Validating a Community-Deployed Risk Stratification and Monitoring Intervention for Maternal Mortality Reduction in Central Brooklyn and the South Bronx: A Proposed Study
Faculty Mentor
Bakry Elmedni
Major/Area of Research
Public Health, Policy and Administration
Description
INTRODUCTION: In New York City, Black women have been up to twelve times more likely to die from pregnancy-related causes than white women. Central Brooklyn and the South Bronx bear the highest maternal mortality rates in the city, with communities like Brownsville experiencing life-threatening pregnancy complications at nearly four times the rate of affluent neighborhoods. Physicians limited to 15-minute prenatal screenings cannot capture the social determinants of health driving these outcomes. This poster proposes a two-phase intervention: a freely accessible 128-question risk stratification tool on the Womb Watch AI platform that applies a fundamentally new lens to maternal risk — capturing everyday environmental exposures, household toxins, economic dynamics, and nutritional realities that existing clinical frameworks overlook — paired with ongoing pregnancy monitoring through Rhythm by Womb Watch AI.
METHOD: The risk stratification tool is built on a social determinants of health framework that goes beyond standard clinical indicators to assess environmental hazards, toxic exposures, household conditions, dietary patterns, and behavioral and economic factors that shape maternal health outcomes in underserved communities. The tool stratifies patient risk based on these indicators across three domains — preterm labor risk, high-risk pregnancy indicators, and nutrition-based risk — producing a composite score on a 0–100 index classified into four tiers: Low, Moderate, High, and Critical. The resulting Maternal Health Risk Profile is designed for direct integration into electronic health records, extending physician lead time beyond the single prenatal visit. Following assessment, participants transition to continuous monitoring via Rhythm by Womb Watch AI, tracking nutritional status, preeclampsia risk, and emerging complications. The proposed study will recruit pregnant individuals from Central Brooklyn and South Bronx zip codes, comparing outcomes between tool users and standard-care controls. IRB approval will be obtained prior to enrollment.
RESULTS: The deployed tool identifies maternal risk factors absent from standard prenatal screenings — everyday environmental exposures, household conditions, economic barriers, and nutritional deficiencies that disproportionately affect women in underserved communities. By stratifying risk based on these overlooked social determinants, the tool provides a clinical picture that a 15-minute visit cannot. The proposed study will measure whether this approach leads to earlier interventions and improved maternal outcomes.
DISCUSSION/CONCLUSION: Knowing why a patient is at risk is the starting point for reducing complications. This research evaluates whether community-deployed risk stratification centered on environmental and social determinants, paired with continuous monitoring and targeted economic interventions, can close the maternal mortality gap by transforming the prenatal encounter into one touchpoint within a comprehensive, data-driven care model.
Validating a Community-Deployed Risk Stratification and Monitoring Intervention for Maternal Mortality Reduction in Central Brooklyn and the South Bronx: A Proposed Study
INTRODUCTION: In New York City, Black women have been up to twelve times more likely to die from pregnancy-related causes than white women. Central Brooklyn and the South Bronx bear the highest maternal mortality rates in the city, with communities like Brownsville experiencing life-threatening pregnancy complications at nearly four times the rate of affluent neighborhoods. Physicians limited to 15-minute prenatal screenings cannot capture the social determinants of health driving these outcomes. This poster proposes a two-phase intervention: a freely accessible 128-question risk stratification tool on the Womb Watch AI platform that applies a fundamentally new lens to maternal risk — capturing everyday environmental exposures, household toxins, economic dynamics, and nutritional realities that existing clinical frameworks overlook — paired with ongoing pregnancy monitoring through Rhythm by Womb Watch AI.
METHOD: The risk stratification tool is built on a social determinants of health framework that goes beyond standard clinical indicators to assess environmental hazards, toxic exposures, household conditions, dietary patterns, and behavioral and economic factors that shape maternal health outcomes in underserved communities. The tool stratifies patient risk based on these indicators across three domains — preterm labor risk, high-risk pregnancy indicators, and nutrition-based risk — producing a composite score on a 0–100 index classified into four tiers: Low, Moderate, High, and Critical. The resulting Maternal Health Risk Profile is designed for direct integration into electronic health records, extending physician lead time beyond the single prenatal visit. Following assessment, participants transition to continuous monitoring via Rhythm by Womb Watch AI, tracking nutritional status, preeclampsia risk, and emerging complications. The proposed study will recruit pregnant individuals from Central Brooklyn and South Bronx zip codes, comparing outcomes between tool users and standard-care controls. IRB approval will be obtained prior to enrollment.
RESULTS: The deployed tool identifies maternal risk factors absent from standard prenatal screenings — everyday environmental exposures, household conditions, economic barriers, and nutritional deficiencies that disproportionately affect women in underserved communities. By stratifying risk based on these overlooked social determinants, the tool provides a clinical picture that a 15-minute visit cannot. The proposed study will measure whether this approach leads to earlier interventions and improved maternal outcomes.
DISCUSSION/CONCLUSION: Knowing why a patient is at risk is the starting point for reducing complications. This research evaluates whether community-deployed risk stratification centered on environmental and social determinants, paired with continuous monitoring and targeted economic interventions, can close the maternal mortality gap by transforming the prenatal encounter into one touchpoint within a comprehensive, data-driven care model.