ) ? min[D(Intzt ), D(Comzt ), D(Regzt )] ComDistzt = D(Regzt ) ? min[D(Comzt ), D(Regzt )]
(2.3a) (2.3b) (2.3c)
The D(·) operator indicates the distance from zip code z at time t to the nearest hospital o?ering a particular level of care. These measures can be thought of as the number of miles saved by choosing the nearest hospital with at least a particular level of care over the nearest hospital with the highest level of care, and therefore get larger as an individual lives closer to a hospital o?ering the particular level of care. When using di?erential distance, the hospital choice decision is modeled as a function of distance to each lower level of care relative to distance to Regional NICU hospitals.21 It emphasizes the fact that mothers make a trade o? when choosing a lower level of care at a closer hospital – they forego potentially higher quality care
OSHPD only provides this data for currently existing facilities. For those facilities for which I do not have exact location, I use the center of the hospital’s zip code obtained in the OSHPD State Utilization File of Hospitals. 21 Cutler (2007) and McClellan et al. (1994) also use di?erential distance as their instruments by subtracting distance to the nearest hospital from distance to the nearest hospital o?ering heart surgery.
20
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in exchange for a shorter travel time.22 Also, these three measures will always take on values greater than or equal to zero due to the min[·] operator in (2.3), and they equal zero if an individual lives closer to a Regional NICU than one of the lower levels of care. This speci?cation captures the fact that if a hospital nearby o?ers a particular level of care, a mother can also receive lower level care by traveling to the same hospital. These distances are not the only way one could specify exposure to NICUs. I utilize this method to best proxy for the cost of obtaining each level of care; although, one could also specify distance based on the distance to the nearest hospital of a speci?c level of care (instead of the nearest hospital with a particular level or higher). Other potential measures of exposure include hospital market shares or the number of hospitals of each level within a given radius. I choose distance so as not to impose potentially endogenous market de?nitions. As mentioned above, the goal is not to estimate structural parameters of hospital choice, but instead to exploit the exogenous variation in distance that directs patients to di?erent levels of care. Table 2.4 provides summary statistics of the four distance measures used to construct the instruments and of the three instruments themselves. On average, mothers of VLBW infants in my sample live 3.7, 5.7, 8.1, and 14.8 miles from the nearest hospital o?ering any birthing services, at least Intermediate care, at least Community care, and Regional care, respectively. The average number of miles saved by traveling to the nearest hospital with no NICU or higher relative to the nearest Regional NICU is 11.2. The average number of miles saved traveling to the nearest hospital with at least an Intermediate NICU or at least a Community NICU is 9.1 and 6.8 miles, respectively. These measures have wide variation, each with
A model with four instruments based on distance to each of the four levels of care would achieve the same goal, as it would condition on distance to the nearest Regional NICU in each ?rst stage regression. Using di?erential distance is equivalent to including all four distance measures separately, but restricting the coe?cient on the Regional distance variable. 2SLS results, not shown here, without this functional form assumption are almost identical to those presented in Section 2.5.
22
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standard deviations around 20 miles, or two to three times their means. I now provide a set of summary statistics supporting the assumption that di?erential distance is uncorrelated with the error term in Equation (2.1) and is therefore independent of unobservable determinants of VLBW mortality. Table 2.5 lists sample means of observable characteristics by the three instruments. If a detailed list of observable characteristics are independent of di?erential distance, it is likely to be the case that unobservable characteristics are as well (Altonji et al., 2005). For each instrument the table shows sample means for three groups: those observations with zero di?erential distance and those with di?erential distance below and above the median, conditional on non-zero di?erential distance. The ?rst three rows show that those individuals living in zip codes below the median typically save between one and ?ve miles by traveling to each of the three lower levels of care, and those with values above the median save between 16 and 32 miles. Other than the proportion of mothers who are black, which is about twice as large for observations at zero di?erential distance compared to individuals above the median for all three instruments, mothers’ demographics show little variation by distance. For example, the percent of mothers covered by Medicaid ranges from 48.3% to 52.1% for the Community distance groups. In contrast, this ?gure had a gap of 13.6 percentage points between No NICU and Community NICU hospitals in Table 2.3. Most importantly, infant health characteristics do not di?er much across distance groups. While the number of prenatal visits is slightly lower for those with zero miles saved, the month prenatal care began is similar across groups and there are no large di?erences in parity, multiple births, birth weight, or gestation. Most individual observable characteristics do not appear to di?er by distance, but there may be other important characteristics that do. The bottom portion of the table presents means of zip code level characteristics. These variables are collected from the 2000 census and merged to the mother’s zip code of residence, and I
35
present means treating each birth as an observation. Here, there are some potentially important di?erences by di?erential distance as median household income increases, percent urban decreases, and population density decreases across columns for each distance variable.23 Despite these di?erences, the variation in di?erential distance is not driven by population density alone. Figure 2.1 displays a map of California and plots the location of Intermediate, Community, and Regional NICUs in 1991. The light gray lines outline counties in the San Francisco Bay, Los Angeles Metro, and San Diego Metro areas. NICUs are clearly clustered around these metropolitan areas, but there does not appear to be any systematic di?erence in where each level of care is located. The geographic distribution of the community distance variable at its 1991 baseline is displayed in Figure 2.2, with Panel A showing the whole state and Panel B zooming in on the ?ve counties comprising the Los Angeles Metro area. The lightest colored zip codes have no births in the VLBW sample and the other zip codes are shaded by the three groups discussed above: those saving zero miles, and those above and below the median conditional on non-zero di?erential distance. The darker zip codes that have the largest di?erential distances, and are therefore closer to Community NICU hospitals, are more likely to be in outlying areas, but there is variation both within the major metropolitan areas and in the suburban areas with many neighboring zip codes of varying distances. Figure 2.3 shows similar maps plotting Intermediate distance. Overall, summary statistics indicate that di?erential distance is uncorrelated with most major observable demographic and infant health characteristics. To the extent that any di?erences in urban concentration are not captured by the individual controls, I examine the robustness of my results to the inclusion of zip code level controls and estimate models with zip code ?xed e?ects in Section 2.6.
23 Cutler (2007) and McClellan et al. (1994) also ?nd that areas with higher di?erential distance to hospitals o?ering heart surgery are less urban.
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2.5 Results
Comparisons of sample means in Section 2.4 revealed unconditionally higher neonatal mortality for VLBW infants born in hospitals with lower levels of care compared to those born in Regional NICU hospitals. However, there are also important di?erences in observable characteristics by level of care. This section estimates OLS speci?cations of the e?ect of level of care on mortality controlling for these observable characteristics and 2SLS estimates that account for any other unobservable determinants of mortality that may be correlated with hospital choice.
2.5.1 OLS Estimates
Table 2.6 presents OLS coe?cient estimates of ? N , ? I , and ? C . Moving across the columns, I progressively add control variables. To account for likely similarities in health conditions and hospital choices at local levels, and because the instruments vary at the zip code level when I estimate 2SLS models, standard errors of all regression estimates in this chapter are clustered by zip code. This clustering allows for arbitrary correlation of the error term within zip codes. The estimates in Column 1 re?ect the unadjusted mortality di?erences by level of care with no additional covariates and replicate the di?erences in sample means from Table 2.3. VLBW infants born in No NICU, Intermediate NICU, and Community NICU hospitals are 7.2, 2.2, and 0.8 percentage points more likely to die, respectively, than those born in Regional NICU hospitals. The Community NICU coe?cient is statistically signi?cant at the 10% level and the other two coe?cients are statistically signi?cant at the 5% level. Column 2 adds controls for long term mortality trends in the form of year dummies and within year mortality cycles in the form of eleven month-of-year dummies and six day-of-week dummies. The estimated e?ect of being born in a hospital with a Community NICU increases to
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1.3 percentage points and is now statistically signi?cant at the 5% level; the other two estimates are similar to the previous column. Column 3 adds controls for mother’s demographic characteristics. The coe?cient estimates decrease from Column 2 but are still positive and precisely estimated. Column 4 adds controls for the infant’s baseline health characteristics and prenatal care. These covariates control for underlying health risk and are similar to controls used in previous studies. This speci?cation estimates “risk-adjusted” mortality differences by level of care and will be treated as the baseline OLS estimates for the remainder of the paper. Except for the No NICU coe?cient, the estimates in Column 4 are slightly larger than those in the previous column, and the coe?cients imply that on average, infants born in hospitals with Community NICUs, Intermediate NICUs, or No NICUs are 1.2, 2.1, or 5.0 percentage points more likely to die than those born in hospitals with Regional NICUs, respectively. Relative to the sample mean mortality rate of 15.7%, these coe?cients imply e?ects of 7.6%, 13.4%, and 31.8%, respectively. OLS estimates lead to the conclusion that infants born in lower level hospitals experience higher risk-adjusted mortality rates, con?rming the previous literature. Infants born in No NICU hospitals have the highest risk-adjusted mortality rate, and most relevant to the trend towards deregionalization, infants born in Intermediate and Community NICU hospitals experience statistically and qualitatively higher mortality rates than those born in Regional NICU hospitals. However, the ?nding that the coe?cient estimates are sensitive to controls implies that observed determinants of mortality are correlated with level of care. The fact that the coe?cient estimates increase or decrease depending on which controls are added reinforces that the direction of any selection is ambiguous. Evidence of selection on the observables emphasizes the importance of accounting for any potential unobserved selection as well.
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2.5.2 First Stage Estimates
This section presents the ?rst stage estimates of the e?ect of distance on level of care chosen speci?ed in Equation (2.2). I provide evidence that the three distance measures are strong instruments and further evidence that they satisfy the exclusion restriction. Table 2.7 presents the results, building up to the baseline speci?cation by progressively adding controls across the columns for each outcome. The coe?cient estimates and standard errors show little to no change across columns. This ?nding implies little correlation between distance and observable characteristics and further supports the hypotheses that the instruments are uncorrelated with unobserved characteristics as well. Columns 4, 8, and 12, present the main ?rst stage speci?cations with all controls included. All of the ?rst stage coe?cient estimates are strongly statistically signi?cant and show the expected substitution patterns. Individuals living closer to a particular level of care are more likely to choose that level of care and less likely to choose the other levels of care. For example, a ten mile increase in ComDist, associated with living ten miles closer to a Community NICU or higher, decreases the probability of choosing a No NICU hospital and an Intermediate NICU hospital by 2.5 and 2.7 percentage points, respectively, and increases the probability of choosing a Community NICU hospital by 7.4 percentage points.24 These coe?cient estimates are equivalent to 33%, 24%, and 31% changes relative to their respective level of care indicator sample means. These are large e?ects given the standard deviations of the distance instruments are around twenty. Qualitatively, distance is an important determinant of the level of care a mother chooses. Below the estimates in each panel I report F-Statistics testing the null that
24 Though not a part of the estimation, there is implicitly a fourth relationship between the probability of choosing a hospital with a Regional NICU and distance. While not shown in the table, this same change decreases the probability of choosing a hospital with a Regional NICU by 2.2 percentage points, con?rming the ?ndings of Haberland et al. (2006) that lower level NICUs divert high-risk births from Regional NICUs.
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the three distance coe?cients are jointly equal to zero. The F-Statistics for the main speci?cations with the full set of controls are 32.46, 44.56, and 38.35, all well above the rule-of-thumb cuto? of 10 typically used to assess ?nite sample bias from weak instruments. Additionally, the fact that each instrument is signi?cant in all three equations and has a particularly large coe?cient estimate in the equation corresponding to its respective level of care, suggests that each of the three instruments provide independent variation to identify the model.
2.5.3 2SLS Estimates
Table 2.8 reports the 2SLS results. Column 1 repeats the baseline OLS results with all controls from Table 2.6. All three 2SLS coe?cient estimates in Column 2 are substantially lower than their counterparts in Column 1. The coe?cient of the No NICU indicator decreases from 0.050 to -0.030, the coe?cient of the Intermediate NICU indicator decreases from 0.021 to 0.009, and the coe?cient of the Community NICU indicator decreases from 0.012 to -0.063. The No NICU and Community NICU coe?cient estimates actually change signs and the Intermediate NICU coe?cient estimate falls by half, but the standard errors increase by a factor of between three and nine. The Community NICU coe?cient estimate is marginally statistically signi?cant (at the 10% level), but neither of the other two estimates in Column 2 are statistically signi?cant.25 Despite the large standard errors, the 2SLS estimates are clearly di?erent from and bounded below the OLS estimates. First, I conduct a Hausman test of the null hypothesis that both the OLS and 2SLS estimates are consistent against the
One might be concerned that some of the infant health and prenatal care controls are endogenous. This would be a concern if, for example, mothers who live close to Regional NICUs also have access to higher quality prenatal care, or if hospitals with di?ering levels of care have di?erent propensities to diagnose various health conditions. To account for this, I also estimate 2SLS regressions excluding this set of controls. The results are similar to those reported in Column 2 of Table 2.8 with coe?cient (standard error) estimates of -0.021 (0.037), 0.014 (0.018), and -0.041 (0.042) for No NICUs, Intermediate NICUs, and Community NICUs, respectively.
25
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alternative that only the 2SLS estimates are consistent.26 The p-value of this test is 0.031, so the null is rejected at the 5% signi?cance level. This test implies that the 2SLS coe?cient estimates are statistically di?erent from the OLS estimates and provide more consistent estimates. Second, even the upper bounds of the 2SLS estimates imply much lower quantitative and qualitative e?ects on mortality than the OLS estimates, at least for the No NICU and Community NICU coe?cients. Figure 2.4 plots the OLS and 2SLS coe?cient estimates scaled by mean neonatal mortality. It also plots one and two standard deviation intervals above the 2SLS coe?cient estimates. The OLS coe?cient estimate of the No NICU coe?cient implies 31.8% higher mortality relative to being born in a Regional NICU hospital. The 2SLS coe?cient estimate is large and negative, one standard deviation above the 2SLS coe?cient estimate is still below zero, and even two standard deviations above implies an e?ect of 17.9% – 44% lower than the OLS estimate. Similarly, one standard deviation above the Community NICU coe?cient estimate is still far below zero, and two standard deviations above implies an e?ect of 4% – 46% lower than the OLS e?ect of 7.4%. One standard deviation above the Intermediate NICU 2SLS coe?cient estimate is above the OLS estimate, but the point estimate is still 55% lower than the OLS point estimate. The 2SLS estimates are not statistically di?erent from zero and are small in magnitude compared to OLS estimates. This ?nding provides evidence that the OLS estimates of higher mortality at the three lower levels of care relative to Regional NICU hospitals are overstated. The dominant form of selection is unobservably higher risk births occurring in lower level hospitals. These results imply that policy
The usual Hausman test also assumes that the OLS estimates are e?cient under the null hypothesis. However, clustered standard errors result in a covariance matrix that is not asymptotically e?cient. Therefore, I construct the Hausman test statistic using a paired bootstrap strategy that samples at the zip code level. My sample has 1,144 zip codes, so I construct 5,000 random samples of my data that each draw 1,144 zip codes with replacement. For each bootstrap sample, I run my OLS and 2SLS regressions and save the coe?cient estimates. I then construct the estimated variance-covariance matrix of the di?erence between the OLS and 2SLS coe?cients based on the distribution of these 5,000 estimates. See Cameron and Trivedi (2005, p. 378) for details.
26
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measures aimed at reversing the e?ects of deregionalization are likely to have a limited impact on mortality. Relocating mothers who would have chosen to give birth in lower level hospitals to Regional NICU hospitals prior to birth would not improve mortality rates because the relocated deliveries would be from the less healthy portion of the distribution. It is important to emphasize that I am estimating how the level of care at the hospital in which an infant is born impacts mortality. My results do not imply that being treated in a hospital with a higher level NICU has no e?ect on outcomes. In fact, a likely mechanism behind my results is that infants born in hospitals with lower levels of care achieve similar outcomes to those born in hospitals with higher levels of care because the former group will be transferred to a higher level hospital after birth if necessary. In my sample 66% of VLBW infants born in hospitals with No NICUs or Intermediate NICUs are transferred to a higher level hospital after birth, and 85% of those that are transferred are sent to a Regional NICU hospital. In order to explore whether the probability of transfer is systematically impacted by distance, I regress an indicator for whether or not an infant is transferred to a Regional NICU hospital on the three distance instruments for the sample of VLBW infants born in No NICU or Intermediate NICU hospitals. To run this regression, I select the sample based on an endogenous variable, but statistically insigni?cant coe?cients on the three distance instruments would suggest that hospitals do not selectively transfer infants based on distance. In other words, this kind of ?nding would imply that transfers occur when medically necessary and are not impacted by where a mother lives in relation to where NICUs are located. Results of this regression do reveal a positive and statistically signi?cant coe?cient of 0.029 on the No NICU distance variable, but the coe?cient estimates on the other two distance instruments are very small and statistically insigni?cant (-0.00008 and 0.008, respectively). The positive coe?cient on No NICU di?eren-
42
tial distance implies that as a mother lives closer to a hospital with No NICU or higher or farther from the nearest Regional NICU, her infant is more likely to be transferred to a Regional NICU. When I instead regress the transfer indicator on all four distance measures instead of the three di?erential distance measures, I ?nd that this coe?cient is being driven by the distance to the nearest Regional NICU hospital. This ?nding is likely a result of using the selected sample of infants born in No NICU or Intermediate NICU hospitals. Infants of mothers who live close to Regional NICU hospitals, but choose not to deliver in the Regional NICU hospital are likely to have healthier infants and less medical need for transfer. Overall, these results suggest that I ?nd no gradient between level of care at the birth hospital and mortality because VLBW infants are transferred to hospitals with higher levels of care when medically necessary, and the location of lower level NICUs does not change the probability of eventually being treated in a hospital with a higher level NICU.
2.6 Robustness Tests
In this section I further test the assumptions that lead to my conclusions and explore the robustness of my ?ndings to various alternative speci?cations. I also examine whether the e?ect of level of care on mortality di?ers among di?erent subsamples of the VLBW infant population and discuss implications of local average treatment e?ects.
2.6.1 Additional Tests of Instrument Validity
The distance instruments are motivated by the supposition that NICUs are not located according to medical need and are likely adopted to attract low-risk obstetric patients. Table 2.9 provides further evidence of this hypothesis by presenting “?rst
43
stage” estimates of the e?ect of distance on mothers’ hospital choice for infants above the VLBW threshold. I display estimates for low birth weight infants (1,500 to 2,500 grams or 3.3 to 5.5 pounds), those just above the low birth weight cuto? (2,500 to 3,000 grams or 5.5 to 6.6 pounds), and the remaining normal birth weight group (3,000 to 4,500 grams 6.6 to 9.9 pounds).27 Distances are strong predictors of level of care for these samples, and the coe?cient estimates and F-Statistics actually increase in absolute value as birth weight increases. This evidence supports the anecdotes that NICUs attract all mothers and the assumption that NICU location is exogenous to the unobserved determinants of VLBW mortality. As a ?nal test of the validity of the instruments, I estimate reduced form regressions of the e?ect of the distance instruments on neonatal mortality, and examine their sensitivity to the addition of controls. The stability of the ?rst stage estimates in Table 2.7 provides evidence in favor of the exclusion restriction. A similar exercise for the reduced form estimates provides a sharper test because it provides evidence on how observable characteristics are correlated with the portion of distance that predicts neonatal mortality. If selection on the unobservables is similar to selection on the observables, and the reduced form estimates are insensitive to controls, the exclusion restriction that E [Dzt ?izt |X] = 0 is likely to hold. Table 2.10 presents the results. The ?rst column includes no controls, the second column adds time dummies, and the ?nal two columns add demographic and health characteristics. The estimates are quite stable across speci?cations. The N oDist and IntDist coe?cient estimates change slightly as controls are added, but they are quite small and statistically insigni?cant across all four columns. The ComDist coe?cient estimate is very stable across speci?cations and in the ?nal column is estimated as a statistically signi?cant -0.004. These reduced form point estimate are also small in magnitude. For example,
27
All samples are subject to the same restrictions described in Section 2.3.
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a one standard deviation change in miles saved to the nearest Community NICU or higher (18.4 miles) only leads to a 0.8 percentage point decrease in mortality. As a comparison, a one standard deviation increase in mother’s age (6.9 years) reduces mortality by 4.5 percentage points, and a one standard deviation increase in number of prenatal care visits (6.1 visits) reduces mortality by 1.8 percentage points. An increase in birth weight from the category just below the mean (900 to 999 grams) to the category just above the mean (1,000 to 1,099 grams) decreases mortality by 3.9 percentage points. All of these e?ects are much larger in magnitude than the e?ects of distance on neonatal mortality. These quantitatively and qualitatively small reduced form estimates are consistent with the small 2SLS estimates of the e?ect of level of care on mortality. 2SLS estimates scale the reduced form estimates by the size of the e?ect of distance on hospital choice. If distance a?ects the level of care chosen but not mortality, level of care cannot a?ect mortality for the population that chooses level of care as a result of distance.
2.6.2 Alternative Speci?cations 2.6.2.1 Zip Code of Residence Controls
Sample means by di?erential distance in Table 2.5 showed that individuals living closer to each of the three lower levels of care relative to Regional NICU hospitals live in zip codes with lower population density and higher income. Though I control for many individual level covariates in my main results, if these zip code level characteristics are conditionally correlated with distance and infant health, 2SLS estimates would be biased. Therefore, I test the robustness of my estimates to controlling for zip code level population density; percent black; percent Hispanic; percent of the population over 25 with no college, some college, a college degree,
45
and more than a college degree; and median household income.28 Additionally, distances may factor into the hospital choice decision di?erently in urban and suburban areas. For example, ?ve miles in downtown Los Angeles may have a much di?erent travel time than ?ve miles in a suburban area. Furthermore, hospitals are located closer to each other in more urban areas than less urban areas. Using di?erential distance and controlling for all three distance variables captures some of these features, but the ?st stage regression may have more predictive power if the e?ect of distance is allowed to vary with population density. I therefore estimate models with interactions of the distance measures and zip code population density added to the instrument set. Table 2.11 presents ?rst stage estimates with the baseline speci?cation repeated in Panel A. In this table each row lists coe?cient estimates from one ?rst stage regression. When zip code level controls are added in the ?rst three rows of Panel B, the magnitudes of the ?rst stage coe?cient estimates change slightly, but they are very similar, highly statistically signi?cant, and the F-Statistics are of similar magnitudes to Panel A. The second portion of Panel B interacts the distance instruments with population density. The coe?cient estimates of the three distance measures decrease a bit in magnitude, but are still highly statistically signi?cant. The density interactions are almost all statistically signi?cant with positive diagonal elements and negative o? diagonal elements, matching the pattern of signs of the distance main e?ects. Thus, the e?ect of distance becomes stronger as population density increases, as would be expected if travel times are longer or travel is more expensive in more densely populated areas. The three added instruments result in similar F-Statistics for the No NICU and Intermediate NICU regressions, but a lower F-Statistic in the Community NICU regression that is still well above 10. The corresponding panels of Table 2.12 present the OLS and 2SLS squares
28 All zip code level variables are calculated from the 2000 Census. Unfortunately, the 1990 census does not provide comparable data at the zip code level.
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results, with each row listing coe?cient estimates from one regression. The OLS and 2SLS coe?cient and standard error estimates in speci?cations controlling for zip code level characteristics are very similar to the baseline estimates. Controlling for di?erences between urban and suburban zip codes does not impact the results. The last row of Panel B presents results when the instrument set includes interactions with population density. The standard errors of these estimates are very similar to the speci?cation with zip code level controls and the baseline speci?cation; however, the coe?cients all move towards zero and none are statistically signi?cant. If anything, allowing the e?ect of distance to di?er with population density results in point estimates that are even closer to zero.
2.6.2.2 Zip Code of Residence Fixed E?ects
Next, I estimate models with zip code of residence ?xed e?ects to control for any other characteristics that are constant within a zip code, but not accounted for by the census data controls. Identi?cation with these ?xed e?ects comes from changes over time in a zip code’s distances to each level of care caused by new, upgraded, or closed NICUs nearby during the sample period. Thus, the variation in distance is directly driven by deregionalization during the sample period. 25% of the VLBW sample lives in a zip code that at some point between 1991 and 2001 experiences a change in Intermediate Distance, and the average change is 4.5 miles. 32% lives in a zip code that experiences a change in Community Distance, and the average change is 3.9 miles. Figure 2.5 maps zip codes that become no closer, slightly closer (changes below the median), and much closer (changes above the median) to Community NICUs, with Panel A showing the whole state and Panel B focusing on the Los Angeles metro area. Zip codes with large changes in distance are more likely to be in outlying areas, but there are many neighboring zip codes experiencing di?erent changes in both urban and suburban areas. Figure 2.6 shows similar maps 47
for Intermediate distance. With ?xed e?ects the instruments are valid if zip code level changes in distance are uncorrelated with zip code level changes in unobserved mortality.29 Even if zip codes at di?erent distances di?er systematically, identi?cation will only be threatened if unobserved mortality trends are conditionally correlated with changes in distance. Figure 2.7 shows that at least trends in mean observable demographic and underlying health variables do not systematically di?er between zip codes experiencing di?erent changes in distance. This ?nding of parallel trends is not surprising given the evidence that deregionalization has not been driven by the health needs of high-risk infants. Panel C of Tables 2.11 and 2.12 show ?rst stage and second stage results with zip code ?xed e?ects, respectively. The instruments are still strong predictors of level of care chosen with large, positive, and statistically signi?cant coe?cients along the diagonal. The F-Statistics are lower than in the cross sectional speci?cations, but they are all above 16 without population density interactions and above 11 with the interactions. OLS results in Table 2.12 are similar to the cross sectional results. The 2SLS estimates are again not statistically signi?cant. The ?xed e?ects lead to much larger standard errors and more negative point estimates of the No NICU and Community NICU coe?cients, but the qualitative results are similar: negative or small point estimates, indicating no di?erence in mortality outcomes by level of care at the birth hospital. When the instruments are allowed to vary with population density, the negative point estimates of the No NICU and Community NICU coef?cients are cut by about two thirds and move towards zero as in the speci?cations without ?xed e?ects. These speci?cations con?rm that the main results are robust to the most complete possible controls for local characteristics. They also show that the cross sectional 2SLS speci?cations estimate similar e?ects to speci?cations
¨ izt ] = 0, where the dots indicate variables in deviation¨ zt ? Formally, the assumption is E [D ¨izt |X from-zip-code-mean-form.
29
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identi?ed directly from changes in distance related to deregionalization.
2.6.2.3 Pooling No NICUs and Intermediate NICUs
I also estimate models where I pool No NICU and Intermediate NICU hospitals into one category. Only 7.6% and 11.1% of the VLBW sample are born in these two types of hospitals, respectively. Thus combining them into one group may provide more precision. Additionally, some of the ?rst stage predictions of these indicators are outside the unit interval. Pooling these two groups reduces the percentage of observations with at least one of their ?rst stage predictions outside the unit interval from 12.8% to 2.7%. It is also likely medically reasonable to pool these two groups. Neither of these types of hospitals is designed to care for VLBW infants, and neither provides mechanical ventilation. Additionally, infants born at both levels of care have very similar transfer patterns. About 60% of VLBW infants born at these two levels of care are transferred to Regional NICUs. In contrast, only 20% of infants born in Community NICU hospitals are transferred to Regional NICUs. Given the likely similarity of care, it is not surprising that a ?2 test that the 2SLS No NICU and Intermediate NICU coe?cient estimates from the main speci?cation are the same does not reject the null hypothesis (p-value=0.24). Panel D of Tables 2.11 indicates that distance is still a strong predictor of level of care with even larger F-statistics than in the original estimation. In Panel D of Table 2.12 OLS estimates are as expected, with a similar Community NICU coef?cient estimate to the baseline speci?cation and coe?cient estimates of the pooled No/Intermediate NICU coe?cient between the original No NICU and Intermediate NICU coe?cient estimates. The precision gains in the 2SLS estimates are not large, but the point estimates are closer to zero, and none of them are statistically signi?cant negative estimates. 49
2.6.2.4 Alternative Control Variables and Clustering
Table 2.13 presents estimate from six other alternative speci?cations, with the baseline speci?cation repeated in Column 1. Columns 2 through 5 test whether the results change of I include various di?erent health related controls. Column 2 adds an indicator for whether the infant was delivered by cesarean section or not. I do not include this control in the main speci?cation because, as a treatment decision, it may be endogenous to the level of neonatal intensive care at the birth hospital. Despite this concern, adding it as a control variable does not appreciably change the OLS or 2SLS estimates. Column 3 and 4 provide evidence that my results are not sensitive to how I control for birth weight. In these two columns, I interact the birth weight indicators with the male dummy and re-specify the birth weight indicators in 50-gram increments instead of 100-gram increments, respectively. Both alternative speci?cations lead to OLS and 2SLS estimates that are similar to the baseline estimates. Column 5 replaces the dummy indicating whether an infant has any of the de?ned clinical conditions with a full set of indicators for each of the nine di?erent conditions.30 Again, the results are quite similar to the baseline estimates. The last two columns of Table 2.13 explore whether the standard error estimates change if the level of clustering is changed. To this point, standard error estimates have been clustered at the zip code level to allow unobserved mortality to be correlated within zip codes. Column 6 allows for more conservative geographic correlation by clustering at the HSA (Hospital Service Area) level. These HSAs are collections of zip codes for which most of their Medicare patients receive care from the same hospital.31 While these areas are calculated only with Medicare patients, they are likely good proxies for general health care markets. My sample includes
30 The nine conditions include hydrops due to isoimmunization, hemolytic disorders, fetal distress, fetus a?ected by maternal condition, oligohydramnios, other high-risk maternal conditions, placenta hemorrhage, premature rupture of membrane, and prolapsed cord (Phibbs et al., 2007). 31 Source:http://gonzo.dartmouth.edu/faq/data.shtm, last accessed May 17, 2010.
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1,144 zip codes which are grouped into 192 HSAs. The standard error estimates remain virtually unchanged when clustering at this larger geographic level. If anything the 2SLS estimate of the community NICU coe?cient becomes a bit more precise.32 Column 7 clusters standard errors by hospital instead of by geography. Allowing unobserved mortality to be correlated within hospitals does slightly in?ate the standard errors beyond those allowing unobserved mortality to be correlated within geographic areas.
2.6.2.5 Alternative Mortality Measures
Results to this point indicate that OLS estimates overstate di?erences in neonatal mortality by level of care. This de?nition of mortality includes all deaths within 28 days of birth or within one year if an infant is continuously hospitalized since birth. It may be the case that results di?er for shorter or longer term measures of mortality. In Table 2.14 I present OLS and 2SLS estimates of the e?ect of level of care on 1-day, 28-day, and 1-year mortality, regardless of hospitalization time. In general results are similar to the baseline speci?cation, repeated in Column 1. OLS estimates reveal higher mortality in lower level hospitals. The point estimates increase as the mortality window increases, but increases in the mean mortality rate as the window lengthens imply the relative magnitudes are similar for each outcome. For all three additional mortality outcomes 2SLS estimates are well below the OLS estimates and statistically insigni?cant. The ?nding that OLS estimates overstate di?erences in mortality is robust to these alternative outcome measures.
Unreported estimates reveal very similar standard error estimates when clustering at the county level. As a caveat, the asymptotics for clustered standard errors require the number of clusters to approach in?nity while the cluster size is ?xed. There are only 39 counties in the data, so this speci?cation has a small number of large clusters.
32
51
2.6.3 Heterogeneity and Local Average Treatment E?ects
Throughout the paper I have assumed a homogeneous e?ect of level of care on mortality for all VLBW infants. However, it is possible that the e?ect may vary by the infant’s characteristics. This is particularly important with instrumental variable estimates because they only estimate the impact of level of care on mortality for the sub-group of infants whose mothers choose level of care based on the instruments. If these “compliers,” who choose their level of care because of distance, are di?erent from the rest of the sample, these estimates will represent a local average treatment e?ect (LATE) (Angrist et al., 1996; Imbens and Angrist, 1994). I cannot directly observe the compliers in my data, but one might be concerned that the 2SLS estimates are driven by a particular group of observations if these compliers di?er from the general population. I therefore estimate my OLS and 2SLS regression equations on various sub-samples based on observable characteristics to ensure the estimates are not being driven by any particular groups. Understanding any heterogeneity in this e?ect is also important for policy implications. If there are sub-groups for whom there is a gradient between level of care and mortality, interventions may be warranted to target these speci?c groups and ensure they are able to deliver in higher level hospitals. Table 2.15 presents results for various subsamples with the baseline estimation from Table 2.8 repeated in Column 1. Overall, the OLS and 2SLS coe?cient estimates are similar across all reported sub-groups. OLS estimates are positive and statistically signi?cant, and 2SLS estimates are small and mostly statistically insigni?cant. Column 2 shows the results for infants of Hispanic mothers. The 2SLS estimate of the e?ect of being born in an Intermediate NICU hospital (0.025) is close to the OLS coe?cient estimate (0.028) for this group, but still statistically insigni?cant. The other two 2SLS coe?cient estimates are negative, statistically insigni?cant, and similar to the baseline sample. 52
Column 3 excludes infants of black mothers from the estimation. Infants with black mothers make up a small subset of the sample, so I do not estimate the regressions for them alone, but excluding them does not have much e?ect on the estimates. This sample also provides a useful robustness check because of the di?erence in percent black by di?erential distance reported in Table 2.5. The estimates for the population of infants with mothers covered by Medicaid in Column 4 are similar to the baseline speci?cation, indicating the results are similar by insurance coverage. The sample of infants whose mothers have no college education in Column 5 has a 2SLS Intermediate NICU coe?cient that is the same as the OLS estimate (0.027), but again it is not statistically signi?cant and the other 2SLS coe?cient estimates are negative. In the previous section I show the results are robust to controlling for population density and allowing the e?ect of distance to di?er by population density. One might also be concerned that the results are driven by either urban or suburban areas, which I address in Column 6. This column presents estimates for the subsample whose zip code population density is below the median. Again, the 2SLS coe?cient on being born in an Intermediate NICU hospital (0.022) is similar to the OLS coe?cient (0.028), but the other estimates are similar to the baseline speci?cation, indicating the results are similar for individuals in urban and suburban areas. Finally, the e?ect of level of care may have changed over time. Mortality rates for VLBW infants decreased during the early 1990s, but leveled o? during the latter part of the decade (Horbar et al., 2002).33 Also, Table 2.2 shows that the di?usion of NICUs leveled o? during the second half of the decade. It is possible that the gradient between level of care and mortality changed during this time period if technology improved, if new NICUs improved over time due to learning, or if the
33 In my sample, mean neonatal mortality fell from 20.08% to 14.80% between 1991 and 1995, but only fell to 13.62% by 2001.
53
propensity for lower level units to transfer infants to higher levels changed over time. Column 7 presents results for births occurring during the ?rst half of the sample from 1991 to 1995. The OLS gradient between level of care is greater during this time period as compared to results for the full time period, but because mean mortality was higher during the earlier period, the relative e?ects are similar. The 2SLS estimates are similarly small and statistically insigni?cant as compared to the baseline estimation. There is no evidence of a di?erential e?ect of level of care on mortality over time. Despite evidence that the e?ect of level of care on mortality does not vary by observable characteristics, there still may be unobserved heterogeneity. If there are heterogeneous treatment e?ects that vary by unobservables, 2SLS would estimate a LATE for a group of compliers that are not identi?able in the data. That being said, because the compliers are the infants whose mothers choose their delivery hospital based on distance, the LATE would in fact be the policy relevant e?ect. Even if the 2SLS estimates do not represent the e?ect of level of care on mortality for the entire population, they still imply that the population that would be impacted by policy measures regarding the geographic distribution of NICUs does not experience di?erent mortality rates by level of care.
2.6.4 Sample Selection
In this section I ensure that my estimates are not sensitive to the sample restrictions discussed in Section 2.3. The ?rst column of Table 2.16 repeats the estimates from the main speci?cation in Table 2.8. Columns 2 through 5 report results including various groups that were excluded from the main analysis sample. Column 2 includes infants in the most rural counties, Column 3 includes infants born in Kaiser hospitals, Column 4 includes infants diagnosed with a congenital anomaly, and Column 5 includes fetal deaths. 54
These estimates reveal that the OLS and 2SLS estimates are not appreciably a?ected by these sample restrictions. If anything, including rural residents results in 2SLS estimates that are closer to zero, although excluding these observations is still probably best, since they are likely to live furthest from all hospitals and may be unobservably di?erent from those living close to all hospitals. Including deliveries in Kaiser hospitals has little e?ect on the estimation as well. Results of ?rst stage regressions for this sample alone, not shown here, reveal that these added observations do not choose hospitals based on distance; therefore, they do not contribute to the 2SLS estimates, so it is not surprising that the results are not a?ected by including them. Including infants with congenital anomalies leads to higher coe?cient estimates in the OLS speci?cation, but similar 2SLS estimates to the baseline speci?cation. Finally, including observations of infants who die before delivery approximately doubles the magnitude of both the OLS and 2SLS coe?cient estimates. The mean mortality rate for this sample is almost twice that of the main analysis sample, so the relative e?ects are very similar. This ?nding indicates that di?erences in level of care do not di?erentially impact the probability of death prior to delivery.
2.7 Conclusion
This chapter estimates the causal e?ects of level of neonatal intensive care at the birth hospital on VLBW mortality. The issue of deregionalization – the increasing number of smaller, community hospitals opening NICUs – has gained attention in the health policy community. Evidence of higher risk-adjusted mortality rates for VLBW infants born in hospitals with lower level NICUs has led advocates to suggest high-risk mothers be referred to more sophisticated hospitals prior to delivery. However, these estimates could be biased in either direction by unobserved selection. To overcome selection concerns, I utilize an instrumental variables strat55
egy that exploits exogenous variation in distance from a mother’s residence to the nearest hospital of each level of care. NICU location has been driven by factors unrelated to the health of VLBW infants, and I provide evidence in my data that distance is uncorrelated with health conditions. My OLS estimates con?rm the previous literature and imply 7.6%, 13.4%, and 31.8% higher risk-adjusted mortality rates for VLBW infants born in Community, Intermediate, and No NICU hospitals, respectively, relative those born in Regional NICUs hospitals. However, my instrumental variables estimates imply that these mortality di?erences are overstated. 2SLS estimates are not statistically di?erent from zero and are small in magnitude. The No NICU and Community NICU 2SLS coe?cient estimates are bounded well below their OLS counterparts, with even two standard deviations above the 2SLS estimates lying about 50% below the OLS estimates. The Intermediate NICU 2SLS coe?cient estimate is not clearly bounded below the OLS estimate, but the point estimate is half the magnitude. My results are robust to controlling for zip code level characteristics and zip code level ?xed e?ects. I also ?nd no evidence that the e?ect of level of care on mortality is heterogeneous by demographics. Even if the e?ect varies on other unobservable dimensions, any unobserved heterogeneity would lead to a local average treatment e?ect directly identi?ed from infants impacted by deregionlization. The fact that the 2SLS estimates are below the OLS estimates, reveals that mothers with higher unobserved risk select into hospitals with lower levels of care. In terms of mortality, these results imply that relocating high-risk deliveries to Regional NICU hospitals prior to birth will not result in improved health outcomes. Instead, Regional hospitals would be treating new patients with higher unobserved acuity. I also show evidence that level of care at the birth hospital does not impact mortality because infants born in No NICU and Intermediate NICU hospitals are often transferred to Regional NICU hospitals, and these transfers are independent
56
of how close mothers live to lower level facilities. Deregionalization does not appear to prevent infants born in No NICU or Intermediate NICU hospitals from eventually receiving care in Regional NICUs. This analysis has addressed the ?rst-order question of how deregionalization has impacted VLBW mortality. Future research is needed to understand the full welfare impacts of this trend. First, while mortality may not vary by level of care at the birth hospital, there may be important di?erences in cost of care. If larger hospitals achieve economies of scale, they may be more e?cient in treating sick infants. Inter-hospital transfers may also be costly, both monetarily and emotionally. Alternatively, more sophisticated facilities may provide more costly procedures with little marginal return. Second, there may be important e?ects of deregionalization on quality and cost of care for healthier infants. Chapter 3 examines one aspect of this question and ?nds that additional short term NICU supply leads to a higher probability of NICU admission for infants above the very low birth weight threshold. Third, if mothers value shorter travel time and more convenient visitation of family members, access to at least some level of intensive care at nearby hospitals may increase utility. Also, more competition in the neonatal intensive care market may lead to lower prices. Ho et al. (2007) study the market for Whipple surgery, a treatment for pancreatic cancer, and ?nd that regionalizing this treatment by consolidating it to the hospitals with the highest volume leads to substantial price increases.34 Finally, further research is warranted to understand the determinants of NICU adoption by hospitals and whether hospitals are able to recoup their ?xed costs by attracting pro?table patients.
These authors do ?nd that regionalization of Whipple surgery can reduce mortality, but price increases cancel out over half of the increased consumer surplus.
34
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Figure 2.1: NICU Location by Level of Care in 1991
Notes: The light gray lines outline counties in the San Fransisco Bay, Los Angeles Metro, and San Diego Metro areas. See text for de?nitions of the levels of care.
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Figure 2.2: Miles Saved to Nearest Community NICU or Higher, 1991
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(a) Full State (b) LA Metro Area Notes: These ?gures shade zip codes based on the number of miles a mother living at the center of the zip code saves by choosing the nearest Community NICU or higher over the nearest Regional NICU. Zip codes shaded in white indicate no very low birth weight births in my analysis sample. Remaining zip codes are divided into three groups: those saving zero miles, and those above and below the median conditional on non-zero di?erential distance. The dark lines in Panel A outline counties in the San Fransisco Bay, Los Angeles Metro, and San Diego Metro areas.
Figure 2.3: Miles Saved to Nearest Intermediate NICU or Higher, 1991
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(a) Full State (b) LA Metro Area Notes: These ?gures shade zip codes based on the number of miles a mother living at the center of the zip code saves by choosing the nearest Intermediate NICU or higher over the nearest Regional NICU. Zip codes shaded in white indicate no very low birth weight births in my analysis sample. Remaining zip codes are divided into three groups: those saving zero miles, and those above and below the median conditional on non-zero di?erential distance. The dark lines in Panel A outline counties in the San Fransisco Bay, Los Angeles Metro, and San Diego Metro areas.
Figure 2.4: Coe?cient Estimate Magnitudes
Notes: This ?gure plots the OLS and 2SLS coe?cient estimates from Table 2.8 divided by mean neonatal mortality (15.7%). The dashed points indicate one and two standard deviation intervals above the 2SLS coe?cient estimates.
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Figure 2.5: Changes in Community Distance, 1991 to 2001
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(a) Full State (b) LA Metro Area Notes: These ?gures shade zip codes based on changes from 1991 to 2001 in the number of miles a mother living at the center of the zip code saves by choosing the nearest Community NICU or higher over the nearest Regional NICU. Zip codes shaded in white indicate no very low birth weight births in my analysis sample. Remaining zip codes are divided into three groups: those that become no closer, slightly closer (changes below the median), and much closer (changes above the median). The dark lines in Panel A outline counties in the San Fransisco Bay, Los Angeles Metro, and San Diego Metro areas.
Figure 2.6: Changes in Intermediate Distance, 1991 to 2001
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(a) Full State (b) LA Metro Area Notes: These ?gures shade zip codes based on changes from 1991 to 2001 in the number of miles a mother living at the center of the zip code saves by choosing the nearest Intermediate NICU or higher over the nearest Regional NICU. Zip codes shaded in white indicate no very low birth weight births in my analysis sample. Remaining zip codes are divided into three groups: those that become no closer, slightly closer (changes below the median), and much closer (changes above the median). The dark lines in Panel A outline counties in the San Fransisco Bay, Los Angeles Metro, and San Diego Metro areas.
Figure 2.7: Demographic and Health Trends by Changes in Distance
(a) Demographics by ? IntDist
(b) Demographics by ? ComDist
(c) Health Characteristics by ? IntDist
(d) Health Characteristics by ? ComDist
Notes: These ?gures plot means of mothers’ demographic and infants’ health characteristics by changes in di?erential distance to Intermediate and Community NICUs. Observations are divided into three groups based on whether the zip code of residence becomes no closer, slightly closer (changes below the median), or much closer (changes above the median) to the respective level of care between 1991 and 2001. N=42,912.
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Table 2.1: Detailed Level of Care De?nitions Level I Care Provided Basic neonatal care for healthy infants No Intensive Care Unit Have an intensive care unit Care for midly ill infants Do not provide mechanical ventilation Provide mechanical ventilation with restrictions (e.g., only for less than 96 hours, or only for infants weighing above 1,000 grams) Provide mechanical ventilation without restrictions Provide major neonatal surgery excluding cardiac surgery requiring bypass and/or extracorporeal membrane oxygenation (ECMO) Provide cardiac surgery requiring bypass and/or ECMO
II
IIIA
IIIB IIIC
IIID
Notes: Level of neonatal care de?nitions from Phibbs et al. (2007). There are three ICD-9 CM codes indicating mechanical ventilation: 96 hours, and duration unknown. Hospitals with NICU beds that do not have occurrences of any of these codes are labeled as Level II. In distinguishing between Level IIIA and IIIB, Phibbs et al. (2007) count units that never provide ventilation for more than 96 hours as IIIA. For units that provide both types but do not provide any surgery, they examine the patterns of ventilation by duration and birth weight to distinguish which appear to have restrictions.
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Table 2.2: California Obstetric Hospitals by Year and Level of Care No Intermediate NICU NICU 161 153 149 147 148 140 141 139 135 130 122 58 52 53 56 49 48 47 45 44 45 45 Community NICU 35 43 45 45 51 54 55 58 60 57 57 Regional NICU 42 44 45 45 46 46 46 46 46 45 45
Year 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Total 296 292 292 293 294 288 289 288 285 277 269
Notes: Author’s tabulations based on data from Phibbs et al. (2007) and OSHPD Annual Utilization Files. See level of care de?nitions in text.
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Table 2.3: Sample Means by Level of Care at Birth Hospital
No NICU Mother’s Demographics Age Black Hispanic Medicaid HMO Self Pay No College Some College College Infant Characteristics Month Prenatal Care Began # of Prenatal Visits Parity Male Multiple Birth Birth Weight (Grams) Gestation (Weeks) Clinical Condition Small for Gest. Large for Gest. Treatment Total Length of Stay Total Charges ($1,000s) Charges/Day ($1,000s) Ventilation Transfer Outcomes 28 Day Mortality 1 Year Mortality Neonatal Mortality 28 Day Readmission 1 Yr Readmission Observations # of Hospitals 25.781 0.098 0.567 0.591 0.148 0.095 0.788 0.151 0.061 2.323 6.692 2.349 0.542 0.167 1067.017 30.079 0.153 0.034 0.008 39.179 156.595 1.656 0.136 0.706 0.202 0.235 0.219 0.043 0.223 3,268 142 Intermediate NICU 26.861 0.205 0.374 0.546 0.213 0.045 0.679 0.199 0.122 2.321 8.209 2.358 0.521 0.210 1063.166 30.083 0.192 0.049 0.009 44.197 158.987 2.894 0.235 0.638 0.150 0.185 0.169 0.036 0.240 4,788 49 Community NICU 27.939 0.128 0.454 0.455 0.276 0.031 0.643 0.195 0.162 2.190 8.718 2.209 0.512 0.218 1064.203 29.836 0.237 0.065 0.012 50.828 204.456 4.059 0.571 0.209 0.139 0.167 0.155 0.011 0.204 10,136 51 Regional NICU 28.084 0.186 0.434 0.508 0.212 0.022 0.654 0.183 0.163 2.202 8.873 2.289 0.511 0.244 1055.371 29.928 0.306 0.055 0.021 53.319 228.216 4.136 0.556 0.114 0.131 0.160 0.147 0.007 0.198 24,720 45
Notes: Columns display sample means for infants delivered in hospitals at four levels of care. Total Length of Stay and Total Charges sum length of stay and hospital charges over all contiguous hospitalizations prior to ?rst being discharged home or dying. Neonatal mortality is mortality within twenty-eight days of birth or within one year if an infant is continuously hospitalized since birth. Number of hospitals indicates the average number of hospitals providing each level of care over the 11-year sample. See Table 2.2 for number of hospitals by year.
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Table 2.4: Summary Statistics of Distance Variables Mean D(No+) D(Int+) D(Com+) D(Reg) NoDist IntDist ComDist N SD
3.673 4.206 5.709 8.065 8.064 11.983 14.830 22.991 11.156 21.723 9.120 20.249 6.766 18.446 42,912
Notes: The ?rst four rows show the mean and standard deviation of distance to the nearest hospital o?ering each level of care or higher. The next three rows show the mean and standard deviation of di?erential distance to the nearest hospital o?ering each level of care or higher relative to the nearest Regional NICU.
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Table 2.5: Sample Means by Distance
By Miles Saved to Nearest No + 0 Distance Miles Saved No+ Miles Saved Int+ Miles Saved Com+ Mother’s Demographics Age Black Hispanic Medicaid HMO Self Pay No College Some College College Infant Characteristics Mth Prenatal Care Began # of Prenatal Visits Parity Male Multiple Birth Birth Weight (Grams) Gestation (Weeks) Clinical Condition Small for Gest. Large for Gest. Zip Code Characteristics Med HH Income ($1,000) Percent Urban Population Density Observations 0.000 0.000 0.000 27.826 0.227 0.434 0.519 0.213 0.031 0.684 0.170 0.146 2.240 8.196 2.390 0.502 0.223 1056.087 30.033 0.281 0.061 0.018 40.511 0.986 8683.053 9,247 Median 26.378 21.695 16.277 27.517 0.107 0.431 0.488 0.227 0.035 0.650 0.198 0.152 2.220 8.679 2.259 0.522 0.236 1060.439 29.905 0.242 0.051 0.013 46.599 0.924 3162.460 16,889 By Miles Saved to Nearest Int + 0 1.605 0.000 0.000 27.787 0.208 0.475 0.524 0.205 0.034 0.694 0.171 0.135 2.241 8.277 2.370 0.506 0.218 1056.993 29.980 0.273 0.058 0.018 40.221 0.978 9185.203 14,585 Median 28.981 25.604 19.219 27.490 0.102 0.436 0.493 0.224 0.033 0.650 0.197 0.153 2.210 8.728 2.246 0.522 0.236 1059.441 29.859 0.236 0.050 0.013 46.690 0.925 3312.801 14,180 By Miles Saved to Nearest Com + 0 4.010 2.177 0.000 27.752 0.207 0.438 0.521 0.211 0.033 0.676 0.178 0.147 2.244 8.537 2.316 0.512 0.227 1060.140 29.975 0.287 0.057 0.018 43.265 0.973 8142.096 21,440 Median 32.040 28.958 25.420 27.411 0.095 0.462 0.499 0.224 0.030 0.661 0.197 0.142 2.194 8.683 2.270 0.523 0.226 1056.107 29.860 0.212 0.050 0.013 44.694 0.923 3456.633 10,753
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Notes: The ?rst three columns display sample means by di?erential distance to the nearest hospital with any obstetric services, the second three columns by di?erential distance to the nearest Intermediate NICU or higher, and the ?nal three columns by di?erential distance to the nearest Community NICU or higher. For each set of columns, the sample is divided into three groups: those with zero di?erential distance, and those above and below the median conditional on non-zero di?erential distance.
Table 2.6: Neonatal Mortality by Level of Care, OLS Estimates Dependent Variable: Neonatal Mortality (1) I(No NICU) 0.072** (0.009) 0.022** (0.007) 0.008* (0.004) (2) 0.072** (0.008) 0.021** (0.006) 0.013** (0.004) X (3) 0.054** (0.009) 0.017** (0.006) 0.010** (0.004) X X (4) 0.050** (0.007) 0.021** (0.005) 0.012** (0.004) X X X
I(Intermediate NICU)
I(Community NICU)
Time FE Demographics Health Controls
Notes: Each column lists estimates with standard errors in parentheses (clustered at the zip code level) from separate regressions of neonatal mortality on indicators for delivery in a hospital with No NICU, an Intermediate NICU, and a Community NICU. Regional NICU is the excluded group. The columns successively add controls. Time ?xed e?ects include year dummies, month-of-year dummies, and day-of-week dummies. Demographics include age, age squared, race, ethnicity, and insurance coverage. Health controls include number of prenatal care visits, month in which prenatal care began, parity, sex, multiple birth status, an indicator for having a clinical condition, indicators for small and large for gestational age, and birth weight dummies at 100 gram increments. N = 42,912; * p