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Acceptance of COVID-19 Vaccines among Adults in Lilongwe, Malawi: A Cross-Sectional Study Based on the Health Belief Model

Department of Global Health, School of Public Health, Peking University, Beijing 100191, China
Global Health Collaborating Centre for Research and Training in Health Sciences, Peking University, P.O. Box 166, Lilongwe 265, Malawi
Institute for Global Health and Development, Peking University, Beijing 100191, China
Author to whom correspondence should be addressed.
Vaccines 2022, 10(5), 760;
Received: 12 April 2022 / Revised: 5 May 2022 / Accepted: 9 May 2022 / Published: 11 May 2022
(This article belongs to the Special Issue Knowledge and Beliefs on Vaccines)


The COVID-19 pandemic has had a significant economic and social impact on Malawi. Promoting vaccination is a key protection measure against COVID-19. Employing the health beliefs model (HBM), this study explores various factors that influence COVID-19 vaccination acceptance (intentions and behavior) among adult residents of Malawi. A semi-structured questionnaire was used for data collection. A field-based survey was conducted among adult residents in Lilongwe, Malawi. Descriptive statistics, linear regression, the Chi-square test, and Pearson’s correlation statistics were used for data analysis. A total of 758 questionnaires were involved. Respondents aged 18–24 (OR = 5.079, 95% CI 2.303–11.202), 25–34 (OR = 2.723, 95% CI 1.363–5.438), urban residents (OR = 1.915, 95% CI 1.151–3.187), graduates/professionals (OR = 1.193, 95% CI 0.857–1.651), health workers (OR = 4.080, 95% CI 1.387–12.000), perceived susceptibility (OR = 1.787, 95% CI 1.226–2.605), perceived benefit (OR = 2.992, 95% CI 1.851–4.834), and action cues (OR = 2.001, 95% CI 1.285–3.115) were predictors for “acceptance of COVID-19 vaccine”. The health belief model structure can be used as a good predictor of vaccine acceptance, especially “perceived susceptibility,” “perceived benefit,” and “action cues”. Strengthening COVID-19 vaccine education in these areas will be an important future intervention.

1. Introduction

The COVID-19 pandemic has triggered an unprecedented and rapid global public health crisis. As one of the most pressing global threats, the pandemic has affected all aspects of life around the world. Countries have implemented strict precautions and controls to contain the outbreak of COVID-19, such as travel bans and lockdowns [1]. However, new variants, such as Delta and Omicron, are making it harder to contain the epidemic. The development and deployment of vaccines is recognized as one of the most promising health intervention strategies and an important new tool in the fight against COVID-19 [2]. Adequate vaccination coverage can help to reduce infection rates and subsequent mortality from COVID-19. To achieve the goal of containing COVID-19 and returning to normal life, countries need to vaccinate at least 70% of the population in order to build herd immunity against COVID-19. Malawi is a low-income country where public health services are challenged [3]. Controlling the COVID-19 pandemic and conducting vaccination campaigns remain huge challenges for Malawi.
As of 4 December 2021, 1,501,147 vaccine doses have been administered in Malawi. 878,471 and 340,249 people have received the first and second doses of the AstraZeneca vaccine, respectively, while 282,427 people have received a full dose of the Johnson & Johnson vaccine, bringing the total number of fully vaccinated people to 622,676. Malawi currently plans to vaccinate 10.97 million people (60% of the population).
Apart from the scarcity and logistical issues of candidate vaccines, vaccination hesitancy is one of the most critical barriers to achieving mass COVID-19 vaccination rates. According to the World Health Organization, vaccine hesitancy is a significant barrier as “even when a COVID-19 vaccine is available, it can be rejected for a variety of reasons.” Reluctance or refusal to vaccinate threatens progress in tackling vaccine-preventable diseases, and vaccine hesitancy is one of the world’s top-10 public health problems. Several factors may influence the acceptance or hesitancy of the COVID-19 vaccine [4]. Previous studies (conducted in the US, UK, Australia, Japan, Nigeria, and other countries) [1,5,6,7,8,9,10,11,12,13] have shown that reduced willingness to vaccinate is associated with females, lower socioeconomic status, unemployment, and less educated respondents. In addition, distrust of vaccines, concerns about unforeseen side effects in the future, and negative discussions about vaccines on social media may also cause people to hesitate about whether to get vaccinated [14,15].
The health belief model (HBM) is a conceptual framework widely used to study health beliefs that explain, predict, and influence behavior. HBM advises people to weigh the severity of the health threat they face (for example, perceived susceptibility and severity) against the perceived benefit or harm of taking a particular action related to that health threat (for example, vaccination) (Figure 1). Their risk assessment can be influenced by various factors, including action cues from trusted information sources and the social context in which they live and with which they interact. These factors have long been considered essential predictors of influenza vaccine uptake, and emerging studies suggest that they may also be necessary for COVID-19 vaccine uptake [16,17,18,19].
There is a real need for more research into the perceptions and acceptance of COVID-19 vaccines among Malawian residents, especially as the government is committed to a mass COVID-19 vaccination program. The purpose of this study was to investigate current vaccination rates for COVID-19 among Malawians, assess the level of COVID-19 vaccine hesitancy among Malawians, and explore the factors influencing vaccination and willingness to be vaccinated against COVID-19. The results of this study have important implications for the health sector when developing best practices for implementing COVID-19 vaccination programs, helping healthcare providers and policymakers to plan targeted education campaigns and vaccination awareness campaigns.

2. Materials and Methods

2.1. Study Design and Data

A cross-sectional design was used for this survey. The fieldwork was conducted in Lilongwe, Malawi, by the Peking University Research and Training Centre in Malawi (PKURTC) from 19 November to 30 November 2021. The target population were adults (aged 18 and above) living in Lilongwe, Malawi. Participants who had difficulties in communication and those who did not consent to the survey were excluded. A sample size of 693 was recommended, with an assumption of a 95% confidence interval (CI) regarding a 5% margin of error and a response rate of 60%. Participation was voluntary and came with no award, and all responses were anonymous. The final sample exceeded this estimate. A total of 758 questionnaires were collected and used for the analysis.
The study adopted a two-stage sampling technique consisting of the selection of residential areas and individuals. For the primary sampling unit, we used simple cluster sampling based on the list of Lilongwe’s administrative divisions (58 areas in total). As a result, 15 areas were selected from the list. Within each selected area, the sample sizes were population-weighted. We used systematic sampling of households according to house numbers and household heads in the survey.
A semi-structured questionnaire was used for the data collection. The questionnaire was deliberate, and some surveys regarding COVID-19 vaccination were conducted in other countries and reviewed by experts. It was initially prepared in English and then translated into Chichewa (see online Appendix A). The questionnaire was digitalized and programmed on tablets using Open Data Kit (ODK) software, version 1.28.4 ( accessed on 11 April 2022). Investigators were assigned to each area and captured individual-level quantifiable indicators face to face.
The survey consisted of three sections: (1) general information and health status, including gender, age, education, residence, occupation, marital status, economic status, chronic disease, and history of vaccine rejection; (2) the health belief model, including two items on perceived susceptibility to COVID-19, two items on perceived severity, two items on the perceived benefits of getting vaccinated against COVID-19, one item on perceived barriers, and four items on action cues; (3) acceptance (intention and behavior) of the COVID-19 vaccine.

2.2. Measures

The dependent variable in this study was the acceptance of the COVID-19 vaccine, which was split into two parts: (1) behavior—taking the COVID-19 vaccine, and (2) intention—willing to get vaccinated, but has not yet received a vaccine. The rest were defined as vaccine unacceptance (had not taken or refused to take the COVID-19 vaccine). Therefore, the outcome variables were assessed with two items: “Have you taken a COVID-19 vaccine?” and “Would you accept or refuse a COVID-19 vaccine if it were offered to you?”.
We constructed independent variables based on the health belief model, including perceived susceptibility, perceived severity, perceived barriers, perceived benefits, action cues, and background factors (sociodemographic and disease history) of the HBM model. Each section consisted of several items, each item was answered yes/no, and each item was individually included in the regression analysis.

2.3. Statistical Analysis

Statistical analyses were performed in SPSS 25. Descriptive statistical analyses were used to characterize the study population. Correlation coefficients were calculated using χ2 to determine the association between the selected possible predictors and vaccination status or willingness to vaccinate. Those independent variables found to be statistically significant were included in the logistic regression model. A two-sided p-value of <0.05 was considered statistically significant. The final model was presented with adjusted odds ratios (OR), 95% confidence intervals (CI), and corresponding p-values.
Consent was sought from Lilongwe’s residents for participation before the questionnaire began. The study was designed and conducted according to the ethical principles established by Peking University. The National Committee on Research in the Social Sciences and Humanities, of The National Commission for Science and Technology, approved this study (P.08/21/593).

3. Results

A total of 758 people were included in the analysis, of which 189 (24.9%) were vaccinated, a further 271 (35.8%) were willing to be vaccinated but had not yet received the vaccine, and 298 (39.3%) refused to be vaccinated. The characteristics of the samples are shown in Table 1 and Table 2.

3.1. Sample Characteristics of Two Independent Classification Variables

3.1.1. Demographic Characteristics

The study subjects comprised 498 (65.7%) females and 679 (89.6%) Christians. Most respondents were married (72.4%) and from rural areas (67.4%). One-third of the study participants were 25–34 years old. Among the respondents, 87.6% had a high school education level or below, while 11.9% had no education. Regarding their occupations, 38% had no job, while 3.6% of the respondents were healthcare workers. One-third of the study participants were in the lowest income category. In terms of health status, most of the population did not have any chronic diseases (79.4%), and only 2.9% considered themselves to be in poor health. A total of 4.5% of the participants reported having had COVID-19 before, while 21% had refused a vaccine recommended by a physician due to doubts.
As seen in Table 1, there were significant differences in COVID-19 vaccine acceptance among people of a different gender, age, education, occupation (health worker), monthly income, urban/rural residence, history of COVID-19 infection, and history of vaccine refusal. Table 2 also reflects a significant difference in COVID-19 vaccine acceptance among people with different attitudes toward the various components of the health belief model (perceived susceptibility, severity, benefits, barriers, and action cues).

3.1.2. Health Benefit Model Characteristics

The majority of respondents agreed on the susceptibility, severity, and benefits of COVID-19 (more than 80%), with 86.8% agreeing that COVID-19 is contagious and 78.4% believing that they are likely to get it. About 92% of participants considered the consequences of COVID-19 to be serious, while 81.1% thought it would be beneficial to be vaccinated against COVID-19 to decrease the chance of contracting COVID-19 or suffering complications and in order to stop the spread of the virus in the community. A total of 76.3% perceived a barrier that prevented them from getting vaccinated. As for the action cues, 35.2% knew someone who had been infected. The majority (62.3%) heard information about vaccines from friends, and nearly half obtained information from the radio, while only 5.9% obtained it from healthcare providers. The results are shown in Table 2.

3.2. Influencing Factors Associated with the Acceptance of the COVID-19 Vaccine

The influencing factors for the acceptance of the COVID-19 vaccine are shown in columns 2–3 of Table 3. A Chi-square analysis of the sociodemographic and health-related variables revealed some significant variables. When entered into a binary logistic regression model, these variables were associated with “acceptance of COVID-19 vaccine”. In the final model, respondents aged 18–24 (OR = 5.079, 95% CI 2.303–11.202), 25–34 (OR = 2.723, 95% CI 1.363–5.438), urban residents (OR = 1.915, 95% CI 1.151–3.187), graduates/professionals (OR = 1.193, 95% CI 0.857–1.651), health workers (OR = 4.080, 95% CI 1.387–12.000), self-reporting health as good (OR = 4.08, 95% CI 1.410–11.840) and fair (OR = 3.145, 95% CI 1.063–9.308), perceived susceptibility (COVID-19 is contagious for you (OR = 1.787, 95% CI 1.226–2.605)), perceived benefit (agree that the vaccine could stop the spread of COVID-19 (OR = 2.992, 95% CI 1.851–4.834)), and action cues (know someone who has been infected by COVID-19 (OR = 2.001, 95% CI 1.285–3.115)) were predictors for the “acceptance of the COVID-19 vaccine”. Meanwhile, the historic rejection of vaccines (OR = 0.160, 95% CI 0.083–0.309) was an inhibitor of the “acceptance of the COVID-19 vaccine”.

3.3. Influencing Factors Associated with Positive Vaccination Intention and Behavior

According to the Chi-square calculation, it can be seen in Table 1 and Table 2 that positive vaccination intention and behavior are statistically correlated with gender, age, urban residents, education, employment, healthcare worker, monthly income, previous diagnosis of COVID-19, historic vaccine rejection, perceived susceptibility to COVID-19, perceived severity of COVID-19, perceived benefits and barrier to getting a COVID-19 vaccine, and action cues. Therefore, in a multinomial regression analysis, we only consider these significantly correlated variables as predictive variables.
As shown in columns 4–5 of Table 3, multinomial logistic regressions found that the promoters of vaccination behavior (Vaccinated) included age 18–24 (OR = 1.118, 95% CI 0.989–1.546), age 25–34 (OR = 1.391, 95% CI 0.853–1.684), urban residents (OR = 1.667, 95% CI 0.868–3.201), monthly income (0–50,000 MWK) (OR = 3.845, 95% CI 2.068–7.148), graduate/professional (OR = 4.343, 95% CI 0.940–20.044), healthcare worker (OR = 2.362, 95% CI 0.068–8.910), perceived susceptibility (COVID-19 is contagious for you) (OR = 1.285, 95% CI 1.147–1.554), perceived severity (OR = 9.959, 95% CI 1.049–95.575), perceived benefit (COVID-19 vaccine can stop the virus from spreading in communities and countries (OR = 2.876, 95% CI 1.057–7.829)), and action cues (know someone who has been infected by COVID-19 (OR = 2.022, 95% CI 1.174–3.480)).
According to columns 6–7 of Table 3, the promoters of vaccination intention (Willing to be vaccinated but not yet) included monthly income (0–50,000 MWK) (OR = 11.604, 95% CI 6.260–21.509), perceived susceptibility (COVID-19 is contagious for you) (OR = 2.532, 95% CI 1.423–4.505), and perceived benefit (COVID-19 vaccine can stop the virus from spreading in communities and countries (OR = 2.450, 95% CI 1.096–5.474)).
The rejection of a historic vaccine (OR = 0.12, v95% CI 0.057–0.250) (OR = 0.482, v95% CI 0.291–0.798) is an inhibitor of vaccination behavior and intention.

4. Discussion

This study explores the predictors of intention and behavior as they pertain to COVID-19 vaccines among adults in Lilongwe, Malawi, and the applicability of the health beliefs model. There are only previous studies about Malawian residents’ knowledge, attitudes, and practices regarding COVID-19 [3] and Malawian healthcare workers’ vaccination status [20].
This study shows that perceived susceptibility and perceived benefit in the HB8M model are essential factors for promoting COVID-19 vaccine acceptance, improving people’s vaccination intention, and promoting people’s vaccination behavior. Perceived severity and crucial action cues such as knowing someone who has had COVID-19 can improve vaccination acceptance by promoting vaccination behavior. Perceived impairment did not play a role in this study. Consistent with previous research [21,22,23], the main dimensions of the HBM model were almost all related to COVID-19 vaccine acceptance. However, our study distinguished between the different facilitation effects of different dimensions on vaccination intention and behavior.
In addition, as background factors that may be involved in vaccination decision making in the HBM model, we also analyzed their potential influence on vaccination intention and behavior. In the current study, those aged between 18 and 34, graduates/professionals, and healthcare workers had more active vaccination behavior. The high acceptance of the COVID-19 vaccine among healthcare workers is consistent with another study on COVID-19 vaccination among healthcare workers in Malawi [20]. Likewise, other studies have found that young people and those with higher education levels are more likely to be vaccinated [24,25]. We presume that this is possibly because they were given more information about vaccines and were better able to make informed decisions. In addition, people with lower monthly incomes have a higher acceptance of the COVID-19 vaccine, which is consistent with some previous studies [26,27,28]. This is widely believed to be due to the government’s policy of free vaccines.
According to the results of this study, the most widely available sources of information about COVID-19 vaccines are the radio and friends. There is little information from doctors and a lot of ignorance or incorrect knowledge about vaccines, which has led to distrust and the rejection of COVID-19 vaccines among Malawians [29]. Therefore, Malawi should be supported in its vaccination outreach and community mobilization campaigns to raise awareness of COVID-19 through radio programs, jingles, and volunteer door-to-door outreach services [30]. The education of the population should be strengthened regarding their vulnerability to COVID-19 infection. People need to be aware of existing health risks, feel at risk, and take protective measures. The benefits of vaccination also need to be highlighted. People need to be aware that vaccines protect them and their communities. Additionally, we can spread information on real-life COVID-19 cases and successful vaccination stories to promote vaccination behavior. We should also track and address rumors/misinformation about COVID-19 vaccines to rebuild public confidence in vaccination. At the same time, Malawi has its own unique cultural and religious background, so it is essential to work with trusted community leaders. Religious leaders can also act as vaccine advocates, using existing trust relationships to advocate for vaccination [31,32].
Urban residents have more active vaccination behavior because it is more challenging to get vaccines for people who live in rural areas compared with urban areas. Thus, Malawi needs to improve access to vaccines for rural residents. We suggest targeted improvements in infrastructure, including logistics for vaccine transport and distribution [33,34], such as “MetaFridge”, a portable ice tub for cryostorage and delivery. The preponderance of convenient vaccination sites should also be increased, especially in rural areas. International organizations and local governments should work together to cover the “last mile” of vaccination. This will also facilitate the establishment of long-term interventions and adaptive infrastructure that can be used for future disease control efforts.
We found that there are still gaps between COVID-19 vaccination intention and behavior. This suggests that real-world conditions may limit vaccination opportunities or that willing individuals may hesitate when vaccines become available. These issues should be addressed when planning vaccination campaigns. Last year, the Malawi government developed a new plan called the National COVID-19 Strategy and Plan—July 2021–June 2022 [35], which builds on the successes achieved and lessons learned from previous plans. The plan includes future control strategies for inter-cluster coordination, health, education, public communication, local governance, protection and social support, employment and labor force protection, transport and logistics, and security and enforcement. It focuses on moving from emergency to longer-term interventions and building from semi-permanent to permanent adaptive infrastructure. Our findings are consistent with ongoing strategies, particularly government-led advocacy, education, and infrastructure development.
This study has several limitations. Firstly, the results of this study may not represent the views or practices of the population as a whole. Secondly, given the cross-sectional nature of the data, the results represent a snapshot of vaccine indecision at one point in time. We cannot explain how attitudes will evolve as the COVID-19 pandemic, vaccine availability, and political discourse change. Thirdly, there is an underlying social desirability bias, according to which participants may react in ways that they think are acceptable. Additionally, we did not assess the impact of rapid mutations of SARS-CoV-2 on COVID-19 vaccine uptake. For example, new mutant strains such as Delta and Omicron may re-infect people who have already been vaccinated with previous vaccines, which may negatively affect people’s views on vaccination [36].

5. Conclusions

Overall, vaccine acceptance (including those who have been vaccinated and those who are willing to be vaccinated) was not high enough among the respondents to protect themselves and their communities. The health belief model structure can be used as a good predictor of vaccine acceptance, especially “perceived susceptibility”, “perceived benefit”, and “action cues”. Strengthening COVID-19 vaccine education in these areas will be an essential future intervention.

Author Contributions

Conceptualization, H.Y. and Q.A.; methodology, Q.A.; formal analysis, Q.A.; data curation, R.O.E. and Q.A.; writing—original draft preparation, Q.A.; writing—review and editing, Q.A., R.O.E., H.Y. and F.C.; supervision, H.Y.; project administration, F.C.; funding acquisition, F.C. All authors have read and agreed to the published version of the manuscript.


This study was supported by the National Nature Science Foundation of China (No.72042014) and the “Double First-Class” Construction Program (BMU2020XY010).

Institutional Review Board Statement

The National Committee on Research in the Social Sciences and Humanities, The National Commission for Science and Technology approved this study (reference number: PROTOCOL P.08/21/593). Informed consent was acquired from the participants before the investigation started.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated during this study are included in this published article and Appendix A.


This report acknowledges the role of the enumerators in the data collection. We also thank the PKURTC team for supporting the research, and gratitude goes to Robert Egolet for leading the team throughout the fieldwork and report writing.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Data Collection Tools

English Questionnaire.
Table A1. COVID-19 vaccination acceptance questionnaire.
Table A1. COVID-19 vaccination acceptance questionnaire.
Interviewer ID: ________ Questionnaire ID: ________ Date: _______________
Section A: General Information and Health Status
1. Gender1.1Male
2. Year of Birth2.118–24
2.555 and above
3. Residence3.1Urban
Indicate area of residence
4. Religion4.1Christian
5. Marital status5.1Married
5.2Never married
6. Education Attained6.1No high school
6.2High school
6.5Not educated
7. Employment Status7.1Government employee
7.2Nongovernment employee
7.7Other (specify)
8. Are you a healthcare worker?8.1Yes
(If Yes)
8.13Health Official,
9. What category (MWK) best fits your overall monthly income?9.10–5000
9.550,000 and above
10. How many people live in your household (HH member should have lived at household for at least six months or more)?10.11–3
10.35 and above
11. How would you describe your house’s condition?
[check multiple answer option]
11.1We have electricity, and it functions at least half a day per day.
11.2We have a safe, clean water source (piped into dwelling or borehole with pump or protected dug well).
11.3We have toilets in good condition (flush or ventilated improved latrine).
11.4We are not crowded (5 or fewer people per room).
11.5We have a firm roo f(tiles or galvanized iron or concrete).
12. What does the household own as a family?
[check multiple answer option]
12.6Mobile phone
12.11Small livestock, e.g., poultry, goats, pigs
12.12Large livestock: cattle
13. Do you suffer from any chronic diseases?13.1Yes
14. How would you perceive your overall health?14.1Good
15. Have you been (or are you currently) infected with COVID-19?15.1Yes
16. Do you personally know someone who has been (or is currently) infected with COVID-19?16.1Yes
17. Have you ever refused a vaccine recommended by a physician due to doubts you had about it?17.1Yes
17.3Have never heard of any vaccine
Section 2: Perceived susceptibility, severity and benefits of COVID-19 vaccine
18.Have you ever heard about a COVID-19 vaccine?18.1Yes
19. Where do you obtain COVID-19 and vaccine information? [check multiple answer option]19.1Internet/TV
19.2Visits to healthcare providers
19.3Family members
19.7Work place
19.9Printed materials from healthcare providers
19.10Vaccine companies and industry
20. To what extent do you agree that COVID-19 is contagious?20.1Strongly agree
20.4Strongly disagree
21. To what extent do you agree that the COVID-19 pandemic poses a risk to people in Malawi?21.1Strongly agree
21.4Strongly disagree
22. Do you agree that the consequences of getting COVID-19 can be serious and can even lead to death?22.1Strongly agree
22.4Strongly disagree
23. Do you think getting COVID-19 is currently a possibility for you?23.1Strongly agree
23.4Strongly disagree
24. Do you agree that a COVID-19 vaccine can decrease the chances of you contracting COVID-19 or suffering from its complications?24.1Strongly agree
24.4Strongly disagree
25. Do you agree that a COVID-19 vaccine can stop the virus from spreading within communities and between countries?25.1Strongly agree
25.4Strongly disagree
26. Do you agree that a COVID-19 vaccine should be compulsory for all citizens and residents in Malawi?26.1Strongly agree
26.4Strongly disagree
27. Do you agree that immunization requirements go against freedom of choice?27.1Strongly agree
27.4Strongly disagree
Section 3: Acceptability and Practice of a COVID-19 Vaccine
28.a Have you been vaccinated with a COVID-19 vaccine? Yes
28.b Why did you choose to get vaccinated?1–10I was forced to do it.
I want to travel outside.
I got sick.
Someone I knew died of COVID-19.
Requirement for me to start job.
Peer pressure.
I was convinced by medical personnel.
I want to protect myself from COVID
I want to protect my family from COVID
28.c Would you accept or refuse a COVID-19 vaccine when if it were available? 28.1I would accept immediately. [Go to Q31]
28.2I would delay the vaccination. [Go to Q29]
28.3I would refuse the vaccination. [Go to Q29]
29. I would delay or refuse the vaccination because-[check multiple answer option]29.1I don’t believe in the existence of COVID-19.
29.2I think the vaccine is a plot.
29.3I am religious and God will protect me.
29.4COVID-19 symptoms are mostly mild so I do not fear COVID-19.
29.5I feel that masks and sanitizers are sufficient for protection.
29.6I think the vaccine will transmit the virus to me.
29.7I think the vaccine will change my genes.
29.8I don’t think that I can afford the vaccine.
29.9The fear of adverse side effects.
29.10Not convinced that it will be effective.
29.11Concern regarding the faulty/fake COVID-19 vaccines.
29.12The speed of developing the vaccine was too fast.
29.13The short duration of clinical trials.
29.14There is no way I trust governments.
29.15Illness or allergy prevented me from getting vaccinated.
16I feel the COVID vaccine is associated with other religious hidden agendas.
30. I would take the COVID-19 vaccine only if-[check multiple answer option]30.1I were given adequate information about it.
30.2The vaccine were taken by many people.
30.3The vaccine’s safety were confirmed.
30.4The vaccine were provided for free.
30.5The doctor advised me to get vaccinated.
30.6The government required me to get vaccinated.
30.7The WHO or UNICEF staff provided me with a vaccine.
30.8No vaccinations at all.
31 How much are you willing to pay for COVID-19 vaccines? MWK
32. How much do you know about and trust the following vaccines?
ManufactureI don’t know at allI’ve heard of it but I don’t trust itI know about it and I trust it
AstraZeneca/Oxford vaccine [UK]
Johnson and Johnson [US]
Moderna [US]
Janssen [US]
Pfizer/BionTech [China]
Sinovac [China]
Zhifei Longcom [China]
Sinopharm Beijing [China]
CanSinoBIO [China]
THE GA Vector State Research Centre of Viralogy and Biotechnology [Russia]
Serum Institute [India]
Chichewa Translated COVID-19 Vaccination Acceptance Questionnaire.
Mafunso wokhudza Kuvomereza Katemera wa COVID-19.
Dzina langa ndine____________________ ndachokela ku ____________ amene tikupanga zakafukufuku pa zomwe mumadziwa pankhani ya Katemera wa COVID-19. Zokambilana zathu zikhala muzigawo ziwiri zotele: gawo la Zokhudza muthu ndi umoyo wake; ndi gawo la Maganizo pakukhudziwa, Muyenso komanso kufunika kwa Katemera wa COVID-19.
Macheza athu atenga pafupifupi mpindi nkhumi ndi zisanu ndipo zonse zimene tikhale tikukambilana zikhala zachinsinsi. Kutengapo mbali kwanu mukafukufuku ameneyu ndikosakakamiza ndipo mukhoza kukana kutenga nawo mbali kapena kusiila panjira macheza amenewa. Ngati mwasankha kuti simutenga nawo mbali pakafukufuku ameneyu simulandila chilango chilichonse kapena kulandidwa Katundu aliyense. Chizindikiilo chanu cha mzika chingowilitsidwa ntchito pakungoonetsa kuti inuyo munavomeleza kuchita nawo kafukufuku ameneyu, koma sichidzagwilitsadwanso ntchito penapaliponse.
Mukuvomeleza kutenga nawo mbali pakafukufuku ameneyi?
Inde Ayi.
Table A2. Mafunso wokhudza Kuvomereza Katemera wa COVID-19. Malonje.
Table A2. Mafunso wokhudza Kuvomereza Katemera wa COVID-19. Malonje.
ID ya Ofunsa Mafunso: ________Questionnaire ID: ________ Tsiku: _______________
Gawo 1: Zokhudza Muthu Ndi Umoyo Wake
1. Mamuna/Mkazi 1.1Mamuna
2. Muli ndi zaka zingati?2.118–24
2.555 kupita mtsogolo
3. Dela lokhala3.1Mtawuni
4. Ndinu achipembedzo chanji?4.1Chikhilisitu
4.3Zina (Tchulani)
5. Muli pa banja?5.1Ndili pa banja
5.3Banja linatha
5.4Wa masiye
6. Maphuzilo anu munalekezera pati?6.1Sanafike sekondale
6.3Kunapita ku Koleji
6.4Anaphunzira ku university
7. Mumagwira ntchito yanji?7.1Amagwira ntchito mu boma
7.2Amagwira ntchito kumabugwe
7.3Anazilemba okha ntchito
7.4Mwana wasukulu
7.5Anapanga litaya
7.6Sali pa ntchito
7.7Zina (Tchulani)
8. Kodi ndinu ogwira ntchito za umoyo (zachipatala)?8.1Eya
(ngati eya)
8.11Owona odwala (Physicians),
8.13Oyang’anira za umoyo (Health Officials),
8.14Opanga zakafukufuku (Researchers),
8.15Zina (Others)
9. Mumapeza ndalama zingati pa mwezi??9.10–5000
9.550,000 kupita mtsogolo
10. Pakhomo pano pamakhala anthu angati (Okhala pakhomo akhale amene wakhala pabanjapo posachepera miyezi isanu ndi umodzi)?10.11–3
10.35 kupita mtsogolo
11. Kodi nyumba yanu mungayifotokoze bwanji? [Mayankho ambiri ndi ololedwa]11.1Tili ndi magetsi, atha kukhala akuyaka mafupifupi theka la tsiku, tsiku lililonse.
11.2Tili ndi kochokera madzi awukhondo (Madzi a muma paipi, Mijigo kapena zitsime zotetezedwa)
11.3Tili ndi zimbudzi za ukhondo komanso zabwino (Zo flasha komanso zopita mphweya).
11.4Sitili odzadzana (Timakhala athu ochepera 5 (asanu) mu chipinda).
11.5Tili ndi denga lolimba (tiles or malata kapena concrete).
12. Kodi Pakhomo pano, muli ndi zinthu ziti ngati banja? [Mayankho ambiri ndi ololedwa]12.1Radio
12.6Mobile phone
12.9Njinga yamoto
12.11Zina (Tchulani)
13. Mumadwala matenda a mgonagona?13.1Yes
14. Mukuona ngati umoyo wanu uli bwanji?14.1Uli bwino
14.2Uli pakatikati
14.3Suli bwino
15. Kodi munapezekako kapena padali pano muli ndi COVID-19?15.1Eya
16. Mukudziwako wina wake amene anali kapena ali ndi COVID-19, mbanja mwanu kapena mdera lanu lino?16.1Eya
17. Kodi munakanako katemera chifukwa chakumukayikira? 17.1Eya
Gawo 2: Maganizo pakukhudziwa, Muyenso komanso kufunika kwa Katemera wa COVID-19
18. Munamvako zaketemera wa COVID-19?18.1Eya
19. Kodi mauthenga okhudza COVID-19 ndi katemera mungaupeze kuti?
[Mayankho ambiri ndi ololedwa]
19.2Kupita Kumalo othandizira odwala
19.3Akubanja ndi azibale
19.5Zolemba kapena ma poster opangidwa ndi achipatala
19.6Ma Kampani opanga katemera
19.8Ku sukulu
19.92Zina (Tchulani)
20. Kodi mukugwirizana nazo bwanji kuti matenda a COVID-19 ndiopatsirana?20.1Ndikugwilizana nazo kwambiri
20.2Ndikugwilizana nazo
20.3Sindikugwilizana nazo
20.4Sindikugwilizana nazo konse
21. Kodi mukugwirizana nazo bwanji kuti matenda a COVID 19 ayika pachiopsezo anthu (mtundu wa) aku Malawi?21.1Ndikugwilizana nazo kwambiri
21.2Ndikugwilizana nazo
21.3Sindikugwilizana nazo
21.4Sindikugwilizana nazo konse
22. Mukugwirizana nazo bwanji kuti zotsatira za matenda a COVID-19 atha kukhala oopsa mpaka munthu kumwalira?22.1Ndikugwilizana nazo kwambiri
22.2Ndikugwilizana nazo
22.3Sindikugwilizana nazo
22.4Sindikugwilizana nazo konse
23. Mukuona ngati padali pano, ndizotheka kutenga matenda a COVID-19?23.1Ndikugwilizana nazo kwambiri
23.2Ndikugwilizana nazo
23.3Sindikugwilizana nazo
23.4Sindikugwilizana nazo konse
24. Mukugwirizana zano bwanji kuti katemera wa COVID 19 atha kuchepetsa kuthekera kotenga matenda a COVID-19 kapena zotsatira zake?24.1Ndikugwilizana nazo kwambiri
24.2Ndikugwilizana nazo
24.3Sindikugwilizana nazo
24.4Sindikugwilizana nazo konse
25. Mukugwirizana nazo bwanji kuti katemera wa COVID-19 atha kuletsa (kusiyiza) kufalikira kwa matendawa mu mmadera amayiko?25.1Ndikugwilizana nazo kwambiri
25.2Ndikugwilizana nazo
25.3Sindikugwilizana nazo
25.4Sindikugwilizana nazo konse
26. Kodi mukugwirizana nazo bwanji kuti Katemera wa COVID-19 azikhala wokakamiza kwa nzika zonse ndi okhala mdziko muno?26.1Ndikugwilizana nazo kwambiri
26.2Ndikugwilizana nazo
26.3Sindikugwilizana nazo
26.4Sindikugwilizana nazo konse
27. Mukugwirizana nazo bwanji ndizakuti zofunika poziteteza zimatsutsana ndi ufulu wachisankho?27.1Ndikugwilizana nazo kwambiri
27.2Ndikugwilizana nazo
27.3Sindikugwilizana nazo
27.4Sindikugwilizana nazo konse
Gawo 3: Kuvomereza komanso zochita kukhudza katemera wa COVID-19
28. Kodi mutha kulola kapena kukana katemera wa COVID-19, atapezeka ?28.1Nditha kulora pompo pompo. [pitan ku Q31]
28.2Nditha kuchedwa kuvomera [pitan ku Q29]
28.3Nditha kukana katemerayi. [pitan ku Q29]
29. Nditha kuchedwa kuvomera ka kukana katemerayi, chifukwa: [Mayankho ambiri ndi ololedwa]29.1Sindikhulupilira kutI COVID-19 ilipo.
29.2I think the vaccine is a plot.
29.3Ndine wachipembedzo, mulungu anditeteza.
29.4Zizindikiro za matenda a COVID-19 amakhala osaopsa kwenikweni, sindiopa za COVID-19.
29.5Ndimaona ngati masiki ndi hand sanitizer ndizokwanira kunditeteza ku matendawa.
29.6Ndikuona ngati katemera atha kundipatsira matenda a COVID-19.
29.7Ndikuona ngati katemera atha kusintha ma genes anga
29.8Sindingakwanitse kulipira katemerayu.
29.9Ndimaopa zotsatira zoopsa za katemerayi.
29.10Sindine okhutitsidwa kuti katemerayu atha kugwira ntchito
29.11Ndimaopa kuti katemera wina atha kukhala wa fake.
29.12Ndikuona ngati katemerayu wapangidwa mwachangu.
29.13Katemerayu sanayezedwe mokwanira.
29.14Boma sindilikhulupilira.
29.15Matenda kapena zowenga zinandiletsa kusabayitsa katemerayi.
30. Nditha kukabayitsa katemera wa COVID-19, pokhapokha: [Mayankho ambiri ndi ololedwa]30.1Ndapatsidwa uphungu ndi uthenga okwanira.
30.2Katemera akubayitsidwa ndi athu ambiri.
30.3Chitetezo cha katemerayi ndichosakayikitsa/chostimikizidwa.
30.4Katemera atakhala waulere.
30.5Adotolo andilangiza kukabayitsa katemera.
30.6Malamulo a bowa akundiyenera kuti ndibayitse katemera.
30.7Mabugwe a WHO kapena UNICEF ndiamene akubaya katemerayu.
30.8Palibe katemerayu ndikomwe.
30.9Zina (Tchulani)
31. Mutha kulipira ndalama zingati, kutakhala kuti katemera wa COVID-19 yu ndiwolipilitsa?
32. Mumadziwa bwanji ndi kukhulupilira mitundu yamakatemera awa?
OpangaSindidziwa kathuNnamvako koma sindimukhulupiliraNdikumudziwa komanso ndimamukhulupirira
AstraZeneca/Oxford vaccine [UK]
Johnson and Johnson [US]
Moderna [US]
Janssen [US]
Pfizer/BionTech [China]
Sinovac [China]
Zhifei Longcom, [China]
Sinopharm Beijing [China]
CanSinoBIO [China]
THE GA Vector State Research Centre of Viralogy and Biotechnology [Russia]
Serum Institute [India]


  1. Adigwe, O.P. COVID-19 vaccine hesitancy and willingness to pay: Emergent factors from a cross-sectional study in Nigeria. Vaccine X 2021, 9, 100112. [Google Scholar] [CrossRef] [PubMed]
  2. Alobaidi, S. Predictors of Intent to Receive the COVID-19 Vaccination Among the Population in the Kingdom of Saudi Arabia: A Survey Study. J. Multidiscip. Healthc. 2021, 14, 1119–1128. [Google Scholar] [CrossRef] [PubMed]
  3. Li, Y.; Liu, G.; Egolet, R.; Yang, R.; Huang, Y.; Zheng, Z. Knowledge, Attitudes, and Practices Related to COVID-19 Among Malawi Adults: A Community-Based Survey. Int. J. Environ. Res. Public Health 2021, 18, 4090. [Google Scholar] [CrossRef] [PubMed]
  4. Biswas, R.; Alzubaidi, M.S.; Shah, U.; Abd-Alrazaq, A.A.; Shah, Z. A Scoping Review to Find Out Worldwide COVID-19 Vaccine Hesitancy and Its Underlying Determinants. Vaccines 2021, 9, 1243. [Google Scholar] [CrossRef]
  5. Perceived Public Health Threat a Key Factor for Willingness to Get the COVID-19 Vaccine in Australia. Available online: (accessed on 11 April 2022).
  6. Mondal, P.; Sinharoy, A.; Su, L. Sociodemographic predictors of COVID-19 vaccine acceptance: A nationwide US-based survey study. Public Health 2021, 198, 252–259. [Google Scholar] [CrossRef]
  7. Okubo, R.; Yoshioka, T.; Ohfuji, S.; Matsuo, T.; Tabuchi, T. COVID-19 Vaccine Hesitancy and Its Associated Factors in Japan. Vaccines 2021, 9, 662. [Google Scholar] [CrossRef]
  8. Reiter, P.L.; Pennell, M.L.; Katz, M.L. Acceptability of a COVID-19 vaccine among adults in the United States: How many people would get vaccinated? Vaccine 2020, 38, 6500–6507. [Google Scholar] [CrossRef]
  9. Rhodes, A.; Hoq, M.; Measey, M.-A.; Danchin, M. Intention to vaccinate against COVID-19 in Australia. Lancet Infect. Dis. 2020, 21, e110. [Google Scholar] [CrossRef]
  10. COVID-19 Vaccination Intention in the UK: Results from the COVID-19 Vaccination Acceptability study (CoVAccS), a Nationally Representative Cross-Sectional Survey. Available online: (accessed on 11 April 2022).
  11. Paul, E.; Steptoe, A.; Fancourt, D. Attitudes towards vaccines and intention to vaccinate against COVID-19: Implications for public health communications. Lancet Reg. Health Eur. 2021, 1, 100012. [Google Scholar] [CrossRef]
  12. Qattan, A.M.N.; Alshareef, N.; Alsharqi, O.; Al Rahahleh, N.; Chirwa, G.C.; Al-Hanawi, M.K. Acceptability of a COVID-19 Vaccine Among Healthcare Workers in the Kingdom of Saudi Arabia. Front. Med. 2021, 8, 644300. [Google Scholar] [CrossRef]
  13. Khalafalla, H.E.; Tumambeng, M.Z.; Halawi, M.H.A.; Masmali, E.M.A.; Tashari, T.B.M.; Arishi, F.H.A.; Shadad, R.H.M.; Alfaraj, S.Z.A.; Fathi, S.M.A.; Mahfouz, M.S. COVID-19 Vaccine Hesitancy Prevalence and Predictors among the Students of Jazan University, Saudi Arabia Using the Health Belief Model: A Cross-Sectional Study. Vaccines 2022, 10, 289. [Google Scholar] [CrossRef]
  14. Factors Associated with COVID-19 Vaccine Hesitancy after Implementation of a Mass Vaccination Campaign. Available online: (accessed on 11 April 2022).
  15. Badr, H.; Zhang, X.; Oluyomi, A.; Woodard, L.D.; Adepoju, O.E.; Raza, S.A.; Amos, C.I. Overcoming COVID-19 Vaccine Hesitancy: Insights from an Online Population-Based Survey in the United States. Vaccines 2021, 9, 1100. [Google Scholar] [CrossRef]
  16. Health Belief Model Perspective on the Control of COVID-19 Vaccine Hesitancy and the Promotion of Vaccination in China: Web-Based Cross-sectional Study. Available online: (accessed on 11 April 2022).
  17. Mahmud, I.; Kabir, R.; Rahman, M.; Alradie-Mohamed, A.; Vinnakota, D.; Al-Mohaimeed, A. The Health Belief Model Predicts Intention to Receive the COVID-19 Vaccine in Saudi Arabia: Results from a Cross-Sectional Survey. Vaccines 2021, 9, 864. [Google Scholar] [CrossRef]
  18. Acceptance of a Third Dose of COVID-19 Vaccine and Associated Factors in China Based on Health Belief Model: A National Cross-Sectional Study. Available online: (accessed on 11 April 2022).
  19. Williams, L.; Gallant, A.J.; Rasmussen, S.; Nicholls, L.A.B.; Cogan, N.; Deakin, K.; Young, D.; Flowers, P. Towards intervention development to increase the uptake of COVID-19 vaccination among those at high risk: Outlining evidence-based and theoretically informed future intervention content. Br. J. Health Psychol. 2020, 25, 1039–1054. [Google Scholar] [CrossRef]
  20. Moucheraud, C.; Phiri, K.; Whitehead, H.S.; Songo, J.; Lungu, E.; Chikuse, E.; Phiri, S.; van Oosterhout, J.J.; Hoffman, R.M. Uptake of the COVID-19 vaccine among healthcare workers in Malawi. Int. Health 2022, ihac007. [Google Scholar] [CrossRef]
  21. Otiti-Sengeri, J.; Andrew, O.B.; Lusobya, R.C.; Atukunda, I.; Nalukenge, C.; Kalinaki, A.; Mukisa, J.; Nakanjako, D.; Colebunders, R. High COVID-19 Vaccine Acceptance among Eye Healthcare Workers in Uganda. Vaccines 2022, 10, 609. [Google Scholar] [CrossRef]
  22. Cai, Z.; Hu, W.; Zheng, S.; Wen, X.; Wu, K. Cognition and Behavior of COVID-19 Vaccination Based on the Health Belief Model: A Cross-Sectional Study. Vaccines 2022, 10, 544. [Google Scholar] [CrossRef]
  23. Acceptance of the COVID-19 Vaccine Based on the Health Belief Model: A Population-Based Survey in Hong Kong. Available online: (accessed on 4 May 2022).
  24. Scognamiglio, F.; Gori, D.; Montalti, M. Vaccine Hesitancy: Lessons Learned and Perspectives for a Post-Pandemic Tomorrow. Vaccines 2022, 10, 551. [Google Scholar] [CrossRef]
  25. COVID-19 Vaccine Hesitancy and Determinants of Acceptance among Healthcare Workers, Academics and Tertiary Students in Nigeria. Available online: (accessed on 4 May 2022).
  26. Al-Mansour, K.; Alyahya, S.; AbuGazalah, F.; Alabdulkareem, K. Factors Affecting COVID-19 Vaccination among the General Population in Saudi Arabia. Healthcare 2021, 9, 1218. [Google Scholar] [CrossRef]
  27. Tran, V.D.; Pak, T.V.; Gribkova, E.I.; Galkina, G.A.; Loskutova, E.E.; Dorofeeva, V.V.; Dewey, R.S.; Nguyen, K.T.; Pham, D.T. Determinants of COVID-19 vaccine acceptance in a high infection-rate country: A cross-sectional study in Russia. Pharm. Pract. (Granada) 2021, 19, 2276. [Google Scholar] [CrossRef]
  28. Li, X.-H.; Chen, L.; Pan, Q.-N.; Liu, J.; Zhang, X.; Yi, J.-J.; Chen, C.-M.; Luo, Q.-H.; Tao, P.-Y.; Pan, X.; et al. Vaccination status, acceptance, and knowledge toward a COVID-19 vaccine among healthcare workers: A cross-sectional survey in China. Hum. Vaccines Immunother. 2021, 17, 4065–4073. [Google Scholar] [CrossRef]
  29. Patwary, M.M.; Alam, A.; Bardhan, M.; Disha, A.S.; Haque, Z.; Billah, S.M.; Kabir, P.; Browning, M.H.E.M.; Rahman, M.; Parsa, A.D.; et al. COVID-19 Vaccine Acceptance among Low- and Lower-Middle-Income Countries: A Rapid Systematic Review and Meta-Analysis. Vaccines 2022, 10, 427. [Google Scholar] [CrossRef]
  30. Kanyanda, S.; Markhof, Y.; Wollburg, P.; Zezza, A. Acceptance of COVID-19 vaccines in sub-Saharan Africa: Evidence from six national phone surveys. BMJ Open 2021, 11, e055159. [Google Scholar] [CrossRef]
  31. Moola, S.; Gudi, N.; Nambiar, D.; Dumka, N.; Ahmed, T.; Sonawane, I.R.; Kotwal, A. A rapid review of evidence on the determinants of and strategies for COVID-19 vaccine acceptance in low- and middle-income countries. J. Glob. Health 2021, 11, 05027. [Google Scholar] [CrossRef]
  32. Davis, T.P.; Yimam, A.K.; Kalam, A.; Tolossa, A.D.; Kanwagi, R.; Bauler, S.; Kulathungam, L.; Larson, H. Behavioural Determinants of COVID-19-Vaccine Acceptance in Rural Areas of Six Lower- and Middle-Income Countries. Vaccines 2022, 10, 214. [Google Scholar] [CrossRef]
  33. Sun, J.; Zhang, M.; Gehl, A.; Fricke, B.; Nawaz, K.; Gluesenkamp, K.; Shen, B.; Munk, J.; Hagerman, J.; Lapsa, M.; et al. Dataset of ultralow temperature refrigeration for COVID 19 vaccine distribution solution. Sci. Data 2022, 9, 67. [Google Scholar] [CrossRef] [PubMed]
  34. Fahrni, M.L.; Ismail, I.A.-N.; Refi, D.M.; Almeman, A.; Yaakob, N.C.; Saman, K.; Mansor, N.F.; Noordin, N.; Babar, Z.-U. Management of COVID-19 vaccines cold chain logistics: A scoping review. J. Pharm. Policy Pr. 2022, 15, 1–14. [Google Scholar] [CrossRef]
  35. National COVID-19 Preparedness and Response Strategy and Plan: July 2021- June 2022. Available online: (accessed on 5 August 2021).
  36. Booster COVID-19 Vaccination Against the SARS-CoV-2 Omicron Variant: A Systematic Review. Available online: (accessed on 4 May 2022).
Figure 1. Conceptual framework of the determinants of COVID-19 vaccine acceptance (based on HBM).
Figure 1. Conceptual framework of the determinants of COVID-19 vaccine acceptance (based on HBM).
Vaccines 10 00760 g001
Table 1. Demographic characteristics and p-values of the samples.
Table 1. Demographic characteristics and p-values of the samples.
VariablesTotal N = 758Vaccine Acceptance N = 460Vaccine Unacceptance N = 189p-Value
VaccinatedWilling to be vaccinated but not yet been vaccinated
N = 189N = 271
Sociodemographic characteristics
Gender 0.012 *
Age <0.001 *
55 and above8010.628352733.82531.3
Residence <0.001 *
Religion 0.275
Other(African traditional religion/Chewa/None)445.8511.42045.51943.2
Marital status 0.089
Never married114153228.141364136
Education <0.001 *
No high school36047.55916.415041.715141.9
High school21428.25827.16831.88841.1
Not educated9011.92022.23437.83640
Employment <0.001 *
Government employee273.62074.113.7622.2
Nongovernment employee709.22637.12231.42231.4
Healthcare worker <0.001 *
Monthly income(MWK) <0.001 *
50,000 and above15820.82415.2159.511939.3
Health characteristics
Chronic disease 0.380
Self-reported health 0.065
Ever diagnosed with COVID-19 0.015 *
Historic vaccine rejection <0.001 *
* p < 0.05.
Table 2. Health benefit model characteristics and p-values of the samples.
Table 2. Health benefit model characteristics and p-values of the samples.
VariablesTotal N = 758Vaccine Acceptance N = 460Vaccine Unacceptance N = 189p-Value
VaccinatedWilling to be vaccinated but not yet been vaccinated87
N = 189N = 271
Perceived susceptibility to COVID-19
Do you agree that COVID-19 is contagious?<0.001 *
Do you think getting COVID-19 is currently a possibility for you?<0.001 *
Perceived severity of COVID-19
Do you agree that the COVID-19 pandemic poses a risk to people in Malawi?<0.001 *
Do you agree that the consequences of getting COVID-19 can be serious and could even lead to death?<0.001 *
Perceived benefits of getting vaccinated against COVID-19
Do you agree that a COVID-19 vaccine can decrease your chances of contracting COVID-19 or suffering from complications?<0.001 *
Do you agree that a COVID-19 vaccine can stop the virus from spreading within communities and between countries?<0.001 *
Perceived barriers to getting vaccinated against COVID-19
Do you agree that immunization requirements go against freedom of choice?0.064
Action cues
Do you know someone who has been infected by COVID-19?<0.001 *
Have you received information about COVID-19 and vaccines from friends?0.001 *
Have you received information about COVID-19 and vaccines from healthcare providers?0.791
Have you received information about COVID-19 and vaccines from the radio?0.042 *
* p < 0.05.
Table 3. Outcomes of logistic regression (ref: Vaccine unacceptance).
Table 3. Outcomes of logistic regression (ref: Vaccine unacceptance).
VariablesBinary Logistic RegressionMultinomial Logistic Regression
Acceptance of COVID-19 VaccineVaccinatedWilling to Be Vaccinated but Not Yet Been Vaccinated
aOR (95% CI)p-ValueOR (95% CI)p-ValueOR (95% CI)p-Value
Demographic characteristics
18–245.079 (2.303–11.202)<0.001 *1.181 (0.989–1.546)0.001 *1.46 (0.621–1.725)0.386
25–342.723 (1.363–5.438)0.005 *1.391 (0.835–1.684)0.044 *0.898 (0.396–2.038)0.798
35–441.057 (0.537–2.079)0.8720.83 (0.372–1.851)0.651.058 (0.450–2.487)0.898
45–541.802 (0.815–3.985)0.1460.584 (0.237–1.440)0.2430.924 (0.355–2.406)0.872
55 and above1 1 1
Urban1.915 (1.151–3.187)0.012 *1.667 (0.868–3.201)0.025 *0.626 (0.341–1.149)0.131
Rural1 1 1
No high school1.634 (0.849–3.137)0.1410.669 (0.302–1.483)0.3220.959 (0.491–1.873)0.902
High school0.994 (0.475–2.080)0.9860.972 (0.397–2.376)0.9501.25 (0.571–2.733)0.577
College0.664 (0.254–1.733)0.4031.508 (0.442–5.057)0.5190.948 (0.300–2.996)0.928
Graduate/Professional1.193 (0.857–1.651)0.008 *4.342 (0.940–20.044)0.040 *1.82 (0.317–10.644)0.502
Not educated1 1 1
Healthcare worker
Yes4.080 (1.387–12.000)0.011 *2.362 (0.602–8.910)0.002 *0.237 (0.034–1.646)0.133
No1 1 1
Monthly income (MWK)
0–50,0001.982 (0.991–4.030)0.0603.845 (2.068–7.148)<0.000 *11.604 (6.260–21.509)<0.000 *
50,000 and above1 1 1
Health status and vaccine history
Self-reported health
Good4.08 (1.410–11.840)0.01 *0.394 (0.098–1.577)0.1881.475 (0.377–5.677)0.576
Fair3.145 (1.063–9.308)0.038 *0.326 (0.081–1.320)0.1160.738 (0.186–2.925)0.665
Poor1 1 1
Historic vaccine rejection
Yes0.160 (0.083–0.309)<0.001 *0.120 (0.057–0.250)<0.000 *0.482 (0.291–0.798)0.005 *
No1 1 1
HBM characteristics
Perceived susceptibility
COVID-19 is contagious for you
Agree1.787 (1.226–2.605)0.003 *2.012 (0.772–5.244)0.013 *2.532 (1.423–4.505)0.002 *
Disagree1 1 1
Perceived severity
COVID-19 can be serious and can even lead to death
Agree2.137 (0.904–4.113)0.0879.959 (1.049–95.575)0.045 *0.925 (0.370–2.316)0.868
Disagree1 1 1
Perceived benefits
A COVID-19 vaccine can stop the virus from spreading within communities and between countries
Agree2.992 (1.851–4.834)<0.001 *2.876 (1.057–7.829)0.039 *2.450 (1.096–5.474)0.029 *
Disagree1 1 1
Action cues
Known someone infected by COVID-19
Yes2.001 (1.285–3.115)0.002 *2.022 (1.174–3.480)0.011 *0.965 (0.587–1.584)0.887
No1 1 1
Abbreviations: OR = odds ratio; aOR = adjusted odds ratio; CI = confidence interval. * p-values < 0.05 were considered statistically significant.
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Ao, Q.; Egolet, R.O.; Yin, H.; Cui, F. Acceptance of COVID-19 Vaccines among Adults in Lilongwe, Malawi: A Cross-Sectional Study Based on the Health Belief Model. Vaccines 2022, 10, 760.

AMA Style

Ao Q, Egolet RO, Yin H, Cui F. Acceptance of COVID-19 Vaccines among Adults in Lilongwe, Malawi: A Cross-Sectional Study Based on the Health Belief Model. Vaccines. 2022; 10(5):760.

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Ao, Qun, Robert Okia Egolet, Hui Yin, and Fuqiang Cui. 2022. "Acceptance of COVID-19 Vaccines among Adults in Lilongwe, Malawi: A Cross-Sectional Study Based on the Health Belief Model" Vaccines 10, no. 5: 760.

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