One of the most commonly observational study design employed in veterinary is the cross-sectional study with binary outcomes. To measure an association with exposure, the use of prevalence ratios (PR) or odds ratios (OR) are possible. 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. 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. 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. An overview of statistical methods and its interpretation using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines was conducted and included a diverse set of peer-reviewed journals among the veterinary science field using PubMed as the search. From each article the statistical method used and the appropriateness of the interpretation of the estimated measure of association were registered. 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. The initial search strategy found 62 articles, in which six articles were excluded and therefore 56 studies were used for the overall analysis. The review showed that independent of the level of prevalence reported, 96% of articles employed logistic regression, thus estimating the OR. Results of the multivariate models indicated that logistic regression was the method that most overestimated the PR. 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.

In the Americas, yellow fever virus transmission is a latent threat due to the proximity between urban and wild environments. Although yellow fever has nearly vanished from North and Central America, there are still 13 countries in the Americas considered endemic by the World Health Organization. Human cases usually occur as a result of the exposure to sylvatic yellow fever in tropical forested environments; but urban outbreaks reported during the last decade demonstrate that the risk in this environment still exists. The objective of this study was to identify spatial patterns and the relationship between key geographic and environmental factors with the distribution of yellow fever human cases in the Americas. An ecological study was carried out to analyze yellow fever human cases reported to the Pan American Health Organization from 2000 to 2014, aggregated by second administrative level subdivisions (counties). Presence of yellow fever by county was used as the outcome variable and eight geo-environmental factors were used as independent variables. Spatial analysis was performed to identify and examine natural settings per county. Subsequently, a multivariable logistic regression model was built. During the study period, 1,164 cases were reported in eight out of the 13 endemic countries. Nearly 83.8% of these cases were concentrated in three countries: Peru (37.4%), Brazil (28.1%) and Colombia (18.4%); and distributed in 57 states/provinces, specifically in 286 counties (3.4% of total counties). Yellow fever presence was significantly associated with altitude, rain, diversity of non-human primate hosts and temperature. A positive spatial autocorrelation revealed a clustered geographic pattern in 138/286 yellow fever positive counties (48.3%). A clustered geographic pattern of yellow fever was identified mostly along the Andes eastern foothills. This risk map could support health policies in endemic countries. Geo-environmental factors associated with presence of yellow fever could help predict and adjust the limits of other risk areas of epidemiological concern. Yellow fever (YF) is a zoonotic disease caused by yellow fever virus (YFV), which is transmitted to humans through the bite of an infected mosquito. Sylvatic and urban cycles have been present in different periods, but currently most cases result from human exposure to jungle or forested environments. The World Health Organization considers 13 countries endemic for YFV in the Americas. The objective of this study was to identify spatial patterns and the relationship between key geographic and environmental factors with the distribution of YF human cases in the Americas. Cases of YF from 2000 to 2014 aggregated by county and eight geo-environmental factors were studied via spatial and statistical analysis. A total of 1,164 cases were reported in this time period, with the majority of them located in Peru, Brazil and Colombia. Yellow fever presence was associated with rain, altitude, diversity of non-human primate hosts and temperature. A large clustered geographic pattern of YF cases was identified along the Andes eastern foothills. Although YF cases can be seen as rare events, the results of this study demonstrate that YF human cases in the Americas are geographically concentrated and are not happening at random, even within areas known to be at risk. Determining the geo-environmental factors related to YFV is essential to delineate risk areas and to consequently improve resource allocation and prevent human cases.

The spread of pathogens in swine populations is in part determined by movements of animals between farms. However, understanding additional characteristics that predict disease outbreaks and uncovering landscape factors related to between-farm spread are crucial steps toward risk mitigation. This study integrates animal movements with environmental risk factors to identify the occurrence of porcine epidemic diarrhea virus (PEDV) outbreaks. Using weekly farm-level incidence data from 332 sow farms, we applied machine-learning algorithms to quantify associations between risk factors and PEDV outbreaks with the ultimate goal of training predictive models and to identify the most important factors associated with PEDV occurrence. Our best algorithm was able to correctly predict whether an outbreak occurred during one-week periods with >80% accuracy. The most important predictors included pig movements into neighboring farms. Other important neighborhood attributes included hog density, environmental and weather factors such as vegetation, wind speed, temperature, and precipitation, and topographical features such as slope. Our neighborhood-based approach allowed us to simultaneously capture disease risks associated with long-distance animal movement as well as local spatial dynamics. The model presented here forms the foundation for near real-time disease mapping and will advance disease surveillance and control for endemic swine pathogens in the United States.

Bovine viral diarrhea virus (BVDV) causes one of the most economically important diseases in cattle, and the virus is found worldwide. A better understanding of the disease associated factors is a crucial step towards the definition of strategies for control and eradication. In this study we trained a random forest (RF) prediction model and performed variable importance analysis to identify factors associated with BVDV occurrence. In addition, we assessed the influence of features selection on RF performance and evaluated its predictive power relative to other popular classifiers and to logistic regression. We found that RF classification model resulted in an average error rate of 32.03% for the negative class (negative for BVDV) and 36.78% for the positive class (positive for BVDV).The RF model presented area under the ROC curve equal to 0.702. Variable importance analysis revealed that important predictors of BVDV occurrence were: a) who inseminates the animals, b) number of neighboring farms that have cattle and c) rectal palpation performed routinely. Our results suggest that the use of machine learning algorithms, especially RF, is a promising methodology for the analysis of cross-sectional studies, presenting a satisfactory predictive power and the ability to identify predictors that represent potential risk factors for BVDV investigation. We examined classical predictors and found some new and hard to control practices that may lead to the spread of this disease within and among farms, mainly regarding poor or neglected reproduction management, which should be considered for disease control and eradication.