Award
National Institute of Environmental Health Sciences 5R01ES033190-05
Ambient Air Pollution, Weather, and Placental Abruption (APWA)
Recipient
Rutgers Biomedical and Health Sciences, Newark, NJ, United States
Award Amount
$592,438.00
Ceiling
$592,438.00
Awarded
July 06, 2026
Identifier
5R01ES033190-05
Research project funded by NIEHS to study the effects of ambient air pollution and weather on placental abruption using advanced statistical methods and large-scale birth data across multiple US states.
Description
This project aims to examine the causal relationships between outdoor exposures to ambient air pollution and weather conditions and placental abruption. It will delineate pathways to abruption, separating the impacts of air pollution and weather implicated in acute and chronic placental abruptions. The study will utilize high-resolution exposure and health outcome data, developing a birth linkage database including hospital discharges linked to stillbirths and live births in California, Florida, Massachusetts, Michigan, and South Carolina (estimated 16 million births, including 155,000 abruption cases) from 2000 to 2016. The project will assign average daily ambient exposure to PM2.5 and its constituents, gaseous pollutants (NO2 and ozone), as well as temperature, humidity, dew point, heat waves, and atmospheric pressure for each residence. It will focus on disentangling the contributions of air pollution and weather on acute abruption through a bi-directional, time-stratified case-crossover design, and on chronic underpinnings using a cohort design. Advanced statistical models such as distributed lag linear and non-linear models, Bayesian Kernel Machine Regression, and causal interaction-mediation decomposition analyses will be employed. The study will consider individual and neighborhood confounders derived from census tracts, correcting for exposure and outcome misclassification and measurement error due to maternal residential mobility. The project aligns with NIH strategic goals in Co-exposures and Data Science and Big Data.