Evidence map›Paper›PMID 36146526›Full record

ArticleVaccines2022

Analysis of Adverse Effects of COVID-19 Vaccines Experienced by Healthcare Workers at Guizhou Provincial Staff Hospital, China.

Yunhua Wei, Yan Wang, Lin Liu, Yan Zha, Yuqi Yang, Yuanlin Wang, Neil Roberts, Yaying Li

Open access · goldAbstract read
In one paragraph

Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 39% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
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 3 institutions in 2 countries.

Yunhua WeiDepartment of Nuclear Medicine, Guizhou Provincial People's Hospital, Affiliated Hospital of Guizhou University, Guiyang 550002, China.
Yan WangDepartment of Nuclear Medicine, Guizhou Provincial People's Hospital, Affiliated Hospital of Guizhou University, Guiyang 550002, China.
Lin LiuDepartment of Nuclear Medicine, Guizhou Provincial People's Hospital, Affiliated Hospital of Guizhou University, Guiyang 550002, China.
Yan ZhaDepartment of Nuclear Medicine, Guizhou Provincial People's Hospital, Affiliated Hospital of Guizhou University, Guiyang 550002, China.
Yuqi YangDepartment of Nuclear Medicine, Guizhou Provincial People's Hospital, Affiliated Hospital of Guizhou University, Guiyang 550002, China.
Yuanlin WangDepartment of Nuclear Medicine, Guizhou Provincial People's Hospital, Affiliated Hospital of Guizhou University, Guiyang 550002, China.
Neil RobertsSchool of Clinical Sciences, The Queen's Medical Research Institute (QMRI), University of Edinburgh, Edinburgh EH8 9YL, UK.
Yaying LiDepartment of Nuclear Medicine, Guizhou Provincial People's Hospital, Affiliated Hospital of Guizhou University, Guiyang 550002, China.
Guizhou University · CNGuizhou Provincial People's Hospital · CNThe Queen's Medical Research Institute · GB

Funding

Yaying Li Doctor Foundation of Guizhou Provincial People's Hospital (GZSYBS[2019]02)Yaying Li Qian Ke He Foundation-ZK [2022] General 253
6 · The paper itself

Abstract

Objective: A retrospective survey was conducted of adverse events following immunization (AEFI) experienced by health care workers (HCWs) in a relatively remote ethnic region in southwest China (Guizhou Province) who received COVID-19 vaccines. Methods: From 18 January 2021 to 21 January 2022, all HCWs of Guizhou Provincial Staff Hospital, China, who received at least one dose of inactivated COVID-19 vaccine (Vero cell), recombinant novel coronavirus vaccine (CHO cell), or one dose of adenovirus type-5 (Ad5) vectored COVID-19 vaccine were asked to complete a self-report questionnaire to provide information on any adverse events that may have occurred in the first 3 days after injection. The frequency of AEFI corresponding to the three types of vaccines were compared and the potential risks of AEFI due to the three different vaccines were predicted by multivariate logistic regression analysis. Results: Of the 904 HCWs who completed the survey, the rates of AEFI were 10.1% (80/794) due to Vero cell, 16.3% (13/80) due to CHO cell, and 46.67% (14/30) due to Ad5 vectored vaccines, and the rates were significantly different (χ2 = 38.7, p < 001) between the three vaccines. Multivariate logistic regression models predict that (1) compared to the Ad 5 vectored group, the risk of AEFI occurrence in the Vero cell group was reduced by about 85.9% (OR = 0.141, 95% CI: 0.065−0.306, p < 0.001) and in the CHO cell group by about 72.1% (OR = 0.279, 95% CI: 0.107−0.723, p = 0.009), (2) the odds for women experiencing AEFI were about 2.1 (OR = 2.093, 95% CI: 1.171−3.742, p = 0.013) times as high as those of men, and (3) the risk of AEFI for HCWs with a Bachelor’s degree or above was about 2.2 (OR = 2.237, 95% CI: 1.434−3.489, p = 0.001) times higher than in HCWs who do not have a Bachelor’s degree. Conclusions: 1. The inactivated COVID-19 vaccine (Vero cell), recombinant novel coronavirus vaccine (CHO cell), and adenovirus type-5 (Ad5) vectored COVID-19 vaccine made in China are safe and relatively broad-spectrum. 2. The prevalence of AEFI is more common in women healthcare workers. 3. The risk of AEFI was higher in those with a Bachelor’s degree or above and may be related to the psychological and social effects triggered by the global COVID-19 pandemic.

Indexed as

adenovirus type-5 (Ad5) vectored COVID-19 vaccineadverse events following immunizationhealthcare workersinactivated COVID-19 vaccine (Vero cell)recombinant novel coronavirus vaccine (CHO cell)

Identifiers

PMID36146526
PMCPMC9502548
OpenAlexW4295094759

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.