Evidence map›Paper›PMID 38990924›Full record

ArticlePLOS global public health2024

Preferences regarding COVID-19 vaccination among 12,000 adults in China: A cross-sectional discrete choice experiment.

Fengyun Yu, Lirui Jiao, Qiushi Chen, Qun Wang, Manuela De Allegri, Zhong Cao, Wenjin Chen, Xuedi Ma, Chao Wang, Jonas Wachinger and 7 more

Abstract read
In one paragraph

Article in PLOS global public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
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

17 authors.

Fengyun YuInterdisciplinary Centre for Scientific Computing, University of Heidelberg, Heidelberg, Germany.
Lirui JiaoDepartment of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Qiushi ChenThe Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
Qun WangFaculty of Humanities and Social Sciences, Dalian University of Technology, Dalian, China.
Manuela De AllegriHeidelberg Institute of Global Health (HIGH), Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.
Zhong CaoState Key Lab of Intelligent Technologies and Systems, Department of Automation, Tsinghua University, Beijing, China.
Wenjin ChenSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Xuedi MaSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0000-0002-6289-7453
Chao WangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Jonas WachingerHeidelberg Institute of Global Health (HIGH), Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.
Zhangfeng JinSchool of Economics, Zhejiang University of Technology, Hangzhou, China.ORCID https://orcid.org/0000-0002-5956-9205
Aditi BunkerHeidelberg Institute of Global Health (HIGH), Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.ORCID https://orcid.org/0000-0001-5906-156X
Pascal GeldsetzerDepartment of Medicine, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, California, United States of America.
Juntao YangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Lan XueInstitute for AI International Governance, Tsinghua University, Beijing, China.
Till BärnighausenHeidelberg Institute of Global Health (HIGH), Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.
Simiao ChenHeidelberg Institute of Global Health (HIGH), Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.ORCID https://orcid.org/0000-0003-0555-2157

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding public preferences concerning vaccination is critical to inform pandemic response strategies. To investigate Chinese adults' preferences regarding COVID-19 vaccine attributes, we conducted a cross-sectional online survey in 12,000 Chinese adults in June-July, 2021. Participants were requested to answer a series of discrete choice questions related to hypothetical COVID-19 vaccines. Using mixed logit models, our analysis revealed that participants had a higher preference for COVID-19 vaccines with longer duration of protection (coefficient: 1.272, 95% confidence interval [1.016 to 1.529]) and higher efficacy (coefficient: 1.063, [0.840, 1.287]). Conversely, participants demonstrated a lower preference associated with higher risk of rare but serious side-effects (coefficient: -1.158, [-1.359, -0.958]), oral administration (coefficient: -0.211, [-0.377, -0.046]), more doses (coefficient: -0.148, [-0.296, 0.000]) and imported origin (coefficient: -0.653, [-0.864, -0.443]). Moreover, preferences were heterogeneous by individual factors: highly educated participants were more sensitive to the negative vaccine attributes including price (coefficient -0.312, [-0.370, -0.253]) and imported vaccine (coefficient -0.941, [-1.186, -0.697]); there was also substantial heterogeneity in vaccine preferences with respect to age group, marital status, work status, income, chronic diagnosis history, COVID-19 vaccination history and geographic regions. As the first study of examining the public preferences for COVID-19 vaccine in China with a large nationwide sample of 12,000 adults, our results indicate that future vaccine should pose lower risk, possess longer protection period, have higher efficacy, be domestically produced, and have lower costs to increase the COVID-19 vaccination coverage. Our current study findings from this study provide insights and recommendations for not only COVID-19 vaccine design but also vaccine attribute preferences to increase vaccine uptake in potential future pandemics.

Identifiers

PMID38990924
PMCPMC11239003

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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.