Leptospirose no Rio Grande do Sul: determinantes, distribuição espacial e temporal entre 2010 e 2019

Abstract

This thesis is composed by two scientific articles, one referring to the social determinants of leptospirosis, and the other to the spatial and temporal distribution and the prediction of leptospirosis cases in the state of Rio Grande do Sul, between 2010 and 2019. Objective is to evaluate the incidence of leptospirosis in the Rio Grande do Sul, Brazil in the period from 2010 to 2019. To develop this study, the database generated by the National System of Notifiable Diseases (SINAN) was analyzed. During the period under analysis, 4,760 cases of human leptospirosis were confirmed, of which 238 resulted in death. The social determinants related to the occurrence of the disease were being a male (87.1%), aged between 20 and 50 years (58.2%), with incomplete elementary education (56.3%) and having white skin color (88.6%). Lethality was higher in darked skin people, with lower level of education. The risk factors statistically associated with death were previous contact with septic tanks or sewers, streams or lakes, direct contact with rodents, farming, grain storage, and garbage and debris. . The health vulnerability index was associated with a higher incidence rate of the disease, especially in municipalities located in the central region of Rio Grande do Sul. In the spatial distribution analysis of leptospirosis, it was observed that the municipalities located in the central region of the state presented higher incidence rates, and the metropolitan region of Porto Alegre was characterized as a cluster of high incidence, with high risk of leptospirosis transmission. A decreased trend of the occurrence of the disease and a seasonality were identified in the months of January, February, March and April. In the time series modeling, the SARIMA model (1,2,1) (1.0.1) was the most suitable, indicating a decrease in incidence in the year of 2020. This technique can be used as a tool, allowing for predictions on the number of future cases.