Secondary Logo

Journal Logo

Epidemiology and Prevention

Social and Environmental Predictors of Plasma HIV RNA Rebound Among Injection Drug Users Treated With Antiretroviral Therapy

Milloy, M-J. PhD*; Kerr, Thomas PhD*,‡; Buxton, Jane MD†,§; Rhodes, Tim PhD; Krusi, Andrea MA*; Guillemi, Silvia MD*; Hogg, Robert PhD*,¶; Montaner, Julio MD*,‡; Wood, Evan MD, PhD*,‡

Author Information
JAIDS Journal of Acquired Immune Deficiency Syndromes: April 1, 2012 - Volume 59 - Issue 4 - p 393-399
doi: 10.1097/QAI.0b013e3182433288
  • Free



Despite the advent of antiretroviral (ART) therapy, HIV-infected individuals who use injection drugs (IDU) continue to experience high levels of HIV/AIDS-related morbidity and mortality.1,2 Central to these suboptimal treatment outcomes are lower rates of access and adherence to ART.3,4 Evidence is urgently required to identify and address barriers to retaining IDU in effective HIV treatment.5

The primary clinical goal of ART is to inhibit viral replication and suppress plasma viral load (PVL) to undetectable levels.6 Longitudinal analyses of clinic-based studies have revealed that although a substantial proportion of individuals are able to achieve viral suppression with ART,7,8 at least 1 in 10 patients will experience at least 1 episode of viral rebound.7 Clinical factors associated with a greater risk of rebound include shorter duration of viral suppression9,10; ART regimen composition7; and nonadherence to ART.11,12

Ongoing illicit drug use represents an added challenge in the medical management of HIV infection.13 Previous studies have identified active alcohol and illicit drug use as risk factors for failure to achieve viral suppression14–17 and avoid viral rebound.12,18 However, the determinants of viral rebound among IDU on ART have not been completely investigated. In particular, consideration of the broader social and environmental factors that have been shown to determine vulnerability to HIV infection19–21 have not been well evaluated as possible determinants of viral outcomes. Thus, given the urgent need to improve treatment access and delivery for HIV-seropositive IDU, we conducted the following study with the primary objective of identifying social and environmental risk factors for viral rebound among IDU on ART.


In these analyses, we used data from the AIDS Care Cohort to evaluate Exposure to Survival Services (ACCESS), an ongoing prospective observational cohort of HIV-seropositive illicit drug users in Vancouver, Canada. The cohort was populated through community recruitment, as detailed previously22–24; briefly, we used snowball sampling and extensive street outreach beginning in 1996 focused on Vancouver's Downtown Eastside neighborhood. The Downtown Eastside includes a large and established open drug market and endemic levels of illicit drug use, poverty, poor housing, and HIV infection.22 Individuals are eligible for ACCESS if they are HIV seropositive; are aged 18 years or older; have used illicit drugs other than cannibinoids in the previous month; and can provide written informed consent. At recruitment and every 6 months, thereafter, individuals answer an interviewer-administered questionnaire, undergo an examination by a study nurse, and provide blood plasma samples for serologic and virologic analysis. Personal data on sociodemographic characteristics, drug-using behaviors, and related exposures are gathered during the interview process by trained study staff. All HIV clinical care is delivered independently of the study, although study staff may provide referrals to clinicians and ancillary social or medical services including support for antiretroviral adherence. The University of British Columbia/Providence Healthcare Research Ethics Board has approved the ACCESS study.

Data gathered during the interview process on sociodemographic drug-using and other characteristics is augmented with comprehensive information on HIV care and treatment outcomes supplied by the Drug Treatment Program of the British Columbia Centre for Excellence in HIV/AIDS (BCCfE), a province-wide centralized highly active antiretroviral therapy (HAART) dispensary and HIV/AIDS clinical monitoring laboratory. For each participant, the BCCfE provides a complete prospective profile of CD4+ cell counts, PVL, and exposure to specific antiretroviral agents (described in detail previously).22–24 Of note is the fact that all HIV care including antiretroviral medications are provided free-of-charge to all HIV-seropositive individuals in the province.

In this study, we included all individuals who were exposed to ART at baseline or who initiated ART over the study period; had at least 1 observation of CD4 cell count and PVL within 12 months of recruitment; and at least 2 consecutive measurements indicating suppression of PVL during the study period. Because the sensitivity of the viral load assays changed over the study period, we defined suppression as any measurement below 500 copies per cubic millimeter before April 1, 1999, and any measurement below 50 copies per cubic millimeter after April 1, 1999.

For all individuals included in these analyses, time zero was defined as the date of the first interview after the second measurement indicating suppression. The primary outcome of interest was confirmed viral rebound, defined as the date of the second of 2 consecutive measurements of PVL above 1000 copies per milliliter, consistent with a previous study from our setting.25 Local treatment guidelines recommend that PVL be assessed at ART initiation, 4 weeks after starting treatment, and every 3 months thereafter. In this study, measures of PVL, CD4 cell count and other clinical indicators could be ordered by the participant's physician and study physicians.

Consistent with previous studies identifying clinical risk factors for viral rebound,9,12,26 we considered the following explanatory variables: PVL at ART initiation (per log10 increase); presence of a protease inhibitor (PI) in the first ART regimen (yes vs. no); experience of participant's HIV physician (<6 patients enrolled BCCfE treatment registry vs. ≥6 patients); CD4 cell count (per 100 cells); the time since ART initiation (per year increase); and adherence to ART (>95% vs. ≤95%). The presence of a PI, PVL at ART initiation, and HIV physician experience were assessed at baseline and were time-invariant variables; the remaining were time-updated exposures and referred to the 6 month period before each participant's interview. CD4 cell count was defined as the mean of all observations in the previous 6 months or, if none were available, the most recent observation. Information on adherence to prescribed ART was gathered using the confidential linkage to the BCCfE's ART dispensation records.3,24 These records contain details on all antiretrovirals used in the province, by recording medications delivered by the centralized dispensary to pharmacies in community and correctional settings. We defined adherence in each 6-month period as the number of days for which ART was dispensed over the number of days an individual was eligible for therapy and dichotomized the resulting proportion at >95% vs. ≤95%. We have previously demonstrated the clinical utility of this validated pharmacy refill measure and shown it reliably predicts viral suppression27–29 and survival.3,24

Sociodemographic characteristics assessed at baseline included the participant's age, gender (female vs. male), whether the participant reported aboriginal ancestry (yes vs. no), and educational attainment (<high school diploma vs. ≥high school diploma). Patterns of illicit drug use were assessed longitudinally and included as time-updated variables. Consistent with a previous study on illicit drug use and viral suppression from our setting,30 we characterized illicit drug use in the last 6 months as a 3-level variable with abstinence as the reference level vs. any illicit drug use (excluding cannibinoids) versus any injection drug use. We also included recent binge drug use, defined as any period of more intense drug use than typical in the previous 6 months (yes vs. no).

As there is a growing interest in the role played by the contextual determinants of HIV vulnerability,21,31 our choice of explanatory variables was informed by the risk environment framework.32,33 This framework is increasingly used to understand the social, environmental, and structural level forces that contribute to the risk of infection with HIV.21 Specifically, we included these time-updated variables as follows: living in unstable housing, defined as being homeless, living in a single-room occupancy hotel room, homeless shelter or transitional housing (yes vs. no); participating in the sex trade, defined as any sexual acts in exchange for money, drugs, or other goods or favors (yes vs. no); engagement in methadone maintenance therapy (yes vs. no); and recent incarceration. Exposure to correctional environments was assessed using a 3-level variable with a reference level of no incarceration overnight or longer in any facility versus any incarceration overnight or longer in pretrial detention versus any incarceration overnight or longer in a provincial prison or federal penitentiary. With the exception of engagement in methadone maintenance therapy (MMT) (yes vs. no), which referred to current status, all other time-updated characteristics referred to the 6-month period before the follow-up interview.

To model the relationship between these explanatory variables and the time to viral rebound, we constructed a series of univariate and multivariate proportional hazards frailty models including a recurrent events framework. Frailty models are a class of survival statistical techniques that consider the effect of time-updated covariates and each individual's unobservable deviation from the baseline hazard function, consistent with each individual's inherent risk of viral rebound. Because each individual could experience multiple periods of viral suppression and viral failure, we included a recurrent events framework. All individuals were coded at risk for the outcome from the first time of suppression to the first rebound, if applicable; from then on, their observations were censored until the individual had 2 consecutive PVL observations indicating suppression at which time they were considered at risk for another failure event. This cycle was continued until the end of all available observations.

As a first step, we considered the relationship between all explanatory variables and the risk of rebound by estimating the hazard ratio (HR) with 95% confidence intervals (95% CIs) and associated P value using univariate frailty models. Next, we constructed a multivariate model including all variables with P values less than 0.05 in univariate analyses except for adherence to prescribed HAART. In a secondary analysis, we fit the same multivariate model, adding the covariate for HAART adherence.


Between May 1996 and November 2008, 762 individuals were recruited into the study. Of these, 538 (70.6%) were ART-exposed, 274 (36.0%) before study recruitment and 264 (34.6%) after recruitment. Two hundred seventy-seven individuals (36.3%) had at least 2 consecutive PVL observations indicating suppression and complete clinical profiles and were included in these analyses. Over the study period, the 277 participants contributed 995 person-years of follow-up with a median follow-up time of 32 months (IQR: 6–64) per participant. One hundred twenty-five participants (45.1%) experienced at least 1 instance of viral rebound over follow-up, equal to a crude incidence of 12.6% (95% CI: 10.5 to 15.0).

The baseline characteristics of the participants, stratified by viral rebound over the study period, are presented in Table 1. Of note, participants who were younger, with less time elapsed on treatment and lower CD4 cell counts at the time of ART initiation had a greater likelihood of failure.

Baseline Characteristics of HIV-Seropositive IDUs on ART With Durably Suppressed HIV RNA Levels (n = 277 Participants)

The unadjusted estimates of the effect of the explanatory variables on the time to rebound are presented in Table 2. Younger individuals [HR = 0.98 (95% CI: 0.97 to 0.99)] and individuals reporting sex-trade participation [HR = 1.45 (95% CI: 1.15 to 1.84)] both faced elevated risks of viral rebound. Engagement in methadone maintenance therapy [HR = 0.75 (95% CI: 0.64 to 0.89)] was protective against treatment failure. Although exposure to pretrial detention facilities was not associated with rebound, incarceration overnight or longer in a provincial prison or federal penitentiary [HR = 1.86 (95% CI: 1.37 to 2.52)] conferred a significant risk of failure. Interestingly, various patterns of illicit drug use, including any use, any injection drug use, and any binge drug use, were not associated with a greater risk of rebound.

Unadjusted Estimates of the Behavioral, Social, and Structural Factors Associated With Viral Rebound Among 277 IDU on ART with Suppressed Viral Loads at Baseline in Vancouver, Canada

The adjusted estimates of factors associated with time to treatment failure are presented in Table 3. In model 1, the multivariate model including all variables significant in univariate analyses, sex-trade participation [adjusted hazard ratio (AHR) = 1.40 (95% CI: 1.08 to 1.82)] and incarcerations in a prison or penitentiary [AHR = 1.83 (95% CI: 1.33 to 2.52)] were each independently associated with treatment failure. Engagement in methadone maintenance therapy [AHR = 0.79 (95% CI: 0.66 to 0.94)] was negatively associated with viral rebound. This model was also adjusted for age and clinical predictors of viral rebound significant in univariate analyses, specifically CD4 cell count, treatment duration, and the presence of a PI in the initial ART regimen. However, in the model including ART adherence (model 2), neither age, sex-trade participation, nor methadone maintenance therapy remained independently associated with viral rebound. The association with provincial or federal incarceration remained, although the effect was substantially attenuated. The significant clinical correlates of rebound remained when adherence was included in the model.

Adjusted Estimates of the Behavioral, Social, and Structural Factors Associated With Viral Rebound Among 277 IDU on ART With Suppressed Viral Loads at Baseline in Vancouver, Canada

In light of the independent relationship between engagement in methadone maintenance therapy, and a recent report identifying OST as a significant determinant of long-term virologic success,34 we conducted a subanalysis identifying the relationship between length of maintenance treatment and the hazard of viral rebound. In a Cox proportional hazards model, we observed that a greater number of consecutive follow-ups on MMT was marginally associated with a lower relative hazard of viral rebound (HR = 0.98, 95% CI: 0.95 to 1.00, P = 0.094.)


In this study, the first to our knowledge to investigate social and environmental determinants of viral rebound among IDU on ART, loss of virologic control after suppression was common, with almost half of participants (45.1%) experiencing at least 1 episode of treatment failure over follow-up. Although this rate of rebound is consistent with previous studies,12,18 we found patterns of illicit drug use were not significant predictors of rebound. Instead, endogenous factors, including recent incarceration, participation in the sex trade, and engagement in methadone maintenance therapy emerged as independent risk factors for rebound. Providing validity to the model, established clinical determinants of viral rebound, specifically CD4 cell count and the length of treatment were also associated in multivariate models.

Comparison of the 2 multivariate models indicates the associations between several exposures and treatment failure are largely driven by poorer adherence to ART within those strata. When adherence to ART is added to the multivariate model (model 2), several associations in model 1, specifically age, participation in the sex trade, and engagement in MMT, are rendered nonsignificant. This is consistent with previous studies that found adherence to ART was typically lower among younger individuals35 and those in the sex trade,36 although engagement in MMT was associated with better adherence.37 Interestingly, although the strength of the effect of recent incarceration in a prison or penitentiary also declined, it remained significantly associated with rebound. This highlights the critical need to improve adherence in criminal justice settings.38,39 Thus, our study supports the provision of increased and improved support for ART adherence among these younger drug users, those in the sex trade and the recently incarcerated, to reduce the risk of viral rebound.

In this study, we used the risk environment framework to analyze HIV disease progression among IDU. In the past, the risk environment framework has informed studies of the factors that shape the risk of HIV acquisition.40–42 Specifically, the framework describes the interplay between exogenous forces, including micro-level and macro-level political, social, economic, and physical effects, and endogenous characteristics, including host and viral attributes, on the production of vulnerability to HIV infection.21 In the current study, we observed that exposures previously linked with a higher risk of HIV infection were independently associated with higher rates of viral rebound, specifically incarceration43 and participation in the sex trade.44 As with HIV infection,44 engagement in MMT was protective. Certainly, the causal pathways between these exposures and HIV infection differ from these exposures to treatment non-adherence and viral rebound. However, this study illustrates how the vulnerability produced by the social and structural context of health care can contribute to HIV disease progression. Thus, the risk environment framework may be a useful model to identify factors contributing to the elevated levels of HIV-related morbidity and mortality among drug users and inform evidence-based interventions in clinical practice, community settings, and at the population level.

Consistent with previous studies from our setting describing how imprisonment complicates adherence39,45 and inhibits suppression,46 incarceration in a prison or penitentiary, but not in pretrial detention, emerged as the strongest nonclinical predictor of viral rebound. Although health services are typically more rudimentary in local pretrial facilities and lack the means to care for chronic conditions, the typically short duration of exposure likely minimizes the clinical consequences of any missed doses. Our finding of a deleterious effect of long-term imprisonment on viral loads contradicts previous prison-based studies of HAART delivery in which prisoners achieved viral suppression.47,48 This is likely related to the barriers to ART access and adherence presented in correctional facilities, including delays in dispensing appropriate antiretrovirals from prison pharmacies; possibly contentious relationships with prison-based health care providers and inmates; and the desire of some individuals to conceal their serostatus from other prisoners.45 Our study underlines the challenges, incarceration, and transition between correctional and noncorrectional environments pose to IDU on ART.49

To better understand the context of these findings, it is important to note that HIV care, including clinical monitoring and all medications, is provided free of charge to all individuals in our setting through the province's publicly-funded health care system. This commitment to universal HIV care was recently reaffirmed by an investment by the provincial government in a seek test and treat intervention to increase the coverage of HAART among IDU.50,51 Our findings highlight the apparent contradiction between government policies which, on one hand, seek to deliver HIV care to IDU and, on the other hand, criminalize drug users and undercut the effectiveness of ART. This conflict is sharpened by recent moves by Canada's federal government to enact mandatory minimum prison sentences for illicit drug-related offenses.52 Future research should focus on the possibly deleterious effect of these social, structural, and environmental exposures on efforts to deploy HIV treatment as prevention among vulnerable and marginalized populations.

Substantial effort has been devoted to the development of prognostic tools to identify individuals on ART at heightened risk of viral failure using routinely collected data.12,53 Our results, specifically the lack of an association with patterns of illicit drug use and the strong link with incarceration, participation in the sex trade, and engagement in methadone maintenance therapy, suggest that these screens could be improved by the inclusion of these and other measures of vulnerability. Further, the finding that abstinent individuals did not significantly differ from active drug users in the likelihood of viral rebound builds on our previous report that ongoing drug use did not prevent viral suppression.30 These studies are evidence against the blanket refusal to provide medically necessary ART to IDU, as is common in many jurisdictions.5

As in all observational studies, our study has several limitations. First, the study sample was not selected at random and our findings should not be generalized to other groups of IDU on ART. However, our use of snowball sampling and other community recruitment methods hopefully minimized the bias resulting from the selection procedures. Similarly, as with all observational studies, the relationships between the explanatory variables and the outcome of interest may be under the influence of unobserved confounding. We have sought to address this bias with multivariate adjustment of the covariate estimates and the selection of a broad set of possible confounders. We also recognize that many of our measures were self-reported and thus may be affected by social desirability bias. However, the key variables emerging as significant in these analyses (sex-trade involvement, recent incarceration and engagement in methadone maintenance therapy) were not likely to be differentially reported by individuals with greater or lesser likelihood of experiencing viral rebound. Finally, for historical reasons, we were forced to use a cut-off for PVL suppression of 500 copies per cubic millimeter. Although we cannot know with certainty, we know of no reason why our results would differ had a cut-off of <50 copies have been possible with our data.

To conclude, we assessed the patterns and predictors of viral rebound among community-recruited drug users on ART with suppressed PVL. Consistent with previous studies finding that exposure to characteristics of the risk environment framework were associated with vulnerability to HIV infection, we found that individuals engaged in the sex trade or recently incarcerated in a prison or penitentiary were at higher risk of viral rebound. Concurrently, active drug use was not associated with viral rebound. Our findings not only demonstrate the utility of the risk environment framework in analyzing patterns of HIV disease progression but also suggest that efforts to engage HIV-seropositive drug users in effective treatment should include consideration of the social, environmental and structural contexts of treatment delivery.


The authors thank the study participants for their contribution to the research and current and past researchers and staff. We would specifically like to thank Deborah Graham, Tricia Collingham, Caitlin Johnston, Steve Kain, and Calvin Lai for their research and administrative assistance.


1. Keiser O, Taffe P, Zwahlen M, et al.. All cause mortality in the Swiss HIV Cohort Study from 1990 to 2001 in comparison with the Swiss population. AIDS. 2004;18:1835–1843.
2. Malta M, Bastos FI, da Silva CM, et al.. Differential survival benefit of universal HAART access in Brazil: a nation-wide comparison of injecting drug users versus men who have sex with men. J Acquir Immune Defic Syndr. 2009;52:629–635.
3. Wood E, Hogg RS, Yip B, et al.. 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 x 10(9) cells/L. Ann Intern Med. 2003;139:810–816.
4. Vlahov D, Celentano DD. Access to highly active antiretroviral therapy for injection drug users: adherence, resistance, and death. Cad Saude Publica. 2006;22:705–718.
5. Wolfe D, Carrieri MP, Shepard D, et al.. Treatment and care for injecting drug users with HIV infection: a review of barriers and ways forward. Lancet. 2010;376:355–366.
6. Thompson MA, Aberg JA, Cahn P, et al.. Antiretroviral treatment of adult HIV infection: 2010 recommendations of the International AIDS Society-USA panel. JAMA. 2010;304:321–333.
7. Gulick RM, Ribaudo HJ, Shikuma CM, et al.. Triple-nucleoside regimens versus efavirenz-containing regimens for the initial treatment of HIV-1 infection. N Engl J Med. 2004;350:1850–1861.
8. Gulick RM, Ribaudo HJ, Shikuma CM, et al.. Three- vs four-drug antiretroviral regimens for the initial treatment of HIV-1 infection: a randomized controlled trial. JAMA. 2006;296:769–781.
9. Lima VD, Bangsberg DR, Harrigan PR, et al.. Risk of viral failure declines with duration of suppression on highly active antiretroviral therapy irrespective of adherence level. J Acquir Immune Defic Syndr. 2010;55:460–465.
10. Smith CJ, Phillips AN, Dauer B, et al.. Factors associated with viral rebound among highly treatment-experienced HIV-positive patients who have achieved viral suppression. HIV Med. 2009;10:19–27.
11. Gardner EM, Sharma S, Peng G, et al.. Differential adherence to combination antiretroviral therapy is associated with virological failure with resistance. AIDS. 2008;22:75–82.
12. Robbins GK, Johnson KL, Chang Y, et al.. Predicting virologic failure in an HIV clinic. Clin Infect Dis. 2010;50:779–786.
13. Altice FL, Kamarulzaman A, Soriano VV, et al.. Treatment of medical, psychiatric, and substance-use comorbidities in people infected with HIV who use drugs. Lancet. 2010;376:367–387.
14. Weber R, Huber M, Rickenbach M, et al.. Uptake of and virological response to antiretroviral therapy among HIV-infected former and current injecting drug users and persons in an opiate substitution treatment programme: the Swiss HIV Cohort Study. HIV Med. 2009;10:407–416.
15. Chander G, Himelhoch S, Fleishman JA, et al.. HAART receipt and viral suppression among HIV-infected patients with co-occurring mental illness and illicit drug use. AIDS Care. 2009;21:655–663.
16. Palepu A, Tyndall M, Yip B, et al.. Impaired virologic response to highly active antiretroviral therapy associated with ongoing injection drug use. J Acquir Immune Defic Syndr. 2003;32:522–526.
17. Lucas GM, Cheever LW, Chaisson RE, et al.. Detrimental effects of continued illicit drug use on the treatment of HIV-1 infection. J Acquir Immune Defic Syndr. 2001;27:251–259.
18. Lucas GM, Chaisson RE, Moore RD, et al.. Highly active antiretroviral therapy in a large urban clinic: risk factors for virologic failure and adverse drug reactions. Ann Intern Med. 1999;131:81–87.
19. Galea S, Vlahov D. Social determinants and the health of drug users: socioeconomic status, homelessness, and incarceration. Public Health Rep. 2002;117:S135–S145.
20. Rhodes T, Singer M, Bourgois P, et al.. The social structural production of HIV risk among injecting drug users. Soc Sci Med. 2005;61:1026–1044.
21. Strathdee SA, Hallett TB, Bobrova N, et al.. HIV and risk environment for injecting drug users: the past, present, and future. Lancet. 2010;376:268–284.
22. Strathdee SA, Palepu A, Cornelisse PG, et al.. Barriers to use of free antiretroviral therapy in injection drug users. JAMA. 1998;280:547–549.
23. Wood E, Hogg RS, Bonner S, et al.. Staging for antiretroviral therapy among HIV-infected drug users. JAMA. 2004;292:1175–1177.
24. Wood E, Hogg RS, Lima VD, et al.. Highly active antiretroviral therapy and survival in HIV-infected injection drug users. JAMA. 2008;300:550–554.
25. Gross R, Yip B, Lo Re V III, et al.. A simple, dynamic measure of antiretroviral therapy adherence predicts failure to maintain HIV-1 suppression. J Infect Dis. 2006;194:1108–1114.
26. Mocroft A, Ruiz L, Reiss P, et al.. Virological rebound after suppression on highly active antiretroviral therapy. AIDS. 2003;17:1741–1751.
27. Low-Beer S, Yip B, O'Shaughnessy MV, et al.. Adherence to triple therapy and viral load response. J Acquir Immune Defic Syndr. 2000;23:360–361.
28. Palepu A, Yip B, Miller C, et al.. Factors associated with the response to antiretroviral therapy among HIV-infected patients with and without a history of injection drug use. AIDS. 2001;15:423–424.
29. Wood E, Montaner JS, Yip B, et al.. Adherence and plasma HIV RNA responses to highly active antiretroviral therapy among HIV-1 infected injection drug users. CMAJ. 2003;169:656–661.
30. Krusi A, Milloy MJ, Kerr T, et al.. Ongoing drug use and outcomes from highly active antiretroviral therapy among injection drug users in a Canadian setting. Antivir Ther. 2010;15:789–796.
31. Krüsi A, Wood E, Montaner J, et al.. Social and structural determinants of HAART access and adherence among injection drug users. Int J Drug Policy. 2010;21:4–9.
32. Rhodes T. The ‘risk environment': a framework for understanding and reducing drug-related harm. Int J Drug Policy. 2002;13:85–94.
33. Rhodes T. Risk environments and drug harms: a social science for harm reduction approach. Int J Drug Policy. 2009;20:193–201.
34. Roux P, Carrieri MP, Cohen J, et al.. Retention in opioid substitution treatment: a major predictor of long-term virological success for HIV-infected injection drug users receiving antiretroviral treatment. Clin Infect Dis. 2009;49:1433–1440.
35. Hinkin CH, Hardy DJ, Mason KI, et al.. Medication adherence in HIV-infected adults: effect of patient age, cognitive status, and substance abuse. AIDS. 2004;18:S19–S25.
36. Shannon K, Bright V, Duddy J, et al.. Access and utilization of HIV treatment and services among women sex workers in Vancouver's Downtown Eastside. J Urban Health. 2005;82:488–497.
37. Uhlmann S, Milloy MJ, Kerr T, et al.. Methadone maintenance therapy promotes initiation of antiretroviral therapy among injection drug users. Addiction. 2010;105:907–913.
38. Kerr T, Marshall A, Walsh J, et al.. Determinants of HAART discontinuation among injection drug users. AIDS Care. 2005;17:539–549.
39. Milloy M-J, Kerr T, Buxton J, et al.. Dose-response effect of incarceration events on non-adherence to HIV antiretroviral therapy among injection drug users. J Infect Dis. 2011;203:1215–1221.
40. Small W, Rhodes T, Wood E, et al.. Public injection settings in Vancouver: physical environment, social context and risk. Int J Drug Policy. 2007;18:27–36.
41. Strathdee SA, Lozada R, Pollini RA, et al.. Individual, social, and environmental influences associated with HIV infection among injection drug users in Tijuana, Mexico. J Acquir Immune Defic Syndr. 2008;47:369–376.
42. Shannon K, Strathdee SA, Shoveller J, et al.. Structural and environmental barriers to condom use negotiation with clients among female sex workers: implications for HIV-prevention strategies and policy. Am J Public Health. 2009;99:659–665.
43. Milloy MJ, Wood E, Small W, et al.. Incarceration experiences in a cohort of active injection drug users. Drug Alcohol Rev. 2008;27:1–7.
44. Cooper JR. Methadone treatment and acquired immunodeficiency syndrome. JAMA. 1989;262:1664–1668.
45. Small W, Wood E, Betteridge G, et al.. The impact of incarceration upon adherence to HIV treatment among HIV-positive injection drug users: a qualitative study. AIDS Care. 2009;21:708–714.
46. Palepu A, Tyndall MW, Chan K, et al.. Initiating highly active antiretroviral therapy and continuity of HIV care: the impact of incarceration and prison release on adherence and HIV treatment outcomes. Antivir Ther. 2004;9:713–719.
47. Babudieri S, Aceti A, D'Offizi GP, et al.. Directly observed therapy to treat HIV infection in prisoners. JAMA. 2000;284:179–180.
48. Springer SA, Pesanti E, Hodges J, et al.. Effectiveness of antiretroviral therapy among HIV-infected prisoners: reincarceration and the lack of sustained benefit after release to the community. Clin Infect Dis. 2004;38:1754–1760.
49. Springer SA, Chen S, Altice FL, et al.. Improved HIV and substance abuse treatment outcomes for released HIV-infected prisoners: the impact of buprenorphine treatment. J Urban Health. 2010;87:592–602.
50. McNeil D. An HIV strategy invites addicts in. The New York Times. February 8, 2011.
51. Montaner JS, Wood E, Kerr T, et al.. Expanded highly active antiretroviral therapy coverage among HIV-positive drug users to improve individual and public health outcomes. J Acquir Immune Defic Syndr. 2010;55:S5–S9.
52. Federal government tries again to set mandatory minimum sentences for some drug offences. HIV AIDS Policy Law Rev. 2009;14:22; 24.
53. Robbins GK, Daniels B, Zheng H, et al.. Predictors of antiretroviral treatment failure in an urban HIV clinic. J Acquir Immune Defic Syndr. 2007;44:30–37.

antiretroviral therapy; human immunodeficiency virus infection; injection drug user; plasma HIV-1 RNA viral load; viral rebound; viral suppression

© 2012 Lippincott Williams & Wilkins, Inc.