title: Classification and regression trees for epidemiologic research: an air pollution example.
abstract: BACKGROUND
  Identifying and characterizing how mixtures of exposures are associated with health endpoints is challenging. We demonstrate how classification and regression trees can be used to generate hypotheses regarding joint effects from exposure mixtures.
  
  
  METHODS
  We illustrate the approach by investigating the joint effects of CO, NO2, O3, and PM2.5 on emergency department visits for pediatric asthma in Atlanta, Georgia. Pollutant concentrations were categorized as quartiles. Days when all pollutants were in the lowest quartile were held out as the referent group (n = 131) and the remaining 3,879 days were used to estimate the regression tree. Pollutants were parameterized as dichotomous variables representing each ordinal split of the quartiles (e.g. comparing CO quartile 1 vs. CO quartiles 2-4) and considered one at a time in a Poisson case-crossover model with control for confounding. The pollutant-split resulting in the smallest P-value was selected as the first split and the dataset was partitioned accordingly. This process repeated for each subset of the data until the P-values for the remaining splits were not below a given alpha, resulting in the formation of a "terminal node". We used the case-crossover model to estimate the adjusted risk ratio for each terminal node compared to the referent group, as well as the likelihood ratio test for the inclusion of the terminal nodes in the final model.
  
  
  RESULTS
  The largest risk ratio corresponded to days when PM2.5 was in the highest quartile and NO2 was in the lowest two quartiles (RR: 1.10, 95% CI: 1.05, 1.16). A simultaneous Wald test for the inclusion of all terminal nodes in the model was significant, with a chi-square statistic of 34.3 (p = 0.001, with 13 degrees of freedom).
  
  
  CONCLUSIONS
  Regression trees can be used to hypothesize about joint effects of exposure mixtures and may be particularly useful in the field of air pollution epidemiology for gaining a better understanding of complex multipollutant exposures.
authors.affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Rd, Atlanta, GA 30322, USA. kgass@emory.edu.
authors.forename: Katherine
authors.initials: K
authors.lastname: Gass
authors.forename: Mitch
authors.initials: M
authors.lastname: Klein
authors.forename: Howard H
authors.initials: HH
authors.lastname: Chang
authors.forename: W Dana
authors.initials: WD
authors.lastname: Flanders
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Strickland
mesh_terms: D000293:Adolescent; D000393:Air Pollutants / Q000032:analysis; D000397:Air Pollution / Q000032:analysis* / Q000706:statistics & numerical data; D000465:Algorithms; D001249:Asthma / Q000453:epidemiology*; D002248:Carbon Monoxide / Q000032:analysis; D002648:Child; D002675:Child, Preschool; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D015340:Epidemiologic Research Design; D005845:Georgia / Q000453:epidemiology; D006801:Humans; D009585:Nitrogen Dioxide / Q000032:analysis; D016017:Odds Ratio; D010126:Ozone / Q000032:analysis; D052638:Particulate Matter / Q000032:analysis; D012044:Regression Analysis; D018709:Statistics, Nonparametric
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter; D010126:Ozone; D002248:Carbon Monoxide; D009585:Nitrogen Dioxide