• Andrea Winquist, Ellen Kirrane, Mitch Klein, Matthew Strickland, Lyndsey A Darrow, Stefanie Ebelt Sarnat, Katherine Gass, James Mulholland, Armistead Russell, and Paige Tolbert.
• aDepartment of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA; bNational Center for Environmental Assessment, Environmental Protection Agency, Durham, NC; cDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA; and dSchool of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA.
• Epidemiology. 2014 Sep 1;25(5):666-73.

BackgroundBecause ambient air pollution exposure occurs as mixtures, consideration of joint effects of multiple pollutants may advance our understanding of the health effects of air pollution.MethodsWe assessed the joint effect of air pollutants on pediatric asthma emergency department visits in Atlanta during 1998-2004. We selected combinations of pollutants that were representative of oxidant gases and secondary, traffic, power plant, and criteria pollutants, constructed using combinations of criteria pollutants and fine particulate matter (PM2.5) components. Joint effects were assessed using multipollutant Poisson generalized linear models controlling for time trends, meteorology, and daily nonasthma upper respiratory emergency department visit counts. Rate ratios (RRs) were calculated for the combined effect of an interquartile range increment in each pollutant's concentration.ResultsIncreases in all of the selected pollutant combinations were associated with increases in warm-season pediatric asthma emergency department visits (eg, joint-effect RR = 1.13 [95% confidence interval = 1.06-1.21] for criteria pollutants, including ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, and PM2.5). Cold-season joint effects from models without nonlinear effects were generally weaker than warm-season effects. Joint-effect estimates from multipollutant models were often smaller than estimates based on single-pollutant models, due to control for confounding. Compared with models without interactions, joint-effect estimates from models including first-order pollutant interactions were largely similar. There was evidence of nonlinear cold-season effects.ConclusionsOur analyses illustrate how consideration of joint effects can add to our understanding of health effects of multipollutant exposures and also illustrate some of the complexities involved in calculating and interpreting joint effects of multiple pollutants.

29 keywords...

hide…