# Project 2: Disparities-Aware Classifiers for Maternal and Infant Health

> **NIH NIH P50** · BAYLOR COLLEGE OF MEDICINE · 2021 · $434,047

## Abstract

ABSTRACT
A woman’s wellbeing during pregnancy affects her short- and long-term health as well as that of her infant. In
fact, preterm delivery is a leading indicator of health in the U.S. because of its devastating impacts on neonatal
mortality and morbidity with estimated economic costs that exceed 25 billion dollars each year. Despite
advances over the last decade in reducing the annual prevalence of preterm birth to under 10%, persistent racial
inequities remain that pose significant challenges to improving maternal and child health among health disparity
populations in the U.S. Hypertensive disorders of pregnancy (HDP), which consist of eclampsia, preeclampsia
and gestational hypertension, affect up to 10% of pregnancies and, like preterm delivery, increase maternal risks
of mortality and morbidity that extend far beyond post-partum periods. Significant racial disparities exist, with
African-American (AA) women at greatest risk for preterm birth and for dying from HDP than any other racial or
ethnic group in the U.S. Traditional risk factors (e.g., maternal sociodemographic characteristics, behaviors,
reproductive history and access to quality health care) do not fully explain racial disparities in preterm birth or
other obstetric outcomes. To elucidate determinants of these racial disparities, we must consider aspects of the
broader biological, physical, social and built environments that may affect women’s health, and move toward an
integrated assessment of these factors to more accurately predict risk. Pregnant women in Houston are an at
risk population. As a petrochemical hub and fourth largest city in the U.S., Houston is a microcosm for identifying
factors of the biological, physical, social, and built environments that constitute the “environmental riskscape”.
Defining riskscape features underlying preterm birth and other obstetric outcomes will inform our understanding
of determinants of racial disparities in these outcomes in Houston and for disparity populations across the nation.
The overall Objective of this Research Project, Disparities-aware Classifiers for Maternal and Infant Health,
is to develop “disparities-aware” classifiers that identify major drivers of preterm birth and HDP using an inclusive
environmental riskscape framework. To do so, we will conduct a study building upon the unique PeriBank
resource at Baylor College of Medicine to acquire data on maternal biological [circulating cell-free RNA (cfRNA)
and microbiome] and chemical (PAH and metal) exposures, as well as features of the social and built
environments. These data will be used to identify informative features that can be integrated into AA and non-
Hispanic white (NHW) disparities-aware classifiers for preterm birth and HDP. Predictive disparities-aware
classifiers that rely on attributes of individual- and place-level stressors have the potential for identifying major
drivers of preterm birth and HDP among AA and NHW women and provide direction for developi...

## Key facts

- **NIH application ID:** 10218043
- **Project number:** 5P50MD015496-02
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** ELAINE SYMANSKI
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $434,047
- **Award type:** 5
- **Project period:** 2020-07-16 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10218043

## Citation

> US National Institutes of Health, RePORTER application 10218043, Project 2: Disparities-Aware Classifiers for Maternal and Infant Health (5P50MD015496-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10218043. Licensed CC0.

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