A Multivariate statistical procedure for investigating relationschips between PM 10 levels and Obstructive Sleep Apnea Syndrome in an urban area

A Multivariate statistical procedure for investigating relationschips between PM 10 levels and Obstructive Sleep Apnea Syndrome in an urban area Giuseppina Anna Giorgio1*, Mariagrazia D’Emilio2, Flaminio Mormile3, Maria Ragosta1, Ivano Salimbene3 1Engineering School, University of Basilicata, Potenza, 85100, Italy 2Institute of Methodologies for Environmental Analysis, IMAA-CNR, Tito Scalo (PZ),85050, Italy 3Dept. Pulmonary Medicine, A. Gemelli University Polyclinic, Rome, 00168, Italy *giusygiorgio_a@libero.it The linkage between high atmospheric particulate matter (PM) concentrations and respiratory and cardiovascular diseases is extensively studied from an epidemiological point of view. In particular, respiratory diseases appear to be increasing worldwide, affecting in particular subjects living in urban areas. Respirable particulate matter could induce adverse effects on airways both directly, causing inflammation and airway hyperresponsiveness, and indirectly, acting as a carrier of other pollutants or allergens. These characteristics could contribute to explain the increase of cases of sensitization to pollen allergens in urban areas. [1-3]. Here we present the application of a multivariate statistical procedure aimed to investigating the relationships between outdoor concentrations of PM10 and occurrence of a particular respiratory disease: the Obstructive Sleep Apnea Syndrome (OSAS). Our database includes daily atmospheric concentrations of PM10 measured in eleven sampling stations displaced in Rome urban area from 2008 to 2011 (ArpaLazio data) and the results of the diagnostic test called PolySomnoGraphy (PSG) performed on about 400 patients by Department of Pulmonary Medicine of Gemelli University Polyclinic in Rome. We applied a multivariate procedure based on cluster analysis and PCA [4] in order to put in evidence the correlation structure of our database. For performing our analysis we also took into account qualitative variables related to risk factors. 1. Karanasiou A., Moreno N., Moreno T., Viana M., de Leeuw F., Querol X. (2012). Health effects from Sahara dust episodes in Europe: literature review and research gap. Environment International 47, 107- 114. 2. Ostro B., Tobias A., Querol X., Alastuey A., Amato F., Pey J., Pérez N., Sunyer J. (2011). The effects of particulate matter sources on daily mortality: a case-crossover study of Barcelona, Spain Environmental Health Perspectives 119, 1781-1787 (2011) 3. D’Amato G., Baena-Cagnani C. E., Cecchi L., Annesi-Maesano I., Nunes C., Ansotegui I., D’Amato M., Liccardi G., Sofia M., Canonica W. G. (2013). Climate change, air pollution and extreme events leading to increasing prevalence of allergic respiratory diseases Multidisciplinary Respiratory Medicine, doi:10.1186/2049-6958-8-12. 4. Ragosta M., Di Leo S. (2011). Multivariate indices for analysing correlation structures in environmental datasets. Proceeding of 58th World Statistics Congress of the International Statistical Institute (ISI), Dublin, Ireland, CPS008:2-01.