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Prevention Science

, Volume 20, Issue 2, pp 246–256 | Cite as

Does Marijuana Use at Ages 16–18 Predict Initiation of Daily Cigarette Smoking in Late Adolescence and Early Adulthood? A Propensity Score Analysis of Add Health Data

  • Trang Quynh NguyenEmail author
  • Cyrus Ebnesajjad
  • Elizabeth A. Stuart
  • Ryan David Kennedy
  • Renee M. Johnson
Article

Abstract

Given the declining trend in adolescent cigarette smoking and increase in general access to marijuana, it is important to examine whether marijuana use in adolescence is a risk factor for subsequent cigarette smoking in late adolescence and early adulthood. Preliminary evidence from a very small number of studies suggests that marijuana use during adolescence is associated with later smoking; however, to control confounding, previously published studies used regression adjustment, which is susceptible to extrapolation when the confounder distributions differ between adolescent marijuana users and non-users. The current study uses propensity score weighting, a causal inference method not previously used in this area of research, to weight participants based on their estimated probability of exposure given confounders (the propensity score) to balance observed confounders between marijuana users and non-users. The sample consists of participants of Add Health (a nationally representative dataset of youth followed into adulthood) who were 16–18, with no history of daily cigarette smoking at baseline (n = 2928 for female and 2731 for male sub-samples). We assessed the effect of adolescent marijuana use (exposure, ascertained at wave 1) on any daily cigarette smoking during the subsequent 13 years (outcome, ascertained at wave 4). Analyses suggest that for females (but not males) who used marijuana in adolescence, marijuana use increased the risk for subsequent daily smoking: OR = 1.71, 95% CI = (1.13, 2.59). We recommend that adolescent marijuana use be viewed as a possible risk factor for subsequent initiation of daily cigarette smoking in women.

Keywords

Marijuana/cannabis Tobacco Adolescence Emerging adulthood Propensity score 

Notes

Funding

The current study is supported by the National Institute on Drug Abuse (K01DA031738, Johnson).

Compliance with Ethical Standards

Conflicts of Interest

The authors declare that they have no conflict of interest.

Research Involving Human Participants and/or Animals

The current project did not involve data collection. We used de-identified data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill.

Informed Consent

Non-applicable, because the current project did not involve data collection.

References

  1. Agrawal, A., Budney, A. J., & Lynskey, M. T. (2012). The co-occurring use and misuse of cannabis and tobacco: A review. Addiction, 107(7), 1221–1233.CrossRefGoogle Scholar
  2. Agrawal, A., & Lynskey, M. T. (2009). Tobacco and cannabis co-occurrence: Does route of administration matter? Drug and Alcohol Dependence, 99(1–3), 240–247.CrossRefGoogle Scholar
  3. Agrawal, A., Scherrer, J. F., Lynskey, M. T., Sartor, C. E., Grant, J. D., Haber, J. R., et al. (2011). Patterns of use, sequence of onsets and correlates of tobacco and cannabis. Addictive Behaviors, 36(12), 1141–1147.CrossRefGoogle Scholar
  4. Azofeifa, A., Mattson, M. E., Schauer, G., McAfee, T., Grant, A., & Lyerla, R. (2016). National estimates of marijuana use and related indicators — National Survey on drug use and health, United States, 2002-2014. Morbidity and Mortality Weekly Report, 65(11).Google Scholar
  5. Badiani, A., Boden, J. M., De Pirro, S., Fergusson, D. M., Horwood, L. J., & Harold, G. T. (2015). Tobacco smoking and cannabis use in a longitudinal birth cohort: Evidence of reciprocal causal relationships. Drug and Alcohol Dependence, 150, 69–76.CrossRefGoogle Scholar
  6. Brook, J. S., Lee, J. Y., & Brook, D. W. (2015). Trajectories of marijuana use beginning in adolescence predict tobacco dependence in adulthood. Substance Abuse, 36(4), 470–477.CrossRefGoogle Scholar
  7. Dunn, L. M., & Dunn, L. M. (1981). Manual for the peabody picture vocabulary test-revised. Circle Pines, MN: American Guidance Service.Google Scholar
  8. Fairman, B. J., Johnson, R. M., & Furr-Holden, C. D. M. (2018). When cannabis is used before tobacco or alcohol: Demographic predictors and associations with heavy use, cannabis use disorder, and other drug outcomes. Prevention Science. Manuscript under review.Google Scholar
  9. Farrelly, M. C., Loomis, B. R., Han, B., Gfroerer, J., Kuiper, N., Couzens, G. L., … Caraballo, R. S.. (2013). A comprehensive examination of the influence of state tobacco control programs and policies on youth smoking. American Journal of Public Health, 103(3), 549–555.Google Scholar
  10. Goodman, E., & Whitaker, R. C. (2002). A prospective study of the role of depression in the development and persistence of adolescent obesity. Pediatrics, 109(3), 497–504.CrossRefGoogle Scholar
  11. Harris, K. M. (2009). The National Longitudinal Study of adolescent to adult health (add health), waves I & II, 1994-1996; wave III, 2001-2002; wave IV, 2007-2009 [machine-readable data file and documentation]. Chapel Hill, NC: Carolina Population Center, Univeristy of Carolina at Chapel Hill.  https://doi.org/10.3886/ICPSR27021.v9
  12. Harris, K. M. (2013). The Add Health Study: Design and Accomplishments. Retrieved from www.cpc.unc.edu/projects/addhealth/documentation/guides/DesignPaperWIIV.pdf
  13. Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis, 15(3), 199–236.CrossRefGoogle Scholar
  14. Hu, M. C., Davies, M., & Kandel, D. B. (2006). Epidemiology and correlates of daily smoking and nicotine dependence among young adults in the United States. American Journal of Public Health, 96(2), 299–308.CrossRefGoogle Scholar
  15. Humfleet, G. L., & Haas, A. L. (2004). Is marijuana use becoming a “gateway” to nicotine dependence? Addiction, 99, 5–6.CrossRefGoogle Scholar
  16. Jamal, A., King, B. A., Neff, L. J., Whitmill, J., Babb, S. D., & Graffunder, C. M. (2016). Current cigarette smoking among adults -- United States, 2005-2015. MMWR. Morbidity and Mortality Weekly Report, 65(44), 1205–1211.CrossRefGoogle Scholar
  17. Johnson, R. M., Brooks-Russell, A., Ma, M., Fairman, B. J., Tolliver, R. L., & Levinson, A. H. (2016). Usual modes of marijuana consumption among high school students in Colorado. Journal of Studies on Alcohol and Drugs, 77(4), 580–588.CrossRefGoogle Scholar
  18. Johnson, R. M., Fairman, B., Gilreath, T., Xuan, Z., Rothman, E. F., Parnham, T., & Furr-Holden, C. D. M. (2015). Past 15-year trends in adolescent marijuana use: Differences by race/ethnicity and sex. Drug and Alcohol Dependence, 155, 8–15.CrossRefGoogle Scholar
  19. Johnson, R. M., Fleming, C. B., Cambron, C., Brighthaupt, S.-C., Dean, L. T., & Guttmannova, K. (2018). Race/ethnicity differences in trends of alcohol, cigarette, and marijuana use among adolescents in Washington state, 2004–2014. Manuscript submitted for publication.Google Scholar
  20. Johnston, L. D., Malley, P. M. O., Miech, R. A., Bachman, J. G., & Schulenberg, J. E. (2016). Monitoring the future national survey results on drug use, 1975-2015: Overview, key findings on adolescent drug use. Ann Arbor: Institute for Social Research, The University of Michigan.  https://doi.org/10.1017/CBO9781107415324.004.
  21. Kostova, D., Ross, H., Blecher, E., & Markowitz, S. (2010). Prices and cigarette demand: Evidence from youth tobacco use in developing countries (NBER working paper series no. 15781). Retrieved from http://www.nber.org/papers/w15781.pdf
  22. Kristman-Valente, A., Hill, K. G., Epstein, M., Kosterman, R., Bailey, J. A., Steeger, C. M., … Hawkins, J. D. (2017). The relationship between marijuana and conventional cigarette smoking behavior from early adolescence to adulthood. Prevention Science.  https://doi.org/10.1007/s11121-017-0774-4.
  23. Lantz, P. M. (2000). Investing in youth tobacco control: A review of smoking prevention and control strategies. Tobacco Control, 9(1), 47–63.CrossRefGoogle Scholar
  24. Lumley, T. (2004). Analysis of complex survey samples. Journal of Statistical Software, 9(8), 1–19.CrossRefGoogle Scholar
  25. Mendel, J. R., Berg, C. J., Windle, R. C., & Windle, M. (2012). Predicting young adulthood smoking among adolescent smokers and nonsmokers. American Journal of Health Behavior, 36(4), 542–554.CrossRefGoogle Scholar
  26. Patton, G. C., Coffey, C., Carlin, J. B., Sawyer, S. M., & Lynskey, M. (2005). Reverse gateways? Frequent cannabis use as a predictor of tobacco initiation and nicotine dependence. Addiction, 100(10), 1518–1525.CrossRefGoogle Scholar
  27. Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401.CrossRefGoogle Scholar
  28. Resnick, M. D., Bearman, P. S., Wm Blum, R., Bauman, K. E., Harris, K. M., Jones, J., et al. (1997). Protecting adolescents from harm findings from the National Longitudinal Study on adolescent health. JAMA, 278(10), 823–832.CrossRefGoogle Scholar
  29. Ridgeway, G., McCaffrey, D. F., Morral, A., Griffin, B. A., & Burgette, L. (2015). Twang: Toolkit for weighting and analysis of nonequivalent groups. Retrieved from https://cran.r-project.org/web/packages/twang/vignettes/twang.pdf
  30. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.CrossRefGoogle Scholar
  31. Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: John Wiley & Sons Inc..CrossRefGoogle Scholar
  32. Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science, 25(1), 1–21.CrossRefGoogle Scholar
  33. Swift, W., Coffey, C., Degenhardt, L., Carlin, J. B., Romaniuk, H., & Patton, G. C. (2012). Cannabis and progression to other substance use in young adults: Findings from a 13-year prospective population-based study. Journal of Epidemiology and Community Health, 66(7), e26.CrossRefGoogle Scholar
  34. Timberlake, D. S., Haberstick, B. C., Hopfer, C. J., Bricker, J., Sakai, J. T., Lessem, J. M., & Hewitt, J. K. (2007). Progression from marijuana use to daily smoking and nicotine dependence in a national sample of U.S. adolescents. Drug and Alcohol Dependence, 88(2–3), 272–281.CrossRefGoogle Scholar
  35. U.S. Department of Health and Human Services. (2012). Preventing tobacco use among youth and young adults a report of the surgeon general executive summary. Atlanta, GA: U.S. 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.Google Scholar
  36. U.S. Department of Health and Human Services. (2014). The health consequences of smoking—50 years of progress: A report of the surgeon general. Atlanta, GA: U.S. 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 http://doi.org/NBK179276 Google Scholar
  37. U.S. Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, & Center for Behavioral Health Statistics and Quality. (2014). National Survey on drug use and health. Retrieved from  https://doi.org/10.3886/ICPSR36361.v1.
  38. US Department of Health and Human Services. (2002). Women and smoking: A report of the Surgeon General. Google Scholar
  39. van Buuren, S., & Groothuis-Oudshoorn, K. (2011). Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3).  https://doi.org/10.18637/jss.v045.i03
  40. VanderWeele, T. J., & Arah, O. a. (2011). Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders. Epidemiology, 22(1), 42–52.CrossRefGoogle Scholar
  41. White, I. R., Royston, P., & Wood, A. M. (2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine, 30(4), 377–399.CrossRefGoogle Scholar

Copyright information

© Society for Prevention Research 2018

Authors and Affiliations

  • Trang Quynh Nguyen
    • 1
    • 2
    Email author
  • Cyrus Ebnesajjad
    • 1
  • Elizabeth A. Stuart
    • 1
    • 2
    • 3
  • Ryan David Kennedy
    • 4
  • Renee M. Johnson
    • 1
  1. 1.Department of Mental HealthJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  2. 2.Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  3. 3.Department of Health Policy and ManagementJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  4. 4.Department of Health, Behavior and SocietyJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA

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