Tekeba et al. BMC Pediatrics(2024) 24:558
https://doi.org/10.1186/s12887-024-05027-z

BMC Pediatrics

RESEARCH

Open Access

Early neonatal mortality and determinantsin Ethiopia: multilevel analysis of Ethiopian
demographic and health survey, 2019
Berhan Tekeba1*, Tadesse Tarik Tamir1, Belayneh Shetie Workneh2, Alebachew Ferede Zegeye3, Almaz Tefera Gonete1,
Tewodros Getaneh Alemu1, Mulugeta Wassie4, Alemneh Tadesse Kassie5, Mohammed Seid Ali1 andEnyew Getaneh Mekonen6
Abstract
Introduction Despite remarkable achievements in improving maternal and child health, early neonatal deaths still
persist, with a sluggish decline in Ethiopia. As a pressing public health issue, it requires frequent and current studies tomake appropriate interventions. Therefore, by using the most recent Ethiopian Mini Demographic Health Survey Dataof 2019, we aimed to assess the magnitude and factors associated with early neonatal mortality in Ethiopia.
Methods Secondary data analysis was conducted based on the demographic and health survey data conductedin Ethiopia in 2019. A total weighted sample of 5,753 live births was included for this study. A multilevel logistic
regression model was used to identify the determinants of early neonatal mortality. The adjusted odds ratio at 95%
Cl was computed to assess the strength and significance of the association between explanatory and outcome
variables. Factors with a p-value of < 0.05 are declared statistically significant.
Results The prevalence of early neonatal mortality in Ethiopia was 26.5 (95% Cl; 22.5–31.08) per 1000 live births.
Maternal age 20–35 (AOR, 0.38; 95% Cl, 0.38–0.69), richer wealth index (AOR, 0.47; 95% Cl, 0.23–0.96), having noantenatal care visit (AOR, 1.86; 95% Cl, 1.05–3.30), first birth order (AOR, 3.41; 95% Cl, 1.54–7.56), multiple pregnancy(AOR, 18.5; 95% Cl 8.8–38.9), presence of less than two number of under-five children (AOR, 5.83; 95% Cl, 1.71–19.79)
and Somali region (AOR, 3.49; 95% Cl, 1.70-12.52) were significantly associated with early neonatal mortality.
Conclusion This study showed that, in comparison to other developing nations, the nation had a higher rate of earlynewborn mortality. Thus, programmers and policymakers should adjust their designs and policies in accordance with
the needs of newborns and children’s health. The Somali region, extreme maternal age, and ANC utilization amongexpectant moms should all be given special consideration.
Keywords Multilevel, Early neonate, Early neonatal mortality, EDHS

*Correspondence:
Berhan Tekeba
berishboss7@gmail.com
1
Department of Pediatrics and Child Health Nursing, School of Nursing,
College of Medicine and Health Sciences, University of Gondar, Gondar,
Ethiopia2
Department of Emergency and Critical Care Nursing, School of Nursing,
College of Medicine and Health Sciences, University of Gondar, Gondar,
Ethiopia3

Department of Medical Nursing, School of Nursing, College of Medicine
and Health Sciences, University of Gondar, Gondar, Ethiopia
4
School of Nursing, College of Medicine and Health Sciences, University
of Gondar, Gondar, Ethiopia
5
Department of Clinical Midwifery, School of Midwifery, College ofMedicine and Health Sciences, University of Gondar, Gondar, Ethiopia
6
Department of Surgical Nursing, School of Nursing, College of Medicine
and Health Sciences, University of Gondar, Gondar, Ethiopia© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified thelicensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation
or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://
creativecommons.org/licenses/by-nc-nd/4.0/.

Tekeba et al. BMC Pediatrics(2024) 24:558

Introduction
Early neonatal mortality is defined as the death of an
infant within seven days of birth [1]. Globally, the number of neonatal deaths declined from 5 million in 1990 to
2.3 million in 2022. The majority of these deaths occurred
during the early neonatal period, which accounts forabout 1.7 million (75%) of the total neonatal deaths, andthe majority (1 million) of early neonatal deaths occurred
within 24 h of birth [2]. The majority of these deaths
occurred in low- and middle-income nations, mostly in
sub-Saharan Africa and South Asia [3–5]. Despite measurement actions and interventions like early initiationof breast feeding, institutional deliveries, immunization,and early newborn care for maternal and child health,neonatal mortality remains declining at a stagnant pacein Ethiopia [6, 7].
The early newborn period is the most vulnerable time
for a child’s survival; neonatal mortality is a measure of
the biological, demographic, and socioeconomic conditions of the nation, as well as the public health system,
health system infrastructure, and pace of population
growth [5, 8]. The etiology of early neonatal death isclosely related to the level of country industrialization[9], which is also the direct way of measuring quality carereceived by a mother during antenatal care and childbirth [10].
Prior research findings illustrate that socio-demographic characteristics of the mother and the children,
teen pregnancy, a low level of maternal education, askilled care provider, cultural practice, multiple births,
and severe infections like sepsis, diarrhea, and asphyxia
are among the major determinants of neonatal mortality [11–13]. However, the determinants of early neonatal
mortality are still uninvestigated in the study area.
The governments of Ethiopia, in collaboration withother stakeholders, have made significant improvements
in reducing maternal and child death in the past two to
three decades by implementing the Health Sector Transformation Plan, Sustainable Development Goal 3, andperforming strong leadership and efforts in community
involvement in the health system. Thus, neonatal mortality decreased from 39 to 29 between 2005 and the 2016
Ethiopian Demographic and Health Survey (EDHS),
but has remained stable after the 2016 EDHS and evenshowed increments to 30 deaths per 1000 live births in
the 2019 EDHS [14].
It is obvious that a reduction in overall under-five
mortality across the globe cannot be achieved without
addressing the growing contribution of neonatal death,particularly early neonatal death, since most of the deaths
were within one week of life [15]. Despite strategies that
promote better survival during the neonatal period andremarkable achievements in improving maternal andchild health in the country, Ethiopia is still under the

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burden of neonatal mortality. For instance, neonatal
mortality decreased from 39 to 29 per 1,000 live births
between 2005 and 2016 but has remained stable since2016 in Ethiopia. Early neonatal mortality, which is thelargest proportion of neonatal mortality, is still uninvestigated in Ethiopia.
Early neonatal death as a critical public health problem requires frequent and current studies to update
information for the concerned bodies so that they might
propose appropriate interventions. In addition, identifying the predictors of early neonatal mortality will help
programmers and policymakers prioritize interventions. Therefore, by using the most recent Ethiopian Mini
Demographic Health Survey Data of 2019, we aimed toassess the magnitude and determinants of early neonatal
mortality in Ethiopia.

MethodStudy setting, design, and period

A population-based cross-sectional study was conductedusing secondary analysis of data from 2019 that was collected from March 21, 2019 to June 28, 2019. The study
was carried out in Ethiopia, a nation in the Horn of Africawith geographic coordinates of 40.48 97° East longitude
and 9.145° N latitude. The estimated total surface areaof the country is 1,126,829 km2. Its neighbors are Djibouti, Eritrea, Kenya, Somalia, South Sudan, Sudan, andSomaliland. Ethiopia is divided into twelve administrative
regions: Sidama, South West Peoples (a newly formed
region), Amhara, Gambela, Harari, Oromia, Somalia, Southern Nation Nationalities and Peoples Region(SNNP), and two city administrations (Addis Ababa andDiredawa). Each region was stratified into urban andrural areas (212 in rural areas and 93 in urban areas).
Data source and sampling procedure

The data for this study was obtained from the 2019 Ethiopian Mini Demographic and Health Survey (EMDHS). It
is the second EMDHS and the fifth implemented in Ethiopia. EMDHS is the country-representative sample survey carried out between the Ethiopian Demographic and
Health Survey (EDHS). It is also designed to provide data
for measuring the up-to-date progress of health sector
goals, such as estimating early childhood mortality [16].
The data was gathered in collaboration with Ethiopia’sCentral Statistical Agency (CSA) and Ministry of Health.
The survey used a two-stage stratified sampling technique. A two-stage stratified cluster sampling technique
was employed, in the first stage, with probability proportional to the enumeration area (EA) size and independent
selection in each sampling stratum. A household listing
operation was carried out for all selected EAs. The generalized list of households was used as a sampling frame
for the second stage of household selection. In the second

Tekeba et al. BMC Pediatrics(2024) 24:558

step of the selection process, a specific number of 30
households in each group were selected proportionallyfrom each enumeration area by using a systematic sampling method. A detailed description of data collection
and sampling procedures has been presented in the 2019
EMDHS final report [17]. In this study, a total weighted
sample of 5753 early neonates from mothers who wereinterviewed about births in the preceding 5 years before
the survey was included in this analysis. For those women
who had given birth to more than one child within thefive years preceding the survey, the most recent birth wasused. Then, using the Kids Record (KR) file dataset, earlynewborn mortality was determined as an outcome variable [17].
Source and study populations

All live births throughout a seven-day period comprised
the source population, whereas all live newborns aged
0–7 days within the enumeration areas (EAs) comprised
the study populations.
Outcome variable

The dependent variable was early neonatal mortality,
which is the death of a newborn within 7 days of birth[18, 19] that the mother encountered five years preceding the survey, and it was dichotomized as “yes” (= 1) for
newborns who had died within seven days of birth and“no” (= 0) for those who were alive within one week oflife. A neonate who died within seven days of birth is said
to have experienced early neonatal mortality.
Independent variables

Both individual and community-level factors werereviewed from different literatures, and these include
types of pregnancy, maternal age and educational level,
place and mode of delivery, types of pregnancy, ANC
visit, PNC visit, and wealth index. Region, residence,
community ANC utilization, community women’s education, and community wealth status were communitylevel factors aggregated from individual-level factors.
Wealth index were categorized as poor, middle, rich
[20]. ANC was classified as optimal if a mother had morethan 4 visits during pregnancy period [21]. Maternal education status was categorized as no formal education, primary, secondary and higher in our study and other EDHS
[22].
Community level variables

Community-level ANC use, community-level women’s
illiteracy, and community-level poverty were aggregated from individual women’s characteristics of ANC
use, education, and wealth index, respectively. Since
the aggregate values for all generated variables have no
meaning at the individual level, they were categorized

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into groups based on median values. Median values wereused to categorize as high and low because all aggregatedvariables were not normally distributed. Similar procedures were applied to all aggregate variables.
Regarding the analysis of aggregation, first the individual-level variables were re-categorized and cross-tabulated with the cluster variables using Stata version 17.
Then, the proportions of ANC use, no education, andpoor wealth index categories for each variable proportion were computed using Microsoft Excel 2010. Then,the proportion of each variable result was imported into
Stata and combined with the original Stata using a oneto-many variable combination. Finally, we categorized
the proportion of each variable as high or low based on
the mean proportion. The details of independent variables and their categorization are described in supplementary Table 1 below. Supplementary Table 1.
Model building for multi-level analysis

In DHS, data variables are nested by clusters, and thosewithin the same cluster show more similarities than thosewith separate clusters. Thus, using the traditional logistic regression model violates the assumptions of independent observation and equal variance across clusters.
Therefore, a multi-level logistic regression analysis was
employed in this study in order to account for the hierarchical nature of DHS data. A bivariate multi-level logistic
regression model was employed in the study to identify
the variables associated with early neonatal death. In theanalysis, four models were fitted. The first (null) model
contains only the outcome variables to test random variability and estimate the intra-cluster correlation coefficient (ICC). The second model contains individual-levelvariables; the third model contains only community-levelvariables; and the fourth model contains both individuallevel and community-level variables [23]. A p-value of
0.05 was used to declare statistical significance. Due tothe hierarchical nature of the model, models were compared using deviation = -2 (log likelihood ratio), and thebest-fit model was determined by taking the model withthe lowest deviance. Adjusted odds ratios with corresponding 95% confidence intervals (CIs) were calculated
to identify independent predictors of early neonatal
death. By calculating the variance inflation factors (VIF),
the variables employed in the models were checked for
multi-collinarity; the results were within acceptable
ranges of one to ten [9, 24, 25].
Parameter estimation method

The likelihood of an early newborn death and independent variables at the individual and community levels were estimated using both fixed effects and random
effect analysis.

Tekeba et al. BMC Pediatrics(2024) 24:558

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The fixed effect or measure of association

Result

For the bivariate and multivariate multilevel logistic
regression analyses, the Stata syntax xtmelogit was used[26].

Socio demographic, reproductive and neonatal relatedcharacteristics of early neonatal mortality

logit (πij) = log [πij/ (1 − πij)] =β0 + β1xij + β2xij . . . . + u0j + e0ij,
Where, πij: the probability of the ith newborn to be died,β0: intercept, βn: regression coefficient Xij: independent
variables u0j: community level error, e0ij: individual level
errors [27].
Random effect analysis

Variation of the outcome variable or random effects
was assessed using the proportional change in variance(PCV), intra-class correlation coefficient (ICC), andmedian odds ratio (MOR) [28, 29].
The ICC shows the variation in early neonatal mortality
due to community characteristics which was calculated
as: ICC = σ2/(σ2 + π2/3), where, σ2, variance of the cluster[30]. The higher the ICC, the more relevant the community characteristics for understanding individual variation in early neonatal mortality.
MOR is the median value of the odds ratio
between the areas with the highest early neonatal death and the area with the lowest earlyneonatal death when randomly picking out two neonates from two√ clusters, which was calculated as:
M O R = exp ( ∗ σ 2*0.6745) ∼ MOR = exp (0.95*σ 2) ,where σ2 is the variance of the cluster. In this study, MOR
shows the extent to which the individual probability ofearly neonatal mortality is determined by residential area
[31].
Furthermore, the PCV illustrates how different factors account for variations in the prevalence of early neonatal mortality and is computed as
P CV = (V null − V cluster)/V null , where Vcluster is thecluster-level variance and Vnull, is the variance of the nullmodel [32, 33].
Ethical consideration

The authors analyzed secondary, publicly available dataobtained from the DHS program database. There was no
additional ethical approval, and informed consent was
obtained by the authors. In order to perform our study,
we registered with the DHS web archive, requested thedataset, and were granted permission to access anddownload the data files. According to the DHS report,
all participant data was anonymized during the collection of the survey data. More details regarding DHS dataand ethical standards are available online at http://www.
dhsprogram.com.

The analysis included a total of 5,753 live births (2,969
males and 2,784 females). More than half of mothers(54.74%) had no formal education. The majority of participants’ household heads were males (79.92%), andmore than half of the children were delivered at home(51.36%). Moreover, the majority of women (93.9%) gavebirth via spontaneous vaginal delivery. In addition, themajority of mothers did not follow postnatal care (PNC)
checkups (87.29%). Nearly two-thirds of women’s antenatal care visits were optimal (73.34%). Table 1.
Prevalence and factors associated with early neonatal
mortality in Ethiopia EDHS, 2019

According to this study, the prevalence of early neonatal
mortality in Ethiopia was 26.5 (95% Cl 22.5–31.08) per
1000 live births. The highest early neonatal mortality rate
was observed in Benishingul Gumuz (54.7 per 1000 live
births), whereas the lowest early neonatal mortality rate
was recorded in Addis Ababa (13.7 per 1000 live births).
Figure 1.
Random effect and fixed effect analysis

The null model was run to determine whether the data
supported assessing randomness at the community
level. The ICC value in the null model indicates 12.5%of the early neonatal mortality was due to the difference
between clusters. In the null model, the median odds
ratio of the odds of early neonatal mortality was 1.9 times
variable between high and low clusters (heterogeneousamong clusters). Regarding the final model PCV, about
92.92% of the variability in the probability of early neonatal mortality was attributed to both individual and community-level factors. The final model (III) was selected
as the best-fitting model since it had the lowest deviance.
Table 2.
Factors associated with early neonatal mortality

In the final model (model III) of the multivariable multilevel logistic regression model, maternal age above 35,
the presence of less than two under-five children, womenwith no ANC visit, first birth order, multiple pregnancies,rich wealth index, and region were significantly associated with early neonatal mortality.
Accordingly, babies born from a maternal age above
35 years were 14% more likely (AOR = 1.14; 95% CI 1.14–
3.6) to die as compared to babies born to a mother aged
20–34. Similarly, babies born from a rich wealth index
family were 52% less likely (AOR = 0.48; 95% Cl, 0.24–
0.98) to die as compared to newborns from poor mothers. First-born babies were three times (AOR = 3.41; 95%
Cl, 1.54–7.56) more likely to die as compared to babies

Tekeba et al. BMC Pediatrics(2024) 24:558

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Table 1 Socio-demographic characteristic of study participants
Variables
Sex of child
Maternal age

Maternal education

Place of delivery
Mode of delivery
Wealth index

ANC visit
Type of pregnancy
Birth order

Postnatal check
No of under-five in household
Community level factors
Community ANC use level
Community poverty level
Community illiteracy level
Place of residence
RegionCommunity ANC
Community poverty level
Community illiteracy
ANC: Antenatal care, No: number

Response
Male
Female< 19 year
20–34 year
> 35 year
No EduPrimary
SecondaryHigherHome
Health facility
Vaginal delivery
Cesarean section
Poor
MiddleRich
Non-optimal
OptimalSingleMultiple
First
SecondThird
Fourth & above
Yes
No
≤2
≥3

Frequency (n)
2969
2784
296
4642
815
3149
1823
480
301
2955
2798
5404
349
2958
805
1990
1044
2918
5586
167
1,261
1,064
838
2,590
466
3512
4,801
952

Percentage (%)
51.61
48.39
5.15
80.69
14.37
54.74
31.69
8.34
5.23
51.36
48.64
93.93
6.07
51.42
13.99
34.59
26.24
73.34
97.1
2.9
21.92
18.49
14.57
45.02
11.71
88.29
83.45
16.55

Low
High
High
Low
High
Low
Urban
Rural
TigrayAfar
AmharaOromo
Somalia
Benishangul
SNNPR
Gambella
Harar
Addis Ababa
Dire-Dawa
Yes
No
Low
High
Low
High

2380
3373
2805
2948
2676
3077
1328
4425
454
652
511
719
637
530
660
450
447
291
402
2380
3373
2805
2948
2676
3077

41.37
58.63
48.76
51.24
46.51
53.49
23.08
76.92
7.89
11.39
8.88
12.5
11.07
9.21
11.47
7.82
7.77
5.06
6.99
41.37
58.63
48.76
51.24
46.51
53.49

Tekeba et al. BMC Pediatrics(2024) 24:558

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Fig. 1 Regional prevalence of early neonatal mortality in Ethiopia

Table 2 Random effect and model comparison for predictors ofearly neonatal mortality in Ethiopia
Parameter
Variance
ICC
MOR
PCV
Model fitness
LLR
Deviance

Null model
0.46
12.5%
1.9
Reference

Model I
0.08
2.38%
1.31
82.6%

Model II
0.30
8.36%
1.68
34.78%

Model III
0.032
0.98%
1.19%
92.92%-776.47
1552.94-404.08808.16-767.761535.52-402.44804.88

ICC: Intra-cluster correlation coefficient, MOR: Median odds ratio, LLR: Loglikelihood ratio, PCV, proportional change in varianceof four and above birth order. Multiple-born babies were
21 times (AOR = 21.12; 95% Cl, 12.99–44.81) more likely
to die as compared to singleton birth. Babies born frommothers with no ANC visit were 92% times (AOR = 1.92;
95% Cl, 1.08–3.4) more likely to die as compared tobabies born from mothers with an ANC visit. Similarly,
the presence of less than two under-five children in thefamily made them six times (AOR = 5.8; 95% CI: 1.72–
19.8) more likely to die, as opposed to women having
more than three under-five children in the family. Babies
born from the Somali region of Ethiopia were three times(AOR = 3.8; 95% Cl, 1.07–13.6) more likely to die as compared to babies born from the Tigray region of Ethiopia.
Table 3.

Discussion
We conducted an analysis of the most recent EDHS
data of 2019, as our findings revealed that the national
prevalence of early neonatal mortality in Ethiopia was
26.5 (95% Cl 22.5–31.08) per 1000 live births. This study
revealed a higher prevalence of early neonatal mortality
in Ethiopia. Thus, it is essential to design newborn initiatives and policy priorities for the prevention of earlynewborn deaths. Special attention should be given tomothers with extreme maternal age, poor mothers, andthe Somali region of Ethiopia.
This study finding is higher than the study done in
Afghanistan, which was 14 per 1000 live births [34], andGhana which was 16 per 1000 live births [35]. This could
be due to the fact that Afghanistan has a better healthcare system than Ethiopia, as evidenced by the hospital
bed-to-patient ratio, which was 39 per 1000 inhabitants,
and the physician-to-patient ratio, which was 25 to 1000
patients as compared to Ethiopia, in which the hospital
bed for 1000 inhabitants was 33 and the physician-topatient ratio was 10 to 1000 patients [36]. In Ghana, thelower rate of early neonatal mortality could be due todifferences in socio-cultural practices in handling earlynewborns. In addition, there are cultural, religious, andgovernment priorities that differ for newborn care anddelivery practices across countries in the world [37].
This study finding is also lower than the study donein Nigeria [38], which was 32 per 1000 live births, andCameroon which was 126 per 1000 live birth [39]. The
possible explanation could be that the Nigerian and
Cameroonian studies were done prior to the Ethiopian
studies. There is a time gap between the above studies
and our study in which the country might have madeimprovements in decreasing early neonatal mortality
but has not reported them. For instance, Ethiopia lowered early neonatal mortality to 26 per 1,000 live births
in recent years. In addition, Cameroon studies utilized
hospital records rather than demographic and healthsurvey data, indicating that hospital records might holdan immediate obstetric, congenital, and infectious causeof early neonatal mortality over DHS data, which mightlower the proportion of early newborn deaths since DHS
data doesn’t incorporate variables indicating the immediate cause of early neonatal death like infectious, obstetric,
asphyxia, and congenital causes of newborn death [40].

Tekeba et al. BMC Pediatrics(2024) 24:558

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Table 3 Multivariable multi-level logistic regression analysis of individual and community level factors associated with early neonatal
mortality in Ethiopia
Individual and community level factors
Maternal age

number of under 5 in householdChild sex
ANC visit
Birth order

Place of delivery
Mode of delivery
PNC follow-up
Mothers education levelTypes of pregnancy
Wealth index

Community level factors
Community poverty level
Community illiteracy level
Community ANC utilization
Place of residence
Region< 20 years
20–35 years> 35 years≤2≥3
FemaleMale
OptimalNon-optimal
First
SecondThird
Fourth & above
Home
Health facility
Vaginal
Cesarean section
Yes
No
No education
Primary
SecondaryHigherSingleMultiple
Poor
MiddleRich

Model I AOR (95% Cl)
1.33 (0.61–2.9)
1
2.03 (1.15–3.57)
5.38 (1.59–18.2)
11
1.16 (0.76–1.77)
1
2.16 (1.28–3.66)
2.14 (1.09–4.20)
1.06 (0.54–2.10)
0.72 (0.31–1.69)
1
1.10 (0.63–1.94)
11
1.78 (0.8–3.82)
1
1.26 (0.59–2.71)
4.11 (0.89-19)
3.8 (0.86–17.07)
1.03 (0.17–6.43)
1
1
22.2 (10.5–47.1)
10.79 (0.40–1.57)
0.71 (0.42–1.23)

Low
High
Low
High
Low
High
Urban
Rural
TigrayAfar
AmharaOromiaSomalia
Benshangul
SNNPR
Gambela
Harar
Addis Ababa
Dire Dawa

Model II AOR (95% Cl)

Model III AOR (95% Cl)
1.23 (0.56–2.7)
1
2.02 (1.14–3.6)*
5.8 (1.72–19.8)*
11
1.16 (0.76–1.77)
1
1.92 (1.08–3.41)*
2.22 (1.13–4.40)
1.03 (0.52–2.07)
0.74 (0.32–1.73)
1
1.14 (0.64–2.04)
11
1.76 (0.81–3.81)
1
1.25 (0.58–2.7)
4.54 (0.95–21.5)
3.88 (0.86–17.4)
1.02 (0.16–6.4)
1
1
21.12 (9.9–44.8)*
10.69 (0.33–1.45)
0.48 (0.24–0.98)*

1
1.12 (0.71–1.77)
10.77 (0.49–1.2)
1.27 (0.8-2.00)
1
1
0.92 (0.55–1.53)
1
1.35 (0.51–3.54)
1.53 (0.60–3.94)
1.52 (0.61–3.77)
2.09 (0.79–5.56)
2.74 (1.13–6.64)
0.95 (0.36–2.52)
1.57 (0.60–4.13)
1.67 (0.63–4.4)
0.67 (0.18–2.51)
1.69 (0.64–4.5)

1
1.6 (0.84–3.05)
10.64 (0.36–1.15)
0.91 (0.49–1.68)
1
10.97 (1.86)
1
1.92 (0.53–6.95)
1.77 (0.53–5.87)
2.28 (0.71–7.32)
3.8 (1.07–13.6)*
2.57 (0.79–8.37)
0.95 (0.26–3.47)
1.43 (0.38–5.38)
1.23 (0.32–4.68)
0.96 (0.19–4.72)
1.95 (0.56–6.86)

ANC: Antenatal Care, PNC: Post natal Care, SNNPR: Southern nation nationality and people*, statistically significant (p-value < 0.05)

In this mixed-effects logistic regression analysis, maternal age, wealth index, birth order, multiple pregnancies,
ANC visits, the number of under-five children, and theSomali region were found to be factors significantly associated with early neonatal mortality in Ethiopia.

Multiple-born babies were more likely to die as compared to single-born babies. This is supported by previous studies done in many countries across the globe[41–44]. The possible explanation could be that multiple
births had a higher prevalence of prematurity [45, 46];
Prematurity is a known risk factor for early neonatal

Tekeba et al. BMC Pediatrics(2024) 24:558

mortality, which increases neonatal death [9]. Moreover, babies born from multiple pregnancies have growth
restrictions, a low Apgar score, and extremely low birthweight [47, 48]. In addition, multiple pregnancies are
more likely to be complicated during pregnancy, labor,
and after birth [49]. Thus, attention should be given to
women with multiple pregnancies during ANC visitswith early ultrasonography identification, special concern
during delivery and post-partum counseling on immediate newborn care, early initiation of breastfeeding, andpracticing infection prevention measures like hand washing, avoiding unnecessary contamination, and a ban on
bottle feeding as much as possible.
In this study, neonates from mothers who had no ANC
visit had an increased odds of early neonatal mortality ascompared to neonates from mothers who had ANC visits. This finding is supported by different studies done in
Bangladesh and developing countries [50, 51]. This is dueto the fact that having no ANC visit during pregnancy
has a negative outcome on newborn birth since early recognition and treatment of danger signs for the motherand newborn will be missed, which predisposes them tolife-threatening complications. In addition, mothers withno ANC visit mightn’t acquire skilled attendants, whichleads them to deliver without assistance, which leads tofurther complications and death [52]. Moreover, mothers
who have not received an ANC visit during pregnancy
are less likely to deliver in a health facility, and postnatal care might be impossible, further predisposing themto death [53]. Therefore, health education, government
prioritization, and community involvement in antenatal
care service utilization shall be promoted.
According to this study, first-birth-order early neonates were more likely to die as compared to fourth- andabove-birth-order neonates. This is consistent with thestudy done in India, in which first-born newborns had
a higher risk of neonatal mortality [1, 54]. The possible
explanations could be the parental child-bearing trend
and the lower utilization of maternity services observed
in developing countries. Moreover, younger women had
experienced pregnancy-related complications that ended
in poor neonatal outcomes during delivery [54, 55].
Maternal age was also significantly associated with
ENM in this study; accordingly, babies from advanced
maternal age (above 35 years) had higher odds of deathas compared to newborns from average reproductive
life (20–34 years). Previous studies also depicted that
extreme maternal age (< 20 and > 35 years) had a paramount influence on neonatal mortality [34, 56]. The
younger maternal age could be due to the fact that mothers have no physical or reproductive capacity to bear achild [57]. Infants born to young mothers were also vulnerable to prematurity, low birth weight, and congenital
anomalies [56, 58]. In addition, younger mothers lack

Page 8 of 10

experience in child care [59, 60]. On the other hand, latematernal age predisposes the newborn to extreme birthweight and complications during pregnancy and delivery,
which increase the odds of early neonatal morbidity andmortality [34, 61, 62].
This study revealed that a higher wealth index is also
negatively associated with early neonatal mortality [63].
This finding is consistent with previous investigations inPakistan [51, 64, 65]. Child mortality has a negative correlation with a higher household wealth index. This is dueto the fact that better living conditions and greater accessto health care services are found in wealthier households.
In wealthier households, this might lessen the chance of
neonatal mortality. Conversely, less wealthy households
are less likely to have access to essential amenities likedrinking water, sanitary toilets, and health facilities [64].
Early neonates from families with less than or equal totwo under-five children were more likely to die as compared to families with three or more under-five children
in the household. This might be related to birth order, inwhich families may have fewer or less than two underfive children if their firstborn and subsequent babies die,since, as our studies and other studies depict, firstborn
babies are more likely to die as compared to high birthorder children that might lower the number of under-fivein the household [66]. Moreover, most mothers who gavebirth to only one child were presumed to be young mothers who suffered one of the risks of prematurity, whichis the immediate cause of newborn death [67]. Furthermore, there might be RH incompatibility in the parents
that preceding newborns might be lost, which lowers thenumber of under-fives in the household.
Newborns from the Somali region had higher odds
of mortality as compared to newborns from the Tigray
region. It is known that the Somali region of Ethiopia
has the highest recorded burden of neonatal death ascompared to other regions of the country [68–70]. This
could be the result of the region’s poor child and neonatal health-seeking behavior, which includes low socioeconomic status, a lack of exposure to health information, afear of modern medical practices, an unhygienic environment in healthcare facilities, and barriers to transportation and distance that contribute to early neonatal deathsin the emerging region [71–73]. Hence, the government,
policymakers, concerned bodies, and other stakeholders should give special attention to the Somali region of
Ethiopia.
This study has its own strengths and limitations. Since
it is based on data from a nationwide survey, the study
has the potential to help programmers and policymakers
develop effective interventions at the national level. But
the DHS is primarily dependent on respondents’ selfreports, so there is a chance of recall bias in this study.
Since the DHS is secondary data, variables related toTekeba et al. BMC Pediatrics(2024) 24:558

obstetrics, birth asphyxia, congenital anomalies, andinfectious-related variables were not incorporated or
missed. In addition, due to the cross-sectional nature of
the data, it cannot establish a cause-and-effect relationship and analyze behavior over a period of time. Thus,
future research considering missed variables will be done.

Conclusion
The study discovered that both individual and community-level characteristics were related to early neonatal mortality. In our study, having no ANC visit, firstbirth order, multiple pregnancies, and socio-economic
inequalities were the major determinants of early neonatal mortality. Thus, policymakers, researchers, healthplanners, and implementers need to give due emphasis to educating women on pregnancy, ANC visits, andimproving household socio-economic status, with special
attention given to the Somali region.
Abbreviations
ANC	Antenatal Care
EA	Enumeration Area
EDHS	Ethiopian Demographic Health Survey
ICC	Inter cluster correlation
ENMR	Early Neonatal Mortality Rate
LR	Logistic Regression
MOR	Median Odds ratio
PCH	Primary Health care
PCV	Proportional Change in Variance
RH	Rhesus iso-immunization

Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s12887-024-05027-z.
Supplementary Material 1
Author contributions
Berhan Tekeba made Conceptualization and drafts the original manuscript.
Tadesse Tarik Tamir made software analysis. Alemneh Tadesse Kassie conceived
and designs the study. Belayneh Shetie Workneh designed run the analysis.
Alebachew Ferede Zegeye made supervision. Almaz Tefera Gonete madedata curation. Mohammed Seid Ali wrote the summery. Mulugeta Wassie
made collecting resources and edits the entire manuscript. Tewdros Getaneh
Alemu checked the analysis and made substantial contributions in reviewing
the design of the study and the draft manuscript. Enyew Getaneh Mekonen
critically reviewed the manuscript for important intellectual content andcontributed to the final approval of the version to be submitted.
Funding
Not applicable.
Data availability
The anonymized data collected is publicly available online at https://www.
dhsprogram.com. The datasets used and/or analyzed during the current study
will be available from the corresponding author on reasonable request.

Declarations
Consent for publication
Not applicable.

Page 9 of 10

Competing interests
The authors declare no competing interests.
Received: 23 November 2023 / Accepted: 22 August 2024

References
1. Kumar C, Singh PK, Rai RK, Singh L. Early neonatal mortality in India,
1990–2006. J Community Health. 2013;38:120–30.
2. https://www.who.int/news-room/fact-sheets/detail/newborn-mortality
3. Sharrow D, Hug L, You D, Alkema L, Black R, Cousens S, et al. Global, regional,
and national trends in under-5 mortality between 1990 and 2019 withscenario-based projections until 2030: a systematic analysis by the UN
Inter-agency Group for Child Mortality Estimation. Lancet Global Health.
2022;10(2):e195–206.
4. Liu L, Oza S, Hogan D, Chu Y, Perin J, Zhu J, et al. Global, regional, andnational causes of under-5 mortality in 2000–15: an updated systematic
analysis with implications for the Sustainable Development Goals. Lancet.
2016;388(10063):3027–35.
5. Målqvist M. Neonatal mortality: an invisible and marginalised trauma. Global
Health Action. 2011;4(1):5724.
6. Bellizzi S, Bassat Q, Ali MM, Sobel HL, Temmerman M. Effect of puerperal
infections on early neonatal mortality: a secondary analysis of six demographic and health surveys. PLoS ONE. 2017;12(1):e0170856.
7. https://ephi.gov.et/wp-content/uploads/2021/06/2B-Executive-Summary_
Reducing-Neonatal-Mortality-in-Ethiopia_20-05-2021-1.pdf
8. Nagalo K, Dao F, Tall FH, Yé D. Ten years morbidity and mortality of newborns
hospitalized at the Clinic El-Fateh Suka (Ouagadougou, Burkina Faso). Pan Afr
Med J. 2013;14:153.
9. Lehtonen L, Gimeno A, Parra-Llorca A, Vento M, editors. Early neonatal death:a challenge worldwide. Seminars in fetal and neonatal medicine. Elsevier;2017.
10. Roy S, Haque MA. Effect of antenatal care and social well-being on earlyneonatal mortality in Bangladesh. BMC Pregnancy Childbirth. 2018;18:1–6.
11. Lawn J, Shibuya K, Stein C. No cry at birth: global estimates of intrapartum
stillbirths and intrapartum-related neonatal deaths. Bull World Health Organ.
2005;83:409–17.
12. Titaley CR, Dibley MJ, Agho K, Roberts CL, Hall J. Determinants of neonatal
mortality in Indonesia. BMC Public Health. 2008;8:1–15.
13. Saugstad OD. Reducing global neonatal mortality is possible. Neonatology.
2011;99(4):250–7.
14. Ethiopia M. Demographic and Health Survey 2019.
15. Collaborators MDS. Causes of neonatal and child mortality in India: a nationally representative mortality survey. Lancet. 2010;376(9755):1853–60.
16. Ephi M. Ethiopian Demographic and Health Survey 2019. Maryland, USA: The
DHS Program ICF.: Rockville; 2021.
17. Ephi I. Ethiopian public health Institute (EPHI)[Ethiopia] and ICF. Ethiopia mini
demographic and health survey. 2019.
18. Kumar C, Singh PK, Rai RK. Singh LJJoch. Early neonatal mortality in India,
1990–2006. 2013;38:120 – 30.
19. Lawn JE, Cousens S, Zupan JJT. 4 million neonatal deaths: when? Where?
Why? 2005;365(9462):891–900.
20. Bitew FH. Spatio-temporal inequalities and predictive models for determinants of Undernutrition among women and children in Ethiopia. The
University of Texas at San Antonio; 2020.
21. Belay AT, Fenta SM, Birhan Biresaw H, Abebaw Moyehodie Y, Melkam Yelam
M, Mekie M. The magnitude of optimal antenatal care utilization and its
associated factors among pregnant women in South Gondar zone, Northwest Ethiopia: a cross-sectional study. International journal of reproductive
medicine. 2022;2022.
22. Neves PA, Barros AJ, Gatica-Domínguez G, Vaz JS, Baker P, Lutter CK. Maternal
education and equity in breastfeeding: trends and patterns in 81 low-andmiddle-income countries between 2000 and 2019. Int J Equity Health.
2021;20:1–13.
23. Sommet N, Morselli D. Keep calm and learn multilevel logistic modeling: a
simplified three-step procedure using Stata, R, mplus, and SPSS. Int Rev Social
Psychol. 2017;30:203–18.
24. Akinyemi JO, Bamgboye EA, Ayeni O. Trends in neonatal mortality in Nigeria
and effects of bio-demographic and maternal characteristics. BMC Pediatr.
2015;15(1):1–12.

Tekeba et al. BMC Pediatrics(2024) 24:558

25. Reyes J, Ramírez RP, Ramos LL, Ruiz LG, Vázquez EB, Patino VR. Neonatal mortality and associated factors in newborn infants admitted to a neonatal careunit. Arch Argent Pediatr. 2018;116(1):42–8.
26. Goldstein H. Multilevel statistical models. Wiley; 2011.
27. Snijders TA, Bosker R. Multilevel analysis: An introduction to basic andadvanced multilevel modeling. 2011.
28. Penny W, Holmes A. Random effects analysis. Statistical parametric mapping:
the analysis of functional brain images. 2007;156:165.
29. Borenstein M, Hedges LV, Higgins JP, Rothstein HR. A basic introduction to
fixed-effect and random‐effects models for meta‐analysis. Res Synthesis
Methods. 2010;1(2):97–111.
30. Rodriguez G, Elo I. Intra-class correlation in random-effects models for binary
data. Stata J. 2003;3(1):32–46.
31. Merlo J, Chaix B, Ohlsson H, Beckman A, Johnell K, Hjerpe P, et al. A brief
conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual
phenomena. J Epidemiol Community Health. 2006;60(4):290–7.
32. Eshete T, Kumera G, Bazezew Y, Mihretie A, Marie T. Determinants of
inadequate minimum dietary diversity among children aged 6–23. Agric
Food Secur. 2018;7(1):1–8. months in Ethiopia: secondary data analysis from
Ethiopian Demographic and Health Survey 2016.
33. Tesema GA, Mekonnen TH, Teshale AB. Individual and community-level
determinants, and spatial distribution of institutional delivery in Ethiopia,
2016: spatial and multilevel analysis. PLoS ONE. 2020;15(11):e0242242.
34. Kibria GMA, Burrowes V, Choudhury A, Sharmeen A, Ghosh S, Mahmud A, etal. Determinants of early neonatal mortality in Afghanistan: an analysis of thedemographic and Health Survey 2015. Globalization Health. 2018;14:1–12.
35. Engmann C, Walega P, Aborigo RA, Adongo P, Moyer CA, Lavasani L, et al.
Stillbirths and early neonatal mortality in rural Northern Ghana. Tropical Med
Int Health. 2012;17(3):272–82.
36. https://www.worlddata.info/country-comparison.
php?country1=AFG&country2=ETH.
37. Sutan R, Berkat S. Does cultural practice affects neonatal survival-a casecontrol study among low birth weight babies in Aceh Province, Indonesia.
BMC Pregnancy Childbirth. 2014;14:1–13.
38. Dahiru T. Determinants of early neonatal mortality in Nigeria: results from2013 Nigeria DHS. J Pediatr Neonatal Care. 2015;2(5):1015406.
39. Koum D, Essomba N, Odile N, Ngwe I, Ndanga M, Ndombo PK, et al. Factors
associated with early neonatal morbidity and mortality in an urban district
hospital. Int J Latest Res Sci Technol. 2015;5(3):9–43.
40. Sankar M, Natarajan C, Das R, Agarwal R, Chandrasekaran A, Paul V. When do
newborns die? A systematic review of timing of overall and cause-specificneonatal deaths in developing countries. J Perinatol. 2016;36(1):S1–11.
41. Jahn A, KYNAST-WOLF G, Kouyaté B, Becher H. Multiple pregnancy in rural
Burkina Faso: frequency, survival, and use of health services. Acta Obstet
Gynecol Scand. 2006;85(1):26–32.
42. Owais A, Faruque ASG, Das SK, Ahmed S, Rahman S, Stein AD. Maternal andantenatal risk factors for stillbirths and neonatal mortality in rural Bangladesh:a case-control study. PLoS ONE. 2013;8(11):e80164.
43. Kayode GA, Ansah E, Agyepong IA, Amoakoh-Coleman M, Grobbee DE,
Klipstein-Grobusch K. Individual and community determinants of neonatal
mortality in Ghana: a multilevel analysis. BMC Pregnancy Childbirth.
2014;14:1–12.
44. Bellizzi S, Sobel H, Betran AP, Temmerman M. Early neonatal mortality in twin
pregnancy: findings from 60 low-and middle-income countries. J Global
Health. 2018;8(1).
45. Giuffrè M, Piro E, Corsello G. Prematurity and twinning. J Maternal-Fetal
Neonatal Med. 2012;25(sup3):6–10.
46. Qazi G. Obstetric and perinatal outcome of multiple pregnancy. J Coll Physicians Surg Pak. 2011;21(3):142–5.
47. Doyle P. The outcome of multiple pregnancy. Hum Reprod.
1996;11(suppl4):110–20.
48. Vogel JP, Torloni MR, Seuc A, Betrán AP, Widmer M, Souza JP, et al. Maternaland perinatal outcomes of twin pregnancy in 23 low-and middle-income
countries. PLoS ONE. 2013;8(8):e70549.
49. Nwankwo T, Aniebue U, Ezenkwele E, Nwafor M. Pregnancy outcome andfactors affecting vaginal delivery of twins at University of Nigeria Teaching
Hospital, Enugu. Niger J Clin Pract. 2013;16(4).

Page 10 of 10

50. Rahman MM, Abidin S. Factors affecting neonatal mortality in Bangladesh. J
Health Manage. 2010;12(2):137–52.
51. Islam MA, Butt ZA, Sathi NJ. Prevalence of neonatal mortality and its Associated factors: a Meta-analysis of demographic and Health Survey Data from 21
developing countries. Dr Sulaiman Al Habib Med J. 2022;4(3):145–52.
52. Adjiwanou V, LeGrand T. Does antenatal care matter in the use of skilled
birth attendance in rural Africa: a multi-country analysis. Soc Sci Med.
2013;86:26–34.
53. Pervin J, Moran A, Rahman M, Razzaque A, Sibley L, Streatfield PK, et al.
Association of antenatal care with facility delivery and perinatal survival–
a population-based study in Bangladesh. BMC Pregnancy Childbirth.
2012;12:1–12.
54. Mishra S, Ram B, Singh A, Yadav A. Birth order, stage of infancy and infantmortality in India. J Biosoc Sci. 2018;50(5):604–25.
55. Singh PK, Rai RK, Alagarajan M, Singh L. Determinants of maternity careservices utilization among married adolescents in rural India. PLoS ONE.
2012;7(2):e31666.
56. Kim Y-N, Choi D-W, Kim DS, Park E-C, Kwon J-Y. Maternal age and risk of earlyneonatal mortality: a national cohort study. Sci Rep. 2021;11(1):814.
57. Fraser AM, Brockert JE, Ward RH. Association of young maternal age withadverse reproductive outcomes. N Engl J Med. 1995;332(17):1113–8.
58. Kang G, Lim JY, Kale AS. Adverse effects of young maternal age on neonatal
outcomes. Singapore Med J. 2015;56(3):157.
59. Kamal SM. What is the association between maternal age and neonatal mortality? An analysis of the 2007 Bangladesh demographic and Health Survey.
Asia Pac J Public Health. 2015;27(2):NP1106–17.
60. Suparmi S, Chiera B, Pradono J. Low birth weights and risk of neonatal mortality in Indonesia. Health Sci J Indonesia. 2016;7(2):113–7.
61. Chaudhary S, Contag S. The effect of maternal age on fetal and neonatal
mortality. J Perinatol. 2017;37(7):800–4.
62. Fretts RC, Schmittdiel J, McLean FH, Usher RH, Goldman MB. Increased maternal age and the risk of fetal death. N Engl J Med. 1995;333(15):953–7.
63. Gebremichael SG, Fenta SM. Factors associated with U5M in the Afar Regionof Ethiopia. Adv Public Health. 2020;2020:1–9.
64. Asif MF, Pervaiz Z, Afridi JR, Safdar R, Abid G, Lassi ZS. Socio-economic determinants of child mortality in Pakistan and the moderating role of household’s
wealth index. BMC Pediatr. 2022;22:1–8.
65. McKinnon B, Harper S, Kaufman JS, Bergevin Y. Socioeconomic inequality inneonatal mortality in countries of low and middle income: a multicountry
analysis. Lancet Global Health. 2014;2(3):e165–73.
66. Wakgari N, Wencheko E. Risk factors of neonatal mortality in Ethiopia. Ethiop
J Health Dev. 2013;27(3):192–9.
67. McDermott J, Steketee R, Wirima J. Perinatal mortality in rural Malawi. Bull
World Health Organ. 1996;74(2):165.
68. Elmi Farah A, Abbas AH, Tahir Ahmed A. Trends of admission and predictorsof neonatal mortality: a hospital based retrospective cohort study in Somali
region of Ethiopia. PLoS ONE. 2018;13(9):e0203314.
69. Tesema GA, Worku MG. Individual-and community-level determinants of
neonatal mortality in the emerging regions of Ethiopia: a multilevel mixedeffect analysis. BMC Pregnancy Childbirth. 2021;21:1–11.
70. Ahmed AT, Farah AE, Ali HN, Ibrahim MO. Determinants of early neonatal
mortality (hospital based retrospective cohort study in Somali region of
Ethiopia). Sci Rep. 2023;13(1):1114.
71. Jalu MT, Ahmed A, Hashi A, Tekilu A. Exploring barriers to reproductive,
maternal, child and neonatal (RMNCH) health-seeking behaviors in Somali
region, Ethiopia. PLoS ONE. 2019;14(3):e0212227.
72. Wondimu H, Dejene K. Determinants of under-five malnutrition, significant
changes, and policy implications in the Ethiopian Demographic Health
Survey, 2019. Discover Sustain. 2022;3(1):16.
73. Carruth L, Mendenhall E. Wasting away: diabetes, food insecurity, and medical insecurity in the Somali Region of Ethiopia. Soc Sci Med. 2019;228:155–63.

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