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. Author manuscript; available in PMC: 2026 Jun 12.
Published in final edited form as: J Subst Use Addict Treat. 2025 Jan 26;171:209629. doi: 10.1016/j.josat.2025.209629

Sexual identity, sexual behavior, and drug use behaviors among people who use drugs in the rural U.S.

Wiley D Jenkins 1, Lauren B Beach 2, John Schneider 3, Samuel R Friedman 4, Mai T Pho 5, Suzan Walters 6, Jerel Ezell 7, April M Young 8, Caitie Hennessy 9, William Miller 10, Vivian F Go 11, Christina Sun 12, David W Seal 13, Ryan P Westergaard 14, Heidi M Crane 15, Rob J Fredericksen 15, Stephanie A Ruderman 15, Scott Fletcher 16, Jimmy Ma 17, JA Delaney 18, Karma Plaisance 19, Judith Feinberg 20, Gordon S Smith 21, P Todd Korthuis 22, Thomas J Stopka 23, Peter D Friedmann 24, William Zule 25, Mike Winer 26
PMCID: PMC13255774  NIHMSID: NIHMS2120384  PMID: 39875013

Abstract

Introduction

People who use drugs (PWUD) are at risk of HIV infection, but the frequency and distribution of transmission-associated behaviors within rural communities is not well understood. Further, while interventions designed to more explicitly affirm individuals’ sexual orientation and behaviors may be more effective, descriptions of behavior variability by orientation are lacking. We sought to describe how disease transmission behaviors and overdose risk vary by sexual orientation and activity among rural PWUD.

Methods

From 01/2018-03/2020, rural PWUD participating in the Rural Opioid Initiative were surveyed across 8 sites. Collected data included: demographics; experiences with drug use, overdose, and healthcare; stigma; gender identity; and sexual orientation and partners. Participants were categorized as: monosexual by orientation and behavior (Mono-only), monosexual by orientation but behaviorally bisexual (Mono/Bi), and bisexual by orientation (Bi+). Analyses included descriptive summaries, bivariate examination (chi-square), and logistic regression (relative risk [RR] and 95% confidence interval [CI]).

Results

The 1455 participants were 84.8% Mono-only, 3.2 % Mono/Bi, and 12.0% Bi+. Compared to Mono-only men, Mono/Bi and Bi+ men had greater risk of transactional sex (RR=9.71, CI=6.66-14.2 and RR=5.09, CI=2.79-9.27, respectively) and sharing syringes for injection (RR=1.58, CI=1.06-2.35 and RR=1.85, CI=1.38-2.47). Compared to Mono-only women, Mono-Bi and Bi+ women had greater risk of transactional sex (RR=4.47, CI=2.68-7.47 and RR=2.63, CI=1.81-3.81); and Bi+ women had greater risk of sharing syringes for injection (RR=1.49, CI=1.23-1.81), sharing syringes to mix drugs (RR=1.44, CI=1.23-1.69), and experiencing an overdose (RR=1.32, CI=1.12-1.56). Bi+ men and women both more frequently reported selling sex as a source of income (versus Mono-only, both p<0.050) and measures of perceived stigma (all p<0.050).

Conclusions

Rural PWUD who are bisexual by orientation or behavior are significantly more likely to engage in behaviors associated with infectious disease transmission and to experience stigma and drug overdose. Given the growing recognition of bisexuality as a distinct orientation that warrants individualized consideration, interventions that are specifically acknowledging and affirming to the circumstances of this group are needed.

INTRODUCTION

The drug overdose epidemic continues to evolve, and has now entered what is considered the 4th wave, characterized by increased co-use of opioids, particularly fentanyl, and stimulants such as methamphetamine and cocaine (Jenkins, 2021; Ciccarone, 2021). This era of co-occurring substance use is also associated with increased risk of overdose and rates of infectious disease such as hepatitis C (HCV) and human immunodeficiency virus (HIV) (Zibbel et al., 2018; Lyss et al., 2020; CDC, 2021). Increased risk of HIV transmission in these circumstances is substantial, as injection drug use (IDU; e.g., sharing syringes for mixing and/or sharing drugs) is associated with approximately 10% of new HIV cases annually and multiple distinct outbreaks in recent years (Lyss et al., 2020; CDCa, 2024; Tookes et al., 2020). Injection drug use is also associated with increased risk and rates of sexually transmitted infections and fungal and bacterial infections (Levitt et al., 2020). Studies have found substance use to be associated with increased frequency of transmission-associated sexual activity (CDCb, 2024; Soe et al., 2018; Feelemeyer et al., 2023).

While these risks are common across the United States, they disproportionately impact minoritized populations, including people living in rural communities as well as lesbian, gay, bisexual, transgender, queer, and other sexual and gender minority (LGBTQ+) individuals (Walters et al., 2023; Chan and Mangla, 2022; Reif et al., 2015; Barger et al., 2018). Despite the risks of infectious disease, rural areas are disadvantaged with lesser availability screening, HIV prevention, and HCV treatment due to challenges such as fewer providers and transportation options (Schranz et al., 2018). Likewise, people identifying as LGBTQ+ face substantial stigmatization and health disparities, which are increased among those engaged in substance use (Schuler et al., 2020; Philbin et al, 2023). Such individuals also face increased risk of infectious disease and other adverse health outcomes within the context of syndemic theory, as income, food insecurity, sexual orientation-based discrimination, and social support are associated with increased polysubstance use and substance use-related problems (Cascalheira et al., 2023). This is further corroborated with findings that sexual minority (SM) individuals are more likely to experience substance use and chemsex drug use (use of drugs to enhance or extend sexual activity), higher levels of social vulnerability, houselessness, and earlier age of entry into commercial sex work (Rosner et al., 2021; Click et al., 2020). Within the National Survey on Drug Use and Health, higher odds of illicit drug use have been reported for gay and bisexual males compared to heterosexual males across all urbanicities; however, these disparities were largest in large urban and in rural areas compared to small urban areas.22 Higher odds of other drug use disorders have also been shown among gay and bisexual males compared to heterosexual male individuals living in rural areas, respectively. These disparities were not found within small urban areas (Dyar and Morgan, 2023).

Variation in sexual orientation measurement can affect the detection of sexual minority substance use disparities. Sexual orientation is a multidimensional social construct that includes sexual identity, sexual attraction, and sexual behavior. While some individuals can be classified consistently as heterosexual, gay or lesbian, or bisexual across all 3 dimensions, (e.g., an individual who reports a heterosexual sexual identity, only different-sex sexual attraction, and only different-sex sexual behavior), other individuals will be classified differently across different dimensions (e.g., an individual who reports a heterosexual identity but who has some same-sex attraction and reports sex with both same-sex and different-sex partners). A 2011 Institute of Medicine report suggests that people identifying as LGBTQ+ in rural areas may be more reluctant to disclose their sexual orientation (IOM, 2011). In the context of illicit drug use, individuals may engage in sexual behaviors with partners of different genders than they otherwise may choose-- due to social power differentials, to obtain needed resources (e.g., food, shelter), or to obtain substances. In the context of drug use it becomes important to study how drug use varies not only by each sexual orientation dimension on its own, but also their overlap. Prior studies have shown that drug use and sexual health related behavior patterns vary when classified by different dimensions of sexual orientation (McCabe et al., 2009). Disparities in substance use impacting people identifying as LGBTQ+ may also vary depending on whether they are classified by self-reported sexual identity or sexual behavior according to partner gender (McCabe et al., 2009). This variation may be illustrated by findings that bisexual individuals, whether categorized by sexual identity or sexual behavior, have worse substance use outcomes of people of any sexual orientation (McCabe et al., 2009; Farmer et al., 2016; Demant et al., 2017; Duncan et al., 2019).

The context of drug use may be associated with coercion into harmful activities. For example, past 12-month coerced sex is reported by 5-9% of nonheterosexual men who inject drugs, with increased risk among those also reporting a paid sex partner (Williams et al., 2019). Drug use has also been associated with sexual victimization and coercion (Turchik and Hassija, 2014). Further, sexual coercion itself is associated with increased risk of drug use and drug use-related harms (Young, Furman and Jones, 2012). Ultimately, not all individuals who use substances experience the same risk environment, and adverse health outcomes are dependent upon each individual’s unique context and experiences. Still, there may be commonalities of circumstances such that interventions to decrease poor sexual health or substance use outcomes may not need to be mutually distinct or unique, but still more-closely tailored to the vulnerabilities associated with specific characteristics and/or behaviors.

As discussed, bisexual individuals who use drugs likely experience adverse health outcome vulnerabilities distinct from those who have a heterosexual or homosexual orientation. Further, the reduced autonomy associated with drug use may result in bisexual behaviors among individuals who do not identify as bisexual. Individuals who report bisexual behaviors but who do not identify as bisexual may experience increased risk of poor sexual health and substance use outcomes that may also vary with urbanicity, but to the best of our knowledge, this has not been investigated. The purpose of this work is to explore how combinations of sexual identity and sexual behavior (e.g., gay, lesbian, or heterosexual identity with bisexual sexual behaviors) are associated with sexual health and substance use outcomes within the context of drug use in rural areas. Results from this study can substantially inform intervention strategies tailored to promote better sexual health, reduced substance use, and associated substance use harms– among people who report bisexual behavior but who identify with a gay or heterosexual sexual identity and who live in rural areas.

METHODS

From January 2018 to March 2020, we conducted a cross-sectional survey of PWUD in nonmetropolitan/rural counties across 8 project areas (“sites”) in ten states participating in the Rural Opioid Initiative (ROI): Illinois [IL]; Kentucky [KY]; North Carolina [NC]; New England [NEng; including Massachusetts, New Hampshire, and Vermont], Ohio [OH], Oregon [OR], West Virginia [WV], Wisconsin [WI]; Figure). All sites obtained local IRB approval for research activities and data sharing. A full description of the ROI’s structure and operations is described elsewhere; and a detailed description of the ROI project sites, their work, and publications, can be found on the ROI Research Consortium Studies website (Jenkins et al., 2022). It should be noted that WI did not collect data regarding sexual orientation and so data from that site were not included in this analysis.

Figure.

Figure.

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Map of the Rural Opioid Initiative sites

Individuals were eligible for inclusion if they lived in a study area, reported any past 30-day (P30) injection drug use or non-injection opioid use “to get high,” were able to communicate in English, and met site-specific age criteria (age ≥15 at two sites and ≥18 at six sites). Participants were recruited between January 2018 and March 2020 using a modified chain-referral based on Respondent Driven Sampling (Rudolph et al., 2024; Heckathorn, 1997). Sites enrolled “seeds” who met eligibility criteria and agreed to recruit 3-6 members of their social network who might be eligible. Seeds were most commonly existing and new participants of each site’s outreach service framework (e.g., harm reduction service organization, primary care, other). Participants received $10-$20 per successfully enrolled network member and $40-$60 for completion of study procedures.

Data sources/ measurement

Following recruitment and informed consent, participants completed a standardized, structured questionnaire, collected by audio computer-assisted self-interview (ACASI) at five sites, computer-assisted personal interview at two sites (REDCap, QDS), and computer-assisted self-interview at one site (Qualtrics). The questionnaire assessed participant self-reported characteristics and behaviors, and typically took 45-75 minutes to complete. Data were transferred to the ROI Data Coordinating Center at the University of Washington for quality review and collation of this multisite analytic dataset.

Variables

Participant characteristics included the following self-reported items plausibly associated with differences in drug use and sexual activity: age, race, gender identity, and stated sexual orientation. Due to small representation of some categories, race was consolidated to two categories (White and BIPOC [Black, Indigenous, and people of color]) from the full selection of White, Black, Native American, and Other [includes Alaskan Native, Asian or Pacific Islander or Native Hawaiian, African, Mixed race, and other race]). Stated sexual orientation (SOstated) was based upon self-report and consolidated to: heterosexual, gay and lesbian, and bisexual plus [includes bisexual, other, and don’t know or not sure]. Behavioral sexual orientation (SObehav) was calculated as the combination of SOstated and past 30-day (P30) sexual activity with men and women, and individuals were organized as: monosexual by orientation and monosexual by behavior with corresponding normative partners (Mono-only; e.g., heterosexual man with only women partners, gay man with only men partners); monosexual by orientation and bisexual by behavior with non-normative partners (Mono-Bi; e.g., heterosexual man with a man partner, gay man with a woman partner); and bisexual or other mixed-gender orientation where any partner might be considered normative (Bi+). While HIV risk varies considerably within these organized categories (e.g., heterosexual versus gay men), our previous work and the extant literature demonstrates considerable variation in behaviors by orientation, especially bisexual orientation, and so we sought to further explore the association with orientation, stated and/or behavioral, with specific behaviors (Jenkins et al., 2023).

Outcomes (personal experiences) were self-reported as past 30-days behaviors plausibly associated with infectious disease transmission and experience with overdose. Sexual activity-related variables included: condomless sex [In the last 30 days, how many times did you have vaginal or anal sex without a condom?]; condomless sex with someone who injects drugs [Of the times you had vaginal or anal sex without a condom in the last 30 days, how many times was that with someone who injected drugs to get high?]; and transactional sex [trading vaginal or anal sex for drugs, money, housing or other needed things]. Drug use-related variables included: experienced overdose (ever; frequency); injection (ever; frequency) and syringe sharing (drug mixing, injection). Behaviors were dichotomized into zero versus any occurrences (0/1+) in the past 30 days (Supplement Table lists all survey instrument items as utilized.).

Other variables included those that may be associated with the outcomes, and/or provide further context for differing aspects of vulnerability. These were self-reported as occurring over the past 30-days or past 6-months and included: naloxone training and possession; stigma scores (based on Latkin et al. (2013)); insurance possession and location of medical care; sources of income and houselessness.

Addressing Potential Bias

Recent theoretical and empirical work has assessed the strengths and weaknesses of Respondent Driven Sampling (RDS) (McCreesh et al., 2012; Rudolph et al., 2014). This work has emphasized the importance of careful selection of seeds from diverse sources and sufficient iterative rounds of recruitment to penetrate further reaches of the larger social networked population being studied. While study sites utilized RDS primarily as an effective means for participant recruitment, criteria required for such sampling to be generally representative of the rural PWUD population (e.g., seed selection and subsequent recruitment waves) were not met. Thus, the sample for this analysis reflects a convenience sample with the biases associated with a lack of systematic sample generation.

Statistical methods

Data were analyzed August-November 2023. We calculated summary statistics for participant characteristic variables overall and stratified by gender and behavioral sexual orientation to assess the variability and the degree of association between participant characteristics and gender and behavioral sexual orientation. Between-variable associations were tested using the t-test for the continuous variable (age) and Pearson’s χ2 test for categorical variables. The overall distributions of transmission behavior (dependent) variables were examined as well as stratified by participant characteristics, and their associations were tested using the χ2 or Fisher’s exact test. Relative risks (RR, with 95% confidence intervals) of each behavior relative to each characteristic were calculated using relative risk regression models adjusted for age and race. We first explored the association of each characteristic with each behavior (bivariate) and then included all exposures in the full model for each outcome (multivariable). Significance was assumed at a P-value <0.05, and analyses were performed using Stata version 18 (StataCorp, College Station, Texas, USA). We reported results in accordance with STROBE guidelines for observational studies (equator, 2023).

RESULTS

Across the 7 sites included, 2057 unique individuals completed the survey instrument. Of those, there were 1455 that reported being sexually active AND indicated either ≥0 men and women partners (i.e., no missing data; Mono-only and Mono/BI) or at least ≥1 partner (Bi+). These individuals had a mean age of 35.2 [9.3] years and they were 86.9% White and 13.1% BIPOC (Table 1). Regarding SOstated, individuals were 85.9% heterosexual, 2.1% gay or lesbian, and 12.0% bisexual. Regarding SObehav (which is used in the subsequent analyses), individuals were 84.8% Mono-only, 3.2 % Mono/Bi, and 12.0% Bi+.

Table 1.

Characteristics of sexually active Rural Opioid Initiative with reported sexual orientation (N=1455)

Variable Overall
Mean [sd]; N (%)		 Men
Mean [sd]; N (%)		 Women
Mean [sd]; N (%)
N (% within gender) 1455 802 (55.1) 653 (44.9)
Age, mean (SD) 35 [9] 36 [10] 34 [9]
 Race  White 1264 (87) 693 (86) 571 (87)
 BIPOC 191 (13) 109 (14) 82 (13)
 Stated Sexual Orientation  Heterosexual 1250 (86) 762 (95) 488 (75)
 Gay/Lesbian 30 (2) 16 (2) 14 (2)
 Bisexual plus 175 (12) 24 (3) 151 (23)
 Behavioral Sexual Orientation  Mono-only 1234 (85) 757 (94) 477 (73)
 Mono/Bi 46 (3) 21 (2) 25 (4)
 Bi+ 175 (12) 24 (4) 151 (23)
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Mono-only = monosexual by orientation and monosexual by behavior with corresponding normative partners

Mono/Bi = monosexual by orientation and bisexual by behavior with non-normative partners

Bi+ = bisexual or other mixed-gender orientation where any partner might be considered normative

We next examined the variation of outcome frequency across gender and SObehav (Tables 2 and 3). Compared to Mono-only men, Mono/Bi and Bi+ men had a greater risk of transactional sex (RR=9.71 [6.66-14.2] and 5.09 [2.79-9.27], respectively) and sharing syringes for injection (RR=1.58 [1.06-2.35] and 1.85 [1.38-2.47]), and Mono/Bi men had a greater risk of condomless sex (RR=1.19 [1.07-1.32]) while Bi+ men had a greater risk of condomless sex with someone who injects drugs (RR=1.67 [1.36-2.04]). Compared to Mono-only women, Mono/Bi and Bi+ women had a greater risk of transactional sex (RR=4.47 [2.68-7.47] and 2.63 [1.81-3.81], respectively) and experiencing at least 3 overdoses (RR=1.75 [1.07-2.88] and 1.44 [1.08-1.92]). Further, Bi+ women had a greater risk than Mono-only women of condomless sex with someone who injects drugs (RR=1.19 [1.02-1.38]), ever experiencing an overdose (RR=1.32 [1.12-1.56]), injecting at least once per day (RR=1.25 [1.08-1.92]), sharing syringes for injection (RR=1.49 [1.23-1.81]), and sharing syringes to mix drugs (RR=1.44 [1.23-1.69]).

Table 2.

Pairwise comparisons of outcomes by gender and behavioral sexual orientation among sexually active Rural Opioid Initiative participants

Variable Men
N (%)		 Women
N (%)
Mono-only Mono/Bi Bi+ Mono-only Mono/Bi Bi+
Column total 757 21 24 477 25 151
Transactional sex 53 (7) 14 (67) ‡ 8 (35) ‡ 50 (11) 11 (44) ‡ 40 (26) ‡
Condomless sex 600 (80) 20 (95) 20 (87) 389 (82) 21 (84) 130 (88)
 …with someone who injects 375 (50) 14 (67) 19 (83) ‡ 246 (52) 16 (64) 94 (64) †
Overdose ever 414 (56) 8 (42) 14 (58) 210 (45) 14 (56) 89 (60) ‡
Overdose ≥3 time1 203 (28) 4 (21) 9 (38) 107 (23) 10 (42) † 48 (33) †
Injection daily or more 437 (58) 10 (48) 15 (63) 246 (52) 13 (52) 100 (66) ‡
Syringe sharing for injecting 269 (36) 12 (57) † 16 (67) ‡ 165 (35) 12 (48) 82 (54) ‡
syringe-mediated syringe sharing 344 (46) 10 (48) 14 (58) 205 (43) 15 (60) 97 (64) ‡
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1

Dichotomized at the mean number of overdoses (3) across the entire cohort

†

p < 0.05 vs. Mono-only

‡

p < 0.01 vs. Mono-only

Table 3.

Relative risk regression for the association between outcomes by gender and behavioral sexual orientation among sexually active Rural Opioid Initiative participants

Variable Men, N=802
RR (95% CI)		 Women, N=653
RR (95% CI)
Mono-only Mono/Bi Bi+ Mono-only Mono/Bi Bi+
Transactional sex 1.0 9.71
(6.66-14.2) ‡ 		5.09
(2.79-9.27) ‡ 		1.0 4.47
(2.68-7.47) ‡ 		2.63
(1.81-3.81) ‡
Condomless sex 1.0 1.19
(1.07-1.32) ‡ 		1.10
(0.93-1.30)		 1.0 1.03
(0.87-1.23)		 1.06
(0.99-1.15)
 ……with someone who injects 1.0 1.34
(0.99-1.82)		 1.67
(1.36-2.04) ‡ 		1.0 1.23
(0.92-1.63)		 1.19
(1.02-1.38) †
Overdose ever 1.0 0.74
(0.44-1.25)		 1.02
(0.73-1.44)		 1.0 1.23
(0.85-1.76)		 1.32
(1.12-1.56) ‡
≥3 overdoses1 1.0 0.75
(0.31-1.80)		 1.35
(0.80-2.27)		 1.0 1.75
(1.07-2.88) † 		1.44
(1.08-1.92) †
Injection daily or more 1.0 0.81
(0.51-1.29)		 1.07
(0.79-1.47)		 1.0 0.98
(0.67-1.44)		 1.25
(1.08-1.44) ‡
Syringe sharing for injection 1.0 1.58
(1.06-2.35) † 		1.85
(1.38-2.47) ‡ 		1.0 1.35
(0.90-2.04)		 1.49
(1.23-1.81) ‡
syringe-mediated syringe sharing 1.0 1.04
(0.65-1.66)		 1.29
(0.92-1.82)		 1.0 1.36
(0.99-1.88)		 1.44
(1.23-1.69) ‡
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Adjusted for age and race (White vs. non-White)

1

Dichotomized at the mean number of overdoses (3) across the entire cohort

†

p < 0.05 vs. Mono-only

‡

p < 0.01 vs. Mono-only

Lastly, we explored other variables that might contribute to, or be a reflection of, the outcomes. Compared to Mono-only men, Bi+ men more frequently reported: experiencing three (of five) aspects of stigma; being unpartnered; and selling sex as a source of income (Table 4). Mono/Bi women less frequently reported feeling ashamed or possessing insurance compared to Mono-only women. Bi+ women differed from Mono-only women across multiple variables, including more frequently reporting feeling they make others uncomfortable, not receiving any healthcare the past 6-months, selling sex and drugs and shoplifting for income, past 6-month houselessness, being unpartnered, and not receiving drug treatment because they didn’t want to be seen at the clinic and fear of disrespect. Both Mono/Bi and Bi+ women more frequently reported current naloxone possession than their Mono-only peers.

Table 4.

Pairwise comparisons of covariates by gender and concordance status among sexually active Rural Opioid Initiative participants

Variable Men Women
Mono-only Mono/Bi Bi+ Mono-only Mono/Bi Bi+
N (%) 757 21 24 477 25 151
Naloxone possession (current) 282 (37) 3 (16) 11 (46) 163 (34) 14 (56) † 69 (47) ‡
Overdose training 344 (46) 12 (63) 14 (58) 252 (54) 16 (64) 75 (52)
Stigma a
 Ashamed 560 (74) 15 (71) 18 (75) 404 (85) 14 (56) ‡ 126 (83)
 Others avoid 511 (68) 15 (71) 21 (88) † 353 (74) 15 (60) 119 (79)
 Lose friends 403 (53) 15 (71) 18 (75) † 291 (61) 15 (60) 92 (61)
 Family rejection 498 (66) 15 (71) 21 (88) † 364 (76) 17 (68) 119 (79)
 Others uncomfortable 482 (64) 14 (67) 18 (75) 321 (67) 13 (52) 118 (78) †
Insurance possession 522 (71) 12 (57) 17 (71) 385 (82) 14 (58) ‡ 113 (77)
Location of medical care (past 6-month)
 Private doctor 168 (22) 7 (33) 4 (17) 144 (30) 8 (32) 38 (25) †
 Community clinic 117 (15) 7 (33) 4 (17) 85 (18) 5 (20) 16 (11) †
 Health dept 26 (3) 0 2 (8) 24 (5) 3 (12) 7 (5) †
 Urgent care 65 (9) 3 (14) 1 (4) 45 (9) 1 (4) 18 (12) †
 Emergency dept 163 (22) 3 (14) 8 (33) 88 (18) 6 (24) 29 (19) †
 Mobile health clinic 3 (0.4) 0 0 0 0 0
 Other 41 (5) 0 1 (4) 14 (3) 1 (4) 12 (8) †
 Have not received 174 (23) 1 (5) 4 (17) 77 (16) 1 (4) 31 (21) †
Houselessness (past 6-month) 379 (50) 11 (52) 15 (68) 208 (44) 15 (60) 103 (68) ‡
No medical treatment (past 30-days) 213 (28) 7 (39) 7 (29) 132 (28) 10 (42) 46 (31)
No treatment – did not want to be seen b 142 (19) 6 (32) 6 (29) 79 (17) 4 (17) 47 (32) ‡
No treatment – feared disrespect c 156 (21) 7 (37) 6 (26) 123 (27) 9 (38) 52 (35) †
Partnership status d
 Unpartnered 490 (68) 11 (61) 19 (90) † 287 (61) 17 (71) 109 (73) ‡
 Partnered 228 (32) 7 (39) 2 (10) † 184 (39) 7 (29) 41 (27) ‡
Sources of income e
 Full time work 175 (23) 4 (19) 6 (25) 55 (12) 3 (12) 9 (6) †
 Part time work 231 (31) 9 (43) 8 (33) 80 (17) 3 (12) 29 (19)
 Retirement check 6 (1) 1 (5) 0 7 (2) 0 0
 Public assistance 40 (5) 0 2 (8) 55 (12) 1 (4) 19 (13)
 Disability check 122 (16) 3 (14) 6 (25) 90 (19) 2 (8) 18 (12) †
 Selling drugs 196 (26) 3 (14) 8 (33) 90 (19) 7 (28) 47 (31) ‡
 Selling sex 13 (2) 1 (5) 2 (8) † 19 (4) 1 (4) 18 (12) ‡
 Theft/shoplifting 17 (9) 1 (5) 5 (21) 56 (12) 4 (16) 30 (20) †
 Someone supports me 182 (24) 5 (24) 7 (29) 213 (46) 11 (44) 77 (51)
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a

Each stigma item considered endorsed as “Somewhat or more”

b

“In the last 6 months I didn’t go for drug treatment because I did not want to be seen at a drug treatment clinic.”

c

“In the last 6 months I didn’t go for drug treatment because I was afraid they’d treat me with disrespect since I use drugs.”

d

Partnered = “married” or “living with partner”; Unpartnered = “widowed”, “divorced”, “separated”, “never married”

e

Each item considered separately as yes/no

†

p < 0.05 vs. Mono-only

‡

p < 0.01 vs. Mono-only

DISCUSSION

Sexually active people who use drugs and who are bisexual by either stated orientation or behavior (i.e., against the normative for their stated orientation) more frequently report sexual and drug use behaviors associated with infectious disease transmission (e.g., condomless and transactional sex; syringe sharing). Bisexual women in particular more frequently report experience with overdose, but also possession of naloxone and training in its use. Research suggests that disparities, such as these are due to structural factors, such as stigma, that impede access to resources that can be used to protect oneself. (e.g.; MacKenzie et al, 2020; Schroeder et al, 2023) Thus, individuals who identify as bisexual, or who may be considered bisexual by reported activity, are at potentially increased risk of infectious disease and overdose compared to heterosexual, gay and lesbian individuals whose sexual partners are normative according to their stated orientation.

Further, individuals who are bisexual by orientation and/or behavior more frequently report other circumstances which may influence their sexual activity and drug use. For example, bisexual PWUD more frequently report experiencing aspects of stigma, which may exacerbate drug use. They also more frequently report lack of health insurance, utilization of non-routine medical care, and avoidance of drug treatment. These data all indicate lesser engagement with systems that may facilitate health seeking and health maintenance. Lastly, increased frequency of houselessness and greater reliance on shoplifting and selling sex and drugs for income indicate potentially increased instability. These are not universally increased across all bisexual groups and each gender, but observed statistical significance may be hampered by the relatively small bisexual populations. While these circumstances do not themselves directly influence disease transmission and overdose risk, they do indicate circumstances of increased vulnerability. Further, while data support that commercial sex workers (CSW) may determine specific guidelines regarding in which activities they will engage, it is not clear that if this is the case among rural PWUD who may not consider themselves CSW. Therefore, their engagement in transactional sex or activities with partners outside the normative for their stated orientation may indicate some degree of decreased personal agency that may influence their context of risk mitigation.

There is growing recognition (e.g., at the National Institutes of Health) that bisexuality as a sexual orientation may confer a distinct combination of experiences that influences an individual’s risk and opportunity environment (NIH, 2023). Our work among rural people who use drugs corroborates previous studies indicating that drug use and sexual behavior patterns vary when classified by different dimensions of sexual orientation (Jenkins et al., 2023). The reasons for these differential patterns and disparities vary. In the context of illicit drug use, individuals may engage in different sexual behaviors with partners of different genders than they otherwise might choose due to social power differentials, to obtain needed resources (e.g., food, shelter), or to obtain drugs. Previous work has found that people identifying as LGBTQ+ in rural areas are at a greater risk for poverty compared to white cisgender heterosexual individuals (Schuler, Prince and Collins, 2021). As a result, these sub-populations who use drugs may be at even higher risk for infectious disease and overdose due to underlying fiscal vulnerability.

Current public health agency epidemiological reporting categories, however, do not comprehensively capture the intersections of sexual identity or sexual behavior for substance use or HIV/STI status (IOM, 2011; Phillips et al, 2023). Due to the social stigma tied to reporting same-gender sexual behavior as well as to the stigmatization of bisexual identity, it is important epidemiologically to investigate combinations of sexual identities and behaviors that contradict social expectations. Continuing work should explore the context of behaviors associated with increased disease and overdose risk, and interventions designed to explicitly acknowledge, affirm and address what may be considered non-normative sexual activity compared to an individual’s sexual identity.

Limitations

There are several limitations associated with this work. First, all data are self-reported, and those items which may be considered socially undesirable (e.g., same-gender sexual activity) may be underreported. Second, the data are cross-sectional, and while we are able to determine outcome relative risk we lack temporal data. Third, data collection methods and eligibility slightly varied across sites, and so there may be differential biases in responses (e.g., not all individuals specifically reported men and women partners). Fourth, the ROI data collection was not designed to examine sexual activity in detail, and so important aspects of individual sexuality are not explored. Finally, there were a substantial number of analyses performed (increasing the chance of finding statistically significant findings by chance) and the data themselves are not necessarily representative of all rural PWUD.

Supplementary Material

Supplement Table

PUBLIC HEALTH IMPLICATIONS.

Our work indicates that bisexual individuals in rural areas who use drugs more frequently engage in sexual and injection use practices which may place them at higher risk of overdose and infectious disease. The profile of risks and opportunities indicates that new interventions and outreach methods should be developed to reduce adverse health outcomes and increase health pursuit. Further, the results directly imply that simple self-reported descriptions of sexual orientation (e.g., heterosexual, gay), and consolidations of non-heterosexual orientations into a single group (e.g., gay and bisexual men) may miss important nuances of infectious disease context, risk, and preventive opportunities. Studies and programs addressing HIV transmission risk reduction should incorporate the collection of more complete aspects of sexual orientation and behavior.

FUNDING ACKNOWLEDGEMENTS

This publication is based upon data collected and/or methods developed as part of the Rural Opioid Initiative (ROI), a multi-site study with the goal of better characterizing the rural opioid epidemic and its consequences and a common protocol that was developed collaboratively by investigators at eight research institutions and at the National Institute on Drug Abuse (NIDA), the Appalachian Regional Commission (ARC), the Centers for Disease Control and Prevention (CDC), and the Substance Abuse and Mental Health Services Administration (SAMHSA). Research presented in this manuscript is the result of secondary data harmonization and analysis and was supported by grant U24DA048538 from NIDA. Primary data collection was supported by grants UG3DA044829/UH3DA044829, UG3DA044798/UH3DA044798, UG3DA044830/UH3DA044830, UG3DA044823/UH3DA044823, UH3DA044822/UH3DA044822, UG3DA044831/UH3DA044831, UG3DA044825, UG3DA044826/UH3DA044826, U24DA044801, and UL1TR002369 co-funded by NIDA, ARC, CDC, and SAMHSA.

The authors thank the other ROI investigators and their teams, community and state partners, and the participants of the individual ROI studies for their valuable contributions.

A full list of participating ROI institutions and other resources can be found at http://ruralopioidinitiative.org.

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