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Aboriginal status is a prognostic factor for mortality among antiretroviral naïve HIV-positive individuals… Lima, Viviane D; Kretz, Patricia; Palepu, Anita; Bonner, Simon; Kerr, Thomas; Moore, David; Daniel, Mark; Montaner, Julio S; Hogg, Robert S May 24, 2006

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ralssBioMed CentAIDS Research and TherapyOpen AcceResearchAboriginal status is a prognostic factor for mortality among antiretroviral naïve HIV-positive individuals first initiating HAARTViviane D Lima1, Patricia Kretz1, Anita Palepu2, Simon Bonner1, Thomas Kerr1, David Moore1, Mark Daniel3, Julio SG Montaner1,2 and Robert S Hogg*1,2Address: 1British Columbia Centre for Excellence in HIV/AIDS, St. Paul's Hospital; Vancouver, Canada, 2Departments of Medicine and/or Healthcare and Epidemiology, Faculty of Medicine, University of British Columbia, Vancouver, Canada and 3Département de médecine sociale et préventive, Université de Montréal et Centre de recherche du Centre hospitalier de l'Université de Montréal, Québec, CanadaEmail: Viviane D Lima - vivianed@interchange.ubc.ca; Patricia Kretz - pkretz@cfenet.ubc.ca; Anita Palepu - anita@sm.hivnet.ubc.ca; Simon Bonner - sbonner@stat.sfu.ca; Thomas Kerr - tkerr@cfenet.ubc.ca; David Moore - dmoore@cfenet.ubc.ca; Mark Daniel - mark.daniel@umontreal.ca; Julio SG Montaner - jmontaner@cfenet.ubc.ca; Robert S Hogg* - bobhogg@cfenet.ubc.ca* Corresponding author    AbstractBackground: Although the impact of Aboriginal status on HIV incidence, HIV disease progression, and accessto treatment has been investigated previously, little is known about the relationship between Aboriginal ethnicityand outcomes associated with highly active antiretroviral therapy (HAART). We undertook the present analysisto determine if Aboriginal and non-Aboriginal persons respond differently to HAART by measuring HIV plasmaviral load response, CD4 cell response and time to all-cause mortality.Methods: A population-based analysis of a cohort of antiretroviral therapy naïve HIV-positive Aboriginal menand women 18 years or older in British Columbia, Canada. Participants were antiretroviral therapy naïve, initiatedtriple combination therapy between August 1, 1996 and September 30, 1999. Participants had to complete abaseline questionnaire as well as have at least two follow-up CD4 and HIV plasma viral load measures. Theprimary endpoints were CD4 and HIV plasma viral load response and all cause mortality. Cox proportionalhazards models were used to determine the association between Aboriginal status and CD4 cell response, HIVplasma viral load response and all-cause mortality while controlling for several confounder variables.Results: A total of 622 participants met the study criteria. Aboriginal status was significantly associated with noAIDS diagnosis at baseline (p = 0.0296), having protease inhibitor in the first therapy (p = 0.0209), lower baselineHIV plasma viral load (p < 0.001), less experienced HIV physicians (P = 0.0133), history of IDU (p < 0.001), notcompleting high school (p = 0.0046), and an income of less than $10,000 per year (p = 0.0115). Cox proportionalhazards models controlling for clinical characteristics found that Aboriginal status had an increased hazard ofmortality (HR = 3.12, 95% CI: 1.77–5.48) but did not with HIV plasma viral load response (HR = 1.15, 95% CI:0.89–1.48) or CD4 cell response (HR = 0.95, 95% CI: 0.73–1.23).Conclusion: Our study demonstrates that HIV-infected Aboriginal persons accessing HAART had similar HIVtreatment response as non-Aboriginal persons but have a shorter survival. This study highlights the need forcontinued research on medical interventions and behavioural changes among HIV-infected Aboriginal and otherPublished: 24 May 2006AIDS Research and Therapy 2006, 3:14 doi:10.1186/1742-6405-3-14Received: 29 September 2005Accepted: 24 May 2006This article is available from: http://www.aidsrestherapy.com/content/3/1/14© 2006 Lima et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 9(page number not for citation purposes)marginalized populations.AIDS Research and Therapy 2006, 3:14 http://www.aidsrestherapy.com/content/3/1/14IntroductionThere are trends for global health concerns to have agreater impact on ethnic minorities [1-5]. For example,the human immunodefeciency virus (HIV) epidemic hasdisproportionately affected Aboriginal persons through-out North America. American Indians and Alaska Nativesmake up 6% of all new HIV infections in the USA, yet theyrepresent less than 1 percent of the population [6]. Studiesattempting to explain this disparity have shown that bothAboriginal men and women are at an increased risk ofantecedent risk factors for HIV infection including sexualabuse, a history of IDU, poverty, poor mental health andinvolvement in the sex trade [7-9] Furthermore, withinrisk groups, for example amongst individuals with a his-tory of injection drug use (IDU), Aboriginal men [10,11]and women [11,12] are at a higher risk of being infectedwith HIV.In addition to a higher risk of HIV infection, it has beenshown that Aboriginal persons were more likely to sufferfrom other morbidities that can complicate HIV diseaseprogression. For example, HIV-infected Aboriginalyouths, were more likely to be co-infected with hepatitis Cvirus [13-15], being depressed [16,17], and having ane-mia [18], all of which have been independently associatedwith morbidity or mortality in HIV-infected individuals[19-21].Further disparity for Aboriginal persons and other mar-ginalized groups is found when considering access totreatment for HIV. In the USA, assessment of state surveil-lance and claims data revealed that access to HIV therapywas influenced by state policies which showed racial ine-quality in pharmaceutical access [22]. In British Colum-bia, Canada, where antiretrovirals are distributed at nocost, Aboriginal ethnicity was negatively associated withreceiving HIV treatment before death [23] and positivelyassociated with leaving the hospital against medicaladvice [24].Although the impact of Aboriginal status on HIV inci-dence, HIV disease progression, and access to treatmenthas been investigated previously, little is known about therelationship between Aboriginal ethnicity and outcomesassociated with highly active antiretroviral therapy(HAART). There have been studies on racial/ethnic (black,white and Hispanic) differences in response to HAART[25-28], yet, to our knowledge, there has been only onesmall study examining this issue with Aboriginal personsin Greenland [29], but none including Aboriginal personsin urban areas. Therefore, the present study examinedwhether Aboriginal and non-Aboriginal persons responddifferently to HAART by measuring HIV plasma viral loadious demographic and clinical characteristics on theassociation between Aboriginal ethnicity and HIV diseaseprogression.MethodsHIV/AIDS drug treatment programThe distribution and the population-based monitoring ofantiretroviral therapy in British Columbia have beenextensively described in the literature [30-32]. The Centredistributes antiretroviral drugs based on guidelines gener-ated by the Therapeutic Guidelines Committee, which ismade-up of physicians, pharmacists, virologists, healthservice researchers and economists. The ProvidenceHealth Care Ethics Committee for Human Experimenta-tion has approved use of the data generated from the pro-gram for research purposes.Data collectionAll antiretroviral treatment recipients in the province areentered into an Oracle-based monitoring and evaluationreporting system that uses standardized indicators to pro-spectively tract the antiretroviral use and clinical andhealth status of HIV-1 positive individuals. Physiciansenrolling an HIV-1 infected individual into the systemmust complete a drug request enrolment prescriptionform, which compiles information on the applicant'saddress, past HIV-specific drug history, CD4 cell counts,plasma HIV-1 RNA, current drug requests, and enrollingphysician data. Typically, persons receiving antiretroviraltherapy are monitored by physicians at intervals no longerthan three months at which time prescriptions arerenewed or modified. At the time of the initial dispensa-tion, participants are asked to provide informed consentfor accessing medical electronic records (which may beused for health utilization studies, but is not relevant tothe analyses in this study), and complete a participant sur-vey, which elicits information on socio-demographiccharacteristics, clinical and health status, and alternativetherapy use. Both the consent form and the participantsurvey are optional and participant's refusal to do eitherwill not limit his or her access to free antiretroviral ther-apy. At the same time, the treating physicians are asked tocomplete a clinical staging form using the World HealthOrganization (WHO) clinical staging system.The Centre recommends that plasma HIV plasma viralload levels and CD4 cell counts be monitored at baseline(time of enrolment), at four weeks after starting antiretro-viral therapy and every three months thereafter. HIVplasma viral load were determined using the RocheAmplicor Monitor assay (Roche Diagnostics, Laval, Que-bec, Canada) using either the standard method or theultrasensitive adaptation. CD4 cell counts were measuredPage 2 of 9(page number not for citation purposes)response, CD4 cell response and time to all-cause mortal-ity. We also examined potential confounder effects of var-by flow cytometry, followed by fluorescent monoclonalAIDS Research and Therapy 2006, 3:14 http://www.aidsrestherapy.com/content/3/1/14antibody analysis (Beckman Coulter, Inc., Mississauga,Ontario, Canada).Study participantsAll HIV-infected men and women in the current studywere entered into the Centre's monitoring and evaluationsystem when they were first prescribed antiretroviralagents. Eligible study participants are persons who were≥18 years old, naïve to antiretroviral therapy when theystarted HAART, and first dispensed triple combinationtherapy consisting of two nucleoside reverse transcriptaseinhibitors and either a protease inhibitor (unboosted) ora non-nucleoside reverse transcriptase inhibitor, betweenAugust 1, 1996 and September 30, 1999. Eligible partici-pants must have completed at least one questionnaire andundertaken at least two follow-up CD4 cell count and HIVplasma viral load tests.Outcome measures and explanatory variablesThere were three primary endpoints of interest in thisstudy: (1) time to HIV plasma viral load suppression asmeasured from the time of starting HAART to the first oftwo consecutive plasma HIV viral loads of < 500 copies/ml; (2) CD4 cell response as measured by the time of start-ing HAART to the first CD4 cell test with an increase of100 cells over baseline; and (3) all cause mortality.Event-free subjects were right censored as of June 30,2003. Participants included in this analysis were not fol-lowed after this date and those lost to follow-up were cen-sored at the date of last known contact with the HIV/AIDSDrug Treatment Program. For the first outcome, individu-als we also censored if the last available HIV plasma viralload test result was the first to be < 500 copies/ml, and thecensoring was applied at the time of the prior test. Deathsoccurring during the follow-up period were identified ona continuous basis from physician reports and throughannual record linkages carried out with the British Colum-bia Division of Vital Statistics.The predictor variable of interest was Aboriginal status. Anumber of potential confounders have been considered:baseline CD4 cell count, baseline plasma HIV viral load,physician's experience with HIV-infected patients, gender,age, income, education, AIDS diagnosis at baseline, pro-tease inhibitor use, year of initiation of therapy, history ofIDU and adherence. Physician experience was estimatedfor the first follow-up physician of each patient. It wasdefined as the cumulative number of HIV-infectedpatients receiving antiretroviral therapy within their prac-tice, by the date of subject's first known eligibility [33]. Inall analysis, in order to get a more parsimonious fit wetransformed this variable by dividing the original value bywas defined as having completed high school. History ofIDU was defined as "ever-injected drugs" (yes or no),which was physician or self-reported. Adherence was esti-mated using pharmacy refill compliance. In brief, wedivided the number of months of HAART dispensed bythe number of months of follow-up in the first year oftherapy. This measure of adherence has been found to beindependently associated with HIV viral suppression andsurvival among HIV-infected persons enrolled in the HIV/AIDS Drug Treatment Program [34,35]. Patients weredefined a priori as non-adherent if they received antiretro-viral medications for less than 95% of the follow-upperiod during the first year of therapy, as in previouslypublished work [36,37].Statistical analysesCox-proportional hazard regression was used to modelthe effect of Aboriginal status and other potential con-founders on the time to virologic suppression, time toCD4 response and survival time [38]. Categorical varia-bles were analyzed using Pearson chi squared statistics,normally distributed continuous variables were analyzedusing t-tests for independent samples, and skewed contin-uous variables were analyzed using Wilcoxon rank sumtests. The assumptions of proportional hazards wereexamined graphically.A number of potential confounders have been included inour analyses. In the selection of important confounders,we applied a method based on the magnitude of changein the coefficient of Aboriginal status [39,40]. Age, sex andadherence were forced into all models. A stepwiseapproach was employed to select additional confounders[41]. Starting with the full model, variables were droppedone at a time, using the relative change in the coefficientfor Aboriginal status as a criterion, until the maximumchange from the full model exceeded 5%, which is a moreconservative approach than the one suggested by Maldo-nado and Greenland [39].Analyses were performed using SAS software version 8.02(SAS, Cary, NC). All tests of significance were 2-sided,with a P value of less than 0.05 indicating that an associa-tion was statistically significant.ResultsBetween August 1st, 1996 and September 30, 1999, a totalof 1,312 antiretroviral naïve participants aged 18 yearsand over initiated triple combination therapy consistingof two nucleosides plus a protease-inhibitor or a non-nucleoside reverse transcriptase inhibitor. Of these, 121(9.2%) were excluded in this analysis for not having bothbaseline CD4 and plasma HIV-1 RNA levels measuresPage 3 of 9(page number not for citation purposes)10. Income was defined as an average yearly income of≥$10,000 or <$10,000 (in Canadian dollars). Educationavailable within six months prior to the start of antiretro-viral therapy or having initiated therapy as part of a clini-AIDS Research and Therapy 2006, 3:14 http://www.aidsrestherapy.com/content/3/1/14cal trial. Among the remaining 1,191 participants a totalof 622 individuals (560 men and 62 women) hadreported their aboriginal status.Study participants were very heterogeneous when com-pared to those excluded from the analysis. Participants inthe study: (1) enrolled mainly in 1996–1997 whilst non-participants enrolled mainly in 1998–1999 (57% versus63%; p-value: < 0.001); (2) were older (median age 36.3versus 37.9 years; p-value 0.0003); (3) had lower baselineCD4 cell count (median CD4 cell count 260 versus 290cells/mm3; p-value: 0.0091); (4) had higher adherencethan non-participants (71% versus 42%; p-value: <0.0001); (5) were treated by more experienced physicians(median experience 55 versus 37 patients; p-value:0.0003). No other differences were noted between the twogroups.At baseline, for all participants, the median age was 38years (interquartile range (IQR): 33–44 years), CD4 cellcount was 260 cells/mm3 (IQR: 110–410 cells/mm3),and HIV plasma viral load was 130,000 copies/ml (IQR:42,000–320,000). A total of 50.0% of participants had anaverage annual income of less than $10,000 dollars,38.9% did not complete high school, 34.2% had a historyof IDU, 17.2% had an AIDS diagnosis, and 14.0%, 41.3%,24.1% and 20.6%, respectively, started therapy in 1996,1997, 1998 and 1999. At baseline, 77% initiated a pro-tease inhibitor based regime as compared to a non-nucle-oside reverse transcriptase regime. During the first year offollow-up 29.4% of participants had adherence level lessthan 95%.Most (N = 568; 91.3%) participants achieved 2 consecu-tive plasma HIV viral loads of < 500 copies/ml before theend of study. Median time to suppression among the 568participants was 3.4 months (IQR: 1.8–7.3 months), and3.6 months (IQR: 1.8–9.0 months) among all patients.Similarly, 560 (90.0%) of participants achieved a CD4 cellrecovery of 100 cells over baseline during follow-upperiod. Median time to recovery among the 560 partici-pants was 5.0 months (IQR: 1.9–12.3 months), and was6.0 months (IQR: 2.0–16.1 months) among all patients.As of June 30, 2003, a total of 67 deaths were identified inthe study population over the follow-up period with anoverall crude mortality rate of 10.8%. Among all causes ofdeath, HIV disease resulting in infectious and parasiticdiseases (excluding acute HIV infection syndrome) (N =32, 47.8%) and injuries due to intentional self-harm (N =13, 19.4%) were responsible for over 67% of all deaths.The remaining deaths (N = 22, 33%) were attributable toa total of twelve causes of deaths. The overall median timeof follow-up was 62.5 months (inter quartile range: 50.3,Among the 622 individuals, 88 (14.1%) described them-selves as being Aboriginal. As noted in Table 1, 8 (9.1%)commenced therapy in 1996, 32 (36.4%) in 1997, 21(23.9%) in 1998, and 27 (30.7%) in 1999. Over half ofthese participants (59; 67.0%) initiated therapy with aprotease-inhibitor, while the rest of the study participants(29; 33.0%) had a regimen that included a non-nucleo-side reverse transcriptase inhibitor. Aboriginal status wasassociated with a history of injection drug use, not com-pleting high school, an annual income < $10,000, nothaving an AIDS diagnosis at baseline, protease inhibitoruse, having lower baseline plasma HIV viral load, and lessexperienced HIV physicians (p < 0.05). Age, gender, yearof initiation of therapy, baseline CD4 cell count, andadherence were not significantly associated with beingAboriginal.Table 2 shows the univariate and multivariate associationsof aboriginal status, clinical and socio-demographic con-founders with time to the first CD4 cell test with anincrease of 100 cells over baseline. Aboriginal status wasnot associated with CD4 cell count response, but onlybaseline HIV plasma viral load, age, physician experienceand adherence.Table 3 shows the results for the univariate and multivar-iate associations of Aboriginal status, clinical and socio-demographic confounders with time to the first of twoconsecutive plasma HIV viral loads of < 500 copies/ml. Inthese analyses Aboriginal status was not associated withHIV plasma viral load response. The multivariate analysisshows that only baseline HIV plasma viral load, age, edu-cation, adherence, and history of IDU were associatedwith HIV plasma viral load response.The univariate and multivariate associations of aboriginalstatus, clinical and socio-demographic confounders withtime to death are displayed in Table 4. Aboriginal statuswas strongly associated with mortality in both univariate(hazard ratio (HR) = 2.87, 95% confidence interval (CI):1.70–4.84) and multivariate analyses (HR = 3.12, 95%CI:1.77–5.48). In addition to Aboriginal status, the multivar-iate analysis shows that age, income, and adherence werealso associated with mortality.DiscussionWe found that there is no significant difference betweenHIV-infected Aboriginal persons and non-Aboriginal per-sons regarding the time to HIV plasma viral load suppres-sion of < 500 copies/ml and time to CD4 cell response of100 cells over baseline after initiation of HAART. Therewas however, a significant higher mortality risk for Abo-riginal persons after the initiation of HAART. After adjust-Page 4 of 9(page number not for citation purposes)71.8 months). ment for confounder variables, Aboriginal persons hadmortality rates 3.12 times higher than non-Aboriginal per-AIDS Research and Therapy 2006, 3:14 http://www.aidsrestherapy.com/content/3/1/14sons. It is interesting to note that the only clinical charac-teristic associated with mortality risk in this populationwas adherence during first year of follow-up. The othervariables associated with mortality were socio-demo-graphic characteristics of the participants (age andincome). We observed that clinical factors were only a sig-nificant predictor when we looked at virologic and immu-nologic responses. Note that among the clinical factors,poor adherence was the strongest predictor of adverse out-ethnicity are closely intertwined with socioeconomic sta-tus and behavioural factors [1,4]. Our method of selectingconfounding factors ensured that the final models wereparsimonious while at the same time the estimates werenot substantially affected by any potential confoundersavailable in the data.Other studies have examined the effect of ethnicity onresponse to HAART but to our knowledge, this was theTable 1: A comparison of baseline socioeconomic and clinical characteristics of Aboriginal and non-Aboriginal participantsVariable Aboriginal Not Aboriginal P-value(n = 88) (n = 534)Year of initiation of therapy, no. (%)1996 8 (9) 79 (15) 0.05881997 32 (36) 225 (42)1998 21 (24) 129 (24)1999 27 (31) 101 (19)Gender, no. (%)Female 13 (15) 49 (9) 0.1044Male 75 (85) 485 (91)AgeMedian 37 38 0.9669Interquartile range 33 – 45 33 – 44AIDS diagnosis, no. (%)Yes 8 (9) 99 (19) 0.0296No 80 (91) 435 (81)Protease inhibitor useYes 59 (67) 418 (78) 0.0209No 29 (33) 116 (22)Baseline CD4 cell count (cells/mm3)Median 275 260 0.6551Interquartile range 150 – 425 110 – 410Baseline plasma HIV viral load (copies/ml)Median 89,350 140,000 < 0.001Interquartile range 21, 750 – 185,000 45,000 – 340, 000Adherence (<95%), no. (%)Yes 58 (66) 381 (71) 0.2995No 30 (34) 153 (29)HIV physician experienceMedian 29 60 0.0133Interquartile range 3 – 116 6 – 166History of IDU, no. (%)Yes 52 (59) 161 (30) < 0.001No 36 (41) 373 (70)Completed high-school, no. (%)Yes 38 (43) 298 (56) 0.0046No 43 (49) 171 (32)Missing 7 (8) 65 (12)Income, no. (%)< $10,000 49 (58) 211 (43) 0.0115>= $10,000 36 (42) 281 (57)Missing 3 (3) 42 (8)Page 5 of 9(page number not for citation purposes)comes in all analyses. To explain this paradox, we consid-ered potential confounding effects, given that race/first study to specifically examine this issue for Aboriginalpersons living in large urban areas. Prior studies looked atAIDS Research and Therapy 2006, 3:14 http://www.aidsrestherapy.com/content/3/1/14potential differences among ethnic groups in response toHAART by measuring short-term virologic and immuno-logic response or differences in survival. In a Danishcohort, race (white versus not white) did not predict dif-ferences in virologic suppression and CD4 cell responseone year after initiating HAART [27]. Race (white versusnot white) also did not independently predict virologicresponse or CD4 response in a group of American menwho have sex with men after 33 months of initiatingHAART [28]. Another study also found that race (His-panic, Black or White) was not a factor in CD4 cell countresponse among American patients who experiencedplasma HIV viral suppression within 6 months of initia-tion of HAART [26]. In a comparison between an Africanand a European cohort both on HAART there were also nodifferences in CD4 response or short term virologicresponse [25]. Racial differences were found in virologicresponse after 9 months however, poorer responses in theTable 3: Univariate and Multivariate Cox proportional hazard models examining the association between being Aboriginal and time to the first of two consecutive plasma HIV viral loads of < 500 copies/mlVariable Unadjusted HR Adjusted HR(95% CI) (95% CI)Aboriginal (Yes versus No) 0.90 (0.69, 1.17) 1.15 (0.89, 1.48)Plasma HIV viral load (per log 10 increase) 0.73 (0.65, 0.81) 0.73 (0.65, 0.82)CD4 cell count (per 100 decrease) 0.98 (0.93, 1.02) --Gender (Female versus Male) 1.25 (0.92, 1.70) 1.09 (0.81, 1.48)Age 1.01 (1.00, 1.02) 1.01 (1.00, 1.02)Physician experience (per 10 patients) 1.02 (1.01, 1.03) --Completed high-school (Yes versus No) 1.64 (1.37, 1.97) 1.37 (1.14, 1.65)Income (<$10,000 versus ≥ $10,000) 0.66 (0.55, 0.79) 0.90 (0.75, 1.09)Baseline Combination (PI versus NNRTI) 0.55 (0.45, 0.68) --Adherence (>= 95% versus <95%) 4.21 (3.32, 5.34) 3.46 (2.77, 4.31)AIDS diagnosis (Yes versus No) 1.14 (0.91, 1.44) --History of IDU (Yes versus No) 0.56 (0.46, 0.68) 0.74 (0.60, 0.92)Year of initiation of therapy1996 1.00 (--) 1.00 (--)1997 0.95 (0.73, 1.23) 1.16 (0.89, 1.51)1998 1.02 (0.77, 1.35) 1.49 (1.10, 2.00)1999 1.06 (0.79, 1.41) 1.79 (1.31, 2.43)Table 2: Univariate and Multivariate Cox proportional hazard models examining the association between being Aboriginal and time to a CD4 cell increase of 100 above baselineVariable Unadjusted HR Adjusted HR(95% CI) (95% CI)Aboriginal (Yes versus No) 0.85 (0.65, 1.11) 0.95 (0.73, 1.23)Plasma HIV viral load (per log 10 increase) 1.21 (1.06, 1.38) 1.27 (1.12, 1.44)CD4 cell count (per 100 decrease) 0.96 (0.92, 1.00) --Gender (Female versus Male) 1.85 (1.30, 2.64) 1.28 (0.94, 1.74)Age 1.00 (0.99, 1.01) 0.99 (0.98, 1.00)Physician experience (per 10 patients) 1.03 (1.02, 1.04) 1.01 (1.01, 1.02)Completed high-school (Yes versus No) 1.03 (0.86, 1.24) --Income (<$10,000 versus ≥ $10,000) 0.83 (0.68, 1.00) 0.97 (0.81, 1.17)Baseline Combination (PI versus NNRTI) 0.81 (0.66, 1.01) 0.79 (0.62, 1.01)Adherence (>= 95% versus <95%) 1.73 (1.39, 2.14) 1.57 (1.28, 1.94)AIDS diagnosis (Yes versus No) 0.96 (0.75, 1.22) --History of IDU (Yes versus No) 0.79 (0.65, 0.96) 0.92 (0.75, 1.13)Year of initiation of therapy1996 1.00 (--) 1.00 (--)1997 0.95 (0.72, 1.26) 1.07 (0.81, 1.40)1998 0.94 (0.69, 1.27) 1.14 (0.84, 1.55)1999 1.03 (0.75, 1.40) 1.10 (0.78, 1.55)Notes: The symbol – means that variable was not included in the analysisPage 6 of 9(page number not for citation purposes)Notes: The symbol – means that variable was not included in the analysisAIDS Research and Therapy 2006, 3:14 http://www.aidsrestherapy.com/content/3/1/14African cohort were thought to be attributable to loweradherence in this group.In accordance with these prior studies, our study alsoshowed no racial differences in virologic or immunologicresponses to HAART. In other words, both Aboriginal per-sons and non-Aboriginal persons in our group on averageachieved a typical response to HAART. This is character-ized by a rapid decrease in HIV viremia to undetectablelevels and a gradual increase in CD4 cell count to levelsapproximating those in uninfected individuals [42].Survival has also been used to examine potential racial/ethnic differences after initiation of HIV treatment. Priorstudies have found an increased mortality risk for HIV-infected racial/ethnic minority groups who had non-sig-nificant differences in clinical management or HIV-treat-ment [43,44]. In an examination of trends in survivalamongst men who have sex with men and who were diag-nosed with AIDS during the HAART era, declines in deathswere smaller among racial minorities (black, Hispanic,Asian/Pacific Islanders, American Indian/Alaskan Native)compared with whites [45]. These studies indicate thatrace/ethnicity has an unfavorable effect on mortality risk,however, none of these studies directly measured theeffects of HAART.This study has a number of limitations. Our measure of ahistory of IDU was self- or physician-reported. Becauseinjection drug use is a stigmatized behaviour, a history ofdrug users who might have become abstinent during fol-low-up [46]. Secondly, although using refill complianceas a measure of adherence has been previously validated,[37,46-49] it may not account for individuals whoreceived their medication but did not actually take it. Inthe two situations described previously, the misclassifica-tion present in the variables history of IDU and adherencecould potentially bias the associations between Aborigi-nal status and the three outcomes of interest. The analysesfor mortality and CD4 cell recovery showed a similar asso-ciation between Aboriginal status in both unadjusted andadjusted regressions. The difference between the coeffi-cients for aboriginal in the univariate and multivariateanalyses ranged from 0.10 (cell recovery analysis) to 0.25(mortality analysis), which indicates that misclassifica-tion bias or residual confounding did not influence theseresults. Note that for the analysis of viral suppression thecoefficient for Aboriginal status, though not statisticallysignificant, changed the direction of association. We con-ducted a more detailed analysis for this outcome to assessthe effect of confounding and misclassification bias onthe relationship of Aboriginal status and viral suppres-sion. We observed that adherence was the strongest con-founder in this analysis, since the hazards for aboriginalstatus across the levels of adherence were substantially dif-ferent, ranging from 0.58 (95%CI: 0.37–0.92) for <95%adherence to 1.34 (95%CI: 1.01–1.78) for ≥95% adher-ence. Therefore, the coefficients for aboriginal just con-trolling for adherence and the coefficient shown in Table3 changed by 0.05, which shows that our results were notTable 4: Univariate and multivariate Cox proportional hazard models examining the association between Aboriginal status and mortalityVariable Unadjusted HR Adjusted HR(95% CI) (95% CI)Aboriginal (Yes versus No) 2.87 (1.70, 4.84) 3.12 (1.77, 5.48)Plasma HIV viral load (per log 10 increase) 1.27 (0.86, 1.86) --CD4 cell count (per 100 decrease) 1.10 (0.97, 1.25) --Gender (Female versus Male) 0.89 (0.41, 1.95) 0.98 (0.41, 2.34)Age 1.04 (1.01, 1.07) 1.06 (1.03, 1.09)Physician experience (per 10 patients) 0.99 (0.96, 1.01) --Completed high-school (Yes versus No) 0.63 (0.39, 1.02) --Income (<$10,000 versus ≥ $10,000) 2.76 (1.59, 4.82) 1.86 (1.03, 3.34)Baseline Combination (PI versus NNRTI) 1.22 (0.65, 2.29) 1.65 (0.82, 3.31)Adherence (>= 95% versus <95%) 0.32 (0.20, 0.52) 0.41 (0.24, 0.71)AIDS diagnosis (Yes versus No) 1.59 (0.91, 2.79) --History of IDU (Yes versus No) 2.18 (1.35, 3.51) 1.47 (0.82, 2.62)Year of initiation of therapy1996 1.00 (--) 1.00 (--)1997 0.49 (0.27, 0.92) --1998 0.67 (0.34, 1.32) --1999 0.34 (0.14, 0.82) --Notes: The symbol – means that variable was not included in the analysisPage 7 of 9(page number not for citation purposes)IDU may be underreported. A history of IDU was also abaseline measure that would not account for injectioninfluenced by misclassification bias or residual confound-ing.AIDS Research and Therapy 2006, 3:14 http://www.aidsrestherapy.com/content/3/1/14The all-cause mortality rate in this cohort was associatedmainly with HIV disease resulting in infectious and para-sitic diseases (48%) and injuries due to intentional self-harm (19%). In addition to Aboriginal status, there wereseveral potential clinical and socio-demographic con-founders taken into account in our study. However, wedid not control for the effect of co-morbidity (e.g., psychi-atric illnesses), co-infections with hepatitis C, anemia,and other lifestyle (e.g., cigarette smoking) characteristicsknown to be related to HIV disease progression [13,14,16-18,21,48]. These factors could have influenced our resultsby biasing our results through residual confounding. Webelieve that controlling for other socio-demographic andlifestyle variables other than age, sex, income, educationand history of IDU would not change our results. In ourcohort we do not have data on co-infections collected lon-gitudinally, other than hepatitis C. If we decided toinclude information on hepatitis C in our study, all ouranalyses and objectives would have to be changed dra-matically mainly because of two reasons: (1) beinginfected with hepatitis C would not be considered a con-founder variable, but it would be a factor influencing thedefinition of our study population; (2) we would needmore study subjects to study the association of being abo-riginal and clinical outcomes separately according to threedistinct disease groups: (i) HIV positive, HCV negative;(ii) HIV negative, HCV positive; and (iii) HIV positive,HCV positive.Finally, the sample size in our study was small. There were569 participants with unknown Aboriginal status thatwere not included in our analyses. Like in all observa-tional studies collecting information on socio-demo-graphic characteristics, there is always a chance formissing information. To date several studies dealt withmissing information via assumptions about the missingdata or via imputation techniques for missing data. In thisstudy, we decided to not include participants with missinginformation on the exposure of main interest. As reportedin our results, there were significant demographic andclinical differences between participants and non-partici-pants. Therefore, we recommend caution when interpret-ing the results from our analyses and extrapolating toother minority populations.Our study highlights the need for continued research onmedical intervention for HIV-infected Aboriginal persons,in particular to determine if providing services for Aborig-inal drug users to address their addictions can improvesurvival after the initiation of HAART. Understanding themechanism by which such health care disparities exist bydetermining what other aspects of being Aboriginalincrease their risk of mortality after initiating HAART canthe understanding of health care inequalities amongstother marginalized populations.Competing interestsThe author(s) declare that they have no competing inter-ests.Authors' contributionsVDL: design, statistical analysis, write-up. PK: design andwrite-up. AP: design and write-up. SB: design and statisti-cal analysis. TK: design and write-up. DM: design andwrite-up. MD: design and write-up. JSGM: design, andwrite-up. RSH: design, data gathering, and write-up.AcknowledgementsWe acknowledge the support from the Michael Smith Foundation for Health Research through a Senior Scholar Awards to Drs Hogg and Palepu from CIHR through an undergraduate co-op studentship to Patricia Kretz from the CIHR-UBC Strategic Training Program for Translational Research in Infectious Diseases. 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