Advertisement

The Interplay Between Marijuana-Specific Risk Factors and Marijuana Use Over the Course of Adolescence

  • Katarina GuttmannovaEmail author
  • Martie L. Skinner
  • Sabrina Oesterle
  • Helene R. White
  • Richard F. Catalano
  • J. David Hawkins
Article

Abstract

Permissive attitudes and norms about marijuana use and perceptions of low harm from use are considered risk factors for adolescent marijuana use. However, the relationship between risk and use may be reciprocal and vary across development and socializing domains. We examined the bidirectional relationships between marijuana-specific risk factors in individual, parent, peer, and community domains and adolescent marijuana use. Longitudinal data came from a sample of 2002 adolescents in 12 communities. Controlling for sociodemographic covariates and communities in which the individuals resided, autoregressive cross-lagged models examined predictive associations between the risk factors and marijuana use. After accounting for concurrent relationships between risk and use and stability in behavior over time, early adolescence and the transition to high school were particularly salient developmental time points. Specifically, higher risk in all four domains in grades 7 and 9 predicted greater use 1 year later. Moreover, youth’s perception of lax community enforcement of laws regarding adolescent use at all time points predicted increases in marijuana use at the subsequent assessment, and perceived low harm from use was a risk factor that prospectively predicted more marijuana use at most of the time points. Finally, greater frequency of marijuana use predicted higher levels of risk factors at the next time point in most socializing domains throughout adolescence. Prevention programs should take into account developmental transitions, especially in early adolescence and during the transition to high school. They also should focus on the reciprocal relationships between use and risk across multiple socializing domains.

Keywords

Marijuana-specific risk factors Marijuana use Adolescence Dynamic relationship 

Notes

Acknowledgements

The authors gratefully acknowledge CYDS panel participants for their continued contribution to the longitudinal study, the Social Development Research Group Survey Research Division for their hard work maintaining high panel retention, and Ms. Tanya Williams and Ms. Diane Christiansen for their editorial and administrative support. An earlier version of this paper was presented at the Society for Longitudinal and Life Course Studies meeting held in Dublin, Ireland, in October 2015; and at the Society for Prevention Research annual meeting held in San Francisco, CA in June 2016.

Funding

Funding for this study was provided by the National Institute on Drug Abuse of the National Institutes of Health under award # R01 DA015183-12 to Dr. Oesterle. These organizations had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Compliance with Ethical Standards

Conflict of Interest

Richard F. Catalano is a board member of Channing Bete Company, distributor of Supporting School Success® and Guiding Good Choices®. Although the intervention effects are not studied here, these programs were tested in the study that produced the data set used in this paper.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Activities related to this study were approved by the University of Washington Institutional Review Board.

Informed Consent

Informed consent was obtained for all participants in the study.

References

  1. Agrawal, A., Madden, P. A., Bucholz, K. K., Heath, A. C., & Lynskey, M. T. (2014). Initial reactions to tobacco and cannabis smoking: A twin study. Addiction, 109, 663–671.CrossRefPubMedPubMedCentralGoogle Scholar
  2. Agresti, A. (2013). Categorical data analysis (3rd ed.). Hoboken: Wiley.Google Scholar
  3. Arthur, M. W., Hawkins, J. D., Pollard, J. A., Catalano, R. F., & Baglioni Jr., A. J. (2002). Measuring risk and protective factors for substance use, delinquency, and other adolescent problem behaviors: The Communities That Care Youth Survey. Evaluation Review, 26, 575–601.PubMedGoogle Scholar
  4. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs: Prentice-Hall.Google Scholar
  5. Becker, S. J., & Curry, J. F. (2014). Testing the effects of peer socialization versus selection on alcohol and marijuana use among treated adolescents. Substance Use & Misuse, 49, 234–242.CrossRefGoogle Scholar
  6. Bentler, P. M. (1990). Comparative fix indexes in structural models. Psychological Bulletin, 107, 238–246.CrossRefPubMedGoogle Scholar
  7. Beyers, J. M., Toumbourou, J. W., Catalano, R. F., Arthur, M. W., & Hawkins, J. D. (2004). A cross-national comparison of risk and protective factors for adolescent substance use: The United States and Australia. Journal of Adolescent Health, 35, 3–16.PubMedGoogle Scholar
  8. Bronfenbrenner, U. (1994). Ecological models of human development. In: International encyclopedia of education (2nd ed., Vol. 3, pp. 1643–1647). Oxford: Elsevier.Google Scholar
  9. Brooks-Russell, A., Levinson, A., Li, Y., Roppolo, R. H., & Bull, S. (2017). What do Colorado adults know about legal use of recreational marijuana after a media campaign? Health Promotion Practice, 18, 193–200.CrossRefPubMedGoogle Scholar
  10. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park: Sage.Google Scholar
  11. Cambron, C., Guttmannova, K., & Fleming, C. B. (2017). State and national contexts in evaluating cannabis laws: A case study of Washington State. Journal of Drug Issues, 47, 74–90.CrossRefPubMedGoogle Scholar
  12. Catalano, R. F., & Hawkins, J. D. (1996). The social development model: A theory of antisocial behavior. In J. D. Hawkins (Ed.), Delinquency and crime: Current theories (pp. 149–197). New York: Cambridge University Press.Google Scholar
  13. Catalano, R. F., Oxford, M. L., Harachi, T. W., Abbott, R. D., & Haggerty, K. P. (1999). A test of the social development model to predict problem behaviour during the elementary school period. Criminal Behaviour and Mental Health, 9, 39–56.CrossRefGoogle Scholar
  14. Catalano, R. F., Fagan, A. A., Gavin, L. E., Greenberg, M. T., Irwin, C. E., Ross, D. A., & Shek, D. T. L. (2012). Worldwide application of the prevention science research base in adolescent health. The Lancet, 379, 1653–1664.CrossRefGoogle Scholar
  15. Cicchetti, D. (1990). A historical perspective on the discipline of developmental psychopathology. In J. E. Rolf, A. S. Masten, D. Cicchetti, K. H. Nuechterlein, & S. Weintraub (Eds.), Risk and protective factors in the development of psychopathology (pp. 2–28). New York: Cambridge University Press.CrossRefGoogle Scholar
  16. Coie, J. D., Watt, N. F., West, S. G., Hawkins, J. D., Asarnow, J. R., Markman, H. J., … Long, B. (1993). The science of prevention: A conceptual framework and some directions for a national research program. American Psychologist, 48, 1013–1022.Google Scholar
  17. Deutsch, A. R., Chernyavskiy, P., Steinley, D., & Slutske, W. S. (2015). Measuring peer socialization for adolescent substance use: A comparison of perceived and actual friends’ substance use effects. Journal of Studies on Alcohol and Drugs, 76, 267–277.CrossRefPubMedPubMedCentralGoogle Scholar
  18. Dever, B. V., Schulenberg, J. E., Dworkin, J. B., O'Malley, P. M., Kloska, D. D., & Bachman, J. G. (2012). Predicting risk-taking with and without substance use: The effects of parental monitoring, school bonding, and sports participation. Prevention Science, 13, 605–615.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Dishion, T. J., & Owen, L. D. (2002). A longitudinal analysis of friendships and substance use: Bidirectional influence from adolescence to adulthood. Developmental Psychology, 38, 480–491.CrossRefPubMedGoogle Scholar
  20. Donovan, J. E. (2004). Adolescent alcohol initiation: A review of psychosocial risk factors. Journal of Adolescent Health, 35, 529.e527–529.e518.CrossRefGoogle Scholar
  21. Ellickson, P. L., Tucker, J. S., Klein, D. J., & Saner, H. (2004). Antecedents and outcomes of marijuana use initiation during adolescence. Preventive Medicine, 39, 976–984.CrossRefPubMedGoogle Scholar
  22. Epstein, M., Hill, K. G., Nevell, A. M., Guttmannova, K., Bailey, J. A., Abbott, R. D., … Hawkins, J. D. (2015). Trajectories of marijuana use from adolescence into adulthood: Environmental and individual correlates. Developmental Psychology, 51, 1650–1663.Google Scholar
  23. Epstein, M., Hill, K. G., Roe, S. S., Bailey, J. A., Iacono, W. G., McGue, M., … Haggerty, K. P. (2017). Time-varying effects of families and peers on adolescent marijuana use: Person-environment interactions across development. Development and Psychopathology, 29, 887–900.Google Scholar
  24. Fergusson, D. M., Horwood, L. J., Lynskey, M. T., & Madden, P. A. F. (2003). Early reactions to cannabis predict later dependence. Archives of General Psychiatry, 60, 1033–1039.CrossRefPubMedGoogle Scholar
  25. Finkel, S. E. (1995). Causal analysis with panel data. Thousand Oaks: Sage.CrossRefGoogle Scholar
  26. Fleary, S. A., Heffer, R. W., McKyer, E. L. J., & Newman, D. A. (2010). Using the bioecological model to predict risk perception of marijuana use and reported marijuana use in adolescence. Addictive Behaviors, 35, 795–798.CrossRefPubMedGoogle Scholar
  27. Fleming, C. B., Guttmannova, K., Cambron, C., Rhew, I. C., & Oesterle, S. (2016). Examination of the divergence in trends for adolescent marijuana use and marijuana-specific risk factors in Washington State. Journal of Adolescent Health, 59, 269–275.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Gardner, M., & Steinberg, L. (2005). Peer influence on risk taking, risk preference, and risky decision making in adolescence and adulthood: An experimental study. Developmental Psychology, 41, 625–635.CrossRefPubMedGoogle Scholar
  29. Grant, J. D., Scherrer, J. F., Lyons, M. J., Tsuang, M., True, W. R., & Bucholz, K. K. (2005). Subjective reactions to cocaine and marijuana are associated with abuse and dependence. Addictive Behaviors, 30, 1574–1586.CrossRefPubMedGoogle Scholar
  30. Guttmannova, K., Wheeler, M. J., Hill, K. G., Evans-Campbell, T. A., Hartigan, L. A., Jones, T. M., … Catalano, R. F. (2017). Assessment of risk and protection in Native American youth: Steps toward conducting culturally relevant, sustainable prevention in Indian Country. Journal of Community Psychology, 43, 346–362.Google Scholar
  31. Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychological Bulletin, 112, 64–105.CrossRefPubMedGoogle Scholar
  32. Hawkins, J. D., Catalano, R. F., Arthur, M. W., Egan, E., Brown, E. C., Abbott, R. D., & Murray, D. M. (2008). Testing communities that care: The rationale, design and behavioral baseline equivalence of the community youth development study. Prevention Science, 9, 178–190.CrossRefPubMedPubMedCentralGoogle Scholar
  33. Hawkins, J. D., Oesterle, S., Brown, E. C., Monahan, K. C., Abbott, R. D., Arthur, M. W., & Catalano, R. F. (2012). Sustained decreases in risk exposure and youth problem behaviors after installation of the Communities That Care prevention system in a randomized trial. Archives of Pediatrics and Adolescent Medicine, 166, 141–148.CrossRefPubMedGoogle Scholar
  34. Hawkins, J. D., Oesterle, S., Brown, E. C., Abbott, R. D., & Catalano, R. F. (2014). Youth problem behaviors 8 years after implementing the Communities That Care prevention system: A community-randomized trial. JAMA Pediatrics, 168, 122–129.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Hill, K. G., Hawkins, J. D., Bailey, J. A., Catalano, R. F., Abbott, R. D., & Shapiro, V. (2010). Person-environment interaction in the prediction of alcohol abuse and alcohol dependence in adulthood. Drug & Alcohol Dependence, 110, 62–69.CrossRefGoogle Scholar
  36. Hox, J. J., Moerbeek, M., & van de Schoot, R. (2010). Multilevel analysis: Techniques and applications. New York: Routledge.Google Scholar
  37. Hughes, A., Lipari, R. N., & Williams, M. (2015). The CBHSQ Report: State estimates of adolescent marijuana use and perceptions of risk of harm from marijuana use: 2013 and 2014. Rockville: Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality.Google Scholar
  38. Jaccard, J., Blanton, H., & Dodge, T. (2005). Peer influences on risk behavior: An analysis of the effects of a close friend. Developmental Psychology, 41, 135–147.CrossRefPubMedGoogle Scholar
  39. Jessor, R., & Jessor, S. L. (1977). Problem behavior and psychological development: A longitudinal study of youth. New York: Academic Press.Google Scholar
  40. Kilmer, J. R., Geisner, I. M., Gasser, M. L., & Lindgren, K. P. (2015). Normative perceptions of non-medical stimulant use: Associations with actual use and hazardous drinking. Addictive Behaviors, 42, 51–56.CrossRefPubMedGoogle Scholar
  41. Kosterman, R., Bailey, J. A., Guttmannova, K., Jones, T. A., Eisenberg, N., Hill, K. G., & Hawkins, J. D. (2016). Marijuana legalization and parents’ attitudes, use, and parenting in Washington State. Journal of Adolescent Health, 59, 450–456.CrossRefPubMedPubMedCentralGoogle Scholar
  42. Lipari, R., Kroutil, L. A., & Pemberton, M. R. (2015). Risk and protective factors and initiation of substance use: Results from the 2014 National Survey on Drug Use and Health. Rockville: Substance Abuse and Mental Health Services Administration.Google Scholar
  43. MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130–149.CrossRefGoogle Scholar
  44. Marsh, H. W., & Hau, K.-T. (1996). Assessing goodness of fit: Is parsimony always desirable? The Journal of Experimental Education, 64, 364–390.CrossRefGoogle Scholar
  45. Martens, M. P., Page, J. C., Mowry, E. S., Damann, K. M., Taylor, K. K., & Cimini, M. D. (2006). Differences between actual and perceived student norms: An examination of alcohol use, drug use, and sexual behavior. Journal of American College Health, 54, 295–300.CrossRefPubMedGoogle Scholar
  46. Mason, W. A., Fleming, C. B., & Haggerty, K. P. (2016). Prevention of marijuana misuse: School-, family-, and community-based approaches. In M. T. Compton (Ed.), Marijuana and mental health (pp. 199–225). Arlington: American Psychiatric Association Publishing.Google Scholar
  47. Mercken, L., Steglich, C., Knibbe, R., & Vries, H. (2012). Dynamics of friendship networks and alcohol use in early and mid-adolescence. Journal of Studies on Alcohol and Drugs, 73, 99–110.CrossRefPubMedGoogle Scholar
  48. Miech, R. A., Johnston, L. D., O'Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2015). Monitoring the Future national survey results on drug use, 1975–2014: Volume I, Secondary school students. Ann Arbor: Institute for Social Research, The University of Michigan.Google Scholar
  49. Monahan, K. C., Oesterle, S., & Hawkins, J. D. (2010). Predictors and consequences of school connectedness: The case for prevention. The Prevention Researcher, 17, 3–6.Google Scholar
  50. Muthén, L. K., & Muthén, B. O. (1998-2013). Mplus user’s guide. Los Angeles: Muthén & Muthén.Google Scholar
  51. Muthén, B. O., du Toit, S. H. C., & Spisic, D. (1997). Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes. http://www.statmodel.com/download/Article_075.pdf.
  52. Pacula, R. L., & Sevigny, E. L. (2014). Marijuana liberalization policies: Why we can’t learn much from policy still in motion. Journal of Policy Analysis and Management, 33, 212–221.CrossRefPubMedPubMedCentralGoogle Scholar
  53. Pacula, R. L., Powell, D., Heaton, P., & Sevigny, E. L. (2015). Assessing the effects of medical marijuana laws on marijuana use: The devil is in the details. Journal of Policy Analysis and Management, 34, 7–31.CrossRefPubMedPubMedCentralGoogle Scholar
  54. Perkins, H. W., Meilman, P. W., Leichliter, J. S., Cashin, J. R., & Presley, C. A. (1999). Misperceptions of the norms for the frequency of alcohol and other drug use on college campuses. Journal of American College Health, 47, 253–258.CrossRefPubMedGoogle Scholar
  55. Rogosa, D. (1980). A critique of cross-lagged correlation. Psychological Bulletin, 88, 245–258.CrossRefGoogle Scholar
  56. Sieving, R. E., Perry, C. L., & Williams, C. L. (2000). Do friendships change behaviors, or do behaviors change friendships? Examining paths of influence in young adolescents’ alcohol use. Journal of Adolescent Health, 26, 27–35.CrossRefPubMedGoogle Scholar
  57. Simons-Morton, B., & Chen, R. S. (2006). Over time relationships between early adolescent and peer substance use. Addictive Behaviors, 31, 1211–1223.CrossRefPubMedGoogle Scholar
  58. Simons-Morton, B. G., & Farhat, T. (2010). Recent findings on peer group influences on adolescent smoking. The Journal of Primary Prevention, 31, 191–208.CrossRefPubMedPubMedCentralGoogle Scholar
  59. Slater, M. D. (2003). Sensation-seeking as a moderator of the effects of peer influences, consistency with personal aspirations and perceived harm on marijuana and cigarette use among younger adolescents. Substance Use & Misuse, 38, 865–880.CrossRefGoogle Scholar
  60. Tang, Z., & Orwin, R. G. (2009). Marijuana initiation among American youth and its risks as dynamic processes: Prospective findings from a national longitudinal study. Substance Use & Misuse, 44, 195–211.CrossRefGoogle Scholar
  61. Thornberry, T. P. (1996). Empirical support for interactional theory: A review of the literature. In J. D. Hawkins (Ed.), Delinquency and crime: Current theories (pp. 198–235). New York: Cambridge University Press.Google Scholar
  62. Wen, H., Hockenberry, J. M., & Druss, B. G. (2018). The effect of medical marijuana laws on marijuana-related attitude and perception among U.S. adolescents and adults. Prevention Science. (in press).Google Scholar
  63. Wu, L.-T., Swartz, M. S., Brady, K. T., & Hoyle, R. H. (2015). Perceived cannabis use norms and cannabis use among adolescents in the United States. Journal of Psychiatric Research, 64, 79–87.CrossRefPubMedPubMedCentralGoogle Scholar
  64. Zaff, J. F., Donlan, A., Gunning, A., Anderson, S. E., McDermott, E., & Sedaca, M. (2016). Factors that promote high school graduation: A review of the literature. Educational Psychology Review, 29, 447–476.CrossRefGoogle Scholar

Copyright information

© Society for Prevention Research 2018

Authors and Affiliations

  • Katarina Guttmannova
    • 1
    Email author
  • Martie L. Skinner
    • 2
  • Sabrina Oesterle
    • 2
  • Helene R. White
    • 3
  • Richard F. Catalano
    • 2
  • J. David Hawkins
    • 2
  1. 1.Center for the Study of Health and Risk Behaviors, Department of Psychiatry and Behavioral SciencesUniversity of WashingtonSeattleUSA
  2. 2.Social Development Research Group, School of Social WorkUniversity of WashingtonSeattleUSA
  3. 3.Center of Alcohol Studies and Sociology DepartmentRutgers – The State University of New JerseyPiscataway TownshipUSA

Personalised recommendations