title: Prescription opioid poisoning across urban and rural areas: identifying vulnerable groups and geographic areas.
abstract: To determine (1) whether prescription opioid poisoning (PO) hospital discharges spread across space over time, (2) the locations of 'hot-spots' of PO-related hospital discharges, (3) how features of the local environment contribute to the growth in PO-related hospital discharges and (4) where each environmental feature makes the strongest contribution.
  
  
  
  Hierarchical Bayesian Poisson space-time analysis to relate annual discharges from community hospitals to postal code characteristics over 10 years.
  
  
  
  California, USA.
  
  
  
  Residents of 18 517 postal codes in California, 2001-11.
  
  
  
  Annual postal code-level counts of hospital discharges due to PO poisoning were related to postal code pharmacy density, measures of medical need for POs (i.e. rates of cancer and arthritis-related hospital discharges), economic stressors (i.e. median household income, percentage of families in poverty and the unemployment rate) and concentration of manual labor industries.
  
  
  
  PO-related hospital discharges spread from rural and suburban/exurban 'hot-spots' to urban areas. They increased more in postal codes with greater pharmacy density [rate ratio (RR) = 1.03; 95% credible interval (CI) = 1.01, 1.05], more arthritis-related hospital discharges (RR = 1.08; 95% CI = 1.06, 1.11), lower income (RR = 0.85; 95% CI = 0.83, 0.87) and more manual labor industries (RR = 1.15; 95% CI = 1.10, 1.19 for construction; RR = 1.12; 95% CI = 1.04, 1.20 for manufacturing industries). Changes in pharmacy density primarily affected PO-related discharges in urban areas, while changes in income and manual labor industries especially affected PO-related discharges in suburban/exurban and rural areas.
  
  
  
  Hospital discharge rates for prescription opioid (PO) poisoning spread from rural and suburban/exurban hot-spots to urban areas, suggesting spatial contagion. The distribution of age-related and work-place-related sources of medical need for POs in rural areas and, to a lesser extent, the availability of POs through pharmacies in urban areas, partly explain the growth of PO poisoning across California, USA.
authors.affiliation: Department of Emergency Medicine, University of California, Davis, Sacramento, CA, USA.
authors.forename: Magdalena
authors.initials: M
authors.lastname: Cerdá
authors.affiliation: Prevention Research Center, Pacific Institute for Research and Evaluation, Oakland, CA, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Gaidus
authors.affiliation: Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
authors.forename: Katherine M
authors.initials: KM
authors.lastname: Keyes
authors.affiliation: Prevention Research Center, Pacific Institute for Research and Evaluation, Oakland, CA, USA.
authors.forename: William
authors.initials: W
authors.lastname: Ponicki
authors.affiliation: Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
authors.forename: Silvia
authors.initials: S
authors.lastname: Martins
authors.affiliation: School of Public Health, Boston University, Boston, MA, USA.
authors.forename: Sandro
authors.initials: S
authors.lastname: Galea
authors.affiliation: Prevention Research Center, Pacific Institute for Research and Evaluation, Oakland, CA, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Gruenewald
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000701:Analgesics, Opioid / Q000506:poisoning*; D001499:Bayes Theorem; D002140:California; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D008875:Middle Aged; D011203:Poverty; D012424:Rural Population / Q000706:statistics & numerical data*; D012959:Socioeconomic Factors; D014505:Urban Population / Q000706:statistics & numerical data*; D055815:Young Adult
chemical_list: D000701:Analgesics, Opioid
keywords: Bayesian space-time models; drug overdose; geography; hospital discharges; prescription opioids; rural and urban