Abstract
Objective: to assess the health impact of air pollution in Brazilian cities and to select effect indicators for surveillance purposes. Methods: based on hospital inpatient and fine particulate matter (PM10) data, a study was performed using time series models, in particular Generalized Additive Models with Poisson regression to estimate the impact of air pollution on health. We analyzed hospital admissions for total respiratory illnesses (TRI), admissions for respiratory diseases in children under 5 years old (RDC) and admissions for cardiovascular diseases in adults (CVD) in 21 cities. The best effect indicator was selected based on the proportion of statistically significant results. Results: we found a significant relationship in 81% of locations for TRI, 89% for RDC and 50% for CVD. Conclusions: significant relationships were found for most cities. RDC were considered the best effect indicator, closely followed by TRI. Both can therefore be used for surveillance purposes.
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