Abstract
Reservation-dwelling American Indian adolescents are at exceedingly high risk for cannabis use. Prevention initiatives to delay the onset and escalation of use are needed. The risk and promotive factors approach to substance use prevention is a well-established framework for identifying the timing and targets for prevention initiatives. This study aimed to develop predictive models for the usage of cannabis using 22 salient risk and promotive factors. Models were developed using data from a cross-sectional study and further validated using data from a separate longitudinal study with three measurement occasions (baseline, 6-month follow-up, 1-year follow-up). Application of the model to longitudinal data showed an acceptable performance contemporaneously but waning prospective predictive utility over time. Despite the model’s high specificity, the sensitivity was low, indicating an effective prediction of non-users but poor performance in correctly identifying users, particularly at the 1-year follow-up. This divergence can have significant implications. For example, a model that misclassifies future adolescent cannabis use could fail to provide necessary intervention for those at risk, leading to negative health and social consequences. Moreover, supplementary analysis points to the importance of considering change in risk and promotive factors over time.
Similar content being viewed by others
Data Availability
Survey data and research materials are available at: https://www.icpsr.umich.edu/web/NAHDAP/studies/37997. The longitudinal data files are not currently publically available. For more information about data availability, please contact the study PIs, Drs. Linda Stanley and Randall Swaim.
References
August, G. J., & Gewirtz, A. (2019). Moving toward a precision-based, personalized framework for prevention science: Introduction to the special issue. Prevention Science, 20, 1–9.
Basuchoudhary, A., Bang, J. T., Sen, T., Basuchoudhary, A., Bang, J. T., & Sen, T. (2017). Predicting Economic Growth: Which Variables Matter. Machine-learning Techniques in Economics: New Tools for Predicting Economic Growth, 37–56.
Bzdok, D., & Meyer-Lindenberg, A. (2018). Machine learning for precision psychiatry: Opportunities and challenges. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(3), 223–230. https://doi.org/10.1016/j.bpsc.2017.11.007
Catalano, R. F., Speaker, E. C., Skinner, M. L., Bailey, J. A., Hong, G., Haggerty, K. P., Guttmannova, K., & Harrop, E. N. (2018). Risk factors for adolescent marijuana use. In K.C Winters & K.A. Sabet (Eds.), Contemporary Health Issues on Marijuana (pp. 219–235). Oxford University Press. https://doi.org/10.1093/med-psych/9780190263072.003.0009
Charmaraman, L., & Grossman, J. M. (2010). Importance of race and ethnicity: An exploration of Asian, Black, Latino, and multiracial adolescent identity. Cultural Diversity and Ethnic Minority Psychology, 16(2), 144–151. https://doi.org/10.1037/a0018668
Connell, C. M., Gilreath, T. D., Aklin, W. M., & Brex, R. A. (2010). Social-ecological influences on patterns of substance use among non-metropolitan high school students. American Journal of Community Psychology, 45(1–2), 36–48. https://doi.org/10.1007/s10464-009-9289-x
Gobbi, G., Atkin, T., Zytynski, T., Wang, S., Askari, S., Boruff, J., Ware, M., Marmorstein, N., Cipriani, A., Dendukuri, N., & Mayo, N. (2019). Association of cannabis use in adolescence and risk of depression, anxiety, and suicidality in young adulthood. JAMA Psychiatry, 76(4), 426-434. https://doi.org/10.1001/jamapsychiatry.2018.4500
Greenwell, B. M., & Boehmke, B. C. (2020). Variable importance plots—An introduction to the vip package. The R Journal, 12(1), 343–366. https://doi.org/10.32614/RJ-2020-013
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
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. https://doi.org/10.1007/s11121-008-0092-y
Henneberger, A. K., Mushonga, D. R., & Preston, A. M. (2021). Peer influence and adolescent substance use: A systematic review of dynamic social network research. Adolescent Research Review, 6, 57–73. https://doi.org/10.1007/s40894-019-00130-0
Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). John Wiley & Sons, Inc. https://doi.org/10.1002/0471722146
Jing, Y., Hu, Z., Fan, P., Xue, Y., Wang, L., Tarter, R. E., ... & Xie, X. Q. (2020). Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder. Drug and alcohol dependence, 206, 107605.
Johnston, L. D., Miech, R. A., O’Malley, P. M., Bachman, J. G., Schulenberg, J. E., & Patrick, M. E. (2019). Monitoring the future national survey results on drug use, 1975–2018: Overview, key findings on adolescent drug use. Institute for Social Research. https://eric.ed.gov/?id=ED594190
Kuhn, M., & Wickham, H. (2020). Tidymodels: A collection of packages for modeling and machine learning using tidyverse principles. https://www.tidymodels.org
Kundu, A., Chaiton, M., Billington, R., Grace, D., Fu, R., Logie, C., Baskerville, B., Yager, C., Mitsakakis, N., & Schwartz, R. (2021). Machine learning applications in mental health and substance use research among the LGBTQ2S+ population: Scoping review. JMIR Medical Informatics, 9(11), e28962. https://doi.org/10.2196/28962
Perski, O., Hébert, E. T., Naughton, F., Hekler, E. B., Brown, J., & Businelle, M. S. (2021). Technology-mediated just-in-time adaptive interventions (JITAIs) to reduce harmful substance use: A systematic review. Addiction, 117(5), 1220–1241. https://doi.org/10.1111/add.15687
R Core Team. (2020). R: A language and environment for statistical computing. https://www.R-project.org/
Rowe, D. C., Vazsonyi, A. T., & Flannery, D. J. (1994). No more than skin deep: Ethnic and racial similarity in developmental process. Psychological Review, 101(3), 396–413. https://doi.org/10.1037/0033-295x.101.3.396
RStudio Team. (2020). RStudio: Integrated development environment for R. http://www.rstudio.com/
Scheier, L. M. (2015). Theoretical models of drug use etiology: Foundations of prevention. In L.M. Scheier (Ed.), Handbook of adolescent drug use prevention: Research, intervention strategies, and practice (pp. 67–83). American Psychological Association. https://doi.org/10.1037/14550-005
Sloboda, Z., Glantz, M. D., & Tarter, R. E. (2012). Revisiting the concepts of risk and protective factors for understanding the etiology and development of substance use and substance use disorders: Implications for prevention. Substance Use & Misuse, 47(8–9), 944–962. https://doi.org/10.3109/10826084.2012.663280
Spillane, N. S., Schick, M. R., Nalven, T., & Kirk-Provencher, K. T. (2021). Three As of American Indian adolescent marijuana use: Availability, acceptability, and approval. Drug and Alcohol Dependence, 219, 108462. https://doi.org/10.1016/j.drugalcdep.2020.108462
Stanley, L. R., Swaim, R. C., Kaholokula, J. K., Kelly, K. J., Belcourt, A., & Allen, J. (2017). The imperative for research to promote health equity in indigenous communities. Prevention Science, 21(S1), 13–21. https://doi.org/10.1007/s11121-017-0850-9
Subbaswamy, A., Adams, R., & Saria, S. (2020). Evaluating model robustness and stability to dataset shift. https://doi.org/10.48550/ARXIV.2010.15100
Swaim, R. C., & Stanley, L. R. (2018). Substance use among American Indian youths on reservations compared with a national sample of US adolescents. JAMA Network Open, 1(1), e180382. https://doi.org/10.1001/jamanetworkopen.2018.0382
Tiffin, P. A., & Paton, L. W. (2018). Rise of the machines? Machine learning approaches and mental health: Opportunities and challenges. The British Journal of Psychiatry, 213(3), 509–510. https://doi.org/10.1192/bjp.2018.105
Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67. https://doi.org/10.18637/jss.v045.i03
Wagner, E. F., & Lewis, N. (2016). Targeted prevention approaches. In R.A. Zucker & S.A. Brown (Eds.), The Oxford handbook of adolescent substance use (pp. 655-674). https://doi.org/10.1093/oxfordhb/9780199735662.013.031
Whitbeck, L. B., Hoyt, D. R., McMorris, B. J., Chen, X., & Stubben, J. D. (2001). Perceived discrimination and early substance abuse among American Indian children. Journal of Health and Social Behavior, 42(4), 405-424. https://doi.org/10.2307/3090187
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … & Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122. https://doi.org/10.1177/1745691617693393
Yip, S. W., Kiluk, B., & Scheinost, D. (2020). Toward addiction prediction: An overview of cross-validated predictive modeling findings and considerations for future neuroimaging research. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5(8), 748–758. https://doi.org/10.1016/j.bpsc.2019.11.001
Zhou, X., Obuchowski, N. A., & McClish, D. K. (2011). Chapter 2. Measures of diagnostic accuracy. Statistical methods in diagnostic medicine. 2nd ed. Hoboken: Wiley, 13–57.
Funding
This research was supported in part by the National Institute on Drug Abuse (R01DA003371).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethics Approval
Approval for this research study was obtained from the ethics committee of Colorado State University. In addition, approval was obtained from local school boards and tribal IRB’s as required by each study location. The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.
Consent to Participate
The study involved research with students who were minors and all data were obtained anonymously. Parents were fully informed about the study and given the opportunity to opt their child out of the study. Less than 1% chose to do so. Prior to completing online surveys, assent was obtained from all participating students.
Conflict of Interest
The authors have no conflicts of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Henry, K.L., Stanley, L.R. & Swaim, R.C. Risk and Promotive Factors Related to Cannabis Use Among American Indian Adolescents. Prev Sci (2024). https://doi.org/10.1007/s11121-024-01649-y
Accepted:
Published:
DOI: https://doi.org/10.1007/s11121-024-01649-y