Abstract
Purpose:
Previous studies have suggested that sexual minorities (SMs), especially racial/ethnic minority youth, have a higher prevalence of substance use and abuse than their heterosexual counterparts. We aimed to understand the associations of SM identity with substance use and disorders among U.S. adults and their variations by race/ethnicity.
Methods:
In 2019, we analyzed data from U.S. adults surveyed in the 2012-2013 National Epidemiological Survey on Alcohol and Related Conditions-III (n=35,981). Participants reported gender, sexual identity, sexual behaviors, and sexual attraction, and were categorized into four SM identities: heterosexual, gay/lesbian, bisexual, and conflicting. Substance (tobacco, alcohol, and marijuana) use and disorders were assessed. Weighted multivariable logistic regressions were used to examine relationships between SM identities and substance use and disorders, stratified by race/ethnicity, adjusting for socio-demographic characteristics.
Results:
SM adults had a higher prevalence of substance use and disorders than their heterosexual counterparts in most substances examined. For example, bisexual adults were more likely than heterosexuals to use marijuana (AOR=3.45, 95% CI=2.64-4.50) and have tobacco use disorder (AOR=2.58, 95% CI=2.02-3.28). These associations were stronger among racial/ethnic minority SMs. For instance, bisexual non-Hispanic Whites were about twice as likely to be current tobacco users than their heterosexual counterparts (AOR=1.99, 95% CI=1.47-2.71); while bisexual non-Hispanic Blacks were three times as likely (AOR=3.17, 95% CI=2.16-4.65).
Conclusion:
SM adults, especially those who are racial/ethnic minorities, experience a significantly higher burden of substance use and disorders than heterosexuals. Efforts to screen and treat substance use and disorders among these adults are needed to reduce these disparities.
Keywords: Sexual Orientation, Race/Ethnicity/Culture, Health Disparities, Substance Use Disorder, Substance Use, Epidemiology, Tobacco Use
INTRODUCTION
Sexual minority (SM) individuals (i.e., those who identify as gay, lesbian, bisexual, or describe their sexual orientation as differing from traditional cultural, societal, or physiological norms) experience a disproportionate burden of substance use and disorders when compared to those who identify as heterosexual (Parker, 2018; Pérez-Stabl, 2016). For example, they have a higher prevalence of cigarette smoking (30.8% for SMs vs. 20.5% for heterosexuals), heavy alcohol use (9.3% for SMs vs. 6.8% for heterosexuals), marijuana use (30.7% for SMs vs. 12.9% for heterosexuals), and substance use disorders than their heterosexual counterparts (Agaku et al., 2014; Grace Medley, 2016).
Research studies have previously examined differences in patterns of substance use and disorders among SM subgroups (Feinstein & Dyar, 2017; Ulrich, 2011). These studies have indicated that bisexuals have a higher prevalence of substance use when compared with both their heterosexual and gay/lesbian counterparts (Green & Feinstein, 2012). Moreover, disparities in substance use observed among SM subgroups may be amplified when SM individuals are also racial/ethnic minorities (Smith et al., 2006). Racial/ethnic minorities often experience an increased burden of substance use and disorders, and a reduced likelihood of completing substance disorders treatment and quitting smoking (Grace Medley, 2016; Saloner & Le Cook, 2013; Trinidad, Perez-Stable, White, Emery, & Messer, 2011). Racial/ethnic minority SMs face a number of risk factors that predispose them to these disparities, as they report encountering sexual-orientation-related discrimination more frequently than white SMs and discrimination from within SM communities (Balsam, Molina, Beadnell, Simoni, & Walters, 2011; Whitfield, Walls, Langenderfer-Magruder, & Clark, 2014). Racial/ethnic variation in perceptions of acceptable masculinity/femininity (e.g., machismo in Hispanic culture), civil liberties for SMs, and acceptance of diverse sexualities may also play a role in these disparities (Bonilla & Porter, 1990; Durell, Chiong, & Battle, 2007). As such, there may be distinct patterns of substance use and disorders among racial/ethnic minority SMs.
However, few studies have examined the intersection between race/ethnicity and SM status on adult substance use and disorders. Talley and colleagues examined youth alcohol use disparities, and found that the associations between SM status and alcohol use behaviors were moderated by race/ethnicity (Talley, Hughes, Aranda, Birkett, & Marshal, 2013). McCabe and colleagues examined adult tobacco use disparities and found that prevalence of any tobacco use, cigarette smoking, and tobacco use disorder varied somewhat by race/ethnicity within each SM subgroup, when stratified by sex (McCabe et al., 2018).
In this study, we expand upon previous research by examining the associations of sexuality with adult substance use and disorders (tobacco, alcohol, and marijuana), and the variation of these associations by race/ethnicity. Additionally, we extended our analysis to include respondents who did not identify themselves as a sexual minorities but expressed same-sex attraction in order to determine whether previously described disparities extend to populations that experience non-heterosexual attraction without explicitly taking on a social identity as a sexual minority. We hypothesized that racial/ethnic minority SMs would experience an increased prevalence of substance use and abuse and that these substance use and abuse differences would be more pronounced among racial/ethnic minorities. We also hypothesized that individuals who expressed non-heterosexual attraction but did not identify themselves as sexual minorities would experience a higher prevalence of substance use and abuse than their heterosexual counterparts, especially among racial/ethnic minorities.
METHODS
Study Design
Data for this study were from the respondents (n=35,981) of the 2012-2013 National Epidemiological Survey on Alcohol and Related Conditions-III (NESARC-III), a nationally representative cross-sectional survey among American adults (≥18 years old) utilizing multistage probability sampling. The NESARC-III was uniquely suited for our research questions because it included information on substance use disorders based on Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria. The 2012-2013 NESARC-III cohort were the latest available data from the study. The NESARC-III utilized structured diagnostic in-household and in-person interviews to collect information on drug and alcohol use and disorders as well as related risk factors (Grant, Chu, et al., 2015). The household response rate was 72%, and an in-person response rate was 84% (Grant, Chu, et al., 2015). Two incentives of $45 were provided prior to and following the completed interview (Grant, Chu, et al., 2015). Data were analyzed in June 2019. This research only involved the use of de-identified data, which is not considered human subjects research and requires no IRB review or approval per National Institutes of Health policy and 45 CFR 46.
Measures
Sexuality
Sexuality was determined using questions about sexual orientation, sexual behavior, and sexual attraction. Respondents were provided with a flashcard to select what best described their sexual identity (heterosexual, gay or lesbian, bisexual, not sure). Sexual behavior was characterized using multiple items, including whether the respondent had had sex in the last 12 months (yes/no) and whether they had had sex with only males, only females, or both males and females during the last 12 months. For sexual attraction, respondents selected an option that best described their sexual interests (only attracted to females, mostly attracted to females, equally attracted to males and females, mostly attracted to males, only attracted to males). Information on sex (male vs. female) was also collected.
Respondents’ sexuality was defined as following. Those who identified as heterosexual, only had sex with heterosexual partners, and were only attracted to opposite sex were classified as heterosexual. Those who identified as gay or lesbian or engaged in sexual behavior with only the same sex were coded as being gay/lesbian. Those who identified as bisexual or engaged in sexual behavior with both sexes were coded as being bisexual. Lastly, those who identified as heterosexual, had engaged in either no sexual behavior or exclusively heterosexual behavior, but reported some levels of same-sex attraction were coded as having conflicting sexuality. This was done to distinguish those who express same-sex attractions/activity but self-identify as heterosexual from their exclusively heterosexual, gay/lesbian, and bisexual counterparts. Individuals that provide conflicting sexual orientation information have been found to have increased substance use and disorders, differing from their heterosexual counterparts (Hughes, Wilsnack, & Kristjanson, 2015; Marshal, Friedman, Stall, & Thompson, 2009). Previous analysis on the NESARC has utilized similar categorization criteria for the purpose of identifying significant differences in substance use and abuse (Boyd, Veliz, Stephenson, Hughes, & McCabe, 2019).
Substance Use
Questions about the lifetime use of each product, use in the last year, recency of last use, and whether a prerequisite number of tobacco products had been consumed were utilized to determine whether respondents were current tobacco users. Those who had consumed a prerequisite number of tobacco products (100+ cigarettes, 50+ cigars, 50+ pipes, or 20+ uses of chewing tobacco) and used the products in the past 30 days were considered current tobacco users. The only exception to these questions was the usage of e-cigarettes, as the NESARC-III did not ask about the amount of e-cigarette use. Those who had used e-cigarettes within the past 30 days were coded as being current tobacco users. Current marijuana use was defined as reported using marijuana in the past 30 days. Binge drinking was assessed with questions about recent alcohol consumption corresponding with the National Institute on Alcohol Abuse and Alcoholism (NIAAA) standards (National Institute on Alcohol Abuse and Alcoholism, 2015). Specifically, respondents who identified as women or as men over 65 and current drinkers were considered binge drinkers if they had, at least once, consumed 4 or more drinks in 2 hours or less within the past year (National Institute on Alcohol Abuse and Alcoholism, 2015). Respondents who identified as men at/under 65 and current drinkers were considered binge drinkers if they had, at least once, consumed 5 or more drinks in 2 hours or less within the past year (National Institute on Alcohol Abuse and Alcoholism, 2015).
Substance Use Disorders
Substance use disorders were defined as meeting DSM-5 criteria for the types of substances examined. The NIAAA Alcohol Use Disorder and Associated Disabilities Interview Schedule- Fifth Version (AUDADIS-5) was included as a component of the NESARC-III to assess the presence of substance use disorders according to DSM-5 criteria (Grant, Goldstein, et al., 2015). Past year diagnosis of tobacco use disorder (TUD), alcohol use disorder (AUD), and cannabis use disorder (CUD) required that ≥2 of 11 standardized criteria be met within the past 12 months (Grant, Goldstein, et al., 2015).
Demographics
Demographic characteristics, including age, education, urbanicity, geographic region, annual household income, and race/ethnicity were assessed. See Table 1 for specific categories.
Table 1.
Demographic Characteristics of Respondents. NESARC-III Survey 2012-2013 (n=35,981)
| Sexual Orientation | |||||
|---|---|---|---|---|---|
|
| |||||
| Total (n = 35981) | Straight (n = 32592) | Gay/Lesbian (n = 1067) | Bisexual (n = 631) | Conflicting (n = 1691) | |
|
|
|||||
| n (Weighted %) | n (Weighted %) | n (Weighted %) | n (Weighted %) | n (Weighted %) | |
| Sex | |||||
| Male | 15726 (48.1%) | 14424 (48.8%) | 601 (59.3%) | 152 (27.2%) | 549 (35.4%) |
| Female | 20255 (51.9%) | 18168 (51.2%) | 466 (40.7%) | 479 (72.8%) | 1142 (64.6%) |
| Age (years) | |||||
| 18-29 | 8075 (21.7%) | 7008 (20.9%) | 319 (27.6%) | 308 (51.7%) | 440 (25.5%) |
| 30-44 | 10058 (25.8%) | 9128 (25.8%) | 312 (28.1%) | 188 (28.1%) | 430 (22.9%) |
| 45-64 | 12119 (35.0%) | 11145 (35.5%) | 363 (35.0%) | 112 (17.3%) | 499 (30.0%) |
| 65+ | 5729 (17.5%) | 5311 (17.8%) | 73 (9.3%) | 23 (2.9%) | 322 (21.5%) |
| Race/Ethnicity | |||||
| Non-Hispanic White | 19048 (66.3%) | 17244 (66.4%) | 553 (64.3%) | 321 (64.3%) | 930 (66.2%) |
| Non-Hispanic Black | 7685 (11.8%) | 6943 (11.7%) | 238 (13.8%) | 167 (16.2%) | 337 (10.9%) |
| Non-Hispanic Other1 | 2279 (7.2%) | 2057 (7.2%) | 51 (5.1%) | 28 (5.3%) | 143 (10.0%) |
| Hispanic | 6969 (14.7%) | 6348 (14.8%) | 225 (16.9%) | 115 (14.2%) | 281 (12.9%) |
| Education Level | |||||
| ≤ High School Degree | 15137 (38.7%) | 13840 (38.9%) | 362 (31.6%) | 251 (39.5%) | 684 (39.6%) |
| Some College | 11994 (33.1%) | 10772 (32.9%) | 387 (34.8%) | 259 (40.7%) | 576 (32.3%) |
| ≥ College Degree | 8850 (28.2%) | 7980 (28.2%) | 318 (33.6%) | 121 (19.8%) | 431 (28.0%) |
| Region | |||||
| Northeast | 5134 (18.3%) | 4590 (18.2%) | 169 (20.5%) | 110 (21.3%) | 265 (18.6%) |
| Midwest | 7505 (21.5%) | 6828 (21.6%) | 182 (17.4%) | 135 (21.7%) | 360 (21.6%) |
| South | 14384 (37.0%) | 13203 (37.5%) | 381 (31.5%) | 228 (33.4%) | 572 (32.1%) |
| West | 8958 (23.2%) | 7971 (22.8%) | 335 (30.6%) | 158 (23.6%) | 494 (27.7%) |
| Urbanicity | |||||
| Urban | 29916 (78.7%) | 26907 (78.1%) | 957 (87.1%) | 567 (86.3%) | 1485 (84.0%) |
| Rural | 6065 (21.3%) | 5685 (21.9%) | 110 (12.9%) | 64 (13.7%) | 206 (16.0%) |
| Annual Household Income | |||||
| < $50,000 | 22630 (53.0%) | 20327 (52.3%) | 638 (51.2%) | 489 (69.2%) | 1176 (62.0%) |
| ≥ $50,000 | 13351 (47.0%) | 12265 (47.7%) | 429 (8.8%) | 142 (0.8%) | 515 (8.0%) |
Non-Hispanic Other combined the responses of Non-Hispanic American Indian/Alaska Native and Non-Hispanic Asian/Native Hawaiian/Other Pacific Islander respondents
Statistical Analysis
All responses were weighted to account for oversampling, nonresponse, and to represent the US noninstitutionalized population as recommended by the NIAAA (Grant, Chu, et al., 2015). Statistical analysis was conducted using SAS® version 9.4 (SAS Institute: Cary, NC). Unweighted frequencies and weighted percentages of sample demographic characteristics were reported stratified by sexuality. Unweighted frequencies and weighted percentages of substance use and disorders were reported by sexuality and stratified by race/ethnicity. Adjusted odds ratios (AORs) and 95% confidence intervals (CIs) were estimated using weighted multivariable logistic regression models for the relationships between the sexuality and substance use and disorders, adjusting for demographics and stratifying by race/ethnicity (Hispanic, non-Hispanic white, and non-Hispanic black). Heterosexual and gay/lesbian respondents were used as the referent groups for Model 1 and Model 2, respectively, in order to allow for a within-group analysis of differences among sexual minority respondents. Statistical significance was set at p<0.05.
RESULTS
Sample Characteristics
Overall, 48.1% of the sample were men and 66.3% were non-Hispanic white (Table 1). 90.6% were heterosexual, 3.0% were gay/lesbian, 1.7% were bisexual, and 4.7% were conflicting. Among Hispanic respondents, 91.1% of them were heterosexual, 3.2% were gay/lesbian, 1.7% were bisexual, and 4.0% were conflicting. Among non-Hispanic white respondents, 90.5% of them were heterosexual, 2.9% were gay/lesbian, 1.7% were bisexual, and 4.9% were conflicting. Among non-Hispanic black respondents, 90.3% of them were heterosexual, 3.1% were gay/lesbian, 2.2% were bisexual, and 4.4% were conflicting.
SM Substance Use and Disorders
All SM subgroups were more likely than their heterosexual counterparts to use and meet disorder criteria for every type of substance examined in the study, except for binge drinking among gay/lesbian adults (Table 2). For example, bisexual adults were more than twice as likely than heterosexual adults to use marijuana (AOR=3.45, 95% CI=2.64-4.50) as well as meet TUD (AOR=2.58, 95% CI=2.02-3.28), AUD (AOR=2.50, 95% CI=2.00-3.13), and CUD criteria (AOR=3.24, 95% CI=2.28-4.58). Additionally, when compared to gay/lesbian adults, bisexual adults were also more likely to use every type of substance, and conflicting adults were more likely to engage in binge drinking.
Table 2:
Prevalence and Adjusted odds ratios of use and abuse of tobacco, alcohol, marijuana based on sexual orientation by race/ethnicity (n=35981), NESARC-III 2012-2013 (n=35,981)
| Overall1 (n = 35981) | Hispanic (n = 6969) | Non-Hispanic White (n = 19048) | Non-Hispanic Black (n = 7685) | ||||||
|---|---|---|---|---|---|---|---|---|---|
|
|
|||||||||
| Sexual Orientation | N (weighted %) | AOR (Weighted 95% CI) | N (weighted %) | AOR (Weighted 95% CI) | N (weighted %) | AOR (Weighted 95% CI) | N (weighted %) | AOR (Weighted 95% CI) | |
| Current Tobacco Use | |||||||||
| Model 1 | Heterosexual | 7944 (24.4%) | Ref | 1052 (16.6%) | Ref | 4708 (27.4%) | Ref | 1795 (25.9%) | Ref |
| Gay/Lesbian | 329 (31.0%) | 1.36 (1.14-1.61) | 72 (32.0%) | 2.50 (1.77-3.53) | 165 (29.9%) | 1.14 (0.90-1.44) | 81 (34.2%) | 1.89 (1.29-2.77) | |
| Bisexual | 282 (44.7%) | 2.58 (2.02-3.28) | 48 (41.7%) | 3.80 (2.29-6.33) | 152 (47.4%) | 1.99 (1.47-2.71) | 73 (43.7%) | 3.17 (2.16-4.65) | |
| Conflicting | 442 (26.2%) | 1.19 (1.03-1.39) | 55 (19.6%) | 1.35 (0.91-1.98) | 258 (27.9%) | 1.03 (0.86-1.24) | 106 (31.5%) | 1.36 (0.99-1.86) | |
|
|
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| Model 2 | Bisexual | 1.90 (1.41-2.57) | 1.52 (0.81-2.85) | 1.75 (1.21-2.54) | 1.68 (1.02-2.77) | ||||
| (Ref.: Gay/Lesbian) | Conflicting | 0.88 (0.70-1.11) | 0.54 (0.34-0.85) | 0.91 (0.68-1.22) | 0.72 (0.44-1.19) | ||||
|
| |||||||||
| Tobacco Use Disorder | |||||||||
| Model 1 | Heterosexual | 6357 (19.5%) | Ref | 739 (11.6%) | Ref | 3948 (22.9%) | Ref | 1363 (19.6%) | Ref |
| Gay/Lesbian | 266 (24.9%) | 1.35 (1.11-1.64) | 52 (23.1%) | 2.38 (1.60-3.53) | 137 (24.8%) | 1.17 (0.88-1.56) | 68 (28.6%) | 2.05 (1.33-3.15) | |
| Bisexual | 243 (38.5%) | 2.42 (1.92-3.05) | 38 (33.0%) | 3.46 (2.15-5.56) | 137 (42.7%) | 1.92 (1.43-2.58) | 61 (36.5%) | 3.14 (2.09-4.71) | |
| Conflicting | 373 (22.1%) | 1.22 (1.05-1.42) | 45 (16.0%) | 1.49 (0.95-2.36) | 225 (24.2%) | 1.04 (0.86-1.25) | 83 (24.6%) | 1.34 (0.96-1.86) | |
|
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| Model 2 | Bisexual | 1.80 (1.32-2.44) | 1.45 (0.79-2.65) | 1.64 (1.10-2.46) | 1.53 (0.83-2.84) | ||||
| (Ref.: Gay/Lesbian) | Conflicting | 0.91 (0.71-1.17) | 0.63 (0.37-1.06) | 0.89 (0.63-1.25) | 0.65 (0.39-1.08) | ||||
|
| |||||||||
| Binge Drinking | |||||||||
| Model 1 | Heterosexual | 4591 (14.3%) | Ref | 1006 (16.1%) | Ref | 2542 (14.9%) | Ref | 775 (11.4%) | Ref |
| Gay/Lesbian | 195 (19.5%) | 1.03 (0.83-1.28) | 57 (27.5%) | 1.60 (1.04-2.46) | 86 (16.6%) | 0.88 (0.66-1.19) | 44 (19.4%) | 1.54 (1.03-2.32) | |
| Bisexual | 187 (30.8%) | 1.95 (1.54-2.48) | 41 (35.9%) | 3.03 (1.90-4.83) | 83 (26.7%) | 1.34 (0.94-1.89) | 54 (34.8%) | 5.02 (3.15-7.98) | |
| Conflicting | 297 (18.1%) | 1.49 (1.24-1.78) | 55 (20.2%) | 1.40 (0.92-2.13) | 167 (18.6%) | 1.41 (1.11-1.78) | 52 (16.0%) | 1.88 (1.24-2.83) | |
|
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| Model 2 | Bisexual | 1.89 (1.34-2.67) | 1.89 (1.01-3.55) | 1.54 (0.94-2.51) | 3.25 (1.77-5.98) | ||||
| (Ref.: Gay/Lesbian) | Conflicting | 1.45 (1.09-1.92) | 0.88 (0.48-1.60) | 1.61 (1.07-2.42) | 1.22 (0.67-2.22) | ||||
|
| |||||||||
| Alcohol Use Disorder | |||||||||
| Model 1 | Heterosexual | 4344 (13.3%) | Ref | 825 (13.0%) | Ref | 2332 (13.5%) | Ref | 936 (13.5%) | Ref |
| Gay/Lesbian | 240 (22.5%) | 1.48 (1.29-1.71) | 54 (24.0%) | 1.63 (1.18-2.25) | 115 (20.8%) | 1.35 (1.09-1.67) | 59 (24.8%) | 2.24 (1.58-3.19) | |
| Bisexual | 201 (31.9%) | 2.50 (2.00-3.13) | 41 (35.7%) | 3.50 (2.13-5.75) | 93 (28.9%) | 1.96 (1.48-2.61) | 56 (33.5%) | 3.67 (2.36-5.72) | |
| Conflicting | 309 (18.3%) | 1.49 (1.25-1.78) | 43 (15.3%) | 1.11 (0.73-1.67) | 169 (18.2%) | 1.44 (1.11-1.86) | 78 (23.2%) | 2.23 (1.58-3.14) | |
|
|
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| Model 2 | Bisexual | 1.69 (1.29-2.21) | 2.15 (1.15-4.03) | 1.46 (1.04-2.03) | 1.64 (0.96-2.80) | ||||
| (Ref.: Gay/Lesbian) | Conflicting | 1.01 (0.81-1.26) | 0.68 (0.40-1.16) | 1.07 (0.78-1.46) | 0.99 (0.61-1.63) | ||||
|
| |||||||||
| Current Marijuana Use | |||||||||
| Model 1 | Heterosexual | 2227 (6.9%) | Ref | 370 (5.9%) | Ref | 1109 (6.5%) | Ref | 628 (9.1%) | Ref |
| Gay/Lesbian | 139 (13.1%) | 1.63 (1.26-2.11) | 34 (15.1%) | 2.49 (1.54-4.04) | 53 (9.6%) | 1.32 (0.87-1.98) | 46 (19.4%) | 2.28 (1.44-3.59) | |
| Bisexual | 144 (23.0%) | 3.45 (2.64-4.50) | 24 (21.1%) | 4.04 (2.26-7.21) | 76 (23.9%) | 3.26 (2.39-4.44) | 40 (23.9%) | 3.55 (2.08-6.08) | |
| Conflicting | 171 (10.2%) | 1.75 (1.40-2.18) | 25 (8.9%) | 1.73 (0.90-3.32) | 99 (10.7%) | 1.82 (1.37-2.41) | 38 (11.4%) | 1.65 (1.00-2.73) | |
|
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| Model 2 | Bisexual | 2.12 (1.45-3.10) | 1.62 (0.82-3.21) | 2.48 (1.47-4.17) | 1.56 (0.75-3.23) | ||||
| (Ref.: Gay/Lesbian) | Conflicting | 1.08 (0.77-1.51) | 0.70 (0.34-1.41) | 1.38 (0.82-2.32) | 0.73 (0.35-1.50) | ||||
|
| |||||||||
| Cannabis Use Disorder* | |||||||||
| Model 1 | Heterosexual | 790 (2.4%) | Ref | 129 (2.0%) | Ref | 367 (2.1%) | Ref | 256 (3.7%) | Ref |
| Gay/Lesbian | 47 (4.4%) | 1.56 (1.03-2.36) | 11 (4.9%) | 1.94 (0.91-4.17) | 13 (2.4%) | 1.06 (0.53-2.11) | 21 (8.8%) | 2.64 (1.31-5.32) | |
| Bisexual | 60 (9.5%) | 3.24 (2.28-4.58) | 15 (13.0%) | 7.32 (3.60-14.89) | 25 (7.8%) | 2.69 (1.69-4.29) | 18 (10.8%) | 2.61 (1.31-5.23) | |
| Conflicting | 70 (4.1%) | 2.04 (1.36-3.05) | 12 (4.3%) | 2.09 (1.10-3.94) | 36 (3.9%) | 2.19 (1.33-3.58) | 17 (5.0%) | 1.47 (0.75-2.88) | |
|
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| Model 2 | Bisexual | 2.07 (1.15-3.73) | 3.77 (1.35-10.48) | 2.56 (1.08-6.04) | 0.99 (0.35-2.81) | ||||
| (Ref.: Gay/Lesbian) | Conflicting | 1.30 (0.73-2.33) | 1.07 (0.42-2.76) | 2.07 (0.89-4.85) | 0.56 (0.19-1.60) | ||||
Note: Model 1 adjusted for substance use and abuse with straight respondents as the reference group. Model 2 adjusted for substance use and abuse with gay/lesbian respondents as the reference group
Although Non-Hispanic Other respondents were included in the demographic analysis, they were excluded from the stratified analysis due to insufficient sample sizes and large confidence intervals
While the NESARC-III refers to marijuana use exclusively, the DSM-5 diagnosis for misuse of marijuana is described as “Cannabis Use Disorder”.
Racial/Ethnic SM Minority Substance Use and Disorders
In analyses stratified by race/ethnicity, we observed that the associations between gay/lesbian and bisexual identity with substance use and disorders were stronger among racial/ethnic minority populations (Table 2). For example, AORs for the associations between being gay/lesbian and current tobacco use were 2.50 (95% CI=1.77-3.53) for Hispanics and 1.89 (95% CI=1.29-2.77) for non-Hispanic blacks, while it was 1.14 (95% CI=0.90-1.44) for the group’s non-Hispanic white counterparts. Likewise, while Hispanic and non-Hispanic black bisexuals were more than three times as likely than their heterosexual counterparts to meet the criteria for TUD (Hispanic: AOR=3.46, 95% CI=2.15-5.56; non-Hispanic black: AOR=4.13, 95% CI=2.09-4.71), this association for non-Hispanic white bisexuals was much weaker (AOR=1.92, 95% CI=1.43-2.58). Adjusted odds ratios for substance use and disorders among respondents with conflicting sexuality were fairly similar across races/ethnicities, except for binge drinking and AUD, in which these associations were the strongest among non-Hispanic blacks (binge drinking: AOR=1.88, 95% CI=1.24-2.83; alcohol use disorder: AOR=2.23; 95% CI=1.58-3.14) as compared to other racial/ethnic groups.
DISCUSSION
Consistent with previous results such as the National Survey on Drug Use and Health, we found that SM groups are more likely to use a variety of substances (e.g., tobacco, alcohol, and marijuana) and have substance use disorders as compared to their heterosexual counterparts (Agaku et al., 2014; Grace Medley, 2016). The findings from our study also uncovered racial/ethnic disparities in substance use and abuse among adult SMs (Hinds, Loukas, & Perry, 2018; McCabe et al., 2018; Talley et al., 2013). Our findings suggested that racial/ethnic SM adults, a group with dual-minority status, experience an increased risk for substance use and disorders as compared to their heterosexual and non-Hispanic white counterparts.
Bisexual individuals were found to have the highest prevalence of substance use and disorders for most substance use and disorders outcomes, in some cases, significantly higher than gay/lesbians. Bisexuals are often considered to “pass” as heterosexual in contexts where their gay/lesbian counterparts may be readily identified as SMs, reducing the likelihood of sexual orientation discrimination (Lingel, 2009). But the experiences of bisexuals within SM spaces may contribute to stress and negative self-perceptions due to intracommunity bias such as biphobia and monosexism (Ulrich, 2011). These experiences, combined with perceived ostracization in both heterosexual and SM social environments, could create stress that results in coping behavior such as substance use. Studying discrimination using tools such as the revised biphobia scale may enable researchers to better understand the risk factors contributing to the significant disparities in substance use and disorders among bisexual individuals (Mulick & Wright, 2011).
Our study also provided support for previous literature suggesting that individuals who provide conflicting sexual orientation information experience increased substance use and disorders (Green & Feinstein, 2012). We defined individuals as the conflicting group if their self-reported sexual attraction deviated from their sexual orientation and/or behavior. Notably, from the findings of our study, conflicting adults were more likely to engage in every form of substance use and disorders as compared to their heterosexual counterparts but not their gay/lesbian counterparts. It is possible that this group’s substance use and disorders could be a form of coping with a complex social identity. Those who describe themselves as heterosexual but engage in same-sex attraction or behavior may experience isolation and exclusion from both exclusively heterosexual and SM communities. More studies are needed to further characterize the risk factors for substance use and disorders faced by this group and their corresponding health outcomes.
The disparities in substance use and disorders observed among racial/ethnic minority SMs may be explained by the unique experiences of this population. For example, in addition to encountering sexual orientation discrimination, racial/ethnic minority SMs may encounter discrimination in the form of racism from within the SM community (as well as racial/ethnic discrimination from the broader society) (Balsam et al., 2011). This experience could create stress for racial/ethnic minority SMs who may struggle to find and benefit from the accepting community spaces to which their non-Hispanic white counterparts have access. These experiences, combined with negative perceptions of SM identities in some racial/ethnic minority communities and socioeconomic factors such as poverty and unemployment, could further lead to substance use and disorders as a form of stress coping behavior. Future research should seek to evaluate the potential impact of racism and other forms of bias from within SM community spaces on substance use and disorders among racial/ethnic minority SMs. Additionally, the tobacco industry has historically singled-out both racial/ethnic minorities and SMs with targeted marketing campaigns, such as Project SCUM (Washington, 2002). Thus, racial/ethnic minority SMs experience a disproportionately high volume of tobacco marketing, with this exposure potentially facilitating the introduction to and subsequent usage of tobacco products (Emory, Buchting, Trinidad, Vera, & Emery, 2019).
Limitations
Firstly, the NESARC-III survey did not include measures to identify transgender or non-binary individuals, despite evidence of these populations having a higher prevalence of substance use and disorders (Hughes et al., 2015; Keuroghlian, Reisner, White, & Weiss, 2015). It is critical that future studies conducting such expansive research (such as the NESARC-IV) include robust samples of gender minority respondents. Inclusion of additional sexual orientation descriptors, such “questioning,” would also help expand the scope and efficacy of research on sexual minority groups who do not describe themselves as being gay, lesbian, or bisexual. Second, questions about sexual orientation were placed in the “medical conditions” section of the NESARC-III survey, which could potentially cause respondents’ discomfort and lead to a failure to disclose a SM identity when responding to the survey.
Second, there is robust literature which suggests that sex is another significant determining factor for substance use issues such as AUD, with differences in alcohol abuse being greater among heterosexual and SM women than their male counterparts (Crane, Swaringen, Foster, & Talley, 2020; Peralta, Victory, & Thompson, 2019).While we acknowledge the sex differences in substance usage, we were unable to complete an analysis of these differences, due to the multiple stratifications of our sample. After stratifying respondents by race/ethnicity and sexual orientation, further stratified analysis by sex led to sub-samples being too small for robust analyses. Future studies should seek to gather a large, diverse sample of LGBTQ+ individuals so that analyses may be conducted on substance use disparities by race, sex, and sexual orientation.
Third, despite the large sample included in the NESARC-III, we were unable to conduct effective analysis of substance use and disorders experienced by Non-Hispanic American Indian/Alaska Natives and Non-Hispanic Asian/Native Hawaiian/Other Pacific Islanders due to a small number of SM respondents. This was likely caused by the limited number of respondents representing these racial/ethnic demographics. Future surveys should seek to acquire a larger sample of SM groups among racial/ethnic minorities to address this issue.
Finally, although our data were collected during 2012-2013, evidence suggests the overall substance use prevalence from this study is consistent with the results from the 2012 and 2017 National Survey on Drug Use and Health (Jonaki Bose, 2018).
CONCLUSION
In conclusion, we found that adult SMs had a higher prevalence of substance use and disorders than their heterosexual counterparts, and the associations between SM identity and substance use and disorders were stronger among racial/ethnic minorities, a result that is consistent with previous literature describing racial/ethnic disparities in SM youth substance use and abuse. These findings imply that substance use and disorders prevention and treatment programs targeting racial/ethnic minority SMs are critically needed to alleviate the disparate risks faced by these communities. It is important for future research to continue examining risk factors and treatment opportunities for reducing substance use and disorders among racial/ethnic minority SMs in order to minimize these health disparities.
Acknowledgements
Data for this study were from the respondents of the 2012-2013 National Epidemiological Survey on Alcohol and Related Conditions-III (NESARC-III), which was sponsored, designed, and directed by the National Institute on Alcohol Abuse and Alcoholism (NIAAA).
This work and Drs. Choi and Julia-Chen-Sankey and Ms. Duarte were supported by the Division of Intramural Research, National Institute on Minority Health and Health Disparities. Mx. Freitag was supported also supported by the Office of the Director, National Institutes of Health. Mr. Ramsey was supported by the National Institute on Minority Health and Health Disparities (NIH Grant Number F31MD014047). Dr. Chen-Sankey was also supported by the National Cancer Institute (NIH grant number K99CA242589). The work of Thomas Freitag was supported by the Amgen Scholars Program at the National Institutes of Health (Summer 2019).
Footnotes
Disclaimer
Comments and opinions expressed in this article belong to the authors and do not necessarily reflect those of the US Government, Department of Health and Human Services, National Institutes of Health, National Cancer Institute, or National Institute on Minority Health and Health Disparities.
This project’s abstract was previously presented at the Society for Research on Nicotine and Tobacco’s Annual Conference in March of 2020.
Conflict of Interest: The authors have no conflicts of interest relevant to this article to disclose.
Financial Disclosure: The authors have no financial relationships relevant to this article to disclose.
Author Disclosure Statement
Mx. Freitag, Dr. Chen-Sankey, and Dr. Choi conceptualized and designed the analysis. Mx. Freitag conducted data analysis and drafted the initial manuscript. All authors reviewed and revised the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
The authors have no conflicts of interest relevant to this article. No competing financial interests exist.
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