What does an epidemiological study show?

Consider an epidemiological study of EMFs and childhood cancer.

The study looks at a certain number of specific children (for example it might look at 200 children with cancer and compare them to 200 children without cancer). Suppose it finds a statistical association between exposure to EMFs and cancer:

There is a statistical association in the study.

One reason for this might just be chance. Perhaps the 200 children, just by chance, included more of the children exposed to EMFs. If we chose a different 200 children, we wouldn’t find the association. The association in our study does not reflect a true association in the population.

Another reason might be bias. Maybe the way we chose the children meant we got more children with cancer exposed to EMFs, or we got fewer comparison children exposed to EMFs. Or maybe there was some systematic bias in the way we measured exposure. So the children in our study are not representative of the whole population, and the association in our study does not exist in the population. More on bias in studies of EMFs.

But if neither of these applies, then:

There is a statistical association in the population.

This still might not mean that EMFs cause cancer. There might be something else in the environment – a factor X. Suppose X causes childhood cancer. But suppose that the children who are exposed to X are also exposed to EMFs. Then it would look as if cancer was associated with EMFs, but this would be a side effect of X. This is called “counfounding” and X is a “confounding factor”.

But if there is no confounding, then:

There is a causal effect

Epidemiologists tend to approach a result in the order we’ve laid it out here. They tend to look at a study which has found an association, and accept it as establishing causation only if the alternatives – chance, bias, confounding – don’t seem likely.

Two things to note: some of these effects can work in either direction. A bias in the measurements, for example, could often mean that the study finds a smaller association than there really is. And there doesn’t have to be just one of these in any one study, you could have some of the association explained by chance and some by bias, for example.

See also more information on:

• How epidemiology fits with other types of research
• The epidemiology of EMFs and different diseases
• The epidemiology of EMFs and childhood cancer in particular
• Three of the key epidemiological studies: the UKCCS, the Ahlbom pooled analysis, and the Draper study

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