The neglected model validation of antimicrobial resistance transmission models - a systematic review.

IF 4.8 2区 医学 Q1 INFECTIOUS DISEASES
Maja L Brinch, Andrea Palladino, Jeroen Geurtsen, Thierry Van Effelterre, Lorenzo Argante, Michael J McConnell, Lene Christiansen, Michelle A Pihl, Natasja K Lund, Tine Hald
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Abstract

Background: In the fight against antimicrobial resistance, mathematical transmission models have been shown as a valuable tool to guide intervention strategies in public health.

Objective: This review investigates the persistence of modelling gaps identified in earlier studies. It expands the scope to include a broader range of control measures, such as monoclonal antibodies, and examines the impact of secondary infections.

Methods: This review was conducted according to the PRISMA guidelines. Gaps in model focus areas, dynamics, and reporting were identified and described. The TRACE paradigm was applied to selected models to discuss model development and documentation to guide future modelling efforts.

Results: We identified 170 transmission studies from 2010 to May 2022; Mycobacterium tuberculosis (n = 39) and Staphylococcus aureus (n = 27) resistance transmission were most commonly modelled, focusing on multi-drug and methicillin resistance, respectively. Forty-one studies examined multiple interventions, predominantly drug therapy and vaccination, showing an increasing trend. Most studies were population-based compartmental models (n = 112). The TRACE framework was applied to 39 studies, showing a general lack of description of test and verification of modelling software and comparison of model outputs with external data.

Conclusion: Despite efforts to model antimicrobial resistance and prevention strategies, significant gaps in scope, geographical coverage, drug-pathogen combinations, and viral-bacterial dynamics persist, along with inadequate documentation, hindering model updates and consistent outcomes for policymakers. This review highlights the need for robust modelling practices to enable model refinement as new data becomes available. Particularly, new data for validating modelling outcomes should be a focal point in future modelling research.

被忽视的抗微生物药物耐药性传播模型的模型验证-系统综述。
背景:在抗微生物药物耐药性的斗争中,数学传播模型已被证明是指导公共卫生干预策略的宝贵工具。目的:本综述调查了早期研究中发现的模型差距的持久性。它扩大了范围,包括更广泛的控制措施,如单克隆抗体,并检查继发感染的影响。方法:本综述按照PRISMA指南进行。确定并描述了模型重点领域、动态和报告中的差距。TRACE范例被应用于选定的模型,以讨论模型开发和文档,以指导未来的建模工作。结果:从2010年到2022年5月,我们确定了170项传播研究;最常见的模型是结核分枝杆菌(39例)和金黄色葡萄球菌(27例)耐药传播,分别关注多药耐药和甲氧西林耐药。41项研究检查了多种干预措施,主要是药物治疗和疫苗接种,显示出增加的趋势。大多数研究是基于人群的区室模型(n = 112)。TRACE框架应用于39项研究,表明普遍缺乏对建模软件的测试和验证以及模型输出与外部数据的比较的描述。结论:尽管努力建立抗微生物药物耐药性模型和预防策略,但在范围、地理覆盖、药物-病原体组合和病毒-细菌动态方面仍然存在重大差距,同时文献不足,阻碍了模型更新和决策者一致的结果。这篇综述强调了需要健壮的建模实践,以便在获得新数据时能够对模型进行改进。特别是,验证模型结果的新数据应该是未来模型研究的重点。
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来源期刊
Antimicrobial Resistance and Infection Control
Antimicrobial Resistance and Infection Control PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH -INFECTIOUS DISEASES
CiteScore
9.70
自引率
3.60%
发文量
140
审稿时长
13 weeks
期刊介绍: Antimicrobial Resistance and Infection Control is a global forum for all those working on the prevention, diagnostic and treatment of health-care associated infections and antimicrobial resistance development in all health-care settings. The journal covers a broad spectrum of preeminent practices and best available data to the top interventional and translational research, and innovative developments in the field of infection control.
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