Social research to understand farmer and agricultural stakeholder attitudes towards bovine tuberculosis vaccination of cattle.

BACKGROUND:This social research study employed a behavioural insights framework, Easy, Attractive, Social, Timely ('EAST'), to identify cues that may influence farmer and stakeholder attitudes towards the deployment of CattleBCG vaccine. METHODS:The EAST framework was employed to develop policy scenarios consisting of several cues likely to affect vaccine uptake. These scenarios consisted of a government-led approach, an individual farmer-led approach, and a third approach, also farmer-led but organised collectively. The government approach was mandatory, while the farmer-led approaches were both voluntary. The scenarios were tested during farmer participatory workshops (n = 8) and stakeholder interviews (n = 35). RESULTS:Overall, the EAST framework provided a useful approach for gathering behavioural insights around attitudes towards cattle vaccination. We found an overall receptiveness towards the idea of vaccinating cattle against bovine tuberculosis, particularly where clear, transparent messaging around the likely efficacy is mobilised, where clarity around potential implications for trading is provided, and where vaccine doses are provided free of charge and administered by veterinarians and veterinary technicians. In general, these factors were a pre-requisite to a mandatory (government-led) national approach, which was the preferred deployment mechanism among farmers and stakeholders. However, these conditions would also likely facilitate a voluntary vaccination programme. LIMITATIONS:Trust in those involved in delivering a vaccine programme and trust in the vaccine itself represent a crucial aspect of farmer and stakeholder attitudes towards cattle vaccination; however, this aspect was not covered by the EAST framework. CONCLUSION:EAST provided a novel framework for examining attitudes towards cattle vaccination with CattleBCG, although we recommend incorporating a 'trust' component in future iterations.