The role of "spillover" in antibiotic resistance.
The role of "spillover" in antibiotic resistance.
复制标题
DOI：
10.1073/pnas.2013694117
复制
发表时间：
2020-11-17
影响因子：
11.1
通讯作者：
Grad YH
中科院分区：
文献类型：
--
作者：
Olesen SW;Lipsitch M;Grad YH
抗生素耐药性可以在人与人之间传播，新的耐药性决定因素在全球迅速传播就是一种现象。抗生素耐药性也可以在家庭成员之间或医院与周围社区之间“溢出”，这样在一个人群中使用抗生素就会选择耐药性，并将其传播到另一个人群中。从理论上讲，一个种群中抗生素的使用可能会蔓延到它的邻居，这使得人们很难理解一个种群自身的行为，比如它的抗生素使用，在多大程度上决定了它的耐药性水平。在这里，我们使用理论建模和观测数据来量化溢出效应，发现即使是种群之间适度的相互作用也可能导致大量的抗性水平共享。抗生素的使用是抗生素耐药性的关键驱动因素。了解抗生素使用和由此产生的耐药性之间的数量关系对于预测未来的抗生素耐药率和设计抗生素管理政策非常重要。然而，耐药关联因“溢出”而变得复杂，即一个群体的抗生素使用水平通过细菌在这些群体之间的传播影响另一个群体的抗药性水平。溢出效应已知会在家庭和医院层面产生影响，但尚不清楚溢出效应是否在更大范围内具有相关性。我们使用数学模型和对观测数据的分析来解决这个问题。首先，我们使用抗生素耐药性的动态模型来预测溢出的影响。虽然完全相互隔离的种群不会经历任何溢出，但我们发现，如果即使1%的互动是在种群之间，那么溢出可能会产生巨大的后果：一个种群中抗生素使用的变化对该种群中抗生素耐药性的影响可能会减少多达50%。然后，我们量化了观察到的抗生素使用溢出和来自美国各州和欧洲国家的三种病原体-抗生素组合的耐药性数据，发现种群之间相互作用的增加与这些种群之间抗生素耐药性的较小差异有关。因此，溢出可能在国家和国家一级产生重要影响，这对预测抗生素耐药性的未来、设计抗生素耐药性管理政策和解释管理干预措施具有重要影响。
Antibiotic resistance can spread from person to person, a phenomenon exemplified by the rapid global spread of novel resistance determinants. Antibiotic resistance can also “spill over” between family members or between a hospital and its surrounding community, such that antibiotic use in one population selects for resistance that is transmitted into the other population. In theory, antibiotic use in one population could spill over into its neighbors, making it difficult to understand to what degree a population’s own behavior, such as its antibiotic use, determines its level of resistance. Here we use theoretical modeling and observational data to quantify spillover, finding that even modest interactions between populations can lead to substantial sharing of resistance levels. Antibiotic use is a key driver of antibiotic resistance. Understanding the quantitative association between antibiotic use and resulting resistance is important for predicting future rates of antibiotic resistance and for designing antibiotic stewardship policy. However, the use–resistance association is complicated by “spillover,” in which one population’s level of antibiotic use affects another population’s level of resistance via the transmission of bacteria between those populations. Spillover is known to have effects at the level of families and hospitals, but it is unclear if spillover is relevant at larger scales. We used mathematical modeling and analysis of observational data to address this question. First, we used dynamical models of antibiotic resistance to predict the effects of spillover. Whereas populations completely isolated from one another do not experience any spillover, we found that if even 1% of interactions are between populations, then spillover may have large consequences: The effect of a change in antibiotic use in one population on antibiotic resistance in that population could be reduced by as much as 50%. Then, we quantified spillover in observational antibiotic use and resistance data from US states and European countries for three pathogen–antibiotic combinations, finding that increased interactions between populations were associated with smaller differences in antibiotic resistance between those populations. Thus, spillover may have an important impact at the level of states and countries, which has ramifications for predicting the future of antibiotic resistance, designing antibiotic resistance stewardship policy, and interpreting stewardship interventions.
登录
查看更多内容
影响因子：
11.8
作者：
Gottesman, Bat-Sheva;Low, Marcelo;Chowers, Michal
通讯作者：
Chowers, Michal
影响因子：
11.8
作者：
de Sande-Bruinsma, Nienke Van;Grundmann, Hajo;Verloo, Didier;Tiemersma, Edine;Monen, Jos;Goossens, Herman;Ferech, Matus
通讯作者：
Ferech, Matus
影响因子：
11.8
作者：
Harbarth, S;Harris, AD;Samore, MH
通讯作者：
Samore, MH
影响因子：
11.8
作者：
Hannah, EL;Angulo, FJ;Samore, MH
通讯作者：
Samore, MH
影响因子：
3.9
作者：
Blanquart, Francois;Lehtinen, Sonja;Fraser, Christophe
通讯作者：
Fraser, Christophe