Healthcare Professionals’ Willingness-to-Pay and Preferences for Covid-19 Vaccines in a Nigerian Tertiary Hospital: A Cross-Sectional Study

Healthcare Professionals’ Willingness-to-Pay and Preferences for Covid-19 Vaccines in a Nigerian Tertiary Hospital: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Healthcare Professionals’ Willingness-to-Pay and Preferences for Covid-19 Vaccines in a Nigerian Tertiary Hospital: A Cross-Sectional Study Chinelo Nneka Ikeanyi, Ifunanya Sandra Ezenwa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6470613/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background : Vaccines remains the strategy to prevent Covid-19 disease since there is currently no available cure. Increase in expenditure and limited resources for healthcare funding has shifted the burden of disease towards the patients. Willingness to pay is an objective measure of the value people place on a product or service. This study assessed healthcare professional’s willingness to pay and preference for the available Covid-19 vaccines in Nigeria. Methods : A cross-sectional study conducted on healthcare professionals in a tertiary healthcare institution using a convenient sampling method with the aid of a structured self-administered two sectioned questionnaires. Section A was to obtain the respondent demographics and other information. Section B gave a description of the COVID-19 vaccines, denoted as Vaccine A, B, C and D, and obtained the respondents willingness to pay and preferred Covid-19 vaccine. Data obtained was entered into a Microsoft excel spreadsheet for data cleaning and analysis to obtain the frequency, percentage, Willingness-to-pay (WTP) and standard deviation. The student T-test and ANOVA were used where appropriate to obtain the relationship between the demographic variables and WTP using graphpad inStat 3.0. Results: Out of the 300 questionnaires that were given out, 279 were returned and properly filled giving a response rate of 93%. A majority (171;61.29%) of the respondents were females, 35 (84.2%) were between the age of 25-44yrs while 210 (75.3%) were married. Less than half of the respondents 122 (43.73%) were doctors, 210 (75.27%) have not been sick with Covid-19 disease while 120 (43.01%) had treated a patient with Covid-19 disease. A few 44 (15.77%) had cared for a relative with Covid-19 while 270 (96.77%) had not been hospitalized due to Covid-19 disease. The willingness to pay for Vaccine A is ₦35103.63± 25067.29, Vaccine B is ₦36442.36± 25440.88, Vaccine C is ₦33557.60± 24948.49 and Vaccine D is ₦35302.29± 25816. Majority of the respondent (45.52%) preferred Vaccine A while 16.13% had no preference. Conclusion : The healthcare professionals preferred Vaccine A with a willingness to pay value of ₦35,103.63 ± 25067.29. The willingness-to-pay for the available Covid-19 vaccine was influenced by the respondent’s demographic characteristics, Covid-19 beliefs and experiences. Covid-19 vaccines Willingness to pay preferences healthcare professionals Figures Figure 1 Background The Covid-19 pandemic which started in Wuhan Province of China in December 2019 was declared a public health emergency of international concern by the WHO on January 11, 2020. The COVID-19 disease is caused by a novel coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is transmitted most commonly through droplets spread from infected persons during coughing, sneezing, or talking [ 1 ]. Coronavirus disease is a respiratory illness with a clinical spectrum of mild to moderate disease (80%), severe disease (15%), and critical illness (5%) with an overall case fatality rate of 0.5–2.8% [ 2 ]. The pandemic had major impact in all aspect of life as economies, academics, businesses were shut down and the healthcare system severely overburdened [ 3 ]. Proven therapeutics for the treatment of COVID − 19 is yet underway but various vaccines have been approved for use by the World Health Organization. The Pfizer-BioNTech, the Moderna, the Johnson and Johnson and the AstraZeneca-University of Oxford COVID-19 vaccines were among the first vaccines given emergency authorization for use by WHO for use [ 4 ]. These vaccines are based upon inactivated or live attenuated viruses, the protein subunits, viral vectors etc. They are considered safe based on accelerated clinical trial data and efficacious (above 50%). The economic consequences of the COVID-19 pandemic, which include a reduction in the employment rate by more than 20%, a reduction in average household income by around one-fifth, and a reduction in the Gross Domestic Product (GDP) growth rates [ 3 ], increase in expenditure for drugs and other health services is a significant challenge to sub–Saharan Africa and Nigeria in Particular [ 5 ]. About 4% of the Nigerian national budget goes to funding of healthcare services, and falls below recommendations by the WHO. Inefficiency of the health system across the country, limited institutional capacity, corruption, unstable and dwindling economic, and lack of political will, have increased the challenges and difficulties associated with the funding of increasing healthcare need [ 6 ]. The rise in incidences of new diseases have raised the burden of diseases and healthcare costs for governments and public institution and diminishing funding for health. The vaccines were initially distributed freely but as resources dwindled, pressure has been put on government and individuals for funding, therefore, individuals will be faced with the responsibility of paying for their healthcare needs and services out-of-pocket [ 7 ]. Limited resources for healthcare funding and increasing emergence of new diseases has also shifted the burden of care towards the patients [ 8 ]. Previous studies revealed that the rate of acceptance of the COVID-19 vaccines is low in most countries in the world [ 9 , 10 ]. Costs of the vaccines have been identified as a predictor of willingness-to-pay [ 11 , 12 ]. Due to the weak economy and scarce healthcare resources, it is important to conduct pharmacoeconomics studies to determine the consumer preferences [ 13 ] and valuation of these vaccines including willingness to pay (WTP) [ 14 ] Health economics and willingness to pay (WTP) is a long-time model to evaluate the choices patients make regarding their health [ 15 ]. Willingness-to-pay is a methodological tool to ascertain the hypothetical monetary value of programs, specific medical interventions, and treatments. Willingness to pay is an objective measure of the value people place on a product or service. It has been used in many areas of marketing management, such as pricing decisions or new product development. Knowledge of a product's willingness to pay on behalf of its potential buyers is critical as it helps to set prices in order to maximize profits while delivering equivalent healthcare value and service [ 16 ]. Healthcare professionals are frontiers in emergency responses such as Covid-19 and are at high risk of getting infected with the corona virus, hence they are of high priority for vaccination [ 17 , 18 ]. Their readiness to part with a fee for the vaccine will promote and validate the immunization against this life-threatening disease among the general population [ 19 , 20 ]. Studies have shown that healthcare providers’ recommendations have the potential to increase WTP for the vaccine [ 15 ] since they are an essential source of evidence for the efficacy of vaccines. Thus, there is need to evaluate the value attached by healthcare professionals to the available Covid-19 vaccines and their preferences. A previous study in Ibadan, southwestern Nigeria revealed healthcare professionals’ positive perception towards Covid-19 vaccines but low (18.2%) willingness to pay. Knowledge of the willingness-to-pay for the Covid-19 vaccines will encourage pharmaceutical companies to invest in the manufacturing of affordable Covid-19 vaccines and in pricing these Covid-19 vaccines in face of the significant economic challenges. It will also enable the government to determine if subsidies will be needed if the selling price of the available vaccines are above the end-user’s willingness-to-pay. In addition, information on healthcare professionals’ preference will enable healthcare policy makers to know the Covid-19 vaccine that should be introduced into the healthcare system at a subsidized rate and have continuous support as this may improve compliance. This study assessed healthcare professional’s willingness to pay and preference for the available Covid-19 vaccines in Nigeria. Methods Study Design This study was a descriptive cross-sectional study conducted on healthcare professionals in a tertiary healthcare institution in Nigeria using a convenient sampling method with the aid of a structured self-administered questionnaire. Study Setting The study was conducted at the Enugu State University Teaching Hospital, Enugu which comprises of five clinical departments, 309 doctors, 396 nurses and 44 pharmacists excluding interns as their current clinical staff. The clinical departments include Obstetrics and gynecology, surgery, internal medicine, pediatrics, and pharmacy departments. Study Instrument The questionnaire used was developed for this study and face validated by two (2) experts each in public health, pharmacoeconomics and clinical pharmacy (uploaded as a Supplementary material). It was a structured questionnaire which had two sections; section A and section B. Section A obtained the respondent demographics and other information such as if the respondent had been sick of Covid-19, had treated a patient who had Covid-19, had been hospitalized for Covid-19 disease etc. Section B gave a description of the COVID-19 vaccines, denoted as Vaccine A, B, C and D, and obtained the respondents willingness to pay and preferred Covid-19 vaccine [ 21 – 26 ] The questions were stated as thus; “VACCINE A: Has 95% efficacy. Given in two doses separated by 3–12 weeks. Stored at a temperature of -80 o C and − 60 oC . Suitable for individuals 16 years and above. Has side effects of pain, redness and swelling at the site of injection; tiredness, headaches, muscle pain, chills, fever and nausea. Write how much you are willing to pay for the two doses of this vaccine? VACCINE B: Has 95% efficacy. Given as two doses, separated by 4weeks. Stored at a temperature of − 50 O C and − 15 O C. Used in individuals 18years and above. Causes pain, redness and swelling at the site of administration; it also causes tiredness, headaches, muscle pain, chills, fever and nausea. Write the amount you are willing to pay for the two doses of this vaccine? VACCINE C: Has 66% efficacy. Given as a single dose. Stored at a temperature of 2 O C and 8 O C. Used in individuals 18 years and above. It causes pain, redness and swelling at the site of administration; it also causes tiredness, headaches, muscle pain, chills, fever and nausea. How much are you willing to pay for the two doses of this vaccine? VACCINE D: Has 70% efficacy. Given in two doses at an interval of 8–12 weeks. Stored at a temperature of 2 o C and 8 oC . Suitable for individuals 18 years and above. It has side effects of blood clots, low platelet count, headaches, fatigue, muscle or joint pain, fever, chills and nausea. State the amount you are willing to pay for this vaccine?” A pilot study was conducted on fifty (50) healthcare professionals in a tertiary institution different from the one used for the study. The questionnaire was found to be usable since it was easily understood and obtained the required information. The reliability and content validity of the questionnaire was evaluated using Cronbach alpha. Inclusion and Exclusion Criteria All doctors, nurses and pharmacists practicing in the Enugu State University Teaching Hospital who are willing to participate in the research and adhere to the study protocol was included. All other health workers both within and outside the hospital were excluded from the study. Study Population/Sample Size The study includes all the doctors, nurses and pharmacists of the Enugu State University Teaching Hospital who consent to be a part of the study. The total number of the doctors, nurses and pharmacist who are staff of the hospital obtained from the personnel unit was seven hundred and forty-nine (749). The sample size was calculated with the aid of raosoft; a sample size calculator and a sample size of two hundred and fifty-five (255) was obtained at 95% confidence interval and error margin of 5%. However, three hundred (300) questionnaires were distributed for attrition, to create room for the questionnaires that may not be properly filled. Data Collection The questionnaires were given to the health workers who consented to be a part of the study during their working hours. They were given few minutes to go through the questions and respond accordingly. After which the questionnaire was retrieved, numbered and kept in an envelope. Data Analysis Data obtained was coded and entered into a Microsoft excel spreadsheet for data cleaning and to obtain the frequency, percentage, Willingness-to-pay (WTP) and standard deviation. The student T-test and ANOVA were used where appropriate to obtain the relationship between the demographic variables and WTP using graphpad inStat 3.0. Ethical Considerations The ethical approval of this study was sobtained from the Medical Advisory Committee of the ESUT Teaching Hospital, Enugu with the reference number ESUTHp/c-MAC/RA/034/Vol.2/181. Informed consent was obtained from each respondent before they were given the questionnaire to fill. The health workers were informed that their involvement in the study was entirely voluntary and that they might leave at any point without facing any repercussions. No identifying information was obtained from the respondents since confidentiality, privacy and anonymity were respected in accordance with the Nuremberg code and Helsinki declarations. Results Out of the 300 questionnaires that were given out, 279 questionnaires were returned and found usable giving a response rate of 93%. A majority (171;61.29%) of the respondents were females while the rest were males. Most (235;84.2%) of the respondents were between the age of 25-44yrs. The married were 210(75.3%) while 3(1.08%) were either widowed or divorced. Less than half 122 (43.73%) were doctors while 64(22.94%) were pharmacists. Most (210; 75.27%) have not been sick with Covid-19 disease while 120 (43.01%) had treated a patient with Covid-19 disease. However, 44 (15.77%) had cared for a relative with Covid-19 while 270 (96.77%) had not been hospitalized due to Covid-19 disease. About 149 (53.41%) believe that Covid-19 exists while 230 (82.44%) do not have health insurance. See details in Table 1 Table 1 Socio-demographic Characteristics of Respondents. (n = 279) S/N Variables Number of Respondents (n) Percentage (%) 1. Gender Male 108 38.71 Female 171 61.29 2. Age 18–24 20 7.17 25–34 133 47.67 35–44 102 36.56 45–54 18 6.45 55–64 6 2.15 3. Marital Status Married 210 75.27 Single 66 23.66 Others 3 1.08 4. Profession Doctor 122 43.73 Pharmacist 64 22.94 Nurse 93 33.33 5. Have you had Covid-19 Yes 69 24.73 No 210 75.27 6. Have you treated a patient with Covid-19 before Yes 120 43.01 No 159 56.99 7. Have you been hospitalized for Covid-19 Yes 9 3.23 No 270 96.77 8. Have you cared for a relative with Covid-19 Yes 44 15.77 No 235 84.23 9. Do you have health Insurance Yes 49 17.56 No 230 82.44 10. Available vaccines effective against Covid-19 Yes 149 53.41 No 130 46.59 11. Vaccine Preference Vaccine A 127 45.52 Vaccine B 92 32.97 Vaccine C 12 4.30 Vaccine D 03 1.08 None 45 16.13 Majority of the respondent (45.52%) preferred Vaccine A while 1.08% preferred Vaccine C and 16.13% have no preferences. Figure 1 has the details. The mean willingness-to-pay values for Vaccine A is ₦35103.63 ± 25067.29, Vaccine B is ₦36442.36 ± 25440.88, Vaccine C is ₦33557.60 ± 24948.49 and Vaccine D is ₦35302.29 ± 25816.39. However, there is no statistical difference between the mean values of these vaccines (P = 0.6070). The male respondents had a higher WTP values for all the Covid-19 vaccines, although this was only statistically significant for vaccines C & D. The WTP values decreased as the age increases. Respondents who are between the ages of 18–44 years had higher WTP. This was statistically significant for vaccine A, B, and D. The widowed and/or divorced respondents were more willing to pay for Covid-19 vaccines than the married ones, although this was significant only for vaccine A. The medical doctors had the highest willingness to pay values for all the Covid-19 vaccines. However, the values were only significant for vaccine B. Health workers who had previously contracted Covid-19 disease will pay higher for vaccine B & D while those who have not been sick with Covid-19 disease will pay higher for vaccine A & C. Though this was not statistically significant. Details is as shown in Table 2 . Table 2 Relationship between Willingness-to-Pay with the respondents’ demographic characteristics S/N Variable Number of Respondents (n) WTP of Vaccine A (₦) WTP of Vaccine B (₦) WTP of Vaccine C (₦) WTP of Vaccine D (₦) 1. Gender Male 108 35962.6± 23510.88 39692.52± 24704.36 38952± 25058.21 39410.04± 26327.47 Female 171 34561.2± 26055.51 34389.63± 25755.44 30150.61± 24340.76 32707.92± 25221.87 P-Value 0.6500 0.0899 0.0039* 0.0344* 2. Age 18–24 20 39087.9± 30382.73 32496.35± 24214.72 39645.1± 32187.65 34064.8± 27292.52 25–34 133 38028.33± 24391.21 39038.38± 24436.08 34763.05± 24186.17 38559.95± 24834.89 35–44 102 342964± 24561.08 36984.64± 25849.69 32540.29± 23225.11 34665.75± 25915.77 45–54 18 22074.89± 22277.18 26906.67± 28296.75 29323.22± 29374.7 23445.67± 27613.57 55–64 6 9803.33± 12918.12 11439± 21594.58 16542.5± 27875.27 13606.67± 240402.96 P-Value 0.0088* 0.0354* 0.2903 0.0351* 3. Marital Status Married 210 33931.31± 25003.43 37344,36± 25669.34 33210.87± 24714.27 3609.11± 25993.11 Single 66 38209.91± 37949.8 33508.05± 32691.79 34491.82± 34428.18 33198.88± 33694 Others 3 48832.33± 9780.29 37857.67± 31545.84 37276± 10984.46 26009.33± 30615.05 P-Value 0.0001* 0.6122 0.9197 0.6478 4. Profession Doctor 122 38517.48± 24011.22 40727.39± 24636.17 35109.86± 22937.38 37606.37± 24382.78 Pharmacist 64 32186.47± 24566.43 33885.45± 24509.44 31795.67± 25506.97 33980.64± 26815.72 Nurse 93 32632.91± 26451.38 32580.74± 26499.03 32733,82± 27152.94 33189.25± 26959.97 P-Value 0.1331 0.0434* 0.1832 0.4157 5. Previous Diagnosis with Covid-19 Yes 69 33957.91± 21769.67 36443.54± 24287.31 31784.49± 24184.82 40451.7± 26730.45 No 210 35480.15± 26096.97 36441.98± 25865.03 34140.2± 25345.94 33610.34± 25345.94 P-Value 0.6625 0.9973 0.4972 0.0560 *Values are significant at p < 0.05 The respondents who had treated a patient with Covid-19 disease will pay higher for the vaccines, however it was only significant for vaccine D. The respondents who had been hospitalized for Covid-19 disease will pay higher for vaccine B and D, those who have not will pay more for vaccine A & C, though only vaccine A had significant values while the respondents who had not cared for a relative with Covid-19 disease will pay higher for covid-19 vaccines. Respondents who had health insurance will pay more for vaccine B, C & D while those who do not have health insurance had higher WTP values for vaccine A. Details is as shown in Table 3 . Table 3 Relationship between respondents’ Covid-19 experiences and willingness to-pay S/N Variable Number of Respondents (n) WTP of Vaccine A (₦) WTP of Vaccine B (₦) WTP of Vaccine C (₦) WTP of Vaccine D (₦) 1. Ever Treated a patient with Covid-19 Yes 120 36303.71± 24877.51 38001± 25163.12 34306.34± 24100.66 3541505± 25081.58 No 159 34198± 25250.07 35265.96± 25664.83 32992.52± 25630.85 35217.18± 26435.88 P-Value 0.4883 0.3749 0.6640 0.0001* 2. Ever been hospitalized with Covid-19 Yes 9 33982.44± 23909.02 40539.22± 20306.32 30454.33± 2247.59 45383.22± 26721.32 No 270 351414.06± 35114.92 36305.8± 36137.46 33661.04± 33703.81 34966.26± 35064.16 P-Value 0.0076* 0.7272 0.7773 0.3785 3. Ever Cared for a relative with Covid-19 Yes 44 30003.16± 24000.47 37902.93± 22472.06 32733.45± 22560.81 32508.82± 24685.64 No 235 36058± 25196.55 36168.89± 25993.6 33711.91± 35412.22 35825.32± 26040.08 P-Value 0.1407 0.999 0.8600 0.4352 4. Do you have health Insurance Yes 49 33450.53± 19449.46 41153.2± 22360.61 35690± 25411.67 40281.86± 21842.31 No 230 35455.87± 26130.9 35438.75± 25983.99 33103.31± 24881.23 34241.42± 26507.23 P-value 0.6120 0.1538 0.5109 0.1373 5. Available Vaccines against Covid-19 Yes 49 33450.53± 19449.46 41153.2± 22360.61 35690± 25411.67 40281.86± 21842.31 No 230 35455.87± 26130.9 35438.75± 25983.99 33103.31± 24881.23 34241.42± 26507.23 P-value 0.6120 0.1538 0.5109 0.1373 *Values are significant at p < 0.05 Discussion In recent years, willingness to pay (WTP) has become a central metric to assess the health choices that customers make and to inform health policy formulations [ 27 ]. Prices of medicines are fixed by pharmaceutical companies to generate profit. However, the consumers purchasing power and willingness-to-pay are usually considered in pricing pharmaceuticals. This study assessed healthcare professional’s willingness-to-pay and preferences for the available Covid-19 vaccines. Most studies had assessed willingness to pay for Covid-19 vaccines but had not assessed the willingness to pay and the preferences for these vaccines based on the individual vaccine’s attributes and characteristics. The preference for any treatment or services is usually a factor of effectiveness, level of side effects, availability and conspiracy stories associated with it. Vaccine hesitancy is always a product of conspiracy theories [ 28 ]. In Nigeria, about 33 conspiracy theories which spread misinformation and mistrust among the public about the pandemic, the government responses and the vaccines were identified causing vaccine hesitance [ 29 ]. Willingness to pay was associated with gender and marital status in this study. The males had high WTP values for all the vaccines, though the differences were significant for only vaccine C and D. Although, several studies have stated that females were more willing to get vaccinated than the males [ 30 , 31 ], which could be due to their high tendency to seek protection against infections and their care giving roles. In many places around the globe, significant disparity exists in the earning potentials of the different genders. Women generally earn around 51.5% less than men in their lifetime work hours though the difference can be as low as 17.3% for women without children and around 68% for women with multiple children [ 32 ]. Even though Nigeria is considered an egalitarian society, significant gap still exists in the earnings between both genders. This could robustly explain the differences in the WTP between males and females in this study. High WTP values for Vaccine A was revealed in respondents who are within the age group of 18–24, while respondents who were aged between 25–34 were more willing to pay for Vaccine B and C. however, low WTP values were observed in older respondents. Risk seeking behaviors could be a potent explanation to higher WTP seen among younger participants. Few academics have examined how people make decisions that involve risk and uncertainty and how such decisions affect behaviors connected to health, with significant policy implications [ 33 ]. Risk preference might have propelled this group to seek out the vaccine and stay clear of the effects of having the disease. Healthcare professionals who had treated a patient with Covid-19 disease had high WTP values for all the vaccines with the highest willingness to pay for vaccine D, likewise those who had been sick with Covid-19. Healthcare professionals who had been hospitalized for Covid-19 had high WTP values for vaccine B and D while those who had not been hospitalized were more willing to pay for vaccine A and C. Risk aversion could be the reason for these findings. Doctors had high WTP for vaccine B. Doctors are the main front line healthcare professionals in many pandemic and emergency responses. This put them at great risk of contracting and dying from the disease especially virulent viruses like Covid-19. This were clearly seen in the Ebola and Dengue fever outbreaks in sub-Saharan Africa. Doctors understand the high level of risk to their lives and so would clearly want to pay more for the safety that the vaccine would offer. Arabyat et al, 2023 reported similar patterns among healthcare professionals in Jordanian hospitals. Healthcare professionals who believe that the available vaccines are effective against Covid-19 disease had high willingness to pay for all the vaccines. Majority of the healthcare professionals in this study preferred vaccine A, though vaccine B had the highest WTP values. This could be due to the specific attributes of Vaccine A, since it is recommended for individuals who are less than 18 years. Lack of trust and confidence in the effective and side effects in the available vaccines was identified as a barrier to the vaccination of medical personnel [ 34 ]. Vaccine acceptance and hesitancy is influenced by cultural, political, social, and economic factors. Other factors include time, place, social behavior of the society and specific vaccines [ 35 , 36 ]. Recommendations There is need for pharmaceutical companies to invest in the research and manufacturing of Vaccine A and vaccines with similar attributes as Vaccine A (that can be used in younger individuals) since it is the most preferred. The government and policy makers in hospital management needs to recommend and introduce Vaccine A in the Nigerian healthcare system and make it available, accessible and affordable to the general public at subsidized rate so as to improve acceptance and adherence in order to achieve herd immunity in the country. Limitations of Study This study may not be fully representative of the health care workers in Enugu State since only one hospital was used. Therefore, caution should be applied in generalizing the findings of this study. The findings of this study may not necessarily be applicable to other occupational groups. This was a cross-sectional study and obtained information at only one-time point, the findings may differ on a long term exposure to various factors and if the study was conducted at a different time. There may be bias,in the responses obtained since this study was a self-reported study conducted with a questionnaire. Conclusion The healthcare professionals preferred Vaccine A with a willingness to pay value of ₦35,103.63 ± 25067.29. The willingness-to-pay for the available Covid-19 vaccine was influenced by the respondents’ demographic characteristics, Covid-19 beliefs and experiences. Abbreviations WTP Willingness–to–pay COVID 19–Coronavirus disease 2019 Declarations Ethical approval and consent to participate The ethical approval of this study was obtained from the Medical Advisory Committee of the ESUT Teaching Hospital, Enugu with the reference number ESUTHp/c-MAC/RA/034/Vol.2/181 . Informed consent was obtained from each participant before they were given the questionnaires to fill. Clinical Trial Number Not Applicable Consent for publication Not applicablwe Availability of data and materials All data generated during this study are included in this manuscript. Competing interests The authors declare that they do not have competing interests. Funding No funding was received for this study Authors Contribution: C.N.I. conceived the project. ISE collected the data. C.N.I. analysed and interpreted data. ISE drafted the original manuscript. C.N.I. critically reviewed the manuscript. All authors read and approved the final manuscript. Acknowledgement Not applicable References Wiersinga WJ, Rhodes A, Cheng AC, et al. Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA. 2020;324: 782–793. Hao YJ, Wang YL, Wang MY, Zhou L, Shi JY, Cao JM, Wang DP. The origins of COVID-19 pandemic: A brief overview. Transboundary and emerging diseases. 2022;69(6):3181–3197. Doi.org/10.1111/tbed.14732 World Bank. 2021; Doi.org/worldbank.org/en/country/jordan/overview. Lalani HS, Nagar S, Sarpatwari A, Barenie RE, Avorn J, Rome BN, Kesselheim AS. US public investment in development of mRNA covid-19 vaccines: retrospective cohort study. BMJ (Clinical research ed.). 2023;380. 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Infect Chemother. 2021;53(2):261. Okediran JO, Ilesanmi OS, Fetuga AA, Onoh I, Afolabi AA, Ogunbode O, Olajide L, Kwaghe AV, Balogun MS. The experiences of healthcare workers during the COVID-19 crisis in Lagos, Nigeria: A qualitative study. Germs. 2020:10(4):356–366. Doi.org/10.18683/germs.2020.1228 World Health Organization. Infodemic. 2021. Doi.org//www.who.int/teams/risk-communication/infodemic - management. World Health Organization. Coronavirus disease (COVID-19) advice for the public. Coronavirus Dis 2019. 2020. Abu-Raddad LJ, Chemaitelly H, Butt AA. Effectiveness of the BNT162b2 Covid-19 Vaccine against the B.1.1.7 and B.1.351 Variants. New England Journal of Medicine 2021;0(0) Andrews N, Tessier E, Stowe J, Gower C, Kirsebom F, Simmons R, et al. Vaccine effectiveness and duration of protection. Angel Y, Spitzer A, Henig O, Saiag E, Sprecher E, Padova H, et al. Association Between Vaccination with BNT162b2 and Incidence of Symptomatic and Asymptomatic SARS-CoV-2 Infections Among Health Care Workers. JAMA 2021. Doi.org //jamanetwork.com/journals/jama/fullarticle/2779853. Azamgarhi T, Hodgkinson M, Shah A, et al. Experience of COVID-19 Vaccination of Healthcare Workers in a Hospital Setting 2021. Doi.org//www.researchsquare.com/article/rs-257937/v1. Bernal JL, Andrews N, Gower C, et al. Effectiveness of COVID-19 vaccines against the B.1.617.2 variant. Epidemiology 2021. Doi,org/10.1101/2021.05.22.21257658. Bernal JL, Andrews N, Gower C, et al. Effectiveness of the Pfizer-BioNTech and Oxford-AstraZeneca vaccines on covid-19 related symptoms, hospital admissions, and mortality in older adults in England: test negative case-control study. BMJ 2021;373:1088. Audureau E, Davis B, Besson MH, Saba J, Ladner J. Willingness to pay for medical treatments in chronic diseases: a multicountry survey of patients and physicians. Journal of comparative effectiveness research. 2019;8(5):357–369. Doi.org/10.2217/cer-2018-0106 Troiano G, Nardi A. Vaccine hesitancy in the era of COVID-19. Public health. 2021;194:245–251. Doi.org/10.1016/j.puhe.2021.02.025 Wonodi C, Obi-Jeff C, Adewumi F, Keluo-Udeke SC, Gur-Arie R, Krubiner C, Jaffe EF, Bamiduro T, Karron R, Faden R. Conspiracy theories and misinformation about COVID-19 in Nigeria: Implications for vaccine demand generation communications. Vaccine. 2022;40(13):2114–2121. Doi.org/10.1016/j.vaccine.2022.02.005 Morgan R, Tan HL, Oveisi N, Memmott C, Korzuchowski A, Hawkins K, et al. Women healthcare workers’ experiences during COVID-19 and other crises: a scoping review. Int J Nurs Stud Adv. 2022;4:100066. Seale H, Heywood AE, Leask J, et al. Examining Australian public perceptions and behaviors towards a future COVID-19 vaccine. BMC Infect Dis. 2021;21(1):21. 39. Glaubitz R, Harnack‐Eber A, Wetter M. ‘The gender gap in lifetime earnings: A microsimulation approach’, LABOUR [Preprint]. 2024. doi:10.1111/labr.12274. Gbogbolu A, Nketiah-Amponsah E. Individual risk preference as a predictor of health behaviour: evidence from the use of condoms against HIV/AIDS in Ghana. BMC public health. 2023;23(1):1657. Doi.org/10.1186/s12889-023-16579-7 Heidari M, Jafari H. Challenges of COVID-19 vaccination in Iran: in the fourth wave of pandemic spread. Prehosp Disaster Med. 2021;36(5):659–60. Omidvar S, Firouzbakht M. Acceptance of COVID-19 vaccine and determinant factors in the Iranian population: a web-based study. BMC Health Serv Res. 2022;22(1):1–8. Patwary MM, Bardhan M, Disha AS, Hasan M, Haque MZ, Sultana R, et al. Determinants of COVID-19 vaccine acceptance among the adult population of Bangladesh using the health belief model and the theory of planned behavior model. Vaccines. 2021;9(12):1393. Additional Declarations No competing interests reported. Supplementary Files Covid19WTPInstrumentforhealthworkers114628.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Jul, 2025 Reviews received at journal 16 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviewers invited by journal 28 May, 2025 Editor assigned by journal 20 May, 2025 Editor invited by journal 06 May, 2025 Submission checks completed at journal 05 May, 2025 First submitted to journal 05 May, 2025 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. 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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-6470613","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463479307,"identity":"502513b4-f283-4f68-9f8b-9523d8359799","order_by":0,"name":"Chinelo Nneka Ikeanyi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYDACCcYGBoYCBgY2HghXDiLMRkiLAUKLMRFaQARQCwNEC0NiAyEt/LOb2x58MLBh4OM5fPBzYZtF+obbvQcYPpQdZjDnX4DdkjsH2w1nGKQxsPG2JUvPbJPI3XDnXALjjHOHGSxnPMCqxUAisU2ax+AwAxs/j4E0L0jLjRwDZt62wwwGNw7g1vIHrIX/82+glnQDkJa/hLQwgLTw9rCBbEkAa2EEaTnfgN0vNxLbJHsM0njYeI6ZWfOckzCcCdRysOdcOo/BDRwhNiP9mcSPChs5+Z7kx7d5yurk+W7kGD74UWYtZ3Aeu8NgABIrjNDoOAAWkUjAqwUK/qC4AL8to2AUjIJRMGIAAOZXWDyiIkptAAAAAElFTkSuQmCC","orcid":"","institution":"University of Nigeria Nsukka","correspondingAuthor":true,"prefix":"","firstName":"Chinelo","middleName":"Nneka","lastName":"Ikeanyi","suffix":""},{"id":463479308,"identity":"b51422f7-fb07-4808-946d-fa24cfb34db4","order_by":1,"name":"Ifunanya Sandra Ezenwa","email":"","orcid":"","institution":"University of Nigeria Nsukka","correspondingAuthor":false,"prefix":"","firstName":"Ifunanya","middleName":"Sandra","lastName":"Ezenwa","suffix":""}],"badges":[],"createdAt":"2025-04-17 10:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6470613/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6470613/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83770968,"identity":"7d9db39d-6138-4f42-9ddb-146296c1b870","added_by":"auto","created_at":"2025-06-02 12:30:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136473,"visible":true,"origin":"","legend":"\u003cp\u003eShows the preferences of health workers for Covid-19 vaccines.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6470613/v1/84d078938d77cb6eee655141.png"},{"id":83771950,"identity":"08766e92-08c7-42b0-a8d0-168d08b5a935","added_by":"auto","created_at":"2025-06-02 12:46:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1154192,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6470613/v1/f16b3d9a-35ad-4240-95e9-843bd65a1023.pdf"},{"id":83770972,"identity":"b5071729-8287-4281-9ed6-b3ef6136b8a3","added_by":"auto","created_at":"2025-06-02 12:30:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":116458,"visible":true,"origin":"","legend":"","description":"","filename":"Covid19WTPInstrumentforhealthworkers114628.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6470613/v1/390a8cd97ff56cc233864b8a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Healthcare Professionals’ Willingness-to-Pay and Preferences for Covid-19 Vaccines in a Nigerian Tertiary Hospital: A Cross-Sectional Study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe Covid-19 pandemic which started in Wuhan Province of China in December 2019 was declared a public health emergency of international concern by the WHO on January 11, 2020. The COVID-19 disease is caused by a novel coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is transmitted most commonly through droplets spread from infected persons during coughing, sneezing, or talking [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Coronavirus disease is a respiratory illness with a clinical spectrum of mild to moderate disease (80%), severe disease (15%), and critical illness (5%) with an overall case fatality rate of 0.5\u0026ndash;2.8% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The pandemic had major impact in all aspect of life as economies, academics, businesses were shut down and the healthcare system severely overburdened [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Proven therapeutics for the treatment of COVID \u0026minus;\u0026thinsp;19 is yet underway but various vaccines have been approved for use by the World Health Organization. The Pfizer-BioNTech, the Moderna, the Johnson and Johnson and the AstraZeneca-University of Oxford COVID-19 vaccines were among the first vaccines given emergency authorization for use by WHO for use [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These vaccines are based upon inactivated or live attenuated viruses, the protein subunits, viral vectors etc. They are considered safe based on accelerated clinical trial data and efficacious (above 50%).\u003c/p\u003e \u003cp\u003eThe economic consequences of the COVID-19 pandemic, which include a reduction in the employment rate by more than 20%, a reduction in average household income by around one-fifth, and a reduction in the Gross Domestic Product (GDP) growth rates [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], increase in expenditure for drugs and other health services is a significant challenge to sub\u0026ndash;Saharan Africa and Nigeria in Particular [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAbout 4% of the Nigerian national budget goes to funding of healthcare services, and falls below recommendations by the WHO. Inefficiency of the health system across the country, limited institutional capacity, corruption, unstable and dwindling economic, and lack of political will, have increased the challenges and difficulties associated with the funding of increasing healthcare need [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The rise in incidences of new diseases have raised the burden of diseases and healthcare costs for governments and public institution and diminishing funding for health. The vaccines were initially distributed freely but as resources dwindled, pressure has been put on government and individuals for funding, therefore, individuals will be faced with the responsibility of paying for their healthcare needs and services out-of-pocket [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Limited resources for healthcare funding and increasing emergence of new diseases has also shifted the burden of care towards the patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous studies revealed that the rate of acceptance of the COVID-19 vaccines is low in most countries in the world [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Costs of the vaccines have been identified as a predictor of willingness-to-pay [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Due to the weak economy and scarce healthcare resources, it is important to conduct pharmacoeconomics studies to determine the consumer preferences [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and valuation of these vaccines including willingness to pay (WTP) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eHealth economics and willingness to pay (WTP) is a long-time model to evaluate the choices patients make regarding their health [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Willingness-to-pay is a methodological tool to ascertain the hypothetical monetary value of programs, specific medical interventions, and treatments. Willingness to pay is an objective measure of the value people place on a product or service. It has been used in many areas of marketing management, such as pricing decisions or new product development. Knowledge of a product's willingness to pay on behalf of its potential buyers is critical as it helps to set prices in order to maximize profits while delivering equivalent healthcare value and service [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHealthcare professionals are frontiers in emergency responses such as Covid-19 and are at high risk of getting infected with the corona virus, hence they are of high priority for vaccination [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Their readiness to part with a fee for the vaccine will promote and validate the immunization against this life-threatening disease among the general population [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Studies have shown that healthcare providers\u0026rsquo; recommendations have the potential to increase WTP for the vaccine [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] since they are an essential source of evidence for the efficacy of vaccines. Thus, there is need to evaluate the value attached by healthcare professionals to the available Covid-19 vaccines and their preferences. A previous study in Ibadan, southwestern Nigeria revealed healthcare professionals\u0026rsquo; positive perception towards Covid-19 vaccines but low (18.2%) willingness to pay. Knowledge of the willingness-to-pay for the Covid-19 vaccines will encourage pharmaceutical companies to invest in the manufacturing of affordable Covid-19 vaccines and in pricing these Covid-19 vaccines in face of the significant economic challenges. It will also enable the government to determine if subsidies will be needed if the selling price of the available vaccines are above the end-user\u0026rsquo;s willingness-to-pay. In addition, information on healthcare professionals\u0026rsquo; preference will enable healthcare policy makers to know the Covid-19 vaccine that should be introduced into the healthcare system at a subsidized rate and have continuous support as this may improve compliance. This study assessed healthcare professional\u0026rsquo;s willingness to pay and preference for the available Covid-19 vaccines in Nigeria.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study was a descriptive cross-sectional study conducted on healthcare professionals in a tertiary healthcare institution in Nigeria using a convenient sampling method with the aid of a structured self-administered questionnaire.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Setting\u003c/h3\u003e\n\u003cp\u003eThe study was conducted at the Enugu State University Teaching Hospital, Enugu which comprises of five clinical departments, 309 doctors, 396 nurses and 44 pharmacists excluding interns as their current clinical staff. The clinical departments include Obstetrics and gynecology, surgery, internal medicine, pediatrics, and pharmacy departments.\u003c/p\u003e\n\u003ch3\u003eStudy Instrument\u003c/h3\u003e\n\u003cp\u003eThe questionnaire used was developed for this study and face validated by two (2) experts each in public health, pharmacoeconomics and clinical pharmacy (uploaded as a Supplementary material). It was a structured questionnaire which had two sections; section A and section B. Section A obtained the respondent demographics and other information such as if the respondent had been sick of Covid-19, had treated a patient who had Covid-19, had been hospitalized for Covid-19 disease etc. Section B gave a description of the COVID-19 vaccines, denoted as Vaccine A, B, C and D, and obtained the respondents willingness to pay and preferred Covid-19 vaccine [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe questions were stated as thus;\u003c/p\u003e \u003cp\u003e\u0026ldquo;VACCINE A: Has 95% efficacy. Given in two doses separated by 3\u0026ndash;12 weeks. Stored at a temperature of -80\u003csup\u003eo\u003c/sup\u003eC and \u0026minus;\u0026thinsp;60\u003csup\u003eoC\u003c/sup\u003e. Suitable for individuals 16 years and above. Has side effects of pain, redness and swelling at the site of injection; tiredness, headaches, muscle pain, chills, fever and nausea. Write how much you are willing to pay for the two doses of this vaccine?\u003c/p\u003e \u003cp\u003eVACCINE B: Has 95% efficacy. Given as two doses, separated by 4weeks. Stored at a temperature of \u0026minus;\u0026thinsp;50\u003csup\u003eO\u003c/sup\u003eC and \u0026minus;\u0026thinsp;15\u003csup\u003eO\u003c/sup\u003eC. Used in individuals 18years and above. Causes pain, redness and swelling at the site of administration; it also causes tiredness, headaches, muscle pain, chills, fever and nausea. Write the amount you are willing to pay for the two doses of this vaccine?\u003c/p\u003e \u003cp\u003eVACCINE C: Has 66% efficacy. Given as a single dose. Stored at a temperature of 2\u003csup\u003eO\u003c/sup\u003eC and 8\u003csup\u003eO\u003c/sup\u003eC. Used in individuals 18 years and above. It causes pain, redness and swelling at the site of administration; it also causes tiredness, headaches, muscle pain, chills, fever and nausea. How much are you willing to pay for the two doses of this vaccine?\u003c/p\u003e \u003cp\u003eVACCINE D: Has 70% efficacy. Given in two doses at an interval of 8\u0026ndash;12 weeks. Stored at a temperature of 2\u003csup\u003eo\u003c/sup\u003eC and 8\u003csup\u003eoC\u003c/sup\u003e. Suitable for individuals 18 years and above. It has side effects of blood clots, low platelet count, headaches, fatigue, muscle or joint pain, fever, chills and nausea. State the amount you are willing to pay for this vaccine?\u0026rdquo;\u003c/p\u003e \u003cp\u003eA pilot study was conducted on fifty (50) healthcare professionals in a tertiary institution different from the one used for the study. The questionnaire was found to be usable since it was easily understood and obtained the required information. The reliability and content validity of the questionnaire was evaluated using Cronbach alpha.\u003c/p\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eAll doctors, nurses and pharmacists practicing in the Enugu State University Teaching Hospital who are willing to participate in the research and adhere to the study protocol was included. All other health workers both within and outside the hospital were excluded from the study.\u003c/p\u003e\n\u003ch3\u003eStudy Population/Sample Size\u003c/h3\u003e\n\u003cp\u003e The study includes all the doctors, nurses and pharmacists of the Enugu State University Teaching Hospital who consent to be a part of the study. The total number of the doctors, nurses and pharmacist who are staff of the hospital obtained from the personnel unit was seven hundred and forty-nine (749). The sample size was calculated with the aid of raosoft; a sample size calculator and a sample size of two hundred and fifty-five (255) was obtained at 95% confidence interval and error margin of 5%.\u003c/p\u003e \u003cp\u003eHowever, three hundred (300) questionnaires were distributed for attrition, to create room for the questionnaires that may not be properly filled.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eThe questionnaires were given to the health workers who consented to be a part of the study during their working hours. They were given few minutes to go through the questions and respond accordingly. After which the questionnaire was retrieved, numbered and kept in an envelope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData obtained was coded and entered into a Microsoft excel spreadsheet for data cleaning and to obtain the frequency, percentage, Willingness-to-pay (WTP) and standard deviation. The student T-test and ANOVA were used where appropriate to obtain the relationship between the demographic variables and WTP using graphpad inStat 3.0.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e The ethical approval of this study was sobtained from the Medical Advisory Committee of the ESUT Teaching Hospital, Enugu with the reference number ESUTHp/c-MAC/RA/034/Vol.2/181. Informed consent was obtained from each respondent before they were given the questionnaire to fill. The health workers were informed that their involvement in the study was entirely voluntary and that they might leave at any point without facing any repercussions. No identifying information was obtained from the respondents since confidentiality, privacy and anonymity were respected in accordance with the Nuremberg code and Helsinki declarations.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOut of the 300 questionnaires that were given out, 279 questionnaires were returned and found usable giving a response rate of 93%. A majority (171;61.29%) of the respondents were females while the rest were males. Most (235;84.2%) of the respondents were between the age of 25-44yrs. The married were 210(75.3%) while 3(1.08%) were either widowed or divorced. Less than half 122 (43.73%) were doctors while 64(22.94%) were pharmacists. Most (210; 75.27%) have not been sick with Covid-19 disease while 120 (43.01%) had treated a patient with Covid-19 disease. However, 44 (15.77%) had cared for a relative with Covid-19 while 270 (96.77%) had not been hospitalized due to Covid-19 disease. About 149 (53.41%) believe that Covid-19 exists while 230 (82.44%) do not have health insurance. See details in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\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\u003eSocio-demographic Characteristics of Respondents. (n\u0026thinsp;=\u0026thinsp;279)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of Respondents\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage (%)\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\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.29\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\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.15\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\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.08\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\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePharmacist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.33\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\u003eHave you had Covid-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.27\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\u003eHave you treated a patient with Covid-19 before\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.99\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\u003eHave you been hospitalized for Covid-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.77\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\u003eHave you cared for a relative with Covid-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.23\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\u003eDo you have health Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.44\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\u003eAvailable vaccines effective against Covid-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.59\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\u003eVaccine Preference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaccine A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaccine B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaccine C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaccine D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.13\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\u003eMajority of the respondent (45.52%) preferred Vaccine A while 1.08% preferred Vaccine C and 16.13% have no preferences. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e has the details.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe mean willingness-to-pay values for Vaccine A is ₦35103.63\u0026thinsp;\u0026plusmn;\u0026thinsp;25067.29, Vaccine B is ₦36442.36\u0026thinsp;\u0026plusmn;\u0026thinsp;25440.88, Vaccine C is ₦33557.60\u0026thinsp;\u0026plusmn;\u0026thinsp;24948.49 and Vaccine D is ₦35302.29\u0026thinsp;\u0026plusmn;\u0026thinsp;25816.39. However, there is no statistical difference between the mean values of these vaccines (P\u0026thinsp;=\u0026thinsp;0.6070).\u003c/p\u003e \u003cp\u003eThe male respondents had a higher WTP values for all the Covid-19 vaccines, although this was only statistically significant for vaccines C \u0026amp; D. The WTP values decreased as the age increases. Respondents who are between the ages of 18\u0026ndash;44 years had higher WTP. This was statistically significant for vaccine A, B, and D. The widowed and/or divorced respondents were more willing to pay for Covid-19 vaccines than the married ones, although this was significant only for vaccine A. The medical doctors had the highest willingness to pay values for all the Covid-19 vaccines. However, the values were only significant for vaccine B. Health workers who had previously contracted Covid-19 disease will pay higher for vaccine B \u0026amp; D while those who have not been sick with Covid-19 disease will pay higher for vaccine A \u0026amp; C. Though this was not statistically significant. Details is as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eRelationship between Willingness-to-Pay with the respondents\u0026rsquo; demographic characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of Respondents\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWTP of Vaccine A (₦)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWTP of Vaccine B (₦)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWTP of Vaccine C (₦)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWTP of Vaccine D (₦)\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\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35962.6\u0026plusmn;\u003c/p\u003e \u003cp\u003e23510.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39692.52\u0026plusmn;\u003c/p\u003e \u003cp\u003e24704.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38952\u0026plusmn;\u003c/p\u003e \u003cp\u003e25058.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39410.04\u0026plusmn;\u003c/p\u003e \u003cp\u003e26327.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34561.2\u0026plusmn;\u003c/p\u003e \u003cp\u003e26055.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34389.63\u0026plusmn;\u003c/p\u003e \u003cp\u003e25755.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30150.61\u0026plusmn;\u003c/p\u003e \u003cp\u003e24340.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32707.92\u0026plusmn;\u003c/p\u003e \u003cp\u003e25221.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0039*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0344*\u003c/b\u003e\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\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39087.9\u0026plusmn;\u003c/p\u003e \u003cp\u003e30382.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32496.35\u0026plusmn;\u003c/p\u003e \u003cp\u003e24214.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39645.1\u0026plusmn;\u003c/p\u003e \u003cp\u003e32187.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34064.8\u0026plusmn;\u003c/p\u003e \u003cp\u003e27292.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38028.33\u0026plusmn;\u003c/p\u003e \u003cp\u003e24391.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39038.38\u0026plusmn;\u003c/p\u003e \u003cp\u003e24436.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34763.05\u0026plusmn;\u003c/p\u003e \u003cp\u003e24186.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38559.95\u0026plusmn;\u003c/p\u003e \u003cp\u003e24834.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e342964\u0026plusmn;\u003c/p\u003e \u003cp\u003e24561.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36984.64\u0026plusmn;\u003c/p\u003e \u003cp\u003e25849.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32540.29\u0026plusmn;\u003c/p\u003e \u003cp\u003e23225.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34665.75\u0026plusmn;\u003c/p\u003e \u003cp\u003e25915.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22074.89\u0026plusmn;\u003c/p\u003e \u003cp\u003e22277.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26906.67\u0026plusmn;\u003c/p\u003e \u003cp\u003e28296.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29323.22\u0026plusmn;\u003c/p\u003e \u003cp\u003e29374.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23445.67\u0026plusmn;\u003c/p\u003e \u003cp\u003e27613.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9803.33\u0026plusmn;\u003c/p\u003e \u003cp\u003e12918.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11439\u0026plusmn;\u003c/p\u003e \u003cp\u003e21594.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16542.5\u0026plusmn;\u003c/p\u003e \u003cp\u003e27875.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13606.67\u0026plusmn;\u003c/p\u003e \u003cp\u003e240402.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0088*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0354*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0351*\u003c/b\u003e\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\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33931.31\u0026plusmn;\u003c/p\u003e \u003cp\u003e25003.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37344,36\u0026plusmn;\u003c/p\u003e \u003cp\u003e25669.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33210.87\u0026plusmn;\u003c/p\u003e \u003cp\u003e24714.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3609.11\u0026plusmn;\u003c/p\u003e \u003cp\u003e25993.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38209.91\u0026plusmn;\u003c/p\u003e \u003cp\u003e37949.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33508.05\u0026plusmn;\u003c/p\u003e \u003cp\u003e32691.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34491.82\u0026plusmn;\u003c/p\u003e \u003cp\u003e34428.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33198.88\u0026plusmn;\u003c/p\u003e \u003cp\u003e33694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48832.33\u0026plusmn;\u003c/p\u003e \u003cp\u003e9780.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37857.67\u0026plusmn;\u003c/p\u003e \u003cp\u003e31545.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37276\u0026plusmn;\u003c/p\u003e \u003cp\u003e10984.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26009.33\u0026plusmn;\u003c/p\u003e \u003cp\u003e30615.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6478\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\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38517.48\u0026plusmn;\u003c/p\u003e \u003cp\u003e24011.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40727.39\u0026plusmn;\u003c/p\u003e \u003cp\u003e24636.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35109.86\u0026plusmn;\u003c/p\u003e \u003cp\u003e22937.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37606.37\u0026plusmn;\u003c/p\u003e \u003cp\u003e24382.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePharmacist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32186.47\u0026plusmn;\u003c/p\u003e \u003cp\u003e24566.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33885.45\u0026plusmn;\u003c/p\u003e \u003cp\u003e24509.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31795.67\u0026plusmn;\u003c/p\u003e \u003cp\u003e25506.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33980.64\u0026plusmn;\u003c/p\u003e \u003cp\u003e26815.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32632.91\u0026plusmn;\u003c/p\u003e \u003cp\u003e26451.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32580.74\u0026plusmn;\u003c/p\u003e \u003cp\u003e26499.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32733,82\u0026plusmn;\u003c/p\u003e \u003cp\u003e27152.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33189.25\u0026plusmn;\u003c/p\u003e \u003cp\u003e26959.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0434*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4157\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\u003ePrevious Diagnosis with Covid-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33957.91\u0026plusmn;\u003c/p\u003e \u003cp\u003e21769.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36443.54\u0026plusmn;\u003c/p\u003e \u003cp\u003e24287.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31784.49\u0026plusmn;\u003c/p\u003e \u003cp\u003e24184.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40451.7\u0026plusmn;\u003c/p\u003e \u003cp\u003e26730.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35480.15\u0026plusmn;\u003c/p\u003e \u003cp\u003e26096.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36441.98\u0026plusmn;\u003c/p\u003e \u003cp\u003e25865.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34140.2\u0026plusmn;\u003c/p\u003e \u003cp\u003e25345.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33610.34\u0026plusmn;\u003c/p\u003e \u003cp\u003e25345.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0560\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Values are significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe respondents who had treated a patient with Covid-19 disease will pay higher for the vaccines, however it was only significant for vaccine D. The respondents who had been hospitalized for Covid-19 disease will pay higher for vaccine B and D, those who have not will pay more for vaccine A \u0026amp; C, though only vaccine A had significant values while the respondents who had not cared for a relative with Covid-19 disease will pay higher for covid-19 vaccines. Respondents who had health insurance will pay more for vaccine B, C \u0026amp; D while those who do not have health insurance had higher WTP values for vaccine A. Details is as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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\u003eRelationship between respondents\u0026rsquo; Covid-19 experiences and willingness to-pay\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of Respondents\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWTP of Vaccine A\u003c/p\u003e \u003cp\u003e(₦)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWTP of Vaccine B (₦)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWTP of Vaccine C (₦)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWTP of Vaccine D (₦)\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\u003eEver Treated a patient with Covid-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36303.71\u0026plusmn;\u003c/p\u003e \u003cp\u003e24877.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38001\u0026plusmn;\u003c/p\u003e \u003cp\u003e25163.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34306.34\u0026plusmn;\u003c/p\u003e \u003cp\u003e24100.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3541505\u0026plusmn;\u003c/p\u003e \u003cp\u003e25081.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34198\u0026plusmn;\u003c/p\u003e \u003cp\u003e25250.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35265.96\u0026plusmn;\u003c/p\u003e \u003cp\u003e25664.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32992.52\u0026plusmn;\u003c/p\u003e \u003cp\u003e25630.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35217.18\u0026plusmn;\u003c/p\u003e \u003cp\u003e26435.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0001*\u003c/b\u003e\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\u003eEver been hospitalized with Covid-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33982.44\u0026plusmn;\u003c/p\u003e \u003cp\u003e23909.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40539.22\u0026plusmn;\u003c/p\u003e \u003cp\u003e20306.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30454.33\u0026plusmn;\u003c/p\u003e \u003cp\u003e2247.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45383.22\u0026plusmn;\u003c/p\u003e \u003cp\u003e26721.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e351414.06\u0026plusmn;\u003c/p\u003e \u003cp\u003e35114.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36305.8\u0026plusmn;\u003c/p\u003e \u003cp\u003e36137.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33661.04\u0026plusmn;\u003c/p\u003e \u003cp\u003e33703.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34966.26\u0026plusmn;\u003c/p\u003e \u003cp\u003e35064.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0076*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3785\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\u003eEver Cared for a relative with Covid-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30003.16\u0026plusmn;\u003c/p\u003e \u003cp\u003e24000.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37902.93\u0026plusmn;\u003c/p\u003e \u003cp\u003e22472.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32733.45\u0026plusmn;\u003c/p\u003e \u003cp\u003e22560.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32508.82\u0026plusmn;\u003c/p\u003e \u003cp\u003e24685.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36058\u0026plusmn;\u003c/p\u003e \u003cp\u003e25196.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36168.89\u0026plusmn;\u003c/p\u003e \u003cp\u003e25993.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33711.91\u0026plusmn;\u003c/p\u003e \u003cp\u003e35412.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35825.32\u0026plusmn;\u003c/p\u003e \u003cp\u003e26040.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4352\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\u003eDo you have health Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33450.53\u0026plusmn;\u003c/p\u003e \u003cp\u003e19449.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41153.2\u0026plusmn;\u003c/p\u003e \u003cp\u003e22360.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35690\u0026plusmn;\u003c/p\u003e \u003cp\u003e25411.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40281.86\u0026plusmn;\u003c/p\u003e \u003cp\u003e21842.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35455.87\u0026plusmn;\u003c/p\u003e \u003cp\u003e26130.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35438.75\u0026plusmn;\u003c/p\u003e \u003cp\u003e25983.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33103.31\u0026plusmn;\u003c/p\u003e \u003cp\u003e24881.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34241.42\u0026plusmn;\u003c/p\u003e \u003cp\u003e26507.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1373\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\u003eAvailable Vaccines against Covid-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33450.53\u0026plusmn;\u003c/p\u003e \u003cp\u003e19449.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41153.2\u0026plusmn;\u003c/p\u003e \u003cp\u003e22360.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35690\u0026plusmn;\u003c/p\u003e \u003cp\u003e25411.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40281.86\u0026plusmn;\u003c/p\u003e \u003cp\u003e21842.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35455.87\u0026plusmn;\u003c/p\u003e \u003cp\u003e26130.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35438.75\u0026plusmn;\u003c/p\u003e \u003cp\u003e25983.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33103.31\u0026plusmn;\u003c/p\u003e \u003cp\u003e24881.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34241.42\u0026plusmn;\u003c/p\u003e \u003cp\u003e26507.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Values are significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent years, willingness to pay (WTP) has become a central metric to assess the health choices that customers make and to inform health policy formulations [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Prices of medicines are fixed by pharmaceutical companies to generate profit. However, the consumers purchasing power and willingness-to-pay are usually considered in pricing pharmaceuticals. This study assessed healthcare professional\u0026rsquo;s willingness-to-pay and preferences for the available Covid-19 vaccines. Most studies had assessed willingness to pay for Covid-19 vaccines but had not assessed the willingness to pay and the preferences for these vaccines based on the individual vaccine\u0026rsquo;s attributes and characteristics. The preference for any treatment or services is usually a factor of effectiveness, level of side effects, availability and conspiracy stories associated with it. Vaccine hesitancy is always a product of conspiracy theories [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In Nigeria, about 33 conspiracy theories which spread misinformation and mistrust among the public about the pandemic, the government responses and the vaccines were identified causing vaccine hesitance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWillingness to pay was associated with gender and marital status in this study. The males had high WTP values for all the vaccines, though the differences were significant for only vaccine C and D. Although, several studies have stated that females were more willing to get vaccinated than the males [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which could be due to their high tendency to seek protection against infections and their care giving roles. In many places around the globe, significant disparity exists in the earning potentials of the different genders. Women generally earn around 51.5% less than men in their lifetime work hours though the difference can be as low as 17.3% for women without children and around 68% for women with multiple children [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Even though Nigeria is considered an egalitarian society, significant gap still exists in the earnings between both genders. This could robustly explain the differences in the WTP between males and females in this study.\u003c/p\u003e \u003cp\u003eHigh WTP values for Vaccine A was revealed in respondents who are within the age group of 18\u0026ndash;24, while respondents who were aged between 25\u0026ndash;34 were more willing to pay for Vaccine B and C. however, low WTP values were observed in older respondents. Risk seeking behaviors could be a potent explanation to higher WTP seen among younger participants. Few academics have examined how people make decisions that involve risk and uncertainty and how such decisions affect behaviors connected to health, with significant policy implications [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Risk preference might have propelled this group to seek out the vaccine and stay clear of the effects of having the disease.\u003c/p\u003e \u003cp\u003eHealthcare professionals who had treated a patient with Covid-19 disease had high WTP values for all the vaccines with the highest willingness to pay for vaccine D, likewise those who had been sick with Covid-19. Healthcare professionals who had been hospitalized for Covid-19 had high WTP values for vaccine B and D while those who had not been hospitalized were more willing to pay for vaccine A and C. Risk aversion could be the reason for these findings.\u003c/p\u003e \u003cp\u003eDoctors had high WTP for vaccine B. Doctors are the main front line healthcare professionals in many pandemic and emergency responses. This put them at great risk of contracting and dying from the disease especially virulent viruses like Covid-19. This were clearly seen in the Ebola and Dengue fever outbreaks in sub-Saharan Africa. Doctors understand the high level of risk to their lives and so would clearly want to pay more for the safety that the vaccine would offer. Arabyat et al, 2023 reported similar patterns among healthcare professionals in Jordanian hospitals.\u003c/p\u003e \u003cp\u003eHealthcare professionals who believe that the available vaccines are effective against Covid-19 disease had high willingness to pay for all the vaccines. Majority of the healthcare professionals in this study preferred vaccine A, though vaccine B had the highest WTP values. This could be due to the specific attributes of Vaccine A, since it is recommended for individuals who are less than 18 years.\u003c/p\u003e \u003cp\u003eLack of trust and confidence in the effective and side effects in the available vaccines was identified as a barrier to the vaccination of medical personnel [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Vaccine acceptance and hesitancy is influenced by cultural, political, social, and economic factors. Other factors include time, place, social behavior of the society and specific vaccines [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations\u003c/h2\u003e \u003cp\u003eThere is need for pharmaceutical companies to invest in the research and manufacturing of Vaccine A and vaccines with similar attributes as Vaccine A (that can be used in younger individuals) since it is the most preferred. The government and policy makers in hospital management needs to recommend and introduce Vaccine A in the Nigerian healthcare system and make it available, accessible and affordable to the general public at subsidized rate so as to improve acceptance and adherence in order to achieve herd immunity in the country.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of Study\u003c/h2\u003e \u003cp\u003eThis study may not be fully representative of the health care workers in Enugu State since only one hospital was used. Therefore, caution should be applied in generalizing the findings of this study. The findings of this study may not necessarily be applicable to other occupational groups.\u003c/p\u003e \u003cp\u003eThis was a cross-sectional study and obtained information at only one-time point, the findings may differ on a long term exposure to various factors and if the study was conducted at a different time. There may be bias,in the responses obtained since this study was a self-reported study conducted with a questionnaire.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe healthcare professionals preferred Vaccine A with a willingness to pay value of ₦35,103.63\u0026thinsp;\u0026plusmn;\u0026thinsp;25067.29. The willingness-to-pay for the available Covid-19 vaccine was influenced by the respondents\u0026rsquo; demographic characteristics, Covid-19 beliefs and experiences.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWTP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWillingness\u0026ndash;to\u0026ndash;pay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOVID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e19\u0026ndash;Coronavirus disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe ethical approval of this study was obtained from the Medical Advisory Committee of the ESUT Teaching Hospital, Enugu with the reference number ESUTHp/c-MAC/RA/034/Vol.2/181\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from each participant before they were given the questionnaires to fill.\u003c/p\u003e\n\u003cp\u003eClinical Trial Number\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicablwe\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eAll data generated during this study are included in this manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they do not have competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study\u003c/p\u003e\n\u003cp\u003eAuthors Contribution:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC.N.I. conceived the project. ISE collected the data. C.N.I. analysed and interpreted data. ISE drafted the original manuscript. C.N.I. critically reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWiersinga WJ, Rhodes A, Cheng AC, et al. Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA. 2020;324: 782\u0026ndash;793.\u003c/li\u003e\n \u003cli\u003eHao YJ, Wang YL, Wang MY, Zhou L, Shi JY, Cao JM, Wang DP. The origins of COVID-19 pandemic: A brief overview. Transboundary and emerging diseases. 2022;69(6):3181\u0026ndash;3197. Doi.org/10.1111/tbed.14732\u003c/li\u003e\n \u003cli\u003eWorld Bank. 2021; Doi.org/worldbank.org/en/country/jordan/overview.\u003c/li\u003e\n \u003cli\u003eLalani HS, Nagar S, Sarpatwari A, Barenie RE, Avorn J, Rome BN, Kesselheim AS. US public investment in development of mRNA covid-19 vaccines: retrospective cohort study. BMJ (Clinical research ed.). 2023;380. Doi.org/10.1136/bmj-2022-073747\u003c/li\u003e\n \u003cli\u003eIfeagwu SC, Yang JC, Parkes-Ratanshi R, Brayne C. Health financing for universal health coverage in Sub-Saharan Africa: a systematic review. Global health research and policy. 2021;6(1), 8. Doi.org/10.1186/s41256-021-00190-7\u003c/li\u003e\n \u003cli\u003eOgbodo OC. \u0026lsquo;Trends and challenges of Health Care Financing in Nigeria\u0026rsquo;, International Journal of Medical Case Reports and Reviews. 2023:2(5):1\u0026ndash;9. Doi.org/10.59657/2837-8172.brs.23.030\u003c/li\u003e\n \u003cli\u003eMoyo E, Mhango M, Moyo P, Dzinamarira T, Chitungo I, Murewanhema G. Emerging infectious disease outbreaks in Sub-Saharan Africa: Learning from the past and present to be better prepared for future outbreaks. Frontiers in public health. 2023:11:1049986. Doi.org/10.3389/fpubh.2023.1049986\u003c/li\u003e\n \u003cli\u003eSteigenberger C, Flatscher-Thoeni M, Siebert U, Leiter AM. Determinants of willingness to pay for health services: a systematic review of contingent valuation studies. The European journal of health economics: HEPAC : health economics in prevention and care. 2022;23(9):1455\u0026ndash;1482. Doi.org/10.1007/s10198-022-01437-x\u003c/li\u003e\n \u003cli\u003eSallam M, Dababseh D, Eid H et al. High rates of COVID-19 vaccine hesitancy and its association with conspiracy beliefs: a study in Jordan and Kuwait among other Arab Countries. Vaccines. 2021;9:42.\u003c/li\u003e\n \u003cli\u003eRaouf M, Elsabbagh D, Wiebelt M. Impact of COVID-19 on the Jordanian Economy: Economic Sectors, Food Systems, and Households\u003cem\u003e\u0026nbsp;\u003c/em\u003e[Internet]. 0 ed. Washington, DC: International Food Policy Research Institute; 2022: Doi.org/10//ebrary.ifpri.org/digital/collection/p15738coll2/id/134132.\u003c/li\u003e\n \u003cli\u003eIlesanmi O, Afolabi A, Uchendu O. The prospective COVID-19 vaccine: willingness to pay and perception of community members in Ibadan, Nigeria. PeerJ. 2021;9:e11153. Doi.org/10.7717/peerj.11153\u003c/li\u003e\n \u003cli\u003eAdigwe OP. COVID-19 vaccine hesitancy and willingness to pay: Emergent factors from a cross-sectional study in Nigeria. Vaccine. 2021;9:100112. Doi.org/10.1016/j.jvacx.2021.100112\u003c/li\u003e\n \u003cli\u003eDias-God\u0026acute;oi IP, Tadeu Rocha Sarmento T, Afonso Reis E et al. Acceptability and willingness to pay for a hypothetical vaccine against SARS CoV-2 by the Brazilian consumer: a cross-sectional study and the implications. Expert Rev Pharmacoecon Outcomes Res. 2021:1\u0026ndash;11, 0.\u003c/li\u003e\n \u003cli\u003eRascati K. Essentials of Pharmacoeconomics. Lippincott Williams \u0026amp; Wilkins; 2013.\u003c/li\u003e\n \u003cli\u003eArabyat RM, Nusair MB, Al-Azzam SI, Amawi HA, El-Hajji FD. Willingness to pay for COVID-19 vaccines: Applying the health belief model. Research in social \u0026amp; administrative pharmacy: RSAP. 2023:19(1):95\u0026ndash;101. Doi.org/10.1016/j.sapharm.2022.09.003\u003c/li\u003e\n \u003cli\u003ePuteh SEW, Ahmad SNA, Aizuddin AN, Zainal R, Ismail R. Patients\u0026apos; willingness to pay for their drugs in primary care clinics in an urbanized setting in Malaysia: a guide on drug charges implementation. Asia Pacific family medicine. 2017;16:5. Doi.org/10.1186/s12930-017-0035-5\u003c/li\u003e\n \u003cli\u003eNoh EB, Nam H-K, Lee H. Which group should be vaccinated frst?: a systematic review. Infect Chemother. 2021;53(2):261.\u003c/li\u003e\n \u003cli\u003eOkediran JO, Ilesanmi OS, Fetuga AA, Onoh I, Afolabi AA, Ogunbode O, Olajide L, Kwaghe AV, Balogun MS. The experiences of healthcare workers during the COVID-19 crisis in Lagos, Nigeria: A qualitative study. Germs. 2020:10(4):356\u0026ndash;366. Doi.org/10.18683/germs.2020.1228\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. Infodemic. 2021. Doi.org//www.who.int/teams/risk-communication/infodemic - management.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. Coronavirus disease (COVID-19) advice for the public. Coronavirus Dis 2019. 2020.\u003c/li\u003e\n \u003cli\u003eAbu-Raddad LJ, Chemaitelly H, Butt AA. Effectiveness of the BNT162b2 Covid-19 Vaccine against the B.1.1.7 and B.1.351 Variants. New England Journal of Medicine 2021;0(0)\u003c/li\u003e\n \u003cli\u003eAndrews N, Tessier E, Stowe J, Gower C, Kirsebom F, Simmons R, et al. Vaccine effectiveness and duration of protection.\u003c/li\u003e\n \u003cli\u003eAngel Y, Spitzer A, Henig O, Saiag E, Sprecher E, Padova H, et al. Association Between Vaccination with BNT162b2 and Incidence of Symptomatic and Asymptomatic SARS-CoV-2 Infections Among Health Care Workers. JAMA 2021. Doi.org //jamanetwork.com/journals/jama/fullarticle/2779853.\u003c/li\u003e\n \u003cli\u003eAzamgarhi T, Hodgkinson M, Shah A, et al. Experience of COVID-19 Vaccination of Healthcare Workers in a Hospital Setting 2021. Doi.org//www.researchsquare.com/article/rs-257937/v1.\u003c/li\u003e\n \u003cli\u003eBernal JL, Andrews N, Gower C, et al. Effectiveness of COVID-19 vaccines against the B.1.617.2 variant. Epidemiology 2021. Doi,org/10.1101/2021.05.22.21257658.\u003c/li\u003e\n \u003cli\u003eBernal JL, Andrews N, Gower C, et al. Effectiveness of the Pfizer-BioNTech and Oxford-AstraZeneca vaccines on covid-19 related symptoms, hospital admissions, and mortality in older adults in England: test negative case-control study. BMJ 2021;373:1088.\u003c/li\u003e\n \u003cli\u003eAudureau E, Davis B, Besson MH, Saba J, Ladner J. Willingness to pay for medical treatments in chronic diseases: a multicountry survey of patients and physicians. Journal of comparative effectiveness research. 2019;8(5):357\u0026ndash;369. Doi.org/10.2217/cer-2018-0106\u003c/li\u003e\n \u003cli\u003eTroiano G, Nardi A. Vaccine hesitancy in the era of COVID-19. Public health. 2021;194:245\u0026ndash;251. Doi.org/10.1016/j.puhe.2021.02.025\u003c/li\u003e\n \u003cli\u003eWonodi C, Obi-Jeff C, Adewumi F, Keluo-Udeke SC, Gur-Arie R, Krubiner C, Jaffe EF, Bamiduro T, Karron R, Faden R. Conspiracy theories and misinformation about COVID-19 in Nigeria: Implications for vaccine demand generation communications. Vaccine. 2022;40(13):2114\u0026ndash;2121. Doi.org/10.1016/j.vaccine.2022.02.005\u003c/li\u003e\n \u003cli\u003eMorgan R, Tan HL, Oveisi N, Memmott C, Korzuchowski A, Hawkins K, et al. Women healthcare workers\u0026rsquo; experiences during COVID-19 and other crises: a scoping review. Int J Nurs Stud Adv. 2022;4:100066.\u003c/li\u003e\n \u003cli\u003eSeale H, Heywood AE, Leask J, et al. Examining Australian public perceptions and behaviors towards a future COVID-19 vaccine. BMC Infect Dis. 2021;21(1):21. 39.\u003c/li\u003e\n \u003cli\u003eGlaubitz R, Harnack‐Eber A, Wetter M. \u0026lsquo;The gender gap in lifetime earnings: A microsimulation approach\u0026rsquo;, LABOUR [Preprint]. 2024. doi:10.1111/labr.12274.\u003c/li\u003e\n \u003cli\u003eGbogbolu A, Nketiah-Amponsah E. Individual risk preference as a predictor of health behaviour: evidence from the use of condoms against HIV/AIDS in Ghana. BMC public health. 2023;23(1):1657. Doi.org/10.1186/s12889-023-16579-7\u003c/li\u003e\n \u003cli\u003eHeidari M, Jafari H. Challenges of COVID-19 vaccination in Iran: in the fourth wave of pandemic spread. Prehosp Disaster Med. 2021;36(5):659\u0026ndash;60.\u003c/li\u003e\n \u003cli\u003eOmidvar S, Firouzbakht M. Acceptance of COVID-19 vaccine and determinant factors in the Iranian population: a web-based study. BMC Health Serv Res. 2022;22(1):1\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003ePatwary MM, Bardhan M, Disha AS, Hasan M, Haque MZ, Sultana R, et al. Determinants of COVID-19 vaccine acceptance among the adult population of Bangladesh using the health belief model and the theory of planned behavior model. Vaccines. 2021;9(12):1393.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Covid-19, vaccines, Willingness to pay, preferences, healthcare professionals","lastPublishedDoi":"10.21203/rs.3.rs-6470613/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6470613/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Vaccines remains the strategy to prevent Covid-19 disease since there is currently no available cure. Increase in expenditure and limited resources for healthcare funding has shifted the burden of disease towards the patients. Willingness to pay is an objective measure of the value people place on a product or service. This study assessed healthcare professional’s willingness to pay and preference for the available Covid-19 vaccines in Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A cross-sectional study conducted on healthcare professionals in a tertiary healthcare institution using a convenient sampling method with the aid of a structured self-administered two sectioned questionnaires. Section A was to obtain the respondent demographics and other information. Section B gave a description of the COVID-19 vaccines, denoted as Vaccine A, B, C and D, and obtained the respondents willingness to pay and preferred Covid-19 vaccine. Data obtained was entered into a Microsoft excel spreadsheet for data cleaning and analysis to obtain the frequency, percentage, Willingness-to-pay (WTP) and standard deviation. The student T-test and ANOVA were used where appropriate to obtain the relationship between the demographic variables and WTP using graphpad inStat 3.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eOut of the 300 questionnaires that were given out, 279 were returned and properly filled giving a response rate of 93%. A majority (171;61.29%) of the respondents were females, 35 (84.2%) were between the age of 25-44yrs while 210 (75.3%) were married. Less than half of the respondents 122 (43.73%) were doctors, 210 (75.27%) have not been sick with Covid-19 disease while 120 (43.01%) had treated a patient with Covid-19 disease. A few 44 (15.77%) had cared for a relative with Covid-19 while 270 (96.77%) had not been hospitalized due to Covid-19 disease. The willingness to pay for Vaccine A is ₦35103.63± 25067.29, Vaccine B is ₦36442.36± 25440.88, Vaccine C is ₦33557.60± 24948.49 and Vaccine D is ₦35302.29± 25816. Majority of the respondent (45.52%) preferred Vaccine A while 16.13% had no preference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The healthcare professionals preferred Vaccine A with a willingness to pay value of ₦35,103.63 ± 25067.29. The willingness-to-pay for the available Covid-19 vaccine was influenced by the respondent’s demographic characteristics, Covid-19 beliefs and experiences.\u003c/p\u003e","manuscriptTitle":"Healthcare Professionals’ Willingness-to-Pay and Preferences for Covid-19 Vaccines in a Nigerian Tertiary Hospital: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 12:30:40","doi":"10.21203/rs.3.rs-6470613/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-03T12:15:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-16T04:44:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-09T22:52:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-09T08:45:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155954277122873369554212576182357376452","date":"2025-06-09T05:21:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298761026837455459138173694038834751240","date":"2025-06-05T10:30:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242696383293904602864157191396068406185","date":"2025-06-03T21:53:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185401385697538828355534644513818809076","date":"2025-06-03T09:03:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-28T09:54:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-20T09:21:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-06T20:53:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-05T22:52:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-05-05T22:51:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b33a418a-5f9f-4366-b99b-a8aea9dffb47","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-02T23:38:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 12:30:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6470613","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6470613","identity":"rs-6470613","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}