title: Regional variations in pediatric medication exposure: Spatial analysis of poison center utilization in western Pennsylvania.
abstract: BACKGROUND
  Medication drug exposures among young children continue to rise despite current poison prevention efforts. These exposures result in increased healthcare utilization and medical costs. New tactics are needed to reduce injuries related to pediatric drug exposures.
  
  
  OBJECTIVE
  We aimed to identify cluster patterns in: (1) calls for pediatric medication drug exposures and (2) a subset of calls that resulted in medical evaluation referrals. We identified and evaluated population characteristics associated with cluster patterns.
  
  
  METHODS
  We analyzed 26,685 pharmaceutical drug exposures involving children <5 years of age based on calls reported to the Pittsburgh Poison Center from 1 January 2006 to 31 December 2010. We performed spatial statistics to assess for clustering. We used logistic regression to estimate population characteristics associated with clustering.
  
  
  RESULTS
  Spatial analysis identified 22 exposure clusters and five referral clusters. Sixty-five percent of 89 ZIP codes in the clusters of drug exposure with healthcare facility (HCF) referral were not identified in the exposure clusters. ZIP codes in the HCF referral clusters were characterized as rural, impoverished, and with high rates of unemployment and school dropouts.
  
  
  CONCLUSIONS
  Our principal findings demonstrate pediatric drug exposures do exist in discrete geographic clusters and with distinct socioeconomic characteristics.
  
  
  CONCLUSIONS
  This study offers a starting point for subsequent investigations into the geographic and social context of pediatric medication drug exposures. This is an important step in revising pediatric poison prevention strategies.
authors.affiliation: a Department of Pediatrics , Division of Emergency Medicine, Rady Children's Hospital, University of California , San Diego , CA , USA.
authors.forename: Margaret B
authors.initials: MB
authors.lastname: Nguyen
authors.affiliation: b Department of Emergency Medicine , Division of Toxicology, University of Pittsburgh School of Medicine , Pittsburgh , PA , USA.
authors.forename: Anthony F
authors.initials: AF
authors.lastname: Pizon
authors.affiliation: c Department of Biostatistics and Epidemiology , School of Medicine, University of Pennsylvania , Philadelphia , PA , USA.
authors.forename: Charles C
authors.initials: CC
authors.lastname: Branas
authors.affiliation: d Department of Epidemiology , Epidemiology Data Center, Graduate School of Public Health, University of Pittsburgh , Pittsburgh , PA , USA.
authors.forename: Anthony
authors.initials: A
authors.lastname: Fabio
mesh_terms: D000061:Accidents, Home / Q000517:prevention & control; D000367:Age Factors; D002675:Child, Preschool; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D010414:Pennsylvania / Q000453:epidemiology; D011039:Poison Control Centers / Q000706:statistics & numerical data*; D011041:Poisoning / Q000175:diagnosis / Q000453:epidemiology* / Q000517:prevention & control / Q000628:therapy; D011203:Poverty; D012017:Referral and Consultation / Q000706:statistics & numerical data; D012111:Residence Characteristics; D012307:Risk Factors; D012424:Rural Population; D012959:Socioeconomic Factors; D013332:Student Dropouts; D013997:Time Factors; D014478:Unemployment
keywords: GIS; injury prevention; socioeconomic; spatial analysis