title: Static and dynamic predictors of criminal involvement among people with heroin dependence: findings from a 3-year longitudinal study.
abstract: BACKGROUND
  The link between heroin use and crime has been well established; however, there has been little opportunity to examine this relationship longitudinally. This study examines the relationship between static and dynamic predictors of criminal involvement, and the degree to which changes in dynamic risk factors moderate the risk of criminal involvement over time.
  
  
  METHODS
  Data were collected as part of the Australian treatment outcome study, a 3-year longitudinal study of 615 people with heroin dependence conducted in Sydney, Australia. Past-month criminal involvement (property crime, drug dealing, fraud, violent crime), demographic, drug use and mental health characteristics were assessed at each interview.
  
  
  RESULTS
  Criminal involvement was consistently and independently predicted by lack of wage/salary as a main source of income, (OR 2.17), meeting diagnostic criteria for anti-social personality disorder (OR 1.91) and major depression (OR 1.41), screening positive for borderline personality disorder (OR 1.47), male sex (OR 1.44), a criminal history (OR 1.33), greater severity of dependence (OR 1.21), more extensive heroin use (OR 1.09), and younger age (OR 0.96) over the 3-year period.
  
  
  CONCLUSIONS
  These findings provide strong evidence of the robust nature of the association between more extensive heroin use, severity of dependence, the co-occurrence of mental health conditions, and an individual's capacity for employment, and criminal involvement. Interventions aimed at increasing an individual's employability and improving mental health in particular, may reduce the risk of criminal involvement among people with heroin dependence.
authors.affiliation: National Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW, Australia. Electronic address: c.marel@unsw.edu.au.
authors.forename: Christina
authors.initials: C
authors.lastname: Marel
authors.forename: Katherine L
authors.initials: KL
authors.lastname: Mills
authors.forename: Shane
authors.initials: S
authors.lastname: Darke
authors.forename: Joanne
authors.initials: J
authors.lastname: Ross
authors.forename: Tim
authors.initials: T
authors.lastname: Slade
authors.forename: Lucy
authors.initials: L
authors.lastname: Burns
authors.forename: Maree
authors.initials: M
authors.lastname: Teesson
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000465:Algorithms; D001315:Australia / Q000453:epidemiology; D003415:Crime / Q000706:statistics & numerical data*; D005260:Female; D005500:Follow-Up Studies; D006556:Heroin Dependence / Q000453:epidemiology* / Q000523:psychology; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008603:Mental Health; D008875:Middle Aged; D011379:Prognosis; D012307:Risk Factors; D012959:Socioeconomic Factors; D019966:Substance-Related Disorders / Q000453:epidemiology / Q000523:psychology; D055815:Young Adult
keywords: Criminal involvement; Heroin dependence; Longitudinal study; Polydrug use; Predictors of crime