Abstract
Background:
Better understanding of factors associated with not ready to stop using substances may inform provider engagement with clients who have an unmet treatment need.
Objectives:
This study explores how treatment barriers, the number of SUD symptoms, and types of substances used are associated with not ready to stop using substances among adults with an unmet treatment need.
Methods:
Data came from the 2015–2019 National Survey on Drug Use and Health. Eligible adults met DSM-IV criteria for substance abuse and dependence, and reported an umnet need for treatment. Among our sample (N=1,017), majority self-identified as male (weighted 59.3%). We employed multivariable logistic regression to examine individual-level factors associated with not being ready to stop using substances.
Results:
About 38% of the respondents reported that they were not ready to stop using substances. Reporting access barriers (aOR=0.44, 95% CI: 0.29, 0.68) and attitudinal barriers (aOR=0.47, 95% CI: 0.28, 0.80) was associated with a lower odds of not ready to stop using. Each additional increase in SUD symptoms was associated with 23% higher odds of not being ready to stop using (aOR=1.22, 95% CI: 1.12, 1.34). Having a diagnosis of alcohol and/or marijuana abuse or dependence was associated with higher odds of not being ready to stop using when compared to respondents without these diagnoses (aOR=2.13, 95% CI: 1.33, 3.40; (aOR=1.82 95% CI: 1.11, 2.99).
Conclusion:
Not ready to stop using substances may be impacted by type of SUD, number of SUD symptoms, and certain barriers, like access and attitude to care.
Keywords: not ready to stop using, substance use disorder, treatment barriers
Introduction
Approximately 20 million adults in the United States (U.S.) meet the criteria for any past-year substance use disorder (SUD) as defined in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) (1). According to the 2019 National Survey on Drug Use and Health (NSDUH), there is a high need for SUD treatment. In fact, 19.3 million American adults met criteria for an SUD with roughly half a million adults (2.6%) reporting concomitant alcohol and illicit drug use (e.g., marijuana) (1). In parallel, drug-related deaths have more than tripled since 2000 (2), and currently one in four deaths is attributable to alcohol, tobacco, and illicit or prescription drug use (2). Despite these statistics, less than one in five individuals with an SUD receive treatment for their disorder (1) mostly due to barriers with accessing treatment (1), and the misconception that treatment is not needed despite meeting the criteria for a SUD diagnosis (3). Recognizing the importance of treatment engagement to reduce harm among populations who need treatment but are not receiving it, it is therefore of high relevance to understand the myriad of systemic and individual barriers to care. There is also growing interest in understanding motivation and behavioral barriers to care among individuals with an SUD (4).
Barriers to care such as treatment availability and affordability, stigma, and preference to self-management have been previously documented (5-7). Personal characteristics - such as sociodemographic characteristics, physical and mental health – are other factors that influence barriers to care (8, 9). One study found that a key obstacle to treatment entry for individuals with an SUD was that they felt they were not ready just yet to stop using substances (4). Whether or not one is ready to stop using substances can be critical for treatment entry as it signals contemplation and the potential start of pre-commitment to recovery (10, 11). There is an apparent discordance between acknowledging a perceived unmet need for treatment and reporting not being ready to stop substance use. This is an example of how motivation for change and motivation for treatment are two different types of motivation that need to be addressed independently to maximize the odds of successful interventions (12). Thus, assessing and understanding motivations to receive treatment, as part of the larger process of change, is considered an integral step towards achieving more favorable recovery outcomes (13, 14). Apart from a focus on tobacco use and readiness for smoking cessation (15, 16), there is a dearth of empirical literature on the topic of readiness to stop using illicit substances (7, 17, 18), especially among individuals who have a perceived unmet need for treatment. The following study extends this literature by focusing on individuals who report an unmet need for treatment and polysubstance use.
Current Study
The current study uses a nation-wide sample to examine factors associated with not being ready to stop using substances_among individuals with a need for substance use treatment. Specifically, we explore how treatment barriers, number of SUD symptoms, and types of substances individuals use are associated with not being ready to stop using substances. Findings may inform providers of the characteristics of individuals who have unique challenges to entering treatment and strategies to develop treatment readiness.
Methods
Data source and sample
Data for this study were obtained from the National Survey on Drug Use and Health (NSDUH), a nationally representative cross-sectional survey of noninstitutionalized U.S. civilians aged 12 or older. The NSDUH provides data on tobacco, alcohol, illicit drug use, mental health and substance use disorders, receipt of substance use treatment, and use of mental health services. The survey utilizes a multilevel stratified hierarchical sampling procedure to interview participants. Interviews are conducted in person, via computer-assisted personal interviewing (CAPI) and audio computer-assisted self-interviewing (ACASI). Detailed information on the survey methodology is available elsewhere (19).
Five-year data (2015-2019) with the same methods for survey sampling and data collection were combined to increase the sample size for the current study. Eligible participants met the following inclusion criteria: 1) 18 years or older; 2) classified as needing treatment for illicit drug or alcohol use in the past year (based on self-reporting of certain abuse, dependence, or treatment criteria); 3) classified as feeling a need for illicit drug or alcohol treatment in the past year (based on personal self-evaluation); and/or 4) needing additional treatment in the past year (i.e., unmet need for substance use treatment). Respondents were classified as needing treatment for illicit drug or alcohol use in the past year if they met at least one of three criteria: (a) dependent on any illicit drug or alcohol in the past year (based on DSM-IV criteria); (b) illicit drug or alcohol abuse in the past year (based on DSM-IV criteria); (c) received treatment for illicit drug or alcohol use at a specialty facility in the past year including a hospital (inpatient), rehabilitation facility (in or outpatient), or mental health center. Respondents were classified as feeling a need for illicit drug or alcohol treatment in the past year if they reported either “feeling a need for” or “feeling a need for additional” illicit drug or alcohol use treatment in the past year, treatment for the use (or misuse for prescription drugs) of one or more illicit drugs in the past year (i.e., marijuana, cocaine, heroin, hallucinogens, inhalants, methamphetamine, pain relievers, tranquilizers, stimulants, or sedatives) or some other drug. For the purposes of this study, we identified our final sample as individuals with a past-year substance use treatment gap despite perceived need. A total of 78 participants were excluded from the sample because they received treatment in the past 12 months but did not perceive a need for additional treatment. The final sample included 1017 participants. Among the final sample, missing data were identified among the following variables of interest: Major depressive episode past year (n = 15 cases); self-perceived general health (n = 1 case). Missing data were handled using listwise deletion, as complete case analysis is recommended when the missing data rate is below 5% (20). Institutional Review Board approval was not required as the secondary dataset was publicly available and de-identified.
Measures
Not ready to stop using substances.
The outcome variable investigated in this study was “not ready to stop using substances.” Among respondents who reported feeling a need for substance use treatment or additional substance use treatment (i.e., a perceived need), NSDUH asked respondents to choose from a list of 15 reasons that explained why they did not get initial or additional treatment. Respondents were able to endorse more than one reason. One response choice was “not ready to stop using.” Respondents who endorsed this option, “not ready to stop using” were coded as 1, otherwise they were coded as 0. Other studies have also used similar item in operationalizing “not ready to stop using” (5, 21). Substance use treatment could be received at a hospital, residential drug or alcohol rehabilitation facility, mental health facility, private physician’s office, in an emergency room, or a self-help group.
Treatment Barriers.
In addition to “not ready to stop using substances,” respondents were able to select 14 other reasons for not receiving necessary substance treatment or additional treatment in the past year despite having a perceived need. For this analysis, we only focused on respondents’ specific reasons and therefore excluded the “some other reason or reasons” option. We then grouped the remaining 13 reasons into mutually exclusive categories to construct the following factors that are conceptualized as treatment barriers: access barriers (e.g., did not know where to go for treatment), affordability barriers (e.g., insurance didn’t cover treatment/cost), stigma barriers (e.g., might have negative effect on job), and attitudinal barriers (e.g., could handle problems without treatment) (5). These four treatment barriers were each measured as a continuous variable. A detailed description of how response options were grouped can be found in Appendix 1.
Number of Substance Use Disorder Symptoms.
This variable was assessed by the DSM-IV’s seven dependence and four abuse symptoms to examine the number of SUD symptoms respondents endorsed (i.e., presence of multiple substance dependence and abuse symptoms at once) was associated with not being ready to stop using substances. The assessment covered a total of 11 different substances, including alcohol, marijuana, cocaine, heroin, hallucinogens, inhalants, methamphetamine, prescription pain relievers, prescription tranquilizers, prescription stimulants, and prescription sedatives. In the analysis, a new binary variable was created to determine if this symptom was endorsed by the participant across those 11 substances asked in NSDUH surveys. An SUD symptoms score was then created by summing the symptoms ranging from 0 to 11, where higher scores indicated higher number of SUD symptoms. NSDUH classifies respondents as having SUDs (either dependence or abuse) if they meet the criteria (i.e., symptoms) specified in the DSM-IV(22). For instance, a respondent who uses alcohol on six or more days in the past year and endorses three or more of the seven dependence criteria (e.g., spent a great deal of time over a period of a month or more getting, using, or getting over the effects of substance) was classified as having alcohol use disorder (AUD). We acknowledge that the term “abuse” is hurtful and harmful. This term is used in this manuscript to remain consistent with the pre-DSM IV criteria used in the past NSDUH survey years.
Substance Use Disorder Types.
We considered past year diagnosis of the following substances: alcohol, marijuana (the term used by NSDUH, versus “cannabis”), and illicit drugs (other than marijuana). Respondents could report multiple diagnoses. Respondents who were diagnosed with having abuse or dependence in the past year of the respective substance were coded as 1, whereas respondents who were not diagnosed with alcohol abuse or dependence in the past year were coded as 0. For illicit drug abuse or dependence, we considered the following nine substances: cocaine, heroin, hallucinogens, inhalants, methamphetamine, prescription pain relivers, prescription tranquilizers, prescription stimulants, and prescription sedatives.
Sociodemographic Characteristics.
Sociodemographic variables include the following: age was coed into “0 = 18-25 years,” “1 = 26-34 years,” “2 = 35-49 years,” and “3 = 50 years or older).” Sex was coded as “0 = Male” versus “1 = Female.” Sexual identity was measured as a nominal variable into the following categories “0 = Heterosexual,” “1 = Lesbian or gay,” and “2 = Bisexual.” Race/ethnicity was measured as a nominal variable into the following categories “0 = Non-Hispanic White,” “1 =. Non-Hispanic Black,” “2 = Hispanic,” and “3 = Non-Hispanic others.” Urbanicity was measured as a nominal variable into the following categories “0 = Large metro,” “1 = Small metro,” and “2 = Non-metro.” SES factors included employment status which was measured as a binary variable and coded 1 if the respondent is employed and 0 if the respondent is not employed. Education was measured as an ordinal variable and coded into the following categories “0 = Less than high school,” “1 = High school,” “2 = Some college/associate,” and “3 = College degree.” Total annual family income was also measured as an ordinal variable into the following categories “0 = Less than $20,000,” “1 = $20,000-$49,999,” “2 = $50,000- $74,999,” and “3 = $75,000 or more.”
Wellbeing Characteristics.
Self-perceived physical health was measured as a binary variable based on the question, “Would you say your health in general is excellent, very good, good, fair, or poor?” Respondent who indicated “excellent,” “very good,” or “good” were grouped together and coded as 0, whereas respondents who indicated “fair” or “poor” were grouped together and coded as 1.
Mental health diagnosis was measured by the presence of major depressive episode (MDE) in the past year. Respondents who indicated the presence of the necessary criteria for MDE during the past year were coded as 1, otherwise they were coded as 0. Severe psychological distress was measured using the K6, a six-item standardized instrument used in screening for non-specific psychological distress (23). Survey respondents were asked to rate how often in the past year they felt: 1) nervous, 2) hopeless, 3) restless or fidgety, 4) so depressed that nothing can cheer them up, 5) that everything was an effort, and 6) worthless. Response options ranged from 0 (None of the time) to 4 (All of the time). Scores on the K6 ranges from 0 to 24 with higher scores indicating severe psychological distress. Following the recommendation of past studies (24-26) and in accordance with the NSDUH data documentation guidelines (27) scores of 13 or higher were considered to be in the clinical range and were recoded as 1, whereas scores less than 13 were considered normal and were recoded as 0. The K6 has been used in several studies, different contexts, and has been found to have strong psychometric properties (24-26).
Statistical analysis
Data were analyzed using descriptive, bivariate, and multivariable analytic techniques. We first conducted descriptive statistics using means and standard deviation for continuous variables and percentages for caterogical variable to examine the general distribution of the study variables. Second, using Pearson chi-square test of association, we examined the bivariate association between all the variabels included in the study with “not being ready to stop using substances”. There were no controls at the bivariate level, and we were unable to ascertain the true effect of each variable on not ready to stop using. Therefore, at the multivariate level we controlled for the effects of all the variables. Multivariable logistic regression was conducted to examine factors associated with “not ready to stop using substances.” We used survey command in Stata to adjust for the complex survey design of NSDUH. The adjusted models included all covariates (e.g., sociodemographic and wellbeing characteristics). The adjusted odds ratio (aOR), and 95% confidence interval (CI) for each factor are presented. All analyses were conducted using Stata MP. Version 16. P-values less than 0.05 were considered statistically significant throughout all the analyses and all analyses were two-tailed.
Results
Sample characteristics
Among the 1,017 respondents in our sample, about 38% (n = 406) reported that they were not ready to stop using at the time being interviewed. A little over one in three respondents (35.9 %) had barriers relating to affordability, 32.5% had barriers relating to access, 27.0% had barriers relating to stigma, and 19.4% had attitudinal barriers. With respect to substance use factors, most of the respondents (63.6 %) had diagnosis of alcohol abuse or dependence, 56.5 % had diagnosis of illicit drug abuse or dependence, and 15.3% had diagnosis of marijuana abuse or dependence. More than half of the sample were male (59.3 %), non-Hispanic White (62.1 %), and employed (57.5 %). About 29% of the respondents perceived their physical health to be poor, 35.8% had a diagnosis of MDE, and 57.7% had symptoms of severe psychological distress in the past year.
At the bivariate level, we found that respondents who did not have affordability barriers were more likely to report not being ready to stop using versus respondents with affordability barriers (χ2 (1) = 27.11, p < 0.001). Similarly, respondents with no attitudinal barriers were also more likely to not be ready to stop using than those reporting attitudinal barriers (χ2(1) = 9.81, p = 0.03). Of the substance use factors examined, respondents with alcohol abuse or dependence were more likely to not be ready to stop using than their peers without alcohol abuse or dependence (χ2(1) = 33.78, p < 0.001). Also, respondents with marijuana abuse or dependence were more likely to not be ready to stop using than those without this diagnosis (χ2(1) = 6.63, p = 0.04). Other factors associated with not ready to stop using substances were employment status, education attainment, and total annual family income. The detailed sample characteristics are presented in Table 1.
Table 1.
Perceived unmet need for substance use treatment in past year (N = 1017).
| Outcome variable | N (Weighted %) |
Weighted % reporting not ready to stop using substances |
Chi-square (sig.) |
|---|---|---|---|
| Not ready to stop using substances | |||
| No | 611 (61.8) | ||
| Yes | 406 (38.2) | ||
| Treatment Barriers | |||
| Access barriers | 8.18 (p = 0.07) | ||
| No | 675 (67.5) | 41.3 | |
| Yes | 342 (32.5) | 31.9 | |
| Affordability barriers | 27.11 (p < .001) | ||
| No | 671 (64.1) | 44.2 | |
| Yes | 346 (35.9) | 27.6 | |
| Stigma barriers | 0.36 (p = 0.66) | ||
| No | 738 (73.0) | 38.8 | |
| Yes | 279 (27.0) | 46.7 | |
| Attitudinal barriers | 9.81 (p = 0.03) | ||
| No | 809 (80.6) | 40.6 | |
| Yes | 208 (19.4) | 28.5 | |
| Substance use factors | −6.44 (p < .001) | ||
| Number of SUD symptoms (weighted Mean, SE; range: 0-11) | 6.77 (0.08) | 6.54 (0.11)1 | |
| 7.58 (0.10)2 | |||
| Alcohol abuse or dependence | 33.78 (p < .001) | ||
| No | 369 (36.4) | 26.5 | |
| Yes | 648 (63.6) | 44.9 | |
| Marijuana abuse or dependence | 6.63 (p = 0.04) | ||
| No | 815 (84.7) | 36.6 | |
| Yes | 202 (15.3) | 47.4 | |
| Illicit drug abuse or dependence | 0.16 (p = 0.79) | ||
| No | 422 (43.5) | 38.9 | |
| Yes | 595 (56.5) | 37.7 | |
| Demographic Variables | |||
| Age | 6.32 (p = 0.36) | ||
| 18-25 years old | 340 (16.8) | 41.2 | |
| 26-34 years old | 285 (26.5) | 36.5 | |
| 35-49 years old | 290 (31.5) | 42.1 | |
| 50 years or Older | 102 (25.3) | 39.2 | |
| Sex | 0.33 (p = 0.72) | ||
| Female | 468 (40.7) | 37.2 | |
| Male | 549 (59.3) | 39.0 | |
| Sexual identity | 1.54 (p = 0.78) | ||
| Heterosexual | 812 (82.9) | 37.5 | |
| Lesbian or gay | 49 (4.4) | 41.0 | |
| Bisexual | 148 (12.0) | 42.9 | |
| Missing | 8 (0.7) | 34.0 | |
| Race/ethnicity | 6.41 (p = 0.46) | ||
| Non-Hispanic White | 622 (62.1) | 40.2 | |
| Non-Hispanic Black | 130 (14.5) | 31.3 | |
| Hispanic | 101 (6.2) | 45.1 | |
| Non-Hispanic others | 164 (17.2) | 34.3 | |
| Urbanicity | 6.79 (p = 0.19) | ||
| Large Metro | 438 (56.9) | 40.5 | |
| Small Metro | 364 (27.9) | 38.5 | |
| Nonmetro | 215 (15.1) | 29.1 | |
| Socioeconomic factors | |||
| Employment status | 13.75 (p = 0.004) | ||
| Employed | 610 (57.5) | 43.1 | |
| Not employed | 407 (42.5) | 31.7 | |
| Education level | 32.68 (p < .001) | ||
| Less than high school | 168 (16.9) | 27.3 | |
| High school | 325 (30.1) | 31.0 | |
| Some college/Associate | 371 (35.8) | 43.2 | |
| College | 153 (16.6) | 51.9 | |
| Total annual family income | 28.35 (p = 0.004) | ||
| Less than $20,000 | 313 (28.9) | 34.1 | |
| $20,000 - $49,999 | 385 (37.6) | 33.6 | |
| $50,000 - $74,999 | 117 (12.8) | 58.4 | |
| $75,000 or More | 202 (20.7) | 39.9 | |
| Health and mental health factors | |||
| Self-perceived physical health | 0.73 (p = 0.49) | ||
| Good | 761 (71.3) | 37.5 | |
| Poor | 255 (28.7) | 40.3 | |
| Major depressive episode | 3.58 (p = 0.22) | ||
| No | 607 (64.2) | 40.5 | |
| Yes | 395 (35.8) | 34.4 | |
| Severe psychological distress, past year | 0.003 (p = 0.97) | ||
| No | 386 (42.3) | 38.3 | |
| Yes | 631 (57.7) | 38.2 |
SUD: Substance Use Disorder; SE: Standard Error; Sig: Significance
Among respondents who reported “ready to stop using substances” (N = 611).
Among respondents who reported “not ready to stop using substances” (N = 406).
Logistic regression findings
Logistic regression models examining factors associated with “not being ready to stop using substances” are presented in Table 2. Based on adjusted odds ratio (aOR), we found that access, attitudinal barriers to treatment, number of SUD symptoms, alcohol abuse or dependence, marijuana abuse or dependence, sexual identity, employment status, education, and household income were significantly associated with “not ready to stop using substances.” Specifically, access barriers (aOR=0.44, 95% CI=0.29, 0.68) or attitudinal barriers (aOR=0.47, 95% CI=0.28, 0.80) respectively were significantly associated with 56% or 53% lower odds of “not being ready to stop using substances.” Each additional increase in SUD symptoms was significantly associated with a 22% higher the odds of “not being ready to stop using substances” (AOR=1.22, 95% CI: 1.12, 1.34). Diagnosis of alcohol abuse or dependence was associated with more than twofold higher odds of “not being ready to stop using substances” when compared to respondents with no such diagnosis (aOR=2.13, 95% CI: 1.33, 3.40). Marijuana abuse or dependence was associated with 1.82 times higher odds of “not being ready to stop using substances” compared to those without the abuse or dependence (aOR=1.82, 95% CI: 1.11, 2.99). Compared to respondents who self-identified as heterosexual, respondents who self-identified as bisexual had 1.55 times higher odds of “not being ready to stop using substance (aOR=1.55, 95% CI: 1.07, 2.25). Respondent who were employed were less likely to report “not beign ready to stop using” (aOR=0.61, 95% CI: 0.41-0.92), compared to the unemployed respondents. Higher educational attainment (some college/associate, aOR= 2.18, 95% CI: 1.10, 4.34; college, aOR= 2.54, 95% CI: 1.20, 5.34) and household income ($50,000-74,999: aOR: 2.26, 95% CI = 1.14-4.47) were positively associated with “not being ready to stop using substances”.
Table 2.
Multivariable logistic regression examining factors associated with not ready to stop using substances
| Variables | aOR | 95% CI | P-value |
|---|---|---|---|
| Treatment Barriers | |||
| Access barriers (Ref: No) | |||
| Yes | 0.44 | (0.29, 0.68) | <.001 |
| Affordability barriers (Ref: No) | |||
| Yes | 0.63 | (0.38, 1.02) | 0.06 |
| Stigma barriers (Ref: No) | |||
| Yes | 1.01 | (0.61, 1.65) | 0.98 |
| Attitudinal barriers (Ref: No) | |||
| Yes | 0.47 | (0.28, 0.80) | 0.01 |
| Substance use factors | |||
| Number of SUD symptoms | 1.22 | (1.12, 1.34) | <.001 |
| Alcohol abuse or dependence (Ref: No) | 2.13 | (1.33, 3.40) | 0.002 |
| Marijuana abuse or dependence (Ref: No) | 1.82 | (1.11, 2.99) | 0.02 |
| Illicit drug abuse or dependence (Ref: No) | 0.98 | (0.59, 1.67) | 0.94 |
| Demographic Variables | |||
| Age (Ref: 18-25 years old) | |||
| 26-34 years old | 0.65 | (0.37, 1.14) | 0.13 |
| 35-49 years old | 1.15 | (0.69, 1.92) | 0.58 |
| 50 years or older | 0.79 | (0.41, 1.52) | 0.47 |
| Sex (Ref: Female) | |||
| Male | 0.87 | (0.53, 1.44) | 0.59 |
| Sexual identity (Ref: Heterosexual) | |||
| Lesbian or gay | 0.93 | (0.35, 2.44) | 0.87 |
| Bisexual | 1.55 | (1.07, 2.25) | 0.02 |
| Missing | 0.25 | (0.03, 1.84) | 0.17 |
| Race/ethnicity (Ref: Non-Hispanic White) | |||
| Non-Hispanic Black | 0.64 | (0.32, 1.25) | 0.19 |
| Hispanic | 1.03 | (0.45, 2.32) | 0.95 |
| Non-Hispanic others | 0.78 | *(0.44, 1.39) | 0.39 |
| Urbanity (Ref: Large Metro) | |||
| Small Metro | 0.99 | (0.64, 1.51) | 0.94 |
| Non-metro | 0.67 | (0.36, 1.23) | 0.19 |
| Socioeconomic factors | |||
| Employment status (Ref: Employed) | |||
| Not employed | 0.61 | (0.41, 0.92) | 0.02 |
| Education level (Ref: Less than high school) | |||
| High school | 1.29 | (0.69, 2.43) | 0.42 |
| Some college/Associate | 2.18 | (1.10, 4.34) | 0.03 |
| College | 2.54 | (1.20, 5.34) | 0.02 |
| Total annual family income (Ref: Less than $20,000) | |||
| $20,000 - $49,999 | 0.88 | (0.55, 1.41) | 0.59 |
| $50,000 - $74,999 | 2.26 | (1.14, 4.47) | 0.02 |
| $75,000 or More | 0.79 | (0.44, 1.39) | 0.40 |
| Health and mental health factors | |||
| Self-perceived physical health (Ref: Good) | |||
| Poor | 1.46 | (0.93, 2.30) | 0.10 |
| Major depressive episode (Ref: No) | 0.68 | (0.40, 1.15) | 0.15 |
| Severe psychological distress, past year (Ref: No) | 0.94 | (0.60, 1.47) | 0.77 |
aOR: adjusted Odds Ratio; CI, Confidence Interval; SUD: Substance Use Disorder.
Bold indicates statistically significant.
Discussion
Using available data from the 2015 to 2019 NSDUH, we examined factors associated with reporting not being ready to stop substance use among individuals with a perceived unmet need for treatment. This study adds to the growing literature addressing and understanding motivation as an important but often-overlooked barrier to care in patients with a need for substance use treatment. It also illustrates the relationship between resource availability and individual characteristics that are associated with not ready to stop using substances. We demonstrate how fewer reported barriers related to access, affordability and attitudes are associated with not ready to stop using substances. Similarly, we describe the association between greater number of SUD symptoms and the self-reporting of alcohol abuse or dependence with not ready to stop using.
Most participants endorsed more than one barrier. The most reported barriers to care were not ready to stop using (38%), followed by affordability (35%), access (32.5%), stigma (27.0%) and attitudinal barriers (19.4%). These proportions are consistent with what has been reported in the literature, with not being ready to stop using being the most prevalent reason for not seeking SUD treatment (5).Yet, our multivariable regression analysis showed that common barriers to treatment entry do not necessarily impact a person’s readiness to stop using. SUD is a complex disease process and treatment engagement is multifactorial, often reflecting the interplay of systemic and individual factors (9) (28). Addressing access and attitudinal barriers to care often results in greater treatment utilization (28, 29). However, other factors such as multispecialty, effective care team, treatment of comorbid conditions, and community outreach affect treatment utilization (28) (30). Additionally, patients may also identify housing, employment, and family care as priorities over receiving treatment (28) Aligning with the transtheoretical model of behavior change, access, affordability, and attitudinal barriers each represent a distinct complicating factor that can impede recovery progress and the weight of each individual barrier on treatment utilization is highly variable among the SUD population (9).
Of note, higher level of education and middle-class income were also associated with higher odds of reporting not being ready to stop using readiness. Together, these findings portray a certain type of user –educated, employed, middle-class individual- as not being ready to stop using substances despite recognizing their need for treatment. As indicated in systematic reviews, higher SES (versus lower SES) is associated with fewer negative substance use-related consequences in part due to moderating effects associated with class (e.g., having available resources)(31) . Consequently, individuals of higher SES may not be motivated to stop their substance misuse. While not possible with the NSDUH dataset, future research could more closely examine the construct “readiness” (i.e., perceiving a need to change a behavior versus being ready to change a behavior) among individuals of higher SES who misuse substances yet are not ready to stop using substances. Also, we found that participants who self-reported as bisexual were significantly more likely to not be ready to stop using versus their heterosexual peers. Available research suggests that bisexual people may be especially susceptible to substance use problems compared to other sexual identity groups, and minority stress may be an important risk factor(32, 33). Thus, providers may acknowledge the unique experience of bisexual identity and minority stress among their SUD patients and the impact on recovery(33).
The type of substance the patient is misusing was also associated with not being ready to stop using substances. Our analysis shows that alcohol dependence or abuse independently doubles the odds of not being ready to stop using substances. This finding is consistent with prior research that found that not being ready to stop using was the primary barrier to treatment entry among young adults with alcohol use disorder (34, 35). Additionally, marijuana abuse or dependence independently nearly doubles the odds of not being ready to stop using substances.The legalization and widespread availability of alcohol, and now marijuana in many states, and the normalization and acceptance of their use in society potentially plays a role in why there is less readiness to stop using alcohol and marijuana versus illicit substance use (36) (37) (38, 39).
Lastly, our findings revealed that each additional increase in SUD symptoms also was associated with higher odds of reporting not being ready to stop using substances. Literature shows that the association between severity of SUD and readiness to stop using may vary depending on the type of substance an individual uses (24) (25) (40). For example, prior studies have demonstrated that severity of a drinking problem is associated with greater perceived need for treatment (41) and readiness to stop using (34). On the other hand, another study found that severe SUD with intravenous drug use was a significant factor associated with ongoing drug use at 6 months follow up (42). This finding can also be explained by concern of experiencing more severe withdrawal symptoms and the need for ongoing drug use for symptom management (26). In regard to practice, providers may discuss with patients what to specifically expect when stopping use and their different treatment options to help mitigate the intensity of withdrawal(43). While we did not find that physical or mental health impacted not being ready to stop using, providers may also help patients to find alternative ways to cope with mental health symptoms or physical pain (44), as substance use is often used to self-medicate.
Engagement in treatment is influenced by motivational and individual considerations and there is mixed data of whether treatment readiness will ultimately result in treatment engagement (45) (40) (46). Thus, providers should consider applying specific motivation strategies to promote action (47-49) and consider small meaningful steps such as harm reduction interventions (50), as some individuals may feel more ready to reduce use. Our findings suggest that individuals with barriers to treatment, especially related to access and attitudes about treatment, are actually less inhibitive to a person’s decision to stop using. On one hand, this may signal that these individuals may have fewer traditional hurdles to clear prior to changing their use; on the other hand, our counterintuitive findings lend that often less-examined barriers (e.g., those that are existential, like social connections made and maintained when using) may be very difficult to overcome. While there is a dearth of literature exploring barriers to treatment and not ready to stop use among persons with a perceived unmet need for care, it is difficult to determine how our findings compare to other studies. Overall, providers and clinical researchers alike may wish to explore with patients their other reasons beyond traditional treatment barriers for why they are not ready to stop using.
It is also important to consider that motivation to change varies depending on the type of substance and, thus, promoting change, specifically stopping use, may be challenging for patients with alcohol abuse or dependence Yet, baseline motivational readiness for change has shown to impact decreases in alcohol use among patients, even as far as three years out from treatment initiation (51-53). Thus, careful history taking, accurate assessment of number of substances used and SUD symptoms, and identification of both traditional and personal barriers to care are crucial when initiating a therapeutic relationship with a patient who is not ready to stop using.
Limitations
Limitations to the study are noted. First, the variables used in this analysis are derived from participant self-report, and there may be at risk of under- or overreporting behaviors. Second, the NSDUH only captures individuals in households; people who have SUDs and who also have unstable housing and/or are incarcerated persons will not be captured in the NSDUH. Third, the data used in this analysis are cross-sectional which limits causal inference regarding the association between individual characteristics among those with a perceived unmet need for treatment and who are not ready to stop using substances. Finally, the type I error rate should be kept in mind when reading the results since there were no multiple comparison corrections employed.
Conclusion
Historically, motivation to enter SUD treatment has been problematic, and the relationship between readiness to change and treatment initiation has not been straightforward This study focuses on a group of participants that reported a perceived unmet need for substance use treatment, noting that a perceived need implies some degree of insight into one's own problematic behavior. However, acknowledging a need for treatment does not translate into seeking treatment. Identifying not being ready to stop using substances as a barrier for treatment captures a group of individuals that are static in the contemplation stage of the process of change. We demonstrate that both alcohol and marijuana dependence or abuse alone nearly double the odds of not being ready to stop using. Education, income level, sexual identity, and number of SUD symptoms were also associated with not ready to stop using substances. Providers should consider ways to address client concerns about cessation of substance use (e.g., onset withdrawal symptoms, impact on social connections) as a strategy to promote treatment uptake.
Financial Support:
This work was supported by the National Institutes of Health (NIH) [Grant No: K02 DA043657 (Dr. Cavazos-Rehg)], and Grants No: T32DA015035 and K12DA041449 from the National Institute on Drug Abuse. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse or the National Institutes of Health.
Appendix I
| Treatment barriers |
|---|
| Access reasons |
| You did not know where to go to get treatment. |
| You didn’t find a program that offered the type of treatment or counseling you wanted. |
| There were no openings in the programs. |
| You had no transportation to a program, or the programs were too far away, or the hours were not convenient. |
| Affordability reasons |
| You had no health care coverage, and you couldn’t afford the cost. |
| You did have health care coverage, but it didn’t cover treatment, or didn’t cover the full cost. |
| Stigma reasons |
| You were concerned that getting treatment or counseling might cause your neighbors or community to have a negative opinion of you. |
| You were concerned that getting treatment or counseling might have a negative effect on your job. |
| You didn't want others to find out that you needed treatment. |
| Attitudinal reasons |
| You didn't think you needed treatment at the time. |
| You thought you could handle the problem without treatment. |
| You didn't think treatment would help. |
| You didn't have time (because of job, childcare, or other commitments). |
Footnotes
Disclosures: Dr. Szlyk has served as a consultant with Google Health. We have no other disclosures to report.
References
- 1.Han B. Key substance use and mental health indicators in the United States: Results from the 2019 National Survey on Drug use and Health. Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. 2020 [Google Scholar]
- 2.Rudd RA, Aleshire N, Zibbell JE, Gladden RM. Increases in drug and opioid overdose deaths—United States, 2000–2014. Morb Mortal Wkly Rep. 2016;64(50 & 51):1378–82. [DOI] [PubMed] [Google Scholar]
- 3.Substance Abuse and Mental Health Services. Treating substance use disorder in older adults. Treatment Improvement Protocol (TIP) Series No. 26. Substance Abuse and Mental Health Services Administration; Rockville, MD; 2020. [Google Scholar]
- 4.Substance Abuse and Mental Health Services. Substance use disorder treatment for people with physical and cognitive disabilities. Advisory. U.S. Department of Health and Human Services, Substance Abuse and Mental Health Services. 2019. [PubMed] [Google Scholar]
- 5.Ali MM, Teich JL, Mutter R. Reasons for not seeking substance use disorder treatment: variations by health insurance coverage. J Behav Health Serv Res. 2017;44(1):63–74. [DOI] [PubMed] [Google Scholar]
- 6.Feder KA, Mojtabai R, Musci RJ, Letourneau EJ. US adults with opioid use disorder living with children: Treatment use and barriers to care. J Subst Abuse Treat. 2018;93:31–7. [DOI] [PubMed] [Google Scholar]
- 7.Urbanoski KA, Cairney J, Bassani DG, Rush BR. Perceived unmet need for mental health care for Canadians with co-occurring mental and substance use disorders. Psychiatr Ser. 2008;59(3):283–9. [DOI] [PubMed] [Google Scholar]
- 8.Priester MA, Browne T, Iachini A, Clone S, DeHart D, Seay KD. Treatment Access Barriers and Disparities Among Individuals with Co-Occurring Mental Health and Substance Use Disorders: An Integrative Literature Review. J Subst Abuse Treat. 2016;61:47–59. Epub 20151031. doi: 10.1016/j.jsat.2015.09.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Substance Abuse and Mental Health Services Administration. Substance Use Disorder Treatment for People With Physical and Cognitive Disabilities. Advisory. U.S. Department of Health and Human Services, Substance Abuse and Mental Health Services Administration. 2019. [PubMed] [Google Scholar]
- 10.Martínez C, Guydish J, Le T, Tajima B, Passalacqua E. Predictors of quit attempts among smokers enrolled in substance abuse treatment. Addict Behav. 2015;40:1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Simpson DD, Joe GW. Motivation as a predictor of early dropout from drug abuse treatment. Psychotherapy: Theory, research, practice, training. 1993;30(2):357. [Google Scholar]
- 12.DiClemente CC. Motivation for change: Implications for substance abuse treatment. Psychol Sci. 1999;10(3):209–13. [Google Scholar]
- 13.Andersson HW, Wenaas M, Nordfjærn T. Relapse after inpatient substance use treatment: A prospective cohort study among users of illicit substances. Addict Behav. 2019;90:222–8. Epub 20181111. doi: 10.1016/j.addbeh.2018.11.008. [DOI] [PubMed] [Google Scholar]
- 14.Cahill MA, Adinoff B, Hosig H, Muller K, Pulliam C. Motivation for treatment preceding and following a substance abuse program. Addict Behav. 2003;28(1):67–79. doi: 10.1016/s0306-4603(01)00217-9. [DOI] [PubMed] [Google Scholar]
- 15.Xie S, Minami H, Selva Kumar D, Hecht J, Litvin Bloom E, Kahler CW, et al. Readiness to quit smoking among smokers in substance use treatment: associations with stress, substance use severity, relapse concerns and gender. J Subst Use. 2021;26(6):669–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Maier LJ, Ramo DE, Kaur M, Meacham MC, Satre DD. Factors associated with readiness to quit smoking among young adults enrolled in a Facebook-based tobacco and alcohol intervention study. Addict Behav. 2020;111:106524. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Aoun S, Pennebaker D, Wood C. Assessing population need for mental health care: a review of approaches and predictors. Ment. Health Serv. Res 2004;6(1):33–46. [DOI] [PubMed] [Google Scholar]
- 18.Ali MM, Nye E, West K. Substance use disorder treatment, perceived need for treatment, and barriers to treatment among parenting women with substance use disorder in US rural counties. J Rural Health. 2022;38(1):70–6. [DOI] [PubMed] [Google Scholar]
- 19.Substance Abuse and Mental Health Services. Results from the 2013 National Survey on Drug Use and Health: Summary of national findings. NSDUH Series H-48, HHS Publication No(SMA) 14-4863. 2014:1–143. [Google Scholar]
- 20.Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999;8(1):3–15. doi: 10.1177/096228029900800102. [DOI] [PubMed] [Google Scholar]
- 21.Apsley HB, Vest N, Knapp KS, Santos-Lozada A, Gray J, Hard G, et al. Non-engagement in substance use treatment among women with an unmet need for treatment: A latent class analysis on multidimensional barriers. Drug Alcohol Depend. 2023;242:109715. Epub 20221205. doi: 10.1016/j.drugalcdep.2022.109715. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Bell CC. DSM-IV: diagnostic and statistical manual of mental disorders. JAMA. 1994;272(10):828–9. [Google Scholar]
- 23.Kessler RC, Barker PR, Colpe LJ, Epstein JF, Gfroerer JC, Hiripi E, et al. Screening for serious mental illness in the general population. Arch Gen Psychiatr. 2003;60(2):184–189. [DOI] [PubMed] [Google Scholar]
- 24.Baiden P, Tarshis S, Antwi-Boasiako K, den Dunnen W. Examining the independent protective effect of subjective well-being on severe psychological distress among Canadian adults with a history of child maltreatment. Child Abuse Negl. 2016;58:129–40. [DOI] [PubMed] [Google Scholar]
- 25.Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek DK, Normand S-L, et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med. 2002;32(6):959–76. [DOI] [PubMed] [Google Scholar]
- 26.McVeigh KH, Galea S, Thorpe LE, Maulsby C, Henning K, Sederer LI. The epidemiology of nonspecific psychological distress in New York City, 2002 and 2003. J Urban Health. 2006;83(3):394–405. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Center for Behavioral Health Statistics and Quality. 2019 National Survey on Drug Use and Health Public Use File Codebook. In: Substance Abuse and Mental Health Services, editors. 2020. [Google Scholar]
- 28.Madras BK, Ahmad NJ, Wen J, Sharfstein JS. Improving access to evidence-based medical treatment for opioid use disorder: Strategies to address key barriers within the treatment system. NAM perspectives. 2020;2020. doi: 10.31478/202004b [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Chan Carusone S, Guta A, Robinson S, Tan DH, Cooper C, O'Leary B, et al. "Maybe if I stop the drugs, then maybe they'd care?"-hospital care experiences of people who use drugs. Harm Reduct J. 2019;16(1):16. Epub 20190213. doi: 10.1186/s12954-019-0285-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Chatterjee A, Yu EJ, Tishberg L. Exploring opioid use disorder, its impact, and treatment among individuals experiencing homelessness as part of a family. Drug Alcohol Depend. 2018;188:161–8. [DOI] [PubMed] [Google Scholar]
- 31.Collins SE. Associations Between Socioeconomic Factors and Alcohol Outcomes. Alcohol Res. 2016;38(1):83–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Capistrant BD, Nakash O. Lesbian, gay, and bisexual adults have higher prevalence of illicit opioid use than heterosexual adults: Evidence from the National Survey on Drug Use and Health, 2015-2017. LGBT Health. 2019;6(6):326–30. doi: 10.1089/lgbt.2019.0060. [DOI] [PubMed] [Google Scholar]
- 33.Schulz CT, Glatt EM, Stamates AL. Risk factors associated with alcohol and drug use among bisexual women: A literature review. Exp Clin Psychopharmacol. 2022;30(5):740–9. Epub 20210610. doi: 10.1037/pha0000480. [DOI] [PubMed] [Google Scholar]
- 34.Alley ES, Velasquez MM, von Sternberg K. Associated factors of readiness to change in young adult risky drinkers. Am J Drug Alcohol Abuse. 2018;44(3):348–357. [DOI] [PubMed] [Google Scholar]
- 35.Wu LT, Pilowsky DJ, Schlenger WE, Hasin D. Alcohol use disorders and the use of treatment services among college-age young adults. Psychiatr Serv. 2007;58(2):192–200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Sznitman SR, Kolobov T, Ter Bogt T, Kuntsche E, Walsh SD, Boniel-Nissim M, et al. Exploring substance use normalization among adolescents: A multilevel study in 35 countries. Soc Sci Med. 2013;97:143–51. [DOI] [PubMed] [Google Scholar]
- 37.Sudhinaraset M, Wigglesworth C, Takeuchi DT. Social and cultural contexts of alcohol use: Influences in a social-ecological framework. Alcohol research: current reviews. 2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Han B, Compton WM, Blanco C, Jones CM. Trends in and correlates of medical marijuana use among adults in the United States. Drug Alcohol Depend. 2018;186:120–9. Epub 20180314. doi: 10.1016/j.drugalcdep.2018.01.022. [DOI] [PubMed] [Google Scholar]
- 39.Chiu V, Hall W, Chan G, Hides L, Leung J. A Systematic Review of Trends in US Attitudes toward Cannabis Legalization. Subst Use Misuse. 2022;57(7):1052–61. Epub 20220418. doi: 10.1080/10826084.2022.2063893. [DOI] [PubMed] [Google Scholar]
- 40.Pantalon MV, Swanson AJ. Use of the University of Rhode Island Change Assessment to measure motivational readiness to change in psychiatric and dually diagnosed individuals. Psychol Addict Behav. 2003;17(2):91. [DOI] [PubMed] [Google Scholar]
- 41.Smith JJ, Spanakis P, Gribble R, Stevelink SA, Rona RJ, Fear NT, et al. Prevalence of at-risk drinking recognition: A systematic review and meta-analysis. Drug Alcohol Depend. 2022:109449. [DOI] [PubMed] [Google Scholar]
- 42.Opsal A, Kristensen Ø, Clausen T. Readiness to change among involuntarily and voluntarily admitted patients with substance use disorders. Subst Abuse: Treat Prev Policy. 2019;14(1):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Day E, Daly C. Clinical management of the alcohol withdrawal syndrome. Addiction. 2022;117(3):804–14. Epub 20210822. doi: 10.1111/add.15647. [DOI] [PubMed] [Google Scholar]
- 44.Volkow ND. Personalizing the Treatment of Substance Use Disorders. Am J Psychiatry. 2020;177(2):113–6. doi: 10.1176/appi.ajp.2019.19121284. [DOI] [PubMed] [Google Scholar]
- 45.Sloas LB, Caudy MS, Taxman FS. Is treatment readiness associated with substance use treatment engagement? An exploratory study. J Drug Educ. 2017;47(1–2):51–67. [DOI] [PubMed] [Google Scholar]
- 46.Ziedonis DM, Trudeau K. Motivation to quit using substances among individuals with schizophrenia: implications for a motivation-based treatment model. Schizophr. Bull 1997;23(2):229–38. [DOI] [PubMed] [Google Scholar]
- 47.Smedslund G, Berg RC, Hammerstrøm KT, Steiro A, Leiknes KA, Dahl HM, et al. Motivational interviewing for substance abuse. Campbell Syst. Rev 2011;7(1): 1–126. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Carroll KM, Ball SA, Nich C, Martino S, Frankforter TL, Farentinos C, et al. Motivational interviewing to improve treatment engagement and outcome in individuals seeking treatment for substance abuse: A multisite effectiveness study. Drug Alcohol Depend. 2006;81(3):301–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Merrill JE, López G, Stevens AK, Singh S, Laws MB, Mastroleo NR, et al. Discussion of alcohol consequences during a brief motivational intervention session: comparing those who do and do not increase readiness to change. Addict Res Theory. 2022:1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Saini J, Johnson B, Qato DM. Self-Reported Treatment Need and Barriers to Care for Adults With Opioid Use Disorder: The US National Survey on Drug Use and Health, 2015 to 2019. Am J of Public Health. 2022;112(2):284–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.DiClemente CC, Carbonari JP, Zweben A and Morrell T,. Motivational readiness to change: A causal chain analysis. Rockville, MD: National Institute on Alcoholism and Alcohol Abuse, 2001. [Google Scholar]
- 52.Project MATCH Group. Matching alcoholism treatments to client heterogeneity: Project MATCH post-treatment drinking outcomes. J Stud Alcohol. 1997;58:7–29. [PubMed] [Google Scholar]
- 53.Project MATCH Group. Matching alcoholism treatments to client heterogeneity: Project MATCH three-year drinking outcomes. Alcohol Clinical Exp Res. 1998. 22(6): 1300–11. [DOI] [PubMed] [Google Scholar]