Multilevel Analysis of Individual and Community Level Factors Associated with Institutional Delivery in Ethiopia,2016 Ethiopian Demography and Health Survey

Abstract

Background

The utilization of skilled care before, during, and after childbirth can significantly reduce maternal mortality and save the lives of women and newborns. However, maternal and child mortality remain high and continue to pose a persistent challenge for low-income countries. Therefore, this study was conducted to identify individual- and community-level factors associated with institutional delivery in Ethiopia.

Methods

A cross-sectional study design with two-stage clustered sampling was employed using data from the 2016 Ethiopian Demographic and Health Survey (EDHS) to identify individual- and community-level factors associated with institutional delivery in Ethiopia. The study included 643 clusters (communities) and 7,091 women aged 15–49 years. Data were analyzed using two-level mixed-effects logistic regression to estimate the fixed effects of individual- and community-level factors and the random intercept for between-cluster variability, using R software version 3.5.3.

Results

In this study, over two-thirds (68%) of women gave birth at home. Institutional delivery was more likely among women with higher education, greater household wealth, frequent antenatal care attendance, and partners with secondary education. Conversely, it was less likely among women with more children and those perceiving long distances to health facilities as a barrier. At the community level, higher average education, media exposure, and recent use of health services were associated with greater institutional delivery use, while rural residence, perceived distance barriers, and regional disparities were linked to lower use.

Conclusions

Both individual- and community-level factors were significantly associated with institutional delivery, in both positive and negative directions. Therefore, the Government of Ethiopia and relevant stakeholders should give due attention to the key factors identified in this study.

Keywords

Communities demographic health survey Ethiopia institutional delivery multilevel analysis

Background

Every day, approximately 830 women die from preventable causes related to pregnancy and childbirth worldwide. Nearly all maternal deaths (99% of global deaths) occur in developing countries, with more than half taking place in sub-Saharan Africa (Onambele et al., 2023). As part of the Sustainable Development Goals, the global target between 2016 and 2030 is to reduce the maternal mortality ratio to fewer than 70 deaths per 100,000 live births. By 2030, no country should have a maternal mortality ratio higher than 140 per 100,000 live births, which is twice the global target (Moran et al., 2016).

Ethiopia has one of the highest maternal mortality ratios in sub-Saharan Africa, with 412 deaths per 100,000 live births, largely due to low maternal health service utilization particularly low rates of institutional delivery (Daniel G Belay, Alemu, Pereira, Lassi & Tessema, 2023). The majority of maternal deaths are preventable, as effective healthcare interventions to prevent or manage pregnancy-related complications are well established. Timely access to skilled care during pregnancy, childbirth, and the postpartum period can significantly reduce the risk of maternal mortality (Mustafa et al., 2016).

All women require access to antenatal care during pregnancy, skilled care during childbirth, and appropriate care and support in the weeks following delivery to ensure their health and well-being, as well as that of their newborns (Pathak et al., 2017). The utilization of skilled care before, during, and after childbirth is critical in reducing maternal mortality and saving the lives of both women and newborns. Skilled attendance during labor, delivery, and the early postpartum period is estimated to prevent between 13% and 33% of maternal deaths (Baral et al., 2010).The major complications responsible for nearly 75% of all maternal deaths include severe bleeding (primarily postpartum hemorrhage), infections (usually occurring after childbirth), high blood pressure during pregnancy (such as pre-eclampsia and eclampsia), complications during delivery, and unsafe abortion (Berhan & Berhan, 2014b).

It is especially crucial that all deliveries are attended by trained health experts, as timely care and treatment can mean the difference between life and death for both the mother and the baby (Ameh & Van Den Broek, 2015). The proportion of births attended by skilled health personnel is a key indicator under Sustainable Development Goal (SDG) 3, specifically Target 3.1, which aims to reduce the global maternal mortality ratio to less than 70 per 100,000 live births by 2030. Skilled attendance at birth is widely recognized as one of the most effective interventions for preventing maternal deaths, as it ensures timely management of complications during childbirth and the immediate postpartum period (Bhowmik et al., 2020). However, the majority of deliveries in Ethiopia particularly in rural communities still occur at home without the assistance of skilled birth attendants. According to a 2024 study, the prevalence of home delivery in rural Ethiopia was 58.72%, with a significant spatial variation across districts (D. B. Belay, Tilahun, Birhan, Tiku & Chen, 2025).

Studies show that skilled delivery is influenced by factors like socio-economic status, education, access to care, and cultural beliefs. While some factors are universal, others depend on local systems and norms (Freedman et al., 2005). Understanding these diverse factors is crucial for designing targeted interventions that can improve maternal healthcare utilization in specific contexts (Hill et al., 2023). In Ethiopia, institutional delivery is limited by poor access to health facilities, especially in rural areas, cultural preference for home births, and concerns over the quality of care (Roro et al., 2014). The factors influencing one behavior often differ from those influencing another, and the most effective interventions are those that target specific behaviors with tailored strategies (Noar et al., 2007).

In Ethiopia, the goal of ensuring that all births occur in health facilities attended by skilled health professionals remains a significant challenge. Institutional delivery service utilization has historically been low (Berhan & Berhan, 2014a) (N. Kebede et al., 2023). For instance, in 2014, only 16% of births were attended in health facilities (Berhan & Berhan, 2014a), a rate significantly lower than in many other developing countries.

Literature Review

Home births remain highly prevalent among poor women in sub-Saharan Africa (SSA), South Asia, and Southeast Asia, where 74.7–89.9% of women in the lowest two wealth quintiles give birth at home. In SSA, the poorest women are more than three times as likely to deliver at home as the richest (77.7% vs. 22.4%). In Ethiopia, only 2% of deliveries in the poorest households were attended by skilled personnel, compared to 46% in the wealthiest (Montagu et al., 2011; UNICEF; Worku, Yalew & Afework, 2013). According to the 2016 EDHS, approximately 74% of births occurred at home without skilled attendance (Demographic, 2016).

Institutional delivery plays a critical role in reducing maternal and perinatal mortality. Studies indicate that up to 14 perinatal deaths per 1,000 births can be averted by delivering at health facilities (Chinkhumba et al., 2014). Moreover, 16–33% of maternal deaths are preventable through skilled attendance that addresses complications such as obstructed labor, eclampsia, puerperal sepsis, and hemorrhage. The presence of skilled health workers, backed by emergency referral systems, is crucial for improving maternal outcomes (Kinney et al., 2010).

However, many deliveries still occur at home. A multi-country DHS analysis across 48 developing countries found that more than half of births take place without skilled attendance (Montagu et al., 2011). Interestingly, countries like Peru, Tunisia, Egypt, and Namibia report relatively low maternal mortality despite low skilled attendance, possibly due to effective emergency care. In contrast, countries such as Ghana, Malawi, Bolivia, and Zambia have high skilled attendance but also high maternal mortality, likely due to poor healthcare quality or unskilled providers. Therefore, skilled attendance must involve both competent professionals and supportive infrastructure (Chinkhumba et al., 2014; Graham et al., 2001).

Numerous individual factors influence institutional delivery. Studies from the Awi and Hadiya zones indicate that younger mothers (under 25 years old) are more likely to deliver at health facilities. In contrast, studies from Cambodia suggest that older women are more likely to use health institutions for delivery. Wealth status is also a significant determinant; women from wealthier households are more likely to utilize health facilities, as evidenced by studies from Wolaita and other regions in Ethiopia. Antenatal care (ANC) is a consistent predictor of institutional delivery. Women who attend ANC particularly those with four or more visits are up to four times more likely to give birth in a health facility (Asseffa et al., 2016; Wolelie et al., 2014).

Education plays a crucial role as well. Women with at least primary education are more likely to deliver in health institutions. Similarly, a husband's educational status influences delivery location; women whose husbands have no formal education are less likely to use health facilities for childbirth. Other individual factors such as prior experience with facility-based delivery, pregnancy planning, and complications in previous pregnancies also increase the likelihood of institutional delivery. Media exposure likewise promotes the use of health facilities (Ahmed et al., 2018; Sagna & Sunil, 2012).

Community-level factors are equally important. Women living in communities with high levels of female education and ANC utilization are significantly more likely to deliver at health institutions (Mekonnen et al., 2015). Moreover, residing in areas with lower poverty rates and greater media exposure is associated with higher rates of institutional delivery.

Overall, improving maternal health outcomes requires addressing both individual and community-level determinants through integrated health and social interventions (Mekonnen et al., 2015; Sagna & Sunil, 2012). However, there is limited recent evidence on the progress made or challenges that persist in institutional delivery utilization. Therefore, this study aimed to identify individual and community-level factors associated with institutional delivery in Ethiopia, using data from the 2016 Ethiopia Demographic and Health Survey. The findings are intended to inform maternal health advocates, policymakers, and planners in designing targeted strategies to improve the utilization of skilled delivery services across the country.

Methods and Materials

Study Design, Period, and Setting

Ethiopia is the second most populous country in Africa after Nigeria, with a population of over 114 million. It is bordered by Eritrea, South Sudan, Sudan, Djibouti, and Somalia (Leong et al., 2018). Administratively, Ethiopia is divided into nine regional states (Tigray, Afar, Amhara, Oromia, Somali, Benishangul-Gumuz, SNNPR, Gambella, and Harari) and two chartered cities, Addis Ababa and Dire Dawa. The country has a three-tier health care delivery system. The first level includes primary care facilities district hospitals, health centers, and their satellite health posts. While district hospitals and health centers provide both curative and preventive services, health posts primarily focus on preventive care. The second level consists of general hospitals, and the third level comprises specialized hospitals, both of which primarily offer curative services. The 2016 Ethiopian Demographic and Health Survey (EDHS) was a nationally representative, population-based cross-sectional study conducted from January 18 to June 27, 2016 (Geremew et al., 2018).

Data Source

In this study, the dataset was obtained from the MEASURE DHS database, available at http://dhsprogram.com/data/(supplemental file 1). Access to the 2016 Ethiopian Demographic and Health Survey (EDHS) dataset was granted through an official authorization letter from the DHS Program Office. The 2016 EDHS is the fourth nationally representative and comprehensive survey conducted in Ethiopia, aimed at providing up-to-date information on population health indicators, including fertility, maternal and child health, nutrition, and health service utilization. The data are collected using standardized methodologies, allowing for both national and regional-level comparisons and supporting evidence-based decision-making in health policy and program development.

Sampling Procedures

In the 2016 Ethiopian Demographic and Health Survey (EDHS), each region of the country was first stratified into urban and rural areas, resulting in 21 distinct sampling strata. The sample was selected using a two-stage stratified sampling design. In the first stage, a total of 645 enumeration areas (also referred to as clusters) 202 urban and 443 rural were randomly selected using probability proportional to the size of households within each stratum. In the second stage, 28 households were systematically selected from each cluster (Daniel Gashaneh Belay et al., 2022).

A representative sample of 18,008 households was initially selected, and interviews were successfully completed in 16,650 households. From these households, 16,583 eligible women aged 15–49 were identified for individual interviews, and completed interviews were conducted with 15,683 of them (Moran et al., 2016). For the purpose of this study, a weighted sample of 7,091 eligible women aged 15–49 years, who had given birth within the five years preceding the 2016 EDHS and who delivered either at a health facility or at home, were included in the analysis.

Study Variables

Dependent variable: The dependent variable is binary, categorized as women who delivered at a health facility (coded as ‘1’) and those who delivered at home (coded as ‘0’).

Independent variables: The independent variables for institutional delivery were selected based on previous literature and the availability of variables in the 2016 EDHS dataset. These variables were broadly categorized into two group's individual-level and community-level factors in line with the multilevel analytical approach.

Individual level variables: Educational level of women, household wealth index, employment status of women's, media exposure, health facility visit in the last 12 months, perceived distance to health facility as a big problem to get medical help, perceived not wanting to go alone to get medical help, husband's educational level, number of living children and number of ANC visits during pregnancy were included as individual level variables (Berhane et al., 2024; Emiru et al., 2020; Nizum et al., 2023).

Community level variables: Region and place of residence were considered as community level variables directly from EDHS but the other variables are not found directly from EDHS (John et al., 2019).

Educational level of women at the community level: The median value of educational attainment at the national level was 5 years. Thus, the median value of the aggregated clusters below 5 were classified as women with low educational level and the median value of the aggregated clusters 5 and more were classified as women with high educational level (Kumar & Dhillon, 2021).

Community level wealth: Similarly, the median value of wealth index of the household at the national level was middle, coded as 3. Then, the aggregated clusters was classified as low wealth and high wealth by considering the national value as a cutoff point (Sibanda & Doctor, 2013).

Perceived distance to health facility as a big problem to get medical help at the community level: At the national level the proportion of perception of distance to health facility to get medical help as a big problem was 0.45. So, clusters were classified as low perception and high perception using the national proportion as a cutoff point after aggregating it (Mekonnen et al., 2015).

Community level employment status: It is classified based on the national proportion of women who had employment. Nationally, 36% of women were working during the survey. Thus, clusters employment status were classified as high, if the aggregated clusters are greater than or equal to 36% and low if it is less than 36% (Okafor, 2019).

Community level media exposure: At the national level 48% of women heard information at least once from radio, Television or magazine/newspaper. Clusters classified as low and high taking the national proportion as a cutoff point after aggregating the media exposure (Xu et al., 2022).

Community level ANC visits: The median value of ANC visit at the national level was 3. Hence, the median value of the clusters having less than 3 ANC visits were classified as low and greater than or equal to 3 ANC visits were classified as high after aggregating the ANC visit (Story, 2014).

Community level visit of health facility: It is classified as high and low by taking 43% cluster members as the reference according to the national data (Tessema et al., 2022).

Data Analysis

Two-level mixed-effects logistic regression analysis was conducted using R software version 3.5.3 to account for the hierarchical nature of the 2016 EDHS data. Initially, bivariate analyses were performed to assess the association between each independent variable and the outcome. Variables with a p-value < 0.25 in the bivariate analysis were included in the final multivariable model. Adjusted odds ratios (AOR) with 95% confidence intervals were used to identify factors independently associated with institutional delivery, with statistical significance set at p < 0.05. Fixed effects estimated associations at the individual and community levels, while random effects assessed between-cluster variation. All analyses were weighted, and frequency tables were generated for both individual- and community-level variables.

Results

Individual Level Characteristics

The study included 7,091 women who had given birth in the five years preceding the 2016 Ethiopian Demographic and Health Survey (EDHS). The majority of women (60.7%) had no formal education, while only 4.4% had higher education. Religiously, 46.6% were Muslim, followed by Orthodox Christians (32.7%). A significant proportion (64.4%) had no media exposure (radio/TV), indicating limited access to health information.

In terms of socioeconomic status, 33.7% belonged to the poorest wealth quintile, while 23.0% were in the richest category. Regarding reproductive history, 41.0% had fewer than three living children, and 8.8% reported a history of abortion. Slightly more than half (51.7%) had visited a health facility in the last 12 months, yet 52.7% perceived distance to health facilities as a major barrier. Additionally, 39.9% reported not wanting to go alone for medical help, highlighting social constraints.

Concerning husband's education, 44.4% had no education, and only 8.0% had higher education. Most women (70.0%) were not employed, which may affect healthcare access. For antenatal care (ANC) visits, 34.6% had no ANC visits, while 36.1% achieved the recommended four or more visits, indicating suboptimal ANC utilization in Ethiopia [Table 1].

Table 1.

Individual Variables of Women who had Birth in the Five Years Preceding the 2016, EDHS (n = 7,091).

Individual level variables	Categories	N	%
Educational level of women	No education	4301	60.7
	Primary	1904	26.9
	Secondary	572	8.1
	Higher	314	4.4
Religion	Orthodox	2321	32.7
	Protestant	1310	18.5
	Muslim	3302	46.6
	Other	158	2.2
Media exposure	No exposure to radio/TV	4570	64.4
	Exposure to both TV& radio	2521	35.6
Wealth index	Poorest	2390	33.7
	Poorer	1163	16.4
	Middle	1007	14.2
	Richer	901	12.7
	Richest	1630	23.0
Number of living children	<3	2907	41.0
	3–5	2756	38.9
	>5	1428	20.1
History of Abortion	No	6465	91.2
	Yes	626	8.8
Visited health facilities in the last 12 months
	No	3422	48.3
	Yes	3669	51.7
Perceived distance to HF as a big problem to get medical help
	Not big problem	3352	47.3
	Big problem	3739	52.7
Perceived not wanting to go alone to get medical help
	Not big problem	4263	60.1
	Big problem	2828	39.9
Husband's educational level
	No education	3149	44.4
	Primary	2117	29.9
	Secondary	736	10.4
	Higher	566	8.0
Employment status of women's
	No	4967	70.0
	Yes	2124	30.0
Number of ANC visits
	None	2455	34.6
	1	339	4.8
	2–3	1729	24.4
	4⁺	2550	36.1

ANC = Antenatal care visits, HF = Health Facility, n = number of study participants, %= percent

Community Level Characteristics

The analysis of community-level factors among 7,091 the highest proportions of women resided in Oromia (14.4%), Amhara (10.6%), and Tigray (10.6%), while urban areas accounted for only 21.2% of respondents, with the vast majority (78.8%) living in rural settings. At the community level, 51.3% of women belonged to clusters with low educational attainment, and 50.9% resided in low-wealth communities, indicating widespread socioeconomic challenges. A substantial 59.8% of women lived in areas where distance to health facilities was perceived as a major problem, potentially limiting healthcare access. Additionally, 63.6% were in communities with low female employment rates, which may restrict economic autonomy and healthcare-seeking behavior.

Media exposure was limited, with 69.8% of women in communities classified as having low media access, likely reducing health awareness. Antenatal care (ANC) utilization was also suboptimal at the community level, with 69.1% of women in clusters with low ANC visit rates. However, a majority (63.4%) lived in areas with higher health facility visitation rates in the past year, suggesting some degree of healthcare engagement [Table 2].

Table 2.

Community Level Variables of Women who had Birth in the Five Years Preceding the 2016 EDHS, [n = 7,091].

Community level variables	Categories	n	%
Region	Tigray	751	10.6
	Afar	647	9.1
	Amhara	749	10.6
	Oromia	1021	14.4
	Somali	805	11.4
	Benishangul	565	8.0
	SNNPR	869	12.3
	Gambela	526	7.4
	Harari	409	5.8
	Diredawa	377	5.3
	Addis Ababa	372	5.2
Place of residence
	Urban	1502	21.2
	Rural	5589	78.8
Women's education at the community level
	Low	3641	51.3
	High	2802	39.5
Wealth index at the community level
	Low	3607	50.9
	High	3484	49.1
Perceived distance to HF as a big problem at the community level
	Not big problem	2854	40.2
	Big problem	4237	59.8
Women's employment status at the community level
	Low	4513	63.6
	High	2578	36.4
Media exposure at the community level
	Low	4947	69.8
	High	2144	30.2
Number of ANC visits at the community level
	Low	4903	69.1
	High	2188	30.9
Visited HF in the last 12 months at the community level
	Low	2597	36.6
	High	4494	63.4

ANC = Antenatal care visits, HF = Health Facility, n = number of study participants, SNNPR = South Nations, Nationalities and People's Region, %= percent.

Two Level Mixed Effects Logistic Regression Results

The between-cluster variability declined progressively across successive models, from 64% in the empty model to 24% in the individual-level only model, 19% in the community-level only model, and 16% in the combined model. Consequently, the combined model incorporating both individual-level and community-level factors was selected for predicting women's place of delivery. The intraclass correlation coefficient (ICC) in the empty model indicated that 64% of the total variance in institutional delivery utilization was attributable to differences across clusters (communities). In Model I, which included only individual factors, results showed that education level, wealth index, perceived distance as a barrier to medical care, husband's education level, number of living children, ANC visits, and health facility visits in the past 12 months were significantly associated with institutional delivery. Model II, incorporating only community-level variables, demonstrated that 24% of variation was attributable to community differences, with individual-level characteristics explaining 63% of the variance (PCV = 63%). This model revealed that region, place of residence, community-level women's education, perceived distance, media exposure, ANC visits, and health facility visits were significantly associated with the outcome variable. The community-level variation accounted for 19% (ICC = 19%) of the dependent variable's variance, with community characteristics explaining 70% of institutional delivery utilization (PCV = 70%). The final model (Model III) included both individual and community-level characteristics simultaneously. Results indicated that women's education level, wealth index, perceived distance, husband's education, number of living children, health facility visits, region, and place of residence remained significantly associated with institutional delivery. After adjustment, women with primary education had 1.37 times higher odds (AOR = 1.37; 95% CI:1.13–1.66) of institutional delivery compared to uneducated women, with progressively stronger effects for secondary (AOR = 2.24; 1.56–3.22) and higher education (AOR = 3.83; 2.0–7.35). Wealthier women showed increased utilization, with the richest having 1.83 times higher odds than the poorest. Women perceiving distance as problematic had 24% lower odds (AOR = 0.76; 0.62–0.93) of institutional delivery. Those with husbands having secondary or higher education were nearly twice as likely to deliver in facilities. Multiparous women showed significantly reduced utilization. A strong dose-response relationship emerged for ANC visits, with women completing 4 + visits having 7.6 times higher odds than those with none. Regional disparities were pronounced, with Afar (AOR = 0.15; 0.06–0.38) and Gambella (AOR = 0.21; 0.09–0.51) showing substantially lower utilization than Addis Ababa. Rural women had 64% lower odds (AOR = 0.36; 0.24–0.55) than urban residents. Community-level education (AOR = 1.36; 1.09–1.71), media exposure (AOR = 1.54; 1.14–2.08), and ANC utilization (AOR = 1.60; 1.18–2.08) all positively influenced institutional delivery rates [Table 3].

The ICC shows that 64% of the variability in institutional delivery was attributable to differences between communities in the empty model. This variability progressively declined with the inclusion of individual- and community-level factors, indicating improved model performance. The proportional change in variance and Akaike Information Criterion values further support the final model (Model III) as the best-fitting model, incorporating both individual and community determinants [Table 4].

Table 3.

Two Level Mixed Effect Regression Results of Individual and Community Level Factors Associated with Institutional Delivery in Ethiopia, 2016 EDHS.

Variables/ characteristics	Model I	Model II	Model III
	Individual characteristics	Community level characteristics	Individual & community level characteristics
	OR (95%CI)	OR (95% CI)	OR (95% CI)
Educational level of women's
No education^(a)	1		1
Primary	1.42 (1.18, 1.71)***		1.37 (1.13, 1.66)**
Secondary	2.63 (1.85, 3.74)***		2.24 (1.56, 3.22)***
Higher	5.32 (2.81, 10.06)***		3.83 (2.0, 7.35)***
Wealth index of household
Poorest^(a)	1		1
Poorer	1.82 (1.44, 2.31)***		1.53 (1.19, 1.97)**
Middle	1.74 (1.35, 2.24)***		1.45 (1.11, 1.90)**
Richer	1.97 (1.50, 2.57)***		1.60 (1.20, 2.14)**
Richest	6.10 (4.53, 8.21)***		1.83 (1.28, 2.63)**
Employment status of women's
No^(a)	1		1
Yes	1.03 (0.86, 1.22)		1.03 (0.86, 1.24)
Media exposure
No exposure to radio/TV^(a)	1		1
Exposure to both TV and radio	1.17 (0.97, 1.40)		1.02 (0.84, 1.23)
Visited HF in the last 12 months
No^(a)	1		1
Yes	1.30 (1.11, 1.51)**		1.16 (0.98, 1.36)
Distance to HF
Not big problem^(a)	1		1
Big problem	0.66 (0.54, 0.80)***		0.76 (0.62, 0.93)**
Not wanting to go alone to HF
Not big problem^(a)	1		1
Big problem	0.98 (0.81, 1.19)		1.03 (0.85, 1.26)
Husband's educational level
No education^(a)	1		1
Primary	1.17 (0.98, 1.41)		1.13 (0.94, 1.36)
Secondary	2.02 (1.52, 2.67)***		1.96 (1.46, 2.62)***
Higher	1.76 (1.22, 2.54)**		1.86 (1.28, 2.71)**
Number of living children
<3^(a)	1		1
3–5	0.59 (0.50, 0.71)***		0.58 (0.48, 0.70)***
>5	0.65 (0.52, 0.81)***		0.65 (0.52, 0.82)***
Number of ANC visits
None^(a)	1		1
1	3.43 (2.38, 4.95)***		2.86 (1.93, 4.25)***
2–3	5.61 (4.46, 7.06)***		4.91 (3.87, 6.24)***
4⁺	9.95 (7.91, 12.51)***		7.60 (6.0, 9.66)***
Region
Tigray		0.70 (0.34, 1.42)	0.88 (0.37, 2.08)
Afar		0.10 (0.04, 0.21)***	0.15 (0.06, 0.38)***
Amhara		0.18 (0.09, 0.37)***	0.22 (0.09, 0.52)**
Oromia		0.23 (0.11, 0.48)***	0.29 (0.12, 0.69)**
Somali		0.22 (0.10, 0.46)***	0.35 (0.14, 0.86)*
Benishangul		0.30 (0.14, 0.63)***	0.28 (0.12, 0.67)**
SNNPR		0.33 (0.16, 0.68)***	0.31 (0.13, 0.73)**
Gambela		0.27 (0.13, 0.55)***	0.21 (0.09, 0.51)**
Harari		0.53 (0.25, 1.14)	0.56 (0.22, 1.37)
Diredawa		0.55 (0.26, 1.19)	0.61 (0.24, 1.50)
Addis Ababa^(a)		1	1
Place of residence
Urban^(a)		1	1
Rural		0.23 (0.16, 0.32)***	0.36 (0.24, 0.55)***
Women's education at the community level
low^(a)		1	1
High		1.44 (1.16, 1.78)**	1.36 (1.09, 1.71)**
Wealth index at the community level
low^(a)		1	1
High		1.28 (0.97, 1.68)	1.0 (0.74, 1.33)
Distance to HF as a big problem at the community level
Not big problem^(a)		1	1
Big problem		0.54 (0.42, 0.71)***	0.73 (0.56, 0.97)*
Women's employment status at the community level
low^(a)		1	1
High		0.98 (0.77, 1.23)	0.98 (0.77, 1.27)
Media exposure at the community level
low^(a)		1	1
High		1.76 (1.32, 2.35)***	1.54 (1.14, 2.08)**
Number of ANC visits at the community level
low^(a)		1	1
High		2.42 (1.84, 3.18)***	1.60 (1.18, 2.08)***
Visited HF in the last 12 months at the community level
Low^(a)		1	1
High		2.08 (1.26, 2.67)***	1.60 (1.23, 2.08)***

(a) = reference category, OR = Odds Ratio, CI = Confidence Interval, TV = Television, HF = Health Facilities, ANC = Antenatal care Visits, SNNPR = Southern Nations, Nationalities and People's Region, *P < 0.05, **P < 0.001, ***P < 0.0001.

Table 4.

Model Fit Statistics and Random Effects for Multilevel Logistic Regression Analysis of Factors Associated with Institutional Delivery in Ethiopia, EDHS 2016.

Random effects	Model 0	Model 1	Model 2	Model 3
ICC (%)	64%	24.0%	19%	16%
PCV	Reference	63%	70%	75%
Model fitness
Log likelihood	−3540.4	−2623.6	−2956.3	−2348.0
AIC	7084.7	5291.1	5952.6	4776.0

AIC = Akaike Information Criterion, ICC = Intera Class Correlation, PCV = Proportional Change in Variances.

Discussion

This study aimed to explore the key factors influencing the use of institutional delivery services in Ethiopia by analyzing data from the 2016 EDHS. Given the hierarchical structure of the data, a multilevel logistic regression analysis was applied to account for variations at different levels and to identify significant predictors of institutional delivery utilization. This study revealed that more than two-thirds (68%) of Ethiopian women did not give birth in a health facility. The finding was lower than reports from Nepal (55%) (Shah et al., 2015), sub-Saharan Africa (66%) (Adde et al., 2020), and Uganda (90.2%) (Mugambe et al., 2021). This difference could be explained by the relatively low educational attainment among women in Ethiopia, as well as limited access to health care facilities and maternal health services compared to those countries. On the other hand, the result was higher than those reported in earlier studies from Ethiopia (Tesema et al., 2020) and Kenya (Tesema et al., 2020). This improvement might be linked to the government's intensified efforts to expand maternal health services through initiatives such as enhancing the Health Extension Program, establishing health development armies, constructing additional health infrastructure, training skilled health professionals, and improving the overall quality of care.

The findings of this study indicate that institutional delivery in Ethiopia is influenced by a range of individual and community-level factors. Positive predictors of institutional delivery included women's level of education, frequency of antenatal care visits, household wealth, husbands’ educational background, media exposure, and the general educational status of the community. Conversely, factors such as region, rural residence, higher number of living children, and perceived distance to health facilities were found to be negatively associated with the use of institutional delivery services. This study found that women residing in communities with higher educational levels were more likely to utilize institutional delivery services compared to those in less-educated communities. Likewise, individual women with higher education had increased odds of delivering in health institutions. This finding aligns with previous studies in Ethiopia and beyond ((Kruk et al., 2015; Mekonnen et al., 2015; Weldemariam et al., 2018). Education likely improves awareness of maternal health, recognition of pregnancy-related risks, and decision-making power (Namdeo, 2017). It is evident that educated women have greater confidence and capability to take action regarding their health. In addition, living in a community with a high or low level of education also plays an important role in the utilization of institutional delivery.

In this study, women living in communities with high antenatal care (ANC) utilization rates were more likely to give birth at health facilities compared to those in communities with low ANC utilization. This finding is consistent with previous studies conducted in Ethiopia and other African countries, where community-level ANC utilization was found to influence individual mothers’ decisions to seek institutional delivery services (Demilew et al., 2016; Kuteyi et al., 2011; Mekonnen et al., 2015; Stephenson et al., 2006). One possible explanation is that ANC visits increase women's awareness of the risks associated with pregnancy and childbirth and promote better birth preparedness, thereby encouraging facility-based deliveries (Kuteyi et al., 2011). Additionally, the health education, counseling, and treatment services provided during ANC visits may lead to positive behavioral changes and enhance the perceived benefits of delivering at health institutions (Mekonnen et al., 2015).

This finding aligns with evidence from other studies (Babalola & Fatusi, 2009; A. Kebede et al., 2016; Mekonnen et al., 2015), which also identified household wealth as a key factor influencing institutional delivery. Similar patterns have been observed, where mothers from wealthier households were more likely to utilize health facilities for childbirth compared to those from poorer households. These may be because better economic status may increase healthcare-seeking behavior and autonomy of healthcare decision-making as they are capable of paying the required medical and transport costs (Mamdani & Bangser, 2004).

Media plays a vital role in promoting health service utilization. In this study, women in communities with high media exposure were more likely to use institutional delivery services than those in low-exposure areas. This aligns with findings from Ethiopia (A. Kebede et al., 2016), highlighting media as a key factor. The possible reason is that health information may improve health-seeking habits via various electronic and print media, as information about what services are available, where and when to get them, as well as the benefits and risks of accessing specific services, can be communicated via such media (Mamdani & Bangser, 2004).

Regional variation significantly affected institutional delivery, with lower utilization observed in peripheral areas. The developing regions of Afar and Gambella had the lowest rates, possibly due to weak health infrastructure, limited resource allocation, and low prioritization capacity (Bobo et al., 2017). Additionally, disparities in infrastructure and literacy levels may hinder access to maternal health services. This finding is consistent with studies from Ethiopia and Bangladesh, which also showed regional differences in health service utilization among women (Kamal, 2013; Mekonnen et al., 2015).

Perceptions of distance to health facilities significantly influenced the use of institutional delivery services. Long travel distances, especially when combined with limited transportation options, create major challenges for pregnant women. Similar findings have been reported in studies conducted in Ethiopia and other African countries (Doctor & Dahiru, 2010; Mekonnen et al., 2015; Utomo et al., 2011). These barriers can prevent timely access to care, even when the decision to seek institutional delivery has been made. In communities where reaching a facility was seen as difficult, fewer women gave birth at health institutions.

This study also revealed that women living in communities with frequent health facility visits in the past 12 months were more likely to utilize institutional delivery. Similar findings were reported in previous studies (Aremu et al., 2012; Yebyo et al., 2014), which showed that visiting health facilities increases the likelihood of giving birth at health institutions. Frequent visits may reflect greater familiarity with health services and provide opportunities to receive important health information from providers, thereby encouraging the use of institutional delivery.

Limitations of the Study

One limitation of this study is the use of clusters as the higher-level unit of analysis, despite some clusters containing too few individual women to reliably generate aggregated community-level characteristics. Additionally, a considerable portion of the variability in the model remains unexplained. Due to data constraints, the study did not include a variable for the distance to health care facilities, which could have provided a better explanation for the variation in institutional delivery rates at the community level.

Implications for Practice

This study underscores critical areas for intervention to improve institutional delivery rates in Ethiopia. Health practitioners, program planners, and policymakers should prioritize targeted efforts to enhance women's education, increase antenatal care attendance, and expand community awareness through media and health service outreach. Addressing geographic and financial barriers especially in rural and underserved regions should be a strategic focus, ensuring equitable access to maternal health services. Engaging male partners in maternal health education and empowering communities through improved service delivery can further encourage institutional births. These findings support the need for integrated, multi-level strategies that respond to both individual needs and broader community contexts to reduce maternal and neonatal mortality.

Conclusion

Our findings revealed that institutional delivery rates in Ethiopia remain suboptimal. The analysis identified both individual and community-level factors significantly influencing delivery location. Institutional delivery utilization showed positive associations with maternal education level, household wealth status, paternal education, ANC visit frequency, media exposure, and regular health facility attendance. Conversely, utilization was reduced by perceived distance barriers, higher parity with three or more children, rural residence, and regional disparities particularly evident in Afar and Gambella regions. The substantial community-level effects underscore the importance of contextual factors in healthcare utilization patterns. We recommend that the Ethiopian government and health partners implement targeted interventions through education campaigns, transportation solutions for remote areas, equitable resource allocation across regions, and strengthened ANC-to-delivery care continuity, with particular focus on high-parity women and rural populations.

Supplemental Material

sj-docx-1-son-10.1177_23779608251358700 - Supplemental material for Multilevel Analysis of Individual and Community Level Factors Associated with Institutional Delivery in Ethiopia, 2016 Ethiopian Demography and Health Survey

Supplemental material, sj-docx-1-son-10.1177_23779608251358700 for Multilevel Analysis of Individual and Community Level Factors Associated with Institutional Delivery in Ethiopia, 2016 Ethiopian Demography and Health Survey by Masrie Getnet, Addis Temie, Tilahun Fufa, Lake Kumlachew and Abraham Teym in SAGE Open Nursing

Footnotes

Acknowledgments

We would like to acknowledge the Demographic Health Survey program office for the realization of this finding by authorizes the data.

ORCID iD

Abraham Teym

Ethics Approval and Consent to Participate

After we have sent a one-page proposal on our finding the Demographic Health Survey program office sent an authorization letter and gave us a permission to access the dataset. The consents are available at .

Author Contributions

MG, AT, TF, LK and AT were involved in conception of the idea, designing methods, analysis, interpretation and drafting of the manuscript. All authors have reviewed and approved the submission of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Availability of Data and Materials

Data is available from MEASURE DHS database at .

Supplemental Material

Supplemental material for this article is available online.

References

Adde

K. S.

Dickson

K. S.

Amu

(2020). Prevalence and determinants of the place of delivery among reproductive age women in sub–saharan Africa. PloS One, 15(12), e0244875. https://doi.org/10.1371/journal.pone.0244875

Ahmed

Demissie

Medhanyie

A. A.

Worku

Berhane

(2018). utilization of institutional delivery Service in a Predominantly Pastoralist Community of Northeast Ethiopia. Ethiopian Journal Of Health Sciences, 28(4), 1–10. https://doi.org/10.4314/ejhs.v28i4.6.

Ameh

C. A.

Van Den Broek

(2015). Making it happen: Training health-care providers in emergency obstetric and newborn care. Best Practice & Research Clinical Obstetrics & Gynaecology, 29(8), 1077–1091. https://doi.org/10.1016/j.bpobgyn.2015.03.019

Aremu

Lawoko

Dalal

(2012). The influence of individual and contextual socioeconomic status on obstetric care utilization in the democratic Republic of Congo: A population-based study. International Journal of Preventive Medicine, 3(4), 278.

Asseffa

N. A.

Bukola

Ayodele

(2016). Determinants of use of health facility for childbirth in rural hadiya zone, southern Ethiopia. BMC Pregnancy And Childbirth, 16(614), 1–9. https://doi.org/10.1186/s12884-016-1151-1

Babalola

Fatusi

(2009). Determinants of use of maternal health services in Nigeria-looking beyond individual and household factors. BMC Pregnancy And Childbirth, 9(43), 1–13. https://doi.org/10.1186/1471-2393-9-43

Baral

Lyons

Skinner

Van Teijlingen

(2010). Determinants of skilled birth attendants for delivery in Nepal. Kathmandu University Medical Journal, 8(3), 325–332. https://doi.org/10.3126/kumj.v8i3.6223

Belay

D. G.

Alemu

M. B.

Pereira

Lassi

Z. S.

Tessema

G. A.

(2023). Determinants and geographic distribution of early newborn care in Ethiopia: Evidence from the 2019 Ethiopian Mini demographic health survey. Scientific Reports, 13(1), 22690. https://doi.org/10.1038/s41598-023-49812-9

Belay

D. G.

Getnet

Akalu

Diress

Gela

Y. Y.

Getahun

A. B.

Belsti

(2022). Spatial distribution and determinants of bottle feeding among children 0–23 months in Ethiopia: Spatial and multi-level analysis based on 2016 EDHS. BMC Pediatrics, 22(1), 120. https://doi.org/10.1186/s12887-022-03181-w

10.

Belay

D. B.

Tilahun

Birhan

N. A.

Tiku

Chen

D.-G.

(2025). Spatial Distribution and Determinants of Home Delivery of Women in Rural Ethiopia. The Open Public Health Journal, 18(1), 1–13. https://orcid.org/10.2174/0118749445361611250110025855

11.

Berhan

(2014a). Antenatal care as a means of increasing birth in the health facility and reducing maternal mortality: A systematic review. Ethiopian Journal Of Health Sciences, 24(5), 93–104. https://doi.org/10.4314/ejhs.v24i0.9S

12.

Berhan

(2014b). Causes of maternal mortality in Ethiopia: A significant decline in abortion related death. Ethiopian Journal Of Health Sciences, 24(5), 15–28. https://doi.org/10.4314/ejhs.v24i0.3S

13.

Berhane

Y. Y.

Tarekegn

Tewahido

Abdelmenan

Fasil

Berhane

H. Y.

Fawzi

(2024). Healthcare Facility Preference among Households in Addis Health and Demographic Surveillance Site (ADDIS-HDSS), Addis Ababa, Ethiopia. Ethiopian Journal Of Health Sciences, 34, 97–104. https://orcid.org/10.4314/ejhs.v34i2.5S

14.

Bhowmik

Biswas

R. K.

Ananna

(2020). Women’s education and coverage of skilled birth attendance: An assessment of sustainable development goal 3.1 in the south and Southeast Asian region. PloS One, 15(4), e0231489. https://doi.org/10.1371/journal.pone.0231489

15.

Bobo

F. T.

Yesuf

E. A.

Woldie

(2017). Inequities in utilization of reproductive and maternal health services in Ethiopia. International Journal For Equity In Health, 16(1), 1–8. https://doi.org/10.1186/s12939-016-0499-1

16.

Chinkhumba

De Allegri

Muula

A. S.

Robberstad

(2014). Maternal and perinatal mortality by place of delivery in sub-saharan Africa: A meta-analysis of population-based cohort studies. BMC Public Health, 14, 1–9. https://doi.org/10.1186/1471-2458-14-1014

17.

Demilew

Y. M.

Gebregergs

G. B.

Negusie

A. A.

(2016). Factors associated with institutional delivery in dangila district, north west Ethiopia: A cross-sectional study. African Health Sciences, 16(1), 10–17. https://doi.org/10.4314/ahs.v16i1.2

18.

Demographic

(2016). Addis Ababa, Ethiopia, and Rockville. CSA and ICF.

19.

Doctor

H. V.

Dahiru

(2010). Utilization of non-skilled birth attendants in northern Nigeria: A rough terrain to the health-related MDGs. African Journal of Reproductive Health, 14(2), 37–45.

20.

Emiru

A. A.

Alene

G. D.

Debelew

G. T.

(2020). Individual, household, and contextual factors influencing the timing of the first antenatal care attendance in northwest Ethiopia: A two-level binary logistic regression analysis. International Journal Of Women's Health, 20(258), 463–471. https://doi.org/10.2147/IJWH.S250832

21.

Freedman

L. P.

Waldman

R. J.

De Pinho

Wirth

M. E.

Chowdhury

A. M. R.

Rosenfield

(2005). Transforming health systems to improve the lives of women and children. The Lancet, 365(9463), 997–1000. https://doi.org/10.1016/S0140-6736(05)71090-4

22.

Geremew

Mengistie

Mellor

Lantagne

D. S.

Alemayehu

Sahilu

(2018). Appropriate household water treatment methods in Ethiopia: Household use and associated factors based on 2005, 2011, and 2016 EDHS data. Environmental Health And Preventive Medicine, 23(908), 1–11. https://doi.org/10.1186/s12199-018-0737-9

23.

Graham

W. J.

Bell

J. S.

Bullough

C. H.

(2001). Can skilled attendance at delivery reduce maternal mortality in developing countries? Safe Motherhood Strategies: A Review Of The Evidence, 17, 97–129.

24.

Hill

Baruah

Beattie

J. M.

Bistola

Castiello

Celutkienė

Mindham

(2023). Culture, ethnicity, and socio-economic status as determinants of the management of patients with advanced heart failure who need palliative care: A clinical consensus statement from the heart failure association (HFA) of the ESC, the ESC patient forum, and the European association of palliative care. European Journal Of Heart Failure, 25(9), 1481–1492. https://doi.org/10.1002/ejhf.2973

25.

John

Binu

Unnikrishnan

(2019). Determinants of antenatal care utilization in India: A spatial evaluation of evidence for public health reforms. Public Health, 166(28), 57–64. https://doi.org/10.1016/j.puhe.2018.09.030

26.

Kamal

S. M.

(2013). Preference for institutional delivery and caesarean sections in Bangladesh. Journal Of Health, Population, And Nutrition, 31(1), 96. https://doi.org/10.3329/jhpn.v31i1.14754

27.

Kebede

Hassen

Nigussie Teklehaymanot

(2016). Factors associated with institutional delivery service utilization in Ethiopia. International Journal Of Women's Health, 16(329), 463–475. https://doi.org/10.2147/IJWH.S109498

28.

Kebede

Kefale

Yigezu

Bogale

E. K.

Zewdie

Wasihun

Adane

(2023). Individual and community level factors associated with modern contraceptive utilization among married women in the emerging region of Ethiopia: A multilevel mixed effects analysis of the 2019 Ethiopia Mini-demographic and health survey. BMC Women's Health, 23(1), 652. https://doi.org/10.1186/s12905-023-02822-1

29.

Kinney

M. V.

Kerber

K. J.

Black

R. E.

Cohen

Nkrumah

Coovadia

Group

C. W

. (2010). Sub-Saharan Africa's mothers, newborns, and children: Where and why do they die? PLoS Medicine, 7(6), e1000294. https://doi.org/10.1371/journal.pmed.1000294

30.

Kruk

M. E.

Hermosilla

Larson

Vail

Chen

Mazuguni

Mbaruku

(2015). Who is left behind on the road to universal facility delivery? A cross-sectional multilevel analysis in rural T anzania. Tropical Medicine & International Health, 20(8), 1057–1066. https://doi.org/10.1111/tmi.12518

31.

Kumar

Dhillon

(2021). Household-and community-level determinants of low-risk caesarean deliveries among women in India. Journal of Biosocial Science, 53(1), 55–70. https://doi.org/10.1017/S0021932020000024

32.

Kuteyi

E. A.

Kuku

Lateef

Ogundipe

Mogbeyteren

Banjo

(2011). Birth preparedness and complication readiness of pregnant women attending the three levels of health facilities in ife central local government, Nigeria. Journal of Community Medicine and Primary Health Care, 23(1–2), 41–54.

33.

Leong

Teo

Rangarajan

Lopez-Jaramillo

Avezum

Jr Orlandini

(2018). World Population Prospects 2019. Department of Economic and Social Affairs Population Dynamics. United Nations; 2019 (https://population. un. org/wpp/Download/, accessed 20 September 2020). The decade of healthy ageing. Geneva: World Health Organization. World, 73(7), 362

34.

Mamdani

Bangser

(2004). Poor people's experiences of health services in Tanzania: A literature review. Reproductive Health Matters, 12(24), 138–153. https://doi.org/10.1016/S0968-8080(04)24135-0

35.

Mekonnen

Z. A.

Lerebo

W. T.

Gebrehiwot

T. G.

Abadura

S. A.

(2015). Multilevel analysis of individual and community level factors associated with institutional delivery in Ethiopia. BMC Research Notes, 8(376), 1–9. https://doi.org/10.1186/1756-0500-8-1

36.

Montagu

Yamey

Visconti

Harding

Yoong

(2011). Where do poor women in developing countries give birth? A multi-country analysis of demographic and health survey data. PloS One, 6(2), e17155. https://doi.org/10.1371/journal.pone.0017155

37.

Moran

A. C.

Jolivet

R. R.

Chou

Dalglish

S. L.

Hill

Ramsey

Say

(2016). A common monitoring framework for ending preventable maternal mortality, 2015–2030: Phase I of a multi-step process. BMC Pregnancy And Childbirth, 16, 1–13. https://doi.org/10.1186/s12884-016-1035-4

38.

Mugambe

R. K.

Yakubu

Wafula

S. T.

Ssekamatte

Kasasa

Isunju

J. B.

Ssempebwa

J. C

. (2021). Factors associated with health facility deliveries among mothers living in hospital catchment areas in rukungiri and kanungu districts, Uganda. BMC Pregnancy And Childbirth, 21, 1–10. https://doi.org/10.1186/s12884-021-03789-3

39.

Mustafa

Yusof

Iiizam

Lukman

Husain

(2016). Maternal health and mortality in developing countries: Challenges of achieving millennium development goals. IOSR J Dent Med Sci, 15(169), 112–117.

40.

Namdeo

R. P.

(2017). Impact of education on decision-making ability of women. Educational Quest-An International Journal of Education and Applied Social Sciences, 8(spl), 431–434. https://doi.org/10.5958/2230-7311.2017.00087.3

41.

Nizum

M. W. R.

Shaun

M. M. A.

Faruk

M. O.

Shuvo

M. A.

Fayeza

Alam

M. F.

Hawlader

M. D. H.

(2023). Factors associated with utilization of antenatal care among rural women in Bangladesh: A community-based cross-sectional study. Clinical Epidemiology and Global Health, 20, 101262. https://doi.org/10.1016/j.cegh.2023.101262

42.

Noar

S. M.

Benac

C. N.

Harris

M. S.

(2007). Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. Psychological Bulletin, 133(4), 673. https://doi.org/10.1037/0033-2909.133.4.673

43.

Okafor

A. T.

(2019). Antenatal Care and Maternal Sociocultural Determinants of Childhood Immunization in Northern Nigeria. Walden University.

44.

Onambele

Guillen-Aguinaga

Ortega-Leon

Montejo

Alas-Brun

Guillen-Grima

(2023). Trends, projections, and regional disparities of maternal mortality in Africa (1990–2030): An ARIMA forecasting approach. Epidemiologia, 4(3), 322–351. https://doi.org/10.3390/epidemiologia4030032

45.

Pathak

Shrestha

Devkota

Thapa

(2017). Factors associated with the utilization of institutional delivery service among mothers.

46.

Roro

M. A.

Hassen

E. M.

Lemma

A. M.

Gebreyesus

S. H.

Afework

M. F.

(2014). Why do women not deliver in health facilities: A qualitative study of the community perspectives in south central Ethiopia? BMC Research Notes, 7, 1–7. https://doi.org/10.1186/1756-0500-7-1

47.

Sagna

M. L.

Sunil

(2012). Effects of individual and neighborhood factors on maternal care in Cambodia. Health & Place, 18(2), 415–423. https://doi.org/10.1016/j.healthplace.2011.12.006

48.

Shah

Rehfuess

E. A.

Maskey

M. K.

Fischer

Bhandari

P. B.

Delius

(2015). Factors affecting institutional delivery in rural chitwan district of Nepal: A community-based cross-sectional study. BMC Pregnancy And Childbirth, 15(27), 1–14. https://doi.org/10.1186/s12884-015-0429-z

49.

Sibanda

Doctor

H. V.

(2013). Measuring health gaps between the rich and the poor: A review of the literature and its implications for health research in Africa. Journal of Public Health in Africa, 4(1), e3. https://doi.org/10.4081/jphia.2013.e3

50.

Stephenson

Baschieri

Clements

Hennink

Madise

(2006). Contextual influences on the use of health facilities for childbirth in Africa. American Journal Of Public Health, 96(1), 84–93. https://doi.org/10.2105/AJPH.2004.057422

51.

Story

W. T.

(2014). Social capital and the utilization of maternal and child health services in India: A multilevel analysis. Health & Place, 28, 73–84. https://doi.org/10.1016/j.healthplace.2014.03.011

52.

Tesema

G. A.

Mekonnen

T. H.

Teshale

A. B.

(2020). Individual and community-level determinants, and spatial distribution of institutional delivery in Ethiopia, 2016: Spatial and multilevel analysis. PloS One, 15(11), e0242242. https://doi.org/10.1371/journal.pone.0242242

53.

Tessema

Z. T.

Tesema

G. A.

Yazachew

(2022). Individual-level and community-level factors associated with eight or more antenatal care contacts in sub-saharan Africa: Evidence from 36 sub-saharan African countries. BMJ Open, 12(3), e049379. https://doi.org/10.1136/bmjopen-2021-049379

54.

Utomo

Sucahya

P. K.

Utami

F. R.

(2011). Priorities and realities: Addressing the rich-poor gaps in health status and service access in Indonesia. International Journal For Equity In Health, 10, 1–14. https://doi.org/10.1186/1475-9276-10-47

55.

Weldemariam

Kiros

Welday

(2018). Utilization of institutional delivery service and associated factors among mothers in north west Ethiopian. BMC Research Notes, 11(194), 1–6. https://doi.org/10.1186/s13104-017-3088-5

56.

Wolelie

Aychiluhm

Awoke

(2014). Institutional delivery service utilization and associated factors in banja district, awie zone, amhara regional sate, Ethiopia. Open Journal of Epidemiology, 4(1), 30–35. https://doi.org/10.4236/ojepi.2014.41006

57.

Worku

A. G.

Yalew

A. W.

Afework

M. F.

(2013). Factors affecting utilization of skilled maternal care in northwest Ethiopia: A multilevel analysis. BMC International Health And Human Rights, 13(18), 1–11. https://doi.org/10.1186/1472-698X-13-20

58.

Peng

M. Y.-P.

Ahuru

R. R.

Anser

M. K.

Osabohien

Aziz

(2022). Individual and community-level factors associated with non-institutional delivery of women of childbearing-age in Nigeria. Humanities and Social Sciences Communications, 9(1), 1–7. https://doi.org/10.1057/s41599-021-01017-z

59.

Yebyo

H. G.

Gebreselassie

M. A.

Kahsay

A. B.

(2014). Individual and community-level predictors of home delivery in Ethiopia: A multilevel mixed-effects analysis of the 2011 Ethiopia National Demographic and Health Survey: ICF international.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.01 MB