Cohort Characteristics and Factors Associated With Cannabis Use Among Adolescents in Canada Using Pattern Discovery and Disentanglement Method

Abstract

Background: COMPASS is a longitudinal, prospective cohort study collecting data annually from students attending high school in jurisdictions across Canada. We aimed to discover significant frequent/rare associations of behavioral factors among Canadian adolescents related to cannabis use.Methods: We use a subset of the COMPASS dataset which contains 18,761 records of students in grades 9 to 12 with 31 selected features (attributes) involving various characteristics, from living habits to academic performance. We then used the Pattern Discovery and Disentanglement (PDD) algorithm to detect strong and rare (yet statistically significant) associations from the dataset.Results: Cohort characteristics and factors associated with cannabis use and other associations detected by PDD show consistent results with common sense and literature surveys. In addition, PDD outperformed methods using other criteria (i.e. support and confidence) popular as reported in the literature. Association results showed that PDD could discover: i) a smaller set of succinct significant associations in clusters; ii) frequent and rare, yet significant, patterns supported by population health relevant study; iii) patterns from a dataset with extremely imbalanced groups (majority class (None-user): minority class (Regular) = 88.3%: 11.7%). Conclusions: Results on the COMPASS dataset have validated PDD’s efficacy in discovering succinct interpretable frequent associations with comprehensive coverage and rare yet significant associations from datasets with extremely imbalanced class distribution without relying on any balancing process. The frequent associations show consistent results with common sense and literature surveys, while the rare patterns show very special cases. The success of PDD on this project indicates that PDD has great potential for population health data analysis.