抗菌素耐药性(AMR)对人类健康的威胁日益增加,促使许多国家采取国家行动计划,以减少农场动物中的抗菌素使用(AMU)。为了实现这一目标,有必要对农场动物中驱动AMU的因素有更深入的了解。虽然以前的研究集中在从农民和兽医的角度更好地了解AMU,不太重视从食品供应链中的多个角度审查影响AMU的系统和环境因素。为此,本文介绍了一种参与式方法,涉及来自两个不同畜牧业部门的多个利益相关者,以确定AMU的潜在驱动因素并探讨其案例特异性。对于每个扇区,我们在四个在线焦点小组中确定了AMU的原因,通过共同创建“问题树”,这导致了超过50名技术人员的鉴定,经济,监管,以及每个部门的社会文化原因和因果联系的探索。在此之后,我们通过内容分析和AMU的聚集原因分析了焦点小组讨论,这些原因涉及17个类别(即AMU的主要驱动因素),然后,我们将其分类为部门级AMU的驱动因素或农场级AMU的驱动因素。最后,我们通过评估是否已经讨论了两个部门的产生类别(即AMU的主要驱动因素)来比较这两个部门,如果是,它们是否涉及相同的原因并具有相同的含义。通过我们的分析,我们在部门层面更好地了解了AMU的几个主要驱动因素,这是由系统和/或上下文原因引起的。由于农民和/或他们的兽医不能总是解决这些问题,我们建议干预措施还应针对与这些原因相关的其他行为者,或考虑它们来帮助实施某些策略。此外,根据我们的比较分析结果,我们认为系统性结构差异,如供应链整合/碎片化的规模和水平,可能会导致动物健康管理方法的差异。这反过来可能会影响AMU的决策和干预措施的有效性,如果它们是通用的,不适合该行业的具体情况。
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.