title: Comparison of tobacco control scenarios: quantifying estimates of long-term health impact using the DYNAMO-HIA modeling tool.
abstract: BACKGROUND
  There 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.
  
  
  RESULTS
  We 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.
  
  
  CONCLUSIONS
  We 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.
authors.affiliation: Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands. m.kulik@erasmusmc.nl
authors.forename: Margarete C
authors.initials: MC
authors.lastname: Kulik
authors.forename: Wilma J
authors.initials: WJ
authors.lastname: Nusselder
authors.forename: Hendriek C
authors.initials: HC
authors.lastname: Boshuizen
authors.forename: Stefan K
authors.initials: SK
authors.lastname: Lhachimi
authors.forename: Esteve
authors.initials: E
authors.lastname: Fernández
authors.forename: Paolo
authors.initials: P
authors.lastname: Baili
authors.forename: Kathleen
authors.initials: K
authors.lastname: Bennett
authors.forename: Johan P
authors.initials: JP
authors.lastname: Mackenbach
authors.forename: H A
authors.initials: HA
authors.lastname: Smit
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D003132:Commerce; D006262:Health; D006293:Health Promotion; D006801:Humans; D009426:Netherlands; D009538:Nicotine; D012907:Smoking / Q000191:economics* / Q000453:epidemiology; D016540:Smoking Cessation / Q000379:methods*; D000074606:Smoking Prevention; D012984:Software; D013997:Time Factors; D014029:Tobacco Use Disorder / Q000191:economics / Q000628:therapy; D014026:Nicotiana
chemical_list: D009538:Nicotine