Health Risks of Ionizing Radiation: - Clark University

8 Introduction
In epidemiological studies researchers often look
for and highlight “significant results”. This is
confusing because the word ‘significant’ has a
statistical meaning that is different from its everyday
meaning. To a person concerned about health risks
from an exposure, any disease, risk or exposure
is significant. When epidemiologists speak of
significance, however, they are talking about a very
specific definition. Statistical significance typically
refers to the circumstance where the results of a
study have less than a 5% likelihood of occurring by
chance. The standard for calling a result significant
can vary, but in any credible scientific report a study
author will always provide the criteria that they use
for significance. In this overview we will frequently
refer to the significance of results; we will use the
scientific definition of the word.
Statistical power depends on large numbers.
Imagine a very small community of two people.
Each person has a small chance of getting cancer and
when they are exposed to a carcinogen this chance
increases. Imagine that these people are exposed to
a small amount of a carcinogen and ten years later
one of them develops cancer. You could fairly say
that half of the community developed cancer. On the
other hand it would be very difficult to show that it
wasn’t just by chance. As a more realistic example
consider a community of several thousand people. In
this community, based on national rates, we expect
that 80 people will get cancer during our study. This
is an uncertain rate, however, varying from place to
place, and so 75 or 85 cases of cancer might not be
unusual. But what if this community is exposed to
radiation from a small nuclear release and in our study
period 87 people get cancer? These are 7 cases more
than we expected, but there is still some possibility
that this increase was just bad luck. Epidemiology
is based on estimating the likelihood that the result
occurred by chance and calculating the significance
of the result. In our example an epidemiologist
might say that there was a 12% chance of getting 87
cancer cases without any exposure. In this case the
result would not be significant. If the epidemiologist
said that there was only a 2% chance of getting this
result, however, it would be significant.
The 5% level is a widely accepted convention
for significance but it is of course an arbitrary cutoff.
Statistical significance can introduce a bias when
the scientific community is less enthusiastic about
publishing statistically inconclusive findings. There
is an even stronger potential bias when a statistically
inconclusive result is held up as evidence that “there
was no effect”. In cases where conditions prevent a
robust epidemiological design, for example a small
community exposed to a small amount of radiation, a
real effect might not be detected and the community
will be faced with a scientific publication implying
that there was no risk from the exposure. In 1991
the National Research Council’s Committee on
Environmental Epidemiology went so far as to
say that conventional approaches to environmental
epidemiology may not only place an unfair burden
on communities for proving causation but may
actually be promoting bad science. The report stated
that “under some circumstances this stipulation
[the high threshold for statistical significance] can
stifle innovation in research when studies that fail to
meet the conventional criteria for a positive finding
are prematurely dismissed”. There is a classic and
important phrase relating to this issue: the absence of
evidence is not evidence of absence. In other words,
it can never be proven that there was “no risk”, even
when we can’t detect a significant increase. We
discuss further the notation of significance in the
‘risk terminology’ section below.
Study designs. There are two categories of
epidemiological study design, descriptive and
analytical. Descriptive studies explore associations
between exposure and disease and sometimes precede
more expensive and time-consuming analytical
studies. Ecologic studies, for example, compare
disease rates between populations based on public
records and use data at the group level (county, town,
etc.) and not the individual level. Descriptive studies
are capable of generating suggestive evidence of a
cause-and-effect relationship but are not very good
at ruling out alternative explanations for an observed
effect.
Analytical studies are usually considered to be
stronger and more reliable than descriptive studies
because they can address confounding variables
and help rule out alternative explanations. The two
analytical study designs are known as case-control
studies and cohort studies. In a case-control design
people who have a particular disease (cases) are
matched with people who do not (controls). The cases