Spatiotemporal trends in and challenges of addressing tuberculosis in Brazil before and after the COVID-19 pandemic, 2001–2023

ABSTRACT

Objective.

To identify spatiotemporal patterns and clusters of tuberculosis (TB) in Brazil between 2001 and 2023, assess the impact of the COVID-19 pandemic on TB trends, and provide recommendations for targeted interventions.

Methods.

This ecological study analyzed secondary data from Brazil’s Notifiable Diseases Information System (known as SINAN), which covers all confirmed TB cases during the study period. Three main indicators were analyzed: TB incidence and two treatment outcomes – cure and loss to follow up. Spatiotemporal cluster analysis was conducted using the Emerging Hot Spot Analysis tool in ArcGIS Pro 2.8 (Esri, Redlands, CA, USA), based on the Getis-Ord Gi* statistic.

Results.

An average of 74 057 TB cases were reported annually. TB incidence declined until 2016 but increased afterward, peaking in 2023. Cure rates declined after 2016, especially following the COVID-19 pandemic, while the rates of loss to follow up increased. Hot spots for incidence and loss to follow up were concentrated in the North, Southeast, and Central–West regions. The South region showed cold spots for cure and loss to follow up. These spatial trends revealed persistent regional disparities in TB outcomes that are closely related to indicators associated with socioeconomic status and access to health care.

Conclusions.

TB continues to present critical public health challenges in Brazil, particularly since the COVID-19 pandemic. Spatiotemporal analysis revealed significant regional clusters of TB burden. Strengthening surveillance systems and improving early diagnosis and treatment adherence strategies, especially in high-burden regions, are essential to mitigate the post-pandemic resurgence of TB and to achieve the World Health Organization’s goal of eliminating TB by 2035.

Tuberculosis (TB) remains a significant public health issue that was further exacerbated by the COVID-19 pandemic. In 2024, an estimated 10.7 million people fell ill with TB worldwide, and about 8.3 million were newly diagnosed and reported to national authorities, the highest number recorded since global TB monitoring began in 1995. TB is currently the world’s leading cause of death from a single infectious agent, ahead of COVID-19 and HIV infection. In 2024, approximately 1.23 million deaths were attributed to TB, and disruptions to TB services during the COVID-19 pandemic and its aftermath are estimated to have resulted in close to 700 000 excess TB deaths between 2020 and 2023 compared with the number expected if pre-pandemic trends had continued (1). Brazil is recognized by the World Health Organization (WHO) as one of 30 countries with a high burden of TB. This acknowledgment underscores the priority given to Brazil in global initiatives aimed at controlling and combating this infectious disease (1).

Although a steady downward trend in TB was observed in Brazil between 2011 and 2016, its incidence increased between 2017 and 2019 (2). In 2020, the peak year of the COVID-19 pandemic in the country, a significant decrease in TB incidence was observed compared with 2019, with new cases dropping from 37.3/100 000 population to 32.7. However, following the global trend, from 2021 onwards, cases began to increase, reaching the highest incidence in recent years, with 38.0 cases/100 000 population in 2023. In line with WHO’s recommendations, by 2035 Brazil is striving to significantly reduce its TB incidence to less than 10 cases/100 000 inhabitants and the number of TB deaths to fewer than 230 annually. However, these challenges seem greater after the COVID-19 pandemic (2–4).

To achieve these objectives, it is essential to enhance strategies for diagnosis, treatment and prevention by incorporating new technologies that can facilitate this process (4, 5). Among technological tools, spatiotemporal analysis stands out for providing a deeper and more comprehensive understanding than unidimensional approaches. It allows for visualization of dynamic temporal patterns and can help to identify more efficient and effective policies for infection control (6, 7).

Geospatial analysis of hot spots and clusters of TB offers substantial benefits. It reveals the spatial distribution of cases and temporal trends, enabling the better allocation of resources such as medical supplies and personnel. It also supports targeted prevention actions, including vaccination and health education. Moreover, continual geospatial monitoring facilitates the early detection of clusters of TB, enabling rapid responses to prevent outbreaks and limit disease spread (8–10).

Spatiotemporal analysis reveals the dynamics of TB dissemination by correlating incidence with environmental and socioeconomic factors, such as population density, poverty and housing conditions. It helps uncover patterns not captured by traditional surveillance, and such understanding enables more targeted public health interventions that can reduce TB incidence and improve outcomes.

This study aimed to leverage advanced spatiotemporal methods to contribute to the global fight against TB and support Brazil’s efforts to achieve the TB elimination goals set by WHO for 2035 (1, 10).

The extensive geographical and population diversity of Brazil are ideal for implementing such an analysis. However, the relative scarcity of spatiotemporal methodologies for analyzing TB indicates a notable gap in the understanding and application of these methods within the field. This scarcity underscores the imperative for further exploration and research (5–7, 10, 11).

The objectives of this study were to map hot spots and clusters of TB in Brazil from 2001 to 2023, investigate the spatiotemporal patterns of TB cases, examine the impact of the COVID-19 pandemic on the spatial distribution of and trends in TB, evaluate the relationship between TB incidence and socioeconomic and environmental indicators, and provide data-driven recommendations for targeted TB control interventions. Additionally, the study aimed to develop geospatial tools and methodologies that could be used for continual TB surveillance.

METHODS

Study design and setting

This ecological study employed a spatiotemporal approach, encompassing all Brazilian municipalities distributed across 26 states and the Federal District, organized into five macro-regions: North, Northeast, Southeast, South and Central–West. Brazil, with a vast territorial expanse of 8 510 345 538 km² and a population of 213.3 million inhabitants, operates the Sistema Único de Saúde (Brazilian Unified Health System), known as SUS, a public health system designed to ensure comprehensive, universal and free access to health care for the entire population (12, 13).

Data sources, study population and study variables

Utilizing secondary data, we examined 2 076 820 reported cases of TB diagnosed between 2001 and 2023, with data from the Sistema de Informação de Agravos de Notificação (Notifiable Diseases Information System), known as SINAN, a database maintained by the Brazilian Ministry of Health. SINAN, established in 1990, compiles notifications for all compulsorily notifiable diseases in the country. Only cases of TB confirmed through clinical or laboratory criteria are notified to this system. The national TB database used in this study is a qualified data set, routinely curated by the Ministry of Health through standardized data-quality procedures, including removing duplicate records, linking notifications belonging to the same treatment episode, and correcting or excluding records with inconsistencies in key identification variables. The national data set is built from information transferred from municipalities and states (14).

Confirmed cases of TB were grouped by year and municipality. The annual incidence was calculated using population estimates from the Instituto Brasileiro de Geografia e Estatística (Brazilian Institute of Geography and Statistics), known as the IBGE (15).

The analysis used three main indicators: TB incidence, calculated as the number of new TB cases divided by the annual population and multiplied by 100 000; cure rate, defined as the proportion of new cases of TB that were cured, obtained by dividing the number of cured cases by the total number of new TB cases and multiplying by 100; and loss to follow up, representing the proportion of new TB cases lost to follow up, calculated by dividing the number of cases lost to follow up by the total number of new TB cases and multiplying by 100.

Operationally, new cases refer to individuals registered in SINAN as new TB cases, excluding those with missing information about their case status (i.e. “not known”) or diagnosed only after death (i.e. “post-obit”) who had never undergone treatment or had received it for fewer than 30 days. A case classified as a cure refers to an individual who completed treatment and either showed clinical improvement or had two negative smear results, one during follow up and another at the end of treatment. Cases with outcomes such as treatment failure, change of therapeutic regimen or drug-resistant TB were excluded from the analysis, as they are monitored through the Sistema de Informação de Tratamentos Especiais da Tuberculose (Tuberculosis Special Treatment Information System, known as SITE-TB), which does not provide final outcomes to SINAN.

The Strengthening the Reporting of Observational Studies in Epidemiology (or STROBE) guidelines were followed for reporting this study (16).

Ethics

This study used secondary data from SINAN about TB cases in Brazil and its states. As the data are publicly available and de-identified, the study was considered exempt from ethical review, in accordance with Resolution 510/2016 of the Brazilian National Health Council (17). Documentation supporting this exemption is available upon request from the corresponding author.

Data analysis

The unit of analysis was the 5 570 Brazilian municipalities. An emerging hot spot analysis (EHA) was performed for each selected TB indicator (Box 1) using ArcGIS PRO 2.8 software (Esri, Redlands, CA, USA). The main objective of an EHA is to identify trends in space and time by using the Getis-Ord-Gi* clustering approach for each selected indicator. The analysis is based on a spatiotemporal cube with a 3-dimensional structure, in which each layer of the Z axis represents a point in time, while the X and Y axes represent the distribution in space (18).

BOX 1. Classification of spatiotemporal patterns of hot spots and cold spots used in the emerging hot spot analysis for tuberculosis indicators, Brazil, 2001–2023^a.

Variable	Representation	Pattern	Description
No pattern detected		No pattern detected	Not classified as any hot or cold spot pattern.
Hot spots		New hot spot	A location that emerges as a statistically significant hot spot only in the final time step, with no prior history of hot spot significance
		Consecutive hot spot	A location showing a continuous sequence of significant hot-spot intervals including in the final time step, with no earlier hot-spot episodes and less than 90% of all intervals classified as hot spots
		Intensifying hot spot	A location that remains a significant hot spot in at least 90% of the intervals, including the final one, and shows a statistically confirmed upward trend in the intensity of high-value clustering over time
		Persistent hot spot	A location classified as a hot spot in at least 90% of the intervals, without any clear upward or downward trend in clustering intensity across the timeline
		Diminishing hot spot	A location that is a hot spot in at least 90% of the intervals, including the final one, and that shows a statistically confirmed decline in clustering intensity over time
		Sporadic hot spot	A location that alternates between being a hot spot and not, with less than 90% of intervals showing hot-spot significance and no intervals classified as cold spots
		Oscillating hot spot	A location that ends as a significant hot spot but was previously a significant cold spot, with less than 90% of all intervals classified as hot spots
		Historical hot spot	A location that is not a hot spot in the final interval but met the criteria for hot-spot significance in at least 90% of all prior intervals
Cold spots		New cold spot	A location that becomes a significant cold spot only in the final time step, with no earlier cold-spot occurrences
		Consecutive cold spot	A location showing a continuous sequence of significant cold-spot intervals including in the final time step, with no earlier cold-spot events and less than 90% of all intervals classified as cold spots
		Intensifying cold spot	A location that is a significant cold spot in at least 90% of the intervals, including the final one, and exhibits a statistically confirmed upward trend in the clustering of low values over time
		Persistent cold spot	A location that meets cold-spot significance in at least 90% of the intervals and shows no clear upward or downward trend in clustering intensity over time
		Diminishing cold spot	A location that is a significant cold spot in at least 90% of the intervals, including the final one, and that shows a statistically verified decline in the clustering of low values over time
		Sporadic cold spot	A location that alternates between being a cold spot and not, with less than 90% of intervals showing cold-spot significance and no intervals classified as hot spots
		Oscillating cold spot	A location that ends as a significant cold spot but was previously a significant hot spot, with less than 90% of all intervals classified as cold spots
		Historical cold spot	A location that is not a cold spot in the final interval but met the criteria for cold-spot significance in at least 90% of the preceding intervals

^aThese categories correspond to the legend used in the hot spot maps and describe how each municipality was classified according to the presence and temporal evolution of statistically significant clusters of high (hot spots) or low (cold spots) values of tuberculosis indicators.

Source: Information developed by the authors based on ArcGIS Pro documentation (19).

Briefly, the geographical information from Brazilian municipalities was distributed on the X and Y axes, while the time data for each TB indicator were distributed on the Z axis. To construct of the space–time cube, each combination of municipality and year corresponded to a single bin, using municipalities as fixed spatial units and annual time steps from 2001 to 2023, consistent with the temporal aggregation adopted for the incidence indicators. This spatial resolution reflects the level at which TB control actions are implemented in Brazil and allows for direct interpretation of clusters for programmatic decision-making.

We selected the EHA over alternative spatial approaches, such as Local Moran’s I, because the EHA simultaneously incorporates the temporal dimension and classifies statistically significant clusters into distinct space–time patterns (for example, new, emerging, intensifying or persistent hot or cold spots), whereas purely spatial statistics would require multiple cross-sectional analyses at different time points, hindering the comparability of trends over time. Finally, the tool scans the three axes in order to find statistically significant space–time clusters. The outcome of the analysis is distributed between two scales: hot spots or cold spots (19). The analysis was performed for both absolute numbers and rates for each indicator presented in Box 1.

RESULTS

The average number of new TB cases registered annually in Brazil from 2001 to 2023 was 74 057. TB incidence in Brazil decreased from 41.6/100 000 inhabitants in 2001 to 34.3 in 2016, then started to rise again, reaching 37.3 in 2019. During the COVID-19 pandemic, notifications dropped to 32.8/100 000 in 2020, but afterward, the incidence reached its highest level in recent years, at 39.8 in 2023 (Figure 1a).

FIGURE 1. Historical trends in tuberculosis, by region and overall, Brazil, 2001–2023^a.

^a Incidence is shown for 2001–2023, whereas cure and loss to follow up are available for 2001–2022.

Source: Figure developed by the authors based on data from SINAN, 2001–2023.

Historically, the states of Rio de Janeiro and Amazonas have had the highest incidence in the country. In Amazonas, incidence remained persistently high, ranging from 77.4/100 000 inhabitants in 2001 to 86.8 in 2023, whereas in Rio de Janeiro it declined from 91.7/100 000 inhabitants to 73.8 over the same period (see Supplementary Table S1).

Cure rates in Brazil have shown temporal and regional variation over the years (Figure 1b). After a gradual increase from 67.8% in 2001 to 72.9% in 2005, the cure rate remained relatively stable at around 73–74% until 2015. A decline began in 2016 and became more pronounced after the COVID-19 pandemic, decreasing to 65.8% in 2022. Cure rates declined across all regions between 2001 and 2022. While the Southeast region showed an overall increase from 2001 to 2016, it experienced a decline in subsequent years. The Central–West and South regions had the sharpest reductions over the period.

Simultaneously, the rate of loss to follow up increased nationwide during the past decade, reaching 13.7% in 2022 (Figure 1c). The highest increases occurred in the Central–West and North regions.

Comprehensive numerical details about incidence, cure and loss to follow up, by region and year, are provided in Supplementary Tables S1–S3.

The analysis of the distribution of new tuberculosis cases (Figure 2a) revealed the presence of hot spots in municipalities primarily located in the Southeast and North regions, indicating a general increase in cases in these areas. A hot spot pattern was observed in 22.0% of municipalities in the Southeast (367/1 668) and in 16.2% of those in the North (73/450). In the South region, only one municipality (0.08%, 1/1 191) exhibited this pattern. The majority of Brazilian municipalities (92.1%, 5 129/5 570) showed no detectable pattern in the absolute number of new TB cases.

FIGURE 2. Clusters of new tuberculosis cases (absolute numbers) and incidence, by Brazilian municipality, 2001–2023.

Source: Figure developed by the authors based on data from SINAN, 2001–2023.

In contrast to the overall number of cases, the clustering of TB incidence revealed regional disparities across Brazil (Figure 2b). In the South, a persistent cold spot pattern was observed in 51.2% (610/1 191) of municipalities, while the Southeast and Northeast had cold spot patterns in approximately 35.2% (587/1 668) and 29.9% (537/1 794) of their municipalities, respectively. In contrast, hot spot patterns were identified in 41.4% (690/1 668) of municipalities in the Southeast and 34.9% (626/1 794) in the Northeast, indicating a balance between clusters of cold and hot spots in these areas.

Notably, the North had hot spot patterns in 34.4% (155/450) of its municipalities, while 30.7% (138/450) presented cold spot patterns. In the Center–West region, cold spot patterns predominated, accounting for 39.1% (182/466) of its municipalities.

When analyzing treatment outcomes in terms of cure rates, hot spots for the absolute numbers of cured patients were identified primarily in the North (16.2%, 73/450 municipalities) and Southeast (22.0%, 367/1 668 municipalities) (Figure 3a), with a clear predominance in the Southeast.

FIGURE 3. Spatiotemporal patterns of hot spots for the absolute number of cures and cure rates among tuberculosis patients, by Brazilian municipality, 2001–2022.

Source: Figure developed by the authors based on data from SINAN, 2001–2022.

Interestingly, in the North region, sporadic hot spots were observed in 44/73 municipalities (60.3%), while intensifying hot spots were identified in 25/73 municipalities (34.2%). In the Southeast region, persistent hot spots were observed in 136/367 municipalities (37.1%) and intensifying hot spots in 225/367 municipalities (61.3%), indicating a sustained and intensified trend of decreasing cure rates in several areas.

No statistically significant hot spots were detected outside of the North and Southeast regions. These findings suggest that efforts to enhance cure rates may be more concentrated or advanced in these areas.

The analysis of patterns of cure rates revealed important regional differences (Figure 3b). In the Southeast, 14.9% (249/1 668) of municipalities had hot spot patterns, with persistent hot spots being the most prevalent subtype, accounting for 64.7% (161/249) of hot spot municipalities. In the South, 1.8% (21/1 191) of municipalities were classified as having historical hot spot patterns. However, the South accounted for 69.7% (1 109/1 591) of all cold spot municipalities nationwide, followed by the Central–West, with 17.8% (283/1 591) and the Southeast with 12.5% (199/1 591).

In terms of loss to follow up for treatment, all regions showed only hot spot patterns, with the Southeast having the highest proportion at 22.3% (371/1 668) of municipalities (Figure 4a). Among these, the highest proportion was observed in the intensifying hot spot category, representing 93.5% of cases (347/371 municipalities).

FIGURE 4. Spatiotemporal patterns of hot spots associated with loss to follow up for treatment and rates of loss to follow up among tuberculosis patients, by Brazilian municipality, 2001–2022.

Source: Figure developed by the authors based on data from SINAN, 2001–2022.

When evaluating the rates of loss to follow up for treatment, the Southeast had the highest proportion of municipalities classified as hot spots at 48.3% (805/1 668); the most prevalent patterns were sporadic hot spots, accounting for 50.9% (410/805 municipalities), and oscillating hot spots, accounting for 37.8% (304/805 municipalities) (Figure 4b). Other regions with hot spot patterns included the Central–West (41.1%, 192/467 municipalities), the South (13.3%, 158/1 191 municipalities), the North (31.8%, 143/450 municipalities) and the Northeast (6.7%, 120/1 794 municipalities).

In contrast, cold spot patterns were found most frequently in the South, in 54.0% (643/1 191) of municipalities, with the sporadic cold spot (41.7%, 268/643) and persistent cold spot (29.7%, 191/643) patterns being the most common. This was followed by the Northeast, with 25.3% (453/1 794) of municipalities showing cold spot patterns, and lower occurrences in the Southeast (6.8%, 113/1 668 municipalities), the North (0.9%, 4/450) and the Central–West (0.4%, 2/467 municipalities).

DISCUSSION

The COVID-19 pandemic disrupted TB control worldwide by affecting surveillance, diagnosis and adherence to treatment (20, 21). Interruptions in health care services and the reallocation of resources contributed to an increased TB burden and delays in diagnosis. According to WHO, between 2020 and 2022 TB-related deaths exceeded pre-pandemic estimates, highlighting significant setbacks in global TB control efforts (22, 23).

Given these disruptions, it is crucial to assess how the numbers of TB cases and treatment outcomes have evolved during this period. This study aimed to analyze the presence of spatiotemporal clusters of TB cases and associated outcomes in Brazil by considering the additional impact of the pandemic on efforts to control TB. The use of geospatial analysis provides a valuable tool to understand the spatial distribution of TB and to help guide targeted interventions in high-burden areas (23, 24).

The spatial patterns of TB in Brazil reveal significant regional inequalities in incidence, cure rates and loss to follow up for treatment. The analysis of the results highlights that the dynamics of TB in the country are influenced by social, economic and structural factors, reflecting different challenges faced across the North, Northeast, Center–West, Southeast and South regions. Our findings align with previous studies that indicated a relationship between areas with low values on the Human Development Index and increased TB burden, particularly in the North, where access to health care services remains a significant challenge. This region has historically had a high incidence of TB, which may be associated with social vulnerabilities, limited health care infrastructure, and difficulties in accessing diagnostic and treatment services (25, 26).

The concentration of hot spots of new TB cases in the North and Southeast regions may reflect ongoing transmission and highlights the need for targeted interventions. In particular, the presence of intensifying hot spots suggests areas where transmission is not yet under control, posing risks to national and international goals to eliminate TB. Nevertheless, such patterns may also be influenced by improvements in surveillance and case notification systems rather than a true increase in incidence. Previous studies have linked enhanced screening and reporting with higher detection rates in areas with a history of underreporting (27, 28).

Cure rates follow a heterogeneous spatial distribution, with a stretch of hot spots extending from the North to the Northeast, indicating improvements in treatment in these regions. Nevertheless, some regions, particularly in the South and parts of the Center–West, Northeast and North, exhibited cold spots, suggesting persistent challenges in ensuring effective TB treatment (27, 28). These findings highlight the need to strengthen policies such as directly observed therapy, social protection programs and health education, which have been shown to be effective in improving TB outcomes in various settings. Evidence from other countries with a high burden of TB indicates that social support mechanisms, including financial incentives and nutritional support, can significantly improve treatment adherence and outcomes (29, 30).

Another concerning finding is the rising trend in the loss to follow up of individuals receiving TB treatment in certain regions, particularly in the North, Southeast and Center–West. High dropout rates in these areas may compromise TB control efforts by increasing the risk of drug resistance and treatment failure. This aligns with global trends showing that treatment interruption is a major contributor to multidrug-resistant TB, which poses significant challenges to public health systems. Interestingly, some areas in the South, Southeast and Northeast had decreasing rates of individuals lost to follow up, despite being surrounded by areas with worsening trends. Further studies are needed to understand which factors contribute to better treatment adherence in these areas and how successful interventions can be replicated elsewhere (31, 32).

The use of geospatial analysis has proven essential in identifying patterns of TB transmission and optimizing the allocation of resources for diagnostics, treatment and health care infrastructure. This study reinforces the importance of using spatial tools to inform public health strategies because they allow for the development of locally tailored interventions that address the specific needs of different areas. Additionally, incorporating predictive models that use spatiotemporal data can enhance TB control efforts by identifying at-risk populations before outbreaks occur (8).

Working at the municipal level also has important implications for interpreting and using this study’s findings. On the one hand, municipalities are the main administrative units responsible for organizing TB control actions within SUS, which means that the identified hot and cold spots can be directly translated into local planning and used to prioritize resources. On the other hand, municipalities differ markedly in population size and urban structure, so the patterns observed in small or very heterogeneous municipalities should be interpreted with caution and, ideally, would be complemented by local contextual information, when available. As an ecological study using municipalities as the unit of aggregation, the findings are also subject to potential ecological bias and cannot be directly interpreted at the individual level, nor can they fully capture within-municipality heterogeneity in terms of TB risk and access to health services. Although these findings provide valuable insights into TB dynamics in Brazil, certain limitations should be acknowledged. Underreporting and incomplete records remain common issues in secondary data collection. However, national estimates indicate that TB data systems capture approximately 87% of cases, which aligns with WHO’s standards. Additionally, this study did not assess TB mortality due to the limitations of available data, which are more accurately captured in the Mortality Information System (Sistema de Informações sobre Mortalidade, known as SIM). Finally, because TB indicators are subject to updates over time, slight variations in the rates of cure and loss to follow up are expected in future assessments (33).

Despite these limitations, our study provides an innovative perspective on TB epidemiology in Brazil by employing a spatiotemporal methodology at a national scale. Understanding regional trends in TB incidence, cure and loss to follow up for treatment is essential for designing effective interventions. Future research should focus on evaluating the long-term impact of the COVID-19 pandemic on TB control efforts, ensuring that policies are adapted to mitigate emerging challenges and to reinforce progress toward eliminating TB (34).

By leveraging geospatial technologies, policy-makers can enhance TB surveillance, improve access to early diagnosis and treatment, and implement targeted strategies to address disparities in TB outcomes across different regions of Brazil. The integration of spatial analysis into national TB programs is a crucial step toward achieving WHO’s goal of eliminating TB by 2035.

Conclusions

This spatiotemporal analysis of TB in Brazil from 2001 to 2023 revealed significant patterns of regional clustering, which highlight the impact of the COVID-19 pandemic on incidence, cure rates and loss to follow up in treatment. While the incidence of TB had declined before the pandemic, these data indicate a concerning increase in cases after 2021, particularly in the North and Southeast regions, where persistent hot spots suggest ongoing transmission. Additionally, the decline in cure rates and the rise in the loss to follow up for treatment, especially in the Central–West and South, underscore the need for targeted interventions to prevent drug resistance and ensure treatment adherence.

In this context, the use of geospatial analysis has proven to be a crucial tool for identifying TB transmission patterns and supporting the design of more effective TB control policies, enabling targeted interventions and improved resource allocation.

Despite the study’s limitations, such as potential underreporting and data variations over time, the findings emphasize the importance of strengthening surveillance systems, expanding access to early diagnosis and implementing predictive strategies to identify high-risk areas and prioritize TB control actions based on spatiotemporal data. Enhancing these efforts will be essential to mitigate the challenges imposed by the pandemic and advance toward the goal of eliminating TB by 2035.

Funding Statement

This study was supported by the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES), Funding Code 001. The funders had no role in study design, data collection, analysis, manuscript preparation or the decision to publish the results.

Disclaimer.

Authors hold sole responsibility for the views expressed in the manuscript, which may not necessarily reflect the opinion or policy of the Revista Panamericana de Salud Pública/Pan American Journal of Public Health or the Pan American Health Organization.