Rachel L Gunn1, Elizabeth R Aston2, Alexander W Sokolovsky2, Helene R White3, Kristina M Jackson2. 1. Center for Alcohol and Addiction Studies, Brown University School of Public Health, Providence, RI 02903, United States. Electronic address: rachel_gunn@brown.edu. 2. Center for Alcohol and Addiction Studies, Brown University School of Public Health, Providence, RI 02903, United States. 3. Center of Alcohol Studies, Rutgers, the State University of NJ, Piscataway, NJ 08854, United States.
Abstract
BACKGROUND: Historically, cannabis researchers have assumed a single mode and product of cannabis (e.g., smoking plant). However, patterns of use, products (e.g., concentrates, edibles), and modes (e.g. blunts, vaporizers) are diversifying. This study sought to: 1) classify cannabis users into groups based on their use of the full range of cannabis products, and 2) examine user group differences on demographics, cannabis consequences and cannabis use disorder (CUD) symptomatology. METHODS: In a sample of college students (data collected in Fall 2017), who used cannabis in the past year (N = 1390), latent class analysis (LCA) was used to characterize cannabis users. We then added demographic characteristics, cannabis consequences, and CUD symptomatology scores separately to LCA models to examine class differences. RESULTS: Five unique classes emerged: high-frequency all-product users, high-frequency plant/moderate-frequency edible and concentrate users, low-frequency plant users, moderate-frequency plant and edible users, and low-frequency edible users. Demographic characteristics, cannabis consequences, and CUD symptomatology differed across classes characterized by frequency as well as product. CONCLUSIONS: Results reflect the increasing variety of cannabis products, modes, and use patterns among college students. In this sample, frequency of use remains a strong predictor of cannabis-related consequences, in addition to type of product. As variation in cannabis use patterns continue to evolve, it is essential for researchers to conduct comprehensive assessments.
BACKGROUND: Historically, cannabis researchers have assumed a single mode and product of cannabis (e.g., smoking plant). However, patterns of use, products (e.g., concentrates, edibles), and modes (e.g. blunts, vaporizers) are diversifying. This study sought to: 1) classify cannabis users into groups based on their use of the full range of cannabis products, and 2) examine user group differences on demographics, cannabis consequences and cannabis use disorder (CUD) symptomatology. METHODS: In a sample of college students (data collected in Fall 2017), who used cannabis in the past year (N = 1390), latent class analysis (LCA) was used to characterize cannabis users. We then added demographic characteristics, cannabis consequences, and CUD symptomatology scores separately to LCA models to examine class differences. RESULTS: Five unique classes emerged: high-frequency all-product users, high-frequency plant/moderate-frequency edible and concentrate users, low-frequency plant users, moderate-frequency plant and edible users, and low-frequency edible users. Demographic characteristics, cannabis consequences, and CUD symptomatology differed across classes characterized by frequency as well as product. CONCLUSIONS: Results reflect the increasing variety of cannabis products, modes, and use patterns among college students. In this sample, frequency of use remains a strong predictor of cannabis-related consequences, in addition to type of product. As variation in cannabis use patterns continue to evolve, it is essential for researchers to conduct comprehensive assessments.
Authors: Tamara L Brown; Kate Flory; Donald R Lynam; Carl Leukefeld; Richard R Clayton Journal: Exp Clin Psychopharmacol Date: 2004-02 Impact factor: 3.157
Authors: Kara Manning; Lorra Garey; Daniel J Paulus; Julia D Buckner; Julianna B D Hogan; Norman B Schmidt; Michael J Zvolensky Journal: Addict Behav Date: 2018-12-10 Impact factor: 3.913
Authors: Johannes G Ramaekers; Gerhold Kauert; Peter van Ruitenbeek; Eef L Theunissen; Erhard Schneider; Manfred R Moeller Journal: Neuropsychopharmacology Date: 2006-03-29 Impact factor: 7.853
Authors: Satomi Odani; Biesse D Soura; Michael A Tynan; Rene Lavinghouze; Brian A King; Israel Agaku Journal: Pediatrics Date: 2019-11-11 Impact factor: 7.124
Authors: Toby T Winton-Brown; Paul Allen; Sagnik Bhattacharyya; Sagnik Bhattacharrya; Stefan J Borgwardt; Paolo Fusar-Poli; Jose A Crippa; Marc L Seal; Rocio Martin-Santos; Dominic Ffytche; Antonio W Zuardi; Zerrin Atakan; Philip K McGuire Journal: Neuropsychopharmacology Date: 2011-03-16 Impact factor: 7.853
Authors: Eric R Pedersen; Caislin L Firth; Anthony Rodriguez; Regina A Shih; Rachana Seelam; Lisa Kraus; Michael S Dunbar; Joan S Tucker; Beau Kilmer; Elizabeth J D'Amico Journal: Am J Addict Date: 2020-12-30
Authors: Christine M Steeger; Leah N Hitchcock; Angela D Bryan; Kent E Hutchison; Karl G Hill; L Cinnamon Bidwell Journal: Int J Drug Policy Date: 2021-05-30
Authors: Angela K Stevens; Elizabeth R Aston; Rachel L Gunn; Alexander W Sokolovsky; Hayley Treloar Padovano; Helene R White; Kristina M Jackson Journal: Alcohol Clin Exp Res Date: 2020-11-26 Impact factor: 3.928