Predicting the Effect of Obstetric Unit Closures on Maternal and Neonatal Outcomes

/SUMMARY: Outcomes for very preterm (VPT; <32 weeks) infants are highly variable across the United States, arising in part from differential implementation of regionalization, which prioritizes targeting high-risk births at hospitals that can provide adequate care. When VPT infants are born at hospitals without advanced neonatal intensive care, they require transport to a higher-level center, increasing their risk of death and disability. Interhospital transports form networks that provide this life-saving care; we have previously characterized infant transports using constructs from the field of network science that describe the structure and flow of patients through the system. Relatively little is known about the complementary transport network for pregnant patients. Despite literature that documents improved outcomes with better access to risk-appropriate care for pregnant patients and VPT infants, obstetric (OB) units are among the first to close when hospitals reduce services. Studies have shown conflicting effects of OB closures on maternal and infant outcomes but have not elucidated why such variation exists. Further, OB closures may unequally impact certain populations, including rural populations and those served by public insurance. The ripple effects of OB closures on the larger perinatal transport network have never been considered. To address this knowledge gap, we will apply sophisticated network science methods to a large, multistate linked administrative and clinical dataset to analyze the obstetric and VPT transport networks in 12 states, with particular attention to how transport network characteristics influence the effects of OB closures on VPT birth mother and infant outcomes. The specific aims are to: (1) Empirically test the ripple effects of OB closures on important outcomes for pregnant patients and their VPT infants; (2) analyze how patient and hospital characteristics differentially impact OB closure effects on perinatal outcomes; and (3) develop and validate a predictive model of resilience in transport networks following closure of different types of units. We will combine advanced biostatistical and econometric techniques with cutting-edge network analysis methods to achieve these aims. Upon completion, the proposed research will elucidate the contribution of patient-, hospital-, transport-, and network-level characteristics to the response of perinatal referral systems to OB closures, and identify concrete approaches for systems-level improvement to improve outcomes in these high-risk patients. Further, these cross-disciplinary methods will be applicable to other types of patient referral systems and may facilitate global improvements in healthcare delivery. Precise measurement and prediction of the domino effects arising from the closure of OB units on the surrounding network will have far-reaching practical implications for maternal and infant health, by encouraging administrators and public health officials to adopt a proactive position in strategically restructuring formal hospital relationships and making decisions regarding unit closures with respect to important neonatal and maternal health outcomes. Project Number: 1R01HD116774-01 | Fiscal Year: 2025 | NIH Institute/Center: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) | Principal Investigator: Sarah Kunz | Institution: BETH ISRAEL DEACONESS MEDICAL CENTER, BOSTON, MA | Award Amount: $790,208 | Activity Code: R01 | Study Section: Special Emphasis Panel[ZRG1 HSS-P (90)] View on NIH RePORTER: https://reporter.nih.gov/project-details/1R01HD11677401