Análise espaço-temporal da malária no estado de Mato Grosso no período de 2003 a 2009

• Ano de defesa: 2013
• Autor(a) principal: Bacani, Dirlene Ada
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Mato Grosso; Brasil; Instituto de Ciências Humanas e Sociais (ICHS); UFMT CUC - Cuiabá; Programa de Pós-Graduação em Geografia
• Palavras-chave em Português: Malária; Análise espacial; Geoprocessamento; CNPQ::CIENCIAS HUMANAS::GEOGRAFIA; Malaria; Spatial analysis; Geoprocessing
• Link de acesso: http://ri.ufmt.br/handle/1/1252

This work aims to identify spatial patterns of occurrence of malaria in Mato Grosso during the years 2003 to 2009, as well as identifying the possible relationship of the risk of disease occurrence with environmental factors, using spatial analysis and logistic regression. The work begins with a descriptive analysis of the epidemiology of malaria occurrence in the study area. Subsequently held exploratory spatial analysis to detect clusters through software SatScan, which were identified spatial clusters of low, medium and high risk, and also space-time clusters. The municipalities of the microregion of Aripuanã, Northwest of State, presented themselves as high-risk clusters in all years of study. Then, to perform the multivariate logistic regression model was constructed a table with ten environmental variables aggregated by municipality (independent factors), taking as dependent variables the relative risk generated by SatScan and API (Annual Parasitic Index). The relative risk did not show satisfactory performance indicators for different cutoff values assigned. The variables that were significant in most years for API (Annual Parasitic Index) were: precipitation, total deforested area by municipality and the employment rate andincome. The variables 'precipitation' and 'employment rate and income' showed positive coefficient, indicating that the higher their rates, higher API of malaria. The variable 'total area deforested', on the contrary, had a negative coefficient, indicating that lower as the deforested area in the municipality, greater is the Annual Parasitic Index of malaria.