Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 1244
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
OCR for page 1245
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
OCR for page 1246
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
OCR for page 1247
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.
OCR for page 1248
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
OCR for page 1249
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
Representative terms from entire chapter:
relevant scientific
