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Abstract: Aims: To inform the transferability of tobacco control‐related economic evidence to resource‐poor countries. Methods: We ran a univariate sensitivity analysis on a return on investment (ROI) model, the European study on Quantifying Utility of Investment in Protection from Tobacco model (EQUIPTMOD), to identify key input values to which the ROI estimates were sensitive. The EQUIPTMOD used a Markov‐based state transition model to estimate the ROI of several tobacco control interventions in five European countries (England, Germany, Spain, Hungary and the Netherlands). Base case ROI estimates were obtained through average values of model inputs (throughout the five countries), which were then replaced one at a time with country‐specific values. Tornado diagrams were used to evaluate the significance of sensitivity, defined as a ≥ 10% difference in ROI estimates from the base case estimates. Results: The ROI estimates were sensitive to 18 (of 46) input values. Examples of model inputs to which ROI estimates were sensitive included: smoking rate, costs of smoking‐related diseases (e.g. lung cancer) and general population attributes. Conclusion: Countries that have limited research time and other resources can adapt EQUIPTMOD to their own settings by choosing to collect data on a small number of model inputs. EQUIPTMOD can therefore facilitate transfer of tobacco control related economic evidence to new jurisdictions. [ABSTRACT FROM AUTHOR]

Abstract: Background and Aims: Although clear benefits are associated with reducing smoking, there is increasing pressure on public health providers to justify investment in tobacco control measures. Decision‐makers need tools to assess the Return on Investment (ROI)/cost‐effectiveness of programmes. The EQUIPT project adapted an ROI tool for England to four European countries (Germany, the Netherlands, Spain and Hungary). EQUIPTMOD, the economic model at the core of the ROI tool, is designed to assess the efficiency of packages of smoking cessation interventions. The objective of this paper is to describe the methods for EQUIPTMOD and identify key outcomes associated with continued and cessation of smoking. Methods: EQUIPTMOD uses a Markov model to estimate life‐time costs, quality‐adjusted life years (QALYs) and life years associated with a current and former smoker. It uses population data on smoking prevalence, disease prevalence, mortality and the impact of smoking combined with associated costs and utility effects of disease. To illustrate the tool's potential, costs, QALYs and life expectancy were estimated for the average current smoker for five countries based on the assumptions that they continue and that they cease smoking over the next 12 months. Costs and effects were discounted at country‐specific rates. Results: For illustration, over a life‐time horizon, not quitting smoking within the next 12 months in England will reduce life expectancy by 0.66, reduce QALYs by 1.09 and result in £4961 higher disease‐related health care costs than if the smoker ceased smoking in the next 12 months. For all age–sex categories, costs were lower and QALYs higher for those who quit smoking in the 12 months than those who continued. Conclusions: EQUIPTMOD facilitates assessment of the cost effectiveness of smoking cessation strategies. The demonstrated results indicate large potential benefits from smoking cessation at both an individual and population level. [ABSTRACT FROM AUTHOR]

Abstract: Background and aims: Estimating ‘return on investment’ (ROI) from smoking cessation interventions requires reach and effectiveness parameters for interventions for use in economic models such as the EQUIPT ROI tool ( http://roi.equipt.eu). This paper describes the derivation of these parameter estimates for England that can be adapted to create ROI models for use by other countries. Methods: Estimates were derived for interventions in terms of their reach and effectiveness in: (1) promoting quit attempts and (2) improving the success of quit attempts (abstinence for at least 12 months). The sources were systematic reviews of efficacy supplemented by individual effectiveness evaluations and national surveys. Findings: Quit attempt rates were estimated to be increased by the following percentages (with reach in parentheses): 20% by tax increases raising the cost of smoking 5% above the cost of living index (100%); 10% by enforced comprehensive indoor public smoking bans (100%); 3% by mass media campaigns achieving 400 gross rating points (100%); 40% by brief opportunistic physician advice (21%); and 110% by use of a licensed nicotine product to reduce cigarette consumption (12%). Quit success rates were estimated to be increased by the following ratios: 60% by single‐form nicotine replacement therapy (NRT) (5%); 114% by NRT patch plus a faster‐acting NRT (2%);124% by prescribed varenicline (5%); 60% by bupropion (1%); 100% by nortriptyline (0%), 10) 298% by cytisine (0%); 40% by individual face‐to‐face behavioural support (2%); 37% by telephone support (0.5%); 88% by group behavioural support (1%); 63% by text messaging (0.5%); and 19% by printed self‐help materials (1%). There was insufficient evidence to obtain reliable, country‐specific estimates for interventions such as websites, smartphone applications and e‐cigarettes. Conclusions: Tax increases, indoor smoking bans, brief opportunistic physician advice and use of nicotine replacement therapy (NRT) for smoking reduction can all increase population quit attempt rates. Quit success rates can be increased by provision of NRT, varenicline, bupropion, nortriptyline, cytisine and behavioural support delivered through a variety of modalities. Parameter estimates for the effectiveness and reach of these interventions can contribute to return on investment estimates in support of national or regional policy decisions. [ABSTRACT FROM AUTHOR]