Evidence map›Paper›PMID 41151800›Full record

ArticleTobacco control2025

SimSmoke simulation models by educational status: past and future US trends and the potential role of policy.

David T Levy, James H Buszkiewicz, Zhe Yuan, Yameng Li, Rafael Meza, Nancy L Fleischer

Abstract read
In one paragraph

Article in Tobacco control, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

David T LevyLombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia, USA dl777@georgetown.edu.ORCID http://orcid.org/0000-0001-5280-3612
James H BuszkiewiczEpidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.ORCID http://orcid.org/0000-0002-7919-7103
Zhe YuanLombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia, USA.
Yameng LiLombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia, USA.
Rafael MezaDepartment of Integrative Oncology, The University of British Columbia Faculty of Medicine, Vancouver, British Columbia, Canada.ORCID http://orcid.org/0000-0002-1076-5037
Nancy L FleischerEpidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.ORCID http://orcid.org/0000-0002-4371-9133

Funding

Research Project 3: Modeling the Impact of Tobacco Control Policies on Polytobacco Use and Associated Health DisparitiesU54CA229974 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI David Theodore Levy, David Mendez Emilien · 2018 to 2026
$39.2M
Comparative Modeling of Lung Cancer Prevention, Early Detection and Treatment InterventionsU01CA253858 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2020 to 2025
$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
The Impact of Tobacco Control Policies on Health Equity in the United States - MERIT ExtensionR37CA214787 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI FLEISCHER, NANCY · 2018 to 2024
$4.2M
NCI NIH HHS R37 CA214787NCI NIH HHS U01 CA152956NCI NIH HHS U01 CA253858NCI NIH HHS U54 CA229974
6 · The paper itself

Abstract

introductionSimulation models are helpful in anticipating future trends and developing effective tobacco control policies to reduce smoking-related inequities. However, few models have systematically analysed smoking trends by education group.

methodsWe developed four separate SimSmoke models by educational group: less than high school, high school, some college and college and above. Education status is based on the US Census estimates, and smoking prevalence is based on the Current Population Survey-Tobacco Use Supplement (CPS-TUS). Following a first-order Markov process, smoking prevalence evolves through yearly initiation, cessation and relapse, subject to tobacco control policies. The models begin in 2006 and incorporate the impact of tobacco control policies implemented through 2023. They are used to project trends in smoking prevalence and smoking-attributable deaths (SADs) and the impact of policies. The smoking prevalence estimates from the model have also been compared with the CPS-TUS in recent years.

resultsAdults with higher educational attainment had the lowest smoking prevalence with the greatest relative decline from 2006 to 2023. Per capita SADs were highest among adults with less education. Price increases, Tobacco 21 laws and smoke-free air laws were most effective in reducing long-term smoking prevalence. The models underestimate the reduction in smoking prevalence relative to CPS-TUS estimates in recent years, especially among youth. DISCUSSION: We found major differences in the initial levels and rates of decline in smoking prevalence by education, leading to widening health inequities. Further study is warranted on education-related policy impacts and the relationship of electronic nicotine delivery systems and other non-cigarette product use to cigarette use.

Indexed as

DisparitiesPublic policySocioeconomic statusSurveillance and monitoring

Identifiers

PMID41151800
PMCPMC13262883

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.