title: Economic evaluation of treatment with orlistat in Italian obese patients.
abstract: The XENDOS study showed that behavioural and pharmacological therapy can decrease the risk of metabolic disorders in obese patients.
  
  
  METHODS
  A probabilistic Bayesian Markov model simulating the outcomes of orlistat treatment on the obese Italian population has been developed with the WinBUGS software. The model integrates an algorithm to estimate cardiovascular risk based on Framingham Heart Study equations. Analyses adopted the societal cost perspective, including direct medical costs borne by both the National Health Service and the patient, since orlistat is not included in the Italian reimbursement list.
  
  
  RESULTS
  The simulation on the Italian obese population estimated an average increase in quality-adjusted life expectancy, a reduction of cardiovascular events and new diabetes cases. The average incremental cost-utility ratio is euro75.3 (7.6-180.6) x 1000/QALY. The subgroup analysis showed that the benefits are relatively greater in older patients and in patients with impaired glucose tolerance (IGT). Two hypotheses have been explored to estimate the impact of a potential reimbursement decision by the Italian NHS: (1) orlistat is given to every obese patient; (2) orlistat is given only to obese IGT patients with a previous glucose tolerance general screening program to assess their eligibility. The cost utility of the strategies are euro42.3 (-22.16-108.7) and euro10.16 (-60.4-38.76) x 1000/QALY, respectively.
  
  
  CONCLUSIONS
  Orlistat shows a good pharmacoeconomic profile and, in particular, the strategy of a screening programme to identify and treat the IGT subgroup has a cost-utility value of about euro10000/QALY. This value is lower than that of several therapeutic strategies commonly accepted and reimbursed in developed countries.
authors.affiliation: Sergio Iannazzo, MBA, Engineer, Advanced Research Srl, Turin, Italy. s.iannazzo@advancedresearch.it
authors.forename: S
authors.initials: S
authors.lastname: Iannazzo
authors.forename: O
authors.initials: O
authors.lastname: Zaniolo
authors.forename: L
authors.initials: L
authors.lastname: Pradelli
mesh_terms: D000368:Aged; D019440:Anti-Obesity Agents / Q000191:economics / Q000627:therapeutic use; D001499:Bayes Theorem; D003198:Computer Simulation; D005260:Female; D017048:Health Care Costs; D006801:Humans; D007558:Italy / Q000453:epidemiology; D007783:Lactones / Q000191:economics* / Q000627:therapeutic use*; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D008954:Models, Biological; D009765:Obesity / Q000188:drug therapy* / Q000191:economics / Q000453:epidemiology; D000077403:Orlistat; D010919:Placebos; D015995:Prevalence; D011788:Quality of Life; D040242:Risk Reduction Behavior; D016896:Treatment Outcome
chemical_list: D019440:Anti-Obesity Agents; D007783:Lactones; D010919:Placebos; D000077403:Orlistat