The Quit Benefits Model: a Markov model for assessing the health benefits and health care cost savings of quitting smoki
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BioMed Central
Open Access
Methodology
The Quit Benefits Model: a Markov model for assessing the health benefits and health care cost savings of quitting smoking Susan F Hurley*1,2 and Jane P Matthews1 Address: 1Bainbridge Consultants, 532 Brunswick St, Fitzroy North, Victoria, 3068, Australia and 2School of Population Health, The University of Melbourne, Parkville, Victoria, 3052, Australia Email: Susan F Hurley* - [email protected]; Jane P Matthews - [email protected] * Corresponding author
Published: 23 January 2007 Cost Effectiveness and Resource Allocation 2007, 5:2
doi:10.1186/1478-7547-5-2
Received: 21 August 2006 Accepted: 23 January 2007
This article is available from: http://www.resource-allocation.com/content/5/1/2 © 2007 Hurley and Matthews; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract Background: In response to the lack of comprehensive information about the health and economic benefits of quitting smoking for Australians, we developed the Quit Benefits Model (QBM). Methods: The QBM is a Markov model, programmed in TreeAge, that assesses the consequences of quitting in terms of cases avoided of the four most common smoking-associated diseases, deaths avoided, and quality-adjusted life-years (QALYs) and health care costs saved (in Australian dollars, A$). Quitting outcomes can be assessed for males and females in 14 five year age-groups from 15–19 to 80–84 years. Exponential models, based on data from large case-control and cohort studies, were developed to estimate the decline over time after quitting in the risk of acute myocardial infarction (AMI), stroke, lung cancer, chronic obstructive pulmonary disease (COPD), and death. Australian data for the year 2001 were sourced for disease incidence and mortality and health care costs. Utility of life estimates were sourced from an international registry and a meta analysis. In this paper, outcomes are reported for simulated subjects followed up for ten years after quitting smoking. Life-years, QALYs and costs were estimated with 0%, 3% and 5% per annum discount rates. Summary results are presented for a group of 1,000 simulated quitters chosen at random from the Australian population of smokers aged between 15 and 74. Results: For every 1,000 males chosen at random from the reference population who quit smoking, there is a an average saving in the first ten years following quitting of A$408,000 in health care costs associated with AMI, COPD, lung cancer and stroke, and a corresponding saving of A$328,000 for every 1,000 female quitters. The average saving per 1,000 random quitters is A$373,000. Overall 40 of these quitters will be spared a diagnosis of AMI, COPD, lung cancer and stroke in the first ten years following quitting, with an estimated saving of 47 l
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