Share this article on:

Implementation and Operational Research: Correlates of Adherence and Treatment Failure Among Kenyan Patients on Long-term Highly Active Antiretroviral Therapy

Ochieng, Washingtone MSc, MBA, PhD; Kitawi, Rose C. BPharm; Nzomo, Timothy J. BSc; Mwatelah, Ruth S. BSc; Kimulwo, Maureen J. BSc; Ochieng, Dorothy J. MSc; Kinyua, Joyceline MSc; Lagat, Nancy BSc; Onyango, Kevin O. MBChB; Lwembe, Raphael M. PhD; Mwamburi, Mkaya MD, PhD; Ogutu, Bernhards R. MBChB, MMed, PhD; Oloo, Florence A. PhD; Aman, Rashid BPharm, PhD

JAIDS Journal of Acquired Immune Deficiency Syndromes: June 1st, 2015 - Volume 69 - Issue 2 - p e49–e56
doi: 10.1097/QAI.0000000000000580
Epidemiology and Prevention

Background: Universal access to highly active antiretroviral therapy (HAART) is still elusive in most developing nations. We asked whether peer support influenced adherence and treatment outcome and if a single viral load (VL) could define treatment failure in a resource-limited setting.

Methods: A multicenter longitudinal and cross-sectional survey of VL, CD4 T cells, and adherence in 546 patients receiving HAART for up to 228 months. VL and CD4 counts were determined using m2000 Abbott RealTime HIV-1 assay and FACS counters, respectively. Adherence was assessed based on pill count and on self-report.

Results: Of the patients, 55.8%, 22.2%, and 22% had good, fair, and poor adherence, respectively. Adherence, peer support, and regimen, but not HIV disclosure, age, or gender, independently correlated with VL and durability of treatment in a multivariate analysis (P < 0.001). Treatment failure was 35.9% using sequential VL but ranged between 27% and 35% using alternate single VL cross-sectional definitions. More patients failed stavudine (41.2%) than zidovudine (37.4%) or tenofovir (28.8%, P = 0.043) treatment arms. Peer support correlated positively with adherence2, P < 0.001), with nonadherence being highest in the stavudine arm. VL before the time of regimen switch was comparable between patients switching and not switching treatment. Moreover, 36% of those switching still failed the second-line regimen.

Conclusion: Weak adherence support and inaccessible VL testing threaten to compromise the success of HAART scale-up in Kenya. To hasten antiretroviral therapy monitoring and decision making, we suggest strengthening patient-focused adherence programs, optimizing and aligning regimen to WHO standards, and a single point-of-care VL testing when multiple tests are unavailable.

Supplemental Digital Content is Available in the Text.

*Center for Research in Therapeutic Sciences and the Institute of Healthcare Management, Strathmore University, Nairobi, Kenya;

Kenya Medical Research Institute, Nairobi, Kenya;

Institute of Tropical Medicine and Infectious Diseases at JKUAT, Nairobi, Kenya;

§MCPHS University, Worcester, MA;

Center for Global Public Health, Tufts University School of Medicine, Boston, MA; and

African Centre for Clinical Trials, Nairobi, Kenya.

Correspondence to: Washingtone Ochieng, MSc, MBA, PhD, Centrer for Research in Therapeutic Sciences, Strathmore University, P.O. Box 59857-00200, Nairobi, Kenya (e-mail:

Supported by the Consortium for National Health Research (Kenya) Grant# RCDG-2012-005, with funds from the Wellcome Trust and the Department for International Development (DFID), UK (Grant ID, WT080883Kokwaro).

The authors have no conflicts of interest to disclose.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (

Received August 21, 2014

Accepted January 13, 2015

Back to Top | Article Outline


Industrialized countries succeeded at controlling HIV-1 through the years, by efficiently implementing highly active antiretroviral therapy (HAART),1,2 as the World Health Organization (WHO) proposed treatment scale-up for poor nations to include human and resource capitalization.3 Most low-income countries now provide integrated HIV-1 management through decentralized and devolved comprehensive services.4–7 Despite these concerted efforts to fast-track treatment, universal coverage and realization of UNAID's strategy of “Getting to Zero” remains elusive.8,9 Constraints include poor or inefficient health care systems to support adherence programs, inaccessible viral load (VL) for treatment monitoring, stringent criteria for antiretroviral therapy (ART) eligibility, delayed linkage and retention to care, and disproportional mechanisms to cover children.10–12 Adherence in children, adolescents, and youths present special challenges, mostly because of variable health care seeking behaviors, stigma, substance use, and reliance on adults for treatment decisions among other variables.13–17 Patients with good adherence show superior virologic and treatment outcomes, including sustained viral suppression and reduced morbidity and mortality.18–20 Treatment successes of developed nations cannot, therefore, be directly replicated in resource-limited settings.6 We feared that treatment failure is largely unrecognized in Kenya years after massive HAART rollout began and sought to provide simplified metrics to assess the effectiveness of the current HAART scale-up campaign. This article describes the associations among adherence, peer support, VL, and regimen in relation with treatment outcomes.

Back to Top | Article Outline


Participants and Data Collection

This study was conducted between October 2012 and May 2014. A total of 546 HIV-1-infected HAART patients between 5 and 73 years of age were recruited at 6 facilities in Coast, Rift Valley, Nairobi, Central, and Nyanza provinces. After obtaining written consent, trained caregivers and study staff administered questionnaires (see CCC enrollment form, Supplemental Digital Content, to collect sociodemographic and treatment data. Five milliliters of EDTA blood was obtained from each patient and used for VL and CD4 T-cell testing. The patients were then handed the partially filled forms to bring to the attending clinician, who alongside project personnel administered the rest of the questionnaires. The clinicians made independent HAART management decisions.

Back to Top | Article Outline

Adherence and Compliance Evaluation

Some care facilities in Kenya facilitate community peer support (CPS) program activities. These groups are mainly run by HIV-positive peer “counselors,” are not monitored or facilitated by the government, and participation is voluntary. Peer counselors encourage patients to adhere to ART schedules and, where necessary, use telephones or personal visits. They also verify pill count at refill and pill burden at home visits. These programs are not available at all country facilities. This study was conducted within facilities offering CPS services. Patients were asked if they were actively, partly, or never involved in CPS and about their HIV disclosure statuses. Adherence assessment was based on residual pill count and on self-report, focusing on dose compliance during the 30 days preceding the latest refill. The number of dose pills at refill was counted and reconciled against the dose counts dispensed at last refill. Additional pill count data were extracted from patient cards for the 4 months before the study period. Nonadherence was determined as the percentage of overdue dose at refill, averaged over a 4-month period and used to assign adherence as good (≤5% dose skipped), fair (6%–15% dose skipped), or poor (>15% dose skipped).20–22

Back to Top | Article Outline

Criteria for Treatment Failure Definition

The WHO provides 3 criteria for defining treatment failure, including clinical, immunologic, and virologic failure (VF).23 Because of its relative sensitivity, we adopted the VF approach, defining failure as VL persistently above 1000 copies per milliliter based on 2 consecutive measurements at least 6 months apart during sustained ART. We also explored alternative approaches to failure diagnosis by applying a cross-sectional single VL (CSVL) framework. Under CSVL strategy, failure was defined alternately as (1) VL >1000 copies plus at least 6 months of uninterrupted HAART, (2) VL >1000 plus at least 12 months of HAART, and (3) VL >1000 plus at least 24 months of HAART. Second VL (VL2) was used for CSVL criteria. Patients with VL2 >1000 but a duration of HAART less than 6, 12, or 24 months were designated as “undetermined virologic responders” (UDVRs) in their respective CSVL categories.

Back to Top | Article Outline

VL Assays

VL tests were done within 2 months of sampling using an automated m2000 Abbott RealTime HIV-1 assay system following manufacturers protocol (Abbott Laboratories, Abbott Park, Illinois). Briefly, internal control RNA was added to 200 µL of plasma and loaded onto the m2000sp instrument for RNA extraction. The limits of detection ranged between 40 and 10,000,000 HIV-1 RNA copies. Undetectable VL was reported as 39 copies while 10,000,001 copies served as the reportable upper limit. All VL assays were conducted at the Early Infant Diagnosis laboratory of the Kenya Medical Research Institute (KEMRI), with 50% of the tests done in duplicate for validation.

Back to Top | Article Outline

CD4 T-cell Assays

CD4 T-cell counts were determined regularly on-site as part of point-of-care ART management. The Alere Pima CD4 Analyser (Alere, Waltham, Massachusetts) was used at 3 of the 6 facilities, whereas the other 3 facilities used FACS counters (Becton Dickinson, Franklin Lakes, New Jersey).

Back to Top | Article Outline

Statistical and Data Analysis

Data were entered into the SPSS platform. Outcome variables were VL, CD4 T-cell counts, duration of treatment, and time to regimen switch. Categorical variables were Age, Sex, HAART regimen, CPS, adherence, HIV disclosure, and HAART duration. Analysis of variance was used as appropriate. Both univariate and multivariate statistics were used to assess associations. For multivariate analyses, scale and categorical variables were first assessed together exclusive of 32 patients in the Assorted HAART arm, and subsequently without patients younger than 18 years. For the univariate tests, 3 levels of adherence and 3 levels of CPS were combined to develop an ACCESS (Adherence and Community Compliance Enhancement Support Systems) matrix of 9 predictors. Patients who were active in CPS (CPS++) and showing good adherence (Ad++) were assigned a variable string of 1. The other components of the matrix were, CPS++/Ad+, CPS++/Ad−, CPS+/Ad++, CPS+/Ad+, CPS+/Ad−, CPS−/Ad++, CPS−/Ad+, CPS−/Ad−, where “+” represented partial CPS or fair adherence and “−” represented no CPS or poor adherence. Tukey post hoc test was applied to determine the differences in VL between the levels of ACCESS matrix. Chi-square statistic was used to assess associations between pairs of independent variables. Cox proportional hazard was used to predict the likelihood of treatment failure, applying duration on HAART or months-to-regimen switch as the time variable. For this latter test, only variables that were significant (P value ≤ 0.05) in the multivariate model were fitted into a forward-looking simple logistics regression.

Back to Top | Article Outline

Ethical Considerations

This study was conducted in accordance with the Helsinki Declaration of 1975 (revised in 2000), after official approvals by the Scientific and the Ethical Review Committees of KEMRI (ERC/SSC protocol #2477). Participation was voluntary upon written informed consent, and all patient data were anonymized.

Back to Top | Article Outline


Patient and Treatment Characteristics

Out of 546 patients, 4 were aged 5–12 years, whereas 6 were 12–17 years old. We could not verify the mode of HIV-1 infection for these 10 subjects. Treatment regimens included various combinations of zidovudine (AZT), lamivudine (3TC), stavudine (D4T), nevirapine (NVP), efavirenz (EFV), and tenofovir (TDF). By regimen, 36.1% were on first-line AZT + 3TC + NVP/EFV (AZT+ arm), 35% on D4T + 3TC + NVP/EFV (D4T+ arm), 23% on TDF + 3TC + NVP/EFV (TDF+ arm), and 5.9% on assorted regimens (Assorted arm). About 46% were active in CPS, whereas 33% and 21% never or only partly participated (Table 1). Median duration of treatment was 48 months, and 92.5% were already treated for more than 12 months. Patients in the D4T+ arm had stayed longest in treatment.



Back to Top | Article Outline

Data Quality Assessment and Study Limitations

Patient enrollment varied minimally across the 6 study sites. The variations in proportions participating in CPS across sites were insignificant (χ2 P = 0.269, SDC table 5, Adherence levels (χ2 P = 0.205) and VL (P = 0.204) were also comparable across sites. Thirty-two of 546 patients were excluded for not receiving a 3-drug HAART regimen. No reasons were on record for this suboptimal ART decision. Loss to VL follow-up at time 1 (VL1) occurred in 96 of the remaining 514 patients. These patients were distributed variously across all sites and were excluded accordingly from relevant section analyses. These strategies for data analysis diminished any bias by assuring intersite comparability. These data should still be interpreted contextually because adherence behaviors are unique to different settings.

Back to Top | Article Outline

Longitudinal Trajectory of VL and Treatment Failure

We assessed treatment failure on the basis of the 2 sequential VL measurements within the AZT, D4T, and TDF treatment arms. Of the 514 patients with second VL data (VL2), 418 also had first VL (VL1) data. Another 96 who lacked VL1 data were excluded from this analysis. Both VLs were comparable across the HAART arms (Table 2). The median duration between VL1 and VL2 tests was 19 months, and VL2 was significantly lower than VL1 (t test, P < 0.001). Defining VF by longitudinal criteria as VL1 and VL2 above 1000 or VL1 below 1000 followed by VL2 above 1000 HIV-1 RNA copies, 35.9% of the patients failed treatment overall. VF was high but comparable across all study sites (SDC table 5, and was significantly higher in the D4T arm (41.2%) than in the TDF (28.8%, P = 0.043) but not the AZT arm. VF patients treated for less than 12 months maintained comparably higher VL (4.69 log10 HIV-1 copies), than failures treated for 12–60 months (4.22 log10 copies) or those treated for longer than 60 months (4.25 log10 copies) (Fig. 1A). Thus, the higher failure rate in the D4T arm was not because of their longer treatment exposure. VF was higher but not significant among male (40.4%) than female (33.7%) patients. There were significantly fewer patients aged younger than 18 years to allow rational comparisons.





Back to Top | Article Outline

Predictors of Treatment Outcome Under Multivariate and Univariate Models

Multivariate tests were modeled to assess independent associations between variables among the 514 patients in the 3 HAART arms. Age, sex, regimen, CPS, adherence, and HIV disclosure were fitted into the stepwise model as predictors, whereas CD4 counts, HAART duration, and VL were fitted as outcome variables. CPS, HAART regimen, and adherence, but not HIV disclosure, age, or sex, were independently and strongly associated with outcomes (Table 3). Specifically, CPS and adherence influenced VL (P ≤ 0.001), whereas regimen was associated with treatment duration. Both CPS and adherence interacted significantly to affect virologic outcome (P < 0.029). CD4 T-cell counts had no relationship with the predictors at all levels.



Model estimated marginal means showed that VL declined significantly with increasing participation in CPS at all levels of adherence (Fig. 1B). Patients actively in CPS had nearly 20 times lower VL (1.87 log10 copies) than those not in CPS (3.13 log10 copies) or those who participated only occasionally (P < 0.001). In an univariate analysis, good adherence was separately associated with lower VL (1.93 log10 HIV-1 copies) than fair (2.51 log10) or poor (3.54 log10, P < 0.001) adherence whether compared for all patients (data not shown) or for only patients failing treatment (Fig. 1C).

Back to Top | Article Outline

Associations of Peer Support Activity, Adherence, and HIV Disclosure

We sought to understand how adherence and CPS might associate to influence treatment outcomes. The cross-tabulation Table 4 shows adherence by proportions of patients in each regimen, HIV disclosure, and CPS groups. Of the 238 patients active in CPS (CPS++), 82.8% had good adherence, as compared, respectively, with 39% and 28.7% who only partly (CPS+) or never (CPS−) participated in CPS. Higher proportions of patients reported poor adherence within CPS− than those in CPS+ or CPS++ arms (χ2 P < 0.001). Prevalence of poor adherence was lowest in the TDF arm compared with D4T or AZT arms, but good adherence was comparable across groups. Adherence was not associated with age group (χ2 P = 0.771). The reasons for adherence behavior were significantly correlated with adherence outcome (χ2 P < 0.001, SDC table 6, Most (69.9%) patients with poor adherence cited “bad” feeling, hopelessness, or inconvenience as reason for adherence behavior. Most (74.2%) patients with good adherence believed that the drugs would prolong their lives and make them healthy. Eight of 514 patients were children aged 5–17 years, and 5 had good adherence. Longevity and health were the main reason for adherence behavior in 6 of 8 patients, but these should be interpreted within veracity limits of guardian disclosure. In a Cox proportional hazard analysis using duration of HAART as time variable and VF as status variable, the risk of treatment failure was low for CPS++ participants but increased comparably in all patients as treatment duration increased (Fig. 1D). The 3 CPS and 3 adherence categories were used to develop a compliance matrix (see Methods). Univariate analysis conducted to compare VL among the various levels of the matrix showed that a combination of CPS++ and good or fair adherence also produced the lowest VL (Fig. 1E, P < 0.001). The same was true for patients in the CPS+ group who had good adherence.



Some (81.5%) of the patients had disclosed their HIV status, including all who were in CPS++. More patients had good adherence compared with those with poor or fair adherence (P = 0.012), although the numbers were comparable across HIV disclosure statuses. Disclosure was associated with peer support2 P < 0.001), but this association was biased by the exclusive disclosure in the CPS++ group. The association disappeared when CPS++ patients were excluded from the analysis. VL was comparable for patients disclosing (mean 2.37) and those not disclosing (mean 2.58) their HIV status (analyses of variance, P = 0.55). Pearson χ2 tests showed no association in treatment response between patients disclosing and those not disclosing status (P = 0.41). Taken together, disclosure had no effect on outcome and appeared unrelated to peer support in this setting.

Back to Top | Article Outline

Virologic Response to Second-line HAART

Switching regimen is an important decision when patients fail first-line treatment. Of the initial 546 patients, only 17.4% switched regimen after a median treatment time of 32 months. Duration before regimen switch was 35, 30.5, 36, and 14 months, respectively, for patients in the AZT, D4T, TDF, or assorted arms and 57, 24, 30.5, or 35 months for those aged 12–17, 18–35, 36–45, or above 45 years. Most (76%) patients switching treatment were in the D4T+ regimen arm, whereas only 9.5% and 13.7% switched, respectively, from primary TDF and AZT arms. VL did not differ significantly between age groups (data not shown) or between regimen categories of patients switching ART. Patients who switched were not more likely to have failed respective first-line regimens before the switch than those not switching. Of the patients switching regimen, 36.2% still failed second-line treatment (Table 2). Considering VL before the time of switch, no difference was observed between patients who switched (3.311 log10 HIV copies) and those not switching regimen (P = 0.969, SDC table 7, Using a Cox proportional hazards analysis, patients in active peer support were more likely to remain longer on treatment before switching regimen and had lower risk of failure than their counterparts in CPS+ or CPS− (Fig. 1F). Together, these data suggest that patients are switched to second-line without proper guidelines, whereas the deserving ones are not promptly linked to care.

Back to Top | Article Outline

Virologic Treatment Failure Based on Cross-sectional Single VL Test

Alternative cost-effective yet reliable VL monitoring strategies are necessary for prompt treatment decisions in settings constrained for resources. Limiting our analysis to 418 patients with complete VL1 and VL2 data, we assessed the effectiveness of a cross-sectional single VL (CSVL) strategy to reliably describe VF, applying only the VL2 for CSVL analysis. Patients who had VL2 of at least 1000 HIV-1 RNA copies but had not been treated for at least 6, 12, or 24 months were assigned UDVR status in the respective CSVL criteria. Using this assessment, 0.7% of the patients were UDVRs under the 6-month CSVL definition, whereas 7.4% and 19.6% were UDVRs under the 12- and 24-month definitions, respectively. Of the patients, 35.2%, 33%, and 27%, respectively, failed treatment under the alternate 6-, 12-, and 24-month CSVL strategies. Failure rates under the 6- and 12-month definition compared well with the 35.9% failure rate observed under the longitudinal definition (see Table S7, Supplemental Digital Content, Thus, a single VL test after 6 or 12 months of HAART defined treatment failure as efficiently as 2 sequential VLs under the standard longitudinal criteria.

Back to Top | Article Outline


We describe factors that influence adherence and VF and provide evidence to inform treatment decisions under conditions of limiting resources. Of the 514 patients in the 3 HAART arms, 35.9% failed first-line regimen by longitudinal strategy and 36% failed second-line after switching regimen. First-line HAART failure was highest among males and in the D4T arm. Industrialized countries have phased out D4T due to toxicities, and the WHO has recommended its discontinuation globally.3,24 Elsewhere, drug resistance was highest among Thai patients initiating D4T-containing regimen.25 In our study, 35% of all patients used D4T in first-line regimen and another 10.5% upon switching treatment. Adherence and peer support (CPS) influenced VL in a multivariate analysis, whereas HAART regimen was associated with length of treatment. Adherence is important to success of HAART,26–28 and in this study, the proportions of patients with poor adherence were highest in the D4T and AZT arms. Poor adherence was in turn associated with higher VL and increased VF. D4T recipients had stayed longer on treatment than patients on alternative regimens, mostly because D4T is comparably cheaper and readily available locally. We did not conduct toxicity assays, but speculate in concurrence with literature that toxicity may have influenced adherence and virologic outcome in the D4T arm.23,29 D4T should, therefore, be eliminated from the current treatment regimens.

A study of South African patients concluded that short-term viral suppression was achievable when adherence was at least 80%.30 Our study is uniquely focused on peer support mechanism, as opposed to non-peer counseling. Good and fair adherence was achieved in 55.6% and 21.6% of our patients, respectively. A combination of active CPS and good adherence resulted in lowest VL (see ACCESS matrix). Noncompliance is not uncommon in Kenya where cultural predisposition and family circumstances affect patients' attitudes toward HIV medication31 and CPS activity clearly enhanced their compliance. This study included a small number of children, and the accuracy of their adherence profiles was limited to the legitimacy of guardian disclosures. HIV status disclosure positively influences care uptake and is correlated with social support.32,33 Disclosure was associated with both adherence and CPS activity in this study, although CPS and adherence, but not disclosure, were independently associated with VL. Sampling bias may have influenced the association between CPS and disclosure, as all patients who were active in CPS also had disclosed status. A recent study did not find any association between treatment outcome and HIV disclosure among children.34 Thus, peer support influenced adherence independent of HIV disclosure in this study.

Patients who fail first-line HAART must promptly switch to second-line regimen to sustain viral suppression. A staggering 54% of ART switches occurred within 12 months of initiating HAART among predominantly urban Kenyans.35 Our study of mostly rural and suburban Kenyans showed that only 17% of the patients switched regimen 32 months after initiating treatment. Patients who switched were not more likely to have failed first-line regimen before the switch than those not switching, and both switchers and non-switchers had comparable VL before the time of switch. Thus, patients may be switched unnecessarily to secondary treatment, whereas others fail to gain timely access to critical treatment decisions.

The standard approach to treatment failure definition relies on sequential VL monitoring, which remains expensive in most developing countries.5,6,12,36,37 Less than 3% of all HAART-eligible patients initiate treatment, and VF diagnosis is delayed in most of those initiating HAART.12,25,38 We asked if a single VL measured in a cross-sectional context (CSVL) could effectively define VF and hasten treatment decision. Of all patients failing treatment under longitudinal criteria, 98% and 92% also failed under the 6- and 12-month CSVL criteria, respectively. Hence, when resources are limited, prompt and reliable treatment decisions can be made with just 1 VL taken between 6 and 12 months after HAART initiation. CD4 T-cell levels had no significant association with predictors, an observation that has also been reported elsewhere.39 Both clinical and immunologic criteria are less sensitive at predicting VF40; hence, overreliance on CD4 T-cell tests for ART decisions in low-income regions may be obscuring treatment failure.

Back to Top | Article Outline


We have demonstrated a high rate of virologic treatment failure among Kenyan HAART patients and shown that peer support enhances adherence to improve treatment outcome. To mitigate failure, we recommend the government, through its various HIV/AIDS control agencies, to (1) institutionalize and support patient-focused peer support within provider facilities; (2) train, empower, employ, and deploy HIV-positive persons as (peer) councilors in community care facilities to facilitate linkage to care and adherence monitoring; (3) scale-up point-of-care VL testing with at least 1 test annually; (4) synchronize HIV care with the current WHO guidelines, including treatment sequencing, optimization, and initiation thresholds; and (5) improve overall counseling methodologies and instruments. These steps should be replicable in other low-income settings.

Back to Top | Article Outline


Dr. Matilu Mwau and Charity Hungu of KEMRI facilitated VL tests. Javan Okendo, Grace Akoth Ochieng, Meshack Ooko, Beatrice Oliech, Rita Ayodi, Paul O. Owuor, Hellen Aloo, Everlyne Githaiga, Zawadi Baya, and Lillian Maina interviewed and collected patient data.

Back to Top | Article Outline


1. Selik RM, Lindegren ML. Changes in deaths reported with human immunodeficiency virus infection among United States children less than thirteen years old, 1987 through 1999. Pediatr Infect Dis J. 2003;22:635–641.
2. Monpoux F, Tricoire J, Lalande M, et al.. Treatment interruption for virological failure or as sparing regimen in children with chronic HIV-1 infection. AIDS. 2004;18:2401–2409.
3. Gilks CF, Crowley S, Ekpini R, et al.. The WHO public-health approach to antiretroviral treatment against HIV in resource-limited settings. Lancet. 2006;368:505–510.
4. Batz HG, Guillerm M, Gonsalves G. Scaling up antiretroviral treatment in resource-poor settings. Lancet. 2006;368:445.
5. Barry O, Powell J, Renner L, et al.. Effectiveness of first-line antiretroviral therapy and correlates of longitudinal changes in CD4 and viral load among HIV-infected children in Ghana. BMC Infect Dis. 2013;13:476.
6. De Luca A, Hamers RL, Schapiro JM. Antiretroviral treatment sequencing strategies to overcome HIV type 1 drug resistance in adolescents and adults in low-middle-income countries. J Infect Dis. 2013;207(suppl 2):S63–S69.
7. Barker PM, McCannon CJ, Mehta N, et al.. Strategies for the scale-up of antiretroviral therapy in South Africa through health system optimization. J Infect Dis. 2007;196(suppl 3):S457–S463.
8. Global update on HIV treatment 2013: results, impact and opportunities: WHO report in partnership with UNICEF and UNAIDS. WHO Library Cataloguing-in-Publication Data; 2013. Geneva, Switzerland.
9. Joint United Nations Programme on HIV/AIDS. Getting to Zero: UNAIDS 2011-2015 Strategy. WHO Library Cataloguing-in-Publication Data: Geneva, Switzerland; 2010.
10. World Health Organization. Global Update on HIV Treatment 2013: Results, Impact and Opportunities. 2013. Geneva, Switzerland.
11. Harries AD, Schouten EJ, Libamba E. Scaling up antiretroviral treatment in resource-poor settings. Lancet. 2006;367:1870–1872.
12. Harries AD, Zachariah R, van Oosterhout JJ, et al.. Diagnosis and management of antiretroviral-therapy failure in resource-limited settings in sub-Saharan Africa: challenges and perspectives. Lancet Infect Dis. 2010;10:60–65.
13. Deng R, Li J, Sringernyuang L, et al.. Drug abuse, HIV/AIDS and stigmatisation in a Dai community in Yunnan, China. Soc Sci Med. 2007;64:1560–1571.
14. Mellins CA, Tassiopoulos K, Malee K, et al.. Behavioral health risks in perinatally HIV-exposed youth: co-occurrence of sexual and drug use behavior, mental health problems, and nonadherence to antiretroviral treatment. AIDS Patient Care STDs. 2011;25:413–422.
15. Stevens SJ, Murphy BS, McKnight K. Traumatic stress and gender differences in relationship to substance abuse, mental health, physical health, and HIV risk behavior in a sample of adolescents enrolled in drug treatment. Child Maltreat. 2003;8:46–57.
16. Haberer J, Mellins C. Pediatric adherence to HIV antiretroviral therapy. Curr HIV/AIDS Rep. 2009;6:194–200.
17. Panel on antiretroviral guidelines for adults and adolescents. Guidelines for the Use of Antiretroviral Agents in HIV-1-Infected Adults and Adolescents. Department of Health and Human Services. Available at: Accessed November 22, 2014.
18. Cingolani A, Antinori A, Rizzo MG, et al.. Usefulness of monitoring HIV drug resistance and adherence in individuals failing highly active antiretroviral therapy: a randomized study (ARGENTA). AIDS. 2002;16:369–379.
19. Paterson DL, Swindells S, Mohr J, et al.. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med. 2000;133:21–30.
20. World Health Organization. Adherence to Long-term Therapies: Evidence for Action. WHO Library Cataloguing-in-Publication Data; 2003. Geneva, Switzerland.
21. Chesney MA, Ickovics JR, Chambers DB, et al.. Self-reported adherence to antiretroviral medications among participants in HIV clinical trials: the AACTG adherence instruments. Patient Care Committee & Adherence Working Group of the Outcomes Committee of the Adult AIDS Clinical Trials Group (AACTG). AIDS Care. 2000;12:255–266.
22. World Health Organization. Antiretroviral Therapy for HIV Infection in Infants and Children: Towards Universal Access: Recommendations for a Public Health Approach—2010 Revision. WHO Library Cataloguing-in-Publication Data; 2010. Geneva, Switzerland.
23. World Health Organization. Consolidated Guidelines on General HIV Care and the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection: Recommendations for a Public Health Approach. WHO Library Cataloguing-in-Publication Data; 2013. Geneva, Switzerland.
24. Phan V, Thai S, Choun K, et al.. Incidence of treatment-limiting toxicity with stavudine-based antiretroviral therapy in Cambodia: a retrospective cohort study. PLoS One. 2012;7:e30647.
25. Sungkanuparph S, Manosuthi W, Kiertiburanakul S, et al.. Options for a second-line antiretroviral regimen for HIV type 1-infected patients whose initial regimen of a fixed-dose combination of stavudine, lamivudine, and nevirapine fails. Clin Infect Dis. 2007;44:447–452.
26. Schneider J, Kaplan SH, Greenfield S, et al.. Better physician-patient relationships are associated with higher reported adherence to antiretroviral therapy in patients with HIV infection. J Gen Intern Med. 2004;19:1096–1103.
27. Marcus EN. The silent epidemic—the health effects of illiteracy. N Engl J Med. 2006;355:339–341.
28. Halkitis PN, Shrem MT, Zade DD, et al.. The physical, emotional and interpersonal impact of HAART: exploring the realities of HIV seropositive individuals on combination therapy. J Health Psychol. 2005;10:345–358.
29. Havlir DV, Koelsch KK, Strain MC, et al.. Predictors of residual viremia in HIV-infected patients successfully treated with efavirenz and lamivudine plus either tenofovir or stavudine. J Infect Dis. 2005;191:1164–1168.
30. El-Khatib Z, Ekstrom AM, Coovadia A, et al.. Adherence and virologic suppression during the first 24 weeks on antiretroviral therapy among women in Johannesburg, South Africa—a prospective cohort study. BMC Public Health. 2011;11:88.
31. Heffron R, Ngure K, Mugo N, et al.. Willingness of Kenyan HIV-1 serodiscordant couples to use antiretroviral-based HIV-1 prevention strategies. J Acquir Immune Defic Syndr. 2012;61:116–119.
32. Battles HB, Wiener LS. From adolescence through young adulthood: psychosocial adjustment associated with long-term survival of HIV. J Adolesc Health. 2002;30:161–168.
33. Arrive E, Dicko F, Amghar H, et al.. HIV status disclosure and retention in care in HIV-infected adolescents on antiretroviral therapy (ART) in West Africa. PLoS One. 2012;7:e33690.
34. Sirikum C, Sophonphan J, Chuanjaroen T, et al.. HIV disclosure and its effect on treatment outcomes in perinatal HIV-infected Thai children. AIDS Care. 2014;26:1144–1149.
35. Hawkins C, Achenbach C, Fryda W, et al.. Antiretroviral durability and tolerability in HIV-infected adults living in urban Kenya. J Acquir Immune Defic Syndr. 2007;45:304–310.
36. Zheng L, Bosch RJ, Chan ES, et al.. Predictors of residual viraemia in patients on long-term suppressive antiretroviral therapy. Antivir Ther. 2013;18:39–43.
37. Bennett DE, Bertagnolio S, Sutherland D, et al.. The World Health Organization's global strategy for prevention and assessment of HIV drug resistance. Antivir Ther. 2008;13(suppl 2):1–13.
38. Gupta RK, Hill A, Sawyer AW, et al.. Virological monitoring and resistance to first-line highly active antiretroviral therapy in adults infected with HIV-1 treated under WHO guidelines: a systematic review and meta-analysis. Lancet Infect Dis. 2009;9:409–417.
39. Chirwa LI, Johnson JA, Niska RW, et al.. CD4+ cell count, viral load, and drug resistance patterns among heterosexual breakthrough HIV infections in a study of oral preexposure prophylaxis. AIDS. 2014;14:28(2):223–226.
40. Rutherford GW, Anglemyer A, Easterbrook PJ, et al.. Predicting treatment failure in adults and children on antiretroviral therapy: a systematic review of the performance characteristics of the 2010 WHO immunologic and clinical criteria for virologic failure. AIDS. 2014;28(suppl 2):S161–S169.

HIV; adherence; treatment failure; resource-limited; peer support

Supplemental Digital Content

Back to Top | Article Outline
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.