Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Lao-Oxford-Mahosot Hospital Wellcome Trust Research Unit, Vientiane, Laos.
Clin Microbiol Infect. 2021 Oct;27(10):1391-1399. doi: 10.1016/j.cmi.2021.05.037. Epub 2021 Jun 7.
Routine microbiology results are a valuable source of antimicrobial resistance (AMR) surveillance data in low- and middle-income countries (LMICs) as well as in high-income countries. Different approaches and strategies are used to generate AMR surveillance data.
We aimed to review strategies for AMR surveillance using routine microbiology results in LMICs and to highlight areas that need support to generate high-quality AMR data.
We searched PubMed for papers that used routine microbiology to describe the epidemiology of AMR and drug-resistant infections in LMICs. We also included papers that, from our perspective, were critical in highlighting the biases and challenges or employed specific strategies to overcome these in reporting AMR surveillance in LMICs.
Topics covered included strategies of identifying AMR cases (including case-finding based on isolates from routine diagnostic specimens and case-based surveillance of clinical syndromes), of collecting data (including cohort, point-prevalence survey, and case-control), of sampling AMR cases (including lot quality assurance surveys), and of processing and analysing data for AMR surveillance in LMICs.
The various AMR surveillance strategies warrant a thorough understanding of their limitations and potential biases to ensure maximum utilization and interpretation of local routine microbiology data across time and space. For instance, surveillance using case-finding based on results from clinical diagnostic specimens is relatively easy to implement and sustain in LMIC settings, but the estimates of incidence and proportion of AMR is at risk of biases due to underuse of microbiology. Case-based surveillance of clinical syndromes generates informative statistics that can be translated to clinical practices but needs financial and technical support as well as locally tailored trainings to sustain. Innovative AMR surveillance strategies that can easily be implemented and sustained with minimal costs will be useful for improving AMR data availability and quality in LMICs.
在中低收入国家(LMICs)和高收入国家,常规微生物学结果是监测抗生素耐药性(AMR)的有价值的数据来源。不同的方法和策略用于生成 AMR 监测数据。
我们旨在审查使用 LMICs 中的常规微生物学结果进行 AMR 监测的策略,并强调需要支持的领域,以生成高质量的 AMR 数据。
我们在 PubMed 上搜索了使用常规微生物学来描述 LMICs 中 AMR 和耐药感染的流行病学的论文。我们还包括了从我们的角度来看,在报告 LMICs 中的 AMR 监测时,突出偏见和挑战或采用特定策略来克服这些偏见和挑战的重要论文。
涵盖的主题包括确定 AMR 病例的策略(包括基于常规诊断标本中的分离物的病例发现和临床综合征的病例监测),收集数据的策略(包括队列、点 prevalence 调查和病例对照),AMR 病例的采样策略(包括批质量保证调查),以及处理和分析 LMICs 中 AMR 监测数据的策略。
各种 AMR 监测策略需要彻底了解其局限性和潜在偏见,以确保在时间和空间上最大限度地利用和解释当地的常规微生物学数据。例如,基于临床诊断标本结果的病例发现监测在 LMIC 环境中相对容易实施和维持,但由于对微生物学的使用不足,AMR 的发病率和比例估计存在偏差的风险。临床综合征的病例监测产生了可以转化为临床实践的信息统计数据,但需要财务和技术支持以及针对当地情况的培训来维持。能够以最小的成本轻松实施和维持的创新性 AMR 监测策略将有助于改善 LMICs 中 AMR 数据的可用性和质量。