Health Risks of Ionizing Radiation: - Clark University

The terminology associated with radiation and
radiation dose is very confusing. There are units of
radioactivity, of energy deposited in matter, and of
biologically relevant dose. In addition, two common
units (rad and rem) have been replaced with larger
units for the same things (grays and sieverts). The
units are:
• Curie (Ci). The curie is a unit used to
measure radioactivity. One curie is a quantity
of a radioactive material that will have
37,000,000,000 transformations, or nuclear
decays, in one second. Often radioactivity is
expressed in smaller units like a thousandth of a
curie (mCi), a millionth of a curie (uCi) or even
a billionth of a curie (nCi).
• Becquerel (Bq). A Becquerel is a unit that
describes one radioactive disintegration per
second and is therefore a much smaller version
of the curie. There are 37,000,000,000 Bq in one
Ci.
• Gray (Gy). The gray is a unit of absorbed dose.
This relates to the amount of energy actually
deposited in some material, and is used for any
type of radiation and any material.
• Rad. The rad (radiation absorbed dose) is the
older unit of absorbed dose. One rad is equal to
0.01 Gy.
• Sievert (Sv). The sievert is used to express
effective dose, or the biological damage
potential of some amount of radiation. Effective
dose is typically calculated by multiplying the
absorbed dose by a factor specific to the type of
radiation. This is usually called a quality factor
or relative biological effectiveness factor. For
low-LET radiations this factor is typically close
to or equal to one so that one Sv is approximately
equal to one Gy. For high-LET radiations like
alpha particles the factor might be as high as
twenty.
• Rem. The rem (roentgen equivalent in man) is
the older unit of effective dose. One rem is equal
to 0.01 Sv.
For this overview we have chosen to use units of Gy
or Sv for dose. Where the primary source used rad
or rem we have made the appropriate conversion.
1.6 Epidemiological methods
Introduction 7
Epidemiology is the statistical study of disease in
human populations. In epidemiological studies
researchers attempt to identify and analyze
relationships between health effects and possible
causes. This is difficult, in general, because there are
many confounding factors in any study that make a
simple cause-and-effect relationship hard to isolate.
In studies of cancer these confounding factors might
include, among other things, genetic predisposition
or exposure to carcinogens other than the one
being studied. If a study population demonstrates
an elevated cancer rate these confounding factors
make it hard to determine the cause. There is also
considerable uncertainty in any epidemiological
study. Researchers can never exactly quantify
exposure or the true background rate of a disease,
for example. Uncertainties in studies of cancer are
compounded by the random nature of cancer: Out
of a group of people exposed to a carcinogen some
might get cancer and some might not; this is partly
determined by chance.
Epidemiological studies of low-dose radiation
present several unique challenges. People are exposed
to radiation from a variety of sources, both natural
and manmade, and as we discussed above there is
considerable variability in this exposure. Although
we can estimate average exposures we never know
exactly how much radiation an individual has been
exposed to or from what sources this radiation
came. When we consider that people are always
experiencing some background radiation exposure
(and exposure to other carcinogens), and that there
is always a background cancer rate, the effects of
small additional doses can be very hard to detect.
In some cases, such as workers at nuclear weapons
facilities, researchers have some idea of individual
exposures from measurements that have been made.
In other cases, for example communities around
nuclear facilities, there are no such measurements.
Statistical significance. Because of these
difficulties epidemiological studies cannot prove
or disprove causation. Instead epidemiological
studies can suggest associations; these associations
carry statistical power based on how strong the
relationship is and how well the study was designed.