Análise especial e espaço-temporal da ocorrência de hanseníase em São José do Rio Preto, SP, Brasil entre 1998 e 2007

• Ano de defesa: 2012
• Autor(a) principal: Cury, Maria Rita de Cassia Oliveira
• Orientador(a): Chiaravalloti Neto, Francisco
• Banca de defesa: Antonio, João Roberto , Zanetta, Dirce Maria Trevisan , Cavasini, Carlos Eugênio , Netto, Antonio Ruffino
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Faculdade de Medicina de São José do Rio Preto
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciências da Saúde::123123123123::600
• Departamento: Medicina Interna; Medicina e Ciências Correlatas::123123123123::600
• País: BR
• Palavras-chave em Português: Hanseníase; Situação Epidemiológica; Geoprocessamento; Análise Espacial
• Palavras-chave em Inglês: Hansen Disease; Leprosy Epidemiological Situation; Geoprocessing; Spatial Analysis; Leprosy
• Palavras-chave em Espanhol: Lepra; Análisis Espacial
• Área do conhecimento CNPq: CNPQ::CIENCIAS DA SAUDE::SAUDE COLETIVA::EPIDEMIOLOGIA::123123123123::600
• Link de acesso: http://bdtd.famerp.br/handle/tede/148

Objectives: To evaluate the relationship between Hansen´s disease and socioeconomic and demographic levels identifying clusters of leprosy cases in space and space-time, in São José do Rio Preto, SP. Material and Methods: Leprosy cases between 1998 and 2007 were geocoded and incidence rates were calculated by census sectors. It was obtained through the technique of main component analysis, a score for socioeconomic classification. The use of ordinary Kriging method allowed the construction of thematic maps for viewing the spatial distribution of leprosy incidence, socioeconomic level and demographic density. Spatial clusters and space-time were identified through the SaTScan program. Three databases were used: the cases, the population of each census sector and the Cartesian coordinates of plane centroids of each census sector. Through the discrete Poisson model, clusters purely spatial and spatial-temporal clusters were identified. Results: While the incidence for the whole city was 10.4 cases per 100,000 inhabitants per year, between 1998 and 2007, the incidences were not homogeneous within the municipality, with values ranging from 0 to 26.9 cases per 100,000 inhabitants per year. There was a high agreement between areas with higher values of incidence and lower socioeconomic levels and among those with lower incidences and higher socioeconomic levels, but the relationship between disease and demographic density was not identified. No relationship between location of patients residence and location of health services was observed. The occurrence of only one purely spatial significant cluster and only one spatiotemporal significant cluster were observed. They were located in the North region its surroundings of the city. Conclusions: The spatial analysis techniques used in this study, in addition to providing important information for the planning of surveillance and control of leprosy, allowed the identification of deficient areas of the municipality, i.e., with higher risk for the disease. With this information, the public authorities can monitor the occurrence of leprosy cases in their territory to identify where and when to prioritize the adoption of surveillance actions. Therefore, they could visualize the reduction of risk of the illness as well as political-administrative measures to minimize the effects of social inequality and raise living standards, hygiene and education of the population, resulting in the reduction of the magnitude of the disease.