Monitoring Vaccine Safety by Studying Temporal Variation of Adverse Events Using Vaccine Adverse Event Reporting System

• Activity Number: 8 - The Best of AOAS
• Type: Invited
• Date/Time: Sunday, August 7, 2022 : 2:00 PM to 3:50 PM
• Sponsor: IMS
• Author(s): Jing Huang* and Yi Cai and Jingcheng Du and Ruosha Li and Susan Ellenberg and Sean Hennessy and Cui Tao and Yong Chen
• Companies: University of Pennsylvania and AT&T Services, Inc. and Melax Tech and The University of Texas Health Science Center at Houston and University of Pennsylvania and University of Pennsylvania and University of Texas Health Science Center at Houston and University of Pennsylvania
• Keywords: Composite likelihood; Heterogeneity; Signal detection; Under-reporting; Vaccine safety outcome

Abstract

The Vaccine Adverse Event Reporting System (VAERS) plays a vital role in vaccine safety surveillance. One of the main missions of VAERS is to monitor increases in reporting rate of adverse events, as such signals can indicate safety issues caused by updates of vaccines or changes in vaccine practices. Existing methods can rarely be used to monitor the temporal variation of reporting adverse events. In this paper, we propose a composite likelihood-based variance component model to study the temporal variation of reporting adverse events using VAERS data. The proposed method is devised to identify safety signals by testing the heterogeneity of reporting rates of adverse events across years. The proposed method accounts for the well known under-reporting of adverse events and the zero-inflation observations in passive surveillance reporting systems. We applied the proposed method to VAERS reports of trivalent influenza virus vaccine and identied 14 adverse events with signicantly heterogeneous reporting rates over years and 2 of them have increasing trend of reporting rates from 1990 to 2013.