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Abstract

Background:

It has been suggested that opioid agonist therapy (OAT) may have a secondary benefit of reducing methamphetamine/amphetamine use. However, current evidence is limited and conflicting, and little is known on the impacts of different OATs on methamphetamine/amphetamine use. The aim of this study was to examine the comparative effectiveness of buprenorphine/naloxone and methadone on methamphetamine/amphetamine use among individuals with opioid use disorder (OUD) initiating OAT in Canada.

Methods:

Data for this study were derived from a 24-week pan-Canadian pragmatic trial conducted between 2017 and 2020 comparing supervised methadone versus flexible take-home dosing buprenorphine/naloxone models of care among OUD. Generalized linear mixed models were used to evaluate the independent effect of treatment (ie, methadone or buprenorphine/naloxone) and time in treatment (ie, week 2 through 24, continuous) on methamphetamine/amphetamine use (measured by urine drug test and self-report using Timeline Follow-Back).

Results:

The sample included 210 participants that initiated OAT, of which 130 (61.9%) were using methamphetamine/amphetamine at baseline. In multivariable analysis, neither treatment nor time in treatment were significantly associated with the odds of methamphetamine/amphetamine use (adjusted odds ratio [AOR] = 0.61, 95% confidence interval [CI] = 0.34-1.08, P = .092; and AOR = 0.73, CI = 0.40-1.28, P = .283, respectively). No interaction between treatment and time in treatment was observed for methamphetamine/amphetamine use (P = .367).

Conclusion:

Methamphetamine/amphetamine use was common among this sample of people with OUD initiating OAT in Canada. Over the 24-week study period, buprenorphine/naloxone and methadone were not associated with a quantifiable change in methamphetamine/amphetamine use among this sample population. The observation of less methamphetamine/amphetamine use in the buprenorphine/naloxone arm warrants further research.

Keywords

opioid use disorder prescription opioids methamphetamine amphetamine methadone buprenorphine/naloxone clinical trial

Highlights

Among adults with opioid use disorder initiating opioid agonist therapy in Canada, nearly two-thirds had evidence of methamphetamine/amphetamine use at baseline.

There was no significant effect of treatment arm (buprenorphine/naloxone vs methadone) on the odds of methamphetamine/amphetamine use.

Time in treatment was not significantly associated with the odds of methamphetamine/amphetamine use.

Introduction

North America is in the midst of a public health emergency of opioid-related morbidity and mortality. More than 80% of opioid-related overdose deaths in 2022 are attributed to fentanyl.^1,2 While highly potent synthetic opioids, such as fentanyl and its analogues, continue to be major drivers of the crisis, stimulants, such as methamphetamine, have become increasingly involved in hospitalizations and overdose deaths,¹ in what has been called the “fourth wave” of the opioid epidemic.^3,4 While reliable Canadian data are scarce, past month methamphetamine use in the United States increased from 18.8% in 2011 to 34.2% in 2017 among treatment-seeking adults who use opioids.⁵ Similar increasing trends were observed in Canada with regard to amphetamine use. For example, in an emergency department in Toronto, Ontario, the proportion of opioid-related visits among amphetamine-related visits increased from 8.4% in 2014 to 19.8% in 2021.⁶ The use of methamphetamine/amphetamine with opioids is associated with a myriad of negative health consequences, including greater risk hepatitis C virus (HCV) and human immunodeficiency virus (HIV) transmission,^7,8 and nonfatal overdose.^9,10

First-line treatment approaches for opioid use disorder (OUD) include oral opioid agonist therapies (OAT), such as methadone and buprenorphine/naloxone. Oral OAT has been associated with improved health and social outcomes, including reductions in unregulated opioid use,¹¹ criminal activity,^12,13 and mortality risk,^14,15 as well as improvements in treatment retention¹¹ and quality of life.¹⁶ However, the majority of efficacy studies were conducted at a time when heroin was the main opioid used, with very limited data on people using other opioids (eg, opioid analgesics, fentanyl and related analogues).^17,18 Existing evidence also suggests that stimulant use while on OAT can negatively impact OUD treatment outcomes. For example, studies have shown that methamphetamine/amphetamine use and use disorder while on OAT is associated with increased rates of unregulated opioid use and decreased rates of treatment retention.^19
-21 Importantly, most studies did not investigate the mechanisms by which stimulant use affects OUD outcomes; therefore, observed associations could be related to a variety of unmeasured factors, including clinic policies and provider judgment.

Given the documented harms of methamphetamine/amphetamine use, including on OAT-related treatment outcomes, there is an important need to address methamphetamine/amphetamine use among people engaged in OAT. While effective pharmacotherapy options for methamphetamine/amphetamine use are limited, some evidence suggests that some medications approved for other conditions (eg, mirtazapine, methylphenidate formulations, and combination of bupropion and naltrexone) may be helpful in improving stimulant use–related outcomes.²² Additionally, other medications (eg, antidepressants, anticonvulsants, psychostimulants, antipsychotics, and opioid antagonists) are being investigated for the treatment of stimulant use disorder, however, primarily in populations without co-occurring OUD.²³ Furthermore, investigations into the therapeutic potential of OAT, such as buprenorphine, for the treatment of co-occurring OUD and methamphetamine use disorder have started.^24,25 While substantial evidence indicates that oral OAT effectively reduces unregulated opioid use,¹¹ evidence for its impact on methamphetamine/amphetamine use is extremely limited and conflicting. Some studies suggest OAT may have a secondary benefit of reducing methamphetamine/amphetamine use,^20,26 others suggest OAT may increase its use (eg, use of stimulants to counter sedating effects of methadone),^26,27 and, finally, other studies report no significant differences in stimulant use (ie, methamphetamine or cocaine).^28,29 Although effective pharmacotherapy options are limited, psychosocial interventions, such as contingency management, have been shown to effectively reduce methamphetamine use among individuals with methamphetamine use disorder.³⁰

Given the high prevalence of concurrent opioid and methamphetamine/amphetamine use and the conflicting evidence, there is an important need to better understand patterns of methamphetamine/amphetamine use among people with OUD engaged in OAT. If a differential impact on concurrent methamphetamine/amphetamine use were to exist for one of the first-line treatment approaches for OUD, this could inform clinical practice and decision-making for people who use both opioids and stimulants and are seeking OAT. Therefore, the aim of this study was to examine the comparative effectiveness of buprenorphine/naloxone and methadone on methamphetamine/amphetamine use among individuals with OUD initiating OAT in Canada.

Methods

Design

The current study is a secondary analysis of OPTIMA (Optimizing Patient Centered-Care: A Pragmatic Randomized Control Trial of Comparing Models of Care in the Management of Prescription Opioid Misuse), a Phase IV multicenter, pragmatic, open-label, two-arm parallel randomized controlled trial conducted in 4 Canadian provinces between 2017 and 2020.^31,32 OPTIMA evaluated the effectiveness of closely supervised methadone versus flexible take-home dosing buprenorphine/naloxone models of care among individuals with OUD. This trial was approved by the Research Ethics Boards at participating clinical sites and was registered at ClinicalTrials.gov (NCT03033732). OPTIMA’s primary outcome was the reduction in unregulated opioid use; given the emergence of methamphetamine/amphetamine use as a public health concern and the mixed evidence on OAT’s impact on methamphetamine/amphetamine use, this secondary analysis examined the comparative effectiveness of buprenorphine/naloxone and methadone on methamphetamine/amphetamine use among individuals with OUD initiating OAT in Canada.

Participants

Treatment-seeking adults (aged 18-64) diagnosed with OUD, based on DSM-V criteria, primarily attributable to prescription-type opioids (eg, licit or illicit, including fentanyl, prescribed or not) were enrolled in the OPTIMA trial. The OUD diagnosis was deemed to be attributable to prescription-type opioids if prescription opioids were self-reported as the most frequently used opioid in the past 30 days. Given that the primary focus of the trial was prescription-type opioid use, individuals were excluded from the study if they self-reported heroin as the most frequently used opioid in the past 30 days. Additionally, individuals were excluded from the trial if they had any unstable psychiatric, medical conditions or other substance use disorders precluding safe participation or the ability to provide fully informed consent. This was based on study physician’s clinical judgment while reviewing medical and psychiatric history, labs, and results of a physical examination. Other exclusion criteria included the following: enrollment in OAT in the 30 days prior to screening; and pregnant, breastfeeding, or planning to conceive. Detailed eligibility criteria have been described elsewhere.^31,32

Procedures

After written informed consent was voluntarily given, eligible participants completed baseline assessments. Participants were randomized, stratified by site and the presence of lifetime heroin use (yes vs no), in a 1:1 ratio to receive 24 weeks of supervised methadone or flexible take-home dosing buprenorphine/naloxone and initiated treatment within 14 days. During the 24-week intervention participants attended biweekly study visits where they completed urine drug tests (UDT) and questionnaires. A Rapid Response^TM Multi-Drug One-step Test Panel was used to analyze urine samples and tested for the presence of morphine, oxycodone, fentanyl, methadone and its metabolite, tramadol, buprenorphine, cocaine, amphetamine, methamphetamine, benzodiazepines, and Δ-9-tetrahydrocannabinol. Single-test strips testing for 6-monoacetylmorphine and hydromorphone were also used. Questionnaires collected information on demographics, medical, and psychiatric comorbidities (eg, HIV, HCV, pain, depression, anxiety), substance use, health care utilization, quality of life, criminal activity, medication dispensation and adverse events.

All interventions followed national and provincial guidelines for the management of OUD.^11,33
-38 In the methadone arm, participants initiated treatment through daily witnessed ingestion with typical maintenance doses of 60 to 120 mg/day and take-home doses (carries) permitted after 2 to 3 months on an individual basis. In the buprenorphine/naloxone arm, participants received a maximum daily dose of 24 mg/6 mg. Once the patient was clinically stable, take-home doses were permitted with 1-week carries suggested within 2 weeks after initiation and 2-week carries suggested within 4-weeks after initiation. Following the pragmatic nature of the trial, induction procedures (eg, traditional or low-dose induction for buprenorphine/naloxone), dosages, and titration speed varied according to clinical judgment and site’s practices. Additionally, reflecting real-world practice, participants were allowed to switch to another OAT if clinically indicated. All participants were compensated up to $560 ($40 per visit) for their participation in the trial.

Analysis Population

This analysis considered all eligible OPTIMA participants who were randomized to a treatment arm, initiated the assigned OAT within 14 days of randomization, and had valid (positive or negative) baseline UDT results or self-report data for methamphetamine or amphetamine.

Measures

Our primary outcome was methamphetamine/amphetamine use at each visit, analyzed as a longitudinal outcome (yes vs no, updated at all study visits). While methamphetamine and amphetamine use are highly prevalent among people who use opioids,^5,6,39,40 their similar chemical structures make it difficult to distinguish the 2 substances based on UDT results. Additionally, as the Timeline Follow-Back (TLFB) used to capture participants’ self-reported substance use grouped both substances under “amphetamines,” we cannot use this information to distinguish between the 2 substances. Thus, both methamphetamine and amphetamine have been included in the primary outcome. At baseline (week 0), methamphetamine/amphetamine use was defined by either having a UDT positive for methamphetamine or amphetamine or self-report of having used either substance within the past 30 days using TLFB.⁴¹ At treatment visits (week 2 through 24), methamphetamine/amphetamine use was defined by either having a UDT positive for methamphetamine or amphetamine or self-report of having used either substance within the past 14 days using TLFB. Thus, a positive UDT or positive self-report was taken as evidence of recent methamphetamine/amphetamine use.

We had 2 main predictor variables: (a) type of treatment (ie, methadone or buprenorphine/naloxone) and (b) time in treatment (ie, week 2 through 24, continuous). Covariates of interested included the following sociodemographic characteristics collected at baseline: age (per year older, continuous); self-identified gender (man vs woman [inclusive of trans women]); ethnicity (white non-Hispanic vs Black, Indigenous or People of Color [inclusive of South Asian/Other Asian/Middle Eastern, Latin American/Hispanic, Black Non-Hispanic, First Nations, Metis, and Other]). Substance use–related variables (defined using UDT and self-report using TLFB) included the following: methamphetamine/amphetamine use within 30 days prior to baseline (yes vs no, time-fixed) and other substance use in the past 14 days (opioids excluding fentanyl, fentanyl only, fentanyl and other opioids, cocaine and cannabis, updated at all follow-up visits). Other variables of interest included are as follows: injection drug use last in the 14 days (yes vs no, updated at all follow-up visits); province (British Columbia [BC] vs Alberta [AB] vs Ontario [ON] vs Quebec [QC], time-fixed); unstable housing in the last 14 days (yes vs no, updated at all follow-up visits); and employment in the last 30 days (employed vs unemployed, updated at every other follow-up visit).

Statistical Analysis

Observed percentages of biweekly methamphetamine/amphetamine use, stratified by type of treatment (ie, methadone or buprenorphine/naloxone), were plotted. Pearson’s χ² was used to test for missingness of methamphetamine/amphetamine results between the 2 groups at each study visit. Baseline characteristics of the study sample, stratified by treatment arm (ie, methadone vs buprenorphine/naloxone), were summarized using Wilcoxon’s rank-sum test for continuous variables, Pearson’s χ² test for categorical variables, and Fisher’s exact test for categorical variables with low cell count (ie, less than 5).

Bivariable and multivariable generalized linear mixed models (GLMM), with a logit-link function to account for the binary outcome, were used to examine unadjusted and adjusted associations between the predictor variables and the outcome, adjusting for all conceptually important secondary covariates. The outcome was considered missing if both UDT and TLFB were missing for a given week. Of the 2520 observations, 401 (15.9%) were missing the outcome and, in this case, missing methamphetamine/amphetamine results were imputed based on the most recent result. We tested the main effects of type of treatment (ie, methadone vs buprenorphine/naloxone) and time in treatment (ie, week 2 through 24), treated as a continuous variable. An interaction term between type of OAT and time in treatment was included to investigate whether the effects of OAT on methamphetamine/amphetamine use varied over time in treatment. If a significant interaction was observed (P < .05), all pairwise within-week comparisons were examined. All predictors and covariates were included in the models as fixed effects, and participant was included as a random effect. Variance inflation factors (VIF) were used to check for multicollinearity in the final, adjusted model. Minimal multicollinearity was present, indicated by VIF <1.5 for all variables.

To assess the robustness of our estimate of the main effects of type of treatment and time in treatment on methamphetamine/amphetamine use, we performed 2 sensitivity analyses: (1) considering missing methamphetamine/amphetamine UDT results as missing and (2) considering missing UDT as positive for methamphetamine/amphetamine. Additionally, we conducted a subanalysis restricted to people who were retained in their assigned treatment. Analyses were performed using R software (version 4.1.1). Two-sided P-values <.05 were considered statistically significant.

Results

Baseline Characteristics

OPTIMA enrolled and randomized 272 eligible individuals with OUD. The median age was 38 (interquartile range [IQR] = 31-46), 176 (64.7%) were men and 183 (67.3%) self-identified as white non-Hispanic. Of these, 210 (77.2%) initiated OAT (107 methadone and 103 buprenorphine/naloxone). Of the 62 individuals that failed to initiate OAT, the majority (82.3%) were using methamphetamine/amphetamine at baseline. The Consolidated Standards of Reporting Trials (CONSORT)⁴² flow diagram of participants included in the present secondary analysis is presented in Figure 1.

Figure 1.

Consolidated Standards of Reporting Trials (CONSORT) flow diagram of OPTIMA participants included in the secondary analyses of the comparative effectiveness of buprenorphine/naloxone and methadone on methamphetamine/amphetamine use among people with OUD in Canada. OUD, opioid use disorder; OPTIMA, Optimizing Patient Centered-Care: A Pragmatic Randomized Control Trial of Comparing Models of Care in the Management of Prescription Opioid Misuse; UDT, urine drug test; TLFB, Timeline Follow-Back.

Table 1 summarizes selected characteristics of the study sample, stratified by treatment arm (ie, methadone vs buprenorphine/naloxone). The median age was 39 (IQR = 31-48), 139 (66.2%) were men and 149 (71.0%) self-identified as white non-Hispanic. Baseline use of substances, as measured by UDT and TLFB, was highly prevalent: 61.9% for methamphetamine/amphetamine, 71.0% for cannabis, 49.5% for cocaine, 57.1% for fentanyl and other opioids, and 36.7% for opioids (excluding fentanyl). There were no statistically significant differences in selected characteristics between the 2 groups.

Table 1.

Baseline Characteristics of 210 People with OUD in the OPTIMA Trial Who Initiated Treatment, Stratified by Treatment Arm.

Characteristic	Total, n (%) (N = 210)	Treatment arm, n (%)		P-value
Characteristic	Total, n (%) (N = 210)	Methadone (n = 107)	BUP/NX (n = 103)	P-value
Socio-demographics
Age (median, IQR)	39 (31-48)	39 (33-48)	38 (31-48)	.705^a
Gender				.089
Man	139 (66.2)	65 (60.7)	74 (71.8)
Woman^b	71 (33.8)	42 (39.3)	29 (28.2)
Race/ethnicity				.409^c
White Non-Hispanic	149 (71.0)	75 (70.1)	74 (71.8)
South Asian/Other Asian/Middle Eastern	3 (1.4)	2 (1.9)	1 (1.0)
Latin American/Hispanic	1 (0.5)	0 (0.0)	1 (1.0)
Black Non-Hispanic	2 (1.0)	1 (0.9)	1 (1.0)
First Nations	33 (15.7)	16 (15.0)	17 (16.5)
Metis	8 (3.8)	3 (2.8)	5 (4.9)
Other	14 (6.7)	10 (9.3)	4 (3.9)
Substance use–related factors
Opioid use last 30 days				.459
Opioids only (excluding fentanyl)	77 (36.7)	37 (34.6)	40 (38.8)
Fentanyl only	13 (6.2)	5 (4.7)	8 (7.8)
Fentanyl and other opioids	120 (57.1)	65 (60.7)	55 (53.4)
Methamphetamine/amphetamine use last 30 days	130 (61.9)	67 (62.6)	63 (61.2)	.829
Cocaine use last 30 days	104 (49.5)	49 (45.8)	55 (53.4)	.271
Cannabis use last 30 days	149 (71.0)	72 (67.3)	77 (74.8)	.233
Health care–related factors
Injection drug use last 30 days	101 (48.1)	48 (44.9)	53 (51.5)	.339
Structural-level factors
Province				.759
British Columbia	52 (24.8)	26 (24.3)	26 (25.2)
Alberta	47 (22.4)	27 (25.2)	20 (19.4)
Ontario	43 (20.5)	20 (18.7)	23 (22.3)
Quebec	68 (32.4)	34 (31.8)	34 (33.0)
Current unstable housing	71 (33.8)	40 (37.4)	31 (30.1)	.265
Employment last 30 days	71 (33.8)	40 (37.4)	31 (30.1)	.265

Abbreviations: BUP/NX, buprenorphine/naloxone; IQR, interquartile range; OUD, opioid use disorder; OPTIMA; Optimizing Patient Centered-Care: A Pragmatic Randomized Control Trial of Comparing Models of Care in the Management of Prescription Opioid Misuse.

Wilcoxon’s rank-sum test; ^bBased on self-report data. Categories were the following: man, woman, transgender (man, woman, neither, both), other, don’t know, choose not to answer; ^cFisher’s exact test.

Figure 2 shows the observed percentages of methamphetamine/amphetamine use at each week stratified by treatment arm (ie, methadone vs buprenorphine/naloxone). At week 0, 62.6%, and 62.1% of participants had methamphetamine/amphetamine use in the methadone and buprenorphine/naloxone arms, respectively. By the end of the study (ie, week 24), observed percentages of methamphetamine/amphetamine were 36.4% in the methadone arm and 31.1% in the buprenorphine/naloxone arm. Missingness of methamphetamine/amphetamine results did not significantly differ between the 2 groups at each study visit.

Figure 2.

Observed percentage of methamphetamine/amphetamine use (UDT or TLFB) by week among OPTIMA participants who initiated treatment (n = 210), stratified by methadone and buprenorphine/naloxone treatment arms. Error bars represent 95% confidence intervals. UDT, urine drug test; TLFB, Timeline Follow-Back; OPTIMA, Optimizing Patient Centered-Care: A Pragmatic Randomized Control Trial of Comparing Models of Care in the Management of Prescription Opioid Misuse.

Impact of Opioid Agonist Therapy on Methamphetamine/Amphetamine Use

Unadjusted and adjusted GLMM of factors associated with methamphetamine/amphetamine use are presented in Table 2. In the adjusted model, neither type of treatment nor time in treatment was significantly associated with the odds of methamphetamine/amphetamine use (P > .05). No interaction between type of OAT and time in treatment was observed for methamphetamine/amphetamine use (P > .05).

Table 2.

Predictors of Methamphetamine/Amphetamine Use from Generalized Linear Mixed Models with Individual-Level Random Effects Among 210 People with OUD in the OPTIMA Trial Who Initiated Treatment.

Variable	Unadjusted		Adjusted
Variable	OR (95% CI)	P-value^a	OR (95% CI)	P-value^a
Predictor variable
BUP/NX (ref.: methadone)	0.46 (0.22, 0.96)	.039	0.61 (0.34, 1.08)	.092
Time in treatment (per 1 week)	0.35 (0.22, 0.57)	<.001	0.73 (0.40, 1.28)	.283
BUP/NX*Time in treatment	0.64 (0.24, 1.69)	.367
Socio-demographics
Age (per 1 year older)	1.01 (0.97, 1.04)	.769	1.01 (0.98, 1.04)	.570
Woman	0.67 (0.30, 1.46)	.308	0.64 (0.35, 1.20)	.167
White non-Hispanic (ref.: BIPOC)	0.27 (0.12, 0.60)	.001	0.58 (0.29, 1.15)	.116
Substance use–related factors
MA/A use at baseline	27.97 (13.32, 58.76)	<.001	6.80 (3.24, 14.28)	<.001
Opioid use last 14 days (ref: no)
Opioids (excluding fentanyl)	2.45 (1.63, 3.68)	<.001	2.06 (1.36, 3.13)	<.001
Fentanyl only	21.27 (13.69, 33.04)	<.001	17.03 (10.89, 26.61)	<.001
Fentanyl and other opioids	31.44 (20.53, 48.13)	<.001	17.66 (11.39, 27.27)	<.001
Cocaine use last 14 days	1.81 (1.34, 2.44)	<.001	1.16 (0.82, 1.65)	.402
Cannabis use last 14 days	2.69 (1.96, 3.70)	<.001	2.00 (1.41, 2.83)	<.001
Health care–related factors
Injection drug use last 14 days	7.96 (5.00, 12.69)	<.001	3.30 (2.00, 5.44)	<.001
Structural-level factors
Province (ref.: British Columbia)
Alberta	0.53 (0.21, 1.35)	.183	1.06 (0.46, 2.46)	.895
Ontario	0.04 (0.02, 0.11)	<.001	0.27 (0.10, 0.74)	.011
Quebec	0.07 (0.03, 0.17)	<.001	1.00 (0.39, 2.59)	.999
Unstable housing last 14 days	0.62 (0.34, 1.13)	.117	0.56 (0.29, 1.07)	.080
Employment last 30 days	0.93 (0.62, 1.38)	.710	1.21 (0.78, 1.89)	.401

Abbreviations: BUP/NX, buprenorphine/naloxone; BIPOC, black, Indigenous, and people of color; MA/A, methamphetamine/amphetamine; OUD, opioid use disorder; OPTIMA; Optimizing Patient Centered-Care: A Pragmatic Randomized Control Trial of Comparing Models of Care in the Management of Prescription Opioid Misuse; OR, odds ratio; CI, confidence interval.

Boldface indicates statistical significance (p < 0.05).

Sensitivity analyses considering missing methamphetamine/amphetamine UDT results as missing and considering missing UDT as positive for methamphetamine/amphetamine did not substantively change results (Online Supplemental Materials 1 and 2). The subanalysis restricted to people who were retained in their assigned treatment did not substantively change results (Table 3).

Table 3.

Predictors of Methamphetamine/Amphetamine Use from Generalized Linear Mixed Models with Individual-Level Random Effects Among 210 People with OUD in the OPTIMA Trial Who Initiated Treatment—Subanalysis Restricted to People Retained in the Assigned Treatment.

Variable	Unadjusted		Adjusted
Variable	OR (95% CI)	P-value^a	OR (95% CI)	P-value^a
Predictor variable
BUP/NX (ref.: methadone)	0.55 (0.19, 1.64)	.287	0.62 (0.30, 1.27)	.189
Time in treatment (per 1 week)	0.69 (0.31, 1.51)	.348	0.91 (0.37, 2.24)	.845
BUP/NX*Time in treatment	0.77 (0.16, 3.77)	.751
Socio-demographics
Age (per 1 year older)	0.96 (0.91, 1.00)	.065	0.99 (0.96, 1.02)	.495
Woman	0.74 (0.21, 2.65)	.644	1.06 (0.46, 2.45)	.885
White non-Hispanic (ref.: BIPOC)	0.42 (0.11, 1.59)	.202	1.47 (0.56, 3.86)	.437
Substance use–related factors
MA/A use at baseline	21.63 (9.65, 48.49)	<.001	13.57 (6.33, 29.07)	<.001
Opioid use last 14 days(ref.: no)
Opioids (excluding fentanyl)	1.89 (1.11, 3.12)	.019	1.68 (1.00, 2.83)	.050
Fentanyl only	13.91 (6.61, 29.27)	<.001	10.83 (5.10, 22.99)	<.001
Fentanyl and other opioids	9.74 (5.11, 18.56)	<.001	6.98 (3.69, 13.20)	<.001
Cocaine use last 14 days	1.31 (0.83, 2.07)	.253	1.07 (0.66, 1.74)	.789
Cannabis use last 14 days	2.02 (1.18, 3.44)	.010	2.58 (1.47, 4.53)	<.001
Health care–related factors
Injection drug use last 14 days	4.69 (2.26, 9.74)	<.001	2.42 (1.20, 4.88)	.014
Structural-level factors
Province (ref.: British Columbia)
Alberta	0.49 (0.06, 4.13)	.512	1.38 (0.28, 6.73)	.688
Ontario	0.03 (0.00, 0.21)	<.001	0.21 (0.04, 0.95)	.043
Quebec	0.04 (0.01, 0.23)	<.001	0.63 (0.14, 2.73)	.534
Unstable housing last 14 days	1.51 (0.50, 4.60)	.464	1.06 (0.33, 3.38)	.924
Employment last 30 days	1.05 (0.63, 1.75)	.848	1.26 (0.75, 2.11)	.391

Boldface indicates statistical significance (p < 0.05)

Discussion

This secondary analysis of the OPTIMA trial demonstrated that methamphetamine/amphetamine use was common among adults with OUD initiating OAT in Canada, with nearly two-thirds (61.9%) of participants having evidence of methamphetamine/amphetamine use at baseline. There were no significant differences in baseline characteristics between the 2 treatment groups. Although slightly lower proportions of methamphetamine/amphetamine use were observed in the buprenorphine/naloxone arm and at later biweekly visits over the 24-week intervention, type of treatment and time in treatment were not significantly associated with reductions in methamphetamine/amphetamine use. No interaction between type of OAT and time in treatment on methamphetamine/amphetamine use was observed.

Methamphetamine/amphetamine use was common in this sample of people with OUD initiating OAT in Canada, which is consistent with Canadian epidemiological data.¹ However, regional differences were observed in the prevalence of use, with Western provinces (ie, BC and AB) reporting higher prevalence. Extant literature suggests that BC and AB had the highest rates of methamphetamine use nationally.^43
-45 Therefore, it is likely that regional differences in the prevalence of methamphetamine/amphetamine use observed in our study are reflective of actual population trends in those regions. Similarly, evidence from the United States indicates that prevalence of methamphetamine use has rapidly increased in recent years, spreading eastward from Western and Southwestern regions of the country.^5,46
-48

In bivariable analysis, buprenorphine/naloxone was associated with reductions in methamphetamine/amphetamine use; however, this finding did not meet the threshold for statistical significance in the adjusted model. It is important to note that other factors, such as high potency fentanyl exposure or lower retention in the buprenorphine/naloxone group, may be masking this association, thus decreasing our ability to detect any beneficial effect on methamphetamine/amphetamine use. Our finding is inconsistent with limited evidence suggesting that buprenorphine may have therapeutic potential in reducing methamphetamine use.²⁰ For example, a study conducted among individuals with OUD in the United States found that at 6-month follow-up, individuals retained in buprenorphine treatment decreased their overall methamphetamine use by an average of 6 days compared with baseline.²⁰ Furthermore, long-acting injectable formulations of buprenorphine have also shown some promise in reducing methamphetamine use among people with OUD and comorbid methamphetamine use.²⁶ That said, in our subanalysis restricted to people who were retained in their assigned treatment there were no significant differences by treatment arm on methamphetamine/amphetamine use over the course of the trial. Similar to our finding, other studies found no evidence that buprenorphine/naloxone significantly reduces methamphetamine/amphetamine use.^27,29 For example, a study conducted in the United States among an unstably housed population with OUD and polysubstance use found no evidence that buprenorphine/naloxone significantly reduced stimulant use, including methamphetamine use.²⁹ Furthermore, a secondary analysis of a randomized controlled trial in the United States among people with OUD (primarily using heroin) found no differences in methamphetamine/amphetamine use between buprenorphine/naloxone and extended-release naltrexone treatment arms.²⁷ We are unaware of any studies specifically investigating methadone’s effect on methamphetamine/amphetamine use; however, qualitative work suggests people may use methamphetamine to cope with the side effects of methadone (eg, sedation, sexual dysfunction).^49,50

Although time in treatment was associated with methamphetamine/amphetamine use in bivariable analysis, this finding was not statistically significant in the adjusted model. One possible explanation for this could be that other factors, including high potency fentanyl exposure, are masking the association. Contrary to our finding, a recent study in the United States conducted among people with OUD (primarily using heroin) treated with buprenorphine/naloxone or extended-release naltrexone found a small, but statistically significant, increase in methamphetamine/amphetamine use at each visit, with a 4% increase in the odds of methamphetamine/amphetamine use compared with the prior visit.²⁷ Additionally, a longitudinal analysis assessing the impact of OAT engagement (ie, methadone or buprenorphine/naloxone) on levels of substance use among people who use drugs in Vancouver, BC, found that after OAT initiation, time was associated with increased odds of methamphetamine use.⁵¹ Therefore, although the effect of time in treatment was not statistically significant in our study, current research suggests longer treatment durations may increase methamphetamine/amphetamine use. While the reasons for this observation have not been elucidated, it is possible that individuals’ treatment goals and motivations may not be abstinence-based or may change over time. Furthermore, as OAT works to block the euphoric effects of other opioids, people may be using methamphetamine/amphetamine to provide a substitute “high” for opioids after stabilization on OAT,^50,52 which may counterbalance any beneficial effect of OAT on stimulant use through other mechanisms. However, it is also possible that other factors, such as other substance use–related factors, may contribute to this finding. For example, another longitudinal analysis conducted among people with likely OUD in Vancouver, BC, demonstrated that the impact of OAT (ie, methadone, buprenorphine/naloxone, slow-release oral morphine, and injectable OAT) on methamphetamine use was conditional on unregulated opioid use.⁵³ In this study, OAT engagement in the absence of unregulated opioid use reduced methamphetamine use, while OAT engagement in the presence of unregulated opioid use increased methamphetamine use.⁵³ While the effect of time in treatment was not observed in our study, it is an important consideration warranting further research. A better understanding of treatment trajectories among people who concurrently use opioids and methamphetamine/amphetamine may inform clinical practice and decision-making for people who use both opioids and stimulants and are seeking OAT.

Our study adds to the growing body of literature and offers novel insights into the potential relationship between OAT and methamphetamine/amphetamine use. To our knowledge, this is the first study examining the comparative effectiveness of methadone and buprenorphine/naloxone on methamphetamine/amphetamine use in a population of people with OUD, primarily attributable to prescription-type opioids (eg, licit or illicit, including fentanyl, prescribed or not). Thus, characteristics of our study population may in part explain discordant results observed in our study. Studies that found statistically significant effects of OAT on methamphetamine/amphetamine use were conducted among populations with OUD primarily attributable to heroin. In contrast, the pragmatic nature of this trial facilitated recruitment of a representative Canadian population with OUD and relatively severe opioid use patterns, including high potency fentanyl exposure.³² Although our results do not suggest differential treatment impacts on methamphetamine/amphetamine use, these findings should be regarded with caution. In our study, although not statistically significant, reductions in methamphetamine/amphetamine use in the buprenorphine/naloxone arm were greater than reductions observed in the methadone arm. Taken together with the existing evidence, this finding suggests that while it is possible that buprenorphine/naloxone may have a therapeutic benefit on methamphetamine/amphetamine use, future research is needed to explore this hypothesis. Currently, a multisite randomized controlled trial is underway in the United States to investigate injectable buprenorphine for the treatment of methamphetamine use disorder among individuals with concurrent opioid use.^24,25

This study is subject to several limitations. First, these were secondary analyses conducted post hoc among a relatively small sample size of treatment-seeking adults with OUD in Canada primarily composed of men and people who self-identified as white non-Hispanic. Thus, it is possible that our results may not be generalizable to other populations with OUD or geographic locations. For example, under Canada’s universal public health system, OAT is covered under provincial drug plans for individuals with low income and available through community pharmacies. Second, there were some missing data for our study outcome (methamphetamine/amphetamine use); however, missingness of methamphetamine/amphetamine results did not significantly differ between the 2 groups at each study visit. Additionally, our 2 sensitivity analyses yielded similar results to the main analysis. Third, as these were secondary analyses of OPTIMA, where the primary focus was opioid use, baseline rates of stimulant use disorder were not captured and are not available in the present analyses. Thus, it is possible that some participants in our study had a co-occurring stimulant use disorder. However, individuals with other substance use disorders (ie, stimulant use disorder) precluding safe participation in the trial, based on the study physician’s clinical judgment, were excluded from the study. Fourth, we cannot distinguish between the treatment impact on use of illicit methamphetamine/amphetamine and prescribed amphetamines (eg, for attention-deficit/hyperactivity disorder or narcolepsy). Nonetheless, it was relatively uncommon for participants to test positive for amphetamine and negative for methamphetamine, as well as self-report no methamphetamine/amphetamine use. Of 2520 total observations, this scenario only occurred 24 times (1.0%); therefore, it is unlikely that the outcome was often misclassified and that this would significantly affect our findings. Finally, it is possible that responses may be subject to response bias as participants self-reported sensitive data, including information on substance use. However, similar self-reported behavioral data have been validated among populations of adults who use drugs.⁵⁴

Conclusion

In conclusion, methamphetamine/amphetamine use was common among this sample of people with OUD initiating OAT in Canada. Over the 24-week study period, buprenorphine/naloxone and methadone were not associated with a quantifiable change in methamphetamine/amphetamine use, thus suggesting no differential treatment impacts. However, given the limited and conflicting evidence, and our observation that lower proportions of methamphetamine/amphetamine use were observed in the buprenorphine/naloxone arm, further research is needed to better understand whether buprenorphine/naloxone has an impact on methamphetamine/amphetamine use.

Supplemental Material

sj-docx-1-saj-10.1177_29767342241298044 – Supplemental material for Comparative Effectiveness of Buprenorphine/Naloxone and Methadone on Methamphetamine/Amphetamine Use Among People with Opioid Use Disorder in Canada

Supplemental material, sj-docx-1-saj-10.1177_29767342241298044 for Comparative Effectiveness of Buprenorphine/Naloxone and Methadone on Methamphetamine/Amphetamine Use Among People with Opioid Use Disorder in Canada by Jenna Langlois, Nadia Fairbairn, Didier Jutras-Aswad, Bernard Le Foll, Keith Ahamad, Ron Lim and M. Eugenia Socías in Substance Use & Addiction Journal

Supplemental Material

sj-docx-2-saj-10.1177_29767342241298044 – Supplemental material for Comparative Effectiveness of Buprenorphine/Naloxone and Methadone on Methamphetamine/Amphetamine Use Among People with Opioid Use Disorder in Canada

Supplemental material, sj-docx-2-saj-10.1177_29767342241298044 for Comparative Effectiveness of Buprenorphine/Naloxone and Methadone on Methamphetamine/Amphetamine Use Among People with Opioid Use Disorder in Canada by Jenna Langlois, Nadia Fairbairn, Didier Jutras-Aswad, Bernard Le Foll, Keith Ahamad, Ron Lim and M. Eugenia Socías in Substance Use & Addiction Journal

Footnotes

The authors are grateful to all study participants for their contributions to research,as well as current and past clinical and research staff. The authors especially thank Jill Fikowski,Denise Adams,Oluwadamilola Akinyemi,Farihah Ali,Katrina Blommaert,Emma Garod,Nirupa Goel,Wendy Mauro-Allard,Kirsten Morin,Benita Okocha,Eve Poirier,Aïssata Sako,Geneviève St-Onge,José Trigo,Angela Wallace,and Amel Zertal for research and administrative assistance.

Author Contributions

JL: Conceptualization,methodology,formal analysis,visualization,writing—original draft,and writing—review and editing. NF: conceptualization,methodology,supervision,and writing—review and editing. DJA,BLF,KA,and RL: Funding acquisition,investigation,project administration,resources,and writing—review and editing. MES: Conceptualization,funding acquisition,investigation,methodology,project administration,resources,supervision,and writing—review and editing.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research,authorship,and/or publication of this article: DJA receives study material from Tetra Bio-Pharma and Cardiol Therapeutics for trials funded by public funding bodies. BLF has received funding from Pfizer (Global Research Awards in Nicotine Dependence Program,including salary support) for investigator-initiated projects and from Indivior (for a clinical trial sponsored by Indivior),and he has received industry funding from Canopy Growth Corporation (through research grants handled by the Centre for Addiction and Mental Health and the University of Toronto),Bioprojet Pharma,Alcohol Countermeasure Systems,Alkermes,and Universal Ibogaine;he has received in-kind donations of cannabis products from Aurora Cannabis Enterprises,Inc and study medication donations from Pfizer (varenicline for smoking cessation) and Bioprojet Pharma;he was provided a coil for a transcranial magnetic stimulation study from Brainsway;he has received in-kind donations of nabiximols from GW Pharmaceuticals for past studies funded by CIHR and NIH;he has participated in a session of a National Advisory Board Meeting (Emerging Trends BUP-XR) for Indivior Canada and is part of Steering Board for a clinical trial for Indivior;and he has served as a consultant for Shinogi. MES has received partial support from Indivior’s investigator-initiated study program for work outside of this study. All other authors have no conflicting interests to declare.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: The OPTIMA study was financially supported by the Canadian Institutes of Health Research (grant numbers SMN-139148,SMN-139149,SMN-139150,SMN-139151) through the Canadian Research Initiative on Substance Misuse (grant numbers CIS-144301,CIS-144302,CIS-144303,CIS-144304). JL is supported by trainee support through the University of British Columbia and the Mitacs Accelerate program. NF is supported by a Philip Owen Professorship in Addiction Medicine at the University of British Columbia,and a Scholar Award from the Michael Smith Foundation for Health Research/St. Paul’s Hospital Foundation. DJA holds a research scholar award from the Fonds de Recherche du Québec en Santé. BLF is supported by the Centre for Addiction and Mental Health (CAMH),Waypoint Centre for Mental Health Care,a clinician-scientist award from the department of Family and Community Medicine of the University of Toronto and a Chair in Addiction Psychiatry from the Department of Psychiatry of University of Toronto. KA is supported by an Embedded Clinician Researcher Salary Award from the Canadian Institutes of Health Research (CIHR). MES is supported by a Scholar Award from the Michael Smith Foundation for Health Research/St. Paul’s Hospital Foundation. All funders had no role in trial design,conduct,analysis,or reporting.

Compliance,Ethical Standards,and Ethical Approval

Study activities were developed to adhere to Good Clinical Practice (GCP) guidelines,the principles laid out in the Declaration of Helsinki,and all other applicable local regulatory requirements. This trial was approved by the Research Ethics Boards at participating clinical sites and was registered at ClinicalTrials.gov (NCT03033732). All participants provided written informed consent form before undergoing any study-related activities.

ORCID iD

M. Eugenia Socías

Supplemental Material

Supplementary material for this article is available online at the SAJ website

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