The socio-environmental exposome and maternal cardiometabolic health in the us: a machine learning approach

Background: There are substantial disparities in American maternal health, with certain groups experiencing disproportionately high rates of pregnancy-related complications and adverse outcomes. Social and Environmental Determinants of Health (SEDH) may play a role in influencing maternal health in pre-pregnancy and gestational scenarios, but remain poorly understood. Objectives: We aimed to investigate the association between SEDH with pre-pregnancy and gestational conditions related to maternal cardiometabolic health throughout the US using a machine learning approach. Methods: We conducted a cross-sectional study analyzing US county-level first live birth data from mothers between 20 and 34 years of age from 2016 to 2022 sourced from the Natality dataset (CDC-WONDER database). We employed the random forest analysis to assess the relationship between 48 SEDH and six distinct outcomes, representing three pre-pregnancy conditions (pre-pregnancy obesity, pre-pregnancy diabetes, and pre-pregnancy hypertension) and three pregnancy conditions (gestational diabetes, gestational hypertension, and eclampsia). Results: Our study included data from 573 US counties. The three most important SEDH identified were per capita income for pre-pregnancy obesity, percentage of Hispanic population for pre-pregnancy hypertension, and severe housing problems for gestational hypertension. We provide prevalence predictions of cardiometabolic maternal risk factors for the majority of US counties (2731 out of 3194), revealing a clustering of these conditions in the southeastern US. Conclusion: We uncovered relevant associations between SEDH and pre-pregnancy/gestational conditions. Our findings help improve understanding of the complex dynamics driving maternal health disparities and emphasize the pressing need to implement targeted interventions to address underlying determinants of health inequities. Condensed abstract: In the US, certain groups experience disproportionately high rates of pregnancy-related complications and adverse outcomes. In this study, we investigate the association between Social and Environmental Determinants of Health (SEDH) with pre-pregnancy and gestational conditions related to maternal cardiometabolic health. We conducted a cross-sectional study analyzing U.S. county-level first live birth data from mothers between 20 and 34 years of age from 2016 to 2022, sourced from the Natality dataset (CDC-WONDER database).We employed the random forest analysis to assess the relationship between 48 SEDH and six distinct outcomes, representing three pre-pregnancy conditions (pre-pregnancy obesity, pre-pregnancy diabetes, and pre-pregnancy hypertension) and three pregnancy conditions (gestational diabetes, gestational hypertension, and eclampsia). Our study included data from 573 US counties. The three most important SEDH identified were per capita income for pre-pregnancy obesity, percentage of Hispanic population for pre-pregnancy hypertension, and severe housing problems for gestational hypertension. Our findings help improve understanding of the complex dynamics driving maternal health disparities and emphasize the pressing need to implement targeted interventions to address underlying determinants of health inequities.