Hierarchical Modeling of Psychosocial, Parental, and Environmental Factors for Susceptibility to Tobacco Product Use in 9–10-Year-Old Children



      Tobacco use during early adolescence can harm brain development and cause adverse health outcomes. Identifying susceptibility in early adolescence before initiation presents an opportunity for tobacco use prevention.


      Data were drawn from the Adolescent Brain and Cognitive Development study that enrolled 9–10-year-old children in 21 US cities between 2016 and 2018 at baseline. Separate nested hierarchical models were performed to incrementally examine the associations of sociodemographic factors, psychosocial influences, parental substance use, immediate social contacts, and perceived neighborhood safety with tobacco use susceptibility among never tobacco users (n = 10,449), overall and stratified by gender.


      A total of 16.6% of youths who have never used tobacco reported susceptibility to tobacco. Females (vs. males, adjusted odds ratio [AOR] [95% confidence interval {CI}] = 0.80 [0.70–0.91]), positive parental monitoring (AOR [95% CI] = 0.76 [0.66–0.87]) and positive school environment (AOR [95% CI] = 0.95 [0.93–0.98]) were associated with reduced susceptibility to tobacco use. Parental education level (high school, AOR [95% CI] = 1.52 [1.02–2.28]; bachelor's degree, AOR [95% CI] = 1.53 [1.03–2.28]; or postgraduate degree, AOR [95% CI] = 1.54 [1.03–2.3] vs. less than high school), youth substance ever use (AOR [95% CI] = 2.24 [1.95–2.58]), internalizing problems (AOR [95% CI] = 1.03 [1–1.06]), and high scores on negative urgency, lack of premeditation, lack of perseverance, sensation seeking, and positive urgency-impulsive behavior scale were associated with increased susceptibility to tobacco use. Stratified analysis showed that parent-perceived neighborhood safety was associated with reduced susceptibility to tobacco use among males but not among females (AOR [95% CI] = 0.89 [0.81–0.99]) vs. (AOR [95% CI] = 1.01 [0.9–1.13]). A positive school environment was associated with lower susceptibility to tobacco use among females but not among males.


      Parental, environmental, and psychosocial factors influence early childhood tobacco susceptibility. Family and school-based tobacco prevention programs should consider integrating these factors into primary school curricula to reduce youth tobacco susceptibility and later initiation.


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        • U.S. Department of Health and Human Services
        Preventing tobacco use among youth and young adults: A report of the surgeon general.
        US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, Atlanta2012
        • Pierce J.P.
        • Chen R.
        • Leas E.C.
        • et al.
        Use of E-cigarettes and other tobacco products and progression to daily cigarette smoking.
        Pediatrics. 2021; 147 (e2020025122)
        • Trinidad D.R.
        • Pierce J.P.
        • Sargent J.D.
        • et al.
        Susceptibility to tobacco product use among youth in wave 1 of the population assessment of tobacco and health (PATH) study.
        Prev Med. 2017; 101: 8-14
        • Johnston L.D.
        • Miech R.A.
        • O'Malley P.M.
        • et al.
        Monitoring the future national survey results on drug use, 1975-2020: Overview, key findings on adolescent drug use.
        Institute for Social Research, University of Michigan, Ann Arbor2021
        • The Centers for Disease Control and Prevention
        National youth tobacco survey (NYTS).
        (Available at:)
        • Volkow N.D.
        • Koob G.F.
        • Croyle R.T.
        • et al.
        The conception of the ABCD study: From substance use to a broad NIH collaboration.
        Dev Cogn Neurosci. 2018; 32: 4-7
        • Pierce J.P.
        • Choi W.S.
        • Gilpin E.A.
        • et al.
        Validation of susceptibility as a predictor of which adolescents take up smoking in the United States.
        Health Psychol. 1996; 15: 355-361
        • Pierce J.P.
        • Farkas A.
        • Evans N.
        • et al.
        California Tobacco Survey 1992: A focus on preventing uptake in adolescents.
        in: California Department of Health Services, Sacramento1993
        • Smith R.E.
        • Swinyard W.R.
        Cognitive response to advertising and trial: Belief strength, belief confidence and product curiosity.
        J Advertising. 1988; 17: 3-14
        • Pierce J.P.
        • Choi W.S.
        • Gilpin E.A.
        • et al.
        Tobacco industry promotion of cigarettes and adolescent smoking.
        JAMA. 1998; 279: 511-515
        • Pierce J.P.
        • Distefan J.M.
        • Kaplan R.M.
        • et al.
        The role of curiosity in smoking initiation.
        Addict Behav. 2005; 30: 685-696
        • Strong D.R.
        • Hartman S.J.
        • Nodora J.
        • et al.
        Predictive validity of the expanded susceptibility to smoke index.
        Nicotine Tob Res. 2015; 17: 862-869
        • Nodora J.
        • Hartman S.J.
        • Strong D.R.
        • et al.
        Curiosity predicts smoking experimentation independent of susceptibility in a US national sample.
        Addict Behav. 2014; 39: 1695-1700
        • Pierce J.P.
        • Sargent J.D.
        • Portnoy D.B.
        • et al.
        Association between receptivity to tobacco advertising and progression to tobacco use in youth and young adults in the PATH study.
        JAMA Pediatr. 2018; 172: 444-451
        • Gentzke A.S.
        • Creamer M.
        • Cullen K.A.
        • et al.
        Vital signs: Tobacco product use among middle and high school students - United States, 2011-2018.
        MMWR Morb Mortal Wkly Rep. 2019; 68: 157-164
        • Gentzke A.S.
        • Wang T.W.
        • Jamal A.
        • et al.
        Tobacco product use among middle and high school students - United States, 2020.
        MMWR Morb Mortal Wkly Rep. 2020; 69: 1881-1888
        • Truth Initiative
        E-cigarettes: Facts, stats and regulations.
        (Available at:)
        • Bold K.W.
        • Kong G.
        • Cavallo D.A.
        • et al.
        E-cigarette susceptibility as a predictor of youth initiation of E-cigarettes.
        Nicotine Tob Res. 2017; 20: 140-144
        • Bandura A.
        Social foundations of thought and action: A social cognitive theory.
        Prentice-hall Englewood Cliffs, NJ1986
        • Richardson J.L.
        • Radziszewska B.
        • Dent C.W.
        • et al.
        Relationship between after-school care of adolescents and substance use, risk taking, depressed mood, and academic achievement.
        Pediatrics. 1993; 92: 32-38
        • Crone M.R.
        • Reijneveld S.A.
        The association of behavioural and emotional problems with tobacco use in adolescence.
        Addict Behav. 2007; 32: 1692-1698
        • Stanton C.A.
        • Highland K.B.
        • Tercyak K.P.
        • et al.
        Authoritative parenting and cigarette smoking among multiethnic preadolescents: The mediating role of anti-tobacco parenting strategies.
        J Pediatr Psychol. 2014; 39: 109-119
        • Ortiz C.
        • Lopez-Cuadrado T.
        • Rodriguez-Blazquez C.
        • et al.
        Physical and social environmental factors related to co-occurrence of unhealthy lifestyle behaviors.
        Health Place. 2022; 75: 102804
        • Meier K.S.
        Tobacco truths: The impact of role models on children's attitudes toward smoking.
        Health Educ Q. 1991; 18: 173-182
        • Cohen E.L.
        • Shumate M.D.
        • Gold A.
        Original: Anti-smoking media campaign messages: Theory and practice.
        Health Commun. 2007; 22: 91-102
        • Svensson R.
        Gender differences in adolescent drug use: The impact of parental monitoring and peer deviance.
        Youth Soc. 2003; 34: 300-329
        • Pierce J.P.
        • Sargent J.D.
        • White M.M.
        • et al.
        Receptivity to tobacco advertising and susceptibility to tobacco products.
        Pediatrics. 2017; 139
        • Garavan H.
        • Bartsch H.
        • Conway K.
        • et al.
        Recruiting the ABCD sample: Design considerations and procedures.
        Dev Cogn Neurosci. 2018; 32: 16-22
        • Barch D.M.
        • Albaugh M.D.
        • Avenevoli S.
        • et al.
        Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: Rationale and description.
        Dev Cogn Neurosci. 2018; 32: 55-66
        • Feldstein Ewing S.W.
        • Chang L.
        • Cottler L.B.
        • et al.
        Approaching retention within the ABCD study.
        Dev Cogn Neurosci. 2018; 32: 130-137
        • Lisdahl K.M.
        • Sher K.J.
        • Conway K.P.
        • et al.
        Adolescent brain cognitive development (ABCD) study: Overview of substance use assessment methods.
        Dev Cogn Neurosci. 2018; 32: 80-96
        • Achenbach T.M.
        The Achenbach system of empirically based assessment (ASEBA): Development, findings, theory, and applications.
        University of Vermont, Research Center for Children, Youth, & Families, Burlington2009
        • Association AP
        DSM 5 diagnostic and statistical manual of mental disorders.
        . 2013; 947: 947
      1. School-Age (CBCL, TRF, YSR, BPM/6-18).
        (Available at:)
        Date: 2021
        Date accessed: September 8, 2022
        • Whiteside S.P.
        • Lynam D.R.
        The five factor model and impulsivity: Using a structural model of personality to understand impulsivity.
        Pers Individ Dif. 2001; 30: 669-689
        • Schaefer E.S.
        Children's reports of parental behavior: An inventory.
        Child Dev. 1965; 36: 413-424
        • Karoly H.C.
        • Callahan T.
        • Schmiege S.J.
        • et al.
        Evaluating the Hispanic paradox in the context of adolescent risky sexual behavior: The role of parent monitoring.
        J Pediatr Psychol. 2016; 41: 429-440
        • Arthur M.W.
        • Briney J.S.
        • Hawkins J.D.
        • et al.
        Measuring risk and protection in communities using the communities that care youth survey.
        Eval Program Plann. 2007; 30: 197-211
        • Echeverria S.E.
        • Diez-Roux A.V.
        • Link B.G.
        Reliability of self-reported neighborhood characteristics.
        J Urban Health. 2004; 81: 682-701
        • Zucker R.A.
        • Gonzalez R.
        • Feldstein Ewing S.W.
        • et al.
        Assessment of culture and environment in the adolescent brain and cognitive development study: Rationale, description of measures, and early data.
        Dev Cogn Neurosci. 2018; 32: 107-120
        • McHugh M.L.
        The chi-square test of independence.
        Biochem Med (Zagreb). 2013; 23: 143-149
        • Heeringa S.G.
        • Berglund P.A.
        A guide for population-based analysis of the adolescent brain cognitive development (ABCD) Study baseline data.
        BioRxiv. 2020;
        • Dai H.
        • Ingram D.G.
        • Taylor J.B.
        Hierarchical and mediation analysis of disparities in very short sleep among sexual minority youth in the US, 2015.
        Behav Sleep Med. 2019; 18: 1-14
        • Scoggins D.
        • Khan A.S.
        • Dai H.
        Hierarchical analysis of disparities in suicidal outcomes with intersection of sexual minority and gender among U.S. Youth, 2017.
        Health Educ Behav. 2021; 49: 569-583
        • Gelman A.
        • Hill J.
        Data analysis using regression and multilevel/hierarchical models.
        Cambridge University Press, New York2006
        • du Prel J.B.
        • Hommel G.
        • Rohrig B.
        • et al.
        Confidence interval or p-value?: Part 4 of a series on evaluation of scientific publications.
        Dtsch Arztebl Int. 2009; 106: 335-339
        • Pierce J.P.
        • Fiore M.C.
        • Novotny T.E.
        • et al.
        Trends in cigarette smoking in the United States. Educational differences are increasing.
        JAMA. 1989; 261: 56-60
        • Biddle B.J.
        • Bank B.J.
        • Marlin M.M.
        Parental and peer influence on adolescents.
        Soc Forces. 1980; 58: 1057-1079
        • Webster R.A.
        • Hunter M.
        • Keats J.A.
        Peer and parental influences on adolescents' substance use: A path analysis.
        Int J Addict. 1994; 29: 647-657
        • Ding D.
        • Sallis J.F.
        • Kerr J.
        • et al.
        Neighborhood environment and physical activity among youth a review.
        Am J Prev Med. 2011; 41: 442-455
        • National Research Council
        The science of adolescent risk-taking: Workshop report.
        National Academies Press, Washington (DC)2011
        • Hackman D.A.
        • Cserbik D.
        • Chen J.C.
        • et al.
        Association of local variation in neighborhood disadvantage in metropolitan areas with youth neurocognition and brain structure.
        JAMA Pediatr. 2021; 175: e210426
        • Brener N.D.
        • Billy J.O.
        • Grady W.R.
        Assessment of factors affecting the validity of self-reported health-risk behavior among adolescents: Evidence from the scientific literature.
        J Adolesc Health. 2003; 33: 436-457