Evidence map›Paper›PMID 37671638›Full record

ArticleNicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco2024

Improving Prediction of Tobacco Use Over Time: Findings from Waves 1-4 of the Population Assessment of Tobacco and Health Study.

Sarah D Mills, Yu Zhang, Christopher A Wiesen, Kristen Hassmiller Lich

Abstract read
In one paragraph

Article in Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Sarah D MillsDepartment of Health Behavior, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.ORCID 0000-0002-0183-6753
Yu ZhangDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
Christopher A WiesenOdum Institute, University of North Carolina, Chapel Hill, NC, USA.
Kristen Hassmiller LichDepartment of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.

Funding

Modeling the public health impact of a national menthol cigarette ban.K01CA242530 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI MILLS, SARAH · 2019 to 2023
$728k
FDA HHSNCI NIH HHS K01 CA242530NIH HHS
6 · The paper itself

Abstract

introductionFirst-order Markov models assume future tobacco use behavior is dependent on current tobacco use and are often used to characterize patterns of tobacco use over time. Higher-order Markov models that assume future behavior is dependent on current and prior tobacco use may better estimate patterns of tobacco use. AIMS AND

methodsThis study compared Markov models of different orders to examine whether incorporating information about tobacco use history improves model estimation of tobacco use and estimated tobacco use transition probabilities. We used data from four waves of the Population Assessment of Tobacco and Health Study. In each Wave, a participant was categorized into one of the following tobacco use states: never smoker, former smoker, menthol cigarette smoker, non-menthol cigarette smoker, or e-cigarette/dual user. We compared first-, second-, and third-order Markov models using multinomial logistic regression and estimated transition probabilities between tobacco use states. `

resultsThe third-order model was the best fit for the data. The percentage of former smokers, menthol cigarette smokers, non-menthol cigarette smokers, and e-cigarette/dual users in Wave 3 that remained in the same tobacco use state in Wave 4 ranged from 63.4% to 97.2%, 29.2% to 89.8%, 34.8% to 89.7%, and 20.5% to 80.0%, respectively, dependent on tobacco use history. Individuals who were current tobacco users, but former smokers in the prior two years, were most likely to quit.

conclusionsTransition probabilities between tobacco use states varied widely depending on tobacco use history. Higher-order Markov models improve estimation of tobacco use over time and can inform understanding of trajectories of tobacco use behavior. IMPLICATIONS: Findings from this study suggest that transition probabilities between tobacco use states vary widely depending on tobacco use history. Tobacco product users (cigarette or e-cigarette/dual users) who were in the same tobacco use state in the prior two years were least likely to quit. Individuals who were current tobacco users, but former smokers in the prior two years, were most likely to quit. Quitting smoking for at least two years is an important milestone in the process of cessation.

Indexed as

Cigarette SmokingElectronic Nicotine Delivery SystemsSmoking CessationTobacco ProductsHumansMentholRisk FactorsTobacco UseUnited StatesMenthol

Identifiers

PMID37671638
PMCPMC10803117

What OpenQuestion holds

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Registered trials

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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.