Literature DB >> 33992138

Global COVID-19 vaccine roll-out: time to randomise vaccine allocation?

Samuel I Watson1, Richard J Lilford2.   

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Year:  2021        PMID: 33992138      PMCID: PMC8118608          DOI: 10.1016/S0140-6736(21)00895-3

Source DB:  PubMed          Journal:  Lancet        ISSN: 0140-6736            Impact factor:   202.731


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The global COVID-19 vaccine roll-out might be the largest public health exercise ever done. COVAX, the vaccines access pillar of the COVID-19 Tools Accelerator, supported by WHO, UNICEF, and others, expects to deliver two billion doses to 190 countries in 1 year. At present, 13 vaccines have received approval in various jurisdictions. The roll-out provides an opportunity, unparalleled in human history, to learn about vaccines. All approved vaccines have shown efficacy in randomised trials; however, there have been no direct comparisons between each vaccine. COVID-19 vaccines, both approved and in development, represent various new and existing technologies including mRNA (Pfizer–BioNTech and Moderna), viral vector (Oxford–AstraZeneca, Janssen, Cansino, and Gamaleya), inactive virus (Sinovac and Sinopharm), attenutated virus (Codagenix), and protein (Novavax and Sanofi–GlaxoSmithKline) vaccines. Most countries have or will have access to more than one vaccine type. In 2000, Lilford and colleagues proposed the so-called tracker studies. Although the COVID-19 vaccine roll-out is on a different scale to most medical technologies, it is illustrative of the rapid change often associated with the development of new treatments in many areas of medicine. Health technologies, particularly devices, are subject to frequent modifications in their design once introduced, and the entry of new competitors with slight or large modifications is frequent. Adoption of these new technologies can happen uncritically1, 2 and independently of their actual effectiveness.3, 4 A tracker study would start early in periods of rapid technological change; patients would be randomised between reasonable alternatives, and new alternatives can be introduced into the randomisation scheme as they become available. Such a design can borrow features from adaptive randomised trials, and it can incorporate features of network meta-analysis that permit the estimation of the difference in effectiveness of two alternatives, even if they were not directly compared. These studies differ from conventional randomised trials that are typically one-off events following preset and rigid protocols. With more vaccines in the pipeline, the global COVID-19 vaccine roll-out is the exact type of situation Lilford and colleagues predicted. At present, there exists sufficient equipoise that it would not be unreasonable to randomise the type of vaccine an individual received. In high-income countries particularly, the public health infrastructure can track disease outcomes at the individual level. Given the unprecedented investment in COVID-19 preventive measures, there is an imperative to maximise our learning, which will become one of the best defences we have against future pandemics. RJL reports support from the UK National Institute for Health Research (NIHR) Applied Research Collaboration West Midlands, NIHR Global Health Research Unit on Improving Health in Slums, and NIHR Research and Innovation for Global Health Transformation. SIW declares no competing interests.
  5 in total

Review 1.  Trials and fast changing technologies: the case for tracker studies.

Authors:  R J Lilford; D A Braunholtz; R Greenhalgh; S J Edwards
Journal:  BMJ       Date:  2000-01-01

2.  The role of hospital payments in the adoption of new medical technologies: an international survey of current practice.

Authors:  Corinna Sorenson; Michael Drummond; Aleksandra Torbica; Giuditta Callea; Ceu Mateus
Journal:  Health Econ Policy Law       Date:  2014-10-17

3.  Socioeconomic Differences in the Adoption of New Medical Technologies.

Authors:  Jame P Smith
Journal:  Am Econ Rev       Date:  2005-05

4.  Technology follies. The uncritical acceptance of medical innovation.

Authors:  D A Grimes
Journal:  JAMA       Date:  1993-06-16       Impact factor: 56.272

5.  Network meta-analysis-highly attractive but more methodological research is needed.

Authors:  Tianjing Li; Milo A Puhan; Swaroop S Vedula; Sonal Singh; Kay Dickersin
Journal:  BMC Med       Date:  2011-06-27       Impact factor: 8.775

  5 in total

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