Evidence map›Paper›PMID 28182748›Full record

ArticlePloS one2017

Beyond experimentation: Five trajectories of cigarette smoking in a longitudinal sample of youth.

Lauren M Dutra, Stanton A Glantz, Nadra E Lisha, Anna V Song

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

39 citing papers in PubMed.

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  10. Predictors of E-cigarette and Cigarette Use Trajectory Classes from Early Adolescence to Emerging Adulthood Across Four Years (2013-2017) of the PATH Study.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2023
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  20. A Longitudinal Examination of Behavioral Transitions among Young Adult Menthol and Non-Menthol Cigarette Smokers Using a Three-State Markov Model.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2021
    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.

Lauren M DutraCenter for Tobacco Control Research and Education, University of California, San Francisco, California, United States of America.
Stanton A GlantzCenter for Tobacco Control Research and Education, University of California, San Francisco, California, United States of America.ORCID http://orcid.org/0000-0003-4620-6672
Nadra E LishaCenter for Tobacco Control Research and Education, University of California, San Francisco, California, United States of America.
Anna V SongPsychological Sciences, Health Sciences Research Institute, University of California, Merced, California, United States of America.

Funding

Training and Education p489-500P50CA180890 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GLANTZ, STANTON ARNOLD · 2013 to 2018
$20.5M
TOBACCO ADVOCACY AT THE STATE LEVELR01CA061021 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GLANTZ, STANTON ARNOLD · 1994 to 2015
$7.4M
Countering Young Adult Tobacco Marketing in BarsU01CA154240 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LING, PAMELA MAY · 2011 to 2015
$6.1M
Postdoctoral training in tobacco controlR25CA113710 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GLANTZ, STANTON ARNOLD · 2006 to 2015
$5.4M
Effect of Tobacco Advocacy at the State LevelR01DA043950 · NIDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI APOLLONIO, DOROTHY ELINOR · 2016 to 2020
$2.6M
Postdoctoral training in tobacco controlT32CA113710 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LING, PAMELA MAY · 2016 to 2020
$2.6M
Small molecular weight eNOS inhibitors for the treatment of pancreatic cancerR21CA153240 · NCI · DUKE UNIVERSITY · PI COUNTER, CHRISTOPHER M · 2011 to 2012
$376k
NCI NIH HHS P50 CA180890NCI NIH HHS R01 CA061021NCI NIH HHS R21 CA153240NCI NIH HHS R25 CA113710NCI NIH HHS T32 CA113710NCI NIH HHS U01 CA154240NIDA NIH HHS R01 DA043950
6 · The paper itself

Abstract

The first goal of this study was to identify the most appropriate measure of cigarette smoking for identifying unique smoking trajectories among adolescents; the second goal was to describe the resulting trajectories and their characteristics. Using 15 annual waves of smoking data in the National Longitudinal Survey of Youth 1997 (NLSY97), we conducted an exploratory latent class growth analysis to determine the best of four outcome variables for yearly smoking (cigarettes per day on days smoked, days smoked per month, mean cigarettes per day, and total cigarettes per month) among individuals aged 12 to 30 (n = 8,791). Days smoked per month was the best outcome variable for identifying unique longitudinal trajectories of smoking and characteristics of these trajectories that could be used to target different types of smokers for prevention and cessation. Objective statistics were used to identify four trajectories in addition to never smokers (34.1%): experimenters (13.6%), quitters (8.1%), early established smokers (39.0%), and late escalators (5.2%). We identified a quitter and late escalator class not identified in the only other comparable latent class growth analysis. Logistic regressions were used to identify the characteristics of individuals in each trajectory. Compared with never smokers, all trajectories except late escalators were less likely to be black; experimenters were more likely to be out of school and unemployed and drink alcohol in adolescence; quitters were more likely to have a mother with a high school degree/GED or higher (versus none) and to use substances in adolescence and less likely to have ever married as a young adult; early established smokers were more likely to have a mother with a high school diploma or GED, be out of school and unemployed, not live with both parents, have used substances, be depressed, and have peers who smoked in adolescence and to have children as young adults and less likely to be Hispanic and to have ever married as young adults; and late escalators were more likely to be Hispanic, drink alcohol, and break rules in adolescence and less likely to have ever married as young adults. Because of the number of waves of data analyzed, this analysis provided a clearer temporal depiction of smoking behavior and more easily distinguishable smoking trajectories than previous analyses. Tobacco control interventions need to move beyond youth-focused approaches to reach all smokers.

Indexed as

AdolescentAdolescent BehaviorAdultDisease ProgressionFemaleHumansLongitudinal StudiesMalePatient Outcome AssessmentPrognosisSmokingSmoking CessationTobacco ProductsTobacco Use DisorderYoung Adult

Identifiers

PMID28182748
PMCPMC5300123

What OpenQuestion holds

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LicenceCC BY
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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.