Rongxia Li1, Eric Weintraub1, Michael M McNeil1, Martin Kulldorff2, Edwin M Lewis3, Jennifer Nelson4, Stanley Xu5, Lei Qian6, Nicola P Klein3, Frank Destefano1. 1. Immunization Safety Office, Centers for Disease Control and Prevention, Atlanta, GA, USA. 2. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA. 3. Kaiser Permanente Vaccine Study Center, Oakland, CA, USA. 4. Group Health Research Institute and University of Washington, Seattle, WA, USA. 5. Institute for Health Research, Kaiser Permanente, Denver, CO, USA. 6. Southern California Kaiser Permanente, Pasadena, CA, USA.
Abstract
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.
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.
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