title: Pharmacoeconomic analysis of antidepressants for major depressive disorder in the United Kingdom.
abstract: OBJECTIVE
  To estimate the cost effectiveness of different classes of antidepressants in the UK National Health Service. DESIGN, PATIENTS AND INTERVENTIONS: The use of the serotonin (5-hydroxytryptamine; 5-HT) and noradrenaline (norepinephrine) reuptake inhibitor (SNRI) venlafaxine was compared with that of selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs) in patients with major depressive disorder (MDD). A meta-analysis determined the clinical success rate, and a decision tree was constructed by interviewing general practitioners and psychiatrists. Adding pharmacological and nonpharmacological treatment costs, meta-analytic rates were applied to the decision tree to calculate the expected cost and outcome for each drug. Cost effectiveness was determined using a composite measure of outcome [symptom-free days (SFD)].
  
  
  RESULTS
  The meta-analysis included data from 44 studies on 4033 patients. The highest overall efficacy rate for outpatients with MDD was with venlafaxine use (73.7%), compared with 61.4% for SSRIs and 59.3% for TCAs. Treatment with venlafaxine yielded the lowest outpatient cost for a SFD (10.53 Pounds), compared with 13.23 Pounds for SSRIs and 15.52 Pounds for TCAs (1998 values).
  
  
  CONCLUSIONS
  Using this economic model, venlafaxine appears to be a cost-effective treatment for outpatients with MDD in the UK.
authors.affiliation: Green College, Oxford, England.
authors.forename: H
authors.initials: H
authors.lastname: Freeman
authors.forename: S
authors.initials: S
authors.lastname: Arikian
authors.forename: A
authors.initials: A
authors.lastname: Lenox-Smith
mesh_terms: D000928:Antidepressive Agents / Q000627:therapeutic use*; D003362:Cost-Benefit Analysis; D003866:Depressive Disorder / Q000188:drug therapy* / Q000191:economics; D006801:Humans; D015201:Meta-Analysis as Topic; D009010:Monte Carlo Method
chemical_list: D000928:Antidepressive Agents