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Comparison of Tobacco Control Scenarios: Quantifying Estimates of Long-Term Health Impact Using the DYNAMO-HIA Modeling Tool
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 starte...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3285691/ https://www.ncbi.nlm.nih.gov/pubmed/22384230 http://dx.doi.org/10.1371/journal.pone.0032363 |
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author | Kulik, Margarete C. Nusselder, Wilma J. Boshuizen, Hendriek C. Lhachimi, Stefan K. Fernández, Esteve Baili, Paolo Bennett, Kathleen Mackenbach, Johan P. Smit, H. A. |
author_facet | Kulik, Margarete C. Nusselder, Wilma J. Boshuizen, Hendriek C. Lhachimi, Stefan K. Fernández, Esteve Baili, Paolo Bennett, Kathleen Mackenbach, Johan P. Smit, H. A. |
author_sort | Kulik, Margarete C. |
collection | PubMed |
description | 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. METHODS AND 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. CONCLUSION: 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. |
format | Online Article Text |
id | pubmed-3285691 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-32856912012-03-01 Comparison of Tobacco Control Scenarios: Quantifying Estimates of Long-Term Health Impact Using the DYNAMO-HIA Modeling Tool Kulik, Margarete C. Nusselder, Wilma J. Boshuizen, Hendriek C. Lhachimi, Stefan K. Fernández, Esteve Baili, Paolo Bennett, Kathleen Mackenbach, Johan P. Smit, H. A. PLoS One Research Article 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. METHODS AND 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. CONCLUSION: 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. Public Library of Science 2012-02-23 /pmc/articles/PMC3285691/ /pubmed/22384230 http://dx.doi.org/10.1371/journal.pone.0032363 Text en Kulik et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Kulik, Margarete C. Nusselder, Wilma J. Boshuizen, Hendriek C. Lhachimi, Stefan K. Fernández, Esteve Baili, Paolo Bennett, Kathleen Mackenbach, Johan P. Smit, H. A. Comparison of Tobacco Control Scenarios: Quantifying Estimates of Long-Term Health Impact Using the DYNAMO-HIA Modeling Tool |
title | Comparison of Tobacco Control Scenarios: Quantifying Estimates of Long-Term Health Impact Using the DYNAMO-HIA Modeling Tool |
title_full | Comparison of Tobacco Control Scenarios: Quantifying Estimates of Long-Term Health Impact Using the DYNAMO-HIA Modeling Tool |
title_fullStr | Comparison of Tobacco Control Scenarios: Quantifying Estimates of Long-Term Health Impact Using the DYNAMO-HIA Modeling Tool |
title_full_unstemmed | Comparison of Tobacco Control Scenarios: Quantifying Estimates of Long-Term Health Impact Using the DYNAMO-HIA Modeling Tool |
title_short | Comparison of Tobacco Control Scenarios: Quantifying Estimates of Long-Term Health Impact Using the DYNAMO-HIA Modeling Tool |
title_sort | comparison of tobacco control scenarios: quantifying estimates of long-term health impact using the dynamo-hia modeling tool |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3285691/ https://www.ncbi.nlm.nih.gov/pubmed/22384230 http://dx.doi.org/10.1371/journal.pone.0032363 |
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