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COELHO-ZANOTTI. Statistical analysis aiming at the predictability of respiratory diseases internment based on meteorological conditions at Sao Paulo city (2007). 196f. Thesis (Doctoral): Instituto de Astronomia Geofisica e Ciencias Atmosfericas da Universidade de Sao Paulo, 2007. The meteorological condition knowledge can provide society prejudice prevention regarding human and material resources. Therefore, the aim of this study the statistical modeling in order to prevent internment of morbidity based on air pollution and meteorological variability. The whole 1997 to 2000 at the city of Sao Paulo. The morbidity data was divided in to three categories: AVAS (upper respiratory airway diseases), AVAI (lower respiratory airway diseases) and IP (Influenza and Pneumonia). These data were obtained from Brazilian Heath Ministry. Air pollution data were obtained from CETESB (Environmental agency) and meteorological data from Parque Estadual das Fontes do Ipiranga. Thermal comfort indexes were also used based on meteorological variables. Poisson regression models as well as Principal Component (PC) models were used in order to evaluate the data through statistical methodology. These models were nominated MBCS (Brazilian Climate and Health Model). Scores from PC statistical analysis were also used in order to compare to multiple regression models. As the first results, AVAS modeling presents association with SO2, CO (both without time lag) and the TEv4a biometeorological index (with 4 days time lag). SO2 presents interesting result due to the fact that it is below the recommended standard, but it still causes AVAS morbidity. Concerning AVAI results, the variables which explain the morbidity were the pollutants MP10, O3 (both without lag) and TEv4 (with 3 days lag). Regarding to the skill of the models, AVAS model presents a 15% average error; AVAI model, 30% and IP model, 44%, during year of 2001. PC analysis corroborated the Poisson models. Regarding PC more weight for AVAS was pollutants. Already AVAI and IP more weight was biometeorological indexes and meteorological variables. The risk results used scores was similar to the MMRP. However for AVAI and IP, the scores of the pollutants and scores of the indexes should be used individually. These results indicate these models can be used as a forecasting internment program, contributing on the public and media decisions, avoiding economical and human unnecessary wastes. Key-words: Poisson regression, respiratory diseases, meteorology variables, bimeteorological indexes, air pollution.

Uma análise estatística com vistas a previsibilidade de internações por doenças respiratórias em função das condições meteorotrópicas na cidade de São Paulo.