背景:抗生素耐药性肠杆菌(ARE)是全球范围内的公共卫生威胁。这些机会性病原体的传播已在医院中进行了大量研究。尽管在世界某些地区的社区中无症状定植的患病率很高,在这种情况下,人们对ARE的获取和传播知之甚少。由于解释社区ARE动态并不简单,数学模型可能是探索潜在现象和进一步评估干预措施对遏制医院外ARE循环的影响的关键。
方法:我们对数学建模研究进行了系统的回顾,重点是AR-E在社区中的传播,不包括仅针对医院的型号。我们提取了模型特征(总体,设置),形式主义(部门,以个人为基础),生物学假设(传播,感染,抗生素的影响,抗性菌株特异性)和主要发现。我们讨论了需要考虑的其他机制,开放的科学问题,和最紧迫的数据需求。
结果:我们确定了18项建模研究,重点关注ARE在社区(n=11)或社区和医院(n=7)中的人传播。旨在(i)了解驱动阻力动态的机制;(ii)识别和量化传播途径;或(iii)评估公共卫生干预措施以减少阻力的模型。为了克服使用经典的两菌株竞争模型在社区中再现观察到的ARE动态的困难,研究建议包括宿主内菌株竞争或强大的宿主种群结构等机制。从纵向托架数据推断模型参数的研究主要基于仅考虑ARE应变的模型。他们显示了ARE运输持续时间的差异,具体取决于获取模式:返回旅行者的运输持续时间明显短于出院住院患者或健康个体。有趣的是,关于公共卫生干预措施成功降低ARE发生率的模型预测取决于病原体,设置,和抗生素耐药机制。对于大肠杆菌,在社区中减少人与人之间的传播比在社区中减少抗生素使用具有更强的效果.对于肺炎克雷伯菌,减少医院抗生素使用比减少社区使用更有效.
结论:本研究提出了数量有限的专门针对ARE在社区传播的建模研究。它强调了模型开发和基于社区的数据收集的必要性,特别是在低收入和中等收入国家,以更好地了解获取途径及其对观察到的ARE水平的相对贡献。这种建模对于正确设计和评估公共卫生干预措施以控制ARE在社区中的传播并进一步减少相关的感染负担至关重要。
BACKGROUND: Antibiotic-resistant Enterobacterales (ARE) are a public health threat worldwide. Dissemination of these opportunistic pathogens has been largely studied in hospitals. Despite high prevalence of asymptomatic colonization in the community in some regions of the world, less is known about ARE acquisition and spread in this setting. As explaining the community ARE dynamics has not been straightforward, mathematical models can be key to explore underlying phenomena and further evaluate the impact of interventions to curb ARE circulation outside of hospitals.
METHODS: We conducted a systematic review of mathematical modeling studies focusing on the transmission of AR-E in the community, excluding models only specific to hospitals. We extracted model features (population, setting), formalism (compartmental, individual-based), biological hypotheses (transmission, infection, antibiotic impact, resistant strain specificities) and main findings. We discussed additional mechanisms to be considered, open scientific questions, and most pressing data needs.
RESULTS: We identified 18 modeling studies focusing on the human transmission of ARE in the community (n=11) or in both community and hospital (n=7). Models aimed at (i) understanding mechanisms driving resistance dynamics; (ii) identifying and quantifying transmission routes; or (iii) evaluating public health interventions to reduce resistance. To overcome the difficulty of reproducing observed ARE dynamics in the community using the classical two-strains competition model, studies proposed to include mechanisms such as within-host strain competition or a strong host population structure. Studies inferring model parameters from longitudinal carriage data were mostly based on models considering the ARE strain only. They showed differences in ARE carriage duration depending on the acquisition mode: returning travelers have a significantly shorter carriage duration than discharged hospitalized patient or healthy individuals. Interestingly, predictions across models regarding the success of public health interventions to reduce ARE rates depended on pathogens, settings, and antibiotic resistance mechanisms. For E. coli, reducing person-to-person transmission in the community had a stronger effect than reducing antibiotic use in the community. For Klebsiella pneumoniae, reducing antibiotic use in hospitals was more efficient than reducing community use.
CONCLUSIONS: This study raises the limited number of modeling studies specifically addressing the transmission of ARE in the community. It highlights the need for model development and community-based data collection especially in low- and middle-income countries to better understand acquisition routes and their relative contribution to observed ARE levels. Such modeling will be critical to correctly design and evaluate public health interventions to control ARE transmission in the community and further reduce the associated infection burden.