Behavioural Surveillance Surveys - The Wisdom of Whores

Another, more complex type of analysis
controls for several confounders at once. This
type of analysis is known as multivariate
analysis.
Multivariate analysis
Multivariate analysis is used to examine the
relationship between a number of different
explanatory variables to an outcome variable.
It looks for interactions between different
explanatory variables.
Logistic regression
The most common form of multivariate
analysis when dealing with categorical variables
such as those most commonly measured in
BSS is logistic regression. Logistic regression
is particularly appropriate when investigating
data sets that include several potential
confounding variables.
Logistic regression uses more sophisticated
statistical techniques to determine whether
the explanatory variable in question has an
independent effect on the outcome variable
after controlling for potential confounding
effects of other variables. The statistical
methods used are too complex to describe
in detail here. Logistic regression is virtually
always performed by computer, and several
standardized software packages are available
to assist in this process.
The output of a logistic regression can be
expressed in terms of an odds ratio. In the
context of BSS, this means the likelihood that
someone of a certain category behaves in a
certain way, compared with someone who is
not in that category. When confounding
factors that may influence the outcome are
controlled for, the result is known as an
adjusted odds ratio. For example, the odds
ratio might describe the likelihood that a sex
worker who earns 100 francs per client used a
condom with her last client, compared with a
sex worker who earns 10 francs per client.
The calculation may control for age, ethnic
origin and educational level of the sex worker,
among other factors.
In general, odds ratios are expressed in
relation to one level of the variable of interest,
which is set at a value of 1.0. This category
is known as the reference group. In the
example above, imagine that sex workers who
earn 10 francs are the reference group, the
odds ratio for sex workers who earn 50 francs
is 1.6, and the odds ratio for sex workers who
earn 100 francs is 2.0. This means that sex
workers who earn 50 francs are 60 percent
(or 1.6 times) more likely to have used a
condom at last sex than sex workers who earn
10 francs. Similarly, sex workers who earn
100 francs are twice as likely as the reference
group to have used a condom at last sex.
Significance tests for logistic regression
Like all types of analysis, logistic regression
analyses include tests for statistical significance.
Statistical software packages that perform
logistic regression can usually produce 95
percent confidence intervals for each odds
ratio, as well as a p-value. This confidence
interval gives an indication of the range in
which you can be 95 percent confident of the
precision of the odds ratio. If the confidence
interval spans from less than one to more than
one, there is no significant difference between
the category investigated and the reference
category. This is because the odds ratio of
1.0 is included in the confidence interval,
signifying no greater odds of association.
In this case, the p-value, which indicates the
probability that the difference between the
two categories occurred by chance, will be
greater than 0.05.
B EHAV I OR A L S U R V EI L L A NC E SURV EY S CHAPTER 7
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