Difference in differences analysis evaluates the effects of the badger control policy on bovine tuberculosis in England.

Persistent tuberculosis (TB) in cattle populations in England has been associated with an exchange of infection with badgers (Meles meles). A badger control policy (BCP) commenced in 2013. Its aim was to decrease TB incidence in cattle by reducing the badger population available to provide a wildlife reservoir for bovine TB. Monitoring data from 52 BCP intervention areas 200-1600 km2 in size, starting over several years, were used to estimate the change in TB incidence rate in cattle herds, which was associated with time since the start of the BCP in each area. A difference in differences analysis addressed the non-random selection and starting sequence of the areas. The herd incidence rate of TB reduced by 56% (95% Confidence Interval 41-69%) up to the fourth year of BCP interventions, with the largest drops in the second and third years. There was insufficient evidence to judge whether the incidence rate reduced further beyond 4 years. These estimates are the most precise for the timing of declines in cattle TB associated with interventions primarily targeting badgers. They are within the range of previous estimates from England and Ireland. This analysis indicates the importance of reducing transmission from badgers to reduce the incidence of TB in cattle, noting that vaccination of badgers, fertility control and on farm biosecurity may also achieve this effect.

Assessing the potential impact of applying a higher sensitivity test to selected cattle populations for the control of bovine tuberculosis in England.

Bovine tuberculosis (bTB) continues to be the costliest, most complex animal health problem in England. The effectiveness of the test-and-slaughter policy is hampered by the imperfect sensitivity of the surveillance tests. Up to half of recurrent incidents within 24 months of a previous one could have been due to undetected infected cattle not being removed. Improving diagnostic testing with more sensitive tests, like the interferon (IFN)-gamma test, is one of the government's top priorities. However, blanket deployment of such tests could result in more false positive results (due to imperfect specificity), together with logistical and cost-efficiency challenges. A targeted application of such tests in higher prevalence scenarios, such as a subpopulation of high-risk herds, could mitigate against these challenges. We developed classification machine learning algorithms (using 80% of 2012-2019 bTB surveillance data as the training set) to evaluate the deployment of IFN-gamma testing in high-risk herds (i.e. those at risk of an incident in England) in two testing data sets: i) the remaining 20% of 2012-19 data, and ii) 2020 bTB surveillance data. The resulting model, classification tree analysis, with an area under a receiver operating characteristic (ROC) curve (AUC) > 95, showed a 73% sensitivity and a 97% specificity in the 2012-2019 test dataset. Used on 2020 data, it predicted eight percent (3 510 of 41 493) of eligible active herds as at-risk of a bTB incident, the majority of them (66% or 2 328 herds) experiencing at least one. Whilst all predicted at-risk herds could have preventive measures applied, the additional application of IFN-gamma test in parallel interpretation to the statutory skin test, if the risk materialises, would have resulted in 8 585 additional IFN-gamma reactors detected (a 217% increase over the 2 710 IFN-gamma reactors already detected by tests carried out). Only 18% (330 of 1 819) of incidents in predicted high-risk herds had the IFN-gamma test applied in 2020. We therefore conclude that this methodology provides a better way of directing the application of the IFN-gamma test towards the high-risk subgroup of herds. Classification tree analysis ensured the systematic identification of high-risk herds to consistently apply additional measures in a targeted way. This could increase the detection of infected cattle more efficiently, preventing recurrence and accelerating efforts to achieve eradication by 2038. This methodology has wider application, like targeting improved biosecurity measures in avian influenza at-risk farms to limit damage to the industry in future outbreaks.

Detection of a local Mycobacterium bovis reservoir using cattle surveillance data.

The incidence of bovine tuberculosis (TB, caused by Mycobacterium bovis) in cattle has been associated with TB in badgers (Meles meles) in parts of England. The aim was to identify badger-associated M. bovis reservoirs in the Edge Area, between the High- and Low-Risk Areas for cattle TB. Data from badger TB surveys were sparse. Therefore, a definition for a local M. bovis reservoir potentially shared by cattle and badgers was developed using cattle TB surveillance data. The performance of the definition was estimated through Latent Class Analysis using badger TB survey data. Spatial units (25 km2 ) in the Edge Area were classified as having a reservoir if they had (i) at least one TB incident in at least three of the previous 7 years, (ii) at least one TB incident in a cattle herd confirmed by post-mortem tests as due to M. bovis infection and not attributable to cattle movements in the previous 2 years and (iii) more confirmed TB incidents than un-confirmed in the previous 2 years. Approximately 20% of the Edge Area was classified as having a local M. bovis reservoir using the cattle-based definition. Assuming 15% TB prevalence in Edge Area badgers, sensitivity for the local M. bovis reservoir definition varied from 25.7% [95% credible interval (CrI): 10.7%-85.1%] to 64.8% (95% CrI: 48.1%-88.0%). Specificity was 91.9% (CrI: 83.6%-97.4%). Over 90% of the local reservoir was in stable endemic TB areas identified through previous work and its spatial distribution was largely consistent with local veterinary knowledge. Uncertainty in the reservoir spatial distribution was explored through its recalculation in spatial units shifted in different directions. We recommend that the definition is re-evaluated as further data on badger infection with M. bovis become available.

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.

Tuberculosis in badgers where the bovine tuberculosis epidemic is expanding in cattle in England.

Bovine tuberculosis (bTB) is an important animal health and economic problem for the cattle industry and a potential zoonotic threat. Wild badgers (Meles meles) play a role on its epidemiology in some areas of high prevalence in cattle, particularly in the UK and Republic of Ireland and increasingly in parts of mainland Europe. However, little is known about the involvement of badgers in areas on the spatial edge of the cattle epidemic, where increasing prevalence in cattle is seen. Here we report the findings of a study of found-dead (mainly road-killed) badgers in six counties on the edge of the English epidemic of bTB in cattle. The overall prevalence of Mycobacterium tuberculosis complex (MTC) infection detected in the study area was 51/610 (8.3%, 95% CI 6.4-11%) with the county-level prevalence ranging from 15 to 4-5%. The MTC spoligotypes of recovered from badgers and cattle varied: in the northern part of the study area spoligotype SB0129 predominated in both cattle and badgers, but elsewhere there was a much wider range of spoligotypes found in badgers than in cattle, in which infection was mostly with the regional cattle spoligotype. The low prevalence of MTC in badgers in much of the study area, and, relative to in cattle, the lower density of sampling, make firm conclusions difficult to draw. However, with the exception of Cheshire (north-west of the study area), little evidence was found to link the expansion of the bTB epidemic in cattle in England to widespread badger infection.

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.

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.

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.

Assessing effects from four years of industry-led badger culling in England on the incidence of bovine tuberculosis in cattle, 2013-2017.

The objective was to measure the association between badger culling and bovine tuberculosis (TB) incidents in cattle herds in three areas of England between 2013-2017 (Gloucestershire and Somerset) and 2015-2017 (Dorset). Farming industry-selected licensed culling areas were matched to comparison areas. A TB incident was detection of new Mycobacterium bovis infection (post-mortem confirmed) in at least one animal in a herd. Intervention and comparison area incidence rates were compared in central zones where culling was conducted and surrounding buffer zones, through multivariable Poisson regression analyses. Central zone incidence rates in Gloucestershire (Incidence rate ratio (IRR) 0.34 (95% CI 0.29 to 0.39, p < 0.001) and Somerset (IRR 0.63 (95% CI 0.58 to 0.69, p < 0.001) were lower and no different in Dorset (IRR 1.10, 95% CI 0.96 to 1.27, p = 0.168) than comparison central zone rates. The buffer zone incidence rate was lower for Gloucestershire (IRR 0.64, 95% CI 0.58 to 0.70, p < 0.001), no different for Somerset (IRR 0.97, 95% CI 0.80 to 1.16, p = 0.767) and lower for Dorset (IRR 0.45, 95% CI 0.37 to 0.54, p < 0.001) than comparison buffer zone rates. Industry-led culling was associated with reductions in cattle TB incidence rates after four years but there were variations in effects between areas.

A study of tuberculosis in road traffic-killed badgers on the edge of the British bovine TB epidemic area.

The role of badgers in the geographic expansion of the bovine tuberculosis (bTB) epidemic in England is unknown: indeed there have been few published studies of bTB in badgers outside of the Southwest of England where the infection is now endemic in cattle. Cheshire is now on the edge of the expanding area of England in which bTB is considered endemic in cattle. Previous studies, over a decade ago when bovine infection was rare in Cheshire, found no or only few infected badgers in the south eastern area of the county. In this study, carried out in 2014, road-killed badgers were collected through a network of local stakeholders (farmers, veterinarians, wildlife groups, government agencies), and Mycobacterium bovis was isolated from 21% (20/94) badger carcasses. Furthermore, there was strong evidence for co-localisation of M. bovis SB0129 (genotype 25) infection in both badgers and cattle herds at a county scale. While these findings suggest that both badgers and cattle are part of the same geographically expanding epidemic, the direction of any cross-species transmission and the drivers of this expansion cannot be determined. The study also demonstrated the utility of using road-killed badgers collected by stakeholders as a means of wildlife TB surveillance.

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

Bovine tuberculosis (TB) is an important animal health issue in many parts of the world. In England and Wales, the primary test to detect infected animals is the single intradermal comparative cervical tuberculin test, which compares immunological responses to bovine and avian tuberculins. Inconclusive test reactors (IRs) are animals that demonstrate a positive reaction to the bovine tuberculin only marginally greater than the avian reaction, so are not classified as reactors and immediately removed. In the absence of reactors in the herd, IRs are isolated, placed under movement restrictions and re-tested after 60 days. Other animals in these herds at the time of the IR result are not usually subject to movement restrictions. This could affect efforts to control TB if undetected infected cattle move out of those herds before the next TB test. To improve our understanding of the importance of IRs, this study aimed to assess whether median survival time and the hazard of a subsequent TB incident differs in herds with only IRs detected compared with negative-testing herds. Survival analysis and extended Cox regression were used, with herds entering the study on the date of the first whole herd test in 2012. An additional analysis was performed using an alternative entry date to try to remove the impact of IR retesting and is presented in the Supplementary Material. Survival analysis showed that the median survival time among IR only herds was half that observed for clear herds (2.1 years and 4.2 years respectively; p < 0.001). Extended Cox regression analysis showed that IR-only herds had 2.7 times the hazard of a subsequent incident compared with negative-testing herds in year one (hazard ratio: 2.69; 95% CI: 2.54, 2.84; p < 0.001), and that this difference in the hazard reduced by 63% per year. After 2.7 years the difference had disappeared. The supplementary analysis supported these findings showing that IR only herds still had a greater hazard of a subsequent incident after the IR re-test, but that the effect was reduced. This emphasizes the importance of careful decision making around the management of IR animals and indicates that re-testing alone may not be sufficient to reduce the risk posed by IR only herds in England and Wales.

Assessing the effects of the first 2 years of industry-led badger culling in England on the incidence of bovine tuberculosis in cattle in 2013-2015.

Culling badgers to control the transmission of bovine tuberculosis (TB) between this wildlife reservoir and cattle has been widely debated. Industry-led culling began in Somerset and Gloucestershire between August and November 2013 to reduce local badger populations. Industry-led culling is not designed to be a randomized and controlled trial of the impact of culling on cattle incidence. Nevertheless, it is important to monitor the effects of the culling and, taking the study limitations into account, perform a cautious evaluation of the impacts. A standardized method for selecting areas matched to culling areas in factors found to affect cattle TB risk has been developed to evaluate the impact of badger culling on cattle TB incidence. The association between cattle TB incidence and badger culling in the first 2 years has been assessed. Descriptive analyses without controlling for confounding showed no association between culling and TB incidence for Somerset, or for either of the buffer areas for the first 2 years since culling began. A weak association was observed in Gloucestershire for Year 1 only. Multivariable analysis adjusting for confounding factors showed that reductions in TB incidence were associated with culling in the first 2 years in both the Somerset and Gloucestershire intervention areas when compared to areas with no culling (incidence rate ratio (IRR): 0.79, 95% CI: 0.72-0.87, p < .001 and IRR: 0.42, 95% CI: 0.34-0.51, p < .001, respectively). An increase in incidence was associated with culling in the 2-km buffer surrounding the Somerset intervention area (IRR: 1.38, 95% CI: 1.09-1.75, p = .008), but not in Gloucestershire (IRR: 0.91, 95% CI: 0.77-1.07, p = .243). As only 2 intervention areas with 2 years of data are available for analysis, and the biological cause-effect relationship behind the statistical associations is difficult to determine, it would be unwise to use these findings to develop generalizable inferences about the effectiveness of the policy at present.