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. Author manuscript; available in PMC: 2021 Apr 1.
Published in final edited form as: Int J Drug Policy. 2020 Mar 2;78:102714. doi: 10.1016/j.drugpo.2020.102714

Individual and Network Factors Associated with HCV Treatment Uptake among People who Inject Drugs

Oluwaseun Falade-Nwulia a, Paul Sacamano b, Sean D McCormick a, Cui Yang b, Greg Kirk b, David Thomas a, Mark Sulkowski a, Carl Latkin b, Shruti H Mehta b
PMCID: PMC7367433  NIHMSID: NIHMS1568655  PMID: 32135398

Abstract

Background:

Hepatitis C virus (HCV) treatment uptake among people who inject drugs (PWID), a population with disproportionately high rates of HCV, remains low. Peers have been shown to positively impact a broad range of health outcomes for PWID. There is, however, limited data on the impact of PWID social network members on HCV treatment.

Methods:

HCV-infected PWID enrolled in an ongoing community-based cohort were recruited as “indexes” to complete an egocentric social network survey. The survey elicited from the index PWID a list of their network members and the index’s perception of network member characteristics. Logistic regression analyses were conducted to compare individual and network factors associated with HCV treatment in the index PWID.

Results:

Among 540 HCV-infected PWID, the mean age was 55.7 years and the majority were black (87.2%) and male (69.8%). PWID reported a mean of 4.4 (Standard Deviation [SD] 3.2) network members, most of whom were relatives (Mean 2.2 [SD 1.5]). In multivariable analysis, increasing index age and HIV infection were positively associated with HCV treatment, while drug use and homelessness in the preceding 6 months were negatively associated with HCV treatment. From a network perspective, having at least one network member who regularly talked with the index about seeing their doctor for HIV care was associated with HCV treatment (Adjusted Odds Ratio [AOR] 2.7; 95% Confidence Interval (CI) [1.3, 5.6]). Conversely, PWID who had at least one network member who helped them understand their HCV care were less likely to have been HCV treated (AOR 0.2; CI [0.1, 0.6).

Conclusion:

HCV treatment uptake in this group of PWID appeared to be positively influenced by discussions with network members living with HIV who were in care and negatively influenced by HCV information sharing within PWID networks. These findings underscore the influence of peers on health seeking behaviors of their network members and emphasizes the importance of well-informed peers.

Keywords: Hepatitis C, people who inject drugs, barriers, facilitators, social network, informational support

Introduction

Hepatitis C virus (HCV) infection is easily transmissible through injection drug use and thus disproportionately impacts people who inject drugs (PWID), with prevalence of 50–90% (Sulkowski & Thomas, 2005). HCV infection is associated with significant morbidity and mortality, including progression to end stage liver disease, hepatocellular cancer, and liver related death (Cousien et al., 2016). HCV-related morbidity and mortality is preventable though treatment with highly tolerable, oral direct acting antiviral agents (DAAs), which are associated with cure rates of 95% or higher (Backus, Belperio, Shahoumian, & Mole, 2018; Falade-Nwulia et al., 2017; Stepanova et al., 2018). The availability of these agents has led the World Health Organization (WHO) to call for elimination of HCV as a public health threat by the year 2030. Specifically, the HCV elimination goals aim for a reduction in HCV incidence by 90% and a reduction in HCV related mortality by 65% by 2030, relative to 2015 rates (World Health Organization: Draft global health sector strategy on viral hepatitis, 2016–2021- The first of it’s kind, 2015). A key milestone for accomplishing this goal is treatment of HCV in 80% of those diagnosed. As PWID are the core of the hepatitis C epidemic in developed countries such as the United States, efforts to eliminate HCV must address HCV treatment uptake in this often marginalized population.

Despite the high prevalence of HCV among PWID and international guidelines recommending treatment for HCV infection among PWID (Robaeys et al., 2013), the majority of PWID remain untreated for HCV even in the oral DAA era. Recent research estimates HCV treatment uptake at below 20% among PWID in different settings (Iversen et al., 2017; Socias et al., 2019; Tsui et al., 2019). PWID continue to bear significant disparities in HCV treatment due to a variety of patient, provider, and system level barriers. Patient level barriers include the perception that HCV is an innocuous disease, fear of liver biopsy, and residual concerns about interferon side effects (Crowley et al., 2018; Mehta et al., 2008; Swan et al., 2010). Interferon, a key component of HCV treatment prior to the advent of oral DAAs, was associated with significant side effects including chills, high grade fevers, depression and in some cases suicidal ideation. Providers are also still less likely to prescribe HCV treatment to PWID due to concerns about poor adherence or risk of reinfection, especially given the high cost of current oral DAAs (Myles, Mugford, Zhao, Krahn, & Wang, 2011). Navigation of the complex US health care system, including the referral and scheduling system, as well as insurance and payment issues also pose significant system level barriers to HCV treatment among PWID (Grebely et al., 2008; Grebely, Oser, Taylor, & Dore, 2013).

There is a body of research supporting the role of peers in improving multiple health outcomes among PWID. For example, among PWID living with HIV, peer support has been strongly associated with both increased access to HIV care and antiretroviral therapy adherence (Knowlton et al., 2007; Knowlton, Hua, & Latkin, 2005). Less is known about the effect of social network members on uptake of hepatitis C treatment.

Aiming to characterize the social networks of PWID and their role in HCV treatment initiation, we administered an egocentric network survey to PWID enrolled in an ongoing community-based cohort in Baltimore, Maryland, USA. We sought PWID perspectives regarding the composition and characteristics of the social network members closest to them. We then assessed the link between individual and social network characteristics on HCV treatment uptake among these PWID. We hypothesized that PWID with greater levels of informational support within their networks would be more likely to have received HCV treatment.

Methods

Individuals enrolled in the AIDS Linked to the IntraVenous Experience (ALIVE) study, a longitudinal cohort of adults 18 years of age or older with a history of injection drug use, were recruited to participate in this study (Vlahov et al., 1991). ALIVE participants have been enrolled in waves since 1988–89, with subsequent enrollment waves in 1994–1995, 1997, 2005–2008, and 2015–2018. Participants complete standardized questionnaires and Audio Computer-Assisted Self-Interview (ACASI) interviews for behavioral, socioeconomic, and clinical parameters, and provide bio-specimens at baseline and semi-annual visits. All ALIVE participants receive HCV and HIV antibody testing, are notified of results, and are offered linkage to care services, if a test result is positive. Between April 1, 2016, and June 30, 2017, all enrolled participants (“indexes”) completed a supplemental face-to-face, 59-question social network survey that was administered by research staff. All study procedures were approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board and conducted in accordance with the 1975 Declaration of Helsinki. All participants provided informed consent.

Participant Selection

To be eligible for participation in this study, individuals had to have been enrolled in the ALIVE cohort, completed the social network inventory, and had evidence of HCV viremia, based on HCV RNA testing performed during the period of enrollment in the ALIVE cohort. Among participants completing the social network inventory, 99% of HCV antibody positive participants had subsequent HCV RNA testing performed. Initial HCV viremia was assessed between 1995 and 2016 in all participants and reconfirmed between 2015 and 2016 in 89% (480/540) of participants included in this study.

Measures

The primary outcome was HCV treatment with a DAA regimen, which was defined as self-report of HCV treatment initiation between 2014 and 2017, in addition to a subsequent undetectable HCV RNA after a previously confirmed detectable HCV RNA. We compared individual and network characteristics of indexes who had ever received HCV treatment with indexes who had not. Drug and alcohol use was assessed based on a report of any illicit drug or alcohol use in the 6 months preceding survey completion. The index-level demographic characteristics explored included age, gender, race, education level, employment status, and homelessness.

The social network inventory utilized a single name-generating question to elicit initials of people who the participant felt closest to by asking the question: “Looking back over the past 6 months, who are the people that have been important to you? Please think about the people you associate with closely including friends, sexual partners, associates and family who are at least 13 years or older”. We focused on close ties as they may have a greater influence on peer health behaviors than less close ties (O’Malley, Arbesman, Steiger, Fowler, & Christakis, 2012; Reis, Collins, & Berscheid, 2000). The size of each participant’s network was the sum of the number of people listed in this question. Participants were then asked to focus on the first five people mentioned, if more than five were mentioned, as the most important network members are regularly nominated first (Burt, 1984; Hickson et al., 2017; Merluzzi & Burt, 2013). For each of these five network members, participants then detailed their characteristics such as gender, age, race, educational level, employment status, drug use status, HIV and HCV infection, care seeking and treatment status. Participants also provided information on the nature of relationship with network members, including duration of relationship, type of relationship (for example: sexual partner, relative, friend), frequency of contact with network member, and degree of closeness on a 5 point scale, ranging from 1 (not close at all) to 5 (very close).

Informational support was measured with two questions: (1) “How often would you say he/she is someone to give you advice/help you understand a situation?” and (2) “How often did he/she help you understand information about your health?” Response options for each of the above questions were: 1 = never; 2 = a little; 3 = sometimes; 4 = frequently; and 5 = always. Network members who were reported by the index as frequently or always fulfilling the function were considered a network member with that characteristic. Disease specific informational support was measured with the following questions: (1) “Does he/she help you to understand care of your hepatitis C?” (asked in reference to all listed network members) and (2) “Does he/she regularly talk to you about seeing their doctor for HIV?” (asked only in reference to network members reported to be living with HIV). Responses to these questions were categorical. We operationalized infectious disease care information sharing as having a network member living with HIV who discussed their HIV medical care with the index in the preceding 6 months.

Participants were also asked which of their listed network members knew one another. This measure was used to calculate network density, which is a measure of connectedness among network members. Density of a network ranges from 0, indicating that no network members know each other, to 1, indicating that all network members know each other.

Statistical Analyses

Descriptive statistics were used to characterize the study sample with respect to demographics, risk behaviors, and number of network members fulfilling specific criteria. We calculated an average for network member age and duration of time index had known their network members. Network density was calculated as the number of actual ties, divided by the number of possible ties. Network calculations were conducted using E-Net (Borgatti, 2006). T-tests and chi-squared statistics were used to evaluate bivariate differences in characteristics between PWID living with HCV who were HCV treated and untreated.

Bivariate and multivariable logistic regression analyses were used to determine odds ratios for factors associated with HCV treatment. Within these logistic regression analyses, network member characteristics were dichotomized as having at least 1 network member with or fulfilling criteria for the specific characteristic versus not having any network member with or fulfilling criteria for the specific characteristic. Factors were considered for inclusion in multivariable analysis if they demonstrated an association with the outcome at the level of p<0.1 in univariate analysis. Factors, including age and race, previously known to be associated with HCV treatment from a review of the literature, were also included in the multivariable analysis (Falade-Nwulia et al., 2016; Kanwal et al., 2016; Marcus et al., 2018; Radwan et al., 2019; Saeed et al., 2017; Socias et al., 2019; Spradling et al., 2018). To account for variations in network size, total network size was included in the multivariable model. All analyses were performed using Stata version 13 (Stata Corp, College Station, Texas).

Results

Individual level characteristics

Among 540 HCV-infected PWID enrolled in the study, the mean age was 55.7 (Standard Deviation [SD] 8.6) years and the majority (87.2%) were black (Table 1). Overall, 30.2% were female, 45.1% had a 12th grade education or higher, 31.7% were HIV co-infected, and 10.9% reported a history of homelessness in the preceding 6 months. Approximately a third reported injection drug use in the preceding 6 months. Heroin alone (17.0%) and speedball (heroin and cocaine together) (16.9%) were the most commonly injected drugs, followed by cocaine (10.4%). Approximately half of the cohort had ever received medication-assisted treatment with methadone (40.7%) or buprenorphine (11.5%).

Table 1:

Index and network characteristics by HCV treatment status among an urban, community-based cohort of 540 PWID

Characteristic Total (%) (n=540) HCV untreated (%) (n=427) HCV treated (%) (n=113) P-value
Index characteristics
Age, mean years (SD) 55.7 (8.6) 54.5 (8.8) 60.1 (6.0) <0.01**
Female gender 163 (30.2) 133 (31.2) 30 (26.6) 0.34
Black race 471 (87.2) 364 (85.3) 107 (94.7) <0.01**
12th grade education, GED, or higher 243 (45.1)a 188 (44.1) 55 (48.7) 0.39
Employed 67 (12.5) a 52 (12.2) 15 (13.4) 0.74
HIV infected 171 (31.7) 118 (27.6) 53 (46.9) <0.01 **
Any homelessness in the past 6 months 59 (10.9) 58 (13.6) 1 (0.9) <0.01 **
Substance use in the past 6 months
 Alcohol use 273 (50.6) 229 (53.6) 44 (38.9) <0.01 **
 Injection drug use 156 (28.9)a 145 (34.0) 11 (9.7) <0.01 **
 Cocaine injection 56 (10.4) 52 (12.2) 4 (3.5) <0.01 **
 Speedball injection 91 (16.9) 84 (19.7) 7 (6.2) <0.01 **
 Heroin injection 92 (17.0) 85 (19.9) 7 (6.2) <0.01 **
Ever prescribed methadone 219 (40.7)a 187 (44.0) 32 (28.3) <0.01 **
Ever prescribed buprenorphine 62 (11.5) 50 (11.7) 12 (10.6) 0.75
Network characteristics
Network size, mean (SD) 4.4 (3.2) 4.3 (3.3) 4.5 (2.7) 0.59
Closeness to network members, mean (SD) 4.5 (0.9) 4.4 (0.9) 4.6 (0.7) 0.09
Network density, mean (SD) 0.4 (0.1)a 0.4 (0.1) 0.4 (0.2) 0.36
Length of time known (years), mean (SD) 32.3 (13.6) 31.9 (13.5) 33.6 (14.0) 0.24
Network member age (years), mean (SD) 50.6 (10.9)a 50.5 (11.3) 51.1 (9.5) 0.53
Number of network members by characteristic
Female, mean (SD) 2.1(1.2)a 2.1 (1.1) 2.3 (1.2) 0.12
Black, mean (SD) 3.1 (1.7) 3.1 (1.7) 3.4 (1.5) 0.06
Employed, mean (SD) 1.9 (1.4)a 1.9 (1.4) 1.9 (1.4) 0.77
Sex partner, mean (SD) 0.4 (0.5) 0.4 (0.5) 0.4 (0.5) 0.44
Relative, mean (SD) 2.2 (1.5) 2.2 (1.5) 2.3 (1.5) 0.73
Professional, mean (SD) 2.2 (1.5) 2.2 (1.5) 2.3 (1.5) 0.73
Communicate at least weekly, mean (SD) 3.1 (1.4) 3.1 (1.4) 3.2 (1.4) 0.41
Give you advice or help you understand a situation, mean (SD) 2.8 (15)a 2.8 (1.6) 2.9 (1.5) 0.48
Help you to understand information about your health, mean (SD) 2.4 (16)a 2.4 (1.7) 2.6 (1.6) 0.13
HIV infected, mean (SD) 0.2 (0.5)a 0.2 (0.5) 0.2 (0.6) 0.15
Regularly talk to you about seeing their doctor for HIV, mean (SD) 0.1 (0.4)a 0.1 (0.4) 0.2 (0.5) 0.03*
HCV infected, mean (SD) 0.3 (0.7)a 0.4 (0.7) 0.2 (0.5) <0.01 **
Been treated for HCV, mean (SD) 0.1 (0.4)a 0.1 (0.4) 0.1 (0.3) 0.07
Receiving care for HCV, mean (SD) 0.2 (0.5)a 0.2 (0.5) 0.1 (0.3) 0.23
Cured of HCV, mean (SD) 0.0 (0.2)a 0.0 (0.2) 0.0 (0.2) 0.76
Help you to understand care of your HCV, mean (SD) 0.2 (06)a 0.3 (0.6) 0.1 (0.2) <0.01 **
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*

p<0.05;

**

p<0.01

a

Data missing on: 12th grade education, GED, or higher (n=1); employed (n=2); injection drug use in the past 6 months (n=1); ever prescribed methadone (n=2); network density (n=23); network member age (n=1); number of female network members (n=4); number of employed network members (n=3); number of network members who give you advice or help you understand a situation (n=1); number of network members who help you to understand information about your health (n=3); number of HIV infected network members (n=1); number of network members who regularly talk to you about seeing their doctor for HIV (n=2); number of HCV infected network members (n=2); number of network members who have been treated for HCV (n=4); number of network members who are receiving care for HCV (n=3); number of network members who have been cured of HCV (n=3); number of network members who help you to understand care of your HCV (n=4)

HIV: Human immunodeficiency virus; HCV: Hepatitis C virus; GED: General Educational Development

Network characteristics

The mean self-reported network size was 4.4 (SD 3.2) network members, with relatives comprising on average 2.2 individuals per network (Table 1). Indexes reported high levels of closeness to network members (average closeness score 4.5 [SD 0.9]), and network density was 0.4, indicating that in an average network, less than half of the possible ties between listed network members existed. Indexes had known their network members on average for 32.3 (SD 13.6) years and communicated at least weekly with majority of their reported network members (3.1 [SD 1.4]).

Characteristics by HCV treatment status

Overall, 113 (20.9%) participants reported receipt of HCV treatment in the DAA era and had an undetectable HCV RNA. An additional 34 participants (6.3%) reported HCV treatment within the DAA era, but did not have an undetectable HCV RNA; these participants were considered untreated for HCV within the analysis. In bivariate analysis, those treated for HCV were more likely to be older, black, and co-infected with HIV (Table 1). There was a negative association between HCV treatment and homelessness, recent alcohol use and recent injection drug use. On the network level, HCV treatment was negatively associated with having a higher number of network members who were perceived to be HCV-infected and a higher number of network members who helped the index understand HCV care. However, having a greater number of network members living with HIV who regularly talked about seeing their doctor for HIV was positively associated with HCV treatment. There was not a statistically significant association between HCV treatment and the number of network members who provided the index advice or the number of network members who helped the index understand information about his/her health.

In the multivariable model (Table 2), individual level factors positively associated with HCV treatment included age (Adjusted Odds Ratio (AOR) 2.5; 95% Confidence Interval (CI) [1.7, 3.6] for each 10 year increment in age) and HIV infection (AOR 2.4; CI [1.4, 3.9]). Conversely, indexes who reported homelessness (AOR 0.1; CI [0.0, 0.8]) and injection drug use (AOR 0.4; CI [0.2, 0.7]) in the last 6 months were less likely to have received HCV treatment. Black race and alcohol use were not significantly associated with HCV treatment in the multivariable model. From the perspective of the network, indexes with at least one network member who helped them understand HCV care were less likely to have received HCV treatment (AOR 0.2; CI [0.1, 0.6]). However, indexes with at least one network member who regularly talked to them about seeing their doctor for HIV care had greater odds of HCV treatment (AOR 2.7; CI [1.3, 5,6]). Sensitivity analyses of network factors associated with HCV treatment utilizing mean numbers of network members having specified characteristics yielded similar findings (Supplementary Table 1).

Table 2:

Univariable and multivariable logistic regression analyses of index and network characteristics associated with HCV treatment initiation among an urban, community-based cohort of 540 PWID

Characteristic Odds Ratio (95% CI) Adjusted Odds Ratio (95% CI)
Index characteristics
Age in 10 year increments, mean (SD) 2.7 (1.9, 3.7)** 2.5 (1.7, 3.6)**
Female gender 0.8 (0.5, 1.3) -
Black race 3.1 (1.3, 7.3)* 0.8 (0.3, 2.2)
12th grade education, GED, or higher 1.2 (0.8, 1.8) -
HIV infected 2.3 (1.5, 3.5)** 2.4 (1.4, 3.9)**
Any homelessness in the past 6 months 0.1 (0.0, 0.4)** 0.1 (0.0, 0.8)*
Substance use in the past 6 months
Alcohol use 0.6 (0.4, 0.8)** 0.6 (0.4, 1.0)*
Injection drug use 0.2 (0.1, 0.4)** 0.4 (0.2, 0.7)**
Network characteristics
Network size 1.0 (1.0, 1.1) 1.0 (0.9, 1.1)
Closeness to network members 1.2 (0.9, 1.6) -
Network density 0.5 (0.1, 2.0) -
Length of time known in 10 year increments (years) 1.1 (0.9, 1.3) -
Network member age in 10 year increments (years) 1.1 (0.9, 1.3) -
At least one network member with characteristics
Give you advice or help you understand a situation 1.9 (0.7, 5.1) -
Help you to understand information about your health 1.8 (0.9, 3.6) 2.0 (1.0, 4.3)
HIV infected 1.5 (0.8, 2.5) -
Regularly talk to you about seeing their doctor for HIV 2.3 (1.3, 4.3)** 2.7 (1.3, 5.6)*
HCV infected 0.6 (0.3, 1.0)* 1.3 (0.6, 3.0)
Receiving care for HCV 0.7 (0.3, 1.4) -
Cured of HCV 1.5 (0.6, 4.1) -
Help you to understand care of your HCV 0.3 (0.1, 0.6)** 0.2 (0.1, 0.6)**
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*

p<0.05;

**

p<0.01

HIV: Human immunodeficiency virus; HCV: Hepatitis C virus; GED: General Educational Development

Discussion

In our analysis, we found that in the oral DAA era, 21% of an urban cohort of PWID had been treated and cured of HCV. This contrasts sharply with the HCV treatment initiation rate of 6% reported in this same cohort in the interferon era (Mehta et al., 2008). Although an encouraging trend, this rate falls significantly short of the 80% HCV treatment uptake required to meet WHO HCV elimination goals. Although high rates of HCV treatment uptake have been reported in some cohorts of people living with HIV (Béguelin et al., 2018; Boerekamps et al., 2018), HCV treatment uptake in other cohorts of PWID in the oral DAA are similar to that found in our study (Iversen et al., 2017; Socias et al., 2019; Tsui et al., 2018).

Our finding of older age and HIV co-infection as individual level factors associated with HCV treatment among PWID is supported by other studies (Radwan et al., 2019; Spradling et al., 2018). PWID living with HIV are more likely to be engaged with primary care in part due to federal and local policies and funding that prioritize HIV treatment and prevention, which in turn increases access to HCV treatment for people living with HIV (PLHIV). Moreover, PLHIV and HCV face increased risk of accelerated progression to hepatocellular cancer and liver failure, which can be reduced through HCV treatment (Benhamou et al., 1999; Kirk et al., 2013; Lo Re et al., 2014). Consequently, HCV treatment has been prioritized among PLHIV.

Consistent with other research, homelessness and recent injection drug use were significantly negatively associated with HCV treatment (Butler, Larney, Day, & Burns, 2019; Charlebois,Lee, Cooper, Mason, & Powis, 2012; Grebely et al., 2011; Rivero-Juarez et al., 2019; Saeed et al., 2017). Despite availability of well tolerated and effective HCV treatment options, the barriers of homelessness and ongoing substance use remain. These findings emphasize the complex social and health related needs of PWID that were not entirely resolved with the advent of DAA regimens, but will need to be addressed as part of comprehensive interventions to increase HCV treatment uptake.

Within our sample, having at least one network member who regularly talked with the index PWID about their HIV care was positively associated with index HCV treatment. To our knowledge, this study is the first to evaluate HIV care information sharing as it relates to HCV treatment and consequently show that it is associated with HCV treatment uptake. In our cohort, we interpret sharing of health related information as having a network member living with HIV who discussed their HIV care with the index, regardless of the index’s HIV status. Given that PLHIV are more likely than people without HIV (particularly those with a history of injection drug use) to be engaged in health care, it is likely they have experience navigating the health care system and may also be more exposed to new information on HCV treatment (Socias et al., 2019). Specifically, because HCV treatment has been prioritized among those with HIV/HCV co-infection, HIV primary care providers may recognize the importance of HCV treatment for PLHIV. As such PLHIV may be more likely to be aware and informed about the effective HCV therapies with few side effects available in the oral DAA era. Knowing that these network members living with HIV are open to discussing their HIV care may further indicate an openness toward discussing HCV treatment with other network members.

While the mechanisms through which network members living with HIV positively impacted HCV treatment in index PWID is not captured in our study, we hypothesize that added knowledge regarding HCV treatments and the health care system possessed by PLHIV may potentiate their ability to provide information about curative treatments for HCV and peer navigation for accessing a clinic for treatment, scheduling an appointment, or obtaining a referral. This assertion is supported by previous literature demonstrating that social network members have the potential to positively improve health outcomes for PWID through provision of critical support, such as peer education, transportation to appointments, emotional, and material support (Latkin et al., 2013). In addition to informational support, social norms may have a role in the health behaviors of social network members (Latkin & Knowlton, 2015). Among PWID living with HIV, peer support is strongly associated with both increased access to HIV care and antiretroviral therapy adherence (Knowlton et al., 2007; Knowlton et al., 2005). These data suggest the potential for interventions focused on PWID living with HIV as peers to promote HCV treatment in their networks. These PWID will, however, require training to enhance their effectiveness in these roles.

In a similar way, this dynamic may help explain the negative association between having at least one network member who talked about HCV treatment and “index” being HCV treated. Despite the advent of DAA regimens with limited side effects, residual knowledge related to the negative side effects, difficult regimens, and low cure rates of interferon-based therapies continues to circulate among social networks (Bryant, Rance, Hull, Mao, & Treloar, 2019). This fear of interferon-based regimens has been a consistent barrier to HCV treatment in previous research (Boglione, Cusato, Cariti, Di Perri, & D’Avolio, 2017; Crowley et al., 2018; McGowan & Fried, 2012). Additionally, significant misinformation exists regarding drug abstinence requirements, which has been shown to be a barrier to HCV treatment (McGowan & Fried, 2012). As recently as 2016, the Maryland Medicaid program mandated abstinence from illicit substance use for 6 months as a requirement for HCV therapy coverage (Roundtable & Innovation, 2016). Fortunately, this sobriety requirement has since been lifted. However, restriction to HCV treatment based on liver fibrosis stage persisted and send the message that HCV treatment should not be prioritized to patients who are denied treatment due to low liver disease fibrosis stage. It is likely that peers continue to exchange this information.

Above all, these findings underscore the influence of peers on health seeking behaviors of their network members and emphasizes the importance of well-informed peers. This study suggests great potential for interventions that utilize well informed peers to facilitate spread of health information, especially about DAA treatments, to improve motivation and facilitate health behavior change through existing information pathways within social networks of PWID. In particular, training of PWID with positive HCV treatment experiences to share their positive experiences with network members may significantly increase HCV treatment uptake in PWID networks.

Well informed peers have been studied in HCV research (Crawford & Bath, 2013; Grebely et al., 2010), but additional focus should be directed towards examining the role of peers, particularly PWID, who are already embedded within these networks. Given a high likelihood of trust between individuals in existing network relationships, interventions that rely on existing social networks have the potential to be more effective and more sustainable than approaches that attempt to create new relationships (Latkin et al., 2013). In our sample, average duration of network relationship was 32.3 years, suggestive of stable relationship with potential to support ongoing behavior change. In addition, peers have potential to provide additional material support such as transportation to appointments that may facilitate uptake of treatment.

Although our study population was older (average age 55 years), these findings have implications for the current opioid epidemic within the United States and its associated increases in new HCV infections among younger PWID (Zibbell et al., 2018; Zibbell et al., 2015). While younger PWID may not have people living with HIV in their networks, they will have other PWID with HCV infection in their social networks. Our finding of a negative association between having at least one network member who helped the index understand HCV care and index HCV treatment highlights a potential negative impact of inaccurate HCV treatment information sharing among PWID. Other data support a potential benefit for increasing HCV treatment uptake in PWID networks through brief interventions that support PWID to provide accurate information on HCV treatment to their drugs use network members (Falade-Nwulia et al., 2020).

Our study is limited by being focused on PWID enrolled in a long standing cohort recruited from a single city (Baltimore). Nonetheless, these findings likely reflect the current barriers and facilitators to HCV treatment that exist for many PWID. Additionally, limiting detailed assessment of network characteristics to only five network members may have prevented us from capturing information on potentially influential network members. However, research indicates that indexes regularly identify the most important network members first (Burt, 1984; Hickson et al., 2017; Merluzzi & Burt, 2013). We are also limited in our ability to confirm treatment status for the 34 participants who reported HCV treatment, but did not have an undetectable HCV viral load. This group could include participants who were reinfected with HCV or had treatment non-response. A major strength of this study is the detailed exploration of social networks and the important role of peer support, particularly informational support, in HCV treatment. The robust sample size, including substantial proportion of females, is also a strength of this study.

Despite advancements in HCV treatment, there remain substantial barriers that impede HCV cure, particularly for PWID. Social networks play an important role in shaping health and have the potential to help overcome HCV treatment barriers among PWID. These data suggest that health information sharing within a social network framework can act as a facilitator or barrier to HCV treatment. Further research should explore the use of informed PWID peers in providing informational support for enhancing HCV treatment uptake and cure.

Supplementary Material

1

Acknowledgements

This research was funded in part by the following National Institutes of Health grants: DA036927, DA048063, K23DA041294, K24DA034621, R37DA013806, R01DA16065, and U01DA036935.

This research was facilitated by the infrastructure and resources provided by a Doris Duke Early Clinician Investigator award and the Johns Hopkins University Center for AIDS Research, a National Institutes of Health funded program [grant number P30AI094189]), which is supported by the following National Institutes of Health Co-Funding and Participating Institutes and Centers: NIAID, NCI, NICHD, NHLBI, NIDA, NIMH, NIA, FIC, NIGMS, NIDDK, and OAR. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Declaration of Interests

MS has the following disclosures:

• PI for research grants - funds paid to Johns Hopkins University: AbbVie, Assembly Bio, Gilead, Proteus Digital Health

• Scientific advisor/Consultant: The terms of these arrangements are being managed by the Johns Hopkins University in accordance with its conflict of interest policies: AbbVie, Arbutus, Gilead

OFN, PS, SDM, CY, GK, DT, CL, and SHM have no interests to declare.

Author Contributions

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