📝 SummarXiv

Abstract

Human exposure to chemicals commonly arises from multiple sources, yet traditional assessments often treat these sources in isolation, overlooking their combined impact. We introduce a Bayesian framework for aggregated chemical exposure assessment that explicitly accounts for these intertwined pathways. By integrating diverse datasets - such as consumption surveys, demographics, chemical measurements, and market presence - our approach addresses typical data challenges, including missing values, limited sample sizes, and inconsistencies, while incorporating relevant prior knowledge. Through a simulation-based strategy that reflects the full spectrum of individual exposure scenarios, we derive robust, population-level estimates of aggregated exposure. We demonstrate the value of this method using titanium dioxide, a chemical found in foods, dietary supplements, medicines, and personal care products. By capturing the complexity of real-world exposures, this comprehensive Bayesian approach provides decision-makers with more reliable probabilistic estimates to inform public health policies.