PROJECT ABSTRACT Differences in hypertensive disorders of pregnancy (HDP) health outcomes based on sociodemographic characteristics are increasing. Compared to their Non-Hispanic White peers, Non-Hispanic Black persons in the US are 79% more likely to suffer from chronic hypertension in pregnancy, 13% more likely to suffer from a HDP, and 37% more likely to experience eclampsia. They are also 94% more likely to experience severe maternal morbidity (SMM) and 60% more likely to die from a pregnancy-related cause. Modifiable risk factors at the individual and population levels, such as socioeconomic status and access to healthcare, have been associated with higher incidence and severity of HDPs, and with the progression of HDPs to SMM. These risk factors, which have also been independently linked to SMMs and pregnancy-related mortality in the absence of HDPs, may have different prevalence across racial/ethnic lines in the US, making the causal pathways more complex. Thus, the complex interactions between these risk factors, HDP, and SMM are not well understood, making it difficult for policymakers to develop effective strategies to address racial/ethnic disparities in the incidence of HDPs in the US. Our long-term goal is to develop interventions that simultaneously reduce the overall prevalence of HDPs and reduce the racial/ethnic disparities in HDPs. Our overall objective for this application is to develop and validate a Markov state-transition decision model of the natural history of HDPs in the US and use that model to evaluate the potential impact of several proposed policy solutions. Our central hypothesis is that differences in the prevalence of modifiable risk factors (e.g., smoking, BMI, and access to high-quality healthcare) influence the observed population-level disparities in HDP and HDP-related SMMs among high-risk groups in the US. Therefore, a decision model that captures these differences will more reliably predict the future prevalence of HDPs in response to different policy interventions. To achieve the project objectives, we will pursue these two specific aims: (1) Develop and validate a Markov state-transition model of the natural history of HDP and its impact on mothers and children; (2) Estimate the potential costs and benefits of primary prevention/treatment strategies that address racial disparities in HDP in the United States. On completion, we expect to have developed a robust decision-support tool that incorporates multilevel risk factors to predict differences in HDP prevalence and that can be used to test the potential effect of proposed interventions. These positive outcomes will enable the development and implementation of evidence-based solutions to reduce the prevalence of HDP in the US.