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

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Alves, Luana Seles ^{[1, 2]} ; dos Santos, Danielle Talita ^[3] ; Moraes Arcoverde, Marcos Augusto ^[1] ; Berra, Thais Zamboni ^[1] ; Arroyo, Luiz Henrique ^[3] ; Vieira Ramos, Antonio Carlos ^[1] ; de Assis, Ivaneliza Simionato ^[1] ; Rego de Queiroz, Ana Angelica ^[4] ; Alonso, Jonas Boldini ^[5] ; Alves, Josilene Dalia ^[3] ; Popolin, Marcela Paschoal ^[6] ; Yamamura, Mellina ^[3] ; Crispim, Juliane de Almeida ^[3] ; Dessunti, Elma Mathias ^[7] ; Palha, Pedro Fredemir ^[5] ; Chiaraval-Neto, Francisco ^[8] ; Nunes, Carla ^[9] ; Arcencio, Ricardo Alexandre ^[5]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo Ribeirao Preto, Nursing Grad Program Publ Hlth Nursing, Coll Nursing, 3900 Ave Bandeirantes, Sao Paulo - Brazil

^[2] Univ Sao Paulo Ribeirao Preto, Coll Nursing, Maternal Infant & Publ Hlth Nursing Dept, Ave Bandeirantes 3900, BR-14040902 Ribeirao Preto, SP - Brazil

^[3] Univ Sao Paulo Ribeirao Preto, Interinst Doctoral Program Nursing, Coll Nursing, Sao Paulo - Brazil

^[4] Publ Hlth, Natal, RN - Brazil

^[5] Univ Sao Paulo Ribeirao Preto, Coll Nursing, Sao Paulo - Brazil

^[6] Fasipe Sch, Dept Nursing, Cuiaba, Mato Grosso - Brazil

^[7] Univ Estadual Londrina, Dept Nursing, Londrina, Parana - Brazil

^[8] Univ Sao Paulo, Sch Publ Hlth, Dept Epidemiol, Sao Paulo - Brazil

^[9] Nova Univ Lisbon, Natl Sch Publ Hlth, Lisbon - Portugal

• Tipo de documento: Artigo Científico
• Fonte: BMC INFECTIOUS DISEASES; v. 19, JUL 17 2019.
• Citações Web of Science: 0

Resumo

BackgroundTuberculosis (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.MethodsThis 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.ResultsFor 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.ConclusionsThere 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. (AU)

• Processo FAPESP: 15/17586-3 - A mortalidade por tuberculose e sua relação com as iniquidades sociais: um estudo multicêntrico
• Beneficiário: Ricardo Alexandre Arcêncio
• Modalidade de apoio: Auxílio à Pesquisa - Regular