Abstract
Objectives : Prevalence odds ratio (POR) is commonly used as a surrogate for relative risk (RR) in crosssectional studies. When prevalences are high, POR may be a poor approximation for RR. Prevalence ratios (PRs) are more easily interpretable when evaluating exposure effects. Our objectives were to compare estimates of PRs and corresponding 95% confidence intervals (CIs) using three different statistical methods on a real data set, furthermore, to report possible practical problems in applying the methods. Methods: Two statistical methods were compared: log-binomial regression and Cox regression. We examined selected high prevalence symptoms: headache, tingling of limbs, and breathing difficulty, and their association with solvent-exposed work tasks in 164 Hebron shoe factory workers. Results: The two methods estimated identical crude point PR estimates and quite similar adjusted estimates. CIs were wider in Cox regression than in log-binominal regression, as exemplified by adjusted estimates for the association between participation in cleaning tasks and tingling of limbs in log-binomial regression (PR=1.78; CI=1.25–2.54), Cox regression (PR=1.76; CI=1.01–3.06). When we used Cox regression with robust variance we obtained narrower CIs (PR=1.76; CI=1.19–2.60). In the log-binomial regression analysis we had to exclude a few subjects with a predicted risk exceeding one. Conclusions: Log-binomial regression is appropriate from a theoretical viewpoint. However, some individuals had a predicted risk larger than one, which caused the computation to abort. Cox regression could produce heavy ties when adjusted for confounders and yielded rather wide CIs, however, by using robust variance we will obtain narrow CIs. In conclusion, the two suggested methods have certain limitations and difficulties. However, Cox regression encountered less serious problems than in the other methods, and is also widely available.
Highlights
The prevalence odds ratio (POR) is commonly used in cross-sectional studies to assess associations between exposures and outcome
Headache was moderately associated with exposure for >24 months in the cleaning task (Table 1)
The crude Prevalence ratios (PRs) were identical in the two methods, but the confidence intervals (CIs) showed the same pattern as for the adjusted estimates
Summary
The prevalence odds ratio (POR) is commonly used in cross-sectional studies to assess associations between exposures and outcome. POR can be used as an approximation of prevalence ratio (PR) and interpreted as a relative risk (RR) in the case of rare diseases assumption (e.g. prevalence of outcomes below 0.1) [1,2,3]. Since many health outcomes are common, the interpretation of an odds ratio as a relative risk is often questionable [4]. Lee and Chia [5] proposed the use of prevalence ratio (PR) instead of POR in cross-sectional studies of common diseases. Others point out that the POR is commonly interpreted incorrectly as a relative risk in cross sectional studies dealing with common diseases such as for example musculoskeletal complaints [4] and other high prevalence outcomes [7]
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