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 update of vaccines or change 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 underreporting 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 identified 14 adverse events with significantly heterogeneous reporting rates over years and two of them have increasing trend of reporting rates from 1990 to 2013. Our findings provide early warning signals that can be more rigorously investigated in future studies of the vaccine.
The authors thank the referees, the associate editor and the editor for their constructive comments that substantially improved the presentation of this work. This work is supported in part by NIH Grants: R01AI130460, R01LM012607 and R01HD099348.
Jing Huang. Yi Cai. Jingcheng Du. Ruosha Li. Susan S. Ellenberg. Sean Hennessy. Cui Tao. Yong Chen. "Monitoring vaccine safety by studying temporal variation of adverse events using vaccine adverse event reporting system." Ann. Appl. Stat. 15 (1) 252 - 269, March 2021. https://doi.org/10.1214/20-AOAS1393