Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/58778
Título: Modelagem GAMLSS espaço temporal da incidência de esquistossomose na região central do Estado de Minas Gerais, Brasil
Palavras-chave: Distribuição espacial
Biomphalaria
Análise de regressão
Análise de dados secundários
Residence characteristics
Regression analysis
Secondary data analysis
Data do documento: 2023
Editor: Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz
Citação: NOGUEIRA, D. A. et al. Modelagem GAMLSS espaço temporal da incidência de esquistossomose na região central do Estado de Minas Gerais, Brasil. Cadernos de Saúde Publica, [S.l.], v. 39,3 n. 6, 2023.
Resumo: In Brazil, millions of people live in areas with risk of schistosomiasis, a neglected chronic disease with high morbidity. The Schistosoma mansoni helminth is present in all macroregions of Brazil, including the State of Minas Gerais, one of the most endemic states. For this reason, the identification of potential foci is essential to support educational and prophylactic public policies to control this disease. This study aims to model schistosomiasis data based on spatial and temporal aspects and assess the importance of some exogenous socioeconomic variables and the presence of the main Biomphalaria species. Considering that, when working with incident cases, a discrete count variable requires an appropriate modeling, the GAMLSS modeling was chosen since it jointly considers a more appropriate distribution for the response variable due to zero inflation and spatial heteroscedasticity. Several municipalities presented high incidence values from 2010 to 2012, and a downward trend was observed until 2020. We also noticed that the distribution of incidence behaves differently in space and time. Municipalities with dams presented risk 2.25 times higher than municipalities without dams. The presence of B. glabrata was associated with the risk of schistosomiasis. On the other hand, the presence of B. straminea represented a lower risk of the disease. Thus, the control and monitoring of B. glabrata snails is essential to control and eliminate schistosomiasis; and the GAMLSS model was effective in the treatment and modeling of spatio-temporal data.
URI: http://repositorio.ufla.br/jspui/handle/1/58778
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