Exploring the dynamic transitions of polysubstance use patterns among Canadian youth using Latent Markov Models on COMPASS dataResearch in context

• Main Authors: Yang Yang (Author), Zahid A. Butt (Author), Scott T. Leatherdale (Author), Plinio P. Morita (Author), Alexander Wong (Author), Laura Rosella (Author), Helen H. Chen (Author)
• Format: Book
• Published: Elsevier, 2022-12-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2667-193X; 10.1016/j.lana.2022.100389
• Keywords: Polysubstance use; Use pattern; Dynamic transition; Risk factor; Canadian adolescents; Latent Markov model; Public aspects of medicine; RA1-1270
• Source: The Lancet Regional Health. Americas, Vol 16, Iss , Pp 100389- (2022)

Summary: Background: 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. Methods: 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. Outcomes: 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. Interpretation: 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. Funding: 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.