Spatial distribution and determinants of solitary childbirth in Ethiopia: Evidence from the 2019 interim demographic and health survey

Tadesse Tarik Tamir; Berhan Tekeba; Alebachew Ferede Zegeye; Deresse Abebe Gebrehana; Mulugeta Wassie; Gebreeyesus Abera Zeleke; Enyew Getaneh Mekonen

doi:10.1371/journal.pone.0333603

. 2025 Sep 26;20(9):e0333603. doi: 10.1371/journal.pone.0333603

Spatial distribution and determinants of solitary childbirth in Ethiopia: Evidence from the 2019 interim demographic and health survey

Tadesse Tarik Tamir ^1,^*, Berhan Tekeba ¹, Alebachew Ferede Zegeye ², Deresse Abebe Gebrehana ³, Mulugeta Wassie ⁴, Gebreeyesus Abera Zeleke ⁵, Enyew Getaneh Mekonen ⁵

Editor: José Antonio Ortega⁶

PMCID: PMC12469165 PMID: 41004544

Abstract

Introduction

Solitary childbirth—giving birth without any form of assistance—remains a serious global public health issue, especially in low-resource settings. It is associated with preventable maternal complications such as hemorrhage and sepsis, and poses significant risks to newborns, including birth asphyxia, infection, and early neonatal death. In Ethiopia, where many births occur outside health facilities, understanding the spatial and socio-demographic patterns of solitary childbirth is vital for informing targeted interventions to improve maternal and child health outcomes. This study aims to identify and map the spatial distribution of solitary childbirth across Ethiopia and to analyze its determinants using data from the 2019 national Interim Demographic and Health Survey.

Method

We analyzed data from the 2019 Interim Ethiopian Demographic and Health Survey to determine the spatial distribution and factors of solitary birth in Ethiopia. A total weighted sample of 3,884 women was included in the analysis. Spatial analysis was used to determine the regional distribution of solitary birth, and multilevel logistic regression was employed to identify its determinants. ArcGIS 10.8 was used for spatial analysis, and Stata 17 was used for multilevel analysis. The fixed effect was analyzed by determining the adjusted odds ratio with a 95% confidence interval.

Result

The prevalence of solitary childbirths in Ethiopia was 12.73%, with a 95% confidence interval spanning from 11.71% to 13.81%. The western and southern parts of Oromia, all of Benishangul-Gumuz, most parts of the SNNPR, and the west of Amhara regions were hotspot areas for solitary birth. Having no formal education, not attending ANC visits, and residing in pastoral regions were significantly associated with higher odds of solitary birth in Ethiopia.

Cocnlusion

A notable proportion of women are experiencing childbirth alone, which highlights a significant aspect of maternal health in the country, reflecting both the challenges and improvements in childbirth practices. The distribution of solitary births exhibited spatial clustering with its hotspot areas located in western and southern parts of Oromia, all of Benishangul-Gumuz, most parts of the SNNPR, and west of Amhara regions. Lack of education, not having an ANC visit, and being a resident of pastoral regions were significant determinants of solitary birth. The implementation of maternal and child health strategies in Ethiopia could benefit from considering the hotspot areas and determinants of solitary birth.

Introduction

Ongoing support during childbirth is linked to better perinatal and infant health outcomes, such as a reduced risk of low birth weight and unplanned cesarean sections, and fewer medical interventions [1,2]. Support persons play a crucial role by communicating with healthcare providers, advocating for the patient’s preferences, and offering both physical and emotional support [3]. Solitary childbirth, defined as a woman delivering a baby alone without any form of assistance, is a significant public health concern in Ethiopia [4]. This phenomenon is particularly prevalent in rural and remote areas, where access to healthcare facilities and support systems is limited1 [4].

Globally, maternal mortality remains unacceptably high, with over 260,000 women dying from pregnancy and childbirth-related complications in 2023 alone, and approximately 92% of these deaths occurring in low- and lower-middle-income countries [5]. Unassisted deliveries significantly contribute to this burden, as they are associated with increased risks of postpartum hemorrhage, infections, obstructed labor, and neonatal death [6]. In Ethiopia, the situation is particularly alarming: the maternal mortality rate stood at 412 per 100,000 live births in 2019, with over half of births occurring at home, often without skilled attendance [7]. These outcomes underscore the need to understand the geographic distribution and determinants of solitary childbirth to inform targeted interventions and reduce preventable maternal and child deaths.

Solitary childbirth remains a significant barrier to achieving global maternal and neonatal health targets, particularly those outlined under Sustainable Development Goal 3 (SDG 3) [8]. SDG 3 aims to “ensure healthy lives and promote well-being for all at all ages,” with specific objectives to reduce the global maternal mortality ratio to less than 70 per 100,000 live births and to end preventable deaths of newborns and children under five years of age by 2030 [8,9]. These targets include reducing neonatal mortality to at least 12 per 1,000 live births and under-five mortality to at least 25 per 1,000 live births [8,9].

Several factors contribute to the high incidence of solitary childbirth in Ethiopia. These include socio-economic determinants such as education level, media exposure, household income, and cultural practices [10–12]. Women with lower educational attainment and those from poorer households are more likely to give birth alone [1]. Additionally, cultural norms and beliefs about childbirth, including the preference for traditional birth practices and the stigma associated with hospital births, play a significant role [10–12].

Ethiopia is characterized by diverse geographical and socio-economic conditions that influence health service accessibility. The country’s healthcare system has made strides in improving maternal health services; however, disparities remain, particularly in rural regions [13–15]. Geographical barriers also significantly impact the spatial distribution of solitary status of childbirth. Regions with difficult terrain and limited infrastructure, such as the Afar and Somali regions, exhibit higher rates of home births without skilled attendants [3,15]. The distance to healthcare facilities and the availability of transportation are critical factors influencing a woman’s decision to seek professional care during childbirth. Moreover, the availability and quality of maternal health services vary across different regions of Ethiopia. Urban areas, such as Addis Ababa, generally have better access to healthcare facilities and skilled birth attendants compared to rural areas [4]. This urban-rural divide highlights the need for region-specific strategies to address the unique challenges faced by women in different parts of the country.

To the best of our knowledge, this study is the first of its kind in Ethiopia to specifically examine the phenomenon of solitary childbirth—defined as giving birth without any form of assistance. While previous research has primarily focused on the type of birth assistance (skilled versus traditional) or the place of delivery (home versus health facility), the unique and critical issue of women delivering entirely alone has received little to no scholarly attention. This gap is particularly concerning given that solitary childbirth remains a common practice in various parts of Ethiopia, often rooted in cultural traditions that encourage women to give birth in isolation or driven by the unavailability of assistance during labor. Addressing this overlooked aspect of maternal health is essential for informing targeted interventions and improving outcomes for both mothers and newborns. Therefore, analyzing the spatial distribution and associated factors of solitary childbirth in Ethiopia is essential for identifying high-risk areas and populations. This analysis can inform the development of targeted interventions aimed at reducing maternal and neonatal mortality and improving overall maternal health outcomes. By addressing the socio-economic, cultural, and geographical barriers to skilled birth attendance, Ethiopia can make significant progress towards achieving its maternal health goals.

Methods and materials

Study design, data source and setting

A secondary data analysis of cross-sectional 2019 Interim Ethiopian Demographic and Health Survey (IEDHS) was conducted. The IEDHS data were accessed through the Monitoring and Evaluation to Assess and Use Results Demographic and Health Survey (MEASURE DHS) program. Specifically, we utilized the Individual Recode (IR) data extracted from the IEDHS dataset. This study was conducted in Ethiopia, located in the Horn of Africa. The country is administratively structured into nine regional states—Tigray, Afar, Amhara, Oromia, Somali, Benishangul-Gumuz, Southern Nations, Nationalities, and Peoples’ Region (SNNPR), Gambela, and Harari—and two chartered city administrations: Addis Ababa and Dire Dawa [16]. Ethiopia operates a decentralized health system organized into three tiers [17,18]. The primary level includes health posts, health centers, and primary hospitals, which serve as the first point of contact for communities and provide essential health services. A primary hospital typically serves a population of around 100,000 and functions as a referral center for health centers, offering emergency, outpatient, and inpatient care. The secondary level comprises general hospitals that act as referral hubs for primary hospitals and serve as training institutions for healthcare professionals. The tertiary level consists of specialized hospitals that provide advanced care and serve as referral centers for general hospitals This structured health system is designed to improve access to care across both urban and rural populations [17,18].

Population and sampling procedure.

The source population for this study consisted of women aged 15–49 years in Ethiopia who gave birth three years preceding the survey. The study population, on the other hand, comprised women aged 15–49 years who had given birth within the three years preceding the survey and were residing in the enumeration areas covered by the survey. The IEDHS employed a stratified two-stage cluster sampling technique to ensure nationally representative data. Firstly, each country is divided into different strata based on relevant characteristics, such as urban/rural location or geographic regions. Within each stratum, enumeration areas (clusters) were randomly selected as the primary sampling units. In the second stage, a sample of households from within each selected enumeration area using either a systematic method was drawn [7]. Sampling weights were applied using the svyset and svy commands to account for the complex survey design of the IEDHS data. This involved utilizing weighting variables, including the sampling weight (v005), primary sampling unit (v021), and strata for sampling design (v023). This approach ensures that the sample is representative of the target population. By incorporating these sampling weights, we aimed to adjust for the unequal probabilities of selection inherent in the survey design, thereby enhancing the accuracy and validity of our findings and allowing for the drawing of valid conclusions. In this study, a total weighted sample of 3884 women were included (Fig 1). ArcGIS 10.8 was used for spatial analysis, and Stata 17 was used for multilevel analysis.

Variables and measurement

In this study, the outcome variable was defined as solitary childbirth. Solitary childbirth refers to the event where a woman gives birth without any assistance from healthcare professionals, midwives, family members, or any other individuals. This includes births that occur in any setting where the mother was entirely alone during the delivery process. The variable was measured as a binary outcome, with a value of 1 indicating a solitary childbirth (no assistance present) and a value of 0 indicating a non-solitary childbirth (assistance present). According to the Guide to DHS Statistics, the types of persons providing delivery assistance are categorized as follows: doctor (m3a), nurse/midwife (m3b), auxiliary midwife (m3c), traditional birth attendant (m3g), relative/other (m3h, m3i, m3j, m3k, m3l, m3m), and no one (m3n) [19]. For this study, solitary childbirth corresponds to the category where no one (m3n) is present. The data for this variable is sourced from the 2019 IEDHS, which includes self-reported information on the presence or absence of assistance during childbirth.

The explanatory variables for this study were chosen based on established guidelines [20] and scholarly literature. These variables were divided into two levels. At the individual level, we included factors such as age, educational attainment, marital status, antenatal care utilization, birth interval, presence of a radio, presence of a TV, sex of the household head, household size, and wealth index. At the community level, we analyzed variables such as residence type, region, women’s illiteracy rate, community poverty rate, and regional characteristics.

Wealth index in the 2019 IEDHS.

Households are classified according to the number and types of consumer goods they possess, which can include items like televisions, bicycles, and automobiles, as well as housing characteristics such as drinking water sources, bathroom facilities, and flooring materials. Principal component analysis is employed to derive these scores. National wealth quintiles are determined by assigning the household score to each usual household member, ranking individuals based on their scores, and dividing the population into five equal segments (poorer, poorest, middle, richer, and rich), each representing 20% of the population [7].

Spatial analysis

Spatial autocorrelation.

The spatial dependency of solitary birth in Ethiopia was assessed using Global Moran’s I, a measure of spatial autocorrelation. This statistic ranges from −1–1, where a value of 0 indicates random distribution, a value near −1 suggests a dispersed pattern, and a value close to 1 signifies clustering. If the Moran’s I value is statistically significant (P < 0.05), it indicates the presence of spatial dependence [21].

Hot spot analysis.

This study employed an optimized hot spot analysis technique to pinpoint areas with high and low solitary birth rates in Ethiopia. The Optimized Hot Spot Analysis tool, which uses the Getis-Ord Gi* statistic, is an advanced version of the traditional Hot Spot Analysis (Getis-Ord Gi*). It identifies statistically significant hot and cold spots in the data and adjusts for multiple testing and spatial dependence using the False Discovery Rate (FDR) correction method [22]. The Getis-Ord Gi* statistic measures spatial clustering by analyzing the distribution of features and their neighboring features [22,23].

Spatial scan statistical analysis.

To identify significant clusters of solitary birth, Scan Statistical Analysis were utilized, employing a circular scanning window that moves across the study area. The analysis incorporated cases, controls, and geographic coordinate data into the Bernoulli model. For each potential cluster, the Log Likelihood Ratio (LLR), Relative Risk (RR), and P-values were calculated to determine if the observed number of cases within the cluster was significantly higher than expected.

Spatial interpolation.

The spatial prediction of solitary birth in unsampled areas of Ethiopia was conducted using Kriging interpolation, based on observed data from sampled regions. Kriging is an interpolation method that estimates the value of a variable in unsampled locations by utilizing observations from neighboring areas [24–26]. This technique minimizes prediction errors and represents geographic variation through a variogram. Named after Danie Krige, Kriging originated in the field of mining geology [25,26].

Multilevel modeling.

This study utilized data from the Demographic and Health Surveys (DHS), which provide information at both household and cluster levels (hierarchical in nature). To tackle the problem of non-independent observations—an essential requirement for standard logistic regression—we employed mixed-effects models with a binary outcome variable. We developed four distinct model specifications: a null model to evaluate random effects and confirm the suitability of multilevel regression; Model I, which included the outcome variable and first-level control variables; Model II, which added second-level control variables; and a comprehensive Model III that encompassed all variables—outcome, first-level controls, and second-level controls. This multilevel modeling approach enabled us to account for the hierarchical structure of the DHS data and more effectively investigate the determinants of solitary childbirth. The regression model was equated as follows [27]:

log (\frac{Π i j}{1 - Π i j}) = β 0 + β 1 x 1 i j + . . . + β n x n i j + γ 0 + γ 1 z 1 i j + . . . + γ m z m i j + u o i j

Where πij represents the probability of solitary birth for the i^th women in the j^th cluster, while (1-πij) denotes the probability of the i^th women in the j^th cluster not experiencing solitary birth. The intercept term β0 characterizes the baseline of the regression equation. The coefficients β1 to βn are linked to the level 1 variables x1ij to xnij, which exert an influence on the response variable at the individual level. The intercept γ0j captures the random effect at level 2, while the coefficients γ1 to γm are associated with the level 2 variables z1ij to zmij, reflecting cluster-level effects. Finally, the error term eij accounts for the random error or residual within the model.

The researchers evaluated both the fixed and random components of the mixed-effects models. The random effect was assessed using variance, the intra-class correlation coefficient (ICC), the median odds ratio (MOR), and the proportional change in variation (PCV). The fixed effect was analyzed by determining the adjusted odds ratio (AOR) with a 95% confidence interval (CI). An association between explanatory variables and solitary childbirth was considered significant if the p-value was below the predetermined significance level of 0.05. For model comparison, the team employed log likelihood, deviance, Akaike’s Information Criterion (AIC), and Bayesian Information Criterion (BIC).

To address multicollinearity—when two or more independent variables in a regression model are highly correlated—the researchers calculated the variance inflation factor (VIF) for each variable, finding VIF values below five, indicating that multicollinearity was not a significant issue. Additionally, multivariable regression techniques were utilized to control for potential confounding factors, allowing researchers to isolate the effect of each independent variable on solitary childbirth while accounting for other relevant influences.

Ethical approval.

This study was based on analysis of existing survey dataset in the public domain that are freely available online with all the identifier information anonymized, no ethical approval was required. The first author obtained authorization for the download and usage of the archive of the IEDHS dataset from MEASURE DHS. The datasets were treated with the utmost confidentiality, and issues related to informed consent, anonymity, and privacy was ethically handled by the MEASURE DHS office. We did not manipulate or apply the microdata beyond the scope of this study.

Results

Descriptive statistics of solitary births by individual and community-level characteristics

The analysis included a total weighted sample of 3,884 women within three years preceding the survey. Table 1 summarizes the association between individual and community-level variables and the occurrence of solitary births. Among individual-level factors, the proportion of solitary births is highest among women aged 35–49 years (17.59%), while the lowest is observed in the 15–19 age group (2.04%), with this association being statistically significant (p < 0.001). Marital status does not show a significant difference in solitary births, with 12.92% of women not in union and 12.71% of those in union reporting solitary births (p = 0.430). Educational level, however, is significantly associated with solitary births; women with no education exhibit the highest proportion (18.16%), compared to only 1.13% among those with secondary or higher education (p < 0.001). Similarly, women in male-headed households report higher rates of solitary births (12.98%) compared to those in female-headed households (10.99%) (p < 0.001). Antenatal care (ANC) visits also play a critical role, with women who had no ANC visits reporting a markedly higher rate of solitary births (22.23%) than those who received ANC (9.42%) (p < 0.001). Household wealth is inversely associated with solitary births; the poorest households exhibit the highest rates (20.99%), while the richest households show the lowest (3.12%) (p < 0.001). Additional individual-level factors significantly associated with solitary births include household size, access to media (radio and television), and birth interval.

Table 1. Descriptive statistics of solitary births by individual and community-level characteristics.

Characteristics		Solitary birth		Total	Chi squared test P value
Characteristics		Yes [n (%)]	No [n (%)]	Total	Chi squared test P value
Individual level characteristics
Women age	15-19	5 (2.04)	222 (97.96)	227 (5.82)	<0.001
	20-34	325 (11.95)	2399 (88.05)	2724 (70.16)
	35-49	164 (17.59)	769 (82.41)	933 (24.02)
Marital status	Not in union	30 (12.92)	202 (87.08)	232 (5.99)	0.430
Marital status	In union	464 (12.71)	3187 (87.29)	3651 (94.01)	0.430
Women educational level	No education	364 (18.16)	1639 (81.84)	2003 (51.57)	<0.001
	Primary	125 (8.94)	1274 (91.06)	1,399 (36.03)
	Secondary & above	5 (1.13)	476 (98.87)	482 (12.40)
Sex of household head	Male	439 (12.98)	2940 (87.02)	3379 (87.01)	<0.001
Sex of household head	Female	55 (10.99)	449 (89.01)	505 (12.99)	<0.001
ANC visits	No visit	223 (22.23)	779 (77.77)	1002 (25.80)	<0.001
ANC visits	Had visit	272 (9.42)	2610 (90.58)	2882 (74.20)	<0.001
Household wealth index	Poorest	173(20.99)	651 (79.01)	824 (21.22)	<0.001
	Poorer	134 (16.48)	680 (83.52)	814 (20.96)
	Middle	84 (11.24)	667 (88.76)	752 (19.36)
	Richer	77 (11.23)	612 (88.77)	689 (17.75)
	Richest	25 (3.12)	779 (96.88)	804 (20.71)
Birth interval	< 24 months	137 (19.05)	581 (80.95)	717 (30.50)	0.116
Birth interval	≥ 24 months	281 (17.18)	1354 (82.82)	1635 (69.50)	0.116
Household has radio	Yes	85 (8.26)	949(91.74	1035 (26.64)	<0.001
Household has radio	No	409 (14.35)	2440 (85.65)	2849 (73.36)	<0.001
Household has television	Yes	12 (1.82)	630 (98.18)	642 (16.52)	<0.001
Household has television	No	483 (14.88)	2760 (85.12)	3242 (83.48)	<0.001
Household size	< 6	255 (9.93)	2312 (90.07)	2567 (66.10)	<0.001
Household size	≥6	239 (18.18)	1077 (81.82)	1317 (33.90)	<0.001
Community level characteristics
Residence	Urban	60 (5.91)	956 (94.09)	1016 (26.16)	<0.001
Residence	Rural	434 (15.14)	2434 (84.86)	2868 (73.84)	<0.001
Community illiteracy rate	Low	204 (9.90)	1860(90.10)	2064 (53.14)	<0.001
Community illiteracy rate	High	290 (15.93)	1530 (84.07)	1820 (46.86)	<0.001
Community poverty rate	Low	234 (9.76)	2161 (90.24)	2395 (61.66)	<0.001
Community poverty rate	High	260 (17.49)	1229 (82.51)	1489 (38.34)	<0.001
Region	Metropolitan	1 (0.90)	155 (99.10)	157 (4.03)	<0.001
	Agrarian	477 (14.05)	2916 (85.95)	3393 (87.37)
	Pastorals	50 (14.97)	284 (85.03)	334 (8.60)

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ANC: Antenatal Care.

At the community level, solitary births are more prevalent in rural areas (15.14%) compared to urban areas (5.91%) (p < 0.001). Communities with higher illiteracy rates report a higher proportion of solitary births (15.93%) compared to those with lower illiteracy rates (9.90%), and similar patterns are observed in relation to community poverty rates, where communities with higher poverty rates have a significantly higher proportion of solitary births (17.49%) compared to their counterparts with lower poverty rates (9.76%) (p < 0.001). Regional disparities are evident, with the highest proportion of solitary births observed in pastoral settings (14.97%) and the lowest in metropolitan areas (0.90%) (p < 0.001) (Table 1).

These findings indicate significant disparities in solitary birth rates based on socioeconomic, demographic, and geographic factors. Most of these associations are statistically significant (p < 0.05), underscoring the need for targeted interventions that address these determinants.

Prevalence of solitary childbirth in Ethiopia

The prevalence of solitary childbirths in Ethiopia was 12.73%, with a 95% confidence interval spanning from 11.71% to 13.81%, as illustrated in Fig 2.

Spatial autocorrelation of solitary birth in Ethiopia.

The spatial autocorrelation analysis indicated significant variation in the distribution of solitary births across Ethiopia, with a Moran’s Index of 0.169016, a Z-score of 3.776489, and a p-value of 0.000159 (Fig 3). This suggests that solitary births are not uniformly distributed throughout the country.

Hot spot analysis of solitary birth in Ethiopia.

Fig 4 illustrates the results of the optimized hot spot analysis of solitary births in Ethiopia. This analysis identifies statistically significant clusters of high and low values (hot spots and cold spots) across the country. The map is color-coded: red indicates hot spots with 99% confidence, orange represents 95% confidence, and blue signifies cold spots at the same confidence levels. Gray spots denote regions where the analysis did not yield significant results. The findings highlight specific regions, such as the western and southern parts of Oromia, throughout Benishangul-Gumuz, most parts of SNNPR, and the northwest of Amhara, where solitary births are concentrated (hot spot areas).

Spatial scan statistical analysis of solitary birth in Ethiopia.

In this study, we conducted a spatial scan statistical analysis of solitary births in Ethiopia, as illustrated in Table 2 and Fig 5. The analysis identified several clusters of solitary births, with the primary cluster comprising 45 enumeration areas located at a latitude of 5.0000° N and a longitude of 37.0000° E, covering a radius of 333.38 km. This primary cluster had a total population of 1008, with 548 recorded cases of solitary births, resulting in a relative risk (RR) of 2.68, indicating a 2.68 times higher risk of solitary birth compared to the women living outside the cluster window. The LLR for this cluster was 67.27, suggesting strong evidence of spatial clustering, and the associated p-value was less than 0.001, confirming the statistical significance of this finding. Additionally, three secondary clusters were identified, with Secondary Cluster 1 encompassing 17 enumeration areas and exhibiting LLR of 46.48. Secondary Cluster 2 included 21 areas with an RR of 2.37, while Secondary Cluster 3 comprised 9 areas with a lower risk. The spatial distribution of these clusters is depicted in Fig 5, which highlights the geographic context, particularly around key regions such as Oromia and SNNPR.

Table 2. Spatial scan statistical analysis of solitary birth in Ethiopia, IEDHS 2019.

Cluster	N	Latitude	Longitude	Radius	Population	Cases	RR	LLR	p-value
Primary	45	5.000000 N	37.000000 E	333.38 km	1008	548	2.68	67.27	<0.001
Secondary 1	17	5.000000 N	37.000000E	247.57 km	573	150	2.53	46.48	<0.001
Secondary 2	3	8.000000 N	37.000000 E	0 km	118	34	2.37	11.24	<0.001
Secondary 3	14	7.000000 N	40.000000 E	156.48 km	477	90	1.60	8.58	0.007

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N: Number of enumeration areas; RR: Relative risk; LLR: Log likelihood ratio.

Interpolation of solitary birth in Ethiopia.

The Kriging interpolation of solitary births across Ethiopia provides a visual representation of predicted risk levels associated with solitary births in various regions. The color gradient ranges from low (indicated in green) to high (represented in red) risk areas, with specific attention to Benishangul-Gumuz, Oromia, SNNPR, and the eastern part of Afar. These regions exhibit varying degrees of risk. The interpolation method employed offers an estimate of solitary birth occurrences, leveraging spatial data to fill in gaps between observed data points. The legend clearly delineates the predicted risk levels, with a higher prediction value of 0.70097 indicating areas of greater concern, while a lower value of −0.04606 suggests minimal risk (Fig 6).

Multivariable multilevel logistic regression of determinants of solitary birth in Ethiopia.

This study employed various metrics to assess model fitness. The log likelihood values indicate a progression from the Null model (−1002.385) to Model III (−690.79), suggesting an improved fit with increasingly complex models. Deviance values also decreased, from 2,004.77 in the Null model to 1,381.58 in Model III, further supporting this trend. Additionally, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) show reductions as model complexity increases, with AIC values decreasing from 2008.77 for the Null model to 1423.57 for Model III, and BIC values dropping from 2021.32 to 1530.66. These findings indicate that more complex models provide a better fit to the data while accounting for the number of parameters. In this regard, Model III was determined to be the best fit for our data (Table 3).

Table 3. Multivariable multilevel logistic regression of determinants of solitary birth in Ethiopia.

Model fitness
Metrics	Null model	Model I	Model II	Model III
Log likelihood	−1002.385	−703.21	−978.03	−690.79
Deviance	2,004.77	1,406.42	1,956.06	1,381.58
AIC	2008.77	1438.422	1970.07	1423.57
BIC	2021.32	1544.64	2014.01	1530.66
Measures of Association (Fixed effect)
Factors		Model I	Model II	Model III
Women age	15-19	2.89 (0.42, 20.09)		3.16 (0.47, 21.31)
	20-34	1.00		1.00
	35-49	1.02 (0.73, 1.43)		0.96 (0.69, 1.35)
Marital status	Not in union	1.00		1.00
Marital status	In union	0.70 (0.29, 1.65)		0.75 (0.31, 1.80)
Women educational level	No education	4.67 (1.23, 17.76)		4.43 (1.15, 17.05)*
	Primary	2.64 (0.69, 10.07)		2.41 (0.63, 9.25)
	Secondary & above	1.00		1.00
Sex of household head	Male	1.56 (0.87, 2.80)		1.34 (0.74, 2.43)
Sex of household head	Female	1.00		1.00
ANC visits	No visit	2.27 (1.60, 3.20)		2.42 (1.71, 3.43)*
ANC visits	Had visit	1.00		1.00
Household wealth index	Poorest	2.53 (0.80, 7.95)		2.64 (0.79, 8.84)
	Poorer	2.15 (0.69, 6.78)		1.93 (0.58, 6.41)
	Middle	1.89 (0.60, 5.92)		1.55 (0.48, 5.06)
	Richer	1.42 (0.46, 4.37)		1.19 (0.38, 3.74)
	Richest	1.00		1.00
Birth interval	< 24 months	1.20 (0.86, 1.68)		1.27 (0.91, 1.77)
Birth interval	≥ 24 months	1.00		1.00
Household has radio	Yes	1.00		1.00
Household has radio	No	1.17 (0.76, 1.79)		1.16 (0.75, 1.78)
Household has television	Yes	1.00		1.00
Household has television	No	2.47 (0.70, 8.63)		1.94 (0.56, 6.80)
Household size	< 6	1.00		1.00
Household size	≥6	0.95(0.68, 1.32)		0.96 (0.69, 1.34)
Residence	Urban		1.00	1.00
Residence	Rural		3.51 (1.37, 9.00)	2.10 (0.68, 6.52)
Community illiteracy rate	Low		1.00	1.00
Community illiteracy rate	High		1.39 (0.75, 2.55)	0.68 (0.35, 1.34)
Community poverty rate	Low		1.00	1.00
Community poverty rate	High		1.40 (0.73, 2.67)	0.67 (0.32, 1.39)
Region	Metropolitan		1.00	1.00
	Agrarian		1.55 (0.58, 4.18)	1.06 (0.34, 3.29)
	Pastorals		4.67 (1.83, 11.94)	3.77 (1.28, 11.17)*
Measures of Variation (Random effects)
Parameters	Null model	Model I	Model II	Model III
Variance (95% CI)	3.862 (2.657, 5.614)	3.507 (2.340, 5.266)	3.120 (2.078, 4.678)	3.013 (2.071, 4.384)
ICC (%, 95% CI)	54.00 (44.68, 6305)	51.63 (41.59, 63.05)	48.66 (38.71-58.71)	47.805 (38.63, 57.13)
MOR (95% CI)	6.47 (4.70, 9.50)	5.92 (4.28, 8.90)	5.35 (3.93, 2.05)	5.20 (3.92, 7.31)
PCV (%)	Reference	9.19	19.21	21.98

Open in a new tab

ANC: antenatal care, AOR: Adusted Odds Ratio, AIC: Akaike’s Information Criterion, BIC: Bayesian Infromation Criterion, CI: Confidence Interval, ICC: Intra-Class Correlation, MOR: Median Odds Ratio, PCV: Proportional Change in Variation.

The results from the fixed effects section of the mixed-effects model revealed that having no formal education, not attending antenatal care (ANC) visits, and residing in pastoral regions were significantly associated with higher odds of solitary birth in Ethiopia. Specifically, the odds of solitary birth were 4.43 times higher among women with no formal education (Adjusted Odds Ratio [AOR] = 4.43, 95% CI: 1.15, 17.05) compared to those with secondary or higher education. Additionally, women who did not attend any ANC visits during their pregnancy had 2.42 times higher odds of solitary birth (AOR = 2.42, 95% CI: 1.71, 3.43) compared to those who did. Furthermore, the likelihood of solitary birth was 3.77 times higher among women living in pastoral regions of Ethiopia (AOR = 3.77, 95% CI: 1.28, 11.17) compared to those residing in metropolitan regions (Table 3).

The random effects section provides a detailed examination of the measures of variation related to solitary childbirth in Ethiopia, highlighting changes in parameters across different statistical models. The Null model indicates a variance of 3.362 (95% CI: 2.257, 5.614), establishing a baseline for understanding variability in solitary childbirth rates across clusters. The intraclass correlation coefficient (ICC) for the Null model is reported at 54.00% (95% CI: 44.18, 63.51), reflecting that a substantial portion of the variability can be attributed to differences between clusters. This percentage evolves across models, illustrating how the addition of predictors influences the clustering effect.

As the models progress from the Null model to Model III, significant changes in unexplained heterogeneity were observed. The median odds ratio (MOR) decreased from 6.47 in the Null model to 5.20 in Model III. This reduction indicates that the variability in solitary childbirth outcomes attributable to cluster-level factors diminishes as more predictors are incorporated. Such a decrease suggests that additional covariates in Model III more effectively explain the differences in solitary childbirth rates, accounting for contextual factors such as socioeconomic status, access to healthcare, and cultural influences. The proportional change in variance (PCV) helps quantify the extent to which each model improves the explanation of variance in solitary childbirth, reinforcing the importance of incorporating relevant contextual factors (Table 3).

Potential biases in the study and how they were Addressed.

Several types of bias could potentially affect this study. First, the data on childbirth experiences were self-reported by women and may be subject to inaccuracies due to social desirability. To minimize this, we relied on standardized data collection procedures used in the 2019 IEDHS, which are designed to enhance reliability and reduce reporting errors. Second, misclassification bias could occur if solitary childbirth is inaccurately reported or misunderstood by respondents. To mitigate this, we used a clear operational definition based on DHS coding, distinguishing solitary childbirth from other forms of delivery assistance. Moreover, confounding is a possibility in observational studies like ours. To address this, we employed multivariable multilevel modeling, which allows for the adjustment of both individual- and community-level variables, helping to control for potential confounders in the analysis.

Discussion

Solitary birth, a phenomenon, where women give birth without any assistance from healthcare professionals or traditional birth attendants, poses serious risks to both maternal and neonatal health. This study disclosed the prevalence, spatial distribution and determinants of solitary birth in Ethiopia using IEDHS 2019.

The prevalence of solitary childbirths in Ethiopia, estimated at 12.73%, with a 95% confidence interval ranging from 11.71% to 13.81%, highlights a significant public health concern. This prevalence indicates that a notable proportion of women are experiencing childbirth alone, which can have profound implications for maternal and infant health outcomes. This statistic also highlights a significant aspect of maternal health in the country, reflecting both the challenges and improvements in childbirth practices.

The results of the spatial autocorrelation analysis revealed significant variation in the distribution of solitary births across Ethiopia, suggesting that these occurrences are not randomly distributed but rather exhibit notable spatial patterns. Socioeconomic factors play a crucial role, as research demonstrates that low-income areas often have limited access to healthcare services, leading to increased rates of solitary births. For instance, a study found that financial constraints and lack of transportation deter low-income women from seeking skilled assistance during childbirth [28]. Additionally, cultural beliefs greatly influence childbirth decisions; many rural communities in Ethiopia prioritize traditional home births, which can result in higher rates of solitary deliveries. A qualitative study highlighted that cultural norms often discourage women from institutional care, contributing to geographic clusters of solitary births [29]. Access to healthcare facilities is another critical factor; the Ethiopian Ministry of Health has reported significant discrepancies in healthcare access between urban and rural areas, with rural regions often lacking adequate maternal healthcare services [30]. This disparity can lead to notable variations in childbirth practices, as women in rural areas may not have the option for institutional delivery, resulting in higher rates of solitary births [30]. Furthermore, the effectiveness of public health interventions varies by region; areas benefiting from targeted maternal health programs, such as community health worker initiatives, often report improved rates of skilled attendance at births. For instance, a study demonstrated that regions with strong community health programs showed lower rates of unassisted births, while areas without such interventions continued to experience high rates of solitary births, thereby contributing to the observed spatial clustering [30].

The findings from the optimized hot spot analysis revealed significant concentrations of solitary births in specific regions of Ethiopia, particularly in the western and southern parts of Oromia, throughout Benishangul-Gumuz, most areas of the SNNPR, and the northwest of Amhara. This clustering suggests that these regions may face systemic challenges that influence maternal health behaviors. Access to healthcare facilities is a critical determinant of maternal health outcomes. Research indicates that rural areas, where many of these hot spots are located, often lack adequate healthcare services, which can lead to higher rates of unassisted births. The Ethiopian Demographic and Health Survey 2016 highlights that limited transportation options and long distances to health facilities can discourage women from seeking assistance during childbirth [31]. Cultural practices and beliefs also significantly impact childbirth decisions. Studies have shown that in many rural communities, traditional norms favor home births and discourage institutional deliveries, leading to higher rates of solitary births [29]. Socioeconomic status is another critical factor; areas with higher poverty rates often exhibit poorer maternal health outcomes. Economic constraints can limit women’s access to healthcare services, making solitary births more likely due to financial barriers or the perceived costs associated with institutional deliveries [32]. The effectiveness of public health interventions varies by region. Areas that have benefited from targeted maternal health programs, such as community health worker initiatives, tend to report lower rates of unassisted births. Research indicates that strong community health programs correlate with improved maternal health outcomes, while regions identified as hot spots may lack such interventions, perpetuating higher rates of solitary births [33]. Additionally, educational attainment significantly influences maternal health behaviors, as women with higher education levels are more likely to seek skilled assistance during childbirth. The EDHS data suggest that educational disparities exist, particularly in rural regions, which may contribute to the clustering of solitary births in these hot spot areas [31].

The multilevel logistic regression of this study revealed that having no formal education, not attending ANC visits, and residing in pastoral regions were significantly associated with higher odds of solitary birth in Ethiopia.

Specifically, Women with no formal education exhibited higher odds of experiencing solitary births compared to those with secondary or higher education. This finding aligns with existing literature that emphasizes the role of education in maternal health outcomes [34–36]. Education empowers women with knowledge about health services, including the importance of skilled attendance during childbirth and the benefits of ANC visits [35]. Studies have shown that educated women are more likely to utilize healthcare services, which can lead to better maternal and neonatal outcomes [34]. In contrast, women without formal education may lack awareness of the risks associated with solitary births and the available healthcare resources.

Additionally, the analysis also revealed that women who did not attend any ANC visits during their pregnancy had significantly higher odds of solitary births. ANC visits are crucial for monitoring the health of both the mother and the fetus, providing essential health education, and preparing for safe delivery. Research indicates that regular ANC attendance is associated with increased likelihood of receiving birth assistance, which is vital for reducing the risks of complications during childbirth [37–39]. The absence of ANC visits may reflect broader issues such as limited access to healthcare services, lack of transportation, or cultural beliefs that discourage seeking medical help during pregnancy [40].

Furthermore, the study found that women living in pastoral regions had a higher likelihood of solitary births compared to those residing in metropolitan areas. Pastoral regions often face unique challenges, including geographical barriers, limited healthcare infrastructure, and cultural practices that may prioritize free births over assisted deliveries [41–43]. Similarly, It has been reported that getting health care services, is difficult for the seasonal migrants who comprise the pastoralist populations [44].These factors contribute to lower utilization of maternal healthcare services in these areas, leading to increased risks associated with solitary births [45]. The disparity in healthcare access between urban and rural or pastoral settings is well-documented, highlighting the need for targeted interventions to improve maternal health services in underserved regions [41–43,45].

Strength and limmitation of the study

A key strength of this study lies in its methodological rigor, particularly the integration of spatial analysis with multilevel modeling. This combined approach offers a robust framework for identifying geographic disparities and examining both individual- and community-level factors associated with solitary childbirth. Moreover, the focus on solitary childbirth—a largely overlooked yet critical public health issue—fills an important gap in the literature and provides novel insights that can inform targeted maternal health interventions. The identification of hotspot regions further supports the development of geographically tailored strategies, such as community-based education and antenatal care outreach, especially in underserved and high-risk areas.

Despite its strengths, the study is not without limitations. First, the use of data from the 2019 IEDHS, which was collected from a reduced number of enumeration areas compared to the standard DHS, may limit the generalizability of the findings to the entire country. Additionally, the absence of key variables—such as distance to health facilities—in the interim dataset restricts a more comprehensive understanding of access-related barriers to assisted childbirth. The cross-sectional nature of the data further limits the ability to establish causal relationships between the identified determinants and solitary childbirth. Moreover, reliance on self-reported data introduces the possibility of recall bias, as women may underreport or misrepresent their childbirth experiences due to social stigma or limited awareness. Lastly, the analysis may not fully capture regional variations in healthcare practices and access, which could influence the prevalence and determinants of solitary childbirth. These limitations should be considered when interpreting the findings and underscore the need for future research, including longitudinal and qualitative studies, to deepen understanding of this issue.

Implication of the study

Implications for maternal health.

The relatively high prevalence of solitary childbirths suggests that many women may lack adequate support during labor and delivery. This absence of support can lead to increased stress and anxiety, potentially complicating the childbirth experience. Support from family members, healthcare providers, or birthing companions is crucial in promoting positive birth experiences. Additionally, the data may reflect broader issues regarding access to healthcare services. Women giving birth alone might indicate barriers such as geographical distance from healthcare facilities, financial constraints, or lack of transportation. Understanding these barriers is essential for developing policies aimed at improving maternal healthcare access. Solitary childbirth may also be influenced by cultural beliefs and practices. In some communities, women may prefer or be expected to give birth alone due to traditional norms. Addressing these cultural dimensions through community engagement and education could promote safer childbirth practices.

Implications for infant health.

The risks associated with solitary childbirth can extend to the newborn. Without professional assistance, there may be delays in addressing complications that could arise during delivery, potentially affecting neonatal outcomes. Ensuring that women have access to skilled birth attendants is crucial for reducing infant morbidity and mortality. Furthermore, women who give birth alone may also face challenges in accessing postnatal care for themselves and their infants. Ensuring follow-up visits and support services can be vital in promoting maternal and child health after delivery.

Recommendations for policy and practice.

There is a need for policies that enhance access to maternal healthcare services, particularly in underserved areas. This includes increasing the number of skilled birth attendants and improving transportation options for expectant mothers. Initiatives aimed at educating communities about the importance of skilled assistance during childbirth can help shift cultural perceptions and encourage more women to seek help. Continued research is essential to monitor trends in solitary childbirth and to understand the underlying factors contributing to this prevalence. This data can inform targeted interventions and policies.

Conclusion

Supporting information

S1 Data. Mini data.

(ZIP)

pone.0333603.s001.zip^{(2MB, zip)}

Abbreviations

AOR: Adjusted Odds Ratio
AIC: Akaike’s Information Criterion
BIC: Bayesian Information Criterion
CI: Confidence Interval
IEDHS: Interim Ethiopian Demographic and Health Survey
FDR: False Discovery Rate
ICC: Intra-class correlation coefficient
MOR: Median odds ratio
LL: Log likelihood
LLR: log likelihood ratio
PCV: Proportional Change in Variance
RR: Relative risk
SDHS: Senegal Demographic and Health Survey
WHO: Worled Health Organization

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0333603.r001

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PONE-D-25-09989Spatial Distribution and Determinants of Solitary Child Birth in Ethiopia: A case of Interim Ethiopian Demographic and Health Survey 2019PLOS ONE

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Additional Editor Comments:

Three experts have reviewed the manuscript noting that the paper requires modifications in terms of its scope, clarity, and methodological details in order to be apt for publication. Note in particular the detailed suggestions of reviewer 2.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Dear Author,

Congratulations for selecting a pertinent topic for your research. However, there are few concerns, which could be addressed for further consideration of the article for publication.

-The author could add phrases for smooth transition of the content from global context to the local Ethiopian focus to improve flow of content and describe the rationale of study.

- The objectives of the study could be written with SMART criteria.

-In the "Variables and Measurement" section, how Wealth Index is constructed could be discussed along with the citation.

- In the methodology section author could explain the reason of choosing the 2019 data set instead of recent one i.e 2024.

- In result section, author may add how the bias was tackled,

- Likewise, Strength, weakness of the study could be written.

-In discussion consider adding more interpretation for regional disparities based on their characteristics.

-In limitation author must clarify why interim data are limiting and can't be generalized.

Regards

Reviewer #2: Spatial Distribution and Determinants of Solitary Child Birth in Ethiopia: A case of Interim Ethiopian Demographic and Health Survey 2019

Peer Review

1. Title

Title: The topic: Spatial Distribution and Determinants of Solitary Child Birth in Ethiopia: A case of Interim Ethiopian Demographic and Health Survey 2019 can be refined

The topic can be refined as “Spatial Distribution and Determinants of Solitary Childbirth in Ethiopia: Evidence from the 2019 Interim Demographic and Health Survey”

This revision of the title above was guided by several considerations:

o Terminological Precision: The phrase “Solitary Child Birth” was corrected to “Solitary Childbirth” to reflect standard usage in academic literature. Additionally, if the term refers to births occurring without skilled attendants or outside health facilities, further refinement—such as “Unattended Births” or “Home Deliveries”—may be warranted to ensure conceptual clarity and international relevance.

o Improved Scholarly Tone: The phrase “A case of” was replaced with “Evidence from” to convey a more rigorous and empirical framing. “Evidence from” signals that the study draws on nationally representative data and supports analytical generalization, which is more appropriate for a peer-reviewed journal.

o Structural Coherence and Citation Norms: The reference to the data source was streamlined from “Interim Ethiopian Demographic and Health Survey 2019” to “2019 Interim Demographic and Health Survey.” This reordering improves readability and aligns with how DHS datasets are typically cited in global health and demographic research. The national context (Ethiopia) is already established earlier in the title, making repetition unnecessary.

Overall, the refined title maintains the original intent while enhancing its clarity, professionalism, and appeal to journal editors and reviewers. It positions the study as a robust, data-driven analysis of maternal health patterns in Ethiopia, grounded in spatial and demographic inquiry.

2. Abstract – Suggestions for Improvement

o The Authors should clarify the problem statement - Refine the opening sentence to more clearly establish the public health significance of solitary childbirth. For example, specify the burden or consequences in terms of maternal morbidity or mortality to strengthen the urgency.

o The Authors should improve flow and structure - Consider restructuring the abstract into clearly labelled segments (e.g., Background, Methods, Results, Conclusion) or ensure smooth transitions between them. This will enhance readability and help readers quickly grasp the study’s scope.

o The Authors should add specificity to the study methods - Briefly mention how solitary childbirth was defined or measured in the DHS dataset. This adds transparency and helps readers understand the operationalization of the key variable.

o The Authors should refine the conclusion for the study impact - Strengthen the final sentences by emphasizing actionable recommendations or policy relevance. For instance, suggest how hotspot identification could inform targeted maternal health interventions or ANC outreach programmes.

3. Introduction

o The Authors should clarify the conceptual definition of “solitary childbirth” early on, distinguishing it from unattended or home births if applicable.

o The Authors should strengthen the rationale by citing recent Ethiopian or regional studies that highlight the public health relevance of solitary births.

o The Authors should consider framing the issue within broader maternal health goals (e.g., SDG 3) to emphasize policy relevance.

o The Authors should streamline the background to focus more sharply on the gap this study addresses.

4. Methods

o The Authors should provide more detail on how solitary childbirth was operationalized in the dataset—what specific survey question or criteria were used?

o The Authors should clarify the sampling strategy and weighting procedures to enhance transparency and reproducibility.

o The Authors should justify the use of multilevel logistic regression by briefly explaining the hierarchical structure of the data.

o The Authors should include ethical considerations or approval details, especially since the study involves human subjects.

5. Results Presentation

• The Authors should use tables and maps to visually support spatial findings—especially hotspot regions.

• The Authors should ensure consistency in reporting confidence intervals and p-values across all results.

• The Authors should highlight key findings with brief interpretive comments to guide the reader.

• The Authors should consider stratifying results by region or demographic group to deepen insights.

6. Discussion

• The Authors should expand on why certain regions (e.g., Benishangul-Gumuz, SNNPR) may have higher rates of solitary birth—link to cultural, infrastructural, or policy factors.

• The Authors should compare findings with similar studies in sub-Saharan Africa to situate the results in a broader context.

• The Authors should address potential confounders or biases that may have influenced the associations.

• The Authors should avoid overgeneralization by acknowledging the limitations of cross-sectional data in establishing causality.

7. Strengths and Limitations of the study

• The Authors should emphasise on the strength: Use of nationally representative DHS data enhances generalizability.

• The Authors should highlight the strength: Integration of spatial and multilevel analysis provides a robust methodological approach.

• The Authors should underscore on the limitation: Potential recall bias in self-reported childbirth experiences.

• The Authors should accentuate on the Limitation: Lack of qualitative data limits understanding of women's lived experiences.

8. Implications of Findings

• The Authors should emphasize how identifying hotspot regions can inform targeted maternal health interventions.

• The Authors should suggest integrating community-based education and ANC outreach in pastoral regions.

• The Authors should highlight the role of policy in addressing structural barriers to assisted childbirth.

• The Authors should recommend collaboration with local health systems to improve birth preparedness and support networks.

9. Conclusion/Recommendations

• The Authors should reiterate the urgency of addressing solitary childbirth as a public health concern.

• The Authors should offer specific, actionable recommendations for health planners and policymakers.

• The Authors should consider proposing future research directions, such as qualitative studies or longitudinal tracking.

• The Authors should ensure the conclusion aligns with the study’s objectives and key findings.

10. Proofreading and Editing

• The Authors should correct some minor typographical errors and ensure consistent formatting.

• The Authors must improve sentence flow by reducing redundancy and tightening phrasing.

• The Authors should standardize terminology (e.g., “solitary childbirth” vs. “solitary birth”) throughout the manuscript.

• The Authors should ensure clarity in transitions between sections to enhance readability.

9. References/In-Citations

• The Authors should verify that all cited studies are current, relevant, and properly formatted according to journal guidelines.

• The Authors should include more regional or country-specific literature to strengthen contextual grounding.

• The Authors should ensure in-text citations match the reference list and are consistently styled.

• The Authors should consider citing WHO or UNFPA reports to support global maternal health framing.

Reviewer #3: Special remarks: (minors)

Reviewer's Report: Overall, the manuscript is clear and well written, and the topic covered is actual.

1. Please add a brief description of the study setting.

2. In the Population and Sampling Procedure section, the population is well defined, but there is a sample.

3. We suggest you create a sample flux diagram to explain how you obtained the sample of 3884, which appears in the results and nowhere in the method.

4. In addition, it would be important to provide the reasons that led you to choose 3-year-old participants and not 5-year-olds.

**********

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Reviewer #1: Yes: Dr Jarina Begum

Reviewer #2: Yes: Monica Ewomazino Akokuwebe

Reviewer #3: No

**********

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Attachment

Submitted filename: Peer Review Comments.docx

pone.0333603.s002.docx^{(23.8KB, docx)}

PLoS One. 2025 Sep 26;20(9):e0333603. doi: 10.1371/journal.pone.0333603.r002

Author response to Decision Letter 1

5 Sep 2025

Response to comments

Subject: Submission of revised manuscript

Manuscript ID: PONE-D-25-09989

Title: Spatial Distribution and Determinants of Solitary Childbirth in Ethiopia: Evidence from the 2019 Interim Demographic and Health Survey

Journal: PLOS One

I hope this letter finds you well. We appreciate the diligent efforts of the editorial team in facilitating the review process for our manuscript. Additionally, we extend our gratitude to the editors and reviewers for their valuable time and thoughtful feedback, which significantly contributed to enhancing the quality of our work.

The constructive comments provided by the reviewers have been instrumental in refining our study. We are pleased to note that the reviewers share our assessment of the scientific significance of our findings. In response to their suggestions, we have meticulously addressed each point raised. Please find our comprehensive responses to the comments below.

Furthermore, I have attached the revised manuscript file separately for your convenience. We believe that the revisions strengthen the manuscript and align it more closely with the journal’s scope and standards.

Thank you for considering our work for publication. We hope that our revised submission meets the high standards set by PLOS One.

Best regards,

Corresponding Author

Comments

Additional Editor Comments:

Response: Dear editor, thank you for the additional comments on our manuscript for modifications and we have addressed the concerns raised by all of the reviewers. Thank you once again.

Reviewers' comments:

Reviewer #1: Dear Author,

Congratulations for selecting a pertinent topic for your research. However, there are few concerns, which could be addressed for further consideration of the article for publication.

-The author could add phrases for smooth transition of the content from global context to the local Ethiopian focus to improve flow of content and describe the rationale of study.

Response: Dear reviewer, we have made modifications to the section as per your suggestion. Kindly see our revised manuscript for the changes.

- The objectives of the study could be written with SMART criteria.

Response: Dear reviewer, thank you for your scientifically sound feedback. We have made modifications to the section as per your suggestion. Kindly see our revised manuscript for the changes.

-In the "Variables and Measurement" section, how Wealth Index is constructed could be discussed along with the citation.

Response: Dear reviewer, you are absolutely right. How Wealth Index was constructed could be discussed along with the citation. In response to your comment, we have addressed the concern as per your suggested. Kindly find the point on page 7 lines 180 - 186 of our revised manuscript.

- In the methodology section author could explain the reason of choosing the 2019 data set instead of recent one i.e 2024.

Response: Dear reviewer, thank you for your invaluable suggestion regarding the dataset used for our study. The main reason for using 2019 dataset is that it is the only most recent DHS available in Ethiopia and 2024 DHS has not been released yet.

- In result section, author may add how the bias was tackled,

Response: Dear reviewer, Thank you for your insightful concern. We have added how the bias was tackled as per your suggestion. Kindly see point on page 20, lines 381 to 391 of our revised manuscript.

- Likewise, Strength, weakness of the study could be written.

Response: Dear reviewer, thank you for your insightful feedback. We have considered re-writing the strength and limitation of the study as per your suggestion. Kindly see the point on pages 23-24, lines 476 -497 of our revised manuscript.

-In discussion consider adding more interpretation for regional disparities based on their characteristics.

Response: Dear reviewer, thank you for your comment. The point has been addressed accordingly.

-In limitation author must clarify why interim data are limiting and can't be generalized.

Response: In response to your comment, we have addressed the concern by clarifying why interim data are limiting and can't be generalized.

Regards

Response: Thank you once again for your input.

Reviewer #2: Spatial Distribution and Determinants of Solitary Child Birth in Ethiopia: A case of Interim Ethiopian Demographic and Health Survey 2019

Peer Review

1. Title

Title: The topic: Spatial Distribution and Determinants of Solitary Child Birth in Ethiopia: A case of Interim Ethiopian Demographic and Health Survey 2019 can be refined

The topic can be refined as “Spatial Distribution and Determinants of Solitary Childbirth in Ethiopia: Evidence from the 2019 Interim Demographic and Health Survey”

This revision of the title above was guided by several considerations:

Response: Dear Reviewer, Thank you for your insightful and detailed review of our manuscript. In response to your invaluable feedback, we have refined the title of our manuscript. Kindly see the title of our revised manuscript for further details.

2. Abstract – Suggestions for Improvement

Response: Dear reviewer, Thank you for your insightful and detailed feedback to our work. We have made modifications to the section as per your suggestion. Kindly see our revised manuscript for the changes.

Response: Dear reviewer, Thank you for your comments regarding the flow and structure of our abstract. We have considered you feedback and the journal guideline in structuring the abstract of our manuscript.

Response: Dear reviewer, thank you for the thoughtful comment regarding the outcome definition. We have provided detailed definition and measurement of outcome in the variables and measurement section of our methods and materials section and our definition aligns with DHS methodology. Kindly see it on pages 6-7, lines 162-179 of our revised manuscript.

Response: In conclusion, this study highlights the spatial disparities and socio-demographic determinants of solitary childbirth in Ethiopia, underscoring its significance as a neglected public health issue. The identification of hotspot regions provides critical insight for health planners and policymakers, enabling the design of geographically targeted maternal health interventions. Specifically, these findings can inform the expansion of antenatal care outreach programs and community-based education initiatives in high-risk and underserved areas. Addressing solitary childbirth requires not only improving access to skilled birth attendants but also tackling the structural and cultural barriers that prevent women from receiving support during delivery. As such, the integration of spatial analysis with multilevel modeling offers a robust foundation for evidence-based policy and programmatic responses aimed at reducing maternal and neonatal risks associated with unassisted births.

3. Introduction

o The Authors should clarify the conceptual definition of “solitary childbirth” early on, distinguishing it from unattended or home births if applicable.

o The Authors should strengthen the rationale by citing recent Ethiopian or regional studies that highlight the public health relevance of solitary births.

o The Authors should consider framing the issue within broader maternal health goals (e.g., SDG 3) to emphasize policy relevance.

Response: Dear reviewer, thank you for the insightful comments and suggestions to improve our introduction. We have made significant changes to the section. Kindly see the introduction of our revised manuscript for the changes.

o The Authors should streamline the background to focus more sharply on the gap this study addresses.

Response: Dear reviewer, thank you for your invaluable feedback. We have addressed the main gap of our study. We kindly invite you to see the point the last two paragraphs of introduction in our revised manuscript.

4. Methods

o The Authors should provide more detail on how solitary childbirth was operationalized in the dataset—what specific survey question or criteria were used?

Response: Dear reviewer, thank you for your suggestion regarding the operationalization of the outcome variable in the dataset. We have detailed the operationalization of the solitary childbirth at variables and measurement section specifically at the outcome variable part.

o The Authors should clarify the sampling strategy and weighting procedures to enhance transparency and reproducibility.

Response: Dear reviewer, we have clarified sampling strategy and weighting procedures to enhance transparency and reproducibility. Kindly find the point on pages 5-6, lines 135-150 of revised manuscript.

o The Authors should justify the use of multilevel logistic regression by briefly explaining the hierarchical structure of the data.

Response: Dear reviewer, we have addressed concern as per your suggestion. Kindly see multilevel modeling section of our revised manuscript for the details.

o The Authors should include ethical considerations or approval details, especially since the study involves human subjects.

Response: Dear reviewer, we have detailed the ethical approval in our revised manuscript. Kindly see the pages 9-10, line numbers 148-155 of our revised work.

5. Results Presentation

• The Authors should use tables and maps to visually support spatial findings—especially hotspot regions.

• The Authors should ensure consistency in reporting confidence intervals and p-values across all results.

• The Authors should highlight key findings with brief interpretive comments to guide the reader.

• The Authors should consider stratifying results by region or demographic group to deepen insights.

Response: We sincerely appreciate the reviewer’s valuable feedback and have carefully addressed all the points raised. To enhance the clarity and impact of our spatial findings, we have incorporated both tables and maps, with particular emphasis on hotspot regions. We have also ensured consistency in the reporting of confidence intervals and p-values throughout the results section to maintain statistical rigor. Key findings are now accompanied by concise interpretive comments to guide the reader and highlight their significance. Furthermore, we have stratified the results by region and relevant demographic groups to provide deeper insights into the patterns and determinants of solitary childbirth. We believe these revisions have substantially improved the manuscript and addressed the reviewer’s concerns effectively.

6. Discussion

• The Authors should expand on why certain regions (e.g., Benishangul-Gumuz, SNNPR) may have higher rates of solitary birth—link to cultural, infrastructural, or policy factors.

• The Authors should compare findings with similar studies in sub-Saharan Africa to situate the results in a broader context.

• The Authors should address potential confounders or biases that may have influenced the associations.

• The Authors should avoid overgeneralization by acknowledging the limitations of cross-sectional data in establishing causality.

Response: Dear reviewer, we are grateful to your thoughtful and constructive feedback, which has helped us enhance the depth and clarity of our manuscript. In response, we have expanded our discussion to explore possible reasons why regions such as Benishangul-Gumuz and SNNPR exhibit higher rates of solitary childbirth, linking these patterns to cultural norms, infrastructural limitations, and disparities in access to maternal health services. We have also compared our findings with similar studies conducted in sub-Saharan Africa to situate our results within a broader regional context. Additionally, we have addressed potential confounders and sources of bias that may have influenced the observed associations, and we have taken care to avoid overgeneralization by clearly acknowledging the limitations of using cross-sectional data, particularly in relation to establishing causal relationships. We believe these revisions have strengthened the manuscript and aligned it more closely with the reviewer’s expectations.

7. Strengths and Limitations of the study

• The Authors should emphasise on the strength: Use of nationally representative DHS data enhances generalizability.

• The Authors should highlight the strength: Integration of spatial and multilevel analysis provides a robust methodological approach.

• The Authors should underscore on the limitation: Potential recall bias in self-reported childbirth experiences.

• The Authors should accentuate on the Limitation: Lack of qualitative data limits understanding of women's lived experiences.

Response: We appreciate the reviewer’s thoughtful observations and have taken steps to incorporate each of the suggested improvements. We have emphasized the strengths of our study by highlighting the use of nationally representative Demographic and Health Survey (DHS) data, which enhances the generalizability of our findings. Additionally, we have underscored the robustness of our methodological approach through the integration of spatial and multilevel analyses. In terms of limitations, we have acknowledged the potential for recall bias inherent in self-reported childbirth experiences, and we have noted the absence of qualitative data as a constraint in fully capturing the lived experiences of women who undergo solitary childbirth. These additions provide a more balanced and transparent presentation of our study’s contributions and limitations.

8. Implications of Findings

• The Authors should emphasize how identifying hotspot reg

Attachment

Submitted filename: Rebuttal Letter.docx

pone.0333603.s004.docx^{(25.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0333603.r003

Decision Letter 1

José Antonio Ortega

16 Sep 2025

Spatial Distribution and Determinants of Solitary Childbirth in Ethiopia: Evidence from the 2019 Interim Demographic and Health Survey

PONE-D-25-09989R1

Dear Dr. Tamir,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

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Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The revision seems to have addressed all the feedback received from the 3 reviewers who suggested a minor revision, in the editor's opinion. It is felt that it is not necessary to send back the manuscript for its acceptance.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0333603.r004

Acceptance letter

José Antonio Ortega

PONE-D-25-09989R1

PLOS ONE

Dear Dr. Tamir,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Data. Mini data.

(ZIP)

pone.0333603.s001.zip^{(2MB, zip)}

Attachment

Submitted filename: Peer Review Comments.docx

pone.0333603.s002.docx^{(23.8KB, docx)}

Attachment

Submitted filename: Rebuttal Letter.docx

pone.0333603.s004.docx^{(25.7KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

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PERMALINK

Spatial distribution and determinants of solitary childbirth in Ethiopia: Evidence from the 2019 interim demographic and health survey

Tadesse Tarik Tamir

Berhan Tekeba

Alebachew Ferede Zegeye

Deresse Abebe Gebrehana

Mulugeta Wassie

Gebreeyesus Abera Zeleke

Enyew Getaneh Mekonen

Roles

Abstract

Introduction

Method

Result

Cocnlusion

Introduction

Methods and materials

Study design, data source and setting

Population and sampling procedure.

Fig 1. Schematic depiction of the sample size determination of the study.

Variables and measurement

Wealth index in the 2019 IEDHS.

Spatial analysis

Spatial autocorrelation.

Hot spot analysis.

Spatial scan statistical analysis.

Spatial interpolation.

Multilevel modeling.

Ethical approval.

Results

Descriptive statistics of solitary births by individual and community-level characteristics

Table 1. Descriptive statistics of solitary births by individual and community-level characteristics.

Prevalence of solitary childbirth in Ethiopia

Fig 2. Prevalence of solitary childbirths in Ethiopia, IEDHS 2019.

Spatial autocorrelation of solitary birth in Ethiopia.

Fig 3. Spatial autocorrelation of solitary birth in Ethiopia, IEDHS 2019.

Hot spot analysis of solitary birth in Ethiopia.

Fig 4. Hot spot analysis of solitary birth in Ethiopia, IEDHS 2019; Source of basemaps URL:https://open.africa/dataset/ethiopia-shapefiles; the figure is similar but not identical to the original image and is therefore for illustrative purposes only.

Spatial scan statistical analysis of solitary birth in Ethiopia.

Table 2. Spatial scan statistical analysis of solitary birth in Ethiopia, IEDHS 2019.

Fig 5. Spatial scan statistical analysis of solitary birth in Ethiopia, IEDHS 2019; Source of basemaps URL: https://open.africa/dataset/ethiopia-shapefiles; the figure is similar but not identical to the original image and is therefore for illustrative purposes only.

Interpolation of solitary birth in Ethiopia.

Fig 6. Kriging interpolation of solitary birth in Ethiopia, EIDHS 2019; Source of basemaps URL: https://open.africa/dataset/ethiopia-shapefiles; the figure is similar but not identical to the original image and is therefore for illustrative purposes only.

Multivariable multilevel logistic regression of determinants of solitary birth in Ethiopia.

Table 3. Multivariable multilevel logistic regression of determinants of solitary birth in Ethiopia.

Potential biases in the study and how they were Addressed.

Discussion

Strength and limmitation of the study

Implication of the study

Implications for maternal health.

Implications for infant health.

Recommendations for policy and practice.

Conclusion

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

José Antonio Ortega

Roles

Author response to Decision Letter 1

Decision Letter 1

José Antonio Ortega

Roles

Acceptance letter

José Antonio Ortega

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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