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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
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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
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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
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136 ARSENIC IN DRINKING WATER: 2001 UPDATE
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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
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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.
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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 .
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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-
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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
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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
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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. REFERENCES Aposhian, H.V., A. Arroyo, M.E. Cebrian, L.M. Del Razo, K.M. Hurlbut, R.C. Dart, D. Gonzalez-Ramirez, H. Kreppel, H. Speisky, A. Smith, M.E. Gonsebatt, P. Ostrosky-Wegman, and M.M. Aposhian. 1997. DMPS-arsenic challenge test. I. Increased urinary excretion of monomethylarsonic acid in humans given dimercaptopropane sulfonate. J. Pharmacol. Exp. Ther. 282~1):192-200. Aposhian, H.V., B. Zheng, M.M. Aposhian, X.C. Le. M.E. Cebrian, W. Cullen, R.A. Zakharyan, M. Ma, R.C. Dart, Z. Cheng, P. Andrewes, L. Yip, G.F. O'Malley, R.M. Maiorino, W. Van Voorhies, S.M. Healy, and A. Titcomb. 2000a. DMPS - Arsenic challenge test. II. Modulation of arsenic species, including monomethylarsonous acid AMMAN, excreted in human urine. Toxicol. Appl. Pharmacol. 165(1):74-83. Aposhian, H.V., E.S. Gurzau, X.C. Le. A. Gurzau, S.M. Healy, X. Lu, M. Ma, L. Yip, R.A. Zakharyan, R.M. Maiorino, R.C. Dart, M.G. Tircus, D. Gonzalez-Ramirez,
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Representative terms from entire chapter: