Indraprastha Institute of Information Technology, Delhi, India.
All India Institute of Medical Sciences, New Delhi, India.
J Glob Antimicrob Resist. 2022 Sep;30:133-142. doi: 10.1016/j.jgar.2022.04.021. Epub 2022 May 6.
Antimicrobial resistance (AMR) is the next big pandemic that threatens humanity. The One Health approach to AMR requires quantification of interactions between health, demographic, socioeconomic, environmental, and geopolitical factors to design interventions. This study is focused on learning health system factors on global AMR.
This study analysed longitudinal data (2004-2017) of AMR having 6 33 820 isolates from 70 middle and high-income countries. We integrated AMR data with the Global Burden of Disease (GBD), Governance (WGI), and Finance data sets to find AMR's unbiased and actionable determinants. We chose a Bayesian decision network (BDN) approach within the causal modelling framework to quantify determinants of AMR. Further, we integrated Bayesian networks' global knowledge discovery approach with discriminative machine learning to predict individual-level antibiotic susceptibility in patients.
From MAR (multiple antibiotic resistance) scores, we found a non-uniform spread pattern of AMR. Components-level analysis revealed that governance, finance, and disease burden variables strongly correlate with AMR. From the Bayesian network analysis, we found that access to immunization, obstetric care, and government effectiveness are strong, actionable factors in reducing AMR, confirmed by what-if analysis. Finally, our discriminative machine learning models achieved an individual-level AUROC (Area under receiver operating characteristic curve) of 0.94 (SE = 0.01) and 0.89 (SE = 0.002) to predict Staphylococcus aureus resistance to ceftaroline and oxacillin, respectively.
Causal machine learning revealed that immunisation strategies and quality of governance are vital, actionable interventions to reduce AMR.
抗菌药物耐药性（AMR）是威胁人类的下一个大流行病。应对 AMR 的“同一健康”方法需要对卫生、人口、社会经济、环境和地缘政治因素之间的相互作用进行量化，以便设计干预措施。本研究专注于了解全球 AMR 中的学习卫生系统因素。
本研究分析了来自 70 个中高收入国家的 633820 株分离株的 AMR 纵向数据（2004-2017 年）。我们将 AMR 数据与全球疾病负担（GBD）、治理（WGI）和财务数据集整合，以发现 AMR 的无偏和可操作的决定因素。我们选择了因果建模框架内的贝叶斯决策网络（BDN）方法来量化 AMR 的决定因素。此外，我们将贝叶斯网络的全球知识发现方法与判别机器学习相结合，以预测患者的个体抗生素敏感性。
从 MAR（多种抗生素耐药性）评分中，我们发现 AMR 的分布模式不均匀。组件级分析表明，治理、金融和疾病负担变量与 AMR 密切相关。从贝叶斯网络分析中，我们发现获得免疫、产科护理和政府效能是减少 AMR 的有力、可操作的因素，这也得到了假设分析的证实。最后，我们的判别机器学习模型在个体水平上分别实现了 0.94（SE=0.01）和 0.89（SE=0.002）的 AUROC（接收者操作特征曲线下面积），以预测金黄色葡萄球菌对头孢洛林和苯唑西林的耐药性。
因果机器学习表明，免疫策略和治理质量是减少 AMR 的重要、可操作的干预措施。
