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Meningococcal conjugate vaccine safety surveillance in the Vaccine Safety Datalink using a tree‐temporal scan data mining method
Author(s) -
Li Rongxia,
Weintraub Eric,
McNeil Michael M.,
Kulldorff Martin,
Lewis Edwin M.,
Nelson Jennifer,
Xu Stanley,
Qian Lei,
Klein Nicola P.,
Destefano Frank
Publication year - 2018
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.4397
Subject(s) - medicine , pharmacovigilance , scan statistic , vaccination , statistic , vaccine safety , adverse effect , statistics , immunology , immunization , mathematics , antigen
Purpose The objective of our study was to conduct a data mining analysis to identify potential adverse events (AEs) following MENACWY‐D using the tree‐temporal scan statistic in the Vaccine Safety Datalink population and demonstrate the feasibility of this method in a large distributed safety data setting. Methods Traditional pharmacovigilance techniques used in vaccine safety are generally geared to detecting AEs based on pre‐defined sets of conditions or diagnoses. Using a newly developed tree‐temporal scan statistic data mining method, we performed a pilot study to evaluate the safety profile of the meningococcal conjugate vaccine Menactra® (MenACWY‐D), screening thousands of potential AE diagnoses and diagnosis groupings. The study cohort included enrolled participants in the Vaccine Safety Datalink aged 11 to 18 years who had received MenACWY‐D vaccination(s) between 2005 and 2014. The tree‐temporal scan statistic was employed to identify statistical associations (signals) of AEs following MENACWY‐D at a 0.05 level of significance, adjusted for multiple testing. Results We detected signals for 2 groups of outcomes: diseases of the skin and subcutaneous tissue, fever, and urticaria. Both groups are known AEs following MENACWY‐D vaccination. We also identified a statistical signal for pleurisy, but further examination suggested it was likely a false signal. No new MENACWY‐D safety concerns were raised. Conclusions As a pilot study, we demonstrated that the tree‐temporal scan statistic data mining method can be successfully applied to screen broadly for a wide range of vaccine‐AE associations within a large health care data network.