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Setodji CM, Martino SC, Dunbar M, Kim KJ, Jenson D, Wong JCS, Shadel WG. Measuring susceptibility to use tobacco in an increasingly complex consumer marketplace: How many questions do we really need? PSYCHOLOGY OF ADDICTIVE BEHAVIORS 2024:2024-59384-001. [PMID: 38421778 PMCID: PMC11358647 DOI: 10.1037/adb0000997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
OBJECTIVE Predicting which young people are likely to use tobacco in the future is critical for prevention and intervention. Although measures for assessing susceptibility to using tobacco have fulfilled this goal for decades, there is almost no standard for the number of items that should be administered, or which items should be administered for which products. This study explored whether brief but psychometrically sound versions of commonly used susceptibility measures can adequately capture the construct relative to longer measures. METHOD A sample of young people (N = 451; Mage = 16.5 years; 64% females; 65% White) completed 33 susceptibility items, which are designed to assess susceptibility to use different types of tobacco products (cigarette, smokeless tobacco, vaping products, and little cigars/cigarillos) of various flavors (tobacco, menthol, and sweet). RESULTS Analysis of these 33 items indicated that asking about the likelihood of using each tobacco product class when a best friend offers it (four items in all) captures 98.5% of information that is captured using the longer set of items; asking the best friend question for each product by each flavor category (11 items in all) captures 99.7% of the information. CONCLUSIONS Depending on research needs, tobacco use susceptibility can be measured with little loss of information by administering a limited set of items assessing the likelihood that a young person will use a tobacco product if a friend offers it for any product-flavor combination. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
| | | | | | | | - Desmond Jenson
- Public Health Law Center, Mitchell Hamline School of Law,
Saint Paul, MN 55105
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Atuegwu NC, Mortensen EM, Krishnan-Sarin S, Laubenbacher RC, Litt MD. Prospective predictors of electronic nicotine delivery system initiation in tobacco naive young adults: A machine learning approach. Prev Med Rep 2023; 32:102148. [PMID: 36865398 PMCID: PMC9971268 DOI: 10.1016/j.pmedr.2023.102148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 01/11/2023] [Accepted: 02/10/2023] [Indexed: 02/15/2023] Open
Abstract
The use of electronic nicotine delivery systems (ENDS) is increasing among young adults. However, there are few studies regarding predictors of ENDS initiation in tobacco-naive young adults. Identifying the risk and protective factors of ENDS initiation that are specific to tobacco-naive young adults will enable the creation of targeted policies and prevention programs. This study used machine learning (ML) to create predictive models, identify risk and protective factors for ENDS initiation for tobacco-naive young adults, and the relationship between these predictors and the prediction of ENDS initiation. We used nationally representative data of tobacco-naive young adults in the U.S drawn from the Population Assessment of Tobacco and Health (PATH) longitudinal cohort survey. Respondents were young adults (18-24 years) who had never used any tobacco products in Wave 4 and who completed Waves 4 and 5 interviews. ML techniques were used to create models and determine predictors at 1-year follow-up from Wave 4 data. Among the 2,746 tobacco-naive young adults at baseline, 309 initiated ENDS use at 1-year follow-up. The top five prospective predictors of ENDS initiation were susceptibility to ENDS, increased days of physical exercise specifically designed to strengthen muscles, frequency of social media use, marijuana use and susceptibility to cigarettes. This study identified previously unreported and emerging predictors of ENDS initiation that warrant further investigation and provided comprehensive information on the predictors of ENDS initiation. Furthermore, this study showed that ML is a promising technique that can aid ENDS monitoring and prevention programs.
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Affiliation(s)
- Nkiruka C. Atuegwu
- Department of Medicine, University of Connecticut School of Medicine, Farmington, CT 06030, USA
- Corresponding author at: University of Connecticut, Department of Medicine, 263 Farmington Avenue, Farmington, CT 06030, USA.
| | - Eric M. Mortensen
- Department of Medicine, University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Suchitra Krishnan-Sarin
- Department of Psychiatry, Yale University School of Medicine, Connecticut Mental Health Center, 34 Park Street, New Haven, CT 06519, USA
| | - Reinhard C. Laubenbacher
- Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Mark D. Litt
- Division of Behavioral Sciences and Community Health, University of Connecticut Health Center, Farmington, CT 06030, USA
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Bluestein MA, Kuk AE, Harrell MB, Chen B, Hébert ET, Pérez A. Longitudinal Transition Patterns of Tobacco Use Among Youth and Young Adults Never Tobacco Product Users: Findings From the Population Assessment of Tobacco and Health Study, 2014-2019. Tob Use Insights 2023; 16:1179173X231161314. [PMID: 36923154 PMCID: PMC10009036 DOI: 10.1177/1179173x231161314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
Abstract
Aims To identify, visualize, and describe the prevalence of within-product patterns of tobacco use behaviors for e-cigarettes, cigarettes, and hookah (TP) by 3 age groups (ie, 12-14-year-old, 15-17-year-old, and 18-20-year-old) with U.S. nationally representative data. Methods In 2014-2015, never users of each (TP) and age group were followed-up longitudinally between 2015-2019 using five transition states: non-susceptible to (TP) use, susceptible to (TP) use, ever (TP) use, past 30-day (TP) use, and discontinued past 30-day (TP) use. Sankey diagrams were used to graphically visualize patterns in tobacco use behaviors across time. Results Among 12-14-year-old who were never users and susceptible to each TP from 2014-2017, 7% initiated ever e-cigarette use and 9.4% first reported past 30-day use by 2018-2019; 5.8% initiated ever cigarette use and 3% first reported past 30-day cigarette use by 2018-2019; and, 4.5% initiated ever hookah use and 1.0% first reported past 30-day hookah use by 2018-2019. Among 15-17-year-old who were never users and susceptible to each TP from 2014-2017, 4.2% initiated ever e-cigarette use and 9.0% first reported past 30-day use by 2018-2019; 4.5% initiated ever cigarette use and 3% first reported past 30-day cigarette use by 2018-2019; and, 4.5% initiated ever hookah use and 2.4% first reported past 30-day hookah use by 2018-2019. Among 18-20-year-old who were never users and susceptible to each TP from 2014-2017, 3.2% initiated ever e-cigarette use and 3.6% first reported past 30-day e-cigarette use by 2018-2019; 3.0% initiated ever cigarette use and 2.3% first reported past 30-day cigarette use; and, 2.8% initiated ever hookah use and 1.0% first reported past 30-day hookah use by 2018-2019. Conclusions From 2014 to 2019, onset and progression of e-cigarette, cigarette, and hookah use occurred more frequently in 12-14 and 15-17-year-old than in young adults 18-20-year-old.
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Affiliation(s)
- Meagan A Bluestein
- Michael & Susan Dell Center for Healthy Living, the University of Texas Health Science Center at Houston (UTHealth), School of Public Health, Austin Campus, Austin, TX, USA
| | - Arnold E Kuk
- Michael & Susan Dell Center for Healthy Living, the University of Texas Health Science Center at Houston (UTHealth), School of Public Health, Austin Campus, Austin, TX, USA
| | - Melissa B Harrell
- Michael & Susan Dell Center for Healthy Living, the University of Texas Health Science Center at Houston (UTHealth), School of Public Health, Austin Campus, Austin, TX, USA.,Department of Epidemiology, Human Genetics and Environmental Sciences, the University of Texas Health Science Center at Houston (UTHealth), School of Public Health in Austin Campus, Austin, TX, USA
| | - Baojiang Chen
- Michael & Susan Dell Center for Healthy Living, the University of Texas Health Science Center at Houston (UTHealth), School of Public Health, Austin Campus, Austin, TX, USA
| | - Emily T Hébert
- Michael & Susan Dell Center for Healthy Living, the University of Texas Health Science Center at Houston (UTHealth), School of Public Health, Austin Campus, Austin, TX, USA.,Department of Health Promotion and Behavioral Sciences, the University of Texas Health Science Center at Houston (UTHealth), School of Public Health, Austin Campus, Austin, TX, USA
| | - Adriana Pérez
- Michael & Susan Dell Center for Healthy Living, the University of Texas Health Science Center at Houston (UTHealth), School of Public Health, Austin Campus, Austin, TX, USA
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Kuk AE, Bluestein MA, Chen B, Harrell M, Spells CE, Atem F, Pérez A. The Effect of Perceptions of Hookah Harmfulness and Addictiveness on the Age of Initiation of Hookah Use among Population Assessment of Tobacco and Health (PATH) Youth. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:5034. [PMID: 35564430 PMCID: PMC9105245 DOI: 10.3390/ijerph19095034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/13/2022] [Accepted: 04/19/2022] [Indexed: 12/15/2022]
Abstract
Despite the negative health consequence of hookah, hookah risk perceptions are misguided among youth. Secondary data analysis of 12-17-year-old never hookah users at their first wave of PATH participation (2013-2019) was performed. The effect of perceptions of hookah harmfulness and addictiveness on the age of initiation ever, past 30-day, and fairly regular hookah use were estimated using interval-censored Cox proportional hazards models. The distribution of the age of initiation of hookah outcomes by perception levels of harmfulness and addictiveness are reported as cumulative incidence and 95% CI. Youth who perceived hookah to be neither harmful nor addictive were 173% more likely to initiate ever, 166% more likely to first report past 30-day use, and 142% more likely to first report fairly regular hookah use at earlier ages compared to youth who considered hookah to be both harmful and addictive. By age 18, 25.5% of youth who perceived hookah as neither harmful nor addictive were estimated to initiate ever hookah use while 9.3% of youth who perceived hookah as harmful and addictive were estimated to initiate ever hookah use. These findings indicate the need to provide prevention and education campaigns to change perceptions of the harmfulness and addictiveness of hookah to delay the age of initiation of hookah use.
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Affiliation(s)
- Arnold E. Kuk
- Michael & Susan Dell Center for Healthy Living, The University of Texas Health Science Center at Houston (UTHealth), Austin, TX 78701, USA; (A.E.K.); (M.A.B.); (B.C.); (M.H.); (C.E.S.)
| | - Meagan A. Bluestein
- Michael & Susan Dell Center for Healthy Living, The University of Texas Health Science Center at Houston (UTHealth), Austin, TX 78701, USA; (A.E.K.); (M.A.B.); (B.C.); (M.H.); (C.E.S.)
| | - Baojiang Chen
- Michael & Susan Dell Center for Healthy Living, The University of Texas Health Science Center at Houston (UTHealth), Austin, TX 78701, USA; (A.E.K.); (M.A.B.); (B.C.); (M.H.); (C.E.S.)
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston (UTHealth), Austin, TX 78701, USA
| | - Melissa Harrell
- Michael & Susan Dell Center for Healthy Living, The University of Texas Health Science Center at Houston (UTHealth), Austin, TX 78701, USA; (A.E.K.); (M.A.B.); (B.C.); (M.H.); (C.E.S.)
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston (UTHealth), Austin, TX 78701, USA
| | - Charles E. Spells
- Michael & Susan Dell Center for Healthy Living, The University of Texas Health Science Center at Houston (UTHealth), Austin, TX 78701, USA; (A.E.K.); (M.A.B.); (B.C.); (M.H.); (C.E.S.)
| | - Folefac Atem
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston (UTHealth), Dallas, TX 75207, USA;
| | - Adriana Pérez
- Michael & Susan Dell Center for Healthy Living, The University of Texas Health Science Center at Houston (UTHealth), Austin, TX 78701, USA; (A.E.K.); (M.A.B.); (B.C.); (M.H.); (C.E.S.)
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston (UTHealth), Austin, TX 78701, USA
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