a Department of Psychology , Colorado State University , Fort Collins , Colorado , USA.
b School of Social Work , Colorado State University , Fort Collins , Colorado , USA.
Subst Use Misuse. 2019;54(11):1799-1811. doi: 10.1080/10826084.2019.1611855. Epub 2019 May 10.
Young adults have elevated risk for negative marijuana use-related outcomes, and there is heterogeneity among users. Identifying risk factors for marijuana user status will improve understanding of different populations of users, which may inform prediction of individuals most likely to experience negative outcomes. To identify predictors of marijuana use initiation in young adults. We simultaneously examined a broad range of potential predictors and all their possible interactions, including constructs that have not been previously studied in substance use initiation research. Data were repeated cross-sectional survey responses from college students in Colorado ( = 4052, 77% White, 61% female, mean age = 22.77). Measures came from the National College Health Assessment, which assesses numerous health and behavioral constructs. We used recursive partitioning and random forest models to identify predictors of ever having used marijuana out of 206 variables. Classification trees identified engagement in increased alcohol use and sexual behavior as salient correlates of marijuana use initiation. Parsimonious recursive partitioning trees explained a substantial amount of variability in marijuana user status (39% in the full model and 24% when alcohol variables were excluded). Random forest models predicted user status with 74.11% and 66.91% accuracy in the full model and when alcohol variables were excluded, respectively. Results support the use of exploratory analyses to explain heterogeneity among marijuana users and non-users. Since engagement in other health-risk behaviors were salient predictors of use initiation, prevention efforts to reduce harm from marijuana use may benefit from targeting risk factors for health-risk behaviors in general.
年轻人有更高的风险产生负面的大麻使用相关后果，而且使用者之间存在异质性。确定大麻使用者身份的风险因素将有助于更好地了解不同使用者群体，从而可以预测最有可能出现负面后果的个体。为了确定年轻人中大麻使用起始的预测因素。我们同时检查了广泛的潜在预测因素及其所有可能的相互作用，包括以前在物质使用起始研究中未研究过的结构。数据来自科罗拉多州大学生的重复横断面调查应答（ = 4052，77%为白人，61%为女性，平均年龄=22.77）。测量来自国家大学生健康评估，评估了许多健康和行为结构。我们使用递归分区和随机森林模型，从 206 个变量中确定了使用大麻的预测因素。分类树确定了饮酒和性行为的增加与大麻使用起始的相关性。简洁的递归分区树解释了大麻使用者身份的大量可变性（全模型中为 39%，排除酒精变量时为 24%）。随机森林模型在全模型和排除酒精变量时，分别以 74.11%和 66.91%的准确率预测了使用者身份。结果支持使用探索性分析来解释大麻使用者和非使用者之间的异质性。由于参与其他健康风险行为是使用起始的显著预测因素，因此，减少大麻使用危害的预防工作可能会从针对一般健康风险行为的危险因素中受益。
