Modelagem Bayesiana dos tempos entre extrapolações do número de internações hospitalares: associação entre queimadas de cana-de-açúcar e doenças respiratórias

Abstract

SICCHIERI, M. P. L. S. Bayesian modelling of the times between peaks of hospital admissions: association between sugar cane plantation burning and respiratory diseases. 2012. 88p. Dissertation (Master degree) Faculty of Medicine of Ribeirao Preto, University of Sao Paulo, Ribeirao Preto, 2012. Relations between respiratory diseases and air pollution has been the goals of many scientific works, but the relation between respiratory diseases and sugar cane burning still is not well studied in the literature. Pre-harvest burning of sugarcane fields used primarily to get rid of the dried leaves is common in most of Sao Paulo state, Southeast Brazil, especially in the Ribeirao Preto region. The locals of pre-harvest sugar cane burning are detected by surveillance satellites of the CPTEC/INPE (Center of Climate Prediction of the Space Research National Institute). In this work, we consider as our data of interest, the time in days, between peaks numbers of hospitalizations due to respiratory diseases. Different statistical models are assumed to analyze the data of pre-harvest burning of sugar cane fields and their relations with hospitalizations due to respiratory diseases. These new models are considered to analyze data sets in presence or not of covariates, representing the numbers of pre-harvest burning of sugar cane fields. Under a Bayesian approach, we get the posterior summaries of interest using MCMC (Markov Chain Monte Carlo) methods. We also use different existing Bayesian discrimination methods to choose the best model. In our case, considering the data of Ribeirao Preto region, we observed that the models in presence of covariates give accurate inferences and good fit for the data. We concluded that there is evidence of a relationship between respiratory diseases and sugar cane burning, that is, larger numbers of pre-harvest sugar cane burning, implies in larger numbers of hospitalizations due to respiratory diseases. In this case, we also observe small times (days) between extra numbers of hospitalizations.