Will COVID-19 be evidence-based medicine's nemesis?

• Published in: PLoS medicine 2020-06, Vol.17 (6), p.e1003266-e1003266
• Main Author: Greenhalgh, Trisha
• Format: Article
• Language: English
• Subjects: Analysis; Betacoronavirus; Biomedical Research - trends; Causality; Clinical trials; Computer and Information Sciences; Coronavirus Infections; Coronaviruses; COVID-19; Disease transmission; Evidence-Based Medicine; Evidence-based practice; Humans; Intervention; Medical research; Medicine; Medicine and Health Sciences; Pandemics; Paradigms; Physical Sciences; Pneumonia, Viral; Population; Problem solving; Public health; Randomized Controlled Trials as Topic; Research and Analysis Methods; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; System theory; Vaccines
• ISSN: 1549-1676; 1549-1277; 1549-1676
• DOI: 10.1371/journal.pmed.1003266

Once defined in rhetorical but ultimately meaningless terms as “the conscientious, judicious and explicit use of current best evidence in making decisions about the care of individual patients” [1], evidence-based medicine rests on certain philosophical assumptions: a singular truth, ascertainable through empirical enquiry; a linear logic of causality in which interventions have particular effect sizes; rigour defined primarily in methodological terms (especially, a hierarchy of preferred study designs and tools for detecting bias); and a deconstructive approach to problem-solving (the evidence base is built by answering focused questions, typically framed as ‘PICO’—population-intervention-comparison-outcome) [2]. When implementing population-wide public health interventions—whether conventional measures such as diet or exercise, or COVID-19 related ones such as handwashing, social distancing and face coverings—we must not only persuade individuals to change their behavior but also adapt the environment to make such changes easier to make and sustain [10–12]. 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]. 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).