Key Elements for Judging the Quality of a Risk Assessment

Abstract

Background:Many reports have been published that contain recommendations for improving the quality, transparency, and usefulness of decision making for risk assessments prepared by agencies of the U.S. federal government. A substantial measure of consensus has emerged regarding the characteristics that high-quality assessments should possess.Objective:The goal was to summarize the key characteristics of a high-quality assessment as identified in the consensus-building process and to integrate them into a guide for use by decision makers, risk assessors, peer reviewers and other interested stakeholders to determine if an assessment meets the criteria for high quality.Discussion:Most of the features cited in the guide are applicable to any type of assessment, whether it encompasses one, two, or all four phases of the risk-assessment paradigm; whether it is qualitative or quantitative; and whether it is screening level or highly sophisticated and complex. Other features are tailored to specific elements of an assessment. Just as agencies at all levels of government are responsible for determining the effectiveness of their programs, so too should they determine the effectiveness of their assessments used in support of their regulatory decisions. Furthermore, if a nongovernmental entity wishes to have its assessments considered in the governmental regulatory decision-making process, then these assessments should be judged in the same rigorous manner and be held to similar standards.Conclusions:The key characteristics of a high-quality assessment can be summarized and integrated into a guide for judging whether an assessment possesses the desired features of high quality, transparency, and usefulness.Citation:Fenner-Crisp PA, Dellarco VL. 2016. Key elements for judging the quality of a risk assessment. Environ Health Perspect 124:1127–1135; http://dx.doi.org/10.1289/ehp.1510483

Human Biomonitoring Data in Health Risk Assessments Published in Peer-Reviewed Journals between 2016 and 2021: Confronting Reality after a Preliminary Review

“…It was designed for this particular review purpose and covers the evaluation of 10 selected HRA elements, namely the assessment context of HRA, dose/exposure-response relationship, exposure setting, exposure sources, exposure duration, exposed population, magnitude of risk, uncertainty of HRA results, options for mitigating/avoiding exposure, and transparency and clarity of the assessment process. These HRA elements are consistent with the core principles of HRA and risk analysis [ 3 , 6 , 12 ] and are among the proposed key elements for judging the quality of HRA [ 22 ]. The evaluation was performed by using a straightforward questionnaire and was, overall, both limited and preliminary.…”

A common approach for ranking of microbiological and chemical hazards in foods based on risk assessment - useful but is it possible?

“…Only mean doses and dose responses for the population of interest were used and this is a limitation especially for listeria where susceptibility may vary widely within risk groups (Pouillot et al 2015). Thus, the mean exposure to bacteria per any serving is not a good estimate for exposure to microbial hazards since it does not reflect an actual scenario, in violation of validity concepts and good risk assessment practices (Burmaster and Anderson 1994;EPA 2000;Lammerding 2007;Fenner-Crisp and Dellarco 2016), and the effect of this on risk ranking is not clear. One potential solution may be to consider only servings contaminated with microorganisms.…”

Applying 'omics technologies in chemicals risk assessment: Report of an ECETOC workshop

“…The biologically plausible sequence of substance-specific key events that are specific to the exposure to a given substance, starting with the definition of the substance and proceeding through the interaction of the substance or its metabolites with a cell, to functional and anatomical changes leading to an observed effect supported by robust experimental observations and mechanistic data (Sonich-Mullin et al, 2001; Boobis et al, 2009; Fenner-Crisp and Dellarco, 2016).…”