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人群水平抗菌药物耐药性的数学建模:系统评价。

Population-level mathematical modeling of antimicrobial resistance: a systematic review.

机构信息

Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.

Present Address: Elsevier Inc., 230 Park Ave, Suite B00, New York, NY, 10169, USA.

出版信息

BMC Med. 2019 Apr 24;17(1):81. doi: 10.1186/s12916-019-1314-9.

Abstract

BACKGROUND

Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work.

METHODS

We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens.

RESULTS

We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals.

CONCLUSIONS

The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.

摘要

背景

数学传播模型越来越多地被用于指导传染病的公共卫生干预措施,特别是在新兴病原体的情况下;然而,建模对日益严重的抗生素耐药性(AMR)问题的贡献仍不清楚。在这里,我们系统地评估了最近一段时间(2006-2016 年)关于 AMR 人群传播模型的出版物,以评估研究现状并确定需要进一步研究的差距。

方法

我们对相关数据库进行了系统的文献检索,以确定病毒、细菌和寄生虫疾病系统中 AMR 的传播研究。我们分析了时间、地理和主题趋势,描述了研究的主要医学和行为干预措施,并确定了与关键病原体有关的中心发现。

结果

我们确定了 273 项建模研究;其中大多数(>70%)集中在 5 种传染病(人类免疫缺陷病毒(HIV)、流感病毒、疟原虫(疟疾)、结核分枝杆菌(TB)和耐甲氧西林金黄色葡萄球菌(MRSA))。流感和医院病原体的 AMR 研究主要在工业化国家进行,而 HIV、TB 和疟疾研究则严重偏向发展中国家。大多数文章仅关注人类的 AMR(89%),无论是在社区(58%)还是医疗保健(27%)环境中。模型系统主要是隔室的(76%)和确定性的(66%)。只有 43%的模型经过流行病学数据校准,很少有模型(14%)经过样本外数据集验证。考虑的干预措施主要是不同药物方案、卫生和感染控制措施、筛查和诊断的影响,而很少有研究涉及新出现的耐药性、疫苗策略、经济或行为改变,以减少人类和动物对抗生素的使用。

结论

AMR 建模文献集中在耐药性已经长期存在的疾病系统上,而很少有研究积极解决新病原体中耐药性的近期上升问题,也没有探索减少抗生素总体消耗的上游策略。值得注意的差距包括研究肠杆菌科和淋病奈瑟菌中的新兴耐药性;动物-人类界面的 AMR 传播,特别是在农业和兽医环境中;医院和社区之间的传播;环境因素在 AMR 传播中的作用;以及疫苗对抗 AMR 的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e565/6480522/3fec715ba589/12916_2019_1314_Fig1_HTML.jpg

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