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OP11 Structural Uncertainty In Economic Modelling For Smoking Cessation

Published online by Cambridge University Press:  12 January 2018

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Abstract

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INTRODUCTION:

Guidance for developing economic models recommend that model structure is carefully considered, and assumptions varied in sensitivity analysis (1). Models in smoking cessation have typically used cohort-level approaches, although recently discrete event simulations (DESs) have been developed (2). DESs allow additional flexibility such as modelling changing risk over time, and recurrent events. Our aim was to explore the impact of varying model structure and assumptions on the cost-effectiveness of smoking cessation programs.

METHODS:

We built a cohort state-transition model which related mortality to smoking status and considered the prevalence (based on smoking status) of five comorbidities associated with smoking, each of which has an associated cost and quality of life decrement. We additionally built a patient-level DES, using the Discretely Integrated Condition Event framework (3). The DES used the same data as the cohort model, except considering incidence for comorbidities rather than prevalence. We considered a population of smokers aged 16 years old and an intervention costing GBP827 on which 27 percent of people quit, compared with no treatment. We produced results using the two models for comparable scenarios, and ran additional scenarios considering different assumptions.

RESULTS:

In the cohort model, the incremental cost-effectiveness ratio (ICER) for intervention versus no treatment was GBP4,000/quality-adjusted life year (QALY). In the DES, modelling mortality linked to smoker status produced an ICER of GBP1,000/QALY and modelling mortality linked to comorbidities produced an ICER of GBP6,000/QALY. In the DES with mortality linked to comorbidities, varying the relative risk of comorbidities with time since quitting gave an ICER of GBP3,000/QALY. Including relapse increased the ICER to GBP21,000/QALY.

CONCLUSIONS:

The ICER for the smoking cessation program changes when model assumptions are varied, although the choice of DES versus cohort model appears to make a relatively small difference. Inclusion of relapse substantially changes the ICER, demonstrating the importance of long-term effects in economic models.

Type
Oral Presentations
Copyright
Copyright © Cambridge University Press 2018 

References

REFERENCES:

1. Roberts, M, Russell, LB, Paltiel, AD, et al. Conceptualizing a model: a Report of the ISPOR-SMDM modeling good research practices task force-2. Value Health. 2012;15:804811.CrossRefGoogle Scholar
2. Berg, ML, Long Cheung, K, Hiligsmann, M, et al. Model-based economic evaluations in smoking cessation and their transferability to new contexts: a systematic review. Addiction. 2017;112 (6):946–67.Google Scholar
3. Caro, JJ. Discretely Integrated Condition Event (DICE) simulation for pharmacoeconomics. Pharmacoeconomics. 2016;34 (7):665–72.Google Scholar