key: cord-0916857-thw4lho9 authors: Greenhalgh, Trisha title: Will COVID-19 be evidence-based medicine’s nemesis? date: 2020-06-30 journal: PLoS Med DOI: 10.1371/journal.pmed.1003266 sha: 976184541f0674a611c9bda8c98944e2ff62d43c doc_id: 916857 cord_uid: thw4lho9 nan policy studies [14] . The report was criticised by epidemiologists for being "non-systematic" and for recommending policy action in the absence of a quantitative estimate of effect size from robust randomized controlled trials [15] . Such criticisms appear to make two questionable assumptions: first, that the precise quantification of impact from this kind of intervention is both possible and desirable, and second, that unless we have randomized trial evidence, we should do nothing. It is surely time to turn to a more fit-for-purpose scientific paradigm. Complex adaptive systems theory proposes that precise quantification of particular cause-effect relationships is both impossible (because such relationships are not constant and cannot be meaningfully isolated) and unnecessary (because what matters is what emerges in a particular real-world situation). This paradigm proposes that where multiple factors are interacting in dynamic and unpredictable ways, naturalistic methods and rapid-cycle evaluation are the preferred study design. The 20 th -century logic of evidence-based medicine, in which scientists pursued the goals of certainty, predictability and linear causality, remains useful in some circumstances (for example, the drug and vaccine trials referred to above). But at a population and system level, we need to embrace 21 st -century epistemology and methods to study how best to cope with uncertainty, unpredictability and non-linear causality [16] . In a complex system, the question driving scientific inquiry is not "what is the effect size and is it statistically significant once other variables have been controlled for?" but "does this intervention contribute, along with other factors, to a desirable outcome?". Multiple interventions might each contribute to an overall beneficial effect through heterogeneous effects on disparate causal pathways, even though none would have a statistically significant impact on any predefined variable [11] . To illuminate such influences, we need to apply research designs that foreground dynamic interactions and emergence. These include in-depth, mixed-method case studies (primary research) and narrative reviews (secondary research) that tease out interconnections and highlight generative causality across the system [16, 17] . Table 1 lists some philosophical contrasts between the evidence-based medicine and complex-systems paradigms. Ogilvie et al have argued that rather than pitting these two paradigms against one another, they should be brought together [9] . As illustrated in (Fig 1) , these authors depict randomized trials (what they call the "evidence-based practice pathway") and natural experiments (the "practice-based evidence pathway") in a complementary and recursive relationship rather than a hierarchical one. They propose that ". . .intervention studies [e.g. trials] should focus on reducing critical uncertainties, that non-randomised study designs should be embraced rather than tolerated and that a more nuanced approach to appraising the utility of diverse types of evidence is required." (page 203) [9] . In the current fast-moving pandemic, where the cost of inaction is counted in the grim mortality figures announced daily, implementing new policy interventions in the absence of randomized trial evidence has become both a scientific and moral imperative. Whilst it is hard to predict anything in real time, history will one day tell us whether adherence to "evidencebased practice" helped or hindered the public health response to Covid-19-or whether an apparent slackening of standards to accommodate "practice-based evidence" was ultimately a more effective strategy. Evidence based medicine: what it is and what it isn't How to Read a Paper: The basics of evidence-based medicine and healthcare ( 6th edition) Covid-19-the search for effective therapy Developing Covid-19 vaccines at pandemic speed COVID in Care Homes -Challenges and Dilemmas in Healthcare Delivery Low-tech solutions for the COVID-19 supply chain crisis Are high-performing health systems resilient against the COVID-19 epidemic? The Lancet Ogilvie et al's model of two complementary modes of evidence generation: evidence-based practice and practice-based evidence Applying principles of behaviour change to reduce SARS-CoV-2 transmission Using natural experimental studies to guide public health action: turning the evidence-based medicine paradigm on its head Behavioral science at the crossroads in public health: extending horizons, envisioning the future. Social science & medicine The need for a complex systems model of evidence for public health Physical interventions to interrupt or reduce the spread of respiratory viruses GRADE: an emerging consensus on rating quality of evidence and strength of recommendations London: Royal Society DELVE (Data Evaluation and Learning for Viral Epidemics) initiative; 2020. Accessed 4th Expert reaction to review of evidence on face masks and face coverings by the Studying complexity in health services research: desperately seeking an overdue paradigm shift Time to challenge the spurious hierarchy of systematic over narrative reviews?