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ORIGINAL ARTICLE

ACCESS TO ANTIRETROVIRAL TREATMENT IN SOUTH AFRICA, 2004 - 2011

Leigh F Johnson, PhD

Centre for Infectious Disease Epidemiology and Research, University of Cape Town

Background. South Africa’s National Strategic Plan (NSP) for 2007 - 2011 aimed to achieve new antiretroviral treatment (ART) enrolment numbers equal to 80% of the number of newly eligible individuals in each year, by 2011.

Objectives. To estimate ART coverage in South Africa and assess whether NSP targets have been met.

Methods. ART data were collected from public and private providers of ART. Estimates of HIV incidence rates were obtained from independent demographic projection models. Adult ART data and incidence estimates were entered into a separate model that estimated rates of progression through CD4 stages, and the model was fitted to South African CD4 data and HIV prevalence data.

Results. By the middle of 2011, the number of patients receiving ART in South Africa had increased to 1.79 million (95% CI 1.65 - 1.93 million). Adult ART coverage, at the previous ART eligibility criterion of CD4 <200/μl, was 79% (95% CI 70 - 85%), but reduced to 52% (95% CI 46 - 57%) when assessed according to the new South African ART eligibility criteria (CD4<350/μl). The number of adults starting ART in 2010/11 was 1.56 times (95% CI 1.08 - 1.97) the number of adults who became ART-eligible in 2010/11, well in excess of the 80% target. However, this ratio was substantially higher in women (1.96, 95% CI 1.33 - 2.51) than in men (1.23, 95% CI 0.83 - 1.58) and children (1.13, 95% CI 0.74 - 1.48).

Conclusion. South Africa has exceeded the ART targets in its 2007 - 2011 NSP, but men and children appear to be accessing ART at a lower rate than women.

Antiretroviral treatment (ART) is a powerful tool for reducing both AIDS mortality1 , 2 and HIV transmission.3 The monitoring of access to ART is therefore critical to the evaluation of the impact of HIV treatment and prevention programmes. Previous monitoring exercises have shown that, since the announcement of a comprehensive care, management and treatment programme by the South African Department of Health in late 2003, access to ART in South Africa has increased dramatically.4 , 5 These assessments suggested that South Africa was on track to meet the targets laid out in the 2007 - 2011 National Strategic Plan (NSP) for HIV/AIDS and Sexually Transmitted Infections, which aimed to achieve new ART enrolment numbers equal to 80% of the number of newly eligible individuals in each year, by 2011.6 However, there has not as yet been any formal assessment of whether this target has been met.

The monitoring of access to ART in South Africa is challenging for several reasons. The interpretation of public sector statistics is complicated by changes in reporting practices in late 2009, with most provinces switching from reporting numbers of patients cumulatively started on ART to numbers of patients currently on ART. Statistics from disease management programmes and programmes run by non-governmental organizations (NGOs) have not been routinely collected and reported. In addition, there is generally a lack of information on the age and sex of patients. This is particularly problematic in view of concerns that ART initiation rates may be lower among men than women.7

The estimation of ART coverage is also hampered by uncertainty regarding the ‘treatment need’, the denominator in the coverage calculation. Mathematical models have been used to estimate numbers of HIV-positive individuals with CD4 counts below different thresholds, but there is substantial uncertainty surrounding the rates of CD4 decline that are assumed in these models, and there is also growing recognition that these rates of CD4 decline may differ between populations.10 There is also concern that cross-sectional measures of ART coverage may fail to give a sense of recent programme performance, which is better reflected in the ratio of the number of patients starting ART in a year to the number of individuals becoming eligible for ART in the same year.11 The latter measure has the advantage of being consistent with the way in which the South African NSP targets are expressed, and is also less sensitive to model assumptions about rates of CD4 decline and ART eligibility criteria.11

The objective of this paper is to assess recent changes in access to ART in South Africa, and to evaluate the extent to which the 2007 - 2011 NSP treatment targets have been met. This study also aims to improve on previous work4 by including more recent programme statistics, by using locally relevant CD4 data in the estimation of the treatment need, by including 95% confidence intervals (CIs) in coverage estimates, and by estimating coverage separately for men, women and children.

Methods

ART programme statistics

Public sector ART programme statistics to the end of June 2011 were obtained from the South African Department of Health, and were adjusted to achieve consistency of definition (cumulative/current), using a previously described formula,4 for each province. Unpublished data on the sex ratio of adult patients enrolled in public ART programmes in four provinces, collected up to March 2009, were used to estimate the sex ratio of adults starting ART in the public sector.

Private sector data and data from NGOs were obtained through surveys conducted every two years, since mid-2006.12 Linear interpolation and extrapolation was used to estimate numbers for programmes with missing data and for years in which no survey was conducted. Estimates of the proportion of private sector patients who were men, women and children were obtained from submissions by medical schemes to the Risk Equalization Fund up to March 2008, and the geographical distribution of private sector patients was estimated from early private sector statistics.13 Detailed data collected from NGO programmes in the 2008 survey were used to determine the profile of NGO patients by age, sex and province.

Mathematical model

To estimate the numbers of adults needing ART, a mathematical model was developed to simulate the growth of the South African population over time, the incidence of HIV and the decline in CD4 counts in HIV-positive adults. The model stratifies the population by age and sex, and projects the change in population in one-year intervals, starting in the middle of 1985. Assumptions regarding the age- and sex-specific population profile, non-HIV mortality, fertility, migration and HIV incidence are based on the ASSA2008 AIDS and Demographic model.14 Once infected, individuals are assumed to progress through a four-stage model of CD4 decline, in the absence of ART (Fig. 1). Individuals are assumed to experience AIDS mortality in the CD4 200 - 349/µl category at a fraction θ of the AIDS mortality rate in the CD4<200/µl category, if untreated. Up to mid-2009, adults of sex g are assumed to start ART only once their CD4 count has dropped below 200/µl, at a rate of r g (t) per annum in year t. Between mid-2009 and mid-2011, the model also allows individuals to start ART in the CD4 200 - 349 category if they develop tuberculosis or become pregnant, following the change in South African ART guidelines in early 2010.15 The r g (t) rates in each year are calculated from the ART programme statistics (further detail is provided in the online appendix).

Adults who start ART are assumed to be lost to the ART programme with probability κ0 during the first 6 months after starting ART, and with probability κ1 for each year after the first 6 months. This does not include individuals who temporarily interrupt ART. Of those leaving the ART programme permanently, a proportion ν are assumed to leave the programme owing to HIV-related mortality, and the remaining proportion (1 – ν) are assumed to stop taking their drugs, after which their mortality risk is assumed to be the same as that of ART-naïve adults with CD4 counts below 200/µl.

Estimates of annual numbers of new paediatric HIV infections were obtained from a separate model of paediatric HIV in South Africa.16 Since paediatric ART guidelines recommend ART initiation in all HIV-infected children aged <12 months, regardless of their immunological or clinical status,17 the annual number of new paediatric HIV infections is used to approximate the annual number of children newly eligible for ART (the denominator in the ART enrolment ratio).

Calibration and uncertainty analysis

The parameters determining the rates of CD4 decline, HIV-related mortality and ART discontinuation are estimated by fitting the model to HIV prevalence data from the 2005 and 2008 Human Sciences Research Council (HSRC) household surveys,18 , 19 and to CD4 data from HIV-positive adults in three South African surveys,20 using a Bayesian melding procedure.23 , 24 A detailed explanation is provided in the online appendix. Briefly, prior distributions are specified to represent uncertainty regarding the parameters of interest, including the range of plausible values for the average time to starting ART after becoming eligible (1/r g (t)). Prior distributions are also specified to represent uncertainty regarding the accuracy of the reported ART programme statistics in each year. This uncertainty and the uncertainty regarding ART attrition rates affect the model ART enrolment inputs. A likelihood function is specified to represent how well the model fits the CD4 data and HIV prevalence data, for a given set of parameter values. The posterior distribution, representing the parameter combinations from the prior distributions that have the highest likelihood values, is then simulated by Sampling Importance Resampling.25

Results

The posterior estimates of the model parameters are summarised in Table 1, and posterior estimates of numbers of patients receiving ART are summarised in Table 2. Over the period mid-2004 to mid-2011, the total number of patients receiving ART in South Africa increased from 47 500 (95% CI 42 900 – 51 800) to 1.79 million (95% CI 1.65 - 1.93 million). Of the latter, 85% were receiving ART through the public health sector, 11% were receiving ART through disease management programmes in the private sector, and the remaining 4% were receiving ART through community treatment programmes run by NGOs. The majority (61%) of patients were women aged 15 or older, men accounted for 31% of patients, and children under the age of 15 comprised the remaining 8% of patients. KwaZulu-Natal and Gauteng were the two provinces with the largest numbers of patients, together accounting for 56% of all patients receiving ART.

Changes over time in numbers of treated and untreated adults in different CD4 stages are shown in Fig. 2. As at mid-2011, untreated HIV-positive adults included 58 000 (95% CI 13 000 – 147 000) individuals who had stopped ART, 385 000 (95% CI 247 000 – 634 000) ART-naive adults with CD4 <200/μl, 1.06 million (95% CI 0.88 - 1.29 million) with CD4 counts of 200 - 349/μl, 0.74 million (95% CI 0.61 - 0.91 million) with CD4 counts of 350 - 500/μl, and 0.94 million (95% CI 0.77 - 1.16 million) with CD4 counts >500/μl. The total unmet need in the middle of 2011 (ART-naïve adults with CD4 <350/μl plus all adults who had stopped ART) was 1.50 million (95% CI 1.24 - 1.84 million), which is 32% lower than the total unmet need four years previously. Estimates of adult ART coverage and ART enrolment ratios are shown in Fig. 3. Using previous CD4 thresholds for defining ART eligibility (CD4 <200/μl), the fraction of adults eligible to receive ART who were actually on ART increased from 5.1% (95% CI 4.2 - 6.1%) in the middle of 2004 to 79% (95% CI 70 - 85%) by the middle of 2011. However, using the new CD4 thresholds for defining ART eligibility (CD4 <350/μl), adult ART coverage by the middle of 2011 was 52% (95% CI 46 - 57%).

As noted previously,11 ART enrolment ratios are similar when using different CD4 thresholds to define ART eligibility. For example, over the period from mid-2010 to mid-2011, the ratio of the number of adults starting ART to the number of adults whose CD4 counts fell below the CD4 threshold was 1.64 (95% CI 1.11 - 2.10) when the CD4 threshold was 200, and 1.56 (95% CI 1.08 - 1.97) when the CD4 threshold was 350. Both ratios are roughly double the target of 80% set in the 2007 - 2011 NSP, and indicate substantial progress in removing the ‘backlog’ of unmet need that accumulated in previous years.

Estimates of ART access are presented separately for men, women and children in Fig. 4. Using the CD4 threshold of 350/μl as the criterion for ART eligibility, the fraction of ART-eligible women who were receiving ART by the middle of 2011 (60%, 95% CI 53 - 65%) was significantly higher than the fraction of ART-eligible men who were on treatment (41%, 95% CI 36 - 46%). A similar difference in magnitude is seen in the ART enrolment ratio over the period mid-2010 to mid-2011: using the same ART eligibility criterion of CD4 <350/μl, the enrolment ratio was 1.96 (95% CI 1.33 - 2.51) in women and 1.23 (95% CI 0.83 - 1.58) in men. Over the same period, the ratio of the number of children starting ART to the number of new infections in children was 1.13 (95% CI 0.74 - 1.48). In most previous years, this ratio was below both the male ART enrolment ratio and the female ART enrolment ratio.

DISCUSSION

South Africa has made impressive progress in the rollout of ART since the start of the public sector ART programme in 2004. The number of patients who started ART in 2010/2011 was well in excess of the number of individuals who became eligible to receive ART over the same period, exceeding the targets set in the 2007 - 2011 NSP. The unmet need for ART was also reduced by 32% between 2007 and 2011. According to the ART initiation criteria that were in place at the time, adult treatment coverage by mid-2011 was close to 80%.

However, there appear to be substantial differences between men, women and children in the rate of ART initiation. The low rate of ART initiation in men relative to women may be a reflection of gender differences in health-seeking behaviour and perceptions that men who seek care are ‘weak’.9 Alternatively, the high rate of ART initiation in women may be due to higher rates of HIV diagnosis through antenatal screening. The relatively low rates of ART initiation in children are probably attributable to the lower rates of HIV testing in children and the greater complexity of paediatric ART relative to adult ART.26 However, it is difficult to compare adult and paediatric measures of ART access meaningfully because the course of HIV infection is so different in children, with many HIV-infected infants dying in the first few months of life before there is an opportunity for testing.

This analysis extends previous work4 by including assessment of uncertainty and by incorporating several new data sources. The 95% CIs that have been estimated reflect uncertainty regarding rates of CD4 decline, rates of mortality and rates of ART retention, and also reflect uncertainty regarding the accuracy of reported ART programme statistics. However, the CIs do not reflect the uncertainty regarding the HIV incidence rates that have been estimated from the ASSA2008 model, and this may lead to some exaggeration of precision. CIs around the ART enrolment ratios are considerably wider in 2009/10 and 2010/11 than in previous years, owing to the change in the way that the Department of Health has reported public sector ART programme statistics.

Various attempts were made to validate the reported ART programme statistics using data from external sources, with limited success. Lamivudine sales figures from Aspen Pharmacare, which until recently supplied 80% of lamivudine in the public sector, were used to obtain crude estimates of numbers of public sector patients on treatment in each quarter. These estimates were not significantly different from the model estimates in Table 2 up to the end of 2008, and from October 2009 to March 2010, but were substantially lower than the model estimates from January to September of 2009. Numbers of viral load tests performed by the National Health Laboratory Service for public sector clinics were also used to obtain theoretical estimates of numbers of patients receiving ART, on the assumption that patients went for viral load testing twice per annum on average. The resulting estimates were slightly higher than the corresponding model estimates up to 2008, but were 18% lower than the model estimates in 2009. Finally, the model estimate of the fraction of the 15 - 49-year-old population on ART in the middle of 2008 was compared with the corresponding proportion estimated in the 2008 HSRC national household survey,27 based on testing for the presence of antiretroviral drugs in blood samples: the model estimate of 1.8% (95% CI 1.6 - 2.0%) was found to be significantly lower than that measured in the survey (3.0%). External data sources therefore do not provide a clear and consistent assessment of the plausibility of the model estimates derived from reported ART programme statistics.

Although attempts were made to produce estimates of ART coverage for each province, it was not possible to produce plausible results for two provinces (Gauteng and Western Cape) because the estimated numbers of patients starting ART in recent years exceeded the estimated numbers of patients eligible to receive ART, in both of these provinces. This could possibly be due to individuals with advanced HIV migrating to urban areas because of the perceived superiority of health services in the major urban centres of Gauteng and Western Cape. The model assumes migration to be independent of HIV status, and may therefore under-estimate the number of HIV-infected ART-eligible individuals who migrate into these provinces. Alternatively, the problems experienced in producing plausible results for Gauteng and Western Cape may be due to assumed HIV incidence rates in these provinces being too low, or reported numbers of ART patients in these provinces being exaggerated.

Many challenges exist, both in achieving future ART rollout targets and in monitoring future progress towards meeting these targets. The new NSP for the 2012 - 2016 period28 proposes targets that are much more ambitious than those in the previous NSP: the ART enrolment target in 2016 is 80% of the new ART need in that year plus 80% of the unmet need from previous years. High levels of HIV testing and counselling, as well as expansion of capacity to deliver ART, will be required to meet these targets. The new NSP for the 2012 - 2016 period proposes several measures to strengthen the monitoring and evaluation of South Africa’s ART programme, including the introduction of a single patient identifier in the health sector and a single registry at the primary care level. It is hoped that these measures will lead to greater precision in the estimation of ART coverage in future, as well as a deeper understanding of the factors determining access to care and retention in care.

Appearing only in the online version of this article is an appendix that provides further detail regarding the method used to model adult ART initiation. It also includes a detailed explanation of the Bayesian melding procedure: the prior distributions and the data sources on which they are based, the method used to define the likelihood function and the method used to simulate the posterior distribution.

Acknowledgements

I am grateful to the many disease management programmes and NGOs that shared data, as well as the National Health Laboratory Service and Aspen Pharmacare for providing data for validation purposes.

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TABLE 1. POSTERIOR ESTIMATES OF MODEL PARAMETERS

Symbol

Mean (95% CI)

Parameters for untreated adults

Annual rate of progression from CD4 >500 to 350 - 500

λ1

0.34 (0.28 - 0.39)

Annual rate of progression from CD4 350 - 500 to 200 - 349

λ2

0.48 (0.40 - 0.58)

Annual rate of progression from CD4 200 - 349 to <200

λ3

0.32 (0.25 - 0.39)

Annual rate of HIV mortality if CD4 <200

λ4

0.21 (0.16 - 0.27)

Ratio of HIV mortality at CD4 200 - 349 to HIV mortality at CD4 <200

θ

0.13 (0.05 - 0.24)

Parameters for treated adults

Probability of permanent loss to care in first 6 months after ART start

κ0

0.078 (0.028 - 0.141)

Annual probability of permanent loss to care after first 6 months of ART

κ1

0.048 (0.018 - 0.087)

Proportion of permanent loss to care that is due to death

ν

0.74 (0.53 - 0.92)

TABLE 2. NUMBERS OF PATIENTS RECEIVING ART IN SOUTH AFRICA

2004

2005

2006

2007

2008

2009

2010

2011

Currently on ART*

Total

47 500

110 900

235 000

382 000

588 000

912 000

1 287 000

1 793 000

By sex/age

Men

17 700

37 500

75 000

120 000

183 000

283 000

396 000

551 000

Women

25 600

63 600

138 000

228 000

354 000

553 000

777 000

1 090 000

Children (<15)

4 200

9 800

22 000

35 000

51 000

76 000

113 000

152 000

By provider

Public sector

9 600

60 600

163 000

290 000

470 000

748 000

1 073 000

1 525 000

Private sector

34 100

43 800

57 000

68 000

86 000

117 000

154 000

190 000

NGO programmes

3 900

6 400

15 000

24 000

32 000

47 000

60 000

78 000

By province

Eastern Cape

5 300

12 600

26 000

43 000

65 000

98 000

137 000

187 000

Free State

2 200

4 900

10 000

18 000

29 000

47 000

66 000

91 000

Gauteng

13 800

30 800

62 000

95 000

145 000

219 000

280 000

439 000

KwaZulu-Natal

12 800

30 300

67 000

110 000

174 000

282 000

409 000

558 000

Limpopo

2 000

4 800

12 000

21 000

36 000

60 000

101 000

124 000

Mpumalanga

3 300

5 800

12 000

24 000

38 000

61 000

96 000

142 000

Northern Cape

400

1 500

3 000

7 000

9 000

13 000

16 000

19 000

North West

2 700

8 800

21 000

34 000

48 000

70 000

96 000

126 000

Western Cape

5 000

11 400

21 000

31 000

45 000

64 000

85 000

107 000

Started ART last year

Men

8 400

22 400

43 000

52 000

75 000

118 000

138 000

189 000

Women

13 700

42 600

84 000

104 000

149 000

235 000

273 000

380 000

Children (<15)

2 700

6 400

13 000

15 000

20 000

29 000

45 000

48 000

Total

24 800

71 300

140 000

172 000

243 000

382 000

456 000

617 000

All numbers are rounded to the nearest 1000 (except in the case of 2004 and 2005 totals, which are rounded to the nearest 100). Due to rounding, some rows may not sum to the total. All estimates are posterior averages (95% confidence intervals not shown).

*Totals reflect numbers at the middle of each year.

Totals reflect ART enrolment over the 12 months up to the middle of the year.

Fig. 1. Multi-state model of decline in CD4 count and ART initiation by HIV-infected adults. All states are stratified by age and sex, and all HIV-infected adults are assumed to experience age-specific mortality unrelated to HIV (not shown).



Fig. 2. Numbers of HIV-positive adults, by CD4 count and ART status. Numbers exclude paediatric HIV infections. Bars represent posterior means (95% confidence intervals not shown).

Fig. 3. Adult ART access. Bars represent posterior means and error bars represent 95% confidence intervals. Dashed line in panel (b) represents 2007 - 2011 National Strategic Plan target.

Fig. 4. Age and sex differences in ART access. Bars represent posterior means and error bars represent 95% confidence intervals. Dashed line in panel (b) represents 2007 - 2011 National Strategic Plan Target.




 

Crossref Citations

1. Barriers to antiretroviral treatment initiation in rural KwaZulu-Natal, South Africa
M Plazy, M-L Newell, J Orne-Gliemann, K Naidu, F Dabis, R Dray-Spira
HIV Medicine  vol: 16  issue: 9  first page: 521  year: 2015  
doi: 10.1111/hiv.12253