Evidence map›Paper›PMID 38831538›Full record

ArticleSubstance use & misuse2024

Perceptions of the Addictiveness of Low-Nicotine Cigarettes Versus Typical Cigarettes and Exposure to Tobacco Industry-Sponsored Corrective Campaign.

Onyema Greg Chido-Amajuoyi, Dale S Mantey, Efehi Igbinomwanhia, Onyinye Omega-Njemnobi, Henry Onyeaka, Robert K Yu, Israel Agaku, Sanjay Shete

Abstract read
In one paragraph

Article in Substance use & misuse, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Onyema Greg Chido-AmajuoyiDepartment of Epidemiology, The University of Texas Maryland Anderson Cancer Center, Houston, TX, USA.
Dale S ManteyDepartment of Health Promotion and Behavioral Science, University of Texas School of Public Health, Austin, TX, USA.
Efehi IgbinomwanhiaDepartment of Cardiology, Case Western Reserve University/Metrohealth, Cleveland, OH, USA.
Onyinye Omega-NjemnobiDepartment of Health Promotion and Behavioral Science, University of Texas School of Public Health, Austin, TX, USA.
Henry OnyeakaDepartment of Psychiatry, Harvard Medical School, Boston, MA, USA.
Robert K YuDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Israel AgakuDepartment of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, Boston, MA, USA.
Sanjay SheteDepartment of Epidemiology, The University of Texas Maryland Anderson Cancer Center, Houston, TX, USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
NCI NIH HHS P30 CA016672
6 · The paper itself

Abstract

backgroundIn 2017, the U.S. Food and Drug Administration (FDA) announced a regulatory plan to reduce the nicotine content of cigarettes. This study examines the association of exposure to industry-sponsored corrective statements on perceptions of the addictiveness of low-nicotine cigarettes relative to typical cigarettes within the general US population.

methodsThe study comprised 4975 US adult respondents of the 2019 Health Information National Trends Survey (HINTS 5, Cycle 3). Multinomial logistic regression models were used to examine associations between exposure to tobacco industry corrective messages and perceptions of the addictiveness of low-nicotine cigarettes relative to typical cigarettes.

resultsIn the overall population, 4.1% reported that low-nicotine cigarettes were much more addictive than typical cigarettes, 67.5% said they were equally addictive, while 28.4% reported they were slightly/much less addictive. Adults exposed to industry-sponsored corrective messages had higher odds of perceiving low-nicotine cigarettes as equally addictive as typical cigarettes (aOR 1.57; 95% CI, 1.13-2.19) than those who saw no corrective messages. Those exposed to the corrective messages specifically about the addictiveness of smoking and nicotine had higher odds of perceiving low-nicotine cigarettes as equally addictive as typical cigarettes (aOR, 1.73; 95% CI, 1.07-2.81) compared to those who saw no corrective message.

conclusionsOur findings suggest that exposure to court-ordered tobacco industry corrective statements may have reinforced perceptions on the addictive potential of nicotine. However, study findings indicate a need for campaigns specifically tailored to address misperceptions observed in this study.

Indexed as

NicotineTobacco IndustryTobacco ProductsAdolescentAdultAgedBehavior, AddictiveFemaleHealth Knowledge, Attitudes, PracticeHumansMaleMiddle AgedUnited StatesYoung AdultNicotineAddictionCigarettesTobaccoTobacco Industry

Identifiers

PMID38831538
PMCPMC11249086

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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.