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In a herd of 100 milking Simmental cows, data of performance and behavior parameters were collected automatically with different systems such as pedometers, an automatic milking system, and automatic weighing troughs for 1 yr. Performance measures were several milking-related parameters, live weight, as well as feed intake. Behavior-associated measures were feeding behavior (e.g. feeding duration, number of visits to the trough, and feeding pace) as well as activity such as lying duration, number of lying bouts, and overall activity. In the same time, lameness status of every cow was assessed with weekly locomotion scoring. According to the score animals were then classified lame (score 4 or 5) or nonlame (score 1, 2, or 3). From these data in total, 25 parameters summarized to daily values were evaluated for their ability to determine the lameness status of a cow. Data were analyzed with a regularized regression method called elastic net with the outcome lame or nonlame. The final model had a high prediction accuracy with an area under the curve of 0.91 [95% confidence interval (CI) = 0.88-0.94]. Specificity was 0.81 (95% CI = 0.73-0.85) and sensitivity was 0.94 (95% CI = 0.88-1.00). The most important factors associated with a cow being lame were number of meals, average feed intake per meal, and average duration of a meal. Lame cows fed in fewer and shorter meals with a decreased intake per meal. Milk yield and lying-behavior-associated parameters were relevant in the model, too, but only as parts of interaction terms demonstrating their strong dependence on other factors. A higher milk yield only resulted in higher risk of being lame if feed intake was decreased. The same accounts for lying duration: only if lying time was below the 50% quantile did an increased milk yield result in a higher risk of being lame. The association of lameness and daily lying duration was influenced by daily feeding duration and feeding duration at daytime. The results of the study give deeper insights on how the association between behavior and performance parameters and lameness is influenced by intrinsic factors in particular and that many of these have to be considered when trying to predict lameness based on such data. The findings lead to a better understanding why, for instance, lying duration or milk yield seem to be highly correlated with lameness in cows but still have not been overly useful as parameters in other lameness detection models.

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The Foodborne Diseases Active Surveillance Network (FoodNet) is currently using a negative binomial (NB) regression model to estimate temporal changes in the incidence of Campylobacter infection. FoodNet active surveillance in 483 counties collected data on 40,212 Campylobacter cases between years 2004 and 2011. We explored models that disaggregated these data to allow us to account for demographic, geographic, and seasonal factors when examining changes in incidence of Campylobacter infection. We hypothesized that modeling structural zeros and including demographic variables would increase the fit of FoodNet's Campylobacter incidence regression models. Five different models were compared: NB without demographic covariates, NB with demographic covariates, hurdle NB with covariates in the count component only, hurdle NB with covariates in both zero and count components, and zero-inflated NB with covariates in the count component only. Of the models evaluated, the nonzero-augmented NB model with demographic variables provided the best fit. Results suggest that even though zero inflation was not present at this level, individualizing the level of aggregation and using different model structures and predictors per site might be required to correctly distinguish between structural and observational zeros and account for risk factors that vary geographically.

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Digital dermatitis (DD) is the most important infectious claw disease in the cattle industry causing outbreaks of lameness. The clinical course of disease can be classified using 5 clinical stages. M-stages represent not only different disease severities but also unique clinical characteristics and outcomes. Monitoring the proportions of cows per M-stage is needed to better understand and address DD and factors influencing risks of DD in a herd. Changes in the proportion of cows per M-stage over time or between groups may be attributed to differences in management, environment, or treatment and can have impact on the future claw health of the herd. Yet trends in claw health regarding DD are not intuitively noticed without statistical analysis of detailed records. Our specific aim was to develop a mobile application (app) for persons with less statistical training, experience or supporting programs that would standardize M-stage records, automate data analysis including trends of M-stages over time, the calculation of predictions and assignments of Cow Types (i.e., Cow Types I-III are assigned to cows without active lesions, single and repeated cases of active DD lesions, respectively). The predictions were the stationary distributions of transitions between DD states (i.e., M-stages or signs of chronicity) in a class-structured multi-state Markov chain population model commonly used to model endemic diseases. We hypothesized that the app can be used at different levels of record detail to discover significant trends in the prevalence of M-stages that help to make informed decisions to prevent and control DD on-farm. Four data sets were used to test the flexibility and value of the DD Check App. The app allows easy recording of M-stages in different environments and is flexible in terms of the users' goals and the level of detail used. Results show that this tool discovers trends in M-stage proportions, predicts potential outbreaks of DD, and makes comparisons among Cow Types, signs of chronicity, scorers or pens. The DD Check App also provides a list of cows that should be treated augmented by individual Cow Types to help guide treatment and determine prognoses. Producers can be proactive instead of reactive in controlling DD in a herd by using this app. The DD Check App serves as an example of how technology makes knowledge and advice of veterinary epidemiology widely available to monitor, control and prevent this complex disease.

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Automatic milking systems (AMS) are implemented in a variety of situations and environments. Consequently, there is a need to characterize individual farming practices and regional challenges to streamline management advice and objectives for producers. Benchmarking is often used in the dairy industry to compare farms by computing percentile ranks of the production values of groups of farms. Grouping for conventional benchmarking is commonly limited to the use of a few factors such as farms' geographic region or breed of cattle. We hypothesized that herds' production data and management information could be clustered in a meaningful way using cluster analysis and that this clustering approach would yield better peer groups of farms than benchmarking methods based on criteria such as country, region, breed, or breed and region. By applying mixed latent-class model-based cluster analysis to 529 North American AMS dairy farms with respect to 18 significant risk factors, 6 clusters were identified. Each cluster (i.e., peer group) represented unique management styles, challenges, and production patterns. When compared with peer groups based on criteria similar to the conventional benchmarking standards, the 6 clusters better predicted milk produced (kilograms) per robot per day. Each cluster represented a unique management and production pattern that requires specialized advice. For example, cluster 1 farms were those that recently installed AMS robots, whereas cluster 3 farms (the most northern farms) fed high amounts of concentrates through the robot to compensate for low-energy feed in the bunk. In addition to general recommendations for farms within a cluster, individual farms can generate their own specific goals by comparing themselves to farms within their cluster. This is very comparable to benchmarking but adds the specific characteristics of the peer group, resulting in better farm management advice. The improvement that cluster analysis allows for is characterized by the multivariable approach and the fact that comparisons between production units can be accomplished within a cluster and between clusters as a choice.

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Automatic milking systems (AMS) are increasingly popular throughout the world. Our objective was to analyze 635 North American dairy farms with AMS for (risk) factors associated with increased milk production per cow per day and milk production per robot per day. We used multivariable generalized mixed linear regressions, which identified several significant risk factors and interactions of risk factors associated with milk production. Free traffic was associated with increased production per cow and per robot per day compared with forced systems, and the presence of a single robot per pen was associated with decreased production per robot per day compared with pens using 2 robots. Retrofitted farms had significantly less production in the first 4 yr since installation compared with production after 4 yr of installation. In contrast, newly built farms did not see a significant change in production over time since installation. Overall, retrofitted farms did not produce significantly more or less milk than newly constructed farms. Detailed knowledge of factors associated with increased production of AMS will help guide future recommendations to producers looking to transition to an AMS and maximize their production.

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OBJECTIVE: To develop transparent and reproducible methods for imputing missing data on disease incidence at national-level for the year 2005. METHODS: We compared several models for imputing missing country-level incidence rates for two foodborne diseases - congenital toxoplasmosis and aflatoxin-related hepatocellular carcinoma. Missing values were assumed to be missing at random. Predictor variables were selected using least absolute shrinkage and selection operator regression. We compared the predictive performance of naive extrapolation approaches and Bayesian random and mixed-effects regression models. Leave-one-out cross-validation was used to evaluate model accuracy. FINDINGS: The predictive accuracy of the Bayesian mixed-effects models was significantly better than that of the naive extrapolation method for one of the two disease models. However, Bayesian mixed-effects models produced wider prediction intervals for both data sets. CONCLUSION: Several approaches are available for imputing missing data at national level. Strengths of a hierarchical regression approach for this type of task are the ability to derive estimates from other similar countries, transparency, computational efficiency and ease of interpretation. The inclusion of informative covariates may improve model performance, but results should be appraised carefully.

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The purpose of this study was to conduct a convenience study for brucellosis prevalence in dairy-producing animals in northern Ecuador. In total, 2,561 cows and 301 goats were tested. Cattle sera were tested using the Rose Bengal card antigen test (RBCT), yielding an overall apparent prevalence of 5.5% (95% confidence interval [95% CI] = 4.7-6.5%) and true prevalence of 7.2% (95% CI = 6.0-8.5%). Prevalence varied by herd size and was highest in larger commercial herds. Polymerase chain reaction was used to test goat milk and lymph nodes, resulting in 9% and 8% positivity, respectively. The RBCTs from goat sera yielded an adjusted true prevalence of 17.8% (95% CI = 6.2-44.2%). Our findings are similar to other overall prevalence estimates for dairy herds but show higher prevalence in commercial herds compared with small groups (less than five animals). We also identify urban milking goats living in metropolitan Quito as a potential source of zoonosis.

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