Researchers propose the tools and frameworks of EBM+ to help overcome the COVID-19 pandemic
In a recent article published in BMJ, researchers proposed the tools and frameworks of mechanistic evidence, sometimes known as evidence-based medicine (EBM)+ combined with traditional EBM, might help overcome the highly protracted coronavirus disease 2019 (COVID-19) pandemic.
EBM+ encompasses conceptual tools and quality frameworks from various fields, including complexity science, engineering, and the social sciences, and a more pluralist approach to fetch 'high-quality' mechanistic evidence.
Traditional methods, such as randomized controlled trials (RCTs) and meta-analyses, contributed significantly to the science of COVID-19. However, these methods have historically answered simple, focused questions in a stable context, such as those about biomedicine. They have significant limitations when extended to complex situations. In this case, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel pathogen, led to chaos across multiple sectors, medical, social, economic, and political dimensions in a fast-changing global context.
SARS-CoV-2 has proved tenacious and shifting. While RCTs and meta-analyses of RCTs remarkably estimated the efficacy of COVID-19 drugs and vaccines, some scholars argue that tackling some of its aspects critically required mechanistic evidence. COVID-19 led to complex situations where multiple variables interacted dynamically with high uncertainty.
Decisions had to be taken in days (urgently), not years, and the consequences of not acting would have been catastrophic (threatening). In such situations, dismissing mechanistic evidence and overvaluing poorly designed or irrelevant RCT findings cost thousands of lives. Overall, the COVID-19 pandemic presented an epistemic opportunity to understand, debate, and embrace EBM+.
About the study
In the present study, researchers argued that while the hierarchy of evidence that places mechanistic evidence at the bottom and meta-analyses of RCTs at the top is a useful heuristic, but inapplicable to all circumstances. There should be enough guidance to apply hierarchy flexibly depending on the nature of the research question and the extent of complexity involved.
Indeed, RCTs are not a panacea; thus, systemic reviews are crucial to interpret primary evidence along with modifications of the hierarchy of evidence to answer contemporary policy questions pertaining to SARS-CoV-2.
Further, the researchers suggested modifications to the hierarchy of evidence for mechanistic evidence. They emphasized combining evidence of mechanisms with probabilistic evidence from clinical trials and non-randomized studies, both comparative and observational, to make a strong case for causality. It's because a plausible mechanism confirms or denies the stability of the causal relationship across settings.
This exercise could ensure that public health interventions would work as they involve both the upstream causes and the 'causes' through which interventions might act. For instance, COVID-19 causes included family structures and their interactions, economic and cultural variations, etc., and attitudes, beliefs, capabilities, and personal resources determined whether public health measures worked. Furthermore, the researchers argued that mechanistic evidence is inherently explanatory.
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Early in the pandemic, COVID-19 scientific research was prematurely fixed and constrained to a population-intervention (or exposure)-comparison-outcome (PICO) format that narrowed predefined outcomes and suppressed scientific imagination. Such studies estimated infections in the mask wearer(s) in statistical terms. Still, they failed to account for unique patterns of SARS-CoV-2 spread (e.g., overdispersion, indoor predominance), all of which could have pointed at its predominantly airborne transmission.
An RCT randomized 6,024 people advised wearing masks outside the home. Its results showed infection with SARS-CoV-2 in 42 and 53 people (1.8% and 2.1%) of the intervention and control arms, respectively, i.e., statistically insignificant. However, EMB supporters argue and evidenced that wearing masks in public does not substantially reduce infection.
The Cochrane Database of Systematic Reviews encouraged policy-makers to adopt the precautionary principle based on the findings of this flawed study because of the urgency of the situation. However, an EBM approach would have accounted for studies elucidating the mechanism(s) by which masking might work. For instance, evidence from:
i) real-world case studies and mathematical modeling studies supporting airborne route of transmission of SARS-CoV-2
ii) Engineering studies showing the filtration properties of different masks
iii) Psychological and socio-cultural evidence of whether people wear masks
Delivering public health interventions and ensuring they are implemented, for instance, face masking, is a complex phenomenon. It has multiple components acting interdependently and individually at multiple levels. Masks could be homemade or produced to formal technical standards. People may mask as socially expected, organisationally required, or legally mandated.
Overall, its adequate implementation is complex in real-world settings for multiple social and behavioral reasons. Thus, complex systems require a new paradigm with designs that could capture dynamic change(s), simultaneously accommodating non-linearity and embracing uncertainty. In support of structural evidence, the researchers discussed how engineering designs account for physical and technical properties and capabilities, social needs, and assessment of impact with the example of personal protective equipment (PPEs).
The use and impact of PPEs are influenced by the quality of indoor air. Yet, an RCT comparing these products with less effective protection to 'prove' their value in protecting against chemical contamination in a lead smelter makes no sense. All required is robust certification systems, standards, and workplace usage protocols for PPEs. Currently used PPEs have already proven a boon against occupational hazards for millions of healthcare workers worldwide.
Professor Susan Michie, an adviser to UK's Scientific Advisory Group on Emergencies, proposed the capability-opportunity-motivation-behavior model. This model helped explain why people prefer or do not prefer wearing a mask. In some cases, they may lack key knowledge (e.g., they may not know whether cloth masks are as effective as surgical masks) or may physically not want to because they are not a good fit. Likewise, they might lack motivation (e.g., conscious beliefs) or opportunity (e.g., employer bans masks). Nevertheless, not exploring these wider influences might produce misleading findings by experimental studies.
In the present study, the authors emphasized more on other sources of evidence, especially mechanistic evidence, to manage the complex and fast-changing COVID-19 pandemic. As the debate regarding EBM+ continues, they hope to contribute further articles on how an EBM+ approach could enhance contribution to pandemic science.
- Greenhalgh, T. et al. (2022) "Adapt or die: how the pandemic made the shift from EBM to EBM+ more urgent", BMJ. Evidence-Based Medicine, 27(5), pp. 253-260. doi: 10.1136/bmjebm-2022-111952. https://ebm.bmj.com/content/27/5/253
Posted in: Medical Science News | Medical Research News | Disease/Infection News
Tags: Biomedicine, Contamination, Coronavirus, Coronavirus Disease COVID-19, covid-19, Drugs, Efficacy, Exercise, Healthcare, Medicine, Pandemic, Pathogen, Personal Protective Equipment, Public Health, Research, Respiratory, SARS, SARS-CoV-2, Severe Acute Respiratory, Severe Acute Respiratory Syndrome, Syndrome
Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.
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