This cross-sectional analytical study was conducted between 25 October 2022 and 18 February 2023 in Kisangani, a city in north-eastern Democratic Republic of Congo (DRC). Kisangani comprises five health zones: two urban (Makiso-Kisangani and Mangobo) and three urban-rural (Tshopo, Kabondo, and Lubunga), with a total of 88 health areas, four general referral hospitals, one provincial hospital, and 88 integrated health centres. All these facilities are covered by the Expanded Programme on Immunisation (EPI).

The study population consisted of households with children aged 12 to 23 months who had resided in Kisangani for at least six months. Only mothers who consented to participate were included. Households were excluded if the mother interrupted the interview for personal reasons.

The minimum sample size was calculated using Daniel Schwartz’s formula, based on an average vaccination dropout rate of 17.5% from the 2021 Kisangani coverage survey [9] . With a 95% confidence level, 5% precision, and a cluster effect of 1.5, the sample size was increased by 10% to account for non-responses, resulting in a target of 370 children aged 12 to 23 months.

A three-stage probability cluster sampling proportional to population size was used:

1) First stage: In each health zone, two clusters were randomly selected from the list of health areas.

2) Second stage: In each selected health area, two neighbourhoods were randomly chosen.

3) Third stage: Within each neighbourhood, households were systematically selected proportional to the number of eligible children.

Data were collected using a pre-tested questionnaire adapted from the WHO Strategic Advisory Group of Experts on Immunisation (SAGE) model [12] . The questionnaire was reviewed by local EPI experts and piloted in a comparable health area. Ten trained surveyors, all epidemiology graduates, conducted face-to-face interviews with mothers. Data completeness and quality were checked daily by the principal investigator, with follow-up visits as needed.

The primary outcome was refusal of vaccination, defined as a mother reporting that she had refused at least an entire vaccine type for her child, either during routine immunisation or mass campaigns.

Potential explanatory variables were grouped as follows:

Data were entered into Excel and analysed using STATA version 13. Descriptive statistics (means, standard deviations, medians, interquartile ranges, and proportions) were used to summarise the sample. Associations between the dependent and independent variables were assessed using contingency tables, Pearson’s chi-square or Fisher’s exact test, and odds ratios (ORs) with 95% confidence intervals (CIs). Variables with p < 0.10 in bivariate analysis were included in a stepwise logistic regression model to adjust for confounders. Model fit was assessed using the Hosmer-Lemeshow test. Statistical significance was set at p < 0.05. This stepwise logistic regression model was chosen because of its ability to effectively identify explanatory variables that are significantly associated with a binary event, while eliminating irrelevant variables. It also optimises the model for predicting the probability of vaccine refusal by retaining only those factors that significantly improve the prediction according to the significance threshold.

The study protocol and questionnaire were approved by the ethics committee of the Faculty of Medicine and Pharmacy, University of Kisangani. Participation was voluntary, with verbal informed consent obtained from all respondents. Data were collected anonymously and confidentiality was maintained throughout.

The prevalence of vaccination refusal among children aged 12 to 23 months in Kisangani was 37.5% ( Figure 1 ).

Table 1 presents the results of bivariate and multivariate analyses examining individual and collective factors linked to vaccination refusal.

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Significant factors included the mother’s age, child’s birth order, pain experienced during vaccination, lack of information from vaccinators, and the belief that some vaccines are more important than others. These findings highlight the importance of maternal perceptions and experiences in vaccine decision-making.

Table 2 details contextual influences on vaccination refusal, including community beliefs, waiting time, lifestyle barriers, and trust in vaccine manufacturers.

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*HCI: health care institution; **Others: media, church, internet, social networks.

It appears that, the belief that vaccinating boys is more important than girls, long waiting times, and the existence of controversial vaccines in the community were strongly associated with refusal. These results underscore the role of social and systemic factors in shaping vaccination behaviours.

Table 3 shows vaccine-specific factors associated with refusal, such as disapproval of the vaccination method, adverse events following immunisation, poor reception by healthcare professionals, and the introduction of new vaccines. These aspects reflect concerns about vaccine safety, administration, and the quality of healthcare interactions.

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*HCI: health care institution; **Others: media, church, internet, social networks.

Several individual, contextual, and vaccine-specific factors were independently associated with vaccine refusal. Notably, mothers older than 24 years were more likely to refuse vaccination for their children (AOR 1.7) ( Table 1 ), a finding that contrasts with some previous studies where younger maternal age was associated with higher hesitancy [13] . This discrepancy may reflect differences in education, exposure to information, and trust in healthcare systems across settings. Birth order was also significant, with first- to third-born children more likely to experience vaccine refusal (AOR 1.9) ( Table 1 ). This result aligns with findings from Cameroon, where birth order influenced vaccination status, but contrasts with studies reporting higher coverage among firstborns. Strong mistrust of vaccine safety, often fuelled by fears of side effects or negative information received through the media or friends and family, is more pronounced among young parents (under 30), who are statistically more likely to hesitate to vaccinate. The influence of cultural beliefs and traditional references, which may be more common among parents with their first children, before experience changes their perceptions for subsequent children. These reasons may be specific to the socio-cultural context and young parents’ perception of vaccination risk [13] - [16] . The reasons for these differences may include parental experience, resource allocation, and cultural factors.

Pain experienced during vaccination (AOR 2.7) and lack of information from vaccinators (AOR 2.3) were important barriers ( Table 1 ). Fear of needles and pain is a well-documented obstacle to vaccination uptake, affecting both children and parents [17] [18] . Insufficient communication from healthcare providers can exacerbate these concerns, highlighting the need for improved counselling and support for parents [19] .

The belief that some vaccines are more important than others (AOR 3.0) and the perception that vaccination is unnecessary if a disease is rare (AOR 1.8) were also associated with refusal ( Table 1 ). These findings reflect gaps in knowledge and risk perception, which can undermine confidence in the full immunisation schedule [18] [20] .

Contextual factors such as the belief that vaccinating boys is more important than girls (AOR 5.1), long waiting times (AOR 3.5), and the mother’s lifestyle (AOR 3.1) were strongly associated with refusal ( Table 2 ). Cultural beliefs about gender, logistical barriers, and competing priorities can all influence vaccination decisions. However, the variable ‘mother’s lifestyle’ measured aspects related to the socio-economic conditions of the child’s mother. Bokoko et al. showed that, among Congolese children, the mother’s lifestyle significantly influences her refusal to vaccinate her child through several mechanisms related in particular to her socio-economic conditions and access to services. Many mothers, especially those living in precarious conditions or in single-parent households, prioritise daily survival (finding food, providing for basic needs) over vaccination, which is perceived as less urgent. Even though vaccination is free, transport costs, the need to take time off work, and the loss of a day’s income are major obstacles. The existence of controversial vaccines in the community (AOR 3.0) and lack of trust in vaccine manufacturers (AOR 1.8) ( Table 2 ) further highlight the impact of misinformation, rumours, and distrust in institutions [21] - [24] .

Vaccine-specific aspects, including disapproval of the vaccination method (AOR 4.1), adverse events following immunisation (AOR 2.0), poor reception by healthcare professionals (AOR 1.9), and the introduction of new vaccines (AOR 1.8), were also significant ( Table 3 ). These findings are consistent with previous research showing that concerns about vaccine safety, administration, and healthcare interactions can deter parents from vaccinating their children [23] - [29] .

The high prevalence of vaccine refusal and the range of associated factors identified in this study suggest that multifaceted interventions are needed. Strategies should include targeted communication to address misconceptions, improved training for healthcare providers in patient-centred communication, and efforts to reduce logistical barriers such as waiting times. Community engagement to address cultural beliefs and build trust in vaccines and healthcare systems is also essential. To combat misconceptions about the supposed prioritization of one gender over another in childhood vaccination, it is essential to develop targeted communication messages that emphasize equality, fairness and the importance of vaccinating all children without discrimination. Finally, messages designed to address parents’ concerns about new vaccines should show that new vaccines are rigorously tested to ensure their safety and efficacy before being offered to children.

This study has several limitations. First, the cross-sectional design precludes causal inference. Second, qualitative analysis was not included, which may have limited the depth of understanding of underlying attitudes and beliefs. Third, the study was conducted during the COVID-19 pandemic, which may have influenced perceptions of vaccines and healthcare. Despite these limitations, the use of a standardised WHO questionnaire and robust sampling methods strengthens the validity of the findings.