Study Design
A matched cohort study among chronic opioid users authorized to use medical cannabis and controls who did not receive authorization for medical cannabis.
Population
Inclusion Criteria: All patients prescribed chronic opioid treatment and authorized for medical cannabis in Alberta (data received from the College of Physicians & Surgeons of Alberta which authorizes all medical use in the province) between March 22, 2013 and March 31, 2018. Participants were adults of any sex, ethnicity, and socioeconomic status who received authorization for medical cannabis for any reason. Chronic opioid treatment was defined as: 1) all patients who had an opioid prescription within 7 days prior to the index date (90% of opioid prescriptions in Alberta are 7 days or less), and 2) either had a total of 120 or more cumulative calendar days of filled opioid prescriptions or 10 or more opioid prescriptions filled, in the year prior to the index date(33-36). The index date for each patient was the first recorded date of medical cannabis authorization. The index date of all eligible controls was the first opioid dispensation date plus one year. The one-year period between the first opioid dispensation date and index date served as the wash period.
Exclusion Criteria: All patients who received medical cannabis but were not registered to receive health benefits in Alberta during the entire study period. Patients who had less than 6 months administrative data before the index date were excluded as changes in weekly average OME could not be reliability calculated. Further, patients who had codeine cough syrups up to one year before the index date were excluded as codeine cough syrup is often prescribed for its antitussive properties, as opposed to pain relief.
Propensity Score Matched Controls
Each authorized medical cannabis patient was matched with one unique control by using high dimensional propensity score (HDPS) matching(37). Controls had to satisfy the same inclusion and exclusion criteria as authorized medical cannabis patients – but without authorization of medical cannabis. Variables incorporated into the HDPS matching method included: sex, age, year of index date (categorical), comorbidities associated with cannabis use (Appendices Tables 1 and 2), and all healthcare resource utilization variables (all within the year prior to the index date). This includes healthcare utilization (all hospitalizations, emergency department visits, physician visits with up to 25 diagnostic codes), and all prescription drug utilized by a patient. To note, our entire healthcare dataset reported greater than 1000 different variables and categories which were included in the HDPS. Administrative data sources (see below) used as the input datasets of the HDPS included: inpatient hospital data, ambulatory visit data and claims data. Similar to those authorized to use medical cannabis, eligible controls had to be users of opioid medication between March 2012 to 2017 and the index date of each control was set to the first opioid use date + 365 days to allow stabilization of therapy. We applied the HDPS matching technique using the SAS Packages proposed by Rassen et al. (2011)(38) and Schneeweiss et al. (2009)(37).
Data Source
The initial medical cannabis patient identifiers were provided by the College of Physicians & Surgeons of Alberta. Using a unique lifetime personnel health number, all patients were linked to the administrative databases of Alberta Health which captures all healthcare utilization for all patients in the province of Alberta as part of the universal healthcare plan for residents. These databases include all inpatient hospitalizations, ambulatory emergence department visits, all community pharmacy drug dispensations, and physician claims data, providing at least one-year of longitudinal follow-up data following the index date for both medical cannabis authorized patients and controls. All data was released as de-identified data to the researchers.
Outcomes
All opioid doses were converted to OME based on each drug’s OME factor, days of supply, dispensation amount and strength. Each daily OME was then converted to a weekly average OME for all medical cannabis patients and matched controls(39, 40) for each week of the study based on the index date. The primary outcome was the difference in the weekly average OME between the medically authorized patients and the control group in the 6 months prior to index and up to 12 months following medical cannabis authorization (or equivalent index date for controls). The average weekly OME in the 6 months prior to authorization (or pseudo index for the matched control) was used as the baseline.
A secondary outcome was the “proportion of patients ceasing opioids”, defined as opioid discontinuation after the index date for 2 distinct circumstances: 1) opioid-free for twice the duration of the previous prescription or; 2) a minimum grace period of 30 opioid-free days. As the duration of opioid prescriptions have been known to be highly variable, we utilized this specific protocol following the guidance of Gomes et al. (2018)(32), although over 90% of all opioid dispensations in Alberta are <7 days.
Patient and Public Involvement
Patients was not involved in the design, conduct and reporting of this research project as it was not applicable to this project. The College of Physicians & Surgeons of Alberta (CPSA) is a public body that was involved in the project in terms of our access to the data. As mentioned in our “Acknowledgements,” although the data was provided by the CPSA, neither the Government of Alberta, Alberta Health, nor CPSA expresses any opinion in relation to this study. Further, the CPSA was not involved in the analysis or reporting of the research project’s outcomes.
Ethics Approval
This study was approved by the University of Alberta Health Research Ethics board (PRO 00084689).
Statistical analysis
All data are expressed descriptively using means (standard deviations [SD]) or count (proportions [%]), as appropriate. To assess the effect of medical cannabis use on weekly average OME, interrupted time series (ITS) analysis was used to assessed the change in trend OME in the 26 weeks (6 months) before and 52 weeks (1 year) after the authorization of medical cannabis (or pseudo index for the matched control). ITS is a quasi-experimental design that allows comparison of trends in an outcome before and after an intervention(41, 42). ITS analysis was selected for its effectiveness in clear differentiation between population-level health pre-intervention and post-intervention periods. The ITS analysis allows cannabis patients to be compared to themselves (i.e. their own control) by modeling their OME trend in the 52 weeks (1 year) after the authorization of medical cannabis relative to the trend they had in the 26 weeks before. However, the basic interrupted time series design cannot exclude confounding due to temporal changes at the population level around the time of the intervention (i.e., cannabis authorization) such as co-interventions. The controlled ITS, which we employed, includes an additional control series to account for temporal changes that may have occurred within the population. By modelling it this way, effects observed within cannabis patients but not among the control series would be supportive evidence that the change in OME was associated with cannabis authorization; conversely, may be stronger to support a causal relationship between the intervention and outcome. Conversely, changes observed in both those authorized medical cannabis and the control series would suggest that other factors may be partially explaining the effect observed within the cannabis patients. Indeed, the controlled interrupted time series(43) has been shown to provide similar results as those observed in RCTs(44, 45), a testament to the validity of the approach(46, 47).
OME was assessed in 7-day windows for each patient (i.e. average OME per week). The absolute effect of medical cannabis authorization on average weekly OME was calculated, which summarizes both the immediate level change (i.e., within a week) and change in trend over the 12 months with the multivariate delta method used to the construct 95% confidence intervals around the estimate(48).
To assess the proportion of patients ceasing opioids after the index date, a logistic regression model was used to compare the odds of opioid discontinuation after medical cannabis authorization between the authorized and unauthorized patients. Covariates adjusted in the logistic regression were age, sex, comorbidities, and overall healthcare utilization in the previous 6 months.
Sensitivity and Stratification Analysis
Further stratification was conducted on both authorized medical cannabis patients (n=5373) and all eligible control (n=24693) patients in 3 subgroups of baseline average OME (based on an average over all 26 weeks before index date): i) OME≤50, ii) OME between 50 and 100, and iii) OME >100. Patients were matched within each category on an average weekly OME ±15.