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Discrete event simulations (DESs) and state-transition models yield similar smoking cessation cost-effectiveness results. Long-term assumptions, not model structure, significantly impact economic evaluations of interventions.

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Area of Science:

  • Health Economics
  • Pharmacoeconomics
  • Modeling and Simulation

Background:

  • Economic evaluations of smoking cessation interventions commonly employ cohort state-transition models.
  • Discrete event simulations (DESs) are proposed as a potentially superior alternative approach.

Purpose of the Study:

  • To compare the cost-effectiveness results of a state-transition model and a DES for smoking cessation interventions.
  • To explore the impact of alternative assumptions on cost-effectiveness using DES through scenario analysis.

Main Methods:

  • Developed and compared a state-transition model and a DES using the discretely integrated condition event (DICE) framework.
  • Estimated costs and quality-adjusted life years (QALYs) from a UK National Health Service perspective over a lifetime horizon.
  • Incorporated five key comorbidities (COPD, MI, CHD, stroke, lung cancer) and used prevalence data for the state-transition model and incidence data for the DES.

Main Results:

  • The state-transition model yielded an incremental cost-effectiveness ratio (ICER) of £3438 for the intervention.
  • The comparable DES scenario resulted in an ICER of £5577.
  • Including long-term relapse in the DES increased the ICER to £18,354.

Conclusions:

  • Model structure did not influence the cost-effectiveness findings for smoking cessation interventions.
  • Long-term assumptions significantly affected the economic evaluation results.
  • Economic models require structures capable of reflecting variations in long-term predictions between interventions.