Computational models used to assess US tobacco control policies

Shari P. Feirman, Allison M. Glasser, Shyanika Rose, Ray Niaura, David B. Abrams, Lyubov Teplitskaya, Andrea C. Villanti

Research output: Research - peer-reviewReview article

Abstract

Introduction: Simulation models can be used to evaluate existing and potential tobacco control interventions, including policies. The purpose of this systematic review was to synthesize evidence from computational models used to project population-level effects of tobacco control interventions. We provide recommendations to strengthen simulation models that evaluate tobacco control interventions. Methods: Studies were eligible for review if they employed a computational model to predict the expected effects of a non-clinical US-based tobacco control intervention. We searched five electronic databases on July 1, 2013 with no date restrictions and synthesized studies qualitatively. Results: Six primary non-clinical intervention types were examined across the 40 studies: taxation, youth prevention, smoke-free policies, mass media campaigns, marketing/advertising restrictions, and product regulation. Simulation models demonstrated the independent and combined effects of these interventions on decreasing projected future smoking prevalence. Taxation effects were the most robust, as studies examining other interventions exhibited substantial heterogeneity with regard to the outcomes and specific policies examined across models. Conclusions: Models should project the impact of interventions on overall tobacco use, including nicotine delivery product use, to estimate preventable health and cost-saving outcomes. Model validation, transparency, more sophisticated models, and modeling policy interactions are also needed to inform policymakers to make decisions that will minimize harm and maximize health. Implications: In this systematic review, evidence from multiple studies demonstrated the independent effect of taxation on decreasing future smoking prevalence, and models for other tobacco control interventions showed that these strategies are expected to decrease smoking, benefit population health, and are reasonable to implement from a cost perspective. Our recommendations aim to help policymakers and researchers minimize harm and maximize overall populationlevel health benefits by considering the real-world context in which tobacco control interventions are implemented.

LanguageEnglish (US)
Article numberntx017
Pages1257-1267
Number of pages11
JournalNicotine and Tobacco Research
Volume19
Issue number11
DOIs
StatePublished - Nov 1 2017

Fingerprint

Tobacco
Taxes
Smoking
Insurance Benefits
Population
Smoke-Free Policy
Mass Media
Tobacco Use
Marketing
Nicotine
Health Care Costs
Research Personnel
Databases
Costs and Cost Analysis
Health

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

Feirman, S. P., Glasser, A. M., Rose, S., Niaura, R., Abrams, D. B., Teplitskaya, L., & Villanti, A. C. (2017). Computational models used to assess US tobacco control policies. Nicotine and Tobacco Research, 19(11), 1257-1267. [ntx017]. DOI: 10.1093/ntr/ntx017

Computational models used to assess US tobacco control policies. / Feirman, Shari P.; Glasser, Allison M.; Rose, Shyanika; Niaura, Ray; Abrams, David B.; Teplitskaya, Lyubov; Villanti, Andrea C.

In: Nicotine and Tobacco Research, Vol. 19, No. 11, ntx017, 01.11.2017, p. 1257-1267.

Research output: Research - peer-reviewReview article

Feirman, SP, Glasser, AM, Rose, S, Niaura, R, Abrams, DB, Teplitskaya, L & Villanti, AC 2017, 'Computational models used to assess US tobacco control policies' Nicotine and Tobacco Research, vol 19, no. 11, ntx017, pp. 1257-1267. DOI: 10.1093/ntr/ntx017
Feirman SP, Glasser AM, Rose S, Niaura R, Abrams DB, Teplitskaya L et al. Computational models used to assess US tobacco control policies. Nicotine and Tobacco Research. 2017 Nov 1;19(11):1257-1267. ntx017. Available from, DOI: 10.1093/ntr/ntx017
Feirman, Shari P. ; Glasser, Allison M. ; Rose, Shyanika ; Niaura, Ray ; Abrams, David B. ; Teplitskaya, Lyubov ; Villanti, Andrea C./ Computational models used to assess US tobacco control policies. In: Nicotine and Tobacco Research. 2017 ; Vol. 19, No. 11. pp. 1257-1267
@article{7d63521d3ad048bc9ea480269d214322,
title = "Computational models used to assess US tobacco control policies",
abstract = "Introduction: Simulation models can be used to evaluate existing and potential tobacco control interventions, including policies. The purpose of this systematic review was to synthesize evidence from computational models used to project population-level effects of tobacco control interventions. We provide recommendations to strengthen simulation models that evaluate tobacco control interventions. Methods: Studies were eligible for review if they employed a computational model to predict the expected effects of a non-clinical US-based tobacco control intervention. We searched five electronic databases on July 1, 2013 with no date restrictions and synthesized studies qualitatively. Results: Six primary non-clinical intervention types were examined across the 40 studies: taxation, youth prevention, smoke-free policies, mass media campaigns, marketing/advertising restrictions, and product regulation. Simulation models demonstrated the independent and combined effects of these interventions on decreasing projected future smoking prevalence. Taxation effects were the most robust, as studies examining other interventions exhibited substantial heterogeneity with regard to the outcomes and specific policies examined across models. Conclusions: Models should project the impact of interventions on overall tobacco use, including nicotine delivery product use, to estimate preventable health and cost-saving outcomes. Model validation, transparency, more sophisticated models, and modeling policy interactions are also needed to inform policymakers to make decisions that will minimize harm and maximize health. Implications: In this systematic review, evidence from multiple studies demonstrated the independent effect of taxation on decreasing future smoking prevalence, and models for other tobacco control interventions showed that these strategies are expected to decrease smoking, benefit population health, and are reasonable to implement from a cost perspective. Our recommendations aim to help policymakers and researchers minimize harm and maximize overall populationlevel health benefits by considering the real-world context in which tobacco control interventions are implemented.",
author = "Feirman, {Shari P.} and Glasser, {Allison M.} and Shyanika Rose and Ray Niaura and Abrams, {David B.} and Lyubov Teplitskaya and Villanti, {Andrea C.}",
year = "2017",
month = "11",
doi = "10.1093/ntr/ntx017",
volume = "19",
pages = "1257--1267",
journal = "Nicotine and Tobacco Research",
issn = "1462-2203",
publisher = "Oxford University Press",
number = "11",

}

TY - JOUR

T1 - Computational models used to assess US tobacco control policies

AU - Feirman,Shari P.

AU - Glasser,Allison M.

AU - Rose,Shyanika

AU - Niaura,Ray

AU - Abrams,David B.

AU - Teplitskaya,Lyubov

AU - Villanti,Andrea C.

PY - 2017/11/1

Y1 - 2017/11/1

N2 - Introduction: Simulation models can be used to evaluate existing and potential tobacco control interventions, including policies. The purpose of this systematic review was to synthesize evidence from computational models used to project population-level effects of tobacco control interventions. We provide recommendations to strengthen simulation models that evaluate tobacco control interventions. Methods: Studies were eligible for review if they employed a computational model to predict the expected effects of a non-clinical US-based tobacco control intervention. We searched five electronic databases on July 1, 2013 with no date restrictions and synthesized studies qualitatively. Results: Six primary non-clinical intervention types were examined across the 40 studies: taxation, youth prevention, smoke-free policies, mass media campaigns, marketing/advertising restrictions, and product regulation. Simulation models demonstrated the independent and combined effects of these interventions on decreasing projected future smoking prevalence. Taxation effects were the most robust, as studies examining other interventions exhibited substantial heterogeneity with regard to the outcomes and specific policies examined across models. Conclusions: Models should project the impact of interventions on overall tobacco use, including nicotine delivery product use, to estimate preventable health and cost-saving outcomes. Model validation, transparency, more sophisticated models, and modeling policy interactions are also needed to inform policymakers to make decisions that will minimize harm and maximize health. Implications: In this systematic review, evidence from multiple studies demonstrated the independent effect of taxation on decreasing future smoking prevalence, and models for other tobacco control interventions showed that these strategies are expected to decrease smoking, benefit population health, and are reasonable to implement from a cost perspective. Our recommendations aim to help policymakers and researchers minimize harm and maximize overall populationlevel health benefits by considering the real-world context in which tobacco control interventions are implemented.

AB - Introduction: Simulation models can be used to evaluate existing and potential tobacco control interventions, including policies. The purpose of this systematic review was to synthesize evidence from computational models used to project population-level effects of tobacco control interventions. We provide recommendations to strengthen simulation models that evaluate tobacco control interventions. Methods: Studies were eligible for review if they employed a computational model to predict the expected effects of a non-clinical US-based tobacco control intervention. We searched five electronic databases on July 1, 2013 with no date restrictions and synthesized studies qualitatively. Results: Six primary non-clinical intervention types were examined across the 40 studies: taxation, youth prevention, smoke-free policies, mass media campaigns, marketing/advertising restrictions, and product regulation. Simulation models demonstrated the independent and combined effects of these interventions on decreasing projected future smoking prevalence. Taxation effects were the most robust, as studies examining other interventions exhibited substantial heterogeneity with regard to the outcomes and specific policies examined across models. Conclusions: Models should project the impact of interventions on overall tobacco use, including nicotine delivery product use, to estimate preventable health and cost-saving outcomes. Model validation, transparency, more sophisticated models, and modeling policy interactions are also needed to inform policymakers to make decisions that will minimize harm and maximize health. Implications: In this systematic review, evidence from multiple studies demonstrated the independent effect of taxation on decreasing future smoking prevalence, and models for other tobacco control interventions showed that these strategies are expected to decrease smoking, benefit population health, and are reasonable to implement from a cost perspective. Our recommendations aim to help policymakers and researchers minimize harm and maximize overall populationlevel health benefits by considering the real-world context in which tobacco control interventions are implemented.

UR - http://www.scopus.com/inward/record.url?scp=85032834871&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032834871&partnerID=8YFLogxK

U2 - 10.1093/ntr/ntx017

DO - 10.1093/ntr/ntx017

M3 - Review article

VL - 19

SP - 1257

EP - 1267

JO - Nicotine and Tobacco Research

T2 - Nicotine and Tobacco Research

JF - Nicotine and Tobacco Research

SN - 1462-2203

IS - 11

M1 - ntx017

ER -