Sherika Hill1, Lilly Shanahan2, E Jane Costello3, William Copeland3. 1. Duke University, Durham, NC. Electronic address: Sherika.Hill@duke.edu. 2. Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland. 3. Duke University, Durham, NC.
Abstract
OBJECTIVE: To identify risk profiles associated with patterns of problematic cannabis use in early adulthood. METHOD: Data came from 1,229 participants in the Great Smoky Mountains Study, a prospective 20-year cohort study from 1993 to 2015 that is representative of western North Carolina with yearly assessments conducted from ages 9 and 16 years, and assessments at ages 19, 21, 26, and 30 years. Patterns of problematic cannabis use (i.e., DSM-5 cannabis use disorder or daily use) in early adulthood included the following: nonproblematic use in late adolescence (ages 19-21) and early adulthood (ages 26-30); limited problematic use in late adolescence only; persistent problematic use in late adolescence and early adulthood; and delayed problematic use in early adulthood only. Multinominal logistic regression models examined pairwise associations between these patterns and risk factors in childhood/early adolescence (ages 9-16) and late adolescence (ages 19-21). Risk factors included psychiatric disorders (e.g., anxiety, depressive), other substance use (smoking, alcohol, illicit drugs), and challenging social factors (e.g., low socioeconomic status, family functioning, peers). Sex and race/ethnicity (white, African American, American Indian) interactions were tested. RESULTS: The persistent pattern (6.7% of sample) was characterized by more anxiety disorders across development and more DSM-5 CUD symptoms during late adolescence compared to the limited pattern (13.3%), which, in turn, had more childhood family instability and dysfunction. The delayed pattern (3.7%) was characterized by more externalizing disorders, maltreatment, and peer bullying in childhood compared to those in nonproblematic users. There were no significant interactions of sex or race/ethnicity. CONCLUSION: Problematic cannabis use patterns during early adulthood have distinctive risk profiles, which may be useful in tailoring targeted interventions.
OBJECTIVE: To identify risk profiles associated with patterns of problematic cannabis use in early adulthood. METHOD: Data came from 1,229 participants in the Great Smoky Mountains Study, a prospective 20-year cohort study from 1993 to 2015 that is representative of western North Carolina with yearly assessments conducted from ages 9 and 16 years, and assessments at ages 19, 21, 26, and 30 years. Patterns of problematic cannabis use (i.e., DSM-5 cannabis use disorder or daily use) in early adulthood included the following: nonproblematic use in late adolescence (ages 19-21) and early adulthood (ages 26-30); limited problematic use in late adolescence only; persistent problematic use in late adolescence and early adulthood; and delayed problematic use in early adulthood only. Multinominal logistic regression models examined pairwise associations between these patterns and risk factors in childhood/early adolescence (ages 9-16) and late adolescence (ages 19-21). Risk factors included psychiatric disorders (e.g., anxiety, depressive), other substance use (smoking, alcohol, illicit drugs), and challenging social factors (e.g., low socioeconomic status, family functioning, peers). Sex and race/ethnicity (white, African American, American Indian) interactions were tested. RESULTS: The persistent pattern (6.7% of sample) was characterized by more anxiety disorders across development and more DSM-5 CUD symptoms during late adolescence compared to the limited pattern (13.3%), which, in turn, had more childhood family instability and dysfunction. The delayed pattern (3.7%) was characterized by more externalizing disorders, maltreatment, and peer bullying in childhood compared to those in nonproblematic users. There were no significant interactions of sex or race/ethnicity. CONCLUSION: Problematic cannabis use patterns during early adulthood have distinctive risk profiles, which may be useful in tailoring targeted interventions.
Authors: Hee-Soon Juon; Kate E Fothergill; Kerry M Green; Elaine E Doherty; Margaret E Ensminger Journal: Drug Alcohol Depend Date: 2011-04-22 Impact factor: 4.492
Authors: T Kraan; E Velthorst; L Koenders; K Zwaart; H K Ising; D van den Berg; L de Haan; M van der Gaag Journal: Psychol Med Date: 2015-11-16 Impact factor: 7.723
Authors: Francesca M Filbey; Sina Aslan; Vince D Calhoun; Jeffrey S Spence; Eswar Damaraju; Arvind Caprihan; Judith Segall Journal: Proc Natl Acad Sci U S A Date: 2014-11-10 Impact factor: 11.205
Authors: William E Copeland; Sherika Hill; E Jane Costello; Lilly Shanahan Journal: J Am Acad Child Adolesc Psychiatry Date: 2016-11-25 Impact factor: 8.829
Authors: Laura Kann; Steve Kinchen; Shari L Shanklin; Katherine H Flint; Joseph Kawkins; William A Harris; Richard Lowry; Emily O'Malley Olsen; Tim McManus; David Chyen; Lisa Whittle; Eboni Taylor; Zewditu Demissie; Nancy Brener; Jemekia Thornton; John Moore; Stephanie Zaza Journal: MMWR Suppl Date: 2014-06-13
Authors: Colleen Stiles-Shields; Joseph Archer; Jim Zhang; Amanda Burnside; Janel Draxler; Lauren M Potthoff; Karen M Reyes; Faith Summersett Williams; Jennifer Westrick; Niranjan S Karnik Journal: Child Psychiatry Hum Dev Date: 2021-11-01
Authors: Elizabeth J Gifford; Lindsey Eldred Kozecke; Megan Golonka; Sherika N Hill; E Jane Costello; Lilly Shanahan; William E Copeland Journal: JAMA Netw Open Date: 2019-08-02