Fuzzy model to estimate the number of hospitalizations for asthma and pneumonia under the effects of air pollution

Abstract

OBJECTIVE: Predict the number of hospitalizations for asthma and pneumonia associated withexposure to air pollutants in the city of São José dos Campos, São Paulo State.METHODS: This is a computational model using fuzzy logic based on Mamdani’s inferencemethod. For the fuzzification of the input variables of particulate matter, ozone, sulfur dioxideand apparent temperature, we considered two relevancy functions for each variable with thelinguistic approach: good and bad. For the output variable number of hospitalizations for asthmaand pneumonia, we considered five relevancy functions: very low, low, medium, high and veryhigh. DATASUS was our source for the number of hospitalizations in the year 2007 and the resultprovided by the model was correlated with the actual data of hospitalization with lag from zeroto two days. The accuracy of the model was estimated by the ROC curve for each pollutant andin those lags.RESULTS: In the year of 2007, 1,710 hospitalizations by pneumonia and asthma were recordedin São José dos Campos, State of São Paulo, with a daily average of 4.9 hospitalizations (SD = 2.9).The model output data showed positive and significant correlation (r = 0.38) with the actualdata; the accuracies evaluated for the model were higher for sulfur dioxide in lag 0 and 2 andfor particulate matter in lag 1.CONCLUSIONS: Fuzzy m odeling p roved a ccurate f or t he p ollutant e xposure e ffects a ndhospitalization for pneumonia and asthma approach