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Abstract

Introduction

Although messenger RNA (mRNA) vaccines have been developed and widely utilized to mitigate the coronavirus disease (COVID-19) pandemic, it is essential to describe the adverse events (AEs) following immunization. This study aimed to identify the patterns associated with serious AE reports after mRNA COVID-19 vaccination in the World Health Organization (WHO)’s global scale database (VigiBase).

Methods

This study performed a latent class analysis (LCA) of reports of serious AEs following mRNA COVID-19 vaccination from VigiBase between December 28, 2020 , and February 28, 2022 (N = 312878). The Medical Dictionary for Regulatory Activities (MedDRA) System Organ Class (SOC) terms were selected for LCA. The reporting characteristics in accordance with the cluster were described. We used a multinomial logistic regression model to estimate the association between potential factors and each cluster.

Results

Five clusters of AE reports were distinguished through LCA: infection AEs (cluster 1), cardiac AEs (cluster 2), respiratory/thrombotic AEs (cluster 3), systemic AEs (cluster 4), and nervous system AEs (cluster 5). Compared to cluster 4, cluster 2 had a higher proportion of males (OR 2.98; 95% confidence interval (CI) 2.87–3.09), and cluster 1 had a longer time to onset than other AEs (≥ 14 days) (OR 16.2; 95% CI 15.5–16.9).

Conclusion

Using LCA, we found five clusters of serious AEs following mRNA COVID-19 vaccination. Each cluster was distinguished by potential factors such as age, gender, region, and time to onset. We suggest that monitoring should carefully consider the patterns of young males with cardiac AEs and elderly individuals with thrombosis after respiratory AEs. Our findings could contribute to enhancing understanding of safety profiles and establishing management strategies for serious AEs of special interest following mRNA COVID-19 vaccination.

Details

Title
Safety Profiles of mRNA COVID-19 Vaccines Using World Health Organization Global Scale Database (VigiBase): A Latent Class Analysis
Pages
443-458
Publication year
2023
Publication date
Feb 2023
Publisher
Springer Nature B.V.
ISSN
21938229
e-ISSN
21936382
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2867136525
Copyright
Copyright Springer Nature B.V. Feb 2023