Evidence map›Paper›PMID 33498688›Full record

ReviewVaccines2021

Influenza Vaccine Effectiveness in Mainland China: A Systematic Review and Meta-Analysis.

Xiaokun Yang, Hongting Zhao, Zhili Li, Aiqin Zhu, Minrui Ren, Mengjie Geng, Yu Li, Ying Qin, Luzhao Feng, Zhibin Peng and 4 more

Abstract readReview
In one paragraph

Review in Vaccines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 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

14 authors.

Xiaokun YangDivision of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.ORCID 0000-0001-9571-1144
Hongting ZhaoDivision of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Zhili LiDivision of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Aiqin ZhuDivision of Infectious Disease Prevention and Disinfection Management, Shanghai Pudong New Area Center for Disease Control and Prevention, Shanghai 200136, China.ORCID 0000-0001-5034-0175
Minrui RenDivision of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Mengjie GengDivision of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Yu LiDivision of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Ying QinDivision of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Luzhao FengSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
Zhibin PengDivision of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Zhijie AnNational Immunization Program, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Jiandong ZhengDivision of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Zhongjie LiDivision of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Zijian FengChinese Center for Disease Control and Prevention, Beijing 102206, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Influenza endangers human health but can be prevented in part by vaccination. Assessing influenza vaccine effectiveness (VE) provides scientific evidence for developing influenza vaccination policy. We conducted a systematic review and meta-analysis of studies that evaluated influenza VE in mainland China. We searched six relevant databases as of 30 August 2019 to identify studies and used Review Manager 5.3 software to analyze the included studies. The Newcastle-Ottawa scale was used to assess the risk of publication bias. We identified 1408 publications, and after removing duplicates and screening full texts, we included 21 studies in the analyses. Studies were conducted in Beijing, Guangzhou, Suzhou, and Zhejiang province from the 2010/11 influenza season through the 2017/18 influenza season. Overall influenza VE for laboratory confirmed influenza was 36% (95% CI: 25-46%). In the subgroup analysis, VE was 45% (95% CI: 18-64%) for children 6-35 months who received one dose of influenza vaccine, and 57% (95% CI: 50-64%) who received two doses. VE was 47% (95% CI: 39-54%) for children 6 months to 8 years, and 18% (95% CI: 0-33%) for adults ≥60 years. For inpatients, VE was 21% (95% CI: -11-44%). We conclude that influenza vaccines that were used in mainland China had a moderate effectiveness, with VE being higher among children than the elderly. Influenza VE should be continuously monitored in mainland China to provide evidence for policy making and improving uptake of the influenza vaccine.

Indexed as

Chinainfluenzameta-analysissystematic reviewvaccine effectiveness

Identifiers

PMID33498688
PMCPMC7912587

What OpenQuestion holds

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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.