Should they stay, or should they go? Relative future risk of bovine tuberculosis for interferon-gamma test-positive cattle left on farms.

Bovine tuberculosis (bTB), caused by Mycobacterium bovis, is a serious infectious disease that remains an ongoing concern for cattle farming worldwide. Tuberculin skin-tests are often used to identify infected animals (reactors) during test-and-cull programs, however, due to relatively poor sensitivity, additional tests can be implemented in parallel. For example, in Northern Ireland interferon-gamma (IFN-g) testing is used in high-risk herds. However, skin-test negative animals which are positive to the IFN-g test are not required by law to be slaughtered - therefore the final choice for these animals' fate is left with the owner. During this study we investigated whether these animals represented a greater risk of becoming a skin reactor, relative to IFN-g test negative animals from the same herds. Our study population included 1107 IFN-g positive animals from 239 herds. A Cox-proportional hazard model indicated that animals which tested IFN-g positive were 2.31 times (95% CI: 1.92-2.79; P < 0.001) more likely to become a reactor compared with IFN-g negative animals. Animals from dairy herds, and from herds in the south-east, were of higher risk than animals from beef herds and other regions, respectively. Our findings suggest that IFN-g positive animals represent a higher risk of failing a skin-test in the future, indicating the value of IFN-g testing for identifying early-stage infected animals. These IFN-g positive animals are not under any disease restriction, and may move freely (trade), which may put recipient herds at increased risk. Our findings provide important evidence for stakeholders engaged in bTB eradication programs.

Test and vaccinate or remove: Methodology and preliminary results from a badger intervention research project.

BACKGROUND: In the British Isles, it is generally accepted that the Eurasian badger (Meles meles) plays a role in the maintenance of bovine tuberculosis (bTB) in cattle. Non-selective culling is the main intervention method deployed in controlling bTB in badgers along with smaller scale Bacillus Calmette-Guérin (BCG) vaccination areas. This paper describes the use of selective badger culling combined with vaccination in a research intervention trial. METHODS: In Northern Ireland, a 100 km2 area was subjected to a test and vaccinate or remove (TVR) badger intervention over a 5-year period. Badgers were individually identified and tested on an annual basis. Physical characteristics and clinical samples were obtained from each unique badger capture event. RESULTS: A total of 824 badgers were trapped with 1520 capture/sampling events. There were no cage-related injuries to the majority of badgers (97%). A low level of badger removal was required (4.1%-16.4% annually), while 1412 BCG vaccinations were administered. A statistically significant downward trend in the proportion of test positive badgers was observed. CONCLUSION: This is the first project to clearly demonstrate the feasibility of cage side testing of badgers. The results provide valuable data on the logistics and resources required to undertake a TVR approach to control Mycobacterium bovis in badgers.

Liver fluke (Fasciola hepatica) co-infection with bovine tuberculosis (bTB) in cattle: A retrospective animal-level assessment of bTB risk in dairy and beef cattle.

Bovine tuberculosis (bTB), caused by Mycobacterium bovis, remains a persistent problem for cattle industries in endemic countries. The frequency, quality, and performance of tests, and the presence of wildlife reservoirs, have been identified as impediments to eradication. Recently, exposure to helminth infection (Fasciola hepatica) has been associated negatively with the disclosure of bTB. Here, for the first time, we assess impact of concurrent infections of Fasciola hepatica and the disclosure of bTB at the animal-level using large surveillance datasets. We utilized a dataset of 138,566 animal records from an abattoir from Northern Ireland (2011-2013). The presence of F. hepatica infection was assessed from macroscopic tissue inspection at abattoir. Multivariable models were developed to assess co-infection associations with bTB status based on: Single Intradermal Comparative Tuberculin Test (SICTT), lesion, bacteriological confirmation, including either all animals, or only skin-test negative animals (lesions at routine slaughter; LRS; confirmed nonreactors at routine slaughter; cNRs) or positive (reactors) animals alone, respectively. The relationship between skin tuberculin reaction sizes and fluke status was also explored for a subset of animals with field recordings (n = 24,680). Controlling for known risk factors (e.g., climatic, herd, and individual level characteristics), we did not find significant associations between the SICTT (standard or severe interpretation), lesion, nor confirmation status of animals and their liver fluke status. The only exception was a negative association between liver fluke positivity, and LRS or cNRs, respectively; though effect-sizes were small (e.g., LRS Odds-Ratio: 0.87; 95% CI: 0.76-1.00). There was limited evidence of a relationship between tuberculin reaction sizes during SICTT testing and liver fluke infection status. These data do not support the contention that the detection of bTB using skin-tests or reactor postmortem follow-up may be compromised by co-infection at a population level, but the relationship with lesion formation (pathogenesis) may indicate an impact for postmortem surveillance.

Liver fluke (Fasciola hepatica) infection in cattle in Northern Ireland: a large-scale epidemiological investigation utilising surveillance data.

BACKGROUND: Liver fluke (Fasciola hepatica) is a widespread parasite of ruminants which can have significant economic impact on cattle production. Fluke infection status at the animal-level is captured during meat inspection of all animals processed for human consumption within Northern Ireland. These national datasets have not been analysed to assess their utility in uncovering patterns in fluke infection at animal- and herd-levels in Northern Ireland. METHODS: We utilised a dataset of 1.2 million animal records from ~18,000 herds across 3 years (2011-2013) to assess animal- and herd-level apparent prevalence and risk-factors associated with fluke infection. Animal-level apparent prevalence was measured as the proportion of animals exhibiting evidence of fluke infection at slaughter; between herd-level infection prevalence was measured by binary categorisation of herds (infected or not). "Within herd" infection prevalence was measured using the proportion of animals within a herd that showed evidence of fluke infection per year (ranging from 0-100%). "Within herd" infection prevalence at the herd level was investigated using multivariable modelling. RESULTS: At the animal level, the proportion of animals slaughtered that exhibited evidence of infection was 21-25% amongst years. Across herds, the proportion of herds with at least one infected animal, varied between 61 and 65%. However, there was a significant sampling effect at the herd-level; all herds where at least 105 animals slaughtered over the study period exhibited evidence of fluke infection (100%). There was significant variation in terms of within-herd infection prevalence. Risk factors included herd type, long-term weather variation, geographic location (region) and the abattoir. CONCLUSIONS: Liver fluke apparent prevalence was high at the herd-level across years. However, there was lower prevalence at the animal level, which may indicate significant variation in the exposure to fluke infection within herds. The proportion infected within-herds varied significantly in time and space, and by abattoir, herd-type and some weather variables. These data are a useful source of information on a widespread endemic disease, despite known limitations in terms of test performance (low sensitivity). As well as informing on the distribution and severity of liver fluke infection, these analyses will be used to investigate the effect of co-infection on risk for bovine tuberculosis.

Risk factors for failure to detect bovine tuberculosis in cattle from infected herds across Northern Ireland (2004-2010).

Correctly identifying animals that are truly infected with a pathogen using ante-mortem tests is the cornerstone of any disease eradication programme. Failure to identify all infected animals will impede the progress towards controlling and eradicating disease and may also have unforeseen consequences when specific prevention measures are in place to avoid animal-to-animal transmission. In the case of bovine tuberculosis (bTB), the screening ante-mortem test, the Single Comparative Intradermal Tuberculin Test (SCITT), can exhibit moderate sensitivity which can result in a "hidden burden" of infection residing within the population. Using an animal-level dataset relating to the disclosure of infected cattle with Mycobacterium bovis, the causative agent of bTB within infected herds in Northern Ireland, we investigated what factors influenced the probability of an animal being a false-negative when truly infected (using post-mortem (PM) microbiological culture confirmation results to assess infection status). We found that different risk factors affected the probability of a test-negative outcome on infected animals depending on the ante-mortem test or their combination (SICTT and/or interferon gamma (IFN-É£) testing). Using multivariable models, SCITT disclosure performance varied significantly by age, location (region), and production type. The IFN-É£ tests were significantly affected by region or season, but these effects depended on the cut-off used during interpretation of the test which affected the tests characteristics. Parallel use of SCITT and IFN-É£ tests resulted in the least number of false-negatives, and their disclosure was affected by season and age-class. Understanding the factors that lead to the non-disclosure of infected animals is essential to optimise large-scale bTB disease eradication programmes.

Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data.

Diagnostic test sensitivity and specificity are probabilistic estimates with far reaching implications for disease control, management and genetic studies. In the absence of 'gold standard' tests, traditional Bayesian latent class models may be used to assess diagnostic test accuracies through the comparison of two or more tests performed on the same groups of individuals. The aim of this study was to extend such models to estimate diagnostic test parameters and true cohort-specific prevalence, using disease surveillance data. The traditional Hui-Walter latent class methodology was extended to allow for features seen in such data, including (i) unrecorded data (i.e. data for a second test available only on a subset of the sampled population) and (ii) cohort-specific sensitivities and specificities. The model was applied with and without the modelling of conditional dependence between tests. The utility of the extended model was demonstrated through application to bovine tuberculosis surveillance data from Northern and the Republic of Ireland. Simulation coupled with re-sampling techniques, demonstrated that the extended model has good predictive power to estimate the diagnostic parameters and true herd-level prevalence from surveillance data. Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.

Genomic prediction for tuberculosis resistance in dairy cattle.

BACKGROUND: The increasing prevalence of bovine tuberculosis (bTB) in the UK and the limitations of the currently available diagnostic and control methods require the development of complementary approaches to assist in the sustainable control of the disease. One potential approach is the identification of animals that are genetically more resistant to bTB, to enable breeding of animals with enhanced resistance. This paper focuses on prediction of resistance to bTB. We explore estimation of direct genomic estimated breeding values (DGVs) for bTB resistance in UK dairy cattle, using dense SNP chip data, and test these genomic predictions for situations when disease phenotypes are not available on selection candidates. METHODOLOGY/PRINCIPAL FINDINGS: We estimated DGVs using genomic best linear unbiased prediction methodology, and assessed their predictive accuracies with a cross validation procedure and receiver operator characteristic (ROC) curves. Furthermore, these results were compared with theoretical expectations for prediction accuracy and area-under-the-ROC-curve (AUC). The dataset comprised 1151 Holstein-Friesian cows (bTB cases or controls). All individuals (592 cases and 559 controls) were genotyped for 727,252 loci (Illumina Bead Chip). The estimated observed heritability of bTB resistance was 0.23±0.06 (0.34 on the liability scale) and five-fold cross validation, replicated six times, provided a prediction accuracy of 0.33 (95% C.I.: 0.26, 0.40). ROC curves, and the resulting AUC, gave a probability of 0.58, averaged across six replicates, of correctly classifying cows as diseased or as healthy based on SNP chip genotype alone using these data. CONCLUSIONS/SIGNIFICANCE: These results provide a first step in the investigation of the potential feasibility of genomic selection for bTB resistance using SNP data. Specifically, they demonstrate that genomic selection is possible, even in populations with no pedigree data and on animals lacking bTB phenotypes. However, a larger training population will be required to improve prediction accuracies.