Distribuição espaço-temporal da leishmaniose tegumentar no estado de São Paulo, com ênfase na região do Oeste Paulista, no período de 1998 a 2020

• Ano de defesa: 2022
• Autor(a) principal: SINATURA, Maíra Antunes
• Orientador(a): Abreu, Marilda Aparecida Milanez Morgado de
• Banca de defesa: Santarém, Vamilton Alvares , D'Andrea, Lourdes Aparecida Zampieri
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade do Oeste Paulista
• Programa de Pós-Graduação: Mestrado em Ciências da Saúde
• Departamento: Mestrado em Ciências da Saúde
• País: Brasil
• Palavras-chave em Português: Leishmaniose Cutânea; Leishmaniose Mucocutânea; Análise Espacial; Desenvolvimento Humano; Epidemiologia
• Palavras-chave em Inglês: Cutaneous Leishmaniasis; Mucocutaneous Leishmaniasis; Spatial Analysis; Human Development; Epidemiology
• Área do conhecimento CNPq: CIENCIAS DA SAUDE::MEDICINA
• Link de acesso: http://bdtd.unoeste.br:8080/jspui/handle/jspui/1502

Introduction: Cutaneous leishmaniasis (CL) is an infectious disease with wide worldwide distribution. In Brazil, it is present in all units of the federation. In the state of São Paulo, it has an endemic character with a higher incidence on the coast and west of São Paulo. It is considered the most neglected among neglected diseases, especially affecting the poorest. The human development index (HDI) is an indicator that can assess the development of a country and be associated with the dispersion of CL. Spatial geography is an important tool to assist managers in taking more efficient measures to control diseases such as leishmaniasis. There are few updated studies evaluating clinical, environmental and socioeconomic aspects of CL and its geographic distribution. Objective: To analyze the spatio-temporal distribution pattern of reported cases of TL, by probable location of infection, in the state of São Paulo, with emphasis on the municipalities of the Regional Health Care Network 11 of Presidente Prudente (RRAS 11) in the period of 1998 to 2020 and analyze its possible correlation with the HDI. Methods: CL cases were obtained from the data available at SINAN-DATASUS. The cumulative incidence and spatio-temporal distribution of CL were determined. The number of cases and the mean incidence of CL in Oeste Paulista and in the state of São Paulo were determined. Thematic maps generated in ArcGIS were prepared to demonstrate the spatial distribution pattern of the CL in the state of São Paulo, with emphasis on RRAS 11 of Presidente Prudente. The mapping of disease identification clusters was performed using the I-Moran method and the Local Moran Index (LISA). The HDI of the municipalities of RRAS 11 was obtained from the Fundação Sistema Estadual de Análises de Dados (SEADE) and its possible correlation with the CL determined. Results: It was found that in the period studied, the state of São Paulo and RRAS 11 had an average incidence of 0.69/100,000 inhabitants per year and 1.67/100,000 inhabitants per year, respectively. The linear regression test did not indicate a significant correlation between the incidence of CL in RRAS 11 and the HDI. Conclusion: The precarious socioeconomic conditions are not determinant for the increased incidence of CL in the Oeste Paulista region. It is suggested that the areas highlighted as permanent hotspots be submitted to future research seeking to identify other factors that may be decisive for the maintenance of this increased incidence.