Evidence map›Paper›PMID 41678524›Full record

ArticlePloS one2026

Development and validation of the Tobacco Use Individual-level Simulation and Tracking (TwIST) Model.

Sarah D Mills, Nicholas Tapp Hughes, Yu Zhang, Kurt M Ribisl, Christopher A Wiesen, Jiaqian Fan, Kristen Hassmiller Lich

Abstract readValidation Study
In one paragraph

Article in PloS one, 2026. 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

7 authors.

Sarah D MillsDepartment of Health Behavior, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID https://orcid.org/0000-0002-0183-6753
Nicholas Tapp HughesDepartment of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Yu ZhangDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID https://orcid.org/0000-0001-5245-5144
Kurt M RibislDepartment of Health Behavior, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Christopher A WiesenOdum Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Jiaqian FanDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Kristen Hassmiller LichDepartment of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Simulation models of tobacco use behavior are useful analytic tools for projecting rates of tobacco use over time and identifying priority areas for intervention. This paper presents the Tobacco Use Individual-level Simulation and Tracking (TwIST) Model, an individual-based simulation model of tobacco use in the adult US population. We describe the model structure, data sources and parameters, and, in addition to future projections, compare modeled estimates of smoking prevalence to those from established surveys. The simulated population and model parameter estimates are informed by the Population Assessment of Tobacco and Health Study and other nationally representative datasets. To simulate tobacco use over time, we estimated 2nd order Markov models using multinomial logistic regression. To validate the model, we compared model estimates of tobacco use to data from three national surveys. The model estimates adult cigarette smoking rates will decline from a prevalence of 12.4% (95% uncertainty interval (95% UI): 12.2-12.8%) in 2020 to 9.6% (95% UI: 9.3-9.9%), 9.1% (95% UI: 8.9-9.4%), and 8.7% (95% UI: 8.5-9.0%) in 2030, 2040, and 2050, respectively. From 2020 through 2050, adults living in poverty are estimated to have a cigarette smoking rate 2.1-2.3 times higher than individuals living above the poverty line. The prevalence of menthol cigarette use will decline at a slower rate than the prevalence of non-menthol cigarette use (21% vs. 38% decline). Model projections of cigarette smoking prevalence typically fall within the 95% confidence intervals of prevalence estimates across three national surveys. Overall, the TwIST Model projects cigarette smoking prevalence rates that are similar to real-world estimates. If tobacco use continues based on current patterns, income-based disparities in smoking will persist and a growing proportion of individuals who smoke will use menthol cigarettes, which are known to be harder to quit.

Indexed as

SmokingTobacco UseAdultComputer SimulationFemaleHumansMarkov ChainsPrevalenceUnited States

Identifiers

PMID41678524
PMCPMC12900341

What OpenQuestion holds

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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.