This manuscript was prepared as part of the Environ-mental
Epidemiology Planning Project of the Health Effects Institute, September
1990 - September 1992.
This work was supported in part by grant R01-HD24659 from
the National Institute for Child Health and Human Development.
Measuring Environmental Exposure and Dose
Concepts
Environmental exposures can occur as a result of contact with a variety
of elements (air, water, soil) that, in turn, influence the pathways for
exposure (inhalation, ingestion, dermal). Individuals' interactions with
these elements are complex, and therefore it is not surprising that exposure
assessment and dose estimation are formidable challenges to those investigating
the health effects of environmental agents.
The concepts of exposure and dose have been elaborated in a series of
recent publications issued by the Board on Environmental Studies and Toxicology
of the National Academy of Sciences (1,2). The term exposure
refers to the concentration of an agent at the boundary between an individual
and the environment as well as the duration of contact between the two,
but dose refers to the amount actually deposited or absorbed in the body
over a given time period. Although internal dose is the ideal measure from
the scientific standpoint, regulation can deal only with external exposures,
and therefore one may want to measure both exposure and dose.
Individuals' exposures may be modified by factors such as activity patterns,
which determine encounters with various sources of exposure; bioavailability
of the agent in time and place; and the rate at which exposure occurs (e.g.,
a relatively constant rate versus a variable rate). From a given exposure,
a person's resultant dose will depend on host characteristics, such as age,
sex, and metabolism. It also will reflect the susceptibility of target tissue
at the time of exposure; any shielding provided by the body (e.g., the placenta,
the blood-brain barrier) or modulation by buildings that attenuate exposure
to electric fields and gamma radiation but can be a source of exposure to
radon; and the effect of concurrent exposures, such as cigarette smoking
or medications. In addition, only particular components of the dose may
be relevant to health effects. For calculating dose-response relationships,
this biologically effective dose is what ought to be quantified. But in
many instances it may be difficult to define what the biologically effective
dose is, much less measure it. In any event, the definition is time-dependent
and subject to change along with the state of scientific knowledge, just
as measurement capabilities change with new technology. Epidemiologists
undoubtedly need to prepare for a new generation of studies in which measurement
of variables will involve data at the level of the gene. A commitment of
resources, such as talent and funding, could improve the state of the art
in exposure and dose assessment and potentially yield better estimation
of exposure-response relationships and more effective measures of environmental
protection.
In the past, the methods used to assign exposures in environmental health
studies were quite crude, and to some extent they still are (e.g, pesticide
usage patterns, residence near a point source of pollution). Even in studies
where disease has been ascertained at the individual level, exposure measures
may be ecologic in nature and based on average levels for a group. When
the group is defined in geographic terms, exposure levels might be estimated
from values recorded by environmental sampling in a subject's general vicinity.
However, recent research has shown that correlations sometimes are weak
between readings from area monitors and subjects' exposures measured using
personal monitors (3), which are presumed to relate more closely
to the true dose. Discrepancies between readings from personal and areawide
samples can result from heterogeneity of exposures, from poor placement
of samplers (e.g., air monitors at elevations well above the breathing zone),
or from failure to take account of human activity patterns and other sources
of exposure.
Exposure monitoring systems can be and are being improved, however. Newer
approaches include sampling the microenvironments where exposure principally
occurs, including indoor environments (e.g., bedrooms and living rooms in
studies of radon and electric and magnetic fields), as well as total exposure
monitoring in which all potentially relevant microenvironments are sampled
(4,5). The latter approach is particularly important for ubiquitous
compounds like the polycyclic aromatic hydrocarbons. To some extent, personal
exposure monitoring is also beginning to be incorporated into environmental
health studies. In addition to these attempts to improve externally derived
measures of exposure, efforts are being made to estimate internal dose using
strategies like empirical dosimetric modeling, pharmacokinetic modeling,
and biologic markers.
Such efforts are important. The failure to assign individual exposure
and dose accurately leads to measurement errors with consequent effects
on measures of association (and, ultimately, risk assessments) that will
differ depending on whether the error is random or systematic and whether
the unit of analysis is the individual or the group. Systematic error in
exposure measurement can introduce bias either toward or away from the null.
Random error tends to bias results toward the null, although exceptions
to the rule can be found in unusual circumstances (6). For ecologic
studies in which exposure is a binary variable derived from combinations
of individual observations, the rule stating random error generally biases
results toward the null may not hold (7).
Given the consequences of error in estimating exposure, it is important
to try to increase accuracy of measurement at the design stage of a study.
How, then, does an investigator decide when the use of a surrogate exposure
measure (i.e., an error-prone measure) is acceptable, and when it is not?
Rosner et al. have shown (8) that for correlations between surrogate
and true measures of exposure less than 0.8, the odds ratios estimated by
logistic regression will differ markedly for the surrogate and the true
exposure measure, while much less bias will occur when correlations between
the two measures are 0.8 or greater. In vivo tibia lead levels measured
by X-ray fluorescence have been proposed as a good surrogate for cumulative
blood lead levels on the basis of a correlation coefficient of 0.84 (9).
For dietary exposures, however, the correlation between food frequency questionnaires
and less error-prone methods (food records, measurements in food or biological
samples) is only around 0.5 (10); yet food frequency questionnaires
continue to be applied in large-scale studies, only occasionally with correction
of risk estimates for error in measurement. On the other hand, the failure
to find a correlation (actual coefficients not given) between current adipose
tissue or serum dioxin levels and surrogate measures of past exposure to
Agent Orange in Vietnam (11,12) affected a decision not to
conduct further research using exposure surrogates based on troop location
and herbicide spraying records. These examples underscore the need to be
explicit about criteria for acceptable surrogate measures, as well as the
need to take error into account when surrogates are used, even while emphasizing
the development of better approaches to exposure-dose assessment.
In the following section, we describe methods designed to reduce error
in exposure measurement insofar as is currently possible (approaches such
as dosimetric modeling, pharmacokinetic modeling, biologic markers, and
use of multiple measures), as well as approaches to assessing the residual
uncertainties in the estimated dose. Even the best of the current methods
will not yield a measure that is completely error-free, and it is therefore
important to recognize and characterize the residual error in measurement
so that it can be considered in analysis of the data.
Measurement Approaches
Exposure or Dose Modeling
Estimating a subject's exposure to an environmental agent involves combining
information about possible sources of exposure (usually obtained from the
subject, from some other respondent, or from records) with an assessment
of the likely degree of exposure from each source.
When an exposure under study is environmental, there may be multiple
pathways by which a person might be exposed and it can be important to consider
all elements and all routes. For example, residents downwind of the Nevada
Test Site could have been exposed to external gamma radiation from the passing
fallout cloud itself, from ingesting contaminated milk or vegetables, or,
in the case of infants, from in utero exposures or breast-feeding. For each
of these pathways, several different radionuclides might need to be considered.
After eliminating pathways that would be expected to make a negligible contribution
to the total dose, one can estimate the likely dose rate per unit of exposure
to each pathway. In the fallout example, this involved consideration of
a) source term, the amount and type of radionuclide released; b)
the environmental transport, dispersion from the source to sites of deposition;
c) rate of radioactive decay and environmental dispersion of the
radionuclides; d) farm management practices leading to contamination
of dairy cattle or vegetables; e) estimates of the uptake of radionuclides
by vegetables and milk; f ) distribution of milk and vegetables to
consumers; and g) uptake by the target organ from ingested radionuclides.
To calculate an individual's dose, this information was then combined with
extensive questionnaire data on breast-feeding and maternal and individual
consumption of milk and vegetables at various ages. For some subjects, modifications
were needed to allow for homegrown vegetables or backyard cows or goats.
For subjects with incomplete exposure information, distributions of default
values specific to their particular circumstances (age, sex, location, etc.)
were developed. Similar calculations were performed for each of over 100
nuclear tests, and the results then were summed to produce estimates of
each subject's total dose (13).
The process described above is far more complex than has been the norm
in environmental epidemiology, but it represents the current state of the
art in environmental dose assessment. Less refined, but perhaps less costly,
approaches to exposure-dose modeling (often for households or geographic
areas rather than for individuals) have been based on Gaussian-dispersion
modeling of airborne emissions (14-16), hydrogeologic modeling
of waterborne exposures (17), and isopleth modeling of soil contaminants
(18). Assuming that dosimetry models are reasonably accurate, such
approaches should decrease bias arising from measurement error and increase
precision. Assessment of the validity of dosimetry models should be made
whenever possible. For example, an environmental dispersion model of emissions
at the time of the accident at the Three Mile Island nuclear plant was validated
by the readings from off-site thermoluminescent dosimeters.
Dosimetric modeling methods are likely to be used more frequently in
future environmental health studies. A question is whether the effort required
both in terms of the information that must be collected from study subjects
and/or by environmental sampling and the effort involved in development
of the dosimetric model itself are warranted by the gain in precision or
reduction in bias of the exposure estimates. Information on this point could
be obtained by comparing the point and interval estimates of associations
observed using gold standard dose estimates with those that would be obtained
using cruder methods. Such comparisons could be made in existing data sets.
Understanding when the gains from dosimetric modeling are substantial and
when they are only marginal would be useful in establishing methodologic
standards of practice.
Some other issues related to dosimetry are exemplified by studies of
cancer and electric and magnetic fields (EMFs). The initial hypothesis about
EMFs was derived from observations showing apparent excesses of leukemia
(and some other cancers) both in children living near electric power lines
that would be expected to generate high magnetic fields (19) and
in certain classes of electrical workers (20). In both the residential
and occupational settings, it has been difficult to establish whether the
magnetic fields are the responsible agent. While subsequent studies have
demonstrated that certain electrical wiring configurations and certain categories
of electrical work are associated with higher than average fields, so far
no convincing associations have been found between leukemia risk and individuals'
exposure to electric or magnetic fields determined by area measurements.
No studies using personal dosimetry have yet been reported.
Four possible explanations are suggested for the failure to establish
a clear association between cancer and measured field strengths. First,
it may be due to their extreme variability in space and time. Any necessarily
short-term measurement (24 hr or a week in a small number of locations)
is a poor surrogate for lifetime dose; under this explanation, household
wiring classifications and job titles may be more stable measures of long-term
exposure. Second, the failure to detect an association with measured fields
may reflect a failure to measure the biologically relevant parameter (e.g.,
peaks, transients, resonance between static and oscillating fields rather
than the time-weighted average). Studies of reproductive outcomes, where
the period of exposure is much shorter than for cancer and where there may
be a particular time window of vulnerability, could help indicate whether
the discrepancy in associations with wire codes and measured fields is due
to their capturing different time frames or different dimensions of EMFs.
A third explanation for the associations of cancer with wiring configurations,
but not with measured fields, relates to selection bias (lower selection
probabilities for controls living near wiring with high current configurations).
Fourth, the surrogate exposure measures (wire codes, job titles) may be
confounded by other correlated risk factors. This controversy is still far
from resolved, but consideration of selection bias and possible confounders
together with careful assessment of all potentially salient aspects of electric
and magnetic fields and of the variability of the different measurements
should shed light on the issue.
The EMF example underscores the need for making multiple measures of
exposure. In particular, it argues for continuing to include surrogate measures
along with gold standard measures in studies of health effects until the
relations between the surrogate and criterion measures are well understood
and there is certainty about the true gold standard (i.e., until the correct
biologic mechanism is known). Substituting an incorrect gold standard for
a surrogate measure can actually increase measurement error. One analytic
approach to using multiple measures that has been proposed as a means of
increasing validity is to restrict analysis to subjects who are classified
as exposed or unexposed by two different, if imperfect, exposure measures
(21). This clearly risks some loss in power since subjects with discordant
results on the two measures are excluded from analysis. Another proposed
approach is to estimate the misclassification probabilities for each measure
and from them to estimate the prevalence of exposure (22).
Some mention of personal monitors should also be made. While these do
not provide a measure of resulting body burden, as biologic markers are
meant to do, personal monitors may measure the intensity of an individual's
total exposure to airborne agents better than fixed-site area monitors.
This is not always the case, however, particularly in studies of long-term
exposures or where areawide concentrations are fairly uniform. The TEAM
study (Total Exposure Assessment Methodology) conducted by the U.S. Environmental
Protection Agency (EPA) found that personal air monitors were acceptable
to subjects from 7 to 85 years of age (23). Investigators studying
effects of exposure to EMFs and indoor air pollutants on children are anxious
to develop personal monitors that can be used with children under age seven,
including toddlers. At present, personal monitors for EMFs are in the form
of wristbands and may not be suitable for very young children. Technology
for personal exposure monitoring is still evolving, but it will rarely be
feasible to apply personal exposure monitoring to all subjects and all relevant
time periods. Therefore, methodologic approaches are needed for combining
collected exposure data with personal samplers and environmental monitors.
Pharmacokinetic Modeling
Pharmacokinetic modeling is an approach to dosimetry that incorporates
information about the internal pharmacologic processes that ensue once an
agent reaches the portal(s) of entry into an individual's body (24).
These include uptake into the circulation; distribution within the body;
and metabolism, storage, and elimination. These models can be simple, involving
only one body compartment, or complex, involving multiple body compartments.
In either case, compartmental rate relationships are used in the model's
equations to estimate concentrations at critical tissues. Such models are
also useful as guides to temporally relevant and efficient ambient sampling
(24). Pharmacokinetic modeling of exposure and dose may be viewed
as a counterpart to biologically based disease models.
Biologic Markers
Because of the difficulty of obtaining accurate and unbiased exposure
information from study subjects and the difficulty of estimating the doses
that such exposures might produce, there has been great interest in the
development of biologic markers. These may be defined as "cellular,
biochemical, or molecular alterations that are measurable in biological
media, such as human tissue, cells, or fluids" (25). If used
appropriately, biologic markers allow for considerable improvement in measurement
of dose. First, they may obviate the errors arising from subjects' lack
of knowledge, memory failure, biased recall, or deliberate misinformation
(26). Second, even when subject reports of exposure are accurate,
individuals may vary considerably in uptake and handling of a material;
the error introduced by such individual variation can be reduced or removed
by using markers that provide an estimate of the dose to a particular individual.
Third, some markers can be used to detect biological interactions between
the exposure of interest and critical tissues; DNA adducts are an example
of this type of marker. In studying environmental tobacco smoke, for instance,
one can--in addition to asking about maternal smoking during pregnancy--actually
measure smoking-related DNA adducts in placentae (27) and, where
the fetus is lost, in critical organs such as fetal lung or liver (28).
Another advantage of biologic markers is that generally they give a quantitative,
or at least semiquantitative, estimate of dose. They also can serve as the
gold standard for other information sources, thus providing a basis for
error allowance procedures in studies that rely on less accurate exposure
measures due to the cost of the marker.
Other Biologic Dosimeters
Certain signs or symptoms can also be viewed as biologic dosimeters.
For example, in the cohort of atomic bomb survivors, it has been reported
that subjects with a history of epilation have a 2.5-fold steeper dose-response
curve for leukemia than those without (29). This can be interpreted
either as an indicator of their greater radiosensitivity or as an indicator
of misestimation of their doses, perhaps as a result of differences in shielding
not accounted for by available dosimetry data.
To be useful in environmental epidemiology studies, a biologic exposure
marker should be clearly better than anamnestic data or environmental measures;
should allow for differentiation between exposure levels; should be applicable
on a large scale; or if too costly for large-scale use, should at least
be acceptable to subjects in a validation substudy. Before markers are used
in epidemiologic research, their sensitivity and specificity should be known
from both the laboratory and epidemiologic perspectives; reproducibility
of results within and between laboratories must also be known; and, very
importantly, the particular time frame they reflect and during which they
can be measured in vivo must be established (25) so that they
provide interpretable data regarding time and dose.
At present, few exposure markers satisfy these requirements. Some markers
may provide a record of cumulative exposure (e.g., bone lead measurement,
mercury or cocaine measurements in hair), but most can assess only relatively
recent exposures. Studies of biologic markers that use a case-control design
and a cross-sectional marker of exposure can be difficult to interpret because
of ambiguity about the temporal sequence of the marker and the disease [e.g.,
whether selenium levels in breast cancer cases are cause or consequence
(30)]. Indeed, such studies can be misleading. Vineis and Caporaso
(31) have described how a case-control study nested in a cohort allowed
Wald and his colleagues (32) to make use of the time between initial
collection of specimens from members of the cohort and subsequent onset
of cancer to clarify the time order in the relationship with blood retinol.
Although analysis considering only the early cases of cancer suggested that
blood retinol might be protective, ultimately it was apparent that some
metabolic change associated with the disease was acting to reduce retinol
levels, rather than vice versa. In addition to such problems in interpretation,
biological measurements are often costly to perform. Furthermore, the need
to obtain specimens can reduce the cooperation of subjects and introduce
the potential for selection bias to occur through initial refusal or later
attrition, although these problems are probably not insurmountable if they
are anticipated and addressed.
Use of Multiple Measures
When the biological basis of an association is poorly understood, it
can be very helpful to have various types of exposure measurements available.
Or, as mentioned previously in connection with personal exposure monitoring,
it may be necessary to rely on another source of exposure information for
portions of the study period. The obvious approach is to analyze each type
of measurement separately, but there may be merit in combining them into
an index, if only to reduce measurement error. Complications can arise if
all measurements are not available on the same subjects. Any associations
observed might be due to differences in the measurements or to differences
in the subgroups of subjects for whom the measurements are available. In
a study of childhood leukemia and electric and magnetic fields, London et
al. (33) reported the results separately for various summaries of
24-hr bedroom dosimetry, spot measurements at various locations, and wiring
configurations. However, drawing on all of these data, they also developed
regression models for magnetic fields at various locations based on attributes
of the wiring and used the values predicted by these models as the time-weighted
average fields for all houses lived in. Thus, predicted values were used
both to replace existing measurements and to impute missing values. The
rationale behind the approach is to avoid the loss of information and possible
selection bias associated with restricting analysis to subjects with data
for all measurements made (34). One alternative is to retain measurements
where they exist and to impute only the missing values, leaving open the
possibility of stratifying on data quality in the analysis. Other approaches
undoubtedly can be devised, and it would be desirable to compare their validity
using data sets in which exposure-response relationships are well understood
and where more than one measure of exposure exists.
Other Issues in Measurement of Exposure
Taking Account of Critical Periods for Exposure
A principal problem in environmental epidemiology has been that the inaccuracy
in measurement generally (although not always) operates in the direction
of overestimating exposure and therefore underestimates risk or perhaps
misses health effects altogether. For example, when assigning the same level
of exposure to all 1000 residents living within five miles of a toxic dump
site when only 100, say, were truly exposed and the other 900 were either
unexposed or exposed at very low levels, one would be certain to calculate
an observed relative risk for exposure that would be lower than the true
risk. Hence the importance of increasing the accuracy of exposure definitions
and measurement is obvious. Rothman and Poole have pointed out (35)
that it is also important to use information on critical periods for exposure,
either in the design phase of a study, in the analysis phase, or in both.
For example, in a study of Down's syndrome, parental exposures occurring
after the fertilization period are presumably irrelevant to the outcome;
in fact, there is mounting evidence that most cases of Down's are traceable
to errors at the time of the first meiotic division in the maternal germ
cell (36). By removing all exposures that are not of biologic consequence
from the estimate of association, one can expect the magnitude of the estimated
association to increase. Moreover, information on known critical periods
might be used to test whether an association appears to be spurious. If
an association were found not only during the critical period but also for
exposure during noncritical periods, then the association might be due to
recall bias, or it could be reflecting autocorrelations in exposure status.
Multivariate analysis of the effects of exposure in various critical and
noncritical periods could, in principle, overcome this problem, provided
there are enough exposed subjects with different temporal patterns of exposure
to be informative.
Taking Account of Migration In and Out of Exposed Areas
The problem of in- and out-migration is frequently raised as an issue
in interpreting results of studies that define exposure in terms of time
and place. Although several studies have considered the effects of population
migration on the validity and precision of estimated associations between
exposure and disease (37) and have described when and in what direction
bias is likely to arise, these issues are still not understood well. Perhaps
more simulations or empirical demonstrations are needed to improve the general
level of comprehension about the effects of population mobility on geographic
studies. In the case of specific studies, it would help to know something
about duration of residence or at least age-specific duration patterns in
an area. One recent suggestion is to estimate by various means the fraction
(f) of time spent by a subject in a particular place and to assign
for the remaining fraction (1-f ) the average exposure for some total
referent area (38).
Assessing Past Exposure
A major problem in many environmental health studies is the difficulty
of estimating past exposures when only present-day measurements are available.
Often, some data on subjects' past exposures can be obtained by questionnaire
or review of existing records. For example, in occupational studies, payroll
records are used to assemble a job history. The use of records from years
past to establish exposure status has the important advantage of obviating
recall bias, although it may introduce its own problems (e.g., missing records
or less specificity in records from early years). Estimating the actual
historical exposure levels is more difficult than simply classifying exposure
status, and it often involves a large degree of judgment. Clearly, the more
historical data there are on variation in exposure levels over time and
place, the better. Study of such patterns of variation can suggest models
for predicting exposures at times for which no measurements are available.
For example, in a study of salivary tumors and dental X-rays, Preston-Martin
et al. (39) reviewed 58 studies that described doses from various
procedures at various times and, while taking into account the dates of
introduction of new technologies, used regression analysis to develop models
for the expected dose as a function of calendar year. In occupational settings,
the subjective experience of long-service workers has been used to compare
current exposures with those in the distant past. Similar strategies (i.e.,
tracking technological developments, use of knowledgeable informants) need
to be applied in the assessment of past environmental exposures. For example,
in a case-control study of colorectal cancer and water chlorination among
women teachers in New York State, Lawrence et al. (40) used current
water sampling in conjunction with records from water treatment plants covering
the previous 20 years in a mathematical model to estimate cumulative exposures
to chloroform in drinking water at home and at work.
Uses of Existing Environmental Databases
One limitation on assessing past environmental exposures is that reviews
of existing data bases at the national and state level srepeatedly have
found them to be inadequate for epidemiologic purposes because of insufficient
data points to assess variability, lack of a standardized Quality Assessment/Quality
Control protocol, incomplete geographic coverage, and missing information
(41). Efforts are underway to modify the major air and water data
bases to make them more useful for future environmental health studies.
Existing environmental data banks could also be used to define strata
within which to conduct sample surveys. Surveys of individuals within these
ecological exposure groupings would help document human activity patterns
and could indicate the distribution of exposure and important confounding
or effect-modifying variables in each stratum. Potentially, such stratified-sample
surveys might provide the basis for constructing an environment-exposure
matrix similar to the job-exposure matrices used in occupational studies.
Such exposure matrices are generally assumed to have a "Berkson error"
structure (42), in which the average of the true doses for all subjects
in an exposure assignment group is equal to the assigned value. As a consequence,
if the true dose-response is linear, the estimated slope of a linear relationship
will not be biased toward the null.
Estimating Dose Uncertainties
A major concern among environmental epidemiologists is the influence
of errors in exposure estimates on associations with disease and methods
of dealing with such errors. The best cure for this problem is to avoid
measurement error in the first place. When this is not feasible (and it
often may not be, particularly in investigating common source exposures
such as toxic dump sites), it is helpful to be able to quantify the direction
and magnitude of the errors. This can be done in a number of ways, including
a) validation studies on a subset of the study sample or a pilot
sample to compare the measurements to be made in the field with a gold standard,
b) replication of measurements to assess within-subject variability,
c) multiple types of measurements to assess validity, and d)
sensitivity analysis to estimate the influence of various unknowns or uncertain
parameters on the estimated doses. The goal might be either to describe
the distribution of exposure errors across the population (or subgroups
there of) or to obtain an estimate of the precision of each subject's exposure
assignment.
Because a gold-standard assay is often not feasible for use in the field
(because of cost, time, acceptability, etc.), validation studies usually
must be limited to a relatively small number of subjects. The resulting
estimates of error distributions may be imprecise (43), although
this will be less of a problem if the data are treated as continuous and
if parameters for sensitivity and specificity do not have to be estimated
(8). Nonetheless, sample sizes for validation studies that are needed
to insure good estimates of the error rates in field measurements should
be calculated carefully. Other considerations are to insure that the measurement
error process in the sample used for validating the field measure is similar
to that in the target population for the full study and to avoid selection
bias in the validation study, which might arise if requirements associated
with use of the gold standard measure are very demanding and participation
rates are consequently low. In the New Jersey case-control study of radon
and lung cancer among women, in-home radon measurements were obtained for
only 40% of the houses targeted, and smoking rates differed among those
with measured and unmeasured homes, raising the possibility of selection
bias (44). If data on disease are collected on validity study participants,
potential selection bias can be examined by testing for heterogeneity in
the risk estimates.
Replicate measurements are useful for describing repeatability (45)
but cannot assess other components of error, such as subjects' tendency
to consistently overreport or underreport exposures. Having different types
of measurements available may be more useful in estimating misclassification
probabilities, even if none of the measures is error-free. See, for instance,
Hui and Walter's maximum likelihood method for estimating error rates with
two independent assessments of exposure (22).
Sensitivity analyses can take a number of forms. The basic idea is to
consider a range of plausible values for each of the unknowns in the exposure
assignment process. If there are only a few unknowns, one might consider
each of them and evaluate their influence on either the individual exposure
assignments or the final dose-response relation. If there are many, one
can estimate the distribution of assigned doses, either analytically or
by Monte Carlo simulation. The latter approach was used in the studies around
the Nevada Test Site because of the complexity of the dosimetry algorithm.
Components of uncertainty that were considered include the source term,
environmental transport, farming practices and distribution, and default
values for individuals' missing data. A series of sensitivity analyses were
also carried out on a mathematical model that estimated the relative geographic
distribution of exposure to accident emissions at Three Mile Island by examining
variations in modeling assumptions for their effect on the base case (46).
Parameters considered were the source term, the degree of plume rise, wind
shifts, and residual error weighting. In addition, a Bayesian analysis was
used to quantify uncertainty about the time-release pattern.
Measuring Outcome of Environmental Exposures
Definitional Issues
As strong effects of environmental exposure have been identified and
dealt with, environmental epidemiology increasingly has become a search
for weaker associations. It is all the more important, therefore, to improve
measurement of outcome through careful definition and avoidance or reduction
of error (35). In defining study end points, the aim should be to
specify the health outcome of interest as precisely as possible in order
to avoid further dilution of a weak association through inclusion of irrelevant
cases. In fact, it may be desirable to consider subgroups of disease that
are etiologically homogeneous and that are believed to be responsive to
the exposure of interest on the basis of theory or prior observations (e.g.,
certain histopathologic types of lung cancer and radon; leukemia types and
subtypes with ionizing radiation and EMFs). This can present something of
a dilemma, however, because statistical power for examining subgroups is
likely to be low unless the difference in effect size among subgroups is
sufficient to offset the reduced sample size.
The virtues of lumping versus splitting frequently come up for discussion
in the context of studies of congenital anomalies. It is unlikely that an
exposure would affect all types of congenital defects. With maternal cocaine
use during pregnancy, for example, defects involving vascular disruption
seem to be implicated. However, a biological basis for positing subgroups
of interest is often lacking; empirical Bayesian approaches may be useful
in helping to formulate relevant subgroupings. In any event, the numbers
in particular case groups are likely to be small for all but a few categories.
If sufficiently large series cannot feasibly be accrued in a single study,
multisite (even multinational) projects may need to be mounted, or more
reliance may need to be placed on meta-analyses combining results from several
studies. Which of these strategies to pursue should be discussed by groups
of investigators studying the same exposure, and their potential funding
sources.
Disease outcomes in environmental epidemiology can be measured on a continuous
scale or categorically as incident or prevalent cases or as deaths. Incidence
data are usually preferable for investigating etiology since prevalence
or mortality data may be influenced by factors affecting duration of disease
and survival as well as those relating to cause. However, incidence data
are often less easily accessed than mortality data, and they can be subject
to artifactual variations in ascertainment--as a result of screening programs,
for example. Whether incidence or mortality is the more reliable indicator
of health status and in what age groups it is reliable have been discussed
extensively but not resolved. See, for example, the recent papers by Doll
(47) and by Davis et al. (48) about cancer time trends. It
might be helpful to have a set of recommended approaches for trend analysis
that were developed by a group of dispassionate methodologists. For etiologic
studies, incidence data seem conceptually superior; when mortality data
are used, consideration needs to be given to accounting for influences on
survival since these might correlate with exposure.
In some areas of research, such as reproduction and development, different
outcomes can occur depending on the timing and dose of exposure. In such
circumstances, it may be important to examine several end points. Extending
population-based registration systems to cover more outcomes than cancer
and birth defects and to cover more geographic areas potentially could be
useful for environmental studies in several respects: in identification
of cases, in validation of self-reported information, and in ascertaining
disease status of migrants.
Biologic Effect Markers and Other Early Indicators of Disease
Biologic effect markers potentially have a number of advantages as study
end points, particularly if they are strongly prognostic of disease in ways
not explained by available exposure information--for example, by reflecting
susceptibility or the action of cofactors (26). While some effect
markers are actually subclinical events (e.g., biochemical tests of occult
pregnancy loss), often markers of effect correlate only weakly with disease.
Serum alpha-fetoprotein is a useful marker for liver cancer as well as a
prenatal marker for neural tube defects. Markers that are not as clearly
predictive of risk, particularly at the individual level, can lead to problems
of interpretation and to needless anxiety for those individuals found to
have elevated levels. The premature application of a poorly standardized
cytological assay on a group of already concerned residents at Love Canal
is a case in point. Calls have been made repeatedly to carry out longitudinal
studies, in experimental animals and humans, that will measure the positive
predictive value of such markers before applying them in field studies;
but these have been largely ignored. The Scandinavian countries, however,
have mounted a collaborative prospective study of cancer in a cohort of
3190 individuals who have been tested for sister chromatid exchanges (SCEs),
structural chromosome aberrations, or both. A report based on a 13-year
follow up of 800 subjects in the Finnish portion of the data (49)
found a moderate, statistically significant positive association between
cancer risk and chromosome aberrations (SMR = 2.65; 95% CI 1.2, 5.0); there
was a positive trend (SMR = 2.06; 95% CI 0.8, 4.2) for SCEs. Additional
prospective studies of this kind are needed to establish the relationships
between markers and disease in order to assure their appropriate use and
interpretation. In addition, determining when a marker could serve as the
basis for preventive health measures directed at a distal end point such
as cancer is an important issue; see Prentice (50) for a useful discussion
of this and a proposed operational criterion for surrogate response variables.
Other potential advantages of biologic effect markers are their use in
classifying disease more precisely and in suggesting mechanisms of action,
such as those relating to susceptible subpopulations. For example, biologic
markers that distinguish slow from fast acetylators have indicated that
the enzyme N-acetyltransferase plays an important role in bladder
cancers induced by exposure to aromatic amines (51,52). Methodologic
needs in the area of effect markers include attention to sources of variability,
both biological and laboratory-related, and to logistical issues, such as
how to achieve reasonable participation rates when the effect marker requires
a demanding regimen. Three current studies of early pregnancy loss illustrate
this latter problem. Two of the studies ask participants for daily urine
samples. The third study uses a modified specimen collection scheme requiring
urine samples only twice monthly, at the beginning of menses. Preliminary
data indicate higher response rates for the study with the simplified collection
protocol. Whether the variability in enrollment is due to the differing
demands on study subjects or to other variable aspects of the three studies
(such as the perceived salience of the topic in the target population) is
not known. Systematic research is needed to determine how to achieve cooperation
in studies that use biologic markers and how to provide for calculating
or estimating the extent and magnitude of selection bias.
Subclinical End Points
What role should physiologic changes (e.g., nerve conduction velocity,
T-cell subsets, sperm count) have in environmental health assessments? It
has been argued that functional alterations and nonspecific symptoms are
likely to be more frequent consequences of low-level environmental exposures
than frank disease (53). However, baseline rates and normal ranges
for such end points may be lacking. Objective methods of assessment to remove
the potential for biased recall may be at an early stage of development,
and interpretation of results in terms of risk to groups and to individuals
frequently is problematic, particularly as assay improvement allows for
discriminating function more and more minutely. These methodologic limitations
can be addressed--semen evaluation is a case in point (although the clinical
significance of altered semen quality is still not clear-cut)--however,
substantial time and effort will be required.
Measuring Confounders and Effect Modifiers
Effect on Risk Estimates If Inadequately Controlled
A confounding variable is one that, if not controlled appropriately,
will tend to distort the exposure-disease association. For example, when
studying whether household exposure to radon is a cause of lung cancer,
one should be concerned about the possible confounding effect of smoking.
Smoking is clearly a major risk factor for lung cancer. If houses with high
radon levels are more likely to be inhabited by smokers, then this would
produce an apparent relationship between radon and lung cancer even if there
were no causal effect. The converse also could happen; if smokers tended
to live in low-radon houses, then one might fail to find an association
between radon and lung cancer if it really were present.
The strategies commonly used by epidemiologists to control confounding
include restriction (e.g., to nonsmokers), matching, or statistical adjustment.
All of these approaches presume that the confounding variable has been correctly
measured. Greenland (54) has pointed out that errors in measurement
of a confounding variable will tend to cause partial loss of an ability
to eliminate confounding bias; for example, if the true odds ratio (adjusted
for the true confounder) is 2.0 and the crude odds ratio (unadjusted) is
4.0, then the odds ratio adjusted for an incorrectly or crudely measured
confounder might be 3.0. This intermediate outcome can only be counted upon
in a case in which the errors in measuring the confounder are random (unrelated
to exposure or disease status); in other cases, the adjusted odds ratio
could be further from the truth than the unadjusted odds ratio. Kupper (55)
has shown that an inaccurate surrogate confounder can produce seriously
misleading inferences.
A factor like smoking, in addition to being a confounder, could also
act as an effect modifier--that is, a variable that modifies the strength
of the association between exposure and disease. A major question in the
radon literature is whether the joint effects of smoking and radon exposure
are multiplicative, additive, or some intermediate possibility. If they
act additively, for example, then radon exposure would produce the same
additional risk of lung cancer in smokers and nonsmokers; but because lung
cancer is rare in nonsmokers, it would follow that radon exposure might
account for a much larger proportion of lung cancers in that group. Conversely,
if the two exposures act multiplicatively, the proportional increase in
lung cancer rates due to radon exposure would be the same in smokers and
nonsmokers; but because of the higher rates in smokers, the absolute increase
would be larger in smokers. This issue therefore has important risk assessment
and public health policy implications. Again, Greenland (54) has
shown that errors in measurement of a covariate can distort its modifying
effect and possibly introduce an apparent interaction where none exists.
Diet and cooking habits in relation to aflatoxin exposure, and showering
habits in relation to radon are additional examples of potentially important
confounding or effect-modifying variables in environmental epidemiology.
Approaches to Measuring Common Confounders and Modifiers
The implications of the previous section are that careful measurement
of strong confounders or modifiers should be given as much attention as
the exposure and disease variables. It follows that some of the same approaches
discussed in the sections on measurement of exposure and disease, such as
use of multiple measures and biologic markers, will pertain here as well.
Continuing with the example of smoking, it is not sufficient simply to
classify subjects by their present status as current, former, or never smokers.
As long as smoking is a risk factor for the disease under study, one usually
tries to obtain information on at least the ages at starting and stopping
and the average daily amount of smoking. These data can be used to compute
pack-years (the product of amount and duration), which is a stronger predictor
of lung cancer risk than current status. In some other cases, however, such
a product term may actually increase error. Better yet, nonlinear multivariate
models could be used to allow for the joint effects of age at starting,
duration and intensity of smoking, and time since quitting. Other modifying
factors might include changes in level of smoking over time, use of filter
cigarettes, and depth of inhalation. However, incorporating multiple modifying
factors into an analysis needs to be done with considerable thought to produce
models that are biologically plausible. Routine inclusion of interaction
terms in a multiple logistic regression analysis can produce models in which
ex-smokers eventually become at lower risk than never smokers, or light
smokers have the same dependence on duration or age at start as heavy smokers.
Use of general risk models based on biologically plausible theories is an
attractive alternative.
Even the most complete smoking history is still likely to be misclassified,
and the errors might well be related to the exposure or disease variables
under study. In an occupational study of radon exposure and lung cancer,
for example, miners with lung cancer might preferentially underreport their
smoking histories to avoid prejudicing a compensation claim. For these reasons,
there has been great interest in developing unbiased methods of assessing
potential confounders. Biological measures, such as urinary cotinine for
smoking or 4-aminobiphenyl-DNA adducts, are very attractive for this purpose.
Other approaches were discussed above, in the section on exposure measurement.
The disadvantage of most of these methods is that they measure only recent
exposure and lifetime exposure will still be misclassified. The development
of methods for combining information from different types of measurements
could be very useful. Also discussed previously in the exposure measurement
section, and equally relevant here, is the need to assess and allow for
measurement error in confounders and effect modifiers whenever possible.
Therefore, consideration should be given to mounting validation substudies
to quantify measurement error in important covariates.
Susceptibility
Variation within a population in sensitivity to an exposure of interest
can be substantial. Khoury et al. (56) estimated the proportion of
susceptible individuals in the population for cigarette-induced cancers
at several sites; the proportions varied from <1% for oral and esophageal
cancer up to 13% for cancer of the lung. Bias in risk estimates will arise
if individuals with similar exposures but different susceptibilities are
treated the same. There are a number of epidemiologic designs for assessing
sensitivity to environmental exposures. As a measurement problem, the central
issue is whether the marker for sensitivity being examined is a measurement
of the genotype itself, some host characteristic, or family history.
The ability to classify genotypes directly has profound implications
for identifying sensitive individuals. The obvious difficulty is that there
are millions of genetic loci, for which only a relatively small number have
probes available and only a few might be relevant to any particular disease.
Thus, some prior knowledge that a locus has a role in the disease process
is essential before embarking on a search for interactions with possible
environmental exposures. Even so, the information for identifying genetically
susceptible individuals may involve invasive and costly tests.
Recognition of phenotypically distinguishable subgroups of the population
that have different baseline risks of disease or sensitivities to environmental
exposures can therefore be very useful for public health protection. The
measurement issues that arise here are essentially no different from those
for any other effect modifier, as discussed above.
For family history as a marker of susceptibility to a disease, the basic
minimal information that needs to be collected is the identification of
the family members with the disease and the number, ages, and relationships
of family members at risk. This information should be collected systematically
for all first-degree relatives (parents, siblings, and offspring), and possibly
for all second-degree relatives. As the objective is to examine family history
as a marker of sensitivity to an environmental exposure, every effort should
be made to obtain exposure information on all relatives, not just the affected
ones.
Psychosocial Stress as Confounder, Effect Modifier, and Mediator
The psychosocial stress that may be associated with exposure to a perceived
environmental hazard can potentially confound, mediate, or modify any associations
between the exposure and disease. Stress might operate indirectly and cause
exposed individuals to alter risk behaviors. Stress also could have an artifactual
association with the end point of concern because of changes in care seeking,
diagnostic practices, or self-reported health states. Alternatively, concern
about environmental exposures could cause adverse outcomes other than those
potentially associated with the perceived hazard. For example, studies around
the Three Mile Island and Chernobyl nuclear plants indicate that the perception
of danger can increase distress levels or clinical states like anxiety and
depression (57,58), irrespective of whether radiation-induced
increases in cancer actually occur.
The issue of stress as a confounder, effect modifier, mediator, indicator
of some methodologic bias--or even as an exposure or outcome--needs to be
explicitly addressed in future environmental epidemiologic research conducted
on sensitized populations. Some relevant methodology has been developed
in studies of communities near toxic wastes to distinguish between biologic
effects of exposure to hazardous substances at such sites and either symptoms
of stress or altered symptom reporting (59,60). These preliminary
efforts include use of a scale to measure hypochondriasis and stratified
analysis of self-reported symptoms to take account of subjects' perception
about the source of pollution. Environmental epidemiologists need to learn
when and how to address the issue of psychosocial stress in order to clarify
interpretation of health effects studies and to estimate the importance
of stress in its own right. Consideration should be given to measuring perceived
stress and physiologic indicators of stress as well as to collecting data
on methodological covariates such as motivation to participate, interest
in receiving health care, and beliefs about the exposure in question as
a cause of adverse health effects.
Methodologic Needs and Recommendations
The aspect of study design that involves measurement of variables is
critical, especially in fields like environmental epidemiology where the
risks from exposure are likely to be small, difficult to detect, and perhaps
not clinically significant, yet may be of public health importance. Methodologic
research in this area should emphasize the further development and application
of dosimetric modeling. Existing data sets representing a range of research
problems within environmental epidemiology could be used to assess the gains
from dosimetry algorithms compared with cruder, more conventional methods
of exposure assessment.
Dosimetry models invariably will use a combination of questionnaire data,
environmental measurements, and biologic markers; this underscores the need
for development and refinement of methods for handling multiple measures.
Biologic markers themselves, as measures of exposure, effect, or susceptibility,
are an area where additional methodologic development would be desirable.
A second important aspect of methodologic research relates to sensitivity
analyses and other approaches for estimating the uncertainty in measurement
of exposure and dose. Included in this category would be validation studies
to compare a gold standard with a more error-prone exposure measurement
in order to allow for correction of bias in the analysis stage of research.
Consideration needs to be given to the costs and benefits of investigating
measurement error in the primary study or in a substudy (which could be
carried out internally or externally in relation to the primary study).
A final area that deserves attention is measurement error in covariates,
which can be as important as measurement error in the exposure or outcome
variables.
