Association of Cannabis Use Reduction With Improved Functional Outcomes: An Exploratory Aggregated Analysis From Seven Cannabis Use Disorder Treatment Trials to Extract Data-Driven Cannabis Reduction Metrics.

Abstract

Objective: This exploratory analysis sought to determine whether decreases in cannabis use are associated with improvements in cannabis-related problems and functional outcomes, and if so, what percentage decrease is associated with improvement.

Methods: Data were aggregated from seven cannabis use disorder treatment trials conducted in the United States (N=920; ages 13 years and older; mean age, 25 years; 30% female, 7% Black, 11% Hispanic/Latinx). Outcome measures included the patient-reported Marijuana Problems Scale (MPS), Health-Related Quality of Life scale (HRQOL), and Pittsburgh Sleep Quality Index and the clinician-rated Clinical Global Impressions (CGI) severity and improvement scales (CGI-S and CGI-I). Generalized estimating equations tested the association between changes in 4-week cannabis use and improvements in functional outcomes. Classification and regression tree (CART) models were developed to determine what reductions in cannabis use could be used as classifiers of improvement.

Results: Decreases in the amount and frequency of cannabis use were significantly associated with improvements in MPS severity and total scores as well as improvements on the CGI-I and in sleep quality, but not improvements on the HRQOL. CART models performed best for CGI-I scores (72%-75% correct classification), while other outcome measures did not perform as well (40%-62% correct classification). CART models showed improvements on the CGI at 74% reduction in use amounts and 47% reduction in use days.

Conclusions: Reductions in cannabis use (∼50% reduction in use days and ∼75% reduction in use amounts) were associated with clinician-assessed improvement, which suggests that cannabis use reduction may yield benefit among individuals with cannabis use disorder. These exploratory results extract a data-driven metric to inform future studies, clinicians, patients, and policy recommendations.