The main decision-making competence for willingness-to-pay towards COVID-19 vaccination: a family-based study in Taizhou, China

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Abstract

Purpose

This research aimed to explore individuals’ willingness to pay (WTP) and studied the role of family decision makers in WTP for COVID-19 vaccines.

Methods

A self-administered online questionnaire evaluating the willingness of community residents to pay for booster vaccination of COVID-19 vaccine was conducted among families in a community in Taizhou, China. The logistic regression model was performed to identify the factors associated with WTP for the COVID-19 vaccines, and all data were analysed by R software, version 4.1.0.

Results

44.2% and 43.7% of 824 community residents were willing to pay for the first two doses and the booster dose of the COVID-19 vaccine, respectively. Decision-makers were more willing to pay for both the first two doses and the boost dose of the COVID-19 vaccines, with OR (95%CI) being 1.75 (1.25–2.47) and 1.89 (1.34–2.67), respectively. Besides, participants’ WTP for COVID-19 vaccines were also associated with their occupation and monthly household income.

Conclusion

This study found that family decision-makers were more willing to pay for both the first two doses and the booster dose of COVID-19 vaccines in Taizhou, China. To improve the WTP for COVID-19 vaccines, public policy programs need to conduct a comprehensive cost-benefit analysis and focus on the role of family decision makers in vaccination.

Key Messages

A study evaluating the willingness of community residents to pay for booster vaccination of COVID-19 vaccine was conducted among families in a community in Taizhou, China.
Family decision-makers were more willing to pay for both the first two doses and the booster dose of COVID-19 vaccines.
To improve the WTP for COVID-19 vaccines, public policy programs need to conduct a comprehensive cost-benefit analysis and focus on the role of family decision-makers in vaccination.

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Most cited references 36

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Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

Qun Li, Xuhua Guan, Peng Wu … (2020)

Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)

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An interactive web-based dashboard to track COVID-19 in real time

Ensheng Dong, Hongru Du, Lauren Gardner (2020)

In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.

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Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review

Sasmita Adhikari, Sha Meng, Yu-Ju Wu … (2020)

Background The coronavirus disease (COVID-19) has been identified as the cause of an outbreak of respiratory illness in Wuhan, Hubei Province, China beginning in December 2019. As of 31 January 2020, this epidemic had spread to 19 countries with 11 791 confirmed cases, including 213 deaths. The World Health Organization has declared it a Public Health Emergency of International Concern. Methods A scoping review was conducted following the methodological framework suggested by Arksey and O’Malley. In this scoping review, 65 research articles published before 31 January 2020 were analyzed and discussed to better understand the epidemiology, causes, clinical diagnosis, prevention and control of this virus. The research domains, dates of publication, journal language, authors’ affiliations, and methodological characteristics were included in the analysis. All the findings and statements in this review regarding the outbreak are based on published information as listed in the references. Results Most of the publications were written using the English language (89.2%). The largest proportion of published articles were related to causes (38.5%) and a majority (67.7%) were published by Chinese scholars. Research articles initially focused on causes, but over time there was an increase of the articles related to prevention and control. Studies thus far have shown that the virus’ origination is in connection to a seafood market in Wuhan, but specific animal associations have not been confirmed. Reported symptoms include fever, cough, fatigue, pneumonia, headache, diarrhea, hemoptysis, and dyspnea. Preventive measures such as masks, hand hygiene practices, avoidance of public contact, case detection, contact tracing, and quarantines have been discussed as ways to reduce transmission. To date, no specific antiviral treatment has proven effective; hence, infected people primarily rely on symptomatic treatment and supportive care. Conclusions There has been a rapid surge in research in response to the outbreak of COVID-19. During this early period, published research primarily explored the epidemiology, causes, clinical manifestation and diagnosis, as well as prevention and control of the novel coronavirus. Although these studies are relevant to control the current public emergency, more high-quality research is needed to provide valid and reliable ways to manage this kind of public health emergency in both the short- and long-term.

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Author and article information

Journal

Journal ID (nlm-ta): Ann Med

Journal ID (iso-abbrev): Ann Med

Title: Annals of Medicine

Publisher: Taylor & Francis

ISSN (Print): 0785-3890

ISSN (Electronic): 1365-2060

Publication date (Electronic, pub): 25 August 2022

Publication date (Electronic, collection): 2022

Publication date PMC-release: 25 August 2022

Volume: 54

Issue: 1

Pages: 2376-2384

Affiliations

[a ]Evidence-Based Medicine Center, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University , Linhai, Zhejiang, China

[b ]Gucheng Street Community Health Service Center , Linhai, Zhejiang, China

[c ]Department of Infectious Diseases, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University , Linhai, Zhejiang, China

Author notes

CONTACT Tao-Hsin Tung ch2876@ 123456yeah.net Evidence-Based Medicine Center, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University , Linhai, Zhejiang, China

Jian-Sheng Zhu zhujs@ 123456enzemed.com Department of Infectious Diseases, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University , Linhai, Zhejiang, China

Author information

Mei-Xian Zhang https://orcid.org/0000-0002-6538-7037

Tao-Hsin Tung https://orcid.org/0000-0003-2097-8375

Jian-Sheng Zhu https://orcid.org/0000-0002-4961-7437

Article

Publisher ID: 2114606

DOI: 10.1080/07853890.2022.2114606

PMC ID: 9423852

PubMed ID: 36004802

SO-VID: 8caf10ec-a415-47d8-abbe-fb1682f18bef

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The main decision-making competence for willingness-to-pay towards COVID-19 vaccination: a family-based study in Taizhou, China

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Abstract

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Novel Coronavirus Disease COVID-19

Most cited references 36

Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

An interactive web-based dashboard to track COVID-19 in real time

Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review

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