Background: We aimed to visualize and classify the time series of COVID-19, tuberculosis (TB) notification, and TB outcomes (cure, treatment abandonment, and death), verify the impact of the new coronavirus pandemic on these indices in Brazil, and verify the presence of spatial autocorrelation between COVID-19 and TB. Methods: This was an ecological time series study that considered TB and COVID-19 cases. Seasonal Trend Decomposition using Loess (STL) was used to trace the temporal trend, Prais–Winsten was used to classify the temporal trend, Interrupted Time Series (ITS) was used to verify the impact of COVID-19 on TB rates, and the Bivariate Moran Index (Global and Local) was used to verify the spatial autocorrelation of events. Results: Brazil and its macro-regions showed an increasing temporal trend for the notification of TB in the pre-pandemic period. Only the Northeast Region showed a decreasing temporal trend for cured cases. For treatment abandonment, all regions except for the Northeast showed an increasing temporal trend, and regarding death, Brazil and the Northeast Region showed an increasing temporal trend. With the ITS, COVID-19 caused a decline in TB notification rates and TB outcome rates. With the global spatial analysis, it was possible to identify the existence of spatial autocorrelation between the notification rate of COVID-19 and the TB notification rate and deaths. With the local analysis, it was possible to map the Brazilian municipalities and classify them according to the relationship between the rates of both diseases and space. Conclusions: COVID-19 influenced the follow-up of and adherence to TB treatment and intensified social vulnerability and, consequently, affected the notification of TB since the relationship between the disease and social determinants of health is already known. The restoration and strengthening of essential services for the prevention and detection of cases and treatment of TB in endemic environments such as Brazil have been oriented as a priority in the global health agenda.

(1) Background: Tuberculosis presents an epidemiological trend toward inequality, especially among people in social exclusion and situations of vulnerability. This study aimed to analyze territories with a concentration of people diagnosed with tuberculosis in a street situation and who partake in chronic use of alcohol, tobacco, and illicit drugs. We also analyzed trends in this health condition in southern Brazil. (2) Methods: Ecological study, developed in the 399 municipalities of Paraná, southern Brazil, with all tuberculosis cases in the homeless population registered in the Information System of Notifiable Diseases between 2014 and 2018. For data analysis, we used descriptive statistics, the Prais–Winsten autoregression method for the time series, and the Getis-Ord Gi technique* for spatial analysis. (3) Results: in total, 560 cases were reported. We found a predominance of alcohol, smoking, and illicit drug users, with an increasing trend in the state and clusters of spatial risk in the East health macro-region. (4) Conclusions: We observed territories with critical levels of highly vulnerable people who use psychoactive substances and are in a street situation. The results highlight the importance of incorporating public policies of social protection for these individuals and resolutive health services that receive these cases and assist in eradicating TB.

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.

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.

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.

The rapid spread of COVID-19 have overwhelmed health systems, especially in the care of chronic disease such as tuberculosis and diabetes. The objective of the study was to analyze the magnitude and relevance of tuberculosis-diabetes and diabetes-COVID-19 comorbidities in spatial risk areas and their factors associated with unfavorable outcomes in the Brazilian population between 2020 and 2022. An ecological study was carried out in Brazilian municipalities. The population was composed by cases of tuberculosis-diabetes and diabetes-COVID-19 comorbidities, registered in the Influenza Epidemiological Surveillance Information System (SIVEP-GRIPE) and in DATASUS from 2020 to 2022. The Scan Statistics technique was used to identify spatial risk clusters. Binary logistic regression was then employed to understand the relationship between outcomes and comorbidities, considering clinical and sociodemographic variables. A total of 24,750 cases of tuberculosis-diabetes comorbidity were identified, which consisted of an incidence of 3.2 cases per 100,000 inhabitants. Risk clusters were identified in the Central-West and North regions. 303,210 cases of diabetes- COVID-19 comorbidity were identified, resulting in an incidence of 0.4 cases per 100,000 inhabitants. São Paulo-SP, Rio de Janeiro-RJ and Belo Horizonte-MG were the municipalities with the highest spatial risk of illness. The analysis of the spatial risk areas revealed distinct patterns in the geographic distribution of comorbidities. Based on the findings, it is concluded that comorbidities between tuberculosis and diabetes, as well as between COVID-19 and diabetes, represent significant challenges for public health in Brazil, deserving attention from health authorities and the scientific community.