DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2014 No. 104

Henock G. Yebyo

Mussie A. Gebreselassie

Alemayehu B. Kahsay

August 2014

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

International Development.

Individual and Community-Level Predictors 

of Home Delivery in Ethiopia:

A Multilevel Mixed-Effects Analysis of the 

2011 Ethiopia National

Demographic and Health Survey
DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2014 No. 104

Henock G. Yebyo

Mussie A. Gebreselassie

Alemayehu B. Kahsay

August 2014

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

International Development.

Individual and Community-Level Predictors 

of Home Delivery in Ethiopia:

A Multilevel Mixed-Effects Analysis of the 

2011 Ethiopia National

Demographic and Health Survey
DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2014 No. 104

Henock G. Yebyo

Mussie A. Gebreselassie

Alemayehu B. Kahsay

August 2014

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

International Development.

Individual and Community-Level Predictors 

of Home Delivery in Ethiopia:

A Multilevel Mixed-Effects Analysis of the 

2011 Ethiopia National

Demographic and Health Survey

Individual and Community-Level Predictors of Home Delivery in Ethiopia: 

A Multilevel Mixed-Effects Analysis of the 2011 Ethiopia National 

Demographic and Health Survey 

Henock G. Yebyo 1

Mussie A. Gebreselassie 1

Alemayehu B. Kahsay 1 

 

ICF International 

Rockville, Maryland, USA 

August 2014 

1

 School of Public Health, College of Health Sciences, Mekelle University, Mekelle, Ethiopia 

Corresponding author: Henock G. Yebyo, School of Public Health, College of Health Sciences, Mekelle University, 

Ethiopia; E-mail: henokyebyo@yahoo.com 
ACKNOWLEDGEMENTS 

The authors would like to thank USAID and ICF International for their technical and 

financial support. We are very grateful for Dr. Wenjuan Wang, Dr. Sarah Staveteig, Yohannes 

Dibaba, Simon Peter Kibira, and Damian Jeremia Damian for their unreserved contribution for 

the completion of our paper. Our special thanks also goes to Rebecca Winter for her detailed 

review of the paper. 

The DHS Working Papers series is an unreviewed prepublication series of papers reporting on research in 

progress that is based on Demographic and Health Surveys (DHS) data. This research is 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 International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA. Phone: +1 

301-407-6500; Fax: +1 301-407-6501; Email: reports@dhsprogram.com; Internet: 

www.dhsprogram.com. 
ABSTRACT 

Introduction: In Ethiopia, despite existent intensive efforts to improve maternal health, 

the proportion of births delivered at home remains high and is still the top priority among the 

national health threats. The study aimed to examine effects of individual and community-level 

factors in women’s decision to deliver at home versus in a health facility. 

Methods: Data for this study were obtained from the nationally representative 2011 

Ethiopia Demographic and Health Survey (EDHS 2011) and focused on a sample from 576 

communities of 7,908 women whose most recent births were within five years preceding the 

survey. The data were analyzed using a two level-mixed-effects logistic regression model to 

determine the individual and community-level factors associated with place of delivery. 

Results: In Ethiopia, 88% of deliveries took place at home. Both individual and 

community-level factors were associated with women’s choice for place of delivery. Lower 

educational levels of mothers (OR=2.74; 95% CI: 1.84, 4.70) and their husbands (OR=2.31; 95% 

CI: 1.68, 3.18) were both positively associated with the odds of giving birth at home. The net 

odds of home delivery among mothers without antenatal care (ANC) visits for their recent 

pregnancy was 3.7 times higher than among mothers who made the recommended four or more 

ANC visits (OR=3.72; 95% CI: 2.85, 4.83). Non-exposure to radio or television messages 

(OR=1.51; 95% CI: 1.13, 2.01), parity of six or more births, (OR=2.68, 95% CI: 1.96, 3.68) and 

perceived problems reaching health facilities due to distance (OR=1.29, 95% CI: 1.03, 1.62) 

were positively associated with home birth. With regard to the community-level characteristics, 

rural communities (OR=4.67, 95% CI: 3.06, 7.11), pastoralist communities (OR=4.53, 95% CI: 

2.81, 7.28), communities with higher poverty levels (OR=1.49; 95% CI: 1.08, 2.22), and those 

with lower ANC utilization (OR=2.01, 95% CI: 1.42, 2.85) and women in areas with perceived 

problem of distance to health facilities (OR=1.29; 95% CI: 1.03, 1.62) had a positive influence 

on women to give birth at home. 

Conclusion: Not only individual characteristics of the women but also community-level 

factors determine women’s decision to deliver at home. Thus efforts to decrease the proportion 

of births delivered at home in Ethiopia should focus both on individuals and on communities. 
Keywords: Community-level factors, Home delivery, Individual factors, Institutional 

delivery, multilevel analysis
ACRONYMS 

ANC Antenatal Care 

CI Confidence Interval 

CMC Century Month Code 

CSA Central Statistical Agency 

EA Enumeration Area 

EDHS Ethiopia Demographic and Health Survey 

HMIS Health Management Information System 

ICC Intra Class Correlation 

MMR Maternal Mortality Ratio 

OR Odds Ratio 

PCA Principal Components Analysis 

PCV Proportional Change in Variance 

SNNP Southern Nations, Nationalities and Peoples 

SSA Sub-Saharan Africa 

VIF Variance Inflation Factor 

WHO World Health Organization 

1 

1 INTRODUCTION 

Globally, the maternal mortality ratio (MMR) has fallen by 45% between 1990 and 2013. 

There were an estimated 289,000 maternal deaths in 2013, yielding a MMR of 210 maternal 

deaths per 100,000 live births. Developing countries accounted for 99% of global maternal 

deaths; sub-Saharan Africa (SSA) alone accounted for 62% of them. The MMR in developing 

countries was 14 times higher than in developed regions. 

Ethiopia is one of the ten countries that comprised 58% of global maternal deaths 

estimated in 2013 (WHO et al. 2013). Based on data in the 2011 Ethiopia Demographic and 

Health Survey (EDHS 2011), the MMR was estimated at 676 maternal deaths per 100,000 live 

births (CSA et al. 2012). This makes it very difficult to achieve the Millennium Development 

Goal (MDG) of 267 maternal deaths per 100,000 live births by 2015 (FMOH 2010). 

Different reasons account for the high MMR in sub-Saharan countries, including 

Ethiopia. A lack of decision-making power among women within the family remains a challenge 

in many countries (Friberg et al. 2010; Kinney et al. 2010). Moreover, low levels of female 

education and little availability of medical services during pregnancy prevent women from 

receiving care and accessing the best choices for themselves and their children’s health, resulting 

in critical delays in receiving care and unnecessary maternal complications and deaths (Carroli et 

al. 2001; Kinney et al. 2010; Koblinsky et al. 2010). These factors could also influence women’s 

decision to give birth at home rather than in a health facility. 

Studies in some countries have reported that home deliveries range from 22% in Senegal 

to 65% in Tanzania, and to 88% in Bangladesh (Rockers et al. 2009; Faye et al. 2011; Wahed et 

al. 2010 ). The EDHS 2011 shows that home delivery comprises 90% of all deliveries in the 

country, regardless of expansion of strategies to increase service utilization and to improve 

maternal and child health (Gurmesa and Abebe 2008). Small studies in different regions of the 

country also support this figure. Similar to studies conducted Bangladesh and Nigeria, small 

studies in Ethiopia show that the most frequent reasons for high rates of home delivery are 

distance to health facility, problems in transportation, lack of decision-making power among 

women, low levels of antenatal care (ANC), and poor educational attainment (Yalem and Miguel 

2010; Paul and Rumsey 2002; Idris et al. 2006; Fikre and Demissie 2012; Mekonnen et al. 2012). 

To overcome the variability of these findings, however, providing representative evidence using 
2 

robust methods is required to identify the effects of individual and community-level factors on 

home delivery in Ethiopia. 

In addition to examining individual women’s characteristics, it is important to consider 

community-level factors in the analysis of women’s decision concerning place of delivery. The 

composition and characteristics of communities may determine whether individual women living 

within the community utilize institutional delivery or not. Few studies have attempted to identify 

the socioeconomic and demographic factors but instead have focused only on effects of 

individual characteristics on women’s decision concerning place of delivery (Yalem and Miguel 

2010; Kyomuhendo 2003; Fikre and Demissie 2012; Mekonnen et al. 2012; Mrisho et al. 2007); 

none of the studies has looked at the effects of community factors. 

Therefore, this study aims to examine the fixed effects of individual and community-level 

factors and the random effect of between-cluster variability on women’s decision to deliver at 

home, using multilevel modeling based on the EDHS 2011 data. The findings of this study 

would present better evidence for policymakers and stakeholders, which in turn may help in 

designing and implementing appropriate interventions at different levels to decrease the 

prevalence of home delivery and to improve the health system in general. 

For this study we used a conceptual framework (Figure 1) to detect the effects of 

individual and community-level factors on place of delivery among women in Ethiopia. The 

EDHS did not capture all factors such as myths and beliefs, culture, and tradition that are deeply 

rooted in the community, and these may be associated with place of delivery. The study 

customized the conceptual framework to the existing data on individual and community-level 

characteristics and access to health facilities in the EDHS that may be associated with women’s 

decision on place of delivery. 
3 

Figure 1. The conceptual framework for women’s decision on place of delivery in Ethiopia 

Community socioeconomic 

characteristics 

- Contextual regions 

- Residence 

- Community poverty 

- Community women’s 

empowerment 

Community access to facility 

- Community exposure to media 

- Community education 

- Community access to ANC 

- Perceived distance to facility 

Individual socioeconomic and 

demographic characteristics 

- Marital status 

- Religion 

- Female and male education 

- Employment status 

- Wealth index 

- Birth order 

- Age at last birth 

Individual access to facility 

- Access to media 

- Perceived distance to health 

facility 

- Perceived transport problem 

- ANC visits 

- Perception to availability of 

 female provider 

Decision on place of 

delivery 
4 

2 OBJECTIVES 

The study aimed to: 

• Determine individual and community-level factors associated with home delivery 

in Ethiopia 

• Estimate the contribution of community-level factors in explaining betweencluster variation in home delivery 
5 

3 METHODS AND DATA 

3.1 Data Source 

We used data from the EDHS 2011, particularly data on individual women. The 

individual-level data included information on socioeconomic and demographic characteristics, 

maternal age at last birth, and perception of access to health facilities among women age 15-49 

whose last birth was within five years preceding the survey. The recode data were accessed at 

www.measuredhs.com on request with the help of ICF International. 

3.2 EDHS Sample Design and Population 

The EDHS 2011 used a two-stage cluster sampling design with rural-urban and regions 

as strata. A sample of 624 clusters was drawn by the Ethiopian Central Statistical Agency from 

its master sampling frame of the 2007 census. Each cluster contained 150-200 households. 

Households were systematically sampled using random selection. All women of reproductive age 

who were either usual members of the selected households or who slept in the household the 

night before the survey were eligible for interview. The EDHS is representative nationally, 

regionally, and by urban-rural residence. The survey was conducted from December 27, 2010 to 

June 3, 2011 in all the nine regions and two City Administrative Councils of Ethiopia. 

3.3 Sample Size 

The EDHS sample design considers different parameters for the indicators to estimate the 

final sample size. Accordingly, 16,515 women were interviewed from 17,817 households 

presumed to represent the Ethiopian population with respect to the selected indicators. But this 

study focused only on 7,908 women (weighted) from 576 clusters (weighted) who had live a 

birth within the five years preceding 2011. For mothers with more than one birth, we used the 

most recent birth for the study.

3.4 Study Variables 

Dependent factor 

Place of delivery: The responses for place of delivery collected in the EDHS 2011 

included: home, different governmental health facilities, different types of private health 
6 

facilities, and others. These places of delivery were grouped into two categories: health 

institution, and home. The prediction of the place of delivery over the individual and communitylevel factors was run with respect to home delivery. 

Independent factors 

From the EDHS data, we considered the following individual and community-level 

factors hypothesized to influence women’s decision on place of delivery. 

Women’s individual factors 

Marital status: We encoded the marital status of women in two categories: living with 

partner; not living with partner. The former included women formally married and women living 

with a partner as married, while the latter contained never-married women as well as those 

widowed, divorced, or separated.

Religion: The dominant religions (Protestant, Orthodox, and Muslim) were encoded 

separately, but the minorities such as Catholic and traditional religions were merged into one 

category and labeled as ‘others’. 

Women’s and partners’ educational level: For both these variables, the original category 

in the EDHS was adopted with no change as: no education; primary education; secondary 

education; higher. However, for the prediction model, we combined the categories secondary and 

higher education, since their proportions were small and with the assumption that mothers who 

attained secondary school and mothers with higher levels of education may not differ 

significantly in the decision as to place of delivery.

Women’s and partner’s employment status in the last 12 months: The EDHS collected 

data on women and partner’s employment as ‘no job’ or as a list of different jobs. The sorts of 

jobs collected for both women and husbands ranged from unskilled manual to professional. In 

the current study, the responses were coded into ‘no job’ or ‘have job’ regardless of the type of 

job. The ‘no job’ category does not mean that women do not perform any activities; rather, 

women are limited to household activities. 

Household wealth quintile: The wealth index classifications were in quintiles: poorest; 

poor; middle; rich; richest. These were computed using principal component analyses (PCA). 
7 

This variable was derived from the different assets of the households in order to assess the 

household’s cumulative living standard. 

Maternal age at last birth: Maternal age at birth was recalculated from the date of the last 

birth and date of birth of the mothers, using the century month code (CMC). The data were 

coded into three groups: underage 20; age 20-34; age 35-49. 

Perceived distance to health facility: The responses for this variable were neither 

quantitative nor specific to services for delivery. The survey question asked women if distance 

was a problem for them to get any medical help from health institutions, not limited to delivery 

care. The responses were coded as: ‘yes, big problem’: or, ‘not big problem’. We used the same 

codes for the current study. 

Perceived transport access: Likewise, this attribute measured whether or not women 

perceived that they had problems taking transport whenever they needed to reach health facilities 

for any medical help. 

Women’s preference of female health provider: Similar to the distance and transport 

variables, this variable was coded as: ‘yes, big problem’; or, ‘not big problem’. This variable 

assesses whether women would prefer a female health professional for medical services. 

Number of ANC visits during pregnancy: This variable aimed to assess how many ANC 

visits women had in their recent pregnancy, regardless of type of provider. It was coded as: none; 

1-3; 4 and more. 

ANC by skilled health provider: This variable measured ANC visits provided by health 

professionals- doctor, midwife, and nurse. Though mothers might have made ANC visits, these 

visits were not counted if the women received ANC services from non-professionals. The 

responses were: ‘yes’; ‘no’. 

Birth order of the last birth: This variable indicated the order of the last birth. Indirectly, 

it pointed out the parity of the mothers. 

Exposure to media: This variable concerned frequency of listening to radio and watching 

television. We categorized it into three parts: no exposure to either media channel; exposure to 

either one of the two channels; exposure to both. The study excluded exposure to magazines and 
8 

newspapers, as the proportion of uneducated women in Ethiopia is high and thus there is little 

exposure to print media. 

Women’s empowerment: We focused on four attributes from the EDHS data to describe 

women as empowered, partially empowered, or not empowered. These were: (i) person who 

usually decides on respondent's health care; (ii) person who usually decides on large household 

purchases; (iii) person who usually decides on visits to family or relatives; and (iv) person who 

usually decides what to do with money husband earns. The categories of the women’s 

empowerment variable were based on the number of these decisions, in which women 

participate, as: involved in four of the decisions; involved in three of the decisions; involved in 

two of the decisions; not involved in any of the decisions. In the current study, a woman was 

considered to be empowered if the decision on any of the above items was made by the woman 

alone or together with her partner. 

Community-level factors 

Contextual region: The interest of the current study was not in the regions delineated for 

administrative purpose, which might not necessarily be related to the health status of the 

population. In the current study the regions were categorized into agrarian, pastoralist, and city, 

based on their settings that may have a strong relationship with health-seeking behavior, 

particularly institutional delivery. The regions of Tigray, Amhara, Oromiya, SNNP, Gambella, 

and Benshangul Gumuz were recorded as agrarian. The Somali and Afar regions were combined 

to form the pastoralist region and the city administrations- Addis Ababa, Dire Dawa, and Hararwere combined as city. Though Gambela and Benshangul Gumuz have been considered as 

pastoralists since recent times, their living settings actually approached the agrarian. 

Place of residence: This variable in the EDHS can explain characteristics of the clusters 

directly. The two categories were: urban; rural. 

Community aggregates 

The EDHS did not capture data that can directly describe the characteristics of the 

clusters. Yet, we created community variables by aggregating the individual mothers’ 

characteristics within their clusters. The aggregates were computed using the mean values of the 

proportions of women in each category of a given variable. Since the aggregate values may not 
9 

have pragmatic meaning, we categorized the aggregate values of a cluster into groups based on 

national median values, since all aggregates were not normally distributed. We used the same 

procedure for all of the following community factors. 

Community education: This is the aggregate value of the educational levels of women 

based on the average of proportions of educational levels in the community. The aggregate could 

show the overall female educational attainment of the clusters. There were two values for this 

variable, with reference to the national median value: higher educational attainment; lower 

educational attainment. 

Community poverty: This variable was created from mean values of wealth index 

categories of the individual mothers. The two values for the community poverty level were: 

higher poverty; lower poverty. 

Community media exposure: With a similar approach, this variable was derived from the 

individual responses for exposure to radio or television. We defined the community media 

exposure variables as: higher exposure to media; lower exposure to media. 

Community ANC utilization: The attribute was derived from the individual values for 

ANC utilization. The aggregate values were grouped into two categories: higher ANC utilizing; 

lower ANC utilizing. 

Community women’s empowerment: The aggregate values of the variable of women’s 

empowerment were computed based on national median values of the aggregates and were coded 

as: higher empowerment; lower empowerment. 

Community distance to health facility: This variable was created from the individual 

women’s characteristics and was customized to a community variable as: ‘distance a big 

problem’; ‘distance not a big problem’. 

3.5 Data Analyses 

Description of individual and community factors 

The data were analyzed using STATA 11 (STATA Corporation, College Station, TX, 

USA). The different characteristics of the women and the communities were described using 

descriptive statistics. The EDHS samples were not self-weighted due to the need for data for 
10 

specific regions or areas of the country that often need to be over-sampled. Thus, we applied 

sample weights to the data when we computed the univariate analyses. 

The sampling procedure for DHS data was a stratified two stage-cluster sampling; that is, 

the selection procedure deviated from the assumption of randomness. To account for the 

underestimation of the standard errors of the final estimates, we analyzed the crude associations 

of individual and community factors with place of delivery by considering sample design of the 

EDHS using cluster and strata as primary and secondary sampling units respectively. 

Cross tabulations 

To examine the crude association between the individual and community-level factors 

separately with the place of delivery, Pearson’s chi-squared test was used. A P-value less than 

0.05 was set for statistical significance of an association. 

Multilevel regression analysis 

Since DHS data are hierarchical, i.e. mothers are nested within households, and 

households are nested within clusters, use of flat models could underestimate standard errors of 

the effect sizes, which consequently affect decision on null hypothesis. With such data, mothers 

within a cluster may be more similar to each other than mothers in the rest of the country. This 

violates the assumption of flat models—independence of observations and equal variance across 

the clusters. This implies a need to consider the between-cluster variability. All these issues 

motivated us to use the multilevel modeling, which was able to compute mixed effects—that is, a 

fixed effect for both the individual and community factors and a random effect for the betweencluster variation simultaneously. As the responses to women’s decision about place of delivery 

were dichotomous, we opted to run a two-level mixed-effects logistic regression model. The two 

levels were at individual and cluster levels. In fact, it would be possible to use the flat logistic 

model by considering the effect of sample design using survey commands or also the cluster 

robust standard error methods, but it is not possible to see the variation between clusters using 

the latter methods. 

We ran four models to estimate both fixed effects of the individual and community-level 

factors and random intercept of between-cluster variation. 
11 

Empty model: This model was run without any factors, to test the random effect of 

between-cluster variability. Derived from the between-cluster and within-cluster variability, 

intra-class correlation coefficient (ICC) was estimated to determine if the data justified using a 

multilevel approach for analyses by depicting the magnitude of between-cluster variability. 

Individual-level factors model: The second model examined effects of individual 

characteristics on women’s decision on place of delivery. Besides, the ICC was estimated and 

observed if there was a decline in the between-cluster variability upon adding individual factors 

to the empty model. 

Community-level factors model: This model contained only characteristics of clusters, not 

individuals. The unit of analysis for this model was the cluster. 

Combined model: The important characteristics of individual women and clusters were 

concurrently fitted to one model to reveal their net fixed and random effects. 

The data were fitted into the model: 

Where 

• πij is probability of the presence of home delivery 

• (1-πij) is probability of institutional delivery 

• β0 is log odds of the intercept 

• β1, ... βn are effect sizes of individual and community-level factors 

• X1ij... Xnij are independent variables of individuals and communities 

• The quantities uOj are random errors at cluster levels 

The distribution of u0j is normal with mean 0 and variance σ

2

u0. The random effect was 

explained using ICC, which was calculated using between-cluster variance and within-cluster 

variance [ICC = σu

2

/ (σu

2

+ π

2

/3)]. In log distribution, the residual variance of women within a 

cluster is zero but variance is considered constant at π

2

/3. The ICC was used to show the level of 

between-cluster correlation within a model and to compare the successive models by looking at 

( ) ij nijn ijj

ij

ij Log X +++++= euoX

















− ββ β

π

π ... 1 11 0
12 

the decline of the ICC value. The Proportional Change in Variance (PCV) was computed for 

each model with respect to the empty model to show power of the factors in the model to explain 

women’s decision about place of delivery. The PCV was calculated by PCV = (Ve-Vmi)/Ve where 

Ve is variance in women’s decision in the empty model and Vmi is variance in successive models. 

We used the Variance Inflator Factor (VIF) to examine instability of effect size of 

predictors as the result of high collinearity among the factors. The fixed-effect sizes of individual 

and community-level factors on place of delivery were expressed using the Odds Ratio (OR) and 

the community effect sizes were estimated using the 95% Confidence Interval (95% CI). 
13 

4 RESULTS 

4.1 Background Characteristics of Individual Women and Clusters 

This study focused on a sample of 7,908 women in Ethiopia for the most recent birth 

within the five years preceding the EDHS 2011. Table 1 shows the distribution of women in the 

sample by background characteristics. 

A large proportion of the woman (67%) had no formal education. Only 5% had attended 

secondary school or higher. Nearly half (45%) of the women were unemployed—that is, they 

were limited to household activities. In contrast, almost all (99%) of their husbands had jobs 

ranging from unskilled manual labor to professional occupations. Estimated using the DHS 

wealth index based on household ownership of selected assets, grouped in five quintiles, the 

proportion of women was nearly equal among the wealth quintiles, from 17% in the richest to 

22% in the poorest (Table 1). 

When any medical help was sought, distance to reach a health facility was reported as a 

big problem among 72% of the women; and 78% of them reported that it was a big problem to 

take transport to reach any health institution for medical service. By media exposure, 41% of the 

women did not listen to radio or watch television, while the rest listened to radio or watched 

television, or both, in the course of a week. The DHS included questions to assess women’s 

empowerment in terms of involvement in major household decisions, either alone or with their 

husband. Ten percent of women were not involved in any of the decisions, while the rest 

participated in at least one decision. Forty-eight were involved in all four household decisions 

asked about in the survey, either alone or together with their husbands (Table 1, Figure 2). 

In addition to the number of ANC visits, the quality of ANC can also be measured by the 

qualifications of the service provider. Nearly 42% of the women had attended ANC at least once 

from any provider, while 34% received ANC from a doctor, nurse, midwife, or health officer. 
14 

Table 1. Background characteristics of women who had a live birth in the five years preceding the 

EDHS 2011, Ethiopia 

Individual characteristics n % 

Marital status 

Not having (living with) partner 722 9.1 

Had (living with) partner 7,185 90.9 

Religion 

Orthodox 3,327 42.1 

Protestant 1,763 22.3 

Muslim 2,563 32.4 

Others (Catholic, traditional,...) 250 3.2 

Educational status of woman 

No education 5,270 66.6 

Primary 2,270 28.7 

Secondary 225 2.8 

Higher 142 1.8 

Educational status of partner 

No education 3,923 50.1 

Primary 3,183 40.6 

Secondary 434 5.5 

Higher 297 3.8 

Employment status of woman 

No 3,585 45.3 

Yes 4,323 54.6 

Employment status of partner 

No 103 1.3 

Yes 7,733 98.7 

Perceived distance to a health facility to get medical help 

Not a big problem 2,142 27.1 

Big problem 5,757 72.9 

Preference for a female health provider 

Not a big problem 3,462 43.8 

Big problem 4,446 56.2 

Perceived transport problem for getting medical help 

Not a big problem 1,783 22.5 

Big problem 6,125 77.4 

Place of delivery 

Health Institution 928 11.7 

Home 6,980 88.3 

(Continued...)

 

 
15 

Table 1. – Continued

Individual characteristics n % 

Age at last birth 

<20 yrs 954 12.0 

20-34 5,645 71.4 

35-49 1,309 16.5 

Number of ANC visits 

None 4,543 57.4 

1-3 1,856 23.4 

4+ 1,508 19.1 

ANC by quality 

No ANC/provided by non-professional 5,234 66.2 

ANC provided by doctor/nurse/midwife 2,674 33.8 

Birth order of the last birth 

1st 1,399 17.7 

2nd or 3rd 2,462 31.1 

4th or 5th 1,814 22.9 

 6th+ 2,233 28.2 

Exposure to mass media 

Did not listen to/watch radio/ TV 3,217 40.7 

Either listened to radio or watched TV 2,599 32.9 

Listened to/watched both radio and TV 2,091 26.4 

Woman’s empowerment based on participation in major decisions 

on household issues1

 

No involvement 677 9.5 

Involved in one decision 723 10.1 

Involved in two decisions 904 12.6 

Involved in three decisions 1,412 19.7 

Involved in four decisions 3,446 48.1 

Wealth Index of household 

Poorest 1,739 22.0 

Poorer 1,696 21.4 

Middle 1,628 20.6 

Richer 1,493 18.9 

Richest 1,351 17.1 

Total 7,908 100.0 

 
16 

4.2 Background Characteristics of Clusters 

The unit of analysis for the characteristics of community-level factors was the cluster. For 

this study, we included 576 clusters in which all the women had lived. In each of the clusters, 

there were 1 to 26 eligible women for this study. Consistent with higher rural than urban 

proportion of the population in Ethiopia, 76% of the clusters were in rural areas. In this study, the 

different regions were categorized into three groups based on their living conditions, regardless 

of their administrative delineations. As such, 92% of the clusters were agrarian regions, while the 

rest were either pastoralist regions or cities. The current study attempted to form communitylevel factors by aggregating values of different individual characteristics. Accordingly, 55% of 

the clusters had higher poverty status and lower levels of women’s empowerment, based on the 

aggregate values derived from the EDHS data on the wealth index and women’s household 

decision-making. 

Nearly half of the clusters had lower female educational attainment and lower community 

media exposure to radio or television. Distance from the community to any health institutions 

seemed to vary across clusters, and in 55% of them distance to get access to a health institution 

was a big problem. Similarly, a higher proportion of the clusters (57%) had lower ANC 

utilization (Table 2). 
17 

Table 2. Characteristics of clusters in EDHS 2011, Ethiopia 

Community characteristics Category % 

Contextual regions Agrarian 91.2 

 Pastoralist 3.2 

 City dwellers 5.6 

 

Place of residence Urban 23.5 

 Rural 76.5 

 

Perceived distance of a community to a health 

facility to get medical service1 Not a big problem 44.8 

Big problem 55.2 

 

Community poverty status Higher 54.9 

 Lower 45.1 

 

Female educational attainment of communities Lower 50.6 

Higher 49.4 

 

Community media exposure Lower 49.2 

 Higher 50.8 

 

Community ANC utilization Lower 57.3 

 Higher 42.7 

 

Women’s empowerment in communities Lower 45.6 

Higher 54.4 

1

 Big problem: a cluster where at least half of the women say distance is a big problem to reach health facility. Not 

a big problem: a cluster where less than half of the women say that distance is a big problem to reach health 

facility. 

4.3 Place of Delivery by Individual Women and Community-Level Characteristics 

In our study, 88% of mothers delivered at home. Only 12% of the women delivered their 

most recent birth in a health institution. Independently, each of the individual and communitylevel factors was tested for presence of association with women’s decision as to place of 

delivery. As Table 3 shows, the education levels of women and their partners were significantly 

associated with place of delivery (P<0.001). Women with no education (or whose partners had 

no education) were more likely to give birth at home, at 95%. The likelihood of giving birth at 

health institutions increased with the education level of women or their partners. As such, among 
18 

women who attended higher education, 76% delivered in a health institution, as did 60% of the 

women whose husbands attended higher education (Table 3). 

The wealth index was highly associated with place of delivery (P<0.001). The proportion 

of women who gave birth at home declined as the level of wealth increased, from poorest (98%) 

to richest (49%). Antenatal care, both by number of visits and quality of care, were found to 

influence women’s decision as to place of delivery (P<0.001). The number of ANC visits was 

negatively correlated with home delivery. About one-third (34%) of women who made at least 

the minimum four ANC visits recommended by WHO delivered in a health institution, compared 

with only 3% who made no ANC visits during pregnancy. Parity was highly significant to 

women’s decision about place of delivery. Multiparous women were more likely to give birth at 

home than uniparous women, at 96% among birth orders six or higher versus 74% among first 

births (Table 3). 

The problems of distance and inaccessibility of transport forgetting any medical services 

were associated with higher chances of home delivery, at 94% and 93% of women respectively, 

compared with women with no problems related to distance and transport. Women’s perception 

of preference for a female health professional was also associated with home delivery. Given that 

most media broadcast promotions about using health services, it is not surprising that women 

with no exposure to television and radio were more likely to give birth at home, while better 

exposure to these media was associated with higher chances of giving birth in a health institution 

(Table 3). In addition, women’s level of involvement in decisions regarding household issues 

had an influence on their decision as to place of delivery (P<0.001). The proportion of women 

who gave birth at home increased as their participation in making household decisions 

decreased; for 97% with no involvement their most recent birth was at home, compared with 

85% who were involved in all of the household decisions (Figure 2). 

Table 4 shows women’s place of delivery by community-level characteristics. The 

likelihood of giving birth at home or delivering in a health facility differed significantly among 

the contextual regions (P<0.001). The proportion of clusters with home delivery was higher in 

rural communities than urban, 95% versus 49%. Similarly, a higher percentage of home births 

occurred in pastoralist communities (92%), followed by agrarian communities (88%). Pertaining 

to the poverty status of the clusters, more home births occurred in high-poverty communities 
19 

than in communities with low poverty levels, 97% versus 69%. By the same token, clusters with 

lower levels of female educational attainment had a higher proportion of home deliveries. 

The presence of more women in a community with good health-seeking practices appears 

to influence the health practices of the other women in their community, both positively and 

negatively. Accordingly, the presence of higher levels of ANC utilization in the clusters had a 

positive influence on women living in those clusters to have institutional delivery. In 

communities with lower levels of ANC utilization, a higher proportion of women delivered at 

home. Similarly, regardless of the exposure of individual women to mass media, merely living in 

communities where a lower proportion of people watched television or listened to the radio was 

associated with a higher proportion of home delivery compared with communities with better 

exposure to television and radio, 97% versus 72% (Table 4). 

Figure 2. Percentage of women with a live birth in the five years preceding EDHS 2011 who 

delivered at home, according to the levels of women’s involvement in household decision-making, 

Ethiopia 

97.22

93.36

92.49

89.92

84.50

Didn't make any major decision

Made only one major decision

Made two major decisions

Made three major decisions

Made four major decisions
20 

Table 3. Percent distribution of women with a live birth in the five years preceding 2011 EDHS by 

place of delivery according individual characteristics, Ethiopia 

Individual characteristics Category

Place of delivery 

X2 -test

Health 

facility Home 

Current marital status Have (or living with) partner 15.8 84.2 12.3** 

Have no (not living with) partner 11.3 88.7 

 

Husband's education1 No education 4.5 95.5 1,198.3*** 

Primary education 11.9 88.1 

 Secondary 40.7 59.3 

 Higher 59.5 40.5 

 

Woman’s education No education 4.8 95.2 1,613.7*** 

 Primary education 17.8 82.1 

 Secondary 70.9 29.1 

 Higher 75.5 24.5 

 

Birth order of the last birth 1st 25.9 74.0 452.9*** 

 2nd - 3rd 14.3 85.7 

 4th - 5th 6.8 93.2 

 6th+ 4.0 96.0 

 

Number of ANC visits None 3.2 96.9 1,064.0*** 

 1-3 14.4 85.6 

 4+ 34.4 65.6 

 

ANC by quality No ANC/ANC by non-health 

professional 

3.1 96.9 1,102.1*** 

 ANC by doctor/nurse/midwife 28.7 71.3 

 

Perceived distance to heath facility2 Not a big problem 26.1 73.9 581.4*** 

 Big problem 6.3 93.7 

 

Perception of women toward the 

preference of female health 

providers 

Not a big problem 16.9 83.1 156.3*** 

Big problem 7.7 92.3 

 

Perceived problem of transport for 

getting medical help 

Big problem 7.0 93.0 577.3*** 

Not a big problem 28.0 72.0 

 

(Continued...)
21 

Table 3. – Continued

Individual characteristics Category

Place of delivery 

X2 -test

Health 

facility Home 

Age at last birth <20 yrs 11.6 88.4 33.1** 

20-34 12.8 87.2 

 >34 7.1 92.9 

 

Media exposure No exposure to radio or TV 3.9 96.1 599.3*** 

Exposure to radio or TV 10.0 90.0 

 Exposure to both radio and TV 26 74.0 

 

Household wealth index Poorest 2.5 97.5 391.2*** 

 Poorer 3.3 96.7 

 Middle 3.3 96.7 

 Richer 7.4 92.6 

 Richest 49.0 51.0 

* P< 0.05, **P< 0.01, *** P< 0.001 

1 69 missing values in husband education 

2

 9 missing values in distance to health facility
22 

Table 4. Distribution of place of delivery according to community-level characteristics, five years 

preceding EDHS 2011, Ethiopia1

Community-level characteristics Category

Place of delivery 

X2 -test 

Health 

facility Home 

Place of residence Urban 50.6 49.4 169.2*** 

 Rural 4.9 95.1 

 

Contextual region Agrarian 12.1 87.9 103.0*** 

 Pastoralist 7.6 92.4 

 City dwellers 77.4 22.6 

 

Community ANC utilization Lower 2.3 97.7 107.9*** 

Higher 33.6 66.4 

 

Community poverty status Lower 2.8 97.2 90.4*** 

Higher 31.3 68.7 

 

Community women’s empowerment Lower 2.2 97.8 68.2*** 

Higher 26.9 73.1 

 

Female educational attainment of 

community 

Lower 3.8 96.2 64.3*** 

Higher 27.7 72.3 

 

Community media exposure Lower 3.4 96.6 65.4*** 

Higher 27.5 72.5 

 

Perceived community distance to a 

health facility to get medical help

Not a big problem 32.7 67.3 106.4*** 

Big problem 1.8 98.2 

1

 The total weighted number of clusters was 576 

 
23 

4.4 Multilevel Analysis of Predictors of Women’s Decision to Place of Delivery 

Table 5 presents the results of multilevel analyses examining the effect of women’s 

individual characteristics and community-level factors in women’s decision concerning delivery. 

Model 1, the empty model, includes only random intercept to capture between-cluster variability. 

In this model, 75% of the total variance in the odds of giving birth at home was accounted for by 

between-cluster variation (ICC=0. 75, σ

2

u0=10. 17, P<0.001). Similarly, the ICC was computed 

in each successive model to understand the relative effects of individual-level and communitylevel factors on women’s decision on place of delivery. The between-cluster variability declined 

over successive models, from 75% in the empty model, to 25% in the individual-level only 

model, 21% in the community-level only model, and 17% in the combined model (Table 5). 

Proportional change in variance indicated that addition of predictors to the empty model better 

explained women’s decision as to place of delivery. Similar to ICC, the combined model showed 

up with higher PCV; that is, 93% of variance in women’s decision about place of delivery could 

be explained by the combined factors at the individual and community levels. Accordingly, the 

combined model of individual-level and community-level factors was selected for predicting 

women’s decision about place of delivery. 

Effect of individual characteristics on place of delivery 

Both the educational level of mothers and of their partners were independently and 

negatively associated with the odds of home delivery. The odds of giving birth at home were 2.7 

times (OR=2.74; 95% CI: 1.84, 4.70) and 2 times (OR=2.05; 95% CI: 1.44, 2.94) higher among 

women with no education compared with those who attended secondary (or higher) school and 

attended primary school, respectively, after adjusting for individual and community-level factors. 

Similarly, women partnered with uneducated husbands had 2 times higher odds of giving birth at 

home after adjusting for individual and community-level factors (OR=2.31; 95% CI: 1.68, 3.18). 

The wealth status of the household affected women’s decision about place of delivery. In all 

wealth quintiles (from rich to poorest), the chances of home birth were higher compared with the 

richest quintile in the individual model; but with the same direction of relationship, only the 

middle quintile was significant in the combined model (Table 5). 

Controlling for the effect of other characteristics, multiparous women had higher odds of 

home delivery than uniparous women. As birth order rose, women were increasingly likely to 
24 

have home delivery. The odds of remaining at home for giving birth among mothers with six or 

more births, 4-5 births, or 2-3 births were 2.7 times (OR=2.68; 95% CI: 1.96, 3.68), 2.5 times 

(OR=2.44, 95% CI: 1.81, 3.28), and 2 times (OR=1.95, 95% CI: 1.53, 2.49) higher, respectively, 

than among uniparous women. Both ANC by quality and ANC by frequency were statistically 

significant predictors of women’s decision concerning place of delivery. The net odds of home 

birth among mothers with no history of ANC visits for their recent pregnancy were 3.7 times 

(OR=3.72; 95% CI: 2.85, 4.83) higher than among mothers who had four or more ANC visits. 

Even mothers who made1-3 ANC visits had higher odds of delivering at home than mothers with 

the recommended four or more visits (Table 5). 

To examine the influence that health promotion in the mass media would have on 

encouraging institutional delivery, the study analyzed the effect of exposure to television or radio 

on women’s decision on place of delivery. Having no exposure to radio or television was 

associated with 51% higher odds (OR=1.51; 95% CI: 1.13, 2.01) of giving birth at home 

compared with mothers who watched television or listened to radio at least once a week. Even 

limited exposure to either radio or television had a positive effect on institutional delivery 

compared with non-exposure. Also, the perceived problem of distance to get any medical service 

deterred mothers from delivering in a health facility (Table 5). 

Age at first birth, women’s perception toward preference for women health providers, 

and the problem of transport to reach medical care was not statistically significant in the 

combined model. As shown, a few other variables were also excluded from the combined model 

as they impaired the prediction power of the model (Table 5). 

Effect of community-level factors on place of delivery 

The study aimed to show if the characteristics of the clusters in which women lived 

would have an effect on their decision about place of delivery, regardless of women’s individual 

characteristics. These results are net effects after controlling for the contribution of all the 

individual and community-level attributes. Related to place of residence, women in rural clusters 

had higher odds of giving birth at home compared with women in urban clusters (OR=4.67, 95% 

CI: 3.06, 7.11). Residence in a pastoralist community had a strong positive net effect on home 

delivery, which exceeded by four-and-a-half times (OR=4.53, 95% CI: 2.81, 7.28) the level 

among city dwellers. Similarly, women living in agrarian communities had greater odds 
25 

(OR=3.22, 95% CI: 2.30, 4.49) of giving birth at home compared with women in city 

communities (Table 5). 

Women in clusters (communities) where distance to a health facility was a big obstacle to 

seeking medical services had greater odds of delivering at home compared with women in 

clusters where distance was not a major obstacle (OR=1.29; 95% CI: 1.03, 1.62) . With regard to 

the cumulative average poverty status of clusters, women in clusters with higher relative poverty 

levels had greater likelihood of giving birth at home (OR=1.49; 95% CI: 1.08, 2.22) versus 

women in clusters with lower poverty levels. In addition, in the model with community-level 

factors only, home delivery was associated positively with low community-level female 

educational attainment. This was not statistically significant in the combined model of individual 

and community-level factors, although the result seems unexpected (Table 5). 

Women’s decision to deliver at home was positively associated with communities having 

lower levels of ANC utilization. After controlling for all other characteristics, results indicated 

that residence in a community with a lower level of women’s ANC utilization had 2 times 

(OR=2.01, 95% CI: 1.42, 2.85) higher odds for home delivery compared with living in a 

community with a higher level of ANC utilization. Unlike the results in the bivariate analysis, 

women’s empowerment and exposure to media did not have a significant effect in the 

community-level-only and combined models on women’s decision-making about place of 

delivery. 

 
26 

Table 5. Two-level mixed effects logistic regression analysis of individual and community factors 

on place of delivery among women with a live birth in five years preceding 2011 EDHS, Ethiopia 

Individual and community 

characteristics 

Model 

empty 

Model II Model III Model IV 

OR (95% CI) OR (95% CI) OR (95% CI) 

Educational level of woman 

No education 2.90*** [ 1.90,4.40] 2.74*** [1.84, 4.7] 

Primary 2.03*** [1.39,2.97] 2.06*** [1.44, 2.94] 

Secondary or higher 1.00 1.00 

Educational level of partner 

No education 2.28*** [1.61, 3.22] 2.31*** [1.68, 3.18] 

Primary 1.79*** [1.33, 2.41] 1.86*** [1.42, 2.44] 

Secondary or higher 1.00 1.00 

Wealth index quintile 

Poorest 8.92*** [6.01, 13.25] 1.15 [0.72, 1.83] 

Poorer 9.38*** [6.17, 14.26] 1.31 [0.82, 2.10] 

Middle 9.71*** [6.44, 14.65] 1.73* [1.09, 2.75] 

Rich 5.65*** [4.06, 7.89] 1.38 [0.97, 1.98] 

Richest 1.00 

Media exposure 

No exposure to TV/radio 1.47* [1.10, 2.00] 1.51** [1.13, 2.01] 

Exposure to either TV/ radio 1.37* [1.07, 1.77] 1.27* [1.02, 1.61] 

Exposure to both TV and radio 1.00 1.00 

Perceived problem of transport 

to get medical help 

Big problem 1.13 [0.84, 1.53] 0.97 [0.71, 1.25 ] 

Not a big problem 1.00 1.00 

Perceived distance to a health 

facility to get medical help 

Big problem 1.78*** [1.32, 2.38] 1.29* [1.03, 1.62] 

Not a big problem 1.00 1.00 

Perception toward preference for 

female health providers 

Not a big problem 1.00 

Big problem 1.01 [0.80, 1.27] 

Birth order of the last birth 

1st 1.00 1.00 

2nd and 3rd 1.85*** [1.42, 2.41] 1.95*** [1.53, 2.49] 

4th and 5th 2.80*** [2.03, 3.85] 2.44*** [1.81, 3.28] 

6th+ 3.08*** [2.21, 4.30] 2.68*** [1.96, 3.68] 

ANC visits 

None 5.10*** [1.43, 2.41] 3.72*** [2.85, 4.83] 

1-3 2.03*** [1.57, 2.61] 1.60*** [1.26, 2.03] 

4+ 1.00 1.00 

(Continued...)
27 

Table 5. – Continued

Individual and community 

characteristics 

Model 

empty 

Model II Model III Model IV 

OR (95% CI) OR (95% CI) OR (95% CI) 

Woman’s empowerment by 

number of major decisions 

Made no major decisions 1.12 [ 0.73, 1.70] 

Made one major decision 1.19 [0.81, 1.75] 

Made two major decisions 1.14 [0.81, 1.61] 

Made three major decisions 1.07 [0.82, 1.39] 

Major four major decisions 1.00 

Contextual region 

Agrarian 3.52*** [2.53, 4.88] 3.22*** [2.30, 4.49] 

Pastoralist 6.26*** [3.85, 10.20] 4.53*** [2.81, 7.28] 

City dwellers 1.00 1.00 

Place of residence 

Urban 1.00 1.00 

Rural 7.73*** [5.16, 11.58] 4.27*** [2.80, 6.50] 

Community distance to heath 

facility 

Big problem 2.11*** [1.51, 2.95] 1.63** [1.16, 2.29] 

Not a big problem 1.00 1.00 

Community poverty status 

Higher poverty 1.52* [1.04, 2.20] 1.49* [1.08, 2.22] 

Lower poverty 1.00 1.00 

Women’s educational attainment 

of community 

Higher 1.00 1.00 

Lower 1.94*** [1.34, 2.79] 1.20 [0.83, 1.73] 

Community ANC utilization 

Higher 1.00 1.00 

Lower 2.82*** [1.98, 3.99] 1.90*** [1.34, 2.60] 

Community media exposure 

Higher 1.00 1.00 

Lower 0.90 [0.63, 1.30] 0.73 [0.50, 1.05] 

Community women’s 

Empowerment 

Higher 1.00 1.00 

Lower 1.12 [0.86, 1.54] 1.13 [0.84, 1.51] 

Random effects 

ICC 0.75 0.25 0.21 0.17 

PCV1

 N/A 88.7% 91% 93% 

* P<0.05; ** P<0.01; *** P<0.001 

1

 PCV was calculated for successive models with reference to null model to look at relative contribution of each 

model to explain women’s decision to place of delivery 
28 

5 DISCUSSION 

About nine births in every ten in Ethiopia are delivered at home1

. This high proportion of 

home delivery reflects different individual and community factors that affect women’s decision 

to deliver at home. At the individual level, lower status of women and partners’ education, nonexposure to mass media, making no visits or only a limited number of ANC visits, higher parity, 

and inaccessibility of health facility in terms of distance are associated with home delivery. At 

the community level, women living in communities in pastoralist regions, rural areas, 

communities with higher poverty levels, those with lower ANC utilization, and areas where the 

distance to a health facility is big problem are more likely to give birth at home. 

When contrasted with the national estimates of Tanzania, Zambia, Kenya, and Uganda, 

and most of SSA, Ethiopia is at the top in terms of the proportion of women delivering at home. 

This may contribute to the higher MMR in Ethiopia since mothers delivering at home could be 

exposed to life-threatening risks such as bleeding, pregnancy-induced hypertensive disorders, 

sepsis, obstructed labor, and others. 

Lower educational levels of mothers and their partners were associated with home 

delivery. This finding is consistent with different studies undertaken in Ethiopia (Abdella et al. 

2012; Alemayehu et al. 2012; Awoke et al. 2013; Abebe et al. 2011), as well as in other countries 

(Jat et al. 2011; Aremu et al. 2012; Lwelamira and Safari 2012; Nair et al. 2012). Though slight, 

the decrease in proportion of home delivery in Ethiopia from 2005 to 2011 could be explained by 

the increasing reach of education in the country, through community mobilization and expansion 

of the schools. Education could influence women’s overall empowerment, enhancing their ability 

to have self-determination, access to information, and financial freedom to support themselves to 

take transport to quality services and (if applicable) to pay for services, as well as to easily 

absorb health messages through the media and from health professionals. These could 

collectively influence mothers’ awareness to seek better medical services, including delivering in 

health facilities. 

 

1

 The place of delivery in this study is 88%, which is slightly below the 90% reported in the EDHS 2011 report. The 

slight discrepancy is attributable to difference in denominator, as the current study considered place of delivery only 

for the recent birth unlike the EDHS report, which measured place of delivery for all children born within five years 

preceding the EDHS 2011. 
29 

This study found that non-exposure to television and radio is associated with mothers 

giving birth at home, while exposure to media is associated with institutional delivery. Even a 

limited exposure to either radio or television has a positive effect on institutional delivery. This 

finding is consistent with other studies in Ethiopia, Pakistan, Indonesia, and India (Begum et al. 

2012; Asmeret 2013; Mahapatro 2012). The effect could be explained by the fact that most 

media programs broadcast promotion of institutional delivery repeatedly, which may influence 

mothers to develop positive behavior toward delivering in a health facility. In Ethiopia, however, 

low levels of electricity distribution in rural areas, where nearly 85% of the population lives, and 

low educational levels could combine to reduce exposure to the media, and thus women could 

fail to get the advantages of health promotions through media. 

The more births that women have, the more likely they are to give birth at home. Other 

studies also support these results (Aremu et al. 2012; Asmeret 2013; Kamal 2013; Nair et al. 

2012). This result could be because multiparous women might think that every subsequent birth 

is safe if they did not have complications in their previous births. Also, women with more births 

are likely to be older, and the vast majority have no education (CSA and ICF International 2012). 

As the result, multiparous women are at high risk of having home delivery. 

Many factors can deter women from getting medical advice or treatment for themselves 

when they need to. Information on such factors is particularly important in understanding and 

addressing the barriers that women may face in seeking care during pregnancy and at the time of 

delivery. The study found that the problem of perceived distance to reach a health facility was 

associated with higher odds of home delivery. This seems to agree with the actual situation in 

Ethiopia. The most decentralized health facilities to the community level are health posts, which 

serve nearly 5,000 people within their catchment areas and are staffed with health extension 

workers- community health workers with one year of training. However, these health facilities 

are not able to render delivery service; rather, they focus only on community mobilization, health 

promotion, and preventive care, including prenatal care. The next nearest structure that offers 

delivery service is the health center. But most of the health centers are not accessible within a 

short distance, especially in the rural areas. With little transport available to travel 10 or 20 

kilometers and mountainous topography, women facing labor pain would find institutional 

delivery to be very challenging and would be likely to stay at home for delivery. In addition, a 
30 

low level of ANC utilization and poor birth preparedness would lead women to have home 

delivery. 

Antenatal care is a proximate predictor of women’s decision about place of delivery. That 

is, women who have had no antenatal care in a health facility are more likely to give birth at 

home. Regardless of who provides the service, making more ANC visits increases institutional 

delivery. Especially, making the minimum number of ANC visits recommended by WHO makes 

it more likely for mothers to give birth in a health facility (Abdella et al. 2012; Alemayehu et al. 

2012; Aremu et al. 2012; Mulumebet et al. 2011; Asmeret 2013; Lwelamira and Safari 2012; 

Kabakyenga et al. 2012). As reported in a DHS analysis of six African countries and in India, 

mothers who seek care for their pregnancy are more likely to seek care for their delivery 

(Stephenson et al. 2006; Nair et al. 2012). Antenatal care is the most favorable contact point for 

mothers to get more information about risks and problems they may encounter during delivery. 

Consistent findings are reported in Tanzania and Rwanda studies (Johnson et al. 2009) in which 

mothers who are informed about pregnancy complications during their ANC visits are more 

likely to deliver in a health facility. 

We also found that characteristics of clusters in which the women live have an effect on 

women’s decision about place of delivery, independent to their individual characteristics. For 

instance, living in a different contextual region makes a difference as to place of delivery. The 11 

regions of Ethiopia, which are basically delineated for administrative purposes, were categorized 

into three contextual regions- pastoralist, agrarian and city- defined based on the basis of the 

living conditions of their population. As such, living in a pastoralist community is associated 

with higher chances of home birth, followed by an agrarian community. Home delivery is to be 

expected in a pastoralist community because pastoralist people are very hard to reach and mostly 

wander to distant areas to look for animal foods, in addition to having poor infrastructure. 

Generally, while the disparity in home delivery across the regions could be attributable to the 

differences in access to health services, media and information, geography, and social and 

cultural attributes, the most important difference is the living conditions of their population. The 

reliability of this finding is assured by its consistency with previous studies conducted in 

Ethiopia (Asmeret 2013) and other African countries (Utomo et al. 2011; Palamuleni 2013; 

Aremu et al. 2012; Kamal 2013). 
31 

As expected, unlike urban communities, rural communities are at high risk of having 

home births, which is similar to findings in other studies (Jat et al. 2011; Alemayehu et al. 2012; 

Mulumebet et al. 2011; Kabakyenga et al. 2012). The nature of urban and rural areas explains 

this discrepancy. Urban areas are accessible to health facilities, with a higher proportion of 

informed and educated people, and better infrastructure. In contrast, rural areas generally have 

poor infrastructure, hold deeply-rooted negative beliefs and myths regarding institutional 

delivery, are physically inaccessible, and have fewer health facilities and inadequate health 

services. Collectively, the better situations of urban communities enhance health-seeking 

behavior of the population in general, not only for institutional delivery. 

Communities where perceived distance to reach a health facility is big problem are 

associated with a high proportion of women delivering at home. If a health facility is not located 

close to the cluster, most mothers living in that cluster invariably are unlikely to give birth in a 

health facility. This finding is supported by the literature, which has reported similarly that 

distance not only reduces the chance of institutional delivery but also reduces the health-seeking 

behavior of the community (Gabrysch et al. 2011; Gebrehiwot et al. 2014; Moore et al. 2011; 

Lwelamira and Safari 2012; Kabakyenga et al. 2012). 

Regardless of the wealth status of individual women, poverty status of the cluster in 

which the women live determines the decisions of women regarding place of delivery. Relatively 

poor clusters are associated with a higher proportion of home delivery, and this finding is similar 

to other studies (Aremu et al. 2012). In addition, women’s decision to give birth at home is 

positively associated with clusters that have lower levels of ANC utilization. Not only does ANC 

utilization by individuals have a positive effect on their going to a health facility for delivery, but 

also a higher community level of ANC utilization may influence other women in the same 

community to give birth in a health facility. If a cluster has a high proportion of women who go 

for ANC, the information they receive during their ANC visits is likely to reach their neighbors 

and influence other women to seek medical services, including institutional delivery. 

Regarding strength of the methodology to handle the hierarchical nature of the data, a 

two-level mixed effects logistic regression was used to handle both the fixed effects of individual 

and community factors and random intercept to explain the between-cluster differences 

simultaneously. Furthermore, it is noted that EDHS often collects individual data; it doesn’t 
32 

capture data that describe the clusters directly except region and place of residence. As the result, 

this study endeavored to generate variables that can characterize communities by aggregating 

individual data into cluster values. This enabled the study to test whether community factors 

could influence the decision as to place of delivery, in addition to individual factors. With 

estimation adjustments for representativeness, such as applying weighting and considering 

sample designs, results produced based on EDHS data are assumed to be representative of the 

Ethiopian population. 

Nevertheless, the study has some limitations. The EDHS didn’t collect all data that may 

direct influence women’s decision about place of delivery. In small-scale studies, for example, 

beliefs and myths, cultural tradition and rituals, unmet community expectations, and shortages of 

skilled human resources were found to be barriers to institutional delivery (Gebrehiwot et al. 

2014). Such variables were not collected in the EDHS. Statistically, the study also indicated that 

there is an unobserved variability that still is not explained by the available data in EDHS. 
33 

6 CONCLUSIONS AND POLICY IMPLICATIONS 

The study revealed that both individual and community-level factors determine women’s 

decision about place of delivery. At the individual level, lower levels of women’s and partners’ 

education, limited or lack of exposure to mass media, making no ANC visits or only a limited 

number of them, higher parity, and inaccessibility of health facility in terms of perceived 

distance are positively associated with home delivery. At the community level, women living in 

pastoralist communities, rural areas, communities with higher poverty and lower ANC 

utilization, and communities where distance is big problem to access health facility are more 

likely to have home delivery. 

To reduce the proportion of women delivering at home, the government of Ethiopia 

should design strategies for improving both the individual and community-level factors. 

Education is a key strategy for making improvements in all health indicators. In particular 

regarding place of delivery, the government of Ethiopia should empower women by increasing 

access to education through community mobilization and affirmative initiatives to send females 

to schools. Since decisions are mostly dominated by husbands, educating men could also help in 

encouraging their wives to access a health facility for delivery. Media is imperative to bring 

health behavior change in general and institutional delivery in particular. Nevertheless, media is 

not accessible to all populations in Ethiopia, as most Ethiopians live in rural areas where 

electricity and media are inaccessible. Therefore, the government should strive to expand access 

to media, particularly radio and television that can reach uneducated women and their partners, 

to discourage home delivery and promote facility-based delivery and maternal and child health 

care. 

Obviously, urban women have a higher proportion of institutional delivery because the 

urban population has better accessible to information, services, education, and infrastructure. In 

fact, only a small percentage of the Ethiopian women have this access, since most people live 

dispersed in remote rural areas of the country. To the benefit of increasing institutional delivery 

and other health, social, and political issues, the government should do more on urbanization of 

rural villages into proximate areas. Most importantly, this would have the advantage of resettling 

the pastoralist communities in permanent residences that make it easier access to health and other 

services. 
34 

The link between receiving ANC and women’s awareness of health is substantiated by 

evidence. Not only is the proportion of ANC utilization in Ethiopia low, but also the quality of 

services is questionable, which may discourage mothers from making visits for ANC. The 

government should intensify efforts to increase ANC through strengthening health extension 

programs and expanding health facilities, particularly health centers that could also improve 

quality. The health management information system (HMIS) in Ethiopia also needs to be 

improved. Currently, the HMIS demands reports about the first ANC visit only. The system does 

not report successive visits while in fact at least the recommended four visits should be reported 

as well. This may limit the efforts of health workers to achieve plans set for first visit, as they are 

accountable only for this visit. 

Although the government of Ethiopia has decentralized health facilities to the community 

within seven kilometers, through expansion of health posts, these institutions are not allowed to 

provide delivery services since they are staffed only with health extension workers. Thus, 

mothers need to go to a health center for delivery. However, physical distance to these 

institutions, the mountainous topography of rural areas, lack of transport, and poor birth 

preparedness collectively deter mothers from delivering in a health facility. As a remedy, the 

government ought to upgrade health posts within the community to train and deploy midwives, 

and equip them with materials for delivery. 

The poorer a community is, the more likely for the people living in the community to 

have poor health-seeking behavior and to make less use of health services. But poverty is 

difficult to resolve in a short time, as it reflects a cumulative effect of many factors. Although it 

may take a longer period of time, integrated efforts through inter-sectoral collaborations with 

health, agricultural, education, political, and social sectors should be in place to decrease 

community poverty. 
35 

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DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2014 No. 104

Henock G. Yebyo

Mussie A. Gebreselassie

Alemayehu B. Kahsay

August 2014

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

International Development.

Individual and Community-Level Predictors 

of Home Delivery in Ethiopia:

A Multilevel Mixed-Effects Analysis of the 

2011 Ethiopia National

Demographic and Health Survey