Alcohol Research Group, Public Health Institute, 6001 Shellmound St #450, Emeryville, CA, 94608, USA.
Department of Epidemiology, University of North Carolina at Chapel Hill Gillings School of Global Public Health, 135 Dauer Drive Campus, Box 7435, Chapel Hill, NC, 27599, USA.
Prev Sci. 2022 Oct;23(7):1276-1286. doi: 10.1007/s11121-022-01378-0. Epub 2022 May 27.
Latent class analysis (LCA) identified subtypes of cannabis marketing exposure among adolescents and assessed whether the classes were associated with three cannabis use outcomes: past 28-day use, poly-cannabis use, and symptoms of cannabis use disorder (CUD). Survey data were from 471 adolescents (aged 15-19 years) who lived in four states with legal non-medical cannabis in 2018. Measures included social media engagement and cannabis outcomes. LCA with robust maximum likelihood estimation identified latent classes. Chi-squared tests assessed whether empirically derived classes differed across demographics, and logistic regression tested associations with cannabis use outcomes. Three classes were identified: digitally engaged (35.5%), digitally unengaged (36.5%), and traditional (28.0%). Both digitally engaged and unengaged classes were exposed to marketing on social media platforms, but youth in the engaged class interacted with posts and brands. Class membership differed by age (χ = 14.89, p < 0.001) and school type, with the digitally engaged group older and not in school or in non-traditional schools (χ = 16.22, p=0.01). As compared to the traditional class, youth in the digitally engaged class had 10.63 times the odds of past 28-day cannabis use (95% CI: 5.25, 21.51), 7.84 times the odds of poly-cannabis use (95% CI: 3.54, 17.33), and 13.85 times the odds of symptoms of CUD (95% CI: 3.96, 48.48). Youth in the digitally engaged class had higher odds of all cannabis use behaviors than the traditional class. These findings point to the possible use of algorithmic marketing to adolescents and suggest a need for monitoring and possible restrictions on digital cannabis marketing.
潜在类别分析(LCA)确定了青少年中大麻营销暴露的亚类,并评估了这些类别是否与三种大麻使用结果相关:过去 28 天的使用、多大麻使用和大麻使用障碍(CUD)的症状。调查数据来自 2018 年居住在四个州的 471 名青少年(年龄在 15-19 岁之间),这些州的非医疗大麻合法。测量包括社交媒体参与度和大麻结果。使用稳健最大似然估计的 LCA 确定了潜在类别。卡方检验评估了经验上得出的类别是否在人口统计学上存在差异,逻辑回归检验了与大麻使用结果的关联。确定了三个类别:数字参与(35.5%)、数字不参与(36.5%)和传统(28.0%)。数字参与和不参与的两类都接触到了社交媒体平台上的营销,但参与类别的年轻人会与帖子和品牌互动。类别的成员差异因年龄(χ²=14.89,p<0.001)和学校类型而异,数字参与组年龄较大,不在学校或非传统学校(χ²=16.22,p=0.01)。与传统类别相比,数字参与类别的年轻人过去 28 天使用大麻的几率高出 10.63 倍(95%CI:5.25,21.51),多使用大麻的几率高出 7.84 倍(95%CI:3.54,17.33),出现大麻使用障碍症状的几率高出 13.85 倍(95%CI:3.96,48.48)。数字参与类别的年轻人出现所有大麻使用行为的几率都高于传统类别。这些发现表明,青少年可能会受到算法营销的影响,并表明需要对数字大麻营销进行监测和可能的限制。