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. Author manuscript; available in PMC: 2025 Jan 1.
Published in final edited form as: J Subst Use. 2023 Sep 6;29(5):836–842. doi: 10.1080/14659891.2023.2254391

Influence of polysubstance use on the health-related quality of life among people who inject drugs undergoing opioid agonist treatment following treatment for hepatitis c virus

Mirinda Ann Gormley 1,2,8,*, Wanfang Zhang 3, Stella Self 4, Joseph A Ewing 1,5, Moonseong Heo 6, Prerana Roth 7, Irene Pericot-Valverde 8, Lior Rennert 6, Matthew J Akiyama 9, Brianna L Norton 9, Alain H Litwin 7,8,10
PMCID: PMC11534293  NIHMSID: NIHMS1929462  PMID: 39502837

Abstract

Background.

Treating hepatitis C virus (HCV) in people who inject drugs (PWID) has been associated with increased health-related quality of life (HRQOL). Polysubstance use (PSU) is common among PWID, but no studies have investigated PSU influence on PWID’s HRQOL HCV treatment.

Methods.

Participants included 150 PWID receiving HCV treatment at opioid agonist treatment clinics in Bronx, NY. The EQ-5D-3L measurement tool assessed five health dimensions producing an index of HRQOL measured at baseline, 4-, 8-, and 12-weeks during treatment and 12- and 24-weeks post-treatment. PSU was determined at baseline. Generalized estimating equations assessed the influence of baseline PSU on changes in mean EQ-5D-3L index over time.

Results.

Of the 150 participants, 46 (30.7%) reported PSU and mean HRQOL overall was 0.655, indicating moderate HRQOL. Mean HRQOL was lower at all time-points for the PSU group compared to the non-PSU group. Though PSU group showed improvements in mean HRQOL from baseline (0.614) to 4-, 12- and follow-up week 24 (0.765, 0.768, and 0.731, respectively), the mean change of HRQOL scores was not significantly associated with PSU (p-value=0.956).

Conclusions.

For individuals with PWID, our study showed no difference in HRQOL between those who did and did not engage in PSU following HCV treatment.

Keywords: Hepatitis C Virus, Polysubstance Use, Health-related quality of life, people who inject drugs, opioid agonist treatment

Introduction

The hepatitis C virus (HCV) is the most commonly reported bloodborne infection in the US (Prevention, 2019). The majority of new cases in the U.S. are due to injection drug use (IDU) with nearly three-quarters (73.0%) of individuals with HCV reporting engaging in IDU (Prevention, 2019). Encouraging treatment of HCV among people who inject drugs (PWID) is likely the best avenue for preventing future infections, as providing HCV treatment can reduce HCV prevalence and incidence among PWID (Hickman et al., 2015, Zelenev et al., 2018).

One model of care that has demonstrated successful delivery of HCV treatment to PWID is providing treatment during opioid agonist treatment (OAT) (Akiyama et al., 2018, Norton et al., 2018, Rosenthal et al., 2020). PWID receiving treatment during OAT have demonstrated high adherence (Schulte et al., 2020, Pericot-Valverde et al., 2020), HCV treatment completion (Schulte et al., 2020), and high rates of sustained virologic response (SVR) (Pericot-Valverde et al., 2020, Schulte et al., 2020, Rosenthal et al., 2020). Additionally, PWID who achieve SVR report increases in their health-related quality of life (HRQOL) following HCV treatment (Gormley et al., 2021, Schulte et al., 2020, Vera-Llonch et al., 2013, Juanbeltz et al., 2018). Individuals with opioid use disorder who continue substance use throughout HCV treatment show improvement in their HRQOL following HCV cure (Spaderna et al., 2023). However, while PWID report increases in their HRQOL post-HCV treatment, on average reported HRQOL still falls below those reported within the general population (Gormley et al., 2021, Vahidnia et al., 2017, Janssen et al., 2019, Fryback et al., 2007). This highlights the need to assess additional clinical factors which may influence HRQOL.

Polysubstance use (PSU) and HCV co-occur at striking rates among PWID (Wagner et al., 2021, Puzhko et al., 2017). Addressing PSU amongst the HCV population is important as engaging in PSU increases an individual’s risk for fatal overdose, and age-adjusted death rates for all substances co-involved with synthetic opioids have increased from 2013–2019 (Mattson et al., 2021). PSU also undermines retention in opiate treatment programs (Blondino et al., 2020) and has been associated with lower quality of life (Kelly et al., 2017, Hagen et al., 2017). However, to our knowledge there are few studies which have examined the influence of PSU on HCV treatment, and no studies which have examined the influence of PSU on HRQOL for individuals receiving HCV treatment.

Understanding the influence of PSU on HRQOL is crucial when considering the necessity of addressing co-occurring substance use during OAT. While a great deal of research has investigated the association of PSU to morbidity and mortality in the general population, to our knowledge, no studies have investigated the relationship between PSU and HRQOL for individuals receiving HCV treatment during OAT. Therefore, the objective of this study was to investigate the impact of baseline PSU on the HRQOL among PWID 24-weeks following HCV treatment.

Methods

Study Design and Sample

Participants were enrolled in the PREVAIL study, a randomized clinical trial investigating intensive models of HCV care for PWID (NCT01857245). Participants were recruited from OAT clinics in the Bronx, New York from October 2013 to May 2016. HCV treatment was based upon guidelines from the American Association for the Study of Liver Diseases (Akiyama et al., 2018). The majority received second-generation direct acting antivirals (DAAs), defined as regimens that were interferon-free, ribavirin-free, or interferon and ribavirin-free, which included: simeprevir and sofosbuvir (SIM/SOF) and sofosbuvir and ledipasvir (SOF/LDV). Few participants received first-generation DAAs, defined as regimens containing ribavirin, interferon, or ribavirin and interferon, and including: sofosbuvir and ribavirin (SOF/RBV), telaprevir, ribavirin, and pegylated interferon (TVR/RBV/PEG), and sofosbuvir, ribavirin, and pegylated interferon (SOF/RBV/PEG).

Study Participants

Adults aged 18 years or older who were HCV treatment naïve (or treatment experienced after 12/3/2014) and spoke English or Spanish were eligible. Individuals were excluded if they had decompensated cirrhosis, could not provide informed consent, were pregnant or breastfeeding, or reported allergies to HCV medication. Information was collected from study participants prior to treatment initiation, and during research visits 4-, 8-, and 12-weeks during treatment and 12- and 24-weeks during treatment. Additional information for the study population and study design are described in detail elsewhere (Akiyama et al., 2019, Akiyama et al., 2018).

Health-Related Quality of Life

The EQ-5D-3L is a measure for HRQOL that provides a simple descriptive profile and single index value for health status (Van Reenen and Oppe, 2015). Although a generic measure not built for any specific disease/disorder, the EQ-5D-3L has frequently been used to measure HRQOL in populations living with HCV (Gormley et al., 2021, Jang et al., 2018, Vera-Llonch et al., 2013, Ng et al., 2019). Five health domains (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) are utilized to measure a respondent’s health state. Health states are determined by the scores reported for each domain, where 1 =“No problems”, 2=“Some problems”, and 3=“Extreme problems” (Van Reenen and Oppe, 2015). Final health states were converted into a summary index using U.S.-specific valuation weights at each level of every domain with a standardized formula provided by the Agency for Healthcare Research and Quality (Shaw et al., 2005). Final EQ-5D-3L scores range from 0 to 1, where 1 is full health and 0 indicates death. Participants were assessed with the EQ-5D-3L at baseline, during treatment (4-, 8-, and 12-weeks), and post treatment (12- and 24-weeks).

Polysubstance use (PSU)

A binary measure of PSU was created using self-reported substance use, which was captured at baseline using a question from the Addiction Severity Index (ASI) 5th edition: “How many days in the past 30 have you used substance?”. Illicit substances captured by the ASI included heroin, methadone, other opioids/analgesics, sedatives, cocaine, amphetamines, cannabis, hallucinogens, and inhalants. Participants were categorized as PSU if they reported at least one day of use for two or more substances at baseline. Non-PSU individuals were participants who reported the misuse of only one substance or reported 0 days of substance misuse.

Sociodemographic and clinical factors

Age, race, sex, high school graduation status, marital status, employment status, DAA regimen, SVR at 12-weeks, treatment group (Modified directly observed therapy (DOT), Group treatment (GRP) or treatment usual treatment (TAU)), liver disease severity, PSU and recent inject drug use were used as predictors and HRQOL score at baseline, study week 4, 8, 12 and follow-up weeks 12 and 24 were used as the outcome. Treatment group refers to the three groups’ patients were randomized into for HCV treatment. Individuals in the modified DOT group were directly observed taking oral doses of HCV medications only on days when they arrived to receive methadone. Individuals in GRP attended weekly sessions with 6 to 12 participants, where they received HCV medication as well as psychosocial support from group members. Individuals assigned to TAU self-administered HCV medications at home.

Statistical Methods

Chi-squared tests for association and t-tests were used to assess differences between the PSU group and the non-PSU group. Prior findings suggest that age, race, sex, high school graduation status, marital status, employment status, type of DAA (Akiyama et al., 2019, Gormley et al., 2021), SVR at 12-weeks (Pericot-Valverde et al., 2020), treatment group (Gormley et al., 2021), liver disease severity (Gormley et al., 2021), PSU and recent IDU (Dalgard O, 2004) may affect HRQOL, and thus all these variables were included in the model. The model was also adjusted for baseline HRQOL. Categorical time variables (week 4, week 8, week 12, follow-up 12 week and follow-up 24 week) and an interaction term between time and PSU were also included as covariates. We examined the relationship with HRQOL between these variables in generalized estimating equations (GEE) model (Zeger and Liang, 1986) using package ‘gee’ in R (Vincent et al., 2014), since the HRQOL was a longitudinal outcome variable correlated across time points.

There were 150 participants included in the analysis with a total of 803 observations of HRQOL. In addition to adding all predictors in GEE model, we also ran an unadjusted model with adding polysubstance use as the only predictor in model. P-values < 0.05 were considered indicative of statistically significant impact.

Results

Of the 150 participants, 104 (69.3%) were negative for PSU at baseline and 46 (30.7%) were positive. The population was predominantly male (64.0%) and Hispanic/Latino (56.0%) with a mean age of 51.2 (range: 25–73). Most had graduated high school (58.7%), were unmarried (62.5%), and unemployed (49.0%). The majority received second generation DAAs (76.9%) and were equally spaced among the directly observed treatment (34.6%), group treatment (29.8%), and treatment as usual (35.6%). Nearly all (93.3%) attained SVR at 12-weeks. Individuals in the PSU group were significantly younger than individuals who did not engage in polysubstance use (47.2 years vs. 52.9 years, p=0.004). Race, sex, education, marital status, treatment type, SVR, treatment group (directly observed treatment, group treatment, treatment as usual), liver disease severity and recent inject drug use were not significantly different for non-PSU group versus PSU group at baseline (Table 1).

Table 1.

Differences in Demographic and Clinical Characteristics by Polysubstance Use

Variables No PSU (N=104) PSU (N=46) Total (N=150) p-value
Age (years)
Mean (SD) 52.9 (9.94) 47.2 (11.1) 51.2 (10.6) 0.004
Median [Min, Max] 53.0 [26.0, 73.0] 47.0 [25.0, 70.0] 52.0 [25.0, 73.0]
Gender
Male 61 (58.7%) 35 (76.1%) 96 (64.0%) 0.11
Female 42 (40.4%) 11 (23.9%) 53 (35.3%)
Race
Non-Hispanic Black 34 (32.7%) 6 (13.0%) 40 (26.7%) 0.076
Hispanic/Latino 53 (51.0%) 31 (67.4%) 84 (56.0%)
Non-Hispanic White 7 (6.7%) 5 (10.9%) 12 (8.0%)
Other 10 (9.6%) 4 (8.7%) 14 (9.3%)
Graduated from high school
No 43 (41.3%) 21 (45.7%) 64 (42.7%) 0.755
Yes 61 (58.7%) 25 (54.3%) 86 (57.3%)
Marital status
Not Married 65 (62.5%) 30 (65.2%) 95 (63.3%) 0.893
Married 39 (37.5%) 16 (34.8%) 55 (36.7%)
Employment
No 51 (49.0%) 28 (60.9%) 79 (52.7%) 0.408
Yes 21 (20.2%) 7 (15.2%) 28 (18.7%)
Disabled 32 (30.8%) 11 (23.9%) 43 (28.7%)
Treatment type
Non-DAA 24 (23.1%) 11 (23.9%) 35 (23.3%) 1
DAA 80 (76.9%) 35 (76.1%) 115 (76.7%)
Sustained virologic response 12
Sustained Virologic Response 97 (93.3%) 44 (95.7%) 141 (94.0%) 0.846
Non-Sustained Virologic Response 7 (6.7%) 2 (4.3%) 9 (6.0%)
Treatment group
Treatment as Usual 37 (35.6%) 14 (30.4%) 51 (34.0%) 0.672
Group 31 (29.8%) 17 (37.0%) 48 (32.0%)
Directly Observed Treatment 36 (34.6%) 15 (32.6%) 51 (34.0%)
Liver disease severity
No Cirrhosis 75 (72.1%) 34 (73.9%) 109 (72.7%) 0.977
Cirrhosis 29 (27.9%) 12 (26.1%) 41 (27.3%)
Medication
SOF/LDV 72 (69.2%) 32 (69.6%) 104 (69.3%) 0.666
SIM/SOF 8 (7.7%) 3 (6.5%) 11 (7.3%)
SOF/RBV/PEG 11 (10.6%) 4 (8.7%) 15 (10.0%)
SOF/RBV 10 (9.6%) 7 (15.2%) 17 (11.3%)
TVR/RBV/PEG 3 (2.9%) 0 (0%) 3 (2.0%)
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DAA=direct acting antivirals, N=sample size, PSU=polysubstance use, SD=standard deviation, SOF/LDV=sofosbuvir and ledipasvir, SIM/SOF=simeprevir and sofosbuvir, SOF/RBV/PEG=sofosbuvir, ribavirin, and pegylated interferon, SOF/RBV=sofosbuvir and ribavirin, TAU=treatment as usual, TVR/RBV/PEG=telaprevir, ribavirin, and pegylated interferon

Figure 1 shows differences in the mean HRQOL throughout time by PSU status and additional information on the values at each time point and the statistical differences between the groups is available in Table 2. The average HRQOL for all participants was 0.655, which is low compared to documented values for United States citizens which fall between 0.83–0.94 (Vahidnia et al., 2017, Janssen et al., 2019, Fryback et al., 2007). The mean HRQOL score was significantly lower for the PSU group at baseline compared to the non-PSU group (0.614 vs. 0.695, p=0.043). There were increases and decreases in mean HRQOL in the 12-weeks of treatment and the 24-weeks of follow-up, but the mean HRQOL were always smaller in the PSU group than in the non-PSU result group. At 24-weeks of follow-up HRQOL for the PSU group was only slightly lower compared to the non-PSU group (0.766 vs. 0.770, p=0.917). Within the PSU group, there was statistically significantly improvements of mean HRQOL, from baseline to week 4 (0.614 to 0.765, p=0.002), and non-significant improvement of mean HRQOL from week 8 to week 12 (0.740 to 0.768, p=0.599), and from follow-up week 12 to follow-up week 24 (0.731 to 0.766, p=0.509).

Figure 1.

Figure 1.

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Differences in mean health related quality of life throughout time by polysubstance use status BL = baseline, FU12 = follow-up week 12, FU24 = follow-up week 24, HRQOL = health related quality of life, PSU = polysubstance use, WK4=treatment week 4, WK8 = treatment week 8, WK12 = treatment week 12 *Indicates values that exhibited a statistical significance from baseline of p < .05.

Table 2.

Health-Related Quality Scores by Polysubstance Use groups during and following HCV treatment

Time N No PSU N PSU P-value
BL 104 0.694596 46 0.614483 0.043
WK4 96 0.797948 46 0.765348 0.394
WK8 90 0.759167 39 0.740846 0.694
WK12 67 0.774507 37 0.768378 0.891
FU12 98 0.788765 42 0.731381 0.202
FU24 97 0.770041 41 0.765756 0.917
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*

P-values are examined with t-test for Health-Related Quality between Polysubstance use group and non-Polysubstance use group

The GEE model (Table 3) examined mean differences in the HRQOL scores for each timepoint compared to treatment week 4, after accounting for potential differences due to baseline HRQOL, sex, race, education, marital status, employment, liver disease severity, sustained virologic response, treatment group, treatment type, polysubstance use, and recent injection drug use. This model showed the mean HRQOL scores had a significant decrease of −0.036 at week 8 (p=0.028) compared to mean HRQOL at week 4 (Table 3) after adjusting for these variables. None of the other time periods showed a statistically significant difference in the mean HRQOL compared to treatment week 4.

Table 3.

Differences in Health-Related Quality of Life Between Time Periodsa

Quantity Estimate (S.E.) p-value
Intercept (corresponds to WK 4)* 0.584 (0.187) <0.001
Adjusted change in HRQOL from WK4 to WK8 −0.036 (0.018) 0.028
Adjusted change in HRQOL from WK4 to WK12 −0.016 (0.019) 0.414
Adjusted change in HRQOL from WK4 to FU12 −0.014 (0.017) 0.414
Adjusted change in HRQOL from WK4 to FU24 −0.016 (0.017) 0.318
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a

Model accounts for baseline HRQOL, age, sex, race, education, marital status, employment, liver disease severity, sustained virologic response, treatment group, treatment type, polysubstance use, and recent injection drug use.

FU12=follow-up week 12, FU24=follow-up week 24, HRQOL=health related quality of life, S.E.=standard error, WK4=treatment week 4, WK8=treatment week 8, WK12=treatment week 12

Discussion

To our knowledge this is the first study to examine the relationship between PSU and HRQOL for PWID on OAT receiving HCV treatment. Individuals with PSU had a significantly lower baseline HRQOL compared to individuals without PSU, however their scores for HRQOL at each time point throughout the study were not significantly different than the non-PSU group. Although individuals in the PSU group reported a lower HRQOL at 24-weeks compared to individuals in the non-PSU group, this relationship was not statistically significant. A lack of a significant detriment in mean HRQOL for individuals engaging in PSU while receiving HCV treatment provides further support for the need to provide HCV treatment to this high-risk population.

Our findings demonstrated that baseline HRQOL had a significant impact on HRQOL at each time point throughout the study. This may be due to the increased prevalence of comorbid conditions among the PSU group. Previous research shows that individuals engaging in PSU have a higher likelihood of experiencing infections(Li et al., 2021), a greater number of psychiatric disorders (Wu et al., 2011), and higher HIV and sexual risk scores, placing them at higher risk for sexually transmitted infections (Wu et al., 2011). Further, engaging in PSU may increase an individual’s deficiencies in social determinants of health (e.g., stable housing, insurance, and food security), which negatively impacts quality of life. Additional research may be necessary to assess the association of these variables on HRQOL among individuals who engage in PSU.

Many researchers have called attention to the importance of addressing PSU for those receiving HCV treatment. First, continued PSU could lead to decreased adherence to HCV treatment. For example, a study amongst individuals receiving HCV treatment in Sydney, Australia reported a higher proportion of individuals with polydrug use were less than <90% adherent to HCV treatment, compared to the non-polydrug group (Read et al., 2019). Additionally, continued injection PSU following HCV treatment increases an individual’s risk of re-infection or could increase the risk of becoming infected with human immunodeficiency virus (HIV) (Puzhko et al., 2017). Continued PSU could also increase risk of attaining co-morbid infections highly prevalent among individuals engaging in injection PSU, such as endocarditis (Li et al., 2021) or bacteremia (Li et al., 2021) and increase the individual’s risk of fatal overdose (Compton et al., 2021).

Our findings provide additional support for the call to address PSU during OAT. Studies have shown that while engaging in OAT was associated with decreased illicit opioid use over time (Carlsen et al., 2020), it does not always lower PSU. For example, one study in Norway reported that OAT was associated with a decrease in opioid use, but not associated with a decrease in PSU, which remained consistently high throughout a year of treatment (Carlsen et al., 2020). This may be due to undertreatment of co-occurring substance use disorders during OAT. For example, in one cohort of PWID where 90% of participants reported co-occurring OUD and methamphetamine use disorder, nearly all participants were treated with some form of OAT, however none of the individuals received contingency management, or an equally effective evidence-based treatment for their methamphetamine use disorder (Li et al., 2021). This may also be due to the “opioid specificity” of most interventions, which limits the ability to address the broader problem of substance use (Compton et al., 2021).

These findings should also bolster support for treating HCV in individuals engaging in PSU. Individuals who inject drugs, many of whom engage in PSU, report facing barriers which ultimately prevent them from accessing HCV treatment (Heard et al., 2021, Madden et al., 2018), despite recommendations that individuals actively engaging in substance misuse be provided with HCV treatment (Grebely et al., 2015). The lack of a significant difference in the mean HRQOL between the PSU group and non-PSU group, especially the marginal difference in HRQOL at 24 weeks follow-up for both groups, illustrate the benefits of engaging individuals who inject drugs into treatment, regardless of PSU status.

Although our results show no statistically significant association between PSU and HRQOL, it is possible our small sample size lacked the statistical power necessary to identify a statistical difference. Future research should be conducted in a larger population to better evaluate the relationship between PSU and HRQOL. While the findings of this study did not show a significant association between PSU and HRQOL following HCV treatment, the results of previous studies should serve to encourage clinicians to consider addressing PSU in patients seeking treatment for substance use disorders and HCV.

Limitations

This study had a few limitations. First, the PREVAIL sample was a small, predominantly male and Hispanic sample of PWID receiving OAT in the Bronx, New York. Thus, these findings may not be generalizable to non-PWID, women, PWID not receiving OAT or rural populations. The self-reported nature of HRQOL may be subject to bias, as it is possible that informing patients of their SVR status may have influenced HRQOL ratings in successive visits. Further it is possible that the self-reported PSU variable may have underestimated the true prevalence of PSU in the population. Additionally, our definition of PSU is limited as it does not account for severity of substance use (number of days used, route of use) or different combinations of substance use. Finally, our analysis only examines the influence of PSU on HRQOL 24-weeks post treatment. Future research is necessary to determine influence of PSU on HRQOL 6 to 12 months following HCV treatment among PWID.

Conclusions

For individuals with PWID, our study showed no difference in HRQOL between those who did and did not engage in PSU following HCV treatment. . Additional research is needed to accurately assess the relationship between PSU and HRQOL during and following HCV treatment.

Acknowledgements

The parent PREVAIL trial is registered in ClinicalTrial.gov (NCT01857245). We are grateful for the collaborative support from the Addiction Medicine Center at the Prisma Health in Greenville, South Carolina. The contents of the work are solely the responsibility of the authors and do not necessarily represent the views of the funding agencies or the U.S. government.

Funding:

This work was supported in part by the National Institute on Drug Abuse (R01DA034086) and Gilead Sciences (IN-337-1779) grants.

Declaration of interests

AHL has served on advisory board for Merck Pharmaceuticals, AbbVie and Gilead Sciences. He has received research grants from Merck Pharmaceuticals and Gilead Sciences. No other authors declared any conflict of interest related to this work.

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