Rapid detection and molecular epidemiology of ß-lactamase producing Enterobacteriaceae isolated from food animals and in-contact humans in Nigeria.

The emergence and spread of ß-lactamase-producing Enterobacteriaceae poses a significant threat to public health, necessitating the rapid detection and investigation of the molecular epidemiology of these pathogens. We modified a multiplex real-time (RT)-PCR to concurrently detect ß-lactamase genes (blaCTX-M, blaTEM, and blaSHV) and Enterobacteriaceae 16S ribosomal RNA. qPCR probes and primers were validated using control isolates, and the sensitivity and specificity assessed. The optimised multiplex qPCR was used to screen 220 non-clinical Enterobacteriaceae from food animals and in-contact humans in Southeast Nigeria selected on cefotaxime-supplemented agar plates. Binary logistic regression was used to explore factors associated with the presence of the blaTEM and blaSHV genes in these isolates, and a subset of isolates from matched sampling sites and host species were whole genome sequenced, and their antimicrobial resistance (AMR) and plasmid profiles determined. The sensitivity and specificity of the qPCR assay was 100%. All isolates (220/220) were positive for Enterobacteriaceae ribosomal 16S rRNA and blaCTX-M, while 66.4% (146/220) and 9% (20/220) were positive for blaTEM and blaSHV, respectively. The prevalence of blaTEM and blaSHV varied across different sampling sites (farm, animal market and abattoirs). Isolates from Abia state were more likely to harbour blaTEM (OR = 2.3, p = 0.04) and blaSHV (OR = 5.12,p = 0.01) than isolates from Ebonyi state; blaTEM was more likely to be detected in isolates from food animals than humans (OR = 2.34, p = 0.03), whereas the reverse was seen for blaSHV (OR = 7.23, p = 0.02). Furthermore, Klebsiella and Enterobacter isolates harboured more AMR genes than Escherichia coli, even though they were isolated from the same sample. We also identified pan resistant Klebsiella harbouring resistance to ten classes of antimicrobials and disinfectant. Therefore, we recommend ESKAPE pathogens are included in AMR surveillance in future and suggest qPCRs be utilised for rapid screening of Enterobacteriaceae from human and animal sources.

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.

Molecular characterization of extended spectrum cephalosporin resistant Escherichia coli isolated from livestock and in-contact humans in Southeast Nigeria.

The rise in antimicrobial resistance (AMR) in bacteria is reducing therapeutic options for livestock and human health, with a paucity of information globally. To fill this gap, a One-Health approach was taken by sampling livestock on farms (n = 52), abattoir (n = 8), and animal markets (n = 10), and in-contact humans in Southeast Nigeria. Extended spectrum cephalosporin (ESC)-resistant (ESC-R) Escherichia coli was selectively cultured from 975 healthy livestock faecal swabs, and hand swabs from in-contact humans. Antimicrobial susceptibility testing (AST) was performed on all ESC-R E. coli. For isolates showing a multi-drug resistance (MDR) phenotype (n = 196), quantitative real-time PCR (qPCR) was performed for confirmation of extended-spectrum ß-lactamase (ESBL) and carbapenemase genes. Whole-genome sequencing (WGS) was performed on a subset (n = 157) for detailed molecular characterisation. The results showed ESC-R E. coli was present in 41.2% of samples, with AST results indicating 48.8% of isolates were phenotypically MDR. qPCR confirmed presence of ESBL genes, with bla CTX-M present in all but others in a subset [bla TEM (62.8%) and bla SHV (0.5%)] of isolates; none harboured transferable carbapenemase genes. Multi-locus sequence typing identified 34 Sequence Types (ST) distributed among different sampling levels; ST196 carrying bla CTX-M-55 was predominant in chickens. Large numbers of single nucleotide polymorphisms (SNPs) in the core genome of isolates, even within the same clade by phylogenetic analysis, indicated high genetic diversity. AMR genotyping indicated the predominant bla CTX-M variant was bla CTX-M-15 (87.9%), although bla CTX-M-55, bla CTX-M-64, and bla CTX-M-65 were present; it was notable that bla CTX-M-1, common in livestock, was absent. Other predominant AMR genes included: sul2, qnrS1, strB, bla TEM-1b, tetA-v2, and dfrA14, with prevalence varying according to host livestock species. A bla CTX-M-15 harbouring plasmid from livestock isolates in Ebonyi showed high sequence identity to one from river/sewage water in India, indicating this ESBL plasmid to be globally disseminated, being present beyond the river environment. In conclusion, ESC-R E. coli was widespread in livestock and in-contact humans from Southeast Nigeria. WGS data indicated the isolates were genetically highly diverse, probably representing true diversity of wild type E. coli; they were likely to be MDR with several harbouring bla CTX-M-15. Surprisingly, human isolates had highest numbers of AMR genes and pigs the least.

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.

Systems-thinking approach to identify and assess feasibility of potential interventions to reduce antibiotic use in tilapia farming in Egypt.

Antibiotics are used in aquaculture to maintain the health and welfare of stocks; however, the emergence and selection of antibiotic resistance in bacteria poses threats to humans, animals and the environment. Mitigation of antibiotic resistance relies on understanding the flow of antibiotics, residues, resistant bacteria and resistance genes through interconnecting systems, so that potential solutions can be identified and issues around their implementation evaluated. Participatory systems-thinking can capture the deep complexity of a system while integrating stakeholder perspectives. In this present study, such an approach was applied to Nile tilapia (Oreochromis niloticus) production in the Nile Delta of Egypt, where disease events caused by antibiotic-resistant pathogens have been reported. A system map was co-produced with aquaculture stakeholders at a workshop in May 2018 and used to identify hotspots of antibiotic use, exposure and fate and to describe approaches that would promote fish health and thus reduce antibiotic use. Antibiotics are introduced into the aquaculture system via direct application for example in medicated feed, but residues may also be introduced into the system through agricultural drainage water, which is the primary source of water for most fish farms in Egypt. A follow-up survey of stakeholders assessed the perceived feasibility, advantages and disadvantages of potential interventions. Interventions that respondents felt could be implemented in the short-term to reduce antibiotic usage effectively included: more frequent water exchanges, regular monitoring of culture water quality parameters, improved storage conditions for feed, use of probiotics and greater access to farmer and service providers training programmes. Other potential interventions included greater access to suitable and rapid diagnostics, high quality feeds, improved biosecurity measures and genetically-improved fish, but these solutions were expected to be achieved as long-term goals, with cost being of one of the noted barriers to implementation. Identifying feasible and sustainable interventions that can be taken to reduce antibiotic use, and understanding implementation barriers, are important for addressing antibiotic resistance and ensuring the continued efficacy of antibiotics. This is vital to ensuring the productivity of the tilapia sector in Egypt. The approach taken in the present study provides a means to identify points in the system where the effectiveness of interventions can be evaluated and thus it may be applied to other food production systems to combat the problem of antibiotic resistance.

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.

Identifying hotspots for antibiotic resistance emergence and selection, and elucidating pathways to human exposure: Application of a systems-thinking approach to aquaculture systems.

Aquaculture systems are highly complex, dynamic and interconnected systems influenced by environmental, biological, cultural, socio-economic and human behavioural factors. Intensification of aquaculture production is likely to drive indiscriminate use of antibiotics to treat or prevent disease and increase productivity, often to compensate for management and husbandry deficiencies. Surveillance or monitoring of antibiotic usage (ABU) and antibiotic resistance (ABR) is often lacking or absent. Consequently, there are knowledge gaps for the risk of ABR emergence and human exposure to ABR in these systems and the wider environment. The aim of this study was to use a systems-thinking approach to map two aquaculture systems in Vietnam - striped catfish and white-leg shrimp - to identify hotspots for emergence and selection of resistance, and human exposure to antibiotics and antibiotic-resistant bacteria. System mapping was conducted by stakeholders at an interdisciplinary workshop in Hanoi, Vietnam during January 2018, and the maps generated were refined until consensus. Thereafter, literature was reviewed to complement and cross-reference information and to validate the final maps. The maps and component interactions with the environment revealed the grow-out phase, where juveniles are cultured to harvest size, to be a key hotspot for emergence of ABR in both systems due to direct and indirect ABU, exposure to water contaminated with antibiotics and antibiotic-resistant bacteria, and duration of this stage. The pathways for human exposure to antibiotics and ABR were characterised as: occupational (on-farm and at different handling points along the value chain), through consumption (bacterial contamination and residues) and by environmental routes. By using systems thinking and mapping by stakeholders to identify hotspots we demonstrate the applicability of an integrated, interdisciplinary approach to characterising ABU in aquaculture. This work provides a foundation to quantify risks at different points, understand interactions between components, and identify stakeholders who can lead and implement change.

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.