Literature DB >> 20176309

Modeling the impact of smoking-cessation treatment policies on quit rates.

David T Levy1, Amanda L Graham, Patricia L Mabry, David B Abrams, C Tracy Orleans.   

Abstract

BACKGROUND: Smoking-cessation treatment policies could yield substantial increases in adult quit rates in the U.S.
PURPOSE: The goals of this paper are to model the effects of individual cessation treatment policies on population quit rates, and to illustrate the potential benefits of combining policies to leverage their synergistic effects.
METHODS: A mathematical model is updated to examine the impact of five cessation treatment policies on quit attempts, treatment use, and treatment effectiveness. Policies include: (1) expand cessation treatment coverage and provider reimbursement; (2) mandate adequate funding for the use and promotion of evidence-based, state-sponsored telephone quitlines; (3) support healthcare system changes to prompt, guide, and incentivize tobacco treatment; (4) support and promote evidence-based treatment via the Internet; and (5) improve individually tailored, stepped-care approaches and the long-term effectiveness of evidence-based treatments.
RESULTS: The annual baseline population quit rate is 4.3% of all current smokers. Implementing any policy in isolation is projected to increase the quit rate to between 4.5% and 6%. By implementing all five policies in combination, the quit rate is projected to increase to 10.9%, or 2.5 times the baseline rate.
CONCLUSIONS: If fully implemented in a coordinated fashion, cessation treatment policies could reduce smoking prevalence from its current rate of 20.5% to 17.2% within 1 year. By modeling the policy impacts on the components of the population quit rate (quit attempts, treatment use, treatment effectiveness), key indicators are identified that need to be analyzed in attempts to improve the effect of cessation treatment policies. 2010 American Journal of Preventive Medicine. All rights reserved.

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Year:  2010        PMID: 20176309      PMCID: PMC2843145          DOI: 10.1016/j.amepre.2009.11.016

Source DB:  PubMed          Journal:  Am J Prev Med        ISSN: 0749-3797            Impact factor:   5.043


  22 in total

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Authors:  Anne N Thorndike; Lois Biener; Nancy A Rigotti
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2.  Variations in treatment benefits influence smoking cessation: results of a randomised controlled trial.

Authors:  H H Schauffler; S McMenamin; K Olson; G Boyce-Smith; J A Rideout; J Kamil
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Review 3.  A meta-analysis of the efficacy of over-the-counter nicotine replacement.

Authors:  J R Hughes; S Shiffman; P Callas; J Zhang
Journal:  Tob Control       Date:  2003-03       Impact factor: 7.552

4.  Smoking cessation among self-quitters.

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5.  Helping smokers quit: understanding the barriers to utilization of smoking cessation services.

Authors:  Sarah E Gollust; Steven A Schroeder; Kenneth E Warner
Journal:  Milbank Q       Date:  2008-12       Impact factor: 4.911

6.  Debunking myths about self-quitting. Evidence from 10 prospective studies of persons who attempt to quit smoking by themselves.

Authors:  S Cohen; E Lichtenstein; J O Prochaska; J S Rossi; E R Gritz; C R Carr; C T Orleans; V J Schoenbach; L Biener; D Abrams
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Review 7.  Healthcare financing systems for increasing the use of tobacco dependence treatment.

Authors:  J Kaper; E J Wagena; J L Severens; C P Van Schayck
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8.  Examining the effects of tobacco treatment policies on smoking rates and smoking related deaths using the SimSmoke computer simulation model.

Authors:  D T Levy; K Friend
Journal:  Tob Control       Date:  2002-03       Impact factor: 7.552

9.  A simulation model of policies directed at treating tobacco use and dependence.

Authors:  David T Levy; Karen Friend
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10.  Impact of over-the-counter sales on effectiveness of pharmaceutical aids for smoking cessation.

Authors:  John P Pierce; Elizabeth A Gilpin
Journal:  JAMA       Date:  2002-09-11       Impact factor: 56.272

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  39 in total

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Authors:  Joseph P Morrissey; Kristen Hassmiller Lich; Rebecca Anhang Price; Jeanne Mandelblatt
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2.  Exploring scenarios to dramatically reduce smoking prevalence: a simulation model of the three-part cessation process.

Authors:  David T Levy; Patricia L Mabry; Amanda L Graham; C Tracy Orleans; David B Abrams
Journal:  Am J Public Health       Date:  2010-05-13       Impact factor: 9.308

3.  Systems science: a revolution in public health policy research.

Authors:  Patricia L Mabry; Stephen E Marcus; Pamela I Clark; Scott J Leischow; David Méndez
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4.  Tobacco Policies in Louisiana: Recommendations for Future Tobacco Control Investment from SimSmoke, a Policy Simulation Model.

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5.  Boosting population quits through evidence-based cessation treatment and policy.

Authors:  David B Abrams; Amanda L Graham; David T Levy; Patricia L Mabry; C Tracy Orleans
Journal:  Am J Prev Med       Date:  2010-03       Impact factor: 5.043

6.  Reaching Healthy People 2010 by 2013: A SimSmoke simulation.

Authors:  David T Levy; Patricia L Mabry; Amanda L Graham; C Tracy Orleans; David B Abrams
Journal:  Am J Prev Med       Date:  2010-03       Impact factor: 5.043

7.  Rethinking health systems strengthening: key systems thinking tools and strategies for transformational change.

Authors:  R Chad Swanson; Adriano Cattaneo; Elizabeth Bradley; Somsak Chunharas; Rifat Atun; Kaja M Abbas; Korina Katsaliaki; Navonil Mustafee; Benjamin Mason Meier; Allan Best
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8.  Cost-effectiveness of extended cessation treatment for older smokers.

Authors:  Paul G Barnett; Wynnie Wong; Abra Jeffers; Ricardo Munoz; Gary Humfleet; Sharon Hall
Journal:  Addiction       Date:  2013-12-13       Impact factor: 6.526

9.  Estimating the Potential Impact of Tobacco Control Policies on Adverse Maternal and Child Health Outcomes in the United States Using the SimSmoke Tobacco Control Policy Simulation Model.

Authors:  David Levy; Mary Katherine Mohlman; Yian Zhang
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10.  The Minnesota SimSmoke Tobacco Control Policy Model of Smokeless Tobacco and Cigarette Use.

Authors:  David T Levy; Zhe Yuan; Yameng Li; Ann W St Claire; Barbara A Schillo
Journal:  Am J Prev Med       Date:  2019-10       Impact factor: 5.043

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