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. Author manuscript; available in PMC: 2022 Dec 1.
Published in final edited form as: J Subst Abuse Treat. 2021 Jul 21;131:108569. doi: 10.1016/j.jsat.2021.108569

Drug and alcohol treatment utilization and barriers among Black, American Indian/Alaskan Native, Latine, Asian/Pacific Islander/Native Hawaiian, and White adults: Findings from NESARC-III

Angela M Haeny 1, Oladunni Oluwoye 2, Rick Cruz 3, Theddeus Iheanacho 1, Asti B Jackson 1, Sycarah Fisher 4, Maria Crouch 1,5, Stephanie O’Malley 1
PMCID: PMC9084614  NIHMSID: NIHMS1732987  PMID: 34393011

Abstract

Background.

Existing epidemiological data suggest differences across racial/ethnic groups in drug and alcohol treatment utilization and barriers to treatment and typically include only Black, Latine, and White adults. The objective of this study was to examine whether disparities remain for DSM-5 lifetime alcohol use disorder (AUD) and drug use disorder (DUD) treatment utilization and barriers across Black, American Indian/Alaska Native (AI/AN), Latine, Asian/Pacific Islander/Native Hawaiian (Asian/PI/NH), and White adults.

Methods.

The current study conducted secondary analyses on data from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC-III). Regression analyses, followed by pairwise comparisons, investigated differences across racial/ethnic groups.

Results.

Analyses indicated differences across racial/ethnic groups in AUD treatment utilization. White and AI/AN adults were more likely to utilize a health care professional than were Black adults. Asian/PI/NH and Latine adults were more likely to endorse language as a barriers to AUD treatment than were White adults. Black adults were more likely to use 12-step programs for DUD treatment utilization than were White and Latine adults, and Black and White adults were more likely to use outpatient programs than were Latine adults. Further, Black adults were more likely than Asian/PI/NH and Latine adults to use specialty DUD treatment. AI/AN, Asian/PI/NH, and White adults were more likely to endorse fear of what others would think as a barrier to DUD treatment relative to Black adults. AI/AN adults were more likely to endorse fear of being hospitalized relative to Black, Latine, and White adults. Asian/PI/NH and Latine adults were more likely to indicate that the hours were inconvenient relative to Black and White adults. White adults were more likely to endorse a family member objected relative to Black adults. AI/AN and White adults were more likely to endorse they stopped on their own relative to Black, Asian/PI/NH, and Latine adults. Further, AI/AN and White adults reported the greatest number of barriers to DUD treatment.

Conclusions.

Differences remain across racial/ethnic group in drug and alcohol treatment utilization and barriers to treatment. Future research aimed at increasing treatment utilization across racial/ethnic groups should focus on social determinants of health.

Keywords: Alcohol use disorder, Drug use disorders, Treatment utilization, Barriers to treatment, Racial disparities

1. Introduction

Alcohol and drug use disorders (AUD and DUDs) are major public health concerns and have significant impacts on the individual, families, and communities (Degenhardt et al., 2018). National epidemiological data estimate that 29.1% and 9.9% of the U.S. population have a lifetime AUD or DUD, respectively, and these prevalence rates vary across racial/ethnic group (Grant et al., 2016; Grant, Goldstein, Saha, et al., 2015). Notably, the lifetime prevalence of AUD (43.4%) and DUD (17.2%) among American Indian/Alaska Native (AI/AN) adults (Grant et al., 2016; Grant, Goldstein, Saha, et al., 2015) has been consistently higher than any other racial/ethnic group (Compton et al., 2007; Grant et al., 2004). The lifetime prevalence of AUD (32.6%) and DUD (10.8%) among White adults is higher relative to Black (AUD: 22.0%; DUD: 9.9%) and Latine1 adults (AUD: 22.9%; DUD: 7.2%; Grant et al., 2016; Grant, Goldstein, Saha, et al., 2015). Asian adults consistently have had the lowest lifetime prevalence of AUD (15.0%) and DUD (4.0%; Compton et al., 2007; Grant et al., 2016; Grant, Goldstein, Saha, et al., 2015).

Among those with lifetime AUD and DUD, only 19.8% and 24.6% report seeking treatment, respectively (Grant et al., 2016; Grant, Goldstein, Saha, et al., 2015). Prior studies investigating substance use disorder (SUD) treatment utilization across racial/ethnic groups are often limited to Black, Latine, and White people and the findings are contradictory (Keyes et al., 2008; Perron et al., 2009; Schmidt et al., 2006; Weisner et al., 2002). Studies that find differences suggest that Black adults are more likely than White adults, and White adults are more likely than Latine adults, to use AUD treatment (Weisner et al., 2002). Black and Latine adults are more likely to utilize DUD treatment relative to White adults (Perron et al., 2009). Prior research suggests that White adults are more likely to have utilized specialty SUD treatment (e.g., SUD rehabilitation program) in the past year than Latine and Black adults (Pinedo, 2019). Black adults were more likely than White and Latine adults to use 12-step, rehabilitation, and outpatient programs; Black adults were more likely than White adults to use inpatient, clergy, and other services; White adults were more likely than Black adults to use professional services; and Black adults reported using more services than Latine and White adults (Perron et al., 2009).

Epidemiological data on SUD treatment utilization among AI, AN, Asian, Pacific Islander (PI), and Native Hawaiian (NH) people are limited (Vaeth et al., 2017). Relative to White people, AI and AN adults were more likely (Emerson et al., 2019; Wu et al., 2003) and Asian and PI people were less likely to utilize SUD treatment (Wu et al., 2003). In a sample of AI people with a lifetime SUD from the Southwest or Northern Plains, the rate of any specialty SUD treatment utilization was higher than national estimates from the general population, and treatment was sought from both traditional healers and Western services (Beals et al., 2003, 2005). Studies that include Asian, PI, and NH participants suggest that they are less likely to utilize specialty drug treatment (Wu et al., 2016, 2017). A smaller study found no differences between Asian, NH, or White people on the most commonly utilized treatments in the sample: self-help groups and the emergency department; however, White people were more likely than Asian and NH people to seek SUD treatment from a mental health provider or physician (Goebert & Nishimura, 2011).

Research also suggests racial disparities in unmet need and barriers to SUD treatment. Research has defined unmet need for SUD treatment as those who meet criteria for SUD but have never sought treatment, or those who report not seeking SUD treatment despite perceiving the need for it (Aoun et al., 2004). Roughly 58% of adults with SUD in the United States go without treatment despite perceiving need for it (Green et al., 2020). For specialty SUD treatment, Latine and Asian adults had higher and Black adults had lower unmet treatment need than White adults (Mulvaney-Day et al., 2012). Earlier epidemiological data indicate no differences in reasons for not seeking treatment or the number of barriers to treatment across Black, Latine, and White adults (Perron et al., 2009). Prior research has examined racial disparities in three types of barriers to treatment: structural (e.g., didn’t know where to go), attitudinal (e.g., afraid of what boss, friends, or family would think), and readiness to change barriers (e.g., wanted to keep drinking/using; Verissimo and Grella, 2017). Black and Latine adults were more likely to endorse structural barriers to AUD treatment than were White adults, and White adults were more likely to endorse attitudinal and readiness to change barriers to AUD and DUD treatment (Verissimo & Grella, 2017). A qualitative study of AI people identified four types of barriers to SUD treatment: personal (e.g., did not want to stop drinking, afraid to seek help), pragmatic (e.g., access, finances, time), concerns about type of available help (e.g., effectiveness), and social network (e.g., no help-seeking supports) (Venner et al., 2012). However, epidemiological studies on barriers to SUD treatment among AN and AI people is lacking relative to other racial/ethnic groups.

Existing studies that document differences in treatment utilization and barriers to treatment across racial/ethnic group are based on older epidemiological data (Mulvaney-Day et al., 2012; Perron et al., 2009; Verissimo & Grella, 2017), use outdated diagnostic criteria (Pinedo, 2019; Verissimo & Grella, 2017), are limited to either alcohol (Vaeth et al., 2017) or drug treatment (Perron et al., 2009), or include only Black, Latine, and White people (Perron et al., 2009; Pinedo, 2019; Verissimo & Grella, 2017). Racial disparities stem from historical and ongoing racism and oppression of People of Color that contribute to inequitable access to opportunities (e.g., quality education; higher-paying jobs) and resources (e.g., safe housing; quality health care; Jones, 2000; Williams et al., 2019; Yearby, 2018). Investigating whether racial disparities in treatment utilization and barriers to treatment remain is essential for identifying steps for mitigating and ultimately eliminating disparities. The current study updates the literature by reporting whether racial/ethnic differences exist in both lifetime AUD and DUD treatment utilization and barriers to treatment based on the most recent diagnostic criteria and including all major racial/ethnic groups in the United States.

2. Methods

2.1. Participants

The current study conducted secondary analyses on the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III; Grant et al., 2016, 2015a), which is a U.S. national sample of noninstitutionalized civilian adults age 18 years and older (N = 36,309). Multistage probability sampling identified participants while oversampling for Black and Latine adults. The NESARC research team obtained informed consent electronically, conducted in-person interviews on drug and alcohol use and related conditions, and compensated participants $90. For the purposes of the current study, the analyses use a subset of the sample with DSM-5 lifetime AUD (N = 10,001, 40.5% female, 17.2% Black, 15.9% Latine, 2.1% AI/AN, 2.8% Asian/PI/NH, 62.0% White) and DUD (N = 3,548, 40.2% female, 21.0% Black, 14.5% Latine, 2.5% AI/AN, 2.1% Asian/PI/NH, 59.9% White). Given that the current study involved secondary analyses of a de-identified dataset, the Yale Institutional Review Board deemed the study exempt.

2.2. Measures

Participants self-reported their demographic information. NESARC researchers created a single race and ethnicity variable (referred to here as racial/ethnic group). The NESARC research team categorized any participant who self-identified as “Hispanic” ethnicity regardless of their self-identified race as Latine. Further, NESARC researchers categorized non-Hispanic Black, AI, AN, Asian, PI, NH, and White people as Black, AI/AN, Asian/PI/NH (combined due to small sample sizes), and White, respectively. Although participants could endorse more than one racial identity, NESARC researchers re-categorized participants who identified as multi-racial into one racial/ethnic group based on an algorithm developed by the U.S. Census Bureau (Grant, Goldstein, Saha, et al., 2015). The analyses used the following categorical variables: race/ethnicity (White, Black, AI/AN, Asian/PI/NH, Latine); age (18–29, 20–44, 45–64, and 65+years); sex (female, male); income (0–19999, 20000–34999, 35000–69999, and 70000+dollars); education level (less than high school, high school, some high school or higher); urbanicity (urban or rural); and medical insurance (uninsured, public insurance, private insurance).

The NESARC research team gathered diagnostic information using the National Institute on Alcohol Abuse and Alcoholism Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5) (Grant, Goldstein, Smith, et al., 2015). The AUDADIS-5 assesses DSM-5 AUD, DUDs (nicotine, sedative, cannabis, opioid, cocaine, stimulant, hallucinogen, inhalant/solvent, club drug, heroin, and other DUD), and selected mood, anxiety, trauma-related, and personality disorders. Notably, the researchers did not include nicotine use disorder when creating the DUD variable. Nine hundred and seventy lay persons with 5 or more years of experience with health-related surveys conducted the interviews. Prior studies provide evidence of test-retest reliability, procedural validity, and concurrent validity of the AUDADIS-5 (Grant et al., 2016; Grant, Goldstein, Saha, et al., 2015).

During the interviews, the NESARC research team asked participants “Have you ever gone anywhere or seen anyone for a reason that was related in any way to your drinking [use of medicines or drugs]—a physician, counselor, Alcoholics Anonymous, or any other community agency or professional?” They then presented participants with a list of (13 for alcohol 14 for drug) types of services utilized and participants responded yes or no as to whether they utilized each service. The NESEARC research team asked participants who endorsed utilizing a service to report the time frame during which they utilized each service (i.e., last 12 months only, before the last 12 months only, both time periods). The NESARC research team also asked participants, “Was there ever a time when you thought you should see a doctor, counselor, or other health professional or seek any other help for your drinking [drug use], but you didn’t go?” Participants who endorsed this question specified whether this happened during the last 12 months and before the last 12 months and checked all that apply from a list of 29 reasons for not seeking treatment. For the purposes of this study, we focused on lifetime use of each service and barriers to treatment. Tables 1–4 provide the list of the treatment services utilized and barriers to treatment. This study defined specialty alcohol treatment as those endorsing inpatient, outpatient, or rehabilitation (Pinedo, 2019), and specialty drug treatment included these services and methadone maintenance programs.

Table 1.

Lifetime treatment utilization by race/ethnic group among those with DSM-5 lifetime alcohol use disorder who reported using at least one type of service: Adjusted models

White (n=1317) Black (n=337) American Indian/Alaska Native (n=78) Asian/Pacific Islander/Native Hawaiian (n=33) Latine (n=304)
Type of Treatment or Help % (SE) % (SE) % (SE) % (SE) % (SE)
Ever sought any alcohol treatment* 20.51 (.53)a,b,c 19.57 (.99)d,e 33.80 (3.43)a,d,f,g 9.86 (1.53)b,e,f,h 17.45 (.88)c,g,h
1. 12-step Program 77.53 (1.46) 80.99 (2.83) 77.01 (5.90) 72.79 (7.75) 77.27 (2.60)
2. Family/social services 19.76 (1.41) 27.63 (3.08) 30.84 (7.37) 7.61 (4.44) 21.88 (3.23)
3. Detoxification 30.25 (1.52) 47.03 (3.23) 33.20 (6.12) 17.69 (7.78) 29.15 (3.41)
4. Other inpatient facility 23.61 (1.34) 27.46 (3.27) 22.56 (5.44) 13.05 (5.99) 18.84 (2.63)
5. Outpatient clinic 32.59 (1.55) 40.57 (4.04) 35.05 (7.54) 28.81 (9.43) 25.41 (3.35)
6. Rehabilitation program 44.65 (1.79) 58.49 (3.22) 49.31 (6.57) 34.38 (9.12) 43.09 (3.59)
7. Emergency department 29.29 (1.45) 24.45 (2.68) 33.73 (5.36) 22.41 (8.38) 26.99 (3.09)
8. Halfway house 8.51 (0.89) 14.41 (2.07) 15.57 (4.58) 17.15 (7.90) 11.33 (2.52)
9. Crisis center 3.82 (0.62) 7.74 (1.62) 9.63 (2.90) 1.90 (1.57) 4.89 (1.46)
10. Employee-assistance program 5.68 (0.73) 9.77 (2.11) 4.97 (2.67) 8.14 (5.84) 6.02 (1.19)
11. Clergy 13.62 (1.13) 18.19 (2.92) 22.68 (4.55) 22.26 (8.02) 18.39 (2.34)
12. Physician or other health care professional* 45.83 (1.81)a 35.49 (3.14)a,b 55.27 (7.05)b,c 27.97 (7.92)c 36.92 (3.36)
13. Any other agency or professional 9.33 (1.01) 8.78 (1.90) 6.54 (2.12) 7.21 (5.16) 8.70 (1.51)
Lifetime use of specialty treatment 55.72 (1.68) 69.50 (2.95) 60.39 (6.94) 46.10 (10.14) 52.93 (3.34)
Number of Services Utilized 3.25 (.12) 3.34 (.16) 3.44 (.42) 2.70 (.42) 3.16 (.21)
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Note. - = frequency was zero.

*

= omnibus Wald F test in unadjusted model significant at p < .05.

Cells with the same letters represent pairwise differences based on unadjusted survey regression models (p < .05). No superscript in a cell means the cell was not different from any other cell.

Table 4.

Reasons for not seeking treatment by race/ethnic group among those with a lifetime drug use disorder who endorsed needing treatment but they did not go: Adjusted models

White (n=445) Black (n=177) American Indian/Alaska Native (n=19) Asian/Pacific Islander/Native Hawaiian (n=16) Latine (n=117)
Needed drug treatment but did not go 20.59 (0.88) 25.58 (2.46) 22.09 (4.17) 19.90 (3.19) 21.77 (2.02)
Number of Barriers Endorsed* 4.70 (.22)a,c,d,e 4.56 (.27)b,c,d,e 5.60 (.34)a,b,c,d,e 6.35 (. 17)a,b,c,d,e 3.23 (.08)a,b,c,d,e
Structural Barriers
1. Health insurance didn’t cover 18.72 (2.47) 12.85 (3.03) 10.74 (7.79) 26.87 (11.26) 12.37 (3.20)
3. Didn’t know any place to go for help 11.17 (1.76) 11.48 (3.16) 7.77 (5.49) 21.51 (10.28) 12.04 (3.16)
4. Couldn’t afford the bill 24.38 (2.49) 12.64 (3.32) 12.83 (8.38) 12.10 (8.96) 12.49 (3.37)
5. Didn’t have a way to get there 5.07 (1.26) 6.16 (1.96) 11.58 (8.10) - 2.69 (1.47)
6. Didn’t have time 9.65 (1.93) 10.20 (2.37) 21.55 (12.63) 2.75 (2.76) 5.90 (1.94)
17. Can’t speak English very well 0.60 (0.60) - - - 1.35 (0.95)
19. Couldn’t arrange for child care 4.98 (1.18) 1.65 (1.09) - - 1.34 (0.98)
20. Had to wait too long to get into a program 6.39 (1.23) 7.19 (2.01) 4.51 (3.88) - 5.01 (2.85)
Attitudinal Barriers
2. Didn’t think anyone could help 15.19 (1.77) 15.38 (2.88) 24.27 (8.61) 1.70 (1.72) 13.59 (4.29)
7. Thought the problem would get better by itself 34.25 (2.97) 38.88 (4.81) 31.23 (7.60) 66.10 (17.17) 30.65 (4.03)
8. Too embarrassed to discuss it with anyone 29.40 (2.36) 24.66 (3.85) 33.67 (9.28) 10.41 (8.12) 20.59 (3.86)
9. Afraid of what boss, friends, family or others would think* 21.62 (2.38)a,b,g 7.58 (2.22)b,c,d 35.29 (12.40)c,e,f 46.95 (17.89)d,e,g,h 10.70 (3.10)a,f,h
10. Thought should be strong enough to handle alone 42.26 (2.98) 33.08 (5.07) 50.68 (10.59) 45.36 (15.54) 37.34 (5.44)
11. Afraid they would put me into the hospital* 13.57 (1.84)a 6.82 (1.96)a,b 30.52 (10.21)b,c 9.34 (8.71) 7.60 (2.43)c
12. Afraid of the treatment they would give me 12.09 (1.81) 6.60 (1.97) 13.78 (7.78) 7.37 (2.26) 4.58 (1.90)
22. Didn’t think drug problem was serious enough 20.48 (2.15) 22.08 (3.85) 38.68 (8.40) 24.52 (11.93) 17.42 (3.56)
23. Didn’t want to go 33.96 (2.53) 24.98 (4.26) 19.34 (8.12) 28.66 (12.00) 25.82 (3.91)
26. Tried getting help before and it didn’t work 10.06 (1.85) 10.73 (3.08) 11.68 (8.24) 2.75 (2.76) 7.49 (2.81)
Readiness to Change Barriers
21. Wanted to keep using drugs 34.83 (2.59) 20.17 (3.72) 37.02 (9.17) 37.56 (14.83) 30.63 (6.39)
24. Stopped using on my own* 24.01 (2.19)a,b,f 10.69 (2.86)b,c 42.87 (9.71)a,c,d,e 12.41 (4.84)e 13.79 (3.04)d,f
25. Friends or family helped me stop using 7.85 (1.61) 3.76 (1.75) - - 4.86 (1.53)
Other Barriers
13. Hated answering personal questions 11.51 (1.62) 8.29 (2.31) 17.18 (8.06) 11.36 (10.35) 11.65 (3.06)
14. Hours were inconvenient* 2.47 (0.69)a,d 0.96 (0.69)b,c - 14.98 (9.74)a,c 4.71 (2.53)b,d
15. A member of my family objected* 1.65 (0.55)a 0.17 (0.17)a - - -
16. Family thought I should go but I didn’t think it was necessary 14.10 (2.08) 13.06 (2.96) 17.07 (7.91) - 10.66 (2.81)
18. Afraid I would lose my job* 11.12 (1.73)a 5.76 (1.82) 15.62 (10.18) 2.75 (2.76) 3.94 (1.29)a
27. Afraid children would be taken away 7.68 (0.82) 4.34 (1.66) 3.02 (3.30) - 6.55 (2.29)
28. Religious beliefs prevent going for treatment - - - - -
29. Other reason 5.48 (1.29) 6.19 (1.59) 3.36 (3.40) - 6.43 (2.47)
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Note. - = frequency was zero.

*

= omnibus Wald F test significant at p < .05.

Cells with the same letters represent pairwise differences based on adjusted survey regression models (p < .05). No superscript in a cell means the cell was not different from any other cell.

2.3. Data analysis

All analyses used SAS software (SAS 9.4; SAS Institute Inc, Cary, North Carolina). The tables provide weighted percentages and means for categorical and continuous variables, respectively, for each variable of interest across racial/ethnic groups. Logistic regressions examine whether treatment utilization and barriers to treatment varied across racial/ethnic group among those with lifetime AUD (N = 10,001) or DUD (N = 3,548). To update Perron and colleagues (2009) report, regression analyses investigate the number of services utilized and barriers to treatment endorsed across racial/ethnic groups separately among those with lifetime AUD or DUD. We found limited evidence for skewness (alcohol: .90; drug: .77) or kurtosis (alcohol: .12; drug: –.04) for the number of treatment services utilized. However, we found evidence of slight skewness (alcohol: 1.99; drug: 1.45) and kurtosis (alcohol: 6.07; drug: 1.87) for the number of barriers to treatment endorsed so we ran sensitivity analyses examining the odds of endorsing a binary variable of barriers to alcohol (0 = 1 barrier [n = 555], 1 = 2+ barriers [n = 795]) and drug treatment (0 = 1–2 barriers [n = 350], 1 = 3+ barriers [n = 420]).2

For all analyses, we first ran unadjusted models. Analyses tested follow-up pairwise comparisons by changing the reference group to facilitate investigating effects across racial/ethnic groups when the omnibus statistic was significant. Additional analyses tested models adjusting for age, sex, income, education, urbanicity, and insurance status for models significant when unadjusted.3 All models used survey procedures, which accounted for the weight, stratum, and cluster variables. The text includes odds ratios and 95% confidence intervals for significant effects.

3. Results

3.1. Alcohol use disorder

3.1.1. Sample characteristics.

Supplementary Table 1 includes demographic characteristics across racial/ethnic groups among participants with lifetime AUD (N = 10,001, 40.5% female, 17.2% Black, 15.9% Latine, 2.1% AI/AN, 2.8% Asian/PI/NH, 62.0% White). AI/AN female participants represented a larger portion of the sample relative to other racial/ethnic groups. The majority of the sample was married/cohabitating, though this was lowest among Black participants relative to the other racial/ethnic groups. Most of the sample had a college degree or higher with the highest rate among Asian/PI/NH participants. The majority of participants held private health insurance with the lowest endorsement among Black and Latine participants. Roughly a quarter of participants met criteria for lifetime DUD with the highest among AI/AN (32%) and the lowest among Asian/PI/NH (18%). Asian/PI/NH participants (32 years) had the oldest mean age they first sought alcohol treatment and Latine participants had the youngest (29 years).

3.1.2. AUD treatment utilization.

Table 1 includes differences across racial/ethnic groups in treatment utilization in adjusted models. AI/AN adults had greater odds of lifetime alcohol treatment utilization relative to all other groups (OR range: 2.02–4.67, 95% CI range: 1.40, 8.19); Asian/PI/NH adults had the lowest odds of lifetime alcohol treatment utilization compared to all other groups (OR range: .21–.52, 95% CI range: .12, .80); and White adults had greater odds of lifetime treatment utilization than Latine adults (OR: 1.20, 95% CI: 1.03, 1.39). All findings remained in adjusted models except the difference between White and Black adults fell in the range of significance such that White adults had greater odds of lifetime alcohol treatment utilization than Black adults (AOR: 1.43, 95% CI: 1.22–1.68) and differences in lifetime alcohol treatment utilization between Black and Asian/PI/NH adults and Latine and Asian/PI/NH adults fell out of the range of significance. In addition, unadjusted models indicated differences in the number of lifetime alcohol treatment services utilized across racial/ethnic group. Specifically, Black adults utilized more services than White, Asian/PI/NH, and Latine adults, and AI/AN adults utilized more services than Asian/PI/NH adults. However, these differences fell out of the range of significance in the adjusted model.

The findings indicated differences across racial/ethnic groups for the specific types of treatment services utilized. AI/AN adults were more likely than Asian/PI/NH adults to utilize family/social services, and Black adults were more likely than White and Asian/PI/NH adults, but these differences did not remain in the adjusted model. Black adults were more likely than White, Latine, and Asian/PI/NH adults to utilize detoxification and rehabilitation programs, but these differences did not remain in adjusted models. Black adults were more likely than White adults to utilize a halfway house, though this did not remain in the adjusted model. AI/AN and Black adults were more likely to utilize a crisis center than White adults, though this did not remain in the adjusted model. AI/AN adults were more likely to utilize clergy than White adults, though this did not remain in the adjusted model. AI/AN and White adults were more likely than Black, Latine, and Asian/PI/NH adults to utilize a physician/health care professional. However, the only differences that remained in adjusted models indicated that White adults (AOR: 1.66, 95% CI: 1.21, 2.27) and AI/AN adults (AOR: 2.31, 95% CI: 1.27, 4.22) were more likely than Black adults and AI/AN adults were more likely than Asian/PI/NH adults (AOR: 2.80, 95% CI: 1.15, 6.58) to use a physician or health care professional for AUD treatment. The results indicated differences across racial/ethnic groups in the use of specialty alcohol treatment in unadjusted models such that Black adults were more likely to utilize specialty alcohol treatment relative to White, Asian/PI/NH, and Latine adults. However, these differences did not remain in the adjusted model.

3.1.3. Reasons for not seeking AUD treatment.

Multiple barriers to treatment were not endorsed by people from specific racial/ethnic groups. Specifically, no Black or AI/AN people endorsed “Can’t speak English very well.” No Asian/PI/NH people endorsed “Couldn’t arrange for childcare”, “Hours were inconvenient”, or “A member of my family objected.” In addition, no AI/AN people endorsed “Religious beliefs prevent going for treatment.” Table 2 presents differences across racial/ethnic groups in barriers to alcohol treatment in adjusted models. We found differences across racial/ethnic groups among those with a lifetime AUD who thought they should seek treatment but did not go such that Asian/PI/NH adults had the lowest endorsement compared to all other groups and AI/AN and Black adults had higher endorsement than White and Latine adults; however, these effects did not remain in the adjusted model.

Table 2.

Barriers to treatment by racial/ethnic group among those with a lifetime alcohol use disorder who endorsed needing treatment but did not go: Adjusted models

White (n=938) Black (n=308) American Indian/Alaskan Native (n=47) Asian/Pacific Islander/Native Hawaiian (n=25) Latine (n=238)
Needed alcohol treatment but did not go 14.22 (.48) 18.71 (1.47) 20.27 (2.30) 8.30 (1.36) 13.79 (.93)
Number of barriers endorsed 3.67 (.16) 3.44 (.21) 4.14 (.64) 3.43 (.75) 2.98 (.19)
Structural Barriers
1. Health insurance didn’t cover 13.22 (1.13) 10.78 (1.98) 12.99 (5.98) 8.33 (6.02) 11.26 (2.70)
3. Didn’t know any place to go for help 9.54 (1.21) 10.48 (2.16) 11.33 (6.00) 18.82 (8.90) 9.69 (2.51)
4. Couldn’t afford the bill 14.87 (1.28) 19.79 (2.55) 9.88 (4.02) 15.50 (8.65) 17.56 (2.71)
5. Didn’t have a way to get there* 3.37 (0.59)a,b 6.82 (2.05)b 14.64 (6.27)a,c,d 1.22 (1.23)d 3.95 (1.32)c
6. Didn’t have time 10.78 (1.38) 10.93 (2.08) 8.80 (4.58) 8.16 (6.61) 13.61 (3.16)
17. Can’t speak English very well* 0.12 (0.05)a,b - - 3.25 (3.19)b 1.36 (1.00)a
19. Couldn’t arrange for childcare 1.25 (0.36) 0.75 (0.60) 2.17 (2.06) - 1.08 (0.56)
20. Had to wait too long to get into a program 2.12 (0.41) 5.46 (1.62) 5.82 (3.45) 4.21 (4.02) 2.04 (1.11)
Attitudinal Barriers
2. Didn’t think anyone could help 16.01 (1.46) 18.66 (2.19) 22.71 (6.94) 11.30 (6.82) 19.60 (2.98)
7. Thought the problem would get better by itself 35.27 (2.39) 35.16 (3.19) 37.08 (7.02) 46.13 (8.71) 37.41 (3.14)
8. Too embarrassed to discuss it with anyone 25.43 (1.87) 25.00 (2.93) 14.53 (4.63) 23.67 (9.21) 18.09 (2.76)
9. Afraid of what boss, friends, family or others would think 13.81 (1.50) 10.11 (2.20) 12.03 (5.03) 10.22 (6.78) 6.97 (1.74)
10. Thought should be strong enough to handle alone* 42.28 (2.01)a 35.88 (2.89) 38.37 (7.65) 32.42 (11.00) 29.80 (2.30)a
11. Afraid they would put me into the hospital 6.78 (0.87) 7.84 (1.67) 11.16 (5.87) 4.38 (4.25) 4.30 (1.38)
12. Afraid of the treatment they would give me 6.48 (0.97) 9.43 (1.96) 6.56 (3.40) 5.64 (4.33) 2.86 (1.18)
22. Didn’t think drinking problem was serious enough 29.89 (2.06) 25.31 (3.02) 25.22 (7.00) 17.55 (7.48) 22.02 (4.15)
23. Didn’t want to go 24.88 (1.79) 22.83 (2.72) 33.27 (7.77) 25.30 (8.43) 19.56 (2.75)
26. Tried getting help before and it didn’t work 8.62 (1.22) 4.60 (1.35) 14.16 (6.05) 7.32 (4.64) 4.40 (1.55)
Readiness to Change Barriers
21. Wanted to keep drinking or got drunk* 26.29 (1.47)a,b 15.02 (1.15)b,c 37.08 (7.04)c,d,e 14.37 (5.62)e 12.28 (2.18)a,d
24. Stopped drinking on my own 24.88 (1.90) 22.70 (3.08) 32.18 (8.22) 18.90 (7.62) 17.24 (3.41)
25. Friends or family helped me stop drinking 6.03 (0.93) 3.19 (1.47) 1.64 (1.63) 13.20 (8.50) 7.76 (2.31)
Other Barriers
13. Hated answering personal questions 11.83 (1.43) 12.94 (2.12) 11.34 (4.41) 14.01 (8.19) 9.49 (2.08)
14. Hours were inconvenient 4.70 (0.91) 4.02 (1.57) 6.60 (4.01) - 1.86 (0.70)
15. A member of my family objected 1.70 (0.42) 0.83 (0.63) 2.52 (2.49) - 2.10 (1.11)
16. Family thought I should go but I didn’t think it was necessary 13.95 (1.22) 11.18 (1.88) 21.54 (7.22) 20.47 (6.58) 11.25 (2.23)
18. Afraid I would lose my job 6.28 (0.90) 4.87 (1.40) 9.08 (5.46) 3.35 (3.29) 4.03 (1.53)
27. Afraid children would be taken away 2.27 (0.52) 2.75 (0.85) 4.58 (3.27) 3.55 (3.49) 1.98 (0.91)
28. Religious beliefs prevent going for treatment 0.35 (0.19) 0.80 (0.58) - 3.55 (3.49) 0.55 (0.41)
29. Other reason* 4.47 (0.84)a 6.29 (1.12) 6.91 (5.26) 8.45 (1.51)a,b 3.62 (1.36)b
Open in a new tab

Note. - = frequency was zero or standard errors not estimable due to small sample size of endorsement.

*

= omnibus Wald F test significant at p < .05.

Cells with the same letters represent pairwise differences based on adjusted survey regression models (p < .05). No superscript in a cell means the cell was not different from any other cell.

The results indicated differences across racial/ethnic groups for several barriers to alcohol treatment. AI/AN adults were more likely than White, Latine, and Asian/PI/NH adults and Black adults were more likely than White adults to endorse “Didn’t have a way to get there”, and these effects remained in adjusted models. White adults were more likely to endorse “Thought should be strong enough to handle alone” than Latine adults, and this remained in adjusted models. Asian/PI/NH and Latine adults were more likely to endorse “Can’t speak English very well” relative to White adults, and this remained in adjusted models. AI/AN adults were more likely than Black, Asian/PI/NH, and Latine adults and White adults were more likely than Black and Latine adults to endorse “Wanted to keep drinking or got drunk”, and these differences remained in adjusted models. Asian/PI/NH adults were more likely than White and Latine adults to endorse “Other reason” and these differences remained in the adjusted model. The findings did not indicate differences across racial/ethnic groups regarding the number of barriers to alcohol treatment endorsed in the unadjusted model when examined in the primary analysis as a continuous outcome or in the sensitivity analysis as a binary outcome.

3.2. Drug use disorder

3.2.1. Sample characteristics.

Supplemental Table 2 reports demographic characteristics across racial/ethnic groups among those with a lifetime DUD (N = 3,548, 40.2% female, 21.0% Black, 14.5% Latine, 2.5% AI/AN, 2.1% Asian/PI/NH, 59.9% White). Asian/PI/NH male participants represented a larger portion of the sample relative to other racial/ethnic groups. Roughly half of participants were married or cohabitating, with the lowest rate among Black participants relative to other racial/ethnic groups. Most participants had some college or higher, with Black (47%) and Latine participants (48%) with the lowest and Asian/PI/NH participants (78%) with the highest rate. Asian/PI/NH and White participants had the highest rates of an annual salary of $70,000 or more and the highest rates of private insurance relative to other racial/ethnic groups. Among those with lifetime DUD, Black adults (64%) had the lowest rate and AI/AN adults (80%) had the highest rate of lifetime AUD. Cannabis use disorder was the most prevalent DUD (63%), followed by cocaine use disorder (24%) and opioid use disorder (21%); this pattern remained true across racial/ethnic groups with the exception of AI/AN adults whose prevalence of cocaine and opioid use disorders was similar (22% vs. 21%, respectively). The mean age ever sought drug treatment was highest among Black adults (31 years) and lowest among Latine adults (25 years).

3.2.2. DUD treatment utilization.

Results identified some services in which no one from a specific racial/ethnic group endorsed. Specifically, no Asian/PI/NH people endorsed utilizing methadone maintenance programs or employee-assistance programs, and no AI/AN people endorsed utilizing any other agency or professional. Table 3 reports differences across racial/ethnic groups in drug treatment utilization in adjusted models. The findings indicate no significant differences across racial/ethnic groups in the lifetime use of at least one drug treatment service in the unadjusted model; however, the results suggest differences across racial/ethnic groups for the types of drug treatment services utilized. Black people were more likely than any other group to utilize 12-step programs; however, only the differences between Black and White (AOR: 2.93, 95% CI: 1.85, 4.66) and Latine adults (AOR: 2.88, 95% CI: 1.46, 5.67) remained in the adjusted model. Further, Black (OR: 2.96, 95% CI: 1.58, 5.54) and White adults (OR: 1.85, 95% CI: 1.17, 2.94) were more likely than Latine adults to utilize outpatient clinics, and these differences remained in adjusted models. In addition, Black adults were more likely than Asian/NH/PI (OR: 4.62, 95% CI: 1.74, 12.22) and Latine adults (OR: 2.17, 95% CI: 1.19, 3.96) to utilize specialty drug treatment and this remained in adjusted models. White adults were more likely than Asian/NH/PI adults to utilize specialty drug treatment, but these differences did not remain in adjusted models. The study found no differences across racial/ethnic groups for number of drug treatment services utilized.

Table 3.

Lifetime treatment utilization by race/ethnic group among those with DSM-5 lifetime drug use disorder who reported using at least one type of service: Adjusted models

White (n=546) Black (n=191) American Indian/Alaska Native (n=23) Asian/Pacific Islander/Native Hawaiian (n=14) Latine (n=119)
Type of Treatment or Help % (SE) % (SE) % (SE) % (SE) % (SE)
Ever sought drug treatment 23.36 (1.07) 24.37 (2.15) 25.86 (7.54) 15.19 (1.26) 21.93 (2.42)
1. 12-step Program* 67.25 (2.45)a 85.60 (2.64)a,b,c,d 62.07 (15.85)d 57.62 (11.82)c 67.53 (5.01)b
2. Family/social services 18.73 (1.97) 25.52 (4.55) 18.01 (8.95) 8.37 (7.19) 11.06 (2.56)
3. Detoxification 36.92 (2.66) 46.68 (5.94) 38.35 (9.82) 39.10 (10.93) 40.15 (5.56)
4. Other inpatient facility 27.28 (2.25) 29.71 (3.70) 20.20 (10.16) 12.24 (11.72) 24.21 (4.93)
5. Outpatient clinic* 39.92 (2.49)a 51.52 (5.98)b 35.22 (9.74) 30.05 (10.28) 26.42 (6.37)a,b
6. Rehabilitation program 50.03 (2.58) 61.79 (4.86) 42.00 (11.55) 41.63 (10.95) 47.06 (7.13)
7. Methadone maintenance program 9.42 (1.39) 6.27 (2.02) 14.43 (4.65) - 10.18 (2.92)
8. Emergency department 23.09 (1.99) 13.33 (2.78) 22.93 (5.94) 9.05 (8.96) 20.37 (4.09)
9. Halfway house 13.10 (1.95) 14.72 (2.95) 10.82 (6.77) 9.44 (7.45) 15.78 (3.45)
10. Crisis center 5.08 (1.07) 8.18 (2.31) 13.12 (6.63) 9.05 (8.96) 7.99 (2.29)
11. Employee-assistance program 4.93 (1.15) 4.04 (1.25) 3.96 (4.08) - 1.15 (0.68)
12. Clergy 12.48 (1.55) 16.13 (2.98) 7.19 (5.11) 21.68 (11.81) 12.41 (3.20)
13. Physician or other health care professional 47.82 (2.10) 38.11 (4.25) 64.98 (8.61) 33.30 (13.80) 40.19 (7.34)
14. Any other agency or professional 5.98 (1.16) 4.72 (1.75) - 17.48 (13.80) 9.98 (2.99)
Lifetime use of specialty treatment* 67.87 (2.66) 76.70 (3.71)a,b 53.81 (14.31) 41.63 (10.95)a 60.26 (5.73)b
Number of Services Utilized 3.62 (.13) 4.06 (.22) 3.53 (.66) 2.89 (.51) 3.34 (.22)
Open in a new tab

Note. - = frequency was zero.

*

= omnibus Wald F test significant at p < .05.

Cells with the same letters represent pairwise differences based on adjusted survey regression models (p < .05). No superscript in a cell means the cell was not different from any other cell.

3.2.3. Reasons for not seeking DUD treatment.

The findings identified several barriers to drug treatment that no one endorsed. Specifically, no participant endorsed “Religious beliefs prevent going for treatment.” No Asian/PI/NH people endorsed “Didn’t have a way to get there.” No Black, AI/AN, or Asian/PI/NH people endorsed “Can’t speak English very well.” No AI/AN or Asian/PI/NH endorsed “Couldn’t arrange for childcare” or “Friends or family helped me stop using.” No AI/AN endorsed “Hours were inconvenient.” No Asian/PI/NH or Latine participants endorsed “A member of my family objected.” No Asian/PI/NH people endorsed “Family thought I should go but I didn’t think it was necessary” or “Other reason.” Table 4 reports differences across racial/ethnic groups in barriers to drug treatment in adjusted models. The findings suggest no significant differences across racial/ethnic group among those with a lifetime DUD who thought they should seek treatment but did not go. However, the results indicated several differences across racial/ethnic group for specific reasons for not seeking DUD treatment when needed. Asian/PI/NH adults were more likely than all other groups and AI/AN and White adults were more likely than Black and Latine adults to report that they were afraid of what boss, friends, family, or others would think; these differences remained in adjusted models. AI/AN adults were more likely than Black and Latine adults and White adults were more likely than Black adults to endorse fear of hospitalization; these differences remained in adjusted models. Asian/PI/NH and Latine adults were more likely to endorse “Hours were inconvenient” relative to White and Black adults, and these differences remained in adjusted models. White adults were more likely to endorse “A family member objected” relative to Black adults and this difference remained in the adjusted model. White adults were more likely to endorse “Afraid I would lose my job” relative to Latine adults and this remained in the adjusted model. AI/AN were more likely than all other groups and White adults were more likely than Latine and Black adults to endorse “Stopped using on my own,” and these differences remained in the adjusted model. AI/AN adults endorsed significantly more barriers to drug treatment than did White, Black, and Latine adults; and White adults endorsed more barriers than Black and Latine adults. All differences remained in the adjusted model except the difference between AI/AN and White adults fell out of the range of significance. The sensitivity analyses examining barriers to treatment as a binary variable replicated these findings, with the exception that differences in endorsing more barriers to treatment fell out of the range of significance between White and Latine adults in adjusted models.

4.1. Discussion

To our knowledge, this study is the first to determine whether differences exist in AUD and DUD treatment utilization and barriers to accessing treatment across Black, AI/AN, Latine, Asian/PI/NH, and White adults and utilizing DSM-5 diagnostic criteria. These findings suggest important differences in AUD and DUD treatment utilization and barriers to treatment across racial/ethnic groups, in many cases above and beyond relevant intersecting sociodemographic factors (e.g., income, education, insurance status). Further, the findings from this study highlight differences in structural, attitudinal, readiness to change, and other barriers across racial/ethnic groups.

4.2. AUD treatment implications

White and AI/AN adults have the highest rates of AUD and are utilizing AUD treatment overall at higher rates than other racial/ethnic groups. However, given racial disparities in negative consequences and health effects of AUD particularly among Black adults (Zapolski et al., 2014), efforts are needed to increase overall treatment utilization among Black, Latine, and Asian/PI/NH adults with AUD as well. Our findings indicate that the differences across racial/ethnic groups that remained in the adjusted model included the use of a physician/health care professional for alcohol treatment and that AI/AN and White adults with AUD most frequently used this option. Researchers are studying approaches to improve screening and referral to SUD treatment in primary care settings (e.g., Hargraves et al., 2017) given the high rate of adults seeking SUD treatment from a physician or other health provider (Grant, Goldstein, Saha, et al., 2015); however, our findings reveal Asian/PI/NH, Black, and Latine adults are least likely to seek AUD treatment in this setting compared to White and AI/AN adults, which highlights potential directions for future research. Although the unadjusted findings in this study suggested that Black adults utilize more services and specialty alcohol treatment services relative to other groups, these differences did not remain in adjusted models, indicating that these differences were better accounted for by sociodemographic factors.

Understanding barriers to treatment can inform approaches to increase alcohol treatment utilization. Specifically, the findings from this study indicate that addressing structural barriers related to transportation can enhance treatment access among AI/AN adults with AUD. Further, these findings also reveal that language remains a significant structural barrier, specifically among Latine and Asian/PI/NH adults with AUD, highlighting the importance of research on culturally adapted evidence-based treatment for these groups (e.g., Ng & Wong, 2018; Paris et al., 2018). Given the heterogeneity of Asian/PI/NH as a racial/ethnic group representing multiple languages spoken and Latine individuals who may speak Spanish or Portuguese, additional research should identify approaches for increasing accessibility to evidence-based AUD treatment offered in various Asian languages and in both Spanish and Portuguese in the United States. White people were more likely to endorse “Thought should be strong enough to handle alone.” White people are more likely to ascribe to an individualistic worldview, which often involves holding a “pull yourself up by your bootstraps” mentality and devaluing seeking help from others. Thus, efforts to de-stigmatize seeking help for AUD when needed could increase treatment utilization among White adults. Further, White and AI/AN adults were more likely to endorse readiness to change barriers driven by the desire to continue drinking. These findings highlight the importance of continued research and implementation of harm reduction and non-abstinence-based treatment approaches, which might be of particular interest to White and AI/AN adults (e.g., Witkiewitz & Tucker, 2020).

4.3. DUD treatment implications

Although the study found no differences across racial/ethnic groups in the lifetime use of drug treatment services, the study did find differences across racial/ethnic groups for the individual services used. For each of the drug treatment services in which differences across racial/ethnic groups remained in adjusted models (i.e., 12-step programs, outpatient clinics, and specialty drug treatment), in all cases, Black adults had the highest rates of utilization. These findings suggest that efforts aiming to improve drug treatment retention, satisfaction, and outcomes among Black adults might consider focusing on 12-step programs, outpatient treatment clinics, and specialty drug treatment given the high rate of use of these services among Black adults.

The majority of people with lifetime AUD and DUD who have utilized alcohol or drug treatment report using 12-step programs with the highest rate of use among Black adults with DUD. Aspects of the 12-step model that may be particularly attractive across racial/ethnic groups are a peer recovery model that links treatment seekers with a social network of individuals who have lived experience of drug addiction, and the integration of spirituality, which may help to explain the higher utilization among Black adults with DUD (Peavy et al., 2017). In addition, prior research has hypothesized that 12-step programs may be more available in areas with greater ethnic/racial diversity (Perron et al., 2009). A review concluded that 12-step programs for illicit drug use are generally as effective as other treatment options, although research on 12-step programs tends to have key design limitations that reduce confidence in the findings (Bøg et al., 2017). Thus, researchers should continue to study 12-step effectiveness with increasing methodological rigor. Researchers may consider identifying the factors that attract people to 12-step programs to inform approaches for improving access to other SUD treatments particularly among Black adults.

The findings from this study also inform potential approaches for enhancing drug treatment utilization through the identification of barriers and whether they differ across racial/ethnic groups. Most notable is the finding that AI/AN adults, who have the highest prevalence of lifetime DUD, also report the most barriers to drug treatment; this finding highlights a need for future research to identify approaches that increase drug treatment access among this group. Of the barriers in which differences across racial/ethnic groups remained in adjusted models, AI/AN adults most frequently endorsed multiple barriers. Specifically, attitudinal barriers including fear of hospitalization, readiness to change barriers including stopped using on their own, and other barriers including fear of losing job, were most highly endorsed by AI/AN adults. Thus, future research aiming to enhance treatment access among AI/AN adults might focus on addressing real concerns about hospitalization and improving and/or creating awareness of employee assistance programs. Further, these findings highlight the importance of natural recovery, an understudied area in which people recover from addiction without formal treatment (e.g., Mocenni & Montefrancesco, 2019; Mohatt et al., 2007; Sobell et al., 2000; Spinelli & Thyer, 2017; Tucker & Simpson, 2010). Additional research should seek to identify how to enhance support for natural recovery among AI/AN adults and others who utilize this option, and how the core elements of natural recovery might help to improve formal treatment approaches.

Our findings also identified that Asian/PI/NH adults were more likely to endorse certain barriers to drug treatment including attitudinal barriers such as fear of what boss, family, friends or others would think and other barriers including the hours were inconvenient. Therefore, addressing DUD stigma in Asian/PI/NH communities and ensuring availability of drug treatment services at various hours may enhance drug treatment utilization among this group. In addition, White adults were more likely to endorse that a family member objected to them seeking treatment. Although the rate of endorsement for this item was rather low overall, subsequent research aimed at enhancing family support for drug treatment among White people may increase treatment utilization among White adults with DUD.

4.4. Systemic factors

Given the pervasiveness and persistence of racism in the United States, resulting in inequitable access to opportunities, goods, and services across racial/ethnic groups, future research aimed at eliminating racial disparities in SUD treatment could benefit from focusing on social determinants of health. For example, People of Color with SUDs are more likely to be funneled into the legal system than the health care system for efficacious SUD treatment (Jordan et al., 2020; Moore & Elkavich, 2008). Being funneled into the legal system instead of the health care system when needed can contribute to increased traumatization while incarcerated and difficulty obtaining stable housing and employment once released, thus increasing the likelihood of returning to problem drug and alcohol use. These same concerns were raised for White people with opioid use disorder during the “opioid epidemic”, and they resulted in efforts to enhance access to health care and increase harm reduction approaches—these same efforts need to be provided to People of Color with SUDs. Further, we need improved mechanisms to ensure anyone with SUD is triaged into the health care system rather than the legal system.

Efforts should eliminate racial discrimination in health care policies and practices that contribute to racial disparities in SUD treatment. Treatment programs can evaluate whether their policies (e.g., no-show/cancellation policy) differentially harm Patients of Color, and if so, develop new policies that result in equitable outcomes for all. In addition, all staff and clinical providers should participate in regular activities to enhance their awareness, knowledge, and skills in providing multicultural care. Training efforts can include teaching clinicians how to assess unique challenges related to racism that Patients of Color experience and approaches for incorporating these assessments into treatment when appropriate (e.g., Cénat, 2020). Addressing the impact of racism on treatment seeking has the potential to enhance treatment satisfaction, retention, and outcomes among Patients of Color. Finally, staff should receive incentives to identify and eliminate racial discrimination among all staff and clinicians in SUD treatment programs.

4.5. Limitations

An important limitation of this study is that these data were collected in 2012–13, so these trends may differ today. However, these are the most recent national data that provide DSM-5 lifetime AUD and DUD diagnoses with a large enough sample to examine treatment utilization and barriers across all major racial/ethnic groups in the United States. We used lifetime assessments of AUD and DUD to increase the sample size across racial/ethnic group; however, lifetime assessments are not as reliable as past year assessments and the lifetime prevalence is likely an underestimate (Haeny et al., 2016). Although we were intentional about including all racial/ethnic groups, and NESARC oversampled for Black and Latine adults, the sample had notably fewer AI/AN and Asian/PI/NH adults with lifetime AUD and DUD. Using weighted data helped with estimating parameters despite, in some cases, very small sample sizes. However, these findings should be considered preliminary and replicated in subsequent studies with larger samples of AI/AN and Asian/PI/NH adults. Further, these preliminary findings suggest that future epidemiological studies should consider oversampling for AI/AN and Asian/PI/NH adults. We must investigate whether differences in SUD treatment utilization across racial/ethnic groups remain to identify approaches for eliminating racial disparities; however, using the broad categories of Black, Latine, AI/AN, Asian/PI/NH, and White negates important within-group differences (e.g., ethnic or tribal identity, sex, nativity) that also inform substance use, treatment utilization, and barriers to treatment. For example, prior work has shown within-group differences in substance use and treatment utilization by immigrant status among Latine people (Mancini et al., 2015); sex and racial/ethnic group differences in barriers to SUD treatment among Black, Latine, and White adults (Verissimo & Grella, 2017); and differences in substance use treatment needs highlight the importance of future research to examine Asian, NH, PI groups separately (Wu & Blazer, 2015). In addition, the data in this study categorized multiracial people into a single racial category, which further contributes to the heterogeneity of each racial/ethnic group. Finally, combining all drugs into one category limits the understanding of which DUDs are associated with specific barriers to treatment utilization especially given that the use of some drugs may be more stigmatizing than the use of others. Future studies should analyze the impact of specific DUDs individually and categorize drugs based on their legal status or stigmatization level on treatment utilization by race/ethnicity.

In summary, the current study elucidated differences across racial/ethnic groups on SUD treatment utilization and barriers to treatment. A major strength of this study was the inclusion of AI/AN and Asian/PI/NH adults with SUDs. Given that these groups are understudied in treatment research, including them allowed us to identify differences in treatment utilization and barriers to treatment relative to other groups. These findings can be used as preliminary data for larger studies aimed at eliminating racial disparities in SUD treatment among AI/AN and Asian/PI/NH adults. Further, we did not limit our pairwise comparisons to using White people as the reference group, a practice consistent with racist ideology that “White” is the standard (e.g., Burlew et al., 2019). The use of epidemiological data based on the most recent diagnostic criteria and reporting treatment utilization and barriers for AUD and DUD separately are also strengths. The current study provides an update of the literature on whether differences in treatment utilization and barriers to treatment remain across Black, AI/AN, Latine, Asian/PI/NH, and White adults. These findings support the need to increase access to SUD treatment across all racial/ethnic groups, identify specific strategies that may be useful for each group, and suggest potential strategies for addressing systemic factors that contribute to racial inequities in SUD treatment.

Supplementary Material

1
2

Highlights.

  • We need to study racial disparities in DSM5 drug and alcohol use disorder treatment

  • Indigenous, Asian, Pacific Islander, and Native Hawaiian adults are often excluded

  • Treatment access/barriers were studied across all major U.S. racial/ethnic groups

  • Differences in treatment utilization/barriers across racial/ethnic group were found

  • These findings inform approaches for increasing access to treatment for each group

Acknowledgements:

Supported by T32DA019426 and R25DA035163 from the National Institutes of Health. This manuscript was prepared using a limited access data set obtained from the National Institute on Alcohol and Alcoholism (NIAAA) and does not reflect the views of NIAAA or the U.S. government.

Footnotes

1

As suggested by Cardemil and colleagues (2019) to include a footnote explaining terminology used, after careful consideration, we decided to use the term Latine because it is a gender-inclusive term that is more accepted and used throughout the Spanish-speaking community than the term Latinx (Cardemil et al., 2019).

2

Participants with lifetime AUD or DUD who endorsed needing treatment but did not go checked all that apply regarding barriers or reasons for not seeking alcohol or drug treatment. Only 7 participants did not endorse any barriers to alcohol treatment and 4 participants did not endorse any barriers to drug treatment which is why 0 is not included in the categorical variables for the sensitivity analyses.

3

In a few cases, drug treatment services and reasons for not seeking drug treatment when needed were not endorsed among some racial/ethnic groups. In these cases, logistic regression models were run excluding the racial/ethnic group(s) with missing data.

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