title: Spatio-temporal ozone variation in a case-crossover analysis of childhood asthma hospital visits in New York City.
abstract: BACKGROUND
  Childhood asthma morbidity has been associated with short-term air pollution exposure. To date, most investigations have used time-series models, and it is not well understood how exposure misclassification arising from unmeasured spatial variation may impact epidemiological effect estimates. Here, we develop case-crossover models integrating temporal and spatial individual-level exposure information, toward reducing exposure misclassification in estimating associations between air pollution and child asthma exacerbations in New York City (NYC).
  
  
  METHODS
  Air pollution data included: (a) highly spatially-resolved intra-urban concentration surfaces for ozone and co-pollutants (nitrogen dioxide and fine particulate matter) from the New York City Community Air Survey (NYCCAS), and (b) daily regulatory monitoring data. Case data included citywide hospital records for years 2005-2011 warm-season (June-August) asthma hospitalizations (n=2353) and Emergency Department (ED) visits (n=11,719) among children aged 5-17 years. Case residential locations were geocoded using a multi-step process to maximize positional accuracy and precision in near-residence exposure estimates. We used conditional logistic regression to model associations between ozone and child asthma exacerbations for lag days 0-6, adjusting for co-pollutant and temperature exposures. To evaluate the effect of increased exposure specificity through spatial air pollution information, we sequentially incorporated spatial variation into daily exposure estimates for ozone, temperature, and co-pollutants.
  
  
  RESULTS
  Percent excess risk per 10ppb ozone exposure in spatio-temporal models were significant on lag days 1 through 5, ranging from 6.5 (95% CI: 0.2-13.1) to 13.0 (6.0-20.6) for inpatient hospitalizations, and from 2.9 (95% CI: 0.1-5.7) to 9.4 (6.3-12.7) for ED visits, with strongest associations consistently observed on lag day 2. Spatio-temporal excess risk estimates were consistently but not statistically significantly higher than temporal-only estimates on lag days 0-3.
  
  
  CONCLUSIONS
  Incorporating case-level spatial exposure variation produced small, non-significant increases in excess risk estimates. Our modeling approach enables a refined understanding of potential measurement error in temporal-only versus spatio-temporal air pollution exposure assessments. As ozone generally varies over much larger spatial scales than that observed within NYC, further work is necessary to evaluate potential reductions in exposure misclassification for populations spanning wider geographic areas, and for other pollutants.
authors: University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, 100 Technology Drive, Ste. 350, Pittsburgh, PA 15219, USA. Electronic address: jlcshmool@pitt.edu.
authors: Jessie Loving Carr
authors: JL
authors: Shmool
authors: University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, 100 Technology Drive, Ste. 350, Pittsburgh, PA 15219, USA. Electronic address: ejk40@pitt.edu.
authors: Ellen
authors: E
authors: Kinnee
authors: Icahn School of Medicine at Mount Sinai, DPM, 1 Gustave L. Levy Pl., Box 1057, New York, NY 10029, USA. Electronic address: perry.sheffield@mssm.edu.
authors: Perry Elizabeth
authors: PE
authors: Sheffield
authors: University of Pittsburgh Graduate School of Public Health, Department of Environmental and Occupational Health, 100 Technology Drive, Ste. 350, Pittsburgh, PA 15219, USA. Electronic address: jcloughe@pitt.edu.
authors: Jane Ellen
authors: JE
authors: Clougherty
mesh_terms: D000293:Adolescent; D000393:Air Pollutants / Q000032:analysis*; D001249:Asthma / Q000139:chemically induced / Q000453:epidemiology*; D002648:Child; D002675:Child, Preschool; D018592:Cross-Over Studies; D004781:Environmental Exposure; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D009519:New York City / Q000453:epidemiology; D010126:Ozone / Q000032:analysis*; D012307:Risk Factors; D012621:Seasons
chemical_list: D000393:Air Pollutants; D010126:Ozone
keywords: Case-crossover; Childhood asthma; Intraurban variation; Ozone; Spatio-temporal