DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2018 No. 139

Augustine Ndaimani

Maxwell Mhlanga

Virgininia Dube-Mawerewere

June 2018

This document was produced for review by the United States Agency for 

International Development.

The Association between Women’s Empowerment and 

Uptake of Child Health Services: A Demographic and 

Health Survey-Based Synthesis

DHS Working Paper No. 139

The Association between Women’s Empowerment and 

Uptake of Child Health Services: A Demographic and 

Health Survey-Based Synthesis

Augustine Ndaimani1

Maxwell Mhlanga1

Virgininia Dube-Mawerewere1

ICF

Rockville, Maryland, USA

June 2018

1 University of Zimbabwe, College of Health Sciences, Harare, Zimbabwe.

Corresponding author: Augustine Ndaimani, International Health and Development, ICF, 530 Gaither 

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

tom.pullum@icf.com
Acknowledgments: A work of this magnitude can never occur in a vacuum. The authors wish to thank 

USAID for funding the research project through the DHS Fellows program implemented by ICF in 2018, 

and the University of Zimbabwe administration for sanctioning the DHS training and allowing time to 

participate in the DHS training that we conducted. There are some individuals who need special mention. 

We are grateful to Dr. Shireen Assaf, Dr. Gheda Temsah, and Ehab Sakr for their painstaking review of the 

scientific content of the paper. We also appreciate the capacity building that we received through Dr. 

Wenjuan Wang and Dr. Shireen Assaf. Our appreciation also goes to “Best!” co-facilitators Ehab Sakr and 

Henock Yebyo, and DHS fellows, for constructive critique instrumental in the shaping of this scientific 

endeavor.

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: 301-572-0950; fax: 

301-572-0999 or 301-407-6501; email: reports@dhsprogram.com; internet: www.dhsprogram.com.

Recommended citation:

Ndaimani, Augustine, Maxwell Mhlanga, and Virgininia Dube-Mawerewere. 2018. The Association 

between Women’s Empowerment and Uptake of Child Health Services: A Demographic and Health SurveyBased Synthesis. DHS Working Paper No. 139. Rockville, Maryland, USA: ICF.
iii

CONTENTS

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

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

ABSTRACT.................................................................................................................................................. ix

1 INTRODUCTION.............................................................................................................................. 1

2 LITERATURE REVIEW ................................................................................................................... 3

2.1 Women’s Empowerment and Its Determinants .................................................................. 3

2.2 Association between Women’s Empowerment and Child Health....................................... 3

2.3 Conceptual Framework ....................................................................................................... 4

3 DATA AND METHODS ................................................................................................................... 7

3.1 Data..................................................................................................................................... 7

3.2 Variables ............................................................................................................................. 7

3.3 Statistical Analysis .............................................................................................................. 8

4 RESULTS ........................................................................................................................................ 9

4.1 Sample Characteristics ....................................................................................................... 9

4.2 Mother’s Empowerment .................................................................................................... 10

4.3 Cross-tabulation of Selected Predictors of Uptake of Child Health Services ................... 13

4.4 Regression Results ........................................................................................................... 14

5 DISCUSSION................................................................................................................................. 17

5.1 Limitations ......................................................................................................................... 19

5.2 Potential Policy Implications ............................................................................................. 19

5.3 Conclusion ........................................................................................................................ 20

REFERENCES............................................................................................................................................ 21

v

TABLES

Table 1 Demographic characteristics of children and their parents among children 

age 12-23 months and among children under age 5 with diarrhea.............................. 9

Table 2 Mother’s empowerment and background characteristics........................................... 12

Table 3 Cross-tabulation of sociodemographic variables, mother’s empowerment, 

and uptake of child health services ............................................................................ 13

Table 4 Results of the multivariable logistic regression of uptake of child health services..... 16

vii

FIGURES

Figure 1 Conceptual framework.................................................................................................. 5

Figure 2 Mother’s empowerment.............................................................................................. 11

ix

ABSTRACT

Globally 1.5 million children die from illnesses that could be prevented through vaccination. Since 2010 

there has been stagnation in global coverage of child vaccination, despite many public health efforts. 

Women, who are caregivers for sick children in most societies, may not have the capacity to make decisions 

such as seeking health care. This study examines the association between mothers’ empowerment and 

uptake of child vaccination and diarrhea treatment, through a secondary analysis of data from the 2016 

Zimbabwe Demographic and Health Survey.

The results show that only 22% of mothers had high levels of decision-making, while 12% had full 

ownership of assets. For uptake of child health services, 76% of children age 12-23 months received basic 

vaccinations, and 42% of children under age 5 were treated for diarrhea. The unadjusted analysis showed a 

significant association between mother’s empowerment and child vaccination. However, no statistically 

significant association was found between mother’s empowerment and child health outcomes, after

controlling for other sociodemographic factors.

The regression analysis showed some statistically significant predictors of child health outcomes, however. 

Predictors that favored completion of basic vaccines included being in the middle wealth quintile compared 

with the poorest wealth quintile, having visited a health facility in the past 12 months, and having health 

insurance. Mothers who visited a health facility in the past 12 months had 50% higher odds of receiving 

treatment for diarrhea for their children under age 5 compared with mothers who did not visit a facility. 

Internet use by the mother also favored receiving treatment for children’s diarrhea.

Universal health insurance may improve child health outcomes. Advising mothers on websites to access 

child health messages, particularly where the household heads are 15-24 years old, may improve uptake of 

health messages.

Key Words: Demographic and Health Survey, treatment of diarrhea, uptake of child health services, 

vaccination, women’s empowerment

1

1 INTRODUCTION

In most societies women are the primary caregivers for the family and the sick—and at times are blamed 

for misfortunes and illness in their families. Women’s disempowerment has been significantly associated 

with poor maternal and child health outcomes and adverse reproductive health outcomes (Godha, 

Hotchkiss, and Gage 2013; Dimbuene et al. 2017). Women’s empowerment is multidimensional and has 

been operationalized using measures such as participation in making household decisions and having access 

to and control over assets (Cunningham et al. 2015; Afifi 2009).

Despite the existing studies, several questions remain unanswered concerning the measurement and 

aggregation of quantitative indicators of women’s empowerment and their associations with measures of 

child health status, such as the effect of women’s decision-making power on child morbidity and mortality,

and the association between women’s asset ownership and their uptake of child health services (Pratley 

2016; Ford et al. 2017). Studies have reported an association between women’s empowerment and child 

mortality, full vaccination, and nutritional status (Cunningham et al. 2015). Women’s empowerment has 

been found to be significantly and positively associated with child health outcomes. A study in Ethiopia 

revealed that low status of women and their disempowerment are highly associated with poor health 

outcomes (Wado 2013).

In Zimbabwe, few studies have tried to measure women’s empowerment quantitatively and to assess its 

association with child health outcomes. While the Zimbabwe Demographic and Health Surveys (ZDHS) 

report on women’s empowerment issues, the data have not been routinely analyzed to explore the 

association between indicators of women’s empowerment and child health outcomes. There has been 

minimal use of Demographic and Health Surveys (DHS) data for further statistical analysis of this topic,

and for interpretation to better inform health programs and health education.

This study assessed the association between women’s (mothers’) empowerment and selected child health 

outcomes, based on analysis of data from the 2015 ZDHS. Variables used to measure women’s 

empowerment include women’s control over their own earnings or their partner’s earnings, ownership of 

assets, and participation in decision-making on their own health. The child health outcomes that were 

assessed in the DHS were vaccination status and treatment status for diarrhea. The study will ultimately 

contribute to the achievement of critical strategic goals in health, such as Sustainable Development Goal 3 

and UNICEF strategic goals on child health, by highlighting significant correlates of child health outcomes.

3

2 LITERATURE REVIEW

2.1 Women’s Empowerment and Its Determinants

Studies have shown that women’s empowerment is a function of both demographic and socioeconomic 

factors in a society. Empowerment of women is the process of enabling them to have the power to navigate 

life’s challenges in all areas of their lives (Mahmud, Shah, and Becker 2012). Many studies have identified 

socioeconomic and demographic factors as determinants of women’s empowerment. These include

maternal age, spousal age difference, women’s education level, exposure to media (Gupta 2008), interspousal education difference, place of residence, household wealth, women’s employment status, women’s 

control over their own earnings and over husband’s earnings, ownership of assets, and family structure 

(Mahmud, Shah, and Becker 2012; Wado 2013).

Both demographic and socioeconomic factors have been reported to affect the decision-making ability of 

women on their own health and that of their children, since they influence women’s health care-seeking 

behavior (Upadhyay and Hindin 2005). Most studies assess women’s participation in decision-making 

through three types of household decision-making: woman’s own health care, major household purchases, 

and visits to family or relatives (Ibrahim, Tripathi, and Kumar 2015).

Factors that measure women’s autonomy, mobility, and control over resources have been reported to be 

direct measures of the level of women’s empowerment (Kabeer 2012). Some studies add a fourth domain 

that assesses the gender-role attitudes of women as a measure of women’s empowerment (Corroon et al.

2014). The backdrop of arguments in favor of making progress toward women’s empowerment is that there 

are inherent positive outcomes for families and societies.

2.2 Association between Women’s Empowerment and Child Health

The literature has demonstrated that women’s empowerment is linked to child health outcomes (Pratley 

2016). Issues around the child’s general hygiene and access to health care services include but are not 

limited to vaccination and long-term nutritional status, which depend upon the mother’s capacity to make 

decisions, including participation in decisions concerning the allocation of household resources (Kabeer 

2012). This then translates to the notion that child survival outcomes are shaped by women’s empowerment.

Studies have attempted to measure quantitatively women’s empowerment and its association with child 

health status. Many studies have acknowledged that the quantification of women’s empowerment is a 

complex process that is highly situational and sensitive to context. A systematic review by Pratley (2016)

revealed a statistically significant positive effect of women’s empowerment on child health outcomes such 

as child mortality, full vaccination, and nutritional status. Similarly, Na and colleagues evaluated the 

association between women’s empowerment and child feeding practices in sub-Saharan Africa by analyzing 

DHS data between 2010 and 2013 (Na et al. 2015). Their review assessed women’s empowerment using 

nine standard items, which were reduced to three main dimensions—economic, socio-familial, and legal. 

The study concluded that the likelihood of meeting recommended infant and young child feeding criteria 

was positively associated with the economic dimension of women’s empowerment.
4

A study in Ghana on women’s empowerment in agriculture and children’s health status yielded a 

statistically nonsignificant association between the two. However, children’s health status was found to be 

significantly associated with control variables such as mother’s education, child’s age, and place of 

residence (Zereyesus et al. 2017). A systematic review of women’s empowerment and child nutritional 

status showed that women’s empowerment was associated with the level of child malnutrition, though the 

findings were mixed (Gupta 2008). This study, as well as similar studies, concluded that women’s

empowerment domains may relate differently to different measures of child nutritional status. These results 

concur with an assessment of the association between women’s empowerment and maternal and child 

health in Benin (Alaofè et al. 2017), which found that efforts to improve nutrition in children can be 

enhanced by promoting women’s self-confidence and decision-making.

From the literature reviewed, a general consensus is that women’s empowerment is associated with child 

health status. However, most of these studies focused on women’s empowerment and child nutrition only. 

Very few sought to determine the association between women’s empowerment and other child health status 

indicators that are hypothesized to have an even closer association with women’s empowerment, such as 

treatment for children who get sick with different conditions, including pneumonia and diarrhea, and health 

care behavioral indicators such as immunization status of children.

This study addresses the gap in the literature on women’s empowerment and child health outcomes in 

Zimbabwe. The purpose of this study is to empirically explore the association between women’s 

empowerment and child health status and outcomes. The study addresses the following research questions:

1. What is the current status of women’s empowerment in Zimbabwe?

2. What is the level of uptake of child health services in Zimbabwe?

3. What is the association between mother’s empowerment and uptake of child health services?

2.3 Conceptual Framework

Child health is conceptualized as an outcome that is dependent on the empowerment of the mother in the 

Zimbabwean context. Mother’s empowerment for this research is operationalized as women having control 

over own earnings and their husband’s earnings, ownership of assets, employment status, and participation 

in household decision-making. The study also conceptualizes this empowerment against a backdrop of other 

covariates or sociodemographic factors that directly affect child health outcomes and also are indirectly 

associated with child health outcomes by their bearing on women’s empowerment. These confounding 

variables include women’s education, age, spousal age difference, marital status, occupation, health 

insurance coverage, and place of residence. Figure 1 illustrates the conceptual framework.
5

Figure 1 Conceptual framework

Women’s empowerment

● Decision-making: (visiting

relatives and friends,

healthcare, household

purchases, husband

earnings)

● Ownership of assets:

(house ownership, land

ownership, title deed

ownership, land title deed

ownership)

Child health

● Completion of basic vaccinations

● Treatment of diarrhea

Socio-demographic 

factors (women)

● Level of education

● Age

● Spousal age difference

● Marital status

● Occupation

● Health insurance

● Place of residence

● Locality

● Number of children

Demographic factors (child)

● Age

● Sex

● Birth order

7

3 DATA AND METHODS

3.1 Data

The study was conducted through secondary analysis of data from the 2015 Zimbabwe Demographic and 

Health Survey (Zimbabwe National Statistics Agency and ICF International 2016). The ZDHS was based 

on data obtained from de facto household females age 15-49 and males age 15-54. In the 2015 ZDHS,

11,196 households were selected through complex sampling, beginning with selection of 166 urban and 

234 rural clusters, followed by random selection of households from the clusters. The survey used the 2012 

Zimbabwe national census as a sampling frame.

The children’s recode file was used for the analysis; therefore, the unit of analysis is the child, and any 

variables referring to the women are identified as the mother’s characteristics. The file has data for children 

born to interviewed women in the previous five years (0-59 months). This includes data on the children’s 

demographic characteristics and such health-related characteristics as immunization and treatment of 

diarrhea, fever, and cough. Moreover, the file also contains data pertaining to the mothers’ 

sociodemographic variables and sexual and obstetric histories. The demographic and occupational 

characteristics of the mothers’ partners are also available in the same file. Since empowerment variables 

included decisions about husband’s/partner’s earnings, the study sample for this analysis included only 

married women—5,145 women, as well as 1,010 children under age 5 who had had diarrhea in the two

weeks before the survey, and 1,263 children age 12-23 months (the vaccination-age category).

3.2 Variables

Women’s empowerment was measured through two indicators: extent of participation in household 

decision-making, and ownership of assets. The women’s empowerment indicators were measured at the 

child level and will therefore also be referred to as the mother’s empowerment in the results. The extent of 

women’s participation in decision-making was determined by their responses to questions about who 

normally decides on social visits to friends and family, women’s own health care, large household 

purchases, and the use of husband’s earnings in the household. Possible responses included: respondent 

alone; respondent and husband/partner jointly; husband alone; or other people. The four responses were

recoded into husband or other (zero points), joint (1 point), and respondent alone (2 points). Similar 

recoding was also done for the variables regarding who decides on social visits, women’s health care, and 

household purchases. The decision-making variable was generated by summing the four decision-making 

items: who decides on social visits; women’s own health care; large household purchases; and the use of 

husband’s earnings. Thus the total decision-making scores ranged from zero to eight points. A score of zero 

showed that the woman was not involved in any decision-making, while a score of eight showed that the 

woman was making all of the decisions herself. We coded zero points as no participation in decisionmaking, 1-5 points as moderate decision-making, and 6-8 points as a high level of decision-making.

Concerning the second indicator, ownership of assets, in the original recode file ownership of land, title 

deeds to land, ownership of a house, and title deed to house are coded as zero for no ownership and one if 

the woman owned the asset alone or jointly with the husband/partner. In our analysis, an indicator for 

ownership of assets was generated by summing land ownership, house ownership, land title deed 
8

ownership, and house title deed ownership. By virtue of the coding of the parameters used to determine 

asset ownership, the overall assets ownership values ranged between zero and four. Thereafter, a sum of 

zero was coded as no ownership, 1-2 as moderate ownership, and 3-4 as full ownership.

Socioeconomic predictors in our study included women’s age, place of residence, level of education, 

religion, age of household head, spousal age difference, ownership of a mobile phone, use of the internet, 

health insurance, marital status, and partner and respondent occupation. In the ZDHS, age was determined 

by asking interviewed women their age at their last birthday. Respondents were asked their highest level of 

school attended: primary, secondary, or higher. They were also asked what their religion was: traditional, 

Roman Catholic, Protestant, Pentecostal, Apostolic sect, other Christian, Muslim, none, or other. In this 

study, religion was classified into Apostolic, Christian, or other. Christians included Roman Catholic, 

Protestant, Pentecostal, or other Christian. Women affiliated with Muslim, other, or no religion were 

classified as being of other religion.

Ownership of a mobile phone was ascertained through the question, “Do you own a mobile phone?” The 

question had a yes or no response. The ZDHS also asked women whether they had ever used the internet. 

For those who had used it, the frequency of internet use in the previous 12 months was also asked. 

Occupation categories were determined based on the International Standard Classification of Occupations 

(ISCO-08). Occupations in ISCO level 1 were classified as unskilled or other, level 2 as moderately skilled, 

and levels 3 and 4 as highly skilled.

In this study the outcome was child health, measured through vaccination and treatment of diarrhea. 

Children were considered to have had basic vaccinations if they had received all of the following vaccines: 

Bacille Calmette Guerin (BCG), three doses of pentavalent vaccine, three doses of oral polio vaccine, and 

one dose of measles vaccine. Vaccination status was calculated for children age 12-23 months. According 

to the Zimbabwe Immunization Code, the above-mentioned vaccines are complete by age 18 months.

The other outcome measure was treatment for children under age 5 with diarrhea symptoms in the last two 

weeks before the survey. Treatment of diarrhea meant getting health care and advice from formal referrals,

which exclude pharmacy, shops, or traditional practitioners. The unit of analysis was children within the 

vaccination age (12-23 months) and children under age 5 who developed diarrhea within two weeks before 

the survey.

3.3 Statistical Analysis

Weighted descriptive statistics and cross-tabulations were computed for all variables for each analysis 

sample for the child health outcomes. Cross-tabulation was also done between demographic variables and 

the mother’s empowerment variables, which included the 5,145 mothers in the child recode file. Before 

fitting any regression, pairwise correlation was conducted to identify the variables to be included in the 

multivariate models. Predictors with a pairwise correlation value above 0.5 were not used in the ultimate 

regression. For this reason, place of residence (for its collinearity with the wealth index), number of children 

(for its collinearity with the mother’s age), and age of the woman’s husband or spouse were removed from 

the final regression models. Adjusted logistic regressions were conducted for mother’s empowerment and 

the two child health outcomes controlling for socioeconomic variables. All analyses accounted for complex 

survey sampling design. Data was analyzed using STATA version 15.
9

4 RESULTS

4.1 Sample Characteristics

Table 1 provides a description of the two samples used for the analysis—one for the vaccination outcome 

and the other for the diarrhea treatment outcome. There were 1,263 children age 12-23 months in the 

vaccination sample, with a mean age of 17.4 months; 76% of the children received basic vaccinations. The

diarrhea sample included 1,010 children under age 5 who had diarrhea in the two weeks before the 2015 

ZDHS; 42% received treatment for it.

Among children age 12-23 months (the age for childhood vaccination), slightly more boys (51%) than girls 

(49%) were vaccinated. About half of the mothers (49%) were age 25-34, and 12% were older than their 

spouses or of equal age. About two-thirds (63%) had attained a secondary education, 64% owned a mobile 

phone, but 81% had never used the internet. Wealth was generally evenly distributed across the wealth 

quintiles, but with 25% of households in the poorest quintile and 15% in richest. Only 7% of mothers had 

health insurance, and 68% had visited a health facility in the previous 12 months. For children under age 5

who had had diarrhea before the survey, the demographic characteristics were similar to those for children 

between 12-23 months of age (see Table 1).

Table 1 Demographic characteristics of children and their parents among children age 12-23 months 

and among children under age 5 with diarrhea

Children age 12-23 months Children under age 5 with diarrhea

Variable Percentage Frequency Percentage Frequency

Sex of child

Male 51.3 648 53.0 535

Female 48.7 615 47.0 475

Birth order of child

1-4 84.0 1,061 86.6 875

5-10 16.0 203 13.1 133

11-14 0.0 0 0.3 3

Age of mother

15-24 35.7 451 36.6 370

25-34 49.3 622 49.6 501

35-49 15.0 190 13.8 139

Mother’s place of residence

Urban 28.9 365 31.4 318

Rural 71.1 898 68.6 693

Mother’s level of education

No education 1.1 14 1.6 16

Primary 32.3 408 32.9 332

Secondary 62.6 791 63.1 638

Higher 4.0 51 02.4 24

Religion of mother

Apostolic 51.5 650 51.4 519

Christian 40.7 514 41.0 414

Other 7.9 99 7.7 78

Age of father

15-24 8.0 87 8.7 75

25-34 45.7 486 5.2 45

35-49 41.0 444 49.8 432

50+ 5.3 57 36.3 315

(Continued...)
10

Table 1—Continued

Children age 12-23 months Children under age 5 with diarrhea

Variable Percentage Frequency Percentage Frequency

Age of household head

15-24 9.7 122 9.6 97

25-34 34.0 430 31.4 317

35-49 38.8 490 37.9 383

50+ 17.5 222 21.1 214

Sex of household head

Male 61.6 778 65.6 663

Female 38.4 486 34.4 348

Age difference between mother and father

Mother older 3.1 34 3.0 26

Equal age 9.0 98 7.0 61

Mother 2-5 years younger 41.4 448 43.2 375

Mother 6-9 years younger 25.8 280 25.3 220

Mother 10+ years younger 20.7 224 21.4 186

Ownership of mobile phone by mother

Yes 63.7 805 62.0 627

No 36.3 458 38.0 384

Internet use by child’s mother

Never 80.6 1,018 81.0 819

Yes 19.4 245 19.0 192

Wealth quintile of mother

Poorest 25.0 316 25.2 254

Poorer 20.1 254 19.1 193

Middle 17.5 221 16.4 166

Richer 22.4 283 24.7 249

Richest 15.0 190 14.7 148

Number of children born to child’s mother

1-3 69.8 882 69.6 704

4-7 29.0 366 28.8 291

8-14 1.2 15 1.6 16

Visit to health facility in past 12 months

Yes 68.3 863 70.1 709

No 31.7 401 29.9 302

Health insurance

Yes 7.4 94 4.2 43

No 92.6 1,170 95.8 968

Occupation of father

Unskilled and other 23.8 256 22.9 197

Moderately skilled 68.6 740 70.7 609

Highly skilled 7.7 83 6.4 55

4.2 Mother’s Empowerment

Figure 2 shows a description of the variables used to construct the mother’s empowerment variable. This 

description is based on the 5,145 mothers who are married and with children under age 5. Generally, 

mothers were somewhat more empowered in decision-making compared with their ownership of assets. 

Still, most mothers exercised only moderate decision-making power. Only 16% of mothers with children 

under age 5 had high participation in decision-making, while 58% had moderate participation, and 31% had 

no participation. Only 13% of the mothers had a high level of ownership of assets, while 49% had moderate 

ownership, and 39% had no ownership of assets.
11

Figure 2 Mother’s empowerment

As Table 2 shows, mother’s empowerment indicators differ by background characteristics. Statistically 

significant correlates of mother’s participation in decision-making include place of residence, level of 

education, sex of household head, ownership of mobile phone, household wealth quintile, health insurance, 

and husband’s occupation. Table 2 also shows that most demographic variables had statistically significant 

associations with ownership of assets, except sex of household head, inter-spousal age difference, health 

insurance coverage, and spouse’s occupation. The largest proportion of mothers with no ownership of

assets, at 60%, were in the richer wealth quintile, followed by the richest wealth quintile, at 47%. Mothers

in the poorest wealth and poorer wealth quintiles were more likely to have high ownership of assets 

compared with mothers in the richest and richer quintiles.

0

20

40

60

80

100

No participation

Moderate decision making

High decision making

No asset ownership

Moderate ownership

Full ownership
12

Table 2 Mother’s empowerment and background characteristics

Participation in decision-making Ownership of assets and title deeds

Variable No Moderate High

Chi-square

p value No Moderate High

Chi-square 

p value

Age

15-24 2.2 77.0 20.7

0.078

51.7 39.9 8.4

< 0.001

25-34 2.4 78.3 19.3 37.0 50.1 13.0

35-49 3.2 72.3 24.5 26.9 56.3 16.8

Place of residence

Urban 1.2 74.6 24.2

0.010

57.7 33.3 8.9

Rural 3.1 77.7 19.2 30.6 55.3 14.1 < 0.001

Mother’s level of education

No education 4.1 72.4 23.5

< 0.000

30.4 61.5 8.1

< 0.001

Primary 4.6 75.3 20.1 32.4 55.4 12.2

Secondary 1.5 77.4 21.1 42.8 44.7 12.5

Higher 0.6 79.3 20.1 37.1 48.2 14.7

Mother’s religion

Apostolic 3.8 77.4 18.8

0.004

32.5 54.0 13.5

< 0.001

Christian 1.1 76.2 22.7 45.4 42.8 11.7

Other 1.5 74.7 23.7 48.6 42.1 9.3

Age of father

15-24 1.9 79.0 19.0

0.007

61.8 33.8 4.4

< 0.001

25-34 1.6 78.4 20.0 46.0 44.5 9.6

35-49 2.8 75.9 21.3 30.7 53.6 15.8

50+ 7.1 68.1 24.8 23.3 57.7 18.9

Age of household head

15-24 2.2 78.7 19.1

0.003

49.4 40.5 10.2

< 0.001

25-34 3.9 75.6 20.4 39.3 49.1 11.5

35-49 1.8 78.7 19.5 31.4 53.6 15.0

50+ 0.9 73.2 25.9 52.1 38.0 9.8

Sex of household head

Male 2.1 79.2 18.7

< 0.000

38.2 49.2 12.6

Female 3.3 71.4 25.3 40.6 47.1 12.3 0.522

Age difference between 

child’s mother and father

Woman older 4.1 74.5 21.4

0.002

38.2 49.7 12.1

0.096

Equal age 1.8 73.5 24.6 43.8 46.0 10.2

Woman 2-5 years younger 1.8 78.7 19.4 41.2 47.0 11.8

Woman 6-9 years younger 1.6 78.6 19.8 38.7 48.8 12.5

Woman 10+ years younger 5.1 71.9 22.9 32.9 52.2 14.9

Ownership of mobile phone 

by mother

Yes 2.0 76.3 21.7

0.053

40.1 47.2 12.7

No 3.4 77.8 18.8 36.7 51.3 12.0 0.233

Internet use by mother

Never 3.0 76.9 20.1

0.001

35.9 50.8 13.3

Yes 0.5 76.0 23.6 51.8 39.1 9.1 < 0.001

Wealth quintile

Poorest 3.1 78.8 18.1

0.073

22.3 63.2 14.5

< 0.001

Poorer 3.2 76.2 20.6 30.0 55.9 14.0

Middle 3.4 76.5 20.0 37.2 49.5 13.3

Richer 2.0 75.3 22.7 60.0 32.2 7.8

Richest 0.4 76.7 22.9 46.5 40.1 13.4

Number of children born to 

child’s mother

1-3 1.6 77.8 20.6

0.000

45.2 44.7 10.1

< 0.001

4-7 3.0 76.2 20.8 27.7 55.2 17.1

8-14 19.9 56.6 23.4 29.4 58.0 12.6

Visit to health facility in past 

12 months

Yes 1.4 76.5 22.1

0.000

39.7 46.7 13.5

No 4.8 77.2 18.0 37.5 52.1 10.4 0.032

Health insurance

Yes 0.2 84.0 15.8

0.000

40.5 46.0 13.5

No 2.7 76.0 21.3 38.8 48.8 12.4 0.777

Occupation of father

Unskilled and other 1.8 79.0 19.2

0.430

36.9 51.5 11.5

0.561

Moderately skilled 2.7 75.7 21.6 39.9 47.6 12.5

Highly skilled 2.4 79.4 18.2 37.1 48.5 14.4
13

4.3 Cross-tabulation of Selected Predictors of Uptake of Child Health

Services

Among the sample, 76% of children received the basic vaccinations, and 61% of children who had diarrhea 

received treatment for it. For most sociodemographic characteristics, there was a generally similar pattern 

in the vaccination rate. Notable disparities occurred for some predictors of vaccination, however. As Table 

3 shows, 92% of the children of mothers with no education and 90% of children of mothers with higher 

than secondary education received the basic vaccinations. Similarly, 86% of children in households in the 

middle wealth quintile and 90% of children whose mothers had health insurance received the basic 

vaccinations. On the contrary, only 27% of children whose mothers had 8-14 children received the basic 

vaccinations. Statistically significant predictors of receiving basic vaccinations were mother’s level of 

education, internet use, number of children born, having visited a health facility in the past 12 months, and 

having health insurance.

Treatment for diarrhea was generally low—overall, 36% and 55% of children with recent diarrhea received 

treatment at a formal health center. The statistically significant predictors of treatment of child diarrhea 

were: age of household head, inter-spousal age difference, internet use, number of children, and visit to a 

health facility in the past 12 months.

Table 3 Cross-tabulation of sociodemographic variables, mother’s empowerment, and uptake of child 

health services

Variable 

Completed the basic 

vaccinations

Received treatment 

for diarrhea

%

Chi-square 

p value %

Chi-square

p value

Age

0.895 0.500

15-24 75.0 42.2

25-34 76.5 38.5

35-49 76.3 35.5

Place of residence

Urban 79.5

0.173

40.6

Rural 74.4 38.9 0.728

Level of education

No education 91.5

0.002

52.3

0.097

Primary 69.5 32.5

Secondary 77.9 42.3

Higher 90.2 49.7

Religion

Apostolic 72.6

0.110

37.7

0.417

Christian 79.1 42.3

Other 80.0 35.2

Age of husband

15-24 78.4

0.212

38.2

0.954

25-34 75.3 37.0

35-49 79.7 41.5

50+ 64.4 39.6

Age of household head

15-24 76.4

0.977

35.9

0.024

25-34 76.6 37.8

35-49 75.1 56.1

50+ 75.7 40.7

Sex of household head

Male 77.3

0.258

37.4

Female 73.6 43.4 0.100

(Continued...)
14

Table 3—Continued

Variable 

Completed the basic 

vaccinations

Received treatment 

for diarrhea

%

Chi-square 

p value %

Chi-square

p value

Age difference between woman and husband

Woman older 81.8

0.037

50.5

0.058

Equal age 73.6 38.5

Woman 2-5 years younger 81.8 33.3

Woman 6-9 years younger 72.1 47.6

Missing 70.6 42.5

Ownership of mobile phone

Yes 77.9

0.076

40.2

No 72.5 38.1 0.598

Internet use

Never 74.5

0.042

36.5

Yes 81.4 52.0 0.002

Wealth quintile

Poorest 70.8

0.009

34.7

0.205

Poorer 69.4 40.1

Middle 85.6 38.8

Richer 77.9 38.1

Richest 78.8 49.6

Number of children born

1-3 79.6

< 0.001

41.2

0.032

4-7 68.7 37.5

8-14 27.3 0.0

Visit to health facility in past 12 months

Yes 81.0

< 0.001

42.1

No 64.4 33.3 0.034

Health insurance

Yes 90.2

0.009

55.4

No 74.7 38.7 0.077

Occupation of husband

Unskilled and other 73.3

0.400

37.4

0.927

Moderately skilled 77.3 39.2

Highly skilled 84.2 38.9

Participation on decision-making

No participation 79.3

0.422

62.1

0.813

Moderate 74.8 40.1

High 77.8 37.2

Ownership of assets

No ownership 80.8

0.072

41.1

0.279

Moderate 73.5 35.7

Full ownership 76.3 45.1

4.4 Regression Results

The study sought to determine the association between mother’s empowerment indicators and uptake of 

child health services. Table 4 shows the adjusted odds ratios for predictors of vaccination and treatment for 

diarrhea. After controlling for the age, sex, birth order of the child, and other socio-demographic predictors, 

there was no statistically significant association between mothers’ empowerment indicators and child health 

outcomes. As Table 4 shows, however, there were some statistically significant associations between 

background characteristics and child health outcomes. Predictors of completion of the basic childhood 

immunizations included being in the middle wealth quintile compared with the poorest quintile (AOR=2.3*, 

95% CI=1.2; 4.3), having visited a health facility in the past 12 months compared with not having visited 

(AOR=2.3***, 95% CI=1.6; 3.3), and having health insurance compared with having none (AOR=3.1*, 

95% CI=1.1; 9.0). Higher birth order was associated with lower odds of receiving vaccination after 

controlling for other factors (AOR=0.3***, 95% CI=0.2; 0.5).
15

Table 4 shows that the odds of receiving treatment for diarrhea for children whose mothers visited a health 

facility in the past 12 months were 1.5 times higher than for children whose mothers did not visit a health 

facility. The mother’s use of the internet favored treatment for childhood diarrhea (AOR=2.1*, 95% CI=1.2;

3.7). Odds of receiving treatment for diarrhea were lower for children with a household head age 25-34 

compared with age 15-24 (AOR=0.3***, 95% CI=0.2;0.6). Other statistically significant predictors of not 

receiving treatment for diarrhea among children under age 5 included birth order of 5-10 compared with 1-

4 (AOR=0.5*, 95% CI=0.3; 0.97); household head age 25-34 compared with age 15-24 (AOR=0.3***, 

95% CI=0.2 ;0.6); household head age 35-49 compared with age 15-24 (AOR= 0.4**, 95% CI=0.2;0.8); 

and household head age 50 or older compared with age 15-24 (AOR=0.4**, 95% CI=0.2;0.8).
16

Table 4 Results of the multivariable logistic regression of uptake of child health services

Variable 

Completed the basic 

vaccinations

Received treatment for 

diarrhea

AOR 95% CI AOR 95% CI

Mother’s participation in decision-making (ref.: no 

participation)

Moderate 2.4 0.8 - 6.8 0.7 0.2 - 2.2

High 2.8 0.9 - 8.7 0.6 0.2 - 2.0

Ownership of assets by mother (ref: no ownership)

Moderate ownership 0.8 0.5 - 1.1 0.9 0.6 - 1.3

Full ownership 0.9 0.5 - 1.7 1.3 0.7 - 2.2

Age of child 0.97 1.0 - 1.1 1.0 0.99 - 1.02

Birth order number of child (ref.: 1-4)

5-10 0.3*** 0.2 - 0.5 0.5* 0.3 - 0.97

Sex of child (ref.: male)

Female 1.1 0.8 - 1.6 1.1 0.8 - 2.1

Age of mother (ref.: 15-24)

25-34 1.3 0.7 - 2.2 1.3 0.8 - 2.0

35-49 2.2 1.0 - 4.9 1.5 0.7 - 3.2

Inter-spousal age difference (ref.: same age)

Woman older 1.6 0.4 - 5.6 1.6 0.5 - 5.3

Spouse 2-5 years older 1.4 0.7 - 2.9 0.7 0.3 - 1.4

Spouse 6-9 years older 0.9 0.4 - 1.8 1.4 0.6 - 3.0

Spouse older by 10 or more years 1.1 0.5 - 2.2 1.01 0.4 - 2.4

Age of household head (ref.: 15-24)

25-34 0.8 0.4 - 2.6 0.3*** 0.2 - 0.6

35-49 0.98 0.4 - 2.6 0.4** 0.2 - 0.8

50-95 0.9 0.4 - 2.3 0.4** 0.2 - 0.8

Mother’s level of education (ref.: no education)

Primary 0.2 0.02 - 2.1 0.1 0.02 - 1.1

Secondary 0.3 0.03 - 2.6 0.2 0.02 - 1.4

Higher 0.3 0.03 - 4.3 0.1 0.008 - 1.04

Religion of mother (ref.: other)

Apostolic 0.5 0.2 - 1.3 1.3 0.6 - 2.6

Christian 0.6 0.2 - 1.6 1.2 0.5 - 2.6

Sex of household head (ref.: male)

Female 0.8 0.5 - 1.3 1.3 0.9 - 1.9

Ownership of mobile phone by mother (ref.: does 

not own a mobile phone)

Yes 1.1 0.7 - 1.7 0.9 0.6 - 1.3

Use of internet by mother (ref.: does not use the 

internet)

Yes 1.3 0.7 - 2.5 2.1* 1.2 - 3.7

Mother’s wealth index (ref.: poorest quintile)

Poorer 0.9 0.5 - 1.5 1.4 0.9 - 2.3

Middle 2.3* 1.2 - 4.3 1.1 0.6 - 2.0

Richer 1.03 0.5 - 2.1 0.8 0.4 - 1.6

Richest 0.6 0.3 - 1.2 1.4 0.7 - 2.8

Visit to health facility in past 12 months (ref.: no 

visit)

Yes 2.3*** 1.6 - 3.3 1.5* 1.01 - 2.2

Health insurance cover (ref.: no insurance coverage)

Yes 3.1* 1.1 - 9.0 1.2 0.5 - 3.1

Mother’s husband or partner's occupation (ref.: 

unskilled)

Moderately skilled 0.9 0.6 - 1.5 1.02 0.7 - 1.6

Highly skilled 1.2 0.4 - 3.6 0.9 0.4 - 2.0

* p<0.05, ** p<0.01, *** p<0.001 
17

5 DISCUSSION

This study sought to determine the levels of mother’s empowerment and to assess the association between 

mother’s empowerment and uptake of child health services in Zimbabwe, through a secondary analysis of 

data from the 2016 Demographic and Health Survey. Mother’s empowerment was assessed by the extent 

of mother’s decision-making power and asset ownership. Decision-making power was a composite 

variable, which was a weighted summation of mother’s ability to decide on social visits, ability to decide 

on own health care, ability to participate in decision-making on household purchases, and ability to decide 

on husband’s earnings. Asset ownership was also a composite variable, being a weighted summation of 

house and land ownership with or without title deeds. Uptake of child-care services was assessed through 

two indicators, namely basic vaccination status of children age 12-23 months and care seeking for treatment 

of diarrhea for children under age 5.

There was no significant association found between mother’s empowerment variables and treatment of 

diarrhea. This can be explained by the fact that diarrheal diseases are generally not considered a serious 

condition by Zimbabwean women. Most women, regardless of their decision-making power, will use home 

remedies and other traditional therapies to try to manage diarrhea. Women will generally report to a health 

facility for professional care if the child has bloody diarrhea or chronic diarrhea, or when the child is 

severely dehydrated. Such poor health-seeking behaviors can be addressed by intensive community 

awareness campaigns on the danger signs of diarrhea and the importance of seeking prompt treatment.

The study hypothesized that mother’s empowerment is determined by sociodemographic characteristics 

such as level of mother’s education, age, inter-spousal age difference, partner’s occupation, place of 

residence, age of household head, wealth index, and number of children born. These assumptions were 

supported by the study results, as these factors were associated with either mother’s decision-making power 

or asset ownership, decision-making on household purchases, or ability to decide on husband’s earnings. 

Some studies have also reported an association between sociodemographic characteristics and women’s 

decision-making autonomy on health care (Pambe, Gnoumou, and Kabore 2013). A study in Nigeria based 

on 2013 DHS data singled out place of residence, age, education, religion, wealth index, occupation, home 

ownership, and husband’s occupation as independent factors associated with women’s decision-making on 

health care (Corroon et al. 2014).

After controlling for sex of the child, age of household head, inter-spousal age difference, number of 

children, health insurance coverage, and respondent’s occupation, significant predictors of mother’s 

participation in decision-making were highest education level of the mother, religion, ownership of mobile 

phone, wealth index, and partner’s occupation. Mothers in the lowest wealth quintile were less likely to 

participate in decision-making compared with mothers in higher wealth quintiles. A related study also 

reported a similar association between wealth quintiles and women’s ability to use and decide on modern 

contraception (Kabeer 2012). In Zimbabwe, women who can make independent or mutual decisions on 

family matters often have a financial muscle and are able to pay for their own health services and their 

children’s. Children from households in which women had no say on their husband’s earnings were less 

likely to receive full basic vaccinations. Half of women in the survey were not working, so consequently 

their ability to spend husband’s earnings was even more critical. A systematic review held in developing 

countries in 2016 reported a statistically significant association between women’s empowerment and child 
18

health outcomes such as child mortality, full vaccination and nutritional status of children (Pratley 2016). 

A study in Kenya reported similar results on women’s empowerment and wealth. Female partner’s 

participation in decision-making was associated with increased wealth in the rural regions (Upadhyay and 

Hindin 2005). There might be a great need to intensify awareness campaigns on gender equality and 

women’s rights as stipulated in the Convention on the Elimination of All Forms of Discrimination and 

Violence against Women. This would improve child health outcomes.

Mothers whose husbands were not working were more likely to control household assets and husband’s 

resources. This result clearly brings out the change of power dynamics in households when men’s status 

goes down. Men who do not work have less financial power and less coercive power in the context of 

Zimbabwe. When the partner/husband is not working, the wife automatically takes the leading role and can 

demand that the husband declare what little income he gets. In African societies the financial burden for a 

household is entirely placed on the husband, and if the situation changes he become more vulnerable, and 

more likely to face a backlash from his wife and to exercise less independent power on spending.

In the study, mothers whose husbands were under age 34 were less likely to own assets compared with 

those whose husbands or partners were older, with the likelihood of asset ownership increasing with 

increasing age groups of partners. This can be explained by the socioeconomic status of the country. The 

majority of the young men and women in Zimbabwe are still dependent on their parents and relatives for 

survival owing to the country’s high unemployment rate. This increases their probability of not owning any 

asset.

Diarrheal diseases among children under age 5 (0-59 months) had a prevalence of 17%. Of the children 

who had diarrhea in the preceding two weeks, only 42% received medical treatment from a professional

service provider. This means that the majority of the children who had diarrhea were treated through other 

means than from a professional service provider (Zimbabwe Demographic Health Survey and ICF 2016). 

Such practices predispose children to further infection and increase the risk of conditions like environmental 

enteropathy, which have been associated with high levels of stunting in children age 0-59 months and poor 

development (Prendergast and Kelly 2016).

Although two-thirds of the mothers in the study had mobile phones, 79% had never used the internet. A lot 

of social marketing for positive behavior change is done on the internet and at times via smart phones. Lack 

of access to these services is disempowering to women. The results are a true reflection of the Zimbabwean 

rural women, who hardly have access to the internet. Those who have mobile phones usually possess an 

outdated version not compatible with new technologies. The study found that internet use increased the 

likelihood of treatment for diarrhea among children age 0-59 months compared with those whose mothers

had no internet access. As for vaccination, this finding strengthens the importance of media and virtual 

social networks in social behavior change communication and promotion of good health care practices. 

This could also explain why women who visited a health facility in the last 12 months were more likely to 

use child health services, because they would receive information and education from the health providers 

at the facility.

Only 9% of the mothers in the study had health insurance coverage. This could partly be explained by the 

fact the half of the mothers were unskilled workers who relied on part-time jobs for survival. This was 

worsened by the fact that another 47% of the participants were not working at all. Failure to have insurance 
19

coverage greatly increases the likelihood that women will not access health care services, and it has a 

negative bearing on child health outcomes, especially in cases of health emergencies. The government of 

Zimbabwe should consider introducing community health insurance models, since they have been proven 

elsewhere to be effective in enhancing community solidary-funding systems for health care in poor-resource

settings.

The study found that having visited a health facility in the last 12 months increased the odds of having a 

child vaccinated by 130%. This shows that continuous interaction with the health facility is empowering to 

mothers on recommended health practices, and this has ripple effects on positive child health outcomes.

Women in the poorest wealth quintile were less likely to get their children fully vaccinated compared with 

those in the middle quintile. This could be due to problems of poor access that are financially related, 

including lack of cash for transport costs and a generalized fear of going to the clinic without appropriate 

clothing for themselves and the child. Women in the richest wealth quintile were less likely to have their 

children vaccinated due to a different reason. In Zimbabwe, women who are rich often do not prioritize 

vaccination because they do not want to queue in public health centers; as a result they get their health 

services from a private service provider. Most of the private health service providers do not prioritize child 

immunization services, since this is a free service offered in the public health sector, while the private sector 

focuses on profit-making health services. The governments in developing countries should monitor the 

implementation of vaccination programs in the private sector and also target the elite women with social 

behavior change strategies. A similar study reported that women's education, wealth quintile, and region 

are the most important determinants for the use of child health care services (Pambe, Gnoumou, and Kabore 

2013).

There was no significant association between mothers’ empowerment variables and basic vaccination status

of children age 12-23 months. A systematic review in 75 countries on gender empowerment also reported 

that child immunization status has no statistically significant association with women’s decision-making 

power (Thorpe et al. 2016).

5.1 Limitations

The study results only represented children of de facto mothers, leaving out other children who might also 

have had diarrhea and who were eligible for basic vaccinations in the same period. The sample size was 

also relatively small for the child health outcomes, especially treatment of diarrhea, which reduced the 

power to detect statistical significance. Finally, the measures of women’s empowerment used for this study 

may not be good measures for Zimbabwe, and new ways of measuring women’s empowerment could be 

employed in future studies.

5.2 Potential Policy Implications

The governments in developing countries need to consider viable and sustainable health insurance coverage

schemes for the general population. Approaches like the community health care funding schemes will go a 

long way not only in cushioning communities on health matters, but also in enhancing community 

ownership and accountability on their health matters.
20

5.3 Conclusion

In this study, an association of mother’s empowerment variables with uptake of child health services for 

vaccination and treatment of diarrhea was not found to be statistically significant after adjusting for other 

variables. However, both women’s empowerment and uptake of child health care services were 

significantly associated with sociodemographic background characteristics. There is need for policymakers 

to strengthen internet-based social behavior change communication strategies, as this will be likely to 

increase uptake of child health services. Policymakers in low-income countries can consider promoting 

community-initiated health insurance programs, such as community revolving funds, to cover children’s 

health care.
21

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