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Early and Late Adolescent Factors that Predict Co-use of Cannabis with Alcohol and Tobacco in Young Adulthood.

Elizabeth J D'AmicoAnthony RodriguezJoan S TuckerMichael S DunbarEric R PedersenRegina A ShihJordan P DavisRachana Seelam
Published in: Prevention science : the official journal of the Society for Prevention Research (2021)
The changing legal landscape of cannabis in the USA has coincided with changes in how cannabis is used, including its co-use with other substances. This study analyzed 10 years of data from a diverse cohort of youth (N = 2429; 54% Hispanic, 16% Asian, 16% white, 3% black, 10% multiracial) to examine predictors in early and late adolescence of co-use of alcohol with cannabis (AC) and tobacco with cannabis (TC) at age 21. Two forms of co-use were examined: concurrent (use of both substances in past month) and sequential (use of one substance right after the other). Analyses focused on four predictor domains: individual (e.g., resistance self-efficacy), peer (e.g., time spent around peers who use), family (e.g., sibling use), and neighborhood (i.e., perceived alcohol and drug problems in neighborhood). For each co-use combination (AC or TC), we estimated parallel process piecewise latent growth models in a structural equation modeling framework using Mplus v8. The final AC and TC co-use models included all predictor variables from the four domains. Increases in positive expectancies and time spent around peers who use AC, as well as steeper decreases in resistance self-efficacy, were all related to a greater likelihood of AC co-use in young adulthood. Increases in sibling TC use and time spent around peers who use TC, as well as steeper decreases in resistance self-efficacy, were all related to a greater likelihood of TC co-use in young adulthood. Overall, findings highlight the importance of addressing peer influence in prevention programming during both early and late adolescence.
Keyphrases
  • depressive symptoms
  • mental health
  • physical activity
  • young adults
  • alcohol consumption
  • social support
  • drinking water
  • early life
  • locally advanced
  • big data
  • artificial intelligence