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Patterns of heroin and cocaine injection and plasma HIV-1 RNA suppression among a long-term cohort of… Kerr, Thomas; Marshall, Brandon David Lewis; Milloy, M-J; Zhang, Ruth; Guillemi, Silvia; Montaner, Julio; Wood, Evan Jan 14, 2012

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Patterns of heroin and cocaine injection and plasma HIV-1 RNAsuppression among a long-term cohort of injection drug usersThomas Kerr1,2, Brandon D. L. Marshall1,3, M-J Milloy1,4, Ruth Zhang1, Silvia Guillemi1,6,Julio S. G. Montaner1,2, and Evan Wood1,21British Columbia Centre for Excellence in HIV/AIDS, 608 - 1081 Burrard Street, Vancouver,British Columbia V6Z 1Y6, Canada2Department of Medicine, University of British Columbia, 2775 Laurel Street, 10th Floor,Vancouver, British Columbia V5Z 1M9, Canada3Department of Epidemiology, Mailman School of Public Health, Columbia University, AllanRosenfield Building, 722 West 168th Street, New York, NY 10032, USA4School of Population and Public Health, University of British Columbia, Vancouver, Canada5Provincial Health Services Authority, 700 - 1380 Burrard Street, Vancouver, British ColumbiaV6Z 2H3, Canada6Department of Family Practice, University of British Columbia, David Strangway Building, 3rdFloor, 5950 University Boulevard, Vancouver, British Columbia V6T 1Z3, CanadaAbstractBackground—Previous studies suggest that active drug use may compromise HIV treatmentamong HIV-positive injection drug users (IDU). However, little is known about the differentialimpacts of cocaine injection, heroin injection, and combined cocaine and heroin injection onplasma HIV-1 RNA suppression.Methods—Data were derived from a longstanding open prospective cohort of HIV-positive IDUin Vancouver, Canada. Kaplan-Meier methods and Cox proportional hazards regression were usedto examine the impacts of different drug use patterns on rates of plasma HIV-1 RNA suppression.© 2011 Elsevier Ireland Ltd. All rights reserved.Send correspondence to: Dr. Thomas Kerr, British Columbia Centre for Excellence in HIV/AIDS, 608 - 1081 Burrard Street,Vancouver, British Columbia V6Z 1Y6, Canada, Phone: 604 806 9116, Fax: 604 806 9044, uhri-ew@cfenet.ubc.ca.ContributorsE. Wood had full access to all of the data in the study and takes full responsibility for the integrity of the data and the accuracy of thedata analysis. T. Kerr, B. Marshall, and E. Wood designed the study and wrote the protocol. R. Zhang conducted the statisticalanalysis, and all authors interpreted the results. T. Kerr wrote the manuscript. E. Wood, B. Marshall, M-J Milloy, S. Guillemi, and J.Montaner critically revised the manuscript and contributed important intellectual content. All authors have read and approved the finalversion of the manuscript.Conflict of InterestJ. Montaner has received educational grants from, has served as an ad hoc advisor to, or has spoken at various events sponsored byAbbott Laboratories, Agouron Pharmaceuticals Inc., Boehringer Ingelheim Pharmaceuticals Inc., Borean Pharma AS, Bristol-MyersSquibb, DuPont Pharma, Gilead Sciences, GlaxoSmithKline, Hoffmann-La Roche, Immune Response Corporation, Incyte, Janssen-Ortho Inc., Kucera Pharmaceutical Company, Merck Frosst Laboratories, Pfizer Canada Inc., Sanofi Pasteur, Shire Biochem Inc.,Tibotec Pharmaceuticals Ltd., and Trimeris Inc. All other authors declare that they have no conflicts of interest.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to ourcustomers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review ofthe resulting proof before it is published in its final citable form. Please note that during the production process errors may bediscovered which could affect the content, and all legal disclaimers that apply to the journal pertain.NIH Public AccessAuthor ManuscriptDrug Alcohol Depend. Author manuscript; available in PMC 2013 July 01.Published in final edited form as:Drug Alcohol Depend. 2012 July 1; 124(1-2): 108–112. doi:10.1016/j.drugalcdep.2011.12.019.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptResults—Between May 1996 and April 2008, 267 antiretroviral (ART) naïve participants wereseen for a median follow-up duration of 50.6 months after initiating ART. The incidence densityof HIV-1 RNA suppression was 65.2 (95%CI: 57.0–74.2) per 100 person-years. In Kaplan-Meieranalyses, compared to those who abstained from injecting, individuals injecting heroin, cocaine, orcombined heroin/cocaine at baseline were significantly less likely to achieve viral suppression (allp < 0.01). However, none of the drug use categories remained associated with a reduced rate ofviral suppression when considered as time-updated variables (all p > 0.05).Conclusions—Active injecting at the time of ART initiation was associated with lower plasmaHIV-1 RNA suppression rates; however, there was no difference in suppression rates when druguse patterns were examined over time. These findings imply that adherence interventions foractive injectors should optimally be applied at the time of ART initiation.Keywordsinjection drug use; antiretroviral therapy; viral suppression1. IntroductionInjection drug users (IDU) continue to account for a substantial proportion of new HIVinfections globally, and in some areas with rapidly growing epidemics of HIV infection,IDU account for the majority of new infections (UNAIDS, 2008). While recent medicaladvances in the treatment of HIV disease have resulted in substantial reductions in AIDS-related morbidity and mortality (Egger et al., 2002; Hammer et al., 1997; Wood et al., 2003),a growing body of evidence indicates that not all populations affected by HIV disease havebenefited equally from available treatments (Aceijas et al., 2006; Chander et al., 2006;Poundstone et al., 2001). For example, IDU experience many barriers to accessing highlyactive antiretroviral therapy (ART) and suffer from low rates of adherence once initiated(Aceijas et al., 2006; Mocroft et al., 1999; Wood et al., 2003b); as a consequence, thispopulation often has poorer AIDS-related outcomes (Egger et al., 2002; Lucas et al., 2001;Palepu et al., 2003).Ongoing illicit drug use among HIV-positive IDU remains a common clinical presentationthat raises significant concerns for healthcare providers considering when to initiate ART(Ding et al., 2005; Loughlin et al., 2004; Maisels et al., 2001). This is due in part to a bodyof evidence indicating that active drug use is associated with lower rates of adherence toART and suppression of HIV RNA (Arnsten et al., 2002; Egger et al., 2002; Lucas et al.,1999; Lucas et al., 2001; Palepu et al., 2003; Weber et al., 2009). However, while negativeimpacts of active drug use on HIV-related clinical outcomes have been shown in severalstudies, little is known about the relationship between specific drugs and patterns of use onoutcomes associated with HIV disease (Krüsi et al., 2010). A small number of studies haverevealed adverse effects of crack cocaine use (Moss et al., 2004; Sullivan et al., 2007)(Baum et al., 2009) and stimulant use more generally (Hinkin et al., 2007; Kapadia et al.,2005) on adherence and progression to AIDS. However, we know of no longitudinal studiesthat have sought to compare the distinct effects of different drugs on outcomes associatedwith the treatment of HIV disease, despite recent calls for this type of research (Krüsi et al.,2010). In addition, questions remain concerning the relative contributions of otherbehavioral factors to poor clinical outcomes among IDU (Krüsi et al., 2010; Wood et al.,2008).Vancouver, Canada, is home to a large injection-drug-related HIV epidemic that is uniquebecause of the widespread availability of several different types of illegal drugs, includinginjection cocaine and heroin (Wood and Kerr, 2006). This unique context and the presenceof a large, longstanding prospective cohort study of HIV-positive IDU conducted within aKerr et al. Page 2Drug Alcohol Depend. Author manuscript; available in PMC 2013 July 01.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscriptcontext of universal access to free healthcare afforded the opportunity to examine the impactof different types of illicit drug use on suppression of HIV-1 RNA.2. MethodsThe AIDS Care Cohort to Evaluate Exposure to Survival Services (ACCESS) is aprospective observational cohort of HIV-seropositive injection drug users in Vancouver,Canada. The cohort has been described in detail previously (Wood et al., 2008) and waspopulated through snowball sampling and extensive street outreach methods in the city’sDowntown Eastside. Individuals were eligible for ACCESS if they were aged 18 years orolder, HIV-seropositive, had used injection drugs at least once in the previous six months,and provided written informed consent. At baseline and semi-annually, participantsanswered a standardized interviewer-administered questionnaire and provided bloodsamples for serologic analysis.As previously described (Strathdee et al., 1998; Wood et al., 2004; Wood et al., 2008), thelocal setting is perhaps unique in that there is a universal healthcare system and a province-wide centralized antiretroviral dispensation program and HIV/AIDS laboratory whichenables a complete prospective profile of all patient CD4 cell count determinations andplasma HIV-1 RNA levels, as well as a complete prospective profile of antiretroviraltherapy use among cohort participants. This includes the specific antiretroviral agents andthe prescribed dose, as well as a validated measure of patient adherence derived fromprescription refill compliance (Wood et al., 2008; Wood et al., 2003a). The guidelines forthe initiation of ART evolved during the study period in accordance with the guidelines setby the International AIDS Society–USA Panel (Carpenter et al., 1998; Hammer et al., 2006).The study has been approved by the Providence Health Care/University of British ColumbiaResearch Ethics Board.In the present study, because updated laboratory data were available throughout the studyperiod, we were able to consider all participants who completed at least one interviewbetween May 1996 and April 2008, were antiretroviral-naïve at baseline, and initiatedtreatment at any point during follow-up. Viral load suppression was defined as the firstinstance of achieving plasma HIV-1 RNA suppression of less than 500 copies/mL. The keyindependent variables of interest were: exclusive cocaine injection (yes vs. no); exclusiveheroin injection (yes vs. no); and heroin and cocaine injection in combination (yes vs. no).Individuals injecting cocaine, heroin, or heroin and cocaine in combination at least once inthe previous six months were included in these categories. For each of these variables, thereference category was no injection drug use (i.e., individuals who did not report anyinjecting in the previous six months).Additional independent variables of interest included: age (per 10 years older); currentmethadone use (yes vs. no); baseline CD4 cell count (cells/μL, per 100-cell increase);baseline plasma HIV-1 RNA (per log10 increase); protease inhibitor (PI) use at ARTinitiation (yes vs. other regimen); year of ART initiation (per year later); and ARTadherence (≥ vs. < 95% adherence). All variable definitions were identical to earlier reports(Uhlmann et al., 2010; Wood et al., 2008). Unless otherwise noted, all behavioral data referto the six-month period prior to the follow-up interview. Plasma HIV-1 RNA was measuredusing the Roche Amplicor Monitor assay (Roche Molecular Systems, Mississauga, Canada).As an initial step, we compared selected demographic (age, gender, Aboriginal ancestry) andclinical (methadone treatment use, HIV-1 viral load, CD4 cell count) characteristics amongthose who did and did not report injecting at baseline. To test for significant differencesamong these groups we used the Pearson’s Chi-square test and the Wilcoxon rank sum test.Cumulative HIV-1 RNA suppression rates were then estimated using Kaplan–MeierKerr et al. Page 3Drug Alcohol Depend. Author manuscript; available in PMC 2013 July 01.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscriptmethods. Here we compared HIV-1 RNA suppression rates among individuals reporting noinjection drug use, exclusive cocaine injecting, exclusive heroin injecting, combined heroin/cocaine injecting, and injecting of drugs other than heroin and cocaine. Survival curves werecompared using the log-rank test. Time zero was defined as the date of ART initiation.Participants who consistently remained unsuppressed were right-censored at the time of theirmost recent HIV-1 RNA plasma measurement.We were interested in comparing the effects of the different drugs (heroin and cocaine eitheralone or in combination) on suppression and what the effect was when these drugs wereinjected after ART was initiated. We were also interested to discern the independent effectof drug use patterns after adjusting for clinical and behavioral characteristics. To achievethis, univariate and multivariate Cox regression was used to assess the independent effectsof different types of drug use during follow-up on HIV-1 RNA suppression rates.In total, three statistical models were constructed. Each model considered each drug usebehavior as a time-updated variable (measuring drug use in the previous six monthsthroughout follow-up). Considering drug use as a time-updated behavior meant thatparticipants could move from injecting to non-injecting, or from one drug use pattern (e.g.,heroin injecting) to another drug use pattern (e.g., combined heroin and cocaine injecting)during study follow-up. In the first model, we estimated the unadjusted association betweenHIV-1 RNA suppression and each drug use variable of interest. In a second fixedmultivariate Cox model, we estimated the association between each drug use variable andHIV-1 RNA suppression, after adjustment for baseline CD4 cell count and baseline plasmaHIV-1 RNA level. In a third confounding model, we estimated the association between eachdrug use variable and HIV-1 RNA suppression, after adjustment for baseline CD4 cellcount, baseline plasma HIV-1 RNA load, age, methadone use, 95% adherence, year of ARTinitiation, and regimen (PI or other) used for initial HAART regimen. To fit the multivariateconfounding model, we employed a conservative backward selection approach. Beginningwith a full model with all covariates included, secondary explanatory variables weredropped one at a time, using the relative change in the regression coefficient for the variablerelated to drug use as a criterion, until the smallest relative change in the coefficient for thedrug use variable exceeded 5%. We then fitted the final model, including the drug usevariable of interest and all remaining secondary explanatory variables as terms in theregression equation. All tests of significance were two-sided, with a p-value of less than 0.05indicating that an association was statistically significant. All statistical analyses wereperformed using SAS software (SAS, Cary, NC).3. ResultsAmong these 267 participants, 124 (46.4%) were female, 105 (39.3%) were of Aboriginalancestry, and the median age at baseline was 36.5 years (interquartile range [IQR] = 29.8–43.5). The median duration of follow-up was 50.6 (IQR = 16.5–95.0) months. The medianCD4 cell count at baseline was 240 (IQR = 140–360) cells/μL, while the median HIV-1viral load was 86,000 (IQR = 36,300–120,000) copies/ml. The cumulative rate of HIV-1RNA suppression for the entire sample was 65.2 (95% confidence interval [CI]: 57.0–74.2)per 100 person-years. As indicated in Table 1, 230 (86.1%) participants reported injecting atbaseline, while 37 (13.9%) reported no injecting. A comparison of participants who did anddid not report injecting at baseline revealed no significant differences between the groupswith respect to any of the socio-demographic or clinical characteristics considered (all p >0.05), with the exception that those who did not report injecting at baseline had a lowermedian HIV-1 viral load than those who did report injecting (61,000 copies/ml vs. 90,500copies/ml, p = 0.032).Kerr et al. Page 4Drug Alcohol Depend. Author manuscript; available in PMC 2013 July 01.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptThe Kaplan-Meier analysis of the cumulative plasma HIV-1 RNA suppression rate stratifiedby baseline drug use is shown in Figure 1. As shown here, at 12 months, the cumulativeHIV-1 suppression rate was not statistically different for those injecting cocaine, heroin, orcocaine and heroin in combination (all log-rank: p > 0.05), with the rate of HIV-1 RNAsuppression at 12 months being 55.5% (95%CI: 44.6%–67.0%) for cocaine injectors, 57.6%(95%CI: 40.6%–75.6%) for heroin injectors, and 55.9% (95%CI: 47.1%–65.1%) for IDUinjecting heroin and cocaine in combination. The rate of HIV-1 RNA suppression for non-injectors at 12 months was 88.8% (95%CI: 76.2%–96.4%). There were statisticallydetectable differences in the rates of HIV-1 RNA suppression between the non-injectinggroup and each of the three drug injecting groups (all pairwise log-rank: p < 0.01).The results of the Cox regression analyses examining the associations between the variousdrug use variables and HIV-1 RNA suppression are shown in Figure 2. As shown here,when the drug use variables were considered as time-updated variables in Model 1, only theunadjusted association between combined cocaine/heroin injection and HIV-1 RNAsuppression (hazard ratio [HR] = 0.67, 95%CI: 0.47–0.97) was statistically significant. InModel 2, none of the drug use variables remained significantly associated with HIV-1 RNAsuppression (all p < 0.05) after adjustment for baseline CD4 cell count (adjusted hazard ratio[AHR] = 0.96, 95%CI: 0.89–1.03), baseline plasma HIV-1 RNA viral load (AHR = 0.75,95%CI: 0.60–0.94), and year of ART initiation (AHR = 1.15, 95%CI: 1.10–1.20). In Model3, a confounding model, none of the drug use variables remained associated withsuppression of HIV-1 RNA after adjustment for baseline CD4 cell count (AHR = 0.95,95%CI: 0.88–1.02), baseline plasma HIV-1 RNA viral load (AHR = 0.61, 95%CI: 0.49–0.77), methadone use (AHR = 1.33, 95%CI: 1.01–1.76), 95% adherence (AHR = 4.00,95%CI: 2.91–5.49), PI use (AHR = 1.35, 95%CI: 1.03–1.77), and year of ART initiation(AHR = 1.10, 95%CI: 1.05–1.15).4. DiscussionThe present study demonstrates lower rates of plasma HIV-1 RNA suppression among IDUwho are actively injecting at the time of ART initiation in comparison to individuals whowere not injecting at this time. The effects of the various drugs (e.g., heroin vs. cocaine) onHIV-1 RNA viral suppression did not differ greatly when baseline drug use was considered.When injection drug use patterns throughout follow-up were considered in time-updatedmodels, differences between abstinent and active injection drug use categories diminished.Suppression of HIV-1 RNA was most strongly predicted by baseline clinical characteristics(e.g., baseline plasma HIV-1 RNA viral load), use of methadone, and adherence to ART.While the comparison of baseline drug use patterns is consistent with past work (Arnsten etal., 2002; Egger et al., 2002; Lucas et al., 1999; Lucas et al., 2001; Palepu et al., 2003;Weber et al., 2009), our comparison of longitudinal drug use patterns stands in contrast toprevious work indicating that active drug use is associated with poorer clinical outcomes(Arnsten et al., 2002; Poundstone et al., 2001). This may be because of differences in thelength of follow-up time, sample sizes, and the measurement of drug use behavior acrossstudies (Baum et al., 2009; Hinkin et al., 2007; Krüsi et al., 2010). To date, only a limitednumber of studies have examined the impact of ongoing drug use longitudinally, and only afew studies have examined the impacts of time-updated drug use behaviors on viralsuppression (Arnsten et al., 2002; Lucas et al., 2001; Lucas et al., 2006). The finding thatactive drug use, when measured as a time-updated behavior, did not predict suppression ofHIV-1 RNA after multivariate adjustment may also reflect the natural history of injectiondrug use, which is typically characterized by periods of varying intensity of injecting as wellas periods of no injecting, rather than trajectories marked by constant levels of injecting thatare fixed at baseline (Bruneau et al., 2004; Galai et al., 2003). These findings may also beKerr et al. Page 5Drug Alcohol Depend. Author manuscript; available in PMC 2013 July 01.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscriptexplained by: evolving characteristics of the local healthcare and social service systems,including housing and adherence support programs (Tyndall et al., 2007), which serve tosupport adherence to ART among active IDU; or the universal healthcare system, whichprovides ART free of charge to local IDU regardless of active drug use (Evans andStrathdee, 2006; Tyndall et al., 2007). Indeed, several previous studies examining the impactof ongoing drug use on ART-related outcomes among IDU have been conducted in thecontext of medical systems with significant barriers to HIV treatment, which maydisproportionally affect IDU (Arnsten et al., 2002; Lucas et al., 1999; Poundstone et al.,2001). Regardless, taken together, these findings imply that adherence interventions shouldbe optimally applied at the time of ART initiation.Of interest, IDU injecting cocaine, heroin, and heroin and cocaine in combination hadsimilar rates of HIV-1 RNA suppression. However, it is notable that individuals injectingheroin and heroin and cocaine in combination had slightly worse outcomes in comparison tothose IDU who injected cocaine exclusively. There have been concerns that stimulantinjection could have a particularly destabilizing effect on ART adherence and associatedclinical outcomes because of the frequency of injections and the short half-life of cocaine, aswell as the psychological sequelae and chaotic behavior that may stem from its use and thelack of an appropriate substitution therapy, as in the case of heroin (Arnsten et al., 2002;Hinkin et al., 2007). While previous research has found active cocaine use to be associatedwith suboptimal adherence and suboptimal virological response (Arnsten et al., 2002; Berget al., 2004; Hinkin et al., 2007), the present study suggests that opioid injectors may haveequally poor, if not worse, outcomes.This study has limitations. Our sample was not randomly selected and therefore may not befully representative of the larger population of treatment-eligible HIV-positive individualswith a history of injection drug use in our setting. Additionally, because of socially desirablereporting, IDU in our study may have underreported recent drug use (Des Jarlais et al.,1999), which could have resulted in a possible underestimation of the influence of ongoingdrug use on suppression of HIV-1 RNA. This is a hypothetical concern, however, and it isnoteworthy that self-reported abstinence at baseline was strongly associated with higherrates of HIV RNA-1 suppression. Further, we may have failed to measure more directbiological mechanisms or processes with potential to mediate the relationship between druguse and suppression of HIV-1 RNA. Finally, while initial response to ART predicts long-term outcomes (Sterne et al., 2005) and it is conventional for clinical trials to look at initialvirological responses, our study did not assess long-term outcomes from ART. It is possiblethat relapses into injecting drug use, while not having an obvious effect on initial ARTresponse, may in fact have impacts on longer-term outcomes.In summary, active drug injecting at the time of ART initiation is associated with lowerplasma HIV-1 RNA suppression rates. Interestingly, when considered longitudinally astime-updated behaviors, there was little association between patterns of drug injecting andplasma HIV-1 RNA suppression rates. These findings imply that adherence interventionsshould be applied at the time of ART initiation for active drug injectors.AcknowledgmentsRole of Funding SourceThis study was supported by US National Institutes of Health (NIH) grant R01DA021525 and Canadian Institutesof Health Research (CIHR) grants MOP-79297 and RAA-79918. T. Kerr is supported by awards from the MichaelSmith Foundation for Health Research (MSFHR) and CIHR. M-J Milloy is supported by awards from CIHR andMSFHR. B. Marshall is supported by a research fellowship award from CIHR and an International AIDS Society/National Institute on Drug Abuse (NIDA) Fellowship in Encouraging HIV and Drug Use Research. J. Montaner issupported by a NIDA Avant-Garde award. None of the funders had any further role in study design; in theKerr et al. Page 6Drug Alcohol Depend. Author manuscript; available in PMC 2013 July 01.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscriptcollection, analysis or interpretation of data; in the writing of the report; or in the decision to submit the paper forpublication.The authors sincerely thank the study participants for their contribution to the research, as well as current and pastresearchers 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.ReferencesAceijas C, Oppenheimer E, Stimson GV, Ashcroft RE, Matic S, Hickman M. Antiretroviral treatmentfor injecting drug users in developing and transitional countries 1 year before the end of the“Treating 3 million by 2005. Making it happen. The WHO strategy” (“3 by 5”). Addiction. 2006;101:1246–1253. 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Author manuscript; available in PMC 2013 July 01.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptUNAIDS. 2008 report on the global AIDS epidemic. UNAIDS; Geneva: 2008.Weber R, Huber M, Rickenbach M, Furrer H, Elzi L, Hirschel B, Cavassini M, Bernasconi E, SchmidP, Ledergerber B. Uptake of and virological response to antiretroviral therapy among HIV-infectedformer and current injecting drug users and persons in an opiate substitution treatment programme:the Swiss HIV Cohort Study. HIV Med. 2009; 10:407–416. [PubMed: 19490174]Wood E, Hogg RS, Bonner S, Kerr T, Li K, Palepu A, Guillemi S, Schechter MT, Montaner JS.Staging for antiretroviral therapy among HIV-infected drug users. JAMA. 2004; 292:1175–1177.[PubMed: 15353528]Wood E, Hogg RS, Lima VD, Kerr T, Yip B, Marshall BD, Montaner JS. Highly active antiretroviraltherapy and survival in HIV-infected injection drug users. JAMA. 2008; 300:550–554. [PubMed:18677027]Wood E, Hogg RS, Yip B, Harrigan PR, O’Shaughnessy MV, Montaner JS. Effect of medicationadherence on survival of HIV-infected adults who start highly active antiretroviral therapy whenthe CD4+ cell count is 0.200 to 0.350 × 10(9) cells/L. Ann Intern Med. 2003a; 139:810–816.[PubMed: 14623618]Wood E, Kerr T. What do you do when you hit rock bottom: responding to drugs in the City ofVancouver. Int J Drug Policy. 2006; 17:55–60.Wood E, Montaner JS, Yip B, Tyndall MW, Schechter MT, O’Shaughnessy MV, Hogg RS.Adherence and plasma HIV RNA responses to highly active antiretroviral therapy among HIV-1infected injection drug users. CMAJ. 2003b; 169:656–661. [PubMed: 14517122]Kerr et al. Page 9Drug Alcohol Depend. Author manuscript; available in PMC 2013 July 01.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptFigure 1.Kaplan–Meier estimates of cumulative rates of plasma HIV-1 RNA suppression within asample of 267 injection drug users who initiated highly active antiretroviral therapy (ART)during the study period. All pairwise comparisons between injecting groups were non-significant (log-rank: p > 0.05). All pairwise comparisons between the non-injecting groupand the three drug-injecting groups were significant (log-rank p < 0.01).Kerr et al. Page 10Drug Alcohol Depend. Author manuscript; available in PMC 2013 July 01.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptFigure 2.The relationship between substance use throughout follow-up and time to viral loadsuppression among a prospective cohort of HIV-positive injection drug users (n = 267).Model 1: Unadjusted association between drug use and viral load suppression.Model 2: Adjusted for baseline CD4 count, baseline log10(viral load).Model 3: Adjusted for baseline CD4 count, baseline log10(viral load), age, currentenrollment in methadone maintenance therapy, adherence (past 6 months), and ARTregimen.Kerr et al. Page 11Drug Alcohol Depend. Author manuscript; available in PMC 2013 July 01.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptKerr et al. Page 12Table 1Baseline characteristics of HIV-positive drug users stratified by injecting versus non-injecting drug use (n =267)CharacteristicInjection drug usep – valueYesn = 230 (86.1%)Non = 37 (13.9%)Age Median (IQR)* 36.4 (30.1 – 43.5) 38.5 (29.4 – 43.0) 0.864Gender Female 103 (83.1) 21 (16.9) 0.175 Male 127 (88.8) 16 (11.2)Aboriginal ancestry Yes 91 (86.7) 14 (13.3) 0.842 No 139 (85.8) 23 (14.2)Methadone treatment Yes 95 (88.8) 12 (11.2) 0.307 No 135 (84.4) 25 (15.6)HIV-1 viral load† Median 90500 (41500 – 150000) 61000 (21100 – 100010) 0.032CD4 count Median (IQR)* 240 (150 – 370) 220 (100 – 320) 0.175*IQR = interquartile range†refers to copies/mlDrug Alcohol Depend. Author manuscript; available in PMC 2013 July 01.


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