Application of a quantitative uncertainty assessment to develop ranges of plausible toxicity values when using observational data in risk assessment: a case study examining associations between PFOA and PFOS exposures and vaccine response

Traditional approaches for quantitatively characterizing uncertainty in risk assessment require adaptation to accommodate increased reliance on observational (vs. experimental) studies in developing toxicity values. Herein, a case study with PFOA and PFOS and vaccine response explores approaches for qualitative and-where possible-quantitative assessments of uncertainty at each step in the toxicity value development process when using observational data, including review and appraisal of individual studies, candidate study selection, dose-response modeling, and application of uncertainty factors. Each of the fifteen studies identified had uncertainties due to risk of bias in confounding, outcome, and exposure ascertainment, likely contributing to the observed inconsistencies within and across studies, and resulting in lack of candidacy for dose-response assessment. Nonetheless, two representative studies were selected to demonstrate possible methods to quantify uncertainty in the remaining steps. Data simulations indicated lack of a clear dose-response relationship; dose-response models fit to representative simulations indicated high uncertainty in both the magnitude and direction of effect with simulated BMDL values varying at least 66- and 86-fold for PFOA and PFOS. Uncertainty factor application added minimal uncertainty. Combined, a high level of uncertainty was observed, precluding the ability to confidently assess causal dose-response relationships with the observational data, alone. This case study highlights the need for quantitative uncertainty analysis when developing toxicity values with observational data and, importantly, emphasizes the need for application of additional techniques to directly assess causality and the specificity of dose-response when relying on studies of association in quantitative risk assessment.