title: Near real-time vaccine safety surveillance using electronic health records-a systematic review of the application of statistical methods.
abstract: OBJECTIVE
  Pre-licensure studies have limited ability to detect rare adverse events (AEs) to vaccines, requiring timely post-licensure studies. With the increasing availability of electronic health records (EHR) near real-time vaccine safety surveillance using these data has emerged as an option. We reviewed methods currently used to inform development of similar systems for countries considering their introduction.
  
  
  METHODS
  Medline, EMBASE and Web of Science were searched, with additional searches of conference abstract books. Questionnaires were sent to organizations worldwide to ascertain unpublished studies. Eligible studies used EHR and regularly assessed pre-specified AE to vaccine(s). Key features of studies were compared descriptively.
  
  
  RESULTS
  From 2779 studies, 31 were included from the USA (23), UK (6), and Taiwan and New Zealand (1 each). These were published/conducted between May 2005 and April 2015. Thirty-eight different vaccines were studied, focusing mainly on influenza (47.4%), especially 2009 H1N1 vaccines. Forty-six analytic approaches were used, reflecting frequency of EHR updates and the AE studied. Poisson-based maximized sequential probability ratio test was the most common (43.5%), followed by its binomial (23.9%) and conditional versions (10.9%). Thirty-seven of 49 analyses (75.5%) mentioned control for confounding, using an adjusted expected rate (51.4% of those adjusting), stratification (16.2%) or a combination of a self-controlled design and stratification (13.5%). Guillain-Barré syndrome (11.9%), meningitis/encephalitis/myelitis (11.9%) and seizures (10.8%) were studied most often.
  
  
  CONCLUSIONS
  Near real-time vaccine safety surveillance using EHR has developed over the past decade but is not yet widely used. As more countries have access to EHR, it will be important that appropriate methods are selected, considering the data available and AE of interest.
authors.affiliation: Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
authors.forename: Andreia
authors.initials: A
authors.lastname: Leite
authors.affiliation: Statistics, Modelling and Economics Department, Public Health England, London, UK.
authors.forename: Nick J
authors.initials: NJ
authors.lastname: Andrews
authors.affiliation: Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
authors.forename: Sara L
authors.initials: SL
authors.lastname: Thomas
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D003257:Consumer Product Safety / Q000592:standards; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006801:Humans; D017891:Pharmacoepidemiology / Q000379:methods* / Q000706:statistics & numerical data*; D014612:Vaccines / Q000009:adverse effects* / Q000592:standards*
chemical_list: D014612:Vaccines
keywords: electronic health records; pharmacoepidemiology; safety; sequential tests; statistical process control; vaccines
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