Evidence map›Paper›PMID 40750790›Full record

ArticleSubstance use & misuse2025

Identifying and Understanding Use Trajectories of Cigarettes and E-Cigarettes.

Nadra E Lisha, Manali Vora, Benjamin W Chaffee, Jing Cheng

Abstract read
In one paragraph

Article in Substance use & misuse, 2025. 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

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2 · The registry

The trial behind it

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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.

Nadra E LishaUCSF Tobacco Center of Regulatory Science, San Francisco, California, USA.
Manali VoraDepartment of Periodontics, School of Dentistry, West Virginia University, Morgantown, West Virginia, USA.
Benjamin W ChaffeeUCSF Tobacco Center of Regulatory Science, San Francisco, California, USA.
Jing ChengUCSF Tobacco Center of Regulatory Science, San Francisco, California, USA.

Funding

PROJECT 5: IMPACT OF CHANGING TOBACCO PRODUCT USE ON HEALTHCARE COSTS FOR GENERAL AND VULNERABLE POPULATIONSU54HL147127 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LING, PAMELA MAY · 2018 to 2022
$20.4M
NHLBI NIH HHS U54 HL147127
6 · The paper itself

Abstract

backgroundThe rising use of e-cigarettes, particularly dual use with cigarettes, necessitates understanding usage patterns and identifying participant and product characteristics that influence behaviors to guide tobacco regulatory decisions.

methodsAnalysis included adults (≥18 years) from the Population Assessment of Tobacco and Health (PATH) study, a nationally representative, longitudinal cohort. Participants were classified into six e-cigarette/cigarette category at each of waves 1-5 (2013-2019). Latent class analysis (LCA) was performed to identify distinct trajectories of cigarette and e-cigarette use patterns over time in Mplus. Random forest was used to examine the importance of baseline participant and product characteristics in predicting the distinct groups of use trajectories. Within each cigarette class, odds ratios and 95% CIs of the top 15 predictors of e-cigarette use group were calculated in multinomial logistic regression controlling for age, race/ethnicity, and education.

resultsFour cigarette-use trajectories were identified: "Cigarette never smokers" (31.3%), "Past experimental cigarette smokers" (23.8%), "Current experimental cigarette smokers" (18.8%), and "Current cigarette smokers" (26.0%). E-cigarette-use trajectories included "Never users" (53.6%), "Past progressors" (32.3%), and "Current progressors" (14.3%). Random forest highlighted common and unique predictors across cigarette-use groups. For example, perceptions of e-cigarette harm relative to cigarettes strongly predicted e-cigarette progression among current smokers, whereas younger age and higher social media use were more relevant among never and past smokers.

conclusionsDistinct cigarette and e-cigarette use trajectories were identified, with predictors varying by cigarette-use group. These findings underscore the importance of targeted regulatory strategies based on subgroup-specific behaviors and characteristics.

Indexed as

Cigarette SmokingElectronic Nicotine Delivery SystemsTobacco ProductsVapingAdolescentAdultFemaleHumansLatent Class AnalysisLongitudinal StudiesMaleMiddle AgedSmokersYoung AdultcigarettesE-cigaretteslatent class analysispatterns of use

Identifiers

PMID40750790
PMCPMC13005670

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.