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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Available from: Jane Matthews, Oct 10, 2015
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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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    ABSTRACT: Tobacco smoking is a risk factor for age-related macular degeneration, but studies of ex-smokers suggest quitting can reduce the risk. We fitted a function predicting the decline in risk of macular degeneration after quitting to data from 7 studies involving 1,488 patients. We assessed the cost-effectiveness of smoking cessation in terms of its impact on macular degeneration-related outcomes for 1,000 randomly selected U.S. smokers. We used a computer simulation model to predict the incidence of macular degeneration and blindness, the number of quality-adjusted life-years (QALYs), and direct costs (in 2004 U.S. dollars) until age 85 years. Cost-effectiveness ratios were based on the cost of the Massachusetts Tobacco Control Program. Costs and QALYs were discounted at 3% per year. If 1,000 smokers quit, our model predicted 48 fewer cases of macular degeneration, 12 fewer cases of blindness, and a gain of 1,600 QALYs. Macular degeneration-related costs would decrease by $2.5 million if the costs of caregivers for people with vision loss were included, or by $1.1 million if caregiver costs were excluded. At a cost of $1,400 per quitter, smoking cessation was cost-saving when caregiver costs were included, and cost about $200 per QALY gained when caregiver costs were excluded. Sensitivity analyses had a negligible impact. The cost per quitter would have to exceed $77,000 for the cost per QALY for smoking cessation to reach $50,000, a threshold above which interventions are sometimes viewed as not cost-effective. Smoking cessation is unequivocally cost-effective in terms of its impact on age-related macular degeneration outcomes alone.
    Cost Effectiveness and Resource Allocation 10/2008; 6:18. DOI:10.1186/1478-7547-6-18 · 0.87 Impact Factor
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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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    ABSTRACT: To support health policy makers in setting priorities, quantifying the potential effects of tobacco control on the burden of disease is useful. However, smoking is related to a variety of diseases and the dynamic effects of smoking cessation on the incidence of these diseases differ. Furthermore, many people who quit smoking relapse, most of them within a relatively short period. In this paper, a method is presented for calculating the effects of smoking cessation interventions on disease incidence that allows to deal with relapse and the effect of time since quitting. A simulation model is described that links smoking to the incidence of 14 smoking related diseases. To demonstrate the model, health effects are estimated of two interventions in which part of current smokers in the Netherlands quits smoking.To illustrate the advantages of the model its results are compared with those of two simpler versions of the model. In one version we assumed no relapse after quitting and equal incidence rates for all former smokers. In the second version, incidence rates depend on time since cessation, but we assumed still no relapse after quitting. Not taking into account time since smoking cessation on disease incidence rates results in biased estimates of the effects of interventions. The immediate public health effects are overestimated, since the health risk of quitters immediately drops to the mean level of all former smokers. However, the long-term public health effects are underestimated since after longer periods of time the effects of past smoking disappear and so surviving quitters start to resemble never smokers. On balance, total health gains of smoking cessation are underestimated if one does not account for the effect of time since cessation on disease incidence rates. Not taking into account relapse of quitters overestimates health gains substantially. The results show that simulation models are sensitive to assumptions made in specifying the model. The model should be specified carefully in accordance with the questions it is supposed to answer. If the aim of the model is to estimate effects of smoking cessation interventions on mortality and morbidity, one should include relapse of quitters and dependency on time since cessation of incidence rates of smoking-related chronic diseases. A drawback of such models is that data requirements are extensive.
    Cost Effectiveness and Resource Allocation 02/2008; 6(1):1. DOI:10.1186/1478-7547-6-1 · 0.87 Impact Factor
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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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