Skip to main content

Understanding the determinants of COVID-19 vaccination intention and willingness to pay: findings from a population-based survey in Bangladesh

Abstract

Background

Several coronavirus disease (COVID-19) vaccines have already been authorized and distributed in different countries all over the world, including Bangladesh. Understanding public acceptance of such a novel vaccine is vital, but little is known about the topic.

Objectives

This study aimed to investigate the determinants of intention to receive a COVID-19 vaccine and willingness to pay (WTP) among people in Bangladesh.

Methods

An anonymous and online-based survey of Bangladeshi people (mean age = 29.96 ± 9.15 years; age range = 18–60 years) was conducted using a self-reported questionnaire consisting of socio-demographics, COVID-19 experience, and vaccination-related information as well as the health belief model (HBM). Multivariable logistic regression was performed to determine the factors influencing COVID-19 vaccination intent and WTP.

Results

Of the 894 participants, 38.5% reported a definite intention to receive a COVID-19 vaccine, whereas 27% had a probable intention, and among this intent group, 42.8% wanted to get vaccinated as soon as possible. Older age, feeling optimistic about the effectiveness of COVID-19 vaccination, believing that vaccination decreases worries and risk of COVID-19 infection, and being less concerned about side effects and safety of COVID-19 vaccination under the HBM construct were found to be significant factors in COVID-19 vaccination intention. Most of the participants (72.9%) were willing to pay for a COVID-19 vaccine, with a median (interquartile range [IQR]) amount of BDT 400/US$ 4.72 (IQR; BDT 200–600/US$ 2.36–7.07) per dose. Factors associated with higher WTP were younger age, being male, having higher education, residing in an urban area, having good self-rated health status, positivity towards COVID-19 vaccination's effectiveness, and being worried about the likelihood of getting infected with COVID-19. Participants who were COVID-19 vaccination intent preferred an imported vaccine over a domestically-made vaccine (22.9% vs. 14.8%), while 28.2% preferred a routine immunization schedule.

Conclusion

The findings indicate a considerable proportion of Bangladeshi people intended to get vaccinated and had WTP for the COVID-19 vaccine. However, urgent education and awareness programs are warranted to alleviate public skepticism regarding the COVID-19 vaccination.

Peer Review reports

Introduction

The coronavirus disease 2019 (COVID-19), which emerged in Wuhan, Hubei Province, China at the end of 2019, has caused a large global outbreak and has become a major public health crisis [1, 2]. COVID-19 is a highly transmittable viral infection caused by a novel strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [3]. On March 11, 2020, the World Health Organization (WHO) declared the emergence of COVID-19 as a pandemic [4] which has affected more than 172 million people worldwide [5]. In Bangladesh, approximately 802,305 confirmed cases of COVID-19 were reported as of June 1, 2021, with a death toll of 12,660 [6]. This pandemic has severely affected people’s physical and psychological well-being [7,8,9,10,11], health system [12, 13] and also caused a major global economic recession [14].

Vaccines are the most effective strategy to protect the population from the devastating outcomes of COVID-19 [15, 16]. More than 287 potential vaccines are being developed and over 102 clinical trials have recently been released [16, 17]. Some have shown positive results, leading to a number of countries approving specific vaccines for implementation in vaccination programs. Meanwhile, by June 1, 2021, over 1.9 billion doses of the COVID-19 vaccine had been administered in 231 locations [18]. Bangladesh began mass vaccination on February 8, 2021 [19]. Despite considerable progress towards the vaccination program, there is some hesitancy about the COVID-19 vaccine [20]. Understanding public perception is crucial in order to achieve high vaccination coverage, especially for newly emerging infectious diseases such as COVID-19 [21,22,23]. According to recent studies on public acceptance of COVID-19 vaccination, the intention to take the vaccine ranged from 67 to 91% across countries such as India, Saudi-Arabia, Canada, the United States, and China [24,25,26,27,28,29]. There are multiple factors that may influence people’s vaccination intentions. Several demographic factors and perception of the disease risk have been found to be significantly associated with COVID-19 vaccination intent [28,29,30]. The health belief model (HBM) is one of the most commonly used models to determine factors associated with vaccination intention [31, 32] and has been used in many previous studies [33,34,35]. The HBM comprises several main constructs: perceived susceptibility, severity, benefits, barriers, self-efficacy to engage in a behavior, and cues to action [31]. Perceived stigma is also used for identifying determinants of vaccination intent [25]. In terms of HBM, perceived benefits (i.e. decreasing the chance of infection and making people less worried about infection) and barriers (i.e., being concerned about their efficacy) to vaccination were found to be significant in affecting vaccination intention [35, 36]. In addition, attitudes and experience regarding vaccination history, and convenience have been shown to be the major predictors of vaccination intention [29, 30].

Willingness-to-pay (WTP) refers to the maximum amount, in monetary terms, that an individual would be willing to allocate to obtain the benefits of a program [37]. The decision to vaccinate depends on the WTP of an individual in order to obtain increased health benefits [38]. HBM constructs have been used to explain WTP for influenza vaccination [34, 39]. In a previous study, the WTP for COVID-19 vaccination was found to be influenced by a variety of socioeconomic factors [36]. In addition, no-affordability barriers [35], as well as being aware of the perceived risks associated with higher WTP [38]. More evidence around public acceptance and WTP for the COVID-19 vaccine is essential to evaluate the success of vaccination programs, and to provide insights into future pricing considerations and demand forecasts.

To date, no research has been carried out in Bangladesh on people’s acceptance of the COVID-19 vaccine, the WTP, and the influencing factors and obstacles to vaccination coverage. The current study is aimed at determining the intention and WTP for a COVID-19 vaccine and other associated factors among people in Bangladesh.

Materials and methods

Study design, participants, and sampling

A cross-sectional online-based survey was carried out between 10 December 2020 and 10 January 2021. The inclusion criteria for participating were age ≥ 18 years, social media users (Facebook, WhatsApp, etc.), and currently living in Bangladesh. Incomplete surveys, individuals below 18 years old, and those who did not consent to the survey were excluded. Participants were not awarded any incentives or remuneration for taking part, and all responses were anonymous.

Study procedure

The study used an online survey tool (Google Forms) to collect data, which was advertised and disseminated across different social media platforms (Facebook, WhatsApp, etc.). Participants were asked, “Are you willing to participate in this study voluntarily?” with “yes/no” responses. If the response was positive, they were given access to the full questionnaire. Otherwise, a blank survey form was submitted automatically. The questionnaire was translated into Bangla (the native language of participants) and then translated back to English and pre-tested with 40 individuals before starting the final data collection for acceptability and clarity. A total of 1032 participants completed the online survey form where 894 participants were included in the final analysis, following quality control and manual check procedures to exclude incomplete and invalid surveys.

Sampling method

The sample size was calculated using the following equation:

$$ n=\frac{z^2 pq}{d^2};n=\frac{1.96^2\times 0.5\times \left(1-0.5\right)}{0.05^2}=384.16\approx 384 $$

Here,

n = number of samples

z = 1.96 (95% confidence level)

p = prevalence estimate (0.5)

q = (1-p)

d = precision limit or proportion of sampling error (0.05)

Assuming a 10% non-response rate, a total of 423.5 ≈ 424 sample size was estimated. However, the final sample exceeded this estimate.

Survey instruments

A self-reported semi-structured questionnaire was developed after reviewing previous studies on COVID-19 vaccine uptake [25, 29, 36]. The survey consisted of questions about (1) socio-demographic information, health status, COVID-19 experience, and vaccination-related information; (2) beliefs about COVID-19 infection and COVID-19 vaccination; (3) intention to receive the COVID-19 vaccine; (4) WTP for the COVID-19 vaccine; and (5) participant’s vaccine preference.

Socio-demographic, health status, COVID-19 experience, and vaccination-related information

Participants’ details, including age, sex, marital status, education level, monthly family income, number of children in the family, and area of residence were recorded. Participants were also asked to rate their overall health status, and whether or not they had any existing chronic diseases. Participants responded to their experience regarding COVID-19, whether or not they perceived COVID-19 vaccination as an effective way to prevent and control COVID-19 and whether or not they perceived a doctor’s recommendation as an important factor for COVID-19 vaccination decision. Information about the history of any vaccine hesitancy was also obtained.

Beliefs about COVID-19 infection and COVID-19 vaccination

Participants’ beliefs about COVID-19 infection and COVID-19 vaccination were measured using HBM [40]. The questions probed perceived stigma of COVID-19 (four items), perceived susceptibility to COVID-19 (three items), perceived severity of COVID-19 (three items), perceived benefits of COVID-19 vaccination (two items), perceived barriers to getting a vaccination against COVID-19 (five items), and cues to action (two items). All construct questions of the health belief model were measured on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) [35, 41]. For simplification, the responses were recoded as “agree” (strongly agree/agree) and “disagree” (strongly disagree/disagree/not sure) during the final analysis.

Intention to receive a COVID-19 vaccine and willingness to pay

Participant’s intention to receive a COVID-19 vaccine was measured by asking “If a vaccine against COVID-19 infection was available, would you be willing to take it?” Response options included “definitely not,” “probably not,” “not sure,” “probably yes,” and “definitely yes.” For our primary outcome, we dichotomized these responses into “yes” (definitely/probably yes) or “no” (all other responses). To assess the WTP for a COVID-19 vaccine, the question was “Would you be willing to pay out-of-pocket for a COVID-19 vaccine?” with “yes/no” responses. Participants who responded positively (yes) were asked “What is the maximum amount you are willing to pay for a dose of the COVID-19 vaccine?” The response options for price per dose were based on a 10-point scale and ranged from BDT 100 (≈ US$ 1.18) to BDT 1000 (≈ US$ 11.79). One United States Dollar (US$) is equivalent to 84.81 Bangladeshi Taka (BDT).

Participant’s vaccine preference

Participants were asked “How soon would you like to receive a COVID-19 vaccine when it becomes available?” with two response options: I will receive the vaccine as soon as possible” or “I will delay”. This was then followed by a question, “Which type of COVID-19 vaccine would you prefer?” with response options: “domestically-made vaccine”, “imported vaccine” or “both are acceptable”. Lastly, participants were asked "What kind of immunization schedule do you prefer for the COVID-19 vaccination?” with response options: “routine immunization”, “emergency vaccination” or “both are acceptable".

Statistical analysis

All statistical analyses were performed using IBM Statistical Package for the Social Sciences software (SPSS; version 25.0). Descriptive analyses, including frequencies, percentages, means, standard deviations, etc. were computed. Bivariate logistic regression analysis was performed on the unadjusted estimates. Variables that were significant (p < 0.05) in the bivariate logistic regression analysis were included in the adjusted multivariable logistic regression model. A p-value less than 0.05 was considered statistically significant.

Results

Socio-demographics

The sample comprised 894 survey responses. The participants’ age ranged from 18 to 60 years with a mean age of 29.96 (SD 9.15) years and approximately half of the participants were female (50.3%). About 57.2% of the participants were unmarried and 55.9% had a bachelor’s degree, 30.4% reported having a monthly family income of > 40,000 BDT and 78.3% resided in urban areas (Table 1).

Table 1 Distribution of all variables and their associations with the intention to receive a COVID-19 vaccine

While the majority of participants reported good health status (70.8%), 27.7% reported having chronic underlying diseases. 18.7% of participants reported having already been diagnosed with COVID-19. More than a quarter of participants (26.3%) responded that their family members had been infected with COVID-19. The majority (89.1%) of participants perceived the doctor’s recommendation as an important factor in their decision to have the COVID-19 vaccine. While 18.6% reported previous vaccine hesitancy (Table 1).

Health beliefs

The distribution of each item of the HBM is presented in Table 1. Approximately 15.9–30.9% agreed with regard to each construct-related stigma of COVID-19. With regards to the perceived susceptibility of contracting COVID-19, 74.7% of respondents disagreed that they had the possibility of contracting COVID-19 in the next few months; 45.4% were concerned about contracting COVID-19, and 33.2% thought that contracting COVID-19 was currently a possibility. Responses to questions about the perceived severity of COVID-19 demonstrate that less than half of respondents (46.1%) thought that complications of COVID-19 were serious and they would be very sick if they contracted COVID-19 (40.8%), or were afraid of contracting COVID-19 (44.9%). While the majority (78.7%) of participants perceived that vaccination was an effective way to prevent and control COVID-19, very few (36.8%) agreed that vaccination would make them feel less worried about contracting COVID-19, and vaccination would decrease their chance of contracting COVID-19 or its complications (42.3%). With regards to perceived barriers to COVID-19 vaccination, the majority of respondents (50.3–60.3%) had concerns about COVID-19 vaccination, including the impact of side-effects on usual activities (53.6%), efficacy (52.6%), safety (54%), affordability (50.3%), and validity (60.3%). In the cues to action section of the survey, over two-thirds of respondents confirmed that they would only take a vaccine if they were provided with adequate information (67.2%) and 43.5% disagreed with taking the COVID-19 vaccine if the vaccine was not taken by many in the public.

COVID-19 vaccination intent

Overall, 65.5% of participants reported a positive intention to receive a COVID-19 vaccine (38.5% definitely yes, and 27.0% probably yes); whilst 34.5% were unwilling or hesitant to be vaccinated against COVID-19 (21.5% not sure, 8.6% probably not, and 4.4% definitely not; Fig. 1). The results of bivariate and multivariable logistic regression of the intention to receive the vaccine are presented in Table 1. Bivariate analysis showed that the intention to receive the vaccine was significantly (p < 0.05) associated with being older, having higher education, having fewer children, having family members not infected with COVID-19, the severe impact of COVID-19 on participant's daily lives, studies/work and physical/mental health, positivity towards COVID-19 vaccination's effectiveness, and perceiving the doctor's recommendation as an important factor in vaccination decision making (Table 1). Multiple logistic regression, using only those variables that were significant in bivariate analysis, retained older age, positivity towards the effectiveness of COVID-19 vaccination, worries about the likelihood of being infected, believing that vaccination will safeguard against catching COVID-19 and decrease the risk of being infected with COVID-19 or its complications, and being less aware of the side-effects and safety of the COVID-19 vaccine (Table 1).

Fig. 1
figure1

COVID-19 vaccination intent (N = 894)

Willingness to pay (WTP)

Almost three-quarters of participants (72.9%) were willing to pay for the COVID-19 vaccine. The median (interquartile range [IQR]) WTP of the willing group was BDT400/US$ 4.72 (IQR; BDT 200–600/US$ 2.35–7.07) per dose (Fig. 2). Bivariate analysis showed that WTP was significantly (p < 0.05) associated with being young, male, being single, having higher education, urban residency, having good self-rated health status, having no chronic underlying diseases, positivity towards the effectiveness of COVID-19 vaccination, perceiving the doctor's recommendation as an important factor in vaccination decision making, being worried about the likelihood of contracting COVID 19, believing that vaccination decreases the chance of contracting COVID-19 or if infected, its complications, and perception of being vaccinated if given enough information about the COVID-19 vaccine (Table 2). Figure 2 represents the amount of money participants WTP for the COVID-19 vaccine. Multiple logistic regression, using only those variables that were significant in bivariate analysis, retained younger age, male, higher education, urban resident, having good self-rated health status, positivity towards the effectiveness of COVID-19 vaccination, and being worried about the likelihood of contracting COVID-19 (Table 2).

Fig. 2
figure2

Willingness to pay for the COVID-19 vaccine (N = 652)

Table 2 Distribution of all studied variables and their associations with the willingness to pay for a COVID-19 vaccine

Vaccine preference

Almost four in every ten participants who were COVID-19 vaccine intet reported that they would receive the vaccine as soon as possible (42.8%); whilst 57.2% reported that they would delay. 14.8% reported a domestically-made vaccine as their preference, 22.9% preferred an imported vaccine and 62.3% had no preference. In terms of immunization schedule, 28.2% preferred routine immunization, 21.5% an emergency vaccination schedule and 50.3% had no preference (Fig. 3).

Fig. 3
figure3

Vaccine preferences (N = 586)

Discussion

Vaccines are a key solution to halting the escalation of pandemics such as COVID-19. The government of Bangladesh began the COVID-19 vaccination roll-out on February 8, 2021 [42]. As with any new vaccine, the COVID-19 vaccine raises concerns. The present study examined how likely people will be to take a COVID-19 vaccine and investigate whether people are willing to pay for it. Our finding represents one of the first estimates of the intention to receive the vaccine among Bangladeshi people and can be used to guide projections of future vaccine uptake and successful implementation of the COVID-19 vaccination program in Bangladesh.

In this study, the majority of participants (65.5%) reported a definite or probable intention to receive a COVID-19 vaccine, which is comparable with recent studies conducted in Saudi Arabia and the United States [25, 28]. A higher proportion of COVID-19 vaccine intention has been reported in similar studies conducted in China, India, Indonesia, and Malaysia, ranging from 83.5 to 94.3% [24, 27, 35, 36]. It may be possible that when the study was conducted, the outbreak of COVID-19 in Bangladesh was largely under control, and also there was a lack of adequate information about the vaccine. Participants in this study had a low level of perceived susceptibility to COVID-19, according to the HBM construct, which is consistent with previous studies [35, 36] and suggests that the Bangladeshi people were not aware of the possibility of the resurgence of COVID-19, making them feel less vulnerable. Our findings suggest that participants’ intention to receive a COVID-19 vaccine was dependent on various socio-demographic factors. In particular, older age was found to be a significant influential factor for the COVID-19 vaccine intention. This finding is justified by the fact that elderly people are at an increased risk of COVID-19 infection both in terms of its severity and also mortality [43]. Our findings highlight the need for education intervention focusing particularly on younger age groups. The participants’ education level was also found to be a significant factor in COVID-19 vaccine intention in the bivariate analysis, although it was not significant in the multivariate analysis. Similar results were shown in other earlier studies in Bangladesh, illustrating that individuals with a higher educational background had more knowledge and awareness regarding COVID-19 [44, 45].

The COVID-19 epidemic has had a significant impact on people all across the world, affecting work, income, and physical and mental health [46,47,48]. The present study found that having family members who had been infected and the perception of COVID-19’s impact on daily life, studies/work, and physical/mental health were significant factors in the bivariate analysis, agreeing with a recent study among Chinese citizens [29]. Majority of the study participants agreed that vaccination is an effective way to prevent and control COVID-19, and this was a significant factor for participant’s intention to receive a vaccine, agreeing with 89.5% of Chinese residents who thought that vaccination is an effective way to prevent and control COVID-19 [29]. This positive attitude towards COVID-19 vaccination and the significant impact that it would have on their life explains the intention to receive a vaccine among people in Bangladesh. Multivariable analysis found that vaccination intention was associated with participant’s beliefs [e.g., Health Belief Model (HBM)] towards COVID-19, consistent with previous studies [34, 36, 49]. In particular, our findings suggest that perceived susceptibility to being infected with COVID-19 and the perceived benefits of and barriers to COVID-19 vaccination are the most important HBM constructs influencing participants’ intention to receive a COVID-19 vaccine. Participants with high perceived susceptibility to being infected with COVID-19 expressed increased vaccination intention, consistent with previous studies [25, 35]. While less than half of the participants (45.4%) were worried about the likelihood of contracting COVID-19, relatively few (25.3%) perceived themselves as at high risk of becoming infected. This indicates the need to increase public education and awareness about risk, in order that preventive actions can be taken to improve COVID-19 pandemic control [50].

The findings of this study also suggest participants' lower perceived benefits of COVID-19 vaccination and relatively higher perceived barriers to getting COVID-19 vaccination. In contrast, a similar study conducted in China showed high perceived benefits and low perceived barriers towards COVID-19 vaccination among the participants [36]. This may be the reason why Bangladeshi people showed a lower intention to receive a COVID-19 vaccine compared to Malaysian and Chinese people [29, 35]. Public health intervention programs that focus on increasing awareness of the benefits of COVID-19 vaccination and reducing the identified barriers are therefore essential. The multivariate analysis found concern about the safety of the COVID-19 vaccination as a significant barrier to vaccination intention, with similar findings reported in other studies related to the new vaccine [51], suggesting that information regarding the safety and efficacy standards should be made available to the general public. Another significant barrier was the worry about possible side effects of the COVID-19 vaccine. Bangladesh has experienced various negative events associated with vaccine malpractices and scandals, which have resulted in the public losing confidence in the COVID-19 vaccines [52], which may be implied in this study, as a considerable proportion of reported concerns regarding the possibility of side-effects of COVID-19 vaccines.

This study revealed that the majority of participants (72.9%) were willing to pay for a COVID-19 vaccine. This finding is comparable with a recent study in Indonesia, which found 78.3% of participants had WTP for a COVID-19 vaccine [38]. Multivariate analysis found that WTP for a vaccine was significantly influenced by socio-demographic factors such as younger age, male sex, higher education level, and residing in an urban area. Younger people reported higher WTP for a COVID-19 vaccine, consistent with a recent study in China [36]. A Malaysian study found higher education levels, professional and managerial occupations, and higher income groups were associated with higher WTP [35]. An Indonesian study found that higher income and high perceived risk among healthcare workers were associated with higher WTP [38]. Good self-rated health status and perceived effectiveness of the vaccine for prevention and control of COVID-19 were also found as significant factors for participants’ WTP for the COVID-19 vaccine. In addition, the perceived severity of the pandemic was also associated with a higher WTP. As HBM constructs were significantly associated with WTP, the HBM model should be used to inform the development of interventions to promote vaccination against COVID-19 as a priority for expenditure.

Over 40% of the participants who intended to receive a COVID-19 vaccine wanted to get vaccinated as soon as possible. Studies conducted in China and India found people’s intention to get prompt COVID-19 vaccination was 52.5 and 65.8% respectively [24, 29]. The majority of vaccine intent participants reported that both types of vaccine (domestically-made or imported) were acceptable, while the imported vaccine was more frequently preferred compared to the domestically-made (22.9% vs 14.8%) in contrast to a study in China which found that the majority of participants preferred a domestically-made vaccine over foreign-made (64.2% vs 11.9%) [36].

Our findings suggest that information about the safety and efficacy of the COVID-19 vaccines should be made public on a regular basis and timely health education and communications by public health and government sources such as healthcare professionals are critical to alleviating public concerns as well as improving confidence and compliance with the COVID-19 vaccine [23, 53].

There are some limitations to the current study that need to be considered when interpreting the results. Firstly, this study is a cross-sectional study design that cannot establish causal inferences. Secondly, the responses were based on self-reporting and may be subject to self-reporting bias and a tendency to report socially desirable responses. Thirdly, the use of an online survey and convenience sampling may result in sampling bias, so results may not apply to the wider community due to a lack of representative samples. Finally, the study was hypothetical in nature as it was conducted before the COVID-19 vaccine became available in Bangladesh, so results may now differ in practice. However, we believe that we have captured some really important information about the COVID-19 vaccine. Further research is needed to gather more data about the COVID-19 vaccine and WTP since over 9.9 million doses of the COVID-19 vaccine have been given in Bangladesh as of June 1, 2021 [18].

Conclusion

This study reflected that a sizeable proportion of Bangladeshi people intended to receive a COVID-19 vaccine. Low perceived susceptibility to being infected with COVID-19, as well as concern about side effects, and the safety of any new vaccine were identified as key factors in people's unwillingness or hesitation to receive a vaccine. Furthermore, the majority of participants had a willingness to pay for a COVID-19 vaccine. This study has important implications for facilitating public health and government authorities to design and deliver targeted intervention programs to enhance public acceptance of the COVID-19 vaccination in Bangladesh.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

COVID-19:

Coronavirus disease 2019

SARS-CoV-2:

Severe Acute Respiratory Syndrome Coronavirus 2

WTP:

Willingness to pay

HBM:

Health belief model

WHO:

World Health Organization

BDT:

Bangladeshi Taka

US$:

United States Dollar

aOR:

Adjusted Odds Ratio

OR:

Odds Ratio

References

  1. 1.

    Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. Lancet. 2020;395(10223):470–3. https://0-doi-org.brum.beds.ac.uk/10.1016/S0140-6736(20)30185-9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Xiang YT, Yang Y, Li W, Zhang L, Zhang Q, Cheung T, et al. Timely mental health care for the 2019 novel coronavirus outbreak is urgently needed. Lancet Psychiatry. 2020;7(3):228–9. https://0-doi-org.brum.beds.ac.uk/10.1016/S2215-0366(20)30046-8.

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Shereen MA, Khan S, Kazmi A, Bashir N, Siddique R. COVID-19 infection: origin, transmission, and characteristics of human coronaviruses. J Adv Res. 2020;24:91–8. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jare.2020.03.005.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Cucinotta D, Vanelli M. WHO declares COVID-19 a pandemic. Acta Biomed. 2020;91:157–60. https://0-doi-org.brum.beds.ac.uk/10.23750/abm.v91i1.9397.

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    World Health Organization (WHO). WHO Coronavirus Disease (COVID-19) Dashboard. 2021. https://covid19.who.int. Accessed 1 June 2021.

    Google Scholar 

  6. 6.

    Corona Tracker COVID-19: Bangladesh overview. 2021. https://www.coronatracker.com/country/bangladesh. Accessed 1 June 2021.

    Google Scholar 

  7. 7.

    Yeasmin S, Banik R, Hossain S, Hossain MN, Mahumud R, Salma N, et al. Impact of COVID-19 pandemic on the mental health of children in Bangladesh: a cross-sectional study. Child Youth Serv Rev. 2020;117:105277. https://0-doi-org.brum.beds.ac.uk/10.1016/j.childyouth.2020.105277.

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N, et al. The psychological impact of quarantine and how to reduce it : rapid review of the evidence. Lancet. 2020;395(10227):912–20. https://0-doi-org.brum.beds.ac.uk/10.1016/S0140-6736(20)30460-8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Zhang Y, Ma ZF. Impact of the COVID-19 pandemic on mental health and quality of life among local residents in Liaoning Province, China: A cross-sectional study. Int J Environ Res Public Health. 2020;17(7):2381. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17072381.

    CAS  Article  PubMed Central  Google Scholar 

  10. 10.

    Rajkumar RP. COVID-19 and mental health: A review of the existing literature. Asian J Psychiatr. 2020;52:102066. https://0-doi-org.brum.beds.ac.uk/10.1016/j.ajp.2020.102066.

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Voitsidis P, Gliatas I, Bairachtari V, Papadopoulou K, Papageorgiou G, Parlapani E, et al. Insomnia during the COVID-19 pandemic in a Greek population. Psychiatry Res. 2020;289:113076. https://0-doi-org.brum.beds.ac.uk/10.1016/j.psychres.2020.113076.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Fahriani M, Anwar S, Yufika A, Bakhtiar B, Wardani E, Winardi W, et al. Disruption of childhood vaccination during the COVID-19 pandemic in Indonesia. Narra J. 2021;1:1–11. https://0-doi-org.brum.beds.ac.uk/10.52225/narraj.v1i1.7.

    Article  Google Scholar 

  13. 13.

    Collins SR, Ph D. Covid-19 — implications for the health care system. N Engl J Med. 2020;383(15):1483–8. https://0-doi-org.brum.beds.ac.uk/10.1056/NEJMsb2021088.

    Article  PubMed  Google Scholar 

  14. 14.

    Thunström L, Newbold SC, Finnoff D, Ashworth M, Shogren JF. The benefits and costs of using social distancing to flatten the curve for COVID-19. J Benefit Cost Anal. 2020;11(2):1–17. https://0-doi-org.brum.beds.ac.uk/10.1017/bca.2020.12.

    Article  Google Scholar 

  15. 15.

    Yang Y, Peng F, Wang R, Guan K, Jiang T, Xu G, et al. The deadly coronaviruses: the 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China. J Autoimmun. 2020;109:102434. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jaut.2020.102434.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Lurie N, Saville M, Hatchett R, Halton J. Developing Covid-19 vaccines at pandemic speed. N Engl J Med. 2020;382(21):1969–73. https://0-doi-org.brum.beds.ac.uk/10.1056/NEJMp2005630.

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    World Health Organization (WHO). DRAFT Landscape of COVID-19 Candidate Vaccine-22 January. 2021. https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines. Accessed 22 Jan 2021.

    Google Scholar 

  18. 18.

    Times F. Covid-19 vaccine tracker: the global race to vaccinate. 2021. https://ig.ft.com/coronavirus-vaccine-tracker. Accessed 1 June 2021.

    Google Scholar 

  19. 19.

    The Daily star. Oxford Vaccines from Serum, India:50 lakh shots arrive today. 2021. https://www.thedailystar.net/frontpage/news/oxford-vaccines-serum-india-50-lakh-shots-arrive-today-2033265. Accessed 25 Jan 2021.

    Google Scholar 

  20. 20.

    The Daily star. Bharat Biotech’s Coronavirus Vaccine: Concerns grow in India over safety. 2021. https://www.thedailystar.net/frontpage/news/bharat-biotechs-coronavirus-vaccine-concerns-grow-india-over-safety-2028549. Accessed 23 Feb 2020.

    Google Scholar 

  21. 21.

    Ullah I, Khan KS, Tahir MJ, Ahmed A, Harapan H. Myths and conspiracy theories on vaccines and COVID-19 : Potential effect on global vaccine. Vacunas. 2021;22(2):1–5. https://0-doi-org.brum.beds.ac.uk/10.1016/j.vacun.2021.01.001.

    Article  Google Scholar 

  22. 22.

    Bell S, Clarke R, Mounier-jack S, Walker JL, Paterson P. Parents ’ and guardians ’ views on the acceptability of a future COVID-19 vaccine : a multi-methods study in England. Vaccine. 2020;38(49):7789–98. https://0-doi-org.brum.beds.ac.uk/10.1016/j.vaccine.2020.10.027.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Dubé E, Macdonald NE. Vaccine acceptance: barriers, perceived risks, benefits, and irrational beliefs. 2nd ed: Elsevier Inc.; 2016. https://0-doi-org.brum.beds.ac.uk/10.1016/B978-0-12-802174-3/00026-6.

  24. 24.

    Tiwari R, Dhama K, Jose B. Covid-19 vaccine acceptance : beliefs and barriers associated with vaccination among the general population in India. J Exp Biol Agric Sci. 2020;8:210–8. https://0-doi-org.brum.beds.ac.uk/10.18006/2020.8(Spl-1-SARS-CoV-2).S210.S218.

    Article  Google Scholar 

  25. 25.

    Reiter PL, Pennell ML, Katz ML. Acceptability of a COVID-19 vaccine among adults in the United States: how many people would get vaccinated? Vaccine. 2020;38(42):6500–7. https://0-doi-org.brum.beds.ac.uk/10.1016/j.vaccine.2020.08.043.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Taylor S, Landry CA, Paluszek MM, Groenewoud R, Rachor GS, Asmundson GJG, et al. A proactive approach for managing COVID-19: the importance of understanding the motivational roots of vaccination hesitancy for SARS-CoV2. Front Psychiatry. 2020;11:1–5. https://0-doi-org.brum.beds.ac.uk/10.3389/fpsyg.2020.575950.

    Article  Google Scholar 

  27. 27.

    Harapan H, Wagner AL, Yufika A, Winardi W, Sofyan H, Mudatsir M. Acceptance of a COVID-19 vaccine in Southeast Asia : a cross-sectional study in Indonesia. Front Public Health. 2020;8:1–8. https://0-doi-org.brum.beds.ac.uk/10.3389/fpsyg.2020.575950.

    Article  Google Scholar 

  28. 28.

    Al-mohaithef M, Padhi BK. Determinants of COVID-19 vaccine acceptance in Saudi Arabia : a web-based National Survey. J Multidiscip Healthc. 2020;13:1657–63. https://0-doi-org.brum.beds.ac.uk/10.2147/JMDH.S276771.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Wang J, Jing R, Lai X, Zhang H, Lyu Y, Knoll MD, et al. Acceptance of COVID-19 vaccination during the COVID-19 pandemic in China. Vaccines. 2020;8(3):1–14. https://0-doi-org.brum.beds.ac.uk/10.3390/vaccines8030482.

    CAS  Article  Google Scholar 

  30. 30.

    Fisher KA, Bloomstone SJ, Walder J, Crawford S, Fouayzi H, Mazor KM. Attitudes toward a potential SARS-CoV-2 vaccine: a survey of U.S. adults. Ann Intern Med. 2020;15(12):1–10. https://0-doi-org.brum.beds.ac.uk/10.7326/M20-3569.

    Article  Google Scholar 

  31. 31.

    Glanz K, Barbara K, Rimer KV. Health behavior and health education: theory, research, and practice. 4th ed. San Francisco: Wiley; 2008.

    Google Scholar 

  32. 32.

    Coe AB, Gatewood SBS, Moczygemba LR, Jean-Venable KR, Goode JO. The use of the health belief model to assess predictors of intent to receive the novel (2009) H1N1 influenza vaccine. Inov Pharm. 2012;3:1–11. https://0-doi-org.brum.beds.ac.uk/10.24926/iip.v3i2.257.

    Article  PubMed  Google Scholar 

  33. 33.

    Brewer NT, Chapman GB, Gibbons FX, Gerrard M, McCaul KD, Weinstein ND. Meta-analysis of the relationship between risk perception and health behavior: the example of vaccination. Health Psychol. 2007;26(2):136–45. https://0-doi-org.brum.beds.ac.uk/10.1037/0278-6133.26.2.136.

    Article  PubMed  Google Scholar 

  34. 34.

    Lin Y, Lin Z, He F, Chen H, Lin X, Zimet GD, et al. HPV vaccination intent and willingness to pay for 2-,4-, and 9-valent HPV vaccines: a study of adult women aged 27–45 years in China. Vaccine. 2020;38(14):3021–30. https://0-doi-org.brum.beds.ac.uk/10.1016/j.vaccine.2020.02.042.

    Article  PubMed  Google Scholar 

  35. 35.

    Wong LP, Alias H, Wong PF, Lee HY, AbuBakar S. The use of the health belief model to assess predictors of intent to receive the COVID-19 vaccine and willingness to pay. Hum Vaccines Immunother. 2020;16(9):2204–14. https://0-doi-org.brum.beds.ac.uk/10.1080/21645515.2020.1790279.

    CAS  Article  Google Scholar 

  36. 36.

    Lin Y, Hu Z, Zhao Q, Alias H, Id MD, Id PW. Understanding COVID-19 vaccine demand and hesitancy : a nationwide online survey in China. PLoS Negl Trop Dis. 2020;14(12):e0008961. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pntd.0008961.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Ess SM, Szucs TD. Economic evaluation of immunization strategies. Clin Infect Dis. 2002;35(3):294–7. https://0-doi-org.brum.beds.ac.uk/10.1086/341419.

    Article  PubMed  Google Scholar 

  38. 38.

    Harapan H, Wagner AL, Yufika A, Winardi W, Anwar S, Gan AK, et al. Willingness-to-pay for a COVID-19 vaccine and its associated determinants in Indonesia. Hum Vaccines Immunother. 2020;16(12):3074–80. https://0-doi-org.brum.beds.ac.uk/10.1080/21645515.2020.1819741.

    CAS  Article  Google Scholar 

  39. 39.

    Iwashita Y, Takemura S. Factors associated with willingness to undergo vaccination against Haemophilus influenzae type b (Hib). Japan J Public Health. 2010;57:381–9. https://0-doi-org.brum.beds.ac.uk/10.11236/jph.57.5_381.

    Article  Google Scholar 

  40. 40.

    Rosenstock IM. The health belief model and preventive health behavior. Health Educ Monogr. 1974;2(4):354–86. https://0-doi-org.brum.beds.ac.uk/10.1177/109019817400200405.

    Article  Google Scholar 

  41. 41.

    Al-Metwali BZ, Al-Jumaili AA, Al-Alag ZA, Sorofman B. Exploring the acceptance of COVID-19 vaccine among healthcare workers and general population using health belief model. J Eval Clin Pract. 2021:1–11. https://0-doi-org.brum.beds.ac.uk/10.1111/jep.13581.

  42. 42.

    Al Jazeera. Bangladesh starts COVID vaccination drive. 2021. https://www.aljazeera.com/news/2021/1/28/bangladesh-starts-covid-vaccination-drive. Accessed 28 Feb 2021.

    Google Scholar 

  43. 43.

    Tian S, Hu N, Lou J, Chen K, Kang X, Xiang Z, et al. Characteristics of COVID-19 infection in Beijing. J Inf Secur. 2020;80(4):401–6. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jinf.2020.02.018.

    CAS  Article  Google Scholar 

  44. 44.

    Banik R, Rahman M, Sikder MT, Rahman QM, Pranta MUR. Knowledge, attitudes, and practices related to the COVID-19 pandemic among Bangladeshi youth: a web-based cross-sectional analysis. J Public Health. 2021. https://0-doi-org.brum.beds.ac.uk/10.1007/s10389-020-01432-7.

  45. 45.

    Farhana K. Knowledge and perception towards novel coronavirus (COVID-19) in Bangladesh. Int Res J Bus Soc Sci. 2020;6:76–9. https://0-doi-org.brum.beds.ac.uk/10.2139/ssrn.3578477.

    Article  Google Scholar 

  46. 46.

    Banik R, Rahman M, Sikder MT, Gozal D. SARS-CoV-2 pandemic: an emerging public health concern for the poorest in Bangladesh. Public Health Pract. 2020;1:100024. https://0-doi-org.brum.beds.ac.uk/10.1016/j.puhip.2020.100024.

    Article  Google Scholar 

  47. 47.

    Mamun MA, Sakib N, Gozal D, Israfil AKM, Hossain S, Al F, et al. The COVID-19 pandemic and serious psychological consequences in Bangladesh : a population-based nationwide study. J Affect Disord. 2021;279:462–72. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jad.2020.10.036.

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    Yasmin S, Alam MK, Ali F, Banik R, Salma N. Psychological Impact of COVID-19 Among People from the Banking Sector in Bangladesh: a Cross-Sectional Study. Int J Ment Health Addict. 2021. https://0-doi-org.brum.beds.ac.uk/10.1007/s11469-020-00456-0.

  49. 49.

    Schmid P, Rauber D, Betsch C, Lidolt G, Denker ML. Barriers of influenza vaccination intention and behavior - a systematic review of influenza vaccine hesitancy, 2005-2016. PLoS One. 2017;12(1):e0170550. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pone.0170550.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Verelst F, Willem L, Beutels P. Behavioural change models for infectious disease transmission: a systematic review (2010-2015). J R Soc Interface. 2016;13(125):20160820. https://0-doi-org.brum.beds.ac.uk/10.1098/rsif.2016.0820.

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Mullard A. COVID-19 vaccine development pipeline gears up. Lancet. 2020;395:1751–2. https://0-doi-org.brum.beds.ac.uk/10.1016/S0140-6736(20)31252-6.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    The Business Standard. Experts urge steps to remove mistrust in vaccines. 2021. https://tbsnews.net/coronavirus-chronicle/covid-19-bangladesh/experts-urge-steps-remove-mistrust-vaccines-185428. Accessed 20 Feb 2020.

    Google Scholar 

  53. 53.

    Larson HJ, Jarrett C, Eckersberger E, Smith DMD, Paterson P. Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: a systematic review of published literature, 2007-2012. Vaccine. 2014;32(19):2150–9. https://0-doi-org.brum.beds.ac.uk/10.1016/j.vaccine.2014.01.081.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The authors would like to express their gratitude to all of the respondents who participated in the study voluntarily and amicably. Furthermore, the authors are also grateful to the people who supported the collection of data online and would like to thank Arfina Akhter Keya, Jannatul Mawa, Jannat Shancharika Shuchi, Sayeda Alvi Khorshed, Anab Anwar, Noyon Chandra Das, Sabiha Naznin, Rion Ahmed Sakhor, Najnin Sultana Rima, Md. Rezwan Ahmed Mahedi, Bashudeb Talukder, Fahima Chowdhury Joya, Fatema Tuz Zohra, Arpita Chakrabarty, Nishrita Devnath Smrity, Safa Akter Ruma, Kifayat Sadmam Ishadi, Adiba for their contribution in data collection.

Funding

The authors declare that no funding has been received for this study from any individuals or organizations.

Author information

Affiliations

Authors

Contributions

RB: Conceptualization, Methodology, Investigation, Data collection, Data curation, Writing - original draft, Editing, Validation., MSI: Data curation, Formal analysis, Writing - original draft, Editing, Validation., MURP: Investigation, Data collection, Validation., QMR: Data collection, Writing - original draft, Validation., MR: Investigation, Editing, Validation., SP: Editing, Validation., RD: Editing, Validation., SH: Editing, Validation., MTS: Conceptualization, Supervision, Investigation, Editing, Validation. The authors read and approved the final manuscript.

Corresponding authors

Correspondence to Rajon Banik or Md. Tajuddin Sikder.

Ethics declarations

Ethics approval and consent to participate

This study maintained ethical standards to the highest possible extent and informed consent was obtained from participants. All procedures followed the 1964 Helsinki declaration. This research was approved by the Biosafety, Biosecurity, and Ethical review board of Jahangirnagar University, Savar, Dhaka-1342, Bangladesh [BBEC, JU/ M 2021/COVID-19/3(1)]. All responses were anonymous to ensure data confidentiality. All participants provided their informed consent to participate in the study after being informed about the purpose of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the publication of this research output.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Banik, R., Islam, M., Pranta, M.U.R. et al. Understanding the determinants of COVID-19 vaccination intention and willingness to pay: findings from a population-based survey in Bangladesh. BMC Infect Dis 21, 892 (2021). https://0-doi-org.brum.beds.ac.uk/10.1186/s12879-021-06406-y

Download citation

Keywords

  • COVID-19 vaccine
  • Health belief model
  • Intention
  • Willingness to pay
  • Bangladesh