Safety profiles of the influenza vaccine and its subtypes are still limited. We aimed to address this knowledge gap using multiple data mining methods and calculated performance measurements to evaluate the precision of different detection methods. We conducted a post-marketing surveillance study between 2005 and 2019 using the Korea Adverse Event Reporting System database. Three data mining methods were applied: (a) proportional reporting ratio, (b) information component, and (c) tree-based scan statistics. We evaluated the performance of each method in comparison with the known adverse events (AEs) described in the labeling information. Compared to other vaccines, we identified 36 safety signals for the influenza vaccine, and 7 safety signals were unlabeled. In subtype-stratified analyses, application site disorders were reported more frequently with quadrivalent and cell-based vaccines, while a wide range of AEs were noted for trivalent and egg-based vaccines. Tree-based scan statistics showed well-balanced performance. Among the detected signals of influenza vaccines, narcolepsy requires special attention. A wider range of AEs were detected as signals for trivalent and egg-based vaccines. Although tree-based scan statistics showed balanced performance, complementary use of other techniques would be beneficial when large noise due to false positives is expected.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.