Comparison of the Count Regression Models in Evaluation of the Effects of Hazelnut Harvest Season Variations on Pulmonary Aspergillus

Abstract

Pulmonary aspergillosis has recently emerged as a worldwide health care problem especially in patients with underlying lung disease. The objective of this study was to compare the Poisson and COM-Poisson regression models and to find the best fitted model for determining the effect of hazelnut harvest season on pulmoner aspergillosis. The data obtained from the state hospital of our cityin the time period of two years, from September, 2012 to August, 2014. A retrospective study was conducted. Respiratory specimens which showed repeated isolation of Aspergillus were included in the study however only one of the samples was analysed. Cases were classified according to revised definitions given by European Organization for Research and Treatment of Cancer/Invasive Mycosis Study Consensus Group (EORT/MSG). Culture positive 36 patients were detected from 3457 patients. Poisson and Conway-Maxwell- Poisson (COM-Poisson) regression models were compared to determine the best fitted model for identifying the number of new pulmonary aspergillosis cases in hazelnut harvest season. To describe the best fitted model of count data, dispersion, deviance and Akaike Information Criteria (AIC) test statistics were used. Based on statistical test for dispersion, the under-dispersion was found non-significant. This results clearly indicate that Poisson regression model is more approtiate for pulmonary aspergillosis data when compared to COM-Poisson regression model. Deviance and AIC values also confirm this result. Poisson regression model and COM- Poisson regression model were compared with statistical tests. According to statistical tests Poisson regression model was found to be the best fit model for pulmonary aspergillosis data.