MEASURE DHS assists countries worldwide in the collection and use of data to monitor and 

evaluate population, health, and nutrition programs. Additional information about the 

MEASURE DHS project can be obtained by contacting Macro International Inc., Demographic 

and Health Research Division, 11785 Beltsville Drive, Suite 300, Calverton, MD 20705 

(telephone: 301-572-0200; fax: 301-572-0999; e-mail: reports@macrointernational.com; 

internet: www.measuredhs.com).

The main objectives of the MEASURE DHS project are: 

to provide decisionmakers in survey countries with information useful for informed 

policy choices; 

to expand the international population and health database; 

to advance survey methodology; and 

to develop in participating countries the skills and resources necessary to conduct highquality demographic and health surveys. 
DHS Comparative Reports No. 22

Levels and Spread of HIV Seroprevalence 

and Associated Factors: Evidence from 

National Household Surveys 

Vinod Mishra1

Amy Medley2

Rathavuth Hong1

Yuan Gu1

Bryant Robey2

Macro International Inc. 

Calverton, Maryland, USA 

February 2009 

Corresponding author: Vinod Mishra, Demographic and Health Research Division, Macro International Inc., 

11785 Beltsville Drive, Suite 300, Calverton, MD 20705; Phone: 301-572-0220; Fax: 301-572-0999; 

Email: vinod.mishra@macrointernational.com. 

1

 Macro International Inc. 

2

 Johns Hopkins University
Editor: Bryant Robey 

Document Production: Yuan Gu 

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

Development (USAID) through the MEASURE DHS project (#GPO-C-00-03-00002-00). The 

views expressed are those of the authors and do not necessarily reflect the views of USAID or 

the United States Government. 

Recommended citation: 

Mishra, Vinod, Amy Medley, Rathavuth Hong, Yuan Gu, and Bryant Robey. 2009. Levels and Spread of 

HIV Seroprevalence and Associated Factors: Evidence from National Household Surveys. DHS 

Comparative Reports No. 22. Calverton, Maryland, USA: Macro International Inc. 
iii

Contents 

Tables v

Figures vii

Preface ix

Executive Summary xi

1 Introduction 1

1.1 Purpose 1

1.2 Brief History and Epidemiology of the HIV Epidemic 1

1.3 Impact of HIV/AIDS 3

1.4 Organization of the Report 4

2 HIV Testing in Surveys 5

2.1 Objectives of the DHS Surveys 5

2.2 Sample Size and Design 5

2.3 Questionnaires 5

2.4 Biomarkers 6

2.5 Voucher System for Voluntary Counseling and Testing 6

2.6 Confidentiality Procedures 6

2.7 Sample Size Needs 7

3 Data and Methods 9

3.1 Data 9

3.2 Indicators Included in the Analysis 9

3.3 Data Limitations 13

3.4 Response Rates 14

4 HIV Prevalence by Demographic Characteristics 17

4.1 Key Findings 17

4.2 Introduction 17

4.3 HIV Prevalence by Age and Sex 17

4.4 HIV Prevalence by Place of Residence 32

4.5 HIV Prevalence by Years Lived in Current Place of Residence 34

4.6 HIV Prevalence by Number of Times Away From Home in the Last 12 Months 37

4.7 HIV Prevalence by Pregnancy Status and Birth in Last 3 Years 39

5 HIV Prevalence by Socioeconomic Characteristics 41

5.1 Key Findings 41

5.2 Introduction 41

5.3 HIV Prevalence by Marital Status 41

5.4 HIV Prevalence by Level of Education 44

5.5 HIV Prevalence by Occupation 46

5.6 HIV Prevalence by Household Wealth 49

5.7 HIV Prevalence by Media Exposure 53

6 HIV Prevalence by HIV/AIDS Knowledge, Misconceptions, and Attitudes 63

6.1 Key Findings 63

6.2 Introduction 63

6.3 HIV Prevalence by Knowledge of HIV Prevention Methods 63

6.4 HIV Prevalence by Knowledge of Mother-to-Child HIV Transmission 66
iv

6.5 HIV Prevalence by Misconceptions Regarding HIV Infection 69

6.6 HIV Prevalence by Attitudes toward People Living with HIV (PLHIV) 71

7 HIV Prevalence by Risk Behaviors 77

7.1 Key Findings 77

7.2 Introduction 77

7.3 HIV Prevalence by Age at First Sex 77

7.4 HIV Prevalence by Number of Sex Partners 80

7.5 HIV Prevalence by Higher-risk Sex Behavior 85

7.6 HIV Prevalence by Alcohol Consumption 89

8 HIV Prevalence among Youth by Sexual Behavior 93

8.1 Key Findings 93

8.2 Introduction 93

8.3 HIV Prevalence among Youth by Sex and Urban/Rural Residence 93

8.4 HIV Prevalence among Youth by Age at First Sexual Intercourse 96

8.5 HIV Prevalence among Youth by Primary Abstinence 100

8.6 HIV Prevalence by Secondary Abstinence 100

9 HIV Prevalence by Protective Behaviors 103

9.1 Key Findings 103

9.2 Introduction 103

9.3 HIV Prevalence by Male Condom Use 103

9.4 HIV Prevalence by Male Circumcision 109

10 HIV Prevalence by Medical Injection Use, Prevalence of Other STIs, and Prior HIV 

Testing Experience 113

10.1 Key Findings 113

10.2 Introduction 113

10.3 HIV Prevalence by Medical Injection Use 113

10.4 HIV Prevalence by STIs or STI Symptoms 116

10.5 HIV Prevalence by Prior HIV Testing Experience 122

11 HIV Prevalence by Women’s Status 125

11.1 Key Findings 125

11.2 Introduction 125

11.3 HIV Prevalence by Women’s Age Gap with Partner 125

11.4 HIV Prevalence by Women’s Ability to Negotiate Safer Sex 127

11.5 HIV Prevalence by Women’s Participation in Household Decision-Making 131

12 Summary and Conclusions 135

12.1 Summary 135

12.2 Key Findings 135

12.3 Program and Policy Implications 136

References 137
v 

Tables 

Table 3.1 Response rates for household selection, individual interview and for HIV testing by 

sex, DHS and AIS surveys with HIV testing 14

Table 4.1 HIV prevalence among women and men age 15-49, by agegroup 18

Table 4.2 HIV prevalence among women and men age 15-49, by urban/rural residence 32

Table 4.3 HIV prevalence among women and men aged 15-49, by number of years live in 

current residence 35

Table 4.4 HIV prevalence among men age 15-49, by number of times away from home in last 

12 months and away from home more than one month 37

Table 4.5 HIV prevalence among women age 15-49, by pregnancy status, birth in last 3 years 

with antenatal care visit 39

Table 5.1 HIV prevalence among women and men age 15-49, by marital status 42

Table 5.2 HIV prevalence among women and men age 15-49, by education level 44

Table 5.3 HIV prevalence among women and men age 15-49, by occupation 47

Table 5.4 HIV prevalence among women and men age 15-49, by household wealth status 49

Table 5.5 HIV prevalence among women and men age 15-49, by frequency of listening to 

radio 53

Table 5.6 HIV prevalence among women and men age 15-49, by frequency watching 

television 56

Table 5.7 HIV prevalence among women and men age 15-49, by frequency of reading 

newspaper and magazine 58

Table 5.8 HIV prevalence among women and men age 15-49, by number of media sources 

exposed to at least once per week 61

Table 6.1 HIV prevalence among women and men age 15-49, by knowledge of AIDS 

prevention methods 64

Table 6.2 HIV prevalence among women and men age 15-49, by knowledge of mother-tochild-transmission (MTCT) of HIV 67

Table 6.3 HIV prevalence among women and men age 15-49, by misconceptions about HIV 

infection 69

Table 6.4 HIV prevalence among women and men age 15-49, by attitude toward people 

living with HIV (PLHIV) 72

Table 7.1 HIV prevalence among women and men age 15-49, by age at fist sexual intercourse 78

Table 7.2 HIV prevalence among women and men age 15-49, by number of sexual partners 

in past 12 months (only among those who ever had sex) and by number of lifetime 

sexual partners 81

Table 7.3 HIV prevalence among women and men age 15-49, by higher risk sexual behavior 

among those who had sex in past 12 months before the survey 86

Table 7.4 HIV prevalence among women and men age 15-49, by alcohol consumption in last 

3 months, and by alcohol consumption before last sex (among those who had sex in 

last 12months) 90

Table 8.1 HIV prevalence among women and men age 15-24, by urban/rural residence 93

Table 8.2 HIV prevalence among women and men age 15-24, by age at first sexual 

intercourse 96

Table 8.3 HIV prevalence among women and men age 15-24, by primary abstinence (never 

had sex) and secondary abstinence (ever had sex, but not in last 12m) 100

Table 9.1 HIV prevalence among women and men age 15-49, by ever use of condom (among 

those who ever had sex) and consistent use of condom in last 12 months (among 

those who had sex in last 12 months) 104
vi 

Table 9.2 HIV prevalence among women and men age 15-49, by condom use at last sex 

(among those who had sex in last 12 months) and condom use at last higher risk 

sex (among those who had higher risk sex) 107

Table 9.3 HIV prevalence among men age 15-49, by male circumcision 109

Table 10.1 HIV prevalence by receipt of medical injections 114

Table 10.2 HIV prevalence among women and men age 15-49 who ever had sex, by having 

STI or STI symptom in the last 12 months 117

Table 10.3 HIV prevalence among women and men age 15-49, by previous HIV testing 

experience, and whether received result of most recent HIV test (among those 

previously tested) 123

Table 11.1 Observed HIV prevalence among women age 15-19, by age-gap with the most 

recent sex partner in last 12 months (among those who had sex in past 12 months) 126

Table 11.2 HIV prevalence among women and men age 15-49 who agreed that wife is justified 

for asking husband to use condom and refusing sex with husband if he has an STI 128

Table 11.3 HIV prevalence among women age 15-49, by their participation in household 

decision-making 128
vii 

Figures 

Figure 3.1 Response rates for HIV testing, men and women in 22 DHS and AIS surveys 16

Figure 4.1a HIV prevalence among tested men and women 15-49 by age group, India 2005/06 21

Figure 4.1b HIV prevalence among tested men and women 15-49 by age group, Hai Phong 

Province, Vietnam 2005 21

Figure 4.1c HIV prevalence among tested men and women 15-49 by age group, Cambodia 2005 22

Figure 4.1d HIV prevalence among tested men and women 15-49 by age group, Senegal 2005 22

Figure 4.1e HIV prevalence among tested men and women 15-49 by age group, Niger 2005 23

Figure 4.1f HIV prevalence among tested men and women 15-49 by age group, Dominican 

Republic 2002 23

Figure 4.1g HIV prevalence among tested men and women 15-49 by age group, Ethiopia 2005 24

Figure 4.1h HIV prevalence among tested men and women 15-49 by age group, Guinea 2005 24

Figure 4.1i HIV prevalence among tested men and women 15-49 by age group, Mali 2001 25

Figure 4.1j HIV prevalence among tested men and women 15-49 by age group, Burkina Faso 

2003 25

Figure 4.1k HIV prevalence among tested men and women 15-49 by age group, Ghana 2003 26

Figure 4.1l HIV prevalence among tested men and women 15-49 by age group, Haiti 2005 26

Figure 4.1m HIV prevalence among tested men and women 15-49 by age group, Rwanda 2005 27

Figure 4.1n HIV prevalence among tested men and women 15-49 by age group, Côte d'Ivoire 

2005 27

Figure 4.1o HIV prevalence among tested men and women 15-49 by age group, Cameroon 

2004 28

Figure 4.1p HIV prevalence among tested men and women 15-49 by age group, Uganda 

2004/05 28

Figure 4.1q HIV prevalence among tested men and women 15-49 by age group, Kenya 2003 29

Figure 4.1r HIV prevalence among tested men and women 15-49 by age group, Tanzania 

2003/04 29

Figure 4.1s HIV prevalence among tested men and women 15-49 by age group, Malawi 2004 30

Figure 4.1t HIV prevalence among tested men and women 15-49 by age group, Zambia 

2001/02 30

Figure 4.1u HIV prevalence among tested men and women 15-49 by age group, Zimbabwe 

2005 31

Figure 4.1v HIV prevalence among tested men and women 15-49 by age group, Lesotho 

2004/05 31

Figure 5.1 HIV prevalence among tested men 15-49 by wealth status 52

Figure 5.2 HIV prevalence among tested women 15-49 by wealth status 52

Figure 7.1 HIV prevalence among tested men 15-49 by number of lifetime sexual partners 84

Figure 7.2 HIV prevalence among tested women 15-49 by number of lifetime sexual partners 84

Figure 7.3 HIV prevalence among tested men 15-49 by sex with a non-spousal partner in last 

12 months 88

Figure 7.4 HIV prevalence among tested women 15-49 by sex with a non-spousal partner in 

last 12 months 88

Figure 8.1 HIV prevalence among tested men 20-24 who had first sexual intercourse before 

age 18 99

Figure 8.2 HIV prevalence among tested women 20-24 who had first sexual intercourse before 

age 18 99

Figure 9.1 HIV prevalence among tested men 15-49 by male circumcision 111

Figure 10.1 HIV prevalence among tested men 15-49 by receipt of 3+ medical injections 115

Figure 10.2 HIV prevalence among tested women 15-49 by receipt of 3+ medical injections 115
viii 

Figure 10.3 HIV prevalence among tested men 15-49 who ever had sex by reported STI or STI 

symptoms in last 12 months 121

Figure 10.4 HIV prevalence among tested women 15-49 who ever had sex by reported STI or 

STI symptoms in last 12 months 121

Figure 11.1 HIV prevalence among tested women 15-19 who had sex in past 12 months by agegap with the most recent sex partner in last 12 months 127

Figure 11.2 HIV prevalence among tested women age 15-49 by their participation in household 

decision-making 134
ix

Preface 

One of the most significant contributions of the MEASURE DHS program is the creation of an 

internationally comparable body of data on the demographic and health characteristics of populations in 

developing countries. The DHS Comparative Reports series examines these data across countries in a 

comparative framework. The DHS Analytical Studies series focuses on specific topics. The principal 

objectives of both series are to provide information for policy formulation at the international level and to 

examine individual country results in an international context. Whereas Comparative Reports are 

primarily descriptive, Analytical Studies have a more analytical approach. 

The Comparative Reports series covers a variable number of countries, depending on the availability of data sets. Where possible, data from previous DHS surveys are used to evaluate trends over 

time. Each report provides detailed tables and graphs organized by region. Survey-related issues such as 

questionnaire comparability, survey procedures, data quality, and methodological approaches are addressed as needed. 

The topics covered in Comparative Reports are selected by MEASURE DHS staff in conjunction 

with the U.S. Agency for International Development. Some reports are updates of previously published 

reports. 

It is anticipated that the availability of comparable information for a large number of developing 

countries will enhance the understanding of important issues in the fields of international population and 

health by analysts and policymakers. 

Ann Way 

Project Director 

xi

Executive Summary 

This report summarizes HIV prevalence and the associations between HIV serostatus and key 

characteristics and behaviors of adult women and men in 22 developing countries, primarily in subSaharan Africa. Data come from Demographic and Health Surveys (DHS) and AIDS Indicator Surveys 

(AIS) conducted between 2001 and 2006. In most of these surveys, nationally representative samples of 

women age 15-49 and men age 15-59 were tested for HIV. Inclusion of HIV testing in DHS and AIS 

surveys provides direct data on the distribution of HIV infection among the general population and 

subgroups and allows individual HIV test results to be linked with information on characteristics of the 

respondents. 

Policymakers and program managers need accurate estimates of the size and scope of a country’s 

HIV epidemic and the distribution of HIV infection within a population in order to identify geographical 

areas with elevated HIV infection rates and higher-risk and vulnerable populations. Moreover, 

understanding the biological, socioeconomic, and behavioral factors associated with HIV infection offers 

insight into appropriate interventions for prevention, care and support, and treatment programs. 

Key findings of the study include: 

Demographic characteristics. HIV prevalence varies considerably among the 22 countries 

studied, from 0.3 percent in India to 23.5 percent in Lesotho. In most countries HIV prevalence is higher 

among women than men, and for all but two countries prevalence is higher in urban than rural areas. 

Among men who were away from their home at least once in the year before the survey, the number of 

times the person left home but not the amount of time spent away from home during an absence is related 

to higher HIV prevalence. 

Socioeconomic characteristics. HIV prevalence is related to marital status, with widowed and 

divorced people at greatest risk, and never-married people at lowest risk. In most countries, HIV 

prevalence is higher among people with more education and those with more household wealth. 

Knowledge and attitudes. Differences in HIV prevalence are small between individuals who 

know or do not know the three main HIV prevention strategies—abstinence, monogamy, and condom 

use. In most countries, knowledge of all three methods of HIV prevention is associated with higher HIV 

prevalence. There is little difference in HIV prevalence among people who have correct information 

about the modes of HIV transmission, suggesting that knowledge about HIV is probably not strongly 

associated with HIV prevalence. 

Risk behaviors. HIV prevalence increases with increasing number of lifetime sex partners, while 

earlier age of sexual debut is also associated with higher HIV prevalence, especially among women. In 

most countries, HIV prevalence is higher among respondents who report having sex with a non-marital, 

non-cohabitating partner in the 12 months preceding the survey. Alcohol use by either or both partners at 

last sex is also associated with higher HIV prevalence, especially among women. 

Youth. In most countries studied for this report, youth who were under age 18 at their first sexual 

encounter have higher rates of HIV prevalence than other youth age 15-24. This finding is true for both 

men and women. With the exception of Lesotho, HIV prevalence is low among youth who have never 

had sex (primary abstinence) and also among youth who did not have sex in the 12 months preceding the 

survey (secondary abstinence). 

Protective behaviors. Ever-use of condoms is associated with higher HIV prevalence, both 

overall and for men and women separately. Generally, the difference in HIV prevalence is small between 
xii 

respondents who do, or do not, consistently use condoms. In the majority of countries, not using condoms 

at last sex, and/or at last sex with a higher-risk partner, is associated with higher HIV prevalence, 

especially among women. There appears no clear pattern of association between male circumcision and 

HIV prevalence—in 8 of 18 countries with data, HIV prevalence is lower among circumcised men, while 

in the remaining 10 countries it is higher. 

Unsafe medical injections, other STIs, and prior HIV testing experience. In most countries 

studied, receiving multiple medical injections in the recent past is positively associated with being HIVinfected, for both women and men. Unsafe medical injections—those given with re-used, non-sterilized 

equipment—have been associated with higher HIV prevalence. Also, STIs, including herpes, syphilis, and 

gonorrhea, are known to increase the risk of HIV transmission. In most countries studied, having an STI 

in the past 12 months is associated with higher HIV prevalence, both overall and for men and women 

separately. In all countries studied, HIV prevalence is higher among respondents who report having been 

previously tested for HIV, both overall and for men and women separately. 

Women’s status. HIV prevalence is higher among both male and female respondents who agree 

that women can negotiate safer sex with their husband if he is infected with an STI, and prevalence also is 

generally higher among women who report participation in household decision making. These positive 

associations between measures of women’s empowerment and HIV prevalence may be due to more 

empowered women having higher education, greater work force participation, and higher wealth status, 

which have been associated with higher prevalence of HIV. 

Several limitations should be kept in mind in interpreting the study’s findings: Because the 

analysis is based on self-reported behaviors, findings may be biased to the extent that respondents may 

misreport these behaviors. Also, because the analysis is based on cross-sectional data, the reported 

associations may not imply causality. Some observed relationships may be due to reverse causality. Also, 

HIV infection may have preceded the behaviors recorded for the 12 months preceding the survey, which 

may bias some of the observed associations. 

Despite the limitations, studies that link HIV prevalence to characteristics of the population can 

provide valuable data for HIV programs and policies. They are more representative of population groups 

and geographic areas than antenatal clinic surveillance and include samples from urban and rural areas, 

men and women, and different age groups. They can help policymakers and program planners identify 

vulnerable areas and groups, understand risk factors, and plan responses to the HIV/AIDS epidemic. 

 
1 

1 Introduction 

1.1 Purpose 

Policymakers and program managers need accurate estimates of the size and scope of a country’s 

HIV epidemic and the distribution of HIV infection within a population in order to identify geographical 

areas with elevated HIV infection rates and higher-risk and vulnerable populations. Moreover, 

understanding the biological, socioeconomic, and behavioral factors associated with HIV infection offers 

insight into appropriate interventions for prevention, care and support, and treatment programs. 

In countries with generalized epidemics, national estimates of HIV prevalence and trends in the 

adult population generally are derived indirectly from HIV surveillance among pregnant women attending 

selected antenatal clinics (UNAIDS/WHO, 2005). While the rate of HIV infection in pregnant women can 

sometimes be a reasonable proxy measure for HIV prevalence in the combined male and female adult 

population, this method has some limitations. Antenatal surveillance data do not include direct 

information about HIV prevalence in men, and assumptions about the male infection rate made from the 

data on pregnant women may or may not be accurate. Also, the system does not capture information on 

HIV prevalence among non-pregnant women, or among pregnant women who do not seek antenatal care. 

Clinic-based surveillance programs also tend to under-represent remote rural populations, who may have 

a different HIV risk profile than urban women. Moreover, pregnant women may be at more risk for HIV 

infection than women who are less sexually active or women who use condoms. Prevalence data from 

antenatal clinics are also somewhat limited because they do not include information on socioeconomic 

characteristics and risk behaviors, a drawback that limits the data’s ability to assess factors important for 

targeting HIV prevention activities. 

Inclusion of HIV testing as part of national Demographic and Health Surveys (DHS) and AIDS 

Indicator Surveys (AIS) provides direct data on the distribution of HIV infection among the general adult 

population, remote rural populations, men, and younger non-pregnant women. In addition, the survey 

protocol allows individual HIV test results to be linked with information collected in the survey on 

demographic, socioeconomic, and behavioral characteristics of the respondents (Mishra et al., 2006). 

This report summarizes HIV seroprevalence levels and the associations between HIV serostatus 

and several key characteristics and behaviors of adult women and men in 22 developing countries, using 

data from nationally representative DHS and AIS Surveys conducted between 2001 and 2006. The 

countries included are, by region: 

Sub-Saharan Africa: Burkina Faso, Cameroon, Côte d’Ivoire, Ethiopia, Ghana, Guinea, 

Kenya, Lesotho, Malawi, Mali, Niger, Rwanda, Senegal, Tanzania, Uganda, Zambia, 

Zimbabwe; 

Latin America and Caribbean: the Dominican Republic, Haiti; 

Asia: Cambodia, India, Vietnam (the Hai Phong province). 

1.2 Brief History and Epidemiology of the HIV Epidemic 

In 1981 a new disease known as the Acquired Immune Deficiency Syndrome (AIDS) was 

identified in the United States among men who have sex with men (MSM). Two years later, in 1983, the 

etiological agent for this new disease was identified as the human immunodeficiency virus (HIV) 

(UNAIDS/WHO, 2003). Over the course of the next 25 years, the number of people infected with HIV 

exploded globally. By the end of 2007, an estimated 33 million people worldwide were living with HIV. 
2 

During that year an estimated 2.7 million people were newly infected with HIV and 2 million people lost 

their lives to AIDS (UNAIDS, 2008). The sheer number of individuals infected with HIV and affected by 

the epidemic marks it as a true global pandemic. 

Within countries, HIV epidemics can be divided into three main types based on HIV prevalence 

both in at-risk sub-populations and in the general population (UNAIDS/WHO, 2003). These types are 

generalized, concentrated, and low epidemics. In countries with a low HIV epidemic, HIV prevalence is 

mainly confined to individuals with higher-risk behavior, such as commercial sex workers, injection drug 

users (IDUs), and men who have sex with men, although the risk networks in these countries are fairly 

diffuse (e.g., low levels of needle/syringe sharing among IDUs). In these countries HIV prevalence does 

not consistently exceed 5 percent in any defined sub-population. 

In concentrated epidemics, the virus is rapidly spreading among defined sub-populations due to 

an active network of risk (e.g., high levels of needle/syringe sharing among IDUs). However, the 

prevalence in the general adult population remains low (less than 1 percent). 

In generalized HIV epidemics, the HIV virus has spread to the general population and is passed 

mainly through sexual networks, independent of higher-risk behavior in the sub-populations. Countries 

are considered to have a generalized HIV epidemic when HIV prevalence in the general population is 

over 1 percent. 

Sub-Saharan Africa remains the region hardest hit by the HIV epidemic. Globally, two-thirds of 

people infected with HIV live in sub-Saharan Africa, and this region accounts for three-quarters of all 

AIDS deaths (UNAIDS, 2008). Currently, 22 million people in this region are estimated to be HIVinfected, the majority of whom (60 percent) are women. Although epidemics in most countries in this 

region are generalized, national adult HIV prevalence varies considerably, from just 1 percent in Senegal 

to 26 percent in Swaziland (UNAIDS, 2008).

Southern Africa has been especially hard hit: seven southern African countries (Botswana, 

Lesotho, Namibia, South Africa, Swaziland, Zambia, and Zimbabwe) have national adult HIV rates at or 

above 15 percent. Most of the HIV epidemics in southern African countries appear to be stabilizing and in 

some cases HIV rates are decreasing. Data from antenatal clinics in Zimbabwe, Botswana, Malawi, 

Zambia, and South Africa suggest a decline in adult HIV prevalence. Unfortunately, in Lesotho and parts 

of Mozambique antenatal data suggest an increase in HIV prevalence. In East Africa, most countries have 

stable or decreasing HIV prevalence. Kenya and Tanzania have witnessed a decrease in HIV prevalence, 

while in Burundi, Eritrea, Ethiopia, Rwanda, and Uganda the HIV epidemics appear to have stabilized. 

West Africa consistently has had lower national HIV prevalence compared with other regions of Africa, 

with most rates between 1 percent and 5 percent. National HIV prevalence has declined in Cote d’Ivoire 

and remained stable in Benin, Burkina Faso, Mali, and Nigeria. 

In Asia, national HIV prevalence varies considerably by country. An estimated 5.0 million people 

in the region are estimated to be HIV-infected. HIV prevalence in Cambodia, Myanmar, and Thailand has 

recently declined. However, Indonesia, Pakistan, Bangladesh, China, and Vietnam have all seen increases 

in HIV prevalence. The epidemic in Vietnam is particularly concerning as the number of people living 

with HIV in that country more than doubled between 2000 and 2005. Despite the recent reduction in the 

estimate of India’s national HIV prevalence, a large number of people remain affected by the HIV 

epidemic due to the sheer population size of the country. An estimated 1.8 million to 3.2 million people in 

the country are currently living with HIV, making India one of the hardest hit countries in terms of 

numbers of people affected, despite its relatively low national HIV prevalence (0.30 percent).
3 

In Latin America and the Caribbean, an estimated 1.9 million people are living with HIV. The 

HIV epidemic is largest in Brazil, where around 640,000 people over the age of 15 are living with the 

virus. However, aggressive prevention and treatment programs by the Brazilian government have reduced 

HIV prevalence among higher-risk groups, such as injection drug users (IDUs), and simultaneously 

reduced the number of deaths due to AIDS. The Bahamas has the largest HIV epidemic in the Caribbean, 

with adult HIV prevalence at 2.2 percent. Unprotected sex among men who have sex with men and 

among commercial sex workers and their clients are the two main modes of HIV transmission in Latin 

America and the Caribbean.

1.3 Impact of HIV/AIDS 

The HIV epidemic has had a devastating impact according to a number of key demographic 

indicators. The International Monetary Fund (2004) estimates that in many of the hardest hit countries life 

expectancy at age 15 has dropped between six and eight years due to HIV/AIDS. The epidemic has also 

reversed gains made in child survival; infant and child mortality rates are an estimated one-third to 

two-thirds higher due to HIV/AIDS (DeCock et al., 2000). A study in Tanzania found that 8.3 percent of 

infant mortality could be directly attributed to HIV/AIDS, either due to vertical transmission of HIV from 

mother to child or through increased risk of child mortality following a maternal death (Ng’weshemi et 

al., 2003). 

The number of children orphaned by the HIV/AIDS epidemic continues to expand. By the year 

2010, it is estimated that as many as 40 million children in developing countries will have lost one or both 

parents to HIV/AIDS (Lyons, 2000). AIDS orphans are likely to have a higher risk of HIV infection, 

further perpetuating the HIV epidemic (UNDP, 2001). 

HIV/AIDS importantly affects children’s life and the families of children’s caregivers. AIDS 

orphans are less likely to attend school (Case et al., 2004; Mishra and Bignami-Van Assche, 2008). 

Children of HIV-infected parents are less likely to receive treatment or care when sick (Mishra et al., 

2007a), as well as suffer from the trauma of sickness and eventual death of a parent and associated 

hardships. The burden of caring for a sick parent often falls on children; many are forced to drop out of 

school and take on adult roles (Case et al., 2004; UNICEF and UNAIDS, 2006). Parental HIV-related 

illness and death often lead to decreased household resources due to treatment costs and job loss (Hunter, 

2003), which may affect health care and the nutritional status of children. Death of even one parent could 

result in changes in living arrangements, displacement, and lack of availability of resources for schooling, 

health care, and food for children. For youth, parental sickness or loss may also translate in risky 

behaviors, sexual exploitation or abuse, as well as being associated with the burden to seek employment 

to support an ailing parent or younger siblings. Orphaned youth are more likely to work and less likely to 

practice sexual abstinence than non-orphaned youth (Mishra and Bignami-Van Assche, 2008). 

The HIV epidemic has placed severe pressure on the health sectors of many countries. The 

estimated costs of treating an HIV-infected patient, not including the costs of antiretroviral treatment, is 

US$30 per capita (UNDP, 2001), three times the US$10 per capita that many countries in sub-Saharan 

Africa spent on total health care in 2005 (WHO, 2006). The huge amount of money spent on HIV/AIDS 

programs leaves few resources left for other crucial public health programs, such as malaria control, 

childhood vaccination, and safe motherhood initiatives (Hunter, 2003). 

In addition, the HIV epidemic interacts with other endemic diseases, magnifying the burden on 

health systems. For example, in most countries with generalized HIV epidemics the incidence of 

tuberculosis has increased rapidly in recent years (WHO, 2007). Thirty percent of all incident tuberculosis 

cases are co-infected with HIV, with the result that tuberculosis becomes much harder to control (WHO, 

2007). In many parts of sub-Saharan Africa malaria is highly endemic (WHO/UNICEF, 2005), and there 
4 

is evidence of an interaction between malaria and HIV. Malaria has been associated with higher HIV viral 

loads and lower CD4 counts (Mermin et al., 2006). Co-infection with HIV and malaria during pregnancy 

has been linked to increased rates of anemia and to an increased risk of adverse birth outcomes (ter Kuile 

et al., 2004). HIV-infected women with placental malaria have increased viral loads, research shows, but 

the effect on mother-to-child transmission of HIV remains unclear (ter Kuile et al., 2004). In many 

countries sexually transmitted infections (STIs), such as syphilis and herpes, are prevalent among both 

men and women (Schulz, 2004). The ulcerative nature of these infections increases the risk of HIV 

transmission, both horizontally and vertically (Cowan et al., 2008; Royce et al., 1997; Gray et al., 2001). 

The HIV epidemic is not only a health problem but also affects every other sector. The HIV 

epidemic has had a huge impact on countries’ ability to reach their development potential. HIV is most 

prevalent among individuals age 15-49, the prime working years (UNAIDS, 2008). Normally, in 

countries without a generalized HIV epidemic adult mortality is low compared with older or younger age 

groups. However, in countries with a high HIV prevalence, as in eastern and southern Africa, the percent 

of adult deaths as a share of all deaths is quite high. The IMF (2004) estimates that, without HIV, only 20 

percent of all deaths in Uganda would be among adults age 15-49. In the face of the HIV epidemic, 

however, 38 percent of all deaths in Uganda are among this age group. 

HIV’s heavy toll on the adult population has severe implications for a country’s labor sector and 

national productivity. Shortages of labor mean a decrease in agricultural production, which threatens the 

country’s food security (UNDP, 2001). Furthermore, reduced productivity from frequent employee 

absenteeism and rapid labor turnover due to HIV-related illnesses, together with high health care costs, 

reduces private-sector profitability and public-sector effectiveness and makes it harder to attract 

investment (UNDP, 2001). 

1.4 Organization of the Report 

Chapter 2 provides a summary of the procedures used to collect the HIV seroprevalence data in 

the DHS and AIS surveys. Chapter 3 describes the data, samples sizes, and response rates, defines the key 

indicators, and discusses limitations of the data. Chapter 4 describes HIV prevalence by basic 

demographic characteristics, while Chapter 5 describes HIV prevalence by broader socioeconomic 

factors. Chapter 6 presents HIV prevalence by selected indicators of knowledge of HIV prevention 

methods, knowledge of mother to child transmission, misconceptions about HIV transmission, and 

attitudes toward people living with HIV. Chapter 7 shows associations between selected risk behaviors 

and HIV serostatus. Chapter 8 focuses on youth sexual behaviors. Chapter 9 shows associations between 

key protective behaviors and HIV serostatus. Chapter 10 presents associations between other modes of 

transmission and HIV serostatus. Chapter 11 presents HIV prevalence by indicators of women’s status. 

Finally, Chapter 12 summarizes the key results and implications of the findings. 
5 

2 HIV Testing in Surveys

2.1 Objectives of the DHS Surveys 

The DHS program has conducted more than 200 national household surveys in more than 80 

developing countries worldwide since 1984. These representative, population-based surveys are primarily 

health interviews with questions on maternal and child health, family planning, nutrition, knowledge and 

behaviors related to HIV/AIDS, and key demographic and socioeconomic factors. Increasingly these 

surveys include collection of biological and clinical data such as anthropometric measurements and 

testing for anemia and HIV. Starting in 2001, more than 2 dozen DHS surveys have included HIV testing. 

The goal of HIV-related surveys is to provide program managers and policymakers involved in 

HIV/AIDS programs with strategic information at the national and sub-national levels needed to monitor 

and evaluate existing programs and to effectively design new strategies for combating the HIV epidemic 

within their country. 

2.2 Sample Size and Design 

The DHS selects random sample clusters from a national sampling frame, usually from the 

national population census. Within the selected clusters, a full listing of all households is made before the 

survey and a systematic random sample of households is taken. Any household members who are absent 

on the day of the interview are re-contacted. An individual is only considered absent from the household 

after three callback attempts. 

2.3 Questionnaires 

Three questionnaires are used in the surveys, a household questionnaire, an individual woman’s 

questionnaire, and an individual man’s questionnaire. The contents of these questionnaires are based on 

the model DHS questionnaires developed by the MEASURE DHS program. The household questionnaire 

lists all the usual members and visitors in the selected households. Its main purpose is to identify women 

and men who are eligible for the individual interviews. 

The household questionnaire collects some basic information on the characteristics of each person 

listed, including age, sex, education, marital status, urban/rural residence, relationship to the head of the 

household, and orphanhood among children under age 18 years. The household questionnaire also 

collects information on characteristics of the household’s dwelling unit, such as the source of water, type 

of toilet facilities, materials used for the floor, ownership of various durable goods, and ownership of 

mosquito nets. It also collects information on whether the household received specific types of care and 

support in the preceding 12 months for any chronically ill adults, any household members who died, and 

any orphans and vulnerable children. The household questionnaire also records respondents’ consent to 

volunteer to give blood samples. The blood collection and testing procedures are described in the next 

section. 

The individual questionnaires collect information from all eligible women and men in the sample 

households. In most countries women age 15-49 and men age 15-59 are eligible for participation. The 

individual questionnaires collect information on demographic and socioeconomic characteristics, 

marriage, sexual activity, knowledge and attitudes toward HIV/AIDS, and knowledge and prevalence of 

other STIs. 
6 

2.4 Biomarkers 

In most surveys discussed in this report, HIV testing was done using dried blood samples (DBS) 

of capillary blood from a finger prick, collected on special filter paper. The only exceptions are the 2002 

Dominican Republic DHS, in which oral mucosal transudate was used, and the 2001-2002 Zambia DHS 

and the 2004-2005 Uganda AIS, in which venous blood was used for testing. The DBS method is 

preferred because it is considered less invasive than a venous blood sample and is easier to store and 

transport. 

For each participant, three to five preprinted circles on the blood-spot collection card are filled 

with blood drops. Samples collected on filter paper are allowed to dry overnight in a drying box with 

desiccant and a humidity indicator card, after which the field worker packs each sample in a low gaspermeable zipper-locked plastic bag with desiccant and a humidity indicator card. All individually-packed 

samples from a cluster are then packed in a larger zipper-locked plastic bag with desiccants and the 

necessary tracking information. Appropriately packed DBS samples are stored in an insulated box and 

transported to a central laboratory for HIV testing (Macro International, 2007a, 2007b). 

A central laboratory is identified in each country to process the DBS samples for HIV testing. 

The testing at this laboratory follows a standard algorithm designed to maximize the sensitivity and 

specificity of HIV test results. The algorithm uses two different HIV antibody enzyme-linked 

immunosorbent assays (ELISA), based on different antigens. All discordant samples that are positive in 

the first test and negative in the second test are retested using both ELISAs. Discordant samples from this 

second round of testing are classified as ―indeterminate‖ and these samples are subjected to a Western 

blot confirmatory test, the result of which is considered final. 

During the sample processing, the laboratory adheres to an approved quality assurance and 

quality control plan with both internal and external components. For internal quality assurance these steps 

are repeated for 5 to 10 percent of randomly selected samples that tested negative on the first test (Macro 

International, 2007a, 2007b). For external quality assessment a subset of DBS samples (usually 5 percent) 

is submitted to an outside reference laboratory for retesting. 

2.5 Voucher System for Voluntary Counseling and Testing 

Participants are not given the results of their HIV test as part of the DHS. However, all survey 

participants are given country-specific information brochures on HIV/AIDS in their local language. Each 

respondent eligible for HIV testing, regardless of whether the respondent agrees to be tested for HIV as 

part of the survey, is also given information on the nearest facility providing voluntary counseling and 

testing and is encouraged to use these services. If local HIV testing services are not free, eligible 

participants are given a voucher that entitles them to go to the closest HIV testing facility for free HIV 

counseling and testing. In countries with limited HIV testing facilities, efforts are made to increase access 

to HIV testing services. 

2.6 Confidentiality Procedures 

To protect the confidentiality of the participants, the data are made anonymous by scrambling the 

cluster and household numbers associated with each participant in such a way as to make it impossible to 

associate an individual data record with a particular place and household. HIV test results are linked to 

data from the questionnaires using barcodes only after the identity codes have been scrambled and the 

files containing the original identity codes have been destroyed. Because the test results cannot be linked 

to a respondent’s identity, there is no possibility of inadvertent disclosure. Any paper records that might 

compromise the confidentiality of the respondents, such as the pages of the questionnaires containing 
7 

barcodes, are also destroyed. All HIV testing procedures are reviewed by the ethical review boards of 

Macro International Inc. (a United States–based company that provides technical assistance to DHS 

surveys around the world), the host country, and any other implementing partners. 

2.7 Sample Size Needs 

To obtain reliable national HIV estimates disaggregated by sex and urban/rural residence, a 

representative sample of at least 3,000 households is required. If, on average, there is one eligible man 

and one eligible woman in each sample household, and if 10 percent of the eligible men and women do 

not participate in the survey, a final sample of roughly 5,400 tested adults is needed. For a population 

with an estimated HIV prevalence of 5 percent, such a sample would provide a 95 percent confidence 

interval of from 4.3 percent to 5.7 percent at the national level. Larger sample sizes are required if HIV 

prevalence is lower or if further disaggregation of HIV estimates is desired. 

9 

3 Data and Methods 

3.1 Data 

This report analyzes data from 22 DHS and AIS surveys that included HIV testing and were 

conducted between 2001 and 2006. In the first three DHS surveys—Mali, the Dominican Republic, and 

Zambia—only age, sex, urban/rural residence, and geographic region of residence for the tested 

individuals were recorded on the blood samples. In these surveys, HIV test results cannot be linked to the 

information in the household and individual questionnaires. In the 19 surveys with linked HIV serostatus 

data—of which 15 are DHS surveys (Burkina Faso, Cambodia, Cameroon, Ethiopia, Ghana, Guinea, 

Haiti, India, Kenya, Lesotho, Malawi, Niger, Rwanda, Senegal, and Zimbabwe) and 4 are AIS surveys 

(Côte d’Ivoire, Tanzania, Uganda, and Vietnam)—HIV test results can be linked anonymously to all the 

respondents’ information collected in the questionnaires, after scrambling the household and cluster 

identification codes. In Vietnam HIV testing was limited to the Hai Phong province. 

In most surveys, nationally representative samples of women age 15-49 and men age 15-59 were 

tested for HIV. The exceptions are Uganda, where women age 15-59 were tested; Tanzania, Côte

d’Ivoire, and Cambodia, where men age 15-49 were tested; and India, Kenya, Malawi, and Zimbabwe, 

where men age 15-54 were tested. The 2004-2005 Uganda survey also tested HIV status among children 

under age 5. In the 22 countries included in this analysis, the numbers eligible for HIV testing ranged 

from 820 men (age 15-49) and 971 women (age 15-49) in the Hai Phong province of Vietnam to 64,175 

men (age 15-54) and 62,182 women (age 15-49) in India. The analysis in this report is limited to women 

and men age 15-49. 

Details about the sampling design and data collection procedures used in each survey are 

available in the individual country reports (Cayemittes et al., 2007; CBS [Kenya], MOH [Kenya], and 

ORC Macro, 2004; CESDEM [Dominican Republic] and ORC Macro, 2003; CPS/MS [Mali], DNSI 

[Mali], and ORC Macro, 2002; CSA [Ethiopia] and ORC Macro, 2006; CSO [Zambia], CBH [Zambia], 

and ORC Macro, 2003; CSO [Zimbabwe] and Macro International 2007; DNS [Guinée] and ORC Macro, 

2006; GSO [Vietnam], NIHE [Vietnam], and ORC Macro, 2006; GSS [Ghana], NMIMR [Ghana], and 

ORC Macro, 2004; IIPS [India] and Macro International, 2007; INS [Cameroun] and ORC Macro, 2004; 

INS [Côte d’Ivoire] and Macro International 2006; INS [Niger] and Macro International, 2007; INSD 

[Burkina Faso], and ORC Macro, 2004; INSR [Rwanda] and ORC Macro, 2006MOHSW [Lesotho], BOS 

[Lesotho] and ORC Macro, 2005; Ndiaye et al., 2006; NIPH [Cambodia], NIS [Cambodia], and ORC 

Macro, 2006; NSO [Malawi] and ORC Macro, 2005; TACAIDS [Tanzania], NBS [Tanzania], and ORC 

Macro, 2005; UBOS [Uganda] and Macro International, 2007) 

3.2 Indicators Included in the Analysis 

Demographic Characteristics 

1. Age in years (15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49) 

2. Sex (male, female) 

3. Residence (urban, rural) 

4. Number of years living in the current place of residence (<3 years, 3–9 years, 10+ years) 

5. Away from home for one month or longer (no, yes) (men only) 
10 

6. Number of times slept away from home in the last 12 months (0, 1–2, 3–4, 5+ times) (men 

only) 

7. Pregnancy status of woman at the time of the survey (not pregnant, pregnant) 

8. Woman gave birth in the past three years and received antenatal care for the last birth (ANC) 

(no birth, last birth without ANC, last birth with ANC) 

Socioeconomic Characteristics 

1. Marital status (never in union, currently in a monogamous union, currently in a polygynous 

union, widowed, divorced/separated) 

2. Education (no education, primary incomplete, primary complete, secondary or higher) 

3. Occupation (not working, professional/white-collar, agriculture, manual/domestic labor) 

4. Household wealth status, measured using a household assets score divided into quintiles 

(lowest, lower, middle, higher, highest) (Rutstein and Johnson, 2004) 

5. Media exposure: 

6. Frequency of listening to the radio (not at all, less than once per week, at least once per week) 

7. Frequency of watching television (not at all, less than once per week, at least once per week) 

8. Frequency of reading a newspaper or magazine (not at all, less than once per week, at least 

once per week) 

9. Number of media sources exposed to at least once per week (none, any one, any two, all 

three) 

HIV Prevention Knowledge, Misconceptions, and Attitudes toward PLHIV 

Knowledge: 

1. Knowledge of abstinence (A): Knowing that HIV infection can be prevented by abstaining 

from sex (no, yes) 

2. Knowledge of being faithful (B): Knowing that HIV infection can be prevented by limiting 

sex to one faithful partner (no, yes). 

3. Knowledge of condom use (C): Knowing that HIV infection can be prevented by always 

using condom when having sex (no, yes) 

4. Knowledge of ABC prevention methods: Knowing that HIV infection can be prevented by 

abstaining from sex, limiting sex to one faithful partner, AND by always using condom when 

having sex (no, yes) 

5. Knowledge of mother-to-child transmission (MTCT): Knowing that HIV infection can be 

transmitted from mother to child through breastfeeding (no, yes) 
11 

6. Knowledge of preventing MTCT: Knowing that there is a special drug that could prevent 

HIV transmission from mother to child (no, yes) 

Misconceptions: 

7. Knowing that a healthy-looking person can have HIV (no, yes) 

8. Knowing that HIV cannot be transmitted by mosquitoes (yes/can, no/cannot) 

Accepting attitudes toward people living with HIV (PLHIV): 

9. Would not want the HIV-positive status of a family member to remain secret (no, yes) 

10. Willingness to care for a family member who has AIDS (no, yes) 

11. Would allow an HIV-infected female teacher to continue teaching (no, yes) (some countries 

do not specify sex of the teacher) 

12. Would buy vegetables from a vendor that has AIDS (no, yes) 

13. Accepting attitudes score: Having accepting attitude toward PLHIV on the above four items 

(none, any one, any two, any three, all four) 

HIV Risk Factors 

1. Age at first sexual intercourse (never had sex, <15, 15-17, 18-19, 20+) 

2. Number of lifetime sex partners (0, 1, 2, 3+) 

3. Number of sex partners in last 12 months, among those who ever had sex (0, 1, 2+) 

4. Higher-risk sex—had sex with non-marital, non-cohabiting partner—in the last 12 months, 

among those who had sex in the past 12 months (no, yes) 

5. Transactional sex: Paid for sex (for men) or received gift/money/favor in exchange for sex 

(for women) in last 12 months, among those who had sex in the past 12 months (no, yes) 

6. Alcohol use in last 3 months (no, yes) 

7. Alcohol used at last sex in the last 12 months, among those who had sex in the past 12 

months (neither partner used, either partner used, both partners used) 

Sexual Behavior of Youth 

1. Had first sex before age 15, among youth age 15-19 (no, yes) 

2. Had first sex before age 18, among youth age 20-24 (no, yes) 

3. Primary abstinence: Never had sex, among youth age 15-24 (no, yes) 

4. Secondary abstinence: Did not have sex in last 12 months, among youth age 15-24 who ever 

had sex (no, yes) 
12 

HIV Protective Factors 

1. Ever used condom, among those who ever had sex (no, yes) 

2. Used condom at last sex in last 12 months, among those who had sex in the past 12 months 

(no, yes) 

3. Used condom consistently: Used condom at last sex with all sexual partners (up to three) in 

last 12 months, among those who had sex in the past 12 months (no, yes) 

4. Used condom at last higher-risk sex in last 12 months, among those who had higher-risk sex 

in the past 12 months (no, yes) 

5. Male circumcision (no, yes) 

Other Modes of HIV Transmission 

1. Number of medical injections in the recent past (0, 1, 2, 3, 4+). The reference period varies 

across countries (3 months, 6 months, or 12 months) 

STI or STI symptoms: 

2. Suffered from an STI in last 12 months, among those who ever had sex (no, yes) 

3. Had an abnormal discharge in last 12 months, among those who ever had sex (no, yes) 

4. Had a genital sore or ulcer in last 12 months, among those who ever had sex (no, yes) 

5. Any STI or STI symptom: Suffered from an STI, had an abnormal discharge, or had a genital 

sore or ulcer in the last 12 months, among those who ever had sex (no, yes) 

Women’s Status 

1. Age gap with partner: Women who had their most recent sex in last 12 months with a partner 

10 or more years older, among women who had sex in the past 12 months 

Negotiate safer sex: 

2. Agrees that a wife is justified to ask husband to use condom if husband has an STI (no, yes) 

3. Agrees that a wife is justified in refusing to have sex with her husband if she knows that the 

husband has an STI (no, yes) 

4. A wife can negotiate safer sex if the response is yes to any of the above two questions (no, 

yes to any one, yes to both) 

Participation in household decision-making:

5. Participates in decisions about own health care (no yes) 

6. Participates in decisions about large household purchases (no yes) 
13 

7. Participates in decisions about household purchases for daily needs (no yes) 

8. Participates in decisions about visit to family or relatives (no yes) 

9. Number of above decisions that woman participates in (none, any one, any two, any three, all 

four) 

Program Coverage 

1. Previously tested for HIV (not tested, tested) 

2. Received result of last test, among those who were previously tested (no, yes) 

3.3 Data Limitations 

While the surveys provide representative data about HIV prevalence according to a range of 

indicators, several limitations should be noted: First, the estimates of HIV prevalence and other data 

presented in this report are based on a de facto household-based sample of the national population. A de 

facto sample assumes that usual residents (de jure household members) who did not spend the previous 

night in their own household are, on average, interviewed in a household they may be visiting. A de facto 

sample maximizes participation rates and avoids potential double counting of respondents. However, HIV 

seroprevalence estimates based on de facto samples may be biased to the extent that some of the de jure 

household members who slept away may not be visiting another household and to the extent that such 

people have differential HIV prevalence. 

Second, because the DHS and AIS are household surveys, they exclude non-household 

population groups, such as those living on the street or in institutions (e.g., prisons, boarding schools, 

military barracks, refugee camps, and brothels). To the extent that these non-household populations have 

higher HIV prevalence than the household population, the estimates of HIV prevalence in this report are 

likely to be underestimated. Given that the proportion of such non-household populations in the total 

population tends to be small, however, any effect of excluding institutional and homeless populations on 

the national estimates obtained from a household-based sample is likely to be small, except possibly in 

low-prevalence countries (Mishra et al., 2008a). 

Third, the analysis is based on self-reported behaviors, and findings may be biased to the extent 

that men and women may misreport these behaviors. There are known biases in reporting sexual 

behaviors. For example, young men tend to overreport some sexual behaviors (Smith, 1992) and 

underreport others (Smith, 1992; Mensch et al., 2003), while young women tend to underreport sexual 

experiences (Buvé et al., 2001). 

Fourth, the analysis is based on cross-sectional data, so the reported associations may not imply 

causality. Fifth, for many HIV-positive adults their infection may have preceded their behaviors recorded 

for the 12 months preceding the survey, which may have also biased some of the observed associations. 

Fifth, some of the observed relationships may be due to reverse causality, or due to confounding. 

Reverse causality could result if either HIV infection or an associated illness results in changes in certain 

characteristics or behaviors. Confounding could result if an observed association between HIV serostatus 

and a certain factor is due to another factor that is correlated both with HIV serostatus and with the first 

factor. 
14 

Sixth, not all data are available for all 22 countries studied, and in some instances the survey 

questions asked are not consistent across countries. Thus for some variables, comparisons may not be 

easy to make. Finally, some of the estimates presented in this report are based on small numbers of cases 

in survey samples, and should be interpreted with caution. 

3.4 Response Rates 

Table 3.1 shows response rates for the household interview, individual interview, and HIV testing 

for the 22 countries included in this study. Response rates for the individual interview and HIV testing are 

presented separately for eligible men and women respondents. Household response rates were very high, 

ranging from 95 percent in Zimbabwe to nearly 100 percent in the Hai Phong province of Vietnam. 

Individual response rates were also very high, especially for women. For all countries, women’s

participation in the individual interviews was higher than men’s and ranged from 100 percent in the Hai 

Phong province of Vietnam to 90 percent in Côte d’Ivoire. The main reason for non-response among both 

eligible men and women was the unavailability of individuals at home, despite repeated visits to the 

household. The lower response rate for men reflects the more frequent and longer absence of men from 

their households. Nonetheless, the response rate among men was over 80 percent for all countries, from 

100 percent in the Hai Phong province of Vietnam to 81 percent in the Dominican Republic. 

Table 3.1. Response rates for household selection, individual interview and for HIV testing by sex, 

DHS and AIS surveys with HIV testing 

Country Household 

response rate

Individual 

response rate

Number eligible 

sex (age) for HIV testing HIV response rate

Burkina Faso 2003 99.4

Male (15-59) 90.5 3,984 85.8

Female (15-49) 96.7 4,575 92.3

Cambodia 2005 98.0

Male (15-49) 93.1 7,229 90.3

Female (15-49) 97.2 8,638 95.1

Cameroon 2004 97.6

Male (15-59) 93.0 5,676 89.8

Female (15-49) 94.5 5,703 92.1

Côte d'Ivoire 2005 95.5

Male (15-49) 87.5 5,148 78.7

Female (15-49) 89.8 5,772 75.8

Dominican Republic 2002 97.9

Male (15-59) 80.5 14,456 80.9

Female (15-49) 92.8 12,514 89.0

Ethiopia 2005 98.5

Male (15-59) 89.0 6,778 75.4

Female (15-49) 95.6 7,142 83.2

Ghana 2003 98.7

Male (15-59) 93.8 5,345 79.9

Female (15-49) 95.7 5,949 89.3

Guinea 2005 99.2

Male (15-59) 94.2 2,848 87.8

Female (15-49) 97.4 4,189 92.5

(Cont’d)
15 

Table 3.1 – cont’d

Country Household 

response rate

Individual 

response rate

Number eligible 

sex (age) for HIV testing HIV response rate

Haiti 2005 99.6

Male (15-59) 97.4 4,505 95.1

Female (15-49) 98.7 5,368 97.8

India 2005/06 97.7

Male (15-54) 87.1 64,175 78.1

Female (15-49) 94.5 62,182 85.0

Kenya 2003 96.3

Male (15-54) 85.5 4,183 70.3

Female (15-49) 94.0 4,303 76.3

Lesotho 2004/05 95.2

Male (15-59) 84.6 3,305 68.0

Female (15-49) 94.2 3,758 80.7

Malawi 2004 97.8

Male (15-54) 85.9 3,797 63.3

Female (15-49) 95.7 12,229 70.4

Mali 2001 97.9

Male (15-59) 83.8 4,062 75.6

Female (15-49) 94.8 4,556 85.2

Niger 2006 97.5

Male (15-59) 92.3 3,379 85.1

Female (15-49) 95.6 4,899 92.0

Rwanda 2005 99.6

Male (15-59) 97.1 4,547 95.4

Female (15-49) 98.2 5,837 97.3

Senegal 2005 98.2

Male (15-59) 85.9 3,963 75.6

Female (15-49) 94.6 5,350 84.5

Tanzania 2003/04 98.5

Male (15-49) 91.3 6,196 77.0

Female (15-49) 95.9 7,154 83.5

Uganda 2004/05 96.8

Male (15-59) 89.2 9,905 83.8

Female (15-59) 94.5 11,454 89.3

Hai Phong, Vietnam 2005 99.9

Male (15-49) 100.0 820 91.7

Female (15-49) 100.0 971 95.1

Zambia 2001/02 98.2

Male (15-59) 88.7 2,418 73.3

Female (15-49) 96.7 2,689 79.4

Zimbabwe 2005 95.0

Male (15-54) 82.1 8,342 63.6

Female (15-49) 90.2 9,870 75.9

Table 3.1 and Figure 3.1 also show a wide variation in the percent of respondents who agreed to 

an HIV test. In most countries, women were more likely than men to agree to an HIV test, except in Côte

d’Ivoire, where men were more likely to agree. Some countries had high response rates for HIV testing 

among both sexes—Rwanda (95 percent among men and 97 percent among women), Cambodia (men 90 

percent, women 95 percent), and the Hai Phong province of Vietnam (men 92 percent, women 95 

percent). However, several countries in sub-Saharan Africa had relatively low levels of agreement to HIV 
16 

testing among both men and women. These countries included Côte d’Ivoire (men 79 percent, women 76 

percent), Kenya (men 70 percent, women 76 percent), Malawi (men 63 percent, women 70 percent), and 

Zambia (men 73 percent, women 79 percent). 

Figure 3.1. Response rates for HIV testing, men and women in 22 DHS and AIS surveys 

90

76

85

81

75

88

76

86

80

95 95

79

90

84

70

77

63

73

64

68

85

95 95

89

83

93

85

98 97

76

92

89

76

84

70

79

76

81

78

92 92

89

85

92

0

20

40

60

80

100

India

Hai Phong, Vietnam

Cambodia

Senegal

Niger

Dominican Republic

Ethiopia

Guinea

Mali

Burkina Faso

Ghana

Haiti

Rwanda

Côte d'Ivoire

Cameroon

Uganda

Kenya

Tanzania

Malawi

Zambia

Zimbabwe

Lesotho Percent

Men Women

Finally, some countries had a wide disparity between the percent of men who accepted HIV 

testing and the percent of women who accepted testing. The difference in HIV testing rates among men 

and women was at least 10 percent in: Lesotho (men 68 percent, women 81 percent), Mali (men 76 

percent, women 85 percent), and Zimbabwe (men 64 percent, women 76 percent). In Ghana the 

differences was 9 percent—men 80 percent, women 89 percent. 
17 

4 HIV Prevalence by Demographic Characteristics 

4.1 Key Findings 

4.2 Introduction 

This chapter presents information on HIV prevalence by selected demographic characteristics for 

the 22 countries included in the study. These characteristics include age, sex, place of residence, and years 

lived at current residence. In addition, data are presented regarding HIV prevalence among male 

household members age 15-49 by whether they were away from home in the past 12 months. Two 

measures are used to examine male migration patterns. These include: (1) whether men were away from 

home for at least one month during the past 12 months, and (2) how many times men reported being away 

from home for any length of time in the past 12 months. Also, HIV prevalence among women is assessed 

by current fertility (currently pregnant) and past fertility (pregnant in the past three years). For women 

who reported being pregnant in the past three years, HIV prevalence is measured by whether women 

reported attending or not attending antenatal care during the last pregnancy. 

4.3 HIV Prevalence by Age and Sex 

HIV prevalence varies considerably by country (Table 4.1). Four countries within sub-Saharan 

Africa report HIV prevalence above 10 percent for the general population: Lesotho (23.5 percent), 

Malawi (11.8 percent), Zambia (15.6 percent), and Zimbabwe (18.1 percent); while three countries in 

Asia report HIV prevalence below 1 percent of the general population: Cambodia (0.6 percent), India (0.3 

percent), and the Hai Phong province of Vietnam (0.5 percent). 

 HIV prevalence varies considerably by country, from 0.3 percent in Vietnam to 23.5 percent in 

Lesotho. In three other countries HIV prevalence is above 10 percent: Malawi (11.8 percent), 

Zambia (15.6 percent), and Zimbabwe (18.1 percent).

 For the majority of countries, HIV prevalence is higher among women than men. The 

exceptions are Burkina Faso, the Dominican Republic, India, Niger, and Vietnam, where HIV 

prevalence is higher among men than women; and Cambodia, where HIV prevalence is the 

same for men and women.

 For all but two countries (Senegal and the Dominican Republic), HIV prevalence is higher in 

urban areas than in rural areas.

 For all countries but India, HIV prevalence is higher among individuals who lived in their 

current residence for less than 10 years compared with those who lived in their same residence 

for 10 years or more. 

 For men who report being away from their home at least once in the past 12 months, the 

number of times the individual left home, but not the amount of time spent away from home 

during an absence, is related to higher levels of HIV prevalence.

 HIV prevalence is higher among women who are not currently pregnant and among women 

who did not give birth in the past three years.
18 

Table 4.1. HIV prevalence among women and men age 15-49, by age group 

Age group

Country/sex 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Total Number

Burkina Faso 2003 0.8 1.3 2.6 3.0 3.4 1.3 1.4 1.8 7,151

Male 0.7 0.6 2.8 3.8 3.0 2.1 2.1 1.9 3,065

Female 0.9 1.8 2.5 2.4 3.6 0.7 0.9 1.8 4,086

Number 1,819 1,248 1,050 880 884 669 601 7,151 7,151

Cambodia 2005 0.0 0.4 1.0 1.0 1.0 0.5 1.1 0.6 14,703

Male 0.1 0.2 0.6 1.2 1.3 0.7 1.3 0.6 6,656

Female 0.0 0.6 1.3 0.8 0.7 0.3 1.0 0.6 8,047

Number 3,298 2,699 1,763 1,821 1,924 1,804 1,395 14,703 14,703

Cameroon 2004 1.4 5.2 7.8 8.9 7.9 5.9 4.6 5.4 9,746

Male 0.6 2.5 5.1 8.1 8.6 5.6 3.9 4.1 4,619

Female 2.1 7.5 10.3 9.7 7.3 6.1 5.3 6.6 5,128

Number 2,477 1,990 1,630 1,260 952 776 662 9,746 9,746

Côte d'Ivoire 2005 0.3 2.5 5.4 10.4 6.7 7.8 7.3 4.7 8,436

Male 0.3 0.3 3.1 5.6 5.0 7.0 4.7 2.9 4,023

Female 0.4 4.5 7.5 14.9 8.4 8.6 10.2 6.4 4,413

Number 1,872 1,878 1,533 1,079 860 649 565 8,436 8,436

Dominican Republic 2002 0.3 0.9 1.2 2.0 0.9 0.9 1.6 1.0 21,437

Male 0.3 0.6 1.2 2.7 1.2 1.1 1.7 1.1 10,706

Female 0.4 1.1 1.1 1.2 0.6 0.8 1.6 0.9 10,731

Number 4,482 3,922 3,362 2,959 2,725 2,244 1,744 21,437 21,437

Ethiopia 2005 0.4 1.1 1.6 1.7 3.2 3.0 0.5 1.4 10,540

Male 0.1 0.4 0.7 1.9 1.8 2.8 0.0 0.9 4,804

Female 0.7 1.7 2.1 1.5 4.4 3.1 0.9 1.9 5,736

Number 2,572 1,954 1,645 1,398 1,231 925 815 10,540 10,540

Ghana 2003 0.3 1.2 2.3 3.6 4.0 3.5 2.2 2.2 9,142

Male 0.2 0.0 1.0 2.8 3.1 4.1 1.9 1.5 4,045

Female 0.5 2.0 3.4 4.2 4.7 3.0 2.5 2.7 5,097

Number 2,070 1,528 1,516 1,253 1,082 855 837 9,142 9,142

Guinea 2005 0.7 1.2 1.6 2.1 1.6 2.0 2.1 1.5 6,319

Male 0.5 0.7 1.2 0.7 0.9 2.8 0.6 0.9 2,577

Female 0.9 1.6 1.8 3.0 2.1 1.4 3.3 1.9 3,742

Number 1,431 1,008 882 822 904 654 616 6,319 6,319

Haiti 2005 0.5 1.7 3.3 3.3 2.9 3.7 2.4 2.2 9,551

Male 0.1 1.1 3.1 2.4 3.7 4.4 3.3 2.0 4,321

Female 0.9 2.3 3.5 4.1 2.2 3.1 1.6 2.3 5,230

Number 2,518 1,871 1,455 1,086 974 816 830 9,551 9,551

(Cont’d)
19 

Table 4.1 – cont’d

Age group

Country/sex 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Total Number

India 2005/06 0.0 0.2 0.4 0.5 0.4 0.3 0.3 0.3 99,838

Male 0.0 0.2 0.4 0.6 0.5 0.4 0.5 0.4 46,506

Female 0.1 0.2 0.3 0.5 0.2 0.2 0.2 0.2 53,332

Number 19,366 17,398 16,074 14,394 13,016 10,884 8,707 99,838 99,838

Kenya 2003 1.6 6.0 10.4 9.4 10.1 9.1 4.4 6.7 6,001

Male 0.4 2.4 7.3 6.6 8.4 8.8 5.2 4.6 2,851

Female 3.0 9.0 12.9 11.7 11.8 9.5 3.9 8.7 3,151

Number 1,456 1,224 950 806 666 535 364 6,001 6,001

Lesotho 2004/05 5.4 19.3 33.3 40.7 41.7 29.6 20.4 23.5 5,019

Male 2.3 11.5 24.5 41.2 38.7 32.3 27.9 19.2 2,001

Female 7.9 24.5 39.3 40.4 43.5 28.6 16.8 26.4 3,018

Number 1,338 1,019 744 633 500 421 364 5,019 5,019

Malawi 2004 2.1 9.5 12.6 19.3 17.7 17.2 11.6 11.8 5,150

Male 0.4 3.9 9.8 20.4 18.4 16.5 9.5 10.2 2,465

Female 3.7 13.2 15.5 18.1 17.0 17.9 13.3 13.3 2,686

Number 967 1,103 986 779 520 477 319 5,150 5,150

Mali 2001 0.8 1.1 2.3 3.5 2.1 1.5 1.7 1.8 6,438

Male 0.3 0.3 0.7 3.8 1.2 1.8 2.6 1.3 2,595

Female 1.1 1.6 3.2 3.3 2.8 1.2 1.0 2.1 3,834

Number 1,369 1,176 995 916 805 675 501 6,438 6,438

Niger 2005 0.0 0.9 0.5 1.2 1.9 0.6 0.6 0.7 7,262

Male 0.0 0.6 0.1 1.2 2.6 0.6 1.1 0.8 2,856

Female 0.0 1.0 0.7 1.1 1.4 0.5 0.1 0.7 4,406

Number 1,389 1,286 1,258 1,038 905 764 621 7,262 7,262

Rwanda 2005 0.5 1.6 2.7 5.1 4.9 7.1 4.3 3.0 9,988

Male 0.3 0.5 2.0 3.8 2.5 8.0 4.7 2.3 4,348

Female 0.7 2.5 3.3 5.9 6.9 6.5 4.0 3.6 5,641

Number 2,408 2,067 1,451 1,297 972 951 841 9,988 9,988

Senegal 2005 0.1 0.5 0.9 1.0 0.7 1.6 1.3 0.7 7,412

Male 0.0 0.2 0.0 1.2 0.8 1.7 0.6 0.5 3,183

Female 0.2 0.8 1.5 0.9 0.6 1.5 1.8 0.9 4,229

Number 1,966 1,405 1,190 935 722 657 537 7,412 7,412

Tanzania 2003/04 2.1 5.2 8.3 10.9 10.7 10.9 6.3 7.0 10,747

Male 2.1 4.2 6.9 8.6 9.8 12.3 6.7 6.3 4,994

Female 2.1 6.0 9.4 12.9 11.6 9.8 5.8 7.7 5,753

Number 2,416 2,056 1,949 1,499 1,233 872 722 10,747 10,747

(Cont’d)
20 

Table 4.1 – cont’d

Age group

Country/sex 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Total Number

Uganda 2004/05 1.5 4.8 7.6 10.3 9.7 8.9 7.6 6.4 16,827

Male 0.3 2.4 5.9 8.1 9.3 9.3 6.9 5.1 7,477

Female 2.6 6.3 8.7 12.2 10.0 8.4 8.3 7.5 9,350

Number 3,973 2,972 2,792 2,503 1,885 1,562 1,141 16,827 16,827

Hai Phong, Vietnam 2005 0.0 0.8 1.1 1.6 0.8 0.0 0.0 0.5 1,675

Male 0.0 1.8 1.2 2.5 1.8 0.0 0.0 0.9 754

Female 0.0 0.0 1.1 0.9 0.0 0.0 0.0 0.2 921

Number 324 253 175 186 229 253 256 1,675 1,675

Zambia 2001/02 4.6 11.4 20.4 25.1 22.5 18.9 16.5 15.6 3,806

Male 1.9 4.4 15.0 20.5 22.4 20.6 20.3 12.9 1,734

Female 6.6 16.3 25.1 29.4 22.6 17.3 13.6 17.8 2,072

Number 863 752 681 534 436 307 232 3,806 3,806

Zimbabwe 2005 4.6 11.6 21.8 32.9 33.4 28.9 21.4 18.1 12,796

Male 3.1 5.8 13.0 29.5 32.1 33.0 26.1 14.5 5,848

Female 6.2 16.3 28.8 35.5 34.5 25.7 18.0 21.1 6,947

Number 3,374 2,765 2,057 1,672 1,201 934 793 12,796 12,796

Note: In this table and in several subsequent tables, an HIV prevalence estimate of 0.0 for certain categories indicates that there are 

no HIV-positive cases in the survey sample for that group of respondents. 

For the majority of countries, HIV prevalence is higher among women than among men. The 

exceptions are Burkina Faso (men 1.9 percent, women 1.8 percent), the Dominican Republic (men 1.1 

percent, women 0.9 percent), India (men 0.4 percent, women 0.2 percent), Niger (men 0.8 percent, 

women 0.7 percent) and the Hai Phong province of Vietnam (men 0.9 percent, women 0.2 percent). In 

Cambodia HIV prevalence is the same for men and women, at 0.6 percent. 

Figures 4.1a to 4.1v show that for most countries, with the exception of Cambodia and Guinea, 

HIV prevalence for both women and men increases with age until it reaches a peak, after which it 

decreases with advancing age. For 13 of the 22 countries, the peak for HIV prevalence among women is 

at a younger age than among men, generally 5-10 years younger. For women in these 13 countries, HIV 

prevalence peaks at age 25-29 (3 countries), 30-34 (6 countries), or 35-39 (4 countries), while for men the

peak is at age 30-34 (1 country), 35-39 (6 countries), and 40-44 (6 countries). 

In 5 of the 22 countries—the Dominican Republic, India, Malawi, Mali, and Niger—men and 

women experience a peak in HIV prevalence at the same age, generally at age 30-34, except in Niger, 

where the peak is at age 35-39. In 3 of the 22 countries—Burkina Faso, Lesotho, and Senegal—HIV 

prevalence peaks five years earlier for men than for women. Finally, in one country—Guinea—there is no 

clear pattern. 
21 

Figure 4.1a HIV prevalence among tested men and women 15-49 

by age group, India 2005/06 

0

1

2

3

4

5

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women

Figure 4.1b HIV prevalence among tested men and women 15-49 

by age group, Hai Phong Province, Vietnam 2005 

0

1

2

3

4

5

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women
22 

Figure 4.1c HIV prevalence among tested men and women 15-49 

by age group, Cambodia 2005 

0

1

2

3

4

5

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women

Figure 4.1d HIV prevalence among tested men and women 15-49 

by age group, Senegal 2005 

0

1

2

3

4

5

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women
23 

Figure 4.1e HIV prevalence among tested men and women 15-49 

by age group, Niger 2005 

0

1

2

3

4

5

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women

Figure 4.1f HIV prevalence among tested men and women 15-49 

by age group, Dominican Republic 2002 

0

1

2

3

4

5

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women
24 

Figure 4.1g HIV prevalence among tested men and women 15-49 

by age group, Ethiopia 2005 

0

1

2

3

4

5

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women

Figure 4.1h HIV prevalence among tested men and women 15-49

by age group, Guinea 2005 

0

1

2

3

4

5

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women
25 

Figure 4.1i HIV prevalence among tested men and women 15-49 

by age group, Mali 2001 

0

1

2

3

4

5

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women

Figure 4.1j HIV prevalence among tested men and women 15-49 

by age group, Burkina Faso 2003 

0

1

2

3

4

5

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women
26 

Figure 4.1k HIV prevalence among tested men and women 15-49 

by age group, Ghana 2003 

0

1

2

3

4

5

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women

Figure 4.1l HIV prevalence among tested men and women 15-49 

by age group, Haiti 2005 

0

1

2

3

4

5

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women
27 

Figure 4.1m HIV prevalence among tested men and women 15-49 

by age group, Rwanda 2005 

0

5

10

15

20

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women

Figure 4.1n HIV prevalence among tested men and women 15-49 

by age group, Côte d'Ivoire 2005 

0

5

10

15

20

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women
28 

Figure 4.1o HIV prevalence among tested men and women 15-49 

by age group, Cameroon 2004 

0

5

10

15

20

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women

Figure 4.1p HIV prevalence among tested men and women 15-49 

by age group, Uganda 2004/05 

0

5

10

15

20

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women
29 

Figure 4.1q HIV prevalence among tested men and women 15-49 

by age group, Kenya 2003 

0

5

10

15

20

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women

Figure 4.1r HIV prevalence among tested men and women 15-49 

by age group, Tanzania 2003/04 

0

5

10

15

20

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women
30 

Figure 4.1s HIV prevalence among tested men and women 15-49 

by age group, Malawi 2004 

0

5

10

15

20

25

30

35

40

45

50

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women

Figure 4.1t HIV prevalence among tested men and women 15-49 

by age group, Zambia 2001/02 

0

5

10

15

20

25

30

35

40

45

50

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women
31 

Figure 4.1u HIV prevalence among tested men and women 15-49 

by age group, Zimbabwe 2005 

0

5

10

15

20

25

30

35

40

45

50

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women

Figure 4.1v HIV prevalence among tested men and women 15-49 

by age group, Lesotho 2004/05 

0

5

10

15

20

25

30

35

40

45

50

15-19 20-24 25-29 30-34 35-39 40-44 45-49

HIV prevalence (%) 

Men

Women
32 

4.4 HIV Prevalence by Place of Residence 

For all but two of the countries, HIV prevalence is higher in urban areas than in rural areas (Table 

4.2). The only two exceptions are the Dominican Republic, where HIV prevalence is slightly higher in 

rural areas, at 1.2 percent compared with 0.9 percent in urban areas, and Senegal, where prevalence is 

equal in urban and rural areas, at 0.7 percent. For some countries, the difference in HIV prevalence 

between urban and rural areas is dramatic: in Burkina Faso, Guinea, Tanzania, and Zambia HIV 

prevalence is at least three times greater in urban areas than in rural areas; in Cambodia, Niger, Rwanda, 

and the Hai Phong province of Vietnam it is four or more times greater. In Ethiopia HIV prevalence is 

eight times greater in urban areas (5.6 percent) than in rural areas (0.7 percent). 

Table 4.2. HIV prevalence among women and men age 15-49, by urban/rural residence 

Residence

Country/sex Urban Rural Total Number

Burkina Faso 2003 3.6 1.3 1.8 7,151

Male 3.2 1.4 1.9 3,065

Female 4.0 1.2 1.8 4,086

Number 1,708 5,443 7,151 7,151

Cambodia 2005 1.4 0.4 0.6 14,703

Male 1.6 0.4 0.6 6,656

Female 1.3 0.5 0.6 8,047

Number 2,550 12,153 14,703 14,703

Cameroon 2004 6.6 4.0 5.4 9,746

Male 4.9 3.0 4.1 4,619

Female 8.2 4.8 6.6 5,128

Number 5,510 4,237 9,746 9,746

Côte d'Ivoire 2005 5.4 4.1 4.7 8,436

Male 3.2 2.5 2.9 4,023

Female 7.4 5.5 6.4 4,413

Number 4,011 4,425 8,436 8,436

Dominican Republic 2002 0.9 1.2 1.0 21,439

Male 1.0 1.3 1.1 10,707

Female 0.9 1.0 0.9 10,732

Number 14,208 7,231 21,439 21,439

Ethiopia 2005 5.6 0.7 1.4 10,540

Male 2.4 0.7 0.9 4,804

Female 7.7 0.7 1.9 5,736

Number 1,664 8,875 10,540 10,540

Ghana 2003 2.3 2.0 2.2 9,142

Male 1.5 1.4 1.5 4,045

Female 2.9 2.5 2.7 5,097

Number 4,291 4,851 9,142 9,142

(Cont’d)
33 

Table 4.2 – cont’d

Residence

Country/sex Urban Rural Total Number

Guinea 2005 2.4 1.0 1.5 6,319

Male 0.7 1.1 0.9 2,577

Female 4.0 0.9 1.9 3,742

Number 2,203 4,115 6,319 6,319

Haiti 2005 2.3 2.1 2.2 9,551

Male 1.8 2.1 2.0 4,321

Female 2.7 2.0 2.3 5,230

Number 4,339 5,212 9,551 9,551

India 2005/06 0.4 0.3 0.3 99,838

Male 0.4 0.3 0.4 46,506

Female 0.3 0.2 0.2 53,332

Number 33,978 65,861 99,838 99,838

Kenya 2003 10.0 5.6 6.7 6,001

Male 7.6 3.6 4.6 2,851

Female 12.3 7.5 8.7 3,151

Number 1,495 4,507 6,001 6,001

Lesotho 2004/05 28.9 22.0 23.5 5,019

Male 21.2 18.6 19.2 2,001

Female 33.1 24.3 26.4 3,018

Number 1,133 3,886 5,019 5,019

Malawi 2004 17.1 10.8 11.8 5,150

Male 16.3 8.8 10.2 2,465

Female 18.0 12.5 13.3 2,686

Number 872 4,279 5,150 5,150

Mali 2001 2.3 1.5 1.8 6,438

Male 1.9 1.0 1.3 2,595

Female 2.5 1.9 2.1 3,834

Number 2,001 4,437 6,438 6,438

Niger 2006 1.5 0.5 0.7 7,262

Male 1.3 0.6 0.8 2,856

Female 1.6 0.5 0.7 4,406

Number 1,628 5,633 7,262 7,262

Rwanda 2005 7.3 2.2 3.0 9,988

Male 5.7 1.5 2.3 4,348

Female 8.6 2.6 3.6 5,641

Number 1,711 8,278 9,988 9,988

(Cont’d)
34 

Table 4.2 – cont’d

Residence

Country/sex Urban Rural Total Number

Senegal 2005 0.7 0.7 0.7 7,412

Male 0.4 0.5 0.5 3,183

Female 0.9 0.8 0.9 4,229

Number 4,009 3,403 7,412 7,412

Tanzania 2003/04 10.9 5.4 7.0 10,747

Male 9.6 4.8 6.3 4,994

Female 12.0 5.8 7.7 5,753

Number 3,276 7,471 10,747 10,747

Uganda 2004/05 10.2 5.8 6.4 16,827

Male 6.7 4.8 5.1 7,477

Female 12.8 6.5 7.5 9,350

Number 2,526 14,302 16,827 16,827

Hai Phong, Vietnam 2005 1.0 0.3 0.5 1,675

Male 2.0 0.4 0.9 754

Female 0.3 0.2 0.2 921

Number 569 1,106 1,675 1,675

Zambia 2001/02 23.1 10.8 15.6 3,806

Male 19.2 8.9 12.9 1,734

Female 26.3 12.4 17.8 2,072

Number 1,483 2,323 3,806 3,806

Zimbabwe 2005 18.9 17.6 18.1 12,796

Male 15.7 13.8 14.5 5,848

Female 21.6 20.8 21.1 6,947

Number 4,990 7,806 12,796 12,796

For all but two countries, the urban-rural difference in HIV prevalence is similar for both men 

and women, with HIV prevalence higher in urban areas than rural areas. In Guinea, however, HIV 

prevalence among men is slightly higher in rural areas than in urban areas (1.1 percent in rural areas 

compared with 0.7 percent in urban areas). In contrast, among Guinean women HIV prevalence is higher 

in urban areas (4.0 percent) than in rural areas (0.9 percent). In Haiti HIV prevalence among men is 

higher in rural areas (2.1 percent) than urban areas (1.8 percent), but among women it is higher in urban 

areas (2.7 percent) than rural areas (2.0 percent). 

4.5 HIV Prevalence by Years Lived in Current Place of Residence 

Table 4.3 shows HIV prevalence among women and men age 15-49 by the number of years lived 

in the current residence. For all countries but India, HIV prevalence is higher among individuals who 

have lived in their current residence for less than 10 years compared with those who have lived in their 

same residence for 10 years or more. In fact, for 12 of the 18 countries with data, HIV prevalence is

highest among individuals who have lived in their residence for less than three years, while for the other 6 

countries it is highest among individuals who have lived in their residence for 3-9 years. In India HIV 
35 

prevalence is the same (0.3 percent) for individuals who have lived in their current residence for 3-9 years 

as for those who have lived in their residence for 10 years or longer. 

Table 4.3. HIV prevalence among women and men aged 15-49, by number of years live in current 

residence 

Number of years in current place of residence

Country/sex <3 3-9 10+ Total Number

Burkina Faso 2003 2.8 1.7 1.7 1.8 7,151

Male 0.6 1.9 1.9 1.9 3,065

Female 4.1 1.6 1.5 1.8 4,086

Number 735 1,209 5,126 7,151 7,151

Cambodia 2005 0.8 1.0 0.5 0.6 14,703

Male 0.9 0.8 0.6 0.6 6,656

Female 0.8 1.2 0.5 0.6 8,047

Number 1,485 2,069 11,125 14,703 14,703

Cameroon 2004 7.5 6.5 4.2 5.4 9,746

Male 5.3 4.8 3.4 4.1 4,619

Female 9.6 8.2 5.0 6.6 5,128

Number 2,038 2,226 5,469 9,746 9,746

Côte d'Ivoire 2005 5.4 4.8 4.2 4.7 8,436

Male 2.7 2.6 3.2 2.9 4,023

Female 7.8 7.0 5.2 6.4 4,413

Number 2,382 2,195 3,845 8,436 8,436

Ethiopia 2005 3.6 2.9 1.2 1.4 10,540

Male 0.9 2.1 0.9 0.9 4,804

Female 5.2 3.3 1.4 1.9 5,736

Number 459 945 9,063 10,540 10,540

Ghana 2003 2.1 2.4 2.1 2.2 9,142

Male 1.6 1.7 1.4 1.5 4,045

Female 2.4 3.0 2.7 2.7 5,097

Number 1,349 2,300 5,379 9,142 9,142

Guinea 2005 1.3 2.0 1.4 1.5 6,319

Male 0.0 1.5 0.9 0.9 2,577

Female 2.2 2.4 1.8 1.9 3,742

Number 495 859 4,944 6,319 6,319

Haiti 2005 3.2 1.9 2.0 2.2 9,551

Male 2.7 1.3 2.1 2.0 4,321

Female 3.5 2.5 1.9 2.3 5,230

Number 1,544 1,876 6,122 9,551 9,551

India 2005/06 0.2 0.3 0.3 0.3 99,838

Male 0.3 0.6 0.3 0.4 46,506

Female 0.2 0.2 0.2 0.2 53,332

Number 11,025 13,824 74,942 99,838 99,838

(Cont’d)
36 

Table 4.3 – cont’d

Number of years in current place of residence

Country/sex <3 3-9 10+ Total Number

Kenya 2003 9.4 8.8 5.0 6.7 6,001

Male 3.9 6.9 4.0 4.6 2,851

Female 12.9 10.1 6.1 8.7 3,151

Number 1,133 1,291 3,465 6,001 6,001

Lesotho 2004/05 23.7 29.8 22.4 23.5 5,019

Male 13.7 18.9 19.8 19.2 2,001

Female 28.6 34.4 24.4 26.4 3,018

Number 510 627 3,827 5,019 5,019

Malawi 2004 14.4 12.0 10.8 11.8 5,150

Male 14.7 12.6 7.6 10.2 2,465

Female 14.2 11.4 13.8 13.3 2,686

Number 1,065 1,211 2,874 5,150 5,150

Niger 2006 1.2 1.1 0.7 0.7 7,262

Male 0.4 1.4 0.8 0.8 2,856

Female 1.8 1.0 0.6 0.7 4,406

Number 550 792 5,892 7,262 7,262

Rwanda 2005 3.1 4.5 2.6 3.0 9,988

Male 2.8 4.0 1.9 2.3 4,348

Female 3.4 4.7 3.3 3.6 5,641

Number 1,274 1,887 6,809 9,988 9,988

Senegal 2005 1.3 0.7 0.6 0.7 7,412

Male 1.1 0.4 0.3 0.5 3,183

Female 1.6 0.9 0.8 0.9 4,229

Number 977 1,331 5,065 7,412 7,412

Tanzania 2003/04 10.0 7.3 5.7 7.0 10,747

Male 8.5 8.1 5.0 6.3 4,994

Female 10.9 6.7 6.6 7.7 5,753

Number 2,411 2,363 5,969 10,747 10,747

Uganda 2004/05 8.4 7.2 5.2 6.4 16,827

Male 5.3 5.7 4.7 5.1 7,477

Female 10.0 8.1 5.6 7.5 9,350

Number 4,021 4,197 8,573 16,827 16,827

Hai Phong, Vietnam 2005 0.9 0.0 0.6 0.5 1,675

Male 2.5 0.0 0.9 0.9 754

Female 0.0 0.0 0.3 0.2 921

Number 122 196 1,358 1,675 1,675

Zimbabwe 2005 17.2 19.8 17.8 18.1 12,796

Male 13.9 13.5 14.9 14.5 5,848

Female 20.4 23.5 20.5 21.1 6,947

Number 1,611 2,429 8,746 12,796 12,796
37 

In 10 of the 19 countries with data HIV prevalence is higher for both men and women who have 

lived for shorter periods of time in their current residence. However, for nine countries there is a 

difference in HIV prevalence between men and women by the number of years living at current residence. 

For example, in eight of these countries—Burkina Faso, Côte d’Ivoire, Ethiopia, Kenya, Lesotho, Niger, 

Uganda, and Zimbabwe—HIV prevalence is highest among men who have lived at their current residence 

for more than three years, while for women it is highest among those who have lived in their residence for 

less than three years. In the Hai Phong province of Vietnam, the pattern is opposite: HIV prevalence is 

highest among men who have lived in their residence for less than 3 years, while for women it is highest 

among those who have lived in their current residence for 10 years or more. 

4.6 HIV Prevalence by Number of Times Away From Home in the Last 12 Months 

HIV prevalence varies according to the number of times male members of the household have 

been away from the home in the past 12 months (Table 4.4). Data are unavailable for four countries 

including Cameroon, Senegal, Tanzania, and Uganda. In 10 of the remaining 15 countries, HIV 

prevalence is highest among men who have not been away from home for more than one month in the 

past 12 months. In the other five countries—India, Lesotho, Malawi, Hai Phong province of Vietnam, and 

Zimbabwe—HIV prevalence is highest among men who have been away from the household for at least 

one month. 

Table 4.4. HIV prevalence among men age 15-49, by number of times away from home in last 12 

months and away from home more than one month 

Away from home 1+ 

months Times away from home last 12 months

Country/sex No Yes 0 1-2 3-4 5+ Total

Burkina Faso 2003

Male 2.0 1.1 2.0 2.5 1.5 0.2 1.9

Number 2,544 515 1,848 665 255 291 3,065

Cambodia 2005

Male 1.3 0.3 0.7 0.5 0.3 0.8 0.6

Number 5,812 844 3,319 1,670 758 901 6,656

Côte d'Ivoire 2005

Male 3.1 2.0 1.9 5.3 2.3 3.0 2.9

Number 3,262 753 2,047 893 400 671 4,012

Ethiopia 2005

Male 1.0 0.8 0.8 0.9 0.5 2.4 0.9

Number 4,512 289 3,389 821 239 352 4,804

Ghana 2003

Male 1.5 1.3 1.4 1.3 1.2 1.9 1.5

Number 3,224 813 1,660 911 655 804 4,029

Guinea 2005

Male 1.1 0.3 1.2 0.8 0.3 0.8 1.0

Number 2,113 455 1,271 703 268 323 2,568

(Cont’d)
38 

Table 4.4 – cont’d

Away from home 1+ 

months Times away from home last 12 months

Country/sex No Yes 0 1-2 3-4 5+ Total

Haiti 2005

Male 2.0 1.5 2.1 2.0 1.4 1.6 2.0

Number 3,897 415 2,812 654 396 444 4,312

India 2005/06

Male 0.3 0.4 0.4 0.2 0.4 0.3 0.4

Number 41,380 5,097 14,455 8,247 7,678 15,996 46,477

Kenya 2003

Male 4.7 3.4 3.2 4.2 5.6 8.2 4.6

Number 2372 470 1,421 655 299 461 2,851

Lesotho 2004/05

Male 18.7 21.0 18.3 19.8 21.1 20.7 19.2

Number 1,592 409 1,180 313 208 299 2,001

Malawi 2004

Male 9.8 13.2 9.2 10.2 9.3 17.9 10.2

Number 2,156 307 1,567 431 216 245 2,465

Niger 2006

Male 1.1 0.3 1.0 0.1 0.5 2.3 0.8

Number 1,859 980 1,234 1,066 233 305 2,839

Rwanda 2005

Male 2.3 2.0 2.2 2.2 3.0 2.4 2.2

Number 3,990 342 3,224 662 237 208 4,333

Hai Phong, Vietnam 2005

Male 0.8 2.0 0.6 2.1 0.0 2.2 0.9

Number 702 53 581 96 29 48 754

Zimbabwe 2005

Male 14.3 15.5 14.4 11.2 14.6 18.2 14.5

Number 4,723 1,118 2,638 1,269 649 1,267 5,841

Note: Some of the HIV prevalence estimates for Hai Phong, Vietnam are based on small numbers of cases. 

In contrast, when examining HIV prevalence by the number of times men reported being away 

from home in the last 12 months, HIV prevalence is higher among men who have never being away from 

home in only two countries (Guinea and Haiti). Instead, in 12 countries HIV prevalence is higher among 

men who have been away from home once or twice (Burkina Faso and Côte d’Ivoire), between three or 

four times (Lesotho and Rwanda), or at least five times in the past 12 months (Cambodia, Ethiopia, 

Ghana, Kenya, Malawi, Niger, the Hai Phong province of Vietnam, and Zimbabwe). These data suggest 

that it is not the duration but rather the frequency of time away from home that is related to higher levels 

of HIV prevalence among men. 
39 

4.7 HIV Prevalence by Pregnancy Status and Birth in Last 3 Years 

In 14 of the 19 countries with data, HIV prevalence is higher among women who were not 

currently pregnant at the time of the survey (Table 4.5). In contrast, in four West African countries—

Burkina Faso, Cameroon, Ghana, Senegal—HIV prevalence is higher among women who reported being 

currently pregnant at the time of the survey. In Niger, HIV prevalence is the same among both groups of 

women. 

Similarly, in 12 of the 19 countries HIV prevalence is higher among women who had not given 

birth in the past 3-years. For women who had given birth in the past three years, in four countries (Ghana, 

Haiti, Rwanda, and Zimbabwe) HIV prevalence is higher among those who did not attend antenatal care 

during the last birth while in three countries (Cambodia, Ethiopia, Kenya) HIV prevalence is higher 

among women who did attend antenatal care. 

Table 4.5. HIV prevalence among women age 15-49, by pregnancy status, birth in last 3 years with 

antenatal care visit 

Pregnancy Birth in last 3 years

Country/sex No Yes No Total

Last birth, 

without 

ANC

Last birth, 

with ANC

Burkina Faso 2003

Female 1.8 2.0 2.1 0.6 1.9 1.8

Number 3,674 412 2,196 533 1,358 4,086

Cambodia 2005

Female 0.6 0.3 0.6 0.4 0.9 0.6

Number 7,570 477 6,066 515 1,466 8,047

Cameroon 2004

Female 6.6 7.3 7.5 2.4 6.0 6.6

Number 4,604 524 3,028 384 1,715 5,128

Côte d'Ivoire 2005

Female 6.6 4.3 7.7 3.5 5.3 6.4

Number 4,060 353 2,704 844 865 4,413

Ethiopia 2005

Female 1.9 1.1 2.0 1.0 3.7 1.9

Number 5,256 480 3,308 1,797 631 5,736

Ghana 2003

Female 2.6 3.6 2.9 3.1 2.2 2.7

Number 4,712 385 3,309 255 1,533 5,097

Guinea 2005

Female 2.0 0.6 2.7 0.2 1.2 1.9

Number 3,371 370 2,053 367 1,322 3,742

Haiti 2005

Female 2.4 1.3 2.3 3.4 2.1 2.3

Number 4,941 289 3,724 217 1,289 5,230

(Cont’d)
40 

Table 4.5 – cont’d

Pregnancy Birth in last 3 years

Country/sex No Yes No Total

Last birth, 

without 

ANC

Last birth, 

with ANC

India 2005/06

Female 0.2 0.1 0.3 0.1 0.1 0.2

Number 50,499 2,833 41,081 2,845 9,406 53,332

Kenya 2003

Female 8.8 7.3 8.8 4.4 8.9 8.7

Number 2,891 260 1,961 120 1,070 3,151

Lesotho 2004/05

Female 26.6 24.3 26.8 21.4 26.1 26.4

Number 2,824 194 2,116 110 792 3,018

Malawi 2004

Female 13.9 9.8 16.6 9.3 10.4 13.3

Number 2,323 362 1,282 55 1,349 2,686

Niger 2006

Female 0.7 0.7 1.0 0.1 0.9 0.7

Number 3,762 644 1,857 1,341 1,208 4,406

Rwanda 2005

Female 3.7 2.2 3.9 8.6 2.9 3.6

Number 5,210 431 3,388 124 2,129 5,641

Senegal 2005

Female 0.8 1.6 1.1 0.0 0.7 0.9

Number 3,895 334 2,658 140 1,431 4,229

Tanzania 2003/04

Female 7.8 6.8 9.1 5.7 6.0 7.7

Number 5,219 533 3,206 352 2,195 5,753

Uganda 2004/05

Female 7.6 6.5 8.6 8.4 6.0 7.5

Number 8,282 1,068 4,854 630 3,866 9,350

Hai Phong, Vietnam 2005

Female 0.2 0.0 0.2 0.0 0.0 0.2

Number 893 28 799 4 117 921

Zimbabwe 2005

Female 21.4 17.5 21.5 24.1 20.2 21.1

Number 6,473 474 4,602 115 2,230 6,947

Note: Some of the HIV prevalence estimates for Hai Phong, Vietnam are based on small numbers of cases. 
41 

5 HIV Prevalence by Socioeconomic Characteristics 

5.1 Key Findings 

5.2 Introduction 

This chapter reports HIV prevalence by the social and economic characteristics of the household 

population, including marital status, education level, occupation, and household wealth. Household 

wealth is measured by an index constructed using household assets data—ownership of consumer items, 

such as television sets, bicycles, and cars, as well as characteristics of the dwelling, such as source of 

drinking water, type of sanitation facilities, and type of flooring material. Surveyed households are ranked 

according to the index and divided into quintiles from one (lowest) to five (highest). In addition, 

frequency of media exposure (newspaper, radio, and television) is used as a proxy measure of 

socioeconomic status. 

5.3 HIV Prevalence by Marital Status 

In the 19 countries with data, HIV prevalence is related to marital status (Table 5.1). HIV 

prevalence is highest among individuals who are widowed. Those who are divorced or separated have an 

intermediate level of HIV infection, while those who have never been in a marital union have relatively 

low HIV prevalence. For example, in Côte d’Ivoire HIV prevalence among widowed individuals is 29.2 

percent compared with 8.1 percent among divorced individuals, and 2.6 percent among the never-married. 

In general, there is little difference in HIV prevalence by whether an individual is in a polygynous or 

monogamous marriage. 

 HIV prevalence is related to marital status, with widowed and divorced individuals at highest 

risk for HIV infection, and never-married individuals at lowest risk. There is little difference 

in HIV prevalence by whether individuals are in monogamous or polygamous marriages.

 In the majority of countries studied, individuals with more education are at a higher risk for 

HIV infection.

 HIV prevalence is related to occupational status, with employees in the professional and 

manual/domestic sectors most at risk for HIV infection. 

 For the majority of countries, HIV prevalence increases with the amount of household wealth. 

Individuals in the top two quintiles of household wealth are at the most risk for HIV infection.

 There is no clear relationship across countries between media exposure to newspapers, radio, 

and television and HIV prevalence.
42 

Table 5.1. HIV prevalence among women and men age 15-49, by marital status 

Marital status

Country/sex Number

Never in 

union

MonogamousPolygynous WidowedDivorced/ 

separated Total

Burkina Faso 2003 1.2 2.5 1.2 7.0 4.2 1.8 7,151

Male 0.7 3.2 1.5 16.1 3.3 1.9 3,065

Female 2.0 2.0 1.2 6.3 4.7 1.8 4,086

Number 2,277 2,742 1,916 104 114 7,151 7,151

Cambodia 2005 0.1 0.8 3.1 1.3 0.6 14,703

Male 0.1 0.9 4.1 2.0 0.6 6,656

Female 0.1 0.7 2.9 1.1 0.6 8,047

Number 5,070 8,769 370 494 14,703 14,703

Cameroon 2004 2.6 6.1 5.2 10.6 5.4 9,746

Male 2.1 5.6 3.5 7.4 4.1 4,619

Female 3.5 6.5 5.5 26.4 15.0 6.6 5,128

Number 3,231 4,341 1,327 308 9,746 9,746

Côte d'Ivoire 2005 2.6 5.1 5.2 29.2 8.1 4.7 8,436

Male 1.3 4.0 1.5 37.2 5.8 2.9 4,023

Female 4.6 6.0 6.4 26.5 9.8 6.4 4,413

Number 3,537 3,306 953 161 478 8,436 8,436

Ethiopia 2005 0.5 1.5 1.4 8.4 4.7 1.4 10,540

Male 0.3 1.4 1.3 13.1 1.9 0.9 4,804

Female 0.7 1.6 1.5 8.1 5.6 1.9 5,736

Number 3,532 5,661 607 222 517 10,540 10,540

Ghana 2003 0.6 2.6 3.0 6.8 5.2 2.2 9,142

Male 0.3 2.4 1.6 7.2 3.3 1.5 4,045

Female 1.1 2.8 3.4 6.8 6.2 2.7 5,097

Number 3,296 4,234 938 109 564 9,142 9,142

Guinea 2005 0.8 1.7 1.3 13.5 1.8 1.5 6,319

Male 0.6 1.3 1.3 0.0 0.0 0.9 2,577

Female 1.2 1.9 1.4 14.8 3.9 1.9 3,742

Number 1,722 2,376 1,943 84 193 6,319 6,319

Haiti 2005 0.6 3.2 3.8 2.6 3.8 2.2 9,551

Male 0.5 3.7 2.5 2.5 4.3 2.0 4,321

Female 0.7 2.8 4.2 2.6 3.5 2.3 5,230

Number 4,015 4,093 753 129 561 9,551 9,551

India 2005/06 0.1 0.3 0.6 1.3 1.4 0.3 99,838

Male 0.2 0.4 1.4 0.1 1.9 0.4 46,506

Female 0.0 0.2 0.3 1.5 1.1 0.2 53,332

Number 27,494 68,029 1,053 2,110 1,153 99,838 99,838

Kenya 2003 2.8 6.9 11.6 31.9 15.1 6.7 6,001

Male 1.6 6.5 11.9 44.2 6.4 4.6 2,851

Female 4.7 7.2 11.5 30.2 19.2 8.7 3,151

Number 2,302 2,800 449 151 299 6,001 6,001

(Cont’d)
43 

Table 5.1 – cont’d

Marital status

Country/sex Number

Never in 

union

MonogamousPolygynous WidowedDivorced/ 

separated Total

Lesotho 2004/05 11.5 28.8 46.5 49.7 23.5 5,019

Male 8.7 32.9 38.3 36.1 19.2 2,001

Female 14.9 26.9 47.3 55.9 26.4 3,018

Number 2,125 2,346 279 264 5,019 5,019

Malawi 2004 3.0 13.1 14.3 35.6 23.3 11.8 5,150

Male 1.8 14.5 10.4 22.2 16.0 10.2 2,465

Female 5.3 11.8 16.0 37.4 25.5 13.3 2,686

Number 1,198 3,050 528 103 272 5,150 5,150

Niger 2006 0.5 0.6 0.6 3.5 5.8 0.7 7,262

Male 0.5 0.9 0.8 2.1 3.6 0.8 2,856

Female 0.4 0.5 0.6 3.9 6.7 0.7 4,406

Number 1,490 3,865 1,672 76 158 7,262 7,262

Rwanda 2005 1.3 3.0 4.2 15.8 10.2 3.0 9,988

Male 0.9 3.5 2.3 14.6 5.1 2.3 4,348

Female 1.6 2.6 4.8 15.9 10.9 3.6 5,641

Number 4,343 4,380 425 248 592 9,988 9,988

Senegal 2005 0.1 0.9 1.0 3.2 3.3 0.7 7,412

Male 0.0 0.9 0.8 27.8 1.1 0.5 3,183

Female 0.3 0.8 1.0 1.5 4.6 0.9 4,229

Number 2,935 2,804 1,334 66 273 7,412 7,412

Tanzania 2003/04 3.4 7.1 9.5 18.4 7.0 10,747

Male 3.1 7.7 9.0 15.0 6.3 4,994

Female 3.8 6.6 9.9 19.8 7.7 5,753

Number 3,459 5,687 634 967 10,747 10,747

Uganda 2004/05 1.6 6.3 6.2 31.4 13.9 6.4 16,827

Male 0.8 6.6 7.6 32.2 10.8 5.1 7,477

Female 2.7 6.1 5.6 31.2 16.0 7.5 9,350

Number 4,985 7,151 2,799 651 1,241 16,827 16,827

Hai Phong, Vietnam 2005 0.5 0.5 0.0 3.6 0.5 1,675

Male 1.1 0.6 0.0 16.6 0.9 754

Female 0.0 0.3 0.0 0.0 0.2 921

Number 537 1,090 22 26 1,675 1,675

Zimbabwe 2005 5.9 20.9 58.8 35.7 18.1 12,796

Male 4.3 22.7 66.7 35.5 14.5 5,848

Female 8.4 19.8 57.7 35.8 21.1 6,947

Number 4,822 6,074 590 764 12,796 12,796

Note: Some of the HIV prevalence estimates for ‘widowed’ and ‘divorced/separated’ are based on small numbers of cases.
44 

With a few notable exceptions, there are no differences between women and men in HIV 

prevalence by marital status. In Cameroon and India, however, widowed women are most likely to be 

HIV infected, while among men in India HIV prevalence is highest among the divorced or separated. In 

Lesotho and Senegal HIV prevalence is highest among divorced women, while for men it is highest 

among widowers. And in Haiti HIV prevalence is highest among divorced or separated men, but for 

women it is highest among those in a polygynous union. 

5.4 HIV Prevalence by Level of Education 

In the majority of countries, HIV prevalence is highest among individuals with either a primary or 

secondary education (Table 5.2). In six countries—Burkina Faso, Cambodia, Ethiopia, Malawi, Rwanda, 

and Tanzania—HIV prevalence is highest among individuals who have completed at least a secondary 

education. For the most part, this is true for both men and women. In Burkina Faso and Cambodia, 

however, men with a secondary or higher education have the highest HIV prevalence, while for women 

HIV prevalence is highest among those with an incomplete primary education. 

Table 5.2. HIV prevalence among women and men age 15-49, by education level 

Education level

Country/sex Total Number

No 

education

Primary 

incomplete

Primary 

complete

Secondary/ 

higher

Burkina Faso 2003 1.6 2.6 1.5 2.8 1.8 7,151

Male 1.7 1.4 1.4 2.9 1.9 3,065

Female 1.5 4.1 1.6 2.8 1.8 4,086

Number 5,150 628 482 891 7,151 7,151

Cambodia 2005 0.7 0.6 0.2 0.7 0.6 14,703

Male 0.3 0.5 0.3 0.9 0.6 6,656

Female 0.8 0.7 0.0 0.5 0.6 8,047

Number 2,204 6,468 1,283 4,749 14,703 14,703

Cameroon 2004 3.2 4.3 7.9 5.9 5.4 9,746

Male 2.8 3.2 5.6 4.3 4.1 4,619

Female 3.3 5.2 9.8 8.0 6.6 5,128

Number 1,566 2,114 1,575 4,492 9,746 9,746

Côte d'Ivoire 2005 4.4 4.7 8.4 4.7 4.7 8,436

Male 2.9 1.5 2.3 3.6 2.9 4,023

Female 5.2 7.5 12.3 7.0 6.4 4,413

Number 3,645 1,969 320 2,502 8,436 8,436

Ethiopia 2005 1.0 1.2 2.2 3.4 1.4 10,540

Male 0.8 0.3 1.4 2.0 0.9 4,804

Female 1.0 2.3 3.7 5.5 1.9 5,736

Number 5,665 2,737 523 1,614 10,540 10,540

Ghana 2003 1.9 2.6 2.5 1.0 2.2 9,142

Male 1.2 1.5 1.9 0.7 1.5 4,045

Female 2.2 3.4 3.1 1.6 2.7 5,097

Number 2,089 1,689 3,838 1,525 9,142 9,142

(Cont’d)
45 

Table 5.2 – cont’d

Education level

Country/sex Total Number

No 

education

Primary 

incomplete

Primary 

complete

Secondary/ 

higher

Guinea 2005 1.3 1.4 3.3 2.2 1.5 6,319

Male 1.2 0.6 0.0 0.8 0.9 2,577

Female 1.3 2.2 5.0 5.1 1.9 3,742

Number 4,121 836 79 1,282 6,319 6,319

Haiti 2005 2.6 2.0 4.5 1.8 2.2 9,551

Male 3.3 2.0 2.3 1.6 2.0 4,321

Female 2.3 1.9 6.3 2.1 2.3 5,230

Number 1,797 3,246 549 3,959 9,551 9,551

India 2005/06 0.3 0.4 0.3 0.2 0.3 99,838

Male 0.5 0.4 0.4 0.2 0.4 46,506

Female 0.3 0.3 0.1 0.1 0.2 53,332

Number 29,665 25,258 17,273 27,625 99,838 99,838

Kenya 2003 3.9 6.4 8.5 6.6 6.7 6,001

Male 2.7 3.4 5.9 5.2 4.6 2,851

Female 4.4 9.3 10.6 8.2 8.7 3,151

Number 552 2,034 1,444 1,972 6,001 6,001

Lesotho 2004/05 27.1 21.5 24.4 24.3 23.5 5,019

Male 26.9 16.7 18.3 19.1 19.2 2,001

Female 27.5 26.0 26.6 26.7 26.4 3,018

Number 398 1,819 1,056 1,741 5,019 5,019

Malawi 2004 12.3 9.7 11.8 15.1 11.8 5,150

Male 9.2 6.5 10.8 14.6 10.2 2,465

Female 13.6 12.3 13.0 15.7 13.3 2,686

Number 962 1,241 2,228 720 5,150 5,150

Niger 2006 0.6 1.1 1.3 1.0 0.7 7,262

Male 0.8 0.5 1.4 0.9 0.8 2,856

Female 0.6 1.7 1.1 1.2 0.7 4,406

Number 5,524 893 114 731 7,262 7,262

Rwanda 2005 3.1 2.5 3.4 5.3 3.0 9,988

Male 2.8 1.6 2.9 4.0 2.3 4,348

Female 3.3 3.2 3.8 6.6 3.6 5,641

Number 1,983 5,759 1,145 1,101 9,988 9,988

Senegal 2005 0.8 0.9 1.1 0.3 0.7 7,412

Male 0.6 0.5 1.2 0.2 0.5 3,183

Female 0.9 1.3 1.0 0.4 0.9 4,229

Number 3,669 1,707 263 1,772 7,412 7,412

Tanzania 2003/04 5.3 5.3 7.9 8.2 7.0 10,747

Male 4.2 4.8 7.0 7.3 6.3 4,994

Female 5.8 5.8 8.8 9.3 7.7 5,753

Number 1,804 1,894 6,030 1,019 10,747 10,747

(Cont’d)
46 

Table 5.2 – cont’d

Education level

Country/sex Total Number

No 

education

Primary 

incomplete

Primary 

complete

Secondary/ 

higher

Uganda 2004/05 6.3 6.3 8.2 5.8 6.4 16,827

Male 7.7 4.6 6.6 4.4 5.1 7,477

Female 5.9 7.7 9.8 7.6 7.5 9,350

Number 2,717 7,860 2,120 4,130 16,827 16,827

Hai Phong, Vietnam 2005 0.0 4.0 0.0 0.4 0.5 1,675

Male 0.0 8.8 0.0 0.7 0.9 754

Female 0.0 0.0 0.0 0.2 0.2 921

Number 6 50 72 1,547 1,675 1,675

Zimbabwe 2005 20.6 19.5 19.1 17.4 18.1 12,796

Male 23.4 15.1 14.9 14.2 14.5 5,848

Female 20.0 22.5 22.3 20.5 21.1 6,947

Number 362 3,152 661 8,621 12,796 12,796

Note: Some of the HIV prevalence estimates for Guinea and Hai Phong, Vietnam are based on small numbers of cases.

In eight countries—Cameroon, Côte d’Ivoire, Guinea, Haiti, Kenya, Niger, Senegal, and 

Uganda—HIV prevalence is highest among people who have completed primary school.

However, there is an inconsistent relationship between HIV prevalence and level of education by 

sex of the respondents. In three of the countries (Côte d’Ivoire, Senegal, and Niger), HIV prevalence is

higher among more educated men, while women with an incomplete primary or no education have the 

highest prevalence of HIV. In contrast, in three other countries (Guinea, Uganda, and Haiti) HIV 

prevalence among women is highest for those with at least a primary education, while among men those 

with no education have the highest HIV prevalence. In the other two countries (Kenya and Cambodia) 

there is no difference in the relationship between HIV prevalence and level of education by sex of the 

respondents. 

In two countries—Lesotho and Zimbabwe—HIV prevalence is highest among individuals with 

no education. In another three countries—Ghana, India, and the Hai Phong province of Vietnam—HIV 

prevalence is highest among individuals with incomplete primary education. Again, there are notable 

differences by sex of the respondents. For example, in Lesotho and Zimbabwe men with no education 

have the highest HIV prevalence, but among women HIV prevalence varies little by education level. 

5.5 HIV Prevalence by Occupation 

HIV prevalence is related to occupational status, with employees in the professional and 

manual/domestic sectors having the highest prevalence (Table 5.3). In none of the 19 countries is overall 

HIV prevalence highest among those not working or those working in the agricultural sector. 
47 

Table 5.3. HIV prevalence among women and men age 15-49, by occupation 

Occupation

Country/sex Not working Total Number

Professional/ 

white collar Agriculture 

Manual/ 

domestic

Burkina Faso 2003 1.6 3.1 1.4 3.5 1.8 7,151

Male 1.3 3.1 1.9 0.0 1.9 3,065

Female 2.0 3.2 1.1 7.1 1.8 4,086

Number 1,063 1,282 4,427 371 7,151 7,151

Cambodia 2005 0.1 0.5 0.7 1.1 0.6 14,703

Male 0.0 0.3 1.6 1.5 0.6 6,656

Female 0.3 0.8 0.4 0.9 0.6 8,047

Number 1,777 5,324 4,824 2,678 14,703 14,703

Cameroon 2004 4.1 8.3 4.4 7.3 5.4 9,746

Male 1.4 8.7 3.5 5.0 4.1 4,619

Female 5.9 7.2 5.2 9.4 6.6 5,128

Number 3,265 990 3,057 2,435 9,746 9,746

Côte d'Ivoire 2005 3.8 6.7 3.7 2.9 4.7 8,436

Male 2.1 3.7 2.7 3.1 2.9 4,023

Female 5.0 8.1 5.3 1.5 6.4 4,413

Number 2,382 2,939 2,378 736 8,436 8,436

Ethiopia 2005 1.4 4.0 0.7 3.7 1.4 10,540

Male 0.0 2.8 0.7 3.9 0.9 4,804

Female 1.6 4.7 0.5 3.4 1.9 5,736

Number 4,342 1,095 4,600 451 10,540 10,540

Ghana 2003 1.0 3.3 2.2 1.8 2.2 9,142

Male 0.3 1.6 2.0 1.7 1.5 4,045

Female 1.6 3.9 2.4 2.0 2.7 5,097

Number 2,119 2,480 2,972 1,479 9,142 9,142

Guinea 2005 0.8 2.9 1.0 1.9 1.5 6,319

Male 0.3 1.4 1.1 1.4 0.9 2,577

Female 1.4 3.5 0.9 2.8 1.9 3,742

Number 1,375 1,482 2,720 694 6,319 6,319

Haiti 2005 1.3 3.2 2.0 2.9 2.2 9,551

Male 0.8 2.9 2.3 2.9 2.0 4,321

Female 1.7 3.3 1.1 3.0 2.3 5,230

Number 3,933 2,781 1,771 1,058 9,551 9,551

India 2005/06 0.1 0.5 0.3 0.4 0.3 99,838

Male 0.1 0.5 0.3 0.4 0.4 46,506

Female 0.2 0.7 0.2 0.2 0.2 53,332

Number 36,413 15,850 26,972 20,533 99,838 99,838

Kenya 2003 4.6 9.8 5.7 9.0 6.7 6,001

Male 1.2 7.1 4.0 6.9 4.6 2,851

Female 6.6 12.1 7.3 13.1 8.7 3,151

Number 1,917 1,274 1,860 951 6,001 6,001

(Cont’d)
48 

Table 5.3 – cont’d

Occupation

Country/sex Not working Total Number

Professional/ 

white collar Agriculture 

Manual/ 

domestic

Lesotho 2004/05 18.0 32.0 23.0 35.8 23.5 5,019

Male 13.6 29.7 17.9 31.9 19.2 2,001

Female 20.9 33.2 27.1 38.5 26.4 3,018

Number 2,776 574 776 893 5,019 5,019

Malawi 2004 9.6 20.6 12.6 10.3 11.8 5,150

Male 6.4 15.3 18.1 9.2 10.2 2,465

Female 11.3 23.0 11.0 28.3 13.3 2,686

Number 1,623 544 1,517 1,467 5,150 5,150

Niger 2006 0.5 1.2 0.6 0.8 0.7 7,262

Male 0.1 1.3 0.6 1.1 0.8 2,856

Female 0.6 1.2 0.6 0.4 0.7 4,406

Number 2,820 1,671 1,900 795 7,262 7,262

Rwanda 2005 2.2 8.9 2.7 4.0 3.0 9,988

Male 1.7 5.5 1.9 3.4 2.3 4,348

Female 2.8 12.1 3.1 5.8 3.6 5,641

Number 3,643 659 4,784 855 9,988 9,988

Senegal 2005 0.5 1.1 0.9 0.4 0.7 7,412

Male 0.4 0.4 0.7 0.5 0.5 3,183

Female 0.6 1.4 1.2 0.3 0.9 4,229

Number 3,159 1,957 1,143 1,104 7,412 7,412

Tanzania 2003/04 5.0 12.4 5.6 8.5 7.0 10,747

Male 3.5 10.1 5.2 9.0 6.3 4,994

Female 6.1 14.0 6.0 6.7 7.7 5,753

Number 1,740 2,033 6,162 812 10,747 10,747

Uganda 2004/05 3.9 10.7 5.9 8.3 6.4 16,827

Male 1.3 6.7 5.5 7.4 5.1 7,477

Female 5.4 14.1 6.3 9.5 7.5 9,350

Number 4,594 2,538 7,392 2,304 16,827 16,827

Hai Phong, Vietnam 2005 0.0 0.3 0.5 1.1 0.5 1,675

Male 0.0 0.0 1.3 1.4 0.9 754

Female 0.0 0.4 0.0 0.6 0.2 921

Number 284 396 573 422 1,675 1,675

Zimbabwe 2005 15.6 22.6 18.4 19.8 18.1 12,796

Male 8.3 19.6 15.4 17.7 14.5 5,848

Female 18.9 25.1 22.6 24.6 21.1 6,947

Number 5,760 2,250 2,501 2,197 12,796 12,796

In 15 of the 19 countries, HIV prevalence is highest among professional or white-collar workers. 

In six of these countries (Côte d’Ivoire, India, Niger, Rwanda, Tanzania, and Zimbabwe), prevalence is 

highest among both male and female professionals. However, in the other nine countries, there are several 
49 

differences by sex of the respondents in the relationship between occupation and HIV prevalence. In six 

countries—Ethiopia, Ghana, Guinea, Haiti, Senegal, and Uganda—HIV prevalence is highest among 

female professionals, but among men prevalence is highest among manual or agricultural workers. 

Conversely, in Cameroon and Kenya HIV prevalence is highest among male professionals, while among 

women it is highest among manual laborers. In Malawi HIV prevalence is highest among men employed 

in the agricultural sector but highest among women who are manual laborers. 

In four countries—Burkina Faso, Cambodia, Lesotho, and the Hai Phong province of Vietnam—

HIV prevalence is highest among manual laborers. Again, there are differences by respondents’ sex. In 

Burkina Faso HIV prevalence is highest among male professionals, but among women it is highest among 

manual laborers. Similarly, in Cambodia among men HIV prevalence is highest among agricultural 

workers, while among women it is highest among manual laborers. In Lesotho and the Hai Phong 

province of Vietnam, there is no difference by respondents’ sex in HIV prevalence and occupational

status. 

5.6 HIV Prevalence by Household Wealth 

For the majority of countries, HIV prevalence increases with the amount of household wealth 

(Table 5.4). In 12 countries HIV prevalence is highest among individuals living in the highest household 

wealth quintile. In five countries—Cameroon, Haiti, India, Lesotho, and Zimbabwe—HIV prevalence is 

highest in the fourth (next-highest) wealth quintile. In Senegal and Ghana HIV prevalence is highest in 

the third quintile. 

Table 5.4. HIV prevalence among women and men age 15-49, by household wealth status 

Wealth status

Country/sex Lowest Second Third Fourth Highest Total Number

Burkina Faso 2003 1.1 1.9 1.4 1.1 3.1 1.8 7,151

Male 1.4 2.9 1.3 0.4 2.7 1.9 3,065

Female 0.9 1.1 1.5 1.7 3.4 1.8 4,086

Number 1,097 1,398 1,631 1,167 1,859 7,151 7,151

Cambodia 2005 0.4 0.3 0.3 0.8 1.2 0.6 14,703

Male 0.3 0.1 0.2 0.7 1.5 0.6 6,656

Female 0.5 0.4 0.4 0.8 0.9 0.6 8,047

Number 2,482 2,713 2,927 3,005 3,576 14,703 14,703

Cameroon 2004 2.4 3.1 6.4 7.2 6.6 5.4 9,746

Male 1.4 2.2 4.5 5.3 5.4 4.1 4,619

Female 3.1 4.0 8.1 9.1 7.9 6.6 5,128

Number 1,575 1,659 1,857 2,156 2,500 9,746 9,746

Côte d'Ivoire 2005 2.7 3.6 5.5 5.2 5.9 4.7 8,436

Male 1.7 3.4 4.3 2.1 2.7 2.9 4,023

Female 3.6 3.9 6.5 8.0 8.8 6.4 4,413

Number 1,436 1,591 1,634 1,723 2,053 8,436 8,436

Ethiopia 2005 0.5 0.7 0.6 0.3 4.3 1.4 10,540

Male 0.7 0.3 0.9 0.4 2.2 0.9 4,804

Female 0.3 1.0 0.4 0.2 6.1 1.9 5,736

Number 1,916 2,057 2,006 2,023 2,538 10,540 10,540

(Cont’d)
50 

Table 5.4 – cont’d

Wealth status

Country/sex Lowest Second Third Fourth Highest Total Number

Ghana 2003 1.4 2.2 3.1 2.2 1.9 2.2 9,142

Male 1.4 1.5 2.0 1.4 1.1 1.5 4,045

Female 1.4 2.7 4.0 3.0 2.4 2.7 5,097

Number 1,567 1,582 1,763 2,001 2,229 9,142 9,142

Guinea 2005 1.7 0.7 0.5 2.1 2.2 1.5 6,319

Male 2.1 0.7 0.4 1.0 0.7 0.9 2,577

Female 1.4 0.7 0.6 2.8 3.6 1.9 3,742

Number 1,191 1,099 1,173 1,269 1,585 6,319 6,319

Haiti 2005 2.1 1.8 2.4 3.1 1.5 2.2 9,551

Male 1.7 2.8 2.4 2.3 0.9 2.0 4,321

Female 2.4 0.9 2.4 3.7 1.9 2.3 5,230

Number 1,470 1,729 1,724 2,165 2,463 9,551 9,551

India 2005/06 0.3 0.2 0.3 0.4 0.2 0.3 99,838

Male 0.4 0.3 0.3 0.5 0.2 0.4 46,506

Female 0.2 0.2 0.3 0.3 0.1 0.2 53,332

Number 16,623 18,956 20,500 21,393 22,366 99,838 99,838

Kenya 2003 3.6 6.5 4.8 7.1 9.8 6.7 6,001

Male 3.4 4.2 2.2 4.3 7.3 4.6 2,851

Female 3.9 8.5 7.1 9.7 12.2 8.7 3,151

Number 937 1,082 1,125 1,287 1,571 6,001 6,001

Lesotho 2004/05 19.0 23.4 24.7 25.0 24.2 23.5 5,019

Male 18.3 16.8 23.7 21.6 14.8 19.2 2,001

Female 19.6 27.9 25.5 27.3 28.9 26.4 3,018

Number 767 945 967 1,093 1,247 5,019 5,019

Malawi 2004 8.3 7.6 12.4 13.2 16.4 11.8 5,150

Male 4.4 4.6 12.1 11.7 14.9 10.2 2,465

Female 10.9 10.3 12.7 14.6 18.0 13.3 2,686

Number 755 1,033 1,149 1,159 1,054 5,150 5,150

Niger 2006 0.3 0.7 0.6 0.6 1.4 0.7 7,262

Male 0.2 1.0 0.8 0.1 1.4 0.8 2,856

Female 0.3 0.5 0.4 0.9 1.3 0.7 4,406

Number 1,140 1,367 1,533 1,447 1,774 7,262 7,262

Rwanda 2005 2.1 2.0 2.8 2.8 5.4 3.0 9,988

Male 1.3 1.7 1.8 2.1 4.1 2.3 4,348

Female 2.6 2.1 3.6 3.4 6.6 3.6 5,641

Number 1,992 1,974 1,918 1,997 2,108 9,988 9,988

Senegal 2005 0.7 0.8 0.9 0.7 0.6 0.7 7,412

Male 0.2 0.6 0.7 0.2 0.6 0.5 3,183

Female 1.0 1.0 1.0 1.0 0.6 0.9 4,229

Number 1,198 1,109 1,322 1,602 2,181 7,412 7,412

(Cont’d)
51 

Table 5.4 – cont’d

Wealth status

Country/sex Lowest Second Third Fourth Highest Total Number

Tanzania 2003/04 3.4 4.5 5.6 9.4 10.5 7.0 10,747

Male 4.1 4.3 4.3 7.7 9.5 6.3 4,994

Female 2.8 4.7 6.8 10.9 11.4 7.7 5,753

Number 1,837 1,966 2,035 2,142 2,766 10,747 10,747

Uganda 2004/05 4.5 5.5 6.0 6.5 8.6 6.4 16,827

Male 4.0 4.2 5.1 5.9 5.6 5.1 7,477

Female 4.9 6.6 6.7 7.0 11.0 7.5 9,350

Number 2,656 3,436 3,164 3,430 4,141 16,827 16,827

Hai Phong, Vietnam 2005 0.0 0.8 0.2 0.2 1.0 0.5 1,675

Male 0.0 0.9 0.5 0.4 1.9 0.9 754

Female 0.0 0.8 0.0 0.0 0.3 0.2 921

Number 21 252 418 504 479 1,675 1,675

Zimbabwe 2005 15.9 18.4 17.9 21.9 15.6 18.1 12,796

Male 13.4 15.1 12.2 17.1 13.5 14.5 5,848

Female 17.7 21.1 22.8 26.9 17.1 21.1 6,947

Number 2,121 2,180 2,281 3,197 3,018 12,796 12,796

Note: HIV prevalence estimates for the lowest wealth quintile for Hai Phong, Vietnam are based on small numbers of cases.

For the most part, there is no difference in the relationship between household wealth status and 

HIV prevalence by sex of respondents—both men and women in the higher wealth quintiles have higher 

HIV infection rates (Figures 5.1 and 5.2). However, in Burkina Faso, Côte d’Ivoire, Guinea, Haiti, 

Lesotho, and Uganda the highest HIV prevalence for men occurs in a lower wealth quintile than for 

women. In Burkina Faso the peak for HIV prevalence among men is in the second wealth quintile, while 

for women it is in the highest (fifth) quintile. Conversely, in Cameroon and in the Hai Phong province of 

Vietnam the peak of HIV prevalence occurs at lower wealth quintiles for women than for men. In the Hai 

Phong province of Vietnam, HIV prevalence among men is highest in the highest wealth quintile, while 

among women there are no HIV-positive cases in the sample in the lowest, third, and fourth wealth 

quintiles. 
52 

Figure 5.1. HIV prevalence among tested men 15-49 

by wealth status 

0

5

10

15

20

25

30

India

Hai Phong, Vietnam

Cambodia

Senegal

Niger

Ethiopia

Guinea

Burkina Faso

Ghana

Haiti

Rwanda

Côte d'Ivoire

Cameroon

Uganda

Kenya

Tanzania

Malawi

Zimbabwe

Lesotho

Wealth index (Q1 - Q5)

HIV prevalence (%)

Note: HIV prevalence estimate for the lowest wealth quintile for Hai Phong, Vietnam is based on a small number of cases.

Figure 5.2. HIV prevalence among tested women 15-49 

by wealth status 

0

5

10

15

20

25

30

India

Cambodia

Senegal

Niger

Ethiopia

Guinea

Burkina Faso

Ghana

Haiti

Rwanda

Côte d'Ivoire

Cameroon

Uganda

Kenya

Tanzania

Malawi

Zimbabwe

Lesotho

Wealth index (Q1 - Q5)

HIV prevalence (%)
53 

5.7 HIV Prevalence by Media Exposure 

Media exposure is measured in three ways: by the frequency of reading newspapers, listening to 

the radio, and/or watching television. The effect of each type of exposure on HIV prevalence is presented 

separately, along with a composite measure of exposure to all three media sources. 

Radio Exposure 

There is no clear pattern across countries between listening to the radio and HIV prevalence 

(Table 5.5). In 10 countries—Burkina Faso, Cameroon, Ethiopia, Guinea, Haiti, Kenya, Niger, Senegal, 

Tanzania, and Uganda—HIV prevalence is highest among respondents who listen to the radio at least 

once a week. However, in Ghana, Lesotho, the Hai Phong province of Vietnam, and Rwanda, HIV 

prevalence is highest among those who listen to the radio less than once a week, and in Cambodia, Côte

d’Ivoire, India, Malawi, and Zimbabwe, HIV prevalence is highest among individuals who never listen to 

the radio. 

There are several differences by sex of respondents in the relationship between HIV prevalence 

and frequency of radio exposure. In Burkina Faso, Malawi, Senegal, and Zimbabwe, HIV prevalence is 

highest among men with more exposure to radio, but highest among women with less radio exposure. 

Conversely, in Cote d’Ivoire, Ghana, Guinea, Haiti, Rwanda, Uganda, and in the Hai Phong province of 

Vietnam, HIV prevalence is highest among women with frequent radio exposure, but highest among men 

with less exposure. In the other eight countries there is no difference in this measure by respondents’ sex. 

Table 5.5. HIV prevalence among women and men age 15-49, by frequency of listening to the radio 

Radio

Country/sex Not at all Total Number

Less than 

once per week

At least once 

per week

Burkina Faso 2003 1.1 1.8 2.2 1.8 7,151

Male 1.4 2.8 1.9 1.9 3,065

Female 1.0 1.4 2.5 1.8 4,086

Number 1,806 1,071 4,262 7,151 7,151

Cambodia 2005 0.7 0.5 0.6 0.6 14,703

Male 0.8 0.4 0.6 0.6 6,656

Female 0.7 0.7 0.5 0.6 8,047

Number 3,654 2,907 8,134 14,703 14,703

Cameroon 2004 4.2 5.4 6.0 5.4 9,746

Male 1.4 3.2 4.7 4.1 4,619

Female 5.1 6.8 7.8 6.6 5,128

Number 2,443 1,454 5,849 9,746 9,746

Côte d'Ivoire 2005 5.1 4.5 4.6 4.7 8,436

Male 3.5 3.9 2.6 2.9 4,023

Female 5.6 4.8 7.7 6.4 4,413

Number 2,457 1,012 4,967 8,436 8,436

Ethiopia 2005 0.7 1.5 2.9 1.4 10,540

Male 0.6 0.7 1.6 0.9 4,804

Female 0.7 2.3 5.1 1.9 5,736

Number 5,085 3,023 2,431 10,540 10,540

(Cont’d)
54 

Table 5.5 – cont’d

Radio

Country/sex Not at all Total Number

Less than 

once per week

At least once 

per week

Ghana 2003 2.4 2.5 2.1 2.2 9,142

Male 2.7 1.7 1.4 1.5 4,045

Female 2.3 2.8 2.8 2.7 5,097

Number 821 937 7,384 9,142 9,142

Guinea 2005 1.3 1.1 1.8 1.5 6,319

Male 1.9 0.4 0.9 0.9 2,577

Female 1.1 1.4 2.7 1.9 3,742

Number 1,428 1,366 3,505 6,319 6,319

Haiti 2005 1.5 2.1 2.2 2.2 9,551

Male 1.6 2.2 2.0 2.0 4,321

Female 1.5 2.1 2.5 2.3 5,230

Number 473 1,369 7,709 9,551 9,551

India 2005/06 0.3 0.2 0.3 0.3 99,838

Male 0.4 0.3 0.3 0.4 46,506

Female 0.3 0.1 0.2 0.2 53,332

Number 43,437 20,347 36,035 99,838 99,838

Kenya 2003 6.1 6.4 6.8 6.7 6,001

Male 2.7 1.4 4.8 4.6 2,851

Female 7.2 8.7 9.0 8.7 3,151

Number 630 384 4,988 6,001 6,001

Lesotho 2004/05 22.1 26.0 24.2 23.5 5,019

Male 16.5 24.5 20.3 19.2 2,001

Female 25.7 27.1 26.9 26.4 3,018

Number 1,953 324 2,742 5,019 5,019

Malawi 2004 12.6 10.8 11.9 11.8 5,150

Male 7.0 8.3 10.6 10.2 2,465

Female 13.8 12.4 13.4 13.3 2,686

Number 685 555 3,911 5,150 5,150

Niger 2006 0.3 0.7 1.0 0.7 7,262

Male 0.7 0.1 0.9 0.8 2,856

Female 0.2 0.9 1.2 0.7 4,406

Number 2,204 1,023 4,034 7,262 7,262

Rwanda 2005 3.2 3.2 2.9 3.0 9,988

Male 1.9 3.0 2.2 2.3 4,348

Female 3.5 3.3 3.8 3.6 5,641

Number 1,632 1,726 6,585 9,988 9,988

Senegal 2005 0.6 0.2 0.8 0.7 7,412

Male 0.4 0.0 0.5 0.5 3,183

Female 1.3 0.2 0.9 0.9 4,229

Number 1,742 463 5,207 7,412 7,412

(Cont’d)
55 

Table 5.5 – cont’d

Radio

Country/sex Not at all Total Number

Less than 

once per week

At least once 

per week

Tanzania 2003/04 4.8 7.3 7.8 7.0 10,747

Male 3.1 5.5 6.9 6.3 4,994

Female 5.4 8.6 8.8 7.7 5,753

Number 2,454 1,049 7,244 10,747 10,747

Uganda 2004/05 5.7 5.5 6.6 6.4 16,827

Male 4.2 5.6 5.1 5.1 7,477

Female 6.1 5.4 8.2 7.5 9,350

Number 2,208 1,633 12,987 16,827 16,827

Hai Phong, Vietnam 2005 0.5 1.1 0.4 0.5 1,675

Male 1.2 1.1 0.7 0.9 754

Female 0.0 1.1 0.2 0.2 921

Number 583 183 909 1,675 1,675

Zimbabwe 2005 20.5 18.5 16.6 18.1 12,796

Male 15.0 15.5 14.2 14.5 5,848

Female 22.9 22.0 19.3 21.1 6,947

Number 4,280 1,396 7,120 12,796 12,796

Television Exposure 

As with radio, there is no clear pattern in the relationship between television viewing and HIV 

prevalence (Table 5.6). In seven countries—Burkina Faso, Côte d’Ivoire, Ethiopia, India, Kenya, Niger, 

and Rwanda—HIV prevalence is highest among individuals who watch television at least once a week. 

However, in Cambodia, Cameroon, Guinea, Lesotho, Senegal, Tanzania, and Uganda, HIV prevalence is 

highest among infrequent television viewers. Similarly, in Ghana, Haiti, Malawi, the Hai Phong province 

of Vietnam, and Zimbabwe, HIV prevalence is highest among those who never watch television. 

There are several differences by respondents’ sex in the relationship between HIV prevalence and 

frequency of television viewing. In Kenya, Niger, Rwanda, Lesotho, Tanzania, Senegal, Malawi, and 

Zimbabwe, HIV prevalence is highest among men with more exposure to television, but among women 

higher HIV prevalence is associated with less television exposure. In contrast, in Burkina Faso, Haiti, and 

in the Hai Phong province of Vietnam, HIV prevalence is highest among women with more exposure to 

television but among men those with limited or no exposure to television have the highest HIV 

prevalence. There are no differences by respondents’ sex in the other eight countries. 
56 

Table 5.6. HIV prevalence among women and men age 15-49, by frequency watching television 

Television

Country/sex Not at all Total Number

Less than once 

per week

At least once 

per week

Burkina Faso 2003 1.5 2.1 2.6 1.8 7151

Male 1.8 2.5 1.8 1.9 3065

Female 1.4 1.5 3.5 1.8 4086

Number 4,663 670 1,813 7,151 7,151

Cambodia 2005 0.5 0.9 0.6 0.6 14,703

Male 0.5 1.0 0.6 0.6 6,656

Female 0.5 0.8 0.6 0.6 8,047

Number 2,421 2,093 10,183 14,703 14,703

Cameroon 2004 4.7 6.0 5.9 5.4 9,746

Male 2.9 4.6 4.6 4.1 4,619

Female 5.5 8.2 7.6 6.6 5,128

Number 3,828 1,765 4,153 9,746 9,746

Côte d'Ivoire 2005 3.9 3.7 5.2 4.7 8,436

Male 2.6 1.4 3.2 2.9 4,023

Female 4.7 6.6 7.3 6.4 4,413

Number 2,336 870 5,230 8,436 8,436

Ethiopia 2005 1.0 1.6 5.0 1.4 10,540

Male 0.7 1.1 1.9 0.9 4,804

Female 1.2 2.4 8.9 1.9 5,736

Number 7,953 1,698 888 10,540 10,540

Ghana 2003 2.5 1.9 2.0 2.2 9,142

Male 1.6 1.4 1.4 1.5 4,045

Female 3.0 2.5 2.5 2.7 5,097

Number 3,217 1,618 4,307 9,142 9,142

Guinea 2005 1.0 3.0 2.0 1.5 6,319

Male 1.0 1.0 0.8 0.9 2,577

Female 1.0 4.6 3.4 1.9 3,742

Number 4,057 632 1,612 6,319 6,319

Haiti 2005 2.5 1.9 2.0 2.2 9,551

Male 3.0 1.1 1.7 2.0 4,321

Female 2.2 2.7 2.2 2.3 5,230

Number 3,795 2,664 3,093 9,551 9,551

India 2005/06 0.2 0.3 0.3 0.3 99,838

Male 0.3 0.3 0.4 0.4 46,506

Female 0.2 0.2 0.3 0.2 53,332

Number 25,969 15,007 58,846 99,838 99,838

Kenya 2003 6.8 6.5 6.8 6.7 6,001

Male 4.3 4.1 5.1 4.6 2,851

Female 8.1 11.9 8.9 8.7 3,151

Number 2,995 962 2,045 6,001 6,001

(Cont’d)
57 

Table 5.6 – cont’d

Television

Country/sex Not at all Total Number

Less than once 

per week

At least once 

per week

Lesotho 2004/05 23.6 25.1 22.9 23.5 5,019

Male 19.4 9.8 21.4 19.2 2,001

Female 26.0 41.7 24.4 26.4 3,018

Number 3,957 249 813 5,019 5,019

Malawi 2004 12.3 10.0 11.0 11.8 5,150

Male 10.3 8.9 11.4 10.2 2,465

Female 13.5 13.8 10.1 13.3 2,686

Number 3,916 600 635 5,150 5,150

Niger 2006 0.6 0.8 1.2 0.7 7,262

Male 0.6 0.6 1.4 0.8 2,856

Female 0.7 1.2 0.9 0.7 4,406

Number 5,731 358 1,173 7,262 7,262

Rwanda 2005 2.8 3.5 4.4 3.0 9,988

Male 2.1 2.0 3.6 2.3 4,348

Female 3.3 6.6 5.6 3.6 5,641

Number 8,190 994 788 9,988 9,988

Senegal 2005 0.8 0.9 0.6 0.7 7,412

Male 0.4 0.9 0.5 0.5 3,183

Female 1.3 0.8 0.7 0.9 4,229

Number 2,480 591 4,341 7,412 7,412

Tanzania 2003/04 6.1 8.9 8.7 7.0 10,747

Male 4.9 7.6 8.1 6.3 4,994

Female 6.9 11.1 9.5 7.7 5,753

Number 6,993 1,341 2,414 10,747 10,747

Uganda 2004/05 6.2 7.9 6.8 6.4 16,827

Male 5.0 5.7 4.6 5.1 7,477

Female 7.0 11.7 9.7 7.5 9,350

Number 13,222 1,500 2,106 16,827 16,827

Hai Phong, Vietnam 2005 5.4 0.0 0.5 0.5 1,675

Male 14.8 0.0 0.8 0.9 754

Female 0.0 0.0 0.2 0.2 921

Number 19 16 1,640 1,675 1,675

Zimbabwe 2005 18.9 17.2 17.3 18.1 12,796

Male 14.3 14.3 14.8 14.5 5,848

Female 21.8 22.6 19.8 21.1 6,947

Number 6,713 1,073 5,009 12,796 12,796

Note: Some of the HIV prevalence estimates for Hai Phong, Vietnam are based on small numbers of cases.
58 

Newspaper/Magazine Exposure 

There is an inconsistent pattern in the relationship between the frequency of newspaper reading 

and HIV prevalence (Table 5.7). For eight countries—Burkina Faso, Ethiopia, Guinea, Kenya, Malawi, 

Niger, Rwanda, and Tanzania—HIV prevalence is highest among those who read a newspaper or 

magazine at least once a week. However, in six countries—Cambodia, Cote d’Ivoire, Lesotho, Senegal, 

the Hai Phong province of Vietnam, and Zimbabwe—prevalence is highest among infrequent newspaper 

readers (those who read a newspaper less than once a week), while in Ghana, Haiti, India, and Uganda, 

HIV prevalence is highest among individuals who never read a newspaper or magazine. 

For the most part, there are no differences by respondents’ sex in the relationship between 

newspaper reading and HIV prevalence. However, in Burkina Faso, Malawi, Cambodia, the Hai Phong 

province of Vietnam, and Uganda, HIV prevalence is highest among men with more exposure to 

newspapers, while it is highest among women with less exposure to newspapers. In contrast, in Ethiopia 

and Côte d’Ivoire HIV prevalence is highest among women who are frequently exposed to newspapers 

but highest among men with less exposure to newspapers. 

Table 5.7. HIV prevalence among women and men age 15-49, by frequency of reading newspaper 

and magazine 

Newspaper or magazine

Country/sex Not at all Total Number

Less than once 

per week

At least once 

per week

Burkina Faso 2003 1.6 3.0 3.9 1.8 7,151

Male 1.6 0.9 4.4 1.9 3,065

Female 1.6 6.0 3.2 1.8 4,086

Number 6,209 377 548 7,151 7,151

Cambodia 2005 0.5 0.9 0.6 0.6 14,703

Male 0.6 0.6 0.9 0.6 6,656

Female 0.5 1.1 0.3 0.6 8,047

Number 9,715 3,024 1,949 14,703 14,703

Cameroon 2004 5.0 6.3 6.0 5.4 9,746

Male 3.7 4.5 4.6 4.1 4,619

Female 5.8 8.5 8.7 6.6 5,128

Number 5,899 1,904 1,943 9,746 9,746

Côte d'Ivoire 2005 4.8 5.7 3.9 4.7 8,436

Male 3.0 2.8 2.6 2.9 4,023

Female 6.0 10.1 6.7 6.4 4,413

Number 5,601 957 1,878 8,436 8,436

Ethiopia 2005 1.2 2.4 2.4 1.4 10,540

Male 0.7 1.5 1.2 0.9 4,804

Female 1.5 3.6 5.4 1.9 5,736

Number 8,119 1,941 480 10,540 10,540

Ghana 2003 2.5 1.6 1.1 2.2 9,142

Male 1.6 1.5 1.2 1.5 4,045

Female 3.1 1.9 1.0 2.7 5,097

Number 6,315 1,078 1,748 9,142 9,142

(Cont’d)
59 

Table 5.7 – cont’d

Newspaper or magazine

Country/sex Not at all Total Number

Less than once 

per week

At least once 

per week

Guinea 2005 1.3 1.8 3.6 1.5 6,319

Male 0.9 0.0 2.0 0.9 2,577

Female 1.5 5.6 6.0 1.9 3,742

Number 5,467 391 414 6,319 6,319

Haiti 2005 2.5 1.9 1.8 2.2 9,551

Male 2.5 1.5 1.3 2.0 4,321

Female 2.4 2.2 2.2 2.3 5,230

Number 5,189 2,153 2,210 9,551 9,551

India 2005/06 0.3 0.2 0.3 0.3 99,838

Male 0.4 0.3 0.3 0.4 46,506

Female 0.3 0.1 0.2 0.2 53,332

Number 48,077 14,851 36,819 99,838 99,838

Kenya 2003 6.7 6.2 7.1 6.7 6,001

Male 3.1 3.9 6.0 4.6 2,851

Female 8.5 8.8 9.2 8.7 3,151

Number 2,704 1,340 1,958 6,001 6,001

Lesotho 2004/05 23.9 30.4 17.8 23.5 5,019

Male 19.1 28.1 15.8 19.2 2,001

Female 27.1 31.6 19.5 26.4 3,018

Number 3,809 428 782 5,019 5,019

Malawi 2004 11.2 12.4 13.2 11.8 5,150

Male 8.9 10.3 12.4 10.2 2,465

Female 12.5 16.0 14.7 13.3 2,686

Number 3,046 1,152 952 5,150 5,150

Niger 2006 0.7 0.8 1.5 0.7 7,262

Male 0.7 0.8 1.4 0.8 2,856

Female 0.7 0.8 1.6 0.7 4,406

Number 6,683 272 307 7,262 7,262

Rwanda 2005 2.9 2.7 4.7 3.0 9,988

Male 2.1 1.7 4.7 2.3 4,348

Female 3.5 3.7 4.8 3.6 5,641

Number 7,080 1,963 924 9,988 9,988

Senegal 2005 0.7 1.1 0.4 0.7 7,412

Male 0.4 0.7 0.5 0.5 3,183

Female 0.9 1.4 0.2 0.9 4,229

Number 5,294 896 1,221 7,412 7,412

Tanzania 2003/04 5.5 8.1 9.5 7.0 10,747

Male 4.4 6.3 8.7 6.3 4,994

Female 6.1 10.2 10.6 7.7 5,753

Number 5,918 1,958 2,872 10,747 10,747

(Cont’d)
60 

Table 5.7 – cont’d

Newspaper or magazine

Country/sex Not at all Total Number

Less than once 

per week

At least once 

per week

Uganda 2004/05 6.6 6.0 6.2 6.4 16,827

Male 5.1 4.6 5.3 5.1 7,477

Female 7.5 7.7 7.5 7.5 9,350

Number 10,577 2,997 3,253 16,827 16,827

Hai Phong, Vietnam 2005 0.3 0.7 0.6 0.5 1,675

Male 0.8 0.6 1.1 0.9 754

Female 0.0 0.7 0.2 0.2 921

Number 592 310 773 1,675 1,675

Zimbabwe 2005 18.8 19.0 16.5 18.1 12,796

Male 13.8 15.3 14.8 14.5 5,848

Female 21.7 22.4 18.7 21.1 6,947

Number 6,127 2,726 3,943 12,796 12,796

Media Exposure (a composite measure) 

Unsurprisingly, given the mixed information above, no clear pattern emerges in the composite 

measure of media exposure and HIV prevalence (Table 5.8). In Ghana, the Hai Phong province of 

Vietnam, and Zimbabwe, HIV prevalence is highest among individuals who have no exposure to any of 

the three types of media. In Haiti, Lesotho, and Senegal, HIV prevalence is highest among individuals 

who are exposed to at least one media source at least once a week, while in Cameroon, Côte d’Ivoire, 

Kenya, and Uganda, HIV prevalence is highest among individuals who are exposed to two media sources 

at least once a week. 

In Cambodia HIV prevalence is highest among individuals exposed to one or two media sources 

at least once a week. In Burkina Faso, Ethiopia, Guinea, Malawi, Niger, Rwanda, and Tanzania, HIV 

prevalence is highest among individuals who are exposed to all three media sources at least once a week. 

In India HIV prevalence varies little by the number of media sources a person is exposed to. 

Generally, there is little difference in the relationship between media exposure and HIV 

prevalence by respondents’ sex. In Lesotho, Malawi, Niger, and Senegal, however, the highest HIV 

prevalence for men occurs at higher levels of media exposure than for women. In contrast, in Kenya and 

in the Hai Phong province of Vietnam, HIV prevalence for women is higher at higher levels of media 

exposure than for men. 
61 

Table 5.8. HIV prevalence among women and men age 15-49, by number of media sources 

exposed to at least once per week 

Number of media source exposed to at least once per week

Country/sex Not at all One media Two media Three media Total Number

Burkina Faso 2003 1.3 1.7 2.6 3.8 1.8 7,151

Male 2.0 1.5 2.0 3.5 1.9 3,065

Female 1.0 2.0 3.3 4.3 1.8 4,086

Number 2,559 2,957 1,242 394 7,151 7,151

Cambodia 2005 0.5 0.7 0.7 0.2 0.6 14,703

Male 0.6 0.5 0.8 0.3 0.6 6,656

Female 0.4 0.9 0.6 0.1 0.6 8,047

Number 2,775 5,041 5,423 1,459 14,703 14,703

Cameroon 2004 4.6 5.3 6.3 5.9 5.4 9,746

Male 2.5 3.8 4.9 4.8 4.1 4,619

Female 5.4 6.9 8.2 8.0 6.6 5,128

Number 3,130 2,624 2,655 1,337 9,746 9,746

Côte d'Ivoire 2005 4.0 5.1 5.7 3.6 4.7 8,436

Male 2.9 2.6 3.4 2.5 2.9 4,023

Female 4.5 7.1 8.3 6.1 6.4 4,413

Number 2,078 2,220 2,555 1,582 8,436 8,436

Ethiopia 2005 0.8 2.8 4.0 4.2 1.4 10,540

Male 0.6 1.5 1.6 1.9 0.9 4,804

Female 1.0 4.7 7.3 11.9 1.9 5,736

Number 7,680 2,093 592 174 10,540 10,540

Ghana 2003 2.8 2.1 2.4 1.1 2.2 9,142

Male 2.4 1.3 1.6 1.2 1.5 4,045

Female 2.9 2.7 3.1 1.0 2.7 5,097

Number 1,415 3,419 2,905 1,403 9,142 9,142

Guinea 2005 1.2 1.3 2.0 3.7 1.5 6,319

Male 1.2 0.8 0.5 2.3 0.9 2,577

Female 1.2 1.8 3.9 6.3 1.9 3,742

Number 2,433 2,540 1,039 304 6,319 6,319

Haiti 2005 1.8 2.6 2.0 1.7 2.2 9,551

Male 1.8 2.5 1.5 1.4 2.0 4,321

Female 1.8 2.7 2.4 2.0 2.3 5,230

Number 1,628 4,206 2,345 1,372 9,551 9,551

India 2005/06 0.2 0.3 0.3 0.3 0.3 99,838

Male 0.3 0.4 0.3 0.3 0.4 46,506

Female 0.2 0.3 0.2 0.2 0.2 53,332

Number 27,713 28,538 27,601 15,987 99,838 99,838

Kenya 2003 6.6 6.4 7.1 7.0 6.7 6,001

Male 2.6 3.2 6.0 5.5 4.6 2,851

Female 7.9 8.9 8.3 9.9 8.7 3,151

Number 875 2,430 1,529 1,167 6,001 6,001

(Cont’d)
62 

Table 5.8 – cont’d

Number of media source exposed to at least once per week

Country/sex Not at all One media Two media Three media Total Number

Lesotho 2004/05 22.7 25.9 22.6 16.9 23.5 5,019

Male 18.2 19.7 21.1 17.9 19.2 2,001

Female 25.6 29.8 23.5 15.6 26.4 3,018

Number 2,070 1,854 799 295 5,019 5,019

Malawi 2004 11.7 11.9 11.4 13.3 11.8 5,150

Male 8.0 10.0 9.7 14.7 10.2 2,465

Female 13.0 13.6 14.7 9.3 13.3 2,686

Number 1,123 2,909 763 354 5,150 5,150

Niger 2006 0.3 1.0 1.0 1.8 0.7 7,262

Male 0.4 0.8 1.1 2.1 0.8 2,856

Female 0.3 1.2 1.0 1.1 0.7 4,406

Number 3,073 3,070 912 207 7,262 7,262

Rwanda 2005 3.2 2.7 3.7 6.5 3.0 9,988

Male 2.6 1.9 2.5 6.3 2.3 4,348

Female 3.4 3.5 4.9 6.7 3.6 5,641

Number 3,225 5,504 979 278 9,988 9,988

Senegal 2005 0.5 1.1 0.8 0.3 0.7 7,412

Male 0.4 0.5 0.6 0.5 0.5 3,183

Female 0.8 1.2 0.9 0.1 0.9 4,229

Number 1,818 1,538 2,936 1,120 7,412 7,412

Tanzania 2003/04 5.3 6.8 7.7 10.1 7.0 10,747

Male 4.1 5.4 7.0 9.5 6.3 4,994

Female 5.9 8.2 8.7 11.1 7.7 5,753

Number 3,170 4,174 1,855 1,549 10,747 10,747

Uganda 2004/05 5.9 6.5 6.7 6.5 6.4 16,827

Male 5.3 4.9 5.3 5.0 5.1 7,477

Female 6.1 7.9 8.5 9.4 7.5 9,350

Number 3,574 9,378 2,659 1,217 16,827 16,827

Hai Phong, Vietnam 2005 4.1 0.2 0.8 0.4 0.5 1,675

Male 12.9 0.0 1.4 0.4 0.9 754

Female 0.0 0.3 0.0 0.3 0.2 921

Number 25 506 616 528 1,675 1,675

Zimbabwe 2005 20.2 17.0 17.5 16.2 18.1 12,796

Male 15.4 13.4 13.7 15.4 14.5 5,848

Female 22.6 20.9 21.1 17.3 21.1 6,947

Number 4,624 2,875 2,693 2,604 12,796 12,796

Note: HIV prevalence estimates for ‘not at all’ category for Hai Phong, Vietnam are based on small numbers of cases.

 
63 

6 HIV Prevalence by HIV/AIDS Knowledge, Misconceptions, and 

Attitudes 

6.1 Key Findings 

6.2 Introduction 

Information from the DHS provides an assessment of levels of knowledge regarding methods of 

preventing HIV transmission, as well misconceptions about how HIV is transmitted. The surveys also 

identify attitudes toward people living with HIV, in order to assess the level of AIDS-related stigma. This 

chapter presents information about the effect of HIV knowledge and attitudes on HIV prevalence. 

Participants are asked if they have heard of abstinence, monogamy, and condom use as HIV prevention 

methods. They are also asked if they know that breastfeeding can transmit HIV from an infected mother 

to her infant and whether they are aware that anti-retroviral drugs can prevent mother-to-child 

transmission of HIV. To identify gaps in HIV knowledge, participants are asked whether a healthy person 

can have AIDS and whether mosquitoes can spread AIDS. Finally, to assess attitudes toward people 

living with HIV, participants are asked whether or not they would: (1) keep the HIV-positive status of a 

family member a secret, (2) care for a family member with AIDS, (3) allow an infected female teacher to 

teach, and (4) buy vegetables from a vendor with AIDS. 

 6.3 HIV Prevalence by Knowledge of HIV Prevention Methods 

In 15 of the 19 countries with data, HIV prevalence is highest among people who know that 

abstinence is an HIV prevention strategy (Table 6.1). In Cameroon, Ghana, and Senegal respondents who 

are unaware of abstinence as an HIV prevention strategy have slightly higher HIV prevalence, although 

differences in prevalence between those who know and those who do not know about abstinence for all 

three countries is less than 1 percent. In the Hai Phong province of Vietnam HIV prevalence does not 

differ between these two groups. (In Burkina Faso data on abstinence as an HIV prevention strategy is 

available only for men.) 

 Differences in HIV prevalence are small between individuals who know the three main HIV 

prevention strategies—abstinence, monogamy, and condom use—and those who do not know. 

In the majority of countries, knowledge of all three methods of HIV prevention is associated 

with higher HIV prevalence. 

 Similarly, there is little difference in HIV prevalence among those who have correct 

information about modes of HIV transmission, both vertically and horizontally—suggesting 

that HIV knowledge may not be strongly associated with HIV prevalence. 

 For most countries, having a more positive attitude towards people living with HIV/AIDS is 

associated with higher HIV prevalence, although differences in HIV prevalence rates across 

measures of HIV-related stigma are small. 

 Generally, there are only small differences in HIV prevalence by any of these measures, 

although the differences vary substantially by country. Notably, there are no differences by 

respondents’ sex in HIV prevalence by any of the measures of knowledge and attitudes 

discussed in this section.
64 

Table 6.1. HIV prevalence among women and men age 15-49, 

by knowledge of AIDS prevention methods 

Abstinence Being faithful Using condom All three

Country/sex No Yes No Yes No Yes No Yes Number

Burkina Faso 2003 n/a n/a 1.2 2.1 1.5 2.1 n/a n/a 7,151

Male 2.2 1.5 1.6 1.9 1.8 1.9 2.0 1.6 3,065

Female n/a n/a 1.1 2.3 1.4 2.4 n/a n/a 4,086

Number 1,533 1,532 2,289 4,862 3,231 3,920 1,942 1,123 7,151

Cambodia 2005 0.4 0.7 0.3 0.7 0.2 0.7 0.3 0.7 14,703

Male 0.2 0.7 0.2 0.7 0.1 0.7 0.1 0.7 6,656

Female 0.5 0.6 0.4 0.7 0.3 0.7 0.5 0.7 8,047

Number 1,599 12,938 1,482 13,061 1,389 13,154 3,338 11,366 14,703

Cameroon 2004 5.9 5.3 4.9 5.5 4.9 5.6 5.6 5.3 9,746

Male 6.0 3.8 5.1 4.0 4.1 4.1 5.2 3.6 4,619

Female 5.9 6.9 4.8 7.0 5.3 7.3 5.9 7.3 5,128

Number 2,033 7,713 1,529 8,217 2,438 7,309 3,962 5,785 9,746

Côte d'Ivoire 2005 4.6 4.8 5.1 4.6 4.7 4.7 4.9 4.6 8,436

Male 2.5 3.0 3.4 2.7 2.5 3.0 3.1 2.7 4,023

Female 6.2 6.5 6.1 6.5 5.9 6.7 6.2 6.6 4,413

Number 2,444 5,992 2,200 6,236 2,650 5,786 3,709 4,727 8,436

Ethiopia 2005 1.2 1.6 0.9 1.7 0.8 2.1 0.9 2.2 10,540

Male 0.4 1.1 0.4 1.1 0.2 1.4 0.3 1.5 4,804

Female 1.5 2.1 1.1 2.4 1.0 3.2 1.3 3.4 5,736

Number 3,101 7,438 3,298 7,242 5,247 5,293 6,371 4,169 10,540

Ghana 2003 2.4 2.1 2.5 2.1 2.0 2.2 2.3 2.1 9,142

Male 1.8 1.4 1.4 1.5 1.0 1.6 1.6 1.4 4,045

Female 2.7 2.7 3.2 2.6 2.5 2.8 2.8 2.7 5,097

Number 1,765 7,377 1,109 8,032 2,097 7,045 3,156 5,985 9,142

Guinea 2005 0.7 1.6 1.0 1.6 1.1 1.6 1.2 1.7 6,319

Male 0.5 1.0 1.6 0.9 0.4 1.1 0.8 1.0 2,577

Female 0.8 2.1 0.8 2.0 1.3 2.1 1.4 2.2 3,742

Number 877 5,442 638 5,681 1,563 4,756 2,056 4,263 6,319

Haiti 2005 1.7 2.3 2.3 2.2 1.7 2.2 2.0 2.2 9,551

Male 1.0 2.1 2.5 2.0 1.3 2.1 1.4 2.1 4,321

Female 2.1 2.4 2.2 2.4 1.9 2.3 2.3 2.3 5,230

Number 1,322 8,172 547 8,956 1,108 8,384 2,267 7,284 9,551

India 2005/06 0.2 0.3 0.2 0.3 0.2 0.3 0.3 0.3 99,838

Male 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4 46,506

Female 0.2 0.3 0.2 0.3 0.2 0.3 0.2 0.2 53,332

Number 46,739 53,098 41,822 58,014 47,529 52,307 57,626 42,212 99,838

Kenya 2003 6.2 6.8 4.4 7.1 5.1 7.5 5.4 7.5 6,001

Male 4.1 4.6 3.2 4.7 3.7 4.9 3.9 4.9 2,851

Female 7.3 9.0 5.1 9.5 6.0 10.3 6.4 10.4 3,151

Number 924 5,078 884 5,118 1,953 4,049 2,257 3,744 6,001

(Cont’d)
65 

Table 6.1 – cont’d

Abstinence Being faithful Using condom All three

Country/sex No Yes No Yes No Yes No Yes Number

Lesotho 2004/05 20.1 24.5 18.1 24.9 19.5 24.8 21.1 25.1 5,019

Male 17.1 19.8 17.0 19.8 17.4 19.9 18.1 20.0 2,001

Female 22.3 27.6 19.1 27.9 21.5 27.7 23.6 28.0 3,018

Number 1,098 3,921 981 4,037 1,199 3,819 1,982 3,036 5,019

Malawi 2004 11.8 11.8 10.7 12.2 12.5 11.5 11.8 11.9 5,150

Male 8.3 10.4 7.9 10.8 11.1 10.0 9.3 10.8 2,465

Female 12.9 13.5 12.2 13.9 13.1 13.5 13.2 13.5 2,686

Number 1,000 4,151 1,358 3,793 1,693 3,457 2,483 2,668 5,150

Niger 2006 0.6 0.8 0.6 0.8 0.6 0.8 0.6 0.9 7,262

Male 0.6 0.8 0.3 0.8 0.3 0.9 0.6 0.9 2,856

Female 0.6 0.9 0.8 0.8 0.8 0.8 0.6 0.9 4,406

Number 1,042 5,224 560 5,718 2,012 4,253 3,537 3,725 7,262

Rwanda 2005 2.8 3.1 3.2 3.0 2.9 3.1 3.0 3.1 9,988

Male 2.2 2.3 2.2 2.3 2.1 2.3 2.2 2.3 4,348

Female 3.1 3.7 3.9 3.6 3.2 3.7 3.4 3.7 5,641

Number 1,558 8,398 1,331 8,641 1,501 8,469 3,208 6,780 9,988

Senegal 2005 0.8 0.7 0.3 0.7 0.3 0.8 0.5 0.8 7,412

Male 0.4 0.5 0.6 0.5 0.1 0.6 0.2 0.6 3,183

Female 1.1 0.8 0.0 0.9 0.5 1.0 0.8 0.9 4,229

Number 809 6,377 425 6,765 1,631 5,557 2,411 5,001 7,412

Tanzania 2003/04 4.9 7.3 6.4 7.1 5.7 7.6 6.0 7.7 10,747

Male 3.7 6.7 5.1 6.4 5.5 6.5 5.5 6.7 4,994

Female 6.0 7.9 7.5 7.7 5.8 8.6 6.4 8.6 5,753

Number 1,352 9,395 1,292 9,455 2,994 7,753 4,140 6,607 10,747

Uganda 2004/05 4.7 6.7 4.3 6.7 4.2 7.3 4.7 7.5 16,827

Male 4.8 5.1 3.5 5.2 4.0 5.3 4.3 5.4 7,477

Female 4.7 7.9 4.8 7.9 4.3 9.0 4.9 9.3 9,350

Number 2,404 14,423 1,837 14,990 4,642 12,185 6,415 10,412 16,827

Hai Phong,

Vietnam 2005 0.5 0.5 1.1 0.5 0.6 0.5 0.4 0.6 1,675

Male 0.8 0.9 3.8 0.8 3.9 0.8 0.7 1.0 754

Female 0.3 0.2 0.0 0.2 0.0 0.3 0.3 0.2 921

Number 435 1,240 95 1,580 166 1,509 497 1,178 1,675

Zimbabwe 2005 17.9 18.2 16.6 18.5 15.5 18.8 16.5 19.1 12,796

Male 13.5 14.8 12.3 15.0 14.0 14.7 13.1 15.3 5,848

Female 20.5 21.4 19.8 21.5 16.5 22.5 18.8 22.8 6,947

Number 1,830 10,752 2,027 10,589 2,522 10,086 4,929 7,866 12,796

Note: Some of the HIV prevalence estimates for ‘being faithful’ for Hai Phong, Vietnam are based on small numbers of cases.
66 

In 14 of the 19 countries, HIV prevalence is highest among respondents who know that 

monogamy or faithfulness to one partner is an effective HIV prevention strategy. In the other five 

countries—Côte d’Ivoire, Ghana, Haiti, Rwanda, and the Hai Phong province of Vietnam—not knowing 

about monogamy as an HIV prevention strategy is associated with higher HIV prevalence. 

In 16 of the 19 countries, knowledge of condom use as an HIV prevention strategy is associated 

with higher HIV prevalence, although differences between HIV prevalence by knowledge of condom use 

are generally slight. In Côte d’Ivoire there is no difference in HIV prevalence between individuals who 

know about condom use as an HIV prevention strategy and those who do not know. Malawi and the Hai 

Phong province of Vietnam are the only two countries where HIV prevalence is highest among people 

who are unaware of condom use as an HIV prevention strategy, but in both cases the difference in HIV 

prevalence by knowledge of condom use is less than 1 percent. 

Finally, in 14 of the 18 countries with data from both women and men, knowing all three methods 

of HIV prevention—abstinence, monogamy, and condom use—is associated with higher HIV prevalence. 

In only three countries—Cameroon, Côte d’Ivoire and Ghana—is HIV prevalence higher among 

individuals who do not know the three HIV prevention strategies, although differences in HIV prevalence 

between those who know and those who do not know about the three methods are slight (less than 0.5 

percent). In India there is no difference in HIV prevalence between people who know and those who do 

not know the three HIV prevention strategies. (In Burkina Faso men who do not know all three methods 

have slightly higher HIV prevalence, reflecting men’s lack of knowledge about abstinence.) 

6.4 HIV Prevalence by Knowledge of Mother-to-Child HIV Transmission 

In 16 of 19 countries with data, HIV prevalence is higher among respondents who are aware that 

breastfeeding can transmit the mother’s HIV infection to her infant (Table 6.2). Only in Burkina Faso and 

the Hai Phong province of Vietnam is HIV prevalence higher among those who are unaware that 

breastfeeding can transmit HIV to the infant. In Haiti there is no difference by this measure. For the most 

part, the difference in HIV prevalence between people who know and those who do not know about 

mother-to-child transmission of HIV is small (1 percentage point or less). The only exceptions, where 

differences in HIV prevalence by knowledge of breastfeeding and HIV transmission are greater than 1 

percentage point, are Zimbabwe (difference of 2.3 percentage points), Malawi (3.9 percentage points), 

and Lesotho (6.1 percentage points). Also, in all 15 countries with data, HIV prevalence is higher among 

individuals who are aware of antiretroviral drugs to prevent mother-to-child transmission. 
67 

Table 6.2. HIV prevalence among women and men age 15-49, by knowledge of mother-to-childtransmission (MTCT) of HIV 

Country/sex

Knows of MTCT of HIV through 

breastfeeding

Know that MTCT can be reduced 

by taking ARVs during pregnancy

No Yes No Yes Number

Burkina Faso 2003 1.9 1.8 n/a n/a 7,151

Male 1.8 1.9 n/a n/a 3,065

Female 1.9 1.7 n/a n/a 4,086

Number 3,645 3,506 n/a n/a 7,151

Cambodia 2005 0.2 0.7 0.4 1.2 14,703

Male 0.0 0.7 0.3 1.6 6,656

Female 0.4 0.6 0.5 0.9 8,047

Number 2,070 12,634 10,521 4,182 14,703

Cameroon 2004 4.9 5.9 n/a n/a 9,746

Male 3.7 4.4 n/a n/a 4,619

Female 5.8 7.5 n/a n/a 5,128

Number 4,565 5,181 n/a n/a 9,746

Côte d'Ivoire 2005 4.1 5.1 3.8 5.9 8,436

Male 2.7 3.0 2.2 3.7 4,023

Female 5.6 6.9 5.3 7.8 4,413

Number 3,322 5,114 4,647 3,789 8,436

Ethiopia 2005 0.8 1.7 1.0 2.9 10,540

Male 0.6 1.1 0.7 1.6 4,804

Female 0.9 2.3 1.2 4.3 5,736

Number 2,887 7,653 7,965 2,575 10,540

Ghana 2003 1.7 2.3 n/a n/a 9,142

Male 1.3 1.5 n/a n/a 4,045

Female 2.0 3.0 n/a n/a 5,097

Number 2,416 6,726 n/a n/a 9,142

Guinea 2005 1.4 1.6 1.5 1.8 6,319

Male 0.9 1.0 0.9 1.0 2,577

Female 1.7 2.1 1.8 2.6 3,742

Number 2,651 3,668 5,547 771 6,319

Haiti 2005 2.2 2.2 2.1 2.2 9,551

Male 1.2 2.1 1.7 2.6 4,321

Female 2.7 2.2 2.5 1.9 5,230

Number 1,425 8,126 6,478 3,072 9,551

Kenya 2003 6.3 6.9 5.9 8.2 6,001

Male 5.0 4.4 3.8 5.9 2,851

Female 7.6 9.1 7.7 10.5 3,151

Number 1,752 4,250 3,868 2,133 6,001

(Cont’d)
68 

Table 6.2 – cont’d

Country/sex

Knows of MTCT of HIV through 

breastfeeding

Know that MTCT can be reduced 

by taking ARVs during pregnancy

No Yes No Yes Number

Lesotho 2004/05 19.1 25.2 20.7 26.7 5,019

Male 16.4 20.4 17.2 22.2 2,001

Female 21.4 28.1 23.7 29.1 3,018

Number 1,354 3,665 2,683 2,336 5,019

Malawi 2004 9.0 12.9 10.3 14.4 5,150

Male 6.7 11.8 8.7 12.9 2,465

Female 11.8 13.8 11.8 15.7 2,686

Number 1,433 3,718 3,205 1,945 5,150

Niger 2006 0.6 0.9 0.7 0.8 7,262

Male 0.8 0.8 0.9 0.7 2,856

Female 0.5 0.9 0.7 1.0 4,406

Number 3,153 4,109 5,624 1,638 7,262

Rwanda 2005 2.3 3.2 1.8 3.4 9,988

Male 2.0 2.3 1.2 2.5 4,348

Female 2.5 3.9 2.2 4.1 5,641

Number 1,922 8,066 2,396 7,592 9,988

Senegal 2005 0.6 0.8 0.7 0.8 7,412

Male 0.5 0.5 0.4 0.6 3,183

Female 0.6 1.0 0.9 0.9 4,229

Number 3,074 4,338 5,396 2,016 7,412

Tanzania 2003/04 6.1 7.5 6.5 9.3 10,747

Male 5.6 6.7 5.8 8.1 4,994

Female 6.7 8.1 7.1 10.6 5,753

Number 3,506 7,241 8,815 1,932 10,747

Uganda 2004/05 5.8 6.9 5.2 7.7 16,827

Male 4.8 5.3 4.4 5.7 7,477

Female 6.6 8.2 5.8 9.4 9,350

Number 7,297 9,530 8,464 8,364 16,827

Hai Phong, Vietnam 2005 0.6 0.4 0.4 0.9 1,675

Male 1.0 0.7 0.8 1.1 754

Female 0.2 0.2 0.0 0.8 921

Number 929 746 1,258 417 1,675

Zimbabwe 2005 16.3 18.6 16.7 19.4 12,796

Male 12.7 15.0 13.8 15.4 5,848

Female 19.5 21.5 19.8 22.1 6,947

Number 2,500 10,296 6,118 6,677 12,796
69 

6.5 HIV Prevalence by Misconceptions Regarding HIV Infection 

In 17 of the 19 countries with data, HIV prevalence is higher among respondents who correctly 

know that a healthy person can be infected with HIV (Table 6.3). Only in Senegal is HIV prevalence 

higher among individuals who have the misconception that a healthy looking person cannot be HIVinfected, although the difference is slight. In India there is no difference by this measure. 

Table 6.3. HIV prevalence among women and men age 15-49, 

by misconceptions about HIV infection 

Country/sex

Healthy looking person can have AIDS Mosquitoes cannot spread AIDS

No Yes Yes, can No, cannot Number

Burkina Faso 2003 1.5 2.1 1.7 2.2 7,151

Male 1.4 2.1 1.7 2.1 3,065

Female 1.5 2.1 1.7 2.4 4,086

Number 2,817 4,335 4,809 2,342 7,151

Cambodia 2005 0.4 0.7 0.3 0.8 14,703

Male 0.5 0.7 0.2 0.8 6,656

Female 0.4 0.7 0.4 0.7 8,047

Number 5,269 9,434 5,074 9,629 14,703

Cameroon 2004 3.7 6.1 6.0 4.9 9,746

Male 3.0 4.4 4.9 3.5 4,619

Female 4.1 7.8 6.9 6.3 5,128

Number 2,536 7,210 4,843 4,903 9,746

Côte d'Ivoire 2005 4.6 4.8 4.9 4.5 8,436

Male 3.3 2.7 3.3 2.4 4,023

Female 5.3 7.1 6.0 6.9 4,413

Number 2,622 5,814 4,635 3,801 8,436

Ethiopia 2005 0.7 2.0 1.2 1.7 10,540

Male 0.3 1.2 0.7 1.2 4,804

Female 0.9 2.9 1.5 2.3 5,736

Number 4,300 6,240 5,305 5,234 10,540

Ghana 2003 1.8 2.2 2.5 1.9 9,142

Male 0.9 1.6 1.9 1.2 4,045

Female 2.2 2.8 2.9 2.5 5,097

Number 1,558 7,584 3,891 5,251 9,142

Guinea 2005 1.2 1.8 1.2 2.0 6,319

Male 0.5 1.2 0.9 1.1 2,577

Female 1.4 2.4 1.4 2.5 3,742

Number 2,744 3,574 3,775 2,544 6,319

(Cont’d)
70 

Table 6.3 – cont’d

Country/sex

Healthy looking person can have AIDS Mosquitoes cannot spread AIDS

No Yes Yes, can No, cannot Number

Haiti 2005 1.4 2.3 2.1 2.2 9,551

Male 1.5 2.0 2.2 1.8 4,321

Female 1.4 2.5 2.1 2.5 5,230

Number 1,350 8,200 4,414 5,137 9,551

India 2005/06 0.3 0.3 0.3 0.3 99,838

Male 0.4 0.4 0.4 0.3 46,506

Female 0.2 0.3 0.2 0.3 53,332

Number 50,992 48,844 83,877 15,959 99,838

Kenya 2003 5.4 6.9 6.7 6.7 6,001

Male 3.4 4.7 3.6 4.9 2,851

Female 6.5 9.1 8.6 8.8 3,151

Number 777 5,224 1,956 4,046 6,001

Lesotho 2004/05 17.7 25.7 26.8 19.2 5,019

Male 14.3 21.4 22.4 14.9 2,001

Female 20.6 28.3 29.7 22.1 3,018

Number 1,360 3,659 2,851 2,167 5,019

Malawi 2004 7.3 12.5 10.6 12.5 5,150

Male 3.7 10.7 7.7 11.3 2,465

Female 8.5 14.5 12.8 13.6 2,686

Number 694 4,456 1,692 3,458 5,150

Niger 2006 0.7 0.8 0.7 0.8 7,262

Male 0.3 1.1 0.7 0.9 2,856

Female 0.8 0.5 0.7 0.7 4,406

Number 3,800 3,462 4,785 2,476 7,262

Rwanda 2005 1.7 3.2 1.9 3.3 9,988

Male 0.6 2.4 1.4 2.5 4,348

Female 2.1 3.9 2.4 3.9 5,641

Number 1,263 8,726 2,074 7,915 9,988

Senegal 2005 1.0 0.6 | 0.7 0.7 7,412

Male 0.2 0.6 | 0.4 0.5 3,183

Female 1.3 0.6 | 1.0 0.8 4,229

Number 2,653 4,759 | 4,391 3,021 7,412

Tanzania 2003/04 4.2 7.7 6.3 7.3 10,747

Male 2.8 6.9 5.4 6.6 4,994

Female 5.1 8.4 7.1 7.9 5,753

Number 1,969 8,778 2,929 7,818 10,747

(Cont’d)
71 

Table 6.3 – cont’d

Country/sex

Healthy looking person can have AIDS Mosquitoes cannot spread AIDS

No Yes Yes, can No, cannot Number

Uganda 2004/05 4.7 6.9 6.6 6.2 16,827

Male 3.8 5.3 5.5 4.7 7,477

Female 5.1 8.3 7.5 7.5 9,350

Number 3,568 13,260 7,197 9,630 16,827

Hai Phong, Vietnam 2005 0.4 0.5 0.5 0.5 1,675

Male 1.4 0.8 1.2 0.8 754

Female 0.0 0.3 0.0 0.3 921

Number 263 1,412 599 1,076 1,675

Zimbabwe 2005 14.2 18.6 21.8 16.9 12,796

Male 9.2 15.0 18.0 13.3 5,848

Female 16.8 21.8 25.1 19.8 6,947

Number 1,471 11,325 3,201 9,595 12,796

There is more variation in the relationship between HIV prevalence and knowledge about 

whether mosquitoes can spread HIV. In nine countries, correctly knowing that mosquitoes cannot 

transmit HIV is associated with higher HIV prevalence. However, in Cameroon, Côte d’Ivoire, Ghana, 

Lesotho, Uganda, and Zimbabwe, having the misconception that HIV can be transmitted by mosquitoes is 

associated with higher HIV prevalence. In India, Kenya, Senegal, and the Hai Phong province of 

Vietnam, there is no difference in HIV prevalence by whether individuals know or do not know about 

mosquitoes and HIV transmission. 

6.6 HIV Prevalence by Attitudes toward People Living with HIV (PLHIV) 

To assess levels of stigma, the surveys ask four questions related to respondents’ attitudes toward 

PLHIV. By the first measure, whether the respondent would keep the HIV status of a family member a 

secret from non-family members, no clear pattern emerges among the 17 countries with data (Table 6.4). 

In seven of these countries—Burkina Faso, Cambodia, Cameroon, Côte d’Ivoire, Haiti, Lesotho, and 

Zimbabwe—respondents who would not want to keep the HIV-infected status of a family member a 

secret have higher HIV prevalence. However, in another seven countries—Ethiopia, Ghana, Malawi, 

Niger, Senegal, Tanzania, and Uganda—the opposite is true: Respondents who would want to keep the 

HIV status of a family member a secret have higher HIV prevalence. In India, Kenya, and the Hai Phong 

province of Vietnam, there is no difference in HIV prevalence by this measure. 
Table 6.4. HIV prevalence among women and men age 15-49, by attitude toward people living with HIV 

Will not keep HIV+ 

status of family 

member secrete

Care for a 

family member 

with AIDS

Allow infected 

female teacher 

to teach

Buy vegetables 

from a vendor 

with AIDS Attitudes toward 

Country/sex Yes No No Yes No Yes No Yes None Any 1 Any 2 

Burkina Faso 2003 2.0 1.7 1.2 2.1 n/a n/a n/a n/a n/a n/a n/a 

Male 2.2 1.7 2.0 1.8 1.2 2.4 n/a n/a n/a n/a n/a 

Female 1.9 1.7 0.7 2.3 n/a n/a n/a n/a n/a n/a n/a 

Number 3,533 3,618 1,887 5,264 5,524 1,627 n/a n/a n/a n/a n/a 

Cambodia 2005 0.7 0.6 0.5 0.6 0.3 0.7 0.3 0.8 1.0 0.1 0.5 

Male 0.7 0.6 0.6 0.6 0.5 0.7 0.4 0.7 1.5 0.1 0.8 

Female 0.7 0.6 0.5 0.7 0.2 0.7 0.2 0.8 0.7 0.1 0.3 

Number 5,452 9,252 3,070 11,634 3,086 11,618 4,835 9,869 315 1339 2668 

Cameroon 2004 6.0 4.8 4.4 5.8 5.1 5.8 4.9 6.1 5.0 4.3 5.9 

Male 3.9 4.3 4.2 4.1 4.1 4.1 3.9 4.4 4.6 3.1 4.7 

Female 7.4 5.5 4.6 7.6 5.8 7.6 5.7 8.0 5.1 5.1 7.1 

Number 5,108 4,639 2,517 7,230 4,903 4,844 5,361 4,386 752 2307 2584 

Côte d'Ivoire 2005 4.9 4.5 4.4 4.8 4.5 4.8 4.2 5.3 3.6 5.1 4.4 

Male 2.5 3.2 3.6 2.7 3.0 2.8 2.9 2.8 2.8 3.1 2.9 

Female 6.4 6.3 4.9 6.9 5.7 7.0 5.2 8.0 4.0 6.5 5.8 

Number 5,093 3,343 1,877 6,559 3,571 4,865 4,552 3,884 956 1488 2060 

Ethiopia 2005 1.0 1.7 0.3 2.2 0.5 2.7 0.9 3.4 0.2 0.4 1.0 

Male 0.7 1.0 0.2 1.3 0.3 1.6 0.6 2.0 0.0 0.2 0.7 

Female 1.1 2.4 0.4 3.1 0.6 4.0 1.2 5.2 0.3 0.6 1.3 

Number 3,445 7,094 4,135 6,405 6,041 4,499 8,376 2,163 1568 2616 2846 

Ghana 2003 1.9 2.3 2.3 2.1 2.1 2.3 2.3 1.7 1.2 2.4 2.4 

Male 1.3 1.5 1.8 1.3 1.1 1.8 1.4 1.5 0.0 1.2 1.9 

Female 2.3 3.0 2.7 2.7 2.7 2.7 3.0 1.9 1.8 3.0 2.7 

Number 3,544 5,598 2,821 6,321 5,308 3,834 6,415 2,727 679 2,492 2,931 

72
Table 6.4 – cont’d

Will not keep HIV+ 

status of family 

member secrete

Care for a 

family member 

with AIDS

Allow infected 

female teacher 

to teach

Buy vegetables 

from a vendor 

with AIDS Attitudes toward 

Country/sex Yes No No Yes No Yes No Yes None Any 1 Any 2 

Guinea 2005 n/a n/a 1.1 1.8 1.5 1.6 1.5 1.8 0.6 1.4 1.9 

Male n/a n/a 0.8 1.0 1.1 0.7 1.0 0.8 1.0 0.9 1.2 

Female n/a n/a 1.3 2.6 1.7 2.6 1.7 3.8 0.5 1.6 2.5 

Number n/a n/a 2,797 3,522 4,718 1,601 5,502 817 594 2,411 2,184 

Haiti 2005 2.5 2.0 2.1 2.2 2.1 2.2 2.1 2.2 2.8 2.1 2.2 

Male 2.1 1.9 1.8 2.1 2.2 1.7 1.9 2.1 3.2 2.0 1.5 

Female 2.8 2.1 2.3 2.3 2.1 2.7 2.3 2.4 2.6 2.1 2.7 

Number 2,531 7,020 3,944 5,607 5,959 3,592 6,566 2,984 606 3,004 2,813 

India 2005/06 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.3 0.2 0.3 0.5 

Male 0.4 0.3 0.4 0.4 0.3 0.4 0.3 0.4 0.3 0.3 0.5 

Female 0.2 0.2 0.2 0.3 0.2 0.3 0.2 0.2 0.1 0.3 0.5 

Number 53,476 46,341 44,948 54,868 47,902 51,911 55,589 44,222 31,943 9,339 12,917 

Kenya 2003 6.7 6.7 5.1 7.0 6.1 7.2 5.8 7.2 5.8 5.0 6.5 

Male 4.0 4.8 2.3 4.9 4.6 4.5 3.1 5.1 3.5 2.8 4.0 

Female 8.5 8.8 7.0 9.0 7.3 9.7 7.3 9.7 7.0 6.4 8.2 

Number 2,085 3,916 912 5,090 2,541 3,461 2,090 3,911 299 738 1230 

Lesotho 2004/05 24.5 22.9 17.9 25.0 n/a n/a 22.9 24.3 n/a n/a n/a 

Male 17.9 19.9 13.7 21.0 n/a n/a 16.7 22.4 n/a n/a n/a 

Female 28.5 25.0 21.9 27.4 n/a n/a 27.2 25.6 n/a n/a n/a 

Number 1,957 3,061 1,056 3,963 n/a n/a 2,756 2,263 n/a n/a n/a 

Malawi 2004 10.8 12.6 6.5 12.1 8.2 13.2 10.4 12.3 2.3 8.8 7.8 

Male 10.2 10.2 4.8 10.4 5.6 11.4 9.7 10.3 0.0 5.4 6.4 

Female 11.6 14.2 7.2 13.8 9.6 15.2 10.7 14.6 3.1 10.6 8.6 

Number 2,211 2,939 265 4,886 1,409 3,741 1,278 3,873 56 261 909 

Niger 2006 0.6 0.8 0.6 0.9 0.5 1.2 0.7 0.8 0.3 0.6 1.1 

Male 1.0 0.6 0.4 0.9 0.5 1.3 0.8 0.8 0.7 0.5 0.8 

Female 0.4 1.0 0.6 0.8 0.6 1.2 0.7 0.9 0.2 0.6 1.3 

Number 3,486 3,775 3,142 4,120 5,138 2,124 5,808 1,454 1,447 2,270 1,911 

73
Table 6.4 – cont’d

Will not keep HIV+ 

status of family 

member secrete

Care for a 

family member 

with AIDS

Allow infected 

female teacher 

to teach

Buy vegetables 

from a vendor 

with AIDS Attitudes toward 

Country/sex Yes No No Yes No Yes No Yes None Any 1 Any 2 

Rwanda 2005 n/a n/a 1.4 3.1 1.7 3.4 1.4 3.6 n/a n/a n/a 

Male n/a n/a 1.2 2.3 1.4 2.5 0.9 2.6 n/a n/a n/a 

Female n/a n/a 1.6 3.8 1.9 4.2 1.6 4.5 n/a n/a n/a 

Number n/a n/a 603 9,386 2,331 7,657 2,599 7,390 n/a n/a n/a 

Senegal 2005 0.6 0.9 1.1 0.6 0.6 0.8 0.6 0.9 0.8 0.8 0.4 

Male 0.3 0.8 0.4 0.5 0.3 0.7 0.2 0.9 0.0 0.2 0.3 

Female 0.9 0.9 1.5 0.6 0.9 0.9 0.9 0.8 1.2 1.1 0.5 

Number 4,890 2,522 1,879 5,533 4,343 3,069 5,141 2,271 846 2,363 2,023 

Tanzania 2003/04 6.9 7.1 3.2 7.5 4.9 7.9 5.7 8.0 2.0 5.3 5.1 

Male 4.9 6.8 3.6 6.5 4.8 6.9 5.6 6.7 1.9 5.5 4.1 

Female 8.1 7.4 2.9 8.4 5.1 8.8 5.8 9.5 2.0 5.1 5.8 

Number 3,683 7,064 1,213 9,534 3,193 7,554 4,628 6,119 318 1,033 2,358 

Uganda 2004/05 6.2 6.6 3.5 6.9 5.3 7.1 5.1 7.1 2.7 4.9 5.7 

Male 4.4 5.6 2.4 5.5 4.4 5.4 3.5 5.7 2.3 3.1 3.9 

Female 7.5 7.5 4.3 8.0 6.1 8.4 6.0 8.5 2.9 5.9 6.9 

Number 8,678 8,149 2,347 14,481 6,316 10,511 5,983 10,845 626 2,260 3,936 

Hai Phong, 

Vietnam 2005 0.5 0.5 1.2 0.5 0.2 0.7 0.5 0.5 2.5 0.0 0.7 

Male 1.2 0.7 3.7 0.8 0.6 1.0 1.4 0.6 8.3 0.0 1.9 

Female 0.0 0.4 0.0 0.2 0.0 0.3 0.0 0.4 0.0 0.0 0.0 

Number 758 917 83 1,592 494 1,181 755 920 41 166 397 

Zimbabwe 2005 18.9 17.3 13.5 19.2 17.2 18.5 18.3 18.0 17.4 16.4 18.1 

Male 15.5 13.4 11.7 15.7 14.8 14.4 14.5 14.6 5.9 14.3 14.8 

Female 21.8 20.3 17.9 21.5 18.9 22.0 20.8 21.4 21.0 18.9 20.8 

Number 6,806 5,990 2,469 10,326 3,577 9,219 5,015 7,780 249 1,346 3,402 

Note: Some of the HIV prevalence estimates for Malawi and Hai Phong, Vietnam are based on small numbers of cases. 

74
75 

By the second measure, whether respondents would be willing to care for an HIV-infected 

relative in their household, in 15 of the 19 countries with data HIV prevalence is higher among those who 

would be willing to care for an HIV-infected relative in their household. However, in Ghana, Senegal, and 

the Hai Phong province of Vietnam, HIV prevalence is slightly higher among respondents who would be 

unwilling to care for an HIV-infected relative. In India there is no difference in HIV prevalence by this 

measure. 

By the third measure, whether HIV-infected female teachers should be allowed to continue 

teaching, in all 17 countries with data HIV prevalence is higher among respondents who say these 

teachers should be allowed to continue teaching. However, the differences in HIV prevalence according 

to this measure are slight, at less than 1 percent for most countries. 

By the fourth measure, whether respondents would buy vegetables from an HIV-infected market 

vendor, in 14 of the 18 countries with data, respondents who would continue to buy produce and other 

goods from an HIV-infected market vendor have higher HIV prevalence. In Ghana and Zimbabwe, 

however, the reverse is true, and in India and the Hai Phong province of Vietnam there is no difference. 

Examining all four measures of accepting attitudes toward PLHIV together, it appears that, for 

most countries, having a more positive attitude toward PLHIV is associated with higher HIV prevalence. 

In 11 of the 17 countries with data on all four measures, having three or all four positive attitudes is 

associated with higher HIV prevalence. In Guinea, India, and Ghana HIV prevalence is highest among 

respondents who have two of the four accepting attitudes toward PLHIV. In only three countries, 

Cambodia, Haiti, and the Hai Phong province of Vietnam, is HIV prevalence highest among respondents 

who have none of these four accepting attitudes. 

77 

7 HIV Prevalence by Risk Behaviors 

7.1 Key Findings 

7.2 Introduction 

This chapter presents information on HIV prevalence by selected risk factors. These include 

number of sex partners, age at first sex, engaging in higher-risk sex, and alcohol consumption. Number of 

sex partners is measured by both the number of partners in the past 12 months and by number of lifetime 

sex partners. Behaviors such as sex with non-marital, non-cohabitating partners, and paying or receiving 

money to have sex are considered to be higher-risk sexual behaviors. Alcohol consumption is measured in 

two ways, as consumption in the past three months and as consumption before last sex. 

7.3 HIV Prevalence by Age at First Sex 

In all 19 countries with data, HIV prevalence is lowest among individuals who report that they 

never have had sex (Table 7.1). Among individuals who have ever had sex, an earlier age of sexual debut 

is generally associated with higher HIV prevalence. For eight countries—Cambodia, Côte d’Ivoire, 

Ethiopia, Ghana, Kenya, Malawi, Tanzania, and Uganda—sexual debut at 17 years or younger is 

associated with higher HIV prevalence. Additionally, in Guinea, Lesotho, Niger, Rwanda, and the Hai 

Phong province of Vietnam, the peak HIV prevalence occurs among individuals whose sexual debut 

occurred at age 18-19. In only three countries—Burkina Faso, Haiti, and India—does the peak HIV 

prevalence occur among individuals whose sexual debut was at age 20 or older. 

For the majority of countries, the peak in HIV prevalence occurs among men who were older at 

their sexual debut. In contrast, the peak of HIV prevalence for women occurs among those who were 

younger at their sexual debut. 

 In general, an earlier age of sexual debut (<17) is associated with higher HIV prevalence. This 

is especially true for women.

 In all countries, HIV prevalence increases with increasing number of lifetime sex partners, both 

overall and for men and women separately.

 In the majority of countries, HIV prevalence is higher among individuals who report having a 

sexual encounter with a non-marital, non-cohabitating partner in the past 12 months. 

 For all but one of the 19 countries with data, alcohol use by either or both partners at last sex is 

associated with higher HIV prevalence. Although true for both men and women, the 

relationship between alcohol use during last sex and HIV prevalence is especially strong among 

women.
78 

Table 7.1. HIV prevalence among women and men age 15-49, by age at fist sexual intercourse 

Age at fist sexual intercourse

Country/sex Number

Never 

had sex <15 15-17 18-19 20+ Total

Burkina Faso 2003 0.6 1.3 1.8 2.2 2.9 1.8 7,151

Male 0.8 0.7 0.8 1.5 3.6 1.9 3,065

Female 0.5 1.5 2.1 2.7 0.3 1.8 4,086

Number 1,371 385 2,779 1,204 1,384 7,151 7,151

Cambodia 2005 0.1 2.2 0.9 0.8 0.9 0.6 14,703

Male 0.0 0.0 0.6 1.1 0.9 0.6 6,656

Female 0.1 2.5 1.1 0.6 0.8 0.6 8,047

Number 4,695 157 1,791 2,713 5,337 14,703 14,703

Cameroon 2004 0.9 5.8 6.5 6.5 5.7 5.4 9,746

Male 1.0 3.7 5.0 5.2 4.7 4.1 4,619

Female 0.7 6.6 7.6 8.0 8.6 6.6 5,128

Number 1,448 1,650 3,817 1,594 1,211 9,746 9,746

Côte d'Ivoire 2005 0.2 5.8 5.0 5.0 5.5 4.7 8,436

Male 0.4 2.9 3.3 2.9 3.4 2.9 4,023

Female 0.0 7.3 6.2 8.0 11.1 6.4 4,413

Number 868 1,419 3,691 1,422 1,024 8,436 8,436

Ethiopia 2005 0.1 1.9 2.6 1.9 1.5 1.4 10,540

Male 0.2 0.2 1.7 1.3 1.3 0.9 4,804

Female 0.1 2.0 2.9 2.5 1.9 1.9 5,736

Number 3,132 1,409 2,309 1,360 2,276 10,540 10,540

Ghana 2003 0.1 4.9 2.3 3.2 2.1 2.2 9,142

Male 0.2 2.0 0.9 2.6 2.1 1.5 4,045

Female 0.0 6.1 2.8 3.6 2.2 2.7 5,097

Number 1,861 584 2,641 1,900 2,145 9,142 9,142

Guinea 2005 0.4 1.6 1.8 2.0 1.2 1.5 6,319

Male 0.6 1.3 0.8 1.1 1.0 0.9 2,577

Female 0.1 1.6 2.1 3.0 2.0 1.9 3,742

Number 730 1,217 2,569 891 892 6,319 6,319

Haiti 2005 0.1 2.7 2.5 2.4 3.0 2.2 9,551

Male 0.2 1.9 2.2 2.4 3.8 2.0 4,321

Female 0.1 4.2 2.7 2.3 2.6 2.3 5,230

Number 1,613 2,403 2,988 1,263 1,262 9,551 9,551

India 2005/06 0.1 0.3 0.3 0.3 0.4 0.3 99,838

Male 0.1 0.3 0.5 0.4 0.5 0.4 46,506

Female 0.0 0.3 0.3 0.2 0.2 0.2 53,332

Number 25,268 10,185 21,282 13,935 29,156 99,838 99,838

Kenya 2003 1.3 8.9 7.6 7.9 6.0 6.7 6,001

Male 0.9 6.0 4.4 4.9 6.0 4.6 2,851

Female 1.6 13.1 10.2 10.2 5.9 8.7 3,151

Number 966 1,281 1,909 1,020 814 6,001 6,001

(Cont’d)
79 

Table 7.1 – cont’d

Age at fist sexual intercourse

Country/sex Number

Never 

had sex <15 15-17 18-19 20+ Total

Lesotho 2004/05 4.4 16.8 25.4 31.2 30.4 23.5 5,019

Male 3.7 8.1 18.7 30.5 27.5 19.2 2,001

Female 5.0 25.4 29.1 31.6 32.8 26.4 3,018

Number 879 361 1,553 1,103 1,105 5,019 5,019

Malawi 2004 2.2 15.0 12.7 13.7 10.8 11.8 5,150

Male 1.9 11.7 10.3 14.1 9.8 10.2 2,465

Female 2.5 17.0 14.4 13.3 12.8 13.3 2,686

Number 514 720 1,982 1,074 852 5,150 5,150

Niger 2006 0.3 0.7 0.6 1.4 1.0 0.7 7,262

Male 0.4 0.7 0.2 1.1 1.1 0.8 2,856

Female 0.2 0.7 0.7 1.7 0.4 0.7 4,406

Number 1,204 1,497 2,245 738 1,546 7,262 7,262

Rwanda 2005 0.6 1.7 4.6 4.6 4.2 3.0 9,988

Male 0.2 0.6 4.1 3.9 3.2 2.3 4,348

Female 0.8 3.3 5.0 5.0 4.9 3.6 5,641

Number 3,097 591 1,476 1,615 3,199 9,988 9,988

Senegal 2005 0.2 1.1 0.8 1.1 0.8 0.7 7,412

Male 0.0 0.0 0.7 0.4 0.9 0.5 3,183

Female 0.3 1.7 0.9 1.6 0.7 0.9 4,229

Number 2,193 769 1,835 955 1,633 7,412 7,412

Tanzania 2003/04 1.9 8.7 8.0 8.0 7.1 7.0 10,747

Male 2.4 8.1 8.2 5.9 6.2 6.3 4,994

Female 1.4 9.2 7.9 9.8 8.4 7.7 5,753

Number 1,560 1,113 3,879 2,300 1,884 10,747 10,747

Uganda 2004/05 0.5 7.9 7.9 6.7 7.1 6.4 16,827

Male 0.2 4.3 6.3 6.1 7.0 5.1 7,477

Female 0.8 9.8 8.8 7.3 7.4 7.5 9,350

Number 2,585 2,624 6,236 3,149 2,203 16,827 16,827

Hai Phong, Vietnam 2005 0.2 0.0 0.0 1.5 0.5 0.5 1,675

Male 0.4 0.0 0.0 1.5 1.1 0.9 754

Female 0.0 0.0 0.0 1.4 0.0 0.2 921

Number 502 4 63 197 908 1,675 1,675

Zimbabwe 2005 3.2 23.5 22.6 23.5 21.9 18.1 12,796

Male 2.7 11.4 18.5 20.4 19.1 14.5 5,848

Female 3.9 28.8 24.9 25.8 25.3 21.1 6,947

Number 2,994 679 3,288 2,681 3,133 12,796 12,796

Note: Some of the HIV prevalence estimates for Hai Phong, Vietnam are based on small numbers of cases.
80 

7.4 HIV Prevalence by Number of Sex Partners 

Number of Lifetime Sex Partners 

In the 13 countries with data for both men and women on the number of lifetime sex partners, 

HIV prevalence is highest among respondents reporting three or more lifetime partners (Table 7.2). This 

is true for both men and women. In two other countries with data only for men, Lesotho and Senegal, HIV 

prevalence is highest among respondents who report more than three lifetime sex partners. Moreover, in 

all 15 countries HIV prevalence increases with increasing number of lifetime sex partners, both overall 

and for men and women separately (Figures 7.1 and 7.2). 
Table 7.2. HIV prevalence among women and men age 15-49, by number of sexual partners in past 12 months (on

ever had sex) and by number of lifetime sexual partners 

# sex partner(s) in last 12 moths

Number

# sex partner(s) in lifetime

Country/sex 0 1 2+ Total 0 1 2 3+ 

Burkina Faso 2003 2.2 2.2 1.6 2.1 5,780 n/a n/a n/a n/a 

Male 3.0 2.4 1.3 2.3 2,260 n/a n/a n/a n/a 

Female 1.9 2.0 5.4 2.1 3,520 n/a n/a n/a n/a 

Number 1,274 4,004 492 5,780 5,780 n/a n/a n/a n/a 

Cambodia 2005 2.3 0.7 2.0 0.9 10,009 0.1 0.5 1.6 2.2 

Male 3.1 0.7 2.0 0.9 4,460 0.0 0.3 0.7 2.1 

Female 2.1 0.6 0.0 0.8 5,548 0.1 0.6 2.6 9.1 

Number 983 8,603 414 10,009 10,009 4,695 7,497 1,062 1,421 

Cameroon 2004 6.5 5.7 7.6 6.2 8,298 0.88 2.3 6.6 7.7 

Male 1.8 3.7 7.1 4.8 3,795 1.00 0.5 2.3 5.6 

Female 8.8 6.9 10.3 7.4 4,503 0.72 2.7 8.2 11.0 

Number 874 5,672 1,736 8,282 8,298 1,448 2,029 1,282 4,970 

Côte d'Ivoire 2005 8.3 4.9 4.1 5.2 7,568 0.2 3.8 3.8 6.3 

Male 3.8 3.2 2.9 3.2 3,553 0.4 2.8 2.0 3.4 

Female 12.0 6.1 11.1 7.0 4,015 0.0 4.1 4.4 11.3 

Number 937 5,467 1,164 7,568 7,568 893 1,748 1,407 4,356 

Ethiopia 2005 4.5 1.6 4.5 2.0 7,407 0.1 1.2 3.4 3.0 

Male 2.1 1.2 4.0 1.4 3,043 0.2 0.6 1.8 2.1 

Female 5.5 1.9 13.2 2.4 4,364 0.1 1.4 4.7 5.6 

Number 904 6,385 119 7,407 7,407 3,132 4,567 1,542 1,259 

Ghana 2003 2.9 2.6 2.5 2.7 7,280 n/a n/a n/a n/a 

Male 1.8 2.0 1.7 1.9 2,975 n/a n/a n/a n/a 

Female 3.5 3.1 8.6 3.2 4,306 n/a n/a n/a n/a 

Number 1,254 5,589 438 7,280 7,280 n/a n/a n/a n/a 

81
Table 7.2 – cont’d

# sex partner(s) in last 12 moths

Number

# sex partner(s) in lifetime

Country/sex 0 1 2+ Total 0 1 2 3+ 

Guinea 2005 1.4 1.7 1.8 1.7 5,588 0.4 1.0 1.7 2.4 

Male 0.4 0.8 1.6 1.0 2,202 0.6 0.9 0.5 1.2 

Female 1.7 2.2 3.3 2.1 3,387 0.1 1.0 2.2 6.7 

Number 1,182 3,641 747 5,588 5,588 730 2,330 1,173 1,948 

Haiti 2005 2.0 2.7 2.6 2.6 7,938 0.1 1.2 2.2 3.4 

Male 1.1 2.5 2.1 2.3 3,766 0.2 0.6 0.1 2.6 

Female 2.6 2.8 9.1 2.9 4,173 0.1 1.3 2.9 5.9 

Number 1,052 5,822 1,058 7,938 7,938 1,613 2,253 1,523 4,065 

India 2005/06 0.7 0.3 0.9 0.4 74,571 0.1 0.3 0.7 0.9 

Male 0.8 0.4 0.7 0.5 32,052 0.1 0.4 0.6 0.9 

Female 0.7 0.2 5.1 0.3 42,519 0.0 0.3 1.0 6.1 

Number 6,523 67,220 676 74,571 74,571 25,266 67,333 4,393 2,580 

Kenya 2003 6.7 7.7 10.4 7.8 5,036 n/a n/a n/a n/a 

Male 1.9 5.4 8.5 5.3 2,399 n/a n/a n/a n/a 

Female 11.2 9.6 21.0 10.1 2,636 n/a n/a n/a n/a 

Number 785 3,866 385 5,036 5,036 n/a n/a n/a n/a 

Lesotho 2004/05 23.0 28.0 30.1 27.6 4,139 n/a n/a n/a n/a 

Male 13.9 23.8 25.2 23.0 1,601 3.7 13.5 19.8 26.1 

Female 27.3 30.1 39.7 30.5 2,538 n/a n/a n/a n/a 

Number 600 2,848 691 4,139 4,139 400 319 219 1007 

Malawi 2004 14.9 12.3 18.0 12.9 4,636 n/a n/a n/a n/a 

Male 7.8 11.0 16.2 11.2 2,199 n/a n/a n/a n/a 

Female 21.0 13.3 43.7 14.4 2,438 n/a n/a n/a n/a 

Number 528 3,849 260 4,636 4,636 n/a n/a n/a n/a 

Niger 2006 0.9 0.7 1.6 0.8 6,058 0.3 0.2 1.4 3.0 

Male 0.5 0.8 1.7 0.9 2,093 0.4 0.2 1.1 1.7 

Female 1.1 0.7 0.0 0.8 3,965 0.2 0.2 1.7 9.5 

Number 492 5,162 377 6,058 6,058 1,204 3,995 1,300 715 

82
Table 7.2 – cont’d

# sex partner(s) in last 12 moths

Number

# sex partner(s) in lifetime

Country/sex 0 1 2+ Total 0 1 2 3+ 

Rwanda 2005 6.5 3.4 4.6 4.1 6,892 0.6 2.4 5.6 7.1 

Male 2.6 3.3 4.1 3.2 3,009 0.2 1.2 2.9 5.6 

Female 9.0 3.5 7.9 4.9 3,883 0.8 3.0 8.1 11.8 

Number 1,629 5,120 140 6,892 6,892 3,100 3,848 1,602 1,430 

Senegal 2005 1.1 0.9 0.8 0.9 5,219 n/a n/a n/a n/a 

Male 0.3 0.7 0.8 0.7 2,207 0.0 0.2 0.4 0.6 

Female 1.8 1.0 0.8 1.1 3,012 n/a n/a n/a n/a 

Number 789 3,932 474 5,219 5,219 977 479 438 1,182 

Tanzania 2003/04 11.2 7.5 7.7 7.9 9,186 1.9 3.8 8.2 10.2 

Male 6.5 7.2 6.9 7.0 4,167 2.4 3.5 5.0 8.2 

Female 15.2 7.6 10.5 8.6 5,019 1.4 3.9 10.1 13.4 

Number 999 6,859 1,328 9,186 9,186 1,560 2,724 1,950 4,414 

Uganda 2004/05 10.3 6.7 8.9 7.5 14,242 0.5 3.6 6.9 10.4 

Male 4.7 5.5 8.1 6.1 6,151 0.2 2.2 3.3 7.8 

Female 14.5 7.3 13.4 8.5 8,091 0.8 4.0 8.6 14.6 

Number 2,010 10,382 1,850 14,242 14,242 2,603 4,374 3,268 6,576 

Hai Phong, Vietnam 2005 0.0 0.7 0.0 0.7 1,173 0.2 0.5 1.0 2.8 

Male 0.0 1.2 0.0 1.1 520 0.4 0.8 1.1 2.8 

Female 0.0 0.3 N/A 0.3 653 0.0 0.3 0.0 0.0 

Number 78 1,084 11 1,173 1,173 502 1,011 93 68 

Zimbabwe 2005 29.9 21.6 18.8 22.7 9,802 3.2 16.1 28.3 27.4 

Male 14.2 20.5 15.1 18.9 4,271 2.7 6.5 14.8 23.3 

Female 40.2 22.3 49.9 25.5 5,531 3.9 18.1 37.1 43.5 

Number 1,484 7,691 614 9,802 9,802 2,994 4,381 1,982 3,359 

Note: Some of the HIV prevalence estimates for Hai Phong, Vietnam are based on small numbers of cases.

83
84 

Figure 7.1. HIV prevalence among tested men 15-49 

by number of lifetime sexual partners 0.10.4 0.0 0.00.4 0.20.6 0.2 0.2 0.41.0 0.2 2.42.73.7 0.40.8 0.3 0.2 0.20.60.9 0.61.22.8 0.52.23.5 6.5 13.5 0.7 0.41.11.8 0.5 0.1 2.9 2.02.33.35.0 14.8 19.8 0.92.8 2.1 2.1 1.22.6 5.6 7.88.2 23.3 26.1 0.61.1 3.4 5.6 0.61.7

0

10

20

30

40

50

India

Hai Phong, Vietnam

Cambodia

Senegal

Niger

Ethiopia

Guinea

Haiti

Rwanda

Côte d'Ivoire

Cameroon

Uganda

Tanzania

Zimbabwe

Lesotho

HIV prevalence (%)

0 1 2 3+ lifetime sexual partners

Note: HIV prevalence estimates for 2 and 3+ lifetime partners for Hai Phong, Vietnam are based on small numbers of cases.

Figure 7.2. HIV prevalence among tested women 15-49 

by number of lifetime sexual partners 0.0 0.1 0.2 0.1 0.1 0.10.8 0.00.7 0.81.4 3.9 0.30.6 0.21.4 1.01.33.04.1 2.74.0 3.9 18.1 1.7 4.7 2.22.9 8.1 4.4 8.28.610.1 37.1 6.1 9.19.5 5.9 11.8 11.3 11.0 43.5 1.02.6 14.6 13.4 5.66.7

0

10

20

30

40

50

India

Cambodia

Niger

Ethiopia

Guinea

Haiti

Rwanda

Côte d'Ivoire

Cameroon

Uganda

Tanzania

Zimbabwe

HIV prevalence (%)

0 1 2 3+ lifetime sexual partners
85 

Number of Sex Partners in the Past Year 

Among the 19 countries with data, there is an inconsistent pattern between number of sex partners 

in the past 12 months and HIV prevalence. In eight of the countries—Cambodia, Côte d’Ivoire, Ghana, 

Rwanda, Senegal, Tanzania, Uganda, and Zimbabwe—HIV prevalence is highest overall among 

respondents who had no sex partners in the past 12 months (Table 7.2). In two countries, Haiti and in the 

Hai Phong province of Vietnam, HIV prevalence is highest among respondents who had one sex partner 

in the past 12 months. In seven countries—Cameroon, Guinea, India, Kenya, Lesotho, Malawi, and 

Niger—HIV prevalence is highest among respondents with two or more sex partners in the past 12 

months. In Burkina Faso HIV prevalence is the same for respondents who had no sex partners in the past 

12 months as for those who had one partner. And in Ethiopia HIV prevalence is the same for respondents 

who had no partners in the past 12 months as for those who had two or more partners. 

There is significant variation by respondents’ sex. In five countries—Burkina Faso, Ghana, Haiti, 

India and Zimbabwe—HIV prevalence for women peaks at a higher number of sex partners than for men. 

In contrast, in six countries—Kenya, Niger, Rwanda, Senegal, Uganda, and Tanzania—the peak HIV 

prevalence for men occurs at higher number of sex partners than for women. In the other eight countries 

the peak HIV prevalence occurs at the same number of sex partners for both men and women. 

7.5 HIV Prevalence by Higher-risk Sex Behavior 

Sex with A Non-Spousal Partner 

In 15 of the 19 countries with data, HIV prevalence is higher among respondents with higher-risk 

sex behavior—that is, respondents who report having sex with a non-marital, non-cohabitating partner in 

the past 12 months (Table 7.3). In Ghana, Malawi, and Zimbabwe HIV prevalence is slightly higher 

among respondents who do not report higher-risk sexual behavior in the past 12 months, although in each 

of these three countries the difference between those who do or do not report a higher-risk sex partner is 

less than 1 percent. Similarly, in Burkina Faso there is no difference in HIV prevalence overall by higherrisk sex behavior, although there is a difference among women. 

HIV prevalence is more strongly related to higher-risk sex behavior among women than among 

men (Figures 7.3 and 7.4). For example, in Ethiopia HIV prevalence is 12.1 percent among women who 

report having a higher-risk sex partner in the past 12 months compared with 1.6 percent among those who 

do not report such behavior. This pattern is also observed among women in Zimbabwe (39.2 percent vs. 

20.3 percent), Cambodia (6.2 percent vs. 0.6 percent), Niger (15.7 percent vs. 0.6 percent), and India (3.5 

percent vs. 0.2 percent). In contrast, among men, HIV prevalence is generally similar for those who do or 

do not report having a higher-risk sexual partner in the past 12 months. 
86 

Table 7.3. HIV prevalence among women and men age 15-49, by higher risk sexual behavior 

among those who had sex in past 12 months before the survey 

Had sex with a non-spousal

partner(s) in last 12 months

Paid (men) or received gift/money/ 

favor (women) in exchange for sex

Country/sex No Yes No Yes Total Number

Burkina Faso 2003 2.1 2.1 n/a n/a 2.1 4,496

Male 2.8 1.0 n/a n/a 2.1 1,902

Female 1.8 4.8 n/a n/a 2.1 2,594

Number 3,465 1,030 n/a n/a 4,496 4,496

Cambodia 2005 0.6 1.8 n/a n/a 0.7 9,025

Male 0.7 1.5 0.7 8.9 0.8 4,238

Female 0.6 6.2 n/a n/a 0.6 4,787

Number 8,408 602 3,829 26 9,025 9,025

Cameroon 2004 5.3 7.2 n/a n/a 6.2 7,408

Male 4.1 5.6 4.8 10.8 5.0 3,514

Female 5.9 10.4 n/a n/a 7.2 3,893

Number 4,095 3,312 3,374 141 7,408 7,408

Côte d'Ivoire 2005 4.5 5.1 n/a n/a 4.8 6,650

Male 2.6 3.4 3.2 0.5 3.1 3,139

Female 5.5 7.7 n/a n/a 6.3 3,510

Number 3,531 3,106 3,039 101 6,650 6,650

Ethiopia 2005 1.5 5.1 n/a n/a 1.7 6,504

Male 1.3 1.7 1.3 0.5 1.3 2,778

Female 1.6 12.1 n/a n/a 1.9 3,726

Number 6,175 329 2,759 25 6,504 6,504

Ghana 2003 2.8 2.3 n/a n/a 2.6 6,027

Male 2.4 1.2 1.7 4.0 1.9 2,562

Female 3.0 3.8 n/a n/a 3.1 3,465

Number 4,361 1,667 2,342 222 6,027 6,027

Guinea 2005 1.5 2.3 n/a n/a 1.7 4,407

Male 1.1 1.1 1.1 3.9 1.1 1,905

Female 1.6 5.5 n/a n/a 2.2 2,502

Number 2,970 1,416 1,880 24 4,407 4,407

Haiti 2005 2.5 2.8 n/a n/a 2.7 6,886

Male 2.7 2.3 2.2 6.8 2.4 3,323

Female 2.5 3.5 n/a n/a 3.0 3,563

Number 2,621 4,256 3,205 118 6,886 6,886

India 2005/06 0.3 0.6 n/a n/a 0.3 68,048

Male 0.4 0.4 0.4 0.4 0.4 29,747

Female 0.2 3.5 n/a n/a 0.2 38,301

Number 66,170 1,733 29,591 155 68,048 68,048

Kenya 2003 7.7 8.8 n/a n/a 8.0 4,251

Male 6.7 4.7 5.6 7.0 5.9 2,025

Female 8.3 17.2 n/a n/a 9.9 2,225

Number 3,046 1,204 1,687 339 4,251 4,251

(Cont’d)
87 

Table 7.3 – cont’d

Had sex with a non-spousal

partner(s) in last 12 months

Paid (men) or received gift/money/ 

favor (women) in exchange for sex

Country/sex No Yes No Yes Total Number

Lesotho 2004/05 27.6 29.3 n/a n/a 28.4 3,539

Male 28.3 22.1 22.9 41.7 24.3 1,409

Female 27.3 37.7 n/a n/a 31.1 2,130

Number 1,840 1,699 1,312 99 3,539 3,539

Malawi 2004 12.7 12.4 n/a n/a 12.6 4,111

Male 12.5 9.1 10.7 15.1 11.6 1,958

Female 12.8 21.9 n/a n/a 13.6 2,153

Number 3,450 659 1,528 430 4,111 4,111

Niger 2006 0.7 5.1 n/a n/a 0.8 5,566

Male 0.8 2.8 0.9 3.3 1.0 1,907

Female 0.6 15.7 n/a n/a 0.7 3,659

Number 5,338 193 1,875 32 5,566 5,566

Rwanda 2005 3.2 5.4 n/a n/a 3.4 5,263

Male 3.5 3.2 3.3 7.7 3.4 2,362

Female 3.0 8.3 n/a n/a 3.5 2,901

Number 4,632 573 2,331 31 5,263 5,263

Senegal 2005 0.8 1.2 n/a n/a 0.9 4,430

Male 0.8 0.7 0.7 0.0 0.7 1,820

Female 0.8 3.4 n/a n/a 1.0 2,611

Number 3,418 982 1,792 28 4,430 4,430

Tanzania 2003/04 7.1 8.3 n/a n/a 7.5 8,188

Male 7.6 6.5 7.1 7.3 7.1 3,711

Female 6.8 11.3 n/a n/a 7.8 4,477

Number 5,386 2,801 3,636 75 8,188 8,188

Uganda 2004/05 6.2 9.6 7.0 10.6 7.0 12,232

Male 6.3 6.4 6.3 10.5 6.3 5,289

Female 6.1 15.4 7.6 11.4 7.6 6,943

Number 9,203 3,003 12,142 91 12,232 12,232

Hai Phong, 

Vietnam 2005 0.6 5.0 n/a n/a 0.7 1,095

Male 0.8 5.5 n/a n/a 1.2 499

Female 0.3 0.0 n/a n/a 0.3 595

Number 1,057 38 n/a n/a 1,095 1,095

Zimbabwe 2005 21.6 20.7 n/a n/a 21.4 8,317

Male 23.9 12.6 20.1 9.6 19.7 3,685

Female 20.3 39.2 n/a n/a 22.7 4,633

Number 6,348 1,963 3,545 140 8,317 8,317

Note: Several of the HIV prevalence estimates for ‘paid sex’ are based on small numbers of cases. Also, the estimates for ‘sex with 

a non-spousal partner’ for Hai Phong, Vietnam are based on small numbers of cases.
88 

Figure 7.3. HIV prevalence among tested men 15-49 

by sex with a non-spousal partner in last 12 months 0.40.8 0.7 0.8 0.81.3 1.1 2.8 2.42.73.5 2.64.1 6.36.77.6 12.5 23.9 28.3 0.4 5.5 1.5 0.7 2.8 1.7 1.1 1.01.22.33.23.4 5.66.4 4.7 6.5 9.1 12.6 22.1

0

10

20

30

40

India

Hai Phong, Vietnam

Cambodia

Senegal

Niger

Ethiopia

Guinea

Burkina Faso

Ghana

Haiti

Rwanda

Côte d'Ivoire

Cameroon

Uganda

Kenya

Tanzania

Malawi

Zimbabwe

Lesotho

HIV prevalence (%)

No Yes, had sex with a non-spousal partner in last 12m

Note: HIV prevalence estimate for sex with a non-spousal partner for Hai Phong, Vietnam is based on a small number of cases.

Figure 7.4. HIV prevalence among tested women 15-49 

by sex with a non-spousal partner in last 12 months 0.20.6 0.8 0.61.6 1.61.83.0 2.53.0 5.55.96.1 8.3 6.8 12.8 20.3 27.3 3.5 6.2 3.4 15.7 12.1 5.5 4.8 3.8 3.5 8.3 7.7 10.4 15.417.2 11.3 21.9 39.2 37.7

0

10

20

30

40

India

Cambodia

Senegal

Niger

Ethiopia

Guinea

Burkina Faso

Ghana

Haiti

Rwanda

Côte d'Ivoire

Cameroon

Uganda

Kenya

Tanzania

Malawi

Zimbabwe

Lesotho

HIV prevalence (%)

No Yes, had sex with a non-spousal partner in last 12m
89 

Transactional Sex 

Data are available for men for 17 countries on HIV prevalence by respondents’ experience with 

transactional sex in the past 12 months—that is, sex in exchange for money paid. In 12 of the 17 

countries, HIV prevalence is higher among men who engaged in transactional sex in the past 12 months 

than those who did not (Table 7.3). For the most part, in these 12 countries the difference in HIV 

prevalence between the two groups is considerable. In 4 of the 17 countries—Côte d’Ivoire, Ethiopia, 

Senegal, and Zimbabwe—HIV prevalence is higher among men who did not engage in transactional sex 

in the past 12 months. In Senegal and Ethiopia the differences in HIV prevalence between the two groups 

are minimal. In Côte d’Ivoire and Zimbabwe, however, HIV prevalence is considerably higher among 

men who did not engage in transactional sex in the past 12 months (Côte d’Ivoire: 3.2 percent vs. 0.5 

percent; Zimbabwe: 20.1 percent vs. 9.6 percent). In India there is no difference in HIV prevalence by 

this measure. 

Data on transactional sex are available for women only for Uganda, where the measure is sex in 

exchange for gifts, money, or favors received. By this measure, HIV prevalence is higher among women 

who had transactional sex in the past 12 months, at 11.4 percent, compared with 7.6 percent among 

women who did not have sex in exchange for gifts, money, or favors. 

7.6 HIV Prevalence by Alcohol Consumption

The influence of alcohol can impair judgment and increase risky sexual behavior, and ultimately 

increase HIV prevalence. To determine the effect of alcohol use on HIV prevalence, surveys ask if 

respondents consumed alcohol in the last three months. In addition, the surveys ask respondents who had 

sex in the 12 months preceding the survey if, for each partner, they or their partner drank alcohol the last 

time they had sex. 

Data for HIV prevalence by alcohol consumption in the last three months are available only for 

four countries—Burkina Faso, India, Kenya, and Lesotho (Table 7.4). For Burkina Faso and India there is 

almost no difference in HIV prevalence by whether or not respondents consumed alcohol in the past three 

months (Burkina Faso: 1.8 percent vs. 1.9 percent; India: 0.3 percent vs. 0.4 percent). This is true for both 

men and women. For Kenya and Lesotho, respondents who consumed alcohol in the past three months 

have higher HIV prevalence than those who did not consume alcohol. This is true for both men and 

women but is especially for women. For example, in Kenya, HIV prevalence among women who drank 

alcohol in the past three months is 17.5 percent compared with 8.2 percent among non-drinkers. In 

Lesotho HIV prevalence among women who drank alcohol in the past three months is 32.2 percent 

compared with 25.4 percent among non-drinkers. 

Data are available for 13 countries on alcohol use at last sex. For all but one country, alcohol use 

by either or both partners is associated with higher HIV prevalence. For many of these countries, the 

difference in HIV prevalence between people who drank at last sex and those who did not is pronounced. 

This is true especially for women. For example, in Zimbabwe, HIV prevalence among women who report 

that both partners drank alcohol at last sex is 75.5 percent compared with 22.1 percent among women 

who report that neither partner drank alcohol. 

The only exception to the association of higher HIV prevalence with alcohol use is Haiti, where 

HIV prevalence is slightly higher among men who did not drink alcohol at last sex. Among women in 

Haiti, however, HIV prevalence is higher among those who drank alcohol at last sex than for those who 

did not (4.4 percent vs. 2.9 percent). 
90 

Table 7.4. HIV prevalence among women and men age 15-49, by alcohol consumption in last 3 

months, and by alcohol consumption before last sex (among those who had sex in last 12months) 

Used alcohol in last 

3 months

Number

Used alcohol last time had sex

Country/sex No Yes No Total Number

Either

partner

Both 

partners

Burkina Faso 2003 1.9 1.8 7,151 n/a n/a n/a n/a n/a

Male 2.0 1.6 3,065 n/a n/a n/a n/a n/a

Female 1.8 2.0 4,086 n/a n/a n/a n/a n/a

Number 5,057 2,087 7,151 n/a n/a n/a n/a n/a

Cameroon 2004 n/a n/a n/a 5.9 6.7 8.1 6.2 7,408

Male n/a n/a n/a 4.8 5.4 6.4 5.0 3,514

Female n/a n/a n/a 6.8 8.3 10.1 7.2 3,893

Number n/a n/a n/a 5,815 1,104 488 7,408 7,408

Ethiopia 2005 n/a n/a n/a 1.5 7.0 0.0 1.7 6,504

Male n/a n/a n/a 1.2 16.1 0.0 1.3 2,778

Female n/a n/a n/a 1.8 4.4 0.0 1.9 3,726

Number n/a n/a n/a 6,343 148 13 6,504 6,504

Ghana 2003 n/a n/a n/a 2.6 2.7 1.8 2.6 6,028

Male n/a n/a n/a 1.9 2.5 0.0 1.9 2,561

Female n/a n/a n/a 3.2 3.0 2.8 3.1 3,466

Number n/a n/a n/a 5,307 633 88 6,028 6,028

Guinea 2005 n/a n/a n/a 1.6 4.1 8.1 1.7 4,388

Male n/a n/a n/a 1.0 2.4 0.0 1.1 1,897

Female n/a n/a n/a 2.1 6.3 12.7 2.2 2,491

Number n/a n/a n/a 4,250 99 16 4,388 4,388

Haiti 2005 n/a n/a n/a 2.7 2.2 2.7 6,880

Male n/a n/a n/a 2.5 1.0 2.4 3,318

Female n/a n/a n/a 2.9 4.4 3.0 3,562

Number n/a n/a n/a 6,536 331 6,880 6,880

India 2005/06 0.3 0.4 99,838 n/a n/a n/a n/a n/a

Male 0.3 0.4 46,506 n/a n/a n/a n/a n/a

Female 0.2 0.3 53,332 n/a n/a n/a n/a n/a

Number 83,521 16,311 99,838 n/a n/a n/a n/a n/a

Kenya 2003* 6.5 8.1 6,001 n/a n/a n/a n/a n/a

Male 3.8 6.3 2,851 n/a n/a n/a n/a n/a

Female 8.2 17.5 3,151 n/a n/a n/a n/a n/a

Number 5,018 984 6,001 n/a n/a n/a n/a n/a

Lesotho 2004/05 22.1 28.1 5,019 22.4 35.1 23.5 5,019

Male 15.0 25.8 2,001 17.9 31.7 19.2 2,001

Female 25.4 32.2 3,018 25.3 37.4 26.4 3,018

Number 3,800 1,218 5,019 4,560 457 5,019 5,019

(Cont’d)
91 

Table 7.4 – cont’d

Used alcohol in last 

3 months

Number

Used alcohol last time had sex

Country/sex No Yes No Total Number

Either 

partner

Both 

partners

Niger 2006 n/a n/a n/a 0.8 11.1 0.8 5,538

Male n/a n/a n/a 1.0 0.0 1.0 1,891

Female n/a n/a n/a 0.7 27.5 0.7 3,647

Number n/a n/a n/a 5,510 13 5,538 5,538

Rwanda 2005 n/a n/a n/a 3.5 4.0 1.9 3.4 5,260

Male n/a n/a n/a 3.4 4.5 0.0 3.4 2,359

Female n/a n/a n/a 3.5 3.6 8.4 3.5 2,901

Number n/a n/a n/a 4,714 413 45 5,260 5,260

Senegal 2005 n/a n/a n/a 0.8 13.3 0.0 0.9 4,406

Male n/a n/a n/a 0.6 12.3 0.0 0.7 1,810

Female n/a n/a n/a 0.9 14.8 0.0 1.0 2,596

Number n/a n/a n/a 4,355 44 6 4,406 4,406

Tanzania 2003/04 n/a n/a n/a 7.0 10.1 12.2 7.5 8,188

Male n/a n/a n/a 6.7 9.3 13.8 7.1 3,711

Female n/a n/a n/a 7.3 10.5 11.0 7.8 4,477

Number n/a n/a n/a 7,023 868 296 8,188 8,188

Uganda 2004/05 n/a n/a n/a 6.8 7.6 7.5 7.0 12,206

Male n/a n/a n/a 5.8 7.6 7.3 6.3 5,269

Female n/a n/a n/a 7.5 7.7 7.6 7.6 6,937

Number n/a n/a n/a 8,478 2,711 1,017 12,206 12,206

Hai Phong, 

Vietnam 2005 n/a n/a n/a 0.5 1.1 0.7 1,095

Male n/a n/a n/a 0.9 1.8 1.2 499

Female n/a n/a n/a 0.2 0.6 0.3 595

Number n/a n/a n/a 750 345 1,095 1,095

Zimbabwe 2005 n/a n/a n/a 21.1 22.6 60.0 21.4 8,306

Male n/a n/a n/a 19.9 18.3 33.7 19.7 3,676

Female n/a n/a n/a 22.1 25.9 75.5 22.7 4,629

Number n/a n/a n/a 7,460 789 35 8,306 8,306

* In Kenya the information was collected for last month only 

Note: Several of the HIV prevalence estimates for ‘alcohol use at last sex’ are based on small numbers of cases.

93 

8 HIV Prevalence among Youth by Sexual Behavior 

8.1 Key Findings 

8.2 Introduction 

UNAIDS estimates that 6,800 new HIV infections occur globally each day (UNAIDS, 2008). 

Forty-five percent of these infections are among youth age 15-24. This chapter presents HIV prevalence 

data among youth age 15-24 by respondents’ sex, urban/rural residence, age at sexual initiation, primary 

abstinence (never had sex), and secondary abstinence (had sex but not in last 12 months). 

8.3 HIV Prevalence among Youth by Sex and Urban/Rural Residence 

For 13 of the 22 countries studied, HIV prevalence among youth is higher in urban areas than in 

rural areas. In five countries, however—Cambodia, Guinea, India, Senegal, and in the Hai Phong 

province of Vietnam—HIV prevalence is the same in both urban and rural areas. In the Dominican 

Republic, Ghana, Mali, and Niger, HIV prevalence is higher in rural areas than in urban areas. 

Table 8.1. HIV prevalence among women and men age 15-24, by urban/rural residence 

Residence

Country/sex Urban Rural Total Number

Burkina Faso 2003 1.8 0.7 1.0 3,067

Male 1.0 0.5 0.7 1,380

Female 2.3 0.9 1.3 1,687

Number 849 2,218 3,067 3,067

Cambodia 2005 0.2 0.2 0.2 5,997

Male 0.0 0.2 0.1 2,856

Female 0.4 0.3 0.3 3,140

Number 1,149 4,848 5,997 5,997

Cameroon 2004 3.5 2.5 3.1 4,466

Male 1.4 1.5 1.5 2,105

Female 5.4 3.4 4.6 2,362

Number 2,668 1,799 4,466 4,466

(Cont’d)

 In most countries, youth age 15-24 who were under age 18 at their first sexual encounter 

have higher rates of HIV prevalence. This is true for both young men and young women. 

 HIV prevalence among youth age 15-24 who have never had sex (primary abstinence) is 

low, ranging from 0.1 percent in Cote d’Ivoire, Cambodia, Ghana, Haiti, and India to 3.8 

percent in Lesotho. 

 Similarly, HIV prevalence among youth age 15-24 reporting no sexual encounters in the past 

12 months (secondary abstinence) is generally low (less than 1 percent), except in Lesotho, 

where 6.2 percent of youth are HIV-infected.
94 

Table 8.1 – cont’d

Residence

Country/sex Urban Rural Total Number

Côte d'Ivoire 2005 1.9 0.9 1.4 3,750

Male 0.4 0.2 0.3 1,706

Female 3.1 1.6 2.4 2,044

Number 1,874 1,875 3,750 3,750

Dominican Republic 2002 0.5 0.8 0.6 8,125

Male 0.2 0.9 0.4 4,230

Female 0.8 0.7 0.8 3,895

Number 5,445 2,680 8,125 8,125

Ethiopia 2005 2.1 0.4 0.7 4,525

Male 0.3 0.2 0.2 2,104

Female 3.3 0.6 1.1 2,422

Number 863 3,662 4,525 4,525

Ghana 2003 0.6 0.8 0.7 3,598

Male 0.2 0.0 0.1 1,651

Female 0.9 1.5 1.2 1,946

Number 1,858 1,740 3,598 3,598

Guinea 2005 0.9 0.9 0.9 2,440

Male 0.0 1.2 0.6 1,101

Female 1.9 0.7 1.2 1,339

Number 1,102 1,338 2,440 2,440

Haiti 2005 1.5 0.7 1.0 4,389

Male 0.8 0.4 0.6 2,069

Female 2.0 1.0 1.5 2,320

Number 2,115 2,274 4,389 4,389

India 2005/06 0.1 0.1 0.1 36,764

Male 0.1 0.1 0.1 16,488

Female 0.2 0.1 0.1 20,276

Number 12,406 24,358 36,764 36,764

Kenya 2003 5.3 3.1 3.6 2,679

Male 2.6 0.8 1.2 1,311

Female 7.6 5.3 5.9 1,369

Number 649 2,030 2,679 2,679

Lesotho 2004/05 15.1 10.5 11.4 2,356

Male 4.7 6.3 6.0 1,021

Female 21.4 14.0 15.5 1,335

Number 428 1,928 2,356 2,356

Malawi 2004 7.2 5.8 6.0 2,071

Male 0.3 2.5 2.1 910

Female 13.3 8.2 9.1 1,161

Number 872 4,279 2,071 2,071

(Cont’d)
95 

Table 8.1 – cont’d

Residence

Country/sex Urban Rural Total Number

Mali 2001 0.7 1.1 0.9 2,545

Male 0.6 0.1 0.3 994

Female 0.8 1.6 1.3 1,545

Number 945 1,600 2,545 2,545

Niger 2006 0.3 0.5 0.4 2,675

Male 0.0 0.4 0.3 1,042

Female 0.5 0.5 0.5 1,633

Number 736 1,939 2,675 2,675

Rwanda 2005 2.8 0.6 1.0 4,476

Male 1.1 0.2 0.4 2,019

Female 4.1 1.0 1.5 2,456

Number 781 3,694 4,476 4,476

Senegal 2005 0.3 0.3 0.3 3,371

Male 0.0 0.2 0.1 1,520

Female 0.5 0.4 0.5 1,851

Number 1,887 1,484 3,371 3,371

Tanzania 2003/04 5.5 2.6 3.5 4,472

Male 5.6 1.8 3.0 2,084

Female 5.5 3.2 4.0 2,388

Number 1,486 2,986 4,472 4,472

Uganda 2004/05 4.8 2.5 2.9 6,944

Male 1.8 0.9 1.1 3,097

Female 6.9 3.8 4.3 3,847

Number 1,200 5,744 6,944 6,944

Hai Phong, Vietnam 2005 0.5 0.3 0.3 577

Male 1.3 0.6 0.8 258

Female 0.0 0.0 0.0 318

Number 577 381 577 577

Zambia 2001/02 10.5 5.8 7.8 1,615

Male 3.7 2.6 3.1 675

Female 15.2 8.2 11.2 940

Number 1,615 945 1,615 1,615

Zimbabwe 2005 8.0 7.6 7.8 6,139

Male 4.4 4.1 4.2 2,939

Female 11.2 10.9 11.0 3,200

Number 2,439 3,700 6,139 6,139
96 

For the most part, the differences in HIV prevalence between urban and rural areas are fairly 

small. In Burkina Faso, Côte d’Ivoire, Haiti, and Tanzania, however, HIV prevalence among youth is 

twice as high in urban areas as in rural areas. In Ethiopia and Rwanda HIV prevalence is five times 

greater in urban areas than in rural areas. In Ethiopia this difference is driven by urban-rural differences in 

HIV prevalence among young women: Among Ethiopian young women, HIV prevalence is 5.5 times 

higher in urban than rural areas compared with 1.5 times higher among young men. 

For most countries, the urban-rural patterns of HIV prevalence are similar for both men and 

women age 15-24. However, in the Dominican Republic and Malawi, they differ. Among Dominican 

young men, HIV prevalence is higher in rural than urban areas, while among young women, prevalence is 

slightly higher in urban than rural areas. Malawi follows the same pattern: HIV prevalence for rural 

young men is 2.5 percent compared with 0.3 percent among urban young men, while prevalence for 

young women is 13.3 percent in urban areas compared with 8.2 percent in rural areas. 

8.4 HIV Prevalence among Youth by Age at First Sexual Intercourse 

Considering HIV prevalence among youth by age at sexual debut, findings differ for youth 

age15-19 and for youth age 20-24. Data among youth age 15-19 present a mixed picture of the association 

between age at first sexual intercourse and HIV prevalence (Table 8.2). In 8 of the 19 countries with 

data—Burkina Faso, Cambodia, Ethiopia, Guinea, India, Lesotho, Rwanda, and Zimbabwe—HIV 

prevalence is higher among youth who were age 15 or older at first sex. However, in 9 of the 19 

countries—Cameroon, Côte d’Ivoire, Ghana, Haiti, Kenya, Malawi, Senegal, Tanzania, and Uganda—

HIV prevalence is higher among youth who were under age 15 at sexual debut. In Niger and in the Hai 

Phong province of Vietnam, none of the youth age 15-19 tested positive for the HIV virus. In general, 

girls who first had sex before age 15 have higher HIV prevalence, while for boys higher HIV prevalence 

occurs among those whose sexual debut occurred at age 15 or older. 

Table 8.2. HIV prevalence among women and men age 15-24, by age at first sexual intercourse 

Youth age 15-19

Number

Youth age 20-24

Country/sex Number

1st sex at 

age < 15 

1st sex at 

age 15 + 

1st sex at 

age < 18 

1st sex at 

age 18 + 

Burkina Faso 2003 0.7 0.8 1,819 1.5 1.1 1,248

Male 0.0 0.7 862 0.8 0.5 518

Female 1.3 0.9 957 1.8 1.8 731

Number 96 1,723 1,819 639 609 1,248

Cambodia 2005 0.0 0.1 3,298 0.2 0.6 2,698

Male 0.1 0.0 1,641 0.0 0.4 1,215

Female 0.0 0.2 1,657 0.4 0.8 1,483

Number 2,999 299 3,298 1,147 1,551 2,698

Cameroon 2004 2.3 1.2 2,477 6.6 3.1 1,990

Male 0.0 0.7 1,198 3.5 1.6 906

Female 3.7 1.8 1,278 8.5 5.1 1,084

Number 387 2,090 2,477 1,206 784 1,990

Côte d'Ivoire 2005 0.8 0.2 1,872 2.6 2.3 1,878

Male 0.0 0.3 807 0.0 0.6 899

Female 1.3 0.2 1,065 4.4 4.7 979

Number 363 1,509 1,872 1,179 699 1,878

(Cont’d)
97 

Table 8.2 – cont’d

Youth age 15-19

Number

Youth age 20-24

Country/sex Number

1st sex at 

age < 15 

1st sex at 

age 15 + 

1st sex at 

age < 18 

1st sex at 

age 18 + 

Ethiopia 2005 0.0 0.5 2,572 1.7 0.8 1,954

Male 0.0 0.1 1,175 0.2 0.4 929

Female 0.0 0.8 1,397 2.1 1.4 1,025

Number 161 2,411 2,572 626 1,328 1,954

Ghana 2003 2.7 0.2 2,070 1.3 1.1 1,528

Male 0.0 0.2 1,035 0.0 0.0 616

Female 4.3 0.2 1,035 1.8 2.0 912

Number 118 1,952 2,070 548 980 1,528

Guinea 2005 0.5 1.2 1,416 1.3 0.8 1,007

Male 0.5 0.5 640 1.1 0.0 455

Female 0.4 1.7 776 1.4 2.2 552

Number 897 519 1,416 761 246 1,007

Haiti 2005 0.6 0.2 2,505 1.9 1.1 1,869

Male 0.1 0.2 1,185 1.2 0.6 870

Female 1.1 0.1 1,320 2.6 1.4 999

Number 1,928 577 2,505 1,451 418 1,869

India 2005/06 0.0 0.1 19,360 0.2 0.2 17,393

Male 0.0 0.0 8,662 0.2 0.2 7,823

Female 0.1 0.1 10,698 0.2 0.1 9,571

Number 16,382 2,978 19,360 11,822 5,572 17,393

Kenya 2003 2.3 1.4 1,456 7.5 4.1 1,224

Male 0.0 0.5 745 2.6 2.0 566

Female 7.1 2.2 711 13.2 5.4 658

Number 347 1,109 1,456 662 561 1,224

Lesotho 2004/05 4.5 5.5 1,338 21.7 17.5 1,019

Male 2.0 2.4 611 11.0 11.9 410

Female 9.3 7.8 726 30.9 20.6 608

Number 168 1,169 1,338 429 589 1,019

Malawi 2004 4.1 1.7 967 11.7 6.4 1,103

Male 0.0 0.5 467 4.8 2.9 442

Female 9.2 2.8 500 15.7 9.3 661

Number 157 810 967 629 475 1,103

Niger 2006 0.0 0.0 1381 1.0 0.6 1,281

Male 0.0 0.0 577 0.9 0.0 461

Female 0.0 0.0 804 1.0 1.2 820

Number 1,040 341 1,381 991 290 1,281

Rwanda 2005 0.4 2.3 2,408 1.6 1.6 2,066

Male 0.1 2.7 1,090 0.6 0.3 929

Female 0.6 2.0 1,318 2.5 2.5 1138

Number 2,233 175 2,408 1,326 741 2,066

(Cont’d)
98 

Table 8.2 – cont’d

Youth age 15-19

Number

Youth age 20-24

Country/sex Number

1st sex at 

age < 15 

1st sex at 

age 15 + 

1st sex at 

age < 18 

1st sex at 

age 18 + 

Senegal 2005 0.1 0.0 1,961 0.6 0.3 1,403

Male 0.0 0.0 906 0.0 0.7 611

Female 0.2 0.0 1,056 1.1 0.0 792

Number 1,623 338 1,961 1,009 394 1,403

Tanzania 2003/04 3.1 2.0 2,415 7.1 3.4 2,056

Male 2.9 2.0 1,181 6.5 2.6 903

Female 3.3 2.0 1,234 7.5 4.3 1,153

Number 267 2,148 2,415 992 1,064 2,056

Uganda 2004/05 3.3 1.2 3,973 5.3 4.1 2,972

Male 0.0 0.3 1,922 2.3 2.5 1,175

Female 7.4 1.9 2,051 6.6 5.7 1,797

Number 571 3,402 3,973 1,681 1,291 2,972

Hai Phong, Vietnam 2005 0.0 0.0 324 0.6 1.1 253

Male 0.0 0.0 148 1.3 2.8 110

Female 0.0 0.0 176 0.0 0.0 143

Number 314 10 324 157 96 253

Zimbabwe 2005 3.0 8.3 3,370 3.4 13.7 2,762

Male 2.9 3.4 1,690 1.4 7.3 1,244

Female 3.1 12.5 1,680 5.9 18.4 1,518

Number 2,341 1,029 3,370 550 2,211 2,762

Note: Some of the HIV prevalence estimates for Burkina Faso and Hai Phong, Vietnam are based on small numbers of cases.

Data for youth age 20-24 present a clearer picture. In majority of countries, youth age 20-24 who 

were under age 18 at first sex have higher HIV prevalence than those whose sexual debut occurred at age 

18 or older. This is true for both young men and women (Figures 8.1 and 8.2). Only in Cambodia, the Hai 

Phong province of Vietnam, and Zimbabwe is HIV prevalence higher among youth who were age 18 or 

older at their sexual debut. In India and Rwanda there was no difference in HIV prevalence by whether 

the youth began sex before or after age 18. 
99 

Figure 8.1. HIV prevalence among tested men 20-24 

who had first sexual intercourse before age 18 0.21.3 0.20.81.2 0.6 3.5 2.32.6 6.5 4.8 1.4 11.0 0.2 2.8 0.4 0.5 0.6 0.31.62.5 2.02.62.9 7.3 11.9

0

10

20

30

40

India

Hai Phong, Vietnam

Ethiopia

Burkina Faso

Haiti

Rwanda

Cameroon

Uganda

Kenya

Tanzania

Malawi

Zimbabwe

Lesotho

HIV prevalence (%)

1st sex at age < 18 1st sex at age 18 + 

Note: HIV prevalence estimate for sex with a non-spousal partner for Hai Phong, Vietnam is based on a small number of cases.

Figure 8.2. HIV prevalence among tested women 20-24 

who had first sexual intercourse before age 18 0.20.41.02.1 1.41.8 1.82.6 2.5 4.4 8.5 6.6 13.2 7.5 15.7 5.9 30.9 0.10.81.21.42.2 1.82.0 1.42.5 4.75.15.7 5.4 4.3 9.3 18.4 20.6

0

10

20

30

40

India

Cambodia

Niger

Ethiopia

Guinea

Burkina Faso

Ghana

Haiti

Rwanda

Côte d'Ivoire

Cameroon

Uganda

Kenya

Tanzania

Malawi

Zimbabwe

Lesotho

HIV prevalence (%)

1st sex at age < 18 1st sex at age 18 + 
100 

8.5 HIV Prevalence among Youth by Primary Abstinence 

HIV prevalence is low among youth age 15-24 who have never had sex (primary abstinence), 

ranging from 0.1 percent in Côte d’Ivoire, Cambodia, Ghana, Haiti, and India to 3.8 percent in Lesotho, 

among the 19 countries with data (Table 8.3). Generally, HIV prevalence is higher among youth age 20-

24 than those age 15-19, although in Burkina Faso, Côte d’Ivoire, Ghana, and Uganda, HIV prevalence is 

slightly higher among youth age 15-19. 

In the majority of countries studied, HIV prevalence is lower for women age 15-24 who have 

never had sex than is the case for young men who have never had sex. In Ghana, Haiti, India, Niger, and 

in the Hai Phong province of Vietnam, there are no HIV cases reported among young women who have 

never had sex. Conversely, in Cambodia, Kenya, Lesotho, Malawi, Rwanda, Senegal, Uganda, and 

Zimbabwe, HIV prevalence is higher among young women who have never had sex than among young 

men who have never had sex. 

8.6 HIV Prevalence by Secondary Abstinence 

HIV prevalence among sexually-experienced youth who have not had sex in the past 12 months 

(secondary abstinence) is generally very low, except in Lesotho where 6.2 percent of youth age 15-24 are 

HIV-infected (Table 8.3). For all but two countries studied, HIV prevalence among youth who report 

secondary abstinence is higher for those age 20-24 than for those age 15-19. In Guinea and Haiti, 

however, HIV prevalence is slightly higher among youth age 15-19 than age 20-24, among those 

reporting secondary abstinence. Generally, HIV prevalence among respondents reporting secondary 

abstinence is higher among young women than young men, while in Cambodia, it is the same. In contrast, 

in India, Tanzania, and the Hai Phong province of Vietnam, HIV prevalence is slightly higher among 

young men than young women reporting secondary abstinence. 
101 

Table 8.3. HIV prevalence among women and men age 15-24, by primary abstinence (never had 

sex) and secondary abstinence (ever had sex, but not in last 12m) 

Never had sex

Number

Ever had sex, but not in last 12m

Country/sex 15-19 20-24 15-24 15-19 20-24 15-24 Number

Burkina Faso 2003 0.8 0.0 0.7 1,312 1.1 1.3 1.2 309

Male 1.0 0.0 0.8 754 0.0 0.0 0.0 104

Female 0.5 0.0 0.5 558 1.8 1.8 1.8 205

Number 1,144 167 1,312 1,312 91 218 309 309

Cambodia 2005 0.0 0.2 0.1 4,146 0.0 0.2 0.1 4,313

Male 0.1 0.0 0.0 2,071 0.1 0.2 0.1 2,161

Female 0.0 0.4 0.1 2,075 0.0 0.3 0.1 2,153

Number 2,999 1,147 4,146 4,146 3,024 1,289 4,313 4,313

Cameroon 2004 0.8 1.6 0.9 1,407 0.8 3.0 1.3 1,793

Male 0.8 2.5 1.0 794 0.7 2.6 1.1 955

Female 0.7 0.0 0.7 613 0.9 3.4 1.5 838

Number 1,218 189 1,407 1,407 1,393 401 1,793 1,793

Côte d'Ivoire 2005 0.1 0.0 0.1 836 0.4 1.6 0.7 1,208

Male 0.2 0.0 0.2 451 0.2 0.0 0.1 655

Female n/a n/a n/a 385 0.6 3.7 1.4 554

Number 715 121 836 836 857 351 1,208 1,208

Ethiopia 2005 0.1 0.2 0.2 2,893 0.1 0.4 0.2 3,119

Male 0.1 0.3 0.2 1,616 0.1 0.3 0.2 1,715

Female 0.1 0.2 0.1 1,277 0.1 0.6 0.3 1,404

Number 2,089 804 2,893 2,893 2,156 963 3,119 3,119

Ghana 2003 0.1 0.0 0.1 1,784 0.1 0.4 0.2 2,160

Male 0.2 0.0 0.2 1,015 0.2 0.0 0.2 1,156

Female 0.0 0.0 0.0 769 0.0 0.8 0.2 1,004

Number 1,455 330 1,784 1,784 1,588 572 2,160 2,160

Guinea 2005 0.4 0.5 0.4 713 0.6 0.4 0.6 1,039

Male 0.7 0.0 0.6 363 0.6 0.0 0.5 471

Female 0.0 1.1 0.1 350 0.7 0.7 0.7 568

Number 626 87 713 713 767 272 1,039 1,039

Haiti 2005 0.0 0.2 0.1 1,490 0.3 0.2 0.3 2,029

Male 0.1 0.7 0.2 527 0.1 0.3 0.1 842

Female 0.0 0.0 0.0 963 0.5 0.1 0.4 1,187

Number 1,220 270 1,490 1,490 1,536 493 2,029 2,029

India 2005/06 0.0 0.1 0.1 21,976 0.0 0.2 0.1 23,157

Male 0.0 0.2 0.1 12,026 0.0 0.2 0.1 12,773

Female 0.0 0.0 0.0 9,950 0.0 0.0 0.0 10,384

Number 15,296 6,680 21,976 21,976 15,698 7,459 23,157 23,157

Kenya 2003 1.3 1.6 1.3 929 1.1 2.2 1.4 1,341

Male 0.7 2.3 1.0 434 0.5 1.3 0.7 692

Female 1.7 1.1 1.6 495 1.8 3.1 2.1 649

Number 769 160 929 929 981 360 1,341 1,341

(Cont’d)
102 

Table 8.3 – cont’d

Never had sex

Number

Ever had sex, but not in last 12m

Country/sex 15-19 20-24 15-24 15-19 20-24 15-24 Number

Kenya 2003 1.3 1.6 1.3 929 1.1 2.2 1.4 1,341

Male 0.7 2.3 1.0 434 0.5 1.3 0.7 692

Female 1.7 1.1 1.6 495 1.8 3.1 2.1 649

Number 769 160 929 929 981 360 1,341 1,341

Lesotho 2004/05 3.7 4.6 3.8 846 3.9 13.0 6.2 1,127

Male 1.8 8.6 2.8 380 1.6 9.1 3.3 482

Female 5.2 0.8 4.7 466 5.8 15.4 8.3 645

Number 728 118 846 846 850 277 1,127 1,127

Malawi 2004 1.5 6.8 2.2 494 1.7 5.8 2.7 746

Male 0.9 6.6 2.0 250 0.7 3.5 1.5 408

Female 2.1 7.2 2.5 244 2.8 9.2 4.3 338

Number 428 67 494 494 551 195 746 746

Niger 2006 0.0 1.0 0.2 1,096 0.0 1.1 0.3 1,289

Male 0.0 1.3 0.4 683 0.0 1.0 0.3 793

Female 0.0 0.0 0.0 413 0.0 1.2 0.3 495

Number 822 274 1,096 1,096 891 398 1289 1,289

Rwanda 2005 0.3 0.9 0.5 2,832 0.4 1.2 0.7 3,517

Male 0.1 0.4 0.2 1,230 0.2 0.4 0.2 1685

Female 0.5 1.3 0.7 1,603 0.6 2.1 1.1 1832

Number 1,992 841 2,832 2,832 2,273 1,243 3,517 3,517

Senegal 2005 0.0 0.7 0.2 1,929 0.1 0.5 0.2 2,234

Male 0.0 0.0 0.0 859 0.0 0.0 0.0 1,075

Female 0.1 1.3 0.4 1,070 0.2 1.1 0.4 1,158

Number 1,421 508 1,929 1,929 1,528 706 2,234 2,234

Tanzania 2003/04 1.6 3.1 1.8 1,495 1.9 4.1 2.4 1,930

Male 1.9 5.3 2.4 784 2.0 4.6 2.6 1,062

Female 1.4 0.0 1.2 711 1.7 3.4 2.1 869

Number 1,301 194 1,495 1,495 1,499 432 1,930 1,930

Uganda 2004/05 0.6 0.4 0.5 2,515 0.6 3.0 1.1 3,374

Male 0.3 0.0 0.2 1,281 0.2 0.9 0.4 1,816

Female 0.9 0.9 0.9 1,235 1.0 6.1 1.9 1,558

Number 2,235 281 2,515 2,515 2,719 656 3,374 3,374

Hai Phong, Vietnam 2005 0.0 0.7 0.2 457 0.0 0.6 0.2 460

Male 0.0 1.3 0.4 218 0.0 1.3 0.4 220

Female 0.0 0.0 0.0 239 0.0 0.0 0.0 240

Number 313 144 457 457 313 147 460 460

Zimbabwe 2005 3.0 3.4 3.1 2,891 3.3 7.3 4.3 3,490

Male 2.9 1.4 2.6 1,519 3.2 2.9 3.1 1,893

Female 3.1 5.9 3.6 1,372 3.3 13.0 5.7 1,596

Number 2,341 550 2,891 2,891 2,571 919 3,490 3,490

Note: Some of the HIV prevalence estimates for Burkina Faso, Guinea, and Malawi are based on small numbers of cases.
103 

9 HIV Prevalence by Protective Behaviors 

9.1 Key Findings 

9.2 Introduction 

This chapter presents data on HIV prevalence by behaviors that have been shown to prevent HIV 

infection. Condom use is a key behavior to prevent HIV infection among sexually active people. This 

behavior is measured in four ways: ever-use of condoms (among those who have ever had sex), condom 

use at last sex (among those who had sex in the past 12 months), condom use at last higher-risk sex

(among those who have had higher-risk sex), and consistent use of condoms (among those who had sex in 

the past 12 months). Because some research has shown a protective effect of male circumcision on the 

transmission of HIV (Agot et al., 2004; Auvert et al., 2001), this chapter also reports HIV prevalence by 

male circumcision status. 

9.3 HIV Prevalence by Male Condom Use 

Ever-Use of Condoms 

Sixteen countries have data on ever-use of male condoms, among respondents who have ever had 

sex (Table 9.1). In four of these countries—Burkina Faso, Cambodia, Cameroon, and Haiti—condom use 

data are available from female respondents only. In all four of these countries, HIV prevalence is higher 

among women who report having ever used a condom. Among the other 12 countries, HIV prevalence 

among respondents who have ever used condoms is higher in 8 countries, both overall and for men and 

women separately. In Senegal there is no difference in HIV prevalence, both overall and for women, 

between those who have ever used a condom and those who have not. For men who have ever used 

condoms, however, HIV prevalence is slightly higher than for those who have never used condoms. In 

Ghana, Kenya, and India, HIV prevalence is higher among respondents who report never using condoms. 

In India this is true for both men and women. In Ghana and Kenya, however, while HIV prevalence is 

higher among men who have never used condoms, it is higher among women who have ever used 

condoms. 

 Ever-use of condoms is associated with higher HIV prevalence, both overall and for men and 

women separately.

 Generally, the difference in HIV prevalence between respondents who do, or do not, 

consistently use condoms is small (less than 1 percent). The exception is Ethiopia, where HIV 

prevalence is considerably higher among those consistently using condoms (7.0 percent vs. 

1.5 percent). 

 In the majority of countries, not using condoms at last sex, and/or at last sex with a higher-risk 

partner, is associated with higher HIV prevalence, especially among women. 

 There appears no clear pattern of association between male circumcision and HIV prevalence. 

In 8 of 18 countries with data, HIV prevalence is lower among circumcised men, while in the 

remaining 10 countries HIV prevalence is higher among circumcised men.
104 

Table 9.1. HIV prevalence among women and men age 15-49, by ever use of condom (among those 

who ever had sex) and consistent use of condom in last 12 months (among those who had sex in 

last 12 months) 

Ever use of condom 

Number

Consistent use of condom in last 12m

Country/sex No Yes Total No Yes Total Number

Burkina Faso 2003 n/a n/a n/a n/a 2.1 2.0 2.1 4,496

Male n/a n/a n/a n/a 2.3 1.7 2.1 1,902

Female 2.0 3.7 2.1 3,520 2.1 2.4 2.1 2,594

Number 2,584 421 3,520 3,520 3,705 790 4,496 4,496

Cambodia 2005 n/a n/a n/a n/a 0.7 1.6 0.7 9,017

Male n/a n/a n/a n/a 0.7 1.7 0.8 4,231

Female 0.7 2.2 0.8 5,548 0.6 1.5 0.6 4,785

Number 5,161 388 5,548 5,548 8,425 586 9,017 9,017

Cameroon 2004 n/a n/a n/a n/a 6.3 5.8 6.2 7,408

Male n/a n/a n/a n/a 5.1 4.9 5.0 3,514

Female 6.1 9.9 8.2 3,876 7.2 7.3 7.2 3,893

Number 2,853 1,650 4,503 3,876 5,860 1,548 7,408 7,408

Côte d'Ivoire 2005 n/a n/a n/a n/a 4.8 4.6 4.8 6,638

Male n/a n/a n/a n/a 2.9 3.6 3.1 3,139

Female n/a n/a n/a n/a 6.3 6.5 6.3 3,499

Number n/a n/a n/a n/a 5,303 1,334 6,638 6,638

Ethiopia 2005 1.8 6.1 2.0 7,407 1.5 7.0 1.7 6,504

Male 1.3 2.3 1.4 3,043 1.3 1.6 1.3 2,778

Female 2.1 23.7 2.4 4,364 1.7 22.4 1.9 3,726

Number 7,053 354 7,407 7,407 6,347 157 6,504 6,504

Ghana 2003 2.8 2.5 2.7 7,280 2.6 2.6 2.6 6,027

Male 2.0 1.9 1.9 2,975 1.9 2.1 1.9 2,560

Female 3.1 3.5 3.2 4,306 3.1 3.5 3.1 3,466

Number 4,747 2,534 7,280 7,280 5,258 768 6,027 6,027

Guinea 2005 1.6 2.0 1.7 5,588 1.7 2.2 1.7 4,388

Male 0.8 1.3 1.0 2,202 1.1 1.1 1.1 1,897

Female 1.9 4.3 2.1 3,387 2.0 5.9 2.2 2,491

Number 4,606 982 5,588 5,588 3,893 487 4,388 4,388

Haiti 2005 n/a n/a n/a n/a 2.8 2.3 2.7 6,880

Male n/a n/a n/a n/a 2.8 1.4 2.4 3,318

Female 2.6 3.9 2.9 4,173 2.8 4.1 3.0 3,562

Number 3,189 946 4,173 4,173 5,612 1,265 6,880 6,880

India 2005/06 0.4 0.3 0.4 74,571 0.3 0.2 0.3 67,896

Male 0.5 0.4 0.5 32,052 0.4 0.4 0.4 29,637

Female 0.3 0.2 0.3 42,519 0.2 0.1 0.2 38,259

Number 58,571 15,910 74,571 74,571 62,863 5,016 67,896 67,896

Kenya 2003 8.4 6.5 7.8 5,036 8.0 7.3 8.0 4,251

Male 5.5 5.0 5.3 2,399 6.2 4.2 5.9 2,025

Female 9.8 11.7 10.1 2,636 9.6 15.4 9.9 2,225

Number 3,469 1,567 5,036 5,036 3,807 444 4,251 4,251

(Cont’d)
105 

Table 9.1 – cont’d

Ever use of condom 

Number

Consistent use of condom in last 12m

Country/sex No Yes Total No Yes Total Number

Lesotho 2004/05 26.2 29.2 27.6 4,139 28.8 27.0 28.4 3,539

Male 22.9 23.1 23.0 1,601 26.8 17.5 24.3 1,409

Female 27.8 34.2 30.5 2,538 30.0 36.0 31.1 2,130

Number 2,188 1,952 4,139 4,139 2,758 781 3,539 3,539

Malawi 2004 12.4 14.1 12.9 4,636 12.6 13.1 12.6 4,111

Male 8.8 13.7 11.2 2,199 12.0 9.2 11.6 1,958

Female 14.3 15.4 14.4 2,438 13.1 22.7 13.6 2,153

Number 3,305 1,332 4,636 4,636 3,735 374 4,111 4,111

Niger 2006 0.7 2.7 0.8 6,058 0.8 2.8 0.8 5,538

Male 0.7 2.6 0.9 2,093 0.9 2.5 1.0 1,891

Female 0.8 4.0 0.8 3,965 0.7 4.8 0.7 3,647

Number 5,830 228 6,058 6,058 5,449 88 5,538 5,538

Rwanda 2005 3.7 9.3 4.1 6,892 2.8 16.9 3.4 5,260

Male 2.3 7.5 3.2 3,009 2.8 12.8 3.4 2,359

Female 4.4 15.5 4.9 3,883 2.9 23.4 3.5 2,901

Number 6,192 699 6,892 6,892 5,027 228 5,260 5,260

Senegal 2005 1.0 1.0 0.9 5,219 0.9 1.2 0.9 4,406

Male 0.5 0.9 0.7 2,207 0.7 0.7 0.7 1,810

Female 1.2 1.2 1.1 3,012 0.9 3.9 1.0 2,596

Number 3,297 1,284 5,219 5,219 3,837 566 4,406 4,406

Tanzania 2003/04 n/a n/a n/a n/a 7.0 10.4 7.5 8,188

Male n/a n/a n/a n/a 6.8 8.4 7.1 3,711

Female n/a n/a n/a n/a 7.1 13.3 7.8 4,477

Number n/a n/a n/a n/a 6,982 1,205 8,188 8,188

Uganda 2004/05 5.5 10.9 6.4 16,827 6.7 9.8 7.0 12,206

Male 4.2 8.5 5.1 7,477 6.4 6.0 6.3 5,269

Female 6.5 13.9 7.5 9,350 6.9 14.4 7.6 6,937

Number 14,064 2,764 16,827 16,827 10,834 1,372 12,206 12,206

Hai Phong, 

Vietnam 2005 n/a n/a n/a n/a 0.4 2.2 0.7 1,095

Male n/a n/a n/a n/a 1.0 1.9 1.2 499

Female n/a n/a n/a n/a 0.0 2.8 0.3 595

Number n/a n/a n/a n/a 920 174 1,095 1,095

Zimbabwe 2005 21.1 24.6 22.7 9,802 21.4 21.2 21.4 8,306

Male 14.6 20.9 18.9 4,271 21.4 15.1 19.7 3,676

Female 23.2 32.1 25.5 5,531 21.4 37.8 22.7 4,629

Number 5,433 4,369 9,802 9,802 6,966 1,339 8,306 8,306

Note: HIV prevalence estimates for ‘consistent condom use’ for Niger are based on small numbers of cases.
106 

Consistent Condom Use 

In 10 of the 19 countries with data on consistent condom use (condom use at last sex with all 

sexual partners (up to three) in the past 12 months)—Cambodia, Ethiopia, Guinea, Malawi, Niger, 

Rwanda, Senegal, Tanzania, Uganda, and the Hai Phong province of Vietnam—HIV prevalence is higher 

among respondents who consistently use condoms, although the difference is often slight. In 8 of the 19 

countries—Burkina Faso, Cameroon, Côte d’Ivoire, Haiti, India, Kenya, Lesotho, and Zimbabwe—HIV 

prevalence is higher among respondents who do not consistently use condoms. In Ghana there is no 

difference in HIV prevalence according to whether or not people consistently use condoms. 

In countries where HIV prevalence is higher among respondents who consistently use condoms, 

this pattern tends to be true for both men and women, except in Malawi and Uganda (Table 9.1). In 

countries where HIV prevalence overall is higher among respondents who do not consistently use 

condoms, however, the pattern often differs by respondents’ sex. Notably, in Burkina Faso, Cameroon, 

Haiti, Kenya, Lesotho, and Zimbabwe, while HIV prevalence is higher among men who do not 

consistently use condoms, prevalence is higher among women who do consistently use them. In Côte 

d’Ivoire, although HIV prevalence is higher among women who do not use condoms consistently, it is 

higher among men who use condoms consistently. 

Condom Use at Last Sex 

In 11 of the 19 countries with data—Cambodia, Cameroon, Ethiopia, Guinea, Niger, Rwanda, 

Senegal, Tanzania, Uganda, the Hai Phong province of Vietnam, and Zimbabwe—HIV prevalence is 

higher among respondents who did not use a condom at last sex (Table 9.2). For the most part, there is no 

difference by respondents’ sex except in Uganda and Zimbabwe, where HIV prevalence for men is higher 

among those who used a condom at last sex, while it is higher among women who did not use a condom 

at last sex. 

In seven countries—Burkina Faso, Côte d’Ivoire, Haiti, India, Kenya, Lesotho, and Malawi—

HIV prevalence is higher among respondents who report using a condom at last sex. For the most part, in 

these countries the difference in HIV prevalence between those who used or did not use a condom at last 

sex is slight, and it varies by respondents’ sex. In Burkina Faso, Haiti, Kenya, Lesotho, and Malawi, HIV 

prevalence is higher among men who used a condom at last sex but is higher among women who did not 

use a condom at last sex. However, in Ghana for both men and women, HIV prevalence is higher among 

people who did not use a condom at last sex. 
107 

Table 9.2. HIV prevalence among women and men age 15-49, by condom use at last sex (among 

those who had sex in last 12 months) and condom use at last higher risk sex (among those who 

had higher risk sex) 

Condom use at last sex

Number

Condom use at last higher risk sex

Country/sex No Yes Total No Yes Total Number

Burkina Faso 2003 1.9 2.2 2.1 4,496 2.9 1.6 2.1 1,030

Male 1.6 2.3 2.1 1,902 1.1 1.0 1.0 743

Female 2.4 2.1 2.1 2,594 6.1 3.6 4.8 287

Number 830 3,665 4,496 4,496 358 672 1,030 1,030

Cambodia 2005 1.6 0.7 0.7 9,025 2.6 1.6 1.8 602

Male 1.6 0.7 0.8 4,238 1.1 1.6 1.5 564

Female 1.5 0.6 0.6 4,787 6.9 0.0 6.2 38

Number 609 8,402 9,025 9,025 133 470 602 602

Cameroon 2004 6.7 6.0 6.2 7,408 7.7 6.8 7.2 3,312

Male 5.7 4.7 5.0 3,514 4.8 6.2 5.6 2,194

Female 8.4 7.0 7.2 3,893 12.2 8.2 10.4 1,118

Number 1,812 5,595 7,408 7,408 1,573 1,740 3,312 3,312

Côte d'Ivoire 2005 4.5 4.9 4.8 6,650 5.5 4.7 5.1 3,106

Male 3.4 3.0 3.1 3,139 2.6 4.2 3.4 1,880

Female 6.7 6.3 6.3 3,510 8.5 6.1 7.7 1,226

Number 1,448 5,189 6,650 6,650 1,698 1,408 3,106 3,106

Ethiopia 2005 6.7 1.5 1.7 6,504 3.0 8.3 5.1 329

Male 1.6 1.3 1.3 2,778 1.2 2.3 1.7 221

Female 19.9 1.7 1.9 3,726 5.6 30.7 12.1 108

Number 164 6,340 6,504 6,504 197 133 329 329

Ghana 2003 2.6 2.6 2.6 6,029 2.6 1.8 2.3 1,667

Male 2.1 1.9 1.9 2,562 1.1 1.3 1.2 960

Female 3.4 3.1 3.1 3,466 4.1 2.9 3.8 707

Number 794 5,234 6,029 6,029 1,022 645 1,667 1,667

Guinea 2005 2.2 1.7 1.7 4,407 2.8 1.2 2.3 1,416

Male 1.1 1.1 1.1 1,905 1.4 0.5 1.1 1,022

Female 5.7 2.0 2.2 2,502 6.1 3.7 5.5 393

Number 490 3,896 4,407 4,407 980 436 1,416 1,416

Haiti 2005 2.3 2.8 2.7 6,886 2.8 2.6 2.8 4,256

Male 1.4 2.8 2.4 3,323 2.7 1.4 2.3 2,548

Female 4.2 2.8 3.0 3,563 3.0 5.5 3.5 1,708

Number 1,267 5,615 6,886 6,886 3,122 1,134 4,256 4,256

India 2005/06 0.2 0.3 0.3 68,048 0.5 0.8 0.6 1,733

Male 0.4 0.4 0.4 29,747 0.2 0.8 0.4 1,625

Female 0.1 0.2 0.2 38,301 4.1 0.0 3.5 108

Number 5,077 62,809 68,048 68,048 1,089 644 1,733 1,733

Kenya 2003 6.9 8.1 8.0 4,251 9.4 7.8 8.8 1,204

Male 4.1 6.3 5.9 2,025 4.2 5.3 4.7 812

Female 15.2 9.6 9.9 2,225 17.2 16.9 17.2 392

Number 483 3,767 4,251 4,251 749 456 1,204 1,204

(Cont’d)
108 

Table 9.2 – cont’d

Condom use at last sex

Number

Condom use at last higher risk sex

Country/sex No Yes Total No Yes Total Number

Lesotho 2004/05 26.9 28.8 28.4 3,539 30.2 27.0 29.0 1,923

Male 17.7 27.1 24.3 1,409 25.7 17.6 22.1 921

Female 36.6 29.8 31.1 2,130 33.5 39.1 35.3 1,002

Number 822 2,717 3,539 3,539 1,197 725 1,923 1,923

Malawi 2004 12.4 12.7 12.6 4,111 11.8 13.3 12.4 659

Male 8.6 12.2 11.6 1,958 8.5 9.8 9.1 488

Female 22.4 13.1 13.6 2,153 19.7 25.9 21.9 171

Number 400 3,709 4,111 4,111 375 284 659 659

Niger 2006 2.8 0.8 0.8 5,566 6.3 3.3 5.1 193

Male 2.5 0.9 1.0 1,907 2.9 2.7 2.8 158

Female 4.8 0.7 0.7 3,659 16.1 13.0 15.7 34

Number 88 5,456 5,566 5,566 119 74 193 193

Rwanda 2005 16.9 2.8 3.4 5,263 4.5 7.6 5.4 573

Male 12.8 2.8 3.4 2,362 3.0 3.4 3.2 322

Female 23.4 2.9 3.5 2,901 6.0 15.9 8.3 251

Number 228 5,027 5,263 5,263 406 166 573 573

Senegal 2005 1.2 0.9 0.9 4,430 1.0 1.3 1.2 982

Male 0.7 0.7 0.7 1,820 0.6 0.8 0.7 804

Female 3.8 0.9 1.0 2,611 2.5 5.0 3.4 179

Number 569 3,834 4,430 4,430 478 504 982 982

Tanzania 2003/04 10.1 7.0 7.5 8,188 7.1 9.7 8.3 2,801

Male 8.2 6.8 7.1 3,711 5.3 7.8 6.5 1,756

Female 13.1 7.1 7.8 4,477 9.6 14.1 11.3 1,045

Number 1,287 6,900 8,188 8,188 1,513 1,288 2,801 2,801

Uganda 2004/05 9.6 6.7 7.0 12,232 10.9 8.4 9.6 3,003

Male 5.9 6.4 6.3 5,289 7.6 5.3 6.4 1,938

Female 14.7 6.8 7.6 6,943 16.1 14.7 15.4 1,065

Number 1,497 10,733 12,232 12,232 1,453 1,550 3,003 3,003

Hai Phong, 

Vietnam 2005 2.2 0.4 0.7 1,095 23.9 0.0 5.0 38

Male 1.9 1.0 1.2 499 37.8 0.0 5.5 34

Female 2.8 0.0 0.3 595 0.0 0.0 0.0 4

Number 174 920 1,095 1,095 8 30 38 38

Zimbabwe 2005 21.7 21.3 21.4 8,317 23.5 18.1 20.7 1,963

Male 15.0 21.3 19.7 3,685 14.2 11.4 12.6 1,364

Female 39.1 21.2 22.7 4,633 38.6 40.0 39.2 599

Number 1,353 6,960 8,317 8,317 944 1,019 1,963 1,963

Note: HIV prevalence estimates for ‘condom use at last higher risk sex’ for Niger and Hai Phong, Vietnam are based on small numbers 

of cases.
109 

Condom Use at Last Higher-Risk Sex 

In 6 of the 19 countries—Ethiopia, India, Malawi, Rwanda, Senegal, and Tanzania—HIV 

prevalence is higher among respondents who report condom use at last sex with a non-marital, noncohabitating partner (Table 9.2). This is true for both men and women in all countries except India, where 

HIV prevalence is higher among women who did not use a condom at last higher-risk sex, while for men 

it is higher among those who did use a condom. 

For the remaining 13 countries, HIV prevalence is higher among people who did not use a 

condom at last higher-risk sex than among those who did. Again, there are several differences by the sex 

of respondents. In Cambodia, Cameroon, Côte d’Ivoire, Ghana, and Kenya, HIV prevalence is higher 

among men who used a condom at their last higher-risk sex, while it is higher among women who did not 

use a condom. In contrast, in Haiti, Lesotho, and Zimbabwe HIV prevalence is higher among men who 

did not use a condom at their last higher-risk sex, while it was higher among women who did use a 

condom. 

9.4 HIV Prevalence by Male Circumcision 

Findings from the 18 countries with data present a mixed picture of the association between male 

circumcision and HIV prevalence (Table 9.3). In eight of the countries (Burkina Faso, Cambodia, Côte

d’Ivoire, Ethiopia, Ghana, India, Kenya, and Uganda), HIV prevalence is higher among men who are not 

circumcised, although the difference between circumcised and non-circumcised men is slight, except in 

Kenya, where the difference is substantial (HIV prevalence of 11.5 percent for non-circumcised men 

compared with 3.1 percent for circumcised men) (Figure 9.1). In 10 of the countries—Cameroon, Guinea, 

Haiti, Lesotho, Malawi, Niger, Rwanda, Senegal, Tanzania, and Zimbabwe—HIV prevalence is higher 

among circumcised men. 

Table 9.3. HIV prevalence among men age 15-49, by male circumcision 

Male circumcision

Country/sex No Yes Total

Burkina Faso 2003

Male 2.9 1.7 1.9

Number 334 2,731 3,065

Cambodia 2005

Male 0.6 0.0 0.6

Number 6,517 138 6,656

Cameroon 2004

Male 1.3 4.3 4.1

Number 317 4,298 4,615

Côte d'Ivoire 2005

Male 3.5 2.8 2.9

Number 173 3,850 4,023

Ethiopia 2005

Male 1.2 0.9 0.9

Number 384 4,420 4,804

(Cont’d)
110 

Table 9.3 – cont’d

Male circumcision

Country/sex No Yes Total

Ghana 2003

Male 1.7 1.4 1.5

Number 181 3,864 4,045

Guinea 2005

Male 0.0 1.0 0.9

Number 18 2,558 2,577

Haiti 2005

Male 1.9 3.9 2.0

Number 4,071 243 4,321

India 2005/06

Male 0.4 0.2 0.4

Number 40,340 5,818 46,506

Kenya 2003

Male 11.5 3.1 4.6

Number 475 2,372 2,851

Lesotho 2004/05

Male 15.4 23.4 19.2

Number 1,046 951 2,001

Malawi 2004

Male 9.4 13.2 10.2

Number 1,906 500 2,405

Niger 2006

Male 0.0 0.8 0.8

Number 14 2,841 2,856

Rwanda 2005

Male 2.1 3.8 2.3

Number 3,908 418 4,348

Senegal 2005

Male 0.0 0.5 0.5

Number 56 3,124 3,183

Tanzania 2003/04

Male 5.6 6.5 6.3

Number 1,529 3,463 4,994

Uganda 2004/05

Male 5.5 3.7 5.1

Number 5,613 1,858 7,477

Zimbabwe 2005

Male 14.2 16.6 14.5

Number 5,235 597 5,848

Note: HIV prevalence estimates for ‘not circumcised’ men for Guinea and Niger are based on small numbers of cases.
111 

Figure 9.1. HIV prevalence among tested men 15-49 

by male circumcision 0.41.2 2.9 1.71.92.1 3.5 1.3 5.5 11.5 5.6 9.4 14.215.4 0.20.91.7 1.4 3.9 3.8 2.8 4.3 3.7 3.1 6.5 13.2 16.6 23.4

0

5

10

15

20

25

India

Ethiopia

Burkina Faso

Ghana

Haiti

Rwanda

Côte d'Ivoire

Cameroon

Uganda

Kenya

Tanzania

Malawi

Zimbabwe

Lesotho

HIV prevalence (%)

No Yes, circumcised

113 

10 HIV Prevalence by Medical Injection Use, Prevalence of Other 

STIs, and Prior HIV Testing Experience 

10.1 Key Findings 

10.2 Introduction 

This chapter presents associations of HIV prevalence with medical injections (injections received 

for a health care need) in the recent past (3 months, 6 months, or 12 months before the survey), and with 

STIs or STI symptoms (including abnormal discharge or a genital sore/ulcer) in the past 12 months. The 

chapter also presents the association between HIV prevalence and prior HIV testing experience, and 

whether the respondent received result of the most recent HIV test. 

Medical injections given with re-used, non-sterilized equipment may increase the risk of HIV and 

other infections (Kane et al., 1999). WHO categorizes up to 50 percent of medical injections in 

developing countries as unsafe, and often as unnecessary (WHO, 2007). Receiving multiple medical 

injections has been shown to be significantly positively associated with being HIV-infected, for both 

women and men (Mishra et al., 2008b; Mishra et al., 2008c). STIs, including herpes, syphilis, and 

gonorrhea, have been associated with increased risk of HIV transmission (Royce et al., 1997; Gray et al., 

2001). 

10.3 HIV Prevalence by Medical Injection Use 

Table 10.1 and Figures 10.1 and 10.2 show that, among the seven countries with data, men who 

received one or more medical injections in the recent past are more likely to be infected with HIV than 

those who did not receive any recent medical injections, in all countries except Ethiopia. The association 

is also positive for women in seven of the eight countries with data on women. Similarly, in all countries 

with data, women and men who received three or more medical injections in the recent past are more 

likely to be infected with HIV than those who received two or fewer injections. 

 In most countries, receiving multiple medical injections is positively associated with being 

HIV-infected, for both women and men. 

 Also, in most countries, having an STI or STI symptoms in the past 12 months is associated 

with higher HIV prevalence, both overall and for men and women separately.

 In all countries studied, HIV prevalence is higher among those who report having been 

previously tested for HIV, both overall and for men and women separately.
114 

Table 10.1. HIV prevalence by receipt of medical injections 

Recently received a medical injection Recently received 3+ medical injections

Country/sex Yes No Yes No

Burkina Faso 2003a

Women 1.4 1.9 2.2 1.8

Men n/a n/a n/a n/a

Cameroon 2004a

Women 9.7 6.1 13.5 6.3

Men 6.2 3.7 6.3 4.0

Tanzania 2003/04

Women 8.1 7.4 n/a n/a

Men 8.7 5.4 n/a n/a

Lesotho 2004a

Women 35.0 24.7 49.2 26.0

Men 44.5 17.6 49.9 18.8

Malawi 2004

Women 15.1 12.6 14.5 13.2

Men 16.6 9.3 14.1 10.1

Uganda 2004/05b

Women 8.5 6.4 10.2 6.1

Men 6.8 3.9 8.5 3.9

Ethiopia 2005

Women 2.3 1.7 3.2 1.6

Men 0.8 1.0 1.4 0.9

Zimbabwe 2006c

Women 27.5 20.0 33.9 20.8

Men 29.1 13.5 36.0 14.1

a

In the last 3 months

bBy a health professional

c

In the last 6 months
115 

Figure 10.1. HIV prevalence among tested men 15-49 

by receipt of 3+ medical injections 

0.9

4.0 3.9

10.1

14.1

18.8

1.4

6.3

8.5

14.1

36.0

49.9

0

10

20

30

40

50

Ethiopia Cameroon Uganda Malawi Zimbabwe Lesotho

HIV prevalence (%)

No Yes, recently received 3+ medical injections

Figure 10.2. HIV prevalence among tested women 15-49 

by receipt of 3+ medical injections 

1.6 1.8

6.3 6.1

13.2

20.8

26.0

3.2

2.2

13.5

10.2

14.5

33.9

49.2

0

10

20

30

40

50

Ethiopia Burkina Faso Cameroon Uganda Malawi Zimbabwe Lesotho

HIV prevalence (%)

No Yes, recently received 3+ medical injections
116 

10.4 HIV Prevalence by STIs or STI Symptoms 

Having an STI in the past 12 months is associated with higher HIV prevalence in 15 of the 19 

countries with data, both overall and for males and females separately (Table 10.2). In India HIV 

prevalence is the same for respondents who reported an STI in the past 12 months and those who did not, 

both overall and for women. Among Indian men, however, HIV prevalence is higher among those who 

reported an STI in the past 12 months. In Ethiopia, Senegal, and the Hai Phong province of Vietnam, HIV 

prevalence is slightly higher among those who did not report an STI in the past 12 months, although for 

all three countries the difference is less than 1 percent. In Senegal and in the Hai Phong province of 

Vietnam, there are no differences by respondents’ sex. In Ethiopia, however, HIV prevalence is slightly 

higher among women who had an STI in the past 12 months, while among men prevalence is slightly 

higher among those who did not have an STI. 
Table 10.2. HIV prevalence among women and men age 15-49 who ever had sex, by having STI or STI symptom i

Country/sex

STI Abnormal discharge Genital Sore/ulcer Any STI or STI symptom

No Yes No Yes No Yes No Yes

Burkina Faso 2003 2.1 6.5 2.1 4.3 2.1 3.6 2.0 4.3 

Male 2.3 2.4 2.3 0.0 2.3 0.0 2.3 0.7 

Female 1.9 8.6 1.9 5.8 2.0 7.6 1.8 6.2 

Number 5,700 80 5,578 202 5,704 76 5,518 262 

Cambodia 2005 0.8 8.0 0.8 4.2 0.9 1.4 0.7 3.5 

Male 0.7 30.9 0.8 15.3 0.9 0.0 0.7 13.0 

Female 0.8 4.0 0.8 2.5 0.8 1.6 0.7 1.9 

Number 9,837 171 9,748 261 9,550 459 9,394 614 

Cameroon 2004 6.1 9.4 6.1 10.3 6.0 9.6 5.9 9.2 

Male 4.6 7.6 4.6 11.2 4.7 6.2 4.5 7.2 

Female 7.2 11.6 7.4 9.6 7.0 11.2 7.0 10.4 

Number 7,840 458 8,046 253 7,658 640 7,399 899 

Côte d'Ivoire 2005 5.1 7.5 5.1 7.3 5.0 6.9 4.8 7.4 

Male 2.9 7.8 3.0 8.5 3.1 4.7 2.8 7.5 

Female 7.0 7.1 7.1 6.8 6.9 7.5 7.0 7.3 

Number 7,257 312 7,222 346 6,634 934 6,413 1,152 

Ethiopia 2005 2.0 1.8 2.0 3.8 2.0 2.6 2.0 3.0 

Male 1.4 0.0 1.4 8.3 1.4 0.2 1.4 2.2 

Female 2.4 2.7 2.4 2.6 2.4 3.8 2.4 3.4 

Number 7,362 46 7,358 50 7,321 86 7,261 137 

Ghana 2003 2.6 5.1 2.6 5.2 2.6 3.6 2.6 4.2 

Male 1.9 1.7 1.9 1.6 1.9 1.1 1.9 2.2 

Female 3.1 7.9 3.1 7.3 3.1 4.4 3.1 4.9 

Number 7,156 127 7,111 172 6,909 374 6,783 496 

117
Table 10.2 – cont’d

Country/sex

STI Abnormal discharge Genital Sore/ulcer Any STI or STI symptom

No Yes No Yes No Yes No Yes

Guinea 2005 1.6 2.5 1.7 1.6 1.6 1.7 1.5 2.1 

Male 0.9 2.1 1.0 0.0 1.1 0.0 1.0 1.4 

Female 2.0 2.6 2.1 1.9 2.1 1.9 2.0 2.2 

Number 5,055 533 5,332 256 4,435 1,153 4,300 1,288 

Haiti 2005 2.6 3.0 2.5 5.2 2.6 2.5 2.5 3.1 

Male 2.2 4.9 2.1 8.7 2.3 2.0 2.1 4.4 

Female 2.9 2.5 2.9 3.3 3.0 2.7 3.0 2.7 

Number 7,423 515 7,619 320 6,854 1,084 6,718 1,220 

India 2005/06 0.4 0.4 0.4 0.3 0.3 0.7 0.4 0.3 

Male 0.5 1.2 0.5 0.4 0.5 0.7 0.5 0.4 

Female 0.3 0.3 0.3 0.3 0.3 0.8 0.3 0.3 

Number 73,588 811 69,114 5,340 72,730 1,719 67,702 6,441 

Kenya 2003 7.7 13.9 7.4 25.8 7.5 16.4 7.4 17.3 

Male 5.0 15.6 4.9 26.3 5.1 11.2 4.9 14.9 

Female 10.0 11.8 9.7 25.4 9.8 19.6 9.7 19.0 

Number 4,942 94 4,941 95 4,904 131 4,842 185 

Lesotho 2004/05 26.8 50.4 26.2 46.3 26.3 37.9 25.5 39.3 

Male 22.4 42.1 21.8 37.9 22.4 29.5 21.9 30.4 

Female 29.6 55.4 28.9 52.0 28.8 41.7 27.8 43.9 

Number 3,995 144 1,006 136 3,670 470 3,506 631 

Malawi 2004 12.7 36.8 12.1 26.3 12.7 19.6 12.0 23.6 

Male 11.0 44.0 10.7 24.8 11.1 14.6 10.7 20.1 

Female 14.1 34.4 13.5 27.0 14.1 22.7 13.3 25.6 

Number 4,587 50 4,383 253 4,491 145 4,277 353 

Niger 2006 0.8 2.7 0.8 0.8 0.8 0.8 0.8 1.3 

Male 0.9 0.0 0.9 1.8 0.9 0.0 0.9 0.8 

Female 0.7 4.3 0.8 0.5 0.8 1.0 0.7 1.5 

Number 6,020 38 5,916 142 5,927 131 5,835 223 

118
Table 10.2 – cont’d

Country/sex

STI Abnormal discharge Genital Sore/ulcer Any STI or STI symptom

No Yes No Yes No Yes No Yes

Rwanda 2005 3.9 25.1 3.8 18.1 3.9 13.7 3.6 15.7 

Male 3.0 19.1 3.0 14.2 3.2 3.4 3.0 9.9 

Female 4.6 28.7 4.4 19.6 4.4 16.5 4.1 18.1 

Number 6,812 80 6,731 161 6,706 186 6,605 287 

Senegal 2005 0.9 0.6 0.9 1.2 0.8 1.9 0.9 1.4 

Male 0.7 0.0 0.7 0.0 0.7 0.0 0.7 0.0 

Female 1.1 0.9 1.1 1.7 1.0 2.2 1.0 1.8 

Number 5,136 84 5,063 156 4,871 348 4,753 466 

Tanzania 2003/04 7.8 12.3 7.7 13.5 7.8 11.0 7.6 12.2 

Male 7.0 9.0 6.9 11.1 7.0 8.9 6.9 9.4 

Female 8.4 17.2 8.4 16.3 8.5 12.7 8.2 15.1 

Number 8,888 298 8,926 260 8,826 360 8,608 578 

Uganda 2004/05 6.4 14.3 6.4 14.2 5.8 14.6 5.2 13.3 

Male 5.1 13.6 5.6 12.8 4.6 14.5 4.2 12.9 

Female 7.4 14.7 7.1 14.6 6.8 14.7 6.1 13.4 

Number 12,234 2,009 12,252 1,990 11,488 2,754 10,210 4,026 

Hai Phong, 

Vietnam 2005 0.7 0.0 0.7 0.0 0.7 0.7 0.7 0.6 

Male 1.1 0.0 1.1 0.0 1.1 0.0 1.1 0.0 

Female 0.3 0.0 0.3 0.0 0.2 0.7 0.2 0.6 

Number 1,154 19 1,124 48 1,021 152 1,007 166 

Zimbabwe 2005 21.7 45.7 21.4 44.8 21.6 40.4 21.0 37.3 

Male 18.3 35.7 17.9 37.8 18.1 39.1 17.7 33.0 

Female 24.3 51.2 24.1 49.7 24.3 40.9 23.7 39.7 

Number 9,401 401 9,273 529 9,227 575 8,829 972 

Note: Some of the HIV prevalence estimates for Burkina Faso, Ethiopia, Kenya, Malawi, Niger, Rwanda, Senegal, and Hai Phong, Vietnam are base

cases. 

119
120 

In 15 of the 19 countries, having an abnormal discharge in the past 12 months is associated with 

higher HIV prevalence. For the most part, this is true for both men and women. However, in Burkina 

Faso, Ghana, and Senegal, HIV prevalence is higher among men who did not have an abnormal 

discharge, while for women it was higher among those who had an abnormal discharge. In Côte d’Ivoire 

this pattern is reversed. In Niger HIV prevalence is the same for respondents having an abnormal 

discharge and those who did not. Among men in Niger, however, HIV prevalence is higher among those 

who did have abnormal discharge, while for women it is slightly higher among those who did not. In 

Guinea, India, and the Hai Phong province of Vietnam, HIV prevalence is slightly higher among those 

without an abnormal discharge in the past 12 months, both overall and among men and women separately. 

In 16 of the 19 countries, respondents reporting a genital sore in the past 12 months tend to have 

higher HIV prevalence (Table 10.2). Although this is generally true for both sexes, in some countries 

there are some notable differences by respondents’ sex. In Burkina Faso, Cambodia, Ethiopia, Ghana, and 

Senegal, HIV prevalence is higher among men who report having a genital sore, but among women HIV 

prevalence is higher among those who did not have a genital sore in the past 12 months. In the Hai Phong 

province of Vietnam, this pattern is reversed. In Niger and the Hai Phong province of Vietnam, there is no 

difference in overall HIV prevalence according to whether or not respondents had a genital sore. 

However, in both countries HIV prevalence is higher among men without a genital sore in the past 12 

months, but higher among women with a genital sore. Haiti is the only country where HIV prevalence is 

higher among respondents who did not report a genital sore, although the difference is small among those 

who did or did not report a genital sore. 

In 17 of the 19 countries, having any STI or STI symptoms (i.e., reporting an STI, abnormal 

discharge, or genital sore or ulcer) is associated with higher HIV prevalence, and the difference is often 

substantial. This is generally true for both men and women (Figures 10.3 and 10.4). However, in Burkina 

Faso, Niger, and Senegal, HIV prevalence is higher among men with no STI or STI symptoms, while it is 

higher among females with an STI or STI symptoms. In Haiti this pattern is reversed. In India and in the 

Hai Phong province of Vietnam, HIV prevalence is marginally higher among those without any STI or 

STI symptoms, although the difference between those with and those without any STI or STI symptoms is 

only 0.1 percent. In both countries, HIV prevalence is higher among men without any STI or STI 

symptoms. Among women in India, however, there is no difference in HIV prevalence by whether or not 

women report any STI or STI symptoms. In the Hai Phong province of Vietnam, HIV prevalence is 

higher among women who report having any STI or STI symptoms in the past 12 months. 
121 

Figure 10.3. HIV prevalence among tested men 15-49 

who ever had sex by reported STI or STI symptoms in last 12 months 0.5 0.70.91.4 1.02.3 1.92.13.0 2.84.5 4.24.9 6.9 10.7 17.7 21.9 0.4 13.0 0.82.2 1.4 0.72.2 4.4 9.9 7.5 7.2 12.914.9 9.4 20.1 33.0 30.4

0

10

20

30

40

50

India

Cambodia

Niger

Ethiopia

Guinea

Burkina Faso

Ghana

Haiti

Rwanda

Côte d'Ivoire

Cameroon

Uganda

Kenya

Tanzania

Malawi

Zimbabwe

Lesotho

HIV prevalence (%)

No Yes, reported STI/STI symptoms in last 12m

Figure 10.4. HIV prevalence among tested women 15-49 

who ever had sex by reported STI or STI symptoms in last 12 months 0.3 0.20.71.0 0.72.4 2.0 1.83.1 3.04.1 7.0 7.0 6.1 9.7 8.2 13.3 23.7 27.8 0.30.61.9 1.8 1.5 3.4 2.2 6.2 4.9 2.7 18.1 7.3 10.4 13.4 19.0 15.1 25.6 39.7 43.9

0

10

20

30

40

50

India

Hai Phong, Vietnam

Cambodia

Senegal

Niger

Ethiopia

Guinea

Burkina Faso

Ghana

Haiti

Rwanda

Côte d'Ivoire

Cameroon

Uganda

Kenya

Tanzania

Malawi

Zimbabwe

Lesotho

HIV prevalence (%)

No Yes, reported STI/STI symptoms in last 12m
122 

10.5 HIV Prevalence by Prior HIV Testing Experience 

Data are available from 18 countries for both men and women on HIV prevalence by prior HIV 

testing experience. In Burkina Faso data are available for men but not women. In all cases HIV 

prevalence is higher among respondents who previously tested for HIV (Table 10.3). 

Among those previously tested for HIV, in 12 of the 18 countries with data for both men and 

women—Cambodia, Côte d’Ivoire, Ghana, Guinea, Lesotho, Niger, Rwanda, Senegal, Tanzania, Uganda, 

the Hai Phong province of Vietnam, and Zimbabwe—receiving the result of a prior test is associated with 

lower HIV prevalence compared with previously tested respondents who did not receive their test results. 

In Burkina Faso HIV prevalence is also lower among previously tested men who received the result of 

their last test. In contrast, in the remaining six countries—Cameroon, Ethiopia, Haiti, India, Kenya, and 

Malawi—receiving the result of a prior HIV test is associated with higher HIV prevalence, among those 

previously tested. 

In general, these patterns hold for both women and men in each country, but there are some 

exceptions. For example, in Côte d’Ivoire among those previously tested, receiving the result of a prior 

test is associated with lower HIV prevalence among men but higher prevalence among women, whereas 

in Ethiopia this pattern is reversed. 
123 

Table 10.3. HIV prevalence among women and men age 15-49, by previous HIV testing experience, 

and whether received result of most recent HIV test (among those previously tested) 

Previously tested for HIV Received result of last test

Country/sex Not tested Tested No Yes Total Number

Burkina Faso 2003 n/a n/a n/a n/a n/a n/a

Male 1.8 2.6 2.7 1.7 1.9 3,065

Female n/a n/a n/a n/a n/a n/a

Number 2,837 228 194 33 3,065 3,065

Cambodia 2005 0.4 2.4 2.4 2.3 0.6 14,703

Male 0.4 2.1 2.1 2.3 0.6 6,656

Female 0.4 2.7 2.7 2.4 0.6 8,047

Number 12,934 1,769 1,624 145 14,703 14,703

Cameroon 2004 4.7 8.8 8.6 9.1 5.4 9,746

Male 3.6 6.8 7.4 2.5 4.1 4,619

Female 5.7 10.2 10.3 10.1 6.6 5,128

Number 7,991 1,755 1,074 681 9,746 9,746

Côte d'Ivoire 2005 4.3 8.5 8.6 8.3 4.7 8,436

Male 2.4 7.3 8.6 0.6 2.9 4,023

Female 6.0 9.4 8.6 14.1 6.4 4,413

Number 7,532 904 765 139 8,436 8,436

Ethiopia 2005 1.4 3.5 2.8 10.8 1.4 10,540

Male 0.9 2.6 1.1 14.5 0.9 4,804

Female 1.8 4.5 4.7 0.6 1.9 5,736

Number 10,083 456 417 39 10,540 10,540

Ghana 2003 2.1 3.1 3.3 2.6 2.2 9,142

Male 1.5 1.3 1.7 0.0 1.5 4,045

Female 2.5 4.4 4.5 4.1 2.7 5,097

Number 8,266 876 672 204 9,142 9,142

Guinea 2005 1.5 2.1 2.4 0.0 1.5 6,319

Male 1.0 0.8 0.9 0.0 0.9 2,577

Female 1.8 4.5 5.9 0.0 1.9 3,742

Number 6,070 249 210 38 6,319 6,319

Haiti 2005 1.8 4.3 4.0 7.8 2.2 9,551

Male 1.8 3.8 3.2 12.4 2.0 4,321

Female 1.8 4.5 4.4 6.0 2.3 5,230

Number 8,152 1,399 1,276 122 9,551 9,551

India 2005/06 0.3 1.0 1.0 1.2 0.3 99,838

Male 0.3 1.5 1.4 1.8 0.4 46,506

Female 0.2 0.5 0.5 0.0 0.2 53,332

Number 96,440 3,390 3,020 358 99,838 99,838

Kenya 2003 6.2 9.8 9.6 11.1 6.7 6,001

Male 4.1 7.3 7.3 6.8 4.6 2,851

Female 8.1 12.2 11.9 14.9 8.7 3,151

Number 5,085 916 827 90 6,001 6,001

(Cont’d)
124 

Table 10.3 – cont’d

Previously tested for HIV Received result of last test

Country/sex Not tested Tested No Yes Total Number

Lesotho 2004/05 21.6 37.1 37.9 33.2 23.5 5,019

Male 17.4 35.7 37.4 23.8 19.2 2,001

Female 24.5 37.7 38.1 35.8 26.4 3,018

Number 4,392 627 520 107 5,019 5,019

Malawi 2004 11.4 14.0 13.7 16.4 11.8 5,150

Male 9.8 12.3 12.2 12.8 10.2 2,465

Female 12.9 16.0 15.5 18.3 13.3 2,686

Number 4,323 828 721 107 5,150 5,150

Niger 2006 0.7 2.3 2.5 1.3 0.7 7,262

Male 0.7 3.1 3.4 1.9 0.8 2,856

Female 0.7 1.2 1.4 0.0 0.7 4,406

Number 7,031 230 192 38 7,262 7,262

Rwanda 2005 2.3 5.5 5.6 4.5 3.0 9,988

Male 1.9 3.8 3.7 4.8 2.3 4,348

Female 2.7 6.6 6.9 4.4 3.6 5,641

Number 7,727 2,262 2,026 235 9,988 9,988

Senegal 2005 0.7 1.3 1.7 0.7 0.7 7,412

Male 0.4 2.3 2.6 0.0 0.5 3,183

Female 0.9 0.8 0.8 0.8 0.9 4,229

Number 7,031 381 241 140 7,412 7,412

Tanzania 2003/04 6.4 10.5 11.0 6.8 7.0 10,747

Male 5.8 8.8 9.2 6.0 6.3 4,994

Female 6.9 11.9 12.7 7.5 7.7 5,753

Number 9,084 1,663 1,435 228 10,747 10,747

Uganda 2004/05 5.7 10.7 11.2 7.3 6.4 16,827

Male 4.8 7.0 7.2 6.2 5.1 7,477

Female 6.5 13.1 13.9 8.0 7.5 9,350

Number 14,544 2,283 1,972 311 16,827 16,827

Hai Phong, Vietnam 2005 0.3 1.9 2.0 0.0 0.5 1,675

Male 0.6 2.8 3.0 0.0 0.9 754

Female 0.1 0.9 1.0 0.0 0.2 921

Number 1,471 204 190 14 1,675 1,675

Zimbabwe 2005 17.0 21.9 22.4 19.0 18.1 12,796

Male 13.9 17.5 17.7 15.8 14.5 5,848

Female 20.0 24.4 25.2 20.5 21.1 6,947

Number 9,904 2,892 2,461 431 12,796 12,796

Note: Some of the HIV prevalence estimates for ‘received result of last test’ for Burkina Faso, Ethiopia, Guinea, Kenya, Niger, and 

Hai Phong, Vietnam are based on small numbers of cases.
125 

11 HIV Prevalence by Women’s Status 

11.1 Key Findings 

11.2 Introduction 

According to UNAIDS, globally women account for half of all individuals over age 15 living 

with HIV. In sub-Saharan Africa, nearly 60 percent of all HIV infections are among women (UNAIDS, 

2008). Although women are biologically more susceptible to HIV infection, women’s unequal status in 

society often contributes to their vulnerability to HIV infection (Gupta, 2002). This chapter presents data 

on the effect of several different measures of women’s empowerment on HIV prevalence. The first 

measure is the age gap between women age 15-24 and their last sex partner. Additionally, women’s 

ability to negotiate safer sex is measured by asking both men and women whether they agree that a wife is 

justified: (1) to ask a husband to use a condom, and (2) to refuse sex if the husband is infected with an 

STI. Another measure of empowerment is women’s participation in major personal and household 

decision-making. 

11.3 HIV Prevalence by Women’s Age Gap with Partner

Examining age differences between young women and their partners is important because young 

women may have less power to negotiate safer sex including condom use with older men than with men 

their own age or younger (Gupta, 2002). Moreover, older men are more likely to be infected with HIV 

(UNAIDS, 2008). To assess the effect of age differences between sexual partners on HIV prevalence, 

women age 15-19 who had sex in the 12 months preceding the survey are asked the ages of all partners in 

the past 12 months. If they do not know the ages of their partners, they are asked if their partners were 

older or younger than they, and if older, whether they were 10 or more years older. 

Data are available for 10 countries regarding the age-gap between women age 15-19 and their last 

sex partner (Table 11.1). In four of these countries—Ghana, Niger, Rwanda, and Senegal—the survey 

samples are too small to measure HIV prevalence among women age 15-19 whose most recent sex 

partner was more than 10 years older. In five of the remaining six countries—Côte d’Ivoire, Haiti, 

Tanzania, Uganda, and Zimbabwe—HIV prevalence is considerably higher among women age 15-19 

whose most recent sex partner was more than 10 years older than the respondent (Figure 11.1). In 

Zimbabwe, for example, HIV prevalence among women whose last partner was at least 10 years older is 

31.7 percent compared with 10.7 percent among women whose last partner was less than 10 years older, 

or was younger. In contrast, in Guinea HIV prevalence is slightly higher among women age 15-19 whose 

last partner was less than 10 years older, or was younger. 

 The findings are mixed regarding the effect of an age gap between sex partners on HIV 

prevalence. In 5 of the 10 countries with data, HIV prevalence is higher among women whose 

last sex partner was at least 10 years older. In the other five countries, HIV prevalence is 

higher among women whose partner was less than 10 years older, or was younger.

 HIV prevalence is higher among both men and women who agree that women can negotiate 

safer sex with their husband if he is infected with an STI. 

 Similarly, HIV prevalence is generally higher among women who report active participation 

in household decision making.
126 

Table 11.1. Observed HIV prevalence among women age 15-19, by age-gap with the most recent 

sex partner in last 12 months (among those who had sex in past 12 months) 

Country/sex

Age-gap with the last sex partner

Total

Partner younger or 

<10 years older Partner 10+ years older

Côte d'Ivoire 2005

Female 2.3 8.8 4.0

Number 142 48 190 

Haiti 2005

Female 1.5 8.4 1.7

Number 2,274 78 2,351 

Ghana 2003

Female 1.4 0.0 1.6

Number 227 9 332 

Guinea 2005

Female 1.2 0.9 1.2

Number 1,358 38 1,396 

Niger 2006

Female 0.5 0.0 0.5

Number 1,417 1 1,418 

Rwanda 2005

Female 2.3 0.0 2.3

Number 950 11 960 

Senegal 2005

Female 0.4 0.0 0.4

Number 1,152 21 1,173 

Tanzania 2003/04

Female 5.4 11.5 6.1

Number 478 67 545 

Uganda 2004/05

Female 3.4 5.5 3.5

Number 1,451 75 1,525 

Zimbabwe 2005

Female 10.7 31.7 12.5

Number 2,437 238 2,675

Note: For most countries, the HIV prevalence estimates for women age 15-19 with ‘10+ years older’ partner are based on small 

numbers of cases.
127 

Figure 11.1. HIV prevalence among tested women 15-19 who had sex in past 12 months by agegap with the most recent sex partner in last 12 months 

1.2 1.5

2.3

3.4

5.4

10.7

0.9

8.4 8.8

5.5

11.5

31.7

0

10

20

30

40

Guinea Haiti Côte d'Ivoire Uganda Tanzania Zimbabwe

HIV prevalence (%)

Partner younger or <10 years older Partner 10+ years older

In Kenya data are available regarding the age gap between women age 15-24 and their first sex 

partner. In this case, the only country with such data, HIV prevalence is higher among women whose first 

sex partner was more than 10 years older with an HIV prevalence of 12.3 percent for women age 15-24 

whose first sex partner was at least 10 years older compared with 7.5 percent when the age gap was less 

than 10 years (data not shown in Table). 

11.4 HIV Prevalence by Women’s Ability to Negotiate Safer Sex 

Women’s ability to negotiate safer sex is measured by asking respondents if a wife is justified to 

ask her husband to use a condom if he is infected with an STI. In 12 countries data are available for both 

men and women. In 11 of these countries, HIV prevalence is higher among respondents who agree with 

the statement that a wife is justified in asking her husband to use a condom if he has an STI (Table 11.2). 

This is true for both men and women in all countries except Kenya, where men who disagree with the 

statement have higher HIV prevalence, while women who agree have higher HIV prevalence. 

In Ghana and India data are available for this measure only among men. These data are mixed, 

with HIV prevalence higher among Ghanaian men who disagree with the statement that a wife is justified 

in requesting condom use, while among Indian men HIV prevalence is higher among those who agree 

with the statement. In Guinea, Niger, and Rwanda, data are available only for women. For all three 

countries, HIV prevalence is higher among women who agree that a wife is justified to ask her husband to 

use a condom if he has an STI. 
128 

Table 11.2. HIV prevalence among women and men age 15-49 who agreed that wife is justified for 

asking husband to use condom and refusing sex with husband if he has an STI 

Country/sex

Wife is justified 

for asking 

husband to use 

condom if he has 

an STI

Wife is justified 

for refusing sex 

with husband

Agreed that women can 

negotiate safer sex

No Yes No Yes No Any 1 Both Total Number 

Burkina Faso 2003 n/a n/a 1.5 1.9 n/a n/a n/a 1.8 7,151

Male n/a n/a 1.5 1.9 n/a n/a n/a 1.9 3,065

Female n/a n/a 1.4 1.9 n/a n/a n/a 1.8 4,086

Number n/a n/a 1,346 5,805 n/a n/a n/a 7,151 7,151

Cambodia 2005 0.4 0.6 n/a n/a n/a n/a n/a 0.6 14,703

Male 0.0 0.6 0.5 0.6 0.0 0.6 0.6 0.6 6,656

Female 0.5 0.6 n/a n/a n/a n/a n/a 0.6 8,047

Number 1,344 13,360 258 6,398 79 229 6,348 14,703 14,703

Cameroon 2004 2.4 6.7 2.2 6.4 1.32 4.77 6.71 5.4 9,746

Male 1.5 5.0 1.5 4.8 1.1 3.0 5.1 4.1 4,619

Female 3.0 8.3 2.8 7.8 1.5 5.7 8.4 6.6 5,128

Number 2,806 6,941 2,186 7,560 1,791 1,409 6,546 9,746 9,746

Côte d'Ivoire 2005 5.5 4.5 4.6 4.7 6.16 3.16 4.78 4.7 8,436

Male 3.2 2.8 2.4 2.9 2.9 2.5 2.9 2.9 4,023

Female 6.3 6.5 5.4 6.8 7.2 3.4 7.2 6.4 4,413

Number 1,670 6,766 1,812 6,624 1,099 1,284 6,053 8,436 8,436

Ethiopia 2005 0.8 2.1 0.6 1.6 0.4 1.0 2.1 1.4 10,540

Male 0.2 1.4 0.2 1.1 0.1 0.2 1.4 0.9 4,804

Female 1.1 3.1 1.0 2.1 0.5 1.4 3.0 1.9 5,736

Number 5,130 5,410 1,784 8,755 1,438 4,038 5,063 10,540 10,540

Ghana 2003 n/a n/a 2.4 2.1 n/a n/a n/a 2.2 9,142

Male 1.6 1.4 0.3 1.6 0.8 1.0 1.5 1.5 4,045

Female n/a n/a 3.5 2.6 n/a n/a n/a 2.7 5,097

Number 339 3,708 1,063 8,081 121 458 3,468 9,142 9,142

Guinea 2005 n/a n/a n/a n/a n/a n/a n/a n/a n/a

Male n/a n/a n/a n/a n/a n/a n/a n/a n/a

Female 1.2 2.4 1.1 2.2 0.9 1.5 2.5 1.9 3,742

Number 1,552 2,190 1,015 2,726 746 1,076 1,920 3,742 3,742

Haiti 2005 1.0 2.4 1.3 2.3 0.9 1.7 2.4 2.2 9,551

Male 0.6 2.3 0.8 2.3 0.5 2.0 2.3 2.0 4,321

Female 1.7 2.4 2.1 2.4 2.6 1.6 2.4 2.3 5,230

Number 1,349 8,202 1,436 8,115 937 910 7,703 9,551 9,551

(Cont’d)
129 

Table 11.2 – cont’d

Country/sex

Wife is justified 

for asking 

husband to use 

condom if he has 

an STI

Wife is justified 

for refusing sex 

with husband

Agreed that women can 

negotiate safer sex

No Yes No Yes No Any 1 Both Total Number 

India 2005/06 n/a n/a 0.3 0.3 n/a n/a n/a 0.3 99,838

Male 0.2 0.4 0.3 0.4 0.2 0.4 0.4 0.4 46,506

Female n/a n/a 0.3 0.2 n/a n/a n/a 0.2 53,332

Number 7,677 38,683 20,409 79,410 5,181 5,714 3,5456 99,838 99,838

Kenya 2003 5.4 7.1 4.9 6.9 3.1 6.2 7.1 6.7 6,001

Male 5.2 4.4 0.7 4.9 1.3 5.1 4.6 4.6 2,851

Female 5.5 9.5 7.0 8.9 4.2 7.1 9.5 8.7 3,151

Number 1,203 4,799 592 5,409 301 1,193 4,507 6,001 6,001

Lesotho 2004/05 15.0 24.8 21.5 24.1 14.97 21.63 24.91 23.5 5,019

Male 12.9 20.5 17.4 19.9 10.5 18.8 20.6 19.2 2,001

Female 17.9 27.3 25.8 26.6 20.5 24.8 27.2 26.4 3,018

Number 629 4,390 1,154 3,865 321 1,140 3,557 5,019 5,019

Malawi 2004 10.2 12.2 9.1 12.6 7.3 10.8 12.7 11.8 5,150

Male 8.2 10.6 7.4 10.9 6.5 8.3 11.2 10.2 2,465

Female 11.9 13.6 10.3 14.4 7.8 13.0 14.2 13.3 2,686

Number 949 4,201 1,140 4,010 364 1,362 3,424 5,150 5,150

Niger 2006 n/a n/a n/a n/a n/a n/a n/a n/a n/a

Male n/a n/a n/a n/a n/a n/a n/a n/a n/a

Female 0.5 0.8 0.7 0.7 0.62 0.54 0.83 0.7 4,406

Number 1,798 2,608 1,210 3,196 907 1,193 2,305 4,406 4,406

Rwanda 2005 n/a n/a 3.4 3.0 n/a n/a n/a 3.0 9,988

Male n/a n/a 1.7 2.3 n/a n/a n/a 2.3 4,348

Female 2.2 3.8 4.0 3.6 1.78 3.54 3.71 3.6 5,641

Number 770 4,871 668 9,320 239 781 4,622 9,988 9,988

Senegal 2005 n/a n/a n/a n/a n/a n/a n/a n/a n/a

Male n/a n/a n/a n/a n/a n/a n/a n/a n/a

Female n/a n/a 0.6 1.0 n/a n/a n/a 0.9 4,229

Number n/a n/a 758 3,471 n/a n/a n/a 4,229 4,229

Tanzania 2003/04 6.1 7.4 4.7 7.5 3.3 7.9 7.4 7.0 10,747

Male 5.6 6.5 3.8 6.7 1.9 8.3 6.4 6.3 4,994

Female 6.4 8.3 5.5 8.2 4.4 7.6 8.3 7.7 5,753

Number 2,945 7,802 1,794 8,953 1,179 2,380 7,187 10,747 10,747

Uganda 2004/05 5.1 6.8 5.5 6.7 4.9 5.8 6.9 6.4 16,827

Male 4.2 5.2 4.5 5.2 3.9 4.9 5.2 5.1 7,477

Female 5.6 8.3 5.9 8.1 5.3 6.2 8.7 7.5 9,350

Number 3,943 12,884 3,862 12,965 2,081 3,643 11,103 16,827 16,827

(Cont’d)
130 

Table 11.2 – cont’d

Country/sex

Wife is justified 

for asking 

husband to use 

condom if he has 

an STI

Wife is justified 

for refusing sex 

with husband

Agreed that women can 

negotiate safer sex

No Yes No Yes No Any 1 Both Total Number 

Hai Phong, 

Vietnam 2005 0.0 0.6 0.3 0.6 0.0 0.4 0.6 0.5 1,675

Male 0.0 0.9 0.0 1.0 0.0 0.0 1.0 0.9 754

Female 0.0 0.3 0.3 0.2 0.0 0.5 0.2 0.2 921

Number 176 1,499 422 1,253 155 287 1,233 1,675 1,675

Zimbabwe 2005 14.1 18.9 15.5 18.8 11.8 17.0 19.1 18.1 12,796

Male 9.2 15.5 13.3 14.9 9.4 13.1 15.5 14.5 5,848

Female 17.7 21.8 17.5 22.0 13.6 20.5 22.1 21.1 6,947

Number 2,016 10,780 2,730 10,065 964 2,817 9,014 12,796 12,796

Women’s ability to negotiate safer sex is also measured by asking respondents whether a wife is 

justified in refusing sex with a husband who is infected with an STI. Data are available from 15 countries 

for both men and women on this measure. In 12 of these countries, HIV prevalence is higher among 

respondents who agree that a woman is justified in refusing sex if her husband has an STI, both overall 

and for men and women separately (Table 11.2). In India there is no difference in HIV prevalence by 

agreement or disagreement with the statement. However, HIV prevalence is slightly higher among men 

who agree that a wife can refuse sex, and slightly higher among women who disagree. In Ghana and 

Rwanda HIV prevalence is higher among those who disagree that a wife is justified to refuse sex with a 

husband who is infected with an STI, but there are differences by respondent’s sex. For men, HIV 

prevalence is higher among those who agree that a wife can refuse sex but higher among women who 

disagree. 

In Cambodia data are available on this measure only for men. In this case, HIV prevalence is 

slightly higher among men who agree that a wife can refuse sex if her husband has an STI. In Guinea, 

Niger, and Senegal, data are available only for women. In Guinea and Senegal HIV prevalence is higher 

among women who agree that a wife can refuse sex, while in Niger there is no difference in HIV 

prevalence among women by whether or not they agree with the statement. 

Participants who agree with either one or both of the above statements about refusal are 

considered to agree that women are justified in negotiating safer sex with their husbands. Data for this 

composite measure are not available from Burkina Faso and Senegal. In all other countries studied, HIV 

prevalence generally is higher among respondents who agree that women can negotiate safer sex. In 10 of 

the countries where data are available for both women and men—Cameroon, Ethiopia, Haiti, Kenya, 

Lesotho, Malawi, Tanzania, Uganda, the Hai Phong province of Vietnam, and Zimbabwe—HIV 

prevalence is higher among respondents who agree that wives are justified in negotiating safe sex. There 

are no differences by respondents’ sex. In Guinea, Niger, and Rwanda, where data are available only for 

women, HIV prevalence is higher among women who agree with either or both statements. In Cambodia, 

Ghana, and India, where data are available only for men, HIV prevalence is higher among men who 

agree. 
131 

11.5 HIV Prevalence by Women’s Participation in Household Decision-Making 

Data are available for 14 countries on HIV prevalence by women’s participation in household 

decision-making. Surveys measure women’s participation in household decision-making in four key 

aspects: (1) health care for themselves, (2) large household purchases, (3) purchases for daily needs, and 

(4) visiting family members or relatives. 

In 12 of the 14 countries, HIV prevalence is higher among women who participate in decisions 

regarding their own health care (Table 11.3). However, in India there is no difference, and in Zimbabwe 

the association is slightly reversed. In the same 12 countries, HIV prevalence is higher among women 

who participate in decisions on large household purchases. In India and Zimbabwe there is no difference 

in HIV prevalence by this measure. In 11 of the 14 countries with data, HIV prevalence is higher among 

women who participate in daily household purchase decisions, while in India there is no difference by this 

measure. In Zimbabwe and Ethiopia HIV prevalence is slightly higher among women who do not 

participate in daily purchase decisions. Finally, in 12 of the 14 countries, HIV prevalence is higher among 

women who usually participate in decisions about visits to family members or relatives. In India there is 

no difference in HIV prevalence by this measure, while in Zimbabwe prevalence is slightly lower among 

women who participate in decisions about visits. 
Table 11.3. HIV prevalence among women age 15-49, by their participation in household decision-m

Country/sex

Decision on 

own health 

care 

Decision on 

large 

household 

purchases 

Decision on 

purchasing 

daily need 

items

Decision on 

visiting family 

and relative

Participation in house

making

No Yes No Yes No Yes No Yes None Any 1 Any 2 

Burkina Faso 2003

Female 1.5 3.1 1.7 2.2 1.8 1.9 1.5 2.4 1.4 2.0 2.5 

Number 4,642 2,509 4,586 2,565 4,223 2,928 3,869 3,282 2,947 1,255 610 

Cameroon 2004

Female 4.7 10.6 5.3 9.5 4.9 8.8 4.7 9.1 3.6 7.5 7.7 

Number 3,409 1,719 3,463 1,665 2,875 2,253 2,849 2,279 2,136 785 636 

Ethiopia 2005

Female 1.7 2.0 1.8 1.9 2.0 1.7 1.8 1.9 2.1 0.2 1.2 

Number 3,335 2,401 3,712 2,024 2,625 3,111 2,802 2,934 2,342 305 632 

Ghana 2003

Female 1.4 3.9 1.7 4.0 1.5 3.8 1.5 3.9 1.1 2.0 2.9 

Number 2,467 2,630 2,825 2,271 2,486 2,610 2,481 2,616 1,739 646 482 

Guinea 2005

Female 1.3 2.7 1.3 2.7 1.4 2.5 1.3 2.6 1.3 1.1 1.1 

Number 2,229 1,513 2,208 1,533 1,969 1,772 1,972 1,770 1,512 427 398 

Haiti 2005

Female 1.8 3.2 1.9 3.1 1.8 2.9 1.5 3.2 1.5 3.4 1.6 

Number 3,307 1,923 3,253 1,977 2,836 2,394 2,635 2,595 2,422 322 435 

India 2005/06

Female 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.2 

Number 28,264 25,067 32,346 20,985 29,507 23,825 29,257 24,074 21,565 5,213 5,520 

Kenya 2003

Female 6.6 10.7 7.3 11.5 6.8 10.8 7.0 10.3 5.8 8.4 8.9 

Number 1,540 1,611 2,117 1,034 1,635 1,516 1,521 1,630 960 551 480 

132
Table 11.3 – cont’d

Country/sex

Decision on 

own health 

care 

Decision on 

large 

household 

purchases 

Decision on 

purchasing 

daily need 

items

Decision on 

visiting family 

and relative

Participation in house

making

No Yes No Yes No Yes No Yes None Any 1 Any 2 

Lesotho 2004/05

Female 20.6 32.9 21.2 34.3 16.6 33.0 18.2 34.3 15.0 21.8 29.9 

Number 1,580 1,438 1,813 1,206 1,208 1,810 1,473 1,545 912 426 381 

Malawi 2004

Female 10.8 18.5 11.2 20.0 10.7 18.2 8.8 16.4 9.3 11.3 15.1 

Number 1,818 867 2,039 647 1,749 936 1,082 1,604 917 737 307 

Niger 2006

Female 0.7 0.9 0.6 1.1 0.7 0.9 0.6 0.9 0.7 0.7 0.4 

Number 3,289 1,116 3,753 653 3,560 846 3,247 1,159 2,591 907 295 

Rwanda 2005

Female 1.7 5.4 2.1 5.4 1.8 5.4 2.0 4.6 1.3 2.8 3.3 

Number 2,742 2,899 3,061 2,580 2,795 2,846 2,235 3,406 1,838 658 554 

Senegal 2005

Female 0.6 1.9 0.7 1.7 0.7 1.5 0.8 1.2 0.7 0.3 1.9 

Number 3,367 862 3,564 665 3,289 940 2,983 1,245 2,485 831 300 

Zimbabwe 2005

Female 21.3 20.9 21.3 21.0 21.4 20.8 21.8 20.6 21.7 18.0 20.7 

Number 3,539 3,409 3,212 3,735 3,302 3,646 3,238 3,709 2,936 125 242 

133
134 

In 12 of 14 countries with data on a composite of all four measures of women’s participation in 

household decisions, HIV prevalence is much higher among women who participate in all four measures 

than among women who do not participate in any of the four measures (Figure 11.2). Ethiopia and 

Zimbabwe are exceptions to this pattern. In Ethiopia HIV prevalence is the same for women who 

participate in none of the four decisions as for women who participate in all four. In Zimbabwe HIV 

prevalence is slightly higher among women who participate in none of the four household decisions (21.7 

percent) than those who participate in all four decisions (20.9 percent). 

Figure 11.2. HIV prevalence among tested women age 15-49

by their participation in household decision-making 0.20.7 0.72.1 1.3 1.4 1.11.5 1.3 3.6 5.8 9.3 21.7 15.0 0.3 2.2 1.42.12.9 2.9 4.3 3.3 5.8 10.8 12.8 22.2 20.9 36.0

0

10

20

30

40

India Senegal Niger Ethiopia Guinea Burkina

Faso

Ghana Haiti Rwanda Cameroon Kenya Malawi Zimbabwe Lesotho

HIV prevalence (%)

None All 4 decisions* 

•The four household decisions include: decision on own health care, decision on large household purchases, decision on purchasing daily need items, and decision on visiting family and relative.
135 

12 Summary and Conclusions 

12.1 Summary 

This report analyzes data from 22 DHS and AIS surveys that included HIV testing and were 

conducted between 2001 and 2006. In most of these surveys, nationally representative samples of women 

age 15-49 and men age 15-59 were tested for HIV. 

 Inclusion of HIV testing as part of national DHS and AIS surveys provides direct representative 

data on the distribution of HIV infection among the population and allows individual HIV test results to 

be linked with information collected in the survey on demographic, socioeconomic, and behavioral 

characteristics of the respondents. These features help to overcome limitations of data obtained from HIV 

surveillance among pregnant women attending selected antenatal clinics. 

Nonetheless, several limitations of the survey data should be kept in mind in interpreting the 

study’s findings. Findings may be biased to the extent that the survey respondents misreport their 

behaviors. Also, the findings reflect associations but cannot determine causality, and some associations 

may be due to reverse causality. In addition, some data on associations may be biased if HIV infection 

preceded the behaviors reported for the 12 months preceding the survey.

12.2 Key Findings 

HIV prevalence varies considerably among the 22 countries studied. In four sub-Saharan African 

countries HIV prevalence is above 10 percent for the general population, while in three Asian countries it 

is below 1 percent. In most countries HIV prevalence is higher among women than men. Widowed and 

divorced individuals are at highest risk for HIV infection, and never-married individuals are at lowest 

risk. 

HIV prevalence is higher in urban areas than in rural areas and also appears to be related to 

socioeconomic status, being higher among people who have more education, work in the professional and 

manual/domestic sectors, and have more household wealth. Such findings may reflect an association 

between these characteristics and sexual risk-taking. Similarly, for men who report being away from their 

home at least once in the past 12 months, the number of times the individual left home, but not the 

amount of time spent away from home during an absence, is related to higher levels of HIV prevalence. 

More absences from home may lead to more exposure to risk-taking and potential HIV infection. 

 There is little difference in HIV prevalence among respondents by whether they have correct 

information about modes of HIV transmission, while knowledge of all three methods of HIV prevention 

is associated with higher HIV prevalence.

As might be expected, higher-risk sexual behaviors are closely related to higher HIV prevalence. 

In general, starting sex earlier, having more lifetime partners, having sex outside of marriage, and having 

sex while under the influence of alcohol are all associated with higher HIV prevalence. Similarly, not 

using condoms at last sex and/or not using condoms at last sex with a higher-risk partner are generally 

associated with higher HIV prevalence, especially among women. Also, youth age 15-24 who avoid sex, 

either by abstaining or delaying the onset of sexual activity, have lower HIV prevalence in the majority of 

countries. 

There appears to be no clear pattern of association between male circumcision and HIV 

prevalence. In 8 of 18 countries with data, as expected, HIV prevalence is lower among circumcised men, 

while in the remaining 10 countries HIV prevalence is higher among circumcised men. 
136 

In most countries, receiving multiple medical injections in the recent past is associated with being 

HIV-infected, for both women and men, probably reflecting the fact that some medical injections are 

given with re-used, non-sterilized equipment. Also, in most countries, having an STI or STI symptoms in 

the past 12 months is associated with higher HIV prevalence, both overall and for men and women 

separately. 

In all countries studied, HIV prevalence is higher among those who report having been previously 

tested for HIV, both overall and for men and women separately. This is to be expected, given that people 

engaging in risky behaviors and suspecting HIV infection are more likely to be HIV-positive and more 

likely to have received HIV testing. 

HIV prevalence is higher among both men and women who agree that women can negotiate safer 

sex with their husband if he is infected with an STI. Similarly, HIV prevalence is generally higher among 

women who report participation in household decision making. These positive associations between 

measures of women’s empowerment and HIV prevalence may be due to more empowered women having 

higher education, greater work force participation, and higher wealth status, which have been associated 

with higher prevalence of HIV (Mishra et al., 2007b). 

12.3 Program and Policy Implications 

HIV-related surveys provide HIV/AIDS program managers and policymakers with valuable 

information at the national and sub-national levels that helps them monitor and evaluate existing 

programs and to effectively design new strategies for addressing the epidemic. Inclusion of HIV testing as 

part of national DHS and AIS surveys provides direct representative data on the distribution of HIV 

infection among the population and allows individual HIV test results to be linked with information 

collected in the survey on demographic, socioeconomic, and behavioral characteristics of the respondents. 

These features help to overcome limitations of data obtained from HIV surveillance among 

pregnant women attending selected antenatal clinics. Additionally, HIV prevalence data from populationbased surveys can help calibrate estimates of HIV prevalence obtained from clinic-based surveillance and 

can substantially improve the accuracy of national estimates of HIV prevalence in countries with 

generalized epidemics. 
137 

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11. Mukuria, Altrena, Casey Aboulafia, and Albert Themme. 2005. The Context of Women’s Health: 

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15. Fort, Alfredo L., Monica T. Kothari, and Noureddine Abderrahim. 2006. Postpartum Care: Levels 

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18. Garenne, M. Michel. 2008. Fertility Changes in Sub-Saharan Africa.

19. Khan, Shane, and Vinod Mishra. 2008. Youth Reproductive and Sexual Health.

20. Kishor, Sunita, and Lekha Subaiya. 2008. Understanding Women’s Empowerment: A 

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22. Mishra, Vinod, Amy Medley, Rathavuth Hong, Yuan Gu, and Bryant Robey. 2009. Levels and 

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