DHS COMPARATIVE

REPORTS 43

FACTORS ASSOCIATED WITH PRIOR 

TESTING AMONG HIV-POSITIVE ADULTS 

IN SUB-SAHARAN AFRICA

August 2016

This publication was produced for review by the United States Agency for International Development (USAID). The report was prepared by

Sarah Staveteig, Sara K. Head, Trevor N. Croft, and Kathryn T. Kampa.

DHS Comparative Reports No. 43 

Factors Associated with Prior Testing among HIV-Positive 

Adults in Sub-Saharan Africa 

Sarah Staveteig1

Sara K. Head2

Trevor N. Croft2

Kathryn T. Kampa3

ICF International 

Rockville, Maryland, USA 

August 2016 

1

 The DHS Program, Avenir Health 

2

 The DHS Program, ICF International 

3

 Independent consultant

Corresponding author: Sarah Staveteig, International Health and Development, ICF International, 530 

Gaither Road, Suite 500, Rockville, MD 20850, USA; telephone: +1 301–572–0577; fax: +1 301–572–

0999; email: sarah.staveteig@icfi.com
Acknowledgments: We thank Mary Mahy and Joy Fishel for their insightful reviews, Lynne Jennrich for 

editing, Natalie LaRoche for formatting, and Sam Estabrook and Matthew Pagan for production of maps. 

Editor: Lynne Jennrich 

Document Production: Natalie LaRoche 

Maps: Sam Estabrook and Matthew Pagan 

This study was carried out with support provided by the United States Agency for International 

Development (USAID) through The DHS Program (#AIDOAA-C-13-00095). The views expressed are 

those of the authors and do not necessarily reflect the views of USAID or the United States Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. For additional information about the DHS Program contact: 

DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA. Phone: +1 

301-407-6500; Fax: +1 301-407-6501; Email: reports@dhsprogram.com; Internet: www.dhsprogram.com. 

Recommended citation: 

Staveteig, Sarah, Sara K. Head, Trevor N. Croft, and Kathryn T. Kampa. 2016. Factors Associated with 

Prior Testing among HIV-Positive Adults in Sub-Saharan Africa. DHS Comparative Reports No. 43. 

Rockville, Maryland, USA: ICF International.
iii

Contents 

Tables ................................................................................................................................................................... v 

Figures ................................................................................................................................................................. v 

Preface ............................................................................................................................................................... vii 

Abstract .............................................................................................................................................................. ix 

Abbreviations ..................................................................................................................................................... xi 

1. Background and Objectives ................................................................................................................. 1 

1.1. Background ................................................................................................................................ 1 

1.2. Study Objectives ........................................................................................................................ 4 

2. Methods and Data ................................................................................................................................. 5 

2.1. HIV Serological Status and Prior Testing ................................................................................. 5 

2.2. Weighting and Significance Testing .......................................................................................... 6 

2.3. Country Selection ...................................................................................................................... 6 

2.4. Estimated Knowledge of HIV Status; Adjustments Using Antiretroviral Therapy Coverage ... 8 

2.5. Covariates Examined ................................................................................................................. 9 

2.6. Limitations ................................................................................................................................. 9 

3. HIV Prevalence and Testing Uptake ................................................................................................. 11 

3.1. HIV Prevalence in Focal Countries ......................................................................................... 11 

3.2. Uptake of HIV Testing by Serological Status ......................................................................... 12 

3.3. Estimated Knowledge of HIV Status and Where to Get Tested .............................................. 14 

4. Testing Uptake During Pregnancy and Birth Among HIV-positive Women ................................ 19 

4.1. Receipt of ANC and Uptake of HIV Testing During ANC ..................................................... 19 

4.2. Uptake at Birth, after Birth ...................................................................................................... 20 

5. HIV Testing Uptake Among PLHIV ................................................................................................. 23 

5.1. Characteristics of the Population of PLHIV ............................................................................ 23

5.2. Factors Associated with Prior Testing ..................................................................................... 26 

6. Discussion and Conclusions ................................................................................................................ 39 

References .......................................................................................................................................................... 41 

Appendix Tables ............................................................................................................................................... 45 

v

Tables 

Table 2.1. Response rate and HIV prevalence ............................................................................................ 7 

Table 3.1. Uptake of HIV testing, by serological status and sex .............................................................. 13 

Table 3.2. ART coverage and estimated knowledge of HIV status .......................................................... 14 

Table 4.1. HIV testing among HIV-positive women during ANC ........................................................... 20 

Table 4.2. HIV testing among HIV-positive women, by date of last birth ............................................... 21 

Table 5.1. Characteristics of PLHIV, by country ..................................................................................... 24 

Table A.1 Characteristics of HIV-negative adults, by country ................................................................. 46 

Table A.2. Characteristics of HIV-positive women, by country ............................................................... 48 

Table A.3. Characteristics of HIV-positive men, by country .................................................................... 49 

Table A.4. HIV testing among PLHIV, by sex and place of residence ..................................................... 50 

Table A.5. HIV testing among HIV-positive women, by age group ......................................................... 51 

Table A.6. HIV testing among HIV-positive men, by age group .............................................................. 52 

Table A.7. HIV testing among HIV-positive women age 15-49, by marital status ................................... 53 

Table A.8. HIV testing among HIV-positive men age 15-49, by marital status ........................................ 54 

Table A.9. HIV testing among HIV-positive women age 15-49, by education ......................................... 55 

Table A.10. HIV testing among HIV-positive men age 15-49, by education ............................................. 56 

Table A.11. HIV testing among HIV-positive women age 15-49, by wealth ............................................. 57 

Table A.12. HIV testing among HIV-positive men age 15-49, by wealth .................................................. 58 

Table A.13. HIV testing among HIV-positive women age 15-49, by lifetime number of 

sexual partners ......................................................................................................................... 59 

Table A.14. HIV testing among HIV-positive men age 15-49, by lifetime number of sexual partners ...... 60 

Table A.15. HIV testing among PLHIV, by self-report of STI symptoms in the past year ........................ 61 

Table A.16. HIV testing among HIV-positive women, by whether they gave birth in the past 2 years ..... 62 

Table A.17. HIV testing among HIV-positive men, by whether they paid for sex in the past year ............ 63

Figures 

Figure 3.1. HIV prevalence in focal countries ........................................................................................... 11 

Figure 3.2. Estimated knowledge of HIV status (adjusted) among PLHIV ............................................... 15 

Figure 3.3. Percentage of PLHIV who are estimated to know their HIV status (adjusted) by year 

and HIV prevalence ................................................................................................................. 16 

Figure 3.4. Knowledge of where to get tested ........................................................................................... 17 

Figure 5.1. HIV testing among PLHIV, by sex.......................................................................................... 27 

Figure 5.2. HIV testing among PLHIV, by age group ............................................................................... 28 

Figure 5.3. HIV testing among PLHIV, by place of residence .................................................................. 29 

Figure 5.4. HIV testing among PLHIV, by education ............................................................................... 30 

Figure 5.5. HIV testing among PLHIV, by marital status.......................................................................... 31 

Figure 5.6. HIV testing among PLHIV, by wealth quintile ....................................................................... 32 

Figure 5.7. HIV testing among PLHIV, by number of lifetime sex partners ............................................. 33 

Figure 5.8. HIV testing among PLHIV, by whether reported STI symptoms in the past year .................. 35 

Figure 5.9. HIV testing among HIV-positive women, by whether given birth in the past 2 years ............ 36 

Figure 5.10. HIV testing among HIV-positive men, by whether they paid for sex in the past year ............ 37 

vii

Preface 

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to provide policymakers and program managers in low- and 

middle-income countries with easily accessible data on levels and trends for a wide range of health and 

demographic indicators. DHS Comparative Reports provide such information, usually for a large number 

of countries in each report. These reports are largely descriptive, without multivariate methods, but when 

possible they include confidence intervals and/or statistical tests. 

The topics in the DHS Comparative Reports series are selected by The DHS Program in consultation with 

the U.S. Agency for International Development. 

It is hoped that the DHS Comparative Reports will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries. 

Sunita Kishor 

Director, The DHS Program 

ix

Abstract 

With evidence on the benefits of the early diagnosis of HIV and the initiation of antiretroviral therapy 

(ART) mounting, HIV testing is a primary entry point for the prevention of HIV transmission. In subSaharan Africa, the scale-up of affordable and readily available HIV testing and treatment has allowed the 

region to make notable progress in the prevention of HIV/AIDS. UNAIDS has recently proposed a set of 

ambitious targets that, if achieved, are predicted to end the AIDS epidemic by 2030. The targets, known as 

90-90-90, call for 90 percent of all people living with HIV (PLHIV) to know their status, 90 percent of 

those to receive antiretroviral therapy (ART), and 90 percent of ART recipients to achieve viral suppression. 

This report focuses on the “first 90” in the 90-90-90 target, and seeks to answer four main questions. First, 

how does testing uptake vary by serological status, sex, and country? Second, what proportion of PLHIV 

are estimated to know their status, and how does this vary by sex, country, timing of the survey, and size 

of the epidemic? Third, what is the role of maternal care in HIV testing uptake among HIV-positive women? 

And fourth, what background and behavioral characteristics are associated with ever being tested for HIV 

among PLHIV? 

This report analyzes data from Demographic and Health Surveys and AIDS Indicator Surveys fielded since 

2006 in 15 sub-Saharan African countries where voluntary serological testing was conducted: Cameroon, 

Congo (Brazzaville), Ethiopia, Gabon, Kenya, Lesotho, Malawi, Mozambique, Namibia, Rwanda, 

Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe. In the countries studied, we find that between 23 

percent and 71 percent of PLHIV are estimated to know their status; on average across countries, after 

adjusting for ART coverage, 51 percent of PLHIV are estimated to know their status. The results reflect 

encouraging progress, but the achievement is far short of the 90 percent goal set by UNAIDS for 2020. 

Several gaps in HIV testing coverage still exist, particularly among adolescents, rural residents, and the 

poorest. While the need continues to target demographic groups at greatest risk of HIV, additional 

interventions focused on reaching the most socially vulnerable populations are essential. 

xi

Abbreviations 

AIS AIDS Indicator Surveys 

ANC antenatal care 

ART antiretroviral therapy 

CDC U.S. Centers for Disease Control and Prevention 

DHS Demographic and Health Surveys 

ELISA enzyme-linked immunoassay 

HCT HIV counseling and testing 

PLHIV people living with HIV 

PMTCT prevention of mother-to-child transmission 

STI sexually transmitted infection 

UNAIDS Joint United Nations Programme on HIV/AIDS 

USAID U.S. Agency for International Development 

WHO World Health Organization 

1

1. Background and Objectives 

In 2015, UNAIDS estimated 1.9 million new HIV infections annually among individuals age 15 and older. 

Of the estimated 36.7 million [34.0 million-39.8 million]1

 people living with HIV (PLHIV) worldwide, 

25.5 million [23.0 million-28.3 million] are living in sub-Saharan Africa, which globally is the region 

hardest hit by the epidemic (UNAIDS 2016b). 

The spread of affordable and readily available HIV tests and antiretroviral therapy (ART) in sub-Saharan 

Africa has had a major impact on HIV prevention efforts. Individuals who know their status are less likely 

to engage in HIV-related risk behaviors (Fonner et al. 2012), and early ART for a person living with HIV 

can lead to a suppressed viral load, thereby reducing the risk of HIV transmission (UNAIDS 2014a). The 

number of AIDS-related deaths globally has fallen by 26 percent since 2010, from an estimated 1.5 million 

in 2010 to 1.1 million in 2015, an outcome directly related to the rapid increase in the number of people on 

ART (UNAIDS 2016d). 

Given the importance of testing and treatment to HIV prevention efforts, in 2014, UNAIDS proposed a 

series of ambitious targets known as 90-90-90: by 2020, 90 percent of all PLHIV will know their status, 90 

percent of people diagnosed with HIV will receive ART, and 90 percent of people receiving ART will have 

viral suppression. UNAIDS predicts that achievement of these targets by 2020 will enable the world to end 

the AIDS epidemic by 2030, creating profound health and economic benefits (UNAIDS 2014a). This report 

focuses on the “first 90”: HIV testing uptake and estimated knowledge of status among PLHIV in subSaharan Africa, and the factors associated with uptake among this population, by country.

1.1. Background 

The evolution of HIV treatment guidelines 

Before antiretroviral therapy (ART) was readily available, treatment guidelines drafted by the World Health 

Organization (WHO) used a conservative definition of treatment eligibility, including a required minimum 

CD4 cell-count threshold. As the availability of ART was scaled-up in the early 2000s and the effectiveness 

and multiple benefits of ART became better understood, the public health case for early initiation of ART 

strengthened considerably and the number and size of treatment programs increased (WHO 2015a). HIV 

treatment can prevent HIV-related illness, avert AIDS-related deaths, and prevent further transmission 

through viral suppression (UNAIDS 2014a). This transmission prevention potential influenced revisions to 

the WHO treatment guidelines and resulted in the recommendation of ART initiation for all people with 

diagnosed HIV infection, without the requirement of a prior CD4 test, also known as universal eligibility. 

Specific changes in the 2015 guidelines include recommended initiation of ART, regardless of CD4 cell 

count, and further recommendations on the use of oral pre-exposure prophylaxis among all populations at 

considerable risk of acquiring HIV (WHO 2015b). 

Universal eligibility for ART means that more people will start ART earlier than in previous years. As this 

expansion occurs, programs must continue to respond to the needs of all patients. The revised 2015 WHO 

recommendations to improve the quality and efficiency of services to PLHIV covers three areas: (1) the 

differentiated care framework to address the diverse needs of PLHIV; (2) alternative strategies for 

community delivery of ART to accommodate the growing number of people on ART; and (3) principles 

for improving the quality of care and providing people-centered care. 

 

1

 Confidence intervals are given in brackets following estimates. 
2

Prior research on factors influencing HIV testing in sub-Saharan Africa 

HIV testing uptake is not uniform across countries or communities in sub-Saharan Africa. Various 

sociodemographic and sexual risk factors have been associated with uptake of HIV testing. Studies 

conducted in the region have identified several variables that may influence an individual’s decision to seek 

or participate in HIV testing; these factors often include sex, residence, and education level. A 2013 analysis 

of HIV testing uptake in sub-Saharan Africa found that in 23 of 29 countries, fewer men than women had 

ever been tested for HIV (Staveteig et al. 2013). The study found that a likely contributor to this discrepancy 

is the critical gateway to HIV testing and counseling that antenatal care (ANC) visits and other maternal 

health services provide for pregnant women, often through integrated opt-out provider-initiated testing and 

counseling. 

Higher percentages of urban residents, compared with rural residents, in sub-Saharan Africa report ever 

having been tested for HIV. Factors such as accessibility of HIV testing services may play a role in these 

urban-rural disparities. The main reason for never having used facility-based testing and counseling 

services, as reported by participants in a Malawi study, was the distance to the testing center (Helleringer 

et al. 2009). Routine testing in hospitals and other health care facilities has significantly increased uptake 

and case finding among attendees of these facilities, but cost and convenience issues often limit the use of 

health care facilities among the lower socioeconomic strata in sub-Saharan countries. Residents of these 

lower income households were significantly less likely to have ever used facility-based HIV testing 

services, compared to the rest of the population in Malawi (Helleringer et al. 2009). 

Education and knowledge also affect the likelihood of testing. Men and women who have attained 

secondary school or higher education are more likely to have ever been tested for HIV, compared with 

women with no education or women who have attended primary school only (Staveteig et al. 2013). 

Knowledge of HIV is related to the likelihood of having ever been tested; however, reverse causality is a 

distinct possibility. For example, inaccurate knowledge about HIV and ART was associated with non-use 

of HIV testing services in Tanzania. Participants in that study with a lower level of HIV knowledge were 

less likely to participate in the uptake of HIV testing; comparable outcomes were found among participants 

with poor ART knowledge. Uptake of HIV testing services was highest among participants with past HIV 

testing experience, compared to participants who never previously used testing services (South et al. 2013). 

Participation in high-risk sexual behavior and personal health concerns can also be motivating factors for 

testing, with concerns about ill health being more prominent among individuals that presented with a late 

seropositive diagnosis (Wanyenze et al. 2011). A study in Malawi found that participants with multiple sex 

partners over the last three years and participants who presented with recent symptoms of sexually 

transmitted infections (STIs) were more likely to participate in home-based testing and counseling 

(Helleringer et al. 2009). Similarly, a study in Zimbabwe found that the proportion of women tested for 

HIV was higher among women who reported more sexual partners. For men, higher odds of testing were 

noted only among men who had two lifetime sexual partners, compared with one lifetime sexual partner. 

Men in Zimbabwe who reported having an STI in the past 12 months were more likely to have been tested 

(Takarinda et al. 2016). A cross-national study found higher levels of testing among HIV-positive adults 

(Staveteig et al. 2013). 

HIV testing and treatment initiatives in sub-Saharan Africa 

To increase HIV testing and treatment, several countries, including the 15 that this report focuses on,2

 have 

implemented initiatives with varying degrees of success. An estimated 60 percent [56%-65%] of PLHIV 

globally know their HIV status, and only 46 percent [43%-50%] of PLHIV are receiving ART, which makes 

 

2

 Cameroon, Congo (Brazzaville), Ethiopia, Gabon, Kenya, Lesotho, Malawi, Mozambique, Namibia, Rwanda, 

Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe. 
3

scaling-up access to testing and treatment services a major priority (UNAIDS 2016d). Finding effective, 

efficient ways to achieve scale-up has been an on-going challenge. 

One approach has been through the integration of prevention of mother-to-child transmission (PMTCT) 

programs with maternal and child health services. Rwanda, for example, began adopting a more familycentered approach in 2003. The approach included strategies designed to reach partners and other family 

members of pregnant women with HIV testing services. Methods that proved especially successful included 

strongly emphasizing counseling to encourage HIV testing, increasing the number of personnel at clinics 

when ANC services are being provided, adopting measures to streamline patient flows, cutting wait times, 

and assuring confidentiality. Uptake of HIV testing and PMTCT services among pregnant women rose to 

above 90 percent at the 18 clinics that implemented the new approaches, and, by 2009, more than 80 percent 

of male partners were also taking HIV tests, compared with 16 percent in 2003 (WHO 2015a). The 

incorporation of male partners of pregnant women attending ANC visits for HIV testing has also been 

highly encouraged in Swaziland, with official recommendations incorporated into the 2006 national HIV 

testing and counseling guidelines. 

Another successful and innovative PMTCT initiative, the Mother-Baby Pack, originated in Lesotho. Local 

health-care workers often found that pregnant women living with HIV were unable to make repeated trips 

to health facilities during and after pregnancy, mainly for reasons related to location and cost. To overcome 

this challenge, they would put medicines in plain brown envelopes for the women to take home and use 

throughout the duration of their pregnancy. In 2007, the Government of Lesotho included the minimum 

packs, as they were called at the time, in their national PMTCT strategy (Lesotho Ministry of Health 2012). 

The Mother-Baby Pack co-packages medications for use starting from 14 weeks of pregnancy until six 

weeks after the birth of the infant. To reduce potential stigma or involuntary disclosure, all women receiving 

ANC are provided one of three different types of packages: (1) for pregnant women who are HIV-negative, 

(2) for pregnant women who are HIV-positive and on prophylaxis, and (3) for pregnant women who are 

HIV-positive and on ART. A twelve-month implementation of the initiative was also planned and launched 

in Cameroon and Zambia in 2010, but due to concerns regarding quality service delivery and a growing 

need to assess community readiness, distribution of the packs ended early (UNICEF 2011). 

Mass testing campaigns have helped increase uptake among people who do not typically use healthcare 

services; this strategy has been particularly common in East African countries. Often, these campaigns use 

community-based testing, which is effective in reaching large numbers of first-time testers and diagnosing 

PLHIV at earlier stages of the disease. Ethiopia has implemented The Millennium AIDS Campaign as an 

innovative scale-up strategy, and the country has experienced a major increase in testing. In the pre-ART 

era, fewer than half a million people were tested per year in the country; however, when the campaign 

began in 2006, that number increased to three million (Seyoum et al. 2009). Community health campaigns 

in Uganda aim to achieve universal testing across a community by removing significant barriers and 

offering rapid HIV testing. These campaigns offer HIV testing within broader service delivery, such as 

hypertension screening for adults and deworming of young children, and thereby normalizing HIV testing 

as a part of routine health care. Testing locations are also decentralized to minimize travel costs and waiting 

time, and community members are encouraged to attend, regardless of their perceived risk. Community 

health campaigns have been successful in identifying people who previously reported no prior testing 

(Chamie et al. 2014). Similar initiatives have been rolled out in Kenya, Namibia, Rwanda, and Zimbabwe 

(Embassy of the United States, Windhoek Namibia 2008; National AIDS and STI Control Programme 

(NASCOP) 2008; Rwanda Ministry of Health 2014; Takarinda et al. 2016). 

Additional community-based interventions targeting hard-to-reach populations that have proved effective 

include mobile testing sites and home-based testing and counseling. While Kenya has experienced an 

increase in availability of stand-alone HIV counseling and testing (HCT) centers, the services remained 
4

concentrated in urban areas, and, in 2007, only 36 percent of adults age 15-49 reported having been tested 

for HIV and received their results. 

To extend testing coverage to population groups with limited access to existing services, new delivery 

models have been developed. These new models put an emphasis on delivering services to rural and hardto-reach populations. This is accomplished through various mobile approaches using trucks, vans, tents, 

and existing community facilities, such as empty school rooms and churches. A 2010 study found that use 

of mobile HCT approaches resulted in greater uptake of HCT services in Kenya, with more than three times 

as many clients accessing HCT through mobile approaches than at stand-alone sites. The addition of 

community-site mobile HCT services was also found to be more cost effective than stand-alone sites 

(Grabbe et al. 2010). Kenya has also implemented home-based testing and counseling, along with Malawi, 

Mozambique, Tanzania, and Uganda. Members of households in the lowest income quartile in Malawi are 

significantly less likely to have ever used facility-based HCT services than the rest of the population. 

Providing home-based testing and counseling has the potential to drastically reduce existing socioeconomic 

gradients in HCT uptake and to help alleviate the impact of AIDS on the most vulnerable households 

(Helleringer et al. 2009). 

The incorporation of ambitious national targets has acted as a motivational strategy in countries such as 

Rwanda and Kenya; however, as these countries strive to achieve massive increases in HIV-related services 

in a relatively short time period, they also recognize that improvements in quality of care and health systems 

are equally important. Ethiopia, Mozambique, and Namibia have also made this a priority, and these 

countries have begun incorporating this aspect into their health-sector initiatives. One of Namibia’s four 

primary health objectives, as described in the country’s 2010 National Strategic Framework for HIV and 

AIDS, is health system strengthening to guarantee that the health system is capable of providing equitable, 

affordable, and high quality services, particularly to disadvantaged and marginalized populations in the 

country (WHO 2011). In Mozambique, a severe shortage of trained human resources and healthcare 

infrastructure inhibited early progress. In 2003, the government attempted to address these needs through 

the development of the National Health Sector Strategic Plan to Combat Sexually Transmitted Infections 

and HIV/AIDS for 2004–2008. The document addressed the existing problems by including plans to train 

2,000 intermediate-level healthcare professionals and, with the anticipation of a massive increase in ART 

coverage, developed a new national drug management and logistics system (WHO 2005). 

Overall, countries have been working with international partners to rapidly scale-up testing and treatment, 

but these services are part of a broader imperative to invest in strengthening and expanding health systems 

and infrastructure. 

1.2. Study Objectives 

HIV testing is an entry point for efforts to reduce morbidity and mortality among PLHIV and prevent the 

transmission of HIV. In light of the recent 90-90-90 targets, the present study seeks to answer four main 

questions for 15 sub-Saharan Africa countries studied. First, how does testing uptake vary by serological 

status, sex, and country? Second, what proportion of the PLHIV population is estimated to know their 

status? How does knowledge vary by HIV prevalence and date of the survey? Third, what is the role of 

maternal care in HIV testing uptake among HIV-positive women? Fourth, what background and behavioral 

characteristics are associated with ever being tested for HIV among PLHIV? 
5

2. Methods and Data 

2.1. HIV Serological Status and Prior Testing 

Demographic and Health Surveys (DHS) and AIDS Indicator Surveys (AIS) are nationally representative, 

cross-sectional surveys conducted in countries worldwide. They are primarily funded by the U.S. Agency 

for International Development (USAID) and implemented with technical assistance from The DHS 

Program at ICF International. Surveys gather both individual and household interviews, as well as a number 

of biomarkers about health and wellbeing. 

In standard DHS and AIS surveys, respondents who say they have heard of HIV are asked, “I don’t want 

to know the results, but have you ever been tested for HIV?” In earlier surveys, the term AIDS virus was 

used in place of HIV. If the respondent answers yes, they are asked a few subsequent questions—timing, 

place of test, and, in some cases, whether testing was voluntary or required—and then asked “I don’t want 

to know the results, but did you get the results of the test?” (ICF International 2011, 2016). 

In three surveys—Malawi 2010, Uganda 2011, and Namibia 2013—respondents who said they had ever 

been tested for HIV were subsequently asked about the results. In Namibia, the question was phrased as 

“Have you ever been tested to see if you have HIV?” In Malawi and Uganda, the term AIDS virus was used 

in place of HIV. If respondents answered yes, after intermediary questions on timing and location of the 

test, they were asked “Did you get the results of the test?”3

Additionally, in the DHS and AIS surveys, respondents to the Woman’s Questionnaire who have given 

birth in the two years before the survey are also asked separate but similar questions about HIV testing in 

the context of ANC and delivery services. If they were tested at any of these points in time they are skipped 

out of the question on ever testing, and instead proceed to questions about timing and receipt of results. 

The results of questions about ever testing and about testing during ANC and delivery described above are 

used to produce the two main indicators in this report: ever tested and tested in the last 12 months. As is 

standard for these indicators, the respondent must have received the results of the last test to be considered 

as having been tested. Respondents are asked additional questions about counseling before and after the 

test, but because these questions may be country-specific and subject to recall bias, we exclude questions 

about counseling and focus only on whether tested and received the results of the last test. 

Later on in the survey, after questions about past testing, in some DHS and in all AIS surveys, respondents 

are asked for consent to be anonymously tested for HIV. HIV testing undertaken during the survey process 

is separate from, and subsequent to, the self-reports of prior HIV testing in the questionnaire. It is possible, 

therefore, for respondents to be both HIV-positive and never tested for HIV; in other words, to have no 

knowledge of their HIV status at the time of the survey. 

HIV testing protocol in DHS and AIS surveys undergoes an ethical review by the host country, by ICF 

International, and—for surveys receiving PEPFAR funding—by the U.S. Centers for Disease Control and 

Prevention (CDC). After a participant consents to be tested, interviewers or a medical staff member 

accompanying the interviewers collects blood drops from a finger prick on filter paper. The collection, 

storage, and testing of these dried blood spot samples follow strict procedures to ensure quality and 

 

3

 Responses to questions about the prior test result among PLHIV in Uganda and Malawi have been analyzed 

elsewhere (Fishel, Barrère, and Kishor 2014). 
6

reliability of the testing.4 Each filter paper has a unique bar code that can be linked to the questionnaire to 

allow for analysis of HIV status by respondents’ background characteristics, but the bar code does not allow 

the results of the HIV test to be identified with the name or location of a specific individual. 

It sometimes happens that the Western blot results for a few samples are indeterminate; in such cases, the 

additional lab HIV testing algorithm used cannot definitively classify a sample as positive or negative. This 

happens in generally five or fewer cases per country. We exclude these respondents from the numerator 

and denominator of our sample, on the grounds that the objectives of this analysis require individuals to 

have a positive or negative classification. The number of indeterminates is too small to produce reliable 

estimates about this group, and, in fact, it is too low for their exclusion to influence the overall results. The 

difference is negligible, but occasionally our estimates of HIV prevalence differ by one-tenth of one percent 

from those presented online and in main DHS reports, which, as a general rule, group indeterminate results 

together with negatives. 

2.2. Weighting and Significance Testing 

DHS and AIS surveys typically use a two-stage cluster sampling design to reach households and, ultimately, 

individuals, as described in the DHS Sampling and Household Listing Manual (ICF International 2012). 

Individual male and female HIV weights were applied to the data to adjust for nonresponse and to restore 

representativeness of the sample. Significance tests and confidence intervals presented in this report use 

complex survey commands in Stata, which adjust for sampling weights, stratification, and intra-cluster 

correlation. This report is primarily descriptive and does not conduct multivariate statistical tests, but 95 

percent confidence intervals around most estimates are shown. When these confidence interval ranges (or, 

on charts, confidence bars) do not overlap, the difference is statistically significant at the p<.05 level; 

occasionally, bars can overlap, but the estimates are still statistically significantly different from each other. 

For that reason, statements of statistical significance in the text about figures for which only a confidence 

interval is shown were checked using logistic regressions with complex survey weights. 

Chapter 5 and the first three appendix tables show the composition of the HIV-positive and HIV-negative 

adult population by key characteristics. If sampling design aspects of the survey design could be ignored, 

tests of the null hypothesis that the HIV-positive and HIV-negative populations have the same composition 

on a categorical covariate could be done with a chi-square statistic. Since chi-square does not allow for 

complex survey adjustments, the tests of the null hypothesis that there is no association between the 

covariate and HIV status in this report are based instead on multinomial logit regressions, with the covariate 

as the outcome and HIV status as the independent variable. Our inferences are based on the p-values of the 

F statistics produced from these regressions. 

2.3. Country Selection 

This report covers countries in sub-Saharan Africa with a DHS or AIS survey conducted in 2006 or after 

that included HIV serological testing. We include only the most recent survey from a country if that survey 

included at least 100 men and 100 women who were classified as HIV-positive, according to the survey 

 

4 The blood spots are dried overnight, packaged, and transported to a laboratory for testing. Although the standard 

DHS HIV testing protocol has recently changed, for the surveys included in this analysis, the following protocol was 

followed. In the laboratory, the samples were tested using an initial ELISA test, and then all positive samples and 5 to 

10 percent of negative samples were retested with a second ELISA. For those tests with discordant results on the two 

ELISA tests, another test, usually a Western blot, was used to determine the result. As external quality control, all 

positive samples and a random sample of about 3 to 5 percent of the negative samples were sent to another lab not 

associated with the survey, and the testing protocol was repeated. The results from the independent lab were checked 

against the results of the main laboratory. 
7

blood test, regardless of the overall HIV prevalence measured in the survey. Fifteen countries had surveys 

that qualified for inclusion. 

Table 2.1 shows the response rate, HIV prevalence, and sample size of HIV-positive adults for the surveys 

included in this report. Consent for HIV testing in the DHS or AIS survey ranged from 85.1 percent in 

Zimbabwe to 99.6 percent in Rwanda. We also show the weighted number of HIV-positive and HIVnegative adults in each survey. 

Table 2.1. Response rate and HIV prevalence 

Adults age 15-49 

Survey 

Number 

interviewed 

and eligible 

for serological 

testinga

Consented 

to testinga

(%) 

Tested, 

valid 

resultsb

HIV 

prevalenceb

(%) 

Numberb

HIVpositive 

Numberb

HIVnegative

Cameroon 2011 13,914 97.0 13,500 4.3 584 12,917 

Congo (Brazzaville) 2009 12,413 97.6 12,109 3.2 382 11,726 

Ethiopia 2011 29,383 93.5 27,254 1.5 400 26,854 

Gabon 2012 10,640 97.7 10,444 4.1 426 10,018 

Kenya 2008-09 7,501 90.3 6,707 6.3 421 6,286 

Lesotho 2014 6,044 97.0 5,819 24.7 1,435 4,384 

Malawi 2010 14,716 93.2 13,586 10.6 1,442 12,144 

Mozambique 2009 9,806 92.1 9,061 11.5 1,039 8,022 

Namibia 2013 8,613 88.8 7,731 14.0 1,085 6,646 

Rwanda 2014-15 12,362 99.6 12,302 3.0 365 11,937 

Swaziland 2006-07 9,143 89.5 8,187 25.9 2,119 6,068 

Tanzania 2011-12 19,319 92.4 17,745 5.1 908 16,837 

Uganda 2011 19,866 98.5 19,556 7.3 1,436 18,120 

Zambia 2013-14 29,941 93.4 27,859 13.3 3,704 24,155 

Zimbabwe 2010-11 16,275 85.1 13,563 15.2 2,064 11,499 

Women age 15-49 

Survey 

Number 

interviewed 

and eligible 

for serological 

testinga

Consented 

to testinga

(%) 

Tested, 

valid 

resultsb

HIV 

prevalenceb

(%) 

Numberb

HIVpositive 

Numberb

HIVnegative 

Cameroon 2011 7,462 97.2 7,219 5.6 402 6,817 

Congo (Brazzaville) 2009 6,550 97.0 6,437 4.1 266 6,172 

Ethiopia 2011 16,515 94.2 14,685 1.9 273 14,412 

Gabon 2012 5,645 97.8 5,459 5.8 317 5,142 

Kenya 2008-09 4,245 90.4 3,641 8.0 291 3,350 

Lesotho 2014 3,418 97.9 3,173 29.7 942 2,230 

Malawi 2010 7,911 94.4 7,090 12.9 913 6,177 

Mozambique 2009 5,647 92.3 5,229 13.1 687 4,543 

Namibia 2013 4,663 90.5 4,051 16.9 683 3,367 

Rwanda 2014-15 6,777 99.6 6,752 3.6 244 6,507 

Swaziland 2006-07 4,987 91.9 4,424 31.1 1,378 3,046 

Tanzania 2011-12 10,967 94.4 9,756 6.2 601 9,156 

Uganda 2011 11,148 98.7 10,883 8.3 907 9,976 

Zambia 2013-14 16,411 94.3 14,719 15.1 2,216 12,503 

Zimbabwe 2010-11 9,171 87.1 7,313 17.7 1,295 6,018 

(Continues)

 
8

Table 2.1—Continued

Men age 15-49 

Survey 

Number 

interviewed 

and eligible 

for serological 

testinga

Consented 

to testinga

(%) 

Tested, 

valid 

resultsb

HIV 

prevalenceb

(%) 

Numberb

HIVpositive 

Numberb

HIVnegative 

Cameroon 2011 6,452 96.8 6,281 2.9 181 6,100 

Congo (Brazzaville) 2009 5,863 98.3 5,671 2.1 117 5,555 

Ethiopia 2011 12,868 92.7 12,569 1.0 127 12,442 

Gabon 2012 4,995 97.5 4,985 2.2 109 4,876 

Kenya 2008-09 3,256 90.0 3,066 4.3 130 2,936 

Lesotho 2014 2,626 95.8 2,646 18.6 493 2,154 

Malawi 2010 6,805 91.8 6,496 8.1 529 5,967 

Mozambique 2009 4,159 91.7 3,832 9.2 352 3,479 

Namibia 2013 3,950 86.8 3,680 10.9 401 3,279 

Rwanda 2014-15 5,585 99.6 5,551 2.2 121 5,430 

Swaziland 2006-07 4,156 86.7 3,763 19.7 741 3,022 

Tanzania 2011-12 8,352 89.8 7,989 3.8 308 7,681 

Uganda 2011 8,718 98.2 8,673 6.1 529 8,144 

Zambia 2013-14 13,530 92.3 13,140 11.3 1,487 11,652 

Zimbabwe 2010-11 7,104 82.5 6,250 12.3 769 5,481 

a Number interviewed and eligible and percentage consenting are unweighted. 

b Weighted with HIV sample weights. 

2.4. Estimated Knowledge of HIV Status; Adjustments Using Antiretroviral Therapy Coverage 

Estimating self-knowledge of HIV status is difficult, particularly among PLHIV. When HIV-positive 

individuals are asked directly about the results of their most recent test, the data suggest substantial 

underreporting (Fishel, Barrère, and Kishor 2014). For this reason, an indirect measure of self-knowledge 

can be an appropriate proxy. 

The proxy for knowledge of HIV status among PLHIV in this analysis is based on self-reported information 

on prior HIV testing. Individuals who report never having been tested or having not received results of the 

most recent test are assumed not to know their HIV status. At the same time, individuals who were tested 

and received the result in the past 12 months are highly likely to know their current status. The percentage 

of PLHIV who are estimated to know their status, therefore, can be considered to range from a lower bound 

equal to the percentage of respondents who have been tested and received their test result in the past 12 

months to an upper bound equal to the percentage of respondents who have ever been tested and received 

their test result. For these reasons, we compute “people living with HIV who are estimated to know their 

status” as the midpoint of this range between the percentage of adults ever tested and the percentage tested 

in the past 12 months. It is equivalent to the UNAIDS indicator “people living with HIV who know their 

status” used for survey data until 2015 (UNAIDS 2014b, 2015). We prefer the addition of the word 

“estimated” as actual knowledge of HIV status among respondents is unobserved. 

To add precision, the estimate can be adjusted for ART coverage among PLHIV. Specifically, ART 

coverage can be an alternate lower bound to testing in the past 12 months because everyone on ART can 

be assumed to know they are HIV-positive. If the percentage of adults on ART is higher than the percentage 

tested in the past 12 months, ART coverage is substituted as the low bound and the midpoint estimate is 

recalculated to produce “people living with HIV who are estimated to know their status (adjusted).” Our 

adjusted indicator is equivalent to the revised definition of “people living with HIV who know their status”

first used by UNAIDS in 2015 and officially introduced in 2016 (UNAIDS 2015, 2016c). 
9

None of the 15 surveys in this analysis included a biomarker for ART use. Instead, the data source on ART 

coverage for countries surveyed in 2010 and after is the UNAIDS AIDSinfo website, with the indicator 

“coverage of people receiving ART” from the year of the survey fieldwork (UNAIDS 2016a). If survey 

fieldwork spanned two years, the estimate for ART coverage in the earlier year was used. Unpublished 

UNAIDS estimates of ART coverage from previous years were obtained through personal correspondence.5

Note that estimates of ART coverage are for the entire adult population ages 15 and older, while survey 

analysis of PLHIV pertains only to adults age 15-49. 

2.5. Covariates Examined 

The 10 covariates examined in this report reflect background characteristics that are associated with risk of 

HIV infection and with access to services, as well as behavioral characteristics and symptoms that elevate 

the risk of HIV infection and which may prompt individuals to seek out testing. The covariates are sex, 

place of residence, age group, marital status, educational attainment, wealth quintile, lifetime number of 

sex partners, self-reported STI symptoms in the past year, whether gave birth in the past two years (women), 

and whether paid anyone in exchange for sexual intercourse in the past year (men). 

Most covariates are self-explanatory, but a few deserve additional explanation. The term “married” includes 

both individuals who are married and those who are living with a partner as if married. “Wealth quintile”

is based on principal components analysis of assets, amenities, and services at the household level and 

divided into quintiles of the household population. It is relative within surveys but not across countries; 

wealthier households may still be categorized as poor on an absolute basis. “Self-reported STI symptoms”

applies to respondents who say they had a sexually transmitted infection or symptoms of an STI (a badsmelling, abnormal discharge from the vagina or penis or a genital sore or ulcer) in the 12 months preceding 

the survey. 

2.6. Limitations 

While DHS and AIS survey data are high quality, this analysis has a few caveats. First, as discussed earlier, 

some non-standardization exists between survey rounds. The question on having been tested for HIV differs 

slightly; in early surveys, women and men were asked if they had ever been tested for the “AIDS virus,” 

and in later surveys they are asked if they have ever been tested for “HIV.” In three surveys—Malawi, 

Namibia, and Uganda—the question about HIV testing is not preceded with the phrase “I don’t want to 

know the results”; PLHIV who did not want to reveal their status may have been more inclined to state that 

they had never been tested. Questions on testing during ANC differ slightly across surveys. 

Second, this is a descriptive report of trends; multivariate tests of significance were not conducted. Patterns 

found in one dimension of HIV testing uptake, such as age, are almost inexorably linked to other factors, 

such as marital status and number of lifetime sexual partners. Third, national HIV testing and treatment 

programs have changed rapidly since the earliest survey in this study was conducted in 2006. Estimates 

from earlier surveys, Swaziland in particular, may not be representative of the situation today. Fourth, 

supplemental data on ART coverage from UNAIDS is useful for estimating the population of PLHIV who 

know their status, but it is drawn from other data sources that may not align perfectly with a nationally 

representative household survey. In particular, our estimates of ever and recently tested are limited to adults 

age 15-49, while ART coverage is measured among people ages 15 and older; coverage may be different 

among adults over age 50. 

Fifth, while population surveys have become important sources of HIV prevalence estimates, it is important 

to note that these estimates may include biases resulting from absence or refusal to participate or testing 

 

5

 Dr. Mary Mahy, e-mail message to first author, April 16, 2016. 
10

error resulting in false positivity. In general, the higher the non-response, the greater the likelihood that the 

survey data may be inaccurate. Separating non-response resulting from absence and refusal is important in 

analyzing the effects of non-response on biomarker estimates. The 2015 UNAIDS/WHO guidelines on 

monitoring HIV impact using population-based surveys states that a non-response rate greater than 25 

percent is considered high, and the collected characteristics that may be related to non-response should be 

further assessed, with all related calculations included in the final survey report (UNAIDS 2015). As Table 

2.1 shows, the consent rate for HIV testing ranged from 85.1 percent in Zimbabwe to 99.6 percent in 

Rwanda; results should be interpreted with caution but are not close to the UNAIDS/WHO level said to be 

of concern (75 percent). An analysis of detailed lab results from 20 DHS and AIS surveys by Fishel and 

Garrett (2016) indicates that it is likely that testing error associated with false positivity on the ELISA tests 

in the HIV testing algorithm is present to some degree in many of the surveys analyzed. The magnitude of 

bias associated with testing error could not be measured by this analysis; however, in many surveys, this 

bias is likely to fall within the bounds of the confidence interval for the HIV prevalence estimate. 

While refusal to be tested may be more common among those who have previously tested positive for HIV, 

the magnitude of refusal bias in HIV prevalence surveys appears to depend on the study protocol. It has 

been found that bias is greater when post-test counseling and the return of HIV test results is a prerequisite 

of study participation (Reniers et al. 2009), which it is not in DHS and AIS surveys. Overall, populationbased surveys, such as DHS and AIS, have been found to provide reliable, nationally representative direct 

estimates of HIV seroprevalence in countries with generalized epidemics. HIV prevalence data from 

population-based surveys can be useful in understanding the size and spread of the epidemic and in 

adjusting estimates from sentinel surveillance (Mishra et al. 2008). 
11

3. HIV Prevalence and Testing Uptake 

This chapter examines the overall HIV prevalence in the 15 countries included in the report. It also examines 

recent HIV testing by HIV serological status, ART coverage, and, using UNAIDS guidelines, the 

percentage of PLHIV estimated to know their HIV status. We present the percentage of PLHIV who know 

where to get an HIV test among PLHIV who have never been tested for HIV. 

3.1. HIV Prevalence in Focal Countries 

Figure 3.1 shows the national HIV prevalence among adults age 15-49 in the 15 countries under study, at 

the time of their most recent survey. Table 2.1 lists the prevalence in percentages. Prevalence is highest in 

countries in Southern Africa, with more than 24 percent of adults infected with HIV in Swaziland and 

Lesotho and more than 10 percent infected in the five other countries under study in this region—Malawi, 

Mozambique, Namibia, Zambia, and Zimbabwe. Countries in East Africa have more moderate prevalence, 

ranging from 1.5 percent in Ethiopia to 7.3 percent in Uganda. The three countries in Central and Western 

Africa—Gabon, Cameroon, and Congo (Brazzaville)—all have a prevalence of 4.3 percent or less. 

Figure 3.1. HIV prevalence in focal countries 
12

3.2. Uptake of HIV Testing by Serological Status 

Table 3.1 presents HIV testing in the past 12 months and ever tested by HIV serological status for adults, 

women, and men. Unadjusted logistic regressions (not shown here) indicate that, with two exceptions for 

men (Gabon 2012 and Congo (Brazzaville) 2009), having ever been tested for HIV is statistically 

significantly higher among HIV-positive individuals than among HIV-negative individuals in all surveys. 

The magnitude of the difference tends to range between 10 to 20 percentage points. Having been tested in 

the past 12 months is also higher among HIV-positive individuals, compared with those who are HIVnegative, but the magnitude of the difference is not as great and the relationship is not as consistently 

significant. Individuals who tested HIV-positive at an earlier date are unlikely to keep testing repeatedly. 

In Cameroon, Kenya, Mozambique, and Swaziland—countries that differ widely in HIV prevalence—both 

ever and recent testing are significantly higher among PLHIV. In only one country do we see HIV-positive 

individuals significantly less likely to test have tested recently than individuals who are HIV-negative. In 

Lesotho, specifically, where HIV prevalence is 29.7 percent among women, HIV-positive women are less 

likely to have recently tested for HIV, compared with women who are HIV-negative. While this difference 

in recent testing could potentially be a cause for concern, it may also indicate individuals who have already 

been identified as HIV-positive more than a year before the survey and see no need to retest. 

As discussed earlier, survey respondents typically are not asked what their test result was, and questions 

about testing are prefaced with the phrase, “I don’t want to know the result.” In three countries, however—

Malawi, Namibia, and Uganda—individuals were asked about their test result, and questions about testing 

were not prefaced in this way. The difference in wording is subtle, but PLHIV who were aware of their 

status in the surveys in these three countries may have been less inclined to state that they had previously 

been tested. 

In most countries, women are substantially more likely than men to have ever tested for HIV, regardless of 

serological status. Except for Ethiopia and Rwanda, where women and men are tested at nearly equal levels, 

the percentage of women who have ever tested for HIV is 5 to 21 percentage points higher than for men. 

Gender patterns are not as evident for HIV testing in the past 12 months. In five countries—Gabon, Lesotho, 

Mozambique, Swaziland, and Zimbabwe—both HIV-positive and HIV-negative women are noticeably 

more likely than men to have recently tested for HIV than their male counterparts (8 percentage points or 

higher). Men appear to be more likely to have recently tested, compared with women in only one country, 

Rwanda, where 45 percent of HIV-positive men recently tested, compared with 37 percent of HIV-positive 

women. 
13 

Table 3.1. Uptake of HIV testing, by serological status and sexAmong women and men age 15-49, percentage tested in the past 12 months and percentage ever tested for HIVa by serological status 

Adults (%) Women (%) Men (%) 

HIVpositive 95% CI HIV- negative 95% CI HIV- positive 95% CI HIV- negative 95% CI HIV- positive 95% CI HIV- negative 95% CI 

Percentage tested in the past 12 months 

Cameroon 2011 30.6 [26.5,34.7] 21.0 [19.8,22.1] 30.2 [25.4,35.0] 21.9 [20.4,23.4] 31.6 [24.2,39.0] 19.9 [18.5,21.4] Congo 

(Brazzaville) 2009 

12.9 [8.4,17.5] 7.6 [6.9,8.3] 14.1 [7.9,20.2] 8.3 [7.2,9.4] 10.3 [3.3,17.3] 6.8 [5.9,7.8]

Ethiopia 2011 29.3 [22.8,35.7] 20.4 [18.8,22.0] 29.4 [20.2,38.7] 20.0 [18.2,21.7] 28.9 [18.3,39.5] 20.9 [19.1,22.7] 

Gabon 2012 31.9 [26.3,37.5] 27.1 [25.4,28.7] 34.8 [28.0,41.6] 31.5 [29.4,33.7] 23.5 [12.2,34.8] 22.3 [20.1,24.5] 

Kenya 2008-09 37.3 [30.2,44.5] 25.5 [23.6,27.5] 38.2 [30.0,46.4] 28.6 [25.7,31.5] 35.4 [25.3,45.6] 22.0 [18.9,25.1] 

Lesotho 2014 48.5 [45.4,51.6] 47.7 [45.7,49.7] 51.2 [47.3,55.0] 59.8 [57.2,62.3] 43.4 [37.3,49.5] 35.2 [32.4,38.0] 

Malawi 2010b b b b b b b b b 30.8 [24.0,37.6] 31.6 [29.6,33.5] 

Mozambique 2009 19.0 [15.9,22.0] 12.7 [11.3,14.2] 21.8 [18.2,25.3] 16.2 [14.2,18.1] 13.6 [9.4,17.9] 8.2 [6.9,9.6]

Namibia 2013 47.6 [43.6,51.6] 43.4 [41.7,45.2] 47.9 [43.0,52.8] 49.4 [47.2,51.5] 47.2 [40.0,54.4] 37.3 [35.0,39.7] 

Rwanda 2014-15 39.3 [33.9,44.8] 37.3 [36.3,38.3] 36.5 [29.9,43.2] 37.9 [36.6,39.1] 45.1 [35.6,54.5] 36.6 [35.1,38.0] 

Swaziland 2006-07 23.2 [21.4,25.1] 13.0 [12.0,14.1] 26.8 [24.2,29.3] 19.5 [17.9,21.1] 16.7 [13.6,19.7] 6.5 [5.5,7.6]

Tanzania 2011-12 33.5 [29.2,37.7] 28.3 [27.0,29.7] 34.3 [29.2,39.4] 30.0 [28.4,31.7] 31.8 [25.7,37.9] 26.3 [24.8,27.9] 

Uganda 2011b b b b b b b b b 26.5 [22.3,30.8] 22.8 [21.4,24.3] 

Zambia 2013-14 43.8 [41.5,46.1] 41.8 [40.8,42.9] 45.1 [42.3,47.9] 46.5 [45.1,48.0] 41.8 [38.6,45.0] 36.8 [35.5,38.1] 

Zimbabwe 2010-11 32.4 [30.1,34.8] 27.6 [26.2,29.1] 35.3 [32.6,38.0] 34.5 [32.8,36.1] 27.6 [23.7,31.5] 20.1 [18.1,22.1] 

Percentage ever tested 

Cameroon 2011 67.7 [63.3,72.1] 44.8 [43.3,46.3] 69.4 [64.6,74.3] 50.0 [48.2,51.9] 63.9 [55.8,72.0] 38.9 [37.1,40.7] Congo 

(Brazzaville) 2009 

30.9 [24.4,37.4] 20.0 [18.9,21.0] 35.2 [26.3,44.1] 22.2 [20.6,23.8] 21.1 [12.7,29.6] 17.5 [16.2,18.9] 

Ethiopia 2011 71.8 [63.9,79.7] 36.4 [34.3,38.6] 72.5 [63.2,81.7] 35.3 [32.8,37.8] 70.3 [59.8,80.8] 37.8 [35.6,39.9] 

Gabon 2012 72.1 [65.3,78.9] 57.5 [55.9,59.2] 76.5 [69.3,83.7] 67.1 [64.7,69.6] 59.4 [46.9,71.9] 47.4 [45.0,49.7] 

Kenya 2008-09 68.9 [63.5,74.3] 48.7 [45.8,51.6] 73.5 [67.1,79.9] 56.5 [53.2,59.7] 58.6 [47.8,69.4] 39.9 [35.9,43.8] 

Lesotho 2014 85.0 [82.8,87.3] 70.6 [68.9,72.3] 91.1 [89.2,93.0] 79.7 [77.6,81.8] 73.4 [68.3,78.4] 61.2 [58.4,64.0] 

Malawi 2010 74.4 [71.6,77.3] 60.9 [59.5,62.3] 81.0 [77.8,84.1] 70.6 [69.0,72.2] 63.1 [57.5,68.7] 50.9 [48.9,52.8] 

Mozambique 2009 38.7 [34.6,42.9] 24.5 [22.2,26.8] 43.2 [38.6,47.8] 31.3 [28.4,34.2] 30.1 [23.8,36.3] 15.6 [13.6,17.6] 

Namibia 2013 88.5 [86.5,90.6] 67.9 [66.4,69.4] 92.8 [90.7,94.9] 77.3 [75.5,79.1] 81.2 [76.7,85.7] 58.3 [56.0,60.5] 

Rwanda 2014-15 94.6 [92.2,96.9] 79.9 [79.1,80.7] 95.9 [93.5,98.3] 82.2 [81.2,83.2] 91.8 [86.7,96.8] 77.2 [75.9,78.4] 

Swaziland 2006-07 38.7 [36.5,40.9] 22.6 [21.2,23.9] 44.0 [41.3,46.7] 31.9 [30.1,33.6] 28.8 [25.1,32.5] 13.2 [11.7,14.7] 

Tanzania 2011-12 69.2 [65.4,73.0] 55.0 [53.5,56.5] 73.4 [69.3,77.6] 61.8 [60.1,63.6] 60.9 [53.7,68.1] 46.9 [45.1,48.7] 

Uganda 2011 69.5 [66.4,72.5] 55.5 [54.0,57.0] 76.5 [73.3,79.8] 64.8 [63.1,66.6] 57.3 [52.3,62.4] 44.0 [42.3,45.8] 

Zambia 2013-14 81.7 [80.0,83.4] 67.9 [67.0,68.9] 88.0 [86.3,89.7] 76.9 [75.8,78.0] 72.3 [69.3,75.3] 58.3 [57.0,59.7] 

Zimbabwe 2010-11 63.7 [61.5,65.8] 45.6 [44.0,47.1] 71.0 [68.6,73.4] 55.9 [54.2,57.7] 51.4 [47.4,55.3] 34.2 [32.0,36.4] a Here and throughout the report, “tested” means tested and received results of the last test. b Due to a problem with the questionnaires for the Malawi 2010 Demographic and Health Survey and Uganda 2011 AIDS Indicator Survey, data on time since 

last HIV test are not available for some women; therefore, these estimates are omitted.
14

3.3. Estimated Knowledge of HIV Status and Where to Get Tested 

Table 3.2 lists three components used to calculate estimated knowledge of HIV status among PLHIV: (1) 

tested for HIV in the past 12 months, (2) ART coverage, and (3) ever tested for HIV, along with the 

percentage of PLHIV who are estimated to know their HIV status, both adjusted for ART coverage and 

unadjusted. By design, the adjusted estimates are similar to the unadjusted estimates. The largest difference 

between estimated knowledge due to adjustment is in Ethiopia, where half of HIV-positive adults were 

estimated to be on ART at the time of the survey; this increases estimated knowledge of HIV status from 

51 percent to 57 percent. In Malawi and Uganda, where an issue with the questionnaires prevented the 

surveys from accurately measuring testing in the past 12 months among all women, only the adjusted 

numbers that provide estimates of knowledge of one’s own status are available. 

Table 3.2. ART coverage and estimated knowledge of HIV status 

Percentage of HIV-positive adults age 15-49 

Tested in 

past 

12 months 

On 

ARTa

Ever 

tested

Estimated to know 

their HIV statusb

Estimated to know 

their HIV status, 

adjustedc

Cameroon 2011 30.6 17.8 67.7 49.2 49.2 

Congo (Brazzaville) 2009 12.9 16.0 30.9 21.9 23.4 

Ethiopia 2011 29.3 41.1 71.8 50.6 56.4 

Gabon 2012 31.9 33.1 72.1 52.0 52.6 

Kenya 2008-09 37.3 21.2 68.9 53.1 53.1 

Lesotho 2014 48.5 35.8 85.0 66.8 66.8 

Malawi 2010 d 25.7 74.4 d 50.1 

Mozambique 2009 19.0 12.8 38.7 28.9 28.9 

Namibia 2013 47.6 48.1 88.5 68.1 68.3 

Rwanda 2014-15 39.3 46.9 94.6 67.0 70.8 

Swaziland 2006-07 23.2 10.6 38.7 31.0 31.0 

Tanzania 2011-12 33.5 18.7 69.2 51.4 51.4 

Uganda 2011 d 25.2 69.5 d 47.3 

Zambia 2013-14 43.8 52.3 81.7 62.8 67.0 

Zimbabwe 2010-11 32.4 26.7 63.7 48.1 48.1 

a Data on antiretroviral therapy (ART) coverage is from independent UNAIDS estimates for adults ages 15 and 

over. See text for details.

b Estimated to know their HIV status is computed as the midpoint between the percentage tested during the 

past 12 months and the percentage ever tested. 

c Estimated to know their HIV status, adjusted uses ART coverage as an alternate lower bound for knowledge 

of HIV status. If ART coverage is higher than the percentage tested in the past 12 months, then it is substituted 

as the lower bound for taking the midpoint with ever tested to estimate knowledge of HIV status. d Due to a problem with the questionnaires for the Malawi 2010 Demographic and Health Survey and Uganda 

2011 AIDS Indicator Survey, data on time since last HIV test are not available for some women; therefore, 

these estimates are omitted. 

The percentage of PLHIV tested in the past 12 months ranges from 13 percent in Congo (Brazzaville) to 

49 percent in Lesotho. The percentage of PLHIV ever tested ranges from 31 percent in Congo (Brazzaville) 

to 95 percent in Rwanda. In all, Congo (Brazzaville), Mozambique, and Swaziland report the lowest 

percentages of recent and ever tested among the countries under study. Less than a quarter of PLHIV in 

each country have recently tested, and less than 40 percent have ever been tested. Lesotho, Namibia, 

Rwanda, and Zambia report the highest levels of recent and ever tested, with ever tested each above 80 

percent. In several countries, there is wide variation in the percentage recently and ever tested; in seven 

countries, the percentage of PLHIV who were recently tested is less than half of the percentage ever tested. 
15

ART coverage estimated for the year of survey fieldwork ranges from 11 percent in Swaziland to 52 percent 

in Zambia. In eight countries, fewer than one-quarter of PLHIV were on ART at the approximate time the 

survey was conducted. Three of the four countries with the highest levels of recent and ever tested also 

report the highest percentage of adults on ART (Namibia, Rwanda, and Zambia: each 47 percent or greater). 

In Ethiopia, which fell mid-range for ever and recently tested, 41 percent of PLHIV were on ART. 

The adjusted percentage of adults estimated to know their HIV status ranges from 23 percent in Congo 

(Brazzaville) to 71 percent in Rwanda (Table 3.2 and Map 3.2). In 9 of 15 countries, more than one-half of 

adults are estimated to know their HIV-positive status. Even so, none of the countries under study have 

achieved the 90 percent target set by UNAIDS for 2020; the closest to that goal are four countries where 

estimated (adjusted) knowledge is just above two-thirds: Rwanda, Namibia, Zambia, and Lesotho. 

Figure 3.2. Estimated knowledge of HIV status (adjusted) among PLHIV 

 
16

Figure 3.3 shows the percentage of PLHIV who are estimated to know their HIV status (adjusted), compared 

to the HIV prevalence by the year of survey. The pattern most evident in Figure 3.3 is the relationship 

between the year in which the survey was conducted and the percentage of PLHIV estimated to know their 

status (adjusted). The four countries where HIV-positive adults are estimated to have the highest knowledge 

of their status are also the four countries with surveys in 2013 and after; a similar cluster forms around 

countries surveyed in 2010-2012. This pattern is evidence of the rapidity of changes in HIV testing and 

treatment. 

In contrast, no specific pattern is apparent in the adjusted percentage of adults estimated to know their HIVpositive status by prevalence, shown in Figure 3.3. Three of the four countries with highest knowledge also 

have relatively high prevalence (Namibia, Zambia, and Lesotho). In Malawi and Zimbabwe (HIV 

prevalence of 10.6 and 15.2 percent, respectively), estimated knowledge is reduced to about one-half. The 

estimate for Mozambique and Swaziland is most troubling; each of these countries has a relatively high 

prevalence (Swaziland, 25.9 percent; Mozambique, 11.5 percent), but less than a third of PLHIV are 

estimated to know their HIV-positive status. An important caveat is that these surveys are among the oldest 

included in the study. Among countries of lower prevalence, Rwanda stands out, with the highest estimate 

of status-awareness in contrast to a relatively low HIV prevalence (3.0 percent). 

Figure 3.3. Percentage of PLHIV who are estimated to know their HIV status (adjusted) by year and 

HIV prevalence 

Critical to awareness of one’s HIV status is knowing where to get an HIV test. Figure 3.4 presents, among 

PLHIV who report no testing prior to the survey, the percentage who know a place where they can be tested 

(in this case, testing is considered to have occurred, even if respondents did not receive the result of their 

most recent test—never tested for HIV or tested and did not receive the result of the last HIV test. Rwanda 

lacked a large enough sample of PLHIV who had never been tested, and, therefore, it was removed from 

the figure. In the remaining 14 countries under study, between 66 and 94 percent of PLHIV who have never 

been tested reported they know a place where they can be tested for HIV. Countries with a higher HIV 

prevalence also reported high levels of knowledge on where to obtain HIV testing; except Mozambique, 

Cameroon

Congo (Brazzaville)

Ethiopia

Gabon

Kenya

Lesotho 

Malawi

Mozambique

Namibia

Rwanda

Swaziland

Tanzania 

Uganda 

Zambia

Zimbabwe

0

10

20

30

40

50

60

70

80

90

100

2004 2006 2008 2010 2012 2014 2016

Bubble size = HIV prevalence among adults 15-49

a See Table 3.2 for information about this measure

Percentage of PLHIV estimated to 

know their HIV status (adjusted)a
17

where knowledge of where to get an HIV test is greater than 80 percent among the southern African 

countries. Congo (Brazzaville) and Mozambique are the only two countries where knowledge of where to 

get tested is less than 70 percent (66 and 69 percent, respectively). In three countries, more than 90 percent 

of PLHIV report knowing where to get a test: Zambia (91 percent), Kenya (93 percent), and Malawi (94 

percent). While 100 percent knowledge of where to be tested remains a goal for all countries, the existence 

of a population of PLHIV who know a place where they can get tested, but have not been tested, suggests 

other barriers at play. In some cases, these adults may not perceive themselves to be at risk of having HIV, 

but a qualitative study in South Africa found that barriers to being tested include stigma around HIV testing 

and fear of knowledge of one’s status (Strauss, Rhodes, and George 2015). 

Figure 3.4. Knowledge of where to get tested 

Among PLHIV age 15-49 who have never been tested for HIV, percentage who know where to get a test 

Notes: Bars indicate 95 percent confidence intervals. 

Rwanda had fewer than 25 unweighted cases of HIV-positive adults never tested and therefore was removed. 

76.8 65.7 81.5 72.2 93.2 86.7 94.4 68.6 87.8 88.3 84.4 82.7 90.6 85.5

0

10

20

30

40

50

60

70

80

90

100

Cameroon 2011

Congo (Brazzaville) 2009

Ethiopia 2011

Gabon 2012

Kenya 2008-09

Lesotho 2014

Malawi 2010

Mozambique 2009

Namibia 2013

Swaziland 2006-07

Tanzania 2011-12

Uganda 2011

Zambia 2013-14

Zimbabwe 2010-11

19

4. Testing Uptake During Pregnancy and Birth Among HIVpositive Women 

4.1. Receipt of ANC and Uptake of HIV Testing During ANC 

This chapter examines HIV testing among HIV-positive women during ANC, an important point at which 

women may access HIV testing, be counseled about HIV, and have the chance to prevent mother-to-child 

transmission of HIV. Staveteig, et al. (2013) found that HIV testing during ANC was a major source of 

prior testing among women. DHS surveys ask women who have given birth in the past two years about 

their receipt of ANC and of HIV tests during their recent pregnancy. 

Table 4.1 lists the percentages of HIV-positive women with a birth in the two years preceding the survey, 

by receipt of ANC and the receipt of HIV testing during ANC. In the majority of countries, less than 10 

percent of HIV-positive women did not receive ANC during their most recent pregnancy—that is, in 11 of 

15 countries, more than 90 percent of HIV-positive women received ANC. For the remaining four countries 

(Congo [Brazzaville], Ethiopia, Mozambique, and Zimbabwe), Ethiopia stands out; more than one-quarter 

of the HIV-positive women did not receive ANC, thereby missing a potential for HIV testing and an 

opportunity to prevent transmission to their child.6

Among HIV-positive women who received ANC, more than two-thirds were tested for HIV in 12 of the 15 

countries. About one-half of HIV-positive women were tested during ANC in Mozambique (49 percent) 

and Swaziland (52 percent); slightly more than one-quarter of HIV-positive women were tested in Congo 

(Brazzaville) (27 percent). 

The percentage of women not tested for HIV during ANC may speak to the degree to which HIV testing is 

fully integrated as a component of ANC services in countries. For example, in Gabon, Swaziland, and 

Tanzania, although receipt of ANC is virtually universal among HIV-positive women, a relatively high 

percentage are not being HIV tested (Gabon, 26 percent; Swaziland, 45 percent, and Tanzania, 21 percent). 

The gap is greatest in Congo (Brazzaville), where 89 percent of HIV-positive women get ANC, but 56 

percent are not tested. Four of the countries reporting the highest receipt of ANC also reported the highest 

levels of HIV testing during ANC. Specifically, in Malawi, Rwanda, Namibia, and Zambia, 97 percent or 

more of HIV-positive women received ANC, and 87 percent or more were tested during ANC. 

 

6

 This estimate is based on fewer than 50 unweighted cases, but it is consistent with the low use of antenatal care 

reported in the 2011 Ethiopia DHS, which found that 57 percent of women who have recently given birth nationwide 

and 23 percent of women who have recently given birth in urban areas did not receive any antenatal care (Central 

Statistical Agency/Ethiopia and ICF International 2012). 
20

Table 4.1. HIV testing among HIV-positive women during ANC 

Among HIV-positive women age 15-49 who gave birth in the two years 

preceding the survey, percentage who: 

Did not 

receive ANCa

Received ANC and: 

Missing Total 

Number of HIVpositive women 

who gave birth 

in past 2 years 

Tested during 

ANCb

Not tested 

during ANC

Cameroon 2011 4.2 77.2 18.0 0.7 100.0 87 

Congo (Brazzaville) 2009 11.0 27.2 55.5 6.3 100.0 57 

Ethiopia 2011 (25.7) (66.7) (7.6) (0.0) 100.0 42 

Gabon 2012 1.0 72.5 26.2 0.3 100.0 92 

Kenya 2008-09 5.4 76.8 17.8 0.0 100.0 81 

Lesotho 2014 9.0 83.2 7.8 0.0 100.0 165 

Malawi 2010 0.4 86.9 12.7 0.0 100.0 234 

Mozambique 2009 14.9 48.8 33.7 2.6 100.0 182 

Namibia 2013 2.8 92.4 4.8 0.0 100.0 147 

Rwanda 2014-15 0.0 91.9 8.1 0.0 100.0 52 

Swaziland 2006-07 2.3 52.4 45.3 0.0 100.0 371 

Tanzania 2011-12 1.0 78.4 20.6 0.0 100.0 135 

Uganda 2011 3.6 66.7 28.9 0.7 100.0 242 

Zambia 2013-14 1.3 91.9 6.9 0.0 100.0 489 

Zimbabwe 2010-11 11.7 72.9 15.1 0.2 100.0 273 

Figures in parentheses are based on 25-49 unweighted cases. 

a Did not have any ANC visits, answered “don’t know” when asked about the number of ANC visits, or were missing 

on this variable. 

b “Tested” indicates tested during ANC and received results. Testing at time of delivery only is excluded. 

4.2. Uptake at Birth, after Birth 

Table 4.2 lists ever tested among HIV-positive women by whether or not they have given birth and by the 

timing of their last birth. For all countries except Ethiopia, the percentage of HIV-positive women who 

have given birth are more likely to have been ever tested for HIV than women who have never given birth. 

In most countries, the difference ranged between 15 and 25 percentage points. The difference is greatest in 

Gabon; the percentage ever tested among women who have given birth two or more years ago was more 

than twice as high, and the percentage ever tested among women who have given birth within the past two 

years was nearly three times as high as that among women who have never given birth. Results from Congo 

(Brazzaville) are equally striking: only 3 percent of HIV-positive women who have never given birth have 

been tested. 

These data underline the importance of HIV testing during ANC as a means to reach women, and potentially 

their partners. Findings also highlight a disparity in HIV testing because women who do not access the 

health care system for ANC may be at a disadvantage. Women who cannot or chose not to have children 

or women who are outside of childbearing age may still be at risk for HIV, but they may be less targeted 

for HIV testing. Women who cannot access ANC for any reason, including for example, cost, distance, or 

permission, may also be less likely to have ever been tested for HIV. 

With only two exceptions—Lesotho and Uganda—the percentage of HIV-positive women who gave birth 

in the past two years appears to have been more likely to have ever tested than women who gave birth more 

than two years ago. In four countries, the difference was small; however, in eight countries, the difference 

ranged between 8 and 17 percentage points. 
21

These data may indicate that in the years preceding these surveys, HIV testing during ANC has been 

increasing or is becoming a more standard component of ANC. Staveteig et al (2013) examined trends in 

HIV testing uptake and found that HIV testing during ANC had increased since 2003 at a rate faster than 

the increase in ANC coverage. 

Table 4.2. HIV testing among HIV-positive women, by date of last birth 

Percentage ever testeda among HIV-positive women 

Never 

given 

birth 

Gave birth 

2 or more 

years ago

Gave birth 

in past 2 

yearsb

All HIVpositive 

women 

Never 

given 

birth 

Gave 

birth 2 or 

more 

years 

ago 

Gave 

birth in 

past 2 

yearsb

All HIVpositive 

women 

Country and year % % % % n n n n 

Cameroon 2011 53.5 68.6 82.8 69.4 59 256 87 402 

Congo (Brazzaville) 2009 (2.9) 42.7 36.5 35.2 41 167 57 266 

Ethiopia 2011 77.3 71.1 (73.8) 72.5 42 189 42 273 

Gabon 2012 (31.1) 78.3 91.1 76.5 37 189 92 317 

Kenya 2008-09 (49.3) 70.7 86.5 73.5 22 188 81 291 

Lesotho 2014 70.3 94.6 93.5 91.1 128 650 165 942 

Malawi 2010 61.3 78.7 92.6 81.0 70 609 234 913 

Mozambique 2009 36.4 40.3 53.1 43.2 94 411 182 687 

Namibia 2013 75.9 93.8 96.0 92.8 56 480 147 683 

Rwanda 2014-15 * 96.8 98.4 95.9 24 169 52 244 

Swaziland 2006-07 31.7 40.8 57.7 44.0 197 810 371 1,378 

Tanzania 2011-12 61.2 71.6 85.3 73.4 71 394 135 601 

Uganda 2011 53.8 81.6 74.6 76.5 104 560 242 907 

Zambia 2013-14 68.2 90.0 92.9 88.0 274 1,453 489 2,216 

Zimbabwe 2010-11 52.9 71.0 79.2 71.0 127 896 273 1295 

An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed. Figures in parentheses 

are based on 25-49 unweighted cases. 

a Here and elsewhere, “tested” means tested and received results of the last test. b Women who gave birth in the past two years are also counted as ever tested if they reported having been tested and 

received results during or after antenatal care, whether or not it was the most recent test. 

23

5. HIV Testing Uptake Among PLHIV 

This chapter examines the background characteristics of PLHIV by country. For each background 

characteristic (sex, residence, age group, education, marital status, wealth, number of lifetime sex partners, 

whether reported STI symptoms in the past year, whether given birth in the past two years (women), and 

whether paid for sex in the past year (men) we quantify differences in the likelihood of having ever been 

tested and received results of the last test. 

5.1. Characteristics of the Population of PLHIV 

Table 5.1 shows the background characteristics of PLHIV by country. Asterisks indicate statistical 

significance of the pseudo chi-squared statistic described in the methods section: the difference in 

composition of the HIV-positive, compared to the HIV-negative adult population. In other words, we test 

the null hypothesis that the characteristic is distributed independently of HIV status. 

The composition of PLHIV reflects both the characteristics of the population in each country, as well as the 

nature of the epidemic. Women comprise the majority of adults in most countries, and they are both socially 

(gender inequality, violence, power) and biologically more vulnerable (during heterosexual transmission) 

to STIs and HIV than men are; therefore, we would expect PLHIV to be majority female in sub-Saharan 

Africa. Even so, the sex composition of PLHIV also reflects differences in risk behavior, age structure, and 

relative survival. 

According to Table 5.1, as expected, the HIV-positive reproductive age population is predominantly female 

in every country. In every country except Zambia, more than 6 in 10 reproductive age adults with HIV are 

women; women comprise three-fourths of the adult HIV-positive population in Gabon. In all 15 countries 

in this study, the distribution of PLHIV by sex is statistically significantly different from that of the HIVnegative adult population (shown in Appendix Table A.1). 

In Cameroon, Congo (Brazzaville), Ethiopia, Gabon, Namibia, and Zambia, the majority of PLHIV live in 

urban areas. Of those six countries, four—Cameroon, Congo (Brazzaville), Gabon, and Namibia—are the 

only countries where the majority of the adult population is urban. Zambia’s adult population is nearly split 

evenly into rural and urban areas; the majority of Ethiopians live in rural areas, but the HIV epidemic is 

concentrated in urban centers. In most countries, compared to their HIV-negative counterparts, PLHIV are 

significantly more likely to reside in urban areas. 

The pseudo chi-square statistic shown in Table 5.1 does not reflect any particular direction of significance, 

but the vast majority of PLHIV—more than 90 percent in many countries and more than 95 percent in a 

few countries—are age 20 and older, and the difference in age composition is statistically significantly 

different from the HIV-negative population. 

The majority of PLHIV in every country except Namibia and Swaziland are currently married. In most 

countries, at least one in five PLHIV are living without a partner, but were formerly married. This is a 

higher than the proportion of formerly married among the HIV-negative population in every country, 

typically by a factor of at least three, and likely reflects both the higher age of PLHIV, as well as a number 

of individuals whose spouses may have died due to HIV-related causes. The composition of marital status 

between the adult HIV-positive and HIV-negative population is statistically significant in every country. 
24

Table 5.1. Characteristics of PLHIV, by country 

Among HIV-positive adults ages 15-49, percentage distribution of each characteristic and whether the distribution is statistically significantly different from the HIV-negative population

Background characteristic 

Cameroon 

2011 

Congo 

(Brazzaville) 

2009 

Ethiopia 

2011 

Gabon 

2012 

Kenya 

2008-09 

Lesotho 

2014 

Malawi 

2010 

Mozambique 

2009 

Namibia 

2013 

Rwanda 

2014-15 

Swaziland 

2006-07 

Tanzania 

2011-12 

Uganda 

2011 

Zambia 

2013-14 

Zimbabwe 

2010-11 

Sex *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 

Male 31.1 30.6 31.7 25.6 31.0 34.3 36.7 33.9 37.0 33.1 35.0 33.9 36.8 40.2 37.2 Female 68.9 69.4 68.3 74.4 69.0 65.7 63.3 66.1 63.0 66.9 65.0 66.1 63.2 59.8 62.8 

Place of residence ** *** *** *** *** *** *** *** *** *** ** 

Urban 60.6 66.8 66.6 88.2 28.3 42.8 33.5 46.4 53.6 41.7 33.3 37.7 24.3 63.4 33.7 Rural 39.4 33.2 33.4 11.8 71.7 57.2 66.5 53.6 46.4 58.3 66.7 62.3 75.7 36.6 66.3 

Age group *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 

15-19 6.7 8.7 2.0 5.0 6.2 5.0 6.0 8.3 3.5 4.2 6.6 4.7 7.3 7.8 5.820-29 31.6 31.9 33.0 29.4 36.0 31.4 26.7 39.0 22.2 26.5 43.5 27.0 29.8 28.2 28.430-39 39.3 30.6 45.9 39.9 35.1 38.7 42.9 35.1 45.3 33.6 34.0 39.6 38.1 38.4 41.940-49 22.4 28.7 19.1 25.7 22.7 24.9 24.5 17.6 29.0 35.7 15.9 28.8 24.8 25.6 24.0

Marital status *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 

Never married 15.6 26.1 8.2 19.2 15.1 21.0 7.8 10.2 45.7 18.9 38.8 13.4 11.4 19.3 11.7 Currently married 63.7 52.7 58.1 60.8 54.7 57.0 68.1 64.8 41.8 58.0 45.9 60.2 61.4 61.0 62.5 Formerly married 20.7 21.2 33.7 20.0 30.2 22.0 24.2 25.1 12.5 23.1 15.3 26.4 27.2 19.7 25.7 

Education ***   ** * *** ** *** ** *** *** *** ** *** 

No education 8.7 3.8 31.8 2.5 5.7 5.0 13.7 17.1 9.0 15.2 10.9 12.9 12.8 5.7 1.6Primary 37.9 20.9 46.1 22.7 62.7 46.4 58.3 63.3 30.8 65.8 35.0 73.7 63.2 40.6 30.3Secondary+ 53.4 75.3 22.2 74.8 31.6 48.6 28.0 19.5 60.2 19.0 54.1 13.4 24.0 53.7 68.1

Wealth quintile *** *** *** *** *** *** *** *** ** *** *** 

Poorest 7.2 17.0 4.1 15.2 11.7 12.9 11.0 9.4 19.8 18.6 15.8 13.0 14.7 9.7 18.8 Poorer 16.7 23.3 5.5 21.8 18.8 14.2 14.6 12.4 27.3 13.3 17.4 13.8 16.1 12.3 18.3 Middle 19.2 18.3 8.9 23.7 15.5 18.9 17.5 15.1 25.8 19.9 19.3 18.0 17.8 17.3 21.6 Richer 26.6 19.6 14.1 23.8 23.8 28.2 20.4 27.7 20.5 11.1 23.4 21.6 22.9 30.5 22.3 Richest 30.3 21.6 67.3 15.5 30.2 25.7 36.4 35.3 6.6 37.1 24.1 33.7 28.4 30.2 19.0 

Lifetime number of sex partners *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 

0 2.4 5.6 3.2 2.0 2.4 2.6 2.3 1.2 1.7 4.3 3.1 3.2 3.8 5.0 4.81-2 20.9 10.8 64.6 8.5 37.4 35.8 46.3 44.6 33.9 62.0 35.9 38.8 32.6 39.5 50.43-4 27.1 25.6 16.8 19.7 34.7 26.6 31.2 26.5 34.0 22.7 29.2 27.7 30.6 30.3 20.35+ 44.9 51.8 13.3 55.4 20.0 33.0 18.2 19.8 24.7 10.9 27.0 28.4 28.7 24.1 21.3DK/Missing 4.7 6.3 2.0 14.4 5.6 2.0 2.0 8.0 5.6 0.0 4.8 1.9 4.3 1.0 3.2 

(Continues) 
25

Table 5.1—Continued

Among HIV-positive adults ages 15-49, percentage distribution of each characteristic and whether the distribution is statistically significantly different from the HIV-negative population

Background characteristic 

Cameroon 

2011 

Congo 

(Brazzaville) 

2009 

Ethiopia 

2011 

Gabon 

2012 

Kenya 

2008-09 

Lesotho 

2014 

Malawi 

2010 

Mozambique 

2009 

Namibia 

2013 

Rwanda 

2014-15 

Swaziland 

2006-07 

Tanzania 

2011-12 

Uganda 

2011 

Zambia 

2013-14 

Zimbabwe 

2010-11 

Self-reported STI symptoms in 

the past year *** *** * *** *** *** *** *** *** *** *** *** *** *** 

Yes 17.0 23.4 6.7 22.5 9.7 16.9 18.1 9.9 11.3 18.5 18.3 13.6 42.4 8.4 14.6 No/DK/Missing 83.0 76.6 93.3 77.5 90.3 83.1 81.9 90.1 88.7 81.5 81.7 86.4 57.6 91.6 85.4 

TOTAL 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

NUMBER 584 382 400 426 421 1,435 1,442 1,039 1,085 365 2,119 908 1,436 3,704 2,064 

WOMEN: Gave birth in the past 

2 years ***   **  *** *** *** *** *** *** *** Yes 21.6 21.6 15.5 28.9 27.9 17.5 25.7 26.4 21.6 21.2 26.9 22.5 26.7 22.1 21.0No 78.4 78.4 84.5 71.1 72.1 82.5 74.3 73.6 78.4 78.8 73.1 77.5 73.3 77.9 79.0

TOTAL 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0NUMBER 402 266 273 317 291 942 913 687 683 244 1,378 601 907 2,216 1,295MEN: Paid sex in the past year *** *** *** *** *** *** *** *** *** *** 

Yes 63.4 24.8 74.8 24.5 69.8 50.8 79.4 65.4 28.4 46.6 68.6 65.6 70.0 71.6 

No 36.6 75.2 25.2 75.5 30.2 49.2 20.6 34.6 71.6 53.4 31.4 34.4 30.0 28.4 

TOTAL 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0NUMBER 181 117 127 109 130 493 529 352 401 121 308 529 1,487 769 Note: ***=p<.01, **=p<.05, *=p<.10 DK = don’t know No data were collected on paid sex in the past year in the 2006-07 Swaziland Demographic and Health Survey.
26

The educational attainment of PLHIV is heterogeneous across countries. In 7 of 15 countries, the majority 

of PLHIV have attained secondary schooling or above, but this largely echoes the overall educational 

attainment of the adult population. PLHIV are disproportionately richer than the HIV-negative population, 

over representing the top wealth quintile in every country except two, Gabon and Namibia. In Cameroon, 

Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Tanzania, and Zambia at least 3 in 10 PLHIV are in the 

top wealth quintile; Ethiopia is an outlier, with about two-thirds of PLHIV in the wealthiest quintile. 

The next row of Table 5.1, the lifetime number of sexual partners reported by PLHIV, is divided into groups 

of 0, 1-2, 3-4, 5+, and don’t know or missing (DK/missing); the latter category reflects respondents who 

say they have lost count and those who are unwilling to state a number. In some countries—Rwanda, 

Tanzania, and Zambia—the DK/missing category is negligible, but in other countries it can be 5 percent or 

even as much as 14 percent of PLHIV in Gabon. 

As expected, PLHIV report higher numbers of lifetime sexual partners than their HIV-negative 

counterparts. PLHIV are disproportionately likely to have had 3-4 or 5+ sexual partners in their lifetime. In 

fact, one striking aspect about the distribution of lifetime sex partners shown in Table 5.1 is the proportion 

of PLHIV who say they have never had sex, ranging from 1 percent in Mozambique 6 six percent in Congo 

(Brazzaville). While the possibility of misreporting cannot be ruled out, this finding suggests a number of 

adults who may have contracted HIV through non-sexual modes of transmission, for example, through 

injections or young adults who contracted HIV from mother-to-child transmission. 

The percentage of PLHIV who self-reported STI symptoms in the past year ranges from 7 percent in 

Ethiopia to more than 40 percent in Uganda. Two additional characteristics, shown at the bottom of the 

table, apply to a single sex: whether women gave birth in the past two years and whether men paid for sex 

in the past year. Generally between 20 and 27 percent of HIV-positive women have given birth in the past 

two years; Ethiopia and Lesotho are below that range, at 16 and 18 percent, respectively, and Kenya and 

Gabon slightly exceed it, at 28 and 29 percent, respectively. 

Among the 14 of 15 countries where men were asked about paid sex (all except Swaziland), between 25 

and 80 percent of HIV-positive men reported having paid someone in exchange for sex in the past year. In 

Congo (Brazzaville), Gabon, and Namibia, fewer than 30 percent reported paying for sex. In Ethiopia, 

Malawi, Zambia, and Zimbabwe, at least 70 percent of HIV-positive men reported paying for sex in the 

past year. In all 14 countries, more HIV-positive men reported having paid for sex in the past year than 

their negative counterparts, and in 10 countries, the difference was statistically significant. 

5.2. Factors Associated with Prior Testing 

This section explores the extent to which having ever been tested and received results differs by background 

characteristics of PLHIV described in the previous sections. Charts have error bars that indicate a 95 percent 

confidence interval around the point estimate. Because “estimated knowledge” is a population-wide 

average, confidence intervals are unknown. Testing in the past 12 months would be rare among people who 

had previously known their status, and this characteristic, in turn, is unobservable. For these reasons the 

focus of this chapter is on the proportion of PLHIV ever tested. Appendix Tables A.2 and A.3 provide 

supplementary information about testing in the past 12 months and estimated knowledge of HIV status by 

background characteristic, disaggregated by sex. 

The “first 90” category of the UNAIDS 90-90-90 target, described earlier, is for 90 percent of HIV-positive 

individuals to know their status. To the extent that seroconversion may have happened after the last test, 

this implies that achieving exactly 90 percent ever tested is insufficient. As discussed in Chapter 3, no 

country has achieved estimated knowledge among PLHIV of 90 percent. Even if we use the 90 percent 

benchmark for ever tested, Figure 5.1 indicates that the only countries that have achieved 90 percent ever 
27

tested among PLHIV are Rwanda among men and women, Lesotho among women only, and Namibia 

among women only. 

As shown in Figure 5.1, HIV testing appears to be higher among women than men in every country studied, 

by an average of about 13 percentage points. The gap is widest, and—according to unadjusted logistic 

regressions not shown here—statistically significant, in 11 of 15 countries: Gabon, Kenya, Lesotho, 

Malawi, Mozambique, Namibia, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe. 

Figure 5.1. HIV testing among PLHIV, by sex 

Among HIV-positive men and women age 15-49, percentage who have ever been tested for HIV 

Note: Bars indicate 95 percent confidence intervals. 

Figure 5.2 shows the testing uptake by age group among PLHIV. Most countries appear to have achieved 

parity in testing among the top three age groups: 20–29, 30–39, and 40–49; however, late adolescents, ages 

15-19, who are HIV-positive typically are much less likely to have ever been tested for HIV. In some cases, 

they appear to be half as likely to have ever been tested as the next age group, ages 20–29. While the 

possibility exists that these young adults were tested at very young ages and either do not remember it or 

do not think it meets the reporting threshold, the reported disparity in ever testing, combined with the 

vulnerability of young adolescents to HIV transmission, is an overall cause for concern. In eight countries, 

unadjusted regressions (not shown here) indicate that HIV-positive late adolescents age 15-19 are 

significantly less likely to have ever been tested than any other age group: Lesotho, Malawi, Namibia, 

0 10 20 30 40 50 60 70 80 90 100

Cameroon 2011

Congo (Brazzaville)

2009

Ethiopia 2011

Gabon 2012

Kenya 2008-09

Lesotho 2014

Malawi 2010

Mozambique 2009

Namibia 2013

Rwanda 2014-15

Swaziland 2006-07

Tanzania 2011-12

Uganda 2011

Zambia 2013-14

Zimbabwe 2010-11

male

female
28

Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe. In four additional countries, the sample size of 

HIV-positive late adolescents ages 15–19 is insufficient to reliably measure testing among this age group. 

In the three remaining countries—Cameroon, Kenya, and Mozambique—a small sample size creates wide 

confidence intervals for testing uptake among late adolescents ages 15-19. 

Figure 5.2. HIV testing among PLHIV, by age group 

Among HIV-positive adults age 15-49 in each age group, percentage who have ever been tested for HIV 

Note: Bars indicate 95 percent confidence intervals. Results based on fewer than 25 unweighted cases have been removed. 

0 20 40 60 80 100

Cameroon 2011

Congo (Brazzaville)

2009

Ethiopia 2011

Gabon 2012

Kenya 2008-09

Lesotho 2014

Malawi 2010

Mozambique 2009

Namibia 2013

Rwanda 2014-15

Swaziland 2006-07

Tanzania 2011-12

Uganda 2011

Zambia 2013-14

Zimbabwe 2010-11

15-19

20-29

30-39

40-49
29

Area of residence affects access to testing services. Figure 5.3 shows testing uptake by place of residence—

either urban or rural—which indicates that in every country, HIV-positive urban residents appear to be 

more likely to have ever been tested than HIV-positive rural residents. In seven countries, unadjusted 

logistic regressions show that the difference is statistically significant: Cameroon, Congo (Brazzaville), 

Ethiopia, Mozambique, Uganda, Zambia, and Zimbabwe. Notably, the three countries with the highest 

levels of testing overall—Lesotho, Namibia, and Rwanda—seem to have achieved near parity in testing 

uptake between urban and rural PLHIV. 

Figure 5.3. HIV testing among PLHIV, by place of residence 

Among HIV-positive adults age 15-49 in each area of residence, percentage who have ever been tested for HIV 

Note: Bars indicate 95 percent confidence intervals. 

Figure 5.4 shows testing uptake by educational attainment of PLHIV. In every country except Zambia, 

testing uptake seems to be highest among PLHIV who have attained secondary education and above. In 

Zambia, testing uptake appears to be highest among PLHIV with no education, but the difference is not 

statistically significant. Gabon and Congo (Brazzaville) had too few PLHIV without education to include 

that category. In Cameroon, Mozambique, Swaziland, Uganda, and Zimbabwe, the difference between 

testing uptake among PLHIV with secondary education and above, compared to their counterparts, is 

statistically significant. 

0 10 20 30 40 50 60 70 80 90 100

Cameroon 2011

Congo (Brazzaville)

2009

Ethiopia 2011

Gabon 2012

Kenya 2008-09

Lesotho 2014

Malawi 2010

Mozambique 2009

Namibia 2013

Rwanda 2014-15

Swaziland 2006-07

Tanzania 2011-12

Uganda 2011

Zambia 2013-14

Zimbabwe 2010-11

urban

rural
30

Figure 5.4. HIV testing among PLHIV, by education 

Among HIV-positive adults age 15-49 with each level of education, percentage who have ever been tested for HIV 

Note: Bars indicate 95 percent confidence intervals. Results based on fewer than 25 unweighted cases have been removed. 

Joining into a marital union may be a time when couples decide to get tested together, or when they are 

encouraged to get tested. Because marriage is the customary context for childbearing in most of subSaharan Africa, we might also expect that more adults, especially women, would be tested if they have been 

married for some time. Additionally, marital status is also related to age. Testing uptake among PLHIV by 

marital status is shown in Figure 5.5. With the exception of Mozambique, currently and formerly married 

PLHIV are descriptively more likely to have ever been tested than their never-married counterparts. In 

Lesotho, Malawi, Namibia, Rwanda, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe, unadjusted 

logistic regressions indicate that the difference in testing uptake between never married PLHIV and 

currently or formerly married PLHIV is statistically significant. In Cameroon, Congo (Brazzaville), and 

0 20 40 60 80 100

Cameroon 2011

Congo (Brazzaville)

2009

Ethiopia 2011

Gabon 2012

Kenya 2008-09

Lesotho 2014

Malawi 2010

Mozambique 2009

Namibia 2013

Rwanda 2014-15

Swaziland 2006-07

Tanzania 2011-12

Uganda 2011

Zambia 2013-14

Zimbabwe 2010-11

No education

Primary

Secondary+
31

Gabon, never married PLHIV are significantly less likely than the formerly married to have been 

ever tested. 

Figure 5.5. HIV testing among PLHIV, by marital status 

Among HIV-positive adults age 15-49 with each marital status, percentage who have ever been tested for HIV 

Note: Bars indicate 95 percent confidence intervals. Married means married or living with a partner as if married. 

Figure 5.6 shows the relationship between wealth quintiles and having ever been tested among PLHIV. The 

survey in Ethiopia had insufficient cases of PLHIV in the second (poorer) quintile to make estimates. 

Overall, the relationship between wealth and testing uptake varies greatly across countries. The general 

trend is toward higher uptake among wealthier PLHIV, but exact levels tend to be uneven. Cameroon, 

Zambia, and Zimbabwe are the only three countries with a strictly monotonic relationship between wealth 

quintile and testing uptake; Congo (Brazzaville), Ethiopia, and Mozambique also exhibit distinct 

differentials in uptake by wealth quintile. In other countries, such as Swaziland, the top four quintiles are 

nearly indistinguishable, and the main difference in testing uptake appears to be between the poorest PLHIV 

and all other groups. 

0 20 40 60 80 100

Cameroon 2011

Congo (Brazzaville)

2009

Ethiopia 2011

Gabon 2012

Kenya 2008-09

Lesotho 2014

Malawi 2010

Mozambique 2009

Namibia 2013

Rwanda 2014-15

Swaziland 2006-07

Tanzania 2011-12

Uganda 2011

Zambia 2013-14

Zimbabwe 2010-11

never

married

currently

married

formerly

married
32

Figure 5.6. HIV testing among PLHIV, by wealth quintile 

Among HIV-positive adults age 15-49 in each wealth quintile, percentage who have ever been tested for HIV 

Note: Bars indicate 95 percent confidence intervals. Results based on fewer than 25 unweighted cases have been removed. 

In four countries, unadjusted logistic regressions indicate that PLHIV in the poorest wealth quintiles are 

significantly less likely than PLHIV in all other wealth quintiles to have ever been tested—Cameroon, 

Lesotho, Mozambique, and Swaziland. In nearly every other country—Congo (Brazzaville), Ethiopia, 

Gabon, Malawi, Namibia, Uganda, Zambia, and Zimbabwe—the difference between the poorest and the 

richest is statistically significant. 

0 20 40 60 80 100

Cameroon 2011

Congo

(Brazzaville) 2009

Ethiopia 2011

Gabon 2012

Kenya 2008-09

Lesotho 2014

Malawi 2010

Mozambique 2009

Namibia 2013

Rwanda 2014-15

Swaziland 2006-07

Tanzania 2011-12

Uganda 2011

Zambia 2013-14

Zimbabwe 2010-

11

poorest

poorer

middle

richer

richest
33

Figure 5.7 shows testing uptake among PLHIV, by reported number of lifetime sex partners. As discussed 

previously, don’t know and missing (DK/Missing) is a separate category, one that is insufficiently large to 

produce estimates of ever tested in Congo (Brazzaville), Ethiopia, Rwanda, and Tanzania. Additionally, 

only six countries had sufficient sample size of PLHIV who reported zero lifetime sex partners to produce 

estimates: Lesotho, Malawi, Swaziland, Uganda, Zambia, and Zimbabwe. 

Figure 5.7. HIV testing among PLHIV, by number of lifetime sex partners 

Among HIV-positive adults age 15-49 with specified number of lifetime sexual partners, 

percentage who have ever been tested for HIV 

Note: Bars indicate 95 percent confidence intervals. Results based on fewer than 25 unweighted cases have been removed. 

0 20 40 60 80 100

Cameroon 2011

Congo

(Brazzaville) 2009

Ethiopia 2011

Gabon 2012

Kenya 2008-09

Lesotho 2014

Malawi 2010

Mozambique 2009

Namibia 2013

Rwanda 2014-15

Swaziland 2006-

07

Tanzania 2011-12

Uganda 2011

Zambia 2013-14

Zimbabwe 2010-

11

0

1-2

3-4

5+

DK/Missing
34

While we might expect those with higher risk behaviors—more than five lifetime sexual partners—to be 

the most likely to have ever been tested, this is not the case, even descriptively, in any country. PLHIV who 

reported between one and two lifetime sex partners are the most likely to have ever been tested in eight 

countries: Kenya, Malawi, Rwanda, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe. In all but one 

of the remaining seven countries, PLHIV who reported between three and four sexual partners were the 

most likely to have ever been tested. Some of this difference may be driven by the fact that men far 

outnumber women in the 5+ category, and men tend to have lower testing uptake overall. 

The DK/Missing response to number of sexual partners may reflect persons in an even higher risk group 

than those in the 5+ category because this group includes respondents who have either lost track of their 

number of lifetime partners or who were unwilling to say.7

 In Cameroon, PLHIV in the DK/Missing 

category on this variable were the most likely to have ever been tested. 

The main statistically significant difference was between PLHIV who reported zero lifetime partners and 

all other groups. In Lesotho, Swaziland, Uganda, Zambia, and Zimbabwe, unadjusted logistic regressions 

indicate that PLHIV who reported zero lifetime sexual partners were significantly less likely than any other 

group to have ever been tested for HIV. In the only other country with sufficient sample size of PLHIV 

who reported zero lifetime partners—Malawi—PLHIV who reported zero partners were significantly less 

likely than any group except the DK/Missing to have ever been tested. 

For adults who are knowledgeable about how HIV is transmitted, symptoms of an STI might typically 

prompt HIV testing, either self-initiated or at a clinic. Figure 5.8 compares testing uptake among PLHIV 

by whether or not they reported STI symptoms in the past 12 months. Here the trend is fairly weak: PLHIV 

who self-reported STI symptoms in the last year appear to be more likely to have ever been tested in 11 of 

15 countries, but the difference was only statistically significant in Malawi, Mozambique, and Zimbabwe. 

 

7

 The respondent can refuse to answer the question for any reason, but one piece of evidence that is consistent with 

higher risk behavior among the DK/Missing group is that the response is more concentrated among PLHIV (see Table 

5.1, compared with Appendix Table A.1). 
35

Figure 5.8. HIV testing among PLHIV, by whether reported STI symptoms in the past year 

Among HIV-positive adults age 15-49 by STI reporting status, percentage who have ever been tested for HIV 

Note: Bars indicate 95 percent confidence intervals. 

 

0 20 40 60 80 100

Cameroon 2011

Congo (Brazzaville)...

Ethiopia 2011

Gabon 2012

Kenya 2008-09

Lesotho 2014

Malawi 2010

Mozambique 2009

Namibia 2013

Rwanda 2014-15

Swaziland 2006-07

Tanzania 2011-12

Uganda 2011

Zambia 2013-14

Zimbabwe 2010-11

Self-reported

STI symptoms

in the past

year

Did not self

report

symptoms, or

DK/missing
36

As discussed in Chapter 4, ANC and labor and delivery are two main points of contact for HIV testing. 

Figure 5.9 shows testing uptake among HIV-positive women by whether they have given birth in the past 

two years. In every country except Uganda, women who have given birth in the past two years appear to be 

more likely to have ever been tested. Unadjusted logistic regressions indicate that this difference is 

statistically significant in eight countries: Cameroon, Gabon, Kenya, Malawi, Mozambique, Swaziland, 

Tanzania, Zambia, and Zimbabwe. Notably, the three countries with the highest levels of testing—Lesotho, 

Malawi, and Rwanda—have achieved near parity between the two groups. 

Figure 5.9. HIV testing among HIV-positive women, by whether given birth in the past two years 

Among HIV-positive women age 15-49 in each group, percentage who have ever been tested for HIV 

Note: Bars indicate 95 percent confidence intervals. 

 

0 20 40 60 80 100

Cameroon 2011

Congo (Brazzaville) 2009

Ethiopia 2011

Gabon 2012

Kenya 2008-09

Lesotho 2014

Malawi 2010

Mozambique 2009

Namibia 2013

Rwanda 2014-15

Swaziland 2006-07

Tanzania 2011-12

Uganda 2011

Zambia 2013-14

Zimbabwe 2010-11

Gave

birth in

the past 2

years

Did not

give birth

in the

past 2

years
37

Figure 5.10 shows HIV testing uptake among HIV-positive men by whether they paid for sex in the past 

year. Of the 14 countries with data available for this indicator—all except Swaziland—men who paid for 

sex seem to be more likely to have ever been tested; however, the difference is statistically significant only 

in Lesotho, Mozambique, Zambia, and Zimbabwe. 

Figure 5.10. HIV testing among HIV-positive men, by whether they paid for sex in the past year 

Among HIV-positive men age 15-49 in each group, percentage who have ever been tested for HIV 

Note: Bars indicate 95 percent confidence intervals. No data were collected on paid sex in the past year in the 2006-07 Swaziland 

DHS. 

0 20 40 60 80 100

Cameroon 2011

Congo (Brazzaville) 2009

Ethiopia 2011

Gabon 2012

Kenya 2008-09

Lesotho 2014

Malawi 2010

Mozambique 2009

Namibia 2013

Rwanda 2014-15

Tanzania 2011-12

Uganda 2011

Zambia 2013-14

Zimbabwe 2010-11

Had paid sex in

the past year

Did not have

paid sex in the

past year, or

DK/Missing

39

6. Discussion and Conclusions 

HIV testing is an essential gateway to treatment, care, and—through provision of ART and counseling on 

behavior—a key part of preventing transmission. As part of its 90-90-90 goal, UNAIDS has called for 90 

percent of all PLHIV to know their status, 90 percent of those who know their status to receive ART, and 

90 percent of ART recipients to achieve viral suppression by 2020. This report has examined factors related 

to the “first 90” target in the region of the world that has been hardest hit by the HIV/AIDS pandemic: HIV 

testing uptake and estimated knowledge of their HIV status among PLHIV in 15 countries in sub-Saharan 

Africa. 

The 15 countries studied in this report have an adult HIV prevalence of between 1.5 percent (Ethiopia) to 

25.9 percent (Swaziland). The study examines three key indicators among PLHIV age 15-49 in every 

country: (1) whether ever tested for HIV, (2) tested in the past 12 months, and (3) estimated to know their 

HIV status. The first two indicators require that the respondent received results of the most recent test. 

Estimated to know their HIV status is, among PLHIV, computed as the midpoint between tested in the past 

12 months and ever tested. It is identical to the pre-2015 UNAIDS indicator, “people living with HIV who 

know their status.” We computed an adjusted version of this indicator, based on UNAIDS estimates of 

ART coverage among the adult population ages 15 and older as an alternate lower bound, which matches 

the revised definition of “people living with HIV who know their status” introduced by UNAIDS in 2016. 

These adjusted figures indicate that between 23 percent (Congo [Brazzaville]) and 71 percent (Rwanda) of 

PLHIV are estimated to know their status. On average, across study countries, after adjusting for ART 

coverage, 51 percent of PLHIV are estimated to know their status. This indicates encouraging progress, but 

is far short of the 90 percent goal set by UNAIDS. Important gaps remain to reach the 2020 targets. 

We examined the following 10 key covariates in relationship to having ever been tested: sex, place of 

residence, age group, marital status, educational attainment, wealth quintile, lifetime number of sex 

partners, self-reported STI symptoms in the past year, whether gave birth in the past two years (women), 

and whether paid anyone in exchange for sexual intercourse in the past year (men). 

Results show that HIV-positive women are more likely than their male counterparts to have ever been 

tested, and the difference is statistically significant in 11 of 15 countries. Women’s contact with the health 

system during pregnancy and birth and the increased efforts to test pregnant women, discussed in Chapter 

4, likely play a role in this difference. 

PLHIV in urban areas are more likely to have ever been tested than PLHIV in rural areas; the difference is 

statistically significant in five countries. Despite the rural nature of the HIV/AIDS pandemic in several 

countries, health systems face the continuing challenge of providing outreach to rural populations and 

providing adequate health infrastructure. At the same time, the vast majority of PLHIV who have never 

been tested know of a place to get a test, which suggests that mere expansion of HIV testing services would 

be insufficient. Additional barriers to testing need to be identified and addressed. 

The analysis has some limitations, described in Chapter 2. The year in which the survey was conducted 

deserves consideration because testing dynamics change rapidly. The three countries with the lowest 

estimated knowledge of HIV status among PLHIV—Congo (Brazzaville), Mozambique, and Swaziland—

are also countries with the earliest surveys. Another consideration is the potential bias resulting from the 

possibility that PLHIV who are aware of their status may refuse serological testing altogether. However, in 

the countries studied, consent for serological testing was well above the 75 percent threshold thought to be 

problematic. In three countries—Malawi, Namibia, and Uganda—the survey question about testing was not 

prefaced with the phrase “I don’t want to know the results,” which may have caused some underreporting 

among PLHIV who were aware of their status. 
40

While progress on testing and treatment of HIV has undoubtedly been achieved, several gaps in coverage 

still exist. This report finds that in the study countries, despite the high risk of HIV young adults face, their 

testing rates are also among the lowest of any age group. This is a particularly important challenge because 

of the broad opportunity for HIV prevention during this critical period in life; however, addressing this 

challenge may require re-examining the age of consent for HIV testing and counseling. The findings also 

show important gaps in coverage among never married, the poorest, and the under-coverage of PLHIV who 

engage in higher-risk behaviors, such as five or more sex partners and paid sex. Taken together, these 

findings suggest that, in addition to efforts to target people at greatest risk of HIV, there is a continued need 

to target interventions toward the most vulnerable people: rural residents, the least educated, young adults, 

and the poorest adults. 

 
41

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45

Appendix Tables 
46 

Table A.1 Characteristics of HIV-negative adults, by country 

Among HIV-negative adults age 15-49, percentage distribution of each characteristic and whether the distribution is statistically significantly different from the HIV-positive population

Background characteristic 

Cameroon 

2011 

Congo 

(Brazzaville) 

2009 

Ethiopia 

2011 

Gabon 

2012 

Kenya 

2008-09 

Lesotho 

2014 

Malawi 

2010 

Mozambique 

2009 

Namibia 

2013 

Rwanda 

2014-15 

Swaziland 

2006-07 

Tanzania 

2011-12 

Uganda 

2011 

Zambia 

2013-14 

Zimbabwe 

2010-11 

Sex *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 

Male 47.2 47.4 46.3 48.7 46.7 49.1 49.1 43.4 49.3 45.5 49.8 45.6 44.9 48.2 47.7Female 52.8 52.6 53.7 51.3 53.3 50.9 50.9 56.6 50.7 54.5 50.2 54.4 55.1 51.8 52.3

Place of residence ** *** *** *** *** *** *** *** *** *** ** 

Urban 54.5 62.9 22.6 88.0 24.5 32.7 18.9 31.8 57.0 19.2 25.3 26.0 20.2 43.8 30.2 Rural 45.5 37.1 77.4 12.0 75.5 67.3 81.1 68.2 43.0 80.8 74.7 74.0 79.8 56.2 69.8 

Age group *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 

15-19 24.5 19.9 24.2 21.3 23.7 30.5 26.0 20.4 24.9 22.0 37.6 24.1 24.0 25.8 26.120-29 37.0 36.0 36.5 34.2 35.9 38.4 38.2 34.7 38.6 35.1 32.6 33.5 34.9 34.9 39.330-39 23.0 27.8 24.0 27.3 23.1 18.8 22.8 27.5 22.3 27.2 15.8 25.2 24.7 24.8 22.140-49 15.5 16.2 15.3 17.2 17.3 12.3 13.0 17.4 14.1 15.6 13.9 17.1 16.4 14.6 12.5

Marital status *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 

Never married 39.5 33.5 34.7 40.7 40.4 51.7 31.9 20.9 66.7 43.0 64.5 34.0 30.9 38.1 37.4 Currently married 54.4 55.6 58.7 50.9 53.6 42.6 61.5 70.4 29.6 51.0 31.3 58.5 60.7 55.5 55.7 Formerly married 6.1 10.9 6.6 8.4 6.1 5.8 6.5 8.7 3.7 6.0 4.3 7.5 8.3 6.4 6.9 

Education ***   ** * *** ** *** ** *** *** *** ** *** 

No education 15.1 3.9 41.1 5.8 6.3 4.0 10.7 21.7 5.9 10.9 6.9 13.7 10.0 6.0 1.6Primary 32.4 23.6 45.2 18.4 54.3 40.0 64.1 59.9 20.6 64.6 34.2 65.6 57.9 44.0 25.5Secondary+ 52.5 72.5 13.7 75.8 39.5 56.0 25.3 18.5 73.5 24.5 58.9 20.6 32.1 49.9 72.9

Wealth quintile *** *** *** *** *** *** *** *** ** *** 

Poorest 16.2 20.3 17.7 14.6 16.4 14.6 16.3 19.2 14.6 16.7 15.4 16.7 17.2 17.2 17.6 Poorer 17.0 19.7 18.7 20.3 17.3 18.2 20.1 20.0 17.8 18.8 16.7 18.4 18.6 18.6 19.4 Middle 18.9 20.2 19.0 22.1 17.4 19.8 20.1 20.3 20.3 19.7 20.6 18.6 18.9 19.1 20.1 Richer 22.4 20.2 20.3 21.6 22.7 22.6 19.6 18.8 24.2 21.5 22.2 20.8 19.9 21.2 21.3 Richest 25.6 19.6 24.4 21.4 26.2 24.7 24.0 21.7 23.1 23.3 25.1 25.6 25.3 23.8 21.6 

Lifetime number of 

sex partners *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 0 16.8 7.0 30.3 7.5 17.7 16.2 15.1 6.8 16.1 27.7 31.4 16.3 16.0 15.9 23.81-2 35.1 21.4 59.2 20.7 45.3 39.9 58.3 49.6 40.2 60.0 36.2 45.6 42.5 48.2 51.03-4 18.2 24.6 7.0 19.6 16.8 20.1 17.4 20.6 21.0 9.1 15.7 19.2 22.4 19.5 12.55+ 28.2 44.0 3.3 41.5 16.5 22.8 8.6 16.6 19.8 3.2 13.8 17.2 17.1 16.0 11.2DK/Missing 1.7 3.1 0.2 10.7 3.6 1.1 0.7 6.4 2.8 0.1 2.8 1.7 2.0 0.4 1.4 

(Continues) 
47 

Table A.1—Continued

Among HIV-negative adults age 15-49, percentage distribution of each characteristic and whether the distribution is statistically significantly different from the HIV-positive population

Background characteristic 

Cameroon 

2011 

Congo 

(Brazzaville) 

2009 

Ethiopia 

2011 

Gabon 

2012 

Kenya 

2008-09 

Lesotho 

2014 

Malawi 

2010 

Mozambique 

2009 

Namibia 

2013 

Rwanda 

2014-15 

Swaziland 

2006-07 

Tanzania 

2011-12 

Uganda 

2011 

Zambia 

2013-14 

Zimbabwe 

2010-11 

Self-reported STI 

symptoms in the past year *** *** * *** *** *** *** *** *** *** *** *** *** *** 

Yes 10.2 18.5 2.0 16.4 3.0 11.1 6.6 5.5 6.5 7.1 6.0 6.2 22.8 3.8 5.9 No/DK/Missing 89.8 81.5 98.0 83.6 97.0 88.9 93.4 94.5 93.5 92.9 94.0 93.8 77.2 96.2 94.1 

TOTAL 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

NUMBER 12,917 11,726 26,854 10,018 6,286 4,384 12,144 8,022 6,646 11,937 6,068 16,837 18,120 24,155 11,499 

WOMEN: Gave birth 

in the past 2 years ***   **  *** *** *** *** *** *** *** Yes 29.9 32.3 27.3 26.5 27.6 22.2 35.5 37.2 21.7 24.7 21.8 33.3 36.4 32.8 28.8No 70.1 67.7 72.7 73.5 72.4 77.8 64.5 62.8 78.3 75.3 78.2 66.7 63.6 67.2 71.2

TOTAL 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

NUMBER 6,817 6,172 14,412 5,142 3,350 2,230 6,177 4,543 3,367 6,507 3,046 9,156 9,976 12,503 6,018 

MEN: Paid sex in the 

past year *** *** *** *** *** *** *** *** *** *** Yes 37.0 19.3 52.8 23.5 47.4 30.5 53.1 59.1 15.4 39.1 54.2 53.5 52.3 48.0No 63.0 80.7 47.2 76.5 52.6 69.5 46.9 40.9 84.6 60.9 45.8 46.5 47.7 52.0

TOTAL 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 NUMBER 6,100 5,555 12,442 4,876 2,936 2,154 5,967 3,479 3,279 5,430 7,681 8,144 11,652 5,481 Note: ***=p<.01, **=p<.05, *=p<.10 No data were collected on paid sex in the past year in the 2006-07 Swaziland Demographic and Health Survey.
48 

Table A.2. Characteristics of HIV-positive women, by country 

Among HIV-positive women age 15-49, percentage distribution of each characteristic and whether the distribution of the characteristic is statistically significantly different from the HIV-negative population 

Cameroon 

2011 

Congo 

(Brazzaville) 

2009 

Ethiopia 

2011 

Gabon 

2012 

Kenya 

2008-09

Lesotho 

2014 

Malawi 

2010 

Mozambique 

2009 

Namibia 

2013 

Rwanda 

2014-15

Swaziland 

2006-07

Tanzania 

2011-12 

Uganda 

2011 

Zambia 

2013-14

Zimbabwe 

2010-11 

Place of residence *** * *** ** *** *** *** *** *** *** *** *** *** **

Urban 61.5 69.9 67.3 89.4 30.9 42.6 34.6 44.7 49.9 40.5 31.3 38.9 27.0 64.6 34.8

Rural 38.5 30.1 32.7 10.6 69.1 57.4 65.4 55.3 50.1 59.5 68.7 61.1 73.0 35.4 65.2

Age Group *** * *** *** *** *** *** *** *** *** *** *** *** *** ***

15-19 8.4 9.1 2.7 5.6 7.1 4.1 7.1 9.1 3.1 4.9 8.4 4.5 7.8 7.1 5.120-29 36.1 37.7 38.7 32.0 39.1 35.6 30.6 44.1 23.1 28.8 48.8 32.2 36.9 30.9 33.030-39 36.4 31.5 43.8 41.1 30.2 37.8 42.3 31.6 46.4 34.7 29.7 37.1 35.1 39.9 41.640-49 19.1 21.7 14.8 21.4 23.6 22.5 20.1 15.2 27.5 31.6 13.0 26.2 20.2 22.1 20.4

Marital Status *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***

Never married 14.5 27.7 7.6 18.1 14.5 18.1 6.4 7.7 46.1 18.1 41.6 13.4 11.0 15.9 9.9

Currently married 59.3 47.1 51.8 61.0 49.2 56.5 61.0 60.1 36.7 51.7 42.7 53.1 55.3 57.4 56.9

Formerly married 26.2 25.2 40.6 20.9 36.3 25.5 32.6 32.2 17.2 30.2 15.7 33.5 33.7 26.7 33.1

Education *** ** * *** *** *** *** *** *** *** ** ***

No education 10.5 5.1 35.4 1.3 6.4 0.9 16.9 21.8 7.5 18.6 10.3 15.4 15.8 7.2 2.0

Primary 39.5 23.3 45.5 25.6 63.9 46.2 58.0 63.4 30.4 64.1 36.3 70.7 63.7 44.8 33.5

Secondary+ 50.0 71.7 19.0 73.1 29.7 52.9 25.1 14.7 62.1 17.3 53.4 13.9 20.5 48.0 64.5

Wealth Quintile *** ** *** *** *** *** *** *** *** *** *** **

Poorest 7.5 14.1 4.5 14.8 13.7 11.9 11.7 9.6 21.9 20.5 16.3 13.1 13.9 10.4 18.2

Poorer 15.6 24.5 4.9 22.6 18.8 14.0 14.2 12.5 26.4 13.8 18.0 13.8 16.4 11.1 17.8

Middle 20.5 16.1 7.2 22.4 14.2 18.9 16.2 14.8 23.6 20.3 19.9 16.1 16.3 18.1 22.4Higher 27.7 19.4 15.7 24.7 20.8 28.6 20.5 28.4 20.8 10.1 22.9 22.8 22.8 30.2 23.2

Highest 28.7 25.8 67.7 15.5 32.5 26.6 37.4 34.7 7.3 35.3 22.9 34.2 30.7 30.2 18.5

Lifetime number of 

sex partners 

*** *** *** *** *** *** *** *** *** *** *** *** *** *** ***

0 2.4 6.1 4.0 0.8 2.5 1.5 2.1 0.7 1.7 3.2 3.0 2.5 2.7 3.4 3.4

1-2 26.4 15.2 77.0 11.3 49.2 47.1 61.0 58.4 41.6 72.2 50.3 49.1 44.3 54.4 69.73-4 33.3 31.2 11.5 22.0 39.4 30.9 30.0 26.7 43.2 21.2 31.9 30.5 35.8 31.9 19.1

5+ 34.1 43.1 5.7 55.0 6.9 19.1 6.6 10.7 10.5 3.5 11.1 17.0 16.1 9.3 6.9

DK/missing 3.9 4.5 1.8 10.9 2.0 1.4 0.4 3.4 3.0 0.0 3.8 0.9 1.1 0.9 1.0

Self-report STI 

symptoms in past year 

*** ** *** *** *** *** *** *** *** *** *** ***

Yes 18.1 29.5 5.4 26.7 11.9 17.6 20.0 11.0 10.4 21.1 17.1 15.9 48.8 7.7 16.3

No or don't know 81.9 70.5 94.6 73.3 88.1 82.4 80.0 89.0 89.6 78.9 82.9 84.1 51.2 92.3 83.7

Gave birth in the past 

2 years 

***   **  *** *** *** *** *** *** ***

Yes 21.6 21.6 15.5 28.9 27.9 17.5 25.7 26.4 21.6 21.2 26.9 22.5 26.7 22.1 21.0

No 78.4 78.4 84.5 71.1 72.1 82.5 74.3 73.6 78.4 78.8 73.1 77.5 73.3 77.9 79.0

TOTAL 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

NUMBER 402 266 273 317 291 942 913 687 683 244 1,378 601 907 2,216 1,295

Note: *** = p<.01, ** = p<.05, * = p<.10 
49 

Table A.3. Characteristics of HIV-positive men, by country 

Among HIV-positive men age 15-49, the percentage distribution of each characteristic and whether the distribution is statistically significantly different from the HIV-negative population 

Cameroon 

2011 

Congo 

(Brazzaville) 

2009 

Ethiopia 

2011 

Gabon 

2012 

Kenya 

2008-09

Lesotho 

2014 

Malawi 

2010 

Mozambique 

2009 

Namibia 

2013 

Rwanda 

2014-15

Swaziland 

2006-07

Tanzania 

2011-12

Uganda 

2011 

Zambia 

2013-14

Zimbabwe 

2010-11 

Place of 

residence 

*** *** *** *** *** *** ** ***

Urban 58.6 59.8 64.9 85.0 22.4 43.1 31.5 49.8 59.7 44.1 36.9 35.2 19.7 61.7 31.8

Rural 41.4 40.2 35.1 15.0 77.6 56.9 68.5 50.2 40.3 55.9 63.1 64.8 80.3 38.3 68.2

Age Group *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***

15-19 3.1 7.9 0.5 3.2 4.3 6.8 4.2 6.5 4.3 2.7 3.2 4.9 6.5 8.9 6.920-29 21.7 18.8 20.6 21.9 29.2 23.3 19.8 29.1 20.7 21.9 33.7 16.8 17.6 24.2 20.630-39 45.6 28.7 50.5 36.6 46.0 40.4 43.9 41.9 43.4 31.4 41.9 44.4 43.1 36.1 42.340-49 29.6 44.6 28.4 38.2 20.5 29.4 32.1 22.4 31.6 44.0 21.2 33.9 32.8 30.8 30.2

Marital Status *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***

Never married 18.1 22.3 9.6 22.6 16.5 26.6 10.1 15.1 45.1 20.5 33.7 13.4 12.2 24.4 14.8

Currently married 73.4 65.6 71.6 60.1 66.9 58.1 80.3 73.8 50.4 70.7 51.9 74.2 71.8 66.4 72.0

Formerly married 8.5 12.0 18.8 17.3 16.6 15.2 9.6 11.1 4.5 8.7 14.4 12.4 16.0 9.3 13.2

Education ** *** *** *** *** *** **

No education 4.8 1.0 23.8 6.0 4.1 12.8 8.2 8.0 11.6 8.3 11.9 8.2 7.6 3.5 1.0

Primary 34.2 15.5 47.3 14.3 60.1 46.7 58.8 63.1 31.3 69.3 32.6 79.6 62.3 34.4 24.8

Secondary+ 61.0 83.5 28.9 79.6 35.9 40.5 33.0 28.9 57.1 22.5 55.5 12.2 30.1 62.1 74.2

Wealth Quintile ** *** ** *** *** *** ***

Poorest 6.5 23.7 3.4 16.3 7.4 15.0 9.9 9.2 16.3 14.7 15.0 12.7 16.2 8.7 19.9

Poorer 19.1 20.7 6.6 19.6 18.8 14.6 15.5 12.3 29.0 12.3 16.3 13.8 15.6 14.1 19.1

Middle 16.4 23.4 12.6 27.6 18.3 19.0 19.6 15.8 29.5 19.2 18.0 21.6 20.5 16.2 20.2Higher 24.2 20.1 10.7 21.1 30.4 27.4 20.2 26.2 19.9 13.1 24.4 19.2 23.1 30.8 20.8

Highest 33.8 12.1 66.6 15.4 25.2 23.9 34.8 36.6 5.3 40.8 26.4 32.7 24.6 30.2 20.0

Lifetime number 

of sex partners 

*** *** *** *** *** *** *** *** *** *** *** *** *** *** ***

0 2.3 4.5 1.6 5.4 2.0 4.7 2.8 2.1 1.8 6.6 3.3 4.6 5.5 7.4 7.3

1-2 8.7 0.8 38.0 0.3 10.9 14.1 21.0 17.7 20.8 41.6 9.0 18.5 12.6 17.5 17.83-4 13.4 12.6 28.3 13.1 24.3 18.4 33.3 25.9 18.4 25.9 24.2 22.4 21.7 28.0 22.4

5+ 68.9 71.5 29.6 56.3 49.1 59.6 38.2 37.5 48.9 25.9 56.8 50.6 50.4 46.1 45.5

DK/missing 6.6 10.6 2.5 24.8 13.7 3.2 4.7 16.8 10.1 0.0 6.6 4.0 9.8 1.1 6.9

Self-report STI 

symptoms in past year

*** *** ** *** *** *** *** *** ** *** *** ***

Yes 14.4 9.6 9.5 10.3 4.8 15.7 14.9 7.8 12.8 13.3 20.5 9.1 31.5 9.5 11.8

No or don't know 85.6 90.4 90.5 89.7 95.2 84.3 85.1 92.2 87.2 86.7 79.5 90.9 68.5 90.5 88.2

Had paid sex in 

the past year

*** *** *** *** *** *** *** *** *** ***

Yes 63.4 24.8 74.8 24.5 69.8 50.8 79.4 65.4 28.4 46.6 68.6 65.6 70.0 71.6

No 36.6 75.2 25.2 75.5 30.2 49.2 20.6 34.6 71.6 53.4 31.4 34.4 30.0 28.4

TOTAL 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

NUMBER 181 117 127 109 130 493 529 352 401 121 741 308 529 1,487 769

Notes: *** = p<.01, ** = p<.05, * = p<.10. No data were collected on paid sex in the past year in the 2006-2007 Swaziland Demographic and Health Survey.
50

Table A.4. HIV testing among PLHIV, by sex and place of residence 

 Women Men 

Urban Rural Urban Rural Urban Rural Urban Rural 

 % n % n 

Estimated to know their HIV statusa

Cameroon 2011 57.4 37.6 248 155 53.8 53.2 106 75 

Congo (Brazzaville) 2009 30.0 12.4 186 80 20.7 16.7 70 47

Ethiopia 2011 60.3 31.7 184 89 54.5 51.4 82 45

Gabon 2012 56.4 49.7 283 34 42.5 40.8 93 16

Kenya 2008-09 56.8 55.4 90 201 46.7 47.3 29 101

Lesotho 2014 72.3 70.3 401 541 63.2 54.8 212 280

Malawi 2010 b b 316 597 50.1 45.5 167 362

Mozambique 2009 42.3 24.6 307 380 27.0 16.8 176 177

Namibia 2013 72.5 68.3 341 342 66.9 60.3 240 161

Rwanda 2014-15 70.7 63.3 99 145 68.7 68.2 53 68

Swaziland 2006-07 35.9 35.2 431 947 23.1 22.9 274 468

Tanzania 2011-12 56.9 52.0 234 367 50.3 47.4 108 199

Uganda 2011 b b 245 662 48.3 45.6 104 424

Zambia 2013-14 68.4 63.2 1432 784 57.1 57.5 918 570

Zimbabwe 2010-11 56.0 51.6 451 844 41.4 43.1 244 525

Tested in the past 12 months 

Cameroon 2011 37.2 18.9 248 155 32.0 30.9 106 75 

Congo (Brazzaville) 2009 16.8 7.7 186 80 13.5 5.5 70 47

Ethiopia 2011 36.8 14.3 184 89 31.1 24.9 82 45

Gabon 2012 35.2 31.8 283 34 24.0 20.7 93 16

Kenya 2008-09 32.8 40.6 90 201 34.5 35.7 29 101

Lesotho 2014 53.8 49.2 401 541 48.0 39.9 212 280

Malawi 2010 b b 316 597 32.2 30.1 167 362

Mozambique 2009 28.7 16.1 307 380 17.8 9.5 176 177

Namibia 2013 50.5 45.3 341 342 50.5 42.3 240 161

Rwanda 2014-15 42.9 32.2 99 145 49.4 41.6 53 68

Swaziland 2006-07 25.9 27.2 431 947 16.9 16.5 274 468

Tanzania 2011-12 38.3 31.7 234 367 35.6 29.7 108 199

Uganda 2011 b b 245 662 30.9 25.5 104 424

Zambia 2013-14 46.5 42.6 1432 784 41.5 42.4 918 570

Zimbabwe 2010-11 36.3 34.8 451 844 25.2 28.7 244 525

Ever tested 

Cameroon 2011 77.6 56.3 248 155 75.5 75.5 106 75 

Congo (Brazzaville) 2009 43.1 17.0 186 80 27.9 27.9 70 47

Ethiopia 2011 83.8 49.0 184 89 77.8 77.8 82 45

Gabon 2012 77.6 67.6 283 34 60.9 60.9 93 16

Kenya 2008-09 80.8 70.2 90 201 58.9 58.9 29 101

Lesotho 2014 90.8 91.4 401 541 78.3 69.6 212 280

Malawi 2010 83.9 79.4 316 597 68.0 60.9 167 362

Mozambique 2009 55.9 33.0 307 380 36.2 24.0 176 177

Namibia 2013 94.5 91.2 341 342 83.3 78.2 240 161

Rwanda 2014-15 98.4 94.3 99 145 87.9 94.8 53 68

Swaziland 2006-07 45.9 43.2 431 947 29.2 29.2 274 468

Tanzania 2011-12 75.4 72.2 234 367 65.0 65.0 108 199

Uganda 2011 80.2 75.2 245 662 65.7 65.7 104 424

Zambia 2013-14 90.3 83.7 1432 784 72.6 72.6 918 570

Zimbabwe 2010-11 75.7 68.4 451 844 57.5 57.5 244 525

 

a “Estimated to know their HIV status” is computed as the midpoint between the percentage tested during the 

past 12 months and the percentage ever tested. 

b Due to a problem with the questionnaires for the Malawi 2010 Demographic and Health Survey and Uganda 

2011 AIDS Indicator Survey, data on time since last HIV test are not available for some women; therefore, these 

estimates are omitted. 
51

Table A.5. HIV testing among HIV-positive women, by age group 

15-19 20-29 30-39 40-49 15-19 20-29 30-39 40-49 

 % n 

Estimated to know their HIV statusa

Cameroon 2011 (39.8) 47.7 56.4 45.8 34 145 147 77

Congo (Brazzaville) 2009 * 31.3 28.7 17.6 24 100 84 58

Ethiopia 2011 * 53.5 51.4 47.6 7 106 120 40

Gabon 2012 * 61.3 61.5 48.4 18 101 130 68

Kenya 2008-09 (41.7) 66.3 53.6 45.9 21 114 88 69

Lesotho 2014 (50.1) 74.1 71.9 69.3 38 335 357 212

Malawi 2010 b b b b 64 280 386 183

Mozambique 2009 35.9 31.0 37.8 23.6 63 303 217 104

Namibia 2013 (50.4) 77.2 71.0 65.8 21 158 317 188

Rwanda 2014-15 * 73.2 63.1 62.3 12 70 85 77

Swaziland 2006-07 18.5 38.2 37.4 31.4 116 673 409 180

Tanzania 2011-12 (49.8) 59.2 55.1 46.3 27 193 223 157

Uganda 2011 b b b b 71 335 318 183

Zambia 2013-14 54.4 71.5 67.6 61.7 157 686 884 490

Zimbabwe 2010-11 40.6 58.0 53.5 47.7 66 427 539 264

Tested in the past 12 months 

Cameroon 2011 (22.2) 29.6 35.8 24.1 34 145 147 77

Congo (Brazzaville) 2009 * 19.2 16.7 7.4 24 100 84 58

Ethiopia 2011 * 38.2 23.6 27.8 7 106 120 40

Gabon 2012 * 42.6 38.1 24.1 18 101 130 68

Kenya 2008-09 (25.2) 46.2 37.7 29.5 21 114 88 69

Lesotho 2014 (40.2) 57.0 50.3 45.5 38 335 357 212

Malawi 2010 b b b b 64 280 386 183

Mozambique 2009 25.0 21.1 25.2 14.5 63 303 217 104

Namibia 2013 (44.7) 59.9 47.1 39.4 21 158 317 188

Rwanda 2014-15 * 50.2 30.1 27.0 12 70 85 77

Swaziland 2006-07 14.2 29.6 27.8 21.9 116 673 409 180

Tanzania 2011-12 (46.7) 35.2 36.3 28.3 27 193 223 157

Uganda 2011 b b b b 71 335 318 183

Zambia 2013-14 46.8 52.5 43.8 36.7 157 686 884 490

Zimbabwe 2010-11 34.1 40.9 34.7 27.8 66 427 539 264

Ever tested 

Cameroon 2011 (57.4) 65.7 76.9 67.5 34 145 147 77

Congo (Brazzaville) 2009 * 43.4 40.7 27.7 24 100 84 58

Ethiopia 2011 * 68.8 79.1 67.4 7 106 120 40

Gabon 2012 * 80.0 84.8 72.7 18 101 130 68

Kenya 2008-09 (58.2) 86.3 69.4 62.2 21 114 88 69

Lesotho 2014 (59.9) 91.1 93.4 93.1 38 335 357 212

Malawi 2010 56.2 84.6 84.6 76.5 64 280 386 183

Mozambique 2009 46.7 40.9 50.4 32.7 63 303 217 104

Namibia 2013 (56.0) 94.4 94.9 92.1 21 158 317 188

Rwanda 2014-15 * 96.2 96.1 97.5 12 70 85 77

Swaziland 2006-07 22.7 46.7 47.0 40.8 116 673 409 180

Tanzania 2011-12 (52.8) 83.2 73.9 64.3 27 193 223 157

Uganda 2011 55.1 79.6 78.8 75.4 71 335 318 183

Zambia 2013-14 61.9 90.5 91.3 86.7 157 686 884 490

Zimbabwe 2010-11 47.0 75.1 72.3 67.6 66 427 539 264

Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed. Figures in 

parentheses are based on 25-49 unweighted cases. 

a “Estimated to know their HIV status” is computed as the midpoint between the percentage tested during the past 12 

months and percentage ever tested. 

b Due to a problem with the questionnaires for the Malawi 2010 Demographic and Health Survey and Uganda 2011 AIDS 

Indicator Survey, data on time since last HIV test are not available for some women; therefore, these estimates are 

omitted. 
52

Table A.6. HIV testing among HIV-positive men, by age group 

15-19 20-29 30-39 40-49 15-19 20-29 30-39 40-49 

 % n 

Estimated to know their HIV statusa

Cameroon 2011 * (57.9) 47.3 45.3 6 39 83 54 

Congo (Brazzaville) 2009 * * (13.6) 15.6 9 22 34 52 

Ethiopia 2011 * (49.8) 52.7 44.9 1 26 64 36 

Gabon 2012 * * 52.7 (46.0) 4 24 40 42 

Kenya 2008-09 * (55.6) 42.5 (49.9) 6 38 60 27 

Lesotho 2014 (19.2) 57.5 60.5 65.3 34 115 199 145 

Malawi 2010 * 44.7 48.4 49.1 22 105 232 170 

Mozambique 2009 (13.6) 18.6 25.8 21.1 23 103 148 79 

Namibia 2013 * 57.4 68.4 67.8 17 83 174 127 

Rwanda 2014-15 * (71.1) (62.4) 73.9 3 26 38 53 

Swaziland 2006-07 * 19.4 24.4 28.3 24 250 310 157 

Tanzania 2011-12 * 49.6 43.6 52.3 15 52 137 104 

Uganda 2011 (30.2) 39.7 44.7 42.0 35 93 228 173 

Zambia 2013-14 22.2 56.7 63.9 59.4 132 360 537 459 

Zimbabwe 2010-11 11.0 34.9 41.3 46.7 53 158 325 232 

Tested in the past 12 months 

Cameroon 2011 * (47.2) 29.7 26.2 6 39 83 54 

Congo (Brazzaville) 2009 * * (4.8) 11.1 9 22 34 52 

Ethiopia 2011 * (28.6) 32.1 23.8 1 26 64 36 

Gabon 2012 * * 25.2 (27.5) 4 24 40 42 

Kenya 2008-09 * (49.5) 28.3 (38.9) 6 38 60 27 

Lesotho 2014 (11.1) 43.3 44.6 49.3 34 115 199 145 

Malawi 2010 * 31.2 30.9 31.2 22 105 232 170 

Mozambique 2009 (5.5) 10.4 17.5 12.9 23 103 148 79 

Namibia 2013 * 41.4 51.8 47.8 17 83 174 127 

Rwanda 2014-15 * (48.2) (38.7) 49.0 3 26 38 53 

Swaziland 2006-07 * 15.0 16.7 21.8 24 250 310 157 

Tanzania 2011-12 * 35.2 26.7 38.5 15 52 137 104 

Uganda 2011 (22.0) 24.7 30.3 23.6 35 93 228 173 

Zambia 2013-14 15.0 43.1 47.3 42.2 132 360 537 459 

Zimbabwe 2010-11 6.3 26.4 30.0 30.0 53 158 325 232 

Ever tested 

Cameroon 2011 * (68.5) 64.8 64.3 6 39 83 54 

Congo (Brazzaville) 2009 * * (22.4) 20.1 9 22 34 52 

Ethiopia 2011 * (70.9) 73.2 65.9 1 26 64 36 

Gabon 2012 * * 80.2 (64.5) 4 24 40 42 

Kenya 2008-09 * (61.7) 56.6 (60.9) 6 38 60 27 

Lesotho 2014 (27.3) 71.6 76.4 81.3 34 115 199 145 

Malawi 2010 * 58.2 65.9 66.9 22 105 232 170 

Mozambique 2009 (21.7) 26.7 34.0 29.3 23 103 148 79 

Namibia 2013 * 73.3 84.9 87.8 17 83 174 127 

Rwanda 2014-15 * (93.9) (86.0) 98.7 3 26 38 53 

Swaziland 2006-07 * 23.8 32.0 34.8 24 250 310 157 

Tanzania 2011-12 * 63.9 60.4 66.1 15 52 137 104 

Uganda 2011 (38.3) 54.7 59.0 60.3 35 93 228 173 

Zambia 2013-14 29.3 70.3 80.5 76.6 132 360 537 459 

Zimbabwe 2010-11 15.6 43.3 52.6 63.3 53 158 325 232 

 

Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed. Figures in 

parentheses are based on 25-49 unweighted cases. 

a “Estimated to know their HIV status” is computed as the midpoint between percentage tested during the past 12 

months and percentage ever tested. 

 
53

Table A.7. HIV testing among HIV-positive women age 15-49, by marital status 

 Never 

married 

Currently 

married 

Formerly 

married 

 Never 

married 

Currently 

married 

Formerly 

married 

 % n 

Estimated to know their HIV statusa

Cameroon 2011 43.2 48.8 55.9 58 239 106 

Congo (Brazzaville) 2009 14.4 26.5 32.6 74 125 67

Ethiopia 2011 (40.9) 54.8 48.0 21 142 111

Gabon 2012 42.7 58.2 59.4 57 194 66

Kenya 2008-09 (53.7) 57.9 54.1 42 143 106

Lesotho 2014 63.4 72.3 74.2 170 532 240

Malawi 2010 b b b 59 557 298

Mozambique 2009 33.7 31.4 34.4 53 413 221

Namibia 2013 70.8 70.4 69.2 315 251 117

Rwanda 2014-15 (68.4) 66.5 64.6 44 126 74

Swaziland 2006-07 31.4 37.1 41.5 573 589 216

Tanzania 2011-12 54.3 49.9 60.0 81 319 201

Uganda 2011 b b b 100 502 306

Zambia 2013-14 58.6 69.2 65.7 352 1272 593

Zimbabwe 2010-11 42.6 54.6 53.9 129 737 429

Tested in the past 12 months 

Cameroon 2011 24.0 30.3 33.3 58 239 106 

Congo (Brazzaville) 2009 9.5 15.5 16.6 74 125 67

Ethiopia 2011 (20.4) 36.6 22.0 21 142 111

Gabon 2012 20.4 38.5 36.4 57 194 66

Kenya 2008-09 (41.7) 38.9 35.9 42 143 106

Lesotho 2014 47.5 50.9 54.4 170 532 240

Malawi 2010 b b b 59 557 298

Mozambique 2009 21.1 21.1 23.2 53 413 221

Namibia 2013 50.3 46.9 43.4 315 251 117

Rwanda 2014-15 (45.1) 34.8 34.3 44 126 74

Swaziland 2006-07 22.9 28.6 32.0 573 589 216

Tanzania 2011-12 34.7 30.2 40.7 81 319 201

Uganda 2011 b b b 100 502 306

Zambia 2013-14 43.4 47.0 42.1 352 1272 593

Zimbabwe 2010-11 30.3 38.0 32.2 129 737 429

Ever tested 

Cameroon 2011 62.3 67.2 78.4 58 239 106 

Congo (Brazzaville) 2009 19.2 37.4 48.6 74 125 67

Ethiopia 2011 (61.4) 72.9 74.0 21 142 111

Gabon 2012 65.0 77.9 82.4 57 194 66

Kenya 2008-09 (65.7) 76.8 72.2 42 143 106

Lesotho 2014 79.2 93.7 93.9 170 532 240

Malawi 2010 58.6 83.0 81.6 59 557 298

Mozambique 2009 46.2 41.6 45.5 53 413 221

Namibia 2013 91.2 93.9 94.9 315 251 117

Rwanda 2014-15 (91.7) 98.1 94.8 44 126 74

Swaziland 2006-07 39.8 45.6 50.9 573 589 216

Tanzania 2011-12 73.8 69.6 79.3 81 319 201

Uganda 2011 64.7 76.8 79.9 100 502 306

Zambia 2013-14 73.7 91.3 89.3 352 1272 593

Zimbabwe 2010-11 54.9 71.2 75.5 129 737 429

Note: Figures in parentheses are based on 25-49 unweighted cases. 

a “Estimated to know their HIV status” is computed as the midpoint between percentage tested during the past 12 

months and percentage ever tested. 

b Due to a problem with the questionnaires for the Malawi 2010 Demographic and Health Survey and Uganda 2011 

AIDS Indicator Survey, data on time since last HIV test are not available for some women; therefore, these 

estimates are omitted. 
54

Table A.8. HIV testing among HIV-positive men age 15-49, by marital status 

 Never 

married 

Currently 

married 

Formerly 

married 

 Never 

married 

Currently 

married 

Formerly 

married 

 % n 

Estimated to know their HIV statusa

Cameroon 2011 (45.3) 49.5 * 33 133 15

Congo (Brazzaville) 2009 (13.5) 15.9 * 26 77 14 

Ethiopia 2011 (25.8) 50.6 (58.4) 12 91 24 

Gabon 2012 * 47.6 * 25 66 19 

Kenya 2008-09 * 49.1 * 22 87 22 

Lesotho 2014 47.3 65.8 49.6 131 286 75 

Malawi 2010 (26.4) 48.8 (53.7) 54 425 51

Mozambique 2009 21.5 21.5 (24.6) 53 260 39 

Namibia 2013 61.0 66.6 * 181 202 18 

Rwanda 2014-15 (60.0) 72.2 * 25 86 11 

Swaziland 2006-07 18.2 25.7 23.1 250 385 107 

Tanzania 2011-12 (32.0) 47.1 (57.4) 41 228 38 

Uganda 2011 32.0 44.0 40.5 64 380 85 

Zambia 2013-14 40.3 62.4 62.9 363 987 138 

Zimbabwe 2010-11 22.9 42.9 39.6 114 554 102 

Tested in the past 12 months 

Cameroon 2011 (32.8) 32.5 * 33 133 15

Congo (Brazzaville) 2009 (9.6) 10.9 * 26 77 14 

Ethiopia 2011 (12.7) 29.1 (36.7) 12 91 24 

Gabon 2012 * 29.7 * 25 66 19 

Kenya 2008-09 * 37.8 * 22 87 22 

Lesotho 2014 36.0 49.3 33.8 131 286 75 

Malawi 2010 (15.8) 32.0 (37.0) 54 425 51

Mozambique 2009 9.6 13.6 (19.2) 53 260 39 

Namibia 2013 47.6 45.8 * 181 202 18 

Rwanda 2014-15 (44.8) 48.7 * 25 86 11 

Swaziland 2006-07 14.3 18.1 17.2 250 385 107 

Tanzania 2011-12 (15.8) 32.2 (46.4) 41 228 38 

Uganda 2011 20.8 28.5 22.2 64 380 85 

Zambia 2013-14 30.0 45.2 48.6 363 987 138 

Zimbabwe 2010-11 16.1 30.1 26.9 114 554 102 

Ever tested 

Cameroon 2011 (57.7) 66.5 * 33 133 15 

Congo (Brazzaville) 2009 (17.3) 20.8 * 26 77 14 

Ethiopia 2011 (38.8) 72.0 (80.1) 12 91 24 

Gabon 2012 * 65.4 * 25 66 19 

Kenya 2008-09 * 60.4 * 22 87 22 

Lesotho 2014 58.6 82.2 65.3 131 286 75

Malawi 2010 (36.9) 65.5 (70.4) 54 425 51 

Mozambique 2009 33.3 29.4 (29.9) 53 260 39 

Namibia 2013 74.3 87.3 * 181 202 18 

Rwanda 2014-15 (75.1) 95.6 * 25 86 11 

Swaziland 2006-07 22.1 33.2 28.9 250 385 107 

Tanzania 2011-12 (48.1) 62.0 (68.4) 41 228 38 

Uganda 2011 43.1 59.4 58.8 64 380 85 

Zambia 2013-14 50.5 79.6 77.2 363 987 138 

Zimbabwe 2010-11 29.7 55.7 52.2 114 554 102 

Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed. Figures 

in parentheses are based on 25-49 unweighted cases. 

a “Estimated to know their HIV status” is computed as the midpoint between percentage tested during the past 12 

months and percentage ever tested. 
55

Table A.9. HIV testing among HIV-positive women age 15-49, by education 

No 

education Primary 

Secondary 

and above 

No 

education Primary 

Secondary 

and above 

 % n 

Estimated to know their HIV statusa

Cameroon 2011 (18.0) 48.0 57.9 42 159 201

Congo (Brazzaville) 2009 * 19.1 28.1 13 62 190

Ethiopia 2011 41.2 52.8 65.1 97 124 52

Gabon 2012 * 47.5 57.9 4 81 232

Kenya 2008-09 (35.2) 55.1 62.1 19 186 86

Lesotho 2014 * 72.3 70.8 9 435 498

Malawi 2010 b b b 154 530 229

Mozambique 2009 26.2 30.7 49.5 150 436 101

Namibia 2013 71.8 65.7 72.4 51 208 424

Rwanda 2014-15 (62.6) 67.2 (66.8) 46 157 42

Swaziland 2006-07 30.5 32.5 38.4 142 500 735

Tanzania 2011-12 42.0 54.6 63.7 92 425 84

Uganda 2011 b b b 144 577 186

Zambia 2013-14 66.4 64.6 68.4 160 992 1064

Zimbabwe 2010-11 (49.4) 45.5 57.3 26 434 835

Tested in the past 12 months 

Cameroon 2011 (0.0) 32.3 34.8 42 159 201

Congo (Brazzaville) 2009 * 7.7 17.2 13 62 190

Ethiopia 2011 23.7 29.4 40.5 97 124 52

Gabon 2012 * 29.2 35.8 4 81 232

Kenya 2008-09 (25.3) 36.6 44.5 19 186 86

Lesotho 2014 * 51.4 51.7 9 435 498

Malawi 2010 b b b 154 530 229

Mozambique 2009 18.2 20.7 31.6 150 436 101

Namibia 2013 50.8 42.7 50.0 51 208 424

Rwanda 2014-15 (30.5) 39.1 (33.6) 46 157 42

Swaziland 2006-07 23.5 24.9 28.7 142 500 735

Tanzania 2011-12 22.2 35.4 42.4 92 425 84

Uganda 2011 b b b 144 577 186

Zambia 2013-14 47.1 43.1 46.7 160 992 1064

Zimbabwe 2010-11 (41.1) 31.8 37.0 26 434 835

Ever tested 

Cameroon 2011 (36.0) 63.7 81.0 42 159 201

Congo (Brazzaville) 2009 * 30.4 38.9 13 62 190

Ethiopia 2011 58.6 76.1 89.7 97 124 52

Gabon 2012 * 65.7 79.9 4 81 232

Kenya 2008-09 (45.0) 73.5 79.7 19 186 86

Lesotho 2014 * 93.1 89.8 9 435 498

Malawi 2010 76.6 78.7 89.2 154 530 229

Mozambique 2009 34.2 40.6 67.3 150 436 101

Namibia 2013 92.8 88.7 94.8 51 208 424

Rwanda 2014-15 (94.6) 95.2 (100.0) 46 157 42

Swaziland 2006-07 37.4 40.0 48.0 142 500 735

Tanzania 2011-12 61.8 73.7 84.9 92 425 84

Uganda 2011 68.3 75.9 85.0 144 577 186

Zambia 2013-14 85.7 86.0 90.1 160 992 1064

Zimbabwe 2010-11 (57.6) 59.2 77.5 26 434 835

Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed. Figures 

in parentheses are based on 25-49 unweighted cases.

a “Estimated to know their HIV status” is computed as the midpoint between percentage tested during the past 12 

months and percentage ever tested. 

b Due to a problem with the questionnaires for the Malawi 2010 Demographic and health Survey and Uganda 2011 

AIDS Indicator Survey, data on time since last HIV test are not available for some women; therefore, these 

estimates are omitted. 
56

Table A.10. HIV testing among HIV-positive men age 15-49, by education 

No 

education Primary 

Secondary 

and above 

No 

education Primary 

Secondary 

and above 

 % n 

Estimated to know their HIV statusa

Cameroon 2011 * 42.7 52.2 9 62 111 

Congo (Brazzaville) 2009 * (15.7) 15.9 1 18 98 

Ethiopia 2011 * 54.2 46.5 30 60 37 

Gabon 2012 * (44.2) 41.4 7 16 87 

Kenya 2008-09 * 41.2 (51.3) 5 78 47 

Lesotho 2014 48.1 55.4 65.2 63 230 199 

Malawi 2010 (40.8) 46.1 50.2 43 311 174 

Mozambique 2009 (1.7) 19.2 33.2 28 223 102 

Namibia 2013 58.7 64.0 65.4 46 126 229 

Rwanda 2014-15 * 64.3 (77.8) 10 84 27 

Swaziland 2006-07 23.0 19.2 24.8 88 242 411 

Tanzania 2011-12 * 46.1 (51.9) 25 245 38 

Uganda 2011 (18.9) 40.6 50.8 40 329 159 

Zambia 2013-14 60.2 55.0 58.1 52 512 923 

Zimbabwe 2010-11 * 28.4 43.7 8 190 570 

Tested in the past 12 months 

Cameroon 2011 * 29.0 34.0 9 62 111 

Congo (Brazzaville) 2009 * (8.3) 10.8 1 18 98 

Ethiopia 2011 * 31.3 24.1 30 60 37 

Gabon 2012 * (34.1) 22.8 7 16 87 

Kenya 2008-09 * 30.1 (37.5) 5 78 47 

Lesotho 2014 36.3 40.5 49.0 63 230 199 

Malawi 2010 (22.9) 32.2 30.4 43 311 174 

Mozambique 2009 (0.0) 12.9 19.0 28 223 102 

Namibia 2013 40.6 49.6 47.1 46 126 229 

Rwanda 2014-15 * 39.5 (57.8) 10 84 27 

Swaziland 2006-07 17.6 14.7 17.6 88 242 411 

Tanzania 2011-12 * 31.1 (43.1) 25 245 38 

Uganda 2011 (9.7) 27.0 30.0 40 329 159 

Zambia 2013-14 38.8 41.3 42.3 52 512 923 

Zimbabwe 2010-11 * 20.1 30.5 8 190 570 

Ever tested 

Cameroon 2011 * 56.4 70.3 9 62 111 

Congo (Brazzaville) 2009 * (23.0) 21.0 1 18 98 

Ethiopia 2011 * 77.1 68.9 30 60 37 

Gabon 2012 * (54.2) 60.0 7 16 87 

Kenya 2008-09 * 52.2 (65.1) 5 78 47 

Lesotho 2014 59.8 70.2 81.3 63 230 199 

Malawi 2010 (58.7) 59.9 69.9 43 311 174 

Mozambique 2009 (3.4) 25.5 47.3 28 223 102 

Namibia 2013 76.7 78.4 83.7 46 126 229 

Rwanda 2014-15 * 89.1 (97.7) 10 84 27 

Swaziland 2006-07 28.4 23.6 32.0 88 242 411 

Tanzania 2011-12 * 61.1 (60.7) 25 245 38 

Uganda 2011 (28.0) 54.1 71.5 40 329 159 

Zambia 2013-14 81.6 68.6 73.9 52 512 923 

Zimbabwe 2010-11 * 36.7 56.8 8 190 570 

Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed. Figures in 

parentheses are based on 25-49 unweighted cases. 

a Calculated as the midpoint of the range between the percentage of adults ever tested and the percentage tested in 

the past 12 months. See Chapter 2 for additional information. 
57

Table A.11. HIV testing among HIV-positive women age 15-49, by wealth

 Poorest Poorer Middle Higher Highest Poorest Poorer Middle Higher Highest

 % n 

Estimated to know their HIV statusa

Cameroon 2011 (15.5) 38.2 48.2 55.5 60.9 30 63 83 112 115

Congo (Brazzaville) 2009 14.5 20.9 (17.8) (24.0) (38.6) 38 65 43 52 69

Ethiopia 2011 * * * (32.3) 59.3 12 13 20 43 185

Gabon 2012 47.4 47.6 (52.0) (66.2) (63.8) 47 72 71 78 49

Kenya 2008-09 (59.4) 57.2 (58.1) 51.7 55.3 40 55 41 61 94

Lesotho 2014 66.6 73.6 74.9 70.6 69.8 112 132 178 270 251

Malawi 2010 b b b b b 107 129 148 188 341

Mozambique 2009 (8.2) 22.0 24.9 33.4 45.5 66 86 102 195 238

Namibia 2013 68.1 67.5 73.8 69.0 (80.5) 150 180 161 142 50

Rwanda 2014-15 64.1 (66.6) (64.5) (66.3) 68.3 50 34 50 25 86

Swaziland 2006-07 29.9 35.3 37.7 37.7 35.1 225 248 275 315 316

Tanzania 2011-12 50.7 48.3 50.0 50.2 61.7 79 83 97 137 205

Uganda 2011 b b b b b 126 148 148 207 278

Zambia 2013-14 60.9 67.0 63.3 70.6 66.3 230 246 400 670 670

Zimbabwe 2010-11 49.8 54.6 51.0 53.5 57.2 236 231 290 300 239

Tested in the past 12 months 

Cameroon 2011 (8.6) 16.9 33.2 31.6 39.5 30 63 83 112 115

Congo (Brazzaville) 2009 11.2 8.4 (12.5) (10.7) (24.7) 38 65 43 52 69

Ethiopia 2011 * * * (6.2) 36.3 12 13 20 43 185

Gabon 2012 29.5 22.9 (37.1) (45.3) (37.3) 47 72 71 78 49

Kenya 2008-09 (46.7) 42.3 (41.9) 35.8 32.2 40 55 41 61 94

Lesotho 2014 47.3 55.7 54.1 49.3 50.4 112 132 178 270 251

Malawi 2010 b b b b b 107 129 148 188 341

Mozambique 2009 (5.9) 13.7 17.8 21.2 31.2 66 86 102 195 238

Namibia 2013 45.2 42.6 53.0 46.2 (63.2) 150 180 161 142 50

Rwanda 2014-15 35.0 (38.4) (33.1) (35.2) 39.0 50 34 50 25 86

Swaziland 2006-07 23.5 25.6 31.6 28.1 24.4 225 248 275 315 316

Tanzania 2011-12 25.1 31.6 25.8 34.7 42.7 79 83 97 137 205

Uganda 2011 b b b b b 126 148 148 207 278

Zambia 2013-14 43.8 46.6 39.2 51.5 42.3 230 246 400 670 670

Zimbabwe 2010-11 35.9 39.0 31.0 34.8 36.9 236 231 290 300 239

Ever tested 

Cameroon 2011 (22.4) 59.5 63.1 79.3 82.2 30 63 83 112 115

Congo (Brazzaville) 2009 17.8 33.4 (23.0) (37.2) (52.5) 38 65 43 52 69

Ethiopia 2011 * * * (58.3) 82.2 12 13 20 43 185

Gabon 2012 65.3 72.3 (66.9) (87.1) (90.3) 47 72 71 78 49

Kenya 2008-09 (72.1) 72.1 (74.3) 67.5 78.4 40 55 41 61 94

Lesotho 2014 85.8 91.5 95.7 91.9 89.2 112 132 178 270 251

Malawi 2010 77.5 77.6 74.9 79.3 86.8 107 129 148 188 341

Mozambique 2009 (10.5) 30.3 32.0 45.6 59.7 66 86 102 195 238

Namibia 2013 90.9 92.4 94.5 91.7 (97.7) 150 180 161 142 50

Rwanda 2014-15 93.1 (94.8) (95.9) (97.4) 97.6 50 34 50 25 86

Swaziland 2006-07 36.3 45.0 43.7 47.3 45.8 225 248 275 315 316

Tanzania 2011-12 76.2 64.9 74.2 65.6 80.6 79 83 97 137 205

Uganda 2011 75.3 67.9 73.2 80.7 80.4 126 148 148 207 278

Zambia 2013-14 77.9 87.4 87.4 89.6 90.3 230 246 400 670 670

Zimbabwe 2010-11 63.7 70.2 70.9 72.2 77.4 236 231 290 300 239

Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed. Figures in 

parentheses are based on 25-49 unweighted cases. 

a Calculated as the midpoint of the range between the percentage of adults ever tested and the percentage tested in 

the past 12 months. See Chapter 2 for additional information. 

b Due to a problem with the questionnaires for the Malawi 2010 Demographic and Health Survey and Uganda 2011 

AIDS Indicator Survey, data on time since last HIV test are not available for some women; therefore, these estimates 

are omitted. 
58

Table A.12. HIV testing among HIV-positive men age 15-49, by wealth 

 Poorest Poorer Middle Higher Highest Poorest Poorer Middle Higher Highest

 % n 

Estimated to know their HIV statusa

Cameroon 2011 * (41.0) (47.4) (51.9) (54.3) 12 35 30 44 61 

Congo (Brazzaville) 2009 (7.0) (17.2) (6.9) * * 28 24 27 23 14 

Ethiopia 2011 * * * * 50.3 4 8 16 14 85 

Gabon 2012 (40.0) (42.9) (38.4) * * 18 21 30 23 17 

Kenya 2008-09 * (27.6) * (59.0) (52.1) 10 25 24 40 33 

Lesotho 2014 46.1 61.1 55.7 64.5 59.7 74 72 94 135 118 

Malawi 2010 45.2 44.9 45.4 50.1 47.4 52 82 104 107 184 

Mozambique 2009 (5.2) (19.0) 12.8 16.7 34.6 32 43 56 92 129 

Namibia 2013 60.4 61.4 63.8 70.9 * 65 116 118 80 21 

Rwanda 2014-15 * * * * 68.3 18 15 23 16 49 

Swaziland 2006-07 14.9 26.0 22.6 24.6 23.8 111 121 133 181 196 

Tanzania 2011-12 (40.4) (61.6) 36.4 44.2 50.1 39 42 66 59 101 

Uganda 2011 43.6 40.2 37.5 36.1 51.2 85 82 108 122 130 

Zambia 2013-14 56.7 55.9 56.1 56.0 59.4 129 209 241 459 450 

Zimbabwe 2010-11 36.3 30.1 36.3 43.2 51.1 153 147 155 160 154 

Tested in the past 12 months 

Cameroon 2011 * (34.2) (36.2) (32.0) (31.3) 12 35 30 44 61 

Congo (Brazzaville) 2009 (5.1) (13.7) (0.0) * * 28 24 27 23 14 

Ethiopia 2011 * * * * 27.4 4 8 16 14 85 

Gabon 2012 (31.9) (18.6) (13.0) * * 18 21 30 23 17 

Kenya 2008-09 * (20.6) * (50.4) (40.1) 10 25 24 40 33 

Lesotho 2014 33.0 46.4 44.1 47.8 42.6 74 72 94 135 118 

Malawi 2010 32.8 29.4 34.5 33.7 27.0 52 82 104 107 184 

Mozambique 2009 (0.0) (10.5) 9.2 11.6 21.4 32 43 56 92 129 

Namibia 2013 43.5 47.1 44.9 53.5 * 65 116 118 80 21 

Rwanda 2014-15 * * * * 48.3 18 15 23 16 49 

Swaziland 2006-07 11.4 19.8 16.8 18.8 15.7 111 121 133 181 196 

Tanzania 2011-12 (27.9) (45.2) 22.2 28.0 36.1 39 42 66 59 101 

Uganda 2011 30.5 26.1 22.5 23.1 30.9 85 82 108 122 130 

Zambia 2013-14 41.9 40.8 40.9 41.3 43.4 129 209 241 459 450 

Zimbabwe 2010-11 25.6 22.2 26.1 28.5 35.4 153 147 155 160 154 

Ever tested 

Cameroon 2011 * (47.7) (58.6) (71.8) (77.3) 26 48 59 72 77 

Congo (Brazzaville) 2009 (8.8) (20.7) (13.8) * * 9 21 14 44 23 

Ethiopia 2011 * * * * 73.2 49 53 72 69 73 

Gabon 2012 (48.0) (67.1) (63.7) * * 48 67 64 50 67 

Kenya 2008-09 * (34.5) * (67.6) (64.1) 46 35 66 68 64 

Lesotho 2014 59.2 75.7 67.2 81.2 76.7 74 72 94 135 118 

Malawi 2010 57.6 60.4 56.3 66.5 67.7 52 82 104 107 184 

Mozambique 2009 (10.4) (27.5) 16.3 21.7 47.7 32 43 56 92 129 

Namibia 2013 77.2 75.6 82.6 88.2 * 65 116 118 80 21 

Rwanda 2014-15 * * * * 88.3 18 15 23 16 49 

Swaziland 2006-07 18.3 32.1 28.3 30.3 31.8 18 32 28 30 32 

Tanzania 2011-12 (52.8) (78.0) 50.6 60.3 64.0 53 78 51 60 64 

Uganda 2011 56.7 54.2 52.4 49.1 71.5 57 54 52 49 72 

Zambia 2013-14 71.4 71.0 71.3 70.7 75.3 71 71 71 71 75 

Zimbabwe 2010-11 46.9 38.0 46.5 57.8 66.8 47 38 47 58 67 

 

Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed. Figures in 

parentheses are based on 25-49 unweighted cases. 

a Calculated as the midpoint of the range between the percentage of adults ever tested and the percentage tested in 

the past 12 months. See Chapter 2 for additional information. 
59

Table A.13. HIV testing among HIV-positive women age 15-49, by lifetime number of sexual partners 

 0 1-2 3-4 5+ DK/missing 0 1-2 3-4 5+ DK/missing

 % n 

Estimated to know their HIV statusa

Cameroon 2011 * 44.2 51.4 54.3 * 10 106 134 137 16

Congo (Brazzaville) 2009 * (29.4) 31.1 23.9 * 16 40 83 114 12

Ethiopia 2011 * 50.4 (66.3) * * 11 210 31 16 5

Gabon 2012 * (53.4) 66.8 57.7 (29.2) 2 36 70 175 34

Kenya 2008-09 * 56.6 57.5 (48.3) * 7 143 114 20 6

Lesotho 2014 * 69.6 74.1 72.6 * 14 444 291 180 13

Malawi 2010 b b b b b 19 557 274 60 4

Mozambique 2009 * 32.3 38.5 20.6 (33.1) 5 401 184 73 24

Namibia 2013 * 71.2 73.2 69.3 * 12 285 295 72 20

Rwanda 2014-15 * 67.1 64.0 * * 8 176 52 8 0

Swaziland 2006-07 (6.3) 36.8 36.3 35.2 31.9 41 693 440 152 52

Tanzania 2011-12 * 50.0 58.0 58.2 * 15 295 183 102 5

Uganda 2011 b b b b b 25 402 324 146 10

Zambia 2013-14 43.6 68.7 66.8 62.8 * 76 1205 708 207 21

Zimbabwe 2010-11 29.3 53.3 55.4 55.2 * 44 902 247 89 13

Tested in the past 12 months 

Cameroon 2011 * 27.0 33.0 31.4 * 10 106 134 137 16

Congo (Brazzaville) 2009 * (22.2) 23.7 7.5 * 16 40 83 114 12

Ethiopia 2011 * 30.2 (39.1) * * 11 210 31 16 5

Gabon 2012 * (35.3) 50.2 32.6 (16.6) 2 36 70 175 34

Kenya 2008-09 * 36.1 41.3 (33.9) * 7 143 114 20 6

Lesotho 2014 * 49.8 54.2 51.1 * 14 444 291 180 13

Malawi 2010 b b b b b 19 557 274 60 4

Mozambique 2009 * 22.6 24.1 11.5 (25.6) 5 401 184 73 24

Namibia 2013 * 49.3 51.0 42.5 * 12 285 295 72 20

Rwanda 2014-15 * 36.5 33.2 * * 8 176 52 8 0

Swaziland 2006-07 (1.9) 28.0 26.4 29.3 24.3 41 693 440 152 52

Tanzania 2011-12 * 28.0 42.2 37.2 * 15 295 183 102 5

Uganda 2011 b b b b b 25 402 324 146 10

Zambia 2013-14 35.8 48.9 43.2 35.9 * 76 1205 708 207 21

Zimbabwe 2010-11 21.3 35.2 36.1 39.2 * 44 902 247 89 13

Ever tested 

Cameroon 2011 * 61.4 69.7 77.2 * 10 106 134 137 16

Congo (Brazzaville) 2009 * (36.5) 38.5 40.3 * 16 40 83 114 12

Ethiopia 2011 * 70.5 (93.5) * * 11 210 31 16 5

Gabon 2012 * (71.5) 83.4 82.7 (41.8) 2 36 70 175 34

Kenya 2008-09 * 77.0 73.7 (62.6) * 7 143 114 20 6

Lesotho 2014 * 89.4 93.9 94.1 * 14 444 291 180 13

Malawi 2010 * 80.6 81.8 95.8 * 19 557 274 60 4

Mozambique 2009 * 41.9 52.8 29.7 (40.5) 5 401 184 73 24

Namibia 2013 * 93.0 95.3 96.1 * 12 285 295 72 20

Rwanda 2014-15 * 97.7 94.7 * * 8 176 52 8 0

Swaziland 2006-07 (10.6) 45.6 46.1 41.1 39.4 41 693 440 152 52

Tanzania 2011-12 * 72.0 73.7 79.2 * 15 295 183 102 5

Uganda 2011 (33.2) 78.5 77.2 76.2 * 25 402 324 146 10

Zambia 2013-14 51.4 88.4 90.3 89.6 * 76 1205 708 207 21

Zimbabwe 2010-11 37.2 71.4 74.6 71.1 * 44 902 247 89 13

Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed. Figures in 

parentheses are based on 25-49 unweighted cases.

a Calculated as the midpoint of the range between the percentage of adults ever tested and the percentage tested in the 

past 12 months. See Chapter 2 for additional information. 

b Due to a problem with the questionnaires for the Malawi 2010 Demographic and Health Survey and Uganda 2011 AIDS 

Indicator Survey, data on time since last HIV test are not available for some women; therefore, these estimates are 

omitted. 
60

Table A.14. HIV testing among HIV-positive men age 15-49, by lifetime number of sexual partners 

 0 1-2 3-4 5+ DK/missing 0 1-2 3-4 5+ DK/missing 

 % n 

Estimated to know their HIV statusa

Cameroon 2011 * * * 45.9 * 4 16 24 125 12 

Congo (Brazzaville) 2009 * * * 13.2 * 5 1 15 84 12 

Ethiopia 2011 * 47.6 (61.8) 42.4 * 2 48 36 38 3 

Gabon 2012 * * * 37.6 (53.2) 6 0 14 62 27 

Kenya 2008-09 * * (47.0) 49.4 * 3 14 32 64 18 

Lesotho 2014 (27.7) 50.7 59.0 63.0 * 23 69 90 293 16 

Malawi 2010 * 45.4 46.9 50.4 (37.4) 15 111 176 202 25 

Mozambique 2009 * 16.0 25.4 22.0 23.7 7 62 91 132 59 

Namibia 2013 * 58.0 61.4 69.9 (58.6) 7 84 74 196 40 

Rwanda 2014-15 * 65.7 (75.6) (68.0) * 8 50 31 31 0 

Swaziland 2006-07 * 20.8 20.7 25.4 20.8 25 67 180 421 49 

Tanzania 2011-12 * (52.1) 41.4 48.3 * 14 57 69 156 12 

Uganda 2011 (23.7) 40.7 44.1 43.4 41.6 29 66 115 266 52 

Zambia 2013-14 23.6 61.3 58.8 60.3 * 110 260 416 686 16 

Zimbabwe 2010-11 16.6 40.8 38.0 42.6 44.7 56 137 172 350 53 

Tested in the past 12 months 

Cameroon 2011 * * * 28.7 * 4 16 24 125 12 

Congo (Brazzaville) 2009 * * * 7.0 * 5 1 15 84 12 

Ethiopia 2011 * 19.4 (49.5) 20.3 * 2 48 36 38 3 

Gabon 2012 * * * 23.6 (35.5) 6 0 14 62 27 

Kenya 2008-09 * * (35.2) 35.0 * 3 14 32 64 18 

Lesotho 2014 (18.0) 34.6 45.5 47.9 * 23 69 90 293 16 

Malawi 2010 * 34.2 30.5 30.2 (23.7) 15 111 176 202 25 

Mozambique 2009 * 11.6 15.0 14.9 12.4 7 62 91 132 59 

Namibia 2013 * 44.6 43.8 52.7 (34.0) 7 84 74 196 40 

Rwanda 2014-15 * 37.9 (53.3) (45.7) * 8 50 31 31 0 

Swaziland 2006-07 * 18.5 15.0 18.5 11.2 25 67 180 421 49 

Tanzania 2011-12 * (33.9) 28.7 34.7 * 14 57 69 156 12 

Uganda 2011 (20.1) 25.2 32.0 25.8 23.6 29 66 115 266 52 

Zambia 2013-14 13.4 49.8 42.9 43.1 * 110 260 416 686 16 

Zimbabwe 2010-11 12.2 27.2 25.6 31.4 26.4 56 137 172 350 53 

Ever tested 

Cameroon 2011 * * * 63.0 * 4 16 24 125 12 

Congo (Brazzaville) 2009 * * * 19.4 * 5 1 15 84 12 

Ethiopia 2011 * 75.7 (74.0) 64.4 * 2 48 36 38 3 

Gabon 2012 * * * 51.5 (70.9) 6 0 14 62 27 

Kenya 2008-09 * * (58.8) 63.7 * 3 14 32 64 18 

Lesotho 2014 (37.4) 66.8 72.5 78.1 * 23 69 90 293 16 

Malawi 2010 * 56.5 63.3 70.6 (51.1) 15 111 176 202 25 

Mozambique 2009 * 20.3 35.7 29.0 35.0 7 62 91 132 59 

Namibia 2013 * 71.3 79.0 87.0 (83.2) 7 84 74 196 40 

Rwanda 2014-15 * 93.5 (97.8) (90.2) * 8 50 31 31 0 

Swaziland 2006-07 * 23.0 26.4 32.2 30.3 25 67 180 421 49 

Tanzania 2011-12 * (70.3) 54.0 61.8 * 14 57 69 156 12 

Uganda 2011 (27.3) 56.1 56.2 61.0 59.5 29 66 115 266 52 

Zambia 2013-14 33.7 72.8 74.7 77.5 * 110 260 416 686 16 

Zimbabwe 2010-11 21.0 54.4 50.3 53.8 62.9 56 137 172 350 53 

 

Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed. Figures in 

parentheses are based on 25-49 unweighted cases. 

a Calculated as the midpoint of the range between the percentage of adults ever tested and the percentage tested in 

the past 12 months. See Chapter 2 for additional information. 
61

Table A.15. HIV testing among PLHIV, by self-report of STI symptoms in the past year

 Women Men 

Yes No Yes No Yes No Yes No 

 % N % n 

Estimated to know their HIV statusa 

Cameroon 2011 56.8 48.3 73 329 (44.5) 48.3 26 155

Congo (Brazzaville) 2009 17.1 27.8 78 187 * 17.3 11 106

Ethiopia 2011 * 50.6 15 259 * 51.4 12 115

Gabon 2012 57.1 55.2 85 233 * 43.8 11 98

Kenya 2008-09 (58.7) 55.5 35 256 * 46.9 6 124

Lesotho 2014 75.4 70.3 166 777 52.8 59.5 113 401

Malawi 2010 b b 182 731 52.5 46.0 79 450

Mozambique 2009 48.8 30.5 76 611 (3.7) 23.4 28 325

Namibia 2013 72.8 70.1 71 612 (60.1) 64.8 51 350

Rwanda 2014-15 65.7 66.4 52 193 * 67.6 31 95

Swaziland 2006-07 37.0 35.1 235 1143 26.5 21.8 152 589

Tanzania 2011-12 54.0 53.9 95 505 (43.8) 46.6 28 280

Uganda 2011 b b 443 464 40.6 42.6 167 362

Zambia 2013-14 70.4 66.2 170 2047 63.4 56.5 141 1347

Zimbabwe 2010-11 60.0 51.9 211 1084 42.9 39.1 91 678

Tested in the past 12 months 

Cameroon 2011 42.3 27.5 73 329 (29.4) 31.9 26 155

Congo (Brazzaville) 2009 7.2 17.0 78 187 * 11.4 11 106

Ethiopia 2011 * 29.8 15 259 * 31.8 12 115

Gabon 2012 37.1 34.0 85 233 * 26.2 11 98

Kenya 2008-09 (46.5) 37.1 35 256 * 34.7 6 124

Lesotho 2014 56.0 50.1 166 777 37.7 44.5 113 401

Malawi 2010 b b 182 731 36.3 29.8 79 450

Mozambique 2009 32.7 20.4 76 611 (0.0) 14.8 28 325

Namibia 2013 52.4 47.3 71 612 (31.1) 49.5 51 350

Rwanda 2014-15 33.3 37.4 52 193 * 42.6 31 95

Swaziland 2006-07 31.8 25.7 235 1143 22.3 15.2 152 589

Tanzania 2011-12 36.1 34.0 95 505 (32.7) 31.7 28 280

Uganda 2011 b b 443 464 23.4 28.0 167 362

Zambia 2013-14 51.3 44.6 170 2047 45.6 41.5 141 1347

Zimbabwe 2010-11 42.7 33.9 211 1084 32.3 27.0 91 678

Ever tested2

Cameroon 2011 71.3 69.0 73 329 (59.6) 64.7 26 155

Congo (Brazzaville) 2009 27.0 38.6 78 187 * 23.2 11 106

Ethiopia 2011 * 71.3 15 259 * 71.0 12 115

Gabon 2012 77.0 76.3 85 233 * 61.3 11 98

Kenya 2008-09 (70.9) 73.9 35 256 * 59.0 6 124

Lesotho 2014 94.7 90.4 166 777 67.8 74.4 77 415

Malawi 2010 85.2 79.9 182 731 68.6 62.1 79 450

Mozambique 2009 64.9 40.5 76 611 (7.4) 32.0 27 325

Namibia 2013 93.2 92.8 71 612 (89.1) 80.1 51 350

Rwanda 2014-15 98.1 95.4 52 193 * 92.6 16 105

Swaziland 2006-07 42.2 44.4 235 1143 30.6 28.4 152 589

Tanzania 2011-12 71.9 73.7 95 505 (54.8) 61.5 28 280

Uganda 2011 77.4 75.7 443 464 57.8 57.1 167 362

Zambia 2013-14 89.4 87.8 170 2047 81.2 71.4 141 1347

Zimbabwe 2010-11 77.3 69.8 211 1084 53.5 51.1 91 678

Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed. 

Figures in parentheses are based on 25-49 unweighted cases.

a Calculated as the midpoint of the range between the percentage of adults ever tested and the percentage

tested in the past 12 months. See Chapter 2 for additional information.

b Due to a problem with the questionnaires for the Malawi 2010 Demographic and Health Survey and Uganda 

2011 AIDS Indicator Survey, data on time since last HIV test are not available for some women; therefore,

these estimates are omitted. 
62

Table A.16. HIV testing among HIV-positive women, by whether they gave birth in 

the past 2 years 

 Yes No Yes No 

 % n 

Estimated to know their HIV statusa 

Cameroon 2011 b b 87 316 

Congo (Brazzaville) 2009 30.1 23.2 57 208 

Ethiopia 2011 (63.5) 48.7 42 231 

Gabon 2012 67.1 51.1 92 226 

Kenya 2008-09 67.9 51.2 81 210 

Lesotho 2014 74.2 70.5 165 778 

Malawi 2010 b b 234 679 

Mozambique 2009 42.2 29.0 182 505 

Namibia 2013 79.6 67.8 147 536 

Rwanda 2014-15 71.2 64.9 52 193 

Swaziland 2006-07 48.8 30.5 371 1007 

Tanzania 2011-12 65.4 50.6 135 465 

Uganda 2011 45.0 52.4 242 665 

Zambia 2013-14 73.7 64.6 489 1728 

Zimbabwe 2010-11 66.4 49.6 273 1023 

Tested in the past 12 months 

Cameroon 2011 b b 87 316 

Congo (Brazzaville) 2009 23.7 11.5 57 208 

Ethiopia 2011 (53.1) 25.1 42 231 

Gabon 2012 43.0 31.5 92 226 

Kenya 2008-09 49.3 33.9 81 210 

Lesotho 2014 54.8 50.4 165 778 

Malawi 2010 b b 234 679 

Mozambique 2009 31.2 18.4 182 505 

Namibia 2013 63.1 43.7 147 536 

Rwanda 2014-15 43.9 34.5 52 193 

Swaziland 2006-07 39.9 21.9 371 1007 

Tanzania 2011-12 45.4 31.1 135 465 

Uganda 2011 15.3 27.6 242 665 

Zambia 2013-14 54.4 42.5 489 1728 

Zimbabwe 2010-11 53.6 30.4 273 1023 

Ever tested 

Cameroon 2011 82.8 65.8 87 316 

Congo (Brazzaville) 2009 36.5 34.8 57 208 

Ethiopia 2011 (73.8) 72.2 42 231 

Gabon 2012 91.1 70.6 92 226 

Kenya 2008-09 86.5 68.5 81 210 

Lesotho 2014 93.5 90.6 165 778 

Malawi 2010 92.6 76.9 234 679 

Mozambique 2009 53.1 39.6 182 505 

Namibia 2013 96.0 91.9 147 536 

Rwanda 2014-15 98.4 95.3 52 193 

Swaziland 2006-07 57.7 39.0 371 1007 

Tanzania 2011-12 85.3 70.0 135 465 

Uganda 2011 74.6 77.2 242 665 

Zambia 2013-14 92.9 86.6 489 1728 

Zimbabwe 2010-11 79.2 68.8 273 1023 

Note: Figures in parentheses are based on 25-49 unweighted cases. 

a Calculated as the midpoint of the range between the percentage of adults ever tested and the 

percentage tested in the past 12 months. See Chapter 2 for additional information. b Due to a problem with the questionnaires for the Malawi 2010 Demographic and Health Survey 

and Uganda 2011 AIDS Indicator Survey, data on time since last HIV test are not available for 

some women; therefore, these estimates are omitted.
63

Table A.17. HIV testing among HIV-positive men, by whether they paid 

for sex in the past year 

Yes No Yes No 

 % n 

Estimated to know their HIV statusa 

Cameroon 2011 50.5 43.1 115 66 

Congo (Brazzaville) 2009 (29.2) 11.3 29 88 

Ethiopia 2011 52.8 40.2 95 32 

Gabon 2012 (44.9) 40.4 27 83 

Kenya 2008-09 46.3 (48.8) 91 39 

Lesotho 2014 64.5 52.1 250 242 

Malawi 2010 48.7 40.2 420 109 

Mozambique 2009 18.8 27.6 230 122 

Namibia 2013 67.0 63.1 114 287 

Rwanda 2014-15 74.8 62.9 56 65 

Tanzania 2011-12 45.4 48.4 211 97 

Uganda 2011 43.7 38.5 347 182 

Zambia 2013-14 61.9 46.0 1041 446 

Zimbabwe 2010-11 42.2 32.7 551 218 

Tested in the past 12 months 

Cameroon 2011 34.1 27.2 115 66 

Congo (Brazzaville) 2009 (25.5) 5.3 29 88 

Ethiopia 2011 33.0 16.9 95 32 

Gabon 2012 (33.6) 20.3 27 83 

Kenya 2008-09 34.4 (37.8) 91 39 

Lesotho 2014 48.7 37.9 250 242 

Malawi 2010 32.7 23.3 420 109 

Mozambique 2009 11.9 16.8 230 122 

Namibia 2013 46.7 47.3 114 287 

Rwanda 2014-15 52.0 39.0 56 65 

Tanzania 2011-12 31.4 32.5 211 97 

Uganda 2011 27.4 24.9 347 182 

Zambia 2013-14 44.8 35.0 1041 446 

Zimbabwe 2010-11 28.7 24.9 551 218 

Ever tested 

Cameroon 2011 66.8 59.0 115 66 

Congo (Brazzaville) 2009 (32.8) 17.3 29 88 

Ethiopia 2011 72.6 63.4 95 32 

Gabon 2012 (56.1) 60.5 27 83 

Kenya 2008-09 58.1 (59.8) 91 39 

Lesotho 2014 80.2 66.2 250 242 

Malawi 2010 64.7 57.0 420 109 

Mozambique 2009 25.6 38.4 230 122 

Namibia 2013 87.2 78.8 114 287 

Rwanda 2014-15 97.5 86.8 56 65 

Tanzania 2011-12 59.3 64.3 211 97 

Uganda 2011 60.0 52.1 347 182 

Zambia 2013-14 78.9 57.0 1041 446 

Zimbabwe 2010-11 55.6 40.5 551 218 

Note: Figures in parentheses are based on 25-49 unweighted cases. 

a Calculated as the midpoint of the range between the percentage of adults ever tested 

and the percentage tested in the past 12 months. See Chapter 2 for additional 

information. 

b No data were collected on paid sex in the past year in the 2006-07 Swaziland 

Demographic and Health Survey.