Disparities in Alcohol Treatment Use at the Intersection of Race,Ethnicity,Gender,and Insurance

Abstract

Background:

Treatment for alcohol use disorder (AUD) has the potential to improve health and quality of life. Little is known about disparities in AUD treatment utilization at the intersection of race and gender. We examined disparities in AUD treatment utilization among those diagnosed with AUD in a community sample, by race, ethnicity, and gender, and whether disparities varied by insurance. We also examined whether criminal legal history and socioeconomic status moderated disparities in treatment.

Methods:

We used data from the nationally representative 2017 to 2019 National Survey on Drug Use and Health, the most recent 3-year period available. The analytic sample included noninstitutionalized adults aged 18 to 64 who met criteria for past year AUD and identified as White, Black, or Latinx (n = 7782). We examined disparities in AUD treatment utilization by race, ethnicity, and gender subgroup and by insurance status, estimating weighted logistic regressions, and adjusting for indicators of clinical need in concordance with the Institute of Medicine definition of healthcare disparity.

Results:

Only 5.4% of adults with AUD in the United States utilized AUD treatment in the past year. AUD treatment utilization did not significantly differ between White males and other racial, ethnic, and gender groups; however, we did identify disparities among Medicaid enrollees and those who were uninsured. Among Medicaid enrollees, Latinx females (3.2%) had lower treatment utilization than White males (9.3%, P < .05). Among uninsured individuals, Latinx males (1.8%) had lower treatment utilization than White males (6.2%, P < .05).

Conclusions:

AUD treatment utilization was extremely low among adults in the United States aged 18 to 64 who met criteria for AUD. Ethnic and gender disparities in treatment utilization were revealed when examining differences in AUD treatment utilization by insurance status. Strategies for improving access to AUD treatment that address structural barriers to care are needed and should consider targeted approaches for Medicaid enrollees and those uninsured.

Keywords

alcohol use disorder treatment disparities race ethnicity gender Medicaid

Highlights

Only 5.4% of adults with AUD in the United States utilized AUD treatment in the past year.

We identified ethnic and gender disparities in treatment utilization when examining differences by insurance status.

Among Medicaid enrollees, Latinx females (3.2%) had lower treatment utilization than White males (9.3%, P < .05), and among uninsured individuals, Latinx males (1.8%) had lower treatment utilization than White males (6.2%, P < .05).

There is a need to improve access to AUD treatment and understand drivers of inequities in AUD treatment access among Medicaid enrollees and those who are uninsured.

Introduction

Alcohol use disorder (AUD) is the most common substance use disorder (SUD) in the United States, affecting over 14 million people,¹ and contributing to serious health and social consequences for individuals, families, and communities. While alcohol treatment is effective in reducing excessive drinking and alcohol-related problems,^2-5 it is vastly underutilized.^6-8

Racial and ethnic minority groups and women are among the least likely to access alcohol treatment.^9-13 Health consequences of AUD, including alcohol-related liver disease, cirrhosis of the liver, and alcohol-related mortality, are experienced at greater rates among Latinx and Black individuals compared to their White counterparts.^14-17 Alcohol-related deaths are accelerating more quickly among the Black population compared to the White population.^18,19 Alcohol use among women has increased over the past decades and gender gaps are narrowing for binge drinking, AUD, and alcohol-related death.^18,20-22 Strategies to improve AUD treatment use and reduce inequities are needed, requiring an examination of structural barriers to treatment utilization for individuals with intersecting identities.

One of the most commonly cited barriers to SUD treatment is the cost of care, which is often directly influenced by access to and type of health insurance.^8,23-25 Among those who needed but did not receive SUD treatment, having insurance does not guarantee being able to afford treatment.²⁶ Policy differences between private insurance and Medicaid (e.g., coverage generosity, utilization management, payment, and provider availability) may influence treatment use and disparities.²⁷

Medicaid is a public health insurance program for individuals with low income in the United States (U.S.), funded jointly by the federal and state governments. Medicaid enrollees generally receive free or low cost care, though eligibility and services covered vary by state. Medicaid serves almost 24% of the U.S. population,²⁸ and a disproportionate share are Latinx and non-Latinx Black adults, and women.^29,30 Medicaid programs are among the most important payers for alcohol treatment services because Medicaid enrollees have higher rates of AUD than the national average.^31,32 Despite the importance of Medicaid coverage for alcohol treatment generally and for racial and ethnic minoritized groups and women specifically, there is little information about differences in AUD treatment use across these intersecting dimensions within Medicaid.

In addition to insurance, criminal legal involvement and socioeconomic factors are likely to be associated with inequities in AUD treatment use. Bias in legal policies and within the criminal legal system, and socioeconomic factors are considered major drivers of racial and ethnic health inequities.^15,33-35 However, the criminal legal system is a major referral source for SUD treatment^36,37 and can lead to SUD treatment access through coercive requirements (e.g., treatment in lieu of jail or prison, or as a condition of probation, parole).^38,39 Whether socioeconomic factors and criminal legal history influence equity in AUD service utilization is unknown.

To address these gaps, our study examined whether disparities in AUD treatment utilization exist at the intersection of race, ethnicity, and gender, and whether disparities varied based on insurance status. We used the Institute of Medicine (IOM; now the National Academy of Medicine) definition of healthcare disparities: differences in the quality of care that are not based on individual preferences, clinical need, or appropriateness of intervention.⁴⁰ Although the original IOM definition referred to racial and ethnic disparities, we conducted an intersectional analysis and examined disparities for groups at the intersection of race, ethnicity, and gender. These more nuanced analyses allowed us to identify patterns that might not be apparent by examining racial and ethnic disparities and gender disparities separately and, therefore, better inform policies and population-level interventions to improve equity in care. We further examined racial, ethnic, and gender disparities by insurance status given the important role that insurance plays in access to care in the United States.

In secondary analyses, we examined the association of criminal legal system involvement and socioeconomic status (SES) with AUD treatment use disparities. Intersectional health disparities research demonstrates that health status is determined by a range of interlocking social positions.^41-44 We hypothesized that disparities in AUD treatment utilization by race, ethnicity, and gender exist, and that disparities would vary by insurance. We also hypothesized that adjusting for criminal legal history and socioeconomic indicators would reduce any disparities.

Methods

Data Source

This cross-sectional study used publicly available, de-identified data from the 2017 to 2019 National Survey on Drug Use and Health (NSDUH).⁴⁵ NSDUH is a U.S. nationally representative survey of noninstitutionalized individuals conducted annually by the Substance Abuse and Mental Health Services Administration (SAMHSA) to gather information on substance use and mental health issues. For nonsensitive questions, field interviewers read the questions and collect responses using a laptop. For sensitive questions (e.g., questions about substance use), participants listen to pre-recorded questions through headphones and record the responses themselves into a laptop without the field interviewer seeing their responses. This data collection method is used to protect participant’s privacy to increase the likelihood of truthful reporting. More details on the NSDUH’s methodology can be found on SAMHSA’s data webpage.⁴⁶ The survey is administered across the 50 states and the District of Columbia. The Brandeis University Institutional Review Board does not consider analysis of these data human subjects research.

Sample

The sample consisted of adults aged 18 to 64 who met Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV)⁴⁷ criteria for alcohol dependence or abuse (AUD) in the past year. Our study focused on adults with Medicaid, private insurance, or those uninsured, who identified as White, Black, or Latinx. There were 9208 respondents aged 18 to 64 who met the criteria for AUD in 2017 to 2019. We excluded 570 individuals who reported other types of insurance only (i.e., Medicare, veterans- or military-based, and other unspecified insurance). Because the sample sizes were too small for reliable comparisons, we excluded Asian (n = 276), American Indian or Alaska Native (n = 178), and Native Hawaiian (n = 51) respondents and those identifying with more than one race (n = 349). Two respondents with missing covariate data were also excluded. Our final analytic sample included 7782 respondents, representing 11 522 693 noninstitutionalized individuals living in the United States, after applying survey weights.

Variables

Outcomes

The outcome variable was AUD treatment utilization in the past year, defined as receiving alcohol treatment in a specialty facility or private doctor’s office in the past 12 months. Specialty facilities included hospitals (inpatient), rehabilitation facilities (inpatient or outpatient), or mental health centers.^9,48 SUD treatment utilization in the past year was included as an outcome in sensitivity analyses, defined as treatment for alcohol and/or a drug use disorder, excluding nicotine, in a specialty facility or in a private doctor’s office.

Race, Ethnicity, and Gender

We created a single, 6-category variable to capture the intersection of race, ethnicity, and gender: Latinx males, Latinx females, White (non-Latinx) males, White (non-Latinx) females, and Black (non-Latinx) males and Black (non-Latinx) females.

The NSDUH follows the standards of the U.S. Department of Health and Human Services of data collection for national surveys,^48,49 which during the years used in this study required that surveys at a minimum ask participants whether they are of Hispanic, Latino, or Spanish origin or descent (from here on Latinx), followed by questions asking which of the following groups best describe them (participants could select more than one): White, Black or African American, American Indian or Alaska Native, Native Hawaiian, Guamanian or Chamorro, Samoan, other Pacific Islander, Asian, or another race. In our analyses, participants who indicated that they were of Latinx origin were categorized as Latinx regardless of their race response. As described earlier, we only included individuals who were: Latinx, non-Latinx Black, and non-Latinx due to small sample sizes in other groups.

NSDUH interviewers are instructed to “Record the respondent’s gender” based on their own assessment, using the categories of male or female.⁴⁸ Since the interviewer was interpreting and recording gender expression, we use the term “gender” instead of “sex.”

Insurance Status

We created 3 mutually exclusive insurance categories: Medicaid, private insurance, and uninsured. Anyone who indicated that they had Medicaid as their insurance, regardless of whether they also had another insurance, was included in the Medicaid category. Private insurance included anyone who had private insurance including those who had additional insurance coverage (other than Medicaid).

Clinical Need

Clinical need was assessed with self-rated fair/poor health status (yes/no), co-occurring SUD, and 2 mental health indicators. Co-occurring SUD was operationalized as an indicator for meeting the DSM-IV criteria for an illicit drug use disorder (e.g., opioid use disorder, cocaine use disorder). The mental health indicators were: (a) whether participants had a past year major depressive episode, and (b) whether participants had past month serious psychological distress based on a score of 13 or higher on the Kessler-6 scale.⁵⁰

Socioeconomic Status (SES)

SES was operationalized as education (less than high school; high school graduate; any college; college graduate) and employment status (working full- or part-time; unemployed; disabled; not in labor force; unknown). Income was not included since Medicaid eligibility is contingent on income.

Criminal Legal History

Criminal legal history was determined as the respondent indicating they had ever been arrested, and/or had been on parole or probation in the past year.

Covariates

Covariates included age, marital status, and survey year.

Statistical Analyses

Data were analyzed from November 2022 to August 2023 using Stata SE v18 (StataCorp, LLC; College Station, Texas, USA). We performed descriptive analyses to examine variation by race, ethnicity, and gender group on participant demographics, clinical need, criminal legal history, SES, and AUD treatment utilization. We tested for differences between White males and other racial, ethnic, and gender groups using tests for proportions, accounting for survey design and applying a Bonferroni correction for multiple comparisons. We chose White males as the reference group for two reasons. In health equity research, one approach is to have the reference group be the one considered the most socially advantaged. In the United States, White males have the highest rates of income,^51,52 wealth,^53-56 healthcare coverage provided by employer,^57,58 and the lowest unemployment rates^51,59 compared to the other groups included in our analysis. Another approach is to choose the group doing best on the outcome measure being used, in this case, the group with the highest alcohol utilization rate. When we assessed disparities in AUD treatment utilization, we found that the predicted rate of alcohol treatment use was higher for White males than the other groups, though not statistically significant.

We estimated a series of 4 multivariable logistic regression models to assess disparities in AUD treatment utilization. Model 1 reflects the IOM definition of disparities, adjusting for clinical need, age, marital status, and survey year.⁹ Next, we examined whether disparities varied by insurance by adding the insurance indicator main effect terms and additional terms representing the interaction between the “race, ethnicity, and gender” variable and insurance type (Model 2). This model allowed us to examine both whether disparities existed within each insurance type, and whether the degree of the disparity was the same across insurance types (difference-in-differences). Finally, to assess whether criminal legal involvement and SES were associated with inequities in AUD treatment utilization, we conducted 2 additional models, first adding criminal legal history (Model 3), and then also adding SES variables (Model 4). We tested for differences in AUD treatment utilization by insurance, between subgroups, and the interaction of insurance and subgroups using predictive margins.⁶⁰ Multicollinearity of predictor variables was examined and not found in any of the models.

As a sensitivity analysis, we examined utilization of any SUD treatment (alcohol and/or another substance) as the outcome. Since people often engage in polysubstance use,⁵³ treatment for drug use may address alcohol use as well.

All analyses were conducted using survey weights to account for the complex survey sampling design and to make nationally representative estimates. We calculated and reported predicted probabilities generated from the predictive margins method for ease of interpretation and to avoid bias generated by interaction terms in nonlinear regression models.⁶⁰ Two-tailed P < .05 was interpreted as statistical significance.

Results

Sample Characteristics

The sample included 7782 respondents representing 11 522 694 adults with AUD. Using weighted data, White males made up the largest proportion of the sample (42%), followed by White females (29%), and Latinx males (12%). Latinx females (7%), Black males (6%), and Black females (5%) made up similar proportions of the sample. About 15.7% of the weighted sample reported having Medicaid insurance coverage, while 68.9% reported private insurance coverage, and 15.4% were uninsured. Racial, ethnic, and gender subgroups differed in sociodemographic and clinical need characteristics, as well as insurance (Table 1). For example, Black and Latinx males and females were less likely to have private insurance (Black males: 46.2%, Black females: 47.8%, Latinx males: 52.0%, Latinx females: 54.2%) and more likely to have Medicaid (Black males: 24.1%, Black females: 37.4%, Latinx males: 17.7%, Latinx females: 29.0%), relative to White males (76.7% had private insurance, and 10.7% had Medicaid). Compared to White males (12.6%), Black (29.8%), and Latinx males (30.3%) were more likely to be uninsured, while White females (9.6%) were less likely to be uninsured. Black males were more likely to report poor health (22.7%), and criminal legal involvement (56.0%) and Latinx males were more likely to report co-occurring SUD (20.4%) compared to White males (12.9% reporting poor health, 44.8% criminal legal involvement, and 15.0% co-occurring SUD). Females from all race and ethnicity groups were more likely to have serious psychological distress (ranging from 42.4% to 44.3%), and less likely to report criminal legal involvement (23.7%-32.8%) than White males (24.7% and 44.8%, respectively).

Table 1.

Prevalence of Sociodemographics, Clinical Need, and Criminal Legal History, by Racial, Ethnic, Gender Group (Weighted n = 11 522 693).

Characteristic	White males (weighted n = 5 057 181), %	White females (weighted n = 3 117 050), %	Black males (weighted n = 768 277), %	Black females (weighted n = 526 334), %	Latinx males (weighted n = 1 434 918), %	Latinx females (weighted n = 1 434 918), %
Health insurance
Medicaid	10.7	14.6	24.1**	37.4**	17.7**	29.0**
Private	76.7	75.9	46.2**	47.8**	52.0**	54.2**
Uninsured	12.6	9.6**	29.8**	14.8	30.3**	16.9
Demographics
Age
18-25	21.5	26.2**	15.5**	22.9	27.8**	42.3**
26-34	24.1	25.1	30.2	30.8	29.9	31.4
35-49	28.4	28.9	29.5	28.7	30.3	20.7**
50-64	26.0	19.8**	24.9	17.6	11.8**	5.5**
Married	37.2	32.0**	21.3**	13.2**	31.1	25.6**
Socioeconomic status
Education
Less than high school	7.8	4.9**	25.2**	13.0	23.9**	10.5
High school graduate	25.0	15.4**	36.5**	29.1	32.0**	22.6
Any college	31.5	38.2**	26.2	35.3	29.6	42.0**
College graduate	35.7	41.6**	12.0**	22.6**	14.4**	25.1**
Employment status
Working full- or part-time	79.6	78.7	64.2**	64.0**	76.6	75.5
Unemployed	5.0	4.1	10.5**	8.5	6.9	6.4
Disabled	4.7	1.8**	10.5	6.2	2.9	2.9
Not in labor force	9.9	15.2**	14.4	19.6**	12.0	14.7
Unknown	0.7	0.3	0.4	1.6	1.8	0.5
Clinical need
Fair/poor self-rated health status	12.9	11.0	22.7**	19.1	15.7	12.4
Have another SUD	15.0	15.8	19.1	18.2	20.4**	18.0
Major depressive episode in past year	15.3	29.3**	13.6	22.8	11.3	25.3**
Serious psychological distress	24.7	43.9**	28.2	42.4**	24.2	44.3**
Criminal legal history^a	44.8	25.5**	56.0**	32.8**	44.8	23.7**

Abbreviation: SUD, substance use disorder.

Criminal legal history refers to respondent indicating that they have ever been arrested, and/or that they have been in parole or probation in the past year; **P < .01 for differences between group and non-Latinx White males.

Disparities in AUD Treatment Utilization

Approximately 5.4% of adults with AUD utilized AUD treatment in the past year (Table 2). Unadjusted treatment utilization rates ranged from 4.7% (Latinx males) to 6.0% (White females). Estimates of AUD treatment utilization changed somewhat when we used the IOM-concordant model to assess disparities, with estimates ranging from 4.1% (Black females) to 5.8% (White males); yet differences between White males and other groups were not statistically significant.

Table 2.

Prevalence of Past Year AUD Treatment Utilization Among Those Who Needed Treatment, by Racial, Ethnic, Gender Group.

Model	Overall	White males	White females	Black males	Black females	Latinx males	Latinx females
Unadjusted	5.4	5.4	6.0	5.5	4.9	4.7	5.4
Adjusted using IOM definition (Model 1)^a	—^b	5.8	5.3	5.2	4.1	5.3	5.4

Predicted probabilities.

Abbreviations: AUD, alcohol use disorder; IOM, Institute of Medicine.

Adjusting for age, marital status, year, and clinical need (self-reported health status, other substance use disorder, major depressive episode in past year, and serious psychological distress in the past month); ^bNot applicable.

Disparities in AUD Treatment Utilization by Insurance

Estimated rates of AUD treatment were 6.8% for Medicaid enrollees, 6.1% for uninsured individual and 5.2% for those with private insurance; differences were not statistically significant between insurance types (results not shown).

Within insurance types, we detected disparities in AUD treatment utilization among Medicaid enrollees and individuals who were uninsured (Figure 1). Among Medicaid enrollees, the IOM-concordant disparity between White males and Latinx females was estimated to be 7.9 percentage points (pps) [95% confidence interval (CI): 1.5, 14.3]. Among uninsured individuals, the disparity between White males and Latinx males was estimated to be 9.3 pps (95% CI: 1.5, 14.3). We did not observe disparities by race, ethnicity, and gender subgroup among the privately insured.

There were significant interactions between insurance and race, ethnicity, and gender group in AUD treatment utilization, specifically between White males and Latinx females with Medicaid insurance vs. private insurance and between White males and Latinx males with Medicaid insurance vs. private insurance. Specifically, the disparity between White males and Latinx females among those with Medicaid (7.9 pps; 95% CI: −14.3, −1.5) was far greater than the disparity between White males and Latinx females among those with private insurance (0.4 pps; 95% CI: −3.9, 3.0), a difference-in-difference of 7.5 pps (95% CI: 0.2, 14.8). The disparity between White males and Latinx males among those with Medicaid (8.2 pps; 95% CI: −16.4, −0.08), was far greater than the disparity (or lack thereof) among those with private insurance (−2.8 pps; 95% CI: −3.4, 9.1), a difference-in-difference of 11.0 pps (95% CI: 1.9, 20.9, P < .05). See online Supplemental Material for full model.

Criminal Legal History and SES

Figure 2 shows disparity estimates based on Models 2 to 4. Although adjusting for criminal legal history and SES generally decreased the disparity between White males and other racial, ethnic, and gender groups among Medicaid enrollees and the uninsured, disparity estimates were not significantly different in these models. Changes in disparities estimates were also not statistically different among those with private insurance or among the uninsured.

Sensitivity Analysis: Disparities in SUD Treatment

About 6.3% of our sample of individuals with AUD reported using SUD treatment in the past year, ranging from 5.0% (Latinx males) to 6.9% (White females). The results of the SUD disparity analyses were similar to the AUD analyses in that we did not detect disparities in SUD treatment utilization in the overall sample, or among those with private insurance, but found disparities among Medicaid enrollees and the uninsured with the same subgroups having significant associations. We additionally found a significant disparity in SUD treatment between White males and Latinx males among Medicaid enrollees (Supplemental Tables 2 and 3 and Supplemental Figure 1).

Discussion

In a community-based, nationally representative sample of White, Black, and Latinx adults aged 18 to 64 with past year AUD, we found that AUD treatment utilization was extremely low (5.4%). Disparities by race, ethnicity, and gender varied by insurance status. We identified disparities in AUD treatment utilization at the intersection of race, ethnicity, and gender among Medicaid enrollees and the uninsured. Among Medicaid enrollees, we found a disparity among Latinx females, and among the uninsured, we found a disparity among Latinx males. Further, disparities between White males and Latinx males and females were larger among those with Medicaid compared to those with private insurance. These findings point to the importance of examining differences in access to AUD treatment by insurance. A nuanced understanding of treatment use may help identify actionable strategies to improve AUD treatment use.

Our findings are generally consistent with research on SUD disparities, which have found that Latinx adults are less likely to access SUD treatment than White individuals.² Research on disparities in access to AUD treatment is generally dated and limited, but also has revealed lower utilization rates among Latinx adults.⁷ Most studies on treatment disparities examine differences separately by race and ethnicity, or by gender, but do not consider differences among intersections of race, ethnicity, and gender. One exception is a study using data through 2010 focusing on lifetime AUD which found disparities in their overall sample even without stratifying by insurance status,¹³ with Latinx females and Black females having lower AUD treatment utilization rates than White females, and Latinx males having lower rates than White males.

Medicaid is a critical payer for substance use treatment.⁶¹ Black and Latinx individuals, women, and women of color are overrepresented in Medicaid.^29,30,62 Therefore, understanding drivers of racial, ethnic, and gender disparities among Medicaid enrollees is critical. State and federal policies can influence Medicaid eligibility,⁶³ Medicaid enrollment, and enrollment continuity.⁶⁴ Variation in these policies may influence whether people are uninsured or able to obtain and maintain Medicaid coverage. Future work should consider how Medicaid-related policies are associated with disparities in access to treatment for Medicaid enrollees and the uninsured.

Insurance coverage generosity and utilization management policies (prior authorizations, limits on coverage), influence the ease of accessing and staying in SUD/AUD treatment on the demand side.^65-67 It is important to examine these factors in states with a larger proportion of Latinx residents (e.g., Texas, Florida). These are also states that have more limited coverage for treatment of opioid use disorder,²⁷ and similar policies might exist for AUD treatment. Future studies should also consider how variations in coverage by Medicaid Managed Care Organizations (MMCOs), might be associated with access to AUD care. Approximately 70% of Medicaid enrollees are covered by MMCOs⁶⁸; yet there is almost no information about MMCO policies related to AUD treatment. Barriers to treatment exist on the supply side in terms of the low availability of providers treating AUD and lack of availability of AUD services. For example, only 35% of psychiatrists accepted new Medicaid patients in 2014 to 2015.⁶⁹ Primary care providers also provide AUD treatment and are more likely to accept Medicaid patients; however, 18% treated no Medicaid patients in 2016.⁷⁰ Additionally, medications for AUD are not often provided in specialty substance use treatment programs.⁷¹

Adjusting for criminal legal involvement and SES did not change AUD treatment disparity estimates. This was the case even where the relative difference in disparity estimates after adjusting for criminal involvement and/or SES was over 30% (e.g., for White and Black females and Black males with Medicaid). Our focus on the intersection of race, ethnicity, and gender and insurance led to small cell sizes, possibly leading to insufficient statistical power to detect differences in estimates. Future studies should examine the role that structural factors have in disparities in AUD treatment using larger and more diverse samples.

Limitations

This study has several limitations. NSDUH does not include those who are unhoused or in jail and prison, populations in which Black and Latinx individuals are overrepresented,^72,73 which may impact generalizability of the findings, and may lead to an underestimate of disparities in alcohol treatment utilization as these groups tend to have high rates of SUD and barriers to engaging in treatment. At the time of this survey, NSDUH did not ask about gender identity. Thus, it was not possible to confirm that the gender assigned by the interviewer matched the respondent’s gender identity or to assess disparities among transgender and non-binary individuals. We used data from the 2017 to 2019 NSDUH; the most recent 3-year period available. Our study questions require 3 years of data to examine smaller subgroups by race and ethnicity and gender, within insurance status. Changes in NSDUH data collection protocols during the first year of the COVID-19 public health emergency (2020),⁷⁴ and changes in questions to determine AUD status (starting 2020),⁷⁴ and treatment utilization (starting in 2022)⁷⁵ do not allow for combining more recent years of data. Future analyses should examine disparities following the COVID-19 pandemic; the current pre-pandemic findings are a valuable baseline and suggest future research directions. We were unable to assess AUD severity. Future research should assess whether disparities exist among those with severe AUD, the people at most immediate need. Finally, we used broad racial and ethnic categories available through the public data set and were unable to include some racial and ethnic groups due to small sample sizes.

Conclusions

Strategies for improving AUD treatment are needed that focus on reducing racial, ethnic, and gender inequities in access to care. Efforts to address inequities in AUD treatment utilization should eliminate barriers to access to care in general, and especially barriers salient to minoritized populations. Consideration should be given to targeting unique barriers within insurance type. There is a particular need to understand drivers of inequities in AUD treatment on the demand and supply side among Medicaid enrollees and uninsured individuals.

Supplemental Material

sj-docx-1-saj-10.1177_29767342241278871 – Supplemental material for Disparities in Alcohol Treatment Use at the Intersection of Race, Ethnicity, Gender, and Insurance

Supplemental material, sj-docx-1-saj-10.1177_29767342241278871 for Disparities in Alcohol Treatment Use at the Intersection of Race, Ethnicity, Gender, and Insurance by Andrea Acevedo, Rachel Sayko Adams, Benjamin Lê Cook, Sage R. Feltus, Lee Panas and Maureen T. Stewart in Substance Use & Addiction Journal

Footnotes

Author Contributions

AA, MS, and RSA originated the project and drafted the initial manuscript. LP conducted the analyses. All authors participated in interpreting the results, contributed to the writing of the manuscript, provided critical feedback to the manuscript, and approved the final manuscript draft for submission.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the National Institute on Alcohol Abuse and Alcoholism and the National Institute on Drug Abuse of the National Institutes of Health under award numbers R01AA029821 and P30DA035772. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health (NIH). NIH had no role in the study design; collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

Compliance,Ethical Standards,and Ethical Approval

Institutional Review Board approval was not required.

ORCID iDs

Andrea Acevedo

Rachel Sayko Adams

Supplemental Material

Supplemental material for this article is available online at the SAJ website

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