The findings of this study indicate that although there are methods that directly estimate PR, many studies in veterinary science do not use these methods and misinterpret the OR estimated by the logistic regression.Ĭross-sectional studies are one of the most frequently designed observational studies in veterinary epidemiology ( 1), likely because they are rapid, inexpensive and of moderate difficulty. Results of the multivariate models indicated that logistic regression was the method that most overestimated the PR. The review showed that independent of the level of prevalence reported, 96% of articles employed logistic regression, thus estimating the OR. The initial search strategy found 62 articles, in which 6 articles were excluded and therefore 56 studies were used for the overall analysis. Additionally, four alternative models for logistic regression that estimate directly PR were tested using our own dataset from a cross-sectional study on bovine viral diarrhea virus. From each article, the statistical method used and the appropriateness of the interpretation of the estimated measure of association were registered. An overview of statistical methods and its interpretation using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted and included a diverse set of peer-reviewed journals among the veterinary science field using PubMed as the search engine. The aims of this study were ( 1) to review articles with cross-sectional designs to assess the statistical method used and the appropriateness of the interpretation of the estimated measure of association and ( 2) to illustrate the use of alternative statistical methods that estimate PR directly. Nonetheless, interpretation of OR is difficult since confusing between risk and odds can lead to incorrect quantitative interpretation of data such as “the risk is X times greater,” commonly reported in studies that use OR.
In human epidemiology, much has been discussed about the use of the OR exclusively for case–control studies and some authors reported that there is no good justification for fitting logistic regression when the prevalence of the disease is high, in which OR overestimate the PR.
To measure an association with exposure, the use of prevalence ratios (PR) or odds ratios (OR) are possible. One of the most commonly observational study designs employed in veterinary is the cross-sectional study with binary outcomes.
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