Evidence map›Paper›PMID 33686812›Full record

ArticleJournal of Korean medical science2021

Estimating Baseline Incidence of Conditions Potentially Associated with Vaccine Adverse Events: a Call for Surveillance System Using the Korean National Health Insurance Claims Data.

Kyungmin Huh, Young Eun Kim, Munkhzul Radnaabaatar, Dae Ho Lee, Dong Wook Kim, Soon Ae Shin, Jaehun Jung

Abstract read
In one paragraph

Article in Journal of Korean medical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 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

7 authors.

Kyungmin Huh *Division of Infectious Diseases, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-5140-3964
Young Eun Kim *Department of Big Data Strategy, National Health Insurance Service, Wonju, Korea.ORCID https://orcid.org/0000-0003-0694-6844
Munkhzul RadnaabaatarArtificial Intelligence and Big-Data Convergence Center, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.ORCID https://orcid.org/0000-0003-4968-5031
Dae Ho LeeDepartment of Internal Medicine, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.ORCID https://orcid.org/0000-0002-8832-3052
Dong Wook KimDepartment of Big Data Strategy, National Health Insurance Service, Wonju, Korea.ORCID https://orcid.org/0000-0002-4478-3794
Soon Ae ShinDepartment of Big Data Strategy, National Health Insurance Service, Wonju, Korea. rosa1026@nhis.or.kr.ORCID https://orcid.org/0000-0001-8858-0801
Jaehun Jung *Artificial Intelligence and Big-Data Convergence Center, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.ORCID https://orcid.org/0000-0002-4856-3668

Funding

Gil Medical Center, Gachon University 2019-11
6 · The paper itself

Abstract

backgroundVaccines against coronavirus disease 2019 (COVID-19) are raising concerns about vaccine safety, particularly in the context of large-scale immunization. To address public concerns, we measured the baseline incidence rates of major conditions potentially related to vaccine-related adverse events (VAEs). We aimed to provide a basis for evaluating VAEs and verifying causality.

methodsConditions of interest were selected from the US Vaccine Adverse Event Reporting System Table of Reportable Events and a recent report from a European consortium on vaccine surveillance. We used the National Health Insurance Service database in Korea to identify the monthly numbers of cases with these conditions. Data from January 2006 to June 2020 were included. Prediction models were constructed from the observed incidences using an autoregressive integrated moving average. We predicted the incidences of the conditions and their respective 95% confidence intervals (CIs) for January through December 2021. In addition, subgroup analysis for the expected vaccination population was conducted.

resultsMean values (95% CIs) of the predicted monthly incidence of vasovagal syncope, anaphylaxis, brachial neuritis, acute disseminated encephalomyelitis, Bell's palsy, Guillain-Barré syndrome, encephalopathy, optic neuritis, transverse myelitis, immune thrombocytopenic purpura, and systemic lupus erythematosus in 2021 were 23.89 (19.81-27.98), 4.72 (3.83-5.61), 57.62 (51.37-63.88), 0.03 (0.01-0.04), 8.58 (7.90-9.26), 0.26 (0.18-0.34), 2.13 (1.42-2.83), 1.65 (1.17-2.13), 0.19 (0.14-0.25), 0.75 (0.61-0.90), and 3.40 (2.79-4.01) cases per 100,000 respectively. The majority of the conditions showed an increasing trend with seasonal variations in their incidences.

conclusionWe measured the incidence of a total of 11 conditions that could potentially be associated with VAEs to predict the monthly incidence in 2021. In Korea, conditions that could potentially be related to VAEs occur on a regular basis, and an increasing trend is observed with seasonality.

Indexed as

AnaphylaxisCOVID-19Databases, FactualHumansIncidenceModels, TheoreticalNational Health ProgramsProduct Surveillance, PostmarketingRepublic of KoreaSARS-CoV-2Syncope, VasovagalVaccinationCOVID-19VaccinationVaccine Adverse EventsVaccine Hesitancy

Identifiers

PMID33686812
PMCPMC7940120

What OpenQuestion holds

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LicenceCC BY-NC
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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.