Evidence map›Paper›PMID 26983616›Full record

ArticleCancer causes & control : CCC2016

The role of public policies in reducing smoking prevalence: results from the Michigan SimSmoke tobacco policy simulation model.

David T Levy, An-Tsun Huang, Joshua S Havumaki, Rafael Meza

Abstract readValidation Study
In one paragraph

Article in Cancer causes & control : CCC, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
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  4. Smoking Histories by State in the U.S.American journal of preventive medicine · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

David T LevyPopulation Sciences, Department of Oncology, Georgetown University, 3300 Whitehaven Street NW, Suite 4100, Washington, DC, 20007, USA. dl777@georgetown.edu.
An-Tsun HuangPopulation Sciences, Department of Oncology, Georgetown University, 3300 Whitehaven Street NW, Suite 4100, Washington, DC, 20007, USA.
Joshua S HavumakiUniversity of Michigan, Ann Arbor, MI, USA.
Rafael MezaUniversity of Michigan, Ann Arbor, MI, USA.

Funding

Comparative Modeling of Lung Cancer Prevention and Control PoliciesU01CA199284 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2015 to 2019
$8.4M
Comparative Modeling of Lung Cancer Control PoliciesU01CA152956 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI DE KONING, HARRY J, KONG, CHUNG YIN · 2010 to 2014
$7.4M
MODELING THE POLICY IMPACT OF CIGARETTE AND SMOKELESS USE ON US MORTALITYR01DA036497 · NIDA · GEORGETOWN UNIVERSITY · PI LEVY, DAVID THEODORE · 2013 to 2016
$2.5M
A Simulation of Tobacco Policy, Smoking and Lung CancerU01CA097450 · NCI · PACIFIC INSTITUTE FOR RES AND EVALUATION · PI LEVY, DAVID THEODORE · 2002 to 2009
$2.1M
NCI NIH HHS U01 CA097450NCI NIH HHS U01 CA152956NCI NIH HHS U01CA152956-03S2NCI NIH HHS U01 CA199284NIDA NIH HHS R01 DA036497
6 · The paper itself

Abstract

introductionMichigan has implemented several of the tobacco control policies recommended by the World Health Organization MPOWER goals. We consider the effect of those policies and additional policies consistent with MPOWER goals on smoking prevalence and smoking-attributable deaths (SADs).

methodsThe SimSmoke tobacco control policy simulation model is used to examine the effect of past policies and a set of additional policies to meet the MPOWER goals. The model is adapted to Michigan using state population, smoking, and policy data starting in 1993. SADs are estimated using standard attribution methods. Upon validating the model, SimSmoke is used to distinguish the effect of policies implemented since 1993 against a counterfactual with policies kept at their 1993 levels. The model is then used to project the effect of implementing stronger policies beginning in 2014.

resultsSimSmoke predicts smoking prevalence accurately between 1993 and 2010. Since 1993, a relative reduction in smoking rates of 22 % by 2013 and of 30 % by 2054 can be attributed to tobacco control policies. Of the 22 % reduction, 44 % is due to taxes, 28 % to smoke-free air laws, 26 % to cessation treatment policies, and 2 % to youth access. Moreover, 234,000 SADs are projected to be averted by 2054. With additional policies consistent with MPOWER goals, the model projects that, by 2054, smoking prevalence can be further reduced by 17 % with 80,000 deaths averted relative to the absence of those policies.

conclusionsMichigan SimSmoke shows that tobacco control policies, including cigarette taxes, smoke-free air laws, and cessation treatment policies, have substantially reduced smoking and SADs. Higher taxes, strong mass media campaigns, and cessation treatment policies would further reduce smoking prevalence and SADs.

Indexed as

Models, TheoreticalAdolescentAdultAgedComputer SimulationFemaleForecastingHealth PolicyHumansMaleMichiganMiddle AgedNicotianaPrevalencePublic PolicySmokingSimSmokeSimulation modelingSmoking-attributable deathsSmoking prevalenceTobacco control

Identifiers

PMID26983616
PMCPMC4840036

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.