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. 2024 Jan-Dec;23(1):53-59.
doi: 10.1080/14760584.2023.2292203. Epub 2023 Dec 14.

Unpacking adverse events and associations post COVID-19 vaccination: a deep dive into vaccine adverse event reporting system data

Yiming Li  1 , Sori K Lundin  1   2 , Jianfu Li  1   3 , Wei Tao  2 , Yifang Dang  1 , Yong Chen  4 , Cui Tao  1   3
Affiliations

Unpacking adverse events and associations post COVID-19 vaccination: a deep dive into vaccine adverse event reporting system data

Yiming Li et al. Expert Rev Vaccines. 2024 Jan-Dec.

Abstract

Introduction: The rapid development of COVID-19 vaccines has provided crucial tools for pandemic control, but the occurrence of vaccine-related adverse events (AEs) underscores the need for comprehensive monitoring.

Methods: This study analyzed the Vaccine Adverse Event Reporting System (VAERS) data from 2020-2022 using statistical methods such as zero-truncated Poisson regression and logistic regression to assess associations with age, gender groups, and vaccine manufacturers.

Results: Logistic regression identified 26 System Organ Classes (SOCs) significantly associated with age and gender. Females displayed especially higher odds in SOC 19 (Pregnancy, puerperium and perinatal conditions), while males had higher odds in SOC 25 (Surgical and medical procedures). Older adults (>65) were more prone to symptoms like Cardiac disorders, whereas those aged 18-65 showed susceptibility to AEs like Skin and subcutaneous tissue disorders. Moderna and Pfizer vaccines induced fewer SOC symptoms compared to Janssen and Novavax. The zero-truncated Poisson regression model estimated an average of 4.243 symptoms per individual.

Conclusion: These findings offer vital insights into vaccine safety, guiding evidence-based vaccination strategies and monitoring programs for precise and effective outcomes.

Keywords: Adverse event; COVID-19; COVID-19 vaccines; VAERS; concept normalization; correlation analysis; natural language processing; vaccine safety monitoring.

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Conflict of interest statement

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Figures

Figure 1
Figure 1
The pairwise correlation matrix of SOCs determined by Spearman’s method
See this image and copyright information in PMC

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