UNASSIGNED:了解哪些因素导致青少年多物质使用(PSU)模式,以及使用模式之间的过渡如何为PSU预防计划的设计和实施提供信息。我们使用机器学习技术从大量加拿大中学生中探索PSU模式的动态。
未经评估:我们对COMPASS数据采用了多元潜在马尔可夫模型(LMM),与一个链接的样本(N=8824)的三年波,WaveI(WI,2016-17,作为基线),第二波(WII,2017-18),和WaveIII(WIII,2018-19)。物质使用指标,即,香烟,电子烟,酒精和大麻,自我报告,并被分类为从未/偶尔/当前使用。
未经评估:确定了四种不同的使用模式:不使用(S1),一次性使用酒精(S2),电子烟和酒精的双重使用(S3),和多用途(S4)。S1在WI的患病率最高(60.5%),然而,S3成为WIII的突出使用模式(32.5%)。随着时间的推移,大多数学生都留在同一个小组中,尤其是S4在整个三波中具有最高的转变概率(0.87).随着时间的推移,那些过渡的人通常会转向更高的使用模式,最可能和最不可能的转变发生在S2→S3(0.45)和S3→S2(<0.01),分别。在所有被检查的协变量中,逃学,由跳过的类的数量来衡量,显著影响从任何低→高的过渡概率(例如,ORS2→S4=2.41,95%CI[2.11,2.72],p<0.00001)和高→低(例如,ORS3→S1=0.38,95%CI[0.33,0.44],p<0.00001)随着时间的推移使用方向。年长的学生,黑人(vs.白人),吃早餐的人不太可能从低到高的使用方向过渡。每周津贴更多的学生,和更多吸烟的朋友在一起,久坐时间较长,和就读于学校,不支持抵制或戒毒/酒精更有可能从低→高使用方向过渡。除了逃学,所有其他协变量对从高→低使用方向的转换概率的影响不一致。
UNASSIGNED:这是第一项研究,旨在通过基于人群的纵向健康调查来确定使用LMM的青年PSU的使用模式和因素的动态,为制定预防青年PSU的计划提供证据。
UNASSIGNED:应用健康科学奖学金;微软AI善款;加拿大健康研究院,加拿大卫生部,加拿大药物滥用问题中心,SickKids基金会,魁北克省的社会部。
UNASSIGNED: Understanding what factors lead to youth polysubstance use (PSU) patterns and how the transitions between use patterns can inform the design and implementation of PSU prevention programs. We explore the dynamics of PSU patterns from a large cohort of Canadian secondary school students using machine learning techniques.
UNASSIGNED: We employed a multivariate latent Markov model (LMM) on COMPASS data, with a linked sample (N = 8824) of three-annual waves, Wave I (WI, 2016-17, as baseline), Wave II (WII, 2017-18), and Wave III (WIII, 2018-19). Substance use indicators, i.e., cigarette, e-cigarette, alcohol and marijuana, were self-reported and were categorized into never/occasional/current use.
UNASSIGNED: Four distinct use patterns were identified: no-use (S1), single-use of alcohol (S2), dual-use of e-cigarettes and alcohol (S3), and multi-use (S4). S1 had the highest prevalence (60.5%) at WI, however, S3 became the prominent use pattern (32.5%) by WIII. Most students remained in the same subgroup over time, particularly S4 had the highest transition probability (0.87) across the three-wave. With time, those who transitioned typically moved towards a higher use pattern, with the most and least likely transition occurring S2→S3 (0.45) and S3→S2 (<0.01), respectively. Among all covariates being examined, truancy, being measured by the # of classes skipped, significantly affected transition probabilities from any low→high (e.g., ORS2→S4 = 2.41, 95% CI [2.11, 2.72], p < 0.00001) and high→low (e.g., ORS3→S1 = 0.38, 95% CI [0.33, 0.44], p < 0.00001) use directions over time. Older students, blacks (vs. whites), and breakfast eaters were less likely to transition from low→high use direction. Students with more weekly allowance, with more friends that smoked, longer sedentary time, and attended attended school unsupportive to resist or quit drug/alcohol were more likely to transition from low→high use direction. Except for truancy, all other covariates had inconsistent effects on the transition probabilities from the high→low use direction.
UNASSIGNED: This is the first study to ascertain the dynamics of use patterns and factors in youth PSU utilizing LMM with population-based longitudinal health surveys, providing evidence in developing programs to prevent youth PSU.
UNASSIGNED: The Applied Health Sciences scholarship; the Microsoft AI for Good grant; the Canadian Institutes of Health Research, Health Canada, the Canadian Centre on Substance Abuse, the SickKids Foundation, the Ministère de la Santé et des Services sociaux of the province of Québec.