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Record W4221013247 · doi:10.3390/ijerph19074137

Cardiovascular Outcomes among Combustible-Tobacco and Electronic Nicotine Delivery System (ENDS) Users in Waves 1 through 5 of the Population Assessment of Tobacco and Health (PATH) Study, 2013–2019

2022· article· en· W4221013247 on OpenAlexaff
Martin C. Mahoney, Cheryl Rivard, Heather L. Kimmel, Hoda T. Hammad, Eva Sharma, Michael J. Halenar, Jim Sargent, K. Michael Cummings, Raymond Niaura, Maciej Ł. Goniewicz, Maansi Bansal‐Travers, Dorothy K. Hatsukami, Diann E. Gaalema, Geoffrey T. Fong, Shannon Gravely, Carol H. Christensen, Ryan M. Haskins, Marushka L. Silveira, Carlos Blanco, Wilson M. Compton, Cassandra A. Stanton, Andrew Hyland

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Waterloo
FundersNational Institute on Drug AbuseCenter for Tobacco ProductsNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsNicotineEnvironmental healthTobacco useMedicinePopulationPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Prior studies have not clearly established risk of cardiovascular disease (CVD) among smokers who switch to exclusive use of electronic nicotine delivery systems (ENDS). We compared cardiovascular disease incidence in combustible-tobacco users, those who transitioned to ENDS use, and those who quit tobacco with never tobacco users. METHODS: This prospective cohort study analyzes five waves of Population Assessment of Tobacco and Health (PATH) Study data, Wave 1 (2013-2014) through Wave 5 (2018-2019). Cardiovascular disease (CVD) incidence was captured over three intervals (Waves 1 to 3, Waves 2 to 4, and Waves 3 to 5). Participants were adults (40+ years old) without a history of CVD for the first two waves of any interval. Change in tobacco use status, from exclusive past 30 day use of any combustible-tobacco product to either exclusive past 30 day ENDS use, dual past 30 day use of ENDS and combustible-tobacco, or no past 30 day use of any tobacco, between the first two waves of an interval was used to predict onset of CVD between the second and third waves in the interval. CVD incidence was defined as a new self-report of being told by a health professional that they had congestive heart failure, stroke, or a myocardial infarction. Generalized estimating equation (GEE) analyses combined 10,548 observations across intervals from 7820 eligible respondents. RESULTS: Overall, there were 191 observations of CVD among 10,548 total observations (1.7%, standard error (SE) = 0.2), with 40 among 3014 never users of tobacco (1.5%, SE = 0.3). In multivariable models, CVD incidence was not significantly different for any tobacco user groups compared to never users. There were 126 observations of CVD among 6263 continuing exclusive combustible-tobacco users (adjusted odds ratio [AOR] = 1.44; 95% confidence interval (CI) 0.87-2.39), 15 observations of CVD among 565 who transitioned to dual use (AOR = 1.85; 0.78-4.37), and 10 observations of CVD among 654 who quit using tobacco (AOR = 1.18; 0.33-4.26). There were no observations of CVD among 53 who transitioned to exclusive ENDS use. CONCLUSIONS: This study found no difference in CVD incidence by tobacco status over three 3 year intervals, even for tobacco quitters. It is possible that additional waves of PATH Study data, combined with information from other large longitudinal cohorts with careful tracking of ENDS use patterns may help to further clarify this relationship.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.382
Teacher spread0.320 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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