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Objective Bayesian methods are available to model inhalation exposures accounting for measurements below the LOD or missing completely. Individual or job-group-based exposure estimates obtained from these models are often assigned to individual workers or to job-groups of workers, respectively, enrolled in epidemiologic studies. This strategy, however, does not account for variability in exposures around the group means, or exposure uncertainty stemming from missing or censored data. The objective of this study is to present a new model that combines Bayesian models for exposure with exposure-response models for a standard continuous health outcome, e.g., spirometry parameters, accounting for uncertainty and variability in exposure.Material and Methods Statistical simulation studies were set up to resemble standard exposure-response scenarios wherein the true value of all exposure and health outcome parameters are known. The standard approach which does not incorporate measurement error was compared to this new approach using different statistical criteria such as bias (distance between estimate and truth), coverage (percentage of uncertainty intervals contain truth), and posterior predictive accuracy (the model’s ability to predict the data).Results Compared to the standard approach, the new method had the lowest bias, more optimal coverage, lower uncertainty interval widths, and slightly better posterior predictive accuracy across scenarios.Conclusion Results suggested that the new approach performed more optimally compared to the standard approach across most statistical criteria. This new approach will enable better understanding of the exposure-response relationships while accounting for exposure variability and uncertainty. Future work will apply this model to a sample of welding workers to understand the relationships between welding fume exposures and spirometry measures.Funding NIGMS: 5U54GM104942-08.