Evaluating Reproductive Health Policy in Medicaid Populations: Application of Novel Methods for Heterogenous Treatment Effect Estimation

PROJECT SUMMARY This NIMHD Mentored Research Scientist Career Development Award (K01) for Dr. Jackie Ellison, an assistant professor of health policy and management at the University of Pittsburgh Graduate School of Public Health, will establish Dr. Ellison as an independent health services and policy researcher with expertise in evaluating heterogeneous effects of reproductive health policies in Medicaid. Research proposed for this K01 award will leverage the timing and geographic variation of emerging reproductive health policies to estimate their effects on contraceptive method use, births, and adverse perinatal health outcomes nationally among persons with Medicaid. Using traditional prespecified subgroup analyses and newer, data-driven methods for causal subgroup identification, Dr. Ellison will also identify the patient populations that are most impacted by these policies. Black, Hispanic, and young women, women living on low incomes, and women with disabilities experience disparities in accessing reproductive care, and disparities in adverse perinatal health outcomes. Consequently, there is an urgent need to not only evaluate the public health effects of emerging reproductive health policies, but to understand the distribution of policy effects across different patient populations. Findings from this research will be disseminated to various stakeholders with the goal of informing evidence-based reproductive health policy at the federal and state-levels, and within state Medicaid programs. This project draws on Dr. Ellison’s quantitative training, knowledge of reproductive health policy, financing, and service delivery, and prior research on health disparities. This work will extend Dr. Ellison’s scholarship into the fields of perinatal health and Medicaid while incorporating new statistical methods for assessing heterogeneous treatment effects. Therefore, for this K01 award, Dr. Ellison will engage in training and career development activities that focus on acquiring expertise in working with the modernized Medicaid data, perinatal health services research, and advanced statistical and machine learning methods for causal inference and causal subgroup identification. This project places an emphasis on research translation and dissemination. With mentorship from a multi-disciplinary team of NIH-funded investigators, Dr. Ellison will also focus on applying training in these areas to health policy research more generally. This training plan complements the proposed research and will equip Dr. Ellison to transition to independence, leveraging the methods and expertise developed in the proposed research with the goal of improving reproductive health outcomes by elucidating the distribution of policy effects across patient populations.