Risk Factors for Asthma Hospitalization among Adults and Elderly.

Joint effect of biological (pollen) and chemical air pollutants on asthma emergency room (ER) visits was analyzed for Szeged region of Southern Hungary. Our database of a nine-year period (1999-2007) includes daily number of asthma emergency room (ER) visits, and daily mean concentrations of CO, PM10, NO, NO2 , O3 and SO2 , furthermore two pollen variables (Ambrosia and total pollen excluding Ambrosia), as well. The analysis was performed for ER visits of asthma bronchiale using two age groups (adults and the elderly) of males and females for three seasons. Factor analysis was performed in order to clarify the relative importance of the pollutant variables affecting asthma ER visits. Asthma ER visits denote notably stronger associations with the pollutants in adult male than in adult female patients both for the pollen season of Ambrosia and the pollen-free season. Furthermore, adults are substantially more sensitive to severe asthma attack than the elderly for the season of total pollen excluding Ambrosia pollen. The joint effect of the chemical and pollen variables is the highest for the asthma ER cases in the pollen season of Ambrosia, basically due to the extra impact of the total pollen excluding Ambrosia pollen and partly due to Ambrosia pollen. A nonparametric regression technique was applied to discriminate between events of ER visit – no ER visit using pollen and chemical pollutants as explaining variables. Based on multiple correlations, the strongest relationships between ER visits and pollutants are observed during the pollen-free season. The elderly group with asthma bronchiale is characterized by weaker relationships between ER visits and pollutants compared to adults. Ratio of the number of correct decisions on the events of ER visit – no ER visit is lowest for the season of total pollen excluding Ambrosia pollen. Otherwise, similar conclusions hold as those received by multiple correlations.</p

A wavelet-based mode decomposition

We propose a wavelet-based method for analyzing non-stationary data. The idea, inspired by the empirical mode decomposition, is to decompose a data set into a finite number of components, well separated in the time-frequency plane, plus a residue, such that each component has a zero mean and is associated to one frequency only. When applied to climatic data, this method gives interesting results