Pediatric emergency department visits and ambient Air pollution in the U.S. State of Georgia: a case-crossover study

Abstract

Background: Estimating the health effects of ambient air pollutant mixtures is necessary to understand the risk of real-life air pollution exposures.

Methods: Pediatric Emergency Department (ED) visit records for asthma or wheeze (n = 148,256), bronchitis (n = 84,597), pneumonia (n = 90,063), otitis media (n = 422,268) and upper respiratory tract infection (URI) (n = 744,942) were obtained from Georgia hospitals during 2002-2008. Spatially-contiguous daily concentrations of 11 ambient air pollutants were estimated from CMAQ model simulations that were fused with ground-based measurements. Using a case-crossover study design, odds ratios for 3-day moving average air pollutant concentrations were estimated using conditional logistic regression, matching on ZIP code, day-of-week, month, and year.

Results: In multipollutant models, the association of highest magnitude observed for the asthma/wheeze outcome was with "oxidant gases" (O₃, NO₂, and SO₂); the joint effect estimate for an IQR increase of this mixture was OR: 1.068 (95% CI: 1.040, 1.097). The group of "secondary pollutants" (O₃ and the PM_2.5 components SO₄^2-, NO^3-, and NH⁴⁺) was strongly associated with bronchitis (OR: 1.090, 95% CI: 1.050, 1.132), pneumonia (OR: 1.085, 95% CI: 1.047, 1.125), and otitis media (OR: 1.059, 95% CI: 1.042, 1.077). ED visits for URI were strongly associated with "oxidant gases," "secondary pollutants," and the "criteria pollutants" (O₃, NO₂, CO, SO₂, and PM_2.5).

Conclusions: Short-term exposures to air pollution mixtures were associated with ED visits for several different pediatric respiratory diseases.

Keywords: Air pollution; CMAQ; Multipollutant model; Pediatric Emergency Department Visits.

Fig. 1

Joint effects of pollutant combinations estimated from multipollutant models. OR estimates comparing air pollutant concentrations at the 75^th percentile with the 25^th percentile for each air pollutant combination from multipollutant models without interaction (triangle), each air pollutant combination from multipollutant models with interactions (circle), and from multipollutant models (no interaction model) including future (tomorrow’s) air pollutant combinations when controlling the 3-day moving average (lag 0-1-2) of current air pollution (cross) were shown