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Health Effects
Understanding Radiation:  

Estimating Risk

Health Effects

Scientists who study the effects of radiation on human health, health physicists, use a combination of existing data and mathematical calculations (models) to estimate effects under a variety of conditions.

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How does EPA estimate the risk of health effects from low levels of exposure?

Since we cannot measure the stochastic effects of very low levels of exposure, health physicists must extrapolate the risks from what they know about high levels of exposure. Most radiation protection organizations estimate the risk of health effects from low levels of exposure, all the way to zero exposure, as proportional to those of high levels of exposure.

This method of estimating risk is called the 'linear no-threshold model.' It assumes there is no 'threshold,' that is to say there is no exposure level below which the risk is zero. It also assumes that the risk increases in proportion to the exposure. If the exposure doubles, the risk also doubles. Some scientists strongly dispute the no-threshold assumption.

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Which characteristics of a radionuclide are used to calculate risk?

Each radionuclide has a set of characteristics that represents a somewhat different risk:

The risk that exposure to a radionuclide will cause a particular health effect also depends on whether exposure is internal or direct (external).

Estimating the risk from radiation exposure

Scientists use several methods to estimate the risks from exposure to a particular substance. Using one method, they compare the number of people who experience a particular health effect in two groups. The groups are the same except that one group has been exposed to the substance and one group, known as the "control group," has not.

A way of refining the risk estimate is to identify two groups of people who have been exposed to the substance. One group is made up of people who are experiencing a particular health effect and the other group consists of people who are not. This method allows scientist to identify other risk factors (such as a family history of the disease) that may make one group more likely to experience the health effect.

For example, estimating the risk of cancer in smokers, requires comparing the occurrence of cancer in a group of smokers to a group of non-smokers. Each group would be similar in age, education, occupation, income, etc.

Identifying other risk factors, such as a family history of the cancer, level of exercise, and alcohol use, can be done by comparing a group of smokers who have cancer with a group of smokers who do not.

We use similar methods to estimate the risks from exposure to ionizing radiation. However, the estimates include uncertainty, because of major challenges in making the estimates:

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