Evidence map›Paper›PMID 39008362›Full record

ArticleJMIR public health and surveillance2024

Adverse Events of COVID-19 Vaccines in the United States: Temporal and Spatial Analysis.

Yiming Li, Jianfu Li, Yifang Dang, Yong Chen, Cui Tao

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yiming LiMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID 0009-0009-8784-1745
Jianfu LiDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, United States.ORCID 0000-0002-9949-7007
Yifang DangMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID 0000-0002-4014-2957
Yong ChenDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.ORCID 0000-0003-0835-0788
Cui TaoDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, United States.ORCID 0000-0002-4267-1924

Funding

VIOLIN 2.0: Vaccine Information and Ontology LInked kNowledgebaseU24AI171008 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Yongqun He, Junguk Hur · 2022 to 2026
$4.2M
Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
NIAID NIH HHS R01 AI130460NIAID NIH HHS U24 AI171008
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic, caused by SARS-CoV-2, has had a profound impact worldwide, leading to widespread morbidity and mortality. Vaccination against COVID-19 is a critical tool in controlling the spread of the virus and reducing the severity of the disease. However, the rapid development and deployment of COVID-19 vaccines have raised concerns about potential adverse events following immunization (AEFIs). Understanding the temporal and spatial patterns of these AEFIs is crucial for an effective public health response and vaccine safety monitoring.

objectiveThis study aimed to analyze the temporal and spatial characteristics of AEFIs associated with COVID-19 vaccines in the United States reported to the Vaccine Adverse Event Reporting System (VAERS), thereby providing insights into the patterns and distributions of the AEFIs, the safety profile of COVID-19 vaccines, and potential risk factors associated with the AEFIs.

methodsWe conducted a retrospective analysis of administration data from the Centers for Disease Control and Prevention (n=663,822,575) and reports from the surveillance system VAERS (n=900,522) between 2020 and 2022. To gain a broader understanding of postvaccination AEFIs reported, we categorized them into system organ classes (SOCs) according to the Medical Dictionary for Regulatory Activities. Additionally, we performed temporal analysis to examine the trends of AEFIs in all VAERS reports, those related to Pfizer-BioNTech and Moderna, and the top 10 AEFI trends in serious reports. We also compared the similarity of symptoms across various regions within the United States.

resultsOur findings revealed that the most frequently reported symptoms following COVID-19 vaccination were headache (n=141,186, 15.68%), pyrexia (n=122,120, 13.56%), and fatigue (n=121,910, 13.54%). The most common symptom combination was chills and pyrexia (n=56,954, 6.32%). Initially, general disorders and administration site conditions (SOC 22) were the most prevalent class reported. Moderna exhibited a higher reporting rate of AEFIs compared to Pfizer-BioNTech. Over time, we observed a decreasing reporting rate of AEFIs associated with COVID-19 vaccines. In addition, the overall rates of AEFIs between the Pfizer-BioNTech and Moderna vaccines were comparable. In terms of spatial analysis, the middle and north regions of the United States displayed a higher reporting rate of AEFIs associated with COVID-19 vaccines, while the southeast and south-central regions showed notable similarity in symptoms reported.

conclusionsThis study provides valuable insights into the temporal and spatial patterns of AEFIs associated with COVID-19 vaccines in the United States. The findings underscore the critical need for increasing vaccination coverage, as well as ongoing surveillance and monitoring of AEFIs. Implementing targeted monitoring programs can facilitate the effective and efficient management of AEFIs, enhancing public confidence in future COVID-19 vaccine campaigns.

Indexed as

COVID-19 VaccinesAdolescentAdultAdverse Drug Reaction Reporting SystemsAgedCOVID-19Drug-Related Side Effects and Adverse ReactionsFemaleHumansMaleMiddle AgedRetrospective StudiesSpatial AnalysisSpatio-Temporal AnalysisUnited StatesYoung AdultCOVID-19 VaccinesADEadverse drug eventadverse event following immunizationAEFICOVID-19COVID-19 vaccinevaccineVaccine Adverse Event Reporting SystemVAERS

Identifiers

PMID39008362
PMCPMC11287098

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.