How Do Epidemiologists Determine Causality?

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Oct 16, 2007, 11:05:36 AM10/16/07
Both the Koch-Henle and the Bradford Hill criteria are used in
Epidemiology in order to determine the cause-and-effect relationship
there exists between an infectious, chemical or physical agent and
disease. In the case of ELF and RF, both have been fulfilled. One only
wonders, where are the decent and honest epidemiologists who work for
the World Health Organization? Where is the Medical Ethics Commission of
the World Health Organization?

The following text has been taken from this site:

I have not written a single word on it. All the due credits are for Dr.

Best regards,

Carlos Sosa

How Do Epidemiologists Determine Causality?

Epidemiology is the branch of medical science that studies the
incidence, distribution, cause, and control of disease in a
population. You often hear the results of epidemiological research
on the six o'clock news. Although epidemiologists prefer to conduct
strong experimental research when possible, oftentimes their
research questions and variables do not lend themselves to
experimental research. It is constructive to look at epidemiology to
learn how to conduct high quality nonexperimental research.
Perhaps the single most important individual in the development of
research methods and analysis in Epidemiology is Sir Austin Bradford
Hill (1897-1991). Bradford Hill developed a list of criteria that
continues to be used today. When using them, don't forget Hill's
"None of these nine viewpoints can bring indisputable evidence for
or against
a cause and effect hypothesis ?. What they can do, with greater or
less strength, is
to help answer the fundamental question?is there any other way of
explaining the set of
facts before us, is there any other answer equally, or more, likely
than cause and
effect?" (Cited in Doll, 1991).
The Bradford Hill Criteria
1: Strength of Association. The stronger the relationship between
the independent variable and the dependent variable, the less likely
it is that the relationship is due to an extraneous variable.
2: Temporality. It is logically necessary for a cause to precede an
effect in time.
3: Consistency. Multiple observations, of an association, with
different people under different circumstances and with different
measurement instruments increase the credibility of a finding.
4: Theoretical Plausibility. It is easier to accept an association
as causal when there is a rational and theoretical basis for such a
5: Coherence. A cause-and-effect interpretation for an association
is clearest when it does not conflict with what is known about the
variables under study and when there are no plausible competing
theories or rival hypotheses. In other words, the association must
be coherent with other knowledge.
6: Specificity in the causes. In the ideal situation, the effect has
only one cause. In other words, showing that an outcome is best
predicted by one primary factor adds credibility to a causal claim.
7: Dose Response Relationship. There should be a direct relationship
between the risk factor (i.e., the independent variable) and
people's status on the disease variable (i.e., the dependent
8: Experimental Evidence. Any related research that is based on
experiments will make a causal inference more plausible.
9: Analogy. Sometimes a commonly accepted phenomenon in one area can
be applied to another area.
In the following example, we apply Hill's criteria to the classic
case of smoking and lung cancer.
1. Strength of Association. The lung cancer rate for smokers was
quite a bit higher than for nonsmokers (e.g., one study estimated
that smokers are about 35% more likely than nonsmokers to get lung
2. Temporality. Smoking in the vast majority of cases preceded the
onset of lung cancer.
3. Consistency. Different methods (e.g., prospective and
retrospective studies) produced the same result. The relationship
also appeared for different kinds of people (e.g., males and females)
4. Theoretical Plausibility. Biological theory of smoking causing
tissue damage which over time results in cancer in the cells was a
highly plausible explanation.
5. Coherence. The conclusion (that smoking causes lung cancer) "made
sense" given the current knowledge about the biology and history of
the disease.
6. Specificity in the causes. Lung cancer is best predicted from the
incidence of smoking.
7. Dose Response Relationship. Data showed a positive, linear
relationship between the amount smoked and the incidence of lung
8. Experimental Evidence. Tar painted on laboratory rabbits' ears
was shown to produce cancer in the ear tissue over time. Hence, it
was clear that carcinogens were present in tobacco tar.
9. Analogy. Induced smoking with laboratory rats showed a causal
relationship. It, therefore, was not a great jump for scientists to
apply this to humans.
Doll, R. (1991). Sir Austin Bradford Hill and the progress of
medical science.
British Medical Journal, 305, 1521-1526.
Hill, B.A. (1965). The environment and disease: Association or
Proceedings of the Royal Society of Medicine, 58, 295-300.
Susser, M. (1977). Judgement and causal inference: Criteria in
epidemiologic studies. American Journal of Epidemiology, 105, 1-15.


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