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Activity Number: 71 - Environmental Applications of Bayesian Methods
Type: Contributed
Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #324468 View Presentation
Title: Bayesian Analysis of Infant's Growth Dynamics with In Utero Exposure to Environmental Toxicants
Author(s): Jonggyu Baek* and Bin Zhu and Peter XK Song
Companies: University of Michigan, Biostatistics and NIH/NCI-DCEG-Biostatistics Branch and University of Michigan,
Keywords: Body mass index ; Markov chain Monte Carlo (MCMC) ; Ornstein-Uhlenbeck process ; prenatal exposure ; semi-parametric stochastic velocity model
Abstract:

The early infancy of at-birth to 3 years is critical for cognitive, emotional and social development. During this period, their developmental tempo and outcomes are potentially impacted by in utero exposure to endocrine disrupting compounds (EDCs). We investigate effects of 10 ubiquitous EDCs on the infant growth dynamics of body mass index (BMI) in a birth cohort study. Modeling growth acceleration is proposed to understand the "force of growth" through a class of semi-parametric stochastic velocity models. The great flexibility of such modeling enables to capture subject-specific dynamics of growth and to assess effects of the EDCs on potential growth delay. We adopted a Bayesian method with the Ornstein-Uhlenbeck process as the prior for the growth rate function, in which the WHO global infant's growth curves were integrated into our analysis. We found that EDCs exposed during the first trimester of pregnancy were inversely associated with BMI growth acceleration, resulting in a delayed achievement of BMI infancy peak. Such early growth deficiency has been reported as being a profound impact on health outcomes in puberty and adulthood (e.g., timing of sexual maturation).


Authors who are presenting talks have a * after their name.

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