Curr HIV/AIDS Rep (2015) 12:196–206 DOI 10.1007/s11904-015-0264-x

THE GLOBAL EPIDEMIC (SH VERMUND, SECTION EDITOR)

Epidemiological Trends for HIV in Southern Africa: Implications for Reaching the Elimination Targets Brian G. Williams 1,2 & Eleanor Gouws 3 & Pierre Somse 3 & Mpho Mmelesi 4 & Chibwe Lwamba 5 & Trouble Chikoko 6 & Erika Fazito 7 & Mohamed Turay 8 & Eva Kiwango 9 & Pepukai Chikukwa 10 & Henry Damisoni 11 & Michael Gboun 12

Published online: 1 May 2015 # Springer Science+Business Media New York 2015

Abstract Southern Africa is the region worst affected by HIV in the world and accounts for one third of the global burden of HIV. Achieving the UNAIDS 90-90-90 target by 2020 and ending the AIDS epidemic by 2030 depend on success in this region. We review epidemiological trends in each country in southern Africa with respect to the prevalence, incidence, mortality, coverage of anti-retroviral therapy (ART) and TB notification rates, to better understand progress in controlling HIV and TB and to determine what needs to be done to reach This article is part of the Topical Collection on The Global Epidemic Electronic supplementary material The online version of this article (doi:10.1007/s11904-015-0264-x) contains supplementary material, which is available to authorized users. * Brian G. Williams [email protected] 1

Reproductive Health and HIV Institute, University of the Witwatersrand, Johannesburg, South Africa

2

South African Centre for Epidemiological Modelling and Analysis, University of Stellenbosch, Stellenbosch, South Africa

3

UNAIDS Regional Support Team for Eastern and Southern Africa, Johannesburg, South Africa

4

UNAIDS Country Office, Gaborone, Botswana

5

UNAIDS Country Office, Maseru, Lesotho

6

UNAIDS Country Office, Lilongwe, Malawi

7

UNAIDS Country Office, Maputo, Mozambique

8

UNAIDS Country Office, Windhoek, Namibia

9

UNAIDS Country Office, Pretoria, South Africa

10

UNAIDS Country Office, Mbabane, Botswana

11

UNAIDS Country Office, Lusaka, Zambia

12

UNAIDS Country Office, Harare, Zimbabwe

the UNAIDS targets. Significant progress has been made in controlling HIV. In all countries in the region, the prevalence of HIV in people not on ART, the incidence of HIV, AIDSrelated mortality and, in most countries, TB notification rates, are falling. In some countries, the risk of infection began to fall before biomedical interventions such as ART became widely available as a result of effective prevention measures or people’s awareness of, and response to, the epidemic but the reasons for these declines remain uncertain. Some countries have achieved better levels of ART coverage than others, but all are in a position to reach the 2020 and 2030 targets if they accelerate the roll-out of ART and of targeted prevention efforts. Achieving the HIV treatment targets will further reduce the incidence of HIV-related TB, but efforts to control TB in HIVnegative people must be improved and strengthened. Keywords HIV . TB . Eastern and Southern Africa . Ending AIDS . UNAIDS targets . ART

Introduction The Joint United Nations Programme on HIV/AIDS (UNAIDS) has set an ambitious 90-90-90 target for 2020 under which 90 % of HIV-positive people should know their status, 90 % of these should be taking ART and 90 % of these should have fully suppressed viral loads [1, 2•]. The intention is to end the AIDS epidemic by 2030 defined, for the present purposes, as having less than one new infection and one AIDS death per thousand adults so that HIV will no longer present a public health threat; this is in line with the UNAIDS definition of reducing incidence and AIDS-related mortality in 2030 by 90 % from the 2010 baseline. Because the nine countries of southern Africa account for 1.6 % of all adults [3] but 36 % of all adults living with HIV in the world [1], reaching the

TB

HIV

Peak NRR Redn. peak ROI/100 CD4

Background

Timing trans. ART Coverage

Change in transmission

R0

Current

Peak

Timing ROI

433 8.9 0.46 0.25

1993.2 1.063 0.976–1.156 0.250 0.240–0.261 0.222 0.208–0.234 6.147 5.643–6.684 0.656 0.529–0.794 2001.0 0.652 0.635–0.672 121

Botswana

611 8.1 0.84 0.24

1995.3 0.855 0.796–0.920 0.218 0.201–0.232 0.217 0.194–0.230 6.234 5.807–6.713 1.387 0.755–1.819 2010.0 0.316 0.314–0.322 207

Lesotho

210 11.9 0.50 0.29

1988.2 0.901 0.724–1.211 0.153 0.146–0.159 0.099 0.091–0.109 2.403 1.930–3.230 0.372 0.293–0.460 2003.9 0.484 0.472–0.500 67

Malawi

179 9.9 0.85 0.27

1998.2 0.429 0.415–0.451 0.107 0.104–0.109 0.093 0.086–0.103 3.955 3.830–4.158 0.308 0.117–0.674 2009.0 0.327 0.318–0.334 87

Mozambique

733 13.6 0.55 0.31

1995.8 0.544 0.522–0.567 0.159 0.154–0.163 0.125 0.119–0.132 4.852 4.649–5.057 0.421 0.356–0.496 2002.0 0.563 0.555–0.575 216

Namibia

630 13.8 0.69 0.31

1996.5 0.955 0.891–1.089 0.162 0.157–0.169 0.157 0.151–0.166 5.558 5.187–6.340 1.000 0.862–1.156 2005.0 0.459 0.448–0.481 176

S. Africa

603 8.8 0.74 0.25

1995.6 0.724 0.673–0.748 0.271 0.256–0.286 0.271 0.256–0.286 6.291 5.850–6.500 1.264 1.047–1.480 2007.2 0.491 0.480–0.505 170

Swaziland

474 15.9 0.55 0.33

1990.1 1.603 1.330–1.865 0.142 0.138–0.146 0.115 0.106–0.124 5.019 4.165–5.840 0.452 0.306–0.643 2006.7 0.484 0.476–0.491 126

Zambia

440 17.8 0.45 0.35

1992.0 0.835 0.623–0.996 0.248 0.235–0.261 0.146 0.132–0.161 5.867 4.374–6.994 0.312 0.260–0.368 1998.0 0.459 0.437–0.484 63

Zimbabwe

Table 1 HIV Timing: the time at which the prevalence of HIV reached half its peak value; ROI: the initial rate of increase in prevalence; Peak: peak prevalence of HIV; Current: current prevalence of HIV among adults aged 15 years of more; R0: case reproduction number; Change in transmission: the change in transmission over and above the natural dynamics of the epidemic and the provision of ART; Timing trans.: the time at which the change in transmission reached half its asymptotic value; ART Coverage: ART coverage in 2013. TB Background: pre-ART notification rate/100 k population/year; Peak: peak notification rate per 100 k population/year; NRR: notification rate ratio for those with and without HIV; Redn. peak: notification rate in 2014 compared to peak; ROI/100 CD4: increase in the TB notification rate for a decline of 100 CD4+ cells/μL. (Numbers based on data in Figs. S1 to S9; ranges give 95 % confidence intervals)

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UNAIDS targets will depend critically on what happens in southern Africa. The natural history of, and the response to, HIV varies substantially among the nine countries (Table 1 and Figs. S1 to S9). For example, the epidemic of HIV reached half of its peak value in 1988 in Malawi but 10 years later, in 1998, in Mozambique. The peak prevalence of HIV among adults aged 15 years or more reached 27 % in Swaziland but 11 % in Mozambique. The prevalence of HIV fell from its peak value of 25 % in Zimbabwe in 1998 to 15 % in 2013 but increased from a plateau of 25 % in Swaziland in 2005 to 27 % in 2013. In 2013, the estimated proportion of people living with HIV who were on anti-retroviral therapy (ART) was 65 % in Botswana but 32 % in Lesotho. In order to gain a better understanding of HIV in southern Africa, we fitted time trends for the prevalence of HIV to a dynamical model [4–8]. We estimate historical trends, project future trends in the prevalence and incidence of HIV, AIDSrelated mortality, and the prevalence and incidence, that is the rate of starting, ART. We fitted time trends for the notification rates for TB to a model of TB driven by HIV [9] to estimate and project TB notification rates. We assess what needs to be done in each country to achieve the levels of ART coverage implicit in the 90-90-90 agenda for 2020 and to end the AIDS epidemic by 2030.

Data and Methods The input data are estimated trends in the prevalence of HIV published by UNAIDS [1] and TB notification rates reported by WHO [10]. UNAIDS derives the HIV estimates by fitting the Spectrum model [11] to temporal data from antenatal clinics in each country, scaled, where necessary, to match estimates of HIV among adults based on national demographic and health surveys. The UNAIDS estimates provide confidence intervals for the fitted curves. One cannot directly estimate corresponding uncertainties in the derived trends and parameters from the curves fitted to the Spectrum model. We therefore simulated errors by generating binomially distributed random numbers with variance chosen to match the reported uncertainty in the fitted prevalence. This does not significantly affect the estimated trends but enables us to determine the fitted and projected uncertainties. The definition of ending the AIDS epidemic used in this paper, i.e. having less than one new infection and one AIDS death per thousand adults, is consistent with the UNAIDS definition of a 90 % reduction in HIV incidence and mortality by 2030, using 2010 as the baseline; in 2010, the estimated adult AIDS mortality per annum among the nine southern African countries ranged from 0.32 to 1.1 % (median 0.6 %), while the incidence ranged from 0.5 to 2.7 % (median 1 %), so that a 90 % reduction should reduce mortality and incidence to less than 0.1 % per annum in the majority of countries.

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The susceptible-infected (SI) model that we use to fit the trends in the prevalence of HIV is described fully in the Supporting Information and in an earlier publication [8]. Briefly, we let the epidemic start in 1970, develop according to a standard SI model, but add a term to allow for heterogeneity in sexual risk so that incidence falls as the prevalence of HIV rises. In some countries, there has been a decline in prevalence, which cannot be attributed to the roll-out of ART and which cannot be captured by the intrinsic dynamics of the epidemic. To account for this, there must have been declines in transmission resulting either from the roll-out of effective prevention programmes or changes in people’s behaviour resulting from their awareness of the epidemic. Here, we will refer to the impact of either of these as ‘changes in transmission’. We allow this ‘transmission’ to decline logistically at a rate, at a time and to an extent that is determined by the data. It is important to note that the incidence of HIV will fall for several reasons: (1) as the number of susceptible people falls and as more infected people die which are inherent in the nonlinearity of the SI model; (2) as those that are at highest risk are infected and cannot be infected again which is inherent in the structure of the sexual network; and (3) as people change their behaviour as awareness of the epidemic grows or other prevention interventions, such as pre-exposure prophylaxis or voluntary male medical circumcision, are rolled out and which we refer to collectively as changes in transmission. Changes in transmission should not be confused with changes in incidence. We allow for the reported coverage of ART. Uncertainty in the fitted curves is estimated using a Markov Chain Monte Carlo method with Gibbs Sampling. Details of the TB model are given in the Supporting Information. We assume that without HIV, TB would be in a steady state but the model could be adapted to allow for a steady decline in the TB notification rate. The model allows for the initial increase in the incidence of TB immediately following the acute phase of HIV infection, the subsequent exponential rise in the incidence of TB with time since infection, and the reduction in the incidence of TB when people are on ART.

Results Southern Africa is the region in the world that is worst affected by HIV. In Botswana, Lesotho and Swaziland, HIV prevalence among adults is still over 20 % while in 2013, South Africa had an estimated 6.3 million people living with HIV, the largest number of any country in the world. However, there has been remarkable progress in the response to the epidemic in recent years and by mid-2014, out of a total of 12.5 million people living with HIV in southern Africa more than 5.7 million were receiving ART. The rates at which treatment programmes have been scaled up, and the declines in incidence and mortality

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Fig. 1 The epidemic of HIV in Zimbabwe among adults aged 15 years or more. a Blue dots: prevalence data; blue line: fitted prevalence; green line: prevalence not on ART; red line: incidence of HIV; brown line: mortality; orange dots: reported prevalence of ART; orange line: fitted prevalence of ART. b Prevalence of HIV and ART. c Prevalence of HIV not on ART.

d AIDS-related mortality. e HIV incidence. f Incidence of ART (the rate at which people start ART). g Blue dots: TB notification rates; blue line: fitted TB notification rates; green line: as blue line for those not on ART; brown line: HIV-negative people. h Decline in transmission. Error bars and bands are 95 % confidence limits

have varied among countries, but the scale-up of ART and programmes to prevent mother-to-child transmission of HIV has led to a substantial decline in the number of new HIV infections and AIDS-related deaths in all countries.

We illustrate the analysis using data for Zimbabwe as shown in Fig. 1; the results for all nine countries are given in the Supporting Information, and the key data are summarized in Fig. 2 and Table 1. The prevalence of HIV in

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Fig. 2 Summary statistics. a The year in which the prevalence of HIV reaches half its peak value. b The peak prevalence of HIV. c The change in transmission. d The year in which the change in transmission reaches

half its asymptotic value. e The adult ART coverage in 2013. f The notification rate ratio for TB among people with and without HIV

Zimbabwe rose early, to about 5 % in 1990, reached a peak of 25 % in 1998 but then fell rapidly to 18 % in 2004 after which ART became available in the public sector (Fig. 1a, b). The pre-ART fall in the prevalence of HIV implies that transmission, over and above any decline due to the natural course of the epidemic or the roll-out of ART, fell by 69 % (63–74 %) between 1990 and 2006 (Fig. 1h). This pre-ART decline in transmission must have been the result of changes in sexual risk which could have included increased use of condoms, fewer sexual partners, delayed onset of sexual debut or changes in inter-generational sex [12•]; the causes of the change have been debated and remain uncertain but must have been in response to an awareness of the epidemic [13, 14, 15•]. A direct consequence of the reduction in transmission is that the incidence of HIV in Zimbabwe peaked in 1993 (Fig. 1a, e) while AIDS-related mortality peaked in 2002 (Fig. 1d). While the total prevalence of HIV levelled off at 14 % (Fig. 1a, b), the prevalence of HIV in those not on ART continued to decline, reaching 7 % in 2014 (Fig. 1a, c), as more people were started on ART (Fig. 1b, f). Projecting the epidemic forward in time shows that if Zimbabwe adopts a policy of immediate treatment for all those infected with HIV, the increased access to ART among those with a high CD4+ cell count will be more than balanced by the decline in transmission so that the number of people starting ART each year will continue to fall (Fig. 1f) and by 2030, Zimbabwe will have reached the target for ending the AIDS epidemic (Fig. 1d, e).

The rising prevalence of HIV in Zimbabwe in the 1990s led the TB notification rate to rise from 63 cases/100 k population/ year in 1985 to a peak of 440 cases/100 k population/year in 2000 (Fig. 1g). The subsequent fall in the prevalence of HIV (Fig. 1a, b) and the roll-out of ART (Fig. 1b) led the TB notification rate (Fig. 1g) to fall to an estimated 225 cases/ 100 k population/year in 2015. ART reduces the incidence of TB by about 60 % [9] so that HIV-positive people who are on ART remain at higher risk of TB than HIV-negative people. Only if they are started on ART soon after HIV infection, at the end of the 2-week acute phase of raised viraemia [16, 17], when the incidence of TB doubles [9], would ART reduce the risk of TB to the levels in HIV-negative people. Everyone with TB, who is also infected with HIV, should therefore start ART as soon as possible [18].

Country Comparisons The data, fits and projections for all the countries in southern Africa, are given in the Supporting Information in Figs. S1 to S9. The key parameters from the fits are summarized in Fig. 2 and Table 1. The data suggest that the epidemic of HIV in southern Africa started first in Malawi, 2 years before Zambia and 10 years before Mozambique (Fig. 2a and Table 1). It then spread to Zambia, Zimbabwe and Botswana, followed by Lesotho, Swaziland, Namibia and South Africa, and finally to Mozambique.

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This is consistent with an early study on the South African gold mines when about 30,000 miners, out of a total workforce of 470,000 [19], were tested for HIV in 1987 [20]; the prevalence among men from Malawi was 4 %, while the prevalence among those from other countries, including South Africa, Swaziland, Lesotho, Namibia and Mozambique, was about 0.03 %. The South African Chamber of Mines then stopped recruiting novices from Malawi [21], and the number of Malawians employed on the South African gold mines, which had reached 120,000 in 1974, fell to 13,000 in 1988 and 2000 in 1989 [22]. The South African Chamber of Mines recruited few men from Rhodesia, now Zimbabwe, apart from a brief period between 1974 and 1982 [19, 23]. At the same time, the number of men recruited from Swaziland increased from 4 thousand in 1972 to 21 thousand in 1988 [19, 23] or about 10 % of the population of adult men aged 15 to 65 years [24]. The draconian restrictions on the movement of black Africans under Apartheid, both within South Africa and to and from neighbouring countries, would have slowed the spread of HIV to South Africa and its immediate neighbours; the civil war in Mozambique, which ended in 1992, may have delayed the spread of HIV to that country. The countries worst affected by HIV, with peak prevalence above 20 %, were Lesotho, Botswana, Zimbabwe and Swaziland (Fig. 2b and Table 1). While the epidemics in Malawi and Zambia were among the earliest to take-off, the adult prevalence of HIV peaked at about 15 % while the epidemics in Zimbabwe and Botswana, which were the next two to takeoff, reached an adult prevalence of HIV of 25 %. The case reproduction number (R0) provides a measure of the number of secondary infections that arise from a primary case of disease in a completely susceptible population (further details in the Supporting Information). Values of R0 estimated from the initial rate of increase in the prevalence of HIV and the life expectancy of people infected with HIV range from 2.4 in Malawi to 6.2 in Lesotho and Swaziland (Table 1). In some, but not all, the countries in the region the prevalence of HIV reached a peak and then fell significantly (Table 1, Fig. 2c and Figs. S1 to S9) and to an extent that cannot be captured by the intrinsic dynamics of the standard SI model. In South Africa and Lesotho, the change in transmission before ART became widely available is not significant. In Botswana Zambia, Namibia, Malawi, Zimbabwe and Mozambique, transmission fell substantially by an estimated 34 % (21–47 %) in Botswana to 69 % (63–74 %) in Zimbabwe. In Swaziland, the prevalence of HIV infection appears to have reached a plateau between 2002 and 2008 after which the prevalence increased from about 25 to 27 % suggesting that transmission may have increased by about 26 % (5–48 %) (Table 1 and Fig. S7h). During this time, HIV incidence continued to fall in Swaziland because of the increasing coverage of ART. It is particularly striking that the fall in transmission was greatest in Zimbabwe and occurred in 1998, 3 years

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before Botswana and 8 years before Zambia (Fig. 2 and Table 1). Previous authors have suggested that behavioural changes which led to a fall in transmission in Zimbabwe were stimulated primarily by increased awareness of AIDS deaths, possibly because of the country’s economic deterioration and the relatively high level of education which would have enabled people to internalize prevention messages [12•, 13, 14]. The coverage of ART varies among the countries (Table 1, Fig. 2e and Figs. S1 to S9). Of all adults infected with HIV in 2013, 32 % were on ART in Lesotho but 65 % were on ART in Botswana. Most countries are on track to reach the 90-90-90 target by 2020 and to eliminate HIV by 2030 provided that they continue to scale-up treatment at about the same or an increased rate to all people living with HIV. By 2015 in some countries, and by 2020 in all countries, the number of people starting ART each year will fall as the prevalence of people who are infected with HIV, but are not on treatment, falls. In order to reach the UNAIDS targets, Lesotho will have to expand their treatment programme substantially and the number of people starting ART each year will have to double by 2020 (Fig. 2e and Fig. S2f). In the last 2 years, Mozambique has rapidly expanded their ART programme (Fig. S4b) and if they maintain this rate of expansion, while improving efforts to retain people in care, the number of people starting ART each year will increase by about four times by 2015 (Fig. S4f) after which the rate will have to be maintained but the numbers starting each year will fall as the number of people not on ART falls (Fig. S4c), Namibia will have to increase their rate of starting people on treatment over the next few years but after that, the number of people starting treatment should fall (Fig. S5f). Since 2010, South Africa has greatly expanded the rate at which people start treatment (Fig. S6b) and if this is maintained and treatment is offered to all HIV-positive people, the number of people starting ART each year should fall as the prevalence of people not on ART falls (Fig. S6f). Swaziland has increased the rate at which people start treatment (Fig. S7b), and if they continue in this way, the numbers needing to start treatment each year should fall (Fig. S7f). In all of southern Africa, the epidemics of HIV initially led to substantial increases in the incidence of TB disease, from 3.5 times in Botswana (Fig. S1g) to 6.9 times in Zimbabwe (Fig. S9g), but the decline in the prevalence of HIV, because of declines in transmission arising from changes in behaviour as well as in the increasing coverage of ART, subsequently led to corresponding declines in the incidence of TB disease (Table 1 and Figs. S1 to S9). The large differences in the impact of HIV on TB is reflected in the notification rate ratios (NRR) for those with and without HIV which vary from about 8 in Botswana, Lesotho, Mozambique and Swaziland to about 17 in Zambia and Zimbabwe (Table 1). The reason for these differences in the impact of HIV on TB in different countries is not clear, but it has been suggested that it could be due to the differences in the CD4+ cell counts in HIV-negative people

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in different countries [9]. The widespread provision of ART has further reduced the incidence of TB disease. A metaanalysis [9] showed that ART reduces the incidence of TB by 61 % (54 to 68 %) irrespective of the CD4+ cell count at which people start treatment so that ART will reduce the incidence of TB but not to the levels in HIVnegative people.

Discussion The countries of southern Africa have experienced the worst epidemics of HIV of any region in the world, but the reasons for this are unclear. The relative timing of the epidemic is not unexpected. While the severe restrictions on people’s movement in South Africa under Apartheid may have delayed the spread of HIV to that country and its immediate neighbours, the system of oscillating migrant labour, on the gold mines in particular, undoubtedly resulted in the break-down of family life and high rates of extra-marital partnerships, often with sex workers who themselves were drawn from all the countries in the region [25–27, 28•, 29], leading to the rapid spread of HIV in time and in space. The civil war in Mozambique may have delayed the spread of HIV within that country although men from southern Mozambique have traditionally been employed on the gold mines in South Africa, resulting in higher rates of HIV transmission in the southern than in the central and northern regions of Mozambique. This analysis raises questions that need further study. Changes in behaviour associated with the risk of transmission, including patterns of partnership formation, may have led to the substantial declines in incidence and prevalence in Malawi, Namibia, Zambia and Zimbabwe and smaller declines in Botswana, Lesotho, Mozambique and South Africa. The model suggests that in Swaziland, behaviour may have changed in a way that led to an increase in prevalence as recently as 2008 but this needs further investigation. The decline in incidence in Zimbabwe is illustrated most dramatically in Harare. In 2002, Hargrove, drawing on data from the ZVITAMBO study, was the first to record the nature and extent of this decline [14]. The prevalence of HIV in women aged 15 to 20 years attending antenatal clinics in Harare reached 22 % in 1996 but fell to 2 % by 2012 [Hargrove, pers. comm.]. The decline in the incidence of HIV in Harare has been attributed to changes in behaviour resulting from an increase in awareness of the dangers of AIDS and the ever more apparent increases in mortality [14]; the relatively high levels of education in Zimbabwe may have contributed to people’s willingness to change behaviour. Other studies in Zimbabwe have reached similar conclusions [13, 15•, 30] but remain to be proven. Awad and Abu-Raddad [12•] found evidence for changes of a similar magnitude to ours in the risk of HIV infection in Botswana, Namibia, Malawi and Zimbabwe but estimate a

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40 % drop in transmission in Lesotho which we do not (Fig. 2c and Table 1). Using data only up to 2010, they did not find evidence for an increase in transmission in Swaziland as appears to be the case in this paper. They hypothesize that small changes in individual behaviour may have collectively reduced aggregate sexual behaviour sufficiently to disrupt the sexual network and hence transmission. The fact that HIV transmission fell significantly before the advent of ART is encouraging, and programmes to ensure that women at high risk, especially young women and sex workers, are able to access pre-exposure prophylaxis [31] and other methods of prevention, intra-venous drug users are able to access clean needles and safe injecting sites [32], young men are offered voluntary medical male circumcision [33], and sex workers and men who have sex with men are encouraged to consistently use condoms during sex [34], must all be expanded. Earlier studies have shown great variation in the prevalence of HIV within countries in South Africa [26], India [35] and Kenya [5, 36] and a better understanding of sub-national variation and the links between different areas, especially cities, arising from migration [28•], may determine the efficiency and effectiveness of the response to the HIV epidemic. More recent mapping studies in the region confirm the importance of understanding HIV epidemics at sub-national level in order to better inform the response at lower geographic levels [37]. Life-saving ART is now available in all countries of the region although the extent to which people have been tested, treated and started on ART varies greatly with Botswana leading the way and Lesotho lagging behind (Fig. 2 and Table 1). In the last few years, Mozambique, South Africa and Swaziland have greatly increased the rate at which people are started on ART and, provided countries adopt a policy of early treatment while retaining people in care to ensure high levels of adherence, it should be possible to reach the UNAIDS 90-9090 target for testing, treatment and suppression of viral load by 2020 and to end the AIDS epidemic by 2030. The most important limitation of this analysis stems from the quality of the data. The HIV prevalence data, on which the Spectrum estimates are based and on which this analysis depends, are often not collected consistently over time or in places, and much better data are needed both to make reliable estimates of national trends and to explore sub-national differences. Patient monitoring is weak in most countries, and the data on ART coverage need to be treated with caution and caveats. Most countries need better routine surveillance and better patient monitoring. The annual antenatal clinic surveys in South Africa [38] provide an example of a strong routine national surveillance system. These surveys have been carried out annually, with a sample of more than 20,000 women for the last 24 years making it possible to map the prevalence of HIV in women by age down to a district level. However, the power of these data

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would be greatly enhanced if they were not only to measure the prevalence of HIV but were to (1) test the women for the presence of anti-retroviral drugs, which would give a good measure of the coverage of ART, (2) do a viral load test, which would give a good measure of compliance and the likelihood of developing drug resistance, (3) measure the incidence of infection using cross-sectional incidence assays [39, 40] and (4) make the raw data available for analysis by the scientific community. The marginal cost of the additional tests would be slight; the contribution to our understanding of the epidemic and the impact of the response would be great. Malawi provides an example of one of the best nationwide patient monitoring systems in the world, as reported by the Department of Health [41•]. The comprehensive national reporting framework is based on quarterly team visits and evaluations of health facilities, while electronic recording systems are used in the larger monitoring sites. The site-visiting teams include national and district level staff, experienced clinicians, nurses, monitoring and evaluation staff, PMTCT and ART coordinators, programme officers from the Ministry of Health and implementing partners. All health facilities with ART services are visited quarterly, records are examined, and staff are supported, advised and encouraged in their work, all of which contribute to successful patient monitoring. These visits also provide a platform for data quality audits to ensure complete and high quality data systems. The audits help to identify deviations from clinical protocols and allow the supervision teams to provide targeted mentoring and clinical advice. Data are entered into a database from which national reports are produced on a regular basis. In addition, data are used for logistics and supply chain management of HIV commodities. While the cost of the patient monitoring system in Malawi is about US$1 million per year, this is only 1 % of the total cost of the HIV programme. Good monitoring and patient follow-up is essential, and it is important to develop a network for sharing best practices in the region so that countries can benefit from other’s experiences in the region. In addition, viral load and drug resistance surveillance will be essential for achieving the treatment targets, but will require resources and need investment in human, technical and financial systems. The benefits of routine viral load testing are clear, and data from sites in Malawi and Mozambique show that 85 % of people on ART have viral suppression within 6 months of starting treatment [42]. The international community should invest in developing analytical capacity in all of these countries. The cost of collecting better data and training people in the region to assemble, analyse, interpret and use this data to improve their HIV control programmes and gain a better understanding of the dynamics of the epidemic and the prospect for control would be a small fraction of the cost of HIV/AIDS to health services, local communities and the state but would dramatically improve our understanding of what needs to be done and strengthen countries’ capacity and ability to implement good control.

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Conclusions The variation in the size of the epidemics and the response to the epidemics among and within countries are still not well understood [5, 26]. HIV incidence has been falling in all countries in the southern Africa region in recent years because of the natural dynamics of the epidemic, behaviour change, the roll-out of ART and other prevention efforts. There is strong evidence for declines in HIV transmission related to behaviour change or other interventions in Malawi, Namibia, Zambia, Zimbabwe and Botswana but much less, if any, in Lesotho, and South Africa, while in Swaziland, there may have been an increase in risky sexual behaviour as recently as 2008 when the adult prevalence of HIV had reached 25 % and AIDSrelated mortality was about 2 % p.a. Continued efforts to strengthen behaviour change programmes and to make them more effective remain important. While some data on behaviour change are available from national surveys, the data are limited because of the biases associated with measuring sexual behaviour and because population surveys are not conducted on a regular basis in the majority of countries. A better understanding of how behaviour has changed across countries in the region and its role in the changing HIV epidemics is urgently needed. The roll-out of anti-retroviral therapy varies across the countries in the region, but most have adopted the 2013 WHO treatment guidelines and are now committed to starting treatment at a CD4+ cell count of 500/μL even though this has different implications for different countries because of the differences in CD4+ cell count distributions in HIV-negative people among countries [43]. Some countries in the region, for example, Zambia, are considering a policy of starting people on treatment as soon as they are found to be HIV-positive [44] offering infected individuals the best prognosis while protecting their partners and the children of pregnant mothers from being infected with HIV. If this is done in all countries, they should all be able to reach the 90-90-90 goal by 2020. However, if the roll-out of ART continues to be expanded, it is essential that people are retained in care, that they stay on treatment and that they adhere to their drug regimens. In Mozambique, for example, only 53 % of patients on ART are currently retained in care 3 years after starting treatment [45]. If this is not rectified, it could lead to viral rebound, ongoing transmission and the development of drug resistance. In Chiradzulu, a rural district in Malawi, encouraging results were found on the cascade of care: among those found to be living with HIV, 78 % had been previously diagnosed, 73 % were linked to care, 65 % were on ART and among those on ART, 91 % had a viral load of less than 1000 copies/mL [46]. The end of AIDS does not imply the end of HIV as all of those people living with HIV who are on ART will have to be kept on ART for the rest of their natural lives, and we cannot expect to end HIV for another 50 years unless a cure is

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developed [47]. While ART restores immune function, and the earlier treatment is started the greater is the benefit [48], it does not do so completely [48, 49]. This is reflected in the observation that ART reduces the incidence of TB by about 61 % (54–68 %) irrespective of how long the person has been infected with HIV [9]. The control of TB in southern Africa, where the prevalence of HIV is high, depends on two key observations. First, as Corbett first showed, the increased incidence of TB in people infected with HIV is more or less balanced by the decrease in the duration of disease [50]; as a result, the increase in TB notification rates, associated with HIV, does not lead to an increase in the prevalence of TB disease and/or of TB transmission. For HIV-negative people, control of TB therefore depends entirely on traditional methods of control. Unfortunately, we still lack a sensitive and specific, but also cheap, point-of-care test for TB and for drug-resistant TB, and patients are treated with toxic drugs that must be administered for 6 months. The development of new tests and drugs for TB disease remains a priority. Second, ART reduces the risk of TB disease by about 61 % but does not reduce it to the level in HIV-negative people. TB patients who are infected with HIV should be started on ART as soon as possible, but further control of TB in HIV-positive people depends on control in HIV-negative people who drive the epidemic. It is of interest that the TB notification rate ratio for people with and without HIV differs widely across the region, for reasons that are not well understood, although it has been suggested that this is because of the wide disparity of CD4+ cell counts within and among different countries in the region [9]; if this were better understood, and if it is also true in HIVnegative people, it could help to improve TB case finding. The response to the HIV epidemic in the southern Africa region in recent years has already led to significant reductions in the number of new infections and AIDS-related deaths. Our analysis which focuses on nine high prevalence countries confirms the results of the recent UNAIDS Fast-Track report that the 90-90-90 treatment target is achievable and that, combined with other prevention efforts, it will be possible to end the AIDS epidemic by 2030 [51]. However, several challenges remain: issues related to human rights as well as stigma and discrimination against people living with HIV need to be dealt with more effectively; the vulnerabilities of women and girls in the region, including gender-based violence and gender inequalities, must be addressed; access to services needs to be expanded to all people living with HIV; resource gaps need to be filled, and countries need to deliver services that are effective, sustainable and of high quality while systemic weaknesses in service delivery need to be addressed; analytical capacity in the region remains weak and must be strengthened. Given flat funding for HIV treatment and prevention, it will be important to track whether the new strategy of the

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President’s Emergency Plan for AIDS Relief (PEPFAR) programme to redirect care and treatment resources from low to high prevalence areas can help southern Africa reach the needed ART expansion and HIV prevention goals that we have outlined in our article [52]. Finally, political commitment to keeping HIV high on the health agenda will ultimately determine the success or otherwise of efforts to control HIV in southern Africa. Disclaimer The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of UNAIDS. Compliance with Ethics Guidelines Conflict of Interest Brian G. Williams, Eleanor Gouws, Pierre Somse, Mpho Mmelesi, Chibwe Lwamba, Trouble Chikoko, Erika Fazito, Mohamed Turay, Eva Kiwango, Pepukai Chikukwa, Henry Damisoni and Michael Gboun declare that they have no conflict of interest. Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.

References Papers of particular interest, published recently, have been highlighted as: • Of importance

1.

2.•

3.

4. 5. 6. 7.

8.

9.

UNAIDS. The Gap Report. Geneva: United Nations Joint Programme on HIV/AIDS. Available from: http://www.unaids. org/en/resources/documents/2014/20140716_UNAIDS_gap_ report (2014). UNAIDS. Ambitious treatment targets: writing the final chapter of the AIDS epidemic. Geneva: United Nations Joint Programme on HIV/AIDS. Available from: http://www.unaids.org/en/media/ unaids/contentassets/documents/unaidspublication/2014/JC2670_ UNAIDS_Treatment_Targets_en.pdf (2014). This documents sets and defines the UNAIDS targets for 2020 and 2030. Department of International Economic and Social Affairs. World demographic estimates and projections, 1950–2025 United Nations, New York 1988. Williams BG. Responding to the AIDS epidemic in Angola. arXiv. Electronic version: http://arxiv.org/pdf/1401.6430v1 (2014). Williams BG. Optimizing control of HIV in Kenya. arXiv. Electronic version: http://arxiv.org/abs/1407.7801 (2014). Williams BG. Managing HIV/AIDS in Malawi. arXiv. Electronic version: http://arxiv.org/abs/1409.4628 (2014). Williams BG. Elimination of HIV in South Africa through expanded access to antiretroviral therapy: cautions, caveats and the importance of parsimony. arXiv. Electronic version: http://arxiv.org/pdf/ 1403.7104 (2014). Williams BG. Fitting and projecting HIV epidemics: data, structure and parsimony. arXiv. Electronic version: http://arxiv.org/abs/1304. 3720 (2014). Williams BG, Granich R, De Cock K, Glaziou P, Sharma A, Dye C. Anti-retroviral therapy for the control of HIV-associated

Curr HIV/AIDS Rep (2015) 12:196–206

10.

11.

12.•

13.

14.

15.•

16.

17.

18.

19.

20. 21. 22.

23.

24. 25.

26.

27.

28.•

tuberculosis: modelling the potential effects in nine African countries. Proc Natl Acad Sci U S A. 2010;107:17853–4. WHO. Global tuberculosis control: surveillance, planning, financing. Geneva: World Health Organization; 2014. Available from: http://apps.who.int/iris/bitstream/10665/137094/1/ 9789241564809_eng.pdf?ua=1. Stover J, Brown T, Marston M. Updates to the Spectrum/Estimation and Projection Package (EPP) model to estimate HIV trends for adults and children. Sex Transm Infect. 2012;88 Suppl 2:i11–6. Awad SF, Abu-Raddad LJ. Could there have been substantial declines in sexual risk behavior across sub-Saharan Africa in the mid1990s? Epidemics. 2014;8:9–17. A similar analysis but with fewer countries and output variables. Halperin DT, Mugurungi O, Hallett TB, Muchini B, Campbell B, Magure T, et al. A surprising prevention success: why did the HIV epidemic decline in Zimbabwe? PLoS Med. 2011;8, e1000414. Hargrove JW, Humphrey JH, Mahomva A, Williams BG, Chidawanyika H, Mutasa K, et al. Declining HIV prevalence and incidence in perinatal women in Harare, Zimbabwe. Epidemics. 2011;3:88–94. Gregson S, Gonese E, Hallett TB, Taruberekera N, Hargrove JW, Lopman B, et al. HIV decline in Zimbabwe due to reductions in risky sex? Evidence from a comprehensive epidemiological review. Int J Epidemiol. 2010;39:1311–23. Attempt to explain the decline in the epidemic of HIV in Zimbabwe. Robb M. RV217: The early capture HIV cohort study (ECHO). Prospective identification of HIV infection prior to acute viraemia among high-risk populations. Poster Abstract 404. Presented at the Keystone Symposium. Whistler, British Columbia, Canada (2011). Robb M. Viral dynamics and immune response in acute infection and their impact on viral set-point. In: Plenary session 02: a new look at the transmission event AIDS 2012 vaccine; Boston, 9–12 September 2012. Abdool Karim SS, Naidoo K, Grobler A, Padayatchi N, Baxter C, Gray AL, et al. Integration of antiretroviral therapy with tuberculosis treatment. N Engl J Med. 2011;365:1492–501. Harington JS, McGlashan ND, Chelkowska EZ. A century of migrant labour in the gold mines of South Africa. J S Afr Inst Min Metall. 2004; 65–71. Brink B, Clausen L. The acquired immune deficiency syndrome. J Mine Med Off Assoc. 1987;63:10–7. James WG. Our precious metal: African labour in South Africa’s gold industry, 1970–1990. Cape Town: David Philip; 1992. Crush J, Jeeves A, Yudelman D. South Africa’s labour empire: a history of black migrancy to the gold mines. Boulder: Westview Press; 1991. Maja, B. Towards a fairer deal for migrants in the South African Economy, Labour Market Review, Department of Labour, Republic of South Africa; 2007 Demographics of Swaziland. Available from: http://en.wikipedia. org/wiki/Demographics_of_Swaziland#Population_2. Williams BG, MacPhail C, Campbell C, Taljaard D, Gouws E, Moema S, et al. The Carletonville-Mothusimpilo project: limiting transmission of HIV through community-based interventions. S Afr J Sci. 2000;96:351–9. Williams BG, Gouws E. The epidemiology of human immunodeficiency virus in South Africa. Philos Trans R Soc Lond Ser B Biol Sci. 2001;356:1077–86. Lurie MN, Williams BG, Zuma K, Mkaya-Mwamburi D, Garnett G, Sturm AW, et al. The impact of migration on HIV-1 transmission in South Africa: a study of migrant and nonmigrant men and their partners. Sex Transm Dis. 2003;30:149–56. Lurie MN, Williams BG. Migration and health in Southern Africa: 100 years and still circulating. Health Psychol Behav Med. 2014;2(1):34–40. Discussion of the role of migration in the spread of HIV in southern Africa.

205 29.

30.

31.

32.

33.

34.

35.

36.

37.

38.

39.

40.

41.•

42.

43.

44.

45. 46.

Williams BG, Gouws E, Lurie M, Crush J. Spaces of vulnerability: migration and HIV/AIDS in South Africa. 2002; Southern African migration project: migration policy series No 24. Cape Town: Queens University; 2002. Available from: Southern Africa Migration Project http://www.queensu.ca/samp/sampresources/ samppublications/policyseries/Acrobat24.pdf Hallett TB, Gregson S, Mugurungi O, Gonese E, Garnett GP. Assessing evidence for behaviour change affecting the course of HIVepidemics: a new mathematical modelling approach and application to data from Zimbabwe. Epidemics. 2009;1:108–17. Alistar SS, Grant PM, Bendavid E. Comparative effectiveness and cost-effectiveness of antiretroviral therapy and pre-exposure prophylaxis for HIV prevention in South Africa. BMC Med. 2014;12:46. Cohen B, Trussell J (Editors) Preventing and mitigating AIDS in Sub-Saharan Africa: research and data priorities for the social and behavioral sciences (Washington, DC) (1996). Auvert B, Marseille E, Korenromp EL, Lloyd-Smith J, Sitta R, Taljaard D, et al. Estimating the resources needed and savings anticipated from roll-out of adult male circumcision in Sub-Saharan Africa. PLoS One. 2008;3, e2679. Batist E, Brown B, Scheibe A, Baral SD, Bekker LG. Outcomes of a community-based HIV-prevention pilot programme for township men who have sex with men in Cape Town, South Africa. J Int AIDS Soc. 2013;16 Suppl 3:18754. Williams BG, Granich R, Chauhan LS, Dharmshaktu NS, Dye C. The impact of HIV/AIDS on the control of tuberculosis in India. Proc Natl Acad Sci U S A. 2005;102:9619–24. WHO. A brief history of tuberculosis control in Kenya. Geneva: World Health Organization; 2009. Available from: http:// whqlibdoc.who.int/publications/2009/9789241596923_eng.pdf. Larmarange J, Bendaud V. HIV estimates at second subnational level from national population-based surveys. AIDS. 2014;28 Suppl 4:S469–76. Anonymous. National Antenatal Sentinel HIV and Syphilis Prevalence Survey in South Africa, 2012. Department of Health, South Africa. Available from: www.doh.gov.za (2012). Kassanjee R, Pilcher CD, Keating SM, Facente SN, McKinney E, Price MA, et al. Independent assessment of candidate HIV incidence assays on specimens in the CEPHIA repository. AIDS. 2014;28:2439–49. Kim AA, Hallett T, Stover J, Gouws E, Musinguzi J, Mureithi PK, et al. Estimating HIV incidence among adults in Kenya and Uganda: a systematic comparison of multiple methods. PLoS One. 2011;6, e17535. Malawi Department of Health. Integrated HIV Program Report: October – December. Blantyre: Ministry of Health, Malawi. Available from: http://www.hivunitmohmw.org/uploads/Main/ Quarterly_HIV_Programme_Report_2012_Q4.pdf (2012). Patient monitoring in Malawi. UNAIDS. Regional consultation to set new targets for the HIV response post-2015 in Eastern and Southern Africa. Johannesburg; 19–20 May 2014. Available from: www.unaidsrstesa.org (2014). Williams BG, Korenromp EL, Gouws E, Schmid GP, Auvert B, Dye C. HIV Infection, antiretroviral therapy, and CD4+ cell count distributions in African populations. J Infect Dis. 2006;194:1450–8. The Ministry of Health and Ministry of Community Development MaCH. Zambia consolidated guidelines for treatment and prevention of HIV infection. Lusaka. Available from: https://xa.yimg.com/ …/Zambia+HIV+Consolidated+Guidelines+−+Feb+2014+ Version+−final+draft-3 (2014). Ministerio da Saude SNdS. Relatório Semestral das Actividades Relacionadas ao HIV/SIDA. Moçambique. 2014. Maputo. Maman D. Chiradzulu HIV impact population survey. Ministry of Health of Malawi, Médecin Sans Frontières; 2013. Available from: http://www.epicentre.msf.org.

206 47.

48.

49.

Curr HIV/AIDS Rep (2015) 12:196–206 Deeks SG, Autran B, Berkhout B, Benkirane M, Cairns S, Chomont N, et al. Towards an HIV cure: a global scientific strategy. Nat Rev Immunol. 2012;12:607–14. Gras L, Kesselring AM, Griffin JT, van Sighem AI, Fraser C, Ghani AC, et al. CD4 cell counts of 800 cells/mm3 or greater after 7 years of highly active antiretroviral therapy are feasible in most patients starting with 350 cells/mm3 or greater. J Acquir Immune Defic Syndr Hum Retrovirol. 2007;45:183–92. Antoniou T, Gillis J, Loutfy MR, Cooper C, Hogg RS, Klein MB, et al. Impact of the data collection on adverse events of anti-HIV drugs cohort study on abacavir prescription among treatment-naive,

50. 51.

52.

HIV-infected patients in Canada. J Int Assoc Provid AIDS Care. 2014;13:153–9. Williams BG, Maher D. Tuberculosis fuelled by HIV: putting out the flames. Am J Respir Crit Care Med. 2007;175:6–8. UNAIDS. Fast-track: ending the AIDS epidemic by 2030. Geneva: United Nations Joint Programme on HIV/AIDS; 2014. Available from: http://www.unaids.org/sites/default/files/media_asset/ JC2686_WAD2014report_en.pdf. PEPFAR 3.0: Controlling the epidemic: delivering on the promise of an AIDS-free Generation. Available at http://www.pepfar.gov/ documents/organization/234744.pdf.