DHS ANALYTICAL 

STUDIES 40

HIV Prevalence Patterns

by Age and sex:

Exploring Differences among 19 Countries

SEPTEMBER 2013

This publication was produced for review by the United States Agency for International Development. It was prepared by 

Chaitra Gopalappa, John Stover, and Carel Pretorius of the Futures Institute, Glastonbury, Connecticut, USA.
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 MEASURE DHS, ICF International, 11785 Beltsville Drive, Suite 300, 

Calverton, MD 20705 (telephone: 301-572-0200; fax: 301-572-0999; e-mail: reports@measuredhs.com; 

internet: www.measuredhs.com). 

The main objectives of the MEASURE DHS project are: 

• to provide decision-makers 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 high-quality demographic 

and health surveys 
DHS Analytical Studies No. 40

HIV Prevalence Patterns by Age and Sex: 

Exploring Differences among 19 Countries 

Chaitra Gopalappa 

John Stover 

Carel Pretorius 

ICF International 

Calverton, Maryland, USA 

September 2013 

Corresponding author: Chaitra Gopalappa, Futures Institute, 41A New London Turnpike, Glastonbury, CT-06033, 

Email: cgopalappa@futuresinstitute.org 
Acknowledgments: We would like to thank Sarah Staveteig, Emily Sonneveldt, and Tom Pullum for 

their abundant assistance with the data, Shea Rutstein and Mary Mahy for their insightful comments on 

the report, and Bryant Robey for the meticulous editing of the paper. 

Editor: Bryant Robey 

Document Production: Yuan Cheng 

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

Development (USAID) through the MEASURE DHS project (#GPO-C-00-08-00008-00). The views 

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

Government. 

Recommended citation: 

Gopalappa, Chaitra, John Stover, and Carel Pretorius. 2013. HIV Prevalence Patterns by Age and Sex: 

Exploring Differences among 19 Countries DHS Analytical Studies No. 40. Calverton, Maryland, USA: 

ICF International. 
iii 

Contents 

List of Tables ............................................................................................................................................... v 

List of Figures ............................................................................................................................................. vi 

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

Executive Summary ................................................................................................................................... ix 

Introduction ................................................................................................................................................. 1 

Methods ........................................................................................................................................................ 3 

HIV Prevalence by Age ......................................................................................................................... 3 

HIV Prevalence Pattern by Age ............................................................................................................ 3 

Identifying Behavioral Variables Correlated with Prevalence Patterns .............................................. 3 

Ratio of Female to Male HIV Prevalence in Each Age Group ............................................................. 4 

Results .......................................................................................................................................................... 5 

HIV Prevalence by Age ......................................................................................................................... 5 

HIV Prevalence Patterns by Age ........................................................................................................ 15 

Correlation between Behavioral Factors and HIV Prevalence Patterns ........................................... 20 

Ratio of Female to Male HIV Prevalence in Each Age Group ........................................................... 39 

Conclusions ................................................................................................................................................ 43 

References .................................................................................................................................................. 45 

v 

List of Tables 

Table 1w. Estimated HIV prevalence and confidence bounds for women ................................................. 12 

Table 1m. Estimated HIV prevalence and confidence bounds for men ...................................................... 13 

Table 2. Significance of difference in HIV prevalence in younger and older age groups .......................... 14 

Table 3.1w. Behavioral data extracted from DHS datasets for women: percent with non-marital 

partners ....................................................................................................................................... 21 

Table 3.2w. Behavioral data extracted from DHS datasets for women: Percent with multiple partners .... 22 

Table 3.3w. Behavioral data extracted from DHS datasets for women: Percent using condom at last 

sex with non-marital partner among those with non-marital partner in past 12 months ............ 23 

Table 3.4w. Behavioral data extracted from DHS datasets for women: Percent using condom at last 

sex with any partner among those with non-marital partner in past 12 months ......................... 24 

Table 3.5w. Behavioral data extracted from DHS datasets for women: Percent using condom at last 

sex with any partner among those who had sex in past 12 months ............................................ 25 

Table 3.6w. Behavioral data extracted from DHS datasets for young women ........................................... 26 

Table 3.1m. Behavioral data extracted from DHS datasets for men: Percent with non-marital partners ... 27 

Table 3.2m. Behavioral data extracted from DHS datasets for men: Percent with multiple partners ........ 28 

Table 3.3m. Behavioral data extracted from DHS datasets for men: Percent using condom at last sex 

with non-marital partner among those with non-marital partners in past 12 months ................. 29 

Table 3.4m. Behavioral data extracted from DHS datasets for men: Percent using condom at last sex 

with any partner among those with non-marital partners in past 12 months .............................. 30 

Table 3.5m. Behavioral data extracted from DHS datasets for men: Percent using condom at last sex 

with any partner among those with multiple partners ................................................................. 31 

Table 3.6m. Behavioral data extracted from DHS datasets for men: Percent using condom at last sex 

with any partner among those who had sex in past 12 months ................................................... 32 

Table 3.7m. Behavioral data extracted from DHS datasets for men: Percent in commercial sex .............. 33 

Table 3.8m. Behavioral data extracted from DHS datasets for young men ................................................ 34 

Table 4w. Simple regression (single independent variable) of relative risks with behavioral factors 

for women ................................................................................................................................... 35 

Table 4m. Simple regression (single independent variable) of relative risks with behavioral factors 

for men ........................................................................................................................................ 36 
vi 

Table 5w. Principal component analysis (PCA) for women ....................................................................... 37 

Table 5m. Principal component analysis (PCA) for men ........................................................................... 38 

List of Figures 

Figure 1w. Estimated HIV Prevalence and 95% confidence bounds for women (x-axis - age group; 

y-axis prevalence) ......................................................................................................................... 6 

Figure 1w. (cont.) Estimated HIV Prevalence and 95% confidence bounds for women (x-axis - age 

group; y-axis prevalence) ............................................................................................................. 7 

Figure 1w. (cont.) Estimated HIV Prevalence and 95% confidence bounds for women (x-axis - age 

group; y-axis prevalence ............................................................................................................... 8 

Figure 1m. Estimated HIV Prevalence and 95% confidence bounds for men (x-axis - age-group; yaxis – prevalence) ......................................................................................................................... 9 

Figure 1m. (cont.) Estimated HIV Prevalence and 95% confidence bounds for men (x-axis - agegroup; y-axis – prevalence) ........................................................................................................ 10 

Figure 1m. (cont.) Estimated HIV Prevalence and 95% confidence bounds for men (x-axis - agegroup; y-axis – prevalence) ........................................................................................................ 11 

Figure 2w. Relative risks of prevalence by age group relative to prevalence in age group 25-29 for 

women (x-axis - age-group; y-axis – prevalence) ...................................................................... 16 

Figure 2w. (cont.) Relative risks of prevalence by age group relative to prevalence in age group 25-

29 for women (x-axis - age-group; y-axis – prevalence) ............................................................ 17 

Figure 2m. Relative risks of prevalence by age group relative to prevalence in age group 25-29 for 

men (x-axis - age-group; y-axis – prevalence) ........................................................................... 18 

Figure 2m. (cont.) Relative risks of prevalence by age group relative to prevalence in age group 25-

29 for men (x-axis - age-group; y-axis – prevalence) ................................................................. 19 

Figure 3. Female to male ratio of HIV prevalence by age group ................................................................ 40 

Figure 3. (cont.) Female to male ratio of HIV prevalence by age group .................................................... 41 
vii 

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 analysis of 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. 

While Comparative Reports are primarily descriptive, Analytical Studies provide in-depth, focused 

studies on a variety of substantive topics. The studies are based on a varying number of data sets, 

depending on the topic being examined. These studies employ a range of methodologies, including 

multivariate statistical techniques. 

MEASURE DHS staff, in conjunction with the U.S. Agency for International Development (USAID), 

selects the topics covered in Analytical Studies. 

It is anticipated that the DHS Analytical Studies will enhance the understanding of analysts and 

policymakers regarding significant issues in the fields of international population and health. 

Sunita Kishor 

Project Director 

ix

Executive Summary 

To make national-level estimates and projections of HIV prevalence over time, UNAIDS uses HIV 

surveillance data from antenatal clinics. These prevalence estimates are further modified by calibrating to 

the proportion of people testing HIV-positive in national surveys, such as the Demographic and Health 

Surveys (DHS). Results are combined across countries by taking the average value as an estimate of the 

general pattern, and this average pattern is applied to all countries with generalized HIV epidemics. 

A key question affecting the accuracy of these estimates and projections is whether HIV prevalence by 

age is similar across all countries with generalized HIV epidemics, or significantly different. And if 

different, are there any age-based behavioral variables that explain those differences? This study seeks to 

determine if HIV prevalence patterns, represented as HIV prevalence in an age group relative to HIV 

prevalence in age 25-29, are significantly different across countries and to identify variables correlated 

with prevalence patterns. The study uses DHS data on HIV prevalence for five-year age groups among 

women and men age 15-49, from 28 surveys covering 19 countries. 

Our analysis finds that an average pattern of HIV prevalence by age generally matches the countryspecific patterns. In some cases, however, mostly in older age groups and in a few cases in the younger 

age groups, HIV prevalence pattern differs from the average pattern, indicating significant variation in 

HIV prevalence pattern in these cases. Our analysis identifies years since the introduction of antiretroviral 

therapy (ART) and maturity of the HIV epidemic as significantly correlated with HIV prevalence pattern 

for women and men in older age groups, and the proportion sexually active or proportion with condom 

use as factors among young women that are significantly correlated with HIV prevalence pattern. The 

significance of ART as a factor in particular points to the need for continuing to identify changes in HIV 

prevalence patterns over time, especially as access to ART may not be similar across all age groups. 

1 

Introduction 

For generalized HIV epidemics, UNAIDS uses HIV surveillance data from antenatal clinics and, using 

the mathematical Estimation and Projection Package (EPP) in the Spectrum software, makes nationallevel estimates of HIV prevalence over time.[1] These prevalence estimates are further modified by 

calibrating to the proportion testing HIV-positive in national surveys, such as the Demographic and 

Health Surveys (DHS).[2] The HIV prevalence estimates from EPP are used in Spectrum to make 

historical estimates and future projections of indicators of interest, such as new infections by age and sex, 

HIV-positive population by age and sex, number of people needing ART, and number of mothers needing 

PMTCT services.[3] 

While estimates from the DHS surveys are a more accurate representation of national prevalence than 

surveillance data, the DHS surveys are conducted only every five years. Also, HIV prevalence can be 

obtained only from recent DHS surveys, as HIV testing was not included in earlier surveys. Thus most 

countries have only one or two DHS surveys available for making HIV prevalence estimates. 

Using surveys for measuring HIV incidence is more challenging than for measuring prevalence, and 

techniques for direct measurement are in the development stage.[4] Therefore, HIV incidence by age is 

estimated using mathematical modeling techniques. To break down new HIV infections by age and sex, 

Spectrum uses patterns of incidence ratios, i.e., ratios of incidence in each age group relative to incidence 

in age 25-29, and ratios of incidence among women relative to incidence among men. To obtain these 

ratios, HIV incidence is estimated using a technique developed by Hallett et al. [5, 6] The technique uses 

HIV prevalence in every five-year age group between age 15 and age 49 at two different time periods 

(i.e., two DHS surveys) and, assuming that the surveys represent the same population, estimates new 

infections as any change in prevalence over this time period, after accounting for mortality.[6] For 

countries with a single survey, the technique can still be applied by assuming that the HIV prevalence 

pattern remains constant over time. Results are combined across countries by taking the average value as 

an estimate of the general pattern, and this average pattern is applied to all countries with generalized 

epidemics. 

This method provides a good approximation, as verified by comparing the incidence pattern it generates 

with the pattern generated by the ALPHA (Analyzing Longitudinal Population-based HIV/AIDS data on 

Africa) network study.[7] Although the same pattern of incidence by age is used in all countries with 

generalized epidemics, the pattern of prevalence by age produced by Spectrum does vary by country as a 

function of the maturity of the epidemic, past trends in incidence, and coverage of ART. 

No formal analysis has been conducted to examine differences in patterns of HIV prevalence by age 

across all countries with at least one DHS survey. While differences in patterns of prevalence by age may 

not directly translate into differences in patterns of incidence by age, identifying any difference in 

prevalence patterns and the factors associated with the differences could identify potential areas for 

further investigation. 

3 

Methods 

HIV Prevalence by Age 

In this report we present HIV prevalence by age and sex, based on DHS survey data and estimate and 

compare patterns of HIV prevalence across countries. We obtain HIV prevalence for five-year age groups 

among women and men age 15-49 from 28 DHS surveys covering 19 countries. While all surveyed 

countries have HIV data for women age 15-49, some countries also have data for men up to age 50-54 or 

55-59. For comparison purpose, we limit the analysis to age 15-49 for both men and women. We use 

STATA software to obtain prevalence (ܲ) and its confidence intervals from DHS data. The 95% 

confidence intervals around the prevalence estimates are obtained using the logit transformation method. 

HIV Prevalence Pattern by Age 

We represent prevalence pattern as relative risk of prevalence. We express the relative risk (ܴܴ) of HIV 

prevalence in an age-group (ܽ ) as the prevalence (ܲ) relative to that in age group 25-29, i.e., 

ܴܴ௔ = ௉ೌ

௉మఱషమవ

, 

and estimate the 95% confidence interval of relative risk as 

[ܴܴ௔݁ିଵ.ଽ଺ௌா, ܴܴ௔݁ାଵ.ଽ଺ௌா]	; ܵܧ = ඨ1−ܲ௔

ܲ௔

+

1−ܲଶହିଶଽ

ܲଶହିଶଽ

Relative risks provide a common base for comparing across countries the variation or pattern in HIV 

prevalence in different age groups. We obtain an average pattern by averaging relative risks across the 

countries and we compare this average with relative risks in each country. 

Identifying Behavioral Variables Correlated with Prevalence Patterns 

We perform simple linear regression and principal component analysis to identify behavioral variables 

significantly correlated with prevalence patterns across countries. We extract all behavioral data from the 

DHS datasets and for behavioral variables of interest examine data in every five-year age group between 

15 and 49, in comparison with a reference group, age 25-29. These variables include the percentage of 

respondents reporting having non-marital partners, having multiple partners, using a condom in last sex 

with non-marital partners among those reporting non-marital partners, using a condom in last sex with 

any partner among those with non-marital partners, using a condom in last sex among those with multiple 

partners, using a condom in last sex among those sexually active and, for men, having commercial sex, 

and being circumcised. 

For each of these variables we test for significant differences across age in each country and, if p-value is 

greater than 0.05, indicating no significant difference by age, we set the relative values to 1 in all age 

groups of the corresponding country. The data show that male circumcision in most countries does not 

significantly differ with age, and hence we exclude this variable from the analysis. 

For age 15-19 and 20-24 we consider additional variables, such as percentage having sex before age 15, 

or before age 18, the percentage unmarried and sexually active, and women reporting partners 10 or more 

years older than themselves (age-mixing), which we use in absolute values instead of relative values. In 

addition, because the surveys were conducted at different times, we also include as independent variables 
4 

the number of years since the introduction of treatment with antiretroviral therapy (ART) at the time of 

survey. These two variables are used for each of the age groups. 

In the simple linear regressions (conducted using STATA software) we set relative risk of HIV 

prevalence as the dependent variable and test for the marginal effect of each behavioral variable on the 

HIV prevalence pattern. As some of the behavioral variables could be correlated, before performing 

multiple regressions to obtain unique effects of the behavioral variables on the HIV prevalence pattern, 

we perform principal component analysis (PCA) that transforms correlated variables into a set of 

uncorrelated components (new independent variables), and varimax rotation to maximize the correlation 

with the original variables. We retain components that account for the most variability and conduct a 

multiple linear regression with relative risk of HIV prevalence as the dependent variable and the 

components as the independent variables. PCA is conducted on standardized data (subtracting mean and 

dividing by standard deviation) because some of the variables have different units of measurement. We 

exclude the reference age group 25-29 from analysis and perform regressions on each age group 

separately. 

Ratio of Female to Male HIV Prevalence in Each Age Group 

In this report we present the relative risk of HIV prevalence in women compared with men by age group. 

Prevalence of HIV is generally lower among men than among women. [8] The doctoral dissertation by 

Hertog, [9] using DHS data, provides a comprehensive analysis on the differences in HIV prevalence 

among women compared with men, and on the factors that could affect these differences across countries. 

In Hertog’s analysis, the factors creating the difference in HIV prevalence between women and men vary 

across countries. Peak incidence of HIV occurs at a younger age for women than for men. The cumulative 

risk of HIV incidence at age 15-49 is higher for women than for men, but the magnitude of the difference 

in cumulative risk is lower than for HIV prevalence. No specific risk factor accounts for the differences 

across countries. None of the explanatory variables (prevalence of male circumcision, sex ratio of 

premarital sex, or gender disparities in education and employment) explains the differences across 

countries. 
5 

Results 

HIV Prevalence by Age 

Figure 1 and Table 1 show HIV prevalence and confidence interval estimates for each country by age and 

sex. Countries with low HIV prevalence mostly have wider confidence intervals compared with highprevalence countries, due to higher relative error. In several countries the confidence intervals are 

narrower in younger age groups and wider in older age groups, due to a smaller number of respondents in 

older age groups compared with younger age groups. In most countries HIV prevalence is lowest in the 

two youngest age groups (15-19 and 20-24), except for Sierra Leone and Burkina Faso, for women, and 

DRC and Ethiopia for men, where HIV prevalence is lowest in the oldest age group, but not significantly 

lower than in the younger age groups (overlapping confidence intervals). 

Pearson’s chi-square test comparing HIV prevalence between younger age groups 15-19, 20-24, and 25-

29 in each country yields p-values of <0.05 in most countries, indicating significantly lower HIV 

prevalence in age 15-19 compared with age 20-24 or age 25-29, or significantly lower prevalence in age 

20-24 compared with age 25-29 (Table 2). Pearson’s chi-square test comparing HIV prevalence between 

age 25-29 and older five-year age groups indicates that for some countries there is not enough statistical 

evidence to conclude significant differences in prevalence across age groups (i.e., p-values >0.05). 

Among countries with significant differences in HIV prevalence by age (p-value <0.05), prevalence peaks 

mostly in age 25-29, 30-34, and 35-39 for women, and age 30-34, 35-39, and 40-44 for men (Table 2). 

 
6 

Figure 1w. Estimated HIV Prevalence and 95% confidence bounds for women (x-axis - age group; 

y-axis prevalence) 
7 

Figure 1w. (cont.) Estimated HIV Prevalence and 95% confidence bounds for women (x-axis - age 

group; y-axis prevalence) 
8 

Figure 1w. (cont.) Estimated HIV Prevalence and 95% confidence bounds for women (x-axis - age 

group; y-axis prevalence 
9 

Figure 1m. Estimated HIV Prevalence and 95% confidence bounds for men (x-axis - age-group; yaxis – prevalence) 
10 

Figure 1m. (cont.) Estimated HIV Prevalence and 95% confidence bounds for men (x-axis - agegroup; y-axis – prevalence) 
11 

Figure 1m. (cont.) Estimated HIV Prevalence and 95% confidence bounds for men (x-axis - agegroup; y-axis – prevalence) 
Table 1w. Estimated HIV prevalence and confidence bounds for women 

Country 

Survey 

Year 

Age group 

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

Burkina Faso 2010 0.2 [0-1] 0.4 [0.2-0.9] 1.2 [0.7-1.9] 2.4 [1.6-3.4] 1.7 [1-3] 2.0 [1.2-3.4] 1.7 [0.9-3.2] Burkina Faso 2003 0.9 [0.4-1.9] 1.8 [1-3.4] 2.5 [1.5-4.2] 2.4 [1.3-4.5] 3.6 [2-6.4] 0.7 [0.3-1.8] 0.9 [0.4-2] Burundi 2010 0.3 [0.1-0.6] 1.5 [0.9-2.6] 1.2 [0.7-2.1] 2.6 [1.6-4.4] 3.7 [2.3-5.9] 3.3 [1.8-5.9] 2.4 [1.4-4.3] Cameroon 2011 2.0 [1.4-2.9] 3.4 [2.6-4.5] 7.6 [6.2-9.3] 7.3 [5.7-9.3] 10.0 [7.8-12.9] 7.1 [5-9.9] 6.4 [4.3-9.3] Cameroon 2004 2.1 [1.4-3.2] 7.5 [6-9.4] 10.3 [8.3-12.7] 9.7 [7.5-12.4] 7.3 [5.2-10.1] 6.1 [4.1-8.9] 5.3 [3.3-8.4] DRC 2007 0.7 [0.3-1.6] 0.4 [0.2-1] 2.9 [1.6-5] 1.6 [0.8-2.9] 2.1 [1.1-3.8] 4.4 [2.1-8.8] 1.1 [0.4-2.8] Ethiopia 2011 0.2 [0.1-0.4] 0.9 [0.5-1.6] 3.0 [2.2-4] 3.7 [2.6-5.3] 3.0 [1.6-5.6] 1.9 [1.1-3.1] 1.8 [1-3.3] Ethiopia 2005 0.7 [0.3-1.6] 1.7 [1-3.1] 2.1 [1.3-3.6] 1.5 [0.7-3.1] 4.4 [2.6-7.4] 3.1 [1.4-6.6] 0.9 [0.2-2.9] Ghana 2003 0.5 [0.2-1.2] 2.0 [1.2-3.2] 3.4 [2.3-5] 4.2 [2.9-6.1] 4.7 [3.2-6.7] 3.0 [1.7-5.2] 2.5 [1.4-4.4] Guinea 2005 0.9 [0.4-2.1] 1.6 [0.9-2.9] 1.8 [1-3.3] 3.0 [1.7-5.3] 2.1 [0.9-4.7] 1.4 [0.6-3.3] 3.3 [1.5-7.1] Kenya 2009 2.7 [1.7-4.4] 6.4 [4.6-8.9] 10.4 [7.6-14.2] 11.0 [7.9-15.2] 8.8 [6.2-12.4] 14.3 [8.2-23.6] 6.4 [4.1-9.9] Kenya 2003 3.0 [1.9-4.6] 9.0 [6.8-11.9] 12.9 [9.9-16.6] 11.7 [8.6-15.7] 11.8 [8.6-15.8] 9.5 [6.1-14.5] 3.9 [1.8-8.1] Lesotho 2009 4.1 [2.9-5.8] 24.1 [20.9-27.7] 35.4 [31-40.1] 40.7 [36-45.6] 42.3 [36.6-48.2] 36.1 [30.2-42.5] 29.5 [24.3-35.3]Lesotho 2003 7.9 [5.9-10.3] 24.5 [20.5-29] 39.0 [33.2-45.1] 40.1 [34-46.4] 43.5 [37.3-49.9] 28.5 [22.6-35.3] 16.8 [12.1-22.9]Liberia 2007 1.3 [0.7-2.4] 2.0 [1.3-3] 2.1 [1.3-3.4] 1.7 [1-2.8] 2.7 [1.8-4.1] 1.7 [0.8-3.3] 2.0 [0.9-4.2] Malawi 2010 4.2 [3-5.7] 6.4 [4.9-8.2] 13.5 [11.2-16.2] 20.8 [17.6-24.4] 23.8 [19.9-28.2] 20.4 [16.6-24.9] 16.1 [12.7-20.2]Malawi 2004 3.7 [2.3-5.8] 13.2 [10.5-16.5] 15.5 [12.4-19.3] 18.1 [14.1-22.8] 17.0 [12.8-22.3] 17.9 [12.8-24.4] 13.3 [9.1-19.2] Mali 2006 0.6 [0.3-1.4] 1.3 [0.6-2.8] 2.0 [0.9-4.6] 2.2 [1.1-4.2] 2.2 [1.1-4.4] 2.0 [0.9-4.3] 1.2 [0.5-3.2] Niger 2006 - 1.0 [0.5-2.1] 0.7 [0.3-1.4] 1.1 [0.5-2.7] 1.4 [0.6-3.3] 0.5 [0.2-1.8] 0.1 [0-0.4] Rwanda 2010 0.8 [0.4-1.3] 2.4 [1.7-3.4] 3.9 [3-5.2] 4.2 [3.1-5.8] 8.0 [6.1-10.3] 6.1 [4.4-8.3] 5.8 [4.1-8.2] Rwanda 2005 0.7 [0.4-1.4] 2.5 [1.8-3.5] 3.3 [2.3-4.9] 5.9 [4.5-7.8] 6.9 [5-9.4] 6.5 [4.7-8.9] 4.0 [2.3-6.6] Senegal 2011 0.2 [0.1-0.4] 0.5 [0.3-1.1] 0.6 [0.2-1.6] 0.9 [0.4-2.2] 1.5 [0.7-3.4] 1.5 [0.8-3] 2.4 [1.3-4.4] Senegal 2005 0.2 [0.1-0.6] 0.8 [0.4-1.8] 1.5 [0.7-3.3] 0.9 [0.4-1.8] 0.6 [0.2-2.1] 1.5 [0.8-3] 1.8 [0.7-4.7] Sierra Leone 2008 1.3 [0.6-2.6] 1.6 [0.8-3] 2.2 [1.3-3.9] 2.4 [1.3-4.5] 1.2 [0.6-2.6] 2.1 [0.7-6.7] 1.0 [0.3-3.1] Swaziland 2007 10.1 [8.3-12.2] 38.4 [35-41.9] 49.2 [45.1-53.3] 45.2 [41-49.6] 37.7 [33.3-42.4] 27.9 [23.3-33] 21.4 [17.4-26] Zambia 2007 5.7 [4.4-7.4] 11.8 [9.8-14.1] 19.9 [16.9-23.4] 26.0 [22.3-30.1] 24.9 [21.2-29.1] 18.3 [14.9-22.3] 12.2 [9-16.3] Zimbabwe 2011 4.2 [3.3-5.4] 10.6 [9-12.3] 20.1 [18-22.4] 29.0 [26-32.2] 29.1 [25.9-32.5] 25.7 [22-29.6] 22.6 [18.9-26.7]Zimbabwe 2005-06 6.2 [4.9-7.8] 16.3 [14.6-18.3] 28.8 [25.6-32.3] 35.5 [31.6-39.6] 34.5 [30.7-38.6] 25.7 [21.8-30.1] 18.0 [14.3-22.4]

12 
Table 1m. Estimated HIV prevalence and confidence bounds for men 

Country 

Survey 

Year 

Age Group 

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

Burkina Faso 2010 0.4 [0.2-1.1] 0.5 [0.2-1.6] 0.5 [0.2-1.7] 1.1 [0.5-2.1] 1.2 [0.6-2.4] 1.4 [0.7-2.6] 1.1 [0.5-2.3] Burkina Faso 2003 0.7 [0.2-2] 0.6 [0.2-2.1] 2.8 [1.5-5.3] 3.8 [1.8-7.7] 3.0 [1.4-6.3] 2.1 [0.9-4.8] 2.1 [0.8-5.4] Burundi 2010 0.3 [0.1-1.2] 0.1 [0-0.5] 0.6 [0.2-2] 1.4 [0.6-3.3] 1.5 [0.7-3.1] 3.3 [1.8-6.2] 2.1 [1-4] Cameroon 2011 0.4 [0.1-1] 0.6 [0.3-1.3] 3.0 [1.6-5.5] 5.3 [3.8-7.4] 5.8 [4.3-7.8] 4.7 [3-7.3] 6.3 [4.3-9.2] Cameroon 2004 0.6 [0.3-1.3] 2.5 [1.6-4] 5.1 [3.7-7] 8.1 [5.9-11] 8.6 [6.2-11.8] 5.6 [3.7-8.5] 3.9 [2.2-6.9] DRC 2007 1.7 [0.6-4.4] 0.3 [0.1-0.9] 0.8 [0.3-1.8] 0.8 [0.2-2.3] 1.8 [0.9-3.5] 0.9 [0.4-2.5] 0.0 [0-0.2] Ethiopia 2011 0.0 [0-0.1] 0.2 [0.1-0.6] 0.9 [0.4-2.2] 1.0 [0.6-1.8] 3.0 [1.8-4.9] 2.1 [1.3-3.5] 1.4 [0.8-2.5] Ethiopia 2005 0.1 [0-0.9] 0.4 [0.2-0.8] 0.7 [0.3-1.8] 1.9 [1.1-3.6] 1.8 [0.9-3.8] 2.8 [1.3-5.9] 0.0 [0-0.1] Ghana 2003 0.2 [0-0.7] - 1.0 [0.4-2.1] 2.8 [1.6-4.8] 3.1 [1.7-5.5] 4.1 [2.2-7.4] 1.9 [0.9-4.1] Guinea 2005 0.5 [0.2-1.5] 0.7 [0.3-2] 1.2 [0.3-4.1] 0.7 [0.2-2.8] 0.9 [0.3-2.5] 2.8 [1.3-5.9] 0.6 [0.1-2.4] Kenya 2009 0.7 [0.3-1.8] 1.5 [0.7-3.1] 6.5 [3.9-10.6] 6.8 [4.6-10] 10.4 [6.9-15.4] 5.7 [3.3-9.7] 4.3 [1.9-9.3] Kenya 2003 0.4 [0.1-1.1] 2.4 [1.4-4.1] 7.3 [4.9-10.7] 6.6 [4.4-9.9] 8.4 [5.5-12.7] 8.8 [5.6-13.6] 5.2 [2.4-10.8] Lesotho 2009 2.9 [1.8-4.7] 5.9 [3.9-8.9] 18.4 [14.6-22.9] 40.2 [34.2-46.5] 35.4 [28.6-42.9] 39.3 [31.1-48] 32.1 [24.3-41] Lesotho 2003 2.3 [1.2-4.6] 11.4 [8.3-15.3] 24.3 [18.8-30.8] 40.7 [32.2-49.9] 38.7 [30.7-47.4] 32.1 [22.9-43] 27.6 [19-38.3] Liberia 2007 0.4 [0.1-1.5] 0.7 [0.3-2] 1.9 [1.1-3.3] 1.7 [0.8-3.4] 1.4 [0.7-2.7] 1.2 [0.5-2.9] 1.9 [1-3.6] Malawi 2010 1.3 [0.7-2.5] 2.8 [1.7-4.5] 6.9 [5-9.5] 10.7 [8.2-13.9] 18.2 [14.5-22.5] 20.8 [16.2-26.2] 15.0 [11.4-19.5]Malawi 2004 0.4 [0.1-1.7] 3.9 [2.2-6.6] 9.8 [7.1-13.4] 20.4 [15.5-26.4] 18.4 [12.9-25.7] 16.5 [11.4-23.2] 9.5 [5.4-16.3] Mali 2006 0.7 [0.3-1.6] 0.8 [0.3-2.2] 0.6 [0.2-2.3] 2.2 [0.9-5.4] 0.6 [0.2-2] 1.9 [0.6-5.6] 0.8 [0.2-2.4] Niger 2006 - 0.6 [0.2-2] 0.1 [0-0.5] 1.2 [0.5-3] 2.6 [1.2-5.5] 0.6 [0.2-1.8] 1.1 [0.3-3.6] Rwanda 2010 0.3 [0.1-0.7] 0.5 [0.2-1.3] 1.8 [1.1-2.8] 3.5 [2.4-5.2] 3.8 [2.5-5.9] 7.5 [5.3-10.6] 5.6 [3.6-8.6] Rwanda 2005 0.3 [0.1-0.9] 0.5 [0.2-1.2] 2.0 [1.2-3.3] 3.8 [2.3-6.2] 2.5 [1.4-4.4] 8.0 [5.7-11.1] 4.7 [3-7.4] Senegal 2011 - 0.1 [0-0.5] 0.5 [0.1-2.5] 0.5 [0.2-1.2] 0.8 [0.3-1.9] 1.6 [0.4-5.9] 1.2 [0.4-3.2] Senegal 2005 - 0.2 [0-1.3] - 1.2 [0.4-3.9] 0.8 [0.3-2.2] 1.7 [0.4-6] 0.6 [0.1-3.2] Sierra Leone 2008 - 1.3 [0.5-3.4] 1.5 [0.6-3.5] 1.8 [0.7-4.5] 1.4 [0.7-3.2] 0.9 [0.3-2.9] 2.1 [0.8-5.6] Swaziland 2007 1.9 [1.2-3] 12.3 [10-15.2] 27.8 [23.9-32.1] 43.8 [37.7-50] 44.9 [39.3-50.7] 40.7 [34.2-47.5] 27.9 [22-34.6] Zambia 2007 3.6 [2.5-5.3] 5.2 [3.7-7.2] 11.4 [9.4-13.9] 17.1 [14.4-20.3] 22.4 [18.9-26.3] 24.1 [19.5-29.3] 18.6 [14.2-24] Zimbabwe 2011 3.4 [2.5-4.6] 3.9 [2.8-5.4] 10.3 [8.4-12.7] 17.4 [14.8-20.3] 25.1 [21.5-29.2] 26.2 [22.3-30.5] 29.9 [25-35.4] Zimbabwe 2005-06 3.1 [2.1-4.4] 5.8 [4.2-8.1] 13 [10.6-15.9] 29.5 [24.9-34.5] 32.1 [27.5-37] 33.0 [27-39.5] 26.1 [21.1-31.7]

13 
14 

Table 2. Significance of difference in HIV prevalence in younger and older age groups 

Country Survey year 

p-value: comparing 

age groups 15-19, 20-

24, and 25-29‡ 

p-value: comparing 

age groups 25-29 and 

older‡ 

Age group of peak 

prevalence 

Women Men Women Men Women Men 

BurkinaFaso 2010 0.0117* 0.8566 0.2735 0.5997 30-34 40-44 

BurkinaFaso 2003 - 0.0104* 0.0224* 0.7882 35-39 30-34 

Burundi 2010 0.002* 0.2787 0.0384* 0.0394* 35-39 40-44 

Cameroon 2011 0* 0* 0.1769 0.1412 35-39 45-49 

Cameroon 2004 0* 0* 0.018* 0.0287* 25-29 35-39 

DRC 2007 0* 0.0465* 0.0764 0.092 40-44 35-39 

Ethiopia 2011 0* 0* 0.255 0.0136* 30-34 35-39 

Ethiopia 2005 0.0453* 0.1534 0.0367* 0.0138* 35-39 40-44 

Ghana 2003 0.0001* 0.0065* 0.3449 0.0347* 35-39 40-44 

Guinea 2005 0.3116 0.5727 0.4744 0.1029 45-49 40-44 

Kenya 2009 0* 0* 0.2188 0.1854 40-44 35-39 

Kenya 2003 0* 0* 0.0282* 0.6896 25-29 40-44 

Lesotho 2009 0* 0* 0.0192* 0* 35-39 30-34 

Lesotho 2003 0* 0* 0* 0.0115* 35-39 30-34 

Liberia 2007 0.4464 0.0346* 0.7215 0.8911 35-39 45-49 

Malawi 2010 0* 0* 0* 0* 35-39 40-44 

Malawi 2004 0* 0* 0.6722 0.0018* 30-34 30-34 

Mali 2006 0.1606 0.9295 0.9073 0.2227 35-39 30-34 

Niger 2006 0.0202* 0.043* 0.1598 0.0091* 35-39 35-39 

Rwanda 2010 0* 0.0004* 0.0018* 0* 35-39 40-44 

Rwanda 2005 0* 0.0002* 0.0186* 0* 35-39 40-44 

Senegal 2011 0.2353 0.0992 0.1389 0.5161 45-49 40-44 

Senegal 2005 0.0136* - 0.541 0.25 45-49 40-44 

SierraLeone 2008 0.4333 0.0597 0.5789 0.8541 30-34 45-49 

Swaziland 2007 0* 0* 0* 0* 25-29 35-39 

Zambia 2007 0* 0* 0* 0* 30-34 40-44 

Zimbabwe 2011 0* 0* 0* 0* 35-39 45-49 

Zimbabwe 2005-06 0* 0* 0* 0* 30-34 40-44 

*Significant at level 0.05 

‡ Pearson's chi-square test to compare prevalence across age groups 

- Not available 
15 

HIV Prevalence Patterns by Age 

Figure 2 presents the relative risks of HIV prevalence by age, relative to prevalence in age 25-29. These 

patterns are similar to those in Figure 1, but the relative values provide a common scale for comparing the 

pattern in age groups across countries. Comparisons of confidence intervals among pairs of countries 

show that, for women, there are some country pairs in each age group where the confidence intervals do 

not overlap, indicating significant differences in prevalence patterns between these country pairs (data not 

shown). For men, the number of such non-overlapping country pairs is much smaller than for women, 

probably because of wider confidence intervals due to smaller samples of survey respondents among men 

than among women. 

Figure 2 compares the confidence intervals for HIV prevalence by age with the average pattern (red line) 

derived from all data sets. For most countries the average pattern falls within the confidence bounds of the 

country-specific prevalence pattern (Figure 2), indicating, with the current data, we cannot conclude 

significance differences in prevalence patterns across countries. Note that, for men, relative risks of HIV 

prevalence in Niger range from 16 to 40 in the older age groups compared with a range of 1 to 4 in most 

other countries; hence the data for Niger were eliminated from the average calculation. 

 
16 

Figure 2w. Relative risks of prevalence by age group relative to prevalence in age group 25-29 for 

women (x-axis - age-group; y-axis – prevalence) 
17 

Figure 2w. (cont.) Relative risks of prevalence by age group relative to prevalence in age group 25-

29 for women (x-axis - age-group; y-axis – prevalence) 
18 

Figure 2m. Relative risks of prevalence by age group relative to prevalence in age group 25-29 for 

men (x-axis - age-group; y-axis – prevalence) 
19 

Figure 2m. (cont.) Relative risks of prevalence by age group relative to prevalence in age group 

25-29 for men (x-axis - age-group; y-axis – prevalence) 
20 

Correlation between Behavioral Factors and HIV Prevalence Patterns 

Tables 3.1.w to 3.8.m present data for the independent variables. For women age 15-19, simple linear 

regression identifies ‘percent unmarried sexually active’ to be positively correlated with relative risk of 

HIV prevalence (Table 4a). As expected, this variable is highly correlated with ‘percent with non-marital 

partners’ in this age group (data not shown), however,the ‘relative value of percent with non-marital 

partners’ (relative to age 25-29) is not statistically significant. The reason is the difference in marital 

status in age 15-19 compared with age 25-29. In the population age 15-19, most are unmarried and thus 

those ‘unmarried and sexually active’ and those with ‘non-marital partners’ are probably equal in this age 

group. In contrast, in the population age 25-29, those with ‘non-marital partners’ could include married as 

well as unmarried men and women. 

For women age 20-24, 30-34, and 35-39, none of the observed variables is statistically significant with 

relative risk of HIV prevalence. For women age 40-44 and 45-49, ‘year of survey’ and ‘year of ART 

introduction’ are positively correlated with HIV prevalence. 

For men age 30-34, the ‘relative value of condom use at last sex among those with multiple partners’ 

(compared with the reference group, age 25-29) is positively correlated with relative risk of HIV 

prevalence, which is counterintuitive (Table 4b). Further analysis reveals that this finding is probably due 

to a positive correlation between absolute values of ‘condom use at last sex among those with multiple 

partners’ and ‘percent with multiple partners’ in age 30-34 but not in the reference group age 25-29 (data 

not shown). 

For men age 45-49, ‘year of survey’ and ‘year of ART introduction’ are positively correlated with HIV 

prevalence. All of the other age categories have no variables significantly associated with relative risk of 

HIV prevalence. 

In all age groups of women and men, from the components generated by PCA, 80 percent to 85 percent of 

the variance in data is accounted for in three to five components with an Eigen value greater than or very 

close to 1. These components are retained for further analysis while the remaining components are 

eliminated from subsequent analysis, as they contribute little information. Each of these new components 

generated by PCA is a linear regression of the standardized original variables, and therefore, is 

representative of the variables with relatively high loadings, i.e., high coefficients usually <-0.5 or >0.5. 

Performing multiple linear regressions with the retained components as the independent variables and 

relative risk of HIV prevalence as the dependent variable identifies components with a significant unique 

association with HIV prevalence. 

In PCA analysis for women age 15-19, ‘percent unmarried sexually active’ and ‘percent sex before 15’ 

both load highly on the retained component c3. This component has a significant positive correlation with 

relative risk of HIV prevalence (Table 5a). For women age 20-24, condom use among those with nonmarital partners has a high loading on component c3, which is significantly correlated with relative risk of 

HIV prevalence. For women age 40-44, ‘year of survey’ and ‘year of ART introduction’ are positively 

correlated with component c1 of PCA, which is significantly correlated with relative risk of HIV 

prevalence. For women in the remaining age groups, 30-34, 35-39, and 45-49, none of the retained 

components has a significant correlation with relative risk of HIV prevalence. 

In all age groups of women, most of the original variables have high loadings on one of the retained 

components, except for ‘age-mixing’ in age 15-19 and ‘relative value of percent with multiple partners’ in 

some of the older age groups, which do not have high loadings, indicating that they add little variance to 

the data. In addition, PCA components indicate that ‘year of survey’ and ‘year of ART introduction’ are 
21 

correlated with each other, and relative values of ‘percent with non-marital partners’ and ‘condom use at 

last sex’ are correlated with each other in all age groups (Table 5a). 

For men, multiple regressions on PCA components identify no components significantly correlated with 

relative risk of HIV prevalence (Table 5b). The variables that do not have high loadings on any of the 

retained components in PCA (indicating that they do not contribute much variance in data) include 

‘percent in commercial sex’ in age 15-19, 20-24, and 30-34, and relative values of ‘percent with multiple 

partners’ and ‘condom use among those with non-marital partners’ for men in some of the older age 

groups. For men, as was the case for women, ‘condom use at last sex’ correlates with ‘percent with nonmarital partners’, but only at age 30-34 and younger age groups. 

Table 3.1w. Behavioral data extracted from DHS datasets for women: percent with non-marital 

partners 

Country 

Year of 

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

Burkina Faso 2010 32.44 14.82 6.09 2.94 1.57 1.07 1.78 

Burkina Faso 2003 39.17 15.78 6.47 3.76 3.62 2.37 1.17 

Burundi 2010 24.13 3.94 3.11 1.78 2.41 3.08 1.13 

Cameroon 2011 60.21 39.81 25.56 18.21 17.13 14.99 14.44 

Cameroon 2004 58.24 38.55 23.49 19.15 18.42 16.70 16.06 

DRC 2007 60.06 26.83 15.09 10.60 7.64 9.78 7.30 

Ethiopia 2011 8.87 7.23 3.46 2.24 3.08 1.39 3.72 

Ethiopia 2005 7.73 6.51 2.21 2.71 1.84 2.35 2.01 

Ghana 2003 77.59 41.96 18.81 8.12 8.11 8.40 7.41 

Guinea 2005 52.03 31.48 13.06 9.73 5.75 7.56 4.70 

Kenya 2008-09 54.62 24.18 14.01 11.65 9.30 12.82 9.28 

Kenya 2003 46.71 21.43 13.78 10.04 10.93 10.95 10.25 

Lesotho 2009 65.46 37.20 30.66 30.22 26.99 29.41 28.20 

Lesotho 2003 58.48 38.22 33.80 29.85 33.87 36.61 30.32 

Liberia 2007 79.76 51.52 31.90 22.12 14.74 15.80 12.15 

Malawi 2010 35.89 9.60 5.30 5.62 4.44 4.54 3.79 

Malawi 2004 28.65 8.10 5.47 5.17 4.41 3.40 3.16 

Mali 2006 20.41 9.86 3.40 2.83 1.04 1.46 1.41 

Niger 2006 0.86 1.34 1.12 0.97 1.11 0.25 0.25 

Rwanda 2010 55.33 14.73 6.66 4.59 5.15 6.92 6.30 

Rwanda 2005 51.99 8.60 5.37 5.04 6.96 6.75 3.44 

Senegal 2011 15.08 8.36 3.92 3.80 4.09 2.51 1.16 

Senegal 2005 13.38 8.81 6.20 4.04 4.06 3.55 1.36 

Sierra Leone 2008 64.02 37.50 18.83 18.77 11.38 12.65 9.66 

Swaziland 2006-07 82.39 60.22 40.19 31.73 21.64 20.14 17.95 

Zambia 2007 51.80 19.73 12.79 11.04 8.22 10.92 4.14 

Zimbabwe 2010-11 21.58 13.20 10.94 10.28 9.00 10.23 8.27 

Zimbabwe 2005-06 24.08 12.97 8.29 9.46 9.45 7.91 6.58 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 
22 

Table 3.2w. Behavioral data extracted from DHS datasets for women: Percent with multiple 

partners 

Country 

Year of 

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

Burkina Faso 2010 0.93 0.95 0.56 0.47 0.24 0.14 0.08 

Burkina Faso 2003 1.63 1.52 0.74 1.33 0.56 0.63 0.11 

Burundi 2010 0.29 0.30 0.42 0.36 0.34 0.11 0.00 

Cameroon 2011 4.05 9.05 8.43 5.48 5.84 4.18 2.58 

Cameroon 2004 5.55 8.42 6.65 5.16 4.69 3.98 4.05 

DRC 2007 3.24 3.96 3.47 2.95 3.48 2.69 3.34 

Ethiopia 2011 0.31 0.80 0.63 0.42 0.44 0.20 0.44 

Ethiopia 2005 13.28 37.60 49.00 52.26 51.40 51.87 54.34 

Ghana 2003 1.54 1.95 0.38 0.61 1.71 0.54 0.25 

Guinea 2005 2.96 2.35 2.68 2.40 1.66 1.58 1.61 

Kenya 2008-09 1.29 1.92 1.40 1.31 2.28 0.47 0.38 

Kenya 2003 1.55 1.80 2.43 1.80 2.07 2.66 2.61 

Lesotho 2009 2.61 7.03 9.92 11.27 9.32 11.15 9.19 

Lesotho 2003 2.83 6.48 9.85 12.44 10.03 9.81 9.88 

Liberia 2007 9.07 10.03 11.02 8.28 7.08 7.75 6.39 

Malawi 2010 0.81 0.96 0.77 0.86 0.74 0.60 0.97 

Malawi 2004 1.02 1.26 0.88 0.58 0.59 0.44 0.22 

Mali 2006 1.97 1.59 0.56 1.98 0.46 1.71 0.78 

Niger 2006 0.71 0.99 1.03 1.13 0.85 1.13 0.24 

Rwanda 2010 0.32 0.88 0.68 0.48 0.58 0.52 0.96 

Rwanda 2005 0.13 0.48 0.40 0.54 0.37 0.22 0.12 

Senegal 2011 0.12 0.50 0.65 0.60 0.91 0.26 0.18 

Senegal 2005 1.02 1.37 1.82 1.52 1.93 2.42 2.81 

Sierra Leone 2008 4.20 5.37 6.54 7.06 7.19 7.25 6.47 

Swaziland 2006-07 1.35 3.05 2.32 1.48 0.73 0.52 0.26 

Zambia 2007 1.90 0.95 1.16 1.20 1.54 1.20 0.15 

Zimbabwe 2010-11 1.68 2.91 2.03 1.60 2.02 3.20 3.53 

Zimbabwe 2005-06 0.90 1.12 1.06 1.09 0.87 0.59 0.34 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 
23 

Table 3.3w. Behavioral data extracted from DHS datasets for women: Percent using condom at 

last sex with non-marital partner among those with non-marital partner in past 12 months 

Country 

Year of 

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

Burkina Faso 2010 52.44 70.39 59.26 55.11 52.12 16.68 22.73 

Burkina Faso 2003 46.43 67.40 56.19 70.57 25.99 38.26 6.62 

Burundi 2010 21.59 32.45 15.10 28.82 * * * 

Cameroon 2011 56.17 56.13 50.79 34.70 32.28 25.44 27.20 

Cameroon 2004 47.09 45.44 39.76 34.23 27.75 23.13 10.72 

DRC 2007 12.41 22.23 14.36 16.90 11.61 15.39 11.68 

Ethiopia 2011 40.63 27.61 28.95 32.57 5.06 * * 

Ethiopia 2005 24.85 23.00 25.53 * * * * 

Ghana 2003 33.50 31.95 27.40 15.40 10.46 10.66 11.92 

Guinea 2005 24.24 28.42 32.22 25.44 17.10 9.40 16.70 

Kenya 2008-09 40.49 37.23 31.86 27.25 29.45 29.45 19.84 

Kenya 2003 23.42 27.63 25.77 22.77 22.96 23.67 0.00 

Lesotho 2009 62.09 65.81 72.86 69.03 65.50 59.01 41.92 

Lesotho 2003 48.09 50.84 47.26 36.83 26.84 23.39 15.54 

Liberia 2007 11.77 16.05 16.74 15.50 7.19 13.27 2.29 

Malawi 2010 43.55 49.26 45.52 21.15 41.67 27.77 51.04 

Malawi 2004 34.88 35.39 26.59 22.89 8.87 13.40 * 

Mali 2006 13.64 19.23 17.65 15.78 18.48 * * 

Niger 2006 * * * * * * * 

Rwanda 2010 40.96 39.26 36.28 38.29 21.43 20.25 19.62 

Rwanda 2005 26.13 24.02 19.43 16.09 13.92 4.54 * 

Senegal 2011 37.69 44.96 41.33 37.87 38.43 53.82 * 

Senegal 2005 30.50 37.66 44.47 32.21 33.94 37.69 * 

Sierra Leone 2008 7.43 11.74 5.51 2.37 4.46 1.13 1.37 

Swaziland 2006-07 51.55 55.41 62.11 52.83 49.10 46.64 33.00 

Zambia 2007 36.27 40.64 43.49 33.31 31.76 30.35 * 

Zimbabwe 2010-11 38.51 53.80 63.82 70.67 68.09 51.18 53.55 

Zimbabwe 2005-06 40.11 41.35 53.18 59.33 38.88 44.73 38.49 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 
24 

Table 3.4w. Behavioral data extracted from DHS datasets for women: Percent using condom at 

last sex with any partner among those with non-marital partner in past 12 months 

Country 

Year of 

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

Burkina Faso 2010 52.44 70.39 59.26 55.11 52.12 13.68 22.73 

Burkina Faso 2003 45.25 65.69 54.84 69.64 24.15 * * 

Burundi 2010 22.22 32.45 18.85 28.82 * * * 

Cameroon 2011 54.96 52.85 45.37 31.02 23.55 21.40 24.19 

Cameroon 2004 47.26 44.31 36.26 32.36 23.13 21.95 8.76 

DRC 2007 11.83 21.07 14.33 17.19 11.38 12.79 11.68 

Ethiopia 2011 40.63 27.61 28.95 32.57 5.06 * * 

Ethiopia 2005 24.95 23.38 5.50 * * * * 

Ghana 2003 33.50 31.95 27.40 15.40 10.46 10.66 11.92 

Guinea 2005 24.07 29.13 29.50 17.86 18.49 9.25 11.86 

Kenya 2008-09 40.83 38.02 31.86 32.04 27.16 29.45 19.84 

Kenya 2003 22.53 25.71 22.43 21.20 21.31 20.31 0.00 

Lesotho 2009 60.59 63.74 62.97 61.90 58.14 52.98 41.51 

Lesotho 2003 46.50 46.38 38.66 25.88 22.38 18.60 9.38 

Liberia 2007 11.72 14.89 13.55 10.22 8.01 5.89 2.29 

Malawi 2010 43.16 48.04 45.33 49.10 40.61 27.77 30.10 

Malawi 2004 34.25 33.78 23.21 22.20 8.87 13.40 * 

Mali 2006 13.50 19.28 16.54 14.93 17.12 * * 

Niger 2006 * * * * * * * 

Rwanda 2010 40.90 39.45 38.27 38.29 21.38 19.97 24.07 

Rwanda 2005 27.61 24.59 21.04 14.11 13.92 5.86 * 

Senegal 2011 37.69 44.40 42.18 35.82 37.56 50.04 * 

Senegal 2005 29.67 36.97 43.57 31.26 35.27 35.71 * 

Sierra Leone 2008 7.43 10.97 4.69 1.44 3.58 1.13 2.88 

Swaziland 2006-07 51.55 54.76 62.36 51.75 48.79 46.64 33.00 

Zambia 2007 35.92 39.50 43.78 33.31 28.93 30.35 * 

Zimbabwe 2010-11 36.78 52.16 62.53 69.47 73.64 54.38 53.55 

Zimbabwe 2005-06 38.39 40.94 53.18 57.31 42.90 44.73 38.49 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 
25 

Table 3.5w. Behavioral data extracted from DHS datasets for women: Percent using condom at 

last sex with any partner among those who had sex in past 12 months 

Country 

Year of 

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

Burkina Faso 2010 21.04 15.73 9.39 5.10 4.56 2.77 2.36 

Burkina Faso 2003 21.42 17.53 7.84 6.44 2.76 2.40 1.42 

Burundi 2010 6.56 2.49 2.98 2.06 1.91 2.82 0.61 

Cameroon 2011 38.81 29.90 19.51 14.57 9.53 7.27 5.74 

Cameroon 2004 32.40 23.63 14.73 10.27 7.32 6.68 3.95 

DRC 2007 8.53 8.42 4.96 3.13 3.70 3.30 3.05 

Ethiopia 2011 4.79 2.79 2.14 1.57 0.82 1.56 1.07 

Ethiopia 2005 1.95 1.65 0.99 0.48 0.69 0.72 0.66 

Ghana 2003 27.16 18.35 8.69 4.96 4.56 2.52 1.89 

Guinea 2005 14.31 11.04 6.13 2.86 2.03 1.45 0.54 

Kenya 2008-09 25.62 13.15 7.24 6.05 5.25 5.46 5.09 

Kenya 2003 13.09 7.38 4.60 4.12 5.38 3.74 1.08 

Lesotho 2009 49.74 43.49 40.21 39.02 34.36 32.80 26.32 

Lesotho 2003 34.07 28.79 23.89 18.98 16.81 12.93 7.00 

Liberia 2007 11.38 10.73 7.26 5.77 3.67 1.98 0.61 

Malawi 2010 22.45 10.17 8.00 8.61 7.13 6.51 5.14 

Malawi 2004 14.33 7.10 4.68 3.04 2.59 1.82 0.94 

Mali 2006 4.33 3.48 1.56 1.24 1.01 0.87 0.28 

Niger 2006 0.39 0.63 0.19 0.41 0.47 0.66 0.20 

Rwanda 2010 27.85 11.06 7.45 6.96 8.33 5.79 5.36 

Rwanda 2005 17.79 3.37 2.71 2.40 1.86 1.65 0.40 

Senegal 2011 8.24 6.08 4.29 3.01 2.74 2.02 1.18 

Senegal 2005 5.66 5.45 4.40 2.66 2.86 2.61 0.11 

Sierra Leone 2008 5.54 5.49 1.97 1.87 1.20 1.79 1.25 

Swaziland 2006-07 48.20 45.15 41.52 36.88 33.19 23.24 17.86 

Zambia 2007 24.63 15.84 12.72 10.61 8.04 9.12 2.67 

Zimbabwe 2010-11 13.11 12.36 12.69 17.70 17.74 19.02 15.49 

Zimbabwe 2005-06 11.83 8.05 6.82 10.29 9.47 7.58 4.98 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 
26 

Table 3.6w. Behavioral data extracted from DHS datasets for young women 

Country 

Year of 

survey 

Percent 

sex 

before 

18 

Percent sex before 

15 

Percent unmarried 

sexually active 

Percent with 

partners 10 or more 

years older 

20-24 15-19 20-24 15-19 20-24 15-19 20-24 

Burkina Faso 2010 60.1 7.7 10.9 17.6 60.0 33.7 39.4 

Burkina Faso 2003 62.0 7.3 7.1 23.9 67.3 NA NA 

Burundi 2010 23.4 3.5 3.1 7.1 18.0 10.7 12.3 

Cameroon 2011 59.6 15.0 18.0 33.1 77.0 27.2 32.1 

Cameroon 2004 68.2 18.0 21.6 32.7 73.4 1.4 0.9 

DRC 2007 62.0 17.9 18.5 36.9 68.4 7.2 3.9 

Ethiopia 2011 42.4 7.1 16.1 2.7 15.1 21.0 22.0 

Ethiopia 2005 48.6 11.1 21.9 2.5 9.7 0.1 NA 

Ghana 2003 43.1 7.4 7.5 29.4 62.7 3.7 0.0 

Guinea 2005 69.7 19.7 25.2 35.5 67.3 5.9 3.5 

Kenya 2008-09 47.8 11.5 10.4 27.4 63.1 2.1 0.9 

Kenya 2003 48.1 14.5 12.8 27.5 58.6 1.7 NA 

Lesotho 2009 45.4 8.5 6.9 35.4 80.3 6.8 13.8 

Lesotho 2003 NA NA NA NA NA NA NA 

Liberia 2007 79.8 18.7 15.8 66.4 97.6 4.2 3.9 

Malawi 2010 59.5 12.1 16.7 24.1 57.1 0.6 0.2 

Malawi 2004 57.1 14.1 15.5 25.2 62.9 0.4 0.5 

Mali 2006 73.0 23.5 26.1 8.1 28.8 5.2 1.8 

Niger 2006 72.9 25.7 33.9 1.1 9.1 0.1 0.0 

Rwanda 2010 16.0 4.8 2.8 11.5 28.1 6.0 11.3 

Rwanda 2005 19.1 5.2 2.6 9.4 22.6 4.6 0.9 

Senegal 2011 NA NA NA NA NA NA NA 

Senegal 2005 36.9 9.1 9.7 3.0 7.9 2.6 1.7 

Sierra Leone 2008 NA NA NA NA NA NA NA 

Swaziland 2006-07 46.3 7.4 6.4 36.8 84.7 5.9 7.4 

Zambia 2007 59.8 12.3 14.8 35.5 72.8 2.3 2.2 

Zimbabwe 2010-11 38.0 3.9 3.7 11.1 40.0 15.3 14.4 

Zimbabwe 2005-06 37.0 4.9 5.8 11.1 42.0 12.5 14.3 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 
27 

Table 3.1m. Behavioral data extracted from DHS datasets for men: Percent with non-marital 

partners 

Country 

Year of 

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

Burkina Faso 2010 94.25 65.98 33.55 21.66 11.21 6.32 4.44 

Burkina Faso 2003 97.64 67.37 46.34 26.10 17.63 15.04 4.32 

Burundi 2010 71.47 18.15 7.41 6.49 3.48 4.56 0.93 

Cameroon 2011 98.28 86.86 65.13 48.90 39.39 35.13 27.58 

Cameroon 2004 96.18 86.76 62.65 48.21 44.13 37.21 35.43 

DRC 2007 93.46 74.60 44.59 29.19 17.11 14.30 15.40 

Ethiopia 2011 72.46 32.16 13.86 5.14 3.03 1.97 2.04 

Ethiopia 2005 68.00 30.80 9.33 4.75 2.11 2.01 0.96 

Ghana 2003 97.93 76.20 50.00 28.57 21.72 13.00 12.01 

Guinea 2005 98.26 90.65 60.33 41.07 30.41 18.56 16.03 

Kenya 2008-09 98.04 75.88 34.40 17.74 18.36 8.94 6.17 

Kenya 2003 96.57 77.15 35.11 22.42 15.02 9.96 7.38 

Lesotho 2009 97.52 84.06 62.42 45.09 40.26 43.91 41.12 

Lesotho 2003 97.07 82.43 55.52 49.85 43.11 42.42 37.87 

Liberia 2007 96.13 82.99 56.56 42.81 34.50 29.86 21.78 

Malawi 2010 92.84 50.75 21.09 14.52 9.88 8.08 11.47 

Malawi 2004 94.49 44.82 20.43 12.79 8.12 4.23 4.29 

Mali 2006 92.49 63.41 31.66 15.00 8.55 8.07 4.25 

Niger 2006 76.27 29.04 10.27 6.13 4.53 2.72 2.63 

Rwanda 2010 96.06 46.67 20.02 9.82 7.60 5.77 4.76 

Rwanda 2005 96.37 39.84 15.98 10.01 8.83 8.14 8.75 

Senegal 2011 92.59 88.12 45.36 25.79 15.38 10.04 8.66 

Senegal 2005 96.31 85.56 54.75 32.81 19.86 16.60 7.11 

Sierra Leone 2008 89.27 77.68 54.67 38.45 30.51 30.23 21.05 

Swaziland 2006-07 98.06 89.89 63.89 46.73 32.54 23.27 26.95 

Zambia 2007 95.85 64.50 40.57 23.96 17.45 12.64 14.15 

Zimbabwe 2010-11 95.13 66.94 36.00 23.18 16.65 12.60 9.72 

Zimbabwe 2005-06 96.24 69.89 34.08 17.85 14.77 11.03 9.89 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 
28 

Table 3.2m. Behavioral data extracted from DHS datasets for men: Percent with multiple partners 

Country 

Year of 

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

Burkina Faso 2010 1.99 11.50 15.90 23.35 24.26 26.11 29.20 

Burkina Faso 2003 4.53 16.09 16.74 18.95 19.37 27.41 17.83 

Burundi 2010 0.75 1.58 3.53 4.46 4.20 7.57 3.26 

Cameroon 2011 9.52 30.45 38.61 38.99 40.72 32.98 31.76 

Cameroon 2004 12.98 34.89 36.62 38.98 38.21 36.27 37.78 

DRC 2007 10.27 20.38 22.65 17.34 18.51 19.61 16.00 

Ethiopia 2011 0.54 1.70 2.88 4.45 6.35 7.33 9.75 

Ethiopia 2005 0.23 1.92 2.26 3.19 4.20 4.84 4.78 

Ghana 2003 2.38 10.73 13.78 13.82 13.37 11.32 10.85 

Guinea 2005 13.95 27.83 31.05 24.64 32.24 25.40 32.06 

Kenya 2008-09 4.31 11.80 10.18 11.82 14.03 10.63 6.72 

Kenya 2003 7.26 16.38 13.08 11.02 14.44 10.53 10.11 

Lesotho 2009 13.78 30.99 28.79 28.06 23.54 25.62 15.65 

Lesotho 2003 11.70 28.06 28.09 29.66 21.72 20.67 12.82 

Liberia 2007 7.66 24.41 25.49 23.43 22.84 18.96 14.71 

Malawi 2010 4.98 8.93 10.97 10.34 10.59 12.50 15.88 

Malawi 2004 4.94 9.38 10.22 11.33 9.20 11.94 11.01 

Mali 2006 4.33 9.59 16.28 18.62 24.80 26.31 29.70 

Niger 2006 2.23 3.75 7.18 16.59 22.71 24.39 25.71 

Rwanda 2010 0.45 3.18 5.70 6.64 5.77 4.65 6.36 

Rwanda 2005 0.27 1.41 3.52 4.63 4.64 4.55 6.09 

Senegal 2011 1.33 3.90 8.56 11.40 10.46 20.50 26.65 

Senegal 2005 4.59 9.32 12.12 10.10 20.21 22.08 31.86 

Sierra Leone 2008 4.90 20.15 26.37 23.72 25.94 26.84 24.11 

Swaziland 2006-07 3.81 18.91 22.80 20.06 15.77 12.73 18.00 

Zambia 2007 4.62 15.19 20.41 19.02 19.33 13.83 17.64 

Zimbabwe 2010-11 3.31 14.66 15.80 16.02 14.06 10.22 10.62 

Zimbabwe 2005-06 2.82 12.76 13.86 10.97 8.86 9.00 9.50 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 
29 

Table 3.3m. Behavioral data extracted from DHS datasets for men: Percent using condom at last 

sex with non-marital partner among those with non-marital partners in past 12 months 

Country 

Year of 

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

Burkina Faso 2010 68.63 79.62 79.98 75.03 76.36 70.18 55.09 

Burkina Faso 2003 61.55 71.35 71.63 79.23 71.07 72.77 * 

Burundi 2010 37.27 52.85 50.90 46.38 * * * 

Cameroon 2011 68.80 72.74 69.09 65.76 60.56 53.90 46.34 

Cameroon 2004 55.66 58.45 60.16 49.99 49.37 40.05 40.51 

DRC 2007 20.91 28.93 28.04 26.84 22.76 16.81 12.31 

Ethiopia 2011 57.16 66.86 63.77 65.33 39.04 53.54 35.80 

Ethiopia 2005 43.97 51.98 46.06 49.70 * * * 

Ghana 2003 46.16 54.44 43.19 39.30 33.46 38.91 38.73 

Guinea 2005 34.09 39.96 43.31 38.88 38.09 50.35 46.22 

Kenya 2008-09 55.32 70.03 64.40 48.03 62.46 42.87 54.79 

Kenya 2003 40.98 50.69 51.53 37.54 39.50 55.12 * 

Lesotho 2009 63.12 66.98 64.32 70.82 62.67 53.60 54.36 

Lesotho 2003 NA NA NA NA NA NA NA 

Liberia 2007 15.89 25.83 26.69 33.02 26.00 28.10 18.67 

Malawi 2010 46.82 59.43 66.12 49.33 45.67 37.94 33.87 

Malawi 2004 35.56 58.47 55.46 40.13 33.18 * * 

Mali 2006 31.04 40.00 37.38 50.31 46.61 40.39 * 

Niger 2006 28.45 38.29 58.87 65.30 * * * 

Rwanda 2010 57.50 68.19 65.32 57.85 49.65 60.65 * 

Rwanda 2005 37.02 38.35 57.83 22.92 26.97 10.91 9.23 

Senegal 2011 55.57 67.89 70.17 59.56 63.00 46.66 * 

Senegal 2005 44.05 59.64 70.66 75.10 73.48 65.42 * 

Sierra Leone 2008 14.41 27.54 22.28 17.25 22.05 18.67 21.20 

Swaziland 2006-07 68.41 69.53 70.46 65.2 62.28 55.43 47.92 

Zambia 2007 42.63 53.16 55.82 49.91 49.75 50.60 42.26 

Zimbabwe 2010-11 66.06 76.12 79.93 76.32 72.62 73.09 79.00 

Zimbabwe 2005-06 54.25 74.47 77.04 77.44 69.01 55.57 51.52 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 
30 

Table 3.4m. Behavioral data extracted from DHS datasets for men: Percent using condom at last 

sex with any partner among those with non-marital partners in past 12 months 

Country 

Year of 

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

Burkina Faso 2010 68.63 79.34 72.46 56.44 55.61 45.49 34.73 

Burkina Faso 2003 62.07 70.19 67.25 59.10 55.37 29.29 * 

Burundi 2010 37.27 51.72 41.36 39.18 * * * 

Cameroon 2011 68.80 69.45 57.45 42.72 37.42 29.86 18.06 

Cameroon 2004 56.14 57.69 50.97 35.69 32.84 23.64 24.27 

DRC 2007 21.04 26.81 21.31 16.63 10.74 9.91 0.14 

Ethiopia 2011 58.60 66.86 61.05 63.48 41.22 38.79 27 

Ethiopia 2005 43.97 48.16 44.99 54.30 * * * 

Ghana 2003 46.16 52.18 40.79 26.04 20.31 16.51 23.24 

Guinea 2005 34.09 38.04 39.93 32.54 21.76 28.06 24.16 

Kenya 2008-09 55.32 68.74 56.78 31.98 33.90 23.26 56.00 

Kenya 2003 40.37 48.76 41.42 25.09 14.31 35.79 * 

Lesotho 2009 61.91 64.43 56.66 57.38 43.05 35.72 43.94 

Lesotho 2003 NA NA NA NA NA NA NA 

Liberia 2007 15.34 22.99 26.46 24.35 18.15 16.63 15.76 

Malawi 2010 45.48 54.83 50.19 29.64 22.44 24.64 26.05 

Malawi 2004 35.56 52.80 47.96 20.65 21.16 * * 

Mali 2006 30.43 37.59 33.70 29.58 25.76 20.89 * 

Niger 2006 30.33 40.85 57.56 47.92 * * * 

Rwanda 2010 57.51 66.42 53.44 42.74 40.07 45.93 * 

Rwanda 2005 37.02 37.03 43.57 13.36 17.50 8.63 4.08 

Senegal 2011 55.01 67.23 66.76 53.54 55.61 30.38 * 

Senegal 2005 44.05 58.89 67.85 62.01 54.45 53.58 * 

Sierra Leone 2008 13.59 26.82 18.33 13.55 15.53 13.27 16.04 

Swaziland 2006-07 68.96 69.78 66.15 58.20 53.54 38.96 30.10 

Zambia 2007 41.68 48.82 41.48 28.11 25.39 23.51 29.47 

Zimbabwe 2010-11 63.96 67.19 58.90 45.41 36.75 45.95 60.84 

Zimbabwe 2005-06 53.16 69.67 54.57 49.51 41.16 29.47 23.89 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 

 
31 

Table 3.5m. Behavioral data extracted from DHS datasets for men: Percent using condom at last 

sex with any partner among those with multiple partners 

Country 

Year of 

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

Burkina Faso 2010 76.36 74.24 44.86 25.24 13.47 8.65 5.81 

Burkina Faso 2003 74.39 69.37 61.72 40.12 26.30 10.55 16.81 

Burundi 2010 * * * * * * * 

Cameroon 2011 69.59 65.25 52.22 33.73 29.91 21.65 9.38 

Cameroon 2004 56.56 56.27 41.75 28.39 24.67 15.06 16.60 

DRC 2007 27.04 19.48 22.74 8.55 5.53 4.96 9.88 

Ethiopia 2011 * 36.93 47.12 15.06 1.93 5.53 0.18 

Ethiopia 2005 * 23.89 6.88 10.39 3.48 * * 

Ghana 2003 59.12 42.75 23.42 17.22 12.71 4.86 2.93 

Guinea 2005 38.25 38.98 32.92 19.50 12.83 11.30 8.72 

Kenya 2008-09 69.05 66.52 39.86 19.92 15.37 9.80 8.67 

Kenya 2003 54.34 50.92 23.18 20.66 9.62 23.64 * 

Lesotho 2009 60.19 60.29 52.46 49.35 41 28.17 38.90 

Lesotho 2003 NA NA NA NA NA NA NA 

Liberia 2007 29.62 27.35 29.15 20.59 12.09 14.73 17.52 

Malawi 2010 36.10 43.95 30.96 13.99 12.95 12.34 8.85 

Malawi 2004 31.44 36.32 22.94 11.86 13.67 4.16 * 

Mali 2006 20.06 33.07 22.16 13.65 8.45 4.94 1.44 

Niger 2006 38.05 45.06 30.34 6.04 1.87 0.00 0.00 

Rwanda 2010 * 53.03 32.45 18.93 14.28 * * 

Rwanda 2005 * * * * * * * 

Senegal 2011 * 44.72 52.07 22.71 9.94 0.79 3.43 

Senegal 2005 56.46 69.09 58.92 33.51 9.31 9.97 3.17 

Sierra Leone 2008 16.65 33.46 22.63 12.78 8.67 10.96 4.15 

Swaziland 2006-07 74.76 64.66 61.24 50.14 50.48 30.88 22.44 

Zambia 2007 48.99 40.61 30.67 19.48 20.87 17.64 17.63 

Zimbabwe 2010-11 66.21 46.99 34.15 20.96 17.00 17.00 32.92 

Zimbabwe 2005-06 70.28 56.21 28.06 23.82 18.25 14.85 6.58 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 
32 

Table 3.6m. Behavioral data extracted from DHS datasets for men: Percent using condom at last 

sex with any partner among those who had sex in past 12 months 

Country 

Year of 

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

Burkina Faso 2010 64.96 55.37 33.57 21.42 12.92 7.67 6.68 

Burkina Faso 2003 60.61 50.24 39.61 25.41 20.46 12.05 6.65 

Burundi 2010 26.64 10.46 5.72 5.54 1.79 4.13 1.64 

Cameroon 2011 67.85 62.00 41.92 27.84 20.18 15.06 8.88 

Cameroon 2004 54.14 51.18 35.32 22.80 19.87 12.45 11.73 

DRC 2007 19.66 20.85 11.62 7.44 3.89 2.56 4.68 

Ethiopia 2011 42.56 21.79 9.23 4.59 2.57 3.27 1.09 

Ethiopia 2005 29.90 14.95 6.35 3.28 1.34 1.77 0.76 

Ghana 2003 45.90 43.98 25.75 14.77 9.54 7.57 7.67 

Guinea 2005 33.93 35.23 26.31 16.02 10.00 7.73 4.66 

Kenya 2008-09 54.86 56.76 26.94 8.93 11.04 8.09 7.26 

Kenya 2003 39.48 39.34 17.23 7.24 4.32 6.39 2.83 

Lesotho 2009 62.25 58.03 47.27 43.69 37.92 34.26 34.04 

Lesotho 2003 NA NA NA NA NA NA NA 

Liberia 2007 15.23 20.44 17.51 14.29 8.90 8.83 5.75 

Malawi 2010 42.79 33.16 19.24 11.74 11.10 9.04 11.28 

Malawi 2004 34.45 30.12 15.40 10.46 8.64 2.61 1.70 

Mali 2006 29.86 28.40 13.55 6.24 4.25 3.29 2.11 

Niger 2006 23.13 11.86 6.72 3.55 1.24 0.25 0.18 

Rwanda 2010 56.42 33.61 15.77 9.51 9.67 8.37 7.17 

Rwanda 2005 35.68 14.81 7.95 2.64 2.80 3.41 1.33 

Senegal 2011 51.64 59.24 31.91 16.40 10.30 5.45 6.45 

Senegal 2005 43.28 50.83 38.10 23.93 13.92 11.12 4.06 

Sierra Leone 2008 12.73 21.70 11.23 7.14 6.32 5.53 4.86 

Swaziland 2006-07 69.03 66.39 55.76 44.25 35.07 30.03 25.85 

Zambia 2007 40.66 37.71 27.56 16.36 16.51 11.51 9.68 

Zimbabwe 2010-11 61.44 48.44 25.77 16.38 13.76 19.22 20.39 

Zimbabwe 2005-06 52.73 51.11 22.86 12.98 14.35 11.23 10.29 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 

 
33 

Table 3.7m. Behavioral data extracted from DHS datasets for men: Percent in commercial sex 

Country 

Year of 

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

Burkina Faso 2010 0.17 1.46 2.53 1.35 0.98 1.25 1.04 

Burkina Faso 2003 0.46 0.12 0.12 0.00 0.00 0.00 0.00 

Burundi 2010 0.08 0.16 0.37 0.97 0.23 0.59 0.17 

Cameroon 2011 1.62 5.56 5.27 5.07 4.38 3.56 4.62 

Cameroon 2004 1.39 5.50 3.68 3.75 3.64 2.13 1.71 

DRC 2007 7.29 13.22 8.15 9.19 5.42 4.48 4.25 

Ethiopia 2011 0.44 1.62 1.68 1.29 1.45 0.58 0.86 

Ethiopia 2005 0.28 0.44 0.63 0.47 0.58 0.01 0.09 

Ghana 2003 0.00 0.16 0.29 0.00 0.21 0.25 0.00 

Guinea 2005 0.28 0.22 0.87 0.30 0.34 0.32 0.00 

Kenya 2008-09 1.38 1.57 1.92 1.21 1.67 0.41 0.95 

Kenya 2003 3.56 3.49 2.57 3.64 1.65 1.01 3.60 

Lesotho 2009 0.77 2.62 1.10 1.86 2.35 3.48 3.54 

Lesotho 2003 0.53 2.04 1.78 1.56 4.35 2.90 0.15 

Liberia 2007 0.83 4.40 2.84 2.84 1.71 1.49 0.53 

Malawi 2010 5.09 4.68 4.20 4.60 3.15 3.30 3.29 

Malawi 2004 8.19 5.81 3.89 6.62 3.88 1.56 1.99 

Mali 2006 0.83 2.42 2.99 0.99 1.27 2.32 0.53 

Niger 2006 1.16 2.41 0.79 1.92 1.22 0.97 0.00 

Rwanda 2010 0.46 1.32 2.26 1.72 1.74 1.71 0.93 

Rwanda 2005 0.24 0.10 0.26 0.17 0.39 0.00 0.64 

Senegal 2011 0.16 0.66 1.64 1.17 0.43 0.88 0.00 

Senegal 2005 0.07 0.00 0.19 1.70 0.75 0.23 0.40 

Sierra Leone 2008 0.53 1.97 2.97 2.39 1.94 2.15 0.00 

Swaziland 2006-07 NA NA NA NA NA NA NA 

Zambia 2007 4.06 4.72 4.25 3.78 2.28 1.05 2.38 

Zimbabwe 2010-11 1.15 4.13 5.50 3.37 3.15 1.65 1.53 

Zimbabwe 2005-06 0.79 4.84 2.84 2.91 2.26 2.48 2.50 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 
34 

Table 3.8m. Behavioral data extracted from DHS datasets for young men 

Country 

Year of 

survey 

Percent sex 

before 18 Percent sex before 15 

Percent unmarried sexually 

active 

20-24 15-19 20-24 15-19 20-24 

Burkina Faso 2010 23.2 1.6 2.3 17.9 66.2 

Burkina Faso 2003 31.8 4.7 2.5 25.6 67.2 

Burundi 2010 19.2 9.3 7.2 16.1 33.4 

Cameroon 2011 49.0 11.2 10.5 37.5 78.2 

Cameroon 2004 50.4 11.5 10.7 37.7 80.0 

DRC 2007 54.6 18.3 17.0 47.4 86.5 

Ethiopia 2011 13.9 1.2 1.3 6.2 24.0 

Ethiopia 2005 14.1 1.7 1.7 5.4 24.1 

Ghana 2003 25.7 3.9 3.9 19.2 61.7 

Guinea 2005 54.4 17.9 16.0 49.1 84.7 

Kenya 2008-09 58.2 22.3 22.0 43.6 85.1 

Kenya 2003 61.2 30.9 26.2 49.5 83.3 

Lesotho 2009 61.0 25.5 17.6 59.0 90.0 

Lesotho 2003 NA NA NA NA NA 

Liberia 2007 53.6 8.6 8.3 46.7 91.9 

Malawi 2010 49.5 26.4 16.0 53.2 77.9 

Malawi 2004 47.7 18.0 9.1 50.8 78.6 

Mali 2006 27.4 6.3 4.2 15.4 36.7 

Niger 2006 22.7 5.3 4.7 13.3 36.2 

Rwanda 2010 26.4 13.3 8.8 21.4 50.6 

Rwanda 2005 26.3 15.3 10.8 22.5 48.2 

Senegal 2011 NA NA NA NA NA 

Senegal 2005 37.9 12.7 11.9 25.5 52.8 

Sierra Leone 2008 NA NA NA NA NA 

Swaziland 2006-07 36.7 4.9 4.7 21.4 76.6 

Zambia 2007 50.6 16.2 15.7 44.0 80.0 

Zimbabwe 2010-11 22.9 3.6 4.2 23.8 62.2 

Zimbabwe 2005-06 26.2 5.2 3.6 27.0 68.7 

*Un-weighted sample size less than 25, and hence, was excluded from analysis; NA: not available 
Table 4w. Simple regression (single independent variable) of relative risks with behavioral factors for women 

Independent variables 

Age groups: women 

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

Coefficient 

pvalue Coefficient p- value Coefficient p- value Coefficient p- value Coefficient p- value Coefficient p- value

Dependent variable: relative risk of HIV prevalence in age group Percent sex before 18 na na 0.03 0.65 na na na na na na na na Percent sex before 15 0.04 0.18 0.07 0.24 na na na na na na na na Percent unmarried sexually active 0.08 0.001* -0.06 0.31 na na na na na na na na Age mixing -0.03 0.35 -0.12 0.09 na na na na na na na na Percent non-marital partners 0.01 0.63 -0.06 0.29 -0.04 0.62 0.23 0.06 0.21 0.06 -0.18 0.25 Percent multiple partners 0.01 0.84 -0.06 0.32 0.00 0.98 0.02 0.87 -0.14 0.22 -0.09 0.55 Condom use at last sex with non-marital partner among those with non-marital -0.02 0.38 0.06 0.22 0.12 0.15 0.03 0.81 0.12 0.31 0.15 0.18 Condom use at last sex with any partner among those with nonmarital partners 0.01 0.68 0.05 0.27 0.09 0.30 0.01 0.94 0.11 0.32 0.06 0.60 Condom use at last sex with any partner among those who had sex 0.00 0.92 0.09 0.11 0.09 0.26 0.13 0.29 -0.01 0.96 -0.18 0.26 Year of survey -0.01 0.77 -0.05 0.37 0.14 0.07 0.20 0.10 0.29 0.008* 0.36 0.016* Years since ART introduction -0.02 0.53 -0.05 0.36 0.13 0.09 0.18 0.15 0.29 0.009* 0.42 0.004* *Significant at level 0.05; na - not applicable 

35 
Table 4m. Simple regression (single independent variable) of relative risks with behavioral factors for men 

Independent variables 

Age groups: men 

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

Coefficient 

pvalue Coefficient p- value Coefficient p- value Coefficient p- value Coefficient p- value Coefficient p- value

Dependent variable: Relative risk of HIV prevalence in age group Percent sex before 18 na na -0.02 0.75 na na na na na na na na Percent sex before 15 -0.01 0.92 -0.04 0.45 na na na na na na na na Percent unmarried sexually active 0.02 0.88 -0.03 0.63 na na na na na na na na Age mixing 0.06 0.54 0.02 0.70 0.01 0.97 0.20 0.19 0.01 0.96 0.36 0.07 Percent non-marital partners -0.02 0.84 -0.01 0.82 -0.10 0.46 -0.24 0.08 -0.18 0.50 0.03 0.86 Percent multiple partners 0.06 0.49 -0.01 0.79 0.27 0.20 0.22 0.29 0.60 0.06 0.07 0.75 Condom use at last sex with non-marital partner among those with nonmarital -0.05 0.62 0.02 0.71 0.19 0.18 -0.28 0.06 -0.28 0.25 -0.06 0.15 Condom use at last sex with any partner among those with non-marital partners 0.02 0.87 0.01 0.84 0.10 0.48 -0.10 0.51 -0.12 0.64 0.12 0.54 Condom use at last sex with any partner among those with multiple partners -0.09 0.48 0.03 0.61 0.33 0.038* -0.02 0.91 -0.27 0.23 0.09 0.60 Condom use at last sex with any partner among those who had sex -0.04 0.69 0.02 0.77 0.04 0.77 -0.04 0.79 0.27 0.35 0.35 0.07 Percent in commercial sex -0.01 0.89 -0.03 0.72 -0.06 0.69 0.10 0.66 -0.01 0.10 0.16 0.94 Year of survey 0.01 0.90 -0.01 0.80 -0.09 0.51 0.21 0.12 0.24 0.36 0.49 0.003* Years since ART introduction 0.07 0.43 0.01 0.90 0.05 0.74 0.21 0.13 0.31 0.22 0.40 0.019* *Significant at level 0.05; na - not applicable 

36 
Table 5w. Principal component analysis (PCA) for women 

Independent variables 

Age groups: women 

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

c1 c2 c3 c4 c1 c2 c3 c4 c5 c1 c2 c3 c1 c2 c3 c1 c2 c3 c1 c2 c3 

Loadings (coefficients) of the original parameters on the PCA components c1, c2, c3, or c4 Percent sex before 18 na na na na 0.28 -0.01 0.00 0.51 -0.23 na na na na na na na na na na na naPercent sex before 15 -0.14 0.06 0.57 0.36 0.45 -0.06 0.06 0.20 -0.41 na na na na na na na na na na na naPercent unmarried sexually active -0.02 -0.07 0.66 -0.05 -0.13 0.03 -0.02 0.78 0.14 na na na na na na na na na na na naAge mixing 0.46 -0.06 -0.09 -0.16 0.04 0.60 0.00 0.10 0.21 na na na na na na na na na na na naPercent non-marital partners 0.06 0.60 -0.03 0.04 0.63 0.09 -0.05 -0.25 0.12 0.02 -0.11 0.69 -0.17 0.02 0.79 -0.05 0.59 -0.04 -0.07 -0.17 0.69Percent multiple partners -0.15 0.01 -0.08 -0.74 0.10 -0.01 0.02 0.10 0.79 -0.53 0.21 -0.10 -0.14 -0.26 -0.11 -0.26 0.50 0.07 -0.17 0.33 0.48Condom use at last sex with non-marital partner among those with non-marital -0.01 0.51 -0.10 0.08 0.00 0.01 0.70 -0.06 -0.03 0.02 0.67 0.00 0.64 0.02 -0.03 0.04 -0.03 0.70 0.04 0.60 0.02Condom use at last sex with any partner among those with non-marital partners -0.32 -0.05 -0.45 0.48 -0.01 -0.01 0.71 0.04 0.05 0.01 0.68 0.03 0.66 -0.02 -0.07 -0.04 0.02 0.71 0.00 0.71 -0.10Condom use at last sex with any partner among those who had sex -0.05 0.60 0.09 -0.12 0.54 -0.07 0.01 0.02 0.20 -0.03 0.13 0.71 0.31 -0.06 0.60 0.23 0.62 0.00 0.32 0.04 0.51Year of survey 0.58 0.06 0.00 0.13 -0.06 0.56 -0.03 -0.02 -0.09 0.59 0.06 0.02 -0.04 0.69 0.06 0.68 0.10 -0.06 0.64 -0.01 0.05Years since ART introduction 0.55 -0.03 -0.04 0.15 0.04 0.55 0.04 -0.06 -0.17 0.61 0.10 -0.10 0.02 0.67 -0.08 0.65 -0.06 0.08 0.67 0.02 -0.09Multiple regression (dependent variable: relative risk of prevalence; Independent variables: PCA components) Coefficient -0.02 0.00 0.05 -0.01 -0.05 -0.06 0.08 0.01 -0.03 0.07 0.09 -0.02 -0.05 0.10 0.12 0.17 0.02 -0.02 0.11 0.06 -0.06p-value 0.34 0.75 0.006* 0.07 0.14 0.08 0.04* 0.82 0.50 0.21 0.12 0.80 0.52 0.19 0.17 0.031* 0.78 0.80 0.17 0.48 0.44*Significant at level 0.01; Bold: Parameters with high loading on the PCA components; na- not applicable 

37 
Table 5m. Principal component analysis (PCA) for men 

Independent variables 

Age groups: men 

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

c1 c2 c3 c4 c1 c2 c3 c4 c1 c2 c3 c4 c1 c2 c3 c1 c2 c3 c4 c1 c2 c3 c4 

Loadings (coefficients) of the original parameters on the PCA components c1, c2, c3, or c4 Percent sex before 18 na na na na 0.51 -0.10 0.00 -0.05 na na na na na na na na na na na na na na naPercent sex before 15 0.60 0.04 0.07 0.07 0.60 0.14 0.11 -0.10 na na na na na na na na na na na na na na naPercent unmarried sexually active 0.54 0.06 -0.13 0.00 0.41 -0.23 -0.02 0.09 na na na na na na na na na na na na na na naPercent non-marital partners 0.06 0.00 0.60 -0.18 0.02 0.60 0.02 -0.21 -0.13 -0.04 0.66 -0.01 0.42 0.04 -0.11 0.50 0.00 -0.16 -0.08 0.32 -0.03 -0.53 0.15Percent multiple partners 0.57 -0.09 0.07 -0.08 0.43 0.07 -0.16 0.15 0.21 -0.11 -0.12 -0.67 -0.45 0.13 0.01 -0.46 0.21 0.12 -0.05 -0.40 0.32 0.05 -0.10Condom use at last sex with non-marital partner among those with non-marital 0.10 -0.02 -0.21 0.52 -0.09 -0.05 -0.09 0.68 0.61 -0.12 0.12 -0.09 -0.18 -0.08 0.48 -0.21 -0.17 0.54 -0.37 0.03 -0.06 0.70 -0.02Condom use at last sex with any partner among those with non-marital partners 0.03 0.14 -0.09 0.57 0.07 0.04 0.15 0.64 0.59 0.09 0.01 0.16 0.13 0.11 0.63 0.07 0.08 0.75 0.17 0.37 0.09 0.47 0.26Condom use at last sex with any partner among those with multiple partners -0.09 -0.15 0.31 0.58 0.02 0.49 -0.21 0.15 0.19 -0.11 -0.10 0.71 0.50 -0.10 0.00 0.41 -0.14 0.29 0.10 0.54 0.00 0.04 -0.36Condom use at last sex with any partner among those who had sex 0.00 0.04 0.68 0.12 0.02 0.54 0.08 0.16 0.16 0.04 0.71 0.00 0.55 0.12 0.12 0.55 0.19 0.14 -0.14 0.55 0.12 -0.05 0.04Percent in commercial sex 0.07 -0.43 -0.08 -0.10 0.11 -0.14 -0.32 0.02 -0.40 -0.14 0.15 0.08 0.01 0.09 -0.58 -0.04 -0.02 0.06 0.89 -0.01 0.00 -0.01 0.87Year of survey 0.05 0.62 0.06 -0.04 0.06 0.03 0.63 0.00 -0.01 0.67 0.05 -0.04 0.04 0.70 -0.02 0.08 0.67 0.02 -0.02 0.07 0.67 -0.01 0.02Years since ART introduction -0.03 0.61 -0.05 -0.01 -0.01 -0.06 0.62 0.05 0.01 0.69 -0.03 0.02 -0.05 0.66 0.03 -0.07 0.64 0.01 0.01 -0.02 0.65 -0.02 -0.01Multiple regression (dependent variable: relative risk of prevalence; Independent variables: PCA components) Coefficient -0.05 0.07 -0.10 -0.03 -0.05 0.00 -0.01 -0.02 -0.04 0.11 -0.12 0.19 -0.04 0.22 -0.23 -0.21 0.14 0.01 0.30 0.15 0.25 -0.04 0.08p-value 0.61 0.46 0.41 0.84 0.23 0.95 0.67 0.83 0.74 0.64 0.37 0.45 0.64 0.06 0.06 0.19 0.41 0.94 0.22 0.28 0.11 0.81 0.66*Significant at level 0.05; Bold: Parameters with high loading on the PCA components; na- not applicable 

38 
39 

Ratio of Female to Male HIV Prevalence in Each Age Group 

The ratio of female to male HIV prevalence in age 15-49 ranges from 0.88 to 2.0 across countries, with all 

but two countries (Niger, 2006, and Burkina Faso, 2003) having a ratio greater than 1, indicating 

significantly higher HIV prevalence among women than men (Figure 3). Comparing female to male ratios 

of HIV prevalence within five-year age groups, younger age groups have a larger ratio than older age 

groups. While in several countries younger age groups have significantly higher HIV prevalence among 

women compared with men, this pattern decreases among older age groups, and at age 45-49 only one 

country shows a significant difference between women and men in HIV prevalence. 

 
40 

Figure 3. Female to male ratio of HIV prevalence by age group 

Note: As confidence bounds are very wide in some countries we have restricted the y-axis scale to provide better 

visual presentation
41 

Figure 3. (cont.) Female to male ratio of HIV prevalence by age group 

Note: As confidence bounds are very wide in some countries we have restricted the y-axis scale to provide better 

visual presentation

43 

Conclusions 

In most countries HIV prevalence is significantly lower among people under age 30, and in most cases 

prevalence is lowest in age 15-19. For age 30-34 and older there is no specific pattern in HIV prevalence. 

Although a few countries show HIV prevalence continuing to increase with age, most show it reaching a 

peak and then decreasing, for both men and women. Prevalence peaks at age 25-29 or 35-39 for women 

and at age 30-34 or 40-44 for men. 

The average of relative risk of HIV prevalence across all countries falls within the confidence bounds of 

relative risk of HIV prevalence in individual countries in most cases, indicating, with the currently 

available data, we cannot conclude significant differences among countries in patterns of HIV prevalence. 

In some cases, however, the average falls outside the confidence bounds in individual countries, mostly in 

the older age groups and, in some instances in the younger age groups, indicating a significant difference 

across countries in these cases. Our analysis identifies a few dependent factors significantly correlated 

with relative risk of HIV prevalence across countries in some of these age groups. Years since ART and 

timing of the survey identifies as significant factors correlated with relative risk of HIV prevalence across 

countries for age 40-44 and age 45-49 for women, and age 45-49 for men. These results probably reflect 

the effects of ART—that is, HIV-positive people receiving treatment are living longer, resulting in higher 

HIV prevalence in older age groups. We would expect these effects to continue in the future, as more 

countries scale up HIV treatment. In the younger age groups, the analysis identifies sexual activity or 

condom use as significant factors correlated with relative risk of HIV prevalence across countries for 

women. For men, however, the analysis did not identify any of the observed variables as significant 

factors. 

This study is subject to certain limitations. The data are obtained from household surveys, which could 

contain errors in recording, over-reporting or under-reporting of behavior among respondents, and errors 

in reporting age and other characteristics. While the analysis focuses only on behavioral variables, other 

factors such as migration may play a role in HIV prevalence patterns. We have a small number of 

observations (19 countries) for comparing prevalence patterns and in some cases up to 13 independent 

variables thus giving a small proportion of observations to variables. However, we used multiple surveys 

increasing the number of observations to 28, which we considered as independent observations as we are 

comparing relative values instead of absolute values of prevalence and including timing of survey as an 

independent variable. Also, use of PCA analysis is suitable under limited observations as it transforms 

original correlated variables into a reduced set of uncorrelated variables. Our final analysis thus consisted 

of 28 observations and up to five independent variables. Limitations also include missing data 

corresponding to non-availability or very small sample size; in the latter case the data are not included in 

the analysis. 

HIV incidence patterns by age are widely used in HIV modeling for strategy analysis. We believe that 

sexual behavioral factors could significantly affect HIV incidence, but since incidence patterns cannot be 

directly measured, it would be valuable to identify the factors that significantly affect HIV prevalence 

patterns, for use in model-based approaches to analyzing incidence patterns. 

The results from this analysis—specifically the significance of sexual activity and condom use on age 

patterns of HIV prevalence in younger age groups—most likely indicates that these factors also play a 

role in HIV incidence patterns. The magnitude of the impact should be explored further. Results from this 

analysis also identify the interdependence of behavior across age groups on HIV prevalence patterns. 

Hence, supplementing these findings from statistical analysis with detailed dynamic modeling should be 

considered in analyzing how behavioral factors affect age patterns of HIV incidence. 
44 

Understanding the role of behavioral factors in age patterns of HIV incidence could become more 

important as countries scale up prevention efforts that include behavioral interventions.[10-13] The 

effectiveness of behavioral interventions could vary across age groups and could have short-term or longterm impacts, thus possibly altering the incidence pattern over time. 

The significance of ART on HIV prevalence patterns underscores the particular need for frequent analysis 

of HIV prevalence patterns. Dynamic models usually capture this change in prevalence from HIV 

infected persons living longer on ART, including in Spectrum where the prevalence patterns change over 

time. However, as HIV-infected persons receiving treatment live longer, it leads to a longer duration of 

exposure for onward transmission.[14, 15] Therefore, a reduction in new infections from prevention 

effects of ART could also be accompanied by delay in new infections, which could alter the incidence 

pattern over time. Changes might become more prominent with early ART and ART delivered to specific 

populations under the 2013 WHO guidelines, including pregnant women, who are likely to be younger, 

and serodiscordant couples.[16] A shift in HIV prevalence patterns over time in itself might have an 

effect on incidence patterns. Timely analysis of prevalence patterns using statistical analysis along with 

dynamic modeling to simulate reduction and delay in new infections due to prevention scale-up could 

provide a better understanding of how HIV incidence patterns could change over time. 
45 

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