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. Author manuscript; available in PMC: 2018 Apr 1.
Published in final edited form as: Drug Alcohol Depend. 2017 Feb 2;173:107–116. doi: 10.1016/j.drugalcdep.2016.12.023

Correlates of willingness to initiate pre-exposure prophylaxis and anticipation of practicing safer drug- and sex-related behaviors among high-risk drug users on methadone treatment*

Roman Shrestha a,b,*, Pramila Karki b,c, Frederick L Altice b,d, Tania B Huedo-Medina b,c, Jaimie P Meyer d, Lynn Madden f, Michael Copenhaver b,c
PMCID: PMC5366273  NIHMSID: NIHMS849283  PMID: 28214391

Abstract

Background

Although people who use drugs (PWUD) are key populations recommended to receive pre-exposure prophylaxis (PrEP) to prevent HIV, few data are available to guide PrEP delivery in this underserved group. We therefore examined the willingness to initiate PrEP and the anticipation of HIV risk reduction while on PrEP among high-risk PWUD.

Methods

In a cross-sectional study of 400 HIV-negative, opioid dependent persons enrolled in a methadone program and reporting recent risk behaviors, we examined independent correlates of being willing to initiate PrEP.

Results

While only 72 (18%) were aware of PrEP, after being given a description of it, 251 (62.7%) were willing to initiate PrEP. This outcome was associated with having neurocognitive impairment (aOR=3.184, p=0.004) and higher perceived HIV risk (aOR=8.044, p<0.001). Among those willing to initiate PrEP, only 12.5% and 28.2%, respectively, indicated that they would always use condoms and not share injection equipment while on PrEP. Consistent condom use was associated with higher income (aOR=8.315, p=0.016), always using condoms with casual partners (aOR=6.597, p=0.001), and inversely associated with ongoing drug injection (aOR=0.323, p=0.027). Consistent safe injection, however, was inversely associated with age (aOR=0.948, p=0.035), ongoing drug injection (aOR=0.342, p<0.001), and perceived HIV risk (aOR=0.191, p=0.019).

Conclusions

While willingness to initiate PrEP was high and correlated with being at elevated risk for HIV, anticipated higher risk behaviors in this group even while on PrEP suggests that the next generation of HIV prevention approaches may need to combine biomedical and behavioral components to sustain HIV risk reduction over time.

Keywords: Pre-Exposure prophylaxis, people who use drugs (PWUD), substance use, methadone maintenance program, HIV prevention, risk compensation

1. Introduction

From the outset of the HIV epidemic, substance use disorders, including the injection of drugs, has fueled HIV transmission and disease progression (Degenhardt et al., 2013; Kamarulzaman and Altice, 2015). Despite people who use drugs (PWUD) contributing less to HIV incidence in the U.S. recently (Centers for Disease Control and Prevention, 2014), they remain a priority population for HIV prevention because of potential HIV transmission associated with preventable drug-related (e.g., needle-sharing) and sex-related (e.g., condomless sex) risk behaviors (Alipour et al., 2013; Marshall et al., 2014; Nadol et al., 2016; Volkow and Montaner, 2011). PWUD are affected by multi-level barriers to treatment and prevention such as stigma, discrimination, and social marginalization, thus posing a formidable challenge to access HIV services (Calabrese et al., 2016; Van Boekel et al., 2013). Failing to effectively intervene with PWUD has resulted in poor individual outcomes and threatens public health by increasing the likelihood continued HIV transmission by PWUD who remain undiagnosed or off treatment with persistent HIV viremia. High-risk PWUD, and the communities in which they live, would greatly benefit from building on existing evidence-based primary HIV prevention interventions and expanding new approaches for HIV prevention.

The recent availability of pre-exposure prophylaxis (PrEP)–the daily self-administration of antiretroviral medication for primary HIV prevention (CDC, 2014)–provides an unprecedented opportunity to curtail the HIV epidemic. Findings from recent PrEP trials have demonstrated that taking PrEP daily significantly reduces HIV transmission among those at substantial risk of acquiring HIV infection, such as men who have sex with men (MSM), people who inject drugs (PWID), sex workers, and transgender people (Baeten et al., 2012; Choopanya et al., 2013; Grant et al., 2010; Thigpen et al., 2012; Van Damme et al., 2012). Consequently, the Centers for Disease Control and Prevention (CDC) recommends PrEP in PWUD and provides clinical practice guidelines on the use of PrEP for HIV prevention (CDC, 2014).

Despite PrEP’s efficacy and coverage by insurance in the U.S., uptake by PWUD has been strikingly low (Kirby and Thornber-Dunwell, 2014). A new PrEP cascade (Liu et al., 2012) suggests that PrEP uptake and optimal protective effect requires a high level of user awareness, willingness to initiate, and ability to remain highly adherent to the medication (Peng et al., 2012). Most recent studies that focus on PrEP uptake factors are concentrated on samples of MSM (Ferrer et al., 2016; Goedel et al., 2016; Gredig et al., 2016; Hoagland et al., 2016; Peng et al., 2012; Young et al., 2013), with limited research among high-risk PWUD (Kuo et al., 2016; Stein et al., 2014). For example, Stein et al. (2014) found that 47% of PWUD reported being willing to use PrEP and that a higher perception of HIV susceptibility was associated with an increased willingness to initiate PrEP (Stein et al., 2014). Among older people who inject drugs (PWID), Kuo et al. (2016) found that only 13.4% had ever heard of PrEP and 71% were likely to take PrEP (Kuo et al., 2016). Furthermore, prior studies have not evaluated how people anticipate their risk-related behaviors will change if they start PrEP. The original PrEP trial affirming its efficacy in PWUD was conducted among PWUD enrolled in a methadone maintenance program (MMP) where high-risk individuals are concentrated and readily available for primary HIV prevention (Choopanya et al., 2013). We therefore sought to better understand factors related to PrEP uptake (e.g., knowledge about and willingness to initiate PrEP) in a sample of PWUD in a MMP. Such findings are necessary to guide future implementation of PrEP among high-risk PWUD in the context of common drug treatment settings.

2. Methods

2.1. Participants

Between June and July 2016, a convenience sample of 400 participants was recruited at Connecticut’s largest MMP. Screening eligibility included: i.) being 18 years or older, ii.) reporting HIV-uninfected, iii.) reporting drug- or sex-related HIV risk behaviors in the past 6 months, and iv.) being able to understand, speak, and read English. All patients were stabilized on methadone to treat opioid dependence. Among the 438 MMP clients approached, 28 did not meet eligibility criteria and an additional 10 either did not agree to study participation or chose not to complete the entire survey, leaving 400 individuals for the final analytical sample.

2.2. Study setting and procedures

Participants were recruited at the APT Foundation, which provides methadone maintenance treatment to over 7,000 patients in the greater New Haven, Connecticut community. Convenience sampling was used to recruit participants through flyers, peers, word-of-mouth, and direct referral from counselors. Screening was conducted by trained research assistants in a private room at APT Foundation or by phone. Individuals who met inclusion criteria and expressed interest in participating completed informed consent procedures in person and were administered a 45-minute survey (range: 40 – 60 minutes) using an audio computer-assisted self-interview (ACASI). All participants were reimbursed for the time and effort needed to participate in the survey. The study protocol was approved by the Institutional Review Board at the University of Connecticut and received board approval from APT Foundation, Inc.

2.3. Measures

Covariate measures included were based on prior research. In addition to demographic and social characteristics, we assessed health insurance status, visits to health care providers in the past 12 months and current methadone dose. We assessed whether participants were prescribed any medication (other than methadone) in the past 30 days and, for those who were, we assessed medication adherence using a self-reported, validated three-item scale developed by Wilson et al. (2016). Summary scales were calculated as the mean of the three individual items with higher score indicating better adherence (0–100) (Wilson et al., 2016). Neurocognitive impairment (NCI) was measured using the Brief Inventory of Neurocognitive Impairment (BINI), which is a brief, 54-item self-reported measure of neuropsychological symptoms (Copenhaver et al., 2016). The overall BINI score, which was obtained by summing responses to all items, was converted to age-adjusted standardized scores (i.e., z-scores) based on normative data. Participants with an age-adjusted z-score ≥ 0.5 were classified as moderately to severely neurocognitively “impaired”, whereas those with a z-score < 0.5 were classified as “not impaired” (Dwan et al., 2015). The overall internal consistency (Cronbach’s alpha) for the BINI scale was 0.97. Depressive symptoms were assessed using the 20-item Center for Epidemiological Studies Depression Scale (CES-D), with ≥ 16 indicative of moderate to severe depression (Radloff, 1977). The overall internal consistency (Cronbach’s alpha) for the scale was 0.92.

Alcohol use disorders were measured using the validated 10-item Alcohol Use Disorders Identification Test (AUDIT), with standard cut-offs ≥ 8 for men and ≥ 4 for women suggestive of an AUD (Babor et al., 2001). The overall international consistency for the AUDIT was 0.92. Current drug- and sex-related risk was assessed for the past 30 days using an adapted version of the HIV risk-taking behavior scale (HRBS) (Ward et al., 1990). Risk perception for HIV was measured by the question “What do you think your current risk of getting HIV is?” with possible options being “no risk as all”, “moderate risk”, or “high risk”. Participants’ satisfaction with previous HIV prevention methods was assessed using the question “Are you satisfied with your current method of HIV protection (e.g., condom use, clean needle use)?” Participants were asked about their awareness and previous use of PrEP. Their willingness to initiate PrEP was assessed after providing a brief description of PrEP (Appendix). After reviewing this description, participants were asked to respond to a statement “I would be interested in taking PrEP to reduce my current risk of HIV infection” on a five-point Likert scale. Their score was further dichotomized as “Yes” (strongly agree and agree) and “No” (strongly disagree, disagree, and neutral). Some further hypothetical questions were asked to assess participants’ anticipation of “always using condoms” and “never sharing injection equipment” while on PrEP: “How confident are you that you would always use condoms while on PrEP?”, and “How confident are you that you would stop sharing needles or works completely while on PrEP?” The 5-point Likert response ranged from “Not at all confident” to “Completely confident”. This variable was further dichotomized as “Yes” (completely confident) and “No” (not at all confident, somewhat confident, moderately confident, and very confident).

2.4. Data analysis

All data analyses were performed using SPSS v. 23 (IBM Corp., 2015), and statistical significance was set at p<0.05. We computed descriptive statistics, including frequencies and percentages for categorical variables, and means and standard deviations for continuous variables. After conducting bivariate analyses for significant associations with the two primary outcomes: 1.) willingness to initiate PrEP; and 2.) anticipation of consistent condom use and not sharing injection equipment while on PrEP), we conducted multivariate logistic regression analyses on bivariate associations found to be significant at p<0.10. Stepwise forward entry and backward elimination methods both showed the same results in examining the independent correlates expressed as adjusted odds ratios (aORs) and their 95% confidence intervals (95% CI). The final model was ultimately selected based on goodness-of-fit using the Hosmer and Lemeshow Test (Hosmer et al., 1997).

3. Results

Table 1 shows participant characteristics as stratified by their willingness to initiate PrEP. Participants were mostly in their early 40s (SD=11.1 years). Most participants reported having taken prescribed medication (other than methadone) in the past 30 days, with a mean medication adherence score of 73.3 (SD=15.4) on a scale of 0–100. Approximately one-third of participants were classified as being neurocognitively impaired, and 74.3% and 47.0% met screening criteria for depression and AUDs, respectively. Self-reported HIV risk behaviors were highly prevalent. Over half of participants reported being satisfied with their current method of HIV prevention and two-thirds perceived that they were at risk of acquiring HIV. Only 18% of participants reported having heard of PrEP as a method to prevent HIV transmission and 1.8% had ever used it. Conversations with friends (6.5%) and health care providers (4.8%) were noted as the top sources of PrEP knowledge (Figure 1).

Table 1.

Characteristics of participants and HIV transmission risk behaviors, stratified by their willingness to initiate PrEP (N = 400)

Variables Entire sample (N = 400) Willingness to initiate OR (95% CI) p
Frequency % No (n = 149) Yes (n = 251)
Characteristics of participants
Age: Mean (SD) 40.9 (11.1) 39.7 (11.4) 41.8 (10.8) 1.017 (.999, 1.037) 0.070
Gender
 Male 234 58.5 94 (23.5) 140 (35.0) - -
 Female 166 41.5 55 (13.8) 111 (27.8) 1.355 (.894, 2.053) 0.152
Sexual orientation
 Heterosexual or straight 345 86.3 137 (34.3) 208 (52.0) - -
 Homosexual, gay, or lesbian 16 4.0 5 (1.3) 11 (2.8) 1.449 (.493, 4.262) 0.500
 Bisexual 39 9.7 7 (1.8) 32 (8.0) 3.011 (1.292, 7.015) 0.011
Ethnicity
 Non-white 147 36.8 44 (11.0) 103 (25.8) - -
 White 253 63.2 105 (26.3) 148 (37.0) .602 (.391, .928) 0.022
Marital status
 Married 83 20.8 32 (8.0) 51 (12.8) - -
 Divorced 111 27.8 33 (8.3) 78 (19.5) 1.483 (.813, 2.705) 0.199
 Widowed 14 3.5 4 (1.0) 10 (2.5) 1.569 (.454, 5.426) 0.477
 Single 192 48.0 80 (20.0) 112 (28.0) .878 (.519, 1.488) 0.630
High school graduate
 No 107 26.8 32 (8.0) 75 (18.8) - -
 Yes 293 73.3 117 (29.3) 176 (44.0) .642 (.399, 1.032) 0.067
Employed
 No 331 82.8 277 (69.3) 54 (13.5) - -
 Yes 69 17.3 51 (12.8) 18 (4.5) 1.054 (.615, 1.807) 0.848
Income level
 < $10,000 312 78.0 254 (63.5) 58 (14.5) - -
 $10,000 – $19,999 57 14.2 52 (13.0) 5 (1.3) .727 (.410, 1.288) 0.274
 ≥$20,000 31 7.8 22 (5.5) 9 (2.3) 1.032 (.478, 2.232) 0.935
Currently have health insurance
 No 19 4.8 15 (3.8) 4 (1.0) - -
 Yes 381 95.2 313 (78.3) 68 (17.0) .768 (.286, 2.066) 0.601
Visited healthcare provider (past 12 months)
 No 34 8.5 29 (7.2) 5 (1.3) - -
 Yes 366 91.5 299 (74.8) 67 (16.8) 1.198 (.586, 2.449) 0.621
Homeless (past 12 months)
 No 198 49.5 172 (43.0) 26 (6.5) - -
 Yes 202 50.5 156 (39.0) 46 (11.5) 1.252 (.834, 1.879) 0.278
Methadone dose: Mean (SD), mg 81.5 (28.4) 81.3 (29.9) 81.3 (27.4) 1.000 (.993, 1.007) 0.982
Taking medication (past 30 days)
 No 92 23.0 38 (9.5) 54 (13.5) - -
 Yes 308 77.0 111 (27.8) 197 (49.3) 1.249 (.776, 2.010) 0.360
Medication adherence: Mean (SD) 73.3 (15.4) 75.4 (15.8) 72.2 (15.2) .986 (.970, 1.002) 0.076
Ever heard of PrEP
 No 328 82.0 120 (30.0) 208 (52.0) - -
 Yes 72 18.0 29 (7.2) 43 (10.8) .855 (.508, 1.441) 0.558
Neurocognitive impairment
 No 279 69.8 122 (30.5) 157 (39.3) - -
 Yes 121 30.3 27 (6.8) 94 (23.5) 2.705 (1.659, 4.411) <0.001
Moderate to Severe Depression
 No 103 25.8 48 (12.0) 55 (13.8) - -
 Yes 297 74.3 101 (25.3) 196 (49.0) 1.694 (1.074, 2.671) 0.023
Alcohol use disorders
 No 212 53.0 87 (21.8) 125 (31.3) - -
 Yes 188 47.0 62 (15.5) 126 (31.5) 1.437 (.940, 2.129) 0.097
HIV transmission risk behaviors
During the past 30 days…
Injected drugs
 No 170 42.5 72 (18.0) 98 (24.5) - -
 Yes 230 57.5 77 (19.3) 153 (38.3) 1.460 (.969, 2.198) 0.070
Shared injection equipment n = 230
 No 80 34.8 32 (13.9) 48 (20.9) - -
 Yes 150 65.2 45 (19.6) 105 (45.7) 1.556 (.882, 2.744) 0.127
Had sex
 No 72 18.0 28 (7.0) 44 (11.0) - -
 Yes 328 82.0 121 (30.3) 207 (51.7) 1.089 (.645, 1.839) 0.751
Number of sexual partners
 1 197 60.1 83 (25.3) 114 (34.8) - -
 2–5 116 35.3 35 (10.7) 81 (24.7) 1.685 (1.035, 2.742) 0.036
 ≥6 15 4.6 3 (0.9) 12 (3.7) 2.912 (.797, 10.647) 0.106
Always used condom with regular partner n = 308
 No 282 91.6 105 (34.1) 177 (57.5) - -
 Yes 26 8.4 8 (2.6) 18 (5.8) 1.335 (.561, 3.177) 0.514
Always used condom with casual partner n = 264
 No 215 81.4 80 (30.3) 135 (51.1) - -
 Yes 49 18.6 12 (4.5) 37 (14.0) 1.827 (.901, 3.707) 0.095
Diagnosed with STIs (past 12 months)
 No 346 86.5 128 (32.0) 218 (54.5) - -
 Yes 54 13.5 21 (5.3) 33 (8.3) .923 (.512, 1.663) 0.789
Perceived risk for HIV infection
 No risk at all 129 32.3 65 (16.3) 64 (16.0) - -
 Moderate 145 36.3 57 (14.2) 88 (22.0) 1.568 (.970, 2.533) 0.065
 High 126 31.5 27 (6.8) 99 (24.8) 3.724 (2.153, 6.441) <0.001
Satisfied with current method of HIV prevention
 No 162 40.5 56 (14.0) 106 (26.5) - -
 Yes 238 59.5 93 (23.3) 145 (36.3) .824 (.544, 1.248) 0.360
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PrEP: Pre-exposure prophylaxis; SD: Standard deviation; OD: Odds ratio; STIs: Sexually transmitted infections; OR: Odds ratio

Note: STIs in the past 12 months

Figure 1.

Figure 1

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Variables of interest related to PrEP among participants (N = 400)

3.1. Willingness to initiate PrEP

Nearly two-thirds of participants (62.7%) reported that they would be willing to initiate PrEP to reduce their risk of HIV infection (Figure 1). While Table 1 shows the bivariate correlates of being willing to initiate PrEP, Table 2 shows the independent correlates associated with this outcome in multivariate modeling. Specifically, being neurocognitively impaired was associated with over a three-fold odds (aOR=3.184, p=0.004) of being willing to initiate PrEP. Additionally, compared to those who did not perceive themselves to be at risk for HIV, those with moderate (aOR=4.439, p<0.001) and high (aOR=8.044, p<0.001) perceived risk were significantly more likely to be willing to initiate PrEP.

Table 2.

Multivariate logistic regression models of factors associated with willingness to initiate PrEP (N = 400)

Variables Willingness to initiate PrEP
aOR 95% CI p
Age 1.017 .986, 1.049 0.280
Sexual orientation
 Heterosexual or straight - - -
 Homosexual, gay, or lesbian 1.378 .279, 6.814 0.694
 Bisexual 2.920 .930, 9.171 0.067
Ethnicity
 Non-white - - -
 White 1.188 .566, 2.496 0.648
High school graduate
 No - - -
 Yes 1.040 .482, 2.240 0.920
Neurocognitive impairment
 No - - -
 Yes 3.184 1.459, 6.949 0.004
Moderate to Severe Depression
 No - - -
 Yes 1.219 .535, 2.779 0.638
Alcohol use disorders
 No - - -
 Yes 1.023 .526, 1.986 0.948
Injected drugs
 No - - -
 Yes .986 .483, 2.012 0.968
Number of sexual partners
 1 - - -
 2–5 .714 .350, 1.455 0.353
 ≥6 .629 .126, 3.139 0.572
Always used condom with casual partner
 No - - -
 Yes 3.401 .940, 6.307 0.062
Perceived risk for getting HIV
 No risk at all - - -
 Moderate 4.439 1.959, 7.060 <0.001
 High 8.044 3.012, 13.481 <0.001
Hosmer and Lemeshow Test: Chi-square = 5.439; p = 0.710
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aOR: Adjusted odds ratio

3.2. Anticipation of safer drug- and sex-related practices while on PrEP

Participants willing to initiate PrEP were asked about their anticipated sexual and drug-related risk behaviors while on PrEP, and only 12.5% indicated that they would consistently use condoms while on PrEP (Figure 1). Though a number of factors were associated with consistent condom use while on PrEP in bivariate analyses (Table 3), in the multivariate model, earning > $20,000 per year (aOR=8.315, p=0.016) and always using condoms with casual partners (aOR=6.597, p=0.001) were significantly correlated with consistent condom use, whereas those currently injecting drugs (aOR=0.323, p=0.027) were less likely to anticipate consistently using condoms while on PrEP (Table 4).

Table 3.

Anticipation of consistent condom use and not sharing of injection equipment while on PrEP (N = 400)

Variables Consistent condom use OR (95% CI) p Not sharing injection equipment OR (95% CI) p
No (n = 350) Yes (n = 50) No (n = 287) Yes (n = 113)
Characteristics of participants
Age: Mean (SD) 41.1 (11.3) 39.9 (9.7) .990 (.964, 1.018) 0.490 41.7 (11.5) 39.1 (9.7) .978 (.959, .998) 0.035
Gender
 Male 206 (51.5) 28 (7.0) - - 172 (43.0) 62 (15.5) - -
 Female 144 (36.0) 22 (5.5) 1.124 (.618, 2.043) 0.701 115 (28.7) 51 (12.8) 1.230 (.793, 1.909) 0.355
Sexual orientation
 Heterosexual or straight 298 (74.5) 47 (11.8) - - 245 (61.3) 100 (25.0) - -
 Homosexual, gay, or lesbian 15 (3.8) 1 (0.3) .423 (.055, 3.275) 0.410 13 (3.3) 3 (0.8) .565 (.158, 2.027) 0.381
 Bisexual 37 (9.3) 2 (0.5) .343 (.080, 1.469) 0.149 29 (7.2) 10 (3.5) .845 (.397, 1.798) 0.662
Ethnicity
 Non-white 127 (31.8) 20 (5.0) - - 108 (27.0) 39 (9.8) - -
 White 223 (55.8) 30 (7.5) .854 (.466, 1.566) 0.611 179 (44.8) 74 (18.5) 1.145 (.726, 1.805) 0.561
Marital status
 Married 71 (17.8) 12 (3.0) - - 57 (14.2) 26 (6.5) - -
 Divorced 96 (24.0) 15 (3.8) .924 (.408, 2.096) 0.851 84 (21.0) 27 (6.8) .705 (.373, 1.330) 0.280
 Widowed 14 (3.5) 0 (0.0) 1 (.999, 1.010) 0.999 12 (3.0) 2 (0.5) .365 (.076, 1.751) 0.208
 Single 169 (42.3) 23 (5.8) .805 (.380, 1.706) 0.572 134 (33.5) 58 (14.5) .949 (.544, 1.656) 0.854
High school graduate
 No 97 (24.3) 10 (2.5) - - 81 (20.3) 26 (6.5) - -
 Yes 253 (63.2) 40 (10.0) 1.534 (.738, 3.187) 0.252 206 (51.5) 87 (21.8) 1.316 (.792, 2.186) 0.290
Employed
 No 277 (69.3) 54 (13.5) - - 235 (58.8) 96 (24.0) - -
 Yes 51 (12.8) 18 (4.5) 1.054 (.615, 1.807) 0.848 52 (13.0) 17 (4.3) .800 (.441, 1.454) 0.464
Income
 < $10,000 280 (70.0) 32 (8.0) - - 231 (57.8) 81 (20.3) - -
 $10,000 – $19,999 50 (12.5) 7 (1.8) 1.225 (.512, 2.928) 0.648 38 (9.5) 19 (4.8) 1.426 (.778, 2.614) 0.251
 ≥$20,000 20 (5.0) 11 (2.8) 4.812 (2.116, 6.945) <0.001 18 (4.5) 13 (3.3) 2.060 (.966, 4.391) 0.061
Currently have health insurance
 No 17 (4.3) 2 (0.5) - - 15 (3.8) 4 (1.0) - -
 Yes 333 (83.3) 48 (12.0) 1.225 (.274, 5.470) 0.790 272 (68.0) 109 (27.3) 1.503 (.488, 4.629) 0.478
Visited healthcare provider (past 12 months)
 No 31 (7.8) 3 (0.8) - - 26 (6.5) 8 (2.0) - -
 Yes 319 (79.8) 47 (11.8) 1.522 (.448, 5.178) 0.501 261 (65.3) 105 (26.3) 1.307 (.573, 2.981) 0.524
Homeless (past 12 months)
 No 170 (42.5) 28 (7.0) - - 140 (35.0) 58 (14.5) - -
 Yes 180 (45.0) 22 (5.5) .742 (.409, 1.347) 0.327 147 (36.8) 55 (13.8) .903 (.584, 1.396) 0.647
Methadone dose: Mean (SD), mg 81.2 (28.1) 82.1 (30.4) 1.001 (.991, 1.012) 0.839 80.5 (28.0) 83.3 (29.3) 1.003 (.996, 1.011) 0.382
Taking medication (past 30 days)
 No 87 (21.8) 5 (1.3) - - 68 (17.0) 24 (6.0) - -
 Yes 263 (65.8) 45 (11.3) 2.977 (1.145, 7.738) 0.025 219 (54.8) 89 (22.3) 1.151 (.680, 1.949) 0.600
Medication adherence: Mean (SD) 72.1 (15.5) 80.4 (13.3) 1.053 (1.021, 1.086) 0.001 71.9 (14.9) 76.8 (16.2) 1.024 (1.005, 1.043) 0.013
Heard about PrEP
 No 287 (71.8) 41 (10.3) - - 234 (58.5) 94 (23.5) - -
 Yes 63 (15.8) 9 (2.3) 1.000 (.462, 2.163) 0.990 53 (13.3) 19 (4.8) .892 (.502, 1.588) 0.699
Neurocognitive impairment
 No 245 (61.3) 34 (8.5) - - 201 (50.2) 78 (19.5) - -
 Yes 105 (26.3) 16 (4.0) 1.098 (.581, 2.075) 0.773 86 (21.5) 35 (8.8) 1.049 (.654, 1.681) 0.843
Moderate to Severe Depression
 No 85 (21.3) 18 (4.5) - - 69 (17.3) 34 (8.5) - -
 Yes 265 (66.3) 32 (8.0) .570 (.305, 1.067) 0.079 218 (54.5) 79 (19.8) .735 (.453, 1.194) 0.214
Alcohol use disorders
 No 181 (45.3) 31 (7.8) - - 143 (35.8) 69 (17.3) - -
 Yes 169 (42.3) 19 (4.8) .656 (.357, 1.206) 0.175 144 (36.0) 44 (11.0) .633 (.406, .987) 0.043
HIV transmission risk behaviors
During the past 30 days…
Injected drugs
 No 138 (34.5) 32 (8.0) - - 100 (25.0) 70 (17.5) - -
 Yes 212 (53.0) 18 (4.5) .366 (.198, .678) 0.001 187 (46.8) 43 (10.8) .328 (.209, .516) <0.001
Shared injection equipment
 No 70 (30.4) 10 (4.3) - - 54 (23.5) 26 (11.3) - -
 Yes 142 (61.7) 8 (3.5) .394 (.149, 1.043) 0.061 133 (57.8) 17 (7.4) .265 (.133, .528) <0.001
Had sex
 No 60 (15.0) 12 (3.0) - - 49 (12.3) 23 (5.8) - -
 Yes 290 (72.5) 38 (9.5) .655 (.323, 1.327) 0.240 238 (59.5) 90 (22.5) .806 (.464, 1.399) 0.442
Number of sexual partners
 1 173 (52.7) 24 (7.3) - - 134 (40.9) 63 (19.2) - -
 2–5 103 (31.4) 13 (4.0) .910 (.444, 1.865) 0.796 90 (27.4) 26 (7.9) .614 (.362, 1.043) 0.071
 ≥6 14 (4..3) 1 (0.3) .515 (.065, 4.093) 0.530 14 (4.3) 1 (0.3) .152 (.020, 1.181) 0.072
Always used condom with regular partner
 No 250 (81.2) 32 (10.4) - - 206 (66.9) 76 (24.7) - -
 Yes 20 (6.5) 6 (1.9) 2.344 (.876, 6.268) 0.090 15 (4.9) 11 (3.6) 1.988 (.874, 4.519) 0.101
Always used condom with casual partner
 No 199 (75.4) 16 (6.1) - - 169 (64.0) 46 (17.4) - -
 Yes 38 (14.4) 11 (4.2) 3.600 (1.551, 8.360) 0.003 30 (11.4) 19 (7.2) 2.327 (1.202, 4.505) 0.012
Diagnosed with STIs (past 12 months)
 No 304 (76.0) 42 (10.5) - - 245 (61.3) 101 (25.3) - -
 Yes 46 (11.5) 8 (2.0) 1.259 (.556, 2.850) 0.581 42 (10.5) 12 (3.0) .693 (.350, 1.371) 0.292
Perceived risk for HIV infection
 No risk at all 107 (26.8) 22 (5.5) - - 78 (19.5) 51 (12.8) - -
 Moderate 132 (33.0) 13 (3.3) .479 (230, .995) 0.049 105 (26.3) 40 (10.0) .583 (.351, .967) 0.037
 High 111 (27.8) 15 (3.8) .657 (.324, 1.334) 0.245 104 (26.0) 22 (5.5) .324 (.181, .578) <0.001
Satisfied with current method of HIV prevention
 No 150 (37.5) 12 (3.0) - - 123 (30.8) 39 (9.8) - -
 Yes 200 (50.0) 38 (9.5) 2.375 (1.200, 4.700) 0.013 164 (41.0) 74 (18.5) 1.423 (.905, 2.238) 0.127
Open in a new tab

PrEP: Pre-exposure prophylaxis; SD: Standard deviation; OD: Odds ratio

Note: STIs: Sexually transmitted infections; STIs in the past 12 months; OR: Odds ratio

Table 4.

Multivariate logistic regression models of factors associated with anticipation of consistent condom use and not sharing of injection equipment while on PrEP (N = 400)

Variables Consistent condom use Variables Not sharing injection equipment
aOR 95% CI p aOR 95% CI p
Income Age, years .948 .902, .996 0.035
 < $10,000 - - - Income
 $10,000 – $19,999 6.409 .925, 12.414 0.060  < $10,000 - - -
 ≥$20,000 8.315 1.719, 15.162 0.016  $10,000 – $19,999 2.243 .489, 10.285 0.299
Taking medication (past 30 days)  ≥$20,000 3.012 .560, 16.207 0.199
 No - - - Medication adherence 1.004 .970, 1.0388 0.837
 Yes 1.035 .993, 1.078 0.103 Alcohol use disorders
Medication adherence 1.016 .967, 1.067 0.527  No - - -
Moderate to Severe Depression  Yes .856 .287, 2.550 0.780
 No - - - Injected drugs
 Yes .943 .446, 1.997 0.879  No - - -
Injected drugs  Yes .342 .217, .539 <0.001
 No - - - Shared injection equipment - - -
 Yes .323 .097, .890 0.027  No - - -
Shared injection equipment  Yes .544 .162, 1.831 0.326
 No - - - Number of sexual partners
 Yes .424 .054, 3.325 0.439  1 - - -
Always uses condom with regular partner  2–5 .476 .153, 1.474 0.198
 No - - -  ≥6 .343 .030, 3.876 0.387
 Yes .436 .023, 8.393 0.583 Always uses condom with casual partner
Always uses condom with casual partner  No - - -
 No - - -  Yes 1.435 .321, 6.411 0.637
 Yes 6.726 2.290, 10.754 0.001 Perceived risk for HIV infection
Perceived risk for HIV infection  No risk at all - - -
 No risk at all - - -  Moderate .760 .442, 1.308 0.322
 Moderate .144 .005, 4.212 0.261  High .488 .261, .911 0.024
 High 1.391 .083, 5.210 0.818 Hosmer and Lemeshow Test: Chi-square = 7.095; p = 0.526
Satisfied with current method of HIV prevention
 No - - -
 Yes .797 .154, 4.131 0.787
Hosmer and Lemeshow Test: Chi-square = 4.869; p = 0.771
Open in a new tab

aOR: Adjusted odds ratio

Regarding drug-related risk, only 28.2% of participants reported that they would not share injection equipment while on PrEP (Figure 1). Again, multiple factors in the bivariate analyses were associated with this outcome, but in the multivariate model (Table 4), participants who were older (aOR=0.948, p=0.035), currently injecting drugs (aOR=0.342, p<0.001), and reported high perceived risk for HIV (aOR=0.191, p=0.019) were less likely to anticipate that they would not share injection equipment while on PrEP.

4. Discussion

Given the dearth of literature examining the interest in or initiation of PrEP among PWUD, we sought to directly assess this risk group for their interest in initiating PrEP and their perceptions about how PrEP might affect their drug and sexual risk behaviors. Several important findings were gleaned from this study that have major implications for PrEP scale-up in MMP settings, where PrEP use among PWUD was originally examined (Choopanya et al., 2013). First, almost none of our participants (<2%) had ever taken PrEP and few (18%) were even aware of PrEP. This is especially concerning given that this is a population at high-risk for HIV, but who have frequent contact with various treatment providers (e.g., through MMPs and elsewhere). This represents missed opportunities to initiate, or at least discuss, PrEP among PWUD. Limited PrEP awareness and use among PWUD here is similar to that reported elsewhere among female sex workers in China (Peng et al., 2012) and among other studies of PWUD in the U.S. (Kuo et al., 2016; Stein et al., 2014), but PrEP awareness was lower than that reported in studies of MSM (Ferrer et al., 2016; Goedel et al., 2016; Hoagland et al., 2016; Young et al., 2013). The higher level of knowledge about PrEP in MSM may stem from a number of PrEP initiatives that have primarily focused on MSM and HIV seronegative partners in sero-discordant couples (Ware et al., 2012). Recent studies have also shown that many addiction treatment providers, with whom MMP patients are in daily contact, have limited awareness of PrEP (Shrestha et al., 2016; Spector et al., 2015). In the context of clinical settings, including MMP patients in this study, treatment providers have great potential to engage their at-risk clients about PrEP through counseling, referrals, research trials, and may also effectively promote adherence to PrEP through counseling and monitoring. Our findings highlight the need for skills training for MMP providers so they can refer clients to PrEP and promote PrEP adherence, as they would for other services (e.g., offer risk reduction items, HIV testing, referral) relevant to HIV prevention. When information deficits about PrEP were corrected by describing its potential benefits, interest in initiating PrEP increased markedly with nearly two-thirds (62.7%) of participants being willing to initiate PrEP. Importantly, those who stand to benefit the most from PrEP (i.e., those at highest risk for HIV) tended to be most interested in it. Specifically, those who accurately perceived themselves as being at higher risk for acquiring HIV were most willing to initiate it, as well as those with neurocognitive impairment, which is associated with higher HIV risk behaviors (Anand et al., 2010; Huedo-Medina et al., 2016; Shrestha and Copenhaver, 2016). Together, these findings support PrEP expansion for PWUD enrolled in MMP.

The combination of high sex- and drug-related risk in MMP patients suggests that PrEP would be ideal for this risk group, just as reported in the original PrEP trial among PWID. In addition to the biomedical prevention benefits of PrEP, the structured nature of MMPs and the requirement for regular counseling suggests that MMP settings could readily support the integration of PrEP into existing evidenced-based behavioral risk reduction strategies.

We found that higher willingness to initiate PrEP was associated with participants having NCI, which is highly prevalent (30%) among this risk group (Shrestha et al., 2016). In prior studies (Anderson et al., 2015; Attonito et al., 2014; Becker et al., 2011), cognitive deficits have been associated with risky behaviors, poor medication adherence, and treatment disengagement (Anand et al., 2010; Shrestha et al., 2015; Verdejo-Garcia and Perez-Garcia, 2007; Vo et al., 2014). Given the relationship between NCI and higher HIV risk behaviors, this is an important group of PWUD who might benefit from PrEP. NCI may also undermine the efficacy of PrEP, however, since high levels of adherence are required for its efficacy (Baeten et al., 2012; Choopanya et al., 2013; Grant et al., 2010; Shrestha et al., 2016; Thigpen et al., 2012; Van Damme et al., 2012). For PWUD with NCI initiating PrEP, it is therefore crucial to couple PrEP with behavioral approaches to support medication adherence, such as cues and reminders or other cognitive remediation strategies (Barlati et al., 2013; Cole-Lewis and Kershaw, 2010; Finitsis et al., 2014; Pop-Eleches et al., 2011).

Participants’ overall perception of HIV risk was relatively high in this cohort of PWUD. We found that individuals who perceived themselves to be at higher risk of contracting HIV reported greater willingness to initiate PrEP, which is consistent with that reported in prior studies among MSM (Eisingerich et al., 2012; Golub et al., 2013; Wheelock et al., 2013; Young et al., 2013). The results suggest that participants are making rational judgment about their own risk levels when considering whether to initiate PrEP. This may indicate not only a concern about risk of HIV infection but also a self-management response to their HIV risk behaviors (Young et al., 2013). Thus, self-management programs, which have been shown to have positive outcomes in a variety of long-term conditions (e.g., diabetes, hypertension, arthritis) (Martin et al., 2014), may be of specific usefulness to promote self-management aspects of HIV prevention, such as HIV risk reduction strategies and PrEP. Alternatively, PrEP may be seen as an important HIV prevention approach in itself if, as our data suggest found, these individuals are unlikely to start using condoms more consistently (Holt et al., 2012). Overall, our findings suggest the need to consider how at-risk PWUD perceive and respond to their HIV risks as this may have a significant impact on the development and roll-out of PrEP-related programs targeting various risk populations.

Similar to other PrEP studies, findings here suggest that those who start PrEP are unlikely to then modify their risk behaviors. While these data do not support risk compensation as an anticipated behavioral response by PWUD, they do suggest that this population is ideal for PrEP and opens opportunities for integrating biomedical and behavioral interventions to enhance adherence and reduce other sexually transmitted infections (STIs) and blood-borne viral infections. Despite variable findings from other PrEP studies showing no risk compensation in clinical trials (Grant et al., 2010; McCormack et al.; Molina et al., 2015) but elevated risk-taking in other observational studies (Golub et al., 2013; Zhou et al., 2012), such responses should be closely examined in further PrEP studies in PWUD. Additionally, participants who reported engaging in prior risk behaviors (i.e., active drug injection, condomless sex) were less likely to anticipate using condoms while on PrEP. Those who perceived themselves to be at high risk of contracting HIV and who engaged in current drug injection were more likely to anticipate sharing injection equipment while on PrEP. This suggests that individuals whose current behaviors increase their perceived risk of HIV are more likely to continue to engage in risky behaviors while on PrEP. This could also place these individuals at increased risk of STIs and blood-borne illnesses due to continued involvement in risky activities that are associated with decreased care engagement and medication adherence. Thus, it will be essential to educate PrEP users about the benefits and limitations of PrEP as a singular HIV prevention approach, and to increase self-efficacy by empowering people to make healthy choices. Combination HIV prevention packages that include teaching HIV risk reduction and PrEP adherence skills, routinely testing for HIV and STIs, and monitoring/supporting PrEP adherence over time may be most beneficial.

Our findings are not without limitations. As with all cross-sectional studies, we are only able to assess associations between variables rather than causal relationships. The use of self-reported measures may have resulted in participant underreporting of socially undesirable behaviors (e.g., drug- and sex-related risk behaviors) or inconsistently reporting (e.g., HIV status) because of stigma or fear of judgment. This is unlikely given the high risks reported by this sample and the use of ACASI for data collection. Additionally, the utilization of self-report measure of HIV status during screening process may have resulted in including individuals who were infected with HIV but were unaware of their status, which would make them ineligible for PrEP. Although a brief explanation about PrEP was provided, we do not know the extent to which participants understood every aspect of PrEP (e.g., effectiveness, cost, side-effects, dispensing venue, adherence, etc.) when providing responses regarding their willingness to initiate PrEP. We used participants’ “interest in taking PrEP” as a proxy measure of their “willingness to initiate PrEP.” The participants in this study were high-risk PWUD enrolled in MMP; thus, our findings may not be generalizable to PWUD in other settings. Finally, the use of the BINI, although a very user-friendly and convenient screening instrument for difficult-to-reach populations, is not designed to measure as many cognitive domains as a comprehensive battery of tests. Despite these limitations, however, the results support the development and implementation of a PrEP program integrated into existing evidence-based HIV prevention efforts that target high risk PWUD.

5. Conclusions

PWUD in MMPs are a high-risk population who would benefit from both biomedical (PrEP) and behavioral intervention strategies to prevent HIV. There have been limited PrEP data on this group, however, to inform primary HIV prevention efforts. Key findings here suggest that PWUD who would benefit from PrEP most were those most interested in receiving it. Moreover, the structured setting of MMPs provides an ideal clinical context in which to integrate biomedical and behavioral interventions in order to optimize HIV prevention efforts.

Supplementary Material

supplement

Highlights.

  • We examined the willingness of people who use drugs (PWUDs) to initiate pre-exposure prophylaxis (PrEP).

  • We also examined the anticipation of HIV risk reduction among PWUDs.

  • 18% reported having heard of PrEP and 62.7% reported being willing to initiate PrEP.

  • PrEP willingness was associated with NCI and higher perceived risk for HIV infection.

  • We found anticipated higher risk behaviors among this sample while on PrEP.

Acknowledgments

Role of funding sources

This work was supported by grants from the National Institute on Drug Abuse for research (R01 DA025943 to FLA) and for career development (K24 DA017072 to FLA; K02 DA033139 to MMC; K23 DA033858 to JPM).

Footnotes

*

Supplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi:...

Conflict of interest

No conflict declared

Contributors

RS, PK, FA, and MC conceptualized and designed the study; RS and PK analyzed the data; RS and PK prepared the first draft; all authors contributed to the interpretation of the data, critically revised the manuscript for important intellectual content, and approved the final manuscript as submitted.

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