School of Public Health, University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.
University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.
Appl Health Econ Health Policy. 2022 Jul;20(4):479-486. doi: 10.1007/s40258-022-00728-x. Epub 2022 Apr 4.
Due to the increasing threat to public health and the economy, governments internationally are interested in models to estimate the future clinical and economic burden of antimicrobial resistance (AMR) and to evaluate the cost-effectiveness of interventions to prevent or control resistance and to inform resource-allocation decision making. A widely cited UK report estimated that 10 million additional deaths will occur globally per annum due to AMR by 2050; however, the utility and accuracy of this prediction has been challenged. The precision of models predicting the future economic burden of AMR is dependent upon the accuracy of predicting future resistance rates. This paper reviews the feasibility and value of modelling to inform policy and resource allocation to manage and curb AMR. Here we describe methods used to estimate future resistance in published burden-of-disease models; the sources of uncertainty are highlighted, which could potentially mislead policy decision-making. While broad assumptions can be made regarding some predictable factors contributing to future resistance rates, the unexpected emergence, establishment and spread of new resistance genes introduces substantial uncertainty into estimates of future economic burden, and in models evaluating the effectiveness of interventions or policies to address AMR. Existing reporting standards for best practice in modelling should be adapted to guide the reporting of AMR economic models, to ensure model transparency and validation for interpretation by policymakers.
由于对公共卫生和经济的威胁不断增加，国际各国政府都有兴趣建立模型来估计未来抗菌素耐药性（AMR）的临床和经济负担，并评估预防或控制耐药性的干预措施的成本效益，为资源分配决策提供信息。一份广受引用的英国报告估计，到 2050 年，全球每年将因 AMR 而额外增加 1000 万人死亡；然而，人们对这一预测的实用性和准确性提出了质疑。预测 AMR 未来经济负担的模型的精确性取决于对未来耐药率的准确预测。本文回顾了用于为管理和遏制 AMR 提供政策和资源分配信息的建模的可行性和价值。在这里，我们描述了在已发表的疾病负担模型中用于估计未来耐药性的方法；强调了不确定性的来源，这些不确定性可能会误导政策决策。虽然可以对一些导致未来耐药率的可预测因素做出广泛的假设，但新的耐药基因的意外出现、确立和传播会给未来经济负担的估计以及评估应对 AMR 的干预措施或政策的有效性的模型带来很大的不确定性。现有的建模最佳实践报告标准应加以调整，以指导 AMR 经济模型的报告，确保模型的透明度和验证，以便政策制定者进行解释。
