Kernel machine and distributed lag models for assessing windows of susceptibility to environmental mixtures in children's health studies

DOI10.1214/21-AOAS1533zbMATH Open1498.62269arXiv1904.12417OpenAlexW3097119804MaRDI QIDQ2154219

Ander Wilson, Brent A. Coull, Hsiao-Hsien Leon Hsu, Robert O. Wright, Rosalind J. Wright, Yueh-Hsiu Mathilda Chiu

Publication date: 14 July 2022

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Abstract: Exposures to environmental chemicals during gestation can alter health status later in life. Most studies of maternal exposure to chemicals during pregnancy have focused on a single chemical exposure observed at high temporal resolution. Recent research has turned to focus on exposure to mixtures of multiple chemicals, generally observed at a single time point. We consider statistical methods for analyzing data on chemical mixtures that are observed at a high temporal resolution. As motivation, we analyze the association between exposure to four ambient air pollutants observed weekly throughout gestation and birth weight in a Boston-area prospective birth cohort. To explore patterns in the data, we first apply methods for analyzing data on (1) a single chemical observed at high temporal resolution, and (2) a mixture measured at a single point in time. We highlight the shortcomings of these approaches for temporally-resolved data on exposure to chemical mixtures. Second, we propose a novel method, a Bayesian kernel machine regression distributed lag model (BKMR-DLM), that simultaneously accounts for nonlinear associations and interactions among time-varying measures of exposure to mixtures. BKMR-DLM uses a functional weight for each exposure that parameterizes the window of susceptibility corresponding to that exposure within a kernel machine framework that captures non-linear and interaction effects of the multivariate exposure on the outcome. In a simulation study, we show that the proposed method can better estimate the exposure-response function and, in high signal settings, can identify critical windows in time during which exposure has an increased association with the outcome. Applying the proposed method to the Boston birth cohort data, we find evidence of a negative association between organic carbon and birth weight and that nitrate modifies the organic carbon, ...

Full work available at URL: https://arxiv.org/abs/1904.12417

zbMATH Keywords

air pollutiondistributed lag modelschildren's healthchemical mixtureskernel machine regressionwindows of susceptibility

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to environmental and related topics (62P12)

Cites Work

• Unnamed Item
• Flexible distributed lag models using random functions with application to estimating mortality displacement from heat-related deaths
• Characterization of weighted quantile sum regression for highly correlated data in a risk analysis setting
• Bayesian profile regression with an application to the National survey of children's health
• Generalized additive distributed lag models: quantifying mortality displacement
• Spatial‐Temporal Modeling of the Association between Air Pollution Exposure and Preterm Birth: Identifying Critical Windows of Exposure
• A Multiple-Index Model and Dimension Reduction
• Semiparametric Regression of Multidimensional Genetic Pathway Data: Least‐Squares Kernel Machines and Linear Mixed Models
• A Penalized Framework for Distributed Lag Non-Linear Models

Related Items (2)

Critical window variable selection for mixtures: estimating the impact of multiple air pollutants on stillbirth ⋮ The impact of correlated exposures and missing data on multiple informant models used to identify critical exposure windows

Recommendations

• Nonlinear predictive latent process models for integrating spatio-temporal exposure data from multiple sources 👍 👎
• Integrative statistical methods for exposure mixtures and health 👍 👎
• Estimating the health effects of environmental mixtures using Bayesian semiparametric regression and sparsity inducing priors 👍 👎
• A Bayesian approach to functional-based multilevel modeling of longitudinal data: applications to environmental epidemiology 👍 👎
• Dynamic Generalized Linear Models with Application to Environmental Epidemiology 👍 👎
• Bayesian Distributed Lag Models: Estimating Effects of Particulate Matter Air Pollution on Daily Mortality 👍 👎
• A Bayesian multivariate analysis of children's exposure to pesticides 👍 👎

This page was built for publication: Kernel machine and distributed lag models for assessing windows of susceptibility to environmental mixtures in children's health studies