Russell C Callaghan1, Marcos Sanches2, Stephen J Kish3. 1. University of Northern British Columbia, Northern Medical Program, 3333 University Way, Prince George, British Columbia, V2N 4Z9, Canada; Centre for Addiction and Mental Health (CAMH), Human Brain Laboratory, 250 College Street, Toronto, Ontario, M5T 1L8, Canada; University of Victoria, Canadian Institute for Substance Use Research (CISUR), 2300 McKenzie Avenue, Victoria, British Columbia, V8N 5M8, Canada. Electronic address: russ.callaghan@unbc.ca. 2. Centre for Addiction and Mental Health (CAMH), Krembil Centre for Neuroinformatics, 250 College Street, Toronto, Ontario, M5T 1L8, Canada. 3. Centre for Addiction and Mental Health (CAMH), Human Brain Laboratory, 250 College Street, Toronto, Ontario, M5T 1L8, Canada.
Abstract
BACKGROUND: In almost all of the literature examining the relation between cannabis use and cannabis-related harms, researchers have neglected to include quantity measures of cannabis use. The study aims to assess whether cannabis: (1) quantity predicts harms; and (2) quantity might interact with other key variables (age, gender, and frequency of use) vis-à-vis the outcomes. METHOD: Using the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III), the current study (n = 36,309; n = 3,339 past-year cannabis users) employed a logistic-regression approach to assess the cross-sectional relations between the continuous variables of cannabis-use quantity and frequency and two Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5) DSM-5-based outcomes: past-year cannabis-use disorder (CUD) and past-year cannabis-related problems (CRP). RESULTS: In the CUD model, the key variables log quantity [OR = 1.98 (95 % CI, 1.64;2.39), p < 0.001], log frequency [OR = 1.78 (95 % CI, 1.62;1.96), p < 0.001] and the log-quantity-by-log-frequency interaction [OR = 0.83 (95 % CI, 0.75;0.93), p = 0.002] were statistically significant. The final CRP model included the following main predictors: log quantity [OR = 2.13 (95 % CI, 1.70;2.66), p = <0.001], log frequency [OR = 1.50 (95 % CI, 1.36;1.65), p = <0.001], and a log-quantity-by-log-frequency interaction [OR = 0.82 (95 % CI, 0.73;0.93), p = 0.002]. CONCLUSIONS: The quantity-by-frequency interactions in both models showed that the relative effect of quantity on cannabis-use disorders and cannabis-related problems decreased as frequency increased, and vice versa.
BACKGROUND: In almost all of the literature examining the relation between cannabis use and cannabis-related harms, researchers have neglected to include quantity measures of cannabis use. The study aims to assess whether cannabis: (1) quantity predicts harms; and (2) quantity might interact with other key variables (age, gender, and frequency of use) vis-à-vis the outcomes. METHOD: Using the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III), the current study (n = 36,309; n = 3,339 past-year cannabis users) employed a logistic-regression approach to assess the cross-sectional relations between the continuous variables of cannabis-use quantity and frequency and two Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5) DSM-5-based outcomes: past-year cannabis-use disorder (CUD) and past-year cannabis-related problems (CRP). RESULTS: In the CUD model, the key variables log quantity [OR = 1.98 (95 % CI, 1.64;2.39), p < 0.001], log frequency [OR = 1.78 (95 % CI, 1.62;1.96), p < 0.001] and the log-quantity-by-log-frequency interaction [OR = 0.83 (95 % CI, 0.75;0.93), p = 0.002] were statistically significant. The final CRP model included the following main predictors: log quantity [OR = 2.13 (95 % CI, 1.70;2.66), p = <0.001], log frequency [OR = 1.50 (95 % CI, 1.36;1.65), p = <0.001], and a log-quantity-by-log-frequency interaction [OR = 0.82 (95 % CI, 0.73;0.93), p = 0.002]. CONCLUSIONS: The quantity-by-frequency interactions in both models showed that the relative effect of quantity on cannabis-use disorders and cannabis-related problems decreased as frequency increased, and vice versa.
Authors: Russell C Callaghan; Marcos Sanches; Robin M Murray; Sarah Konefal; Bridget Maloney-Hall; Stephen J Kish Journal: Can J Psychiatry Date: 2022-01-12 Impact factor: 5.321