Evidence map›Paper›PMID 30222998›Full record

ArticlePreventive medicine2018

Understanding individual differences in vulnerability to cigarette smoking is enhanced by attention to the intersection of common risk factors.

Diann E Gaalema, Adam M Leventhal, Jeffrey S Priest, Stephen T Higgins

Abstract read
In one paragraph

Article in Preventive medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Article
  4. Article
  5. Article
  6. The Impact of the Covid-19 Pandemic on Smoking Among Vulnerable Populations.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2023
    Article
  7. Article
  8. Observational
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Editorial: 5Preventive medicine · 2018
    Article
  16. 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.

Diann E GaalemaVermont Center on Tobacco Regulatory Science, University of Vermont, Burlington, VT, United States of America; Department of Psychiatry, University of Vermont, Burlington, VT, United States of America; Department of Psychology, University of Vermont, Burlington, VT, United States of America. Electronic address: dgaalema@uvm.edu.
Adam M LeventhalDepartment of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, CA, United States of America.
Jeffrey S PriestMedical Biostatistics, University of Vermont, Burlington, VT, United States of America.
Stephen T HigginsVermont Center on Tobacco Regulatory Science, University of Vermont, Burlington, VT, United States of America; Department of Psychiatry, University of Vermont, Burlington, VT, United States of America; Department of Psychology, University of Vermont, Burlington, VT, United States of America.

Funding

Vermont Center on Behavior and HealthP20GM103644 · NIGMS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI SIGMON, STACEY C · 2013 to 2022
$21.3M
USC Tobacco Center of Regulatory Science (TCORS) for Vulnerable PopulationsP50CA180905 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI PENTZ, MARY ANN, SAMET, JONATHAN M · 2013 to 2017
$20.1M
University of Vermont Tobacco Center of Regulatory ScienceU54DA036114 · NIDA · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI HIGGINS, STEPHEN T · 2018 to 2022
$19.4M
Vermont Center on Tobacco Regulatory ScienceP50DA036114 · NIDA · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI HIGGINS, STEPHEN T · 2013 to 2017
$19.1M
NCI NIH HHS P50 CA180905NIDA NIH HHS P50 DA036114NIDA NIH HHS U54 DA036114NIGMS NIH HHS P20 GM103644
6 · The paper itself

Abstract

While smoking prevalence in the U.S. and other industrialized countries has decreased substantially, this change has been unevenly distributed, with dramatic decreases in certain subpopulations but little change or even increases in others. Accordingly, considerable attention has been fruitfully devoted to identifying important risk factors for smoking (e.g., mental illness, other substance use disorders). However, there has been little research on the intersection of these risk factors. As risk factors rarely occur in isolation, it is important to examine risk-factor profiles as is commonly done in studying other chronic conditions (e.g., cardiovascular disease). The purpose of this Commentary is to encourage greater interest in the intersection of multiple risk factors using cigarette smoking as an exemplar. We focus on the intersection of eight well-established risk factors for smoking (age, gender, race/ethnicity, educational attainment, poverty, drug abuse/dependence, alcohol abuse/dependence, mental illness). Studying the intersection of risk factors is likely to require use of innovative data-analytic methods. We illustrate, using years 2011-2016 of the US National Household Survey on Drug Use and Health, how Classification and Regression Tree (CART) analysis can be an effective tool for identifying risk profiles for smoking. Examination of the intersection of these risk factors elucidates a series of risk profiles with associated, orderly gradations in vulnerability to current smoking, including the striking and reliable strength of a college education as a stand-alone profile predicting low risk for current smoking, and illustrating the potentially increasing importance of drug abuse/dependence as a risk factor.

Indexed as

IndividualityAdultAgedAlcoholismEthnicityFemaleHealth SurveysHumansMaleMiddle AgedPovertyPrevalenceRisk FactorsSmokingSubstance-Related DisordersUnited StatesCigarette smokingClassification and regression tree analysisRisk factorsRisk profilesTobacco useVulnerabilityVulnerable populations

Identifiers

PMID30222998
PMCPMC6234036

What OpenQuestion holds

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Read underepoch 390

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.