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Introduction
11. Multicausality: Confounding
Accounting for the multicausal nature of disease – secondary associations and their
control
When "modern epidemiology" developed in the 1970s, Olli Miettinen organized sources of bias into
three major categories: selection bias, information bias, and confounding bias. If our focus is the
crude association between two factors, selection bias can lead us to observe an association that
differs from that which exists in the population we believe we are studying (the target population).
Similarly, information bias can cause the observed association to differ from what it actually is.
Confounding differs from these other types of bias, however, because confounding does not alter
the crude association. Instead, concern for confounding comes into play for the interpretation of
the observed association.
We have already considered confounding, without referring to it by that term, in the chapter on age
standardization. The comparison of crude mortality rates can be misleading, not because the rates
are biased, but because they are greatly affected by the age distributions in the groups being
compared. Thus, in order to be able to interpret the comparison of mortality rates we needed to
examine age-specific and age-standardized rates in order avoid or equalize the influence of age. Had
we attemped to interpret the crude rates, our interpretation would have been confounded by age
differences in the populations being compared. We therefore controlled for the effects of age in
order to remove the confounding. In this chapter we will delve into the mechanics of confounding
and review the repertoire of strategies to avoid or control it.
Counterfactual reasoning
Epidemiologic research, whether descriptive or analytic, etiologic or evaluative, generally seeks to
make causal interpretations. An association between two factors prompts the question what is
responsible for it (or in the opposite case, what is responsible for our not seeing an association we
expect). Causal reasoning about associations, even those not the focus of investigation, is part of
the process of making sense out of data. So the ability to infer causal relationships from observed
associations is a fundamental one.
In an "epidemiologists' ideal world", we could infer causality by comparing a health outcome for a
person exposed to a factor of interest to what the outcome would have been in the absence of
exposure. A comparison of what would occur with exposure to what would occur in the absence of
exposure is called counterfactual, because one side of the comparison is contrary to fact (see
Rothman and Greenland, p49, who attribute this concept to Hume's work in the 18th century). This
counterfactual comparison provides a sound logical basis for inferring causality, because the effect
of the exposure can be isolated from the influence of other factors.
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www.epidemiolog.net, © Victor J. Schoenbach 2000 11. Multicausality: Confounding - 335
rev. 10/28/2000, 11/2/2000, 5/11/2001