title: Association of daily asthma emergency department visits and hospital admissions with ambient air pollutants among the pediatric Medicaid population in Detroit: time-series and time-stratified case-crossover analyses with threshold effects.
abstract: BACKGROUND
  Asthma morbidity has been associated with ambient air pollutants in time-series and case-crossover studies. In such study designs, threshold effects of air pollutants on asthma outcomes have been relatively unexplored, which are of potential interest for exploring concentration-response relationships.
  
  
  METHODS
  This study analyzes daily data on the asthma morbidity experienced by the pediatric Medicaid population (ages 2-18 years) of Detroit, Michigan and concentrations of pollutants fine particles (PM2.5), CO, NO2 and SO2 for the 2004-2006 period, using both time-series and case-crossover designs. We use a simple, testable and readily implementable profile likelihood-based approach to estimate threshold parameters in both designs.
  
  
  RESULTS
  Evidence of significant increases in daily acute asthma events was found for SO2 and PM2.5, and a significant threshold effect was estimated for PM2.5 at 13 and 11 μg m(-3) using generalized additive models and conditional logistic regression models, respectively. Stronger effect sizes above the threshold were typically noted compared to standard linear relationship, e.g., in the time series analysis, an interquartile range increase (9.2 μg m(-3)) in PM2.5 (5-day-moving average) had a risk ratio of 1.030 (95% CI: 1.001, 1.061) in the generalized additive models, and 1.066 (95% CI: 1.031, 1.102) in the threshold generalized additive models. The corresponding estimates for the case-crossover design were 1.039 (95% CI: 1.013, 1.066) in the conditional logistic regression, and 1.054 (95% CI: 1.023, 1.086) in the threshold conditional logistic regression.
  
  
  CONCLUSIONS
  This study indicates that the associations of SO2 and PM2.5 concentrations with asthma emergency department visits and hospitalizations, as well as the estimated PM2.5 threshold were fairly consistent across time-series and case-crossover analyses, and suggests that effect estimates based on linear models (without thresholds) may underestimate the true risk.
authors.affiliation: Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029, USA.
authors.forename: Shi
authors.initials: S
authors.lastname: Li
authors.forename: Stuart
authors.initials: S
authors.lastname: Batterman
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Wasilevich
authors.forename: Robert
authors.initials: R
authors.lastname: Wahl
authors.forename: Julie
authors.initials: J
authors.lastname: Wirth
authors.forename: Feng-Chiao
authors.initials: FC
authors.lastname: Su
authors.forename: Bhramar
authors.initials: B
authors.lastname: Mukherjee
mesh_terms: D000293:Adolescent; D000393:Air Pollutants / Q000032:analysis / Q000633:toxicity*; D001249:Asthma / Q000628:therapy*; D002648:Child; D002675:Child, Preschool; D018592:Cross-Over Studies; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D006801:Humans; D008484:Medicaid; D008824:Michigan; D010316:Particle Size; D010343:Patient Admission; D014481:United States
chemical_list: D000393:Air Pollutants