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. Author manuscript; available in PMC: 2016 Apr 27.
Published in final edited form as: AIDS Care. 2015 Apr 27;27(9):1128–1136. doi: 10.1080/09540121.2015.1032205

Factors linked to transitions in adherence to antiretroviral therapy among HIV-infected illicit drug users in a Canadian setting

Brenden Joseph 1, Thomas Kerr 1,2, Cathy M Puskas 1,3, Julio Montaner 1,2, Evan Wood 1,2, M-J Milloy 1,2
PMCID: PMC4830351  NIHMSID: NIHMS773431  PMID: 25915438

Abstract

HIV-positive people who use illicit drugs typically achieve lower levels of adherence to antiretroviral therapy and experience higher rates of sub-optimal HIV/AIDS treatment outcomes. Given the dearth of longitudinal research into ART adherence dynamics, we sought to identify factors associated with transitioning into and out of optimal adherence to ART in a longitudinal study of HIV-infected people who use illicit drugs (PWUD) in a setting of universal no-cost HIV/AIDS treatment. Using data from a prospective cohort of community-recruited HIV-positive illicit drug users confidentially linked to comprehensive HIV/AIDS treatment records, we estimated longitudinal factors associated with losing or gaining ≥ 95% adherence in the previous six months using two generalized linear mixed-effects models. Among 703 HIV-infected ART-exposed PWUD, becoming non-adherent was associated with periods of homelessness (Adjusted Odds Ratio [AOR] = 2.52, 95% Confidence Interval [95% CI]: 1.56 - 4.07), active injection drug use (AOR = 1.25, 95% CI: 1.01 - 1.56) and incarceration (AOR = 1.54, 95% CI: 1.10 - 2.17). Periods of sex work (AOR = 0.51, 95% CI: 0.34 - 0.75) and injection drug use (AOR = 0.62, 95% CI: 0.50 - 0.77) were barriers to becoming optimally adherent. Methadone maintenance therapy (MMT) was associated with becoming optimally adherent (AOR = 1.87, 95% CI: 1.50 - 2.33) and was protective against becoming non-adherent (AOR = 0.52, 95% CI: 0.41 - 0.65). In conclusion, we identified several behavioural, social and structural factors that shape adherence patterns among PWUD. Our findings highlight the need to consider these contextual factors in interventions that support the effective delivery of ART to this population.

Keywords: Antiretroviral therapy, adherence, drug users, methadone, incarceration, sex work

Introduction

The advent of antiretroviral therapy (ART) has led to substantial declines in HIV/AIDS-associated morbidity and mortality among people living with HIV (PLWH) worldwide (Beck et al., 1999; Murphy et al., 2001; Palella et al., 1998; Panos et al., 2008). Following initiation of treatment, optimal adherence to ART is strongly associated with suppression of plasma HIV-1 RNA viral load, preventing disease progression to AIDS and premature death, as well as virtually eliminating onward viral transmission (Baeten et al., 2012; Donnell et al., 2010; He et al., 2013; Ledergerber et al., 1999; McGowan & Shah, 2000; Quinn et al., 2000).

Despite the clear benefits of ART on HIV disease progression and transmission, access remains heterogeneous worldwide and some groups have not experienced the full benefits of ART (Baeten et al., 2012; Donnell et al., 2010; He et al., 2013). In particular, studies of PLWH who use illicit drugs such as cocaine, heroin and methamphetamine have described elevated rates of sub-optimal HIV/AIDS treatment outcomes (Barash et al., 2007; Lucas et al., 2006; Lucas et al., 2002). Fortunately, PLWH who use illicit drugs have been shown to attain ART outcomes comparable to individuals in other risk categories given adequate levels of ≥ 95% adherence (Wood, Montaner, et al., 2003b; Wood et al., 2004). Thus, there is an urgent need to promote access and adherence to ART and to introduce interventions that reduce the clinical, behavioural, social, and structural barriers to effective HIV/AIDS treatment and care faced by people who use illicit drugs (PWUD) (Milloy et al., 2012; Wolfe et al., 2010).

Research has revealed a broad range of behavioural, social, and structural risk factors for non-adherence to ART (Malta et al., 2008; Wood, Kerr, Tyndall, & Montaner, 2008). Systematic reviews of studies of adherence among PWUD reveal that almost all studies are cross-sectional and dichotomize individuals as adherent or non-adherent at a given point in time and thus cannot consider changes in adherence over time (Krusi, Wood, Montaner, & Kerr, 2010; Malta et al., 2008; Wood et al., 2008). Increasingly, adherence is understood as a dynamic and complex process and renewed HIV/AIDS prevention and treatment efforts are focused on retaining PLWH in care over the long term. Thus, using data from a long-running community-recruited cohort of HIV-infected PWUD, we sought to identify the behavioural, social, and structural factors associated with losing or attaining ≥95% adherence to prescribed ART among PWUD.

Methods

In this study, we used data from the AIDS Care Cohort to evaluate Exposure to Survival Services (ACCESS), an ongoing prospective observational cohort of illicit drug users living with HIV/AIDS in Vancouver, British Columbia (BC), Canada. Described in detail elsewhere (Milloy et al., 2011; Strathdee et al., 1998), recruitment for the cohort began in May, 1996, and focused on the city's Downtown Eastside neighbourhood, a post-industrial area with an open drug market and high levels of illicit drug use, poverty, and HIV infection (Strathdee et al., 1997; Tyndall et al., 2003). Eligibility criteria for participation in ACCESS are HIV-serostatus, aged ≥ 18 years, use of illicit drugs other than cannabis in the previous month, and written informed consent. At the baseline interview, and each biannual interview thereafter, participants respond to an interviewer-administered questionnaire, are examined by a study nurse, and provide blood for serologic analyses. At recruitment, participants provide their personal health number (PHN), a unique and persistent identifier issued for billing and tracking purposes to all residents of BC by the government-run universal and no-cost medical system. The ACCESS study has been approved by the University of British Columbia/Providence Health Care Research Ethics Board.

Information gathered through the interview and examination process is augmented by data on HIV treatment and clinical outcomes. This information is available through a confidential linkage with the provincial Drug Treatment Program (DTP). As described in detail elsewhere (Strathdee et al., 1998), the DTP provides ART and related care free of charge to all PLWH in BC and is administered by the British Columbia Centre for Excellence in HIV/AIDS (BCCfE) according to the International Antiviral Society's current therapeutic guidelines (Thompson et al., 2012). Data collected through the DTP provides a complete retrospective and prospective clinical profile for each participant including CD4+ cell counts, plasma HIV-1 RNA viral load, and all ART dispensations. Due to BC's province-wide free healthcare including all HIV/AIDS medications and care, we are able to evaluate the effects of various exposures on treatment uptake and adherence free of the confounding influence of treatment cost.

The present analyses included all participants who had initiated ART at least one day prior to a baseline or follow-up interview. Participants who initiated ART after the baseline were included from the follow-up after initiation onward. To be eligible for these analyses, individuals had to have contributed ≥ 2 survey interviews following ART initiation and had to have at least one CD4+ cell count observation within ± 180 days of the baseline interview.

Using pharmacy refill data, adherence to ART was calculated as the ratio of the number of days for which ART was dispensed over the number of days since the previous interview or since ART initiation, up to a maximum of 180 days. Optimal adherence was defined as equal to or greater than 95%, meaning participants had been dispensed enough ART for ≥95% of the days since the previous interview or since ART initiation. Pharmacy refill compliance has been previously validated and shown to be a reliable measure of ART adherence, with a demonstrated association with both virologic suppression and survival (Grossberg, Zhang, & Gross, 2004; Wood et al., 2004; Wood, Hogg, et al., 2003a).

The primary outcomes of interest in this study were transitions in adherence to ART. Transitions were dichotomized as either out of optimal adherence or into optimal adherence. Transitions out of optimal adherence occurred when a participant's ART dispensation data indicated a drop in actual dispensations from ≥95% of eligible dispensations in the previous six-month period to below 95% in the following six-month period. Transitions into optimal adherence occurred when a participant's ART dispensation data indicated a rise in actual dispensations from below 95% of eligible dispensations in the previous six-month period to ≥95% in the following six-month period.

We considered a range of explanatory variables including: age (per year older), gender (female vs. male), self-reported Caucasian ethnicity (yes vs. no), level of education attained (≥ high school diploma vs. < high school diploma), homelessness (yes vs. no), employment (formal employment including regular, temporary, or self-employment vs. none or non-formal employment), injection drug use (yes vs. no), non-injection drug use (yes vs. no), self-reported binge drug use (runs or binges in which participant reported using more drugs than usual; yes vs. no), involvement in sex work (yes vs. no), incarceration (yes vs. no), and engagement in methadone maintenance therapy (MMT; yes vs. no). Clinical variables considered were CD4+ cell count (per 100 cells/mL) and physician HIV-related experience (< 6 vs. ≥ 6 patients previously enrolled in the province-wide HIV registry). CD4+ cell counts were conducted through the ACCESS study in addition to any measurements conducted outside of the study setting, for example, by a participant's personal physician. CD4+ data was calculated as a mean of all observations in the last six months or, if no observations were made during the six-month period, the most recent measure. All explanatory variables were time-updated and referred to the six-month period in which a transition in adherence was observed, except homelessness and MMT, which referred to current status at the time of the interview, and physician experience, which was time-fixed.

We compared the characteristics of the study sample at the baseline interview by level of adherence to ART (≥95% vs. <95%) in order to present the study sample and identify clinical, structural, and social differences between optimally adherent and non-adherent individuals. This comparison was conducted using the χ2 test for categorical variables and the Wilcoxon rank-sum test for continuous variables.

We constructed multivariate models of factors associated with transitioning into or out of optimal adherence using generalized linear mixed-effects modeling. This method was used as it accounts for the correlation inherent in serial measures from the same individual. To prepare the models, we used an a priori-defined backwards stepwise procedure. First, we fit a full model including all explanatory variables with p-values < 0.4 in bivariable analyses. After noting the value of the Akaike Information Criterion (AIC) for the full model, we dropped the covariate associated with the largest p-value and fit a reduced model. We continued this procedure until zero explanatory variables were left in the model, and chose the final model that minimized the AIC. To account for HIV disease progression and clinical eligibility for ART, we included CD4+ cell count as an explanatory covariate in all multivariable models.

Results

Between May, 1996 and March, 2012, 1036 PLWH who use illicit drugs were recruited and provided informed consent for the ACCESS study. Of these, 703 had at least two interviews following ART initiation and at least one CD4+ cell count within 180 days of the baseline interview. These 703 individuals were included in this study and contributed 5422 interviews during the study period, or a median of 5 (Interquartile Range [IQR] = 3 – 10) per participant. For the analysis of transitioning into optimal adherence, 1813 observation periods among 509 individuals met our inclusion criteria of containing < 95% adherence in the previous follow-up. For the analysis of transitioning out of optimal adherence, 2674 observation periods among 436 individuals met our inclusion criteria of containing ≥ 95% adherence in the previous follow-up.

Select socio-demographic, behavioural, structural, and clinical characteristics of the participants at baseline interview stratified by level of adherence to ART (≥95% vs. <95%) are presented in Table 1. Of the 703 individuals included in our analyses, 190 (27.0%) participants had optimal adherence to ART at baseline. Individuals exhibiting optimal adherence were older (Odds Ratio [OR] = 1.02, 95% Confidence Interval [95% CI]: 1.01 – 1.02), more likely to be engaged in MMT (OR = 1.56, 95% CI: 1.12 – 2.19), more likely to have a high school diploma (OR = 1.51, 95% CI: 1.08 - 2.10,), and had higher CD4+ cell counts (OR = 1.27, 95% CI: 1.17 – 1.38). In contrast, individuals exhibiting non-adherence were more likely to be female (OR = 0.44, 95% CI: 0.30 – 0.65), non-Caucasian (OR = 2.17, 95% CI: 1.52 – 3.10), binge drug users (OR = 0.42, 95% CI: 0.28 – 0.64), injection drug users (OR = 0.52, 95% CI: 0.36 – 0.77), and were more likely to have been recently incarcerated (OR = 0.57, 95% CI: 0.34 – 0.94) and involved in sex work (OR = 0.33, 95% CI: 0.17 – 0.63).

Table 1.

Characteristics of 703 PLWH who use illicit drugs stratified by ≥95% adherence to ART in the six-month period prior to the baseline interview.

Characteristic <95% adherence n (%) 513 (73.0) ≥95% adherence n (%) 190 (27.0) OR1 95% CI2 p-value
Age
    Per year older 38.9 (33.1 – 44.6) 46.0 (39.6 – 51.4) 1.02 1.01 - 1.02 < 0.001
Gender
    Male 302 (58.9) 145 (76.3) 1.00
    Female 211 (41.1) 45 (23.7) 0.44 0.30 - 0.65 0.001
Caucasian
    No 244 (47.6) 56 (29.5) 1.00
    Yes 269 (52.4) 134 (70.5) 2.17 1.52 - 3.10 < 0.001
Binge drug use
    No 342 (66.7) 157 (82.6)
    Yes 171 (33.3) 33 (17.4) 0.42 0.28 - 0.64 < 0.001
Non-injection drug use
    No 154 (30.0) 28 (14.7) 1.00
    Yes 359 (70.0) 162 (85.3) 2.48 1.59 - 3.87 < 0.001
Sex work
    No 432 (84.2) 179 (94.2) 1.00
    Yes 81 (15.8) 11 (5.8) 0.33 0.17 - 0.63 < 0.001
Injection drug use
    No 98 (19.1) 59 (31.1) 1.00
    Yes 415 (80.9) 131 (68.9) 0.52 0.36 - 0.77 0.001
MMT4,5
    No 328 (63.9) 101 (53.2) 1.00
    Yes 185 (36.1) 89 (46.8) 1.56 1.12 - 2.19 0.011
Education
    < High school diploma 316 (61.6) 98 (51.6)
    ≥ High school diploma 197 (38.4) 92 (48.4) 1.51 1.08 - 2.10 0.020
Incarceration
    No 421 (82.1) 169 (88.9) 1.00
    Yes 92 (17.9) 21 (11.1) 0.57 0.34 - 0.94 0.029
Homeless5
    No 465 (90.6) 180 (94.7)
    Yes 48 (9.4) 10 (5.3) 0.54 0.27 - 1.09 0.090
HIV MD experience
    ≥ 6 patients 425 (82.8) 160 (84.2) 1.00
    < 6 patients 88 (17.2) 30 (15.8) 0.91 0.58 - 1.42 0.734
Employment
    None or non-formal 498 (97.1) 184 (96.8)
    Formal 15 (2.9) 6 (3.2) 1.09 0.41 - 2.83 0.808
CD4+ cell count
    Per 100 cells/ml 2.7 (1.7 – 4.0) 3.8 (2.6 – 5.1) 1.27 1.17 - 1.38 < 0.001
Open in a new tab
1

Odds Ratio

2

95% Confidence Interval

3. Refers to 180 day period prior to the baseline interview

4

Methadone maintenance therapy

5

Refers to current status

The crude and adjusted longitudinal estimates of the odds of transitioning out of optimal adherence to ART are presented in Table 2. In an adjusted model, individuals who transitioned out of optimal adherence tended to be younger (Adjusted Odds Ratio [AOR] = 0.95, 95% CI: 0.94 – 0.97) with lower CD4+ cell counts (AOR = 0.87, 95% CI: 0.82 – 0.97), and were likely to have been homeless (AOR = 2.52, 95% CI: 1.56 – 4.07) or incarcerated (AOR = 1.54, 95% CI: 1.10 – 2.17) during the period of the transition. Those individuals reporting injection drug use (AOR = 1.25, 95% CI: 1.01 – 1.56) during this period were also likely to transition out of optimal adherence. Of special note, engagement in MMT (AOR = 0.52, 95% CI: 0.41 – 0.65) was the sole factor protective against the loss of optimal adherence in the model.

Table 2.

Bivariate and multivariate factors associated with transitioning out of optimal (≥95%) adherence during a six-month period among 436 PLWH who use illicit drugs

Characteristic OR1 95% CI2 p-value AOR3 95% CI2 p-value
Age
    Per year older 0.94 0.93 - 0.96 < 0.001 0.95 0.94 - 0.97 < 0.001
Gender
    Female vs. male 1.27 0.96 - 1.69 0.099
Caucasian
    Yes vs. no 0.88 0.67 - 1.15 0.353
MMT4,5
    Yes vs. no 0.54 0.43 - 0.67 < 0.001 0.52 0.41 - 0.65 < 0.001
Homeless5
    Yes vs. no 3.62 2.29 - 5.74 < 0.001 2.52 1.56 - 4.07 < 0.001
Incarceration
    Yes vs. no 2.12 1.53 - 2.94 < 0.001 1.54 1.10 - 2.17 0.013
Injection drug use
    Yes vs. no 1.47 1.19 - 1.82 < 0.001 1.25 1.01 - 1.56 0.045
Sex work
    Yes vs. no 1.83 1.28 - 2.63 0.001 1.44 0.98 - 2.10 0.060
HIV MD experience
    <6 patients vs. ≥6 1.38 0.96 - 1.99 0.081 1.37 0.96 - 1.95 0.084
Non-injection drug use
    Yes vs. no 1.08 0.86 - 1.35 0.518
Employment
    Yes vs. no 0.76 0.45 - 1.29 0.31
Education
    ≥HS6 vs. <HS 0.84 0.66 - 1.06 0.149
Binge drug use
    Yes vs. no 1.21 0.94 - 1.55 0.146
CD4+ cell count
    Per 100 cells/ml 0.85 0.8 - 0.9 < 0.001 0.87 0.82 - 0.92 < 0.001
Open in a new tab
1

Odds Ratio

2

95% Confidence Interval

3

Adjusted Odds Ratio

4

Methadone maintenance therapy

5

Refers to current status

6

High School

Table 3 shows the crude and adjusted longitudinal estimates of the odds of transitioning into optimal adherence to ART. Individuals who transitioned into optimal adherence were older (AOR = 1.08, 95% CI: 1.06 – 1.09) with higher CD4+ cell counts (AOR = 1.25, 95% CI: 1.19 – 1.32), and were more likely to be male (AOR = 0.69, 95% CI: 0.51 – 0.93). Injection drug use (AOR = 0.62, 95% CI: 0.50 – 0.77), and involvement in sex work (AOR = 0.51, 95% CI: 0.34 – 0.75) were significant barriers to becoming optimally adherent to ART. Engagement in MMT (AOR = 1.87, 95% CI: 1.50 – 2.33) was the sole factor facilitating attaining optimal adherence in the model.

Table 3.

Bivariate and multivariate factors associated with transitioning into optimal (≥95%) adherence during a six-month period among 509 PLWH who use illicit drugs

Characteristic OR1 95% CI2 p-value AOR3 95% CI2 p-value
Age
    Per year older 1.1 1.08 - 1.11 < 0.001 1.08 1.06 - 1.09 < 0.001
Gender
    Female vs. male 0.53 0.4 - 0.7 < 0.001 0.69 0.51 - 0.93 0.017
Caucasian
    Yes vs. no 1.34 1.02 - 1.76 0.034 1.30 0.97 - 1.76 0.079
MMT4,5
    Yes vs. no 1.77 1.44 - 2.19 < 0.001 1.87 1.50 - 2.33 < 0.001
Injection drug use
    Yes vs. no 0.51 0.42 - 0.62 < 0.001 0.62 0.50 - 0.77 < 0.001
Sex work
    Yes vs. no 0.31 0.21 - 0.45 < 0.001 0.51 0.34 - 0.75 0.001
Homeless5
    Yes vs. no 0.42 0.27 - 0.66 < 0.001 0.63 0.40 - 1.01 0.054
Incarceration
    Yes vs. no 0.52 0.39 - 0.71 < 0.001 0.78 0.57 - 1.07 0.126
Non-injection drug use
    Yes vs. no 0.94 0.76 - 1.16 0.553
Employment
    Yes vs. no 1.21 0.75 - 1.96 0.432
Education
    ≥HS6 vs. <HS 1.13 0.91 - 1.40 0.271
HIV MD experience
    <6 patients vs. ≥6 0.74 0.52 - 1.07 0.113
Binge drug use
    Yes vs. no 0.74 0.59 - 0.93 0.011
CD4+ cell count
    Per 100 cells/ml 1.27 1.20 - 1.33 < 0.001 1.25 1.19 - 1.32 < 0.001
Open in a new tab
1

Odds Ratio

2

95% Confidence Interval

3

Adjusted Odds Ratio

4

Methadone maintenance therapy

5

Refers to current status

6

High School

Discussion

In this longitudinal study of factors associated with transitioning into or out of optimal adherence to ART among PLWH who use illicit drugs, we observed a high level of non-adherence, with over 70% of individuals exhibiting non-adherence at baseline. In multivariate models adjusted for illicit drug use patterns, sex work was a barrier to becoming optimally adherent while homelessness and incarceration were both risk factors for becoming non-adherent. Engagement in MMT was predictive of optimal adherence at baseline and was the sole protective factor against becoming non-adherent as well as the sole facilitating factor for becoming optimally adherent in multivariate models.

Recent studies have revealed strong links between incarceration and non-adherence to ART in many settings (Chen et al., 2011; Milloy et al., 2011). Several barriers to treatment within correctional systems have been described, such as sub-standard healthcare facilities, a lack of care for HIV-related co-morbidities such as addiction, and an emphasis on public security over public health (Fazel & Baillargeon, 2011; Flanigan & Beckwith, 2011; Jürgens, Nowak, & Day, 2011). In addition, studies have shown how poor continuity of care between correctional and non-correctional care settings is a barrier to maintenance of treatment (Baillargeon et al., 2009; Small, Wood, Betteridge, Montaner, & Kerr, 2009). Elimination of these barriers may be key to maintaining optimal adherence levels among HIV-infected PWUD involved in the criminal justice system.

Sex workers have been well described as a highly marginalized population with elevated rates of drug use, unstable housing, and elevated risk of HIV infection (Kral et al., 2001; Rekart, 2005; Vanwesenbeeck, 2001). Given the established vulnerabilities in this group, it is not surprising that sex work involvement was a significant barrier to becoming optimally adherent in our analyses. Sex workers carry an elevated HIV risk burden due to a complex structure of interacting sex and drug related risk behaviours (Miller et al., 2002; Spittal et al., 2003) and are a hard-to-reach group facing multiple barriers to accessing health care and treatment, including social stigma, incompatible service office hours, and mental health issues (Kurtz, Surratt, Kiley, & Inciardi, 2005). Effective interventions for the promotion of ART adherence and elimination of barriers to care in this group are urgently needed.

Research has revealed strong associations between unstable housing and deleterious adherence patterns (Bangsberg et al., 2000; Kidder, Wolitski, Campsmith, & Nakamura, 2007; Kushel et al., 2006; Moss et al., 2004). Compared to housed individuals, the unstably housed or homeless have increased rates of drug and alcohol abuse, as well as higher rates of HIV infection and HIV/AIDS-related morbidity and mortality (Milloy et al., 2012; Palepu et al., 2006). ART adherence may be negatively affected through stigma, poor physical and mental health, or a lack of routine, privacy, and storage for medications (Palepu et al., 2006). Our analyses show that homeless individuals are at risk of becoming non-adherent, emphasizing an already established need for housing interventions.

Our findings show clear adherence benefits from MMT engagement in both facilitating and maintaining optimal ART adherence. Although this study sample is marked by prevalent poly-drug use, including high levels of opioid (heroin, prescription opioid) and stimulant (cocaine, methamphetamine) use, we should note that we did not screen participants for opioid or stimulant dependence. Nevertheless, for opioid users, MMT has been shown to be associated with lower frequency of heroin injection drug use (Caplehorn, Bell, Kleinbaum, & Gebski, 1993; Joseph, Stancliff, & Langrod, 2000) and positive clinical outcomes in association with ART, including viral load suppression (Palepu et al., 2006; Uhlmann et al., 2010).

Current medication regimes for HIV infection require at least daily adherence over the long term to promote virologic non-detectability, stall disease progression and prolong survival. However, there are very few studies among HIV-positive drug users assessing adherence over the long term using longitudinal observations (Malta et al., 2010). In this study, the use of repeated measures every six months has allowed us to isolate periods characterized by loss or gain of ≥ 95% adherence. Our findings echo previous cross-sectional work which, for example, identified the important effects of incarceration and engagement in opioid substitution therapy on adherence (Palepu et al, 2011; Milloy et al., 2011). This study reveals their impact on adherence dynamics over time and identifies factors relevant to efforts to retain individuals in HIV treatment.

This research has several limitations. First, our analytic sample cannot be seen as representative of all PLWH who use illicit drugs due to non-random participant recruitment and the free availability of ART in our setting. Secondly, the possibility of confounding variable influence cannot be excluded. To minimize the effects of confounding variables, we used multivariate modeling of data from a long standing cohort of HIV-infected PWUD. Thirdly, several explanatory variables such as injection drug use, sex work involvement, and incarceration were derived from participant self-report, which might be susceptible to bias. For example, a social desirability bias might engender the under-reporting of participant incarceration or sex work involvement, potentially diminishing an observed relationship to ART adherence. Lastly, the MMT explanatory variable refers to a maintenance therapy undertaken to treat opioid dependence specifically, and may therefore be biased towards opioid users within the study group. Despite these potential limitations, within the context of BC's free healthcare and universal access to ART, confounding due to financial barriers to healthcare access has been reduced.

Within the context of an ongoing prospective cohort of PLWH who use illicit drugs, we have identified several factors associated with transitions between optimal adherence and non-adherence. Active injection drug use and sex work were found to be barriers to becoming optimally adherent, whereas homelessness, incarceration, and injection drug use were risk factors for becoming non-adherent. MMT was the sole protective factor against becoming non-adherent as well as the sole facilitating factor of becoming optimally adherent. Our findings emphasize the importance of considering social and structural determinants of ART adherence dynamics and highlight the role of MMT in the protection and maintenance of optimal adherence to ART among opioid-dependent PWUD. Given the high levels of non-adherence among PWUD, more research is urgently needed to better understand ART adherence dynamics, to investigate possible protective factors among non-opiate using PWUD, and to develop effective strategies against threats to optimal ART adherence.

Acknowledgements

The authors thank the study participants for their contributions to the research, as well as current and past researchers and staff. We would specifically like to thank: Kristie Starr, Deborah Graham, Tricia Collingham, Carmen Rock, Brandon Marshall, Caitlin Johnston, Steve Kain, Benita Yip and Guillaume Colley for their research and administrative assistance.

Funding: The study is supported by the US National Institutes of Health (R01-DA021525) and the Canadian Institutes of Health Research (MOP-79297 and RAA-79918.) The funders had no role in the design and conduct of this study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Disclosure statement: Dr. Milloy is supported in part by the United States National Institutes of Health. This work was supported in part by a Tier 1 Canada Research Chair in Inner-City Medicine awarded to Dr. Wood. Dr. Montaner is supported by the British Columbia Ministry of Health and through an Avant-Garde Award (No. 1DP1DA026182) from the National Institute of Drug Abuse (NIDA), at the US National Institutes of Health (NIH). He has also received financial support from the International AIDS Society, United Nations AIDS Program, World Health Organization, National Institutes of Health Research-Office of AIDS Research, National Institute of Allergy & Infectious Diseases, The United States President's Emergency Plan for AIDS Relief (PEPfAR), UNICEF, the University of British Columbia, Simon Fraser University, Providence Health Care and Vancouver Coastal Health Authority.

References

  1. Arnsten JH, Demas PA, Grant RW, Gourevitch MN, Farzadegan H, Howard AA, Schoenbaum EE. Impact of active drug use on antiretroviral therapy adherence and viral suppression in HIV-infected drug users. Journal of General Internal Medicine. 2002;17(5):377–381. doi: 10.1046/j.1525-1497.2002.10644.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Baeten JM, Donnell D, Ndase P, Mugo NR, Campbell JD, Wangisi J, et al. Antiretroviral prophylaxis for HIV prevention in heterosexual men and women. New England Journal of Medicine. 2012;367(5):399–410. doi: 10.1056/NEJMoa1108524. doi:10.1056/NEJMoa1108524. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Baillargeon J, Giordano TP, Rich JD, Wu ZH, Wells K, Pollock BH, Paar DP. Accessing antiretroviral therapy following release from prison. JAMA : the Journal of the American Medical Association. 2009;301(8):848–857. doi: 10.1001/jama.2009.202. doi:10.1001/jama.2009.202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bangsberg DR, Hecht FM, Charlebois ED, Zolopa AR, Holodniy M, Sheiner L, et al. Adherence to protease inhibitors, HIV-1 viral load, and development of drug resistance in an indigent population. AIDS (London, England) 2000;14(4):357–366. doi: 10.1097/00002030-200003100-00008. [DOI] [PubMed] [Google Scholar]
  5. Barash ET, Hanson DL, Buskin SE, Teshale E. HIV-infected Injection Drug Users: Health Care Utilization and Morbidity. Journal of Health Care for the Poor and Underserved. 2007;18(3):675–686. doi: 10.1353/hpu.2007.0053. doi:10.1353/hpu.2007.0053. [DOI] [PubMed] [Google Scholar]
  6. Beck EJ, Mandalia S, Williams I, Power A, Newson R, Molesworth A, et al. Decreased morbidity and use of hospital services in English HIV-infected individuals with increased uptake of anti-retroviral therapy 1996-1997. National Prospective Monitoring System Steering Group. AIDS (London, England) 1999;13(15):2157–2164. doi: 10.1097/00002030-199910220-00020. [DOI] [PubMed] [Google Scholar]
  7. Caplehorn JR, Bell J, Kleinbaum DG, Gebski VJ. Methadone dose and heroin use during maintenance treatment. Addiction (Abingdon, England) 1993;88(1):119–124. doi: 10.1111/j.1360-0443.1993.tb02769.x. [DOI] [PubMed] [Google Scholar]
  8. Chen NE, Meyer JP, Avery AK, Draine J, Flanigan TP, Lincoln T, et al. Adherence to HIV Treatment and Care Among Previously Homeless Jail Detainees. AIDS and Behavior. 2011 doi: 10.1007/s10461-011-0080-2. doi:10.1007/s10461-011-0080-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Donnell D, Baeten JM, Kiarie J, Thomas KK, Stevens W, Cohen CR, et al. Heterosexual HIV-1 transmission after initiation of antiretroviral therapy: a prospective cohort analysis. The Lancet. 2010;375(9731):2092–2098. doi: 10.1016/S0140-6736(10)60705-2. doi:10.1016/S0140-6736(10)60705-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Fazel S, Baillargeon J. The health of prisoners. Lancet. 2011;377(9769):956–965. doi: 10.1016/S0140-6736(10)61053-7. [DOI] [PubMed] [Google Scholar]
  11. Flanigan TP, Beckwith CG. The intertwined epidemics of HIV infection, incarceration, and substance abuse: a call to action. The Journal of Infectious Diseases. 2011;203(9):1201–1203. doi: 10.1093/infdis/jir034. doi:10.1093/infdis/jir034. [DOI] [PubMed] [Google Scholar]
  12. Grossberg R, Zhang Y, Gross R. A time-to-prescription-refill measure of antiretroviral adherence predicted changes in viral load in HIV. Journal of Clinical Epidemiology. 2004;57(10):1107–1110. doi: 10.1016/j.jclinepi.2004.04.002. doi:10.1016/j.jclinepi.2004.04.002. [DOI] [PubMed] [Google Scholar]
  13. He N, Duan S, Ding Y, Rou K, McGoogan JM, Jia M, et al. Antiretroviral therapy reduces HIV transmission in discordant couples in rural Yunnan, China. PloS One. 2013;8(11):e77981. doi: 10.1371/journal.pone.0077981. doi:10.1371/journal.pone.0077981. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Joseph H, Stancliff S, Langrod J. Methadone maintenance treatment (MMT): a review of historical and clinical issues. The Mount Sinai Journal of Medicine, New York. 2000;67(5-6):347–364. [PubMed] [Google Scholar]
  15. Jürgens R, Nowak M, Day M. HIV and incarceration: prisons and detention. Journal of the International AIDS Society. 2011;14(1):26. doi: 10.1186/1758-2652-14-26. doi:10.1186/1758-2652-14-26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Kidder DP, Wolitski RJ, Campsmith ML, Nakamura GV. Health status, health care use, medication use, and medication adherence among homeless and housed people living with HIV/AIDS. American Journal of Public Health. 2007;97(12):2238–2245. doi: 10.2105/AJPH.2006.090209. doi:10.2105/AJPH.2006.090209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Kral AH, Bluthenthal RN, Lorvick J, Gee L, Bacchetti P, Edlin BR. Sexual transmission of HIV-1 among injection drug users in San Francisco, USA: risk-factor analysis. Lancet. 2001;357(9266):1397–1401. doi: 10.1016/S0140-6736(00)04562-1. doi:10.1016/S0140-6736(00)04562-1. [DOI] [PubMed] [Google Scholar]
  18. Krusi A, Wood E, Montaner J, Kerr T. Social and structural determinants of HAART access and adherence among injection drug users. The International Journal on Drug Policy. 2010;21(1):4–9. doi: 10.1016/j.drugpo.2009.08.003. doi:10.1016/j.drugpo.2009.08.003. [DOI] [PubMed] [Google Scholar]
  19. Kurtz SP, Surratt HL, Kiley MC, Inciardi JA. Barriers to health and social services for street-based sex workers. Journal of Health Care for the Poor and Underserved. 2005;16(2):345–361. doi: 10.1353/hpu.2005.0038. doi:10.1353/hpu.2005.0038. [DOI] [PubMed] [Google Scholar]
  20. Kushel MB, Colfax G, Ragland K, Heineman A, Palacio H, Bangsberg DR. Case management is associated with improved antiretroviral adherence and CD4+ cell counts in homeless and marginally housed individuals with HIV infection. Clinical Infectious Diseases. 2006;43(2):234–242. doi: 10.1086/505212. doi:10.1086/505212. [DOI] [PubMed] [Google Scholar]
  21. Ledergerber B, Egger M, Opravil M, Telenti A, Hirschel B, Battegay M, et al. Clinical progression and virological failure on highly active antiretroviral therapy in HIV-1 patients: a prospective cohort study. Swiss HIV Cohort Study. Lancet. 1999;353(9156):863–868. doi: 10.1016/s0140-6736(99)01122-8. [DOI] [PubMed] [Google Scholar]
  22. Lucas GM, Gebo KA, Chaisson RE, Moore RD. Longitudinal assessment of the effects of drug and alcohol abuse on HIV-1 treatment outcomes in an urban clinic. AIDS (London, England) 2002;16(5):767–774. doi: 10.1097/00002030-200203290-00012. [DOI] [PubMed] [Google Scholar]
  23. Lucas GM, Griswold M, Gebo KA, Keruly J, Chaisson RE, Moore RD. Illicit drug use and HIV-1 disease progression: a longitudinal study in the era of highly active antiretroviral therapy. American Journal of Epidemiology. 2006;163(5):412–420. doi: 10.1093/aje/kwj059. doi:10.1093/aje/kwj059. [DOI] [PubMed] [Google Scholar]
  24. Malta M, Magnanini MMF, Strathdee SA, Bastos FI. Adherence to Antiretroviral Therapy Among HIV-Infected Drug Users: A Meta-Analysis. AIDS and Behavior. 2010;14(4):731–747. doi: 10.1007/s10461-008-9489-7. doi: 10.1007/s10461-008-9489-7. [DOI] [PubMed] [Google Scholar]
  25. Malta M, Strathdee SA, Magnanini MMF, Bastos FI. Adherence to antiretroviral therapy for human immunodeficiency virus/acquired immune deficiency syndrome among drug users: a systematic review. Addiction (Abingdon, England) 2008;103(8):1242–1257. doi: 10.1111/j.1360-0443.2008.02269.x. doi:10.1111/j.1360-0443.2008.02269.x. [DOI] [PubMed] [Google Scholar]
  26. McGowan JP, Shah SS. Prevention of perinatal HIV transmission during pregnancy. Journal of Antimicrobial Chemotherapy. 2000;46(5):657–668. doi: 10.1093/jac/46.5.657. [DOI] [PubMed] [Google Scholar]
  27. Miller CL, Spittal PM, Laliberte N, Li K, Tyndall MW, O'shaughnessy MV, Schechter MT. Females experiencing sexual and drug vulnerabilities are at elevated risk for HIV infection among youth who use injection drugs. JAIDS Journal of Acquired Immune Deficiency Syndromes. 2002;30(3):335–41. doi: 10.1097/00126334-200207010-00010. [DOI] [PubMed] [Google Scholar]
  28. Milloy M-JS, Marshall BDL, Kerr T, Buxton J, Rhodes T, Montaner J, Wood E. Social and structural factors associated with HIV disease progression among illicit drug users: a systematic review. AIDS (London, England) 2012;26(9):1049–1063. doi: 10.1097/QAD.0b013e32835221cc. doi:10.1097/QAD.0b013e32835221cc. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Milloy M-J, Kerr T, Buxton J, Rhodes T, Guillemi S, Hogg R, et al. Dose-response Effect of Incarceration Events on Nonadherence to HIV Antiretroviral Therapy Among Injection Drug Users. The Journal of Infectious Diseases. 2011;203(9):1215–1221. doi: 10.1093/infdis/jir032. doi:10.1093/infdis/jir032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Moss AR, Hahn JA, Perry S, Charlebois ED, Guzman D, Clark RA, Bangsberg DR. Adherence to highly active antiretroviral therapy in the homeless population in San Francisco: a prospective study. Clinical Infectious Diseases. 2004;39(8):1190–1198. doi: 10.1086/424008. doi:10.1086/424008. [DOI] [PubMed] [Google Scholar]
  31. Murphy EL, Collier AC, Kalish LA, Assmann SF, Para MF, Flanigan TP, et al. Highly active antiretroviral therapy decreases mortality and morbidity in patients with advanced HIV disease. Annals of Internal Medicine. 2001;135(1):17–26. doi: 10.7326/0003-4819-135-1-200107030-00005. [DOI] [PubMed] [Google Scholar]
  32. Nolan S, Milloy M-J, Zhang R, Kerr T, Hogg RS, Montaner JSG, Wood E. Adherence and plasma HIV RNA response to antiretroviral therapy among HIV-seropositive injection drug users in a Canadian setting. AIDS Care. 2011;23(8):980–987. doi: 10.1080/09540121.2010.543882. doi:10.1080/09540121.2010.543882. [DOI] [PubMed] [Google Scholar]
  33. Palella FJ, Delaney KM, Moorman AC, Loveless MO, Fuhrer J, Satten GA, et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. New England Journal of Medicine. 1998;338(13):853–860. doi: 10.1056/NEJM199803263381301. doi:10.1056/NEJM199803263381301. [DOI] [PubMed] [Google Scholar]
  34. Palepu A, Milloy M-J, Kerr T, Zhang R, Wood E. Homelessness and Adherence to Antiretroviral Therapy among a Cohort of HIV-Infected Injection Drug Users. Journal of Urban Health. 2011;88(3):545–555. doi: 10.1007/s11524-011-9562-9. doi:10.1007/s11524-011-9562-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Palepu A, Tyndall MW, Joy R, Kerr T, Wood E, Press N, et al. Antiretroviral adherence and HIV treatment outcomes among HIV/HCV co-infected injection drug users: the role of methadone maintenance therapy. Drug and Alcohol Dependence. 2006;84(2):188–194. doi: 10.1016/j.drugalcdep.2006.02.003. doi:10.1016/j.drugalcdep.2006.02.003. [DOI] [PubMed] [Google Scholar]
  36. Panos G, Samonis G, Alexiou VG, Kavarnou GA, Charatsis G, Falagas ME. Mortality and morbidity of HIV infected patients receiving HAART: a cohort study. Current HIV Research. 2008;6(3):257–260. doi: 10.2174/157016208784324976. [DOI] [PubMed] [Google Scholar]
  37. Quinn TC, Wawer MJ, Sewankambo N, Serwadda D, Li C, Wabwire-Mangen F, et al. Viral load and heterosexual transmission of human immunodeficiency virus type 1. Rakai Project Study Group. The New England Journal of Medicine. 2000;342(13):921–929. doi: 10.1056/NEJM200003303421303. doi:10.1056/NEJM200003303421303. [DOI] [PubMed] [Google Scholar]
  38. Rekart ML. Sex-work harm reduction. Lancet. 2005;366(9503):2123–2134. doi: 10.1016/S0140-6736(05)67732-X. doi:10.1016/S0140-6736(05)67732-X. [DOI] [PubMed] [Google Scholar]
  39. Small W, Wood E, Betteridge G, Montaner J, Kerr T. The impact of incarceration upon adherence to HIV treatment among HIV-positive injection drug users: a qualitative study. AIDS Care. 2009;21(6):708–714. doi: 10.1080/09540120802511869. doi:10.1080/09540120802511869. [DOI] [PubMed] [Google Scholar]
  40. Spittal PM, Bruneau J, Craib KJP, Miller C, Lamothe F, Weber AE, et al. Surviving the sex trade: A comparison of HIV risk behaviours among street-involved women in two Canadian cities who inject drugs. AIDS Care. 2003;15(2):187–195. doi: 10.1080/0954012031000068335. doi:10.1080/0954012031000068335. [DOI] [PubMed] [Google Scholar]
  41. Strathdee S, Palepu A, Cornelisse PG, Yip B, O'shaughnessy MV, Montaner JS, et al. Barriers to use of free antiretroviral therapy in injection drug users. JAMA : the Journal of the American Medical Association. 1998;280(6):547–549. doi: 10.1001/jama.280.6.547. [DOI] [PubMed] [Google Scholar]
  42. Strathdee S, Patrick DM, Currie SL, Cornelisse PG, Rekart ML, Montaner JS, et al. Needle exchange is not enough: lessons from the Vancouver injecting drug use study. AIDS. 1997;11(8):F59–F65. doi: 10.1097/00002030-199708000-00001. doi:10.1097/00002030-199708000-00001. [DOI] [PubMed] [Google Scholar]
  43. Thompson MA, Aberg JA, Hoy JF, Telenti A, Benson C, Cahn P, et al. Antiretroviral treatment of adult HIV infection: 2012 recommendations of the International Antiviral Society-USA panel. JAMA : the Journal of the American Medical Association. 2012;308(4):387–402. doi: 10.1001/jama.2012.7961. doi:10.1001/jama.2012.7961. [DOI] [PubMed] [Google Scholar]
  44. Tyndall MW, Currie S, Spittal P, Li K, Wood E, O'shaughnessy MV, et al. Intensive injection cocaine use as the primary risk factor in the Vancouver HIV-1 epidemic. AIDS. 2003;17(6):887–893. doi: 10.1097/00002030-200304110-00014. doi:10.1097/00002030-200304110-00014. [DOI] [PubMed] [Google Scholar]
  45. Uhlmann S, Milloy M-J, Kerr T, Zhang R, Guillemi S, Marsh D, et al. Methadone maintenance therapy promotes initiation of antiretroviral therapy among injection drug users. Addiction (Abingdon, England) 2010;105(5):907–913. doi: 10.1111/j.1360-0443.2010.02905.x. doi:10.1111/j.1360-0443.2010.02905.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Vanwesenbeeck I. Another decade of social scientific work on sex work: a review of research 1990-2000. Annual Review of Sex Research. 2001;12:242–289. [PubMed] [Google Scholar]
  47. Weber R, Ledergerber B, Opravil M, Siegenthaler W, Lüthy R. Progression of HIV infection in misusers of injected drugs who stop injecting or follow a programme of maintenance treatment with methadone. Bmj. 1990;301(6765):1362–1365. doi: 10.1136/bmj.301.6765.1362. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Wolfe D, Carrieri MP, Shepard D. Treatment and care for injecting drug users with HIV infection: a review of barriers and ways forward. The Lancet. 2010;376(9738):355–366. doi: 10.1016/S0140-6736(10)60832-X. doi:10.1016/S0140-6736(10)60832-X. [DOI] [PubMed] [Google Scholar]
  49. Wong K-H, Lee S-S, Lim W-L, Low H-K. Adherence to methadone is associated with a lower level of HIV-related risk behaviors in drug users. Journal of Substance Abuse Treatment. 2003;24(3):233–239. doi: 10.1016/s0740-5472(03)00029-1. [DOI] [PubMed] [Google Scholar]
  50. Wood E, Hogg RS, Yip B, Harrigan PR, O'shaughnessy MV, Montaner JSG. Effect of medication adherence on survival of HIV-infected adults who start highly active antiretroviral therapy when the CD4+ cell count is 0.200 to 0.350 × 10(9) cells/L. Annals of Internal Medicine. 2003a;139(10):810–816. doi: 10.7326/0003-4819-139-10-200311180-00008. [DOI] [PubMed] [Google Scholar]
  51. Wood E, Kerr T, Tyndall MW, Montaner JSG. A review of barriers and facilitators of HIV treatment among injection drug users. AIDS (London, England) 2008;22(11):1247–1256. doi: 10.1097/QAD.0b013e3282fbd1ed. doi:10.1097/QAD.0b013e3282fbd1ed. [DOI] [PubMed] [Google Scholar]
  52. Wood E, Montaner JSG, Yip B, Tyndall MW, Schechter MT, O'shaughnessy MV, Hogg RS. Adherence and plasma HIV RNA responses to highly active antiretroviral therapy among HIV-1 infected injection drug users. CMAJ : Canadian Medical Association Journal = Journal De l'Association Medicale Canadienne. 2003b;169(7):656–661. [PMC free article] [PubMed] [Google Scholar]
  53. Wood E, Montaner JSG, Yip B, Tyndall MW, Schechter MT, O'shaughnessy MV, Hogg RS. Adherence to antiretroviral therapy and CD4 T-cell count responses among HIV-infected injection drug users. Antiviral Therapy. 2004;9(2):229–235. [PubMed] [Google Scholar]

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