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OCR for page 200
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.
OCR for page 228
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.
OCR for page 229
.
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)
-
..
OCR for page 230
"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.
OCR for page 231
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.
OCR for page 232
232
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Representative terms from entire chapter:
lifetime risk
