PMID- 38063069
OWN - NLM
STAT- MEDLINE
DCOM- 20231220
LR  - 20240218
IS  - 1744-8395 (Electronic)
IS  - 1476-0584 (Print)
IS  - 1476-0584 (Linking)
VI  - 23
IP  - 1
DP  - 2024 Jan-Dec
TI  - Unpacking adverse events and associations post COVID-19 vaccination: a deep dive 
      into vaccine adverse event reporting system data.
PG  - 53-59
LID - 10.1080/14760584.2023.2292203 [doi]
AB  - 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.
FAU - Li, Yiming
AU  - Li Y
AD  - McWilliams School of Biomedical Informatics, the University of Texas Health 
      Science Center at Houston, Houston, TX, USA.
FAU - Lundin, Sori K
AU  - Lundin SK
AD  - McWilliams School of Biomedical Informatics, the University of Texas Health 
      Science Center at Houston, Houston, TX, USA.
AD  - Department of Biostatistics & Data Science, School of Public Health, the 
      University of Texas Health Science Center at Houston, Houston, TX, USA.
FAU - Li, Jianfu
AU  - Li J
AD  - McWilliams School of Biomedical Informatics, the University of Texas Health 
      Science Center at Houston, Houston, TX, USA.
AD  - Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, 
      FL, USA.
FAU - Tao, Wei
AU  - Tao W
AD  - Department of Biostatistics & Data Science, School of Public Health, the 
      University of Texas Health Science Center at Houston, Houston, TX, USA.
FAU - Dang, Yifang
AU  - Dang Y
AD  - McWilliams School of Biomedical Informatics, the University of Texas Health 
      Science Center at Houston, Houston, TX, USA.
FAU - Chen, Yong
AU  - Chen Y
AD  - Department of Biostatistics, Epidemiology, and Informatics, Perelman School of 
      Medicine, University of Pennsylvania, Philadelphia, PA, USA.
FAU - Tao, Cui
AU  - Tao C
AD  - McWilliams School of Biomedical Informatics, the University of Texas Health 
      Science Center at Houston, Houston, TX, USA.
AD  - Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, 
      FL, USA.
LA  - eng
GR  - R01 AI130460/AI/NIAID NIH HHS/United States
GR  - U24 AI171008/AI/NIAID NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20231214
PL  - England
TA  - Expert Rev Vaccines
JT  - Expert review of vaccines
JID - 101155475
RN  - 0 (COVID-19 Vaccines)
RN  - 0 (Vaccines)
SB  - IM
MH  - Aged
MH  - Female
MH  - Humans
MH  - Male
MH  - Pregnancy
MH  - Adverse Drug Reaction Reporting Systems
MH  - *COVID-19/epidemiology/prevention & control
MH  - *COVID-19 Vaccines/adverse effects
MH  - United States
MH  - Vaccination/adverse effects
MH  - *Vaccines/adverse effects
PMC - PMC10872386
MID - NIHMS1951133
OTO - NOTNLM
OT  - Adverse event
OT  - COVID-19
OT  - COVID-19 vaccines
OT  - VAERS
OT  - concept normalization
OT  - correlation analysis
OT  - natural language processing
OT  - vaccine safety monitoring
COIS- 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.
EDAT- 2023/12/08 06:41
MHDA- 2023/12/17 13:19
PMCR- 2025/01/01
CRDT- 2023/12/08 05:28
PHST- 2025/01/01 00:00 [pmc-release]
PHST- 2023/12/17 13:19 [medline]
PHST- 2023/12/08 06:41 [pubmed]
PHST- 2023/12/08 05:28 [entrez]
AID - 10.1080/14760584.2023.2292203 [doi]
PST - ppublish
SO  - Expert Rev Vaccines. 2024 Jan-Dec;23(1):53-59. doi: 
      10.1080/14760584.2023.2292203. Epub 2023 Dec 14.