Farming on the edge: farmer attitudes to bovine tuberculosis in newly endemic areas

Enticott, Gareth; Maye, Damian; Carmody, Paul; Naylor, Rhiannon; Ward, Kim; Hinchliffe, Steve; Wint, William; Alexander, Neil; Elgin, R.; Ashton, Adam; Upton, Paul; Nicholson, R.; Goodchild, T.; Brunton, Lucy A.; Broughan, Jennifer M.

doi:10.1136/vr.103187

Cited by 18 publications

(13 citation statements)

References 9 publications

(16 reference statements)

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“…The SPSS "Extract" command was used to extract variables describing these seven components for the regression analysis. Detailed descriptions of farmer perceptions and attitudes towards bTB are presented elsewhere (Enticott, Maye et al 2015).…”

Section: Discussionmentioning

confidence: 99%

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Farm characteristics and farmer perceptions associated with bovine tuberculosis incidents in areas of emerging endemic spread

Broughan

Maye

Carmody

et al. 2016

Preventive Veterinary Medicine

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DisclaimerThe University of Gloucestershire has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material.The University of Gloucestershire makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited.The University of Gloucestershire makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights.The University of Gloucestershire accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement. Please cite this article as: Broughan

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Section: Discussionmentioning

confidence: 99%

“…The survey design and implementation has been described in detail by (Enticott, Maye et al 2015). The survey was thus designed to build on and extend work completed in previous epidemiological and social science research phases, combining social risk factors with farm practice and physical factors.…”

Section: Questionnaire Designmentioning

confidence: 99%

Farm characteristics and farmer perceptions associated with bovine tuberculosis incidents in areas of emerging endemic spread

Broughan

Maye

Carmody

et al. 2016

Preventive Veterinary Medicine

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“…This relatively weak influence of global prevalence, as opposed to local prevalence, has been also reported for farmers' trading behaviors. Although some farmers, but not all, avoid purchasing livestock from a high disease risk area for both endemic ( 32 , 33 ) and exotic diseases ( 34 , 35 ), it is unclear whether or not farmers are engaged in the risk-averse trading in response to disease prevalence. In fact, a UK study showed that the proportion of farmers from low bTB risk areas who mentioned they do not purchase cattle from high risk areas was larger than that of farmers from high bTB risk areas; however, farmers listed maintaining an existing trade channel as the main reason for this behavior ( 36 ).…”

Section: Disease-related Factors Relevant To Farmers' Dynamic Behaviomentioning

confidence: 99%

“…One of the most studied factors that contribute to forming farmers' descriptive belief may be actual disease experience. Enticott et al investigated practices and attitudes toward bTB among farmers in an area which had been recently designated as bTB endemic ( 32 ). This study found that the proportions of farmers that avoid purchasing from a high bTB risk area were similar between those previously had a bTB breakdown and those not ( 32 ), which may suggest a direct bTB experience may not necessarily change farmers' trading behaviors.…”

Section: Disease-related Factors Relevant To Farmers' Dynamic Behaviomentioning

confidence: 99%

Modeling Dynamic Human Behavioral Changes in Animal Disease Models: Challenges and Opportunities for Addressing Bias

et al. 2018

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Over the past several decades, infectious disease modeling has become an essential tool for creating counterfactual scenarios that allow the effectiveness of different disease control policies to be evaluated prior to implementation in the real world. For livestock diseases, these models have become increasingly sophisticated as researchers have gained access to rich national livestock traceability databases, which enables inclusion of explicit spatial and temporal patterns in animal movements through network-based approaches. However, there are still many limitations in how we currently model animal disease dynamics. Critical among these is that many models make the assumption that human behaviors remain constant over time. As many studies have shown, livestock owners change their behaviors around trading, on-farm biosecurity, and disease management in response to complex factors such as increased awareness of disease risks, pressure to conform with social expectations, and the direct imposition of new national animal health regulations; all of which may significantly influence how a disease spreads within and between farms. Failing to account for these dynamics may produce a substantial layer of bias in infectious disease models, yet surprisingly little is currently known about the effects on model inferences. Here, we review the growing evidence on why these assumptions matter. We summarize the current knowledge about farmers' behavioral change in on-farm biosecurity and livestock trading practices and highlight the knowledge gaps that prohibit these behavioral changes from being incorporated into disease modeling frameworks. We suggest this knowledge gap can be filled only by more empirical longitudinal studies on farmers' behavioral change as well as theoretical modeling studies that can help to identify human behavioral changes that are important in disease transmission dynamics. Moreover, we contend it is time to shift our research approach: from modeling a single disease to modeling interactions between multiple diseases and from modeling a single farmer behavior to modeling interdependencies between multiple behaviors. In order to solve these challenges, there is a strong need for interdisciplinary collaboration across a wide range of fields including animal health, epidemiology, sociology, and animal welfare.

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“…Many of the stakeholders in this problem have polarised views and as such the problem has political dimensions that are similar to those around the debate about climatic change (Caplan, 2012;Nisbet & Markowitz, 2015). Stakeholders are broadly divided into those that feel that a wild animal reservoir host (in GB this would be badgers) is the principal cause of disease outbreaks by reintroducing infection to otherwise clean cattle herds, and those who feel that it is to cattle that attention needs to be directed and thus there is a conflict (King et al, 2007;Brooks-Pollock et al, 2014;Macdonald & Feber, 2015) -see also farmers view (Cowie et al, 2015;Enticott et al, 2015;O'Hagan et al, in press). The role of science in this debate should be to provide objective advice and guidance on the best way to tackle the disease.…”

Section: Introductionmentioning

confidence: 99%

A big-data spatial, temporal and network analysis of bovine tuberculosis between wildlife (badgers) and cattle

Moustakas

Evans

2016

Stoch Environ Res Risk Assess

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Bovine tuberculosis (TB) poses a serious threat for agricultural industry in several countries, it involves potential interactions between wildlife and cattle and creates societal problems in terms of human-wildlife conflict. This study addresses connectedness network analysis, the spatial, and temporal dynamics of TB between cattle in farms and the European badger (Meles meles) using a large dataset generated by a calibrated agent based model. Results showed that infected network connectedness was lower in badgers than in cattle. The contribution of an infected individual to the mean distance of disease spread over time was considerably lower for badger than cattle; badgers mainly spread the disease locally while cattle infected both locally and across longer distances. The majority of badger-induced infections occurred when individual badgers leave their home sett, and this was positively correlated with badger population growth rates. Point pattern analysis indicated aggregation in the spatial pattern of TB prevalence in badger setts across all scales. The spatial distribution of farms that were not TB free was aggregated at different scales than the spatial distribution of infected badgers and became random at larger scales. The spatial cross correlation between infected badger setts and infected farms revealed that generally infected setts and farms do not coexist except at few scales. Temporal autocorrelation detected a two year infection cycle for badgers, while there was both within the year and longer cycles for infected cattle. Temporal cross correlation indicated that infection cycles in badgers and cattle are negatively correlated. The implications of these results for understanding the dynamics of the disease are discussed.

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Farming on the edge: farmer attitudes to bovine tuberculosis in newly endemic areas

Cited by 18 publications

References 9 publications

Farm characteristics and farmer perceptions associated with bovine tuberculosis incidents in areas of emerging endemic spread

Farm characteristics and farmer perceptions associated with bovine tuberculosis incidents in areas of emerging endemic spread

Modeling Dynamic Human Behavioral Changes in Animal Disease Models: Challenges and Opportunities for Addressing Bias

A big-data spatial, temporal and network analysis of bovine tuberculosis between wildlife (badgers) and cattle

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