Epidemiol Rev. 2022 Jan 14;43(1):53-64. doi: 10.1093/epirev/mxab001.
The increased focus on the public health burden of antimicrobial resistance (AMR) raises conceptual challenges, such as determining how much harm multidrug-resistant organisms do compared to what, or how to establish the burden. Here, we present a counterfactual framework and provide guidance to harmonize methodologies and optimize study quality. In AMR-burden studies, 2 counterfactual approaches have been applied: the harm of drug-resistant infections relative to the harm of the same drug-susceptible infections (the susceptible-infection counterfactual); and the total harm of drug-resistant infections relative to a situation where such infections were prevented (the no-infection counterfactual). We propose to use an intervention-based causal approach to determine the most appropriate counterfactual. We show that intervention scenarios, species of interest, and types of infections influence the choice of counterfactual. We recommend using purpose-designed cohort studies to apply this counterfactual framework, whereby the selection of cohorts (patients with drug-resistant, drug-susceptible infections, and those with no infection) should be based on matching on time to infection through exposure density sampling to avoid biased estimates. Application of survival methods is preferred, considering competing events. We conclude by advocating estimation of the burden of AMR by using the no-infection and susceptible-infection counterfactuals. The resulting numbers will provide policy-relevant information about the upper and lower bound of future interventions designed to control AMR. The counterfactuals should be applied in cohort studies, whereby selection of the unexposed cohorts should be based on exposure density sampling, applying methods avoiding time-dependent bias and confounding.
对抗微生物药物耐药性(AMR)公共卫生负担的日益关注带来了概念性挑战,例如确定多药耐药生物与其他因素相比造成了多大的危害,或者如何确定负担。在这里,我们提出了一个反事实框架,并提供了指导,以协调方法和优化研究质量。在 AMR 负担研究中,已经应用了 2 种反事实方法:耐药感染的危害相对于相同药物敏感感染的危害(敏感感染反事实);以及耐药感染的总危害相对于这种感染被预防的情况(无感染反事实)。我们建议使用基于干预的因果方法来确定最合适的反事实。我们表明,干预方案、感兴趣的物种和感染类型会影响反事实的选择。我们建议使用专门设计的队列研究来应用这种反事实框架,其中队列的选择(耐药、敏感感染和无感染的患者)应基于通过暴露密度采样匹配感染时间,以避免有偏估计。考虑到竞争事件,建议应用生存方法。最后,我们提倡通过应用无感染和敏感感染反事实来估计 AMR 的负担。所得数字将为旨在控制 AMR 的未来干预措施的上限和下限提供与政策相关的信息。反事实应在队列研究中应用,其中未暴露队列的选择应基于暴露密度采样,应用避免时变偏差和混杂的方法。