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. Author manuscript; available in PMC: 2017 Dec 1.
Published in final edited form as: Drug Alcohol Depend. 2016 Oct 27;169:171–179. doi: 10.1016/j.drugalcdep.2016.10.027

Substance Use and STI Acquisition: Secondary Analysis from the AWARE Study*

Daniel J Feaster, Carrigan L Parish, Lauren Gooden, Tim Matheson, Pedro C Castellon, Rui Duan, Yue Pan, Louise F Haynes, Bruce R Schackman, C Kevin Malotte, Raul N Mandler, Grant N Colfax, Lisa R Metsch
PMCID: PMC5140686  NIHMSID: NIHMS829396  PMID: 27837708

Abstract

Objectives

Sexually transmitted infections (STIs) are significant public health and financial burdens in the United States. This manuscript examines the relationship between substance use and prevalent and incident STIs in HIV-negative adult patients at STI clinics.

Methods

A secondary analysis of Project AWARE was performed based on 5,012 patients from 9 STI clinics. STIs were assessed by laboratory assay and substance use by self-report. Patterns of substance use were assessed using latent class analysis. The relationship of latent class to STI rates was investigated using Poisson regression by population groups at high risk for STIs defined by participant’s and partner’s gender

Results

Drug use patterns differed by risk group and substance use was related to STI rates with the relationships varying by risk behavior group. Substance use treatment participation was associated with increased STI rates

Conclusions

Substance use focused interventions may be useful in STI clinics to reduce morbidity associated with substance use. Conversely, gender-specific sexual health interventions may be useful in substance use treatment.

Keywords: sexually transmitted infections, substance use, substance use treatment, gender differences

1. INTRODUCTION

Despite efforts to promote sexual risk reduction (Centers for Disease Control and Prevention (CDC), 2014; Owusu-Edusei et al., 2013; Satterwhite et al., 2013), sexually transmitted infections (STIs) still remain a significant financial and public health burden in the United States (U.S.). Current estimates indicate that STIs in the U.S. are increasing (Bowen et al., 2015; Patton et al., 2014) with an overall prevalence of 110 million infections and incidence of 20 million new cases per year in the U.S. (CDC, 2013; Satterwhite et al., 2013). Substance users are at increased risk for STIs including HIV (Belani et al., 2012, Des Jarlais, et al., 2011).

Substance using STI clinic patients have demonstrated higher rates of condomless sex, multiple partners and STI diagnoses compared with their counterparts who do not have a history of substance use (Cachay et al., 2004; Cook et al., 2006; Scott-Sheldon et al., 2009). The type of drug used influences the relationship between substance use and sexual behavior, depending on its pharmacological effects and reason for use (Leigh, 2002; Leigh et al., 2008; Rawson et al., 2002, Scott-Sheldon et al., 2009). For example, in one study, only use of amphetamines, but not alcohol, cocaine or marijuana, was associated with decreased condom use (Leigh et al., 2008). Yet in other studies, crack/cocaine use but not heroin use was associated with higher risk behavior scores (Lejuez et al., 2005). Crack cocaine use, in particular, has been found to be associated with increased number of sexual partners (Harzke et al., 2009) and STIs (Ross et al., 2002). Thus evidence points to the importance of type of substance but variability in results point to the potential importance of other factors.

There are gender differences in levels of drug use and drug of choice and the relationships among drug use, sexual risk and STIs. In general, the substance use literature has shown that men are more likely to use substances than women (Cotto et al., 2010, Huang et al., 2006) and men show higher rates of abuse and dependence of alcohol and marijuana, however, women have higher rates of dependence on cocaine and psychotherapeutics (pain relievers, stimulants, tranquilizers and sedatives; Cotto et al., 2010). Crack/Cocaine use and dependence has been found to be particularly elevated for inner-city female substance users relative to males (Lam et al., 2004, Lejuez et al. 2007). Gender power imbalances, such as intimate partner violence, and specific types of drug markets associated with specific sexual networks, such as crack and heroin markets, have shown to be associated with differentially heightened STI risks that are both sexual- and drug-related (Decker et al., 2014; Jennings et al., 2013).

The relationship between substance use and sexual risk is further compounded by evidence that level of drug use and type of drug used, sexual risk behavior, STIs and their interrelationships differ not only by gender but also by partner gender and that STI transmission rates vary by contact type and partnership networks (Adams et al., 2013; Kopetz et al., 2014). Both men who have sex with men (MSM) and women who have sex with women (WSW) have higher rates of substance use than their heterosexual counterparts (Cochran et al., 2004, McCabe et al., 2009) and the relationship between particular substances used and sexual risk varies across gender and partner gender (LaBrie et al., 2005; Leigh et al., 2008; Scott-Sheldon et al., 2009). A 2016 study found that heavy alcohol use is associated with multiple partners for women and men who have sex with men (MSM) only, but not for heterosexual men, demonstrating how partner gender may impact the extent to which specific drugs increase sexual risk behaviors (Carey et al., 2016). A literature review exclusive to MSM showed that binge drinking and methamphetamine use are consistently associated with risky sexual behaviors and concluded that not enough research had been conducted with other substance types (Vosburgh et al., 2012). Therefore, while prior studies have studied the association between substance use and STI incidence within a few particular subgroups defined by type of drug, STI, and/or gender/partner gender, few studies have investigated across multiple types of substances used, gender and partner gender and risk of STIs in a single analysis. It is within this context that the objective of this study was to assess the relationship across subgroups of substance classes and sexuality groups to identify gender-specific and substance-specific risk factors associated with STI prevalence and incidence.

In this secondary analysis, we evaluate the effect of substance use patterns on STI acquisition and hypothesize that 1) substance use patterns will differ by gender and partner gender, 2) more severe substance use patterns will be associated with higher STI prevalence at baseline and 6-month cumulative incidence of STIs and 3) there will be differences in the relationship between patterns of substance use and STIs across gender and partner gender. Compared to other types of substance use, we expect methamphetamine and club drugs to have higher impact for MSM (Pappas and Halkitis, 2011; Senn et al., 2009; Shoptaw, 2006; Shoptaw and Reback, 2007; Theodore et al., 2014; Vosburgh et al., 2012), and marijuana, alcohol and cocaine, particularly crack cocaine, to have higher impact in women and men who have sex with women (MSW; Cavanaugh et al., 2011; Kopetz et al., 2014; Lejuez et al., 2007). We also examine the impact of participation in substance use treatment on STIs.

2. METHODS

2.1 Participants

Project AWARE (Metsch et al., 2013), recruited 5,012 patients seeking services from STI clinics in nine U.S. cities between April and December, 2010. After baseline behavioral assessment and STI testing, patients were randomized to either: (1) HIV testing with brief risk reduction counseling, or (2) a control condition where patients received HIV testing with information only about HIV and HIV testing. Six months later, participants returned for repeated assessment and STI testing. The study was approved by the Institutional Review Boards at each site. All participants provided written informed consent prior to study activities and were compensated up to $90 for study participation. Eligibility criteria included being: (1) 18 years or older, (2) able to communicate in English, (3) HIV-negative or unknown HIV status, (4) able to provide contact information and consent for STI and HIV testing, HIPAA and/or medical record releases, and (5) able to confirm they had no plans to leave the area for 6-months.

2.2 Measures

For the present analysis, participants were classified into four risk behavior groups: MSM, MSW, women who have sex with men only (WSM) and women who have sex with women (WSW). Transgender individuals were grouped with males if they had a penis due to the similarity of possible behaviors. Males who reported episodes of anal or oral sex with another male were classified as MSM. All other males were classified as MSW. Similarly, women who reported sex with women were classified as WSW.

Specimens for STI testing were collected at baseline and 6-months. Urine samples from MSM and MSW, rectal samples from MSM, and cervical/vaginal samples from female participants were collected and tested for Neisseria gonorrhea (GC) and Chlamydia trachomatis (CT) in all participants and Trichomonas vaginalis (TV) in women. Blood serum samples were collected from all participants for testing syphilis and Herpes Simplex-2 (HSV-2, tested by Elisa, confirmed by Western blot). Finger-stick blood was used for HIV rapid testing.

History of STI in the 6 months before baseline was assessed by self-report. A participant with a positive baseline laboratory STI test was considered to have a prevalent STI. Cumulative 6-month STI incidence was ascertained by laboratory STI tests at follow-up and medical record abstraction for only the STIs listed above. Participants positive for STIs at baseline were considered an incident case only if their positive diagnosis occurred after adequate treatment. Incident HSV-2 and/or HIV required the patient be negative at baseline.

Self-reported sexual risk and substance use behaviors over the prior 6-months were collected at baseline using an Audio Computer-Assisted Self-Interview (ACASI). Sexual behavior was assessed through query about participants’ vaginal and/or anal sexual episodes and partners with and without condoms. Drug use severity was measured by the Drug Abuse Screening Test (DAST-10; Yudko et al., 2007). Moderate and substantial severity were defined by DAST-10 score equal or greater than three but less than six, and equal or greater than six, respectively. Alcohol misuse was categorized as moderate or severe based on the Alcohol Use Disorders Identification Test (AUDIT-C), with scores equal or greater than 3 and less than 6 (in women) or equal or greater than 4 and less than 7 (in men) considered moderate and scores greater than 6 or 7 considered severe. Participants were asked to indicate, from a given list of specific substances, which drugs they had used in the prior six months, then about their frequency of use in the prior six months. Frequency of use was asked as none, less than once a month, 1–3 times a month, once a week, more than once a week but less than daily, and daily. These substances included: amphetamines, cocaine (separately for powder and crack form), MDMA (ecstasy), Ketamine (Special K), GHB and inhalants, heroin and pain pills, hallucinogens, PCP, tranquilizers/barbiturates and marijuana (with or without prescription). Participants were also asked to indicate if they had injected any of these drugs in the prior six months. The higher categories of frequency of use were subsequently combined to weekly or more to create four-ordered categories for the latent-class analysis. Uptake of substance use treatment in the 6-months prior to baseline was asked separately for alcoholics or narcotics anonymous (AA/NA) or professional treatment (including outpatient, inpatient, and detoxification).

2.3 Analyses

Descriptive tables are stratified by risk behavior group (MSM, MSW, WSM and WSW). Substance use was analyzed using latent class analysis (LCA) in Mplus V7.4 (Muthén and Muthén, 2012). In this analysis, MSW, MSM and women were treated as multiple groups. WSW were not treated as a separate group in the latent class due to the smaller number of WSW. WSW status was used as a covariate to predict latent class membership, thus allowing the proportions in each class to differ between WSM and WSW. Analyses with different numbers of classes were compared and the solution with the smallest Adjusted Bayesian Information Criteria (aBIC; Sclove, 1987) was chosen (Dziak et al., 2014). Invariance in thresholds was tested to determine whether the proportions of different drugs within a class varied across risk groups. Individual’s probabilities of class membership were used to take 20 pseudo-class draws (Bandeen-Roche et al., 1997; Wang et al., 2005) from each individual’s multinomial distribution using SAS 9.3. The following analyses were run 20 times, once for each pseudo-class draw, and combined using Rubin’s combining rules for multiple imputation (Li et al., 1991; Rubin, 1987) to account for the uncertainty of class membership and ensure appropriate standard errors. STI rates were directly estimated using Poisson regression with a log link function to examine risk ratios as well as risk levels (Barros and Hirakata, 2003). Unadjusted models were estimated to test for simple risk differences by risk behavior group for prevalent and incident STI as a function of substance use class. Adjusted risk differences controlled for age, race/ethnicity, education, marital status, site of recruitment, randomized treatment, number of unprotected sex acts and unprotected partners. Rates of substance use treatment by substance use class and the relationship of treatment to STI rates are reported using three categories: no treatment, attendance at alcoholics anonymous or narcotics anonymous (AA/NA) meetings or use of professional substance use treatment (with possible concurrent AA/NA attendance). The Benjamini-Hochberg procedure (Benjamini and Hochberg, 1995) is used to control the family-wise error rate to .05 within each hypothesis.

3. RESULTS

3.1 Sample Characteristics

The sample of 5,012 patients were 45.1% black, 31.8% white, and 15.3% Hispanic (Table 1). The majority of the sample (61.5%) had more than a high school education, while approximately one-quarter (25.8%) had a high school education and 12.7% had less than a high school education. Almost three-quarters (74%) were single. Over one-third (36.7%) of the sample was in the lowest income bracket ($0–5,000), and also over one-third (35.0%) was in the second lowest income bracket ($5,001–20,000). The overall sample also included MSW (38.1%), MSM (27.9%), WSM (29.8%) and WSW (4.2%). Table 1 shows all of the sample characteristics for these four groups.

Table 1.

Sample Characteristics by Gender and Partner Gender

Demographics: MSW Only MSM WSM WSW Total
n=1908 n=1400 n=1492 n=212 n=5012
n % n % n % N % n %
Race/Ethnicity χ2(9) = 757.78 p < .001
 Hispanic 167 8.8 409 29.2 156 10.5 34 16.0 766 15.3
 Black 1076 56.4 225 16.1 873 58.5 85 40.1 2259 45.1
 White 539 28.2 602 43.0 368 24.6 83 39.2 1592 31.7
 Other 126 6.6 164 11.7 95 6.4 10 4.7 395 7.9
Education χ2(6) = 329.78 p < .001
 < High School 322 16.9 70 5.0 216 14.5 28 13.2 636 12.7
 High School 623 32.7 204 14.6 424 28.4 43 20.3 1294 25.8
 > High School 963 50.5 1126 80.4 852 57.1 141 66.5 3082 61.5
Marital Status1 χ2(6) = 119.01 p < .001
 Married or Partnered 271 14.2 221 15.8 175 11.8 30 14.2 697 13.9
 Divorced/Sep./Wid. 298 15.6 60 4.3 221 14.9 24 11.3 603 12.1
 Single 1338 70.2 1117 80.0 1092 73.4 158 74.5 3705 74.0
Income Category2 χ2(4) = 566.49 p < .001
 $0–$5,000 767 41.0 271 19.7 674 46.5 85 40.7 1797 36.7
 $5,001–$20,000 661 35.3 416 30.3 550 37.9 88 42.1 1715 35.0
 $20.001–40,000 286 15.3 330 24.0 162 11.2 26 12.4 804 16.4
 $40,001 and up 157 8.4 356 25.9 64 4.41 10 4.78 587 12.0
Age [Mean | SD]3 34.2 (12.1) 31.0 (10.0) 30.4 (10.6) 28.8 (8.0) 32.0 (11.1)
Unprotected Partners [Mean I SE]4 1.6 (0.1) 3.26 (0.1) 1.6 (0.1) 2.0 (0.2) 2.1 (0.04)
Unprotected Sex Acts [Mean I SE]4 25.7 (1.2) 16.8 (0.9) 24.7 (1.29) 36.3 (5.0) 23.3 (0.7)
Sexually Transmitted Infections5 Each p < .001
 Self-Report prior 6 Months 354/1908 18.6 332/1399 23.7 409/1491 27.4 45/212 21.2 1140/5010 22.8
 Prevalent 718/1874 38.3 504/1292 39.0 829/1455 57.0 90/210 42.9 2141/4831 44.3
 Incident 93/1518 6.1 174/1134 15.3 194/1245 15.6 15/174 8.62 476/4071 11.7
Substance Use6: n=1901 n=1392 n=1469 n=211 n=4973
 Any Drug Use 1137 59.8 765 55.0 726 49.4 153 72.5 2781 55.9
 Any Drug excl. Marijuana 531 27.9 487 35.0 349 23.7 99 46.9 1466 29.4
 Heroin 28 1.5 15 1.1 22 1.5 8 3.8 73 1.5
 Crack Cocaine 156 8.2 45 3.2 106 7.2 26 12.3 333 6.7
 Powder Cocaine 164 8.6 198 14.2 101 6.9 31 14.7 494 9.9
 Amphetamines 39 2.1 118 8.5 29 2.0 10 4.7 196 3.9
 Ecstasy 117 6.2 179 12.9 87 5.9 36 17.1 419 8.4
 Hallucinogens 58 3.1 78 5.6 30 2.0 25 11.9 191 3.8
 Ketamine 13 0.7 40 2.9 9 0.6 8 3.8 70 1.4
 GHB 4 0.2 91 6.5 4 0.3 5 2.4 104 2.1
 Inhalants 7 0.4 253 18.2 3 0.2 4 1.9 267 5.4
 Pain Pills 110 5.8 100 7.2 74 5.0 34 16.1 318 6.4
 Tranquilizers 46 2.4 65 4.7 54 3.7 21 10.0 186 3.7
 PCP 4 0.2 3 0.2 2 0.1 5 2.4 14 0.3
Substance Use Severity:
 Current Injectors (0–6 mos)7 107/1908 5.6 86/1398 6.2 87/1491 5.8 25/212 11.8 305/5009 6.1
 Alcohol Problem Severity χ2(6) = 103.07 p < .001
  Moderate 445/1898 23.5 358/1391 25.7 463/1484 31.2 99/212 46.7 1396/4985 27.4
  Substantial 274/1898 14.4 160/1391 11.5 106/1484 7.1 26/212 12.3 566/4985 11.4
 Drug Use Severity χ2(6) = 67.13 p < .001
  Moderate 340/1884 18.1 203/1371 14.8 148/1453 10.2 32/211 15.2 723/4919 14.7
  Substantial 211/1884 11.2 113/1371 8.2 156/1453 10.7 40/211 19 520/4919 10.6
 Substance Use Treatment: χ2(6) = 69.90 p < .001
  AA/NA Only 103/1907 5.4 52/1397 3.7 50/1490 3.4 7/212 3.3 212/5006 4.2
  Professional Treatment8 225/1906 11.8 66/1397 4.7 176/1490 11.8 27/212 12.7 494/5006 9.9
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1

Sample sizes vary due to missing data: MSW N=1907, MSM N=1398, WSM N=1488, WSW N=212

2

Sample sizes vary due to missing data: MSW N=1871, MSM N=1373, WSM N=1450, WSW N=209

3

F(3,5008)=46.65, p<.001

4

Estimates are from a negative binomial model and the appropriate measure of dispersion is the standard error. Unprotected Sex Acts: χ2(3)=52.42, p<.001, Unprotected Partners χ2(3)=290.68, p<.001

5

Self-Report: χ2(3) =38.75, Prevalent: χ2(3) = 136.78, Incident: χ2(3) = 80.02, Repeated: χ2(2) = 107.43

6

All substance use comparisons are significant with p<.001

7

χ2(3) = 13.01, p<.005

8

May also participate in AA/NA

STI data showed that 22.8% of the overall sample self-reported an STI in the six months prior to baseline. Furthermore, 44.3% had a confirmed prevalent STI at baseline and 11.7% presented with a cumulative incident STI.

Overall, 55.9% of all participants reported having used at least one drug (not including alcohol), 29.4% reported having used a drug other than marijuana, and 6.1% of the sample reported injecting drugs within the last six months. Use of each drug differed significantly by risk behavior group. Generally, WSM were the least likely to report using drugs, while WSW, followed by MSM, were most likely to report drug use other than marijuana. MSM had much lower crack cocaine use than MSW and WSM; WSW showed the highest level of crack use. Overall attendance at substance use treatment was low in the sample, with 4.2% reporting AA/NA and 9.9% reporting some professional treatment.

3.2 Hypothesis 1—Latent Classes of Substance Use

A four class solution had the lowest aBIC score and an entropy score of .91, indicative of good class separation. The class measurement across risk groups (MSW, MSM, WSM and WSW) was significantly different (χ2(320)=1188.26, p < .001); however, there were very common patterns of use across risk groups within classes allowing similar interpretation of the classes across groups. Figure 1 shows the four classes with each risk group plotted within each class plot. Panel a) shows the Low Use substance use class (SUC) with a slightly more than 20% probability of marijuana use and for MSM a small probability of ecstasy or inhalant use. Panel b) shows the Mostly Marijuana SUC in which nearly all members are marijuana users and around 60% show at least moderately severe alcohol and drug use problems. There is also some use of powder cocaine and club drugs in this class. Panel c) is the Severe Club SUC with high levels of powder cocaine, ecstasy, GHB, and hallucinogens (all frequently used when attending dance clubs) as well as pain pills and tranquilizers/sedatives. The final panel, d) shows the Severe Street SUC in which injection, heroin and crack cocaine are at their highest of all the classes. Nearly all in this final class show at least moderate drug use severity. Across these classes, patterns of use for MSW and all women (WSM and WSW) are quite similar. MSM, in contrast, show elevated levels of use of many of the substances within each class. There are also quantitative differences in the proportions of each risk group in each class in addition to these qualitative differences among classes. Table 2 shows the proportion of each risk group in each of the classes which differed significantly overall and between all pairwise comparisons of risk group (p < .001, all significant after multiple testing correction). WSW have the lowest proportion in the Low Use SUC and the highest proportion in each of the other SUCs. MSW, WSM and WSW have higher proportions in the Severe Street SUC than do MSM.

Figure 1. Profiles of Substance Use within Classes.

Figure 1

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Lines plot the probability any use of the particular substance (or type of use for Injectors and Mod-Sev Dast)within each of the four plots. MSM are represented by the darkest line and WSM+WSW are represented by the lightest line.

Table 2.

Substance Use Class Membership by Risk Groups1

MSW MSM WSM WSW Totals
Substance Use Class (SUC) Count % Count % Count % Count % Count %
Low Use 1144.7 60.0 927.3 66.3 1001 67.1 80.2 37.8 3153.2 62.9
Mostly Marijuana 476.8 25.0 322.9 23.1 297.5 19.9 75.4 35.6 1172.6 23.4
Severe Club Use 63.6 3.3 74.6 5.3 42.98 2.9 32.0 15.1 213.1 4.3
Severe Street Use 222.9 11.7 74.3 5.3 150.5 10.1 24.4 11.5 472.1 9.4
Totals 1908 1399 1492 212 5011
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1

Counts are based on the sum of posterior probabilities of class membership across the sample.

3.3 STIs

Table 3 shows the rates of prevalent and incident STIs by substance use class and risk group in both unadjusted and adjusted models (See Supplemental Table 11 for statistical significance of model terms). In general, adjusting for demographics and sexual risk decreases predicted STI rates at least slightly for all groups other than MSM where adjustment increases rates in nearly all STI/SUC combinations. To address hypotheses 2 and 3 we examined both contrasts of different SUCs across and within risk group.

Table 3.

Rates of Sexually Transmitted Infections by Substance Use Class Membership and Risk Groups

MSW MSM WSM WSW
STI Rates Unadjusted Adjusted UnAdjusted Adjusted UnAdjusted Adjusted UnAdjusted Adjusted
 SUC % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI)
Baseline Prevalence
 Low Use 39.7 (36.9, 42.7) 28.3 (25.7, 31.2) 35.8 (32.7, 39.3) 45.9 (41.2, 51.2) 55.4 (52.2, 58.7) 42.8 (39.3, 46.7) 34.0 (24.5, 47.1) 30.2 (22.0, 41.6)
 Marijuana 31.4 (27.1, 36.4) 25.3 (21.7, 29.6) 41.5 (36.0, 47.9) 57.1 (48.6, 66.9) 53.0 (46.7, 60.2) 46.7 (41.0, 53.1) 37.9 (27.3, 52.6) 41.6 (29.9, 57.9)
 Severe Club 23.1 (14.2, 37.4) 25.9 (15.1, 44.3) 51.3 (40.5, 65.1) 74.0 (55.4, 98.7) 43.4 (28.7, 65.5) 49.8 (33.1, 74.9) 40.2 (25.5, 63.4) 46.9 (26.8, 82.3)
 Severe Street 49.3 (42.4, 57.2) 28.9 (24.3, 34.4) 52.2 (40.7, 66.8) 49.4 (39.1, 62.4) 81.3 (74.2, 89.1) 54.6 (48.5, 61.4) 90.2 (77.6, 100.0) 61.7 (50.8, 74.9)
6-Month Cumulative Incidence
 Low Use 6.1 (4.6, 8.) 4.2 (3.0, 5.9) 13.7 (11.4, 16.4) 14.8 (11.3, 19.3) 13.9 (11.7, 16.6) 9.2 (7.0, 12.1) 4.9 (1.3, 18.9) 4.2 (1.1, 16.2)
 Marijuana 7.7 (5.1, 11.7) 4.9 (3.0, 8.1) 16.6 (12.3, 22.4) 20.0 (13.9, 28.9) 8.8 (2.1, 37.2) 5.8 (1.0, 35.2) 9.2 (2.5, 33.9) 11.0 (2.8, 42.5)
 Severe Club 4.5 (1.0, 20.6) 5.5 (1.2, 24.3) 28.7 (18.9, 43.6) 35.5 (22.0, 57.2) 17.4 (13.0, 23.2) 12.8 (9.0, 18.1) 8.9 (3.7, 21.4) 8.2 (3.4, 20.0)
 Severe Street 3.4 (1.3, 8.7) 2.3 (0.8, 7.0) 17.0 (8.7, 33.3) 16.6 (7.3, 37.5) 23.0 (16.3, 32.4) 19.6 (13.1, 29.3) 17.1 (5.9, 50.1) 7.6 (1.3, 46.)
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3.3.1 Hypothesis 2—Severity of Substance Use and STI Rates across Risk Groups

All comparisons with p<.0083 were statistically significant after multiple testing correction. The Severe Street SUC was associated with significantly higher rates of prevalent STIs (RR=1.63, (1.44, 1.84); aRR=1.30, (1.15, 1.47)) relative to the Low Use SUC controlling for risk group. In the unadjusted model for prevalent STIs, Severe Street SUC had significantly higher rates of STI relative to both the Severe Club SUC (RR=174, (1.39, 2.18))) and the Mostly Marijuana SUC (RR=1.64, (1.42, 1.99)) controlling for risk group.

3.3.2 Hypothesis 3—SUC Differences within Risk Group

All contrasts with p < .015 were statistically significant after multiple testing correction. MSM within the Severe Club SUC relative to the Low Use SUC were at elevated risk for prevalent (RR=1.43, (1.11, 1.85), aRR=1.61, (1.20, 2.17)) and incident STIs ((RR=2.10, (1.34, 3.31.85), aRR=2.41, (1.49, 3.88)). WSM within the Severe Street SUC relative to WSM in the Low Use SUC were also at elevated risk for prevalent (RR=1.47, (1.32, 1.63), aRR=1.27, (1.14, 1.43)) and incident STIs (RR=1.65, (1.12, 2.43), aRR=2.12, (1.36, 3.31)). WSW in the Severe Street SUC relative to WSW in the Low Use SUC had elevated prevalent (RR=2.66, (1.84, 3.83), aRR=2.04, (1.42, 2.93)). MSW in the Severe Street SUC had significantly higher risk of prevalent STIs in unadjusted analyses relative to MSW in Severe Club SUC (RR=2.14, (1.29, 3.55), the Mostly Marijuana SUC (RR=1.57 (1.26, 1.96)) and the Low Use SUC (RR=1.24, (1.05, 1.47)). There were no significant differences in incident STIs across SUCs for MSW. This same pattern of unadjusted prevalent STI rates held for both WSM and WSW (Supplemental Table 22). Supplemental Table 33 shows risk ratios among risk groups within SUCs. Within the Severe Street SUC both MSM and MSW have lower rates of STIs than do WSW and WSM.

3.4 Substance Use Treatment

The proportion of participants reporting substance use treatment increased with the increasing severity of the SUC (Supplemental Table 44). Rates of professional treatment were high within the Severe Street SUC for WSM (58.3%) and WSW (70.8%). Rates of substance use treatment in the Severe Club SUC were lower (12.1% across gender and partner gender). MSM had the lowest rates of professional substance abuse treatment within the Severe Club (10.0%) and Severe Street (30.7%) SUCs. Adding substance use treatment as a predictor of STI rates (Table 4) showed that the No Treatment group had significantly lower rates of prevalent STIs than either the AA/NA only group (RR=0.79, (0.69, 0.90)) or the professional treatment group (RR=0.85, (0.77, 0.94)).

Table 4.

Rates of Sexually Transmitted Infections by Substance Use Treatment

Prevalent Incident
Substance Use Treatment Unadjusted1 Adjusted Unadjusted1 Adjusted
 Category % (95%CI) % (95%CI) % (95%CI) % (95%CI)
No Treatment 42.7 (40.0, 45.7) 41.7 (38.2, 45.6) 10.2 (8.2, 12.7) 8.2 (6.1, 11.0)
AA/NA 54.2 (47.6, 61.7) 44.4 (38.2, 51.7) 11.8 (8.1, 17.4) 12.9 (8.5, 19.6)
Professional Treatment 50.4 (45.6, 55.7) 43.5 (38.7, 48.8) 11.8 (8.8, 16.0) 10.3 (7.1, 14.9)
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1

Table 4 is based on most likely class assignment for each participant rather than the sum of posterior probabilities (Table 2 enumerates the classes based on posterior probabilities). These two approaches to assigning class membership do vary in counts per class.

4. DISCUSSION

Substance use was high in this sample of STI patients from 9 STD clinics throughout the U.S. Over half of the sample reported use of any drugs and 29.4% reported using drugs other than marijuana. Over one quarter showed moderate to severe levels of substance use severity. These rates are considerably higher than the general population; namely the 2013 National Survey on Drug Use and Health (Substance Abuse Mental Health Services Administration, 2014) reported that 16% of the U.S. population over 12 years of age reported any illicit drug use in the last year (8.2% illicit drugs other than marijuana). Substance use treatment in the sample was low as is true nationally (Compton et al., 2007). Among individuals in the Severe Street class, substance use treatment was higher but still only around 50%. MSM had particularly low rates of treatment.

Our latent class analysis confirmed our hypothesis that across different risk groups, based on gender and partner-gender, there are different patterns of substance use: WSW had the highest proportions of all risk groups in both the Severe Club SUCs. MSM had the lowest proportion of all risk groups in the Severe Street and the highest in the Severe Club SUCs. There was little difference in proportion of MSW, WSM, and WSW 10.1% to 11.7 % in the Severe Street SUC (10.1%–11.7%).

Hypothesis 2, predicting an association between more severe substance use patterns and higher STI rates, was confirmed with Severe Street SUC having significantly higher prevalent STIs controlling for risk group; however, Hypotheses 3 showed that this pattern differed across risk group with MSM in the Severe Club SUC showing elevations in both STI measures relative to MSM in the Low Use SUC. Higher levels of STIs were seen in Severe Street SUC relative to the Low Use SUC for both WSM (prevalent and incident) and WSW (prevalent). MSW showed the least relationship between substance use and STIs with only unadjusted prevalent STIs being different in the Severe Street SUC than in all other SUCs. STIs were also lowest in MSW.

Much research has pointed to how use of a particular substance within a particular risk group is related to sexual risk behavior or STIs (Logan et al., 1998; Nuttbrock et al., 2000; Pappas and Halkitis, 2011; Shoptaw, 2006; Shoptaw and Reback, 2007; Stahlman et al., 2013; Theodore et al., 2014; Vosburgh et al., 2012; Wechsberg et al., 2015). The current investigation was able to compare and statistically test differences among four risk-groups—MSM, MSW, WSM and WSW—across empirically determined SUCs in their risk for a uniformly tested battery of STIs. Overall, substance use was related to STIs, but this differed by risk behavior groups suggesting that interventions might differentially target these groups, delineated by gender and partner gender, depending on their substance use patterns. For WSM and WSW, and a lesser extent MSW, the Severe Street SUC typified by high levels of crack cocaine use was the primary risk for STIs, whereas for MSM the Severe Club SUC typified by powder cocaine, methamphetamine, ecstasy, hallucinogens as well as pain pill use showed elevated risk. These relationships were apparent even after controlling for the level of sexual risk, suggesting that the increased risk may work through drug users’ sexual networks rather than changes in risky behaviors (Schneider et al., 2013; Tobin et al., 2011). Use of club drugs and methamphetamines is particularly high in MSM, and has been associated with heightened disease transmission (Cheng et al., 2010; Colfax and Guzman, 2006; Green and Halkitis, 2006; Mansergh et al., 2006; Purcell et al., 2005; Semple et al., 2011; Thiede et al., 2009; Wohl et al., 2008; Zuckerman and Boyer, 2012). Furthermore, previous studies have documented high crack cocaine and other cocaine use and dependence among women compared to men, and associated this with high levels of STIs/HIV due to factors like trading sex tfor money or drugs with anonymous or recently incarcerated partners (Absalon et al., 2006; Cavanaugh et al., 2011; Kopetz et al., 2014; Lejuez et al., 2007; Logan et al., 1998; Nuttbrock et al., 2000; Stahlman et al., 2013). Tailored interventions can specifically target women who use drugs by addressing the proximal and distal factors that contribute to women’s drug use, engagement in sex trade, and HIV risk, including childhood abuse, homelessness and unemployment (Edwards et al., 2006; El-Bassel and Strathdee, 2015; Wechsberg et al., 2015).

Substance use treatment, particularly AA/NA, was associated with higher level of STIs controlling for class of substance use indicating sustained risk of STIs after entering treatment or lack of attention to STIs in treatment settings. In light of these findings, it may be useful to improve sexual behavioral interventions and modify the way in which sexual health is addressed within the context of substance use treatment, certainly for women and MSM who had higher STI rates than MSW. This may likely include the integration of targeted repeated HIV and STI testing services and gender-specific interventions that address sexual risk reduction and sexual health in substance use treatment given the association between risk behaviors and substance use (Calsyn et al., 2009; Tross et al., 2008; Wechsberg et al., 2015).

4.1 Limitations

These results should be interpreted in light of several limitations. First, clinics were not randomly selected and findings may not be generalizable to all STI clinics in or outside the U.S. Second, we did not assess any pharyngeal STIs nor rectal infections in female participants. Third, risk remained after controlling for level of sexual risk indicating that at least part of the increased risk of STIs is due to elevated risk of substance users’ sexual networks; data are not available to explore this further. Fourth, we had relatively few WSW in the sample and confidence intervals for their risk levels are quite wide. Fifth, these are observational relationships as participants were not randomly assigned to substance use treatment. Finally, other studies have analyzed sexual orientation by three dimensions – identity, attraction, and behavior – and shown that risk for substance misuse is not uniform across all sexual minorities but instead varies based upon how sexual orientation is defined (McCabe et al., 2009; McCabe et al., 2013); we only have data on behavior.

4.2 Public Health Implications

Studies have shown that efforts to facilitate behavior change in STI clinics through risk reduction counseling, such as promoting use of protection and monogamy, are often limited and ineffective (Anderson et al., 2013; Erbelding et al., 2004; Lin et al., 2008; Proude et al., 2004). Many patients are returning with reoccurring STIs, indicating that there are likely underlying, unmet needs and comorbidities, often related to their gender, partner gender, and substance type, which must also be addressed through targeted screening and interventions to promote behavior change and limit STI incidence and reoccurrence (Erbelding et al., 2004; Newman et al., 2006; Senn et al., 2010). STI clinics are potentially important settings for identifying and treating patients with substance use issues when the context of each patient’s environment is considered (Scott-Sheldon et al., 2009). However, our data also indicate that specific attention to sexual health may be necessary to reduce STI risk during the substance use treatment course.

Substance use screening, referral and linkage for substance use treatment (Madras et al., 2009) may be useful in this setting in light of our finding that only 9.9% of our sample reported current or recent professional substance use treatment, despite high reported substance use. The data presented herein would be of use in planning this type of research and tailoring screening instruments to the client’s context. Of note, however, the ASPIRE randomized controlled trial showed that brief negotiated interviewing and motivational interviewing were not effective in reducing drug use among primary care patients identified by SBIRT programs (Saitz et al., 2014, see also Saitz, 2014 ). Therefore, further research is needed to evaluate more targeted SBIRT and targeted intervention approaches specific to this STI setting and these particularly vulnerable subpopulations where incidence of substance use and risk for STI acquisition is high (Gunn et al., 2000; Newman et al., 2006; Richert et al., 1993). Specifically, the beneficial impact of such prevention and treatment services on STI incidence rates and repeated STI diagnoses need to be assessed, in light of feasibility, cost-effectiveness, and lack of evidence supporting its effectiveness specific to this potentially resource-limited venue.

Supplementary Material

supplement

HIGHLIGHT.

  • We studied relationships between drug use and prevalent and incident sexually transmitted infections (STIs).

  • Substance use was related to STI rates and varied by gender and partner gender.

  • Participation in substance use treatment was associated with increased STI rates.

  • Focused interventions may be useful in STD clinics to reduce drug-related morbidity.

  • Gender-specific sexual health interventions may be useful in substance use treatment.

Acknowledgments

Role of Funding: This work was supported by the Office of the Director, National Institutes of Health (OD) (RC2DA028973), and the National Institute on Drug Abuse (R21DA038641 and R01DA027379). The infrastructure of the National Drug Abuse Treatment Clinical Trials Network was used as a platform in conducting the Project AWARE trial (U10DA13720). Support from the University of Miami Center for AIDS Research (CFAR) is also acknowledged (P30 AI073961). The NIH had no further role in study design, in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

None to report.

Footnotes

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Conflict of Interest: None of the authors have declared any conflict of interest.

Contributors: Dr. Feaster conceptualized the study, analyzed the data and contributed to the overall writing and editing of the manuscript. He had full access to all data and participated in the design, execution, and analysis of the article. Dr. Parish contributed to the overall writing, editing and submission of the manuscript. Dr. Gooden, Dr. Matheson, Mr. Castellon, Mr. Duan, Mr. Pan, Ms. Haynes, Dr. Schackman, Dr. Malotte, Dr. Mandler, and Dr. Colfax all contributed to the editing of the manuscript. Dr. Metsch was a principal investigator for Project Aware (with Drs. Colfax and Feaster), conceptualized the study and contributed to the writing of the manuscript. All authors have reviewed and approved of the final manuscript.

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