Hostname: page-component-cc8bf7c57-qfg88 Total loading time: 0 Render date: 2024-12-11T23:09:04.922Z Has data issue: false hasContentIssue false

Polygenic Score × Intervention Moderation: An application of discrete-time survival analysis to modeling the timing of first tobacco use among urban youth

Published online by Cambridge University Press:  02 February 2015

Rashelle J. Musci*
Affiliation:
Johns Hopkins University Bloomberg School of Public Health
Katherine E. Masyn
Affiliation:
Harvard University Graduate School of Education
George Uhl
Affiliation:
NIH-IRP NIDA Molecular Neurobiology Branch
Brion Maher
Affiliation:
Johns Hopkins University Bloomberg School of Public Health
Sheppard G. Kellam
Affiliation:
Johns Hopkins University Bloomberg School of Public Health
Nicholas S. Ialongo
Affiliation:
Johns Hopkins University Bloomberg School of Public Health
*
Address correspondence and reprint requests to: Rashelle J. Musci, Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, 624 North Broadway, Baltimore, MD 21205; E-mail: [email protected].

Abstract

The present study examines the interaction between a polygenic score and an elementary school-based universal preventive intervention trial. The polygenic score reflects the contribution of multiple genes and has been shown in prior research to be predictive of smoking cessation and tobacco use (Uhl et al., 2014). Using data from a longitudinal preventive intervention study, we examined age of first tobacco use from sixth grade to age 18. Genetic data were collected during emerging adulthood and were genotyped using the Affymetrix 6.0 microarray. The polygenic score was computed using these data. Discrete-time survival analysis was employed to test for intervention main and interaction effects with the polygenic score. We found a main effect of the intervention, with the intervention participants reporting their first cigarette smoked at an age significantly later than controls. We also found an Intervention × Polygenic Score interaction, with participants at the higher end of the polygenic score benefitting the most from the intervention in terms of delayed age of first use. These results are consistent with Belsky and colleagues' (e.g., Belsky, Bakermans-Kranenburg, & van IJzendoorn, 2007; Belsky & Pleuss, 2009, 2013; Ellis, Boyce, Belsky, Bakermans-Kranenburg, & van IJzendoorn, 2011) differential susceptibility hypothesis and the concept of “for better or worse,” wherein the expression of genetic variants are optimally realized in the context of an enriched environment, such as provided by a preventive intervention.

Type
Special Section Articles
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Audrain-McGovern, J., Lerman, C., Wileyto, E. P., Rodriguez, D., & Shields, P. G. (2004). Interacting effects of genetic predisposition and depression on adolescent smoking progression. American Journal of Psychiatry, 161, 12241230.CrossRefGoogle ScholarPubMed
Bakermans-Kranenburg, M. J., van IJzendoorn, M. H., Pijlman, F. T. A., Mesman, J., & Juffer, F. (2008). Experimental evidence for differential susceptibility: Dopamine D4 receptor polymorphism (DRD4 VNTR) moderates intervention effects on toddlers' externalizing behavior in a randomized controlled trial. Developmental Psychology, 44, 293300.Google Scholar
Barrish, H. H., Saunders, M., & Wolf, M. M. (1969). Good behavior game: Effects of individual contingencies for group consequences on disruptive behavior in a classroom. Journal of Applied Behavior Analysis, 2, 119124. doi:10.1901/jaba.1969.2-119Google Scholar
Beach, S., Brody, G. H., Lei, M.-K., & Philbert, R. (2010). Differential susceptibility to parenting among African American youths: Testing the DRD4 hypothesis. Journal of Family Psychology, 24, 513521. doi:10.1037/a0020835Google Scholar
Belsky, J., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2007). For better and for worse: Differential susceptibility to environmental influences. Current Directions in Psychological Science, 16, 300304.Google Scholar
Belsky, D. W., Moffitt, T. E., Baker, T. B., Biddle, A. K., Evans, J. P., Harrington, H., et al. (2013). Polygenic risk and the developmental progression to heavy, persistent smoking and nicotine dependence: Evidence from a 4-decade longitudinal study. JAMA Psychiatry, 70, 534542.CrossRefGoogle ScholarPubMed
Belsky, J., & Pluess, M. (2009). Beyond diathesis–stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135, 885908.Google Scholar
Belsky, J., & Pluess, M. (2013). Beyond risk, resilience and dysregulation: Phenotypic plasticity and human development. Development and Psychopathology, 25, 12431261.Google Scholar
Bierut, L .J. (2009). Nicotine dependence and genetic variation in the nicotinic receptors. Drug and Alcohol Dependence, 104, S64S69.Google Scholar
Bloom, A. J., Baker, T. B., Chen, L.-S., Breslau, N., Hatsukamu, D., Bierut, L. J., et al. (2014). Variants in two adjacent genes, EGLN2 and CYP2A6, influence smoking behavior related to disease risk via different mechanisms. Human Molecular Genetics, 23, 555561.Google Scholar
Bradshaw, C. P., Zmuda, J. H., Kellam, S. G., & Ialongo, N. S. (2009). Longitudinal impact of two universal preventive interventions in first grade on educational outcomes in high school. Journal of Educational Psychology, 101, 926937. doi:10.1037/a0016586Google Scholar
Breslau, N., Fenn, N., & Peterson, E. L. (1993). Early smoking initiation and nicotine dependence in a cohort of young adults. Drug Alcohol Dependence, 33, 129137.Google Scholar
Breslau, N., & Peterson, E. L. (1996). Smoking cessation in young adults: Age at initiation of cigarette smoking and other suspected influences. American Journal of Public Health, 86, 214220.Google Scholar
Brody, G. H., Beach, S. R. H., Philibert, R. A., Chen, Y.-F., & Murray, V. M. (2009). Prevention effects moderate the association of 5-HTTLPR and youth risk behavior initiation: Gene × Environment hypotheses tested via a randomized prevention design. Child Development, 80, 645661. doi:10.1111/j. 1467-8624.2009.01288.xGoogle Scholar
Brody, G. H., Chen, Y., Yu, T., Beach, S. R. H., Kogan, S. M., Simons, R. L., et al. (2012). Life stress, the dopamine receptor gene, and emerging adult drug use trajectories: A longitudinal, multilevel, mediated moderation analysis. Development and Psychopathology, 24, 941951.Google Scholar
Brody, G. H., Murry, V. M., Gerrard, M., Gibbons, F. X., Molgaard, V., McNair, L. D., et al. (2004). The Strong African American Families program: Translating research into prevention programming. Child Development, 75, 900917. doi:10.1111/j.1467-8624.2004.00713.xGoogle Scholar
Brook, J. S., Brook, D. W., Zhang, C., & Cohen, P. (2009). Pathways from adolescent parent–child conflict to substance use disorders in the fourth decade of life. American Journal on Addictions, 18, 235242.Google Scholar
Chen, L. S., Anthony, J. C., & Crum, R. M. (1999). Perceived cognitive competence, depressive symptoms and the incidence of alcohol-related problems in urban school children. Journal of Child and Adolescent Substance Abuse, 8, 3753.Google Scholar
Cicchetti, D., & Schneider-Rosen, K. (1984). Toward a transactional model of childhood depression. In Cicchetti, D. & Schneider-Rosen, K. (Eds.), Childhood depression a developmental perspective (pp. 528). San Francisco, CA: Jossey–Bass.Google Scholar
Corrigall, W. A., Coen, K. M., & Adamson, K. L. (1994). Self-administered nicotine activates the mesolimbic dopamine system through the ventral tegmental area. Brain Research, 653, 278284.Google Scholar
D'Avanzo, B., La Vecchia, C., & Negri, E. (1994). Age at starting smoking and number of cigarettes smoked. Annals of Epidemiology, 4, 455459.Google Scholar
Doherty, E. E., Green, K. M., Reisinger, H. S., & Ensminger, M. E. (2008). Long term patterns of drug use among an urban African-American cohort: The role of gender and family. Journal of Urban Health, 85, 250267.Google Scholar
Dolan, L. J., Kellam, S. G., Brown, C. H., Werthamer-Larsson, L., Rebok, G. W., Mayer, L. S., et al. (1993). The short-term impact of two classroom-based preventive interventions on aggressive and shy behaviors and poor achievement. Journal of Applied Developmental Psychology, 14, 317345.Google Scholar
Ducci, F., Kaakinen, M., Pouta, A., Hartikainen, A.-L., Veijola, J., Isohanni, M., et al. (2011). TTC12-ANKK1-DRD2 and CHRNA5-CHRNA3-chrnb4 influence different pathways leading to smoking behavior from adolescence to mid-adulthood. Biological Psychiatry, 69, 650660.Google Scholar
Duncan, L. E., Pollastri, A. R., & Smoller, J. W. (2014). Mind the gap: Why many geneticists and psychological scientists have discrepant views about gene–environment interaction (G × E) research. American Psychologist, 69, 249268.Google Scholar
Ellis, B. J., Boyce, W. T., Belsky, J., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2011). Differential susceptibility to the environment: A neurodevelopmental theory. Development and Psychopathology, 23, 728.Google Scholar
Ensminger, M. E., Forrest, C. B., Riley, A. W., Kang, M., Green, B. F., & Starfield, B. (2000). The validity of measures of socioeconomic status of adolescents. Journal of Adolescent Research, 15, 392419. doi:10.1177/0743558400153005Google Scholar
Furr-Holden, C., Ialongo, N., Anthony, J. C., Petras, H., & Kellam, S. (2004). Developmentally inspired drug prevention: Middle school outcomes in a school-based randomized prevention trial. Drug and Alcohol Dependence, 73, 149158.Google Scholar
Gabrielsen, M. E., Romundstad, P., Langhammer, A., Krokan, H. E., & Skorpen, F. S. (2013). Association between a 15q25 gene variant, nicotine-related habits, lung cancer and COPD among 56 307 individuals from the HUNT study in Norway. European Journal of Human Genetics, 21, 12931299.Google Scholar
Granic, I., & Patterson, G. R. (2006). Toward a comprehensive model of antisocial development: A dynamic systems approach. Psychological Review, 113, 101131.Google Scholar
Haberstick, B. C., Zeiger, J. S., Corley, R. P., Hopfer, C. J., Stallings, M. C., Rhee, S. H., et al. (2011). Common and drug-specific genetic influences on subjective effects to alcohol, tobacco and marijuana use. Addiction, 106, 215224.CrossRefGoogle ScholarPubMed
Horimoto, A., Oliveira, C. M., Giolo, S. R., Soler, J. P., Andrade, M., Krieger, J. E., et al. (2012). Genetic analyses of smoking initiation, persistence, quantity, and age-at-onset of regular cigarette use in Brazilian families: The Baependi Heart Study. BMC Medical Genetics, 13, 9.Google Scholar
Horwitz, B. N., & Neiderhiser, J. M. (2011). Gene–environment interplay, family relationships, and child adjustment, Journal of Marriage and the Family, 73, 804816.Google Scholar
Ialongo, N., Poduska, J., Werthamer, L., & Kellam, S. (2001). The distal impact of two first grade preventive interventions on conduct problems and disorder in early adolescence. Journal of Emotional and Behavioral Disorders, 9, 146160. doi:10.1177/106342660100900301Google Scholar
Ialongo, N. S., Rogosch, F. A., Cicchetti, D., Toth, S. L., Buckley, J., Petras, H., et al. (2006). A developmental psychopathology approach to the prevention of mental health disorders. In Cicchetti, D. (Ed.), Developmental psychopathology (2nd ed., pp. 9681018). Hoboken, NJ: Wiley.Google Scholar
Ialongo, N. S., Werthamer, L., Kellam, S. G., Brown, C. H., Wang, S., & Lin, Y. (1999). Proximal impact of two first-grade preventive interventions on the early risk behaviors for later substance abuse, depression, and antisocial behavior. American Journal of Community Psychology, 27, 599641. doi:10.1023/A:1022137920532Google Scholar
Jaffee, S. R., & Price, T. S. (2007). Gene–environment correlations: A review of the evidence and implications for prevention of mental illness. Molecular Psychiatry, 12, 432442.Google Scholar
Johnston, L. D., O'Malley, P., & Bachman, J. G. (1995). National survey results on drug use from the Monitoring the Future study, 1975–1994: Vol. 1. Secondary school students (Publication No. 95-4026). Washington, DC: US NIH, PHS, DHHS, NIH.Google Scholar
Kan, K. J., Dolan, C. V., Nivard, M., Middeldorp, C. M., van Beijsterveldt, C. E., Willemsen, G., et al. (2013). Genetic and environmental stability in attention problems across the lifespan: Evidence from the Netherlands Twin register. Journal of the American Academy of Child & Adolescent Psychiatry, 52, 1225.Google Scholar
Kegel, C. A. T., Bus, A. G., & van IJzendoorn, M. H. (2011). Differential susceptibility in early literacy instruction through computer games: The role of the dopamine D4 receptor gene (DRD4). Mind, Brain, and Education, 5, 7178. doi:10.1111/j.1751-228X.2011.01112.xCrossRefGoogle Scholar
Kellam, S., & Anthony, J. (1998). Targeting early antecedents to prevent tobacco smoking: Findings from an epidemiologically-based randomized field trial. American Journal of Public Health, 88, 14901495.Google Scholar
Kellam, S. G., & Rebok, G. W. (1992). Building developmental and etiological theory through epidemiologically based preventive intervention trials. In McCord, J. & Tremblay, R. E. (Eds.), Preventing antisocial behavior: Interventions from birth through adolescence (pp. 162195). New York: Guilford Press.Google Scholar
Kellam, S. G., Brown, C. H., Poduska, J. M., Ialongo, N., Wang, W., Toyinbo, P., et al. (2008). Effects of a universal classroom behavior management program in first and second grades on young adult behavioral, psychiatric, and social outcomes. Drug and Alcohol Dependance, 95(Suppl. 1), S5S28. doi:10.1016/j.drugalcdep.2008.01.004Google Scholar
Kendler, K. S., & Prescott, C. A. (2008). Genes, environment, and psychopathology: Understanding the causes of psychiatric and substance use disorders. New York: Guilford Press.Google Scholar
Kendler, K. S., Prescott, C. A., Myers, J., & Neale, M. C. (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archives of General Psychiatry, 60, 929937.CrossRefGoogle ScholarPubMed
Kim, H. K., Capaldi, D. M., Pears, K. C., Kerr, D. C. R., & Owen, L. D. (2009). Intergenerational transmission of internalising and externalising behaviors across three generations: Gender specific pathways. Criminal Behaviour and Mental Health, 19, 125141.Google Scholar
Knafo, A., & Jaffee, S. R. (2013). The implications of genotype–environment correlation for establishing causal processes in psychopathology. Development and Psychopathology, 25, 16.Google Scholar
Kraemer, H. C., Kazdin, A. E., Offord, D. R., Kessler, R. C., Jensen, P. S., & Kupfer, D. J. (1999). Measuring the potency of risk factors for clinical or policy significance. Psychological Methods, 3, 257271.Google Scholar
La Greca, A. M., & Moore Harrison, H. (2005). Adolescent peer relations, friendships, and romantic relationships: Do they predict social anxiety and depression? Journal of Clinical Child and Adolescent Psychology, 34, 4961.CrossRefGoogle ScholarPubMed
Laviolette, S. R., & Van de Kooy, D. (2004). The neurobiology of nicotine addiction: Bridging the gap from molecules to behavior. Nature Reviews Neuroscience, 5, 5565.Google Scholar
Lessov, C. N., Martin, N. G., Statham, D. J., Todorov, A. A., Slutske, W. S., Bucholz, K. K., et al. (2004). Defining nicotine dependence for genetic research: Evidence from Australian twins. Psychological Medicine, 34, 865879.Google Scholar
Little, R. J., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). New York: Wiley.Google Scholar
Liu, J. Z., Tozzi, F., Waterworth, D. M., Pillai, S. G., Muglia, P., Midleton, L., et al. (2010). Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nature Genetics, 42, 436440.Google Scholar
MacQueen, D. A., Heckman, B. W., Blank, M. D., Van Rensburg, K. J., Park, J. Y., Drobes, D. J., et al. (2014). Variation in the α 5 nicotinic acetylcholine receptor subunit gene predicts cigarette smoking intensity as a function of nicotine content. Pharmacogenomics Journal, 14, 7076.Google Scholar
Masyn, K. E. (2014). Discrete-time survival analysis in prevention science. In Sloboda, Z. & Petras, H. (Eds.), Defining prevention science, Advances in Prevention Science (pp. 513535). New York: Springer Science + Business Media.Google Scholar
Montana, G., & Pritchard, J. K. (2004). Statistical tests for admixture maping with case control and cases-only. American Journal of Human Genetics, 75, 771789.Google Scholar
Muthen, B., & Asparouhov, T. (2008). Growth mixture modeling: Analysis with non-Gaussian random effects. In Fitzmaurice, G., Davidian, M., Verbeke, G., & Molenberghs, G. (Eds.), Advances in longitudinal data analysis (pp. 143165). Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Muthén, B., & Masyn, K. (2005). Discrete-time survival mixture analysis. Journal of Educational and Behavioral Statistics, 30, 2758.Google Scholar
Muthén, B., & Muthén, L. (1998–2013). Mplus users guide. Los Angeles: Author.Google Scholar
Muthén, B., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463469. doi:10.1111/j.0006-341X.1999.00463.xGoogle Scholar
Narusyte, J., Neiderhiser, J. M., Andershed, A.-K, D'Onofrio, B. M., Reiss, D., Spotts, E., et al. (2011). Parental criticism and externalizing behavior problems in adolescents: The role of environment and genotype–environment correlation. Journal of Abnormal Psychology, 120, 365376.Google Scholar
Okoli, C. T. C., Kelly, T., & Hahn, E. J. (2007). Second hand smoke and nicotine exposure: A brief review. Addictive Behaviors, 32, 19771988.Google Scholar
Patterson, G. R., Reid, J., & Dishion, T. (1992). A social learning approach: Vol. 4. Antisocial boys. Eugene, OR: Castalia.Google Scholar
Petras, H., Chilcoat, H., Leaf, P. J., Ialongo, N. S., & Kellam, S. G. (2004). Utility of TOCA-R scores during the elementary school years in identifying later violence among adolescent males. Journal of the American Academy of Child & Adolescent Psychiatry, 43, 8896. doi:10.1097/01.CHI.0000096625.64367.e6Google Scholar
Petras, H., Kellam, S. G., Brown, C. H., Muthén, B. O., Ialongo, N. S., & Poduska, J. M. (2008). Developmental epidemiological courses leading to antisocial personality disorder and violent and criminal behavior: Effects by young adulthood of a universal preventive intervention in first- and second-grade classrooms. Drug and Alcohol Dependence, 95S, S45S59.CrossRefGoogle Scholar
Petras, H., Masyn, K. E., Buckley, J. A., Ialongo, N. S., & Kellam, S. (2011). Who is most at risk for school removal? A multilevel discrete-time survival analysis of individual and context-level influences. Journal of Educational Psychology, 103, 223237. doi:10.1037/a0021545Google Scholar
Petras, H., Masyn, K. E., & Ialongo, N. S. (2011). The developmental impact of two first grade preventive interventions on aggressive/disruptive behavior in childhood and adolescence: An application of latent transition growth mixture modeling. Prevention Science, 12, 300313. doi:10.1007/s11121-011-0216-7Google Scholar
Pritchard, J. K., & Rosenberg, N. A. (1999). Use of unlinked genetic markers to detect population stratification in association studies. American Journal of Human Genetics, 65, 220228.Google Scholar
Reboussin, B. A., Hubbard, S., & Ialongo, N. S. (2007). Marijuana use patterns among African-American middle school students: A longitudinal latent class regression analysis. Drug and Alcohol Dependence, 90, 1224.Google Scholar
Reboussin, B. A., & Ialongo, N. S. (2010). Latent transition models with latent class predictors: ADHD subtypes and high-school marijuana use. Journal of the Royal Statistical Society, 173, 145164.CrossRefGoogle ScholarPubMed
Rose, J. E., Behm, F., Drgon, T., Johnson, C., & Uhl, G. R. (2010). Personalized smoking cessation: Interactions between nicotine dose, dependence and quit-success genotype score. Molecular Medicine, 16, 247253.Google Scholar
SAMHSA, Office of Applied Studies. (2001). National household survey on drug abuse: Population estimates, 1999. Ann Arbor, MI: Inter-university Consortium for Political and Social Research.Google Scholar
Sankararaman, S., Sridhar, K., & Halperin, E. (2008). Estimating local ancestry in admixed populations. American Journal of Human Genetics, 82, 290303.Google Scholar
Schaeffer, C., Petras, H., Ialongo, N., Poduska, J., & Kellam, S. (2003). Modeling growth in boys aggressive behavior across elementary school: Links to later criminal involvement, conduct disorder, and antisocial personality disorder. Developmental Psychology, 39, 10201035.Google Scholar
Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147177.Google Scholar
Shivola, E., Rose, R. J., Dick, D. M., Pulkkinen, L., Martunnen, M., & Kaprio, J. (2008). Early-onset depressive disorders predict the use of addictive substances in adolescence: A prospective study of adolescent Finnish twins. Addiction, 103, 20452053.Google Scholar
Storr, C. L., Ialongo, N. S., Kellam, S. G., & Anthony, J. C. (2002). A randomized controlled trial of two primary school intervention strategies to prevent early onset tobacco smoking. Drug and Alcohol Dependence, 66, 5160. doi: 10.1016/S0376-8716(01)00184-3Google Scholar
Stringaris, A., Zavos, H., Leibenluft, E., Maughan, B., & Eley, T. (2012). Adolescent irritability: Phenotypic associations and genetic links with depressed mood. American Journal of Psychiatry, 169, 4754.Google Scholar
Sullivan, P. F., & Kendler, K. S. (1999). The genetic epidemiology of smoking. Nicotine and Tobacco Research, 1(Suppl. 2), S51S57.Google Scholar
Tapper, A. R., Nashmi, R., & Lester, H. A. (2006). Neuronal nicotinic acetylcholine receptors and nicotine dependence. In Madras, B. K., Colvis, C. M., Pollock, J. D., Rutter, J. L., Shurtleff, D., & Zastrow, M. von (Eds.), Cell biology of addiction (pp. 179190). Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press.Google Scholar
Tobacco and Genetics Consortium. (2010). Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nature Genetics, 42, 441447.Google Scholar
Uhl, G. R., Drgon, T., Johnson, C., Ramoni, M., Behm, F. M., & Rose, J. E. (2010). Genome-wide association for smoking cessation success in a trial of precessation nicotine replacement. Molecular Medicine, 16, 512526.Google Scholar
Uhl, G. R., Drgon, T., Johnson, C., Walther, D., David, S. P., Aveyard, P., et al. (2010). Geomone-wide association for smoking cessation success: Participants in the Patch in Practice trial of nicotine replacement. Pharmacogenomics, 11, 357367.Google Scholar
Uhl, G., Walther, D., Bem, F., & Rose, J. (2011). Menthol preference among smokers: Association with trpa1 variants. Nicotine and Tobacco Research, 13, 13111315.Google Scholar
Uhl, G., Walther, D., Musci, R., Fisher, C., Anthony, J., Storr, C., et al. (2014). Smoking quit success genotype score v1.0 predicts quit success and distinct patterns of developmental involvement with common addictive substances. Molecular Psychiatry, 19, 5054. doi:10.1038/mp.2012.155Google Scholar
Wang, Y., Browne, D., Petras, H., Stuart, E., Wagner, F., Lambert, S., et al. (2009). Depressed mood and the effect of two universal first grade preventive interventions on survival to the first tobacco cigarette smoked among urban youth. Drug and Alcohol Dependence, 100, 194203.Google Scholar
Wang, Y., Storr, C., Green, K., Zhu, S., Stuart, E., Lynne-Landsman, P. H., et al. (2012). The effect of two elementary school-based prevention interventions on being offered tobacco and the transition to smoking. Drug and Alcohol Dependence, 120, 202208.Google Scholar
Werthamer-Larsson, L., Kellam, S., & Wheeler, L. (1991). Effect of first-grade classroom environment on shy behavior, aggressive behavior, and concentration problems. American Journal of Community Psychology, 19, 585602. doi:10.1007/BF00937993Google Scholar
Wilkinson, A. V., Bondy, M. L., Wu, X., Wang, J., Dong, Q., & D'Amelio, A. M. Jr. (2012). Cigarette experimentation in Mexican origin youth: Psychosocial and genetic determinants. Cancer Epidemiology Biomarkers Prevention, 21, 228238.Google Scholar