Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Nov 1.
Published in final edited form as: Am J Prev Med. 2020 Sep 24;59(5):686–696. doi: 10.1016/j.amepre.2020.05.025

Medical, Nonmedical, and Illegal Stimulant Use by Sexual Identity and Gender

Morgan M Philbin 1, Emily R Greene 2, Silvia S Martins 2, Natalie LaBossier 3, Pia M Mauro 2
PMCID: PMC7577928  NIHMSID: NIHMS1610168  PMID: 32981768

Abstract

Introduction:

Major knowledge gaps regarding medical and nonmedical prescription stimulant use and illegal stimulant use (i.e., cocaine/crack/methamphetamine) by sexual identity and gender have implications for individuals’ health and well-being. This study improves stimulant use measurement by differentiating type of stimulant use and focusing on lesbian, gay, and bisexual subpopulations.

Methods:

Data were pooled for adults in the 2015–2017 National Survey on Drug Use and Health (n=126,463; analyzed in 2019). Gender-stratified logistic regression models examined associations between sexual identity and past-year illegal stimulant use. Gender-stratified multinomial logistic regression models estimated odds of: (1) medical use only versus no past-year prescription stimulant use, (2) any nomnedical stimulant use versus no past-year use, and (3) any nonmedical stimulant use versus medical use only.

Results:

Illegal stimulant use varied by sexual identity (men: gay, 9.2%; bisexual, 7.5%; heterosexual, 3.2%; women: gay/lesbian, 3.2%; bisexual, 7.8%; heterosexual, 1.5%), as did nonmedical prescription stimulant use. Relative to same-gender heterosexuals, gay (AOR=2.61, 95% CI=2.00, 3.40) and bisexual men (AOR=1.70, 95% CI=1.24, 2.33) had higher odds of past-year illegal stimulant use, as did gay/lesbian (AOR=1.63, 95% CI=1.16, 2.28) and bisexual women (AOR=2.70, 95% CI=2.23, 3.26). Sexual minorities reported higher odds of nonmedical prescription stimulant use than heterosexuals. Any nonmedical prescription opioid use was reported by 26.4% of people who reported nonmedical stimulant use, and 27.0% of people who reported illegal stimulant use.

Conclusions:

Lesbian, gay, and bisexual individuals had a higher prevalence stimulant use than their heterosexual counterparts. This has important implications for health disparities, particularly given high levels of polysubstance use. Taking a multilevel approach is crucial to reduce stimulant-related harms for lesbian, gay, and bisexual individuals.

INTRODUCTION

Prescription stimulant use has increased in the U.S. during the last decade1–3 and the number of prescribed stimulants doubled from 2006 to 2016.2,4 Though medically indicated for conditions such as attention-deficit/hyperactivity disorder, medical stimulant use is associated with emotional problems5,6 and poorer cardiovascular health.7 Nonmedical prescription stimulants use (i.e., use of prescription stimulants in ways not directed by a doctor) has also increased: Approximately 3.1% of U.S. adults8 and 10% of college students report past-year nonmedical use.9,10 Nonmedical prescription stimulant use is higher among individuals who are male,8,11–13 of higher SES,14 aged 18–25 years,4,8,15 and white.4,8,15 Although the media often portrays nonmedical prescription stimulant use as enhancing academic performance, it is associated with lower grades,18,19 drug dependence,4,20 and other substance use (i.e., polysubstance use),4,21,22 including illegal stimulants.19,21,23 Illegal stimulant use (e.g., cocaine/crack, methamphetamine) is also higher among individuals who are male,24 aged 18–25 years,25 and without a college education.25 Illegal stimulant use is associated with an increased risk for infectious diseases (e.g., HIV),26 lower economic participation,27 poor mental health outcomes,28 substance use disorder,20 and unintentional overdose.29 Because of these negative consequences, identifying populations with higher nonmedical or illegal stimulant use, such as sexual minorities, can lead to targeted interventions to minimize stimulant use-related harm.

Lesbian, gay, and bisexual (LGB) individuals report higher substance use (e.g., alcohol, marijuana, cigarettes, and other drugs30–32) and substance use disorders than their heterosexual counterparts.30–33 Though LGB individuals report higher nonmedical prescription drug use,31 that is rarely disaggregated by type of prescription (i.e., stimulant versus opioids). Illegal stimulant use is disproportionately high among LGB individuals: Past-year cocaine use was 6.6% (vs 2.0% among heterosexuals) and methamphetamines was 2.5% (vs 0.69% among heterosexuals).24 However, these national-level data have yet to be disaggregated by LGB subgroup and gender. Higher drug use among LGB individuals is likely a result of minority stress—that is, exposure to stigma and discrimination based on sexual orientation results in health disparities.34 Structural stigma (e.g., employment or housing discrimination) drives psychological and physical health morbidities among LGB populations, and perceived stigma is associated with cocaine use.35 Bisexuals experience “double discrimination” from heterosexuals and lesbian and gay communities,36,37 which may account for the particularly high substance use among bisexual individuals.

Major knowledge gaps remain about the medical and nonmedical use of prescription stimulants, and illegal stimulant use, by sexual identity. Although existing research suggests that LGB individuals are at higher risk for prescription stimulant use than heterosexuals,38–40 this research has important measurement limitations. First, studies focusing on specific LGB subpopulations, such as methamphetamine use among gay men,41,42 exclude other sexual identities. Studies also explore stimulant use separately (i.e., medical use versus nonmedical use), instead of characterizing patterns by type of use.43 Relatedly, most studies combine all forms of prescription drug use (e.g., stimulants and opioids),32,44 which obscures important patterns. Lastly, despite women reporting a lower prevalence of use, methamphetamine use has increased significantly among women since 2017.24 Given these discrepancies, this study explores separate analyses by gender.

The current study aims to improve the measurement of stimulant use by differentiating medical, nonmedical, and illegal stimulant use, and focusing on LGB subpopulations, who are at elevated risk of use. The 2015–2017 National Survey on Drug Use and Health (NSDUH) is used to: (1) describe the prevalence of medical prescription stimulant use, nonmedical prescription stimulant use, and illegal stimulant use by sexual identity and gender in a national sample of U.S. adults and (2) describe differences in overlapping stimulant use and polysubstance use by sexual identity. Stimulant use outcomes are expected to be higher among LGB individuals compared with heterosexuals, and highest among bisexual women and gay men compared with other sexual identity and gender subgroups. Characterizing LGB-based disparities among adults can help identify different points for multilevel interventions, such as increased screening and access to treatment at the clinical-level, and policy-level legislation to increase healthcare access and minimize LGB-focused housing and workforce discrimination, winch are associated with substance use.45

METHODS

Study Sample

The 2015–2017 NSDUH included data from annual cross-sectional surveys assessing substance use among a nationally representative sample of the U.S. civilian population. Data were collected using face-to-face household interviews, computer-assisted interviewing, and audio computer-assisted survey instruments to maximize participant privacy. Additional details can be found elsewhere.24,46 The weighted interview response rates among adults for 2015–2017 ranged from 66.3% to 68.4%.46

Observations were pooled across the three years (N=170,319), adding a year indicator and dividing yearly survey weights by three. Individuals who lacked sexual identity data, including individuals aged 12–17 years (n=41,579; 9.2%), were excluded, as were adults who responded don’t know/refuse to the sexual identity item (n=2,277; 1.7%). The final analytic sample included 126,463 adults.

Measures

Participants were asked: Which of the following do you consider yourself to be? Response categories included heterosexual, that is, straight; lesbian or gay; bisexual; don’ know; and refuse to answer. A three-level variable was created to describe mutually exclusive sexual identities, including heterosexual, gay/lesbian, or bisexual adults. Individuals who selected don’t know or refused to answer were excluded.

Participants were asked if they had used prescription stimulants (e.g., Adderall, Ritalin, Dextroamphetamine) in the past 12 months. People who reported any past-year prescription stimulant use were further asked if they had used prescription stimulants in a way other than the doctor had directed in the past 12 months, which identified people reporting nonmedical prescription stimulant use. A three-level categorical variable was created to reflect past-year use: (1) no use, (2) medical use only (i.e., prescription stimulant use but no self-reported nonmedical use), or (3) any nonmedical use of prescription stimulants.

Participants were asked if they had used cocaine or methamphetamine in the past 12 months. A dichotomous variable of illegal stimulant use was created to indicate any cocaine or methamphetamine use in the past year.

To assess the overlap between past year nonmedical and illegal stimulant use, the three-level categorical variable included: (1) only nonmedical prescription stimulant use, (2) only illegal stimulant use, and (3) use of both nonmedical prescription simulants and illegal stimulants.

Participants reported whether they drank any alcohol, used marijuana, prescription opioids, or heroin in the past year. Past-month binge drinking was defined as five or more (males) or four or more (females) drinks in a single occasion.

Sociodemographic variables included age (18–25, 26–34, 35–49, ≥50 years), race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic any race, non-Hispanic other), annual household income (<$20,000, $20,000–$49,999, $50,000–$74,999, ≥$75,000), population density (large metro, small metro, non-metro), and survey year indicators. The NSDUH computer-assisted interview guide determines gender by asking the interviewer to “record respondent’s gender: male or female.”

Statistical Analysis

The weighted prevalence of (1) medical prescription stimulants, (2) nonmedical prescription stimulants, and (3) illegal stimulants (i.e., cocaine or methamphetamine) was calculated by sexual identity and gender. Demographic characteristics were described for individuals who reported using any stimulant nonmedically or illegally, differentiating past-year nonmedical use only, illegal use only, or both. Gender-stratified multinomial logistic regression models estimated odds of: (1) medical prescription stimulant use only versus no past-year prescription stimulant use, (2) any nonmedical stimulant use versus no nonmedical use, and (3) any nonmedical stimulant use versus medical use only. A gender-stratified logistic regression model examined the association between sexual identity and past-year illegal stimulant use. All models adjusted for sociodemographics. Analyses were conducted in SAS, version 9.4 and accounted for the NSDUH complex survey design using sampling weights. All statistical tests were two-sided and considered statistically significant at the Bonferroni-adjusted p-value of <0.0125. These data, analyzed in 2019, were deemed non-human subjects research.

RESULTS

The sample was 48.4% male and majority white (64.8%), followed by Hispanic (15.6%) and black (11.8%). The majority (52.5%) reported an annual income of ≥$50,000 and lived in a large metro area (53.8%) (Table 1).

Table 1.

Sociodemographic Characteristics of Adults by Sexual Identity and Medical and Non-Medical Prescription Stimulant and Illegal Stimulant Use

Factor Overall N (wt col %) Heterosexual (N=118,222)
Wt row % (SE)		 Gay/Lesbian (N=2,731)
Wt row % (SE)		 Bisexual (N=5,510)
Wt row % (SE)
Prescription stimulant use
 Illegal stimulant use Prescription stimulant use
 Illegal stimulant use Prescription stimulant use
 Illegal stimulant use
Any Medical only Non-medical Any Medical only Non-medical Any Medical only Non-medical
Overall, N (wt %) 10,005 (6.5) 6,172 (4.5) 3,833 (2.0) 3,774 (2.3) 379 (11.5) 220 (7.0) 159 (4.5) 211 (6.6) 898 (14.2) 462 (7.4) 436 (6.8) 451 (7.7)
Gender
  Male 58,815 (48.4) 6.5 (0.1) 4.1 (0.1) 2.4 (0.1) 3.2 (0.1) 12.0 (1.0) 6.6 (0.7) 5.4 (0.8) 9.2 (1.1) 13.0 (1.1) 6.4 (0.8) 6.6 (0.9) 7.5 (5.4)
  Female 67,648 (51.6) 6.5 (0.1) 4.9 (0.1) 1.6 (0.1) 1.5 (0.1) 10.7 (1.2) 7.4 (1.1) 3.3 (0.5) 3.2 (0.5) 14.7 (0.7) 7.9 (0.4) 6.8 (0.7) 7.8 (0.6)
Age, years
  18–25 41,379 (14.2) 14.1 (0.2) 6.8 (0.2) 7.3 (0.2) 5.7 (0.2) 19.2 (1.4) 9.7 (1.1) 9.4 (1.0) 11.1 (1.2) 18.0 (0.8) 8.3 (0.6) 9.7 (0.5) 9.8 (0.7)
  26–34 26,114 (15.9) 9.2 (0.3) 5.6 (0.2) 3.6 (0.2) 4.4 (0.2) 16.4 (2.3) 9.1 (1.3) 7.3 (1.4) 10.4 (1.6) 15.5 (1.6) 7.9 (1.0) 7.6 (1.0) 7.4 (1.0)
  35–49 33,090 (24.7) 6.1 (0.1) 4.9 (0.1) 1.2 (0.1) 1.9 (0.1) 10.2 (1.5) 6.4 (1.0) 3.8 (1.0) 6.6 (1.4) 11.5 (1.2) 7.0 (1.0) 4.5 (0.9) 6.7 (0.9)
  ≥50 25,880 (45.2) 3.6 (0.1) 3.3 (0.1) 0.3 (0.1) 0.9 (0.1) 5.2 (1.1) 4.7 (1.1) 0.5 (0.4) 1.9 (0.8) 6.1 (1.7) 5.0 (1.6) 1.1 (0.6) 4.5 (1.5)
Race/Ethnicity
  NH white 77,100 (64.8) 7.7 (0.1) 5.3 (0.1) 2.4 (0.1) 2.5 (0.1) 12.3 (1.3) 7.1 (0.8) 5.2 (0.7) 5.8 (0.9) 16.5 (0.9) 8.8 (0.7) 7.7 (0.7) 8.6 (0.7)
  NH Black 15,888 (11.8) 3.5 (0.2) 2.9 (0.2) 0.6 (0.1) 2.0 (0.2) 7.1 (0.5) 3.5 (0.8) 3.6 (1.2) 8.7 (1.9) 7.8 (1.5) 4.1 (1.0) 3.7 (0.8) 4.8 (1.2)
  Hispanic 21,186 (15.6) 4.7 (0.2) 3.3 (0.2) 1.4 (0.1) 2.3 (0.2) 13.5 (2.1) 10.1 (1.7) 3.4 (0.8) 8.3 (1.5) 12.2 (1.5) 5.6 (1.1) 6.6 (1.1) 7.2 (1.2)
  Other 12,289 (7.8) 4.5 (0.2) 3.0 (0.2) 1.5 (0.1) 1.9 (0.2) 7.5 (2.0) 5.2 (1.7) 2.3 (0.8) 5.3 (1.9) 11.4 (1.5) 6.3 (1.1) 5.1 (0.9) 6.9 (1.3)
Income
  <$20,000 26,280 (16.8) 7.8 (0.2) 4.9 (0.2) 2.9 (0.2) 4.2 (0.2) 12.4 (1.7) 8.1 (1.4) 4.3 (1.1) 10.0 (1.8) 14.5 (1.1) 7.6 (0.9) 6.9 (0.9) 10.8 (1.1)
  $20,000–$49,999 39,996 (29.7) 5.7 (0.2) 4.1 (0.1) 1.6 (0.1) 2.4 (0.1) 11.5 (1.7) 7.4 (1.4) 4.1 (0.8) 5.1 (0.9) 14.9 (1.0) 7.7 (0.8) 7.2 (0.7) 6.9 (0.9)
  $50,000–$74,999 38,548 (16.2) 6.3 (0.2) 4.6 (0.2) 1.7 (0.1) 2.1 (0.1) 9.8 (1.9) 5.0 (1.3) 4.8 (1.4) 5.0 (1.3) 11.7 (1.5) 6.4 (1.1) 5.3 (1.0) 5.5 (1.1)
  ≥$75,000 40,435 (37.3) 6.6 (0.2) 4.7 (0.2) 1.9 (0.1) 1.6 (0.1) 11.7 (1.1) 7.0 (0.8) 4.7 (0.7) 6.5 (1.3) 14.3 (1.1) 7.5 (0.8) 6.8 (0.9) 6.6 (0.9)
Population density
  Large metro 53,653 (53.8) 6.4 (0.1) 4.4 (0.1) 2.0 (0.1) 2.4 (0.1) 12.2 (1.1) 7.3 (0.7) 4.9 (0.7) 7.5 (1.0) 13.6 (0.9) 6.5 (0.7) 7.1 (0.6) 8.5 (0.8)
  Small metro 62,958 (40.4) 6.7 (0.1) 4.7 (0.1) 2.0 (0.1) 2.3 (0.1) 9.8 (1.1) 6.3 (0.8) 3.5 (0.6) 4.5 (0.7) 14.8 (1.0) 8.4 (0.8) 6.4 (0.6) 6.8 (0.6)
  Rural 9,852 (5.8) 5.3 (0.3) 4.1 (0.2) 1.1 (0.1) 1.7 (0.2) 15.0 (3.6) 8.1 (3.1) 6.9 (2.4) 10.2 (3.9) 16.3 (3.3) 10.8 (2.5) 5.5 (2.3) 5.6 (1.7)
Open in a new tab

SE, survey adjusted standard error; wt. col %, survey weighted column percentage; wt. row %, survey weighted row percentage; NH, non-Hispanic.

The prevalence of past-year medical stimulant use for U.S. adults was 4.5% among heterosexuals, 7.0% among gays/lesbians, and 7.4% among bisexuals; prevalence of non-medical use was 1.9%, 4.5%, and 6.8% respectively (Table 1). Prevalence of illegal stimulant use was 2.3% for heterosexuals, 6.6% for gays/lesbians, and 7.7% for bisexual adults.

Differences in past year prescription stimulant use, nonmedical prescription stimulant use and illegal stimulant use emerged by gender and sexual identity (Figure 1). Past-year medical stimulant use was higher for women (4.9%–7.9%) compared with men (4.1%–6.4%).

Figure 1.

Figure 1.

Open in a new tab

Prevalence of medical and nonmedical prescription stimulant, and illegal stimulant use: sexual identity and gender.

Past-year nonmedical stimulant use was higher among gay men than gay/lesbian women (5.4% vs 3.3%), whereas there were no gender differences among heterosexuals or bisexuals. Illegal stimulant use was twofold higher among heterosexual men than women (3.2% vs 1.5%), nearly threefold higher among gay men than gay/lesbian women (9.2% vs 3.2%), and consistent across bisexual men and women (7.5% vs 7.8%).

There were gender and sexual identity specific disparities in patterns of medical and nonmedical prescription stimulant use. In adjusted models, gay men were more likely than heterosexual men to report medical stimulant use (adjusted relative OR [AROR]=1.62, 95% CI=1.25, 1.10) (Table 2). Similar patterns emerged for bisexual women (AROR=1.39, 95% CI=1.18, 1.63) but not gay/lesbian women. Compared with heterosexual men, bisexual men (AROR=1.86, 95% CI=1.40, 2.47) and gay men (AROR=2.01, 95% CI=1.48, 2.73) were more likely to report nonmedical versus no prescription stimulant use. Similarly, bisexual (AROR=2.05, 95% CI=1.73, 2.43) and gay/lesbian women (AROR=1.70, 95% CI=1.21, 2.38) were more likely than heterosexual women to report nonmedical prescription stimulant use. Among people who reported any prescription stimulant use, only bisexual women were more likely than heterosexual women to report nonmedical use (AROR=1.47, 95% CI=1.20, 1.81) than medical stimulant use. There were no statistically significant gender-specific differences comparing type of prescriptions stimulant use between bisexual and gay/lesbian men and women.

Table 2.

Medical and Non-Medical Prescription Stimulant Use and Illegal Stimulant Use by Sexual Identity and Gender

Prescription stimulantsa Illegal stimulantsb
Variable Medical use ONLY vs no past-year prescription stimulant usec,d Any non-medical use vs no past-year prescription stimulant usec,d Any non-medical use vs medical use ONLYd,e Any past-year illegal stimulant use vs no past-year illegal stimulant use
Men AROR (95% CI) Women AROR (95% CI) Men AROR (95% CI) Women AROR (95% CI) Men AROR (95% CI) Women AROR (95% CI) Men AROR (95% CI) Women AROR (95% CI)
Heterosexual ref ref ref ref ref ref ref ref
Bisexualc 1.38 (1.05, 1.82) 1.39 (1.18, 1.63) 1.86 (1.40, 2.47) 2.05 (1.73, 2.43) 1.34 (0.91, 1.97) 1.47 (1.20, 1.81) 1.70 (1.24, 2.33) 2.70 (2.23, 3.26)
Lesbian/Gayc 1.62 (1.25, 2.10) 1.47 (1.08, 2.02) 2.01 (1.48, 2.73) 1.70 (1.21, 2.38) 1.24 (0.84, 1.84) 1.15 (0.79, 1.68) 2.61 (2.00, 3.40) 1.63 (1.16, 2.28)
Lesbian/Gay ref ref ref ref ref ref ref ref
Bisexualf 0.86 (0.61, 1.20) 0.94 (0.67, 1.33) 0.92 (0.66, 1.31) 1.21 (0.90, 1.63) 1.08 (0.67, 1.74) 1.06 (0.75, 1.50) 0.65 (0.42, 1.00) 1.65 (1.12, 2.44)
Open in a new tab

Notes: Boldface indicates statistical significance at Bonferroni adjusted p<0.0125.

a

Multinomial logistic regression model.

b

Binary logistic regression model.

c

References: sexual identity: heterosexual, use: no past-year use.

d

Models adjust for survey year, age, race/ethnicity, annual household income, urbanicity.

e

References: sexual identity: heterosexual, use: past-year medical use

f

Sexual identity: lesbian/gay, use: no past-year use.

AROR, adjusted relative OR.

Patterns of illegal stimulant use differed by gender and sexual identity. Compared with heterosexual men, bisexual men were more likely to report past-year illegal use (AOR=1.70, 95% CI=1.24, 2.33), as were gay men (AOR=2.61, 95% CI=2.00, 3.40). Bisexual (AOR=2.70, 95% CI=2.23, 3.26) and gay/lesbian women (AOR=1.63, 95% CI=1.16, 2.28) were more likely to report past-year illegal stimulant use than their heterosexual counterparts.

The study also explored patterns of polysubstance use by sociodemographic characteristics among adults who use stimulants, differentiating by use of nonmedical prescription stimulants only, illegal stimulant use, or both. Among adults who reported nonmedical stimulant use, 1.1% reported heroin use and 52.3% nonmedical prescription opioid use. Among adults who reported only illegal stimulant use, 9.6% also reported heroin use, while 58.2% also reported nonmedical prescription opioid use. Among adults who reported both nonmedical prescription stimulant and illegal stimulant use, 10.4% reported heroin use, while 44.5% also reported nonmedical prescription opioid use. Gender differences emerged by LGB status for individuals who reported nonmedical use of prescription stimulants or illegal stimulant use or both (Table 3).

Table 3.

Sociodemographic Characteristics of U.S. Adults Reporting Past-Year Non-Medical or Illegal Stimulant Use by Sexual Identity

Heterosexual (N=6,410)
Wt col % (SE)		 Gay/Lesbian (N=311)
Wt col % (SE)		 Bisexual (N=718)
Wt col % (SE)
Factor Non-medical stimulant use only Illegal stimulant use only Both non-medical and illegal stimulant use χ2 p-value Non-medical stimulant use only Illegal stimulant use only Both non-medical and illegal stimulant use χ2 p-value Non-medical stimulant use only Illegal stimulant use only Both non-medical and illegal stimulant use χ2 p-value
Overall N (wt %) 2,636 (36.2) 2,577 (47.0) 1,197 (16.8) 100 (29.0) 152 (51.5) 59 (19.5) 267 (34.2) 282 (42.2) 169 (23.6)
Sex <0.0001 0.0020 0.2669
   Male 56.7 (1.6) 68.7 (1.4) 64.6 (1.8) 56.4 (7.3) 76.7 (4.0) 85.1 (4.1) 23.9 (4.1) 24.8 (4.1) 33.6 (4.9)
 Female 43.3 (1.6) 31.3 (1.4) 35.4 (1.8) 43.6 (7.3) 23.3 (4.0) 14.9 (4.1) 76.1 (4.1) 75.2 (4.1) 66.3 (4.9)
Age, years <0.0001 0.4968 0.0237
   18–25 50.3 (1.4) 26.5 (1.0) 50.0 (2.1) 39.5 (5.7) 29.0 (4.3) 45.3 (8.6) 56.7 (3.8) 46.4 (3.5) 52.2 (5.4)
   26–34 26.4 (1.3) 27.9 (1.3) 32.3 (1.8) 38.4 (7.0) 35.6 (4.8) 29.5 (8.3) 28.6 (4.4) 22.0 (3.6) 30.7 (5.3)
   35–49 16.0 (1.1) 23.5 (1.0) 13.0 (1.6) 18.5 (6.4) 23.1 (4.4) 19.4 (6.4) 12.2 (3.3) 18.6 (2.3) 14.3 (4.0)
   ≥50 7.3 (1.1) 22.1 (1.4) 4.7 (1.3) 3.6 (3.4) 12.3 (5.7) 5.8 (54) 2.5 (1.7) 13.0 (4.2) 2.8 (2.6)
Race/ Ethnicity <0.0001 0.0141 0.1833
   NH White 77.9 (0.9) 63.4 (1.6) 82.1 (1.6) 79.1 (5.1) 52.4 (6.6) 63.4 (7.3) 65.0 (4.0) 63.2 (4.0) 75.3 (4.0)
   NH Black 4.1 (0.4) 12.9 (1.2) 2.1 (0.5) 6.7 (2.8) 18.2 (3.7) 16.9 (6.2) 10.1 (2.5) 10.9 (2.9) 3.1 (1.5)
   Hispanic 11.7 (0.8) 17.1 (1.2) 10.0 (1.2) 11.7 (3.9) 23.3 (4.8) 14.0 (4.6) 17.2 (2.7) 16.4 (2.9) 15.4 (4.0)
   Other 6.3 (0.6) 6.5 (0.7) 5.7 (0.9) 2.5 (14) 6.0 (2.4) 5.6 (3.4) 7.7 (1.7) 9.5 (1.9) 6.1 (1.2)
Education <0.0001 0.2823 0.0039
   Less than high school 7.4 (0.7) 18.1 (1.0) 8.6 (1.2) 3.9 (1.8) 13.5 (4.9) 10.4 (5.2) 3.7 (1.1) 16.7 (3.0) 6.8 (2.1)
   High school/GED 17.6 (1.0) 29.5 (1.1) 20.5 (1.7) 16.3 (5.3) 23.4 (5.0) 14.5 (6.0) 29.2 (3.9) 27.3 (3.7) 23.3 (4.9)
   Some college 43.3 (1.4) 34.3 (1.1) 41.6 (1.9) 41.3 (5.8) 30.5 (3.8) 43.3 (8.6) 48.4 (4.7) 36.4 (3.9) 42.6 (5.3)
   College graduate 31.6 (1.3) 18.1 (0.9) 29.3 (2.4) 38.5 (6.7) 32.6 (3.8) 30.8 (6.2) 18.6 (3.7) 19.6 (3.9) 27.3 (4.2)
Income <0.0001 0.0472 0.0259
   <$20,000 22.8 (1.1) 30.5 (1.4) 27.9 (2.2) 9.6 (2.8) 30.1 (4.6) 34.4 (7.6) 22.6 (3.1) 39.7 (4.3) 35.0 (5.1)
   $20,000–$49,999 23.8 (1.1) 31.8 (1.4) 24.9 (1.5) 32.0 (6.1) 24.1 (5.6) 17.6 (6.0) 42.3 (3.7) 32.7 (4.1) 27.4 (4.1)
   $50,000–$74,999 14.1 (1.0) 14.4 (1.0) 14.0 (1.3) 20.6 (5.3) 12.1 (3.7) 14.7 (6.8) 9.4 (2.0) 8.3 (1.9) 13.3 (4.0)
   ≥$75,000 39.3 (1.2) 23.3 (1.0) 33.2 (1.8) 37.8 (5.9) 33.7 (7.2) 33.3 (7.2) 25.7 (3.8) 19.3 (3.0) 24.3 (4.9)
Urbanicity 0.3015 0.4700 0.7498
   Large metro 54.6 (1.3) 55.9 (1.7) 57.4 (2.1) 66.1 (6.3) 71.8 (4.1) 70.9 (7.1) 56.2 (3.9) 61.9 (4.2) 63.9 (5.1)
   Small metro 42.2 (1.2) 39.6 (1.8) 38.8 (1.8) 31.2 (6.3) 24.6 (4.3) 21.4 (5.9) 40.0 (3.8) 34.9 (4.1) 33.7 (5.0)
   Rural 3.2 (0.4) 4.5 (0.5) 3.8 (0.7) 2.6 (1.5) 3.6 (1.6) 7.6 (4.1) 3.8 (1.8) 3.2 (1.1) 2.3 (1.3)
Other substance use
   Any alcohol use 92.5 (0.8) 91.5 (0.8) 96.1 (1.1) 0.0107 90.3 (3.8) 89.7 (3.5) 92.5 (5.2) 0.8996 96.7 (2.4) 89.3 (2.9) 99.6 (0.3) 0.0039
   Binge drinking, past month 64.3 (1.3) 67.1 (1.3) 77.5 (1.8) <0.0001 65.6 (7.9) 65.3 (5.5) 73.6 (7.4) 0.7241 63.1 (4.1) 67.7 (3.8) 77.2 (3.8) 0.0868
   Marijuana use 63.0(1.3) 73.3(1.1) 89.4(1.4) <0.0001 50.2(6.8) 70.3(6.3) 87.6(5.7) 0.0057 74.4(3.3) 78.4(3.4) 87.4(3.5) 0.0665
   Non-medical prescription opioids 25.4 (1.1) 27.1 (1.0) 43.7 (1.6) <0.0001 36.0 (6.4) 22.6 (4.9) 55.9 (8.2) 0.0020 32.8 (4.2) 28.9 (3.2) 45.5 (5.1) 0.0272
   Heroin 1.1 (0.3) 9.8 (0.8) 10.5 (1.2) <0.0001 0 (0.0) 3.7 (1.6) 6.8 (3.1) – 0.9 (0.5) 10.2 (2.8) 11.7 (3.3) 0.0002
Open in a new tab

Note: Boldface indicates statistical significance (p<0.05).

wt col %: weighted column percent.

DISCUSSION

This study is the first to use a nationally representative sample of U.S. adults to describe LGB-related disparities in the use of medical and nonmedical prescription stimulants and illegal stimulants. Patterns of nonmedical use and illegal use differed by gender and sexual identity: LGB adults reported higher medical and nonmedical prescription stimulant use and illegal stimulant use than their heterosexual counterparts. Though additional work is needed to explore potential differences in this relationship by gender, findings are consistent with the minority stress model.47,48 This model may be particularly salient for bisexual individuals, who can face discrimination from both heterosexual and sexual minority communities,36,37,49 though such communities can also be sources of support. This highlights the need for future harm reduction interventions to target stimulant use among LGB populations. The findings have important implications across sexual identities, particularly related to polysubstance use, as 25%–50% of people reporting nonmedical and illegal stimulant use also used other substances (e.g., nonmedical prescription opioid use).

The magnitude of LGB disparities in illegal stimulant use is concerning: Bisexual women’s illegal stimulant use was fivefold that of heterosexual women, while gay men’s use was threefold that of heterosexual men. By contrast, gay/lesbian women reported lower illegal stimulant use than bisexuals or gay men, but prevalence was still twofold higher than heterosexual women. This study builds on past research reporting disproportionately high rates of illegal26,50,51 (and nonmedical prescription52–54) stimulant use among men who have sex with men (MSM). The findings extend these associations to include sexual identity, not sexual behavioral (e.g., bisexual or gay men versus MSM). Owing to the past focus on MSM and illegal stimulant use, little is known about the patterns of LGB women’s use. Studies frequently collapse LGB women, eliminating the potential to tease out differences between bisexual and lesbian/gay women, as the authors have done. In a recent study, bisexual adult women reported higher rates of all substance use measures than lesbian/gay women,55 though they did not measure stimulant use. Bisexual women have reported higher past-year and daily marijuana use than gay/lesbian women,31,56 and gay/lesbian women report higher lifetime cocaine use than heterosexual women.57 The current findings begin to build the evidence to fill this knowledge gap.

Though beyond the scope of this study, understanding drivers of illegal and nonmedical stimulant use merits further attention. Substantial research has explored drivers of illegal stimulant for MSM,42,58 primarily in the context of HIV.26,50 These include experiences of social discrimination,51 sexuality-related stigma,51 and racism. 59 Though the drivers of illegal stimulant use among bisexual women are likely similar—for example, homophobia and social and gender discrimination—future work should explicitly explore them in this understudied subgroup. Differences in illegal stimulant use may also result from differences in overall patterns of drug use (e.g., there are fewer gender differences among sexual minorities for past-year nonmedical opioid use60 and past-year and daily marijuana use).50

The high prevalence of illegal stimulant use, combined with nonmedical stimulant use, could increase LGB individuals’ risks for negative consequences related to stimulant use. These potential consequences include substance use disorder and overdose61,62, given increases in fentanyl contamination in illegally produced pills63 and cocaine and methamphetamine.64,65 Moreover, a high percentage of individuals who reported illegal and nonmedical stimulant use also reported prescription opioid use. Some experts warn that stimulant use disorders could be the next epidemic, indicating the need for research to understand who is most at risk for exposure to stimulant-related adverse outcomes.62–66

Nonmedical use of prescription stimulants was significantly higher for LGB individuals. This extends previous findings to adults, as most existing evidence was based on college students39,40 or youth.32,40 Bisexual women reported twofold higher nonmedical use and illegal stimulant use than gay/lesbian women. Findings addressed past study measurement limitations among bisexuals, such as combining all nonmedical prescription drug use (i.e., stimulants and opioid use31) or folding illegal stimulant use in an “any illegal drug” category, which made it difficult to tease apart drivers potentially unique to stimulant use.31

Future research should explore structural drivers of nonmedical and illegal stimulant use for LGB individuals to better understand causes of the differences by gender and sexual identity (particularly gay men and bisexuals), such as state-level factors that might drive substances use (e.g., homophobia35,67 or regulations of physician prescribing patterns).2,68,69 Individual-level analyses that also include state-level variables should control for state-level differences in laws relating to sexual minority discrimination (e.g., housing- and employment-based discrimination), which may affect substance use. Work should also explore reasons why: (1) gay/lesbian women have a lower prevalence than their sexual minority peers, (2) medical stimulant use was higher for LGB individuals compared with heterosexuals, and (3) few differences in medical use existed within LGB individuals. Work should also explore stimulant use patterns among individuals who chose the don’t know category of sexual identity. Patterns of use among youth remain of interest, as many prescriptions for stimulants begin at age 12–17 years.

Limitations

This study used nationally representative data to compare and disaggregate stimulant use by sexual identity and gender. Limitations include that sexual identity and substance use measures were self-reported, which may lead to recall bias or socially desirable reporting.70 This exploratory study did not adjust for multiple comparisons. Owing to NSDUH’s question framing, the authors could not differentiate between only nonmedical use and both nonmedical and medical use. The NSDUH started assessing sexual identity among adults in 2015, so these relationships could not be examined in earlier years or among adolescents. The NSDUH options for gender include only “male” or “female” and thus did not allow researchers to differentiate between transgender and cis-gender individuals. The NSDUH did not explicitly oversample LGB populations, so findings may not be representative of all LGB adults; this also meant that the authors had to exclude the don’t know category of sexual identity owing to lack of power to estimate gender-specific effects. The NSDUH does not assess sexual behavior, so this study only captured associations based on individuals’ sexual identity. The NSDUH excluded individuals who are incarcerated or homeless,46 among whom LGB individuals are over-represented.71

CONCLUSIONS

In this study, LGB individuals had uniformly higher stimulant use than their heterosexual peers. Exploring the drivers of stimulant use is important for health disparities, given the risk for disordered use and overdose, and in the context of increased fentanyl contamination of illegal stimulants.29,65 Widespread stimulant use—alongside opioid use—is referred to as the “fourth wave” of the epidemic.66 Multilevel interventions to minimize stimulant-related harms, many of which require clinical support, should pay particular attention to LGB populations. Providers who focus on LGB communities should screen for and discuss substance use, including stimulants, as the U.S. Preventive Services Task Force updated its draft recommendations for substance use screening to demonstrate moderate evidence.72 Drug screening and discussions with providers remain low,73 indicating that additional training and resources may be required to increase discussions about stimulant use and related treatment options tailored for LGB adults. Communities and providers can scale up access to medication disposal and harm reduction services. Structural-level targets include reducing unnecessary prescribing, offering non-stigmatizing and affordable treatment when clinically indicated,74 and addressing fentanyl contamination through harm reduction approaches, such as providing fentanyl test strips.75 Taking such a multilevel approach is crucial to reduce unintended stimulant-related harms that could disproportionately impact LGB adults.

Supplementary Material

1

ACKNOWLEDGMENTS

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. No members of the NIH, Substance Abuse and Mental Health Services Administration, or National Survey on Drug Use and Health played a role in the analysis, interpretation of results, or the decision to submit this manuscript. Research was supported by the NIH/National Institute on Drug Abuse: K01DA039804A (Philbin), K01DA045224 (Mauro), R01DA037866-S2 (Martins).

Publicly available de-identified data does not constitute human subjects research and was therefore not required to undergo review by the Columbia University IRB

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

All authors are responsible for this reported research. MMP, PMM, and SSM conceptualized and designed the study, and ERG conducted the statistical analyses. MMP, ERG, and NLaB drafted the initial manuscript. All authors interpreted results, and critically reviewed and revised the manuscript. All authors approved the final manuscript as submitted. No financial disclosures or conflicts of interest were reported by the authors of this paper.

REFERENCES

  • 1.Arria AM, DuPont RL. Prescription stimulant use and misuse: implications for responsible prescribing practices. Am J Psychiatry. 2018; 175(8):707–708. 10.1176/appi.ajp.2018.18050596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Piper BJ, Ogden CL, Simoyan OM, et al. Trends in use of prescription stimulants in the United States and Territories, 2006 to 2016. PLoS ONE. 2018;13(11):e0206100 10.1371/journal.pone.0206100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Safer DJ. Recent trends in stimulant usage. J Atten Disord. 2016;20(6):471–477. 10.1177/1087054715605915. [DOI] [PubMed] [Google Scholar]
  • 4.Han B, Jones CM, Blanco C, Compton WM. National trends in and correlates of nonmedical use of prescription stimulants, nonmedical use frequency, and use disorders. J Clin Psychiatry. 2017;78(9): 1250–1258. 10.4088/jcp.17m11760. [DOI] [PubMed] [Google Scholar]
  • 5.Molina BSG, Hinshaw SP, Eugene Arnold L, et al. Adolescent substance use in the multimodal treatment study of attention-deficit/hyperactivity disorder (ADHD) (MTA) as a function of childhood ADHD, random assignment to childhood treatments, and subsequent medication. J Am Acad Child Adolesc Psychiatry. 2013;52(3):250–263. 10.1016/j.jaac.2012.12.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Schachar RJ, Tannock R, Cunningham C, Corkum PV. Behavioral, situational, and temporal effects of treatment of ADHD with methylphenidate. J Am Acad Child Adolesc Psychiatry. 1997;36(6):754–763. 10.1097/00004583-199706000-00011. [DOI] [PubMed] [Google Scholar]
  • 7.Nissen SE. ADHD drugs and cardiovascular risk. N Engl J Med. 2006;354( 14):1445–1448. 10.1056/nejmp068049. [DOI] [PubMed] [Google Scholar]
  • 8.Chen L-Y, Strain EC, Alexandre PK, Alexander GC, Mojtabai R, Martins SS. Correlates of nonmedical use of stimulants and methamphetamine use in a national sample. Addict Behav. 2014;39(5):829—836. 10.1016/j.addbeh.2014.01.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Johnston LD, O’Malley PM, Miech RA, Bachman JG, Schulenberg JE. Monitoring the Future national survey results on drug use, 1975–2015: overview, key findings on adolescent drug use. Ann Arbor, MI: Institute for Social Research; 2016. 10.3998/2027.42/148123. [DOI] [Google Scholar]
  • 10.McCabe SE, Teter CJ, Boyd CJ, Wilens TE, Schepis TS. Sources of prescription medication misuse among young adults in the United States: the role of educational status. J Clin Psychiatry. 2018;79(2): 17m11958 10.4088/jcp.17m11958. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Benson K, Flory K, Humphreys KL, Lee SS. Misuse of stimulant medication among college students: a comprehensive review and meta-analysis. Clin Child Fam Psychol Rev. 2015; 18( 1): 50–76. 10.1007/s10567-014-0177-z. [DOI] [PubMed] [Google Scholar]
  • 12.DeSantis AD, Webb EM, Noar SM. Illicit use of prescription ADHD medications on a college campus: a multimethodological approach. J Am Coll Health. 2008;57(3): 315–324. 10.3200/jach.57.3.315-324. [DOI] [PubMed] [Google Scholar]
  • 13.Dussault CL, Weyandt LL. An examination of prescription stimulant misuse and psychological variables among sorority and fraternity college populations. J Atten Disord. 2013;17(2):87–97. 10.1177/1087054711428740. [DOI] [PubMed] [Google Scholar]
  • 14.Arria AM, Wilcox HC, Caldeira KM, Vincent KB, Garnier-Dykstra LM, O’Grady KE. Dispelling the myth of “smart drugs”: cannabis and alcohol use problems predict nonmedical use of prescription stimulants for studying. Addict Behav. 2013;38(3): 1643–1650. 10.1016/j.addbeh.2012.10.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Pilkinton M, Cannatella A. Nonmedical use of prescription stimulants: age, race, gender, and educational attainment patterns. J Hum Behav Soc Environ. 2012;22(4):409–420. 10.1080/10911359.2012.664968. [DOI] [Google Scholar]
  • 16.Lakhan SE, Kirchgessner A. Prescription stimulants in individuals with and without attention deficit hyperactivity disorder: misuse, cognitive impact, and adverse effects. Brain Behav. 2012;2(5):661–677. 10.1002/brb3.78. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Partridge BJ, Bell SK, Lucke JC, Yeates S, Hall WD. Smart drugs “as common as coffee”: media hype about neuroenhancement. PLoS One. 2011. ;6(11 ):e28416 10.1371/journal.pone.0028416. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Arria AM, Caldeira KM, O’Grady KE, Vincent KB, Johnson EP, Wish ED. Nonmedical use of prescription stimulants among college students: associations with attention-deficit-hyperactivity disorder and polydrug use. Pharmacother J Hum Pharmacol Drug Ther. 2008;28(2): 156–169. 10.1592/phco.28.2.156. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.McCabe SE, Knight JR, Teter CJ, Wechsler H. Non-medical use of prescription stimulants among US college students: prevalence and correlates from a national survey. Addiction. 2005; 100( 1): 96–106. 10.1111/j.1360-0443.2005.00944.x. [DOI] [PubMed] [Google Scholar]
  • 20.National Institute on Drag Abuse. Prescription Stimulants: What are prescription stimulants. www.drugabuse.gov/publications/drugfacts/prescription-stimulants Updated June 2018 Accessed June 9, 2020.
  • 21.Arria AM, DuPont RL. Nonmedical prescription stimulant use among college students: why we need to do something and what we need to do. J Addict Dis. 2010;29(4):417–426. 10.1080/10550887.2010.509273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Barrett SP, Darredeau C, Bordy LE, Pihl RO. Characteristics of methylphenidate misuse in a university student sample. Can J Psychiatry. 2005;50(8):457–461. 10.1177/070674370505000805. [DOI] [PubMed] [Google Scholar]
  • 23.Sweeney CT, Sembower MA, Ertischek MD, Shiftman S, Schnoll SH. Nonmedical use of prescription ADHD stimulants and preexisting patterns of drug abuse. J Addict Dis. 2013. ;32 (1): 1–10. 10.1080/10550887.2012.759858. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Bose J, Hedden SL, Lipari RN, Park-Lee E. Key Substance use and mental health indicators in the United States: results from the 2017 National Survey on Drug Use and Health. Rockville, MD: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration, 2018. www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHFFR2017/NSDUHFFR2017.pdf. Accessed June 9, 2020. [Google Scholar]
  • 25.Durell TM, Kroutil LA, Crits-Christoph P, Barchha N, Van Brunt DL. Prevalence of nonmedical methamphetamine use in the United States. Subst Abuse Treat Prev Policy. 2008;3:19 10.1186/1747-597x-3-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Bourne A, Reid D, Hickson F, Torres-Rueda S, Weatherburn P. Illicit drug use in sexual settings (‘chemsex’) and HIV/STI transmission risk behaviour among gay men in South London: findings from a qualitative study. Sex Transm Infect. 2015;91(8):564–568. 10.1136/sextrans-2015-052052. [DOI] [PubMed] [Google Scholar]
  • 27.DeSimone J Illegal drug use and employment. J Labor Econ. 2002;20(4): 952–977. 10.1086/342893. [DOI] [Google Scholar]
  • 28.Degenhardt L, Hall W. Addiction 1: Extent of illicit drug use and dependence, and their contribution to the global burden of disease. Lancet Lond. 2012;379(9810): 55–70. 10.1016/s0140-6736(11)61138-0. [DOI] [PubMed] [Google Scholar]
  • 29.LaRue L, Twillman RK, Dawson E, et al. Rate of fentanyl positivity among urine drug test results positive for cocaine or methamphetamine. JML4 Netw Open. 2019;2(4):e192851 10.1001/jamanetworkopen.2019.2851. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.McCabe SE, West BT, Elughes TL, Boyd CJ. Sexual orientation and substance abuse treatment utilization in the United States: results from a national survey. J Subst Abuse Treat. 2013;44( 1 ):4–12. 10.1016/j.jsat.2012.01.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Schuler MS, Rice CE, Evans-Polce RJ, Collins RL. Disparities in substance use behaviors and disorders among adult sexual minorities by age, gender, and sexual identity. Drug Alcohol Depend. 2018;189:139–146. 10.1016/j.drugalcdep.2018.05.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Zaza S, Kami L, Barrios LC. Lesbian, gay, and bisexual adolescents: population estimate and prevalence of health behaviors. JAMA. 2016;316(22)2355–2356. 10.1001/jama.2016.11683. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Medley G, Lipari R, Bose J, Cribb DS, Kroutil LA, McHenry G. Sexual orientation and estimates of adult substance use and mental health: results from the 2015 National Survey on Dmg Use and Health. Rockville, MD: Substance Abuse and Mental Health Services Administration, 2016. www.samhsa.gov/data/sites/default/files/NSDUH-SexualOrientation-2015/NSDUH-SexualOrientation-2015/NSDUH-SexualOrientation-2015.htm. Accessed June 9, 2020. [Google Scholar]
  • 34.Meyer IH. Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence. Psychol Bull. 2003;129(5):674–697. 10.1037/0033-2909.129.5.674. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Lea T, de Wit J, Reynolds R. Minority stress in lesbian, gay, and bisexual young adults in Australia: associations with psychological distress, suicidality, and substance use. Arch Sex Behav. 2014;43(8): 1571–1578. 10.1007/s10508-014-0266-6. [DOI] [PubMed] [Google Scholar]
  • 36.Friedman MR, Dodge B, Schick V, et al. From bias to bisexual health disparities: attitudes toward bisexual men and women in the United States. LGBT Health. 2014;1(4):309–318. 10.1089/lgbt.2014.0005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Roberts TS, Home SG, Hoyt WT. Between a gay and a straight place: bisexual individuals’ experiences with monosexism. J Bisexuality. 2015;15(4):554–569. 10.1080/15299716.2015.1111183. [DOI] [Google Scholar]
  • 38.Hedden SL, Kennet J, Lipari R, et al. Behavioral health trends in the United States: results from the 2014 National Survey on Drug Use and Health. Rockville, MD: Center for Behavioral Health Statistics and Quality, 2015. www.samhsa.gov/data/sites/default/files/NSDUH-FRR1-2014/NSDUH-FRR1-2014.pdf. Accessed June 9, 2020. [Google Scholar]
  • 39.Kerr DL, Ding K, Chaya J. Substance use of lesbian, gay, bisexual and heterosexual college students. Am J Health Behav. 2014;38(6):951–962. 10.5993/ajhb.38.6.17. [DOI] [PubMed] [Google Scholar]
  • 40.Shadick R, Dagirmanjian FB, Trub L, Dawson H. Sexual orientation and first-year college students’ nonmedical use of prescription drugs. J Am Coll Health. 2016;64(4):292–299. 10.1080/07448481.2015.1117469. [DOI] [PubMed] [Google Scholar]
  • 41.Kecojevic A, Corliss HL, Lankenau SE. Motivations for prescription drug misuse among young men who have sex with men (YMSM) in Philadelphia. Int J Drug Policy. 2015:26181:764–771. 10.1016/j.drugpo.2015.03.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Newcomb ME, Ryan DT, Greene GJ, Garofalo R, Mustanski B. Prevalence and patterns of smoking, alcohol use, and illicit drug use in young men who have sex with men. Drug Alcohol Depend. 2014;141:65–71. 10.1016/j.drugalcdep.2014.05.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.McCabe SE, Hughes TL, Bostwick WB, West BT, Boyd CJ. Sexual orientation, substance use behaviors and substance dependence in the United States. Addiction. 2009; 104(8): 1333–1345. 10.1111/j.1360-0443.2009.02596.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Li DH, Turner BC, Mustanski B, Phillips GL. Sexual orientation disparities in prescription drug misuse among a nationally representative sample of adolescents: prevalence and correlates. Addict Behav. 2018;77:143–151. 10.1016/j.addbeh.2017.09.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Hatzenbuehler ML. Structural stigma and the health of lesbian, gay, and bisexual populations. Curr Dir Psychol Sci.2014;23(2): 127–132. 10.1177/0963721414523775. [DOI] [Google Scholar]
  • 46.Center for Behavioral Health Statistics and Quality. 2017 National Survey on Drag Use and Health: Methodological Summary and Definitions. Rockville, MD: Substance Abuse and Mental Health Services Administration, 2018www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHMethodSummDefs2017/NSDUHMethodSummDefs2017.htm. Accessed June 9, 2020. [Google Scholar]
  • 47.Lehavot K, Simoni JM. The impact of minority stress on mental health and substance use among sexual minority women. J Consult Clin Psychol. 2011;79(2): 159–170. 10.1037/a0022839. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Mereish EH, Goldbach JT, Burgess C, DiBello AM. Sexual orientation, minority stress, social norms, and substance use among racially diverse adolescents. Drug Alcohol Depend. 2017;178:49–56. 10.1016/j.drugalcdep.2017.04.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Feinstein BA, Dyar C. Bisexuality minority stress, and health. Curr Sex Health Rep. 2017;9(1):42–49. 10.1007/s11930-017-0096-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Plankey MW, Ostrow DG, Stall R, et al. The relationship between methamphetamine and popper use and risk of HIV seroconversion in the multicenter AIDS cohort study. J Acquir Immune Defic Syndr. 2007;45(1): 85–92. 10.1097/qai.0b013e3180417c99. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Colfax G, Coates TJ, Husnik MJ, et al. Longitudinal patterns of methamphetamine, popper (amyl nitrite), and cocaine use and high-risk sexual behavior among a cohort of San Francisco men who have sex with men. J Urban Health. 2005;82:i62 10.1093/jurban/jti025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Swartz JA, McCarty-Caplan D. A study of the longitudinal patterns of stimulant and amyl nitrite use and sexual behavior pre- and post-HIV seroconversion among MSM. AIDS Behav. 2018;22(4): 1395–1409. 10.1007/s10461-017-2008-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Kelly BC, Parsons JT. Prevalence and predictors of non-medical prescription drug use among men who have sex with men. Addict Behav. 2010;35(4):312–317. 10.1016/j.addbeh.2009.11.002. [DOI] [PubMed] [Google Scholar]
  • 54.Benotsch EG, Martin AM, Koester S, Cejka A, Luckman D. Nonmedical use of prescription drugs and HIV risk behavior in gay and bisexual men. Sex Transm Dis. 2011;38(2): 105–110. 10.1097/olq.0b013e3181f0bc4b. [DOI] [PubMed] [Google Scholar]
  • 55.Schuler MS, Collins RL. Sexual minority substance use disparities: bisexual women at elevated risk relative to other sexual minority groups. Drug Alcohol Depend. 2020;206:107755 10.1016/j.drugalcdep.2019.107755. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Philbin MM, Mauro PM, Green E, Martins SS. State-level marijuana policies and marijuana use and marijuana use disorder among a nationally representative sample of adults in the United States, 2015–2017: sexual identity and gender matter. Drug Alcohol Depend. 2019;204:107506 10.1016/j.drugalcdep.2019.06.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Cochran SD, Ackerman D, Mays VM, Ross MW. Prevalence of non-medical drug use and dependence among homosexually active men and women in the US population. Addiction. 2004;99(8):989–998. 10.1111/j.1360-0443.2004.00759.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Kecojevic A, Wong CF, Corliss HL, Lankenau SE. Risk factors for high levels of prescription drug misuse and illicit drug use among substance-using young men who have sex with men (YMSM). Drug Alcohol Depend. 2015;150:156–163. 10.1016/j.drugalcdep.2015.02.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Gerrard M, Stock ML, Roberts ME, et al. Coping with racial discrimination: the role of substance use. Psychol Addict Behav. 2012;26(3):550–560. 10.1037/a0027711. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Mauro PM, Philbin MM, Greene E, Martins SS. Gender and sexual identity differences in prescription opioid use by medical marijuana law status in the United States. Poster session at the 11th International Women’s and Children’s Health and Gender (InWomen’s) Group Conference, San Antonio, TX. [Google Scholar]
  • 61.CDC. 2018 Annual Surveillance Report of Drag-Related Risks and Outcomes, United States. Atlanta, GA: CDC, HHS, 2018. www.cdc.gov/drugoverdose/pdf/pubs/2018-cdc-drug-surveillance-report.pdf. Accessed June 9, 2020. [Google Scholar]
  • 62.Seth P, Scholl L, Rudd RA, Bacon S. Overdose deaths involving opioids, cocaine, and psychostimulants — United States, 2015–2016. MMWR Morb Mortal Wkly Rep. 2018;67( 12): 349–358. 10.15585/mmwr.mm6712a1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Peterson AB. Increases in fentanyl-related overdose deaths — Florida and Ohio, 2013–2015. MMWR Morb Mortal Wkly Rep. 2016;65(33):844–849. 10.15585/mmwr.mm6533a3. [DOI] [PubMed] [Google Scholar]
  • 64.Daniulaityte R, Juhascik MP, Strayer KE, et al. Trends in fentanyl and fentanyl analogue-related overdose deaths – Montgomery County, Ohio, 2015–2017. Drug Alcohol Depend. 2019;198:116–120. 10.1016/j.drugalcdep.2019.01.045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Nolan ML, Shamasunder S, Colon-Berezin C, Kunins HV, Paone D. Increased presence of fentanyl in cocaine-involved fatal overdoses: implications for prevention. J Urban Health. 2019;96:49–54. 10.1007/s11524-018-00343-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Manz J National Academy for State Health Policy. Federal Funding Change that Includes Stimulants Allows States to Expand their Substance Use Disorder Initiatives – The National Academy for State Health Policy, https://nashp.org/federal-funding-change-that-includes-stimulants-allows-states-to-expand-their-substance-use-disorder-initiatives/ Published February 3, 2020 Accessed June 9, 2020.
  • 67.Hatzenbuehler ML, Jun H-J, Corliss HL, Austin SB. Structural stigma and sexual orientation disparities in adolescent drug use. Addict Behav. 2015;46:14–18. 10.1016/j.addbeh.2015.02.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Dowell D, Zhang K, Noonan RK, Hockenberry JM. Mandatory provider review and pain clinic laws reduce the amounts of opioids prescribed and overdose death rates. Health Aff (Millwood). 2016;35( 10): 1876–1883. 10.1377/hlthaff.2016.0448. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Meara E, Horwitz JR, Powell W, et al. State legal restrictions and prescription-opioid use among disabled Adults. N Engl J Med. 2016;375(l):44–53. 10.1056/nejmsa1514387. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Steenkamp J-BE, De Jong MG, Baumgartner H. Socially desirable response tendencies in survey research. J Mark Res. 2010;47(2): 199–214. 10.1509/jmkr.47.2.199. [DOI] [Google Scholar]
  • 71.HHS, Substance Abuse and Mental Health Services Administration. Learning From the Field: Programs Serving Youth Who Are LGBTQI2-S and Experiencing Homelessness. www.samhsa.gov/sites/default/files/programs_campaigns/homelessness_programs_resources/learning-field-programs-servingyouth-lgbtqi2s-experiencing-homelessness.pdf. Published 2011. Accessed June 9, 2020.
  • 72.Draft Recommendation | United States Preventive Services Taskforce. www.uspreventiveservicestaskforce.org/Page/Document/draft-recommendation-statement/drug-use-in-adolescents-and-adults-including-pregnant-women-screening. Accessed January 27, 2020.
  • 73.Mauro PM, Samples H, Klein KS, Martins SS. Discussing drug use with health care providers is associated with perceived need and receipt of drug treatment among adults in the United States: We need to talk. Med Care. 2020;In Press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Philbin MM, Mauro PM. Measuring polysubstance use over the life course: implications for multilevel interventions. Lancet Psychiatry. 2019;6( 10):797–798. 10.1016/s2215-0366(19)30328-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Peiper NC, Clarke SD, Vincent LB, Ciccarone D, Kral AH, Zibbell JE. Fentanyl test strips as an opioid overdose prevention strategy: findings from a syringe services program in the Southeastern United States. Int J Drug Policy. 2019;63:122–128. 10.1016/j.drugpo.2018.08.007. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1610168-supplement-1.pdf (58.9KB, pdf)

RESOURCES