title: Do drug seizures predict drug-related emergency department presentations or arrests for drug use and possession?
abstract: BACKGROUND
  Direct evidence of the effect of drug seizures on drug use and drug-related harm is fairly sparse. The aim of this study was to see whether seizures of heroin, cocaine and ATS predict the number of people arrested for use and possession of these drugs and the number overdosing on them.
  
  
  METHODS
  We examined the effect of seizure frequency and seizure weight on arrests for drug use and possession and on the frequency of drug overdose with autoregressive distributed lag (ARDL) models. Granger causality tests were used to test for simultaneity.
  
  
  RESULTS
  Over the short term (i.e. up to 4 months), increases in the intensity of high-level drug law enforcement (as measured by seizure weight and frequency) directed at ATS, cocaine and heroin did not appear to have any suppression effect on emergency department (ED) presentations relating to ATS, cocaine and heroin, or on arrests for use and/or possession of these drugs. A significant negative contemporaneous relationship was found between the heroin seizure weight and arrests for use and/or possession of heroin. However no evidence emerged of a contemporaneous or lagged relationship between heroin seizures and heroin ED presentations.
  
  
  CONCLUSIONS
  The balance of evidence suggests that, in the Australian context, increases in the monthly seizure frequency and quantity of ATS, cocaine and heroin are signals of increased rather than reduced supply.
authors.affiliation: NSW Bureau of Crime Statistics and Research, Sydney, Australia.
authors.forename: Wai-Yin
authors.initials: WY
authors.lastname: Wan
authors.affiliation: NSW Bureau of Crime Statistics and Research, Sydney, Australia. Electronic address: Don_J_Weatherburn@agd.nsw.gov.au.
authors.forename: Don
authors.initials: D
authors.lastname: Weatherburn
authors.affiliation: National Security College, Australian National University, Canberra, Australia.
authors.forename: Grant
authors.initials: G
authors.lastname: Wardlaw
authors.affiliation: Department of Econometrics and Business Statistics, Monash University, Caulfield, Australia.
authors.forename: Vasilis
authors.initials: V
authors.lastname: Sarafidis
authors.affiliation: NSW Ministry of Health, Sydney, Australia; Discipline of Psychiatry, Sydney Medical School University of Sydney, Australia.
authors.forename: Grant
authors.initials: G
authors.lastname: Sara
mesh_terms: D000662:Amphetamines / Q000009:adverse effects / Q000600:supply & distribution; D001315:Australia / Q000453:epidemiology; D003042:Cocaine / Q000009:adverse effects / Q000600:supply & distribution; D003415:Crime / Q000706:statistics & numerical data; D062787:Drug Overdose / Q000453:epidemiology*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D003932:Heroin / Q000009:adverse effects / Q000600:supply & distribution; D006801:Humans; D013287:Illicit Drugs / Q000009:adverse effects / Q000600:supply & distribution; D035846:Law Enforcement; D015233:Models, Statistical; D019966:Substance-Related Disorders / Q000453:epidemiology*
chemical_list: D000662:Amphetamines; D013287:Illicit Drugs; D003932:Heroin; D003042:Cocaine
keywords: ARDL models; Amphetamine-type substance; Cocaine; Drug possession arrests; Heroin; Overdose; Supply control