Behavioural Surveillance Surveys - The Wisdom of Whores

False conclusions: the danger of confounders
in bivariate analysis
It is particularly important to be aware of
confounding in bivariate analysis. Confounding
can happen when one of the variables that is
being treated as an explanatory variable in
the analysis is associated with the outcome
variable, but is independently associated with
another explanatory variable which may be
the true cause of the observed association.
A simple example may help to explain this.
Let’s say there were cross-sectional surveys
looking at tuberculosis in Jakarta, with the
prevalence of TB as the outcome variable of
the study, and various socio-demographic
factors as the explanatory variables. If ownership
of a Mercedes Benz were included
among the explanatory variables, investigators
would almost certainly find an inverse
association between Mercedes ownership
and TB. Owners of Mercedes would be less
likely to have TB than people who did not
own Mercedes, and the associations would
probably be highly statistically significant.
Does that mean than owning a Mercedes
Benz protects against TB? No. This is a classic
case of confounding. TB is significantly
associated with socio-economic status (SES)
and the poor housing conditions, poor diets
and other lifestyle factors that accompany
low SES. People who are poor are more
likely than people who are rich to have TB.
They are also far less likely than people
who are rich to own a Mercedes Benz.
The relationship between being rich and
owning a Mercedes Benz is independent
of the relationship between being rich and
having TB. So if you were looking for a
relationship between Mercedes Benz ownership
and TB, you would probably find one. But this
relationship would have been confounded
by the association of both the explanatory
variable (Mercedes ownership) and the outcome
variable (TB) with socio-economic status.
This means that once bivariate analysis has
been performed, it is wise to put the variables
that have been shown to be associated with
the outcome of interest through other types of
tests, which control for potential confounders.
There are two ways to do this.
If a confounder or potential confounders
are known in advance, then a type of χ 2 test
known as the Mantel-Haenszel test can be
used. This splits the confounding variable into
its various categories, and compares each
category with an outcome variable, using two
by two tables, to control for confounders.
In the above example, to investigate the
relationship between Mercedes ownership
and TB, you would want to control for the
potential confounding effect of SES by looking
at the association between Mercedes ownership
and TB for each wealth group. If there were
three SES groups in the study, (e.g. earnings
over 10,000 a year, earnings between 5,000 -
9,999 a year, and earnings < 5000 a year),
then a two by two table would be constructed
for each SES group and the Mantel-Haenszel
chi-square statistic could then be calculated
to see whether SES is a confounding factor.
Performing this test in the example given
above would almost certainly give a result
that was not statistically significant. In other
words, it would lead investigators to conclude
that once the level of SES (the potential
confounder in this example) has been controlled
for, there is no significant association between
Mercedes Benz ownership and the likelihood
of being infected with TB.
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C H A PTER 7 B EHAV I OR A L S U R V EI L L A NC E S U R V EY S