Application of qualitative and quantitative uncertainty assessment tools in developing ranges of plausible toxicity values for 2,3,7,8-tetrachlorodibenzo-p-dioxin.
Daniele S WikoffLaurie HawsCaroline L RingRobert BudinskyPublished in: Journal of applied toxicology : JAT (2019)
Increasing interest in characterizing risk assessment uncertainty is highlighted by recent recommendations from the National Academy of Sciences. In this paper we demonstrate the utility of applying qualitative and quantitative methods for assessing uncertainty to enhance risk-based decision-making for 2,3,7,8-tetrachlorodibenzo-p-dioxin. The approach involved deconstructing the reference dose (RfD) via evaluation of the different assumptions, options, models and methods associated with derivation of the value, culminating in the development of a plausible range of potential values based on such areas of uncertainty. The results demonstrate that overall RfD uncertainty was high based on limitations in the process for selection (e.g., compliance with inclusion criteria related to internal validity of the co-critical studies, consistency with other studies), external validity (e.g., generalizing findings of acute, high-dose exposure scenarios to the general population), and selection and classification of the point of departure using data from the individual studies (e.g., lack of statistical and clinical significance). Building on sensitivity analyses conducted by the US Environmental Protection Agency in 2012, the resulting estimates of RfD values that account for the uncertainties ranged from ~1.5 to 179 pg/kg/day. It is anticipated that the range of RfDs presented herein, along with the characterization of uncertainties, will improve risk assessments of dioxins and provide important information to risk managers, because reliance on a single toxicity value limits the information needed for making decisions and gives a false sense of precision and accuracy.
Keyphrases
- risk assessment
- high dose
- decision making
- oxidative stress
- human health
- high resolution
- systematic review
- case control
- climate change
- deep learning
- low dose
- heavy metals
- drug induced
- intensive care unit
- electronic health record
- quality improvement
- hepatitis b virus
- acute respiratory distress syndrome
- respiratory failure