Abstract
Purpose
Despite proven effectiveness in reducing HIV transmission, PrEP use remains low among those who meet the recommended CDC guidance for PrEP use.
Methods
Data are from a U.S. national cohort of men and trans persons who have sex with men (2017–2018). Logistic regression analyses were used to determine individual and partner factors associated with intentions to use PrEP among non-PrEP-using participants reporting a main partner (n = 1,671).
Results
Prior PrEP use among participants (14.2%) and their partners (7.7%) was low. Participants’ prior PrEP use and main partner’s HIV-positive status were both positively associated with intentions to use PrEP.
Conclusion
HIV prevention interventions incorporating main partners may be effective in increasing PrEP use.
Keywords: HIV, partner characteristics, PrEP, dyads, sero-discordant
INTRODUCTION
In the United States (U.S.), HIV incidence remains a significant public health concern, and much of the national HIV burden is among young men who have sex with men (MSM) who accounted for 66% of the nearly 39,000 new infections in 2017 (Centers for Disease Control and Prevention, 2016, 2018a, 2019). One promising tool for preventing HIV transmission is pre-exposure prophylaxis (PrEP)—an HIV prophylactic for HIV-negative individuals (Centers for Disease Control and Prevention, 2017; Golub & Myers, 2019). PrEP has been found to be highly effective at reducing HIV acquisition from sexual contact (Fonner et al., 2016), however PrEP uptake in the U.S. has been slower and lower than anticipated (Centers for Disease Control and Prevention, 2018b; Siegler et al., 2018). In the fourth quarter of 2017, Siegler et al (2018) estimated that only 10% of persons meeting the CDC guidance for PrEP eligibility were using PrEP (Siegler et al., 2018). The total number of persons meeting guidance for PrEP use—including MSM, vulnerable women, and injection drug users—was estimated to be 1.2 million persons in 2015 with nearly 500,000 of those individuals being MSM (Smith et al., 2015).
Current literature suggests that social norms and partner characteristics influence sexual health practices, including risk and protective behaviors (Carlos et al., 2010; Karney et al., 2010; Peterson, Rothenberg, Kraft, Beeker, & Trotter, 2008). For example, studies support that positive peer norms about condoms are associated with greater use (Carlos et al., 2010; Latkin, Forman, Knowlton, & Sherman, 2003; Peterson et al., 2008). Latkin et al. (2003) found that peer discussion about condom use also facilitated use among those at risk for HIV. Additionally, studies suggest that utilization of HIV prevention strategies relies heavily on dyadic characteristics (Hoff et al., 2015; Karney et al., 2010; Mitchell et al., 2016; Mitchell & Petroll, 2013). For example, one study found that HIV-positive individuals were particularly supportive of partners using PrEP, with favorable views on PrEP use within the context of primary relationships (Hoff et al., 2015). Conversely, dyads can also present unique barriers to PrEP use. For instance, a qualitative study found that partners acknowledged the benefits of PrEP for others, but not necessarily their own relationship (Mitchell et al., 2016). That is, couples expressed concern about how it could negatively impact a hypothetical relationship agreement (cheating, increasing “risky” behavior because of feeling “invincible”) or how it would be useful in a hypothetical relationship where only one partner was HIV-positive (Mitchell et al., 2016).
Building on existing literature, further understanding of the dyadic characteristics associated with intentions to use PrEP is warranted. The purpose of this study was to assess individual and partner factors—importantly, partner’s HIV status—associated with intentions to use PrEP.
METHODS
The Together 5000 Cohort
Data were collected as part of enrollment for Together 5000 (T5K), a U.S. national, internet-based cohort study of men and trans persons vulnerable for HIV acquisition. Briefly, potential participants were recruited using ads on men-for-men geosocial sexual networking smartphone applications from fall 2017 to spring 2018. Core eligibility criteria specified that participants be between the ages of 16 and 49; have had at least two males sex partners in the past 90 days; not be participating in any clinical trials for HIV vaccines or other PrEP; not be currently taking PrEP; not be HIV-positive; identify as male, trans male, or trans female; live in the U.S.; be able to receive an at-home HIV test kit in the mail; be willing to be contacted by the study; and meet at least one other criteria indicating higher risk for HIV including sexual behaviors and substance use. Inclusion criteria were adapted from CDC guidance for PrEP candidacy (Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, & National Center for HIV/AIDS, 2020). The cohort, its enrollment procedures, and full eligibility criteria have been fully described elsewhere (Grov et al., 2019; Nash et al., 2019).
Nearly 23,000 potential participants completed the screening survey (no compensation), and, of those, 8,764 met our eligibility criteria and invited to enroll in the cohort. These participants were then sent a link to a second survey that included additional measures on sexual behaviors, partners, substance use, and psychosocial measures. Participants received a $15 gift card by email for completing this secondary survey. For the present study, we restricted analyses to participants who completed this secondary survey (n = 6,270) and reported having a main partner (final n = 1,671).
Measures
Intentions to use PrEP
The outcome of interest for the current analyses was intentions to use PrEP assessed using the following question, “PrEP is currently available with a prescription from your doctor, and research has shown that most insurance companies cover most or all of the costs of PrEP. Do you plan to begin PrEP?” At the time of data collection, PrEP was considered in the form of once-daily Tenofovir disoproxil fumarate/Emtricitabine (TDF/FTC). Participants were categorized as reporting that they intended to use PrEP (yes) or that they had little/no intentions of using PrEP.
Main partner characteristics
Participants were asked to report demographic characteristics (age, gender), HIV status, and PrEP experience of their main partners. Partner gender was defined as cisgender male, cisgender female, and gender minority that included reported partner identities other than cisgender male or female. We assessed the frequency of condomless anal or vaginal sex with their main partner. Partner HIV status was ascertained by asking participants, “What is your main partner’s HIV status?” Responses were coded as, “I know or think my partner is HIV-positive,” “I do not know my partner’s status,” or “I know or think my partner is HIV-negative.” For participants who reported an HIV-positive main partner, we also asked, “Is your main partner’s HIV viral load [herein ‘VL’] undetectable?” which was coded as, “I know or think my partner’s VL is undetectable,” “I do not know if my partner’s VL is undetectable,” and “I know or think my partner’s VL is detectable.” PrEP experience was collected by asking, “Is your main partner on [PrEP]?” Responses were categorized into “yes” or “no or I do not know.”
Other covariates
Finally, we included other demographic, socioeconomic, and behavioral characteristics known to be associated with PrEP use as covariates in analyses. These factors were (participant’s) age, gender, race/ethnicity, sexual orientation, whether or not they identified as polyamorous, employment status, highest level of education, annual income, marital status, health insurance coverage (yes, no), having experienced housing instability (past 5 years), having engaged in recent sex work (past 3 months), ever having been incarcerated, self-reported HIV status, and PrEP experience. PrEP experience was assessed on the screening questionnaire by asking participants “Have you ever been prescribed HIV medications (e.g., Truvada) for use as PrEP (Pre-Exposure Prophalyxis)?”. Participants were able to choose a) “I don’t know what PrEP is,” b) “No, never taken PrEP,” c) “Yes, I am currently on PrEP” (these individuals were not eligible for enrollment in the cohort), and d) “Yes, but I am not currently taking PrEP.” Due to the small number of responses in the current study sample (n = 72 of 1,675 or 4.3%), those who did not know of PrEP were combined with those who reported never having taken PrEP to form a “PrEP-naïve” category.
Analyses
The purpose of this study was to identify individual and main partner factors associated with self-reported of intent to begin using PrEP for HIV prevention. We calculated descriptive statistics for demographic characteristics and tested for differences between participants who intended to use PrEP and those with little or no intentions to use PrEP using chi-squared and t-tests, as appropriate. Based on factors identified as associated with intended PrEP using initial tests of differences (p ≤ 0.05), we then used a multivariable logistic regression model to determine direction and magnitude of factors related to participants’ PrEP intentions. For the final logistic regression model, we report adjusted odds ratios (aORs) and 95% confidence intervals (95% CIs). All analyses were completed using SAS 9.4.
RESULTS
Description of Together 5000 participants and their main partners
As noted, participants selected for analyses all reported having a main partner. Among these participants, approximately half (53.1%) were 25–35 years old, most (97.7%) identified as cisgender men, just over half (56%) identified as white, and most (84.7%) identified as gay, queer, or homosexual. Just over a quarter (29.4%) reported being married and 18.3% reported being polyamorous. Many (65.1%) reported being employed full-time, 42.1% reported having at least some college, and 38.9% reported an annual income between $20,000 and $49,999. Three-quarters (74.7%) reported having health insurance. Nineteen (18.7%) percent reported having experienced housing instability in the past 5 years, 13.9% reported having engaged in sex work in the past 3 months, and 13.2% reported ever being incarcerated. Forty percent (39.8%) self-reported not knowing their HIV status and most (85.8%) were PrEP-naive. However, 49.9% reported intending to start PrEP.
Many participants (46.1%) reported that their main partners were between 25–35 years old. Most (95.4%) partners were cisgender men. Nine percent (9.4%) reported knowing or thinking that their main partners were HIV-positive and, of participants reporting that they knew or thought their main partners were HIV-negative, 7.7% reported that their main partners had prior experience with PrEP. Fifty-eight percent of participants reported that they had condomless anal or vaginal sex with their main partner once a week. Among participants reporting an HIV-positive partner, 71.4% reported that their partners had an undetectable VL. Bivariate analyses indicated differences between PrEP intentions for several individual and partner factors (Table 1). Also, we found associations (p-value = 0.04) between partner’s HIV status and participants’ prior PrEP use—known or presumed HIV-positive partner (16.7%), unknown partner’s status (8.7%), known or suspected HIV-negative (15%)—but no statistically significant differences (p-value = 0.93) between participant’s prior PrEP use by partner’s viral suppression status—virally suppressed (17.3%), not virally suppressed (16.7%), unknown status (14.9%) (results not presented in table).
Table 1.
Descriptive statistics of participant characteristics (total and by intent to use PrEP) and associations of characteristics with intention to use PrEP among those reporting a main partner, Together 5000 study, 2017–2018 (n = 1,671).
| Participant intentions to use PrEP | Participant intentions to use PrEP | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total n = 1671 | Intend to use PrEP (Yes) n = 833 | Little or No Intentions n = 838 | ref = Little or no intentions | |||||||||||
| Frequency | (%) | Frequency | (Col %) | (Row %) | Frequency | (Col %) | (Row %) | Chi-squared | p | Estimate | aOR | 95% Confidence | p | |
| Participant characteristics | ||||||||||||||
| Age | 9.3 | 0.03 | ||||||||||||
| 16–24 years old | 288 | (17.2) | 163 | (19.6) | (56.8) | 125 | (14.9) | (43.3) | ||||||
| 25–35 years old | 888 | (53.1) | 439 | (52.7) | (49.4) | 449 | (53.6) | (50.6) | ||||||
| 36–45 years old | 396 | (23.7) | 191 | (22.9) | (48.2) | 205 | (24.5) | (51.8) | ||||||
| 46–55 years old | 99 | (5.9) | 40 | (4.8) | (40.4) | 59 | (7.0) | (59.6) | ||||||
| Gender | 1.7 | 0.25 | ||||||||||||
| Male | 1633 | (97.7) | 818 | (98.2) | (50.1) | 815 | (97.3) | (49.9) | ||||||
| Gender minority | 38 | (2.3) | 15 | (1.8) | (39.5) | 23 | (2.7) | (60.5) | ||||||
| Race/Ethnicity | 27.1 | <.0001 | ||||||||||||
| White | 936 | (56.0) | 424 | (50.9) | (45.3) | 512 | (61.1) | (54.7) | Ref. | |||||
| Black or African American | 129 | (7.7) | 72 | (8.6) | (55.8) | 57 | (6.8) | (44.2) | 0.29 | 1.33 | 0.91 -- 1.96 | 0.15 | ||
| Latino | 374 | (22.4) | 206 | (24.7) | (54.9) | 168 | (20.1) | (45.1) | 0.34 | 1.40 | 1.09 -- 1.79 | 0.01 | ||
| Asian or Pacific Islander | 80 | (4.8) | 56 | (6.7) | (70.0) | 24 | (2.9) | (30.0) | 1.00 | 2.71 | 1.63 -- 4.51 | 0.0001 | ||
| Other or multiple races/ethnicities | 152 | (9.1) | 75 | (9.0) | (49.7) | 77 | (9.2) | (50.3) | 0.09 | 1.09 | 0.77 -- 1.56 | 0.63 | ||
| Sexual orientation | 0.5 | 0.77 | ||||||||||||
| Gay, Queer, Homosexual | 1415 | (84.7) | 706 | (84.8) | (49.9) | 709 | (84.6) | (50.1) | ||||||
| Bisexual | 239 | (14.3) | 120 | (14.4) | (50.0) | 119 | (14.2) | (50.0) | ||||||
| Other | 17 | (1.0) | 7 | (0.8) | (41.2) | 10 | (1.2) | (58.8) | ||||||
| Polyamorous | 8.1 | 0.01 | ||||||||||||
| No | 1365 | (81.7) | 658 | (79.0) | (48.2) | 707 | (84.4) | (51.8) | Ref. | |||||
| Yes | 306 | (18.3) | 175 | (21.0) | (57.2) | 131 | (15.6) | (42.8) | 0.33 | 1.39 | 1.07 -- 1.80 | 0.01 | ||
| Employment status | 1.1 | 0.77 | ||||||||||||
| Full-time (40 hours per week) | 1088 | (65.1) | 551 | (66.2) | (50.7) | 537 | (64.1) | (49.3) | ||||||
| Part-time (less than 40 hours per week) | 214 | (12.8) | 102 | (12.2) | (47.7) | 112 | (13.4) | (52.3) | ||||||
| Working or full-time student | 210 | (12.6) | 105 | (12.6) | (49.8) | 105 | (12.5) | (50.2) | ||||||
| Unemployed/Other | 159 | (9.5) | 75 | (9.0) | (47.2) | 84 | (10.0) | (52.8) | ||||||
| Highest level of education | 12.0 | 0.01 | ||||||||||||
| < High school diploma | 32 | (1.9) | 19 | (2.3) | (58.8) | 13 | (1.6) | (41.2) | ||||||
| High school diploma or GED | 236 | (14.1) | 132 | (15.9) | (55.9) | 104 | (12.4) | (44.1) | ||||||
| Some college or technical school training | 704 | (42.1) | 366 | (43.9) | (51.9) | 338 | (40.3) | (48.1) | ||||||
| At least a 4-year college degree | 699 | (41.8) | 316 | (37.9) | (45.3) | 383 | (45.7) | (54.7) | ||||||
| Annual income | 15.2 | 0.001 | ||||||||||||
| Less than $20,000 | 467 | (28.0) | 244 | (29.3) | (52.2) | 223 | (26.6) | (47.8) | 0.28 | 1.33 | 1.01 -- 1.74 | 0.04 | ||
| $20,000–$49,999 | 650 | (38.9) | 350 | (42.0) | (53.8) | 300 | (35.8) | (46.2) | 0.37 | 1.45 | 1.14 -- 1.84 | 0.002 | ||
| $50,000 or more | 554 | (33.2) | 239 | (28.7) | (43.1) | 315 | (37.6) | (56.9) | Ref. | |||||
| Martial status | 0.0 | 0.91 | ||||||||||||
| Yes | 492 | (29.4) | 244 | (29.3) | (49.5) | 248 | (29.6) | (50.5) | ||||||
| No | 1179 | (70.6) | 589 | (70.7) | (50.0) | 590 | (70.4) | (50.0) | ||||||
| Health insurance | 1.3 | 0.26 | ||||||||||||
| Yes | 1248 | (74.7) | 612 | (73.5) | (49.0) | 636 | (75.9) | (51.0) | ||||||
| No | 423 | (25.3) | 221 | (26.5) | (52.2) | 202 | (24.1) | (47.8) | ||||||
| Experience with housing instability (< 5 years) | 4.8 | 0.03 | ||||||||||||
| No/Not within last 5 years | 1359 | (81.3) | 660 | (79.2) | (48.6) | 699 | (83.4) | (51.4) | ||||||
| Yes, within last five years | 312 | (18.7) | 173 | (20.8) | (55.3) | 139 | (16.6) | (44.7) | ||||||
| Engaged in sex work (< 3 months) | 3.8 | 0.06 | ||||||||||||
| No | 1438 | (86.1) | 703 | (84.4) | (48.9) | 735 | (87.7) | (51.1) | ||||||
| Yes | 233 | (13.9) | 130 | (15.6) | (55.7) | 103 | (12.3) | (44.3) | ||||||
| Incarceration status (ever) | 0.1 | 0.83 | ||||||||||||
| No | 1450 | (86.8) | 721 | (86.6) | (49.7) | 729 | (87.0) | (50.3) | ||||||
| Yes | 221 | (13.2) | 112 | (13.5) | (50.9) | 109 | (13.0) | (49.1) | ||||||
| Self-reported HIV status | 0.1 | 0.76 | ||||||||||||
| HIV-negative | 1006 | (60.2) | 498 | (59.8) | (49.6) | 508 | (60.6) | (50.5) | ||||||
| I don’t know; I am unsure | 665 | (39.8) | 335 | (40.2) | (50.3) | 330 | (39.4) | (49.7) | ||||||
| PrEP experience | 18.1 | <.0001 | ||||||||||||
| PrEP-naïve | 1433 | (85.8) | 684 | (82.1) | (47.7) | 749 | (89.4) | (52.3) | Ref. | |||||
| Yes, but not currently taking PrEP | 238 | (14.2) | 149 | (17.9) | (62.8) | 89 | (10.6) | (37.2) | 0.56 | 1.75 | 1.31 -- 2.35 | 0.0002 | ||
| Main partner’s characteristics | ||||||||||||||
| Main partner’s age | 8.5 | 0.07 | ||||||||||||
| 16–24 years old | 298 | (17.8) | 160 | (19.2) | (53.9) | 138 | (16.5) | (46.2) | Ref. | |||||
| 25–35 years old | 770 | (46.1) | 390 | (46.8) | (50.7) | 380 | (45.4) | (49.4) | −0.11 | 0.89 | 0.67 -- 1.18 | 0.42 | ||
| 36–45 years old | 347 | (20.8) | 152 | (18.3) | (43.7) | 195 | (23.3) | (56.3) | −0.40 | 0.67 | 0.48 -- 0.93 | 0.02 | ||
| 46–55 years old | 206 | (12.3) | 109 | (13.1) | (53.1) | 97 | (11.6) | (46.9) | −0.01 | 0.99 | 0.68 -- 1.44 | 0.94 | ||
| 55+ years old | 50 | (3.0) | 22 | (2.6) | (43.1) | 28 | (3.3) | (56.9) | −0.57 | 0.56 | 0.30 -- 1.06 | 0.08 | ||
| Main partner’s gender | 6.8 | 0.03 | ||||||||||||
| Female | 68 | (4.1) | 31 | (3.7) | (45.6) | 37 | (4.4) | (54.4) | −0.11 | 0.89 | 0.54 -- 1.47 | 0.66 | ||
| Male | 1594 | (95.4) | 801 | (96.2) | (50.3) | 793 | (94.6) | (49.8) | Ref. | |||||
| Gender minority | 9 | (0.5) | 1 | (0.1) | (11.1) | 8 | (1.0) | (88.9) | −2.21 | 0.11 | 0.01 -- 0.90 | 0.04 | ||
| Number of condomless anal or vaginal sex acts with main partner | 8.8 | 0.07 | ||||||||||||
| None | 252 | (15.5) | 147 | (17.6) | (57.4) | 109 | (13.0) | (42.6) | ||||||
| Once a week | 937 | (57.7) | 479 | (57.4) | (49.6) | 487 | (58.0) | (50.4) | ||||||
| > 1 weekly, <= 2 times a week | 228 | (14.0) | 108 | (12.9) | (46.0) | 127 | (15.1) | (54.0) | ||||||
| > 2 weekly, <= 3 times a week | 91 | (5.6) | 44 | (5.3) | (44.0) | 56 | (6.7) | (56.0) | ||||||
| > 3 times a week | 116 | (7.1) | 57 | (6.8) | (48.3) | 61 | (7.3) | (51.7) | ||||||
| Main partner’s HIV status | 12.5 | 0.002 | ||||||||||||
| I know or think my partner is HIV+ | 157 | (9.4) | 99 | (11.9) | (62.7) | 58 | (6.9) | (37.3) | 0.68 | 1.97 | 1.37 -- 2.81 | 0.0002 | ||
| I do not know my partner’s status | 118 | (7.1) | 60 | (7.2) | (51.3) | 58 | (6.9) | (48.7) | 0.02 | 1.02 | 0.69 -- 1.51 | 0.91 | ||
| I know or think my partner is HIV− | 1396 | (83.5) | 674 | (80.9) | (48.3) | 722 | (86.2) | (51.7) | Ref. | |||||
| Main partner’s PrEP experience | 5.0 | 0.08 | ||||||||||||
| No | 1208 | (86.5) | 569 | (84.4) | (47.1) | 639 | (88.5) | (52.9) | ||||||
| Yes | 108 | (7.7) | 61 | (9.1) | (56.5) | 47 | (6.5) | (43.5) | ||||||
| I don’t know | 80 | (5.7) | 44 | (6.5) | (55.0) | 36 | (5.0) | (45.0) | ||||||
Relationship influences on PrEP intentions
Of our participants with main partners, those who intended to start using PrEP had a higher odds of identifying as Latinx (aOR = 1.4, 95% CI: 1.09–1.79 ref. white) or Asian or Pacific Islander (aOR = 2.71, 95% CI: 1.63–4.51), reporting being polyamorous (aOR = 1.39, 95% CI: 1.07–1.8 ref. no), reporting lower levels of annual income (Less than $20,000 aOR = 1.33, 95% CI: 1.01–1.74; $20,000-$49,999 aOR = 1.45, 95% CI: 1.14–1.84 ref. $50,000 or more), reporting prior PrEP use (aOR = 1.75, 95% CI: 1.31–2.35 ref. PrEP-naïve), and reporting that they knew or thought their main partner was HIV-positive (aOR = 1.97, 95% CI: 1.37–2.81, ref. know or think partner is HIV-negative). Participants who reported that their main partners were between the ages of 36 and 45 (aOR = 0.67, 95% CI: 0.48–0.93 ref. partner’s age 16–24) and that their main partners identified as a gender minority (aOR = 0.11, 95% CI: 0.01–0.9 ref. male partner) had a lower odds of intending to begin PrEP use.
DISCUSSION
The purpose of this report was to identify individual and partner factors associated with intentions to begin using PrEP. We found that prior PrEP use was low among HIV-vulnerable participants who reported not currently using PrEP—all of whom met CDC-indicated guidance for PrEP treatment—and their main partners. Low PrEP uptake and adherence among those at highest risk for HIV acquisition continues to be a public health conundrum given the effectiveness of PrEP at reducing HIV transmission among vulnerable populations. For participants with HIV-positive partners, PrEP use could be dependent on partner’s known viral suppression, however we found no differences between participants’ prior PrEP use and partner’s viral suppression status. Importantly, a low proportion of participants who reported that they did not know their main partner’s HIV status (8.7%) reported prior PrEP use. Supposing that some of these main partners were HIV-positive—diagnosed or not—this suggests a critical missed opportunity for HIV prevention via PrEP.
Multivariable logistic regression analyses identified two important factors associated with intentions to use PrEP: participants’ prior PrEP experience and their partner’s HIV status. First, results indicate that participants who previously took PrEP had a significantly higher odds of intending to restart PrEP. This might suggest that PrEP discontinuation, or perhaps long lapses in PrEP use, are due to barriers other than a participant’s desire to stop using PrEP. Reasons could include changes in perceptions (reductions) of risk that lead participants to discontinuing PrEP (Whitfield, John, Rendina, Grov, & Parsons, 2018). Previous research has noted commonly held misperceptions about the medication’s effectiveness and perceived barriers to obtaining and maintaining PrEP prescriptions (e.g., costs, numerous medical appointments) (Holloway et al., 2017; Marcus et al., 2019; Morgan, Ryan, Newcomb, & Mustanski, 2018). There may also be economic (Westmoreland et al., 2019) or structural issues that impact PrEP continuation. Finally, we found that participants who had a racial/ethnic background of Latinx and Asian/Pacific Islander had a higher odds of intending to start PrEP compared to white participants. There continue to be racial/ethnic disparities in PrEP use that mimic the HIV epidemic in the U.S. (Kanny et al., 2019). Our results suggest that there are other factors—potentially individual, economic, and structural—hindering PrEP uptake among racial/ethnic minorities despite intentions to use PrEP.
Second, our results highlight the importance of partner-level factors that may influence PrEP intentions. Our research highlights partner’s HIV status as a potential motivating factor for prior PrEP use; however, the proportion of PrEP use was still low. Previous research has indicated the influence of sexual partners on sexual behaviors including condomless anal sex (CAS) and partner concurrency (i.e. outside sexual partners) (Mitchell & Petroll, 2013). Individuals and their partners may recognize the benefits and added protection of PrEP if they identify as polyamorous or for those who have partnership agreements allowing outside sex especially in partnership desiring condomless sex. Indeed, our results suggest that participants who identify as polyamorous and were (at the time) currently in a relationship with more than one person had a higher odds of intending to use PrEP. For couples who have conversations about CAS and outside partners, incorporating PrEP into a sexual health plan could be an attractive option (Mitchell et al., 2016). Few interventions focus on HIV prevention and promoting good sexual health within relationships, however our research and evidence from these prevention programs highlight the potential benefits of unique and tailored relationship-centered health programs (Macapagal, Feinstein, Puckett, & Newcomb, 2019).
Results should be interpreted in light of the study’s limitations. First, this study relies on self-reported data which can be subject to reporting biases. In our case, this may be particularly challenging as we asked participants to not only report on their own behaviors but their partners’. Next, this data is cross-sectional and cannot be used to determine causal relationships. Also, the data contains no current PrEP users at enrollment and, as such, we are unable to determine partner influences on current PrEP use in this study. We are also unable to assess factors associated with never having heard of PrEP due to the limited number of respondents who selected this response. Samples for HIV testing were self-collected and thus it is theoretically possible the samples provided came from those other than the participant (i.e., a friend/partner). As a study focused on HIV risk, many of our measures assessed behaviors that would put one at risk for HIV (such as condomless sex). Given the brief cross-sectional nature of our survey, we lack nuanced data on a full range of risk reduction strategies (e.g., sex with condoms, serosorting, strategic positioning). Additionally, we do not have dyad-specific data to confirm partnerships nor to assess the handling of extradyadic sexual encounters among these partnerships. Finally, our cohort was recruited via the internet and potentially vulnerable to duplicate and fraudulent participants; however, we used various methods (e.g., restricting enrollment to unique IP addresses) to reduce this. These are discussed in-depth elsewhere (Grov et al., 2019; Nash et al., 2019).
CONCLUSION
PrEP uptake in the U.S. has been slow and researchers continue to explore potential barriers related to PrEP use. Social and relationship contexts have been proven to be influential for sexual decision making and for PrEP. Our study results highlight the importance of relationship factors, notably partner’s HIV status, in plans to use PrEP and support the incorporation of partner-related considerations in HIV prevention interventions.
Acknowledgements
Special thanks to additional members of the T5K study team: David Pantalone, Sarit A. Golub, Viraj V. Patel, Gregorio Millett, Don Hoover, Sarah Kulkarni, Matthew Stief, Chloe Mirzayi, Corey Morrison, Javier Lopez-Rios, & Pedro B. Carneiro. Thank you to the program staff at NIH: Gerald Sharp, Sonia Lee, and Michael Stirratt. And thank you to the members of our Scientific Advisory Board: Adam Carrico, Michael Camacho, Demetre Daskalakis, Sabina Hirshfield, Jeremiah Johnson, Claude Mellins, and Milo Santos. Finally, a very special thanks to Meredith Ray for her coding and programming expertise. While the NIH financially supported this research, the content is the responsibility of the authors and does not necessarily reflect official views of the NIH. Parts of this paper were presented at the 2019 American Public Health Association Annual Meeting November 2–6 in Philadelphia, PA.
Funding
Together 5,000 was funded by the National Institutes for Health (UG3 AI 133675 - PI Grov). Other forms of support include the CUNY Institute for Implementation Science in Population Health, the Einstein, Rockefeller, CUNY Center for AIDS Research (ERC CFAR, P30 AI124414).
Footnotes
Disclosure statement
No competing financial interests exist.
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