Participatory identification of the causes of antimicrobial use and how they may vary according to differences in sector structure : The case of the Flemish pork and veal sectors

Summary

The increasing threat of antimicrobial resistance (AMR) to human health has prompted many countries to adopt national action plans to reduce antimicrobial use (AMU) in farm animals. To achieve this goal, it is necessary to gain a deeper understanding of the factors driving AMU in farm animals. While previous research has focused on gaining a better understanding of AMU from the perspective of farmers and veterinarians, less emphasis has been placed on examining the systemic and contextual factors that influence AMU from multiple viewpoints within the food supply chain. To this end, this paper describes a participatory approach involving multiple stakeholders from two distinct livestock sectors to identify the underlying drivers of AMU and explore their case-specificity. For each sector, we identified causes of AMU during four online focus groups, by co-creating a “problem tree”, which resulted in the identification of over 50 technical, economic, regulatory, and sociocultural causes per sector and exploration of causal links. Following this, we analysed the focus group discussion through a content analysis and clustered causes of AMU that were related into 17 categories (i.e. main drivers of AMU), that we then classified as drivers of AMU at sector level or drivers of AMU at farm level. Finally, we compared the two sectors by assessing whether the generated categories (i.e. main drivers for AMU) had been discussed for both sectors and, if so, whether they involved the same causes and had the same implications. Through our analysis, we gained a better understanding of several main drivers of AMU at sector level, that result from systemic and/or contextual causes. As these cannot always be addressed by farmers and/or their veterinarian, we suggest that interventions should also target other actors related to these causes or consider them to help implement certain strategies. Furthermore, based on the results of our comparative analysis, we suggest that systemic structural differences, such as size and level of supply chain integration/fragmentation, may lead to differences in how animal health management is approached. This in turn may influence AMU's decision-making and the effectiveness of interventions, if they are generic and not tailored to the specificities of the sector.