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March 2019 Bayesian analysis of infant’s growth dynamics with in utero exposure to environmental toxicants
Jonggyu Baek, Bin Zhu, Peter X. K. Song
Ann. Appl. Stat. 13(1): 297-320 (March 2019). DOI: 10.1214/18-AOAS1199


Early infancy from at-birth to 3 years is critical for cognitive, emotional and social development of infants. During this period, infant’s developmental tempo and outcomes are potentially impacted by in utero exposure to endocrine disrupting compounds (EDCs), such as bisphenol A (BPA) and phthalates. We investigate effects of ten 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 semiparametric stochastic velocity models. The great flexibility of such a dynamic model enables us to capture subject-specific dynamics of growth trajectories and to assess effects of the EDCs on potential delay of growth. We adopted a Bayesian method with the Ornstein–Uhlenbeck process as the prior for the growth rate function, in which the World Health Organization global infant’s growth curves were integrated into our analysis. We found that BPA and most of phthalates exposed during the first trimester of pregnancy were inversely associated with BMI growth acceleration, resulting in a delayed achievement of infant BMI peak. Such early growth deficiency has been reported as a profound impact on health outcomes in puberty (e.g., timing of sexual maturation) and adulthood.


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Jonggyu Baek. Bin Zhu. Peter X. K. Song. "Bayesian analysis of infant’s growth dynamics with in utero exposure to environmental toxicants." Ann. Appl. Stat. 13 (1) 297 - 320, March 2019.


Received: 1 July 2017; Revised: 1 May 2018; Published: March 2019
First available in Project Euclid: 10 April 2019

zbMATH: 07057429
MathSciNet: MR3937430
Digital Object Identifier: 10.1214/18-AOAS1199

Keywords: Body mass index , Markov chain Monte Carlo (MCMC) , Ornstein–Uhlenbeck process , prenatal exposure , semiparametric stochastic velocity model

Rights: Copyright © 2019 Institute of Mathematical Statistics

Vol.13 • No. 1 • March 2019
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