title: Integrating nine prescription opioid analgesics and/or four signal detection systems to summarize statewide prescription drug abuse in the United States in 2007.
abstract: OBJECTIVE
  Integrate statewide rankings of abuse across different drugs and/or signal detection systems to summarize prescription drug abuse in each state in 2007.
  
  
  METHODS
  Four signal detection systems (Opioid Treatment Programs, Key Informants, Drug Diversion, and Poison Centers) that covered heterogeneous populations collected data on the abuse of nine opioids: hydrocodone, immediate-release oxycodone, tramadol, extended-release [ER] oxycodone, fentanyl, morphine, methadone, hydromorphone, and buprenorphine). We introduce here linearized maps which integrate nine drugs within each system; four systems for each drug; or all drugs and systems.
  
  
  RESULTS
  When rankings were integrated across drugs, Rhode Island, New Hampshire, Maine, West Virginia, and Michigan were in the highest tertile of abuse in three systems. When rankings were integrated across signal detection systems, there was a geographic clustering of states with the highest rates for ER oxycodone (in Tennessee, Mississippi, Kentucky, Ohio, Indiana, Michigan, and in Massachusetts, New Hampshire, Maine, and Vermont) and methadone (Massachusetts, Rhode Island, New Hampshire, Maine, Vermont, Connecticut, and New Jersey). When rankings were integrated across both drugs and signal detection systems, states with 3-digit ZIP codes below 269 (i.e., from Massachusetts to West Virginia): Massachusetts, New Hampshire, Maine, Vermont, Washington DC, Virginia, and West Virginia were in the highest tertile and only Delaware was in the lowest tertile.
  
  
  CONCLUSIONS
  We have presented methods to integrate data on prescription opioid abuse collected by signal detection systems covering different populations. Linearized maps are effective graphical summaries that depict differences in the level of prescription opioid abuse at the state level.
authors.affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. mschneid@jhsph.edu
authors.forename: Michael F
authors.initials: MF
authors.lastname: Schneider
authors.forename: J Elise
authors.initials: JE
authors.lastname: Bailey
authors.forename: Theodore J
authors.initials: TJ
authors.lastname: Cicero
authors.forename: Richard C
authors.initials: RC
authors.lastname: Dart
authors.forename: James A
authors.initials: JA
authors.lastname: Inciardi
authors.forename: Mark
authors.initials: M
authors.lastname: Parrino
authors.forename: Alvaro
authors.initials: A
authors.lastname: Muñoz
mesh_terms: D000701:Analgesics, Opioid; D003625:Data Collection; D006801:Humans; D055553:Prescription Drugs; D018570:Risk Assessment; D012308:Risk Management; D019966:Substance-Related Disorders / Q000453:epidemiology*; D014481:United States / Q000453:epidemiology
chemical_list: D000701:Analgesics, Opioid; D055553:Prescription Drugs