DHS ANALYTICAL 

STUDIES 35

HIV Status and Cohabitation 

in Sub-Saharan Africa

DECEMBER 2013

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

Thomas Pullum and Sarah Staveteig of ICF International, Rockville, Maryland, USA.

DHS Analytical Studies No. 35 

HIV Status and Cohabitation 

in Sub-Saharan Africa 

Thomas Pullum 

Sarah Staveteig 

ICF International 

Rockville, Maryland, USA 

December 2013 

Corresponding author: Thomas Pullum, International Health and Development, ICF International, 530 

Gaither Road, Suite 500, Rockville, MD 20850, USA; Phone +1 301-572-0220; Fax +1 301-407-6501; 

Email: tom.pullum@icfi.com
Acknowledgments: Early drafts of this report benefited from contributions by Shanxiao Wang, Astou 

Coly, and Vinod Mishra. The authors are grateful to Georges Reniers for his thoughtful review. 

Editor: Sidney Moore 

Document Production: Yuan Cheng 

Maps: Thea Roy 

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: 

Pullum, Thomas, and Sarah Staveteig. 2013. HIV Status and Cohabitation in Sub-Saharan Africa. DHS 

Analytical Studies No. 35. Rockville, Maryland, USA: ICF International. 
iii 

Contents 

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

List of Figures ........................................................................................................................................... viii 

List of Maps .............................................................................................................................................. viii 

Preface ......................................................................................................................................................... ix 

Executive Summary ................................................................................................................................... xi 

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

2 Data ...................................................................................................................................................... 5 

3 HIV Prevalence and Cohabiting Partner Status .............................................................................. 9 

3.1 Association between HIV Status and Cohabiting Partner Status ................................................... 9 

3.2 General Pattern of Association by Age and Sex ......................................................................... 23

4 HIV Concordance, Discordance, Seroconversion, and Risk ......................................................... 29 

4.1 Levels of Concordance and Discordance ................................................................................... 29 

4.2 Selection and Seroconversion ..................................................................................................... 39 

4.3 Discordance and the Risk of Future Seroconversion ................................................................. 44 

5 Conclusions ........................................................................................................................................ 49 

References .................................................................................................................................................. 53 

Appendix .................................................................................................................................................... 57 

v 

List of Tables 

Table 3.1 Percentage of respondents who are HIV-positive and percentage of respondents who have 

cohabiting partners by sex, and significance level for the difference between men and 

women, according to urban-rural residence, 10 DHS surveys in sub-Saharan Africa, 2006-

2012 ............................................................................................................................................ 11 

Table 3.2 Percentage HIV-positive among men and women who do not have a cohabiting partner, 

and percentage HIV-positive among men and women who do have a cohabiting partner, 

by sex, and significance level for the difference between men and women, according to 

urban-rural residence, 10 DHS surveys in sub-Saharan Africa, 2006-2012 ............................... 15 

Table 3.3 Percentage with a cohabiting partner among men and women who are HIV-negative, and 

percentage with a cohabiting partner among men and women who are HIV-positive, by 

sex, and significance level for the difference between men and women, according to 

urban-rural residence, 10 DHS surveys in sub-Saharan Africa, 2006-2012 (restricted to 

respondents who were tested and who live in households in which both men and women 

were interviewed) ....................................................................................................................... 19 

Table 3.4 Odds ratios for HIV status and cohabiting partner status: that men and women are HIVpositive and have a cohabiting partner or HIV-negative and do not have a cohabiting 

partner, calculated separately for men and women, and significance level for the difference 

between men and women, according to urban-rural residence, 10 DHS surveys in subSaharan Africa, 2006-2012 ......................................................................................................... 22 

Table 4.1 Among cohabiting couples, the percent distribution of observed and expected couples 

(M=man, W=woman) by four combinations of couple HIV serostatus (negative 

concordance, female positive discordance, male positive discordance, and positive 

concordance), according to urban-rural residence, 10 DHS surveys in sub-Saharan Africa, 

2006-2012 (restricted to cohabiting couples in which both the man and the woman were 

tested for HIV) ............................................................................................................................ 32 

Table 4.2 HIV prevalence among men and women who have cohabiting partners, and measures of 

selection and HIV seroconversion, according to urban-rural residence, 10 DHS surveys in 

sub-Saharan Africa, 2006-2012 .................................................................................................. 36 

Table 4.3 Percentage of HIV-negative men and women who are at risk of infection from an HIVpositive cohabiting partner, and relative risk for women compared with men, by three risk 

categories, according to urban-rural residence, 10 DHS surveys in sub-Saharan Africa, 

2006-2012 ................................................................................................................................... 47 

Table A1.1 Percentage of men and women who are HIV-positive and percentage of men and women 

who have cohabiting partners, by sex, and significance level for the difference between 

men and women, according to age, 10 DHS surveys in sub-Saharan Africa, 2006-2012 .......... 57 
vi 

Table A1.2 Percentage HIV-positive among men and women who do not have a cohabiting partner, 

and percentage HIV-positive among men and women who do have a cohabiting partner, 

by sex, and significance level for the difference between men and women, according to 

age, 10 DHS surveys in sub-Saharan Africa, 2006-2012 ........................................................... 61 

Table A1.3 Percentage with a cohabiting partner among men and women who are HIV-negative, and 

percentage with a cohabiting partner among men and women who are HIV-positive, by 

sex, and significance level for the difference between men and women, according to age, 

10 DHS surveys in sub-Saharan Africa, 2006-2012 (restricted to respondents who were 

tested and who live in households in which both men and women were interviewed) .............. 65 

Table A1.4 Among cohabiting couples, the percent distribution of observed and expected couples 

(M=man, W=woman) by four combinations of couple HIV serostatus (negative 

concordance, female positive discordance, male positive discordance, and positive 

concordance), according to age (age of woman, age of man, and age difference between 

man and woman), 10 DHS surveys in sub-Saharan Africa, 2006-2012 (restricted to 

cohabiting couples in which both the man and the woman were tested for HIV) ...................... 69 

Table A1.5 HIV prevalence among men and women who have cohabiting partners, and measures of 

selection and HIV seroconversion, according to age (age of woman, age of man, and age 

difference between man and woman), 10 DHS surveys in sub-Saharan Africa, 2006-2012 ...... 77 

Table A1.6 Percentage of HIV-negative men and women who are at risk of infection from an HIVpositive cohabiting partner, and relative risk for women compared with men, by three 

categories of risk, according to age, 10 DHS surveys in sub-Saharan Africa, 2006-2012 ......... 84 

Table A2.1 Percentage of men and women who are HIV-positive and percentage of men and women 

who have cohabiting partners, by sex, and significance level for the difference between 

men and women, according to education (none, primary, secondary or higher), 10 DHS 

surveys in sub-Saharan Africa, 2006-2012 ................................................................................. 87 

Table A2.2 Percentage HIV-positive among men and women who do not have a cohabiting partner, 

and percentage HIV-positive among men and women who do have a cohabiting partner, 

by sex, and significance level for the difference between men and women, according to 

education (none, primary, secondary or higher), 10 DHS surveys in sub-Saharan Africa, 

2006-2012 ................................................................................................................................... 89 

Table A2.3 Percentage with a cohabiting partner among men and women who are HIV-negative, and 

percentage with a cohabiting partner among men and women who are HIV-positive, by 

sex, and significance level for the difference between men and women, according to 

education (none, primary, secondary or higher), 10 DHS surveys in sub-Saharan Africa, 

2006-2012 (restricted to respondents who were tested and who live in households in which 

both men and women were interviewed) .................................................................................... 91 

Table A2.4 Among cohabiting couples, the percent distribution of observed and expected couples 

(M=man, W=woman) by four combinations of couple HIV serostatus (negative 

concordance, female positive discordance, male positive discordance, and positive 
vii 

concordance), according to education (none, primary, secondary or higher), 10 DHS 

surveys in sub-Saharan Africa, 2006-2012 (restricted to cohabiting couples in which both 

the man and the woman were tested for HIV) ............................................................................ 93 

Table A2.5 HIV prevalence among men and women who have cohabiting partners, and measures of 

selection and HIV seroconversion, according to education (none, primary, secondary or 

higher), 10 DHS surveys in sub-Saharan Africa, 2006-2012 ..................................................... 97 

Table A2.6 Percentage of HIV-negative men and women who are at risk of infection from an HIVpositive cohabiting partner, and relative risk for women compared with men, by three 

categories of risk, according to education (none, primary, secondary or higher), 10 DHS 

surveys in sub-Saharan Africa, 2006-2012 ............................................................................... 101 

Table A3.1 Percentage of men and women who are HIV-positive and percentage of men and women 

who have cohabiting partners, by sex, and significance level for the difference between 

men and women, according to wealth status (quintiles: lowest, second, middle, fourth, 

highest), 10 DHS surveys in sub-Saharan Africa, 2006-2012 .................................................. 103 

Table A3.2 Percentage HIV-positive among men and women who do not have a cohabiting partner, 

and percentage HIV-positive among men and women who do have a cohabiting partner, 

by sex, and significance level for the difference between men and women, according to 

wealth status (quintiles: lowest, second, middle, fourth, highest), 10 DHS surveys in subSaharan Africa, 2006-2012 ....................................................................................................... 106 

Table A3.3 Percentage with a cohabiting partner among men and women who are HIV-negative, and 

percentage with a cohabiting partner among men and women who are HIV-positive, by 

sex, and significance level for the difference between men and women, according to 

wealth status (quintiles: lowest, second, middle, fourth, highest), 10 DHS surveys in subSaharan Africa, 2006-2012 (restricted to respondents who were tested and who live in 

households in which both men and women were interviewed) ................................................ 109 

Table A3.4 Among cohabiting couples, the percent distribution of observed and expected couples 

(M=man, W=woman) by four combinations of couple HIV serostatus (negative 

concordance, female positive discordance, male positive discordance, and positive 

concordance), according to wealth status (quintiles: lowest, second, middle, fourth, 

highest), 10 DHS surveys in sub-Saharan Africa, 2006-2012 (restricted to cohabiting 

couples in which both the man and the woman were tested for HIV) ...................................... 112 

Table A3.5 HIV prevalence among men and women who have cohabiting partners, and measures of 

selection and HIV seroconversion, according to wealth status (quintiles: lowest, second, 

middle, fourth, highest), 10 DHS surveys in sub-Saharan Africa, 2006-2012 ......................... 114 

Table A3.6 Percentage of HIV-negative men and women who are at risk of infection from an HIVpositive cohabiting partner, and relative risk for women compared with men, by three 

categories of risk, according to wealth status (quintiles: lowest, second, middle, fourth, 

highest), 10 DHS surveys in sub-Saharan Africa, 2006-2012 .................................................. 116 
viii 

List of Figures 

Figure 3.1 HIV prevalence among men and women in urban and rural areas, 10 DHS surveys in subSaharan Africa, 2006-2012 ......................................................................................................... 14 

Figure 3.2 HIV prevalence by sex and cohabiting partner status (with or without cohabiting partner), 

10 DHS surveys in sub-Saharan Africa, 2006-2012 ................................................................... 17 

Figure 3.3 HIV prevalence by five-year age interval for men and women; pooled data from 10 DHS 

surveys in sub-Saharan Africa, 2006-2012 ................................................................................. 24 

Figure 3.4 HIV prevalence by five-year age interval for men and women, with men shifted down by 

five years of age; pooled data from 10 DHS surveys in sub-Saharan Africa, 2006-2012 .......... 25 

Figure 3.5 HIV prevalence by five-year age interval for men without a cohabiting partner, men with 

a cohabiting partner, women without a cohabiting partner, and women with a cohabiting 

partner; pooled data from 10 DHS surveys in sub-Saharan Africa, 2006-2012 ......................... 27 

Figure 3.6 Number of men without a cohabiting partner, number of men with a cohabiting partner, 

number of women without a cohabiting partner, and number of women with a cohabiting 

partner, by five-year age interval; pooled data from 10 DHS surveys in sub-Saharan 

Africa, 2006-2012 ....................................................................................................................... 28 

Figure 4.1 Possible combinations of the man’s HIV status and the woman’s HIV status, their 

observed proportions (ܘ(, and their expected proportions (ܘෝ) ) if couple formation were 

independent of HIV status .......................................................................................................... 30 

Figure 4.2 HIV serostatus of cohabiting couples age 15-49, excluding cohabiting couples that are 

both HIV-negative, 10 DHS surveys in sub-Saharan Africa, 2006-2012 ................................... 31 

Figure 4.3 HIV prevalence among men and women with cohabiting partners in urban and rural areas, 

10 DHS surveys in sub-Saharan Africa, 2006-2012 ................................................................... 37 

Figure 4.4 Observed values of delta and HIV prevalence in urban and rural areas, 10 DHS surveys in 

sub-Saharan Africa, 2006-2012 .................................................................................................. 43 

List of Maps 

Map 1.1 HIV prevalence among adults age 15-49 in 10 sub-Saharan countries, 2006-2012 ..................... 3 

Map 4.1 Percentage of cohabiting couples age 15-49 that are HIV discordant, 10 DHS surveys in 

sub-Saharan Africa, 2006-2012 ................................................................................................ 334 
ix 

Preface 

One of the most significant contributions of The 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. 

DHS Program 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 

Director, The DHS Program 

xi 

Executive Summary 

The purpose of this analytical study is to advance our understanding of the role of HIV discordance in 

HIV epidemics, particularly as it relates to discordance within the subpopulation of cohabiting partners. 

HIV discordance between partners could be defined for a broader population of sexual partners, but our 

definition is the only one for which DHS data can be used. 

Data for the analysis come from DHS surveys in 10 countries in sub-Saharan Africa: Cameroon, Kenya, 

Lesotho, Malawi, Mozambique, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe. The countries 

were selected because their HIV prevalence exceeds 4%. Their most recent surveys were conducted 

between 2006 and 2012. 

Before focusing on pairs of cohabiting men and women, the study looks at patterns of HIV prevalence 

and cohabitation among men and women as individuals. In the general population represented by these 10 

surveys, HIV prevalence is higher among women than among men—typically at least 50% higher. Much 

of this excess infection can be traced to women under age 35. In a simple pooling of data, the age 

distribution of HIV prevalence peaks about five years earlier for women than for men, but otherwise the 

patterns are similar across ages 15-49. 

Living with a partner or spouse, in a cohabiting partner relationship, is more common for men than for 

women. Only in Cameroon are women significantly more likely than men to have a cohabiting partner. 

This difference is important because having a cohabiting partner is associated with HIV prevalence, but 

differently for men and women. Typically, men with a cohabiting partner have higher levels of HIV 

prevalence than men without a cohabiting partner. In contrast, women with a cohabiting partner have 

lower levels of HIV prevalence than women without a cohabiting partner. 

For men and women who do not have a cohabiting partner, the higher levels of HIV among women than 

men are of particular concern because of the magnitude of the difference. HIV prevalence is typically two 

to three times higher among women without a cohabiting partner than among men without a cohabiting 

partner. By contrast, for men and women who have a cohabiting partner, the difference in HIV prevalence 

is usually small and not statistically significant. This pattern of HIV prevalence does not necessarily mean 

that having a cohabiting partner is protective for women and risky for men, although that is a superficial 

implication. Such inferences are seriously constrained by the cross-sectional nature of the data and 

various kinds of selectivity, most importantly from the higher mortality of persons with HIV/AIDS. 

Within the subpopulation of men and women who have cohabiting partners we next match the associated 

partners into pairs (couples) and use the couple as the unit of analysis. There are four possible 

combinations of HIV status: negative concordant, positive concordant, and two types of discordance. The 

observed distribution across these four combinations can be compared with a hypothetical distribution 

derived from an assumption of independence of the partners’ HIV statuses. Under independence, the 

probabilities of the four combinations can be determined from the HIV prevalence of men and women in 

the subpopulation of cohabiting couples. The null hypothesis of independence provides a baseline for 

assessing the correspondence between the HIV status of the man and the HIV status of the woman. 

The observed data always show a statistically significant excess of concordant couples and a deficit of 

both types of discordant couples. The excess number of concordant couples could have resulted from 

indirect selection in which HIV-negative men and women tend to select one another, and HIV-positive 

men and women tend to select one another. The pattern is referred to as indirect selection because it is 

probably not explicitly based on HIV status when the cohabitation began. Most people in the survey 

countries did not know their initial HIV status or that of their partner; partner selection is typically based 
xii 

on factors such as similarity of residence (urban-rural), level of education, wealth, etc., but these factors 

are associated with HIV prevalence. Stratification in the tables has reduced the role of selection but 

probably not eliminated it. 

Another possible source of departure from the assumption of independence of the partners’ HIV statuses 

may be HIV seroconversion, through which the HIV-negative partner in a discordant pair becomes HIVpositive and the pair transitions to positive concordance. Each such infection will simultaneously reduce 

the number of discordant couples by one and increase the number of positive concordant couples by one. 

Alternatively, the relative excess of concordant couples could also arise because discordant pairs are more 

likely than concordant pairs to separate and return to the population of men and women without a 

cohabiting partner. With DHS data we are unable to distinguish between seroconversion and separation, 

but we will assume that there has been substantial seroconversion, consistent with the usual interpretation 

that the HIV-negative partner has an elevated risk of HIV infection. 

We measure the difference between the observed distribution and the expected distribution in two ways. 

The first measure is the arithmetic difference between the observed percentage of positive concordant 

pairs and the expected percentage. The amount of this excess is found to have a nearly perfect correlation 

with HIV prevalence: higher overall prevalence implies a higher level of concordance. The second 

measure is Cohen’s kappa, which could range from zero, if the partners’ HIV statuses were indeed 

independent, to a maximum of 100 if there were complete concordance. The observed kappa is almost 

always in a range from 30 to 70, with a median near 50. That is, the data are generally about halfway 

between the two extremes of independence and perfect concordance. Two measures derived from kappa 

indicate the relative deficit in each of the two discordant cells, in terms of men and women, respectively. 

Among the 10 urban and 10 rural subgroups analyzed, only three showed that men and women in 

cohabiting couples differ significantly in their HIV prevalence. In urban Kenya, women in cohabiting 

couples 1) tend to have higher HIV prevalence than men, 2) are less likely than men to be in a discordant 

partnership, and 3) appear to have seroconverted through discordance at a higher rate than men. The 

opposite pattern was seen in rural Zambia and rural Zimbabwe. These three sectors are the main 

exceptions to the finding that discordance is generally very symmetric with respect to men and women. 

The report also presents measures of the risk of future HIV infection as a result of discordance. Our 

preferred indicator of the collective risk compares HIV-negative men and women in discordant couples 

with the total population of HIV-negative men and women. By that measure, the percentage of HIVnegative individuals who have elevated risk of seroconversion because of discordance with a cohabiting 

partner ranges from 1% to 4%. The risks are similar for men and women. This type of risk is highest for 

men and women in urban Mozambique (4%), for men in urban Swaziland (4%), and for women in urban 

Zambia (4%). 
1 

1 Introduction 

In a generalized HIV epidemic it is well known that HIV transmission occurs primarily as the result of 

unprotected sexual activity between men and women. Much of the effort to reduce sexual transmission of 

the disease is directed at counseling and testing of couples, with a particular focus on serodiscordant 

couples, that is, couples in which one partner is HIV-positive and the other is HIV-negative (UNAIDS, 

2012; WHO, 2012). 

The literature indicates that many countries with HIV epidemics have marriage customs that show a 

pattern of later age at first marriage associated with a longer period of premarital sexual activity 

(Bongaarts, 2007). These findings are of concern to researchers because multiple sexual partners before, 

during, or after marriage increases the risk of HIV transmission. At the same time, there may be some 

amount of reverse causation taking place. Young people in countries with HIV epidemics may, to some 

extent, be avoiding marriage. Women in particular may perceive themselves to be at greater risk of 

contracting HIV within marriage than outside of marriage. Recent studies of female-discordant 

partnerships (i.e., the woman is HIV-positive and the man is HIV-negative) indicate that marriage is a 

risky institution for both partners. A study carried out in urban areas of Rwanda and Zambia estimated 

that 60% to 94% of recent HIV infections occurred among cohabiting couples (Dunkle et al., 2008). Data 

from DHS surveys indicate that among the sub-Saharan countries for which data are available, a median 

of less than one in four adults has ever been tested for HIV and received the test results1

 (Staveteig et al., 

2013). This low level of testing makes it unlikely that many HIV discordant couples are aware of their 

HIV status, much less take measures to avoid seroconversion (i.e., transmission of HIV from the HIVpositive partner to the HIV-negative partner). Learning more about the nature of couple discordance in 

higher-prevalence countries, even on a bivariate basis, is useful in the design of programmatic 

interventions that target persons most at risk of HIV infection. 

This report examines the association between HIV status (i.e., whether the respondent is HIV-positive or 

negative) and cohabiting partner status (i.e., whether the respondent is living (cohabiting) with a partner 

or spouse). The study uses data from 10 national sample surveys carried out in sub-Saharan Africa 

between 2006 and 2012 by the Demographic and Health Surveys (DHS) project. Basic information on 

HIV status and cohabiting partner status is collected at the time of the survey; however, the data do not 

include important information such as how long the cohabiting partner relationship has applied or which 

came first (the cohabiting partner relationship or seroconversion of one of the partners). For that reason 

we have limited the analysis to describing the association between HIV status and cohabiting partner 

status. The analytical strategy is described in detail in the report; however, because it utilizes two distinct 

perspectives it will be helpful to give an overview here. 

The first perspective treats individual men and women as the units of analysis and examines the 

association between HIV status and a simple binary version of cohabiting partner status, namely, whether 

or not the respondent has a cohabiting partner. The question here is whether a person who has a 

cohabiting partner is more likely or less likely to be HIV-positive, compared with a person who does not 

have a cohabiting partner. 

The second perspective focuses on cohabiting couples as the units of analysis and examines the 

association between HIV status of the man and HIV status of the woman. It deals exclusively with 

respondents who have a cohabiting partner and combines the man and the woman into a pair (couple). In 

 

1 Ever been tested for HIV refers to the respondent’s report of HIV testing received prior to the DHS interview. This 

testing is separate from the HIV test done as part of the DHS survey, which is the basis of the HIV status (HIVnegative or HIV-positive) analyzed in this report. 
2 

DHS and AIS surveys (i.e., standard Demographic and Health surveys and AIDS Indicator Surveys) it is 

possible to link men and women who are cohabiting partners, both of whom have been tested, and to 

classify these couples according to the HIV status of the partners. Using this information, it is possible to 

estimate the probability of each of four possible combinations of HIV status occurring in cohabiting 

couples—two types of concordance (HIV-negative/HIV-negative; HIV-positive/HIV-positive) and two 

types of discordance (HIV-negative/HIV-positive; HIV-positive/HIV-negative). The findings presented 

are based primarily on interpretation of the differences between the observed and expected distributions 

of these four combinations of HIV status in cohabiting couples. 

This report is closely related to four other DHS reports. Joy Fishel et al. (2011) looks at HIV status and 

serodiscordance in the 2009 Mozambique AIS survey, one of the ten surveys included here. That study, 

which was funded by CDC as well as USAID, focuses on whether the respondents were aware of their 

HIV status, and goes into greater detail than is feasible here. That report also includes an excellent 

overview of the literature on discordance. Staveteig et al. (2013) examines levels and trends in HIV 

testing in 29 countries in sub-Saharan Africa, including all of the countries and surveys discussed in this 

report. Gopalappa et al. (2013) describes the age and sex patterns of HIV prevalence in sub-Saharan 

Africa. MacQuarrie et al. (2013) looks at the linkage between HIV status and domestic violence in five 

countries in sub-Saharan Africa. It includes four surveys in the present report that used the domestic 

violence module. 

HIV serodiscordance is widespread among couples in sub-Saharan Africa, putting millions of uninfected 

partners at high risk of HIV infection (Grabbe and Bunnell, 2010; Malamba et al., 2005). HIV 

discordance is an inherently unstable condition. Without prevention efforts, the HIV-negative partner in a 

sexually active couple is continually at risk of becoming HIV-positive. Some individuals remain HIVnegative in spite of repeated exposure to HIV, but many do not. From a biological perspective, whether an 

individual remains uninfected after being exposed to HIV may depend on factors such as the route of 

exposure, the viral load, phenotype of virus, and genetic factors (Bienzle et al., 2000). 

Several socio-demographic, behavioral, and biomedical factors have been reported to be associated with 

HIV discordance or concordance. The presence of other sexually transmitted infections (STIs) and lack of 

awareness of antiretroviral therapies (ARTs) indicate greater risk for HIV concordance (Guthrie et al., 

2007; Ruzagira et al., 2011). Uncircumcised men are at increased risk for HIV concordance because male 

circumcision is partially protective against female-to-male transmission (Auvert et al., 2005; Babalola, 

2011; Bailey et al., 2007; Gray et al., 2007). Behavioral risk factors for concordance include higher use of 

alcohol and lower use of condoms (Guthrie et al., 2007). Employment, education, and wealth have also 

been associated with HIV concordance (Allen et al., 2003; Kamali et al., 2003). 

The finding that HIV-discordant couples in sub-Saharan Africa tend to be female positive as often, or 

more often, than male positive (de Walque, 2007; Eyawo et al., 2010) has been met with surprise. The 

finding appears to contradict conventional ideas about early marriage and monogamy among women in 

sub-Saharan Africa and about men’s propensity for premarital and extra-marital sexual activity. Yet, 

statistically speaking, a high proportion of female-positive couples would not be surprising because HIV 

prevalence is higher for women of reproductive age (15-49) than men in all 10 countries surveyed. If 

couples formed at random, more would be female positive than male positive. Similarly, the finding that a 

larger proportion of couples in countries with higher HIV prevalence are HIV-positive concordant 

(Chemaitelly et al., 2012) is statistically predictable: the joint probability of being HIV-positive 

concordant increases in proportion to increases in HIV prevalence among men and women. Both of these 

findings will be addressed and modified in this report, 

We address the inherent predictability of observed patterns in HIV discordance and concordance by 

introducing new metrics that account for expected HIV status among couples. Using data from recent 
3 

Demographic and Health Surveys (DHS) and AIDS Indicator Surveys (AIS) in 10 DHS surveys in subSaharan Africa, 2006-2012 with most recent adult HIV prevalence above 4%—Cameroon, Kenya, 

Lesotho, Malawi, Mozambique, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe—we examine 

factors associated with differences between observed and expected HIV status of cohabiting partners. The 

national prevalence for men and women combined in these 10 countries is shown in Map 1.1. 

Map 1.1 HIV prevalence among adults age 15-49 in 10 sub-Saharan countries, 2006-2012 

In this report, the couple’s HIV status is represented by four possible combinations of the man’s HIV 

status and the woman’s HIV status, as follows: (Type 1) man negative/woman negative, (Type 2) man 

negative/woman positive, (Type 3) man positive/woman negative, and (Type 4) man positive/woman 

positive. Type 1 and Type 4 couples, in which both partners are negative or both are positive, are 
4 

described as concordant (negative concordant and positive concordant, respectively). Type 2 and Type 3 

couples, in which one partner is positive and the other is negative, are described as discordant, and are of 

particular interest to HIV programs. The HIV-negative partner in a discordant cohabiting couple is 

especially vulnerable to infection. Interventions may be directed at identifying such couples and 

modifying behavior in order to reduce the probability that the negative partner will be infected by the 

positive partner. 

If couples could be followed over time, some transitions would be observed from Type 1 to Type 2 or 

Type 3 and some transitions would also be observed from Type 2 or Type 3 to Type 4. Because our data 

are only a snapshot in time, the history of such transitions is not known. When only one partner is 

seropositive, then we can be sure that the infection must have come from outside the union. When both 

partners are seropositive, it is possible that the second partner to become seropositive was infected by the 

first partner—although we do not know who was first and who was second—but it is also possible that 

both infections were from outside the union. The duration of the HIV statuses and the duration of the 

union are not known. 

There is potential selectivity in the couples that are observed. With longitudinal data, some couples would 

be lost to observation because the couple separates. New couples may form and can be of any of the four 

possible types at the time of formation. Couples may dissolve because of the death of one or both 

partners, which is more likely for partners who are HIV-positive. They may also dissolve because of 

separation/divorce. Longitudinal research by Porter et al. (2004) found that separation is more common 

among discordant couples, particularly Type 2 (man negative/woman positive), than among other 

couples. Because the data are cross-sectional, with no information about the source of infection, the 

duration of infection, the duration of the partnership, or the degree of selectivity, our analysis can only be 

descriptive. 

This report is, in large part, about the association among three characteristics of the survey respondents: 

HIV status, whether they have a cohabiting partner or spouse, and whether the respondent is male or 

female. The analysis is driven by three related sets of questions. For each set of questions, variation across 

countries and covariates are described. All inferences from these cross-sectional data should be 

considered tentative. 

Questions about individuals are the subject of Chapter 3: For individual men and women, what is the 

association between having a cohabiting partner and being HIV-positive? That is, are individuals who 

have a cohabiting partner more likely to be HIV-positive? Equally, because the sequencing is not known, 

are individuals who are HIV-positive more likely to have a cohabiting partner? Is the association between 

HIV status and cohabiting partner status the same for men and women? 

Questions about couples are the subject of Chapter 4: For men and women who are paired cohabiting 

partners (couples), do the HIV statuses of the partners tend to be the same (concordant) or different 

(discordant)? How does the degree of discordance compare with what would be expected under a model 

of independence? 

Questions about the risk of HIV transmission are also included in Chapter 4: What is the aggregate level 

of risk of HIV infection that is due specifically to HIV discordance in cohabiting partners—that is, the 

risk that an HIV-negative partner will seroconvert to HIV-positive status because of cohabiting with an 

HIV-positive partner? What is the difference between men and women in the level of risk? 

Chapters 3 and 4 include descriptions of the methodology used in the analysis and a summary of the 

findings. Residence (urban-rural) is the only covariate presented explicitly in the main tables, although 

similar tables for age, education, and wealth are included in the Appendix. 
5 

2 Data 

The study is based on nationally-representative samples of cohabiting couples from 10 recent 

Demographic and Health Surveys and AIDS Indicator Surveys in sub-Saharan Africa. These surveys, 

conducted during the period 2006-2012, collected socio-demographic and behavioral data from 

individuals and households and are representative of the population living in households. 

Except for HIV status and cohabiting partner status, which are the key outcomes, all of the variables used 

here come from the household surveys. This information includes residence (urban or rural), age (fiveyear intervals beginning with age 15, or wider intervals), education (none, primary, secondary and 

higher), and wealth status (quintiles: lowest, second, middle, fourth, highest). Residence is a cluster-level 

variable while wealth is a household-level variable; both of these variables are the same for all members 

of the same household. The four variables (residence, age, education, and wealth) are the only covariates 

in the analysis except, in the case of couples, where we include the difference between the man and the 

woman in age and education. 

The information needed to establish cohabiting partner status comes from interviews with men and 

women, rather than from the household survey itself. In all surveys, all women age 15-49 were 

interviewed. (The Uganda survey included women up to age 59, but we only include women 15-49 in the 

analysis.) The interview included questions about whether the woman had a male partner and, if so, 

whether he resided in the same household and, if so, to identify him.2

 In most of the surveys, all men 

within a specified age range were also interviewed. The interview included questions about whether the 

man had a female partner and, if so, whether she resided in the same household and, if so, to identify her. 

All men and women who could be matched were then put into a “couples” file in which the units were 

cohabiting pairs. To be accepted as a match, there had to be agreement between the woman’s 

identification of her partner and the man’s identification of his partner. 

It should be emphasized that in this report the phrase, “has a cohabiting partner,” means that a cohabiting 

partner has been identified and matched. A respondent may have a regular partner, perhaps even a spouse 

who resides de jure in the same household, but if that person is not a de facto cohabiting partner at the 

time of the survey—perhaps because of temporary work-related migration—then the respondent is 

classified as “has no cohabiting partner.” If the partner is absent, then we know virtually nothing about his 

or her characteristics. 

The degree to which cohabiting couples are representative of all couples—including couples who are 

sexual partners but are not actually cohabiting—is not explored here. However, a recent DHS study of 

serodiscordant couples in Mozambique found that married individuals who were cohabiting with their 

partner were not significantly different from married individuals who were not cohabiting with their 

partner, in terms of HIV prevalence, age, education, wealth, residence, region, and province (Fishel et al., 

2011). 

The original age range for men is not the same in all surveys. The age range for men was 15-49 for the 

surveys of Mozambique, Swaziland, and Tanzania; 15-54 for Kenya, Malawi, and Zimbabwe; and 15-59 

for Cameroon, Lesotho, Uganda, and Zambia. Variation in age ranges could have some effect on the 

matching of partners. If the upper limit for men is lower, somewhat fewer women can be matched with 

their male partners. For example, if a woman is age 48 and her partner is age 52, they would be included 

as a couple in seven of the countries but not in Mozambique, Swaziland, or Tanzania. For better 

 

2

 During data processing, the respondent’s identification (by name) is converted into a line number that can be 

matched. 
6 

correspondence between the samples of men and women, and better comparability across countries, in 

this report the ages of men are limited to 15-49. Thus, in surveys of men for which eligibility went up to 

54 or 59, men over age 49 have been dropped. This modification corresponds to the UNAIDS age range 

for HIV prevalence estimates. It should be noted that any differences between estimates of men’s HIV 

prevalence in this report and the main DHS survey report are largely because the interval used in the 

survey report was wider than 15-49.3

 

In four of the ten surveys, a subsample of men was interviewed. In the Cameroon sample, for example, 

the number of men is about half the number of women. Such subsampling was done at the level of the 

household. A variable in the household survey, hv027, was coded 1 for everyone in the household if all 

the adult men in the household were eligible to be interviewed. Otherwise, the variable was coded 0 for 

everyone in the household. In addition to Cameroon, men were subsampled in Kenya, Lesotho, and 

Malawi. 

In households with hv027=0, because no adult men were interviewed, it is impossible to identify couples. 

In those households, the woman could identify a cohabiting male partner (with a line number in the 

household roster), but this identification could not be corroborated by the man and the man’s covariates 

would be missing. This is important for comparing cohabiting partner status across countries because if 

only a subset of men were interviewed, it could appear that fewer women had partners. To adjust for this 

effect, we only use cohabiting partner data for women who were in households with hv027=1. 

Adults in sampled households and in the eligible age range were tested for HIV with dried blood spot 

(DBS) samples collected on a special filter paper using capillary blood from a finger prick. Participation 

in HIV testing was voluntary, and each selected participant was asked to provide informed consent before 

blood samples were collected. Informed consent for the interview itself was obtained separately. In each 

country, HIV testing was conducted in a central laboratory following a standard testing algorithm, 

designed to maximize the sensitivity and specificity of HIV test results, and an approved quality 

assurance and quality control plan. All HIV testing procedures were reviewed by the ethical review 

boards of ICF and the host country. In order to ensure confidentiality, the HIV test results were 

anonymously linked to individual and household questionnaire information through bar codes, after 

scrambling the household and cluster identifiers. 

More than 20 countries in sub-Saharan Africa have had recent surveys covering the same information that 

is analyzed here, including couples who can be classified as HIV concordant or discordant. This study 

focuses on surveys in 10 of the 11 countries with most recent adult HIV prevalence levels of 4% or 

greater, with datasets released prior to July 2013. The cutoff of 4% was used because surveys with lower 

prevalence levels include too few couples (particularly discordant couples) to permit useful findings. 

Throughout most of the analysis, countries are described separately, rather than being pooled, and men 

and women are described separately, rather than being pooled. As described below, in Chapter 3, there is 

 

3

 DHS data may include up to three reports of age for adults. In the household survey, the household respondent 

gives the age of every household member. This is saved as hv105 and is a basis for eligibility for the survey of 

women or the survey of men. If the man is eligible for the men’s interview, because hv105 is in the range 15-59, 

say, then he is asked about his age, under the assumption that he will tend to be more accurate than the household 

respondent. If he says he is older than 59, then the interview will not continue. If he is retained, then his report is 

saved as mv012 in the men’s survey. For women, the corresponding variable in the women’s survey is v012. There 

is not a correction of hv105. Men and women are also asked their age on the day of HIV testing, and the response is 

coded as hb1 (for men) or ha1 (for women). Following general practice for DHS reports, the age variable used in 

this report is mv012 (for men) or v012 (for women). 
7 

some multivariate analysis which includes all 10 countries and both men and women, but that analysis 

includes coefficients (fixed effects) for the countries. 

The following is a list of the 10 surveys discussed in this report, including the years of fieldwork, the type 

of survey, the specific version of the data files that was used in the analysis, and the reference for the final 

report: 

• Cameroon 2011 DHS, CM60-61 (INS and ICF International, 2012) 

• Kenya 2008-09 DHS, KE51-52 (KNBS and ICF Macro, 2010) 

• Lesotho 2009 DHS, LS60 (MOHSW and ICF Macro, 2010) 

• Malawi 2010 DHS, MW61 (NSO and ICF Macro, 2011) 

• Mozambique 2009 AIS, MZ51 (INS, INE and ICF Macro, 2010) 

• Swaziland 2006-07 DHS, SZ51-52 (CSO and Macro International, 2008) 

• Tanzania 2011-12 AIS, TZ6A (TACAIDS et al., 2013) 

• Uganda 2011 AIS, UG6A (MOH and ICF International, 2012) 

• Zambia 2007 DHS, ZM51 (CSO and Macro International, 2009) 

• Zimbabwe 2010-11 DHS, ZW62 (ZIMSTAT and ICF International, 2012) 

Final reports for all of these surveys are available on the DHS website (www.dhsprogram.com). The 

reports provide information about sample design, numbers of cases, response rates, data quality, and main 

findings, which need not be repeated here. The analysis required some merging of the data files (the PR, 

IR, MR, CR, and AR files) for each country. In the Tanzania and Mozambique surveys, a few men 

reported more than one cohabiting partner. Following standard DHS practice, those men appear in the 

couples file two or more times, once with each partner named. The repetition of those men causes a slight 

over-representation of men in polygynous unions (in the couples file but not in the file of men). The only 

plausible alternative would be to include each man only once, perhaps with the partner listed first. That 

option would lead to under-representation of women in polygynous unions (in the couples file but not in 

the file of women) and some discarding of data, which is always undesirable. 

DHS uses a two-stage cluster design that requires complex weights (hv005 in the household data) to 

adjust for differential probabilities of selection and nonresponse rates. Slightly different weights (hiv05) 

are required when using HIV data to account for higher nonresponse among subgroups, including men, 

urban residents, and more educated persons. Following DHS practice, the male partner’s weight (hiv05) 

was used during analysis of the couples’ data because of the typically higher nonresponse levels for men. 

We adjusted for the complex sample design and weights in the DHS and AIS data. 

The report contains a number of estimates of HIV prevalence that may differ slightly from one another as 

well as from estimates that appear in DHS final reports. Usually these differences will be very small, and 

well within the range of sampling error, but it may be useful to give two general reasons why even small 

discrepancies are occasionally observed. The first reason is that most of our estimates are conditioned on 

the presence of other specific variables (that is, those other variables must not be missing) and often on a 

specific value of another variable. To measure the association between HIV status and cohabiting partner 

status, both of those variables must be non-missing in the data. For example, estimated HIV prevalence 

may be slightly different for: 1) women who were tested, 2) women who were tested and for whom 
8 

cohabiting partner status is non-missing, 3) women who were tested and have a cohabiting partner, and 4) 

women who were tested and have a cohabiting partner whose HIV status is known. 

Another reason for occasional minor discrepancies in HIV prevalence estimates is that a number of 

essentially arbitrary decisions must be made during data processing. DHS general practice and our 

practice here are not always identical—although we have made every effort to be internally consistent and 

explicit. For example, as mentioned previously, it is general practice to repeat, in the DHS couples file, 

the men who have multiple cohabiting partners. We have consistently followed that practice. However, 

we are aware of at least one table in a DHS report on couples in which the man appears in only one 

couple, with the first woman listed. This restriction was not noted in that table or the text, leading us to 

differ slightly from that report’s estimate of the number of cohabiting partners. It is also normal DHS 

practice to include, in the denominator of an HIV prevalence estimate, any cases in which the test result 

was indeterminate, and to count them as HIV-negative. There are never more than a handful of such 

cases, but we found at least one survey in which this rule was not followed. In this report we have chosen 

to treat indeterminate cases as missing rather than HIV-negative, leading to potential minor differences 

from DHS reports. 

Similarly, there may be minor variations in the numbers of cases, the percentages of respondents with a 

cohabiting partner, and the levels of concordance and discordance that can be traced to the same kinds of 

sources and that have no material effect on the conclusions. 

 
9 

3 HIV Prevalence and Cohabiting Partner Status 

In this chapter, the individual respondents in the surveys of men and women are treated as the units of 

analysis and are described in terms of their HIV status and their cohabiting partner status. The goal is to 

position the men and women whose HIV status is known and who have matched cohabiting partners 

within the larger context of all men and women. HIV concordance and discordance are characteristics of 

cohabiting couples which, as units of analysis, are described in Chapter 4. The discussion of the tables 

emphasizes general patterns of association among respondents living with a cohabiting partner, HIV 

status, and the sex of respondents. There are substantial variations in HIV prevalence and cohabiting 

partner status within the surveys; these topics have been described elsewhere in detail, particularly in the 

main reports, and so are not discussed here. 

It is important to avoid any statement that HIV status is a consequence of having a cohabiting partner, or 

that having a cohabiting partner is a consequence of HIV status. The data do not allow us to infer 

causation. We will establish in this report that there is typically a strong association between HIV status 

and cohabiting partner status, but the association is usually different for men and for women. 

A woman age 15-49 has a cohabiting partner if she is in a union with a man age 15-49 who lives in the 

same household and was also interviewed. Such a woman will appear in the couples subsample and is 

assigned a cohabiting partner status code of CP=1. If she does not have a cohabiting partner, or if her 

partner is older than 49, or is absent, or did not consent to HIV testing, then CP=0. A woman can have a 

missing code for CP. If she was in one of the randomly selected households in which men were not 

interviewed, then we do not know whether she had a cohabiting partner. For men, CP is never missing. If 

a respondent is in a union but their partner is not a de facto member of the same household or is outside 

the age range, then they will be coded as not having a cohabiting partner. As a result, there is probably a 

bias in the direction of underestimating the percentage of individuals with a cohabiting partner. 

3.1 Association between HIV Status and Cohabiting Partner Status 

Methods 

In this chapter to repeat, the units of analysis are individual men and women, and the interest is in the 

association among three binary characteristics: sex of the respondent; whether the respondent is HIVpositive or HIV-negative; and whether the respondent does or does not have a cohabiting partner. 

Detailed results for each of the 10 surveys are presented in Tables 3.1-3.4, according to urban-rural 

residence; additional tables for age, education, and wealth are presented in the Appendix. The association 

is described with conditional probabilities and odds ratios. Statistical tests are provided. The methodology 

used in this chapter will be familiar to most readers. 

The analysis compares the propensity of men and women to be in a union, although such comparison 

must be done carefully. Say, for example, in a DHS survey there are M men and W women, subsets of 

whom have been matched in C couples. We could calculate the propensity of men to be in a couple as 

C/M, and the propensity of women to be in a couple as C/W. The relative propensity, for men versus 

women, say, would then be (C/M)/(C/W) = W/M, a ratio that does not actually depend at all on the value 

of C, and is therefore useless. (The difference between the two propensities would not have this defect, 

and the ratios given in this chapter do not have this defect.) There are subtle relationships among M, W, 

and C, and their pattern of proportionality has been debated by demographers (see, for example, 

Matthews, 2012 and Schoen, 1981). 

 
10 

Findings 

This section focuses on the four main tables (Tables 3.1-3.4), with columns for men and women and rows 

for the total, urban, and rural populations in each survey. Tables 3.1, 3.2, and 3.3 include corresponding 

percentages for men and women, and then significance levels for the difference between men and women. 

To simplify the presentation, labels for significance levels indicate both direction and strength. If, in a 

pair of percentages, the percentage is higher for men than for women, the label begins with “M.” If higher 

for women, it begins with “W.” The p-value is indicated by one, two, or three “+” signs. A single “+” 

indicates that the area in the tail of the sampling distribution of the test statistics, on the side represented 

by “M” or “W,” is less than .025. A double “++” indicates that the area is less than .005, and a triple 

“+++” indicates that it is less than .0005. Thus, the labels match with the conventional labels “*”, “**”, 

and “***”, for .05, .01, and .001 two-tailed tests, respectively. These will be referred to as .05, .01, and 

.001 tests, but the labels also indicate the direction by beginning with “M” or “W”. Table 3.4 shows 

corresponding direction and strength with the labels “+”, “++”, “+++”, and “-”. “--”. “---” indicating 

whether an odds ratio is significantly greater than 1.00 or significantly less than 1.00. The purpose of 

these labels is to make it easier to scan large numbers of coefficients rapidly. The tables include weighted 

Ns, which consist of the total number of cases in the cells that are being compared with a percentage 

difference or a ratio. All calculations use the sample weights and all test statistics are robust and adjusted 

for clustering.4

Table 3.1 shows the percentages of men and women in the selected surveys who are HIV-positive and the 

percentages that have a cohabiting partner. The percentages are based on the maximum available number 

of cases: all persons who received an HIV test and all persons who were in a household that included 

hv027 (adult men in the household eligible to be interviewed), respectively. The remaining tables in this 

chapter, however, require information about both the HIV test results and the cohabiting partner status, 

and therefore include fewer cases. 

 

4

 We do not include a survey adjustment for strata when calculating statistical significance. When that adjustment is 

included test statistics tend to become unstable because of small cell sizes. 
Table 3.1 Percentage of respondents who are HIV-positivea and percentage of respondents who have cohabiting partnersb by sex, and 

significance level for the difference between men and women, according to urban-rural residence, 10 DHS surveys in sub-Saharan Africa, 2006-2012 

Survey Category 

Percent HIV-positive Percent with a cohabiting partner 

Men Women 

W/M 

ratio N Sig. Men Women 

W/M 

ratio N Sig. 

Cameroon 2011 DHS All 2.9 5.6 193 13,449 W+++ 34.5 40.1 116 13,449 W+++ Cameroon 2011 DHS Urban 3.0 6.4 212 7,256 W+++ 28.0 31.9 114 7,256 W+++ Cameroon 2011 DHS Rural 2.7 4.7 171 6,194 W+++ 42.4 49.4 116 6,194 W+++ Kenya 2008-09 DHS All 4.3 8.1 189 6,734 W+++ 39.0 35.3 90 6,734 M++ Kenya 2008-09 DHS Urban 3.6 10.6 290 1,617 W+++ 44.5 41.5 93 1,617 ns Kenya 2008-09 DHS Rural 4.5 7.4 163 5,116 W+++ 37.2 33.4 90 5,116 M+ Lesotho 2009 DHS All 17.9 26.7 149 6,567 W+++ 26.2 22.1 84 6,567 M+++ Lesotho 2009 DHS Urban 21.3 31.1 146 1,940 W+++ 31.3 21.6 69 1,940 M+++ Lesotho 2009 DHS Rural 16.7 24.7 148 4,627 W+++ 24.4 22.3 91 4,627 M+ Malawi 2010 DHS All 8.1 12.8 159 13,528 W+++ 50.8 48.7 96 13,528 M+ Malawi 2010 DHS Urban 12.0 22.7 190 2,711 W+++ 38.0 39.2 103 2,711 ns Malawi 2010 DHS Rural 7.0 10.5 149 10,817 W+++ 54.2 51.0 94 10,817 M++ Mozambique 2009 AIS All 9.1 13.1 143 9,100 W+++ 59.1 53.4 91 9,100 M+++ Mozambique 2009 AIS Urban 12.8 18.5 144 2,969 W+++ 44.5 43.0 97 2,969 ns Mozambique 2009 AIS Rural 7.2 10.7 149 6,132 W+++ 66.9 58.1 87 6,132 M+++ Swaziland 2006-07 DHS All 19.5 31.0 159 8,210 W+++ 18.9 15.7 83 8,210 M+++ Swaziland 2006-07 DHS Urban 25.5 36.8 144 2,072 W+++ 22.3 19.3 87 2,072 M+ Swaziland 2006-07 DHS Rural 17.4 29.1 167 6,138 W+++ 17.6 14.5 82 6,138 M+++ Tanzania 2011-12 AIS All 3.8 6.2 162 17,711 W+++ 46.6 36.9 79 17,711 M+++ Tanzania 2011-12 AIS Urban 5.2 9.0 173 4,561 W++ 37.5 28.1 75 4,561 M+++ Tanzania 2011-12 AIS Rural 3.4 5.1 153 13,149 W+++ 49.6 40.1 81 13,149 M+++ Uganda 2011 AIS All 6.1 8.3 137 19,562 W+++ 47.6 41.6 87 19,562 M+++ Uganda 2011 AIS Urban 6.1 10.6 174 4,013 W+++ 34.4 27.6 80 4,013 M+++ Uganda 2011 AIS Rural 6.1 7.7 127 15,549 W+++ 50.9 45.4 89 15,549 M+++ Zambia 2007 DHS All 12.2 15.9 131 10,337 W+++ 47.3 45.6 96 10,337 ns 

(Continued...) 

11 
Table 3.1. – Continued

Survey Category 

Percent HIV-positive Percent with a cohabiting partner 

Men Women 

W/M 

ratio N Sig. Men Women 

W/M 

ratio N Sig. 

Zambia 2007 DHS Urban 16.0 23.0 144 4,258 W+++ 35.5 36.4 103 4,258 ns Zambia 2007 DHS Rural 9.5 11.0 116 6,079 ns 55.6 52.0 94 6,079 M++ Zimbabwe 2010-11 DHS All 12.2 17.7 145 13,669 W+++ 40.5 34.4 85 13,669 M+++ Zimbabwe 2010-11 DHS Urban 12.9 19.5 151 4,667 W+++ 37.6 28.4 76 4,667 M+++ Zimbabwe 2010-11 DHS Rural 11.9 16.8 141 9,002 W+++ 42.2 38.2 91 9,002 M+++ Note: All calculations use the sample weights and all test statistics are robust and adjusted for clustering. a: restricted to respondents who were tested b: restricted to respondents in households in which both men and women were interviewed Ratios are calculated as 100*(% for men)/(% for women) n is weighted Significance refers to a test of the null hypothesis that men and women have the same HIV prevalence or the same cohabiting partnership rates M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

12 
13 

Inferences about HIV prevalence that can be drawn from Table 3.1 are well known but will be restated 

here to provide context. Looking at the panel on the left side of the table, and the lines for each country 

(All) and for urban and rural areas, HIV prevalence is always higher for women than for men and almost 

always higher in urban areas than in rural areas. The percentage of women who are HIV-positive is as 

high as one and a half times the percentage of men who are HIV-positive. The greatest imbalance is in 

urban Kenya, where HIV prevalence among women (11%) is more than twice that among men (4%). 

With just two exceptions, the excess for women is significant at the .001 level in all countries. The 

exceptions are urban Tanzania, significant at the .01 level, and rural Zambia, where the excess is not 

significant. In every survey, for both men and women—except for men in Kenya—HIV prevalence is 

higher in urban areas than in rural areas. With the exception of Swaziland, the ratio of female to male 

HIV prevalence is always somewhat higher in urban areas than in rural areas. 

Patterns of HIV prevalence by background characteristics (age, education, and wealth) are shown in the 

Appendix (Tables A1.1, A2.1 and A3.1, respectively). In general, the patterns observed in Table 3.1 are 

repeated for the categories of education and wealth, except that HIV prevalence is low for men and 

women with no education and the pattern is more pronounced for the higher wealth quintiles. The pattern 

by age is more nuanced. Table A1.1 shows that the age pattern is similar for men and women, both for 

people who have a cohabiting partner and for people who do not have a cohabiting partner. However, 

there is one important difference: comparing men and women, the age distribution of HIV prevalence 

shifts to the right for men. That is, prevalence tends to peak about five years later for men than for 

women. This same pattern is seen for men and women with cohabiting partners and men and women 

without cohabiting partners. Indeed, even the magnitude of HIV prevalence is similar for men and women 

who have a cohabiting partner and for those who do not. The principal difference between men and 

women who have the same cohabiting partner status is the five-year age shift. The shift of approximately 

five years is presumably related to the tendency for men to be older than their partners—whether 

cohabiting or not (cf. Glynn et al., 2001; Gregson et al., 2002; Kelly et al., 2003; Luke and Kurz, 2002). 

The relationship with age will be described further in section 3.2. 

Returning to Table 3.1, the panel on the right side of the table shows the percentages of men and women 

who have a cohabiting partner. There is considerable variation across surveys but, overall, the medians 

are 44% for men and 38% for women. Variations in cohabiting partner status are important for assessing 

the level of risk of HIV infection from a partner. The lowest levels of cohabiting partners are in rural 

Swaziland, and the highest levels are in rural Mozambique. In most countries, levels of cohabiting 

partners are somewhat higher in rural areas than in urban areas for both men and women. In Kenya and 

Swaziland, the pattern is reversed: having a cohabiting partner is less likely in rural areas than in urban 

areas. In Lesotho, having a cohabiting partner is less likely for men in rural areas but not for women in 

rural areas. Patterns of cohabiting partner status by age, education, and wealth are shown in the Appendix 

(Tables A3.1, A3.2 and A3.3, respectively). 

The most consistent pattern observed is that having a cohabiting partner is more common among men 

than among women. Expressed as a ratio of the percentage for women with a cohabiting partner divided 

by the percentage of men with a cohabiting partner—or, the relative risk of having a cohabiting partner—

the disparity between men and women is greatest in urban Lesotho where the level of cohabiting partners 

for women is just 70% of the level for men. 

To summarize, Table 3.1 shows two strong patterns regarding HIV status and cohabiting partner status for 

men and women in the data from 10 sub-Saharan countries: 1) women are more likely than men to be 

HIV-positive, and 2) women are less likely than men to have a cohabiting partner. Figure 3.1 shows HIV 

prevalence among men and women, by rural-urban residence (blue dots: urban, red dots: rural) for the 10 

sub-Saharan countries. The dots are all to the right of the line of equality. 
14 

Figure 3.1 HIV prevalence among men and women in urban and rural areas, 10 DHS surveys in 

sub-Saharan Africa, 2006-2012. Note: The vertical and horizontal axes show the HIV prevalence of 

men and women, respectively. The diagonal line indicates equal prevalence for men and women. 

The remainder of Chapter 3 focuses on men and women for whom information on both HIV status and 

cohabiting partner status was obtained. Table 3.2 shows HIV status by sex of the respondent and whether 

the respondent has a cohabiting partner, according to urban-rural residence. (It is an expansion of the left 

panel of Table 3.1, which did not consider partnership status.) In Table 3.2, the left panel (first four 

columns) shows men and women who do not have a cohabiting partner, while the right panel (next four 

columns) shows men and women who do have a cohabiting partner. 
Table 3.2 Percentage HIV-positive among men and women who do not have a cohabiting partner, and percentage HIV-positive among 

men and women who do have a cohabiting partner, by sex, and significance level for the difference between men and women, according to urban-rural residence, 10 DHS surveys in sub-Saharan Africa, 2006-2012

Survey Category 

No cohabiting partner: % HIV-positive Has cohabiting partner: % HIV-positive 

Men Women 

W/M 

ratio N Sig. Men Women 

W/M 

ratio N Sig. 

Cameroon 2011 DHS All 1.9 6.2 333 8,404 W+++ 4.8 4.6 96 5,046 ns Cameroon 2011 DHS Urban 1.8 6.3 343 5,074 W+++ 6.0 6.5 109 2,182 ns Cameroon 2011 DHS Rural 1.9 6.1 318 3,330 W+++ 3.8 3.2 83 2,864 ns Kenya 2008-09 DHS All 3.4 8.9 260 4,250 W+++ 5.7 6.6 117 2,483 ns Kenya 2008-09 DHS Urban 3.9 11.8 301 924 W++ 3.3 8.9 268 693 W++ Kenya 2008-09 DHS Rural 3.3 8.2 249 3,326 W+++ 6.6 5.8 88 1,790 ns Lesotho 2009 DHS All 13.9 27.0 195 5,004 W+++ 29.2 25.6 88 1,563 ns Lesotho 2009 DHS Urban 15.9 31.9 200 1,450 W+++ 33.0 28.4 86 490 ns Lesotho 2009 DHS Rural 13.2 24.7 187 3,554 W+++ 27.4 24.4 89 1,074 ns Malawi 2010 DHS All 5.3 15.3 288 6,806 W+++ 10.7 10.2 95 6,722 ns Malawi 2010 DHS Urban 8.3 26.3 319 1,665 W+++ 18.0 16.9 94 1,046 ns Malawi 2010 DHS Rural 4.3 12.1 281 5,141 W+++ 9.4 9.0 96 5,676 ns Mozambique 2009 AIS All 7.8 17.1 219 4,021 W+++ 10.1 9.6 95 5,080 ns Mozambique 2009 AIS Urban 11.0 20.7 189 1,672 W+++ 15.1 15.6 103 1,296 ns Mozambique 2009 AIS Rural 5.0 14.9 298 2,348 W+++ 8.2 7.6 92 3,783 ns Swaziland 2006-07 DHS All 15.6 29.9 191 6,807 W+++ 36.2 36.9 102 1,402 ns Swaziland 2006-07 DHS Urban 21.3 35.2 165 1,643 W+++ 40.2 43.3 108 429 ns Swaziland 2006-07 DHS Rural 13.7 28.3 206 5,164 W+++ 34.4 34.1 99 973 ns Tanzania 2011-12 AIS All 2.8 7.1 260 10,452 W+++ 5.0 4.5 89 7,258 ns Tanzania 2011-12 AIS Urban 3.9 10.1 261 3,107 W+++ 7.4 6.3 85 1,455 ns Tanzania 2011-12 AIS Rural 2.3 5.9 256 7,346 W+++ 4.4 4.0 90 5,804 ns Uganda 2011 AIS All 5.4 9.7 179 10,908 W+++ 6.8 6.4 94 8,654 ns Uganda 2011 AIS Urban 4.4 11.7 264 2,793 W+++ 9.3 7.9 85 1,220 ns Uganda 2011 AIS Rural 5.7 9.0 157 8,115 W+++ 6.4 6.2 96 7,434 ns Zambia 2007 DHS All 9.2 18.4 201 5,546 W+++ 15.5 13.0 84 4,791 M++ 

(Continued...) 

15 
Table 3.2 – Continued

Survey Category 

No cohabiting partner: % HIV-positive Has cohabiting partner: % HIV-positive 

Men Women 

W/M 

ratio N Sig. Men Women 

W/M 

ratio N Sig. 

Zambia 2007 DHS Urban 11.3 22.7 201 2,724 W+++ 24.7 23.7 96 1,534 ns Zambia 2007 DHS Rural 7.1 14.5 206 2,822 W+++ 11.4 7.7 68 3,257 M+++ Zimbabwe 2010-11 DHS All 8.7 19.3 222 8,535 W+++ 17.3 14.8 86 5,134 M++ Zimbabwe 2010-11 DHS Urban 9.2 20.6 224 3,151 W+++ 19.0 16.8 88 1,516 ns Zimbabwe 2010-11 DHS Rural 8.4 18.5 220 5,385 W+++ 16.6 14.0 85 3,618 M++ Note: Restricted to respondents who were tested and who lived in households in which both men and women were interviewed. Ratios are calculated as 100*(% for men)/(% for women) N is weighted Significance refers to a test of the null hypothesis that men and women have the same HIV prevalence, controlling for partnership status (no cohabiting partner or has cohabiting partner) M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

16 
17 

Figure 3.2 presents national levels of HIV prevalence for men and women by cohabiting partner status 

(from Table 3.2). In every country, men are more likely to be HIV-positive if they have a cohabiting 

partner. Among women, the pattern is the opposite: in every country except Swaziland, women without a 

cohabiting partner are more likely to be HIV-positive than women with a cohabiting partner. These 

findings are correlative rather than causal and may relate to patterns of widowhood, divorce, and 

remarriage among men and women in these countries (de Walque and Kline, 2012) rather than to any 

protective effect of marriage. 

Figure 3.2 HIV prevalence by sex and cohabiting partner status (with or without cohabiting 

partner), 10 DHS surveys in sub-Saharan Africa, 2006-2012 

Women’s HIV prevalence is higher than men’s, in the general population and whether or not they have a 

cohabiting partner. However, as Figure 3.2 illustrates, the disparity in HIV prevalence between women 

and men is much smaller among women with a cohabiting partner than among those without a cohabiting 

partner. Table 3.2 presents the survey results on HIV prevalence for men and women by cohabiting 

partner status, according to urban-rural residence. The left panel of Table 3.2 shows that among men and 

women who do not have a cohabiting partner, women are much more likely than men to be HIV-positive 

and the difference is statistically significant at the .001 level. This corresponds with the higher levels of 

HIV prevalence among women in the general population. 

The right panel of Table 3.2 shows that among men and women who do have a cohabiting partner there is 

almost never a significant difference between their levels of HIV prevalence, with three exceptions: rural 

men in Zambia and Zimbabwe with cohabiting partners have significantly higher HIV prevalence than 

**

**

**

**

**

**

**

*

**

**

**

**

**

**

*

**

**

40.0 30.0 20.0 10.0 0.0 10.0 20.0 30.0 40.0 50.0

Swaziland 2006-07

 Lesotho 2009

 Zimbabwe 2010-11

 Zambia 2007

 Malawi 2010

 Mozambique 2009

 Uganda 2011

 Kenya 2008-09

 Tanzania 2011-12

 Cameroon 2011

HIV Prevalence (%)

Women without cohabiting

Women with cohabiting1 partner

Men without cohabiting partner

Men with cohabiting1 partner(s)

1. Respondent identifies someone in the household as a husband/wife/partner, and the partner was also interviewed separately and identified 

respondent as husband/wife/partner, and both partners consented to an HIV test and had valid results.between all/matched: **=p<.01, *=p<.05
18 

rural women with cohabiting partners; and, only in urban Kenya, women have significantly higher HIV 

prevalence than their male counterparts—by a margin of more than 2:1. Presumably, the overall lack of 

significant difference between men and women with cohabiting partners reflects some degree of 

seroconversion and perhaps of homogamy among cohabiting couples, a possibility to be discussed more 

in the next chapter. 

HIV prevalence tends to be slightly lower for men with no cohabiting partner than for all men, and 

slightly higher for women with no cohabiting partner than for all women. As a result, the ratio of female 

to male HIV prevalence among those with no cohabiting partner is higher than that in the general 

population. There are many surveys in which prevalence for women is more than twice that for men (that 

is, the ratio is >200). All of the ratios (or differences) are statistically significant at the .001 level. 

Table 3.3 shows another way to examine the association between HIV status and cohabiting partner 

status. It looks at the cohabiting partner rate (the percentage of men or women with a cohabiting partner) 

separately for HIV-negative and HIV-positive men and women. The most consistent pattern of 

differentials is for men and women who are HIV-positive (Table 3.3, right panel). For them, men have a 

much higher cohabiting partner rate than women. In urban Kenya, the difference follows the general 

pattern but is not significant, perhaps only because of the smaller sample size. 

Among men and women who are HIV-negative, the percentage of men who have a cohabiting partner is 

almost always the same as, or larger than, the percentage of women who have a cohabiting partner, but 

the difference is often less than for HIV-positive men and women, and often the difference is not 

significant. Urban Malawi, urban Zambia, and urban and rural Cameroon are the only subgroups in which 

HIV-negative women are significantly more likely than HIV-negative men to have a cohabiting partner. 

In Appendix Table A2.3 (education) and Table A3.3 (wealth), Cameroon is the principal exception. 

There is a general pattern of association among HIV status, cohabiting partner status, and sex of 

respondents that can be seen in Table 3.2 and Table 3.3; that is, the combination of having a cohabiting 

partner and being HIV-positive is much more common among men than among women. Conversely, the 

combination of not having a cohabiting partner and being HIV-positive is more common among women 

than among men. This trend is consistent with observations from analyses of DHS survey data that found: 

1) elevated HIV prevalence among formerly married women, and 2) higher proportions of remarriage 

among formerly married men than among formerly married women (de Walque and Kline, 2012). 
Table 3.3 Percentage with a cohabiting partner among men and women who are HIV-negative, and percentage with a cohabiting partner 

among men and women who are HIV-positive, by sex, and significance level for the difference between men and women, according tourban-rural residence, 10 DHS surveys in sub-Saharan Africa, 2006-2012 (restricted to respondents who were tested and who live in 

households in which both men and women were interviewed)

Survey Category 

HIV-negative: % with a cohabiting partner HIV-positive: % with a cohabiting partner 

Men Women 

W/M 

ratio N Sig. Men Women 

W/M 

ratio N Sig. 

Cameroon 2011 DHS All 33.8 40.5 120 12,868 W+++ 57.7 33.2 58 581 M+++ Cameroon 2011 DHS Urban 27.1 31.9 117 6,909 W+++ 56.2 32.8 58 347 M++ Cameroon 2011 DHS Rural 41.9 50.1 120 5,960 W+++ 59.7 33.9 57 234 M+++ Kenya 2008-09 DHS All 38.5 35.8 93 6,298 ns 51.4 28.8 56 436 M+++ Kenya 2008-09 DHS Urban 44.6 42.3 95 1,497 ns 40.4 35.0 87 120 ns Kenya 2008-09 DHS Rural 36.4 34.0 93 4,801 ns 54.4 26.2 48 316 M+++ Lesotho 2009 DHS All 22.6 22.4 99 5,058 ns 42.7 21.1 50 1,509 M+++ Lesotho 2009 DHS Urban 26.6 22.5 84 1,408 ns 48.5 19.7 41 532 M+++ Lesotho 2009 DHS Rural 21.2 22.4 105 3,650 ns 40.1 22.0 55 978 M+++ Malawi 2010 DHS All 49.3 50.2 102 12,087 ns 67.4 38.8 58 1,441 M+++ Malawi 2010 DHS Urban 35.4 42.1 119 2,236 W++ 57.2 29.2 51 476 M+++ Malawi 2010 DHS Rural 52.8 51.9 98 9,852 ns 72.0 43.7 61 965 M+++ Mozambique 2009 AIS All 58.5 55.6 95 8,061 ns 65.0 39.2 60 1,040 M+++ Mozambique 2009 AIS Urban 43.3 44.5 103 2,496 ns 52.6 36.2 69 473 M+++ Mozambique 2009 AIS Rural 66.1 60.1 91 5,565 M++ 76.9 41.5 54 566 M+++ Swaziland 2006-07 DHS All 15.0 14.3 96 6,081 ns 35.0 18.6 53 2,128 M+++ Swaziland 2006-07 DHS Urban 17.9 17.3 97 1,416 ns 35.2 22.8 65 656 M+++ Swaziland 2006-07 DHS Rural 14.0 13.5 96 4,665 ns 34.9 17.0 49 1,473 M+++ Tanzania 2011-12 AIS All 46.0 37.6 82 16,795 M+++ 61.4 26.7 44 915 M+++ Tanzania 2011-12 AIS Urban 36.6 28.9 79 4,221 M+++ 53.6 19.6 37 341 M+++ Tanzania 2011-12 AIS Rural 49.1 40.6 83 12,575 M+++ 65.4 31.2 48 575 M+++ Uganda 2011 AIS All 47.3 42.5 90 18,123 M+++ 53.4 32.0 60 1,439 M+++ Uganda 2011 AIS Urban 33.2 28.4 85 3,662 M++ 52.5 20.5 39 351 M+++ Uganda 2011 AIS Rural 50.7 46.1 91 14,461 M+++ 53.7 36.2 67 1,088 M+++ 

(Continued...) 

19 
Table 3.3 – Continued

Survey Category 

HIV-negative: % with a cohabiting partner HIV-positive: % with a cohabiting partner 

Men Women 

W/M 

ratio N Sig. Men Women 

W/M 

ratio N Sig. 

Zambia 2007 DHS All 45.5 47.2 104 8,866 ns 60.2 37.1 62 1,471 M+++ Zambia 2007 DHS Urban 31.8 36.2 114 3,412 W++ 54.6 37.5 69 846 M+++ Zambia 2007 DHS Rural 54.4 53.9 99 5,453 ns 66.8 36.4 54 625 M+++ Zimbabwe 2010-11 DHS All 38.8 36.2 93 11,562 M++ 58.2 29.2 50 2,107 M+++ Zimbabwe 2010-11 DHS Urban 35.3 29.8 85 3,880 M+++ 55.8 24.8 45 787 M+++ Zimbabwe 2010-11 DHS Rural 40.4 39.6 98 7,682 ns 59.4 32.0 54 1,320 M+++ Notes: Restricted to respondents who were tested and who lived in households in which both men and women were interviewed. Ratios are calculated as 100*(% for men)/(% for women) N is weighted Significance refers to a test of the null hypothesis that men and women have the same cohabiting partnership rates, controlling for HIV status (negative or positive) M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

20 
21 

Table 3.4 describes the association between HIV status and cohabiting partner status using odds ratios, 

i.e., the odds ratio of HIV status and cohabiting partner status. The data are shown separately for men (left 

panel) and for women (right panel). An odds ratio greater than one indicates a positive association: 

people with cohabiting partners tend to be HIV-positive and people without cohabiting partners tend to be 

HIV-negative. An odds ratio less than one indicates that people with cohabiting partners tend to be HIVnegative and people without cohabiting partners tend to be HIV-positive. 

For both men and women, there are several areas in which the associations are not significant, in either a 

positive or a negative direction. In general, the association between HIV status and cohabiting partner 

status is positive for men and negative or not significant for women. For men, the exceptions are nonsignificance in Kenya, Mozambique, urban Tanzania, and rural Uganda. For women, the outstanding 

exception is Swaziland, where there is a strong positive association (although it is only statistically 

significant when the urban and rural areas are combined). For women, the odds ratios are not significant 

in urban Cameroon, Kenya, Lesotho, Swaziland, and urban Zambia. Otherwise, except for Swaziland, 

they are negative. 

Table 3.4 describes the association between HIV status and cohabiting partner status, separately for men 

and women. The association is measured with an odds ratio (OR) that can be interpreted in either of two 

ways: 

	 = 	݋݅ݐܽݎ	ݏܱ݀݀

ܱ݀݀ݏ)being	HIV-positive	|	has	a	cohabiting	partner)

ܱ݀݀ݏ)being	HIV-positive	|	no	cohabiting	partner)

or 

	 = 	݋݅ݐܽݎ	ݏܱ݀݀

ܱ݀݀ݏ)having	cohabiting	partner	|	HIV-positive)

ܱ݀݀ݏ)having	cohabiting	partner	|	HIV-negative)

These two expressions may appear to be different, but algebraically they are the same. The odds ratio can 

be calculated from either of two logit regressions. Logit regression provides an easy mechanism for 

adjusting for sample weights and clustering and producing standard errors and test statistics. If HIV status 

(0/1) is regressed on cohabiting partner status (0/1)—or the other way around—the slope coefficient will 

be the same, and its exponential will be the odds ratio. 

In Table 3.4, the panel on the left is for men and the panel on the right is for women. For men, the odds 

ratios are almost always very significantly greater than 1. The odds ratio is often greater than 2, as in 

Lesotho, Malawi, and Zimbabwe, and even greater than 3 in rural Swaziland. That is, of the four possible 

combinations of HIV status and cohabiting partner status, the dominant combinations for men are: 1) 

HIV-positive and does have a cohabiting partner and 2) HIV-negative and does not have a cohabiting 

partner. The other two combinations are substantially less common. 

Among women, the association is generally very different. The only setting in which the odds ratio is 

positive is in Swaziland, where it was also strongly positive among men. Urban Zambia and urban 

Cameroon show odds ratios of slightly more than 1, but not significantly greater. Otherwise, the odds 

ratio is less than 1 (the association is negative) among both urban and rural women, even when it is not 

significantly less than 1. Thus, for women, the dominant combinations of HIV status and cohabiting 

partner status are the reverse of those for men: 1) HIV-positive and does not have a cohabiting partner and 

2) HIV-negative and does have a cohabiting partner. 
22 

Table 3.4 Odds ratios for HIV status and cohabiting partner status: that men and women are HIVpositive and have a cohabiting partner or HIV-negative and do not have a cohabiting partner, 

calculated separately for men and women, and significance level for the difference between men 

and women, according to urban-rural residence, 10 DHS surveys in sub-Saharan Africa, 2006-2012 

Survey Category 

Men Women 

Odds ratio N Sig. Odds ratio N Sig. 

Cameroon 2011 DHS All 2.7 6,226 +++ 0.7 7,223 - 

Cameroon 2011 DHS Urban 3.4 3,427 +++ 1.0 3,829 ns 

Cameroon 2011 DHS Rural 2.0 2,799 ++ 0.5 3,394 --- 

Kenya 2008-09 DHS All 1.7 2,899 + 0.7 3,835 ns 

Kenya 2008-09 DHS Urban 0.8 737 ns 0.7 880 ns 

Kenya 2008-09 DHS Rural 2.1 2,162 + 0.7 2,955 ns 

Lesotho 2009 DHS All 2.6 2,779 +++ 0.9 3,789 ns 

Lesotho 2009 DHS Urban 2.6 732 +++ 0.9 1,208 ns 

Lesotho 2009 DHS Rural 2.5 2,047 +++ 1.0 2,581 ns 

Malawi 2010 DHS All 2.1 6,206 +++ 0.6 7,322 --- 

Malawi 2010 DHS Urban 2.4 1,296 +++ 0.6 1,415 - 

Malawi 2010 DHS Rural 2.3 4,910 +++ 0.7 5,907 -- 

Mozambique 2009 AIS All 1.3 3,849 ns 0.5 5,252 --- 

Mozambique 2009 AIS Urban 1.5 1,343 ns 0.7 1,626 - 

Mozambique 2009 AIS Rural 1.7 2,506 ns 0.5 3,625 --- 

Swaziland 2006-07 DHS All 3.1 3,621 +++ 1.4 4,589 ++ 

Swaziland 2006-07 DHS Urban 2.5 946 +++ 1.4 1,126 ns 

Swaziland 2006-07 DHS Rural 3.3 2,675 +++ 1.3 3,462 ns 

Tanzania 2011-12 AIS All 1.9 7,438 +++ 0.6 10,273 --- 

Tanzania 2011-12 AIS Urban 2.0 1,846 ns 0.6 2,715 ns 

Tanzania 2011-12 AIS Rural 2.0 5,592 +++ 0.7 7,557 -- 

Uganda 2011 AIS All 1.3 8,558 + 0.6 11,004 --- 

Uganda 2011 AIS Urban 2.2 1,687 ++ 0.6 2,326 - 

Uganda 2011 AIS Rural 1.1 6,871 ns 0.7 8,678 --- 

Zambia 2007 DHS All 1.8 4,674 +++ 0.7 5,663 --- 

Zambia 2007 DHS Urban 2.6 1,930 +++ 1.1 2,329 ns 

Zambia 2007 DHS Rural 1.7 2,744 +++ 0.5 3,335 --- 

Zimbabwe 2010-11 DHS All 2.2 5,769 +++ 0.7 7,900 --- 

Zimbabwe 2010-11 DHS Urban 2.3 1,862 +++ 0.8 2,805 - 

Zimbabwe 2010-11 DHS Rural 2.2 3,907 +++ 0.7 5,095 --- 

Note: Restricted to respondents who were tested and who lived in households in which both men and women were 

interviewed. 

An odds ratio greater than 1.00 indicates that the combination of being HIV-positive and having a cohabiting 

partner (or, conversely, being HIV-negative and not having a cohabiting partner) occurs more often than would be 

expected. 

Significance refers to a test of the null hypothesis that HIV status and cohabiting partner status are independent 

+, ++, +++: odds ratio is significantly greater than 1.00, at the .05, .01, or .001 level 

ns: the odds ratio is not significantly different from 1.00 

-, --, ---: odds ratio is significantly less than 1.00, at the .05, .01, or .001 level 
23 

The Swaziland survey stands out for having the strongest association between HIV status and cohabiting 

partner status, and it is positive for both men and women. 

Residence (urban-rural) is the only covariate included in the four tables presented in this chapter (Tables 

3.1, 3.2, 3.3, and 3.4). Additional tables for the covariates of age, education, and wealth, are presented in 

the Appendix (A1.1-A1.3, A2.1-A2.3, and A3.1-A3.3). 

3.2 General Pattern of Association by Age and Sex 

The three-way pattern of association described above is difficult to interpret, even though it appears to be 

surprisingly consistent across countries and according to urban-rural residence. In an effort to clarify the 

pattern, we pooled the survey data from the 10 countries.5

 Pooling the data is informative because it 

highlights a general pattern shared by the surveys, but interpretation of the results is essentially 

qualitative. The key characteristic that facilitates interpretation is age of the respondent. 

Tables 3.1-3.4 showed that HIV status and cohabiting partner status are associated—with a great deal of 

consistency across countries—but differently for men and women. At the same time, identifying patterns 

has been difficult. In an effort to identify a general pattern in the association among HIV status, 

cohabiting partner status, age, and sex, we shift to a different strategy, one that utilizes a series of figures 

that describe all men and women in the 10 DHS surveys combined. The combined file includes a total of 

115,794 men and women for whom: 1) HIV seroprevalence results are known, 2) cohabiting partner 

status is known, and 3) no other variables in the model are missing. 

First, in the combined file, the percentage HIV-positive (i.e., HIV prevalence) is calculated for each 

combination of age and sex, as shown in Table A1.1. This percentage is plotted in two lines in Figure 

3.3—one for men and one for women. The line on the left in the figure, which rises more quickly and has 

a higher and earlier peak, refers to women; the other line refers to men. For men and women in all 

countries there is a tendency for prevalence to be very low before age 20, then to increase with a peak in 

the age range 30 to 39, and then to decline. From about age 40 onwards, male and female prevalence 

levels are nearly identical. 

Increases in HIV prevalence from the teens to the thirties are due to the cumulative nature of exposure. 

All things being equal, within a birth cohort accumulated exposure to risk must increase monotonically 

with age. The decline after about age 40 is partly due to the higher mortality among infected persons and 

partly due to the historical trajectory of HIV incidence. The shift of approximately five years to the right 

in the age distribution for men, relative to women, is presumably due to the tendency for men to be older 

than their partners—whether a cohabiting partner or not. This pattern was identified earlier in the 

discussion of Table 3.1. 

Note that the pronounced higher HIV prevalence among women, compared with men, is in the younger 

age groups, which include the majority of respondents. The later ages, at which men and women have 

approximately the same HIV prevalence, actually include a relatively small proportion of the data. 

 

 

5

 It is common for multiple surveys to be combined in a single data file, but then they are usually analyzed one at a 

time as distinct entities. Here the surveys are analyzed together. 
24 

Figure 3.3 HIV prevalence by five-year age interval for men and women; pooled data from 10 DHS 

surveys in sub-Saharan Africa, 2006-2012. Note: The blue line refers to men and the red line 

refers to women. 

A logit regression was carried out to accompany Figure 3.3, using the binary form of HIV status (HIVnegative and HIV-positive are coded 0 and 1, respectively) as the outcome. The model was estimated with 

additive categorical covariates for residence, education, wealth, and survey, plus age and sex, also 

included as categorical variables. In this model, the odds ratio for women, compared with men, was 1.55. 

That is, the odds of being HIV-positive rather than HIV-negative are 55% greater for women than for 

men, after additive adjustments for age and the other covariates. 

Much of this excess prevalence for women is due to the age pattern of HIV. In the interest of simplicity 

we just assume a lag of five years. (The lag could be treated as a parameter to be optimized.) Figure 3.4 

displaces the men five years to the left, so that age 15 on the scale becomes age 20 for men, etc., and men 

under 20 are dropped. In figure 3.4, the age range for women is 15-49 and for men is 20-49, but translated 

to 15-44. 
25 

Figure 3.4 HIV prevalence by five-year age interval for men and women, with men shifted down by 

five years of age; pooled data from 10 DHS surveys in sub-Saharan Africa, 2006-2012. Note: the 

blue line refers to men and the red line refers to women. 

The age distributions of prevalence in Figure 3.4 show a remarkably consistent pattern for men and 

women. After the first age interval, prevalence is consistently higher for women than for men, but if we 

repeat the logit regression done above in connection with Figure 3.3, the odds ratio falls from 1.55 to 

1.16. That is, after the age translation the odds that a woman will be HIV-positive rather than HIVnegative are only about 16% greater than the odds for a man. This finding suggests that much of the 

excess prevalence among women, compared with men, is due to the difference in the age distributions of 

prevalence for men and women, and the fact that countries with high prevalence also tend to have a young 

age distribution. 

Thus, most of the difference between the HIV prevalences of men and women in the pooled file can be 

described as a five-year lag for men, with identical and low prevalence of about 4% in the first age 

interval (age 15-19 for women and 20-24 for men), after which prevalence rises steadily for about 15 to 

20 years and then gradually declines because of lower incidence rates in the later ages and higher 

mortality among people who are HIV-positive. 

A case could be made for analyzing the data further after making the shift, or translation, of men’s ages in 

Figure 3.4, but this will not be done here. Instead, we return to Figure 3.3, with no age shift, to distinguish 

between whether someone does or does not report a cohabiting partner, the main characteristic of interest 

in this chapter. 
26 

Figures 3.5 and 3.6 describe graphically four groups: 1) men without a cohabiting partner, 2) men with a 

cohabiting partner, 3) women without a cohabiting partner, and 4) women with a cohabiting partner. 

Figure 3.5 shows the four groups in terms of the age pattern of HIV prevalence, while Figure 3.6 shows 

the four groups in terms of the numbers of men and women by five-year age interval. 

Figure 3.5 includes four lines with similar shapes, which can be distinguished by whether the peak is 

early or late, and whether the peak is low or high. The four shapes are associated with the four types of 

respondents as follows: 

• men without a cohabiting partner: peak prevalence is high and late 

• men with a cohabiting partner: peak prevalence is low and late 

• women without a cohabiting partner: peak prevalence is high and early 

• women with a cohabiting partner: peak prevalence is low and early. 

Certainly, cohabiting partner status is not permanent. The four lines in Figure 3.5 that represent four types 

of respondents should not be interpreted as life course trajectories of men and women who fall neatly into 

two groups—those who have a partner throughout their entire adult lives and those who never have a 

partner. The lines are simply composite snapshots of combinations of sex, age, HIV status, and cohabiting 

partner status at the time of the surveys. Even so, the four lines tell a story. First, the displacement of five 

years (approximately) between men and women, observed in Figure 3.3, carries over to Figure 3.5. That 

is, the age profile of HIV prevalence for men is delayed by about five years, compared with the profile for 

women, whether or not there is a cohabiting partner. Second, the peak prevalence is much lower if there is 

currently a cohabiting partner—whether the respondent is a man or a woman. The peaks are similar for 

men and women, except that, as a third observation, the peak for women is greater than the peak for men, 

particularly for women without a partner. The four profiles are basically similar, distinguished mainly by 

1) earlier HIV infection among women than among men, 2) lower HIV prevalence among persons with a 

cohabiting partner than among those without a cohabiting partner, and 3) highest overall HIV prevalence 

among women without a cohabiting partner. The findings suggest that having a cohabiting partner is 

protective, particularly for women. 

 
27 

Figure 3.5 HIV prevalence by five-year age interval for men without a cohabiting partner, men with 

a cohabiting partner, women without a cohabiting partner, and women with a cohabiting partner; 

pooled data from 10 DHS surveys in sub-Saharan Africa, 2006-2012 

Figure 3.6 plots the age distribution of the four groups, rather than their HIV prevalence. The two lines 

that start high in the first age group are the numbers of men and women without a cohabiting partner. The 

two lines that start low are the numbers of men and women with a cohabiting partner. After age 30 or so, 

the numbers of people in three of these groups are similar; however, the number of men without a 

cohabiting partner is much lower, corresponding with the higher levels of cohabiting partner status for 

men than for women that are seen in Table 3.1 and in the Appendix (Tables A1.1, A1.2, and A1.3). 

These patterns invite a more elaborate statistical model (for consideration at a later date). Now we move 

on to Chapter 4 and an analysis of HIV status in cohabiting couples. 
28 

Figure 3.6 Number of men without a cohabiting partner, number of men with a cohabiting partner, 

number of women without a cohabiting partner, and number of women with a cohabiting partner, 

by five-year age interval; pooled data from 10 DHS surveys in sub-Saharan Africa, 2006-2012 

 
29 

4 HIV Concordance, Discordance, Seroconversion, and Risk 

The previous chapter examined the relationship between an individual man’s or woman’s HIV status and 

cohabiting partner status in each of the 10 DHS surveys, according to urban-rural residence. Further 

analysis of the relationship according to age, education, and wealth status is presented in the Appendix 

tables. The analysis next moves to an examination of the correspondence between the woman’s HIV 

status and the man’s HIV status within a couple, specifically, matched cohabiting couples with nonmissing HIV test results for both the man and the woman. The preceding chapter established a context for 

the couple-level analysis; now the couples will be the units of analysis. Note that in this framework the 

number of men, the number of women, and the number of couples are identical. The chapter is divided 

into three parts— levels of concordance and discordance, selection and seroconversion, and discordance 

and the risk of future seroconversion—each of which presents methods and findings. 

4.1 Levels of Concordance and Discordance 

Methods 

Rather than focusing just on discordant couples, the analysis compares the observed distribution of the 

couple’s HIV status across the four possible combinations or types of serostatus with the hypothetical 

distribution that would be observed if the HIV status of the man and the HIV status of the woman were 

statistically independent. As described earlier, there are four possible combinations of the man’s HIV 

status and the woman’s HIV status, which will be referred to as the couple’s HIV status: 

Negative concordant (-/-): both the man and the woman are HIV-negative 

Female positive discordant (-/+): the man is HIV-negative and the woman is HIV-positive 

Male positive discordant (+/-): the man is HIV-positive and the woman is HIV-negative 

Positive concordant (+/+): both the man and the woman are HIV-positive 

These combinations represent the cells of a cross-tabulation of the HIV status of the man (two rows) 

versus the HIV status of the woman (two columns), as shown in Figure 4.1. Algebraically, couple status, 

resultc, is a linear function of the binary (0/1) variables resultm and resultw, for men and women, 

respectively, calculated as = ݎ݁ݑݏ݈ݓݐ + 2 ∗ ݎ݁ݑݏ݈ݐ݉ . In Figure 4.1, pm and pw are the probabilities that a 

man or a woman, respectively, is HIV-positive. In order to generate the expected probabilities of each of 

the four combinations inside the 2x2 table, those marginal probabilities refer specifically to the men and 

women who make up the tested couples. 

A common baseline when working with two variables is to assume that they are statistically independent, 

or uncorrelated, and then to examine patterns of deviations from that assumption. This assumption of 

independence is known to be false but it provides a baseline, hypothetical distribution of joint couple 

status. If the man’s and woman’s HIV statuses are independent, then the probabilities of each of the four 

types is calculated by multiplying together the marginal probabilities, as shown in the interior of Figure 

4.1. For example, the probability that a couple will be positive concordant is the probability that the man 

is HIV-positive multiplied by the probability that the woman is HIV-positive, and so on. 

 
30 

Figure 4.1 Possible combinations of the man’s HIV status and the woman’s HIV status, their 

observed proportions (ܘ(, and their expected proportions (ܘෝ) if couple formation were independent 

of HIV status 

HIV status Woman HIV-negative 

(௪݌ − 1)

Woman HIV-positive 

௪݌

Man HIV-negative 

(௠݌ − 1)

Negative concordant (-/-) 

Couple HIV status resultc = 1 

̂݌(௪݌ − 1)(௠݌1−) = ଵ

Female positive discordant (-/+) 

Couple HIV status resultc = 2 

̂݌

௪݌(௠݌1−) = ଶ

Man HIV-positive 

௠݌

Male positive discordant (+/-) 

Couple HIV status resultc = 3 

̂݌(௪݌ − 1)௠݌ = ଷ

Positive concordant (+/+) 

Couple HIV status resultc = 4 

̂݌

௪݌௠݌ = ସ

Although Figure 4.1 presents the calculation of probabilities with a simple 2x2 table, the actual 

calculation is done with logit regression, a method that incorporates sampling weights and other 

covariates. Using a file of couples in which both the man and the woman have been tested, a logit 

regression is first done for the male member of the couple, with binary outcome 1 if he is HIV-positive 

and 0 if he is HIV-negative. Parallel logit regressions are also done for the female member of the couple, 

with binary outcome 1 if she is HIV-positive and 0 if she is HIV-negative. Such logit regressions are done 

for all cohabiting couples who were tested for HIV, and then for couples within subgroups defined by 

residence, age, education, and wealth. 

The logit regression for the male member of the couple produces a fitted or estimated probability that the 

man is HIV-positive, using the covariates included in the model. This probability may vary substantially 

from one man to another, based on which category of a covariate he is in. For example, within his 

residential subgroup, HIV prevalence may be 0.10 or 10%, so his individual probability is 0.10. Within 

his age group, HIV prevalence may be 0.20 or 20%, so his individual probability is 0.20. The label for the 

fitted probability is pm. The fitted probability that the man is HIV-negative will be 1-pm. Similarly, the 

logit regression for the female member of the couple produces pw, an estimate of the probability that she 

is HIV-positive. The fitted probability that she is HIV-negative is 1-pw. 

Findings 

The covariates continue to be residence, age, education, and wealth. Residence (urban-rural) and wealth 

(quintiles: lowest, second, middle, fourth, highest) are household-level variables, and therefore are the 

same for both partners. By contrast, age and education can be different for the man and the woman. Two 

new variables are constructed—agediff and eddiff—to describe the disparity between the partners’ ages or 

their levels of education. Both variables are categorical, with categories to be defined below. 

 
31 

For the total (All) and for urban and rural areas, Table 4.1 shows the observed percentages of cohabiting 

couples in each of the four combinations/categories of couple HIV serostatus and the expected 

percentages (under the model of independence). The distribution of cohabiting couples by HIV serostatus 

from Table 4.1 (excluding couples in which both members are HIV-negative) is shown in Figure 4.2. 

Figure 4.2 HIV serostatus of cohabiting couples age 15-49, excluding cohabiting couples that are 

both HIV-negative, 10 DHS surveys in sub-Saharan Africa, 2006-2012 

1.5 2.4 3.1 3.4 4.9 6.3

8.0

10.3

18.9

28.8

3.1 2.0

3.2 3.0

5.2 3.8

4.6

4.5

7.6

8.7

2.8 2.6

2.8 3.3

5.1 4.7

6.6

6.7

9.5

7.7

Cameroon Tanzania Kenya Uganda Mozambique Malawi Zambia Zimbabwe Lesotho Swaziland

Male-Positive Discordant

Female-Positive Discordant

Both Positive
32 

Table 4.1 Among cohabiting couples, the percent distribution of observed and expected couples 

(M=man, W=woman) by four combinations of couple HIV serostatus (negative concordance, 

female positive discordance, male positive discordance, and positive concordance), according to 

urban-rural residence, 10 DHS surveys in sub-Saharan Africa, 2006-2012 (restricted to cohabiting 

couples in which both the man and the woman were tested for HIV) 

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Cameroon 2011 DHS All 92.1 90.7 3.3 4.6 3.1 4.4 1.5 0.2 

Cameroon 2011 DHS Urban 89.2 87.8 4.9 6.3 4.1 5.5 1.8 0.4 

Cameroon 2011 DHS Rural 94.2 93.0 2.1 3.3 2.3 3.6 1.4 0.1 

Kenya 2008-09 DHS All 91.2 88.4 3.1 5.9 2.5 5.3 3.2 0.4 

Kenya 2008-09 DHS Urban 91.1 88.7 5.4 7.8 0.8 3.2 2.7 0.3 

Kenya 2008-09 DHS Rural 91.3 88.3 2.2 5.1 3.2 6.2 3.4 0.4 

Lesotho 2009 DHS All 62.7 51.4 7.9 19.2 10.1 21.4 19.3 8.0 

Lesotho 2009 DHS Urban 58.7 44.0 7.1 21.8 8.1 22.8 26.0 11.3 

Lesotho 2009 DHS Rural 64.5 55.0 8.3 17.8 11.1 20.6 16.2 6.7 

Malawi 2010 DHS All 85.5 80.5 3.9 8.9 4.6 9.6 6.0 1.0 

Malawi 2010 DHS Urban 77.5 68.4 4.0 13.1 6.5 15.6 12.1 3.0 

Malawi 2010 DHS Rural 87.0 82.8 3.8 8.0 4.3 8.4 4.9 0.8 

Mozambique 2009 AIS All 85.0 81.3 5.2 8.9 5.1 8.8 4.7 1.0 

Mozambique 2009 AIS Urban 76.9 71.8 8.5 13.5 7.3 12.3 7.4 2.3 

Mozambique 2009 AIS Rural 87.8 84.6 4.1 7.2 4.4 7.5 3.8 0.6 

Swaziland 2006-07 DHS All 54.8 39.7 8.7 23.8 7.7 22.8 28.8 13.7 

Swaziland 2006-07 DHS Urban 47.7 32.7 11.9 26.9 7.2 22.2 33.2 18.3 

Swaziland 2006-07 DHS Rural 58.3 43.3 7.1 22.1 8.0 22.9 26.6 11.7 

Tanzania 2011-12 AIS All 93.0 90.9 2.0 4.2 2.6 4.8 2.3 0.2 

Tanzania 2011-12 AIS Urban 89.2 87.1 3.7 5.8 4.6 6.7 2.6 0.4 

Tanzania 2011-12 AIS Rural 93.9 91.8 1.6 3.7 2.2 4.3 2.3 0.2 

Uganda 2011 AIS All 90.2 87.1 3.0 6.1 3.2 6.3 3.5 0.4 

Uganda 2011 AIS Urban 88.1 83.8 2.6 6.9 4.4 8.6 5.0 0.7 

Uganda 2011 AIS Rural 90.5 87.6 3.1 6.0 3.1 6.0 3.3 0.4 

Zambia 2007 DHS All 80.2 73.9 4.7 11.0 6.8 13.1 8.2 2.0 

Zambia 2007 DHS Urban 66.6 56.9 9.0 18.7 8.7 18.4 15.7 6.0 

Zambia 2007 DHS Rural 86.2 82.1 2.8 6.9 6.0 10.1 4.9 0.9 

Zimbabwe 2010-11 DHS All 78.4 70.7 4.6 12.3 6.8 14.5 10.2 2.5 

Zimbabwe 2010-11 DHS Urban 77.6 67.9 3.6 13.3 6.0 15.7 12.8 3.1 

Zimbabwe 2010-11 DHS Rural 78.6 71.6 4.9 11.9 7.1 14.1 9.4 2.4 

Note: Weighted frequencies for the rows in this table are given in Table 4.2 

 
33 

The great majority of cohabiting couples are negative concordant. In Cameroon, Kenya, Tanzania, and 

Uganda, fewer than 10% of couples have one or both partners HIV-positive. In three additional surveys 

(Malawi, Mozambique, and Zambia) 10% to 20% of couples have any HIV infection. However, there are 

three surveys (Lesotho, Swaziland, and Zimbabwe) in which at least 20% of cohabiting couples have one 

or both partners HIV-positive. The observed level of positive concordance is generally 10% or less, with 

the following exceptions: urban, rural, and total in Swaziland (33%, 27%, and 29%, respectively); urban, 

rural, and total in Lesotho (24%, 17%, and 19%, respectively); and the urban areas of Malawi (13%), 

Zambia (14%), and Zimbabwe (13%). In each country, more couples are observed to be concordant (both 

positive concordant and negative concordant) than would be expected under the model of independence. 

As a corollary of this observation, fewer couples are observed to be discordant—either female positive 

discordant or male positive discordant—than would be expected. The observed percentage discordant is 

typically about half the expected value. In about half of the 20 urban and rural subgroups, fewer than 10% 

of cohabiting couples are discordant; in the remaining subgroups (more often urban than rural), at least 

10% of cohabiting couples are discordant. The maximum observed percentage of discordant cohabiting 

couples is 19% in rural Lesotho and urban Swaziland. The other urban and rural subgroups in which 10% 

or more of cohabiting couples are discordant are the urban areas of Zambia (16%), Mozambique (15%), 

Lesotho (14%), and Malawi and Zimbabwe (10%); and the rural areas of Swaziland (15%) and Zimbabwe 

(12%). The total percentage of cohabiting couples that are HIV discordant is shown in Map 4.1. As 

expected, the percent discordant is higher in countries where HIV prevalence is higher. 

Three tables in the Appendix (Tables A1.4, A2.4, and A3.4) provide observed and expected percentages 

of cohabiting couples in the four categories of couple HIV serostatus, according to age, education, and 

wealth. The patterns are similar to those in Table 4.1, with the same countries standing out because of 

high levels of discordance and positive concordance. 
34 

Map 4.1 Percentage of cohabiting couples age 15-49 that are HIV discordant, 10 DHS surveys in 

sub-Saharan Africa, 2006-2012 

 
35 

Additional discussion of the HIV prevalence of men and women in the matched couples 

We now take a closer look at the HIV prevalences of the men and women who make up the couples 

described in Table 4.1. Levels and differences between men and women who have a cohabiting partner 

were discussed in the last chapter, but that discussion will be extended here because the focus has 

narrowed to the smaller number of men and women who were actually matched with one another as 

couples. In Chapter 3, “has a cohabiting partner” did not require 1) that the cohabiting partner was 

included in the data, 2) that the two partners had mutually identified each other, or 3) that both partners 

had been tested for HIV. For cohabiting couples in this chapter, all of those conditions must be met. 

Table 4.2 presents several measures relevant to interpreting the data in Table 4.1. Most of this discussion 

is reserved for the next section, but the first two columns of Table 4.2—the HIV prevalences of men and 

women who have a cohabiting partner—are discussed here. These are the marginal percentages (in the 

2x2 table) used to calculate the expected percentages given in Table 4.1. These numbers in Table 4.2 and 

the corresponding numbers in Appendix tables A1.5, A2.5, and A3.5 will be discussed in considerable 

detail because they are crucial for interpreting the differences between the observed and expected percent 

distributions shown in Tables 4.1, A1.4, A2.4 and A3.4. 

Table 4.2 shows that in the subgroup of cohabiting partners there is considerable similarity in HIV 

prevalence between men and women, by urban-rural residence. At the same time, the overall range in 

prevalence is substantial—from a low in rural Cameroon of 4% for men and 3% for women to a high in 

urban Swaziland of 40% for men and 45% for women. Figure 4.3 illustrates the similarity in HIV 

prevalence between cohabiting men and women, with blue dots for those living in urban areas and red 

dots for those living in rural areas. The dots for all 20 subgroups (10 urban and 10 rural) are very close to 

the 45 degree line that would indicate exactly equal prevalence for men and women. 

Although the similarity of HIV prevalence for men and women is the main finding from the first two 

columns of Table 4.2, prevalence tends to be slightly higher for men than for women. This pattern differs 

slightly from an earlier finding that HIV-discordant couples in sub-Saharan Africa tend to be female 

positive as often, or more often, than male positive (de Walque, 2007; Eyawo et al., 2010). In all 10 rural 

subgroups, and five of the urban subgroups, prevalence is higher for men than for women. The exceptions 

(the blue dots below the diagonal line in Figure 4.3) are urban areas in Cameroon, Kenya, Mozambique, 

Swaziland, and Zambia. Urban Kenya and Swaziland are the only residential subgroups in which HIV 

prevalence for women is more than 1% higher than that for men. 

The pattern of HIV prevalence among men and women who have a cohabiting partner is substantially 

different from the pattern in the general population. The contrast can be seen by comparing Figure 4.3 

with the earlier Figure 3.1. The difference in HIV prevalence between men and women in the general 

population is almost entirely due to differences in HIV prevalence in the non-cohabiting subgroup. Within 

the cohabiting subgroup—where concordance and discordance are relevant—HIV prevalence is generally 

similar for men and women. The higher prevalence observed for women compared with men in the 

general population is largely nullified, or even reversed, in cohabiting partners. 

In Table 4.2, the significance column (“Sig.”) indicates whether the difference in HIV prevalence 

between men and women who have a cohabiting partner is statistically significant. Among the 20 urban 

and rural subgroups, only three show differences that are significant: HIV prevalence is significantly 

higher for men than for women in rural Zambia and rural Zimbabwe. Because Zambia and Zimbabwe are 

predominantly rural, the significantly higher prevalence for men than for women in those two countries 

carries over to the national level (All). Urban Kenya is the only area where HIV prevalence is 

significantly higher for women (8%) than for men (4%). 
36 

Table 4.2 HIV prevalence among men and women who have cohabiting partners, and measures of 

selection and HIV seroconversion, according to urban-rural residence, 10 DHS surveys in subSaharan Africa, 2006-2012 

Survey Category 

HIV prevalence 

Selection/Seroconversion 

measures 

Men Women Delta kap_SC SCm SCw Sig. N 

Cameroon 2011 DHS All 4.7 4.8 1.3 29.3 28.7 29.9 ns 2,289 

Cameroon 2011 DHS Urban 5.9 6.7 1.4 23.6 22.0 25.3 ns 984 

Cameroon 2011 DHS Rural 3.7 3.4 1.3 36.1 37.7 34.6 ns 1,305 

Kenya 2008-09 DHS All 5.7 6.2 2.8 50.1 47.8 52.7 ns 1,064 

Kenya 2008-09 DHS Urban 3.5 8.1 2.4 43.9 31.0 75.5 W++ 305 

Kenya 2008-09 DHS Rural 6.6 5.5 3.0 52.7 58.3 48.2 ns 759 

Lesotho 2009 DHS All 29.4 27.2 11.3 55.6 58.8 52.7 ns 689 

Lesotho 2009 DHS Urban 34.2 33.1 14.7 65.8 67.3 64.4 ns 218 

Lesotho 2009 DHS Rural 27.2 24.4 9.5 49.6 53.5 46.2 ns 471 

Malawi 2010 DHS All 10.6 9.9 5.0 54.1 56.4 52.1 ns 2,987 

Malawi 2010 DHS Urban 18.5 16.1 9.1 63.6 69.6 58.5 ns 460 

Malawi 2010 DHS Rural 9.2 8.8 4.1 50.5 51.8 49.3 ns 2,527 

Mozambique 2009 AIS All 9.8 9.9 3.7 42.0 41.7 42.2 ns 2,322 

Mozambique 2009 AIS Urban 14.6 15.9 5.1 39.1 37.3 41.0 ns 589 

Mozambique 2009 AIS Rural 8.1 7.9 3.1 42.6 43.5 41.8 ns 1,733 

Swaziland 2006-07 DHS All 36.5 37.5 15.1 64.8 63.5 66.2 ns 626 

Swaziland 2006-07 DHS Urban 40.4 45.2 15.0 61.0 55.6 67.5 ns 204 

Swaziland 2006-07 DHS Rural 34.6 33.8 15.0 66.5 67.7 65.3 ns 422 

Tanzania 2011-12 AIS All 5.0 4.4 2.1 47.8 51.4 44.7 ns 3,302 

Tanzania 2011-12 AIS Urban 7.2 6.2 2.1 34.0 36.7 31.7 ns 648 

Tanzania 2011-12 AIS Rural 4.4 3.9 2.1 52.9 56.7 49.6 ns 2,654 

Uganda 2011 AIS All 6.8 6.6 3.1 49.7 50.6 48.8 ns 3,972 

Uganda 2011 AIS Urban 9.3 7.6 4.3 55.1 62.3 49.5 ns 562 

Uganda 2011 AIS Rural 6.4 6.4 2.9 48.5 48.3 48.6 ns 3,410 

Zambia 2007 DHS All 15.1 12.9 6.3 52.1 57.1 47.9 M+ 2,007 

Zambia 2007 DHS Urban 24.4 24.7 9.7 52.2 51.7 52.6 ns 612 

Zambia 2007 DHS Rural 11.0 7.8 4.1 48.1 59.1 40.5 M+++ 1,395 

Zimbabwe 2010-11 DHS All 17.0 14.8 7.7 57.3 62.5 52.9 M++ 2,180 

Zimbabwe 2010-11 DHS Urban 18.7 16.4 9.7 66.9 72.8 61.9 ns 495 

Zimbabwe 2010-11 DHS Rural 16.5 14.3 7.1 54.1 59.1 49.9 M+ 1,685 

Note: Selection/seroconversion measures are defined in the text 

N is weighted 

Significance refers to a test of the null hypothesis that selection/seroconversion is the same for men and women 

M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level 

ns: the difference is not statistically significant 

W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 
37 

The Appendix tables that correspond to Table 4.2 show only a few examples of significant differences in 

HIV prevalence between men and women who have a cohabiting partner by age, education, and wealth 

(Tables A1.5, A2.5, and A3.5, respectively). In Table A1.5, some differences in HIV prevalence 

according to age are nominally significant at the .05 level. However, because the cases are equally 

divided between higher prevalence for men and higher prevalence for women, and they are seen even less 

often than expected with a .05 test—5% of comparisons would be significant by chance with a .05 test—

we do not attach any importance to them. 

Table A2.5 shows one example of a significant difference in HIV prevalence between men and women 

who have cohabiting partners according to education. In the Malawi survey, men in the highest education 

category (secondary or higher) have an HIV prevalence of 13%, compared with 9% for women in the 

same education category. HIV prevalence for men in this category is significantly higher (at the .01 level) 

than HIV prevalence for women. A difference at the .01 level of significance is unlikely to be a statistical 

fluke and may merit further investigation. 

Figure 4.3 HIV prevalence among men and women with cohabiting partners in urban and rural 

areas, 10 DHS surveys in sub-Saharan Africa, 2006-2012. Note: The vertical and horizontal axes 

show the prevalence of men and women, respectively. The diagonal line indicates equal 

prevalence for men and women. Blue and red dots identify urban and rural areas, respectively. 

 

Table A3.5 presents differences in HIV prevalence between men and women who have a cohabiting 

partner according to wealth status (quintiles). There do not appear to be convincing differences in HIV 

prevalence between men and women by wealth quintile. Only one difference is significant, and that is at 
38 

the .05 level; in 50 comparisons we would have expected 50*.05=2.5 significant differences at this 

nominal level. 

The dominant finding is that in all 10 surveys and for almost all categories of the covariates, differences 

in HIV prevalence between men and women in the subgroup of cohabiting couples are generally small 

and not statistically significant. 

A coarse categorization of the partners’ differences in age and education is included in Appendix tables 

A1.5 and A2.5, respectively. It could be hypothesized that when men have more education or are older 

than their partners, there will tend to be an exacerbated power difference that could translate into a 

preponderance of discordant couples in which the man is HIV-positive and the woman is HIV-negative. 

The data do not support this hypothesis. 

To elaborate, the analysis looks first at the age difference between men and women who are cohabiting 

partners (Table A1.5), using four categories based on single year of age reported for the man and the 

woman: 1) the woman is older than the man; 2) the man is 0-4 years older than the woman; 3) the man is 

5-9 years older than the woman; and 4) the man is 10 or more years older than the woman. There is only 

one case in which an age difference is accompanied by a significant difference in HIV prevalence 

between men and women. In Zimbabwe, when the man is 5-9 years older than the woman, the man is 

significantly (at the .05 level) more likely than the woman to be HIV-positive. In this subgroup, HIV 

prevalence is 18% for men and 14% for women, (with more decimal places, the difference is 3.5%). 

Approximately the same difference is observed in couples in which the man is 10+ years older than the 

woman, although the difference does not achieve statistical significance. As noted before, Zimbabwe is 

one of the two surveys in which men with cohabiting partners have significantly higher HIV prevalence 

than women, and the majority of couples have an age difference of 5-9 or 10+ years. Evidence that an age 

difference has a systematic effect on the couple’s HIV status is weak at best. 

The education difference between men and women who are cohabiting partners (Table A2.5) is 

constructed using three categories: 1) the woman has more education than the man; 2) the man and 

woman are in the same education category; and 3) the man has more education than the woman. The only 

support for the education power difference hypothesis stated above comes from the Malawi survey. 

There, when the man is better educated than the woman, the man is more likely to be HIV-positive, 

although only at the .05 level of significance (Table A2.5). It is likely that this finding is mainly a 

manifestation of the difference noted above for Malawi, i.e., that HIV prevalence among men is 

significantly greater (at the .01 level) than HIV prevalence among women, together with the generally 

positive association between education and HIV prevalence in Malawi. Simply because of that pattern, 

couples in which the man is more educated than the woman will tend to combine men with higher HIV 

prevalence and women with lower HIV prevalence. In Malawi it is found that when the woman has more 

education than the man, the woman is more likely than the man to be HIV-positive. Among couples of 

this type, HIV prevalence among female partners is 16% and among male partners it is 10%, a substantial 

difference. Only about 12% of cohabiting couples in Malawi consist of women with more education than 

their male partner, but further investigation of the higher HIV prevalence of women in these couples 

would be useful, because it reverses the role of the man and the woman in the hypothesized pattern. 

Again, the dominant finding is that differences in HIV prevalence between men and women in the 

subgroup of cohabiting couples, according to differences in age and education, are generally small and 

not statistically significant, with the possible exception of age differences in Zimbabwe and education 

differences in Malawi. 
39 

4.2 Selection and Seroconversion 

A positive association is always observed between the HIV statuses of the man and the woman in 

cohabiting couples. Comparing these results with a random model of paired men and women, why do the 

data always show more concordant couples than expected? 

There are at least two plausible explanations for this finding. The first arises from the general tendency 

for a man and woman who have formed a couple to be similar at initial couple formation, partly due to 

preferences and partly due to patterns of social contact. This similarity appears regarding a number of 

background characteristics such as residence (urban or rural), age—although often the man is a little older 

than the woman—education, wealth, religion, ethnicity, etc. Similarity of marital partners because of 

similarity of backgrounds is sometimes described as “homogamy.” Matched couples in DHS surveys 

need not be formally married so the term “selection” is used to describe the process of couple formation; 

selection here refers to the mutual selection process whereby men and women become partners. There are 

two possible types of selection: direct and indirect. “Direct” selection describes choices made on the basis 

of HIV status. “Indirect” selection refers to choices made on the basis of characteristics such as education 

and place of residence that are associated with HIV status. 

HIV testing is increasing rapidly throughout Africa (Staveteig et al., 2013) and some faith-based 

organizations in a few countries have begun encouraging or mandating premarital testing (cf. Rennie and 

Mupenda, 2008; Uneke et al., 2007). In theory, increased awareness of HIV status and expanded 

opportunities for premarital testing increase the opportunity for HIV-negative adults to select HIVnegative partners. So far, however, in sub-Saharan Africa, documentation of 1) the extent to which 

couple-based testing occurs prior to marriage and 2) the extent to which intentional serosorting (selection) 

of partners of the same HIV status occurs, is limited. The motivation of an HIV-positive individual to 

select an HIV-positive partner may be altruistic (so as not to expose an HIV-negative partner to the 

disease) or may be to guard against the possible negative consequences of disclosure, but, with the 

exception of Sully (2013), the evidence for serosorting tends to be anecdotal (see Reniers and Helleringer 

(2011) for a cogent summary). 

It is likely that some amount of indirect selection is taking place in the formation of these couples. For 

example, in most settings, urban and better-educated persons are more likely to be HIV-positive than rural 

and less-educated persons. The tendency to prefer partners who are similar in terms of background 

characteristics such as residence (urban or rural) and education, variables that are themselves associated 

with HIV status, could induce an excess of concordant couples (Type 1 and Type 4) and a deficit of 

discordant couples (Type 2 and Type 3). Thus, selection could include possible similarity of HIV status at 

initial couple formation, because of indirect selection according to background characteristics. In the 

analysis, an attempt is made to adjust for indirect selection, giving results separately for characteristics of 

both partners (urban-rural residence and wealth quintile), or one partner at a time (age and education). It 

is likely that this strategy only partially adjusts for indirect selection because we do not adjust for 

homogamy on religion, ethnicity, language group, etc. 

A second plausible explanation for the larger-than-expected proportion of HIV-positive concordant 

couples is HIV seroconversion. The transition in an interval of time from Type 2 (female discordant) to 

Type 4 (positive concordant) may occur because a man has been infected by his partner; a transition from 

Type 3 (male discordant) to Type 4 (positive concordant) may occur because a woman has been infected 

by her partner. (It should also be noted that some transitions occur because of infections from outside the 

couple—third party infections.) Such transitions reduce the proportions of Type 2 and Type 3 couples and 

increase the proportion of Type 4 couples. Transitions in an interval of time from Type 1 to Type 2 or 

Type 3 can only occur as the result of infection from outside the couple. 
40 

If a cohort of, say, 1000 couples could be followed, the number of Type 1 couples (negative concordant) 

would necessarily decline monotonically over time because of new infections, deaths (from any cause), 

and dissolutions of unions. Type 2 and Type 3 (discordant) couples are transitional to Type 4 (positive 

concordant) in specific cases, but the overall numbers of Type 2 and Type 3 couples could remain fairly 

steady over time because some couples transition in from Type 1 and some transition out to Type 4. Type 

4 is the “absorbing” serostatus category; it tends to include increasing numbers of couples over time, 

except for the countervailing influence of higher mortality. 

The excess of positive concordant cases is equivalent to a deficit of discordant cases, and it is possible 

that the pattern arises in part from a higher dissolution rate for couples that are discordant, through a 

combination of separation and death, than for couples who are concordant—positive concordant as well 

as negative concordant. There is indeed some evidence, from Rakai, Uganda (Porter et al., 2004) of 

higher separation rates for discordant couples than for HIV negative concordant couples. A complete 

analysis would also include dissolution through the death of one or both partners, which should be most 

common for HIV positive concordant couples. We will not attempt to measure the roles of separation and 

death, but to the extent that they play a role, this analysis will over-estimate the importance of selection 

and seroconversion. 

Methods 

In the data, it is consistently found that the observed proportion of couples who are positive concordant is 

always greater than the expected proportion. We define ∆= ݌ସ − ݌̂

ସ to be the deviation that describes the 

excess in cell 4 (positive concordant). Because the deviations must add to zero in each row and column of 

Figure 4.1, this same number will appear as a deficit in cells 2 and 3 (discordant) and an excess in cell 1 

(negative concordant). 

A familiar measure of the correspondence between two variables—in this case the man’s HIV status and 

the woman’s HIV status—is Cohen’s kappa, a simple measure of agreement that positions the observed 

data between a) what would be observed under the null hypothesis of independence and b) what would be 

observed if there were complete concordance. Kappa is defined as follows: 

̂݌) − (ସ݌ + ଵ݌) = ݇

̂݌ + ଵ

ସ)

̂݌) − 1

̂݌ + ଵ

ସ)

If the observed and expected proportions were equal in all cells, then the numerator would be zero and 

kappa would be zero. On the other hand, if all cases were on the main diagonal, so that (݌ଵ + ݌ସ) = 1, 

then kappa would be one. It is mathematically possible for kappa to be negative, but for our data it is 

always positive, so kappa effectively measures positive association on a scale from 0 to 1. For a 2x2 table, 

kappa is essentially the same as M.G. Kendall’s measure tau-b, to describe the association between two 

ordinal variables. 

As an example, in the Zambia 2007 DHS survey (unweighted) there are 1894 + 181 = 2075 concordant 

couples out of a total of 2330 couples, an observed proportion ݌= 

ଶ଴଻ହ

ଶଷଷ଴

= .89. Under independence, 

there would be 173.5 + 40.5 = 1794 concordant couples, a proportion ݌̂ =ଵ଻ଽହ

ଶଷଷ଴

= .77 . Kappa is 

݇ = .଼ଽି.଻଻

ଵି.଻଻

= .52. 
41 

Kappa can only reach its theoretical maximum of 1 if the row and column distributions are the same, i.e., 

if male and female prevalence within the sample of cohabiting couples are the same. The formula for 

kappa can be adjusted so the theoretical maximum is always 1, but that variant will not be used here. 

An alternative expression for kappa is the combined excess of couples in the two concordant cells, 

relative to the expected number of couples in the two discordant cells: 

̂݌) − (ସ݌ + ଵ݌) = ݇

̂݌ + ଵ

ସ)

̂݌) − 1

̂݌ + ଵ

̂݌ − ଵ݌) = (ସ̂݌ − ସ݌) + (ଵ

ସ)

̂݌)

̂݌ + ଵ̂݌ + ଶ̂݌ + ଷ

̂݌) − (ସ

̂݌ + ଵ

ସ) = 2∆

̂݌

̂݌ + ଶ

ଷ

If, say, two tables produce the same value of kappa, then it follows that Δ is proportional to the sum of the 

expected proportion of discordant couples. In this chapter it will be seen that kappa is very similar across 

subgroups, and this type of proportionality is approximately what is observed. 

In a 2x2 table, a test of the significance of kappa is just a test of the significance of a positive association 

between the man’s HIV status and the woman’s HV status. Because the region of rejection is all on one 

side, the p-value of the usual chi-square statistic will be half of the nominal value. For example, if the 

nominal p-value for a chi-square test is .05, the p-value for kappa would be .025. Tests were carried out 

but need not be provided in this report because in all cases kappa was significantly different from 0 (and 

positive) at the .001 level. 

We propose that kappa be interpreted in this context as a measure of the two possible sources of 

association between the HIV statuses of partners: selection and seroconversion. In other words, kappa can 

be called a “Selection/Conversion Index,” or SC, with the value 0 if there is no selection or 

seroconversion, and the value 1 if one mechanism or the other, or both mechanisms working together, 

produce a perfect positive association. This report will not attempt to distinguish between the two 

mechanisms, each of which is subject to a number of unobserved sources of variation.6

 

In addition to SC, we propose two sex-specific measures, SCm (Selection/Conversion for men) and SCw

(Selection/Conversion for women), which describe the deficits in Type 2 and Type 3 couples, 

respectively. Each index can be thought of as the ratio of the excess in Type 4 couples, relative to the 

expected number of Type 2 or Type 3 couples, respectively: ܵܥ݉∆ = 

௣ොమ

∆ = ݓܥܵ and 

௣ොయ

. 

SCm is the deficit in Type 2 couples (man negative, woman positive), relative to the expected number of 

couples of this type. When multiplied by 100, it is the percent reduction when the observed number of 

Type 2 discordant couples is compared with the expected number. SCw is the analogous measure for 

Type 3 discordant couples (man positive, woman negative), and describes the shortfall in the number of 

women observed in the discordant combination that represents risk for women. 

Because of the algebraic relationship between SC (=kappa) and SCm and SCw, the value of SC will 

always be intermediate to SCm and SCw. The ratio does not depend on delta: 

ௌ஼௠

ௌ஼௪ = ௣ොయ

௣ොమ

= ௉௥(ெା)∗௉௥(ௐି)

௉௥(ெି)∗୔୰	(ௐା)=୔୰(ெା)/୔୰	(ெି)୔୰(ௐା)/୔୰	(ௐି). 

 

6 For example, the distribution of union duration within the population. At the point of couple formation, similarity 

will be due entirely to selection; afterwards there is a monotonic increase in the possibility of similarity through 

seroconversion.
42 

That is, SCm/SCw is just an odds ratio: the odds that a man is HIV-positive divided by the odds that a 

woman is HIV-positive, in the population of men and women in cohabiting couples, ignoring the HIV 

status of the actual partner. A test of whether SCm and SCw are different from each other (in the 

population of all couples) is equivalent to a test of whether HIV prevalence is different for men in couples 

compared with women in couples. Details on this test are provided later in the report; the tables include 

the significance level indicated by the test. 

Findings 

As anticipated in the discussion of methods, the excess percentage of couples who are HIV concordant 

(observed minus expected) and the deficit of discordant couples are interpreted as the results of the 

combined effects of selection and seroconversion. Selection refers to assortative matching of men and 

women. If prospective partners were aware of each other’s serostatus, it is likely that the HIV-negative 

person would specifically avoid a relationship with someone who was HIV-positive, a preference that 

would produce a deficit of discordant couples. There is known to be homophily on residence, education, 

and possibly other background characteristics associated with HIV status, which could indirectly produce 

some positive association on HIV status. 

There is a monotonically increasing risk over time that a couple will convert from HIV discordant to 

positive concordant. If there are outside partners, then there is also a risk of seroconversion from negative 

concordant to discordant or from discordant to positive concordant. Couples may be dissolved through 

separation or the death of either partner, at rates that probably differ across the four possible 

combinations. We have no information about such transitions. 

Returning to Table 4.2, the third column in the table (Delta) provides one way to summarize the 

deviations from independence of male and female prevalence detailed in Table 4.1. Delta is the excess

percentage in each of the two concordant cells, that is, the observed percentage minus the percentage that 

would be expected under the null hypothesis of independence. Delta is also the deficit in each of the two 

discordant cells. A comparison of delta with the prevalences of men and women in the first two columns 

of Table 4.2 shows a clear association, positive and strong, consistent with a pattern of HIV transmission 

that moves couples out of discordance and into positive concordance. 

Delta has a very close linear relationship with the prevalence levels of the men and women in the 

subgroup of cohabiting couples. Figure 4.4 shows the correspondence graphically. In the figure, the 

vertical axis is delta and the horizontal axis is the average prevalence for men and women. If delta is 

regressed on this mean HIV prevalence, with the 20 urban and rural areas as units, the slope of the line is 

b=0.418. The fit is excellent; both the unadjusted R2

 and the adjusted R2 are 0.98.7

This empirical regularity suggests that an epidemiological model incorporating information about rates of 

couple formation and dissolution, HIV transmission, and mortality could potentially fit the pattern of 

concordance and discordance across a wide range of HIV prevalence levels. However, it must be noted 

that DHS data do not include the information necessary to develop such a model. 

The next three columns in Table 4.2 (kap_SC, SCm, SCw) attempt to quantify the degree of selection and 

seroconversion with measures that include “SC” in their labels. All of them were defined earlier, with 

delta (Δ) in the numerator. 

 

7

 The regression line is forced to go through the origin (0, 0) because delta must be 0 if prevalence is 0. If delta is 

regressed on both male HIV prevalence and female HIV prevalence, expressed as the mean and the difference to 

avoid collinearity, the coefficient for the difference is significant but the rounded value of the adjusted R2

 remains 

0.98. 
43 

Figure 4.4 Observed values of delta and HIV prevalence in urban and rural areas, 10 DHS surveys 

in sub-Saharan Africa, 2006-2012. Note: The vertical axis shows delta and the horizontal axis 

shows the average prevalence for men and women. Blue and red dots identify urban and rural 

areas, respectively. The regression line goes through the origin, with slope 0.418 and R2

=0.98. 

Kappa, introduced in Chapter 2 and labeled “kap_SC,” is a well-established measure of concordance. It 

could range from 0—if the man’s HIV status and the woman’s HIV status were statistically 

independent—to a maximum of 100 with complete concordance. As shown in Chapter 2, in the present 

context it can be described verbally as the combined deficit, or shortfall, in the two discordant categories 

of couple prevalence, divided by the combined expectations in those two categories, under the null 

hypothesis of independence. With the multiplier of 100, we are able to interpret kap_SC as the percent 

reduction in discordance, relative to the expected level of discordance, which must be due to a 

combination of selection and seroconversion. For example, a value of 40 could be interpreted to mean that 

the observed number of discordant couples—both types combined—is 40% less than expected under the 

model of independence. 

In Table 4.2, the level of kap_SC (or kappa) is substantial. It ranges from 25 in urban Cameroon to 68 in 

urban Lesotho and 67 in rural Swaziland. The high levels are about two-thirds of the distance from 

independence to complete concordance. 

Table 4.2 includes the two variants of kap_SC, referred to as SCm and SCw. SCm is the relative deficit in 

the Type 2 female-positive discordant (man negative, woman positive) category, and suggests 

transmission from the woman to the man. SCw is the relative deficit in the Type 3 male-positive 

discordant (man positive, woman negative) category and suggests transmission from the man to the 
44 

woman. Because of the way they are defined, their values are very close to kap_SC. Table 4.2 shows only 

three residential (urban-rural) subgroups in which SCm and SCw are significantly different, 

corresponding to whether there is a significant difference between the number of Type 2 and Type 3 

discordant couples. In urban Kenya, SCm=33 and SCw=76. Looking back at this sector in Table 4.1, 

5.8% of couples were Type 2 discordant (female-positive), compared with an expected percentage of 

8.6%. The deficit, relative to the expected percentage, is (8.6-5.8)/8.6. With more decimals, and 

multiplied by a factor of 100, this is SCm=33. The number of couples of this type is 33% less than 

expected. Similarly, 0.9% of couples were Type 3 discordant (male-positive), compared with an expected 

percentage of 3.8%. The relative deficit is (3.8-0.9)/3.8, the source of SCw=76. Apart from rounding 

error, the numerators of these measures are the same, delta, but because the expected number of malepositive discordant couples was smaller than the expected number of female-positive discordant couples, 

SCm and SCw are quite different. 

The significance tests for 1) the difference between SCm and SCw in Table 4.2, 2) the difference between 

the HIV prevalences of men and women in the same table, and 3) the difference between the observed 

numbers of discordant couples of Type 2 (female-positive) and Type 3 (male-positive) in Table 4.1, are 

equivalent. The finding in all three is that in 17 of 20 residential (urban-rural) subgroups, there is no 

difference in HIV prevalence between men and women. Of the three significant differences indicated in 

Table 4.2, one suggests that women in urban Kenya have higher HIV prevalence than men; have higher 

rates of seroconversion, from discordance into positive concordance, than men; and remain in fewer 

discordant relationships than men. The other two significant differences suggest the opposite in rural 

Zambia and rural Zimbabwe. 

Section 4.1 above presented a discussion of significant differences in HIV prevalence between men and 

women who have cohabiting partners, by age, education, and wealth, from the Appendix tables (A1.5, 

A2.5, and A3.5, respectively). Differences noted there—and there were only a few—apply also to the 

balance between Type 2 (female-positive) and Type 3 (male-positive) discordance, and the differences in 

seroconversion, because the test statistic is the same for all three comparisons.8

4.3 Discordance and the Risk of Future Seroconversion 

A man or woman who is HIV-negative and has an HIV-positive partner has a risk of HIV infection unless 

some consistent intervention is in place. This chapter will briefly assess the level of risk, with particular 

emphasis on how it differs for men and women. 

Methods 

The indicators of selection and seroconversion in Section 4.2 should be treated cautiously, but in any case 

they refer to the past. Looking toward the future, indices of the risk of future HIV infection due 

specifically to discordance can be constructed. Three interpretations of risk are examined. For each 

interpretation we calculate the percentage of men who are at risk; the percentage of women who are at 

 

8

 A test of the null hypothesis that the HIV prevalences of men and women in cohabiting couples are equal is much 

different from the statistical tests in Chapter 3. In those tests, the men and women were not linked and were treated 

as independent samples. When the men and women are linked as couples (“matched pairs”), the test is different and 

more powerful. Both of these tests can be done with chi-square. The test for the independent samples approach is the 

usual chi-square statistic for a 2x2 table. The test for the matched pairs is known as McNemar’s test and is widely 

documented under that name. The logit format is equivalent to chi-square, but can be applied to couple-level data 

and can include adjustments for weights. This matched pairs test is appropriate for any hypothesis about a sex 

difference in concordance/discordance, including a test of whether the two types of discordance are equally likely. 
45 

risk; and the ratio of those two numbers, i.e., the relative risk for men and women. The relative risk is 

expressed as the number of women at risk per 100 men at risk. 

First, of all men who have a cohabiting partner, the percentage who are HIV-negative with an HIVpositive partner is 100݌ଶ. Similarly, of all women who have a cohabiting partner, the percentage who are 

HIV-negative with an HIV-positive partner is 100݌ଷ. These two percentages are referred to as Rm1 and 

Rw1, respectively. The two add to the percentage of all couples that are HIV discordant. The 

denominators of Rm1 and Rw1 are all men who have a cohabiting partner and all women who have a 

cohabiting partner, respectively. 

Second, of all men who have a cohabiting partner and are HIV-negative, the percentage who have an 

HIV-positive partner and are at risk of infection from that partner is 100݌ଶ/(	݌ଵ + ݌ଶ). Similarly, of all 

women who have a cohabiting partner and are HIV-negative, the percentage who have an HIV-positive 

partner and are at risk of infection from that partner is 100݌ଷ/(	݌ଵ + ݌ଷ). These percentages are referred 

to as Rm2 and Rw2, respectively. The numerators of Rm2 and Rw2 are the same as the numerators of Rm1

and Rw1, but the denominators of Rm2 and Rw2 are limited to HIV-negative men and women, 

respectively. We consider Rm2 and Rw2 to be more descriptive of risk than Rm1 and Rw1 because men 

and women who are already HIV-positive have no risk of infection and, if included, they artificially 

inflate the denominators. 

Third, define ܥି ௠and ܥି௪ to be the percentages of HIV-negative men and HIV-negative women, 

respectively, who have a cohabiting partner. (A minus sign is included in the subscripts to convey that 

cohabiting partner rates are conditional on being HIV-negative.) These are given as percentages in Table 

3.3, which describes cohabiting partner status conditional on HIV status. If these percentages are 

multiplied by the respective percentages in the preceding paragraph (and then divided by 100), we get the 

estimated percentages of all HIV-negative men or all HIV-negative women who are at risk of infection 

from this source. To summarize: 

• Of all men with a cohabiting partner, the percentage who are HIV-negative and have an HIVpositive partner: ܴ݉1 = 100݌ଶ

• Of all women with a cohabiting partner, the percentage who are HIV-negative and have an HIVpositive partner: ܴݓ1 = 100݌ଷ

• Of all men who are HIV-negative and have a cohabiting partner, the percentage whose partner is 

HIV-positive: ܴ݉2 = 100݌ଶ/(	݌ଵ + ݌ଶ)

• Of all women who are HIV-negative and have a cohabiting partner, the percentage whose partner 

is HIV-positive: ܴݓ2 = 100݌ଷ/(	݌ଵ + ݌ଷ)

• Of all men who are HIV-negative, the percentage who have an HIV-positive cohabiting partner: 

(ଶ݌ + ଵ݌	)/ଶ݌௠ିܥ = ܴ݉3

• Of all women who are HIV-negative, the percentage who have an HIV-positive cohabiting 

 .(ଷ݌ + ଵ݌	)/ଷ݌௪ିܥ = 3ݓܴ :partner

The relative risk of men and women for seroconversion from an HIV-positive cohabiting partner can be 

calculated in three ways, which differ only in how the denominator of risk is defined. 
46 

The first measure of relative risk is limited to the HIV-negative men and HIV-negative women who are in 

discordant couples, and is defined as ܴܴ1 = 100ܴݓ1/ܴ݉1 = 100݌ଷ/݌ଶ. It is simply the ratio of the 

number of Type 3 (male-positive discordant) couples to the number of Type 2 (female-positive 

discordant) couples, multiplied by 100. This ratio is easy to calculate but is limited to discordant couples. 

The second measure of relative risk expands the denominator of men’s risk and women’s risk to include 

all HIV-negative men and HIV-negative women who have a cohabiting partner, thus adding couples in 

which both partners are HIV-negative: ܴܴ2 = 100ܴݓ2/ܴ݉2. 

The third measure of relative risk includes all men and all women who are HIV-negative, not just those 

who have a cohabiting partner: ܴܴ3 = 100ܴݓ3/ܴ݉3. 

For all three ways to measure the relative risk of HIV for men versus women, a value greater than 100 

indicates higher risk for women than for men, and a value less than 100 indicates higher risk for men than 

for women, from an HIV discordant relationship, and within the subgroups defined by the denominators 

of the risk measures. 

Findings 

Table 4.3 gives these measures of the risk of seroconversion due to HIV discordance, for men and for 

women, and also the ratio of the risk for women to the risk for men, or relative risk (RR).9

 For men, the 

first measure of risk ranges from a low of 2% in rural Tanzania (several other urban/rural areas are also 

around 2%) to a high of 12% in urban Swaziland. For women, the range is from a low of 1% in urban 

Kenya to a high of 11% in rural Lesotho. The risk is generally higher for women than for men 

(RR1>100). In all rural areas, the risk of seroconversion from a cohabiting partner is higher for women 

than for men. The highest relative risk is in rural Zambia, where there are 220 women at risk of HIV 

infection for every 100 men at risk. The only urban/rural areas in which women have less risk than men 

are the urban areas of Cameroon, Kenya, Mozambique, Swaziland, and Zambia. 

The pattern for the second measure of risk is similar but with higher levels because the individuals who 

are already HIV-positive are removed from the denominator. The highest level of risk of HIV infection is 

again for urban men in Swaziland (20%): fully one-fifth of HIV-negative men with a cohabiting partner in 

urban Swaziland have a partner who is HIV-positive. This level of risk is by far the highest in Table 4.3, 

although two other countries (Lesotho and Zambia) show risk values for men of about 11%. 

The relative risks for men and women are almost the same for the second measure of risk as for the first. 

Again, women have higher risk than men in the rural areas of all 10 countries and in the urban areas of 

half of the countries.

 

9

 The sex ratios given earlier in this report followed the demographic tradition of males in the numerator and females 

in the denominator. The risk ratios in this part of the report are expressed as women’s risk divided by men’s risk, 

simply because there is typically more concert about women’s risk. 
47 

Table 4.3 Percentage of HIV-negative men and women who are at risk of infection from an HIVpositive cohabiting partner, and relative risk for women compared with men, by three risk 

categories, according to urban-rural residence, 10 DHS surveys in sub-Saharan Africa, 2006-2012 

Survey Category 

Risk category #1 Risk category #2 Risk category #3 

Men Women RR Men Women RR Men Women RR 

Cameroon 2011 DHS All 3.3 3.1 94 3.5 3.3 94 1.2 1.3 113 

Cameroon 2011 DHS Urban 4.9 4.1 83 5.2 4.4 84 1.4 1.4 99 

Cameroon 2011 DHS Rural 2.1 2.4 114 2.1 2.4 114 0.9 1.2 136 

Kenya 2008-09 DHS All 3.1 2.5 82 3.3 2.7 83 1.3 1.0 77 

Kenya 2008-09 DHS Urban 5.4 0.8 15 5.6 0.9 15 2.5 0.4 14 

Kenya 2008-09 DHS Rural 2.1 3.2 150 2.3 3.4 148 0.8 1.2 139 

Lesotho 2009 DHS All 7.9 10.1 128 11.2 13.9 124 2.5 3.1 123 

Lesotho 2009 DHS Urban 7.1 8.1 114 10.8 12.2 112 2.9 2.7 95 

Lesotho 2009 DHS Rural 8.3 11.1 134 11.4 14.6 129 2.4 3.3 136 

Malawi 2010 DHS All 3.9 4.6 119 4.3 5.1 118 2.1 2.6 120 

Malawi 2010 DHS Urban 4.0 6.5 162 4.9 7.7 158 1.7 3.2 187 

Malawi 2010 DHS Rural 3.8 4.3 111 4.2 4.7 110 2.2 2.4 108 

Mozambique 2009 AIS All 5.2 5.1 98 5.8 5.7 98 3.4 3.1 93 

Mozambique 2009 AIS Urban 8.5 7.3 86 9.9 8.6 87 4.3 3.8 89 

Mozambique 2009 AIS Rural 4.1 4.4 107 4.4 4.7 107 2.9 2.8 97 

Swaziland 2006-07 DHS All 8.7 7.7 89 13.7 12.3 90 2.1 1.8 86 

Swaziland 2006-07 DHS Urban 11.9 7.2 60 20.0 13.1 65 3.6 2.3 63 

Swaziland 2006-07 DHS Rural 7.1 8.0 112 10.9 12.0 110 1.5 1.6 106 

Tanzania 2011-12 AIS All 2.0 2.6 131 2.1 2.8 130 1.0 1.0 106 

Tanzania 2011-12 AIS Urban 3.7 4.6 125 3.9 4.9 124 1.4 1.4 98 

Tanzania 2011-12 AIS Rural 1.6 2.2 133 1.7 2.2 133 0.8 0.9 110 

Uganda 2011 AIS All 3.0 3.2 107 3.2 3.5 107 1.5 1.5 96 

Uganda 2011 AIS Urban 2.6 4.4 169 2.9 4.7 165 0.9 1.3 141 

Uganda 2011 AIS Rural 3.1 3.1 99 3.3 3.3 99 1.7 1.5 90 

Zambia 2007 DHS All 4.7 6.8 145 5.6 7.9 141 2.5 3.7 147 

Zambia 2007 DHS Urban 9.0 8.7 97 11.9 11.6 97 3.8 4.2 110 

Zambia 2007 DHS Rural 2.8 6.0 212 3.2 6.5 205 1.7 3.5 203 

Zimbabwe 2010-11 DHS All 4.6 6.8 149 5.5 8.0 145 2.1 2.9 135 

Zimbabwe 2010-11 DHS Urban 3.6 6.0 165 4.5 7.1 160 1.6 2.1 135 

Zimbabwe 2010-11 DHS Rural 4.9 7.1 145 5.8 8.3 141 2.4 3.3 139 

Note: Relative risk (RR) is expressed as number of women at risk per 100 men at risk. 
48 

The third measure best describes the risk of HIV infection through HIV discordance with a cohabiting 

partner because it includes all HIV-negative men and women regardless of whether they have a 

cohabiting partner. It is always lower than the other two measures and shows a somewhat different pattern 

because of variations in cohabiting partner status across countries, according to urban-rural residence. The 

percentage of HIV-negative men and women who are at risk of HIV infection under this measure ranges 

from 1% to 4% for both men and women. In most countries, only 1% or 2% of all HIV-negative men or 

women are at risk of infection from a cohabiting partner who is HIV-positive. Looking at urban-rural 

residence, the risks are highest for men and women in urban Mozambique (4%), for men in urban 

Swaziland (4%), and for women in urban Zambia (4%). In eight of the ten countries, the risk for women 

in rural areas is greater than the risk for men; the exceptions are rural Mozambique and Uganda. The 

urban areas are approximately equally divided in terms of whether women or men have more risk. At the 

national level, the ratio of women at risk to 100 men at risk, the relative risk (RR), is in a range of 100 +/- 

20. 

By far the lowest HIV risk and relative risk for women is in urban Kenya, where it is one-sixth the level 

for men (Table 4.3). At the same time, urban Kenya is the only residential subgroup that showed 

significantly higher seroconversion rates for women than for men (Table 4.2). It may seem contradictory 

that seroconversion is higher for women than for men, while future risk is lower for women than for men. 

However, this is not a contradiction. Among cohabiting couples, HIV prevalence is 4% for men and 9%, 

much higher, for women. The expected proportion of couples that are Type 2 (female-positive discordant) 

(see Figure 4.1) is (1-.04) (.09), or 9%, and the expected proportion that are Type 3 (male-positive 

discordant) is (1-.09) (.04), or 4%. The observed percentages in these two types of discordance are 6% 

and 1%, respectively, because of past seroconversion that occurred disproportionately among women. 

The remaining percentages of men and women who are at risk of future seroconversion, 6% and 1%, are 

substantially out of balance; men now have greater risk of contracting HIV from this source than women. 
49 

5 Conclusions 

The purpose of this analytical study is to advance our understanding of the role of HIV discordance in 

HIV epidemics, particularly as it relates to discordance in cohabiting partners (couples). Data for the 

analysis come from DHS surveys in 10 countries in sub-Saharan Africa. Although not the direct focus of 

the study, information on the relative risk of HIV infection among cohabiting partners by age, education, 

and wealth is presented in tables in the Appendix. The study assessed discordance only within the 

subpopulation of cohabiting partners, which, of course, is a subset of the possible pairings of men and 

women that pose a risk of HIV infection. From this perspective, HIV discordance in cohabiting partners 

could have a much broader definition than the one obtained using these data. 

A number of generalizations have been possible. First, in the general population, HIV prevalence is 

higher among women than among men—typically at least 50% higher. Much of this excess infection can 

be traced to women under age 35. In a simple pooling of data from the 10 surveys the age distribution of 

HIV prevalence peaks about five years earlier for women than for men, but otherwise the patterns are 

similar. 

Second, living together with a partner or spouse (in a cohabiting partner relationship) is more common for 

men than for women. Only in Cameroon were women significantly more likely than men to have a 

cohabiting partner. This difference is important because having a cohabiting partner is associated with 

HIV prevalence, but differently for men and women. Typically, men with a cohabiting partner have higher 

levels of HIV prevalence than men without a cohabiting partner; in contrast, women with a cohabiting 

partner have lower levels of HIV prevalence than women without a cohabiting partner. 

In the subpopulation of men and women who do not have a cohabiting partner, the higher levels of HIV 

among women than men are of particular concern because of the magnitude of the difference: HIV 

prevalence is typically two to three times higher among women without a cohabiting partner than among 

men without a cohabiting partner. Interestingly, in the subpopulation of men and women who do have a 

cohabiting partner, the difference in HIV prevalence is usually small and not significant. It should be 

noted that this pattern of HIV prevalence does not necessarily mean that having a cohabiting partner is 

protective for women and risky for men, although that is a superficial implication. Such inferences are 

seriously constrained by the cross-sectional nature of the data. 

The length of the cohabiting partner relationship is another factor to be considered. As units of analysis, 

cohabiting partners (couples) are less stable than individual men and women and are not as well defined 

in the data. We do not know, for example, what the HIV status of the man and the woman was prior to 

their becoming cohabiting partners; we only know their current status. Additionally, after a partner 

becomes HIV-positive, he or she has a higher probability of dying and, if either partner dies, the couple, 

by definition, no longer exists. 

Infection of either cohabiting partner, whether before or after the other partner, can come from a third 

person. Sophisticated models of all of the possible routes of transmission have been developed by other 

researchers, but because we do not have information about when the couple was formed and when or how 

infection may have occurred, this analysis is largely descriptive. 

Within the subpopulation of men and women who have cohabiting partners we matched the associated 

partners into pairs (couples) and used the couple as the unit of analysis in the study. There are four 

possible combinations of HIV status: negative concordant, positive concordant, and two types of 

discordance. The observed distribution across these four types of HIV status was compared with a 

hypothetical distribution derived from an assumption of independence of the partners’ HIV statuses. 
50 

Under independence, the probabilities of the four combinations occurring can be determined from the 

HIV prevalence of men and women in the subpopulation of cohabiting couples. The null hypothesis of 

independence never fits the data satisfactorily, but it provides a baseline for comparison. 

The observed data always show a statistically significant excess of concordant couples and a deficit of 

both types of discordant couples. The excess number of concordant couples could have resulted from 

indirect selection in which HIV-negative men and women tend to select one another, and HIV-positive 

men and women tend to select one another. The pattern is referred to as indirect selection because it is 

probably not explicit. Most people in the survey countries do not know their HIV status or the HIV status 

of their partner; therefore, partner selection is typically based on factors such as similarity of residence 

(urban-rural), level of education, wealth status, etc., factors associated with HIV prevalence. Stratification 

in the tables has reduced the role of selection but probably not eliminated it. 

The principal source of significant departure from the assumption of independence of the partners’ HIV 

statuses is thought to be HIV seroconversion, through which the HIV-negative partner in a discordant pair 

becomes HIV-positive and the pair transitions to positive concordance. Each such infection will 

simultaneously reduce the number of discordant couples by one and increase the number of positive 

concordant couples by one. 

We measured the difference between the observed distribution and the expected distribution in two ways. 

The first measure is delta (Δ), the arithmetic difference between the observed proportion of positive 

concordant pairs and the expected proportion. Because a 2x2 table has only one degree of freedom, delta 

is also the excess in the negative concordant combination, and the negative of delta is the deficit in each 

of the discordant combinations. Although the value of delta varies a great deal from one subgroup to 

another, it was found to have a nearly perfect correlation with HIV prevalence in the urban/rural sector. In 

general, delta is about 40% of the mean prevalence for men and women. For example, as a rule of thumb, 

if the mean prevalence for men and women is 20%, then we would expect about 4% (.2*.2=.04) of 

couples to be positive concordant, but delta would be about 8% (.2*.4=.08), so the observed percentage 

positive concordant would be about 12% (.04+.08=.12). Each of the negative concordant combinations 

would include about 4% of couples (.2*.8-.12=.04). 

The second measure is Cohen’s kappa (with a factor of 100), which could range from zero, if the partners’ 

HIV statuses were indeed independent, to a maximum of 100 (approximately) if there were complete 

concordance. The observed kappa is almost always in a range from 30 to 70, with a median around 50. 

That is, the data are generally about halfway between the two extremes of independence and perfect 

concordance. 

It was shown that kappa can be expressed as the total deficit in the two discordant cells, divided by the 

expected numbers in those two cells. That is, kappa can also be interpreted as the relative deficit

(observed frequency minus expected frequency, divided by expected frequency) in those two cells. Under 

this interpretation, approximately 30% to 70% of couples expected to be discordant are positive 

concordant because of a combination of partner selection and HIV seroconversion. Extending that 

interpretation, two measures derived from kappa were also presented, SCm and SCw, to indicate the 

relative deficit in the two discordant cells, in terms of men and women, respectively. 

Among the 20 (10 each) urban and rural subgroups analyzed, only three showed significant results 

indicating that men and women in cohabiting couples differ in terms of their HIV prevalence or their type 

of discordance or their rates of seroconversion. In urban Kenya, women in cohabiting couples 1) tend to 

have higher prevalence than men, 2) tend to not be in a discordant partnership, compared with men, and 

3) may have seroconverted through discordance at a higher rate than men. The third inference must be 

tentative. It is based on the relative sizes of the two discordant cells and the positive concordant cell, 
51 

which reflect a combination of seroconversion and various kinds of selectivity. The opposite pattern was 

seen in rural Zambia and rural Zimbabwe. The finding that discordance is generally very symmetric with 

respect to men and women reinforces earlier findings by Eyawo et al. (2010) and Fishel et al. (2011). 

Interpretation of discordance in this study has also examined future risk of HIV seroconversion and how 

levels of risk differ for men and women. Three alternative measures of risk were calculated, based on 

different specifications of the population at risk. Our preferred indicator of the collective risk arising from 

discordance is the third measure, which compares HIV-negative men and women in discordant couples 

with the total population of HIV-negative men and women. By that measure, the percentage of HIVnegative individuals who have elevated risk of seroconversion because of discordance with a cohabiting 

partner ranges from 1% to 4%. It is similar for both men and women, although in a majority of the urban 

and rural subgroups included in this study women have somewhat higher risk of seroconversion than 

men. This type of risk is highest for men and women in urban Mozambique (4%), for men in urban 

Swaziland (4%), and for women in urban Zambia (4%). These measures of risk are limited to couples 

who were cohabiting and were matched, and both the man and the woman agreed to participate in the 

HIV testing in the respective DHS surveys. The risk of HIV infection because of discordance would be 

somewhat higher if a more expansive definition of couples were possible with DHS data. 

Because of major improvements in HIV testing in recent years (Staveteig et al., 2013), the opportunity to 

identify individuals at risk of contracting HIV is increasing rapidly. DHS data and the analysis presented 

here can assist policymakers with assessing the need for interventions designed to avert the transmission 

of HIV between cohabiting partners and measuring the impact of those interventions after 

implementation. 

53 

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Appendix 

Table A1.1 Percentage of men and women who are HIV-positivea and percentage of men and women who have cohabiting partnersb, by 

sex, and significance level for the difference between men and women, according to age, 10 DHS surveys in sub-Saharan Africa, 2006-

2012 

Survey Category 

Percent HIV-positive Percent with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Cameroon 2011 DHS All 2.9 5.6 193 13,449 W+++ 34.5 40.1 116 13,449 W+++ Cameroon 2011 DHS 15-19 0.4 2.0 565 3,194 W++ 0.7 12.7 1,849 3,194 W+++ Cameroon 2011 DHS 20-24 0.6 3.4 534 2,653 W+++ 9.3 36.6 392 2,653 W+++ Cameroon 2011 DHS 25-29 3.0 7.6 254 2,278 W++ 35.5 54.1 152 2,278 W+++ Cameroon 2011 DHS 30-34 5.3 7.3 137 1,702 ns 59.6 56.5 95 1,702 ns Cameroon 2011 DHS 35-39 5.8 9.9 171 1,491 W+ 73.0 56.6 78 1,491 M+++ Cameroon 2011 DHS 40-44 4.7 7.1 151 1,158 ns 69.8 48.0 69 1,158 M+++ Cameroon 2011 DHS 45-49 6.3 6.4 101 975 ns 69.8 40.5 58 975 M+++ Kenya 2008-09 DHS All 4.3 8.1 189 6,734 W+++ 39.0 35.3 90 6,734 M++ Kenya 2008-09 DHS 15-19 0.7 2.9 400 1,520 W+ 0.1 6.1 4,896 1,520 W+++ Kenya 2008-09 DHS 20-24 1.5 6.5 428 1,321 W+++ 13.1 38.6 295 1,321 W+++ Kenya 2008-09 DHS 25-29 6.6 10.6 162 1,102 ns 48.9 53.1 109 1,102 ns Kenya 2008-09 DHS 30-34 6.9 11.1 160 950 ns 70.4 47.8 68 950 M+++ Kenya 2008-09 DHS 35-39 10.5 9.0 86 653 ns 68.5 43.6 64 653 M+++ Kenya 2008-09 DHS 40-44 5.8 14.3 247 638 W+ 71.0 38.8 55 638 M+++ Kenya 2008-09 DHS 45-49 4.3 6.6 154 549 ns 77.0 26.7 35 549 M+++ Lesotho 2009 DHS All 17.9 26.7 149 6,567 W+++ 26.2 22.1 84 6,567 M+++ Lesotho 2009 DHS 15-19 2.8 4.1 146 1,681 ns 0.8 6.4 754 1,681 W+++ Lesotho 2009 DHS 20-24 5.9 24.3 413 1,381 W+++ 12.2 24.7 203 1,381 W+++ Lesotho 2009 DHS 25-29 18.4 35.4 192 1,012 W+++ 34.7 31.5 91 1,012 ns Lesotho 2009 DHS 30-34 40.1 40.8 102 843 ns 48.6 27.3 56 843 M+++ Lesotho 2009 DHS 35-39 35.2 42.1 120 662 ns 57.0 22.8 40 662 M+++ Lesotho 2009 DHS 40-44 39.4 36.3 92 498 ns 52.4 27.7 53 498 M+++ 

(Continued...) 

57 
Table A1.1 – Continued

Survey Category 

Percent HIV-positive Percent with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Lesotho 2009 DHS 45-49 32.2 29.6 92 489 ns 48.7 27.4 56 489 M+++ Malawi 2010 DHS All 8.1 12.8 159 13,528 W+++ 50.8 48.7 96 13,528 M+ Malawi 2010 DHS 15-19 1.3 4.1 318 3,226 W++ 1.7 17.2 998 3,226 W+++ Malawi 2010 DHS 20-24 2.8 6.4 231 2,566 W++ 32.3 58.2 180 2,566 W+++ Malawi 2010 DHS 25-29 6.9 13.4 194 2,443 W+++ 70.8 62.6 88 2,443 M+ Malawi 2010 DHS 30-34 10.7 20.9 196 1,811 W+++ 83.3 58.8 71 1,811 M+++ Malawi 2010 DHS 35-39 17.9 23.7 133 1,554 W+ 84.4 59.7 71 1,554 M+++ Malawi 2010 DHS 40-44 20.5 20.4 99 1,037 ns 82.7 51.5 62 1,037 M+++ Malawi 2010 DHS 45-49 14.8 16.0 108 892 ns 83.7 41.1 49 892 M+++ Mozambique 2009 AIS All 9.1 13.1 143 9,100 W+++ 59.1 53.4 91 9,100 M+++ Mozambique 2009 AIS 15-19 2.7 7.1 261 1,721 W+++ 4.6 32.6 706 1,721 W+++ Mozambique 2009 AIS 20-24 4.9 14.4 293 1,688 W+++ 48.9 55.5 114 1,688 ns Mozambique 2009 AIS 25-29 11.6 16.9 145 1,512 W+ 74.9 63.0 84 1,512 M+++ Mozambique 2009 AIS 30-34 13.5 15.3 114 1,406 ns 80.6 61.4 76 1,406 M+++ Mozambique 2009 AIS 35-39 13.9 13.3 96 1,182 ns 86.2 58.4 68 1,182 M+++ Mozambique 2009 AIS 40-44 12.4 12.9 103 773 ns 82.8 52.1 63 773 M+++ Mozambique 2009 AIS 45-49 10.7 10.1 94 818 ns 83.9 49.4 59 818 M+++ Swaziland 2006-07 DHS All 19.5 31.0 159 8,210 W+++ 18.9 15.7 83 8,210 M+++ Swaziland 2006-07 DHS 15-19 1.9 10.0 540 2,444 W+++ 0.1 4.2 4,261 2,444 W+++ 

Swaziland 2006-07 DHS 20-24 12.4 38.4 310 1,706 W+++ 5.5 16.4 297 1,706 W+++ Swaziland 2006-07 DHS 25-29 27.7 49.1 177 1,192 W+++ 23.7 25.3 107 1,192 ns Swaziland 2006-07 DHS 30-34 43.7 45.0 103 912 ns 39.4 26.3 67 912 M+++ Swaziland 2006-07 DHS 35-39 44.7 37.6 84 763 ns 43.7 22.3 51 763 M+++ Swaziland 2006-07 DHS 40-44 40.9 27.7 68 617 M+++ 51.9 17.6 34 617 M+++ Swaziland 2006-07 DHS 45-49 27.9 21.5 77 575 ns 57.9 7.4 13 575 M+++ Tanzania 2011-12 AIS All 3.8 6.2 162 17,711 W+++ 46.6 36.9 79 17,711 M+++ Tanzania 2011-12 AIS 15-19 0.8 1.3 165 4,086 ns 1.4 15.5 1,091 4,086 W+++ Tanzania 2011-12 AIS 20-24 1.7 4.4 255 3,135 W++ 24.6 41.4 169 3,135 W+++ Tanzania 2011-12 AIS 25-29 2.5 7.0 275 2,766 W+++ 56.6 52.1 92 2,766 ns 

(Continued...) 

58 
Table A1.1 – Continued

Survey Category 

Percent HIV-positive Percent with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Tanzania 2011-12 AIS 30-34 6.2 9.2 148 2,334 ns 71.0 55.0 78 2,334 M+++ Tanzania 2011-12 AIS 35-39 7.1 8.0 112 2,261 ns 81.2 45.6 56 2,261 M+++ Tanzania 2011-12 AIS 40-44 6.9 9.2 133 1,776 ns 80.1 33.7 42 1,776 M+++ Tanzania 2011-12 AIS 45-49 6.6 10.3 156 1,353 ns 78.4 10.7 14 1,353 M+++ Uganda 2011 AIS All 6.1 8.3 137 19,562 W+++ 47.6 41.6 87 19,562 M+++ Uganda 2011 AIS 15-19 1.7 3.0 176 4,450 W+ 1.5 13.5 921 4,450 W+++ Uganda 2011 AIS 20-24 2.8 7.1 255 3,512 W+++ 28.2 46.5 165 3,512 W+++ Uganda 2011 AIS 25-29 4.0 9.8 243 3,247 W+++ 59.9 54.2 91 3,247 M++ Uganda 2011 AIS 30-34 9.1 11.0 121 2,578 ns 71.8 52.3 73 2,578 M+++ Uganda 2011 AIS 35-39 11.0 12.1 110 2,448 ns 74.5 54.4 73 2,448 M+++ Uganda 2011 AIS 40-44 11.3 10.7 95 1,804 ns 77.7 46.2 60 1,804 M+++ Uganda 2011 AIS 45-49 10.2 10.5 103 1,524 ns 76.8 37.6 49 1,524 M+++ Zambia 2007 DHS All 12.2 15.9 131 10,337 W+++ 47.3 45.6 96 10,337 ns Zambia 2007 DHS 15-19 3.6 5.7 161 2,326 W+ 0.9 14.0 1,479 2,326 W+++ Zambia 2007 DHS 20-24 5.0 11.6 231 1,868 W+++ 22.4 49.8 223 1,868 W+++ Zambia 2007 DHS 25-29 11.3 19.7 175 1,839 W+++ 58.5 57.3 98 1,839 ns Zambia 2007 DHS 30-34 17.2 25.7 150 1,593 W++ 72.8 57.2 79 1,593 M+++ Zambia 2007 DHS 35-39 22.1 24.6 111 1,183 ns 81.6 60.3 74 1,183 M+++ Zambia 2007 DHS 40-44 23.8 18.0 76 847 ns 81.6 52.0 64 847 M+++ Zambia 2007 DHS 45-49 18.5 12.2 66 681 ns 81.5 45.9 56 681 M+++ Zimbabwe 2010-11 DHS All 12.2 17.7 145 13,669 W+++ 40.5 34.4 85 13,669 M+++ Zimbabwe 2010-11 DHS 15-19 3.4 4.3 129 3,113 ns 0.6 13.5 2,090 3,113 W+++ Zimbabwe 2010-11 DHS 20-24 3.9 10.5 272 2,710 W+++ 20.8 39.2 188 2,710 W+++ Zimbabwe 2010-11 DHS 25-29 10.2 20.1 198 2,469 W+++ 53.3 45.3 85 2,469 M+++ Zimbabwe 2010-11 DHS 30-34 16.9 29.3 173 1,877 W+++ 66.8 45.9 69 1,877 M+++ Zimbabwe 2010-11 DHS 35-39 25.1 29.3 117 1,554 ns 72.7 40.4 56 1,554 M+++ Zimbabwe 2010-11 DHS 40-44 25.7 25.5 99 1,110 ns 70.7 34.5 49 1,110 M+++ Zimbabwe 2010-11 DHS 45-49 30.6 22.9 75 837 M+ 68.8 22.5 33 837 M+++ 

(Continued...) 

59 
Table A1.1 – Continued

Note: Ratios are calculated as 100*(% for men)/(% for women) 

a: restricted to respondents who were tested b: restricted to respondents in households in which both men and women were interviewed N is weighted Significance refers to a test of the null hypothesis that men and women have the same HIV prevalence or the same cohabiting partner rates M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

60 
Table A1.2 Percentage HIV-positive among men and women who do not have a cohabiting partner, and percentage HIV-positive among 

men and women who do have a cohabiting partner, by sex, and significance level for the difference between men and women, according to age, 10 DHS surveys in sub-Saharan Africa, 2006-2012 

Survey Category 

No cohabiting partner: % HIV-positive Has cohabiting partner: % HIV-positive 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Cameroon 2011 DHS All 1.9 6.2 333 8,404 W+++ 4.8 4.6 96 5,046 ns Cameroon 2011 DHS 15-19 0.4 1.8 499 2,974 W++ 0.0 3.6 220 ns Cameroon 2011 DHS 20-24 0.7 3.6 546 2,006 W+++ 0.5 3.1 678 646 ns Cameroon 2011 DHS 25-29 2.8 10.1 360 1,238 W+ 3.4 5.5 164 1,040 ns Cameroon 2011 DHS 30-34 6.4 10.7 165 716 ns 4.6 4.7 103 986 ns Cameroon 2011 DHS 35-39 5.1 14.8 289 536 W++ 6.1 6.2 102 955 ns Cameroon 2011 DHS 40-44 2.6 9.5 368 483 W+ 5.6 4.4 80 675 ns Cameroon 2011 DHS 45-49 7.7 8.6 112 451 ns 5.7 3.1 54 524 ns Kenya 2008-09 DHS All 3.4 8.9 260 4,250 W+++ 5.7 6.6 117 2,483 ns Kenya 2008-09 DHS 15-19 0.7 2.0 275 1,470 ns 0.0 16.5 50 ns Kenya 2008-09 DHS 20-24 1.5 6.9 462 952 W++ 1.8 6.0 338 369 ns Kenya 2008-09 DHS 25-29 5.4 15.1 280 535 W+ 7.8 6.7 86 568 ns Kenya 2008-09 DHS 30-34 9.6 13.9 144 401 ns 5.8 8.0 138 548 ns Kenya 2008-09 DHS 35-39 22.2 11.2 51 301 ns 5.0 6.1 120 352 ns Kenya 2008-09 DHS 40-44 3.2 20.2 636 301 W++ 6.9 5.1 75 337 ns Kenya 2008-09 DHS 45-49 5.8 8.1 139 289 ns 3.8 2.7 70 260 ns Lesotho 2009 DHS All 13.9 27.0 195 5,004 W+++ 29.2 25.6 88 1,563 ns Lesotho 2009 DHS 15-19 2.8 3.8 135 1,617 ns 0.0 8.1 63 ns Lesotho 2009 DHS 20-24 5.9 25.4 427 1,113 W+++ 5.5 21.0 382 268 W++ Lesotho 2009 DHS 25-29 18.9 37.8 200 680 W+++ 17.5 30.2 173 332 W+ Lesotho 2009 DHS 30-34 38.0 45.5 120 537 ns 42.3 28.3 67 307 M+ Lesotho 2009 DHS 35-39 36.2 46.1 127 421 ns 34.4 28.7 84 241 ns Lesotho 2009 DHS 40-44 47.9 37.2 78 318 ns 31.6 34.0 108 181 ns Lesotho 2009 DHS 45-49 31.2 32.5 104 318 ns 33.4 21.8 65 171 ns Malawi 2010 DHS All 5.3 15.3 288 6,806 W+++ 10.7 10.2 95 6,722 ns Malawi 2010 DHS 15-19 1.3 3.3 253 2,922 W+ 0.0 7.9 303 ns 

(Continued...) 

61 
Table A1.2 – Continued

Survey Category 

No cohabiting partner: % HIV-positive Has cohabiting partner: % HIV-positive 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Malawi 2010 DHS 20-24 2.1 8.5 406 1,363 W+++ 4.2 4.8 117 1,202 ns Malawi 2010 DHS 25-29 9.8 21.5 219 832 W++ 5.8 8.6 150 1,611 ns Malawi 2010 DHS 30-34 20.0 29.9 149 539 ns 8.8 14.6 166 1,272 W+ Malawi 2010 DHS 35-39 24.4 32.8 135 448 ns 16.7 17.6 105 1,106 ns Malawi 2010 DHS 40-44 30.3 26.6 88 352 ns 18.5 14.5 78 685 ns Malawi 2010 DHS 45-49 24.6 22.8 93 350 ns 12.9 6.2 48 542 ns Mozambique 2009 AIS All 7.8 17.1 219 4,021 W+++ 10.1 9.6 95 5,080 ns Mozambique 2009 AIS 15-19 2.9 7.1 250 1,396 W++ 0.0 7.1 325 ns Mozambique 2009 AIS 20-24 5.6 17.9 322 792 W+++ 4.3 11.6 273 895 W++ Mozambique 2009 AIS 25-29 14.7 25.6 174 487 ns 10.6 11.7 111 1,026 ns Mozambique 2009 AIS 30-34 21.4 22.9 107 434 ns 11.5 10.5 91 973 ns Mozambique 2009 AIS 35-39 21.9 19.6 90 352 ns 12.7 8.8 70 830 ns Mozambique 2009 AIS 40-44 23.5 19.9 85 265 ns 10.1 6.4 63 508 ns Mozambique 2009 AIS 45-49 12.2 15.1 124 295 ns 10.5 4.9 47 523 M+ Swaziland 2006-07 DHS All 15.6 29.9 191 6,807 W+++ 36.2 36.9 102 1,402 ns Swaziland 2006-07 DHS 15-19 1.9 8.9 479 2,393 W+++ 0.0 35.8 51 ns Swaziland 2006-07 DHS 20-24 10.9 36.9 338 1,508 W+++ 37.4 45.9 123 198 ns Swaziland 2006-07 DHS 25-29 26.1 50.8 194 899 W+++ 32.7 44.3 135 293 W+ Swaziland 2006-07 DHS 30-34 42.5 48.2 113 625 ns 45.6 36.1 79 287 ns Swaziland 2006-07 DHS 35-39 45.8 40.7 89 528 ns 43.3 26.7 62 235 M+ Swaziland 2006-07 DHS 40-44 45.7 27.8 61 433 M+++ 36.4 26.8 74 184 ns Swaziland 2006-07 DHS 45-49 37.1 22.6 61 422 M+ 21.3 8.3 39 153 ns Tanzania 2011-12 AIS All 2.8 7.1 260 10,452 W+++ 5.0 4.5 89 7,258 ns Tanzania 2011-12 AIS 15-19 0.8 1.4 174 3,707 ns 0.0 0.8 378 ns Tanzania 2011-12 AIS 20-24 1.5 5.5 362 2,064 W+++ 2.5 3.0 121 1,071 ns Tanzania 2011-12 AIS 25-29 2.1 9.9 472 1,280 W+++ 2.9 4.3 149 1,486 ns Tanzania 2011-12 AIS 30-34 8.1 12.6 157 899 ns 5.5 6.4 116 1,435 ns Tanzania 2011-12 AIS 35-39 8.2 10.1 124 900 ns 6.9 5.4 79 1,361 ns 

(Continued...)

62 
Table A1.2 – Continued

Survey Category 

No cohabiting partner: % HIV-positive Has cohabiting partner: % HIV-positive 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Tanzania 2011-12 AIS 40-44 10.2 11.2 109 794 ns 6.1 5.3 87 982 ns Tanzania 2011-12 AIS 45-49 14.0 10.7 76 808 ns 4.5 7.2 158 545 ns Uganda 2011 AIS All 5.4 9.7 179 10,908 W+++ 6.8 6.4 94 8,654 ns Uganda 2011 AIS 15-19 1.6 3.0 181 4,093 W+ 4.2 2.8 66 356 ns Uganda 2011 AIS 20-24 2.5 7.8 315 2,132 W+++ 3.6 6.3 175 1,380 ns Uganda 2011 AIS 25-29 5.3 13.4 255 1,411 W+++ 3.2 6.7 210 1,836 W++ Uganda 2011 AIS 30-34 13.9 14.0 101 1,012 ns 7.2 8.2 114 1,566 ns Uganda 2011 AIS 35-39 16.5 18.8 114 892 ns 9.2 6.5 71 1,556 ns Uganda 2011 AIS 40-44 19.6 14.3 73 695 ns 8.9 6.6 74 1,109 ns Uganda 2011 AIS 45-49 17.3 14.1 82 673 ns 8.0 4.5 57 851 ns Zambia 2007 DHS All 9.2 18.4 201 5,546 W+++ 15.5 13.0 84 4,791 M++ Zambia 2007 DHS 15-19 3.4 5.4 159 2,143 ns 17.7 7.7 43 183 ns Zambia 2007 DHS 20-24 4.4 13.7 311 1,162 W+++ 7.3 9.5 132 706 ns Zambia 2007 DHS 25-29 11.8 26.3 223 777 W+++ 10.9 14.9 136 1,062 ns Zambia 2007 DHS 30-34 23.3 35.9 155 565 W+ 14.9 18.1 121 1,028 ns Zambia 2007 DHS 35-39 30.8 39.3 128 347 ns 20.1 14.9 74 836 ns Zambia 2007 DHS 40-44 38.2 24.8 65 291 ns 20.5 11.8 57 556 M+ Zambia 2007 DHS 45-49 30.9 18.1 59 262 ns 15.7 5.2 33 419 M++ Zimbabwe 2010-11 DHS All 8.7 19.3 222 8,535 W+++ 17.3 14.8 86 5,134 M++ Zimbabwe 2010-11 DHS 15-19 3.4 4.1 122 2,870 ns 0.0 5.6 243 ns Zimbabwe 2010-11 DHS 20-24 3.9 11.7 297 1,840 W+++ 3.6 8.8 245 870 W+ Zimbabwe 2010-11 DHS 25-29 8.6 24.0 279 1,250 W+++ 11.5 15.4 135 1,218 ns Zimbabwe 2010-11 DHS 30-34 18.0 35.1 195 831 W+++ 16.5 22.5 137 1,045 W+ Zimbabwe 2010-11 DHS 35-39 34.5 37.0 107 701 ns 21.7 18.4 85 853 ns Zimbabwe 2010-11 DHS 40-44 28.1 29.8 106 552 ns 24.8 17.2 69 558 ns Zimbabwe 2010-11 DHS 45-49 39.1 25.6 66 491 M+ 27.5 13.9 51 346 M+ 

(Continued...) 

63 
Table A1.2 – Continued

Note: Restricted to respondents who were tested and who lived in households in which both men and women were interviewed. 

Ratios are calculated as 100*(% for men)/(% for women) N is weighted Significance refers to a test of the null hypothesis that men and women have the same HIV prevalence, controlling for cohabiting partner status (no partner or has 

a partner) M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

64 
Table A1.3 Percentage with a cohabiting partner among men and women who are HIV-negative, and percentage with a cohabiting 

partner among men and women who are HIV-positive, by sex, and significance level for the difference between men and women, according to age, 10 DHS surveys in sub-Saharan Africa, 2006-2012 (restricted to respondents who were tested and who live in households in which both men and women were interviewed) 

Survey Category 

HIV-negative: % with a cohabiting partner HIV-positive: % with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Cameroon 2011 DHS All 33.8 40.5 120 12,868 W+++ 57.7 33.2 58 581 M+++ Cameroon 2011 DHS 15-19 0.7 12.5 1,813 3,155 W+++ 0.0 22.5 39 ns Cameroon 2011 DHS 20-24 9.3 36.7 392 2,595 W+++ 6.7 33.1 498 58 ns Cameroon 2011 DHS 25-29 35.3 55.3 156 2,152 W+++ 39.8 39.2 99 126 ns Cameroon 2011 DHS 30-34 60.0 58.1 97 1,593 ns 50.9 36.4 71 108 ns Cameroon 2011 DHS 35-39 72.8 58.9 81 1,371 M+++ 76.3 35.2 46 120 M+++ Cameroon 2011 DHS 40-44 69.1 49.4 72 1,089 M+++ 83.4 30.3 36 69 M+++ Cameroon 2011 DHS 45-49 70.2 41.9 60 913 M+++ 63.2 19.7 31 62 M+ Kenya 2008-09 DHS All 38.5 35.8 93 6,298 ns 51.4 28.8 56 436 M+++ Kenya 2008-09 DHS 15-19 0.1 5.3 4,180 1,492 W+++ 0.0 35.3 28 ns Kenya 2008-09 DHS 20-24 13.1 38.8 297 1,263 W+++ 15.2 35.3 233 58 ns Kenya 2008-09 DHS 25-29 48.3 55.4 115 1,003 ns 58.1 33.5 58 100 ns Kenya 2008-09 DHS 30-34 71.2 49.4 69 862 M+++ 58.7 34.5 59 88 ns Kenya 2008-09 DHS 35-39 72.6 45.0 62 591 M+++ 33.1 29.5 89 63 ns Kenya 2008-09 DHS 40-44 70.2 43.0 61 570 M+++ 84.1 13.9 17 68 M+++ Kenya 2008-09 DHS 45-49 77.4 27.8 36 518 M+++ 68.8 10.7 16 31 M+ Lesotho 2009 DHS All 22.6 22.4 99 5,058 ns 42.7 21.1 50 1,509 M+++ Lesotho 2009 DHS 15-19 0.9 6.1 702 1,622 W+++ 0.0 12.6 59 ns Lesotho 2009 DHS 20-24 12.3 25.8 210 1,153 W+++ 11.4 21.4 187 228 ns Lesotho 2009 DHS 25-29 35.1 34.0 97 727 ns 32.9 26.8 82 285 ns Lesotho 2009 DHS 30-34 46.9 33.0 70 502 M+ 51.3 18.9 37 341 M+++ Lesotho 2009 DHS 35-39 57.6 28.1 49 402 M+++ 55.7 15.6 28 261 M+++ Lesotho 2009 DHS 40-44 59.2 28.7 49 312 M+++ 42.1 26.0 62 186 M+ Lesotho 2009 DHS 45-49 47.9 30.4 64 340 M++ 50.4 20.1 40 149 M+++ Malawi 2010 DHS All 49.3 50.2 102 12,087 ns 67.4 38.8 58 1,441 M+++ 

(Continued...) 

65 
Table A1.3 – Continued

Survey Category 

HIV-negative: % with a cohabiting partner HIV-positive: % with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Malawi 2010 DHS 15-19 1.7 16.5 946 3,139 W+++ 0.0 33.1 87 ns Malawi 2010 DHS 20-24 31.9 59.1 186 2,443 W+++ 48.8 44.4 91 123 ns Malawi 2010 DHS 25-29 71.7 66.1 92 2,180 ns 58.7 40.3 69 264 ns Malawi 2010 DHS 30-34 85.1 63.5 75 1,519 M+++ 68.6 41.1 60 292 M++ Malawi 2010 DHS 35-39 85.6 64.5 75 1,228 M+++ 78.7 44.3 56 327 M+++ Malawi 2010 DHS 40-44 84.8 55.3 65 825 M+++ 74.4 36.6 49 212 M+++ Malawi 2010 DHS 45-49 85.6 45.9 54 754 M+++ 72.9 16.0 22 138 M+++ Mozambique 2009 AIS All 58.5 55.6 95 8,061 ns 65.0 39.2 60 1,040 M+++ Mozambique 2009 AIS 15-19 4.7 32.6 687 1,635 W+++ 0.0 32.4 86 ns Mozambique 2009 AIS 20-24 49.2 57.4 117 1,504 ns 42.3 44.8 106 183 ns Mozambique 2009 AIS 25-29 75.8 66.9 88 1,290 M++ 68.1 43.7 64 223 M+ Mozambique 2009 AIS 30-34 82.3 64.8 79 1,202 M+++ 69.1 42.1 61 205 M++ Mozambique 2009 AIS 35-39 87.5 61.4 70 1,021 M+++ 78.3 38.7 49 160 M+++ Mozambique 2009 AIS 40-44 85.0 56.0 66 675 M+++ 67.4 25.8 38 98 M+++ Mozambique 2009 AIS 45-49 84.2 52.2 62 733 M+++ 81.8 24.1 30 85 M+++ Swaziland 2006-07 DHS All 15.0 14.3 96 6,081 ns 35.0 18.6 53 2,128 M+++ Swaziland 2006-07 DHS 15-19 0.1 3.0 2,985 2,301 W++ 0.0 14.9 144 ns Swaziland 2006-07 DHS 20-24 3.9 14.4 364 1,247 W+++ 16.7 19.6 117 459 ns Swaziland 2006-07 DHS 25-29 22.0 27.7 126 719 ns 28.0 22.8 82 473 ns Swaziland 2006-07 DHS 30-34 38.1 30.6 80 506 ns 41.1 21.0 51 406 M+++ Swaziland 2006-07 DHS 35-39 44.8 26.1 58 455 M+++ 42.4 15.8 37 308 M+++ Swaziland 2006-07 DHS 40-44 55.8 17.8 32 417 M+++ 46.2 17.1 37 200 M+++ Swaziland 2006-07 DHS 45-49 63.3 8.7 14 437 M+++ 44.2 2.9 7 138 M+++ Tanzania 2011-12 AIS All 46.0 37.6 82 16,795 M+++ 61.4 26.7 44 915 M+++ Tanzania 2011-12 AIS 15-19 1.4 15.6 1,088 4,042 W+++ 0.0 9.6 43 ns Tanzania 2011-12 AIS 20-24 24.4 42.0 172 3,032 W+++ 34.6 27.8 80 103 ns Tanzania 2011-12 AIS 25-29 56.5 53.6 95 2,617 ns 64.2 32.1 50 149 M+ Tanzania 2011-12 AIS 30-34 71.5 56.7 79 2,148 M+++ 62.4 38.1 61 186 M+ 

(Continued...)

66 
Table A1.3 – Continued

Survey Category 

HIV-negative: % with a cohabiting partner HIV-positive: % with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Tanzania 2011-12 AIS 35-39 81.5 46.9 58 2,088 M+++ 78.5 31.0 40 173 M+++ Tanzania 2011-12 AIS 40-44 80.8 35.2 44 1,632 M+++ 70.5 19.5 28 144 M+++ Tanzania 2011-12 AIS 45-49 80.1 11.1 14 1,236 M+++ 54.0 7.4 14 117 M+++ Uganda 2011 AIS All 47.3 42.5 90 18,123 M+++ 53.4 32.0 60 1,439 M+++ Uganda 2011 AIS 15-19 1.4 13.5 947 4,344 W+++ 3.7 12.8 347 106 ns 

Uganda 2011 AIS 20-24 28.0 46.8 167 3,322 W+++ 36.4 41.2 113 189 ns 

Uganda 2011 AIS 25-29 60.5 56.1 93 3,006 ns 47.6 37.1 78 241 ns Uganda 2011 AIS 30-34 73.2 53.8 74 2,316 M+++ 56.9 39.0 69 263 M+ Uganda 2011 AIS 35-39 76.2 57.9 76 2,164 M+++ 62.0 29.3 47 284 M+++ Uganda 2011 AIS 40-44 79.8 48.4 61 1,606 M+++ 61.2 28.5 47 198 M+++ Uganda 2011 AIS 45-49 78.5 40.0 51 1,366 M+++ 60.4 16.2 27 158 M+++ Zambia 2007 DHS All 45.5 47.2 104 8,866 ns 60.2 37.1 62 1,471 M+++ Zambia 2007 DHS 15-19 0.8 13.7 1,698 2,216 W+++ 4.7 18.6 396 110 ns 

Zambia 2007 DHS 20-24 21.8 51.0 233 1,705 W+++ 32.2 40.9 127 163 ns Zambia 2007 DHS 25-29 58.7 60.8 104 1,539 ns 56.6 43.2 76 299 ns Zambia 2007 DHS 30-34 74.8 63.1 84 1,248 M+++ 63.2 40.2 64 346 M+++ Zambia 2007 DHS 35-39 83.6 68.1 81 907 M+++ 74.3 36.5 49 277 M+++ Zambia 2007 DHS 40-44 85.0 56.1 66 672 M+++ 70.4 34.1 48 175 M+++ Zambia 2007 DHS 45-49 84.3 49.6 59 579 M+++ 69.1 19.6 28 102 M+++ Zimbabwe 2010-11 DHS All 38.8 36.2 93 11,562 M++ 58.2 29.2 50 2,107 M+++ Zimbabwe 2010-11 DHS 15-19 0.8 13.6 1,684 2,992 W+++ 0.0 17.9 121 ns Zimbabwe 2010-11 DHS 20-24 20.6 40.9 198 2,499 W+++ 19.2 33.7 175 211 ns Zimbabwe 2010-11 DHS 25-29 53.6 48.6 91 2,073 ns 61.4 35.3 58 396 M+++ Zimbabwe 2010-11 DHS 30-34 69.3 50.6 73 1,424 M+++ 67.0 35.5 53 452 M+++ Zimbabwe 2010-11 DHS 35-39 77.0 47.7 62 1,127 M+++ 63.7 25.9 41 428 M+++ 

Zimbabwe 2010-11 DHS 40-44 72.8 38.0 52 826 M+++ 69.3 23.1 33 284 M+++ Zimbabwe 2010-11 DHS 45-49 76.7 25.7 34 622 M+++ 66.1 14.0 21 215 M+++ 

(Continued...) 

67 
Table A1.3 – Continued

Note: Restricted to respondents who were tested and who lived in households in which both men and women were interviewed. 

Ratios are calculated as 100*(% for men)/(% for women) N is weighted Significance refers to a test of the null hypothesis that men and women have the same cohabiting partner rates, controlling for HIV status (negative or positive) 

M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

68 
Table A1.4 Among cohabiting couples, the percent distribution of observed and expected couples (M=man, W=woman) by four 

combinations of couple HIV serostatus (negative concordance, female positive discordance, male positive discordance, and positive 

concordance), according to age (age of woman, age of man, and age difference between man and woman), 10 DHS surveys in subSaharan Africa, 2006-2012 (restricted to cohabiting couples in which both the man and the woman were tested for HIV) 

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Cameroon 2011 DHS All All 92.1 90.7 3.3 4.6 3.1 4.4 1.5 0.2 Cameroon 2011 DHS Age of woman 15-19 95.3 93.5 0.8 2.5 2.1 3.8 1.9 0.1 Cameroon 2011 DHS Age of woman 20-24 95.1 94.4 2.0 2.6 2.3 2.9 0.7 0.1 Cameroon 2011 DHS Age of woman 25-29 90.4 88.6 3.8 5.7 3.6 5.4 2.2 0.3 

Cameroon 2011 DHS Age of woman 30-34 93.0 91.8 3.0 4.3 2.5 3.8 1.4 0.2 

Cameroon 2011 DHS Age of woman 35-39 88.7 87.3 5.8 7.3 3.6 5.0 1.9 0.4 

Cameroon 2011 DHS Age of woman 40-44 87.2 86.0 4.7 5.9 6.3 7.6 1.8 0.5 Cameroon 2011 DHS Age of woman 45-49 95.4 95.4 2.0 1.9 2.7 2.6 0.0 0.1 Cameroon 2011 DHS Age of man 15-19 Cameroon 2011 DHS Age of man 20-24 96.8 96.4 2.7 3.1 0.0 0.4 0.5 0.0 

Cameroon 2011 DHS Age of man 25-29 94.0 93.5 2.8 3.3 2.7 3.2 0.6 0.1 

Cameroon 2011 DHS Age of man 30-34 92.7 90.7 2.9 4.9 2.1 4.2 2.2 0.2 

Cameroon 2011 DHS Age of man 35-39 90.5 89.3 3.9 5.1 4.1 5.3 1.5 0.3 

Cameroon 2011 DHS Age of man 40-44 90.4 88.4 3.8 5.7 3.5 5.5 2.3 0.4 

Cameroon 2011 DHS Age of man 45-49 91.7 90.7 3.2 4.1 4.0 4.9 1.2 0.2 

Cameroon 2011 DHS Age difference M<W 88.2 87.7 6.6 7.2 4.3 4.8 0.9 0.4 Cameroon 2011 DHS Age difference M-W 0-4 90.9 90.2 4.6 5.4 3.5 4.2 1.0 0.3 

Cameroon 2011 DHS Age difference M-W 5-9 93.1 91.6 2.8 4.3 2.4 3.9 1.7 0.2 

Cameroon 2011 DHS Age difference M-W 10+ 92.7 90.9 1.9 3.7 3.3 5.2 2.0 0.2 

Kenya 2008-09 DHS All All 91.2 88.4 3.1 5.9 2.5 5.3 3.2 0.4 Kenya 2008-09 DHS Age of woman 15-19 82.4 76.7 6.1 11.8 4.3 10.0 7.3 1.5 

Kenya 2008-09 DHS Age of woman 20-24 90.0 87.2 3.3 6.1 3.5 6.3 3.2 0.4 Kenya 2008-09 DHS Age of woman 25-29 91.9 89.3 3.5 6.1 1.7 4.3 2.9 0.3 Kenya 2008-09 DHS Age of woman 30-34 91.9 88.9 3.1 6.1 1.7 4.7 3.3 0.3 

(Continued...) 

69 
Table A1.4 – Continued

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Kenya 2008-09 DHS Age of woman 35-39 92.3 88.7 0.6 4.2 3.1 6.7 3.9 0.3 Kenya 2008-09 DHS Age of woman 40-44 91.8 91.3 3.9 4.4 3.6 4.1 0.7 0.2 

Kenya 2008-09 DHS Age of woman 45-49 Kenya 2008-09 DHS Age of man 15-19 Kenya 2008-09 DHS Age of man 20-24 97.7 97.7 0.6 0.6 1.7 1.7 0.0 0.0 

Kenya 2008-09 DHS Age of man 25-29 87.6 83.5 4.5 8.6 3.0 7.2 4.8 0.7 

Kenya 2008-09 DHS Age of man 30-34 90.3 87.2 3.8 6.8 2.5 5.6 3.5 0.4 

Kenya 2008-09 DHS Age of man 35-39 92.3 89.7 2.8 5.4 2.0 4.6 2.9 0.3 

Kenya 2008-09 DHS Age of man 40-44 91.7 88.7 1.6 4.6 3.4 6.4 3.3 0.3 

Kenya 2008-09 DHS Age of man 45-49 92.8 90.8 3.3 5.2 1.8 3.7 2.2 0.2 

Kenya 2008-09 DHS Age difference M<W 96.8 96.2 1.0 1.5 1.6 2.2 0.6 0.0 Kenya 2008-09 DHS Age difference M-W 0-4 90.5 87.4 3.8 6.9 2.2 5.3 3.5 0.4 

Kenya 2008-09 DHS Age difference M-W 5-9 92.4 89.3 2.0 5.1 2.2 5.3 3.4 0.3 Kenya 2008-09 DHS Age difference M-W 10+ 87.0 84.3 5.2 7.9 4.4 7.1 3.4 0.7 Lesotho 2009 DHS All All 62.7 51.4 7.9 19.2 10.1 21.4 19.3 8.0 Lesotho 2009 DHS Age of woman 15-19 90.2 88.5 6.0 7.7 1.7 3.5 2.0 0.3 

Lesotho 2009 DHS Age of woman 20-24 70.9 58.8 4.6 16.6 7.1 19.1 17.4 5.4 

Lesotho 2009 DHS Age of woman 25-29 56.7 44.0 8.3 21.1 10.9 23.6 24.1 11.3 Lesotho 2009 DHS Age of woman 30-34 53.5 46.3 12.4 19.5 16.9 24.0 17.3 10.1 Lesotho 2009 DHS Age of woman 35-39 57.4 44.7 7.8 20.5 11.2 23.9 23.6 10.9 Lesotho 2009 DHS Age of woman 40-44 58.4 48.5 12.7 22.6 9.8 19.7 19.1 9.2 

Lesotho 2009 DHS Age of woman 45-49 Lesotho 2009 DHS Age of man 15-19 Lesotho 2009 DHS Age of man 20-24 91.1 86.1 3.4 8.4 0.0 5.0 5.5 0.5 

Lesotho 2009 DHS Age of man 25-29 76.6 66.7 6.0 15.9 4.2 14.0 13.2 3.3 

Lesotho 2009 DHS Age of man 30-34 52.7 40.2 4.4 17.0 17.6 30.1 25.2 12.7 Lesotho 2009 DHS Age of man 35-39 55.7 43.4 9.7 22.0 10.7 23.0 23.9 11.6 

(Continued...) 

70 
Table A1.4 – Continued

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Lesotho 2009 DHS Age of man 40-44 57.8 49.0 10.1 18.8 14.5 23.2 17.7 8.9 Lesotho 2009 DHS Age of man 45-49 48.9 38.4 17.2 27.7 9.3 19.7 24.6 14.2 

Lesotho 2009 DHS Age difference M<W 55.3 37.7 2.9 20.5 9.5 27.1 32.3 14.7 

Lesotho 2009 DHS Age difference M-W 0-4 67.7 57.2 6.2 16.6 9.9 20.3 16.3 5.9 Lesotho 2009 DHS Age difference M-W 5-9 63.6 51.9 8.2 19.9 8.8 20.4 19.4 7.8 Lesotho 2009 DHS Age difference M-W 10+ 46.3 39.0 16.3 23.5 16.1 23.4 21.4 14.1 Malawi 2010 DHS All All 85.5 80.5 3.9 8.9 4.6 9.6 6.0 1.0 Malawi 2010 DHS Age of woman 15-19 89.4 87.6 6.2 8.1 2.1 4.0 2.3 0.4 

Malawi 2010 DHS Age of woman 20-24 91.7 89.4 2.3 4.6 3.4 5.7 2.6 0.3 

Malawi 2010 DHS Age of woman 25-29 87.3 82.7 3.2 7.8 4.1 8.6 5.4 0.8 

Malawi 2010 DHS Age of woman 30-34 80.0 73.6 5.3 11.7 6.3 12.7 8.5 2.0 

Malawi 2010 DHS Age of woman 35-39 77.6 68.5 4.3 13.4 6.1 15.2 12.0 3.0 

Malawi 2010 DHS Age of woman 40-44 80.3 74.0 4.2 10.5 7.3 13.6 8.1 1.9 

Malawi 2010 DHS Age of woman 45-49 85.5 82.1 6.2 9.6 4.1 7.4 4.2 0.9 

Malawi 2010 DHS Age of man 15-19 98.1 98.1 1.9 1.9 0.0 0.0 0.0 0.0 

Malawi 2010 DHS Age of man 20-24 91.3 90.8 4.6 5.1 3.3 3.9 0.8 0.2 

Malawi 2010 DHS Age of man 25-29 91.6 88.3 2.9 6.2 1.9 5.2 3.6 0.4 

Malawi 2010 DHS Age of man 30-34 88.1 83.9 3.0 7.2 4.1 8.2 4.9 0.7 

Malawi 2010 DHS Age of man 35-39 79.3 72.3 4.6 11.6 6.9 13.9 9.2 2.2 

Malawi 2010 DHS Age of man 40-44 77.7 68.7 3.5 12.4 7.0 15.9 11.8 2.9 

Malawi 2010 DHS Age of man 45-49 81.0 75.7 6.1 11.5 5.8 11.2 7.0 1.7 

Malawi 2010 DHS Age difference M<W 78.3 72.6 6.1 11.8 7.7 13.4 7.9 2.2 

Malawi 2010 DHS Age difference M-W 0-4 89.1 85.2 3.1 7.0 3.3 7.2 4.5 0.6 

Malawi 2010 DHS Age difference M-W 5-9 85.0 79.5 4.3 9.8 4.1 9.5 6.7 1.2 Malawi 2010 DHS Age difference M-W 10+ 77.6 70.9 4.0 10.7 9.3 16.0 9.1 2.4 

Mozambique 2009 AIS All All 85.0 81.3 5.2 8.9 5.1 8.8 4.7 1.0 Mozambique 2009 AIS Age of woman 15-19 90.7 88.3 4.2 6.6 2.4 4.8 2.7 0.4 

(Continued...) 

71 
Table A1.4 – Continued

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Mozambique 2009 AIS Age of woman 20-24 82.4 79.1 7.1 10.4 6.0 9.3 4.5 1.2 Mozambique 2009 AIS Age of woman 25-29 83.8 79.3 5.0 9.5 5.5 10.0 5.8 1.2 

Mozambique 2009 AIS Age of woman 30-34 82.1 77.8 5.5 9.7 6.8 11.1 5.6 1.4 

Mozambique 2009 AIS Age of woman 35-39 89.1 85.3 3.8 7.6 2.8 6.6 4.4 0.6 

Mozambique 2009 AIS Age of woman 40-44 87.9 85.2 3.6 6.3 5.2 7.9 3.3 0.6 Mozambique 2009 AIS Age of woman 45-49 87.1 83.6 3.6 7.1 5.1 8.6 4.2 0.7 Mozambique 2009 AIS Age of man 15-19 86.9 86.9 13.1 13.1 0.0 0.0 0.0 0.0 Mozambique 2009 AIS Age of man 20-24 88.0 86.7 7.7 9.1 2.5 3.8 1.8 0.4 

Mozambique 2009 AIS Age of man 25-29 84.2 80.2 5.0 8.9 5.9 9.8 5.0 1.1 

Mozambique 2009 AIS Age of man 30-34 82.9 79.2 6.1 9.8 6.0 9.8 5.0 1.2 

Mozambique 2009 AIS Age of man 35-39 84.8 80.5 3.2 7.6 6.6 11.0 5.4 1.0 Mozambique 2009 AIS Age of man 40-44 85.1 82.4 5.3 8.0 6.1 8.8 3.6 0.9 Mozambique 2009 AIS Age of man 45-49 86.4 80.0 3.4 9.8 2.7 9.1 7.5 1.1 Mozambique 2009 AIS Age difference M<W 81.3 76.8 7.0 11.4 5.8 10.3 5.9 1.5 Mozambique 2009 AIS Age difference M-W 0-4 86.2 83.7 5.6 8.1 4.9 7.5 3.3 0.7 

Mozambique 2009 AIS Age difference M-W 5-9 88.0 85.4 4.6 7.1 4.3 6.9 3.1 0.6 

Mozambique 2009 AIS Age difference M-W 10+ 79.5 71.9 4.2 11.8 6.3 14.0 9.9 2.3 

Swaziland 2006-07 DHS All All 54.8 39.7 8.7 23.8 7.7 22.8 28.8 13.7 

Swaziland 2006-07 DHS Age of woman 15-19 49.9 40.3 14.9 24.5 12.3 21.9 22.9 13.3 

Swaziland 2006-07 DHS Age of woman 20-24 47.7 31.8 11.9 27.8 5.6 21.5 34.8 18.9 

Swaziland 2006-07 DHS Age of woman 25-29 47.1 30.4 8.9 25.6 7.2 23.9 36.8 20.1 

Swaziland 2006-07 DHS Age of woman 30-34 57.6 42.5 9.2 24.2 6.2 21.2 27.1 12.1 

Swaziland 2006-07 DHS Age of woman 35-39 61.1 50.0 5.8 16.9 13.7 24.8 19.4 8.3 Swaziland 2006-07 DHS Age of woman 40-44 63.7 48.1 4.0 19.5 7.5 23.0 24.9 9.3 

Swaziland 2006-07 DHS Age of woman 45-49 Swaziland 2006-07 DHS Age of man 15-19 Swaziland 2006-07 DHS Age of man 20-24 43.2 33.1 19.9 30.0 9.2 19.4 27.7 17.6 

(Continued...) 

72 
Table A1.4 – Continued

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Swaziland 2006-07 DHS Age of man 25-29 52.9 38.0 14.1 29.0 3.8 18.8 29.2 14.3 Swaziland 2006-07 DHS Age of man 30-34 44.9 28.3 8.2 24.8 8.4 25.0 38.5 21.9 

Swaziland 2006-07 DHS Age of man 35-39 51.5 33.8 4.7 22.4 8.6 26.4 35.2 17.5 

Swaziland 2006-07 DHS Age of man 40-44 54.7 41.0 9.2 22.9 9.4 23.2 26.7 13.0 

Swaziland 2006-07 DHS Age of man 45-49 75.2 65.9 4.1 13.4 7.8 17.2 12.9 3.5 

Swaziland 2006-07 DHS Age difference M<W 45.8 28.4 8.1 25.6 6.7 24.2 39.3 21.8 

Swaziland 2006-07 DHS Age difference M-W 0-4 59.4 44.3 10.4 25.4 4.2 19.2 26.1 11.0 

Swaziland 2006-07 DHS Age difference M-W 5-9 55.7 40.5 6.3 21.5 9.6 24.8 28.3 13.2 

Swaziland 2006-07 DHS Age difference M-W 10+ 48.6 35.1 10.9 24.4 10.4 23.9 30.1 16.6 Tanzania 2011-12 AIS All All 93.0 90.9 2.0 4.2 2.6 4.8 2.3 0.2 

Tanzania 2011-12 AIS Age of woman 15-19 97.9 97.8 0.6 0.8 1.2 1.4 0.2 0.0 Tanzania 2011-12 AIS Age of woman 20-24 95.0 93.8 1.3 2.5 2.4 3.6 1.3 0.1 Tanzania 2011-12 AIS Age of woman 25-29 93.7 91.1 1.5 4.1 2.1 4.6 2.8 0.2 Tanzania 2011-12 AIS Age of woman 30-34 91.2 87.9 2.2 5.5 2.9 6.2 3.7 0.4 Tanzania 2011-12 AIS Age of woman 35-39 90.4 88.1 2.9 5.2 4.0 6.3 2.7 0.4 

Tanzania 2011-12 AIS Age of woman 40-44 91.5 89.9 3.6 5.2 3.0 4.7 1.9 0.3 

Tanzania 2011-12 AIS Age of woman 45-49 88.4 85.9 5.2 7.7 3.3 5.8 3.0 0.5 Tanzania 2011-12 AIS Age of man 15-19 Tanzania 2011-12 AIS Age of man 20-24 96.8 96.7 0.8 0.9 2.3 2.4 0.1 0.0 Tanzania 2011-12 AIS Age of man 25-29 96.0 94.2 1.0 2.8 1.1 2.9 1.9 0.1 Tanzania 2011-12 AIS Age of man 30-34 93.6 92.1 1.3 2.8 3.4 4.9 1.6 0.2 Tanzania 2011-12 AIS Age of man 35-39 90.7 87.8 2.4 5.3 3.6 6.5 3.3 0.4 

Tanzania 2011-12 AIS Age of man 40-44 90.5 87.3 3.3 6.6 2.5 5.8 3.7 0.4 

Tanzania 2011-12 AIS Age of man 45-49 92.8 91.0 2.8 4.6 2.3 4.2 2.1 0.2 Tanzania 2011-12 AIS Age difference M<W 87.9 84.8 4.6 7.8 3.6 6.8 3.8 0.6 Tanzania 2011-12 AIS Age difference M-W 0-4 94.1 92.6 1.3 2.9 2.9 4.4 1.7 0.1 

Tanzania 2011-12 AIS Age difference M-W 5-9 93.9 92.4 2.0 3.5 2.4 3.9 1.7 0.2 

(Continued...) 

73 
Table A1.4 – Continued

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Tanzania 2011-12 AIS Age difference M-W 10+ 91.5 87.8 2.1 5.8 2.3 6.0 4.1 0.4 Uganda 2011 AIS All All 90.2 87.1 3.0 6.1 3.2 6.3 3.5 0.4 

Uganda 2011 AIS Age of woman 15-19 94.2 92.0 0.4 2.7 3.0 5.2 2.4 0.2 

Uganda 2011 AIS Age of woman 20-24 91.3 89.0 3.8 6.0 2.4 4.7 2.6 0.3 Uganda 2011 AIS Age of woman 25-29 90.4 87.3 3.1 6.2 3.0 6.1 3.5 0.4 Uganda 2011 AIS Age of woman 30-34 87.6 82.9 3.0 7.6 4.0 8.7 5.4 0.8 Uganda 2011 AIS Age of woman 35-39 89.9 87.3 2.8 5.5 4.1 6.8 3.1 0.4 

Uganda 2011 AIS Age of woman 40-44 87.8 84.1 3.6 7.3 4.2 7.9 4.4 0.7 

Uganda 2011 AIS Age of woman 45-49 91.6 88.2 3.2 6.6 1.4 4.8 3.8 0.4 

Uganda 2011 AIS Age of man 15-19 95.8 95.8 0.0 0.0 4.2 4.2 0.0 0.0 

Uganda 2011 AIS Age of man 20-24 93.2 91.2 3.2 5.2 1.4 3.4 2.3 0.2 Uganda 2011 AIS Age of man 25-29 93.6 91.9 3.2 4.9 1.4 3.1 1.9 0.2 Uganda 2011 AIS Age of man 30-34 90.3 86.8 2.6 6.0 3.3 6.7 3.9 0.5 Uganda 2011 AIS Age of man 35-39 88.5 84.2 2.4 6.8 4.0 8.4 5.0 0.7 Uganda 2011 AIS Age of man 40-44 87.3 84.3 3.8 6.8 5.3 8.2 3.6 0.7 

Uganda 2011 AIS Age of man 45-49 88.6 85.0 3.5 7.2 3.6 7.2 4.3 0.6 

Uganda 2011 AIS Age difference M<W 88.0 82.6 3.3 8.6 2.6 7.9 6.2 0.8 

Uganda 2011 AIS Age difference M-W 0-4 92.3 89.8 3.0 5.5 2.0 4.5 2.7 0.3 Uganda 2011 AIS Age difference M-W 5-9 91.3 89.2 2.7 4.8 3.6 5.7 2.5 0.3 Uganda 2011 AIS Age difference M-W 10+ 85.5 80.8 3.6 8.3 5.3 9.9 5.6 1.0 

Zambia 2007 DHS All All 80.2 73.9 4.7 11.0 6.8 13.1 8.2 2.0 Zambia 2007 DHS Age of woman 15-19 87.0 83.1 3.5 7.4 4.9 8.8 4.7 0.8 

Zambia 2007 DHS Age of woman 20-24 85.9 81.7 3.6 7.8 5.4 9.6 5.1 0.9 

Zambia 2007 DHS Age of woman 25-29 77.8 71.5 4.8 11.2 8.6 15.0 8.7 2.3 Zambia 2007 DHS Age of woman 30-34 77.4 68.6 5.8 14.7 4.9 13.8 11.8 3.0 Zambia 2007 DHS Age of woman 35-39 77.3 70.9 4.9 11.3 9.0 15.4 8.8 2.5 Zambia 2007 DHS Age of woman 40-44 76.6 69.7 6.1 13.0 7.6 14.5 9.6 2.7 

(Continued...) 

74 
Table A1.4 – Continued

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Zambia 2007 DHS Age of woman 45-49 83.0 78.6 3.3 7.8 7.9 12.4 5.7 1.2 Zambia 2007 DHS Age of man 15-19 Zambia 2007 DHS Age of man 20-24 92.8 89.5 0.6 3.9 3.0 6.3 3.6 0.3 

Zambia 2007 DHS Age of man 25-29 84.5 81.6 4.3 7.2 7.4 10.4 3.8 0.9 Zambia 2007 DHS Age of man 30-34 81.1 74.4 4.1 10.8 6.3 12.9 8.6 1.9 Zambia 2007 DHS Age of man 35-39 73.7 65.2 7.0 15.5 7.1 15.6 12.2 3.7 Zambia 2007 DHS Age of man 40-44 77.2 69.2 4.3 12.3 7.7 15.7 10.8 2.8 Zambia 2007 DHS Age of man 45-49 78.4 73.2 5.9 11.1 8.4 13.6 7.2 2.1 

Zambia 2007 DHS Age difference M<W 76.3 69.9 8.5 14.9 6.1 12.5 9.1 2.7 

Zambia 2007 DHS Age difference M-W 0-4 80.6 74.2 4.0 10.4 7.1 13.5 8.3 1.9 

Zambia 2007 DHS Age difference M-W 5-9 83.2 77.4 3.8 9.5 5.9 11.6 7.2 1.4 

Zambia 2007 DHS Age difference M-W 10+ 71.2 64.0 7.8 14.9 9.9 17.0 11.1 4.0 Zimbabwe 2010-11 DHS All All 78.4 70.7 4.6 12.3 6.8 14.5 10.2 2.5 

Zimbabwe 2010-11 DHS Age of woman 15-19 90.6 88.6 1.6 3.5 5.6 7.5 2.3 0.3 

Zimbabwe 2010-11 DHS Age of woman 20-24 87.0 81.6 1.9 7.3 4.9 10.3 6.3 0.9 

Zimbabwe 2010-11 DHS Age of woman 25-29 77.6 69.3 4.8 13.2 6.4 14.8 11.1 2.8 

Zimbabwe 2010-11 DHS Age of woman 30-34 71.0 61.4 7.6 17.2 7.1 16.7 14.3 4.7 

Zimbabwe 2010-11 DHS Age of woman 35-39 71.1 62.9 5.7 13.9 10.9 19.0 12.4 4.2 

Zimbabwe 2010-11 DHS Age of woman 40-44 72.3 62.6 6.5 16.2 7.1 16.9 14.1 4.4 

Zimbabwe 2010-11 DHS Age of woman 45-49 60.4 50.9 8.7 18.2 13.2 22.8 17.6 8.1 

Zimbabwe 2010-11 DHS Age of man 15-19 Zimbabwe 2010-11 DHS Age of man 20-24 93.5 91.5 2.6 4.6 1.7 3.7 2.2 0.2 

Zimbabwe 2010-11 DHS Age of man 25-29 85.8 80.5 3.3 8.6 4.6 9.9 6.4 1.0 

Zimbabwe 2010-11 DHS Age of man 30-34 77.9 71.1 5.6 12.5 7.2 14.0 9.3 2.5 

Zimbabwe 2010-11 DHS Age of man 35-39 75.3 64.2 3.6 14.8 6.0 17.1 15.0 3.9 Zimbabwe 2010-11 DHS Age of man 40-44 66.3 59.1 8.0 15.1 13.3 20.5 12.4 5.3 

Zimbabwe 2010-11 DHS Age of man 45-49 69.0 57.9 4.8 15.9 9.5 20.6 16.7 5.6 

(Continued...)

75 
Table A1.4 – Continued

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Zimbabwe 2010-11 DHS Age difference M<W 74.6 68.2 9.3 15.6 6.8 13.2 9.4 3.0 Zimbabwe 2010-11 DHS Age difference M-W 0-4 82.7 76.2 4.2 10.7 5.0 11.5 8.1 1.6 

Zimbabwe 2010-11 DHS Age difference M-W 5-9 78.4 70.2 3.7 12.0 7.0 15.3 10.9 2.6 

Zimbabwe 2010-11 DHS Age difference M-W 10+ 69.9 60.5 4.8 14.1 11.2 20.5 14.1 4.8 

Note: Weighted frequencies for the rows in this table are given in Table 4.2. 

76 
Table A1.5 HIV prevalence among men and women who have cohabiting partners, and measures of selection and HIV seroconversion, 

according to age (age of woman, age of man, and age difference between man and woman), 10 DHS surveys in sub-Saharan Africa, 2006-2012 

Survey Category 

HIV prevalence Selection/Seroconversion measures 

Men Women Delta kap_SC SCm SCw Sig. N 

Cameroon 2011 DHS All All 4.7 4.8 1.3 29.3 28.7 29.9 ns 2,289 Cameroon 2011 DHS Age of woman 15-19 4.0 2.6 1.8 55.1 69.9 45.5 ns 201 Cameroon 2011 DHS Age of woman 20-24 3.0 2.7 0.6 22.9 24.4 21.7 ns 509 Cameroon 2011 DHS Age of woman 25-29 5.8 6.0 1.8 32.8 32.0 33.5 ns 621 Cameroon 2011 DHS Age of woman 30-34 3.9 4.4 1.2 30.8 29.0 32.8 ns 441 Cameroon 2011 DHS Age of woman 35-39 5.4 7.7 1.4 23.4 19.8 28.7 ns 334 Cameroon 2011 DHS Age of woman 40-44 8.1 6.4 1.2 18.3 20.9 16.3 ns 135 

Cameroon 2011 DHS Age of woman 45-49 2.7 2.0 -0.1 -2.3 -2.7 -2.0 ns 48 Cameroon 2011 DHS Age of man 15-19 . 11 

Cameroon 2011 DHS Age of man 20-24 0.5 3.2 0.4 24.8 14.1 100.0 ns 109 

Cameroon 2011 DHS Age of man 25-29 3.3 3.4 0.5 15.4 15.1 15.6 ns 381 

Cameroon 2011 DHS Age of man 30-34 4.4 5.2 2.0 44.1 40.6 48.4 ns 493 

Cameroon 2011 DHS Age of man 35-39 5.6 5.4 1.2 23.5 24.0 23.1 ns 547 

Cameroon 2011 DHS Age of man 40-44 5.8 6.1 1.9 34.5 33.7 35.3 ns 412 

Cameroon 2011 DHS Age of man 45-49 5.2 4.3 0.9 21.0 23.2 19.2 ns 337 

Cameroon 2011 DHS Age difference M<W 5.2 7.5 0.5 8.6 7.2 10.7 ns 185 

Cameroon 2011 DHS Age difference M-W 0-4 4.5 5.6 0.8 15.9 14.2 18.1 ns 561 Cameroon 2011 DHS Age difference M-W 5-9 4.1 4.5 1.5 36.0 34.4 37.7 ns 855 Cameroon 2011 DHS Age difference M-W 10+ 5.4 4.0 1.8 40.9 48.6 35.3 ns 688 Kenya 2008-09 DHS All All 5.7 6.2 2.8 50.1 47.8 52.7 ns 1,064 Kenya 2008-09 DHS Age of woman 15-19 11.5 13.4 5.8 52.7 48.6 57.5 ns 44 Kenya 2008-09 DHS Age of woman 20-24 6.7 6.5 2.8 45.1 46.0 44.3 ns 252 Kenya 2008-09 DHS Age of woman 25-29 4.6 6.4 2.6 50.4 42.8 61.3 ns 299 Kenya 2008-09 DHS Age of woman 30-34 5.0 6.4 3.0 56.0 49.6 64.4 ns 220 Kenya 2008-09 DHS Age of woman 35-39 7.0 4.6 3.6 65.7 84.7 53.6 ns 134 Kenya 2008-09 DHS Age of woman 40-44 4.3 4.6 0.5 11.6 11.1 12.1 ns 94 

(Continued...) 

77 
Table A1.5 – Continued

Survey Category 

HIV prevalence Selection/Seroconversion measures 

Men Women Delta kap_SC SCm SCw Sig. N 

Kenya 2008-09 DHS Age of woman 45-49 . 21 Kenya 2008-09 DHS Age of man 15-19 . 1 Kenya 2008-09 DHS Age of man 20-24 1.7 0.6 0.0 -0.9 -1.8 -0.6 ns 69 Kenya 2008-09 DHS Age of man 25-29 7.9 9.3 4.1 52.0 47.6 57.3 ns 193 

Kenya 2008-09 DHS Age of man 30-34 6.0 7.2 3.0 49.1 44.7 54.5 ns 280 

Kenya 2008-09 DHS Age of man 35-39 4.9 5.7 2.6 52.7 48.6 57.5 ns 169 

Kenya 2008-09 DHS Age of man 40-44 6.8 4.9 3.0 54.2 65.1 46.4 ns 186 

Kenya 2008-09 DHS Age of man 45-49 4.0 5.4 1.9 43.4 37.3 51.9 ns 165 

Kenya 2008-09 DHS Age difference M<W 2.2 1.6 0.6 29.4 35.5 25.0 ns 104 Kenya 2008-09 DHS Age difference M-W 0-4 5.7 7.3 3.1 50.8 44.8 58.7 ns 371 Kenya 2008-09 DHS Age difference M-W 5-9 5.6 5.4 3.1 60.0 61.4 58.8 ns 406 Kenya 2008-09 DHS Age difference M-W 10+ 7.8 8.6 2.7 35.8 34.1 37.8 ns 184 Lesotho 2009 DHS All All 29.4 27.2 11.3 55.6 58.8 52.7 ns 689 Lesotho 2009 DHS Age of woman 15-19 3.8 8.0 1.7 30.9 22.4 49.9 ns 56 

Lesotho 2009 DHS Age of woman 20-24 24.5 22.0 12.0 67.3 72.5 62.9 ns 184 

Lesotho 2009 DHS Age of woman 25-29 35.0 32.4 12.7 57.0 60.4 53.9 ns 167 

Lesotho 2009 DHS Age of woman 30-34 34.2 29.7 7.2 32.9 36.7 29.8 ns 118 

Lesotho 2009 DHS Age of woman 35-39 34.8 31.4 12.6 57.1 61.7 53.0 ns 81 

Lesotho 2009 DHS Age of woman 40-44 28.9 31.8 9.9 46.8 43.8 50.3 ns 62 

Lesotho 2009 DHS Age of woman 45-49 . 21 Lesotho 2009 DHS Age of man 15-19 . 6 Lesotho 2009 DHS Age of man 20-24 5.5 8.9 5.0 74.9 59.9 100.0 ns 68 

Lesotho 2009 DHS Age of man 25-29 17.4 19.3 9.9 66.0 62.1 70.4 ns 144 

Lesotho 2009 DHS Age of man 30-34 42.8 29.6 12.5 53.2 73.8 41.6 M++ 165 

Lesotho 2009 DHS Age of man 35-39 34.6 33.6 12.2 54.5 55.7 53.3 ns 141 

Lesotho 2009 DHS Age of man 40-44 32.2 27.8 8.8 41.6 46.4 37.7 ns 84 

Lesotho 2009 DHS Age of man 45-49 33.9 41.9 10.4 44.0 37.7 52.9 ns 81 

Lesotho 2009 DHS Age difference M<W 41.8 35.2 17.6 73.9 85.8 65.0 ns 51 

(Continued...) 

78 
Table A1.5 – Continued

Survey Category 

HIV prevalence Selection/Seroconversion measures 

Men Women Delta kap_SC SCm SCw Sig. N 

Lesotho 2009 DHS Age difference M-W 0-4 26.2 22.5 10.4 56.5 62.7 51.3 ns 291 Lesotho 2009 DHS Age difference M-W 5-9 28.2 27.7 11.6 57.8 58.5 57.1 ns 267 Lesotho 2009 DHS Age difference M-W 10+ 37.5 37.6 7.3 31.0 30.9 31.1 ns 80 

Malawi 2010 DHS All All 10.6 9.9 5.0 54.1 56.4 52.1 ns 2,987 Malawi 2010 DHS Age of woman 15-19 4.4 8.5 1.9 31.6 23.6 47.9 ns 248 

Malawi 2010 DHS Age of woman 20-24 6.0 4.9 2.3 45.1 50.4 40.8 ns 746 

Malawi 2010 DHS Age of woman 25-29 9.5 8.6 4.6 55.7 58.6 53.0 ns 819 

Malawi 2010 DHS Age of woman 30-34 14.7 13.8 6.4 52.7 54.9 50.7 ns 501 

Malawi 2010 DHS Age of woman 35-39 18.1 16.3 9.1 63.7 67.9 59.9 ns 418 

Malawi 2010 DHS Age of woman 40-44 15.5 12.4 6.2 51.8 59.5 45.9 ns 193 

Malawi 2010 DHS Age of woman 45-49 8.3 10.4 3.4 39.5 35.1 45.1 ns 63 

Malawi 2010 DHS Age of man 15-19 0.0 1.9 0.0 0.0 0.0 ns 28 Malawi 2010 DHS Age of man 20-24 4.1 5.3 0.6 12.2 10.8 14.2 ns 344 

Malawi 2010 DHS Age of man 25-29 5.6 6.5 3.3 57.5 52.9 62.8 ns 665 

Malawi 2010 DHS Age of man 30-34 8.9 7.9 4.2 54.1 58.1 50.6 ns 666 

Malawi 2010 DHS Age of man 35-39 16.1 13.8 7.0 54.7 60.2 50.1 ns 583 

Malawi 2010 DHS Age of man 40-44 18.8 15.3 8.9 62.8 71.7 55.9 ns 387 

Malawi 2010 DHS Age of man 45-49 12.9 13.2 5.3 47.2 46.5 47.9 ns 315 

Malawi 2010 DHS Age difference M<W 15.6 14.0 5.7 45.2 48.2 42.6 ns 212 

Malawi 2010 DHS Age difference M-W 0-4 7.8 7.6 3.9 54.8 55.9 53.8 ns 1,335 Malawi 2010 DHS Age difference M-W 5-9 10.7 11.0 5.5 56.6 55.8 57.4 ns 1,103 Malawi 2010 DHS Age difference M-W 10+ 18.4 13.1 6.7 49.9 62.3 41.6 M+ 337 

Mozambique 2009 AIS All All 9.8 9.9 3.7 42.0 41.7 42.2 ns 2,322 Mozambique 2009 AIS Age of woman 15-19 5.1 6.9 2.4 41.8 36.1 49.7 ns 275 Mozambique 2009 AIS Age of woman 20-24 10.5 11.6 3.3 33.3 31.5 35.3 ns 550 Mozambique 2009 AIS Age of woman 25-29 11.2 10.7 4.6 46.6 47.8 45.5 ns 521 Mozambique 2009 AIS Age of woman 30-34 12.5 11.1 4.2 40.7 43.6 38.2 ns 448 Mozambique 2009 AIS Age of woman 35-39 7.2 8.2 3.8 53.8 50.2 58.0 ns 312 

(Continued...) 

79 
Table A1.5 – Continued

Survey Category 

HIV prevalence Selection/Seroconversion measures 

Men Women Delta kap_SC SCm SCw Sig. N 

Mozambique 2009 AIS Age of woman 40-44 8.5 6.9 2.7 38.5 43.2 34.7 ns 142 Mozambique 2009 AIS Age of woman 45-49 9.3 7.8 3.5 44.3 49.0 40.5 ns 74 Mozambique 2009 AIS Age of man 15-19 0.0 13.1 0.0 0.0 0.0 ns 37 Mozambique 2009 AIS Age of man 20-24 4.3 9.5 1.4 21.4 15.2 35.8 ns 318 Mozambique 2009 AIS Age of man 25-29 10.9 10.0 3.9 41.9 43.9 40.0 ns 469 Mozambique 2009 AIS Age of man 30-34 11.0 11.1 3.8 38.3 38.2 38.4 ns 470 Mozambique 2009 AIS Age of man 35-39 12.0 8.6 4.4 47.3 57.9 39.9 ns 449 Mozambique 2009 AIS Age of man 40-44 9.6 8.9 2.7 32.5 34.1 31.1 ns 285 Mozambique 2009 AIS Age of man 45-49 10.2 10.9 6.4 67.4 64.9 70.1 ns 295 Mozambique 2009 AIS Age difference M<W 11.8 12.9 4.4 40.7 38.7 43.0 ns 293 Mozambique 2009 AIS Age difference M-W 0-4 8.2 8.9 2.6 32.9 31.7 34.3 ns 930 Mozambique 2009 AIS Age difference M-W 5-9 7.5 7.7 2.5 36.5 35.9 37.0 ns 708 Mozambique 2009 AIS Age difference M-W 10+ 16.3 14.1 7.6 59.1 64.5 54.6 ns 390 Swaziland 2006-07 DHS All All 36.5 37.5 15.1 64.8 63.5 66.2 ns 626 Swaziland 2006-07 DHS Age of woman 15-19 35.2 37.8 9.6 41.3 39.1 43.9 ns 41 

Swaziland 2006-07 DHS Age of woman 20-24 40.4 46.7 15.9 64.6 57.3 74.0 ns 130 

Swaziland 2006-07 DHS Age of woman 25-29 44.0 45.7 16.7 67.5 65.3 69.8 ns 152 

Swaziland 2006-07 DHS Age of woman 30-34 33.2 36.3 15.0 66.2 62.0 70.9 ns 125 

Swaziland 2006-07 DHS Age of woman 35-39 33.1 25.2 11.1 53.2 65.7 44.7 ns 91 

Swaziland 2006-07 DHS Age of woman 40-44 32.4 28.9 15.5 73.1 79.6 67.5 ns 64 

Swaziland 2006-07 DHS Age of woman 45-49 . 23 Swaziland 2006-07 DHS Age of man 15-19 . 1 Swaziland 2006-07 DHS Age of man 20-24 36.9 47.5 10.1 41.1 33.8 52.4 ns 38 Swaziland 2006-07 DHS Age of man 25-29 33.0 43.2 14.9 62.5 51.5 79.5 W+ 115 Swaziland 2006-07 DHS Age of man 30-34 46.9 46.7 16.6 66.7 67.0 66.4 ns 132 Swaziland 2006-07 DHS Age of man 35-39 43.8 39.8 17.7 72.7 79.1 67.2 ns 123 Swaziland 2006-07 DHS Age of man 40-44 36.1 35.9 13.7 59.6 59.9 59.2 ns 102 Swaziland 2006-07 DHS Age of man 45-49 20.7 17.0 9.4 61.1 69.6 54.4 ns 114 

(Continued...) 

80 
Table A1.5 – Continued

Survey Category 

HIV prevalence Selection/Seroconversion measures 

Men Women Delta kap_SC SCm SCw Sig. N 

Swaziland 2006-07 DHS Age difference M<W 46.0 47.5 17.5 70.2 68.2 72.2 ns 50 Swaziland 2006-07 DHS Age difference M-W 0-4 30.3 36.4 15.0 67.4 59.2 78.2 W+ 210 

Swaziland 2006-07 DHS Age difference M-W 5-9 38.0 34.7 15.2 65.5 70.6 61.2 ns 247 

Swaziland 2006-07 DHS Age difference M-W 10+ 40.5 41.0 13.5 55.9 55.3 56.5 ns 118 

Tanzania 2011-12 AIS All All 5.0 4.4 2.1 47.8 51.4 44.7 ns 3,302 Tanzania 2011-12 AIS Age of woman 15-19 1.4 0.8 0.2 18.1 24.8 14.2 ns 311 Tanzania 2011-12 AIS Age of woman 20-24 3.7 2.6 1.2 39.6 48.1 33.6 ns 637 Tanzania 2011-12 AIS Age of woman 25-29 4.8 4.3 2.6 58.9 62.8 55.4 ns 802 Tanzania 2011-12 AIS Age of woman 30-34 6.6 5.9 3.3 56.1 59.4 53.2 ns 674 Tanzania 2011-12 AIS Age of woman 35-39 6.7 5.6 2.3 40.3 44.4 36.9 ns 529 Tanzania 2011-12 AIS Age of woman 40-44 4.9 5.5 1.6 32.5 30.8 34.5 ns 280 Tanzania 2011-12 AIS Age of woman 45-49 6.3 8.2 2.5 36.5 32.1 42.4 ns 69 Tanzania 2011-12 AIS Age of man 15-19 . 24 

Tanzania 2011-12 AIS Age of man 20-24 2.4 0.9 0.1 4.7 8.8 3.2 ns 309 Tanzania 2011-12 AIS Age of man 25-29 3.0 2.9 1.8 63.0 64.3 61.7 ns 534 Tanzania 2011-12 AIS Age of man 30-34 5.1 3.0 1.5 38.5 53.0 30.3 ns 622 Tanzania 2011-12 AIS Age of man 35-39 6.9 5.7 2.9 49.2 54.8 44.6 ns 724 Tanzania 2011-12 AIS Age of man 40-44 6.2 7.0 3.3 53.3 50.1 56.9 ns 644 Tanzania 2011-12 AIS Age of man 45-49 4.4 4.8 1.9 42.3 40.2 44.6 ns 445 Tanzania 2011-12 AIS Age difference M<W 7.4 8.4 3.2 43.4 40.6 46.6 ns 295 Tanzania 2011-12 AIS Age difference M-W 0-4 4.5 3.0 1.5 42.2 53.3 34.9 M+ 1,139 Tanzania 2011-12 AIS Age difference M-W 5-9 4.1 3.7 1.6 41.7 44.0 39.7 ns 1,226 Tanzania 2011-12 AIS Age difference M-W 10+ 6.4 6.2 3.7 63.0 64.2 61.9 ns 642 Uganda 2011 AIS All All 6.8 6.6 3.1 49.7 50.6 48.8 ns 3,972 Uganda 2011 AIS Age of woman 15-19 5.3 2.8 2.3 57.3 84.4 43.3 ns 321 

Uganda 2011 AIS Age of woman 20-24 5.0 6.3 2.2 42.0 37.3 48.2 ns 953 

Uganda 2011 AIS Age of woman 25-29 6.5 6.6 3.1 50.0 49.4 50.5 ns 991 

Uganda 2011 AIS Age of woman 30-34 9.5 8.4 4.7 57.1 60.9 53.7 ns 698 

(Continued...) 

81 
Table A1.5 – Continued

Survey Category 

HIV prevalence Selection/Seroconversion measures 

Men Women Delta kap_SC SCm SCw Sig. N 

Uganda 2011 AIS Age of woman 35-39 7.2 5.9 2.7 43.5 48.8 39.3 ns 605 Uganda 2011 AIS Age of woman 40-44 8.6 8.0 3.8 49.2 51.2 47.4 ns 289 

Uganda 2011 AIS Age of woman 45-49 5.2 7.0 3.4 59.5 51.5 70.6 ns 116 

Uganda 2011 AIS Age of man 15-19 4.2 0.0 0.0 0.0 0.0 ns 29 Uganda 2011 AIS Age of man 20-24 3.6 5.4 2.1 47.7 39.4 60.5 ns 383 Uganda 2011 AIS Age of man 25-29 3.2 5.0 1.7 42.9 35.0 55.3 W+ 777 Uganda 2011 AIS Age of man 30-34 7.2 6.4 3.4 54.0 57.3 51.1 ns 793 Uganda 2011 AIS Age of man 35-39 9.0 7.4 4.3 57.4 64.1 52.0 ns 810 Uganda 2011 AIS Age of man 40-44 8.9 7.4 3.0 39.6 43.8 36.1 ns 655 Uganda 2011 AIS Age of man 45-49 7.8 7.8 3.6 50.5 50.8 50.3 ns 526 Uganda 2011 AIS Age difference M<W 8.8 9.4 5.3 64.7 62.2 67.5 ns 442 

Uganda 2011 AIS Age difference M-W 0-4 4.7 5.7 2.5 49.5 45.0 55.1 ns 1,328 

Uganda 2011 AIS Age difference M-W 5-9 6.0 5.1 2.2 40.7 44.4 37.5 ns 1,478 

Uganda 2011 AIS Age difference M-W 10+ 10.9 9.3 4.6 51.0 56.1 46.8 ns 723 

Zambia 2007 DHS All All 15.1 12.9 6.3 52.1 57.1 47.9 M+ 2,007 Zambia 2007 DHS Age of woman 15-19 9.5 8.2 3.9 48.1 52.5 44.3 ns 147 Zambia 2007 DHS Age of woman 20-24 10.5 8.7 4.2 48.2 53.7 43.7 ns 456 Zambia 2007 DHS Age of woman 25-29 17.3 13.5 6.3 48.5 56.8 42.3 M+ 531 Zambia 2007 DHS Age of woman 30-34 16.7 17.6 8.8 62.1 60.3 64.1 ns 413 Zambia 2007 DHS Age of woman 35-39 17.9 13.7 6.4 47.9 56.6 41.5 ns 284 Zambia 2007 DHS Age of woman 40-44 17.2 15.7 6.9 50.1 52.9 47.5 ns 130 Zambia 2007 DHS Age of woman 45-49 13.6 9.0 4.4 44.0 57.0 35.9 ns 45 Zambia 2007 DHS Age of man 15-19 . 10 Zambia 2007 DHS Age of man 20-24 6.6 4.2 3.3 64.9 84.0 52.8 ns 157 

Zambia 2007 DHS Age of man 25-29 11.3 8.1 2.9 33.3 40.7 28.1 ns 398 

Zambia 2007 DHS Age of man 30-34 14.8 12.6 6.7 56.5 62.3 51.8 ns 496 

Zambia 2007 DHS Age of man 35-39 19.3 19.2 8.5 54.5 54.6 54.3 ns 431 

Zambia 2007 DHS Age of man 40-44 18.5 15.1 8.0 57.2 65.1 51.0 ns 287 

(Continued...) 

82 
Table A1.5 – Continued

Survey Category 

HIV prevalence Selection/Seroconversion measures 

Men Women Delta kap_SC SCm SCw Sig. N 

Zambia 2007 DHS Age of man 45-49 15.7 13.2 5.2 41.8 46.5 38.0 ns 228 Zambia 2007 DHS Age difference M<W 15.2 17.6 6.4 46.5 42.8 51.0 ns 147 Zambia 2007 DHS Age difference M-W 0-4 15.4 12.3 6.4 53.8 61.7 47.7 M+ 751 Zambia 2007 DHS Age difference M-W 5-9 13.0 10.9 5.7 54.2 60.1 49.4 ns 857 Zambia 2007 DHS Age difference M-W 10+ 21.0 18.9 7.1 44.6 47.8 41.9 ns 253 Zimbabwe 2010-11 DHS All All 17.0 14.8 7.7 57.3 62.5 52.9 M++ 2,180 

Zimbabwe 2010-11 DHS Age of woman 15-19 7.8 3.8 2.0 35.5 55.3 26.1 ns 191 

Zimbabwe 2010-11 DHS Age of woman 20-24 11.2 8.2 5.4 61.7 74.4 52.6 M++ 549 

Zimbabwe 2010-11 DHS Age of woman 25-29 17.6 16.0 8.3 59.7 63.4 56.5 ns 560 

Zimbabwe 2010-11 DHS Age of woman 30-34 21.4 21.9 9.6 56.5 55.7 57.4 ns 414 

Zimbabwe 2010-11 DHS Age of woman 35-39 23.2 18.1 8.2 49.6 58.8 42.9 M+ 304 

Zimbabwe 2010-11 DHS Age of woman 40-44 21.2 20.6 9.8 59.0 60.2 57.9 ns 134 

Zimbabwe 2010-11 DHS Age of woman 45-49 30.9 26.3 9.5 46.5 52.3 41.9 ns 28 

Zimbabwe 2010-11 DHS Age of man 15-19 . 10 Zimbabwe 2010-11 DHS Age of man 20-24 3.8 4.8 2.0 48.3 43.2 54.7 ns 217 Zimbabwe 2010-11 DHS Age of man 25-29 11.0 9.6 5.3 57.5 62.0 53.7 ns 504 Zimbabwe 2010-11 DHS Age of man 30-34 16.5 14.9 6.8 51.6 54.8 48.8 ns 497 Zimbabwe 2010-11 DHS Age of man 35-39 21.1 18.7 11.1 69.6 75.3 64.8 ns 443 Zimbabwe 2010-11 DHS Age of man 40-44 25.8 20.4 7.2 40.3 47.5 35.1 ns 301 Zimbabwe 2010-11 DHS Age of man 45-49 26.2 21.5 11.1 60.6 69.7 53.7 ns 208 Zimbabwe 2010-11 DHS Age difference M<W 16.2 18.6 6.4 44.2 40.7 48.3 ns 219 

Zimbabwe 2010-11 DHS Age difference M-W 0-4 13.1 12.3 6.5 58.6 60.9 56.5 ns 817 

Zimbabwe 2010-11 DHS Age difference M-W 5-9 17.9 14.6 8.3 60.8 69.1 54.3 M++ 828 

Zimbabwe 2010-11 DHS Age difference M-W 10+ 25.3 19.0 9.4 53.9 66.0 45.6 M++ 316 

Note: Selection/seroconversion measures are defined in the text 

N is weighted Significance refers to a test of the null hypothesis that selection/seroconversion is the same for men and women M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

83 
Table A1.6 Percentage of HIV-negative men and women who are at risk of infection from an HIV-positive cohabiting partner, and relative 

risk for women compared with men, by three categories of risk, according to age, 10 DHS surveys in sub-Saharan Africa, 2006-2012 

 Risk category #1 Risk category #2 Risk category #3 Survey Category Men Women RR Men Women RR Men Women RR Cameroon 2011 DHS All 3.3 3.1 94 3.5 3.3 94 1.2 1.3 113 Cameroon 2011 DHS 15-19 0.0 2.1 0.0 2.2 0.0 0.3 Cameroon 2011 DHS 20-24 2.7 2.3 84 2.7 2.3 86 0.3 0.9 340 Cameroon 2011 DHS 25-29 2.8 3.6 130 2.8 3.8 134 1.0 2.1 210 Cameroon 2011 DHS 30-34 2.9 2.5 86 3.1 2.7 86 1.8 1.5 83 Cameroon 2011 DHS 35-39 3.9 3.6 92 4.1 3.9 95 3.0 2.3 76 Cameroon 2011 DHS 40-44 3.8 6.3 167 4.0 6.8 168 2.8 3.4 120 Cameroon 2011 DHS 45-49 3.2 2.7 84 3.3 2.7 81 2.3 1.1 48 Kenya 2008-09 DHS All 3.1 2.5 82 3.3 2.7 83 1.3 1.0 77 Kenya 2008-09 DHS 15-19 0.0 4.2 0.0 4.9 0.0 0.3 Kenya 2008-09 DHS 20-24 0.6 3.5 623 0.6 3.8 654 0.1 1.5 1,938 Kenya 2008-09 DHS 25-29 4.5 1.7 37 4.9 1.8 36 2.4 1.0 42 Kenya 2008-09 DHS 30-34 3.8 1.7 44 4.0 1.8 45 2.8 0.9 31 Kenya 2008-09 DHS 35-39 2.8 3.1 111 2.9 3.3 111 2.1 1.5 69 Kenya 2008-09 DHS 40-44 1.6 3.6 224 1.7 3.7 219 1.2 1.6 134 Kenya 2008-09 DHS 45-49 3.3 0.0 0 3.4 0.0 0 2.6 0.0 0 Lesotho 2009 DHS All 7.9 10.1 128 11.2 13.9 124 2.5 3.1 123 Lesotho 2009 DHS 15-19 0.0 1.7 0.0 1.9 0.0 0.1 Lesotho 2009 DHS 20-24 3.4 7.1 212 3.6 9.1 256 0.4 2.4 538 Lesotho 2009 DHS 25-29 6.0 10.9 181 7.3 16.1 221 2.6 5.5 214 Lesotho 2009 DHS 30-34 4.4 16.9 380 7.8 24.0 309 3.6 7.9 217 Lesotho 2009 DHS 35-39 9.7 11.2 115 14.9 16.3 110 8.6 4.6 54 Lesotho 2009 DHS 40-44 10.1 9.8 97 14.9 14.4 97 8.8 4.1 47 Lesotho 2009 DHS 45-49 17.2 11.6 67 26.1 18.6 71 12.5 5.7 45 Malawi 2010 DHS All 3.9 4.6 119 4.3 5.1 118 2.1 2.6 120 Malawi 2010 DHS 15-19 1.9 2.1 108 1.9 2.3 117 0.0 0.4 1,140 

(Continued...)

84 
Table A1.6 – Continued

 Risk category #1 Risk category #2 Risk category #3 

Survey Category Men Women RR Men Women RR Men Women RR Malawi 2010 DHS 20-24 4.6 3.4 74 4.8 3.6 75 1.5 2.1 139 Malawi 2010 DHS 25-29 2.9 4.1 140 3.1 4.5 145 2.2 2.9 134 Malawi 2010 DHS 30-34 3.0 6.3 208 3.3 7.3 220 2.8 4.6 164 Malawi 2010 DHS 35-39 4.6 6.1 132 5.5 7.3 132 4.7 4.7 100 Malawi 2010 DHS 40-44 3.5 7.4 209 4.3 8.4 194 3.7 4.6 126 Malawi 2010 DHS 45-49 6.1 4.1 67 7.1 4.6 65 6.0 2.1 35 Mozambique 2009 AIS All 5.2 5.1 98 5.8 5.7 98 3.4 3.1 93 Mozambique 2009 AIS 15-19 13.1 2.4 18 13.1 2.6 20 0.6 0.8 137 Mozambique 2009 AIS 20-24 7.7 6.0 78 8.0 6.8 84 4.0 3.9 99 Mozambique 2009 AIS 25-29 5.0 5.5 109 5.6 6.1 109 4.2 4.1 96 Mozambique 2009 AIS 30-34 6.1 6.8 112 6.8 7.7 113 5.6 5.0 89 Mozambique 2009 AIS 35-39 3.2 2.8 87 3.6 3.0 83 3.2 1.8 58 Mozambique 2009 AIS 40-44 5.3 5.2 98 5.8 5.5 95 5.0 3.1 62 Mozambique 2009 AIS 45-49 3.4 5.1 148 3.8 5.5 144 3.2 2.9 89 Swaziland 2006-07 DHS All 8.7 7.7 89 13.7 12.3 90 2.1 1.8 86 Swaziland 2006-07 DHS 15-19 0.0 12.3 0.0 19.8 0.0 0.6 Swaziland 2006-07 DHS 20-24 19.8 5.6 28 31.5 10.5 33 1.2 1.5 123 Swaziland 2006-07 DHS 25-29 14.0 7.2 51 21.0 13.3 63 4.6 3.7 80 

Swaziland 2006-07 DHS 30-34 8.2 6.2 75 15.4 9.7 63 5.9 3.0 50 Swaziland 2006-07 DHS 35-39 4.7 13.7 293 8.3 18.3 220 3.7 4.8 128 Swaziland 2006-07 DHS 40-44 9.2 7.5 81 14.4 10.5 73 8.0 1.9 23 Swaziland 2006-07 DHS 45-49 4.1 0.0 0 5.2 0.0 0 3.3 0.0 0 Tanzania 2011-12 AIS All 2.0 2.6 131 2.1 2.8 130 1.0 1.0 106 Tanzania 2011-12 AIS 15-19 0.0 1.2 0.0 1.2 0.0 0.2 Tanzania 2011-12 AIS 20-24 0.8 2.4 303 0.8 2.5 304 0.2 1.0 523 Tanzania 2011-12 AIS 25-29 1.0 2.1 206 1.0 2.2 209 0.6 1.2 198 Tanzania 2011-12 AIS 30-34 1.3 2.9 218 1.4 3.1 220 1.0 1.7 175 Tanzania 2011-12 AIS 35-39 2.4 4.0 167 2.6 4.2 165 2.1 2.0 95 

(Continued...)

85 
Table A1.6 – Continued

 Risk category #1 Risk category #2 Risk category #3 

Survey Category Men Women RR Men Women RR Men Women RR Tanzania 2011-12 AIS 40-44 3.3 3.0 93 3.5 3.2 93 2.8 1.1 40 Tanzania 2011-12 AIS 45-49 2.8 3.4 121 2.9 3.7 126 2.3 0.4 17 Uganda 2011 AIS All 3.0 3.2 107 3.2 3.5 107 1.5 1.5 96 Uganda 2011 AIS 15-19 0.0 2.9 0.0 3.0 0.0 0.4 Uganda 2011 AIS 20-24 3.2 2.4 76 3.3 2.6 78 0.9 1.2 130 Uganda 2011 AIS 25-29 3.2 3.0 95 3.3 3.2 98 2.0 1.8 91 Uganda 2011 AIS 30-34 2.6 4.0 157 2.8 4.4 159 2.0 2.4 117 Uganda 2011 AIS 35-39 2.4 4.1 170 2.7 4.4 165 2.0 2.5 125 Uganda 2011 AIS 40-44 3.8 4.2 109 4.2 4.5 108 3.3 2.2 66 Uganda 2011 AIS 45-49 3.5 1.4 40 3.8 1.5 40 3.0 0.6 20 Zambia 2007 DHS All 4.7 6.8 145 5.6 7.9 141 2.5 3.7 147 Zambia 2007 DHS 15-19 4.9 4.9 100 5.9 5.3 90 0.0 0.7 1,539 

Zambia 2007 DHS 20-24 0.6 5.4 857 0.7 5.9 878 0.1 3.0 2,053 

Zambia 2007 DHS 25-29 4.2 8.6 204 4.8 10.0 209 2.8 6.1 216 Zambia 2007 DHS 30-34 4.1 4.9 122 4.8 6.0 126 3.6 3.8 106 Zambia 2007 DHS 35-39 7.0 9.0 128 8.7 10.4 120 7.3 7.1 98 Zambia 2007 DHS 40-44 4.3 7.6 177 5.3 9.0 171 4.5 5.1 113 Zambia 2007 DHS 45-49 6.0 8.0 134 7.1 8.7 124 6.0 4.3 73 Zimbabwe 2010-11 DHS All 4.6 6.8 149 5.5 8.0 145 2.1 2.9 135 Zimbabwe 2010-11 DHS 15-19 0.0 5.6 0.0 5.8 0.0 0.8 Zimbabwe 2010-11 DHS 20-24 2.6 4.9 185 2.7 5.3 193 0.6 2.2 384 Zimbabwe 2010-11 DHS 25-29 3.3 6.4 197 3.7 7.7 209 2.0 3.7 190 Zimbabwe 2010-11 DHS 30-34 5.6 7.1 126 6.7 9.1 135 4.7 4.6 99 Zimbabwe 2010-11 DHS 35-39 3.6 10.9 298 4.6 13.3 287 3.6 6.3 178 Zimbabwe 2010-11 DHS 40-44 8.0 7.1 89 10.7 8.9 83 7.8 3.4 43 Zimbabwe 2010-11 DHS 45-49 4.8 13.2 275 6.5 18.0 275 5.0 4.6 92 Note: Relative risk (RR) is expressed as number of women at risk per 100 men at risk. 

86 
Table A2.1 Percentage of men and women who are HIV-positivea and percentage of men and women who have cohabiting partnersb, by 

sex, and significance level for the difference between men and women, according to education (none, primary, secondary or higher), 10 DHS surveys in sub-Saharan Africa, 2006-2012 

Survey Category 

Percent HIV-positive Percent with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Cameroon 2011 DHS All 2.9 5.6 193 13,449 W+++ 34.5 40.1 116 13,449 W+++ Cameroon 2011 DHS None 1.7 2.8 167 2,014 ns 55.5 58.2 105 2,014 ns Cameroon 2011 DHS Primary 3.1 6.7 215 4,422 W+++ 42.8 46.7 109 4,422 W+ Cameroon 2011 DHS Sec+ 2.9 6.0 205 7,013 W+++ 27.1 27.1 100 7,013 ns Kenya 2008-09 DHS All 4.3 8.1 189 6,734 W+++ 39.0 35.3 90 6,734 M++ Kenya 2008-09 DHS None 5.5 6.1 111 420 ns 59.9 32.6 55 420 M+++ Kenya 2008-09 DHS Primary 4.9 9.1 184 3,715 W+++ 37.7 37.6 100 3,715 ns Kenya 2008-09 DHS Sec+ 3.4 7.0 203 2,598 W++ 39.1 32.0 82 2,598 M++ Lesotho 2009 DHS All 17.9 26.7 149 6,567 W+++ 26.2 22.1 84 6,567 M+++ Lesotho 2009 DHS None 27.4 20.7 76 364 ns 41.8 36.9 88 364 ns Lesotho 2009 DHS Primary 17.2 30.1 175 3,171 W+++ 26.5 24.8 94 3,171 ns 

Lesotho 2009 DHS Sec+ 15.9 23.7 149 3,032 W+++ 21.2 19.1 90 3,032 ns Malawi 2010 DHS All 8.1 12.8 159 13,528 W+++ 50.8 48.7 96 13,528 M+ Malawi 2010 DHS None 11.0 14.1 129 1,506 ns 70.2 56.8 81 1,506 M++ Malawi 2010 DHS Primary 7.6 11.5 152 8,610 W+++ 52.0 50.5 97 8,610 ns Malawi 2010 DHS Sec+ 8.4 16.1 191 3,413 W+++ 44.7 36.8 82 3,413 M+++ 

Mozambique 2009 AIS All 9.1 13.1 143 9,100 W+++ 59.1 53.4 91 9,100 M+++ Mozambique 2009 AIS None 7.1 9.7 136 1,955 ns 76.2 63.5 83 1,955 M+++ Mozambique 2009 AIS Primary 9.1 14.5 159 5,479 W+++ 63.9 53.1 83 5,479 M+++ Mozambique 2009 AIS Sec+ 10.1 14.9 148 1,666 W+ 40.5 31.3 77 1,666 M+++ Swaziland 2006-07 DHS All 19.5 31.0 159 8,210 W+++ 18.9 15.7 83 8,210 M+++ Swaziland 2006-07 DHS None 31.1 39.0 125 656 ns 32.4 24.6 76 656 M+ Swaziland 2006-07 DHS Primary 17.9 33.6 188 2,853 W+++ 16.5 15.0 91 2,853 ns 

Swaziland 2006-07 DHS Sec+ 19.0 28.3 149 4,701 W+++ 18.6 14.8 80 4,701 M+++ 

Tanzania 2011-12 AIS All 3.8 6.2 162 17,711 W+++ 46.6 36.9 79 17,711 M+++ 

(Continued...) 

87 
Table A2.1. – Continued

Survey Category 

Percent HIV-positive Percent with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Tanzania 2011-12 AIS None 3.3 5.4 164 2,479 ns 60.9 44.0 72 2,479 M+++ Tanzania 2011-12 AIS Primary 4.5 6.7 148 11,697 W+++ 53.2 40.4 76 11,697 M+++ Tanzania 2011-12 AIS Sec+ 2.0 5.0 246 3,534 W+++ 22.1 16.7 76 3,534 M+++ Uganda 2011 AIS All 6.1 8.3 137 19,562 W+++ 47.6 41.6 87 19,562 M+++ Uganda 2011 AIS None 8.5 9.5 112 2,004 ns 61.1 50.7 83 2,004 M+++ Uganda 2011 AIS Primary 6.7 8.9 134 11,398 W+++ 52.2 45.5 87 11,398 M+++ Uganda 2011 AIS Sec+ 4.9 6.4 131 6,161 W+ 38.7 28.4 73 6,161 M+++ Zambia 2007 DHS All 12.2 15.9 131 10,337 W+++ 47.3 45.6 96 10,337 ns Zambia 2007 DHS None 7.6 10.7 141 785 ns 65.3 57.2 88 785 ns Zambia 2007 DHS Primary 10.8 15.6 144 5,205 W+++ 53.3 51.8 97 5,205 ns Zambia 2007 DHS Sec+ 13.8 18.0 130 4,347 W+++ 40.0 33.1 83 4,347 M+++ Zimbabwe 2010-11 DHS All 12.2 17.7 145 13,669 W+++ 40.5 34.4 85 13,669 M+++ 

Zimbabwe 2010-11 DHS None 15.0 15.8 105 221 ns 41.3 34.9 85 221 ns Zimbabwe 2010-11 DHS Primary 13.6 20.4 151 3,521 W+++ 42.6 40.8 96 3,521 ns Zimbabwe 2010-11 DHS Sec+ 11.8 16.7 142 9,928 W+++ 40.0 31.9 80 9,928 M+++ Note: Ratios are calculated as 100*(% for men)/(% for women) 

a: restricted to respondents who were tested b: restricted to respondents in households in which both men and women were interviewed N is weighted Significance refers to a test of the null hypothesis that men and women have the same HIV prevalence or the same cohabiting partner rates M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

88 
Table A2.2 Percentage HIV-positive among men and women who do not have a cohabiting partner, and percentage HIV-positive among 

men and women who do have a cohabiting partner, by sex, and significance level for the difference between men and women, according to education (none, primary, secondary or higher), 10 DHS surveys in sub-Saharan Africa, 2006-2012 

Survey Category 

No cohabiting partner: % HIV-positive Has cohabiting partner: % HIV-positive 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Cameroon 2011 DHS All 1.9 6.2 333 8,404 W+++ 4.8 4.6 96 5,046 ns Cameroon 2011 DHS None 1.6 4.3 263 857 ns 1.7 1.7 100 1,158 ns Cameroon 2011 DHS Primary 2.3 8.0 346 2,436 W+++ 4.1 5.2 125 1,987 ns 

Cameroon 2011 DHS Sec+ 1.7 5.8 341 5,112 W+++ 6.3 6.7 107 1,901 ns 

Kenya 2008-09 DHS All 3.4 8.9 260 4,250 W+++ 5.7 6.6 117 2,483 ns Kenya 2008-09 DHS None 4.2 7.1 169 258 ns 6.3 4.0 63 162 ns Kenya 2008-09 DHS Primary 3.7 10.3 276 2,318 W+++ 6.9 7.0 101 1,398 ns Kenya 2008-09 DHS Sec+ 3.0 7.2 239 1,675 W+ 4.1 6.6 160 923 ns Lesotho 2009 DHS All 13.9 27.0 195 5,004 W+++ 29.2 25.6 88 1,563 ns Lesotho 2009 DHS None 21.9 25.5 116 214 ns 35.0 12.4 35 150 ns Lesotho 2009 DHS Primary 13.8 31.9 231 2,360 W+++ 26.6 24.8 93 811 ns 

Lesotho 2009 DHS Sec+ 12.2 22.9 188 2,430 W+++ 29.9 27.2 91 602 ns Malawi 2010 DHS All 5.3 15.3 288 6,806 W+++ 10.7 10.2 95 6,722 ns Malawi 2010 DHS None 15.5 18.5 119 600 ns 9.0 10.8 120 906 ns Malawi 2010 DHS Primary 5.0 13.8 278 4,204 W+++ 10.0 9.3 93 4,405 ns Malawi 2010 DHS Sec+ 4.9 17.6 359 2,002 W+++ 12.8 13.4 105 1,411 ns 

Mozambique 2009 AIS All 7.8 17.1 219 4,021 W+++ 10.1 9.6 95 5,080 ns 

Mozambique 2009 AIS None 9.5 14.4 151 663 ns 6.4 7.0 110 1,292 ns Mozambique 2009 AIS Primary 8.1 19.0 233 2,304 W+++ 9.6 10.5 109 3,175 ns Mozambique 2009 AIS Sec+ 7.1 14.8 209 1,053 W++ 14.5 15.1 104 613 ns Swaziland 2006-07 DHS All 15.6 29.9 191 6,807 W+++ 36.2 36.9 102 1,402 ns Swaziland 2006-07 DHS None 26.4 39.6 150 473 W+ 40.8 37.1 91 183 ns Swaziland 2006-07 DHS Primary 14.2 32.1 227 2,406 W+++ 36.7 42.0 115 447 ns 

Swaziland 2006-07 DHS Sec+ 15.4 27.4 178 3,929 W+++ 34.8 33.8 97 772 ns 

Tanzania 2011-12 AIS All 2.8 7.1 260 10,452 W+++ 5.0 4.5 89 7,258 ns 

(Continued...) 

89 
Table A2.2. – Continued

Survey Category 

No cohabiting partner: % HIV-positive Has cohabiting partner: % HIV-positive 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Tanzania 2011-12 AIS None 1.4 6.0 423 1,275 W+ 4.5 4.6 102 1,205 ns Tanzania 2011-12 AIS Primary 3.7 8.1 217 6,329 W+++ 5.2 4.5 88 5,369 ns Tanzania 2011-12 AIS Sec+ 1.3 5.3 407 2,849 W+++ 4.6 3.5 77 685 ns Uganda 2011 AIS All 5.4 9.7 179 10,908 W+++ 6.8 6.4 94 8,654 ns Uganda 2011 AIS None 10.8 12.4 115 939 ns 7.0 6.6 94 1,065 ns Uganda 2011 AIS Primary 5.9 11.4 193 5,892 W+++ 7.4 6.0 82 5,506 ns 

Uganda 2011 AIS Sec+ 4.4 6.0 137 4,078 ns 5.7 7.5 132 2,083 ns Zambia 2007 DHS All 9.2 18.4 201 5,546 W+++ 15.5 13.0 84 4,791 M++ Zambia 2007 DHS None 8.9 14.8 168 319 ns 6.9 7.6 110 466 ns Zambia 2007 DHS Primary 9.6 20.4 213 2,477 W+++ 11.9 11.1 93 2,728 ns Zambia 2007 DHS Sec+ 8.9 17.0 190 2,750 W+++ 21.2 20.0 94 1,597 ns Zimbabwe 2010-11 DHS All 8.7 19.3 222 8,535 W+++ 17.3 14.8 86 5,134 M++ 

Zimbabwe 2010-11 DHS None 11.5 18.9 164 141 ns 19.6 9.8 50 80 ns Zimbabwe 2010-11 DHS Primary 9.6 24.4 255 2,049 W+++ 18.9 14.7 78 1,472 M+ Zimbabwe 2010-11 DHS Sec+ 8.4 17.5 208 6,346 W+++ 16.8 15.1 90 3,582 ns Note: Restricted to respondents who were tested and who lived in households in which both men and women were interviewed. 

Ratios are calculated as 100*(% for men)/(% for women) N is weighted Significance refers to a test of the null hypothesis that men and women have the same HIV prevalence, controlling for cohabiting partner status (no partner or has a partner) M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

90 
Table A2.3 Percentage with a cohabiting partner among men and women who are HIV-negative, and percentage with a cohabiting 

partner among men and women who are HIV-positive, by sex, and significance level for the difference between men and women, according to education (none, primary, secondary or higher), 10 DHS surveys in sub-Saharan Africa, 2006-2012 (restricted to respondents who were tested and who live in households in which both men and women were interviewed) 

Survey Category 

HIV-negative: % with a cohabiting partner HIV-positive: % with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Cameroon 2011 DHS All 33.8 40.5 120 12,868 W+++ 57.7 33.2 58 581 M+++ Cameroon 2011 DHS None 55.4 58.8 106 1,963 ns 56.7 35.9 63 51 ns Cameroon 2011 DHS Primary 42.3 47.5 112 4,200 W++ 57.3 36.3 63 223 M++ Cameroon 2011 DHS Sec+ 26.2 26.9 103 6,705 ns 58.0 30.1 52 308 M+++ Kenya 2008-09 DHS All 38.5 35.8 93 6,298 ns 51.4 28.8 56 436 M+++ Kenya 2008-09 DHS None 59.3 33.4 56 395 M+++ 69.3 21.4 31 25 ns Kenya 2008-09 DHS Primary 36.9 38.4 104 3,441 ns 52.9 29.0 55 274 M++ Kenya 2008-09 DHS Sec+ 38.9 32.2 83 2,462 M++ 46.7 30.0 64 136 ns Lesotho 2009 DHS All 22.6 22.4 99 5,058 ns 42.7 21.1 50 1,509 M+++ Lesotho 2009 DHS None 37.4 40.7 109 267 ns 53.4 22.1 41 97 ns Lesotho 2009 DHS Primary 23.5 26.7 114 2,393 ns 41.0 20.4 50 778 M+++ Lesotho 2009 DHS Sec+ 17.7 18.3 103 2,397 ns 39.7 21.9 55 635 M+++ Malawi 2010 DHS All 49.3 50.2 102 12,087 ns 67.4 38.8 58 1,441 M+++ Malawi 2010 DHS None 71.6 59.0 82 1,305 M++ 57.7 43.6 76 201 ns Malawi 2010 DHS Primary 50.6 51.8 102 7,769 ns 68.6 40.8 60 840 M+++ Malawi 2010 DHS Sec+ 42.6 38.0 89 3,013 M+ 67.7 30.8 45 400 M+++ Mozambique 2009 AIS All 58.5 55.6 95 8,061 ns 65.0 39.2 60 1,040 M+++ Mozambique 2009 AIS None 76.8 65.4 85 1,776 M++ 68.2 45.8 67 179 ns Mozambique 2009 AIS Primary 63.5 55.6 88 4,819 M+++ 67.7 38.5 57 661 M+++ Mozambique 2009 AIS Sec+ 38.5 31.2 81 1,466 M++ 58.2 31.7 55 200 M+++ Swaziland 2006-07 DHS All 15.0 14.3 96 6,081 ns 35.0 18.6 53 2,128 M+++ Swaziland 2006-07 DHS None 27.9 25.4 91 422 ns 42.6 23.4 55 234 M++ Swaziland 2006-07 DHS Primary 12.7 13.1 103 2,100 ns 33.8 18.7 55 753 M+++ Swaziland 2006-07 DHS Sec+ 14.9 13.7 92 3,559 ns 34.0 17.7 52 1,141 M+++ 

(Continued...) 

91 
Table A2.3. – Continued

Survey Category 

HIV-negative: % with a cohabiting partner HIV-positive: % with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Tanzania 2011-12 AIS All 46.0 37.6 82 16,795 M+++ 61.4 26.7 44 915 M+++ Tanzania 2011-12 AIS None 60.2 44.4 74 2,359 M+++ 83.1 37.4 45 120 M+++ Tanzania 2011-12 AIS Primary 52.8 41.3 78 11,026 M+++ 61.0 27.4 45 671 M+++ Tanzania 2011-12 AIS Sec+ 21.5 17.0 79 3,410 M++ 50.1 11.8 24 124 M++ Uganda 2011 AIS All 47.3 42.5 90 18,123 M+++ 53.4 32.0 60 1,439 M+++ Uganda 2011 AIS None 62.1 52.3 84 1,819 M++ 50.6 35.3 70 185 ns Uganda 2011 AIS Primary 51.8 46.9 91 10,488 M+++ 57.8 30.7 53 909 M+++ Uganda 2011 AIS Sec+ 38.5 28.0 73 5,816 M+++ 45.2 33.3 74 345 ns Zambia 2007 DHS All 45.5 47.2 104 8,866 ns 60.2 37.1 62 1,471 M+++ Zambia 2007 DHS None 65.7 59.2 90 708 ns 59.4 40.6 68 77 ns Zambia 2007 DHS Primary 52.6 54.5 104 4,497 ns 58.7 36.9 63 708 M+++ Zambia 2007 DHS Sec+ 36.6 32.3 88 3,661 M++ 61.4 36.7 60 686 M+++ Zimbabwe 2010-11 DHS All 38.8 36.2 93 11,562 M++ 58.2 29.2 50 2,107 M+++ Zimbabwe 2010-11 DHS None 40.1 37.1 93 187 ns 55.6 21.6 39 34 ns Zimbabwe 2010-11 DHS Primary 40.1 44.3 111 2,889 ns 59.5 29.7 50 632 M+++ Zimbabwe 2010-11 DHS Sec+ 38.4 33.0 86 8,487 M+++ 57.8 29.2 51 1,441 M+++ Note: Restricted to respondents who were tested and who lived in households in which both men and women were interviewed. Ratios are calculated as 100*(% for men)/(% for women) N is weighted Significance refers to a test of the null hypothesis that men and women have the same cohabiting partner rates, controlling for HIV status (negative or positive) 

M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

92 
Table A2.4 Among cohabiting couples, the percent distribution of observed and expected couples (M=man, W=woman) by four 

combinations of couple HIV serostatus (negative concordance, female positive discordance, male positive discordance, and positive 

concordance), according to education (none, primary, secondary or higher), 10 DHS surveys in sub-Saharan Africa, 2006-2012 (restricted to cohabiting couples in which both the man and the woman were tested for HIV) 

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Cameroon 2011 DHS All All 92.1 90.7 3.3 4.6 3.1 4.4 1.5 0.2 Cameroon 2011 DHS Age of woman None 95.8 95.5 1.7 2.0 2.2 2.4 0.3 0.1 

Cameroon 2011 DHS Age of woman Primary 91.3 90.0 3.7 4.9 3.5 4.8 1.5 0.3 Cameroon 2011 DHS Age of woman Sec+ 89.9 87.7 4.1 6.3 3.3 5.6 2.7 0.4 Cameroon 2011 DHS Age of man None 97.6 96.8 0.8 1.6 0.8 1.6 0.8 0.0 Cameroon 2011 DHS Age of man Primary 91.8 90.8 4.0 5.0 3.0 4.0 1.2 0.2 Cameroon 2011 DHS Age of man Sec+ 90.4 88.7 3.5 5.3 4.0 5.8 2.1 0.3 Cameroon 2011 DHS Age difference M<W 92.1 90.7 3.8 5.2 2.5 3.9 1.6 0.2 Cameroon 2011 DHS Age difference M=W 92.0 90.3 3.4 5.1 2.6 4.3 2.0 0.3 Cameroon 2011 DHS Age difference M>W 92.1 91.5 3.0 3.5 4.2 4.8 0.8 0.2 Kenya 2008-09 DHS All All 91.2 88.4 3.1 5.9 2.5 5.3 3.2 0.4 Kenya 2008-09 DHS Age of woman None 95.4 93.2 1.7 3.9 0.6 2.8 2.3 0.1 Kenya 2008-09 DHS Age of woman Primary 90.5 87.6 3.2 6.2 2.9 5.8 3.4 0.4 Kenya 2008-09 DHS Age of woman Sec+ 91.6 89.0 3.1 5.8 2.3 5.0 3.0 0.3 Kenya 2008-09 DHS Age of man None 90.8 84.2 2.0 8.6 0.0 6.6 7.2 0.7 Kenya 2008-09 DHS Age of man Primary 90.4 87.5 2.8 5.7 3.4 6.3 3.3 0.4 Kenya 2008-09 DHS Age of man Sec+ 92.2 89.9 3.5 5.8 1.8 4.0 2.5 0.3 Kenya 2008-09 DHS Age difference M<W 84.5 78.1 3.6 10.0 4.2 10.6 7.8 1.4 Kenya 2008-09 DHS Age difference M=W 92.1 89.7 2.7 5.1 2.5 4.9 2.7 0.3 Kenya 2008-09 DHS Age difference M>W 91.1 88.5 4.0 6.6 2.0 4.6 3.0 0.3 Lesotho 2009 DHS All All 62.7 51.4 7.9 19.2 10.1 21.4 19.3 8.0 Lesotho 2009 DHS Age of woman None Lesotho 2009 DHS Age of woman Primary 63.6 52.2 7.0 18.4 10.3 21.7 19.0 7.7 Lesotho 2009 DHS Age of woman Sec+ 61.9 50.4 9.0 20.5 9.2 20.7 19.9 8.4 

(Continued...) 

93 
Table A2.4. – Continued

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Lesotho 2009 DHS Age of man None 59.3 48.7 6.7 17.3 14.6 25.1 19.5 8.9 Lesotho 2009 DHS Age of man Primary 63.6 52.4 9.2 20.5 8.3 19.5 18.9 7.6 

Lesotho 2009 DHS Age of man Sec+ 63.1 51.3 6.6 18.4 10.5 22.4 19.8 8.0 Lesotho 2009 DHS Age difference M<W 59.4 47.5 9.0 20.9 10.1 22.0 21.5 9.6 Lesotho 2009 DHS Age difference M=W 64.7 54.2 7.9 18.5 9.8 20.4 17.5 6.9 Lesotho 2009 DHS Age difference M>W 60.5 46.1 1.8 16.3 13.4 27.8 24.3 9.8 Malawi 2010 DHS All All 85.5 80.5 3.9 8.9 4.6 9.6 6.0 1.0 Malawi 2010 DHS Age of woman None 80.7 74.6 4.2 10.4 7.1 13.2 8.0 1.8 Malawi 2010 DHS Age of woman Primary 87.5 83.1 3.4 7.8 3.9 8.3 5.2 0.8 Malawi 2010 DHS Age of woman Sec+ 82.2 76.3 5.3 11.3 4.9 10.8 7.6 1.6 Malawi 2010 DHS Age of man None 88.4 83.4 3.6 8.5 2.4 7.3 5.7 0.7 Malawi 2010 DHS Age of man Primary 85.2 80.4 4.6 9.5 4.2 9.1 5.9 1.1 Malawi 2010 DHS Age of man Sec+ 85.3 80.0 2.2 7.6 6.1 11.4 6.4 1.1 Malawi 2010 DHS Age difference M<W 80.9 75.3 9.3 14.9 2.5 8.2 7.3 1.6 Malawi 2010 DHS Age difference M=W 87.0 82.2 3.3 8.1 4.0 8.8 5.7 0.9 Malawi 2010 DHS Age difference M>W 83.5 78.4 3.5 8.6 6.6 11.7 6.4 1.3 Mozambique 2009 AIS All All 85.0 81.3 5.2 8.9 5.1 8.8 4.7 1.0 Mozambique 2009 AIS Age of woman None 88.1 85.1 3.7 6.7 4.7 7.7 3.6 0.6 Mozambique 2009 AIS Age of woman Primary 84.1 80.2 5.7 9.6 5.2 9.1 5.0 1.1 Mozambique 2009 AIS Age of woman Sec+ 78.7 73.4 7.9 13.3 6.0 11.3 7.4 2.0 Mozambique 2009 AIS Age of man None 86.8 85.2 7.1 8.6 4.0 5.6 2.1 0.6 Mozambique 2009 AIS Age of man Primary 86.0 82.4 4.7 8.3 4.9 8.5 4.4 0.9 Mozambique 2009 AIS Age of man Sec+ 79.8 74.1 5.9 11.6 6.7 12.4 7.6 1.9 Mozambique 2009 AIS Age difference M<W 73.5 68.1 14.2 19.6 4.2 9.5 8.1 2.7 Mozambique 2009 AIS Age difference M=W 85.7 82.7 5.4 8.4 5.0 8.0 3.9 0.8 Mozambique 2009 AIS Age difference M>W 85.0 80.3 3.8 8.5 5.4 10.1 5.8 1.1 Swaziland 2006-07 DHS All All 54.8 39.7 8.7 23.8 7.7 22.8 28.8 13.7 

(Continued...) 

94 
Table A2.4. – Continued

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Swaziland 2006-07 DHS Age of woman None 53.1 41.9 16.1 27.3 7.4 18.6 23.4 12.2 Swaziland 2006-07 DHS Age of woman Primary 49.6 34.5 9.9 25.1 8.2 23.4 32.2 17.0 

Swaziland 2006-07 DHS Age of woman Sec+ 58.3 42.5 6.2 22.0 7.5 23.4 28.0 12.1 Swaziland 2006-07 DHS Age of man None 50.7 38.2 8.9 21.4 13.3 25.8 27.0 14.5 Swaziland 2006-07 DHS Age of man Primary 52.7 38.8 10.6 24.5 8.6 22.5 28.1 14.2 Swaziland 2006-07 DHS Age of man Sec+ 56.9 40.5 7.6 24.0 5.9 22.3 29.6 13.2 Swaziland 2006-07 DHS Age difference M<W 49.8 35.6 6.5 20.7 13.4 27.6 30.3 16.0 Swaziland 2006-07 DHS Age difference M=W 58.8 43.1 7.4 23.1 6.3 22.0 27.5 11.8 Swaziland 2006-07 DHS Age difference M>W 47.5 33.6 14.5 28.4 6.7 20.6 31.3 17.4 Tanzania 2011-12 AIS All All 93.0 90.9 2.0 4.2 2.6 4.8 2.3 0.2 Tanzania 2011-12 AIS Age of woman None 93.2 90.3 1.6 4.5 2.0 5.0 3.2 0.3 Tanzania 2011-12 AIS Age of woman Primary 93.0 91.0 2.2 4.1 2.7 4.6 2.1 0.2 Tanzania 2011-12 AIS Age of woman Sec+ 92.6 90.8 1.6 3.5 3.7 5.5 2.1 0.2 Tanzania 2011-12 AIS Age of man None 93.6 91.3 1.6 4.0 2.1 4.5 2.6 0.2 Tanzania 2011-12 AIS Age of man Primary 92.8 90.5 2.0 4.2 2.7 5.0 2.5 0.2 Tanzania 2011-12 AIS Age of man Sec+ 93.6 92.8 2.9 3.7 2.7 3.5 0.9 0.1 Tanzania 2011-12 AIS Age difference M<W 93.5 91.6 1.9 3.8 2.5 4.4 2.1 0.2 Tanzania 2011-12 AIS Age difference M=W 92.8 90.6 2.0 4.1 2.9 5.0 2.4 0.2 Tanzania 2011-12 AIS Age difference M>W 93.5 91.4 2.2 4.4 1.9 4.0 2.3 0.2 Uganda 2011 AIS All All 90.2 87.1 3.0 6.1 3.2 6.3 3.5 0.4 Uganda 2011 AIS Age of woman None 88.4 84.8 2.7 6.3 4.7 8.3 4.3 0.6 Uganda 2011 AIS Age of woman Primary 90.5 87.3 2.6 5.8 3.2 6.5 3.7 0.4 Uganda 2011 AIS Age of woman Sec+ 90.4 88.1 4.9 7.1 2.1 4.4 2.6 0.3 Uganda 2011 AIS Age of man None 91.6 88.1 2.1 5.6 2.5 5.9 3.8 0.4 Uganda 2011 AIS Age of man Primary 89.8 86.4 2.8 6.2 3.5 6.9 3.9 0.5 Uganda 2011 AIS Age of man Sec+ 90.6 88.3 3.7 6.1 3.0 5.3 2.7 0.4 Uganda 2011 AIS Age difference M<W 88.0 84.4 4.5 8.1 3.3 6.8 4.3 0.7 

(Continued...) 

95 
Table A2.4. – Continued

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Uganda 2011 AIS Age difference M=W 91.4 88.6 2.8 5.7 2.6 5.4 3.2 0.3 Uganda 2011 AIS Age difference M>W 88.6 85.1 2.9 6.3 4.5 8.0 4.1 0.6 

Zambia 2007 DHS All All 80.2 73.9 4.7 11.0 6.8 13.1 8.2 2.0 Zambia 2007 DHS Age of woman None 87.8 82.4 2.4 7.8 3.6 9.0 6.2 0.8 Zambia 2007 DHS Age of woman Primary 81.7 76.6 4.6 9.7 7.1 12.1 6.6 1.5 Zambia 2007 DHS Age of woman Sec+ 73.2 64.2 6.0 15.0 7.8 16.8 13.0 3.9 Zambia 2007 DHS Age of man None 90.0 86.1 2.3 6.3 3.1 7.1 4.5 0.5 Zambia 2007 DHS Age of man Primary 84.9 79.7 3.5 8.7 5.3 10.4 6.3 1.1 Zambia 2007 DHS Age of man Sec+ 73.1 65.6 6.5 14.0 9.3 16.8 11.1 3.6 Zambia 2007 DHS Age difference M<W 84.4 77.6 2.0 8.9 5.3 12.1 8.2 1.4 Zambia 2007 DHS Age difference M=W 80.3 74.1 5.1 11.3 6.4 12.7 8.2 1.9 Zambia 2007 DHS Age difference M>W 78.7 72.5 4.8 11.0 8.1 14.3 8.3 2.2 Zimbabwe 2010-11 DHS All All 78.4 70.7 4.6 12.3 6.8 14.5 10.2 2.5 Zimbabwe 2010-11 DHS Age of woman None 79.8 76.6 7.3 10.4 8.2 11.4 4.7 1.5 Zimbabwe 2010-11 DHS Age of woman Primary 77.8 70.6 5.3 12.5 7.2 14.4 9.8 2.5 Zimbabwe 2010-11 DHS Age of woman Sec+ 78.7 70.7 4.2 12.2 6.6 14.6 10.6 2.5 Zimbabwe 2010-11 DHS Age of man None Zimbabwe 2010-11 DHS Age of man Primary 75.8 68.0 5.8 13.6 7.5 15.3 10.9 3.1 Zimbabwe 2010-11 DHS Age of man Sec+ 79.2 71.6 4.3 11.9 6.6 14.2 10.0 2.3 Zimbabwe 2010-11 DHS Age difference M<W 74.7 67.4 6.1 13.3 8.8 16.1 10.4 3.2 Zimbabwe 2010-11 DHS Age difference M=W 78.8 70.7 4.1 12.2 6.5 14.6 10.6 2.5 Zimbabwe 2010-11 DHS Age difference M>W 78.8 72.4 5.5 11.9 7.2 13.5 8.6 2.2 Note: Weighted frequencies for the rows in this table are provided in Table 4.2 

96 
Table A2.5 HIV prevalence among men and women who have cohabiting partners, and measures of selection and HIV seroconversion, 

according to education (none, primary, secondary or higher), 10 DHS surveys in sub-Saharan Africa, 2006-2012 

Survey Category 

HIV prevalence Selection/Seroconversion measures 

Men Women Delta kap_SC SCm SCw Sig. N 

Cameroon 2011 DHS All All 4.7 4.8 1.3 29.3 28.7 29.9 ns 2,289 Cameroon 2011 DHS Age of woman None 2.5 2.0 0.2 10.4 11.7 9.5 ns 632 Cameroon 2011 DHS Age of woman Primary 5.1 5.2 1.2 25.3 24.9 25.7 ns 887 Cameroon 2011 DHS Age of woman Sec+ 6.0 6.8 2.3 37.9 35.6 40.5 ns 770 

Cameroon 2011 DHS Age of man None 1.6 1.6 0.8 48.2 48.5 48.0 ns 341 Cameroon 2011 DHS Age of man Primary 4.2 5.2 1.0 22.4 20.1 25.4 ns 919 Cameroon 2011 DHS Age of man Sec+ 6.1 5.6 1.8 31.9 33.5 30.5 ns 1,030 

Cameroon 2011 DHS Age difference M<W 4.1 5.4 1.4 29.9 26.2 34.7 ns 239 Cameroon 2011 DHS Age difference M=W 4.6 5.4 1.7 36.7 33.9 40.0 ns 1,335 

Cameroon 2011 DHS Age difference M>W 5.0 3.7 0.6 13.5 16.0 11.7 ns 715 Kenya 2008-09 DHS All All 5.7 6.2 2.8 50.1 47.8 52.7 ns 1,064 Kenya 2008-09 DHS Age of woman None 2.9 4.0 2.2 65.3 56.1 78.3 ns 76 Kenya 2008-09 DHS Age of woman Primary 6.3 6.6 3.0 49.4 48.0 50.9 ns 635 Kenya 2008-09 DHS Age of woman Sec+ 5.3 6.1 2.6 49.3 45.9 53.3 ns 354 Kenya 2008-09 DHS Age of man None 7.2 9.2 6.6 86.8 76.7 100.0 ns 49 Kenya 2008-09 DHS Age of man Primary 6.8 6.2 2.9 48.5 51.1 46.2 ns 543 Kenya 2008-09 DHS Age of man Sec+ 4.3 6.0 2.3 46.5 39.5 56.5 ns 473 

Kenya 2008-09 DHS Age difference M<W 11.9 11.4 6.4 62.5 64.4 60.8 ns 93 Kenya 2008-09 DHS Age difference M=W 5.2 5.4 2.4 47.7 46.6 48.8 ns 744 Kenya 2008-09 DHS Age difference M>W 4.9 7.0 2.6 46.7 39.4 57.2 ns 228 Lesotho 2009 DHS All All 29.4 27.2 11.3 55.6 58.8 52.7 ns 689 Lesotho 2009 DHS Age of woman None . 13 Lesotho 2009 DHS Age of woman Primary 29.4 26.1 11.4 56.7 61.8 52.4 ns 353 Lesotho 2009 DHS Age of woman Sec+ 29.1 28.9 11.4 55.6 55.9 55.3 ns 323 Lesotho 2009 DHS Age of man None 34.0 26.2 10.6 50.0 61.4 42.1 ns 125 Lesotho 2009 DHS Age of man Primary 27.1 28.1 11.2 56.2 54.9 57.6 ns 344 

(Continued...) 

97 
Table A2.5. – Continued

Survey Category 

HIV prevalence Selection/Seroconversion measures 

Men Women Delta kap_SC Men Women Sig. N 

Lesotho 2009 DHS Age of man Sec+ 30.4 26.4 11.8 58.0 64.3 52.9 ns 220 Lesotho 2009 DHS Age difference M<W 31.6 30.5 11.9 55.5 56.9 54.1 ns 233 Lesotho 2009 DHS Age difference M=W 27.3 25.4 10.6 54.3 57.0 51.8 ns 412 Lesotho 2009 DHS Age difference M>W 37.6 26.1 14.4 65.5 88.8 51.9 M+ 43 Malawi 2010 DHS All All 10.6 9.9 5.0 54.1 56.4 52.1 ns 2,987 Malawi 2010 DHS Age of woman None 15.1 12.2 6.1 51.9 59.1 46.3 ns 509 Malawi 2010 DHS Age of woman Primary 9.1 8.6 4.4 54.7 56.4 53.1 ns 2,009 Malawi 2010 DHS Age of woman Sec+ 12.4 12.9 5.9 53.8 52.7 54.9 ns 469 

Malawi 2010 DHS Age of man None 8.1 9.2 4.9 62.1 57.8 67.1 ns 244 Malawi 2010 DHS Age of man Primary 10.1 10.6 4.8 52.2 51.1 53.4 ns 1,901 Malawi 2010 DHS Age of man Sec+ 12.5 8.6 5.4 56.5 70.9 47.0 M++ 842 Malawi 2010 DHS Age difference M<W 9.8 16.6 5.6 48.7 37.7 68.9 W++ 256 Malawi 2010 DHS Age difference M=W 9.7 9.0 4.8 57.0 59.5 54.7 ns 1,891 

Malawi 2010 DHS Age difference M>W 13.0 9.9 5.2 50.6 59.7 44.0 ns 841 Mozambique 2009 AIS All All 9.8 9.9 3.7 42.0 41.7 42.2 ns 2,322 

Mozambique 2009 AIS Age of woman None 8.3 7.3 3.0 41.7 44.7 39.1 ns 798 

Mozambique 2009 AIS Age of woman Primary 10.2 10.7 3.9 41.5 40.5 42.5 ns 1,332 Mozambique 2009 AIS Age of woman Sec+ 13.3 15.3 5.3 43.3 40.1 47.1 ns 193 Mozambique 2009 AIS Age of man None 6.1 9.2 1.5 21.6 17.8 27.7 ns 300 Mozambique 2009 AIS Age of man Primary 9.3 9.1 3.6 42.8 43.3 42.2 ns 1,612 Mozambique 2009 AIS Age of man Sec+ 14.3 13.5 5.7 47.6 49.3 46.0 ns 410 Mozambique 2009 AIS Age difference M<W 12.3 22.3 5.4 36.9 27.5 56.4 W+ 93 Mozambique 2009 AIS Age difference M=W 8.9 9.3 3.1 37.1 36.2 38.0 ns 1,445 Mozambique 2009 AIS Age difference M>W 11.2 9.6 4.7 50.6 55.5 46.5 ns 784 Swaziland 2006-07 DHS All All 36.5 37.5 15.1 64.8 63.5 66.2 ns 626 Swaziland 2006-07 DHS Age of woman None 30.8 39.5 11.2 48.9 41.2 60.3 ns 81 Swaziland 2006-07 DHS Age of woman Primary 40.4 42.2 15.2 62.6 60.4 64.9 ns 205 Swaziland 2006-07 DHS Age of woman Sec+ 35.5 34.2 15.9 69.9 72.0 68.0 ns 340 

(Continued...) 

98 
Table A2.5. – Continued

Survey Category 

HIV prevalence Selection/Seroconversion measures 

Men Women Delta kap_SC Men Women Sig. N 

Swaziland 2006-07 DHS Age of man None 40.3 35.9 12.5 52.9 58.3 48.4 ns 82 Swaziland 2006-07 DHS Age of man Primary 36.7 38.7 13.9 59.3 56.9 61.9 ns 196 Swaziland 2006-07 DHS Age of man Sec+ 35.5 37.2 16.4 70.8 68.3 73.5 ns 348 Swaziland 2006-07 DHS Age difference M<W 43.7 36.8 14.2 58.8 68.6 51.5 ns 118 Swaziland 2006-07 DHS Age difference M=W 33.8 34.9 15.7 69.6 67.8 71.4 ns 382 Swaziland 2006-07 DHS Age difference M>W 38.0 45.8 13.9 56.6 48.9 67.3 ns 126 Tanzania 2011-12 AIS All All 5.0 4.4 2.1 47.8 51.4 44.7 ns 3,302 

Tanzania 2011-12 AIS Age of woman None 5.2 4.7 2.9 62.0 65.3 59.1 ns 674 

Tanzania 2011-12 AIS Age of woman Primary 4.8 4.3 1.9 44.2 46.8 41.8 ns 2,362 Tanzania 2011-12 AIS Age of woman Sec+ 5.8 3.7 1.9 41.4 53.5 33.8 ns 266 

Tanzania 2011-12 AIS Age of man None 4.7 4.2 2.4 55.6 59.0 52.6 ns 385 

Tanzania 2011-12 AIS Age of man Primary 5.2 4.5 2.3 49.5 53.9 45.8 ns 2,555 

Tanzania 2011-12 AIS Age of man Sec+ 3.6 3.8 0.8 22.3 21.7 23.0 ns 361 Tanzania 2011-12 AIS Age difference M<W 4.6 4.0 1.9 46.3 49.8 43.2 ns 308 Tanzania 2011-12 AIS Age difference M=W 5.3 4.4 2.2 47.2 52.3 43.0 ns 2,316 Tanzania 2011-12 AIS Age difference M>W 4.2 4.6 2.1 50.7 48.8 52.7 ns 678 Uganda 2011 AIS All All 6.8 6.6 3.1 49.7 50.6 48.8 ns 3,972 Uganda 2011 AIS Age of woman None 8.9 6.9 3.7 49.8 57.7 43.9 ns 601 Uganda 2011 AIS Age of woman Primary 6.9 6.2 3.2 52.5 55.5 49.8 ns 2,613 

Uganda 2011 AIS Age of woman Sec+ 4.7 7.5 2.3 39.2 31.7 51.5 W+ 758 Uganda 2011 AIS Age of man None 6.3 6.0 3.5 60.1 62.0 58.3 ns 279 Uganda 2011 AIS Age of man Primary 7.4 6.7 3.4 52.2 55.0 49.6 ns 2,475 

Uganda 2011 AIS Age of man Sec+ 5.7 6.4 2.3 41.4 38.8 44.4 ns 1,218 Uganda 2011 AIS Age difference M<W 7.5 8.8 3.6 48.0 44.2 52.4 ns 431 Uganda 2011 AIS Age difference M=W 5.8 6.0 2.8 50.9 49.9 51.8 ns 2,394 

Uganda 2011 AIS Age difference M>W 8.6 6.9 3.5 48.3 54.6 43.3 ns 1,148 

Zambia 2007 DHS All All 15.1 12.9 6.3 52.1 57.1 47.9 M+ 2,007 Zambia 2007 DHS Age of woman None 9.8 8.6 5.3 63.8 68.8 59.4 ns 247 

(Continued...) 

99 
Table A2.5. – Continued

Survey Category 

HIV prevalence Selection/Seroconversion measures 

Men Women Delta kap_SC Men Women Sig. N 

Zambia 2007 DHS Age of woman Primary 13.7 11.3 5.1 46.5 52.3 41.8 M+ 1,234 Zambia 2007 DHS Age of woman Sec+ 20.8 19.0 9.0 56.7 60.1 53.6 ns 526 

Zambia 2007 DHS Age of man None 7.6 6.8 4.0 59.3 63.0 55.9 ns 120 Zambia 2007 DHS Age of man Primary 11.6 9.8 5.2 54.0 59.4 49.6 ns 1,038 Zambia 2007 DHS Age of man Sec+ 20.4 17.6 7.5 48.7 53.6 44.7 ns 848 Zambia 2007 DHS Age difference M<W 13.5 10.3 6.8 64.9 76.9 56.1 ns 181 Zambia 2007 DHS Age difference M=W 14.6 13.3 6.2 52.1 55.2 49.3 ns 1,222 Zambia 2007 DHS Age difference M>W 16.5 13.2 6.2 48.8 56.1 43.2 M+ 604 Zimbabwe 2010-11 DHS All All 17.0 14.8 7.7 57.3 62.5 52.9 M++ 2,180 Zimbabwe 2010-11 DHS Age of woman None 12.9 12.0 3.2 29.1 30.4 28.0 ns 40 Zimbabwe 2010-11 DHS Age of woman Primary 16.9 15.1 7.2 53.7 57.7 50.2 ns 750 Zimbabwe 2010-11 DHS Age of woman Sec+ 17.2 14.7 8.0 59.8 65.9 54.8 M++ 1,390 Zimbabwe 2010-11 DHS Age of man None . 19 Zimbabwe 2010-11 DHS Age of man Primary 18.4 16.7 7.8 54.1 57.4 51.2 ns 522 Zimbabwe 2010-11 DHS Age of man Sec+ 16.5 14.2 7.6 58.4 64.1 53.6 M++ 1,640 Zimbabwe 2010-11 DHS Age difference M<W 19.2 16.5 7.2 49.2 54.3 45.0 ns 206 Zimbabwe 2010-11 DHS Age difference M=W 17.1 14.8 8.1 60.5 66.4 55.7 M++ 1,507 Zimbabwe 2010-11 DHS Age difference M>W 15.7 14.1 6.4 50.1 53.6 47.1 ns 467 Note: Selection/seroconversion measures are defined in the text 

N is weighted Significance refers to a test of the null hypothesis that selection/seroconversion is the same for men and women M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

100 
101 

Table A2.6 Percentage of HIV-negative men and women who are at risk of infection from an HIVpositive cohabiting partner, and relative risk for women compared with men, by three categories 

of risk, according to education (none, primary, secondary or higher), 10 DHS surveys in subSaharan Africa, 2006-2012 

 Risk category #1 Risk category #2 Risk category #3 

Survey Category Men Women RR Men Women RR Men Women RR 

Cameroon 2011 DHS All 3.3 3.1 94 3.5 3.3 94 1.2 1.3 113 

Cameroon 2011 DHS None 0.8 2.2 269 0.8 2.2 270 0.5 1.3 287 

Cameroon 2011 DHS Primary 4.0 3.6 89 4.2 3.7 90 1.8 1.8 101 

Cameroon 2011 DHS Sec+ 3.5 3.3 95 3.7 3.6 96 1.0 1.0 98 

Kenya 2008-09 DHS All 3.1 2.5 82 3.3 2.7 83 1.3 1.0 77 

Kenya 2008-09 DHS None 2.0 0.6 30 2.1 0.6 29 1.3 0.2 17 

Kenya 2008-09 DHS Primary 2.8 2.9 102 3.0 3.1 102 1.1 1.2 106 

Kenya 2008-09 DHS Sec+ 3.5 2.3 66 3.6 2.5 68 1.4 0.8 56 

Lesotho 2009 DHS All 7.9 10.1 128 11.2 13.9 124 2.5 3.1 123 

Lesotho 2009 DHS None 6.7 26.8 402 10.1 31.7 314 3.8 12.9 341 

Lesotho 2009 DHS Primary 9.2 10.3 112 12.7 14.0 110 3.0 3.7 125 

Lesotho 2009 DHS Sec+ 6.6 9.2 141 9.4 13.0 138 1.7 2.4 143 

Malawi 2010 DHS All 3.9 4.6 119 4.3 5.1 118 2.1 2.6 120 

Malawi 2010 DHS None 3.6 7.1 198 3.9 8.1 207 2.8 4.8 171 

Malawi 2010 DHS Primary 4.6 3.9 84 5.2 4.3 83 2.6 2.2 85 

Malawi 2010 DHS Sec+ 2.2 4.9 223 2.5 5.6 224 1.1 2.1 200 

Mozambique 2009 AIS All 5.2 5.1 98 5.8 5.7 98 3.4 3.1 93 

Mozambique 2009 AIS None 7.1 4.7 65 7.6 5.0 66 5.8 3.3 56 

Mozambique 2009 AIS Primary 4.7 5.2 112 5.2 5.9 114 3.3 3.3 100 

Mozambique 2009 AIS Sec+ 5.9 6.0 102 6.8 7.0 103 2.6 2.2 83 

Swaziland 2006-07 DHS All 8.7 7.7 89 13.7 12.3 90 2.1 1.8 86 

Swaziland 2006-07 DHS None 8.9 7.4 83 15.0 12.2 82 4.2 3.1 74 

Swaziland 2006-07 DHS Primary 10.5 8.2 78 16.7 14.2 85 2.1 1.9 88 

Swaziland 2006-07 DHS Sec+ 7.6 7.5 99 11.8 11.4 97 1.8 1.6 89 

Tanzania 2011-12 AIS All 2.0 2.6 131 2.1 2.8 130 1.0 1.0 106 

Tanzania 2011-12 AIS None 1.6 2.0 123 1.7 2.1 123 1.0 0.9 91 

Tanzania 2011-12 AIS Primary 2.0 2.7 138 2.1 2.8 136 1.1 1.2 107 

Tanzania 2011-12 AIS Sec+ 2.9 3.7 128 3.0 3.8 128 0.6 0.6 102 

Uganda 2011 AIS All 3.0 3.2 107 3.2 3.5 107 1.5 1.5 96 

Uganda 2011 AIS None 2.1 4.7 220 2.3 5.0 221 1.4 2.6 187 

Uganda 2011 AIS Primary 2.8 3.2 116 3.0 3.5 115 1.6 1.6 104 

Uganda 2011 AIS Sec+ 3.7 2.1 57 3.9 2.3 58 1.5 0.6 43 

Zambia 2007 DHS All 4.7 6.8 145 5.6 7.9 141 2.5 3.7 147 

Zambia 2007 DHS None 2.3 3.6 156 2.5 4.0 158 1.7 2.4 142 

Zambia 2007 DHS Primary 3.5 7.1 200 4.0 8.0 199 2.1 4.3 207 

Zambia 2007 DHS Sec+ 6.5 7.8 120 8.2 9.6 118 3.0 3.1 104 

Zimbabwe 2010-11 DHS All 4.6 6.8 149 5.5 8.0 145 2.1 2.9 135 

(Continued...)
102 

Table A2.6 – Continued

 Risk category #1 Risk category #2 Risk category #3 

Survey Category Men Women RR Men Women RR Men Women RR 

Zimbabwe 2010-11 DHS None 0.0 8.2 0.0 9.3 0.0 3.4 

Zimbabwe 2010-11 DHS Primary 5.8 7.1 123 7.1 8.4 118 2.9 3.7 131 

Zimbabwe 2010-11 DHS Sec+ 4.3 6.6 155 5.1 7.8 152 2.0 2.6 131 

Note: Relative risk (RR) is expressed as number of women at risk per 100 men at risk. 
Table A3.1 Percentage of men and women who are HIV-positivea and percentage of men and women who have cohabiting partnersb, by 

sex, and significance level for the difference between men and women, according to wealth status (quintiles: lowest, second, middle, fourth, highest), 10 DHS surveys in sub-Saharan Africa, 2006-2012 

Survey Category 

Percent HIV-positive Percent with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Cameroon 2011 DHS All 2.9 5.6 193 13,449 W+++ 34.5 40.1 116 13,449 W+++ Cameroon 2011 DHS Lowest 1.2 2.6 208 2,150 W+ 46.7 57.2 123 2,150 W+++ Cameroon 2011 DHS Second 3.5 4.9 142 2,324 ns 42.4 45.3 107 2,324 ns 

Cameroon 2011 DHS Middle 2.6 5.9 225 2,563 W+++ 33.8 37.5 111 2,563 W+ 

Cameroon 2011 DHS Fourth 3.1 6.9 227 3,011 W+++ 29.3 32.8 112 3,011 W+ Cameroon 2011 DHS Highest 3.5 6.6 188 3,402 W++ 27.6 33.2 120 3,402 W+++ Kenya 2008-09 DHS All 4.3 8.1 189 6,734 W+++ 39.0 35.3 90 6,734 M++ Kenya 2008-09 DHS Lowest 2.3 6.3 273 1,097 W++ 45.7 34.5 76 1,097 M+++ Kenya 2008-09 DHS Second 4.5 9.0 198 1,191 W++ 39.2 35.1 90 1,191 ns Kenya 2008-09 DHS Middle 4.5 6.8 150 1,175 ns 34.4 33.3 97 1,175 ns 

Kenya 2008-09 DHS Fourth 5.6 7.5 133 1,527 ns 33.3 29.6 89 1,527 ns 

Kenya 2008-09 DHS Highest 3.9 10.3 267 1,743 W+++ 43.4 42.3 97 1,743 ns Lesotho 2009 DHS All 17.9 26.7 149 6,567 W+++ 26.2 22.1 84 6,567 M+++ Lesotho 2009 DHS Lowest 16.3 20.3 125 989 ns 33.0 28.3 86 989 M+ Lesotho 2009 DHS Second 17.5 26.1 149 1,183 W++ 27.7 27.3 98 1,183 ns Lesotho 2009 DHS Middle 17.7 27.5 156 1,316 W+++ 19.3 20.4 106 1,316 ns 

Lesotho 2009 DHS Fourth 17.9 31.5 177 1,520 W+++ 26.9 18.3 68 1,520 M+++ Lesotho 2009 DHS Highest 19.6 25.6 131 1,560 W+ 26.6 19.8 75 1,560 M+++ Malawi 2010 DHS All 8.1 12.8 159 13,528 W+++ 50.8 48.7 96 13,528 M+ Malawi 2010 DHS Lowest 5.6 8.9 158 2,133 W+ 55.3 43.7 79 2,133 M+++ Malawi 2010 DHS Second 6.5 9.3 144 2,643 W+ 57.8 55.2 95 2,643 ns Malawi 2010 DHS Middle 8.0 10.7 134 2,685 W+ 57.1 54.7 96 2,685 ns Malawi 2010 DHS Fourth 8.1 13.6 167 2,662 W+++ 51.4 52.4 102 2,662 ns 

Malawi 2010 DHS Highest 10.6 19.6 185 3,405 W+++ 37.8 39.4 104 3,405 ns 

Mozambique 2009 AIS All 9.1 13.1 143 9,100 W+++ 59.1 53.4 91 9,100 M+++ 

(Continued...) 

103 
Table A3.1. – Continued

Survey Category 

Percent HIV-positive Percent with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Mozambique 2009 AIS Lowest 5.0 6.7 135 1,644 ns 76.0 60.7 80 1,644 M+++ Mozambique 2009 AIS Second 5.9 8.7 147 1,758 ns 69.7 64.4 92 1,758 ns Mozambique 2009 AIS Middle 7.2 9.8 135 1,821 ns 64.9 62.1 96 1,821 ns Mozambique 2009 AIS Fourth 12.7 18.5 146 1,798 W+++ 55.2 46.4 84 1,798 M+++ Mozambique 2009 AIS Highest 13.5 20.6 152 2,080 W+++ 36.9 36.2 98 2,080 ns 

Swaziland 2006-07 DHS All 19.5 31.0 159 8,210 W+++ 18.9 15.7 83 8,210 M+++ Swaziland 2006-07 DHS Lowest 19.8 31.5 159 1,315 W+++ 21.0 16.7 80 1,315 M+++ Swaziland 2006-07 DHS Second 19.8 31.9 161 1,420 W+++ 19.7 14.5 74 1,420 M+++ Swaziland 2006-07 DHS Middle 16.9 31.3 185 1,681 W+++ 13.7 12.6 92 1,681 ns Swaziland 2006-07 DHS Fourth 20.8 31.5 151 1,833 W+++ 15.9 13.5 85 1,833 M+ Swaziland 2006-07 DHS Highest 20.2 29.2 145 1,960 W+++ 24.2 20.5 85 1,960 M++ Tanzania 2011-12 AIS All 3.8 6.2 162 17,711 W+++ 46.6 36.9 79 17,711 M+++ Tanzania 2011-12 AIS Lowest 3.1 4.7 153 2,950 ns 54.6 42.1 77 2,950 M+++ Tanzania 2011-12 AIS Second 2.9 4.7 166 3,222 W++ 54.6 43.7 80 3,222 M+++ Tanzania 2011-12 AIS Middle 4.2 5.5 130 3,299 ns 48.6 41.0 84 3,299 M+++ Tanzania 2011-12 AIS Fourth 3.5 6.7 192 3,689 W+++ 44.0 35.5 81 3,689 M+++ Tanzania 2011-12 AIS Highest 4.9 8.1 164 4,551 W++ 36.1 27.2 75 4,551 M+++ Uganda 2011 AIS All 6.1 8.3 137 19,562 W+++ 47.6 41.6 87 19,562 M+++ Uganda 2011 AIS Lowest 5.7 6.8 120 3,322 ns 55.3 47.8 87 3,322 M+++ Uganda 2011 AIS Second 5.1 7.5 148 3,604 W++ 56.4 50.6 90 3,604 M+++ Uganda 2011 AIS Middle 6.5 7.3 113 3,691 ns 51.9 47.2 91 3,691 M+++ Uganda 2011 AIS Fourth 7.2 9.2 128 3,938 ns 42.8 37.6 88 3,938 M+++ Uganda 2011 AIS Highest 5.9 9.9 167 5,007 W+++ 36.3 30.3 84 5,007 M+++ Zambia 2007 DHS All 12.2 15.9 131 10,337 W+++ 47.3 45.6 96 10,337 ns Zambia 2007 DHS Lowest 6.8 8.6 128 1,914 ns 61.7 62.3 101 1,914 ns 

Zambia 2007 DHS Second 9.7 9.8 101 1,721 ns 59.3 47.2 80 1,721 M+++ Zambia 2007 DHS Middle 10.8 13.2 122 1,869 ns 50.9 49.9 98 1,869 ns Zambia 2007 DHS Fourth 17.9 22.8 127 2,264 W++ 40.3 41.4 103 2,264 ns 

(Continued...) 

104 
Table A3.1. – Continued

Survey Category 

Percent HIV-positive Percent with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Zambia 2007 DHS Highest 13.7 21.6 157 2,569 W+++ 32.4 33.0 102 2,569 ns Zimbabwe 2010-11 DHS All 12.2 17.7 145 13,669 W+++ 40.5 34.4 85 13,669 M+++ Zimbabwe 2010-11 DHS Lowest 14.6 17.1 117 2,340 ns 53.0 41.3 78 2,340 M+++ Zimbabwe 2010-11 DHS Second 12.2 16.5 135 2,489 W++ 44.8 38.6 86 2,489 M+++ Zimbabwe 2010-11 DHS Middle 12.1 20.0 165 2,675 W+++ 38.5 34.3 89 2,675 M+ 

Zimbabwe 2010-11 DHS Fourth 11.6 19.9 171 3,037 W+++ 39.6 34.3 87 3,037 M+++ Zimbabwe 2010-11 DHS Highest 11.2 15.4 138 3,128 W++ 32.6 27.0 83 3,128 M+++ Note: Ratios are calculated as 100*(% for men)/(% for women) 

a: restricted to respondents who were tested b: restricted to respondents in households in which both men and women were interviewed N is weighted Significance refers to a test of the null hypothesis that men and women have the same HIV prevalence or the same cohabiting partner rates M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

105 
Table A3.2 Percentage HIV-positive among men and women who do not have a cohabiting partner, and percentage HIV-positive among 

men and women who do have a cohabiting partner, by sex, and significance level for the difference between men and women, according to wealth status (quintiles: lowest, second, middle, fourth, highest), 10 DHS surveys in sub-Saharan Africa, 2006-2012 

Survey Category 

No cohabiting partner: % HIV-positive Has cohabiting partner: % HIV-positive 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Cameroon 2011 DHS All 1.9 6.2 333 8,404 W+++ 4.8 4.6 96 5,046 ns Cameroon 2011 DHS Lowest 1.2 3.5 294 1,021 W+ 1.3 1.9 146 1,129 ns Cameroon 2011 DHS Second 2.7 5.9 216 1,301 W++ 4.5 3.7 84 1,023 ns 

Cameroon 2011 DHS Middle 1.9 6.3 331 1,645 W+++ 4.0 5.2 130 918 ns Cameroon 2011 DHS Fourth 1.9 7.5 396 2,071 W+++ 5.9 5.9 99 939 ns Cameroon 2011 DHS Highest 1.7 6.4 378 2,366 W++ 8.3 7.1 86 1,036 ns Kenya 2008-09 DHS All 3.4 8.9 260 4,250 W+++ 5.7 6.6 117 2,483 ns Kenya 2008-09 DHS Lowest 1.0 7.0 694 673 W++ 3.9 5.1 131 424 ns Kenya 2008-09 DHS Second 2.7 9.2 340 751 W+ 7.4 8.5 116 440 ns Kenya 2008-09 DHS Middle 3.5 8.4 236 778 W+ 6.4 3.6 57 397 ns Kenya 2008-09 DHS Fourth 5.1 8.1 160 1,050 ns 6.8 6.0 89 477 ns Kenya 2008-09 DHS Highest 3.4 11.7 343 998 W+++ 4.5 8.5 190 746 ns Lesotho 2009 DHS All 13.9 27.0 195 5,004 W+++ 29.2 25.6 88 1,563 ns Lesotho 2009 DHS Lowest 13.6 21.8 160 689 W++ 21.6 16.4 76 300 ns Lesotho 2009 DHS Second 12.4 27.1 218 858 W+++ 31.0 23.4 76 325 ns Lesotho 2009 DHS Middle 14.0 27.1 193 1,054 W+++ 33.0 29.1 88 261 ns 

Lesotho 2009 DHS Fourth 11.8 30.6 259 1,192 W+++ 34.3 35.7 104 327 ns 

Lesotho 2009 DHS Highest 17.2 26.1 152 1,210 W++ 26.3 23.7 90 350 ns Malawi 2010 DHS All 5.3 15.3 288 6,806 W+++ 10.7 10.2 95 6,722 ns Malawi 2010 DHS Lowest 3.0 10.4 347 1,098 W++ 7.8 6.9 89 1,035 ns Malawi 2010 DHS Second 4.6 11.6 253 1,153 W+++ 7.8 7.5 96 1,490 ns Malawi 2010 DHS Middle 5.9 12.3 209 1,187 W++ 9.5 9.3 98 1,498 ns Malawi 2010 DHS Fourth 3.8 15.6 405 1,280 W+++ 12.2 11.7 96 1,382 ns 

Malawi 2010 DHS Highest 7.2 22.5 310 2,089 W+++ 16.2 15.3 94 1,316 ns 

(Continued...) 

106 
Table A3.2. – Continued

Survey 

Category 

No cohabiting partner: % HIV-positive Has cohabiting partner: % HIV-positive 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Mozambique 2009 AIS All 7.8 17.1 219 4,021 W+++ 10.1 9.6 95 5,080 ns Mozambique 2009 AIS Lowest 5.1 6.4 127 546 ns 4.9 6.9 140 1,098 ns Mozambique 2009 AIS Second 5.0 12.7 251 587 W+ 6.3 6.4 103 1,171 ns Mozambique 2009 AIS Middle 5.5 13.7 250 667 W++ 8.2 7.3 90 1,153 ns Mozambique 2009 AIS Fourth 7.4 23.0 309 899 W+++ 16.9 13.3 79 898 ns Mozambique 2009 AIS Highest 10.9 22.2 204 1,321 W+++ 17.9 17.6 98 759 ns 

Swaziland 2006-07 DHS All 15.6 29.9 191 6,807 W+++ 36.2 36.9 102 1,402 ns Swaziland 2006-07 DHS Lowest 16.8 31.3 187 1,072 W+++ 31.2 32.5 104 243 ns Swaziland 2006-07 DHS Second 13.6 30.1 221 1,183 W+++ 45.1 42.3 94 237 ns Swaziland 2006-07 DHS Middle 14.4 30.9 215 1,461 W+++ 33.3 34.0 102 219 ns Swaziland 2006-07 DHS Fourth 16.4 29.3 178 1,566 W+++ 43.7 45.1 103 268 ns Swaziland 2006-07 DHS Highest 16.8 28.3 168 1,526 W+++ 30.9 32.8 106 435 ns 

Tanzania 2011-12 AIS All 2.8 7.1 260 10,452 W+++ 5.0 4.5 89 7,258 ns 

Tanzania 2011-12 AIS Lowest 1.8 5.3 286 1,558 W+ 4.1 3.9 95 1,392 ns Tanzania 2011-12 AIS Second 1.3 5.0 392 1,665 W++ 4.2 4.4 106 1,557 ns Tanzania 2011-12 AIS Middle 3.1 6.2 197 1,836 ns 5.4 4.5 84 1,463 ns Tanzania 2011-12 AIS Fourth 3.1 8.5 276 2,246 W+++ 4.0 3.4 85 1,443 ns Tanzania 2011-12 AIS Highest 3.5 8.8 256 3,147 W+++ 7.5 6.1 81 1,404 ns Uganda 2011 AIS All 5.4 9.7 179 10,908 W+++ 6.8 6.4 94 8,654 ns Uganda 2011 AIS Lowest 5.6 8.9 160 1,623 W+ 5.8 4.5 78 1,700 ns Uganda 2011 AIS Second 4.0 9.3 230 1,687 W+++ 5.9 5.8 99 1,918 ns Uganda 2011 AIS Middle 6.8 8.3 122 1,870 ns 6.2 6.3 101 1,821 ns Uganda 2011 AIS Fourth 6.4 10.1 157 2,370 W+ 8.4 7.8 94 1,568 ns Uganda 2011 AIS Highest 4.6 10.9 237 3,359 W+++ 8.3 7.7 93 1,648 ns Zambia 2007 DHS All 9.2 18.4 201 5,546 W+++ 15.5 13.0 84 4,791 M++ 

Zambia 2007 DHS Lowest 5.3 14.8 282 727 W+++ 7.7 4.9 64 1,187 M+ Zambia 2007 DHS Second 7.9 11.6 146 825 ns 10.9 7.8 71 896 ns Zambia 2007 DHS Middle 7.4 16.7 225 928 W+++ 14.1 9.8 69 941 M+ 

(Continued...) 

111 107 
Table A3.2. – Continued

Survey Category 

No cohabiting partner: % HIV-positive Has cohabiting partner: % HIV-positive 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Zambia 2007 DHS Fourth 15.0 24.1 161 1,337 W++ 22.3 20.9 94 927 ns Zambia 2007 DHS Highest 7.7 20.5 264 1,728 W+++ 26.3 23.9 91 841 ns Zimbabwe 2010-11 DHS All 8.7 19.3 222 8,535 W+++ 17.3 14.8 86 5,134 M++ Zimbabwe 2010-11 DHS Lowest 12.1 19.3 160 1,251 W++ 16.8 13.9 83 1,090 ns Zimbabwe 2010-11 DHS Second 7.8 19.1 246 1,469 W+++ 17.7 12.2 69 1,020 M++ 

Zimbabwe 2010-11 DHS Middle 8.2 21.9 267 1,695 W+++ 18.2 16.5 91 980 ns 

Zimbabwe 2010-11 DHS Fourth 8.0 21.1 265 1,916 W+++ 17.4 17.5 101 1,121 ns Zimbabwe 2010-11 DHS Highest 8.7 16.0 185 2,205 W+++ 16.3 13.7 84 923 ns Note: Restricted to respondents who were tested and who lived in households in which both men and women were interviewed. 

Ratios are calculated as 100*(% for men)/(% for women) N is weighted Significance refers to a test of the null hypothesis that men and women have the same HIV prevalence, controlling for cohabiting partner status (no partner or has 

a partner) M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

108 
Table A3.3 Percentage with a cohabiting partner among men and women who are HIV-negative, and percentage with a cohabiting 

partner among men and women who are HIV-positive, by sex, and significance level for the difference between men and women, according to wealth status (quintiles: lowest, second, middle, fourth, highest), 10 DHS surveys in sub-Saharan Africa, 2006-2012 

(restricted to respondents who were tested and who live in households in which both men and women were interviewed) 

Survey Category 

HIV-negative: % with a cohabiting partner HIV-positive: % with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Cameroon 2011 DHS All 33.8 40.5 120 12,868 W+++ 57.7 33.2 58 581 M+++ Cameroon 2011 DHS Lowest 46.7 57.6 124 2,107 W+++ 48.2 41.4 86 43 ns Cameroon 2011 DHS Second 41.9 45.9 109 2,225 W+ 54.7 34.4 63 99 ns Cameroon 2011 DHS Middle 33.3 37.8 114 2,450 W+ 51.8 33.2 64 113 ns Cameroon 2011 DHS Fourth 28.5 33.2 117 2,857 W+++ 56.5 27.8 49 154 M++ Cameroon 2011 DHS Highest 26.3 33.1 126 3,230 W+++ 65.1 35.6 55 172 M+ Kenya 2008-09 DHS All 38.5 35.8 93 6,298 ns 51.4 28.8 56 436 M+++ Kenya 2008-09 DHS Lowest 45.0 35.0 78 1,044 M++ 76.5 27.8 36 53 M++ Kenya 2008-09 DHS Second 38.0 35.3 93 1,108 ns 63.7 33.5 53 83 ns Kenya 2008-09 DHS Middle 33.7 34.4 102 1,106 ns 48.6 17.9 37 68 M++ Kenya 2008-09 DHS Fourth 32.9 30.1 92 1,425 ns 40.0 23.8 60 102 ns Kenya 2008-09 DHS Highest 43.1 43.1 100 1,614 ns 50.2 34.8 69 129 ns Lesotho 2009 DHS All 22.6 22.4 99 5,058 ns 42.7 21.1 50 1,509 M+++ Lesotho 2009 DHS Lowest 30.9 29.7 96 806 ns 43.7 22.8 52 183 M+++ Lesotho 2009 DHS Second 23.2 28.3 122 921 W+ 48.9 24.5 50 262 M+++ Lesotho 2009 DHS Middle 15.7 20.0 128 1,015 W++ 35.9 21.6 60 301 M++ Lesotho 2009 DHS Fourth 21.5 17.2 80 1,120 ns 51.6 20.7 40 400 M+++ Lesotho 2009 DHS Highest 24.4 20.3 84 1,197 ns 35.7 18.4 51 363 M+++ Malawi 2010 DHS All 49.3 50.2 102 12,087 ns 67.4 38.8 58 1,441 M+++ Malawi 2010 DHS Lowest 54.0 44.6 83 1,972 M+++ 76.1 33.9 45 160 M+++ Malawi 2010 DHS Second 57.0 56.3 99 2,431 ns 70.1 44.4 63 212 M++ Malawi 2010 DHS Middle 56.1 55.5 99 2,432 ns 68.2 47.6 70 253 M+ Malawi 2010 DHS Fourth 49.1 53.5 109 2,369 W+ 77.0 45.3 59 293 M+++ Malawi 2010 DHS Highest 35.5 41.5 117 2,883 W+++ 57.6 30.7 53 523 M+++ 

(Continued...) 

109 
Table A3.3. – Continued

Survey Category 

HIV-negative: % with a cohabiting partner HIV-positive: % with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Mozambique 2009 AIS All 58.5 55.6 95 8,061 ns 65.0 39.2 60 1,040 M+++ Mozambique 2009 AIS Lowest 76.0 60.6 80 1,545 M+++ 75.5 62.4 83 99 ns Mozambique 2009 AIS Second 69.4 65.9 95 1,627 ns 74.0 47.9 65 131 M+ Mozambique 2009 AIS Middle 64.3 63.8 99 1,663 ns 73.3 46.7 64 158 M+++ Mozambique 2009 AIS Fourth 52.5 49.3 94 1,508 ns 73.8 33.3 45 290 M+++ Mozambique 2009 AIS Highest 35.0 37.5 107 1,719 ns 49.0 30.9 63 362 M+++ Swaziland 2006-07 DHS All 15.0 14.3 96 6,081 ns 35.0 18.6 53 2,128 M+++ Swaziland 2006-07 DHS Lowest 18.0 16.5 92 966 ns 33.1 17.2 52 349 M+++ Swaziland 2006-07 DHS Second 13.5 12.3 91 1,040 ns 44.8 19.3 43 380 M+++ Swaziland 2006-07 DHS Middle 11.0 12.1 110 1,265 ns 26.9 13.6 51 416 M+++ Swaziland 2006-07 DHS Fourth 11.3 10.8 96 1,343 ns 33.5 19.4 58 490 M+++ Swaziland 2006-07 DHS Highest 21.0 19.5 93 1,467 ns 37.0 23.0 62 493 M+++ Tanzania 2011-12 AIS All 46.0 37.6 82 16,795 M+++ 61.4 26.7 44 915 M+++ Tanzania 2011-12 AIS Lowest 54.0 42.4 79 2,832 M+++ 72.7 35.0 48 119 M++ Tanzania 2011-12 AIS Second 53.8 43.8 82 3,095 M+++ 79.8 40.8 51 127 M+++ Tanzania 2011-12 AIS Middle 48.0 41.5 86 3,136 M+++ 61.9 33.7 54 163 M++ Tanzania 2011-12 AIS Fourth 43.8 36.7 84 3,491 M+++ 50.6 18.0 36 198 M+++ Tanzania 2011-12 AIS Highest 35.1 27.8 79 4,242 M+++ 55.1 20.4 37 309 M+++ Uganda 2011 AIS All 47.3 42.5 90 18,123 M+++ 53.4 32.0 60 1,439 M+++ Uganda 2011 AIS Lowest 55.3 49.0 89 3,112 M+++ 56.4 31.8 56 211 M+++ Uganda 2011 AIS Second 55.9 51.6 92 3,373 M++ 65.4 39.2 60 231 M+++ Uganda 2011 AIS Middle 52.1 47.7 92 3,434 M++ 49.5 40.4 82 257 ns Uganda 2011 AIS Fourth 42.3 38.2 90 3,608 M+ 49.4 32.0 65 330 M++ Uganda 2011 AIS Highest 35.4 31.1 88 4,597 M+++ 51.0 23.7 46 410 M+++ Zambia 2007 DHS All 45.5 47.2 104 8,866 ns 60.2 37.1 62 1,471 M+++ Zambia 2007 DHS Lowest 61.1 64.8 106 1,766 ns 70.1 35.2 50 148 M+++ Zambia 2007 DHS Second 58.5 48.2 83 1,553 M+++ 66.7 37.5 56 168 M+++ Zambia 2007 DHS Middle 49.0 51.9 106 1,642 ns 66.4 36.9 56 227 M+++ 

(Continued...) 

110 
Table A3.3. – Continued

Survey Category 

HIV-negative: % with a cohabiting partner HIV-positive: % with a cohabiting partner 

Men Women Ratio N Sig. Men Women Ratio N Sig. 

Zambia 2007 DHS Fourth 38.2 42.5 111 1,799 ns 50.1 38.1 76 465 ns Zambia 2007 DHS Highest 27.7 32.0 116 2,105 W++ 61.9 36.5 59 464 M+++ Zimbabwe 2010-11 DHS All 38.8 36.2 93 11,562 M++ 58.2 29.2 50 2,107 M+++ Zimbabwe 2010-11 DHS Lowest 52.8 43.1 82 1,963 M+++ 62.2 33.8 54 377 M+++ Zimbabwe 2010-11 DHS Second 41.8 40.3 96 2,124 ns 64.6 28.4 44 364 M+++ Zimbabwe 2010-11 DHS Middle 36.4 36.2 100 2,232 ns 58.7 28.7 49 443 M+++ Zimbabwe 2010-11 DHS Fourth 36.5 36.3 100 2,541 ns 58.3 31.2 54 495 M+++ Zimbabwe 2010-11 DHS Highest 31.1 27.6 89 2,701 M+ 48.0 24.1 50 427 M+++ Note: Restricted to respondents who were tested and who lived in households in which both men and women were interviewed. Ratios are calculated as 100*(% for men)/(% for women) N is weighted Significance refers to a test of the null hypothesis that men and women have the same cohabiting partner rates, controlling for HIV status (negative or positive) 

M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level ns: the difference is not statistically significant W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 

111 
112 

Table A3.4 Among cohabiting couples, the percent distribution of observed and expected couples 

(M=man, W=woman) by four combinations of couple HIV serostatus (negative concordance, 

female positive discordance, male positive discordance, and positive concordance), according to 

wealth status (quintiles: lowest, second, middle, fourth, highest), 10 DHS surveys in sub-Saharan 

Africa, 2006-2012 (restricted to cohabiting couples in which both the man and the woman were 

tested for HIV) 

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Cameroon 2011 DHS All 92.1 90.7 3.3 4.6 3.1 4.4 1.5 0.2 

Cameroon 2011 DHS Lowest 96.9 96.4 2.0 2.4 0.7 1.1 0.4 0.0 

Cameroon 2011 DHS Second 93.6 92.3 2.3 3.7 2.5 3.9 1.5 0.2 

Cameroon 2011 DHS Middle 91.0 89.8 4.4 5.7 3.0 4.3 1.5 0.3 

Cameroon 2011 DHS Fourth 89.9 89.1 4.4 5.3 4.5 5.4 1.2 0.3 

Cameroon 2011 DHS Highest 88.0 85.5 3.8 6.3 5.1 7.6 3.1 0.6 

Kenya 2008-09 DHS All 91.2 88.4 3.1 5.9 2.5 5.3 3.2 0.4 

Kenya 2008-09 DHS Lowest 93.3 91.6 3.3 5.0 1.6 3.3 1.9 0.2 

Kenya 2008-09 DHS Second 90.6 86.4 2.0 6.1 2.8 6.9 4.7 0.5 

Kenya 2008-09 DHS Middle 92.9 89.7 0.4 3.6 3.2 6.5 3.5 0.3 

Kenya 2008-09 DHS Fourth 91.1 87.8 2.0 5.3 3.2 6.5 3.7 0.4 

Kenya 2008-09 DHS Highest 89.8 87.7 5.7 7.8 2.1 4.2 2.5 0.4 

Lesotho 2009 DHS All 62.7 51.4 7.9 19.2 10.1 21.4 19.3 8.0 

Lesotho 2009 DHS Lowest 74.3 65.1 4.5 13.7 8.3 17.5 12.9 3.7 

Lesotho 2009 DHS Second 62.5 52.3 6.8 17.0 13.0 23.1 17.7 7.5 

Lesotho 2009 DHS Middle 60.6 47.3 6.5 19.7 10.0 23.3 22.9 9.7 

Lesotho 2009 DHS Fourth 56.0 41.3 8.5 23.2 8.0 22.7 27.5 12.8 

Lesotho 2009 DHS Highest 60.7 52.5 12.4 20.5 11.3 19.4 15.7 7.6 

Malawi 2010 DHS All 85.5 80.5 3.9 8.9 4.6 9.6 6.0 1.0 

Malawi 2010 DHS Lowest 88.3 85.0 3.6 6.9 4.1 7.4 3.9 0.6 

Malawi 2010 DHS Second 88.1 85.4 4.0 6.7 4.6 7.3 3.3 0.6 

Malawi 2010 DHS Middle 86.7 82.2 3.7 8.3 4.2 8.7 5.4 0.9 

Malawi 2010 DHS Fourth 84.0 78.2 4.1 9.9 4.8 10.6 7.1 1.3 

Malawi 2010 DHS Highest 80.5 72.3 3.8 12.0 5.3 13.5 10.4 2.2 

Mozambique 2009 AIS All 85.0 81.3 5.2 8.9 5.1 8.8 4.7 1.0 

Mozambique 2009 AIS Lowest 89.7 88.2 5.7 7.2 2.8 4.3 1.9 0.3 

Mozambique 2009 AIS Second 89.3 87.6 4.2 6.0 4.3 6.1 2.2 0.4 

Mozambique 2009 AIS Middle 88.7 85.4 3.0 6.3 4.4 7.7 3.9 0.6 

Mozambique 2009 AIS Fourth 77.6 70.7 5.2 12.0 7.9 14.8 9.3 2.5 

Mozambique 2009 AIS Highest 73.8 68.0 9.6 15.4 7.8 13.6 8.9 3.1 

Swaziland 2006-07 DHS All 54.8 39.7 8.7 23.8 7.7 22.8 28.8 13.7 

Swaziland 2006-07 DHS Lowest 57.6 46.2 10.7 22.1 10.1 21.5 21.6 10.3 

Swaziland 2006-07 DHS Second 45.6 30.8 8.4 23.2 11.4 26.2 34.6 19.8 

Swaziland 2006-07 DHS Middle 58.8 43.4 7.8 23.2 6.3 21.8 27.1 11.6 

Swaziland 2006-07 DHS Fourth 46.3 28.4 8.7 26.7 5.1 23.1 39.8 21.8 

(Continued...)
113 

Table A3.4 – Continued

Survey Category 

M negative 

W negative 

M negative 

W positive 

M positive 

W negative 

M positive 

W positive 

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. 

Swaziland 2006-07 DHS Highest 61.3 47.2 8.2 22.3 6.7 20.8 23.9 9.8 

Tanzania 2011-12 AIS All 93.0 90.9 2.0 4.2 2.6 4.8 2.3 0.2 

Tanzania 2011-12 AIS Lowest 94.2 92.1 1.7 3.7 2.0 4.0 2.2 0.2 

Tanzania 2011-12 AIS Second 93.7 91.3 1.7 4.2 2.0 4.4 2.7 0.2 

Tanzania 2011-12 AIS Middle 92.4 90.3 2.1 4.3 3.0 5.2 2.4 0.2 

Tanzania 2011-12 AIS Fourth 94.4 92.6 1.5 3.3 2.1 3.9 2.0 0.1 

Tanzania 2011-12 AIS Highest 90.2 88.0 3.1 5.3 4.2 6.4 2.5 0.4 

Uganda 2011 AIS All 90.2 87.1 3.0 6.1 3.2 6.3 3.5 0.4 

Uganda 2011 AIS Lowest 92.8 89.8 1.5 4.5 2.5 5.5 3.3 0.3 

Uganda 2011 AIS Second 91.7 88.6 2.5 5.7 2.3 5.5 3.5 0.3 

Uganda 2011 AIS Middle 90.6 87.7 3.4 6.3 2.7 5.6 3.3 0.4 

Uganda 2011 AIS Fourth 87.4 84.1 4.2 7.5 4.5 7.8 4.0 0.7 

Uganda 2011 AIS Highest 87.8 84.8 3.8 6.9 4.6 7.7 3.7 0.6 

Zambia 2007 DHS All 80.2 73.9 4.7 11.0 6.8 13.1 8.2 2.0 

Zambia 2007 DHS Lowest 90.3 87.4 2.1 4.9 4.4 7.2 3.3 0.4 

Zambia 2007 DHS Second 86.5 83.4 3.3 6.4 6.3 9.4 3.8 0.7 

Zambia 2007 DHS Middle 82.8 77.6 3.7 8.9 6.9 12.1 6.6 1.4 

Zambia 2007 DHS Fourth 70.2 61.3 7.4 16.4 8.7 17.6 13.7 4.7 

Zambia 2007 DHS Highest 66.1 56.0 8.5 18.6 9.0 19.1 16.5 6.3 

Zimbabwe 2010-11 DHS All 78.4 70.7 4.6 12.3 6.8 14.5 10.2 2.5 

Zimbabwe 2010-11 DHS Lowest 80.0 71.6 3.4 11.7 6.0 14.3 10.7 2.3 

Zimbabwe 2010-11 DHS Second 78.2 72.2 4.6 10.5 9.1 15.0 8.1 2.2 

Zimbabwe 2010-11 DHS Middle 75.9 68.2 5.8 13.5 7.6 15.3 10.7 3.0 

Zimbabwe 2010-11 DHS Fourth 79.0 70.4 4.8 13.4 5.0 13.6 11.2 2.6 

Zimbabwe 2010-11 DHS Highest 78.8 71.0 4.7 12.5 6.3 14.1 10.2 2.5 

Note: Weighted frequencies for the rows in this table are provided in Table 4.2 
114 

Table A3.5 HIV prevalence among men and women who have cohabiting partners, and measures 

of selection and HIV seroconversion, according to wealth status (quintiles: lowest, second, 

middle, fourth, highest), 10 DHS surveys in sub-Saharan Africa, 2006-2012 

Survey Category 

HIV 

prevalence 

Selection/seroconversion 

measures 

Men Women Delta kap_SC SCm SCw Sig. N 

Cameroon 2011 DHS All 4.7 4.8 1.3 29.3 28.7 29.9 ns 2,289 

Cameroon 2011 DHS Lowest 1.1 2.5 0.4 24.1 17.4 38.8 ns 509 

Cameroon 2011 DHS Second 4.1 3.8 1.4 36.6 38.0 35.3 ns 480 

Cameroon 2011 DHS Middle 4.6 5.9 1.3 25.2 22.2 29.2 ns 402 

Cameroon 2011 DHS Fourth 5.7 5.6 0.9 16.1 16.3 16.0 ns 429 

Cameroon 2011 DHS Highest 8.2 6.9 2.5 36.6 40.3 33.5 ns 469 

Kenya 2008-09 DHS All 5.7 6.2 2.8 50.1 47.8 52.7 ns 1,064 

Kenya 2008-09 DHS Lowest 3.5 5.2 1.7 41.2 34.1 52.0 ns 176 

Kenya 2008-09 DHS Second 7.4 6.6 4.2 63.7 67.9 60.0 ns 191 

Kenya 2008-09 DHS Middle 6.7 3.9 3.2 63.9 88.9 49.8 ns 164 

Kenya 2008-09 DHS Fourth 6.9 5.7 3.3 55.9 62.4 50.7 ns 208 

Kenya 2008-09 DHS Highest 4.6 8.1 2.1 35.4 27.3 50.5 ns 325 

Lesotho 2009 DHS All 29.4 27.2 11.3 55.6 58.8 52.7 ns 689 

Lesotho 2009 DHS Lowest 21.2 17.4 9.2 59.2 67.3 52.8 ns 133 

Lesotho 2009 DHS Second 30.7 24.5 10.2 50.6 59.7 43.9 ns 142 

Lesotho 2009 DHS Middle 33.0 29.4 13.2 61.6 67.1 56.9 ns 115 

Lesotho 2009 DHS Fourth 35.5 36.0 14.8 64.2 63.5 65.0 ns 146 

Lesotho 2009 DHS Highest 26.9 28.1 8.1 40.6 39.5 41.8 ns 153 

Malawi 2010 DHS All 10.6 9.9 5.0 54.1 56.4 52.1 ns 2,987 

Malawi 2010 DHS Lowest 8.0 7.6 3.3 46.2 47.8 44.7 ns 460 

Malawi 2010 DHS Second 7.9 7.3 2.7 38.6 40.2 37.1 ns 662 

Malawi 2010 DHS Middle 9.6 9.1 4.5 53.4 54.9 52.0 ns 675 

Malawi 2010 DHS Fourth 11.9 11.2 5.8 56.7 58.6 55.0 ns 607 

Malawi 2010 DHS Highest 15.7 14.2 8.2 64.4 68.5 60.8 ns 584 

Mozambique 2009 AIS All 9.8 9.9 3.7 42.0 41.7 42.2 ns 2,322 

Mozambique 2009 AIS Lowest 4.6 7.5 1.5 26.2 20.9 35.1 W+ 523 

Mozambique 2009 AIS Second 6.5 6.4 1.8 29.7 30.0 29.4 ns 545 

Mozambique 2009 AIS Middle 8.3 6.9 3.4 47.8 53.1 43.4 ns 521 

Mozambique 2009 AIS Fourth 17.3 14.5 6.8 51.0 56.8 46.2 ns 397 

Mozambique 2009 AIS Highest 16.6 18.5 5.8 39.9 37.6 42.6 ns 336 

Swaziland 2006-07 DHS All 36.5 37.5 15.1 64.8 63.5 66.2 ns 626 

Swaziland 2006-07 DHS Lowest 31.7 32.3 11.4 52.3 51.5 53.0 ns 108 

Swaziland 2006-07 DHS Second 46.0 43.0 14.8 59.9 63.8 56.4 ns 104 

Swaziland 2006-07 DHS Middle 33.4 34.9 15.5 68.7 66.6 71.0 ns 97 

Swaziland 2006-07 DHS Fourth 44.9 48.5 18.0 72.2 67.3 77.8 ns 119 

Swaziland 2006-07 DHS Highest 30.5 32.1 14.1 65.4 63.2 67.8 ns 198 

Tanzania 2011-12 AIS All 5.0 4.4 2.1 47.8 51.4 44.7 ns 3,302 

(Continued...)
115 

Table A3.5 – Continued

Survey Category 

HIV 

prevalence 

Selection/seroconversion 

measures 

Men Women Delta kap_SC SCm SCw Sig. N 

Tanzania 2011-12 AIS Lowest 4.2 3.9 2.0 52.9 55.0 51.0 ns 632 

Tanzania 2011-12 AIS Second 4.6 4.4 2.5 57.2 58.8 55.6 ns 711 

Tanzania 2011-12 AIS Middle 5.4 4.5 2.1 45.0 49.7 41.0 ns 681 

Tanzania 2011-12 AIS Fourth 4.1 3.5 1.8 50.6 54.8 47.0 ns 653 

Tanzania 2011-12 AIS Highest 6.8 5.6 2.1 36.9 40.9 33.7 ns 625 

Uganda 2011 AIS All 6.8 6.6 3.1 49.7 50.6 48.8 ns 3,972 

Uganda 2011 AIS Lowest 5.8 4.8 3.0 60.0 66.6 54.5 ns 787 

Uganda 2011 AIS Second 5.8 6.0 3.2 57.5 56.5 58.5 ns 886 

Uganda 2011 AIS Middle 6.0 6.7 2.9 48.9 46.3 51.9 ns 836 

Uganda 2011 AIS Fourth 8.4 8.2 3.3 42.9 43.8 42.1 ns 699 

Uganda 2011 AIS Highest 8.3 7.5 3.1 42.3 44.7 40.1 ns 764 

Zambia 2007 DHS All 15.1 12.9 6.3 52.1 57.1 47.9 M+ 2,007 

Zambia 2007 DHS Lowest 7.6 5.4 2.8 46.6 57.3 39.3 M+ 510 

Zambia 2007 DHS Second 10.2 7.2 3.1 39.4 48.6 33.1 ns 381 

Zambia 2007 DHS Middle 13.5 10.3 5.2 49.8 58.9 43.2 ns 402 

Zambia 2007 DHS Fourth 22.4 21.1 8.9 52.6 54.7 50.7 ns 367 

Zambia 2007 DHS Highest 25.5 25.0 10.1 53.6 54.3 52.9 ns 347 

Zimbabwe 2010-11 DHS All 17.0 14.8 7.7 57.3 62.5 52.9 M++ 2,180 

Zimbabwe 2010-11 DHS Lowest 16.7 14.1 8.4 64.2 71.2 58.4 ns 513 

Zimbabwe 2010-11 DHS Second 17.2 12.7 5.9 46.5 56.4 39.5 M+ 477 

Zimbabwe 2010-11 DHS Middle 18.3 16.5 7.7 53.5 57.1 50.3 ns 437 

Zimbabwe 2010-11 DHS Fourth 16.2 16.0 8.6 63.9 64.5 63.4 ns 430 

Zimbabwe 2010-11 DHS Highest 16.5 14.9 7.8 58.5 62.2 55.1 ns 323 

Note: Selection/seroconversion measures are defined in the text 

N is weighted 

Significance refers to a test of the null hypothesis that selection/seroconversion is the same for men and women 

M+, M++, M+++: percentage is significantly greater for men than for women, at the .05, .01, or .001 level 

ns: the difference is not statistically significant 

W+, W++, W+++: percentage is significantly greater for women than for men, at the .05, .01, or .001 level 
116 

Table A3.6 Percentage of HIV-negative men and women who are at risk of infection from an HIVpositive cohabiting partner, and relative risk for women compared with men, by three categories 

of risk, according to wealth status (quintiles: lowest, second, middle, fourth, highest), 10 DHS 

surveys in sub-Saharan Africa, 2006-2012 

 Risk category #1 Risk category #2 Risk category #3 

Survey Category Men Women RR Men Women RR Men Women RR 

Cameroon 2011 DHS All 3.3 3.1 94 3.5 3.3 94 1.2 1.3 113 

Cameroon 2011 DHS Lowest 2.0 0.7 33 2.0 0.7 34 1.0 0.4 42 

Cameroon 2011 DHS Second 2.3 2.6 113 2.4 2.7 112 1.0 1.2 123 

Cameroon 2011 DHS Middle 4.4 3.0 69 4.6 3.2 70 1.5 1.2 80 

Cameroon 2011 DHS Fourth 4.4 4.5 102 4.7 4.8 102 1.3 1.6 119 

Cameroon 2011 DHS Highest 3.8 5.1 134 4.1 5.4 132 1.1 1.8 167 

Kenya 2008-09 DHS All 3.1 2.5 82 3.3 2.7 83 1.3 1.0 77 

Kenya 2008-09 DHS Lowest 3.3 1.6 48 3.4 1.7 49 1.5 0.6 38 

Kenya 2008-09 DHS Second 2.0 2.8 141 2.1 3.0 140 0.8 1.1 130 

Kenya 2008-09 DHS Middle 0.4 3.2 810 0.4 3.4 786 0.1 1.2 803 

Kenya 2008-09 DHS Fourth 2.0 3.2 162 2.1 3.4 160 0.7 1.0 146 

Kenya 2008-09 DHS Highest 5.7 2.1 37 5.9 2.3 38 2.6 1.0 38 

Lesotho 2009 DHS All 7.9 10.1 128 11.2 13.9 124 2.5 3.1 123 

Lesotho 2009 DHS Lowest 4.5 8.3 184 5.7 10.0 176 1.8 3.0 169 

Lesotho 2009 DHS Second 6.8 13.0 190 9.9 17.2 174 2.3 4.9 213 

Lesotho 2009 DHS Middle 6.5 10.0 155 9.7 14.2 147 1.5 2.8 187 

Lesotho 2009 DHS Fourth 8.5 8.0 94 13.1 12.4 95 2.8 2.1 76 

Lesotho 2009 DHS Highest 12.4 11.3 91 17.0 15.7 92 4.1 3.2 77 

Malawi 2010 DHS All 3.9 4.6 119 4.3 5.1 118 2.1 2.6 120 

Malawi 2010 DHS Lowest 3.6 4.1 113 3.9 4.4 113 2.1 2.0 93 

Malawi 2010 DHS Second 4.0 4.6 114 4.4 5.0 113 2.5 2.8 112 

Malawi 2010 DHS Middle 3.7 4.2 112 4.1 4.6 112 2.3 2.6 111 

Malawi 2010 DHS Fourth 4.1 4.8 116 4.6 5.4 115 2.3 2.9 125 

Malawi 2010 DHS Highest 3.8 5.3 140 4.5 6.2 138 1.6 2.6 161 

Mozambique 2009 AIS All 5.2 5.1 98 5.8 5.7 98 3.4 3.1 93 

Mozambique 2009 AIS Lowest 5.7 2.8 49 6.0 3.0 50 4.5 1.8 40 

Mozambique 2009 AIS Second 4.2 4.3 103 4.5 4.6 103 3.1 3.0 98 

Mozambique 2009 AIS Middle 3.0 4.4 148 3.2 4.7 145 2.1 3.0 144 

Mozambique 2009 AIS Fourth 5.2 7.9 153 6.3 9.3 148 3.3 4.6 139 

Mozambique 2009 AIS Highest 9.6 7.8 81 11.5 9.5 83 4.0 3.6 89 

Swaziland 2006-07 DHS All 8.7 7.7 89 13.7 12.3 90 2.1 1.8 86 

Swaziland 2006-07 DHS Lowest 10.7 10.1 94 15.7 14.9 95 2.8 2.5 87 

Swaziland 2006-07 DHS Second 8.4 11.4 136 15.6 20.1 129 2.1 2.5 118 

Swaziland 2006-07 DHS Middle 7.7 6.3 82 11.6 9.7 83 1.3 1.2 92 

Swaziland 2006-07 DHS Fourth 8.7 5.1 59 15.8 10.0 63 1.8 1.1 60 

Swaziland 2006-07 DHS Highest 8.2 6.7 81 11.8 9.8 83 2.5 1.9 77 

Tanzania 2011-12 AIS All 2.0 2.6 131 2.1 2.8 130 1.0 1.0 106 

(Continued...)
117 

Table A3.6 – Continued

 Risk category #1 Risk category #2 Risk category #3 

Survey Category Men Women RR Men Women RR Men Women RR 

Tanzania 2011-12 AIS Lowest 1.7 2.0 117 1.7 2.0 117 0.9 0.9 92 

Tanzania 2011-12 AIS Second 1.7 1.9 114 1.8 2.0 114 1.0 0.9 93 

Tanzania 2011-12 AIS Middle 2.1 3.0 142 2.3 3.2 141 1.1 1.3 122 

Tanzania 2011-12 AIS Fourth 1.5 2.1 137 1.6 2.1 136 0.7 0.8 114 

Tanzania 2011-12 AIS Highest 3.1 4.2 136 3.3 4.5 135 1.2 1.2 107 

Uganda 2011 AIS All 3.0 3.2 107 3.2 3.5 107 1.5 1.5 96 

Uganda 2011 AIS Lowest 1.5 2.5 167 1.6 2.6 165 0.9 1.3 146 

Uganda 2011 AIS Second 2.5 2.3 92 2.6 2.4 92 1.5 1.2 85 

Uganda 2011 AIS Middle 3.4 2.7 80 3.6 2.9 81 1.9 1.4 74 

Uganda 2011 AIS Fourth 4.2 4.5 107 4.6 4.9 107 1.9 1.9 96 

Uganda 2011 AIS Highest 3.8 4.6 121 4.2 5.0 120 1.5 1.6 105 

Zambia 2007 DHS All 4.7 6.8 145 5.6 7.9 141 2.5 3.7 147 

Zambia 2007 DHS Lowest 2.1 4.4 207 2.3 4.6 202 1.4 3.0 215 

Zambia 2007 DHS Second 3.3 6.3 191 3.7 6.8 185 2.2 3.3 153 

Zambia 2007 DHS Middle 3.7 6.9 188 4.2 7.7 181 2.1 4.0 192 

Zambia 2007 DHS Fourth 7.4 8.7 117 9.5 11.0 115 3.6 4.7 128 

Zambia 2007 DHS Highest 8.5 9.0 106 11.4 12.0 105 3.2 3.8 122 

Zimbabwe 2010-11 DHS All 4.6 6.8 149 5.5 8.0 145 2.1 2.9 135 

Zimbabwe 2010-11 DHS Lowest 3.4 6.0 176 4.1 6.9 171 2.1 3.0 140 

Zimbabwe 2010-11 DHS Second 4.6 9.1 198 5.5 10.4 188 2.3 4.2 181 

Zimbabwe 2010-11 DHS Middle 5.8 7.6 131 7.1 9.1 128 2.6 3.3 128 

Zimbabwe 2010-11 DHS Fourth 4.8 5.0 105 5.7 5.9 105 2.1 2.2 104 

Zimbabwe 2010-11 DHS Highest 4.7 6.3 134 5.6 7.4 132 1.8 2.0 117 

Note: Relative risk (RR) is expressed as number of women at risk per 100 men at risk.