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COVID-19 Vaccine Coverage and Factors Associated with Vaccine Hesitancy: A Cross-Sectional Survey in the City of Kinshasa, Democratic Republic of Congo

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24 January 2024

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26 January 2024

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Abstract
: Background: Vaccination against COVID-19 has been the main strategy used by most countries to limit the spread of the virus. However, vaccine uptake has been low in Africa, leading to the implementation of several interventions in order to improve vaccine coverage. This study was conducted due to the lack of information about COVID-19 vaccine coverage and the factors associated with vaccine hesitancy. Methods: This cross-sectional study was carried out in Kinshasa city using multi-stage random sampling. A total of 2160 households were included in the study. The data were analyzed using Stata 15 software. The means and standard deviations were calculated for normally distributed continuous variables, while proportions with their 95% confidence intervals (CIs) were calculated for categorical variables. Pearson’s chi-square test or Fisher’s exact test was used to test the associations between dependent variables and each independent variable. Logistic regression was used to identify the factors associated with hesitancy receiving the COVID-19 vaccination. Results: The majority of respondents were aged between 25 and 34 and 35 and 49 (28.9%). During this study, 15% (95% CI [13.25–17.9]) of respondents had received at least one dose of the COVID-19 vaccine. Among the reasons given for refusing to be vaccinated, most respondents cited concerns about the vaccine being unsafe or causing adverse reactions (45%). Among the reasons given for accepting the vaccine, 26% thought that the vaccine prevented superinfection. The factors associated with hesitancy toward the COVID-19 vaccine were female gender, an age of less than 35 years, and living in non-slum households. Conclusions: Despite the interventions implemented across the country, the reluctance to be vaccinated remains a problem; this could lead to poor health outcomes, especially among the elderly and those with pre-existing conditions. It is important to step up awareness-raising campaigns in the community in order to increase the uptake of vaccination.
Keywords: 
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Preprints on COVID-19 and SARS-CoV-2

1. Introduction

Vaccination against COVID-19 has been the main strategy used in the majority of countries to limit the spread of COVID-19 and combat its mortality [1,2,3,4,5]. Studies have been carried out in several developed countries to find effective vaccines that are capable of limiting the spread of the disease [6]. However, acceptance of the vaccine has been low in certain European countries and especially in Africa, leading to the implementation of several interventions in order to improve vaccination coverage [1].
In Africa, the WHO, through the Covax mechanism, has supplied vaccines to a number of countries that were unable to purchase them [7]. This was in order to make vaccines available even to poor populations. It has been difficult for African countries to reach the WHO target, with only 9% of people being fully vaccinated by the end of 2021 [7]. More than 900 million doses of vaccine were needed to be administered to bring the continent’s coverage to around 40% [7].
Hesitancy to adopt this strategy has been noted in several countries around the world, including Africa [8,9,10]. The main reasons for this, such as a fear of the effects of the vaccine or an imbalance between the risks and benefits, as well as certain cultural and religious factors, have prevented the vaccine from being accepted [11,12]. In addition, there is a lack of knowledge about both the disease and the vaccine [8,13,14].
Most countries have implemented measures to speed up the administration and acceptance of vaccines; these include making it compulsory to be vaccinated before travelling or taking part in certain competitions, especially sporting events [15]. In some countries, one must be vaccinated before entering public places such as shops, supermarkets, etc., resulting in many people getting vaccinated because other barriers have been lifted [15].
In the DRC, several vaccines have been made available to combat COVID-19. The number of people who are vaccinated against COVID-19 is around 10% of the general population, and around 8% of the population has been fully vaccinated [16,17]. Like in other countries, hesitancy to be vaccinated is also a problem. Ditekemena et al., assessed the willingness of the population to be vaccinated if offered a vaccine, and found that 55.9% of participants were willing to be vaccinated against COVID-19 [16]. This could also be linked to the population’s general level of knowledge about COVID-19, as knowledge is a major determinant of adherence to COVID-19 control measures [18].
A number of measures have been implemented throughout the country in order to increase public support for this strategy and combat the spread of the disease [19]. Awareness campaigns were carried out throughout the country to improve access to information and combat all the rumors that were circulating. In addition to these media awareness campaigns, all travelers were asked to be tested on arrival at the airport, except those who were vaccinated [19]. Only fully vaccinated travelers could travel throughout the country without being tested for COVID-19 [19].
However, despite all these interventions, it is difficult to obtain an idea of the factors that lead to low vaccination coverage in order to improve the control strategies that are implemented. Most of the studies carried out have analyzed the intention to be vaccinated, and some have focused on the perspective of healthcare workers [20]. These analyses did not include factors that are associated with vaccination coverage and the reasons given for refusing vaccination, as these studies were limited to an assessment of individuals’ intention to be vaccinated. For this reason, this study aimed to assess COVID-19 vaccine coverage and identify the reasons that individuals are hesitant toward these different vaccines within the population.

2. Materials and Methods

2.1. Settings

According to the Ministry of Health’s 2017 statistical annual that was published in 2019, the city–province of Kinshasa has 35 health zones with 9 general referral hospitals. The city is subdivided into 393 health areas and has 712 first-level structures. However, almost half the population has no access to basic social needs such as water and hygiene, and there is also malnutrition and food insecurity [18].

2.2. Study Design and Sampling

A cross-sectional study was conducted from 27 July to 03 August 2022 in the city of Kinshasa. The study was carried out in households and the respondents to the questionnaires were either the heads of household or an adult member of the household. All households that were within the selected areas of the city of Kinshasa and had the following characteristics were included in this study.
Sampling adapted from Lot Quality Assurance Sampling (LQAS) was used to select the households. At the first level, the province of Kinshasa was selected on a purposive basis according to the guidelines of the study sponsor and the location of the Kinshasa School of Public Health. At the second level, the health zones were selected using a stratified sampling technique. Based on the list of all health zones, two strata were created; these were made up of urban and rural health zones. Within each stratum, enumeration areas of three health zones were formed; in each, three health zones with the same geographical and socio-demographic characteristics were grouped. Then, in each enumeration area, a single health zone was sampled in a simple random fashion using a random number generator that was provided by Microsoft Excel. In the third stage, in each selected health zone, three health areas were selected in a simple random fashion using the random number generator that is provided by MS Excel. In the fourth stage, the households to be surveyed were selected via systematic sampling after a plot survey was conducted; this enabled households to be enumerated and a sampling frame to be drawn up according to the eligibility criteria. This sampling frame included all the plots in the neighborhood that were selected for the group, which contained at least one eligible statistical unit.
A total of 2160 households were included in the study in order to assess the COVID-19 vaccination coverage and the factors associated with non-vaccination.

2.3. Measures

The data on socio-demographic characteristics included the area of residence, gender, age, marital status, religion, level of education, income level, household size, and whether housing was in informal settlements (slums) [18]. The level of education was considered ‘low’ if secondary education or vocational training had not been completed, ‘medium’ if it had been completed, and ‘high’ if higher or university education had been completed. The socio-economic level was determined by a wealth index that had been constructed from a set of household assets (radio, tape recorder, television set, bicycle, torch, horse or donkey cart), housing conditions (roof material, number of rooms, type of wall, windows, availability and type of latrines) and the ownership of domestic animals. Participants in the study were classified according to their wealth index score, which was divided into quintiles.
Data on hesitancy toward COVID-19 vaccination included questions relating to individuals’ COVID-19 vaccination status, reasons for being vaccinated and reasons for not being vaccinated.

2.4. Data Collection

The data were collected using tablets that were configured with the surveyCTO application. After three days of training on the objectives of the survey and a review of the data collection tools used and ethical aspects of the study, 126 interviewers (students on the Master’s program in public health) were supervised in the field by assistants from the Kinshasa School of Public Health during the data collection. The interviews were conducted in the language commonly spoken in Kinshasa, Lingala, or in French for those who wished to do so.

2.5. Statistical Analysis

The data were collected and sent to the dedicated server. After a quality and consistency check, the data were exported to Stata 17 (StataCorp, College Station, TX) for analysis. Descriptive statistics were used to describe the basic characteristics of the study data. Means and standard deviations (SDs) were calculated for normally distributed continuous variables, while proportions with their 95% CIs were calculated for categorical variables. Pearson’s chi-square test or Fisher’s exact test were used to test the associations between the dependent variables and each independent variable. Logistic regression was used to identify the factors that are associated with hesitancy regarding COVID-19 vaccination. Variance inflation factors (VIF) were estimated to assess multicollinearity, and a VIF threshold of less than 4 was considered as no multicollinearity. A significance level of α = 0.05 was used for all tests.

2.6. Ethical Approval

The protocol used in this study received ethical approval from the School of Health Ethics Committee (reference number: ESP/CE/71B/2022). Participants were informed that their participation was voluntary and that they could withdraw from the study without any consequences. It should be noted that oral informed consent was obtained from each participant. Participants were informed that participating in this survey would not guarantee any immediate benefit and that the results of this study could help the Expanded Programme on Immunisation and the Multisectoral Committee for the Control of COVID-19 to implement evidence-based interventions for the prevention and control of the COVID-19 pandemic. Confidentiality was ensured by maintaining the anonymity of the study participants.

3. Results

3.1. Characteristics of Participants

Table 1 presents the socio-demographic characteristics of the study participants. The majority of respondents fell within the age groups of 25–34 and 35–49, accounting for 28.9% each. Women constituted a significant portion of the participants, making up 69% of the sample. Among religious affiliations, 48.7% of the participants were associated with revivalist churches, followed by the Catholic church at 19.2%. A substantial proportion of the respondents had attained a secondary education level (43.1%). Notably, nearly half of the respondents (49.3%) were occupied as housewives, students, or pupils.

3.2. COVID-19 Vaccination Uptake

Overall, 15% (CI 95%: 13.25–17.9) of respondents had received at least one dose of a COVID-19 vaccine.

3.3. Hesitancy toward COVID-19 Vaccine

Table 2 presents data on the hesitancy to receive the COVID-19 vaccine based on the socio-demographic characteristics of the participants. It indicates that younger respondents exhibited higher levels of vaccine hesitancy (74.4%) compared to older individuals. Additionally, women demonstrated greater vaccine hesitancy than men, and this difference was statistically significant (p < 0.001). Surprisingly, those who did not reside in slum areas showed the highest level of vaccine hesitancy.

3.4. Reasons of Refusal and Acceptance of COVID-19 Vaccine

Figure 1 illustrates the reasons that the participants gave for refusing vaccination. The reason given most often by the respondents was concerns about the vaccine’s safety and potential adverse effects, accounting for 45% of refusals. This was followed by doubts about the vaccine’s effectiveness (11%) and religious objections (9%).
Regarding the reasons for accepting the vaccine (Figure 2), 26% of respondents believed that the vaccine offered protection against reinfection, while 22% considered it safe. Additionally, approximately 7% felt compelled to accept the vaccine due to their close contact with vulnerable individuals whom they wished to protect.
As shown in Figure 3, respondents under the age of 25 were 2.15 times more likely to exhibit vaccine hesitancy (95% CI: 1.43–3.24; p < 0.001) compared to those aged 65 years and older. Similarly, respondents aged 25–34 were 1.86 times more likely to exhibit vaccine hesitancy (95% CI: 1.27–2.73; p = 0.001) compared to those aged 65 and older. Female respondents were 1.37 times more likely to exhibit vaccine hesitancy (95% CI: 1.11–1.70; p = 0.004) compared to male respondents. Non-slum households were 1.39 times more likely to exhibit vaccine hesitancy (95% CI: 1.09–1.75; p = 0.006) compared to slum households.
This community-based cross-sectional survey, carried out in Kinshasa, DR Congo, aimed to assess COVID-19 vaccine coverage and identify the reasons why hesitancy is shown toward these vaccines within the population. This study found that less than a quarter of respondents had received at least one dose of a COVID-19 vaccine. Among the reasons given for refusing to be vaccinated, most respondents cited concerns about the vaccine’s safety or adverse reactions. Among the reasons given for accepting the vaccine, a quarter of respondents believed that it prevented reinfection. The factors associated with vaccine hesitancy included female gender, an age of less than 35 years and living in non-slum households.
This study’s findings regarding vaccine coverage were much lower than those of a study conducted by Ditekemena et al., which found that approximately 41% of the population in the city of Kinshasa were willing to be vaccinated if offered the vaccine [16]. This proportion is significantly lower than that observed in our study, where only a small number of people in Kinshasa had received at least one dose. However, it should be noted that a small proportion of the population was obligated to accept the vaccine due to the requirements of most European countries [21,22]. The socio-economic level of the majority of the population did not allow them to travel internationally, and they did not want to be vaccinated.
The main reason given for vaccine hesitancy in our study was concerns about the vaccine’s safety or adverse reactions. Our results align with those of other studies, including that conducted by Okubo et al. in Japan; this study found that adverse reactions were the main reason for individuals exhibiting hesitancy toward vaccination [23]. A study conducted by Campelo in Brazil also found that the fear of adverse reactions was the primary obstacle to vaccine uptake [24]. We believe that this may not only be due to rumors about the adverse effects of certain vaccines, such as Astra Zeneca, but also due to the widespread media coverage of these rumors. In the DRC, for example, this situation led to an initial suspension of vaccination before it resumed. This information was sometimes disseminated by the scientific communities as well [20].
In Egypt, medical students stated that they were not vaccinated due to their fear of adverse effects, vaccine ineffectiveness, and a lack of information about different vaccines [24]. Moreover, the vaccine was accepted so as to avoid reinfection. Belief in the existence of the disease is a prerequisite for the acceptance of all preventive measures. Therefore, it is important to reinforce communication strategies in order to help people understand the benefits of vaccination. Farooq Ahmad et al. found that vaccination was accepted in their study due to the belief that it would stop the pandemic [24]. This highlights the importance of effective communication strategies. Effective strategies should also be implemented in our context, as the majority of the population did not believe in the existence of the disease, making it difficult for them to adhere to various interventions.
In our study, females, people under the age of 35, and those living in non-slum households were more likely to exhibit vaccine hesitancy. The female gender and an age of less than 35 have been found to be associated with hesitancy in several studies [23,25]. A study conducted by Soares in Portugal found that young people were more hesitant to be vaccinated compared to older people [25]. In China, Xiao et al. found that young adults aged between 18 and 34 were the most reluctant to be vaccinated [26]. We believe that this may be because the impacts that were experienced by the younger age group during the pandemic were less severe in most countries, even though they were able to transmit the disease to at-risk people, including the elderly or people with comorbidities such as diabetes, hypertension, and other chronic diseases. This younger population often did not perceive themselves as being at risk. Additionally, at the start of the COVID-19 vaccination programme in the DRC, certain priority groups were targeted first, including the elderly, healthcare professionals, and people with comorbidities [20]. As a result, it was difficult to vaccinate enough young people.
Females were more likely to exhibit reluctance with regard to vaccination than males. We think that this could be explained by the fact that women are more likely to believe rumors and are more fearful than men. Rumors about vaccination appeared to have a greater impact on women, who were more afraid of the potential adverse effects of the vaccine [27]. A similar reluctance regarding vaccination among women has been observed in other developing countries like Senegal and Ethiopia [28,29].
Rumors generally circulate among those who are the most informed and those who use social networks [30]. These channels have been a vehicle for many rumors about the COVID-19 vaccine [31]. This could explain why individuals from non-slum households, who have a higher socio-economic status and more access to these media, are more hesitant to be vaccinated. On the other hand, people living in non-slum areas may be more likely to accept the information conveyed by community health workers and adhere to vaccination.
These findings are subject to several limitations. Although we cannot exactly determine why higher estimates were obtained from our survey compared to those from the vaccine administration data (PEV), there are several possible explanations. First, regarding the survey data, the COVID-19 vaccination status was based on self-reports, which may not have accurately reflected the actual vaccination status of some respondents. Second, the residents of Kinshasa Province who received their vaccinations elsewhere may have been excluded from the Kinshasa vaccine administration data (PEV). This could explain some of the larger differences observed between the immunization data and survey data. Third, it is possible that there COVID-19 vaccinations taking place during the current campaign (from 20 July 2022) that have not been reported in the administration data (PEV), which would artificially lower the coverage estimates based on the vaccine administration data (PEV); however, this may be a very small number. Fourth, the PEV does not contain accurate data about the population size (the last census occurred in 1984). Fifth, these findings are relevant to the July data only, and any bias may have subsequently changed.
Finally, social desirability bias could lead to some unvaccinated individuals claiming that they are vaccinated. It is, thus, likely that the population survey overestimates vaccination coverage due to survey respondents misreporting their vaccination status. However, this study gives a clear picture of COVID-19 vaccination coverage while the vaccine was available and while all awareness-raising strategies were in place. This study will thus enable the Ministry and all stakeholders to improve the strategies that are implemented and thus increase vaccination coverage based on the factors associated with the population’s hesitancy regarding vaccination.

4. Conclusions

The COVID-19 pandemic was a humanitarian crisis that had a significant impact on people’s lifestyles. Vaccination has been an important intervention in limiting the spread of this pandemic. Despite the interventions implemented across the country, hesitancy regarding vaccination remains a problem. It is important to step up awareness-raising campaigns in the community in order to increase vaccination uptake. Future studies should address the population’s perceptions of the COVID-19 vaccine. Similar studies would be important at the national level, and should include all provinces or, better yet, all health zones, to better describe this phenomenon at the national level.

Author Contributions

Conceptualization, APZ and MDK.; methodology, APZ, TNT, BS, EL, KB, KF, KDM and MDK.; software, APZ, TNT, KF and KDM.; validation, APZ, TNT, BS, EL, KB, KF, KDM and MDK.; formal analysis APZ, TNT, KF, KDM and MDK.; investigation, APZ, TNT, BS, EL, KB, KF, KDM and MDK.; resources, APZ, BS, EL, KB, KF, KDM and MDK.; data curation, APZ, KB, KF, KDM and MDK.; writing—original draft preparation, APZ, TNT, BS, EL, KB, KF, KDM and MDK.; writing—review and editing, APZ, TNT, BS, EL, KB, KF, KDM and MDK.; visualization, APZ, TNT, KF, KDM and MDK.; supervision, APZ, TNT and MDK project administration, APZ and MDK.; funding acquisition, APZ and MDK. All authors have read and agreed to the published version of the manuscript.

Funding

The authors have received no specific funding for this work.

Institutional Review Board Statement

Ethical clearance was obtained from the Ethics Committee of the KSPH (reference number: ESP/CE/71B/2022). Consent was obtained from each respondent during data collection. Privacy and confidentiality were maintained throughout the study.

Informed Consent Statement

All co-authors consented to the publication of the last version of the present article.

Data Availability Statement

The dataset used for analysis can be obtained upon reasonable request by writing an email to the corresponding author.

Acknowledgments

We thank all individuals who participated in this study. We also thank the public health students involved in the field data collection for their commitment and tireless efforts in ensuring that the data collection was well executed.

Conflicts of Interest

All authors declare that they have no competing interests.

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Figure 1. Reasons for refusing the COVID-19 vaccine (in %).
Figure 1. Reasons for refusing the COVID-19 vaccine (in %).
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Figure 2. Reasons for accepting the COVID-19 vaccine. 3.5. Factors Associated to the Hesitancy of COVID-19 Vaccine (in %).
Figure 2. Reasons for accepting the COVID-19 vaccine. 3.5. Factors Associated to the Hesitancy of COVID-19 Vaccine (in %).
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Figure 3. Forest Plot: factors associated with hesitancy toward the COVID-19 vaccine and discussion.
Figure 3. Forest Plot: factors associated with hesitancy toward the COVID-19 vaccine and discussion.
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Table 1. Characteristics of the participants.
Table 1. Characteristics of the participants.
No Slum Slum Ensemble p
n % n % n %
Age 0.299
  <25 94 14.7 238 17.6 332 16.6
  25–34 189 29.5 387 28.6 576 28.9
  35–49 180 28.1 396 29.2 576 28.9
  50–64 112 17.5 221 16.3 333 16.7
  ≥65 66 10.3 112 8.3 178 8.9
Gender 0.492
  Male 196 30.0 430 31.5 626 31.0
  Female 458 70.0 936 68.5 1394 69.0
Level of Education <0.001
  None/Primary School 110 16.9 592 43.4 702 34.8
  Secondary School 299 45.9 570 41.8 869 43.1
  University/High 243 37.3 202 14.8 445 22.1
Religion of respondent <0.001
  Catholic 160 24.5 228 16.7 388 19.2
  Protestant 111 17.0 172 12.6 283 14.0
  Revival Church 284 43.4 699 51.2 983 48.7
  Others 99 15.1 267 19.5 366 18.1
Employment <0.001
  No occupation/housewife/student or pupil 313 47.9 683 50.0 996 49.3
  Public sector employee with a regular monthly salary 84 12.8 122 8.9 206 10.2
  Private sector employee with a regular monthly salary 41 6.3 65 4.8 106 5.2
  Self-employed in the private sector (self-employed) 119 18.2 170 12.4 289 14.3
  Worker in the informal sector and small trade 71 10.9 262 19.2 333 16.5
  Agropastoral and fishing 3 0.5 12 0.9 15 0.7
  Other 23 3.5 52 3.8 75 3.7
Household size <0.001
  ≤6 548 83.8 966 70.7 1514 75.0
  ≥7 106 16.2 400 29.3 506 25.0
Sufficient living space (not overcrowded) <0.001
  Overcrowding 875 29.1 3167 42.9 4042 39.0
  Sufficient Living Area 2127 70.9 4208 57.1 6335 61.0
Income Quintiles <0.001
  Very low 44 6.7 360 26.4 404 20.0
  Low 74 11.3 330 24.2 404 20.0
  Middle 124 19.0 281 20.6 405 20.0
  High 159 24.3 244 17.9 403 20.0
  Very High 253 38.7 151 11.1 404 20.0
Table 2. Hesitancy to receive the COVID-19 vaccine.
Table 2. Hesitancy to receive the COVID-19 vaccine.
No Yes p
n % n %
Age <0.001
  <25 85 25.6 247 74.4
  25–34 161 28.0 415 72.0
  35–49 199 34.5 377 65.5
  50–64 137 41.1 196 58.9
  ≥65 76 42.7 102 57.3
Gender <0.001
  Male 242 38.7 384 61.3
  Female 424 30.4 970 69.6
Level of Education 0.023
  None/Primary School 256 36.5 446 63.5
  Secondary School 260 29.9 609 70.1
  University/High 148 33.3 297 66.7
  Religion of respondent 0.674
  Catholic 128 33.0 260 67.0
  Protestant 97 34.3 186 65.7
  Revival Church 330 33.6 653 66.4
  Others 111 30.3 255 69.7
Employment 0.262
  No occupation/housewife/student or pupil 306 30.7 690 69.3
  Public sector employee with a regular monthly salary 75 36.4 131 63.6
  Private sector employee with a regular monthly salary 39 36.8 67 63.2
  Self-employed in the private sector (self-employed) 99 34.3 190 65.7
  Worker in the informal sector and small trade 121 36.3 212 63.7
  Agropastoral and fishing 6 40.0 9 60.0
  Other 20 26.7 55 73.3
Household size 0.184
  ≤6 487 32.2 1027 67.8
  ≥7 179 35.4 327 64.6
Sufficient living space (not overcrowded) 0.770
  Overcrowding 197 33.4 392 66.6
  Sufficient Living Area 469 32.8 962 67.2
Income Quintiles 0.158
  Very low 154 38.1 250 61.9
  Low 128 31.7 276 68.3
  Middle 132 32.6 273 67.4
  High 122 30.3 281 69.7
  Very High 130 32.2 274 67.8
Slum Household 0.001
  No 183 28.0 471 72.0
  Yes 483 35.4 883 64.6
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