Highlights
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Alcohol and cannabis use disorders were most prevalent among sexual minorities.
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Bisexual and lesbian/gay women had increased odds of most SUDs.
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Black bisexual, black lesbian/gay and Hispanic bisexual women had high SUD burdens.
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Among men, fewer SUD disparities across multiple identities were identified.
Keywords: Substance abuse, Sexual minority, LGB, Intersectionality
Abstract
Objective
This study examines the impact of the intersection of gender, racial/ethnic identity, and sexual orientation among adults on substance use disorders (SUDs) from 2021 to 2022.
Method
We conducted an analysis of persons (ages 18 and older) who responded to the 2021 and 2022 National Survey on Drug Use and Health (NSDUH). Logistic regression models were constructed to examine odds of past-year SUDs at the intersection of gender, sexuality, and race/ethnicity. All analyses were design-corrected to enhance population representativeness and generalizability.
Results
Prevalence varied by race/ethnicity and sexual orientation across both sexes (total n = 83,722). Non-Hispanic multiracial lesbian/gay individuals had the highest prevalence of any SUD in both sexes (46.6 % in women, 52.3 % in men). Bisexual women showed consistently elevated odds of SUD across most racial/ethnic groups (aORs 1.48–2.99) compared to White heterosexual women. Men had higher prevalence of SUD than women (21.1 % compared to 15.0 %, p < 0.0001). Only White gay and bisexual men had significantly increased odds for any SUD compared to heterosexual White men (aOR 1.73 and 1.57, respectively). White bisexual men had higher odds of reporting cannabis use disorder (CUD; aOR 1.87). Hispanic men demonstrated lower odds of any SUD or CUD (aORs 0.85 and 0.71, respectively).
Conclusion
Women demonstrated more pronounced SUD disparities between intersectional identity. While men had higher SUD prevalence overall, few disparities were observed between intersectional identities. To effectively address these disparities and their consequences (e.g., differential minority stress and mental/physical health outcomes), prevention and intervention efforts should prioritize an intersectionality approach.
1. Introduction
Lesbian, gay, and bisexual (LGB) individuals experience disproportionately high rates of substance use disorders (SUDs) compared to the general population (Krueger et al., 2020, McCabe et al., 2010, Solberg et al., 2023). Minority stress, a term encompassing the chronic burden of stigma, prejudice, and discrimination experienced by marginalized groups, is a key contributor to this disparity (Meyer, 2003). For LGB individuals, minority stress involves both shared experiences with other marginalized groups, such as discrimination and victimization, and unique stressors like internalized stigma, identity concealment, and the impact of anti-LGBT policies and laws (Hatzenbuehler, 2016, Meyer, 2003, Outland, 2016). These diverse forms of stress have been demonstrably linked to increased substance use among LGB individuals (Goldbach et al., 2014, Lee et al., 2016, Parent et al., 2019).
For individuals belonging to multiple marginalized groups, like LGB individuals within racial/ethnic minorities, the experience of minority stress intensifies and intertwines. Discrimination targets not just their sexual identity but also their race/ethnicity. Crenshaw's (1989) theory of intersectionality illuminates this phenomenon, providing a vital lens to understand how multiple identities intersect and shape experiences of oppression. Building upon its foundation in Black feminist thought, the intersectionality framework has expanded from its initial focus on gender and race (Crenshaw, 1989) to encompass the experiences of other marginalized groups (Bowleg, 2012), including sexual minorities (Flentje et al., 2024, Mereish and Bradford, 2015, Schuler et al., 2018, Schuler et al., 2020, Vu et al., 2019). Initially, health research applying an intersectional framework presumed an additive effect, suggesting that each additional minority identity would linearly increase health risk (Bowleg, 2012). However, as the understanding of intersectionality has evolved, the lived realities of minority identities were determined to be not simply additive, but rather intricately interwoven and mutually reinforcing. Viewing minority identities solely as risk factors ignores the dynamic interplay between social positions and the evidence that multiple identities can buffer risk factors or act as protective factors within specific social contexts (del Pino et al., 2023, Nicholson et al., 2022, Taylor et al., 2022).
Presently, several studies using national data have examined intersections of identity and substance use, revealing important yet incomplete insights. While promising, these studies leave significant gaps in our understanding. For example, Schuler et al. (2018) explored gender, age, and sexual orientation, uncovering unique vulnerabilities for bisexual women across all age groups, but neglecting race/ethnicity and focusing solely on alcohol use disorder (AUD) and any SUD. Building on this, Schuler et al. (2020) investigated gender, race/ethnicity, and sexual orientation, identifying disparities in smoking, heavy episodic drinking, and marijuana use for lesbian/gay and bisexual women across racial/ethnic groups, with limited disparities observed for men. However, these focused on substance use behaviors (e.g., any use of a substance), which are different from SUD in severity, consequences, and treatment, as SUD refers to a medical condition characterized by compulsive substance seeking and use despite harm. Similarly, Rodriguez-Seijas et al. (2019) found associations between sexual minority status and AUD, nicotine use disorder, and cannabis use disorder (CUD) among Black and Hispanic adults, but aggregated diverse sexual minorities together, limiting insights into specific experiences. These studies highlight the need for more comprehensive investigations.
The purpose of this study was to examine the intersection of gender, race/ethnicity, and sexual orientation to reveal the nuanced vulnerabilities associated with SUDs. Leveraging data from the 2021 and 2022 National Survey on Drug Use and Health (NSDUH), the study focuses on SUDs, allowing for the uncovering of disparities in outcomes rather than behavioral differences. Furthermore, the study disaggregates sexual minority data to investigate individual groups within this diverse population, revealing tailored insights that would remain unseen when sexual minority status is treated dichotomously. To the authors’ knowledge, this level of granularity makes this the first study to comprehensively examine SUDs at the intersection of these three critical identities, potentially paving the way for targeted interventions and personalized treatment approaches that effectively address the unique needs of each group.
2. Methods
2.1. Sample
Data for this study were derived from the pooled 2021 and 2022 National Survey on Drug Use and Health (NSDUH), a nationally representative survey of U.S. civilians residing in non-institutionalized settings, aged 12 and older. Conducted annually across all 50 states and the District of Columbia, the NSDUH collects data through in-person and web-based interviews. Participants responded to comprehensive questionnaires encompassing demographics and substance use.
For this study, the analytical sample was restricted to adults aged 18 or older, as respondents under 18 were not queried about their sexual orientation. Additionally, individuals from Asian, Native American, Alaska Native, Hawaiian Native, and other Pacific Islander backgrounds were excluded due to insufficient sample sizes to produce stable estimates of SUD.
2.2. Measures
2.2.1. Past-year substance use disorders
Participants were assessed for past-year AUD, CUD, opioid use disorder (OUD), and stimulant use disorders (StimUD) using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria (American Psychiatric Association, 2013). Each SUD was coded as 0 (no) or 1 (yes). A participant was considered to have multiple SUDs (mSUDs) if they met the criteria for two or more disorders, coded as 0 (no) or 1 (yes). Conversely, any positive diagnosis, regardless of the specific substance(s), resulted in a coding of 1 (yes) for any SUD (aSUD).
2.2.2. Intersecting identities
Gender was assessed with the question: “Are you male or female?” Race/ethnicity was categorized into four groups: White (non-Hispanic), Black (non-Hispanic), multiracial (non-Hispanic), and Hispanic. Sexual orientation was assessed with a single item: “Which one of the following do you consider yourself to be?” with valid response options including “Heterosexual, that is straight,” “lesbian or gay,” and “bisexual.”
2.2.3. Demographics and Covariates
Demographic variables were coded categorically and included age (18–24, 25–34, 35–49, 50 + ), gender (male, female), education (less than high school, high school, some college, college or higher), insurance status (uninsured, only public insurance, private insurance), marital status (married, widowed, divorced, or separated, and never married), income (less than $10,000. $10,000-$19,999, $20,000-$29,999, $30,000-$39,999, $40,000-$49,999, $50,000-$74,999, $75,000 or more), and employment (full-time or part-time employee or student, or not employed). These categories were selected based on availability of data in the NSDUH, and we chose to minimize modification of categories to better enable future comparison and meta-analysis across studies.
Past-year major depressive episode was coded dichotomously. Briefly, adults were recorded as having a major depressive episode in the last year if they reported meeting five of nine criteria for major depressive episode, including a depressed mood or loss of pleasure in daily activities.
2.3. Analysis
Data cleaning and analysis were performed using SAS 9.4 (SAS Institute, Cary, NC) using survey procedures for complex sampling designs. Statistical weighting to account for complex sampling addressed nonresponse, noncoverage, and oversampling of some groups, thereby enhancing the generalizability of the study’s findings to the national population. To address missing data, variables with imputed values were preferentially selected. Nevertheless, the NOMCAR option was incorporated in the logistic regression models to reduce potential bias from missingness. The NOMCAR option avoids listwise deletion of cases with missing values, ensuring that full data is used in the calculation of the standard errors for weighted analyses. Individuals who did not provide a response to the question about sexual orientation were excluded from analyses.
We calculated unweighted frequencies and weighted percentages for all demographic, covariate, and outcome variables and compared them by using Rao-Scott X2. Multiple logistic regression models were then used to determine the odds of past-year SUD by intersectional identity. First, we separated the sample by gender and then included each intersectional identity as a dummy-coded variable (e.g., Black heterosexual, Hispanic lesbian/gay) so that women of each intersectional identity group were compared to heterosexual White women and men from each intersectional identity group were compared to heterosexual White men. This reference group was chosen to facilitate comparison to extant literature. We adjusted for all demographic variables and past year major depressive episode in each model. Results from the logistic regressions are reported as adjusted odds ratios (aOR) with 95 % confidence intervals (CI). Statistical significance was determined by CI, where CI that did not include 1 are reported as statistically significant. For groups with cell sizes < 10, estimates are not reported. Due to low overall prevalence of OUD and StimUD, we did not report intersectional data for these substances, as low cell counts contributed to model instability. In addition, we found relatively low cell counts for multiracial individuals on mSUD and Asians on all SUD categories; thus those group was excluded from models for which they demonstrated rare outcomes (<10 cell count). For all frequencies, we report the unweighted n with the weighted percents to aid in interpretation.
3. Results
3.1. Study sample
The sample comprised of 83,722 participants, with 46,711 women and 37,011 men. Among women, 1,360 (2.2 %) identified as lesbian/gay, 6,070 (8.1 %) identified as bisexual, and 39,821 (weighted 93.5 %) identified as heterosexual. Among men, 1,219 (3.2 %) identified as gay, 1,727 (3.3 %) identified as bisexual, and 34,065 (93.5 %) identified as heterosexual. Notably, the racial/ethnic composition was relatively similar across sex, race, and sexual orientation (see Table 1). Overall, men had significantly higher prevalence of aSUD than women (21.1 % compared to 15.0 %, p < 0.0001) and demonstrated higher prevalence with individual substances including AUD (13.8 % compared to 9.6 %, p < 0.0001), CUD (8.0 % compared to 5.2 %, p < 0.0001), StimUD (2.0 % compared to 1.3 %, p < 0.0001), and mSUD (in a similar pattern (3.2 % compared to 2.2 %, p < 0.0001), but not for OUD where rates were similar (2.2 % compared to 2.1 %, p = 0.8216).
Table 1.
Demographic frequencies (unweighted n and weighted %) from the 2021–2022 National Survey on Drug Use and Health by Sexual Orientation and Gender (N = 83,722).
| Women (n = 46,711) |
Men (n = 37,011) |
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|---|---|---|---|---|---|---|---|---|
| Sample Characteristics | Lesbian/Gay (n = 1,360) |
Bisexual (n = 6,070) |
Heterosexual (n = 39,281) |
p-value | Gay (n = 1,219) |
Bisexual (n = 1,727) |
Heterosexual (n = 34,065) |
p-value |
| Age (years) | < 0.0001 | < 0.0001 | ||||||
| 18–24 | 594 (27.6) | 3,450 (43.2) | 9,242 (9.9) | 433 (14.8) | 951 (33.9) | 10,153 (12.8) | ||
| 25–34 | 306 (21.5) | 1,555 (29.5) | 7,770 (13.7) | 284 (19.9) | 374 (24.0) | 6,601 (15.4) | ||
| 35–49 | 300 (21.3) | 916 (19.7) | 11,495 (23.8) | 292 (26.6) | 255 (18.7) | 8,905 (24.7) | ||
| 50+ | 160 (29.6) | 149 (7.6) | 10,774 (52.6) | 210 (38.7) | 147 (23.4) | 8,406 (47.0) | ||
| Race/ethnicity | < 0.0001 | 0.2135 | ||||||
| Non-Hispanic White | 860 (61.3) | 3,952 (66.5) | 25,981 (66.7) | 808 (66.2) | 1,223 (69.1) | 22,772 (67.5) | ||
| Non-Hispanic Black | 199 (17.8) | 618 (11.7) | 4,944 (13.6) | 122 (12.4) | 126 (8.7) | 4,041 (12.3) | ||
| Non- Hispanic Multiracial | 69 (2.9) | 410 (4.2) | 1,429 (2.0) | 62 (2.1) | 91 (2.7) | 1,354 (1.8) | ||
| Hispanic | 232 (18.0) | 1,090 (17.7) | 6,927 (17.7) | 227 (19.3) | 287 (19.6) | 5,898 (18.5) | ||
| Ed ucation | < 0.0001 | < 0.0001 | ||||||
| Less than high school | 126 (9.1) | 643 (9.3) | 3,319 (9.2) | 72 (4.4) | 194 (9.2) | 3,705 (11.0) | ||
| High school | 320 (22.9) | 1,631 (26.1) | 8,553 (26.5) | 253 (21.1) | 509 (28.4) | 9,573 (29.6) | ||
| Some college | 456 (35.5) | 2,208 (38.3) | 12,285 (31.6) | 389 (32.0) | 582 (35.2) | 9,660 (29.6) | ||
| College or higher | 458 (32.5) | 1,588 (26.3) | 15,124 (32.8) | 505 (42.5) | 442 (27.2) | 11,127 (29.8) | ||
| Insurance status | < 0.0001 | 0.1119 | ||||||
| Uninsured | 158 (9.8) | 687 (11.5) | 3,219 (7.5) | 117 (9.1) | 271 (15.2) | 4,220 (11.6) | ||
| Public insurance | 344 (26.2) | 1,999 (34.4) | 10,783 (31.0) | 270 (25.6) | 402 (25.4) | 7,588 (25.5) | ||
| Private insurance | 820 (64.0) | 3,212 (54.0) | 24,266 (61.5) | 802 (65.3) | 1,004 (59.5) | 21,359 (62.9) | ||
| Marital Status | < 0.0001 | < 0.0001 | ||||||
| Married | 303 (23.7) | 1,256 (23.1) | 17,914 (49.1) | 212 (25.6) | 318 (22.8) | 14,939 (51.3) | ||
| Widowed, divorced, or separated | 141 (15.2) | 604 (13.6) | 6,744 (26.6) | 76 (10.0) | 120 (12.4) | 3,545 (16.9) | ||
| Never married (single) | 916 (61.2) | 4,210 (63.3) | 14,623 (24.3) | 931 (64.4) | 1,289 (64.8) | 15,581 (31.8) | ||
| Income | < 0.0001 | < 0.0001 | ||||||
| Less than $10,000 | 417 (26.0) | 2,368 (35.5) | 9,969 (22.7) | 284 (20.0) | 574 (27.1) | 6,528 (14.2) | ||
| $10,000-$19,999 | 256 (18.7) | 1,433 (23.4) | 6,912 (20.2) | 199 (16.8) | 344 (18.0) | 5,073 (15.0) | ||
| $20,000-$29,999 | 173 (13.1) | 835 (12.9) | 4,908 (13.0) | 148 (12.7) | 238 (14.5) | 3,858 (11.4) | ||
| $30,000-$39.999 | 155 (11.8) | 583 (9.5) | 3,963 (10.3) | 114 (8.7) | 168 (9.0) | 3,513 (10.7) | ||
| $40,000-$49,999 | 94 (6.4) | 358 (6.0) | 3,292 (8.6) | 89 (7.0) | 111 (9.3) | 3,136 (10.1) | ||
| $50,000-$74,999 | 135 (12.0) | 367 (6.4) | 4,773 (11.7) | 164 (14.4) | 134 (9.0) | 4,926 (16.2) | ||
| $75,000 or more | 130 (12.1) | 333 (6.4) | 5,464 (13.5) | 221 (20.2) | 158 (13.1) | 7,031 (22.5) | ||
| Employment | < 0.0001 | 0.1877 | ||||||
| Full- or part-time and/or student | 965 (68.6) | 4,194 (67.8) | 24,836 (53.2) | 888 (65.3) | 1,218 (69.2) | 24,162 (65.1) | ||
| Not employed | 380 (31.4) | 1,819 (32.2) | 13,940 (46.8) | 316 (34.7) | 513 (30.8) | 9,476 (34.9) | ||
3.2. Substance use disorder prevalence among women
Among women in this study, SUD prevalence varied by sexual orientation and race/ethnicity (see Table 2). Multiracial lesbian/gay women experienced the highest burden of aSUD (46.6 %) among women. Hispanic lesbian/gay women experienced the highest prevalence of AUD (28.2 %) among women, followed closely by White bisexual women (21.5 %) and Black lesbian/gay women (20.2 %). Multiracial lesbian/gay women (39.1 %) experienced CUD at nearly twice the rate of the next highest groups: multiracial bisexual women (27.1 %) and White bisexual women (22.2 %). StimUD rates were generally lower, with multiracial lesbian/gay women at 11.1 %. OUD was also relatively low, with Black lesbian/gay women again reporting the highest prevalence (5.2 %). Notably, multiracial bisexual women also had the highest rate of mSUDs (10.1 %), followed by White bisexual and Black bisexual women (8.8 % and 8.6 %, respectively).
Table 2.
Prevalence (unweighted n and weighted %) of past-year substance use disorder by gender, sexual orientation, and race.
| Substance Use Disorder | Women |
Men |
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|---|---|---|---|---|---|---|
| Lesbian/Gay | Bisexual | Heterosexual | Gay | Bisexual | Heterosexual | |
| Any Substance Use Disorder | ||||||
| Non-Hispanic White | 246 (20.2) | 1,447 (38.8) | 3,820 (12.9) | 260 (32.0) | 477 (38.8) | 4,964 (19.4) |
| Non-Hispanic Black | 64 (32.1) | 216 (32.2) | 700 (12.5) | 37 (29.7) | 42 (32.7) | 965 (22.5) |
| Non-Hispanic Multiracial | 25 (46.6) | 170 (30.2) | 294 (22.9) | 23 (52.3) | 32 (33.2) | 363 (27.8) |
| Hispanic | 73 (34.5) | 394 (38.4) | 934 (10.7) | 79 (33.8) | 99 (31.5) | 1,380 (20.5) |
| Alcohol Use Disorder | ||||||
| Non-Hispanic White | 129 (10.6) | 776 (21.5) | 2,612 (8.9) | 161 (19.9) | 249 (19.5) | 3,325 (12.9) |
| Non-Hispanic Black | 38 (20.2) | 125 (18.3) | 438 (7.9) | 22 (12.2) | 20 (14.9) | 554 (14.3) |
| Non-Hispanic Multiracial | 12 (7.8) | 77 (12.3) | 173 (12.4) | 13 (32.2) | 17 (21.5) | 210 (16.1) |
| Hispanic | 46 (28.2) | 205 (19.7) | 613 (7.2) | 51 (24.3) | 56 (15.4) | 888 (14.2) |
| Cannabis Use Disorder | ||||||
| Non-Hispanic White | 116 (9.1) | 845 (22.2) | 1,119 (3.2) | 111 (11.1) | 272 (21.7) | 1,980 (6.9) |
| Non-Hispanic Black | 32 (13.6) | 144 (20.1) | 300 (4.4) | 23 (18.5) | 28 (20.4) | 540 (9.5) |
| Non-Hispanic Multiracial | 15 (39.1) | 129 (27.1) | 133 (9.6) | 12 (33.4) | 22 (13.6) | 203 (15.3) |
| Hispanic | 36 (10.1) | 253 (21.8) | 338 (3.4) | 34 (12.0) | 63 (20.0) | 653 (7.3) |
| Stimulant Use Disorder | ||||||
| Non-Hispanic White | 30 (2.6) | 164 (4.9) | 377 (1.1) | 35 (5.2) | 64 (4.4) | 416 (1.7) |
| Non-Hispanic Black | 2 (5.0) | 12 (0.9) | 29 (0.3) | 4 (4.9) | 2 (2.4) | 50 (1.7) |
| Non-Hispanic Multiracial | 2 (11.1) | 20 (2.4) | 32 (1.7) | 5 (12.4) | 2 (0.9) | 42 (2.6) |
| Hispanic | 6 (0.8) | 45 (5.5) | 71 (0.8) | 11 (5.1) | 10 (3.0) | 101 (2.3) |
| Opioid Use Disorder | ||||||
| Non-Hispanic White | 28 (2.6) | 125 (3.8) | 435 (2.1) | 18 (3.9) | 43 (4.0) | 403 (2.0) |
| Non-Hispanic Black | 6 (5.2) | 11 (2.9) | 99 (2.3) | 2 (0.76) | 5 (3.8) | 84 (3.0) |
| Non-Hispanic Multiracial | 4 (5.0) | 15 (1.7) | 41 (4.3) | 0 | 3 (0.9) | 33 (2.7) |
| Hispanic | 4 (0.5) | 25 (2.7) | 97 (1.7) | 5 (2.8) | 4 (6.9) | 93 (2.1) |
| Multiple Substance Use Disorders | ||||||
| Non-Hispanic White | 34 (2.2) | 313 (8.8) | 457 (1.5) | 51 (6.6) | 102 (7.2) | 814 (2.6) |
| Non-Hispanic Black | 11 (5.9) | 65 (8.6) | 125 (1.9) | 11 (4.7) | 10 (4.4) | 194 (4.1) |
| Non-Hispanic Multiracial | 6 (5.0) | 49 (10.1) | 61 (3.9) | 6 (24.9) | 9 (2.6) | 89 (5.9) |
| Hispanic | 14 (4.9) | 99 (7.3) | 131 (1.6) | 14 (5.5) | 27 (5.9) | 261 (3.3) |
3.3. Substance use disorder prevalence among men
SUD prevalence also varied by sexual orientation and race/ethnicity for men (see Table 2). Multiracial gay men had the highest aSUD rate (52.3 %), followed by White gay (38.8 %), Hispanic gay (33.8 %), and multiracial bisexual men (33.2 %). Among specific SUDs, alcohol and cannabis were most common, with multiracial gay men exhibiting highest prevalence for both AUD (32.2 %%) and CUD (33.4 %). OUD rates were generally low, with Hispanic gay men showing the highest prevalence (6.9 %), though low cell sizes for this suggest caution in interpretation. For mSUDs, multiracial gay men exhibited the highest frequency (24.9 %), followed by White bisexual (7.2 %) and gay men (6.6 %).
3.4. Intersectional comparisons of SUD among women
We further investigated the odds of SUD among women, with intersectional identities comprised of sexual orientation and race/ethnicity compared to White heterosexual women (see Table 3). In general, bisexual and lesbian/gay women had increased odds of most SUDs, though this varied across racial groups. At the intersection of gender, race/ethnicity, and sexual orientation, Black bisexual women, Black lesbian/gay women, and Hispanic bisexual women exhibited elevated odds across aSUD, AUD, and CUD (ORs 1.73–6.87); this is particularly notable as heterosexual Black and Hispanic women exhibited significantly lower odds of reporting aSUD or AUD compared to the reference group (ORs 0.71–0.82).
Table 3.
Odds of substance use disorder by for women by sexual orientation and race.
| Lesbian/Gay |
Bisexual |
Heterosexual |
||||
|---|---|---|---|---|---|---|
| aOR | 95 % CI | aOR | 95 % CI | aOR | 95 % CI | |
| Any Substance Use Disorder | ||||||
| Non-Hispanic White | 1.73 | 1.24, 2.41 | 2.25 | 1.95, 2.60 | REF | |
| Non-Hispanic Black | 2.17 | 1.20, 3.93 | 1.87 | 1.40, 2.50 | 0.82 | 0.70, 0.97 |
| Non-Hispanic Multiracial | 3.53 | 1.03, 12.13 | 1.27 | 0.84, 1.92 | 1.79 | 1.29, 2.47 |
| Hispanic | 2.42 | 1.28, 4.58 | 2.27 | 1.74, 2.97 | 0.71 | 0.60, 0.84 |
| Alcohol Use Disorder | ||||||
| Non-Hispanic White | 0.79 | 0.54, 1.17 | 1.65 | 1.36, 1.99 | REF | |
| Non-Hispanic Black | 2.24 | 1.17, 4.29 | 1.63 | 1.24, 2.14 | 0.83 | 0.69, 0.99 |
| Non-Hispanic Multiracial | 0.45 | 0.13, 1.54 | 0.79 | 0.47, 1.33 | 1.38 | 0.92, 2.06 |
| Hispanic | 2.96 | 1.46, 6.02 | 1.59 | 1.17, 2.16 | 0.79 | 0.68, 0.93 |
| Cannabis Use Disorder | ||||||
| Non-Hispanic White | 1.43 | 1.02, 1.99 | 2.87 | 2.32, 3.55 | REF | |
| Non-Hispanic Black | 1.99 | 1.12, 3.55 | 2.53 | 1.69, 3.80 | 1.00 | 0.77, 1.30 |
| Non-Hispanic Multiracial | 6.87 | 1.46, 32.26 | 2.99 | 1.91, 4.69 | 2.36 | 1.42, 3.93 |
| Hispanic | 1.50 | 0.80, 2.82 | 2.63 | 1.92, 3.62 | 0.68 | 0.56, 1.01 |
| Multiple Substance Use Disorders | ||||||
| Non-Hispanic White | 0.67 | 0.35, 1.29 | 1.84 | 1.27, 2.67 | REF | |
| Non-Hispanic Black | 1.96 | 0.53, 7.21 | 1.89 | 1.12, 3.20 | 0.94 | 0.66, 1.33 |
| Hispanic | 1.37 | 0.51, 3.66 | 1.48 | 1.04, 2.12 | 0.73 | 0.53, 1.02 |
Note: Sample sizes for the models were 44,803 for ‘multiple substance use disorders’ and 46,711 for all others. Boldface indicates significant result.
Multiracial lesbian/gay women exhibited the highest odds of aSUD (aOR = 3.53, 95 % CI = 1.03. 12.13), followed by Hispanic lesbian/gay (aOR = 2.42, 95 % CI = 1.28, 4.58), then Hispanic bisexual women (aOR = 2.27, 95 % CI = 1.74, 2.97). For AUD, Hispanic lesbian/gay women were nearly 3 times as likely to report AUD (aOR = 2.96, 95 % CI = 1.46, 6.02). Black lesbian/gay women reported the second-highest odds of AUD (aOR = 2.24, 95 % CI = 1.17, 4.29), followed by White bisexual women (aOR = 1.65, 95 % CI = 1.13, 3.34) and Black bisexual women (aOR = 1.63, 95 % CI = 1.24, 2.14).
Odds of reporting CUD were elevated for multiracial lesbian/gay women (aOR = 6.87, 95 % CI = 1.46, 32.26) though the wide confidence intervals suggest this estimate should be interpreted with caution. Bisexual women exhibited elevated odds of reporting CUD across racial groups (aORs 2.53–2.99) compared to the reference group, as did Black and White lesbian/gay women (aOR 1.99 and aOR 1.43, respectively). Regarding mSUDs, bisexual women had significantly higher odds across racial groups (aORs 1.48–1.89) compared to White heterosexual women.
3.5. Intersectional Comparisons of SUD among men
Fewer intersectional identity groups demonstrated significantly different odds of SUD compared to White heterosexual men (see Table 4). White gay (aOR = 1.73, 95 % CI = 1.24, 2.42) and bisexual (aOR = 1.57, 95 % CI = 1.28, 1.98) men reported higher odds of experiencing aSUD. For AUD, no significant differences were found among intersectional identity groups. However, for CUD, White bisexual men were the only group to exhibit increased odds, being 1.87 times more likely to report CUD compared to Non-Hispanic White heterosexual men (95 % CI = 1.41, 2.47), while Hispanic heterosexual men were less likely to report CUD (aOR = 0.71, 95 % CI = 0.58, 0.88). White gay men were the only group with significantly elevated odds of mSUD, being 2.15 times more likely to report mSUDs (95 % CI = 1.03, 4.46). No other intersectional identity groups showed significantly different odds compared to reference group.
Table 4.
Odds of Substance use disorder by for men by sexual orientation and race.
| Gay |
Bisexual |
Heterosexual |
||||
|---|---|---|---|---|---|---|
| aOR | 95 % CI | aOR | 95 % CI | aOR | 95 % CI | |
| Any Substance Use Disorder | ||||||
| Non-Hispanic White | 1.73 | 1.24, 2.42 | 1.57 | 1.28, 1.98 | REF | |
| Non-Hispanic Black | 1.15 | 0.49, 2.71 | 1.12 | 0.61, 2.06 | 0.99 | 0.84, 1.16 |
| Non-Hispanic Multiracial | 1.89 | 0.68, 5.26 | 1.10 | 0.45, 2.67 | 1.20 | 0.87, 1.67 |
| Hispanic | 1.48 | 0.88, 2.49 | 1.25 | 0.62, 2.52 | 0.85 | 0.72, 1.00 |
| Alcohol Use Disorder | ||||||
| Non-Hispanic White | 1.47 | 1.00, 2.16 | 1.07 | 0.83, 1.39 | REF | |
| Non-Hispanic Black | 0.77 | 0.35, 1.67 | 0.73 | 0.29, 1.81 | 1.04 | 0.88, 1.25 |
| Non-Hispanic Multiracial | 1.72 | 0.48, 6.17 | 1.26 | 0.45, 3.57 | 1.08 | 0.72, 1.62 |
| Hispanic | 1.65 | 0.95, 2.86 | 0.93 | 0.42, 2.06 | 0.97 | 0.82, 1.50 |
| Cannabis Use Disorder | ||||||
| Non-Hispanic White | 1.43 | 0.80, 2.56 | 1.87 | 1.41, 2.47 | REF | |
| Non-Hispanic Black | 1.55 | 0.53, 4.55 | 1.50 | 0.68, 3.32 | 0.97 | 0.74, 1.27 |
| Non-Hispanic Multiracial | 2.11 | 0.63, 7.06 | 0.73 | 0.31, 1.72 | 1.59 | 1.02, 2.48 |
| Hispanic | 1.00 | 0.47, 2.10 | 1.67 | 0.69, 4.07 | 0.71 | 0.58, 0.88 |
| Multiple Substance Use Disorders | ||||||
| Non-Hispanic White | 2.15 | 1.03, 4.46 | 1.34 | 0.92, 1.97 | REF | |
| Non-Hispanic Black | 0.86 | 0.41, 1.79 | 0.67 | 0.29, 1.57 | 1.10 | 0.92, 1.51 |
| Hispanic | 1.14 | 0.44, 2.96 | 1.09 | 0.51, 2.34 | 0.87 | 0.67, 1.13 |
Note: Sample sizes were 35,504 for the ‘multiple substance use disorders’ model and 37,011 for all other models. Boldface indicates significant result.
4. Discussion
Previous research explored links between broader substance use behavior and the intersections of gender, race/ethnicity, and/or sexual orientation (e.g., Schuler et al., 2020). Our study delves deeper, examining the associations between intersectional identities and multiple SUDs. We reveal stark racial and sexual orientation disparities, particularly among women, with bisexual women across all races/ethnicities and multiracial women across sexual orientations also show consistently elevated odds for most SUDs. Black lesbian/gay, Black bisexual, and Hispanic lesbian/gay women also faced high SUD burdens. Interestingly, men show fewer disparities across identities, despite high overall prevalence of SUDs, which may be driven by higher rates of SUD in the reference group (White heterosexual men). This underscores the critical role of an intersectional approach in investigating substance use.
The intersection of gender and sexual orientation also reveals pronounced disparities in SUDs, particularly among bisexual women. Bisexual women consistently show elevated odds of SUDs compared to other orientations which aligns with previous findings (Ford et al., 2023, McCabe et al., 2022, Schuler and Collins, 2020) and underscores the importance of an intersectional approach. Several factors contribute to this vulnerability, including the persistent invisibility of bisexuality within mainstream culture and pervasive biphobia (Monro et al., 2017, Zivony and Saguy, 2018). Bisexuality's challenge to the rigid binary model of sexual orientation (attraction solely to the same or opposite sex) often renders it overlooked in society (Schuler and Collins, 2020, Taylor, 2018). Further compounding the issue, both heterosexual individuals and members of the LGBTQ+ community often harbor negative stereotypes about bisexual people, questioning their authenticity, labeling them as “confused,” and stereotyping them as sexually promiscuous (Burke and LaFrance, 2018, Feinstein et al., 2017). These pervasive anti-bisexual stigmas, both internalized and external, act as potent minority stressors and are linked to increased substance use (Feinstein et al., 2017, Ford et al., 2023, Vu et al., 2019). Relative invisibility, stigmatization, and discrimination translates to a lack of culturally specific resources and support networks for bisexual individuals, further isolating them and reducing their sense of belonging and connection to the LGBTQ+ community (Bostwick and Dodge, 2019, Feinstein et al., 2017). Notably, the impact of such stressors differs across genders; women experiencing minority stress exhibit higher rates of both mental health issues and substance use, while for men, only mental health is affected (Lehavot and Simoni, 2011, Vu et al., 2019). Further research is needed to explore how biphobia differentially affects bisexual women and men, and to investigate whether bisexual-specific stressors have a stronger association with substance use in bisexual women compared to bisexual men.
Pronounced SUD disparities were noted at the intersection of gender, race/ethnicity, and sexual orientation, with multiracial lesbian/gay, multiracial bisexual, Black lesbian/gay, Black bisexual, and Hispanic lesbian/gay women in our study exhibiting higher odds of reporting SUD. This finding mirrors the conclusions of prior research (Demant et al., 2018, Greene et al., 2020, Krueger et al., 2020, Mereish and Bradford, 2015, Schuler et al., 2020), further highlighting the complex interplay of identities in determining vulnerabilities to SUDs. Minority racial/ethnic LGB women face a unique confluence of heterosexism, racism, and misogyny that amplifies vulnerability to SUDs. This “triple jeopardy” effect creates compounded stress, discrimination, and limited resources, often leaving them navigating complex trauma, economic hardship, and a lack of safe and affirming spaces (Krueger et al., 2020, McCabe et al., 2010). Compared to individuals experiencing only one or two of these marginalized identities, their challenges in seeking support and coping with adversity can be greatly magnified, potentially leading to higher rates of self-medicating with substances (Mereish & Bradford, 2015). Further research is crucial to fully understand and address how intersectional identity formation and identification contribute to health outcomes and resilience. Additionally, more refined measures of multiracial identity are needed to evaluate the effects of this complex phenomenon on health and SUD prevalence in persons who identify as multiracial, as multiracial individuals may experience racism differently than members of the groups from which they derive their identity (Grilo et al, 2023).
Interestingly, this study reveals a high burden of CUD across multiple intersectional groups of women compared to heterosexual White women, including high prevalence and sharp disparities among people of varying races and sexual orientations. Cannabis is the most used substance in the United States, which may contribute to this. While cannabis remains illegal federally, 24 states and the District of Columbia have legalized marijuana for recreational and medical use, while 14 additional states have legalized it for medical use only (National Council of State Legislatures, 2024). Due to the rising legalization of cannabis, more individuals may be increasing their consumption generally. Of note, and for the first time, past-year marijuana use was significantly higher in young adult women compared to young adult men, which may also contribute to this finding (Patrick et al., 2024). Furthermore, bisexual women in our study consistently exhibited elevated odds for CUD compared to White heterosexual women, which is consistent with previous findings (e.g., Boyd et al., 2020, Krueger et al., 2020).
Our study reveals a unique pattern in men. While White gay and bisexual men demonstrated elevated odds of reporting an SUD compared to heterosexual White men, sexual minority men of color showed no difference. This unexpected finding suggests potential resilience among sexual minority men of color, possibly built from navigating the dual stressors of race and sexual orientation. Previous research supports this notion, highlighting adaptive coping mechanisms and resilience as potential contributors (Bowleg, 2013, Mereish and Bradford, 2015, Meyer, 2010). The findings suggest that sexual minority men of color may have developed effective coping mechanisms to navigate the combined stressors of racism and heterosexism, at least regarding the development of SUDs (Schuler et al., 2020, Vu et al., 2019). This resilience manifests in their lower odds of developing SUDs compared to heterosexual men. Applying an intersectionality framework, we recognize that the intertwined identities of gender, race/ethnicity, and sexual orientation may position these men in complex social locations, both privileged (e.g., masculinity) and marginalized. This paradoxical reality, where advantages coexist with oppression, likely contributes to their resilience. However, a deeper investigation of both disparities and resilience is crucial, considering the interplay and nuances within these intersecting identities. Such an exploration would provide a more complete understanding of their current social positions and experiences.
Taken together, these findings support the need to develop and advocate for social and organizational policies to reduce discrimination and promote greater understanding of these subpopulations, thereby alleviating minority stress and lowering reliance on substance use as a coping mechanism, thus preventing the development of SUDs. Additionally, primary care providers can focus on screening and referral for those at risk of SUDs using culturally humble approaches. Substance use prevention and treatment programs must be inclusive and provide a safe space for those with marginalized identities. Recent recommendations from LGBTQ+ individuals to make substance use prevention and treatment programs affirming include non-discrimination policies, regular staff sensitivity training, and LGBTQ+ -specific sessions (Paschen-Wolff et al., 2024). Future research should additionally focus on the development of novel interventions for these high-risk groups of women.
4.1. Limitations
Several limitations should be considered when interpreting the findings of this study. While nationally representative, data is limited to the U.S. and may not be generalizable to other countries with different cultural, social, and policy contexts influencing substance use. Small sample sizes limited our ability to examine several marginalized racial/ethnic groups including First Nations peoples who have historically experienced sharp burdens related to SUD prevalence and treatment gaps. The cross-sectional design precludes any conclusions about causality or the temporal sequence of events. As the NSDUH does not collect data on diverse gender identities (e.g., transgender and non-binary individuals), our ability to examine gender was limited to the binary (men/women). This restricted scope limits the generalizability of findings to the broader LGBTQ+ populations. Furthermore, the LBG sample included only individuals who self-identify as out, potentially neglecting those who are closeted or in earlier stages of identity exploration. This could introduce selection bias, as the population classified as LGB may have different experiences and vulnerabilities related to substance use compared to the overall LGBTQ+ community. Small cell sizes, particularly for racial minorities within specific sexual orientation categories, hinder our ability to draw robust conclusions about some groups. Oversampling by race and sexual orientation in future studies would provide more reliable data to analyze and understand potential disparities in substance use outcomes among diverse LGBTQ+ subgroups. While minority stress undoubtedly plays a significant role in SUDs among sexual minority individuals, the NSDUH does not fully capture the nuanced experiences of proximal and distal stressors that vary widely across different subpopulations. This limitation hinders our ability to comprehensively understand the specific minority stressors contributing to SUDs within this diverse group. Finally, the study relies on self-reported diagnoses of SUDs, which are susceptible to social desirability or recall bias. This could contribute to an underestimation of SUD prevalence, thereby influencing the observed relationships between sexual orientation and substance use.
5. Conclusion
This was the first study to examine SUDs based on DSM-5 criteria at the intersection of gender, race/ethnicity, and sexual orientation. The findings revealed striking disparities in reports of SUDs between intersectional groups of women but surprisingly fewer significant differences in SUDs among intersectional identities of men. These results identify new areas for future studies. Research attention should focus on prevention and intervention strategies for the higher risk subpopulations of women and explore both the risk and protective factors of intersectional identities in men.
CRediT authorship contribution statement
Marvin A. Schilt-Solberg: Writing – original draft, Project administration, Methodology, Funding acquisition, Conceptualization. Lisa M. Blair: Writing – original draft, Methodology, Formal analysis. Julie A.M.J. Kurzer: Writing – original draft.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
Data analysis and manuscript preparation were supported by a research grant from the National Institute of Drug Abuse (3R01DA016575-21S2). The study sponsor had no role in the study design, collection, analysis, or interpretation of the data, writing of the manuscript, or the decision to submit the paper for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the study sponsors.
Contributor Information
Marvin A. Schilt-Solberg, Email: solberma@umich.edu.
Lisa M. Blair, Email: lblair@wayne.edu.
Julie A.M.J. Kurzer, Email: hk3993@wayne.edu.
Data availability
Data will be made available on request.
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Associated Data
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Data Availability Statement
Data will be made available on request.