We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
ANÁLISE ESPACIAL DE CASOS PROVÁVEIS DE DENGUE NO MUNICÍPIO DE SÃO LUÍS, MARANHÃO, BRASIL.
- Authors
Vieira da Costa, Flávia Regina; dos Remédios Freitas Carvalho Branco, Maria; Aquino Junior, José; da Silva Brito Costa, Silmery; Soraya Araujo, Adriana; Barros Câmara, Ana Patrícia; do Socorro da Silva, Maria; de Sousa Queiroz, Rejane Christine; Moura da Silva, Antônio Augusto; dos Santos, Alcione Miranda; Ribeiro Rodrigues, Zulimar Márita; da Silva Soeiro, Vanessa Moreira
- Abstract
INTRODUCTION: Dengue is considered one of the main arboviruses in the world, characterized in Brazil by the increase in severe cases and deaths. OBJECTIVE: to perform spatial analysis of probable dengue cases in São Luís - MA. METHODS: Population-based ecological study of probable dengue cases, reported in the Notifiable Diseases Information System (SINAN) in 2015 and 2016, which took place in the city of São Luís - MA. 4,681 probable dengue cases were georeferenced by census sectors, incidence rates were calculated and adjusted using the local empirical Bayesian estimator. The Kernel and Moran Global and Local density estimator was used for spatial analysis. RESULTS: Hot areas (high-density) in the census sectors of the northwest region of the municipality were evidenced through the Kernel density estimator. Incidence rates were adjusted by applying the local empirical Bayesian method, identifying a greater number of sectors with medium and high incidence. From the global Moran index, statistically significant positive spatial autocorrelation was evidenced for the dengue incidence rates (I = 0.69; p <0.001) and for the incidence rates adjusted by the Bayesian method (I = 0.80; p <0.001). According to the local Moran index, clusters of sectors with a high incidence of dengue were identified in areas with high population density in the northeast and northwest regions of the municipality. CONCLUSION: The research demonstrated that Bayesian estimators helped to minimize the problems of underreporting and the influence of population size on census tracts.
- Publication
Arquivos de Ciências da Saúde da UNIPAR, 2022, Vol 26, Issue 3, p693
- ISSN
1415-076X
- Publication type
Article
- DOI
10.25110/arqsaude.v26i3.2022.8792