Epidemiology and quantitative risk assessment: a bridge from science to policy

Abstract

Quantitative risk assessment provides formalized scientific input to regulatory agencies that set occupational and environmental standards for potentially toxic exposures. Current practice relies heavily on statistical extrapolation from high-dose animal studies. Human data obviate the need for interspecies extrapolation and reduce the range of high-to-low dose extrapolation. This paper proposes a framework for classifying individual epidemiologic studies as to their adequacy for use in dose-response extrapolation. The framework considers five criteria: (1) a stable positive association with an adverse health outcome; (2) high overall study quality; (3) no substantial confounding; (4) quantitative exposure assessment for individuals; (5) evidence of a dose-response relationship. With these criteria, studies can be categorized as (1) suitable to serve as a basis for extrapolation; (2) inadequate to be the basis for direct extrapolation but appropriate to use for evaluating the plausibility of animal-derived risk estimates; or (3) useful only for hazard identification, not for dose-response assessment. Methods for using studies in the first two categories are briefly described. The emphasis is not on establishing rigid rules, but rather on ensuring a consistent, reliable process that makes optimum use of available data.