title: Near real-time vaccine safety surveillance with partially accrued data.
abstract: OBJECTIVE
  The Vaccine Safety Datalink (VSD) Project conducts near real-time vaccine safety surveillance using sequential analytic methods. Timely surveillance is critical in identifying potential safety problems and preventing additional exposure before most vaccines are administered. For vaccines that are administered during a short period, such as influenza vaccines, timeliness can be improved by undertaking analyses while risk windows following vaccination are ongoing and by accommodating predictable and unpredictable data accrual delays. We describe practical solutions to these challenges, which were adopted by the VSD Project during pandemic and seasonal influenza vaccine safety surveillance in 2009/2010.
  
  
  METHODS
  Adjustments were made to two sequential analytic approaches. The Poisson-based approach compared the number of pre-defined adverse events observed following vaccination with the number expected using historical data. The expected number was adjusted for the proportion of the risk window elapsed and the proportion of inpatient data estimated to have accrued. The binomial-based approach used a self-controlled design, comparing the observed numbers of events in risk versus comparison windows. Events were included in analysis only if they occurred during a week that had already passed for both windows.
  
  
  RESULTS
  Analyzing data before risk windows fully elapsed improved the timeliness of safety surveillance. Adjustments for data accrual lags were tailored to each data source and avoided biasing analyses away from detecting a potential safety problem, particularly early during surveillance.
  
  
  CONCLUSIONS
  The timeliness of vaccine and drug safety surveillance can be improved by properly accounting for partially elapsed windows and data accrual delays.
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA. Sharon_Greene@harvardpilgrim.org
authors.forename: Sharon K
authors.initials: SK
authors.lastname: Greene
authors.forename: Martin
authors.initials: M
authors.lastname: Kulldorff
authors.forename: Ruihua
authors.initials: R
authors.lastname: Yin
authors.forename: W Katherine
authors.initials: WK
authors.lastname: Yih
authors.forename: Tracy A
authors.initials: TA
authors.lastname: Lieu
authors.forename: Eric S
authors.initials: ES
authors.lastname: Weintraub
authors.forename: Grace M
authors.initials: GM
authors.lastname: Lee
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D015982:Bias; D006801:Humans; D007252:Influenza Vaccines / Q000009:adverse effects*; D007251:Influenza, Human / Q000517:prevention & control; D016012:Poisson Distribution; D011358:Product Surveillance, Postmarketing / Q000379:methods*; D012306:Risk; D013997:Time Factors
chemical_list: D007252:Influenza Vaccines