Exploring the Fate of Cattle Herds With Inconclusive Reactors to the Tuberculin Skin Test

Mycobacterium tuberculosis complex detection in rural goat herds in South Africa using Bayesian latent class analysis

Animal tuberculosis affects a wide range of domestic and wild animal species, including goats (Capra hircus). In South Africa, Mycobacterium tuberculosis complex (MTBC) testing and surveillance in domestic goats is not widely applied, potentially leading to under recognition of goats as a potential source of M. bovis spread to cattle as well as humans and wildlife. The aim of this study was to estimate diagnostic test performance for four assays and determine whether M. bovis infection was present in goats sharing communal pastures with M. bovis positive cattle in the Umkhanyakude district of Northern Zululand, KwaZulu Natal. In 2019, 137 M. bovis-exposed goats were screened for MTBC infection with four diagnostic tests: the in vivo single intradermal comparative cervical tuberculin test (SICCT), in vitro QuantiFERON®-TB Gold (QFT) bovine interferon-gamma release assay (IGRA), QFT bovine interferon gamma induced protein 10 (IP-10) release assay (IPRA), and nasal swabs tested with the Cepheid GeneXpert® MTB/RIF Ultra (GXU) assay for detection of MTBC DNA. A Bayesian latent class analysis was used to estimate MTBC prevalence and diagnostic test sensitivity and specificity. Among the 137 M. bovis-exposed goats, positive test results were identified in 15/136 (11.0%) goats by the SICCT; 4/128 (3.1%) goats by the IPRA; 2/128 (1.6%) goats by the IGRA; and 26/134 (19.4%) nasal swabs by the GXU. True prevalence was estimated by our model to be 1.1%, suggesting that goats in these communal herds are infected with MTBC at a low level. Estimated posterior means across the four evaluated assays ranged from 62.7% to 80.9% for diagnostic sensitivity and from 82.9% to 97.9% for diagnostic specificity, albeit estimates of the former (diagnostic sensitivity) were dependent on model assumptions. The application of a Bayesian latent class analysis and multiple ante-mortem test results may improve detection of MTBC, especially when prevalence is low. Our results provide a foundation for further investigation to confirm infection in communal goat herds and identify previously unrecognized sources of intra- and inter-species transmission of MTBC.

Machine learning classification methods informing the management of inconclusive reactors at bovine tuberculosis surveillance tests in England

Bovine tuberculosis (bTB) remains one of the most complex, challenging, and costly animal health problems in England. Identifying and promptly removing all infected cattle from affected herds is key to its eradication strategy; the imperfect sensitivity of the diagnostic testing regime remaining a serious obstacle. The main diagnostic test for bTB in cattle in England, the Single Intradermal Comparative Cervical Tuberculin Test (SICCT: also known as the skin test), can produce inconclusive results below the reactor threshold. The immediate isolation of inconclusive reactor (IR) animals followed by a 60-day retest may not prevent lateral spread within the herd (if it is substandard, allowing transmission) or transmission to wildlife. Over half of IR-only herds that went on to have a positive skin test result (a bTB herd 'incident') in 2020, had it triggered by at least one IR not clearing their 60-day retest, instead of by another test within the previous 15 months. Machine learning classification algorithms (classification tree analysis and random forest), applied to England's 2012-2020 IR-only surveillance herd tests, identified at-risk tests for an incident at the IRs' 60-day retest. In this period, 4 739 out of 22 946 (21 %) IR-only surveillance tests disclosing 6 296 out of 42 685 total IRs, had an incident at retest (2 716 IRs became reactors and 3 580 IRs became two-time IRs). Both models showed an AUC above 80 % in the 2012-2019 dataset. Classification tree analysis was preferred due to its easy-to-interpret outputs, 70 % sensitivity, and 93 % specificity in the 20 % of 2019-2020 testing dataset. The paper aimed to identify IR-only surveillance tests at-risk of an incident at the 60-day retest to target them with appropriate measures to mitigate the IRs' risk. Sixteen percent (341 out of 2 177) of IR-only herd tests were identified as high-risk in the 2020 dataset, with 265 (78 %) of these having at least one reactor or IR at retest. Severe-level reinterpretation of the high-risk IR-only disclosing tests identified in this dataset would turn 68 out of the 590 (12 %) IRs into reactors, generating 23 incidents, the majority (19 or 83 %) part of the 265 incidents that would have been declared at the retest. Classification tree analysis used to identify IR-only high-risk tests in herds eligible for severe interpretation would enhance the sensitivity of the test-and-slaughter regime, cornerstone of the bTB eradication programme in England, further mitigating the risk of disease spread posed by IRs.

Occupational exposure and challenges in tackling M. bovis at human-animal interface: a narrative review

Zoonotic tuberculosis caused by Mycobacterium bovis (M. bovis), a member of Mycobacterium tuberculosis complex (MTBC) has increasingly gathered attention as a public health risk, particularly in developing countries with higher disease prevalence. M. bovis is capable of infecting multiple hosts encompassing a number of domestic animals, in particular cattle as well as a broad range of wildlife reservoirs. Humans are the incidental hosts of M. bovis whereby its transmission to humans is primarily through the consumption of cattle products such as unpasteurized milk or raw meat products that have been contaminated with M. bovis or the transmission could be due to close contact with infected cattle. Also, the transmission could occur through aerosol inhalation of infective droplets or infected body fluids or tissues in the presence of wound from infected animals. The zoonotic risk of M. bovis in humans exemplified by miscellaneous studies across different countries suggested the risk of occupational exposure towards M. bovis infection, especially those animal handlers that have close and unreserved contact with cattle and wildlife populations These animal handlers comprising of livestock farmers, abattoir workers, veterinarians and their assistants, hunters, wildlife workers as well as other animal handlers are at different risk of contracting M. bovis infection, depending on the nature of their jobs and how close is their interaction with infected animals. It is crucial to identify the underlying transmission risk factors and probable transmission pathways involved in the zoonotic transmission of M. bovis from animals to humans for better designation and development of specific preventive measures and guidelines that could reduce the risk of transmission and to protect these different occupational-related/populations at risk. Effective control and disease management of zoonotic tuberculosis caused by M. bovis in humans are also hindered by various challenges and factors involved at animal–human interface. A closer look into factors affecting proper disease control and management of M. bovis are therefore warranted. Hence, in this narrative review, we have gathered a number of different studies to highlight the risk of occupational exposure to M. bovis infection and addressed the limitations and challenges underlying this context. This review also shed lights on various components and approaches in tackling M. bovis infection at animal–human interface.

Cell-Mediated Immunological Biomarkers and Their Diagnostic Application in Livestock and Wildlife Infected With Mycobacterium bovis

Mycobacterium bovis has the largest host range of the Mycobacterium tuberculosis complex and infects domestic animal species, wildlife, and humans. The presence of global wildlife maintenance hosts complicates bovine tuberculosis (bTB) control efforts and further threatens livestock and wildlife-related industries. Thus, it is imperative that early and accurate detection of M. bovis in all affected animal species is achieved. Further, an improved understanding of the complex species-specific host immune responses to M. bovis could enable the development of diagnostic tests that not only identify infected animals but distinguish between infection and active disease. The primary bTB screening standard worldwide remains the tuberculin skin test (TST) that presents several test performance and logistical limitations. Hence additional tests are used, most commonly an interferon-gamma (IFN-γ) release assay (IGRA) that, similar to the TST, measures a cell-mediated immune (CMI) response to M. bovis. There are various cytokines and chemokines, in addition to IFN-γ, involved in the CMI component of host adaptive immunity. Due to the dominance of CMI-based responses to mycobacterial infection, cytokine and chemokine biomarkers have become a focus for diagnostic tests in livestock and wildlife. Therefore, this review describes the current understanding of host immune responses to M. bovis as it pertains to the development of diagnostic tools using CMI-based biomarkers in both gene expression and protein release assays, and their limitations. Although the study of CMI biomarkers has advanced fundamental understanding of the complex host-M. bovis interplay and bTB progression, resulting in development of several promising diagnostic assays, most of this research remains limited to cattle. Considering differences in host susceptibility, transmission and immune responses, and the wide variety of M. bovis-affected animal species, knowledge gaps continue to pose some of the biggest challenges to the improvement of M. bovis and bTB diagnosis.

A comparison of the value of two machine learning predictive models to support bovine tuberculosis disease control in England

Nearly a decade into Defra's current eradication strategy, bovine tuberculosis (bTB) remains a serious animal health problem in England, with c.30,000 cattle slaughtered annually in the fight against this insidious disease. There is an urgent need to improve our understanding of bTB risk in order to enhance the current disease control policy. Machine learning approaches applied to big datasets offer a potential way to do this. Regularized regression and random forest machine learning methodologies were implemented using 2016 herd-level data to generate the best possible predictive models for a bTB incident in England and its three surveillance risk areas (High-risk area [HRA], Edge area [EA] and Low-risk area [LRA]). Their predictive performance was compared and the best models in each area were used to characterize herds according to risk. While all models provided excellent discrimination, random forest models achieved the highest balanced accuracy (i.e. average of sensitivity and specificity) in England, HRA and LRA, whereas the regularized regression LASSO model did so in the EA. The time since the last confirmed incident was resolved was the only variable in the top-ten ranking in all areas according to both types of models, which highlights the importance of bTB history as a predictor of a new incident. Risk categorisation based on Receiver Operating Characteristic (ROC) analysis was carried out using the best predictive models in each area setting a 99 % threshold value for sensitivity and specificity (97 % in the LRA). Thirteen percent of herds in the whole of England as well as in its HRA, 14 % in its EA and 31 % in its LRA were classified as high-risk. These could be selected for the deployment of additional disease control measures at national or area level. In this way, low-risk herds within the area considered would not be penalised unnecessarily by blanket control measures and limited resources be used more efficiently. The methodology presented in this paper demonstrates a way to accurately identify high-risk farms to inform a targeted disease control and prevention strategy in England that supplements existing population strategies.

Optimisation of the tuberculin skin test for detection of Mycobacterium bovis in African buffaloes (Syncerus caffer)

Effective screening methods are critical for preventing the spread of bovine tuberculosis (bTB) among livestock and wildlife species. The tuberculin skin test (TST) remains the primary test for bTB globally, although performance is suboptimal. African buffaloes (Syncerus caffer) are a maintenance host of Mycobacterium bovis in South Africa, tested using the single intradermal tuberculin test (SITT) or comparative test (SICTT). The interpretation of these tests has been based on cattle thresholds due to the lack of species-specific cut-off values for African buffaloes. Therefore, the aims of this study were to calculate buffalo-specific thresholds for different TST criteria (SITT, SICTT, and SICTT_72h that calculates the differential change at 72 h only) and compare performance using these cut-off values. The results confirm that 3 mm best discriminates M. bovis-infected from unexposed control buffaloes with sensitivities of 69 % (95 % CI 60-78; SITT and SICTT) and 76 % (95 % CI 65-83; SICTT_72h), and specificities of 86 % (95 % CI 80-90; SITT), 96 % (95 % CI 92-98; SICTT_72h) and 97 % (95 % CI 93-99; SICTT), respectively. A comparison between TST criteria using buffalo-specific thresholds demonstrates that the comparative TST performs better than the SITT, although sensitivity remains suboptimal. Therefore, further research and the addition of ancillary tests, such as cytokine release assays, are necessary to improve M. bovis detection in African buffaloes.

Optimized interferon-gamma release assays for detection of Mycobacterium bovis infection in African buffaloes (Syncerus caffer)

The African buffalo (Syncerus caffer) is an economically and ecologically important wildlife species in South Africa; it is also a primary wildlife maintenance host of Mycobacterium bovis. Accurate and early detection of M. bovis infection in buffaloes is important for controlling transmission. Assays that detect cell-mediated immune responses to M. bovis in buffaloes have been developed although these often display suboptimal sensitivity or specificity. Therefore, the aim of this study was to evaluate the newly available Mabtech bovine interferon-gamma (IFN-γ) ELISA^PRO kit and optimize its use for detection of buffalo IFN-γ in whole blood samples stimulated with the QuantiFERON® TB Gold Plus antigens. Additionally, the test performance of the Mabtech IFN-γ release assay (IGRA) was compared to the currently used Cattletype® IGRA by determining buffalo-specific cut-off values for the two IGRAs and using gold standard-positive (M. bovis culture-confirmed) and M. bovis-unexposed negative cohorts. Validation of the Mabtech ELISA revealed negligible matrix interference and a linear and parallel response for recombinant bovine and native buffalo IFN-γ in the range 1.95-250 pg/mL. Intra- and inter-assay reproducibility produced coefficients of variation <5.5 % and <6.1 %, respectively, with a limit of detection at 3.2 pg/mL. Using receiver operator characteristic curve analyses, buffalo-specific cut-off values were calculated as 8 pg/mL for the Mabtech IGRA and 5 % (signal to positive control ratio) for the Cattletype® IGRA. The sensitivities were 89 % and 83 % for the Mabtech and Cattletype IGRAs with specificities of 94 % and 97 %, respectively. Although the species-specific cut-off values require further evaluation in a relevant test group, the results suggest that the Mabtech IGRA is a promising, sensitive and specific diagnostic tool for M. bovis detection in African buffaloes.

Epidemiological Study of Mycobacterium bovis Infection in Buffalo and Cattle in Amazonas, Brazil

Bovine Tuberculosis (BTB) is an endemic disease in about one hundred countries, affecting the economy causing a decrease in productivity, condemnation of meat, and damaging the credibility on international trade. Additionally, Mycobacterium bovis the major causative agent for BTB can also infect humans causing a variety of clinical presentations. The aim of this study was to determine BTB prevalence and the main risk factors for the Mycobacterium bovis prevalence in cattle and buffalos in Amazonas State, Brazil. Tissue samples from 151 animals (45 buffalo and 106 cattle from five herds with buffalo only, 22 herds with cattle only, and 12 herds with buffalo and cattle) were obtained from slaughterhouses under State Veterinary Inspection. M. bovis were isolated on Stonebrink medium. The positive cultures were confirmed by polymerase chain reaction (PCR) testing. The apparent herd and animal prevalence rates were 56.4 and 5.40%, respectively. Regarding animal species, the apparent prevalence rates were 3% in cattle and 11.8% in buffalo. Generalized Linear Mixed Models (GLMM) with random effect were used to assess the association with risk factors on the prevalence. Species (buffalo), herds size (>100 animals) and the presence of both species (buffalo and cattle) in the herd were the major risk factors for the infection by Mycobacterium bovis in the region. The findings reveal an urgent need for evidence-based effective intervention to reduce BTB prevalence in cattle and buffalo and prevent its spread to the human population. Studies are needed to understand why buffalo are more likely to be infected by M. bovis than cattle in Amazon. Recommendations for zoning, use of data from the inspection services to generate information regarding BTB focus, adoption of epidemiological tools, and discouragement of practices that promote the mixing of cattle and buffalo, were made.

Exploring the Risk Posed by Animals with an Inconclusive Reaction to the Bovine Tuberculosis Skin Test in England and Wales

The single intradermal comparative cervical tuberculin (SICCT) test is the primary test for ante-mortem diagnosis of bovine tuberculosis (TB) in England and Wales. When an animal is first classified as an inconclusive reactor (IR) using this test, it is not subject to compulsory slaughter, but it must be isolated from the rest of the herd. To understand the risk posed by these animals, a case-control study was conducted to measure the association between IR status of animals and the odds of them becoming a reactor to the SICCT at a subsequent test. The study included all animals from herds in which only IR animals were found at the first whole herd test in 2012 and used data from subsequent tests up until the end of 2016. Separate mixed-effects logistic regression models were developed to examine the relationship between IR status and subsequent reactor status for each risk area of England and for Wales, adjusting for other explanatory variables. The odds of an animal becoming a subsequent reactor during the study period were greater for IR animals than for negative animals in the high-risk area (odds ratio (OR): 6.85 (5.98-7.86)) and edge area (OR: 8.79 (5.92-13.04)) of England and in Wales (OR: 6.87 (5.75-8.22)). In the low-risk area of England, the odds were 23 times greater, although the confidence interval around this estimate was larger due to the smaller sample size (11-48, p < 0.001). These findings support the need to explore differential controls for IR animals to reduce the spread of TB, and they highlight the importance of area-specific policies.

Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making

Identifying and understanding the risk factors for endemic bovine tuberculosis (TB) in cattle herds is critical for the control of this disease. Exploratory machine learning techniques can uncover complex non-linear relationships and interactions within disease causation webs, and enhance our knowledge of TB risk factors and how they are interrelated. Classification tree analysis was used to reveal associations between predictors of TB in England and each of the three surveillance risk areas (High Risk, Edge, and Low Risk) in 2016, identifying the highest risk herds. The main classifying predictor for farms in England overall related to the TB prevalence in the 100 nearest cattle herds. In the High Risk and Edge areas it was the number of slaughterhouse destinations and in the Low Risk area it was the number of cattle tested in surveillance tests. How long ago the last confirmed incident was resolved was the most frequent classifier in trees; if within two years, leading to the highest risk group of herds in the High Risk and Low Risk areas. At least two different slaughterhouse destinations led to the highest risk group of herds in England, whereas in the Edge area it was a combination of no contiguous low-risk neighbours (i.e. in a 1 km radius) and a minimum proportion of 6-23 month-old cattle in November. A threshold value of prevalence in 100 nearest neighbours increased the risk in all areas, although the value was specific to each area. Having low-risk contiguous neighbours reduced the risk in the Edge and High Risk areas, whereas high-risk ones increased the risk in England overall and in the Edge area specifically. The best classification tree models informed multivariable binomial logistic regression models in each area, adding statistical inference outputs. These two approaches showed similar predictive performance although there were some disparities regarding what constituted high-risk predictors. Decision tree machine learning approaches can identify risk factors from webs of causation: information which may then be used to inform decision making for disease control purposes.