Investigating housing, education, and employment conditions and maternal and child health outcomes: a geospatial and data science approach

University Of Chicago, Chicago IL

Investigators

Abstract

The United States ranks among the lowest of all developed countries in perinatal health, with high rates of maternal and infant mortality, particularly among women with lower socioeconomic status, those residing in rural regions, and across racial/ethnic backgrounds. Perinatal outcomes are influenced by an interplay of clinical, individual, and social factors, and there is growing interest in how place-based conditions affect maternal and child health (MCH) outcomes. The social, economic, and physical conditions outside the medical system that shape health, have been widely explored particularly through a multitude of place-based measures. However, many of these measures do not fully capture local housing, education, and employment conditions that shape health-related resources and risks. In addition, despite evidence that these place-based factors vary widely across regions in the US and some groups are more sensitive to their effects, the geospatial patterning of housing, education, and employment conditions and their association with MCH health is also poorly understood. Understanding the spatial patterning of housing, education, and work conditions and their association to MCH outcomes 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 MCH. 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 housing, education, and employment conditions using data science and machine-learning approaches (Aim1). Aim 2 will investigate the association between these typologies and preconception health among women of reproductive age using causal inference methods and multilevel modeling. Aim 3 will explore associations between county-level housing, education, and employment typologies and perinatal health outcomes using spatial multilevel models. Ultimately, this research seeks to contribute to a comprehensive understanding of how local conditions shape MCH outcomes to improve MCH across 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.