1. INTRODUCTION

Poliomyelitis (polio) is an acute viral disease caused by three serotypes of poliovirus. Highly contagious, it affects both adults and, primarily, children under the age of five. Transmission occurs from person to person, mainly via the fecal-oral route, but also, although less frequently, through contaminated water or food, or by the oral-oral route through droplets expelled when speaking, coughing, or sneezing. The poliovirus multiplies in the entry sites of the body, such as the mouth, throat, and intestines, and can later enter the bloodstream, affecting the nervous system and causing paralysis. (1-3).

Most infected individuals are asymptomatic or experience mild symptoms such as fever, malaise, headache, sore throat, nausea, vomiting, diarrhea, constipation, or meningeal signs. Approximately 1% of infected individuals develop the paralytic form of the disease, which typically manifests asymmetrically in the lower limbs, with reduced muscle strength and deep tendon reflexes, while maintaining sensitivity in the affected limb. Between 5% and 10% of patients with paralysis progress to death due to respiratory muscle failure (1, 2, 4). There is no treatment for poliomyelitis; prevention is solely through vaccination.

In 1994, Brazil received certification for the eradication of wild poliovirus circulation. However, epidemiological surveillance and immunization continue to be recommended, as some countries still serve as reservoirs of wild poliovirus, posing a risk of reintroduction (5). Although national immunization campaigns remain in place, vaccination coverage for various diseases has declined, falling below the 95% target set by the Ministry of Health.

One possible explanation for this decline is that the eradication of several infectious diseases has made them less well-known, leading some people to neglect vaccination and increasing the risk of reintroducing previously controlled or eradicated diseases (6). The decrease in vaccination coverage is also associated with the false belief that people will not become ill, or if they do, they will only experience mild symptoms. As a result, the complications of the disease are often perceived as less significant than the risks of potential adverse events related to vaccination.

Contributing to the decline in vaccination coverage was the coronavirus pandemic that began in 2019 (COVID-19), which resulted in about a 20% reduction in childhood vaccination rates (7). Hotez (2020) points out that the anti-vaccine movement, which had already been rising in the United States and around the world, likely gained momentum during the pandemic, fueled by new conspiracy theories. Various conjectures were spread, including the idea that COVID-19 vaccines would implant a chip under the skin for global surveillance (8).

This study aimed to analyze the immunization coverage rate (ICV) for poliomyelitis in Brazil from 2012 to 2023, with the goal of projecting future trends in vaccination coverage. The research sought to evaluate the impact of the COVID-19 pandemic and identify the main challenges to maintaining high vaccination coverage.

2. MATERIAL AND METHODS

This retrospective ecological study analyzed the evolution of poliomyelitis vaccination coverage in Brazil from 2012 to 2023. Data from 2012 to 2022 were obtained from the Department of Information and Informatics of the Unified Health System (DATASUS), accessing the National Immunization Program (PNI) database. The data selection included the variable “region” as rows, “year” as columns, and “poliomyelitis” as the measure. Data from 2023 were provided by the Department of Monitoring, Evaluation, and Dissemination of Strategic Health Information (DEMAS) of the Department of Information and Digital Health (SEIDIGI), accessed through the INFOMS system via the link https://infoms.saude.gov.br/extensions/SEIDIGI_DEMAS_VACINACAO_CALENDARIO_NACIONAL_COBERTURA_RESIDENCIA/SEIDIGI_DEMAS_VACINACAO_CALENDARIO_NACIONAL_COBERTURA_RESIDENCIA.html. In this database, the filters “Vaccination Coverage by Municipality of Residence,” year “2023,” and “Injectable Polio Vaccine (VIP)” were applied.

The vaccination coverage data used in this study were obtained from the National Immunization Program Information System (SI-PNI), based on the address registered in the National Health Card (CNS) of the individuals. The collection included detailed information about the state, municipality, and health facility where the vaccination took place. Given the dynamic nature of vaccination data, the Vaccination Coverage Index (ICV) may vary after collection due to the continuous update of information in the system. The ICV is an indicator that estimates the proportion of the target population that received the final dose of the vaccination schedule of interest, calculated as the ratio between the total number of doses administered and the estimated population for that vaccine, multiplied by 100.

The data were collected between June and July 2024 and organized into Microsoft Excel spreadsheets. They were then subjected to statistical analysis using SPSS version 17.0 software. To assess the relationship between variables, simple linear regression analysis was employed. Statistical significance for the regressions was considered for p-values < 0.05, while observing the basic assumptions for the application of this model. To compare the means of independent groups, the Student’s t-test was used.

3. RESULTS

The data on poliomyelitis vaccination coverage in Brazil from 2012 to 2023 were collected and organized as presented in Table 1. The analysis of the data revealed that in 2012, national vaccination coverage exceeded 96%, with variations between regions, although all regions had values above 94%. From 2013, there was a period of high vaccination uptake. However, starting in 2016, a trend of continuous decline in vaccination coverage was observed, reaching its lowest point in 2021. In 2023, a modest recovery in coverage rates was recorded.

Tab. 1: Poliomyelitis vaccination coverage in Brazil and its regions by year.

In order to predict the evolution of poliomyelitis vaccination coverage in the subsequent years, a simple linear regression analysis was performed for Brazil as a whole and for each of its regions. The regression results for Brazil indicated a significant model fit [F(1,10) = 20.692, p = 0.001], with a coefficient of determination (R²) of 0.674, suggesting that approximately 67.4% of the variability in vaccination coverage can be explained by the linear trend observed over time. The regression equation obtained, presented in Equation 1, allows for estimating vaccination coverage for future years, considering the historical trend. The graphical representation of this relationship is shown in Graph 1.

Equation 1 – Forecast of poliomyelitis vaccination coverage in Brazil.

Vaccination Coverage = 4434.437 – 2.155 × Year

Graph 1 – Poliomyelitis vaccination coverage in Brazil – Forecast based on Linear Regression.

Legend: blue dots – Actual values; red dots – predicted values by linear regression; dashed line – trendline.

By applying simple linear regression analysis to the data from the North region, a statistically significant model was obtained [F(1,10) = 16.443, p = 0.002] to predict poliomyelitis vaccination coverage. The coefficient of determination (R²) of 0.622 indicates that approximately 62.2% of the variability in vaccination coverage can be explained by the linear trend observed over the period. The resulting regression equation, presented in Equation 2, allows for estimating vaccination coverage for future years, considering the historical trend specific to the North region. The graphical representation of this relationship is shown in Graph 2.

Equation 2 – Forecast of poliomyelitis vaccination coverage in the North region

Vaccination Coverage in the North = 5012.373 – 2.445 × Year

Graph 2 – Poliomyelitis vaccination coverage in the North region – Forecast based on Linear Regression.

Legend: blue dots – Actual values; red dots – predicted values by linear regression; dashed line – trendline.

For the Northeast region, the simple linear regression analysis did not reveal a statistically significant model [F(1,10) = 11.318, p = 0.002; R² = 0.531] to predict poliomyelitis vaccination coverage in the subsequent years. The p-value greater than 0.05 indicates that the linear relationship between time and vaccination coverage is not statistically significant, limiting the predictive ability of the model.

In contrast, for the Southeast region, the simple linear regression analysis showed a significant model fit [F(1,10) = 25.132, p = 0.001; R² = 0.715], indicating that approximately 71.5% of the variability in vaccination coverage can be explained by the linear trend observed over time. The resulting regression equation, presented in Equation 3, allows for estimating vaccination coverage for future years, considering the historical trend specific to the Southeast region. The graphical representation of this relationship is shown in Graph 3.

Equation 3 – Forecast of poliomyelitis vaccination coverage in the Southeast region:

Vaccination Coverage in the Southeast = 4727.387 – 2.3 × Year

Graph 3 – Poliomyelitis vaccination coverage in the Southeast region – Forecast based on Linear Regression.

Legend: blue dots – Actual values; red dots – predicted values by linear regression; dashed line – trendline.

By applying simple linear regression analysis to the data from the South region, a statistically significant model was obtained [F(1,10) = 16.152, p = 0.002] to predict poliomyelitis vaccination coverage. The coefficient of determination (R²) of 0.618 indicates that approximately 61.8% of the variability in vaccination coverage can be explained by the linear trend observed over the period. The resulting regression equation, presented in Equation 4, allows for estimating vaccination coverage for future years, considering the historical trend specific to the South region. The graphical representation of this relationship is shown in Graph 4.

Equation 4 – Forecast of poliomyelitis vaccination coverage in the South region.

Vaccination Coverage in the South = 2751.196 – 1.319 × Year

Graph 4 – Poliomyelitis vaccination coverage in the South region – Forecast based on Linear Regression

Legend: blue dots – Actual values; red dots – predicted values by linear regression; dashed line – trendline.

By applying simple linear regression analysis to the data from the Central-West region, a highly significant statistical model was obtained [F(1,10) = 33.680, p < 0.001] to predict poliomyelitis vaccination coverage. The coefficient of determination (R²) of 0.771 indicates that approximately 77.1% of the variability in vaccination coverage can be explained by the linear trend observed over the period. The resulting regression equation, presented in Equation 5, allows for estimating vaccination coverage for future years, considering the historical trend specific to the Central-West region. The graphical representation of this relationship is shown in Graph 5.

Equation 5 – Forecast of poliomyelitis vaccination coverage in the Central-West region.

Vaccination Coverage in the Central-West = 5351.328 – 2.608 × Year

Graph 5 – Poliomyelitis vaccination coverage in the Central-West region – Forecast based on Linear Regression.

Legend: blue dots – Actual values; red dots – predicted values by linear regression; dashed line – trendline

To assess the impact of the COVID-19 pandemic on the trend of poliomyelitis vaccination coverage in Brazil, a simple linear regression analysis was conducted using data from the pre-pandemic period (2012-2019). The regression model showed a significant fit [F(1,6) = 249.978, p = 0.04; R² = 0.531], allowing for the prediction of an annual reduction of approximately 2.27% in vaccination coverage. However, when comparing the model’s predictions with the actual data from 2020 and 2021, a significant divergence was observed. The average annual decline in vaccination coverage during the pandemic years was 6.58%, indicating a more pronounced decline than predicted by the pre-pandemic model (t = 1.139; p = 0.031). This difference is visualized in Graph 6, suggesting that the COVID-19 pandemic accelerated the decline in poliomyelitis vaccination coverage in Brazil.

Equation 6 – Forecast of poliomyelitis vaccination coverage in Brazil, considering data from 2012 to 2019.

Vaccination Coverage = 5008.256 – 2.44 × Year

Graph 6 – Comparison of poliomyelitis vaccination coverage in Brazil: Pre-pandemic forecast vs. actual values.

Legend: blue dots – Actual values; red dots – predicted values by linear regression; dashed line – trendline

Results of Linear Regression (2012-2019)

The linear regression analysis revealed a declining trend in poliomyelitis vaccination coverage in Brazil from 2012 to 2019. The regression equation showed an annual decline rate of approximately 2.27%, as estimated by Equation 6, with reasonable statistical significance [F(1,6) = 249.978, p = 0.04; R² = 0.531]. This indicates that the regression explained about 53.1% of the variation in vaccination coverage during the pre-pandemic period.

However, despite the adequacy of the model to predict historical data, the forecast for 2020 and 2021 significantly underestimated the actual magnitude of the decline.

Comparison of Vaccination Coverage Means: Prediction vs. Actual Values (obtained from the studied databases)

After obtaining the forecasts for 2020 and 2021 from the linear regression equation, the actual vaccination coverage values for these two years were compared to the predictions using the independent samples t-test. The t-test evaluated whether there was a statistically significant difference between the predicted means and the observed values, revealing a significant discrepancy.

The forecast estimated a continuous annual decline of 2.27% in vaccination coverage, which would be consistent with the trend observed from 2012 to 2019. However, the actual values recorded a much larger average decline of 6.58% per year during 2020 and 2021, resulting in a significant difference between the prediction and reality (t = 1.139; p = 0.031). The p-value of 0.031 indicates that the probability of this difference occurring by chance is low (less than 5%), confirming that the COVID-19 pandemic exacerbated the decline in vaccination coverage more sharply than predicted by the model.

Analysis of the Slope Coefficients of the Regressions

The slope coefficients of the linear regressions represent the annual rate of change in vaccination coverage. For the 2012-2019 period, the estimated slope coefficient was -2.27%, reflecting a moderate declining trend in vaccination coverage, but still within acceptable levels.

On the other hand, when observing the actual values of 2020 and 2021, the implicit slope coefficient (calculated based on the average real decline values) was -6.58%. This more negative coefficient reflects the impact of the COVID-19 pandemic, which may have contributed to greater challenges in maintaining vaccination coverage, whether due to misinformation, interruptions in healthcare services, or other logistical and social barriers.

DISCUSSION

Brief Historical Account of the Evolution of Poliomyelitis in the World and Brazil
The first clinical description of poliomyelitis was made in 1789 by British physician Michael Underwood. Later, the disease was formally recognized in 1840 by German physician Jakob Heine.
Throughout the second half of the 19th century, cases of the disease were identified in Norway, Sweden, and France. In 1890, poliomyelitis was first reported as an epidemic by Swedish pediatrician Karl Oskar Medin, after an epidemic with 44 cases in Stockholm during the summer of 1887. Medin was responsible for recognizing the systemic phase of the disease, which mostly did not progress to neurological manifestations.

In the late 19th and early 20th centuries, several poliomyelitis epidemics were recorded in various parts of the world, with one of the most notable being the 1916 epidemic in the northeastern United States, which resulted in 6,000 deaths and over 27,000 cases of paralysis. During these epidemics, it was concluded that poliomyelitis was a contagious disease that primarily affected children, adolescents, young adults, and infants, especially in economically privileged populations and regions with low population density. Additionally, the disease was identified in its mild form, without involvement of the central nervous system. These observations were crucial in understanding the nature of poliomyelitis, especially the fact that the disease could occur without paralytic symptoms.

In the late 19th century, sporadic cases of the disease were described in Brazil. However, in the early 20th century, the disease was observed more frequently, especially in the Southeast region.
The first poliomyelitis outbreak described occurred in the city of Rio de Janeiro in 1911. Shortly after, another outbreak was recorded in the city of Americana, in the state of São Paulo, in 1917, which led to the creation of a law-making poliomyelitis a mandatory reporting disease in the state of São Paulo.

It was not until the 1930s and 1940s that larger outbreaks were recorded, in cities such as Porto Alegre (1935 and 1945), São Paulo and Rio de Janeiro (1939), Belém (1943), and Recife (1946). In the 1950s, the largest recorded epidemic occurred in Rio de Janeiro in 1953, with 746 cases and a rate of 21.5 cases per 100,000 inhabitants.

The first inactivated poliomyelitis vaccine (IPV) was produced by Jonas Salk and adopted in 1955 in the United States, significantly reducing the number of cases per 100,000 inhabitants. One disadvantage of the Salk vaccine was the higher circulation of wild poliovirus and its implications for outbreaks. In Brazil, the Salk vaccine was also used starting in 1955 by some pediatricians, although vaccination coverage was limited.

In the 1950s, Albert Sabin developed the oral poliomyelitis vaccine (OPV), using an attenuated version of the poliovirus. After several tests, the vaccine was approved in 1959 in the Soviet Union and in 1961 in the United States. Although effective, the main disadvantage of OPV was the risk of developing vaccine-associated paralytic poliomyelitis (VAPP). To mitigate this risk, a strategy of pre-immunization with the IPV in children at 2 and 4 months of age, followed by administration of OPV at 18 months, 4 years, and 6 years, was implemented. However, this approach did not eliminate the risk of VAPP entirely.

In Brazil, Sabin’s OPV was introduced into the routine vaccination schedule in 1961 by the Ministry of Health, with the first mass vaccination experiences conducted in the cities of Santo André (SP) and Petrópolis (RJ). However, these initiatives were not sufficiently widespread or continuous to control the disease effectively. Only in 1973, with the creation of the National Immunization Program (PNI), did the country establish a robust and integrated strategy for controlling not only poliomyelitis, but also other diseases such as measles, tuberculosis, diphtheria, tetanus, and whooping cough, while maintaining the eradication of smallpox.

Until 1974, there was no active surveillance with investigation and diagnosis of reported cases. Only from 1975, with the implementation of the National Epidemiological Surveillance System (SNVE), was there a systematization of case and outbreak investigation, including laboratory confirmation of diagnosis and evaluation of sequelae, providing better knowledge about the epidemiological behavior of poliomyelitis and its subsequent eradication in the country.

The last isolation of wild poliovirus in Brazil occurred in 1989 from a case of paralysis in Souza-PB. Mass campaigns using OPV were used as strategies to eliminate the virus in the country, as this vaccine provides individual immunity, as well as increasing immunity in population groups in general, with the spread of the vaccine-derived poliovirus in the environment within a short period of time.

Since 2012, IPV has been part of the poliomyelitis vaccination schedule in Brazil with two doses, expanded to three doses (at 2, 4, and 6 months of age) in 2016, with OPV boosters at 15 months and 4 years. Starting in 2024, a new recommendation was established for only one IPV booster at 15 months, with OPV (popularly known as the “droplet”) gradually being replaced by IPV. Table 1 shows the temporal evolution of the immunization methods for the Brazilian population.

Table 1: Timeline of the Poliomyelitis Vaccination Schedule in Brazil

Legend: OPV: Oral Polio Vaccine; IPV: Inactivated Polio Vaccine.

Discussion of Results Obtained in the Study

Regarding the results obtained in this study, vaccination coverage in Brazil has tended to decline since 2012, when the coverage rate was 96.55%. In 2021, the lowest coverage was recorded, at 71.04%. A slight recovery was observed in 2022, reaching 85.53% in 2023, a level higher than in 2019. However, analyzing previous trends, projections for the coming years are concerning, with an estimated coverage of only 68.4% in 2026, unless containment measures are adopted.

The estimated decline in polio vaccination coverage is reflected in all regions. The data from this study show that the Northern region faced the most critical situation, with the worst projected vaccination coverage rate (ICV) of only 58.803% in 2026, a 20% drop compared to 2023, well below the Ministry of Health’s target of 95% (14). The Southern region has the best vaccination coverage forecast for the coming years, with an estimate of 78.9% in 2026, though still below the Ministry’s goal.

Furthermore, analyzing vaccination coverage during the most critical years of COVID-19, 2020 and 2021, a more significant decline was observed compared to previous years, with the average annual decline in ICV increasing from 2.27% to 6.58%. The decline in vaccination coverage is not unique to Brazil. In 2022, the Pan American Health Organization (PAHO) considered the Americas, including Brazil and seven other Latin American countries, to be at high risk for the return of the disease (13).

Several studies warn of the possible risk of reintroduction and circulation of wild poliovirus worldwide. Donalisio et al. (2023) evaluated polio vaccination in Brazil from 2011 to 2021 and concluded that vaccination coverage declined across all regions over the period, with a more significant drop during the pandemic, particularly in the North and Northeast regions (15), findings consistent with those of this study, which extended the research period. Maciel et al. (2023) analyzed the temporal and spatial distribution of polio vaccination coverage between 1997 and 2021 and found a strong downward trend in coverage, with the Northern region presenting the lowest rates (16). These data highlight the vulnerability of the North and Northeast regions to low vaccination coverage and a higher risk of disease or circulating virus reintroduction, necessitating greater attention from public health services.

Although Brazil has a robust vaccination strategy and a history of vaccination campaigns, a general decline in immunizations has been observed since 2012, not limited to the polio vaccine. Data from June 2024 show that BCG coverage was 77.81%, the second dose of the MMR vaccine was 77.19%, and varicella vaccine coverage was 68.73%.

Possible Factors Associated with the Decline in ICV

The anti-vaccine movement has led to a phenomenon called vaccine hesitancy, which predates the COVID-19 pandemic. Many people fail to vaccinate themselves or their children due to a lack of accurate information or an overload of fraudulent information.

Janz and Becker (1984) established the Health Belief Model, which consists of five domains. The first component is perceived susceptibility, referring to an individual’s belief in the likelihood of developing a particular health condition. The second is perceived severity, which is the belief in the seriousness of the health issue and its potential consequences. The third factor is perceived benefits, which reflects the belief in the effectiveness of preventive or therapeutic measures to reduce the risk or severity of the health problem. The fourth is perceived barriers, referring to the perceived obstacles or costs associated with adopting these measures, whether financial, emotional, or logistical. Finally, cues to action are stimuli or triggers that motivate people to act, such as the onset of symptoms, media awareness campaigns, or recommendations by healthcare professionals (17).

Thus, three major factors influence vaccine hesitancy: a) Demographic and socioeconomic factors, including age, gender/sex, geographic location, education level, ethnicity, health knowledge, immigration history, and income, which can affect vaccine perception and acceptance; b) Health belief model components, including perceived susceptibility, severity, benefits, barriers, and cues to action, which shape individuals’ willingness to seek and accept vaccination; and c) Modes of action, related to individual behavior influenced by positive actions such as government and media campaigns and recommendations from health professionals, as well as the impact of misinformation, which can fuel vaccine hesitancy and reduce vaccine uptake.

These three factors interact in complex ways, influencing vaccine hesitancy and vaccine acceptance in the population. Understanding these dynamics is essential for developing effective communication strategies and public health interventions aimed at increasing vaccine acceptance (18, 19).

Influences of the COVID-19 Pandemic on the Reduction of ICV Against Polio

Several factors may explain the decline in vaccination coverage in the country after the pandemic that claimed nearly 700,000 lives, but some aspects are considered particularly relevant:

Interruptions in Health Services: During the COVID-19 pandemic, most medical efforts were redirected to care for those affected by the coronavirus. This resulted in a prioritization of hospital beds and outpatient and emergency care for infected patients. Consequently, many health services, including routine vaccination campaigns, faced significant interruptions. The need to mobilize resources to fight the pandemic led to a reduction in vaccination services, negatively impacting the immunization of both children and adults and compromising control over other infectious diseases that had been fully or partially controlled.

Fear of Seeking Health Services: Social isolation implemented to control the spread of COVID-19, combined with widespread fear of contracting the virus in healthcare settings, resulted in a significant reduction in the public’s demand for healthcare services. This hesitation led many people to avoid not only elective treatments but also preventive care, such as vaccinations. The perceived risk in healthcare settings, where crowds could occur and the likelihood of exposure to the virus was higher, led many to avoid medical appointments and vaccination campaigns.

Economic Decline: During the period of social isolation, many people faced job losses and reductions in their income sources, directly impacting their ability to access healthcare services. This economic decline made it even harder to seek out medical services related to infectious disease prevention, including vaccinations. Financial insecurity led many individuals and families to prioritize their expenses, often placing healthcare and vaccinations lower on the list of priorities. Additionally, the lack of financial resources may have increased dependence on public health systems, which, due to the pandemic, were already overwhelmed and had limited services.

Misinformation and Vaccine Hesitancy: The social isolation caused by the COVID-19 pandemic created a daily scenario with a significant increase in internet and social media usage. This change facilitated the spread of misinformation about vaccines, contributing to the circulation of fake news regarding potential severe adverse effects of vaccines. The impact of this misinformation was broad and negative, affecting public perception of vaccine safety.

Furthermore, some healthcare professionals, including doctors, became vectors of anti-scientific misinformation, warning patients about the risks of adverse effects from the COVID-19 vaccine. These actions fueled pre-existing anti-vaccine movements, resulting in increased vaccine hesitancy and even refusal to vaccinate. The public’s ignorance about the importance of vaccination, even being discouraged by doctors and political agents who supported the anti-vaccine movement, highlights a lack of rationality and good faith, representing a historical regression against science. The false news disseminated on social media and by some “public figures” ignored the fact that adverse events from vaccines are far fewer than the manifestations of the disease. The perception that infectious diseases covered by childhood vaccination pose low risk leads many caregivers to skip vaccinating children, ignoring the high morbidity and mortality rates when the disease sets in (17, 20).

CONCLUSIONS

The analyses in this study revealed that, in addition to the existing trend of declining vaccination coverage against polio before the pandemic, the emergence of COVID-19 significantly accelerated this decline. The difference between the projections and actual data indicates that the impact of the pandemic was much deeper than expected, likely due to a combination of factors.

The Pan American Health Organization (PAHO) warns of the risk of outbreaks due to the decline in vaccination coverage, coupled with inefficient surveillance of acute flaccid paralysis (AFP) and the continued use of the oral polio vaccine (OPV). As it contains live attenuated virus, OPV becomes susceptible to mutations and may regain neurovirulence (18, 21).

It is crucial that health institutions and regulatory bodies implement public policies that ensure the dissemination of accurate and up-to-date vaccine information, debunking myths and combating misinformation. Transparency in communicating the risks and benefits of vaccines is key to strengthening trust in immunization efforts and increasing vaccination coverage.