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Methodological frontiers in vaccine safety: qualifying available evidence for rare events, use of distributed data networks to monitor vaccine safety issues, and monitoring the safety of pregnancy interventions
  1. Caitlin Dodd1,
  2. Nick Andrews2,
  3. Helen Petousis-Harris3,
  4. Miriam Sturkenboom1,
  5. Saad B Omer4,
  6. Steven Black5
  1. 1Julius Center, UMC Utrecht, Utrecht, The Netherlands
  2. 2Statistics Modelling and Economics Department, Public Health England, London, UK
  3. 3Department of General Practice and Primary Health Care, The University of Auckland, Auckland, New Zealand
  4. 4Institute for Global Health, Yale University, New Haven, Connecticut, USA
  5. 5Global Vaccine Data Network, Berkeley, California, USA
  1. Correspondence to Dr Caitlin Dodd; c.n.dodd{at}umcutrecht.nl

Abstract

While vaccines are rigorously tested for safety and efficacy in clinical trials, these trials do not include enough subjects to detect rare adverse events, and they generally exclude special populations such as pregnant women. It is therefore necessary to conduct postmarketing vaccine safety assessments using observational data sources. The study of rare events has been enabled in through large linked databases and distributed data networks, in combination with development of case-centred methods. Distributed data networks necessitate common protocols, definitions, data models and analytics and the processes of developing and employing these tools are rapidly evolving. Assessment of vaccine safety in pregnancy is complicated by physiological changes, the challenges of mother-child linkage and the need for long-term infant follow-up. Potential sources of bias including differential access to and utilisation of antenatal care, immortal time bias, seasonal timing of pregnancy and unmeasured determinants of pregnancy outcomes have yet to be fully explored. Available tools for assessment of evidence generated in postmarketing studies may downgrade evidence from observational data and prioritise evidence from randomised controlled trials. However, real-world evidence based on real-world data is increasingly being used for safety assessments, and new tools for evaluating real-world evidence have been developed. The future of vaccine safety surveillance, particularly for rare events and in special populations, comprises the use of big data in single countries as well as in collaborative networks. This move towards the use of real-world data requires continued development of methodologies to generate and assess real world evidence.

  • vaccines
  • epidemiology
  • review

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There are no data in this work.

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Data availability statement

There are no data in this work.

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Footnotes

  • Handling editor Seye Abimbola

  • Contributors All authors contributed to conceptualisation, drafting and revision of the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests HPH and SB are co-directors of the Global Vaccine Data Network.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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