Evidence map›Paper›PMID 34171345›Full record

SynthesisPreventive medicine2021

Vaccination against COVID-19: A systematic review and meta-analysis of acceptability and its predictors.

Qiang Wang, Liuqing Yang, Hui Jin, Leesa Lin

Registry-linked trialOpen access · greenAbstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Preventive medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05424952 (The Effectiveness of Conversational AI Service), which is not on this map. Cited by 190 papers, 18 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
190citing papers in PubMed, 18 pooled it
34.2field-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.

NCT05424952 nacompletednot on this mapstarted 2022, after this paper: background citation

The Effectiveness of Conversational AI Service (Chatbot) Utilization on Vaccine Confidence

TypeinterventionalSponsorThe University of Hong KongRan2022 to 2023Enrolled1,000ConditionsVaccination HesitancyArmsConversational AI Service (Chatbot)
3 · Its place in the literature

Who cites it

190 citing papers in PubMed, 18 syntheses or guidelines pooled it, 311 citations in OpenAlex.

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130 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

4 authors at 2 institutions in 2 countries.

Qiang WangDepartment of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.
Liuqing YangDepartment of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.
Hui JinDepartment of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China. Electronic address: jinhui_hld@163.com.
Leesa LinDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, SAR, China.
Southeast University · CNLondon School of Hygiene & Tropical Medicine · GB

Funding

Wellcome Trust 215373/A/19/Z
6 · The paper itself

Abstract

We aimed to estimate the coronavirus disease 2019 (COVID-19) vaccine acceptance rate and identify predictors associated with acceptance. To this end, we searched PubMed, Web of Science, Cochrane Library, and Embase databases until November 4, 2020. Meta-analyses were performed to estimate the rate with 95% confidence intervals (CI). Predictors were identified to be associated with vaccination intention based on the health belief model framework. Thirty-eight articles, with 81,173 individuals, were included. The pooled COVID-19 vaccine acceptance rate was 73.31% (95%CI: 70.52, 76.01). Studies using representative samples reported a rate of 73.16%. The pooled acceptance rate among the general population (81.65%) was higher than that among healthcare workers (65.65%). Gender, educational level, influenza vaccination history, and trust in the government were strong predictors of COVID-19 vaccination willingness. People who received an influenza vaccination in the last year were more likely to accept COVID-19 vaccination (odds ratio: 3.165; 95%CI: 1.842, 5.464). Protecting oneself or others was the main reason for willingness, and concerns about side effects and safety were the main reasons for unwillingness. National- and individual-level interventions can be implemented to improve COVID-19 vaccine acceptance before large-scale vaccine rollout. Greater efforts could be put into addressing negative predictors associated with willingness.

Indexed as

COVID-19COVID-19 VaccinesCross-Sectional StudiesHumansSARS-CoV-2VaccinationCOVID-19 VaccinesAcceptanceCOVID-19PredictorsSystematic reviewVaccine

Identifiers

PMID34171345
PMCPMC8217737
OpenAlexW3173610445

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.