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Record W4417406691 · doi:10.64898/2025.12.15.25342290

Transitions in cigarette and ENDS use in the PATH Study: a multistate transition model analysis of adults in 2021–2022 compared to previous years

2025· article· en· W4417406691 on OpenAlexaff
Andrew F. Brouwer, Olivia Roberts, Jihyoun Jeon, Evelyn Jiménez-Mendoza, Stephanie R. Land, Neal D. Freedman, Rossana Torres‐Álvarez, Ritesh Mistry, David T. Levy, Rafael Meza

Bibliographic record

VenuemedRxiv · 2025
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsInstitute of Population and Public HealthUniversity of British Columbia
FundersNational Cancer InstituteNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsPath (computing)Transition (genetics)Public healthPath analysis (statistics)Series (stratigraphy)

Abstract

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Abstract Introduction Electronic nicotine delivery systems (ENDS) products continue to evolve, and so ongoing analysis of transition rates over time is important for tracking real-world associations between ENDS and cigarette use and for providing the information necessary to project future public health outcomes. Methods Using the Population Assessment of Tobacco and Health (PATH) Study Waves 6–7 (2021–2022), we applied a Markov multistate transition model to estimate transition rates for initiation and cessation of each product. We estimated one-year transition probabilities for each transition. These results were compared to estimated rates and probabilities in Waves 1–6 (2014– 2021). Results The fraction adopting ENDS use in 2021–22 among those who had never previously established tobacco product use, those not currently using tobacco products, and those currently smoking cigarettes increased to 0.5% (95% confidence intervals [CI]: 0.4, 0.6%), 2.7% (95% CI: 2.3, 3.2%), and 6.8% (95% CI: 6.1, 7.6%), respectively. These increases were driven by young adults (ages 18–24), with respective transition fractions of 2.7% (95% CI: 2.3, 3.2%), 23.6% (95% CI: 20.2, 27.0%), and 19.2% (95% CI: 14.0, 24.5%). The fraction of adults who transitioned from dual cigarette and ENDS use to cigarette-only use remained around 25% (26.2% [95% CI: 21.7, 30.7%]), while the fraction who transitioned to ENDS-only use increased to 24.2% (95%CI: 20.5, 27.9%). The increase in the dual to ENDS-only use transition was also driven by young adults (34.4% [95% CI: 26.2, 42.6%]) and adults ages 25–34 (29.4% [95% CI: 23.1, 35.7%]). Conclusion Public health efforts are needed to promote cigarette cessation among older adults, specifically. What this paper adds What is already known on this topic Transitions in cigarette and ENDS use have been changing over time. Young adults have been early adopters of ENDS, with older adults less likely to try ENDS or to completely switch from cigarettes to ENDS. Frequency of product use likely impacts the likelihood of product quitting or switching. What this study adds We found increasing adoption of ENDS among adults who have never smoked, those not currently using cigarettes or ENDS, and those using cigarettes only. These patterns were driven by young adults, with little cigarette cessation or switching to ENDS among older adults. Daily (vs non-daily) use of ENDS facilitated cigarette cessation among those using cigarettes and ENDS, but it was a barrier to ENDS cessation among those using ENDS only. How this study might affect research, practice, or policy Public health efforts are needed to promote cigarette cessation among older adults who smoke, many of whom may already be experiencing the health effects of tobacco use. Studies are needed to develop strategies for leveraging ENDS to maximize smoking cessation while also helping those who successfully quit smoking to avoid long-term ENDS use.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.305
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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