Abstract The United States ranks among the worst of all developed countries in perinatal health, including high rates of maternal and infant mortality and pervasive disparities among women who are racial/ethnic minorities, have lower SES, and reside in rural regions. Perinatal outcomes are influenced by an interplay of clinical, individual, and social factors, but the role of social and structural determinants has been increasingly investigated as a driver of maternal and child health (MCH) disparities. Social determinants of health (SDOH), the social, economic, and physical conditions outside the medical system that shape health, have been widely explored particularly through a multitude of place-based SDOH measures. However, these measures fail to comprehensively capture the structural drivers of MCH- the policies, macroeconomic forces, institutions and systems, and culture that generate socioeconomic inequalities across places and populations. In addition, despite evidence that structural drivers vary widely across regions in the US and that the health of marginalized groups is more sensitive to place-based factors, the geospatial patterning of structural drivers and their association with MCH disparities is also poorly understood. Understanding the spatial patterning of structural drivers and their association to MCH disparities would inform the development of tailored, place- based, policies and multilevel interventions. The candidate, Dr. Martinez-Cardoso, is applying for this K01 award in order to develop advanced methodological training to address these research gaps. Dr. Martinez- Cardoso is a well-trained public health researcher with complementary expertise in quantitative data analysis and health disparities. The training component of the award includes formal/informal training in big data science, geospatial analytics, and causal inference, paired with a high-caliber mentor and advisory committee. This training will be applied to research characterizing county-level typologies of structural drivers using data science and machine-learning approaches (Aim1). Aim 2 will investigate the association between structural drivers and racial/ethnic health disparities among women of reproductive age using causal inference methods and multilevel modeling. Aim 3 will explore associations between structural drivers and racial/ethnic perinatal health disparities using spatial multilevel models. Ultimately, this research seeks to contribute to a comprehensive understanding of the structural drivers shaping MCH outcomes to effectively reduce disparities and promote equitable MCH across diverse geographic regions and racial/ethnic groups in the United States. The award will also catalyze the candidate’s long-term goal of becoming an independent investigator focused on improving MCH using novel data science tools and innovative multilevel interventions and policies.

Narrative In the US, disparities in maternal and child health (MCH) across socioeconomic status, race/ethnicity, and geography are pervasive with little evidence of improvements despite significant investments. Addressing these disparities requires characterizing the structural drivers of MCH and their association to health to inform the design of multi-level, upstream interventions and policies. The goal of this project is to apply novel and rigorous data science and analytic tools to address significant gaps in our understanding of how structural drivers are geographically patterned and generate racial/ethnic MCH disparities from preconception to birth.