Data Mining for Prospective Early Detection of Safety Signals in the Vaccine Adverse Event Reporting System (VAERS): A Case Study of Febrile Seizures after a 2010–2011 Seasonal Influenza Virus Vaccine

Abstract

Empirical Bayesian data mining in VAERS prospectively detected the safety signal for febrile seizures after Fluzone(®) 2010-2011 in young children. The EB05 threshold, database restrictions, adjustment and baseline data mining were strategies adopted a priori to enhance the specificity of the 2010-2011 influenza vaccine data mining analyses. A database restriction used to separate live vaccines resulted in a reduced EB05. Adjustment of data mining analyses had a larger effect on estimates of disproportionality than the MGPS algorithm. Masking did not appear to influence our findings. This case study illustrates the value of VAERS data mining for vaccine safety monitoring.

Safety monitoring in the Vaccine Adverse Event Reporting System (VAERS)

“…VAERS has successfully detected safety signals that required further evaluation [36,52-59] and has also provided reassurance on the safety of vaccines [60-63]. One of the earliest successes in signal detection and assessment in VAERS involved the first rotavirus vaccine, RotaShield ® .…”

“…Given the known underreporting of adverse events to VAERS, these findings were concerning enough for CDC to suspend its recommendation for RotaShield ® vaccination and initiate further investigation [64]; shortly thereafter the vaccine was withdrawn from the market by the manufacturer [65]. More recently, VAERS detected disproportional reporting for febrile seizures in young children following an inactivated influenza vaccine during the 2010-2011 influenza season [58,59]. Clinical review of the VAERS reports indicated the cases were typical of uncomplicated febrile seizures and all children fully recovered.…”

Major Birth Defects after Vaccination Reported to the Vaccine Adverse Event Reporting System (VAERS), 1990 to 2014

“…We used empirical Bayesian (EB) data mining (DuMouchel, 1999) to identify birth defects reported more frequently than expected following any vaccine in the VAERS database. We used published criteria (Szarfman, 2002;Martin et al, 2013) to identify, with a high degree of confidence, vaccine-event pairs reported at least twice as frequently as would be expected (i.e., lower bound of the 90% confidence interval surrounding the EB geometric mean [EB05] >2). We clinically reviewed those reports containing preferred terms for birth defects that exceeded the data mining threshold noted above.…”

Enhancing vaccine safety capacity globally: A lifecycle perspective

“…Empirical Bayesian data mining to detect disproportional reporting has already been incorporated into routine VAERS surveillance (55-57) and natural language processing (58) and text mining (59) hold early promise for rapid case classification in both active and passive surveillance. In the VSD system, rapid cycle sequential analytic methods have been developed to conduct near real-time (weekly) monitoring for pre-specified conditions following individual vaccines and types of vaccine (39, 60-62).…”