{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Keil AP"],"funding":["NIEHS NIH HHS","National Institutes of Health"],"pagination":["2647-2657"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8796809"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["190(12)"],"pubmed_abstract":["The importance of studying the health impacts of exposure mixtures is increasingly being recognized, but such research presents many methodological and interpretation difficulties. We used Bayesian g-computation to estimate effects of a simulated public health action on exposure mixtures and birth weights in Milwaukee, Wisconsin, in 2011-2013. We linked data from birth records with census-tract-level air toxics data from the Environmental Protection Agency's National Air Toxics Assessment model. We estimated the difference between observed and expected birth weights that theoretically would have followed a hypothetical intervention to reduce exposure to 6 airborne metals by decommissioning 3 coal-fired power plants in Milwaukee County prior to 2010. Using Bayesian g-computation, we estimated a 68-g (95% credible interval: 25, 135) increase in birth weight following this hypothetical intervention. This example demonstrates the utility of our approach for using observational data to evaluate and contrast possible public health actions. Additionally, Bayesian g-computation offers a flexible strategy for estimating the effects of highly correlated exposures, addressing statistical issues such as variance inflation, and addressing conceptual issues such as the lack of interpretability of independent effects."],"journal":["American journal of epidemiology"],"pubmed_title":["Bayesian G-Computation for Estimating Impacts of Interventions on Exposure Mixtures: Demonstration With Metals From Coal-Fired Power Plants and Birth Weight."],"pmcid":["PMC8796809"],"funding_grant_id":["L40 ES032257","R01 ES030078","R01 ES029531"],"pubmed_authors":["Keil AP","Buckley JP","Kalkbrenner AE"],"additional_accession":[]},"is_claimable":false,"name":"Bayesian G-Computation for Estimating Impacts of Interventions on Exposure Mixtures: Demonstration With Metals From Coal-Fired Power Plants and Birth Weight.","description":"The importance of studying the health impacts of exposure mixtures is increasingly being recognized, but such research presents many methodological and interpretation difficulties. We used Bayesian g-computation to estimate effects of a simulated public health action on exposure mixtures and birth weights in Milwaukee, Wisconsin, in 2011-2013. We linked data from birth records with census-tract-level air toxics data from the Environmental Protection Agency's National Air Toxics Assessment model. We estimated the difference between observed and expected birth weights that theoretically would have followed a hypothetical intervention to reduce exposure to 6 airborne metals by decommissioning 3 coal-fired power plants in Milwaukee County prior to 2010. Using Bayesian g-computation, we estimated a 68-g (95% credible interval: 25, 135) increase in birth weight following this hypothetical intervention. This example demonstrates the utility of our approach for using observational data to evaluate and contrast possible public health actions. Additionally, Bayesian g-computation offers a flexible strategy for estimating the effects of highly correlated exposures, addressing statistical issues such as variance inflation, and addressing conceptual issues such as the lack of interpretability of independent effects.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Dec","modification":"2025-04-05T13:24:13.181Z","creation":"2025-04-05T13:24:13.181Z"},"accession":"S-EPMC8796809","cross_references":{"pubmed":["33751055"],"doi":["10.1093/aje/kwab053"]}}