{"title":"Modeling the transmission of antibiotic-resistant Enterobacterales in the community: A systematic review","authors":"Eve Rahbé , Philippe Glaser , Lulla Opatowski","doi":"10.1016/j.epidem.2024.100783","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>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.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>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 <em>E. coli</em>, reducing person-to-person transmission in the community had a stronger effect than reducing antibiotic use in the community. For <em>Klebsiella pneumoniae</em>, reducing antibiotic use in hospitals was more efficient than reducing community use.</p></div><div><h3>Conclusions</h3><p>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.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436524000446/pdfft?md5=6fcf3dc9c59e75dcc65b20f9e031f69d&pid=1-s2.0-S1755436524000446-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epidemics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1755436524000446","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
引用次数: 0
Abstract
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.
背景:耐抗生素肠杆菌(ARE)是全球公共卫生的一个威胁。对这些机会性病原体传播的研究主要集中在医院。尽管在世界上的一些地区,无症状菌落在社区中的流行率很高,但人们对 ARE 在这种环境中的感染和传播却知之甚少。由于对社区 ARE 动态的解释并不直截了当,因此数学模型是探索潜在现象和进一步评估干预措施对遏制 ARE 在医院外传播的影响的关键:我们对有关 AR-E 在社区传播的数学模型研究进行了系统回顾,排除了仅针对医院的模型。我们提取了模型的特征(人群、环境)、形式(分区、基于个体)、生物学假设(传播、感染、抗生素影响、耐药菌株特异性)和主要发现。我们还讨论了需要考虑的其他机制、有待解决的科学问题以及最迫切的数据需求:结果:我们确定了 18 项建模研究,重点关注 ARE 在社区(11 项)或社区和医院(7 项)的人类传播。这些模型旨在:(i) 了解耐药性动态的驱动机制;(ii) 识别和量化传播途径;或 (iii) 评估减少耐药性的公共卫生干预措施。经典的双菌株竞争模型难以再现社区中观察到的耐药性动态,为了克服这一困难,研究建议加入一些机制,如宿主内部的菌株竞争或强大的宿主种群结构。从纵向携带数据推断模型参数的研究大多基于只考虑 ARE 菌株的模型。这些研究显示,ARE携带持续时间因感染模式而异:返乡旅行者的携带持续时间明显短于出院的住院病人或健康人。有趣的是,不同模型对降低 ARE 感染率的公共卫生干预措施成功与否的预测取决于病原体、环境和抗生素耐药机制。就大肠杆菌而言,减少社区中人与人之间的传播比减少社区中抗生素的使用更有效。对于肺炎克雷伯氏菌,减少医院使用抗生素比减少社区使用抗生素更有效:本研究提出,专门针对 ARE 在社区传播的建模研究数量有限。它强调了模型开发和社区数据收集的必要性,尤其是在低收入和中等收入国家,以便更好地了解感染途径及其对观察到的 ARE 水平的相对贡献。这种模型对于正确设计和评估公共卫生干预措施以控制 ARE 在社区的传播和进一步减轻相关的感染负担至关重要。
期刊介绍:
Epidemics publishes papers on infectious disease dynamics in the broadest sense. Its scope covers both within-host dynamics of infectious agents and dynamics at the population level, particularly the interaction between the two. Areas of emphasis include: spread, transmission, persistence, implications and population dynamics of infectious diseases; population and public health as well as policy aspects of control and prevention; dynamics at the individual level; interaction with the environment, ecology and evolution of infectious diseases, as well as population genetics of infectious agents.