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. Author manuscript; available in PMC: 2022 Nov 15.
Published in final edited form as: Arch Sex Behav. 2021 Nov 9;50(8):3551–3561. doi: 10.1007/s10508-021-02128-z

Medical and Non-Medical Opioid Use at the Intersection of Gender and Sexual Identity: Associations with State Medical Cannabis Law Status in a U.S. National Sample of Adults

Pia M Mauro 1, Morgan M Philbin 2, Emily R Greene 1, Melanie S Askari 1, Silvia S Martins 1
PMCID: PMC8729119  NIHMSID: NIHMS1760835  PMID: 34751862

Abstract

At the structural level, medical cannabis laws (MCLs) have been negatively associated with opioid prescribing practices, and sexual minority adults report disproportionately high non-medical prescription opioid use. We examined medical/non-medical prescription opioid use by intersecting sexual identity and gender and explored associations with MCLs using the 2015–2017 National Survey on Drug Use and Health, which captured sexual identity and MCL state residence for adults 18+ years (N = 126,463). Survey-weighted gender-stratified multinomial logistic models estimated adjusted relative risk ratios (aRRR) of medical vs. no prescription opioid use, and any non-medical vs. no prescription opioid use, by sexual identity and MCL, and tested moderation by MCL. Past-year medical prescription opioid use was higher among women than men across sexual identities (e.g., bisexual: 38.5% women vs. 30.2% men). Non-medical prescription opioid use was lower among women than men, except for bisexual adults (12.4% women vs. 7.6% men). MCL was associated with lower medical prescription opioid vs. no use among heterosexual women (aRRR = 0.86, 95% confidence interval [CI] = 0.81–0.91), bisexual women (aRRR = 0.74, 95% CI = 0.62–0.89), and heterosexual men (aRRR = 0.91, 95% CI = 0.85–0.97). Living in an MCL state was associated with lower non-medical vs. no use among heterosexual and bisexual women, but not among men or lesbian/gay women. MCL status did not moderate associations between sexual identity and prescription opioid outcomes. Future studies should assess whether implementing MCLs could particularly affect bisexual women who reported the highest prescription opioid use and may need targeted services.

Keywords: Prescription opioid use, Non-medical prescription opioid use, Medical cannabis laws, Gender, Sexual identity, Sexual orientation

INTRODUCTION

Non-medical prescription opioid use is disproportionately high in specific sexual minority populations, particularly bisexual women (Duncan et al., 2019; Feinstein & Dyar, 2017; Schuler et al., 2018). This pattern extends to other opioid use and related outcomes: in a nationally representative sample in the United States, 12.6% of bisexual women reported past-year non-medical prescription opioid or heroin use, compared to 3.5% of heterosexual women; bisexual women also had more than twice the odds of past-year opioid use disorder than heterosexual women (Schuler et al., 2019). This is particularly concerning, as non-medical prescription opioid use is associated with a variety of negative health consequences beyond prescription opioid use disorder (Center for Behavioral Health Statistics and Quality (CBHSQ), 2014), including other substance use and disorders (Grigsby & Howard, 2019; Liebling et al., 2018), hospitalizations and subsequent comorbidities (Substance Abuse and Mental Health Services Administration, 2013; Sumetsky et al., 2019; Unick et al., 2013), and non-fatal and fatal overdoses (Hall et al., 2008; Yokell et al., 2014).

Minority stress theory proposes that substance use disparities by sexual minority identity can be partially attributed to the increased levels of stigma, discrimination, and victimization experienced by sexual minority individuals, particularly bisexual individuals, who may face discrimination from both heterosexual and gay/lesbian individuals (Feinstein & Dyar, 2017; Katz-Wise & Hyde, 2012; Lee et al., 2016; Meyer, 2003). Intersectionality theory, introduced by Crenshaw (Crenshaw, 1989, 1991), further indicates that systems of oppression and privilege may differentially impact people with intersecting identities, particularly when those are marginalized such as female gender and a non-heterosexual sexual identity. From this perspective, we focus on medical and non-medical prescription opioid use among people with intersecting gender and sexual identities, to assess differences among people with more marginalized intersecting identities.

Structural stigma, which can include policies and cultural norms that limit opportunities for, and the well-being of, stigmatized groups (Hatzenbuehler & Link, 2014) has been associated with various health outcomes and can disproportionally affect sexual minority adults (Hatzenbuehler & Link, 2014). For example, state-level policies that discriminate against sexual minority adults (e.g., not including sexual orientation as a protected class in hate crime policies) can differentially increase their risk of negative outcomes from substance use (Hatzenbuehler et al., 2014; Pachankis et al., 2014) and mental health (Hatzenbuehler et al., 2009; Raifman et al., 2018). Recent work suggests that state cannabis policies, which do not necessarily target sexual minorities, could still be differentially impacting sexual minority populations (Philbin et al., 2019). Understanding structural-level drivers of medical and non-medical prescription opioid use across sexual minority subgroups is part of a multi-level, intersectional research approach (Bauer, 2014) that will support public health program planners’ ability to properly distribute resources and tailor interventions toward the most marginalized populations.

In light of the ongoing opioid overdose crisis in the U.S. (Kerr, 2019; Scholl et al., 2018), researchers and policymakers have been considering medical cannabis laws (MCLs; also known as medical marijuana laws) as a potential structural intervention to reduce harms associated with prescription opioid use (Powell et al., 2018). MCLs have been associated with lower state-level non-medical prescription opioid use (Powell et al., 2018) and lower state-level overdose mortality (Bachhuber et al., 2014; Powell et al., 2018), as well as lower opioid prescribing in administrative records of Medicaid (Bradford & Bradford, 2017) and Medicare enrollees (Bradford & Bradford, 2016). However, MCL enactment was not associated with a reduction in individual-level non-medical prescription opioid use (Segura et al., 2019). Thus, the role MCLs may play in reducing opioid-related harm, particularly among sub-populations with marginalized intersecting identities, needs further investigation.

State policies such as MCLs could affect medical and non-medical prescription opioid use across different subgroups of adults based on gender and sexual identity. More than half of people in the U.S. live in states with MCLs: As of June 2020, 33 states had MCLs and 11 states had recreational cannabis laws (NCSL, 2019). One study found that the effects of MCL among young adults can vary by gender (Mauro et al., 2019). Cross-sectionally, MCLs have been associated with differences in cannabis use by gender and sexual minority status among adults (Philbin et al., 2019). A key unanswered question is whether similar associations exist between MCL and opioid outcomes, distinguishing between medical and non-medical prescription opioid use. If MCLs are associated with differences in opioid prescribing practices and other opioid-related outcomes, then already marginalized subgroups who report high prevalence of opioid use, such as sexual minority women, could be disproportionately affected.

To help elucidate these gaps in the literature, we estimated medical prescription opioid and non-medical prescription opioid use by intersecting sexual identity and gender, comparing adults residing in MCL versus non-MCL states. We hypothesized that: (1) Medical and non-medical prescription opioid use would be higher among sexual minorities than among their heterosexual counterparts; (2) With the exception of higher use among bisexual women regardless of state status, medical prescription opioid use would be lower in MCL than non-MCL states; and (3) MCL status would not be associated with non-medical prescription opioid use across sexual identity status. Understanding these relationships has public health implications. Identifying whether medical and non-medical prescription opioid use is disproportionally high in states with different MCL exposures could inform whether MCLs could be part of a structural-level policy toolbox to intervene on negative prescription opioid outcomes across various subgroups of people and potentially mitigate health disparities in already marginalized groups.

METHOD

Participants

We obtained data from the 2015–2017 National Survey on Drug Use and Health (NSDUH) public use files. This annual cross-sectional survey assessed behavioral health indicators in nationally representative samples of the non-institutionalized civilian population in the United States ages 12 and older. The survey included individuals ages 12 and older in each of the 50 states and Washington DC, oversampling younger age groups (CBHSQ, 2016, 2017, 2018). Data were collected via face-to-face household interviews using computer assisted interviewing and audio computer assisted survey instruments to maximize privacy while reporting sensitive information. Weighted interview response rates among adults for the years 2015–2017, which included measures on sexual identity, ranged from 66.3%−68.4% (CBHSQ, 2016, 2017, 2018). Sampling weights accounted for selection probability, non-response, and population distribution. Additional details of the NSDUH sampling methods and survey techniques can be found elsewhere (CBHSQ, 2016, 2017, 2018).

We pooled 170,319 observations across 2015–2017, adding a year indicator. We excluded adolescents ages 12–17 who were not asked about sexual identity (n = 41,579). We also excluded adults who responded to the sexual identity question with “don’t know” (n = 776), “refuse to answer” (n = 1,440), left it blank (n = 54), or unusable data (n = 7). Demographic comparisons between the final analytic sample of 126,463 participants ages 18 and older and the 2,277 adults who were excluded are described in Supplemental Table 1.

Measures

Sexual identity was assessed by asking, “Which of the following do you consider yourself to be?” with possible response categories of “Heterosexual, that is straight,” “Lesbian or gay,” “Bisexual,” “Don’t know,” and “Refuse to answer.” After excluding those who refused or selected “don’t know,” participants were categorized into three mutually exclusive sexual identity groups: heterosexual, gay/lesbian, and bisexual. Gender was collected as a binary variable differentiating female and male participants, based on interviewer observation.

Residence in an MCL state was defined as whether participants were coded as living in an MCL state at the time of the interview. MCL status indicated that the state of residence had a law approving cannabis for medical use before the date of interview; otherwise, the participant was coded as living in a state that did not permit medical cannabis use (i.e., in a non-MCL state).

Medical or non-medical use of prescription opioids was assessed by asking participants who reported past-year use of prescription opioid pain relievers (e.g., Vicodin, Hydrocodone) if they had used prescription opioids in a way other than the doctor had directed during this time. Any prescription opioid use other than directed in the past-year indicated non-medical prescription opioid use. Medical prescription opioid use indicated reporting prescription opioid use in the past-year but not reporting using in a way other than directed. Due to questionnaire design, we could not distinguish people reporting both non-medical and medical prescription opioid use from those reporting only non-medical prescription opioid use. Past-year prescription opioid use was therefore categorized into three mutually exclusive categories: no prescription opioid use, medical prescription opioid use only (i.e., no non-medical prescription opioid use), or any non-medical prescription opioid use.

To assess sources of most recent prescription opioids used non-medically, participants reporting past-year non-medical prescription opioid use were asked about the source of the non-medical prescription opioid use at last use. Possible response categories were “Got from one doctor,” “Got from more than one doctor,” “Stole from doctor office, clinic, hospital, or pharmacy,” “Got from friend or relative,” “Bought from friend or relative,” “Took from friend or relative without asking,” “Bought from drug dealer or other stranger,” or “Got some other way.”

To assess primary reason for most recent non-medical use of prescription opioids, participants were asked about all the reasons for using prescription opioids non-medically at last use, and then asked to select the primary reason for non-medical use at last use. Reasons included to relieve physical pain (i.e., “To relieve physical pain”), relax/sleep (i.e., “To relax or relieve tension,” “To help with my sleep”), feelings/emotions (i.e., “To help me with my feelings or emotions”), get high/other (i.e., “To experiment or to see what they’re like,” “To feel good or get high,” “To increase or decrease the effect(s) of some other drug,” “Because I am “hooked” or I have to have it,” or “The other reason I reported”).

Other covariates included past-year cannabis use (yes, no), age (18–25, 26–34, 35–49, 50 and older), 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 or more), population density (large metro, small metro, non-metro), and survey year indicators.

Data Analysis

First, we describe prevalence of medical prescription opioid use and non-medical prescription opioid use by sexual identity and other covariates using survey weights. We divided the survey weights by three to derive nationally representative estimates from the three years of data. Second, we used weighted multivariable multinomial logistic regression to examine the association between sexual identity and past-year medical prescription opioid use and non-medical prescription opioid use, stratifying by gender and adjusting for sociodemographic characteristics and past-year cannabis use. These models also tested for the moderating effect of MCL state by including an interaction term between sexual identity and MCL state status. Third, we described the most recent source and primary reason for most recent use among people reporting non-medical prescription opioid use by gender, sexual identity, and MCL status. Statistical analyses were conducted in SAS 9.4 (SAS, 2014) and adjusted for the complex survey design. All statistical tests were two-sided and p-values < .05 were considered statistically significant.

RESULTS

Prevalence of Medical and Nonmedical Prescription Opioid Use by Gender and Sexual Identity

The prevalence of medical prescription opioid use was 31.8% among heterosexual adults, 33.7% among gay/lesbian adults, and 36.1% among bisexual adults (Table 1). Prevalence of non-medical prescription opioid use was 4.1% among heterosexual adults, 7.7% among gay/lesbian adults, and 11.1% among bisexual adults. Any past-year medical prescription opioid use was higher among women, ranging from 34.8% to 38.5% by sexual identity, compared to men, which ranged from 28.6% to 30.2% by sexual identity. Though differences emerged by sexual identity, medical prescription opioid use was higher among women than men; in contrast, non-medical prescription opioid use was higher among men than women except for bisexual adults (Fig. 1).

Table 1.

Selected sociodemographic characteristics of U.S. adults by sexual identity and prescription opioid use type

Heterosexual (N = 118,222) Gay/Lesbian (N = 2,731) Bisexual (N = 5,510)
Overall
N (%)		 Medical prescription opioid use only
Wt row % (95% CI)		 Non-medical prescription opioid use
Wt row % (95% CI)		 Medical prescription opioid use only
Wt row % (95% CI)		 Non-medical prescription opioid use
Wt row % (95% CI)		 Medical prescription opioid use only
Wt row % (95% CI)		 Non-medical prescription opioid use
Wt row % (95% CI)
All adults 18 + 35,059 (31.8%) 6,211 (4.1%) 885 (33.7%) 257 (7.7%) 1,881 (36.1%) 706 (11.1%)
Gender
Male 58,815 (48.1%) 28.6 (28.0–29.2) 4.9 (4.6–5.1) 30.0 (26.3–33.8) 8.3 (6.3–10.2) 30.2 (26.6–33.7) 7.6 (5.6–9.5)
Female 67,648 (51.9%) 34.8 (34.3–35.4) 3.4 (3.3–3.6) 38.5 (34.0–42.9) 6.9 (5.1–8.7) 38.5 (36.1–40.9) 12.4 (11.0–13.8)
Age Category
18–25 41,379 (14.2%) 23.4 (22.8–24.0) 7.0 (6.6–7.3) 27.3 (24.4–30.1) 13.6 (11.4–15.8) 28.9 (47.0–30.5) 13.1 (11.6–14.6)
26–34 26,114 (15.5%) 28.1 (27.5–28.7) 6.6 (6.2–7.1) 29.8 (24.1–35.5) 8.0 (4.8–11.1) 36.6 (33.2–40.0) 12.9 (10.7–15.2)
35–49 33,090 (24.7%) 31.7 (31.0–32.4) 4.5 (4.2–4.7) 32.9 (28.2–37.6) 8.1 (5.7–10.6) 43.6 (38.4–48.8) 8.8 (6.1–11.4)
50+ 25,880 (45.2%) 35.5 (34.7–36.3) 2.3 (2.1–2.5) 40.0 (35.5–44.5) 4.0 (1.8–6.2) 43.3 (36.8–49.9) 5.9 (3.0–8.7)
Race/Ethnicity
Non-Hispanic White 77,100 (64.8%) 33.7 (33.2–34.2) 4.4 (4.2–4.6) 36.2 (33.3–39.1) 7.7 (5.7–9.6) 37.7 (35.2–40.2) 12.1 (10.7–13.6)
Non-Hispanic Black 15,888 (11.8%) 33.7 (32.5–35.0) 3.6 (3.2–4.0) 35.0 (27.6–42.4) 6.7 (3.7–9.7) 40.3 (35.5–45.0) 9.9 (7.3–12.6)
Hispanic 21,869 (15.6%) 25.4 (24.5–26.2) 4.1 (3.7–4.5) 27.5 (21.6–33.4) 10.2 (6.4–14.1) 32.4 (28.0–36.8) 9.8 (7.4–12.1)
Other 12,289 (7.8%) 25.6 (24.1–27.2) 3.1 (2.6–3.6) 24.2 (14.6–33.8) 3.5 (1.3–5.7) 26.5 (20.6–32.3) 8.2 (5.4–11.1)
Income
< $20,000 26,280 (16.8%) 33.5 (32.5–34.5) 5.5 (5.1–5.9) 39.2 (33.9–44.5) 7.8 (5.2–10.4) 36.3 (32.0–40.6) 13.0 (10.4–15.5)
$20,000-$49,900 39,996 (29.7%) 32.6 (31.8–33.4) 4.3 (4.0–4.6) 34.3 (29.9–38.7) 9.1 (6.2–11.9) 38.6 (35.7–41.6) 10.6 (8.5–12.6)
$50,000-$74,900 19,752 (16.2%) 31.8 (30.9–32.8) 4.1 (3.7–4.4) 34.4 (26.7–42.1) 5.7 (2.8–8.5) 33.4 (29.1–37.7) 10.9 (7.9–14.0)
$75,000 + 40,435 (37.3%) 30.4 (29.8–31.0) 3.5 (3.2–3.7) 29.5 (24.9–34.2) 7.4 (4.8–10.0) 34.0 (30.5–37.4) 9.7 (7.7–11.7)
Population Density
Large Metro 53,653 (53.9%) 30.0 (29.4–30.7) 4.1 (3.9–4.3) 31.5 (27.8–35.2) 7.8 (5.9–9.7) 35.0 (32.1–37.9) 10.1 (8.7–11.5)
Small Metro 62,958 (40.4%) 33.9 (33.3–34.5) 4.2 (4.1–4.4) 36.9 (32.6–41.1) 6.8 (4.7–8.9) 37.5 (34.4–40.5) 12.6 (10.6–14.7)
Rural 9,852 (5.8%) 33.6 (32.3–34.9) 3.9 (3.4–4.4) 43.6 (32.9–54.3) 14.3 (3.9–24.6) 39.1 (31.5–46.7) 9.0 (4.8–13.1)
MCL State
Yes 67,799 (53.2%) 30.0 (29.5–30.6) 4.2 (4.0–4.3) 32.7 (29.1–36.3) 8.0 (6.2–9.7) 33.7 (31.3–36.0) 10.7 (9.4–12.1)
No 58,664 (46.8%) 33.8 (33.2–34.4) 4.1 (3.9–4.3) 35.3 (31.4–39.2) 7.2 (5.2–9.2) 39.5 (36.6–42.5) 11.5 (9.8–13.2)
Any Past-Year Cannabis Use
Yes 25,662 (14.5%) 36.2 (33.3–39.0) 19.1 (16.8–21.4) 35.9 (30.6–41.2) 16.8 (12.9–20.6) 36.2 (33.3–39.0) 19.1 (16.8–21.4)
No 100,801 (85.5%) 36.1 (33.7–38.4) 6.3 (5.1–7.5) 32.9 (29.4–36.4) 4.1 (3.0–5.2) 36.1 (33.7–38.4) 6.3 (5.1–7.5)
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Note: N = 126,463; wt row %: survey weighted row percentage; 95% CI: 95% Confidence Interval; MCL: Medical Cannabis Laws

Figure 1.

Figure 1.

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Medical and non-medical prescription opioid use by gender and sexual identity among US adults (N = 126,463)

Associations Between Medical and Nonmedical Prescription Opioid Use, MCL Status, and Sexual Identity by Gender

Multinomial logistic regression models estimated relative risk ratios comparing (1) medical prescription opioid use vs. no prescription opioid use; (2) non-medical prescription opioid use vs. no prescription opioid use; and (3) non-medical prescription opioid use vs. medical prescription opioid (Table 2). Results are reported first among women and men, differentiating by sexual identity and MCL status within gender. Compared to heterosexual women, bisexual women were significantly more likely to report medical prescription opioid use (adjusted relative risk ratio [aRRR] = 1.56, 95% confidence interval [CI] = 1.32–1.82) and non-medical prescription opioid use (aRRR = 2.47, 95% CI = 2.00–3.04) compared to no prescription opioid use. Adult women in MCL states had lower adjusted risk of medical prescription opioid use (vs. no prescription opioid use) than those in non-MCL states, both among heterosexual (aRRR = 0.86, 95% CI = 0.81–0.91) and bisexual adults (aRRR = 0.74, 95% CI = 0.62, 0.89, respectively). Similarly, women in MCL states had lower adjusted risk of non-medical prescription opioid use (vs. no prescription opioid use) than in non-MCL states among heterosexual and bisexual women (aRRR = 0.88, 95% CI = 0.79–0.98; aRRR = 0.74, 95% CI = 0.57–0.97, respectively). There were no significant differences by MCL status comparing non-medical prescription opioid use to medical prescription opioid use by sexual identity among women.

Table 2.

Multinomial logistic regression of medical and non-medical prescription opioid use stratified by gender among U.S. adults

Medical prescription opioid use only vs. No prescription opioid usea,b Non-medical prescription opioid use vs. No prescription opioid usea,b Non-medical prescription opioid use vs. Medical prescription opioid use onlyb,c
Men Women Men Women Men Women
aRRR (95% CI) aRRR (95% CI) aRRR (95% CI) aRRR (95% CI) aRRR (95% CI) aRRR (95% CI)
Sexual identity
Heterosexual Ref Ref Ref Ref Ref Ref
Bisexuala 1.24 (0.97–1.60) 1.56 (1.32–1.82) 1.26 (0.80–1.98) 2.47 (2.00–3.04) 1.01 (0.63–1.62) 1.59 (1.31–1.92)
Lesbian/Gaya 1.21 (0.97–1.51) 1.07 (0.84–1.36) 1.32 (0.89–1.94) 1.31 (0.90–1.90) 1.09 (0.68–1.73) 1.22 (0.83–1.81)
MCL by sexual identity
Heterosexual
MCL 0.91 (0.85–0.97) 0.86 (0.81–0.91) 0.93 (0.84–1.04) 0.88 (0.79–0.98) 1.03 (0.91–1.17) 1.03 (0.93–1.14)
non-MCL Ref Ref Ref Ref Ref Ref
Bisexual
MCL 0.90 (0.60–1.34) 0.74 (0.62–0.89) 0.86 (0.48–1.51) 0.74 (0.57–0.97) 0.95 (0.50–1.81) 1.01 (0.78–1.30)
non-MCL Ref Ref Ref Ref Ref Ref
Lesbian/Gay
MCL 0.85 (0.61–1.18) 1.07 (0.76–1.53) 1.04 (0.61–1.76) 1.14 (0.67–1.94) 1.22 (0.68–2.21) 1.06 (0.61–1.84)
non-MCL Ref Ref Ref Ref Ref Ref
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Note: N = 126,463;

a

References: Sexual Identity: Heterosexual, Use: No Past-Year Use;

b

Models adjust for survey year, age, race/ethnicity, annual household income, population density, and past-year marijuana use;

c

References: Sexual Identity: Heterosexual, Use: Past-Year Medical Use;

aRRR = adjusted relative risk ratio; 95% CI: 95% Confidence Interval; MCL: Medical Cannabis Laws; Bold indicates statistically significant result;

*

Indicates statistically significant differences between sexual minority and heterosexual or MCL state status (p-value < .05).

Among men, we did not detect statistically significant associations between sexual identity and adjusted likelihood of medical prescription opioid use compared to no past-year prescription opioid use. Lower medical prescription opioid use vs. no prescription opioid use observed in MCL states (aRRR = 0.91, 95% CI = 0.85–0.97) was limited to heterosexual men; there were no significant differences in opioid use outcomes among sexual minority men by MCL status. We did not find any significant associations between non-medical prescription opioid use vs. no prescription opioid use or medical prescription opioid use by sexual identity or MCL status among men (Table 2).

There was no evidence that MCL modified the association between sexual minority status and prescription opioid use outcomes for women or men.

Source of and Primary Reason for Most Recent Prescription Opioid Used Non-Medically by Gender and MCL Status

Most adults (53%) who reported non-medical prescription opioid use said that their most recent source was family or friends. Variations emerged in source of most recent prescription opioid use by gender, sexual identity, and state MCL status (see Table 3). Overall, the majority of sexual minority women listed physical pain as the primary reason for non-medical prescription opioid use (Table 3). Two-thirds of women (67.1%) and 60.7% of men reported that the primary reason for recent non-medical prescription opioid use was to relieve physical pain. However, when differentiating by sexual identity and MCL status, only one-third (34.9%) of gay/lesbian women in non-MCL states reported pain as the primary reason for non-medical prescription opioid use. In this subgroup, 31.3% reported use to reduce tension or sleep and 13.7% reported use to help with feelings or emotions. Similarly, 12% of bisexual women in non-MCL states reported non-medical prescription opioid use to help with feelings or emotions, compared to 4.6% of bisexual women in MCL states.

Table 3.

Source and primary reason for most recent non-medical use of prescription opioids by sexual identity, gender, and state Medical Canabis Law status among U.S. adults

Heterosexual Gay/Lesbian Bisexual
Men† (N = 3,352)
By MCL, % (95% CI)		 Women† (N = 2,603)
By MCL, % (95% CI)		 Men† (N = 129)
By MCL, % (95% CI)		 Women† (N = 122)
By MCL, % (95% CI)		 Men† (N = 107)
By MCL, % (95% CI)		 Women† (N = 567)
By MCL, % (95% CI)
Source Yes (N = 1,795) No (N = 1,557) Yes (N = 1,357) No (N = 1,246) Yes (N = 80) No (N = 49) Yes (N = 75) No (N = 47) Yes (N = 65) No (N = 42) Yes (N = 317) No (N = 250)
Medical 37.4 (33.3–41.5) 32.6 (29.2–36.0) 42.2 (38.1–46.3) 41.8 (38.0–45.6) 45.7 (30.7–60.7) 29.3 (14.2–44.4) 32.7 (14.7–50.7) 25.3 (10.7–39.8) 31.3 (16.3–46.3) 13.3 (2.7–23.9) 29.2 (21.4–36.9) 30.6 (21.4–39.7)
Friend or family 50.8 (46.6–54.9) 55.7 (52.2–59.1) 51.3 (47.8–54.9) 51.8 (48.3–55.3) 46.8 (32.2–61.5) 60.0 (43.4–76.6) 64.2 (45.6–82.7) 63.0 (45.9–80.1) 55.5 (39.5–71.3) 75.4 (61.0–89.7) 60.7 (52.4–69.1) 62.0 (53.2–70.9)
Drug dealer or stranger 6.8 (5.2–8.3) 7.4 (6.1–8.8) 2 .9 (1.9–3.8) 3.2 (1.9–4.5) 4.7 (0.6–8.7) 7.7 (0.0–17.6) 2.6 (0.0–5.9) 11.7 (2.1–21.4) 12.0 (0.0–24.6) 4.9 (0.0–12.5) 7.5 (3.7–11.2) 2.9 (0.8–5.0)
Other 5.0 (3.2–6.8) 4.3 (3.0–5.6) 3.6 (1.8–5.3) 3.2 (1.8–4.6) 2.8 (0.0–5.8) 3.0 (0.0–7.7) 0.5 (0.0–1.4) 0 1.2 (0.0–2.5) 6.4 (2.7–10.1) 2.6 (0.7–4.6) 4.5 (0.6–8.4)
Men† (N = 3,435)
By MCL, % (95% CI)		 Women† (N = 2,652)
By MCL, % (95% CI)		 Men† (N = 129)
By MCL, % (95% CI)		 Women† (N = 125)
By MCL, % (95% CI)		 Men† (N = 110)
By MCL, % (95% CI)		 Women† (N = 582)
By MCL, % (95% CI)
Primary reason Yes (N = 1,841) No (N = 1,594) Yes (N = 1,376) No (N = 1,276) Yes (N = 80) No (N = 49) Yes (N = 77) No (N = 48) Yes (N = 69) No (N = 41) Yes (N = 326) No (N = 256)
Relieve physical pain 61.5 (58.1–64.8) 60.6 (57.8–63.5) 68.2 (64.7–71.7) 70.6 (66.9–74.2) 54.2 (38.0–70.4) 44.9 (26.6–63.1) 58.9 (43.1–74.7) 34.9 (10.4–59.5) 65.7 (49.6–81.8) 46.5 (24.9–68.0) 58.2 (49.8–66.6) 52.9 (45.2–60.6)
Relax/sleep 13.8 (11.2–16.3) 13.7 (11.3–16.2) 14.5 (11.1–17.8) 14.5 (11.7–17.3) 14.0 (0.0–28.3) 24.8 (5.1–44.4) 17.2 (2.4–31.9) 31.3 (10.6–52.1) 3.1 (0.0–6.3) 12.4 (0.6–24.3) 17.4 (10.9–24.0) 14.2 (8.6–19.8)
Feel good, get high, experiment 17.1 (14.8–19.5) 19.1 (16.8–21.3) 9.9 (7.8–12.0) 8.0 (6.4–9.6) 20.8 (9.4–32.1) 26.7 (9.7–43.7) 14.7 (5.0–24.4) 10.6 (2.1–19.1) 19.2 (5.3–33.1) 33.3 (13.1–53.5) 14.6 (9.7–19.5) 15.4 (9.3–21.5)
Help with feelings or emotions 2.3 (1.3–3.2) 2.9 (1.5–4.2) 3.5 (2.1–5.0) 3.2 (1.8–4.6) 3.3 (0.0–6.7) 1.4 (0.0–4.3) 9.2 (2.9–15.5) 13.7 (0.7–26.8) 2.3 (0.0–5.3) 5.3 (0.0–13.5) 4.6 (1.9–7.2) 12.0 (5.7–18.3)
Get high/ other reason 5.3 (4.2–6.4) 3.7 (2.6–4.7) 3.9 (2.3–5.6) 3.7 (2.4–5.0) 7.7 (0.0–15.6) 2.2 (0.0–5.3) 0 9.5 (0.0–21.0) 9.7 (0.0–20.6) 2.5 (0.0–7.5) 5.2 (2.6–7.8) 5.5 (0.9–10.0)
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a) N = 6,880; b) N = 7,033;

†

Weighted column percentages (95% Confidence Interval);

MCL: States with Medical Cannabis Laws; Non-MCL: States without Medical Cannabis Laws

Fewer than half of gay and bisexual men in non-MCL states reported physical pain as the primary reason for non-medical use, compared to 54.2% of gay and 65.7% of bisexual men in MCL states. A third of bisexual men in non-MCL states used prescription opioid non-medically to feel good or get high, compared to 19.2% of bisexual men in MCL states (Table 3).

DISCUSSION

In this nationally representative cross-sectional study, we first estimated medical and non-medical prescription opioid use by sexual identity and gender. A third of adults reported medical prescription opioid use across sexual identities. More than half of bisexual women reported any prescription opioid use, of whom one in four reported non-medical prescription opioid use. Prevalence of non-medical prescription opioid use was four times higher among bisexual women than their heterosexual counterparts. We then estimated differences by MCL residence, accounting for individual-level differences in use by sexual identity. Medical prescription opioid use was lower for heterosexual men, heterosexual women, and bisexual women in MCL states than non-MCL states. MCLs were associated with lower non-medical prescription opioid use among heterosexual or bisexual women, but not men. Our study expands the literature that differentiates subgroups’ use of medical prescription opioid and non-medical prescription opioid use, particularly by gender and sexual identity. It also assessed differences in opioid use by MCL, which could help identify intervention targets to prevent non-medical prescription opioid use and related problems. Unique contributions of our work include measuring medical prescription opioid use, reporting sources, and reasons for non-medical prescription opioid use, as well as estimating associations with medical cannabis laws by sexual identity and gender.

Overall, sexual minority women had significantly higher medical prescription opioid use than heterosexual women. In contrast, there was no significant medical prescription opioid use difference among men. Our findings were consistent with a recent study indicating higher non-medical prescription opioid use among bisexual women than their heterosexual counterparts (Duncan et al., 2019; Schuler et al., 2019). Observed gender differences in the associations between sexual minority identity and substance use were consistent with previous studies (Duncan et al., 2019; McCabe et al., 2013; Philbin et al., 2019). As gender differences in substance use by sexual minority status emerge early in life, particularly among bisexual females (Corliss et al., 2010; Marshal et al., 2008), future studies should assess the effects of MCLs on sexual minority youth.

Sexual minority adults reported for non-medical prescription opioid use for different reasons in MCL versus non-MCL states. A higher proportion of sexual minority adults in non-MCL states reported non-medical prescription opioid use to “reduce tension, relax, or sleep”; sexual minority women reporting non-medical prescription opioid use to “help with feelings or emotions” could be indicative of additional burdens experienced by sexual minorities in states without access to medical cannabis. For example, additional supports (e.g., screening and intervention) for women with intersecting minority identity may be needed, as 12–14% of those who lived in non-MCL states used prescription opioid non-medically to help with feelings or emotions. However, we also found commonalities across all sexual identities. For example, regardless of sexual identity, the majority of adults reported the most recent past-year non-medical prescription opioid use source to be a family or friend, followed by medical source, which is consistent with various other studies (Cicero et al., 2008; Martins et al., 2009; Saloner et al., 2017). The multiple reasons of use and sources of substances call for a multi-pronged approach to address the complex pathways that connect opioid use for medical reasons to later non-medical prescription opioid use and other negative health outcomes. Future studies should formally test whether our findings are the result of minority stress or whether they are consistent with other mechanisms. Nonetheless, as substance use disorder treatment is low among adults, including sexual minorities (McCabe et al., 2013; Krasnova et al., 2021), higher medical and non-medical prescription opioid use among sexual minorities calls for efforts to prevent substance use disorder onset and ensure adequate treatment access, particularly in non-MCL states.

Importantly, medical prescription opioid use was lower in MCL states for both heterosexual men and women, consistent with lower medical prescription opioid use indicators in other studies (Bradford & Bradford, 2016, 2017). Bisexual women also had significantly lower medical prescription opioid use in MCL states than non-MCL states, but these differences did not extend to sexual minority men. Our cross-sectional study findings were consistent with findings from studies of Medicare/Medicaid (Bradford & Bradford, 2016, 2017), and suggest that MCL status may be associated with differences in medical prescription opioid use. However, questions remain as to whether differences extend to non-medical prescription opioid use, as a recent study found no causal relationship between MCL and individual-level non-medical prescription opioid use in the overall population (Segura et al., 2019). The passage of state-level MCLs may be correlated with other structural factors that could affect burden of opioid-related negative outcomes among sexual minority women. Future work should explore the associations between MCL, prescription opioid policies, and prescription opioid use to determine which structural drivers are most salient in these populations. Lower prevalence of medical prescription opioid use across genders in MCL states could indicate opportunities to reduce unnecessary exposure to opioids, which could lead to subsequent opioid-related harms. As there are various considerations when incorporating this type of intersectionality into research (Bauer, 2014), future studies should continue to assess whether and how the effects of social and substance use policies differ across subgroups particularly among bisexual women, who had the highest medical and non-medical prescription opioid use prevalence. Future work should also incorporate state-level prescription opioid policies (e.g., prescription drug monitoring programs) to determine their impact on this relationship.

As this was a cross-sectional study, we explored average differences by gender and sexual identity comparing people in MCL states and non-MCL states, but we were unable to directly examine the causal effects of MCLs on medical or non-medical prescription opioid use. Importantly, being restricted to the public-use data meant that we were unable to see if the implementation of an MCL led to changes in opioid use prevalence within these intersecting identity groups due to lack of state-level indicators in the public-use data. Relying on public use data also meant that were unable to control for state-level differences, such as state-level number of opioid prescriptions filled, opioid prescribing practices policies, dosage and duration of prescribed opioids when used medically, or access to prescription drug monitoring programs. We categorized people who used opioids but did not report non-medical use of opioids as engaging in medical use of opioids. This could lead to misclassification if people are inadvertently using the medication in a way other than how it was prescribed. Our findings may not be generalizable to people with other sexual identities or statuses not included in this study, such as adults who reported “don’t know” or refused to answer the sexual identity measure, adults with discordant sexual identity and behavior (Gattis et al., 2012) or adults with various gender identities (Newcomb, 2020). Nonetheless, the large sample combining 2015–2017 survey data allowed us to estimate policy-relevant opioid use measures and focus on an understudied population of adults of different sexual identities.

Our nationally representative cross-sectional study is an important step to understand the intersectional relationship between gender, sexual identity, and medical and non-medical prescription opioid use, testing the potential moderating effects of living in a state with an MCL. We found high medical prescription opioid use across sexual identity groups of U.S. adults, and higher medical prescription opioid use and non-medical prescription opioid use among bisexual women than their heterosexual counterparts. In this study, MCLs were associated with lower medical prescription opioid use for heterosexual adults and bisexual women, which could be due to unobserved state-level differences or potential upstream effects of MCLs on prescription opioid use. This could be a particular opportunity to expand screening and treatment services for bisexual women, who both have the highest medical and non-medical prescription opioid use, and high perceived treatment need for substance use disorder (Krasnova et al., 2021). Future studies should assess whether these observed associations in sexual minority and majority populations are causally related to MCL enactment and implementation.

Supplementary Material

Supplemental

Funding:

This study was partially funded by the National Institute on Drug Abuse- National Institutes of Health grants K01DA045224 (PI: Mauro), K01DA039804A (PI: Philbin) and R01DA037866-S2 (PI: Martins). The funders had no role in design, conduct of the study, analysis, interpretation of the results of this study, or preparation of this manuscript.

Footnotes

Conflicts of interest/Competing interests: The authors have no conflicts of interest to disclose.

Availability of data and material: The data reported herein were obtained from the National Survey on Drug Use and Health public use files, https://www.datafiles.samhsa.gov/data-sources

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