title: Who goes to alcohol and drug treatment? Understanding utilization within the context of insurance.
abstract: OBJECTIVE
  This study assesses the differential roles of demographic characteristics, problem severity, motivational factors and insurance in entrance into alcohol and drug treatment.
  
  
  METHODS
  A probability sample of adult problem and dependent drinkers from a Northem California county (N = 672) and consecutive adult intakes from the county's public (n = 298) and private (n = 628) alcohol and drug treatment programs. Interviewers conducted telephone screening for individuals meeting problem-drinking criteria in the general population; in-person interviews were conducted for those who screened positive, and also for individuals entering treatment.
  
  
  RESULTS
  Insurance did not play an independent role in treatment entry. In logistic regression analysis, using a model from the medical utilization literature, black ethnicity (odds ratio [OR] = 2.98, p < .001); older age (OR = 4.67, p < .001); less education (OR = 1.81, p < .01); legal (OR = 7.46, p < .001) and work (OR = 3.57,p <.001) pressures; higher psychiatric (OR = 4.03,p <.001) and drug (OR = 3.04,p <.001) severity; and social consequences (OR = 1.35, p < .01) predicted treatment entry. Interventions from legal sources (OR = 6.22, p < .01) were related to entering treatment for the uninsured; legal (OR = 7.02, p < .001), workplace (OR = 6.01, p < .001) and family (OR = 1.62, p < .05) pressures were related for the insured.
  
  
  CONCLUSIONS
  Social consequences, rather than severity of dependence, in both insured and uninsured problem drinkers are important predictors of treatment entry when traditional utilization measures are controlled. Public programs play a crucial part in the alcohol and drug treatment system, and their viability is important as health policy changes.
authors.affiliation: Department of Psychiatry, University of California, San Francisco, San Francisco, California, USA. conniew@lppi.ucsf.edu
authors.forename: Constance
authors.initials: C
authors.lastname: Weisner
authors.forename: Helen
authors.initials: H
authors.lastname: Matzger
authors.forename: Tammy
authors.initials: T
authors.lastname: Tam
authors.forename: Laura
authors.initials: L
authors.lastname: Schmidt
mesh_terms: D000328:Adult; D000367:Age Factors; D000704:Analysis of Variance; D016009:Chi-Square Distribution; D005260:Female; D006801:Humans; D007342:Insurance Benefits / Q000379:methods / Q000706:statistics & numerical data* / Q000639:trends; D008297:Male; D017057:Medically Uninsured / Q000706:statistics & numerical data*; D008875:Middle Aged; D016017:Odds Ratio; D012959:Socioeconomic Factors; D016320:Substance Abuse Treatment Centers / Q000191:economics / Q000706:statistics & numerical data*; D019966:Substance-Related Disorders / Q000191:economics* / Q000628:therapy*