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Pesticides in the Diets of Infants and Children 8 Estimating the Risks THE PRECEDING CHAPTERS have demonstrated that infants and children may have special sensitivities to certain toxic insults. Children also consume notably more of certain foods relative to their body weight than do adults. Thus, their ingestion of pesticide residues on these foods may be proportionately higher than that of adults. For certain chronic toxic effects such as cancer, exposures occurring early in life may pose greater risks than those occurring later in life. For these reasons, risk assessment methods that have traditionally been used for adults may require modification when applied to infants and children. To evaluate the potential risks from dietary intake of pesticide residues by infants and children, some familiarity with basic toxicological approaches to risk assessment is required. In the first part of this chapter, the committee reviews principles of toxicological risk assessment, including interspecies extrapolation, forms of toxicity, mathematical models for assessing cancer risk, low-dose linearity, and multiple exposures. Special characteristics of infants and young children that must be considered when assessing their risks from dietary exposures to pesticides are then discussed. The fundamental purpose of regulatory toxicology is the determination of risks (including zero risk) at various possible levels of human exposure to toxicants present in the environment. For risk assessment applications, it is useful to distinguish between toxic processes that are either stochastic or nonstochastic in nature. Stochastic processes such as carcinogenesis result from the random occurrence of one or more biological events in specific individuals. Thus, whether or not a particular individual will develop a cancerous lesion under specified conditions or exposure cannot
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Pesticides in the Diets of Infants and Children be predicted with certainty. Nonstochastic events such as enzyme inhibition following exposure to a certain toxicant are more predictable. Nonstochastic effects may not be seen if lifetime exposures are below a certain threshold concentration, whereas stochastic effects such as carcinogenesis may have no threshold. Different approaches to risk assessment are usually used for these two categories of response. Information on health risks may be obtained directly from epidemiological and other studies of humans or indirectly through toxicological experiments conducted in animal models. Although results of laboratory studies are applicable to humans only indirectly, they can be used to predict potential health hazards in advance of actual human exposure and thus continue to be used widely to identify substances with potential toxicity. Three major problems are inherent in the translation of results of animal tests to humans: Laboratory tests are conducted at relatively high doses to induce measurable rates of response in a small sample of animals. These results must often extrapolated to lower doses that correspond to anticipated human exposure levels. Interspecies differences must be considered when extrapolating between the animal model and humans. It may be necessary to extrapolate from a route of exposure chosen for experimental practicality to a different but more likely route of human exposure. General Principles of Risk Assessment Toxicological Risk Assessment Classical methods in toxicological risk assessment are applicable with nonstochastic toxic effects. For many kinds of agents and end points, toxicity is manifest only after the depletion of a physiological reserve. In addition, the biological repair capacity of many tissues can accommodate a certain degree of damage by reversible toxic processes (Aldridge, 1986; Klaassen, 1986). Above this threshold, however, the compensatory mechanisms that maintain normal biological function may be overwhelmed, leading to organ dysfunction. The objective of classical toxicological risk assessment, which has focused on nonstochastic end points, has been to establish a threshold dose below which adverse health effects were expected to be rare or absent. Historically, the threshold concept was introduced by Lehman and Fitzhugh (1954), who proposed that an acceptable daily intake (ADI) could be calculated for chemical contaminants in human food. This concept was
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Pesticides in the Diets of Infants and Children endorsed by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) in 1961 and subsequently adopted by the Joint FAO/WHO Meeting of Experts on Pesticide Residues (JMPR) in 1962 (Lu, 1988). Formally, the ADI was defined as: ADI = NOEL/SF, where the NOEL is the no-observed-effect level in toxicity studies, usually in the most sensitive species, and SF is the safety factor. Weight loss, reduction in weight gain, alteration in organ weight, and inhibition of cholinesterase activity are indicative of specific adverse effects that may be considered when establishing the NOEL (Babich and Davis, 1981). The safety factor makes allowances for the type of effect, the severity or reversibility of the effect, and variability among and within species (NRC, 1970). In 1977, the National Research Council's Safe Drinking Water Committee reviewed the methods that had evolved for establishing ADIs and made several important recommendations. First, the committee proposed that the NOEL be expressed in mg/kg of body weight rather than mg/kg of diet to adjust for differences in dietary consumption patterns. Since children consume proportionally more food relative to body weight than do adults, ADIs established on a body weight basis would be more protective of children than would ADIs established as dietary concentrations. Second, the committee explicitly supported the use of only a 10-fold safety factor (now called uncertainty factor) in the presence of adequate dose-response data derived from human studies. And third, the committee proposed an additional 10-fold safety factor in the absence of adequate toxicity data, for an overall safety factor of 100. The use of these factors was later supported in a report by the Safe Drinking Water Committee in a reexamination of the earlier risk assessment practices (NRC, 1986). For carcinogenesis, that committee proposed the following definitions for two levels of safety factors: 10: When studies in humans involving prolonged ingestion have been conducted with no indication of carcinogenicity. 100: When chronic toxicity studies have been conducted in one or more species with no indication of carcinogenicity. Data on humans are either unavailable or scanty. With cholinesterase inhibitors, an uncertainty factor of 10 is applied, since toxicity in humans and animals has been shown to be similar, and because the effect is generally reversible. An uncertainty factor of 100 is more commonly used for compounds with other toxic end points, assuming that humans are 10 times more sensitive than the most sensitive test animal and that the most sensitive humans are 10 times more sensitive
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Pesticides in the Diets of Infants and Children than the average human. The interspecies uncertainty factor is based on studies in adult laboratory animals and, therefore, is applicable to adult human beings, but studies in adult laboratory animals are not necessarily good for predicting the response of human infants and children. The other 10-fold intraspecies uncertainty factor is meant to cover variations within human populations, including genetic predisposition, poor nutrition, disease status, and age (Babich and Davis, 1981). A factor of 10 for intraspecies variation in susceptibility may be sufficient for any one element of interpersonal difference but may not be sufficient for multiple elements. Thus, as presently determined, an uncertainty factor of 100 may not be sufficient to account for the potential increased sensitivity of infants and children. When insufficient toxicity data are available, an uncertainty factor of 1,000 may be applied. The ADI is then converted to the maximal permitted intake (MPI), which is the product of the ADI and the average body weight of an adult, considered to be 60 kg, or sometimes 70 kg, by the U.S. Environmental Protection Agency (EPA) and 70 kg by the Food and Drug Administration (FDA) (Babich and Davis, 1981). Uncertainty factors used in toxicological risk assessment have some widely recognized limitations. Since the ADI is based on an estimate of the population threshold or true no-effect level (NEL), it does not provide absolute assurance of safety (Crump, 1984b). Larger and better studies can demonstrate effects at lower doses, but the size of the uncertainty factor is not directly related to sample size. Therefore, smaller and poorer experiments tend to lead to larger ADIs (Schneiderman and Mantel, 1973). Although a factor of 10 is used to accommodate variation in sensitivity among species, and another factor of 10 is used for variation within a species, it cannot be guaranteed that a combined uncertainty factor of 100 will afford adequate protection in all cases. Nor are these factors of 10 based on validated biological models. There is additional uncertainty, usually small, about whether some observed biological responses are adverse effects or innocuous. Thus, the ADI is not intended to have a high degree of mathematical precision. Rather, it is a guide to human exposure levels that are not expected to present serious health risks. The EPA has recommended using the term uncertainty factor (UF) rather than safety factor in recognition of the fact that the ADI does not guarantee absolute safety consistent with more recent recommendations of the Safe Drinking Water Committee (NRC, 1986). The agency has also adopted the reference dose (RfD) as a replacement for the ADI (Barnes and Dourson, 1988; EPA, 1988). In addition, EPA introduced the concept of a modifying factor (MF) to be applied to the UF in recognition of the possible special circumstances surrounding the establishment of a specific RfD. The RfD
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Pesticides in the Diets of Infants and Children is determined by use of the equation: RfD = NOAEL/(UF x MF), where the NOAEL (no-observed-adverse-effect level) is the highest dose at which there is no statistically significant adverse effect in the test animals beyond that exhibited by a control group, and the UF accommodates uncertainties in the extrapolation of dose-threshold data to humans. The MF is applied when scientific uncertainties in the study are not accommodated by the UF. When the data do not demonstrate a NOAEL, a LOAEL (lowest-observed-adverse-effect level) may be used. This is the lowest dose at which a statistically significant adverse effect is observed. The NOAEL and the LOAEL depend on the design of the study, particularly on the selection of the experimental dose groups. If the dose groups are far apart, then the LOAEL may be significantly higher than the true concentration at which adverse effects occur and the NOAEL may be much lower than the minimum concentration producing an adverse effect. In the present context, the term adverse effect is defined as any effect that results in a functional impairment or pathological lesion that may affect the performance of the whole organism, or that reduces the ability of the organism to respond to additional challenges (Dourson, 1986). By definition, a UF is a number that reflects the degree of uncertainty that must be considered when experimental data are extrapolated to humans (Dourson and Stara, 1983; Barnes and Dourson, 1988). When the critical study (the one with the best available dose-response data) is selected for calculation of the reference dose, five factors may contribute to the composite uncertainty factor: the need to extrapolate from animal data to humans when human exposure data are unavailable or inadequate; the need to accommodate human response variability to include sensitive subgroups; the nature, severity, and chronicity of the effect; the need to accommodate the necessity of using LOAEL rather than NOAEL data; and the need to extrapolate from a data base that is inadequate or incomplete. The overall UF may vary from 1 to 10,000, depending on the combination of these individual factors, but usually does not exceed 100. An MF between 1 and 10 may be used to account for scientific uncertainties, either in the study or in the data base, that are not explicitly taken into consideration in any of the five factors listed above (Barnes and Dourson,
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Pesticides in the Diets of Infants and Children 1988). For example, application of the MF may be regarded as a professional judgment that each test group contained too few animals. Cancer Risk Estimation The quantitative estimation of risks associated with low levels of exposure to carcinogens present in the environment is an important part of the regulatory process. At present, risk estimation methods are usually based on the assumption that the dose-response curve for carcinogenesis is linear in the low-dose region. This position is reflected in the principles proposed by the Office of Science and Technology Policy (1986), which stated: When data and information are limited … and when much uncertainty exists regarding the mechanism of carcinogenic action, models or procedures which incorporate low-dose linearity are preferred. (OSTP, 1986) This position is also reflected in the EPA's (1986) Carcinogen Risk Assessment Guidelines. Although the Guidelines emphasize risk estimates derived using some form of linear extrapolation, they are based on the assumption that such estimates may be more appropriately viewed as plausible upper limits on risk and that the lower limit may well be effectively zero. The Guidelines also state that procedures for obtaining a best estimate lying somewhere between these two extremes generally do not exist at present, but that it may be possible to move away from a linearized extrapolation to obtain an upper limit in some circumstances and instead, use a threshold approach. For example, the EPA has recently taken a step in this direction by considering the possibility of a threshold for the induction of thyroid tumors (Paynter et al., 1988). The EPA now uses the linearized multistage model for low-dose cancer risk estimation. The most important aspect of this practice is not the choice of the multistage model itself for risk estimation purposes but, rather, the linearized form of the model. Because the model is constrained to be linear at low doses, it is expected to yield risk estimates comparable to those based on other linear extrapolation procedures, including those proposed by Gaylor et al. (1987) and Krewski et al. (1990). Additivity to Background and Low-Dose Linearity Additivity to background is often cited in support of the assumption of low-dose linearity in carcinogenic risk assessment. In the additive background model proposed by Crump et al. (1976) and Peto (1978),
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Pesticides in the Diets of Infants and Children spontaneous tumors are associated with an effective background dose, to which exposure from environmental carcinogens is added. According to Crump and colleagues: If carcinogenesis by an external agent acts additively with an already ongoing process, then under almost any model the response will be linear at low doses. (Crump et al., 1976) Hoel (1980) subsequently demonstrated that this result also holds for partial additivity. This is expressed as follows in the current Guidelines: If a carcinogenic agent acts by accelerating the same carcinogenic process that leads to the background occurrence of cancer, the added effect of the carcinogenic process at low doses is expected to be virtually linear. (EPA, 1986) The basic idea behind the additive background model is illustrated in Table 8-1. Here the spontaneous response rate is considered to arise as a consequence of a background dose d, and the effects of the test chemical administered at dose d are additive in a dosewise fashion. The linearity of the excess risk over background P(d + d) - P(d) at low doses follows from the fact that the secant between doses of d and (d + d) converges to the tangent to the dose-response curve as the dosed of the test compound becomes small. Within the framework of this model, the only condition required for this result to hold is that the probability of tumor occurrence be a smooth, strictly increasing function of dose. No further assumptions are required concerning either the mathematical form of the dose-response relationship or the toxicological mechanism by which tumors are induced. The low-dose linearity implied by this model refers to the slope of TABLE 8-1 Effect of Background Rate on Accuracy of Additivity Approximation Under the Assumption of the Multistage Modela Spontaneous Background Rate Exposure Level for Both Agents Deviation of Additivity Approximation from True Risk After Subtracting Background, % 0.1 0.01 -0.73 0.001 -0.10 0.01 0.01 -3.39 0.001 -0.34 0.001 0.01 -11.23 0.001 -1.18 a From NRC, 1988, p. 196.
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Pesticides in the Diets of Infants and Children the dose-response curve at an applied dose of zero. Without additional assumptions, no further general statements can be made about the magnitude of this slope or about the range of low doses over which this linear approximation will be sustained reasonably well. The linear approximation in the multistage model holds well, even at doses that double the background tumor rate if that rate is not trivially small (Crump et al., 1976). When attempting to fit curves in the average regulatory setting, however, the lowest dose tested under usual experimental conditions is often one-quarter or a smaller fraction of the MTD (NRC, 1993). Compared to actual dietary concentrations, one-quarter of the MTD will often be very high, and linear interpolation between a tumor response at one-quarter or less of the MTD and the tumor response of the controls may still overestimate the response at the (low) regulatory dose and the low dose to which it is anticipated that humans will be exposed. Nonlinearity at High Doses Linearity at low doses does not imply that the dose-response curve will also be linear at high doses. In particular, curvature at high doses can result from factors such as saturation of absorption or elimination pathways or the alteration of pharmacokinetic processes involved in metabolic activation (Hoel et al., 1983). Nonlinearity at high doses can also be attributable to saturation of DNA repair systems or the induction of cellular proliferation. Dose-response curves for chemicals that can both cause DNA damage and induce cellular proliferation can be subject to a high degree of upward curvature, as with the hockey-stick-shaped dose-response curves for tumors of the urinary bladder induced by 2-acetylaminofluorene (2-AAF) (Cohen and Ellwein, 1990). Similarly, secondary carcinogens may act only at relatively high doses. Nonlinearity due to saturation of elimination pathways results in upward curvature (as with methylene chloride), whereas saturation of activation processes leads to downward curvature (as with vinyl chloride). For processes that saturate in accordance with Michaelis-Menten kinetics, however, the amount of the proximate carcinogen formed at low doses will be directly proportional to the administered dose, since such processes are essentially first order at low doses (Murdoch et al., 1987). But again there is no way to estimate the shape or the range of the linearity. If the pharmacokinetic model governing metabolic activation is known, dose-response may be assessed in terms of the dose delivered to the target tissue (NRC, 1987a). This may result in a more nearly linear dose-response curve, which greatly facilitates statistical extrapolation to low doses (Hoel et al., 1983; Krewski et al., 1986). To estimate delivered dose, however, it is important to consider the uncertainty associated with the parameters
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Pesticides in the Diets of Infants and Children used in the pharmacokinetic model (Portier and Kaplan, 1989). Several additional parameters introduced in the modeling when pharmacokinetics is considered will introduce more uncertainty. Molecular Dosimetry Different chemicals may induce different kinds of DNA lesions involving anywhere from two to 13 sites on molecular DNA (NRC, 1989). At the same time, there is evidence that spontaneously occurring DNA lesions (i.e., those with no known cause) can differ from those caused by exposure to alkylating agents. This suggests that fingerprinting of DNA damage in exposed and unexposed individuals may provide a practical means for distinguishing between additive and independent backgrounds. The dose-response curve for tumor induction can be linear or nonlinear for a genotoxic agent that acts completely independently of background. If neoplastic conversion can result from a single mutagenic DNA lesion, the linearity of adduct formation at low doses implies linearity with respect to tumor induction (Lutz et al., 1990). If two or more mutagenic lesions are required to create a malignant cancer cell, however, the dose-response curve will be nonlinear with an effective threshold at low doses. This is essentially a multihit, independent background model in which the response is proportional to dose raised to a power equal to the number of DNA lesions required for neoplastic conversion. Mathematical Modeling of Cancer Risk Mathematical modeling has been a dominant feature of the quantitation of cancer risk. However, no model has been adequately validated in practice, and none account for all possible situations. Following are brief general descriptions of the more important risk assessment models that have been proposed and used over the years as background for the assessment of the risk to children from dietary exposures to pesticides. Linear Extrapolation Approaches. In the absence of evidence to the contrary, existing arguments support the adoption of low-dose linearity for cancer risk assessment. Various methods for low-dose linear extrapolation have been proposed by several investigators. Gross et al. (1970) suggested discarding data starting at the highest dose until a linear model provided an adequate description of the remaining data. Van Ryzin (1980) proposed the use of any dose-response model that fits the data reasonably well to estimate the dose producing an excess risk of 1% followed by simple linear extrapolation to lower doses. Gaylor and Kodell (1980) suggested fitting a dose-response model to obtain an upper confidence
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Pesticides in the Diets of Infants and Children limit on the probability of tumor incidence at the lowest experimental dose and then using linear extrapolation at doses between zero and that dose. Since estimates at low doses might be unduly influenced by the choice of the dose-response model, Farmer et al. (1982) recommended linear extrapolation below the lowest experimental dose, or from the dose corresponding to an excess risk of 1%, whichever is larger. Krewski et al. (1984) proposed what is called a model-free procedure using a linear interpolation from the response at the lowest dose showing any increase, down to the response at zero dose (i.e., control response). This approach is simple, but is not strictly model free, since the use of a linear interpolation implies a model of response directly proportional to the dose or exposure. Krewski et al. (1986) modified this procedure to consider the upper confidence limits of the low-dose slopes for each dose showing no statistically significant increase in tumor incidence above background. The shallowest slope was then selected for low-dose risk estimation. Gaylor (1987) recommended using the smallest upper confidence limit where there is evidence to believe that the lowest dose is in a portion of the dose-response curve that is likely to lie between linearity and some upward curvature. Only rarely is it known with any exactness where this region is located, and often it is not certain that it exists at all. Because of the difficulties in specifying and implementing a fully biologically based model of carcinogenesis for purposes of risk assessment (NRC, 1993), simple model-free approaches to low-dose risk estimation are attractive, especially when little is known about the process of tumor induction. Krewski et al. (1991) compared the behavior of the upper confidence limits of the slopes obtained from the model-free extrapolation (MFX) procedure with the upper confidence limit of the low-dose slope estimate obtained from the linearized multistage (LMS) procedure of Crump (1984a), as described below in the section on ''Estimates of Carcinogenic Potency." Comparisons were obtained for 572 bioassays taken from a compilation in the Carcinogenic Potency Database by Gold et al. (1984). Bioassays were restricted to rodent experiments exhibiting definite carcinogenicity. The median of the ratio of the upper 95% confidence limits on low-dose slopes for MFX: LMS was 1.3. In 433 of the 572 bioassays, the MFX estimate was within a factor of two of the LMS estimate. In eight cases, the MFX estimate exceeded the LMS estimate by a factor of 10. In these cases, there was a leveling off or decrease in the slope of the dose-response curve at higher doses, which tended to reduce the LMS values. Biologically Based Cancer Models. The multistage model has a long history of use in theoretical descriptions of carcinogenesis (Whittemore and Keller, 1978; Brown and Koziol, 1983; Armitage, 1985). As described
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Pesticides in the Diets of Infants and Children by Armitage and Doll (1961), a stem cell must sustain a series of mutations in order to give rise to a malignant cancer cell. This model predicts that the age-specific cancer incidence rates should increase in proportion to age raised to a power related to the number of stages in the model, and provides a good description of many forms of human cancer by allowing for two to six stages. The biological basis for the multistage model is incomplete in that it does not incorporate tissue growth or cell kinetics. Furthermore, as many as six stages may be required to describe dose-response curves with high upward curvatures, raising questions of biological interpretation. For these reasons, dose-response models that do reflect certain biological processes (e.g., cell deaths, cell turnover) have received considerable attention in recent years. Perhaps most widely discussed is the two-stage mutation-birth-death model, which was developed by Moolgavkar, Venzen, and Knudson and is therefore known as the MVK model (Moolgavkar, 1968a,b). The MVK model is a biologically motivated model of carcinogenesis based on the hypothesis that a tumor may be initiated following genetic damage in one or more cells in the target tissue as a result of exposure to a compound called an initiator (Moolgavkar, 1986a,b; Thorslund et al., 1987). The initiated cells may then undergo a further transformation to give rise to a cancerous lesion. The rate at which such lesions occur may be increased by subsequent exposure to a promoter, which increases the pool of initiated cells through clonal expansion. Mathematical formulations of this process have been developed by Greenfield et al. (1984) and Moolgavkar et al. (1988). The MVK model assumes that two mutations are necessary for a normal cell to become malignant. Initiating activity may be quantified in terms of the rate of occurrence of the first mutation. The rate of occurrence of the second mutation quantifies progression to a fully differentiated cancerous lesion. Promotional activity is measured by the difference in the birth and death rates of initiated cells. In the absence of promotional effects and variability in the pool of normal cells, the two-stage mutation-birth-death model reduces to a classic two-stage model. The MVK model provides a convenient context for the quantitative description of the initiation/promotion mechanisms of carcinogenesis (Moolgavkar, 1986a). As described above, initiator increases the rate of occurrence of the first mutation, whereas a promoter increases the pool of initiated cells. Thus, the term progressor is used to describe an agent that increases the rate of occurrence of the second mutation, resulting in malignant transformation to a cancerous cell (EPA, 1987). It is possible that the same agent could play two or even all three of these roles (initiator, promoter, progressor), thereby enhancing both initiation and progression.
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Pesticides in the Diets of Infants and Children The committee recommends development and validation of animal test systems to evaluate toxicity to developing organ systems. These tests should be designed to evaluate the unique developmental waypoints of infants and children, including assessment of the central nervous system, immune system, endocrine system, and reproductive system. Estimates of cancer risk should take into account both the higher exposures of infants and children to certain pesticides and the earlier age at which these exposures occur in comparison to adults. These factors can be taken into account using cancer risk estimation methods that allow for time-dependent exposure patterns and toxicological testing paradigms that include early exposures. The use of biologically based models of carcinogenesis that take into account the special physiological characteristics of infants and children should be developed. Biologically based models such as the two-stage clonal expansion model of carcinogenesis provide a realistic approach to cancer risk estimation. If the rate of clonal expansion of initiated cells could be determined as a function of age, this information could be used in developing biologically based models of carcinogenesis for use in risk estimation. The use of the benchmark dose for risk assessment applications involving infants and children should be explored. Although not yet widely applied in risk assessment, the benchmark dose approach offers certain advantages over the NOEL. In particular, this approach uses all the available dose-response data, better reflects the slope of the dose-response curve, and provides an explicit indication of risk at doses at or below the benchmark dose. The benchmark dose may also be used as a means of integrating toxicological and carcinogenic risk assessment methodologies. The use of risk distributions rather than a point estimate such as a mean, median, or outer bound should be used where possible to provide a more complete characterization of risk. In this report, the notion of combining distributions of individual food consumption levels with distributions of pesticide residue levels in food to obtain a distribution of exposure levels was used extensively and is illustrated by the examples presented in Chapter 7. Data on the potency of a particular pesticide (expressed either in terms of a reference dose or measure of carcinogenic potency) can then combined with data on the distribution of exposures to estimate a distribution of risks across a population of infants and children. Successful application of this method will
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Representative terms from entire chapter: