Rajet Vatsa

Dissertation Title: "Advancing Evidence-Based Maternity Care: Empirical Studies of Technologies, Policies, and Clinical Practices in the U.S. and Abroad"

While maternal and newborn health (MNH) outcomes have improved globally over the past couple decades, progress has been slow or stagnant in many low- and lower-middle-income settings. In Kenya, the maternal mortality rate remains well over five times the United Nations’ Sustainable Development Goal and high rates of severe maternal and neonatal morbidity persist. Meanwhile, the United States trails almost all high-income nations with respect to MNH care, including variable family planning support across states and minimal recent declines, if not increases, in rates of adverse outcomes like maternal and perinatal mortality. Against this backdrop, global maternity care utilization has been on the rise, with increases in the use of antepartum and postpartum support tools, contraception, and advanced imaging during pregnancy. This dissertation explores such phenomena and expands the evidence base on 1) a patient-facing digital health tool implemented in health facilities across Kenya; 2) recent Medicaid payment policies regarding provision of immediate postpartum long-acting reversible contraception (LARC); and 3) the utility of electronic-health-record- (EHR) and machine-learning- (ML)-based risk stratification models to identify and guide management for individuals at high risk of adverse outcomes like late stillbirth. 

In Chapter 1, jointly conducted with Wei Chang, Sharon Akinyi, Sarah Little, Catherine Gakii, John Mungai, Cynthia Kahumbura, Anneka Wickramanayake, Sathyanath Rajasekharan, Jessica Cohen, and Margaret McConnell, I describe a parallel arm cluster randomized controlled trial carried out in 40 health facilities in Kenya to evaluate the impact of a low-cost, digital health platform called PROMPTS. Developed by Jacaranda Health, a leading MNH nonprofit, PROMPTS consists of informational messages, appointment reminders, and a two-way clinical helpdesk. Using longitudinal surveys of participants, we find that individuals recruited from facilities offering PROMPTS exhibited modest but consistent improvements across the pregnancy-postpartum care continuum, including in knowledge, preparedness, routine and danger sign care seeking, newborn care, and postpartum care content. We identify notable advances in the postpartum setting, for both mothers and newborns, which has important implications for efforts to improve postpartum care quality in Kenya.

In Chapter 2, jointly conducted with Maria Steenland, Benjamin Sommers, and Jessica Cohen, I evaluate Medicaid payment policies in Georgia and New York aimed at expanding contraceptive choice through separate reimbursement of immediate postpartum LARC. Using statewide hospital discharge data from the Healthcare Cost and Utilization Project and an interrupted time series design, we find significant, post-policy increases in the provision of immediate postpartum LARC, with accompanying reductions in the rate of subsequent, short-interval birth. We also find coincident decreases in rates of immediate postpartum sterilization, suggesting some amount of substitution between highly effective contraceptive methods. In subgroup analyses of adolescent individuals, Hispanic individuals, and non-Hispanic Black individuals – all of whom have higher rates of unintended pregnancy – we identify larger than average increases in immediate postpartum LARC provision and more pronounced reductions in the rate of subsequent birth within 21 months. Ultimately, our findings suggest that the payment policies led to new use of LARC and likely some reduction in the rate of unintended pregnancy. 

In Chapter 3, jointly conducted with Jessica Cohen and Mark Clapp, I describe the development of an ML-based risk stratification model to prospectively identify individuals at high risk of adverse perinatal outcomes. Using features readily available in the EHR of a large, Massachusetts-based health care system, we first demonstrate the improvement in risk stratification from supplementing traditional, clinical risk factors with additional clinical, geographic, and sociodemographic features. We then leverage model predictions to generate insights about utilization of outpatient antenatal fetal surveillance (AFS) and its association with rates of late stillbirth, across risk groups. We find that rates of AFS use increased with predicted risk, suggesting that providers conducted surveillance at least in part based on similar factors to our model. Finally, we prospectively identified a predictably high-risk set of pregnancies for which AFS was associated with a nearly 80% lower rate of late stillbirth, though – given our observational approach – we could not causally attribute the lower outcome rate to AFS. Descriptively, we find that high-predicted-risk individuals who did not receive AFS lived further from the hospital and got less prenatal care but had lower rates of nearly all traditional clinical risk factors, including at the end of pregnancy.