REVIEW PAPER
Comparing effects of tobacco use prevention modalities: need for complex system models
 
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1
Departments of Preventive Medicine and Psychology, University of Southern California, Los Angeles, USA
 
2
Department of Oncology, Georgetown University, Washington, DC, USA
 
3
University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Chapel Hill, USA
 
4
University of North Carolina, School of Medicine, Chapel Hill, USA
 
5
University of North Carolina, Cecil G. Sheps Center for Health Services Research and the NCTRaCS Institute, Chapel Hill, USA
 
6
Institute for Health Research and Policy, Health Policy Center, University of Illinois, Chicago, USA
 
 
Submission date: 2012-09-24
 
 
Acceptance date: 2013-01-17
 
 
Publication date: 2013-01-22
 
 
Corresponding author
Steve Sussman   

Departments of Preventive Medicine and Psychology, University of Southern California, Soto Street Building 302A, 2001 N. Soto Street, Los Angeles, CA 90033-9045, USA
 
 
Tobacco Induced Diseases 2013;11(January):2
 
KEYWORDS
ABSTRACT
Many modalities of tobacco use prevention programming have been implemented including various policy regulations (tax increases, warning labels, limits on access, smoke-free policies, and restrictions on marketing), mass media programming, school-based classroom education, family involvement, and involvement of community agents (i.e., medical, social, political). The present manuscript provides a glance at these modalities to compare relative and combined impact of them on youth tobacco use. In a majority of trials, community-wide programming, which includes multiple modalities, has not been found to achieve impacts greater than single modality programming. Possibly, the most effective means of prevention involves a careful selection of program type combinations. Also, it is likely that a mechanism for coordinating maximally across program types (e.g., staging of programming) is needed to encourage a synergistic impact. Studying tobacco use prevention as a complex system is considered as a means to maximize effects from combinations of prevention types. Future studies will need to more systematically consider the role of combined programming.
 
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