A qualitative content analysis of factors influencing British dairy farmers’ willingness to share antibiotic usage data

A qualitative content analysis of factors influencing British dairy farmers’ willingness to share antibiotic usage data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A qualitative content analysis of factors influencing British dairy farmers’ willingness to share antibiotic usage data Camilla Strang, Lucy Brunton, Pablo Alarcon, Jacqueline M Cardwell This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7037458/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Centralised capture of accurate farm-level data on antibiotic usage (ABU) is needed for surveillance of antibiotic resistance. The objectives of this study were to describe the factors influencing British dairy farmers willingness to 1) submit their ABU data to a centralised medicine hub (MH) and 2) allow third-party access to such data. An inductive qualitative content analysis was undertaken on data collected in person from 94 dairy farmers in South West England and Wales. Participants answered a closed survey question on use of a centralised MH, and provided an explanation for their response. Factors affecting participants’ willingness to share ABU by using a centralised system were that they had nothing to hide, they trusted their veterinarian with their data, and perceived that it would provide useful information for farmers and veterinarians. However, participants had a fear of losing control over decision-making and therefore wanted to control access to their data. They also felt overwhelmed by data demands, but suggested that data sharing is already happening and inevitable. Participants in this study were more likely to have positive viewpoints of sharing ABU due to the self-selection process. These findings suggest that, overall, farmers in this study are happy to share their ABU data, and recognise the potential benefits it could bring for herd health. However, they also highlight challenges that need to be addressed to ensure successful implementation of a centralised data collection system. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research 1. Introduction The UK livestock industry has successfully reduced antibiotic usage (ABU) since 2015 1–8 . Despite this success, it has been recognised that antibiotics need to be monitored in each livestock species to optimise usage. Optimal monitoring requires the development of species-specific data capture systems using computerised methods. This has been achieved in the pig, poultry and aquaculture sectors on a voluntary basis with high levels of surveillance coverage (≥ 90%) (8,9). Monitoring of ABU also involves the setting of annual targets for each livestock sector to meet. Targets have been introduced to minimise but maintain appropriate usage in response to the critical concern of antibiotic resistance 9 . Where sectors have good ABU data availability through surveillance systems, for example the SDa in the Netherlands and the UK electronic Medicine Book – Pigs’ (eMB-Pigs), targets are generally met 10 , 11 . However, when there is minimal data, or supplied datasets are not robust, then sector progress is difficult to demonstrate as results are limited in giving a representative picture of usage. This is the situation for the UK dairy sector. Annual ABU in the dairy sector seems to be much lower than in the pig sector. However, the pig sector has 95% surveillance coverage, whereas dairy has only 28% coverage from a voluntary sample, meaning that this estimate is less accurate 12 . The reality of ABU in the dairy sector therefore remains unclear. The high level of coverage in the UK pig sector has been achieved through the development of the eMB-Pigs by the Agriculture and Horticulture Development Board (AHDB). The system was introduced in 2016; over the subsequent four-year period coverage increased from 17–95% and has been maintained since. In 2021, the AHDB adapted and introduced this electronic medicine book for the ruminant sector, where it is known as the Medicine Hub (MH). By collating national sector-level data, the MH is anticipated to promote the reputation of the industry and support trade, provide evidence to support the RUMA (Responsible Use of Medicines in Agriculture Alliance) targets, and help implement the UK’s future approach to veterinary medicines regulation. It is believed that accurate ABU data will become fundamental for successful trade. The hub will also provide consistency through benchmarking, which is perceived to give value to farmers. Benchmarking allows farmers to measure their business against the rest of their sector, and develop farm-specific herd health plans with their veterinarian to improve management practices and monitor their herds health and welfare. This tool is also perceived as a valuable communication aid between farmers and veterinarians, driving behaviour change and therefore progression 1 , 13 . However, as for any group where policy interventions seek to drive behaviour change, it is recognized that farmers do not always behave in the way government, scientists and veterinarians would like them to 14 , 15 . Motivations for changing behaviour or decision-making vary from farmer to farmer, depending on their values and experience, and are often passed down through generations 14 . Therefore, understanding the rationale for behaviours is often key to implementing successful policy and interventions involving behaviour change. On this basis, the objectives of this study were to explore dairy farmers’ willingness to 1) submit their ABU data to a centralised MH and 2) allow third-party access to such data. 2. Results 2.1 Participant characteristics A total of 103 dairy farmers completed the questionnaire. Of these, 58 (56%) used software for their medicine records and 26 (25%) were on an aligned milk contract. The median herd size was 180 (range 45–1250). Further details on enterprise and management characteristics can be found in a previous publication 16 . All participants provided a response to the closed question about use of a centralised MH and third-party access to their ABU data. Ninety-four (91.2%) participants provided an explanation of their response. Comparison of participants that did not respond found that the highest proportion of non-responses was from those aged 60 or above, with 62.5% not providing an explanation. As age increased, participants were less likely to respond, with 20.8% from those aged 50–59 not responding, compared to 7.7% from those aged 40–49, and 2.8% from those aged less than 40 (p-value = 0.01). 2.2 Factors influencing willingness to share data and use the MH 2.2.1 Farmers have nothing to hide Some participants responded that they had nothing to hide when asked how they would feel if their antibiotic records were disclosed to the MH and third parties. This was because all antibiotics given were regarded as necessary to maintain animal welfare, and therefore none of the treatments were deemed inappropriate. In particular, as the use of highest priority critically important antibiotics (HP-CIA) had been restricted under the 2017 Red Tractor Assurance Scheme Standards for Dairy, Beef and Sheep in conjunction with the BCVA 2 , participants considered that all treatments were appropriate providing such antibiotics had not been used: ‘…not worried, especially as we’re not using critically important antibiotics’ (Farmer, ID 79) and ‘not concerned…all treatments are necessary’ (Farm manager, ID 19). Some participants had no concerns about sharing their data, especially amongst those who already share their data with their veterinarian and their supply chain: ‘Vets and Tesco ask for antibiotic usage so already supplying, therefore no issue’ (Farmer, ID 58); ‘ already give information to my milk buyer quarterly ’ (Farmer, ID 99). Considered in this light, sharing the data with a centralised MH was perceived as just another evolutionary development in farm data, rather than a complete revolution in data recording: ‘No objections to using. Industry always evolving, new things coming up’ (Family member, ID 67). Even those who had yet to share their data felt it was inevitable, and were resigned to it: ‘ it’s inevitable – we will have to do it ’ (Farmer, ID 66); ‘ nothing to hide and understand will come into place, it’s marketing for selling your product ’ (Farmer, ID 73). These views often went further than a straightforward openness to sharing data. Participants also wanted a level playing field, and there was a perception that other farmers sometimes did not play by the rules: ‘Farmers have to be more honest’ (Farmer, ID 42) and ‘Some farms need a kick up the arse’ (Farmer, ID 32). Some felt that others would need an incentive to use a centralised MH, such as it being a requirement: ‘Nothing to hide…if it becomes compulsory then it’s not a problem (Farmer, ID 15). Sharing veterinary sales data was suggested by some as a better way to achieve a level playing field: ‘vet data is more comparable, level playing field as more accurate data’ (Farmer, ID 89). However, overall, the MH was felt to be a way to progress, having the potential to drive transparency: ‘no objection to using…important to be transparent…with the majority of farmers, there is an interest to reduce antibiotic usage, to reduce costs and there’s nothing to hide. Farmers pay for the privilege of using medicines’ (Farmer, ID 68) and ‘fair enough…nothing to lose…can progress by being open and honest. The benefits outstrip detriments and it shows you’re operating in a positive manner’ (Farmer, ID 32). Transparency was seen as a benefit for farmers, giving them an advantage in a competitive market for their milk and maintaining consumer confidence. One farmer’s view on data sharing had changed because of benefits arising from such transparency: ‘I would have been guarded a few years ago about sharing but since HP-CIA regulations and usage being questioned, it has resulted in lower antibiotic usage and a financial benefit’ (Farmer, ID 96). In addition, it was felt transparency would provide further evidence that farmers can be trusted to treat animals without a veterinarians permission each time. Participants recognised this as a financial benefit to farmers when compared to other EU countries, such as the Netherlands, where they reported that such restrictions apply, with a strong reluctance for that to occur in the UK 10 . In the UK, whilst antibiotics are prescription-only medicines and can only be prescribed by veterinarians to ‘animals under their care’, farmers do have a level of autonomy over the decision to treat 21 . The Royal College of Veterinary Surgeons interprets this meaning of ‘animals under their care’ as animals must have been seen ‘recently enough or often enough for the veterinary surgeon to have personal knowledge of the condition of the animal or current health status of the herd or flock to make a diagnosis and prescribe’ 22 . ‘Recently enough’ is deemed a matter of professional judgement of the veterinary surgeon involved. When placed in the context of dairy farms, most have semi-regular visits for fertility work, therefore the health status of the herd is likely to be known to the veterinarian. 2.2.2 Farmers trust their vet with their data Veterinarians and veterinary practices were identified as the key trusted recipients of shared data. Vets were seen as a source of help for farmers and with regard to ABU: ‘access to records would be useful for vets, they are there to help and see if drugs are working’ (Farmer, ID 20). Indeed, veterinarians often had access to antibiotic records already, which was deemed ‘useful, as they can pick out issues and highlight if you’re using too much’ (Farmer, ID 64). As the veterinarian’s role was to care for the farm’s animals, there was an understanding that they would interpret data in a fair way to benefit both the farmer and the animal. There was an established relationship between a farmer and their veterinarian and, on this basis, they were more comfortable being queried on ABU by them: ‘I have a good relationship with the practice, therefore I’m happy to be kicked and queried by them but not by an anonymous person or body’ (Farmer, ID 83). Farmers saw discussions on ABU as a private matter, often unwilling for data to be accessed and interpreted by other persons or industry bodies: ‘Nothing to hide, but then the whole business becomes public and not just between farmer and vet’ (Family member, ID 67). Some participants even suggested that the vet instead of the farmer should enter their ABU and purchase data into the MH. It was felt this would give a more accurate picture of what was going on, along with alleviating time constraints: ‘happy for vets to have access and enter records as time constraint…why can’t they put in purchase data, more accurate and getting true picture and know everything up to date. If there’s mismatch, can ask if all has been allocated’ (Farmer, ID 48). 2.2.3 The hub will provide useful information for farmers and vets Access to records by vets, and the reporting of ABU and benchmarking at farm level, was thus deemed useful for both farmers and veterinarians, and essential if participants were going to use a centralised MH: ‘another job to do, must be able to learn from it and get something back’ (Farmer, ID 83). Veterinarians could use the data to identify health issues early on to prevent a wider problem within the herd, and a farmer could rely on them to do so: ‘If there was an [health] issue, vets would pick it up’ (Farmer, ID 29); ‘benefit for vet…able to pick up problems like non-responders and trends before becomes a larger issue’ (Family member, ID 16). For farmers, having data collated in one place with an automated summary report would save time and provide information on their ABU that they might not otherwise have the time or resources to obtain: ‘useful to have feedback on antibiotic usage as I may not have noticed a problem – I would trial if available’ (Farmer, ID 95). Participants also reported that benchmarking – comparing their own data with previous years and other farmers – can be a positive experience, as well as identifying issues and seeing how the industry is doing: ‘ useful to know where you are and reassuring to know not too bad…chore to analyse records’ (Farmer, ID 79). Many farmers already participated in farm discussion groups, benchmarking various aspects of farm management and giving farmer-to-farmer feedback. This was seen as an informal learning opportunity for farmers: ‘a lready in a discussion group for benchmarking all farm issues (e.g. fuel, insurance) which gives useful feedback’ (Farmer, ID 95). 2.2.4 Farmers fear losing control over decision-making Despite recognizing the value of benchmarking, there were concerns that it could be used to restrict ABU, the need for which could vary from season to season depending on circumstances such as inclement weather and increased disease levels. Participants, whilst keen to reduce ABU, wanted to retain autonomy to treat their animals when necessary, without being penalized, and therefore wanted recognition that circumstances can arise requiring significant usage: ‘a s long as it’s not used as stick to beat you with if a health event or problem and we’re given time to sort [it] with our vet’ (Family member, ID 40). A related concern was the growing influence of supply chain bodies – particularly milk buyers and retailers – who were gaining access to farmers’ ABU data. Many felt these entities were increasingly dictating how farms should be managed, requiring performance and productivity data: ‘ Milk buyers are looking too much into farmers’ business…they should leave us to identify and manage issues. [Milk buyers] use the excuse of health and welfare and customer satisfaction but it’s a financial aspect really’ [Farmer ID 6]. Some farmers cited excessive data demands, such as being asked to provide details on crop yields despite the focus being on milk production: ‘…so they want to know for the last [milk buyer scheme], how many hectors of wheat and barley we grow on the farm as well, and what that yielded. What's that got to do with milk that I'm sending them?’ [Farmer ID 5]. Others noted that supply chains increasingly sought broader financial and operational information: ‘I think they’re getting too much power or trying to run our businesses for us…you’re getting [supermarket] that don’t only want that information [on medicine use]. They want all your financial information and whatever’ [Farmer, ID 15]. Participants felt that these demands were leading to a loss of independence, with supply chains exerting too much control over farm operations: ‘the milk buyer has too much control…taking decisions away from the farmer. They don’t trust the farmer’ (Farmer, ID 11); ‘ too ‘Big Brother’, not allowing us to do what we want to do’ [Farmer ID 17]. It was also argued that there were limitations with sharing ABU data in isolation, as this single measure would be insufficient for assessing farm performance: ‘it doesn’t give the full picture on a situation...just looking at one section of information is not accurate’ [Farmer ID 17]. Because farmers are accustomed to making their own decisions, they felt these increasing data demands were gradually eroding their autonomy: ‘Farmers have brains, allow them to make decisions’ [Farmer ID 17]. There was a desire for some kind of reciprocal information exchange between the supply chain and farmer; if one party has to share information about their business, there is the expectation that the other party should follow suit: ‘needs to be a two-way flow of information, they don’t say anything about their business’ (Farmer, ID 23). 2.2.5 Farmers want to control who has access to their data Participants argued that as they own their data and it has a value, it would be important for them to have control over who could access it through the MH. One farmer questioned the extent of data requests ‘Why do they need it? Information given for free and is of value. Poor information, people making poor decisions, for example ‘he has low antibiotic usage, why don’t you?’ (Farmer, ID 2). They do not want to be taken for granted, but rather treated with respect with regard to appropriate ABU: ‘Happy for vets to have open access but more cautious about retailers and milk buyers. What are they looking at? What would they want to do with the information? […] I have concerns over direct access...they can access with my permission on a day-by-day basis. I want to have some control as I have concerns over interpretation’ (Farmer, ID 90). Farmers also felt vulnerable to being misunderstood by individuals who lack the full context of farm-specific circumstances, especially when antibiotic use spikes during disease outbreaks or adverse weather conditions: ‘ happy to be open but concerns about data falling into the wrong hands, being misinterpreted, misquoted and manipulated depending on the person evaluating…could be miscommunicated to the public’ (Family member, ID 14). Farmers emphasized the importance of controlling the narrative, since they – and their veterinarians – best understand the rationale behind treatment decisions. They were also concerned about being penalised or viewed as reticent if records were not fully up to date or interpreted incorrectly: ‘ there’s the perception there is something to hide, which isn’t the case…we’d be losing control over information’ (Farmer, ID 3). Many suspected that supply chain actors might use ABU data to influence the milk prices or otherwise disadvantage farmers, contrasting with the trust they place in veterinarians to work collaboratively for improved animal health. 2.3.6 Farmers feel overwhelmed by data demands Many participants felt overwhelmed by increasing data demands and ABU targets. Different industry bodies were setting their own targets, and the idea of having further targets to meet with another data collection system was a concern: ‘benchmarking interesting but have enough targets from my veterinary practice’ (Farmer, ID 83). The need for ABU data to be shared with different organisations in different formats was already leading to duplication of effort, making the upkeep of medicine records an expanding chore: ‘…time constraints for farmers submitting information to multiple sources’ (Family member, ID 49). Those currently recording antibiotic use electronically suggested that data systems should be linked, to avoid spending additional time entering data into the MH: ‘no concerns with using the medicine hub but it would need data linkage. I don’t want to be duplicating [data entry]’ (Farmer, ID 30). Alternatively, the MH should become the single point of data entry, to save time and resources: ‘duplication is the worse aspect with general paperwork, would be easier if all in one place…always the same thing for different bodies, [supermarkets], Red Tractor…’ (Farmer, ID 81). Some participants were also concerned about their lack of technical ability with computers and software, asserting that dairy farmers and employees are practical people, not computer people. A new electronic system would cause worry, so reassurances of consistency and reliability would be needed: ‘paper trail won’t get wiped…fear of starting one programme and then having to switch. Would prefer one central system and government to recommend one programme that covers all’ (Farmer, ID 42). Participants were worried about the time taken to enter data into an unfamiliar system, and some did not have access to a computer: ‘…small business, why can’t we carry on recording using paper? Accountant gets annoyed, bank is now online and I don’t have a bank card. I feel discriminated against for not having a computer’ (Farmer, ID 11). However, others were dismissive of this view: ‘if you lack IT skills, employ someone to do it or ask your vet’ (Farmer, ID 32). Language barriers with farm staff could also impact data accuracy, and having the MH readily available to staff for direct input of treatments at time of administration, if being used as a full time medicine book, was considered potentially problematic: ‘treatment difficult as initially in the diary…no use having software at the dairy unit as there’s language barriers, and if several people have access, more room for error’ (Farmer, ID 48). 3. Discussion Key findings of this qualitative content analysis were that, overall, participating dairy farmers were prepared to share their antibiotic treatment data with the MH, and did see the potential benefits that could arise from this with regard to providing reassurance and facilitating early identification of herd health issues. For many, data sharing was already happening, and therefore development of a centralised MH was perceived as an inevitable ‘evolution’ of this. However, there was a sense amongst participants of feeling overwhelmed by this next step in sharing data, with worries about their lack of capacity and lack of ability to carry out such data entry. Although participants felt they had ‘nothing to hide’, there were concerns about the extent of third-party access to such a system; access by veterinarians to review data was readily accepted whereas access by the supply chain was met with more resistance. There was a desire for farmers to have some control over data access. Thus, these findings suggest there are several areas that could be addressed by the AHDB to reassure and support farmers to maximise successful implementation of the MH. Farmer attitudes to ABU remain sensitive, due to the global public health threat of antibiotic resistance 9 . Farmers often feel they are victims of ‘other-blaming’ in society and the media regarding overuse of antibiotics and resultant antibiotic resistance 23 – 25 . Often, a defensive response was given when participants were asked about ABU, emphasizing that all treatments given on-farm are necessary to maintain animal welfare and are being carried out appropriately. The acceptability of third-party access was variable and dependent on the relationship context. Access by veterinarians was widely accepted, aligning with previous research that veterinarians in private practice, and other farmers, carry the most weight with farmers 26 . This highlights the opportunity to leverage the established farmer-veterinarian relationship to encourage uptake of the MH. Initiatives such as the RCVS Knowledge Farm Vet Champions and the Welsh Veterinary Prescribing Champion (VPC) networks under the Arwain DGC (Responsible Antimicrobial Use) programme in Wales are notable examples 27 , 28 . These projects have successfully supported veterinarins through ongoing, free training and soft skill development to engage with farmers in reducing ABU. A similar model could be extended to MH implementation, offering ongoing support and training to farmers, especially those with limited capacity or confidence with IT systems. Arwain DGC also demonstrates use of technology on proof-of-concept farms to improve health and welfare, thereby reducing ABU. MH could function in a similar capacity, enabling earlier identification and resolution of health issues. In contrast, the relationship between farmers and the supply chain is more anonymous. Here, access was perceived as intrusive, with concerns that data might be misinterpreted or used to manipulate milk prices. These negative perceptions may stem from a broader sense of power imbalance between dairy farmers and the supply chain. The Grocery Code Adjudicator (GCA) and DEFRA 29 have highlighted transparency issues in some contracts. In addition, there have been several publicised data breaches within the agricultural community, including one where production data were unlawfully shared amongst processors to reduce payments 30 . However, such feelings of powerlessness, a desire for privacy and concerns over misinterpretation, either unintentional or deliberate, are not restricted to dairy farmers and their supply chain. These concerns about data sharing are common in participants involved in human health research. Studies in human health have found that patients are more willing for data to be shared with health professionals directly delivering healthcare than with private organisations and government agencies 31 32 . In research ethics, one of the core principles is respect for persons 33 . This should be extended to non-research organisations. Providing farmers with clear consent procedures for data-sharing that include their views in the flow of data originating from their farm, would show respect. Although there is no known evidence to date suggesting a causal relationship between participant permissions and acceptance of data sharing, providing farmers with the ability to control who can access their data could encourage participation 34 . Whilst the primary aim of the MH is to provide national level data on ABU in the dairy industry, participants were willing to share their ABU data when they regarded the summarised results as beneficial to farmers and veterinarians. Therefore, recognising farmers as data creators and providing a form of feedback specific to the farm will be important 34 , 35 . In this study, participants believed this could take the form of benchmarking. It is well known that farmers have significant strains on resources and see medicine recording as a chore 36 , 37 . They have difficulty extracting useful information from records due to their inconsistent format and multiple lines of data entry 37 . However, this study does appear to highlight a changing perspective regarding the usefulness of records to identify issues, possibly due to automated transformation of data into information. Farmers recognised that veterinarians could use information to problem solve and identify issues early on to prevent wider issues occurring, such as prolonged disease outbreaks. This would have both financial and animal welfare benefits. Participants also saw themselves taking an interest in the outputs, being able to self-identify trends, anticipate problems, and benchmark their farm against the rest of the industry as a management tool. Data could also be used to promote the industry. This contrasts with previous findings that animal care is monitored by regular visual inspection of animals only, and records being an administrative task for assurance scheme compliance 37 – 39 . Whilst perceiving data sharing as inevitable, participants did report feeling overwhelmed by submitting data to the MH, due to lack of capacity or ability. Some viewed it as an additional burden in an industry where new legislation, increasing paperwork, media criticism, heavy workload and time pressures are risk factors for occupational stress and eventual burnout; burnout in the agricultural industries is above international norms 25 , 40 , 41 . Increasing paperwork is worsened by duplication to multiple sources in different formats, along with limited technological capability. This could result in emotional exhaustion, associated with negative behaviour and attitudes towards data sharing 41 . For the MH uptake to be a success, especially if it is to remain voluntary, consideration should be given to minimising duplication of effort, for example by data linkage. Furthermore, AHDB confirming farmer antibiotic recording compliance with auditors, and providing appropriate technology support systems, such as training or options for outsourcing data entry, is likely to encourage and support voluntary MH uptake. Farmers reactions to the MH and associated data sharing are consistent with Lazarus’ coping theory 42 . This framework examines responses to events perceived as disruptive. Coping is defined as ‘cognitive and behavioural efforts exerted to manage specific external and/or internal demands that are appraised as taxing or exceeding the resources of the person 43 . Internal demands relate to personal goals; external ones to job requirements or social pressures 44 . When demands exceed coping resources, disruption is felt 44 . Coping responses involve two appraisals; primary and secondary 45 . Primary appraisal evaluates a person’s initial reaction to a disruptive event: Is this event positive, threatening or challenging 46 ? Secondary appraisal evaluates coping options. If one has some control over the event, problem-focused strategies (e.g. training, advocacy, adaptation) will prevail; where not, emotion-focused response (e.g. withdrawal) may dominate, which has been found to be the case with farmers dealing with farm-related problems 47 . As the MH is voluntary, understanding and supporting positive coping strategies is critical. Participants expressed a wide variety of responses towards the willingness to share data using the MH. Some viewed the MH and data sharing in a straightforward, positive way and believed that the tool would enable transparency. Others felt threatened citing control loss, IT difficulties, worries about the associated time burden or concerns over duplication. Others viewed it as a challenge, a potential for gain; the tool could improve market access and promote the end product and be an easy interaction platform with veterinarians. Some viewed it as both a threat and a challenge; being open and honest means progression, but lack of control over access was a concern. Where the MH was viewed as a threat or a challenge, the coping solutions suggested for compliance were data linkage, software training, control over data access, and provision of benefits such as benchmarking and detailed individual feedback. However, for others the coping strategy was not to engage, instead feeling discriminated against for their lack of ability with IT, and fearing the implementation of the MH. The findings of this study have been drawn from the responses to further explanation of a closed question on the usefulness of sharing ABU data, and explanation of the planned implementation of a centralised MH. As the study was conducted in person with participants, there was opportunity for clarification and discussion of their responses with the researcher, and with other farm staff or family members present at the time of the visit. While this was valuable, and maximized understanding between interviewer and participant, the participants views may have been influenced by the discussion with others. Information given to participants about the interviewer’s veterinary and dairy farming background varied, depending on how recruitment had happened and how conversations unfolded during the visit. Participants who were aware of the interviewer’s veterinary and dairy farming background may have been more inclined to engage in open and frank discussion because of a perception of support or empathy, or conversely, they may have been less inclined because of a perception of being judged. Qualitative research does not aim for statistical representativeness, but instead seeks to develop a detailed understanding of how people experience and interpret specific issues within particular contexts. The findings of our study reflect the views of a group of dairy farmers in South West England and Wales, most of whom were under 60. As in any research that depends on voluntary participation, whether qualitative or quantitative, farmers who were more engaged or held positive views were probably more inclined to take part. This self-selection influences which viewpoints are accessible to researchers, but does not diminish the value of the detailed, contextual understanding that qualitative research provides. Our recruitment method aimed to capture diversity by including farmers with varied herd sizes, milk contract types, and record-keeping approaches. This helped provide a nuanced understanding of the topic and contributes to the credibility of the findings. Future research could explore how common these views are across the wider population of dairy farmers. The AHDB will become the gatekeeper of farm data. A proactive communication strategy is essential to build trust and encourage uptake. This should include clear guidelines on data access, sample reports, and expectation setting. An IT support system should be available for those lacking digital skills. Where farms already keep electronic records or supply ABU data to the supply chain, integrating these sources into the MH can minimize duplication and avoid a significant time burden. This outlined collaborative approach to data sharing between dairy farmers and the AHDB is recommended to ensure successful implementation of a centralised MH. 4. Materials and Methods This study was approved by the Royal Veterinary College’s Social Sciences Research Ethical Review Board (SR2018-1621). 4.1 Study population, data collection and data analysis Participants in this study were dairy farmers, or persons heavily involved in the dairy farm, such as employees or family members. Participants were located in South West England and Wales, and visited in person, by author CS, a veterinarian and PhD student, between April 2019 and October 2019 to answer a questionnaire on their enterprise, management and ABU and provide access to their farm medicine records, as reported elsewhere 16 . This study focusses on their response to a closed question on the use of a centralised MH, and their explanation to this response. The question was read out verbatim, providing further explanation if required. Responses were recorded on the paper questionnaire with their oversight. The planned implementation by AHDB of a centralised MH for the dairy industry with the primary aim of collecting ABU data at farm level to report at national level, was explained to participants. They were also told the MH would provide ABU reporting and benchmarking at farm level. Then they answered a closed question: ‘ Would you find it useful if people requesting your data (e.g. your veterianarian, retailers, milk buyers) could have direct access to your records with your permission? ’, and were asked explain their response. Questionnaire completion and resulting open discussions were not audio-recorded, but discussions were captured in field notes at the time. This contextualized questionnaire data and provided additional insight into dairy farmers opinions about ABU and recording. On completion of data gathering, data from field notes were linked to questionnaire responses via a unique respondent ID code, using a relational database. Frequency distributions created in R (version 3.6.20) were used to summarise demographic factors, recording method, type of milk contract and herd size 17 . Additional data on farm management factors and enterprise size of study participants have been published elsewhere 16 . Using pseudonmyised data, participant demographics were compared between those who provided an explanation for their response to the closed question, and those who did not, using the Chi-square or Fisher’s exact test. Participants’ responses to the question on data sharing and use of a centralised MH were imported into NVivo for qualitative content analysis, a method used to describe textual data by organizing it into meaningful categories 18 . As this study was exploratory, a data-led, inductive approach was used to develop codes and identify patterns and categories that captured participants’ perspectives and opinions 19 . Such codes were developed by assigning key words or short phrases to describe what was being conveyed 20 . Codes with a similar meaning were then grouped into overarching codes, and categories developed through an iterative process of reviewing and refining codes to ensure the analysis remained grounded in the data. Quotations from the data have been used to provide examples of the developed categories, and are presented in italics and inverted comma’s throughout. Declarations Data Availability Statement: The anonymised dataset is available from the corresponding author on request. Acknowledgments: Many thanks to all the farmers who gave their time to participate in this study, to Eamon Watson and colleagues at National Milk Records (NMR) and Alasdair Moffett and colleagues at Synergy Farm Health for their help with recruitment of farmers and feedback on methods. Author contributions: Conceptualization, C.S., L.B., P.A., and J.C.; study design and methods, C.S., and J.C.; formal analysis, C.S.; interpretation, C.S., L.B., and J.C.; data curation, C.S.; writing—original draft preparation, C.S.; writing—review and editing, C.S., L.B., and J.C.; visualization, C.S.; supervision, L.B., P.A., and J.C.; project administration, C.S. and L.B.; funding acquisition, C.S., J.C., P.A. and L.B. All authors have reviewed and agreed to the published version of the manuscript. Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding: This study was funded by the Royal Veterinary College, with additional funding for farmer interviews provided by Antibiotic Research UK (grant number ANTSRG 01/2018). The funders played no role in study design, data collection, analysis and interpretation of data, or the writing of the manuscript. Informed Consent Statement: Informed consent was obtained from all participants. References Responsible Use of Medicines in Agriculture Alliance. Targets Task Force Report 2020. Responsible Use of Antibiotics in UK Farming. Progress against 2020 Targets. New Targets 2021 - 2024 . (2020). UK-VARSS. UK Veterinary Antibiotic Resistance and Sales Surveillance Report (UK-VARSS 2016) . https://www.gov.uk/government/publications/veterinary-antimicrobial-resistance-and-sales-surveillance-2016 (2017). European Medicines Agency. Sales of Veterinary Antimicrobial Agents in 31 European Countries in 2022 - Trends from 2010 to 2022 - Thirteenth ESVAC Report . https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32004R0726 (2023) doi:10.2809/766171. UK-VARSS. UK Veterinary Antibiotic Resistance and Sales Surveillance Report (UK-VARSS 2015) . https://www.gov.uk/government/publications/veterinary-antimicrobial-resistance-and-sales-surveillance-2015 (2016). UK-VARSS. UK Veterinary Antibiotic Resistance and Sales Surveillance Report. (UK-VARSS 2017) . https://www.gov.uk/government/publications/veterinary-antimicrobial-resistance-and-sales-surveillance-2017 (2018). UK-VARSS. UK Veterinary Antibiotic Resistance and Sales Surveillance Report (UK-VARSS 2018) . https://www.gov.uk/government/publications/veterinary-antimicrobial-resistance-and-sales-surveillance-2018 (2019). UK-VARSS. UK Veterinary Antibiotic Resistance and Sales Surveillance Report (UK-VARSS 2019) . https://www.gov.uk/government/publications/veterinary-antimicrobial-resistance-and-sales-surveillance-2019 (2020). UK-VARSS. UK Veterinary Antibiotic Resistance and Sales Surveillance Report (UK-VARSS 2020) . https://www.gov.uk/government/publications/veterinary-antimicrobial-resistance-and-sales-surveillance-2020 (2021). O’Neill, J. Tackling Drug-Resistant Infections Globally: Final Report and Recommendations . Review on Antimicrobial Resistance (2016) doi:10.1016/j.jpha.2015.11.005. Speksnijder, D. C., Mevius, D. J., Bruschke, C. J. M. & Wagenaar, J. A. Reduction of veterinary antimicrobial use in the Netherlands. The dutch success model. Zoonoses Public Health 62 , 79–87 (2015). Responsible Use of Medicines in Agriculture Alliance. Targets Task Force Report 2020. Responsible Use of Antibiotics in UK Farming. Progress against 2020 Targets. New Targets 2021 - 2024 . https://www.ruma.org.uk/targets-task-force-2021-2024/ (2020). UK-VARSS. Veterinary Antibiotic Resistance and Sales Surveillance Report (UK-VARSS 2022) . https://www.gov.uk/government/publications/veterinary-antimicrobial-resistance-and-sales-surveillance-2022 (2023). AHDB. A medicine hub update for veterinary professionals. (2021). Garforth, C. Livestock Keepers’ Reasons for Doing and Not Doing Things Which Governments, Vets and Scientists Would Like Them to Do. Zoonoses Public Health 62 , 29–38 (2015). Tummers, L. Public Policy and Behavior Change. Public Adm Rev 79 , 925–930 (2019). Strang, C., Alarcon, P., Cardwell, J. M. & Brunton, L. Assessing antibiotic usage data capture accuracy on dairy farms in England and Wales. Veterinary Record 193 , (2023). R Core Team. R: A language and environment for statistical computing. Preprint at (2019). Elo, S. & Kyngäs, H. The qualitative content analysis process. J Adv Nurs 62 , 107–115 (2008). Burnard, P., Gill, P., Stewart, K., Treasure, E. & Chadwick, B. Analysing and presenting qualitative data. Br Dent J 204 , 429–432 (2008). Spence, K. L., Cardwell, J. M., Slater, J. & Rosanowski, S. M. Preliminary insight into horse owners’ perceptions of, and attitudes towards, exotic diseases in the United Kingdom. BMC Vet Res 15 , 338 (2019). UK Government. The Veterinary Medicines Regulations . (Acts of Parliament, 2013). Royal College of Veterinary Surgeons (RCVS). Code of Professional Conduct for Veterinary Surgeons . https://www.rcvs.org.uk/setting-standards/advice-and-guidance/code-of-professional-conduct-for-veterinary-surgeons/supporting-guidance/veterinary-medicines/ (2021). Golding, S. E., Ogden, J. & Higgins, H. M. Shared Goals, Different Barriers: A Qualitative Study of UK Veterinarians’ and Farmers’ Beliefs About Antimicrobial Resistance and Stewardship. Front Vet Sci 6 , (2019). Kallioniemi, M. K., Simola, A., Kaseva, J. & Kymäläinen, H. R. Stress and Burnout Among Finnish Dairy Farmers. J Agromedicine 21 , 259–268 (2016). Booth, N. J. & Lloyd, K. Stress in farmers. International Journal of Social Psychiatry 46 , 67–73 (2000). Garforth, C. Livestock Keepers’ Reasons for Doing and Not Doing Things Which Governments, Vets and Scientists Would Like Them to Do. Zoonoses Public Health 62 , 29–38 (2015). Arwain DGC. Leading on the responsible use of antimicrobials in Wales. www.rhaglenni.mentera.cymru/arwaindgc/en/home/ (2025). RCVS Knowledge. Farm Vet Champions. https://knowledge.rcvs.org.uk/amr/farm-vet-champions/ (2025). DEFRA. Consultation: Contractual Relationships in the UK Dairy Industry . (2020). Wiseman, L., Sanderson, J., Zhang, A. & Jakku, E. Farmers and their data: An examination of farmers’ reluctance to share their data through the lens of the laws impacting smart farming. NJAS - Wageningen Journal of Life Sciences 90–91 , 100301 (2019). Ghafur, S., Van Dael, J., Leis, M., Darzi, A. & Sheikh, A. Public perceptions on data sharing: key insights from the UK and the USA. The Lancet Digital Health vol. 2 e444–e446 Preprint at https://doi.org/10.1016/S2589-7500(20)30161-8 (2020). Whiddett, R., Hunter, I., Engelbrecht, J. & Handy, J. Patients’ attitudes towards sharing their health information. Int J Med Inform 75 , 530–541 (2006). Ross, M. W., Iguchi, M. Y. & Panicker, S. Ethical aspects of data sharing and research participant protections. American Psychologist 73 , 138–145 (2018). Howe, N., Giles, E., Newbury-Birch, D. & McColl, E. Systematic review of participants’ attitudes towards data sharing: A thematic synthesis. Journal of Health Services Research and Policy vol. 23 123–133 Preprint at https://doi.org/10.1177/1355819617751555 (2018). Alter, G. & Gonzalez, R. Responsible practices for data sharing. American Psychologist 73 , 146–156 (2018). Crowe, C. & Oxtoby, T. Strengthening the vet-farmer relationship. In Pract 41 , 275–277 (2019). Escobar, M. P. Perceptions and practices of farm record-keeping and their implications for animal welfare and regulation. 40 (2015). Burton, R. J. F. Seeing through the ‘good farmer’s’ eyes: Towards developing an understanding of the social symbolic value of ‘productivist’ behaviour. Sociol Ruralis (2004) doi:10.1111/j.1467-9523.2004.00270.x. Naylor, R., Hamilton-Webb, A., Little, R. & Maye, D. The ‘Good Farmer’: Farmer Identities and the Control of Exotic Livestock Disease in England. Sociol Ruralis 58 , 3–19 (2018). Reissig, L., Crameri, A. & von Wyl, A. Prevalence and predictors of burnout in Swiss farmers – Burnout in the context of interrelation of work and household. Ment Health Prev 14 , 200157 (2019). Jones-Bitton, A., Hagen, B., Fleming, S. J. & Hoy, S. Farmer Burnout in Canada. Int J Environ Res Public Health 16 , 5074 (2019). Lazarus, R. S. Toward better research on stress and coping. American Psychologist 55 , 665–673 (2000). Lazarus, R. S. & Folkman, S. Stress, Appraisal and Coping . (Springer New York LLC, 1984). Bhattacherjee, A., Davis, C. J., Connolly, A. J. & Hikmet, N. User response to mandatory IT use: a coping theory perspective. European Journal of Information Systems 27 , 395–414 (2018). Lazarus, R. S. & Folkman, S. Stress, Appraisal and Coping . (Springer New York LLC, 1984). Bhattacherjee, A., Davis, C. J., Connolly, A. J. & Hikmet, N. User response to mandatory IT use: a coping theory perspective. European Journal of Information Systems 27 , 395–414 (2018). Fennell, K. M., Kettler, L. J., Skaczkowski, G. & Turnbull, D. A. Farmers’ stress and coping in a time of drought. 12 , (2012). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7037458","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":484980932,"identity":"e8aecb81-9707-4dd9-b34c-e04dcca2fe79","order_by":0,"name":"Camilla Strang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBADOThLgrBiZjBpTLqWxAaitfD3nz/24eeO2vTtM5IfMPyoYUic2UBAi8SNZOaZvWeO5865kWbA2HOMIXE2IVsMJJiZGXjbjuXO4DnDwMDbwJA4j6AW/sPMjH/bjqVLALUw/iVKC0MyMzNvW02CBHsPAzPIFoIOA/rFmFm27YDhDPY2g8MyxySMCXqfv//gY8a3bXXyQD89fPimxkZ2xgFC1kDAYTB5gKiIhII6olWOglEwCkbBCAQANRY44H+oUDwAAAAASUVORK5CYII=","orcid":"","institution":"Royal Veterinary College","correspondingAuthor":true,"prefix":"","firstName":"Camilla","middleName":"","lastName":"Strang","suffix":""},{"id":484980933,"identity":"09d5b242-2230-4d6d-8757-ed9dbf417738","order_by":1,"name":"Lucy Brunton","email":"","orcid":"","institution":"Royal Veterinary College","correspondingAuthor":false,"prefix":"","firstName":"Lucy","middleName":"","lastName":"Brunton","suffix":""},{"id":484980934,"identity":"3661f67b-04ad-422e-9ded-447d59804c9a","order_by":2,"name":"Pablo Alarcon","email":"","orcid":"","institution":"Royal Veterinary College","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Alarcon","suffix":""},{"id":484980937,"identity":"c5a848ae-8e06-4bcd-b6aa-585ae5302446","order_by":3,"name":"Jacqueline M Cardwell","email":"","orcid":"","institution":"Royal Veterinary College","correspondingAuthor":false,"prefix":"","firstName":"Jacqueline","middleName":"M","lastName":"Cardwell","suffix":""}],"badges":[],"createdAt":"2025-07-03 11:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7037458/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7037458/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86801297,"identity":"c4064c6d-d93f-464b-a4b5-e64de6bbc7f5","added_by":"auto","created_at":"2025-07-15 17:06:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":669773,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7037458/v1/830d9060-5558-4656-856c-02bcfb7a8978.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A qualitative content analysis of factors influencing British dairy farmers’ willingness to share antibiotic usage data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe UK livestock industry has successfully reduced antibiotic usage (ABU) since 2015 \u003csup\u003e1\u0026ndash;8\u003c/sup\u003e. Despite this success, it has been recognised that antibiotics need to be monitored in each livestock species to optimise usage. Optimal monitoring requires the development of species-specific data capture systems using computerised methods. This has been achieved in the pig, poultry and aquaculture sectors on a voluntary basis with high levels of surveillance coverage (\u0026ge;\u0026thinsp;90%) (8,9). Monitoring of ABU also involves the setting of annual targets for each livestock sector to meet. Targets have been introduced to minimise but maintain appropriate usage in response to the critical concern of antibiotic resistance \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Where sectors have good ABU data availability through surveillance systems, for example the SDa in the Netherlands and the UK electronic Medicine Book \u0026ndash; Pigs\u0026rsquo; (eMB-Pigs), targets are generally met \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, when there is minimal data, or supplied datasets are not robust, then sector progress is difficult to demonstrate as results are limited in giving a representative picture of usage. This is the situation for the UK dairy sector. Annual ABU in the dairy sector seems to be much lower than in the pig sector. However, the pig sector has 95% surveillance coverage, whereas dairy has only 28% coverage from a voluntary sample, meaning that this estimate is less accurate \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The reality of ABU in the dairy sector therefore remains unclear.\u003c/p\u003e\u003cp\u003eThe high level of coverage in the UK pig sector has been achieved through the development of the eMB-Pigs by the Agriculture and Horticulture Development Board (AHDB). The system was introduced in 2016; over the subsequent four-year period coverage increased from 17\u0026ndash;95% and has been maintained since. In 2021, the AHDB adapted and introduced this electronic medicine book for the ruminant sector, where it is known as the Medicine Hub (MH). By collating national sector-level data, the MH is anticipated to promote the reputation of the industry and support trade, provide evidence to support the RUMA (Responsible Use of Medicines in Agriculture Alliance) targets, and help implement the UK\u0026rsquo;s future approach to veterinary medicines regulation. It is believed that accurate ABU data will become fundamental for successful trade. The hub will also provide consistency through benchmarking, which is perceived to give value to farmers. Benchmarking allows farmers to measure their business against the rest of their sector, and develop farm-specific herd health plans with their veterinarian to improve management practices and monitor their herds health and welfare. This tool is also perceived as a valuable communication aid between farmers and veterinarians, driving behaviour change and therefore progression \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, as for any group where policy interventions seek to drive behaviour change, it is recognized that farmers do not always behave in the way government, scientists and veterinarians would like them to \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Motivations for changing behaviour or decision-making vary from farmer to farmer, depending on their values and experience, and are often passed down through generations \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Therefore, understanding the rationale for behaviours is often key to implementing successful policy and interventions involving behaviour change. On this basis, the objectives of this study were to explore dairy farmers\u0026rsquo; willingness to 1) submit their ABU data to a centralised MH and 2) allow third-party access to such data.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participant characteristics\u003c/h2\u003e\u003cp\u003eA total of 103 dairy farmers completed the questionnaire. Of these, 58 (56%) used software for their medicine records and 26 (25%) were on an aligned milk contract. The median herd size was 180 (range 45\u0026ndash;1250). Further details on enterprise and management characteristics can be found in a previous publication \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. All participants provided a response to the closed question about use of a centralised MH and third-party access to their ABU data. Ninety-four (91.2%) participants provided an explanation of their response. Comparison of participants that did not respond found that the highest proportion of non-responses was from those aged 60 or above, with 62.5% not providing an explanation. As age increased, participants were less likely to respond, with 20.8% from those aged 50\u0026ndash;59 not responding, compared to 7.7% from those aged 40\u0026ndash;49, and 2.8% from those aged less than 40 (p-value\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Factors influencing willingness to share data and use the MH\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Farmers have nothing to hide\u003c/h2\u003e\u003cp\u003eSome participants responded that they had nothing to hide when asked how they would feel if their antibiotic records were disclosed to the MH and third parties. This was because all antibiotics given were regarded as necessary to maintain animal welfare, and therefore none of the treatments were deemed inappropriate. In particular, as the use of highest priority critically important antibiotics (HP-CIA) had been restricted under the 2017 Red Tractor Assurance Scheme Standards for Dairy, Beef and Sheep in conjunction with the BCVA \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, participants considered that all treatments were appropriate providing such antibiotics had not been used: \u003cem\u003e\u0026lsquo;\u0026hellip;not worried, especially as we\u0026rsquo;re not using critically important antibiotics\u0026rsquo;\u003c/em\u003e (Farmer, ID 79) and \u003cem\u003e\u0026lsquo;not concerned\u0026hellip;all treatments are necessary\u0026rsquo;\u003c/em\u003e (Farm manager, ID 19). Some participants had no concerns about sharing their data, especially amongst those who already share their data with their veterinarian and their supply chain: \u003cem\u003e\u0026lsquo;Vets and Tesco ask for antibiotic usage so already supplying, therefore no issue\u0026rsquo;\u003c/em\u003e (Farmer, ID 58); \u0026lsquo;\u003cem\u003ealready give information to my milk buyer quarterly\u003c/em\u003e\u0026rsquo; (Farmer, ID 99). Considered in this light, sharing the data with a centralised MH was perceived as just another evolutionary development in farm data, rather than a complete revolution in data recording: \u003cem\u003e\u0026lsquo;No objections to using. Industry always evolving, new things coming up\u0026rsquo;\u003c/em\u003e (Family member, ID 67). Even those who had yet to share their data felt it was inevitable, and were resigned to it: \u0026lsquo;\u003cem\u003eit\u0026rsquo;s inevitable \u0026ndash; we will have to do it\u003c/em\u003e\u0026rsquo; (Farmer, ID 66); \u0026lsquo;\u003cem\u003enothing to hide and understand will come into place, it\u0026rsquo;s marketing for selling your product\u003c/em\u003e\u0026rsquo; (Farmer, ID 73).\u003c/p\u003e\u003cp\u003eThese views often went further than a straightforward openness to sharing data. Participants also wanted a level playing field, and there was a perception that other farmers sometimes did not play by the rules: \u003cem\u003e\u0026lsquo;Farmers have to be more honest\u0026rsquo;\u003c/em\u003e (Farmer, ID 42) and \u003cem\u003e\u0026lsquo;Some farms need a kick up the arse\u0026rsquo;\u003c/em\u003e (Farmer, ID 32). Some felt that others would need an incentive to use a centralised MH, such as it being a requirement: \u003cem\u003e\u0026lsquo;Nothing to hide\u0026hellip;if it becomes compulsory then it\u0026rsquo;s not a problem\u003c/em\u003e (Farmer, ID 15). Sharing veterinary sales data was suggested by some as a better way to achieve a level playing field: \u003cem\u003e\u0026lsquo;vet data is more comparable, level playing field as more accurate data\u0026rsquo;\u003c/em\u003e (Farmer, ID 89). However, overall, the MH was felt to be a way to progress, having the potential to drive transparency: \u003cem\u003e\u0026lsquo;no objection to using\u0026hellip;important to be transparent\u0026hellip;with the majority of farmers, there is an interest to reduce antibiotic usage, to reduce costs and there\u0026rsquo;s nothing to hide. Farmers pay for the privilege of using medicines\u0026rsquo;\u003c/em\u003e (Farmer, ID 68) and \u003cem\u003e\u0026lsquo;fair enough\u0026hellip;nothing to lose\u0026hellip;can progress by being open and honest. The benefits outstrip detriments and it shows you\u0026rsquo;re operating in a positive manner\u0026rsquo;\u003c/em\u003e (Farmer, ID 32). Transparency was seen as a benefit for farmers, giving them an advantage in a competitive market for their milk and maintaining consumer confidence. One farmer\u0026rsquo;s view on data sharing had changed because of benefits arising from such transparency: \u003cem\u003e\u0026lsquo;I would have been guarded a few years ago about sharing but since HP-CIA regulations and usage being questioned, it has resulted in lower antibiotic usage and a financial benefit\u0026rsquo;\u003c/em\u003e (Farmer, ID 96). In addition, it was felt transparency would provide further evidence that farmers can be trusted to treat animals without a veterinarians permission each time. Participants recognised this as a financial benefit to farmers when compared to other EU countries, such as the Netherlands, where they reported that such restrictions apply, with a strong reluctance for that to occur in the UK \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In the UK, whilst antibiotics are prescription-only medicines and can only be prescribed by veterinarians to \u0026lsquo;animals under their care\u0026rsquo;, farmers do have a level of autonomy over the decision to treat \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The Royal College of Veterinary Surgeons interprets this meaning of \u0026lsquo;animals under their care\u0026rsquo; as animals must have been seen \u0026lsquo;recently enough or often enough for the veterinary surgeon to have personal knowledge of the condition of the animal or current health status of the herd or flock to make a diagnosis and prescribe\u0026rsquo; \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. \u0026lsquo;Recently enough\u0026rsquo; is deemed a matter of professional judgement of the veterinary surgeon involved. When placed in the context of dairy farms, most have semi-regular visits for fertility work, therefore the health status of the herd is likely to be known to the veterinarian.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Farmers trust their vet with their data\u003c/h2\u003e\u003cp\u003eVeterinarians and veterinary practices were identified as the key trusted recipients of shared data. Vets were seen as a source of help for farmers and with regard to ABU: \u003cem\u003e\u0026lsquo;access to records would be useful for vets, they are there to help and see if drugs are working\u0026rsquo;\u003c/em\u003e (Farmer, ID 20). Indeed, veterinarians often had access to antibiotic records already, which was deemed \u003cem\u003e\u0026lsquo;useful, as they can pick out issues and highlight if you\u0026rsquo;re using too much\u0026rsquo;\u003c/em\u003e (Farmer, ID 64). As the veterinarian\u0026rsquo;s role was to care for the farm\u0026rsquo;s animals, there was an understanding that they would interpret data in a fair way to benefit both the farmer and the animal. There was an established relationship between a farmer and their veterinarian and, on this basis, they were more comfortable being queried on ABU by them: \u003cem\u003e\u0026lsquo;I have a good relationship with the practice, therefore I\u0026rsquo;m happy to be kicked and queried by them but not by an anonymous person or body\u0026rsquo;\u003c/em\u003e (Farmer, ID 83). Farmers saw discussions on ABU as a private matter, often unwilling for data to be accessed and interpreted by other persons or industry bodies: \u003cem\u003e\u0026lsquo;Nothing to hide, but then the whole business becomes public and not just between farmer and vet\u0026rsquo;\u003c/em\u003e (Family member, ID 67).\u003c/p\u003e\u003cp\u003eSome participants even suggested that the vet instead of the farmer should enter their ABU and purchase data into the MH. It was felt this would give a more accurate picture of what was going on, along with alleviating time constraints: \u003cem\u003e\u0026lsquo;happy for vets to have access and enter records as time constraint\u0026hellip;why can\u0026rsquo;t they put in purchase data, more accurate and getting true picture and know everything up to date. If there\u0026rsquo;s mismatch, can ask if all has been allocated\u0026rsquo;\u003c/em\u003e (Farmer, ID 48).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 The hub will provide useful information for farmers and vets\u003c/h2\u003e\u003cp\u003eAccess to records by vets, and the reporting of ABU and benchmarking at farm level, was thus deemed useful for both farmers and veterinarians, and essential if participants were going to use a centralised MH: \u003cem\u003e\u0026lsquo;another job to do, must be able to learn from it and get something back\u0026rsquo;\u003c/em\u003e (Farmer, ID 83). Veterinarians could use the data to identify health issues early on to prevent a wider problem within the herd, and a farmer could rely on them to do so: \u003cem\u003e\u0026lsquo;If there was an [health] issue, vets would pick it up\u0026rsquo;\u003c/em\u003e (Farmer, ID 29); \u003cem\u003e\u0026lsquo;benefit for vet\u0026hellip;able to pick up problems like non-responders and trends before becomes a larger issue\u0026rsquo;\u003c/em\u003e (Family member, ID 16). For farmers, having data collated in one place with an automated summary report would save time and provide information on their ABU that they might not otherwise have the time or resources to obtain: \u003cem\u003e\u0026lsquo;useful to have feedback on antibiotic usage as I may not have noticed a problem \u0026ndash; I would trial if available\u0026rsquo;\u003c/em\u003e (Farmer, ID 95). Participants also reported that benchmarking \u0026ndash; comparing their own data with previous years and other farmers \u0026ndash; can be a positive experience, as well as identifying issues and seeing how the industry is doing: \u0026lsquo;\u003cem\u003euseful to know where you are and reassuring to know not too bad\u0026hellip;chore to analyse records\u0026rsquo;\u003c/em\u003e (Farmer, ID 79). Many farmers already participated in farm discussion groups, benchmarking various aspects of farm management and giving farmer-to-farmer feedback. This was seen as an informal learning opportunity for farmers: \u0026lsquo;a\u003cem\u003elready in a discussion group for benchmarking all farm issues (e.g. fuel, insurance) which gives useful feedback\u0026rsquo;\u003c/em\u003e (Farmer, ID 95).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4 Farmers fear losing control over decision-making\u003c/h2\u003e\u003cp\u003eDespite recognizing the value of benchmarking, there were concerns that it could be used to restrict ABU, the need for which could vary from season to season depending on circumstances such as inclement weather and increased disease levels. Participants, whilst keen to reduce ABU, wanted to retain autonomy to treat their animals when necessary, without being penalized, and therefore wanted recognition that circumstances can arise requiring significant usage: \u0026lsquo;a\u003cem\u003es long as it\u0026rsquo;s not used as stick to beat you with if a health event or problem and we\u0026rsquo;re given time to sort [it] with our vet\u0026rsquo;\u003c/em\u003e (Family member, ID 40).\u003c/p\u003e\u003cp\u003eA related concern was the growing influence of supply chain bodies \u0026ndash; particularly milk buyers and retailers \u0026ndash; who were gaining access to farmers\u0026rsquo; ABU data. Many felt these entities were increasingly dictating how farms should be managed, requiring performance and productivity data: \u0026lsquo;\u003cem\u003eMilk buyers are looking too much into farmers\u0026rsquo; business\u0026hellip;they should leave us to identify and manage issues. [Milk buyers] use the excuse of health and welfare and customer satisfaction but it\u0026rsquo;s a financial aspect really\u0026rsquo;\u003c/em\u003e [Farmer ID 6].\u003c/p\u003e\u003cp\u003eSome farmers cited excessive data demands, such as being asked to provide details on crop yields despite the focus being on milk production: \u003cem\u003e\u0026lsquo;\u0026hellip;so they want to know for the last [milk buyer scheme], how many hectors of wheat and barley we grow on the farm as well, and what that yielded. What's that got to do with milk that I'm sending them?\u0026rsquo;\u003c/em\u003e [Farmer ID 5]. Others noted that supply chains increasingly sought broader financial and operational information: \u003cem\u003e\u0026lsquo;I think they\u0026rsquo;re getting too much power or trying to run our businesses for us\u0026hellip;you\u0026rsquo;re getting [supermarket] that don\u0026rsquo;t only want that information [on medicine use]. They want all your financial information and whatever\u0026rsquo;\u003c/em\u003e [Farmer, ID 15]. Participants felt that these demands were leading to a loss of independence, with supply chains exerting too much control over farm operations: \u003cem\u003e\u0026lsquo;the milk buyer has too much control\u0026hellip;taking decisions away from the farmer. They don\u0026rsquo;t trust the farmer\u0026rsquo;\u003c/em\u003e (Farmer, ID 11); \u0026lsquo;\u003cem\u003etoo \u0026lsquo;Big Brother\u0026rsquo;, not allowing us to do what we want to do\u0026rsquo;\u003c/em\u003e [Farmer ID 17].\u003c/p\u003e\u003cp\u003eIt was also argued that there were limitations with sharing ABU data in isolation, as this single measure would be insufficient for assessing farm performance: \u003cem\u003e\u0026lsquo;it doesn\u0026rsquo;t give the full picture on a situation...just looking at one section of information is not accurate\u0026rsquo;\u003c/em\u003e [Farmer ID 17]. Because farmers are accustomed to making their own decisions, they felt these increasing data demands were gradually eroding their autonomy: \u003cem\u003e\u0026lsquo;Farmers have brains, allow them to make decisions\u0026rsquo;\u003c/em\u003e [Farmer ID 17]. There was a desire for some kind of reciprocal information exchange between the supply chain and farmer; if one party has to share information about their business, there is the expectation that the other party should follow suit: \u003cem\u003e\u0026lsquo;needs to be a two-way flow of information, they don\u0026rsquo;t say anything about their business\u0026rsquo;\u003c/em\u003e (Farmer, ID 23).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.5 Farmers want to control who has access to their data\u003c/h2\u003e\u003cp\u003eParticipants argued that as they own their data and it has a value, it would be important for them to have control over who could access it through the MH. One farmer questioned the extent of data requests \u003cem\u003e\u0026lsquo;Why do they need it? Information given for free and is of value. Poor information, people making poor decisions, for example \u0026lsquo;he has low antibiotic usage, why don\u0026rsquo;t you?\u0026rsquo;\u003c/em\u003e (Farmer, ID 2). They do not want to be taken for granted, but rather treated with respect with regard to appropriate ABU: \u003cem\u003e\u0026lsquo;Happy for vets to have open access but more cautious about retailers and milk buyers. What are they looking at? What would they want to do with the information? [\u0026hellip;] I have concerns over direct access...they can access with my permission on a day-by-day basis. I want to have some control as I have concerns over interpretation\u0026rsquo;\u003c/em\u003e (Farmer, ID 90).\u003c/p\u003e\u003cp\u003eFarmers also felt vulnerable to being misunderstood by individuals who lack the full context of farm-specific circumstances, especially when antibiotic use spikes during disease outbreaks or adverse weather conditions: \u0026lsquo;\u003cem\u003ehappy to be open but concerns about data falling into the wrong hands, being misinterpreted, misquoted and manipulated depending on the person evaluating\u0026hellip;could be miscommunicated to the public\u0026rsquo;\u003c/em\u003e (Family member, ID 14). Farmers emphasized the importance of controlling the narrative, since they \u0026ndash; and their veterinarians \u0026ndash; best understand the rationale behind treatment decisions. They were also concerned about being penalised or viewed as reticent if records were not fully up to date or interpreted incorrectly: \u0026lsquo;\u003cem\u003ethere\u0026rsquo;s the perception there is something to hide, which isn\u0026rsquo;t the case\u0026hellip;we\u0026rsquo;d be losing control over information\u0026rsquo;\u003c/em\u003e (Farmer, ID 3).\u003c/p\u003e\u003cp\u003eMany suspected that supply chain actors might use ABU data to influence the milk prices or otherwise disadvantage farmers, contrasting with the trust they place in veterinarians to work collaboratively for improved animal health.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.3.6 Farmers feel overwhelmed by data demands\u003c/h2\u003e\u003cp\u003eMany participants felt overwhelmed by increasing data demands and ABU targets. Different industry bodies were setting their own targets, and the idea of having further targets to meet with another data collection system was a concern: \u003cem\u003e\u0026lsquo;benchmarking interesting but have enough targets from my veterinary practice\u0026rsquo;\u003c/em\u003e (Farmer, ID 83). The need for ABU data to be shared with different organisations in different formats was already leading to duplication of effort, making the upkeep of medicine records an expanding chore: \u003cem\u003e\u0026lsquo;\u0026hellip;time constraints for farmers submitting information to multiple sources\u0026rsquo;\u003c/em\u003e (Family member, ID 49). Those currently recording antibiotic use electronically suggested that data systems should be linked, to avoid spending additional time entering data into the MH: \u003cem\u003e\u0026lsquo;no concerns with using the medicine hub but it would need data linkage. I don\u0026rsquo;t want to be duplicating [data entry]\u0026rsquo;\u003c/em\u003e (Farmer, ID 30). Alternatively, the MH should become the single point of data entry, to save time and resources: \u003cem\u003e\u0026lsquo;duplication is the worse aspect with general paperwork, would be easier if all in one place\u0026hellip;always the same thing for different bodies, [supermarkets], Red Tractor\u0026hellip;\u0026rsquo;\u003c/em\u003e (Farmer, ID 81).\u003c/p\u003e\u003cp\u003eSome participants were also concerned about their lack of technical ability with computers and software, asserting that dairy farmers and employees are practical people, not computer people. A new electronic system would cause worry, so reassurances of consistency and reliability would be needed: \u003cem\u003e\u0026lsquo;paper trail won\u0026rsquo;t get wiped\u0026hellip;fear of starting one programme and then having to switch. Would prefer one central system and government to recommend one programme that covers all\u0026rsquo;\u003c/em\u003e (Farmer, ID 42). Participants were worried about the time taken to enter data into an unfamiliar system, and some did not have access to a computer: \u003cem\u003e\u0026lsquo;\u0026hellip;small business, why can\u0026rsquo;t we carry on recording using paper? Accountant gets annoyed, bank is now online and I don\u0026rsquo;t have a bank card. I feel discriminated against for not having a computer\u0026rsquo;\u003c/em\u003e (Farmer, ID 11). However, others were dismissive of this view: \u003cem\u003e\u0026lsquo;if you lack IT skills, employ someone to do it or ask your vet\u0026rsquo;\u003c/em\u003e (Farmer, ID 32). Language barriers with farm staff could also impact data accuracy, and having the MH readily available to staff for direct input of treatments at time of administration, if being used as a full time medicine book, was considered potentially problematic: \u003cem\u003e\u0026lsquo;treatment difficult as initially in the diary\u0026hellip;no use having software at the dairy unit as there\u0026rsquo;s language barriers, and if several people have access, more room for error\u0026rsquo;\u003c/em\u003e (Farmer, ID 48).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003e Key findings of this qualitative content analysis were that, overall, participating dairy farmers were prepared to share their antibiotic treatment data with the MH, and did see the potential benefits that could arise from this with regard to providing reassurance and facilitating early identification of herd health issues. For many, data sharing was already happening, and therefore development of a centralised MH was perceived as an inevitable \u0026lsquo;evolution\u0026rsquo; of this. However, there was a sense amongst participants of feeling overwhelmed by this next step in sharing data, with worries about their lack of capacity and lack of ability to carry out such data entry. Although participants felt they had \u0026lsquo;nothing to hide\u0026rsquo;, there were concerns about the extent of third-party access to such a system; access by veterinarians to review data was readily accepted whereas access by the supply chain was met with more resistance. There was a desire for farmers to have some control over data access. Thus, these findings suggest there are several areas that could be addressed by the AHDB to reassure and support farmers to maximise successful implementation of the MH.\u003c/p\u003e\u003cp\u003eFarmer attitudes to ABU remain sensitive, due to the global public health threat of antibiotic resistance\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Farmers often feel they are victims of \u0026lsquo;other-blaming\u0026rsquo; in society and the media regarding overuse of antibiotics and resultant antibiotic resistance \u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Often, a defensive response was given when participants were asked about ABU, emphasizing that all treatments given on-farm are necessary to maintain animal welfare and are being carried out appropriately.\u003c/p\u003e\u003cp\u003eThe acceptability of third-party access was variable and dependent on the relationship context. Access by veterinarians was widely accepted, aligning with previous research that veterinarians in private practice, and other farmers, carry the most weight with farmers \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This highlights the opportunity to leverage the established farmer-veterinarian relationship to encourage uptake of the MH. Initiatives such as the RCVS Knowledge Farm Vet Champions and the Welsh Veterinary Prescribing Champion (VPC) networks under the Arwain DGC (Responsible Antimicrobial Use) programme in Wales are notable examples \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. These projects have successfully supported veterinarins through ongoing, free training and soft skill development to engage with farmers in reducing ABU. A similar model could be extended to MH implementation, offering ongoing support and training to farmers, especially those with limited capacity or confidence with IT systems. Arwain DGC also demonstrates use of technology on proof-of-concept farms to improve health and welfare, thereby reducing ABU. MH could function in a similar capacity, enabling earlier identification and resolution of health issues.\u003c/p\u003e\u003cp\u003eIn contrast, the relationship between farmers and the supply chain is more anonymous. Here, access was perceived as intrusive, with concerns that data might be misinterpreted or used to manipulate milk prices. These negative perceptions may stem from a broader sense of power imbalance between dairy farmers and the supply chain. The Grocery Code Adjudicator (GCA) and DEFRA\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e have highlighted transparency issues in some contracts. In addition, there have been several publicised data breaches within the agricultural community, including one where production data were unlawfully shared amongst processors to reduce payments \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, such feelings of powerlessness, a desire for privacy and concerns over misinterpretation, either unintentional or deliberate, are not restricted to dairy farmers and their supply chain. These concerns about data sharing are common in participants involved in human health research. Studies in human health have found that patients are more willing for data to be shared with health professionals directly delivering healthcare than with private organisations and government agencies \u003csup\u003e31 32\u003c/sup\u003e. In research ethics, one of the core principles is respect for persons \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This should be extended to non-research organisations. Providing farmers with clear consent procedures for data-sharing that include their views in the flow of data originating from their farm, would show respect. Although there is no known evidence to date suggesting a causal relationship between participant permissions and acceptance of data sharing, providing farmers with the ability to control who can access their data could encourage participation \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhilst the primary aim of the MH is to provide national level data on ABU in the dairy industry, participants were willing to share their ABU data when they regarded the summarised results as beneficial to farmers and veterinarians. Therefore, recognising farmers as data creators and providing a form of feedback specific to the farm will be important\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In this study, participants believed this could take the form of benchmarking. It is well known that farmers have significant strains on resources and see medicine recording as a chore\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. They have difficulty extracting useful information from records due to their inconsistent format and multiple lines of data entry\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. However, this study does appear to highlight a changing perspective regarding the usefulness of records to identify issues, possibly due to automated transformation of data into information. Farmers recognised that veterinarians could use information to problem solve and identify issues early on to prevent wider issues occurring, such as prolonged disease outbreaks. This would have both financial and animal welfare benefits. Participants also saw themselves taking an interest in the outputs, being able to self-identify trends, anticipate problems, and benchmark their farm against the rest of the industry as a management tool. Data could also be used to promote the industry. This contrasts with previous findings that animal care is monitored by regular visual inspection of animals only, and records being an administrative task for assurance scheme compliance\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Whilst perceiving data sharing as inevitable, participants did report feeling overwhelmed by submitting data to the MH, due to lack of capacity or ability. Some viewed it as an additional burden in an industry where new legislation, increasing paperwork, media criticism, heavy workload and time pressures are risk factors for occupational stress and eventual burnout; burnout in the agricultural industries is above international norms \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Increasing paperwork is worsened by duplication to multiple sources in different formats, along with limited technological capability. This could result in emotional exhaustion, associated with negative behaviour and attitudes towards data sharing\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. For the MH uptake to be a success, especially if it is to remain voluntary, consideration should be given to minimising duplication of effort, for example by data linkage. Furthermore, AHDB confirming farmer antibiotic recording compliance with auditors, and providing appropriate technology support systems, such as training or options for outsourcing data entry, is likely to encourage and support voluntary MH uptake.\u003c/p\u003e\u003cp\u003eFarmers reactions to the MH and associated data sharing are consistent with Lazarus\u0026rsquo; coping theory\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. This framework examines responses to events perceived as disruptive. Coping is defined as \u0026lsquo;cognitive and behavioural efforts exerted to manage specific external and/or internal demands that are appraised as taxing or exceeding the resources of the person\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Internal demands relate to personal goals; external ones to job requirements or social pressures \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. When demands exceed coping resources, disruption is felt \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCoping responses involve two appraisals; primary and secondary \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Primary appraisal evaluates a person\u0026rsquo;s initial reaction to a disruptive event: Is this event positive, threatening or challenging \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e? Secondary appraisal evaluates coping options. If one has some control over the event, problem-focused strategies (e.g. training, advocacy, adaptation) will prevail; where not, emotion-focused response (e.g. withdrawal) may dominate, which has been found to be the case with farmers dealing with farm-related problems\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. As the MH is voluntary, understanding and supporting positive coping strategies is critical.\u003c/p\u003e\u003cp\u003eParticipants expressed a wide variety of responses towards the willingness to share data using the MH. Some viewed the MH and data sharing in a straightforward, positive way and believed that the tool would enable transparency. Others felt threatened citing control loss, IT difficulties, worries about the associated time burden or concerns over duplication. Others viewed it as a challenge, a potential for gain; the tool could improve market access and promote the end product and be an easy interaction platform with veterinarians. Some viewed it as both a threat and a challenge; being open and honest means progression, but lack of control over access was a concern. Where the MH was viewed as a threat or a challenge, the coping solutions suggested for compliance were data linkage, software training, control over data access, and provision of benefits such as benchmarking and detailed individual feedback. However, for others the coping strategy was not to engage, instead feeling discriminated against for their lack of ability with IT, and fearing the implementation of the MH.\u003c/p\u003e\u003cp\u003eThe findings of this study have been drawn from the responses to further explanation of a closed question on the usefulness of sharing ABU data, and explanation of the planned implementation of a centralised MH. As the study was conducted in person with participants, there was opportunity for clarification and discussion of their responses with the researcher, and with other farm staff or family members present at the time of the visit. While this was valuable, and maximized understanding between interviewer and participant, the participants views may have been influenced by the discussion with others. Information given to participants about the interviewer\u0026rsquo;s veterinary and dairy farming background varied, depending on how recruitment had happened and how conversations unfolded during the visit. Participants who were aware of the interviewer\u0026rsquo;s veterinary and dairy farming background may have been more inclined to engage in open and frank discussion because of a perception of support or empathy, or conversely, they may have been less inclined because of a perception of being judged.\u003c/p\u003e\u003cp\u003eQualitative research does not aim for statistical representativeness, but instead seeks to develop a detailed understanding of how people experience and interpret specific issues within particular contexts. The findings of our study reflect the views of a group of dairy farmers in South West England and Wales, most of whom were under 60. As in any research that depends on voluntary participation, whether qualitative or quantitative, farmers who were more engaged or held positive views were probably more inclined to take part. This self-selection influences which viewpoints are accessible to researchers, but does not diminish the value of the detailed, contextual understanding that qualitative research provides. Our recruitment method aimed to capture diversity by including farmers with varied herd sizes, milk contract types, and record-keeping approaches. This helped provide a nuanced understanding of the topic and contributes to the credibility of the findings. Future research could explore how common these views are across the wider population of dairy farmers.\u003c/p\u003e\u003cp\u003eThe AHDB will become the gatekeeper of farm data. A proactive communication strategy is essential to build trust and encourage uptake. This should include clear guidelines on data access, sample reports, and expectation setting. An IT support system should be available for those lacking digital skills. Where farms already keep electronic records or supply ABU data to the supply chain, integrating these sources into the MH can minimize duplication and avoid a significant time burden. This outlined collaborative approach to data sharing between dairy farmers and the AHDB is recommended to ensure successful implementation of a centralised MH.\u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cp\u003e This study was approved by the Royal Veterinary College\u0026rsquo;s Social Sciences Research Ethical Review Board (SR2018-1621).\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Study population, data collection and data analysis\u003c/h2\u003e\u003cp\u003eParticipants in this study were dairy farmers, or persons heavily involved in the dairy farm, such as employees or family members. Participants were located in South West England and Wales, and visited in person, by author CS, a veterinarian and PhD student, between April 2019 and October 2019 to answer a questionnaire on their enterprise, management and ABU and provide access to their farm medicine records, as reported elsewhere \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. This study focusses on their response to a closed question on the use of a centralised MH, and their explanation to this response. The question was read out verbatim, providing further explanation if required. Responses were recorded on the paper questionnaire with their oversight. The planned implementation by AHDB of a centralised MH for the dairy industry with the primary aim of collecting ABU data at farm level to report at national level, was explained to participants. They were also told the MH would provide ABU reporting and benchmarking at farm level. Then they answered a closed question: \u0026lsquo;\u003cem\u003eWould you find it useful if people requesting your data (e.g. your veterianarian, retailers, milk buyers) could have direct access to your records with your permission?\u003c/em\u003e\u0026rsquo;, and were asked explain their response. Questionnaire completion and resulting open discussions were not audio-recorded, but discussions were captured in field notes at the time. This contextualized questionnaire data and provided additional insight into dairy farmers opinions about ABU and recording.\u003c/p\u003e\u003cp\u003eOn completion of data gathering, data from field notes were linked to questionnaire responses via a unique respondent ID code, using a relational database. Frequency distributions created in R (version 3.6.20) were used to summarise demographic factors, recording method, type of milk contract and herd size \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Additional data on farm management factors and enterprise size of study participants have been published elsewhere \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Using pseudonmyised data, participant demographics were compared between those who provided an explanation for their response to the closed question, and those who did not, using the Chi-square or Fisher\u0026rsquo;s exact test. Participants\u0026rsquo; responses to the question on data sharing and use of a centralised MH were imported into NVivo for qualitative content analysis, a method used to describe textual data by organizing it into meaningful categories \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. As this study was exploratory, a data-led, inductive approach was used to develop codes and identify patterns and categories that captured participants\u0026rsquo; perspectives and opinions \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Such codes were developed by assigning key words or short phrases to describe what was being conveyed \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Codes with a similar meaning were then grouped into overarching codes, and categories developed through an iterative process of reviewing and refining codes to ensure the analysis remained grounded in the data. Quotations from the data have been used to provide examples of the developed categories, and are presented in italics and inverted comma\u0026rsquo;s throughout.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The anonymised dataset is available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e Many thanks to all the farmers who gave their time to participate in this study, to Eamon Watson and colleagues at National Milk Records (NMR) and Alasdair Moffett and colleagues at Synergy Farm Health for their help with recruitment of farmers and feedback on methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, C.S., L.B., P.A., and J.C.; study design and methods, C.S., and J.C.; formal analysis, C.S.; interpretation, C.S., L.B., and J.C.; data curation, C.S.; writing—original draft preparation, C.S.; writing—review and editing, C.S., L.B., and J.C.; visualization, C.S.; supervision, L.B., P.A., and J.C.; project administration, C.S. and L.B.; funding acquisition, C.S., J.C., P.A. and L.B. All authors have reviewed and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was funded by the Royal Veterinary College, with additional funding for farmer interviews provided by Antibiotic Research UK (grant number ANTSRG 01/2018). The funders played no role in study design, data collection, analysis and interpretation of data, or the writing of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Informed consent was obtained from all participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eResponsible Use of Medicines in Agriculture Alliance. \u003cem\u003eTargets Task Force Report 2020. Responsible Use of Antibiotics in UK Farming. Progress against 2020 Targets. New Targets 2021 - 2024\u003c/em\u003e. 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(Springer New York LLC, 1984).\u003c/li\u003e\n\u003cli\u003eBhattacherjee, A., Davis, C. J., Connolly, A. J. \u0026amp; Hikmet, N. User response to mandatory IT use: a coping theory perspective. \u003cem\u003eEuropean Journal of Information Systems\u003c/em\u003e\u003cstrong\u003e27\u003c/strong\u003e, 395\u0026ndash;414 (2018).\u003c/li\u003e\n\u003cli\u003eFennell, K. M., Kettler, L. J., Skaczkowski, G. \u0026amp; Turnbull, D. A. Farmers\u0026rsquo; stress and coping in a time of drought. \u003cstrong\u003e12\u003c/strong\u003e, (2012).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-antimicrobials-and-resistance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjamar","sideBox":"Learn more about [npj Antimicrobials and Resistance](http://www.nature.com/npjamar/)","snPcode":"44259","submissionUrl":"https://submission.springernature.com/new-submission/44259/3","title":"npj Antimicrobials and Resistance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7037458/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7037458/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCentralised capture of accurate farm-level data on antibiotic usage (ABU) is needed for surveillance of antibiotic resistance. The objectives of this study were to describe the factors influencing British dairy farmers willingness to 1) submit their ABU data to a centralised medicine hub (MH) and 2) allow third-party access to such data. An inductive qualitative content analysis was undertaken on data collected in person from 94 dairy farmers in South West England and Wales. Participants answered a closed survey question on use of a centralised MH, and provided an explanation for their response. Factors affecting participants\u0026rsquo; willingness to share ABU by using a centralised system were that they had nothing to hide, they trusted their veterinarian with their data, and perceived that it would provide useful information for farmers and veterinarians. However, participants had a fear of losing control over decision-making and therefore wanted to control access to their data. They also felt overwhelmed by data demands, but suggested that data sharing is already happening and inevitable. Participants in this study were more likely to have positive viewpoints of sharing ABU due to the self-selection process. These findings suggest that, overall, farmers in this study are happy to share their ABU data, and recognise the potential benefits it could bring for herd health. 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