DHS ANALYTICAL 

STUDIES 64

Does men’s involvement improve

the Health outcomes of their

Partners and children?

July 2018

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

Shireen Assaf and Lwendo Moonzwe Davis.

DHS Analytical Studies No. 64

Does Men’s Involvement Improve the Health Outcomes 

of Their Partners and Children?

Shireen Assaf1,2

Lwendo Moonzwe Davis1

ICF

Rockville, Maryland, USA

July 2018

1

ICF

2 The DHS Program

Corresponding author: Shireen Assaf, International Health and Development, ICF, 530 Gaither Road, 

Suite 500, Rockville, MD 20850, USA; phone: 301-572-0950; fax: 301-572-0999; email: 

shireen.assaf@icf.com
Acknowledgments: The authors wish to thank Tim Shand, Christopher Hook, and Joy Fishel for 

their valuable comments on an earlier version of the report.

Editor: Bryant Robey

Document Production: Joan Wardell

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

Development (USAID) through The DHS Program (#AID-OAA-C-13-00095). The views 

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

States Government.

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

evaluate population, health, and nutrition programs. For additional information about The DHS 

Program contact: DHS Program, ICF, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA;

phone: +1 301-572-0950; fax: +1 301-572-0999 or +1 301-407-6501; email: 

reports@dhsprogram.com; internet: www.dhsprogram.com.

Recommended citation:

Assaf, Shireen, and Lwendo Moonzwe Davis. 2018. Does Men’s Involvement Improve the Health 

Outcomes of Their Partners and Children? DHS Analytical Studies No. 64. Rockville, Maryland, 

USA: ICF.
iii

CONTENTS

TABLES........................................................................................................................................................ v

FIGURES .................................................................................................................................................... vii

PREFACE .................................................................................................................................................... ix

ABSTRACT.................................................................................................................................................. xi

1 BACKGROUND............................................................................................................................... 1

1.1 Introduction ......................................................................................................................... 1

2 DATA AND METHODS ................................................................................................................... 7

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

2.2 Outcome Variables.............................................................................................................. 7

2.3 Independent Variables ........................................................................................................ 9

2.4 Analysis............................................................................................................................. 10

3 RESULTS ...................................................................................................................................... 13

3.1 Description of Outcomes................................................................................................... 13

3.2 Description of Main Independent Variables ...................................................................... 16

3.3 Regression Results ........................................................................................................... 20

4 DISCUSSION................................................................................................................................. 37

REFERENCES............................................................................................................................................ 41

APPENDIX.................................................................................................................................................. 47

v

TABLES

Table 1 Sample sizes and number of women and men interviewed in the surveys 

used in the analysis ...................................................................................................... 7

Appendix Table 1 Percentage with 95% confidence intervals for the outcomes used in the 

analysis....................................................................................................................... 48

Appendix Table 2 Percentage with 95% confidence intervals for the men’s involvement variables 

used in the model ....................................................................................................... 49

Appendix Table 3 Adjusted odds ratios with 95% confidence intervals for women’s unmet need 

for family planning by men’s involvement variables ................................................... 50

Appendix Table 4 Adjusted odds ratios with 95% confidence intervals for women’s modern 

contraceptive use by men’s involvement variables .................................................... 51

Appendix Table 5 Adjusted odds ratios with 95% confidence intervals for having at least 4 antenatal 

care visits by men’s involvement variables ................................................................ 52

Appendix Table 6 Adjusted odds ratios with 95% confidence intervals for having antenatal care 

visit before 4 months of pregnancy by men’s involvement variables ......................... 53

Appendix Table 7 Adjusted odds ratios with 95% confidence intervals for delivering in a health 

facility by men’s involvement variables ...................................................................... 54

Appendix Table 8 Adjusted odds ratios with 95% confidence intervals for the youngest child 12-23 

months with all three doses of DPT vaccine by men’s involvement variables ........... 55

Appendix Table 9 Adjusted odds ratios with 95% confidence intervals for the youngest child 

under age 2 with diarrhea that had treatment sought for them by men’s 

involvement variables ................................................................................................. 56

vii

FIGURES

Figure 1 Family planning outcomes: percentage of women age 15-49 currently using a 

modern contraceptive method and percentage with an unmet need for family 

planning ...................................................................................................................... 13

Figure 2 Antenatal care (ANC) outcomes: percentage of women age 15-49 who attended 

at least four ANC visits, and percentage who attended an ANC visit before 4 

months of pregnancy for their most recent pregnancy in the last 2 years before the 

survey ....................................................................................................................... 14

Figure 3 Delivery outcome: percentage of women age 15-49 who delivered in a health 

facility for their most recent pregnancy in the last 2 years before the survey ............ 15

Figure 4 Child health outcomes: percentage of children age 12-23 months who received 

all three doses of DPT vaccine, and the percentage of children under age 2 with 

diarrhea for whom treatment was sought ................................................................... 16

Figure 5 Men’s knowledge variables ........................................................................................ 17

Figure 6 Men’s attitude variables.............................................................................................. 18

Figure 7 Men’s behavior variables on antenatal care and family planning............................... 19

Figure 8 Joint decision on how to spend men’s earnings......................................................... 20

Figure 9 Forest plot of adjusted odds ratios for unmet need for family planning by men’s 

knowledge of fertile period.......................................................................................... 21

Figure 10 Forest plot of adjusted odds ratios for unmet need for family planning by men’s 

attitudes toward contraceptive use............................................................................. 22

Figure 11 Forest plot of adjusted odds ratios for unmet need for family planning by men’s 

behaviors .................................................................................................................... 23

Figure 12 Forest plot of adjusted odds ratios for modern contraceptive use by men’s 

knowledge of the fertile period ................................................................................... 24

Figure 13 Forest plot of adjusted odds ratios for modern contraceptive use by men’s 

attitudes toward contraceptive use............................................................................. 25

Figure 14 Forest plot of adjusted odds ratios for modern contraceptive use by men’s 

behaviors .................................................................................................................... 26

Figure 15 Forest plot of adjusted odds ratios for women attending at least four antenatal 

care visits by men’s behaviors ................................................................................... 27

Figure 16 Forest plot of adjusted odds ratios for women attending the first antenatal care 

visit before 4 months of pregnancy by men’s behaviors ............................................ 28

Figure 17 Forest plot of adjusted odds ratios for delivery in a health facility by men’s 

behaviors .................................................................................................................... 29

Figure 18 Forest plot of adjusted odds ratios for most recent child completing three doses 

of DPT vaccine by men’s behavior of making joint decisions on earnings ................ 30

Figure 19 Forest plot of adjusted odds ratios for most recent child receiving treatment of 

diarrhea by men’s knowledge to give more fluids when the child has diarrhea ......... 31

Figure 20 Forest plot of adjusted odds ratios for treatment of diarrhea in children by men’s 

behavior of making joint decisions on earnings.......................................................... 32

Figure 21 Summary of significant findings from all the regression models ................................ 34

Figure 22 Number of times each country showed a significant adjusted odds ratio from 

a total of 19 possible hypotheses ............................................................................... 36

ix

PREFACE

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

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

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

One of the objectives of The DHS Program is to analyze DHS data and provide findings that will be useful 

to policymakers and program managers in low- and middle-income countries. DHS Analytical Studies serve 

this objective by providing in-depth research on a wide range of topics, typically including several 

countries, and applying multivariate statistical tools and models. These reports are also intended to illustrate 

research methods and applications of DHS data that may build the capacity of other researchers.

The topics in the DHS Analytical Studies series are selected by The DHS Program in consultation with the 

U.S. Agency for International Development.

It is hoped that the DHS Analytical Studies will be useful to researchers, policymakers, and survey 

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

Sunita Kishor

Director, The DHS Program

xi

ABSTRACT

Men’s involvement in the health of their partners and children can play an important role in improving 

health outcomes. Many interventions in reproductive, maternal, and child health adopt strategies that seek 

to increase men’s positive, equitable, and supportive involvement. This study used data from men’s 

responses in Demographic and Health Surveys (DHS) in 33 countries. We linked this information with 

information from women’s survey responses on outcomes for family planning, maternal, and child health 

outcomes. The objective was to learn whether men’s involvement, defined as having correct knowledge, 

positive attitudes, and supportive behaviors toward the health of their partners and children, has an 

association with specific reproductive, maternal, and child health outcomes. Adjusted logistic regression 

models were run for each outcome and its corresponding independent variable of interest, adjusting for 

control variables. Although in general few countries exhibited significant findings, the analysis found some 

significance in the associations between men’s involvement variables and outcomes related to family 

planning, antenatal care, and child health. The behavior of men discussing family planning with a health 

care worker showed significant association with their partners’ modern contraceptive use in most countries. 

Some countries exhibited a greater number of significant findings compared with other countries, which 

suggests the need for qualitative studies that could help explain these country-specific findings. The study 

also points to the need for strengthened measures within DHS surveys to capture men’s knowledge, 

attitudes, and behavior related to reproductive, maternal, and child health.

KEY WORDS: men’s involvement, men’s knowledge, men’s attitudes, men’s behavior, family planning, 

maternal health, child health

1

1 BACKGROUND

1.1 Introduction

For several decades advocates for improved maternal and child health have promoted men’s involvement 

in all aspects of health care for women and children (Gallen, Liskin, and Kak 1986; Green, Cohen, and 

Belhadj-El Ghouayel 1995; Wegner et al. 1998). In many parts of the world, men play a central role in 

decision-making regarding the health of women and children; therefore, male involvement is critical in 

improving maternal and child health outcomes. Although engaging men in health care for women and 

children is not a universal strategy, several maternal health interventions have advocated for male 

involvement as a means to improve outcomes for women’s sexual and reproductive health, and for maternal, 

newborn, and child health (Barker, Ricardo, and Nascimento 2007; Barker et al. 2010; Kraft et al. 2014; 

Yargawa and Leonardi-Bee 2015). However, these approaches often have been short-term and small-scale. 

Additionally, men’s involvement in their children’s health is associated with better overall health outcomes 

for the children, including their cognitive, emotional, and social development (Allen and Daly 2007; Aluisio 

et al. 2011; Sarkadi et al. 2008). We also know that men’s greater involvement in the lives of their children 

is good for men themselves, and for supporting more gender-equitable societies (Levtov et al. 2015). Thus, 

several interventions and programs have sought to include approaches that increase fathers’ involvement in 

maternal and child health (Greene et al. 2006; Kululanga, Sundby, and Chirwa 2011; Sternberg and Hubley 

2004). The effect of men’s involvement on health outcomes for women and children is directly linked to 

men’s knowledge and education, their attitudes, and their behaviors (Mkandawire and Hendriks 2018; 

Nasreen et al. 2012; Rammohan, Awofeso, and Fernandez 2012; Shattuck et al. 2011; Sternberg and Hubley 

2004).

1.1.1 Men’s knowledge and education

Several studies have shown that interventions to educate men about family planning, antenatal care (ANC), 

and maternal and newborn health improved maternal and child health outcomes (Mullany, Becker, and 

Hindin 2006; Shattuck et al. 2011; Tokhi et al. 2018). Men’s knowledge of contraception is linked to their 

life course and to socioeconomic factors (Duze and Mohammed 2006; MacQuarrie et al. 2015). For 

example, a study across 18 countries in Africa, Asia, and Latin America and the Caribbean found that men 

with no children and never-married men had the lowest levels of contraceptive knowledge, while in most

of the countries men’s contraceptive knowledge was also positively and significantly associated with their

level of education; it was highest among men age 35-45 (MacQuarrie et al. 2015).

A study in Southeast Nigeria found that men’s awareness of and support for contraceptive use were 

significantly associated with their wives’ desire to use contraception (Ezeanolue et al. 2015). The study 

showed that men who were aware of female contraception had three times greater odds of having spouses 

who wanted to use contraception compared with men who were not aware of female contraception

(Ezeanolue et al. 2015). Further, in the Malawi Motivator Project, when married men were educated through 

their peers about the importance of using contraceptives, their contraceptives uptake increased (Shattuck et 

al. 2011). The Malawi Motivator Project highlights the success of the information-motivation-behavior 

(IMB) model. In its application of the IMB model, married men who were given information about modern 

family planning methods were motivated to act on this information and to follow better practices and 
2

behavior skills related to family planning (Shattuck et al. 2011). While before the intervention all 

participants in both the intervention (the Malawi Motivator Project) and the control group reported not 

using contraceptives, after the intervention 78% in the intervention group reported using family planning

methods compared with 59% in the control group. When asked about the perceived benefits, beyond the 

financial aspects, men discussed the health benefits for their wives, girlfriends, and children (Shattuck et 

al. 2011).

A study in Ethiopia found that involving men in discussions of family planning with their wives made a 

significant difference in modern contraceptive use; twice as many members of the intervention group were 

using a modern contraceptive method compared with the control group (Terefe and Larson 1993). Another 

study conducted in Ethiopia also found that men who participated in a family planning intervention for 

couples were more likely to be actively involved in family planning compared with men who did not 

participate (Tilahun et al. 2015). Similarly, a randomized control trial (RCT) among couples in Burkina 

Faso, which focused on educating husbands on their role in pregnancy and maternity care, including 

counseling sessions for couples, found that couples who participated in the intervention were more likely 

to use effective contraception eight months postpartum (Daniele et al. 2018).

A systematic review on the effectiveness of interventions that engage men in maternal and newborn health 

during the prenatal, delivery, and postpartum period identified several studies showing that interventions 

that engage and educate men resulted in more positive maternal and newborn health outcomes (Tokhi et al. 

2018). In rural Tanzania, training male and female safe motherhood promoters to conduct home visits to 

educate husbands of pregnant women on danger signs and pregnancy complications resulted in a significant 

increase in birth attendance by skilled health providers, at 51% for the intervention compared with 34% for 

the control group (Mushi, Mpembeni, and Jahn 2010). In Indonesia a social mobilization campaign 

targeting husbands, midwives, and community leaders also resulted in a significant increase in skilled birth 

attendance, at 70% for the intervention compared with 44% for the control group (Sood et al. 2004). A 

complex primary health care program that included outreach to male partners and community leaders for 

Afghani refugees in Pakistan found significant improvements in ANC coverage and postnatal care visits, 

as well as a reduction in the maternal mortality ratio (Purdin, Khan, and Saucier 2009).

The father’s level of education has been found to be a key factor in facilitating men’s positive and active 

involvement in the health of their children (Levtov et al. 2015). Findings from the RCT among couples in 

Burkina Faso found that couples who participated in the education-focused intervention were also more 

likely to be exclusively breastfeeding three months postpartum (Daniele et al. 2018). In another study, data 

from DHS surveys were analyzed for the six countries with the lowest proportions of children vaccinated 

against measles: India, Indonesia, Pakistan, Nigeria, Democratic Republic of Congo, and Ethiopia; the 

analysis found that in all six countries the proportion of children vaccinated increased significantly as 

father’s education increased, independently of the mother’s education (Rammohan, Awofeso, and 

Fernandez 2012). The study also demonstrated that when both the mother and father are educated, the 

proportion of children vaccinated is higher. Another study in Haitian villages found that the presence of 

fathers’ clubs had a positive effect on children’s health as measured through vaccination status, growth 

monitoring, and vitamin A supplementation. Fathers met regularly to talk with the village nurse or health 

workers and to learn about child and family health (Sloand, Astone, and Gebrian 2010). Results showed

that children age 2 in villages with a fathers’ club had 2.6 times higher odds to be fully vaccinated, 3.75 
3

times higher odds to receive vitamin A supplementation by age 1, and significantly more growth monitoring 

than children in villages with no fathers’ club (Sloand, Astone, and Gebrian 2010).

1.1.2 Men’s attitudes and perceptions

Changing men’s attitudes and perceptions is also important to achieving better outcomes in maternal and 

child health; knowledge and education alone may not be enough. For example, interviews conducted among 

the Hausa people in Northern Nigeria showed that most men were aware of contraceptive methods (nearly 

two-thirds reported knowing at least one method), but 85% of men were not willing to allow their spouses 

to use family planning, even when they were not able financially to take care of their current number of 

children (Duze and Mohammed 2006). In the interviews, men reported that the reasons for their negative 

attitudes and perceptions toward using contraceptives were that their religion discourages the use of 

contraceptives to prevent childbearing and that a child’s birth is accompanied by wealth, so it is desirable 

to have many children (Duze and Mohammed 2006). In a qualitative study conducted in Uganda, men 

discussed several negative aspects of women’s contraceptive use, including reduced sexual pleasure for 

women, increased infertility risks, and fear of engagement in extramarital sexual relationships, in a belief 

that women who use contraception might become promiscuous if they could avoid becoming pregnant 

(Kabagenyi et al. 2014). A study based on data from 58 DHS surveys in 18 countries in Africa, Asia, and 

Latin America and the Caribbean found that most men in most of the countries agreed that contraceptive 

use is associated with promiscuity, even though more than half of respondents disagreed with the statement 

that “contraception is a woman’s business” (MacQuarrie et al. 2015).

Men’s perceptions also affect their wives’ use of skilled birth attendants (SBAs) and prenatal care. A study 

using DHS data from Kenya showed that about half of women whose husbands had a positive perception 

of woman’s health care were attended by an SBA during delivery compared with 41% of women whose 

husbands had a negative perception (Mangeni et al. 2012). Another study in South West Ethiopia showed 

that among women who reported that their husbands approved of prenatal care, 80% used prenatal care 

compared with only 40% of women whose husbands disapproved (Biratu and Lindstrom 2016). The 

relationship between husband’s approval and wife’s use of prenatal care is stronger than the relationship 

between use of prenatal care and the woman’s own characteristics, including education and desire for the 

pregnancy (Biratu and Lindstrom 2016).

Gender-transformative approaches—that is, interventions or programs that promote understanding and 

challenge social norms and attitudes that perpetuate inequalities between men and women—have proven 

effective in changing gender inequalities and improving health-related behaviors (Doyle et al. 2018; Health 

Communication Capacity Collaborative (Hc3) 2014). An assessment of a gender-transformative 

intervention for couples on several behavioral and health-related outcomes that were influenced by gender 

norms and power relations found that couples that participated in the intervention had greater attendance 

and male participation in ANC, lower levels of child abuse, greater use of modern contraception, higher 

levels of men’s participation in child care, and less dominance of men in decision-making (Doyle et al. 

2018).

1.1.3 Men’s behavior and decision-making

Concerning men’s behavior toward maternal and child health, a systematic review of men’s accompanying 

their partners during ANC visits found positive impacts on women’s knowledge of danger signs during 
4

pregnancy, increases in institutional delivery and skilled birth attendance, and higher uptake of postnatal 

care services (Aguiar and Jennings 2015). Another systematic review and meta-analysis of male 

involvement found increases in skilled birth attendance and use of postnatal care (Yargawa and LeonardiBee (2015). In this study male involvement included: active participation in maternal health services and 

care, characterized by husband’s ANC attendance, presence in the delivery room, and support for health 

during pregnancy, delivery and postpartum; financial support for expenses related to pregnancy and 

childbirth; and shared decision-making with wife on maternal health (Yargawa and Leonardi-Bee 2015). 

The researchers also found that male involvement was significantly associated with reduced odds of 

postpartum depression among women.

In Kenya, approximately two-thirds of women whose husbands attended at least one ANC visit delivered 

with an SBA compared with less than half (43%) of women whose husbands did not attend (Mangeni et al. 

2012). Similarly, in Uttar Pradesh, West Bengal, and Maharashtra, India, pregnant women who reported 

having their husbands present at ANC visits had 1.4 times greater odds of delivering in a health facility 

compared with women whose husbands did not attend ANC visits (Chattopadhyay 2012). Another study 

conducted in Nepal found that having husbands present during childbirth was positively associated with the 

extent to which women felt in control of their labor, which positively impacts a new mother’s emotional 

well-being (Sapkota et al. 2012).

Education received during men’s attendance during ANC visits was also shown to improve maternal 

outcomes. In India, for example, after men and women received facility-based education, whether 

individually or as a couple during ANC visits, significantly fewer complications were reported during 

pregnancy or postpartum (23% for the intervention compared with 38% for the control group) (Varkey et 

al. 2004). In Nepal, pregnant women who received maternal and neonatal education with their husbands at 

ANC visits were more likely to attend postpartum visits compared with women who received education 

without their husbands present or who received no education, and had twice the odds of reporting more 

than three birth preparations compared with women who received education without their husbands being 

present (Mullany, Becker, and Hindin 2006).

Several studies have also examined decision-making in relation to family planning outcomes. A study 

conducted in Mozambique found that couples who made joint decisions concerning their health had greater

intention to use contraceptives compared with couples where only the husband made the health decisions 

(Mboane and Bhatta 2015). A study in Pakistan found that joint decision-making for couples (based on 13 

questions regarding various aspects of decision-making) was a stronger determinant of contraceptive use 

compared with women making the decisions on their own. The study also found that educated men were

more likely to make joint decisions with their wives about contraceptive use (65%) compared with illiterate 

men (55%) (Hameed et al. 2014). An RCT in Rwanda found that participation in a gender-transformative 

couples intervention increased the likelihood of reporting modern contraceptive use; this association was 

hypothesized to be due to the intervention’s ability not only to provide information about contraceptives 

but also to strengthen couple communication, support, and joint decision-making (Doyle et al. 2018).

This report examines men’s knowledge, attitudes, and behaviors toward various aspects of their partners’

and children’s health, based on men’s responses to questions asked in DHS surveys. We linked these data 

with DHS data on reproductive, maternal, and child health outcomes to learn if there is a significant 

association. The descriptions of the variables used to measure male involvement are summarized in section 
5

2.3. These variables arise from the only questions available in the DHS surveys that ask men directly about 

the topics not related to their own health, but rather related to the health of their partners and/or children. 

The outcomes related to the men’s partners and children are described in section 2.2. The hypothesis is that 

a high level of knowledge, nondiscriminatory and positive attitudes, and positive behaviors among men 

would be associated with improved health outcomes for their partners and children, controlling for other 

factors.

7

2 DATA AND METHODS

2.1 Data

The analysis uses data from the most recent DHS survey in 33 countries, as shown in Table 1. The selection 

of countries was based on the availability of data on men’s involvement. Countries that included all of the 

DHS survey questions on men’s involvement, as described in section 2.3, and that had a recent DHS survey 

(from 2009 to 2016), were selected. Some countries with the DHS Men’s Questionnaire did not include all 

of the questions shown in section 2.3 and therefore were not included in this analysis.

Table 1 Sample sizes and number of women and men interviewed in the surveys used in the analysis

Country Survey year

Households 

interviewed

Women 

age 15-49 

interviewed

Men 

interviewed

Afghanistan 2015 24,395 29,461 10,760a

Armenia 2015-16 7,893 6,116 2,755a

Angola 2015-16 16,109 14,379 5,684b

Burkina Faso 2010 14,424 17,087 7,307c

Benin 2011-12 17,422 16,599 5,180d

Burundi 2010 8,596 9,389 4,280c

Chad 2014-15 17,233 17,719 5,248c

Comoros 2012 4,482 5,329 2,167c

Congo Democratic Republic 2013-14 18,171 18,827 8,656c

Côte d’Ivoire 2011-12 9,686 10,060 5,135c

Ethiopia 2016 16,650 15,683 12,688c

Gambia 2013 6,217 10,233 3,821c

Ghana 2014 11,835 9,396 4,388c

Kenya 2014 17,409 14,741 12,819b

Kyrgyz Republic 2012 8,040 8,208 2,413a

Lesotho 2014 9,402 6,621 2,931c

Liberia 2013 9,333 9,239 4,118a

Malawi 2015-16 26,361 24,562 7,478b

Mali 2012-13 10,105 10,424 3,816a

Mozambique 2011 13,919 13,745 4,035d

Myanmar 2015-16 12,500 12,885 4,737a

Namibia 2013 9,849 9,176 4,481d

Nepal 2016 11,040 12,862 4,063a

Nigeria 2013 38,522 38,948 17,359a

Rwanda 2014-15 12,699 13,497 6,217c

Senegal 2016 4,437 8,865 3,527c

Sierra Leone 2013 12,629 16,658 7,262a

Tanzania 2015-16 12,563 13,266 3,514a

Timor-Leste 2009-10 11,463 13,137 4,076a

Togo 2013-14 9,549 9,480 4,476c

Uganda 2016 19,588 18,506 5,336b

Zambia 2013-14 15,920 16,411 14,773c

Zimbabwe 2015 10,534 9,955 8,396b

Note: Ages of men interviewed: a - age 15-49; b - age 15-54; c - age 15-59; d - age 15-64

2.2 Outcome Variables

Six outcomes that cover indicators in family planning, delivery care, and child health were used in the 

analysis. The operational definitions of these outcomes are described below.
8

Unmet need for family planning

Among women age 15-49 currently in a union, the proportion who have an unmet need for family planning. 

This combines unmet need for spacing and for limiting.

This variable was coded as a binary variable, with 1 for women who had an unmet need and 0 if they did 

not (i.e., the variable is coded as a negative outcome). All other outcomes were coded as binary variables 

in the positive direction, i.e., a code of 1 indicated they had the positive outcome described in the definitions 

below. For this outcome, it is hypothesized that high level of knowledge, non-discriminatory and positive 

attitudes, and positive behaviors in men would be associated with lower likelihood of unmet need for family 

planning for the men’s partners. For all other outcomes we expect the men’s involvement variables to be 

associated with greater likelihood of the outcome.

Contraceptive prevalence rate for modern methods (mCPR)

Among women age 15-49 currently in a union, the proportion who are using a modern contraceptive 

method. Modern contraceptive methods include pills, IUD, injections, implants, diaphragm, female and 

male condoms, female and male sterilization, foam or jelly, and the lactational amenorrhea method (LAM). 

The mCPR may also include other modern contraceptive methods that are country-specific or less common 

but were reported by the respondent and identified in the datasets as modern methods.

Four or more visits for antenatal care (ANC)

Among women age 15-49 who had a birth in the past two years, the proportion who had at least four ANC 

visits for the most recent birth.

First ANC visit before four months of pregnancy

Among women age 15-49 who had a birth in the past two years, the proportion who had their first ANC 

visit for their most recent birth before four months of pregnancy.

Delivery in a health facility

Among women age 15-49 who had a birth in the past two years, the proportion whose most recent birth 

was delivered in a health facility. This indicator distinguishes between home deliveries and facility 

deliveries. Health facilities could be government, private, or another type of facility, such as a polyclinic or 

dispensary.

Completing three doses of DPT vaccine (DPT3)

The proportion of children age 12-23 months who have received all three doses of the DPT vaccine. The 

DPT3 immunization is selected for the indicator because children who receive this vaccine generally have 

received all of the other recommended immunizations.

Treatment for diarrhea

The proportion of children under age 2 who had diarrhea in the two weeks before the survey and for whom 

advice or treatment was sought from a health facility or provider. In the DHS final reports, this indicator is 
9

reported for children under age 5. For this report, this was changed to children under age 2 to correspond 

to the variable on knowledge of diarrhea that was only asked for men who had children under age 2.

For the two child health outcomes of DPT3 and treatment for diarrhea, the indicators were computed for 

the youngest living child age 2 or younger. This was due to the restriction of the men’s involvement 

questions to children age 2 or younger in the Men’s Questionnaire of the DHS survey.

2.3 Independent Variables

The main independent variables of interest are derived from questions in the DHS Men’s Questionnaire 

that ask about the respondent’s involvement in different aspects of their partner’s or children’s health. These 

questions can be grouped into three domains: men’s knowledge, men’s attitude, and men’s behavior. These 

variables were coded in the positive direction, i.e., correct knowledge, positive attitudes, and positive 

behaviors. The study’s outcomes are for women and children according to the independent variables related 

to men’s knowledge, attitudes, and behaviors.

Knowledge:

Correct knowledge of fertile period: The proportion of men who have correct knowledge of a 

woman’s fertile period. To have correct knowledge, the respondent should give the answer of 

halfway between two periods. The other options for this question are: just before her period begins, 

during her period, right after her period has ended, other, and don’t know.

Correct knowledge of fluid intake for child with diarrhea: The proportion of men who have a 

child under age 2 and who responded in the DHS survey that a child should be given more to drink 

than usual when they have diarrhea.

Attitude:

Two indicators were used to measure men’s attitudes, both related to contraception. These 

indicators are derived from a survey question that asks men if they agree with the following 

statements:

1. Contraception is a woman’s business and a man should not have to worry about it.

2. Women who use contraception may become promiscuous.

The two indicators were coded in the positive direction for involvement in women’s health—that 

is, men who do not agree with each of these statements.

Behavior:

Present during ANC visit: Among men with a partner who attended an ANC visit for their most 

recent child under age 2, the proportion present during the ANC visit.

Discussed family planning with health care worker: The proportion of men who stated in the DHS 

survey that they discussed family planning with a health worker in the last few months. This is 

considered as a behavior variable, not a knowledge variable, because it measures the act of 

discussing with the health worker and not the man’s level of knowledge.
10

Joint decision on how to spend men’s earnings: The proportion of men who responded that the 

decision on how to use the money they earned is a joint decision between them and their wife. The 

DHS survey also contains a question asked of both women and men on who decides on the 

respondent’s own health care. However, the responses from the wife on who decides on her own 

health care reflects her perception, not necessarily that of her husband, and thus might not be 

consistent with the other variables examined in the analysis, which are based on men’s responses 

about their knowledge, attitudes, and behaviors. Therefore this was not included in the analysis.

Control variables

The control variables used in the regression include men’s age (continuous variable), men’s education (none 

and primary, secondary and above), place of residence (urban or rural), and wealth quintile (lowest, second, 

middle, fourth, highest). Men’s age was grouped as 15-49, 15-54, or 15-59 depending on the survey (see 

Table 1). The wife’s age was highly correlated with her husband’s age, and the wife’s education level was 

highly correlated with her husband’s education level. Therefore, these characteristics of the women could 

not be included in the regression models.

2.4 Analysis

Men and women interviewed in the DHS survey were required to declare each other as partners to be 

included in this analysis. For men who had multiple wives, one wife was randomly selected to avoid bias 

in the models. The unit of analysis is therefore the women.

Adjusted logistic regressions were fit for each outcome and its corresponding main independent variable of 

interest as follows. All the models were adjusted for the control variables listed above.

Unmet need for family planning and modern contraceptive prevalence (mCPR)

Five separate logistic regression models were fit for these two outcomes. A separate model was fit that 

included the independent variables of: 1. correct knowledge of fertile period; 2. does not agree that 

contraception is only a woman’s business; 3. does not agree that women who use contraceptives are 

promiscuous; 4. discussed family planning with a health worker; and 5. made joint decision with their 

partner on how to spend their earnings.

Four or more ANC visits, first ANC visit before four months of pregnancy, and delivery in 

health facility

Two separate logistic regression models were fit for these outcomes. A separate model was fit to include 

the independent variables of: 1. being present during ANC visit; and 2. made joint decision on how to spend 

earnings.

DPT3 vaccination

Only one logistic regression model was fit for this outcome. This model included the independent variable 

of joint decision on how to spend earnings. There were no questions that asked men about their knowledge, 

attitude, or behavior toward the vaccination of their children.
11

Treatment of diarrhea

Two separate logistic regression models were fit for this outcome. A separate model was fit to include the 

independent variables of: 1. knowing that more fluids should be given to a child with diarrhea; and 2. made 

joint decision on how to spend earnings.

The logistic regressions accounted for the DHS sampling design and stratification. For some surveys, the 

denominator for the outcome was based on fewer than 100 observations and, therefore, the regression 

models were not fit for these surveys. This was mainly the case for the treatment of diarrhea symptoms 

outcome since this is restricted to children under age 2 who had symptoms of diarrhea in the two weeks 

before the survey. This restriction reduced the sample size, and for Armenia, Benin, Ghana, the Kyrgyz 

Republic, Lesotho, Myanmar, Namibia, and Nepal there were fewer than 100 observations. There were also 

fewer than 100 observations for the DPT vaccine outcome in Lesotho.

The adjusted odds ratios from the regressions are summarized using forest plots with the use of the metan

macro in Stata 15 (Harris et al. 2008). Stata 15 was used for all the analysis.

13

3 RESULTS

3.1 Description of Outcomes

The percentage of women using a modern contraceptive method and the level of unmet need for family 

planning varied across the 33 countries. As Figure 1 shows, the percentage of women age 15-59 currently 

using a modern contraceptive method ranged from 69% in Zimbabwe to 7% in Chad. In general, there was 

an inverse relationship between modern contraceptive use and unmet need for family planning; that is, 

countries with higher prevalence of modern contraceptive use have lower rates of unmet need. As such, 

Zimbabwe, with the highest level of modern contraceptive use, had the lowest percentage of women with 

unmet need, at 8%. The highest level of unmet need for family planning was in Angola, at 37%. Unmet 

need was also 30% or greater in Benin, Burundi, Ghana, Comoros, Liberia, Togo, and Timor-Leste.

Figure 1 Family planning outcomes: percentage of women age 15-49 currently using a modern 

contraceptive method and percentage with an unmet need for family planning

Figure 2 shows that Armenia had the highest percentage of women attending at least four ANC visits and 

also the highest percentage attending ANC before four months of pregnancy, at 97% and 93% respectively. 

In both Ghana and the Kyrgyz Republic, over 80% of women attended at least four ANC visits, but in 

Ghana a lower percentage attended ANC before four months of pregnancy than in the Kyrgyz Republic, at 

65% and 80% respectively. Other countries with a high percentage of women attending at least four ANC 

visits were Gambia, Zimbabwe, Sierra Leone, and Liberia, at 70-80%. In general, more women attended at 

least four ANC visits than attended an ANC visit before four months of pregnancy, with a few exceptions. 

In Rwanda, Comoros, Burkina Faso, and Afghanistan, a higher percentage of women attended their first 

ANC visit before four months than attended at least four ANC visits—with the greatest difference in 

0

10

20

30

40

50

60

70

80

90

100

Modern contraceptive use Unmet for family planning
14

Comoros, where 44% of women attended at least four ANC visits while 55% made their first visit before 

four months of pregnancy. Countries with the lowest percentages of women attending their first visit before 

four months of pregnancy were Mozambique, Democratic Republic of Congo, and Nigeria, all below 20%.

Figure 2 Antenatal care (ANC) outcomes: percentage of women age 15-49 who attended at least four ANC 

visits, and percentage who attended an ANC visit before 4 months of pregnancy for their most 

recent pregnancy in the last 2 years before the survey

As Figure 3 shows, the percentage of women who delivered in a health facility for their most recent 

pregnancy was nearly universal in the Kyrgyz Republic and Armenia. Malawi and Rwanda also had high 

percentages of facility deliveries, at 93% and 92% respectively, and the percentage was over 80% in Benin, 

Namibia, and Zimbabwe. The lowest percentages of facility deliveries were in Chad (22%) and TimorLeste (25%). Nigeria and Ethiopia also had low rates, with less than 40% of women delivering in a health 

facility.

0

10

20

30

40

50

60

70

80

90

100

Had at least 4 ANC visits Had first ANC visit before 4 months of pregnancy
15

Figure 3 Delivery outcome: percentage of women age 15-49 who delivered in a health facility for their 

most recent pregnancy in the last 2 years before the survey

Overall, as Figure 4 shows, the percentage of children age 12-23 months receiving all three doses of DPT 

vaccine was high, at over 90% of children in Burkina Faso, Ghana, Tanzania, Malawi, Burundi, and highest 

in Rwanda, at 99%. Angola, Chad, and Nigeria had the lowest rates, at about 40%. Treatment seeking for 

children age 0-23 months with diarrhea symptoms ranged from about 30% in four countries—Senegal 

(28%), Togo (29%), Côte d’Ivoire (30%), and Benin (30%)—to over 70% in Timor-Leste, Zambia, and 

Gambia.

0

10

20

30

40

50

60

70

80

90

100
16

Figure 4 Child health outcomes: percentage of children age 12-23 months who received all three doses of

DPT vaccine, and the percentage of children under age 2 with diarrhea for whom treatment was 

sought

Appendix Table 1 provides a summary of these seven indicators as well as the number of observations and 

confidence intervals.

3.2 Description of Main Independent Variables

Figure 5 summarizes the two independent variables used to represent men’s knowledge: knowledge to give 

a child more to drink when the child has diarrhea, and correct knowledge of woman’s fertile period. Correct 

knowledge that a child should drink more when the child has diarrhea was highest in Comoros, at 79% of 

men, while in Malawi and Nepal knowledge rates were about 70%. Men in Timor-Leste had the lowest 

levels of correct knowledge concerning proper treatment for diarrhea in children, at 13%, followed by 

Ghana (29%), Côte d’Ivoire (28%), and Nigeria (27%). Overall, men’s correct knowledge of woman’s 

fertile period was less than 50% across all countries. The percentages were highest in Comoros and 

Democratic Republic of Congo, at 47% and 48% respectively. Men in Mozambique and Liberia had the 

lowest level of correct knowledge of woman’s fertile period, at just 3% each.

0

10

20

30

40

50

60

70

80

90

100

Had all three doses of DPT vaccine Treatment was sought for chilld with diarrhea
17

Figure 5 Men’s knowledge variables

Figure 6 presents the two variables measuring men’s attitude. Overall, a higher percentage of men did not 

agree that contraception was only a woman’s business than the percentage of men who did not agree that 

women who use contraception might become promiscuous. The vast majority of men in Burundi (97%) and 

Rwanda (96%) did not agree that contraception is only a woman’s business. This perception was also high 

in Ethiopia, Burkina Faso, Senegal, Nepal, Kenya, and Togo, all above 85%, and was lowest in Afghanistan 

(43%) and Angola (46%). Rwanda, Ethiopia, and Mozambique had the highest percentage of men, all above 

85%, who did not agree that women who use contraception might become promiscuous; Lesotho and 

Myanmar had the lowest percentage, at below 35%. In a few countries—Mozambique, the Kyrgyz 

Republic, Angola, and Afghanistan—a higher percentage of men did not agree that women who use 

contraception might become promiscuous than the percentage of men who did not agree that contraception 

was only a woman’s business.

0

10

20

30

40

50

60

70

80

90

100

Know to give more to drink when child has diarrhea Have correct knowledge of women's fertile period
18

Figure 6 Men’s attitude variables

For the behavior variables, which are presented in Figure 7, a larger percentage of men were present during 

ANC visits than discussed family planning with a health worker in the last few months before the survey. 

Rwanda had both the highest percentage of men who were present during ANC visits (95%) and the highest 

percentage of men who discussed family planning with a health worker (49%). Myanmar (78%) and Zambia 

(71%) also had a high percentage of men who were present during ANC visits, while the lowest percentages 

were in Senegal (21%) and Burundi (18%) . With the exception of Rwanda and Ethiopia, the percentage of 

men who recently discussed family planning with the health worker was low, at less than 30% across all 

countries, and lowest in Armenia at only 4%.

Appendix Table 2 provides a summary of the men’s involvement variables as well as the number of 

observations and confidence intervals.

0

10

20

30

40

50

60

70

80

90

100

Does not agree that contraception is only a women's business

Does not agree that women who use contraception become promiscuous
19

Figure 7 Men’s behavior variables on antenatal care and family planning

Figure 8 shows the proportion of men who stated that the decision on how to use the money they earned 

was a joint decision with their wife. The percentage was highest in Zimbabwe, Ethiopia, and Lesotho, all 

above 80%. Rwanda and Timor-Leste also had a high percentage of men reporting joint decision-making, 

at 79% and 76% respectively. Very few men in Mali (3%) and Benin (9%) said they made decisions jointly 

on how to use the money they earned. Senegal, Togo, Nigeria, Chad, and Burkina Faso also had low 

percentages, all below 20%.

0

10

20

30

40

50

60

70

80

90

100

Present during ANC check-up Discussed family planning with health worker in the last few months
20

Figure 8 Joint decision on how to spend men’s earnings

3.3 Regression Results

The results from the regression models for each outcome are described below. This is followed by a 

summary of the results for all the outcomes and countries. Forest plots are used to illustrate the adjusted 

odds ratios (AOR) for the main independent variables of interest for each outcome. The AORs and their 

corresponding confidence intervals are also summarized in Appendix Tables 3-9.

3.3.1 Family planning outcomes

Two outcomes related to family planning were examined in association with several men’s involvement 

variables related to family planning. These were married women age 15-49 with an unmet need for family 

planning, and women age 15-49 currently using a modern contraceptive method. The men’s involvement 

variables for these two outcomes covered all selected domains of knowledge, attitudes, and behavior related 

to family planning.

Unmet need for family planning

In Figure 9, we see the results of the model predicting unmet need for family planning among women 

according to husband’s knowledge of woman’s fertile period. We would expect to see a lower likelihood of 

unmet need among women whose partners have correct knowledge of their fertile period. Figure 9 and 

Appendix Table 3 show that five countries—Angola, Armenia, Benin, Liberia, and Mali—had significant 

odds ratios for this association. In Angola, Armenia, and Liberia the odds ratios were in the opposite of the 

expected direction. In these three countries men who had correct knowledge of the woman’s fertile period 

0

10

20

30

40

50

60

70

80

90

100
21

had partners with higher likelihood of unmet need compared with men with incorrect knowledge. In Benin 

and Mali, men who had correct knowledge had partners with lower likelihood of unmet need compared 

with men with incorrect knowledge.

Figure 9 Forest plot of adjusted odds ratios for unmet need for family planning by men’s knowledge of 

fertile period

Figure 10 summarizes the adjusted odds ratios for unmet need among women according to men’s attitudes 

toward women using contraceptive methods. In Armenia, the Kyrgyz Republic, Myanmar, and Tanzania, 

men who did not agree that contraception was a woman’s business and a man should not have to worry 

about it had partners with lower likelihood of unmet need compared with men who agreed. For Mozambique 

the odds ratio was in the opposite of the expected direction. In Burkina Faso, the Kyrgyz Republic, 

Tanzania, and Zambia, men who did not agree that women who used contraception might become 

promiscuous had partners with lower likelihood of unmet need compared with men who agreed. For 

Armenia and Nigeria the odds ratios were in the opposite of the expected direction.
22

Figure 10 Forest plot of adjusted odds ratios for unmet need for family planning by men’s attitudes toward 

contraceptive use

a. Adjusted odds ratios of men who do not agree that 

contraception is a woman’s business and men should not 

have to worry about it compared with men who do agree

b. Adjusted odds ratios of men who do not agree that 

contraception might make a woman promiscuous compared 

with men who do agree

Figure 11 shows the adjusted odds ratios for women’s unmet need for family planning according to men’s 

behavior-related variables. For most countries, discussing family planning with a health worker was not a 

significant predictor of their partners having unmet need. In Afghanistan, we see the expected relationship, 

with men who discussed family planning with a health worker having partners with lower likelihood of 

unmet need compared with men who did not discuss. In Mali and Nigeria the significant relationship was 

in the opposite of the expected direction. Figure 11b shows that in Chad, Côte d’Ivoire, the Kyrgyz 

Republic, Myanmar, Tanzania, and Uganda, men who made joint decisions on how to spend their earnings 

had partners with lower likelihood of unmet need compared with men who did not make joint decisions.
23

Figure 11 Forest plot of adjusted odds ratios for unmet need for family planning by men’s behaviors

a. Adjusted odds ratios of men who discussed family 

planning with a health worker compared with men 

who did not

b. Adjusted odds ratios of men who make joint decisions with 

their partner on how to spend their earnings compared with 

men who do not

Modern contraceptive use

Figure 12 shows that only in Sierra Leone did men with correct knowledge of woman’s fertile period have 

partners with significantly higher odds of modern contraceptive use compared with men who had incorrect 

knowledge. The odds ratio was also significant for Namibia, but in the opposite of the expected direction.
24

Figure 12 Forest plot of adjusted odds ratios for modern contraceptive use by men’s knowledge of the 

fertile period

Figure 13 shows the adjusted odds ratios for women’s modern contraceptive use according to men’s 

attitudes toward women using contraceptive methods. In Figure 13a we see that in Angola, Benin, Nigeria, 

and Togo, men who did not agree that contraceptive use was only women’s business had partners with a 

greater likelihood of modern contraceptive use compared with men who agreed. Figure 13b shows that in 

seven countries—Burkina Faso, Burundi, Kenya, Nigeria, Senegal, Zambia, and Zimbabwe—men who did 

not agree that women who used contraception might become promiscuous had partners with greater 

likelihood of modern contraceptive use compared with men who agreed. For Armenia, the odds ratio was 

in the opposite of the expected direction, as was also found for the unmet need outcome.
25

Figure 13 Forest plot of adjusted odds ratios for modern contraceptive use by men’s attitudes toward 

contraceptive use

a. Adjusted odds ratios of men who do not agree that 

contraception is a woman’s business and a man should not 

have to worry about it compared with men who agree

b. Adjusted odds ratios of men who do not agree that 

contraception may make a woman promiscuous compared 

with men who agree

Figure 14 shows the adjusted odds ratios for women’s modern contraceptive use according to men’s 

behaviors. In Figure 14a we see that in 13 countries men who recently discussed family planning with a 

health worker were significantly more likely to have partners using a modern contraceptive method 

compared with men who did not discuss. These countries were Afghanistan, Burkina Faso, Burundi, Chad, 

Comoros, Democratic Republic of Congo, Côte d’Ivoire, Ghana, Liberia, Rwanda, Togo, Zambia, and 

Zimbabwe. No countries were significant in the opposite of the expected direction. In addition, for 

Afghanistan, the odds ratio for men’s family planning discussion behavior was significant and in the 

expected direction for both the unmet need and the modern contraceptive use outcomes.

Figure 14b shows that in Afghanistan, the Kyrgyz Republic, Tanzania, and Uganda, men who made joint 

decisions with their partners on how to spend their earnings had partners who were more likely to use a 

modern contraceptive method compared with men who did not make the decision jointly. In Burundi, 

Liberia, and Rwanda, the odds were also significant but not in the expected direction.
26

Figure 14 Forest plot of adjusted odds ratios for modern contraceptive use by men’s behaviors

a. Adjusted odds ratios of men who recently discussed family 

planning with a health worker compared with men 

who did not

b. Adjusted odds ratios of men who make joint decisions with 

their partners on how to spend their earnings compared with 

men who do not

3.3.2 ANC and delivery outcomes

Two outcomes related to ANC visits and one outcome related to delivery in a health facility were examined 

in association with several men’s involvement variables. The ANC outcomes were women age 15-49 who 

attended at least four ANC visits for their most recent birth in the last two years before the survey, and 

women age 15-49 who went for their first ANC visit for their most recent pregnancy before four months of 

pregnancy. The facility delivery outcome was women age 15-49 who delivered their most recent birth in a 

health facility. The men’s involvement variables only covered the behaviors of being present during ANC 

visits and of making a joint decision on how to spend their earnings. There were no knowledge or attitude 

variables related to ANC visits or facility delivery.

At least four ANC visits

Figure 15a shows that in seven countries—Comoros, Gambia, Kenya, Mali, Nepal, Senegal, and Uganda—

men who were present during an ANC visit had partners with significantly higher odds of attending at least 

four ANC visits compared with men who were not present. In Timor-Leste, however, the odds were 

significant in the opposite of the expected direction, which indicates that men who were present during 

ANC had partners with lower odds of having at least four ANC visits compared with men who were not 

present. Figure 15b shows that in Armenia, Comoros, the Kyrgyz Republic, and Nigeria, men’s partners 

had greater odds of attending at least four ANC visits if the men made a joint decision on how to spend 

their earnings compared with men who did not make a joint decision. In Burundi the odds were in the 

opposite of the expected direction.
27

Figure 15 Forest plot of adjusted odds ratios for women attending at least four antenatal care visits by 

men’s behaviors

a. Adjusted odds ratios of men who were present at an ANC 

visit compared with men who were not

b. Adjusted odds ratios of men who make joint decisions with 

their partners on how to spend their earnings compared with 

men who do not

ANC visit before four months of pregnancy

As Figure 16a shows, men in Burundi and Sierra Leone had partners with significantly higher odds of 

attending an ANC visit before four months of pregnancy if they were present during an ANC visit compared 

with not being present. Figure 16b shows that men in Benin and the Kyrgyz Republic had partners with 

higher odds of attending an ANC visit before four months of pregnancy if they made a joint decision on 

how to spend their earnings compared with not making a joint decision. For Burundi and Kenya, the odds 

were in the opposite of the expected direction, with lower odds of the outcome.
28

Figure 16 Forest plot of adjusted odds ratios for women attending the first antenatal care visit before 4

months of pregnancy by men’s behaviors

a. Adjusted odds ratios of men who were present during an 

ANC visit compared with men who were not

b. Adjusted odds ratios of men who make joint decisions with 

their partners on how to spend their earnings compared with 

men who do not

Delivery in a health facility

Figure 17a shows that in seven countries —Burundi, Liberia, Myanmar, Nigeria, Tanzania, Uganda, and 

Zambia—men had partners with significantly higher odds of delivering in a health facility if they were 

present during an ANC visit compared with not being present. Figure 17b shows that only men in Lesotho 

and Nigeria had partners with higher odds of facility delivery if they made a joint decision on how to spend 

their earnings compared with not making a joint decision. For Myanmar the odds were lower, and in the 

opposite of the expected direction.
29

Figure 17 Forest plot of adjusted odds ratios for delivery in a health facility by men’s behaviors

a. Adjusted odds ratios of men who were present during an 

ANC visit compared with men who were not

b. Adjusted odds ratios of men who make joint decisions with 

their partners on how to spend their earnings compared with 

men who do not

3.3.3 Child health outcomes

Two child health outcomes were examined in association with men’s involvement variables. These were 

the most recent child age 12-23 months receiving all three doses of DPT vaccine (DPT3), and treatment 

sought from a health provider or health facility for the most recent child under age 2 with diarrhea. The 

DPT3 outcome did not have any corresponding variable related to men’s knowledge, attitudes, or behavior. 

For this outcome, only joint decision-making on earnings was examined. For treatment of diarrhea, an 

additional variable was examined for men’s correct knowledge of amount of fluid to give to a child with 

diarrhea. Estimates could not be produced for some surveys due to low counts or missing data, and this was 

the case for more surveys for the treatment of diarrhea outcome than for the DPT3 outcome, as shown in 

Appendix Tables 8 and 9.

DPT3 vaccination

Figure 18 shows the adjusted odds ratios for the most recent child completing three doses of DPT vaccine 

according to whether their fathers make joint decisions on their earnings. In Nigeria, Senegal, and Tanzania,

the odds of complete DPT vaccination were higher for children of men who made joint decisions on 

earnings compared with men who did not. For all other surveys the adjusted odds ratios were not significant.
30

Figure 18 Forest plot of adjusted odds ratios for most recent child completing three doses of DPT vaccine 

by men’s behavior of making joint decisions on earnings

Treatment for diarrhea

Figure 19 summarizes the adjusted odds ratios for the most recent child under age 2 receiving treatment for 

diarrhea by men’s knowledge to give more fluids when a child has diarrhea. Afghanistan, Democratic 

Republic of Congo, and Togo had significant odds ratios in the expected direction—that is, men who knew 

to give a child more fluids when they have diarrhea had children with higher odds of having treatment 

sought for them compared with men with incorrect knowledge. Nigeria also had a significant odds ratio but 

it was not in the expected direction.
31

Figure 19 Forest plot of adjusted odds ratios for most recent child receiving treatment of diarrhea by 

men’s knowledge to give more fluids when the child has diarrhea

Figure 20 shows that only men in Uganda who made joint decisions on how to spend their earnings had 

children with higher odds of treatment for diarrhea compared with men who did not make joint decisions. 

The odds ratios were also significant in Ethiopia and Mozambique, but not in the expected direction.
32

Figure 20 Forest plot of adjusted odds ratios for treatment of diarrhea in children by men’s behavior of 

making joint decisions on earnings

3.3.4 Summary

Figure 21 presents a summary of the results from the forest plots in Figures 9-20. The figure shows which 

countries were significant for each outcome and independent variable in the analysis. The countries in green 

indicate that the odds ratio was in the expected direction, while the countries in red indicate that the odds 

were in the opposite of the expected direction. It is difficult to assess these findings across all the outcomes 

since only the outcomes related to family planning had male involvement variables that covered all selected 

domains of knowledge, attitude, and behavior. Only the variable on joint decision-making on men’s 

earnings cut across all outcomes.

When we compare the two family planning outcomes, we see that modern contraceptive use had more 

significant findings and in the expected direction than was the case for unmet need for family planning. The 

summary also shows that the behavior of men discussing family planning with a health worker in the last 

few months is a significant predictor of modern contraceptive use in the most countries compared with any 

other involvement variable and outcome. For the two men’s attitudes related to family planning, the attitude 

that women who use contraception may become promiscuous was a significant predictor of modern 

contraceptive use in more countries than was the attitude that contraception is only women’s business. Only 

Nigeria showed significance in the expected direction for both of the attitude variables with modern 

contraceptive use. For unmet need, the Kyrgyz Republic and Tanzania showed significance in the expected 

direction for both of the attitude variables.
33

For the indicators related to ANC visits and health facility delivery, Figure 21 shows that men’s presence 

during ANC was an important predictor of the outcomes of at least four ANC visits and delivery in a health 

facility in several countries. However, men’s presence during ANC was a significant predictor for both of 

these outcomes only in Uganda. Men’s presence during ANC significantly predicted having an ANC visit 

before four months of pregnancy in just two countries.

For the child health outcomes, correct knowledge of fluid intake for a child with diarrhea was only 

significant in the expected direction in three countries. There was no knowledge-related variable for child 

vaccination.

The behavior of making a joint decision on how to spend the men’s earnings was used in the models for all 

of the outcomes in the analysis. In general, Figure 21 shows that this was not a significant predictor in many 

countries for any of the outcomes. It was significant and in the expected direction in the Kyrgyz Republic 

for the family planning outcomes and for the two ANC indicators. For Uganda and Tanzania, it was 

significant in the expected direction for the family planning outcomes and for one of the child health 

outcomes. For Nigeria, it was significant and in the expected direction for the outcomes of four or more 

ANC visits, facility delivery, and three doses of DPT vaccine. All other countries varied in the significance 

of this behavior variable according to the outcome.
34

Figure 21 Summary of significant findings from all the regression models

Unmet for family 

planning

Modern contraceptive 

use

Had at least 4 ANC 

visits

Had first ANC visit 

before 4 months of 

pregnancy

Delivered in a 

health facility

Child had 

doses o

vacci

Knowledge

Have correct knowledge of 

women’s fertile period 

(compared with incorrect 

knowledge)

Angola +

Armenia +

Benin -

Liberia +

Mali -

Namibia -

Sierra Leone +

Know to give more to drink 

when child has diarrhea 

(compared with did not 

know)

Attitudes

Does not agree that 

contraception is only 

women’s business 

(compared with agree)

Armenia -

Kyrgyz Republic -

Mozambique +

Myanmar -

Tanzania -

Angola +

Benin +

Nigeria +

Togo +

Does not agree that women 

who use contraception may 

become promiscuous 

(compared with agree)

Armenia +

Burkina Faso -

Kyrgyz Republic -

Tanzania -

Zambia -

Nigeria +

Armenia -

Burkina Faso +

Burundi +

Kenya +

Nigeria +

Senegal +

Zambia +

Zimbabwe +

Behavior

Discussed family planning 

with health worker in the 

last few months (compared 

with did not discuss)

Afghanistan -

Mali +

Nigeria +

Afghanistan +

Burkina Faso +

Burundi +

Chad +

Comoros + 

Congo DR +

Côte d’Ivoire +

Ghana +

Liberia +

Rwanda +

Togo +

Zambia +

Zimbabwe +
35

Unmet for family 

planning

Modern contraceptive 

use

Had at least 4 ANC 

visits

Had first ANC visit 

before 4 months of 

pregnancy

Delivered in a 

health facility

Child had 

doses o

vacci

Behavior (continued)

Present during ANC visit 

(compared with was not 

present)

Comoros +

Gambia +

Kenya +

Mali +

Nepal +

Senegal +

Timor-Leste -

Uganda +

Burundi +

Sierra Leone +

Burundi +

Liberia +

Myanmar +

Nigeria +

Tanzania +

Uganda +

Zambia +

Joint decision on how to 

spend respondent’s 

earnings (compared with 

decision not made jointly)

Chad -

Côte D’Ivoire -

Kyrgyz Republic -

Myanmar -

Tanzania -

Uganda -

Afghanistan +

Burundi -

Kyrgyz Republic +

Liberia -

Rwanda -

Tanzania +

Uganda +

Armenia +

Burundi -

Comoros +

Kyrgyz Republic +

Nigeria +

Benin +

Burundi -

Kenya -

Kyrgyz Republic +

Lesotho +

Myanmar -

Nigeria +

Niger

Seneg

Tanzan

Note: Red indicates that the AOR was in the opposite of the expected direction, green indicates an AOR in the expected direction. A + sign indicates a pos

and a - sign indicates a negative association (OR less than 1). 

1. Lesotho was excluded from the analysis of DPT vaccine due to low number of observations. 

2. Armenia, Benin, Ghana, Kyrgyz Republic, Lesotho, Myanmar, Namibia, and Nepal were excluded from the analysis of treatment for diarrhea due to low 
36

Figure 22 shows the number of times a country showed a significant odds ratio from the 19 possible 

hypotheses tested (if we count all of the models fit for each independent variable and each outcome). 

Nigeria, the Kyrgyz Republic, and Tanzania had the most significant results in the expected direction (six). 

Nigeria also had three odds ratios in the opposite of the expected direction. Twenty countries had no more 

than two odds ratios that were significant in the expected direction, and most of these countries also had 

odds ratios in the opposite of the expected direction.

Figure 22 Number of times each country showed a significant adjusted odds ratio from a total of 19 

possible hypotheses

0

1

2

3

4

5

6

7

Significant AOR in the expected direction Significant AOR was not in the expected direction
37

4 DISCUSSION

This report used data from the most recent DHS survey in 33 countries with availability of data on men’s 

involvement. These surveys asked men questions (or proxies) on their knowledge, attitude, and behavior 

toward selected health-related outcomes of their partners and children. However, most of these questions

were asked only in relation to family planning. There were no attitude questions related to maternal or child 

health, only one knowledge question on child health (amount of fluid to give a child with diarrhea), and 

only one behavior-specific question related to maternal health (present during ANC visit). Therefore, it was 

difficult to compare the level of male involvement across the outcomes.

For the available data, results were found to be mainly country-specific. In general, few countries exhibited 

significant findings based on the available questions, as summarized in Figures 21 and 22, and for a few 

countries the significant findings were in the opposite of the expected direction. The lack of significance 

found in some of these involvement variables could be due to the nature of the survey question. For some,

there is a direct link between the question and the outcome, such as men discussing family planning with a 

health worker in the last few months and current modern contraceptive use by their partners. For other 

questions, however, such as having correct knowledge of a woman’s fertile period, the link with the 

outcomes is not direct. In general, we did not find many significant associations between the knowledge 

variables and the outcomes for family planning and treatment for childhood diarrhea. Perhaps more suitable 

questions directly related to men’s knowledge on unmet need for family planning and on modern 

contraceptive use would have been more appropriate for the analysis than the question on knowledge of the

fertile period. However, such questions were not available in the DHS Men’s Questionnaire.

Four countries—Nigeria, the Kyrgyz Republic, Tanzania, and Uganda—had a high number of significant 

findings in the expected direction compared with the other countries (see Figure 22). While in the case of 

Nigeria this finding could be for analytical reasons (the large sample size), this is not the case for the other 

three countries. In these countries it appears that the measures of male involvement used in the analysis 

may have a more positive effect on the health outcomes of their partners and children. Qualitative studies 

and in-depth country-specific analysis are needed to understand these country-specific findings and whether 

cultural factors could explain the differences between countries; for instance, why some countries are 

showing significance in the opposite of the expected direction, or why some countries are showing a greater 

number of significant findings compared to others.

The summary also shows that the behavior of men discussing family planning with a health worker in the 

last few months is a significant predictor of a positive health outcome in more countries than for any other 

men’s involvement variable (significant in 13 countries, all in the expected direction). Other studies have 

also found that men who participated in male-only or couples counseling sessions with health care providers 

were more likely to report using modern contraception as a couple compared with men who did not 

participate in such sessions (Daniele et al. 2018; Shattuck et al. 2011; Tilahun et al. 2015). However, there 

is a limitation to this model, since women (whether themselves or their partner) who are already using a 

modern contraceptive method might not need to discuss family planning with a health worker. In that case, 

however, we would have expected to see some significance in the opposite of the expected direction, since 

those who did not discuss family planning would be more likely to be already using modern contraception. 

However, this was not the case in the analysis, as none of the countries were significant in the opposite of 
38

the expected direction, indicating that the timing between the variables is not a major limitation. The quality 

of the discussion with the health worker could also be factor for not observing more significant findings. If 

the quality of the discussion or counseling was poor, this could have a negative impact on the likelihood of 

using a modern method. Several studies have shown that good quality of counseling in family planning can 

increase the likelihood of contraceptive use and continuation (Abdel-Tawab and Roter 2002; Daniele et al. 

2018; Dehlendorf et al. 2016; Kamhawi et al. 2013; Lee et al. 2011). However, these studies only looked at 

counseling provided to women.

Joint decision-making on how to spend men’s earnings was the only variable that was examined for all 

outcomes. Our analysis used joint decision-making on earnings as a proxy for the level of partner 

communication. However, this variable may not have captured its intended purpose. Joint decision-making 

on earnings showed significance in the expected direction in only a few surveys. In addition, a few surveys 

showed results in the opposite of the expected direction, suggesting that making a joint decision on men’s 

earnings caused worse health-related outcomes compared with not making a decision jointly. There are 

several limitations for this variable. The analysis did not distinguish between the partner or others making 

the decision alone and the women making the decision alone. While the latter is usually found infrequently, 

it is possible that women who make decisions alone are more likely to attend ANC visits compared with 

women who make a decision jointly with their partners, therefore causing some findings in the opposite 

direction to the expectation (Allendorf 2007; Haque et al. 2012). It is important to note that joint decisionmaking was only examined in relation to men’s earnings, while in several countries decision-making around 

income is seen as being within “man’s domain.” As Figure 8 shows, in 20 of the 33 countries in the analysis 

less than 50% of men reported making decisions jointly on how to spend their earnings. Therefore, it is 

possible that the variable is not a good measure of the level of communication or involvement within 

couples. The other limitation is that the variable on men’s earnings is not directly linked to any of the health 

outcomes. It possible that some funds are needed for women to obtain family planning, go to an ANC visit, 

or treat the child for diarrhea, for instance; however, this is not expected to be a large enough burden to 

affect the outcomes in our analysis.

We found very little significance for the male-involvement variables and the two outcomes related to child 

health—DPT3 vaccination and treatment for diarrhea. In this study we were not able to look at men’s 

attitudes and behaviors for child health outcomes, because in general few studies have examined the impact 

of male involvement on direct child health measures such as immunizations and seeking medical treatment. 

One limitation of the vaccine outcome is that vaccination may be a requirement in most countries, and 

therefore men’s involvement would most likely be irrelevant—the child would be vaccinated regardless of 

whether the father was involved. What might be more important is male involvement in groups in which 

men are able to discuss child and family health with health care providers. In Haiti, fathers’ participation in 

fathers’ clubs significantly increased children’s vaccination status (Sloand, Astone, and Gebrian 2010). In 

addition, it is possible that child health care, including vaccination, is viewed as the mother’s role. In 

Tanzania, a qualitative assessment found that respondents felt that it was the mother’s role to take the child 

to be vaccinated so that she can soothe the child afterwards by breastfeeding, and also because it would be 

the mother’s responsibility to provide care if the child got sick due to not being vaccinated (Mushi et al. 

2008). In addition, it is likely that children’s vaccination is performed as part of the mother’s postpartum 

care and therefore does not require men’s involvement.
39

The main limitation for the outcome concerning treatment for diarrhea is primarily an analytical issue, since 

the outcome has only a few observations for most surveys. In fact, eight countries were removed from the 

analysis for this outcome because of small sample size. Having a low number of observations could reduce 

the power to detect significance in the regression models. Another limitation is that we do not know who 

took the child for treatment, and therefore the link between the father and the treatment is unclear.

One of the main limitations of this analysis is that our data are cross-sectional. In contrast, many of the 

studies discussed in the literature review that have shown positive effects of men’s involvement are

prospective case-control studies. For instance, four studies (Daniele et al. 2018; Shattuck et al. 2011; Terefe 

and Larson 1993; Tilahun et al. 2015) found that discussions or education with the male partner on family 

planning increased contraceptive use in an intervention group compared with the control group. Gendertransformative interventions that include educating men through discussion with health care providers have 

also been found to result in greater attendance and male participation in ANC (Doyle et al. 2018). With the 

DHS data, we cannot directly measure the impact of discussion with the health worker, which could have 

happened before or perhaps after the women began using a contraceptive method. This is also the case for 

men being present during ANC visits. We do not know the frequency or timing of these ANC visits and 

therefore what relationship they had with the two ANC outcomes. This is not the case for the facility 

delivery outcome, since we know that delivery follows ANC, and a positive association was seen in a 

number of countries. The lack of significance between men being present during ANC and the outcome of 

delivery in the health facility could be due to normative behavior in which men might report that they 

attended ANC visits in order to appear favorable to the interviewer, when in fact they were not present. This 

could also have been the case for the other men’s variables related to their knowledge, attitudes, and 

behavior, which could have biased the results.

The analysis found some significant findings in the association between men’s involvement variables and 

the outcomes for family planning, maternal health, and child health. The highest number of significant 

findings was between discussion of family planning with a health worker and modern contraceptive use. 

Variables related to men’s knowledge appeared to have the weakest link to health outcomes, although a 

knowledge component was only available in relation to family planning outcomes and treatment for 

diarrhea. In addition, the lack of questions in the DHS surveys related to men’s knowledge, attitudes, and 

behaviors in relation to maternal and child health outcomes made it difficult to compare our results across 

outcomes. It is recommended that more appropriate and cross-cutting questions to measure men’s 

involvement be included in the DHS questionnaires, and especially in relation to maternal and child health. 

The country-specific findings and the fact that some countries show more significant findings than others 

suggest the need for qualitative studies to understand the underlying cultural factors that may affect these 

results.

41

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APPENDIX
48

Appendix Table 1 Percentage with 95% confidence intervals for the outcomes used in the analysis

Unmet need Modern CPR At least 4 ANC visits

Had ANC visit 

before 4 months of 

pregnancy

Delivered in a health 

facility

Child h

dos

Country

Survey 

year % [95% C.I.] N % [95% C.I.] N % [95% C.I.] N % [95% C.I.] N % [95% C.I.] N % [95% 

Afghanistan 2015 23.9 [22.4,25.5] 10155 21.3 [19.3,23.3] 10155 19.2 [17.0,21.6] 4526 30.4 [27.6,33.3] 4526 55.5 [51.3,59.6] 4526 57.3 [51

Armenia 2015-16 9.0 [7.6,10.7] 1469 32.2 [29.7,34.8] 1469 96.9 [93.9,98.4] 271 92.7 [89.3,95.1] 271 99.4 [97.3,99.9] 271 88.1 [79

Angola 2015-16 37.3 [34.7,40.1] 2348 11.7 [9.5,14.2] 2348 59.5 [55.4,63.5] 1148 38.1 [34.6,41.8] 1148 46.6 [42.8,50.4] 1148 41.1 [35

Burkina Faso 2010 24.0 [22.5,25.6] 4153 16.4 [15.1,17.8] 4153 33.1 [30.6,35.7] 1902 42.9 [40.0,45.9] 1902 75.0 [71.6,78.1] 1902 91.1 [88

Benin 2011-12 34.9 [32.7,37.2] 2589 7.7 [6.5,9.1] 2589 58.8 [55.6,62.0] 1159 48.1 [45.0,51.3] 1159 86.1 [83.7,88.2] 1159 53.7 [48

Burundi 2010 33.2 [30.8,35.7] 2184 18.8 [16.8,21.0] 2184 34.4 [31.1,37.9] 1176 21.3 [18.8,24.1] 1176 66.5 [62.6,70.1] 1176 95.4 [92

Congo 

Democratic 

Republic 2013-14 27.2 [25.0,29.6] 4286 7.9 [6.6,9.5] 4286 45.0 [41.7,48.3] 2264 16.3 [13.7,19.2] 2264 78.0 [74.4,81.2] 2264 55.3 [50

Côte d’Ivoire 2011-12 27.0 [24.3,29.9] 1906 13.2 [11.4,15.3] 1906 40.8 [36.7,45.1] 803 28.2 [24.1,32.7] 803 54.0 [48.9,59.0] 803 62.6 [56

Ethiopia 2016 21.5 [19.6,23.4] 6654 37.5 [34.8,40.4] 6654 32.0 [28.9,35.3] 2778 20.4 [18.1,22.9] 2778 34.6 [30.2,39.2] 2778 54.6 [50

Ghana 2014 30.1 [27.6,32.8] 1710 25.2 [22.1,28.6] 1710 88.5 [84.9,91.4] 664 64.9 [59.3,70.1] 664 74.7 [70.2,78.8] 664 91.9 [87

Gambia 2013 23.7 [21.2,26.4] 1154 11.3 [7.8,16.2] 1154 79.4 [75.2,83.0] 620 37.6 [33.3,42.1] 620 62.3 [56.5,67.9] 620 84.9 [77

Kenya 2014 15.6 [14.3,17.0] 5157 56.8 [54.8,58.9] 5157 56.3 [53.3,59.2] 1859 20.3 [17.9,23.0] 1859 64.8 [61.9,67.7] 1859 90.3 [87

Comoros 2012 32.6 [28.8,36.5] 938 15.4 [12.8,18.4] 938 43.7 [38.0,49.7] 419 55.4 [48.8,61.9] 419 79.4 [73.9,84.0] 419 73.2 [65

Kyrgyz 

Republic 2012 18.1 [15.5,21.0] 1388 36.9 [34.0,40.0] 1388 85.6 [81.6,88.8] 474 79.7 [75.4,83.5] 474 99.7 [97.5,100.0] 474 88.2 [80

Liberia 2013 34.5 [31.4,37.7] 1770 19.5 [16.3,23.1] 1770 72.9 [67.6,77.6] 681 67.4 [62.9,71.5] 681 55.2 [48.7,61.5] 681 72.4 [64

Lesotho 2014 14.2 [11.3,17.5] 730 65.1 [60.5,69.5] 730 69.1 [60.3,76.7] 189 45.4 [38.0,53.1] 189 75.6 [67.4,82.3] 189 88.9 [81

Mali 2012-13 24.9 [22.7,27.2] 2606 9.9 [8.5,11.5] 2606 39.6 [36.0,43.4] 1227 34.5 [31.2,38.0] 1227 55.5 [51.1,59.9] 1227 63.6 [58

Myanmar 2015-16 13.3 [11.7,15.1] 2661 55.9 [53.5,58.3] 2661 57.9 [53.1,62.5] 606 40.0 [34.8,45.4] 606 43.8 [38.3,49.4] 606 58.0 [51

Malawi 2015-16 16.0 [14.6,17.5] 3884 61.8 [59.8,63.7] 3884 48.4 [45.1,51.7] 1376 25.7 [22.9,28.6] 1376 92.7 [90.7,94.2] 1376 92.5 [89

Mozambique 2011 23.2 [21.1,25.5] 2239 11.2 [9.8,12.7] 2239 45.3 [41.0,49.6] 1022 11.5 [9.4,14.1] 1022 55.2 [50.3,60.0] 1022 75.8 [70

Nigeria 2013 16.8 [15.7,18.0] 7837 10.9 [9.8,12.2] 7837 52.4 [49.9,54.9] 3783 17.5 [15.9,19.2] 3783 38.7 [36.2,41.2] 3783 38.2 [35

Namibia 2013 14.4 [12.3,16.8] 1149 57.9 [53.8,61.8] 988 60.4 [53.6,66.8] 267 39.8 [33.2,46.8] 267 84.4 [79.5,88.3] 267 87.7 [78

Nepal 2016 9.3 [7.9,11.1] 2376 54.5 [51.9,57.1] 2376 67.6 [61.8,73.0] 459 67.6 [62.0,72.7] 459 63.2 [57.2,68.9] 459 86.0 [77

Rwanda 2014-15 17.4 [16.0,19.0] 2964 48.5 [46.4,50.7] 2964 45.9 [42.9,48.9] 1159 57.4 [54.4,60.3] 1159 91.7 [89.7,93.4] 1159 99.0 [97

Sierra Leone 2013 24.4 [22.5,26.3] 3391 16.9 [15.2,18.7] 3391 77.3 [73.8,80.5] 1298 45.5 [42.0,49.0] 1298 55.6 [50.8,60.2] 1298 77.9 [72

Senegal 2016 22.7 [19.7,26.0] 1184 28.5 [24.8,32.6] 1184 53.1 [47.4,58.7] 496 52.9 [46.8,58.9] 496 74.9 [70.0,79.1] 496 86.5 [79

Chad 2014-15 22.7 [20.7,24.7] 2755 6.7 [5.3,8.5] 2755 35.1 [32.0,38.4] 1377 31.2 [28.1,34.6] 1377 21.9 [19.0,25.1] 1377 38.8 [34

Togo 2013-14 32.3 [30.0,34.7] 1973 19.1 [17.2,21.1] 1973 55.5 [51.5,59.4] 803 26.3 [23.3,29.5] 803 75.2 [71.2,78.7] 803 86.2 [81

Timor-Leste 2009-10 30.9 [28.7,33.1] 2001 22.9 [20.7,25.2] 2001 55.2 [51.4,58.9] 1025 45.1 [41.7,48.6] 1025 25.0 [21.7,28.7] 1025 66.4 [61

Tanzania 2015-16 21.3 [18.8,24.0] 1558 33.7 [30.8,36.7] 1558 52.3 [47.7,56.8] 704 21.4 [18.0,25.3] 704 68.5 [63.9,72.8] 704 92.5 [88

Uganda 2016 27.2 [25.2,29.4] 2408 37.1 [34.8,39.4] 2408 59.6 [55.8,63.3] 1090 28.5 [25.4,31.9] 1090 71.8 [68.1,75.2] 1090 80.9 [76

Zambia 2013-14 19.7 [18.6,20.9] 7092 46.3 [44.7,47.8] 7092 53.5 [51.1,55.9] 2987 24.3 [22.2,26.6] 2987 71.1 [68.5,73.5] 2987 85.9 [83

Zimbabwe 2015 7.7 [6.6,8.9] 3499 68.5 [66.5,70.5] 3499 77.5 [73.9,80.8] 1173 39.2 [35.8,42.8] 1173 81.9 [78.2,85.1] 1173 86.7 [82

Note: a - Regression models were not fit due to small number of observations.
49

Appendix Table 2 Percentage with 95% confidence intervals for the men’s involvement variables used in the model

Knowledge Attitude Be

Survey 

year

Know to give more to 

drink when child has 

diarrhea

Correct knowledge 

of fertile period

Does not agree that 

contraception is 

only a women’s 

business

Does not agree that 

women who use 

contraception 

become 

promiscuous

Discussed family 

planning with health 

worker in the last few 

months

Presen

ch

Country % [95% C.I.] N % [95% C.I.] N % [95% C.I.] N % [95% C.I.] N % [95% C.I.] N % [95% 

Afghanistan 2015 38.4 [34.8,42.1] 4344 7.2 [6.4,8.0] 10155 43.1 [40.4,45.9]

1015

5 45.6 [42.9,48.4] 10155 16.2 [14.5,17.9] 10155 63.8 [57

Armenia 2015-16 46.2 [37.1,55.5] 269 34.5 [29.9,39.4] 1469 83.8 [81.1,86.2] 1469 45.4 [40.7,50.2] 1469 4.0 [3.0,5.2] 1469 60.5 [51

Angola 2015-16 50.8 [46.0,55.7] 1010 6.4 [5.0,8.2] 2348 45.9 [41.4,50.5] 2348 50.0 [45.7,54.4] 2348 23.6 [20.4,27.1] 2348 62.3 [57

Burkina Faso 2010 38.4 [35.0,42.0] 1819 36.3 [33.4,39.3] 4153 89.3 [87.7,90.7] 4153 75.3 [72.9,77.6] 4153 12.5 [11.1,13.9] 4153 43.8 [40

Benin 2011-12 47.5 [43.1,51.9] 953 36.3 [33.9,38.9] 2589 66.4 [63.7,69.0] 2589 50.1 [47.5,52.7] 2589 10.9 [9.5,12.5] 2589 38.7 [34

Burundi 2010 59.4 [55.4,63.3] 1119 20.5 [18.0,23.2] 2184 96.6 [95.3,97.6] 2184 71.1 [68.2,73.9] 2184 14.9 [13.2,16.7] 2184 17.6 [14

Congo 

Democratic 

Republic 2013-14 52.6 [48.1,57.1] 2158 47.0 [43.7,50.4] 4286 70.3 [67.2,73.2] 4286 62.9 [59.7,65.9] 4286 8.3 [7.1,9.7] 4286 29.8 [26

Côte d’Ivoire 2011-12 28.3 [23.9,33.1] 749 15.7 [13.4,18.3] 1906 67.6 [64.2,70.8] 1906 51.4 [47.5,55.3] 1906 5.3 [4.1,6.9] 1906 49.4 [43

Ethiopia 2016 39.1 [35.6,42.6] 2587 15.1 [13.2,17.2] 6654 89.4 [87.8,90.8] 6654 88.9 [87.3,90.4] 6654 43.2 [40.5,45.9] 6654 57.9 [54

Ghana 2014 28.8 [24.2,33.8] 650 27.6 [23.2,32.4] 1710 75.6 [70.5,80.0] 1710 51.0 [46.4,55.6] 1710 9.8 [7.9,11.9] 1710 46.2 [38

Gambia 2013 65.7 [57.8,72.8] 597 16.1 [12.8,20.2] 1154 72.8 [67.4,77.6] 1154 47.4 [41.9,53.1] 1154 20.7 [17.2,24.6] 1154 38.1 [32

Kenya 2014 60.2 [57.1,63.2] 1783 17.3 [15.7,19.1] 5157 87.0 [85.6,88.4] 5157 68.9 [66.9,70.9] 5157 16.3 [15.0,17.6] 5157 34.1 [30

Comoros 2012 79.1 [73.2,84.0] 394 47.9 [42.5,53.4] 938 63.8 [57.5,69.7] 938 43.3 [37.5,49.3] 938 11.9 [9.1,15.4] 938 49.3 [41

Kyrgyz Republic 2012 41.3 [35.7,47.1] 459 31.9 [28.3,35.8] 1388 64.0 [60.4,67.4] 1388 69.0 [65.0,72.8] 1388 6.8 [5.2,8.7] 1388 54.2 [49

Liberia 2013 40.4 [34.3,47.0] 630 2.8 [1.5,5.0] 1770 69.1 [64.0,73.7] 1770 65.8 [61.2,70.1] 1770 22.0 [18.6,25.8] 1770 44.4 [38

Lesotho 2014 38.7 [30.4,47.6] 169 19.0 [15.7,22.8] 730 54.4 [50.5,58.4] 730 31.5 [27.6,35.5] 730 8.9 [6.7,11.7] 730 40.7 [30

Mali 2012-13 64.3 [60.7,67.8] 1100 24.8 [22.1,27.8] 2606 74.4 [71.6,77.0] 2606 63.1 [59.7,66.3] 2606 11.3 [10.0,12.8] 2606 42.7 [38

Myanmar 2015-16 31.5 [26.9,36.5] 591 6.1 [5.0,7.3] 2661 54.5 [51.7,57.2] 2661 33.6 [31.2,36.0] 2661 6.1 [5.1,7.3] 2661 78.1 [73

Malawi 2015-16 70.0 [66.6,73.3] 1271 10.1 [8.9,11.4] 3884 69.8 [67.7,71.8] 3884 66.7 [64.6,68.7] 3884 26.3 [24.3,28.3] 3884 65.9 [62

Mozambique 2011 50.3 [46.1,54.5] 932 2.8 [2.0,3.7] 2239 82.9 [80.5,85.0] 2239 90.2 [88.4,91.7] 2239 5.8 [4.5,7.4] 2239 41.8 [37

Nigeria 2013 27.2 [25.1,29.4] 3490 11.6 [10.5,12.8] 7837 73.6 [71.7,75.3] 7837 52.0 [50.0,54.0] 7837 7.5 [6.8,8.4] 7837 45.3 [42

Namibia 2013 36.6 [29.7,44.2] 237 4.4 [3.0,6.3] 1149 53.5 [49.9,57.0] 1149 44.9 [41.6,48.3] 1149 7.4 [5.5,9.9] 1149 50.9 [43

Nepal 2016 70.4 [65.0,75.3] 438 30.1 [27.1,33.4] 2376 87.4 [85.1,89.4] 2376 74.6 [71.9,77.1] 2376 11.6 [9.8,13.6] 2376 53.8 [47

Rwanda 2014-15 67.3 [64.1,70.4] 1120 28.2 [26.1,30.5] 2964 96.1 [95.2,96.8] 2964 96.5 [95.6,97.2] 2964 49.0 [46.7,51.2] 2964 94.8 [93

Sierra Leone 2013 31.0 [27.0,35.3] 1181 19.8 [16.8,23.2] 3391 51.1 [47.2,55.1] 3391 48.9 [44.9,52.8] 3391 18.0 [15.5,20.8] 3391 51.7 [47

Senegal 2016 39.1 [32.6,45.9] 482 10.7 [8.3,13.6] 1184 88.9 [86.4,91.0] 1184 67.5 [63.4,71.3] 1184 5.6 [3.7,8.4] 1184 20.6 [16

Chad 2014-15 35.9 [31.8,40.2] 1293 35.7 [31.8,39.8] 2755 58.4 [54.9,61.9] 2755 45.8 [42.3,49.4] 2755 6.3 [5.0,8.0] 2755 36.7 [32

Togo 2013-14 53.0 [48.1,57.9] 780 36.8 [33.7,40.1] 1973 86.9 [84.4,89.1] 1973 71.0 [67.7,74.0] 1973 12.8 [11.1,14.8] 1973 47.9 [43

Timor-Leste 2009-10 13.3 [11.0,16.0] 1008 7.8 [6.3,9.7] 2001 64.4 [61.4,67.2] 2001 53.7 [50.9,56.5] 2001 10.0 [8.4,11.9] 2001 59.1 [55

Tanzania 2015-16 43.1 [38.8,47.6] 665 13.2 [10.9,15.8] 1558 58.5 [54.9,62.0] 1558 48.2 [45.1,51.5] 1558 12.5 [10.5,14.9] 1558 55.8 [51

Uganda 2016 66.3 [62.7,69.8] 972 16.1 [14.2,18.2] 2408 78.8 [76.6,80.9] 2408 50.5 [48.0,52.9] 2408 18.2 [16.4,20.0] 2408 58.7 [54

Zambia 2013-14 54.9 [52.1,57.8] 2879 16.1 [14.4,18.0] 7092 78.5 [76.8,80.1] 7092 73.6 [72.0,75.2] 7092 20.6 [19.2,22.0] 7092 70.6 [68

Zimbabwe 2015 65.2 [61.7,68.5] 1091 14.1 [12.7,15.8] 3499 79.1 [77.3,80.7] 3499 66.2 [64.3,68.1] 3499 16.6 [15.0,18.4] 3499 57.1 [53
50

Appendix Table 3 Adjusted odds ratios with 95% confidence intervals for women’s unmet need for family 

planning by men’s involvement variables

Knowledge Attitude Behavior

Survey 

year

Correct 

knowledge of 

fertile period 

versus 

incorrect

Does not agree 

that 

contraception is 

only a women’s 

business versus 

agrees

Does not agree 

that women 

who use 

contraception 

become 

promiscuous 

versus agrees

Discussed 

family planning 

with health 

worker in the 

last few months 

versus did not 

discuss

Joint decision 

on how to 

spend 

respondent’s 

earnings versus 

not joint

Country AOR 95% C.I. AOR 95% C.I. AOR 95% C.I. AOR 95% C.I. AOR 95% C.I.

Afghanistan 2015 1.1 [0.8,1.5] 1.2 [1.0,1.4] 1.1 [0.9,1.2] 0.8* [0.7,1.0] 0.9 [0.7,1.1]

Angola 2015-16 1.6* [1.0,2.6] 0.9 [0.7,1.2] 1.0 [0.8,1.2] 1.0 [0.7,1.3] 1.2 [0.9,1.6]

Armenia 2015-16 1.8** [1.2,2.7] 0.6* [0.4,0.9] 1.6* [1.1,2.3] 0.7 [0.2,1.8] 1.6 [1.0,2.7]

Benin 2011-12 0.7** [0.6,0.9] 0.9 [0.8,1.1] 1.1 [1.0,1.4] 0.9 [0.7,1.2] 0.8 [0.6,1.1]

Burkina Faso 2010 0.9 [0.7,1.0] 1.1 [0.9,1.5] 0.8** [0.6,0.9] 1.0 [0.8,1.3] 1.0 [0.7,1.4]

Burundi 2010 1.1 [0.8,1.4] 0.9 [0.5,1.7] 0.9 [0.7,1.2] 1.1 [0.8,1.4] 1.1 [0.8,1.4]

Chad 2014-15 1.1 [0.9,1.3] 1.1 [0.9,1.4] 1.0 [0.8,1.2] 1.1 [0.7,1.9] 0.6** [0.4,0.9]

Comoros 2012 1.2 [0.8,1.8] 0.9 [0.6,1.4] 1.1 [0.8,1.6] 0.9 [0.5,1.5] 0.8 [0.4,1.4]

Congo Democratic 

Republic 2013-14 0.9 [0.7,1.1] 1.2 [0.9,1.5] 1.0 [0.9,1.3] 1.0 [0.7,1.4] 0.9 [0.7,1.1]

Côte d’Ivoire 2011-12 1.3 [0.9,1.8] 1.2 [0.9,1.6] 0.9 [0.7,1.1] 1.0 [0.5,1.8] 0.7* [0.5,1.0]

Ethiopia 2016 1.0 [0.7,1.3] 1.2 [0.8,1.7] 1.2 [0.9,1.6] 1.1 [0.9,1.3] 0.9 [0.6,1.5]

Gambia 2013 0.7 [0.5,1.1] 0.9 [0.6,1.2] 0.9 [0.7,1.3] 1.1 [0.8,1.6] 1.4 [0.9,2.1]

Ghana 2014 0.9 [0.7,1.2] 1.0 [0.8,1.3] 0.8 [0.6,1.1] 0.9 [0.6,1.4] 0.9 [0.7,1.1]

Kenya 2014 0.9 [0.7,1.2] 1.1 [0.8,1.5] 0.9 [0.7,1.1] 1.2 [0.9,1.6] 0.9 [0.7,1.2]

Kyrgyz Republic 2012 1.0 [0.7,1.4] 0.7* [0.5,1.0] 0.6* [0.4,1.0] 0.9 [0.4,2.1] 0.6* [0.4,0.9]

Lesotho 2014 1.0 [0.5,2.0] 1.5 [0.9,2.6] 1.3 [0.8,2.2] 0.8 [0.3,1.8] 1.8 [0.7,4.6]

Liberia 2013 3.4* [1.3,8.8] 0.9 [0.6,1.2] 1.1 [0.8,1.4] 0.7 [0.4,1.1] 1.1 [0.8,1.6]

Malawi 2015-16 1.0 [0.7,1.3] 0.9 [0.7,1.1] 1.0 [0.8,1.2] 1.0 [0.8,1.3] 1.0 [0.7,1.3]

Mali 2012-13 0.7** [0.5,0.9] 0.8 [0.6,1.1] 0.9 [0.7,1.1] 1.3* [1.0,1.8] 1.7 [0.6,4.4]

Mozambique 2011 1.5 [0.9,2.7] 1.5* [1.1,2.2] 1.3 [0.9,1.8] 0.8 [0.5,1.3] 0.9 [0.7,1.3]

Myanmar 2015-16 1.0 [0.5,1.9] 0.7** [0.5,0.9] 0.9 [0.7,1.2] 0.6 [0.3,1.1] 0.7** [0.5,0.9]

Namibia 2013 2.2 [0.9,5.2] 1.5 [1.0,2.3] 1.2 [0.8,1.7] 0.8 [0.4,1.7] 0.9 [0.6,1.4]

Nepal 2016 1.3 [0.9,1.8] 0.9 [0.6,1.5] 1.0 [0.7,1.4] 1.1 [0.6,1.8] 0.9 [0.6,1.3]

Nigeria 2013 1.0 [0.8,1.3] 1.0 [0.8,1.2] 1.2* [1.0,1.4] 1.3* [1.0,1.6] 1.1 [0.9,1.3]

Rwanda 2014-15 1.1 [0.8,1.3] 1.1 [0.7,1.8] 0.9 [0.6,1.5] 0.8 [0.7,1.0] 1.1 [0.8,1.5]

Senegal 2016 1.0 [0.5,2.0] 1.5 [0.9,2.5] 0.9 [0.6,1.3] 0.7 [0.3,1.4] 0.7 [0.4,1.2]

Sierra Leone 2013 1.0 [0.8,1.3] 0.9 [0.8,1.1] 0.8 [0.7,1.0] 1.0 [0.8,1.3] 0.9 [0.7,1.1]

Timor-Leste 2009-10 0.9 [0.6,1.4] 0.9 [0.8,1.2] 1.0 [0.8,1.3] 1.1 [0.8,1.5] 1.1 [0.6,2.0]

Tanzania 2015-16 0.8 [0.5,1.4] 0.7* [0.5,1.0] 0.7* [0.5,0.9] 1.0 [0.7,1.5] 0.7* [0.5,1.0]

Togo 2013-14 1.1 [0.9,1.4] 1.0 [0.7,1.3] 1.0 [0.8,1.3] 0.8 [0.6,1.2] 0.9 [0.6,1.2]

Uganda 2016 0.9 [0.6,1.2] 1.0 [0.8,1.3] 0.9 [0.7,1.1] 1.1 [0.9,1.5] 0.8* [0.6,1.0]

Zambia 2013-14 1.1 [0.9,1.4] 0.9 [0.8,1.0] 0.8* [0.7,1.0] 1.0 [0.8,1.1] 1.0 [0.8,1.1]

Zimbabwe 2015 1.1 [0.7,1.7] 0.9 [0.6,1.4] 0.9 [0.6,1.3] 0.9 [0.6,1.3] 0.7 [0.5,1.1]

Note: * p<0.05, ** p<0.01, *** p<0.001
51

Appendix Table 4 Adjusted odds ratios with 95% confidence intervals for women’s modern contraceptive 

use by men’s involvement variables

Knowledge Attitude Behavior

Survey 

year

Correct 

knowledge of 

fertile period 

versus incorrect

Does not agree 

that 

contraception is 

only a women’s 

business versus 

agrees

Does not agree 

that women who 

use 

contraception 

become 

promiscuous 

versus agrees

Discussed family 

planning with 

health worker in 

the last few 

months versus 

did not discuss

Joint decision on 

how to spend 

respondents 

earnings versus 

not joint

Country AOR 95% C.I. AOR 95% C.I. AOR 95% C.I. AOR 95% C.I. AOR 95% C.I.

Afghanistan 2015 1.3 [1.0,1.7] 1.0 [0.8,1.2] 1.1 [0.8,1.4] 1.5** [1.1,1.9] 1.3* [1.0,1.7]

Angola 2015-16 0.9 [0.3,2.4] 1.6* [1.0,2.4] 0.9 [0.5,1.4] 1.1 [0.7,1.9] 1.4 [0.8,2.4]

Armenia 2015-16 1.0 [0.7,1.3] 1.1 [0.8,1.5] 0.7** [0.5,0.9] 0.9 [0.5,1.6] 0.8 [0.6,1.0]

Benin 2011-12 1.2 [0.9,1.7] 1.8* [1.1,2.9] 1.4 [1.0,1.9] 1.0 [0.6,1.9] 1.1 [0.6,1.9]

Burkina Faso 2010 1.1 [0.9,1.4] 1.0 [0.7,1.4] 1.3* [1.0,1.6] 1.5*** [1.2,1.9] 1.1 [0.8,1.6]

Burundi 2010 1.0 [0.7,1.3] 1.3 [0.7,2.6] 1.3* [1.0,1.8] 1.4* [1.0,2.0] 0.6* [0.4,0.9]

Chad 2014-15 0.8 [0.5,1.3] 0.6 [0.4,1.0] 0.7 [0.4,1.1] 2.7*** [1.6,4.6] 0.8 [0.4,1.7]

Comoros 2012 0.9 [0.6,1.5] 0.8 [0.5,1.3] 1.2 [0.8,1.8] 2.0** [1.2,3.3] 1.0 [0.6,1.9]

Congo 

Democratic 

Republic 2013-14 1.0 [0.7,1.4] 1.0 [0.7,1.5] 1.2 [0.8,1.8] 1.8** [1.2,2.9] 1.1 [0.8,1.7]

Côte d’Ivoire 2011-12 0.9 [0.6,1.5] 1.2 [0.8,1.7] 1.2 [0.8,1.7] 2.3* [1.2,4.6] 0.9 [0.6,1.4]

Ethiopia 2016 1.1 [0.9,1.3] 0.9 [0.7,1.2] 1.0 [0.8,1.4] 1.2 [1.0,1.5] 1.2 [0.8,1.7]

Gambia 2013 0.9 [0.4,2.1] 1.1 [0.5,2.2] 1.6 [0.9,2.8] 0.9 [0.4,1.7] 0.7 [0.3,1.4]

Ghana 2014 0.8 [0.6,1.1] 0.8 [0.5,1.1] 1.2 [0.9,1.8] 1.8** [1.2,2.9] 1.0 [0.7,1.3]

Kenya 2014 0.9 [0.7,1.2] 0.9 [0.7,1.1] 1.3*** [1.1,1.6] 1.0 [0.8,1.3] 1.2 [1.0,1.4]

Kyrgyz Republic 2012 1.0 [0.8,1.4] 1.0 [0.8,1.4] 1.2 [0.9,1.6] 0.8 [0.4,1.4] 1.4* [1.0,1.8]

Lesotho 2014 1.2 [0.7,1.8] 1.0 [0.7,1.4] 1.1 [0.8,1.6] 1.7 [0.9,3.3] 0.7 [0.4,1.4]

Liberia 2013 1.0 [0.4,2.6] 0.9 [0.6,1.5] 1.0 [0.7,1.6] 1.6* [1.1,2.3] 0.6* [0.3,1.0]

Malawi 2015-16 1.1 [0.8,1.4] 1.1 [1.0,1.3] 1.1 [0.9,1.3] 1.1 [0.9,1.3] 1.1 [0.9,1.4]

Mali 2012-13 1.1 [0.8,1.6] 0.9 [0.6,1.3] 1.0 [0.8,1.4] 0.8 [0.5,1.3] 0.8 [0.3,2.3]

Mozambique 2011 0.5 [0.2,1.1] 1.3 [0.9,2.1] 0.8 [0.5,1.2] 1.3 [0.8,2.3] 1.2 [0.9,1.7]

Myanmar 2015-16 0.9 [0.6,1.4] 1.1 [0.9,1.3] 1.0 [0.8,1.2] 1.1 [0.7,1.6] 1.2 [1.0,1.4]

Namibia 2013 0.4* [0.2,0.9] 0.9 [0.6,1.3] 0.8 [0.6,1.1] 0.7 [0.4,1.2] 0.9 [0.7,1.3]

Nepal 2016 1.2 [1.0,1.5] 1.0 [0.8,1.3] 1.1 [0.9,1.4] 1.0 [0.8,1.4] 1.2 [0.9,1.6]

Nigeria 2013 0.9 [0.7,1.3] 1.4** [1.1,1.9] 1.4** [1.1,1.7] 1.2 [0.9,1.5] 1.2 [0.9,1.6]

Rwanda 2014-15 0.9 [0.7,1.1] 0.9 [0.6,1.3] 1.3 [0.9,2.0] 1.2* [1.0,1.3] 0.8* [0.7,1.0]

Senegal 2016 0.9 [0.4,1.9] 1.8 [1.0,3.3] 1.6* [1.0,2.4] 0.8 [0.5,1.4] 0.7 [0.5,1.1]

Sierra Leone 2013 1.3* [1.0,1.8] 1.0 [0.8,1.3] 1.0 [0.8,1.3] 1.0 [0.7,1.3] 0.8 [0.6,1.1]

Timor-Leste 2009-10 1.3 [0.9,1.9] 0.8 [0.6,1.0] 1.1 [0.9,1.5] 1.1 [0.8,1.6] 1.1 [0.7,1.7]

Tanzania 2015-16 1.2 [0.8,1.8] 1.1 [0.8,1.4] 1.1 [0.8,1.4] 1.1 [0.7,1.5] 1.4* [1.0,1.8]

Togo 2013-14 0.9 [0.7,1.2] 1.8* [1.1,2.7] 1.3 [1.0,1.7] 2.0*** [1.5,2.8] 1.1 [0.7,1.7]

Uganda 2016 1.0 [0.7,1.3] 1.0 [0.8,1.3] 1.2 [0.9,1.4] 1.1 [0.9,1.4] 1.4** [1.1,1.7]

Zambia 2013-14 1.0 [0.8,1.1] 1.0 [0.9,1.2] 1.2* [1.0,1.3] 1.2* [1.0,1.4] 1.0 [0.9,1.1]

Zimbabwe 2015 1.0 [0.8,1.3] 1.2 [0.9,1.4] 1.4** [1.1,1.7] 1.3* [1.0,1.6] 1.2 [1.0,1.6]

Note: * p<0.05, ** p<0.01, *** p<0.001
52

Appendix Table 5 Adjusted odds ratios with 95% confidence intervals for having at least 4 antenatal care

visits by men’s involvement variables

Behavior

Survey 

year

Present during

ANC check-up 

versus not 

present

Joint decision on 

how to spend 

respondent’s 

earnings versus 

not joint

Country AOR 95% C.I. AOR 95% C.I.

Afghanistan 2015 1.2 [0.9,1.6] 1.4 [1.0,1.9]

Angola 2015-16 1.1 [0.8,1.7] 0.7 [0.5,1.2]

Armenia 2015-16 2.5 [0.6,10.3] 21.9** [2.4,201.7]

Benin 2011-12 0.8 [0.5,1.1] 1.1 [0.7,1.9]

Burkina Faso 2010 1.0 [0.8,1.2] 1.1 [0.7,1.8]

Burundi 2010 1.4 [1.0,2.0] 0.5* [0.3,0.9]

Chad 2014-15 1.1 [0.8,1.6] 1.3 [0.8,2.3]

Comoros 2012 2.2* [1.2,4.2] 2.2* [1.1,4.1]

Congo Democratic Republic 2013-14 1.2 [0.9,1.6] 1.1 [0.9,1.4]

Côte d’Ivoire 2011-12 0.8 [0.5,1.3] 1.0 [0.6,1.6]

Ethiopia 2016 1.2 [0.9,1.6] 1.1 [0.7,1.7]

Gambia 2013 1.8* [1.1,3.1] 1.0 [0.6,1.6]

Ghana 2014 1.1 [0.6,2.0] 1.0 [0.5,1.8]

Kenya 2014 1.4* [1.0,1.8] 0.9 [0.6,1.1]

Kyrgyz Republic 2012 0.9 [0.5,1.6] 2.0* [1.0,3.8]

Lesotho 2014 0.9 [0.2,3.2] 1.5 [0.4,5.3]

Liberia 2013 1.1 [0.6,1.8] 1.5 [0.8,2.8]

Malawi 2015-16 0.9 [0.7,1.2] 1.0 [0.7,1.4]

Mali 2012-13 1.6** [1.2,2.3] 1.9 [0.8,4.8]

Mozambique 2011 1.2 [0.9,1.8] 1.2 [0.8,1.8]

Myanmar 2015-16 1.0 [0.6,1.7] 1.2 [0.8,1.8]

Namibia 2013 0.7 [0.4,1.6] 1.3 [0.6,2.6]

Nepal 2016 2.3** [1.2,4.2] 1.4 [0.8,2.5]

Nigeria 2013 1.1 [0.9,1.4] 1.7*** [1.3,2.1]

Rwanda 2014-15 0.9 [0.5,1.6] 1.1 [0.8,1.6]

Senegal 2016 2.2** [1.3,3.7] 1.7 [0.9,3.3]

Sierra Leone 2013 1.1 [0.8,1.5] 1.5 [0.9,2.5]

Timor-Leste 2009-10 0.7** [0.5,0.9] 1.3 [0.8,2.3]

Tanzania 2015-16 1.1 [0.7,1.5] 1.1 [0.7,1.6]

Togo 2013-14 1.1 [0.8,1.6] 1.3 [0.7,2.2]

Uganda 2016 1.4* [1.0,2.0] 1.0 [0.7,1.4]

Zambia 2013-14 1.0 [0.8,1.2] 1.2 [0.9,1.4]

Zimbabwe 2015 1.0 [0.7,1.4] 0.9 [0.6,1.5]

Note: * p<0.05, ** p<0.01, *** p<0.001
53

Appendix Table 6 Adjusted odds ratios with 95% confidence intervals for having antenatal care visit 

before 4 months of pregnancy by men’s involvement variables

Behavior

Survey 

year

Present during

ANC check-up 

versus not 

present

Joint decision on 

how to spend 

respondent’s 

earnings versus 

not joint

Country AOR 95% C.I. AOR 95% C.I.

Afghanistan 2015 0.8 [0.6,1.2] 1.1 [0.7,1.6]

Angola 2015-16 0.9 [0.6,1.3] 0.8 [0.5,1.3]

Armenia 2015-16 1.2 [0.4,3.8] 1.4 [0.4,5.0]

Benin 2011-12 1.1 [0.8,1.5] 1.7* [1.0,2.7]

Burkina Faso 2010 0.8 [0.7,1.1] 0.8 [0.5,1.2]

Burundi 2010 1.7** [1.1,2.5] 0.6* [0.4,1.0]

Chad 2014-15 1.1 [0.8,1.6] 1.2 [0.6,2.3]

Comoros 2012 1.1 [0.6,1.9] 0.7 [0.4,1.4]

Congo Democratic Republic 2013-14 1.3 [0.9,1.8] 0.9 [0.6,1.4]

Côte d’Ivoire 2011-12 1.4 [1.0,2.2] 1.5 [0.9,2.4]

Ethiopia 2016 1.2 [0.9,1.7] 0.9 [0.5,1.6]

Gambia 2013 0.9 [0.6,1.4] 0.9 [0.6,1.4]

Ghana 2014 0.8 [0.5,1.2] 0.9 [0.6,1.4]

Kenya 2014 1.1 [0.8,1.7] 0.7* [0.5,0.9]

Kyrgyz Republic 2012 1.0 [0.6,1.7] 1.9* [1.1,3.2]

Lesotho 2014 1.0 [0.3,3.2] 1.8 [0.6,5.6]

Liberia 2013 1.4 [0.9,2.1] 1.5 [0.8,2.6]

Malawi 2015-16 1.1 [0.8,1.5] 1.4 [0.9,2.0]

Mali 2012-13 0.9 [0.6,1.2] 1.2 [0.4,3.4]

Mozambique 2011 1.4 [0.8,2.4] 1.4 [0.8,2.6]

Myanmar 2015-16 1.0 [0.6,1.8] 1.0 [0.6,1.5]

Namibia 2013 0.6 [0.3,1.2] 1.0 [0.5,2.1]

Nepal 2016 1.4 [0.8,2.4] 1.9 [1.0,3.7]

Nigeria 2013 1.1 [0.8,1.4] 1.3 [1.0,1.7]

Rwanda 2014-15 0.9 [0.5,1.7] 0.9 [0.6,1.2]

Senegal 2016 1.1 [0.5,2.1] 1.5 [0.8,2.7]

Sierra Leone 2013 1.4* [1.0,1.8] 1.5 [1.0,2.2]

Timor-Leste 2009-10 0.9 [0.7,1.2] 1.5 [0.7,3.0]

Tanzania 2015-16 1.1 [0.7,1.7] 1.3 [0.8,2.1]

Togo 2013-14 1.2 [0.8,1.8] 1.3 [0.8,2.3]

Uganda 2016 1.3 [1,1.8] 1.2 [0.9,1.7]

Zambia 2013-14 1.3 [1.0,1.6] 1.0 [0.8,1.3]

Zimbabwe 2015 1.4 [1.0,1.9] 1.1 [0.7,1.7]

Note: * p<0.05, ** p<0.01, *** p<0.001
54

Appendix Table 7 Adjusted odds ratios with 95% confidence intervals for delivering in a health facility by 

men’s involvement variables

Behavior

Survey 

year

Present during

ANC check-up 

versus not 

present

Joint decision 

on how to spend 

respondent’s 

earnings versus 

not joint

Country AOR 95% C.I. AOR 95% C.I.

Afghanistan 2015 1.1 [0.8,1.4] 1.0 [0.8,1.3]

Angola 2015-16 1.3 [0.8,2.1] 0.8 [0.5,1.2]

Armenia 2015-16 NC NC NC NC

Benin 2011-12 0.8 [0.5,1.4] 1.3 [0.6,2.7]

Burkina Faso 2010 1.1 [0.8,1.5] 0.7 [0.4,1.4]

Burundi 2010 1.8* [1.1,2.7] 1.0 [0.6,1.6]

Chad 2014-15 0.7 [0.5,1.1] 1.2 [0.6,2.4]

Comoros 2012 1.2 [0.6,2.5] 1.0 [0.5,2.3]

Congo Democratic 

Republic 2013-14 0.7 [0.5,1.0] 1.3 [0.9,1.8]

Côte d’Ivoire 2011-12 0.8 [0.5,1.3] 1.3 [0.8,2.1]

Ethiopia 2016 1.2 [0.9,1.6] 1.3 [0.7,2.5]

Gambia 2013 1.0 [0.7,1.5] 0.9 [0.5,1.5]

Ghana 2014 0.9 [0.5,1.6] 0.9 [0.5,1.5]

Kenya 2014 1.2 [0.9,1.7] 1.1 [0.8,1.5]

Kyrgyz Republic 2012 NC NC NC NC

Lesotho 2014 1.4 [0.4,4.3] 7.5** [2,28.3]

Liberia 2013 1.6* [1.1,2.4] 0.9 [0.5,1.8]

Malawi 2015-16 0.9 [0.5,1.6] 0.8 [0.4,1.6]

Mali 2012-13 1.2 [0.9,1.8] 1.1 [0.3,3.7]

Mozambique 2011 1.0 [0.7,1.6] 0.9 [0.5,1.5]

Myanmar 2015-16 1.8* [1.1,3.0] 0.6* [0.4,1.0]

Namibia 2013 1.5 [0.5,3.9] 2.2 [0.9,5.0]

Nepal 2016 1.0 [0.6,1.8] 1.1 [0.7,1.9]

Nigeria 2013 1.6*** [1.3,2.0] 1.7*** [1.3,2.2]

Rwanda 2014-15 1.6 [0.7,3.5] 1.3 [0.7,2.4]

Senegal 2016 1.0 [0.6,1.7] 1.2 [0.6,2.5]

Sierra Leone 2013 1.1 [0.7,1.5] 1.1 [0.7,1.7]

Timor-Leste 2009-10 1.1 [0.7,1.7] 0.7 [0.3,1.4]

Tanzania 2015-16 1.7* [1.1,2.6] 1.1 [0.7,1.7]

Togo 2013-14 1.3 [0.8,2.0] 0.9 [0.4,1.9]

Uganda 2016 1.9*** [1.3,2.6] 1.2 [0.8,1.8]

Zambia 2013-14 1.6*** [1.2,2.1] 0.9 [0.7,1.1]

Zimbabwe 2015 0.7 [0.5,1.2] 1.0 [0.6,1.6]

Note: * p<0.05, ** p<0.01, *** p<0.001; NC: not computed due to small number of 

observations or missing data
55

Appendix Table 8 Adjusted odds ratios with 95% confidence intervals for the youngest child 12-23 months 

with all three doses of DPT vaccine by men’s involvement variables

Behavior

Survey 

year

Joint decision on 

how to spend 

respondent’s 

earnings versus 

not joint

Country AOR 95% C.I.

Afghanistan 2015 0.8 [0.5,1.2]

Angola 2015-16 1.2 [0.6,2.3]

Armenia 2015-16 0.5 [0.1,1.7]

Benin 2011-12 0.9 [0.4,1.7]

Burkina Faso 2010 0.6 [0.2,1.4]

Burundi 2010 0.1 [0.0,1.0]

Chad 2014-15 1.1 [0.5,2.5]

Comoros 2012 1.8 [0.5,6.6]

Congo Democratic Republic 2013-14 1.0 [0.7,1.6]

Côte d’Ivoire 2011-12 2.1 [0.9,5.1]

Ethiopia 2016 1.3 [0.5,3.1]

Gambia 2013 1.9 [0.5,7.0]

Ghana 2014 1.6 [0.7,3.6]

Kenya 2014 0.7 [0.4,1.5]

Kyrgyz Republic 2012 2.6 [0.8,8.8]

Lesotho 2014 NC NC

Liberia 2013 0.6 [0.3,1.4]

Malawi 2015-16 1.3 [0.5,3.2]

Mali 2012-13 0.4 [0.1,1.4]

Mozambique 2011 0.5 [0.3,1.1]

Myanmar 2015-16 1.0 [0.6,1.9]

Namibia 2013 1.2 [0.3,5.2]

Nepal 2016 0.9 [0.2,3.9]

Nigeria 2013 1.9** [1.3,2.8]

Rwanda 2014-15 NC NC

Senegal 2016 9.5** [1.9,48.3]

Sierra Leone 2013 1.1 [0.5,2.5]

Tanzania 2015-16 2.9* [1.1,7.6]

Timor-Leste 2009-10 1.1 [0.3,3.9]

Togo 2013-14 1.4 [0.5,3.9]

Uganda 2016 1.4 [0.7,2.5]

Zambia 2013-14 1.4 [0.9,2.0]

Zimbabwe 2015 1.3 [0.6,2.9]

Note: * p<0.05, ** p<0.01, *** p<0.001; NC: not computed due to 

small number of observations
56

Appendix Table 9 Adjusted odds ratios with 95% confidence intervals for the youngest child under age 2 

with diarrhea that had treatment sought for them by men’s involvement variables

Knowledge Behavior

Survey 

year

Know to give 

more to drink 

when child has 

diarrhea

Joint decision on 

how to spend 

respondent’s 

earnings versus 

not joint

Country AOR 95% C.I. AOR 95% C.I.

Afghanistan 2015 1.9* [1.1,3.2] 1.1 [0.7,1.7]

Angola 2015-16 1.5 [0.7,3.3] 2.3 [0.9,6.2]

Armenia 2015-16 NC NC NC NC

Benin 2011-12 NC NC NC NC

Burkina Faso 2010 1.1 [0.6,1.8] 1.1 [0.4,3.5]

Burundi 2010 1.3 [0.8,2.1] 0.6 [0.3,1.2]

Chad 2014-15 1.6 [0.9,2.9] 1.0 [0.4,2.7]

Comoros 2012 5.1 [0.6,44.7] 1.7 [0.4,8.0]

Congo Democratic 

Republic 2013-14 1.6* [1.0,2.6] 1.0 [0.6,1.7]

Côte d’Ivoire 2011-12 0.5 [0.2,1.2] 0.9 [0.3,2.6]

Ethiopia 2016 1.0 [0.6,1.6] 0.1** [00,0.4]

Gambia 2013 0.8 [0.3,2] 1.6 [0.5,4.5]

Ghana 2014 NC NC NC NC

Kenya 2014 0.8 [0.5,1.4] 1.0 [0.6,1.7]

Kyrgyz Republic 2012 NC NC NC NC

Lesotho 2014 NC NC NC NC

Liberia 2013 0.6 [0.3,1.3] 0.7 [0.3,2.0]

Malawi 2015-16 1.4 [0.8,2.3] 1.0 [0.5,2.0]

Mali 2012-13 2.1 [0.7,6.6] 2.1 [0.2,19.0]

Mozambique 2011 1.3 [0.5,3.1] 0.2* [0.1,0.8]

Myanmar 2015-16 NC NC NC NC

Namibia 2013 NC NC NC NC

Nepal 2016 NC NC NC NC

Nigeria 2013 0.5* [0.3,0.9] 0.7 [0.4,1.2]

Rwanda 2014-15 1.0 [0.6,1.9] 0.7 [0.3,1.5]

Senegal 2016 1.7 [0.5,5.6] 4.0 [0.6,24.3]

Sierra Leone 2013 2.3 [0.7,8.1] 8.2 [0.6,103.4]

Timor-Leste 2009-10 0.9 [0.3,2.7] 1.1 [0.2,6.2]

Tanzania 2015-16 0.7 [0.3,1.7] 1.2 [0.4,3.3]

Togo 2013-14 2.5* [1.2,5.2] 1.1 [0.3,3.5]

Uganda 2016 0.8 [0.5,1.2] 2.2* [1.2,4.2]

Zambia 2013-14 0.9 [0.6,1.4] 0.8 [0.5,1.4]

Zimbabwe 2015 0.8 [0.5,1.3] 1.5 [0.6,3.5]

Note: * p<0.05, ** p<0.01, *** p<0.001; NC: not computed due to small number of 

observations