Unpacking adverse events and associations post COVID-19 vaccination: a deep dive into vaccine adverse event reporting system data
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 fr...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Expert Review of Vaccines |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/14760584.2023.2292203 |
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| _version_ | 1846141399561404416 |
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| author | Yiming Li Sori K Lundin Jianfu Li Wei Tao Yifang Dang Yong Chen Cui Tao |
| author_facet | Yiming Li Sori K Lundin Jianfu Li Wei Tao Yifang Dang Yong Chen Cui Tao |
| author_sort | Yiming Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2ae28cb19064458f94c1f4493bfd2e12 |
| institution | Kabale University |
| issn | 1476-0584 1744-8395 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Expert Review of Vaccines |
| spelling | doaj-art-2ae28cb19064458f94c1f4493bfd2e122024-12-04T09:49:48ZengTaylor & Francis GroupExpert Review of Vaccines1476-05841744-83952024-12-01231535910.1080/14760584.2023.2292203Unpacking adverse events and associations post COVID-19 vaccination: a deep dive into vaccine adverse event reporting system dataYiming Li0Sori K Lundin1Jianfu Li2Wei Tao3Yifang Dang4Yong Chen5Cui Tao6McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USAMcWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USAMcWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USADepartment of Biostatistics & Data Science, School of Public Health, the University of Texas Health Science Center at Houston, Houston, TX, USAMcWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USADepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USAMcWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USAIntroduction 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.https://www.tandfonline.com/doi/10.1080/14760584.2023.2292203Adverse eventconcept normalizationcorrelation analysisCOVID-19COVID-19 vaccinesnatural language processing |
| spellingShingle | Yiming Li Sori K Lundin Jianfu Li Wei Tao Yifang Dang Yong Chen Cui Tao Unpacking adverse events and associations post COVID-19 vaccination: a deep dive into vaccine adverse event reporting system data Expert Review of Vaccines Adverse event concept normalization correlation analysis COVID-19 COVID-19 vaccines natural language processing |
| title | Unpacking adverse events and associations post COVID-19 vaccination: a deep dive into vaccine adverse event reporting system data |
| title_full | Unpacking adverse events and associations post COVID-19 vaccination: a deep dive into vaccine adverse event reporting system data |
| title_fullStr | Unpacking adverse events and associations post COVID-19 vaccination: a deep dive into vaccine adverse event reporting system data |
| title_full_unstemmed | Unpacking adverse events and associations post COVID-19 vaccination: a deep dive into vaccine adverse event reporting system data |
| title_short | Unpacking adverse events and associations post COVID-19 vaccination: a deep dive into vaccine adverse event reporting system data |
| title_sort | unpacking adverse events and associations post covid 19 vaccination a deep dive into vaccine adverse event reporting system data |
| topic | Adverse event concept normalization correlation analysis COVID-19 COVID-19 vaccines natural language processing |
| url | https://www.tandfonline.com/doi/10.1080/14760584.2023.2292203 |
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