Tuberculosis (TB) is the infectious disease that kills the most people worldwide. The use of geoepidemiological techniques to demonstrate the dynamics of the disease in vulnerable communities is essential for its control. Thus, this study aimed to identify risk clusters for TB deaths and their variation over time. This ecological study considered cases of TB deaths in residents of Londrina, Brazil between 2008 and 2015. We used standard, isotonic scan statistics for the detection of spatial risk clusters. The Poisson discrete model was adopted with the high and low rates option used for 10, 30 and 50% of the population at risk, with circular format windows and 999 replications considered the maximum cluster size. Getis-Ord Gi* (Gi*) statistics were used to diagnose hotspot areas for TB mortality. Kernel density was used to identify whether the clusters changed over time. For the standard version, spatial risk clusters for 10, 30 and 50% of the exposed population were 4.9 (95% CI 2.6–9.4), 3.2 (95% CI: 2.1–5.7) and 3.2 (95% CI: 2.1–5.7), respectively. For the isotonic spatial statistics, the risk clusters for 10, 30 and 50% of the exposed population were 2.8 (95% CI: 1.5–5.1), 2.7 (95% CI: 1.6–4.4), 2.2 (95% CI: 1.4–3.9), respectively. All risk clusters were located in the eastern and northern regions of the municipality. Additionally, through Gi*, hotspot areas were identified in the eastern and western regions. There were important risk areas for tuberculosis mortality in the eastern and northern regions of the municipality. Risk clusters for tuberculosis deaths were observed in areas where TB mortality was supposedly a non-problem. The isotonic and Gi* statistics were more sensitive for the detection of clusters in areas with a low number of cases; however, their applicability in public health is still restricted.

Background: International migration is a global phenomenon with significant implications on the health–disease process due to exposures along transit routes and local/destination epidemiological indicators. We aimed to analyze the transmission and spread of tuberculosis among international migrants and refugees from a spatiotemporal perspective and the associated factors. Method: This was an ecological study of cases of tuberculosis in international migrants in Brazil, between 2010 and 2021. Annual incidence rates were calculated and spatiotemporal scan techniques were used to identify municipalities at risk. Multiple logistic regression was used to identify factors associated with tuberculosis in international migrants. Results: A total of 4037 cases of tuberculosis were reported in Brazil in international migrants. Municipalities at risk for this event were identified using the spatiotemporal scan technique, and a cluster was identified with ITT: +52.01% and ETT: +25.60%. A higher probability of TB infection was identified in municipalities with a TB incidence rate >14.40 cases/100 inhabitants, population >11,042 inhabitants, Gini index >0.49, and illiteracy rate >13.12%. A lower probability was found in municipalities with average per capita household income >BRL 456.43. Conclusions: It is recommended that health authorities implement monitoring and rigorous follow-up in affected areas to ensure proper diagnosis and treatment completion for international migrants, preventing disease spread to other communities.

Introduction: Vaccines are a significant public health achievement, which are crucial for child survival and disease control globally. In Brazil, the National Immunization Program (PNI) manages vaccination schedules, including essential vaccines like BCG and Hepatitis B, administered at birth. Despite achieving over 95% coverage for years, vaccination rates have declined since 2016, a trend exacerbated by the COVID-19 pandemic. This study aims to analyze spatial and temporal trends in BCG and Hepatitis B vaccination coverage at birth, identify areas with spatial variation in these trends, classify the identified trends, and investigate the pandemic’s impact on vaccination adherence. Methods: This is an ecological study with real-world data from Brazil, focusing on vaccination coverage from 2014 to 2023. Utilizing the Spatial Variation in Temporal Trends (SVTT) technique, the study identifies municipalities’ vaccination trends. It also employs time series analysis and Interrupted Time Series methods to evaluate the pandemic’s impact on vaccination rates, using data from the PNI and the Information System on Live Births (SINASC). Results: Between January 2014 and December 2023, Brazil administered 25,902,207 doses of the BCG vaccine to children at birth, with 3911 municipalities (70.24%) showing declining trends, particularly in Florianópolis. Similarly, 22,962,434 doses of the Hepatitis B vaccine were administered, with 3284 municipalities also experiencing declines. Conclusions: It is crucial that public health policies be reevaluated to address regional disparities in vaccination coverage, particularly in more vulnerable areas. Focused interventions, such as awareness campaigns, improved access to vaccination services, and strengthened monitoring, are fundamental to reversing this trend.

The early identification of the Breathing Symptoms within the scope of Primary Health Care is recommended, and is also one of the strategies of national sanitary authorities for reaching the elimination of tuberculosis. The purpose of this study is to consider which attributes and which territories have shown the most significant progress in Primary Health Care, in terms of coordination of Health Care Networks, and also check if those areas of Primary Health Care that are most critical regarding coordination, there were more or less cases of avoidable hospitalizations for tuberculosis. This is an ecological study that uses primary and secondary data. For analysis, coropletic maps were developed through the ArcGIS software, version 10.2. There was also the calculation of gross annual and Bayesian rates for hospitalizations for tuberculosis, for each Primary Health Care territory. There were satisfactory results for attributes such as Population (n = 37; 80.4 %), Primary Health Care (n = 43; 93.5 %), Support System (n = 45; 97.8 %); the exceptions were Logistics System (n = 32; 76.0 %) and Governance System, with fewer units in good condition (n = 31; 67.3 %). There is no evidence of any connection between networks’ coordination by Primary Health Care and tuberculosis avoidable admissions. The results show that progress has been made regarding the coordination of the Health Care Networks, and a positive trend has been shown, even though the levels are not excellent. It was found no relationship between the critical areas of Primary Health Care and tuberculosis avoidable hospitalizations, possibly because other variables necessary to comprehend the phenomena.

The COVID-19 pandemic disrupted tuberculosis (TB) control, increasing mortality and potentially worsening disparities. This study aimed to analyze the temporal trends of TB mortality in Brazil and to trace the COVID-19 pandemic’s effect using a Bayesian approach, focusing on nationwide data. An ecological study of TB deaths recorded in the Mortality Information System (SIM) from 2012 to 2022 was conducted. Trends and percentage changes in the mortality were estimated. A Bayesian Structural Time Series model combined with an Autoregressive Integrated Moving Average model was used to assess the pandemic’s effect on TB. A total of 51,809 TB deaths were identified, with a mortality rate of 2.27 per 100,000. Higher rates were found among the elderly (6.86), indigenous populations (5.58), and black individuals (4.21). The Bayesian model estimated a 9.9% (CI 8.8–11%) increase in TB mortality due to COVID-19. The Midwest region showed the highest increase (30%, 25–35%). Females experienced a greater post-pandemic monthly increase (2.80%) in mortality than males (0.72%). The Bayesian analysis revealed a significant rise in TB mortality during the COVID-19 pandemic, with notable disparities affecting females, the elderly, the indigenous, and the black populations. These findings highlight the pandemic’s long-term impact on TB and stress the need for equity-focused, data-driven public health responses in Brazil.

Propôs-se verificar como a incidência e mortalidade por tuberculose (TB) se comportam em áreas cobertas pela Estratégia Saúde da Família e Bolsa Família, com ou sem desigualdade de renda. Estudo ecológico dos 26 Estados federados e o Distrito Federal do Brasil. Foram empregadas análises univariadas, multivariadas e análises espaciais. Na análise univariada, calculou-se a medida de posição (mediana) e a medida de dispersão (amplitude) das variáveis. Na análise multivariada, identificou-se sete grupos através da análise de agrupamento pelo método hierárquico. A construção dos mapas temáticos demonstrou uma distribuição heterogênea entre os grupos, devido às características particulares. O estudo evidenciou a realidade epidemiológica da TB no Brasil e seu comportamento em áreas cobertas pela Estratégia Saúde da Família e Programa Bolsa Família. Não observou diferenças no que se refere à incidência e mortalidade quando satisfatória cobertura desses programas, o que evidencia a relação da doença a outros determinantes.