Since the large-scale rollout of public-sector antiretroviral therapy (ART) programs in resource-limited settings, over 5 million patients have been initiated onto ART, representing 36% of those in need in 2009 . Over the past decade, numerous treatment programs have demonstrated successful treatment outcomes in programs at scale [2–4], and subsequent survival impacts [5,6]. Still, substantial losses from care in the pre-ART period [7–11], low CD4 cell counts at ART initiation [12–15], increased mortality during early ART [16–18] and sustained attrition from HIV programs [12,14] continue to prove difficult challenges for those implementing treatment programs in resource-constrained settings.
As HIV treatment programs mature, ever more patients are initiating ART. In some settings, the accumulated experience managing and treating HIV has translated into better patient outcomes , whereas in others increasing demands on providers may prevent these gains from being realized. At the same time, to maintain the benefits associated with treatment, attention must be paid to keeping stable patients in care and adherent long-term. Given these competing needs are occurring in a setting of finite resources, decisions must be made as to where to focus efforts and new strategies must be developed to manage patients more efficiently.
To fully describe the short-term and long-term experience managing and treating HIV at one of the largest HIV treatment programs in the world, we report HIV treatment outcomes over 7 years in a public-sector ART treatment program at the Themba Lethu Clinic (TLC) in Johannesburg, South Africa.
TLC is located at Helen Joseph Hospital in Johannesburg, South Africa. Care at TLC follows the national guidelines [19,20]. During the period of this analysis, patients initiated ART with a CD4 cell count less than 200 cells/μl or a WHO stage IV condition.
Under the 2004 guidelines, standard first-line ART regimens included stavudine or zidovudine with lamivudine and either efavirenz or nevirapine. Switching to second-line ART was recommended after documented first-line treatment failure (two consecutive viral loads >1000 copies/ml). Standard second-line therapy included zidovudine, didanosine and lopinavir–ritonavir. As other regimens are sometimes used, we defined second-line as a protease inhibitor-based regimen with at least one change in nucleoside reverse transcriptase inhibitor.
We included all ART-naive adults (≥18 years) initiating first-line three-drug ART at TLC between 1 April 2004 and 31 March 2010. Patient data is collected using an electronic patient management system  to record data on patient demographics, clinical examinations, conditions, treatment regimens and laboratory results. Although initially data was collected on paper records and entered into the electronic system, since 2007 data is entered by clinicians during patient encounters, except for visit booking and attendance, which is entered by clinic staff upon arrival at the clinic.
Prior to ART initiation, patients are monitored every 3–6 months. On ART patients are seen monthly for the first 6 months on therapy, then every 2 months if stable. Pharmacy visits occur at every visit, whereas medical visits occur every 6 months or as clinically indicated. CD4 cell count and viral load monitoring was scheduled at 6 months, and six-monthly thereafter.
We define loss to follow-up (LTF) as at least 3 months late for a scheduled visit with no later visit. Deaths are identified through attempts by clinic counselors to trace lost patients. For patients who provide a valid South African National ID number (55%), we also ascertain deaths from the National Vital Registration system (last linked in September 2011) . Patients who die are included in the analysis until the date of death even if this occurs after leaving care and are coded as deaths, not losses.
The dataset was closed on 2 August 2011. Person-time began at ART initiation and ended at death or the earliest of LTF; 7 years of follow-up; or dataset closure. Comparisons by yearly cohorts (1 April of each year till 31 March of the subsequent year) were conducted on 1-year outcomes to allow cohorts to be comparable. We analyzed predictors of death and LTF with log-binomial regression using modified Poisson regression and robust error estimation . We then describe 7-year outcomes for those initiated in the first cohort.
Approval for analysis was granted by the Human Research Ethics Committee of the University of the Witwatersrand and by the Institutional Review Board of Boston University.
Thirteen thousand, two hundred and twenty-seven adult patients initiated treatment at TLC. In 2004, HIV treatment services rapidly scaled-up, with 1794 patients initiating ART between April 2004 and March 2005 (Table 1). Yearly cohorts after that ranged from 1996 to 2617 patients. The median CD4 cell count at ART initiation for all cohorts was less than 120 cells/μl, but a small, nearly consistent yearly increase was observed. From 2004/2005 to 2009/2010, the median CD4 cell count increased by 39% (from 82 to 114 cells/μl, P<0.0001). About 75–80% of all ART patients achieved viral suppression in a median [interquartile range (IQR)] of 3.9 (3.7–5.0) months and 90% when limited to those with at least 6 months of follow-up (Table 1).
Much of the negative outcomes on ART occurred in the first year (Fig. 1). Death was highest in the first 12 months at 24.3 per 100 person-years [95% confidence interval (CI) 21.6–27.4] in month 1 and 5.4 per 100 person-years (95% CI 4.1–7.3) in month 12, but remained stable at under 5 per 100 person-years for each month thereafter. LTF (which cannot occur before month 4) was also elevated in the first year (month 4: 53.2 per 100 person-years; 95% CI 49.0–57.9; month 12: 6.6 per 100 person-years; 95% CI 5.1–8.6) then stabilized at under 10 per 100 person-years through 72 months.
One-year treatment outcomes
After a year on treatment, 76.0% (n = 10 051) of patients were alive and in care; 9.1% (n = 1201 of 13 227) of patients died within 1 year in a median (IQR) of 2.9 (1.1–6.1) person-months. One-year mortality was below 11% (7.5–10.6%) in all years. We identified several predictors of mortality at ART initiation over the first year on ART (supplementary material, https://links.lww.com/QAD/A250), including CD4 cell count less than 50 [risk ratio (RR) 2.66, 95% CI 2.06–3.44] and 50–99 cells/μl (RR 1.68, 95% CI 1.28–2.21) compared with more than 200 cells/μl, anemia (RR severe vs. none: 2.79, 95% CI 2.24–3.49), low BMI (RR low vs. normal: 1.70, 95% CI 1.49–1.94) and older age (RR ≥45 vs. 18–29: 1.61, 95% CI 1.35–1.92).
LTF over 1 year on ART occurred in 11.2% (n = 1477 of 13 227) of patients in a median (IQR) of 5.0 (4.0–7.6) person-months. Although not perfectly monotonic, 1-year losses increased from 8.5% in 2004/2005 to 12.1% in 2009/2010 (RR 1.42, 95% CI 1.18–1.71). Anemia was a strong predictor of LTF (RR severe vs. none: 1.73, 95% CI 1.45–2.06) (supplementary material, https://links.lww.com/QAD/A250). Male sex (RR 1.28, 95% CI 1.15–1.43), young age (RR 18–29 vs. >45: 1.40, 95% CI 1.20–1.64), low BMI (RR low vs. normal: 1.33, 95% CI 1.16–1.52) and initiating on a non-d4T-based regimen (RR other vs. d4T-3TC-EFV: 1.35, 95% CI 1.18–1.55) were also significant predictors of loss.
One thousand, seven hundred and ninety-four patients initiated treatment during the first year of the treatment rollout in South Africa (2004/2005) of whom 48.4% are in care, 9.9% transferred to another facility, 25.4% were lost and 16.3% have died. These patients have been followed for 8172 person-years and a median (IQR) of 6.4 (1.7–6.8) years on treatment per-person. This gave us a rate of 3.59 deaths (95% CI 3.20–4.02) and 5.57 losses per 100 person-years (95% CI 5.08–6.10). When including patients from all cohorts in a survival analysis we found overall 37.8% (36.2–39.4%) of patients were alive and in care at 7 years.
Nearly half (46%, n = 825) experienced at least one single drug substitution exclusive of switching to second-line. Of the 1577 who completed at least 6 months of follow-up, 213 (13.5%) failed first-line in a median (IQR) of 25.9 (15.8–41.4) months. Of those who failed, 141 (66.2%) switched to second-line for a rate of 48.5 per 100 person-years (95% CI 41.1–57.2). Switching occurred in a median (IQR) of 5.0 (1.8–16.8) months after failure. The median gain in CD4 cell count from baseline to last CD4 cell count conducted was 339 cells/μl.
In South Africa, as with many resource-limited countries, scale-up of the national ART program began in 2004 . Over the past 7 years, the program has grown to include roughly 1.5 million patients on ART . As South Africa's national ART programs continue to mature, the challenges of maintaining patients who have been on ART for a substantial period are being balanced against the demands of increasing patient volumes, higher ART initiation thresholds and changes to preferred ART regimens [20,23].
In our cohort of over 13 000 patients, we demonstrated positive 1-year outcomes (mortality, LTF, suppression, etc.) over the six cohorts, despite increasing clinic size from under 2000 patients in 2004/2005 to more than 9000 in 2009/2010. We found most patients (90% of those with ≥6 months of follow-up) suppressed the virus, and 76.0% (n = 10 051) were still alive and in care after a year on treatment. One-year mortality was below 11% in all cohorts. After the first 12 months on treatment, monthly rates of death and loss decreased strongly and remained fairly constant, further underscoring the need for interventions targeted toward getting patients through the critical first year.
Among patients on treatment since the first year since the clinic's inception, roughly half are still in care and a further 10% have been transferred to another facility. Although this is less than ideal, given the emergency mindset of getting as many patients on treatment as possible at that time, this is still a remarkable success. Our long-term findings are in line with another South African cohort . Like that program, we saw increasing LTF over time and increasing CD4 cell counts at ART initiation. However, unlike that program we did not observe decreasing mortality over time. It is, however, possible they were able to identify more deaths than we were among those lost using linkage to the registry.
Our study, which includes roughly twice as many patients as the Khayelitsha analysis, confirms that the benefits of antiretroviral treatment can continue to be realized even as programs expand. As TLC has grown in patient size, the size of the clinical staff has remained stable over the past years with roughly six to eight doctors and two to three nurses, whereas funding for the clinic has not increased at the same pace. Instead new strategies have been devised to cope with the increasing patient load and maintain outcomes. These include rearrangement of the patient flow of the clinic, implementing short message service reminder services, use of an electronic patient record system to track patients through the clinics and down-referral of stable patients to clinics supported by nurses [24,25]. Future efforts to reduce costs will need to be explored to achieve universal access.
Our study limitations include missing data and limited information on treatment adherence and other potentially important covariates. Also, there is probably some misclassification of death leading to underestimated mortality rates and overestimated LTF rates. In addition, we can only track patients after ART initiation and cannot comment on pre-ART outcomes. Finally, this study represents only one clinic and results may not be generalizable to all settings.
Although the future of HIV treatment funding remains unclear, the benefits of massive investment in treatment are clearly evident. This 7-year follow-up evaluation demonstrates that resources being invested in large-scale HIV treatment are achieving substantial gains.
M.P.F. designed the study and drafted the manuscript. K.S. designed and conducted the analysis. I.S., M.M., W.M., P.M. and P.M. had significant input into the design of the study, suggested additional analyses and edited the manuscript.
The content is solely the responsibility of the authors and does not necessarily represent the official views of USAID, the National Institute of Allergy and Infectious Diseases or the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The opinions expressed herein are those of the authors and do not necessarily reflect the views of the funders or the study site.
Funding for this study was provided by the South Africa Mission of the US Agency for International Development (USAID) under the terms of Cooperative Agreement 674-A-00–09–00018–00 to Boston University and Cooperative Agreement 674-A-00–02–00018 to Right to Care. M.P.F. was also supported by Award Number K01AI083097 from the National Institute of Allergy and Infectious Diseases.
Conflicts of interest
There are no conflicts of interest.
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