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7 Risk Assessment Exposure, laboratory, and epidemiological data provided earlier in this report are used in this chapter to make quantitative and qualitative (or comparative) assessments of risks from exposure to asbestifonm fibers. To place the discussion in context, the chapter begins with a brief general discussion of risk assessment and a few special considerations concerning asbestos and related fibrous materials. Various difficulties often limit the accuracy and precision with which risk to human health can be estimated. Nevertheless, when the data base is good, the risk est imate s can be suf f ic lent ly informal ive to aid policy judgments. Some of the factors that enhance the usefulness of the data include dose-response information based on several accurately known exposure levels; knowledge of physiologic and metabolic factors that affect exposure of body tissues; an understanding of the mechanism by which the substance results in toxicity; knowledge of the extent to which experimental systems mimic the human response; and an understanding of the properties of a complex and variable substance that account for its toxicity. Many of there issues apply in the assessment of risk from asbestiform fibers, which have varying physical and chemical properties. Some members of the class, the commonly used naturally occurring forms of asbestos, have been clearly shown to cause fibrosis of the lung and pleura as well as cancer of the lung, mesothelium, and possibly the gastrointestinal eract in humans. Some occupational data on other fibers are also available, and considerable numbers of experimental studies have been conducted. It is reasonable from a biological viewpoint to use data from occupational studies to derive estimates of risk from nonoccupational exposure. However, differences in route of exposure, type and characteristics of fiber, exposure levels, and time patterns must be considered. Moreover, because working populations are generally healthier than the public at large, the latter may contain a higher proportion of more susceptible individuals. THE PROCESS 0F RISK ASSESSMENT The principles guiding the assesament of health risks from environmental substances were recently reviewed by a committee of the 200

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201 4 National Research Council ~1983~. These principles are summarized here to provide a framework for assessing the health risks from exposure to asbestiform fibers. The numerous terms uset to describe different aspects of risk assessment include 'hazard a~sesament," "hazard identification," "risk assessment," "qualitative risk a~sesament," "dose-response asee~sment," "comparative risk assesament," "quantitative risk assessment," and "risk characterization. " The use of these terms has not been standardized. Three concepts are generally incorporated into the risk assessment process. First is the identification of the kinds of harmful health effects, e.g., anemia, birth defects, or cancer, thee can result from sufficient exposure to a substance. Second is the do~e-response curve for a particular effect, i.e., the severity of damage and/or the percentage of people or animals likely to be at various exposure levels. Third is the number of people in a particular population, e.g., residents of the United States or workers in a particular industry, likely to be harmed under past, present, or projected levels and conditions of exposure. In this report, the committee has used "risk asses~mene" as a broad term encompassing all three of these concepts. "Hazard identification" refers to the first concept, "dose-response" curves or relationships are used in discussions of particular sets of data, and 'tquantiteeive rink assessment" refers to the estimates of risk to humans derived by mathematical extrapolations from there data. "Population risk estimates" describe the expected frequency or incidence of a harmful effect in a specific group of humans under defined conditions of exposure . The amount and complexity of informal ion needed increase as we progress from hazard identification to dose-response assessment to population risk estimation, although each step builds on the preceding one. Hazard identification characterizes the nature of toxic effects that a substance in capable of causing in laboratory animals or humans. Dose-response curves based on experimental or epidemiological observations define the frequency and sometimes the severity of these toxic effects at several levels of exposure. The dose-response information is used in quant itat ive risk estimation. Through mathematical modeling and application of known biological principles, attempts are often made to estimate risk for dose levels, exposure conditions, or species other than those for which do~e-response data have been obtained. For example, quantitative risk assessments often rely on dose-response data from studies of laboratory animals exposed to relatively high exposure levels in order to estimate the risk to humans exposed to lower levels. Assumptions and uncertainties involved in the application of quantitative risk assesament to cancer induction have been discussed extensively (Food

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202 Safety Council, 1980; International Regulatory Liaison Group, 1979; Office of Technology Assessment, 1981~. Population risk estimates bring together quantitative risk estimates and data on exposure of a specific group of humane to identify their risk under actual or anticipated exposure conditions. The most relevant information for categorizing the hazard or the dose-response for humans is derived from studies of exposes humans. Unfortunately, evidence from this source is often unavailable or inconclusive at times when decisions about acceptable exposure must be made. Humans are exposed to so many dif ferent substances through food, medicines, air, water, household materials, and occupational environments that sorting out the causes of harmful effects on health is often difficult. Perhaps of most importance is the fact that evidence of human health hazards from substances introduced into our environment cannot be obtained directly from observations in humans until people have been harmed. For these reasons, evidence from laboratory animals or from other biological test systems is often used as an alternative or as a supplement to data on humans . A substantial body of evidence has demonstrated the utility of these experimental systems (Doull et al., 1980; National Research Council, 1977; Richmond et al., 1981~. A variety of mathematical models have been developed for using data at high doses, usually only available from studies in animals, to est imate risks for humans at low doses (Armitage, 1982; Cornfield et al., 1978; Crump et al., 1976; Fishbein, 1980; Food Safety Council, 1980; Krewski and Van Ryzin, 1981; Van Ryzin, 1980~. Because there are extensive data on the effects of asbestos and some other fibers in humans, the quantitative risk assessments in this chapter are based exclusively on data from epidemiological studies in humans, whereas the comparative risk assesement~ also take into consideration data from laboratory studies. Every scientific study or technique has some lower limit to its sensitivity. A sensitive method in analytical chemistry may be capable of detecting a few molecules of a particular chemical among a billion other kinds of molecules but incapable of detecting a few among a trillion. The sensitivity of an animal test for toxicity in limited by many factors, such as the number of animals that it is practical to study, the subtlety of the effect of interest, the occurrence of similar effects in animals not exposed to the material under test, and limitations on the Amounts of material that can be administered and on the methods used to administer them. Other difficulties limit the power of epidemiological studies. For example, it is often difficult to select appropriate control groups, estimate exposure, or detect health effects from the exposures of concern, especially if the exposures are much lower than those that occur among occupat tonal groups .

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203 . 1 1 Several kinds of information are use fut for estimating risks at low exposure leirele on the basis of observations at higher exposures. These inc. lude the shape of the dose-re sponge curve in the range of exposure studied, knowledge of the mechanism by which the type of toxic effect occurs , and informal ion on dose-related changes in the uptake, distribution, chemical or physical modification, and exereeion of the substance, i.e., phanmacokinetica. - Substances vary "Arkedly both in the quantity required to produce a toxic effect and in the rapidity with which the incidence of toxic effects decreases with decreasing dose, i.e., the shape of the dose-response curve. In an experiment covering a sufficiently wide range of exposure levels, it is possible to find some levels that are toxic and some lower leve Is at which no toxic ity is observed. me highest dose at which no toxicity is seen is often called the "no-observed-effect level, " or NOEL (Klaassen and Doull, 1980) . However, any experiment will have some limit in its sensitivity to Small effects, ant the true no~effect-level, if any, may be below the NOEL in a part ice tar experiment . The fundamental assumption underlying the NOEL safety factor approach is that Some minimal level of a toxic substance is required to cause damage and that the substance is not toxic below that level. The NOEL type of experiment is used to f ind that leve 1. The maximum dose at which no toxicity would occur is called the "threshold" for that substance. However, several mathematical models for quantitative estimation of cancer rick assume that there is no threshold; risk diminishes with decreasing dose, but some risk in assumed to remain as long as there is any exposure. The determination of which of these two assumptions is correct will probably depend on the nature of the toxic effect. Thus, understanding the mechanism of toxicity can provide guidance in seeting acceptable exposure levels. For a substance that exerts its toxic effect by inactivating an enzyme present in abundance in each cell, it is reasonable to assu~ that a threshold would exist. Inactivation of a few molecules of the enzyme is unlikely to damage the cell. On the other hand, a chemical that is mutagenic or carcinogenic because it damages some critical site on a DNA molecule that starts the carcinogenic process can reasonably be aced not to have a threshold. The likelihood that a critical site would be damaged would decrease with decreasing dose, but the possibility that this damage could occur rema ins at any exposure above zero . For many effects, the severity of the toxic effect, as well as the probability that it will occur, also decreases with dose. For example, a dose that damages a high proportion of cells in the liver may be lethal; one that damages a moderate number may cause severe illness but not death; a Small dose that causes damage to a few cells may not lead

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204 to any clinical symptoms. The error in assuming a threshold if none truisr existed would generally not be expected to lead to serious cases of disease in this situat ion. By contrast, the severity of cancer and of mutat ions is not re lated to the dose of the substance causing them. Low tose exposure to x-rays or cigarette smoke causes fewer cancers than does high dose exposure, but the resulting cancers are just as lethal. Emus, although there may be some substances that show a threshold for cancer induction (Hoer et al., 1983), an error in assuming a threshold when none really exists would severe ly harm those persons who got the disease despite a low exposure . Accurate documentation of exposure is important for determining the dose-response curves for toxicity in animals or humans and also for estimating population risks. Errors in the estimation of exposure will lead to errors in He f ining the dose-re spouse curve and in making quantitative risk estimates for individuals or specific populations. The amount of a toxic substance or its active metabolize that reaches the body site that is susceptible to its effect is the exposure that accounts for toxicity, but such measures are almost never available (Hoer et al., 1983~. Other measurements, such as amounts in the blood, amounts entering the body, or concentrations in the air or water of a community, are often use ful surrogates , but as noted earlier in this report, they are also often unavailable. The sensitivity of the exposed population is another consideration in the risk estimation process. Some individuals may be more sensitive than others to specific environmental insults because of nutritional deficiencies, genetic predisposition, and for children, small body size, developmental immaturity, and increased metabolic and respiratory rates (Calabrese, 197B, 1980) . With their rapid metabolic rate, children consume proportionately more food and inhale greater volumes of air than an adult for a given body weight. Thus, they would also consume or inhale proportionately more of any contaminants that are present (8abich and Davis, 1981~. Human infants do not have mature hepatic detoxification systems until they reach 2 to 3 months of age (Pelkonen et at., 1973; Rane and Ackerman, 1972~. Serum in~munoglobulin does not attain adult levels until children are 10 to 12 years old (Calabrese, 1978~. Studies in animals have also demonstrated a greater sensitivity among the young after exposure to chemicals by a variety of routes (Goldenthal, 19711. Children's lunge may also be especially sensitive to environmental pollutants. Tager et al. (1983) have observed measurable differences In lung function between children of smoking mothers and children whose mothers did not smoke.

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205 i Population risk estimation is based on all the preceding steps. First, the exposure of the study population must be known. Heterogeneity of the population with respect to level of exposure or sensitivity to the toxic material should also be considered in the calculations. Exposure, dose-response curares, distribut ion of sensitivity factors, and the size of the population are then used to estimate the number of people likely to suffer toxic effects from the substance of interest. If the material causes more than one type of toxic effect, each effect requires separate calculations. Ideally, calculation of risk is an objective, scientific activity devoid of policy judgmenta. The latter are made separately when deciding the acceptable level of exposure. However, policy decisions can seldom be divorced completely from the process of risk asse~ement. The reason for this lies in the uncertainty of many of the scientific judgments required. For example, if one experimental species is more susceptible to the toxicity of a material than another and data on humans are unavailable, which species should be used for estimating human risk? Which mathematical model should be applied to the data? These and many other questions of judgment were discussed in the recent National Research Council (1983) report. In the following sections, the committee has used epidemiological data, mostly from occupational settings, to develop a quantitative model of the relationship between fiber dose and carcinogenic response for a generalized "asbestos" exposure resulting in em er lung cancer or mesothelioma. That dose-response relationship is then applied to a hypothetical, but reasonable, exposure level to show potential population risk levels in populations of arbitrary size. In the final section, the committee assesses ricks for other types of fibers and, in same cases, for other tiaeases by qualitative comparisons with the base case of a generalized asbestos exposure. QUANTITATIVE RISK ASSESSMENT In the previous chapters, the commit tee extent ive ly reviewed information on the health effects of asbestos and other asbestiform fibers. In preparing this section, it also reviewed several risk assesomento for asbestos in the open literature and in government documents. On the basis of its evaluation of the quality and coverage of the information and the as~esoment techniques, the committee decided that a quantitative assessment of the risk- for mesothelioma and lung cancer from nonoccupational exposures to asbestos would be meaningful. It also concluded that the information base was insufficient for useful quantitative assesamenta for other fiber types and tiaeasea, but that in some cases a qualitative, comparative assesoment was feasible and useful. me se decisions do not mean that the asbestos assesoment is without major uncertainties nor does it mean that the comparative assessments are of poor quality. In both cases, the ob jective is to

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206 present information useful for evaluating the health risks of asbestiform fibers in nonoccupational settings. First, an overview of mathematical models for carcinogenic risk assessment in presented to provide a context for the assessments for lung cancer and mesothelioma, which are of principal interest. Next, there is a review of several assessments for asbestos that were based on such models. Finally, these assessments and the committee.e own analyses are applied to the information presented in earlier chapters to produce quantitative risk estimates for nonoccupational exposures to asbestos in ambient air. Mathematical Model for Carcinogenic Risk Estimate As explained earlier, it is not necessary to use data on asbestos exposure from animal experiments to estimate risks for humans, but it is necessary to extrapolate from the health effects observed at high occupational levels of exposure to much lower nonoccupational exposures. Occupational epidemiology makes it possible to describe the probability of dying from a particular type of cancer as a function of age at first exposure, level and duration of exposure, and current age. Mathematical extrapolation models based on the multistage theory of carcinogene~is make it possible to estimate the probability of dying from that type of cancer for different ages at first exposure, different (lower) exposure levels, and different (often longer) duration of exposure, also as a function of current age. By considering the cumulative probability throughout a lifetime, the "lifetime risk" of cancer mortality can be computed. At any age, an individual faces some probability of reaching an end point that is related to cancer in the next year, for example, dying of lung cancer. Suppose that at a given age, a, the probability is given by pta,d), where d is the dose of the carcinogen--in this case, asbestos. When d = 0, p~a,O) is the probability of the end point for unexposed people. If t is some age of interest, then the cumulative probability P(t,d) of reaching the end point before that age is given by the sum of the annual probabilities up to that age: P(t,d) ~ the sum of plead) over all ages, a, At. (1) Reaching the end point by time t is analogous to the "failure time" for a generalized system that is no longer effective after time t. General mathematical analysis can be used to show thee the probability of failure as a function of time can be written as follows: P(t,d) = 1 - e -I(t,d) (2) where I(t,d) represents the cumulative incidence function (or cumulative hazard function) of occurrence of the observable failure prior to time t.

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207 Armitage and Doll (1961), Peto et al. (1982), Kalbfieisch and Prentice (1980), Hartley and Sielken (1977), Hartley et al. (1981), and RalLfieisch et al. ~1983) have applied this mode} to carcinogenesis. If the cumulative incidence Itt,d) is small, then equation (2) may be simplified to P(t,d) ~ I(t,d), where - means approximate ly. (3) In carcinogenic risk assessment, attention is usually focussed on the cumulative incidence function I(t,t) rather than on the probability function P(t,d). The Armitage-Doll (1961) multistage theory of carcinogenesis suggests that I(t,d) can be written as a product of two terma--g~d) , depending only on dose, and hits, depending only on time. That is, I(t,d) ~ gods hits. 1 (4) If there are k dose-dependent stages in the process of carcinogenesis and the rate of transformation from one stage to the next in assumed to be a linear function of dose, the function gods would be a polynomial of degree k in the done. The function htt) depends only on time. This mode! and its generalization and justification have been discussed by Grump et al. (1976), Hartley et al. (1981), and Kalbfleisch et al. 1983~. To determine the values of the constants in the polynomial gods and the functional form for htt), the cumulative incidence function must be fitted to daea--preferably to data based on observations in human populations. The multistage model described above has been fitted successfully to many sets of cancer data, including data on asbestos, and appears at present to be a generally adequate model for assessing cancer risk. Fitting equation (4) to data involves estimating the constants in the mode! for some suitably determined function htt). This mode! has been applied to both mesothelioma and lung cancer data on asbestos~exposed workers. The form of htt) and ache values of the constants from those studies will be discusses in the next section. The function g~d)--and thus the cumulative excess incidence function I(t,d)--can be approximated as a linear function of dose in the low-dose range that equals O when d ~ 0. This relationship can be used for extrapolating from high to low doses and has ache following form: I(t,d) ~ cdh(t). (5) Lois form assumes that there is at least one do~e-depentent stage of cancer development. The argument for a linear (with respect to dose) approximation for low-dose exposures has been justifies on the basis that the exposure dose d is added to a background leve ~ (Hoe I, 1980; Peto, 1978~. This assumption may not always be justified in application

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t 208 (see Cornfield et al. ~ 1978 and Van Ryzin, 1981), but it should lead to an appropriate upper bound for the co~ittee's risk assesements for asbestos. Furthermore, and more importantly, ruling out a linear dose term for asbestos exposure does not seem justifies by the data now available (Nicholson, 1983; Peto, 1982; Schneiderman et al., 1981~. Thus, the mode ~ adopted for risk assessment in the next three see t ions of this chapter is based on the cancer mortality incidence calculated by equse ion (5 ~ . PUBLISHED RISK ASSESSMENTS This section reviews some published risk assessments for lung cancer ant mesotheliorea. me se assessments helped the committee select a functional form for hi t) for the two diseases and to establish the value of the constant c in equation (5~. Lung Cancer Rick from Nonoccupat tonal Environmental Exposures The following summary of risk a~sesamento for lung cancer from asbestos exposures is based on data on exposure of worker populations. These data suggest that the function I(t,&) in equation (5) tee corset I(t,d) ~ c*TodIof t), (6) where To is the durat ion of exposure to asbestos at dose d, Ion t ~ is the cumulative mortality incidence for lung cancer up to age t for those who have not been exposed to asbestos, and c* is a constant that depends on the cohort under study, but not on dose or age. As used in equation (6) and in the remainder of this section, d in the concentration of fibers in the workplace air, usually measured in fibero/cm3. Although d is referred to as dose, some authors would call it done rate and would refer to the product Ted as (cumulative) dose. Equat ion (6), derived by Peto ( 1982), is consistent with his earlier studies of chrysotile workers (Peso, 1978~. This equation is also supported by four studies reviewed by Nicholson (1983), who noted that the relative rick of lung cancer deaths for asbestos workers compared to a similar population was linearly related to the accumulated dose years, i.e., fibers/cm3 x years, or (fibers/cm3)yr. In equation (6), the underlying incidence rate Iott) is consider- ably different for smokers and nonsmokers of each sex. Therefore, the risks for each of these groups must be assessed separately. Another consequence of equation (6) is that the relative risk of lung cancer due to asbestos exposure does not depend on age at first exposure. Thus, lifelong risk of lung cancer resulting from exposure to asbestos can be calculated quite simply by using equation (6~. As an example, consider the following calculation given by Peto (1982~. -

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209 Consider the effect of 10 years of exposure at 1 fiber/cm3. If we assume thee the relative risk for lung cancer among insulation workers increased approximately fourfold tHa~ond et al. ( 1979) reported 4.2 for nonsmokers ant 3.9 for smoke ret ~ and that this risk is based on a cumulative dose of 600 fibers/cm3 (20 years at 30 fibers/cm3), then 10 years of exposure to 1 fiber/cm3 will increase the relative risk by 4.0 x 10/600 ~ 0.067. Since approximately 15% of lifelong smokers die of lung cancer, this mortality rate will increase to 0.15 x 1.067 x 100, or 16%. mus, the difference (1X) is the excess due to asbestos as predicted by the equation. Since only 0. 5% of nonsmokers die of lung cancer, this would become 0.533% (0.005 x 1.067 x 100) for an added risk of 0.033: due to asbestos exposure. Mesothelioma Risk from Nonoccupational Environmental Exposures The committee reviewed two estimations of mesothelioma risk, one by Peto and his colleagues (Peso, 1982; Peto et al., 1982) and the other by Nicholson (1983~. These analyses and their consequences are summarized in this section. Using the data of Selikoff et al. ( 1979) on mortality among 17, 800 members of the Internat tonal Assoc fat ion of Heat and Frost Insulators and Asbestos Workers, Peto et al . ( 1982 ) showed that the mortality rate from mesothelioma in these workers was dependent on the time since first exposure, but did not depend on the age at first exposure. From this finding, and the application of the multistage theory of carcinogenesis through equation (5), the cumulative incidence function becomes: I(t,d) = edit - talk, (7) where t - to represents time since first exposure at age to. For any group of workers exposed at the same dose leve 1 d, the produc t cd = b is a constant depending on the type of asbestos exposure . Equat ion ~ 7 suggests Chat the risk for mesothelioma is primarily dependent on the time since first exposure (t - to). This same phenomenon was noted by Schneidennan et al. (1981) and Nicholson (1983~. Fitting equation (7) with b = cd to the data of Selikoff et al. ( 1979) for men up to age 80 by the method of maximum likelihood estimation resulted in an estimate of k = 3.2 with a standard error of + 0.36 and b = 4.37 x 10-8. Using this calculation, Peto et al. (1982) estimated the lifelong mesothelioma risk for this worker group to be 15:, It, and 3% for age at first exposures of 20, 30, and 40 years, respective ly. These figures have been adjusted for other competing causes of teeth. Using equation (7) witch k a 3.2, Peto and colleagues determined that b x 108 ranges in value from 2.94 to 5.15 for four other sets of data (see Table 7-~. Using k ~ 3. 5, PeCo (1982) computed a lifetime mesoehelioma rate of 1 in 100,000 children exposed from age 12 to age 18

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210 TARlE 7-~. Mesothelioma Death Rates in Various Studies and Predictions of Riska Study Population Relative Risk and Reference (b s 108) Corresponding Lifetime Risk (~)b byAgeat First Exposure (yes ) 20 30 40 North American insulation 4.37 15 7 3 workers (mixed exposure) Selikoff et al., 1979 Factory workers (mixed 4.95 17 ~3 exposure) Newhouse and Berry, 1976 Chrysotile textile 2.94 factory workers Pe to, l980b 10 5 2 Australian crocidolite 5.15 17 ~3 miners Hobbs et al., 1980 U.S. amosite factory 4.91 17 ~3 workers Seid - n _ al., 1979 aAdapted from Peto et al. (1982~. The death rate at time t - to since first exposure at age to is proportional to b, obtained by fitting equation (7) with k ~ 3.2. bThe calculation of "lifetime risk," i.e., the percentage of similarly exposed men who would die of mesothelioma before age 80, is based on an actuarial calculation using 1977 U.S. rates for white males for all causes of death other than mesothelioma inflated by a factor of 1.26, the observed relative risk among insulation workers (Selikoff et al., 1979~. (i.e., 6 years of school age), assuming the fiber level was 0.003 fiber/cm3 (~/l, 000 of the exposure of the insulation workers). A second risk assessment was done by Nicholson (1983), who criticized the Peto et al. (1982) analysis for fitting equation (7) to only those men who died of mesothelioma up to age 80. By including all insulation workers, he estimated k to be 5.0. 1

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226 Scoring Considerations Production. If all other factors were equivalent, a greater production volume (or U.S. consumption level, if that is significantly different) would result in a greater level of exposure and a correspondingly greater population risk. If natural occurrence is important, it can be used here as another surrogate for exposure. Use Pattern. Several concepts are embodied here. Al] have to do with the degree to which production, consumption, or natural occurrence will lead to actual human exposures. If the fibers are used only in products where they are tightly bound into a matrix, relatively little exposure will occur at least until final disposal, whereas loose fiber use in consumer applications would lead to relatively heady and immediate esposurea. Products such as talcum powder, which are intended for direct human use, will lead to higher exposures per unit production than those that are not. Geography. This a core applies to the spatial distribution of sources including natural deposits, mills or production facilities, fiber product manufacturing sites, use sites, and disposal sites. Concentrated sources tend to imply higher exposures of fewer people. This classification can also be used as a basis for evaluating such factors as the likelihood of fibers reaching drinking water. Population. The size of the population at risk determines the extent of the hazard for a given level of individual risk. A type of fiber that yields exposures to many people, such as a constituent of a common cons''=er product, has more potential for producing adverse health effects than one that affects only a few people, such as a naturally occurring but noncommercial fiber that is present only in selected, sparsely populated regions. Trends. Exposure is a dynamic process that changes with changes in total production volume, production processes, une patterns, population distribution and habits, and many other factors that do not remain static. Thus, the risk that would apply to a steady state of exposure at current levels con be misleading both for currently observed effects or for future occurrence of effects. The sharp downtrend in asbestos exposures tends to ameliorate the population risks that might otherwise be asseased, whereas new fiber types may present enormously higher exposures in the future than they do at present. Fiber Size. Two counteracting influences are at work with fiber size. The clearest is their respirability, which declines markedly as fiber diameter increases, becoming essentially zero above 3 or 4 pm. It is likely that length also eventually affects respirability and, especially, transport potential within the body. On the other hand, short fibers are probably more easily removed from the body by phagocytes; thinner ones may be more easily dissolved, coated, or gelled

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227 by body fluids; and small fibers in general may not act biologically the - same as large fibers, which con disturb eany cells at once. Furthermore, am^31 fibers may be more likely to be exhaled with the tics, volume and, thus, not retained in the lung. The overall significance of fiber size may therefore be represented as a potency that is greatest for fibers around 0.2 am diameter and 20 am in length (Pott, 1978~. Morphology. Whatever the response to fiber size, it seems likely that long, thin fibers that have strength, durability, flexibility, and a high aspect ratio are more likely to cause adverse health effects than are fibers without these characteristics. The curIlness of chrysotile fiber bundles may increase their effective aerodynamic diameter, thus decreasing their respirability below that expected on the basis of fiber diameter alone. Chemistry. Although little is known about the influence of fiber chemistry on potential for health effects, it seems possible that the chemical properties of fibers play some role, especially with respect to surface chemistry. Another feature of surface chemlatry, i.e., the ability to adsorb carcinogenic substances, is included under "aynergism." Penetration. The ability of a fiber to penetrate to the site where effects are developed, for example, to the pleura or peritoneum in the development of mesothelioma, is clearly important to its potential for causing disease. Thin category includes all fiber properties that facilitate such penetration. It is closely related to fiber size, morphology, and stability. Stability. Some experimental evidence suggests that the longer a fiber remains in a tissue, the greater is its opportunity for inducing its biological effects, for example, stimulating cell hyperplasia when a transformed cell is present. In this case, the important factor is not the resistance to tranalocation but the resistance to chemical or physical degradation such as dissolution or gelling. H''=~n Studies. This category include e both clinical and epidemiological observations in Herman populations. Animal Studies. The demonatration of significant biological effects in a well-desig~ed animal experiment is considered evidence that the test substance has a potential for causing similar effects in hark. In Vitro Studies. Although the meaningfulness of short-term, in vitro experiments with respect to the effects of fibers is questionable, it is known that asbestos and some other fibers demonstrate some cellular-level effects such as hemolysis. The ability to cause such effects is considered a weak, but not entirely worthless, argument for health effects potential.

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228 Synergism. Information on synergistic effects would markedly affect assessment of comparative risk. The only such information available involves asbestos and cigarette smoking. Other. This catchall category could be applied to any influence on overall risk, including exposure, biodisposition, and effects. For example, if a particular fiber is found to be more likely than the others to reach young children and if the effect in question is most prevalent in children or if it increases in incidence with time after flrat exposure as with mesothelioma, then the comparative risk estimate would be increased. Discussion of Comparative Risks Table 7-7 anm-=rizes from a different perapectlve the information in Appendis H. No cell of the fiber/effect/route matrix approaches the population risk levels associated with the prime cell (chrysotile/lung cancer/i~halation). As noted in the quantitative assesament, the mesothelloma risk from lifetime exposure to asbestos is potentially much greater than the lung cancer risk. Although some researchers question Whether chrysotile is as potent as other asbestos varieties in causing mesothelioma, the committee has assumed that even exposure ondy to chrysotile continously since birth would cause more mesothelioma then lung cancer. Chrysotile has been extensively used in the past and thus alto provider a source of in-place exposure. Of the other combinations, the commlLtee believes the ones most worth watching in the near term are fibrous glass any attapulgite for lung cancer by inhalation. The risks for effects of crocidolite and other asbestos varieties are reasonably well understood, and measures taken to reduce occupational exposures in the future may also keep the nonoccupational exposures to a mininum. However, general population exposures to crocidolite already in place could be subatantial, especially in connection with its disposal. The other cells seem to entail significantly lese population risk (more than 10 times less) than the prime cell. In several cases, this Judgment is based principalig on current exposure or biodisposition rather than on definitive evidence that the fibers have low intrinsic health effects potential. For example, both ceramic and carbon fibers can be found in Despicable size ranges and may well have biological properties similar to those of asbestos. However, they are produced in law volumes and are used in limited, generally contained applications. Population risks could become substantial if these facts ~hanged. Most fibrous glass and mineral wool is produced in nonrespirable sizes, and some evidence from epldemlological and animal studies suggests that their biological toxicity is low. Thus, risk levels for these substances are rated low despite the substantial potential for exposure.

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. Factor Higher Siailar Lo~er 229 T2UL~ 7-~. Sw~ry of Coaperative ai~ Aseeseacot Coopared trith Chrycotile/Lung Cancer/~helation, Dats on the Factor Suesest that Populat ion Rick Should bc Much Lover Production Fibrous glese Minere1 wool Crocidolite Attepulgite Other ashestos Carbon f iber Ceramic f iber Use pattern Fibrous glass Other asbestos Crocidolite Cesemic fiber "tepulgite Carbon fiber Minere1 vool Cnrysotile/ingest ion Geogrephy Fibrous glass Other asbestos Crocidolite Minere1 wool At tepulgite Carbon fiber Ceremic fiber Cnrysot ile/ ingest ion Population Fibrous glass Crocidolite Carbon fiber Attapulgitc Other sabestos Ceramic fiber Minere1 vool Trend e Fibrous glase Other ashestos Crocidolite At tepu lgi te Mine re 1 woo 1 Carbon f ibe r Ceramic fiber Fiber sisc Crocidolite Minere1 vool Fibrous glese Other asbestos Attapulgite Carbon fiber Ceremic f iber Morphology Croc ido 1 ite t`1 1 others Chemistry No clear effect of cheeistry evident Penetration Crocidolite Cerbon fiber Minera1 vool Fibrous glass Other asbesto. Ceremic fiber Chrlrsotile/ingestion Atespulgiee Stabilit~r Crocidolite All otbere Fibrous g1~e Other asbesto. (continuet on next pege) - ..

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"B~ 7-7 (COSIt. ) 230 Compared trite Cbrysotile/L~ng Cancer/Inb~a,~eion, Beta on the Factor Sumest that Population Blak Should be Factor ~br ~Siallar Lower Much I~wer Epideeiological Crocldolite/ Crocidolite/ Fibrous glass studies aesothelio~a lung cancer Ceramic fiber Mineral wool ~ners1 wool "i - 1 studies Crocidolite 4~, others Other asbestos In vitro studiesa Synergism Otherb Overall popt't A tion rifle aQuantltati~re differences in activity not apparent. No other factor ~s sufficiently striking for l~clualon. All others Fibrous Gil 88 Chrysotlle/ ~sothelioma/ ingestion Crocidolite Attapulgitc/ lung cancer Fibrous glass Carbon fiber Ceramic fiber At~pulgite/ aesothelioo~a Other asbestos/ other cancer For any combination of fiber type, effect, and route of exposure not assessed, even for comparative risk, the committee believes either that risks are at most of marginal significance or that there is insufficient information on which to base such a comparison. Most of the combinations fall into the former category. Carcinogenic effects other than Burg cancer or mesothelioma constitute examples of the insufficient information category for several fibers. SUMMARY AND RECOMMENDATIONS The committee has made quantitative risk assessments for nonoccupational exposures to asbestos and qualitative (or comparative) risk assessments for a variety of asbestiform fibers. Lung cancer and mesothelioma from inhaled materials received the greatest consideration.

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231 l ] .a .. i . i .i For the quantitative risk assessment, a linear model for low dose extrapolation was used. When quantifying risk from nonoccupational exposures, uncertainties are introduced not only by the selection of mathematical models but also because the characteristics of fibrous materials in the ambient environment differ from those in the workplace. By converting mass concentrations measured in the environment to equivalent numbers of fibers in the workplace, the committee assumed a median population exposure of 0.0004 fibers/cm3 air throughout a 73-year lifetime. Based on this and various other assumptions, the individual lifetime risk for lung cancer was estimated to be between 3 in a million for female nonsmokers and 64 in a million for male smokers, and for me~othelioma it was approximately nine in a million, regardless of smoking habits or sex. However, other assumptions could decrease the risks essentially to zero, or could increase them. The finding that the risk for mesotheliama is greater than that for lung cancer among nonsmokers is due to the strong dependence of mesothelioma risk on time since first exposure. Thus, a given exposure in childhood markedly increases the lifetime risk of mesothelioma compared with an equivalent dose later. It should be remembered that these risk estimates were based on data obtained from worker cohorts. Smokers runs a substantially higher risk of malignant disease from asbestos than do nonsmokers; for smokers, lung cancer is a greater risk than mesothelioma. Studies should be conducted to learn more precisely the dependence of mesothelioma and lung cancer mortality on time since first exposure and on the characteristics of the exposure. Such efforts should include studies in animal models and follow-up studies of occupationally exposed cohorts. For the comparative risk assessment, population risks (as opposed to individual risks) were considered. The risks were based on three major factors: exposure levels, biodi~position, and evidence of adverse health effects. m e potential for exposure was a dominant factor. Emus, risk estimates for substances of equal biological potency may be widely divergent if the populations exposed to them differ greatly. Two points follow from this. First, some individuals may be exposed to high levels of a fiber for which the overall population exposure is low. Second, the overall population risk would change if use patterns change. Current population risk from exposures to the various substances considered, including fibrous glass, attapulgite, and carbon fibers, appears to be much less than for the risk from asbestos, especially chrysotile. However, further information is needed to evaluate the possible adverse effects of exposures to fine fibrous glass and attapulgite.

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