COVID-19 booster vaccine acceptance in North Central Nigeria: An application of the Integrated Behavioural Model

Abiodun Ebenezer Kolapo^1,2, Olubusayo Elizabeth Fakunle³, Michael Sunday Oguntoye⁴, Muhammad Shakir Balogun⁵

 

¹Nigeria Field Epidemiology and Laboratory Training Programme, ²Kwara State Primary Health Care Development Agency, ³Department of Environmental Health Sciences, Faculty of Public Health, College of Medicine, University of Ibadan, Nigeria, ⁴Kwara State Primary Health Care Development Agency, Kwara State, Nigeria, ⁵Africa Field Epidemiology Network, Abuja Nigeria

 

 

^&Corresponding author
Abiodun Ebenezer Kolapo, Southern Africa Regional Coordinating Centre, Africa CDC, Stand 1186, Cnr Addis Ababa Drive & Chaoli Road, Lusaka, 10101, Zambia.

 

 

 

 

The global COVID-19 pandemic which has its origin in Wuhan, Hubei province of China, in December 2019, has left in its wake catastrophic public health, social and economic consequences [1,2]. As of 26 April 2023, more than 764 million people have been infected with close to 7 million deaths [3]. A range of diverse non-pharmacological and precautionary measures, including social distancing, wearing of facemasks, hand hygiene and avoidance of physical contact was insufficient as a lasting solution to the pandemic [4]. The scourge has only been slowed by the rapid development of vaccines, which were mostly granted approval for use under the World Health Organisation Emergency Use Listing procedure (EUL) [5]. However, the immunity provided by the primary vaccines against COVID-19 is short-lived, necessitating the urgent need for boosters to achieve longterm immunity, reduce transmissibility and severity of the disease [6,7]. Currently, the World Health Organisation Strategic Advisory Group of Experts (WHO-SAGE) recommends a booster dose of COVID-19 vaccine for individuals aged 18 years or older, 4-6 months after completion of the primary vaccination series [8,9].

 

A booster dose can be defined as an extra dose of vaccine administered following the completion of a primary vaccination series [8]. The rationale for the proposition of booster doses was contingent upon the realization that the decline of humoral immunity occurred over time after completion of a primary course [10-12]. Furthermore, the emergence and threat of variants of concerns (VoC), such as the Delta and Omicron variants, with greater infectivity and immune evasion mechanisms have heightened the urgency for booster vaccines [5,13]. Each successive wave of the COVID-19 pandemic has seen the emergence of a new SARS-CoV-2 variant as the dominant circulating variant, compounding the challenge of slow rates of primary vaccination and vaccine hesitancy in some settings [14,15]. Against the backdrop of these reasons, Israel became the first country in the world to roll out booster vaccines for her vulnerable citizens on 30 July 2021 [16]. While the eligible population for booster vaccine has since been expanded to the general population in many countries of the world [13,17], the WHO-SAGE currently recommends national governments to prioritise the administration of the vaccine for those at the risk of severe disease and death [18].

 

Booster vaccine campaign officially commenced in Nigeria on 10 December 2021, following the discovery of the Omicron variant of the COVID-19 virus in the country [19]. Despite the National Primary Health Care Development Agency recommending booster vaccines for all Nigerians 18 years and older who have been fully vaccinated, as of 19 March 2023, only 12.3 % of the eligible Nigerian population had received at least a booster [20]. This may be due to the fact that the success of any vaccination programme depends to a large extent on the readiness of the population to accept the vaccine. In this study, booster acceptance is defined as timely receipt of the booster vaccine as recommended in the timetable, while booster hesitancy refers to delays in receiving boosters on the recommended timetable and refusal to receive boosters [21]. A lot of work has been done on the determinants of COVID-19 primary vaccine series acceptance in Nigeria, and vaccine hesitancy has been widely blamed for the rather slow pace of uptake in the country [22-25]. However, very little is known about the behavioural factors influencing the acceptance of booster vaccine.

 

Theoretical models of health behavior and risk perception are important tools used in understanding factors influencing and inhibiting health behavior. The Health Belief Model (HBM) and the Theory of Planned Behaviour (TPB), among others, are two of the most widely used in studying vaccine acceptance and hesitancy. Both theories have been previously used to investigate vaccination behaviour towards several vaccine-preventable diseases [26,27]. Vaccination studies combining the theories have shown that the integrative models tend to explain variance in the intention to be vaccinated much more than when the models are used separately[28-30].

 

After a thorough literature search and review, it became apparent that this study is one of the first in Nigeria that has sought to explain booster vaccine acceptance using an integrative approach of the theoretical models. The information derived from these conceptual behavioural frameworks will be useful to guide public health efforts in optimizing COVID-19 booster vaccine acceptance and uptake. Hence, this study aimed to investigate the determinants of the acceptance of COVID-19 booster doses among respondents who have been fully vaccinated at the time of the survey using constructs derived from integration of the Health Belief Model and the Theory of Planned Behaviour.

 

 

Theoretical Framework

 

The Integrated Behavioural Model combines constructs from popular theories, including the Health Belief Model (HBM), the Theory of Reasoned Action (TRA) and the Theory of Planned Behaviour, among others [31]. The HBM posits that an individuals' engagement (or lack of engagement) in health-seeking behaviour can be explained by their beliefs about health problems, namely the individual's perceived susceptibility and severity of a disease, as well as the perceived effectiveness, benefits of the intervention and barriers to the intervention [32,33]. Perceived susceptibility refers to the individual's perception regarding the likelihood of contracting a particular disease [33]. Perceived severity refers to the individual's belief as to difficulties and problems that may ensue should the individual be infected with a disease [33]. Perceived barriers refer to the individual's perceived negative aspects related to the action of getting vaccinated, such as costs, physical pain, psychological considerations or a logistic lack of access [33]. Cues to action refer to strategies and incentives such as health messaging from authoritative sources, that encourage people to behave in a specific way [34].

 

The TRA posits that behavioral intentions, which directly precede actions, are shaped by salient information or beliefs regarding the likelihood that engaging in a specific behavior will result in a particular outcome.[35] The TPB builds upon the TRA by incorporating the concept of perceived behavioural control into the model to partly explain why all intentions do not translate into behavior. It argues that intention and behavior are the core aspects of the TPB, and that intention to carry out a behavior is ultimately determined by the individual´s beliefs [26,36]. Additional predictors, such as the concepts of self-efficacy and anticipated regret have been added to the model to enhance the predictive power [36,37]. Attitude towards a behaviour is defined as the extent to which an individual has a negative or positive disposition towards a particular behavior [38]. Subjective norm refers to the degree to which significant others (e.g., friends, family, and society members) influence an individual´s perception of judgement in engaging in a specific behavior [27].

 

Perceived behavioural control refers to the perceived difficulty in performing the behaviour [27,38]. Self-efficacy refers to an individual´s degree in performing a certain behaviour despite the difficulties [31]. Perceived behavioural control and self-efficacy represent an individual´s personal agency in the model[31]. Anticipated regret is the expectation of feeling regret when an individual fails to conduct certain behaviour [36,37].

 

In developing our conceptual model, we integrated constructs from both the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB), along with socio-demographic and COVID-19-related variables, as outlined in Figure 1. In the context of COVID-19, the Health Belief Model (HBM) encompasses perceived severity and susceptibility to COVID-19; perceived benefits and barriers to receiving the booster vaccine; and cues to action, such as recommendations from doctors, immigration authorities, and workplaces, to receive the booster.

 

The TPB model proposes that the intention and behavior of receiving the booster vaccine depends on: attitude toward COVID-19 booster vaccine (perceived necessity, benefit, and effectiveness), subjective norms (the extent to which significant others support getting booster vaccine), and perceived behavioral control (extent to which an individual perceives COVID-19 booster vaccination as being within their control), self- efficacy (the degree of freedom in receiving a booster vaccine) and anticipated regret for not taking COVID-19 booster vaccine.

 

Study Design and Population

 

The research was a cross-sectional sub-population study nested within a larger survey. The parent study was conducted to investigate the determinants of COVID-19 vaccine acceptance among an eligible population in Ilorin, a city in North Central Nigeria. This larger study adopted a three-stage cross-sectional cluster research design in investigating respondents in the study area. All consenting adults who had lived in the study area for at least three months were included in the primary research. The nested study employed a purposive sampling technique and included all 339 respondents who had completed the primary vaccine series. Data collection for the nested study occurred immediately after the primary assessment for each eligible respondent.

 

Study Instrument

 

The questionnaire consisted of the following sections: (1) socio-demographic predictor variables, (2) COVID-19 related variables, (3) COVID-19 booster vaccination history, (4) intention to receive a booster vaccine, (5) HBM predictor variables and (6) TPB predictor variables.

 

Following an extensive review of the literature, we adopted and modified items of the HBM and TPB theories from works by Hossain et al [26] and Shmueli [39]. The questionnaire was deployed on KoboCollect, and administered face-to-face by the researchers and assistants.

 

Variables and Measurements

 

Dependent variable

 

We used the following two questions to measure COVID-19 booster vaccine acceptance among the respondents: (a) Have you received at least a booster dose? (response options of Yes and No) and (b) Do you have intention of receiving a booster dose in the future? (response options: Yes, Undecided, and No). Question (b) was limited to those respondents who had indicated a ‘No’ in question (a). We combined these two items and recoded all “Yes” responses as ‘booster vaccine acceptance’, and all “No” and “Undecided” as ‘booster vaccine hesitancy’.

 

Independent variables

 

The independent variables were grouped into four blocks:

 

Socio-demographic predictor variables

 

These included age, gender, highest level of education, marital status, religion and occupation. The age variable was transformed into age categories (18-39; 40-59, ≥60).

 

COVID-19 related variables

 

The COVID -19 variables included belief in COVID-19 existence, knowing someone infected with COVID-19, history of COVID-19 infection, awareness of COVID-19 booster vaccine, and COVID-19 booster vaccination history.

 

The HBM constructs

 

The HBM constructs consisted of the following five domains: perceived susceptibility (included two items, α = 0.775), perceived severity (included three items, α = 0.739), perceived benefits (included three items, α = 0.909), perceived barriers (included six items, α = 0.718), and cues to action (included three items, α = 0.774) (Table 1).

 

The TPB constructs

 

The TPB constructs consisted of five domains: self-efficacy (included two items, α = 0.479), attitude towards vaccine (included two items, α = 0.488), subjective norms (include four items, α = 0.912), perceived behavioural control (included three items, α = 0.533), and anticipated regret which had only an item (Table 1).

 

Items in the HBM and TPB constructs were measured on a 3-point Likert scale (1= Disagree, 2= Neutral, 3= Agree). Negative items were reverse-coded and identified as (R) (Table 1).- Reverse coding ensured consistency in the interpretation of responses, so that higher values always reflect higher levels of the construct being measured.

 

The questionnaire consisted of 36 questions and took less than 10 minutes to fill.

 

Data Analysis

 

Data was cleaned with MS Excel and analysed with Stata MP Version 15. We reported descriptive statistics using frequency and percentages for categorical variables and mean and standard deviations for continuous variables. We examined associations between the primary outcome of interest (“COVID-19 booster vaccine acceptance”) and independent variables using the Chi Square test. We examined relationships between independent samples (COVID-19 booster vaccine acceptance vs. COVID-19 booster vaccine hesitancy) using t-test. We performed hierarchical logistic regression analysis to investigate the predictors of booster vaccine acceptance. We reported Nagelkerke Pseudo-R squared as the measure of variance in intention to be vaccinated. Only the independent variables that correlated significantly (p < 0.05) with booster vaccine acceptance in the bivariate analyses were included in the regression model.

 

Ethical considerations

 

We obtained ethical approval for the research from the Research and Ethics Committee of the Kwara State Ministry of Health, Nigeria with the approval I.D ERC/MOH/2021/10/004. All procedures performed in this study complied with the institutional and/or national research committee ethical standards, the 1964 Helsinki Declaration and subsequent amendments. We provided all the participants with the detailed objectives of the survey; communicated the benefits of the research; and received verbal informed consent before enrollment in the survey. We ensured strict anonymity for every respondent and clearly spelt out choice of voluntary participation or withdrawal.

 

 

The study involved a total of 339 individuals. The majority (224/339(66.3%)) were married and worked in non- health related occupations (301/339(89%)). Only 3.9% (13/339) individuals reported a history of COVID-19 infection while 71/339 (21%) reported unawareness of COVID-19 booster vaccine (Table 2).

 

Covariates that had statistical significance on booster vaccine acceptance included being married, having a health-related occupation, belief in COVID-19 existence, knowledge of someone who had COVID-19 and awareness of COVID-19 booster vaccine (Table 2).

 

Results of the univariate analyses between HBM and TPB variables and acceptance of COVID-19 booster vaccine are reported in (Table 3). The results reveal that participants who accepted the booster vaccine tended to have higher levels of perceived vulnerability to the infection and perceived greater benefits of vaccination. In the HBM, respondents reported that perceptions of barriers to vaccination did not significantly influence the acceptance of the vaccine. According to the TPB, participants who accepted the vaccine reported greater levels of self-efficacy, perceived control, subjective norms, anticipated regret, and positive attitudes to vaccination.

 

Model 1 explained 36.5% of the variance in acceptance of the booster vaccine (Ar² = 0.365) (Table 4; model 1). The most important components of the hierarchical regression were the HBM dimensions, which added 36.3% to the explained variance, on top of the 0.19% explained by the socio-demographic and COVID-19- related characteristics (Table 4).

 

According to Model 1, participants who were aware of COVID-19 booster were 4.45 times more likely to accept the vaccine as those who were unaware (OR: 4.45, 95% CI: 2.14 - 9.24). A higher perception of vaccine benefits was associated with 30% lower odds of vaccine acceptance (OR: 0.70, 95% CI: 0.60-0.81 (OR: 0.70, 95% CI:(0.60 - 0.81). Model 2 showed that awareness of the vaccine and the TPB constructs of self-efficacy, subjective norms and anticipated regret were predictive of vaccine acceptance (Table 4).

 

In Model 3, all statistically significant relationships in the previous models remained constant except for anticipated regret. The addition of TPB covariates to the hierarchical regression model added 5.8% to the overall variance of 42.3% (Table 4).

 

 

The present study used integrated behavioural theoretical constructs to investigate determinants of COVID-19 booster vaccine acceptance among eligible adults who have been fully vaccinated. The prevalence of COVID-19 booster acceptance was high (83%). This acceptance rate possibly reflected public confidence in the safety and efficacy of the COVID-19 booster, and suggested that a large portion of the population had enhanced immunity which is critical to reducing the spread of COVID-19.

 

The acceptance rate stands in marked contrast to the national average currently put at less than 10% as at the time of publication. However, it is important to note that the population of our study area represents only a microcosm of the estimated national population of approximately 216 million. [40]. Hence, the appreciable difference between the acceptance rate in our study and the national average might be attributed to several factors, including differences in regional awareness, local public health initiatives, and demographic or socio-economic characteristics. Additionally, our study area may have benefited from targeted vaccination campaigns or stronger community engagement efforts, which could have resulted in higher acceptance rates compared to the national average.

 

The finding, however, aligns with those of similar researches in Indonesia, Italy, and the USA with high acceptance rates of 89.3%, 85.7% and 96.2% respectively [41-43]. The high rates in these three countries could possibly be attributed to higher risk perceptions, confidence in the government and healthcare system, robust public health messaging, efficient vaccine supply and availability and a more developed infrastructure.

 

The integrated model could explain 42.3% of the variance in the acceptance of booster vaccine. This variance was higher than those resulting from either of the standalone models. This finding is consistent with two studies conducted in Israel to assess the intention to accept COVID-19 vaccine, which reported that combined models could predict vaccination intentions better than could independent models [39,44]. The level of variance our model explained is considered significant and demonstrates a strong predictive power, as it captures a substantial portion of the factors influencing individuals' decision-making processes and effectively identified risk key determinants [45]. These insights can help public health officials design more targeted interventions to increase vaccine acceptance and uptake. However, the remaining 58% of unexplained variance suggests the presence of additional influencing factors, underscoring the need for further research and refinement.

 

The HBM model contributed a significant portion of the variance (R=36.2 %.) in the model. Although individual items within the block - except for perceived severity- were not statistically significant, the block´s overall contribution suggests that the combined influence of these health beliefs is important in predicting vaccine acceptance. This may indicate that these perceptions work synergistically rather than independently. This findings accords with many other previous studies that have used the model in investigating booster vaccine acceptance [28,30,36,39]. Our study aligns with a systematic review that showed that the influence of severity, self-efficacy, and cues to action on vaccination intention declined with COVID-19 booster vaccine globally. Possibly, other contextual factors played a role in convincing them of the need for a booster. In comparison to the HBM, the TPB model contributes a variance of 5.8% to the overall model. These findings align with an Israeli study that demonstrated that the HBM explained greater variance in the intention to receive the booster vaccine than the TPB.

 

Respondents who worked in the health-related professions were more likely to accept the booster than those in the non-health professions, though this association was not statistically significant in our study. However, this trend aligns with findings from South African and American researches where HCWs demonstrated significantly higher booster vaccine acceptance than non-HCWs. HCWs have greater access to information on vaccines and their benefits, and are thus expected to have higher acceptance rates than non-HCWs. The lack of statistical significance in our study may be attributed to personal concerns about safety, side effects or other contextual factors that warrant further investigation.

 

The finding that awareness of COVID-19 booster vaccine was predictive of booster vaccine acceptance may have implications for the design and implementation of programmes against COVID-19 [46]. Therefore, public health programmes aiming at increasing the booster vaccination acceptance should prioritise risk communication; address the community benefits of the booster vaccine, promote clear, accessible information about the vaccine, simplify logistics for vaccination, enhance sharing of success stories to build confidence and offer counselling services to address concerns. Additionally, vaccination initiatives should leverage trusted community, political and religious leaders, influencers, and peer networks to promote vaccination as a socially accepted behavior [47].

 

After an extensive literature search and review, we found that this study is unique in that it was built upon a primary study that utilised a cluster design. The design facilitated access to a diverse range of respondents and therefore strengthens the generalizability of our study findings. Secondly, while several previous studies investigated correlates of the intention to accept the booster vaccines before roll out our study investigated both the intention and actual acceptance of the boosters at a time when the booster vaccines had already become available. The significance of this lies in the fact that individuals often make real life decisions based on learning and assimilation processes, unlike when faced with hypothetical situations, where cognitive biases and heuristics tend to influence their intentions [48].

 

Limitations

 

Our study has certain limitations which must be borne in mind when interpreting the results. First, we relied on self- report of booster vaccination. This could lead to bias since we did not have access to their health record nor requested for the vaccination slip as evidence. However, the research was carried out only a year after the roll out of the booster doses, hence limiting the possibility of delayed recall. Second, there was the possibility of social desirability bias. We encouraged honest responses by limiting the use of leading questions .as much as possible. Lastly, we excluded from this research all persons who had not completed their primary COVID-19 vaccine series. This could introduce selection bias into our study.

 

 

In conclusion, the study demonstrated that integrated HBM and TPB models explained greater variance in the acceptance of the booster vaccine than could have independent models. Policy makers and public authorities can leverage the strength of the integrated model to plan and guide future interventions.

 

 

The authors declare no competing interest.

 

 

Conceptualisation: A.E.K; Writing- original draft: A.E.K; Writing – critical review and editing: OEF, MSO. Final critical review: A.E.K, M.S.B. All authors approved the final version of this manuscript.

 

 

We wish to appreciate the work of Totalad Concepts, Ilorin, Kwara State, Nigeria, for coordinating the data collection for this project.

 

 

Table 1: Responses and internal consistency of the items of the Health Belief Model and Theory of Panned Behaviour

Table 2: Characteristics of respondents by COVID-19 booster dose acceptance status

Table 3: Univariate analyses between HBM and TPB models and COVID-19 booster acceptance

Table 4: Hierarchical multiple logistic regression of COVID-19 booster acceptance

Figure 1: Conceptual framework

 

 

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