# Source-specific multi-pollutant exposures and the neighborhood context in disparities in stillbirth

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2024 · $533,173

## Abstract

ABSTRACT
The origin of racial and ethnic disparities in obstetric outcomes does not appear to be genetic nor fully explained
by individual-level factors. Hence, we must look toward the broader context in the physical, social and built
environments to understand and mitigate disparities in maternal and child health. In the physical, social and built
environments, air pollution, neighborhood disadvantage, and residential greenness may play important roles. As
compared to low birth weight, fetal growth restriction and preterm birth, considerably less is known about air
pollution exposure's impact on stillbirth, defined as an intrauterine fetal death that occurs at or after 20 weeks of
gestation. Urban air pollution is a complex mixture of particle and gaseous pollutants, often correlated because
they are influenced by common nearby sources and meteorology and thus, simultaneously studying exposure
to multiple air pollutants is challenging. Previous studies on the risk of stillbirth associated with air pollution
exposure have largely focused on pollutant-specific effects, including our studies examining ozone and metal
constituents of PM in the fourth largest city in the U.S., Houston, Texas. Using source apportionment to assess
air pollutant exposures takes advantage of the highly correlated nature of the air pollutant mixture, quantifies the
contributions of different source categories and provides information that allows more targeted control strategies
to be developed. To date, we are aware of only one study that applied source apportionment for fine particulate
matter, and it used the Positive Matrix Factorization (PMF) receptor modeling. Yet, that investigation used
estimated source contributions as if they were true exposures without accounting for spatial misalignment error
and inherent uncertainty in the method itself. Addressing these shortcomings, we will apply an innovative
approach that integrates Bayesian spatial multivariate receptor models (BSMRM) with land-use and traffic data
as well as accounting for exposure measurement errors in health effects estimation. Like air pollution, the social
and built environments cluster neighborhood-level exposures that adversely impact health. These stressors are
often greater among minority and low-income populations who suffer what has been termed “double jeopardy”.
The objective of the proposed study is three-fold. First, we wish to evaluate the impact of source-specific
multipollutant exposures on stillbirth using an innovative approach that integrates BSMRM with land-use and
traffic data and accounting for exposure measurement errors in health effects estimation. We hypothesize that
increased air pollutant source-apportioned concentrations will be associated with increased risk of stillbirth and
that effect sizes will be different for different source categories. Second, we wish to examine the impact of the
neighborhood context on risk of stillbirth. Third, given the priority of health disparit...

## Key facts

- **NIH application ID:** 10850676
- **Project number:** 5R01ES031990-04
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Eun Sug Park
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $533,173
- **Award type:** 5
- **Project period:** 2021-09-24 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10850676

## Citation

> US National Institutes of Health, RePORTER application 10850676, Source-specific multi-pollutant exposures and the neighborhood context in disparities in stillbirth (5R01ES031990-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10850676. Licensed CC0.

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