Dynamic Regression Model for Evaluating the Association Between Atmospheric Conditions and Deaths due to Respiratory Diseases in São Paulo, Brazil

Modelo Dinâmico de Regressão Para Avaliar a Associação Entre as Condições Atmosféricas e as Mortes por Doenças Respiratórias em São Paulo, Brasil

Abstract

The article reports the modeling of mortality due to respiratory diseases emanating from atmospheric conditions, capturing significant associations and verifying the ability of stochastic modeling to predict deaths arising from the relationship between weather conditions and air pollution. The statistical methods used in the analysis were cross-correlation and pre-whitening, in addition to dynamic regression modeling combining the dynamics of time series and the effect of explanatory variables. The results show there are significant associations between mortality and sulfur dioxide, air temperature, atmospheric pressure, relative humidity, and autoregressive structure. The cross-correlations captured significant lags between atmospheric variables and deaths, of two months for SO₂ and relative humidity, eleven months for PM₁₀, seven months for O₃, and eight months for air temperature and the cross-correlation without lag with NO₂. With CO variables, precipitation and atmospheric pressure, cross-correlations were not detected. Stochastic modeling showed that deaths due to respiratory diseases can be predicted from the combination of meteorological and air pollution variables, especially considering the existing trend and seasonality.

Keywords:
forecasting; mortality; meteorology; air pollution