Access to antiretroviral treatment (ART) is increasing in resource-constrained settings.1 Such therapy can successfully reduce HIV-related morbidity and mortality,2 but only if high levels of adherence are maintained.3 Although recent evidence suggests that lower adherence levels may be acceptable, particularly for nonnucleoside reverse transcriptase regimens,4,5 suboptimal adherence is associated with viral resistance, virological and immunological failure, and disease progression.3,6,7
Levels of adherence to ART in sub-Saharan Africa are generally greater than in high-income countries.8-12 However, some studies in Africa have shown variable ART adherence and patient retention,13-16 as evidenced by a recent meta-analysis in which more than a third of patients reported suboptimal adherence in 8 of the 27 studies included.8
Strategies such as patient education, practical medication management skills, building self-efficacy, and support from treatment buddies have been used to optimize ART adherence.17-27 Mostly, evaluation of these strategies has occurred in high-income countries. Adherence studies that have taken place in Africa have focused on providing home-based support,28,29 assessing the role costs of ART plays in adherence29,30 and on building self-efficacy.9,31 Modified directly observed therapy (m-DOT), adapted from Tuberculosis Directly Observed Treatment Short-course (TB-DOTS),32,33 is one approach to promote adherence in which a proportion of drug taking is observed, whereas the remainder are self-administered. M-DOT has been evaluated among specific target groups in the United States, largely in patients with poor adherence, active drug use, or incarceration.34-39 Overall, these studies, including a randomized trial,36 reported promising findings. However, a randomized trial among patients attending public clinics in the United States found no effects on adherence or virological outcomes with a 6-month community-based m-DOT intervention.40 In resource-constrained settings, a study in Haiti was the first to document that a home-based m-DOT strategy using community health workers (CHW) could effectively support adherence29,41 and a randomized trial of a 6-week community-based m-DOT intervention delivered by peer educators in Mozambique showed promising results as well.42
The primary objective of our study was to determine whether an m-DOT intervention would increase adherence to ART in adults in the first 24 weeks after initiating therapy. The study also aimed to ascertain whether the intervention would be effective in sustaining higher adherence in a 48-week postintervention period.
Participants were recruited at 3 outpatient HIV treatment clinics in Mombasa, Kenya, from September 2003 to November 2004: a provincial referral hospital (n = 167); a private, not-for-profit clinic (n = 59); and a district hospital (n = 8). The study cohort was followed for 72 weeks. Study activities and the m-DOT intervention took place within routine clinical services. HIV prevalence among pregnant women who access antenatal services and HIV testing at the provincial referral hospital was 11.4% (211/1858) in 2004 and 11.1% (177/1594) in 2005 similar to levels at the district hospital (9.3%, 46/497 in 2004 and 10.8%, 51/472 in 2005). Access to ART at these sites began around the time of the study.
ART naive adults (>18 years) residing in Mombasa who were eligible for ART (CD4 cell count < 200 cells/mm3 or WHO clinical stage 3 or 4) were invited to participate. Clinic nurses informed potential participants of the study who were then contacted by research staff. Written informed consent was provided by all participants. Ethical approval was obtained from the Kenyatta National Hospital Ethics and Research Committee and Institutional Review Board of the Population Council.
Study Design and Sampling Techniques
Computer-generated random number assignment was used, allocating an equal number of participants to treatment and control groups. Allocation concealment was maintained with sequentially numbered, opaque sealed envelopes. Before ART initiation, participants were randomly assigned to study groups, in blocks of 40. Only laboratory personnel were blinded to study group allocation. Standardized data collection tools, staff training, and regular supervision by the study coordinator aimed to ensure that study activities were uniform across sites.
A sample size of 230 was chosen to detect a 20% difference in adherence between study groups (80% adherence with m-DOT vs 60% in controls) assuming 40% death or loss to follow-up, an alpha of 0.05 and power of 0.80.
All patients received first-line treatment regimens consisting of single-formulation stavudine, lamivudine, and either nevirapine or efavirenz. As standard care, all participants attended 3 one-on-one adherence counseling sessions before initiating ART. In these, trained nurse counselors emphasized the importance of adherence; educated patients on the treatment regimen, dosing instructions, side effects, and dietary considerations; tailored the regimen to daily activities; and identified social issues like living conditions and family support.43 After ART initiation, patients visited treatment centers every 4 weeks for follow-up. At the first 2 follow-up visits, general adherence counseling was provided (review of the topics discussed during preparatory counseling), as was discussion of specific emerging problems with side effects or medication intake43; thereafter, counseling was tailored to individual needs. All patients were encouraged to bring a family member or friend to clinic visits and counseling sessions.
In addition to standard care, patients in the intervention arm received m-DOT services for the first 24 weeks of ART. Although consideration was given to provision of home-based m-DOT, formative research revealed that patients were concerned this would undermine their privacy and confidentiality. They also believed that clinic visits would provide beneficial access to health care workers, so m-DOT services were provided during twice weekly clinic visits.44 To enhance convenience, participants could select 1 of 6 health centers for their m-DOT visits (the 3 recruitment sites and 3 primary health clinics). At each visit, participants met with m-DOT observers (nurses) who observed ingestion of the patient's ART dose, inquired about difficulties encountered, and provided individualized adherence support. Used medication containers were also collected, pill counts recorded, and medication dispensed for the subsequent 3 or 4 days.
During the monthly clinic visits, efforts were made to ensure that participants in the m-DOT and control group received equal contact time with study staff to minimize the likelihood that any differences observed would be due to nonspecific effects of increased attention given to the intervention group. CHW traced m-DOT participants who missed visits and brought medications to participants who were unable to visit the facility. After cessation of m-DOT at week 24, patients had no further contact with m-DOT observation centers. Like the control group, they collected monthly medication refills at recruitment clinics and received standard adherence support during weeks 25-72. No compensation was given for study participation, though travel costs were reimbursed for 21 m-DOT participants with financial constraints (US$0.65 per visit).
Data Collection and Study Variables
Data were collected using structured questionnaires administered in Swahili by research assistants. Age was grouped by tertiles (<33, 33-40, and >40 years). Variables were categorized as follows: education as never attended school, received primary education, and attended secondary or tertiary education; marital status as never married, married or cohabiting, divorced or separated, and widowed; living arrangements as lives alone and lives with others; and employment into currently employed and unemployed. HIV-related hospitalization, a binary variable, was based on participant's self-report of HIV-related hospitalization in the preceding year. Four socioeconomic categories were derived by totaling scores from questions on dwelling characteristics, access to water and electricity services, and ownership of assets like television, radio, and bicycle. Each question had equal weighting and was scored from 1 to 4. Perceived internalized stigma was assessed at baseline and 12 weeks using a 16-item scale (Cronbach's alpha of adapted scale: 0.81) derived from Berger's HIV stigma scale (Cronbach's alpha: 0.96)45 and field tested before use. This covered 3 domains: disclosure concerns (6 items), negative self-image (5 items), and concerns with public attitudes (5 items). Patients responded on a 4-item Likert scale from strongly agree to strongly disagree. Total scores (possible range 16-64) were categorized as minimal or low (16-40), moderate (41-52), or high stigma (53-64). Depression was assessed at baseline and weeks 24, 48, and 72 (4 data points) using a culturally adapted 21-item Beck's Depression Inventory (BDI) version I (Cronbach's alpha: 0.86) translated into Swahili (Cronbach's alpha for the Swahili BDI: 0.84). Depression was categorized as none (0-9), mild (10-18), moderate (19-29), and severe (30-63) as per BDI guidelines.46 Body weight was collected monthly and body mass index (BMI) is presented (weight in kilogram per height in square meters). CD4 cell counts were determined at baseline and weeks 24, 48, and 72 using PARTEC (Partec-GmBH, Münster, Germany) and FACS counters (Becton & Dickinson Immunocytometry Systems, San Jose, CA). CD4 measures were extracted from medical records by nurses and the study coordinator. For analysis, cell counts at baseline and week 24 were dichotomized above and below the median. Plasma viral load (Roche Amplicor HIV-1 Monitor test version 1.5; Roche Molecular systems, Branchburg, NJ) was measured at 48 and 72 weeks. Viral load was dichotomized to those with, and those without, viral suppression (ie, <400 copies/mL3).
Adherence measures consisted of self-reported adherence47 and clinic-based pill counts.48 Self-reported adherence (4-day recall) was assessed 7.0 times (8 weekly from 1 to 48 weeks plus at week 72) and was dichotomized into those having missed or not having missed doses in the past 4 days.47 Pill counts were made at each m-DOT visit between weeks 4 and 24 when used medication containers were returned (twice weekly). These counts were aggregated to produce a 4-week measure. From weeks 25-48 and for participants in the control arm (weeks 4-48), pill counts were measured every 4 weeks and at week 72 (a total of 13 data points for both groups). Pill counts, measured as a percentage and dichotomized to >95% and <95% adherence, were calculated as:
Participants who died or dropped out of the study were, for the purposes of analysis, considered nonadherent for that reporting period and were counted as missing thereafter. Those who attended clinic visits but failed to bring back bottles were counted as missing for that reporting period. Some came back with fewer pills than expected due to misplaced pills, repeated ingestion if pills had been vomited, or “pill dumping” (as occasionally admitted to counselors and CHW). This produces values of adherence >100% and these patients were given an adherence value equal to 100 minus the excess percent adherence (eg, a 102% pill count was given the value 98%).
Annual incremental costs for the health system for each m-DOT patient was calculated from the additional resources required: staff time (patient-flow analysis) at health facilities to observe the clients, staff time of CHW who tracked patients through home visits, and additional transportation and communication expenses for CHW trackers.
Data were double entered by separate clerks in a Microsoft Access 2003 database and analyzed using Intercooled Stata 8.0 (Stata Corporation, College Station, TX). Patients were analyzed within the group to which they were originally assigned, regardless of whether they received the study intervention.
Levels of adherence in the 2 groups was compared in the first 24 weeks to assess effectiveness of the intervention and then compared during weeks 25-48 and week 72 to detect any sustained effects on adherence. In these 2 periods, self-reported adherence with m-DOT and the proportion of 4 weekly periods in which patients in the m-DOT group were >95% adherent was compared with controls.
Generalized estimating equations were constructed to evaluate repeated pill-count adherence measures in the 2 study groups, controlled for intraclient clustering and baseline variables, which may have differed between the intervention and the control group. An exchangeable correlation structure was chosen, which assumes that the correlation between each pair of observations for an individual is the same. Variables associated with the repeated pill-count measures (each capturing a 4-week period) in bivariate analysis or in previous studies were entered in the multivariate model in a forward stepwise manner. Variables with P < 0.10 or those whose removal markedly altered the model fit were retained in the final model. Interaction between study group and other explanatory variables was assessed.
Secondary outcomes were evaluated as follows: virological suppression, increases in CD4 cell counts, and changes in weight and BMI. For these analyses, the Mann-Whitney U test was used to assess differences between groups as continuous variables had a nonnormal distribution and χ2 test were used to detect differences in categorical variables.
Subject Characteristics and Retention
One hundred sixteen participants were assigned to the intervention arm, 118 to the control arm, and 15 declined participation (Fig. 1). Seven participants were enrolled but did not receive the intervention (3 due to death before initiating ART, 1 due to severe illness, and 3 due to withdrawal). The m-DOT and control groups had similar baseline characteristics (Table 1). About two thirds were female. At baseline, a substantial proportion of participants reported mild (34.8%, 78/224) or moderate to severe depression (31.7%, 71/224). Median CD4 cell count was 99 cells/mm3, with 25.4% (59/232) having <50 cells/mm3.
The majority (78.6%, 181/230) initiated an efavirenz-based regimen with the remainder receiving nevirapine-based treatment. During the study, 1 patient changed to a protease inhibitor-containing regimen (lopinavir/ritonavir, zidovudine, and didanosine). Participants in the intervention arm attended a median of 42 of 48 scheduled m-DOT visits (interquartile range = 28-45). The annual incremental cost per client of the m-DOT intervention was approximately US$31 (1US$ = 70 KSh). Additional staff time accounted for 80% of costs.
Of 234 people enrolled, 217 (92.7%) had a study endpoint at 24 weeks and 210 (89.7%) at 72 weeks (183 completed the study protocol and 27 died). More than two thirds of deaths occurred within 24 weeks of enrollment (19/27, 70%).
Effects of M-DOT on Adherence and Viral Load Measures
Within the 24-week intervention period, 9.1% (9/99) of participants in the m-DOT arm self-reported missed doses compared with 19.1% (20/105) in the control arm (P = 0.04). During the same period, 96.5% (551/571) of the monthly pill-count measures (6 data assessment points) in the m-DOT arm showed adherence levels >95% compared with 86.1% (445/517) of controls [adjusted odds ratio (AOR) = 4.4; 95% CI = 2.6 to 7.5; P < 0.001] (Table 2). Similar results were seen using a pill-count threshold of ≥90% to define adherence: 97.7% (558/571) of adherence measures were ≥90% in the m-DOT group compared with 93.4% (483/517) in controls (AOR = 2.9; 95% CI = 1.4 to 6.0; P = 0.004). Viral load was not available for the period 1-24 weeks.
Analysis of other covariates in weeks 1-24 showed adherence was higher among participants aged 33-40 years compared with those <33 years, among people who lived with others, and among those with a pre-ART CD4 cell count above the median. Gender, employment, and site of recruitment were not associated with adherence during the intervention or subsequent period. With multivariate analysis, those in the m-DOT group were 4.8 times (95% CI = 2.7 to 8.6; P < 0.001) as likely to be adherent as controls in weeks 1-24. Depression (results shown below) and having an HIV-related hospitalization in the year preceding ART (AOR = 1.7; 95% CI = 0.9 to 3.1; P = 0.08) were retained in the final generalized estimating equations model, though a significant association between these factors and adherence was not noted.
From weeks 25 to 48, after m-DOT discontinuation, no differences were noted in self-reported adherence: 12.9% (12/93) of those receiving m-DOT reported missed doses compared with 13.4% (13/97) in the control arm (P = 0.92). During this period, 80.7% (415/514) of pill-count measures (6 data assessment points) in the m-DOT arm were >95% compared with 457/545 (83.9%) in controls (AOR = 0.8; 95% CI = 0.6 to 1.2; P = 0.36). Undetectable viral load (Table 3) was observed in a larger proportion of patients in the m-DOT arm compared with the control arm at 48 weeks, but this difference was not statistically significant (OR = 2.0; 95% CI = 0.80 to 5.2; P = 0.13).
At 72 weeks, there was no difference in self-reported adherence between groups: 2.6% (2/77) of patients in the m-DOT arm reported missed doses compared with 4.7% (4/89) of controls (P = 0.49). Although a higher proportion of participants in the m-DOT group had >95% adherence on pill counts compared with controls, the difference was not statistically significant (70.7% vs 58.8%; OR = 1.7; 95% CI = 0.9 to 3.3; P = 0.12). A similar proportion in both study groups had undetectable viral load.
Effects of M-DOT on Immunological and Clinical Measures
After 6 months of ART, the median increase in CD4 cell counts for all participants was 173 cells/mm3 (interquartile range = 95-261 cells/mm3) similar in both groups (169 cells/mm3 with m-DOT vs 175 cells/mm3 in controls). Similarly, immunological changes after 18 months of ART did not differ between groups (median CD4 cell increase in m-DOT = 220 vs 234 cells/mm3 in controls; P = 0.65). However, compared with controls, patients in the m-DOT group had a larger mean increase in BMI during weeks 1-24 (2.2 kg/m2, SD = 2.2 vs 1.4 kg/m2, SD = 2.1; P = 0.014) and weeks 1-48 (2.4 kg/m2, SD = 3.2 vs 1.5 kg/m2, SD = 2.6; P = 0.047). No increase in BMI was detected at 72 weeks (2.4 kg/m2, SD = 3.7 vs 1.6 kg/m2, SD = 3.2; P = 0.13). No difference in survival at week 72 was noted: 87.1% (101/116) and 89.8% (106/118; P = 0.51) in the m-DOT and control arm, respectively.
Subgroup Analysis: Effects of M-DOT on Adherence Among Those With Depression
In the multivariate model for the period 1-24 weeks (shown above), after adjusting for study group and HIV-related hospitalization, adjusted odds of adherence in those with mild depression was 0.6 (95% CI = 0.3 to 1.1; P = 0.08) and those with moderate/severe depression was 0.6 (95% CI = 0.3 to 1.1; P = 0.11), compared with participants without depression.
When comparing m-DOT with controls, no decreases in depression scores were detected at week 24 (median decrease of 6 in m-DOT vs 4 in controls; P = 0.13). However, increased reductions in scores were seen at week 48 (median decreases in m-DOT 10.5 vs 6 in controls; P = 0.04) and week 72 (median decreases in m-DOT 10 vs 6.5 in controls; P = 0.03). To further explore this, we investigated the effects of m-DOT among participants who had moderate or severe depression at study entry. Among these participants, those who received m-DOT were 7.0 times as likely to be adherent than controls in the 1- to 24-week period (95% CI = 2.7 to 18.0; P < 0.001). Differences in adherence in this subgroup were not noted after week 24. However, participants in the m-DOT group with baseline moderate or severe depression were more likely to have viral suppression at 48 weeks than similar controls (21/25 vs 13/22; P = 0.057) and at 72 weeks (27/30 vs 15/23; P = 0.027).
The m-DOT intervention was effective in increasing adherence to ART. These effects were, however, not sustained after cessation of the intervention. Although higher rates of viral suppression were observed at 48 weeks among m-DOT participants, these differences were not significant. Other secondary measures such as increasing BMI and decreasing depression scores suggest clinical benefits for those receiving the intervention. Our findings are similar to a randomized controlled study that evaluated a peer-delivered m-DOT intervention in Mozambique,46 which showed that although self-reported levels of adherence were higher among those receiving the intervention, no differences were noted in immunological outcomes.46
Although the study findings suggest that m-DOT increased adherence in these settings, high levels of adherence were also noted among participants in the control group who received only basic support measures. This supports the view that intensive adherence interventions like m-DOT should optimally target subgroups with, or at high risk for, poor adherence.49 Targeting subgroups of patients at risk for nonadherence has been the dominant strategy in m-DOT research. Our study further suggests that m-DOT may be particularly useful among patients with moderate to severe depression, which is common among HIV-infected persons,50 and negatively impacts adherence and mortality.3,51,52 This finding may in part be as a result of regular interaction with an empathetic and supportive counselor. Further research is needed to confirm this finding.
Although there may be a potential role for m-DOT within ART programs in sub-Saharan Africa, it should be considered a component of multifaceted adherence support. A Cochrane review20 highlights the importance of practical management skills (tailored drug schedules, reminder devices, and medication dossettes) and interventions which help patients identify and address barriers to adherence. These practical strategies, with conceptual similarities to m-DOT, were found to have better success than more complex interventions. The review also found interventions provided for longer periods of time (>12 weeks) were more successful than shorter interventions.
The optimal duration of m-DOT is unknown and warrants further investigation. Adherence declined over time in both the intervention and the control groups. Other studies have also shown dissipation in effectiveness after cessation of the interventions.53,54 Abrupt/sudden cessation of m-DOT at 24 weeks, with the subsequent introduction of a new follow-up routine, may have contributed to a marked decline in adherence in the m-DOT group. This suggests that a gradual reduction in intensity of m-DOT intervention or booster sessions tailored to the specific need of patients may be required.
Attendance at m-DOT visits was high even without financial incentives suggesting high user acceptability of m-DOT services in this setting. The feasibility of implementing the m-DOT intervention in this study has also been described.55 A similarly high acceptability and feasibility of m-DOT were reported from Mozambique.56 In addition to a favorable acceptability profile, m-DOT has a relatively low-cost profile.
We used, a priori, a 95% cut off for adherence in this study. Given that recent evidence suggests that patients receiving nonnucleoside reverse transcriptase-containing regimens may achieve undetectable viral loads with lower levels of adherence,4,5 we reanalyzed our data using a 90% adherence cut off and found no difference in study conclusions. Because of the generally high adherence levels, we had insufficient nonadherent subjects for meaningful analysis at lower adherence cut offs. Regardless, we would caution against the promotion of different levels of adherence for different treatment regimens as some patients inevitably move to treatment regimens containing protease inhibitors.
The present m-DOT intervention took place in a clinic setting where participants had greater overall contact time with health providers compared with the control group, thereby increasing the opportunity for the intervention group to access health services. A previous study found that greater utilization of medical services associated with receiving m-DOT resulted in improved virological outcomes.57 The outcomes reported here could, similarly, be due to nonspecific effects of the increased attention given to the intervention group.
We report adherence levels in a research study setting and it is possible that adherence would be lower in routine care settings. However, as this study took place within the context of routine clinical services, we feel that this increases the ability to generalize findings to similar settings. Further, almost all eligible participants were enrolled, reducing the likelihood of selection bias. As the study began when ART programs were first introduced in Kenya, treatment was available for a limited number of patients. With this constraint, the study lacks power to detect smaller but nevertheless clinically important differences in outcomes. Differences in the way pill counts were measured for the 2 groups during the first 24 weeks could have introduced bias in these measures. However, self-reported adherence, measured identically in both groups, was in the same direction as pill-count measures, supporting study conclusions. The use of additional clinical, virological, and immunological endpoints also strengthens the ability to draw conclusions about the effectiveness of the intervention. Though field tested, the adapted depression and stigma scales have not been validated in this setting, possibly introducing measurement bias. Finally, adherence with single-formulation medications, as used in this study, with a higher pill burden, may differ from fixed-dose combinations.58
In conclusion, this randomized controlled trial of a m-DOT strategy showed that this intervention can increase adherence. Although adherence declined to control levels after cessation of the intervention, the results suggest that m-DOT could potentially be a useful strategy to improve adherence in resource-constrained settings, particularly among depressed patients and others at highest risk for nonadherence.
We thank the Ministry of Health, Government of Kenya, for their support. We acknowledge the staff at Coast Province General Hospital, Port Reitz District Hospital, Bomu Medical Centre, Magongo Health Centre, Likoni Health Centre and Bamburi Health Centre, and the CHW for their active support and participation. We thank Rick Homan for the cost analysis. Last, we thank all study participants for their invaluable contribution. Scott Kellerman made incisive comments improving the paper markedly. The opinions expressed herein are those of the authors and do not necessarily reflect the views of US Agency for International Development.
1. WHO, UNAIDS, UNICEF. Towards universal access: scaling up priority HIV/AIDS interventions in the health sector: progress report. Available at: http://www.who.int/hiv/mediacentre/universal_access_progress_report_en.pdf
. Accessed April 3, 2008.
2. Crum NF, Riffenburgh RH, Wegner S, et al. Comparisons of causes of death and mortality rates among HIV-infected persons: analysis of the pre-, early, and late HAART (highly active antiretroviral therapy) eras. J Acquir Immune Defic Syndr
3. Paterson DL, Swindells S, Mohr J, et al. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med
4. Maggiolo F, Ravasio L, Ripamonti D, et al. Similar adherence rates favor different virologic outcomes for patients treated with nonnucleoside analogues or protease inhibitors. Clin Infect Dis
5. Weiser SD, Guzman D, Riley ED, et al. Higher rates of viral suppression with nonnucleoside reverse transcriptase inhibitors compared to single protease inhibitors are not explained by better adherence. HIV Clin Trials
6. Bangsberg DR, Hecht FM, Charlebois ED, et al. Adherence to protease inhibitors, HIV-1 viral load, and development of drug resistance in an indigent population. Aids
7. Bangsberg DR, Perry S, Charlebois ED, et al. Non-adherence to highly active antiretroviral therapy predicts progression to AIDS. Aids
8. Mills EJ, Nachega JB, Bangsberg DR, et al. Adherence to HAART: a systematic review of developed and developing nation patient-reported barriers and facilitators. PLoS Med
9. Ramadhani HO, Thielman NM, Landman KZ, et al. Predictors of incomplete adherence, virologic failure, and antiviral drug resistance among HIV-infected adults receiving antiretroviral therapy in Tanzania. Clin Infect Dis
10. Nachega JB, Hislop M, Dowdy DW, et al. Adherence to highly active antiretroviral therapy assessed by pharmacy claims predicts survival in HIV-infected South African adults. J Acquir Immune Defic Syndr
11. Orrell C, Bangsberg DR, Badri M, et al. Adherence is not a barrier to successful antiretroviral therapy in South Africa. AIDS
12. Oyugi JH, Byakika-Tusiime J, Ragland K, et al. Treatment interruptions predict resistance in HIV-positive individuals purchasing fixed-dose combination antiretroviral therapy in Kampala, Uganda. AIDS
13. Aboubacrine SA, Niamba P, Boileau C, et al. Inadequate adherence to antiretroviral treatment and prevention in hospital and community sites in Burkina Faso and Mali: a study by the ATARAO group. Int J STD AIDS
14. Eholie SP, Tanon A, Polneau S, et al. Field adherence to highly active antiretroviral therapy in HIV-infected adults in Abidjan, Cote d'Ivoire. J Acquir Immune Defic Syndr
15. Gill CJ, Hamer DH, Simon JL, et al. No room for complacency about adherence to antiretroviral therapy in sub-Saharan Africa. AIDS
16. Rosen S, Fox MP, Gill CJ. Patient retention in antiretroviral therapy programs in sub-Saharan Africa: a systematic review. PLoS Med
17. Nachega JB, Knowlton AR, Deluca A, et al. Treatment supporter to improve adherence to antiretroviral therapy in HIV-infected South African adults. A qualitative study. J Acquir Immune Defic Syndr
. 2006;43 (suppl 1):S127-S133.
18. Wong IY, Lawrence NV, Struthers H, et al. Development and assessment of an innovative culturally sensitive educational videotape to improve adherence to highly active antiretroviral therapy in Soweto, South Africa. J Acquir Immune Defic Syndr
. 2006;43(Suppl 1):S142-S148.
19. Ickovics JR, Meade CS. Adherence to HAART among patients with HIV: breakthroughs and barriers. AIDS Care
20. Rueda S, Park-Wyllie LY, Bayoumi AM, et al. Patient support and education for promoting adherence to highly active antiretroviral therapy for HIV/AIDS. Cochrane Database Syst Rev
21. Remien RH, Stirratt MJ, Dolezal C, et al. Couple-focused support to improve HIV medication adherence: a randomized controlled trial. AIDS
22. Safren SA, Hendriksen ES, Desousa N, et al. Use of an on-line pager system to increase adherence to antiretroviral medications. AIDS Care
23. Safren SA, Otto MW, Worth JL, et al. Two strategies to increase adherence to HIV antiretroviral medication: life-steps and medication monitoring. Behav Res Ther
24. Samet JH, Horton NJ, Meli S, et al. A randomized controlled trial to enhance antiretroviral therapy adherence in patients with a history of alcohol problems. Antivir Ther
25. Smith SR, Rublein JC, Marcus C, et al. A medication self-management program to improve adherence to HIV therapy regimens. Patient Educ Couns
26. Tuldra A, Fumaz CR, Ferrer MJ, et al. Prospective randomized two-Arm controlled study to determine the efficacy of a specific intervention to improve long-term adherence to highly active antiretroviral therapy. J Acquir Immune Defic Syndr
27. Weber R, Christen L, Christen S, et al. Effect of individual cognitive behaviour intervention on adherence to antiretroviral therapy: prospective randomized trial. Antivir Ther
28. Weidle PJ, Wamai N, Solberg P, et al. Adherence to antiretroviral therapy in a home-based AIDS care programme in rural Uganda. Lancet
29. Mukherjee JS, Ivers L, Leandre F, et al. Antiretroviral therapy in resource-poor settings. Decreasing barriers to access and promoting adherence. J Acquir Immune Defic Syndr
. 2006;43(Suppl 1):S123-S126.
30. Hardon AP, Akurut D, Comoro C, et al. Hunger, waiting time and transport costs: time to confront challenges to ART adherence in Africa. AIDS Care
31. Diabate S, Alary M, Koffi CK. Determinants of adherence to highly active antiretroviral therapy among HIV-1-infected patients in Cote d'Ivoire. AIDS
32. Chaulk CP, Kazandjian VA. Directly observed therapy for treatment completion of pulmonary tuberculosis: Consensus Statement of the Public Health Tuberculosis Guidelines Panel. JAMA
33. Davies GR, Connolly C, Sturm AW, et al. Twice-weekly, directly observed treatment for HIV-infected and uninfected tuberculosis patients: cohort study in rural South Africa. AIDS
34. Wohl DA, Stephenson BL, Golin CE, et al. Adherence to directly observed antiretroviral therapy among human immunodeficiency virus-infected prison inmates. Clin Infect Dis
35. Mitty JA, Huang D, Loewenthal HG, et al. Modified directly observed therapy: sustained self-reported adherence and HIV health status. AIDS Patient Care STDS
36. Altice FL, Maru DS, Bruce RD, et al. Superiority of directly administered antiretroviral therapy over self-administered therapy among HIV-infected drug users: a prospective, randomized, controlled trial. Clin Infect Dis
37. Fischl M, Castro J, Monroig R, et al. Impact of directly observed therapy on long-term outcomes in HIV clinical trials [Abstract 528]. In 8th Conference of Retroviruses and Opportunistic Infections, Chicago, IL. February 4-8, 2001
. Available at http://www.retroconference.org/2001/Abstracts/Abstracts/Abstracts/528.htm
. Accessed June 27, 2008.
38. Macalino GE, Mitty JA, Bazerman LB, et al. Modified directly observed therapy for the treatment of HIV-seropositive substance users: lessons learned from a pilot study. Clin Infect Dis
. 2004;38(Suppl 5):S393-S397.
39. Mitchell CG, Freels S, Creticos CM, et al. Preliminary findings of an intervention integrating modified directly observed therapy and risk reduction counseling. AIDS Care
40. Wohl AR, Garland WH, Valencia R, et al. A randomized trial of directly administered antiretroviral therapy and adherence case management intervention. Clin Infect Dis
41. Farmer P, Leandre F, Mukherjee J, et al. Community-based treatment of advanced HIV disease: introducing DOT-HAART (directly observed therapy with highly active antiretroviral therapy). Bull World Health Organ
42. Pearson CR, Micek MA, Simoni JM, et al. Randomized control trial of peer-delivered, modified directly observed therapy for HAART in Mozambique. J Acquir Immune Defic Syndr
43. Population Council/Horizons, International Centre for Reproductive Health, and Coast Province General Hospital (Ministry of Health, Kenya). Adherence to Antiretroviral Therapy in Adults: A Guide for Trainers
. Nairobi, Kenya: Population Council; 2004. Available at: http://www.popcouncil.org/arvadhrnctrngguide.pdf
44. Sarna A, Kalibala S, Kaai S, et al. Acceptability of A Modified Directly Observed Therapy Approach to Improve Adherence to Antiretroviral Therapy
. Horizons Research summary. Washington, DC:Population Council. 2004.
45. Berger BE, Ferrans CE, Lashley FR. Measuring stigma in people with HIV: psychometric assessment of the HIV stigma scale. Res Nurs Health
46. Beck AT, Ward C, Mendelson M. et al. An inventory for measuring depression. Arch Gen Psychiatry
47. 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
48. Miller LG, Hays RD. Adherence to combination antiretroviral therapy: synthesis of the literature and clinical implications. AIDS Read
49. Smith-Rohrberg D, Altice FL. Randomized, controlled trials of directly administered antiretroviral therapy for HIV-infected patients: questions about study population and analytical approach. Clin Infect Dis
. 2006;43:1221-1222; author reply 1222-1223.
50. Ciesla JA, Roberts JE. Meta-analysis of the relationship between HIV infection and risk for depressive disorders. Am J Psychiatry
51. Gordillo V, del Amo J, Soriano V, et al. Sociodemographic and psychological variables influencing adherence to antiretroviral therapy. AIDS
52. Lima VD, Geller J, Bangsberg DR, et al. The effect of adherence on the association between depressive symptoms and mortality among HIV-infected individuals first initiating HAART. AIDS
53. Wagner GJ, Kanouse DE, Golinelli D, et al. Cognitive-behavioral intervention to enhance adherence to antiretroviral therapy: a randomized controlled trial (CCTG 578). AIDS
54. Simoni JM, Pearson CR, Pantalone DW, et al. Efficacy of interventions in improving highly active antiretroviral therapy adherence and HIV-1 RNA viral load. A meta-analytic review of randomized controlled trials. J Acquir Immune Defic Syndr
. 2006;43(Suppl 1):S23-S35.
55. Munyao P, Sarna A, Luchters S, et al. How Feasible is a DAART Strategy to Promote Adherence to ART: Lessons from Mombasa, Kenya
. Washington, DC: Population Council; 2005.
56. Pearson CR, Micek M, Simoni JM, et al. Modified directly observed therapy to facilitate highly active antiretroviral therapy adherence in Beira, Mozambique. Development and implementation. J Acquir Immune Defic Syndr
. 2006;43(Suppl 1):S134-S141.
57. Smith-Rohrberg D, Mezger J, Walton M, et al. Impact of enhanced services on virologic outcomes in a directly administered antiretroviral therapy trial for HIV-infected drug users. J Acquir Immune Defic Syndr
. 2006;43(Suppl 1):S48-S53.
58. Maggiolo F, Ripamonti D, Arici C, et al. Simpler regimens may enhance adherence to antiretrovirals in HIV-infected patients. HIV Clin Trials