Département de médecine sociale et préventive, École de Santé Publique, University of Montreal, Montreal, Quebec, Canada.
Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada.
BMJ Open. 2023 Mar 16;13(3):e069022. doi: 10.1136/bmjopen-2022-069022.
Antimicrobial resistance (AMR) is a complex problem that requires the One Health approach, that is, a collaboration among various disciplines working in different sectors (animal, human and environment) to resolve it. Mathematical and statistical models have been used to understand AMR development, emergence, dissemination, prediction and forecasting. A review of the published models of AMR will help consolidate our knowledge of the dynamics of AMR and will also facilitate decision-makers and researchers in evaluating the credibility, generalisability and interpretation of the results and aspects of AMR models. The study objective is to identify and synthesise knowledge on mathematical and statistical models of AMR among bacteria in animals, humans and environmental compartments.
Eligibility criteria: Original research studies reporting mathematical and statistical models of AMR among bacteria in animal, human and environmental compartments that were published until 2022 in English, French and Spanish will be included in this study.
Database of PubMed, Agricola (Ovid), Centre for Agriculture and Bioscience Direct (CABI), Web of Science (Clarivate), Cumulative Index to Nursing and Allied Health Literature (CINAHL) and MathScinet. Data charting: Metadata of the study, the context of the study, model structure, model process and reporting quality will be extracted. A narrative summary of this information, gaps and recommendations will be prepared and reported in One Health decision-making context.
Research ethics board approval was not obtained for this study as neither human participation nor unpublished human data were used in this study. The study findings will be widely disseminated among the One Health Modelling Network for Emerging Infections network and stakeholders by means of conferences, and publication in open-access peer-reviewed journals.
抗菌药物耐药性(AMR)是一个复杂的问题,需要采取“同一健康”方法,即需要动物、人类和环境等不同领域的各种学科合作来解决这一问题。数学和统计模型已被用于了解 AMR 的发展、出现、传播、预测和预报。对 AMR 模型的综述将有助于巩固我们对抗生素耐药性动态的认识,也将有助于决策者和研究人员评估 AMR 模型的结果和方面的可信度、可推广性和解释。本研究的目的是确定并综合动物、人类和环境中细菌的 AMR 数学和统计模型方面的知识。
纳入标准:本研究将纳入截至 2022 年发表于英文、法文和西班牙文的关于动物、人类和环境中细菌的 AMR 数学和统计模型的原始研究报告。
将检索 PubMed、Agricola(Ovid)、Centre for Agriculture and Bioscience Direct(CABI)、Web of Science(Clarivate)、Cumulative Index to Nursing and Allied Health Literature(CINAHL)和 MathScinet 数据库。数据图表:将提取研究的元数据、研究背景、模型结构、模型过程和报告质量。将以叙述性摘要的形式总结这些信息、差距和建议,并在同一健康决策背景下报告。
本研究未获得研究伦理委员会的批准,因为本研究既没有涉及人类参与,也没有使用未发表的人类数据。研究结果将通过会议和在开放获取同行评议期刊上发表,在同一健康建模网络新兴传染病网络和利益攸关方中广泛传播。