Association between Air Pollutants and Hospitalizations for Cardiovascular Diseases: Time-Series Analysis in S\~ao Paulo, 2010-2019

Abstract

Cardiovascular diseases (CVD) remain one of the leading causes of hospitalization in Brazil. Exposure to air pollutants such as PM$_{10}$ $\mu$m, NO$_2$, and SO$_2$ has been associated with the worsening of these diseases, especially in urban areas. This study evaluated the association between the daily concentration of these pollutants and daily hospitalizations for acute myocardial infarction and cerebrovascular diseases in S\~ao Paulo (2010-2019), using generalized additive models with a lag of 0 to 4 days. Two approaches for choosing the degrees of freedom in temporal smoothing were compared: based on pollutant prediction and based on outcome prediction (hospitalizations). Data were obtained from official government databases. The modeling used the quasi-Poisson family in R software (v. 4.4.0). Models with exposure-based smoothing generated more consistent estimates. For PM10{\mu}m, the cumulative risk estimate for exposure was 1.08%, while for hospitalization, it was 1.20%. For NO$_2$, the estimated risk was 1.47% (exposure) versus 1.33% (hospitalization). For SO$_2$, a striking difference was observed: 7.66% (exposure) versus 14.31% (hospitalization). The significant lags were on days 0, 1, and 2. The results show that smoothing based on outcome prediction can generate bias, masking the true effect of pollutants. The appropriate choice of df in the smoothing function is crucial. Smoothing by the pollutant series was more robust and accurate, contributing to methodological improvements in time-series studies and reinforcing the importance of public policies for pollution control.