Abstract
Objective: To establish tuberculosis endemicity levels in the Brazilian Amazon and identify municipalities relevant to sustaining disease transmission. Methods: This was an ecological and analytical study using data on newly reported tuberculosis cases from 2009 to 2024 in municipalities within the Brazilian Amazon. Tuberculosis notification and demographic data were obtained from the Notifiable Diseases Information System and the Brazilian Institute of Geography and Statistics. Incidence rates were calculated, outliers were identified and excluded (incidence >69.11 per 100,000 population), and the unsupervised k-means learning algorithm was applied for clustering. Cluster validity was assessed using the Silhouette Index (SI) and Davies–Bouldin Index (DBI). Results: Five endemicity levels were identified: low (<13.60), moderate (13.60–22.91), high (22.91–33.70), very high (33.70–48.79), and hyperendemic (>48.79 per 100,000 population). Validation indicated well-defined clusters (DBI = 0.59 and SI values predominantly above 0.50). Seven of the nine state capitals were among the hyperendemic municipalities, and an additional 232 municipalities were classified as having very high or hyperendemic levels. Conclusion: The proposed classification provides a robust tool for epidemiological surveillance and public health planning, enabling the identification of the municipalities most vulnerable to tuberculosis.
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