Abstract
Objectives. To evaluate the impact of frequent cannabis use on injection cessation and injection relapse among people who inject drugs (PWID).
Methods. Three prospective cohorts of PWID from Vancouver, Canada, provided the data for these analyses. We used extended Cox regression analysis with time-updated covariates to analyze the association between cannabis use and injection cessation and injection relapse.
Results. Between 2005 and 2018, at-least-daily cannabis use was associated with swifter rates of injection cessation (adjusted hazard ratio [AHR] = 1.16; 95% confidence interval [CI] = 1.03, 1.30). A subanalysis revealed that this association was only significant for opioid injection cessation (AHR = 1.26; 95% CI = 1.12, 1.41). At-least-daily cannabis use was not significantly associated with injection relapse (AHR = 1.08; 95% CI = 0.95, 1.23).
Conclusions. We observed that at-least-daily cannabis use was associated with a 16% increase in the hazard rate of injection cessation, and this effect was restricted to the cessation of injection opioids. This finding is encouraging given the uncertainty surrounding the impact of cannabis policies on PWID during the ongoing opioid overdose crisis in many settings in the United States and Canada.
In the past decade, many jurisdictions throughout the United States and Canada have legalized nonmedical (recreational) cannabis use by adults.1 These reforms have proceeded despite arguments from some experts that cannabis has not been subjected to the same rigorous scientific evaluation typically applied to other drugs undergoing significant changes in legalization and regulation.1 For example, the potential effects of cannabis legalization or nonmedical cannabis use among members of vulnerable groups, including people living with mental illness or those of lower socioeconomic status, have not been fully investigated.2
One longstanding concern regarding cannabis is how its use might influence the use of other psychoactive substances and the progression to other forms of high-risk substance use such as injection drug use.3,4 The potential for cannabis to exert so-called gateway effects has been questioned by recent evidence indicating that trajectories to high-risk forms of substance use are largely driven by environmental, psychosocial, and genetic risk factors.3,4 At the same time, some preliminary studies have linked cannabis use or cannabis legalization to a number of positive effects related to substance use behaviors and health outcomes associated with substance use. For example, an interrupted time-series analysis of population-level data from Colorado found that recreational cannabis legalization was associated with a 6.5% decrease in opioid-related deaths (b = –0.68; 95% confidence interval [CI] = –1.34, –0.03). These results appear to represent a reversal of the increases in opioid-related deaths that occurred over a 14-year period preceding recreational cannabis legalization.5
At the individual level among higher-risk people who use drugs, such as people who use illicit and injection drugs, cannabis use has been associated with decreased drug-seeking behavior and reduced use of substances such as crack cocaine, decreased exposure to high-risk opioids such as fentanyl, and retention in opioid agonist therapy.6,7 In our setting, we recently reported that frequent cannabis use was associated with lower rates of injection initiation among street-involved youths and documented the intentional use of cannabis by them to mitigate drug-related harms.7–9
Given the serious harms associated with injection drug use and the outstanding questions surrounding cannabis and other forms of substance use, there is a need to better understand the relationship between cannabis and high-risk drug use, including injection drug use. Thus, the 2 objectives of the present study were to evaluate the impact of frequent cannabis use on cessation of injection drug use and to evaluate the impact of frequent cannabis use on injection relapse among 3 prospective cohorts of people who use illicit drugs in Vancouver, Canada, during a period of de facto cannabis decriminalization.
METHODS
We derived the data for these analyses from 3 community-recruited open prospective cohort studies of people who use illicit drugs: the At-Risk Youth Study (ARYS), the Vancouver Injection Drug Users Study (VIDUS), and the AIDS Care Cohort to Evaluate Exposure to Survival Services (ACCESS). These studies apply harmonized recruitment, follow-up, and data collection procedures and have been described in detail previously.10,11 As a brief overview, participants were recruited through extensive street outreach and self-referral in the Downtown Eastside and Downtown South neighborhoods of Vancouver, Canada. These areas experience high levels of drug-related harms, including overdose and HIV transmission, as well as marginalization and criminalization.12
The City of Vancouver has also been a site of de facto decriminalization of nonmedical cannabis use, with the Vancouver Police Department exercising their discretion to not pursue charges of personal cannabis possession or use without a complaint since 2006.13 Beginning in 2015, the City of Vancouver allowed the establishment and operation of retail cannabis stores, and recreational cannabis use was legalized federally in October 2018.14
Eligibility in each of these cohort studies is contingent on using illicit drugs other than or in addition to cannabis in the previous month, residing in the Greater Vancouver Regional District, and providing written informed consent. ARYS includes street-involved youths (defined as being without stable housing or having accessed street-based youth services in the past 6 months) aged 14 to 16 years; VIDUS includes adults who have injected illicit drugs in the month preceding enrollment and tested seronegative for HIV at the time of enrollment; ACCESS includes HIV-positive adults. Participants in the VIDUS cohort who seroconverted to HIV-positive during follow-up were transferred to the ACCESS cohort. At baseline and semiannually over follow up, participants completed an interviewer-administered questionnaire that collected data including sociodemographic information, substance use patterns, HIV risk behaviors, and engagement with health and social services. Participants also provided blood samples for HIV and hepatitis C serological analysis. Participants are remunerated CA $40 for their time at each study visit.
The analytical sample for the first objective (i.e., evaluate the impact of frequent cannabis use on injection cessation) was restricted to all ARYS, VIDUS, and ACCESS participants who were aged 18 years or older, reported injection drug use at baseline or over follow-up, and completed at least 1 study visit between September 2005 and November 2018. The outcome of interest was time to injection cessation, defined as a period of 6 months without any injection drug use. This outcome is consistent with previous studies and defined as responding “No” to the item, “In the last six months, have you used a needle to chip, fix, or muscle even once?”15 The analytical sample for the second objective (i.e., evaluate the impact of frequent cannabis use on injection relapse) was restricted to all ARYS, VIDUS, and ACCESS participants who were aged 18 years or older; provided a report of injection drug use at baseline or over follow-up, which was followed by a 6-month period of no injection drug use (injection cessation); and completed at least 1 study visit between September 2005 and November 2018. The second outcome of interest was time to relapse of injection drug use. This outcome was defined as any injection drug use in the past 6 months that occurred after a period of injection cessation (i.e., a 6-month period with no injection drug use). Both outcomes (injection cessation and injection relapse) were defined as the midpoint between the first interview during which the outcome was reported and the preceding interview before the outcome was reported.
The primary explanatory variable of interest was frequent cannabis use in the past 6 months (≥ daily vs < daily). Cannabis use was measured on the basis of the item, “In the last six months, how often have you used marijuana?” The response options were “0 = less than once a month”; “1 = 1–3 times per month”; “2 = about once per week”; “3 = 2–3 times per week”; and “4 = at least daily.” These categories were collapsed to at least daily versus less than daily because of the low prevalence of occasional users (e.g., “less than once a month” and “1–3 times per month”), and analyzing these as individual categories can produce unstable estimates of effect size. Previous investigations of this sample have also found that there are significant differences in the reasons for use between at-least-daily users and less-than-daily users. At-least-daily users appear to use cannabis for a specific therapeutic purpose and were significantly more likely than less-than-daily users to report using cannabis to reduce pain, insomnia, stress, and nausea or loss of appetite.16 For these reasons, we opted to combine the less-than-daily categories and create the less-than-daily versus at-least-daily variable.
On the basis of previous studies of injection cessation and relapse, we selected a range of sociodemographic, substance use, and drug treatment variables hypothesized to confound the association between cannabis use and injection cessation and injection relapse.17,18 These variables were sex (male vs female); age (per year older); race/ethnicity (White vs non-White); being in a relationship (i.e., legally married, common-law, or regular partner vs others); recent incarceration (yes vs no); licit employment (i.e., having a regular, temporary, or self-employed work vs none); enrollment in opioid agonist therapy (yes vs no); engagement with alcohol or drug treatment other than opioid agonist therapy (yes vs no); homelessness (yes vs no); binge drug use, defined as a period of using drugs more often than usual (yes vs no); noninjection heroin use (≥ daily vs < daily); noninjection cocaine use (≥ daily vs < daily); noninjection crack-cocaine use (≥ daily vs < daily); noninjection crystal methamphetamine use (≥ daily vs < daily); involvement in drug dealing (yes vs no); having tried but been unable to access addiction treatment services (yes vs no or never tried); and cohort designation (i.e., ACCESS vs VIDUS, ARYS vs VIDUS).
Additional covariates included in the analysis of injection relapse were experiencing barriers to accessing alcohol or drug treatment (yes vs no), involvement in sex work (yes vs no), any history of childhood sexual abuse (yes vs no), and experiencing physical or sexual violence (yes vs no). The primary explanatory variable, the outcome variables, and each of the time-varying secondary covariates were assessed at baseline and every 6 months thereafter via the interviewer-administered questionnaire completed. We treated all of these variables as time-varying covariates with the exception of sex, race/ethnicity, and history of childhood sexual abuse, which were assessed at baseline only. These variable definitions were consistent with previous studies and each behavioral variable referred to the previous 6-month period.11
We analyzed the characteristics of the study sample at baseline, stratified by cannabis use, using the χ2 test for binary variables and the Wilcoxon rank sum test for continuous variables. We calculated the incidence density and the 95% CI of each outcome (injection cessation and injection relapse) based on the Poisson distribution. We applied Kaplan–Meier methods to calculate the cumulative hazard of the first injection cessation and relapse event, stratified by daily cannabis use at baseline. We used extended Cox regression models to estimate the unadjusted and adjusted relative hazards and 95% CIs for variables associated with injection cessation and injection relapse. We applied an a priori multivariable model building protocol to an extended Cox regression model for recurrent events to fit the adjusted models. First, we constructed a full multivariable model that included all explanatory variables. In a manual stepwise manner, we removed covariates that produced the smallest relative change in the cannabis use coefficient 1 at a time. We discontinued this process once the minimum change in the cannabis use coefficient exceeded 5%. We selected a 5% threshold because simulation studies have shown that confounder-selection strategies using the “change in estimate” approach provided the least-biased effect estimates when lower thresholds were selected.19 This method has been used in several previous studies as a confounder-selection strategy.20,21
This method is designed to retain covariates that represent the most significant confounders of the association between cannabis use and injection cessation and relapse.19 Given that cannabis use has produced distinct effects on opioid and stimulant use in previous studies, we also performed subanalyses on the cessation of injection opioids versus stimulants, and relapse of injection opioids versus stimulants.1,8 Because each of the variables in the analysis were time-updated every 6 months and a very low proportion of values was missing during the study period (< 2%), we excluded missing observations from the analysis. Participants were right censored if their last study visit was conducted more than 3 years before the end of the study period. The distribution of the censored data is provided in Appendix A, Table A (available as a supplement to the online version of this article at http://www.ajph.org).
Injection opioid use comprised heroin, fentanyl, and prescription opioids, and injection stimulant use comprised cocaine, crack cocaine, and crystal methamphetamine. These subanalyses followed the same model building protocol described previously for the whole-sample analysis. We used SAS version 9.4 (SAS Institute, Cary, NC) to perform all statistical analyses. All P values were 2-sided with a significance threshold of .025 (.05/2) because we tested 2 independent hypotheses (the association between cannabis use and injection cessation and the association between cannabis use and injection relapse).
RESULTS
From September 2005 to November 2018, a total of 2459 people who inject drugs (PWID) were enrolled and completed at least 1 follow-up interview in the ARYS (n = 570; 23.2%), VIDUS (n = 1152; 46.8%), or ACCESS (n = 737; 30.0%) studies during the study period and were included in the analysis of injection cessation. Of this sample, 2110 participants were included in the analysis of injection relapse from the ARYS (n = 836; 39.6%), VIDUS (n = 715; 33.9%), or ACCESS (n = 559; 26.5%) studies. The baseline characteristics of the study sample (n = 2459) are presented in Table 1 and Appendix A, Tables B, C, and D. At baseline, the median age of the participants was 36.8 years (interquartile range [IQR] = 25.9–45.7), 863 (35.1%) were female, and 1466 (59.6%) were White. At-least-daily cannabis use was reported by 666 (27.0%) of the study sample at the time of enrollment. The median follow-up time per participant was 49.8 months (IQR = 18.2–109.8). During the 12-year study period, 1371 (55.8%) participants reported at least 1 injection cessation event, resulting in an incidence density of 17.9 events per 100 person-years (95% CI = 17.1, 18.7). From study enrollment, the median time to the first report of injection cessation was 16.4 months (IQR = 5.0–39.9). Among those who reported injection cessation, 1151 (54.6%) participants reported at least 1 injection relapse event over the study period for an incidence density of 25.9 events per 100 person-years (95% CI = 24.2, 27.7). The median time to injection relapse was 8.4 months (IQR = 3.1–21.5).
TABLE 1—
Baseline Characteristics Stratified by Daily Cannabis Use Among People Who Inject Drugs: Vancouver, BC, Canada, 2005–2018
| Daily Cannabis Use |
||||
| Characteristic | Total No. (%) or Median (IQR) | Yes (n = 666), No. (%) or Median (IQR) | No (n = 1793), No. (%) or Median (IQR) | P |
| Age, y | 36.8 (25.9–45.7) | 32.8 (23.8–43.2) | 38.3 (27.7–46.5) | < .001 |
| Sex | < .001 | |||
| Male | 1595 (64.9) | 492 (73.9) | 1103 (61.5) | |
| Female | 863 (35.1) | 174 (26.1) | 689 (38.4) | |
| Race/ethnicity | .62 | |||
| White | 1466 (59.6) | 402 (60.4) | 1064 (59.3) | |
| Other | 988 (40.2) | 262 (39.3) | 726 (40.5) | |
| Stable relationship | .75 | |||
| Legally married, common law, or regular partner | 736 (29.9) | 203 (30.5) | 533 (29.7) | |
| Others | 1703 (69.3) | 459 (68.9) | 1244 (69.4) | |
| Incarcerationa | .61 | |||
| Yes | 457 (18.6) | 119 (17.9) | 338 (18.9) | |
| No | 1983 (80.6) | 540 (81.1) | 1443 (80.5) | |
| Employmenta | .018 | |||
| Yes | 667 (27.1) | 204 (30.6) | 463 (25.8) | |
| No | 1791 (72.8) | 462 (69.4) | 1329 (74.1) | |
| Opioid agonist therapya | < .001 | |||
| Yes | 944 (38.4) | 218 (32.7) | 726 (40.5) | |
| No | 1494 (60.8) | 440 (66.1) | 1054 (58.8) | |
| Participation in drug or alcohol treatmenta | .06 | |||
| Yes | 315 (12.8) | 99 (14.9) | 216 (12.0) | |
| No | 2123 (86.3) | 559 (83.9) | 1564 (87.2) | |
| Homelessnessa | .005 | |||
| Yes | 1122 (45.6) | 336 (50.5) | 786 (43.8) | |
| No | 1323 (53.8) | 329 (49.4) | 994 (55.4) | |
| Binge drug usea | ||||
| Yes | 1205 (49.0) | 342 (51.4) | 863 (48.1) | .11 |
| No | 1240 (50.4) | 316 (47.4) | 924 (51.5) | |
| Noninjection heroin usea | .92 | |||
| ≥ daily | 102 (4.1) | 28 (4.2) | 74 (4.1) | |
| < daily | 2353 (95.7) | 635 (95.3) | 1718 (95.8) | |
| Noninjection cocaine usea | .11 | |||
| ≥ daily | 27 (1.1) | 11 (1.7) | 16 (0.9) | |
| < daily | 2431 (98.9) | 654 (98.2) | 1777 (99.1) | |
| Noninjection crack cocaine usea | .004 | |||
| ≥ daily | 777 (31.6) | 181 (27.2) | 596 (33.2) | |
| < daily | 1680 (68.3) | 484 (72.7) | 1196 (66.7) | |
| Noninjection methamphetamine usea | < .001 | |||
| ≥ daily | 163 (6.6) | 64 (9.6) | 99 (5.5) | |
| < daily | 2294 (93.3) | 600 (90.1) | 1694 (94.5) | |
| Participation in selling illicit drugsa | .25 | |||
| Yes | 928 (37.7) | 264 (39.6) | 664 (37.0) | |
| No | 1528 (62.1) | 402 (60.4) | 1126 (62.8) | |
| Unable to access addiction treatmenta | .036 | |||
| Yes | 454 (18.5) | 141 (21.2) | 313 (17.5) | |
| No | 1951 (79.3) | 511 (76.7) | 1440 (80.3) | |
| Experiencing barriers to treatment accessa | .029 | |||
| Yes | 131 (6.2) | 55 (7.8) | 76 (5.4) | |
| No | 1962 (93.0) | 642 (91.2) | 1320 (93.9) | |
| Involvement in sex worka | .13 | |||
| Yes | 151 (7.2) | 42 (6.0) | 109 (7.8) | |
| No | 1954 (92.6) | 660 (93.8) | 1294 (92.0) | |
| History of childhood sexual abuse | .006 | |||
| Yes | 560 (26.5) | 160 (22.7) | 400 (28.4) | |
| No | 1487 (70.5) | 521 (74.0) | 966 (68.7) | |
| Experience of violencea | < .001 | |||
| Yes | 484 (22.9) | 228 (32.4) | 256 (18.2) | |
| No | 1600 (75.8) | 469 (66.6) | 1131 (80.4) | |
| Cohort | < .001 | |||
| VIDUS | 1152 (46.8) | 265 (39.8) | 887 (49.5) | |
| ACCESS | 737 (30.0) | 176 (26.4) | 561 (31.3) | |
| ARYS | 570 (23.2) | 225 (33.8) | 345 (19.2) | |
Note. ACCESS = AIDS Care Cohort to Evaluate Exposure to Survival Services; ARYS = At-Risk Youth Study; IQR = interquartile range; VIDUS = Vancouver Injection Drug Users Study. The sample size was n = 2459.
Refers to activities in the 6 months before the follow-up interview.
The adjusted hazard ratios (AHRs) of injection cessation are presented in Table 2. In the adjusted analysis, at-least-daily cannabis use was significantly associated with increased rates of injection cessation (AHR = 1.16; 95% CI = 1.03, 1.30; P = .017). The subanalysis indicated that this association was only significant for opioid injection cessation (AHR = 1.26; 95% CI = 1.12, 1.41; P < .001); cannabis use was not significantly associated with the cessation of stimulant injecting (AHR = 0.93; 95% CI = 0.83, 1.04; P = .216; Table 3). At-least-daily cannabis use was not significantly associated with time to injection relapse (AHR = 1.08; 95% CI = 0.95, 1.23; P = .236; Table 4). This association was also nonsignificant for opioid injection relapse (AHR = 1.05; 95% CI = 0.92, 1.19; P = .476) and stimulant injection relapse (AHR = 1.00; 95% CI = 0.88, 1.14; P = .969; Table 4). The unadjusted associations between each of the covariates and injection cessation and injection relapse are included in Appendix A, Table E). Substance use trends over follow-up and the Kaplan–Meier analyses of injection cessation and injection relapse are also presented in Appendix A, Figures A through D.
TABLE 2—
Extended Cox Regression Analysis of Factors Associated With Time to Injection Cessation and Injection Relapse: Vancouver, BC, Canada, 2005–2018
| Characteristic | Injection Cessation (n = 2459), AHR (95% CI) | Injection Relapse (n = 2110), AHR (95% CI) |
| Daily cannabis usea (yes vs no) | 1.16 (1.03, 1.30) | 1.08 (0.95, 1.23) |
| Age (HR per year older) | 1.01 (1.00, 1.01) | 1.00 (0.99, 1.00) |
| Sex (male vs female) | 0.86 (0.76, 0.97) | |
| Incarcerationa (yes vs no) | 0.61 (0.51, 0.73) | |
| Employmenta (yes vs no) | 0.73 (0.64, 0.83) | |
| Opioid agonist therapya (yes vs no) | 1.32 (1.17, 1.48) | 1.81 (1.57, 2.08) |
| Participation in alcohol or drug treatmenta (yes vs no) | 1.84 (1.59, 2.14) | 1.07 (0.90, 1.26) |
| Binge drug usea (yes vs no) | 0.34 (0.31, 0.38) | 2.17 (1.94, 2.43) |
| Participation in selling illicit drugsa (yes vs no) | 0.38 (0.33, 0.44) | 1.70 (1.49, 1.95) |
| Involvement in sex worka (yes vs no) | . . . | 1.58 (1.31, 1.91) |
| Cohorta | ||
| ACCESS vs VIDUS | . . . | 0.94 (0.81, 1.08) |
| ARYS vs VIDUS | . . . | 0.42 (0.33, 0.54) |
Note. ACCESS = AIDS Care Cohort to Evaluate Exposure to Survival Services; AHR = adjusted hazard ratio; ARYS = At-Risk Youth Study; CI = confidence interval; HR = hazard ratio; VIDUS = Vancouver Injection Drug Users Study. Variables included in the analyses but not retained in the final adjusted models were race/ethnicity (White vs other), stable relationship (yes vs no), homelessness (yes vs no), noninjection heroin use (≥ daily vs < daily), noninjection cocaine use (≥ daily vs < daily), noninjection crack cocaine use (≥ daily vs < daily), noninjection methamphetamine use (≥ daily vs < daily), being unable to access addiction treatment (yes vs no), experiencing barriers to treatment access (yes vs no), history of childhood sexual abuse (yes vs no), and experience of violence (yes vs no).
Refers to activities in the 6 months before the follow-up interview.
TABLE 3—
Subanalysis of Factors Associated With Time to Cessation of Opioid Injection Versus Stimulant Injection: Vancouver, BC, Canada, 2005–2018
| Characteristic | Opioid Injection Cessation (n = 1469), AHR (95% CI) | Stimulant Injection Cessation (n = 1579), AHR (95% CI) |
| Daily cannabis usea (yes vs no) | 1.26 (1.12, 1.41) | 0.93 (0.83, 1.04) |
| Age (HR per year older) | 1.01 (1.01, 1.02) | . . . |
| Sex (male vs female) | . . . | 0.82 (0.73, 0.90) |
| Opioid agonist therapya (yes vs no) | . . . | 1.21 (1.09, 1.35) |
| Participation in alcohol or drug treatmenta (yes vs no) | . . . | 1.32 (1.14, 1.52) |
| Noninjection methamphetamine usea (yes vs no) | . . . | 0.71 (0.58, 0.87) |
| Participation in selling illicit drugsa (yes vs no) | . . . | 0.74 (0.66, 0.82) |
| Unable to access addiction treatmenta (yes vs no) | . . . | 0.76 (0.66, 0.87) |
Note. ACCESS = AIDS Care Cohort to Evaluate Exposure to Survival Services; AHR = adjusted hazard ratio; ARYS = At-Risk Youth Study; CI = confidence interval; HR = hazard ratio; VIDUS = Vancouver Injection Drug Users Study. Variables included in the analyses but not retained in the final adjusted model were race/ethnicity (White vs other), stable relationship (yes vs no), incarceration (yes vs no), employment (yes vs no), homelessness (yes vs no), binge drug use (yes vs no), noninjection heroin use (≥ daily vs < daily), noninjection cocaine use (≥ daily vs < daily), noninjection crack cocaine use, and study cohort (ACCESS vs VIDUS; ARYS vs VIDUS).
Refers to activities in the 6 months before the follow-up interview.
TABLE 4—
Extended Cox Regression Analysis of Factors Associated With Time to Relapse of Opioid Injecting Versus Stimulant Injecting: Vancouver, BC, Canada, 2005–2018
| Characteristic | Opioid Injection Relapse (n = 1210), AHR (95% CI) | Stimulant Injection Relapse (n = 1349), AHR (95% CI) |
| Daily cannabis usea (yes vs no) | 1.05 (0.92, 1.19) | 1.00 (0.88, 1.14) |
| Age (HR per year older) | 0.99 (0.98, 1.00) | 1.00 (0.99, 1.01) |
| Incarcerationa (yes vs no) | . . . | 1.26 (1.09, 1.46) |
| Employmenta (yes vs no) | 0.79 (0.69, 0.90) | 0.76 (0.68, 0.86) |
| Opioid agonist therapya (yes vs no) | 2.58 (2.26, 2.96) | 1.31 (1.17, 1.47) |
| Participation in alcohol or drug treatmenta (yes vs no) | 1.15 (0.95, 1.38) | . . . |
| Homelessnessa (yes vs no) | . . . | 1.17 (1.04, 1.32) |
| Binge drug usea (yes vs no) | 1.97 (1.78, 2.18) | 1.98 (1.80, 2.19) |
| Noninjection heroin usea (≥ daily vs < daily) | 1.30 (0.96, 1.75) | |
| Noninjection crack cocaine usea (≥ daily vs < daily) | . . . | 1.09 (0.96, 1.25) |
| Noninjection methamphetamine usea (yes vs no) | . . . | 1.61 (1.30, 1.99) |
| Participation in selling illicit drugsa (yes vs no) | 1.80 (1.58, 2.04) | 1.35 (1.20, 1.52) |
| Experiencing barriers to treatment accessa (yes vs no) | . . . | 1.22 (1.02, 1.46) |
| Involvement in sex worka (yes vs no) | 1.44 (1.21, 1.72) | 1.29 (1.09, 1.53) |
| History of childhood sexual abuse (yes vs no) | . . . | 1.03 (0.91, 1.18) |
| Experience of violencea (yes vs no) | 1.18 (1.03, 1.34) | 1.22 (1.08, 1.38) |
| Cohorta | ||
| ACCESS vs VIDUS | 0.85 (0.75, 0.98) | 1.04 (0.91, 1.19) |
| ARYS vs VIDUS | 0.42 (0.33, 0.53) | 0.50 (0.39, 0.62) |
Note. ACCESS = AIDS Care Cohort to Evaluate Exposure to Survival Services; AHR = adjusted hazard ratio; ARYS = At-Risk Youth Study; CI = confidence interval; HR = hazard ratio; VIDUS = Vancouver Injection Drug Users Study. Variables included in the analyses but not retained in the final adjusted model were sex (male vs female), race/ethnicity (White vs other), stable relationship (yes vs no), noninjection cocaine use (≥ daily vs < daily), and being unable to access addiction treatment.
Refers to activities in the 6 months before the follow-up interview.
DISCUSSION
In the present study, we observed that at-least-daily cannabis use was associated with a 16% increase in the hazard rate of injection cessation, and the subanalysis showed that this effect was restricted to the cessation of injection opioids. We also found that at-least-daily cannabis use was not significantly associated with relapse to injection drug use, and this association remained nonsignificant in subanalyses of opioid injection relapse and stimulant injection relapse.
Previous studies of injection cessation have identified several individual, behavioral, and socio-structural factors associated with injection cessation, including living in stable housing (adjusted odds ratio [AOR] = 1.30; 95% CI = 1.13, 1.48), having formal employment (AOR = 1.12; 95% CI = 1.01, 1.23), social support (AOR = 1.22; 95% CI = 1.10, 1.35), access to health and social services (AOR = 1.21; 95% CI = 1.09, 1.34), younger age (adjusted time ratio [ATR] = 0.79; 95% CI = 0.65, 0.94), and HIV seropositivity (ATR = 0.83; 95% CI = 0.73, 0.96).9,22 Daily injection drug use (ATR = 1.55; 95% CI = 1.35, 1.79), speedball (heroin and cocaine) injection (AOR = 1.39; 95% CI = 1.20, 1.62), homelessness (AOR = 1.36; 95% CI = 1.12, 1.65), illegal income activities (AHR = 0.19; 95% CI = 0.06, 0.61), and history of sexual abuse (AHR = 0.44; 95% CI = 0.27, 0.71) have been negatively associated with injection cessation.9,15,17,23,24 Two previous studies observed that injection cessation was often paralleled by increases in cannabis use, although cannabis use was not identified as a predictor of injection cessation.17,25
To our knowledge, this is the first longitudinal study to identify a positive association between cannabis use and cessation of injection drug use. This observation is supported by a recent study showing that frequent cannabis use was associated with decreased illicit opioid use among people who use drugs with chronic pain, a common comorbidity among this population.16 Preliminary trials in humans have reported reduced severity of opioid withdrawal associated with synthetic oral tetrahydrocannabinol (THC) administration (dronabinol), although some mild dose-related side effects were reported in 1 trial.26–28 THC produces feelings of reward by binding to cannabinoid receptor type 1 (CB1R), which are colocalized with μ opioid receptors.1,28,29 Endocannabinoids such as THC have been shown to influence opioid peptide levels and enhance the sensitivity and reward associated with other substances, which may explain the ability of cannabis to moderate opioid withdrawal.1,28
Although cannabidiol (CBD), a nonintoxicating phytocannabinoid, is not rewarding, it has also been implicated in the treatment of substance use disorders and opioid use disorder specifically.1 Animal models and preliminary human studies indicate that CBD attenuated the reward associated with opioids and reduced withdrawal symptoms and cue-induced cravings among heroin-dependent individuals.28,30 In both animal models and human studies, the reduced craving associated with CBD was observed up to 1 week after the final CBD administration.28,30 A randomized clinical trial of CBD use among individuals with heroin use disorder found that CBD administration for 3 consecutive days significantly reduced the drug craving and anxiety associated with salient drug cues compared with placebo.31 These benefits have important implications for substance use disorders given that drug craving increases with the duration of drug abstinence.1 These effects may be attributed to the ability of CBD to normalize opioid-induced impairment of CB1R receptors in the striatum, which plays a central role in the processing of reward, reinforcement, motivation, and decision-making.1,30 CBD also decreases activation in the amygdala during the processing of negative emotions, and the amygdala processes conditioned cues associated with substance use that provoke drug-seeking behaviors.1 The neurophysiological effects of THC and CBD and the potential to attenuate opioid withdrawal may account, in part, for our observation that daily cannabis use was associated with the cessation of injection opioid use among PWID.
Existing studies of injection relapse have identified several risk factors including younger age, male gender, homelessness, HIV seropositivity, noninjection stimulant use, and incarceration.17,18,22,23,32 Only 2 of these studies analyzed the impact of cannabis use, and, similar to our study, the association between cannabis use and injection relapse was not statistically significant.22,32 Predictors of injection relapse have been understudied relative to injection initiation and injection cessation. Given that substance use dependence is recognized as a chronic condition involving recurring cycles of relapse and recovery, additional research evaluating predictors of injection relapse in the context of an opioid overdose crisis will be important to mitigate the drug-related harm associated with injection drug use.33
Limitations
The limitations of this study included the measurement of drug use behaviors via self-report, although the reliability and validity of self-report measures among PWID have been demonstrated in previous studies.34 Underreporting of stigmatized and criminalized behaviors such as illicit injection drug use may have attenuated the effect sizes observed in the present study. Residual confounding may have also influenced the results as this was an observational design, and these findings may not be generalizable to other groups of PWID as these cohorts do not include random samples. Furthermore, we did not collect data on the types of cannabis used by study participants during the entire study period, including details of the relative concentrations of bioactive molecules (i.e., THC and CBD).
Although cannabis use was positively associated with injection cessation, it should be noted that the magnitude of the effect size (AHR = 1.16) was smaller than other than factors including opioid agonist therapy (AHR = 1.32) and alcohol and drug treatment (excluding opioid agonist therapy; AHR = 1.84). Nevertheless, the association between cannabis use and the cessation of opioid injection is important given the recent regulatory changes to nonmedical cannabis use and the ongoing opioid overdose epidemic in North America.
Public Health Implications
In conclusion, we found that at-least-daily cannabis use was associated with an increased rate of injection cessation, and this effect was restricted to the cessation of opioid injection. We did not observe a significant association between at-least-daily cannabis use and injection relapse. These observations are encouraging given the uncertainty surrounding the impact of cannabis legalization policies during the ongoing opioid overdose crisis in many settings in the United States and Canada, particularly among PWID who are at increased risk for drug-related harm. The accumulating evidence from preclinical and epidemiological studies linking cannabis use to opioid use behaviors further supports the evaluation of the therapeutic benefits of cannabis and specific cannabinoids (e.g., CBD and THC) for people living with opioid use disorder.
ACKNOWLEDGMENTS
The study was supported by the US National Institutes of Health (U01-DA038886, U01-DA0251525) and the Canadian Institutes of Health Research (CIHR; MOP–286532). This research was undertaken, in part, thanks to funding from the Canada Research Chairs program through a Tier 1 Canada Research Chair in Inner City Medicine. This study was supported by the CIHR Canadian HIV Trials Network (CTN 222). H. Reddon is supported by a Sponsor/CTN Postdoctoral Fellowship Award. K. DeBeck is supported by a Michael Smith Foundation of Health Research (MSFHR)/St Paul’s Hospital Foundation–Providence Health Care Career Scholar Award and a CIHR New Investigator Award. M-J. Milloy is supported in part by the US National Institutes of Health (U01-DA021525), a New Investigator Award from CIHR, and a Scholar Award from MSFHR. His institution has received an unstructured gift to support him from NG Biomed Ltd, a private firm applying for a government license to produce cannabis. He is the Canopy Growth professor of cannabis science, a position established through unstructured gifts to the University of British Columbia from Canopy Growth, a licensed producer of cannabis, and the Ministry of Mental Health and Addictions of the Government of British Columbia. K. Hayashi is supported by a CIHR New Investigator Award (MSH-141971), a MSFHR Scholar Award, and the St Paul’s Foundation. M. E. S. is supported by a MSFHR/St Paul’s Foundation Scholar Award. S. Lake is supported through doctoral award funding from the CIHR and the Pierre Elliott Trudeau Foundation. M. Karamouzian is supported by Vanier Canada Graduate Scholarship and Pierre Elliott Trudeau Foundation Doctoral Scholarships.
The authors thank the study participants for their contribution to the research, as well as current and past researchers and staff. We would specifically like to thank Carly Hoy, Jennifer Matthews, Peter Vann, Steve Kain, Lorena Mota, and Ana Prado for their research and administrative support. All authors respectfully acknowledge that they live and work on the unceded traditional territory of the Coast Salish Peoples, including the traditional territories of xʷməθkwəy̓əm (Musqueam), Sḵwx̱wú7mesh (Squamish), and Səl̓ ílwətaɬ (Tsleil-Waututh) Nations.
CONFLICTS OF INTEREST
The authors have no conflicts to declare.
HUMAN PARTICIPANT PROTECTION
Ethical approval for the At-Risk Youth Study, Vancouver Injection Drug Users Study, and AIDS Care Cohort to Evaluate Exposure to Survival Services has been obtained from the Providence Health Care/University of British Columbia Research Ethics Board on an annual basis.
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