metadata: authors: 0: author: Fraser-Hurt,Nicole
1: author: Prestidge,Marelize
2: author: Harimurti,Pandu
3: author: Taaffe,Jessica Elizabeth
count: World
docna: 0: docna: A comprehensive review of empirical and
            modeled HIV incidence trends (1990-2012)
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Demographics,Public Health Promotion,HIV AIDS,Cholera,Communicable Diseases,Leprosy,Law and Justice Institutions
teratopic: Health, Nutrition and Population,Gender,Private Sector Development
abstracts: cdata!: An accurate measurement of HIV incidence
            is a key for policy makers and HIV program managers
            directing national HIV response. However, there is no
            perfect method to measure or estimate the rate at which new
            HIV infections occur in a population. This review compiles
            and triangulates longitudinal HIV incidence and prevalence
            data from published studies and trials, national reports and
            surveys, and the Joint United Nations Programme on HIV/AIDS
            estimates from the Spectrum model, focusing on 20 countries
            in Sub-Saharan Africa with generalized HIV epidemics. Three
            main points can be taken from this analysis of HIV incidence
            trends. First, modeled HIV incidence and nationally reported
            HIV prevalence levels in young females suggest that national
            HIV incidence has declined since 2000 in all except three
            countries analyzed (stable estimated HIV trends in Burkina
            Faso, Burundi, and Uganda), but trial and survey data
            suggest that in some demographics, HIV incidence remains
            critically high. Second, all modeled national HIV incidence
            curves and most empirically observed trends commenced a
            downward trajectory prior to the introduction of
            anti-retroviral therapy programs around 2004, suggesting the
            contribution of other factors, such as HIV prevention
            programs and natural epidemic dynamics, to this decline.
            Third, modeled HIV incidence estimates, including the
            incidence peaks in the past, exhibit much variation between
            Spectrum model versions and when new data are added,
            emphasizing the uncertainty of model outputs and the need to
            use incidence estimates with caution.
repnme: repnme: A comprehensive review of empirical and
            modeled HIV incidence trends (1990-2012)
display_title: A comprehensive review of empirical
            and modeled HIV incidence trends (1990-2012)
listing_relative_url: /research/2014/09/20229050/comprehensive-review-empirical-modeled-hiv-incidence-trends-1990-2012
topicv3: Demographic Change m1327040 1448,Pandemics m1327520 2167,HIV/AIDS m1327522 1416,Mortality m1504365 3661,Health m1504661 3480
publishtoextweb_dt: 2014-09-01T00:00:00Z
historic_topic: Health, Nutrition and Population,Gender,Private Sector Development
no_of_pages: 83
author_keys: 0: author_key: 660415
1: author_key: 664365
2: author_key: 784715
3: author_key: 1259130
model_extractions: WPS7042 Policy Research Working Paper 7042 A Comprehensive Review of Empirical and Modeled HIV Incidence Trends (1990–2012) _Jessica Taaffe Nicole Fraser-Hurt Marelize Gorgens Pandu Harimurti_ Health Nutrition and Population Global Practice Group September 2014
Policy Research Working Paper 7042

# **Abstract**

An accurate measurement of HIV incidence is a key for policy makers and HIV program managers directing national HIV response. However, there is no perfect method to measure or estimate the rate at which new HIV infections occur in a population. This review compiles and triangulates longitudinal HIV incidence and prevalence data from published studies and trials, national reports and surveys, and the Joint United Nations Programme on HIV/ AIDS estimates from the Spectrum model, focusing on 20 countries in Sub-Saharan Africa with generalized HIV epidemics. Three main points can be taken from this analysis of HIV incidence trends. First, modeled HIV incidence and nationally reported HIV prevalence levels in young females suggest that national HIV incidence has declined since 2000 in all except three countries analyzed (stable estimated HIV trends in Burkina Faso, Burundi, and Uganda), but trial and survey data suggest that in some demographics, HIV incidence remains critically high. Second, all modeled national HIV incidence curves and most empirically observed trends commenced a downward trajectory prior to the introduction of anti-retroviral therapy programs around 2004, suggesting the contribution of other factors, such as HIV prevention programs and natural epidemic dynamics, to this decline. Third, modeled HIV incidence estimates, including the incidence peaks in the past, exhibit much variation between Spectrum model versions and when new data are added, emphasizing the uncertainty of model outputs and the need to use incidence estimates with caution.

This paper is a product of the Health Nutrition and Population Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mgorgens@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# A Comprehensive Review of Empirical and Modeled HIV Incidence Trends (1990– 2012)

_Jessica Taaffe Nicole Fraser-Hurt Marelize Gorgens_<sup>_1_</sup> _Pandu Harimurti_ <u>Keywords: HIV/AIDS, incidence prevalence estimates, epidemic model</u> **JEL code** : Other

> 1 The authors are staff or consultants with the Global HIV and AIDS Program, HNP, Human Development Network. The authors wish to thank and acknowledge Jeff Eaton, Dan Hogan, Kim Powers, Mary Mahy, and Laith Jamal Abu Raddad for reviewing and providing useful comments and insights on this document. The authors would also like to acknowledge the support of David Wilson, Director of the Global HIV/AIDS Program of the World Bank.
|**Table of Contents**|
|---|
|Summary.........................................................................................................................................5|
|List of Acronyms............................................................................................................................7|
|List of Tables..................................................................................................................................7|
|List of Figures................................................................................................................................8|
|1. Rationale...............................................................................................................................10|
|2. Introduction..........................................................................................................................11|
|2.1. Monitoring incidence...................................................................................................11|
|2.1.1. Empirical measurements of HIV prevalence used to inform incidence<br>estimates ...............................................................................................................12|
|2.1.1.1.<br>ANC Sentinel Surveillance...................................................................12|
|2.1.1.2.<br>Demographic and Health Surveys/AIDS Indicator Surveys............13|
|2.1.2. Empirical measurement of HIV incidence ....................................................14|
|2.1.2.1.<br>Cohort studies and ALPHA Network..................................................14|
|2.1.2.2.<br>Laboratory assays................................................................................15|
|2.1.3. Modelled incidence .........................................................................................15|
|2.2. Effect of ART on prevalence data...............................................................................18|
|3. Methods of analysis.............................................................................................................20|
|4. Analysis of incidence trends...............................................................................................20|
|4.1. Countries with a stabilized incidence trend..............................................................22|
|4.1.1. Burkina Faso.......................................................................................................22|
|4.1.2. Burundi...............................................................................................................23|
|4.1.3. Uganda................................................................................................................24|
|4.2. Countries with a modest downward trend ..............................................................25|
|4.2.1. Central African Republic..................................................................................26|
|4.2.2. Congo.................................................................................................................27|
|4.2.3. Ethiopia..............................................................................................................28|
|4.2.4. Ghana.................................................................................................................29|
|4.2.5. Kenya..................................................................................................................30|
|4.2.6. Nigeria................................................................................................................31|
|4.2.7. Rwanda..............................................................................................................32|
|4.2.8. Tanzania.............................................................................................................33|
|4.3. Countries with a moderate downward trend .........................................................34|
|4.3.1. Lesotho..............................................................................................................34|
|4.3.2. South Africa.......................................................................................................35|
|4.3.3. Zimbabwe..........................................................................................................38|
|4.4. Countries with a steep downward trend .................................................................39|
|4.4.1. Botswana...........................................................................................................39|
|4.4.2. Malawi...............................................................................................................40|
|4.4.3. Mozambique.....................................................................................................41|
|4.4.4. Namibia..............................................................................................................42|

3
|4.4.5. Swaziland............................................................................................................43|
|---|
|4.4.6. Zambia ...............................................................................................................44|
|5. Discussion..............................................................................................................................46|
|6. Conclusion.............................................................................................................................51|
|Annex 1: Country and year specific incidence and prevalence data, including sources...52|
|Annex 2: Changes to Spectrum data curves from 2012 to 2013..........................................66|
|References...................................................................................................................................69|

4
# **SUMMARY**

Understanding and monitoring longitudinal HIV incidence trends is imperative for tracking progress towards HIV prevention goals, including how incidence trends have changed through increasing antiretroviral therapy (ART) coverage. There are several ways that HIV incidence can be assessed, empirical and modeled, and each method has its limitations. Incidence can be directly measured from prospective cohort studies, but logistical and financial restraints make this difficult to carry out at a scale that provides sufficient precision of the incidence estimate. Sampling of specimen within a population and using laboratory assays and testing algorithms that identify recent HIV infection can infer HIV incidence, though this method requires local validation against HIV incidence estimates obtained by other methods. Proxies of HIV incidence, such as HIV prevalence trends in young females, can also be used to infer incidence trends, as most HIV infections within this demographic are recent. However, this method requires repeat cross-sectional measurements over several years in order to infer any incidence trend. Finally, HIV incidence can be obtained through back-calculation models that use various HIV epidemiological data, including HIV prevalence and mortality rates, to estimate incidence within the adult population. Here, estimated incidence chiefly relies on prevalence estimates and factors that affect survival, including the effect of age and cofactors of transmission as well as the model’s accurate representation of sexual behavior and transmission dynamics.

Modeled incidence estimates provided by UNAIDS are key information that policy makers and HIV program managers use to monitor the dynamics of the HIV epidemics in various countries. Therefore, it is important to evaluate how these HIV incidence and proxies of it (HIV prevalence trends in young females) correspond to modeled incidence data. To date, no report has comprehensively triangulated and compared modelled HIV incidence against several forms and sources of directly and indirectly measured incidence across multiple countries.

We therefore compared UNAIDS incidence estimates from the Spectrum model to directly measured incidence from published studies and trials. We also compared UNAIDS modeled estimates to HIV prevalence trends in young females, as a proxy of incidence, using cross-sectional data from published studies and trials and national surveys, such as from antenatal sentinel surveillance and population-based surveys (Demographic and Health Survey/AIDS Indicator Survey data). This report represents a comprehensive source of both modeled and empirical incidence trends in several countries.

A select group of countries were chosen for this analysis due to their known generalized HIV epidemics (epidemics in which HIV is highly disseminated within the general population): Botswana, Burundi, Burkina Faso, Congo, Central African Republic, Ethiopia, Ghana, Kenya, Lesotho, Namibia, Nigeria, Malawi, Mozambique, Rwanda, South Africa, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe. Within each country, multiple forms and sources of incidence data, modeled and empirical, were graphed alongside ART coverage data, and longitudinal trends are described. Specifically, incidence trends 5
were evaluated for 1) rising or falling curves and rates of change between 1990 and 2012, 2) how well empirical and modeled UNAIDS data match each other, and 3) dynamics before and during the ART scale-up period (2004 –2012).

Briefly, it was found that the countries analyzed experienced overall decreases in UNAIDS modeled incidence since 2000, with the exception of Burkina Faso, Burundi, and Uganda. Modeled incidence in these three countries has shown very little change, and in some cases, a slight upward trend through some years. There is great variation in the slope of downward trends across countries since 2004, with some having small and moderate declines (slope -0.01 to -0.06), and others with steeper and more dramatic declines (slope -0.08 to -0.14). Available antenatal sentinel surveillance and populationbased survey data in young females support these HIV trends, though the dynamics may slightly differ by rates of change and/or timing. In Nigeria, the Spectrum-estimated HIV incidence decline is not supported by the ANC-derived HIV prevalence in young females in the time period 2005-2010. Relatively few directly measured incidence data have been reported from HIV intervention trials or observational studies (we found 43 sources of measured incidence data from 10 of the selected countries); however, the reported HIV incidence levels tend to be higher than modelled national incidence estimates and are likely due to intentional targeting of high risk populations in research. We observed that modeled HIV incidence had begun to decline prior to ART scale-up, and in most cases where HIV data prior to 2004 were available, HIV prevalence trends in young females 15-24 supported this incidence decline prior to ART scale-up. This analysis, however, was not designed to assess the impact of the increasing coverage of ART on HIV incidence trends in the selected countries. Therefore, it is difficult to determine if or how much ART scale-up has contributed to incidence decline, though its effect cannot be ruled out, as recent data show that increased ART coverage reduces transmission risk in a South African hyperendemic HIV situation (Tanser et al. 2013). At the same time, new analyses from South Africa (Shisana et al. 2014) suggest no change in HIV incidence in two inter-survey periods stretching from 2005 to 2012, despite significant ART scale-up in that time period.

In conclusion, three main points can be taken from this analysis of HIV incidence trends in Sub-Saharan Africa: 1) Modeled HIV incidence and nationally reported HIV prevalence levels in young females suggest that national HIV incidence has declined since 2000 in all except three countries analyzed (stable estimated HIV trends in Burkina Faso, Burundi, and Uganda), but trial and survey data suggest that in some demographics, HIV incidence remains critically high; 2) All modelled national HIV incidence curves and most empirically observed trends commenced a downward trajectory prior to the introduction of ART programs around 2004, suggesting the contribution of other factors, such as HIV prevention programs and natural epidemic dynamics, to this decline; and 3) Modelled HIV incidence estimates including the incidence peaks in the past, exhibit much variation between Spectrum model versions and when new data is added, emphasizing the uncertainty of model outputs and the need to use incidence estimates with caution.

6
# **List of Acronyms**

AIDS Acquired Immune Deficiency Syndrome ANC Antenatal Clinic AIS AIDS Indicator Survey ART Antiretroviral Therapy BAIS Botswana AIDS Impact Survey DHS Demographic and Health Surveys GARPR Global AIDS Response Progress Reporting HH Household HIV Human Immunodeficiency Virus UNAIDS United Nations Joint Program on AIDS UNGASS United Nations General Assembly

# **List of Tables**

**Table 1** . Types of HIV incidence data to be analyzed and comparisons made.......................21

**Table 2** . Summary of HIV incidence trends and ART coverage: Countries with a stabilized trend 2004-2012.........................................................................................22

**Table 3** . Summary of HIV incidence trends and ART coverage: Countries with a modest downward trend 2004-2012 .................................................................. 26

**Table 4.** Summary of HIV incidence trends and ART coverage: Countries with a moderate downward trend 2004-2012.............................................................. 34

**Table 5.** Summary of HIV incidence trends and ART coverage: Countries with a steep downward trend 2004-2012 ................................................................... 39

7
# **List of Figures**

|**Figure 1.**Countries for which 2010 Spectrum software shows greater change 2001-2009............17|
|---|
|**Figure 2**. Countries for which 2012 Spectrum software shows greater change 2001-2009............17|
|**Figure 3.**Burkina Faso: HIV prevalence (females 15-24) and modelled incidence (adults 15-49).23|
|**Figure 4.**Burkina Faso: HIV prevalence (females 15-24) and national ART coverage.......................23|
|**Figure 5.**Burundi: HIV prevalence (females 15-24) and modelled incidence (adults 15-49)..........24|
|**Figure 6**. Burundi: HIV prevalence (females 15-24) and national ART coverage................................24|
|**Figure 7**. Uganda: HIV prevalence (young females) and incidence (adults).........................................25|
|**Figure 8**. Uganda: HIV prevalence (young females) and national ART coverage.................................25|
|**Figure 9.**Uganda: HIV incidence (adults) and national ART coverage....................................................25|
|**Figure 10.**Central African Republic: HIV prevalence (females 15-24)<br>and modelled incidence (adults 15-49)...........................................................................................................27|
|**Figure 11**. Central African Republic: HIV prevalence (females 15-24)<br>and national ART coverage..................................................................................................................................27|
|**Figure 12.**Congo: HIV prevalence (females 15-24) and modelled incidence (adults 15-49)...........27|
|**Figure 13**. Congo: HIV prevalence (females 15-24) and national ART coverage..................................27|
|**Figure 14.**Ethiopia: HIV prevalence (females 15-24) and modelled incidence (adults 15-49).......28|
|**Figure 15.**Ethiopia: HIV prevalence (females 15-24) and national ART coverage..............................28|
|**Figure 16.**Ghana: HIV prevalence (females 15-24) and incidence (adults).........................................29|
|**Figure 17**. Ghana: HIV prevalence (females 15-24), incidence (adults)<br>and national ART coverage.................................................................................................................................29|
|**Figure 18**. Kenya: HIV prevalence (young females) and incidence (adults)..........................................30|
|**Figure 19.**Kenya: HIV prevalence (young females), incidence (adults),<br>|
|and national ART coverage..................................................................................................................................30|
|**Figure 20**. Nigeria: HIV prevalence (females 15-24) and modelled incidence (adults 15-49).........31|
|**Figure 21**. Nigeria: HIV prevalence (females 15-24) and national ART coverage................................31|
|**Figure 22**. Rwanda: HIV prevalence (females 15-24) and incidence (adults)......................................32|
|**Figure 23.**Rwanda: HIV prevalence (females 15-24), incidence (adults),|
|and national ART coverage................................................................................................................................32|
|**Figure 24**. Tanzania: HIV prevalence (young females) and incidence (adults).................................. 33|
|**Figure 25.**Tanzania: HIV prevalence (young females), incidence (adults),|
|and national ART coverage.................................................................................................................................33|
|**Figure 26.**Lesotho: HIV prevalence (females 15-24) and modelled incidence (adults 15-49).......35|
|**Figure 27**. Lesotho: HIV prevalence (females 15-24) and national ART coverage..............................35|
|**Figure 28.**South Africa: HIV prevalence (young females) and incidence (adults).............................36|
|**Figure 29.**South Africa: HIV prevalence (young females) and national ART coverage.....................36|
|**Figure 30.**South Africa: HIV incidence (adults) and national ART coverage.......................................36|
|**Figure 31.**South Africa: Modelled and Directly Measured HIV incidence (adults)..........................37|
|**Figure 32.**Zimbabwe: HIV prevalence (young females) and incidence (adults).................................38|
|<br>**Figure 33**. Zimbabwe: HIV prevalence (young females), incidence (adults)|
|and national ART coverage.................................................................................................................................38|
|**Figure 34**. Botswana: HIV prevalence (females 15-24) and modelled incidence (adults 15-49)....40|
|**Figure 35.**Botswana: Prevalence (females 15-24) and national ART coverage..................................40|
|**Figure 36**. Malawi: HIV prevalence (young females) and incidence (adults).......................................41|

8
|**Figure 37**. Malawi: HIV prevalence (young females), incidence (adults),|
|---|
|and national ART coverage.................................................................................................................................41|
|**Figure 38**. Mozambique: HIV prevalence (females 15-24)<br>and modelled incidence (adults 15-49)..........................................................................................................42|
|**Figure 39.**Mozambique: HIV prevalence (females 15-24) and national ART coverage...................42|
|**Figure 40.**Namibia: HIV prevalence (females 15-24) and modelled incidence (adults 15-49).......43|
|**Figure 41.**Namibia: HIV prevalence (females 15-24) and national ART coverage.............................43|
|**Figure 42**. Swaziland: HIV prevalence (females 15-24), and modelled incidence (adults 15-49)...44|
|**Figure 43.**Swaziland: HIV prevalence (females 15-24) and national ART coverage..........................44|
|**Figure 44.**Zambia: HIV prevalence (young females) and incidence (adults) ......................................45|
|**Figure 45.**Zambia: HIV prevalence (young females), incidence (adults),<br>and national ART coverage..................................................................................................................................45|

9
# **1. RATIONALE**

Accurate and representative HIV incidence estimates are imperative for policy makers and program managers to be able to assess progress in meeting HIV programmatic goals and make decisions in regard to allocation of financial and health resources. While modeled UNAIDS Spectrum incidence estimates are valuable, it is important to remember that these estimates are derived from ANC and DHS prevalence data and rely on multiple assumptions, which are imperfect for the reasons to be discussed further in this document.

Triangulation of multiple forms of incidence data, including from published studies and trials, provides a comprehensive illustration of HIV incidence trends, though few reports have done so. Some reports of incidence trends at the country level exist, but are restricted to those using sentinel surveillance and DHS/AIS data, with little, if any measured incidence data from clinical trials or other studies. Those that have done this for multiple countries within Sub-Saharan Africa have concentrated on comparing ANC, DHS, and Spectrum data (Mahy, Garcia-Calleja, and Marsh 2012; Montana, Mishra, and Hong 2008). None of these reports have also described these incidence trends in relation to ART scale-up. In a technical meeting on the large estimated declines in HIV incidence in countries with generalized HIV epidemics in 2013, the importance of going back to observational HIV data and drawing comparisons between such real-world data and modeled incidence trends was emphasized (HIV Modelling Consortium, 18-19 Sept 2013 London).

As such, the rationale for this data review was to triangulate multiple types of HIV incidence data and describe trends prior to and through ART scale-up within a select group of Sub-Saharan countries. Specifically, this document focuses on assessing incidence trends between 1990 and 2012, comparing directly measured incidence from clinical and observational cohort studies, indirectly measured incidence proxies (prevalence among females age 15-24), and modeled incidence estimates (Spectrum).

10
# **2. INTRODUCTION**

To address and meet Millennium Development Goal (MDG) 6 targets of reversing HIV/AIDS spread by 2015, a firm commitment to HIV prevention efforts must be maintained globally and nationally. Indeed, national and regional strategies call for a 50% reduction in the number of new HIV infections by 2015 (Southern African Development Community 2009; Assembly 2012) and national-level indicators and targets have been established to monitor progress towards this goal.

As we near 2015, it is important to assess progress that has been made in reaching HIV prevention goals. Reducing HIV incidence is the main goal and indicator of successful HIV prevention, and therefore, accurate monitoring and assessment of temporal HIV incidence trends at national and sub-national levels is imperative. A number of methods exist in which the rate at which new HIV infections occur can be determined, though each method has its limitations and biases. Additionally, increasing ART coverage in many countries has introduced further complications of inferring incidence levels from HIV prevalence data. Since each of the current methods is imperfect at determining HIV incidence, it is useful to triangulate and compare different incidence estimates to gain an understanding of the rate new infections occur in a population.

The next sections describe the different methods and structures in place used to monitor incidence and how ART scale-up has influenced these estimates. This is followed by a discussion of methods and data sources used in this review and description of incidence trends country by country. Sub-Saharan countries with generalized HIV epidemics were chosen for this review, including: Botswana, Burundi, Burkina Faso, Congo, Central African Republic, Ethiopia, Ghana, Kenya, Lesotho, Namibia, Nigeria, Malawi, Mozambique, Rwanda, South Africa, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe.

## **<u>2.1. Monitoring HIV incidence</u>**

National HIV prevalence trends within the overall population have been used in the past to assess the success of HIV prevention efforts, with prevalence increases clearly indicating failure to prevent new infections. However, in an era of increasing ART coverage, this method is no longer suitable to accurately understand the dynamics of the HIV epidemic. More individuals infected with HIV are living longer lives due to increased access to and improved ART drugs, thereby influencing an upward trend in HIV prevalence, as they remain longer in the HIV+ population (Zaidi et al. 2013). Indeed, Rehle et al. showed that “excess HIV prevalence,” due to longer survival on ART, added on average 2.1 percentage points to prevalence within the population aged 15-49 in South Africa’s 2008 HH Survey (Rehle et al. 2010). As such, longitudinal prevalence 11
trends, especially as ART scale-up increases, can be misleading about the rate of acquisition of new infections in the overall population.

However, prevalence trends within specific population strata can be used as a proxy for incidence within larger populations and have been established as national-level indicator data. Estimating HIV incidence from prevalence within the entire HIV positive population is challenging, as the long average duration of HIV infection makes it difficult to detect changes in new infections within older age groups and these estimate are largely dependent on assumptions made on mortality, which are becoming more complex as ART prolongs survival. Therefore, prevalence trends within the younger population (adults 15-24) are regarded as a proxy for HIV incidence within the entire population, and the highest rates of new infections typically fall within young adults, especially females (UNAIDS 2012a, 2013a). For generalized epidemics, such as seen in the majority of Sub-Saharan Africa, HIV surveillance among antenatal clinic (ANC) attendees and routine Demographic and Health or AIDS Indicator Surveys (DHS/AIS) provide important HIV prevalence data to track HIV trends at national and global levels, including for the United Nations General Assembly Special Session on HIV/AIDS (UNGASS) and the Global AIDS Response Progress Report (GARPR) data collection. It is worth noting, however, that using HIV prevalence trends within youth may become more complicated as long-term survivors of mother-to-child transmission of HIV age, and therefore may bias prevalence increases within this indicator population, such as has been seen in Eastern Zimbabwe (Eaton et al. 2013).

# **2.1.1. Empirical measurements of HIV prevalence used to inform HIV incidence estimates**

## **2.1.1.1. ANC Sentinel Surveillance**

Anonymous and unlinked testing of samples from women attending antenatal clinics is used to measure HIV prevalence within a cross-section of the sexually active population and is used to generate HIV incidence estimates. These surveys have been completed every 1-3 years and thus, can be used to longitudinally monitor HIV epidemic trends, including being the primary (and until recently, with repeat DHS, the only available data on HIV infections over time in Africa) source of prevalence trends in young people for UNGASS and GARPR indicators.

However, sentinel surveillance has many limitations and biases. First, ANC data are limited to pregnant women using ANC sentinel site clinics, biasing the data upward, as these women are sexually active and at risk for sexual transmission of HIV, and HIV prevalence data are greatly influenced by changes within this demographic. Prior to the ART era, the ANC-derived HIV prevalence was chiefly affected by lower fertility in women with advanced HIV infections and by a selection bias for unprotected sexual activity especially among younger women. Now, ART scale-up has helped restore 12
fertility in HIV+ women (Kim Powers, personal communication). Family planning promotion in HIV+ women may reduce the numbers of them attending antenatal clinics. Second, ANC survey data represent only the geographical regions covered by participating antenatal sentinel sites. It is possible that those sites that were added in later years to the sentinel system are at an earlier HIV epidemic stage than those included early or represent lower level epidemics, if more highly affected areas were chosen first for surveillance. Finally, in sites having trouble reaching necessary sample size, or wanting better estimates for the incidence proxy, oversampling among 15-24 year olds has been recommended (WHO, UNAIDS, and CDC 2003). Any modifications in sampling/oversampling will bias the longitudinal HIV prevalence trend. The direction in which it would do so is difficult to predict and is largely dependent on compositional changes happening within ANC population sites, with regard to age, geographic residency, treatment coverage, fertility behaviors, etc.

# **2.1.1.2 Demographic and Health Surveys/AIDS Indicator Surveys**

Data from national population-based surveys, including DHS/AIS, have also been used to estimate HIV incidence and supplement prevalence data from antenatal clinics. These surveys are typically done by sampling and testing for HIV within households for which the residents have given their individual consent. Although they have the advantage of including both male and female HIV data, population-based surveys are more expensive and logistically difficult to carry out, and therefore, are performed less frequently. Additionally, these data can be biased by non-response (either by individual refusal to participate or household absence at time of survey). As voluntary counseling and testing services become increasingly available, more non-response bias from individuals already knowing their HIV status, including those previously testing positive for HIV, may be introduced into DHS/AIS surveys (Kim Powers, personal communication), bringing in considerable uncertainty around the estimates when non-response is high. However, preliminary work suggests that DHS response rates are not declining, despite potential increases in testing, and therefore it is unlikely that any potential bias in survey data is increasing (Mark McGovern, personal communication).

DHS/AIS prevalence data obtained across two household sero-surveys can be used to mathematically estimate incidence, also incorporating cohort mortality or survival after infection data into the equation (Hallett et al. 2010; Hallett et al. 2008). These methods for inferring incidence are limited by the number of sero-surveys conducted, as incidence can only be estimated for the inter-survey period. Assessing temporal incidence trends will be only possible when countries have completed at least three of these surveys. In the meantime, as we have done here, focusing on DHS/AIS prevalence trends within females 15-24 is another way of descriptively inferring incidence, as most infections within this group are likely to be recent.

13
# **2.1.2. Empirical measurements of HIV incidence**

## **2.1.2.1. Cohort studies and ALPHA Network**

While ideal, directly and accurately measuring HIV incidence on a population level is challenging. Doing so requires prospectively following a cohort of HIV-negative individuals and diagnosing new HIV infections. This type of study is logistically complex and expensive and thus often considered to be unrealistic for measuring national incidence in resource poor settings. Importantly, enrollment into a cohort can influence behavior changes and bias HIV incidence estimates. Additionally, individuals or populations participating in biomedical trials may have been chosen for their high HIV risk.

Despite the challenges of maintaining them, several long-term cohorts have been established in various parts of Sub-Saharan Africa, generating invaluable epidemiological data. To facilitate meta-analysis of multiple cohort data, collaborations between groups with established HIV cohorts and comparable data sets have been facilitated through the ALPHA Network (Analysing Longitudinal Population-based HIV/AIDS data on Africa). Set up in 2005 with funding from The Wellcome Trust and coordinated by the Department of Population Health at the London School of Hygiene & Tropical Medicine, the ALPHA Network “aims to maximize the usefulness of data generated in communitybased longitudinal HIV studies in Sub-Saharan Africa for national and international agencies involved in designing or monitoring interventions and epidemiological forecasting” (http://www.lshtm.ac.uk/eph/dph/research/alpha/index.html). The Network links 10 previously existing sites spread across Kenya (African Health Population Research Centre; Kisumu), Malawi (Karonga), South Africa (uMkhanyakude; Agincourt), Tanzania (Kisesa; Ifakara), Uganda (Masaka; Rakai), and Zimbabwe (Manicaland).

Prior to forming the Network, each site had been individually conducting its own studies on HIV spread and risk, prevention and treatment, and developing field and analytical methods. Now together, these sites have been able to replicate technical methods and analyses across cohorts and share the results of their studies in regular teaching and analytical workshops. A key finding from a joint HIV incidence analysis of ALPHA Network sites revealed a common age-sex distribution pattern of HIV incidence among young ages at all sites, but more inter-site variation at older ages (Todd et al. 2008). Further analysis revealed a secondary HIV incidence peak occurring among older participants in some of the sites, prompting investigation of HIV risk factors at all ages, not just among youth (Barnighausen et al. 2007; Barnighausen et al. 2008; Lopman et al. 2008). Additionally, more recent data from ALPHA Network sites have been used to inform the Spectrum model’s assumptions on the age-pattern of HIV incidence and survival (UNAIDS Reference Group on Estimates 2012). However, no comprehensive 14
triangulation and comparison of these ALPHA site data against nationally reported HIV prevalence and modeled Spectrum incidence trends has been published. **2.1.2.2. Laboratory Assays and Laboratory-based Testing Algorithms** HIV incidence can be inferred in clinical trials and cohort studies through the use of laboratory assays detecting recent infection, including the BED capture enzyme immunoassay (BED-CEIA), which was, until recently, the only commercially and globally available assay. This assay measures a biomarker that rises during early infection, and can be used cross-sectionally to discriminate between recent (less than six months) and older HIV infections. However, this assay is prone to misclassification of individuals with older infections (Hargrove et al. 2008) and cross-sectional BED incidence estimates have been higher than those obtained from longitudinal follow-up (Karita et al. 2007; McDougal et al. 2006), thus raising concerns on the use of this assay to accurately measure incidence despite locally established correction factors.

Other laboratory-based assays used for measuring incidence exist, and can be used in combination with the BED-CEIA. Instead of measuring only biomarker quantity, the strength, or avidity, of the biomarker in an assay can also be measured, as avidity increases through early HIV infection. The Multi-Assay Algorithm (MAA) measures both avidity and quantity of the biomarker/antibody response, using the BED-CEIA assay along with a second assay that specifically measures binding strength. Additionally, the Limiting Antigen Avidity (LAg-Avidity) assay has been recently developed, improving upon the measurement of low and high avidity antibodies and using a more crossreactive marker to eliminate bias from populations with different HIV subtypes (an issue with the BED-CEIA). Both the MAA and LAg-Avidity assays have been shown to have lower false recent rate than the BED-CEIA (Eshleman et al. 2013; Laeyendecker et al. 2013; WHO and UNAIDS 2013). The LAg-Avidity assay is now commercially available, and the United States Centers for Disease Control and Prevention (CDC) Division of Global HIV/AIDS (DGHA) has been recommended it for incidence surveillance, instead of the BED_CEIA, in countries outside of the US supported by President’s Emergency Plan For AIDS Relief (PEPFAR) funding (WHO and UNAIDS 2013). In the 2012 HIV populationlevel HIV survey in South Africa, a HIV incidence algorithm was used based on the LagAvidity assay in combination with a negative ARV test and a viral load >1,000 copies/ml (Shisana et al., 2014). At the same time, indirect HIV incidence estimates were generated using the inter-survey mathematical modelling method according to Hallett et al. 2008 and 2010.

Current research and development efforts are focused on developing assays able to use dried blood spot specimens in validated incidence assays, which would make HIV detection more rapid and logistically feasible in resource-limited settings.

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# **2.1.3. Modeled incidence**

HIV epidemic dynamics, including incidence, can be inferred through mathematical modeling, taking into account multiple factors associated with the epidemic (demographics, risk factors, prevalence, probability of transmission, ART coverage, etc.). Several modeling approaches that estimate new HIV infections and incidence exist from individual research groups and organizations, including the Modes of Transmission <u>(http://www.who.int/bulletin/volumes/90/11/12-102574/en/index.html) and Actuarial</u> Society of South Africa models (http://aids.actuarialsociety.org.za/404.asp?pageid=3145). UNAIDS has been using Spectrum modeling software for over a decade to estimate national HIV epidemic trends, including recent reporting of incidence trends. Using a set of input parameters and model equations, the Estimation and Projection Package (EPP) with Spectrum estimates HIV incidence by fitting prevalence trends in ANC data (Brown et al. 2010; Bao et al. 2012), incorporating inputs such as ART coverage and mortality rates. These projections are then calibrated to national population-based survey estimates of HIV prevalence to generate national HIV prevalence and incidence estimates, and then stratified by age and sex based on demographic data. The most updated version of Spectrum (as of January 2014) now included DHS/AIS in the fitting process and has eliminated the second calibration step.

It is important to keep in mind that EPP/Spectrum generates estimates for both national HIV prevalence and incidence, which may or may not be in agreement with empirical measurements, including nationally reported data. Preliminary work from Eaton et al. suggested that when comparing EPP prevalence trends to that from DHS/HH survey data, prevalence declines may have been overestimated by the modelling process in southern African countries (Jeff Eaton, personal communication).

EPP and Spectrum are updated frequently, adding in new features to improve user interface and refine the model. These updates to the model include better assumptions and the addition of recent data that should improve upon previous estimates and more accurately model HIV population dynamics. While these improvements to the software package are positive, one must be aware that updates to the model can change previous estimates and outputs generated by earlier versions of Spectrum. Comparing estimates from 2010 and 2013 UNAIDS Global HIV/AIDS Reports (the 2012 Spectrum version represents an update from its 2010 version), there is no consistent trend in how the different versions influence historical incidence trajectories between 2001 and 2009 ( **Figure 1** and **2** ). Out of a group of Sub-Saharan African countries, some countries (Burkina Faso, Central African Republic, Namibia, Rwanda, South Africa, Swaziland, Zimbabwe) show a greater decrease in incidence rate from 2001-2009 using the 2010 Spectrum estimates ( **Figure 1** ); other countries (Botswana, Congo, Ghana, Kenya, Lesotho, Malawi, Mozambique, Nigeria, Tanzania, Uganda, Zambia) show greater decreases using 2012 Spectrum incidence rate estimates ( **Figure 2** ). Figures 1 and 2 are only meant to provide an illustration of how much incidence estimates can change 16
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curves accessed from the AIDSinfo Online Database from the 2012 and 2013 UNAIDS reports, only differing by one more years of data, several countries showed considerable difference between the two incidence curves. In many cases estimated peak incidence was higher from the 2013 report (12 out of 19 countries had higher incidence peaks using the 2013 report data; median difference between highest incidence from 2013 to 2012 report among these 12 countries, 0.33, but as high as 1.37) sometimes occurring in a different year than the 2012 report, and these changes were evident as far back as 1990 (Annex 2).

Still, changes in incidence, estimated by Spectrum, have been important for measuring progress in the global fight against HIV/AIDS at regional and country levels. The 2012 Global AIDS report grouped countries by overall changes in incidence between 20012011, with those falling into categories of increasing incidence over 25%, stable incidence (less than 25% change in incidence, up or down), and those with reductions in incidence greater than 25% and 50%. Although the majority of countries in the 2012 report have estimated incidence reductions over 25%, a third of them are estimated to have experienced stable incidence, or worse, incidence increases over 25% between 2001 and 2011 (including Congo, Lesotho, Nigeria, Tanzania, and Lesotho, which have all experienced stable incidence over that period).

# **<u>2.2. Effect of ART on prevalence data, mortality, and infectiousness</u>**

Achieving universal access to ART (reaching 80% of those in need of treatment) is a global priority, and the provision of ARVs is a key component of combination HIV prevention strategies, as ART reduces the likelihood of both sexual and mother-to-child transmission of HIV. Following the hallmark HPTN052 study showing that early HIV treatment of HIV positive partners among sero-discordant couples significantly reduced transmission to their partners (Cohen et al. 2011), there has been a push in the global community for using HIV treatment as prevention on a population level ('Treatment as prevention for HIV' 2011). As progress towards achieving universal access is made (global ART coverage was estimated at 65% in 2012), the effects of ART on national HIV epidemics and measurements of it will increasingly become more prominent.

HIV prevalence is determined by HIV incidence and mortality, and the availability of ART makes inferring incidence from prevalence – in the absence of good mortality data - more challenging. As already mentioned, ART also influences fertility patterns, and the tracking of ANC trends is complicated by the effects of ART and declining HIV incidence on the age-distribution of HIV prevalence, shifting it towards less fertile age-groups. Another factor to be considered is the influence of ART patient demographics, for instance on Spectrum projected incidence. HIV incidence is commonly estimated for the adult population 15-49, but not all patients on ART are within this demographic (10-20% of ART patients are already over 50, and this percentage will increase as the ART patient cohort ages. This factor affected the 2012 UNAIDS estimates, and this assumption was 18
changed for the 2013 round of UNAIDS estimates (which will be released later this year) to account for the increasing fraction of persons on ART who are over 50 (Jeff Eaton, personal communication).

Epidemic models allowing for ART effects on survival and infectiousness do so with the current incomplete understanding of such effects and can be affected by inaccuracies in reported numbers of individuals on ART. As ART is scaled-up and ART eligibility broadened, more error in the reporting of patient numbers may occur in the stretched ART programs. Additionally, self-reported adherence to therapy may be higher than actual adherence, thus potentially overestimating the effect of ART use on patients’ infectiousness. Spectrum currently sets ART’s transmission reduction effect at 92% (based on HPTN052 clinical trial), whereas real-world programmatic data suggest a lower effect, with 15-30% of patients on ART maintaining viral loads at infectious levels (Kranzer et al. 2013; National AIDS and STI Control Programme 2013; Justman et al. 2013). In the Spectrum model, assumptions around ART are most likely to affect the HIV incidence estimates of the most recent years, since HIV prevalence data are not sufficient to robustly infer HIV incidence in these recent years.

This brief account of how HIV incidence can be estimated through different direct and indirect methods, compared and tracked over time, illustrates that there is no perfect method, but that there are increasingly reliable ways to estimate this key metric of the HIV response.

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# **3. METHODS OF ANALYSIS**

For each of the 20 included countries, the following questions were posed:

- 1) Is modeled UNAIDS incidence rising/falling, and at what relative rate of change?

- 2) How well does empirical data (ANC, DHS/AIS, clinical trials and observational studies, including ALPHA Network) match modeled incidence trends?

- 3) How have these incidence trends changed prior to and during ART scale-up?

To address these questions, multiple forms and sources of incidence data have been used here: 1) Nationally reported prevalence data from ANC reports and DHS/AIS, focusing on females 15-24, 2) HIV prevalence and incidence data within young females obtained from ALPHA Network studies, 3) Directly measured incidence data from published clinical trials and/or observational studies (unrelated to ALPHA network), and 4) Modeled incidence and ART coverage data obtained from Spectrum.

Nationally reported HIV prevalence data in females 15-24 were obtained directly through ANC sentinel surveillance reports available online or from DHS/AIS reports found through MeasureDHS website’s, http://www.measuredhs.com/ and http://www.statcompiler.com/. UNAIDS/GARPR Indicator 1.6 (HIV prevalence in young people) data were accessed from the AIDSinfo Online Database (http://www.aidsinfoonline.org/ ) and used to supplement when ANC or DHS/AIS reports were not available for later years. Although Indicator 1.6 data guidelines request antenatal clinic attendees data (aged 15-24) be used, the inclusion of population based survey data, for both sexes, is also allowed.

Directly measured HIV prevalence and incidence data were identified through PubMed and Google Scholar searches of published HIV trials or observational studies using keywords “HIV epidemiology” or “HIV incidence” paired with the name of the included countries. For ALPHA Network site data, the names of the various ALPHA sites combined with “HIV” were used in searches, and recent publications from the ALPHA Network were used to find older publications as per their citation lists.

Modeled incidence estimates from Spectrum were downloaded directly from the UNAIDS AIDSinfo website (http://www.unaids.org/en/dataanalysis/datatools/aidsinfo/ ). Estimated ART coverage among those eligible for treatment was obtained from a variety of sources: for the years 2004-2007 - UNAIDS Report on the global HIV/AIDS epidemic 2008 (based on ART eligibility CD4 at or <200); for the years 2009-2011 - UNAIDS Report on the global AIDS epidemic 2012 (based on ART eligibility CD4 at or <350) and http://aidsinfo.unaids.org (accessed June 2013); for 2012 – WHO Global update on HIV treatment 2013: results, impact and opportunities and <u>http://aidsinfo.unaids.org (accessed October 2013) based on ART eligibility CD4 at or</u> <350).

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||New<br>infections<br>ee15 s<br>ages 15+|oot|Modeled <br>| nore|incidence fro<br>|ache<br>2000-2012 ||m Spectrum<br>ait<br>**|**<br> 2004-2012|sbenanae <br>2004-2012|ART<br>20**1**2 <br>20 2|Observations from<br>measured incidence!<br> P**AL**P**H**A **n**eworkstudie**s**,<br>P A etwork studie|
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|SouthAfrica)|346,034|1.596|1.37|-0.08|“0.05|“30%|80%|Considerbly higher<br>measured incidence rates<br>from published studies|
|Zimbabwe ||59,418|145|||0.05|-34%|79%, |<br>“Lackofstu|Incidence rates higher in<br>trial studies<br>diesreportingthisdata|
<!-- Start of picture text --> 30 6 30 70<br>5 5 5 ; so<br>__=<br>=Foy = 50 =<br>S a 4 — 70 2<br>5 = 8 (40 ©<br>a 15 338 5 15 ba<br>> ‘BDc a> 10068o<br>@ 410 z—- @ 410 =<br>o o (20<br>5 1 5 40 <<br>o- . . , . oO o- . . . . . 0<br>1990 1995 2000 2005 2010 2000 2002 2004 2006 2008 2010 2012<br>me Modelled incidence (%), adults 15-49, from Spectrum software<br>Prevalence (%), females 15-24 (source: national DHS/AIS reports)<br>men Prevalence (%), females 15-24 (source: national ANC reports)<br>ees Prevalence (%), young people, UNAIDS Indicator 1.6 (source: AIDSinfo database)<br>ees ART coverage (%)(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013<br><!-- End of picture text -->
<!-- Start of picture text --> mn Modelled incidence (%), adults 15-49,<br>from Spectrum software 5 carton oe<br>Prevalence eras in eveentoricky il prepacsticns -<br>(source: (%), fomales 15-24. 40 a. 14<br>South African National HIV Survey reports) 35 sCesirol werenHiskiaain rricrobicide bea! 12<br>moms Prevalence (%), females 15-24 io 7 _ vette a me te Pn<br>(source: national ANC reports) o ava Datu tad 10 =<br>Incidence uo ee ea ee a<br>rate (per 100 person years), adults 5 a 3<br>(source: Tanser et al} Ton secon eona Monae TM~ : =oo<br>VWorvan nicotene<br>ees Prevalence (%), young females Fa 1s z=ial preamps TELS.ar: : _<br>meee (source:Incidence publishedrate (per 100studies, personseeyears), Annex}adults a "a crateie eranPomeie rekcreblcidseteferenceie intersprisa! oepersiong—,tices trig! ad POeenaingen!aicxCesta woreTgin<br>mms (source:ART coverage published (%)(source: studies,UNAIDS see Annex)Global Report on HIV! 5QoO = 20<br>AIDS, 2008, 2010, 2012, 2013 ian 1386 ana 2006 2010<br><!-- End of picture text --> <!-- Start of picture text --> 45 a0 Vi eet a0<br>Derken ee erage! verae 18-35<br>4 a ao 414 ao<br>35 reer ws eervadcda ia Hiebive<br>— aee eee TO= 42 Goninal worean in= reborabisicke trial NS<br>3°, 30 eo ces 50 gy ero Sastatigis ue aDoe<br>as mn 8 Hlncates mares ins en iniflcige ral ><br>5 so & c so &<br>acd 20 * 08a aoa,=] Midas Wome—inprwentionbart tial = Mos-pragauntror woment a a0 oo2<br>ooBis vecunentoeiesln ganar —= Owens baeuiein 505fe E 6 Weren[rienEater in micro bicreCope — afilparstica [piste pant worsenwoosrerbeb a PrP teal1B-: 21-—<br>0 Foprabatian a 4 ae eee trod seoarvenin micrebicite trial om<br>20 dehuscesbesy -— —a —i— 2oet<br>5 10 2 ae asad are hb sa<br>pope<br>a a 0 a<br>2000 202 2004 2008 2008 2010 mz 2000 apea voo4 one 2008 aoid eae<br>“Horzontal error bars demote time period in which prevalence or incidence was measured. Value is at midpoint of time period.<br><!-- End of picture text -->
<!-- Start of picture text --> 4.5 5 6<br>4A<br>5<br>3.5 ‘<br>_— = “<br>= 3 Za x<br>® a a<br>g7 = 3 = ~<br>2 a iad<br>o E = :<br>3 3 _ — é<br>e165 2 2. —=—=—_—_-_» =F<br>1 =———— a<br>0.5 1 Ec ee = =< ry<br>& - ry<br>G 0<br>2002 2004 2006 2008 2010 2012 2002 2004 2008 2008 9010 2012<br>=H WNational HI Survey Modelled:Total 15-49 —6— National HIV Survey Modelled:Total 15-49<br>——Spectrum modelled incadence: Adults 15-49 —@ National HIV Survey Modelled: Males 15-49<br>® National HI¥ Survey Direct Measurement: Total 15-49 &— National HIV Survey Modelled: Females 15-49<br><a= National HIV Survey Modelled: Total 15-24<br>- a - .<br>“Modelled2005-2008,incidence 2009-2012)was andplotted Y-errorat midpoint bars raprasent of inter-survey 95% Confidencepenods (2002-2005, Intervals =de &# =NationalHNoE Nokona HIV HIV SurveyBunmy Modelled:Males. 15-24Modeled:" Females 15-24<br>“HNon-response or testing refusal in Nabonal Hiv Surveys was higher<br>females and may bias modelled incidence estimates; im males than @ SpectrumNational HIVmodelledSurvey Directincidence: Measurement Adults 15-48Total 15419<br>®@ National HIV Survey Direct Measurement Male 15-49<br>2002— 40.2% Male: 35.4% Female @ National HI Survey Direct Measurement Female 15-16<br>2005- 34.6% Male: 302% Female 4 National HIV Survey Direct Measurement Total 15-24<br>2008— 31.0% Male; 26.65 Female 4 National HIV Survey Direct MeasuramentMale 15-24<br>& National HIV Survey Direct Measurement Female 15-24<br><!-- End of picture text --> Sources: South Affcan National HIV Survey reports and Spectrum software
<!-- Start of picture text --> 35 7 7 35 30<br>30 8 Ea)<br>6 & 30<br>== 25 omen La Harareclinics and Ghitungiza 5 = 25 60 e2<br>& = ‘Wollagn from antenatal clinics 3 =] n<br>= 20 eet — Ghitungwiza 4 = 20 so &<br>= emearal pulation wanton ‘ol women In microbicidestud’ o aa 1 o<br>g 15 \ Racine “ig TF Harare a = 15 - Mankelan a ~ 40 8<br>a See NY t SeeoreremiayMe ° ra in c Tory 3 natal<br>a 10 enenal Popul = mat Kesey 2= 85 Wayman| Wayricinna = - 30 o<br>Women Maniclanal — o Harare at<br>5 In ganeral population mlHarare 1 Woman en antndata5 Sianclinics Harare Chintungwiza<br>Employocs ie pravention bl onarel women in microblclde shady _<br>voluMiary trom businesses In Hee and Chiturgwiza — | 10<br>0 counseling and testing study Workin trom “eee ontral women in microbirice study<br>1990 1995 2000 2005 2010, a Volunted}2000couneoling ared«2002 terting 2004iv = 2006, 2008 )= 2010s, a<br>sees Modelled incidence (%), adults 15-49, from Spectrum software<br>Prevalence (%), females 15-24 (source: national DHS/AIS reports)<br>aeee= Prevalence (%), females 15-24 (source: national ANC reports)<br>eee Prevalence (%), young females (source: published studies, see Annex)<br>=== Incidence rate (per 100 person years}, adults (source: published studies, see Annex)<br>eee ART coverage (%)(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013<br><!-- End of picture text -->
||infections<br>||Modeled<br>|incidencefro<br><br>|mSpectrum<br>||ART<br>|measuredincidence!<br>|
|---|---|---|---|---|---|---|---|---|
||aoe15p<br>ages 15+|ons||<br>pore|<br>|x<br>Boe<br>2000-2012|<br>onSabe,**|**<br>2004-2012|Zoctange <br>2004-2012|2012,<br>2012|<br>P**A**LP**H**Anewo**r**studies,<br>LP Anetwo k studies|
|.||||||||<br>Incidence rates higher in|
|_<br>Namibia|9.650|1.53||“0.15||“ATG|90%|Aulagnier et al shows.<br>higher incidence in<br>Winhosk adult general<br>population|
|Zambia|46,587|1.49|0.79|“0.08|-0.08|ATM|7To%<br>“Lackofstu|Abdool et al shows higher<br>incidence rate in women<br>fram microbicide trial<br>diesreportingthisdata|
<!-- Start of picture text --> 40) MM 7? 40 5 100<br>a5 | 6 35 + a0<br>=— H+ = = 30 = = a0 =<br>= 1 5 3 70 D<br>‘254 oo 25 a<br>3 226 sg<br>& 0 , 3 5 20 50) $<br>a 15 J é 5 15 oo<br>a 0) 3 a7 w 30 elce<br>20 «t<br>54 jd 5 10<br>0 0 0 ' it)<br>1990 1995 2000 2005 2010 2000 «27007 «2004 2006 2008 2010 2012<br>=O5 ART Coverage<br><!-- End of picture text -->

ames Modelled incidence (%), adults 15-49, from Spectrum software Prevalence (%), females 15-24 (source: national DHS/AIS reports) mms Prevalence (9), females 15-24 (source: national ANC reports) om ART coverage ()(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013
<!-- Start of picture text --> 20 Blantyre 6 20.3 ao?00— oom | «BO<br>18 antral womenin<br>Women from parinataland microbicide sbudy 8 18 70<br>16 + trenily planning clinics jie —— —I | 5 2 46 + T _<br>= Women eo and Rena aca aT) - 60 se<br>a4 family planning clinics "~~ 4 £ 144 | S<br>fe SSKaron ga a 908- 50<br>a 10 Karanga 3B = 10 = Karonga > 40 4<br>7> 4 natal ollie = renin | ze BT rratat fromctinecs oo<br>a nite ental clinics a hd 30<br>a § — Cc 6 a<br>4 Lilongwe 2 20is<br>So bes 1 a 4 ee and conan <<br>a+ : Fs 3 family planming clinics microbicide study Pregnant 10<br>O° ao . Fhman ten eesina __4 —__<br>1990 71995 2000 2005 2010 oO 02000 ame2dbe peeing2004 Gontral2006 women In2008 microbéchoe2010 study 2012a<br>“Horizontal error bars denote time period in which prevalence or incidence was measured. Value is at midpoint of time period.<br>ees Modelled incidence (%), adults 15-49, from Spectrum software<br>Prevalence (%), females 15-24 (source: national DHS/AIS reports)<br>ees Prevalence (%), females 15-24 (source: national ANC reports)<br>mes Prevalence (%), young people, UNAIDS Indicator 1.6 (source: AIDSinfo database)<br>men PreValence (%), young females (source: published studies, see Annex)<br>een (NCidence rate (per 100 person years), adults (source: published studies, see Annex)<br>ees ART coverage (%)(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013<br><!-- End of picture text -->
<!-- Start of picture text --> 18 2 18 80<br>16 > 1.8 16 > + 70<br>; 16<br>_ _<br>= i ep =<br>=o iz 1 o == —EA<br>2 12 0 o so 8<br>2 10, 5 2 10 5<br>S 1 os Ss, 40 ><br>e ; 0.8 2 S 30 O<br>a 6 0.8 e 6 rE<br>4 a4: a 4 op <<br>2 o2 2 10<br>0 + T T 7 + O Oo + uy _ r +g<br>1985 #1990 1995 2000 2005 2010 2015 2000 2002 2004 27006 2008 2010 2012<br>ae Modelled incidence (%), adults 15-49, from Spectrum software<br>Prevalence (%), females 15-24 (source: national DHS/AIS reports)<br>eae Prevalence (%), females 15-24 (source: national ANC reports)<br>ames ART coverage (%)(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013<br><!-- End of picture text -->
<!-- Start of picture text --> 16 OO orc OOoWindhook g «COT<br>14 > enera population 5 5 4 20<br>— 2- | = 12 + =<br>S 3 = 7 Oy<br>10 = | a 3 10 = t mn<br>2 e & so 8g<br>a 3 (15 og = 4 5 @<br>a ‘oe = 8<br>z 6 , = @ 6 a 0<br>a 4 & 4 “ be<br>2 0.5 z Windhook ; 20<br>o 2 General population 10<br>nV) i) o 0 _ 0<br>1990 1995 2000 2005 2010 2000 202 2004 2006 2005 2010 2012<br>>OS% ART Coverage<br>“Horizontal error bars denote time period in which prevalence or incidence was measured. Value is at midpoint of time period.<br><!-- End of picture text -->

mms MOdelled incidence (%), adults 15-49, from Spectrum software oon Prevalence (%), females 15-24 (source: national ANC reports) aan Incidence rate (per 100 person years), adults (source: published studies, see Annex) om ART coverage (%)(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013
<!-- Start of picture text --> 45 Gi 45 90<br>5 =<br>35 35 7O <s<br>= 40 “VA, 340 a0=<br>w 4 oo = 4 60<br>825 28®<br>=2; 5 2 25 50 5<br>s 20 =2 —§ 20 4 o6<br>@ 15 2 @ 15 cr<br>a a fia<br>10 10 20<br>1<br>5 5 10<br>a+ 7 7 0 a+ T + 7 — oO<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>ems Modelled incidence (%), adults 15-49, from Spectrum software<br>Prevalence (%), females 15-24 (source: national DHS/AIS reports)<br>mms Prevalence (%), females 15-24 (source: national ANC reports)<br>moms ART coverage (%)(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013<br><!-- End of picture text -->
<!-- Start of picture text --> 43 ctrlLusaka Lusakawomen in 35 90<br>microbicide trial _ ea ,<br>30 Weonmen in microbicihe —a— 35 30 Women in microbicide trial preparation ao<br>—_ Lusaka 3 70 =<br>xe— 25 7 ‘Warner in-microbicidetriat preparation — se 25 =<br>8 ii -75 eo = 60 a<br>o 20 | e 8<br>c o 20 sg &<br>2 > 3® ¢c o><br>mw i154 o iS - — 410<br>a i56£ 2 o<br>pa > 30<br>o@ 10° = L 4 a 10 - oe<br>20<br>5] 0.5 5 Wane ; i0<br>Pee eee<br>o- . : fi 0 preparation : D<br>4990 1995 2000 2005 2010 2000 2002 2004 2006 2008 2010 2012<br>“Horizontal error bars denote time period in which prevalence or incidence was measured. Value is at midpoint of time period.<br>ees Modelled incidence (%), adults 15-49, from Spectrum software<br>Prevalence (%), females 15-24 (source: national DHS/AIS reports)<br>mon PReValence (%), females 15-24 (source: national ANC reports)<br>men PReValence (%), young people, UNAIDS Indicator 1.6 (source: AlIDSinfo database)<br>mes Prevalence (4), young females (source: published studies, see Annex)<br>een Incidence rate (per 100 person years), adults (source: published studies, see Annex)<br>ees ART coverage (")(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013<br><!-- End of picture text -->
# **5. DISCUSSION**

The purpose of this review was to comprehensively triangulate, describe, and analyze longitudinal HIV incidence trends in select Sub-Saharan African countries between 1990 and 2012. Proxies of HIV incidence (HIV prevalence in young females) from nationally reported sources and published studies were compared to each other, and UNAIDS modeled HIV incidence curves were compared to HIV incidence empirically obtained from trials and studies. In addition, HIV incidence trends were related to temporal trends in ART scale-up from 2004 to 2012.

One challenge encountered in this review was the scarcity of _measured_ HIV incidence data in many countries. There is a dearth of published studies reporting directly measured HIV incidence, including those from the ALPHA Network, and for many countries, no measured HIV incidence data could be identified. The scarcity of data is most profound before 2001, which presents a challenge to the certainty in which national HIV epidemics can be modeled prior to this year. Uganda, Zimbabwe, and South Africa are exceptions to this general lack of measured incidence data, as a number of studies reporting HIV incidence rates from intervention trials or observational studies have been carried out in these countries. In general, measured HIV incidence rates tend to be higher in sub-national studies in specific demographics than corresponding UNAIDS incidence estimates for these three countries, though incidence rates measured within the general population (not specific demographics or high-risk groups) of Uganda tend to be similar to the UNAIDS estimates and the overall incidence trend in Zimbabwe mirrors that projected by Spectrum.

Another difficulty in longitudinally assessing incidence trends was the scarcity of _repeat_ cross-sectional, nationally representative HIV prevalence surveys. In many cases, only a few ANC and DHS/AIS reports through the years of interest could be readily found, leaving few data points from which to infer long-term incidence trends. While many of the individual reports from sentinel surveillance or DHS/AIS surveys could be obtained from Measure DHS, governmental websites, or other online sources, a central repository for these reports, even within the UNAIDS website, would be a useful resource for those conducting similar analyses. Recently (as of late 2013, before compilation of data in this review), national Spectrum files used to estimate prevalence were made available by UNAIDS through request at <u>http://apps.unaids.org/spectrum/.</u> ANC data was more abundant than DHS/AIS data, which comes as no surprise due to the logistical and financial challenges of carrying out household-based surveys. However, as pointed out by Montana et al., in the cases of Ethiopia and Tanzania (Mahy, GarciaCalleja, and Marsh 2012), and also found here in other countries (Lesotho, South Africa, and Botswana), HIV prevalence in young ANC clients aged 15-24 is higher than prevalence measured in females from DHS/AIS in the same age bracket, a chief reason being different sexual behaviors in these two groups. Also, ANC data are subject to a 46
number of influencers and biases, including natural and HIV/ART related fertility changes within a population and the varying composition of the surveillance sample as new sites are included in surveys. Data from DHS also carry their own unique biases such as test refusal; therefore, the sources of data available and used in the Spectrum model, along with their inherent biases, will greatly influence UNAIDS estimates, potentially in a way that may not accurately reflect incidence within the general population. It is difficult to predict in what direction these biases may affect the resulting modeled incidence trends, as the overall effect will likely be multi-factorial and change over time. However, preliminary work is being conducted to specifically investigate the effects of biases on HIV prevalence data, including underestimation of HIV prevalence due to testing refusal of survey respondents knowing their positive status.

In general, most nationally reported HIV prevalence trends within females 15-24 are in agreement with the _direction_ of modeled incidence trends from Spectrum (e.g. Central African Republic, Congo, Ethiopia, Ghana, Kenya, Tanzania, Lesotho, South Africa, Zimbabwe, Botswana, and Namibia), though the _relative amount_ of change may be quite different in these two data sources (e.g. Tanzania, Swaziland, Zambia), or the _timing_ of changes may (e.g. Burundi and Kenya) may not match. Since ANC and DHS/AIS prevalence data inform the Spectrum model, it is not surprising that most of these trends reflect each other.

Few research trials or studies exist in which measured HIV incidence trends have been assessed over long –periods of time; most incidence rates were measured over a few years to assess the impact of an intervention or suitability of a site for an intervention trial. A consistent finding in countries with directly measured HIV incidence from trials or studies was that these empirical incidence rates were higher than the modeled UNAIDS estimates. This is likely due to such studies focusing on higher-risk demographics (ex. high-risk young women, discordant couples), or geographic areas with higher HIV transmission intensity than the national average situation. Most studies included in this review are not representative of national incidence, as very few have included more than one trial site or region within a country<sup>5</sup> . Trial and study sites/populations are also atypical, as they have more resources with regard to service provision and ability to implement ART scale-up and other interventions. Altogether, these findings suggest that modeled national incidence estimates are not indicative of incidence within specific demographics and/or regions. The wide range of HIV incidence levels in different demographics is well illustrated by the South African 2012 national HIV incidence estimates (Shisana et al., 2014): At 1.72% for adults aged 15.49 years, but 0.55% in young men, 2.54% in young women, 3.08% in adults cohabiting, and 4.54% in

> 5 One important exception is the SHIMS study, whose directly measured incidence value of 2.4% in 2011 is in close accordance with the Spectrum incidence estimate of 2.47%. The SHIMS incidence survey was designed to be nationally representative.

47
Black African females aged 20-34 years (Spectrum estimate national level for 2012 at 1.37%).

In all countries included here, there has been a considerable decrease in modeled HIV incidence from peak levels, with variations in how much and at what rate this fall in modeled incidence may have occurred in different countries. Although HIV prevention progress can be assessed by a few measures of incidence reduction (percent change, value in 2012, rate of change), it is important to consider all of them on a country-bycountry basis, as only using one may provide the full picture of the progress that has been made in addressing the national HIV epidemic or its current state. High or low HIV incidence rates can translate into very different number of new HIV infections depending on the countries’ population sizes. Ethiopia had a very low incidence rate in 2012 (0.03%), but it still experienced an estimated 10,503 new infections that same year. Nigeria had a lower estimated incidence rate than Tanzania (0.23% vs. 0.32%), but it had nearly three times as many estimated new infections in 2012 (199,921 vs. 68,683). On the other hand, Swaziland, a much less populous country, had a high estimated incidence in 2012 (2.04%), but the number of new HIV cases in adults was much smaller at 10,287. Historical numbers of new HIV infections determine current ART needs and expenditures, while current (and future) new infections determine the additional, longterm treatment liabilities in countries.

Percent reduction of modeled incidence is another useful measure to assess progress in addressing the HIV epidemic, but it, too, cannot provide the whole picture. For example, while Ghana’s modeled incidence dropped 70%, from 0.18% to 0.05%, the slope of Botswana’s decline (2.23% to 1.25%) from 2004-2012 is steeper, despite a lower percent reduction in incidence (44%). Additionally, this measure is strongly influenced by its _historical_ incidence data and the height of the incidence peaks in the past, which can be problematic, as different versions of Spectrum can provide varying estimated levels, resulting in different percent reductions across software versions. When comparing longitudinal incidence trends using estimates from both 2010 and 2012 Spectrum software, it was found that incidence reduction was greater for some countries using 2010 Spectrum estimates (Burkina Faso, Central African Republic, Namibia, Rwanda, South Africa, Swaziland, Zimbabwe), and greater for others (Botswana, Congo, Ghana, Kenya, Lesotho, Malawi, Mozambique, Nigeria, Tanzania, Uganda, Zambia) using 2012 Spectrum (Fig. 1 and 2). These differences in incidence reduction comparing 2012 vs 2010 Spectrum estimates are quite extreme in some cases: Ghana (56% vs 17%), Nigeria (49% vs 3%), and Uganda (39% vs 4%).

If different Spectrum versions or the addition of one more year of data into the current Spectrum software (see Annex 2) result in different incidence estimates and historical peaks, this suggests some uncertainty in past estimations and future projections of HIV incidence, and complicates the assessment of the HIV epidemic. This is especially important as UNAIDS determines national progress using modeled HIV incidence changes, categorized by increasing incidence over 25%, stable incidence (less than 25% 48
change in incidence, up or down), and incidence reduction greater than 25% and 50%. Additionally, variable HIV incidence data across years may over/underestimate those in need of treatment in the present and future. In some cases, recent modeled HIV incidence estimates and trends may be too optimistic. ANC data from Nigeria suggest that HIV incidence has stabilized, whereas Spectrum estimates continue to decline. Also, in cases where incidence has been directly measured in trials or studies, these values are often higher than modeled incidence (a large caveat being that many of these studies have been done in high-risk populations). Because of this potential uncertainty in Spectrum estimates and its recent trends, it will be very important in the future to continue to collect and analyze empirical incidence data, directly and indirectly measured.

Thus, it was chosen to group countries and assess overall progress in reducing incidence by the rate of change in modeled incidence between 2004 and 2012. By this measure, Botswana and Swaziland have made the most recent progress in reducing national incidence. In these cases, and the other countries with steep Spectrum modeled incidence declines (Malawi, Mozambique, Namibia, and Zambia) the downward trend in HIV incidence began prior to ART scale-up; thus, additional mechanisms are likely responsible for the reduction before and through this period. Investigation of what factors influenced the decline, including prevention programs and/or natural epidemic declines, in these countries is warranted (an analysis of epidemic, behavioral and program data to understand reasons for epidemic change in selected countries is being conducted by Imperial College London and funded by the World Bank).

Three countries in this analysis have shown very little change in, or stabilized, modeled incidence between 2004 and 2011. These countries include Burkina Faso, Burundi, and Uganda. Both Burkina Faso and Burundi support small HIV epidemics (no more than 4,502 estimated new infections in 2012) with recently low estimated incidence levels, so the stabilized trend is not as concerning compared to Uganda’s large epidemic. (122,806 new estimated infections in Uganda for 2012).

A focus of this review was to describe how incidence trends changed, if at all, during ART scale-up, with the acknowledged caveat that doing so is complicated by the inclusion of ART into the Spectrum estimation process, and the resulting incidence curves cannot be interpreted to assess ART effects on national-level HIV incidence. Additionally, the extent to which ART scale-up contributed to incidence trends cannot be determined without examining the effects that other HIV prevention programs (male circumcision and voluntary counseling and testing), HIV risk behavior changes in the population, or natural epidemic dynamics, could have also had (as previously mentioned, the Imperial College analysis addresses this issue). Despite these significant limitations to our analysis, it is evident that in all countries, modeled HIV incidence had started to decline prior to ART scale-up, and in most of these cases, the steepest declines happened well before 2004. In some cases, there was very little change or even an upward trend in modeled incidence during ART scale-up (2004-2012), but the design 49
of this assessment is unable to determine what effect, if any, ART had on national HIV incidence trends. It is interesting to note that the inter-survey HIV incidence levels in South Africa are level at 1.9% for 2005-08 (ART coverage in the range of 20-30% [CD4 200 threshold]) and equally 1.9% for 2008-12 (ART coverage rising to 66% in 2011 [CD4 350 threshold]), hence showing no clear impact of the significant ART scale-up on model-inferred HIV incidence. While unable to conclude through this analysis, it is possible that ART might have had an influence on sustaining the downward trajectory of the epidemics in some countries.

50
# **6. CONCLUSION**

The purpose of this document was to longitudinally describe and analyze HIV incidence trends in 20 Sub-Saharan African countries, using multiple sources and forms of data, empirical and modeled, and to assess how these data related to each other. It was found that modeled incidence trends are generally in congruence with nationally reported incidence proxy data from ANC and DHS/AIS reports, and both sets of data indicate that incidence has been on the decline in all countries analyzed here. However, measured incidence rates and prevalence coming from published studies and ALPHA network sites show variation from the modeled and nationally reported data, more often with higher values. A caveat is that these data come from discrete field sites and regions, and too few studies have provided temporal data across multiple sites for comparison with nationally reported data to help validate modeled HIV incidence trends. More focus should be given to efforts to longitudinally measure and monitor incidence within and across geographical areas.

This analysis also sought to investigate the dynamics of incidence trends prior to and during ART scale-up. In all countries, a downward trend in modeled incidence had begun prior to scale-up, suggesting other factors responsible for reduced incidence, such as HIV risk behavior changes or the natural rises and falls of an epidemic. While it cannot be ruled out that increased national ART coverage helped maintain downward modeled incidence trends, but concluding so was beyond the scope of this review. Furthermore, it is likely too early for the effects of ART scale-up to be observed at the population level, even for descriptive purposes, and current efforts are going into investigating what led to incidence declines prior to scale-up (previously mentioned Imperial College studies, funded by the World Bank).

In conclusion, while modeled incidence trends are encouraging and suggest a positive evolution towards lower national HIV incidence levels, maintaining this progress or rejuvenating efforts to break stagnant incidence trends will be helped with accurate and representative incidence data and a better understanding of how and why incidence began to decline within each country (and whether these declines have stabilized or continue to decline in recent years). This requires continual updates to Spectrum software based on the latest understanding of how various factors influence HIV epidemic dynamics (ART’s and other interventions’ effects on prevalence data and risk of transmission, biases from prevalence data, ART adherence and efficacy estimates, and changing/aging HIV+ population dynamics). Finally, increased availability and measurement of empirical HIV epidemiological data (as for instance through the ongoing TasP trials) will greatly improve the understanding of the HIV epidemic on the ground and provide further information to improve the Spectrum model.

51
**ANNEX 1: Country and year specific incidence and prevalence data, including sources**

# **Botswana**

||**Prevalence amon**<br>|**g females 15-24(%)**<br>|**Spectrum**<br>|**estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|1.95|-|
|**1991**|-|-|2.77|-|
|**1992**|-|-|3.73|-|
|**1993**|-|-|4.65|-|
|**1994**|-|-|5.35|-|
|**1995**|-|-|5.75|-|
|**1996**|-|-|5.73|-|
|**1997**|-|-|5.50|-|
|**1998**|-|-|5.01|-|
|**1999**|-|-|4.42|-|
|**2000**|-|-|3.85|-|
|**2001**|35.1|-|3.35|-|
|**2002**|31.6|-|2.94|-|
|**2003**|32.5|-|2.56|-|
|**2004**|-|18.2|2.23|44|
|**2005**|26.1|-|1.98|64|
|**2006**|25.0|-|1.85|76|
|**2007**|24.6|-|1.74|79|
|**2008**|-|10.7|1.72|-|
|**2009**|20.8|-|1.59|90|
|**2010**|-|-|1.38|93|
|**2011**|16.1|-|1.30|>95|
|**2012**|-|-|1.25|95|

<u>ANC data sources: Botswana Second Generation HIV/AIDS Antenatal Sentinel Surveillance Technical Report 2009 and</u> 2011 <u>National DHS and AIS data sources: Botswana AIDS Impact Survey 2004 and 2008</u>

# **Burkina Faso**

||**Prevalence amo**<br>|**ng females 15-24(%)**<br>|**Spectrum**<br>|**estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|0.73|-|
|**1991**|-|-|0.70|-|
|**1992**|-|-|0.65|-|
|**1993**|-|-|0.56|-|
|**1994**|-|-|0.45|-|
|**1995**|-|-|0.35|-|
|**1996**|-|-|0.27|-|
|**1997**|-|-|0.20|-|
|**1998**|-|-|0.14|-|
|**1999**|-|-|0.10|-|
|**2000**|-|-|0.07|-|
|**2001**|-|-|0.06|-|
|**2002**|-|-|0.06|-|
|**2003**|1.9|1.3|0.06|-|
|**2004**|1.5|-|0.06|7|
|**2005**|1.4|-|0.06|19|
|**2006**|1.0|-|0.06|31|
|**2007**|1.3|-|0.06|35|
|**2008**|0.4|-|0.07|-|
|**2009**|1.3|-|0.06|44|
|**2010**|0.7|0.3|0.07|50|
|**2011**|-|-|0.06|57|
|**2012**|-|-|0.05|70|

<u>ANC data sources: Burkina Faso UNGASS Report 2008 and Burkina Faso GARPR 2012</u> <u>National DHS and AIS data sources: Burkina Faso DHS 2005 and 2010</u> 52
# **Burundi**

<!-- Start of picture text --> Prevalence among females 15-24 (%) Spectrum estimates<br>National Antenatal Sentinel National Demographic and Incidence in Antiretroviral<br>Surveillance Surveys Health or AIDS Impact Surveys adults 15-49 (%) coverage (%)<br>1990 - - 0.61 -<br>1991 - - 0.77 -<br>1992 - - 0.90 -<br>1993 - - 0.95 -<br>1994 - - 0.87 -<br>1995 - - 0.71 -<br>1996 - - 0.51 -<br>1997 - - 0.34 -<br>1998 - - 0.17 -<br>1999 - - 0.11 -<br>2000 7.1 - 0.07 -<br>2001 - - 0.06 -<br>2002 - 3.8 0.06 -<br>2003 3.9 - 0.08 -<br>2004 - - 0.09 6<br>2005 - - 0.09 13<br>2006 - - 0.09 17<br>2007 - - 0.10 23<br>2008 - - 0.10 -<br>2009 1.3 - 0.08 35<br>2010 0.8 0.8 0.08 45<br>2011 - - 0.08 54<br>2012 - - 0.06 58<br><!-- End of picture text --> <u>ANC data sources: HIV and AIDS epidemiological surveillance report for the WHO African region: 2007 update, Burundi</u> UNGASS Report 2010, Burundi Bilan des Realisation du Plan d'Action National de Lutte contre le SIDA 2011 <u>National DHS and AIS data sources: Burundi HH 2002 and DHS 2010</u>

# **Central African Republic**

||**Prevalence amo**<br>|**ng females 15-24(%)**<br>|**Spectrum**<br>|**estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|1.18|-|
|**1991**|-|-|1.17|-|
|**1992**|-|-|1.18|-|
|**1993**|-|-|1.17|-|
|**1994**|-|-|1.15|-|
|**1995**|-|-|1.13|-|
|**1996**|-|-|1.07|-|
|**1997**|-|-|1.00|-|
|**1998**|-|-|0.91|-|
|**1999**|-|-|0.81|-|
|**2000**|-|-|0.74|-|
|**2001**|-|-|0.67|-|
|**2002**|-|-|0.62|-|
|**2003**|-|-|0.58|-|
|**2004**|-|-|0.53|3|
|**2005**|-|-|0.48|4|
|**2006**|-|-|0.44|6|
|**2007**|8.21|-|0.40|21|
|**2008**|-|-|0.37|-|
|**2009**|7.22|-|0.34|23|
|**2010**|-|-|0.32|24|
|**2011**|4.83|-|0.29|22|

<u>ANC data sources: Central African Republic UNGASS Report 2008 and 2010, Comite National de Lutte Contre le SIDA</u> 2012 Rapport d'Activite au Niveau du Pays Republique Centrafricaine 53
# **Congo**

||**Prevalence amon**<br>**National Antenatal**|**g females 15-24(%)**|**Spectrum**|**estimates**||
|---|---|---|---|---|---|
||**Sentinel**|**National Demorahic**|||**Indicator 16 - HIV**|
||<br>**Surveillance**<br>**Surveys **|**gp**<br>**and Health or AIDS**<br>**Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|**.**<br>**prevalence in young**<br>**people 15-24(%)**|
|**1990**|-|-|0.81|-|-|
|**1991**|-|-|0.84|-|-|
|**1992**|-|-|0.81|-|-|
|**1993**|-|-|0.82|-|-|
|**1994**|-|-|0.76|-|-|
|**1995**|-|-|0.71|-|-|
|**1996**|-|-|0.66|-|-|
|**1997**|-|-|0.62|-|-|
|**1998**|-|-|0.54|-|-|
|**1999**|-|-|0.53|-|-|
|**2000**|-|-|0.49|-|-|
|**2001**|-|-|0.44|-|-|
|**2002**|-|-|0.39|-|-|
|**2003**|-|-|0.36|-|-|
|**2004**|-|-|0.34|2|-|
|**2005**|2.6|-|0.31|11|-|
|**2006**|-|-|0.29|12|-|
|**2007**|-|-|0.27|17|2.6|
|**2008**|-|-|0.26|-|-|
|**2009**|-|2.4|0.24|24|1.9|
|**2010**|-|-|0.21|41|-|
|**2011**|-|-|0.18|44|1.79|
|**2012**|-|-|0.16|39|-|

<u>ANC data source: Congo UNGASS Report 2008</u> <u>National DHS and AIS data source: Congo AIS 2009</u>

# **Ethiopia**

||**Prevalence amon**<br>|**g females 15-24(%)**<br>|**Spectr**<br>|**um estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral coverage**<br>**(%)**|
|**1990**|-|-|0.36|-|
|**1991**|-|-|0.44|-|
|**1992**|-|-|0.52|-|
|**1993**|-|-|0.55|-|
|**1994**|-|-|0.56|-|
|**1995**|-|-|0.56|-|
|**1996**|-|-|0.54|-|
|**1997**|-|-|0.51|-|
|**1998**|-|-|0.46|-|
|**1999**|-|-|0.41|-|
|**2000**|-|-|0.35|-|
|**2001**|12.4|-|0.29|-|
|**2002**|11.0|-|0.22|-|
|**2003**|8.6|-|0.15|-|
|**2004**|-|-|0.11|4|
|**2005**|5.6|1.1|0.08|7|
|**2006**|-|-|0.06|18|
|**2007**|3.5|-|0.04|29|
|**2008**|-|-|0.04|-|
|**2009**|2.6|-|0.03|36|
|**2010**|-|-|0.03|47|
|**2011**|-|0.5|0.02|56|
|**2012**|-|-|0.03|60|

<u>ANC data source: Report on the 2009 Round Antenatal Care Sentinel HIV Surveillance in Ethiopia</u> <u>National DHS and AIS data sources: Ethiopia DHS 2005 and 2011</u> 54
# **Ghana**

<!-- Start of picture text --> Prevalence among females 15-24 (%) Spectrum estimates<br>National Antenatal Sentinel National Demographic and Incidence in Antiretroviral<br>Surveillance Surveys Health or AIDS Impact Surveys adults 15-49 (%) coverage (%)<br>1990 - - 0.22 -<br>1991 - - 0.25 -<br>1992 - - 0.29 -<br>1993 - - 0.34 -<br>1994 - - 0.36 -<br>1995 - - 0.37 -<br>1996 - - 0.37 -<br>1997 - - 0.34 -<br>1998 - - 0.31 -<br>1999 - - 0.28 -<br>2000 - - 0.25 -<br>2001 - - 0.23 -<br>2002 3.8 - 0.20 -<br>2003 3.2 1.2 0.18 -<br>2004 2.5 - 0.17 3<br>2005 1.9 - 0.15 5<br>2006 2.5 - 0.14 12<br>2007 2.6 - 0.13 15<br>2008 1.9 - 0.11 -<br>2009 2.1 - 0.10 28<br>2010 1.5 - 0.08 36<br>2011 1.7 - 0.07 47<br>2012 - - 0.05 58<br><!-- End of picture text --> <u>ANC data sources: Ghana HIV Sentinel Survey Report 2007, Ghana UNGASS Report 2008, Ghana Country AIDS</u> Progress Report 2012 <u>National DHS and AIS data source: Ghana DHS 2003</u>

|**Study reference**|**Incidence rate/100**<br>**personyears**|**Time frame of**<br>**study**|**Study/Trial**<br>**population**|**Region**|
|---|---|---|---|---|
|Petersonetal.,PLoS One2007|1.09|2004-2006|Women in microbicidetrial|Kumasi and<br>Accra|

55
# **Kenya**

||**Prevalence amon**|**g females 15-24(%)**|**Spectrum**|**estimates**|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|1.07|-|
|**1991**|-|-|1.53|-|
|**1992**|-|-|1.99|-|
|**1993**|-|-|2.24|-|
|**1994**|-|-|2.15|-|
|**1995**|-|-|1.80|-|
|**1996**|-|-|1.40|-|
|**1997**|-|-|1.07|-|
|**1998**|-|-|0.84|-|
|**1999**|-|-|0.70|-|
|**2000**|18.0|-|0.72|-|
|**2001**|13.1|-|0.66|-|
|**2002**|10.8|-|0.61|-|
|**2003**|9.1|5.9|0.58|-|
|**2004**|6.2|-|0.54|6|
|**2005**|6.8|-|0.52|14|
|**2006**|5.6|-|0.51|27|
|**2007**|-|-|0.52|38|
|**2008**|5.7|-|0.53|-|
|**2009**|-|4.5|0.49|51|
|**2010**|4.6|-|0.46|61|
|**2011**|-|-|0.44|72|
|**2012**|-|-|0.41|73|

<u>ANC data sources: Sentinel Surveillance of HIV & STDS in Kenya Report 2006 and Sentinel Surveillance of HIV &</u> Syphilis from Antenatal Clinics in Kenya Report 2010 <u>National DHS and AIS data sources: Kenya DHS 2003 and 2008-2009</u>

|**Study reference**|**Prevalence**<br>**among**<br>**females 15-24**<br>**(%)**|**Incidence**<br>**rate/100**<br>**personyears**|**Time frame of**<br>**study**|**Study/Trial**<br>**population**|**Region**|
|---|---|---|---|---|---|
|Brubaker et al., HIV<br>Med2011||4.6|2005-2007|Discordant couples|Kisumu|
|Chege et al., J Infect<br>DevCtries2012|17.24 (18-24)||2007-2008|Adult general<br>population|Kisumu|
|Van Damme, NEJM<br>2012||4.7|2009-2011|PrEP among women<br>18-35 (control)|Bondo|
|Baeten et al., NEJM<br>2012<br>ALPHA network study||1.9|2008-2010|PrEP in discordant<br>couples(placebo)|Eldoret, Kisumu,<br>Nairobi,Thika|

56
# **Lesotho**

||**Prevalence among**<br>|**females 15-24(%)**<br>|**Spectrum e**|**stimates**|**Indicator 1.6 -**<br>|
|---|---|---|---|---|---|
||**National Antenatal**|**National Demographic**|||**HIV prevalence**|
||<br>**Sentinel Surveillance**<br>**Surveys **|<br>**and Health or AIDS**<br>**Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|<br>**in young people**<br>**15-24(%)**|
|**1990**|-|-|0.49|-|-|
|**1991**|-|-|0.93|-|-|
|**1992**|-|-|1.62|-|-|
|**1993**|-|-|2.52|-|-|
|**1994**|-|-|3.57|-|-|
|**1995**|-|-|4.46|-|-|
|**1996**|-|-|4.98|-|-|
|**1997**|-|-|5.04|-|-|
|**1998**|-|-|4.72|-|-|
|**1999**|-|-|4.20|-|-|
|**2000**|21.7|-|3.75|-|-|
|**2001**|-|-|3.32|-|-|
|**2002**|-|-|2.95|-|-|
|**2003**|24.2|-|2.77|-|-|
|**2004**|-|15.4|2.66|4|-|
|**2005**|20.5|-|2.66|11|-|
|**2006**|-|-|2.67|22|-|
|**2007**|18.7|-|2.77|26|18.7|
|**2008**|-|-|2.85|-|-|
|**2009**|-|13.6|2.69|50|19.6|
|**2010**|-|-|2.38|56|-|
|**2011**|-|-|2.26|58|16.88|
|**2012**|-|-|2.26|54|-|

<u>ANC data sources: Lesotho ANC HIV and Syphilis Sentinel Surveillance Synopsis 2003, 2005, and 2007</u> <u>National DHS or AIS data sources: Lesotho DHS 2004 and 2009</u>

# **Malawi**

||**Prevalence among**<br>|**females 15-24(%)**<br>|**Spectrum e**|**stimates**|**Indicator 1.6 -**<br>|
|---|---|---|---|---|---|
||**National Antenatal**<br>**Sentinel Surveillance**<br>**Surveys **|**National Demographic**<br>**and Health or AIDS**<br>**Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|**HIV prevalence**<br>**in young people**<br>**15-24(%)**|
|**1990**|-|-|2.36|-|-|
|**1991**|-|-|2.44|-|-|
|**1992**|-|-|2.45|-|-|
|**1993**|-|-|2.49|-|-|
|**1994**|-|-|2.51|-|-|
|**1995**|-|-|2.48|-|-|
|**1996**|-|-|2.41|-|-|
|**1997**|-|-|2.30|-|-|
|**1998**|-|-|1.85|-|-|
|**1999**|-|-|2.03|-|-|
|**2000**|-|-|1.91|-|-|
|**2001**|9.3|-|1.76|-|-|
|**2002**|-|-|1.63|-|-|
|**2003**|18.3|-|1.49|-|-|
|**2004**|-|9.1|1.37|5|-|
|**2005**|14.3|-|1.25|11|-|
|**2006**|-|-|1.15|21|-|
|**2007**|12.3|-|1.04|35|12.3|
|**2008**|-|-|0.79|-|-|
|**2009**|-|-|0.88|47|12.3|
|**2010**|-|5.2|0.82|54|-|
|**2011**|-|-|0.79|67|8.21|
|**2012**|-|-|0.77|69|-|

<u>ANC data source: Malawi HIV and Syphilis Sero–Survey and National HIV Prevalence and AIDS Estimates Report for</u> 2007 <u>National DHS or AIS data sources: Malawi DHS 2004 and 2010</u> 57
# **Malawi**

||**Prevalence**<br>**among females**<br>**15-24(%)**|**Incidence rate/100**<br>**personyears**|**Time frame of**<br>**study**|**Study/Trial**<br>**population**|**Region**|
|---|---|---|---|---|---|
|Crampin_et al.,_<br>AIDS 2003|8.9 (ANC),<br>11.4 (general<br>population)||1998-2001|Women from<br>antenatal clinics and<br>studies of<br>mycobacterial<br>disease (control)|Karonga|
|Kumwenda et al.,<br>Sex Transm Dis<br>2006||4.86|1999-2001|Women from<br>postnatal or family<br>planning clinics|Blantyre|
|Kumwenda et al.,<br>Sex Transm Dis<br>2006||4.2|1999-2001|Women from<br>postnatal or family<br>planning clinics|Lilongwe|
|Crampin et al., J<br>Acquir Immune<br>Defic Syndr 2008|8.8 (1999-2001),<br>6.0 (2002-2004)||1999-2004|Women from<br>antenatal clinics|Karonga|
|Abdool et al., AIDS<br>2011||1.78|2005-2009|Women in<br>microbicide study<br>(control)|Lilongwe|
|Abdool et al., AIDS<br>2011||4.9|2005-2009|Women in<br>microbicide study<br>(control)|Blantyre|
|Keating et al., PLoS<br>One2011||4|2009|Pregnant women<br>Lilongwe|Lilongwe|
|ALPHA network study||||||

# **Mozambique**

||**Prevalence amo**|**ng females 15-24(%)**|**Spectrum e**|**stimates**|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|0.27|-|
|**1991**|-|-|0.38|-|
|**1992**|-|-|0.49|-|
|**1993**|-|-|0.61|-|
|**1994**|-|-|0.74|-|
|**1995**|-|-|0.90|-|
|**1996**|-|-|1.09|-|
|**1997**|-|-|1.29|-|
|**1998**|-|-|1.50|-|
|**1999**|-|-|1.67|-|
|**2000**|-|-|1.79|-|
|**2001**|12.5|-|1.83|-|
|**2002**|13.1|-|1.83|-|
|**2003**|-|-|1.74|-|
|**2004**|15.6|-|1.64|3|
|**2005**|-|-|1.52|7|
|**2006**|-|-|1.39|12|
|**2007**|11.3|-|1.29|24|
|**2008**|-|-|1.19|-|
|**2009**|12.0|11.1|1.13|34|
|**2010**|-|-|1.09|41|
|**2011**|-|-|1.02|46|
|**2012**|-|-|1.03|45|

<u>ANC data sources: Mozambique 2012 GARPR</u> <u>National DHS or AIS data source: Mozambique AIS 2009</u> 58
# **Namibia**

||**Prevalence amon**|**g females 15-24(%)**|**Spec**<br>**Incidence in**|**trum estimates**|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**adults 15-49**<br>**(%)**|**Antiretroviral coverage**<br>**(%)**|
|**1990**|-|-|0.49|-|
|**1991**|-|-|0.72|-|
|**1992**|-|-|1.02|-|
|**1993**|-|-|1.39|-|
|**1994**|-|-|1.78|-|
|**1995**|-|-|2.15|-|
|**1996**|-|-|2.44|-|
|**1997**|-|-|2.64|-|
|**1998**|-|-|2.70|-|
|**1999**|-|-|2.64|-|
|**2000**|-|-|2.50|-|
|**2001**|-|-|2.29|-|
|**2002**|-|-|2.03|-|
|**2003**|-|-|1.78|-|
|**2004**|15.2|-|1.53|22|
|**2005**|-|-|1.31|62|
|**2006**|14.2|-|1.12|64|
|**2007**|-|-|0.96|88|
|**2008**|10.6|-|0.88|-|
|**2009**|-|-|0.81|78|
|**2010**|10.3|-|0.78|90|
|**2011**|-|-|0.82|>95|
|**2012**|-|-|0.81|90|

ANC data source: Namibia UNGASS Report 2010

||**Incidence rate/100**<br>**personyears**|**Time frame of**<br>**study**|**Study/Trialpopulation**|**Region**|
|---|---|---|---|---|
|Aulagnieretal.,PLoS One2011|2.4|2007-2009|Adultgeneralpopulation|Windhoek|
|ALPHA network study|||||

# **Nigeria**

||**Prevalence amon**<br>|**g females 15-24(%)**<br>|**Spectrum**<br>|**estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|0.22|-|
|**1991**|-|-|0.26|-|
|**1992**|-|-|0.30|-|
|**1993**|-|-|0.34|-|
|**1994**|-|-|0.39|-|
|**1995**|-|-|0.43|-|
|**1996**|-|-|0.48|-|
|**1997**|-|-|0.51|-|
|**1998**|-|-|0.53|-|
|**1999**|-|-|0.56|-|
|**2000**|-|-|0.55|-|
|**2001**|6.0|-|0.54|-|
|**2002**|-|-|0.51|-|
|**2003**|5.3|-|0.49|-|
|**2004**|-|-|0.45|2|
|**2005**|4.3|-|0.40|6|
|**2006**|-|-|0.37|13|
|**2007**|-|-|0.34|26|
|**2008**|4.2|-|0.31|-|
|**2009**|-|-|0.28|25|
|**2010**|4.1|-|0.27|27|
|**2011**|-|-|0.26|30|
|**2012**|-|-|0.23|32|

<u>ANC data source: 2010 Nigeria National HIV Sero-prevalence Sentinel Survey</u> 59
# **Rwanda**

||**Prevalence among**<br>|**females 15-24(%)**<br>|**Spectrum**|**estimates**|**Indicator 1.6 -**<br>|
|---|---|---|---|---|---|
||**National Antenatal**|**National Demographic**|||**HIV prevalence**|
||**Sentinel Surveillance**<br>**Surveys **|<br>**and Health or AIDS**<br>**Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|<br>**in young people**<br>**15-24(%)**|
|**1990**|-|-|0.92|-|-|
|**1991**|-|-|0.96|-|-|
|**1992**|-|-|0.87|-|-|
|**1993**|-|-|0.76|-|-|
|**1994**|-|-|0.67|-|-|
|**1995**|-|-|0.60|-|-|
|**1996**|-|-|0.57|-|-|
|**1997**|-|-|0.51|-|-|
|**1998**|-|-|0.45|-|-|
|**1999**|-|-|0.39|-|-|
|**2000**|-|-|0.34|-|-|
|**2001**|-|-|0.27|-|-|
|**2002**|-|-|0.25|-|-|
|**2003**|-|-|0.23|-|-|
|**2004**|-|-|0.22|10|-|
|**2005**|-|1.5|0.21|29|-|
|**2006**|-|-|0.18|52|-|
|**2007**|-|-|0.17|71|3.5|
|**2008**|-|-|0.18|-|-|
|**2009**|-|-|0.16|77|3.7|
|**2010**|-|1.5|0.13|84|-|
|**2011**|-|-|0.13|82|1.86|
|**2012**|-|-|0.13|87|-|

|National DHS or AIS data sources:<br>**Study reference**|Rwanda DHS 2005 and<br>**Incidence rate/100**<br>**personyears**|2010/2011<br>**Time frame of study**|**Study/Trial**<br>**population**|**Region**|
|---|---|---|---|---|
|Braunstein et al., Sex Transm Dis 2011|3.5 (2006-2007),<br>2.1(2007-2008)|2006-2008|Female sex workers|-|

# **South Africa**

||**Prevalence amon**<br>|**g females 15-24(%)**<br>|**Spectrum**<br>|**estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|0.15|-|
|**1991**|-|-|0.30|-|
|**1992**|-|-|0.55|-|
|**1993**|-|-|0.93|-|
|**1994**|-|-|1.41|-|
|**1995**|-|-|1.93|-|
|**1996**|-|-|2.40|-|
|**1997**|-|-|2.72|-|
|**1998**|-|-|2.85|-|
|**1999**|-|-|2.81|-|
|**2000**|-|-|2.67|-|
|**2001**|23.1|-|2.49|-|
|**2002**|23.5|-|2.26|-|
|**2003**|24.8|-|2.09|-|
|**2004**|25.1|-|1.96|4|
|**2005**|24.9|16.9|1.85|15|
|**2006**|22.4|-|1.79|21|
|**2007**|22.1|-|1.73|28|
|**2008**|21.7|13.9|1.72|-|
|**2009**|21.7|-|1.75|42|
|**2010**|21.8|-|1.78|57|
|**2011**|20.5|-|1.72|66|
|**2012**|-|-|1.37|80|

<u>ANC data sources: The 2011 National Antenatal Sentinel HIV & Syphilis Prevalence Survey in South Africa, The 2010</u> National Antenatal Sentinel HIV & Syphilis Prevalence Survey in South Africa <u>National DHS or AIS data sources: South African National HIV Prevalence, HIV Incidence, Behaviour and Communication</u> Survey 2005 and 2008 60
# **South Africa**

|**Study reference**|**Prevalance**<br>**in young**<br>**females(%)**|**Incidence rate/100**<br>**personyears**|**Time frame**<br>**of study**|**Study/Trial**<br>**population**|**Region**|
|---|---|---|---|---|---|
|Coleman et al.,<br>J Acquir Immune Defic Syndr Hum<br>Retrovirol 1997|6.3 (1992),<br>9.7 (1993),<br>14(1995)||1992-1995|Women from<br>antenatal clinics|Hlabisa, Durban|
|Auvert et al.,<br>PLoSMed2005||2.11|2002-2004|Young men (18-24)<br>incircumcision trial<br>|Orange Farm and<br>surrounding areas|
|Jewkes et al.,<br>BMJ 2008||4.07|2003-2004|Young adults (15-26)<br>in intervention trial<br>(control)|Eastern Cape|
|Mavedzenge et al.,<br>J Acquir Immune Defic Syndr 2011||6.75|2003-2005|Women in<br>prevention trial|Durban|
|Mavedzenge et al.,<br>JAcquir ImmuneDefic Syndr 2011||3.33|2003-2005|Women in<br>prevention trial<br>|Johannesburg|
|Ramjee et al.,<br>J Acquir Immune Defic Syndr 2008|39.2 (18-25)|5|2003-2005|Women in<br>preparatory study for<br>microbicidetrial|Durban|
|Ramjee et al.,<br>J Acquir Immune Defic Syndr 2008|36.8 (18-25)|6|2003-2005|Women in<br>preparatory study for<br>microbicidetrial|Hlabisa|
|Rice et al.,<br>BMC Public Health 2007|30.3 (ANC),<br>15.2 (general<br>population)||2005|Women from<br>antenatal clinics and<br>ingeneralpopulation|Hlabisa|
|Moodley et al.,<br>AIDS2009||10.7|2006-2007|Pregnant women|Mpumalanga, Eastern<br>Cape andFree State|
|Abdool et al.,<br>AIDS 2011||3.82|2005-2009|Women in<br>microbicide study<br>(control)|Durban|
|Abdool et al.,<br>AIDS 2011||11.5|2005-2009|Women in<br>microbicide study<br>(control)|Hlabisa|
|Nel et al.,<br>PLoS One2012||14.8|2007-2009|Non pregrant women<br>(18-35)|<br>Ladysmith|
|Nel et al.,<br>PLoS One2012||6.3|2007-2009|Non pregrant women<br>(18-35)|<br>Edendale|
|Nel et al.,<br>PLoS One2012||7.2|2007-2009|Non pregrant women<br>(18-35)|<br>Pinetown|
|Abdool et al.,<br>Science 2010||9.1|2007-2010|Women in<br>microbicide trial<br>(placebo)|KwaZulu-Natal|
|Van Damme,<br>NEJM 2012||3.4|2009-2011|PrEP among women<br>18-35 (control)|Bloemfontein|
|Van Damme,<br>NEJM 2012||6|2009-2011|PrEP among women<br>18-35 (control)|Praetoria|
|Gómez-Olivé<br>etal. AIDS Care2013<br>ALPHA network study|18.40||2010-2011|Adult general<br>population|Ehlanseni|

<u>South African National HIV Incidence, Prevalence, and Behaviour Survey, 2012, Data and Estimates</u>

||||**HIV Incidence**<br>|**(%)**<br>|
|---|---|---|---|---|
|**Survey Year or Inter-**<br>**Survey Period**|**Age Group and**<br>**Demographic**||**95% Confidence Interval**<br>**(Low)**|**95% Confidence Interval**<br>**(High)**|
||**Total 15–49**|2.20|0.90|4.00|
||**Males 15-49**|1.20|0.10|3.00|
|**2002–2005**|**Females 15-49**|3.20|1.80|5.00|
||**Total 15–24**|2.80|1.70|4.20|
||**Males 15-24**|0.60|0.10|1.60|
||**Females 15-24**|5.30|3.60|7.10|
||**Total 15–49**|1.90|0.80|3.30|
||**Males 15-49**|1.60|0.60|3.00|
|**Modeled**<br>**2005–2008**|**Females 15-49**|2.20|1.00|3.60|
|<br>|**Total 15–24**|2.30|1.20|3.50|
||**Males 15-24**|1.40|0.50|2.30|
||**Females 15-24**|3.50|2.10|4.90|
||**Total 15–49**|1.90|0.80|3.10|
||**Males 15-49**|1.60|0.60|2.70|
|**2008–2012**|**Females 15-49**|2.10|1.00|3.40|
||**Total 15–24**|1.50|0.80|2.30|
||**Males 15-24**|1.00|0.40|1.60|
||**Females 15-24**|2.10|1.20|3.10|
||**Total 15–49**|1.72|1.38|2.06|
||**Males 15-49**|1.21|0.97|1.45|
|**Direct**<br>**2012**|**Females 15-49**|2.28|1.84|2.74|
|**Measurement**<br>|**Total 15–24**|1.49|1.21|1.88|
||**Males 15-24**|0.55|0.45|0.65|
||**Females 15-24**|2.54|2.04|3.04|
|||||61|
62
# **Swaziland**

||**Prevalence amon**<br>|**g females 15-24(%)**|**Spectrum**|**estimates**|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|0.54|-|
|**1991**|-|-|1.16|-|
|**1992**|-|-|2.12|-|
|**1993**|-|-|3.27|-|
|**1994**|-|-|4.30|-|
|**1995**|-|-|4.92|-|
|**1996**|-|-|5.07|-|
|**1997**|-|-|4.84|-|
|**1998**|-|-|4.45|-|
|**1999**|-|-|4.07|-|
|**2000**|35.4|-|3.75|-|
|**2001**|-|-|3.48|-|
|**2002**|39.4|-|3.36|-|
|**2003**|-|-|3.27|-|
|**2004**|39.4|-|3.29|14|
|**2005**|-|-|3.21|27|
|**2006**|34.6|-|3.03|35|
|**2007**|-|22.7|2.85|42|
|**2008**|38.1|-|2.74|-|
|**2009**|-|-|2.57|64|
|**2010**|34.7|-|2.50|73|
|**2011**|34.0|-|2.47|83|
|**2012**|-|-|2.04|82|

<u>ANC data source: Swaziland Country Report on Monitoring the Political Declaration on HIV/AIDS 2012 National DHS and AIS data source: Swaziland DHS 2006-2007</u>

# **Tanzania**

||**Prevalence amo**<br>|**ng females 15-24(%)**<br>|**Spectrum**<br>|**estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|1.25|-|
|**1991**|-|-|1.33|-|
|**1992**|-|-|1.34|-|
|**1993**|-|-|1.29|-|
|**1994**|-|-|1.21|-|
|**1995**|-|-|1.08|-|
|**1996**|-|-|0.97|-|
|**1997**|-|-|0.87|-|
|**1998**|-|-|0.76|-|
|**1999**|-|-|0.71|-|
|**2000**|-|-|0.64|-|
|**2001**|-|-|0.59|-|
|**2002**|-|-|0.55|-|
|**2003**|-|-|0.53|-|
|**2004**|7.4|4.0|0.50|<1|
|**2005**|-|-|0.48|5|
|**2006**|6.8|-|0.46|14|
|**2007**|-|-|0.42|31|
|**2008**|5.3|3.6|0.40|-|
|**2009**|-|-|0.38|32|
|**2010**|-|-|0.34|38|
|**2011**|-|-|0.34|40|
|**2012**|-|-|0.32|61|

<u>ANC data sources: Tanzania Surveillance of HIV and Syphilis Infections Among Antenatal Clinic Attendees 2003/4,</u> 2005/06, 2008 <u>National DHS and AIS data sources: Tanzania AIS 2003-2004, Tanzania AIS/Malaria Indicator Survey 2007-2008 and</u> 2011-2012 63
# **Tanzania**

||**Prevalence**<br>**among females**<br>**15-24(%)**|**Incidence**<br>**rate/100 person**<br>**years**|**Time frame of**<br>**study**|**Study/Trial**<br>**population**|**Region**|
|---|---|---|---|---|---|
|Boerma et al.,<br>Sex Transm Dis2003|5.32||1996-1997|Adult general<br>population|Kisesa|
|Wambura et al.,<br>J Acquir Immune Defic<br>Syndr 2007||.81 (1994-1997)<br>1.24 (1996-2000)<br>1.13 (1999-2004)|1994-2003|Adult general<br>population|Kisesa|
|Bloom et al.,<br>Sex Transm Infect 2002|5.4 (1994-1995)|0.84 Females<br>(1996-1997), 0.73<br>Males(1996-1997)|1994-1995,<br>1996-1997|Adult general<br>population|Kisesa|
|Ramjee et al.,<br>J Acquir Immune Defic<br>Syndr 2008|11 (Baseline<br>Prevalence 18-25)|1.3|2003-2005|Women in<br>preparatory study<br>for microbicidetrial|Moshi|
|Geis et al.,<br>J Acquir Immune Defic<br>Syndr 2011||1.35|2002-2006|Adult general<br>population|Mbeya|
|Watson-Jones et al.,<br>NEJM 2008||4.12|2004-2006|HSV-2+ women<br>from STD treatment<br>trial(placebo)|North-<br>western<br>Tanzania|
|Munseri et al.,<br>BMCPublicHealth 2013||0.85|2007-2008|Police officers|Dar es<br>Salaam|
|Kapiga et al.,<br>PLoS One, 2013||3.7|2008-2010|Women working in<br>food and<br>recreational facilities|Northern<br>Tanzania|
|ALPHA network study||||||

# **Uganda**

||**Prevalence amo**<br>|**ng females 15-24(%)**<br>|**Spectrum**<br>|**estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|2.71|-|
|**1991**|-|-|2.06|-|
|**1992**|-|-|1.48|-|
|**1993**|-|-|1.01|-|
|**1994**|-|-|0.73|-|
|**1995**|-|-|0.55|-|
|**1996**|-|-|0.44|-|
|**1997**|-|-|0.47|-|
|**1998**|-|-|0.51|-|
|**1999**|-|-|0.55|-|
|**2000**|-|-|0.62|-|
|**2001**|-|-|0.64|-|
|**2002**|-|-|0.67|-|
|**2003**|-|-|0.73|-|
|**2004**|-|-|0.75|12|
|**2005**|-|4.3|0.79|21|
|**2006**|-|-|0.84|27|
|**2007**|-|-|0.90|33|
|**2008**|-|-|0.90|-|
|**2009**|-|-|0.89|41|
|**2010**|-|-|0.87|45|
|**2011**|-|4.9|0.83|54|
|**2012**|-|-|0.77|64|

<u>National DHS and AIS data sources: Uganda HIV/AIDS Sero-behavioural survey 2004-2005, Uganda AIS 2011</u> 64
# **Uganda**

|**Study reference**|**Prevalence**<br>**among females**<br>**15-24(%)**|**Incidence**<br>**rate/100**<br>**personyears**|**Time frame of**<br>**study**|**Study/Trial**<br>**population**|**Region**|
|---|---|---|---|---|---|
|Ahmed et al.,<br>AIDS2001||1.71|1994-1998|Community HIV<br>prevention trial|Rakai|
|Mulder et al.,<br>BMJ 1995|Ages 13-24:<br>9.9 (1989),<br>7.3 (1994)|0.73<br>(1989-1991),<br>0.71<br>(1992-1994)|1989-1994|Adult general<br>population|Masaka|
|Wawer et al.,<br>AIDS 1997|Adults 15-24: 20.6<br>(1990), 16.45<br>(1992)|2.1<br>(1990-1991),<br>2<br>(1991-1992)|1990-1992|Adult general<br>population|Rakai|
|Kengeya-Kayondo et al.,<br>Int J Epidemiol 1996||0.82 (1990),<br>0.69 (1991),<br>0.57 (1992),<br>0.89 (1993),<br>0.45 (1994)|1990-1994|Adult general<br>population|Masaka|
|Arroyo et al.,<br>J Acquir Immune Defic<br>Syndr 2006||Ages 14-90:<br>1.27|1999-2002|Adult general<br>population|Rakai|
|Brown et al.,<br>AIDS2007||1.6|1999-2004|Women 18-35|Kampala|
|Gray et al.,<br>Lancet 2007||1.33|2004-2006|Control men in<br>circumcision trial|Rakai|
|Tobian et al.,<br>AIDS2009||1.09|2004-2006|Men undergoing<br>circumcision|Rakai|
|Ruzagira et al.,<br>PLoS ONE 2011|Ages 18-24: 10.3<br>(2004)|1.04|2004-2007|Adult general<br>population|Masaka|
|Guwatudde et al.,<br>PLoS ONE 2009|Adults 15-24: 5.8|0.77|2006-2007|Adult general<br>population|Kayunga|
|Seeley et al.,<br>Sex Transm Dis2012||4.9|2009- 2011|Fishing<br>communities|Lake Victoria|
|Baeten et al.,<br>NEJM 2012||2.07|2008-2011|PrEP in discordant<br>couples (placebo|Jinja, Kabwoh,<br>Kampala, Mbale,|
|||||group)|Tororo|
|ALPHA network study||||||

# **Zambia**

||**Prevalence amo**|**ng females 15-24(%)**|**Spectrum e**|**stimates**|**Indicator 1.6 -**<br>|
|---|---|---|---|---|---|
||||||**HIV**|
||**National Antenatal**<br>**Sentinel Surveillance**<br>**Surveys **|**National Demographic and**<br>**Health or AIDS Impact**<br>**Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|**prevalence in**<br>**young people**<br>**15-24(%)**|
|**1990**|-|-|2.50|-|-|
|**1991**|-|-|2.53|-|-|
|**1992**|-|-|2.59|-|-|
|**1993**|-|-|2.51|-|-|
|**1994**|-|-|2.43|-|-|
|**1995**|-|-|2.33|-|-|
|**1996**|-|-|2.23|-|-|
|**1997**|-|-|2.10|-|-|
|**1998**|-|-|1.98|-|-|
|**1999**|-|-|1.88|-|-|
|**2000**|-|-|1.79|-|-|
|**2001**|-|-|1.75|-|-|
|**2002**|16.3|11.1|1.66|-|-|
|**2003**|-|-|1.59|-|-|
|**2004**|11.5|-|1.49|7|-|
|**2005**|-|-|1.38|16|-|
|**2006**|14.6|-|1.35|26|-|
|**2007**|15.6|8.5|1.27|46|16.8|
|**2008**|-|-|1.15|-|-|
|**2009**|-|-|1.02|81|13.8|
|**2010**|-|-|0.99|72|-|
|**2011**|-|-|0.96|82|-|
|**2012**|-|-|079|79|-|
|<br>ANC da<br>HIV and<br>Nationa<br>**Zamb**|<br>ta sources: Zambia UNGA<br>AIDS and the Universal A<br>l DHS and AIS data source<br>**ia**|<br>SS Report 2008, Zambia Country<br>ccess 2010, ANC Sentinel Surve<br>s: Zambia DHS 2001-2002 and 2|.<br>Report: Monitoring the<br>illance of HIV/Syphilis t<br>007|<br>Declaration of Co<br>rends in Zambia 19|<br>mmitment on<br>94-2002|

65
|**Study reference**|**Incidence rate/100**<br>**personyears**|**Time frame of**<br>**study**|**Study/Trialpopulation**|**Region**|
|---|---|---|---|---|
|Abdool et al.,<br>AIDS 2011|3.57|2005-2009|Women in microbicide study<br>(control)|Lusaka|

# **Zimbabwe**

||**Prevalence amo**<br>**National Antenatal Sentinel**<br>**Surveillance Surveys **|**ng females 15-24(%)**<br>**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Spectrum**<br>**Incidence in**<br>**adults 15-49(%)**|**estimates**<br>**Antiretroviral**<br>**coverage (%)**|
|---|---|---|---|---|
|**1990**|-|-|4.03|-|
|**1991**|-|-|4.89|-|
|**1992**|-|-|5.49|-|
|**1993**|-|-|5.84|-|
|**1994**|-|-|5.78|-|
|**1995**|-|-|5.47|-|
|**1996**|-|-|4.86|-|
|**1997**|-|-|4.16|-|
|**1998**|-|-|3.48|-|
|**1999**|-|-|2.87|-|
|**2000**|29.0|-|2.38|-|
|**2001**|25.2|-|1.98|-|
|**2002**|20.8|-|1.72|-|
|**2003**|-|-|1.56|-|
|**2004**|17.4|-|1.45|1|
|**2005**|-|-|1.35|4|
|**2006**|12.5|10.9|1.31|11|
|**2007**|-|-|1.29|18|
|**2008**|-|-|1.28|-|
|**2009**|11.6|-|1.25|37|
|**2010**|-|-|1.22|54|
|**2011**|-|7.3|1.16|77|
|**2012**|-|-|0.96|79|

<u>ANC data sources: Zimbabwe UNGASS Report 2008, Zimbabwe GARPR 2012 National DHS or AIS data sources: Zimbabwe DHS 2005-2006 and 2010-2011</u>

|**Study reference**<br>|**Prevalence**<br>**among females**<br>**15-24(%)**|**Incidence**<br>**rate/100**<br>**personyears**|**Time frame of**<br>**study**|**Study/Trial**<br>**population**|**Region**|
|---|---|---|---|---|---|
|Gregson et al.,<br>AIDS1997|Ages 15-29: 21.8||1993-1994|Adult general population|Manicaland|
|Boerma et al.,<br> Sex Transm Dis2003|11.67||1998-2000|Adult general population|Manicaland|
|Kumwenda et al.,<br>Sex Transm Dis 2006||4.78|1999-2001|Women attending<br>postnatal or family<br>planning clinics|Harare|
|Brown et al.,<br>AIDS2007||4.1|1999-2004|Women (18-35)|Harare and<br>Chitungwiza|
|Marsh et al.,<br>J Int AIDS Soc 2011|||1998-2005|Women from antenatal<br>clinics and in general<br>population|Manicaland|
|Corbett et al.,<br>AIDS 2007||1.2|2002-2004|Employees from<br>businesses included in<br>VCTstudy|Harare|
|Mavedzenge et al.,<br>J Acquir Immune Defic<br>Syndr 2011||2.72|2003-2005|Women in prevention trial|Harare|
|Abdool et al.,<br>AIDS 2011||2.71|2005-2009|Women in microbicide<br>study (control)|Harare|
|Abdool et al.,<br>AIDS2011||3.29|2005-2009|Women in microbicide<br>tdtl|Chitungwiza|
|||||suy (conro)||
|ALPHA network study||||||

66 **ANNEX 2: Changes to Spectrum data curves from 2012 to 2013**
<!-- Start of picture text --> Congo<br>0.9<br>08<br>0.7<br>g os0.6<br>32 0.40.<br>£ 0.3<br>0.2<br>0.1<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Burkina Faso<br>0.8<br>07<br>0.6<br>8 0.5<br>Bosso<br>£0.3<br>0.2<br>0.1<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Nigeria<br>0.7<br>0.6<br>0.5<br>g<br>= 0.4<br>3<br>=<br>0.2<br>0.1<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Tanzania<br>1.6<br>1.4<br>12<br>gt<br>e<br>Jos<br>zoe<br>0.4<br>0.2<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Zambia<br>3<br>2.5<br>2<br>vo<br>eo<br>i=<br>B15<br>3<br>£<br>1<br>0.5<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text -->
<!-- Start of picture text --> Botswana<br>7<br>6<br>5<br>o<br>eo 4<br>i=<br>3<br>=<br>2<br>1<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Ghana<br>0.4<br>0.35<br>0.3<br>@ 0.25<br>eo<br>i=<br>B02<br>= 0.15<br>0.1<br>0.05<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Malawi<br>3<br>2.5<br>9g ><br>i=<br>wis<br>£<br>1<br>0s<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Mozambique<br>2<br>1.8<br>16<br>i=$1.21.4<br>31<br>£08<br>0.6<br>0.4<br>0.2<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Rwanda<br>1.2<br>1<br>0.8<br>voeo<br>i=<br>B06<br>£<br>0.4<br>0.2<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Swaziland<br>6<br>5<br>4<br>voeo<br>i=<br>33<br>£<br>2<br>1<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Uganda<br>3<br>2.5<br>2<br>4<br>Sis<br>3<br>1<br>0.5<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Zimbabwe<br>7<br>6<br>5<br>24<br>3<br>33<br>2<br>1<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text -->
<!-- Start of picture text --> Burundi<br>1.2<br>1<br>0.8<br>g<br>B06<br>es<br>0.4<br>0.2<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Ethiopia<br>oe<br>os<br>os<br>g<br>5 os<br>—<br>a<br>aL<br>os — x0 — 20<br><!-- End of picture text --> <!-- Start of picture text --> Kenya<br>2.5<br>2<br>g LS<br>a<br>Zz<br>2<br>0.5<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Lesotho<br>7<br>6<br>5<br>84<br>o<br>a=]<br>Bs<br>2<br>1<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text -->
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