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


Journal

PLOS global public health
ISSN: 2767-3375
Titre abrégé: PLOS Glob Public Health
Pays: United States
ID NLM: 9918283779606676

Informations de publication

Date de publication:
2024
Historique:
received: 20 12 2023
accepted: 28 05 2024
medline: 11 7 2024
pubmed: 11 7 2024
entrez: 11 7 2024
Statut: epublish

Résumé

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.

Identifiants

pubmed: 38990924
doi: 10.1371/journal.pgph.0003387
pii: PGPH-D-23-02386
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e0003387

Informations de copyright

Copyright: © 2024 Yu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exists.

Auteurs

Fengyun Yu (F)

Interdisciplinary Centre for Scientific Computing, University of Heidelberg, Heidelberg, Germany.

Lirui Jiao (L)

Department 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 Chen (Q)

The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America.

Qun Wang (Q)

Faculty of Humanities and Social Sciences, Dalian University of Technology, Dalian, China.

Manuela De Allegri (M)

Heidelberg Institute of Global Health (HIGH), Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.

Zhong Cao (Z)

State Key Lab of Intelligent Technologies and Systems, Department of Automation, Tsinghua University, Beijing, China.

Wenjin Chen (W)

School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Xuedi Ma (X)

School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Chao Wang (C)

School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Jonas Wachinger (J)

Heidelberg Institute of Global Health (HIGH), Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.

Zhangfeng Jin (Z)

School of Economics, Zhejiang University of Technology, Hangzhou, China.

Aditi Bunker (A)

Heidelberg Institute of Global Health (HIGH), Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.

Pascal Geldsetzer (P)

Department of Medicine, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, California, United States of America.
Chan Zuckerberg Biohub - San Francisco, San Francisco, California, United States of America.

Juntao Yang (J)

School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Lan Xue (L)

Institute for AI International Governance, Tsinghua University, Beijing, China.
School of Public Policy and Management, Tsinghua University, Beijing, China.

Till Bärnighausen (T)

Heidelberg Institute of Global Health (HIGH), Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Simiao Chen (S)

Heidelberg Institute of Global Health (HIGH), Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Classifications MeSH