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Epidemiology and Social

The continuum of HIV care in South Africa

implications for achieving the second and third UNAIDS 90-90-90 targets

Takuva, Simbarashe; Brown, Alison E.; Pillay, Yogan; Delpech, Valerie; Puren, Adrian J.

Author Information
doi: 10.1097/QAD.0000000000001340



South Africa has the largest HIV epidemic globally, with over 6 million people estimated to be living with the virus by the end of 2012 [1]. The scale of the response to the South African HIV epidemic has been substantial; in 2011, the National Department of Health launched the HIV Counselling and Testing (HCT) campaign. In the following 20 months, approximately 20 million tests were conducted, and the antiretroviral therapy (ART) roll-out has resulted in massive increase in reports of treatment, from around 50 000 in 2004 to over 2.4 million in 2014 [2]. This increase in ART and consequent improved survival of persons living with HIV (PLHIV)/AIDS has contributed to increasingly high prevalence rates [3,4]. However, the estimated number of newly acquired HIV infections remains very high at an estimated 400 000 in 2012 [3].

Successful treatment with ART, so that an individual's viral load is suppressed to undetectable levels, is key to reducing new HIV infections. In the landmark HPTN052 study, early ART led to a 96% reduction of sexual transmission of HIV in serodiscordant couples [5]. In addition, a large population-based prospective cohort study in rural KwaZulu-Natal, South Africa, demonstrated that individual HIV acquisition risk declined significantly as ART coverage rose in the surrounding local community [6]. However, the roll-out of ART can only be an effective public health intervention if it is integrated within a programme that ensures that those living with HIV are diagnosed and rapidly linked and retained in HIV care.

The HIV treatment cascade is a convenient tool for assessing the extent that PLHIV are integrated at different stages of HIV care pathway, that is it measures the extent that PLHIV are diagnosed, in care, on treatment and virally suppressed, and therefore noninfectious [7]. In the context of the South African epidemic, understanding engagement in the different stages of HIV care is crucial as it not only provides a summary measure of the success of the ART programme in terms of population level viral suppression, but also highlights the specific stages of the continuum of care at which people are lost to care, risking death, morbidity and onward transmission. In addition, measure is in line with the UNAIDS 90-90-90 targets. These targets aim to eliminate HIV-related mortality and reduce transmission through achieving standards so that 90% of all PLHIV know their status, 90% of those with diagnosed HIV infection to receive ART and 90% of all people receiving ART to be virally suppressed [7]. South Africa has adopted the 90-90-90 global target and aims for the ART target to be reached by 2020.

In this study, we construct and characterize selected stages of the HIV care continuum for 2012 to monitor the effectiveness of the HIV programme and identify areas where intensive effort is needed if the 90-90-90 targets are to be met. We use published estimates of the number of PLHIV/AIDS in combination with routine data collected through the National Health Laboratory Service to produce a HIV care continuum of South Africa, highlighting differences by demographic characteristics. We combine national prevalence estimates together with data from the national laboratory system to quantify, for the first time, the number of persons accessing in HIV care in 2012, and of those, the number receiving ART and number virally suppressed.


Sources of data

We combined information from three data sources. First, the national household survey conducted in 2012 that measures point estimates of prevalence in the South African population [1]. Second, mid-year population estimates for South Africa published annually by vital registration (Statistics South Africa) [8]. Third, laboratory data from the National Health Laboratory Service Corporate Data Warehouse (CDW). The National Health Laboratory Service is the largest diagnostic pathology laboratory service in South Africa, with 349 laboratories across the nine provinces supporting the 4000 clinics and 400 hospitals in the South African public healthcare system [9]. The CDW is a repository for all public sector laboratory measurements in South Africa (over 80% of all facilities, >43 million people), which includes all HIV-related laboratory tests conducted across public health laboratories since 2004. All HIV-related laboratory tests, that is CD4+ cell counts, viral load measurements conducted in 2012 are captured in the system, and these were used to estimate access to and retention in care and viral suppression.

Deduplication of HIV patients accessing care multiple times

Over 3.9 million CD4+ cell count and viral load measurements for persons enrolled in the South African Public Sector HIV Treatment programme in 2012 were extracted from the CDW. A laboratory request form with demographic data accompanies each specimen and is recorded onto the laboratory information system. Once testing is completed, all available results are recorded alongside the demographic data. As laboratory tests can and do relate to the same individual having multiple tests over time and between sites, data linkage was performed using a probabilistic algorithm to determine the records that relate to the same individual. In probabilistic record linkage, personal identifiers are used together to determine how likely every single pair of records relates to the same individual [10–12]. First, records are directly linked in which surnames, first names and dates of birth were an exact match. After this, probabilistic matching, which uses an algorithm to assess the probability that a set of records are linked to the same individual, is conducted. The resulting probabilities are weighted and averaged, according to the frequency within the population, to provide a final score on which matching is based and sequentially numbered [13]. A database was constructed to enable a single-record-per-person within year and a chronological laboratory history for each person across years. An overview of this patient linking algorithm is supplied as Supplementary material,

Estimating the continuum of care

Estimates of HIV prevalence from the household survey were combined with mid-year population estimates to determine national HIV prevalence. These estimates were aligned to CDW laboratory data; laboratory markers were used as proxies for engagement in care and successful ART treatment. Data were analysed by sex and age group.

Statistical analysis

Number of PLHIV was calculated using age-specific and sex-specific estimates of the national household survey HIV prevalence data for 2012 and the corresponding mid-year population estimates, that is estimated number of HIV-infected = HIV prevalence (%) × mid-year population estimate [1,8].

Number of persons linked to HIV care was estimated using the presence of at least one CD4+ cell count test result in the CDW during 2012, following deduplication of multiple records relating to one patient. During 2012, clinical guidelines recommended that a CD4+ cell count be collected 6-month intervals among patients attending HIV services.

Number of persons on ART was estimated using the number of viral load measurements as a proxy. In accordance with South African HIV treatment guidelines, patients receiving ART have a viral load at 6 and 12 months after starting treatment and yearly thereafter [14]. Viral load measurements are not indicated for patients not yet receiving ART for the study period of interest. To account for treatment among patients seen for care in 2012, we included the viral load data for first 6 months from 1 January 2012 to 31 July 2013 (allowing another 1 month for persons late for care).

Number of persons with viral suppression was estimated using a viral load cut-off less than 400 copies/ml among all persons with information available. For persons with more than one viral load measurement during 2012, we used the last measurement in 2012.

We calculated number and proportion of PLHIV, in HIV care, on ART and with viral suppression. All estimates are rounded off to the nearest 1000. We further stratified analysis by sex and age group. Modified Poisson regression with a robust error variance was used to assess prevalence ratios and 95% confidence intervals (CIs) for associations among sex, age group and viral suppression among persons on ART. All analyses were conducted with STATA version 12 (Stata Corp., College Station, Texas, USA).

Ethics statement

The study was conducted using surveillance data for public health purposes; anonymized data were taken from laboratory datasets, which means that informed consent was not required. The study complies with the South African Medical Research Council's Guidelines on Ethics for Medical Research and the Declaration of Helsinki. Permission to analyse the data was granted by the National Health Laboratory Service.


The electronic laboratory CD4+ and viral load datasets had a total of 3.9 million CD4+ cell count measurements each, and 2.7 million viral load measurements were conducted during the specified period. After deduplication, the final analytic datasets consisted of 3.3 million CD4+ cell count measurements and 2.1 million viral load measurements for each individual. Overall, around 135 700 unique records had only a viral load measurement during this period but no CD4+ cell count measurement. Values for sex were unspecified for 54 000 records (1.6%) in the CD4+ dataset and for 40 000 records (1.9%) in the viral load dataset. Age was missing or of spurious value, that is age more than 100 years, for 178 000 records (5.4%) in the CD4+ dataset and 129 000 records (6.0%) in the viral load dataset. No viral load tests were available for about 36 000 persons with indication of a measurement being done in the database.

Table 1 shows the age-specific and sex-specific population size and HIV prevalence estimates. Among 6511 000 persons estimated to be living with HIV/AIDS in South Africa in 2012, an estimated 3300 000 individuals (50.7%) accessed care and an estimated 2140 000 (32.9%) received ART. Although the viral suppression rate was 73.7% (1550 000 out of 2103 000) among the population on ART in 2012, the overall percentage of persons with viral suppression among the estimated HIV-infected population in South Africa was 23.8% (1550 000 out of 6511 000). This corresponds to an approximate estimate of 5 million persons whose infection is uncontrolled and who are potentially infectious (Fig. 1).

Table 1:
Estimated number of persons living with HIV by sex and age group in 2012 in South Africa.
Fig. 1:
Estimated HIV continuum of care in 2012 in South Africa.HIV-infected, n = 6511 000; linked to HIV care, n = 3300 000; on ART, n = 2140 000 and viral suppression (viral load <400 copies/ml), n = 1550 000. All numbers are rounded off to the nearest 1000.

Women comprised 60.4% of PLHIV. Just under 60% of all women living with HIV/AIDS accessed care in 2012 (57.2%, n = 2248 000) compared with 38.5% of all men living with HIV/AIDS (n = 994 000). Among persons in care, the proportion of women on ART was 63.1 vs. 68.4% for men. Viral suppression rates among women on ART were 74.0 and 69.1% among men on ART. Figure 2 shows the HIV continuum of care stratified by sex in South Africa in 2012. Viral suppression rate among all PLHIV was 26.7% in women compared with 18.2% in men.

Fig. 2:
Estimated HIV continuum of care in 2012 in South Africa stratified by sex.Age group 0–14 years: HIV-infected, n = 363 000; linked to HIV care, n = 171 000; on ART, n = 156 000 and viral suppression (viral load <400 copies/ml), n = 92 800. Age group 15–49 years: HIV-infected, n = 5520 000; linked to HIV care, n = 2595 000; on ART, n = 1566 000 and viral suppression (viral load <400 copies/ml), n = 1139 000. Age group above 50 years: HIV-infected, n = 628 000; linked to HIV care, n = 353 000; on ART, n = 289 000 and viral suppression (viral load <400 copies/ml), n = 227 000. Missing/unspecified values: CD4+ dataset – sex, n = 54 000 (1.6%) and age, n = 178 000 (5.4%). Viral load dataset – sex, n = 40 000 (1.9%) and age, n = 129 000 (6.0%). All numbers are rounded off to the nearest 1000.

Access to care was largely similar between the 0–14 and 15–49 years age groups (47.1 vs. 47.0%, respectively) but higher in above 50years age group (56.2%). Among the persons accessing care in 2012, the proportion of those aged 0–14 years who received ART was high (91.2%); equivalent figures were 81.9% among persons aged above 50 years and 60.3% among persons aged 15–49 years. However, rates of viral suppression among persons on ART indicate that only 60.3% of those aged 0–14 years were suppressed compared with 74.2 and 79.6% of those aged 15–49 and above 50 years, respectively. Among all, PLHIV viral suppression rates for the 0–14, 15–49 and above 50 years age groups were 25.6, 20.6 and 36.1%, respectively. Figure 3 illustrates the HIV care continuum disaggregated by age groups 0–14, 15–49 and above 50 years.

Fig. 3:
Estimated HIV continuum of care in 2012 in South Africa stratified by age group.Age group 0–14 years: HIV-infected, n = 363 000; linked to HIV care, n = 171 000; on ART, n = 156 000 and viral suppression (viral load <400 copies/ml), n = 92 800. Age group 15–49 years: HIV-infected, n = 5520 000; linked to HIV care, n = 2595 000; on ART, n = 1566 000 and viral suppression (viral load <400 copies/ml), n = 1139 000. Age group above 50 years: HIV-infected, n = 628 000; linked to HIV care, n = 353 000; on ART, n = 289 000 and viral suppression (viral load <400 copies/ml), n = 227 000. Missing/unspecified values: CD4+ dataset – sex, n = 54 000 (1.6%) and age, n = 178 000 (5.4%). Viral load dataset – sex, n = 40 000 (1.9%) and age, n = 129 000 (6.0%). All numbers are rounded off to the nearest 1000.

Among individuals on ART, the overall viral suppression rate was 73.7%. In unadjusted analysis, male sex and younger age was associated with poorer chances of viral suppression. After controlling for these factors in multivariate regression models, the same association was maintained: men, adjusted prevalence ratio = 0.940, 95% CI 0.938–0.942, P less than 0.001 compared with women; adjusted prevalence ratio = 0.928, 95% CI 0.926–0.930, P less than 0.001; adjusted prevalence ratio = 0.759, 95% CI 0.756–0.762, P less than 0.001 for age groups 15–49 and less than 15 years vs. above 50 years, respectively.


For the first time, we have provided national estimates of the extent that PLHIV in South Africa are accessing care, receiving treatment and are virally suppressed. Despite the considerable expansion of the South African public sector HIV treatment programme over the decade, we estimate that half of PLHIV/AIDS in South Africa during 2012 accessed HIV care, and overall just below 25% achieved viral suppression. Current data indicate implications for not only mortality and morbidity, but also ongoing HIV transmission; approximately three in four PLHIV in South Africa are potentially infectious. Further intensification of efforts to improve HIV testing, access to care and treatment is needed if the 90-90-90 targets, including the central target, are to be reached.

The South African National Strategic Plan for HIV, STIs and TB 2012–2016 aims to reduce new HIV infections by at least 50% using combination prevention approaches. As part of this approach, the South African Government has embarked on a deliberate effort to scale up and strengthen the quality of HCT at all health facilities and nonhealth facilities. HCT has become increasingly available in South African public health facilities in recent years. The proportion of people who have ever had an HIV test and are aware of their status rose from 50% in 2008 to 66.5% in 2012 [15]. Although HCT coverage has increased, concerted efforts are needed to continue to expand HCT, coupled with linkage to care, for all population groups with additional focus on men and young people. Knowledge of HIV status is critical to these prevention and treatment goals, and HCT is the key entry point to a comprehensive continuum of care.

About 50% of all PLHIV accessed care in 2012. Once on treatment, the ART programme is effective, with three-quarters achieving viral suppression; however, our work indicates that younger people and men may need further intervention to improve levels of viral suppression. Identification of HIV infection and successful linkage to care is the largest impediment to engagement in the continuum of care as compared with ART initiation and viral suppression. Generally, in the context of health care, women have more opportunities than men for contact with health facilities mainly through reproductive and child health services [16]. Introduction of HIV care (from testing to treatment) in antenatal care facilities has greatly increased the percentage of women tested in antenatal care settings as part of the prevention of mother-to-child transmission of HIV programme and also in promoting the health of the mother [17–20]. Facility-based HCT alone will not be adequate to achieve high HIV testing rates. Recent data show that community-based models of HCT (mobile including workplace programmes, testing campaigns and home-based) are capable of reaching a wider range of groups including hard-to-reach sections like men and young persons. In addition, barriers seen with facility-based HCT models such as time, cost and distance are potentially avoided [21–26]. In a study conducted as part of the larger NIMH HPTN 043 randomized trial investigating mobile HIV testing in South Africa, van Rooyen et al.[22] demonstrated that mobile voluntary counseling and testing located in convenient and accessible community locations attracted more men than women to HIV testing and both the urban and rural sites had significantly more men testing for the first time as compared with women. Emerging evidence also indicates that programmes integrated into the workplace and programmes that offer peer education may be successful at engaging African men in HIV testing, care and treatment [27,28]. In addition, other strategies are shown to be effective in enhancing linkage to care include, providing on-site or immediate CD4+ testing with same-day results, involvement of trained lay providers who are peers and act as community outreach workers to facilitate linking to care after testing, promoting partner testing as may approaches in PMTCT settings that encourage male involvement [22,29–35]. Other studies have shown that, when appropriately regulated, self–HIV testing may increase the uptake of HIV testing among people not reached by other HIV testing services, many of whom are first-time testers [24–26]. Self-HIV testing gives people the opportunity to test discreetly and conveniently. However, mechanisms to ensure engagement with care should be established.

A recent report has shown that about 70.2% of HIV-infected people in a larger population-based survey in Botswana had virological suppression, close to the UNAIDS target of 73%. This was the first study to demonstrate that UNAIDS 90-90-90 targets are achievable even in resource-constrained settings with high HIV burden [36]. However, our national estimates mirror other recent reports from sub-Saharan Africa. It is estimated that approximately 32% of all adults living with HIV/AIDS in sub-Saharan Africa are on ART and approximately 24% (16–28%) of them have not achieved viral suppression [37,38]. Little systematic data are available for the proportion of PLHIV who are linked to care. Similarly, in a longitudinal treatment cascade constructed using demographic health surveillance data from a large HIV prevalence area in KwaZulu Natal, South Africa, 52% of HIV-infected persons in the population have been linked to care, 42% retained in care and 38% initiated ART. Viral suppression rates were not reported but 26% had achieved CD4+ cell count of more than 500 over the course of 10 years since diagnosis [39]. It is critical to investigate better working approaches to optimize linkage and retention in care and therefore maximize patient ART outcomes.

Our work represents the first attempt to estimate the treatment cascade within South Africa, and as such some limitations remain. Our data exclude the nonpublic sector in South Africa. An estimated 15% of all ART provision in 2011 was through the private sector and non-governmental organization (NGO) programmes. Assuming that linkage to care, treatment and viral suppression is 100% in the private and NGO sector, our estimates could potentially underestimate by around 15% due to the exclusion of nonpublic sector data sources [40]. Our assumptions are also sensitive to guideline adherence by clinicians and by patients; hence, persons completely missing laboratory visits over the 1-year period are not counted. In addition, as the NHLS data are specimen-based, it do not contain ART-specific data, presenting challenges in determining whether patients were receiving ART or not. In the context of current HIV treatment and monitoring guidelines, we used the presence of a viral load measurement as a proxy for ART status after deduplication. Reassuringly, our estimates on persons on ART using laboratory data are similar to those reported from the National Department of Health ART programme data and modelling estimates (for 2012: adjusted estimates from the National Department of Health District Health Information System – 1933 799, THEMBISA mathematical model – 2322 000 and UNGASS Report – 2150 880) [2,41,42]. Although the TIER.Net system suggests that under half of viral loads are reported, NHLS data are highly likely to be more complete as these measurements are captured from the laboratories using automated methods, whereas the TIER.Net data are captured at the clinic level, a process which has demonstrated less than 40% compliance [43–45]. Finally, we cannot estimate how many people are aware of their HIV diagnosis; hence, the cascade we construct has a step missing. It is important to understand how many everdiagnosed make it into care. With our current approach, we cannot differentiate between the undiagnosed and the diagnosed, but never in care.

The use of routine laboratory data can be used as a cost-effective yet robust surveillance system to monitor the public health response to HIV at the national level. This approach to national surveillance can be adopted in other resource-limited setting as implementation is cheap because it builds on existing systems using standard matching algorithms.


S.T., A.J.P., A.E.B. and V.D. conceived the overall study. S.T. led data analysis with contributions from A.E.B. All authors participated in data interpretation, contributed to subsequent drafts of the manuscript, and read and approved the final manuscript. Data were provided by the Corporate Data Warehouse, National Health Laboratory Service (NHLS), South Africa. We express our gratitude to Sue Candy and team (NHLS Corporate Data Warehouse) for the patient linking algorithm and for technical support during the duration of this project. We also thank William Macleod (Health Economics and Epidemiology Research Office and Boston University) for valuable inputs with regard to improving the matching algorithms of the database.

Funding for this study was provided by NICD, PHE-NICD agreement.

The opinions expressed herein are those of the authors and do not necessarily reflect the views of NICD, NHLS, PHE or the NDoH.

Conflicts of interest

There are no conflicts of interest.


1. Shisana O, Rhele T, Simbayi LC, Zuma K, Jooste S, Zungu N, et al. South African National HIV Prevalence, Incidence and Behaviour Survey. 2012; [Accessed 20 October 2016].
2. South African National AIDS Council (SANAC). National Strategic Plan for HIV, STIs and TB, 2012-2016. 2011 [Accessed 20 October 2016].
3. South African National AIDS Coucil (SANAC). Progress report on the National Strategic Plan for HIV, TB and STIs (2012–2016). 2014.
4. Zaidi J, Grapsa E, Tanser F, Newell M-L, Bärnighausen T. Dramatic increase in HIV prevalence after scale-up of antiretroviral treatment. AIDS 2013; 27:2301–2305.
5. Cohen MS, Chen YQ, McCauley M, Gamble T, Hosseinipour MC, Kumarasamy N, et al. Prevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med 2011; 365:493–505.
6. Tanser F, Bärnighausen T, Grapsa E, Zaidi J, Newell M-L. High coverage of ART associated with decline in risk of HIV acquisition in rural KwaZulu-Natal, South Africa. Science 2013; 339:966–971.
7. Gardner EM, McLees MP, Steiner JF, Del Rio C, Burman WJ. The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection. Clin Infect Dis 2011; 52:793–800.
8. Statistics South Africa. 2013. Mid-year population estimates, 2013. Pretoria: Statistics South Africa. [Accessed 19 July 2015].
9. National Health Laboratory Services. NHL Strategic Plan 2010–2015. 2010; 1–50.
10. Goetz MB, Hoang T, Kan VL, Rimland D R-BM. Development and validation of an algorithm to identify patients newly diagnosed with HIV infection from electronic health records. AIDS Res Hum Retroviruses 2014; 30:626–633.
11. Kabudula CW, Clark BD, Gómez-Olivé FX, Tollman S, Menken J, Reniers G. The promise of record linkage for assessing the uptake of health services in resource constrained settings: a pilot study from South Africa. BMC Med Res Methodol 2014; 14:71.
12. Sengayi M, Spoerri A, Egger M, Kielkowski D, Crankshaw T, Cloete C, et al. Record linkage to correct under-ascertainment of cancers in HIV cohorts: the Sinikithemba HIV clinic linkage project. Int J Cancer 2016; 139:1209–1216.
13. Grobler J. National Health Laboratory Service Corporate Data Warehouse patient linking technical document. 2014.
14. National Department of Health. The South African antiretroviral treatment guidelines. Pretoria: National Department of Health; 2013.
15. National Department of Health. National HIV counselling and testing policy guidelines. Pretoria: National Department of Health; 2015.
16. Mills EJ, Beyrer C, Birungi J, Dybul MR. Engaging men in prevention and care for HIV/AIDS in Africa. PLoS Med 2012; 9:e1001167.
17. Tsague L, Tsiouris FO, Carter RJ, Mugisha V, Tene G, Nyankesha E, et al. Comparing two service delivery models for the prevention of mother-to-child transmission (PMTCT) of HIV during transition from single-dose nevirapine to multidrug antiretroviral regimens. BMC Public Health 2010; 10:753.
18. Pfeiffer J, Montoya P, Baptista AJ, Karagianis M, Pugas M de M, Micek M, et al. Integration of HIV/AIDS services into African primary healthcare: lessons learned for health system strengthening in Mozambique – a case study. J Int AIDS Soc 2010; 13:3.
19. Killam WP, Tambatamba BC, Chintu N, Rouse D, Stringer E, Bweupe M, et al. Antiretroviral therapy in antenatal care to increase treatment initiation in HIV-infected pregnant women: a stepped-wedge evaluation. AIDS 2010; 24:85–91.
20. van der Merwe K, Chersich MF, Technau K, Umurungi Y, Conradie F, Coovadia A. Integration of antiretroviral treatment within antenatal care in Gauteng Province, South Africa. J Acquir Immune Defic Syndr 2006; 43:577–581.
21. Makusha T, Knight L, Taegtmeyer M, Tulloch O, Davids A, Lim J, et al. HIV self-testing could ‘revolutionize testing in South Africa, but it has got to be done properly’: perceptions of key stakeholders. PLoS One 2015; 10:e0122783.
22. van Rooyen H, McGrath N, Chirowodza A, Joseph P, Fiamma A, Gray G, et al. Mobile VCT: reaching men and young people in urban and rural South African pilot studies (NIMH Project Accept, HPTN 043). AIDS Behav 2013; 17:2946–2953.
23. Choko AT, Desmond N, Webb EL, Chavula K, Napierala-Mavedzenge S, Gaydos CA, et al. The uptake and accuracy of oral kits for HIV self-testing in high HIV prevalence setting: a cross-sectional feasibility study in Blantyre, Malawi. PLoS Med 2011; 8:e1001102.
24. Ganguli I, Bassett IV, Dong KL, Walensky RP. Home testing for HIV infection in resource-limited settings. Curr HIV/AIDS Rep 2009; 6:217–223.
25. Merson MH, Feldman EA, Bayer R, Stryker J. Rapid self testing for HIV infection. Lancet 1997; 349:352–353.
26. Wright AA, Katz IT. Home testing for HIV. N Engl J Med 2006; 354:437–440.
27. Kuwane B, Appiah K, Felix M, Grant A, Churchyard G. Expanding HIV care in Africa: making men matter in Johannesburg. Lancet 2009; 374:1329.
28. Sweat M, Morin S, Celentano D, Mulawa M, Singh B, Mbwambo J, et al. Community-based intervention to increase HIV testing and case detection in people aged 16–32 years in Tanzania, Zimbabwe, and Thailand (NIMH Project Accept, HPTN 043): a randomised study. Lancet Infect Dis 2011; 11:525–532.
29. Larson BA, Schnippel K, Ndibongo B, Xulu T, Brennan A, Long L, et al. Rapid point-of-care CD4 testing at mobile HIV testing sites to increase linkage to care: an evaluation of a pilot program in South Africa. J Acquir Immune Defic Syndr 2012; 61:e13–e17.
30. Hyle EP, Jani IV, Lehe J, Su AE, Wood R, Quevedo J, et al. The clinical and economic impact of point-of-care CD4 testing in Mozambique and other resource-limited settings: a cost-effectiveness analysis. PLoS Med 2014; 11:e1001725.
31. Jani IV, Sitoe NE, Alfai ER, Chongo PL, Quevedo JI, Rocha BM, et al. Effect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: an observational cohort study. Lancet 2011; 378:1572–1579.
32. Wynberg E, Cooke G, Shroufi A, Reid SD, Ford N. Impact of point-of-care CD4 testing on linkage to HIV care: a systematic review. J Int AIDS Soc 2014; 17:18809.
33. Decroo T, Rasschaert F, Telfer B, Remartinez D, Laga M, Ford N. Community-based antiretroviral therapy programs can overcome barriers to retention of patients and decongest health services in sub-Saharan Africa: a systematic review. Int Health 2013; 5:169–179.
34. McCollum ED, Preidis GA, Kabue MM, Singogo EBM, Mwansambo C, Kazembe PN, et al. Task shifting routine inpatient pediatric HIV testing improves program outcomes in urban Malawi: a retrospective observational study. PLoS One 2010; 5:e9626.
35. Nyondo AL, Choko AT, Chimwaza AF, Muula AS. Invitation cards during pregnancy enhance male partner involvement in prevention of mother to child transmission (PMTCT) of human immunodeficiency virus (HIV) in Blantyre, Malawi: a randomized controlled open label trial. PLoS One 2015; 10:e0119273.
36. Gaolathe T, Wirth KE, Holme MP, Makhema J, Moyo S, Chakalisa U, et al. Botswana's progress toward achieving the 2020 UNAIDS 90-90-90 antiretroviral therapy and virological suppression goals: a population-based survey. Lancet HIV 2016; 3:e221–e230.
37. UNAIDS. Access to antiretroviral therapy in Africa, 2013. Status Rep Prog Towar 2015 targets. 2013; 1–10.
38. Barth RE, van der Loeff MFS, Schuurman R, Hoepelman AIM, Wensing AMJ. Virological follow-up of adult patients in antiretroviral treatment programmes in sub-Saharan Africa: a systematic review. Lancet Infect Dis 2010; 10:155–166.
39. Haber N, Chimbindi N, Herbst K, Tanser F, Pillay D, Bärnighausen T. Longitudinal HIV treatment cascade in KwaZulu-Natal, South Africa. International AIDS Conference. Melbourne, Australia; 2014. p. Poster TUPE426.
40. Johnson LF. Access to antiretroviral treatment in South Africa, 2004–2011. South Afr J HIV Med 2012; 13:22–27.
41. Johnson L. THEMBISA version 1.0: a model for evaluating the impact of HIV/AIDS in South Africa. Cape Town: Centre for Infectious Diseases Epidemiology and Research, University of Cape Town; 2014.
42. UNAIDS. Global AIDS Response Progress Report 2012 South Africa; 2012 [Accessed 20 October 2016].
43. National Department of Health, National Department of Health. TIER T1 and T2 ART Monitoring and Evaluation Implementation Plan. 2011.
44. Young T, Galloway M. Integrated systems analysis of clinic-laboratory interface: understanding the nature and limitation of the preanalytical and postanalytical phases of specialist laboratory tests at South African primary healthcare clinics. 2010.
45. National Department of Health, MacLeod W, Bor J, Crawford K, Carmona S. Spatial clustering analysis of viral load suppression by South African province, district, sub-district and facility (April 2014–March 2015). 2015.

cascade; CD4+ cell count; continuum of care; HIV testing; HIV treatment; viral suppression

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