This section includes all pertinent epidemiological studies
(see Part A, Section 4).
Studies of biomarkers are included when they are relevant
to an evaluation of carcinogenicity to humans.
(a) Types of study considered
Several types of epidemiological study contribute to
the assessment of carcinogenicity in humans - cohort studies,
case-control studies, correlation (or ecological) studies
and intervention studies. Rarely, results from randomized
trials may be available. Case reports and case series
of cancer in humans may also be reviewed.
Cohort and case-control studies relate individual exposures
under study to the occurrence of cancer in individuals
and provide an estimate of effect (such as relative risk)
as the main measure of association. Intervention studies
may provide strong evidence for making causal inferences,
as exemplified by cessation of smoking and the subsequent
decrease in risk for lung cancer.
In correlation studies, the units of investigation are
usually whole populations (e.g. in particular geographical
areas or at particular times), and cancer frequency is
related to a summary measure of the exposure of the population
to the agent under study. In correlation studies, individual
exposure is not documented, which renders this kind of
study more prone to confounding. In some circumstances,
however, correlation studies may be more informative than
analytical study designs (see, for example, the Monograph
on arsenic in drinking-water; IARC, 2004).
In some instances, case reports and case series have
provided important information about the carcinogenicity
of an agent. These types of study generally arise from
a suspicion, based on clinical experience, that the concurrence
of two events - that is, a particular exposure and occurrence
of a cancer - has happened rather more frequently than
would be expected by chance. Case reports and case series
usually lack complete ascertainment of cases in any population,
definition or enumeration of the population at risk and
estimation of the expected number of cases in the absence
of exposure.
The uncertainties that surround the interpretation of
case reports, case series and correlation studies make
them inadequate, except in rare instances, to form the
sole basis for inferring a causal relationship. When taken
together with case-control and cohort studies, however,
these types of study may add materially to the judgement
that a causal relationship exists.
Epidemiological studies of benign neoplasms, presumed
preneoplastic lesions and other end-points thought to
be relevant to cancer are also reviewed. They may, in
some instances, strengthen inferences drawn from studies
of cancer itself.
(b) Quality of studies considered
It is necessary to take into account the possible roles
of bias, confounding and chance in the interpretation
of epidemiological studies. Bias is the effect of factors
in study design or execution that lead erroneously to
a stronger or weaker association than in fact exists between
an agent and disease. Confounding is a form of bias that
occurs when the relationship with disease is made to appear
stronger or weaker than it truly is as a result of an
association between the apparent causal factor and another
factor that is associated with either an increase or decrease
in the incidence of the disease. The role of chance is
related to biological variability and the influence of
sample size on the precision of estimates of effect.
In evaluating the extent to which these factors have
been minimized in an individual study, consideration is
given to a number of aspects of design and analysis as
described in the report of the study. For example, when
suspicion of carcinogenicity arises largely from a single
small study, careful consideration is given when interpreting
subsequent studies that included these data in an enlarged
population. Most of these considerations apply equally
to case-control, cohort and correlation studies. Lack
of clarity of any of these aspects in the reporting of
a study can decrease its credibility and the weight given
to it in the final evaluation of the exposure.
Firstly, the study population, disease (or diseases)
and exposure should have been well defined by the authors.
Cases of disease in the study population should have been
identified in a way that was independent of the exposure
of interest, and exposure should have been assessed in
a way that was not related to disease status.
Secondly, the authors should have taken into account
- in the study design and analysis - other variables that
can influence the risk of disease and may have been related
to the exposure of interest. Potential confounding by
such variables should have been dealt with either in the
design of the study, such as by matching, or in the analysis,
by statistical adjustment. In cohort studies, comparisons
with local rates of disease may or may not be more appropriate
than those with national rates. Internal comparisons of
frequency of disease among individuals at different levels
of exposure are also desirable in cohort studies, since
they minimize the potential for confounding related to
the difference in risk factors between an external reference
group and the study population.
Thirdly, the authors should have reported the basic data
on which the conclusions are founded, even if sophisticated
statistical analyses were employed. At the very least,
they should have given the numbers of exposed and unexposed
cases and controls in a case-control study and the numbers
of cases observed and expected in a cohort study. Further
tabulations by time since exposure began and other temporal
factors are also important. In a cohort study, data on
all cancer sites and all causes of death should have been
given, to reveal the possibility of reporting bias. In
a case-control study, the effects of investigated factors
other than the exposure of interest should have been reported.
Finally, the statistical methods used to obtain estimates
of relative risk, absolute rates of cancer, confidence
intervals and significance tests, and to adjust for confounding
should have been clearly stated by the authors. These
methods have been reviewed for case-control studies (Breslow
& Day, 1980) and for cohort studies (Breslow &
Day, 1987).
(c) Meta-analyses and pooled analyses
Independent epidemiological studies of the same agent
may lead to results that are difficult to interpret. Combined
analyses of data from multiple studies are a means of
resolving this ambiguity, and well-conducted analyses
can be considered. There are two types of combined analysis.
The first involves combining summary statistics such as
relative risks from individual studies (meta-analysis)
and the second involves a pooled analysis of the raw data
from the individual studies (pooled analysis) (Greenland,
1998).
The advantages of combined analyses are increased precision
due to increased sample size and the opportunity to explore
potential confounders, interactions and modifying effects
that may explain heterogeneity among studies in more detail.
A disadvantage of combined analyses is the possible lack
of compatibility of data from various studies due to differences
in subject recruitment, procedures of data collection,
methods of measurement and effects of unmeasured co-variates
that may differ among studies. Despite these limitations,
well-conducted combined analyses may provide a firmer
basis than individual studies for drawing conclusions
about the potential carcinogenicity of agents.
IARC may commission a meta-analysis or pooled analysis
that is pertinent to a particular Monograph (see Part
A, Section 4). Additionally, as a means of gaining
insight from the results of multiple individual studies,
ad-hoc calculations that combine data from different studies
may be conducted by the Working Group during the course
of a Monograph meeting. The results of such original
calculations, which would be specified in the text by
presentation in square brackets, might involve updates
of previously conducted analyses that incorporate the
results of more recent studies or de-novo analyses. Irrespective
of the source of data for the meta-analyses and pooled
analyses, it is important that the same criteria for data
quality be applied as those that would be applied to individual
studies and to ensure also that sources of heterogeneity
between studies be taken into account.
(d) Temporal effects
Detailed analyses of both relative and absolute risks
in relation to temporal variables, such as age at first
exposure, time since first exposure, duration of exposure,
cumulative exposure, peak exposure (when appropriate)
and time since cessation of exposure, are reviewed and
summarized when available. Analyses of temporal relationships
may be useful in making causal inferences. In addition,
such analyses may suggest whether a carcinogen acts early
or late in the process of carcinogenesis, although, at
best, they allow only indirect inferences about mechanisms
of carcinogenesis.
(e) Use of biomarkers in epidemiological studies
Biomarkers indicate molecular, cellular or other biological
changes and are increasingly used in epidemiological studies
for various purposes (IARC, 1991; Vainio et al.,
1992; Toniolo et al., 1997; Vineis et al.,
1999; Buffler et al., 2004). These may include
evidence of exposure, of early effects, of cellular, tissue
or organism responses, of individual susceptibility or
host responses, and inference of a mechanism (see Part
B, Section 4b). This is a rapidly evolving field that
encompasses developments in genomics, epigenomics and
other emerging technologies.
Molecular epidemiological data that identify associations
between genetic polymorphisms and interindividual differences
in susceptibility to the agent(s) being evaluated may
contribute to the identification of carcinogenic hazards
to humans. If the polymorphism has been demonstrated experimentally
to modify the functional activity of the gene product
in a manner that is consistent with increased susceptibility,
these data may be useful in making causal inferences.
Similarly, molecular epidemiological studies that measure
cell functions, enzymes or metabolites that are thought
to be the basis of susceptibility may provide evidence
that reinforces biological plausibility. It should be
noted, however, that when data on genetic susceptibility
originate from multiple comparisons that arise from subgroup
analyses, this can generate false-positive results and
inconsistencies across studies, and such data therefore
require careful evaluation. If the known phenotype of
a genetic polymorphism can explain the carcinogenic mechanism
of the agent being evaluated, data on this phenotype may
be useful in making causal inferences.
(f) Criteria for causality
After the quality of individual epidemiological studies
of cancer has been summarized and assessed, a judgement
is made concerning the strength of evidence that the agent
in question is carcinogenic to humans. In making its judgement,
the Working Group considers several criteria for causality
(Hill, 1965). A strong association (e.g. a large relative
risk) is more likely to indicate causality than a weak
association, although it is recognized that estimates
of effect of small magnitude do not imply lack of causality
and may be important if the disease or exposure is common.
Associations that are replicated in several studies of
the same design or that use different epidemiological
approaches or under different circumstances of exposure
are more likely to represent a causal relationship than
isolated observations from single studies. If there are
inconsistent results among investigations, possible reasons
are sought (such as differences in exposure), and results
of studies that are judged to be of high quality are given
more weight than those of studies that are judged to be
methodologically less sound.
If the risk increases with the exposure, this is considered
to be a strong indication of causality, although the absence
of a graded response is not necessarily evidence against
a causal relationship. The demonstration of a decline
in risk after cessation of or reduction in exposure in
individuals or in whole populations also supports a causal
interpretation of the findings.
A number of scenarios may increase confidence in a causal
relationship. On the one hand, an agent may be specific
in causing tumours at one site or of one morphological
type. On the other, carcinogenicity may be evident through
the causation of multiple tumour types. Temporality, precision
of estimates of effect, biological plausibility and coherence
of the overall database are considered. Data on biomarkers
may be employed in an assessment of the biological plausibility
of epidemiological observations.
Although rarely available, results from randomized trials
that show different rates of cancer among exposed and
unexposed individuals provide particularly strong evidence
for causality.
When several epidemiological studies show little or no
indication of an association between an exposure and cancer,
a judgement may be made that, in the aggregate, they show
evidence of lack of carcinogenicity. Such a judgement
requires firstly that the studies meet, to a sufficient
degree, the standards of design and analysis described
above. Specifically, the possibility that bias, confounding
or misclassification of exposure or outcome could explain
the observed results should be considered and excluded
with reasonable certainty. In addition, all studies that
are judged to be methodologically sound should (a) be
consistent with an estimate of effect of unity for any
observed level of exposure, (b) when considered together,
provide a pooled estimate of relative risk that is at
or near to unity, and (c) have a narrow confidence interval,
due to sufficient population size. Moreover, no individual
study nor the pooled results of all the studies should
show any consistent tendency that the relative risk of
cancer increases with increasing level of exposure. It
is important to note that evidence of lack of carcinogenicity
obtained from several epidemiological studies can apply
only to the type(s) of cancer studied, to the dose levels
reported, and to the intervals between first exposure
and disease onset observed in these studies. Experience
with human cancer indicates that the period from first
exposure to the development of clinical cancer is sometimes
longer than 20 years; latent periods substantially shorter
than 30 years cannot provide evidence for lack of carcinogenicity.
Posted 23 January 2006
