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Age and vaccine information sources drive vaccine hesitancy: A household survey in Central-Western Brazil

  • Ana Isabel do Nascimento,

    Roles Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Programa de Pós-Graduação em Doenças Infecciosas e Parasitárias, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Danilo dos Santos Conrado,

    Roles Investigation, Writing – review & editing

    Affiliation Programa de Pós-Graduação em Doenças Infecciosas e Parasitárias, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Lisany Krug Mareto,

    Roles Formal analysis, Investigation, Writing – review & editing

    Affiliation Programa de Pós-Graduação em Doenças Infecciosas e Parasitárias, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Micael Viana de Azevedo,

    Roles Investigation, Methodology, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • João Cesar Pereira da Cunha,

    Roles Investigation, Methodology, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Gabriel Serrano Ramires Koch,

    Roles Investigation, Methodology, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Laysa Gomes Osório,

    Roles Investigation, Methodology, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Samara Tessari Pires,

    Roles Investigation, Methodology, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Letícia Suemi Arakaki,

    Roles Investigation, Methodology, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Sara Raquel Pinto Borges,

    Roles Investigation, Methodology, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Robson Franca Gomes e Silva,

    Roles Investigation, Methodology, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Rodrigo Mayer Pucci,

    Roles Investigation, Methodology, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • João Guilherme de Novaes Corrêa,

    Roles Investigation, Methodology, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • João Vitor Barrio,

    Roles Investigation, Methodology, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Maria Eduarda de Souza Rodrigues,

    Roles Investigation, Methodology, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Artur Jorge Bianchi,

    Roles Investigation, Methodology

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Márcio José de Medeiros,

    Roles Conceptualization, Data curation, Investigation, Methodology, Writing – review & editing

    Affiliation Instituto Politécnico, Universidade Federal do Rio de Janeiro, Macaé, RJ, Brasil

  • Ana Paula Sayuri Sato,

    Roles Conceptualization, Methodology, Writing – review & editing

    Affiliation Faculdade de Saúde Pública, Universidade de São Paulo, São Paulo, SP, Brasil

  • Maria Elizabeth Araújo Ajalla,

    Roles Conceptualization, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  • Cláudia Du Bocage Santos-Pinto,

    Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

  •  [ ... ],
  • Everton Falcão de Oliveira

    Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    everton.falcao@ufms.br

    Affiliations Programa de Pós-Graduação em Doenças Infecciosas e Parasitárias, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil, Faculdade de Medicina, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brasil

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Abstract

In recent decades, the decline in vaccination coverage has garnered global attention, and the impact of vaccine hesitancy has become a significant concern for public health policymakers worldwide. This study aims to measure vaccine hesitancy and its associated factors among residents of Campo Grande, Mato Grosso do Sul, Brazil. From September 2022 to October 2023, a cross-sectional study aligned with a household survey was conducted to measure vaccination coverage among residents of Campo Grande municipality in Brazil. Data were collected through face-to-face interviews using the WHO. Reasons for vaccine hesitancy were classified under the 3C conceptual model of vaccine hesitancy determinants. Descriptive statistics were employed to characterize the study population, and univariate and multivariate logistic regression analyses were conducted to assess the association between hesitant and non-hesitant participants and the study variables. We included 467 households in the study, with a total of 518 participants interviewed. Over half of the participants hesitated to get vaccinated (50.2%), with the COVID-19 vaccines being the most hesitated (55.4%). The majority of reported reasons for hesitancy were related to a lack of confidence (62.3%). The hesitant participants in our study were younger than the non-hesitant participants (aOR = 0.98; 95% CI: 0.97, 0.99), were less likely to believe that vaccines could protect themselves and their children from serious diseases (aOR = 0.23; 95% CI: 0.06, 0.66), and were less likely to get information from healthcare workers or official health organizations’ online profiles (aOR = 0.39; 95% CI: 0.17, 0.86). We observed a high prevalence of vaccine hesitancy in Campo Grande. The results highlight the potential impact of the COVID-19 pandemic and the infodemic in increasing negative feelings about vaccines.

Introduction

Vaccination has been recognized as one of the best methods for controlling the transmission of infectious diseases [1]. In Brazil, the Brazilian National Immunization Program (Programa Nacional de Imunizações, PNI), was established as the main institutional strategy for implementing vaccination as a public health policy, building on earlier successful initiatives such as the variola eradication campaign, which aimed to control and eradicate smallpox [2]. This achievement occurred in an adverse national context marked by political instability, limited resources for preventive health measures, and profound social and demographic changes, including rapid urbanization driven by internal migration and persistent sanitation challenges [2]. In response, the Brazilian National Immunization Program (PNI) was formulated in 1973 and formally established in 1975, following the success of the smallpox eradication campaign, to ensure sustained vaccination capacity and strengthen the foundations of the Brazilian Unified Health System, one of the largest public health systems in the world [2,3].

Since its establishment, the PNI has ensured nationwide access to vaccines free of charge, achieving substantial operational and structural advances [3]. These efforts have led to the control of multiple vaccine-preventable diseases, including poliomyelitis, tetanus, and urban yellow fever, and have contributed significantly to reductions in infant mortality across the country [4]. The PNI also developed widely recognized communication strategies, such as partnerships with media figures, mass radio and television campaigns, and the creation of the nationally recognized character Zé Gotinha, which played a key role in the successful control of poliomyelitis [3].

Nevertheless, vaccination coverage has declined globally in recent years [5]. In Brazil, despite historically high coverage levels, a sustained downward trend has been observed since 2016 [6]. This decline has coincided with the growing influence of anti-vaccine discourse, the politicization of vaccination during the COVID-19 pandemic, and the widespread circulation of misinformation in digital environments, factors that may have contributed to increasing mistrust and changes in public perceptions of immunization.

Understanding the reasons behind the choice to vaccinate or not – and interpreting these reasons regarding the context and the perceptions about vaccines and illnesses, considering sociodemographic factors – is crucial for sustaining high vaccine demand [7]. This concern led to the development of the concept of “vaccination hesitancy”, a phenomenon declared as one of the global threats to public health in 2019 and broadened the theoretical framework surrounding vaccination attitudes [8]. This concept highlights the diversity within the vaccine-hesitant population, which spans a spectrum from complete acceptance to outright refusal of vaccines. The World Health Organization (WHO) defines, in 2015, vaccination hesitancy as the “delay in acceptance or refusal of vaccines despite the availability of vaccination services” [9,10].

Vaccine hesitancy fundamentally stems from a decision-making process influenced by a variety of factors, summarized in the 3C model of vaccine hesitancy: complacency, confidence, and convenience. Complacency refers to the underestimation of disease risk and the perceived need for vaccination. Confidence-related determinants encompass mistrust in the vaccine, the health system distributing it, its professionals, and the policymakers governing vaccine policies. Lastly, determinants related to convenience involve issues of geographical access, availability, comprehensibility of vaccine-related information, quality of health services, and other access-related obstacles [10]. Subsequently, the 5C conceptual model was developed to incorporate additional behavioral aspects of vaccination, such as collective responsibility and risk calculation [11], alongside the renewal of the conceptual model, the WHO redefined vaccination hesitancy as “a motivational state of being conflicted about or opposed to, getting vaccinated”, which included intentions and willingness [12].

Following the global trend of declining vaccination coverage rates [6], Campo Grande, the capital of Mato Grosso do Sul, located in a tri-border region (Brazil-Bolivia-Paraguay), has experienced a decline in vaccination coverage since 2019 [13]. Based on this context, this study aimed to measure vaccination hesitancy and identify its associated factors among residents of Campo Grande.

Materials and methods

Study design and period

This cross-sectional study used data collected through a household survey conducted in Campo Grande, Mato Grosso do Sul, Brazil, between September 2022 and October 2023, with the primary objective of estimating vaccination coverage in the municipality. For the purposes of this analysis, data on vaccine hesitancy were also collected and analysed.

Study site

Campo Grande, the capital state of Mato Grosso do Sul, by the time of our study, comprised a population of 898,100 inhabitants. The municipality holds a population mostly female and young (sex ratio = 92.2; age median = 34 years) [14]. Mato Grosso do Sul shares borders with Bolivia and Paraguay, holding, alongside Mato Grosso, over 60% of the immigrant population of the Brazilian Central-West region between 2022 and 2023 [15]. In the same period, Mato Grosso do Sul provided the second-highest amount of refugee concessions in the region. The state received four times the migrant population of 2013 [15]. In 2022, 5,614 migrant workers were received in the municipality [15]. Therefore, healthcare actions to promote vaccination are common in the municipality of Campo Grande, targeting immigrants and refugees.

Sampling

The sampling method was cluster sampling, according to the one proposed by the WHO, in 2018, for studies that aim to estimate vaccination coverage [16]. Further details of the sampling method and calculation are described in the S1 Appendix. This method is based on two stages: (1) cluster selection and (2) households’ selection.

  1. (1). Definition and selection of clusters: Assuming that the expected average vaccination coverage in Campo Grande, for all vaccines available in the PNI, is 90%, with a confidence interval around the estimates of 8% (i.e., 90% ± 8% coverage estimate), with an alpha (type I error) of 5%, the effective sample size – based on an assumption of simple random sampling – was n = 101. To determine the average number of people eligible for the study (individuals aged 12 years or older), a pilot study was conducted. For this, one cluster (census tract) was randomly selected. Considering that on average, in Brazil, each census tract has approximately 300 households, for the pilot study 10% of the households contained in the selected cluster were drawn, which resulted in 31 households. After completing the pilot study, the average number of respondents per cluster, within a 3-hour interval, with a field team of 6 researchers, distributed in pairs, was 10. Assuming that the intracluster correlation is 0.33 [16], the design effect size was set at 3. Applying the formula proposed by the WHO, the estimated number of clusters was 30.3, which was rounded to 30. The selection of clusters was done by simple random sampling without replacement, using the cartographic base of census sectors from the IBGE of 2021. The cluster where the pilot study was conducted was included in the study. Therefore, an additional 29 clusters were sampled afterward. Clusters that primarily contained institutionalized populations (prisons and long-term care facilities, such as nursing homes) were immediately replaced when drawn. Clusters containing large condominiums or gated communities that did not allow the study team entry for data collection after initial contact by the researchers were also replaced.
  2. (2). The definition of the number and selection of the households was based on the pilot study. The number of residencies visited to find an eligible participant averaged 1.5, the inflation factor to account for refusals and non-respondent residences was 1.05, and the average number of respondents per day of data collection was 10. Therefore, the number of households per cluster was defined as 15. Those households were selected by random simple probabilistic sampling.

The random sampling and spatial allocation of clusters and households were performed using sf package from software R 3.4.2.

Study population and data collection

All residents of the Campo Grande municipality, aged 12 years or more, who consented to participate in this study, were eligible. The study was based on data collected through an interview using the SAGE Work Group questionnaire from 2015, which was translated into Portuguese and linguistically adapted to fit our context, without deviating from its original meaning [9,10,17]. Questions about socioeconomic and demographic variables, along with access to health units with vaccination facilities, were included in this interview and data collection instrument. During the interviews, participants were informed about the concept of vaccination hesitancy, proposed by the WHO in 2015, since this study’s instrument was developed based on that definition [10], and answered about the vaccine hesitancy related to their own vaccination and their reasons for the hesitation. Finally, we classified the reasons for hesitancy under the 3C conceptual model for vaccine hesitancy, as the data collection questionnaire that we used was built based on this model (S3 Appendix). When possible, these reasons were also interpreted in light of the ‘risk calculation’ dimension (the individual assessment of the risks and benefits of vaccination) from the 5C model of vaccine hesitancy [11].

Statistical analysis

Descriptive statistics was employed to characterize the studied population. The study data were analysed according to the occurrence of vaccine hesitancy. Therefore, the study population was divided into hesitant (HP) and non-hesitant (NHP) participants. The continuous variables were reported as mean values and standard deviation (SD) and were compared with the Welch t-test. The categorical variables were reported as a frequency and were compared with the chi-square and/or Fisher’s exact tests in this stage.

We conducted a multivariate logistic regression model to assess the relationship between vaccine hesitancy and the covariates found to have a p-value of 0.20 or less were included in the previous stage of the analysis. The stepwise algorithm (considering both backward and forward directions) and the Akaike Information Criterion (AIC) were used for variable selection, controlling for potential confounders, and determining the best-fitting model. The presence of multicollinearity was assessed using the variance inflation factor (VIF). The Hosmer-Lemeshow test was used as a measure of fit.

To explore heterogeneity across age groups, we conducted additional analyses using categorized age. Age was grouped into 20-year intervals to ensure sufficient sample size within each category. Descriptive analyses compared the distribution of vaccine hesitancy across age groups, and a multivariable logistic regression model was re-estimated using age as a categorical variable, applying the same modelling strategy as in the primary analysis (see S2 Appendix). This complementary approach was adopted to facilitate interpretation of potential generational patterns, while age was retained as a continuous variable in the main model to preserve statistical power.

The significance level adopted for all hypothesis tests was 5% (α = 0.05). The analysis was performed using R software version 4.3.2 (https://www.r-project.org/), and the following packages were used: tidyverse, descr, and generalhoslem.

Ethics approval and consent to participate

The study was approved by the Research Ethics Committee (CEP) of the Federal University of Mato Grosso do Sul (CAEE: 47947821.0.0000.0021), according to opinion nº: 5.200.726. All the participants signed a written informed consent form before the data collection. When adults the Informed Consent Form (ICF) was applied, and the Assent Form (AF) was applied for minors (under 18 years old), along with the consent of the responsible guardian present during the application of the questionnaire, who also signed the ICF.

Results

A total of 467 households were included and 518 participants were interviewed. The medium of respondents was 1,1 (SD = 0.38) per household, and 17 respondents per cluster (SD = 4).

Most of the participants were female (321/518; 62.0%), and non-white (362/518; 69.9%). The mean age of the population was 46,7 years, most participants were between 52–71 (179/518; 34.6%), and 32–51 years old (174/518; 33.6%). The mean of years of study was 9.7. Most of the respondents were not classified as low income (334/518; 64,5%), and the resided households had an average of 3.1 residents per household. Most participants lived in houses with access to piped water (494/518; 95.4%), and sanitary sewer treatment (273/518; 52.7%), but the majority didn’t have health insurance (363/518; 70.1%). The socioeconomics and demographic characteristics of the participants of the study are presented in Table 1.

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Table 1. Study data according to the occurrence of vaccine hesitancy.

https://doi.org/10.1371/journal.pone.0348412.t001

Regarding access to health units with vaccination facilities and the availability of vaccines in these locations, 23.0% (119/518) of the respondents reported barriers. Most of the mentioned reasons were related to the lack of vaccines (63/119; 52.9%) and the waiting time at the health unit (50/119; 42.0%). Other reasons for not getting vaccinated in the recommended schedule were reported by 6.2% (32/518) of the participants, as medical counterindication to get vaccinated due to illness or allergies, lack of professionals, lack of support to get to the health unit, bad reception in the health unit, and refusal of the professionals to vaccinate them. A large percentage of respondents affirmed to have had received guidance from health professionals about vaccination (392/518; 75.8%). Regarding the relationship with health professionals, most of the participants had a great or good relationship with the health professionals and workers from the attended health unit (367/518; 69.7%).

Over half of the respondents (71.8%, 372/518) said to believe that most people who live with them have themselves vaccinated with all the recommended vaccines. And 77.6% (402/518) reported to not have changed their beliefs after the beginning of the pandemic of COVID-19. However, the HP had a greater percentage of respondents who believed that most people who live with them do not get vaccinated as recommended by the PNI (28.9%, 75/260) when compared to the NHP (19.8, 51/258) with difference between groups (p = 0.020).

The majority of respondents did not believe that there are reasons for not vaccinating people in the community (87.3%. 452/518). Nonetheless, the HP had a greater percentage of participants who did believe in reasons for not vaccinating the population (16.2%, 42/260) when compared to the NHP (9.3%, 24/258) (p = 0.027). A greater percentage of the HP also reported to have developed this belief after the onset of the pandemic of COVID-19 (50.0%, 21/42) (p = 0.035). The reasons reported by the NHP for the non-vaccination of the community were mainly related to medical counterindication and illnesses (66.7%, 16/24) whilst, among the HP, the reasons described were the lack of safety in the vaccination and the short time for its development (35.7%, 15/42) and the freedom for the individual choice (28.6%, 12/42).

As to other aspects related to the community’s vaccination, almost half the participants did not believe there were difficulties for ethnical or religious groups in the community to get vaccinated (49.6%, 257/518). Of the 28.2% (146/518) that reported difficulties from those groups to get vaccinated, the main reason described was the self-choice to not get vaccinated (62.3%, 104/167), and 21.6% of those reasons were related to the lack of active research for those communities by the health units (36/167.). The great majority of participants reported having never experienced any community leader discouraging vaccination (81.1%, 420/518). The most reported leaders who discouraged vaccination were religious leaders (54.6%, 30/55) and politicians (20.0%, 11/55).

Among the study participants, 50,2% (260/518) reported to have hesitated to get one or more vaccines. Among the hesitant respondents, 62.9% (127/260) hesitated to vaccinate only after the beginning of the COVID-19 pandemic. In total, 19.7% (102/518) have refused to get vaccinated, and 56.9% (58/102) of the respondents who refused to get vaccinated with one or more vaccines have refused after the beginning of the COVID-19 pandemic.

The most hesitated vaccine were the COVID-19 vaccines (144/260; 55.4%), followed by the influenza vaccine (48/260; 18.5%). Other mentioned vaccines were DTP or DTP (27/260; 10.38%), Hepatitis B (12/260; 4.61%), meningitis (9/260; 3.46%), yellow fever (8/260; 3.07%), MMR (7/260; 2.69%), BCG (5/260; 1.92%), pneumococcal (4/260; 1.53%), IPV or OPV (2/260; 0.76%) vaccines. Among the hesitant respondents, 87 participants (87/260; 33.5%) reported to have delayed their vaccination schedule or to have not been vaccinated for a long time. Most of them informed to not recall which immunizers were out of date, to no longer hold their immunization booklet, or to not remember where it was kept.

Regarding reasons for hesitancy, over half were related to lack of confidence (162/260; 62.3%), and all of those were reported to have occurred after the onset of the COVID-19 pandemic. The low perception of safety or worries related to side effects were the most prevalent reason for lack of confidence (92/162; 56.8%), followed by the low perception of efficacy of the vaccine (68/162; 42.0%) and to have read or listened to negative news about the vaccine in various types of media (57/162; 35.2%). Other reasons for hesitation were related to complacency (115/260; 42.7%). The perception that the vaccination was not necessary (72/115; 62.6%), forgetting the date of the vaccine shot (35/115; 30.4%), and demotivation or laziness (13/115; 11.3%) were the main complacency motives informed, but, among those, only the perception that the vaccine was not needed was reported mainly due to the pandemic, moreover, other less mentioned reasons, such as political (4/115; 3.5%) and religious reasons (4/115; 4.4%) were also reported mostly because of the pandemic. Regarding the lack of convenience (93/260; 35.8%), 50.5% (47/93) could not get vaccinated because of their work schedule, and 32.3% (30/93) reported difficulties in finding reliable information about the hesitated vaccines, which was mostly given to the pandemic (Fig 1).

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Fig 1. Reasons for hesitation according to the 3C conceptual model of determinants of vaccination hesitancy.

https://doi.org/10.1371/journal.pone.0348412.g001

When comparing hesitant participants (HP) to non-hesitant ones, the HP was younger than the NHP (p < 0.001). When compared to the NHP, the hesitant participants believed less in the ability of vaccines to protect themselves and children from serious illnesses (p = 0.008). Among those who believed that there were reasons for people to not get vaccinated, the HP was the majority (p = 0.027). The HP and NHP got informed about vaccination mostly through television, however, the HP also used social media as the main source of information or did not get actively informed about the topic, while the NHP got more informed through health care professionals or organizations (p < 0.001).

The covariates that remained in the final logistic regression model were: mean age, having access to health insurance, positive answer to “vaccines can protect you and children from serious illnesses”, believing there are reasons for people not getting vaccinated, having difficulties accessing vaccines and vaccination services, getting informed mainly through health care professionals, or through social media, or through other sources (friends, family or not getting informed about vaccination at all).

Among these covariates, the mean age was significantly negatively associated with hesitancy. In the age-stratified analysis, individuals aged 52–71 showed lower levels of hesitancy (OR = 0.59; 95%CI = 0.36–0.95; p = 0.031) compared with other age groups (Table B in S2 Appendix). Believing that vaccines can protect oneself and children had a moderated negative association with hesitancy. Getting informed mainly through social media had a positive moderated association with hesitancy. The other variables showed no significant association with the increase in vaccine hesitancy, but they contributed to the fitting of the best model. The p-value for the Hosmer and Lemeshow test was 0.485, showing a good model adjustment (Table 2).

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Table 2. The final model for the occurrence of vaccine hesitancy.

https://doi.org/10.1371/journal.pone.0348412.t002

Discussion

This study observed a high prevalence of vaccine hesitancy among participants, higher than that found in other studies conducted in the country and in countries with similar health systems before the COVID-19 pandemic [18,19]. This result highlights not only the possible impact of the pandemic on vaccination hesitancy in the country, compared to the pre-pandemic era, but also the higher hesitancy compared to other countries with similar universal health systems.

Regarding VH in Latin American countries, most studies took place in Brazilian municipalities. Most studies developed abroad, however, don’t address general adult vaccination; instead, they investigate specific vaccine-hesitation, focusing on pregnant women and the elderly population. Nevertheless, COVID-19 vaccine hesitancy has been largely studied. In a scope review of COVID-19-specific hesitation in Africa and Latin America, the highest percentage of hesitancy was 67.2% in Egypt and 73.0% in Ecuador, higher than the percentage of hesitation observed in our study (27.8% of the total population). Anyhow, the lack of homogeneity in the methodology of studying this phenomenon in Latin American and African populations underscores the need for further understanding of the vaccination hesitancy in the global south.

The association between age and hesitancy was also identified in other studies [18,20]. This phenomenon is described in the literature as the paradox of immunization, experienced among younger individuals who exhibit more complacent behaviour due to a lack of memory of epidemics and pandemics in their lifetime [21,22]. This was further supported by our age-stratified analysis, in which participants aged 52–71 years showed lower hesitancy compared with younger individuals.

Other contextual determinants related to vaccination hesitancy, particularly regarding COVID-19 vaccination, were described in two systematic reviews, where factors such as being female, non-white, having lower income, and lower education levels were determinants of higher vaccination hesitancy [23,24]. This profile is similar to that found in our study. Although variables other than age were not related to vaccine hesitancy, the absence of association underscores the importance of exploratory and periodic studies on factors related to hesitancy. COVID-19 vaccine-specific hesitancy was higher in our study compared to other studies conducted in the country [25,26]. Our study collected data through face-to-face interviews, which may have contributed to the higher prevalence of COVID-19-specific vaccine hesitancy observed. During the pandemic, many studies used online data collection methods, potentially limiting the participation of lower-income individuals and leading to an under-representation of hesitancy rates in the country [27].

The distrust related to the COVID-19 vaccine may be linked to and exacerbated by the COVID-19 infodemic [28]. The reduction in social contact due to social distancing has increased global screen time, leading to increased searches for information about vaccines amid the growing production of misinformation and disinformation regarding COVID-19 vaccines on various social platforms [29,30]. Despite the continued general recognition of the importance of vaccination observed in our study, confidence and feelings of collective responsibility may have diminished due to the pandemic [31,32].

Therefore, the HP in our study may have been more influenced by negative information about vaccines than the NHP. This result may be due to the increasing amount of unverified and negative online information during the pandemic, which may have intensified negative feelings toward the vaccines among these respondents, as observed in other studies [33,34].

Regarding influenza vaccine-specific hesitancy, other studies across the country found similarly low hesitancy, especially among the elderly population, who are the main target of vaccination campaigns [35,36]. This may be due to the widespread diffusion of the National Immunization Program (PNI) influenza immunization campaigns targeting the population annually [37].

Other reasons related to complacency, as reported by the participants, may be linked to the paradoxical success of vaccination, as described previously and recently observed with COVID-19 booster doses [38]. Some authors also report that complacent determinants are driven by the neoliberal system widely spread across the globe [39,40]. Neoliberalism stands for the 70’s renewing of Adam Smith’s liberalism, which defended minimal government intervention and rolling over economic matters, to grow the market [41]. Individualism, free market solutions through privatization, decentralization, and deregulation are pillars of the neoliberal mindset, increasingly incorporated in the rhetorical arguments of vaccine reluctant or hesitant advocates [39].

Forgetting about the vaccination [42,43] and not recording specific hesitated vaccines are described here as hesitation-related behaviors. Forgetfulness may result from a prioritization process where certain tasks are deemed more important than others, leading to the neglect of less prioritized tasks [44,45]. Methods for recalling vaccination doses, especially for long intervals between doses in adult and adolescent vaccination schedules, may be useful for increasing vaccination rates [46].

In addition, the association between risk perception and vaccination behavior has been well established in the scientific literature. Risk perception is commonly described as comprising three dimensions: the perceived likelihood of harm, perceived individual vulnerability to the hazard, and perceived severity of its consequences. Accordingly, higher levels of risk perception are associated with greater vaccine uptake [47]. Based on this framework, we hypothesize that complacent adults’ management of their own vaccination may be partly explained by lower levels of perceived risk within this population.

The negative view towards mandatory vaccination found among the participants has been previously described in the Brazilian population and in some European countries [48,49]. This perception must be addressed, especially considering that the political scenario in Brazil may have influenced public opinion about vaccination during the pandemic [50].

The perception that most people who lived with the HP (friends, family, and neighbours) did not get vaccinated with all recommended vaccines evokes discussions about vaccination as a social norm. As an intrinsically social process, individual vaccination can influence not only vaccination coverage but also the behavior of other individuals within social circles as a social norm [51]. Therefore, valuing patients and communities who get vaccinated may enhance vaccination uptake among relatives, friends, and neighbours, and strengthen resilience against anti-vaccine sentiments [51].

Despite the low prevalence of obstacles to vaccination, the lack of vaccines and other access difficulties have been described in previous literature in the Global South and in Brazil [52,53]. However, this issue appears to be more related to a shortage of vaccines during the pandemic, which was a global occurrence. During the pandemic, the expectation that Brazil would be an independent source to supply itself and other Latin American countries did not materialize, highlighting the country’s lack of self-sufficiency in vaccine manufacturing during a public health emergency [54].

Regarding the good relationship with healthcare professionals among the participants in our study, healthcare professionals are the most important and reliable source of information about vaccination, and they play a crucial role in enhancing health literacy among the population [55]. In recent years, primary healthcare attention has been strengthened in Brazil, which may have improved the study population’s perception of healthcare. Nevertheless, the strengthening of the patient-healthcare professional is crucial to decrease hesitancy in the studied population. The development of strategies to quickly respond to health emergencies that may decrease vaccine uptake, with honesty and transparency, to enhance popular trust in the health system [56].

The use of traditional mass media, such as television, in combination with educational interventions, has been shown to improve perceptions of vaccination. At the same time, the widespread influence of social media and other digital communication technologies underscores the need for these platforms to be strategically incorporated into vaccination promotion and advocacy within healthcare system [57].

Our findings should be interpreted within a broader context of risk perception, institutional trust, and health communication. The WHO SAGE Working Group has emphasized that vaccine hesitancy is shaped not only by individual beliefs but also by perceived risk, confidence in health authorities, and the credibility of information disseminated to the public [10] (WHO, 2014). International evidence supports this perspective, as a global survey across 67 countries demonstrated substantial variation in vaccine confidence, with institutional trust and political context playing a particularly important role in middle-income settings [58]. During the COVID-19 pandemic, the World Health Organization further highlighted the emergence of an “infodemic,” in which the rapid spread of misinformation through social and traditional media challenged effective risk communication and public adherence to health recommendations [59]. Although our study was not designed to directly assess political determinants or exposure to misinformation, the observed associations between vaccine hesitancy, younger age, and reliance on non-institutional information sources are consistent with this broader literature, underscoring the importance of strengthening trustworthy health communication and institutional credibility.

Our study has some limitations, including a sample that may not accurately represent the city’s true demographics. Specifically, there was an underrepresentation of teenagers and an overrepresentation of the elderly compared to the actual population of Campo Grande. Additionally, there were few participants per household, and access to wealthier residents was restricted due to entry denial into gated communities and apartment buildings. Nevertheless, few studies have comprehensively addressed vaccination hesitancy across all vaccines and included a wide age range during the transition towards the end of the COVID-19 pandemic. Furthermore, our study was conducted through face-to-face interviews, potentially biasing towards a higher representation of the lower-income population in Campo Grande.

Conclusions

We observed a high prevalence of vaccine hesitancy in Campo Grande, likely exacerbated by the COVID-19 pandemic and infodemic, which have contributed to heightened negative perceptions of vaccines. While access barriers were noted to a lesser extent, they remain significant for achieving adequate vaccination coverage. Additionally, complacency and forgetfulness regarding vaccination importance may also impact vaccination rates in the municipality. Addressing these challenges at both individual and community levels is crucial. Effective use of technology for communication and combating misinformation is essential. Furthermore, promoting health education to enhance individual autonomy and community resilience is key to countering current and future anti-vaccine movements.

Supporting information

S1 Appendix. Sampling strategy and sample size calculation using the WHO two-stage cluster sampling method.

https://doi.org/10.1371/journal.pone.0348412.s001

(PDF)

S2 Appendix. Analysis of vaccine hesitancy by age groups (20-year intervals): descriptive comparisons and multivariable logistic regression.

https://doi.org/10.1371/journal.pone.0348412.s002

(PDF)

S3 Appendix. Data collection instrument: adapted WHO SAGE questionnaire, additional variables, and 3C model framework for vaccine hesitancy.

https://doi.org/10.1371/journal.pone.0348412.s003

(PDF)

Acknowledgments

We sincerely thank the residents of Campo Grande who graciously welcomed our study team and participated in this research. Your valuable insights and cooperation have been instrumental in advancing our understanding of vaccination hesitancy in the community.

References

  1. 1. Andreoni M, Sticchi L, Nozza S, Sarmati L, Gori A, Tavio M, et al. Recommendations of the Italian society for infectious and tropical diseases (SIMIT) for adult vaccinations. Hum Vaccin Immunother. 2021;17(11):4265–82. pmid:34524945
  2. 2. Pôrto Â, Ponte CF. Vacinas e campanhas: as imagens de uma história a ser contada. Vaccines and campaigns: images with a story to tell. 2003;10:725–42.
  3. 3. Minakawa MM, Frazão P. The Trajectory of Brazilian Immunization Program between 1980 and 2018: From the Virtuous Cycle to the Vaccine Coverage Decline. Vaccines (Basel). 2023;11(7):1189. pmid:37515005
  4. 4. Pércio J, Fernandes EG, Maciel EL, Lima NVT de. 50 years of the Brazilian National Immunization Program and the Immunization Agenda 2030. Epidemiol Serv Saude. 2023;32(3):e20231009. pmid:38055500
  5. 5. Sweileh WM. Bibliometric analysis of global scientific literature on vaccine hesitancy in peer-reviewed journals (1990-2019). BMC Public Health. 2020;20(1):1252. pmid:32807154
  6. 6. Césare N, Mota TF, Lopes FFL, Claudia A, Lima M, Luzardo R. Longitudinal profiling of the vaccination coverage in Brazil reveals a recent change in the patterns hallmarked by differential reduction across regions. Int J Infect Dis. 2020.
  7. 7. Nichter M. Vaccinations in the third world: A consideration of community demand. 1995;41.
  8. 8. Larson HJ, Broniatowski DA. Volatility of vaccine confidence. Science. 2021;371(6536):1289. pmid:33766861
  9. 9. MacDonald NE, SAGE Working Group on Vaccine Hesitancy. Vaccine hesitancy: Definition, scope and determinants. Vaccine. 2015;33(34):4161–4. pmid:25896383
  10. 10. WHO. REPORT OF THE SAGE WORKING GROUP ON VACCINE HESITANCY. WHO COVID-19 Glob data. 2015; 64. Available: https://www.who.int/immunization/sage/meetings/2014/october/1_Report_WORKING_GROUP_vaccine_hesitancy_final.pdf
  11. 11. Betsch C, Schmid P, Heinemeier D, Korn L, Holtmann C, Böhm R. Beyond confidence: Development of a measure assessing the 5C psychological antecedents of vaccination. PLoS One. 2018;13(12):e0208601. pmid:30532274
  12. 12. WHO. Understanding the behavioural and social drivers of vaccine uptake: WHO position paper – May 2022. Wkly Epidemiol Rec. 2022;97:209–24.
  13. 13. Saúde) S (Secretaria de E de. Secretaria de Saúde desenvolve ações com objetivo de elevar coberturas vacinais no Estado. 2023. Available: https://www.saude.ms.gov.br/secretaria-de-saude-desenvolve-acoes-com-objetivo-de-elevar-coberturas-vacinais-no-estado/
  14. 14. IBGE. Panorama censo 2022. 2022. Available: https://cidades.ibge.gov.br/brasil/ms/campo-grande/pesquisa/10101/97905
  15. 15. Brasil. As Dinâmicas Migratórias Nas Macrorregiões Do Brasil. Brasília; 2024.
  16. 16. WHO. World Health Organization Vaccination Coverage Cluster Surveys: Reference Manual. World Health Organization. 2018. pp. 1–234. Available: http://www.who.int/immunization/documents/who_ivb_18.09/en/
  17. 17. Sato APS. Qual a importância da hesitação vacinal na queda das coberturas vacinais no Brasil?. Revista de Saúde Pública. 2018;52:1–9.
  18. 18. Brown AL, Sperandio M, Turssi CP, Leite RMA, Berton VF, Succi RM, et al. Vaccine confidence and hesitancy in Brazil. Cad Saude Publica. 2018;34(9):e00011618. pmid:30281705
  19. 19. Nizigiyimana A, Acharya D, Morillon GF, Poder TG. Predictors of vaccine acceptance, confidence, and hesitancy in general, and COVID-19 vaccination refusal in the province of Quebec, Canada. Patient Prefer Adherence. 2022;16:2181–202.
  20. 20. Sypsa V, Roussos S, Engeli V, Paraskevis D, Tsiodras S, Hatzakis A. Trends in COVID-19 Vaccination Intent, Determinants and Reasons for Vaccine Hesitancy: Results from Repeated Cross-Sectional Surveys in the Adult General Population of Greece during November 2020-June 2021. Vaccines (Basel). 2022;10(3):470. pmid:35335102
  21. 21. Lega F. What we need to know. Health Serv Manage Res. 2018;31(4):179. pmid:30369257
  22. 22. Jacobson RM, St. Sauver JL, Finney Rutten LJ. Vaccine hesitancy. Mayo Clin Proc. 2015;90:1562–8.
  23. 23. Kafadar AH, Tekeli GG, Jones KA, Stephan B, Dening T. Determinants for COVID-19 vaccine hesitancy in the general population: a systematic review of reviews. J Public Health (Berl). 2022;31(11):1829–45.
  24. 24. Lin C, Tu P, Beitsch LM. Confidence and Receptivity for COVID-19 Vaccines: A Rapid Systematic Review. Vaccines (Basel). 2020;9(1):16. pmid:33396832
  25. 25. Nery N Jr, Ticona JPA, Cardoso CW, Prates APPB, Vieira HCA, Salvador de Almeida A, et al. COVID-19 vaccine hesitancy and associated factors according to sex: A population-based survey in Salvador, Brazil. PLoS One. 2022;17(1):e0262649. pmid:35061811
  26. 26. Olbrich Neto J, Olbrich SRLR. Attitudes, hesitancy, concerns, and inconsistencies regarding vaccines reported by parents of preschool children. Rev Paul Pediatr. 2023;41:e2022009. pmid:36921172
  27. 27. Aguilar Ticona JP, Nery N Jr, Victoriano R, Fofana MO, Ribeiro GS, Giorgi E, et al. Willingness to Get the COVID-19 Vaccine among Residents of Slum Settlements. Vaccines (Basel). 2021;9(9):951. pmid:34579188
  28. 28. Bin Naeem S, Kamel Boulos MN. COVID-19 Misinformation Online and Health Literacy: A Brief Overview. Int J Environ Res Public Health. 2021;18(15):8091. pmid:34360384
  29. 29. Ramsey N, Obeidallah M, Abraham A. Impact of COVID-19 on adolescent health and use of social media. Curr Opin Pediatr. 2023;35(3):362–7. pmid:37036294
  30. 30. Skafle I, Nordahl-Hansen A, Quintana DS, Wynn R, Gabarron E. Misinformation about COVID-19 vaccines on social media: rapid review. J Med Internet Res. 2022;24:e37367.
  31. 31. Wagner AL, Masters NB, Domek GJ, Mathew JL, Sun X, Asturias EJ, et al. Comparisons of Vaccine Hesitancy across Five Low- and Middle-Income Countries. Vaccines (Basel). 2019;7(4):155. pmid:31635270
  32. 32. McRee A-L, Gower AL, Kiss DE, Reiter PL. Has the COVID-19 pandemic affected general vaccination hesitancy? Findings from a national study. J Behav Med. 2023;46(1–2):9–14. pmid:35635594
  33. 33. Chopra H, Vashishtha A, Pal R, Ashima, Tyagi A, Sethi T. Mining Trends of COVID-19 Vaccine Beliefs on Twitter With Lexical Embeddings: Longitudinal Observational Study. JMIR Infodemiology. 2023;3:e34315. https://doi.org/10.2196/34315 37192952
  34. 34. Gramacho W, Turgeon M, Santos Mundim P, Pereira I. Why did Brazil fail to vaccinate children against COVID-19 during the pandemic? An assessment of attitudinal and behavioral determinants. Vaccine. 2024;42(2):315–21. pmid:38061957
  35. 35. Sato APS, Antunes JLF, Moura RF, de Andrade FB, Duarte YAO, Lebrão ML. Factors associated to vaccination against influenza among elderly in a large Brazilian metropolis. PLoS One. 2015;10(4):e0123840. pmid:25874953
  36. 36. Francisco PMSB, Barros MB de A, Cordeiro MRD. Vacinação contra influenza em idosos: prevalência, fatores associados e motivos da não-adesão em Campinas, São Paulo, Brasil. Cad Saude Publica. 2011;27:417–26.
  37. 37. González-Block MÁ, Gutiérrez-Calderón E, Pelcastre-Villafuerte BE, Arroyo-Laguna J, Comes Y, Crocco P, et al. Influenza vaccination hesitancy in five countries of South America. Confidence, complacency and convenience as determinants of immunization rates. PLoS One. 2020;15(12):e0243833. pmid:33306744
  38. 38. Limbu YB, Huhmann BA. Why Some People Are Hesitant to Receive COVID-19 Boosters: A Systematic Review. Trop Med Infect Dis. 2023;8(3):159. pmid:36977160
  39. 39. Sanders C, Burnett K. The neoliberal roots of modern vaccine hesitancy. J Heal Soc Sci. 2019;4:149–56.
  40. 40. Schmitt HJ, Jimenez T, Young IF. Pandemic precarity: A multi‐level study of neoliberal precarity and COVID‐Related outcomes in the United States. Social & Personality Psych. 2023;17(12).
  41. 41. Mcgregor S. Neoliberalism and health care. 2001; 82–9.
  42. 42. Barros MGM, Santos MC da S, Bertolini RPT, Pontes Netto VB, Andrade MS. Perda de oportunidade de vacinação: aspectos relacionados à atuação da atenção primária em Recife, Pernambuco, 2012. Epidemiol e Serviços Saúde. 2015;24:701–10.
  43. 43. Bălan A, Ruță SM. Influenza Vaccination of Romanian Medical Students and Resident Physicians-A Matter of Accessibility. Vaccines (Basel). 2023;11(10):1551. pmid:37896954
  44. 44. Castel AD, Rhodes MG, McCabe DP, Soderstrom NC, Loaiza VM. The fate of being forgotten: information that is initially forgotten is judged as less important. Q J Exp Psychol (Hove). 2012;65(12):2281–7. pmid:23163866
  45. 45. Alves MVC, Bueno OFA. Interferência retroativa: o esquecimento como uma interrupção na consolidação da memória. Temas Psicol. 2017;25(3):1043–54.
  46. 46. Dufour L, Carrouel F, Dussart C. Human papillomaviruses in adolescents: Knowledge, attitudes, and practices of pharmacists regarding virus and vaccination in France. Viruses. 2023;15.
  47. 47. Brewer NT, Chapman GB, Gibbons FX, Gerrard M, McCaul KD, Weinstein ND. Meta-analysis of the relationship between risk perception and health behavior: the example of vaccination. Health Psychol. 2007;26(2):136–45. pmid:17385964
  48. 48. Kuznetsova L, Cortassa G, Trilla A. Effectiveness of Mandatory and Incentive-Based Routine Childhood Immunization Programs in Europe: A Systematic Review of the Literature. Vaccines (Basel). 2021;9(10):1173. pmid:34696280
  49. 49. Martinez EZ, Zucoloto ML, Ramos VP, Dutra CDC, de Jesus GJ, Esteves AVF, et al. Brazilian Adults’ Attitudes and Practices Regarding the Mandatory COVID-19 Vaccination and Their Hesitancy towards Childhood Vaccination. Vaccines (Basel). 2022;10(11):1853. pmid:36366361
  50. 50. Gramacho WG, Turgeon M. When politics collides with public health: COVID-19 vaccine country of origin and vaccination acceptance in Brazil. Vaccine. 2021;39(19):2608–12. pmid:33846045
  51. 51. Brewer NT, Chapman GB, Rothman AJ, Leask J, Kempe A. Increasing vaccination: putting psychological science into action. Psychol Sci Public Interes. 2017;18:149–207.
  52. 52. Bhanu C, Gopal DP, Walters K, Chaudhry UAR. Vaccination uptake amongst older adults from minority ethnic backgrounds: A systematic review. PLoS Med. 2021;18(11):e1003826. pmid:34735440
  53. 53. Faria CGF, de Matos UMA, Llado-Medina L, Pereira-Sanchez V, Freire R, Nardi AE. Understanding and addressing COVID-19 vaccine hesitancy in low and middle income countries and in people with severe mental illness: Overview and recommendations for Latin America and the Caribbean. Front Psychiatry. 2022;13:910410. pmid:36177216
  54. 54. Hotez PJ. Global vaccine access demands combating both inequity and hesitancy. Health Aff. 2023;42:1681–8.
  55. 55. Marín-Cos A, Marbán-Castro E, Nedic I, Ferrari M, Crespo-Mirasol E, Ventura LF, et al. “Maternal Vaccination Greatly Depends on Your Trust in the Healthcare System”: A Qualitative Study on the Acceptability of Maternal Vaccines among Pregnant Women and Healthcare Workers in Barcelona, Spain. Vaccines (Basel). 2022;10(12):2015. pmid:36560425
  56. 56. Sondagar C, Xu R, MacDonald NE, Dubé E. Vaccine acceptance: How to build and maintain trust in immunization. Can Commun Dis Rep. 2020;46(5):155–9. pmid:32558811
  57. 57. Rosselli R, Martini M, Bragazzi NL. The old and the new: vaccine hesitancy in the era of the Web 2.0. Challenges and opportunities. J Prev Med Hyg. 2016;57(1):E47-50. pmid:27346940
  58. 58. Larson HJ, Figueiredo AD e, Xiahong Z, Schulz WS, Verger P, Johnston IG. The state of vaccine confidence 2016: global insights through a 67-country survey. EBioMedicine. 2016;12:295–301.
  59. 59. Zarocostas J. How to fight an infodemic. Lancet. 2020;395(10225):676. pmid:32113495