Summary
Objectives:
According to results from the epidemiological literature, it can be expected that
the prevalence odds ratio (POR) and the prevalence ratio (PR) differ with increasing
disease prevalence. We illustrate different concepts to calculate these effect measures
in cross-sectional studies and discuss their advantages and weaknesses, using actual
data from the ISAAC Phase III cross-sectional survey in Münster, Germany.
Methods:
We analyzed data on the association between self-reported traffic density and wheeze
and asthma by means of the POR, obtained from a logistic regression, and the PR, which
was estimated from a log-linear binomial model and from different variants of a Poisson
regression.
Results:
The analysis based on the less frequent disease, i.e. asthma with an overall prevalence
of 7.8%, yielded similar results for all estimates. When wheezing with a prevalence
of 17.5% was analyzed, the POR produced the highest estimates with the widest confidence
intervals. While the point estimates were similar in the log-binomial model and Poisson
regression, the latter showed wider confidence intervals. When we calculated the Poisson
regression with robust variances, confidence intervals narrowed.
Conclusions:
Since cross-sectional studies often deal with frequent diseases, we encourage analyzing
cross-sectional data based on log-linear binomial models, which is the ‘natural method’
for estimating prevalence ratios. If algorithms fail to converge, a useful alternative
is to define appropriate starting values or, if models still do not converge, to calculate
a Poisson regression with robust estimates to control for overestimation of errors
in the binomial data.
Keywords
Cross-sectional studies - generalized linear model - Poisson regression - prevalence
odds ratio - prevalence ratio
