Abstract
BackgroundMany epidemiologic studies report the odds ratio as a measure of association for cross-sectional studies with common outcomes. In such cases, the prevalence ratios may not be inferred from the estimated odds ratios. This paper overviews the most commonly used procedures to obtain adjusted prevalence ratios and extends the discussion to the analysis of clustered cross-sectional studies.MethodsPrevalence ratios(PR) were estimated using logistic models with random effects. Their 95% confidence intervals were obtained using delta method and clustered bootstrap. The performance of these approaches was evaluated through simulation studies. Using data from two studies with health-related outcomes in children, we discuss the interpretation of the measures of association and their implications.ResultsThe results from data analysis highlighted major differences between estimated OR and PR. Results from simulation studies indicate an improved performance of delta method compared to bootstrap when there are small number of clusters.ConclusionWe recommend the use of logistic model with random effects for analysis of clustered data. The choice of method to estimate confidence intervals for PR (delta or bootstrap method) should be based on study design.
Highlights
Many epidemiologic studies report the odds ratio as a measure of association for cross-sectional studies with common outcomes
More recent studies examining the differences between OR and PR, according to variations in the prevalence of exposure and disease, have shown that differences between odds ratios and proportions ratios, relative risks or prevalence ratios increase with increasing disease frequency [15]
We estimated the effect of maternal mental health status on asthma occurrence using random effects logistic regression, considering two standardization methods and three approaches for getting the confidence intervals for PR
Summary
Many epidemiologic studies report the odds ratio as a measure of association for cross-sectional studies with common outcomes. In such cases, the prevalence ratios may not be inferred from the estimated odds ratios. Clustered cross-sectional studies have become increasingly popular in epidemiology, especially when the use of simple random sample designs is not feasible. In such case, the analysis must take into account the degree of similarity between subjects within clusters [19]. In the present paper we have evaluated methods for estimating adjusted PR in clustered cross-sectional studies using random-effects models
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