One of the most commonly observational study designs 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 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. 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 6 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.

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.

BackgroundIn 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.Methodology/Principal findingsAn 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%).Conclusions/SignificanceA 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.

Many aspects of the porcine reproductive and respiratory syndrome virus (PRRSV) between‐farm transmission dynamics have been investigated, but uncertainty remains about the significance of farm type and different transmission routes on PRRSV spread. We developed a stochastic epidemiological model calibrated on weekly PRRSV outbreaks accounting for the population dynamics in different pig production phases, breeding herds, gilt development units, nurseries and finisher farms, of three hog producer companies. Our model accounted for indirect contacts by the close distance between farms (local transmission), between‐farm animal movements (pig flow) and reinfection of sow farms (re‐break). The fitted model was used to examine the effectiveness of vaccination strategies and complementary interventions such as enhanced PRRSV detection and vaccination delays and forecast the spatial distribution of PRRSV outbreak. The results of our analysis indicated that for sow farms, 59% of the simulated infections were related to local transmission (e.g. airborne, feed deliveries, shared equipment) whereas 36% and 5% were related to animal movements and re‐break, respectively. For nursery farms, 80% of infections were related to animal movements and 20% to local transmission; while at finisher farms, it was split between local transmission and animal movements. Assuming that the current vaccines are 1% effective in mitigating between‐farm PRRSV transmission, weaned pigs vaccination would reduce the incidence of PRRSV outbreaks by 3%, indeed under any scenario vaccination alone was insufficient for completely controlling PRRSV spread. Our results also showed that intensifying PRRSV detection and/or vaccination pigs at placement increased the effectiveness of all simulated vaccination strategies. Our model reproduced the incidence and PRRSV spatial distribution; therefore, this model could also be used to map current and future farms at‐risk. Finally, this model could be a useful tool for veterinarians, allowing them to identify the effect of transmission routes and different vaccination interventions to control PRRSV spread.

Ruminant pestiviruses can infect cattle populations worldwide and cause significant economic losses due to their impact on productivity and health. Knowledge of pestivirus diversity is important for control programs and vaccine development and for determining probable sources of infection. In this paper, we describe a search for ruminant pestiviruses with RT-PCR in sera of 9078 calves from 6 to 12 months of age. The calves were first analyzed in pools and then analyzed individually. Thirty-three RT-PCR positive animals were detected (0.36%) from 6.9% (24) of the 346 herds. The sequencing analysis of the 5' non-coding region and N terminal autoprotease showed the presence of BVDV-1a (15 isolates), -1b (3), -1d (1) and -2b (14), with a higher frequency (42.4%) of BVDV-2 in comparison with other countries. The presence of sheep was significantly associated with BVDV infection. Our results also suggested that a BVDV control program based only on the investigation of cattle would not be successful, especially in regions with farms harboring multiple animal species. This study may also serve as a reference for future control programs in Southern Brazil because it reports the prevalence of cattle with active infections and the genetic background of the circulating strains.

BackgroundLeptospirosis is an epidemic-prone neglected disease that affects humans and animals, mostly in vulnerable populations. The One Health approach is a recommended strategy to identify drivers of the disease and plan for its prevention and control. In that context, the aim of this study was to analyze the distribution of human cases of leptospirosis in the State of Rio Grande do Sul, Brazil, and to explore possible drivers. Additionally, it sought to provide further evidence to support interventions and to identify hypotheses for new research at the human-animal-ecosystem interface.Methodology and findingsThe risk for human infection was described in relation to environmental, socioeconomic, and livestock variables. This ecological study used aggregated data by municipality (all 496). Data were extracted from secondary, publicly available sources. Thematic maps were constructed and univariate analysis performed for all variables. Negative binomial regression was used for multivariable statistical analysis of leptospirosis cases. An annual average of 428 human cases of leptospirosis was reported in the state from 2008 to 2012. The cumulative incidence in rural populations was eight times higher than in urban populations. Variables significantly associated with leptospirosis cases in the final model were: Parana/Paraiba ecoregion (RR: 2.25; CI95%: 2.03–2.49); Neossolo Litolítico soil (RR: 1.93; CI95%: 1.26–2.96); and, to a lesser extent, the production of tobacco (RR: 1.10; CI95%: 1.09–1.11) and rice (RR: 1.003; CI95%: 1.002–1.04).ConclusionUrban cases were concentrated in the capital and rural cases in a specific ecoregion. The major drivers identified in this study were related to environmental and production processes that are permanent features of the state. This study contributes to the basic knowledge on leptospirosis distribution and drivers in the state and encourages a comprehensive approach to address the disease in the animal-human-ecosystem interface.

Porcine reproductive and respiratory syndrome virus (PRRSV) remains among the most costly diseases in North America (Holtkamp

No abstract