Tuberculose no estado de Mato Grosso: uma análise geoespacial de territórios vulneráveis

Abstract

Introduction: Tuberculosis (TB) has persisted in Brazil and worldwide over time. Brazil is one of the countries with the highest number of TB cases in the world, making the disease a priority issue on the political agenda of the Ministry of Health (MS) since 2003. Objectives: The objective of this study was to evaluate the spatial pattern of TB and identify municipalities with greater vulnerability related to the indicators of the disease in the state of Mato Grosso. Methods: The cases registered in the municipalities of Mato Grosso in the period from 2011 to 2020 were evaluated. The data were collected by the Sistema de Informação de Agravos de Notificação (SINAN), through the Data Repository of the Information Systems of the Mato Grosso State Department of Health (DwWeb/SES-MT). After collection, we validated the database and then extracted the information related to the characterization of the reported cases. Information about the resident population was obtained from the 2010 demographic census of the Brazilian Institute of Geography and Statistics (IBGE). We calculated the indicators and their spatial distribution according to the municipalities of the state of Mato Grosso. The Getis-Ord Gi* analysis was employed to identify risk areas for the occurrence of the investigated indicators. Then, thematic maps were elaborated whose most evident colors represent the most critical areas of the represented indicator. Results: A total of 16,293 patients affected by the disease were included in this study. The highest occurrence was among men, individuals of mixed color and aged between 15 and 59. Regarding education, 45.73% attended school from 1 to 8 years, and 5.90% had no schooling. The spatial analysis allowed us to identify municipalities more vulnerable to the disease that showed high rates of incidence (295.88 cases per 100,000 inhabitants), abandonment of treatment (50%), and mortality (7.48 deaths per 100,000 inhabitants). Furthermore, risk clusters were found spread heterogeneously in the state with reliability between 90 and 99%. Conclusion: The research allowed us to know the profile of individuals who have been affected by TB in the state of Mato Grosso. In addition, the geographic visualization of the most vulnerable territories with the highest risk for the occurrence of cases, treatment abandonment and deaths can serve as a subsidy for the planning, implementation and evaluation of programs and policies aimed at TB in the most critical regions.