Evidence map›Paper›PMID 34315454›Full record

SynthesisBMC medicine2021

Evaluation of the safety profile of COVID-19 vaccines: a rapid review.

Qianhui Wu, Matthew Z Dudley, Xinghui Chen, Xufang Bai, Kaige Dong, Tingyu Zhuang, Daniel Salmon, Hongjie Yu

Open access · goldAbstract readMeta-AnalysisReview
In one paragraph

Synthesis in BMC medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
125citing papers in PubMed, 4 pooled it
15.0field-weighted citation impact, top 1% of its field
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

125 citing papers in PubMed, 4 syntheses or guidelines pooled it, 247 citations in OpenAlex.

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65 more citing papers are in PubMed but not listed here.

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

8 authors at 2 institutions in 2 countries.

Qianhui WuSchool of Public Health, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.
Matthew Z DudleyInstitute for Vaccine Safety, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.
Xinghui ChenSchool of Public Health, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.
Xufang BaiSchool of Public Health, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.
Kaige DongSchool of Public Health, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.
Tingyu ZhuangSchool of Public Health, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.
Daniel SalmonInstitute for Vaccine Safety, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.
Hongjie YuSchool of Public Health, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China. yhj@fudan.edu.cn.
Fudan University · CNJohns Hopkins University · US

Funding

China National Funds for Distinguished Young Scientists No. 81525023Key Emergency Project of Shanghai Science and Technology Committee No 20411950100
6 · The paper itself

Abstract

backgroundThe rapid process of research and development and lack of follow-up time post-vaccination aroused great public concern about the safety profile of COVID-19 vaccine candidates. To provide comprehensive overview of the safety profile of COVID-19 vaccines by using meta-analysis technique.

methodsEnglish-language articles and results posted on PubMed, Embase, Web of Science, PMC, official regulatory websites, and post-authorization safety surveillance data were searched through June 12, 2021. Publications disclosing safety data of COVID-19 candidate vaccines in humans were included. A meta-analysis of proportions was performed to estimate the pooled incidence and the pooled rate ratio (RR) of safety outcomes of COVID-19 vaccines using different platforms.

resultsA total of 87 publications with safety data from clinical trials and post-authorization studies of 19 COVID-19 vaccines on 6 different platforms were included. The pooled rates of local and systemic reactions were significantly lower among inactivated vaccines (23.7%, 21.0%), protein subunit vaccines (33.0%, 22.3%), and DNA vaccines (39.5%, 29.3%), compared to RNA vaccines (89.4%, 83.3%), non-replicating vector vaccines (55.9%, 66.3%), and virus-like particle vaccines (100.0%, 78.9%). Solicited injection-site pain was the most common local reactions, and fatigue and headache were the most common systemic reactions. The frequency of vaccine-related serious adverse events was low (< 0.1%) and balanced between treatment groups. Vaccine platforms and age groups of vaccine recipients accounted for much of the heterogeneity in safety profiles between COVID-19 vaccines. Reporting rates of adverse events from post-authorization observational studies were similar to results from clinical trials. Crude reporting rates of adverse events from post-authorization safety monitoring (passive surveillance) were lower than in clinical trials and varied between countries.

conclusionsAvailable evidence indicates that eligible COVID-19 vaccines have an acceptable short-term safety profile. Additional studies and long-term population-level surveillance are strongly encouraged to further define the safety profile of COVID-19 vaccines.

Indexed as

COVID-19VaccinesAdolescentAgedAged, 80 and overBNT162 VaccineChAdOx1 nCoV-19COVID-19 VaccinesHumansMiddle AgedSARS-CoV-2Vaccines, DNABNT162 VaccineChAdOx1 nCoV-19COVID-19 Vaccinesreluscovtogene ralaplasmidVaccinesVaccines, DNANovel coronavirus diseases 2019ReviewSafety profileSevere acute respiratory syndrome coronavirus 2Vaccine

Identifiers

PMID34315454
PMCPMC8315897
OpenAlexW3185137851

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.