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OCR for page 133
4
Variability and Uncertainty
VARIABILITY VERSUS UNCERTAINTY
Quantitative health risk assessments rely on mathematical models to combine
data and assumptions with the goal of providing useful information to the
decision-maker about health risks. In the context of developing a dose-re-
sponse relationship, risk assessors evaluate the weight of the scientific evi-
dence of an effect. Given sufficient evidence, they combine data from multi-
ple studies in the same species and from epidemiological and animal studies
to estimate the health risks. These risks are estimated in the dose range for
which observations have been made and are then extrapolated from these dose
ranges to Tower doses that might be of concern for regulation. Science and
Judgment in RiskAssessment MARC 1994) emphasized the important distinc-
tions between variability and uncertainty in risks, highlighting their very
different ramifications for risk management in the following statement:
Uncertainty forces decision-makers to judge how probable it is that
risks will be overestimated or underestimated for every member ofthe
exposed population, whereas variability forces them to cope with the
certainty that different individuals will be subjected to risks both
above and below any reference point one chooses (NRC 1994, p.
237~.
133
OCR for page 134
134 ARSENICINDR1NKING WATER. 2001 UPDATE
Uncertainty refers to a lack of knowledge in the underlying science. Variabil-
ity in a risk assessment means that some individuals in a population have more
or less risk than others because of differences in exposure, dose-response
relationship, or both. Arsenic in Drinking Water MARC ~999) discussed the
variability and uncertainty associated with an arsenic risk assessment. This
chapter focuses on data that have become available since that report. These
data provide further insight into the variability and uncertainty in the risk
assessment and allow a better characterization of how they affect the overall
characterization of risk. This chapter is divided into two sections, one that
primarily discusses variability and one that primarily discusses uncertainty.
It is important, however, to recognize that uncertainty might exist about vari-
able quantities and that variability and uncertainty are best defined and under-
stood within a decision-making context ARC ~ 994; Thompson and Graham
1996).
VARIABILITY AND UNCERTAINTIES
DISCUSSED IN TTIE 1999 REPORT
In its discussion of variability in an arsenic risk assessment, the 1999
Subcommittee on Arsenic in Drinking Water concluded that characteristics of
different individuals could contribute to variability in risk estimates. Human
susceptibility to inorganic arsenic could vary because of differences in genet-
ics, metabolism, diet, health status, and sex.
In the 1999 NRC report, the subcommittee presented risk estimates that
were calculated using different statistical models, which provide some indica-
tion ofthe impact ofthe model on the estimates, and concluded that the statis-
tical model used to analyze the data can have a major impact on the estimated
cancer risks at Tow-dose exposures. The ~ 999 NRC report quantified some of
the impacts of different dose-response mode! choices, but did not quantify the
impacts of variability in the population of the risk estimates.
In most epidemiological studies of arsenic, biomarkers of individual ar-
senic exposure have not been used; therefore, assumptions must be made
about the amount and source of wafer consumed to estimate exposures. Those
assumptions add to the uncertainty in the risk assessment. There is the poten-
tial for bias in the results of the Taiwanese studies (Chen et al. 1985, 1988,
1992; Wu et al. 1989) because ofthe uncertainty in exposure estimates. How-
ever, similar associations between arsenic exposure and cancers (lung and
bladder) were seen in studies in Chile (Smith et al. 1998) and Argentina
(Hopenhayn-Rich et al. 1998~; therefore, the uncertainty in the Taiwanese
OCR for page 135
VARIABILITYAND UNCERTAINTY 135
data, and the subsequent risk assessment using those data, is decreased ARC
1999~. Additional uncertainty comes from the lack of knowledge of the pre-
cise intake of arsenic from food in both the U.S. and Taiwanese populations.
Differences in the intake between the populations could affect the assessment
of risk from exposure to arsenic in Taiwan and the extrapolation of that risk
to the U.S. population. The lack of knowledge regarding population differ-
ences adds to the uncertainty in the risk estimates.
SOURCES OF VARIABILITY
Exposure
Different individuals or subpopulations are exposed to different amounts
of arsenic, leading to variability in the risk estimates that could be calculated
for a given population. Many factors can affect the amount of arsenic to
which different individuals are exposed through drinking water, including
differences in the amount of arsenic present in drinking water, the amount of
water that individuals consume, and physical factors, such as sex, age, and
body weight. Exposure to arsenic through sources other than drinking water
can also lead to variability in risk estimates. Environmental arsenic concentra-
tions affect exposures that do not come from drinking water, and those con-
centrations vary among locations because of different natural background
levels and local anthropogenic sources. In addition, different foods contain
varying concentrations of arsenic (IOM 2001~; therefore, an individuals food
preferences will affect arsenic intake. Although this update focuses on risk
from exposure to arsenic in drinking water, other factors that affect arsenic
exposure from all sources are important to consider when interpreting the
dose-response modeling based on use of available epidemiological data. This
section discusses those sources of variability in exposure estimates and their
potential impacts on an arsenic risk assessment. Many of those sources are
further explored and discussed in Chapter 5.
Per Capita Water Consumption
Water Consumption in the U.S. Population
One reason a subpopulation might be at higher risk for the adverse effects
of arsenic in drinking water is that they have a higher than average water
OCR for page 136
136 ARSENIC IN DRINKING WATER: 2001 UPDATE
OCR for page 137
VARIABILI7YAND UNCERTAINTY 13 7
For many of these permutations, EPA (2000b) provided the raw data that
could be used to fit empincal distributions to the data set. Table 4-! provides
an example ofthe date provided, end Figure 4-1 shows the plot ofthe dis~ibu-
tions.
The data clearly show vanability in water consumption with age, with the
highest daily total water ingestion rates per unit of body weight occurring in
infants and young children. In its risk assessment, EPA took the reported
ingestion rates (given in milliliters per day) from an earlier draft of the EPA
(2000b) report. EPA developed distributions of body weight from national
census data to get the distribution of inking-water rates on the basis of per
unit of body weight. Ultimately, the EPA (2000b) report on drinking-water
TABLE 4-1 Example of the Distribution of the Water Intakes Represented As Se-
lected Percentiles Reported by EPA (2000b) for Bow Sexes, Total Water (from All
Sources), and Direct and Indirect Pathways Averaged over 2 Nonconsecutive Days
(mL/kg/day)
F~ne-age Percentile
Category
(year) Mean 1% 5% 10% 25% 50% 75% 90% 95% 99%
<0.5 92 2 7 14 31 87 139 169 196 239
0.5-0.9 65 2 6a 11 26 58 88 120 164 185
1 -3 31 1 4 7 15 26 40 60 74 118
4-6 27 1a 5 8 14 23 36 51 68 97
7-10 20 1a 4 6 10 17 26 36 44 70
11-14 16 1a 3 4 8 14 21 33 40 60
15-19 15 2 4 7 12 19 29 38 66
20-24 18 2 4 8 14 22 34 44 86a
25-54 20 1 4 6 11 17 26 37 46 69
55-64 20 2 6 8 12 18 26 35 42 59
21 4 7 9 13 19 27 34 39 54
Allages 21 1 4 6 11 17 26 38 50 87
a Sample size does not meet the minimum reporting requirements.
Source: EPA (2000b, p. IV-19~.
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138 ARSENIC IN DRINKING WATER: 2001 UPDATE
rates also presented the distribution in per unit of body weight, and the distri-
butions were very similar. In its analysis, EPA computed lifetime average
water consumption by randomly selecting from ( 1 ) the water consumption and
body-weight distributions for each sex and age category, and (2) randomly
selecting a sex for the simulated individual, then multiplying the values drawn
by the number of years the individual male or female spent in each age cate-
gory, and finally summing over the age categories to simulate an entire life-
time. This method did not make any assumptions about consistency (or corre-
lation) in individual body weights or water-consumption rates over time.
The very short sample time of the water intake survey deserves additional
discussion. Although the data on water-consumption rate provide a large
amount of information about water ingestion for a very short time (2 days),
several important uncertainties arise when using these short-term data to repre-
sent the long-term average. To compute the average lifetime ingestion rate
from those data, the changes in individual drinking-water behaviors over time
must be assumed. It must be also assumed that a random cross-sectional sam-
ple for 2 nonconsecutive days accurately estimates water consumption over a
lifetime. The later assumption might be valid if the 2 days are representative,
because it might be expected that intraindividual variability in water consump-
tion over time will be lower than interindividual variability over time (i.e.,
people who drink a lot of water ~ year will probably drink a lot of water other
years). However, the data are lacking to test this assumption, and the extrapo-
lation from a 2-day sample to a longer time period might be misleading if some
people participate in the survey during a hot summer week and others respond
in the winter, neither giving an estimate of typical consumption. In general,
some regression to the mean should be expected as a longer period of time is
sampled. Biases associated with the survey and participant compliance also
add to the uncertainties in the data. Despite those caveats, EPA's data are
useful for risk analysis and for characterizing the variability in population
drinking-water rates per unit of body weight. Those data are used in the risk
calculations done by the subcommittee to determine the sensitivity of the risk
estimates to drinking-water consumption values (presented in Chapter 5~.
Water Consumption in the Taiwanese Study Population
In its earlier risk assessment, EPA (1988) did not consider variability in
water consumption per unit of body weight in the study populations when
interpreting the epidemiological data on arsenic carcinogenicity in Taiwan or
OCR for page 139
FARIAB~7YAND UNCERTAINTY 139
(a)
(b)
So%
~ 70%
-
50%
40%
30%
20%
10%
0%
100%
/
Ad/
Of/
0.5 - 0.9
4-6
7-10
- - - -11 -14
All ages
0 50 100 150 200 250
Water consumed, mL/kg/day
80%
Anon
/,7
- 15-19
- - 20 - 24
- 25-54
—55 - 64
—~ 65~
All ages
0 50 100 150 200
Water consumed, mL/kg/day
250
FIGURE 4-1 Cumulative distributions of water intake data by age. (a) Cumulative
distributions at ages less than 0.5, 0.5-0.9, 1-3, 4-6, 7-10, 11-14, and all ages. (b)
Cumulative distributions at ages 15-19, 20-24, 25-54, 55-64, 65 and above, and all
ages.
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~ 40 ARSENIC IN DRINKING WA TER: 2001 UPDA TE
the dose-response relationship; the previous subcommittee also did not quan-
tify that variability TIC 1999~. EPA (1988) and NRC (1999) assumed that
a typical Taiwanese male and female weigh 55 kg and 50 kg, respectively, and
drink 3.5 L/day and 2.0 L/day, respectively. Those values correspond to a
daily per kilogram intake of water of approximately 60~0 mL/kg/day, much
more than the 21-28 mElkg/day that Americans are estimated to consume.
The assumptions suggest considerable differences between Taiwanese and
American populations, and the assumptions about Taiwanese water intake
have been questioned previously (Mushak and Crocetti ~ 995~. Unfortunately,
uncertainty remains about how much water the Taiwanese drink, and no ap-
propriate data on the distribution of water consumption in the Taiwanese study
populations are available at this time. When such data become available, they
could be used to better estimate exposures in the Taiwanese study populations,
reducing the uncertainty in the risk assessment. However, it seems likely that
the water consumption pattern of the people in southwestern Taiwan, where
many of the previous epidemiological studies were carried out, has changed
with time as the socioeconomic situation has improved. Therefore, informa-
tion on current water intakes might not be useful for reevaluation of the situa-
tion 20~0 years ago. In the absence of reliable data on water consumption in
the Taiwanese study populations, the sensitivity of the risk estimates to those
assumptions should be assessed to quantify some ofthe uncertainty in the risk
assessment. Some analyses ofthe sensitivity ofthe risk estimates to different
Taiwanese water-intake assumptions are presented in Chapter 5.
Given that there is variability in daily drinking-water ingestion rates,
which can be partially characterized based on available data, the subcommit-
tee believes that the EPA risk assessment should explicitly address the impacts
of this variability. In particular, the subcommittee notes that the recent ap-
proach of EPA (2001) to account for variability in quantity of water consumed
by the United States population without simultaneously accounting for vari-
ability in water consumption among the Taiwanese would have the result of
underestimating the upper bound of risk attributable to arsenic in drinking
water in the quantitative risk assessment. It is reasonable to assume that the
Taiwanese had a variable intake of water as well, and biostatistical analysis
should consider that the southwestern Taiwanese cohort consisted of a signif~-
cant proportion of males and females whose direct water consumption was
less than 3.5 and 2.0 L/day, respectively.
OCR for page 141
VARIABILITY AND UNCERTAINTY 141
Consideration of Infants and Children
Within a given population, certain individuals or subpopulations can be
more susceptible to the toxic effects of a substance, either because they have
a higher exposure to the substance or because they are intrinsically more
susceptible to it. Infants and children are often considered more susceptible
to the adverse effects oftoxic substances than adults (NRC 1993), but suscep-
tibility must be assessed on a case-by-case basis (~ST 1992~.
Susceptibility from Exposure Differences
EPA (2000b) noted that "when considering water-ingestion rates in units
of milliliters per kilogram of body weight per day, this analysis shows that the
mean per capita ingestion rates for babies younger than one year are estimated
to be three to four times higher than the mean rates for the population as a
whole." That increased exposure (on a body-weight basis) indicates that
infants and young children might be at increased risk. Therefore, the sub-
committee considered whether there is any evidence that infants and children
are more susceptible than adults to the toxic effects of arsenic in drinking
water. Calderon et al. (1999) investigated the excretion of arsenic in urine.
They found increased concentrations of arsenic metabolites (measured as total
arsenic) in individuals less than 18 years of age compared with individuals
greater than ~ ~ years of age and suggested that the difference might be due to
differences in water consumption. Although these data might support the
hypothesis that children are at higher risk to the effects of arsenic in drinking
water, the impact of the increased exposure in infants and young children on
the risk assessment depends on what pattern of exposure correlates with the
effect and whether the increased exposure is intrinsically accounted for in the
design of the epidemiological studies used in the risk assessment. Those
issues are discussed further in the section Dose Metrics and Model Uncer-
tainty.
lit should be noted that the variation in water intake of infants is dependent on the
frequency of breast feeding.
t
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~ 42 ARSENIC IN DRINKING WA TER: 2001 UPDA TE
Intrinsic Susceptibility
It is also important to consider whether infants and children are intrinsi-
cally more susceptible to arsenic toxicity. Differences in the metabolism of
arsenic in infants, children, and adults could make one ofthose subpopulations
more susceptible to the toxic effects of arsenic. Previous studies indicate that
young individuals might have a lower rate of methylation of inorganic arsenic
than adults (NRC 1999~. More recently, indigenous Andean children have
been found to have about 45% oftheir urinary arsenic as dimethylarsinic acid
(DMA) (Concha et al. 1998~. In a study of people (2-83 years of age) exposed
to arsenic in drinking water in Finland, the percentage of arsenic in urine as
DMA increased with age (from 59% in people less than 30 years of age to
74% in people more than 50 years of age) (Kurttio et al. ~ 998~. Similar results
were seen in a study of arsenic-exposed people in northeastern Taiwan (Hsu
et al. 1997~. in contrast, a recent study from Mexico showed that children in
Region Lagunera had normal fractions of DMA in urine (an average of about
70°/0; De! Razo et al. 1999~. Therefore, the data on variation in arsenic
methylation with age are not consistent, and it is not known how such differ-
ences in methylation would affect arsenic toxicity. As discussed in Chapter
3, the methylation of inorganic arsenic can affect its toxicity, but that methyla-
tion is no longer thought to be entirely a detoxification process.
Variability in Arsenic Metabolism
Some subsections or subpopulations of a given population could be more
or less susceptible to arsenic toxicity because of differences in arsenic metabo-
lism. In particular, there are marked differences in arsenic methylation among
mammalian species, population groups, and individuals. As discussed in
Chapter 3, the valence state and extent of methylation of arsenic can affect its
toxicity. The main products in the methylation of inorganic arsenic, pentava-
lent monomethylarsonic acid (MMAV) and dimethylarsinic acid (DMAV), are
readily excreted in urine. Therefore, more efficient methylation, especially to
DMAV, means faster overall excretion of arsenic (Vahter 1999a). The
methylation of arsenic, however, is not entirely a detoxifying process. The
initial reduction of pentavalent inorganic arsenic (ASV) to trivalent inorganic
arsenic (AsIII), as well as the formation and distribution of methylated trivalent
arsenic metabolites in tissues, results in increased toxicity. MMAIII is highly
.
OCR for page 143
VAR]ABILI7Y AND UNCERTAIN7Y 143
reactive with cellular constituents, especially sulflhydryl groups, and, in gen-
eral, exhibits a greater toxicity than inorganic Astir (see Chapter 3~. Variations
in the metabolism of arsenic, therefore, are likely to be associated with vana-
tions in susceptibility to arsenic. The extent to which such metabolic varia-
tions might affect an individuaT's susceptibility to cancer or systemic toxicity
with arsenic exposure is an important uncertainty.
Because methylated forms of arsenic are readily excreted in urine, evalua-
tion of arsenic methylation efficiency is generally based on the relative
amounts of the different metabolites detected in urine (Vahter ~ 999a). Cur-
rently, however, there are no data on the association between urinary concen-
trations of MMAi~ or DMA~ and their formation and concentrations in tis-
sues. Those metabolites are highly reactive and retained intracellularly,
whereas DMAV is the main form of arsenic excreted from the cells (Styblo et
al. 2000) (see Chapter 3~. Therefore, the concentrations of MA and
DMA~ in the urine are less likely to reflect the formation of those metabolites
in tissues. However, MA has been detected in human urine, although the
site of reduction of MMAV to MMA~i is not known. Because MMAi~T is pro-
duced fromMMAV, it seems probable that the tissue concentrations of EMMA
would increase with the total production of MMA and, therefore, with increas-
ing total MMA in urine.
Genetic Polymorphisms Related to Arsenic Metabolism
Glutathione (GSH) S-transferases (GST) constitute a large family of de-
toxifying enzymes that catalyze the conjugation of reduced GSH with a wide
range of compounds. Because GSH levels, which are essential for the reduc-
tion and methylation of arsenic, are influenced by GSTs, the latter have been
considered as possible candidates for influencing the metabolism and the
toxicity of arsenic. A study from the area in northeastern Taiwan where ar-
senic is endemic showed that subjects with the null genotype of GST M! had
a slightly increased percentage of inorganic arsenic in urine (Chiou et al.
1997~. In addition, there was a tendency for an increased percentage of DMA
in urine of subjects with the null genotype of GST T1. Whether those geno-
types might influence susceptibility to arsenic is not known. As discussed in
Chapter 3, recent studies indicate that human MMAV reductase is identical to
human glutathione-s-transferase omega class 1-1 (hGSTO 1-1~. Therefore,
polymorphisms in that GST also might affect both cellular protective mecha-
OCR for page 158
158 ARSENICINDRINKING WATER: 2001 UPDATE
sonic remains highly uncertain, even as more is learned about the underlying
mechanisms of disease. In addition, the variability in latency of different
types of arsenic-related diseases has not been explored. These might arise
from a difference in the exposure level, duration of exposure, age of exposure,
susceptibility (that might lead some people to respond more quickly than
others), or other unknown risk factors.
In assessing the risks from arsenic, it is nearly impossible to entirely rule
out the possibility that genetics, lifestyle differences (e.g., smoking, food
preferences, cooking habits), and exposure to other environmental factors
might play a role in explaining variability in the risks. A few investigations
have suggested that there might be an interaction between arsenic exposure
and smoking in cancer causation. A case-control study by Bates et al. (1995)
examined the relationship between arsenic exposure in drinking water and
bladder cancer and found a positive association between arsenic and bladder
cancer only among smokers. The findings of two studies (Ferreccio et al.
2000; Tsuda et al. 1995) suggest that there might be a synergistic effect be-
tween the arsenic ingestion and smoking on the risk of lung cancer. A syner-
gistic interaction for lung cancer between smoking and inhaled arsenic has
been noted in occupational cohorts (Hertz-Picciotto et al. 1992~. Further
confirmation and characterization of an interaction between arsenic ingestion
and smoking in the causation of cancer are necessary before this potential
effect can be accounted for in a quantitative risk assessment.
Nevertheless, it is important to emphasize that there is little reason to
suspect that smoking is a significant confounder of the association between
arsenic ingestion and lung or bladder cancer. In the case-control study of lung
cancer in Chile by Ferreccio et al. (2000) described in Chapter 2, high odds
ratios for lung cancer with increasing arsenic exposure were observed in
models that adjusted for smoking history. In the cohort study of incident
bladder cancer in northeastern Taiwan reported by Chiou et al (2001), the
observed association with arsenic exposure also persisted after adjusting for
smoking history. As noted by NRC (1999), an ecological study by Smith et
al. (1998) reported standardized mortality ratios (SMRs) for lung and bladder
cancer in a region of northern Chile (Region IT) for past exposures to high
arsenic concentrations in drinking water. The SMR for chronic obstructive
pulmonary disease (COPD) in females in that region was 0.6 (95% CI = 0.4-
0.7~. Since COPC is overwhelmingly due to smoking, it is apparent that the
region contained few female smokers relative to the rest of Chile. Yet the
female lung cancer SMR in that region was 3.1 (95% CT = 2.7-3.7), and the
female bladder cancer SMR was 8.2 (6.3-10.5). As nosed by the authors, there
was a large risk of arsenic associated lung and bladder cancer that was not
OCR for page 159
VARIAB~ITY AND UNCERTAINTY ~ 59
subject to confounding by smoking. A similar finding emerged for males.
The high SMRs for Jung cancer and bladder mortality in the region of south-
west Taiwan where arsenic is endemic were unlikely to be subject to signifi-
cant confounding by smoking or other factors (see description of Tsai et al.
(1999) in Chapter 2~. Neither mates nor females in the area where arsenic is
endemic had elevated SMRs for emphysema, a cause of death almost entirely
attributable to smoking.
VALUE-OF-INFORMATION APPROACH
Future research might reveal that the manifestations of arsenic toxicity are
influenced by gene-environment interactions or that some populations might
be at relatively increased risk of developing cancer from exposure to arsenic
based on their genetics or their behaviors (e.g., smokers). The implications of
this type of variability will raise important policy considerations for risk man-
agers, andresearch to support the efforts of decision-makers to deal with these
issues should be initiated.
In a ~ 996 report, the National Research Council discussed the importance
of using an analytic-deliberative process in the context of managing risks
(NRC ~ 996~. As noted in the report of the Presidential/Congressional Com-
mission on Risk Assessment and Risk Management ~ ~ 997), better risk-man-
agement decisions will be made when the process has the capacity for itera-
tions "if new information is developed that changes the need for or nature of
risk management." A value-of-information approach can be used to decide if
it is more appropriate to collect additional information or make a decision
based on a risk assessment with specified uncertainties. Given the existing
uncertainties that remain for arsenic, EPA should explore whether implement-
ing a value-of-information approach to prioritizing research efforts would be
helpful in supporting its process for regulating arsenic. As discussed by NRC
(1996),
Value-of-information methods address whether potential reductions
in uncertainty would make a difference in the decision; they suggest
priorities among reducible uncertainties on the basis of how much
difference the expected reduction might make (p. ~ 10~.
Although the decision to implement a value-of-information approach reflects
a policy choice, it might help to focus research efforts on those key uncertain-
ties that would have the largest impact on our understanding of the magnitude
of arsenic risks.
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60 ARSENIC IN DRINKING WA TER: 2001 UPDA TE
SUMMARY AND CONCLUSIONS
The subcommittee commends EPA for considering variability and uncer-
tainty in its risk assessment for arsenic and hopes that EPA will continue to
refine and update its assessment. New data developed since the 1999 report
provide more insight into how sources of variability and uncertainty can be
addressed in a risk assessment for arsenic. For example, variability in
drin~ng-water rates per unit of body weight can now be quantitatively ana-
Tyzed. The subcommittee explores the implications of using that type of infor-
mation on variability in risk assessment in Chapter 5. However, there are still
a number of key sources of uncertainty that have not been addressed. They
include the following:
· The mechanisms by which arsenic causes cancer are not well under-
stood. It is unclear what the shape of its dose-response curve is at low doses
and whether the magnitude of the dose or the duration of exposure is more
important in cancer risk.
· There is a lack of concordance in the results of animal and human
studies.
· Geographical and cultural differences in arsenic exposures make it
difficult to relate consumption habits in Taiwan with those in the United
States. To make better comparisons and account for differences, more infor-
mation is needed on arsenic content in foods, water-ingestion rates, food-
preparation practices, and nutritional status of the populations. Dietary and
nutntional variability between population groups might result in a different
proportion of the populations being at risk for arsenic-induced health effects,
but the risk will be increased in all those who share the same pattern of expo-
sure and susceptibility.
· It is unclear whether infants and young children might be more sus-
ceptible to arsenic-induced health effects, particularly those for noncancer end
points where less-than-lifetime exposures are important and children's greater
water consumption per unit of body weight might put them at relatively
greater risk.
· The metabolism and extent of methylation of arsenic is not fully
understood. For example,
There is considerable interindividual variation in arsenic metabolism,
particularly in the production of MMA and DMA. Some data indicate that
methylation is influenced more by genetic factors than by environmental
factors. Recent studies, however, confirm the previous findings that methyla-
tion of arsenic, especially the second methylation step in which MMA is
OCR for page 161
VARIABILITYAND UNCERTAINTY 1 61
converted to DMA, is inhibited at higher doses of arsenic. Data also indicate
less-efficient methylation of arsenic to DMA in children compared with
adults, but those data are not conclusive.
.
Experimental studies indicate that exposure to other pollutants (e.g.,
vanadium, selenium, cadmium, mercury, and lead) might inhibit the second
arsenic methylation step. Administration of chelating agents, such as DMPS,
has been found to decrease the urinary excretion of DMA and increase that of
MMA.
Arsenic metabolites vary considerably in their toxicity; thus, variation
in the metabolism of arsenic is likely to be associated with variations in sus-
ceptibility to arsenic. Animal and human data indicate that efficient methyla-
tion of inorganic arsenic to DMAV results in faster overall excretion of ar-
senic. Also, there is increasing evidence that individuals with increased MMA
production and retention (mainly EMMA retain more arsenic and are more
prone to at least some toxic effects of arsenic.
RECOMMENDATIONS
· EPA should explore research opportunities to reduce the key uncer-
tainties identified and should support research efforts that might resolve im-
portant uncertainties. EPA should also explore using an iterative value-of-
information approach to prioritize future research efforts targeted at resolving
uncertainties in the arsenic risk assessment. When better data and information
are obtained, they should be factored in the risk assessment.
· In considering how variability in the amount of drinking water con-
sumed would affect quantitative risk assessment for arsenic, EPA should
adjust for variability in consumption in the population that was the source of
the data (e.g., the southwestern Taiwanese), as well as in the general U.S.
population.
Key sources of uncertainty that may be subject to substantial reduc-
tion include the following:
Better understanding of the differences in arsenic exposure between
U. S. and Taiwanese populations. This information could be obtained through
surveys of the Taiwan drinking-water consumption, water usage for cooking,
and body weights. However, it is unclear whether data obtained at the present
time could be applied retrospectively to a study population whose key expo-
sure occurred decades in the past.
Improved characterization of the form and bioavailability of the ar-
senic present in the raw foods and of the arsenic incorporated into food from
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~ 62 ARSENIC IN DRINKING WA TER: 2001 UPDA TE
drinking water during cooking and food preparation. Such characterization
will enhance knowledge of valid means to account for this arsenic in quantita-
tive risk assessment, which is particularly relevant to EPA's exposure assess-
ment efforts.
Clarification of the mechanisms of action of arsenic and development
of an appropriate animal model for studying the effects of arsenic. Such
clarification and development would help to reduce the uncertainty about dose
metrics and the relevance of animal data for human risk assessments.
· Further characterization ofthe long-term drinking-waterconsumption
patterns in the general population of the United States could narrow the cur-
rent characterization of variability in drinking-water rate s. However, although
it might reduce the overall variability in the estimated risks, it is unlikely to
dramatically change the risk estimates.
· More information is needed on the variability in the metabolism of
arsenic among individuals and the effect ofthat variability on an individuaT's
susceptibility to cancer and systemic toxicity. In particular, the impact of
variability in MA formation and distribution in human tissues is needed.
Factors influencing the variability, genetic as well as environmental, should
be studied.
· More data are needed to better understand the susceptibility of chil-
dren to arsenic-induced toxicity, particularly for noncancer effects, and their
arsenic-methylation efficiency.
· The influence of nutritional and dietary factors on the risk for arsenic-
induced health effects should be subject to further research.
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Representative terms from entire chapter:
drinking water
