Abstract
In the United States, high rates of HIV infection among persons who engage in transactional sex are partially driven by substance use. Little is known about transactional sex among rural populations of people who inject drugs (PWID). Using data from a 2018 survey of 420 rural PWID in West Virginia, we used logistic regression to identify correlates of recent transactional sex (past 6 months). Most study participants were male (61.2%), white (83.6%), and reported having injected heroin (81.0%) in the past 6 months. Nearly one-fifth (18.3%) reported engaging in recent transactional sex. Independent correlates of transactional sex were: being female [adjusted odds ratio (aOR) 3.90; 95% CI 2.12–7.16]; being a sexual minority (aOR 3.07; 95% CI 1.60–5.87); being single (aOR 3.22; 95% CI 1.73–6.01); receptive syringe sharing (aOR 3.13; 95% CI 1.73–5.66); and number of injections per day (aOR 1.08; 95% CI 1.01–1.15). Rural PWID who engage in transactional sex are characterized by multiple vulnerabilities that increase their HIV risk.
Keywords: HIV, Transactional sex, Rural health, People who inject drugs
Introduction
Injection drug use and transactional sex work carry significant risks for blood borne infections (e.g., HIV and viral hepatitis) and other sexually transmitted infections (STIs) [1–7]. Transactional sex work involves the exchange of sex for money or other goods, such as drugs, and may take on many forms, ranging from informal survival sex work to persons who view sex work as an occupation [8–12]. Research has shown that women who engage in transactional sex work experience HIV risks that are 14 times greater than women in the general population and that HIV prevalence is greater among women who engage in transactional sex than all US females (17.3% and 0.19%, respectively) [1, 13, 14]. Engagement in both injection drug use and transactional sex work are strongly correlated and may exacerbate risks for HIV/STIs [15–20]. These elevated risks are often driven by sociostructural vulnerabilities (e.g., food insecurity, poverty), the illicit nature of sex work and injection drug use, and stigma [4–6, 21]. Research has also shown that women who engage in transactional sex work experience high rates of violence and may use drugs as a coping mechanism [22]. While the existing literature is commendable, it primarily reflects studies conducted in urban areas and among women who engage in transactional sex work. No research has been conducted to explore correlates of transactional sex work among rural populations of male and female people who inject drugs (PWID). Better understanding this population is critically important given the magnitude of the opioid crisis in rural areas of the United States.
Rural communities are disproportionately affected by the opioid crisis; for example, a recent analysis identified 220 counties throughout the United States that were vulnerable to injection drug use-associated HIV/HCV outbreaks with the majority concentrated in rural Appalachia [23]. These risk vulnerabilities are underscored by surveillance data from four predominantly rural states (Kentucky, Tennessee, Virginia, and West Virginia) that indicate a 364% increase in acute HCV infections from 2006 to 2012 [24]. The opioid crisis has also precipitated dramatic increases in overdose fatalities; according to the Centers for Disease Control and Prevention, in 2017, there were 70,237 drug overdose fatalities in the United States [25]. The impacts of this crisis are especially felt by West Virginia, a rural state with the highest age-adjusted drug overdose death rate in the country [25]. Notably, 28 of the 55 counties in West Virginia were also identified as vulnerable to an injection drug use-associated HIV/HCV outbreak [23].
The effects of the opioid crisis in West Virginia may be amplified by endemic socio-structural vulnerabilities (e.g., poverty, lack of economic opportunities). For example, a 2018 study found that there were an estimated 1857 PWID in Cabell County (WV), reflecting an estimated 2.4% population prevalence of recent (past 6 months) injection drug use [26]. Among these individuals, the majority (57%) reported being homeless, unemployed (66%), and going to bed hungry at least once per week (64%). Rural communities may also lack access to basic HIV and HCV testing, as well as provision of harm reduction services; thus, increasing risks for both HIV/HCV outbreaks and overdose fatalities over time. The synergistic relationship between injection drug use and transactional sex work paired with the impacts of the opioid crisis in rural communities may create an environment that impedes the public health of PWID who also engage in transactional sex work. Better understanding factors associated with transactional sex work among rural PWID populations would provide an evidence-base for tailoring HIV/HCV and overdose prevention initiatives. The purpose of this research is to explore factors (e.g., sociodemographics, structural vulnerabilities, substance use patterns) associated with transactional sex work among rural PWID.
Methods
Data from a rural PWID population estimation study were used for this research [26]. For brevity, we provide an overview of the methods used in the parent study [26]. In June and July 2018, a capture-recapture population estimation study was implemented in Cabell County, West Virginia to quantify the size and characteristics of the PWID population. While Cabell County contains the city of Huntington, it is primarily rural with 86.2% of its land space characterized as rural by the US Census Bureau [27]. Cabell County was also identified as vulnerable to an injection drug use-associated HIV/HCV outbreak [23]. In 2016, Cabell County led West Virginia in heroin-related overdose fatalities [28]. Cabell County also made National news in the Fall of 2016 when 26 overdoses linked to persons using heroin laced with fentanyl and carfentanil were reported in a mere 5-h period [29]. In recent years, Cabell County implemented several initiatives to prevent overdose, leading to a 40% reduction in EMS calls for overdoses in 2018 [30].
During the capture phase of the study, PWID were recruited at the Cabell Huntington Harm Reduction Program, a harm reduction program implemented by the Cabell-Huntington Health Department in 2015 in response to the growing opioid crisis. The Cabell-Huntington Harm Reduction Program provides a variety of services to PWID, including: access to sterile injection equipment, drug treatment referrals, vaccinations, overdose prevention resources (e.g., naloxone), and HIV/STI testing. During the recapture phase, PWID were recruited in community locations where PWID were known to congregate, such as: public parks, transit locations, green spaces, neighborhoods known for drug-related activities, parking lots (apartments and businesses), gas stations, homeless encampments, on the stoops of abandoned properties, and on sidewalks. These locations were identified via a series of geospatial analyses of indicators related to drug use as well as discussions with community stakeholders [26].
Due to the pervasive stigma surrounding illicit drug use, eligibility criteria were broad: (1) to be aged at least 18 years old; and (2) to have ever used drugs by any route of administration. All persons that were approached to participate received a verbal description of the study and staff answered any questions persons may have surrounding their participation. Staff then verbally screened individuals who expressed interest in participating. All data were collected anonymously through audio computer assisted self-interview (ACASI). As an incentive for their participation, persons received a snack bag or a $10 grocery gift card during the capture and recapture phases, respectively [26]. The Institutional Review Board at the Johns Hopkins Bloomberg School of Public Health approved this research.
Measures
Dependent Variable
The outcome of interest was recent (past 6 months) engagement in transactional sex work, which was assessed with the following question: “In the past 6 months, did you sell sex? By selling sex, I mean trading or exchanging oral, vaginal, or anal sex for things like money, food, drugs, or a place to stay.”
Socio-Demographics
Age was measured continuously. For analytical purposes, race was collapsed into a binary variable (White, non-Hispanic vs. all others, including: Hispanic, Black, multiracial, and other). Relationship status was also dichotomized to those who reported being single and those who reported being married or in a relationship. Sexual minority status was dichotomized to heterosexual and all others, including: gay, lesbian, bisexual, and other.
Structural Vulnerabilities
Structural vulnerability measures included: current homelessness; food insecurity (defined as going to bed hungry at least once per week); and limited education (defined as not completing high school). Employment status was also measured and dichotomized to those who reported being unemployed versus all others, which included persons who reported a variety of employment situations (e.g., full-time, part-time, temporary or seasonal work). We also asked persons if they had been arrested in the past 6 months.
Substance Use
Persons were asked to report the number of times they inject drugs on a typical day. Receptive injection equipment sharing was measured via, “In the past 6 months, did you use any of the following items that you knew had been used by someone else?” with answer options including: syringes or needles; cookers; cottons; and rinse water. Participants were asked to report if their recent (past 6 months) drug use had increased, decreased, or stayed the same. These data were dichotomized to those who reported that their drug use had increased versus those who reported their drug use had decreased or stayed the same. Persons also reported if they had ever accessed services at the Cabell-Huntington Harm Reduction Program. Participants reported if they had recently (past 6 months) wanted to start drug treatment but were unable to get into a program. Overdose experiences in the past 6 months were measured continuously but collapsed to a binary variable indicating those who had and had not overdosed to the point of passing out in the past 6 months. Persons also reported substances they used in the past 6 months (e.g., cocaine, heroin, marijuana, speedball, crystal methamphetamine, prescription medications [e.g., oxycontin, percocet, codeine, darvon, percodan, dilaudid, demerol], fentanyl, and buprenorphine/suboxone) and by route(s) of administration (i.e., ingest, smoke, snort, inject).
For analytical purposes, we report injection drug use measures individually and a composite measure for non-injection drug use, which reflects persons ingesting, smoking, and/or snorting each drug.
Statistical Analysis
The analytical sample consisted of persons recruited in Cabell County who reported injection drug use in the past 6 months (n = 421). One participant identified as transgender; to preserve anonymity, they were excluded from the analysis. Prevalence estimates were calculated for the variables of interest. Initial tests for association with transactional sex work were calculated using Pearson’s Chi square tests and independent samples t-tests, with p < 0.05 indicating statistical significance. Due to the exploratory nature of this research, we executed bivariate logistic regression models of the factors significant at p < 0.10 in initial tests for association to identify covariates for consideration in multivariable logistic regression modeling. We also used post hoc Pearson’s Chi square tests to test the associations between the injection drug use measures as well as between the covariates and homelessness and food insecurity (i.e., going to bed hungry at least once per week). Given associations between the injection drug use measures, we did not retain drug-specific injection drug use measures, but rather the average number of injections per day. Similarly, in terms of receptive injection equipment sharing, we retained receptive syringe sharing given its strong association with HIV transmission. Homelessness and going to bed hungry at least once per week were also correlated with other covariates and not retained. All analyses were conducted using SAS Enterprise Guide v5.1.
Results
The average age of study participants was 35.8 years (Table 1). The majority was male (61.2%), White, non-Hispanic (83.6%), single (53.8%), currently unemployed (66.0%), had at least a high school education (72.6%), went to bed hungry at least once per week (64.8%), and considered themselves homeless (56.0%). A small proportion (17.4%) identified as sexual minorities. One-third (33.6%) reported having been recently arrested. In terms of injection drug use in the past 6 months, heroin was most commonly reported (81.0%), followed by crystal methamphetamine (71.1%) and fentanyl (54.8%). On average, persons reported injecting 4.4 times per day. Slightly more than half (56.2%) reported having ever accessed services at the Cabell-Huntington Harm Reduction Program. Relatively large proportions reported receptive syringe sharing (42.9%) in the past 6 months and having overdosed in the past 6 months (42.6%). Nearly one in four (24.8%) reported that their drug use had increased in the past 6 months. A large proportion (36.5%) reported wanting to start drug treatment but unable to get into a program. For recent non-injection drug use, prescription medications were most commonly reported (63.6%), followed by marijuana (62.4%), cocaine (59.8%), and crystal methamphetamine (55.7%). A minority (18.3%) reported engaging in transactional sex work in the past 6 months.
Table 1.
Characteristics by transactional sex engagement in the last 6 months (n=420).
| Variable | Total (n=420), N(%) | Not engaged in transactional sex work (n=343) N (%) | Engaged in Transactional Sex (n=77) N(%) | p-value |
|---|---|---|---|---|
| Socio-demographics | ||||
| Age, mean (SD) | 35.8 (8.5) | 36.3 (8.6) | 33.3 (7.8) | 0.005a |
| Gender | <0.001 | |||
| Male | 257 (61.2) | 229 (66.8) | 28 (36.4) | |
| Female | 163 (38.8) | 114 (33.2) | 49 (63.6) | |
| Race/Ethnicity | 0.650 | |||
| White, non-Hispanic | 341 (83.6) | 277 (83.2) | 64 (85.3) | |
| Other | 67 (16.4) | 56 (16.8) | 11 (14.7) | |
| Relationship Status | 0.002 | |||
| Single | 225 (53.8) | 171 (50.1) | 54 (70.1) | |
| Married/In a relationship | 193 (46.2) | 170 (49.9) | 23 (29.9) | |
| Sexual Minority | 73 (17.4) | 45 (13.2) | 28 (36.4) | <0.001 |
| Structural Vulnerabilities | ||||
| Education | 0.749 | |||
| Didn’t graduate high school | 115 (27.4) | 95 (27.8) | 20 (26.0) | |
| High school graduate or GED or more | 304 (72.6) | 247 (72.2) | 57 (74.0) | |
| Consider self homeless | 235 (56.0) | 184 (53.6) | 51 (66.2) | 0.044 |
| Unemployed | 277 (66.0) | 221 (64.4) | 56 (72.7) | 0.165 |
| Food insecurity | 272 (64.8) | 211 (61.5) | 61 (79.2) | 0.003 |
| Arrested, past 6 months | 141 (33.6) | 115 (33.5) | 26 (33.8) | 0.968 |
| Substance Use | ||||
| Number of injections per day, mean (SD) | 4.4 (3.9) | 4.1 (3.7) | 5.7 (4.4) | <0.001a |
| Receptive injection equipment sharing, past 6 months | ||||
| Syringes | 180 (42.9) | 130 (37.9) | 50 (64.9) | <0.001 |
| Cookers | 184 (43.8) | 134 (39.1) | 50 (64.9) | <0.001 |
| Cottons | 156 (37.1) | 118 (34.4) | 38 (49.4) | 0.014 |
| Rinse Water | 176 (41.9) | 128 (37.3) | 48 (62.3) | <0.001 |
| Drug use level, past 6 months | 0.009 | |||
| Decreased or stayed the same | 316 (75.2) | 267 (77.8) | 49 (63.6) | |
| Increased | 104 (24.8) | 76 (22.2) | 28 (36.4) | |
| Accessed harm reduction services at Cabell-Huntington Health Department | 231 (56.2) | 182 (54.3) | 49 (64.5) | 0.108 |
| Wanted treatment, unable to get services, past 6 months | 152 (36.5) | 118 (34.7) | 34 (44.2) | 0.120 |
| Overdose, last 6 months | 179 (42.6) | 144 (42.0) | 35 (45.5) | 0.578 |
| Injection drug use, last 6 months | ||||
| Cocaine | 144 (34.3) | 108 (31.5) | 36 (46.8) | 0.011 |
| Heroin | 340 (81.0) | 270 (78.7) | 70 (90.9) | 0.014 |
| Speedball | 161 (38.3) | 122 (35.6) | 39 (50.6) | 0.014 |
| Crystal Methamphetamine | 298 (71.1) | 236 (69.0) | 62 (80.5) | 0.044 |
| Painkillers | 99 (23.6) | 75 (21.9) | 24 (31.2) | 0.082 |
| Fentanyl | 230 (54.8) | 176 (51.3) | 54 (70.1) | 0.003 |
| Suboxone or Buprenorphine | 127 (30.2) | 102 (29.7) | 25 (32.5) | 0.637 |
| Non-injection drug use, past 6 months | ||||
| Cocaine | 251 (59.8) | 200 (58.3) | 51 (66.2) | 0.200 |
| Heroin | 141 (33.6) | 115 (33.5) | 26 (33.8) | 0.968 |
| Marijuana | 262 (62.4) | 215 (62.7) | 47 (61.0) | 0.788 |
| Crystal Methamphetamine | 234 (55.7) | 190 (55.4) | 44 (57.1) | 0.780 |
| Prescription medications | 267 (63.6) | 213 (62.1) | 54 (70.1) | 0.186 |
Student’s t-test
There were significant differences (p < 0.05) between rural PWID that had and had not engaged in recent transactional sex work (Table 1). Compared to their non-transactional sex work engaging counterparts, rural PWID that reported transactional sex work were more likely to report being: female (63.6% and 33.2%, respectively), single (70.1% and 50.1%, respectively), a sexual minority (36.4% and 13.2%, respectively), homeless (66.2% and 53.6%, respectively), and going to bed hungry at least once per week (79.2% and 61.5%, respectively). There were also differences in HIV risk behaviors with those reporting transactional sex work more likely than their non-transactional sex work engaging counterparts to report receptive injection equipment sharing, including syringes (64.9% and 37.9%, respectively). Rural PWID that reported transactional sex work were also more likely than their counterparts to report injecting (in the past 6 months): cocaine (46.8% and 31.5%, respectively); heroin (90.9% and 78.7%); speedball (50.7% and 35.6%); crystal methamphetamine (80.5% and 69.0%); and fentanyl (70.1% and 51.3%). Relatedly, rural PWID that reported transactional sex work also injected more frequently per day than their non-transactional sex work engaging counterparts (5.7 and 4.1 times per day, respectively).
Unadjusted and adjusted associations of transactional sex work are shown in Table 2. In unadjusted analyses, factors significantly associated with transactional sex work included: being female [unadjusted odds ratio (uOR) 3.52, 95% confidence interval (95% CI) 2.10–5.89], being single (uOR 2.33, 95% CI 1.37–3.97), being a sexual minority (uOR 3.77, 95% CI 2.15–6.60), number of injections per day (uOR 1.10, 95% CI 1.03 1.16), receptive syringe sharing in the past 6 months (uOR 3.03, 95% CI 1.81–5.09), drug use having increased in the past 6 months (uOR 2.01, 95% CI 1.18–3.41), and age (uOR 0.96, 95% CI 0.93–0.99). In multivariate adjusted analyses, factors significantly associated with transactional sex work included: being female [adjusted odds ratio (aOR) 3.90, 95% CI 2.12–7.16); being single (aOR 3.22, 95% CI 1.73–6.01), being a sexual minority (aOR 3.07, 95% CI 1.60–5.87); number of injections per day (aOR 1.08, 95% CI 1.01–1.15); and receptive syringe sharing in the past 6 months (aOR 3.13, 95% CI 1.73–5.66).
Table 2.
Bivariate and multivariable logistic regressions for predictors of recent transactional sex work engagement
| Unadjusted | Adjusted | |||||
|---|---|---|---|---|---|---|
| Variable | OR | 95% CI | p-value | aOR | 95% CI | p-value |
| Socio-demographics | ||||||
| Age | 0.96 | 0.93 – 0.99 | 0.006 | 0.98 | 0.95 – 1.02 | 0.350 |
| Gender | ||||||
| Male | 1 | Ref | -- | 1 | Ref | -- |
| Female | 3.52 | 2.10 – 5.89 | <0.001 | 3.90 | 2.12 – 7.16 | <0.001 |
| Relationship Status | ||||||
| Married/In a relationship | 1 | Ref | -- | 1 | Ref | -- |
| Single | 2.33 | 1.37 – 3.97 | 0.002 | 3.22 | 1.73 – 6.01 | <0.001 |
| Sexual Minority | 3.77 | 2.15 – 6.60 | <0.001 | 3.07 | 1.60 – 5.87 | 0.001 |
| Substance Use | ||||||
| Number of injections per day | 1.10 | 1.03 – 1.16 | 0.002 | 1.08 | 1.01 – 1.15 | 0.017 |
| Receptive Syringe sharing, past 6 months | 3.03 | 1.81 – 5.09 | <0.001 | 3.13 | 1.73 – 5.66 | <0.001 |
| Drug use level, past 6 months | ||||||
| Decreased or stayed about the same | 1 | Ref | -- | 1 | Ref | -- |
| Increased | 2.01 | 1.18 – 3.41 | 0.010 | 1.37 | 0.73 – 2.54 | 0.323 |
Discussion
The results of this study suggest a unique constellation of factors are associated with engaging in transactional sex work among rural PWID and that PWID who engage in transactional sex work experience risks for HIV and other comorbidities that exceed those of their non-transactional sex work engaging PWID counterparts. Among this sample of rural PWID, those who reported engaging in transactional sex work reported greater levels of homelessness and food insecurity than their counterpart PWID. Further, PWID who engaged in transactional sex work also reported injecting drugs with greater frequency than their non-transactional sex work engaging peers as well as greater use of multiple injectable drugs, including: cocaine, heroin, crystal methamphetamine, speedball, and fentanyl. Given the disproportionate impact of the opioid epidemic in rural communities, future research should explore how to meet the unique infectious disease and overdose prevention needs among rural PWID who engage in transactional sex work.
The high prevalence practice of receptive syringe sharing paired with greater numbers of injections per day among rural PWID that engage in transactional sex work may warrant immediate public health response. These data are especially concerning when one considers that a greater proportion of rural PWID who engage in transactional sex work also reported that their drug use had increased in the past 6 months compared to their nontransactional sex work engaging counterparts. Rural PWID who engage in transactional sex work are at very high risk of HIV infection and may be a bridging population to other groups. Although most persons in the sample reported having accessed harm reduction services, efforts should be undertaken to increase access to sterile injection equipment as well as other HIV prevention strategies, such as pre-exposure prophylaxis (PrEP).
Our findings should be contextualized within the broader risk environment of rural PWID as well as the nexus of accumulated and interacting vulnerabilities stemming from transactional sex work (e.g., illegality of transactional sex work, housing instability). PWID who engage in transactional sex work in rural communities face multiple layers of stigma associated with injection drug use as well as transactional sex work, potentially exacerbating risks for HIV acquisition and overdose. Rural PWID that engage in transactional sex work who are also sexual minorities may face additional stigma and barriers to service access. Although not significantly different, we found that large proportions of both rural PWID who engage in transactional sex work and their non-transactional sex work counterparts reported wanting drug treatment but were unable to access a program. Low access to essential health and human services, such as drug treatment, may leave persons with few options to seek help and place persons at sustained risk for a multiplicity of adverse health consequences. Interventions should be developed that not only ameliorate the structural vulnerabilities experienced by rural PWID who engage in transactional sex work, but also address cultural stigmas against this population. Importantly, our finding that women were nearly four times more likely to report engaging in transactional sex work suggests that work should be done to address the unique challenges this population faces, particularly around HIV prevention as research has shown that women who engage in transactional sex work have HIV risks 14 times greater than that of women in the general population [1].
There are several study limitations that should be considered when interpreting our findings. First, our definition of transactional sex work was broad and did not allow us to ascertain the nuanced motives persons had for exchanging sex (e.g., drugs, housing, pleasure). This is an important limitation as there are a variety of reasons why persons may be engaging in transactional sex work. Another limitation is that we were not able to measure the degree to which the population interacted with law enforcement beyond whether or not they had been arrested in the past six months. Existing research has documented that persons who engage in transactional sex work and who have adverse interactions with law enforcement may be at increased risk for HIV and other comorbidities [31]. Another limitation is that most of our sample identified as White and, as a result, analyses were limited to comparisons between White and all other individuals. While this limitation is not surprising given the relative racial and ethnic homogeneity in our study setting, future work in rural communities should strive to oversample minority communities. Despite these limitations, our research was characterized by several strengths. To our knowledge, this study is the first that examines correlates of transactional sex work among a rural sample of male and female PWID. Another strength of this study is that our sample reflected a large number of rural PWID, a notable accomplishment given the rurality of the study setting and stigma associated with illicit drug use and transactional sex work. Additionally, our recruitment strategy spanned a two-month period and a large number of areas known for drug use, thus enhancing the representativeness of our findings.
In conclusion, addressing risks for HIV infection and overdose experiences among rural PWID in the era of the modern opioid crisis will require novel interventions tailored to the needs of subpopulations of PWID that are most vulnerable, including those who engage in transactional sex work. There is an urgent need for expanded access to harm reduction services and other health and human services among rural PWID that engage in transactional sex work. Addressing the unique needs of this population will require innovation in intervention design to overcome not only issues associated with rurality (e.g., low access to services, geographic dispersion), but also the constellation of vulnerabilities associated with transactional sex work.
Acknowledgements
This research was supported by a Grant from the Bloomberg American Health Initiative at the Johns Hopkins Bloomberg School of Public Health to Dr. Sean T. Allen. This research has been facilitated by the infrastructure and resources provided by the Johns Hopkins University Center for AIDS Research, an NIH funded program (P30AI094189), which is supported by the following NIH Co-Funding and Participating Institutes and Centers: NIAID, NCI, NICHD, NHLBI, NIDA, NIMH, NIA, FIC, NIGMS, NIDDK, and OAR. STA is also supported by the National Institutes of Health (K01DA046234). The funders had no role in study design, data collection, or in analysis and interpretation of the results, and this paper does not necessarily reflect views or opinions of the funders. We are grateful to the collaboration of the Cabell Huntington Health Department, without whom, this project would not have been possible. We are especially grateful to Thommy Hill, Tyler Deering, Kathleen Napier, Jeff Keatley, Michelle Perdue, Chad Helig, and Charles “CK” Babcock for all their support throughout the study implementation. We are also grateful for the hard work of the West Virginia COUNTS! research team: Megan Keith, Anne Maynard, Aspen McCorkle, Terrance Purnell, Ronaldo Ramirez, Kayla Rodriguez, Lauren Shappell, Kristin Schneider, Brad Silberzahn, Dominic Thomas, Kevin Williams, and Hayat Yusuf. We gratefully acknowledge the West Virginia Department of Health and Human Resources. We also wish to acknowledge Josh Sharfstein, Michelle Spencer, Dori Henry, and Akola Francis for their support throughout each phase of the study. Most importantly, we are grateful to our study participants.
References
- 1.Baral S, Beyrer C, Muessig K, et al. Burden of HIV among female sex workers in low-income and middle-income countries: a systematic review and meta-analysis. Lancet Infect Dis. 2012;12(7):538–49. [DOI] [PubMed] [Google Scholar]
- 2.Patterson TL, Semple SJ, Staines H, et al. Prevalence and correlates of HIV infection among female sex workers in 2 Mexico-US border cities. J Infect Dis. 2008;197(5):728–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Platt L, Rhodes T, Judd A, et al. Effects of sex work on the prevalence of syphilis among injection drug users in 3 Russian cities. Am J Public Health. 2007;97(3):478–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Kerrigan D, Wirtz A, Semini I, et al. The global HIV epidemics among sex workers. Washington DC: The World Bank; 2012. [Google Scholar]
- 5.Ulibarri MD, Roesch S, Rangel MG, Staines H, Amaro H, Strathdee SA. “Amar te Duele” (“love hurts”): sexual relationship power, intimate partner violence, depression symptoms and HIV risk among female sex workers who use drugs and their non-commercial, steady partners in Mexico. AIDS Behav. 2015;19(1):9–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.El-Bassel N, Witte SS, Wada T, Gilbert L, Wallace J. Correlates of partner violence among female street-based sex workers: substance abuse, history of childhood abuse, and HIV risks. AIDS Patient Care STDS. 2001;15(1):41–51. [DOI] [PubMed] [Google Scholar]
- 7.Wojcicki JM, Malala J. Condom use, power and HIV/AIDS risk: sex-workers bargain for survival in Hillbrow/Joubert Park/Berea, Johannesburg. Soc Sci Med. 2001;53(1):99–121. [DOI] [PubMed] [Google Scholar]
- 8.McMillan K, Worth H, Rawstorne P. Usage of the terms prostitution, sex work, transactional sex, and survival sex: their utility in HIV prevention research. Arch Sex Beh. 2018;47(5):1517–27. [DOI] [PubMed] [Google Scholar]
- 9.Chatterji M, Murray N, London D, Anglewicz P. The factors influencing transactional sex among young men and women in 12 sub-Saharan African countries. Soc Biol. 2005;52(1–2):56–72. [DOI] [PubMed] [Google Scholar]
- 10.Maganja RK, Maman S, Groves A, Mbwambo JK. Skinning the goat and pulling the load: transactional sex among youth in Dar es Salaam, Tanzania. AIDS Care. 2007;19(8):974–81. [DOI] [PubMed] [Google Scholar]
- 11.Greene JM, Ennett ST, Ringwalt CL. Prevalence and correlates of survival sex among runaway and homeless youth. Am J Public Health. 1999;89(9):1406–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Reuben J, Serio-Chapman C, Welsh C, Matens R, Sherman SG. Correlates of current transactional sex among a sample of female exotic dancers in Baltimore, MD. J Urban Health. 2011;88(2):342–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Paz-Bailey G, Noble M, Salo K, Tregear SJ. Prevalence of HIV among U.S. female sex workers: systematic review and metaanalysis. AIDS Behav. 2016;20(10):2318–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Centers for Disease Control and Prevention. Estimated HIV incidence and prevalence in the United States, 2010–2015. HIV Surveillance Supplemental Report 2018. https://www.cdc.gov/hiv/pdf/library/reports/surveillance/cdc-hiv-surveillance-supplemental-report-vol-23-1.pdf. Accessed Mar 2018. [Google Scholar]
- 15.Morris MD, Lemus H, Wagner KD, et al. Factors associated with pathways toward concurrent sex work and injection drug use among female sex workers who inject drugs in northern Mexico. Addiction. 2013;108(1):161–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Shannon K, Kerr T, Bright V, Gibson K, Tyndall MW. Drug sharing with clients as a risk marker for increased violence and sexual and drug-related harms among survival sex workers. AIDS Care. 2008;20(2):228–34. [DOI] [PubMed] [Google Scholar]
- 17.Strathdee SA, Philbin MM, Semple SJ, Pu M, Orozovich P, Martinez G, et al. Correlates of injection drug use among female sex workers in two Mexico-U.S. border cities. Drug Alcohol Depend. 2008;92(1–3):132–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Tran TN, Detels R, Long HT, Lan HP. Drug use among female sex workers in Hanoi, Vietnam. Addiction. 2005;100(5):619–25. [DOI] [PubMed] [Google Scholar]
- 19.El-Bassel N, Wechsberg WM, Shaw SA. Dual HIV risk and vulnerabilities among women who use or inject drugs: no single prevention strategy is the answer. Curr Opin HIV AIDS. 2012;7(4):326–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Roy E, Richer I, Morissette C, et al. Temporal changes in risk factors associated with HIV seroconversion among injection drug users in eastern central Canada. AIDS. 2011;25(15):1897–903. [DOI] [PubMed] [Google Scholar]
- 21.UNAIDS. UNAIDS guidance note on HIV and sex work. Geneva, Switzerland: UNAIDS; 2012. http://www.unaids.org/sites/default/files/sub_landing/files/JC2306_UNAIDS-guidance-note-HIVsex-work_en.pdf. Accessed 15 Mar 2019. [Google Scholar]
- 22.Decker MR, Wirtz AL, Moguilnyi V, et al. Female sex workers in three cities in Russia: HIV prevalence, risk factors and experience with targeted HIV prevention. AIDS Behav. 2014;18(3):562–72. [DOI] [PubMed] [Google Scholar]
- 23.Van Handel MM, Rose CE, Hallisey EJ, et al. County-level vulnerability assessment for rapid dissemination of HIV or HCV infections among persons who inject drugs, United States. J Acquir Immune Defic Syndr. 2016;73(3):323–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Zibbell JE, Iqbal K, Patel RC, et al. Increases in hepatitis C virus infection related to injection drug use among persons aged ≤ 30 years - Kentucky, Tennessee, Virginia, and West Virginia, 2006–2012. MMWR Morb Mortal Wkly Rep. 2015;64(17):453–8. [PMC free article] [PubMed] [Google Scholar]
- 25.Hedegaard H, Minino AM, Warner M. Drug overdose deaths in the United States, 1999–2017. NCHS Data Brief. 2018;329:1–8. [PubMed] [Google Scholar]
- 26.Allen ST, O’Rourke A, White RH, Schneider KE, Kilkenny M, Sherman SG. Estimating the number of people who inject drugs in a rural county in Appalachia. Am J Public Health. 2019;109(3):445–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.US Census Bureau. 2010 Census Urban and Rural Classification and Urban Area Criteria. US Census Bureau; 2012. [Google Scholar]
- 28.West Virginia Drug Overdose Deaths Historical Overview 2001–2015. Bureau for Public Health, West Virginia Department of Health & Human Resources; 2017. http://dhhr.wv.gov/oeps/disease/ob/documents/opioid/wv-drug-overdoses-2001_2015.pdf. Accessed 15 Mar 2019. [Google Scholar]
- 29.Kersey L. 26 overdoses over 5 hours in one town, but nobody got referred to treatment. Charleston Gazette-Mail. 2017. July 29, 2017. [Google Scholar]
- 30.Nash B. Cabell OD totals down 40% through nine months. The Herald Dispatch. 10/02/2018. [Google Scholar]
- 31.Footer KHA, Park JN, Allen ST, et al. Police-related correlates of client-perpetrated violence among female sex workers in Baltimore city, Maryland. Am J Public Health. 2019;109(2):289–95. [DOI] [PMC free article] [PubMed] [Google Scholar]