Spatial, Spatiotemporal Patterns, and Sociodemographic Aspects of Mortality From Chagas Disease in the State of Alagoas, Northeast Brazil: A 21-Year Population-Based Study.

This study aimed to analyse the magnitude, temporal trend, and spatial pattern of Chagas disease mortality in the state of Alagoas, providing a detailed epidemiological profile and identifying priority areas for intervention. A retrospective, population-based study was conducted using data on Chagas disease-related deaths from 1999 to 2019, obtained from the Mortality Information System. Descriptive analyses were applied to characterise deaths by sex, age, education level, and geographic distribution. The temporal trend was assessed using joinpoint regression, with calculation of the annual percent change. For spatial analysis, the global Moran's I index and space-time scan statistics were applied to identify high-risk clusters. The results showed higher mortality in males (56%-64%), a predominance of deaths in individuals over 50 years old, and a concentration of the chronic cardiac form of Chagas disease in approximately 80% of the records. Low educational attainment was associated with higher mortality, indicating social vulnerability. The spatial distribution revealed that 95.0% of municipalities reported Chagas disease-related deaths, with risk clusters primarily located in the Zona da Mata region, particularly in the 1st, 2nd, 3rd, and 4th Regional Health Superintendencies. These areas have a history of high vector infestation and socio-environmental conditions conducive to transmission. Although the overall temporal trend remained stable or showed a slight decline, the persistence of endemic areas and clusters underscores the need for targeted actions. Thus, it is concluded that Alagoas remains an endemic area for Chagas disease, with mortality influenced by social and environmental factors. This study highlights the importance of strengthening surveillance, diagnosis, and control in priority regions and suggests that future research incorporate predictive models integrating social determinants to improve disease management.