Using examination performance data and focus groups to inform teaching - a case study from final year students of veterinary medicine.

BACKGROUND: Student feedback has played an important role in the maintenance of quality and standards in higher education. Perhaps the most commonly used method to capture feedback is a series of questions or statements where students indicate their degree of satisfaction or agreement. Focus groups offer an alternative means of capturing 'richer' qualitative data relating to students' thoughts on course structure. Aside from student evaluations, student examination performance has been used as a method to evaluate the efficacy of curriculum changes at programme level. However, this data is utilised less so at a 'finer detail' level to identify specific issues with the delivery of teaching. CASE PRESENTATION: The purpose of this report was to outline the approach taken using qualitative and quantitative data to identify problems with a specific area of teaching, inform a new teaching approach and to assess the impact of those changes. Following quantitative and qualitative analysis, a practical class on dairy herd fertility performance was highlighted as an area for improvement. After the introduction of the newly formatted practical class with a greater focus on self-directed learning, there was a significant increase in the average score (p < 0.001) and a decrease in the proportion of students failing (p < 0.001) the question that assessed the analysis of dairy herd fertility data. In addition, the R-squared value between students' performance in the fertility question and their performance in the overall examination increased from 0.06 to 0.11. CONCLUSIONS: The combination of qualitative focus group data and quantitative analysis of examination performance data represent robust methods for identifying problems associated with specific aspects of veterinary teaching.

Schmallenberg virus: a systematic international literature review (2011-2019) from an Irish perspective.

In Autumn 2011, nonspecific clinical signs of pyrexia, diarrhoea, and drop in milk yield were observed in dairy cattle near the German town of Schmallenberg at the Dutch/German border. Targeted veterinary diagnostic investigations for classical endemic and emerging viruses could not identify a causal agent. Blood samples were collected from animals with clinical signs and subjected to metagenomic analysis; a novel orthobunyavirus was identified and named Schmallenberg virus (SBV). In late 2011/early 2012, an epidemic of abortions and congenital malformations in calves, lambs and goat kids, characterised by arthrogryposis and hydranencephaly were reported in continental Europe. Subsequently, SBV RNA was confirmed in both aborted and congenitally malformed foetuses and also in Culicoides species biting midges. It soon became evident that SBV was an arthropod-borne teratogenic virus affecting domestic ruminants. SBV rapidly achieved a pan-European distribution with most countries confirming SBV infection within a year or two of the initial emergence. The first Irish case of SBV was confirmed in the south of the country in late 2012 in a bovine foetus. Since SBV was first identified in 2011, a considerable body of scientific research has been conducted internationally describing this novel emerging virus. The aim of this systematic review is to provide a comprehensive synopsis of the most up-to-date scientific literature regarding the origin of SBV and the spread of the Schmallenberg epidemic, in addition to describing the species affected, clinical signs, pathogenesis, transmission, risk factors, impact, diagnostics, surveillance methods and control measures. This review also highlights current knowledge gaps in the scientific literature regarding SBV, most notably the requirement for further research to determine if, and to what extent, SBV circulation occurred in Europe and internationally during 2017 and 2018. Moreover, recommendations are also made regarding future arbovirus surveillance in Europe, specifically the establishment of a European-wide sentinel herd surveillance program, which incorporates bovine serology and Culicoides entomology and virology studies, at national and international level to monitor for the emergence and re-emergence of arboviruses such as SBV, bluetongue virus and other novel Culicoides-borne arboviruses.

Routine Herd Health Data as Cow-Based Risk Factors Associated with Lameness in Pasture-Based, Spring Calving Irish Dairy Cows.

Herd-level risk factors related to the cow's environment have been associated with lameness. Uncomfortable stall surface and inadequate depth of bedding as well as abrasive alley way surface are contributing factors to increased levels of lameness. Access to pasture has been found as having a beneficial effect on cows' locomotion. However, dairy cattle managed under grazing conditions are exposed to a different set of risk factors for lameness, mainly associated with cow tracks. Cow-based risk factors for lameness are not as clearly defined as the herd level risk factors. The objective of the present study was to use routine herd health monitoring data to identify cow-based risk factors for lameness and utilise this information to indicate cows at risk of developing lameness in the first 150 days of lactation. Lameness data were gathered from 10 pasture-based dairy herds. A total of 1715 cows were monitored, of which 1675 cows were available for analysis. Associations between lameness status and potential cow-level risk factors were determined using multivariable logistic regression. Parity 3 and 4 + cows showed odd ratios (OR's) for lameness of 3.92 and 8.60 respectively (95% confidence interval (CI) 2.46-6.24; 5.68-13.0). Maximum loss of Body condition score (BCS) after calving exhibits OR's for lameness of 1.49 (95% CI 1.08-2.04) if cows lost 0.5 in BCS after calving and 2.26 (95% CI 1.30-3.95) for cows losing more than 0.5 BCS. Animals calving in BCS 3.25 and ≥ 3.5 had correlating OR's of 0.54 (95% CI 0.34-0.87) and 0.33 (95% CI 0.16-0.65) for being lame compared to cows calving with BCS ≤ 2.75. Data gathered as part of herd health monitoring can be used in conjunction with lameness records to identify shortcomings in lameness management. Findings and recommendations on lameness management can be formulated from readily available information on cow-based risk factors for lameness.

Genome-wide association study of endo-parasite phenotypes using imputed whole-genome sequence data in dairy and beef cattle.

BACKGROUND: Quantitative genetic studies suggest the existence of variation at the genome level that affects the ability of cattle to resist to parasitic diseases. The objective of the current study was to identify regions of the bovine genome that are associated with resistance to endo-parasites. METHODS: Individual cattle records were available for Fasciola hepatica-damaged liver from 18 abattoirs. Deregressed estimated breeding values (EBV) for F. hepatica-damaged liver were generated for genotyped animals with a record for F. hepatica-damaged liver and for genotyped sires with a least one progeny record for F. hepatica-damaged liver; 3702 animals were available. In addition, individual cow records for antibody response to F. hepatica on 6388 genotyped dairy cows, antibody response to Ostertagia ostertagi on 8334 genotyped dairy cows and antibody response to Neospora caninum on 4597 genotyped dairy cows were adjusted for non-genetic effects. Genotypes were imputed to whole-sequence; after edits, 14,190,141 single nucleotide polymorphisms (SNPs) and 16,603,644 SNPs were available for cattle with deregressed EBV for F. hepatica-damaged liver and cows with an antibody response to a parasitic disease, respectively. Association analyses were undertaken using linear regression on one SNP at a time, in which a genomic relationship matrix accounted for the relationships between animals. RESULTS: Genomic regions for F. hepatica-damaged liver were located on Bos taurus autosomes (BTA) 1, 8, 11, 16, 17 and 18; each region included at least one SNP with a p value lower than 10-6. Five SNPs were identified as significant (q value < 0.05) for antibody response to N. caninum and were located on BTA21 or 25. For antibody response to F. hepatica and O. ostertagi, six and nine quantitative trait loci (QTL) regions that included at least one SNP with a p value lower than 10-6 were identified, respectively. Gene set enrichment analysis revealed a significant association between functional annotations related to the olfactory system and QTL that were suggestively associated with endo-parasite phenotypes. CONCLUSIONS: A number of novel genomic regions were suggestively associated with endo-parasite phenotypes across the bovine genome and two genomic regions on BTA21 and 25 were associated with antibody response to N. caninum.

A review of paratuberculosis in dairy herds - Part 1: Epidemiology.

Bovine paratuberculosis is a chronic infectious disease of cattle caused by Mycobacterium avium subspecies paratuberculosis (MAP). This is the first in a two-part review of the epidemiology and control of paratuberculosis in dairy herds. Paratuberculosis was originally described in 1895 and is now considered endemic among farmed cattle worldwide. MAP has been isolated from a wide range of non-ruminant wildlife as well as humans and non-human primates. In dairy herds, MAP is assumed to be introduced predominantly through the purchase of infected stock with additional factors modulating the risk of persistence or fade-out once an infected animal is introduced. Faecal shedding may vary widely between individuals and recent modelling work has shed some light on the role of super-shedding animals in the transmission of MAP within herds. Recent experimental work has revisited many of the assumptions around age susceptibility, faecal shedding in calves and calf-to-calf transmission. Further efforts to elucidate the relative contributions of different transmission routes to the dissemination of infection in endemic herds will aid in the prioritisation of efforts for control on farm.

A review of paratuberculosis in dairy herds - Part 2: On-farm control.

Bovine paratuberculosis is a chronic infectious disease of cattle, caused by Mycobacterium avium subspecies paratuberculosis (MAP). This is the second in a two-part review of the epidemiology and control of paratuberculosis in dairy herds. Several negative production effects associated with MAP infection have been described, but perhaps the most significant concern in relation to the importance of paratuberculosis as a disease of dairy cattle is the potential link with Crohn's disease in humans. Milk is considered a potential transmission route to humans and it is recognised that pasteurisation does not necessarily eliminate the bacterium. Therefore, control must also include reduction of the levels of MAP in bulk milk supplied from dairy farms. There is little field evidence in support of specific control measures, although several studies seem to show a decreased prevalence associated with the implementation of a combined management and test-and-cull programme. Improvements in vaccination efficacy and reduced tuberculosis (TB) test interference may increase uptake of vaccination as a control option. Farmer adoption of best practice recommendations at farm level for the control of endemic diseases can be challenging. Improved understanding of farmer behaviour and decision making will help in developing improved communication strategies which may be more efficacious in affecting behavioural change on farm.

Low accuracy of Bayesian latent class analysis for estimation of herd-level true prevalence under certain disease characteristics-An analysis using simulated data.

Estimation of the true prevalence of infected individuals involves the application of a diagnostic test to a population and adjusting according to test performance, sensitivity and specificity. Bayesian latent class analysis for the estimation of herd and animal-level true prevalence, has become increasingly used in veterinary epidemiology and is particularly useful in incorporating uncertainty and variability into analyses in a flexible framework. However, the approach has not yet been evaluated using simulated data where the true prevalence is known. Furthermore, using this approach, the within-herd true prevalence is often assumed to follow a beta distribution, the parameters of which may be modelled using hyperpriors to incorporate both uncertainty and variability associated with this parameter. Recently however, the authors of the current study highlighted a potential issue with this approach, in particular, with fitting the distributions and a tendency for the resulting distribution to invert and become clustered at zero. Therefore, the objective of the present study was to evaluate commonly specified models using simulated datasets where the herd-level true prevalence was known. The specific purpose was to compare findings from models using hyperpriors to those using a simple beta distribution to model within-herd prevalence. A second objective was to investigate sources of error by varying characteristics of the simulated dataset. Mycobacterium avium subspecies paratuberculosis infection was used as an example for the baseline dataset. Data were simulated for 1000 herds across a range of herd-level true prevalence scenarios, and models were fitted using priors from recently published studies. The results demonstrated poor performance of these latent class models for diseases characterised by poor diagnostic test sensitivity and low within-herd true prevalence. All variations of the model appeared to be sensitive to the prior and tended to overestimate herd-level true prevalence. Estimates were substantially improved in different infection scenarios by increasing test sensitivity and within-herd true prevalence. The results of this study raise questions about the accuracy of published estimates for the herd-level true prevalence of paratuberculosis based on serological testing, using latent class analysis. This study highlights the importance of conducting more rigorous sensitivity analyses than have been carried out in previous analyses published to date.

Genetic parameters for variability in the birth of persistently infected cattle following likely in utero exposure to bovine viral diarrhea virus.

Genetic selection is an inexpensive and complementary strategy to traditional methods of improving animal health and welfare. Nonetheless, endeavors to incorporate animal health and welfare traits in international breeding programs have been hampered by the availability of informative phenotypes. The recent eradication program for bovine viral diarrhea (BVD) in the Republic of Ireland has provided an opportunity to quantify the potential benefits that genetic selection could offer BVD eradication programs elsewhere, as well as inform possible eradication programs for other diseases in the Republic of Ireland. Using a dataset of 188,085 Irish calves, the estimated direct and maternal heritability estimates for the birth of persistently infected calves following likely in utero exposure to BVD virus ranged from not different from zero (linear model) to 0.29 (SE = 0.075; threshold model) and from essentially zero (linear model) to 0.04 (SE = 0.033; threshold model), respectively. The corresponding genetic SD for the direct and maternal effect of the binary trait (0, 1) ranged from 0.005 (linear model) to 0.56 (threshold model) units and ranged from 0.00008 (linear model) to 0.20 (threshold model) units, respectively. The coefficient of direct genetic variation based on the linear model was 2.56% indicating considerable genetic variation could be exploited. Based on results from the linear model in the present study, there is the potential to reduce the incidence of persistent infection in cattle by on average 0.11 percentage units per year which is cumulative and permanent. Therefore, genetic selection can contribute to reducing the incidence of persistent infection in cattle. Moreover, where populations are free from persistent infection, inclusion of the estimated genetic merit for BVD in national breeding indexes could contribute to a preservation of a BVD-free status. Results from the present study can be used to inform breeding programs of the potential genetic gains achievable. Moreover, the approaches used in the present study can be applied to other diseases when data become available.

Culicoides species composition and abundance on Irish cattle farms: implications for arboviral disease transmission.

BACKGROUND: Following the emergence of Schmallenberg virus (SBV) in Ireland in 2012, a sentinel herd surveillance program was established in the south of Ireland with the primary aim of investigating the species composition and abundance of Culicoides on livestock farms in the region. METHODS: Ultraviolet-light trapping for Culicoides was carried out on 10 sentinel farms. Each site was sampled fortnightly over 16 weeks (21st July to 5th November 2014). One Onderstepoort Veterinary Institute UV light trap was run overnight at each site and catches were transferred immediately into 70% ethanol. Culicoides were morphologically identified to species level. Collection site habitats were characterised using the Phase 1 habitat survey technique (Joint Nature Conservation Committee). RESULTS: A total of 23,929 individual Culicoides from 20 species was identified, including one species identified in Ireland for the first time, Culicoides cameroni. The most abundant species identified were Culicoides obsoletus/Culicoides scoticus (38%), Culicoides dewulfi (36%), Culicoides pulicaris (9%), Culicoides chiopterus (5%) and Culicoides punctatus (5%), comprising 93% of all Culicoides specimens identified. Collection site habitats were dominated by improved grassland and a combination of broadleaf woodland and native woodland species. CONCLUSIONS: The most abundant species of Culicoides identified were the putative vectors of bluetongue virus (BTV) and SBV in northern Europe. Their presence and abundance demonstrates the potential for future transmission of arboviruses among livestock in this region.

Little genetic variability in resilience among cattle exists for a range of performance traits across herds in Ireland differing in Fasciola hepatica prevalence.

It is anticipated that in the future, livestock will be exposed to a greater risk of infection from parasitic diseases. Therefore, future breeding strategies for livestock, which are generally long-term strategies for change, should target animals adaptable to environments with a high parasitic load. Covariance components were estimated in the present study for a selection of dairy and beef performance traits over herd-years differing in Fasciola hepatica load using random regression sire models. Herd-year prevalence of F. hepatica was determined by using F. hepatica-damaged liver phenotypes which were recorded in abattoirs nationally. The data analyzed consisted up to 83,821 lactation records from dairy cows for a range of milk production and fertility traits, as well as 105,054 young animals with carcass-related information obtained at slaughter. Reaction norms for individual sires were derived from the random regression coefficients. The heritability and additive genetic standard deviations for all traits analyzed remained relatively constant as herd-year F. hepatica prevalence gradient increased up to a prevalence level of 0.7; although there was a large increase in heritability and additive genetic standard deviation for milk and fertility traits in the observed F. hepatica prevalence levels >0.7, only 5% of the data existed in herd-year prevalence levels >0.7. Very little rescaling, therefore, exists across differing herd-year F. hepatica prevalence levels. Within-trait genetic correlations among the performance traits across different herd-year F. hepatica prevalence levels were less than unity for all traits. Nevertheless, within-trait genetic correlations for milk production and carcass traits were all >0.8 for F. hepatica prevalence levels between 0.2 and 0.8. The lowest estimate of within-trait genetic correlations for the different fertility traits ranged from -0.03 (SE = 1.09) in age of first calving to 0.54 (SE = 0.22) for calving to first service interval. Therefore, there was reranking of sires for fertility traits across different F. hepatica prevalence levels. In conclusion, there was little or no genetic variability in sensitivity to F. hepatica prevalence levels among cattle for milk production and carcass traits. But, some genetic variability in sensitivity among dairy cows did exist for fertility traits measured across herds differing in F. hepatica prevalence.

Genetic correlations between endo-parasite phenotypes and economically important traits in dairy and beef cattle.

Parasitic diseases have economic consequences in cattle production systems. Although breeding for parasite resistance can complement current control practices to reduce the prevalence globally, there is little knowledge of the implications of such a strategy on other performance traits. Records on individual animal antibody responses to Fasciola hepatica, Ostertagia ostertagi, and Neospora caninum were available from cows in 68 dairy herds (study herds); national abattoir data on F. hepatica-damaged livers were also available from dairy and beef cattle. After data edits, 9,271 dairy cows remained in the study herd dataset, whereas 19,542 dairy cows and 68,048 young dairy and beef animals had a record for the presence or absence of F. hepatica-damaged liver in the national dataset. Milk, reproductive, and carcass phenotypes were also available for a proportion of these animals as well as their contemporaries. Linear mixed models were used to estimate variance components of antibody responses to the three parasites; covariance components were estimated between the parasite phenotypes and economically important traits. Heritability of antibody responses to the different parasites, when treated as a continuous trait, ranged from 0.07 (O. ostertagi) to 0.13 (F. hepatica), whereas the coefficient of genetic variation ranged from 4% (O. ostertagi) to 20% (F. hepatica). The antibody response to N. caninum was genetically correlated with the antibody response to both F. hepatica (-0.29) and O. ostertagi (-0.67); a moderately positive genetic correlation existed between the antibody response to F. hepatica and O. ostertagi (0.66). Genetic correlations between the parasite phenotypes and the milk production traits were all close to zero (-0.14 to 0.10), as were the genetic correlations between F. hepatica-damaged livers and the carcass traits of carcass weight, conformation, and fat score evaluated in cows and young animals (0.00 to 0.16). The genetic correlation between F. hepatica-damaged livers in cows and milk somatic cell score was 0.32 (SE = 0.20). Antibody responses to F. hepatica and O. ostertagi had favorable genetic correlations with fertility traits, but conversely, antibody response to N. caninum and F. hepatica-damaged livers were unfavorably genetically correlated with fertility. This study provides the necessary information to undertake national multitrait genetic evaluations for parasite phenotypes.

A discussion of calibration techniques for evaluating binary and categorical predictive models.

Modelling of binary and categorical events is a commonly used tool to simulate epidemiological processes in veterinary research. Logistic and multinomial regression, naïve Bayes, decision trees and support vector machines are popular data mining techniques used to predict the probabilities of events with two or more outcomes. Thorough evaluation of a predictive model is important to validate its ability for use in decision-support or broader simulation modelling. Measures of discrimination, such as sensitivity, specificity and receiver operating characteristics, are commonly used to evaluate how well the model can distinguish between the possible outcomes. However, these discrimination tests cannot confirm that the predicted probabilities are accurate and without bias. This paper describes a range of calibration tests, which typically measure the accuracy of predicted probabilities by comparing them to mean event occurrence rates within groups of similar test records. These include overall goodness-of-fit statistics in the form of the Hosmer-Lemeshow and Brier tests. Visual assessment of prediction accuracy is carried out using plots of calibration and deviance (the difference between the outcome and its predicted probability). The slope and intercept of the calibration plot are compared to the perfect diagonal using the unreliability test. Mean absolute calibration error provides an estimate of the level of predictive error. This paper uses sample predictions from a binary logistic regression model to illustrate the use of calibration techniques. Code is provided to perform the tests in the R statistical programming language. The benefits and disadvantages of each test are described. Discrimination tests are useful for establishing a model's diagnostic abilities, but may not suitably assess the model's usefulness for other predictive applications, such as stochastic simulation. Calibration tests may be more informative than discrimination tests for evaluating models with a narrow range of predicted probabilities or overall prevalence close to 50%, which are common in epidemiological applications. Using a suite of calibration tests alongside discrimination tests allows model builders to thoroughly measure their model's predictive capabilities.

The creation and evaluation of a model to simulate the probability of conception in seasonal-calving pasture-based dairy heifers.

BACKGROUND: Herd fertility in pasture-based dairy farms is a key driver of farm economics. Models for predicting nulliparous reproductive outcomes are rare, but age, genetics, weight, and BCS have been identified as factors influencing heifer conception. The aim of this study was to create a simulation model of heifer conception to service with thorough evaluation. METHODS: Artificial Insemination service records from two research herds and ten commercial herds were provided to build and evaluate the models. All were managed as spring-calving pasture-based systems. The factors studied were related to age, genetics, and time of service. The data were split into training and testing sets and bootstrapping was used to train the models. Logistic regression (with and without random effects) and generalised additive modelling were selected as the model-building techniques. Two types of evaluation were used to test the predictive ability of the models: discrimination and calibration. Discrimination, which includes sensitivity, specificity, accuracy and ROC analysis, measures a model's ability to distinguish between positive and negative outcomes. Calibration measures the accuracy of the predicted probabilities with the Hosmer-Lemeshow goodness-of-fit, calibration plot and calibration error. RESULTS: After data cleaning and the removal of services with missing values, 1396 services remained to train the models and 597 were left for testing. Age, breed, genetic predicted transmitting ability for calving interval, month and year were significant in the multivariate models. The regression models also included an interaction between age and month. Year within herd was a random effect in the mixed regression model. Overall prediction accuracy was between 77.1% and 78.9%. All three models had very high sensitivity, but low specificity. The two regression models were very well-calibrated. The mean absolute calibration errors were all below 4%. CONCLUSION: Because the models were not adept at identifying unsuccessful services, they are not suggested for use in predicting the outcome of individual heifer services. Instead, they are useful for the comparison of services with different covariate values or as sub-models in whole-farm simulations. The mixed regression model was identified as the best model for prediction, as the random effects can be ignored and the other variables can be easily obtained or simulated.

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows.

The aim of this study was to build and compare predictive models of calving difficulty in dairy heifers and cows for the purpose of decision support and simulation modeling. Models to predict 3 levels of calving difficulty (unassisted, slight assistance, and considerable or veterinary assistance) were created using 4 machine learning techniques: multinomial regression, decision trees, random forests, and neural networks. The data used were sourced from 2,076 calving records in 10 Irish dairy herds. In total, 19.9 and 5.9% of calving events required slight assistance and considerable or veterinary assistance, respectively. Variables related to parity, genetics, BCS, breed, previous calving, and reproductive events and the calf were included in the analysis. Based on a stepwise regression modeling process, the variables included in the models were the dam's direct and maternal calving difficulty predicted transmitting abilities (PTA), BCS at calving, parity; calving assistance or difficulty at the previous calving; proportion of Holstein breed; sire breed; sire direct calving difficulty PTA; twinning; and 2-way interactions between calving BCS and previous calving difficulty and the direct calving difficulty PTA of dam and sire. The models were built using bootstrapping procedures on 70% of the data set. The held-back 30% of the data was used to evaluate the predictive performance of the models in terms of discrimination and calibration. The decision tree and random forest models omitted the effect of twinning and included only subsets of sire breeds. Only multinomial regression and neural networks explicitly included the modeled interactions. Calving BCS, calving difficulty PTA, and previous calving assistance ranked as highly important variables for all 4 models. The area under the receiver operating characteristic curve (ranging from 0.64 to 0.79) indicates that all of the models had good overall discriminatory power. The neural network and multinomial regression models performed best, correctly classifying 75% of calving cases and showing superior calibration, with an average error in predicted probability of 3.7 and 4.5%, respectively. The neural network and multinomial regression models developed are both suitable for use in decision-support and simulation modeling.

Relative importance of herd-level risk factors for probability of infection with paratuberculosis in Irish dairy herds.

Control of paratuberculosis is challenging due to the relatively poor performance of diagnostic tests, a prolonged incubation period, and protracted environmental survival. Prioritization of herd-level interventions is not possible because putative risk factors are often not supported by risk factor studies. The objective for this study was to investigate the relative importance of risk factors for an increased probability of herd paratuberculosis infection. Risk assessment data, comprehensive animal purchase history, and diagnostic test data were available for 936 Irish dairy herds. Both logistic regression and a Bayesian ß regression on the outcome of a latent class analysis were conducted. Population attributable fractions and proportional reduction in variance explained were calculated for each variable in the logistic and Bayesian models, respectively. Routine use of the calving area for sick or lame cows was found to be a significant explanatory covariate in both models. Purchasing behavior for the previous 10 yr was not found to be significant. For the logistic model, length of time calves spend in the calving pen (25%) and routine use of the calving pen for sick or lame animals (14%) had the highest attributable fractions. For the Bayesian model, the overall R2 was 16%. Dry cow cleanliness (7%) and routine use of the calving area for sick or lame cows (6%) and had the highest proportional reduction in variance explained. These findings provide support for several management practices commonly recommended as part of paratuberculosis control programs; however, a large proportion of the observed variation in probability of infection remained unexplained, suggesting other important risks factors may exist.

Johne's disease in the eyes of Irish cattle farmers: A qualitative narrative research approach to understanding implications for disease management.

Bovine Johne's Disease (JD) is a disease characterised by chronic granulomatous enteritis which manifests clinically as a protein-losing enteropathy causing diarrhoea, hypoproteinaemia, emaciation and, eventually death. Some research exists to suggest that the aetiologic pathogen Mycobacterium avium subspecies paratuberculosis may pose a zoonotic risk. Nationally coordinated control programmes have been introduced in many of the major milk producing countries across the world. However, JD is challenging to control in infected herds owing to limitations of diagnostic tests and the long incubation period of the disease. Internationally, research increasingly recognises that improved understanding of farmers' subjective views and behaviours may inform and enhance disease management strategies and support the identification and implementation of best practice at farm level. The aim of this study was to use qualitative research methods to explore the values and knowledges of farmers relative to the control of JD at farm level. The Biographical Narrative Interpretive Method (BNIM) was used to generate data from both infected and presumed uninfected farms in Ireland. Qualitative analysis revealed that cultural and social capital informed farmers' decisions on whether to introduce control and preventive measures. Cultural capital refers to the pride and esteem farmers associate with particular objects and actions whereas social capital is the value that farmers associate with social relationships with others. On-farm controls were often evaluated by farmers as impractical and were frequently at odds with farmers' knowledge of calf management. Knowledge from farmers of infected herds did not disseminate among peer farmers. Owners of herds believed to be uninfected expressed a view that controls and preventive measures were not worthy of adoption until there was clear evidence of JD in the herd. These findings highlight important barriers and potential aids to prevention and control in both infected and uninfected herds.

The creation and evaluation of a model predicting the probability of conception in seasonal-calving, pasture-based dairy cows.

Reproductive performance in pasture-based production systems has a fundamentally important effect on economic efficiency. The individual factors affecting the probability of submission and conception are multifaceted and have been extensively researched. The present study analyzed some of these factors in relation to service-level probability of conception in seasonal-calving pasture-based dairy cows to develop a predictive model of conception. Data relating to 2,966 services from 737 cows on 2 research farms were used for model development and data from 9 commercial dairy farms were used for model testing, comprising 4,212 services from 1,471 cows. The data spanned a 15-yr period and originated from seasonal-calving pasture-based dairy herds in Ireland. The calving season for the study herds extended from January to June, with peak calving in February and March. A base mixed-effects logistic regression model was created using a stepwise model-building strategy and incorporated parity, days in milk, interservice interval, calving difficulty, and predicted transmitting abilities for calving interval and milk production traits. To attempt to further improve the predictive capability of the model, the addition of effects that were not statistically significant was considered, resulting in a final model composed of the base model with the inclusion of BCS at service. The models' predictions were evaluated using discrimination to measure their ability to correctly classify positive and negative cases. Precision, recall, F-score, and area under the receiver operating characteristic curve (AUC) were calculated. Calibration tests measured the accuracy of the predicted probabilities. These included tests of overall goodness-of-fit, bias, and calibration error. Both models performed better than using the population average probability of conception. Neither of the models showed high levels of discrimination (base model AUC 0.61, final model AUC 0.62), possibly because of the narrow central range of conception rates in the study herds. The final model was found to reliably predict the probability of conception without bias when evaluated against the full external data set, with a mean absolute calibration error of 2.4%. The chosen model could be used to support a farmer's decision-making and in stochastic simulation of fertility in seasonal-calving pasture-based dairy cows.

An investigative framework to facilitate epidemiological thinking during herd problem-solving.

Veterinary clinicians and students commonly use diagnostic approaches appropriate for individual cases when conducting herd problem-solving. However, these approaches can be problematic, in part because they make limited use of epidemiological principles and methods, which has clear application during the investigation of herd problems. In this paper, we provide an overview of diagnostic approaches that are used when investigating individual animal cases, and the challenges faced when these approaches are directly translated from the individual to the herd. Further, we propose an investigative framework to facilitate epidemiological thinking during herd problem-solving. A number of different approaches are used when making a diagnosis on an individual animal, including pattern recognition, hypothetico-deductive reasoning, and the key abnormality method. Methods commonly applied to individuals are often adapted for herd problem-solving: 'comparison with best practice' being a herd-level adaptation of pattern recognition, and 'differential diagnoses' a herd-level adaptation of hypothetico-deductive reasoning. These approaches can be effective, however, challenges can arise. Herds are complex; a collection of individual cows, but also additional layers relating to environment, management, feeding etc. It is unrealistic to expect seamless translation of diagnostic approaches from the individual to the herd. Comparison with best practice is time-consuming and prioritisation of actions can be problematic, whereas differential diagnoses can lead to 'pathogen hunting', particularly in complex cases. Epidemiology is the science of understanding disease in populations. The focus is on the population, underpinned by principles and utilising methods that seek to allow us to generate solid conclusions from apparently uncontrolled situations. In this paper, we argue for the inclusion of epidemiological principles and methods as an additional tool for herd problem-solving, and outline an investigative framework, with examples, to effectively incorporate these principles and methods with other diagnostic approaches during herd problem-solving. Relevant measures of performance are identified, and measures of case frequencies are calculated and compared across time, in space and among animal groupings, to identify patterns, clues and plausible hypotheses, consistent with potential biological processes. With this knowledge, the subsequent investigation (relevant on-farm activities, diagnostic testing and other examinations) can be focused, and actions prioritised (specifically, those actions that are likely to make the greatest difference in addressing the problem if enacted). In our experience, this investigative framework is an effective teaching tool, facilitating epidemiological thinking among students during herd problem-solving. It is a generic and robust process, suited to many herd-based problems.