The Quit Benefits Model: A Markov model for assessing the health benefits and health care cost savings of quitting smoking

Bainbridge Consultants, 532 Brunswick St, Fitzroy North, Victoria, 3068, Australia.
Cost Effectiveness and Resource Allocation (Impact Factor: 0.87). 02/2007; 5(2):2. DOI: 10.1186/1478-7547-5-2
Source: PubMed


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).
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.
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 life-years and 75 QALYs. Sensitivity analyses indicated that QBM predictions were robust to variations of +/- 10% in parameter estimates.
The QBM can answer many of the questions posed by Australian policy-makers and health program funders about the benefits of quitting, and is a useful tool to evaluate tobacco control programs. It can easily be re-programmed with updated information or a set of epidemiologic data from another country.

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    • "Quitters' mortality probabilities were estimated by applying a function that described the decline in the risk of death from all causes for quitters relative to smokers[4] to the probability of death for smokers. The function was based on data from the ACS CPS-II. "
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    • "It includes 28 chronic diseases and several risk factors amongst which smoking, Body Mass Index, and physical inactivity. In modeling diseases explicitly, the structure of the model is similar to the Prevent model [16] and the recently presented Quit Benefits model [10,17]. An important difference with the Prevent model is that also different risk factor classes are modeled. "
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    • "Hoje há, em vários países, trabalhos publicados nos mais diversos segmentos dá area da saúde que lançaram mão dessa técnica. Como exemplo podemos citar: utilização de stents na cardiologia Polanczyk, Wainstein, and Ribeiro (2007), métodos de vasectomia Seamans and Harner-Jay (2007) e benefícios resultantes do ato de parar de fumar Hurley and Matthews (2007). Ná area da saúde, definidos os recursos utilizados nos tratamentos A e P e o número de resultados clínicos positivos obtidos como conseqüência de cada um desses tratamentos, o objetivo dessa técnicá e encontrar o tratamento que possibilita obter o maior número de unidades de bons resultados clínicos para cada unidade monetária investida. "
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