A background factor is something that we are not directly interested in. For example, when comparing IgG levels in various ethnic groups we are not interested in age, sex, social class, eating habits etc. etc.
Sometimes background factors can get in the way and make addressing the
research question more complicated.
Background factors for which...
- The groups differ on the background factor AND
- The background factor itself influences outcome
…are known as confounding factors
Confounding factors 'get in the way' of the comparison between groups that we want to make. Confounding is defined as "a situation in which the effects of two processes are not separated". The word comes from the Latin 'confundere' which means 'to mix together'.
a) Suppose IgG levels differ between ethnic groups but some ethnic groups tend to be older and we know that IgG is also associated with age; we don't know if differences in IgG are due to age differences or ethnicity, since:
- The (ethnic) groups differ on the background factor (age) AND
- The background factor (age) itself influences outcome (IgG level)
age is a confounding factor.
Both criteria need to be fulfilled for age to be a confounder: If the ethnic groups differed in their age distributions but age did not affect IgG level (criteria (2) not satisfied), then age would not get in the way of our comparison of IgG levels in different ethnic groups.
Similarly, if age affected IgG levels, but the ethnic groups were of the same age (criteria (1) not satisfied), there would be no problem.
b) In a study to compare respiratory compliance in preterm infants who require assisted ventilation with those who do not, birthweight may be a confounding factor in that it may:
- Differ between the groups AND
- Influence outcome (respiratory compliance)
c) In a randomized controlled trial to compare two treatments for
eczema, severity of rash at presentation will be a confounder if:
- Patients allocated to one treatment tended to have worse severity at presentation AND
- Severity at presentation is linked to the final outcome (severity after treatment).
The effect of confounding can be avoided by appropriate study design, or by adjusting for these factors in the analysis. Sometimes studies are designed specifically to avoid potential confounders becoming actual confounders by ensuring that criterion (1) is not satisfied. For example, if age-matched ethnic groups were selected, then any differences in IgG level between the ethnic groups could not be due to age differences and age could not be a confounding factor in our comparison of IgG levels.
When planning a study, it is not always clear which factors are potential confounders. If there is doubt as to whether a factor is a confounder or not then information should be collected on it so that adjustment can be made if necessary. For example if we collect details of the birthweights and a measure of initial disease severity for the examples (b) and (c) given above, then we may be able to correct for differences between groups (assisted ventilation/ not and treatment 1/treatment 2 respectively) in the analysis. If the birthweight or severity information is not recorded then it will be impossible to discount their influence on the results or to correct for any influence they may have.
Whatever type of comparative study is undertaken, whether it is observational or experimental, ideally the groups being compared should not differ in ways that may affect outcome, apart from the grouping variable (usually disease or treatment). It will then be possible to attribute differences in outcome to that grouping variable.
Any feature that differs between the groups and is associated with outcome will act as a confounder.
Failure to recognise confounders can lead to wrong conclusions. Confounding factors can mask associations or create spurious ones.