Quantifying uncertainty in a risk assessment using human data

A call for risk assessment approaches that better characterize and quantify uncertainty has been made by the scientific and regulatory community. This paper responds to that call by demonstrating a distributional approach tha t draws upon human data to derive potency estimates and to identify and qua ntify important sources of uncertainty. The approach is rooted in the scien ce of decision analysis and employs an influence diagram, a decision tree, probabilistic weights, and a distribution of point estimates of carcinogeni c potency. Its results estimate the likelihood of different carcinogenic ri sks (potencies) for a chemical under a specific scenario. For this exercise , human data on formaldehyde were employed to demonstrate the approach. Sen sitivity analyses were performed to determine the relative impact of specif ic levels and alternatives on the potency distribution. The resulting poten cy estimates are compared with the results of an exercise using animal data on formaldehyde. The paper demonstrates that distributional risk assessmen t is readily adapted to situations in which epidemiologic data serve as the basis for potency estimates. Strengths and weaknesses of the distributiona l approach are discussed. Areas for further application and research are re commended.