Leveraging Causal Inference and Machine Learning Methods to Advance Evidence-Based Maternal Care and Improve Newborn Health Outcomes - PROJECT SUMMARY/ABSTRACT The United States (U.S.) trails behind almost all high-income nations with respect to newborn health, with rates of adverse outcomes, including perinatal mortality, preterm birth, and low birthweight, having demonstrated minimal progress in recent years. Significant disparities underlie such outcomes, with higher rates among racial and ethnic minorities and individuals of low socioeconomic status. While such populations frequently face barriers in accessing adequate care, other studies suggest that certain low-risk populations experience over-provision of maternal care. Rising trends in prenatal care and imaging, for example, are tempered by limited rigorous evidence that increases in utilization have meaningfully improved outcomes. This reality necessitates a renewed approach to maternity care, including: 1) generation of evidence-based policies and guidelines, particularly ones that benefit vulnerable populations; and 2) better assessment of interventions with ambiguous benefit to improve targeting of care. Two specific areas of policy and clinical relevance include access to immediate postpartum contraception and antepartum fetal surveillance, strategies with potential to improve newborn outcomes but for which existing evidence is limited. Accordingly, the overall goal of this proposal is to produce rigorous evidence on: 1) a Medicaid policy incentivizing provision of immediate postpartum long-acting reversible contraception (IPP-LARC); and 2) the scope and effect of antepartum fetal surveillance. Aim 1 utilizes state inpatient discharge data from three states and a synthetic control design to estimate the impact of Medicaid provider payments for provision of IPP-LARC on short birth intervals, preterm birth, and low birthweight. Aim 2 leverages electronic health record data from a large, integrated health system and a machine-learning-based propensity score design to assess the scope and effect of antepartum fetal surveillance on perinatal mortality and other related newborn outcomes. The proposed research aligns closely with the National Institute of Child Health and Human Development’s (NICHD) research priorities to promote reproductive health, better understand the health impacts of contraception, empower healthy pregnancies, and address health disparities. The overall fellowship training plan will be supported by a multidisciplinary mentorship team and a collaborative training environment dedicated to research, professional, and clinical skills development. Through bridging methods in machine learning and quasi-experimental evaluation that have rarely been used in the evaluation of maternal care policies and guidelines, this proposal will be instrumental in informing data-driven clinical practice around immediate postpartum contraception and antepartum fetal surveillance. Together, such evidence will help advance equitable newborn outcomes in the U.S.
