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L Options for Dealing with Uncertainties Methods for dealing with uncertainties in scientific data are generally understood by working scientists and require no special discussion here except to point out that such uncertainties should be explicitly acknowl- edged and taken into account whenever a risk assessment is undertaken. More subtle and difficult problems are created by uncertainties associated with some of the inferences that must be made in the absence of directly applicable data; much confusion and inconsistency can result if they are not recognized and dealt with in advance of undertaking a risk assessment. The most significant inference uncertainties arise in risk assessments whenever attempts are made to answer the following questions (NRC, 1994): • What sets of hazard and dose–response data (for a given substance) should be used to characterize risk in the population of interest? • If animal data are to be used for risk characterization, which end- points for adverse effects should be considered? • If animal data are to be used for risk characterization, what measure of dose (e.g., dose per unit body weight, body surface, or dietary intake) should be used for scaling between animals and humans? • What is the expected variability in dose–response between animals and humans? • If human data are to be used for risk characterization, which adverse effects should be used? • What is the expected variability in dose–response among members of the human population? 1244

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1245 A PPENDIX L • How should data from subchronic exposure studies be used to esti- mate chronic effects? • How should problems of differences in route of exposure within and between species be dealt with? • How should the threshold dose be estimated for the human population? • If a threshold in the dose–response relationship seems unlikely, how should a low-dose risk be modeled? • What model should be chosen to represent the distribution of exposures in the population of interest when data relating to exposures are limited? • When interspecies extrapolations are required, what should be assumed about relative rates of absorption from the gastrointestinal tract of animals and of humans? • For which percentiles on the distribution of population exposures should risks be characterized? At least partial, empirically based answers to some of these questions may be available for some of the nutrients under review, but in no case is scientific information likely to be sufficient to provide a highly certain answer; in many cases there will be no relevant data for the nutrient in question. It should be recognized that for several of these questions, certain infer- ences have been widespread for long periods of time; thus, it may seem unnecessary to raise these uncertainties anew. When several sets of animal toxicology data are available, for example, and data are not sufficient for identifying the set (i.e., species, strain, and adverse effects endpoint) that best predicts human response, it has become traditional to select that set in which toxic responses occur at the lowest dose (the most sensitive set). In the absence of definitive empirical data applicable to a specific case, it is generally assumed that there will not be more than a tenfold variation in response among members of the human population. In the absence of absorption data, it is generally assumed that humans will absorb the chemi- cal at the same rate as the animal species used to model human risk. In the absence of complete understanding of biological mechanisms, it is gener- ally assumed that, except possibly for certain carcinogens, a threshold dose must be exceeded before toxicity is expressed. These types of long-standing assumptions, which are necessary to complete a risk assessment, are recog- nized by risk assessors as attempts to deal with uncertainties (NRC, 1994). A past National Research Council (NRC) report (1983) recommended adoption of the concepts and definitions that have been discussed in this report. The NRC committee recognized that throughout a risk assessment, data and basic knowledge will be lacking and risk assessors will be faced with several scientifically plausible options (called inference options by the NRC) for dealing with questions such as those presented above. For

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1246 DIETARY REFERENCE INTAKES example, several scientifically supportable options for dose scaling across species and for high- to low-dose extrapolation will exist, but there will be no ready means to identify those that are clearly best supported. The NRC committee recommended that regulatory agencies in the United States identify the needed inference options in risk assessment and specify, through written risk assessment guidelines, the specific options that will be used for all assessments. Agencies in the United States have identified the specific models to be used to fill gaps in data and knowledge; these have come to be called default options (EPA, 1986). The use of defaults to fill knowledge and data gaps in risk assessment has the advantage of ensuring consistency in approach (the same defaults are used for each assessment) and minimizing or eliminating case-by-case manipulations of the conduct of risk assessment to meet predetermined risk management objectives. The major disadvantage of the use of defaults is the potential for displacement of scientific judgment by excessively rigid guidelines. A remedy for this disadvantage was also suggested by the NRC committee: risk assessors should be allowed to replace defaults with alter- native factors in specific cases of chemicals for which relevant scientific data are available to support alternatives. The risk assessors’ obligation in such cases is to provide explicit justification for any such departure. Guide- lines for risk assessment issued by the U.S. Environmental Protection Agency (EPA, 1986), for example, specifically allow for such departures. The use of preselected defaults is not the only way to deal with model uncertainties. Another option is to allow risk assessors complete freedom to pursue whatever approaches they judge applicable in specific cases. Because many of the uncertainties cannot be resolved scientifically, case- by-case judgments without some guidance on how to deal with them will lead to difficulties in achieving scientific consensus, and the results of the assessment may not be credible. Another option for dealing with uncertainties is to allow risk assessors to develop a range of estimates based on application of both defaults and alternative inferences that, in specific cases, have some degree of scientific support. Indeed, appropriate analysis of uncertainties seems to require such a presentation of risk results. Although presenting a number of plausible risk estimates has the advantage that it would seem to more faith- fully reflect the true state of scientific understanding, there are no well- established criteria for using such complex results in risk management. The various approaches to dealing with uncertainties inherent in risk assessment are summarized in Table L-1. As can be seen in the nutrient chapters, specific default assumptions for assessing nutrient risks have not been recommended. Rather, the approach calls for case-by-case judgments, with the recommendation that the basis

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1247 A PPENDIX L for the choices made be explicitly stated. Some general guidelines for making these choices are, however, offered. REFERENCES EPA (U.S. Environmental Protection Agency). 1986. Proposed guidelines for car- cinogen risk assessment; Notice. Fed Regis 61:17960–18011. NRC (National Research Council). 1983. Risk Assessment in the Federal Government: Managing the Process. Washington, DC: National Academy Press. NRC. 1994. S cience and Judgment in Risk Assessment. W ashington, DC: National Academy Press.

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1248 DIETARY REFERENCE INTAKES TABLE L-1 Approaches for Dealing with Uncertainties in a Risk Assessment Program Program Model Advantages Case-by-case judgments by Flexibility; high potential to experts maximize use of most relevant scientific information bearing on specific issues Written guidelines specifying Consistent treatment of different defaults for data and model issues; maximization of uncertainties (with allowance transparency of process; resolution for departures in specific of scientific disagreements possible cases) by resorting to defaults Presentation of full array of Maximization of use of scientific estimates by assessors from all information; reasonably reliable scientifically plausible models portrayal of true state of scientific understanding

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1249 A PPENDIX L Disadvantages Potential for inconsistent treatment of different issues; difficulty in achieving consensus; need to agree on defaults Possible difficulty in justifying departure or achieving consensus among scientists that departures are justified in specific cases; danger that uncertainties will be overlooked Highly complex characterization of risk, with no easy way to discriminate among estimates; size of required effort may not be commensurate with utility of the outcome