Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-06T22:46:27.524Z Has data issue: false hasContentIssue false
  • English
  • Français

Trajectories of marijuana use from late childhood to late adolescence: Can Temperament × Experience interactions discriminate different trajectories of marijuana use?

Published online by Cambridge University Press:  20 June 2016

Matthew D. Scalco  and
Craig R. Colder
Matthew D. Scalco*
Affiliation:
State University of New York at Buffalo
Craig R. Colder
Affiliation:
State University of New York at Buffalo
*
Address correspondence and reprint requests to: Matthew D. Scalco, Department of Psychology, 204 Park Hall, State University of New York at Buffalo, Buffalo, NY 14260-4110; E-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Informed by developmental ecological and epigenetic theory, the current study examined three aims concerning adolescent marijuana use with a large community sample (N = 755; gender = 53% female) and six annual assessments that spanned 11–18 years of age. First, the natural history of adolescent marijuana use was modeled using a two-part latent growth curve analysis. Second, the validity of the mixtures was examined with a broad array of known correlates of adolescent marijuana use. Third, temperament (e.g., surgency, effortful control, and negative affect) was tested as individual differences that would enter into statistical interactions with peer substance use and prior alcohol and cigarette use to distinguish trajectories of marijuana use. The results suggested that escalations in marijuana use were observed for some youth who initiated marijuana use early in adolescence. Youth whose marijuana use did escalate substantially (10%) were distinguished on temperament, conduct disorder, peer delinquency, and pubertal development at baseline. Furthermore, hypothesized interactions between surgency and both peer substance use and prior substance use discriminated different patterns of marijuana use. The findings are discussed with respect to strategies for timing and content of preventive interventions.

Type
Regular Articles
Information
Development and Psychopathology , Volume 29 , Issue 3 , August 2017 , pp. 775 - 790
Copyright
Copyright © Cambridge University Press 2016 

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.)

Footnotes

This research was funded by National Institute on Drug Abuse Grants R01 DA020171 and R01 DA019631 (to C.R.C.).

References

Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: 3-step approaches using Mplus. Structural Equation Modeling, 21, 329–341.CrossRefGoogle Scholar
Bauer, D. J., & Curran, P. J. (2003). Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods, 8, 338–363.Google Scholar
Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Cambridge, MA: Harvard University Press.Google Scholar
Chassin, L., Colder, C. R., Hussong, A., & Sher, K. (2015). Substance use and substance use disorders. In Sameroff, A. J. (Ed.), Developmental psychopathology (3rd ed.). Hoboken, NJ: Wiley.Google Scholar
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.Google Scholar
Colder, C. R., Chassin, L., Lee, M. R., & Villalta, I. K. (2010). Developmental perspectives: Affect and adolescent substance use. In Kassel, J. D. (Eds.), Substance abuse and emotion (pp. 109–135). Washington, DC: American Psychological Association.Google Scholar
Colder, C. R., Hawk, L. W., Lengua, L. J., Wiezcorek, W., Eiden, R. D., & Read, J. P. (2013). Trajectories of reinforcement sensitivity during adolescence and risk for substance use. Journal of Research on Adolescence, 23, 345–356.Google Scholar
Colder, C. R., Scalco, M. D., Trucco, E. M., Read, J. P., Lengua, L. J., & Wieczorek, W. F. (2013). Prospective associations of internalizing and externalizing problems and their co-occurrence with early adolescent substance use. Journal of Abnormal Child Psychology, 41, 667–677.Google Scholar
Colder, C. R., Trucco, E. M., Lopez, H. I., Hawk, L. W., Read, J. P., Lengua, L. J., et al. (2011). Revised reinforcement sensitivity theory and laboratory assessment of BIS and BAS in children. Journal of Research in Personality, 45, 198–207.Google Scholar
Corr, P. J. (2008). Reinforcement Sensitivity Theory (RST): Introduction. In Corr, P. (Eds.), The reinforcement sensitivity theory of personality (pp. 1–43). New York: Cambridge University Press.Google Scholar
Creemers, H. E., Dijkstra, J. K., Vollebergh, W. M., Ormel, J., Verhulst, F. C., & Huizink, A. C. (2010). Predicting life-time and regular cannabis use during adolescence; the roles of temperament and peer substance use: The TRAILS study. Addiction, 105, 699–708.Google Scholar
Creemers, H. E., Korhonen, T., Kaprio, J., Vollebergh, W. M., Ormel, J., Verhulst, F. C., et al. (2009). The role of temperament in the relationship between early onset of cigarette and cannabis use: The TRAILS study. Drug and Alcohol Dependence, 104, 113–118.Google Scholar
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52, 281–302.Google Scholar
Cruz, J. E., Emery, R. E., & Turkheimer, E. (2012). Peer network drinking predicts increased alcohol use from adolescence to early adulthood after controlling for genetic and shared environmental selection. Developmental Psychology, 48, 1390–1402.CrossRefGoogle ScholarPubMed
Cumming, G. (2008). Replication and p intervals: p values predict the future only vaguely, but confidence intervals do much better. Perspectives on Psychological Science, 3, 286–300.Google Scholar
Cyders, M. A., & Smith, G. T. (2007). Mood-based rash action and its components: Positive and negative urgency. Personality and Individual Differences, 43, 839–850.Google Scholar
Dawes, M. A., Antelman, S. M., Vanyukov, M. M., Giancola, P., Tarter, R. E., Susman, E. J., et al. (2000). Developmental sources of variation in liability to adolescent substance use disorders. Drug and Alcohol Dependence, 61, 3–14.Google Scholar
Dishion, T. J., & Medici Skaggs, N. (2000). An ecological analysis of monthly “bursts” in early adolescent substance use. Applied Developmental Science, 4, 89–97.Google Scholar
Dishion, T. J., Spracklen, K. M., Andrews, D. W., & Patterson, G. R. (1996). Deviancy training in male adolescents friendships. Behavior Therapy, 27, 373–390.Google Scholar
Ellickson, P. L., D'Amico, E. J., Collins, R. L., & Klein, D. J. (2005). Marijuana use and later problems: When frequency of recent use explains age of initiation effects (and when it does not). Substance Use & Misuse, 40, 343–359.Google Scholar
Ellickson, P. L., Martino, S. C., & Collins, R. L. (2004). Marijuana use from adolescence to young adulthood: Multiple developmental trajectories and their associated outcomes. Health Psychology, 23, 299–307.Google Scholar
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.Google Scholar
Elliott, D. S., & Huizinga, D. (1983). Social group and delinquent behavior in a national youth panel. Criminology, 21, 149–177.Google Scholar
Ellis, L. K., & Rothbart, M. K. (2001). Revision of the Early Adolescent Temperament Questionnaire. Poster presented at the Society for Research in Child Development Biennial Meeting, Minneapolis, MN, April 19–22.Google Scholar
Ernst, M., & Fudge, J. L. (2009). Developmental neurobiological model of motivated behavior: Anatomy, connectivity and ontogeny of the triadic nodes. Neuroscience & Biobehavioral Reviews, 33, 367–382.CrossRefGoogle ScholarPubMed
Ferguson, D. M., Woodward, L. J., & Horwood, L. J. (1999). Childhood peer relationship problems and young people's involvement with deviant peers in adolescence. Journal of Abnormal Child Psychology, 27, 357–370.Google Scholar
Fields, H. L., Hjelmstad, G. O., Margolis, E. B., & Nicola, S. M. (2007). Ventral tegmental area neurons in learned appetitive behavior and positive reinforcement. Annual Review of Neuroscience, 30, 289–316.Google Scholar
Flory, K., Lynam, D., Milich, R., Leukefeld, C., & Clayton, R. (2004). Early adolescent through young adult alcohol and marijuana use trajectories: Early predictors, young adult outcomes, and predictive utility. Developmental Psychopathology, 16, 193–213.Google Scholar
Franken, I. A., & Muris, P. (2006). Gray's impulsivity dimension: A distinction between reward sensitivity versus rash impulsiveness. Personality and Individual Differences, 40, 1337–1347.CrossRefGoogle Scholar
Gottlieb, G., & Willoughby, M. T. (2006). Probabilistic epigenesis of psychopathology. In Cicchetti, D. & Cohen, D. J. (Eds.), Developmental psychopathology: Vol. 1. Theory and method (2nd ed., pp. 673–700). Hoboken, NJ: Wiley.Google Scholar
Green, B. E., & Ritter, C. (2000). Marijuana use and depression. Journal of Health and Social Behavior, 41, 40–49.Google Scholar
Haegerich, T. M., & Tolan, P. H. (2008). Core competencies and the prevention of adolescent substance use. New Directions for Child and Adolescent Development, 2008, 47–60.Google Scholar
Hendriks, V., van der Schee, E., & Blanken, P. (2012). Matching adolescents with a cannabis disorder to multidimensional family therapy or cognitive behavioral therapy: Treatment effect moderators in a randomized controlled trail. Drug and Alcohol Dependence, 125, 119–126.Google Scholar
Hix-Small, H., Duncan, T. E., Duncan, S. C., & Okut, H. (2004). A multivariate associative finite growth mixture modeling approach examining adolescent alcohol and marijuana use. Journal of Psychopathology and Behavioral Assessment, 26, 255–270.Google Scholar
Hussong, A. M., Jones, D. J., Stein, G. L., Baucom, D. H., & Boeding, S. (2011). An internalizing pathway to alcohol use and disorder. Psychology of Addictive Behaviors, 25, 390–404.Google Scholar
Johnston, L. D., O'Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2011). Monitoring the Future national survey results on drug use, 1975–2010: Vol. 1. Secondary school students. Ann Arbor, MI: University of Michigan, Institute for Social Research.Google Scholar
Johnston, L. D., O'Malley, P. M., Miech, R. A., Bachman, J. G., & Schulenberg, J. E. (2015). Monitoring the Future national survey results on drug use: 1975–2014: Overview, key findings on adolescent drug use. Ann Arbor, MI: University of Michigan, Institute for Social Research.Google Scholar
Kandel, D. (1975). Stages in adolescent involvement in drug use. Science, 190, 912–914.CrossRefGoogle ScholarPubMed
Kandel, D. B., & Chen, K. (2000). Types of marijuana users by longitudinal course. Journal of Studies on Alcohol, 61, 367–378.Google Scholar
Khantzian, E. J. (1997). The self-medication hypothesis of substance use disorders: A reconsideration and recent applications. Harvard Review of Psychiatry, 4, 231–244.Google Scholar
King, K. M., Iacono, W. G., & McGue, M. (2004). Childhood externalizing and internalizing psychopathology in the prediction of early substance use. Addiction, 99, 1548–1559.Google Scholar
Kline, R. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford Press.Google Scholar
Koob, G. F., & Le Moal, M. (2008). Addiction and the brain antireward system. Annual Review of Psychology, 59, 29–53.Google Scholar
Lanza, S. T., Tan, X., & Bray, B. C. (2013). Latent class analysis with distal outcomes: A flexible model-based approach. Structural Equation Modeling, 20, 1–26.Google Scholar
Liddle, H. A. (2010). Multidimensional family therapy: A science-based treatment system. Australian and New Zealand Journal of Family Therapy, 31, 138–148.Google Scholar
Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767–778.CrossRefGoogle Scholar
Maldonado-Molina, M. M., Collins, L. M., & Lanza, S. T. (2005). Using latent transition analysis to test the gateway hypothesis of drug use onset in th Add Health data (Tech. Report 02-54). State College, PA: Pennsylvania State University, Methodology Center. Retrieved from http://methodology.psu.edu/media/techreports/02-54.pdf Google Scholar
Maldonado-Molina, M. M., & Lanza, S. T. (2010). A framework to examine gateway relations in drug use: An application of latent transition analysis. Journal of Drug Issues, 40, 901–924.Google Scholar
Mason, W. A., Hitchings, J. E., & Spoth, R. L. (2008). The interaction of conduct problems and depressed mood in relation to adolescent substance involvement and peer substance use. Drug and Alcohol Dependence, 96, 233–248.Google Scholar
McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York: Wiley.Google Scholar
Meehl, P. E. (1997). The problem is epistemology, not statistics: Replace significance tests by confidence intervals and quantify accuracy of risky numerical predictions. In Harlow, L. L., Mulaik, S. A., & Steiger, J. H. (Eds.), What if there were no significance tests? (pp. 393–425). Mahwah, NJ: Erlbaum.Google Scholar
Miller, S., Siegel, J. T., Hohman, Z. P., & Crano, W. D. (2013). Factors mediating the association of the recency of parent's marijuana use and their adolescent children's subsequent initiation. Psychology of Addictive Behaviors, 27, 848–853.Google Scholar
Miller, W. R., Tonigan, J. S., & Longabaugh, R. (1995). The Drinker Inventory of Consequences (DrInc): An instrument for assessing adverse consequences of alcohol abuse. Test Manual (Vol. 4, Project Match Monograph Series). Rockville, MD: National Institute on Alcohol Abuse and Alcoholism.Google Scholar
Murphy, A., Taylor, E., & Elliott, R. (2012). The detrimental effects of emotional process dysregulation on decision-making in substance dependence. Frontiers in Integrative Neuroscience, 6, 1–24.Google Scholar
Muthén, B. (2001). Two-part growth mixture modeling. Unpublished manuscript. Retrieved from http://www.statmodel.com/bmuthen/articles/Article_094.pdf Google Scholar
Muthen, B. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In Kaplan, D. (Ed.), Handbook of quantitative methodology for the social sciences. Newbury Park, CA: Sage.Google Scholar
Muthén, L. K., & Muthén, B. O. (1998–2012). Mplus user's guide (7th ed.). Los Angeles: Author.Google Scholar
Nagin, D. S. (1999). Analyzing developmental trajectories: A semiparametric, group-based approach. Psychological Methods, 4, 139–157.Google Scholar
New York State Office Alcoholism and Substance Abuse Services. (2009). The New York State Youth Development Survey 2008 Report. Albany, NY: Author.Google Scholar
Olsen, M. K., & Schafer, J. L. (2001). A two-part random-effects model for semicontinuous longitudinal data. Journal of the American Statistical Association, 96, 730–745.Google Scholar
Peterson, A. C., Crockett, L., Richards, M., & Boxer, A. (1988). A self-report measure of pubertal status: Reliability, validity, and initial norms. Journal of Youth and Adolescence, 17, 117–133.CrossRefGoogle Scholar
Pillow, D. R., Pelham, W. E. Jr., Hoza, B., Molina, B. S., & Shultz, C. H. (1998). Confirmatory factor analyses examining attention deficit hyperactivity disorder symptoms and other childhood disruptive behaviors. Journal of Abnormal Child Psychology, 26, 293–309.Google Scholar
Ragan, D., Osgood, W., Moody, J., & Gest, S. (2014). Clarifying peer selection and influence processes for adolescent delinquency and alcohol use: A comparison of three analytic approaches. Paper presented at the Society for Research on Adolescence Biennial Meeting, Austin, TX.Google Scholar
Robinson, T. E., & Berridge, K. C. (2003). Addiction. Annual Review of Psychology, 54, 25–53.CrossRefGoogle ScholarPubMed
Rothbart, M. K., & Bates, J. E. (2006). Temperament. In Eisenberg, N. (Eds.), Handbook of child psychology (6th ed., pp. 99–166). Hoboken, NJ: Wiley.Google Scholar
Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100, 322–331.Google Scholar
Scalco, M. D., Colder, C. R., Hawk, L. W. Jr., Read, J. P., Wieczorek, W. F., & Lengua, L. J. (2014). Internalizing and externalizing problem behavior and early adolescent substance use: A test of a latent variable interaction and conditional indirect effects. Psychology of Addictive Behaviors, 28, 828.Google Scholar
Scalco, M. D., Colder, C. R., & Lengua, L. J. (2015, May). Validity and higher order factor structure of the early adolescent temperament questionnaire—Revised. Poster presented at the Association for Psychological Science, New York.Google Scholar
Scalco, M. D., Trucco, E. M., Coffman, D. L., & Colder, C. R. (2015). Selection and socialization effects in early adolescent alcohol use: A propensity score analysis. Journal of Abnormal Child Psychology. Advance online publication.Google Scholar
Schafer, J. L. (1997). Analysis of incomplete multivariate data. Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177.Google Scholar
Sher, K. J., Jackson, K. M., & Steinley, D. (2011). Alcohol use trajectories and the ubiquitous cat's cradle: Cause for concern? Journal of Abnormal Psychology, 120, 322–335.Google Scholar
Sobell, L. C., & Sobell, M. B. (2012). Timeline follow-back. In Litten, R. Z. & Allen, J. P. (Eds.), Measuring alcohol consumption: Psychosocial and biochemical methods (pp. 41–72). New York: Springer.Google Scholar
Spear, L. P. (2011). Rewards, aversions and affect in adolescence: Emerging convergences across laboratory animal and human data. Developmental Cognitive Neuroscience, 1, 390–403.Google Scholar
Steinberg, L. (2008). A social neuroscience perspective on adolescent risk taking. Developmental Review, 28, 78–106.Google Scholar
Sung, M., Erkanli, A., Angold, A., & Costello, E. J. (2004). Effects of age at first substance use and psychiatric comorbidity on the development of substance use disorders. Drug and Alcohol Dependence, 75, 287–299.CrossRefGoogle ScholarPubMed
Susman, E. J., & Dorn, L. D. (2009). Puberty: Its role in development. In Lerner, R. M. & Steinberg, L. (Eds.), Handbook of adolescent psychology (3rd ed., Vol. 1, pp. 116–151). Hoboken, NJ: Wiley.Google Scholar
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston: Pearson Education.Google Scholar
Vanyukov, M. M., Tarter, R. E., Kirillova, G. P., Kirisci, L., Reynolds, M. D., Kreek, M. J., et al. (2012). Common liability to addiction and “gateway hypothesis”: Theoretical, empirical and evolutionary perspective. Drug and Alcohol Dependence, 123S, S3–S17.Google Scholar
Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18, 450–469.Google Scholar
Waldron, H. B., & Turner, C. W. (2008). Evidence-based psychosocial treatments for adolescent substance use. Journal of Clinical Child and Adolescent Psychology, 37, 238–261.Google Scholar
Wiers, R. W., Bartholow, B. D., van den Wildenberg, E., Thush, C., Engels, R. C., Sher, K. J., et al. (2007). Automatic and controlled processes and the development of addictive behaviors in adolescents: A review and a model. Pharmacology Biochemistry and Behavior, 86, 263–283.Google Scholar
Whiteside, S. P., & Lynam, D. R. (2001). The five-factor model and impulsivity: Using a structural model to understand impulsivity. Personality and Individual Differences, 30, 669–689.Google Scholar
Winters, K. C., Stinchfield, R. D., Henly, G. A., & Schwartz, R. H. (1991). Validity of adolescent self-report of alcohol and other drug involvement. International Journal of the Addictions, 25, 1379–1395.Google Scholar