Relationship of long-term air pollution exposure with asthma and rhinitis in Italy: an innovative multipollutant approach

Background: air pollution is a complex mixture; novel multipollutant approaches could help understanding the health effects of multiple concomitant exposures to air pollutants. Aim: to assess the relationship of long-term air pollution exposure with the prevalence of respiratory/allergic symptoms and diseases in an Italian multicenter study using single and multipollutant approaches. Methods: 14420 adults living in 6 Italian cities (Ancona, Pavia, Pisa, Sassari, Turin, Verona) were investigated in 2005-2011 within 11 different study cohorts. Questionnaire information about risk factors and health outcomes was collected. Machine learning derived mean annual concentrations of PM10, PM2.5, NO2 and mean summer concentrations of O3 (μg/m3) at residential level (1-km resolution) were used for the period 2013-2015. The associations between the four pollutants and respiratory/allergic symptoms/diseases were assessed using two approaches: a) logistic regression models (single-pollutant models), b) principal component logistic regression models (multipollutant models). All the models were adjusted for age, sex, education level, smoking habits, season of interview, climatic index and included a random intercept for cohorts. Results: the three-year average (± standard deviation) pollutants concentrations at residential level were: 20.3 ± 6.8 μg/m3 for PM2.5, 29.2 ± 7.0 μg/m3 for PM10, 28.0 ± 11.2 μg/m3 for NO2, and 70.9 ± 4.3 μg/m3 for summer O3. Through the multipollutant models the following associations emerged: PM10 and PM2.5 were related to 14-25% increased odds of rhinitis, 23-34% of asthma and 30-33% of night awakening; NO2 was related to 6-9% increased odds of rhinitis, 7-8% of asthma and 12% of night awakening; O3 was associated with 37% increased odds of asthma attacks. Overall, the Odds Ratios estimated through the multipollutant models were attenuated when compared to those of the single-pollutant models. Conclusions: this study enabled to obtain new information about the health effects of air pollution on respiratory/allergic outcomes in adults, applying innovative methods for exposure assessment and multipollutant analyses.