Simulation Modeling and Tobacco Control

Creating More Robust Public Health Policies

Bibliographic Data

ID11027371
AuthorsDavid T Levy (0000-0001-5280-3612, David T. Levy is with the Pacific Institute for Research and Evaluation of Calverton, Md, and the University of Baltimore, Baltimore, Md. Joseph E. Bauer is with the Roswell Park Cancer Institute of Buffalo, NY. Hye-ryeon Lee is with the Department of Speech at the University of Hawaii, Manoa.), Joseph E Bauer (0000-0003-4939-8120, David T. Levy is with the Pacific Institute for Research and Evaluation of Calverton, Md, and the University of Baltimore, Baltimore, Md. Joseph E. Bauer is with the Roswell Park Cancer Institute of Buffalo, NY. Hye-ryeon Lee is with the Department of Speech at the University of Hawaii, Manoa.), Hye-Ryeon Lee (0000-0001-9957-5522, David T. Levy is with the Pacific Institute for Research and Evaluation of Calverton, Md, and the University of Baltimore, Baltimore, Md. Joseph E. Bauer is with the Roswell Park Cancer Institute of Buffalo, NY. Hye-ryeon Lee is with the Department of Speech at the University of Hawaii, Manoa.), Hye‐ryeon Lee (0000-0002-8427-3174, University of Hawaiʻi at Mānoa)
Year2006
Volume96
Issue3
Pages494-498
Publication date2006-03-01
Open AccessNo
TypeARTICLE
VenueAmerican Journal of Public Health (JOURNAL)
Journal identifiersISSN: 0090-0036 • E-ISSN: 1541-0048
PublisherAmerican Public Health Association (PUBLISHER • US)
DOI10.2105/ajph.2005.063974
PMID16449585
PMCIDPMC1470526
OpenAlexW2054954466
LanguageEN
Citations received30
References cited25

Although previous empirical studies have shown that tobacco control policies are effective at reducing smoking rates, such studies have proven of limited effectiveness in distinguishing how the effect of policies depend on the other policies in place, the length of adjustment period, the way the policy is implemented, and the demographic groups considered. An alternative and complementary approach to purely statistical equations is simulation models. We describe the SimSmoke simulation model and how we used it to assess tobacco control policy in a specific case study. Simulation models are not only useful for policy prediction and planning but also may help to broaden our understanding of the role of different public health policies within a complex, dynamic social system

Control (management) · Econometrics · Economic growth · Economics · Environmental health · Public economics · Public health · Public health policy · Public policy · Tobacco control · Artificial Intelligence · Computer Science · demographic modeling and climate adaptation · Health disparities and outcomes · Medicine · Smoking Behavior and Cessation · Health Policy

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Open DOISci-HubOpen Access