Comparison of Tobacco Control Scenarios: Quantifying Estimates of Long-Term Health Impact Using the DYNAMO-HIA Modeling Tool

BackgroundThere are several types of tobacco control interventions/policies which can change future smoking exposure. The most basic intervention types are 1) smoking cessation interventions 2) preventing smoking initiation and 3) implementation of a nationwide policy affecting quitters and starters simultaneously. The possibility for dynamic quantification of such different interventions is key for comparing the timing and size of their effects. Methods and ResultsWe developed a software tool, DYNAMO-HIA, which allows for a quantitative comparison of the health impact of different policy scenarios. We illustrate the outcomes of the tool for the three typical types of tobacco control interventions if these were applied in the Netherlands. The tool was used to model the effects of different types of smoking interventions on future smoking prevalence and on health outcomes, comparing these three scenarios with the business-as-usual scenario. The necessary data input was obtained from the DYNAMO-HIA database which was assembled as part of this project. All smoking interventions will be effective in the long run. The population-wide strategy will be most effective in both the short and long term. The smoking cessation scenario will be second-most effective in the short run, though in the long run the smoking initiation scenario will become almost as effective. Interventions aimed at preventing the initiation of smoking need a long time horizon to become manifest in terms of health effects. The outcomes strongly depend on the groups targeted by the intervention. ConclusionWe calculated how much more effective the population-wide strategy is, in both the short and long term, compared to quit smoking interventions and measures aimed at preventing the initiation of smoking. By allowing a great variety of user-specified choices, the DYNAMO-HIA tool is a powerful instrument by which the consequences of different tobacco control policies and interventions can be assessed.研究背景：现有多种可改变未来吸烟暴露水平的烟草控制干预措施与政策。其中最基础的干预类型包括：1）戒烟干预；2）预防吸烟起始；3）同时针对戒烟者与新吸烟者的全国性政策实施。对上述不同干预措施开展动态量化分析，是对比其干预时机与效果规模的核心要点。 研究方法与结果：本研究开发了一款DYNAMO-HIA软件工具，可实现不同政策场景下健康影响的量化对比。本研究以荷兰为应用场景，演示了该工具针对三类典型烟草控制干预措施的应用效果。研究利用该工具建模不同吸烟干预措施对未来吸烟流行率与健康结局的影响，并将上述三类干预场景与常规现状（business-as-usual）情景进行对比。本研究所需的输入数据来自本项目组建的DYNAMO-HIA数据库。所有吸烟干预措施在长期范围内均能发挥效果。全人群干预策略在短期与长期均为最有效的干预方式。戒烟干预场景在短期内效果次之，而从长期来看，预防吸烟起始干预场景的效果将几乎与之持平。旨在预防吸烟起始的干预措施，需要较长的时间周期才能在健康结局中显现其效果。干预效果在很大程度上取决于干预措施的目标人群。 研究结论：本研究量化对比了全人群干预策略与戒烟干预、预防吸烟起始措施在短期与长期范围内的效果优势。该工具支持用户自定义多种参数选项，因此可作为一款强大的评估工具，用于评估不同烟草控制政策与干预措施的实施后果。