ArticleStatistics in medicine2025
Modeling Joint Health Effects of Environmental Exposure Mixtures With Bayesian Additive Regression Trees.
Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Multi-Tree Model for Precision Environmental Health with Longitudinally Assessed Mixture Exposure.Journal of the Royal Statistical Society. Series C, Applied statistics · 2026Article
- Causal analysis of traditional and environmental risk factors for long-term development of type 2 diabetes using a conditional survival Bayesian network: evidence from the Korean Genome and Epidemiology Study.BMC public health · 2026Article
- Flexible statistical approaches for modeling nonlinear relationships in diabetes prediction using splines, Bayesian kernel regression and Bayesian regression trees.Discover analytics · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
Studying the association between mixtures of environmental exposures and health outcomes can be challenging due to issues such as correlation among the exposures and non-linearities or interactions in the exposure-response function. For this reason, one common strategy is to fit flexible nonparametric models to capture the true exposure-response surface. However, once such a model is fit, further decisions are required when it comes to summarizing the marginal and joint effects of the mixture on the outcome. In this work, we describe the use of soft Bayesian additive regression trees (BART) to estimate the exposure-risk surface describing the effect of mixtures of chemical air pollutants and temperature on asthma-related emergency department (ED) visits during the warm season in Atlanta, Georgia, from 2011 to 2018. BART is chosen for its ability to handle large datasets and for its flexibility to be incorporated as a single component of a larger model. We then summarize the results using a strategy known as accumulated local effects to extract meaningful insights into the mixture effects on asthma-related morbidity. Notably, we observe negative associations between
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