Who Seeks Help? A Sociodemographic Analysis of Cannabis Use Disorder Treatment in New York

Abstract

Introduction: Cannabis use disorder (CUD) is being increasingly diagnosed in the United States, but access to treatment remains unequal, particularly in New York. Identifying the factors that contribute to disparities in receiving treatment for CUD among different population groups is essential for ensuring effective and targeted interventions. This study explores the sociodemographic factors influencing treatment utilization for CUD in New York. Methods: Data for this study were retrieved from the 2018 Treatment Episode Data Set—Discharges (TEDS-D) of the U.S. Substance Abuse and Mental Health Services Administration (SAMHSA). Sample size for the study is 422,319 people with CUD. Logistic regression analysis was performed to examine the odds of receiving treatment for CUD based on demographic and socioeconomic factors, as well as the type of treatment setting. Results: The results revealed significant disparities in treatment utilization. Asians/Pacific Islanders and Hawaiian Natives had lower odds of receiving treatment compared to African Americans (OR = 0.367, 95% CI 0.341–0.394). Similarly, Caucasians had the lowest odds of receiving treatment (OR = 0.270, 95% CI 0.266–0.275). Females were less likely to receive treatment compared to males (OR = 0.756, 95% CI 0.744–0.768). Those with higher educational attainment (over four years of college) had the lowest odds of receiving treatment, while individuals with 9–11th grade education had the highest odds. Employment status also influenced treatment access, with the unemployed having the highest odds, and full-time employees having the lowest. Additionally, individuals with no source of income had significantly lower odds of receiving treatment. Conclusions: This study highlights significant disparities in the provision of treatment for CUD in New York, influenced by sociodemographic factors such as race, gender, age, education, and employment status. These findings emphasize the need for targeted interventions to reduce these disparities and improve treatment access for underserved populations.

1. Introduction

Cannabis ranks as the most widely used psychoactive substance in the United States, with millions of Americans using cannabis for medicinal or recreational purposes [1,2]. The recognition of the medicinal properties of cannabis has also generated concerns about cannabis use disorder (CUD), which is associated with a public health burden both nationally and internationally [3,4]. CUD is the problematic, compulsive use of cannabis with negative health and social consequences. Cannabis is legal for medical and/or recreational use in an increasing number of states, but as use becomes more prevalent, so do dependence and misuse [5]. The rise in cannabis use over the years can be chiefly attributed to the lifting of state policies decriminalizing or legalizing its use. In 2019, an estimated 48.2 million Americans, roughly 13–18% of US population, reported consumption of cannabis and cannabis products, highlighting the increasing prevalence of its usage [6]. New York and 23 other states have legalized cannabis for therapeutic and recreational use. Over the recent two decades, public attitudes towards cannabis have changed dramatically: negative perceptions about cannabis have decreased and the acceptance of cannabis use has increased [7]. Despite the movement toward loosening laws around recreational use, the growing rates of dependence on cannabis are cause for concern, particularly as anecdotal reports of its use in treating mental illnesses (including psychosis and mood disorders) have surfaced, and the decreased reliance on clinical evidence as to its efficacy in these diseases has emerged [8].

CUD continues to be a common psychiatric disorder. Estimates show that 1.5% of Americans meet the criteria for CUD, and approximately 10% of individuals who use cannabis will become addicted [9]. The problems of problematic cannabis use are not just health-related, including significant social and economic costs. People with CUD often experience lost wages, reduced work productivity, and social isolation [9,10]. Furthermore, the use of cannabis during pregnancy and adolescence has been linked to a range of medical, behavioral, and mental health complications, such as developmental deficits, cognitive function impairments, and elevated risk of mental health disorders such as anxiety and depression [11]. One of the more troubling trends has been higher CUD rates among US veterans, particularly younger veterans, and those with chronic mental health diagnoses; for this population, the disorder more than doubled in prevalence between 2005 and 2014 [12,13]. The economic and societal burden of CUD is substantial. Untreated CUD has long-term harmful consequences for an individual’s health, employment, and social stability, according to research [14]. The urgent need for effective treatment interventions has therefore never been greater. Research has shown that CUD is treatable, with improved retention and reduction in cannabis use, as well as improved quality of life among treated individuals [9,15]. Unfortunately, even with the demonstrated benefits of treatment, many individuals with CUD do not pursue or attain the necessary care [16]. This disparity in the availability of treatment has created an increased demand for both an understanding of barriers to care as well as support systems in place for those affected [17]. A key gap in the literature to date is that we do not yet have an understanding of the sociodemographic trends of those seeking treatment for cannabis use.

Although several studies have characterized the prevalence of cannabis use and CUD among various populations [9,18], relatively few studies have focused on the specific sociodemographic characteristics that influence an individual’s decision to seek treatment. While treatment utilization of diverse health conditions is affected by age, sex, income, race, and geographic location, there remains limited understanding of how these factors intersect with CUD treatment. Recognizing these differences is essential to designing targeted interventions and policies that can work to lower barriers to care [9,18,19]. New York is an especially interesting environment in which to examine the utilization of CUD treatment. New York has been one of the first states where both recreational and medicinal cannabis have been legalized, with the implications of this most significantly reflected in changes in cannabis-related behaviors and attitudes [20]. Though legalization may have been associated with increased cannabis use [5], it is unknown whether this has translated to increased treatment-seeking behaviors. Moreover, it is important to grasp the extent sociodemographic variables in New York modulate treatment-seeking for CUD in underserved communities.

Some population groups, like low-income individuals, racial and ethnic minorities and people in rural areas, may find accessing appropriate treatment services more difficult than others [21]. This study addresses this gap in the literature and explores the sociodemographic factors impacting treatment utilization for CUD in New York. More specifically, we will examine how various social and demographic characteristics like age, gender, race and socioeconomic status affect the probability of receiving treatment. The study aims to identify these differences to explain why some people may experience barriers to treatment whereas others may be more inclined to seek help. Then, the study will clarify the factors that determine the utilization of CUD treatment in a quickly evolving legal and social environment. Understanding the available treatment options and sociodemographic disparities in treatment within New York can allow policymakers, healthcare providers, and community organizations to collaborate to better address these barriers to treatment. It is designed to provide insights for potential solutions that can be implemented to address against the disparities in receipt of treatment, and to ensure that all individuals with CUD are able to access the care and support necessary to address their condition and achieve a better quality of life.

2. Methods

2.1. Study Design

This study employed a cross-sectional design to examine factors related to receipt of CUD treatment. We used the Treatment Episode Data Set—Discharges (TEDS-D), a national data set that includes substance abuse treatment discharge records for all the 50 U.S. states, Washington D.C., and Puerto Rico. Data were extracted specifically for individuals discharged from treatment for substance use in 2018, providing a large and representative sample of substance use disorder treatment participants. The TEDS-D system draws upon data from many sources for a rich view of treatment episodes, as well as patterns across a range of demographic and clinical characteristics. It was limited to people who had been diagnosed with CUD and then determined which socio-demographic and treatment-related factors were associated with a high treatment rate for this condition. Stratified analyses were conducted for individuals diagnosed with CUD versus those not receiving cannabis treatment. The stratification was useful in summarizing the predictors of treatment receipt and with respect to how socio-economic variables and demographic variables interact with treatment settings. Results: The dependent variable, or primary endpoint for the analysis, was whether the individual received treatment for CUD, and the analysis explored the independent variables that were associated with receiving treatment.

2.2. Sample Population

We included 2,952,105 individuals, a racially and ethnically diverse clinical cohort from the Treatment Episode Data Set—Discharges (TEDS-D) The sample comprised all individuals exiting substance use treatment programs across the US in 2018, specifically focusing on those with a diagnosis of CUD. For analysis, a nationally representative, large sample was acquired through the Treatment Episode for Data Set—Discharge, or the TEDS-D data set, which reflects 83% of all eligible drug and alcohol treatment admissions across the United States according to the substance abuse and mental health services administration (SAMHSA, n.d.). Data from New York was included in order to provide a broad state-level view of treatment trends for cannabis use disorder. The sample was further classified by treatment for CUD (yes/no). Demographic variables were gathered, including age, sex, marital status, race, education and employment status. Stratifying these treatments into categories as per the face-to-face analysis permitted a more granular examination into the association of various determinants (such as socio-economic status of patients or type of treatment setting) with overall treatment. By examining these characteristics, we were able to create more complete profiles of populations at varying levels of risk and treatment need for CUD.

2.3. Measures of Study

The primary endpoint of the study was the receipt of treatment for CUD, which was recorded as a binary variable (i.e., whether or not the individual received treatment). Independent variables included demographic and socio-economic characteristics such as age, sex, marital status, employment status, education, race, primary source of income, and the type and setting of treatment received. Specific age groups (12–24, 25–49, and >50 years) were used as categorical variables to measure the influence of age on treatment likelihood. Marital status was categorized as never married, now married, or other, while employment status was classified as full-time, part-time, unemployed, or not in the labor force.

Further, education was categorized based on the highest level of schooling achieved, with groups including less than one school grade, grades 9–11, grade 12 or GED, 1–3 years of college, and 4 or more years of college. The primary source of income was classified into categories such as wages or salary, public assistance, retirement or pension, disability, or other. Treatment setting variables included detoxification (hospital inpatient or residential), rehabilitation (short-term or long-term), and ambulatory (intensive outpatient). These variables were used to determine the odds of receiving treatment for CUD based on a variety of demographic and socio-economic factors.

2.4. Data Collection

Data were obtained from the Treatment Episode Data Set—Discharges (TEDS-D), a standard data system developed by the Substance Abuse and Mental Health Services Administration (SAMHSA) to compile episodes of treatment data for all 50 U.S. states, Washington D.C., and Puerto Rico. In this study, data collection targeted individuals who were discharged from substance use treatment during 2018. TEDS-D collects a spectrum of treatment-related data, including client demographics (e.g., sex, age, education level), substance use (e.g., primary and secondary substances used, route of consumption), employment status, and treatment settings. Data submitted to the federal government is standardized so that it is consistent and comparable across states. These data focused on the individuals with CUD and considered demographic and socio-economic factors. These factors were based on detailed reports from treatment providers so as to ensure the data set was as accurate and thorough as possible. Demographic data on age, sex, race, marital status, and employment status were obtained directly from treatment intake forms. Information about the type and setting of treatment received, including whether treatment consisted of detoxification, rehabilitation or ambulatory care, was also obtained. Using the standardized TEDS-D system enabled the collection of comparable data on a heterogeneous population across six U.S. sites.

2.5. Data Analysis

Data analysis was conducted using SAS 9.4 statistical software (SAS, Cary, NC, USA), with a focus on determining the odds of receiving treatment for CUD based on the independent variables. Logistic regression analysis was the primary statistical technique used, allowing for the examination of binary outcomes (i.e., treatment vs. no treatment) in relation to a variety of demographic and treatment-related factors. Independent variables, including age, marital status, employment status, education, race, and treatment setting, were entered into the logistic model to determine their association with the likelihood of receiving treatment. Reference groups were selected for each categorical variable based on common patterns observed in the population. For instance, the reference group for age was individuals aged 12–24 years, while the reference group for marital status was those who were never married. Additionally, male sex, African American race, low education (i.e., less than or equal to grade 8), and treatment received in an ambulatory or intensive outpatient setting were used as reference categories.

3. Results

Table 1. Sociodemographic attributes of New York Sample (N = 422,319).

Demographic Characteristics	Cannabis Use Disorder (N)	Cannabis Use Disorder (%)
Age
12–24	148,242	35.10%
25–49	245,055	58.03%
50	29,022	6.87%
Race
Alaska Native/American Indian	3080	0.80%
Asian/Pacific islander/Hawaiian	1691	0.45%
Black/African American	196,481	52.54%
white	134,933	36.02%
Other single race/2/more race	38,400	10.25%
Gender
Male	3,215,423	76.14%
Female	100,776	23.86%
Employment
Full time	61,363	14.5%
Part time	34,745	8.23%
Unemployed	116,931	27.7%
Not in labor force	208,971	49.5%
Marital status
Never married	341,185	83.09%
Currently married	39,512	9.62%
Divorced/separated	29,923	7.295%
Education
<1st grade–8th grade	33,633	7.96%
Grade 9–11	173,852	41.17%
Grade 12	147,657	34.97%
1–3 years of college	59,704	14.14%
>4 years of college	7426	1.76%
Source of income/support
Salary/wages	86,951	27.21%
Public assistance	65,097	20.37%
Retirement/disability	398	0.10%
Other	70,381	22.02%
none	96,725	30.26%
Type of treatment setting
Detox, (hospital inpatient, residential)	3415	1.81%
Rehab (both short term and long-term rehab)	23,069	12.21%
Ambulatory, Intensive outpatient	162,479	85.98%
Living arrangement
Homeless	37,307	8.8%
Dependent living	35,832	8.4%
Independent living	349,134	82.6%

presents the percentages of the New York study population who reported receiving treatment for CUD, as well as the percentages of individuals who reported having CUD and subsequently received treatment, categorized by various independent variables.

Table 2. Adjusted odds ratio estimate for sociodemographic attributes and cannabis use disorder.

Characteristics		Adjusted Odds Ratio (95% CI)
Age (a)
12–24 (a)	Ref
25–49 (a)	0.220	0.217–0.224	<0.001
>50 (a)	0.045	0.044–0.047	<0.001
Race (b)
Alaskan Native/American Indian (b)	0.408	0.378–0.440	<0.001
Asian/Pacific Islander/Hawaiian Native (b)	0.367	0341–0.394	<0.001
Black/African American (b)	Ref
White (b)	0.270	0.266–0.275	<0.001
Other single race/two or more races (b)	0.498	0.489–0.508	<0.001
Gender (c)
Male (c)	Ref
Female (c)	0.756	0.744–0.768	<0.001
Education (d)
No education–8th grade (d)	Ref
Grades 9–11 (d)	1.044	1.013–1.076	<0.001
Grade 12 (d)	0.707	0.686–0.729	<0.001
1–3 years of college (d)	0.506	0.490–0.526	<0.001
>4 years (d)	0.280	0.267–0.294	<0.001
Employment status (e)
Full-time (e)	0.882	0.813–0.843	<0.001
Part-time (e)	1.101	1.063–1.141	<0.001
Unemployed (e)	Ref
Not in the labor force (e)	0.828	0.813–0.843	<0.001
Marital Status (f)
Never married (f)	Ref
Currently married (f)	0.741	0.724–0.757	<0.001
Divorced/separated/widowed/(f)	0.684	0.668–0.701	<0.001
Source of income
Wages/salaried (g)	Ref
Public assistance (g)	0.2248	0.0180–155.969	<0.001
Retirement/pension, disability (g)	0.1134	0.0951–1.4239	0.2328
Other (g)	0.2208	0.0163–182.416	<0.001
None (g)	0.1319	0.0174–57.322	<0.001

Table 2 Logistic regression predicting treatment for cannabis use disorder based on age, race, sex, education, employment status, marital status, source of income and type or treatment setting at admission. (a) Age, comparing each group to 12–24 years; (b) race, comparing each group to Black/African American; (c) comparing reported gender, female versus male; (d) educational status, comparing each group to less than first grade education, no schooling, nursery school or kindergarten to 8th grade education; (e) employment status, comparing each group to unemployed; (f) marital status, comparing each group to never married; (g) primary source of income comparing each group to individuals on wages and salary.

provides a breakdown of the sociodemographic characteristics of the total study population, segmented by cannabis use status. A total of 2,952,105 participants were enrolled in the study, with 422,319 individuals identified as having CUD. Among the cannabis users, the majority (58.03%) were aged 25–49 years, 35.10% were aged 12–24 years, and 6.87% were aged over 50 years. The male cannabis use population accounted for 76.14%, while females made up 23.86%. In terms of race, 52.45% of cannabis users were Black or African American, 36.02% were White, 10.25% identified as mixed race or another race, 0.82% were Alaska Native or American Indian, and 0.45% were Asian or Pacific Islander. Employment data revealed that 14.54% of cannabis users were employed full-time, 8.23% were employed part-time, and 77.23% were either unemployed or not in the labor force. In terms of marital status, 83.09% of cannabis users were unmarried or never married, 9.62% were currently married, and 7.29% were divorced or separated. Regarding income sources, 20.63% of cannabis users reported salaries or wages as their primary income, 15.45% received public assistance, 0.09% received retirement or disability income, 26.48% had “other” forms of income, and 37.34% had no income. Finally, living arrangements showed that 8.83% of cannabis users were homeless, 8.49% lived in dependent housing, and 82.68% reported living independently.

Table 2 presents the adjusted odds ratio estimates for the likelihood of receiving CUD treatment in New York. Using African Americans as the reference group, the results indicate that Caucasians had the lowest odds of receiving treatment (OR 0.270, 95% CI 0.266–0.275, p < 0.001), followed by American Indians and Alaska Natives (OR 0.367, 95% CI 0.341–0.394). Mixed race or other racial groups had odds of 0.498 (95% CI 0.489–0.508), and Asians and Pacific Islanders had odds of 0.408 (95% CI 0.378–0.440) for receiving treatment. Regarding marital status, married individuals had lower odds of receiving treatment (OR 0.741, 95% CI 0.724–0.757) compared to single individuals, while the divorced or separated population had even lower odds (OR 0.684, 95% CI 0.668–0.701). In terms of educational attainment, individuals with more than 4 years of college education had the lowest odds of receiving treatment (OR 0.280, 95% CI 0.267–0.294), followed by those with 1–3 years of college education (OR 0.506, 95% CI 0.490–0.526). Conversely, individuals with educational levels between grades 9–11 had the highest odds of receiving treatment (OR 1.044, 95% CI 1.014–1.076). Employment status analysis showed that part-time employees had higher odds of receiving treatment (OR 1.101, 95% CI 1.063–1.141), while full-time employees had lower odds (OR 0.882, 95% CI 0.813–0.843). Individuals not in the labor force, including students, retirees, inmates, and disabled individuals, had the lowest odds of receiving treatment (OR 0.828, 95% CI 0.813–0.843) compared to the unemployed. Finally, in terms of marital status, currently married individuals had odds of 0.761 (95% CI 0.724–0.757), while divorced, separated, or widowed individuals had odds of 0.684 (95% CI 0.668–0.701).

4. Discussion

The rising rates of CUD throughout the United States have become parallel with the increase in cannabis products available, variability in product potency, and legislative changes aimed at decriminalization and recreational use [2,5,9]. These elements have led to an increase in problematic cannabis use, which is a relevant public health issue. The present study was conducted on a large scale and attempted to identify the sociodemographic, behavioral, and clinical correlations of cannabis-treatment-seeking behavior from the New York population, the results of which might serve as useful insight into the determinants of treatment-seeking behavior for CUD.

Our study reveals important sociodemographic differences in treatment utilization, including by race, gender, marital status, education, and employment, thereby identifying areas for further work. In our study, the underuse of medicinal cannabis was particularly frequent among Caucasian people, women, married individuals, and those with higher educational levels [22]. Notably, these individuals may also have more access to alternative treatments or be more influenced by social norms that discourage the use of cannabis for medical purposes. Additionally, higher education levels may correlate with more cautious health behaviors or skepticism toward cannabis use, even when medically indicated. As evidenced in the literature, such differences in treatment seeking behavior could arise from different factors such as socioeconomic class, stigma for accessing substance use treatment, unawareness of the harms from cannabis use, employment status, as well as race [23]. Other important factors include the living situation, healthcare access, and the availability of social support that can influence whether those who have been diagnosed with CUD seek help to manage their condition. These findings are in line with past research, which indicates that treatment-seeking behavior for CUD is frequently impacted by a combination of individual, social, ecological factors [24].

CUD, according to DSM-5, consists of a problematic pattern of cannabis use leading to significant impairment or distress in an individual’s life [25], which could be described as mild, moderate, or severe, depending on how many of the diagnostic criteria are met. In the United States, a significant portion of this population does not seek treatment despite the availability of numerous treatment services for CUD [26]. Approximately 30% of people who use cannabis meet the diagnostic criteria for CUD, but many do not use or receive appropriate treatment [26,27]. Our study also found significant demographic differences in CUD prevalence. Compared with NHW, NHB, Native American, and mixed-race individuals are disproportionately affected by CUD [28]. Moreover, a greater prevalence of CUD has been observed among young males, particularly those who are younger than 25 years old and without higher education [22,29]. An increased risk for CUD has been associated with daily or near-daily cannabis use at for recreational use in adolescents [5,30]. In fact, the lack of standardization in cannabis potency, and differences in user behavior, can complicate attempts to quantify cannabis consumption [31]. Differences in potency of cannabis products between flower products and concentrated resin products result in different risks and potential health consequences and need to be considered when evaluating CUD treatment needs [31]. As a result of legalization of recreational cannabis in New York, the perceived dangers of cannabis use are decreased which may also increase the use of CUD treatment services in future years [5,32].

In terms of who sought treatment, we did find that Black individuals were more likely to seek treatment for CUD, which aligns with previous literature, noting that increased availability of medical via dispensaries was associated with greater treatment utilization [33]. Yet, problematic use of cannabis is more common in areas with greater density of cannabis dispensaries, and increased potency of cannabis is associated with greater risk of intoxication and psychological dependence [33]. Visits to the emergency room linked to cannabis intoxication, withdrawal symptoms, and other cannabis-related health complications are a vital entry into the care pathway, since they frequently proceed treatment for CUD [34,35]. Moreover, the ever-potent nature of cannabis compounded by health-related repercussions highlight the demand for accessible and effective treatment to those with CUD. Additionally, the increased cannabis availability is experienced by vulnerable populations of interest, including youth aged less than 25 years, pregnant women, and individuals with co-morbid psychiatric disorders [5], resulting in increased incidence of cannabis-attributable emergency department presentations [36] and wide range in severity of CUD [37].

Our study also found that young Black males, especially unmarried and unemployed, live independently, as these males are more likely to seek treatment for CUD. However, there remains barriers that are significant to seeking treatment. Notably, evidence in the literature suggests key barriers in seeking help included: no health insurance, stigma associated with drug use treatment, and a general lack of knowledge regarding the harmful effects of marijuana use [38]. The general reduction in the perceived harms associated with cannabis use has also contributed to low levels of treatment utilization, which has been mirrored by a decrease in treatment-seeking, compounding the issue of treated CUD [39]. Socioeconomic factors linked to the underutilization of cannabis treatment resources include low income, financial strain and low levels of education [40]. Moreover, concerns about being disempowered during treatment, limited awareness of the treatment options, and negative stereotypes about people who seek out substance use help have all played a part in the ambivalence around treatment seeking [40]. Further complicating attempts to educate the public on the risks associated with cannabis use and on the availability of treatment has been marred by the influence a variety of media outlets have had on public perceptions of cannabis and its effects [41].

Notably, recognizing individuals with severe CUD early—especially those attending emergency departments—improves the chances of achieving positive treatment outcomes [38]. Our results validate findings from Wu et al. who also reported an underuse of treatment in women, married individuals, and people with higher educational levels [28]. Although our study specifically examined differences among Caucasians, Wu et al.’s research showed that Asian Americans are underutilizing cannabis treatment in the U.S. compared with other racial groups as well. Conversely, our results emphasized that Caucasians specifically were scarce in treatment usage for CUD. Our study, along with other prior research, highlights that treatment utilization for CUD in the United States is low, even with the resources available. It is important to develop more targeted treatment interventions for specific populations, such as adolescents, young adults, and pregnant women, to address the rising prevalence of CUD and disparities among cannabis products available that differ in potency. Healthcare personnel need to be trained to identify CUD criteria and to triage individuals into treatment programs to ensure that positive outcomes are maximized.

As cannabis use has become a continuing, growing problem in states where it is legalized, including New York State, the need for CUD treatment has never been greater. There are treatment options for CUD; however, many individuals remain untreated, especially within marginalized populations. Educational needs in both healthcare providers and the general public around CUDs randomized treatment criteria, treatment options, and cannabis harm reduction initiatives are apparent. This study demonstrates that there are treatment underutilization disparities, with Caucasian, female, and more educated individuals being less likely to utilize treatment services. Further reducing stigma and increasing awareness regarding the efficacy of evidence-based culturally relevant interventions are important to further seeking treatment of CUD, especially among at-risk groups including adolescents and young adults and vulnerable populations like pregnant women. Making treatment more accessible, affordable and culturally relevant will help us drive positive social change to overcome such barriers. Therefore, access to CUD treatment will help not only in living a healthy life but also in building a healthy society which decreases the long-term cost burden of cannabis addiction on society and individuals.

The findings from our study have huge implications to policy and research. There is a concerning increased prevalence of CUD in the U.S., particularly against the background of expanding availability of cannabis products, variation in potency, and incremental legalization of cannabis use in some states. Originating in New York State, the study emphasizes the need to tackle treatment-seeking behavior disparities by assessing sociodemographic, behavioral, and clinical correlates of individuals with opioid use disorders involving prescription or illicit opioids. Such findings highlight the need for public health efforts that center on equitable access to care, culturally competent interventions, and messaging targeting groups already under-represented in COVID-19 data, including young men, racial and ethnic minorities, individuals with lower levels of education, and those experiencing socioeconomic disadvantage. The underuse of treatment among Caucasians, women, and highly educated individuals also highlights the need for increasing stigma resistance, raising awareness about the harms of cannabis, and working to broaden public knowledge about treatment options. The evolving cannabis landscape warrants policy evolution that includes standardized potency regulations, screening for CUD by primary and emergency care providers, and the enhanced availability of treatment services for vulnerable populations such as adolescents, pregnant women, and those with co-occurring mental health disorders. Additionally, the results also call for the development of evidence-based prevention and harm reduction campaigns and for healthcare professionals to be trained to recognize and treat CUD, which is essential to alleviating individual distress and reducing the psychological and social burden of untreated cannabis addiction in the population at large. This study contributes to positive social change by demonstrating the lack of treatment-seeking by those most at risk for CUD and providing a foundation for recommendations toward ensuring equitable, culturally relevant, and accessible services. Findings will help identify at-risk populations and barriers, informing targeted public health initiatives and policy reforms that decrease stigma and enhance outreach.

One of the major strengths of this study lies in its cross-sectional nature, allowing a broad overview of the sociodemographic, behavioral, and clinical factors related to CUD treatment-seeking behavior in a large and diverse sample of New Yorkers. This design facilitates the identification of patterns and differences in treatment utilization, which can inform public health intervention planning and provide opportunities for target population interventions. However, one of the main limitations is that it does not allow for establishing causality or the ordered appearance of the examined variables. Moreover, the self-reported data may be susceptible to recall or social desirability bias, and the results may not be extrapolated outside the studied demographic or geographic population.

5. Conclusions

This study underscores significant treatment-seeking behavior disparities in CUD among the New York population, indicating that despite the availability of services, many remain untreated, especially those from marginalized, socioeconomically disadvantaged backgrounds. The results suggest that CUD has a higher prevalence when it comes to racial minorities, younger males, as well as less educated and employed people. At the same time, a surprisingly low treatment usage was observed among Caucasians, women, and those better-educated. The patterns above suggest that structural and social barriers, including stigma, lack of awareness, and limited access to care, persistently inhibit help-seeking behaviors. The findings support the need for public health efforts that are sensitive to the most vulnerable cultural, economic, and psychosocial realities. In addition, early diagnosis and treatment, particularly through emergency care settings, can lead to better treatment outcomes. In summary, our study highlights strong evidence supporting comprehensive, culturally sensitive, and accessible treatment approaches for CUD, as well as the need for continued public education and the formation of policy aimed at mitigating the rising impact of cannabis use amidst an increasingly permissive legal landscape.