Biostatistical methods for estimating the cumulative impact of environmental contaminant exposures on preterm birth

Population and public health, Occupational and environmental health
Posted: January 11, 2017
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Principal Investigators

McCandless, L

Lanphear, B

Co-investigators

Braun J

Fraser W

Moodie E

Platt R

Stephens D

Woods M

Funding

$198,330 - CIHR Operating Grant Preterm Birth

Duration

2017-2019

Abstract

The causes of many preterm births are unknown. A growing body of research points to the role of environmental contaminant exposures during pregnancy as a possible cause of adverse pregnancy outcomes, including preterm birth. Of particular concern is cumulative exposure to multiple contaminants. While each contaminant may shorten pregnancy by only a few days, the cumulative impact of many contaminants may be substantial. The interest in environmental factors that influence pregnancy outcomes has arisen in tandem with new and important birth cohort studies in Canada and beyond. However, analyzing biomarkers in birth cohort studies presents extraordinary challenges to biostatistical research. Numerous methods have been proposed for modelling cumulative exposures including Bayesian methods and machine learning techniques. However, the differences between methods are not well understood by stakeholders in terms of ability to obtain better estimates with less bias and higher precision. The overall goal of this project is to estimate the effect of environmental contaminant exposures during pregnancy on preterm birth using data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study and the Health Outcome and Measures of the Environment (HOME) Study. Additionally, we will develop and investigate novel biostatistical methods for estimating the health effects of cumulative exposures. This grant submission builds on an emerging Canadian collaboration between the disciplines of biostatistics and reproductive health epidemiology, with input from knowledge users and trainees. We will develop state-of-the-art biostatistical methods to obtain better evidence that improves our understanding of the environmental causes and mechanistic pathways leading to preterm birth.