National Institute of Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Diseases, Imperial College London, London, W12 0NN, UK.
Data Analytics, The Health Foundation, London, UK.
BMC Med. 2018 Aug 23;16(1):137. doi: 10.1186/s12916-018-1121-8.
Antibiotic-resistant bacteria (ARB) are selected by the use of antibiotics. The rational design of interventions to reduce levels of antibiotic resistance requires a greater understanding of how and where ARB are acquired. Our aim was to determine whether acquisition of ARB occurs more often in the community or hospital setting.
We used a mathematical model of the natural history of ARB to estimate how many ARB were acquired in each of these two environments, as well as to determine key parameters for further investigation. To do this, we explored a range of realistic parameter combinations and considered a case study of parameters for an important subset of resistant strains in England.
If we consider all people with ARB in the total population (community and hospital), the majority, under most clinically derived parameter combinations, acquired their resistance in the community, despite higher levels of antibiotic use and transmission of ARB in the hospital. However, if we focus on just the hospital population, under most parameter combinations a greater proportion of this population acquired ARB in the hospital.
It is likely that the majority of ARB are being acquired in the community, suggesting that efforts to reduce overall ARB carriage should focus on reducing antibiotic usage and transmission in the community setting. However, our framework highlights the need for better pathogen-specific data on antibiotic exposure, ARB clearance and transmission parameters, as well as the link between carriage of ARB and health impact. This is important to determine whether interventions should target total ARB carriage or hospital-acquired ARB carriage, as the latter often dominated in hospital populations.
抗生素耐药菌(ARB)是由抗生素的使用选择出来的。为了降低抗生素耐药水平,需要更深入地了解 ARB 的获取途径和地点,这就需要对干预措施进行合理设计。我们的目的是确定 ARB 是在社区环境还是医院环境中更容易获得。
我们使用了 ARB 自然史的数学模型来估计在这两种环境中分别有多少 ARB 被获得,以及确定进一步研究的关键参数。为此,我们探索了一系列现实的参数组合,并考虑了英格兰一组重要耐药菌株的参数案例研究。
如果我们考虑所有人群中具有 ARB 的人(社区和医院),在大多数临床衍生的参数组合下,尽管医院中抗生素使用和 ARB 传播水平较高,但大多数人在社区中获得了耐药性。然而,如果我们只关注医院人群,在大多数参数组合下,该人群中更多的人在医院中获得了 ARB。
很可能大多数 ARB 是在社区中获得的,这表明减少总体 ARB 携带量的努力应集中在减少社区环境中的抗生素使用和传播上。然而,我们的框架强调需要更好的针对特定病原体的抗生素暴露、ARB 清除和传播参数的数据,以及 ARB 携带与健康影响之间的联系。这对于确定干预措施是应该针对总 ARB 携带量还是医院获得性 ARB 携带量很重要,因为后者在医院人群中往往占主导地位。