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. Author manuscript; available in PMC: 2018 Dec 28.
Published in final edited form as: Drug Alcohol Depend. 2017 Oct 18;181:223–228. doi: 10.1016/j.drugalcdep.2017.09.032

Predictors of treatment utilization and barriers to treatment utilization among individuals with lifetime cannabis use disorder in the United States

Bradley T Kerridge a,*, Pia M Mauro b, S Patricia Chou a, Tulshi D Saha a, Roger P Pickering a, Amy Z Fan a, Bridget F Grant a, Deborah S Hasin b,c
PMCID: PMC6310167  NIHMSID: NIHMS1000314  PMID: 29107786

Abstract

Objective:

To present information on predictors of treatment utilization and barriers to treatment utilization among individuals with lifetime DSM-5 cannabis use disorder (CUD).

Method:

Face-to-face survey of a representative sample of the adult US general population (n = 36,309).

Results:

Treatment rates for CUD were low in this general population survey (13.7%). Severity of CUD and comorbidity of other lifetime drug use disorders were significant predictors of lifetime treatment utilization for CUD. Preference for self-reliance, minimizing problems, fear of stigma, and financial and structural issues were among the most frequently endorsed reasons for respondents not seeking treatment when they perceived the need for treatment among individuals with lifetime CUD, regardless of whether they eventually utilized treatment at some time in their lives.

Conclusions:

Given the rising prevalence of CUD in the US over the past decade and currently low treatment rates for CUD, increased provision for services for CUD appears critically needed, especially those that screen for and treat, when present, other drug use disorders. Programs to reduce stigma and financial barriers are needed, as well as programs to increase awareness among the general public, health care professionals about the nature and seriousness of CUD, and the availability and effectiveness of treatment for this disorder.

Keywords: Cannabis use disorder, Treatment, Barriers to cannabis use disorder treatment, Predictors of cannabis use disorder treatment

1. Introduction

Americans increasingly see cannabis use as harmless (Compton et al., 2016; Pacek et al., 2015). While some individuals can use cannabis without harm, its use does involve risk for various adverse health consequences including cannabis use disorder (CUD) (Hasin et al., 2016). CUD is associated with considerable impairment and comorbidity (Stinson et al., 2006), and its prevalence has increased substantially in the United States (US) and in clinical samples over the last 15 years (Bonn-Miller et al., 2012; Charilaou et al., 2017; Gubatan et al., 2016). However, despite the fact that evidence-based treatments for CUD are available (Copeland et al., 2014; Danovitch and Gorelick, 2012; Dutra et al., 2008; Marshall et al., 2014), CUD goes largely untreated in the US. Current national estimates of cannabis-specific treatment among adults with lifetime CUD are very low (13.7%; Hasin et al., 2016).

Little is known about predictors of and barriers to CUD treatment that can increase our understanding of access to treatment at a time when treatment rates for CUD are so low. Based on the broader health services utilization literature, characteristics associated with receiving cannabis-specific treatment and barriers to treatment can be partitioned into predisposing, enabling and need factors (Aday and Anderson, 1974; Andersen and Newman, 1973; Andersen, 2008; Andersen et al., 2013). Predisposing factors are individual characteristics such as sociodemographic variables that influence treatment utilization. Enabling factors are available resources that can facilitate the use of services (e.g., health insurance). In this paper, we refer to such factors as “enabling/impeding”, since such factors may impede use of services rather than enable them. Need factors are conditions that individuals or others recognize as requiring treatment (e.g., severity of CUD). Information about the characteristics associated with CUD treatment utilization is needed in order to achieve equitable access to treatment services.

Two studies on barriers to seeking treatment compared small numbers of patients in cannabis treatment with non-treatment heavy cannabis users or cannabis dependent individuals in the community (Gates et al., 2012; van der Pol et al., 2013). In general, these comparative treatment studies found that CUD severity, not wanting to stop using cannabis, low treatment awareness, psychiatric comorbidity, and fear of stigma were important barriers to CUD treatment. A national survey conducted in 2001–2002 (Khan et al., 2013) assessed treatment utilization and/or barriers to seeking any drug treatment among individuals with CUD, regardless of whether treatment was specifically sought for cannabis. These studies identified psychiatric comorbidity as an important predictor of treatment utilization, and identified barriers including minimizing CUD problems, self-reliance, and fear of stigma. While informative, the national survey was conducted several years ago, was not based on disorders diagnosed with DSM-5 criteria, and did not address treatment specifically for CUD. Thus, current information is lacking at the national level on predictors of treatment utilization specifically for CUD, which is acutely needed given the increases in cannabis use and CUD in conjunction with the public perception that cannabis is a harmless substance (Hasin et al., 2015b).

The purpose of this study was therefore to present current information on predisposing, enabling/impeding, and need factors as predictors of and barriers to treatment among individuals with lifetime Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5: American Psychiatric Association, 2013) CUD using a large nationally representative sample of US adults.

2. Methods

2.1. Sample

The present study utilized data from the 2012–2013 National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III), described in detail elsewhere (Grant et al., 2014). The NESARC-III target population was the US noninstitutionalized adult civilian population, including residents of selected group quarters. Respondents were selected through multistage probability sampling. Sample size was 36,309, with a household response rate of 72%, and person-level response rate of 84%, giving a total response rate of 60.1%, which is comparable to other contemporary national surveys (Centers for Disease Control and Prevention, 2014; Substance Abuse and Mental Health Services Administration, 2014). The study protocol was approved by institutional review boards of the National Institutes of Health and Westat.

2.2. Assessments

The diagnostic interview was the Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5) (Grant et al., 2011). The AUDADIS-5 measures DSM-5 alcohol, drug, and nicotine use and disorders, as well as common psychiatric disorders in the last 12-months and prior to the last 12-months. DSM-5 lifetime CUD diagnoses required ≥ 2 of 11 criteria in the last 12-months or prior to the last 12-months.

Test-retest reliabilities of CUD diagnoses (kappa = 0.41, 0.41) and their dimensional criteria scales (intraclass coefficients ([ICC] = 0.70, 0.71) were fair to substantial in a general population sample (Grant et al., 2015b). Procedural validity was established through blind reappraisal using the semi-structured, clinician-administered Psychiatric Research Interview for Substance and Mental Disorders, DSM-5 version (PRISM-5) (Hasin et al., 2011). AUDADIS-5/PRISM-5 concordance was moderate for CUD (kappa = 0.60, 0.51) and substantial for its dimensional criteria scales (ICC = 0.79, 0.78) (Hasin et al., 2015a).

2.3. Treatment

Respondents were asked if they ever went anywhere or saw anyone for problems related to their cannabis use in the last 12-months or prior to last 12 months. This included 14 potential sources of help for CUD, including professional inpatient and outpatient settings, and 12-step programs. Twelve-month and prior to last 12-month CUD treatment were combined to form a single measure of lifetime CUD treatment.

2.4. Predictors of treatment utilization

Predictors of treatment utilization were organized into three categories: predisposing, enabling/impeding, and need factors (Andersen, 2008, 2013;Andersen et al., 2013; Andersen and Newman, 1973). Predisposing factors included sex, age, race-ethnicity, education, marital status, and family history of drug problems. Enabling/impeding factors included urbanicity, family income, employment status and health insurance in the last 12 months. Need factors included CUD severity level (mild, moderate, severe, as defined in DSM-5), cannabis withdrawal, age at onset of cannabis use, age of onset of CUD, frequency of cannabis use during period of heaviest use, duration of longest episode (in months), lifetime mood, anxiety, personality, other substance use disorders, and presence of three or more medical conditions in the past year.

2.5. Reasons for not seeking treatment

Respondents were asked if there was ever a time when they thought they should see a doctor, counselor or any other health professional for any reason that was related to their drug use, even though they failed to go. Those who responded positively were asked whether any of 27 items listing reasons for not seeking treatment pertained to them. Reasons for not seeking treatment were also organized into more refined conceptual groupings, including predisposing (fear of stigma/social consequences, fear of treatment, treatment pessimism), enabling/impeding (financial and logistical/structural) and perceived need (self-reliance, minimizing problems), factors consistent with conceptual and factor analytic results on treatment barriers among substance users (Mojtabai and Crum, 2013; Rap et al., 2006).

2.6. Statistical analyses

Weighted percentages were computed for predictors of lifetime cannabis-specific treatment utilization among those with lifetime CUD. Bivariate odds ratios (ORs) were computed for each predictor and cannabis-specific treatment utilization. Adjusted ORs estimated from a multivariable logistic regression indicated associations between each predisposing, enabling and need factor and cannabis-specific treatment utilization, adjusted for all others. Weighted percentages were also calculated for each reason for not seeking treatment among all individuals with lifetime CUD and separately by whether respondents indicated seeking treatment at some point in their lifetime. All analyses were restricted to lifetime measures based on sample size considerations. To account for the complex survey design of the NESARC-III, SUDAAN 11.0.1 (Research Triangle Institute, 2012) was used to produce standard errors and confidence limits for estimates presented here.

3. Results

The weighted prevalence of DSM-5 lifetime CUD was 6.3% (n = 2242). The prevalence of cannabis-specific treatment among those with lifetime CUD was 13.7% (n = 300). Bivariate ORs reflected associations between predictors and cannabis-specific treatment utilization (Table 1). Compared to married individuals, the odds of cannabis-specific treatment utilization were significantly greater among those previously married (widowed, separated or divorced; OR = 1.64). Having a family history of drug problems also increased the odds of cannabis-specific treatment utilization (OR = 1.65), as did being in the lowest income category compared to the highest income (OR = 1.75). Compared to those with mild CUD, respondents with moderate or severe DSM-5 CUD had greater odds of cannabis-specific treatment utilization (ORs = 1.70, 4.10). Odds of cannabis-specific treatment utilization were also greater among respondents experiencing cannabis withdrawal (OR = 1.86) and among respondents with a lifetime histories of mood disorder (OR = 1.85), anxiety disorder (OR = 1.55), personality disorder (OR = 2.13), another drug use disorder (OR = 2.21) or three or more medical conditions (OR = 1.44).

Table 1.

Prevalence, Bivariate Odds Ratios and Adjusted Odds Ratios (AORs) of Lifetime Cannabis-Specific Treatment Utilization Among Individuals with Lifetime Cannabis Use Disorder (n = 2242).

Factor Prevalence (SE) Bivariate OR (95% CI) AORsb(95% CI
PREDISPOSING
Men 14.69 (1.08) 1.28 (0.94–1.73) 1.32 (0.94–1.85)
 Women 11.90 (1.34) Ref Ref
Age: 18–29 11.63 (1.41) 2.25 (0.56–8.99) 2.73 (0.53–14.14)
 30–44 13.44 (1.60) 2.65 (0.70–10.09) 2.88 (0.65–12.83)
 45–60 17.42 (1.55) 3.60 (0.91–14.31) 3.47 (0.75–15.95)
 > 60 5.53 (3.51) Ref Ref
Race-ethnicity: White 13.74 (0.99) Ref Ref
 Black 12.71 (2.27) 0.91 (0.59–1.41) 0.81 (0.52–1.25)
 Native American 27.32 (10.79) 2.36 (0.78–7.15) 1.78 (0.49–6.45)
 Asian/Pacific Islander 10.99 (4.20) 0.78 (0.33–1.83) 0.98 (0.38–2.52)
 Hispanic 11.62 (2.13) 0.83 (0.53–1.27) 0.88 (0.55–1.43)
Education: < high school 14.60 (2.85) 1.19 (0.73–1.94) 0.90 (0.57–1.42)
 High school 15.44 (1.88) 1.27 (0.89–1.80) 1.28 (0.88–1.86)
 Some college 12.59 (1.01) Ref Ref
Marital status: married/cohabiting 12.60 (1.40) Ref Ref
 Widowed/separated/divorced 19.13 (2.40) 1.64 (1.06–2.53) 1.44 (0.88–2.34)
 Never married 12.62 (1.49) 1.00 (0.68–2.40) 1.21 (0.74–1.97)
Family history of drug problems 15.59 (1.24) 1.65 (1.14–2.40) 1.29 (0.85–1.96)
 No family history of drug problems 10.06 (1.33) Ref Ref
–
ENABLING
Urban 13.60 (0.89) 0.96 (0.66–1.39) 0.99 (0.69–1.42)
 Rural 14.10 (2.15) Ref Ref
Family income: $0-$19,999 17.57 (1.92) 1.75 (1.12–2.73) 1.19 (0.68–2.09)
 $20,000-$34,999 10.83 (1.85) 1.00 (0.59–1.68) 0.85 (0.48–1.49)
 $35,000-$69,999 13.75 (1.56) 1.31 (0.83–2.08) 1.13 (0.69–1.83)
 ≥$70,000 10.85 (1.67) Ref Ref
Employed last 12-months 12.46 (0.95) 0.62(0.43–0.88) 0.72 (0.46–1.13)
 Not employed last 12-months 18.69 (2.29) Ref Ref
Any health insurance 12-months 14.02 (1.09) 1.13 (0.75–1.71) 1.27 (0.80–2.01)
 No health insurance 12-months 12.58 (1.85) Ref Ref
–
NEED
CUD-5 severity: Mild 7.26 (0.90) Ref Ref
 Moderate 11.72 (1.86) 1.70 (1.07–2.69) 1.46 (0.90–2.37)
 Severe 24.27 (1.91) 4.10 (2.98–5.63) 2.90 (1.97–4.27)
Withdrawal 18.94 (1.56) 1.86 (1.40–2.48) 1.11 (0.80–1.55)
 No withdrawal 11.17 (1.02) Ref Ref
Age at onset of cannabis usea 15.46 (0.48) 0.95 (0.86–1.05) 1.00 (0.95–1.05)
Age at onset of CUDa 21.08 (0.63) 0.99 (0.97–1.01) 1.01 (0.99–1.03)
Frequency of cannabis use, heaviest perioda 313.60 (8.05) 1.00 (1.00–1.00)
Duration of longest episodea 77.89 (8.02) 1.00 (1.00–1.01) 1.00 (1.00–1.00)
Lifetime mood disorder 17.42 (1.28) 1.85 (1.34–2.56) 1.42 (0.91–2.20)
 No lifetime mood disorder 10.24 (1.21) Ref Ref
Lifetime anxiety disorder 17.34 (1.53) 1.55 (1.18–2.04) 1.05 (0.74–1.50)
 No lifetime anxiety disorder 11.90 (0.98) Ref Ref
Lifetime personality disorder 18.51 (1.54) 2.13 (1.58–2.87) 1.32 (0.93–1.89)
 No lifetime personality disorder 9.63 (0.94) Ref Ref
Lifetime alcohol use disorder 14.35 (0.98) 1.28 (0.87–1.89) 1.01 (0.65–1.57)
 No lifetime alcohol use disorder 11.53 (1.80) Ref Ref
Other lifetime DUD 20.66 (1.78) 2.21 (1.62–3.01) 1.56 (1.11–2.20)
 No other lifetime DUD 10.55 (1.01) Ref Ref
Medical conditions > 3, 12-months 17.07 (1.68) 1.44 (1.06–1.96) 0.81 (0.58–1.13)
 Medical conditions ≤3, 12-months 12.46 (1.02) Ref Ref
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a

= Mean (SE).

b

Multivariable model adjusted for all predisposing, enabling and need factors.

Relative to those with mild CUD (i.e., 2–3 symptoms), adjusted odds of lifetime cannabis-specific treatment utilization were greater among individuals with severe lifetime CUD (i.e., 6+ symptoms) (OR = 2.9). The odds of lifetime cannabis-specific treatment utilization was also greater among those with another lifetime drug use disorder relative to those without another lifetime drug use disorder (OR = 1.56) (Table 1).

Table 2 presents the distribution of reasons for not seeking treatment among all individuals with lifetime CUD who ever perceived a need for treatment but did not go at some point in their life (n = 351). We also distinguished barriers reported among those who perceived a need for treatment but did not go at some point in their lives, but then eventually did seek cannabis treatment (n = 126), and those individuals that perceived such a need and never sought treatment (n = 225). Among all individuals with lifetime CUD, the most frequently reported reasons for not seeking treatment were related to ideas of self-reliance, including “thought I should be strong enough to handle it alone” (45.71%; SE = 3.94) and several reasons reflecting minimizing CUD problems, including “wanting to keep using” (33.64%; SE = 3.05), “didn’t want to go” (33.38%; SE = 3.19) and “thought the problem was not serious enough” (25.77%; SE = 2.97). Fear of stigma was also frequently reported, with 27.2% (SE = 2.80) of the total sample of individuals with lifetime CUD being “too embarrassed to discuss their problems” and 21.47% (SE = 2.99) of respondents “being afraid of what others might think.” Financial barriers were less frequently reported, including, “could not afford to pay” (18.52%; SE = 2.76), and “insurance did not cover treatment” (16.23%; SE = 2.75). Logistical/structural barriers included “did not know any place to go for help” (12.75%; SE = 2.10) or “did not have time” (10.04%; SE = 2.21). Similar rankings for reasons for not seeking treatment were seen among subsamples of individuals with lifetime CUD who did or did not seek treatment in their lifetime.

Table 2.

Barriers to Treatment Utilization Among individuals with Lifetime Cannabis Use Disorder who Perceived the Need for Treatment but Did Not Go.

Factor Sought Treatment in Lifetime (n = 126) % (SE) Never sought Treatment in Lifetime (n = 225) % (SE) Total (n = 351) % (SE)
PREDISPOSING
Fear of Stigma/Social Consequences
 Too embarrassed to discuss it 32.15 (5.91) 24.32 (2.791) 27.23 (2.801)
 Afraid of what others would think 27.96 (5.82) 17.63 (3.21) 21.47 (2.99)
 Hated answering personal questions 12.91 (3.56) 13.38 (2.62) 13.20 (2.06)
 Afraid would lose job 13.15 (3.82) 8.66 (2.30) 10.33 (2.22)
 Was afraid children would be taken away 4.36 (1.63) 6.14 (1.34) 5.48 (1.01)
 Family member objected 0.95 (0.94) 0.98 (0.53) 0.97 (0.49)
Fear of Treatment
 Afraid would be put into the hospital 17.92 (4.60) 15.14 (3.01) 16.17 (2.51)
 Afraid of the treatment 15.62 (4.25) 10.93 (3.28) 12.67 (2.46)
Treatment Pessimism
 Did not think anyone could help 27.85 (5.06) 11.62 (2.41) 17.66 (2.73)
 Tried to get help before and it didn’t work 17.83 (3.84) 7.48 (2.17) 11.33 (2.08)
ENABLING
Financial
 Could not afford to pay 19.07 (4.63) 18.19 (2.87) 18.52 (2.76)
 Health insurance didn’t pay for it 20.01 (4.48) 13.99 (3.47) 16.23 (2.75)
 Could not arrange child care 0.66 (0.49) 1.61 (1.12) 1.26 (0.73)
Logistical/Structural
 Did not know any place to go for help 10.89 (3.23) 13.86 (2.70) 12.75 (2.10)
 Did not have time 15.50 (4.29) 6.81 (2.06) 10.04 (2.21)
 Did not have a way to get there 10.46 (3.77) 4.91 (1.33) 6.97 (1.65)
 Had to wait too long 8.10 (3.22) 2.45 (0.93) 4.55 (1.40)
 Inconvenient hours 4.70 (2.17) 1.91 (1.17) 2.95 (1.10)
 Could not arrange child care 0.66 (0.49) 1.61 (1.12) 1.26 (0.73)
 Cannot speak English well 2.51 (2.48) 0.29 (0.29) 1.11 (0.94)
PERCEIVED NEED
Self-Reliance/Minimizing Problems
 Thought should be strong enough to handle it alone 47.96 (6.27) 44.38 (4.56) 45.71 (3.94)
 Thought the problem would get better by itself 41.34 (6.43) 39.36 (3.84) 40.10 (3.41)
 Wanted to keep using 37.85 (5.86) 31.14 (3.33) 33.64 (3.05)
 Didn’t want to go 36.38 (4.93) 31.59 (3.52) 33.38 (3.19)
 Thought problem was not serious enough 23.17 (5.11) 27.30 (3.44) 25.77 (2.97)
 Stopped using on my own 16.43 (4.24) 26.64 (3.58) 22.85 (2.59)
 Did not think it was necessary (despite family requests) 23.78 (4.72) 10.34 (2.45) 15.34 (2.50)
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4. Discussion

A major finding of this study was that treatment utilization for cannabis use disorder was strikingly low: among those with a lifetime diagnosis of CUD, only 13.7% ever received any professional or peer-led help. While drug use disorders have long been under-treated in the US (Compton et al., 2007; Grant et al., 2015a), the rate of treatment utilization specifically for CUD was even lower, only half that of treatment utilization for drug use disorders generally.

Severity of CUD and presence of another drug use disorder were shown to be significant predictors of CUD treatment in adjusted models, consistent with the few treatment utilization studies of alcohol use disorders (Chartier et al., 2016; Grant 1996; Hasin, 1994; Hasin and Glick, 1992; Hasin and Grant, 1995) and overall drug use disorders (Blanco et al., 2015; Grella et al., 2009). However, these prior studies also showed that predisposing and enabling factors (e.g., income, education) impact treatment utilization for alcohol and other drug use disorders (Chartier et al., 2016; Grella et al., 2009; Grant, 1996). An important premise of the behavioral model underlying the organizational framework of this study is the public policy concept of equitable distribution– that is, treatment services should be distributed solely on the basis of treatment need9 (Andersen, 2008; Andersen et al., 2013). Although CUD treatment utilization was shown to be predicted by only need factors in this study, the apparent equitable distribution of cannabis treatment services must be balanced by the extremely low treatment utilization rates among individuals with lifetime CUD.

Reasons for not seeking treatment among those with lifetime CUD become even more important in understanding access to cannabis treatment when treatment utilization rates are low, as different barriers could lead to distinct intervention approaches. Similar to factors predicting lack of service use for alcohol (Chartier et al., 2016; Cohen et al., 2007; Grant 1995, 1996) and drug use disorders (Perron et al., 2009), this study found that need factors, including preference for self-reliance and minimizing problems associated with cannabis use (i.e., beliefs that problems were not serious enough, would resolve on their own or treatment wasn’t necessary, or wanted to keep using) were among the most frequently endorsed barriers to treatment among individuals with lifetime CUD (Khan et al., 2013; van der Pol et al., 2013). Self-reliance and minimizing problems may serve not only to impede the treatment seeking process, but to substantially reduce the perception of a cannabis problem altogether. Evidence-based treatments (Danovitch and Gorelick, 2012; Dutra et al., 2008; Marshall et al., 2014) are available for CUD (Copeland et al., 2014). Public and professional education about treatment efficacy and availability may encourage individuals with CUD to seek treatment.

Despite strong evidence that stigma towards individuals with an alcohol use disorder adversely impacts treatment utilization (Keyes et al., 2010; Link and Cullen, 1986; Phelan et al., 2000), little is known about how strongly stigma is related to CUD. As shown in this study, reasons for not seeking treatment reflecting embarrassment and fear of what others might think were endorsed by over a quarter of individuals with lifetime CUD, regardless of whether they had sought treatment at some time in their lives. Although studies have documented reductions in stigma toward mental illness after interventions aimed toward health care providers, police, community workers and employers (Corrigan, 2004; Estroff et al., 2004; Holmes et al., 1999; Thompson et al., 2002), no such initiatives have targeted CUD and overall, the severe stigma accompanying substance use disorders remains an international phenomenon (Yang et al., 2017). Our results indicate the need for such programs focused on the reduction of stigma to increase treatment utilization among individuals with CUD.

Consistent with the alcohol (Chartier et al., 2016; Grant, 1995; Perron et al., 2009) and cannabis (Khan et al., 2013) literatures, enabling factors most frequently endorsed among individuals with lifetime cannabis use disorder included inability to pay for services and several logistical/structural issues (e.g., time, transportation). Financial and logistical/structural barriers may be more amenable to change in the short-term (e.g., through policy interventions that increase access to insurance or transportation) than changes aimed at altering attitudes and beliefs, especially those aimed at stigma.

Strengths of the study include the NESARC-III’s large sample size, reliable and valid measures of CUD, rigorous survey methodology, and the inclusion of questions directly measuring cannabis-specific treatment utilization. Limitations are noted. NESARC-III was a cross-sectional survey and stability over time in estimates of service use could not be ascertained. Similar to the alcohol and drug treatment literatures, this study focused largely on individual barriers to CUD treatment; future research should also examine organizational, environmental and health policy factors that may facilitate or impede CUD treatment utilization.

As the prevalence of cannabis users increase in the US general population (Hasin et al., 2015a,b), the population burden associated with CUD may increase as well. As is the case for alcohol and other drug use disorders, most individuals with CUD do not receive services, even among those who do perceive the need for treatment. Severity of CUD and presence of another drug use disorder were predictors of CUD treatment utilization underscoring the need to screen for and treat, if present, drug use disorders among those seeking treatment for CUD and educating the public and healthcare providers about CUD before problems with cannabis become too severe. Barriers to seeking treatment identified in this study highlight the need to educate the public about the effectiveness of CUD treatment, develop targeted programs to reduce stigma of CUD, and reduce financial and logistic barriers that impede access to care.

Acknowledgments

Role of funding source

The National Epidemiologic Survey on Alcohol and Related Conditions was sponsored by the National Institute of Alcohol Abuse and Alcoholism, with supplemental support from the National Institute on Drug Abuse. The sponsor had no role in the study design, in the collection, analysis or interpretation of the data, in the writing of the report, or in the decision to submit the article for publication. Support is also acknowledged for grant R01DA034244 from the National Institutes of Health and from the New York State Psychiatric Institute (Dr. Hasin) and T32DA031099 (Dr. Mauro).

Footnotes

Conflicts of interest

None.

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