PPGTA

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Navegando PPGTA por Assunto "Agrotóxicos"

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    Análise espacial da exposição humana aos agrotóxicos e a mortalidade por câncer na microrregião Sul do estado de Mato Grosso, Brasil
    (Universidade Federal de Mato Grosso, 2019-09-13) Pimentel, Cauê Felipe; Olinda, Ricardo Alves de; 029.035.004-28; http://lattes.cnpq.br/7767223263366578; Olinda, Ricardo Alves de; 029.035.004-28; http://lattes.cnpq.br/7767223263366578; Barbosa, Domingos Sávio; 702.907.921-34; http://lattes.cnpq.br/6896725721269796; Santos, Débora Aparecida da Silva; 707.499.571-15; http://lattes.cnpq.br/9193787723474678
    The use of pesticides represents a major public health problem in developing countries, especially those with economies based on agribusiness such as Brazil. It is inherent the possibility of presence in the environment in general, exposing humans to direct and indirect contact with these substances, which through the conceptual model of risk, it is possible to establish the correlation between agricultural production, human exposure to pesticides and cancer. This study aimed to perform the spatial analysis of human exposure to pesticides and to establish a correlation with cancer mortality in the Mato Grosso southern micro region. This is an ecological, cross-sectional epidemiological study through spatial analysis of indicators of agricultural productivity (percentage of planted area by soybean, corn and cotton) and the correlation with health indicators (mortality and cancer mortality rate) and socioeconomic (Population Density, HDI and Gini Index) from 2006 to 2016. In the spatial analysis an exploratory analysis was performed to characterize the distribution of cancer mortality in the study micro region. Next spatial statistical techniques, spatial autocorrelation, Geary C statistics, Moran Index and Local Indicators of Spacial Association (LISA), Global Moral Index, and Moran scattering diagram were used. To verify the best explanatory model of variables association, four regression models were applied: the classical regression model, the autoregressive spatial model, the spatial error model and the Durbin spatial model, using Akaike’s information criterion to select the model that best fits the data adjustment. The analyzes were performed using the R statistical software. The results showed the highest significance in the Spearman correlation test at 5% for the percentage of cotton planted area, followed by the percentage area planted by soybean as a function of cancer mortality. Spatial regression analysis allowed a statistically significant correlation between the agricultural productivity variable (percentage of soybean planted area) and the socioeconomic variable (population density) with cancer mortality in the micro region under study. Therefore, the use of epidemiology and spatial statistics contributes to the establishment of the correlation between the environment and exposure to pesticides and provides useful information in environmental management combined with the processes of producing a green economy and in the health management and surveillance process.