For any study design, three essential issues should be considered when making casual inferences –bias, confounding, and interaction. Bias should be keenly considered in any epidemiological study design. It refers to any form of organized error in a study that leads to a wrong estimate of the effect of an exposure on the likelihood of disease. There are mainly two forms of bias in epidemiological studies- selection bias and information bias. If how cases were picked is such that an apparent association is observed, the apparent association is a bias selection. Bias in information is a result of a flawed process that does not put subjects on the uniform requirement. Confounding, one of the most critical problems in observational epidemiological studies, attempts to explain inferences that may be thought to be causal when, in fact, they are not (Celentano & Szklo, 2019). An example of a confounder is also discussed in this paper.
Criteria to Establish a Factor as a Confounder
When determining whether a specified primary factor causes a specified disease, we refer to a different tertiary factor as a confounder if:
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The specified disease is directly linked to the tertiary factor.
The primary factor and the tertiary factor relate, but the tertiary factor is not a consequence of the primary factor.
If the two criteria checks, then the tertiary factor is a confounder
Example
When establishing whether alcohol consumption causes heart disease, we might consider smoking an important confounding factor. This is because a correlation exists between smoking and alcohol consumption. Moreover, smoking is not a consequence of alcohol consumption but is also associated with heart disease.
Way to Adjust for a Confounding Relationship in the Study Design
In a study design, we can adjust for a confounding relationship by matching the cases to the controls. The matching process enables the identification of possible risk factors that qualify as confounders.
References
Celentano, D. D., & Szklo, M. (2019). Gordis epidemiology (6th ed.). Elsevier.
