title: Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining.
abstract: Traditional studies on effects of outdoor pollution on asthma have been criticized for questionable statistical validity and inefficacy in exploring the effects of multiple air pollutants, alone and in combination. Association rule mining (ARM), a method easily interpretable and suitable for the analysis of the effects of multiple exposures, could be of use, but the traditional interest metrics of support and confidence need to be substituted with metrics that focus on risk variations caused by different exposures.
  
  
  
  We present an ARM-based methodology that produces rules associated with relevant odds ratios and limits the number of final rules even at very low support levels (0.5%), thanks to post-pruning criteria that limit rule redundancy and control for statistical significance. The methodology has been applied to a case-crossover study to explore the effects of multiple air pollutants on risk of asthma in pediatric subjects.
  
  
  
  We identified 27 rules with interesting odds ratio among more than 10,000 having the required support. The only rule including only one chemical is exposure to ozone on the previous day of the reported asthma attack (OR=1.14). 26 combinatory rules highlight the limitations of air quality policies based on single pollutant thresholds and suggest that exposure to mixtures of chemicals is more harmful, with odds ratio as high as 1.54 (associated with the combination day0 SO2, day0 NO, day0 NO2, day1 PM).
  
  
  
  The proposed method can be used to analyze risk variations caused by single and multiple exposures. The method is reliable and requires fewer assumptions on the data than parametric approaches. Rules including more than one pollutant highlight interactions that deserve further investigation, while helping to limit the search field.
authors.affiliation: Department of Computer Science, University of Houston, Philip Guthrie Hoffman Hall, 3551 Cullen Blvd., Room 501, Houston, TX 77204-3010, USA. Electronic address: giulia.toti@kcl.ac.uk.
authors.forename: Giulia
authors.initials: G
authors.lastname: Toti
authors.affiliation: Department of Computer Science, University of Houston, Philip Guthrie Hoffman Hall, 3551 Cullen Blvd., Room 501, Houston, TX 77204-3010, USA.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Vilalta
authors.affiliation: Honors College, University of Houston, M.D Anderson Library, 4333 University Dr, Room 212, Houston, TX 77204-2001, USA.
authors.forename: Peggy
authors.initials: P
authors.lastname: Lindner
authors.affiliation: Department of Earth and Atmospheric Sciences, University of Houston, Science & Research Building 1, 3507 Cullen Blvd, Room 312, Houston, TX 77204-5007, USA; Now at: Earth Sciences Division, NASA Headquarters, 300 E St SW, Washington, DC 20546, USA.
authors.forename: Barry
authors.initials: B
authors.lastname: Lefer
authors.affiliation: Department of Pediatrics, Baylor College of Medicine/Texas Children's Hospital, One Baylor Plaza, Houston, TX 77030, USA.
authors.forename: Charles
authors.initials: C
authors.lastname: Macias
authors.affiliation: Honors College, University of Houston, M.D Anderson Library, 4333 University Dr, Room 212, Houston, TX 77204-2001, USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: Price
mesh_terms: D000397:Air Pollution / Q000009:adverse effects*; D001249:Asthma; D002648:Child; D018592:Cross-Over Studies; D057225:Data Mining; D004784:Environmental Monitoring; D006801:Humans; D000069550:Machine Learning
keywords: Association rule mining; Multiple exposures; Outdoor pollution; Pediatric asthma; Risk assessment; Rule redundancy