Colliding crises: antimicrobial resistance and ageing

危机碰撞：抗菌素耐药性和衰老

• 批准号: MR/W026643/1
• 负责人: Gwenan Knight
• 金额: $ 144.58万
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FW026643%2F1
• 关键词: Colliding; crises; antimicrobial; resistance; ageing

Antimicrobial resistance (AMR) is increasing and is making infections harder to treat and prevent. This public health crisis collides with an ageing human population. More than 1 in 6 people in the world in 2050 are predicted to be over 60 years old and they have a 20-fold increase in bacterial infection incidence compared to younger adults. However, the likelihood that infections are caused by resistant bacteria varies by the age of the person, the bacterial species and the antibiotic in a currently unexplained way. A simple analysis of the proportion of isolates resistant to different antibiotics by age in European infection data has shown that resistance to methicillin in Staphylococcus aureus (MRSA) is ~18% in those age 5-18 years old, but over 40% in the elderly (65 years and older). Whereas resistance to vancomycin in Enterococci is roughly equal by age at ~3%. My research on multidrug-resistant Mycobacterium tuberculosis infection suggests that globally there is a peak in infection in those aged 15-25 years old. Understanding these trends by age matters as this would provide an insight into the underlying mechanisms of AMR selection and transmission which could then inform improved patient care and prescribing regimens (e.g., more subtle antibiotic use recommendations by age). Addressing this knowledge gap requires the quantitative analysis I will use here to systematically map, for the first time, AMR patterns in infections by age using global datasets. I will ask whether the patterns could be due to changes in clinical care or to recent or past differences in antibiotic usage, to then improve antibiotic prescribing and interventions against AMR. Specifically, I will pair data analysis of trends in AMR prevalence by age from global open access datasets with a mathematical model to simulate the underlying processes. This two-pronged approach will map the patterns and use modelling to determine the contribution of underlying processes such as transmission in hospitals and duration of carriage of resistant bacteria. It will require careful consideration of data collection processes and both period and cohort effects. To understand these trends in AMR, one must also consider antibiotic usage variation by age. However, global antibiotic usage data is hard to find and is usually not available segregated by age nor at the individual patient level. In this project I will use a unique dataset from England that links antibiotic usage with microbiology information at the individual patient level to determine the relative importance of recent (in the past year) versus past antibiotic usage to risk of infection with an antibiotic resistant bacterium. This is important as healthcare usage and hence antibiotic exposure varies with age, so any association of age and AMR could be masking underlying antibiotic use trends. Using this information, I will then assess interventions for AMR control by investigating the impact of age-targeted interventions in the mathematical model developed to simulate the underlying processes. I will also adapt an existing tool for informing empiric antibiotic prescribing to account for age-based or antibiotic usage associations with AMR. Empiric prescribing, when antibiotics are given in the absence of microbiological information, is the most common use of antibiotics globally and hence increased subtlety in use has enormous potential to reduce AMR selection and spread.By dissecting AMR complexity by age, this research will provide novel insights into the risks of infection with AMR bacteria and the origins of AMR to support evidence-based policy making. The broader impact will be a fundamental shift in our understanding of the heterogeneity and trends in AMR by host age which could enable long-term improvements in patient care.

抗生素耐药性（AMR）正在增加，使感染更难治疗和预防。这场公共卫生危机与人口老龄化相冲突。预计到2050年，世界上超过六分之一的人将超过60岁，与年轻人相比，他们的细菌感染发病率增加了20倍。然而，感染由耐药细菌引起的可能性因人的年龄，细菌种类和抗生素而异，目前无法解释。对欧洲感染数据中不同年龄的不同抗生素耐药菌株比例的简单分析表明，金黄色葡萄球菌（MRSA）对甲氧西林的耐药性在5-18岁的人群中约为18%，但在老年人（65岁及以上）中超过40%。而肠球菌对万古霉素的耐药性与年龄大致相同，约为3%。我对耐多药结核分枝杆菌感染的研究表明，在全球范围内，15-25岁的人群感染率最高。通过年龄了解这些趋势很重要，因为这将提供对AMR选择和传播的潜在机制的洞察，然后可以为改善患者护理和处方方案提供信息（例如，更微妙的抗生素使用建议的年龄）。解决这一知识缺口需要定量分析，我将在这里使用全球数据集首次系统地绘制不同年龄感染的AMR模式。我会问这种模式是否可能是由于临床护理的变化，或者是由于最近或过去抗生素使用的差异，从而改善抗生素处方和针对AMR的干预措施。具体来说，我将对来自全球开放获取数据集的AMR患病率趋势进行数据分析，并使用数学模型模拟潜在过程。这种双管齐下的方法将绘制模式并使用建模来确定潜在过程的贡献，例如医院中的传播和耐药细菌携带的持续时间。这将需要仔细考虑数据收集过程以及时期和群组效应。为了了解AMR的这些趋势，还必须考虑不同年龄的抗生素使用差异。然而，全球抗生素使用数据很难找到，通常无法按年龄或个体患者水平进行分类。在这个项目中，我将使用来自英国的独特数据集，该数据集将抗生素使用与个体患者水平的微生物学信息联系起来，以确定最近（过去一年）与过去抗生素使用对抗生素耐药细菌感染风险的相对重要性。这一点很重要，因为医疗保健的使用和抗生素暴露随年龄而变化，因此年龄和AMR的任何关联都可能掩盖潜在的抗生素使用趋势。利用这些信息，我将通过研究针对年龄的干预措施在模拟潜在过程的数学模型中的影响来评估AMR控制的干预措施。我还将调整现有的工具，用于告知经验性抗生素处方，以说明基于年龄或抗生素使用与AMR的关联。在缺乏微生物信息的情况下使用抗生素的经验性处方是全球最常见的抗生素使用方式，因此使用中增加的微妙性具有减少AMR选择和传播的巨大潜力。通过按年龄解剖AMR复杂性，这项研究将为AMR细菌感染的风险和AMR的起源提供新的见解，以支持循证政策制定。更广泛的影响将是我们对不同宿主年龄的耐药性异质性和趋势的理解发生根本性转变，这可能会使患者护理得到长期改善。

项目成果

Global burden of disease due to rifampicin-resistant tuberculosis: a mathematical modeling analysis.

• DOI: 10.1038/s41467-023-41937-9
• 发表时间: 2023-10-04
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Menzies, Nicolas A.;Allwood, Brian W.;Dean, Anna S.;Dodd, Pete J.;Houben, Rein M. G. J.;James, Lyndon P.;Knight, Gwenan M.;Meghji, Jamilah;Nguyen, Linh N.;Rachow, Andrea;Schumacher, Samuel G.;Mirzayev, Fuad;Cohen, Ted
• 通讯作者: Cohen, Ted

Extended Data - Cumulative Plots

扩展数据 - 累积图

• DOI: 10.6084/m9.figshare.25418230
• 发表时间: 2024
• 作者: Wildfire J

Extended data - Further Results.docx

扩展数据 - 进一步结果.docx

• DOI: 10.6084/m9.figshare.25417615
• 发表时间: 2024
• 作者: Wildfire J