A discrete choice experiment for evidence-based decision-making to Explore Willingness to pay for Covid-19 vaccination

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The longtime solution to prevent the spread of virus is to develop an effective vaccine. To com (WTP). The objective of the present study was to explore the WTP of people aged over 18 for Covid-19 vaccination through a discrete choice experiment (DCE) for evidence-based decision-making. A cross-sectional survey was performed during two mid-weeks in May 2020 in six southeastern provinces in Vietnam. A self-design three-part questionnaire was used to investigate the community. A DCE was designed with twelve vaccine profiles, each one involved four attributes, protection efficacy, duration, side effects and out-of-pocket cost. A binary logistic regression model was applied to predict the probability of choosing a given vaccine. Protection duration posed the highest effect on vaccine choice (prevalence weight 1.2109). The marginal WTP for 10-year protection is US $ 531.77 (95% CI: 284.31–1485.58). If the vaccine improved to 95% protection for 10 years and had no side effects, the WTP increased to $ 1466.79. When the self-paid cost increased from US $ 12.5 to 200, the probability decrease dramatically decreased approximately 21%. This study resulted a high value that southern Vietnamese residents were willing to pay for a vaccine against Covid-19. These findings support decision makers in the implementation of vaccine program in the future. Covid-19 Health Economic Pandemic Vaccine Vietnam Willingness To Pay WTP Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Coronavirus is a name of a group of viruses targeting the respiratory system. In recent years, people saw previous lethal outbreaks of coronavirus, including the severe acute respiratory syndrome (SARS-CoV) and the Middle East respiratory syndrome (MERS-CoV). On the last days of 2019 in Wuhan City in China, the cluster of pneumonia cases was reported with unknown origin. Epidemiologically, these patients were linked to Wuhan’s Huanan Seafood Wholesale Market (Bogoch et al. 2020 ; Lu et al. 2020 ). In early January 2020, a novel coronavirus had been detected in patient-obtained samples before that newly analyzed viral genetic chain was suggested to be the origin of the outbreak. The World Health Organization (WHO) announced a Public Health Emergency of International Concern for this novel type of coronavirus. Subsequently, the name Covid-19 was published by WHO, with “Co” referring to corona, “vi” to virus, “d” to disease and “19” to 2019 (WHO 2020). WHO has been operating the WHO Coronavirus Disease Dashboard, which has statistics of every single country; daily situation reports have also been published (GOV.UK. 2020). On 7 May 2020, there have been 4,534,731 confirmed cases of Covid-19 including 307,537 deaths reported to WHO globally (WHO 2020). In Vietnam, the government confirmed the spread of Covid-19 on 23 January 2020. There were only 16 cases detected between 23 January and 13 February 2020, then saw 22 days without new infection. Although the number of new reported cases has been increasing daily, the rate of the increase has been still under 15% since the first recorded case (Toan et al. 2020). Up to 18 May 2020, there have been 320 cases of Covid-19 confirmed in total, including 260 recovered cases and no deaths confirmed in Vietnam (Ministry of Health in Vietnam 2020 ). The unprecedented pandemic has caused a massive challenge for global healthcare systems. Doctors and other healthcare staffs have been at risk and overloaded as well. Medical facilities and protective equipment, especially medical masks, have been exhausted. Medical systems have become unbalanced; other patients with other diseases and health problems have been neglected. Additionally, it is challenging to diagnose, quarantine and treat confirmed cases (Haleem et al. 2020 ). Obviously, the pandemic has caused a high burden not only to healthcare system but also on all the area of socioeconomics due to quarantine. Therefore, a longtime solution to prevent the spread of Covid-19 is to develop an effective vaccine. Since the beginning of pandemic, research and development have been making a run to develop vaccines, thereby control the pandemic (Sehgal, 2020 ). A broad variety of platforms are being investigated, including DNA, mRNA, recombinant protein, and adenoviral vector, centered on MERS and SARS vaccine production pathway (Wang et al. 2020 ; Jiang et al 2020 ). More than 40 pharmaceutical firms, corporations and research institutions from several countries have successfully produced Covid-19 vaccines. Some candidates have entered efficacy evaluation on animals and clinical trials (Zhang et al. 2020 ). A phase 3 clinical trial has been planned to operate in July 2020 (Ben 2020 ). In order to compute the benefits of a program, e.g. vaccination program, researchers calculate the number that the individual is willing to give up to get the benefit, called willingness-to-pay (WTP).WTP for wellbeing is a concern for patient and community health care decision taking. The word typically applies to the ability of people to invest resources for their own, i.e. “out of pocket”, achieving health benefits for themselves or preventing financial problems or what safety threats for themselves (Florian et al. 2020 ). Stated preference (SP) and revealed preference methods are two kinds of preference-based outcome measurements. In SP methods, the total economic value can be measured, with non-use value and option value being incorporated, therefore, valuing hypothetical goods and intervention could be possible. Among SP methods, discrete choice experience (DCE) is an approach to assess the value pf every attributes of the good or services, differentiating with contingent valuation which assess the value of the whole product (Kjaer, 2005). The basis of the DCE is quite complicated as it merges some of the economic theories. The DCE is focused on the principle of probabilistic preference, named random utility principle, and is ideal for Lancaster 's economic benefit theory and neoclassical economies (Kjaer, 2005). Random utility theory provides researchers with access to sophisticated multidimensional goods preferences, from which preferences models can be estimated. The foundation of probabilistic choice theory is that when an person has options it becomes uncertain, such that the choices cannot be predicted correctly (Kjaer, 2005). In DCE models, they ascribe a probability to every alternative to be chosen instead of classifying one alternative as the chosen option (Kjaer, 2005). The objective of the present study was to explore the preferences and willingness to pay of people aged over 18 for Covid-19 vaccination through a discrete choice experiment for evidence-based decision-making. 2. METHODOLOGY 2.1 Study design A cross-sectional community-based survey was performed during two mid-weeks in May 2020 in six southeastern provinces in Vietnam. Target population was estimated at17,074,300 in 2018 (General Statistic Office of Vietnam, 2020 ). The study questions were as follows: 1. How could vaccine attributes influence the WTP for Covid-19 vaccination? 2. How cost of the vaccine effects to the probability to take the vaccine? 2.2 Sampling procedure Simple random sampling technique was used to recruit study participants, who regularly passed by in the major streets, parks, shopping malls and restaurants. Participants who met the inclusion criteria were enrolled: i) were at least 18 years old and declare to have an income; ii) had Vietnamese nationality and were able to communicate fluently; iii) had no specific symptoms or diagnosis of influenza, Covid-19 or flu-like syndromes. Those who disagreed to participate or gave ambiguous answers were eliminated. Sample size was determined using the population proportion sample size formula (Dowdy et al. 2011 ) where z value equal to 1.96, according to alpha error equal to 5%with a two-side significance; total population N = 17,074,300; rate of respondent p assumed at 85%; and standard error d assumed at 0.05. The formula resulted in a sample size of 196 adults. Assuming the probability of 30% missing data, 255 questionnaires were released. Data was collected using a structured, close-ended, 20-item questionnaire prepared in Vietnamese language. Each question was verbally elaborated and explained in the local language to the respondents at the time of interview. The data collection was done by three graduated pharmacy students after one-day training course. These interviewers were follow an arrangement to distribute to all six provinces. The completeness, consistency, and accuracy of the data were checked at the end of every single day by major investigators. 2.3 Study instrument The questionnaire used for data collection had three parts. The first part of the questionnaire related to demographic characteristics of the participants. The next two questions mentioned in the hearing and seeking for finding Covid-19 vaccine information section and the final eight questions were related to measure WTP on DCE method. The questionnaire was first pilot-tested over 30 respondents to check for any inconsistencies. Necessary adjustments were made accordingly prior to the realistic stage of the study. The pilot study was basically conducted to investigate the response rate of the patients and identify the most important attributes to be included in the DCE survey. It was not part of the validation process but was aimed at confirming that this particular instrument could be employed easily with maximum response rate from the specific population subset. 2.4 DCE design Due to no available Covid-19 vaccine in the market, choice modeling on vaccines against other virus such as human immunodeficiency virus, human papilloma virus and dengue virus were reviewed in order to identify vaccine attributes and levels for DCE. Based on these previous studies and the preliminary qualitative research, four Covid-19 vaccine attributes were selected eventually, including protection efficacy, protection duration, side effect, and cost. The protection efficacy or protection against Covid-19 levels was performed in percentage referred to the risk reduction (50 versus 95). The protection duration levels were performed in years (1 versus 10). The side effect levels, which referred to the potential side effects after the administration of the Covid-19 vaccine, were performed in qualitative frequency (minor versus major). The cost or out-of-pocket cost levels, which referred to all the value that the consumers self-paid to get vaccinated such a vaccine, and were performed in US dollars (12.5/ 25/ 50/ 100/ 200). The highest price of the vaccine, i.e. US $ 200, was set approximately to the monthly gross domestic products of Vietnam in 2019 (The World Bank, 2019), then was halved for each lower price. Presenting respondents with all possible choices, i.e. full factorial design, is impractical since it generates too many choice sets (2 × 2 × 2 × 5 = 40 hypothetical vaccine profiles). Thus, fractional factorial designs are used to reduce the profiles for which preferences are elicited. An orthogonal design was run by JMP Pro software to produce twelve hypothetical vaccine profiles, allowing preferences for all vaccine profiles to be identified (Table 1 ). In addition, two debriefing questions occur naturally in the question frame in order to categorize respondents into those who understand that they were asked to do and those who did not (Pearce et al. 2020 ). Such questions provided an option whether one alternative is unambiguously superior for all attributes to test whether the respondent chooses the dominant alternative within the collection (Johnson et al. 2019 ). Respondents who chose inferior alternative in both debriefing questions were also excluded. Table 1 Vaccine profiles and choice set Profile number Protection efficacy (%) Protection duration (year) Side effect Cost (US $ ) 1 50 1 Minor 50 2 95 1 Minor 200 3 50 1 Minor 12.5 4 95 1 No 100 5 50 1 No 200 6 95 10 Minor 100 7 50 1 No 50 8 50 10 No 25 9 95 1 Minor 25 10 95 10 Minor 100 11 95 10 No 12.5 12 95 10 Minor 50 DCE question Choice set Vaccine profile (choose one only) 1 Choice set 1 of 6 Vaccine 1 Vaccine 2 2 Choice set 2 of 6 Vaccine 3 Vaccine 4 3 Debriefing question 4 Choice set 3 of 6 Vaccine 5 Vaccine 6 5 Debriefing question 6 Choice set 4 of 6 Vaccine 7 Vaccine 8 7 Choice set 5 of 6 Vaccine 9 Vaccine 10 8 Choice set 6 of 6 Vaccine 11 Vaccine 12 2.5 Statistical analysis DCE data was generated by applying theory of the random utility model (Ryan et al. 2012 ), where individual is assumed to choose from a range of possibilities, i.e. vaccine profiles in this case, opting for the one with for the highest utility. The utility ( U ) to individual n associated with vaccine i can be specified as: U ni = V ni + ε ni where V is deterministic observable component and ε is the random unobservable component. Since the utility of any given vaccine cannot directly observe, DCE data were therefore modeled within a probabilistic framework. When an individual was presented a pair of vaccine, the probability ( P ) he or she chose vaccine a over vaccine b was estimated as: P na = Pr[U na > U nb ] = Pr[ε na – ε nb > V nb – V na ]. A binary logistic regression model was applied to predict the probability of choosing a given vaccine. Assuming that the probability of choosing a given vaccine is determined by the indirect utility, it is additive and of the form: U = β 0 + β 1 x 1 + β 2 x 2 + β 3 x 3 + β 4 x 4 + ε (2) where betas (β i ) provided quantitative preference weight for each attribute level, x i referred to four attributes and ε referred to error terms as mentioned above. Within the context of Covid-19 vaccine, WTP was defined as the monetary value of the attributes of a vaccine, i.e. how much a respondent would be willing to pay to have an improvement in any level of the attributes, which could be estimated by: Where x 4 referred to cost and x i referred to remained attributes. In addition, the change in logit probability due to change in cost as long as all other attributes remained equal was given by: Data were entered into Microsoft Excel 2013 software program by using double check procedure, and analyzed by using SPSS 22.0 software. 2.6 Ethical consideration The questionnaire had a brief introduction explaining the aims and purpose of the study. After providing a clear explanation, the inform written consent was obtained voluntarily prior to the participation. Participants were given a chance to quit the study whenever they felt unpleased. The anonymity of the details gathered was ensured, so the name and address of the participants were not reported in the format for data collection. 3. RESULTS A total of 251 completed questionnaires were collected. Eleven of those were excluded due to wrong answer in debriefing questions. The final sample used in the analysis comprised 240 respondents, each providing response to six completed choice sets and resulting in 2880 observations (240 individuals × 6 choice sets × 2 options for each choice set). Table 2 General information of respondents (N = 240) Characteristics n(%) Age Mean (SD) 30 (9.8) Min-Max 16–68 Median (Q1-Q3) 29 (23–34) Gender Male 85 (35.4) Female 155 (64.6) Province HCMC 105 (43.8) Others a 135 (56.3) Living area Urban 223 (92.9) Rural 17 (7.1) Education High school diploma or lower 19 (7.9) Bachelor's degree 178 (74.2) Post-graduated 43 (17.9) Marital status Single/Widowed/Divorced 141 (58.8) Married/Living with partner 98 (40.8) Occupation Blue collar 3 (1.3) White collar 42 (17.5) Healthcare staff 123 (51.3) Student 66 (27.5) Housewife/Retired/Unemployed 5 (2.1) Monthly income (million VND) b < 4.5 52 (21.7) 4.5 - <7.5 43 (17.9) 7.5 - <10.5 33 (13.8) 10.5 - <15.0 39 (16.3) 15.0 - <30.0 39 (16.3) ≥ 30.0 27 (11.3) Self-rated risk of getting Covid-19 Low 122 (50.8) Moderate 87 (36.3) High 29 (12.1) Note : Data presented as n (%) unless stated otherwise. a Ba Ria-Vung Tau, Tay Ninh, Binh Duong, Binh Phuoc, Dong Nai b 1US$ = 23,124 VND Abbreviation : HCMC, Ho Chi Minh City; Q1, 25th quartile; Q3, 75th quartile; SD, standard deviation. The demographic characteristics of the participants were presented in Table 2 .The majority of respondents lived in urban areas (92.9%).The educational attainment of the respondents was mostly bachelor’s degree (74.2%). The monthly income accounted for the highest rate of less than 4.5 million VND. When participants gave a self-assessment of the risk of getting Covid-19, 50.8% thought that the level of risk was low. According to Fig. 1 , the materials form internet was the highest source that brought the information related to Covid-19 vaccine (74.6%) as well as the source that respondents use to seek information about the Covid-19 vaccine (61.3%). According to Fig. 1 , the materials form internet was the highest source that brought the information related to Covid-19 vaccine (74.6%) as well as the source that respondents use to seek information about the Covid-19 vaccine (61.3%). As could be seen in the Table 3 , logistic regression model presented that protection efficacy, protection duration and side effect positively associated with the probability of choosing vaccine. Meanwhile, out-of-pocket cost affected negatively. Protection duration posed the highest effect on vaccine choice (prevalence weight 1.2109). The marginal WTP for 10-year protection is US $ 531.77 (95% CI: 284.31–1485.58). Table 3 Coefficient estimates for attributes using binary logistic regression and marginal willingness to pay for attributes of Covid-19 vaccination Attributes Preference weight β (95% CI) Standard error P-value Marginal WTP (95% CI) Protection against COVID-19 1.2035 (1.0052 to 1.4018) 0.1012 < 0.001 528.51 (277.97 to 1494.16) Protection duration 1.2109 (1.0281 to 1.3937) 0.0933 < 0.001 531.77 (284.31 to 1485.58) Side effects 0.9257 (0.7404 to 1.111) 0.0945 < 0.001 406.52 (204.75 to 1184.2) Out-of-pocket cost -0.0023 (-0.0009 to -0.0036) 0.0007 0.001 Constant 1.5471 0.1085 < 0.001 Number of respondents 240 Number of observations 2880 Log likelihood 3426.117 Pseudo R square 0.2381 Figure 2 shows the change in WTP for Covid-19 vaccine as long as attributes improved. Start at the baseline vaccine profiles, i.e. 50% protection in 1 year with minor side effect, if the vaccine improved to 95% protection for 10 years and had no side effects, the WTP significantly increased to $ 1466.79. Figure 3 illustrates the probability of using the vaccine would decrease if out-of-pocket costs increased as long as all other attributes remain equal. When the self-paid cost increased from US $ 12.5 to 200, the probability decrease dramatically decreased approximately 21%. 4. DISCUSSION This study was conducted in the context that Covid-19 pandemic had devastated the global society and economy. A run to develop an efficacious vaccine has been more competitive than ever. In Vietnam, the pandemic put a high pressure on healthcare system and social welfare during the first three months of lunar new year. However, opportune quarantine proved to be a significant solution to reduce the spread out of infection. Despite causing huge difficulties on national economy, the unite of government and citizen help Vietnam stay away from brutality of the virus. Up to July 2020, Vietnam has reported no case of death due to Covid-19 and no community transmission. However, quarantine and social-distancing has caused various consequences on all aspect of socioeconomics. During long social-distancing periods, people have had no access to places for entertainment such as cinemas, gymnasiums, beer clubs, and travel or visit their relatives. As a result, there has been undue stress among the population. Students had to study at home, which could not give them sufficient knowledge in some cases. Examinations and sports tournaments had to be cancelled (Haleem et al 2020 ). The service sector could not provide proper; hotels, restaurants, cafes, museums, stadiums have been closed. Factories could not be operated to manufacture certain goods, and the supply chain of products has been disrupted as a result. National and international business have been lost; cash has been flown in the market poorly, which has made the revenue growth slow down significantly (Haleem et al 2020 ). On the other hand, re-infection of SARS-Cov-2 continued to threaten the health system. In this case, vaccines are always the best choice to control the outbreak. This study offers insight into the WTP of a futureCovid-19 vaccine in Vietnam. The research will help decision-makers consider how much of the population can afford the Covid-19 vaccine when launching national vaccination campaigns. Moreover, the WTP report shows vaccine makers with a more explicit scene of people’s viewpoints of the Covid-19. Recent simulations have shown that household demand for Covid-19 vaccine is sensitive to income and price, indicating that respondents have taken the hypothetical buying scenario seriously. These results suggest that there is a potential for a private market for Covid-19 vaccination in Vietnam and that sales could be robust if the price of vaccines is lower than the median estimates in our research; a higher price of vaccines that restrict access to the rate of the population who can afford to purchase, thus decreasing the efficacy of campaigns of vaccination. Other studies found that people tended to show lower WTP when having more time to consider a new vaccination product and their budget constraints. As the respondents were not bound by their specified purchase decisions, likely, they would not act as they indicated. The challenges have forced WTP and other pharmacoeconomic research to wait for decisions, including (i) Patients will prefer using drugs directly when infected instead of a vaccine preventing an outbreak; (ii) These vaccines must be mandatory and available for everyone, including people in developing countries, instead of those who are willing to pay with the highest prices. Therefore, vaccine developers should face challenges when dealing with government agencies, with commercial risks increasing; (iii) The market is oversized with a great deal of research; however, the number of vaccines approved should be limited. As a result, there is plenty of unused vaccine development leading to commercial risk (Veugelers et al. 2020). The findings of the present study need to be considered in the context of some limitations. First, the dataon Covid-19 information and WTP are self-reported and could be subject to individual bias. In addition, this is a cross-sectional study conducted in one site in the Southern, and therefore results of the research may not be representative for all settings of the entire Vietnamese population. Last but not least, this is the first pharmacoeconomic studies conducted regarding Covid-19 vaccine according to our best review. Therefore, there is no previous study to compare with our findings, causing difficulty to ensure the consistency of the results. 5. CONCLUSION Covid-19 pandemic has emerged the essential demand of an efficacy vaccine. This study resulted a high value that southern Vietnamese residents were willing to pay for a vaccine against Covid-19. Protection duration affects the most on the probability to choose a such vaccine. These findings support decision makers in the implementation of vaccine program in the future. Declarations Ethical Statements: Funding: Not applicable Conflict of interest The authors declare that they have no conflict of interest. Human and Animal Rights This article does not contain any studies with human or animal subjects performed by any of the authors. Informed Consent Informed consent was obtained from all individual participants included in the study. Consent to participate: Not applicable Consent for publication: Not applicable Availability of data and material: The data that support the findings of this study are available from the corresponding author upon reasonable request. Authors’ contributions TQV agreed on the content of the study. QVT, TTHN, HTN, BTN, VNHP, LA, TLV, ANPT,HTTN,CDQN, PVN, NXV, UMTT, HKT, NDP and TQV collected all the data for analysis. TQV agreed on the methodology. QVT, TTHN, HTN, BTN, VNHP, LA, TLV, ANPT,HTTN,CDQN, PVN, NXV, UMTT, HKT, NDP and TQV completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript. Acknowledgements: Not applicable Authors' information: Quang Vinh Tran, Tram Thi Huyen Nguyen, Hiep Thanh Nguyen, Binh Thanh Nguyen, Van Nu Hanh Pham, Luerat Anuratpanich, Truong Lam Vu, Anh Ngoc Phuong Ta, Hieu Thi Thanh Nguyen, Chau Duc Quynh Nguyen, Pol Van Nguyen, Nam Xuan Vo, Uyen My Thuc Truong, Hong Kim Tang, Nhat Duc Phung, Trung Quang Vo. References Ben, F. Moderna delivers first human data for a coronavirus vaccine. (2020). 2020/5/18 [cited 2020 18/06]; Available from: https://www.biopharmadive.com/news/moderna-coronvirus-vaccine-first-study-results/578109/. Bogoch, II, et al., (2020) Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel. J Travel Med, 27(2), taaa008. Dowdy, S., S. Wearden, and D. Chilko, (2011) Statistics for research. Vol. 512. John Wiley & Sons. Florian, B., et al. (2020) Willingness to Pay for a for a Potential Vaccine Against SARS-CoV-2 / COVID-19 Among Adult Persons. [cited 2020 May 15]; Available from: https://assets.researchsquare.com/files/rs-32595/v1/2d6a53f3-37b4-49aa-8b79-b5b717b445cb.pdf. General Statistic Office of Vietnam. (2020) Statistics: Population and Employment. [June 15]. Available from: https://www.gso.gov.vn/default.aspx?tabid=714. GOV.UK. COVID-19: (2020) epidemiology, virology and clinical features. 2020 [cited 2020 18/05]; Available from: https://www.gov.uk/government/publications/wuhan-novel-coronavirus-background-information/wuhan-novel-coronavirus-epidemiology-virology-and-clinical-features. Haleem, A., M. Javaid, and R. Vaishya, (2020) Effects of COVID 19 pandemic in daily life. Curr Med Res Pract, 10(2),78–79. Jiang, S., L. Du, and Z. Shi, (2020) An emerging coronavirus causing pneumonia outbreak in Wuhan, China: calling for developing therapeutic and prophylactic strategies. J Emerging microbes infections, 9(1),275-277. Johnson, F.R., J.-C. Yang, and S.D. Reed, (2019) The internal validity of discrete choice experiment data: a testing tool for quantitative assessments. Value in Health, 22(2),157-160. Kjær, T., (2005) A review of the discrete choice experiment-with emphasis on its application in health care. Health Economics - University Of Southern Denmark. p. 143. Lu, H., C.W. Stratton, and Y.W. Tang, (2020) Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle. J Med Virol, 92(4), 401-402. Ministry of Health in Vietnam. (2020) Epidermiology of COVID-19 in Vietnam. 18/05/2020 [cited 2020 16/06]; Available from: https://ncov.moh.gov.vn/. Pearce, A.M., et al., (2020) How are debriefing questions used in health discrete choice experiments? An online survey. Value in Health, 23(3), 289-293. Ryan, M., et al., (2012) How to conduct a discrete choice experiment for health workforce recruitment and retention in remote and rural areas: a user guide with case studies. World Health Organization & CapacityPlus: World Bank, 2012. Sehgal, D., (2020) Analysis of Vaccines to tackle COVID-19 with Patent Review. SocArXiv. Toan, L.D.H., (2020) The COVID-19 risk perception: A survey on socioeconomics and media attention. Data Brief, 40(1),105530. The World Bank. (2020) GDP per capita (current US$) - Vietnam. 2019 [cited 2020 15/06]; Available from: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=VN. Veugelers R, Zachmann G. (2020) Racing against COVID-19: a vaccines strategy for Europe. Policy Contribution, 7, 1-19. Wang, N., et al., (2020) Subunit vaccines against emerging pathogenic human coronaviruses. J Frontiers in microbiology, 11, 298. World Health Organization. (2020) Rolling updates on coronavirus disease (COVID-19). 2020/6/11 [cited 2020 18/06]; Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen. World Health Organization. (2020) WHO Coronavirus Disease (COVID-19) Dashboard. 2020 2020/6/15 [cited 2020 16/05]; Available from: https://covid19.who.int/. Zhang, N., et al., (2020) Current development of COVID-19 diagnostics, vaccines and therapeutics. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3852449","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266601152,"identity":"b209e007-cff8-48cb-8aee-fc0f0909444c","order_by":0,"name":"Quang Vinh Tran","email":"","orcid":"","institution":"Pham Ngoc Thach University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Quang","middleName":"Vinh","lastName":"Tran","suffix":""},{"id":266601153,"identity":"4520564f-ae29-41a0-9a44-34327fd01ee0","order_by":1,"name":"Tram Thi Huyen Nguyen","email":"","orcid":"","institution":"Ear-Nose-Throat Hospital in Ho Chi Minh 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Thanh Nguyen","email":"","orcid":"","institution":"Pham Ngoc Thach University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hieu","middleName":"Thi Thanh","lastName":"Nguyen","suffix":""},{"id":266601161,"identity":"6a70cdc3-898c-43f2-8775-f64600965ea7","order_by":9,"name":"Chau Duc Quynh Nguyen","email":"","orcid":"","institution":"Pham Ngoc Thach University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chau","middleName":"Duc Quynh","lastName":"Nguyen","suffix":""},{"id":266601162,"identity":"79388162-7885-4f77-be4b-b617d8868680","order_by":10,"name":"Pol Nguyen","email":"","orcid":"","institution":"Pham Ngoc Thach University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Pol","middleName":"","lastName":"Nguyen","suffix":""},{"id":266601163,"identity":"a6f74dde-d1a0-41fb-8185-80239edfa651","order_by":11,"name":"Nam Xuan Vo","email":"","orcid":"","institution":"Ton Duc Thang University","correspondingAuthor":false,"prefix":"","firstName":"Nam","middleName":"Xuan","lastName":"Vo","suffix":""},{"id":266601164,"identity":"7bd15a0b-8f62-48f1-94f3-0da223d19ef4","order_by":12,"name":"Uyen My Thuc Truong","email":"","orcid":"","institution":"Ear-Nose-Throat Hospital in Ho Chi Minh city","correspondingAuthor":false,"prefix":"","firstName":"Uyen","middleName":"My Thuc","lastName":"Truong","suffix":""},{"id":266601165,"identity":"e31717f4-28c9-4f3e-8d17-ffc9f46cebba","order_by":13,"name":"Hong Kim Tang","email":"","orcid":"","institution":"Pham Ngoc Thach University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"Kim","lastName":"Tang","suffix":""},{"id":266601166,"identity":"70a9103c-8725-44f8-a8e0-c2d29f1bb14f","order_by":14,"name":"Nhat Duc Phung","email":"","orcid":"","institution":"Pham Ngoc Thach University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Nhat","middleName":"Duc","lastName":"Phung","suffix":""},{"id":266601167,"identity":"046d4bd7-1a97-443b-98b3-8193b338476f","order_by":15,"name":"Trung Quang Vo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYAiAA4z9ICqhALcSHgwtMxtAWgxI0bLhAIjGo8Ve+ozZwy9lh+XN+dcYPmDccUd28/nViR8eGDDI84sdwG4LX465scy5w4Y7Z7wxNmA888x42423myWADjOcOTsBuxYeHjNpybbDjBtuHEuTYGw7nLjtxtkNIC0JBrfxa7GHa9k84+zmH4S0SH4EqtxwvvkYWMsG/t5t+G05w1YmzXAuPXnDDebDBolnDhvPuMG7zSLBQAKnX9h7mLdJ/iiztt1w/mDjg487Dsv295/dfPNHhY08vzR2LSDAzMMGJCWAChIboAwgiVM5CDD+AGnhPwBkNUAZo2AUjIJRMAqQAAC+MWcm3nlyNQAAAABJRU5ErkJggg==","orcid":"","institution":"Pham Ngoc Thach University of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Trung","middleName":"Quang","lastName":"Vo","suffix":""}],"badges":[],"createdAt":"2024-01-11 05:44:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3852449/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3852449/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49710856,"identity":"8499db05-952f-46e5-ac31-53e11438caa7","added_by":"auto","created_at":"2024-01-16 19:47:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58398,"visible":true,"origin":"","legend":"\u003cp\u003eInformation source of Covid-19 vaccine\u003c/p\u003e\n\u003cp\u003e(A) Which sources that brought information related to Covid-19 vaccine toward you?\u003c/p\u003e\n\u003cp\u003e(B) Which sources that you used to seek information related to Covid-19 vaccine?\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3852449/v1/5d4fc407044605b2a63967ab.png"},{"id":49710858,"identity":"44c99590-5a4f-4d48-990b-85dd2a6a0e33","added_by":"auto","created_at":"2024-01-16 19:47:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56961,"visible":true,"origin":"","legend":"\u003cp\u003eChange in willingness to pay for Covid-19 vaccination as attributes of the vaccine improve (*Baseline: 50% protection in 1 year with minor side effect)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3852449/v1/4a8501735622cd59a2505ad3.png"},{"id":49711194,"identity":"5b9dc803-71de-42c2-ba5a-66a97800b637","added_by":"auto","created_at":"2024-01-16 19:55:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38159,"visible":true,"origin":"","legend":"\u003cp\u003eDecrease of probabilities of taking vaccine along with increase of out-of-pocket cost as long as all other attributes remain equal\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3852449/v1/0a9c962d06a8a71e7b2f556c.png"},{"id":52246769,"identity":"704480f8-a8fd-4512-9a47-257956eda0d0","added_by":"auto","created_at":"2024-03-08 08:38:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":583007,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3852449/v1/5f3f411b-5a04-4be9-858b-1af77f134b74.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A discrete choice experiment for evidence-based decision-making to Explore Willingness to pay for Covid-19 vaccination","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eCoronavirus is a name of a group of viruses targeting the respiratory system. In recent years, people saw previous lethal outbreaks of coronavirus, including the severe acute respiratory syndrome (SARS-CoV) and the Middle East respiratory syndrome (MERS-CoV). On the last days of 2019 in Wuhan City in China, the cluster of pneumonia cases was reported with unknown origin. Epidemiologically, these patients were linked to Wuhan\u0026rsquo;s Huanan Seafood Wholesale Market (Bogoch et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In early January 2020, a novel coronavirus had been detected in patient-obtained samples before that newly analyzed viral genetic chain was suggested to be the origin of the outbreak. The World Health Organization (WHO) announced a Public Health Emergency of International Concern for this novel type of coronavirus. Subsequently, the name Covid-19 was published by WHO, with \u0026ldquo;Co\u0026rdquo; referring to corona, \u0026ldquo;vi\u0026rdquo; to virus, \u0026ldquo;d\u0026rdquo; to disease and \u0026ldquo;19\u0026rdquo; to 2019 (WHO 2020). WHO has been operating the WHO Coronavirus Disease Dashboard, which has statistics of every single country; daily situation reports have also been published (GOV.UK. 2020). On 7 May 2020, there have been 4,534,731 confirmed cases of Covid-19 including 307,537 deaths reported to WHO globally (WHO 2020).\u003c/p\u003e \u003cp\u003eIn Vietnam, the government confirmed the spread of Covid-19 on 23 January 2020. There were only 16 cases detected between 23 January and 13 February 2020, then saw 22 days without new infection. Although the number of new reported cases has been increasing daily, the rate of the increase has been still under 15% since the first recorded case (Toan et al. 2020). Up to 18 May 2020, there have been 320 cases of Covid-19 confirmed in total, including 260 recovered cases and no deaths confirmed in Vietnam (Ministry of Health in Vietnam \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe unprecedented pandemic has caused a massive challenge for global healthcare systems. Doctors and other healthcare staffs have been at risk and overloaded as well. Medical facilities and protective equipment, especially medical masks, have been exhausted. Medical systems have become unbalanced; other patients with other diseases and health problems have been neglected. Additionally, it is challenging to diagnose, quarantine and treat confirmed cases (Haleem et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eObviously, the pandemic has caused a high burden not only to healthcare system but also on all the area of socioeconomics due to quarantine.\u003c/p\u003e \u003cp\u003eTherefore, a longtime solution to prevent the spread of Covid-19 is to develop an effective vaccine. Since the beginning of pandemic, research and development have been making a run to develop vaccines, thereby control the pandemic (Sehgal, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A broad variety of platforms are being investigated, including DNA, mRNA, recombinant protein, and adenoviral vector, centered on MERS and SARS vaccine production pathway (Wang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jiang et al \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). More than 40 pharmaceutical firms, corporations and research institutions from several countries have successfully produced Covid-19 vaccines. Some candidates have entered efficacy evaluation on animals and clinical trials (Zhang et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A phase 3 clinical trial has been planned to operate in July 2020 (Ben \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn order to compute the benefits of a program, e.g. vaccination program, researchers calculate the number that the individual is willing to give up to get the benefit, called willingness-to-pay (WTP).WTP for wellbeing is a concern for patient and community health care decision taking. The word typically applies to the ability of people to invest resources for their own, i.e. \u0026ldquo;out of pocket\u0026rdquo;, achieving health benefits for themselves or preventing financial problems or what safety threats for themselves (Florian et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStated preference (SP) and revealed preference methods are two kinds of preference-based outcome measurements. In SP methods, the total economic value can be measured, with non-use value and option value being incorporated, therefore, valuing hypothetical goods and intervention could be possible. Among SP methods, discrete choice experience (DCE) is an approach to assess the value pf every attributes of the good or services, differentiating with contingent valuation which assess the value of the whole product (Kjaer, 2005). The basis of the DCE is quite complicated as it merges some of the economic theories. The DCE is focused on the principle of probabilistic preference, named random utility principle, and is ideal for Lancaster 's economic benefit theory and neoclassical economies (Kjaer, 2005). Random utility theory provides researchers with access to sophisticated multidimensional goods preferences, from which preferences models can be estimated. The foundation of probabilistic choice theory is that when an person has options it becomes uncertain, such that the choices cannot be predicted correctly (Kjaer, 2005). In DCE models, they ascribe a probability to every alternative to be chosen instead of classifying one alternative as the chosen option (Kjaer, 2005).\u003c/p\u003e \u003cp\u003eThe objective of the present study was to explore the preferences and willingness to pay of people aged over 18 for Covid-19 vaccination through a discrete choice experiment for evidence-based decision-making.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eA cross-sectional community-based survey was performed during two mid-weeks in May 2020 in six southeastern provinces in Vietnam. Target population was estimated at17,074,300 in 2018 (General Statistic Office of Vietnam, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The study questions were as follows:\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e1. How could vaccine attributes influence the WTP for Covid-19 vaccination?\u003c/p\u003e\n\u003cp\u003e2. How cost of the vaccine effects to the probability to take the vaccine?\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sampling procedure\u003c/h2\u003e \u003cp\u003eSimple random sampling technique was used to recruit study participants, who regularly passed by in the major streets, parks, shopping malls and restaurants. Participants who met the inclusion criteria were enrolled: i) were at least 18 years old and declare to have an income; ii) had Vietnamese nationality and were able to communicate fluently; iii) had no specific symptoms or diagnosis of influenza, Covid-19 or flu-like syndromes. Those who disagreed to participate or gave ambiguous answers were eliminated.\u003c/p\u003e \u003cp\u003eSample size was determined using the population proportion sample size formula (Dowdy et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"480\" height=\"41\"\u003e\u003c/p\u003e\u003cp\u003ewhere z value equal to 1.96, according to alpha error equal to 5%with a two-side significance; total population N\u0026thinsp;=\u0026thinsp;17,074,300; rate of respondent p assumed at 85%; and standard error d assumed at 0.05. The formula resulted in a sample size of 196 adults. Assuming the probability of 30% missing data, 255 questionnaires were released.\u003c/p\u003e \u003cp\u003eData was collected using a structured, close-ended, 20-item questionnaire prepared in Vietnamese language. Each question was verbally elaborated and explained in the local language to the respondents at the time of interview. The data collection was done by three graduated pharmacy students after one-day training course. These interviewers were follow an arrangement to distribute to all six provinces. The completeness, consistency, and accuracy of the data were checked at the end of every single day by major investigators.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Study instrument\u003c/h2\u003e \u003cp\u003eThe questionnaire used for data collection had three parts. The first part of the questionnaire related to demographic characteristics of the participants. The next two questions mentioned in the hearing and seeking for finding Covid-19 vaccine information section and the final eight questions were related to measure WTP on DCE method.\u003c/p\u003e \u003cp\u003eThe questionnaire was first pilot-tested over 30 respondents to check for any inconsistencies. Necessary adjustments were made accordingly prior to the realistic stage of the study. The pilot study was basically conducted to investigate the response rate of the patients and identify the most important attributes to be included in the DCE survey. It was not part of the validation process but was aimed at confirming that this particular instrument could be employed easily with maximum response rate from the specific population subset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 DCE design\u003c/h2\u003e \u003cp\u003eDue to no available Covid-19 vaccine in the market, choice modeling on vaccines against other virus such as human immunodeficiency virus, human papilloma virus and dengue virus were reviewed in order to identify vaccine attributes and levels for DCE. Based on these previous studies and the preliminary qualitative research, four Covid-19 vaccine attributes were selected eventually, including protection efficacy, protection duration, side effect, and cost. The protection efficacy or protection against Covid-19 levels was performed in percentage referred to the risk reduction (50 versus 95). The protection duration levels were performed in years (1 versus 10). The side effect levels, which referred to the potential side effects after the administration of the Covid-19 vaccine, were performed in qualitative frequency (minor versus major). The cost or out-of-pocket cost levels, which referred to all the value that the consumers self-paid to get vaccinated such a vaccine, and were performed in US dollars (12.5/ 25/ 50/ 100/ 200). The highest price of the vaccine, i.e. US\u003cspan\u003e$\u003c/span\u003e200, was set approximately to the monthly gross domestic products of Vietnam in 2019 (The World Bank, 2019), then was halved for each lower price.\u003c/p\u003e \u003cp\u003ePresenting respondents with all possible choices, i.e. full factorial design, is impractical since it generates too many choice sets (2 \u0026times; 2 \u0026times; 2 \u0026times; 5\u0026thinsp;=\u0026thinsp;40 hypothetical vaccine profiles). Thus, fractional factorial designs are used to reduce the profiles for which preferences are elicited. An orthogonal design was run by JMP Pro software to produce twelve hypothetical vaccine profiles, allowing preferences for all vaccine profiles to be identified (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, two debriefing questions occur naturally in the question frame in order to categorize respondents into those who understand that they were asked to do and those who did not (Pearce et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such questions provided an option whether one alternative is unambiguously superior for all attributes to test whether the respondent chooses the dominant alternative within the collection (Johnson et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Respondents who chose inferior alternative in both debriefing questions were also excluded.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVaccine profiles and choice set\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtection \u003c/p\u003e \u003cp\u003eefficacy (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtection duration (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSide \u003c/p\u003e \u003cp\u003eeffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCost (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDCE question\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChoice set\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eVaccine profile (choose one only)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChoice set 1 of 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaccine 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVaccine 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChoice set 2 of 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaccine 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVaccine 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDebriefing question\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChoice set 3 of 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaccine 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVaccine 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDebriefing question\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChoice set 4 of 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaccine 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVaccine 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChoice set 5 of 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaccine 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVaccine 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChoice set 6 of 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaccine 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVaccine 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eDCE data was generated by applying theory of the random utility model (Ryan et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), where individual is assumed to choose from a range of possibilities, i.e. vaccine profiles in this case, opting for the one with for the highest utility. The utility (\u003cem\u003eU\u003c/em\u003e) to individual n associated with vaccine i can be specified as: \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eni\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e= V\u003c/em\u003e\u003csub\u003e\u003cem\u003eni\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ε\u003c/em\u003e\u003csub\u003e\u003cem\u003eni\u003c/em\u003e\u003c/sub\u003e where \u003cem\u003eV\u003c/em\u003e is deterministic observable component and \u003cem\u003eε\u003c/em\u003e is the random unobservable component. Since the utility of any given vaccine cannot directly observe, DCE data were therefore modeled within a probabilistic framework. When an individual was presented a pair of vaccine, the probability (\u003cem\u003eP\u003c/em\u003e) he or she chose vaccine a over vaccine b was estimated as: \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ena\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e= Pr[U\u003c/em\u003e\u003csub\u003e\u003cem\u003ena\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026gt; U\u003c/em\u003e\u003csub\u003e\u003cem\u003enb\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e]\u0026thinsp;=\u0026thinsp;Pr[ε\u003c/em\u003e\u003csub\u003e\u003cem\u003ena\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026ndash; ε\u003c/em\u003e\u003csub\u003e\u003cem\u003enb\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e\u0026gt;\u0026thinsp;V\u003c/em\u003e\u003csub\u003e\u003cem\u003enb\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026ndash; V\u003c/em\u003e\u003csub\u003e\u003cem\u003ena\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e].\u003c/em\u003e\u003c/p\u003e \u003cp\u003eA binary logistic regression model was applied to predict the probability of choosing a given vaccine. Assuming that the probability of choosing a given vaccine is determined by the indirect utility, it is additive and of the form:\u003c/p\u003e \u003cp\u003e \u003cem\u003eU\u0026thinsp;=\u0026thinsp;β\u003c/em\u003e \u003csub\u003e \u003cem\u003e0\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ε\u003c/em\u003e (2)\u003c/p\u003e \u003cp\u003ewhere betas (β\u003csub\u003ei\u003c/sub\u003e) provided quantitative preference weight for each attribute level, x\u003csub\u003ei\u003c/sub\u003e referred to four attributes and ε referred to error terms as mentioned above. Within the context of Covid-19 vaccine, WTP was defined as the monetary value of the attributes of a vaccine, i.e. how much a respondent would be willing to pay to have an improvement in any level of the attributes, which could be estimated by:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e referred to cost and \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e referred to remained attributes. In addition, the change in logit probability due to change in cost as long as all other attributes remained equal was given by:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"424\" height=\"68\"\u003e\u003c/p\u003e \u003cp\u003eData were entered into Microsoft Excel 2013 software program by using double check procedure, and analyzed by using SPSS 22.0 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Ethical consideration\u003c/h2\u003e \u003cp\u003eThe questionnaire had a brief introduction explaining the aims and purpose of the study. After providing a clear explanation, the inform written consent was obtained voluntarily prior to the participation. Participants were given a chance to quit the study whenever they felt unpleased. The anonymity of the details gathered was ensured, so the name and address of the participants were not reported in the format for data collection.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eA total of 251 completed questionnaires were collected. Eleven of those were excluded due to wrong answer in debriefing questions. The final sample used in the analysis comprised 240 respondents, each providing response to six completed choice sets and resulting in 2880 observations (240 individuals \u0026times; 6 choice sets \u0026times; 2 options for each choice set).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral information of respondents (N\u0026thinsp;=\u0026thinsp;240)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (9.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin-Max\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u0026ndash;68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (23\u0026ndash;34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (35.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (64.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProvince\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (43.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (56.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223 (92.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (7.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school diploma or lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (7.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor's degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (74.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-graduated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (17.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle/Widowed/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141 (58.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried/Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (40.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlue collar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite collar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (17.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthcare staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123 (51.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (27.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife/Retired/Unemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly income (million VND)\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (21.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5 - \u0026lt;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (17.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5 - \u0026lt;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (13.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5 - \u0026lt;15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (16.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.0 - \u0026lt;30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (16.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (11.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-rated risk of getting Covid-19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122 (50.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (36.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (12.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003eData presented as n (%) unless stated otherwise.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eBa Ria-Vung Tau, Tay Ninh, Binh Duong, Binh Phuoc, Dong Nai\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e1US$ = 23,124 VND\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAbbreviation\u003c/b\u003e: \u003cem\u003eHCMC, Ho Chi Minh City; Q1, 25th quartile; Q3, 75th quartile; SD, standard deviation.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe demographic characteristics of the participants were presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.The majority of respondents lived in urban areas (92.9%).The educational attainment of the respondents was mostly bachelor\u0026rsquo;s degree (74.2%). The monthly income accounted for the highest rate of less than 4.5\u0026nbsp;million VND. When participants gave a self-assessment of the risk of getting Covid-19, 50.8% thought that the level of risk was low.\u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the materials form internet was the highest source that brought the information related to Covid-19 vaccine (74.6%) as well as the source that respondents use to seek information about the Covid-19 vaccine (61.3%).\u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the materials form internet was the highest source that brought the information related to Covid-19 vaccine (74.6%) as well as the source that respondents use to seek information about the Covid-19 vaccine (61.3%).\u003c/p\u003e \u003cp\u003eAs could be seen in the Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, logistic regression model presented that protection efficacy, protection duration and side effect positively associated with the probability of choosing vaccine. Meanwhile, out-of-pocket cost affected negatively. Protection duration posed the highest effect on vaccine choice (prevalence weight 1.2109). The marginal WTP for 10-year protection is US\u003cspan\u003e$\u003c/span\u003e531.77 (95% CI: 284.31\u0026ndash;1485.58).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficient estimates for attributes using binary logistic regression and marginal willingness to pay for attributes of Covid-19 vaccination\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttributes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreference weight β \u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarginal WTP (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtection against COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2035 \u003c/p\u003e \u003cp\u003e(1.0052 to 1.4018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e528.51 \u003c/p\u003e \u003cp\u003e(277.97 to 1494.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtection duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2109 \u003c/p\u003e \u003cp\u003e(1.0281 to 1.3937)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e531.77 \u003c/p\u003e \u003cp\u003e(284.31 to 1485.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSide effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9257 \u003c/p\u003e \u003cp\u003e(0.7404 to 1.111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e406.52 \u003c/p\u003e \u003cp\u003e(204.75 to 1184.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOut-of-pocket cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0023 \u003c/p\u003e \u003cp\u003e(-0.0009 to -0.0036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of respondents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of observations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3426.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the change in WTP for Covid-19 vaccine as long as attributes improved. Start at the baseline vaccine profiles, i.e. 50% protection in 1 year with minor side effect, if the vaccine improved to 95% protection for 10 years and had no side effects, the WTP significantly increased to \u003cspan\u003e$\u003c/span\u003e1466.79.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the probability of using the vaccine would decrease if out-of-pocket costs increased as long as all other attributes remain equal. When the self-paid cost increased from US\u003cspan\u003e$\u003c/span\u003e12.5 to 200, the probability decrease dramatically decreased approximately 21%.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis study was conducted in the context that Covid-19 pandemic had devastated the global society and economy. A run to develop an efficacious vaccine has been more competitive than ever. In Vietnam, the pandemic put a high pressure on healthcare system and social welfare during the first three months of lunar new year. However, opportune quarantine proved to be a significant solution to reduce the spread out of infection. Despite causing huge difficulties on national economy, the unite of government and citizen help Vietnam stay away from brutality of the virus. Up to July 2020, Vietnam has reported no case of death due to Covid-19 and no community transmission.\u003c/p\u003e \u003cp\u003eHowever, quarantine and social-distancing has caused various consequences on all aspect of socioeconomics. During long social-distancing periods, people have had no access to places for entertainment such as cinemas, gymnasiums, beer clubs, and travel or visit their relatives. As a result, there has been undue stress among the population. Students had to study at home, which could not give them sufficient knowledge in some cases. Examinations and sports tournaments had to be cancelled (Haleem et al \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The service sector could not provide proper; hotels, restaurants, cafes, museums, stadiums have been closed. Factories could not be operated to manufacture certain goods, and the supply chain of products has been disrupted as a result. National and international business have been lost; cash has been flown in the market poorly, which has made the revenue growth slow down significantly (Haleem et al \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, re-infection of SARS-Cov-2 continued to threaten the health system. In this case, vaccines are always the best choice to control the outbreak. This study offers insight into the WTP of a futureCovid-19 vaccine in Vietnam. The research will help decision-makers consider how much of the population can afford the Covid-19 vaccine when launching national vaccination campaigns. Moreover, the WTP report shows vaccine makers with a more explicit scene of people\u0026rsquo;s viewpoints of the Covid-19.\u003c/p\u003e \u003cp\u003eRecent simulations have shown that household demand for Covid-19 vaccine is sensitive to income and price, indicating that respondents have taken the hypothetical buying scenario seriously. These results suggest that there is a potential for a private market for Covid-19 vaccination in Vietnam and that sales could be robust if the price of vaccines is lower than the median estimates in our research; a higher price of vaccines that restrict access to the rate of the population who can afford to purchase, thus decreasing the efficacy of campaigns of vaccination. Other studies found that people tended to show lower WTP when having more time to consider a new vaccination product and their budget constraints. As the respondents were not bound by their specified purchase decisions, likely, they would not act as they indicated. The challenges have forced WTP and other pharmacoeconomic research to wait for decisions, including (i) Patients will prefer using drugs directly when infected instead of a vaccine preventing an outbreak; (ii) These vaccines must be mandatory and available for everyone, including people in developing countries, instead of those who are willing to pay with the highest prices. Therefore, vaccine developers should face challenges when dealing with government agencies, with commercial risks increasing; (iii) The market is oversized with a great deal of research; however, the number of vaccines approved should be limited. As a result, there is plenty of unused vaccine development leading to commercial risk (Veugelers et al. 2020).\u003c/p\u003e \u003cp\u003eThe findings of the present study need to be considered in the context of some limitations. First, the dataon Covid-19 information and WTP are self-reported and could be subject to individual bias. In addition, this is a cross-sectional study conducted in one site in the Southern, and therefore results of the research may not be representative for all settings of the entire Vietnamese population. Last but not least, this is the first pharmacoeconomic studies conducted regarding Covid-19 vaccine according to our best review. Therefore, there is no previous study to compare with our findings, causing difficulty to ensure the consistency of the results.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eCovid-19 pandemic has emerged the essential demand of an efficacy vaccine. This study resulted a high value that southern Vietnamese residents were willing to pay for a vaccine against Covid-19. Protection duration affects the most on the probability to choose a such vaccine. These findings support decision makers in the implementation of vaccine program in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Statements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding:\u003c/em\u003e\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of interest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHuman and Animal Rights\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human or animal subjects performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInformed Consent\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to participate:\u003c/em\u003e\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication:\u003c/em\u003e\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and material:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTQV agreed on the content of the study. QVT, TTHN, HTN, BTN, VNHP, LA, TLV, ANPT,HTTN,CDQN, PVN, NXV, UMTT, HKT, NDP and TQV collected all the data for analysis. TQV agreed on the methodology. QVT, TTHN, HTN, BTN, VNHP, LA, TLV, ANPT,HTTN,CDQN, PVN, NXV, UMTT, HKT, NDP and TQV completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements:\u003c/em\u003e\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; information:\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eQuang Vinh Tran, Tram Thi Huyen Nguyen, Hiep Thanh Nguyen, Binh Thanh Nguyen, Van Nu Hanh Pham, Luerat Anuratpanich, Truong Lam Vu, Anh Ngoc Phuong Ta, Hieu Thi Thanh Nguyen, Chau Duc Quynh Nguyen, Pol Van Nguyen, Nam Xuan Vo, Uyen My Thuc Truong, Hong Kim Tang, Nhat Duc Phung, Trung Quang Vo.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBen, F. Moderna delivers first human data for a coronavirus vaccine. (2020). 2020/5/18 [cited 2020 18/06]; Available from: https://www.biopharmadive.com/news/moderna-coronvirus-vaccine-first-study-results/578109/.\u003c/li\u003e\n\u003cli\u003eBogoch, II, et al., (2020) Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel. J Travel Med, 27(2), taaa008.\u003c/li\u003e\n\u003cli\u003eDowdy, S., S. Wearden, and D. Chilko, (2011) Statistics for research. Vol. 512. John Wiley \u0026amp; Sons.\u003c/li\u003e\n\u003cli\u003eFlorian, B., et al. (2020) Willingness to Pay for a for a Potential Vaccine Against SARS-CoV-2 / COVID-19 Among Adult Persons. [cited 2020 May 15]; Available from: https://assets.researchsquare.com/files/rs-32595/v1/2d6a53f3-37b4-49aa-8b79-b5b717b445cb.pdf.\u003c/li\u003e\n\u003cli\u003eGeneral Statistic Office of Vietnam. (2020) Statistics: Population and Employment. [June 15]. Available from: https://www.gso.gov.vn/default.aspx?tabid=714.\u003c/li\u003e\n\u003cli\u003eGOV.UK. COVID-19: (2020) epidemiology, virology and clinical features. 2020 [cited 2020 18/05]; Available from: https://www.gov.uk/government/publications/wuhan-novel-coronavirus-background-information/wuhan-novel-coronavirus-epidemiology-virology-and-clinical-features.\u003c/li\u003e\n\u003cli\u003eHaleem, A., M. Javaid, and R. Vaishya, (2020) Effects of COVID 19 pandemic in daily life. Curr Med Res Pract, 10(2),78\u0026ndash;79.\u003c/li\u003e\n\u003cli\u003eJiang, S., L. Du, and Z. Shi, (2020) An emerging coronavirus causing pneumonia outbreak in Wuhan, China: calling for developing therapeutic and prophylactic strategies. J Emerging microbes infections, 9(1),275-277.\u003c/li\u003e\n\u003cli\u003eJohnson, F.R., J.-C. Yang, and S.D. Reed, (2019) The internal validity of discrete choice experiment data: a testing tool for quantitative assessments. Value in Health, 22(2),157-160.\u003c/li\u003e\n\u003cli\u003eKj\u0026aelig;r, T., (2005) A review of the discrete choice experiment-with emphasis on its application in health care. Health Economics - University Of Southern Denmark. p. 143.\u003c/li\u003e\n\u003cli\u003eLu, H., C.W. Stratton, and Y.W. Tang, (2020) Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle. J Med Virol, 92(4), 401-402.\u003c/li\u003e\n\u003cli\u003eMinistry of Health in Vietnam. (2020) Epidermiology of COVID-19 in Vietnam. 18/05/2020 [cited 2020 16/06]; Available from: https://ncov.moh.gov.vn/.\u003c/li\u003e\n\u003cli\u003ePearce, A.M., et al., (2020) How are debriefing questions used in health discrete choice experiments? An online survey. Value in Health, 23(3), 289-293.\u003c/li\u003e\n\u003cli\u003eRyan, M., et al., (2012) How to conduct a discrete choice experiment for health workforce recruitment and retention in remote and rural areas: a user guide with case studies. World Health Organization \u0026amp; CapacityPlus: World Bank, 2012.\u003c/li\u003e\n\u003cli\u003eSehgal, D., (2020) Analysis of Vaccines to tackle COVID-19 with Patent Review. SocArXiv.\u003c/li\u003e\n\u003cli\u003eToan, L.D.H., (2020) The COVID-19 risk perception: A survey on socioeconomics and media attention. Data Brief, 40(1),105530.\u003c/li\u003e\n\u003cli\u003eThe World Bank. (2020) GDP per capita (current US$) - Vietnam. 2019 [cited 2020 15/06]; Available from: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=VN.\u003c/li\u003e\n\u003cli\u003eVeugelers R, Zachmann G. (2020) Racing against COVID-19: a vaccines strategy for Europe. Policy Contribution, 7, 1-19.\u003c/li\u003e\n\u003cli\u003eWang, N., et al., (2020) Subunit vaccines against emerging pathogenic human coronaviruses. J Frontiers in microbiology, 11, 298.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2020) Rolling updates on coronavirus disease (COVID-19). 2020/6/11 [cited 2020 18/06]; Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2020) WHO Coronavirus Disease (COVID-19) Dashboard. 2020 2020/6/15 [cited 2020 16/05]; Available from: https://covid19.who.int/.\u003c/li\u003e\n\u003cli\u003eZhang, N., et al., (2020) Current development of COVID-19 diagnostics, vaccines and therapeutics.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Covid-19, Health Economic, Pandemic, Vaccine, Vietnam, Willingness To Pay, WTP","lastPublishedDoi":"10.21203/rs.3.rs-3852449/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3852449/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCovid-19 pandemic has caused a massive challenge for global healthcare systems. The longtime solution to prevent the spread of virus is to develop an effective vaccine. To com (WTP). The objective of the present study was to explore the WTP of people aged over 18 for Covid-19 vaccination through a discrete choice experiment (DCE) for evidence-based decision-making. A cross-sectional survey was performed during two mid-weeks in May 2020 in six southeastern provinces in Vietnam. A self-design three-part questionnaire was used to investigate the community. A DCE was designed with twelve vaccine profiles, each one involved four attributes, protection efficacy, duration, side effects and out-of-pocket cost. A binary logistic regression model was applied to predict the probability of choosing a given vaccine. Protection duration posed the highest effect on vaccine choice (prevalence weight 1.2109). The marginal WTP for 10-year protection is US\u003cspan\u003e$\u003c/span\u003e531.77 (95% CI: 284.31\u0026ndash;1485.58). If the vaccine improved to 95% protection for 10 years and had no side effects, the WTP increased to \u003cspan\u003e$\u003c/span\u003e1466.79. When the self-paid cost increased from US\u003cspan\u003e$\u003c/span\u003e12.5 to 200, the probability decrease dramatically decreased approximately 21%. This study resulted a high value that southern Vietnamese residents were willing to pay for a vaccine against Covid-19. These findings support decision makers in the implementation of vaccine program in the future.\u003c/p\u003e","manuscriptTitle":"A discrete choice experiment for evidence-based decision-making to Explore Willingness to pay for Covid-19 vaccination","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-16 19:47:27","doi":"10.21203/rs.3.rs-3852449/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fcfefe08-89e6-4eb0-818f-f0b7b418395a","owner":[],"postedDate":"January 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-08T08:37:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-16 19:47:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3852449","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3852449","identity":"rs-3852449","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}