Greenhalgh Trisha, Fisman David, Cane Danielle J, Oliver Matthew, Macintyre Chandini Raina
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
BMJ Evid Based Med. 2022 Jul 19;27(5):253-60. doi: 10.1136/bmjebm-2022-111952.
Evidence-based medicine (EBM's) traditional methods, especially randomised controlled trials (RCTs) and meta-analyses, along with risk-of-bias tools and checklists, have contributed significantly to the science of COVID-19. But these methods and tools were designed primarily to answer simple, focused questions in a stable context where yesterday's research can be mapped more or less unproblematically onto today's clinical and policy questions. They have significant limitations when extended to complex questions about a novel pathogen causing chaos across multiple sectors in a fast-changing global context. Non-pharmaceutical interventions which combine material artefacts, human behaviour, organisational directives, occupational health and safety, and the built environment are a case in point: EBM's experimental, intervention-focused, checklist-driven, effect-size-oriented and deductive approach has sometimes confused rather than informed debate. While RCTs are important, exclusion of other study designs and evidence sources has been particularly problematic in a context where rapid decision making is needed in order to save lives and protect health. It is time to bring in a wider range of evidence and a more pluralist approach to defining what counts as 'high-quality' evidence. We introduce some conceptual tools and quality frameworks from various fields involving what is known as mechanistic research, including complexity science, engineering and the social sciences. We propose that the tools and frameworks of mechanistic evidence, sometimes known as 'EBM+' when combined with traditional EBM, might be used to develop and evaluate the interdisciplinary evidence base needed to take us out of this protracted pandemic. Further articles in this series will apply pluralistic methods to specific research questions.
循证医学(EBM)的传统方法,尤其是随机对照试验(RCT)和荟萃分析,以及偏倚风险工具和清单,对新冠疫情科学做出了重大贡献。但这些方法和工具主要是为了在稳定的背景下回答简单、聚焦的问题,在这种背景下,昨天的研究或多或少可以毫无问题地应用于今天的临床和政策问题。当扩展到关于一种新型病原体在快速变化的全球背景下造成多个部门混乱的复杂问题时,它们有很大的局限性。结合物质制品、人类行为、组织指令、职业健康与安全以及建筑环境的非药物干预就是一个例子:EBM以实验为导向、以干预为重点、由清单驱动、以效应大小为导向且演绎的方法有时使辩论变得混乱而非提供信息。虽然随机对照试验很重要,但在需要迅速做出决策以拯救生命和保护健康的背景下,排除其他研究设计和证据来源尤其成问题。现在是时候引入更广泛的证据和更多元的方法来定义什么算作“高质量”证据了。我们介绍了来自包括复杂性科学、工程学和社会科学等各种涉及所谓机制研究领域的一些概念工具和质量框架。我们建议,机制证据的工具和框架,当与传统循证医学结合时有时被称为“EBM+”,可用于开发和评估使我们摆脱这场持久大流行所需的跨学科证据基础。本系列的后续文章将把多元方法应用于具体的研究问题。