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

population, health, and nutrition programs. Additional information about the MEASURE DHS project can 

be obtained by contacting MEASURE DHS, ICF International, 11785 Beltsville Drive, Suite 300, 

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

internet: www.measuredhs.com). 

The main objectives of the MEASURE DHS project are: 

• to provide decision-makers in survey countries with information useful for informed policy choices 

• to expand the international population and health database 

• to advance survey methodology and 

• to develop in participating countries the skills and resources necessary to conduct high-quality 

demographic and health surveys 
DHS Analytical Studies No. 36

Spousal Violence and HIV: 

Exploring the Linkages in Five Sub-Saharan African Countries 

Kerry L.D. MacQuarrie 

Rebecca Winter 

Sunita Kishor 

ICF International 

Calverton, Maryland, USA 

September 2013 

Corresponding author: Kerry L.D. MacQuarrie, International Health and Development, ICF International, 11785 

Beltsville Drive, Calverton, Maryland 20705, USA; Phone: 301-572-0200; Fax 301-572-0999; Email: 

kerry.macquarrie@icfi.com 
Acknowledgment: The authors would like to thank Dr. Georges Reniers and Dr. Heidi Kar for their 

helpful comments. An earlier version of this report was presented at the XXVII IUSSP International 

Population Conference in Busan, Korea in 2013. 

Editor: Bryant Robey 

Document Production: Yuan Cheng 

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

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

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

Government. 

Recommended citation: 

MacQuarrie, Kerry L.D., Rebecca Winter, and Sunita Kishor. 2013. Spousal Violence and HIV: Exploring 

the Linkages in Five Sub-Saharan African Countries. DHS Analytical Studies No. 36. Calverton, 

Maryland, USA: ICF International. 
iii 

Contents 

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

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

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

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

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

1.1 Gender Framework: Clusterings of HIV Risk Factors and Experience of Spousal Violence ...... 3 

1.2 Conceptual Framework ................................................................................................................ 4 

2 Data, Measures, and Analytical Approach ....................................................................................... 7 

2.1 Data .............................................................................................................................................. 7 

2.2 Measures ....................................................................................................................................... 9 

2.3 Analytical Approach ................................................................................................................... 10 

3 Results ................................................................................................................................................ 15 

3.1 Forms of Spousal Violence ......................................................................................................... 15 

3.2 Profile of Spousal Violence and HIV .......................................................................................... 18 

3.3 Associations between Spousal Violence and HIV ....................................................................... 19 

3.4 Relationship between HIV Status and Risk Factors ................................................................... 21 

3.5 Relationship between HIV Risk Factors and Spousal Violence ................................................. 24 

3.6 Nuanced Relationship between Spousal Violence and HIV ........................................................ 30 

4 Conclusions and Discussion ............................................................................................................. 45 

References .................................................................................................................................................. 47 

Appendix .................................................................................................................................................... 51 

v 

List of Tables 

Table 1. Derivation of the analytic sample ................................................................................................. 8 

Table 2. Pattern of spousal violence factors and item loadings ................................................................ 16 

Table 3. Internal reliability and eigenvalues for five spousal violence factors ......................................... 17 

Table 4. Prevalence of spousal violence, controlling behaviors, and women's HIV status in the 

analytic sample ........................................................................................................................... 18 

Table 5. Unadjusted and adjusted associations between forms of spousal violence and women's 

HIV status in the analytic sample: odds ratios from separate logistic regressions ..................... 20 

Table 6. Adjusted associations between women's HIV status (dependent variable) and wives' risk 

factors, husbands' HIV status, and husbands' risk factors in the analytic sample: odds ratios 

from separate logistic regressions ............................................................................................... 21 

Table 7. Adjusted associations between women's HIV risk factors and spousal violence in the 

analytic sample: odds ratios from separate logistic regressions ................................................. 25 

Table 8. Adjusted linear associations between spousal violence (dependent variable) and husbands' 

HIV risk factors in the analytic sample: coefficients from linear regressions ............................ 27 

Table 9a. Multivariate associations between women's HIV status (dependent variable) and suspicion 

in the analytic sample: odds ratios from a sequence of logistic regressions ............................... 30 

Table 9b. Multivariate associations between women's HIV status (dependent variable) and isolation 

in the analytic sample: odds ratios from a sequence of logistic regressions ............................... 33 

Table 9c. Multivariate associations between women's HIV status (dependent variable) and emotional 

violence in the analytic sample: odds ratios from a sequence of logistic regressions ................ 36 

Table 9d. Multivariate associations between women's HIV status (dependent variable) and physical 

violence in the analytic sample: odds ratios from a sequence of logistic regressions ................ 39 

Table A1.1. Items and oblique rotated factor loadings: Kenya 2008-09 .................................................... 51 

Table A1.2. Items and oblique rotated factor loadings: Malawi 2010 ........................................................ 52 

Table A1.3. Items and oblique rotated factor loadings: Rwanda 2005 ....................................................... 53 

Table A1.4. Items and oblique rotated factor loadings: Rwanda 2010 ....................................................... 54 

Table A1.5. Items and oblique rotated factor loadings: Zambia 2007 ........................................................ 55 

Table A1.6. Items and oblique rotated factor loadings: Zimbabwe 2010-11 .............................................. 56 
vi 

Table A2. Percent distribution of demographic characteristics of each partner and household-level 

characteristics in the analytic sample ......................................................................................... 57 

Table A3. Percent distribution of wives' and husbands' HIV risk factors in the analytic sample .............. 58 

List of Figures 

Figure 1. Conceptual pathways from spousal violence to HIV .................................................................... 4 
vii 

Preface 

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

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

developing countries. 

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

The DHS Analytical Studies series focuses on analysis of specific topics. The principal objectives of both 

series are to provide information for policy formulation at the international level and to examine 

individual country results in an international context. 

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

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

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

multivariate statistical techniques. 

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

selects the topics covered in Analytical Studies. 

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

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

Sunita Kishor 

Project Director 

ix

Executive Summary 

Over the past decade a consensus has been growing that intimate partner violence contributes to women’s 

vulnerability to HIV. A diverse body of research has explored this association, mostly in the developing 

world. Studies based on women who present at health clinics often report a significantly higher 

prevalence of intimate partner violence among HIV-positive women compared with HIV-negative 

women. Moreover, six of seven studies using nationally representative samples reported a significant 

association between some form of violence and HIV status. 

The usual interpretation is that spousal violence increases the risk of HIV for women. Yet a direct effect 

on HIV status is unlikely, since there is no apparent direct causal pathway leading from most forms of 

spousal violence to the acquisition of HIV. This study contributes to an understanding of the relationship 

between spousal violence and HIV by taking advantage of data from both members of a couple and using 

discrete, nuanced measures of spousal violence to better specify the associated pathways through which 

violence influences HIV. We propose a gender-based conceptual framework in which the association 

between a woman’s experience of spousal violence and her HIV status is mediated by two primary 

pathways: First, the HIV risk behaviors/factors of her husband and, second, her own behavioral and 

situational HIV risk factors. Both of these factors have been associated with violence experienced by 

women and perpetrated by men. 

This study uses data on married couples from six Demographic and Health Surveys (DHS) in five subSaharan countries: Kenya 2008-09, Malawi 2010, Rwanda 2005, Rwanda 2010, Zambia 2007, and 

Zimbabwe 2010-11. These surveys included HIV testing for both women and men and also the domestic 

violence module, thus providing a subsample of married or cohabiting couples by their experience of 

violence and their HIV status. 

We examine the direct or indirect linkages between spousal violence and women’s HIV status. We also 

explore the association between spousal violence and wives’ and husbands’ HIV risk factors. Specifically, 

we include the following factors: lifetime number of sexual partners; STI or STI symptoms in the last 12 

months; and for husbands only, non-marital sex in the past 12 months; having paid for sex; alcohol use; 

and husband’s HIV status. We consider several forms of spousal violence (emotional, physical, and 

sexual violence) and husbands’ controlling behaviors. In keeping with the conceptual framework, we 

develop a series of statistical analyses to test the direct effect of spousal violence on women’s HIV status 

and the role of HIV risk factors as mediators. 

The results reveal a strikingly common structure of what constitutes violence across the five countries. 

Five factors emerge in each country: (1) suspicion, (2) isolation, (3) emotional violence, (4) physical 

violence, and (5) sexual violence. These five factors account for 57 to 66 percent of the variance among 

the items in each country. Our factor analysis upholds the validity of experts’ assignment of the various 

acts of spousal violence to the categories of emotional, physical, or sexual violence. An important 

additional insight is that the six items typically categorized as controlling behavior actually represent not 

one construct, but two separate constructs—suspicion and isolation—which are distinct from emotional, 

physical, or sexual violence. 

The study finds a significant association between multiple forms of violence and women’s HIV status, 

after adjusting for wives’ and husbands’ socio-demographic characteristics but not risk factors. Yet, no 

single form of spousal violence is consistently associated with women’s HIV status in all five countries. 

A significant relationship is found with women’s HIV status for the controlling behaviors suspicion and 

isolation in Zambia and Zimbabwe; for emotional violence in Kenya, Rwanda and Zimbabwe; for 

physical violence, in Kenya, Rwanda, and Zimbabwe; and in no country for sexual violence, the least 
x

prevalent form of violence among study couples. In Malawi no form of violence is associated with a 

wife’s risk of having HIV. 

In all five countries both HIV risk factors for women—lifetime number of sexual partners and recent STI 

or STI symptoms—are significantly associated with their having HIV, after controlling for background 

characteristics but not for each other. Most of the spousal violence measures are associated with both 

wives’ HIV risk factors in each country. 

The most prominent predictor of a woman’s HIV status is her husband’s HIV status, among all the men’s 

factors considered. Numerous husbands’ HIV risk factors are associated with their wives’ HIV status, but 

far less consistently than either husbands’ HIV status or women’s risk factors. The association between 

the experience of spousal violence and husbands’ risk factors, too, is weaker and less consistent than with 

women’s risk factors. Nevertheless, multiple relationships between spousal violence and wives’ and their 

husbands’ risk factors on one hand, and between wives’ and husbands’ risk factors and women’s HIV 

status on the other, suggest that there are several possible mediators between various forms of spousal 

violence and women’s HIV status. 

Indeed, when either wives’ risk factors or husbands’ risk factors, or both combined, are added to our 

models, most spousal violence factors are no longer a significant predictor of women’s HIV status. The 

only form of spousal violence that appears to have a direct net association with HIV is physical violence, 

which remains significant in all models in Kenya and Zimbabwe. For almost all forms of violence 

(physical violence being the exception) and in all five countries, any observed significant relationship of 

spousal violence with a woman’s HIV status is explained away by wives’ or husband’s HIV risk factors. 

The study provides evidence that there is no direct effect of most forms of spousal violence on women’s 

HIV status, only an indirect effect through selected behavioral and other factors commonly considered to 

put an individual at high risk of HIV. The finding that sexual violence is not associated with women’s 

HIV status, even before considering any mediating risk factors, deserves further exploration. Similarly, 

investigation is warranted to ascertain why physical violence continues to be associated with women’s 

HIV status after controlling for these risk factors. 
1

1 Introduction 

Over the past decade there has been increasing international interest in how women’s experience of 

spousal violence affects the risk of acquiring HIV, and consensus is growing that women’s experience of 

intimate partner violence—alongside gender inequality more broadly—contributes to vulnerability to HIV 

infection. In 2011 the World Health Organization (WHO) included the reduction of gender-based 

vulnerability to HIV infection as part of its Global Health Sector Strategy on HIV/AIDS 2011-2015 

(World Health Organization 2011). 

It is not intuitively obvious, however, that there should be a relationship between women’s experience of 

intimate partner violence and their risk of HIV. With the exception of sexual violence, there is no direct 

causal pathway leading from most forms of spousal violence to HIV infection. This study examines the 

direct or indirect linkage between spousal violence—a key component of intimate partner violence—and 

women’s HIV status. It also explores the association between spousal violence and wives’ and husbands’ 

risk factors, which may more directly influence women’s HIV status. The study is based on data from 

recent Demographic and Health Surveys (DHS) in five sub-Saharan countries—Kenya, Malawi, Rwanda, 

Zambia, and Zimbabwe. Note that the term “husbands” also includes partners cohabiting as if married. 

A diverse body of literature, mostly in the developing world, has explored the association between 

intimate partner violence, with various degrees of emphasis on causal pathways. With a few notable 

exceptions, these studies have reported a significant association between some form of violence and 

women’s HIV status. To date, the studies that have suggested a positive association between women’s 

experience of intimate partner violence and risk of HIV fall into two categories: studies using clinic-based 

samples or other non-representative samples, or cross-sectional studies using population-based samples. 

Studies based on women who present at health clinics (ranging from antenatal care clinics to VCT clinics 

to enrollees in HIV-prevention interventions) often have reported a significantly higher prevalence of 

violence among HIV-positive women compared with HIV-negative women (Dunkle et al. 2004; Fonck et 

al. 2005; Maman et al. 2010; Maman et al. 2002; Prabhu et al. 2011). These clinic-based studies have 

found women’s HIV status to be associated with their experience of physical violence (Maman et al. 

2002), physical or sexual violence (Maman et al. 2002), any intimate partner violence (Dunkle et al. 

2004), and spousal control (Dunkle et al. 2004). In a recent study that sought to isolate the direction of 

causality in the association between intimate partner violence and HIV status, Maman and colleagues 

(2010) followed a cohort of South African women (age 15-26) enrolled in an HIV-prevention 

intervention. They found that women’s report at baseline of one or more incidents of intimate partner 

violence was a significant predictor of HIV incidence during the follow-up period. 

Several rigorous, non-household-based studies have also examined the association between forms of 

spousal violence and women’s HIV status. One cohort study among young women in South Africa 

(Jewkes et al. 2010) reported a significant association between incident HIV infection and both intimate 

partner violence and relationship power inequity. A second cohort study in seven African countries, 

however, reported no evidence of an association between sero-conversion and the experience of intimate 

partner violence prior to acquiring HIV, based on an analysis of discordant couples enrolled in a clinical 

trial of a herpes simplex virus type 2 suppressive therapy (Were et al. 2011). Finally, a cluster randomized 

control trial of an HIV behavioral intervention among women age 15-26 in South Africa reported that the 

significant bivariate association between women’s experience of intimate partner violence and HIV status 

did not retain its significance after adjusting for HIV risk behaviors (including risk factors for both the 

female and male partners) (Jewkes, Nduna, et al. 2006). 
2

Most studies using population-based cross-sectional data have found a significant association between 

women’s experience of intimate partner violence and HIV status. Overall, of the seven studies identified 

that use nationally-representative samples, six reported a significant association between some form of 

violence and HIV status (Andersson and Cockcroft 2012; Decker, Seage, Hemenway, Raj, et al. 2009; 

Ghosh et al. 2011; Kayibanda, Bitera, and Alary 2012; Sareen, Pagura, and Grant 2009; Silverman et al. 

2008). Three of these six studies used data from India’s third National Family Health Survey (NFHS-3) 

(Decker, Seage, Hemenway, Raj, et al. 2009; Ghosh et al. 2011; Silverman et al. 2008). One study used 

data from the Rwanda Demographic and Health Survey (DHS) (Kayibanda, Bitera, and Alary 2012), 

another examined a non-DHS nationally representative sample from Botswana, Namibia, and Swaziland 

(Andersson and Cockcroft 2012), and the sixth study reporting a positive association used data from the 

United States (Sareen, Pagura, and Grant 2009). The only nationally representative study identified that 

did not report a significant association between some form of spousal violence and HIV status used DHS 

data on ever-married women from 10 developing countries (Harling, Msisha, and Subramanian 2010). 

While six of the seven studies reported significant findings, their samples, methodologies, and findings 

are noteworthy for their differences. As mentioned above, three of the six studies with significant findings 

relied on the same data from India, and the experience of couples in India may not be representative of 

couples’ experience in other countries or cultures. Also, while all studies were population-based, the 

analytic samples varied considerably, ranging from all men and women regardless of marital status 

(Andersson and Cockcroft 2012), to currently married women (Silverman et al. 2008), ever-married 

women (Harling, Msisha, and Subramanian 2010), women who were in any romantic relationship in the 

last year (Sareen, Pagura, and Grant 2009), and currently cohabitating couples (Decker, Seage, 

Hemenway, Raj, et al. 2009; Ghosh et al. 2011; Kayibanda, Bitera, and Alary 2012). 

Definitions of violence varied as well, from bivariate measures of any violence versus none (Decker, 

Seage, Hemenway, Gupta, et al. 2009), to summary scales counting the number of violent items 

experienced (Kayibanda, Bitera, and Alary 2012), to categorical variables identifying women who 

experienced no violence, just physical violence, or physical and sexual violence (Silverman et al. 2008), 

just to name a few variations. Some studies looked only at sexual violence (Ghosh et al. 2011), others 

physical and sexual violence (Sareen, Pagura, and Grant 2009; Silverman et al. 2008), while some 

included emotional violence (Kayibanda, Bitera, and Alary 2012), and a few included spousal controlling 

behaviors as well (Kayibanda, Bitera, and Alary 2012). The manner in which these studies assessed 

intimate partner violence and the forms of violence they examined may have influence both the reporting 

of violence and the detection of associations with other factors, including HIV prevalence (Lary et al. 

2004; O’Leary and Kar 2010). Finally, the inconsistent inclusion of HIV risk behaviors across studies 

makes it difficult to compare their findings. 

Given that the studies’ methodologies and definitions vary substantially, it is not surprising that their 

findings also vary. Silverman and colleagues (2008) found that women who have experienced both 

physical and sexual spousal violence were 3.92 times more likely (p=.01) to be HIV-positive, but found 

no significant association between the experience of other combinations of spousal violence and HIV 

status. In contrast, Kayibanda and colleagues (2012) found that emotional violence—but not physical 

violence, sexual violence, or controlling behaviors—was significantly associated with women’s HIV 

status, among currently married/cohabitating women in Rwanda, after adjusting for socio-demographic 

characteristics. Harling and colleagues (2010) examined combinations of physical and sexual violence 

among ever-married women and, as mentioned, found no association. The range in findings may well 

depend on variations across studies in the conceptualization of violence (for example, the study by 

Harling and colleagues did not consider domains of emotional violence and controlling behaviors), 

differences in analytic methodologies, and the range of covariates included in multivariate models (such 

as the problematic inclusion of risk factors that could be part of the pathways between spousal violence 

and HIV). 
3

Our study adds to the literature on the association between spousal violence and HIV status by focusing 

on cross-sectional data from married couples, in the five countries mentioned. A focus on couples permits 

a more comprehensive examination of the link between HIV and spousal violence, since information is 

available on not only women at risk of spousal violence but also the spouses who perpetrate the violence. 

Specifically, this research considers information on both spouses’ characteristics, HIV status, and risk 

behaviors in assessing the mechanisms through which women who experience spousal violence may have 

increased risk of being HIV-positive. 

The remainder of this chapter attempts to specify the conceptual linkages between spousal violence, 

wives’ and husbands’ HIV risk factors, and women’s HIV status. Subsequent chapters of the report detail 

the data and analytical approach and give results from our analyses. Results are reported in several 

separate sections, first describing levels and domains of spousal violence, then showing associations 

among the different segments of our conceptual framework, and finally presenting multivariate analyses 

testing for the presence of a direct effect of spousal violence on women’s HIV status in the presence of 

women’s and their husbands’ HIV risk factors and husbands’ HIV status. A final chapter provides some 

conclusions. 

1.1 Gender Framework: Clusterings of HIV Risk Factors and Experience of Spousal Violence 

Many of the population-based studies exploring the link between forms of intimate partner violence and 

HIV status have not explicitly articulated, let alone modeled, the pathways through which intimate partner 

violence influences HIV status. The conclusions from these studies may differ due to the variable 

inclusion or exclusion of intervening factors through which violence affects HIV status. This report seeks 

to clarify the ways in which the experience of spousal violence may lead to increased odds of having HIV 

among married women, using data from both wives and their husbands and guided by a conceptual 

framework depicting possible pathways by which violence could indirectly affect HIV status. 

The usual interpretation given to an association, if detected, is that spousal violence increases the risk of 

HIV for women1

. Yet, a direct effect on HIV status is unlikely. While incidents of sexual violence can 

directly lead to HIV infection, the low rate of transmission in any given sexual encounter makes unlikely 

a large, statistically significant direct association between sexual violence and a woman’s HIV status 

unless that violence is perpetual and frequent. It is more likely that sexual violence affects HIV status 

indirectly via similar mechanisms as emotional or physical violence. If spousal violence is to increase the 

odds of a woman having HIV, it likely does so indirectly through some intervening mechanisms, by 

association with her own risk behaviors, those of her spouse, or her spouse’s HIV status. 

We propose a gender-based conceptual framework in which the association between women’s experience 

of spousal violence and her HIV status is mediated by two primary pathways. First, among men, the welldocumented association between men’s perpetration of violence and HIV behavioral risk factors points to 

an underlying traditional masculinity, associated with violent behavior toward female partners, maledominant attitudes, multiple sexual partners, potential infidelity, risk-taking tendencies, and alcohol 

consumption, among other “male” behaviors (Townsend et al. 2011). In this view, men who are violent 

toward their spouses are also more likely to have the other behaviors associated with traditional masculine 

identity, and thereby are more likely to have acquired HIV. 

Second, among women, there is also a gendered story to tell about the clustering of women’s experience 

of spousal violence with higher prevalence of HIV risk behaviors. As Jewkes (2006) states, “the 

 

1 The reverse causal direction, that having HIV leads to domestic violence, cannot be ruled out with cross-sectional 

data, and a spurious correlation is a possibility. However, the panel study conducted by Maman and colleagues 

(2010) lends credibility to the standard interpretation. 
4

experience of violence...reinforces gendered power inequalities that impact on women’s HIV risk.” 

Several studies examine the psychological mechanisms through which the experience of violence could 

contribute to women’s HIV risk behaviors—for example, through altering women’s self-efficacy or 

diminishing women’s power in the relationship (Jewkes, Dunkle, et al. 2006; Mittal, Senn, and Carey 

2011; Raj, Silverman, and Amaro 2004). One study that examined the association between women’s 

experience of violence and condom negotiation efficacy, in a sample of incarcerated women in three US 

states, found that women who had experienced violence had significantly lower confidence to negotiate 

condom use with a partner (Swan and O'Connell 2011). 

It has also been theorized that this link could partially be explained by depressive symptoms resulting 

from intimate partner violence, and the associated increases in alcohol and drug consumption (Mittal, 

Senn, and Carey 2011). In a sample of women in the US, the experience of intimate partner violence in 

the last three months was found to be associated with having had more episodes of unprotected sex with a 

steady partner, with drug use before sex, and with depressive symptoms (Mittal, Senn, and Carey 2011). 

Pitpitan and colleagues (2013) found that the experience of physical violence was strongly associated 

with women’s alcohol consumption, and that even after controlling for women’s alcohol use, the 

experience of physical spousal violence remained significantly associated with women’s sexual risk 

behaviors, including the number of male sex partners, the likelihood of unprotected sex, the occurrence of 

sex under the influence of alcohol, and the occurrence of a recent sexually transmitted infection (STI). 

Some authors highlight the potential for direct transmission of HIV via forced sex (Andersson and 

Cockcroft 2012; Stockman et al. 2013). Others document that, in addition to the fact that men who 

perpetrate intimate partner violence have higher HIV prevalence, within couples where the man is HIVpositive the odds that the female partner contracts HIV are higher where the male partner has perpetrated 

intimate partner violence (Decker, Seage, Hemenway, Raj, et al. 2009). Possible explanations for this 

added risk include psychological trauma, reduced immunity due to stress, and potentially higher risk 

behaviors that increase the changes for HIV transmission (Campbell et al. 2008). 

1.2 Conceptual Framework 

This paper seeks to clarify the ways in which the experience of spousal violence may lead to increased 

odds of having HIV among married women. It does so using data from both wives and their husbands and 

nuanced measures of spousal violence, and is guided by a conceptual framework depicting possible 

pathways by which spousal violence could affect HIV status. 

Figure 1. Conceptual pathways from spousal violence to HIV 

 

Spousal violence 

• Controlling 

behaviors 

• Emotional 

• Physical 

• Sexual 

Woman’s HIV high-risk 

behaviors/factors 

Husband’s HIV high-risk 

behaviors/factors

Husband’s HIV status

Woman’s HIV status

? 

Background 

characteristics 

• Woman’s 

• Husband’s 
5

As illustrated above, we consider multiple forms of spousal violence: emotional, physical, and sexual 

violence. We also consider controlling behaviors by husbands toward their wives—such as acting jealous 

or suspicious, or trying to limit their actions (see “Analytical Approach”)—which the literature alternately 

conceptualizes as a separate form or a predictor of spousal violence or as a component of (usually 

emotional) violence (e.g. Tjaden 2004; Watts and Mayhew 2004). 

We question the plausibility of a direct effect of spousal violence on women’s HIV status and instead 

posit two indirect mechanisms for such an association: husbands’ HIV high-risk behaviors/factors and 

wives’ high-risk behaviors/factors. Husbands who perpetrate violence against their wives may exhibit 

riskier sexual behaviors, abuse alcohol or drugs, or be HIV-positive (Campbell et al. 2008; Decker, Seage, 

Hemenway, Raj, et al. 2009; Dunkle and Decker 2013; Fals-Stewart and Kennedy 2005; Lary et al. 2004; 

Makayoto et al. 2013; Maman et al. 2010; Silverman et al. 2008; Teti et al. 2006). This association may 

be the result of adherence to traditional masculine norms, discussed above, or gender-inequitable attitudes 

that devalue women and perpetuate sexual double standards (Dunkle and Decker 2013). Since these are 

co-occurring behaviors—being violent is not likely to cause sexual risk-taking, nor is engaging in risky 

sexual behaviors likely to cause spousal violence—except insofar as risky sexual behaviors, such as 

infidelity, may cause marital conflict (Lary et al. 2004; Schensul et al. 2006)). No causal direction is 

implied in the Conceptual Framework, as indicated by the curved line in the figure above. 

Women who experience violence may engage in riskier behaviors of their own, as discussed above. They 

are likely to experience lower levels of personal empowerment and agency, and thus their ability to 

demand fidelity from their partner may be compromised. They also may be less able to negotiate the 

terms of sexual activity, such as insisting upon condom use or refusing sex when the husband as an STI or 

its symptoms. That is, women who experience spousal violence face a loss of efficacy or a “choice 

disability” (Andersson, Cockcroft, and Shea 2008; Stockman et al. 2013). Furthermore, as mentioned, the 

experience of spousal violence is associated with poor mental health outcomes, low self-esteem and selfworth, alcohol and drug use, which are in turn associated with riskier sexual behaviors (González-Guarda, 

Florom-Smith, and Thomas 2011; Meyer, Springer, and Altice 2001; Teti et al. 2006). Several studies 

have noted that sexual abuse, in particular, earlier in life is associated with riskier sexual behaviors in 

adulthood (Andersson, Cockcroft, and Shea 2008; Stockman et al. 2013). The riskier behaviors lead to an 

increased likelihood of a woman herself being HIV-positive. As for men, no causal direction is attributed 

to the association between spousal violence and women’s HIV risk factors, as these may operate in both 

directions, or may co-occur. 

This paper addresses the following questions about the potential pathways through which the experience 

of spousal violence may affect women’s HIV status: 

• Are all forms of spousal violence equally and similarly associated with women’s HIV status? For 

which forms of spousal violence does this association exist? 

• How strong is the relationship between spousal violence and individual/partner risk factors? Is 

spousal violence more strongly associated with women’s or their husbands’ risk factors? 

• Does the relationship between spousal violence and women’s HIV status disappear when risk 

factors and their husbands’ HIV status are accounted for? 

7

2 Data, Measures, and Analytical Approach 

2.1 Data 

The data for this study come from six recent Demographic and Health Surveys (DHS) in sub-Saharan 

Africa: Kenya 2008-09, Malawi 2010, Rwanda 2005, Rwanda 2010, Zambia 2007, and Zimbabwe 2010-

11. The DHS is a nationally representative, population-based household survey that monitors reproductive 

health behaviors, attitudes, and outcomes, demographic trends, maternal and child health, and social and 

demographic characteristics of women and men of reproductive age. Many DHS surveys also collect 

biomarker data to test for HIV, malaria parasitemia, and anemia levels. DHS data are collected in face-toface household interviews, and three standard core questionnaires—household, woman’s and man’s—are 

included in each survey, enabling analysis across countries. 

The DHS uses multistage cluster sampling techniques to obtain nationally representative samples. In the 

countries included in this report, in the first sampling stage each country was stratified into major regions 

(or districts, in the case of Malawi 2010 and Rwanda 2010). Census-based geographic enumeration areas 

were selected from these regions, with a probability of selection proportional to their size. The 

enumeration areas were then mapped and all households listed. In the second sampling stage, households 

were randomly selected from a list of all households within each selected enumeration area. Urban areas 

and less populated areas were typically oversampled to enable representative regional and rural-urban 

comparisons. 

All analyses are weighted using the domestic violence weight calculated in the DHS datasets. 

Additionally, we use the svy commands available within Stata to account for the complex sampling 

design and estimate robust standard errors. 

In addition to the core questionnaire, the DHS has several additional modules that countries can elect to 

include in a survey. This study takes advantage of the DHS domestic violence module, which was first 

developed and standardized in 2000 and has, to date, been included in 79 surveys to collect data on 

spousal violence against women. The portion of the module specific to spousal violence uses a greatly 

modified version of the conflict tactics scales (CTS) (Straus 1979, 1990). The module includes questions 

that ask women whether their current or most recent (if divorced, separated, or widowed) husband/partner 

ever perpetrated any of a series of behaviorally specific acts of physical or sexual violence. Women who 

say yes to a particular item are then asked about the frequency of perpetration in the 12 months preceding 

the interview. The module also includes questions about a series of controlling behaviors that apply to the 

respondent’s relationship with her current or most recent husband/partner. 

This study uses information from women’s responses to having ever experienced each type of violent act 

by their current husband, as well as their responses to questions about whether their current husband 

exhibits controlling behaviors. Note that, as mentioned, throughout this report the term husband refers to 

men who are legally married to the respondent or who live with her as if married. 

Recent sub-Saharan African surveys were considered for inclusion in the study if: (1) they were 

conducted in 2005 or later and the data were publicly available by June 2013; (2) they included the 

domestic violence module, (3) they included HIV testing for both women and men; and (4) there was an 

overlap between the HIV-tested subsample and the subsample selected for the domestic violence module. 

Of the nine countries that met these criteria, four were excluded due to the small sample size available for 

analysis, which greatly limited the power to detect key associations (Burkina Faso, Liberia, Mali, and Sao 

Tomé and Principe). While the intention was to include only the most recent survey in any given country, 

we included both the Rwanda 2005 and Rwanda 2010 DHS surveys, since the 2010 survey did not collect 
8

information on several key measures (emotional violence and controlling behaviors), while the 2005 

survey contained this information. 

Table 1 displays the derivation of the analytic sample. From the total number of eligible women age 15-

49 with completed interviews, the sample was first restricted to women who were administered the 

domestic violence module. Whereas all women age 15-49 in a household are eligible for the DHS 

woman’s questionnaire, only one randomly selected eligible woman per household is administered the 

domestic violence module. This practice is in accordance with WHO guidelines (2001) on the ethical 

conduct of domestic violence research, in order to maintain confidentiality and maximize the safety of the 

respondents. Further, the domestic violence module may be administered in only a subsample of all 

sampled households. In Kenya, Zambia, and Zimbabwe, the domestic violence module was administered 

in all households. In Malawi, however, it was administered in every third household, and in both Rwanda 

surveys, in ever second household. 

The sample for analysis was further restricted to women who provided blood for HIV testing and had a 

valid test result. HIV testing was conducted for all women who were eligible to be interviewed and who 

provided consent in all households in the Zambia and Zimbabwe surveys, in every second household in 

the Kenya and both Rwanda surveys, and in every third household in the Malawi survey. Finally, the 

sample was restricted to matched married couples for whom the husband also had a valid HIV test result, 

and both spouses had complete information for key background characteristics and risk factors. The 

eligibility criteria for men varied slightly across study countries. Men age 15-54 were eligible for 

interview in Kenya, Malawi, and Zimbabwe, while in Zambia and both Rwanda surveys men age 15-59 

were eligible for interview. 

The final sample used for analysis included 873 Kenyan couples, 2,627 Malawian couples, 1,452 

Rwandan couples (2005 DHS), 2,013 Rwandan couples (2010 DHS), 1,611 Zambian couples, and 1,711 

Zimbabwean couples (Table 1). Couples include both those who are married and those who are 

cohabiting as if married. 

Table 1. Derivation of the analytic sample 

Country 

Survey 

year 

Total unweighted 

number of women 

Total unweighted 

number of women 

with domestic 

violence module 

administered 

Total unweighted 

number of women 

who also had HIV 

testing done and 

have a valid result 

Total unweighted 

number of women 

who were 

successfully 

matched with a 

husband with 

valid HIV results 

Kenya 2008-9 8,444 6,318 2,862 1,071 

Malawi 2010 23,020 6,229 5,836 2,994 

Rwanda 2005 11,321 4,066 4,021 1,848 

Rwanda 2010 13,671 5,008 4,986 2,438 

Zambia 2007 7,146 5,236 4,192 1,983 

Zimbabwe 2010-11 9,171 6,542 5,644 1,986 
9

2.2 Measures 

The key outcome measure in this study is women’s current HIV status. HIV testing has been included as 

part of MEASURE DHS surveys in at least 31 countries since 2001. Standard testing protocol provides 

for anonymous, informed, and voluntary testing of women and men. Response rates for HIV testing in 

study countries range from 79.4 percent among men in Zambia to 99.6 percent among women in Rwanda 

(2010). Blood spots for HIV testing are collected on filter paper from a finger prick and are then 

transported to a laboratory. The laboratory protocol is typically based on an initial ELISA test, and then a 

retest of all positive samples with a second ELISA. The protocol in all countries specifies that 5 to 10 

percent of the samples that are negative on the first ELISA test are retested. For samples with discordant 

results on the two ELISA tests, a new ELISA or a Western Blot is performed. The protocol for HIV 

testing undergoes ethical reviews in the host countries and in the United States. 

Spousal violence, the key predictor variables, was measured using five domains of violence and 

controlling behaviors that consistently emerged in a factor analysis across all study countries, as will be 

described in more detail below (see Analytic Approach). The five domains include women’s report of 

spousal emotional violence, spousal physical violence, and spousal sexual violence ever perpetrated by 

their current husbands; and women’s report of two domains of controlling behaviors ever exhibited by 

their current husbands, which we label “suspicion” and “isolation.” For all regression models, the five 

domains of violence and controlling behavior are measured as continuous variables, using the factor 

scores generated in the factor analysis. For several descriptive tables, three dichotomous summary 

variables are used to identify couples in which the wife reported any experience of each type of spousal 

violence by her husband; and two dichotomous summary variables to identify couples in which the wife 

reported that her husband exhibits at least one of the controlling behaviors in each domain. 

The study also examined wives’ and husbands’ HIV risk factors, insofar as these could be important 

intermediary variables that might partially explain the observed association between spousal violence and 

HIV status. The HIV risk behaviors listed below have all been associated with HIV-positive status for 

women and men: 

• Lifetime number of sexual partners. For women, the indicator has three categories: women 

who reported that they had sex with one partner, women who reported two partners, and women 

who reported three or more partners in their lifetime. For their husbands, the indicator has five 

categories: men who reported one partner, men who reported two partners, men who reported 

three partners, men who reported four or more partners, and men who reported that they did not 

know how many partners they had. The fifth category is included because a substantial number of 

husbands in several countries reported “don’t know,” which could indicate having many lifetime 

partners. Because our analysis is restricted to couples, and all respondents reported at least one 

sexual partner, there is no category for zero partners for either women or men. Women and men 

with missing responses are excluded from the analysis. 

• Sex with non-spousal, non-cohabiting partner. This variable identifies women/men who 

reported sexual partners in the past 12 months other than their spouses or cohabitating partners. 

Men and women with missing information and women who responded that they did not know are 

excluded from the analysis. 

• Had an STI or STI symptoms in last 12 months. During the interview, male and female 

respondents are asked three questions about sexually transmitted infections: whether in the 12 

months preceding the survey they had a) an STI; b) a bad smelling abnormal genital discharge; 

and c) a genital sore or ulcer. This summary variable identifies women/men who reported either 
10

that they had an STI, a genital sore or ulcer, or an unusual discharge in the past 12 months. 

Respondents with missing information for all three questions are excluded. 

For an additional two high-risk behaviors included in the analysis, information is available only for men: 

• Ever paid for sex. Men who reported that they have ever paid for sex are compared with men 

who reported that they have never paid for sex. In the Kenya and Zambia surveys studied, 

information was only collected about whether men paid for sex in the last 12 months. In these two 

cases, paying for sex in the last 12 months is used as a proxy for having ever paid for sex. 

• Husband’s alcohol use. This indicator, based on the wife’s report, has three categories: husband 

does not drink or drinks but never gets drunk, husband drinks and sometimes gets drunk, and 

husband drinks and often gets drunk. Alcohol consumption has been associated with both 

perpetrating spousal violence and being infected with HIV. In cases where a wife did not provide 

the information on her husband’s drinking habits, this variable is excluded.

Our analysis adjusted for demographic factors that, per the literature, would be likely to confound the 

association between spousal violence and women’s HIV status. In selecting control variables, we were 

careful to avoid including variables that could be part of causal pathways between spousal violence and 

HIV, such as men’s and women’s HIV risk factors. From a full list of potential confounders, variables 

were selected for inclusion only if they had a significant bivariate association with women’s HIV status 

and at least one violence factor score in at least one country. Three variables did not meet these criteria 

and were not included in the analysis: marital status (married versus cohabiting), whether the 

respondent’s father beat her mother, and the spousal age difference. The controls chosen for inclusion in 

the analysis are the standard DHS household wealth quintiles, household place of residence (urban-rural), 

geographic region of the country, women’s educational attainment (none, primary, secondary or higher), 

men’s educational attainment (none, primary, secondary or higher), women’s total number of children 

ever born (measured continuously), and wife’s age at first marriage (measured continuously). 

2.3 Analytical Approach 

The DHS domestic violence module, as mentioned, uses a greatly shortened and modified conflict tactics 

scale (CTS) (Straus 1979) to measure different forms of spousal violence. One benefit of a CTS-style 

instrument for comparative research is that these items refer to specific behavioral acts, regardless of 

whether they are understood to constitute violence in a given cultural setting (Kishor and Bradley 2012). 

The many different types of acts and behaviors asked about in the DHS are organized into categories of 

physical, emotional, and sexual violence and controlling behaviors. Assigning different acts/behaviors 

into these four types of behavior is based on the face validity of the items according to experts in the field. 

However, we are unaware of any analysis that determines conclusively whether these groupings are 

validated by the data in the range of cultural settings in which the DHS module has been applied—for 

example, whether acts that other researchers consider to be physical violence share have more in common 

with each other than with acts that we consider to be emotional or sexual violence. 

Additionally, for measuring prevalence or conducting analysis, the different items have to be summarized 

in some way to provide one or more indicators of violence. One common approach has been to assume 

that women in DHS surveys can be counted as having experienced violence if they respond yes to having 

experienced even one act (or some other predefined number of acts), thereby converting a large number 

of questions into a single dichotomous indicator. This indicator is based on the assumption that the 

experience of any act/behavior versus no act/behavior is more meaningful than the specific act/behavior 

or how many acts/behaviors are experienced. Another approach with similar assumptions uses a simple 
11

additive index (sometimes referred to as a naïve index), which gives equal weight to each act/behavior 

(DiStefano, Zhu, and Mindrila 2009). 

In this report we use factor analysis to understand the underlying structure of items related to spousal 

violence in the data and determine if these structures are similar or dissimilar across study countries. We 

then define violence measures derived from the factor scores resulting from this analysis. A factor score is 

essentially a weighted index in which the respondent’s value on each item is weighted by the importance 

or influence of that item in the overall factor, as measured by its factor loading score (Pett, Lackey, and 

Sullivan 2003). These factor scores hold several advantages for measuring violence compared with other 

commonly used summary indicators. Factor scores are linear combinations of the observed variables 

produced by a multivariate procedure that accounts for correlations among factors (DiStefano, Zhu, and 

Mindrila 2009). By assessing the shared variance and uniqueness of items, the use of factor scores 

eliminates the need for arbitrary assumptions about how to combine the different items and how to weight 

them. 

The factor analysis was conducted on the full sample of women to whom the domestic violence module 

was administered, rather than the restricted sample of women in couples in which both members had valid 

HIV test results. The larger, unrestricted sample was used in all study countries in order to uncover the 

relationship among observed spousal violence variables in the broadest sample possible. 

We conducted exploratory factor analysis (EFA) using principal component factor extraction technique 

with oblique (Promax) rotation of factor loadings, as no strong assumptions about the independence of 

factors could be asserted (Pett, Lackey, and Sullivan 2003). Separate EFA solutions were sought for each 

survey sample, rather than pooling countries together. Factors were retained based on a combination of 

screeplots and a minimum eigenvalue of approximately 1.0 (Pett, Lackey, and Sullivan 2003). A strict 

restriction of an eigenvalue ≥ 1.0 was relaxed so as to detect any common structure across countries that 

might lie just below this threshold. 

Sixteen spousal violence items, including six describing controlling behaviors, were included in the factor 

analysis, as follows: 

Husband: 

• Is jealous or angry if respondent talks with other men 

• Frequently accuses respondent of being unfaithful 

• Insists on knowing where respondent is at all times 

• Does not permit respondent to meet her female friends 

• Tries to limit respondent's contact with family 

• Does not trust respondent with any money 

• Ever says or does something to humiliate respondent in front of others 

• Ever threatens to hurt or harm respondent or someone close to her 

• Ever insults respondent or makes her feel bad about herself 

• Ever pushes, shakes, or throws something at respondent 

• Ever slaps respondent 

• Ever punches respondent with his fist or hits with something that could hurt her 
12

• Ever kicks, drags, or beats up respondent 

• Ever tries to choke or burn respondent on purpose 

• Ever physically forces respondent to have sexual intercourse with him even when she does not 

want to 

• Ever forces respondent to perform any sexual acts she does not want to 

A seventeenth item describing violence with a weapon2

 was excluded from the analysis because the 

wording of the question was inconsistent across countries; the loading score for this item was both poor 

and sensitive to the variation in wording. The Rwanda 2010 survey included neither items describing 

controlling behaviors nor the three items describing humiliation, threats, and insults. 

Items with factor loadings >0.40 were retained (Kootstra 2004; Pett, Lackey, and Sullivan 2003). 

Cronbach’s alpha was calculated for each factor as a measure of inter-item reliability. Finally, factors 

were tested for correlation and, since they were slightly correlated, the oblique rotation was retained 

(Kootstra 2004). The final factor scores are estimated for each case and saved as new variables in the 

dataset. These factor scores, generated from the sample of women completing the domestic violence 

module, are subsequently used as latent variables in the regression analyses of spousal violence and HIV, 

which uses the more restricted sample of matched couples with spousal violence data from the wife and 

HIV status data for both members of the couple. 

Using the factor regression scores as the key independent spousal violence variables, we analyze the 

relationship, if any, between spousal violence and women’s HIV status. We run a sequence of 

multivariate regression models separately for each of the five countries (six surveys). For each, we first 

run unadjusted models by regressing women’s HIV status on each of the spousal violence variables, and 

then we run adjusted models with controls for urban-rural residence, region, wealth quintile, women’s 

education, husbands’ education, women’s occupation, husbands’ occupation, women’s age, husbands’ 

age, total number of children ever born, and women’s age at first birth (Table 5). The adjusted model 

serves as the base model for each country and indicates whether there is a relationship between the 

dependent variable, HIV-positive status, and the independent variables of interest. 

We then run partial logistic regressions that model, in sections, each pathway of our conceptual model. 

Each of these models includes the same socio-demographic control variables as the corresponding base 

model. In sequence we regress women’s HIV status on (a) their husbands’ HIV status and HIV risk 

factors and (b) women’s HIV risk factors (Table 6). We then regress women’s risk factors on the spousal 

violence variables (Table 7), and the spousal violence variables on their husbands’ risk factors (Table 8), 

with the spousal violence variables expressed as factor scores in both sets of regressions. This sequence of 

models helps to establish relationships between variables in each segment of the pathway(s) through 

which we expect spousal violence to affect women’s HIV status. 

A preferable analytical strategy to adopt for this purpose might be to simultaneously estimate a system of 

regression equations, but the cross-sectional nature of the survey data does not allow us to temporally 

sequence each of the variables to facilitate such an analysis. Therefore, we are restricted to making 

conclusions about associations rather than causation between HIV status, spousal violence, and factors 

along the conceptual pathway. 

 

2

 Some DHS questionnaires had an item about “threats or attacks with a knife or gun or any other weapon,” while 

others asked about “threats with a knife or gun” only. In one survey, respondents were asked about threats and 

attacks in separate items. 
13

Next, we run a series of additive models. To our base model of women’s experience of spousal violence 

(again expressed as factor scores) and socio-demographic controls (Model 1), we first add husbands’ HIV 

status and risk factors (Model 2). In Model 3 we replace husbands’ risk factors with women’s risk factors 

and, in Model 4 we include both sets of risk factors. This modeling approach allows us to distinguish any 

direct effect of spousal violence variables on women’s HIV status from their indirect effects through HIV 

risk factors with which they are associated. A weakening of the significance of and/or reduction in the 

magnitude of the odds ratio for spousal violence variables in the presence of risk factors is taken as 

evidence of an indirect effect. Any residual significant odds ratio after controlling for risk factors is taken 

as evidence of a direct association between spousal violence and women’s HIV status. All analyses are 

conducted using the domestic violence weights, unless otherwise indicated.

15

3 Results 

3.1 Forms of Spousal Violence 

We conducted factor analysis to determine how many violence-related factors emerge and if the same 

factors emerge in different settings. The results of the factor analysis using 16 violence-related items 

reveal a strikingly common structure of what constitutes different types of violence across the five 

countries included in this report. 

Five factors emerge in each country, which we labeled as: (1) suspicion, (2) isolation, (3) emotional 

violence, (4) physical violence, and (5) sexual violence. These five factors account for 57 to 66 percent of 

the variance among the items in each country. All items load onto these factors in an identical pattern 

across the countries, as illustrated in Table 2. In the Rwanda 2010 survey, which did not include all 16 

items, just two factors emerge—physical violence and sexual violence—but with the same items loading 

onto these factors as in the other countries. Detailed data on the factors and their item loading scores for 

each country can be found in Appendix Tables A1.1-A1.6. 
16

Table 2. Pattern of spousal violence factors and item loadings 

 

Physical 

violence 

Emotional 

violence Suspicion Isolation 

Sexual 

violence 

Husband/partner: 

Is jealous or angry if respondent talks with 

other men ▪ 

Frequently accuses respondent of being 

unfaithful ▪ 

Insists on knowing where respondent is at all 

times ▪ 

Does not permit respondent to meet her 

female friends ▪

Tries to limit respondent's contact with family ▪

Does not trust respondent with any money ▪

Ever says or does something to humiliate 

respondent in front of others ▪ 

Ever threatens to hurt or harm respondent or 

someone close to her ▪ 

Ever insults respondent or makes her feel bad 

about herself ▪ 

Ever pushes, shakes, or throws something at 

respondent ▪ 

Ever slaps respondent ▪

Ever punches respondent with his fist or hits 

with something that could hurt her ▪ 

Ever kicks, drags, or beats up respondent ▪

Ever tries to choke or burn respondent on 

purpose ▪

Ever spits in respondent's face* ▪

Ever physically forces respondent to have 

sexual intercourse with him even when she 

does not want to ▪

Ever forces respondent to perform any sexual 

acts she does not want to ▪

*Rwanda 2005 only 

 
17

Table 3 shows the Cronbach’s alpha internal reliability measure for each of the factors and the eigenvalue 

for the five factor solution, all of which approach 1.0, among the five countries (six surveys). In all 

countries the first factor that emerges is the physical violence factor, followed by the emotional violence 

factors. Physical violence is composed of five items, with a sixth item, “husband/partner ever spit in 

respondent’s face,” included only in the Rwanda 2005 survey, also loading on the physical violence factor 

in that sample. The Cronbach’s alpha for this factor ranges from 0.73 in Zimbabwe to 0.84 in Malawi. 

Table 3. Internal reliability and eigenvalues for five spousal violence factors 

Kenya Malawi 

Rwanda 

2005 

Rwanda 

2010 Zambia Zimbabwe

Cronbach's alpha Suspicion 0.6759 0.6546 0.6088 na 0.6815 0.6277 

Cronbach's alpha Isolation 0.5815 0.6609 0.6750 na 0.5765 0.6375 

Cronbach's alpha Emotional Violence 0.7619 0.7963 0.6844 na 0.7423 0.6466 

Cronbach's alpha Physical Violence 0.7795 0.8384 0.8312 0.7801 0.7437 0.7273 

Cronbach's alpha Sexual Violence 0.5821 0.6474 0.6700 0.6653 0.7530 0.7079 

Eigenvalue for the five factor solution 0.8618 1.0077 1.0364 0.9811 0.9344 0.8997 

na: not available 

Emotional violence is composed of three items capturing whether a woman’s husband ever humiliated her 

in front of others, threatened to hurt her, or insulted her. The Cronbach’s alpha for this factor ranges from 

0.65 in Zimbabwe to 0.80 in Malawi. Sexual violence, with its two items, consistently remains as a 

separate factor, and its alpha ranges from 0.58 in Kenya to 0.75 in Zambia. This apparent structure 

regarding these three factors largely validates the three forms of violence commonly conceptualized by 

experts in this field. 

Two factors, rather than one, consistently emerge to capture controlling behaviors. The first, termed 

“suspicion”, includes three items for husband is jealous if respondent talks with other men, accuses her of 

infidelity, and insists on knowing where she is. For this factor, the Cronbach’s alpha ranges from 0.61 in 

Rwanda (2005) to 0.68 in Zambia. The second controlling behavior factor, “isolation”, includes three 

items for husband prevents respondent from meeting with female friends, limits her contact with family, 

and does not trust her with money; and its alpha ranges from 0.58 in Zambia and Kenya to 0.68 in 

Rwanda (2005). 

This finding of two factors for the controlling behaviors offers new insight into the nature of spousal 

violence, in two ways. First, it indicates that controlling behaviors are separate factors from emotional 

violence or any other violence factor, rather than being a component of any of these forms of violence. 

Second, it suggests that suspicion and isolation are distinct forms of controlling behaviors. 

Most factors are only modestly correlated with one another, with a correlation coefficient ranging from 

0.2 to 0.6 (not shown). Correlations are weakest between the sexual violence factor and either of the two 

controlling behavior factors, suspicion and isolation. They are highest between physical violence and 

emotional violence, ranging from 0.54 in Rwanda (2005) to 0.61 in Kenya. This pattern persists in all five 

countries. Because factors are somewhat correlated, an oblique rotation was retained for the solution that 

produced the factor scores for these five factors. 
18

3.2 Profile of Spousal Violence and HIV 

As discussed, findings from the factor analysis suggest that there are two consistent domains of 

controlling behaviors (isolation and suspicion) and three consistent domains of spousal violence 

(emotional violence, physical violence, and sexual violence) across the five study countries. Table 4 

presents the prevalence of each domain of spousal violence and controlling behaviors, as well as HIV 

prevalence among women in the study population (married couples with complete information on spousal 

violence and HIV status). Prevalence is here defined as experiencing at least one item in that domain of 

spousal violence. For successive analyses, spousal violence factors scores are used rather than this 

measure. A profile of the countries in terms of their background characteristics and risk factors can be 

found in Appendix Tables A2 and A3. 

Table 4. Prevalence of spousal violence, controlling behaviors, and women's HIV status in the 

analytic sample 

 Kenya Malawi 

Rwanda 

2005 

Rwanda 

2010 Zambia Zimbabwe 

 N=873 N=2,627 N=1,452 N=2,013 N=1,611 N=1,711 

 % % % % % % 

Prevalence of spousal violence

Controlling behaviors 

Suspicion* 55.9 58.1 46.1 na 74.9 62.2 

Isolation** 33.0 18.3 24.5 na 36.2 21.3 

Emotional violence*** 27.6 23.2 9.3 na 22.2 26.5 

Physical violence^ 33.7 19.6 32.9 53.9 44.8 30.0 

Sexual violence^^ 14.5 15.3 11.7 14.8 16.7 16.6 

Women's HIV prevalence 6.1 9.5 1.9 3.0 12.6 15.1 

na: not available 

* Identifies couples for whom the wife reports that her current husband exhibits at least one of the following 

behaviors: he is jealous if she talks with other men, accuses her of unfaithfulness, or insists on knowing where she 

is. 

** Identifies couples for whom the wife reports that her current husband exhibits at least one of the following 

behaviors: he does not permit her to meet her female friends, tries to limit her contact with her family, or does not 

trust her with money. 

*** Identifies couples for whom the wife reports any lifetime experience of the following items of violence with her 

current husband: the partner ever said or did something to humiliate the respondent in front of others, threatened 

to hurt or harm respondent or someone close to her, or ever insulted respondent or made her feel bad. 

^ Identifies couples for whom the wife reports any lifetime experience of the following items of violence by her 

current husband: he ever pushed, shook her, or threw something at her, ever slapped her, ever punched 

respondent with his fist or hit with something that could hurt her, ever kicked, dragged, or beat up respondent, ever 

twisted respondent's arm or pulled hair, ever spit on her, or tried to choke or burn respondent on purpose. 

^^ Identifies couples for whom the wife reports any lifetime experience of the following items of violence by her 

current husband: ever physically forced her to have sexual intercourse with him even when she did not want to, or 

ever forced her to perform any sexual acts she did not want to. 
19

Of the five domains of violence and controlling behaviors, the cluster of suspicion-related controlling 

behaviors is the most prevalent form among couples in the five countries studied, ranging from 46 percent 

of wives in Rwanda (2005) to 75 percent in Zambia who reported that their husbands exhibit at least one 

of the three controlling behaviors in this domain (the husband is jealous or angry if she talks with other 

men, the husband accuses her of unfaithfulness, or insists on knowing where she is at all times). The 

second domain of controlling behaviors, which identifies women whose husbands do not permit them to 

meet their female friends, try to limit their contact with their family, or do not trust them with money, is 

far less prevalent, ranging from 18 percent of wives in Malawi to 36 percent in Zambia who reported that 

their husbands exhibit at least one of these controlling behaviors. 

Of the three traditional forms of spousal violence, physical violence is consistently the most prevalent 

among study couples, followed by emotional violence and then sexual violence. Women’s reported 

lifetime experience of any spousal physical violence ranges from 20 percent in Malawi to 54 percent in 

Rwanda (2010). About one-third of women in Kenya, Rwanda (2005), and Zimbabwe reported 

experiencing any physical violence, and nearly half of women in Zambia did. In all countries except 

Rwanda, between 20 and 30 percent of women reported any lifetime experience of spousal emotional 

violence; in Rwanda less than 10 percent of women reported any spousal emotional violence. In the five 

study countries the percentage of women who reported any lifetime experience of spousal sexual violence 

ranged from 12 percent in Rwanda (2005) to 17 percent in Zambia and Zimbabwe. 

Table 4 also presents HIV prevalence among women in the study couples. HIV prevalence varies across 

the six surveys—at 2 percent in Rwanda 2005 and 3 percent in Rwanda 2010, 6 percent in Kenya, 10 

percent in Malawi, 13 percent in Zambia, and 15 percent in Zimbabwe. Recall that these estimates are not 

representative of all women in the study countries, but rather a unique subsample of couples of 

reproductive age with both spouses successfully interviewed and tested for HIV with a valid result. 

3.3 Associations between Spousal Violence and HIV 

Table 5 presents unadjusted and adjusted odds ratios comparing the odds within study couples of the 

woman being HIV-positive, regressed on different forms of spousal violence. In the adjusted models the 

associations between each form of violence and women’s HIV status were modeled separately, 

controlling for key demographic characteristics including the couple’s place of residence, geographic 

region, and wealth quintile, both spouses’ level of education, occupation, and age, the total number of 

children born to the wife, and the woman's age at first marriage/cohabitation. 

Domestic violence 

Woman’s risk 

factors 

Husband’s risk factors Husband’s HIV status 

Woman’s HIV status 
20

Table 5. Unadjusted and adjusted associations between forms of spousal violence and women's 

HIV status in the analytic sample: odds ratios from separate logistic regressions 

 Kenya Malawi Rwanda 2005 Rwanda 2010 Zambia Zimbabwe 

 N=873 N=2,627 N=1,452 N=2,013 N=1,611 N=1,711 

 uOR aOR uOR aOR uOR aOR uOR aOR uOR aOR uOR aOR 

Controlling 

behaviors 

Suspicion 1.02 1.05 1.00 0.95 1.11 0.94 na na 1.22* 1.18† 1.20** 1.31*** 

Isolation 1.03 1.10 1.00 1.02 1.23 1.00 na na 1.23* 1.19†

 1.13† 1.20* 

Emotional 1.23 1.31† 1.02 0.97 1.41* 1.32† na na 1.28** 1.19 1.13* 1.21** 

Physical 1.27* 1.36* 1.11 1.08 1.15 1.28 1.18 1.38* 1.18†

 1.16 1.28*** 1.39*** 

Sexual 1.05 1.00 0.95 0.97 1.13 1.01 1.03 1.12 1.10 1.04 1.05 1.13 

*** p<=0.001; ** p<=0.01; * p<=0.05; †

 p<=0.10 

uOR: unadjusted odds ratio; aOR: adjusted odds ratio 

na: Not available 

Note: the adjusted model control for place of residence (urban/rural), region, wealth (quintiles), women's 

education (none/primary/secondary+), men's education (none/primary/secondary+), women's occupation 

(unemployed/employed in agriculture/employed in non-agriculture), men's occupation (unemployed/employed in 

agriculture/employed in non-agriculture), women's age (4 categories), men's age (4 categories), total children 

ever born (continuous), women's age at first cohabitation (continuous). 

In four of the six surveys, a significant bivariate association is observed between at least one form of 

violence and women’s HIV status. In the adjusted model, spousal physical violence is significantly 

associated with women’s HIV status in three of six surveys (Kenya, Rwanda 2010, and Zimbabwe). In 

Zimbabwe, notably, four of the five violence domains are significant. In Rwanda 2005 and Zambia, the 

association is not significant but the direction and magnitude of association are consistent, such that in all 

six surveys the odds of being HIV-positive are greater among women with higher scores on the physical 

violence scale. In Kenya, for example, each unit increase on the physical violence scale (which ranges 

from -1.1 to 4.2) is associated with a 36 percent increase in the adjusted odds of being HIV-positive. 

Emotional violence is significantly associated with women’s HIV status in the adjusted model in 

Zimbabwe, and is borderline significant (p<0.10) in Kenya and Rwanda 2005, such that scoring higher on 

the spousal emotional violence scale is associated with increased adjusted odds of being HIV-positive. In 

Zimbabwe each one unit increase on the emotional violence scale is associated with a 21 percent increase 

in the odds of being HIV-positive. 

The association between spousal sexual violence—the least prevalent form of violence among study 

couples—and women’s HIV status is not statistically significant in any of the six surveys. 

The two domains of controlling behaviors are strongly and significantly associated with women’s HIV 

status in Zimbabwe only. With each one-unit increase on the suspicion and isolation scales (scales range 

from -1.8 to 2.3 for suspicion, and -1.1 to 3.4 for isolation), the odds that a Zimbabwean woman is HIVpositive increase by 31 percent (suspicion) and 20 percent (isolation), respectively. In Zambia, too, 

suspicion and isolation are associated with women’s HIV status, but the significance is only marginal. 
21

In sum, after adjusting for likely confounders, at least one domain of spousal violence remains 

significantly associated with women’s HIV status in Kenya, Rwanda (2010), and Zimbabwe. The most 

consistent associations appear to be between spousal physical and emotional violence and women’s HIV 

status. Given that there is no direct causal pathway between any of the forms of spousal violence and 

women’s HIV status, subsequent sections of the report will attempt to better understand indirect pathways 

that could explain the observed significant associations. 

3.4 Relationship between HIV Status and Risk Factors 

In order to understand the potential indirect effect that spousal violence may have on women’s HIV 

status, we first need to assess the magnitude of the relationship between this outcome and the factors 

through which we expect spousal violence to exert influence on women’s HIV status. Table 6 presents 

adjusted odds ratios obtained by regressing the wife’s HIV status on known HIV risk behaviors and 

factors reported by women and by their husbands, controlling for a range of background characteristics. 

We considered two variables representing HIV risk factors for women in separate models: lifetime 

number of partners and STI and STI symptoms in the last 12 months. A third risk factor, sex with a nonspousal partner, was omitted because reports of this behavior were exceedingly rare in most countries 

(See Appendix Table A3). A more extensive set of HIV risk factors for husbands, as well as the 

husband’s HIV status, were also examined for an association with the wife’s HIV status, again in separate 

models. 

Table 6. Adjusted associations between women's HIV status (dependent variable) and wives' risk 

factors, husbands' HIV status, and husbands' risk factors in the analytic sample: odds ratios from 

separate logistic regressions 

 Kenya Malawi 

Rwanda 

2005 

Rwanda 

2010 Zambia Zimbabwe

 N=873 N=2,627 N=1,452 N=2,013 N=1,611 N=1,711 

 aOR aOR aOR aOR aOR aOR 

Women's risk factors^ 

Lifetime number of partners 

One (reference) 1.00 1.00 1.00 1.00 1.00 1.00

Two 1.71 3.47 *** 2.38 * 3.88 *** 2.44 *** 4.02 *** 

Three or more 4.14 *** 9.13 *** 2.31 6.32 *** 3.16 *** 6.78 *** 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00 1.00 1.00 1.00 1.00

STI 3.42 * 1.83 ** 4.55 ** 4.06 *** 1.87 2.87 *** 

Men's HIV status 

HIV negative (reference) 1.00 1.00 1.00 1.00 1.00 1.00

HIV positive 74.23 *** 31.06 *** 252.47 *** 432.39 *** 16.90 *** 32.67 *** 

(Continued...) 
22

Table 6. – Continued 

 Kenya Malawi 

Rwanda 

2005 

Rwanda 

2010 Zambia Zimbabwe

 N=873 N=2,627 N=1,452 N=2,013 N=1,611 N=1,711 

 aOR aOR aOR aOR aOR aOR 

Men's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00 1.00 1.00 1.00 1.00

Two 0.83 2.60 * 1.78 4.62 ** 5.04 ** 1.78 † 

Three 0.97 5.32 *** 2.40 † 5.60 ** 4.67 * 2.27 * 

Four or more 0.66 6.12 *** 2.62 † 7.84 *** 8.20 *** 3.29 *** 

Don't know 2.12 10.62 *** 63.10 ** 7.25 ** 5.67 *** 

Sex with nonspousal, noncohabiting 

partner in last 12 months 

No (reference) 1.00 1.00 1.00 1.00 1.00 1.00

Yes 0.83 0.81 0.79 5.75 *** 2.09 ** 0.89

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00 1.00 1.00 1.00 1.00

STI 1.06 2.33 ** 12.68 *** 4.16 *** 2.73 ** 2.60 *** 

Ever paid for sex 

No (reference) na 1.00 1.00 1.00 1.00 1.00

Yes na 1.02 1.42 4.03 *** 1.80 1.57 ** 

Alcohol use 

Husband doesn't drink or drinks but 

never drunk (reference) 1.00 1.00 1.00 na 1.00 1.00 

Husband drinks and sometimes drunk 1.29 1.28 0.99 na 1.58 * 1.18

Husband drinks and often drunk 2.54 † 1.64 † 0.63 na 2.14 ** 1.56 † 

*** p<=0.001; ** p<=0.01; * p<=0.05; † p<=0.10 

^ Women's risk factors are each run in separate models, adjusting for the following key control variables: place of 

residence (urban/rural), region, wealth (quintiles), women's education (none/primary/secondary+), men's education 

(none/primary/secondary+), women's occupation (unemployed/employed in agriculture/employed in nonagriculture), men's occupation (unemployed/employed in agriculture/employed in non-agriculture), women's age (4 

categories), men's age (4 categories), total children ever born (continuous), women's age at first cohabitation 

(continuous). 

 
23

Women’s risk factors and women’s HIV status 

In general, in all countries both of the HIV risk factors for women that we examine are significantly 

associated with women being HIV-positive, controlling for background characteristics but not for each 

other. In Kenya, the odds of being HIV-positive are more than 400 percent higher among women who 

have had three or more sexual partners in their lifetime compared with women with one partner, but there 

is no significant difference between women with two lifetime partners and one lifetime partner. 

Curiously, among women in Rwanda (2005), having two partners in their lifetime is significantly 

associated with being HIV-positive, but having three or more partners is not. For the other surveys, 

women having either two partners or three or more partners in their lifetime are each significantly 

associated with increased odds of being HIV-positive. Similarly, in four countries the odds of being HIVpositive are 183 to 406 percent greater among women who had an STI or reported an STI or STI 

symptoms in the last 12 months. In Zambia no significant association is found between having an STI or 

STI symptoms and being HIV-positive. 

Men’s HIV status and women’s HIV status 

The most prominent predictor of a woman’s HIV status, among all the factors considered in separate 

models, is her husband’s HIV status. The odds ratio for a woman whose husband is HIV-positive being 

HIV-positive herself range from 16.9 times higher in Zambia to 432.4 times higher in Rwanda, compared 

with women whose husbands are not HIV-positive. 

Men’s risk factors and women’s HIV status 

 

Domestic violence 

Woman’s risk 

factors 

Husband’s risk factors Husband’s HIV status 

Woman’s HIV status 

Domestic violence 

Woman’s risk 

factors

Husband’s risk factors Husband’s HIV status 

Woman’s HIV status 
24

Husbands’ other HIV risk factors are far less consistent in their adjusted associations with their wives’ 

HIV status than are either husbands’ HIV status or women’s risk factors. In Kenya, none of the husbands’ 

risk factors examined are significantly associated with their wives being HIV-positive, except for 

husbands’ alcohol use, which is marginally significantly associated (p<0.10) with women’s HIV status 

only for women whose husbands are frequently drunk. In the other countries, the husbands’ risk factors 

that are most commonly associated with women’s HIV status are experience of STI or STI symptoms in 

the last 12 months and lifetime number of partners, which are significant in four of the five countries. The 

odds of being HIV-positive are approximately 200 percent higher among women whose husbands have 

had an STI or STI symptom in the last 12 months compared with women whose husbands have had no 

symptoms, in most samples; this figure is even higher in both Rwanda surveys. In all countries except 

Kenya, the odds of the wife having HIV increases steadily with the lifetime number of partners her 

husband has had. However, this association is only borderline significant in the Rwanda 2005 survey. 

The other husbands’ risk factors are less frequently associated with women’s HIV status. In Rwanda 

(2010) and Zambia the odds of being HIV-positive are higher among women whose husbands reported 

having had sex with a non-spousal, non-cohabitating partner in the last 12 months, and in Rwanda 2010 

and Zimbabwe among women whose husbands reported ever paying for sex. In Zambia women whose 

husbands drink and are sometimes or often drunk have increased odds of being HIV-positive, In Malawi 

and Zimbabwe, as in Kenya, and this association is also borderline significant among women whose 

husbands are often drunk. 

3.5 Relationship between HIV Risk Factors and Spousal Violence 

In this section we explore the potential indirect effect of spousal violence on women’s HIV status by 

investigating the association between spousal violence and women’s and their husbands’ HIV risk factors, 

many of which are significantly associated with women’s HIV-positive status, as demonstrated in the 

previous section. Table 7 shows the adjusted associations between spousal violence and both women’s 

HIV risk factors—first, having an STI or STI symptoms in the last 12 months and, second, having two or 

more sexual partners in their lifetime. For each risk factor, the effect of the three forms of spousal 

violence and both forms of controlling behaviors are estimated in separate models, controlling for 

background characteristics. Table 8 shows the adjusted coefficients in ordinary least squares models in 

which women’s spousal violence and controlling behavior factor scores (one adjusted model for each 

factor) are regressed on husbands’ risk factors, with background characteristics as covariates. 

 

Domestic violence 

Woman’s risk 

factors

Husband’s risk factors Husband’s HIV status 

Woman’s HIV status 
25

Spousal violence and women’s HIV risk factors 

As Table 7 shows, most of the spousal violence measures are significantly associated with both risk 

factors for women. These associations are weakest in Kenya. 

Table 7. Adjusted associations^ between women's HIV risk factors and spousal violence in the 

analytic sample: odds ratios from separate logistic regressions 

 Kenya Malawi 

Rwanda 

2005 

Rwanda 

2010 Zambia Zimbabwe

 N=873 N=2,627 N=1,452 N=2,013 N=1,611 N=1,711 

 aOR aOR aOR aOR aOR aOR 

Dependent variable 1: woman had 

STI or STI symptoms in last 12 

months 

Controlling behaviors 

Suspicion 0.88 1.41 *** 1.37 ** na 1.32 * 1.32 ** 

Isolation 0.98 1.32 *** 1.31 * na 1.12 1.24 * 

Emotional 1.46 * 1.47 *** 1.33 * na 1.50 *** 1.33 ** 

Physical 1.28 † 1.24 ** 1.45 *** 1.51 *** 1.51 *** 1.41 *** 

Sexual 1.37 ** 1.27 *** 1.29 ** 1.39 *** 1.33 ** 1.32 *** 

Dependent variable 2: woman 

reported 2 or more lifetime sexual 

partners^^ 

Controlling behaviors 

Suspicion 1.49 *** 1.20 *** 1.24 ** na 1.20 ** 1.38 *** 

Isolation 1.16 1.07 1.20 * na 1.19 ** 1.16 * 

Emotional 1.38 ** 1.12 * 1.15 † na 1.29 *** 1.21 ** 

Physical 1.40 ** 1.13 * 1.17 * 1.33 *** 1.34 *** 1.25 *** 

Sexual 1.00 1.17 ** 0.91 1.11 † 1.15 * 1.09 

*** p<=0.001; ** p<=0.01; * p<=0.05; †

 p<=0.10 

^ Forms of violence are run in separate logistic models, adjusting for the following key control variables: place of 

residence (urban/rural), region, wealth (quintiles), women's education (none/primary/secondary+), men's 

education (none/primary/secondary+), women's occupation (unemployed/employed in agriculture/employed in nonagriculture), men's occupation (unemployed/employed in agriculture/employed in non-agriculture), women's age (4 

categories), men's age (4 categories), total children ever born (continuous), women's age at first cohabitation 

(continuous). 

^^ Women's lifetime number of sexual partners was collapsed to create a binary indicator comparing women with 

two or more lifetime sexual partners to women with 1 reported sexual partner, so that comparable logistic 

regression models could be run for both risk factors. 
26

Women in Malawi, Rwanda, Zambia and Zimbabwe face 32 to 41 percent higher odds of having an STI 

or STI symptom in the last 12 months with each unit increase in the experience of suspicious controlling 

behavior by their husbands. A similar increase in odds is apparent for women who experience isolating 

behaviors by their husbands in Malawi, Rwanda, and Zimbabwe, but not Zambia. These associations were 

not found in Kenya. 

The experience of emotional, physical, or sexual violence is each associated with higher odds of having 

had an STI or STI symptom in the last 12 months, in all five countries. The magnitude of this association 

is largest for emotional and physical violence, although in Kenya the experience of spousal physical 

violence is only marginally significantly associated with reported STI or STI symptoms. 

Similarly, with regard to the lifetime number of sexual partners, in Rwanda (2005), Zambia, and 

Zimbabwe both controlling behavior measures are associated with greater odds of having two or more 

partners, while in Kenya and Malawi experiencing suspicious controlling behaviors, but not isolating 

behaviors, is associated with having two or more partners. In all five countries women who experience a 

greater degree of either emotional or physical violence by their husbands also are more likely to have had 

two or more sexual partners in their lifetime. However, a significant association between the experience 

of sexual violence and the lifetime number of partners is apparent in only three of the six surveys 

(Malawi, Rwanda 2010, and Zambia), and the magnitude of the odds ratio is smaller than for other 

violence measures. 

In summary, several measures of spousal violence are frequently associated with women’s HIV risk 

factors. The relationship between husbands’ controlling behaviors, especially isolation, and women’s risk 

factors is less consistent than with other forms of violence. The association between experience of sexual 

violence and a wife’s lifetime number of partners is evident in fewer countries. 

Spousal violence and husbands’ HIV risk factors 

In general, the association between women’s experience of spousal violence and husbands’ HIV risk 

factors is weaker and less consistent than with women’s HIV risk factors (Table 8). Alcohol use is the risk 

factor most strongly and consistently associated with the experience of spousal violence. The likelihood 

of experiencing every form of spousal violence and controlling behavior is much greater among women 

whose husbands are often drunk compared with women whose husbands do not drink at all or who drink 

but are never drunk. 
27

Table 8. Adjusted linear associations^ between spousal violence (dependent variable) and 

husbands' HIV risk factors in the analytic sample: coefficients from linear regressions 

 Kenya Malawi 

Rwanda 

2005 

Rwanda 

2010 Zambia Zimbabwe

 aβ aβ aβ aβ aβ aβ

Dependent variable 1: suspicion 

controlling behavior 

Husband's lifetime number of partners 

One (reference) 1.00 1.00 1.00 na 1.00 1.00

Two -0.18 0.21 ** 0.04 na 0.03 0.26 ** 

Three -0.24 0.10 0.04 na 0.17 † 0.22 * 

Four or more 0.01 0.16 * 0.03 na 0.20 * 0.15 †

Don't know 0.02 0.35 * na na 0.10 0.39 ** 

Husband had sex with nonspousal, 

noncohabiting partner in last 12 months 

(Y/N) -0.03 -0.07 0.15 † na -0.06 -0.02

Husband had STI symptoms in last 12 

months (Y/N) 0.30 0.15 0.05 na 0.12 0.13

Husband ever paid for sex (Y/N) 0.34 0.05 0.10 na 0.05 0.05

Husband's alcohol use 

Husband doesn't drink or drinks but 

never drunk (reference) 1.00 1.00 1.00 na 1.00 1.00

Husband drinks and sometimes drunk 0.28 * 0.12 * 0.12 * na 0.20 ** 0.19 *** 

Husband drinks and often drunk 0.52 *** 0.49 *** 0.65 *** na 0.42 *** 0.55 *** 

Dependent variable 2: isolation 

controlling behavior 

Husband's lifetime number of partners 

One (reference) 1.00 1.00 1.00 na 1.00 1.00

Two -0.22 0.14 ** 0.04 na -0.07 0.19 * 

Three -0.33 0.17 * 0.06 na -0.05 0.14 † 

Four or more -0.31 0.17 ** 0.01 na -0.02 0.14 † 

Don't know -0.31 0.01 na na 0.01 0.38 * 

Husband had sex with nonspousal, 

noncohabiting partner in last 12 months 

(Y/N) -0.05 -0.06 0.24 * na 0.14 * -0.06

Husband had STI symptoms in last 12 

months (Y/N) -0.09 0.03 0.01 na 0.00 0.12

Husband ever paid for sex (Y/N) -0.03 0.01 0.11 na -0.19 0.01

(Continued...) 
28

Table 8. – Continued

 Kenya Malawi 

Rwanda 

2005 

Rwanda 

2010 Zambia Zimbabwe

 aβ aβ aβ aβ aβ aβ

Husband's alcohol use 

Husband doesn't drink or drinks but 

never drunk (reference) 1.00 1.00 1.00 na 1.00 1.00

Husband drinks and sometimes drunk 0.10 0.08 0.06 na 0.03 0.10 † 

Husband drinks and often drunk 0.56 *** 0.39 *** 0.53 *** na 0.22 ** 0.41 *** 

Dependent variable 3: emotional 

spousal violence 

Husband's lifetime number of partners 

One (reference) 1.00 1.00 1.00 na 1.00 1.00

Two -0.11 0.10 † 0.07 na -0.02 0.07

Three -0.09 0.00 0.09 na 0.01 0.08

Four or more 0.11 0.08 0.16 * na 0.10 0.09

Don't know 0.19 0.05 na na 0.10 0.30

Husband had STI symptoms in last 12 

months (Y/N) 0.38 0.27 ** 0.09 na -0.04 0.02

Husband's alcohol use 

Husband drinks and sometimes drunk 0.01 0.16 ** 0.08 * na 0.08 0.17 ** 

Dependent variable 4: physical spousal 

violence 

Husband's lifetime number of partners 

One (reference) 1.00 1.00 1.00 1.00 1.00 1.00

Two 0.00 0.10 † 0.11 * 0.15 ** 0.03 0.16 * 

Three -0.01 0.04 0.19 ** 0.05 0.05 0.16 † 

Four or more 0.14 0.09 † 0.26 *** 0.14 * 0.10 0.17 * 

Don't know 0.00 0.03 na 0.60 * 0.19 0.45 ** 

Husband had sex with nonspousal, 

noncohabiting partner in last 12 months 

(Y/N) 0.14 0.23 * 0.05 0.41 *** 0.04 0.12

Husband had STI symptoms in last 12 

months (Y/N) 0.11 0.19 † -0.02 0.01 0.19 0.05

Husband ever paid for sex (Y/N) -0.25 -0.02 -0.16 † -0.15 † 0.34 -0.15 * 

Husband's alcohol use 

Husband doesn't drink or drinks but 

never drunk (reference) 1.00 1.00 1.00 na 1.00 1.00

Husband drinks and sometimes drunk 0.05 0.10 * 0.17 *** na 0.10 * 0.09 † 

Husband drinks and often drunk 1.10 *** 0.85 *** 1.01 *** na 0.72 *** 0.65 *** 

(Continued...) 
29

Table 8. – Continued

 Kenya Malawi 

Rwanda 

2005 

Rwanda 

2010 Zambia Zimbabwe

 aβ aβ aβ aβ aβ aβ

Dependent variable 5: sexual spousal 

violence 

Husband's lifetime number of partners 

One (reference) 1.00 1.00 1.00 1.00 1.00 1.00

Two -0.04 0.12 * 0.03 0.08 -0.07 -0.05

Three 0.06 0.15 * 0.16 * -0.04 0.09 0.24 * 

Four or more -0.15 0.18 ** -0.03 0.00 0.11 0.01

Don't know -0.12 0.16 -0.23 0.30 0.32 * 

Husband had sex with nonspousal, 

noncohabiting partner in last 12 months 

(Y/N) -0.06 -0.11 0.14 0.11 0.02 -0.13 † 

Husband had STI symptoms in last 12 

months (Y/N) 0.02 0.08 0.30 † 0.09 0.23 0.02

Husband ever paid for sex (Y/N) 0.07 0.13 † 0.14 0.16 0.06 -0.07

Husband's alcohol use 

Husband doesn't drink or drinks but 

never drunk (reference) 1.00 1.00 1.00 na 1.00 1.00

Husband drinks and sometimes drunk 0.13 0.05 0.10 * na 0.01 -0.01

Husband drinks and often drunk 0.87 *** 0.51 *** 0.39 *** na 0.39 *** 0.41 *** 

*** p<=0.001; ** p<=0.01; * p<=0.05; †

 p<=0.10 

na: not available 

^ Husband's risk factors are run together in one model, adjusting for the following key control variables: place of 

residence (urban/rural), region, wealth (quintiles), women's education (none/primary/secondary+), men's education

(none/primary/secondary+), women's occupation (unemployed/employed in agriculture/employed in nonagriculture), men's occupation (unemployed/employed in agriculture/employed in non-agriculture), women's age (4 

categories), men's age (4 categories), total children ever born (continuous), women's age at first cohabitation 

(continuous). 

Husbands’ lifetime number of partners is associated with suspicion, physical violence, and sexual 

violence in three of the countries, and with isolation and emotional violence in two countries. The 

association of the experience of spousal violence with husbands’ other HIV risk factors varies. For no 

form of spousal violence is there a consistent association with any other HIV risk factor in a majority of 

countries. For example, women whose husbands reported having an STI or STI symptoms in the last 12 

months experience greater spousal emotional violence and physical violence in Malawi, but not 

elsewhere. 
30

3.6 Nuanced Relationship between Spousal Violence and HIV 

In order to determine whether the experience of spousal violence exerts an indirect effect on women’s 

HIV status through their own or their husbands’ risk factors, or has a net direct effect even controlling for 

these factors, a sequence of logistic regression models was run. In the first model women’s HIV status is 

regressed on the experience of spousal violence and a range of background characteristics (also shown in 

Table 5). In Model 2 we add to this base model husbands’ HIV status and HIV risk factors. We substitute 

women’s own risk factors for their husbands’ risk factors in Model 3; and in Model 4 we combine both 

sets of risk factors in a complete model of the projected pathways through which the experience of 

spousal violence may influence women’s HIV status. Rather than presenting an exhaustive series of 

regressions for all five forms of violence and controlling behaviors for all five countries, we estimate this 

series of four models only for those forms of violence that initially have a significant association with 

women’s HIV status in the base model (Model 1). 

Suspicion and women’s HIV status 

As Table 9a shows, the odds of a woman being HIV-positive are 18 percent higher in Zambia and 31 

percent higher in Zimbabwe with each unit increase in her suspicion factor score. However, in Zambia, 

this observed association disappears with the inclusion of either or both her own risk factors and her 

husband’s risk factors and HIV status. This is in spite of the fact that few risk factors are independently 

associated with women’s HIV status in either the spouse-specific models (Models 2 and 3) or the 

combined model (Model 4). Only women whose husbands are often drunk and who themselves have had 

more than two sexual partners in their lifetime have increased odds of being HIV-positive. 

Table 9a. Multivariate associations^ between women's HIV status (dependent variable) and 

suspicion in the analytic sample: odds ratios from a sequence of logistic regressions^^ 

 Model 1 Model 2 Model 3 Model 4 

 aOR aOR aOR aOR 

Zambia (WN=1,611) 

Controlling behavior--suspicion factor score 1.18 † 1.11 1.13 1.08

Male partner's HIV status 

Negative (reference) 1.00 1.00 1.00

Positive 15.85 *** 16.25 *** 15.72 *** 

(Continued...) 

Domestic violence 

Woman’s risk 

factors

Husband’s risk factors Husband’s HIV status 

Woman’s HIV status 
31

Table 9a. – Continued 

 Model 1 Model 2 Model 3 Model 4 

 aOR aOR aOR aOR 

 

Men's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 3.13 2.68

Three 2.26 1.87

Four or more 3.49 † 2.85

Don't know 2.83 2.43

Sex with nonspousal, noncohabiting partner in last 12 

months 

No (reference) 1.00 1.00

Yes 1.47 1.41

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 1.31 1.18

Ever paid for sex 

No (reference) 1.00 1.00

Yes 1.32 1.47

Alcohol use 

Husband doesn't drink or drinks but never drunk 

(reference) 1.00 1.00

Husband drinks and sometimes drunk 1.48 1.44

Husband drinks and often drunk 1.86 ** 1.71 * 

Women's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 2.28 *** 2.09 ** 

Three or more 2.80 *** 2.47 ** 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 1.32 1.12

Zimbabwe (WN=1,711) 

Controlling behavior--suspicion factor score 1.31 *** 1.23 * 1.14 1.11

Male partner's HIV status 

Negative (reference) 1.00 1.00 1.00

Positive 31.89 *** 33.30 *** 34.01 *** 

(Continued...) 

 

 
32

Table 9a. – Continued 

 Model 1 Model 2 Model 3 Model 4 

 aOR aOR aOR aOR 

Men's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 1.23 1.12

Three 2.32 * 2.40 † 

Four or more 2.44 * 1.83

Don't know 4.34 ** 4.19 ** 

Sex with nonspousal, noncohabiting partner in last 12 

months 

No (reference) 1.00 1.00

Yes 0.54 * 0.52 * 

STI symptoms in last 12 months . .

No STI (reference) 1.00 1.00

STI 2.10 ** 2.18 ** 

Ever paid for sex . .

No (reference) 1.00 1.00

Yes 0.88 1.00

Alcohol use . .

Husband doesn't drink or drinks but never drunk 

(reference) 1.00 1.00

Husband drinks and sometimes drunk 1.08 1.07

Husband drinks and often drunk 1.52 1.42

Women's risk factors 

Lifetime number of partners . .

One (reference) 1.00 1.00

Two 4.37 *** 4.43 *** 

Three or more 6.23 *** 5.99 *** 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 2.62 *** 2.70 *** 

*** p<=0.001; ** p<=0.01; * p<=0.05; †

 p<=0.10 

^ Models control for the following key control variables: place of residence (urban/rural), region, wealth (quintiles), 

women's education (none/primary/secondary+), men's education (none/primary/secondary+), women's occupation 

(unemployed/employed in agriculture/employed in non-agriculture), men's occupation (unemployed/employed in 

agriculture/employed in non-agriculture), women's age (4 categories), men's age (4 categories), total children ever 

born (continuous), women's age at first cohabitation (continuous). Model 1: Adjusts only for the control variables; 

Model 2: Adjusts for control variables, husband's HIV risk factors, and husband's HIV status; Model 3: Adjusts for 

control variables, the wife's HIV risk factors, and husband's HIV stats; Model 4: Adjusts for control variables, the 

husband and wife's HIV risk factors, and husband's HIV status. 

^^ The model sequence is presented for all surveys in which the suspicion factor score was significantly associated 

with women's HIV status (p<.10) after adjusting for key control variables (see Table 5). 
33

The findings appear to be slightly different in Zimbabwe. The highly significant direct effect of the 

experience of suspicious controlling behaviors is explained away not by the husbands’ HIV risk factors 

but by women’s own risk factors. Additionally, Models 2 and 3 indicate that more risk factors for both 

members of the couple are associated with women’s HIV status. Both of the women’s risk factors, and 

three of their husbands’ risk factors—lifetime number of partners, having had an STI or STI symptoms, 

and having had a non-marital sexual partner—but not husbands’ alcohol use—are associated with higher 

odds of women being HIV-positive. In both Zambia and Zimbabwe, their husbands’ HIV status is, 

unsurprisingly, a significant predictor of women’s own HIV status, and the odds ratios indicate this effect 

to be sizable. 

Isolating controlling behaviors and women’s HIV status 

As Table 9b shows for Zambia and Zimbabwe, women’s experience of isolating controlling behaviors by 

their husbands is associated with higher odds of having HIV, as was the case for husbands’ suspicion. The 

pattern of this association across the series of models is also much the same. While women in both 

countries face approximately 20 percent greater odds of being HIV-positive with a unit increase in 

isolation scores when controlling only for background characteristics, this effect is no longer significant 

in models that control for women’s own HIV risk factors and their husbands’ risk factors. 

Table 9b. Multivariate associations^ between women's HIV status (dependent variable) and 

isolation in the analytic sample: odds ratios from a sequence of logistic regressions^^ 

 Model 1 Model 2 Model 3 Model 4 

 aOR aOR aOR aOR 

Zambia (WN=1,611) 

Controlling behavior--isolation factor score 1.19 † 1.11 1.11 1.08

Male partner's HIV status 

Negative (reference) 1.00 1.00 1.00

Positive 15.69 *** 16.13 *** 15.61 *** 

 

Men's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 3.17 2.69

Three 2.33 1.91

Four or more 3.58 † 2.89

Don't know 2.83 2.41

Sex with nonspousal, noncohabiting partner in last 12 

months 

No (reference) 1.00 1.00

Yes 1.45 1.39

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 1.31 1.18

(Continued...) 
34

Table 9b. – Continued 

 Model 1 Model 2 Model 3 Model 4 

 aOR aOR aOR aOR 

Ever paid for sex 

No (reference) 1.00 1.00

Yes 1.35 1.49

Alcohol use 

Husband doesn't drink or drinks but never drunk 

(reference) 1.00 1.00

Husband drinks and sometimes drunk 1.51 † 1.46

Husband drinks and often drunk 1.89 ** 1.73 * 

Women's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 2.28 *** 2.08 ** 

Three or more 2.83 *** 2.48 ** 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 1.32 1.12

Zimbabwe (WN=1,711) 

Controlling behavior--isolation factor score 1.2 * 1.14 1.11 1.08

Male partner's HIV status 

Negative (reference) 1.00 1.00 1.00

Positive 32.04 *** 33.37 *** 33.98 *** 

 

Men's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 1.29 1.15

Three 2.43 * 2.46 * 

Four or more 2.48 * 1.84

Don't know 4.43 ** 4.21 ** 

Sex with nonspousal, noncohabiting partner in last 12 

months 

No (reference) 1.00 1.00

Yes 0.56 * 0.53 * 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 2.14 ** 2.2 ** 

(Continued...) 

 

 
35

Table 9b. – Continued 

 Model 1 Model 2 Model 3 Model 4 

 aOR aOR aOR aOR 

Ever paid for sex . .

No (reference) 1.00 1.00

Yes 0.9 1.01

Alcohol use 

Husband doesn't drink or drinks but never drunk 

(reference) 1.00 1.00

Husband drinks and sometimes drunk 1.12 1.09

Husband drinks and often drunk 1.56 1.43

Women's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 4.44 *** 4.49 *** 

Three or more 6.43 *** 6.14 *** 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 2.63 *** 2.71 *** 

*** p<=0.001; ** p<=0.01; * p<=0.05; †

 p<=0.10 

^ Models control for the following key control variables: place of residence (urban/rural), region, wealth (quintiles), 

women's education (none/primary/secondary+), men's education (none/primary/secondary+), women's occupation 

(unemployed/employed in agriculture/employed in non-agriculture), men's occupation (unemployed/employed in 

agriculture/employed in non-agriculture), women's age (4 categories), men's age (4 categories), total children ever 

born (continuous), women's age at first cohabitation (continuous). Model 1: Adjusts only for the control variables; 

Model 2: Adjusts for control variables, husband's HIV risk factors, and husband's HIV status; Model 3: Adjusts for 

control variables, the wife's HIV risk factors, and husband's HIV stats; Model 4: Adjusts for control variables, the 

husband and wife's HIV risk factors, and husband's HIV status. 

^^ The model sequence is presented for all surveys in which the suspicion factor score was significantly associated 

with women's HIV status (p<.10) after adjusting for key control variables (see Table 5). 

In these models, too, a similar set of women’s and husbands’ HIV risk factors are associated with 

women’s HIV status, as is the case for the models that included the suspicion factor. Notable differences 

include that the isolation factor in Zimbabwe is no longer significantly associated with the wife’s HIV 

status when her husband’s risk factors are controlled for (Model 2). 

Emotional violence and women’s HIV status 

The emotional violence factor is significantly associated with higher odds of having HIV for women in 

Kenya, Rwanda (2005), and Zimbabwe (Table 9c). The odds of being HIV-positive are roughly 30 

percent higher in Kenya and Rwanda (2005) and 20 percent higher in Zimbabwe with each unit increase 

in the emotional violence factor score. 

 
36

Table 9c. Multivariate associations^ between women's HIV status (dependent variable) and 

emotional violence in the analytic sample: odds ratios from a sequence of logistic regressions^^ 

 Model 1 Model 2 Model 3 Model 4 

 aOR aOR aOR aOR 

Kenya (WN=873) 

Emotional violence factor score 1.31 † 1.34 1.39 1.28

Male partner's HIV status 

Negative (reference) 1.00 1.00 1.00

Positive 126.72 *** 82.3 *** 144.66 *** 

 

Men's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 0.59 0.69

Three 0.79 1.05

Four or more 0.28 0.28

Don't know 1.08 1.03

Sex with nonspousal, noncohabiting partner in last 12 

months 

No (reference) 1.00 1.00

Yes 0.30 0.21

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 1.63 1.18

Alcohol use 

Husband doesn't drink or drinks but never drunk 

(reference) 1.00 1.00

Husband drinks and sometimes drunk 1.08 1.02

Husband drinks and often drunk 4.52 * 4.26 †

Women's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 1.59 2.06

Three or more 4.09 ** 5.64 *** 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 2.91 2.44

Rwanda 2005 (WN= 2,013) 

Emotional violence factor score 1.32 †

 1.18 0.98 1.1

(Continued...) 
37

Table 9c. – Continued 

 Model 1 Model 2 Model 3 Model 4 

 aOR aOR aOR aOR 

Male partner's HIV status 

Negative (reference) 1.00 1.00 1.00

Positive 259.07 *** 349.97 *** 401.37 *** 

 

Men's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 1.90 1.68

Three 2.38 2.41

Four or more 2.13 1.53

Don't know 

Sex with nonspousal, noncohabiting partner in last 12 

months 

No (reference) 1.00 1.00

Yes 0.49 0.56

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 12.53 *** 11.31 *** 

Ever paid for sex 

No (reference) 1.00 1.00

Yes 1.11 1.48

Alcohol use 

Husband doesn't drink or drinks but never drunk 

(reference) 1.00 1.00

Husband drinks and sometimes drunk 0.65 0.57

Husband drinks and often drunk 0.19 * 0.18 †

Women's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 5.27 ** 5.23 ** 

Three or more 1.61 1.61

STI symptoms in last 12 months 

No STI (reference) 

STI 3.01 2.79

Zimbabwe (WN=1,711) 

Emotional violence factor score 1.21 ** 1.19 * 1.16 † 1.12

Male partner's HIV status 

Negative (reference) 1.00 1.00 1.00

Positive 32.36 *** 33.71 *** 34.18 *** 

(Continued...) 
38

Table 9c. – Continued 

 Model 1 Model 2 Model 3 Model 4 

 aOR aOR aOR aOR 

Men's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 1.30 1.14

Three 2.38 * 2.40 † 

Four or more 2.46 * 1.82

Don't know 4.43 ** 4.18 ** 

Sex with nonspousal, noncohabiting partner in last 12 

months 

No (reference) 1.00 1.00

Yes 0.55 * 0.53 * 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 2.13 ** 2.21 ** 

Ever paid for sex 

No (reference) 1.00 1.00

Yes 0.90 1.01

Alcohol use 

Husband doesn't drink or drinks but never drunk 

(reference) 1.00 1.00

Husband drinks and sometimes drunk 1.08 1.07

Husband drinks and often drunk 1.45 1.35

Women's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 4.39 *** 4.45 *** 

Three or more 6.34 *** 6.10 *** 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 2.60 *** 2.70 *** 

*** p<=0.001; ** p<=0.01; * p<=0.05; †

 p<=0.10 

^ Models control for the following key control variables: place of residence (urban/rural), region, wealth (quintiles), 

women's education (none/primary/secondary+), men's education (none/primary/secondary+), women's occupation 

(unemployed/employed in agriculture/employed in non-agriculture), men's occupation (unemployed/employed in 

agriculture/employed in non-agriculture), women's age (4 categories), men's age (4 categories), total children ever 

born (continuous), women's age at first cohabitation (continuous). Model 1: Adjusts only for the control variables; 

Model 2: Adjusts for control variables, husband's HIV risk factors, and husband's HIV status; Model 3: Adjusts for 

control variables, the wife's HIV risk factors, and husband's HIV stats; Model 4: Adjusts for control variables, the 

husband and wife's HIV risk factors, and husband's HIV status. 

^^ The model sequence is presented for all surveys in which the suspicion factor score was significantly associated 

with women's HIV status (p<.10) after adjusting for key control variables (see Table 5). 
39

In both Kenya and Rwanda (2005), any independent effect of the experience of emotional violence is 

absent in models controlling for either husbands’ HIV risk factors or women’s risk factors, or both. This 

is the case although only a few risk factors predict women’s HIV status. These are: women’s lifetime 

number of partners, husbands’ alcohol use, and, in Rwanda only, husbands’ reports of STI or STI 

symptoms. 

Zimbabwe shows slightly different results: the odds ratio for emotional violence remains little changed 

with the addition of either husbands’ or women’s HIV risk factors, and it maintains its significant 

association with women’s HIV status. Only when both sets of risk factors are controlled for is the 

experience of emotional violence no longer significantly associated with women’s HIV status. Zimbabwe 

is also set apart by the fact that both of the women’s risk factors and a greater number of and different 

husbands’ risk factors are associated with women’s HIV status. 

Physical violence and women’s HIV status 

We examined physical violence more closely in the three countries where it has a significant association 

with women’s HIV status: Kenya, Rwanda (2010), and Zimbabwe. In these countries the odds of women 

being HIV-positive are 36 to 39 percent higher with each unit increase in their physical violence score 

(Table 9d). 

Table 9d. Multivariate associations^ between women's HIV status (dependent variable) and 

physical violence in the analytic sample: odds ratios from a sequence of logistic regressions^^ 

 Model 1 Model 2 Model 3 Model 4 

 aOR aOR aOR aOR 

Kenya (WN=873) 

Physical violence factor score 1.36 * 1.51 * 1.59 ** 1.44 † 

Male partner's HIV status 

Negative (reference) 1.00 1.00 1.00

Positive 126.67 *** 87.31 *** 138.98 *** 

 

Men's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 0.63 0.75

Three 0.82 1.10

Four or more 0.31 0.32

Don't know 1.29 1.26

Sex with nonspousal, noncohabiting partner in last 12 

months 

No (reference) 1.00 1.00

Yes 0.29 0.22

(Continued...) 

 
40

Table 9d. – Continued 

 Model 1 Model 2 Model 3 Model 4 

 aOR aOR aOR aOR 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 1.77 1.24

Alcohol use 

Husband doesn't drink or drinks but never drunk 

(reference) 1.00 1.00

Husband drinks and sometimes drunk 1.05 1.00

Husband drinks and often drunk 3.52 3.33

Women's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 1.68 2.06

Three or more 4.07 ** 5.43 *** 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 2.87 2.48

Rwanda 2005 (WN= 2,013) 

Physical violence factor score 1.38 * 0.92 0.90 0.85

Male partner's HIV status 

Negative (reference) 1.00 1.00 1.00

Positive 448.69 *** 340.68 *** 363.11 *** 

 

Men's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 2.07 1.81

Three 1.97 1.62

Four or more 1.06 0.77

Don't know 0.49 0.40

Sex with nonspousal, noncohabiting partner in last 12 

months 

No (reference) 1.00 1.00

Yes 6.67 ** 6.59 ** 

(Continued...) 

 

 
41

Table 9d. – Continued 

 Model 1 Model 2 Model 3 Model 4 

 aOR aOR aOR aOR 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 1.20 1.36

Ever paid for sex 

No (reference) 1.00 1.00

Yes 1.46 1.60

Alcohol use 

Husband doesn't drink or drinks but never drunk 

(reference) 

Husband drinks and sometimes drunk 

Husband drinks and often drunk . . 

Women's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 3.19 * 3.36 ** 

Three or more 2.20 2.53

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 2.29 † 2.10

Zimbabwe (WN=1,711) 

Physical violence factor score 1.39 *** 1.54 *** 1.47 *** 1.45 *** 

Male partner's HIV status 

Negative (reference) 1.00 1.00 1.00

Positive 34.68 *** 36.73 *** 36.48 *** 

 

Men's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 1.21 1.06

Three 2.19 † 2.18 † 

Four or more 2.35 * 1.73

Don't know 3.65 ** 3.40 * 

Sex with nonspousal, noncohabiting partner in last 12 

months 

No (reference) 1.00 1.00

Yes 0.54 * 0.52 * 

(Continued...) 
42

Table 9d. – Continued 

 Model 1 Model 2 Model 3 Model 4 

 aOR aOR aOR aOR 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 2.18 ** 2.29 ** 

Ever paid for sex 

No (reference) 1.00 1.00

Yes 0.97 1.10

Alcohol use 

Husband doesn't drink or drinks but never drunk 

(reference) 1.00 1.00

Husband drinks and sometimes drunk 1.05 1.03

Husband drinks and often drunk 1.11 1.06

Women's risk factors 

Lifetime number of partners 

One (reference) 1.00 1.00

Two 4.34 *** 4.37 *** 

Three or more 6.15 *** 5.97 *** 

STI symptoms in last 12 months 

No STI (reference) 1.00 1.00

STI 2.43 *** 2.56 *** 

*** p<=0.001; ** p<=0.01; * p<=0.05; †

 p<=0.10 

^ Models control for the following key control variables: place of residence (urban/rural), region, wealth (quintiles), 

women's education (none/primary/secondary+), men's education (none/primary/secondary+), women's occupation 

(unemployed/employed in agriculture/employed in non-agriculture), men's occupation (unemployed/employed in 

agriculture/employed in non-agriculture), women's age (4 categories), men's age (4 categories), total children ever 

born (continuous), women's age at first cohabitation (continuous). Model 1: Adjusts only for the control variables; 

Model 2: Adjusts for control variables, husband's HIV risk factors, and husband's HIV status; Model 3: Adjusts for 

control variables, the wife's HIV risk factors, and husband's HIV stats; Model 4: Adjusts for control variables, the 

husband and wife's HIV risk factors, and husband's HIV status. 

^^ The model sequence is presented for all surveys in which the suspicion factor score was significantly associated 

with women's HIV status (p<.10) after adjusting for key control variables (see Table 5). 

In Kenya and Zimbabwe the experience of spousal physical violence maintains its significant association 

with women’s HIV status as their husbands’ HIV status and husbands’ or women’s HIV risk factors are 

added to the model, either separately or combined (although the level of significance diminishes 

somewhat in Kenya). Furthermore, a comparison of the odds ratios across the models shows that the 

effect of the physical violence factor increases when incorporating HIV risk factors. In the comprehensive 

model (Model 4) in Kenya and Zimbabwe, the odds of women being HIV-positive are 44 and 45 percent 

higher, respectively, with each unit increase in their physical violence score. 

The results from these two countries differ from one another in minor ways. Whereas in Kenya none of 

the HIV risk factors husbands are significant predictors of women’s HIV status in the presence of 

women’s experience of physical violence and husbands’ HIV status (Models 2 and 4), several of the 
43

husbands’ risk factors remain significant in Zimbabwe—lifetime number of sexual partners, non-marital 

sex, and STI or STI symptoms. Additionally, women’s HIV risk factors are more strongly predictive of 

their HIV status in Zimbabwe than in Kenya. 

Compared with Kenya and Zimbabwe, results of the 2010 Rwanda survey indicate a different pattern 

altogether. Initially significant, the association between women’s HIV status and experience of physical 

violence loses its association when either husbands’ or women’s HIV risk factors and husbands’ HIV 

status are entered into the model. The non-significant odds ratio also reverses direction. The only HIV 

risk factor for husbands that predicts women’s HIV status is sex with a non-marital partner in the last 12 

months (Models 2 and 4). Women’s lifetime number of partners and their experience of an STI or STI 

symptoms in the last 12 months are both associated with women’s HIV status, although only women’s 

lifetime number of partners (specifically, having two partners) retains significance in the comprehensive 

model (Model 4). 

Sexual violence and women’s HIV status 

In none of the five countries is there an initially significant relationship between sexual violence and 

women’s HIV status, controlling for basic background characteristics. Therefore, no further analysis was 

performed to examine its association while controlling for women’s and their husbands’ HIV risk factors. 

45

4 Conclusions and Discussion 

In spite of great variation in the prevalence of the various forms of spousal violence reported by married 

women among sampled couples in the five countries studied, there is remarkable consistency in the 

structure of spousal violence across countries. Notably, our factor analysis unequivocally upheld the face 

validity of the categories of emotional, physical, or sexual violence that experts have assigned to the 

different acts of violence asked about in the DHS domestic violence module. The factor analysis provides 

another important insight: that the six items typically categorized as controlling behaviors actually 

represent not one, but two separate concepts, which we label “suspicion” and “isolation” in this report. 

These terms describe husbands’ behaviors that represent suspicion of their wives and behaviors that aim 

to isolate them. Both are distinct from any of the three categories of emotional, physical, and sexual 

spousal violence. 

The items in these two controlling behavior factors are similar to those in the dominance/isolation 

subscale of the Psychological Maltreatment of Women Inventory (PMWI) (Tolman 1999). While they 

may be similar in underlying construct to the “jealousy” and “dominance” factors identified in other 

research using PMWI-type measures (Kar and O’Leary 2013; Kasian and Painter 1992), we apply 

different labels because the sets of items comprising “suspicion” and “isolation” differ both in number 

and in wording from those comprising “jealousy” and “dominance”. Additional psychometric testing 

would be needed to determine whether these differences are meaningful and represent distinct constructs, 

or whether they are immaterial to assessing the same latent construct. 

Despite the striking consistency in the structure of spousal violence across countries, there is substantial 

variation in the relationships between the various forms of violence and women’s HIV status, and in their 

associations with the two pathways through which violence exerts influence. No single form of spousal 

violence is consistently associated with a woman’s risk of HIV in all five countries studied. In Malawi no 

form of violence is associated with a wife’s risk of having HIV. A significant relationship was found with 

women’s HIV status for the factors of suspicion and isolation in Zambia and Zimbabwe; for emotional 

violence in Kenya, Rwanda, and Zimbabwe; and for physical violence in Kenya, Rwanda, and Zimbabwe; 

and in no country was a significant relationship found between sexual violence and women’s HIV status. 

It is somewhat surprising that there is no apparent association in any of the countries in this report 

between spousal sexual violence and women’s HIV status, even in the base model, as this form of 

violence is the only one for which there is a conceptual basis for a direct effect on women’s HIV status. 

Of all the forms of violence and controlling behaviors, sexual violence is the least prevalent in all 

countries. One possibility is that, given the low prevalence, the sample sizes are insufficient to detect any 

significant effect. Another possibility is that there is truly no relationship between sexual violence and 

women’s HIV status, either directly or indirectly. Sexual violence is associated with women’s experience 

of an STI or STI symptom in the last 12 months, women’s lifetime number of sexual partners, husbands’ 

alcohol use, and husbands’ lifetime number of partners in multiple countries. However, compared with 

other forms of spousal violence, sexual violence is associated with fewer HIV risk factors for women and 

their husbands, and in fewer countries. Where an association is found, the magnitude of the odds ratio is 

smaller than for other forms of violence. These findings suggest that, if sexual violence is to influence 

women’s HIV status through the same pathways as other forms of violence, these linkages are relatively 

weak. 

The investigation into the pathways through which the different forms of violence may be associated with 

a woman’s risk of HIV is also revealing. For almost all forms of spousal violence, with the exception of 

physical violence, and in all countries, any observed significant relationship between spousal violence and 

a woman’s risk of HIV, is explained away by women’s or their husbands’ HIV risk factors, or both. In 
46

other words, a woman’s experience of different forms of spousal violence is positively associated with her 

risk of HIV because either a) her own high-risk behaviors or STI status are affected by her experience of 

violence and in turn affect her risk of HIV; or b) her husband’s HIV and STI status along with his highrisk behaviors are also positively associated with her risk of violence and her risk of HIV; or because of 

both (a) and (b). 

Thus, this report provides evidence that there is no direct effect of most forms of spousal violence on 

women’s HIV status, only an indirect effect through selected behavioral and other factors commonly 

considered to put an individual at high risk of HIV. 

The only form of violence that appears to have a direct net association with HIV is physical violence. 

Physical violence remains significant in all models in Kenya and Zimbabwe. In Zimbabwe, even with 

controls for all high-risk factors, a unit increase in the physical violence factor score increases the odds of 

the wife being HIV-positive by 45 percent, and this relationship remains highly significant for the entire 

analysis. In Kenya, by contrast, although a similar relationship is observed, the significance is greatly 

reduced if all risk factors are controlled for. 

Several potential explanations can be proposed for this finding. It could be that the net positive direct 

association of the experience of physical violence with the risk of having HIV remains because some key 

variables that represent additional indirect pathways through which physical violence influences women’s 

HIV status are absent from this analysis. Alternately, perhaps, the direct relationship is capturing a 

simultaneous association between spousal violence and women’s HIV status in the reverse causal 

direction. That is, at the same time that the experience of physical violence increases the risk of a woman 

having HIV (through multiple risk factors), being HIV-positive may be a trigger for episodes of physical 

violence. Or, finally, it may be that physical violence does in fact have a direct effect, perhaps by 

triggering increased levels of stress that compromise a woman’s immune system, leaving her more 

susceptible to HIV infection (Campbell et al. 2008). 

This study has some limitations, primarily the limitations imposed by the cross-sectional nature of DHS 

data. We use retrospective measures of the experience of spousal violence and data on the prevalence—

but not incidence—of HIV. As such, and like the vast majority of empirical research on this question, we 

do not know whether experience of spousal violence preceded infection with HIV or if infection with HIV 

preceded spousal violence, or both. This constraint prevents us from interpreting any causal direction to 

the associations we find between spousal violence and women’s HIV status, or between spousal violence 

and wives’ and husbands’ HIV risk factors. 

The use of data on both members of a couple is a strength of this report. However, the use of couple data 

presents some trade-offs. These data rely on couples in which both members could be successfully 

interviewed and for whom there are valid HIV test results and in which the wife was administered the 

domestic violence module of the DHS questionnaire. Thus, we exclude couples in which either the wife 

or the husband was not present in the household, was unavailable for interview, or declined HIV testing. 

Additionally, both HIV and spousal violence may contribute to dissolution of marriages, through death of 

one spouse, divorce, or separation. As a result, our sample of couples may or may not be fully 

representative of all marriages in which spousal violence occurs. Finally, spousal violence and several of 

the personal risk factors included in our analyses may be subject to underreporting, due to recall error, 

embarrassment, or social desirability bias. It is not fully known how any underreporting of these variables 

may impact the results we observe. 

Despite these limitations, the study contributes to an understanding of the relationship between spousal 

violence and HIV. It takes advantage of data from both members of a couple and uses discrete, nuanced 

measures of violence to better specify the associated pathways through which the various forms of 

spousal violence influence women’s HIV status. 
47

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51 

Appendix 

Table A1.1. Items and oblique rotated factor loadings: Kenya 2008-09 

 Suspicion Isolation 

Emotional 

violence 

Physical 

violence 

Sexual 

violence Uniqueness

Husband/partner: 

Is jealous or angry if respondent talks 

with other men 0.8436 0.3203 

Frequently accuses respondent of 

being unfaithful 0.4904 0.5112 

Insists on knowing where respondent 

is at all times 0.7140 0.3929 

Does not permit respondent to meet 

her female friends 0.6434 0.3973 

Tries to limit respondent's contact with 

family 0.7211 0.3864 

Does not trust respondent with any 

money 0.5618 0.5522 

Ever say or do something to humiliate 

respondent in front of others 0.8364 0.3254 

Ever threaten to hurt or harm 

respondent or someone close to her 0.6624 0.3968 

Ever insult respondent or make her 

feel bad about herself 0.7510 0.3413 

Ever push, shake, or throw something 

at respondent 0.5906 0.4050 

Ever slap respondent 0.6272 0.3886 

Ever punch respondent with his fist or 

hit with something that could hurt her 0.8141 0.3358 

Ever kick, drag, or beat up respondent 0.7669 0.3635 

Ever try to choke or burn respondent 

on purpose 0.6018 0.4428 

Ever physically force respondent to 

have sexual intercourse with him even 

when she did not want to 0.7172 0.3285 

Ever force respondent to perform any 

sexual acts she did not want to 0.9368 0.1931 

Cronbach's alpha 0.6759 0.5815 0.7619 0.7795 0.5821 

Unweighted n 4,740 

Eigenvalue 0.8618 

Variance explained 62% 

 
52 

Table A1.2. Items and oblique rotated factor loadings: Malawi 2010 

 Suspicion Isolation 

Emotional 

violence 

Physical 

violence 

Sexual 

violence Uniqueness

Husband/partner: 

Is jealous or angry if respondent talks 

with other men 0.8398 0.2979 

Frequently accuses respondent of 

being unfaithful 0.4106 0.4816 

Insists on knowing where respondent 

is at all times 0.8707 0.3471 

Does not permit respondent to meet 

her female friends 0.8157 0.3482 

Tries to limit respondent's contact with 

family 0.8100 0.3134 

Does not trust respondent with any 

money 0.6732 0.5278 

Ever say or do something to humiliate 

respondent in front of others 0.8925 0.2699 

Ever threaten to hurt or harm 

respondent or someone close to her 0.7036 0.3233 

Ever insult respondent or make her 

feel bad about herself 0.8530 0.2713 

Ever push, shake, or throw something 

at respondent 0.7235 0.3836 

Ever slap respondent 0.6662 0.4037 

Ever punch respondent with his fist or 

hit with something that could hurt her 0.8681 0.2738 

Ever kick, drag, or beat up respondent 0.8495 0.2822 

Ever try to choke or burn respondent 

on purpose 0.7395 0.4579 

Ever physically force respondent to 

have sexual intercourse with him even 

when she did not want to 0.7972 0.2742 

Ever force respondent to perform any 

sexual acts she did not want to 0.9171 0.2071 

Cronbach's alpha 0.6609 0.6546 0.7963 0.8384 0.6474 

Unweighted n 5,321 

Eigenvalue 1.0077 

Variance explained 66% 
53 

Table A1.3. Items and oblique rotated factor loadings: Rwanda 2005 

 

Controlling 

behavior 

Emotional 

violence 

Physical 

violence 

Sexual 

violence Uniqueness

Husband/partner: 

Is jealous or angry if respondent talks with 

other men 0.6606 0.4950 

Frequently accuses respondent of being 

unfaithful 0.5958 0.5326 

Insists on knowing where respondent is at all 

times 0.7008 0.3952 

Does not permit respondent to meet her 

female friends 0.6288 0.3940 

Tries to limit respondent's contact with family 0.7228 0.4910 

Does not trust respondent with any money 0.4994 0.6129 

Ever say or do something to humiliate 

respondent in front of others 0.6293 0.3391 

Ever threaten to hurt or harm respondent or 

someone close to her 0.7551 0.3324 

Ever push, shake, or throw something at 

respondent 0.7704 0.3388 

Ever slap respondent 0.7759 0.3959 

Ever punch respondent with his fist or hit with 

something that could hurt her 0.8002 0.3581 

Ever kick, drag, or beat up respondent 0.6991 0.4539 

Ever try to choke or burn respondent on 

purpose 0.4353 0.7283 

Ever spit in the face of respondent 0.7259 0.4172 

Ever physically force respondent to have 

sexual intercourse with him even when she did 

not want to 0.8121 0.2588 

Ever force respondent to perform any sexual 

acts she did not want to 0.8885 0.2252 

Cronbach's alpha 0.7375 0.6844 0.8312 0.6700 

Unweighted n 2,470 

Eigenvalue 1.0364 

Variance explained 58% 

(Continued...) 
54 

Table A1.3. – Continued

 Suspicion Isolation Uniqueness 

Husband/partner: 

Is jealous or angry if respondent talks with 

other men 0.8157 

Frequently accuses respondent of being 

unfaithful 0.9192 

Insists on knowing where respondent is at all 

times 0.5553 

Does not permit respondent to meet her 

female friends 0.8991 

Tries to limit respondent's contact with family 0.7513 

Does not trust respondent with any money 0.5535 

Cronbach's alpha 0.6088 0.6750 

Unweighted n 2,470 

Eigenvalue 0.9140 

Variance explained 60% 

Table A1.4. Items and oblique rotated factor loadings: Rwanda 2010 

 Isolation 

Emotional 

violence 

Physical 

violence 

Sexual 

violence Uniqueness

Husband/partner: 

Ever push, shake, or throw something at 

respondent 0.7376 0.4547 

Ever slap respondent 0.8730 0.3357 

Ever punch respondent with his fist or hit 

with something that could hurt her 0.6069 0.5481 

Ever kick, drag, or beat up respondent 0.7365 0.4588 

Ever try to choke or burn respondent on 

purpose 0.6292 0.4499 

Ever spit in respondent's face na 

Ever physically force respondent to have 

sexual intercourse with him even when she 

did not want to 0.7869 0.2717 

Ever force respondent to perform any sexual 

acts she did not want to 0.9543 0.1890 

Cronbach's alpha 0.6653 

Unweighted n 3,461 

Eigenvalue 0.9811 

Variance explained 61% 
55 

Table A1.5. Items and oblique rotated factor loadings: Zambia 2007 

 Suspicion Isolation 

Emotional 

violence 

Physical 

violence 

Sexual 

violence Uniqueness 

Husband/partner: 

Is jealous or angry if respondent 

talks with other men 0.8680 0.2986 

Frequently accuses respondent 

of being unfaithful 0.6358 0.4269 

Insists on knowing where 

respondent is at all times 0.6613 0.4169 

Does not permit respondent to 

meet her female friends 0.6374 0.3479 

Tries to limit respondent's 

contact with family 0.8163 0.4508 

Does not trust respondent with 

any money 0.6600 0.5804 

Ever say or do something to 

humiliate respondent in front of 

others 0.8618 0.3127 

Ever threaten to hurt or harm 

respondent or someone close to 

her 0.6875 0.4001 

Ever insult respondent or make 

her feel bad about herself 0.8236 0.2893 

Ever push, shake, or throw 

something at respondent 0.7300 0.4015 

Ever slap respondent 0.4671 0.5603 

Ever punch respondent with his 

fist or hit with something that 

could hurt her 0.8413 0.3585 

Ever kick, drag, or beat up 

respondent 0.8370 0.3471 

Ever try to choke or burn 

respondent on purpose 0.5886 0.5442 

Ever physically force 

respondent to have sexual 

intercourse with him even when 

she did not want to 0.8950 0.1984 

Ever force respondent to 

perform any sexual acts she did 

not want to 0.9077 0.1956 

Cronbach's alpha 0.6815 0.5765 0.7437 0.7437 0.7530 

Unweighted n 4,115 

Eigenvalue 0.9344 

Variance explained 62% 
56 

Table A1.6. Items and oblique rotated factor loadings: Zimbabwe 2010-11 

 Suspicion Isolation 

Emotional 

violence 

Physical 

violence 

Sexual 

violence Uniqueness

Husband/partner: 

Is jealous or angry if respondent 

talks with other men 0.8589 0.3039 

Frequently accuses respondent 

of being unfaithful 0.4547 0.5591 

Insists on knowing where 

respondent is at all times 0.7286 0.3620 

Does not permit respondent to 

meet her female friends 0.7654 0.3520 

Tries to limit respondent's 

contact with family 0.7736 0.3940 

Does not trust respondent with 

any money 0.6349 0.5949 

Ever say or do something to 

humiliate respondent in front of 

others 0.8431 0.3725 

Ever threaten to hurt or harm 

respondent or someone close to 

her 0.6303 0.4634 

Ever insult respondent or make 

her feel bad about herself 0.7462 0.3818 

Ever push, shake, or throw 

something at respondent 0.6519 0.4364 

Ever slap respondent 0.6030 0.4641 

Ever punch respondent with his 

fist or hit with something that 

could hurt her 0.8140 0.3840 

Ever kick, drag, or beat up 

respondent 0.8185 0.3718 

Ever try to choke or burn 

respondent on purpose 0.5051 0.6412 

Ever physically force respondent 

to have sexual intercourse with 

him even when she did not want 

to 0.8664 0.2286 

Ever force respondent to 

perform any sexual acts she did 

not want to 0.8804 0.2212 

Cronbach's alpha 0.6277 0.6375 0.6466 0.7273 0.7079 

Unweighted n 5,193 

Eigenvalue 0.8997 

Variance explained 59% 
57 

Table A2. Percent distribution of demographic characteristics of each partner and household-level 

characteristics in the analytic sample 

 Kenya Malawi 

Rwanda 

2005 

Rwanda 

2010 Zambia Zimbabwe 

 N=873 N=2,627 N=1,452 N=2,013 N=1,611 N=1,711 

 % % % % % % 

Wealth quintile 

poorest 17.30 15.20 20.90 17.50 25.30 22.20 

poorer 18.70 22.10 21.80 19.80 19.30 21.20 

middle 16.80 22.30 20.00 21.90 20.10 20.00 

richer 18.70 21.00 22.80 22.70 19.40 20.70 

richest 28.50 19.40 14.60 18.20 16.00 15.90 

Place of residence 

urban 27.00 15.70 10.80 12.40 30.40 25.40 

rural 73.00 84.30 89.20 87.60 69.60 74.60 

Respondent's occupation 

unemployed 34.90 23.60 21.90 10.00 38.80 57.50 

agric-employed 29.10 46.50 71.90 77.70 36.50 13.90 

non-ag employed 35.90 29.90 6.20 12.30 24.70 28.60 

Husband's occupation 

unemployed 1.10 2.30 36.00 0.60 1.90 17.20 

agric-employed 35.60 48.80 43.60 71.70 56.50 29.40 

non-ag employed 63.30 48.90 20.40 27.70 41.60 53.40 

Woman's education 

None 7.20 18.70 28.50 19.40 13.00 2.60 

Primary 60.80 66.90 64.30 71.50 61.30 34.10 

Secondary+ 32.00 14.40 7.20 9.10 25.70 63.30 

Husband's education 

None 4.60 8.80 22.50 17.70 5.90 1.20 

Primary 51.60 63.80 67.00 70.70 52.10 26.70 

Secondary+ 43.80 27.40 10.50 11.60 42.00 72.10 

Respondent's age 

15-19 3.90 7.40 1.00 1.40 6.80 7.90 

20-29 49.60 48.50 40.20 42.60 44.20 50.10 

30-39 30.50 29.70 35.40 36.10 33.10 30.10 

40-49 16.10 14.50 23.30 19.80 15.80 11.80 

(Continued...) 

 

 
58 

Table A2. – Continued 

 Kenya Malawi 

Rwanda 

2005 

Rwanda 

2010 Zambia Zimbabwe 

 N=873 N=2,627 N=1,452 N=2,013 N=1,611 N=1,711 

 % % % % % % 

Husband's age 

15-24 7.40 11.30 7.10 7.30 8.60 9.90 

25-34 42.00 40.80 34.70 42.30 39.20 44.20 

35-44 29.80 29.30 33.80 28.10 31.30 30.50 

45+ 20.70 18.60 24.40 22.30 20.90 15.40 

Total children ever born 

0 7.60 4.60 5.00 6.00 6.20 7.60 

1 14.20 13.60 12.00 16.20 11.50 21.00 

2 19.40 16.70 13.90 16.00 16.10 25.40 

3 18.00 17.80 13.60 14.60 14.20 19.00 

4 or more 40.70 47.30 55.50 47.10 51.90 27.00 

Mean total children ever 

born 3.40 3.80 4.30 3.80 4.10 2.60 

Age at first cohabitation 

under age 15 9.20 12.90 2.30 2.30 11.80 6.30 

15-17 23.20 42.80 18.30 16.20 42.70 34.20 

18-20 37.20 33.20 39.50 35.40 28.60 36.10 

21-23 15.20 8.40 27.00 29.20 10.50 13.60 

over age 23 15.20 2.70 13.00 16.90 6.30 9.90 

Mean age at first 

cohabitation 19.30 17.40 20.00 20.50 17.70 18.80 

Total 100.00 100.00 100.00 100.00 100.00 100.00 

Table A3. Percent distribution of wives' and husbands' HIV risk factors in the analytic sample 

 Kenya Malawi 

Rwanda 

2005 

Rwanda 

2010 Zambia Zimbabwe 

WN=873 WN=2,627 WN=1,452 WN=2,013 WN=1,611 WN=1,711 

 % % % % % % 

Wives' risk factors 

Lifetime number of 

partners 

One 42.30 59.60 78.40 80.40 50.20 71.90 

Two 32.90 29.70 16.70 15.40 28.80 19.90 

Three or more 24.80 10.70 4.90 4.20 21.00 8.20 

Mean 2.00 1.60 1.30 1.30 1.90 1.80 

(Continued...) 
59 

Table A3. – Continued

 Kenya Malawi 

Rwanda 

2005 

Rwanda 

2010 Zambia Zimbabwe 

WN=873 WN=2,627 WN=1,452 WN=2,013 WN=1,611 WN=1,711 

 % % % % % % 

Sex with nonspousal, noncohabiting 

partner in last 12 months 

No 99.10 99.40 99.90 99.50 99.40 99.60 

Yes 0.90 0.60 0.10 0.50 0.60 0.40 

STI symptoms in last 12 

months 

No STI 93.60 88.40 94.90 91.50 94.70 88.80 

STI 6.40 11.60 5.10 8.50 5.30 11.20 

Husbands' risk factors 

Lifetime number of partners 

One 9.70 16.50 32.40 40.80 9.60 13.80 

Two 14.30 24.10 28.20 28.10 15.50 18.00 

Three 13.20 22.80 18.70 14.10 15.90 17.40 

Four or more 51.40 34.70 20.70 16.80 56.70 46.60 

Don't know 11.50 2.00 0.00 0.20 2.30 4.30 

Mean 7.10 4.00 3.00 2.70 6.60 6.20 

Sex with nonspousal, noncohabiting 

partner in last 12 months 

No 95.10 91.10 95.20 96.30 84.00 88.10 

Yes 4.90 8.90 4.80 3.70 16.00 11.90 

STI symptoms in last 12 

months 

No STI 97.70 94.30 97.60 92.60 94.80 92.60 

STI 2.30 5.70 2.40 7.40 5.20 7.40 

Ever paid for sex 

No 99.00 84.60 94.70 94.80 97.30 74.40 

Yes 1.00 15.40 5.30 5.20 2.70 25.60 

Alcohol use 

Husband doesn't drink 

or drinks but never 

drunk 65.60 66.20 43.20 na 51.20 53.40 

Husband drinks and 

sometimes drunk 24.00 24.20 45.10 na 32.00 36.00 

Husband drinks and 

often drunk 10.40 9.60 11.70 na 16.90 10.60 

Total 100.00 100.00 100.00 100.00 100.00 100.00