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Air pollutants and low birth weight


Exposure to air pollutants has to be associated with several deleterious effects on people's health, even at levels considered safe by environmental legislation. Among the consequences harmful to health, is the low birth weight, which has been the target of several epidemiological studies in order to identify its risk factors in an attempt to develop interventions that can reduce these factors and prevent their occurrence, including those related to the environment, such as air pollution arising from outbreaks of fires in the Brazilian Amazon region. Therefore, this study aims to estimate the association between air pollutants and low birth weight in the city of Cuiaba - MT.This is a retrospective cohort study. The population is composed by living in the city of Cuiaba-MT born in the period between 2012 and 2013. The data of live births are obtained from the Live Birth Information System (SINASC) of the Department of the Unified Health System ( DATASUS). The value of birth weight will be dichotomized into low birth weight (less than 2,500g) and proper weight (2500 g or more).The extent of exposure to air pollution (independent variable) will be obtained from the concentration of air pollutants (CO and PM2.5) and fire outbreaks.The average daily concentration of these pollutants will be estimated from an operational monitoring system in real time, using 3D transport model online CATT-BRAMS (Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System ) coupled to a model emissions. In addition to the CATT-BRAMS, data on fire outbreaks will be obtained from the Environmental Information System (SISAM).The exposure assessment will be based on the date of birth to obtain the average concentration of pollutants in the first and third trimesters of pregnancy. The data of live births, the mean concentration of pollutants (CO and PM2.5) and fire outbreaks will be organized in a single database.Statistical analyzes will be performed by logistic regression Univariate and multiple.The following software will be used: Excel and Stata 9.0. (AU)

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