CBD Oil And Methadone

CBDISTILLERY

Buy CBD Oil Online

Impact of Cannabis Use During Stabilization on Methadone Maintenance Treatment Illicit drug use, particularly of cannabis, is common among opiate-dependent individuals, and has the potential to Association between cannabis use and methadone maintenance treatment outcomes: an investigation into sex differences Open Access This article is distributed under the terms of the Creative

Impact of Cannabis Use During Stabilization on Methadone Maintenance Treatment

Illicit drug use, particularly of cannabis, is common among opiate-dependent individuals, and has the potential to impact treatment in a negative manner.

Methods

To examine this, patterns of cannabis use prior to and during methadone maintenance treatment (MMT) were examined to assess possible cannabis-related effects on MMT, particularly during methadone stabilization. Retrospective chart analysis was used to examine outpatient records of patients undergoing MMT (n=91), focusing specifically on past and present cannabis use and its association with opiate abstinence, methadone dose stabilization, and treatment compliance.

Results

Objective rates of cannabis use were high during methadone induction, dropping significantly following dose stabilization. History of cannabis use correlated with cannabis use during MMT, but did not negatively impact the methadone induction process. Pilot data also suggested that objective ratings of opiate withdrawal decrease in MMT patients using cannabis during stabilization.

Conclusions and Scientific Significance

The present findings may point to novel interventions to be employed during treatment for opiate dependence that specifically target cannabinoid-opioid system interactions.

1. Introduction

Methadone maintenance treatment (MMT) is an opiate agonist pharmacotherapy prescribed for opiate-dependent individuals as a means of extinguishing illicit opiate use and reducing associated risk behaviors 1, 2 . Initiation on methadone maintenance treatment is a federally regulated process that requires slow and careful titration of methadone dosing to avoid risk of overmedication. Dose titration proceeds through initiation, induction, and stabilization phases prior to reaching a maintenance phase 3–5 . The initial period of methadone dose stabilization is one of particular vulnerability to relapse due to the persistence of low-moderate grade opiate withdrawal and associated stress during dose titration 6–10 . Non-opiate illicit drug use often persists during the early phases of treatment for opiate dependence 11–18 , promoted at least in part by the experience of withdrawal and craving. There is a limited amount of research focused on the impact of continued non-opiate substance use on methadone dose titration and overall treatment compliance.

Cannabis (marijuana) is commonly used in combination with heroin or oral opiates, in addition to other substances such as cocaine and benzodiazepines 19–21 . In several studies of non-opiate illicit drug use in opiate-dependent individuals, rates of cannabis use were shown to remain high during treatment for opiate dependence 15, 22–25 . Several groups have investigated the impact of cannabis use on various measures of treatment outcome and success, such as retention in treatment or compliance 16, 23–28 . However, very limited research has focused specifically on the impact of cannabis exposure during the process of methadone dose titration (referred to herein as methadone induction) and the early stabilization phase. The present study sought to examine the trends in cannabis use of individuals during the early phases of MMT initiation in order to test the hypothesis that cannabis use may impact illicit drug use, and subsequently methadone stabilization. Based on findings from experimental models of opiate dependence, it is hypothesized that the use of cannabinoids, via its interaction with the opioid system, may impact opioid signaling in the brain 29–36 . Multiple groups have reported synergy between cannabinoids and opiates when administered concurrently 10, 37–40 . The present study attempted to investigate whether similar evidence of cannabinoid-opioid interactions can be found in the clinical setting among treatment-seeking opiate-dependent individuals. The authors hypothesized that cannabis use may increase during dose titration, and that this elevated use may impact methadone induction and stabilization on MMT. Additionally, associations between pre-treatment history of opiate and cannabis use were examined as they relate to methadone dosing and titration, and illicit drug use during treatment.

2. Materials and Methods

The data presented in this retrospective study were collected from outpatient charts belonging to individuals enrolled in the Narcotic Addiction Rehabilitation Program (NARP) at Thomas Jefferson University in Philadelphia, PA. This urban, publicly funded and university-sponsored clinic offers medication-assisted treatment and intensive outpatient programs for opiate dependence. Criteria for admission to treatment included a minimum of one-year documented history of opiate use, being at least 18 years of age, and a positive opiate urine drug screen (UDS) at admission. Approximately 365 individuals were enrolled in NARP at any given point in time over the past decade. This study was approved by the Institutional Review Board of Thomas Jefferson University.

2.1. Sample Criteria

Criteria for study inclusion were a minimum of nine months in treatment and the presence of the following data in the outpatient chart: monthly urinary drug screen results, treatment compliance (daily attendance), and medical intake evaluation information. In order to capture data from the weeks both prior to and immediately following methadone dose stabilization, the time in treatment criteria was established at a minimum of nine months. Charts were sampled from individuals that were enrolled in NARP between December 1, 2005 and July 1, 2009 (approximately 500 in total). The authors collected and analyzed data from all criteria-meeting patients that had at least one cannabis-positive urinary drug screen (UDS) during treatment (n=56). An additional random sample of 35 non-cannabis using individuals from the same enrollment period was used as a comparison group. These charts were pulled using a random number generator and were selected for the study if all inclusion criteria were met.

2.2. Treatment enrollment and structure

As part of the intake process, individuals seeking admission participated in a structured clinical interview and medical evaluation where detailed drug use histories were collected. Individuals were then initiated on methadone maintenance therapy according to federal guidelines, with assigned daily medication times. Dosing was slowly titrated until a blocking dose of methadone was achieved and opiate craving and use were controlled. For the purposes of this project, dose stabilization was defined as a period of eight weeks at a constant dose of methadone. Acquisition of this dose marked the transition from methadone induction to early stabilization phase in patients. Progress was monitored through regular meetings with counselors and medical staff, as well as UDS performed at least once-monthly on a random basis. During the titration period, in which low to moderate-grade opiate withdrawal symptoms were typically experienced, the Clinical Opiate Withdrawal Scale (COWS) (21) was administered by the medical staff as needed to help assess the need for dose increases, and periodically thereafter when dose changes were requested or needed.

2.3. Plan of Analysis

Study data was analyzed as follows using SPSS 16.0 Graduate Pack software. The authors first examined whether a history of pre-treatment cannabis use was associated with proxy measures of opiate addiction and severity (i.e., daily opiate expenditure, number of previous opiate dependence treatment episodes, and cumulative years of opiate use). Next, the patterns and effects of both pre-treatment and in-treatment cannabis use on methadone induction and opiate use were studied. Recent cannabis history (dichotomized as Yes/No) referred to any self-reported history of cannabis use in the month prior to enrollment in treatment. Data on in-treatment cannabis use was recorded from urinalyses conducted during the initial 9 months of MMT enrollment. To test the hypothesis that cannabis use impacts the process of methadone induction, a series of ANOVAs were conducted using either recent cannabis history or cannabis use during methadone induction as independent variables. Because illicit drug use during stabilization could have potentially complicated the dose titration process, the dependent measures included: 1. rates of cannabis and opiate drug use during the methadone induction phase and early stabilization phase, 2. the number of weeks required to complete methadone dose titration (induction), 3. the methadone blocking dose upon stabilization, and 4. medication compliance (attendance for daily methadone administration). Parallel ANCOVAs were conducted for each operationalization of the independent variable, using daily opiate expenditure as a covariate to control for pre-treatment opiate use/severity of dependence. In this manner, it was possible to study how the patterns of cannabis use interact with opiate dependence prior to treatment, and also the possible impact of cannabis use on MMT and opiate use during two critical phases of treatment: methadone induction and early stabilization.

3. Results

3.1. Sample Statistics

Data from a total of 91 individuals were recorded. Due to the limited availability of certain data in the clinical record, some analyses were performed on a smaller subset of cases. Average age at admission was 39 ± 11 years, and ranged from 20-62 years of age. Sixty percent (n=55) of subjects were male. Almost eighty percent (n=70) of subjects were Caucasian, the remainder were African American (n=12, 13.5%), and Hispanic (n=6, 6.7%). The majority of the sample were intravenous drug users (n=61, 67.0%), but oral narcotic (n=44, 50%) and intranasal administration (n=43, 49.4%) was also commonly reported. In the month prior to entering treatment, almost half of the individuals (46.6%) were using multiple substances (benzodiazepines, cannabis, or stimulants) in addition to opiates. (See Table 1 )

Table 1

Variable N Values
Age
Mean 91 39.37
Standard Deviation ± 11.29
Range 20–62
Stable Methadone Blocking Dose
Mean 85 * 111.98mg/day
Standard Deviation ± 60.80
Range 20–330
Weeks to Stabilization
Mean 91 11.01
Standard Deviation ± 9.38
Range 1–56 **
Variable N Percentage
Gender
Female 55 60.4%
Male 36 39.6%
Race/Ethnicity
African America 12 13.5%
Caucasian 70 78.7%
Hispanic 6 6.7%
Other 1 1.1%
Opiate Route of Administration
Intravenous 67.8 %
Oral 91 50%
Intranasal 49.4%
Combination of ≥ 2 of the Above 38.9%
History of Illicit Drug Use: Lifetime + ⋄
Marijuana 91 71.5%
Stimulants 64.8%
Benzodiazepines 70.3%
Combination of ≥ 2 of the Above 80.2%
History of Illicit Drug Use: Recent + ⋄
Marijuana 42% ++
Stimulants 91 39.3%
Benzodiazepines 51.7%
Combination of ≥ 2 of the Above 46.6%

Analysis of stabilization involved examination of two distinct treatment phases during the time period from enrollment through month nine of treatment. “Methadone induction phase” was used to refer to the period of time during which an individual’s methadone dose was titrated to a stable blocking dose, while “early stabilization phase” denoted the remainder of the nine month study period following acquisition of a blocking dose. Almost all subjects (n=85) were able to achieve a stable blocking dose of methadone (x=112.42±61.03mg) within an average of 11±9.4 weeks. For a variety of reasons, 6 of the 91 subjects were unable to achieve a stable blocking dose during the study timeframe. In the nine months following enrollment, subjects on average missed daily dosing 18.5±24.6 times. Stabilization on MMT, operationalized in this study as 8 weeks on a stable methadone dose, was associated with a significant decrease in the percentage of opiate-positive UDS. A within-subjects comparison of opiate use between methadone induction and early stabilization treatment phases revealed an approximately 50% decrease in the mean percentage of opiate-positive UDS [57.4 to 28.2%, t(82)=6.58, p

3.2. Cannabis use: pre-treatment history and use during methadone induction

Data on self-reported pre-treatment cannabis use was gathered from information recorded during the intake medical evaluation. Within the sample, 28.6% (n=26) individuals reported no history of cannabis use, past or recent (i.e., 30 days prior to enrollment). While 31.9% (n=29) of subjects reported past but no recent cannabis use, almost 40% (n=36) reported past and recent cannabis use. During the nine-month study period, 38.5% were cannabis-abstinent (n=35), while 61.5% (n=56) used cannabis at least once. Not surprisingly, when examining the relationship between pre-admission reports of marijuana use and urine results in the first nine-months of treatment, a strong positive effect was observed (r=0.736, p< 0.001). Evidence of ongoing cannabis use was also examined. Based on monthly UDS results during the first nine months of enrollment, cannabis using individuals were classified as occasional users (1-3 months cannabis-positive, n=27) or frequent users (> 3 months cannabis, n = 29). There was a positive correlation between rates of cannabis use and illicit benzodiazepine use during the initial nine months in treatment: r(91)=0.374, p

Table 2

Patterns of Cannabis Use in the Sample

Cannabis use history n Percentage Frequency of cannabis use in-treatment n Percentage
No Lifetime/No Recent 26 28.6% Abstinent 35 38.5%
Yes Lifetime/No Recent 29 31.9% Occasional 27 29.7%
Yes Lifetime/Yes Recent 36 39.6% Heavy 29 31.9%
Rate of Cannabis Use + Change & SD Statistics
Individuals with lifetime cannabis history
Methadone induction phase 48.8% −11.2±42.9% t(56) = 1.96
Early stabilization phase 37.6% p = 0.055
Individuals with recent cannabis history
Methadone induction phase 74.6% −25.3±45.2% t(33) = 3.27
Early stabilization phase 49.3% p = 0.003
Correlations r value p value
Cannabis use: prior to & during treatment r(80)=0.736 p
Pre-stabilization cannabis use & unfavorable discharge status r(80)=0.069 p=0.567
Pre-stabilization cannabis use & MMT attendance r(65)=0.151 p=0.230
Rate of cannabis use & opiate use (during treatment) r(82)=0.018 p=0.873
Cannabis use during treatment & age r(91)= −0.210 p=0.047
Rate of cannabis use & daily opiate expenditure r(49)= −0.313 p=0.028

3.3. Role of drug use history and proxy measures of addiction severity in cannabis use

Opiate addiction history and severity of dependence at treatment intake was assessed via several proxy measures reported in the intake medical evaluation: years of opiate abuse, number of previous treatment episodes for opiate dependence (excluding Narcotics Anonymous), and daily opiate expenditure. The following analyses included data from all individuals in the sample providing complete information. In addition to having 2.42 ±1.70 (n=51) previous episodes in treatment, individuals had an average of a 15.68 ±10.71 (n=71) year history of opiate abuse upon presentation to the clinic for treatment, and spent an average of $108.00 ±65.00 (n=49) per day on opiates.

To test the hypothesis that cannabis users may actually use less opiates and possibly constitute a unique subset of opiate-dependent individuals, analyses were performed to examine whether pre-treatment cannabis use was associated with any of the proxy measures of opiate addiction severity. Neither years of opiate abuse nor number of previous treatment episodes differed based on history of recent cannabis use [t(71)=0.026, p=0.796 and t(51)=01.360, p=0.178, respectively]. Interestingly, decreased daily opiate expenditure spent on opiates (i.e. pre-treatment opiate use) was associated with a history of recent cannabis use (Mn=$85.00) when compared to those with no recent cannabis use (Mn=$126.25) [t(49)= 2.373, p=0.022]. ( Figure 1 ) These data indicate that cannabis users appear to spend less per day on the purchase of opiates, and may in turn use a lesser amount of opiates daily.

Subsequent analyses included daily opiate expenditure as a covariate in an attempt to control for variation based upon the amount of pre-treatment opiate use. For all subsequent analyses involving daily opiate expenditure (Mn=$107.65±64.56, n=51), imputation with the mean was performed in cases where this information was missing in patient records. To verify that this procedure did not alter the findings of associations with cannabis use, analyses were repeated using the imputed form of daily opiate expenditure (Mn=$107.65±48.12, n=91). A recent history of cannabis use was again associated with decreased daily opiate expenditure [t(89)=2.368, p=0.020]. ( Figure 1 )

3.4. Effect of cannabis use history on methadone induction

ANOVAs were next used to determine whether a history of cannabis use was associated with changes in rates of drug use during the methadone induction phase and early stabilization phase. Analyses first examined whether past cannabis use was associated with increased cannabis use during methadone induction or early stabilization. There was a significant interaction between treatment phase (methadone induction/early stabilization) and cannabis history on rates of cannabis use during treatment [F(1,80)=14.669, p

Analogous mixed-effects analyses crossing recent cannabis history and early MMT treatment phase (methadone induction/early stabilization) were then conducted employing opiate use as the outcome measure. Supportive of treatment efficacy, a main effect of treatment phase demonstrated that the rates of opiate use declined significantly between the induction and early stabilization phase [F(1,81)=141.338, p

In addition to rates of illicit drug use, the analyses also sought to uncover any potential associations between recent cannabis history and several other measures of stabilization difficulty such as time required to complete methadone dose titration (methadone induction), methadone blocking dose upon stabilization, and medication compliance. Cannabis use prior to treatment was not associated with any changes in the time required to complete methadone induction [t(83)=0.875, p=0.384], or the eventual stabilization dose [t(82)=0.219, p=0.827]. Increases in the total daily medication absences over the initial nine months of the MMT program were also not associated with recent cannabis use [t(68)=0.982, p=0.330].

3.5. Effects of cannabis use during methadone induction

To address potential detrimental effects of cannabis on MMT stabilization, in-treatment cannabis use was examined in several ways to reveal any potential associations with illicit opiate use or measures of stabilization difficulty. Rates of cannabis-positive UDS and opiate-positive UDS did not correlate during either phase of treatment [methadone induction: r(82)=0.104, p=0.332; early stabilization: r(82)=0.038, p=0.734]. Dichotomized (yes/no) in-treatment cannabis use was not associated with change in opiate use during any treatment phase [F(1, 81)=0.999, p=0.321], and ANCOVA controlling for daily opiate expenditure yielded similar results [F(1,80)=0.087, p=0.769]. Cannabis use during methadone induction was not associated with any significant differences in time required for dose titration [t(80)=0.150, p=0.881], blocking dose [t(79)=0.847, p=0.399], or medication compliance [t(63)=1.212, p=0.230]. Furthermore, cannabis use did not significantly affect premature discharge status [X 2 (1)=3.009, p=0.222]. ( Figure 3 )

3.5.1. Preliminary data on cannabis and opiate withdrawal severity

To examine whether cannabis intake during MMT treatment could be related to opiate withdrawal symptoms, associations between cannabis use and severity of opiate withdrawal were investigated using data from the Clinical Opiate Withdrawal Scale (COWS), an index designed to serve as an objective measure of opiate withdrawal 41 . Effective in January of 2007, the COWS was added as an additional clinical assessment tool, administered in response to patients’ complaints of opiate withdrawal symptoms and craving. While induction COWS data were only available for a subset of the sample (n=40), when subjects were categorized as either low (n=29) or moderate withdrawal severity (n=11), a significant relationship with cannabis use was observed. Specifically, a 2×2 contingency table revealed that cannabis users preferentially fell into the low-severity withdrawal category while those that abstained from cannabis were more often in the moderate-level withdrawal category [X 2 (1)=7.54, p=0.006]. When further characterizing in-treatment cannabis users as abstinent, occasional or frequent use, 3×2 chi-square analysis demonstrated an inverse association between frequency of cannabis use and opiate withdrawal severity [X 2 (2)=6.71, p=0.035]. Further prospective studies are needed to assess this effect in a more controlled manner. ( Figure 3 )

4. Discussion

4.1. Association between cannabis and opiate use in treatment-seeking individuals

Cannabis-using opiate-dependent individuals presenting for MMT reported significantly less daily expenditure on acquisition of opiates. When considering this observation, note that the proxy measures of opiate addiction severity used in this study were selected based on available information in the patient record, and therefore lack the control of prospective assessment of addiction severity. Nonetheless, these findings highlighted a potentially interesting trend associated with concurrent cannabis and opiate use. A possible explanation for this finding may be that cannabis users in this study were less “severe” opiate addicts, or required lesser opiate intake. However, cannabis-using individuals did not differ from cannabis-abstinent individuals based on other proxy measures of severity of opiate dependence that included one’s cumulative years of opiate use and number of previous treatment episodes. Interaction between the molecular targets of opiates and cannabis in the brain may underlie the observation that those concurrently using both cannabis and opiates actually purchase and use less opiates 37, 39, 40, 42, 43 .

While cannabis users appeared to purchase (and presumably used) less opiates than cannabis-abstinent individuals at the time of program enrollment, rates of persistent illicit opiate use during MMT were not found to differ based on cannabis use. Data from this sample demonstrated no cannabis effects on dose titration, induction time, attendance, or unfavorable early discharge. These findings were in agreement with several previous studies concerning cannabis effects on MMT. In a large retrospective analysis of MMT, cannabis use was not associated with treatment retention, opiate/cocaine use, or any measure of treatment outcome 27 . Similarly, no risk or harm to treatment outcome was associated with cannabis use in additional studies of patients on MMT 15, 44, 45 or buprenorphine 25 . Intermittent cannabis users were found to have improved retention and outcomes in antagonist treatment for opiate dependence 16, 28 . In a study examining post-discharge cannabis use following inpatient treatment, using cannabis was associated with relapse to alcohol and cocaine use, but not with relapse to heroin use 46 .However, negative aspects of cannabis use on treatment for opiate dependence have also been reported. Several groups have demonstrated the association of cannabis use with likelihood of poly-drug use 24, 45 and increased risk for heroin relapse 47 . Overall, studies of cannabis use on heroin intake in clinical populations did not support this trend 16, 26–28, 48 .

4.2. Decreased cannabis use upon completion of methadone induction

Interestingly, upon acquisition of a blocking dose of methadone, there was a concurrent decline in cannabis use in the sample as a whole. Although this could possibly have been a direct effect of methadone, methadone dose was not found to be related to cannabis use rates in our sample. In a study comparing detection of substance use over the first year of heroin-maintenance and MMT, similar but less dramatic decreases in cannabis use were observed among methadone-maintained patients. Both heroin and methadone-maintenance resulted in dramatic reduction of illicit opiate use despite common cannabis use 49 . Although our group has demonstrated the decline in multiple types of illicit drug use with long-term MMT in the past 11–13 , the present findings were to our knowledge the first to specifically examine patterns of cannabis use over time during the critical early stages of MMT.

4.2.1. Potential role for cannabis in reduction of opiate withdrawal

The transition from methadone induction to the early stabilization phase of treatment was expected to be accompanied by a decline in opiate craving and withdrawal 1, 9, 10 . Decreases in the rate of cannabis-positive UDS were also observed during this transition, but it is unknown if this decline in cannabis use was related to diminished withdrawal symptoms, as clinical data regarding this phenomenon is limited. One group found that cannabis use was positively associated with lower plasma methadone concentrations, and while cannabis use could have caused metabolic changes that resulted in this finding, it is also possible that “cannabis use may be a compensatory response to opioid withdrawal symptoms in some individuals with more rapid methadone clearance” 50 . In a study on the efficacy of non-opioid drugs for opiate withdrawal, cannabis was reported by patients to be less effective in reduction of symptoms than benzodiazepines, but more effective than cocaine, alcohol and nicotine 18 . There was a positive correlation between rates of cannabis and benzodiazepine use (based on monthly UDS results) in our sample. Further studies will be required to determine how the effects of benzodiazepine may interact with those of cannabis during methadone induction. However, numerous studies of cannabinoid-opioid interactions in animal models of opiate addiction have provided strong evidence for an ameliorative effect of cannabinoids on opiate withdrawal symptoms 30, 34, 51–54 .

The current study used objective measures (COWS and UDS) to examine this relationship in a pilot data set, where increased cannabis use was found to be associated with lower severity of withdrawal in a subset of the sample with available chart data. These results suggested a potential role for cannabis in the reduction of withdrawal severity during methadone induction, however prospective studies will be required to verify these initial findings.

4.3. Limitations and Prospective Studies

Due to the study design, the information gleaned from this retrospective chart analysis was descriptive in nature and interpretation of its findings must be cautiously considered. The challenges presented by the nature of the data included lessened control over inherent confounds in studies of drug use, and missing or limited chart information reduced the sample size for certain analyses. While UDS data provided an objective view of drug use during treatment, tests were required to be administered only once a month. Weekly quantitative drug screens detailing the specific amount of drug use would have been optimal and should be employed in prospective studies. Additionally, the study data on substance use history prior to treatment was limited to self-reported information present in the medical record. Optimally, more detailed objective analysis of pre-treatment substance use would be undertaken.

More extensive studies will be necessary to elucidate whether cannabis does indeed alleviate withdrawal signs during stabilization and whether it may be associated with treatment prognosis. Additionally, many individuals within this sample concurrently used cannabis and illicit benzodiazepines during MMT. Unfortunately, the nature of the data made it impossible to control for benzodiazepine use. Therefore, carefully-controlled studies will be essential to determine whether concurrent use of cannabis and benzodiazepines during methadone induction results in additive, subtractive, or synergistic decreases in opiate withdrawal signs. Additionally, further studies will be necessary to examine the specific patterns and effects of cannabis use in individuals on other types of therapeutic interventions for opiate dependence, such as antagonist or buprenorphine treatment.

Although the retrospective data presents limitations, this approach offered the opportunity to uncover patterns of cannabinoid-opiate associations in the existing data, so that this information may be used to guide the design of future prospective studies. Poly-drug abuse is extremely common among opiate-dependent individuals, and use of multiple substances often persists during substance abuse treatment. By maintaining a particular focus on the stabilization process during initiation of MMT, it was possible to examine whether cannabis use affected progress during initiation on to MMT, a critical time point in the treatment for opiate dependence.

Acknowledgments

The authors acknowledge the support of grants: DA02019 (Dr. Van Bockstaele) and DA023755 (Dr. Sterling) from the National Institutes of Health, Bethesda, MD.

The authors would like to thank the staff at the Narcotic Addiction Rehabilitation Program for their assistance with the data collection process.

Footnotes

Declaration of Interest:

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this paper.

References

1. Dole VP, Nyswander M. A Medical Treatment for Diacetylmorphine (Heroin) Addiction. a Clinical Trial with Methadone Hydrochloride. Jama. 1965 Aug 23; 193 :646–650. [PubMed] [Google Scholar]

2. Nichols AW, Salwen MB, Torrens PR. Outpatient induction to methadone maintenance treatment for heroin addiction. Arch Intern Med. 1971 May; 127 (5):903–909. [PubMed] [Google Scholar]

3. Leavitt SB. Methadone Dosing & Safety in the Treatment of Opiate Addiction. Addiction Treatment Forum. 2003 [Google Scholar]

4. Nicholls L, Bragaw L, Ruetsch C. Opioid dependence treatment and guidelines. J Manag Care Pharm. 2010 Feb; 16 (1 Suppl B):S14–21. [PubMed] [Google Scholar]

5. Bell J, Zador D. A risk-benefit analysis of methadone maintenance treatment. Drug Saf. 2000 Mar; 22 (3):179–190. [PubMed] [Google Scholar]

6. Langrod J, Lowinson J, Ruiz P. Methadone treatment and physical complaints: a clinical analysis. Int J Addict. 1981 Jul; 16 (5):947–952. [PubMed] [Google Scholar]

7. Dyer KR, White JM. Patterns of symptom complaints in methadone maintenance patients. Addiction. 1997 Nov; 92 (11):1445–1455. [PubMed] [Google Scholar]

8. Elkader AK, Brands B, Callaghan R, Sproule BA. Exploring the relationship between perceived inter-dose opioid withdrawal and patient characteristics in methadone maintenance treatment. Drug Alcohol Depend. 2009 Dec 1; 105 (3):209–214. [PubMed] [Google Scholar]

9. Belding MA, Iguchi MY, Lamb RJ, Lakin M, Terry R. Coping strategies and continued drug use among methadone maintenance patients. Addict Behav. 1996 May-Jun; 21 (3):389–401. [PubMed] [Google Scholar]

10. Barta WD, Kurth ME, Stein MD, Tennen H, Kiene SM. Craving and self-efficacy in the first five weeks of methadone maintenance therapy: a daily process study. J Stud Alcohol Drugs. 2009 Sep; 70 (5):735–740. [PubMed] [Google Scholar]

11. Gottheil E, Sterling RC, Weinstein SP. Diminished illicit drug use as a consequence of long-term methadone maintenance. J Addict Dis. 1993; 12 (4):45–57. [PubMed] [Google Scholar]

12. Weinstein SP, Gottheil E, Sterling RC, DeMaria PA., Jr Long-term methadone maintenance treatment: some clinical examples. J Subst Abuse Treat. 1993 May-Jun; 10 (3):277–281. [PubMed] [Google Scholar]

13. DeMaria PA, Jr, Sterling R, Weinstein SP. The effect of stimulant and sedative use on treatment outcome of patients admitted to methadone maintenance treatment. Am J Addict. 2000 Spring; 9 (2):145–153. [PubMed] [Google Scholar]

14. Nurco DN, Kinlock TW, Hanlon TE, Ball JC. Nonnarcotic drug use over an addiction career–a study of heroin addicts in Baltimore and New York City. Compr Psychiatry. 1988 Sep-Oct; 29 (5):450–459. [PubMed] [Google Scholar]

15. Nirenberg TD, Liepman MR, Cellucci T, Swift RM, Sirota AD. Cannabis versus other illicit drug use among methadone maintenance patients. Psychology of Addictive Behaviors. 1996; 10 :222–227. [Google Scholar]

16. Church SH, Rothenberg JL, Sullivan MA, Bornstein G, Nunes EV. Concurrent substance use and outcome in combined behavioral and naltrexone therapy for opiate dependence. Am J Drug Alcohol Abuse. 2001 Aug; 27 (3):441–452. [PubMed] [Google Scholar]

17. San L, Torrens M, Castillo C, Porta M, de la Torre R. Consumption of buprenorphine and other drugs among heroin addicts under ambulatory treatment: results from cross-sectional studies in 1988 and 1990. Addiction. 1993 Oct; 88 (10):1341–1349. [PubMed] [Google Scholar]

18. Hermann D, Klages E, Welzel H, Mann K, Croissant B. Low efficacy of non-opioid drugs in opioid withdrawal symptoms. Addict Biol. 2005 Jun; 10 (2):165–169. [PubMed] [Google Scholar]

19. Degenhardt L, Hall W, Lynskey M. The relationship between cannabis use and other substance use in the general population. Drug Alcohol Depend. 2001 Nov 1; 64 (3):319–327. [PubMed] [Google Scholar]

20. Frauger E, Vigneau C, Orleans V, et al. Consumption of cannabis among subjects with history of abuse/dependence or under an opiate maintenance therapy: OPPIDUM data in 2006 and main trends since 2004. Therapie. 2008 Mar-Apr; 63 (2):119–127. [PubMed] [Google Scholar]

21. Maremmani I, Stefania C, Pacini M, et al. Differential substance abuse patterns distribute according to gender in heroin addicts. J Psychoactive Drugs. 2010 Mar; 42 (1):89–95. [PubMed] [Google Scholar]

22. Saxon AJ, Calsyn DA, Blaes PA, Haver VM, Greenberg DM. Marijuana use by methadone maintenance patients. NIDA Res Monogr. 1990; 105 :306–307. [PubMed] [Google Scholar]

23. Ellner M. Marijuana use by heroin abusers as a factor in program retention. J Consult Clin Psychol. 1977 Aug; 45 (4):709–710. [PubMed] [Google Scholar]

24. DuPont RL, Saylor KE. Marijuana and benzodiazepines in patients receiving methadone treatment. Jama. 1989 Jun 16; 261 (23):3409. [PubMed] [Google Scholar]

25. Budney AJ, Bickel WK, Amass L. Marijuana use and treatment outcome among opioid-dependent patients. Addiction. 1998 Apr; 93 (4):493–503. [PubMed] [Google Scholar]

26. Nixon LN. Cannabis use and treatment outcome in methadone maintenance. Addiction. 2003 Sep; 98 (9):1321–1322. author reply 1322–1323. [PubMed] [Google Scholar]

27. Epstein DH, Preston KL. Does cannabis use predict poor outcome for heroin-dependent patients on maintenance treatment? Past findings and more evidence against. Addiction. 2003 Mar; 98 (3):269–279. [PMC free article] [PubMed] [Google Scholar]

28. Raby WN, Carpenter KM, Rothenberg J, et al. Intermittent marijuana use is associated with improved retention in naltrexone treatment for opiate-dependence. Am J Addict. 2009 Jul-Aug; 18 (4):301–308. [PMC free article] [PubMed] [Google Scholar]

29. De Vries TJ, Homberg JR, Binnekade R, Raaso H, Schoffelmeer AN. Cannabinoid modulation of the reinforcing and motivational properties of heroin and heroin-associated cues in rats. Psychopharmacology (Berl) 2003 Jul; 168 (1–2):164–169. [PubMed] [Google Scholar]

30. Lichtman AH, Sheikh SM, Loh HH, Martin BR. Opioid and cannabinoid modulation of precipitated withdrawal in delta(9)-tetrahydrocannabinol and morphine-dependent mice. J Pharmacol Exp Ther. 2001 Sep; 298 (3):1007–1014. [PubMed] [Google Scholar]

31. Navarro M, Carrera MR, Fratta W, et al. Functional interaction between opioid and cannabinoid receptors in drug self-administration. J Neurosci. 2001 Jul 15; 21 (14):5344–5350. [PMC free article] [PubMed] [Google Scholar]

32. Cichewicz DL. Synergistic interactions between cannabinoid and opioid analgesics. Life Sci. 2004 Jan 30; 74 (11):1317–1324. [PubMed] [Google Scholar]

33. Manzanares J, Corchero J, Romero J, Fernandez-Ruiz JJ, Ramos JA, Fuentes JA. Pharmacological and biochemical interactions between opioids and cannabinoids. Trends Pharmacol Sci. 1999 Jul; 20 (7):287–294. [PubMed] [Google Scholar]

34. Valverde O, Noble F, Beslot F, Dauge V, Fournie-Zaluski MC, Roques BP. Delta9-tetrahydrocannabinol releases and facilitates the effects of endogenous enkephalins: reduction in morphine withdrawal syndrome without change in rewarding effect. Eur J Neurosci. 2001 May; 13 (9):1816–1824. [PubMed] [Google Scholar]

35. Fattore L, Vigano D, Fadda P, Rubino T, Fratta W, Parolaro D. Bidirectional regulation of mu-opioid and CB1-cannabinoid receptor in rats self-administering heroin or WIN 55,212-2. Eur J Neurosci. 2007 Apr; 25 (7):2191–2200. [PubMed] [Google Scholar]

36. Fattore L, Spano S, Cossu G, Deiana S, Fadda P, Fratta W. Cannabinoid CB(1) antagonist SR 141716A attenuates reinstatement of heroin self-administration in heroin-abstinent rats. Neuropharmacology. 2005 Jun; 48 (8):1097–1104. [PubMed] [Google Scholar]

37. Tham SM, Angus JA, Tudor EM, Wright CE. Synergistic and additive interactions of the cannabinoid agonist CP55,940 with mu opioid receptor and alpha2-adrenoceptor agonists in acute pain models in mice. Br J Pharmacol. 2005 Mar; 144 (6):875–884. [PMC free article] [PubMed] [Google Scholar]

38. Cox ML, Haller VL, Welch SP. Synergy between delta9-tetrahydrocannabinol and morphine in the arthritic rat. Eur J Pharmacol. 2007 Jul 12; 567 (1–2):125–130. [PubMed] [Google Scholar]

39. Roberts JD, Gennings C, Shih M. Synergistic affective analgesic interaction between delta-9-tetrahydrocannabinol and morphine. Eur J Pharmacol. 2006 Jan 13; 530 (1–2):54–58. [PubMed] [Google Scholar]

40. Cichewicz DL, McCarthy EA. Antinociceptive synergy between delta(9)-tetrahydrocannabinol and opioids after oral administration. J Pharmacol Exp Ther. 2003 Mar; 304 (3):1010–1015. [PubMed] [Google Scholar]

41. Wesson DR, Ling W. The Clinical Opiate Withdrawal Scale (COWS) J Psychoactive Drugs. 2003 Apr-Jun; 35 (2):253–259. [PubMed] [Google Scholar]

42. Parolaro D, Rubino T, Vigano D, Massi P, Guidali C, Realini N. Cellular mechanisms underlying the interaction between cannabinoid and opioid system. Curr Drug Targets. 2010; 11 (4):393–405. [PubMed] [Google Scholar]

43. Vigano D, Rubino T, Parolaro D. Molecular and cellular basis of cannabinoid and opioid interactions. Pharmacol Biochem Behav. 2005 Jun; 81 (2):360–368. [PubMed] [Google Scholar]

44. Saxon AJ, Calsyn DA, Greenberg D, Blaes P, Haver VM, Stanton V. Urine screening for marijuana among methadone-maintained patients. American Journal on Addictions. 1993; 2 :207–211. [Google Scholar]

45. Weizman T, Gelkopf M, Melamed Y, Adelson M, Bleich A. Cannabis abuse is not a risk factor for treatment outcome in methadone maintenance treatment: a 1-year prospective study in an Israeli clinic. Aust N Z J Psychiatry. 2004 Jan-Feb; 38 (1–2):42–46. [PubMed] [Google Scholar]

46. Aharonovich E, Liu X, Samet S, Nunes E, Waxman R, Hasin D. Postdischarge cannabis use and its relationship to cocaine, alcohol, and heroin use: a prospective study. Am J Psychiatry. 2005 Aug; 162 (8):1507–1514. [PubMed] [Google Scholar]

47. Wasserman DA, Weinstein MG, Havassy BE, Hall SM. Factors associated with lapses to heroin use during methadone maintenance. Drug Alcohol Depend. 1998 Nov 1; 52 (3):183–192. [PubMed] [Google Scholar]

48. Nava F, Manzato E, Lucchini A. Chronic cannabis use does not affect the normalization of hypothalamic-pituitary-adrenal (HPA) axis induced by methadone in heroin addicts. Prog Neuropsychopharmacol Biol Psychiatry. 2007 Jun 30; 31 (5):1089–1094. [PubMed] [Google Scholar]

49. Musshoff F, Trafkowski J, Lichtermann D, Madea B. Comparison of urine results concerning co-consumption of illicit heroin and other drugs in heroin and methadone maintenance programs. Int J Legal Med. 2009 Aug 12; [PubMed] [Google Scholar]

50. Hallinan R, Crettol S, Agho K, et al. Cannabis and benzodiazepines as determinants of methadone trough plasma concentration variability in maintenance treatment: a transnational study. Eur J Clin Pharmacol. 2009 Nov; 65 (11):1113–1120. [PubMed] [Google Scholar]

51. Frederickson RC, Hewes CR, Aiken JW. Correlation between the in vivo and an in vitro expression of opiate withdrawal precipitated by naloxone: their antagonism by l-(-)-delta9-tetrahydrocannabinol. J Pharmacol Exp Ther. 1976 Nov; 199 (2):375–384. [PubMed] [Google Scholar]

52. Yamaguchi T, Hagiwara Y, Tanaka H, et al. Endogenous cannabinoid, 2-arachidonoylglycerol, attenuates naloxone-precipitated withdrawal signs in morphine-dependent mice. Brain Res. 2001 Aug 3; 909 (1–2):121–126. [PubMed] [Google Scholar]

53. Cichewicz DL, Welch SP. Modulation of oral morphine antinociceptive tolerance and naloxone-precipitated withdrawal signs by oral Delta 9-tetrahydrocannabinol. J Pharmacol Exp Ther. 2003 Jun; 305 (3):812–817. [PubMed] [Google Scholar]

54. Vela G, Ruiz-Gayo M, Fuentes JA. Anandamide decreases naloxone-precipitated withdrawal signs in mice chronically treated with morphine. Neuropharmacology. 1995 Jun; 34 (6):665–668. [PubMed] [Google Scholar]

Association between cannabis use and methadone maintenance treatment outcomes: an investigation into sex differences

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Associated Data

The dataset for the current study is available from the corresponding author upon request.

Abstract

Background

Cannabis will soon become legalized in Canada, and it is currently unclear how this will impact public health. Methadone maintenance treatment (MMT) is the most common pharmacological treatment for opioid use disorder (OUD), and despite its documented effectiveness, a large number of patients respond poorly and experience relapse to illicit opioids. Some studies implicate cannabis use as a risk factor for poor MMT response. Although it is well established that substance-use behaviors differ by sex, few of these studies have considered sex as a potential moderator. The current study aims to investigate sex differences in the association between cannabis use and illicit opioid use in a cohort of MMT patients.

Methods

This multicentre study recruited participants on MMT for OUD from Canadian Addiction Treatment Centre sites in Ontario, Canada. Sex differences in the association between any cannabis use and illicit opioid use were investigated using multivariable logistic regression. A secondary analysis was conducted to investigate the association with heaviness of cannabis use.

Results

The study included 414 men and 363 women with OUD receiving MMT. Cannabis use was significantly associated with illicit opioid use in women only (OR = 1.82, 95% CI 1.18, 2.82, p = 0.007). Heaviness of cannabis use was not associated with illicit opioid use in men or women.

Conclusions

This is the largest study to date examining the association between cannabis use and illicit opioid use. Cannabis use may be a sex-specific predictor of poor response to MMT, such that women are more likely to use illicit opioids if they also use cannabis during treatment. Women may show improved treatment outcomes if cannabis use is addressed during MMT.

Background

Canada is currently developing legislation for the legalization of cannabis [1]. The rationale is that legalization would have social and economic advantages by generating revenue and deterring such crimes as illegal drug dealing [2]. Prohibition has been ineffective, with data suggesting that this policy option has created more societal costs by way of excessive incarceration, largely involving already marginalized individuals [3], and no evidence to suggest that these criminal penalties have any substantial effect on public health [4].

Colorado, USA, has recently legalized cannabis, and while it remains premature to assess the public health impact of this policy, data show that the commercialization of medical marijuana in 2009 led to a 20% increase in college age (18–25 years) monthly marijuana use and a 36% increase in adult (26+ years) monthly marijuana use in the following 3 years [5]. Legalizing cannabis will almost certainly increase its availability and accessibility; plausible mechanisms for increasing recreational use include reduced prices, ease of access, criminal penalties no longer acting as a deterrent, and increased social acceptability [6]. It is reasonable to expect that Canada will observe a similar increase in the prevalence of cannabis use, though its public health impact remains uncertain.

Despite the commonly held perception that cannabis is relatively harmless [7], its use has been linked to adverse consequences such as cognitive impairment, lower life satisfaction, respiratory problems, and increased risk of developing psychotic episodes and disorders [8]. Those with a history of psychiatric or substance-use disorders can experience worsened symptoms from cannabis use [1]. Cannabis users are also at heightened risk for developing other substance-use disorders [9]. However, the current system of criminalization is similarly associated with individual and public risks. For example, individuals with a criminal record from minor possession charges often experience considerable difficulties in finding employment or housing leading to further social and health risks [1]. Public costs of criminalization are also substantial, with an estimated $2.3 billion spent annually on enforcement and prosecution [1].

While public health risks of cannabis legalization may by and large be minimal, certain vulnerable populations are more susceptible to the deleterious effects of its use. One such population are those with substance-use disorders. North America is currently in the midst of an opioid crisis [10], in which we are witnessing a dramatic increase in non-medical use of opioids and subsequently the incidence of opioid use disorder (OUD). While opioid abuse is associated with serious adverse outcomes, it has been shown that the development of addiction is a major driver in the increase in opioid-related morbidity and mortality [11], indicating the extent to which OUD negatively impacts public health.

Because of the ongoing opioid epidemic in Canada, we must remain mindful of how increasing accessibility of cannabis will impact this population, in particular. Currently, the most commonly prescribed treatment for OUD is methadone maintenance treatment (MMT), an opioid substitution therapy [12]. MMT has proven to be effective in retaining patients in treatment and reducing opioid use and mortality [13], and this effectiveness has led to a steep increase in patients on MMT. In Ontario, Canada, the number of patients receiving MMT has nearly doubled since 2010 [12]. Despite its effectiveness, a significant number of patients respond poorly to treatment and experience relapse [14]. Illicit opioid use in combination with MMT is of immense concern, as it is a substantial risk factor for overdose and death [15].

Recent studies point to a changing landscape of OUD and those in treatment, one that includes a higher percentage of women, older aged patients, and more individuals abusing prescription opioids rather than heroin [16]. These sociodemographic changes warrant a re-evaluation of risk factors associated with poor MMT outcomes.

Compared to the general population, patients on MMT show a higher prevalence of cannabis use [16], and because of its documented association with polysubstance use [9, 17], psychiatric disorders [18], and overall worse quality of life [19], represents a potential risk factor for poor MMT outcomes. Several studies have investigated the influence of cannabis use on MMT outcomes in humans, though the results are mixed. Some studies have indicated cannabis use is associated with poorer treatment outcomes [20–22] while others looking at illicit opioid use found no significant association [23–26]. Although this is the case, confidence in these diverging results is reduced by methodological limitations such as small sample size and subjective outcome measures, making further investigations merited.

Furthermore, few studies have considered sex as a potential moderator. It is well established that substance-use behaviors differ by sex and different social and biological factors contribute to the development of substance-use disorders between men and women [27]. Although a higher proportion of men use cannabis, women who use cannabis are more likely to experience adverse outcomes such as development of cannabis use disorder, and may also be more likely to show negative outcomes from cannabis in other domains such as more severe cannabis withdrawal symptoms and [28] and worse mental health and social functioning [29]. A large survey of cannabis users, for example, found that a larger proportion of men use cannabis for recreational purposes while more women reported using it for purposes of self-medication [30]. Thus, motivational processes for drug use may differ between men and women.

The objective for this study is to investigate sex differences in the association between cannabis use and illicit opioid use during methadone maintenance treatment. We will build on previous research by including a large, representative sample of MMT patients to ensure adequate power and generalizability of findings. Our secondary objective is to determine whether heaviness of cannabis use is associated with illicit opioid use among male and female cannabis users.

Methods

Participants and procedure

Data were collected as part of the Genetics of Opioid Addiction (GENOA) program, an ongoing prospective cohort study conducted in collaboration with the Population Genomics Program at McMaster University, and the Canadian Addiction Treatment Centre (CATC) [31]. We recruited participants from 16 CATC sites across Ontario, Canada, from 2013 to 2016. Patients were eligible for participation if they were ≥18 years old, on methadone maintenance treatment for OUD, and able to provide informed written consent. Individuals were excluded if they did not speak English, were on an opioid substitution therapy other than methadone, or refused to provide blood or urine samples (Fig. 1 ). If individuals were deemed eligible for participation, they were provided with a written consent form to read and sign. Eligible participants provided informed written consent, upon which they underwent a face-to-face interview administered by trained research staff. Participants were compensated with a 5$ coffee shop gift card. This study was approved by the Hamilton Integrated Research Ethics Board (HIREB; Study ID 11-056).

Flow diagram for eligibility and screening of participants

Data collection

The study participants provided sociodemographic and clinical information during the face-to-face interview. Participants were asked to report their biological sex, and all participants reported either male or female. We also collected information regarding current methadone maintenance treatment, methadone dose, duration of current treatment, and information about any past treatments for opioid use disorder.

The Maudsley Addiction Profile (MAP) [32] was administered to retrieve information about substance-use, health risk behaviors, physical and psychological health, and personal and social functioning in the past 30 days. Substance-use data included information on number of days used in the past 30, typical dose used, and route of administration. We also used the physical and psychological health sections of the MAP to compare general health and well-being among participants. These sections comprised of eight questions each and were scored using a Likert scale ranging from 0 to 4 (never-always) to produce a maximum score of 40 per section.

All study data were collected and managed by trained researchers using REDCap electronic data capture tools [33].

Drug use measurements

In addition to self-reported use of drugs using the MAP, all study participants underwent routine weekly or biweekly urine toxicology screens at the clinical sites part of routine clinical care as per CATC management protocol.

Cannabis use

Cannabis use, the primary predictor variable, was measured using urinalysis (cut-off = 50 ng/ml for tetrahydrocannabinol) in the past 3 months. Unfortunately, several clinics discontinued screening for cannabis during urine testing, so only 45.0% of participants had any cannabis urine screens. Therefore, we opted to use self-reported cannabis use from the MAP. To verify the validity of self-reports, we calculated the sensitivity and specificity using participants who had data for both urinalysis and MAP (n = 349). The sensitivity was 79.9% (95% CI 72.7, 85.8) and specificity was 80.0% (95% CI 73.6, 85.4), and thus we deemed self-reported cannabis use an appropriate measure of cannabis use. Sensitivity and specificity values did not significantly differ between men and women, and there were no significant differences between false negatives and false positives.

For the primary regression analysis, we dichotomized cannabis use as any reported use versus no use in the past 30 days for our main predictor variable. We defined heaviness of cannabis use as the product of number of days used in the past 30 days by the typical dose per use (measured in grams) as reported on the MAP.

To quantify cannabis heaviness for participants who reported doses in values other than grams, we utilized the quantification of common “marijuana measurements” as determined and reported by Mariani et al. [34]. Many participants reported values such as “less than one joint” or “couple of puffs of a joint”, and we coded all of these reports as equivalent to one half of a joint (0.33 g). For all other reported quantities, we consulted an addiction expert to estimate the average dose per route of administration based on clinical experience. We used the following quantifications: bowl = 0.25 g and cookie = 2 g.

Illicit opioid use

Illicit opioid use during MMT was the primary outcome which was measured in the 3 months prior to baseline interview using urinalysis, with participants averaging 16 screens per 3 months. The cut-off concentration was 300 ng/mL for opiates and 100 ng/mL for oxycodone. We dichotomized illicit opioid use to reflect no positive screens versus any positive screens during a 3-month duration. This dichotomized variable is a patient-important treatment outcome, as the ultimate goal of MMT is complete abstinence of opioids. Individuals were excluded from analysis if they were currently prescribed any opioid medications, as these compromise the results of urine screens.

Statistical analysis

Descriptive statistics were reported to compare demographic characteristics between men and women. Continuous variables were expressed as mean (standard deviation) and categorical variables were expressed as number (percent). We employed a Student’s t test to test significant differences between continuous variables, and a chi-square test for categorical variables.

A multivariable logistic regression analysis was performed to investigate the association between cannabis and illicit opioid use, including an interaction term, sex by cannabis use, to investigate between-group sex differences. In the analysis, we controlled for age, sex, methadone dose, and treatment duration. Two multivariable logistic regression analyses were also performed for men and women separately to investigate within-group sex differences, controlling for the same covariates.

We conducted a secondary analysis on cannabis users to determine whether it is only the presence of cannabis use that influences treatment outcome or the heaviness of use that drives the association. For this, we replaced the binary cannabis variable with the continuous measurement of cannabis use heaviness. Multivariable logistic regression analyses were employed for male and female users, controlling for the same covariates as in the initial analysis.

Variables were assessed for collinearity using the variance inflation factor (VIF), and variables with VIF > 10 were excluded from the analysis. Adjusted odds ratios (OR), 95% confidence intervals (CI), and p values generated from the regression models are reported. The level of significance for hypothesis testing was set at alpha = 0.05 for the main analysis and alpha = 0.025 for analyses performed separately on men and women.

The general requirement for logistic regression is to have a minimum of 10 events per predictor variable [35]. We included 212 men and 183 women with the event (presence of at least one positive opioid urine screen), and we included four predictor variables therefore the study was adequately powered for analysis. When isolating cannabis users for the secondary analysis, there were 133 men and 91 women with the event, demonstrating adequate power.

All analyses were performed using IBM SPSS version 20. This study is reported in adherence to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [36].

Results

Participants’ characteristics

The total sample comprised of 777 participants including 414 men and 363 women (Fig. 1 ). Ages varied from 18 to 65 years with a mean age of 38.05 years (SD =11.11). The mean daily methadone dose was 75.44 mg (SD = 45.84), and the average duration of current MMT was 48.55 months (SD = 49.53).

Demographic and clinical characteristics comparing men and women are reported in Table 1 . 59.7 of males and 43.5% of females reported using cannabis. Furthermore, men on average used cannabis more often in the past 30 days and at a higher average dose. Women also had significantly worse physical and psychological functioning compared to men. A comparison of cannabis users and non-users can be found in Appendix 1.

Table 1

Demographic and clinical characteristics of men and women on MMT

Variable Men (n = 414) Women (n = 363) p value
Age in years (SD) 39.07 (11.72) 36.88 (10.27) 0.006
Ethnicity (% Caucasian) 347 (84.6%) 288 (80.2%) 0.127
Marital status
Never married (%) 203 (49.0%) 158 (43.5%) 0.079
Married/common law/living with partner (%) 129 (31.2%) 109 (30.0%)
Widowed/separated/divorced (%) 82 (19.8%) 96 (26.4%)
Education
Less than grade 9 (%) 88 (21.4%) 68 (18.9%) 0.008
Grade 9–12 (%) 233 (56.6%) 177 (49.2%)
Trade school, college, university (%) 91 (22.1%) 115 (31.9%)
Employment (% currently working) 175 (42.3%) 98 (27.0%)
Smoking status (% current smoker) 336 (81.2%) 320 (88.2%) 0.007
Age of onset of opioid use in years (SD) 24.90 (8.90) 25.00 (8.11) 0.881
Methadone dose in mg/day (SD) 78.15 (48.36) 72.34 (42.63) 0.079
Current treatment duration in years (SD) 4.10 (4.11) 3.98 (4.15) 0.704
Physical functioning (SD) 14.45 (7.74) 16.79 (7.38)
Psychological functioning (SD) 12.33 (8.82) 15.11 (9.36)
Cannabis use (% cannabis users) 247 (59.7%) 158 (43.5%)
Days cannabis use in last 30 (SD) 11.97 (13.54) 7.44 (12.02)
Average cannabis dose in g/day (SD) 1.48 (1.71) 1.04 (1.03) 0.004

Maximum score for the MAP physical and psychological functioning is 40, with higher scores indicating worse functioning

SD standard deviation

Cannabis use

The primary logistic regression analysis did not yield a significant association between cannabis use and illicit opioid use, after adjusting for age, sex, methadone dose, and treatment duration (OR = 1.16, 95% CI 0.77, 1.75, p = 0.49). The interaction of sex and cannabis use also did not show a significant association with illicit opioid use in the regression model (OR = 1.52, 95% CI 0.84, 2.77, p = 0.17) (Table ​ (Table2 2 ).

Table 2

Multivariable logistic regression analysis on predictors of illicit opioid use

Predictor Odds ratio 95% CI p value
Cannabis use 1.16 0.77–1.75 0.485
Sex*cannabis use 1.52 0.84–2.77 0.169
Age 1.00 0.99–1.02 0.857
Sex 0.83 0.54–1.28 0.399
Methadone dose 0.96* 0.93–0.99 0.023
Duration of treatment 0.91* 0.87–0.95

Age and duration of treatment interpreted as a one-point increase. Methadone dose interpreted as a 10-point increase

OR odds ratio, CI confidence interval

Sex differences

After adjusting for age, methadone dose, and treatment duration, any cannabis use in the past 30 days was significantly associated with illicit opioid use (OR = 1.82, 95% CI 1.18, 2.82, p = 0.007) in women but not in men (OR = 1.11, 95% CI 0.73, 1.69, p = 0.62) (Table 3 ).

Table 3

Multivariable logistic regression analysis on predictors of illicit opioid use by sex

Men Women
Predictor Odds ratio 95% CI p value Odds ratio 95% CI p value
Cannabis use 1.11 0.73–1.69 0.618 1.82* 1.18–2.82 0.007
Age 0.99 0.98–1.01 0.588 1.01 0.99–1.03 0.356
Methadone dose 0.94* 0.90–0.99 0.010 0.99 0.94–1.04 0.634
Duration of treatment 0.92* 0.87–0.97 0.004 0.90* 0. 84–0.95

Age and duration of treatment interpreted as a one-point increase. Methadone dose interpreted as a ten-point increase

OR odds ratio, CI confidence interval

Heaviness of cannabis use

Among cannabis users, the mean number of days of cannabis use in the past 30 days was 18.91 days (SD = 12.46) and the mean daily dose was 1.31 g (SD = 1.50), varying from 0.10 to 14.00 g. The logistic regression analysis showed the heaviness of cannabis use to be unrelated to illicit opioid use in both women (OR = 1.00, 95% CI 0.99, 1.01, p = 0.92) and men (OR = 1.01, 95% CI 1.00–1.01, p = 0.07) (Table 4 ).

Table 4

Multivariable logistic regression analysis on predictors of illicit opioid use among cannabis users by sex

Men Women
Predictor Odds ratio 95% CI p value Odds ratio 95% CI p value
Cannabis use heaviness 1.01 1.00–1.01 0.072 1.00 0.99–1.01 0.917
Age 0.99 0.97–1.02 0.476 1.02 0.98–1.05 0.449
Methadone dose 0.92* 0.87–0.98 0.016 1.02 0.94–1.11 0.662
Duration of treatment 0.91 0.84–0.99 0.037 0.91 0.83–0.99 0.035

Cannabis use heaviness, age, and duration of treatment interpreted as a one-point increase. Methadone dose interpreted as a 10-point increase

Discussion

The current study sought to investigate sex differences in the association between cannabis use and illicit opioid use in a cohort of MMT patients. Our results suggest that cannabis use during treatment may be a predictor of illicit opioid use in women. This could help explain why previous studies investigating this relationship provided conflicting results due to the lack of consideration of sex effect on the association between cannabis use and continued opioid use in MMT [23, 37].

To our knowledge, this is the largest study conducted to date investigating the relationship between cannabis use and illicit opioid use in men and women on MMT. While some studies have indicated that cannabis use is associated with poor MMT treatment outcomes [20–22], several previous studies looking at illicit opioid use have not found significant results [23, 24, 26]. These inconsistent reports could be explained by methodological limitations such as the selection of the study participants [23] and insufficient investigations into sex differences in cannabis use and MMT treatment outcomes. For example, the external validity of the studies reporting no association may be low, as two were secondary analyses of RCTs with restrictive inclusion criteria [23, 26], and one study analyzed a sample of predominantly men [24]. In this case, it is unlikely these findings apply to a current sample of MMT patients which contain about 50% women.

Despite the well-documented sex differences in the sociodemographic and clinical profiles of patients in MMT [38], there has been little research conducted on sex-specific predictors of MMT outcomes. Women are more sensitive to the subjective effects of cannabis (i.e., subjective ratings of intoxication and other drug effects like altered mood and sociability) and consequently show a faster trajectory to cannabis use disorder [28], indicating they may be have a higher proclivity to problematic cannabis use. Furthermore, cannabis use has consistently been shown to be associated with worse mental health outcomes in women compared to men [19, 39].

Preclinical research points to many important developmental and biological sex differences which suggest females are more susceptible to the deleterious effects of cannabis use. Studies in rodents have found that females exposed to ∆9-tetrahydrocannabinol (THC) were more susceptible to the reinforcing effects of cannabinoids, such that female rats more quickly acquired self-administration and were more sensitive to drug- and cue-induced reinstatement of the drug [40]. These behavioral observations may be explained by the findings that prolonged exposure to THC led to a much greater cannabinoid receptor desensitization in female rats compared to their male counterparts [40]. It was also found significantly greater concentrations of THC and its metabolites in the female rat brain compared to males [41]. Despite this evidence, there is a paucity of research looking into the sexually dimorphic effects of cannabis in humans [42].

While there is reason to consider biological mechanisms as explanation for the differential consequences of cannabis use in men and women, other clinical and social factors should not be overlooked. Women in MMT tend to show a higher prevalence of comorbid psychiatric and physical illnesses [16, 43, 44], as well as more severe opioid craving upon treatment entry [45] which may represent confounding factors that serve to increase rates of both cannabis and opioid use during MMT. As such, these patients may have motivation to use both drugs for purposes of self-medication. Indeed a survey of cannabis users found men were more likely to use cannabis recreationally while women were more likely to use it for purposes of self-medication for conditions such as anxiety and headaches [30]. As we only classified participants based on biological sex, further work should evaluate gender constructs and their influence on treatment response to determine whether the observed sex differences can be explained by biological or social mechanisms, or a combination of the two.

Unexpectedly, when looking at cannabis users only, we failed to find an association between heaviness of cannabis use and illicit opioid use in either sex. It is currently unclear why this is the case. A study by Saxon et al. [46] found that MMT patients who had intermittent positive cannabis urine screens had a significantly higher percentage of positive screens for other drugs of abuse compared to those who consistently had positive screens. Thus, the relationship between cannabis use heaviness and illicit opioid use may not be linear. On the other hand, this observation may simply be the result of our rough approximation of cannabis use heaviness and slang terminology reported in the interviews, rather than reflecting the true effect.

Several studies also indicate a distinct difference between recreational cannabis users and those with cannabis use disorder, regardless of frequency of use, such that patients with a cannabis use disorder actually show less polysubstance use during MMT [23, 47, 48]. It is unclear why this is the case, but it may represent a confounding effect such as having cannabis use disorder may be associated with lack of means to obtain further drugs and lack of will or time to use other drugs while on MMT. In this study, we did not find a significant association between the amount or frequency of cannabis use and illicit opioid use. However, our study lacks the ability to distinguish cannabis use disorder from recreational use.

Another consideration is to account for the potency of cannabis used by patients, which was not measured in this study. Research on opioid-dependent rats suggests cannabidiol (CBD) and THC, the two main active ingredients in cannabis, actually generate opposing response. Administration of CBD extinguishes cue-induced heroin-seeking behaviors following periods of abstinence [49], whereas THC administration seems to heighten opioid sensitivity and increase heroin self-administration [50, 51]. This antagonism is further supported by imaging studies in humans, which suggest that CBD attenuates the neurotoxic and adverse psychiatric effects of THC [52, 53]. Because of these differential effects, those who use cannabis for medicinal purposes may choose higher CBD concentrations while those who use it for recreational purposes may prefer greater amounts of THC. Therefore, depending on ratio of CBD to THC in the ingested cannabis, an individual may become more or less susceptible to further drug use, and this distinction should be investigated further.

Some limitations of this study should be noted. The cross-sectional nature of the analysis prevents any causal inferences from being made. Self-reported cannabis use, despite its adequate sensitivity and specificity may also be a biased estimate. Particularly in chronic cannabis users, short-term memory and recall may be impaired [54, 55] which could affect the accuracy of retrospective self-reports even further. Conversely, there is evidence to suggest self-report use may be a more valid and sensitive indicator of cannabis use compared to urine screening. For example, patients enrolled in methadone maintenance treatment are required to provide urine samples at least one or two times per week; however, studies have shown the average time for the first negative result in urine screening for THC metabolites following a single dose of THC was 8.5 days following ingestion for infrequent users and 19.1 days for chronic users [56]. This suggests that urine data may overestimate the frequency of cannabis use.

Conclusions

This study suggests that cannabis use is a potential sex-specific predictor of poor outcome during MMT. It will be important to look at the impact of cannabis use on women by systematically screening for cannabis use in women with OUD and providing addiction counseling to address not only opioid use but also cannabis use in this vulnerable group. This study also showed that women with OUD experienced physical and psychological symptoms more frequently than men; these symptoms may be the underlying cause of cannabis use in women in this study and addiction services should consider sex-specific treatment programs to manage symptoms and co-substance use.

Acknowledgements

The authors would like to extend their gratitude to Jackie Hudson and Sheelagh Rutherford for their ongoing dedication and contributions to GENOA. We would like to thank the CATC staff and management for their collaboration with this research project, as well as all the GENOA team members for their valuable contributions and expertise that made this project possible. We would also like to acknowledge all students who helped out with data collection, entry, and management for this project. Finally, we would like to thank the study participants who generously volunteered their time and data, without which, none of this would be possible.

Funding

This work was supported by the Canadian Institute for Health Research, the Chanchlani Research Centre, and Peter Boris Centre for Addictions Research. The funding agencies had no role in the design of the study, review process, or publication of results.

Availability of data and materials

The dataset for the current study is available from the corresponding author upon request.

Authors’ contributions

LZ was responsible for conception and design of the study, acquisition of data, analysis and interpretation of data, manuscript writing, and critical revision of the manuscript. MB and NS contributed to acquisition of data, manuscript writing, and critical revision of the manuscript. CP, AW, MV, JD, GP, DM, and DD were responsible for data collection, communication with CATC clinics, and critical revision of the manuscript. JM, MS, and SM were responsible for analysis and interpretation of data, and critical revision of the manuscript. LT assisted with statistical analysis and critical revision of the manuscript. ZS contributed to the conception and design of the study, analysis and interpretation of data, and critical revision of the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publications

Ethics approval and consent to participate

This study was approved by the Hamilton Integrated Research Ethics Board (HIREB; Study ID 11-056). All participants in this study provided informed written consent.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abbreviations

CATC Canadian Addiction Treatment Centre
CBD Cannabidiol
CI Confidence interval
GENOA Genetics of Opioid Addiction
HIREB Hamilton Integrated Research Ethics Board
MAP Maudsley Addiction Profile
MMT Methadone maintenance treatment
OR Odds ratio
OUD Opioid use disorder
SD Standard deviation
STROBE Strengthening the reporting of observational studies in epidemiology
THC ∆9-tetrahydrocannabinol
VIF Variance inflation factor

Appendix 1

Table 5

Demographic and clinical characteristics of cannabis users and non-users on MMT

Variable Cannabis non-users (n = 372) Cannabis users (n = 405) p value
Age in years (SD) 39.78 (11.05) 36.46 (10.94)
Sex (% female) 205 (55.1%) 158 (39.0%)
Ethnicity (% Caucasian) 306 (83.4%) 329 (81.8%) 0.634
Marital status
Never married (%) 150 (40.3%) 211 (52.1%) 0.004
Married/common law/living with partner (%) 126 (33.9%) 112 (27.7%)
Widowed/separated/divorced (%) 96 (25.8%) 82 (20.2%)
Education
Less than grade 9 (%) 67 (18.2%) 89 (22.0%) 0.087
Grade 9–12 (%) 190 (51.6%) 220 (54.5%)
Trade school, college, university (%) 111 (30.2%) 95 (23.5%)
Employment (% currently working) 132 (35.5%) 141 (34.8%) 0.880
Smoking status (% current smoker) 301 (80.9%) 355 (87.7%) 0.010
Age of onset of opioid use in years (SD) 26.12 (9.08) 23.86 (7.86)
Methadone dose in mg per day (SD) 78.77 (46.54) 72.36 (45.02) 0.053
Current treatment duration in years (SD) 4.26 (4.35) 3.85 (3.91) 0.164
Physical functioning (SD) 15.06 (7.92) 16.02 (7.38) 0.085
Psychological functioning (SD) 12.90 (9.57) 14.27 (8.76) 0.040

Maximum score for the MAP physical and psychological functioning is 40, with higher scores indicating worse functioning

SD standard deviation

Contributor Information

Zainab Samaan, Phone: 905 522 1155, Email: [email protected] .

References

1. Task Force on Marijuana Legalization and Regulation. Toward the legalization, regulation and restriction of access to marijuana: discussion paper. 2016.

2. Hajizadeh M. Legalizing and regulating marijuana in Canada: review of potential economic, social, and health impacts. Int J Heal Policy Manag. 2016; 5 :453–6. doi: 10.15171/ijhpm.2016.63. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

3. Rehm J, Fischer B. Cannabis legalization with strict regulation, the overall superior policy option for public health. Clin Pharmacol Ther. 2015; 97 :541–4. doi: 10.1002/cpt.93. [PubMed] [CrossRef] [Google Scholar]

4. Room R, Reuter P. How well do international drug conventions protect public health? Lancet. 2012; 379 :84–91. doi: 10.1016/S0140-6736(11)61423-2. [PubMed] [CrossRef] [Google Scholar]

5. Rocky Mountain High Intensity Drug Trafficking Area. The legalization of marijuana in Colorado: the impact. 2014.

6. Hall W, Lynskey M. Evaluating the public health impacts of legalizing recreational cannabis use in the United States. Addiction. 2016; 111 :1764–73. doi: 10.1111/add.13428. [PubMed] [CrossRef] [Google Scholar]

7. Porath-Waller A, Brown J, Frigon AP, Clark H. What Canadian youth think about cannabis [Internet]. Can. Cent. Subst. Abus. 2013;1-57. Available from: http://www.ccsa.ca/Resource%20Library/CCSA-What-Canadian-Youth-Think-about-Cannabis-2013-en.pdf.

8. Volkow ND, Baler RD, Compton WM, Weiss SRB. Adverse health effects of marijuana use. N Engl J Med. 2014; 370 :2219–27. doi: 10.1056/NEJMra1402309. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

9. Blanco C, Hasin DS, Wall MM, Flórez-Salamanca L, Hoertel N, Wang S, et al. Cannabis use and risk of psychiatric disorders: prospective evidence from a US national longitudinal study. JAMA Psychiat. 2016;73:1–8. [PubMed]

10. Nelson LS, Juurlink DN, Perrone J. Addressing the opioid epidemic. JAMA. 2015; 314 :1453–4. doi: 10.1001/jama.2015.12397. [PubMed] [CrossRef] [Google Scholar]

11. Kolodny A, Courtwright DT, Hwang CS, Kreiner P, Eadie JL, Clark TW, et al. The prescription opioid and heroin crisis: a public health approach to an epidemic of addiction. Annu Rev Public Health. 2015; 36 :559–74. doi: 10.1146/annurev-publhealth-031914-122957. [PubMed] [CrossRef] [Google Scholar]

12. Fischer B, Kurdyak P, Goldner E, Tyndall M, Rehm J. Treatment of prescription opioid disorders in Canada: looking at the “other epidemic”? Subst Abuse Treat Prev Policy. 2016;11:1-4. [PMC free article] [PubMed]

13. Mattick RP, Breen C, Kimber J, Davoli M. Methadone maintenance therapy versus no opioid replacement therapy for opioid dependence. Cochrane Database Syst Rev. 2009;3:CD002209. [PMC free article] [PubMed]

14. Lions C, Carrieri MP, Michel L, Mora M, Marcellin F, Morel A, et al. Predictors of non-prescribed opioid use after one year of methadone treatment: an attributable-risk approach (ANRS-methaville trial) Drug Alcohol Depend. 2014; 135 :1–8. doi: 10.1016/j.drugalcdep.2013.10.018. [PubMed] [CrossRef] [Google Scholar]

15. Bohnert ASB, Ilgen MA, Trafton JA, Kerns RD, Eisenberg A, Ganoczy D, et al. Trends and regional variation in opioid overdose mortality among Veterans Health Administration patients, fiscal year 2001 to 2009. Clin J Pain. 2014;30:605–12. [PubMed]

16. Bawor M, Dennis BB, Varenbut M, Daiter J, Marsh DC, Plater C, et al. Sex differences in substance use, health, and social functioning among opioid users receiving methadone treatment: a multicenter cohort study. Biol Sex Differ. 2015; 6 :21. doi: 10.1186/s13293-015-0038-6. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

17. Degenhardt L, Hall W, Lynskey M. The relationship between cannabis use and other substance use in the general population. Drug Alcohol Depend. 2001; 64 :319–27. doi: 10.1016/S0376-8716(01)00130-2. [PubMed] [CrossRef] [Google Scholar]

18. Moore THM, Zammit S, Lingford-Hughes A, Barnes TRE, Jones PB, Burke M, et al. Cannabis use and risk of psychotic or affective mental health outcomes: a systematic review. Lancet. 2007; 370 :319–28. doi: 10.1016/S0140-6736(07)61162-3. [PubMed] [CrossRef] [Google Scholar]

19. Lev-Ran S, Imtiaz S, Taylor BJ, Shield KD, Rehm J, Le Foll B. Gender differences in health-related quality of life among cannabis users: results from the national epidemiologic survey on alcohol and related conditions. Drug Alcohol Depend. 2012; 123 :190–200. doi: 10.1016/j.drugalcdep.2011.11.010. [PubMed] [CrossRef] [Google Scholar]

20. Wasserman DA, Weinstein MG, Havassy BE, Hall SM. Factors associated with lapses to heroin use during methadone maintenance. Drug Alcohol Depend. 1998; 52 :183–92. doi: 10.1016/S0376-8716(98)00092-1. [PubMed] [CrossRef] [Google Scholar]

21. Proctor SL, Copeland AL, Kopak AM, Hoffmann NG, Herschman PL, Polukhina N. Outcome predictors for patients receiving methadone maintenance treatment: findings from a retrospective multi-site study. J Subst Use. 2016;21:1–13.

22. Roux P, Carrieri PM, Cohen J, Ravaux I, Spire B, Gossop M, et al. Non-medical use of opioids among HIV-infected opioid dependent individuals on opioid maintenance treatment: the need for a more comprehensive approach. Harm Reduct J. 2011; 8 :31. doi: 10.1186/1477-7517-8-31. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

23. Epstein DH, Preston KL. Does cannabis use predict poor outcome for heroin-dependent patients on maintenance treatment? Past findings and more evidence against. Addiction. 2003; 98 :269–79. doi: 10.1046/j.1360-0443.2003.00310.x. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

24. Nirenberg TD, Cellucci T, Liepman MR, Swift RM, Sirota AD. Cannabis versus other illicit drug use among methadone maintenance patients. Psychol Addict Behav. 1996; 10 :222–7. doi: 10.1037/0893-164X.10.4.222. [CrossRef] [Google Scholar]

25. Calsyn DA, Saxon AJ. An innovative approach to reducing cannabis use in a subset of methadone maintenance clients. Drug Alcohol Depend. 1999; 53 :167–9. doi: 10.1016/S0376-8716(98)00121-5. [PubMed] [CrossRef] [Google Scholar]

26. Saxon AJ, Wells EA, Fleming C, Jackson TR, Calsyn DA. Pre-treatment characteristics, program philosophy and level of ancillary services as predictors of methadone maintenance treatment outcome. Addiction. 1996; 91 :1197–209. doi: 10.1046/j.1360-0443.1996.918119711.x. [PubMed] [CrossRef] [Google Scholar]

27. Fattore L, Melis M, Fadda P, Fratta W. Sex differences in addictive disorders. Front Neuroendocrinol. 2014; 35 :272–84. doi: 10.1016/j.yfrne.2014.04.003. [PubMed] [CrossRef] [Google Scholar]

28. Cooper ZD, Haney M. Investigation of sex-dependent effects of cannabis in daily cannabis smokers. Drug Alcohol Depend. 2014; 136 :85–91. doi: 10.1016/j.drugalcdep.2013.12.013. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

29. Aspis I, Feingold D, Weiser M, Rehm J, Shoval G, Lev-Ran S. Cannabis use and mental health-related quality of life among individuals with depressive disorders. Psychiatry Res. 2015; 230 :341–9. doi: 10.1016/j.psychres.2015.09.014. [PubMed] [CrossRef] [Google Scholar]

30. Cuttler C, Mischley LK, Sexton M. Sex differences in cannabis use and effects: a cross-sectional survey of cannabis users. Cannabis Cannabinoid Res. 2016; 1 :166–75. doi: 10.1089/can.2016.0010. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

31. Samaan Z, Bawor M, Dennis BB, Plater C, Varenbut M, Daiter J, et al. Genetic influence on methadone treatment outcomes in patients undergoing methadone maintenance treatment for opioid addiction: a pilot study. Neuropsychiatr Dis Treat. 2014; 10 :1503–8. doi: 10.2147/NDT.S66234. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

32. Marsden J, Gossop M, Stewart D, Best D, Farrell M, Strang J. The Maudsley Addiction Profile Development and User manual. Natl. Addict. Centre/Institute Psychiatry. 1998; 1–40.

33. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009; 42 :377–81. doi: 10.1016/j.jbi.2008.08.010. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

34. Mariani JJ, Brooks D, Haney M, Levin FR. Quantification and comparison of marijuana smoking practices: blunts, joints, and pipes. Drug Alcohol Depend. 2011; 113 :249–51. doi: 10.1016/j.drugalcdep.2010.08.008. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

35. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstem AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996; 49 :1373–9. doi: 10.1016/S0895-4356(96)00236-3. [PubMed] [CrossRef] [Google Scholar]

36. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61:344–9. [PubMed]

37. Scavone JL, Sterling RC, Weinstein SP, Van Bockstaele EJ. Impact of cannabis use during stabilization on methadone maintenance treatment. Am J Addict. 2013; 22 :344–51. doi: 10.1111/j.1521-0391.2013.12044.x. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

38. Bawor M, Dennis BB, Bhalerao A, Plater C, Worster A, Varenbut M, et al.. Sex differences in outcomes of methadone maintenance treatment for opioid addiction: a systematic review and meta-analysis. C. Open. 2015;3:E344-E351. [PMC free article] [PubMed]

39. van Gastel WA, MacCabe JH, Schubart CD, van Otterdijk E, Kahn RS, Boks MPM. Cannabis use is a better indicator of poor mental health in women than in men: a cross-sectional study in young adults from the general population. Community Ment Health J. 2014;50:823–30. [PubMed]

40. Craft RM, Marusich JA, Wiley JL. Sex differences in cannabinoid pharmacology: a reflection of differences in the endocannabinoid system? Life Sci. 2013; 92 :476–81. doi: 10.1016/j.lfs.2012.06.009. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

41. Tseng AH, Harding JW, Craft RM. Pharmacokinetic factors in sex differences in ∆9- tetrahydrocannabinol-induced behavioral effects in rats. Behav Brain Res. 2004; 154 :77–83. doi: 10.1016/j.bbr.2004.01.029. [PubMed] [CrossRef] [Google Scholar]

42. Fattore L. Considering gender in cannabinoid research: a step towards personalized treatment of marijuana addicts. Drug Test Anal. 2013; 5 :57–61. doi: 10.1002/dta.1401. [PubMed] [CrossRef] [Google Scholar]

43. Evans E, Kelleghan A, Li L, Min J, Huang D, Urada D, et al. Gender differences in mortality among treated opioid dependent patients. Drug Alcohol Depend. 2015; 155 :228–35. doi: 10.1016/j.drugalcdep.2015.07.010. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

44. Peles E, Schreiber S, Naumovsky Y, Adelson M. Depression in methadone maintenance treatment patients: rate and risk factors. J Affect Disord. 2007; 99 :213–20. doi: 10.1016/j.jad.2006.09.017. [PubMed] [CrossRef] [Google Scholar]

45. Back SE, Payne RL, Wahlquist AH, Carter RE, Stroud Z, Haynes L, et al. Comparative profiles of men and women with opioid dependence: results from a national multisite effectiveness trial. Am J Drug Alcohol Abuse. 2011; 37 :313–23. doi: 10.3109/00952990.2011.596982. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

46. Saxon AJ, Calsyn DA, Greenberg D, Blaes P, Haver VM, Stanton V. Urine screening for marijuana among methadone-maintained patients. Am J Addict. 1993; 2 :207–11. doi: 10.1111/j.1521-0391.1993.tb00421.x. [CrossRef] [Google Scholar]

47. Peirce JM, Petry NM, Roll JM, Kolodner K, Krasnansky J, Stabile PQ, et al. Correlates of stimulant treatment outcome across treatment modalities. Am J Drug Alcohol Abuse. 2009; 35 :48–53. doi: 10.1080/00952990802455444. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

48. Best D, Gossop M, Greenwood J, Marsden J, Lehmann P, Strang J. Cannabis use in relation to illicit drug use and health problems among opiate misusers in treatment. Drug Alcohol Rev. 1999; 18 :31–8. doi: 10.1080/09595239996734. [CrossRef] [Google Scholar]

49. Ren Y, Whittard J, Higuera-Matas A, Morris CV, Hurd YL. Cannabidiol, a nonpsychotropic component of cannabis, inhibits cue-induced heroin-seeking and normalizes discrete mesolimbic neuronal disturbances. J Neurosci. 2009;29:14764–9. [PMC free article] [PubMed]

50. Ellgren M, Spano SM, Hurd YL. Adolescent cannabis exposure alters opiate intake and opioid limbic neuronal populations in adult rats. Neuropsychopharmacology. 2007; 32 :607–15. doi: 10.1038/sj.npp.1301127. [PubMed] [CrossRef] [Google Scholar]

51. Solinas M, Panlilio LV, Goldberg SR. Exposure to delta-9-tetrahydrocannabinol (THC) increases subsequent heroin taking but not heroin’s reinforcing efficacy: a self-administration study in rats. Neuropsychopharmacology. 2004; 29 :1301–11. doi: 10.1038/sj.npp.1300431. [PubMed] [CrossRef] [Google Scholar]

52. Demirakca T, Sartorius A, Ende G, Meyer N, Welzel H, Skopp G, et al. Diminished gray matter in the hippocampus of cannabis users: Possible protective effects of cannabidiol. Drug Alcohol Depend. 2011; 114 :242–5. [PubMed] [Google Scholar]

53. Schubart CD, Sommer IEC, van Gastel WA, Goetgebuer RL, Kahn RS, Boks MPM. Cannabis with high cannabidiol content is associated with fewer psychotic experiences. Schizophr Res. 2011; 130 :216–21. doi: 10.1016/j.schres.2011.04.017. [PubMed] [CrossRef] [Google Scholar]

54. Volkow ND, Swanson JM, Evins AE, DeLisi LE, Meier MH, Gonzalez R, et al. Effects of cannabis use on human behavior, including cognition, motivation, and psychosis: a review. JAMA Psychiatry. 2016;73:292-97. [PubMed]

55. Crane NA, Schuster RM, Fusar-Poli P, Gonzalez R. Effects of cannabis on neurocognitive functioning: recent advances, neurodevelopmental influences, and sex differences. Neuropsychol Rev. 2013; 23 :117–37. doi: 10.1007/s11065-012-9222-1. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

56. Grotenhermen F. Pharmacokinetics and pharmacodynamics of cannabinoids. Clin Pharmacokinet. 2003; 42 :327–60. doi: 10.2165/00003088-200342040-00003. [PubMed] [CrossRef] [Google Scholar]

How useful was this post?

Click on a star to rate it!

Average rating 3 / 5. Vote count: 1

No votes so far! Be the first to rate this post.

See also  CBD Gummies Death