Alamo, Stella T. MD, MDC*; Colebunders, Robert MD, PhD†,‡; Ouma, Joseph BStat, MStat§; Sunday, Pamela BScQE‖; Wagner, Glenn PhD¶; Wabwire-Mangen, Fred MD, PhD#; Laga, Marie MD, PhD**
Retention in antiretroviral therapy (ART) programs is an important factor in determining clinical outcomes of patients.1–3 However, there is a substantive and increasing loss-to-follow-up (LTFU) of patients in many ART programs in resource-limited settings.4–7
Several studies have sought to explain the reasons for LTFU. Poverty, including the costs of frequent clinic visits, transport costs, and a fee for service have been cited as major causes.3,8–10 The risk of death5,6,11,12 and self-transfer to other facilities13,14 have also been well documented.
Closely supervised community-based care using peer workers bring services closer to patients and provide a support system in the community, which could address many cited barriers to retention.15,16 Reasons for LTFU from community-based programs may therefore be different from those commonly cited and understanding the reasons for LTFU from these programs is urgently needed.
In a recent study, we estimated the incidence of, and risk factors for LTFU and death in 6645 HIV-infected patients enrolled in Reach Out Mbuya HIV/AIDS Initiative (ROM), which offers comprehensive community-based ART. After 10 years, only 41% of the patients were still retained, with 74% of those not retained being LTFU.17 This study was performed to gain an in-depth understanding of the risk factors for LTFU at ROM.
This study was conducted at Reach Out Mbuya HIV /AIDS initiative which serves the urban poor in Kampala—the capital city of Uganda and has been described previously.18–20 ROM's strategies to optimize retention include the following: (1) community-based care, with 47% of its staff being patients. The expert patients branded “community ART and tuberculosis treatment supporters” (CATTS) visit patents at home every 1–4 weeks and conduct same-day household tracing of patients who miss a clinic appointment20; (2) provision of free services within geographically accessible clinics; (3) provision of services within a defined catchment area and verification of residency before enrolment; (4) provision of financial and nonfinancial supports17; (5) task shifting (Nurses and CATTS taking on roles traditionally held by doctors and nurses, respectively); (6) mandatory multiple pre-ART counseling sessions, HIV status disclosure to household members, allocation of a CATTS and signing of a treatment agreement before ART initiation; and (7) family-based care.
Patient Tracing and Outcomes Ascertainment
By 2006, it was evident that there was increasing LTFU at ROM18 and the community follow-up procedures which focused on ensuring adherence and retention in ART patients were revised to include pre-ART patients.20 In 2008, electronic medical records and same-day tracing of patients who miss a clinic appointment was implemented and significantly reduced LTFU.21 A more recent evaluation found high attrition rates17; and we implemented in the current study a tracing approach to ascertain the outcomes of patients who were deemed LTFU. The tracers were a research team of 10 who were not ROM staff (to ensure anonymity and objectivity). If the patient was not found at home, family members or neighbors were contacted for further information. The tracers returned to the houses of those not found at home a second time to verify information. Patients found alive received a semistructured interview and were encouraged to return ROM for care.
“LTFU” is defined as absence from the clinic for 90 days after the expected last clinic visit in a patient who is not known to be dead or formally transferred to another facility. “Stopped care” is a patient who was LTFU from ROM, was traced and found alive, but was not continuing care from anywhere else. “Self-transferred” is a patient who was LTFU from ROM, was traced, found alive and was continuing care in another facility. “Discontinued care” refers to patients who have either stopped care or self-transferred from ROM. Most ART programs do not trace patients who are LTFU because of costs, lack of proper addresses and unreliable phone contacts. Stopped care and self-transferred are therefore normally classified simply as LTFU. Although “self transfer” may be a desirable outcome, there are no systems in Uganda to track in-between facility movements and to estimate the proportion of patients who “self transfer” and in fact “transfer in” to another treatment site and what their outcomes are within the new treatment site. “Dead” is a patient who was LTFU and following the tracing was found dead. “Returned to care” is a patient who was LTFU, was traced, found alive, and re-enrolled into care at ROM.
A retrospective cohort analysis of all patients aged ≥18 years enrolled in ROM was done to identify all patients who were LTFU between May 31, 2001, and May 31, 2010. We used both qualitative and quantitative techniques simultaneously to understand reasons for LTFU.
Data Collection and Analysis
Previous studies have found a mortality of 20%–60%5,7 among those LTFU. We estimated a mortality of 10% among our LTFU group. With a power of 95% and a significance level of 0.05, a total of 579 patients were randomly sampled [every 7(th) patient from the list of all patients LTFU] and then traced to get our predicated estimates.
Baseline demographic and socialeconomic characteristics are collected at home for every patient as part of the enrollment procedures. The data is updated and verified annually. Demographic, socialeconomic, and clinical variables were extracted from the patient registration and clinic electronic records, respectively, and exported into STATA version 11 (Stata Corp, TX) for analysis. We determined the proportion of LTFU patients who had died, stopped care, self-transferred, and returned into care. Baseline characteristics were described using medians and interquartile range (IQR) for continuous variables and counts and percentages for categorical variables. Bivariate comparisons were done using χ2 test or Fisher exact test for categorical variables, whereas the student t test and Wilcoxon rank sum test were used for comparing data with normal and nonnormal distributions, respectively. The incidence of LTFU was calculated using time 0 as the enrollment date and LTFU date as the time when the patient had not returned to the clinic for 90 days after the last clinic visit. Patients returning to the clinic after 90 days and subsequently defaulting again were counted only once. Using “stopped care” as the hazard and “self-transferred” as the comparator, we examined in a Cox proportional multivariable model risk factors for stopping care. All variables were included in the model but we used a step-wise approach initially to keep variables with probability of minimum acceptable errors <0.3. After identifying such variables, we run the model keeping age and sex in. All reported P values are exact and 2-tailed, and P less than 0.05 was considered significant.
The qualitative interviews were pretested on a sample of patients from the AIDS Support Organization Uganda which has a similar model of care to ROM. Pretested interviews were not included in the analysis. The pretested semistructured interview was administered to all 481 patients found at home to explore for reasons for discontinuation of care. In addition, those who returned after the tracing received an interview exploring for the reasons for their return. A total of 6 focus group discussions (FGD) were conducted with the following: (1) patients who discontinued care because of religious reasons; (2) male patients who stopped care; (3) female patients who stopped care; (4) patients who self-transferred; (5) patients who returned into care; and (6) ROM staff.
Each FGD had 7–10 participants purposeful sampled after a preliminary analysis of the quantitative data and semistructured interviews and explored themes that emerged from the preliminary analysis to gain additional insights into barriers to retention. Interviews were contacted in English or the local language best known to the patient. All FGDs were tape recorded, transcribed, double checked for accuracy (2 coders coded the data and author no 1 checked for rate agreement) and translated. Data were analyzed using thematic analysis.22 First, we used text management software (ATLAS.ti) to mark contiguous blocks of transcript text that pertained to the major topical domains of interest (socioeconomic or programatic barriers) and the 3 patient categories (stopped care, self referred or returned to care). We then pulled out all text associated with a particular domain, printed the quotes and sorted them into piles based on their thematic similarities. We then named each thematic category and developed an explicit codebook to describe each category. In the next step, a 2-person team matched each quote in a domain with a specific subcategory. We then examined the degree to which these themes were distributed across demographic characteristics. Representative quotes were selected to illustrate identified themes.
Description of Study Cohort
Between May 31, 2001, and May 31, 2010, 2933 of 3954 (74%) of patients who had not been retained at ROM were considered LTFU. Of 579 of 2933 (19.0%) of patients who were sampled for tracing, 32 (5.5%) were untraceable, 66 (11.4 %) were confirmed dead, and 481(83.0%) were found alive. Of those found alive, 232 (40.0%) had stopped care and 249 (43.0 %) had self-transferred, whereas 61 (12.7%) returned to care (Fig. 1). The median time from enrollment to LTFU was 1.14 years (IQR: 0.46–2.48 years). The median time from LTFU to return into care was 911 days (IQR: 525–1546 days), with that for patients who stopped care being 973 days (IQR: 648–1726 days), and for patients who self-transferred being 807 days (IQR: 455–1489 days).
Baseline socialeconomic, demographic, and clinic characteristics are shown in Table 1. Compared with those found alive, patients who died or were untraceable, were more likely to be: unemployed, married, less educated, without children, and had not disclosed their HIV status.
TABLE 1-a Baseline C...Image Tools
In Table 2, we compare the risk factors for discontinuing care for found alive. In adjusted hazards ratios, being of born again religion, originating from outside Kampala, resident in Kampala for <5 years but >1 year, having school-age going children who are out of school, non-HIV status disclosure, CD4 counts >250 cells per cubic millimeter and pre-ART were associated with an increased risk of stopping care.
Of the 61 patients who returned into care, their median CD4 count at the time of LTFU was significantly higher than at the time of return [401/mm3 (IQR; 324–584) vs. 205/mm3 (IQR; 172–440), P < 0.0001]. The major reasons why patients discontinued and returned to care are shown in Tables 3A, B.
Reasons for Discontinuation of and Return to Care
Overall, 86.0% of ROMs' patients originated from outside Kampala with 38.2% of them having migrated in search of health care. Other reasons for migration include search of work (27.5%), war (11.9%), marriage (10.3%), and family move (9.3%). In the 12 months before the evaluation, 406 (6.8%) of the patients interviewed had ever traveled in and out of Kampala with 174 (39.0%) of them having traveled 10 or more times. The main reasons for travel were business 169 (35.1%), work 73 (15.0%), looking for accommodation 42 (8.7%), prayers 35 (7.3%), and burial 27 (5.6%).
One hundred thirteen (23.5%) of the patients believed that prayer heals HIV, and 45 (8.8 %) of them discontinued care because of religious beliefs with the majority, (38%) being “born again”, 39 (34.1%) Protestants, 23 (20.4%) Catholics, and 8 (7.1%) Muslims. The FGDs confirmed the influence of religion on retention. “I will not go to ROM anymore. I feel healthier now with prayers than when I was when taking ARVs. My pastor is kind and treats me better than the staff at ROM used to” (FGD 1-female patient). Some staff suggested that many patients are desperate and vulnerable when they learn they are HIV positive and resort to prayer as a coping mechanism.
Forty-nine (9.7 %) of the patients discontinued care because of the inconvenience to their work with 41 (91.4%) having self-transferred. “I got a job as a truck driver. I travel long distances and sometimes I am away from Kampala for several months. I used to miss my clinic appointments and yet ROM would not give me drugs for more than one month. I went to another clinic which is not as strict as ROM” (FGD 2-male patient).
HIV Status Disclosure
Overall, 160 (33.3%) patients had a CD4 count <250 cells per cubic millimeter and were eligible for ART at their last clinic visit but had not initiated ART because of lack of HIV status disclosure. In addition, 31 (6.1%) of the patients discontinued care at ROM because of lack of HIV status disclosure of which 21 stopped care.
One hundred twenty-three (21%) of the patients reported that their children were out of school, and lack of money for fees was cited as the main reason.
The median household size was 7 (range: 1–18). Three hundred twenty (55.3%) of the patients lived in rented houses and 290 (90%) of them could barely afford the rent, which on average is US $30 per month “I took my children to my mother in the village because I could not afford to look after them in Kampala. I had to travel to check on them every month and eventually I could no longer afford the transport back and forth and remained in the village” FGD 3-female patient.
Five patients returned to care for the need of school fees, whereas 39 (7.7%) discontinued care because they had failed to pay back the loans they had received from ROM. During the FGD, 7 of 10 of the patients who self-transferred highlighted that they felt the system to determine who receives financial or nonfinancial benefits were unfair. The staff on the other hand said some patients moved into ROMs catchment area to get benefits “some patients brought children of their relatives so that they could benefit from the school fees and when they did not receive these services they left ” FGD 6-male CATTS.
Catchment Area Restrictions
Only 26.4% of the patients found alive were still resident within ROMs catchment area. Of these, 175 (49.9%) had stopped care. Fourteen (22.0%) of the patients returned to care because the catchment area had been expanded to include their villages. The FGD responses revealed several challenges regarding the catchment area restrictions. Nine of the 10 patients who self-transferred mentioned that they could not afford the cost of rent within ROMs catchment area anymore. “I liked the services at ROM, but I could not afford the rent anymore. I failed to get a cheap house within the catchment area so I moved and the staff at ROM would not allow me continue getting my drugs” FGD 5-male patient. The staff on the other hand felt the catchment-area restrictions were good because it allowed for easy patient follow-up. However, others said it delayed enrollment into care and very sick patients may die before they get care.
Almost all, 533 (92.1%) of the patients walked to ROM clinics. None of the interviewed patients mentioned lack of transport or distance as a barrier to clinic attendance. Majority, 108 (43.5%) of those who self-transferred went to private clinics and 9/10 of the patients mentioned that they would return to ROM because of the high transport costs to other facilities. Moreover, those who returned to care cited the easy access to ROMs clinics as the reason for their return.
Ninety-three (19.3%) of the patients discontinued care because of long waiting times with 33 (33.0%) having stopped. However, 2 patients returned to care because they experienced longer waiting times elsewhere. “I got a job and was tired of reporting for work late. I came to the clinic at 6 AM but still left after 11 AM”.
Ninety (18.7 %) of the patients discontinued care because of the strict follow-up procedures. Of these, 73% (66) self-transferred. During the FGD, 9 of the 10 patients who self-transferred said they disliked the routine visits by the CATTS. “I dislike being treated like a child. The CATTS comes to my house every week to count my pills. When I go to the clinic they also count my pills. Sometimes, they make mistakes in counting my pills and they harass me about not taking my drugs well” FGD 2-male patient. The patients disclosed that there was forced HIV status disclosure by the CATTS, who are well known within the communities “My CATTS comes to my house wearing the ROM T-shirt and people around get to know that I am HIV positive and they start stigmatizing me”. FGD 2-male patient “I had just tested HIV positive and I had not yet disclosed to my new husband. The ROM staff insisted on coming to my house. My husband beat me up claiming I had infected him with HIV. Since then I have not seen him”. FGD 3-female patient. Additionally, they disliked the monthly clinic visits and desired longer appointment schedules. “I have taken septrin for a long time. It is cheap and I can easily get it in the drug shops. I do not see why I should come and line up in the clinic every month to see a doctor to prescribe for me septrin” FGD 2-Male patient.
Strict ART Eligibility Criteria and Delayed Initiation of ART
The patients felt the lengthy and many pre-ART adherence counseling sessions, demands for HIV-status disclosure, and a family treatment supporter in addition to a CATTS from ROM was too demanding.
Rapid scale-up of ART has improved the quality of life of People Living with HIV/AIDS, but challenges regarding retention in resource-limited settings remain. Quality of service, cost, poverty, long travel distances to clinics, low education levels, and stigma are commonly cited barriers to retention.3,8–10,23 Medication toxicity, alcohol abuse, and comorbidities are also frequently cited barriers.24–29 Several strategies have been proposed to optimize retention some of which include the following: a free service,23 peer-led support including outreaches and home visits,30,31 active patient tracing systems,32 and decentralized ART delivery with functional referral systems.33
We found reasons for LTFU from ROM, which confirms the aforementioned barriers to retention, but we also found reasons that challenge some of the cited barriers.
Like in other studies,34 we found substantial misclassification of patients as LTFU highlighting the challenges that many programs with increasing caseloads face in patient follow-up.35
Our key finding is the need to return to a normal life after an improved quality of life as revealed by the qualitative interviews. “Normalization” does not imply that patients will return to their earlier life. People living with HIV know that their new “normal” life has changed because it is punctuated with taking medicine and regular professional interventions, but they strive to manage and live with their condition, adjust to the disruption of “normal” routines, and so “normalize” their everyday lives, work and leisure activities, and relationships.
We found a high risk of stopping care in patients who have been in the program for 1–4 years. This may be explained by the stabilization of patients, who had migrated to receive care and had acute illnesses at enrollment and returned back home when their quality of life improved. Providing services within a defined catchment area was found to be prohibitive to access and continuity of care by the poor patients who are mobile or threatened by the increasing costs of living in the area. An alternative model of following up patients even when they are not in the targeted area needs to be sought, for example, through the mobile phone.
High poverty and low education makes it very hard for ROMs patients to understand and comply with certain aspects of care and treatment which include the influence of religion and the need for HIV-status disclosure. Religious leaders may not understand the biomedical aspects of ART. There is need to target them with discussions on the benefits of ART. A substantial number of patients who were already eligible for ART at their last clinic visit had not initiated ART because of nondisclosure, highlighting the need for strategies to enforce disclosure including couple and home based counseling and testing or removal of disclosure as a requirement for ART initiation for some patients. In addition, there is need to review the procedures for initiation of ART including the timing and number of pre-ART counseling sessions. Ironically, though only 6% of the interviewed patients mentioned stigma as the reason for discontinued care. This may be attributed the peer model of care which has about 95% of its community workers drawn from its clients. The model encourages patient testimonies; formation of community support groups and every patient is assigned a community supporter who visits them at home to enforce adherence and retention.
The higher rate of discontinuation of care by pre-ART patients may be due to the perceived good health and broadly focused adherence counseling for these patients. Broadly focused interventions are often less effective than individually tailored interventions which strategically target specific individual barriers.36
Forty-three percent of the patients self-transferred and is comparable to that reported by Geng et al37 in a cohort in rural Uganda confirming that as the number of facilities offering ART expands, more patients reported as LTFU may have transferred informally to another facility. Worrying though is the big number of patients who self-transfer to private clinics where there may be unregulated ART prescription practices. Majority of the patients in our study self-transferred because they had returned to work or back home after their quality of life improved. It is, therefore, important that the pace of ART rollout to rural facilities be increased to match the needs of those returning home.
Engagement of peer community health workers has been identified as important in continuation in follow-up.20,35 However, our findings reveal that close follow-up through home visits are a barrier to continuity of care with patients citing the need for confidentiality and their busy work schedules. Additionally, patients cited poor staff attitudes and long waiting times as a barrier. We attribute this to the increasing case load, which can be solved by efficiency improvement interventions.21 Stable patients could be allowed to participate in scheduling their appointments with provision for longer appointment schedules and pharmacy only visits. However, there is need to identify patients at risk of stopping treatment who should be a priority for home visits and tracing.
About 30% of Uganda's population lives below US $1 a day,38 yet the costs of long-term care associated with HIV, combined with the loss of productivity and income resulting from chronic ill health perpetuate poverty. Many patients have therefore discontinued care because of the competing needs for food, shelter, and affordable transport. ROM has identified strategies aimed at protecting poor households from the impoverishment associated with ill health and the benefits of provision of financial and nonfinancial support in reducing mortality and LTFU in this cohort have been documented.17 However, the positive findings obscure the realities for those who discontinue care despite having received these benefits. With improved quality of life and the return to work, factors that affect satisfaction with health care including waiting times and provider attitudes may override financial and nonfinancial benefits and patients will shop around for the best options.
Mortality amongst our LTFU group was low compared with what other studies have found,5,6,11,12 which is attributed to the presence of an organized community system of care which facilitates early diagnosis, recognition, and management of side-effects and prompt referrals. Additionally, the same day tracing of patients who are LTFU20 ensures that those who have died are documented within few days of a missed clinic appointment.
The strength of our study is that only 5.5% of the patients were untraceable. However, patients who could not be traced may have died leading to underestimation of mortality. Furthermore, we used program data, which were collected for patient follow-up and not for research purposes.
Reach Out addresses many barriers related to poor retention, but major challenges remain ahead. Many patients on ART have an improved quality of life. HIV care should therefore be normalized and managed as a chronic disease with the patients taking a central role in the management of their health while identifying those in need of close follow-up. Efforts should be made to improve referrals and mechanisms to track patients referred between sites. Additionally, tracing of patients who stop treatment is required to convince them to return to care.
1. Sterne JA, Hernan MA, Ledergerber B, et al.. Long-term effectiveness of potent antiretroviral therapy in preventing AIDS and death: a prospective cohort study. Lancet. 2005;366:378–384.
2. Egger M, May M, Chene G, et al.. Prognosis of HIV-1-infected patients starting highly active antiretroviral therapy: a collaborative analysis of prospective studies. Lancet. 2002;360:119–129.
3. Coetzee D, Hildebrand K, Boulle A, et al.. Outcomes after two years of providing antiretroviral treatment in Khayelitsha, South Africa. AIDS. 2004;18:887–895.
4. Braitstein P, Brinkhof MW, Dabis F, et al.. Mortality of HIV-1 infected patients in the first year of antiretroviral therapy: comparison between low-income and high-income countries. Lancet. 2006;367:817–824.
5. Rosen S, Fox MP, Gill CJ. Patient retention in antiretroviral therapy programs in sub-Saharan Africa: a systematic review. PLoS Med. 2007;4:e298.
6. Brinkoff MW, Dabis F, Myer L, et al.. Early loss to program in HIV-Infected patients starting potent antiretroviral therapy in lower income countries. Bull world health organ. 2008;85:559–567.
7. Fox MP, Brennan A, Maskew M, et al.. Using Vital Registration Data to Update Mortality among Patients Lost to Follow-up from ART Programs: Evidence from the Themba Lethu Clinic, South Africa. Trop Med Int Health. 2010;15:405–413.
8. Wallman S, Baker M. Which resources pay for treatment? A model for estimating the informal economy of health. Soc Sci Med. 1996;42:671–679.
9. Goudge J, Govender V. A Review of Experience Concerning Household Ability to Cope With the Resource Demands of Ill-Health and Health Care Utilization. Regional Network for Equity in Health in Southern Africa (Equinet) Policy series No 3. Harare, Zimbabwe: Training and Resource Centre; 2000.
10. Weiser S, Wolfe W, Bangsberg D, et al.. Barriers to antiretroviral adherence for patients living with HIV infection and AIDS in Botswana. J Acquir Immune Defic Syndr. 2003;34:281–288.
11. Anglaret X, Toure S, Gourvellec G, et al.. Impact of vital status investigation procedures on estimates of survival in cohorts of HIVinfected patients from Sub-Saharan Africa. J Acquir Immune Defic Syndr. 2004;35:320–323.
12. Brinkhof MW, Pujades-Rodriguez M, Egger M. Mortality of patients lost to follow-up in antiretroviral treatment programmes in resource-limited settings: systematic review and meta-analysis. PLoS ONE. 2009;4:e5790.
13. Geng EH, Bangsberg DR, Musinguzi N, et al.. Understanding Reasons for and Outcomes of Patients Lost to Follow-Up in Antiretroviral Therapy Programs in Africa Through a Sampling-Based Approach. J Acquir Immune Defic Syndr. 2010;53:405–411.
14. Bisson GP, Groos R, Bellamy S, et al.. Overestimates of survival after HAART: implications for global scale-up efforts. PLoS ONE. 2008;3:e1725.
15. Stringer JS, Zulu I, Levy J, et al.. Rapid scale-up of antiretroviral therapy at primary care sites in Zambia: feasibility and early outcomes. JAMA. 2006;296:782–793.
16. Zachariah R, Teck R, Buhendwa L, et al.. Community support is associated with better antiretroviral treatment outcomes in a resource-limited rural district in Malawi. Trans R Soc Trop Med Hyg. 2007;101:79–84.
17. Alamo ST, Colebunders R, Ouma J, et al.. Socio-economic support reduces non-retention in care in a comprehensive community based program in Uganda. J Acquir Immune Defic Syndr. 2012;59:e52–e59.
18. Larry WC, Alamo S, Guma S, et al.. Two year virologic outcomes of an alternative AIDS care model: evaluation of a peer health worker and nurse-staffed community-based programme in Uganda. J Acquir Immune defic Syndr. 2009;50:276–282.
19. Amanyire G, Wanyenze R, Stella A, et al.. Client and provider perspectives of the efficiency and quality of care in the context of rapid scale-up of antiretroviral therapy. AIDS Patient Care STDS. 2010;24:441–446.
20. Alamo S, Wabwire-Mangen F, Kenneth E, et al.. Task-shifting to community health workers: evaluation of the performance of a peer-led model in an antiretroviral program in Uganda. AIDS Patient Care and STDs. 2012;26:101–107.
21. Alamo ST, Wagner GJ, Sunday P, et al.. Electronic medical records and patient tracing improve adherence to clinic appointments and clinic efficiency in a resource limited setting. AIDS Behav. 2012;16:368–374.
22. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:77–101.
23. Merten S, Kenter E, McKenzie O, et al.. Patient-reported barriers and drivers of adherence to antiretrovirals in sub-Saharan Africa: a meta-ethnography. Trop Med Int Health. 2010;15(suppl 1):16–33.
24. Rougemont M, Stoll BE, Elia N, et al.. Antiretroviral treatment adherence and its determinants in Sub-Saharan Africa: a prospective study at Yaounde Central Hospital, Cameroon. AIDS Res Ther. 2009;6:21.
25. Peltzer K, Friend-du Preez N, Ramlagan S, et al.. Antiretroviral treatment adherence among HIV patients in KwaZulu-Natal, South Africa. BMC Public Health. 2010;10:111.
26. Olowookere SA, Fatiregun AA, Akinyemi JO, et al.. Prevalence and determinants of nonadherence to highly active antiretroviral therapy among people living with HIV/AIDS in Ibadan, Nigeria. J Infect Dev Ctries. 2008;2:369–372.
27. Unge C, Sodergard B, Marrone G, et al.. Long-term adherence to antiretroviral treatment and program drop-out in a high-risk urban setting in sub-Saharan Africa: a prospective cohort study. PLoS One. 2010;5:e13613.
28. Jaquet A, Ekouevi DK, Bashi J, et al.. Alcohol use and non-adherence to antiretroviral therapy in HIV-infected patients in West Africa. Addiction. 2010;105:1416–1421.
29. 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. 2006;3:e438.
30. WHO. Antiretroviral Therapy for HIV Infection in Adults and Adolescents: Recommendations for a Public Health Approach (2010 version). Geneva, Switzerland: WHO; 2010.
31. Mugavero MJ. Missed Visits Predict Mortality in Patients Establishing Initial HIV Care. Newark, NJ:2008.
32. Zwarenstein M, Treweek S, Gagnier JJ, et al.. Improving the reporting of pragmatic trials: an extension of the CONSORT statement. BMJ. 2008;337:A2390.
33. Harries AD, Zachariah R, Lawn SD, et al.. Strategies to improve patient retention on antiretroviral therapy in sub-Saharan Africa. Trop Med Int Health. 2010;15(suppl 1):70–75.
34. Rosen S, Fox MP, Gill CJ. Patient retention in antiretroviral therapy programs in sub-Saharan Africa: a systematic review. PLoS Med. 2007;4:e298.
35. Geng EH, Emenyonu N, Bwana MB, et al.. Sampling-based approach to determining outcomes of patients lost to follow-up in antiretroviral therapy scale-up programs in Africa. JAMA. 2008;300:506–507.
36. Simoni JM, Amico KR, Smith L, et al.. Antiretroviral adherence interventions: translating research findings to the real world clinic. Curr HIV/AIDS Rep. 2010;7:44–51.
37. Geng EH, Bangsberg DR, Musinguzi N, et al.. Understanding reasons for and outcomes of patients lost to follow-up in antiretroviral therapy programs in Africa through a sampling-based approach. J Acquir Immune Defic Syndr. 2010;53:405–411.
© 2012 Lippincott Williams & Wilkins, Inc.