Detection of risk clusters for deaths due to tuberculosis specifically in areas of southern Brazil where the disease was supposedly a non-problem

• Autor(a) principal: Alves, Luana Seles
• Data de Publicação: 2019
• Outros Autores: Dos Santos, Danielle Talita, Arcoverde, Marcos Augusto Moraes, Berra, Thais Zamboni, Arroyo, Luiz Henrique, Ramos, Antônio Carlos Vieira, De Assis, Ivaneliza Simionato, De Queiroz, Ana Angélica Rêgo, Alonso, Jonas Boldini, Alves, Josilene Dália, Popolin, Marcela Paschoal, Yamamura, Mellina, De Almeida Crispim, Juliane, Dessunti, Elma Mathias, Palha, Pedro Fredemir, Chiaraval-Neto, Francisco, Nunes, Carla, Arcêncio, Ricardo Alexandre
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://doi.org/10.1186/s12879-019-4263-1
• topic: Cluster detection; Death; Isotonic regression; Scan statistics; Spatial analysis; Tuberculosis; Infectious Diseases; SDG 3 - Good Health and Well-being

Background: 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. Methods: 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. Results: 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. Conclusions: 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.