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Use of routinely collected electronic healthcare data for postlicensure vaccine safety signal detection: a systematic review
  1. Yonatan Moges Mesfin,
  2. Allen Cheng,
  3. Jock Lawrie,
  4. Jim Buttery
  1. School of Population Health and Preventive Medicine, Monash University, Melbourne, Clayton, Victoria, Australia
  1. Correspondence to Yonatan Moges Mesfin; Yonatan.Mesfin{at}monash.edu

Abstract

Background Concerns regarding adverse events following vaccination (AEFIs) are a key challenge for public confidence in vaccination. Robust postlicensure vaccine safety monitoring remains critical to detect adverse events, including those not identified in prelicensure studies, and to ensure public safety and public confidence in vaccination. We summarise the literature examined AEFI signal detection using electronic healthcare data, regarding data sources, methodological approach and statistical analysis techniques used.

Methods We performed a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Five databases (PubMed/Medline, EMBASE, CINAHL, the Cochrane Library and Web of Science) were searched for studies on AEFIs monitoring published up to 25 September 2017. Studies were appraised for methodological quality, and results were synthesised narratively.

Result We included 47 articles describing AEFI signal detection using electronic healthcare data. All studies involved linked diagnostic healthcare data, from the emergency department, inpatient and outpatient setting and immunisation records. Statistical analysis methodologies used included non-sequential analysis in 33 studies, group sequential analysis in two studies and 12 studies used continuous sequential analysis. Partially elapsed risk window and data accrual lags were the most cited barriers to monitor AEFIs in near real-time.

Conclusion Routinely collected electronic healthcare data are increasingly used to detect AEFI signals in near real-time. Further research is required to check the utility of non-coded complaints and encounters, such as telephone medical helpline calls, to enhance AEFI signal detection.

Trial registration number CRD42017072741

  • electronic healthcare records
  • post-licensure safety surveillance
  • adverse event following immunization
  • systematic review

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Handling editor Seye Abimbola

  • Contributors YMM conceived the original research idea with guidance from JB. All authors contributed to the design of the study. YMM searched and screened the studies, extracted the data and wrote the initial drafts of the paper. JB, AC and JL revised the article critically. All authors contributed to and approved the final 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 None declared.

  • Patient consent for publication Not required.

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

  • Data availability statement The authors are happy to provide further data up on request.

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