Signal Monitoring for Adverse Events Following Immunisation with COVID-19 Vaccines During the SARS-CoV-2 Pandemic: An Evaluation of the South African Surveillance System

Introduction Monitoring of adverse events following immunisation (AEFI) is recommended for post-licensure surveillance. We investigated whether the South African surveillance system could detect signals of disproportionate reporting and whether these signals aligned with globally identified AEFI and adverse events of special interest (AESI) post-coronavirus disease-2019 (COVID-19) vaccination. Methods This retrospective pharmacovigilance study undertook disproportionality analysis of the National Department of Health AEFI database from the start of the COVID-19 vaccine rollout on 17 May 2021 to 31 December 2022. We complemented this with AEFI reports for vaccines not on the routine Expanded Programme on Immunisation schedule, to address potential masking of signals due to the high reporting rate of COVID-19 vaccine AEFI. Results During the study period, 3846 AEFI were reported for 37,537,009 doses of COVID-19 vaccines (BNT162b2 and Ad26.COV2.S) administered. The overall reporting rate was 10.2 per 100,000 doses, 18.1/100,000 and 7.9/100,000 for Ad26.COV2.S and BNT162b2, respectively. Comparison with other countries suggests underreporting. Disproportionate reporting signals were obtained for three and seven AEFI following BNT162b2 and Ad26.COV2.S vaccines, respectively. An additional three AEFI signals from Ad26.COV2.S emerged in the augmented dataset, indicating masking. All Ad26.COV2.S signals, and one BNT162b2 signal, appear in the vaccines’ product information. Among nine AESI evaluated, myocarditis/pericarditis presented as a signal of disproportionate reporting following BNT162b2 vaccination. Conclusions This study is one of the first from a lower-middle-income country, using a spontaneous reporting system for signal detection post-COVID-19 vaccination. Signals aligned with those reported globally. The study highlights the need to further investigate underreporting, masking, and system attributes for system strengthening.