(2024) 24:560
Tessema and Geda ﻿BMC Pediatrics
https://doi.org/10.1186/s12887-024-05019-z

BMC Pediatrics

RESEARCH

Open AccessChild health outcomes and associatedfactors among under five years children
in Ethiopia: a population attributable fractions
analysis of Ethiopia demographic and healthsurvey (2005–2016)
Negussie Shiferaw Tessema1* and Nigatu Regassa Geda1,2

Abstract
Introduction In Ethiopia, more than half (57%) of children aged 6–59 months were estimated to be anemic in 2016
alone. The country had about 37% of under-five children suffering from stunting and under-five mortality rate of 59
deaths per 1000 live births in 2019. The main purpose of this paper was to estimate the proportion of under-fivechildren prevented from childhood undernutrition, anemia, and under-five mortality by removing the risk factors
or inequalities.
Method This cross-sectional study was based on a pooled total sample of 29,831 children aged 0–59 months
drawn from three rounds of the Ethiopian Demography and Health Surveys (2005–2016). We employed multiple
logistic regression analysis to identify the modifiable risk factors associated with childhood anemia, undernutrition,and under-five mortality among under-five children. We also used Population Attributable Fractions (PAFs) to estimate
the proportion of under-five children that could be prevented from childhood undernutrition, anemia, and under-five
mortality by removing inequalities.
Result PAF analyses of risk factors of childhood anemia confirmed that 38.5% of occurrence of childhood anemia
was attributed to five selected risk factors, which include having a large household size (5+), being in a poor household, being born from anemic and unemployed mothers, and being breastfed for less than six months. About 45.6%
of occurrences of childhood undernutrition were attributed to unimproved toilet facility, solid cooking fuel, and homedelivery. About 72% of the reported under-five mortality could possibly be averted by removing the use of unimproved toilet facilities, early age childbirth (< 18 years old mothers), and a large number of children ever born to mothers and less than six months breastfeeding practice at the population level.
Conclusion The present study suggests that a substantial reduction in the prevalence of childhood anemia, undernutrition, and under-five mortality in the country is attainable if child survival-focused program interventions and policies target households and mothers with low socioeconomic status and those who have low awareness of childhealthcare, including breastfeeding practice and use of safe sanitation facilities.
Keywords Anemia, Childhood, Ethiopia, Mortality, Under-five, Undernutrition*Correspondence:
Negussie Shiferaw Tessema
ntshiferaw@gmail.com
Full list of author information is available at the end of the article© 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 longas 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 the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
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Tessema and Geda ﻿BMC Pediatrics(2024) 24:560

Introduction
Child health outcomes, including childhood anemia,
undernutrition, and under-five mortality, continue to besignificant public health concerns in developing countries [22, 34]. According to the World Health Organization (WHO), childhood anemia is a blood hemoglobin
concentration of less than 11 g/dl [41]. Undernutrition is
described as insufficient intake of nutritious food, leading
to stunting (low height-for-age), wasting (low weight-forheight), and being underweight (low weight-for-age) [42].
Under-five mortality refers to the death of a child before
the age of five [43].
Despite the progress and achievements made in thepast two decades, a large number of children are sufferingfrom childhood anemia, undernutrition, and under-five
mortality worldwide. Globally, an estimated 148.1 million under-five children were affected by stunting in 2022
alone. Of these, 43% of stunted children lived in Africa[39]. In addition, about 269 under-five children wereanemic in 2019, and more than 60% of anemic children
also resided in Africa [32]. In addition, about 5.0 million
children died before reaching their fifth birthday at the
global level in 2021 [38]. Worryingly, children born in
sub-Saharan Africa are subject to the highest under-five
mortality rate of 74 deaths per 1000 live births, which is
15 times higher than the risk for children in Europe andNorthern America and 19 times higher than children
born in the region of Australia and New Zealand in 2021
[39]. Currently, the global Child Health Task Force is urging 54 African countries, including Ethiopia, to accelerate efforts to achieve the Sustainable Development Goal
(SDG) 2030 targets of under-five mortality of 25 deathsor fewer per 1000 live births [6].
In Ethiopia, more than half (57%) of children aged 6–59
months were estimated to be anemic in 2016 [8]. The
country had about 37% of under-five children sufferingfrom stunting and under-five mortality rate of 59 deaths
per 1000 live births in 2019 [11]. Based on the reported
burden of global under-five mortality, the country ranked
third in Africa and tenth in the world [10]. The Sustainable Development Index (SDI) is a measure that aims toassess a country’s progress toward achieving SDGs in an
integrated manner. It considers economic, social, andenvironmental dimensions of development, providing amore holistic view of a nation’s development compared
to GDP. According to the SDI score, Ethiopia is ranked
144 out of 166 countries with a 54.5 score of the SDI
based on the 2023 Sustainable Development Goal monitoring report [29]. Understanding the key factors andtheir contribution to childhood morbidity (undernutrition and anemia) and mortality (under-five mortality)
will be helpful to accelerate the reduction in childhood

Page 2 of 11

anemia, undernutrition, and under-five mortality, andfurther to achieve SDG targets related to child morbidity
and mortality.
Previous studies [9, 16, 25, 30, 31, 40, 45] pointed out
geographic location, place of residence, household assetbased wealth index, household size, maternal education,maternal age at birth, maternal employment status, andchild size at birth as the key factors significantly associated with childhood undernutrition in Ethiopia. Someother studies [1, 18, 20, 26] documented that childhood
anemia is significantly associated with location, placeof residence, household asset-based wealth index, thehousehold size, maternal education, maternal anemia andemployment status, place of birth, age of child in months,
and child size at birth as reported subjectively by mothers. Other studies [2, 3, 33, 44] also identified the household size, source of drinking water, parental and maternal
education, maternal age at birth and employment status,
child’s size at birth, and duration of breastfeeding as the
key associated factors with under-five mortality.
While substantial evidence exists on the major determinants of child survival, our understanding of how thesefactors contribute to the overall burden of childhood anemia, undernutrition, and under-five mortality in Ethiopia
remains limited. Therefore, this study poses the question“How much is the proportional reduction in childhood
undernutrition, anemia, and under-five mortality byremoving the key risk factors?” The findings of the study
will help identify the risk factors contributing to childhood undernutrition, anemia, and under-five mortality.
This information will be valuable for development partners and policymakers to design and implement targeted,risk-specific interventions in the country.

Data source and methods
The study context

Ethiopia has a federal system with twelve regional states,
including three recently emerged regions from the previous Southern Nations, Nationalities, and Peoples’
Region (SNNPR), and two chartered city administrations.
According to the 2022 United Nations Development
Program (UNDP) estimate, the country’s population
was about 123 million with a 2.6% annual growth rate,
and this makes the country the second most populous
country in Africa [35]. Ethiopia is prone to natural andmanmade shocks and stress that affect households, communities, and environmental system resilience [37]. The
periodic occurrence of communicable disease outbreaks
and the rise in non-communicable diseases present significant challenges to the country’s health system [23].
The government of Ethiopia has shown commitment
to global agendas, including Sustainable Development

Tessema and Geda ﻿BMC Pediatrics(2024) 24:560

Goals and Seqota Declarations to achieve child survival
targets and end childhood undernutrition by 2030 [23].
To accelerate the reduction in childhood undernutrition,
the country has developed and endorsed food and nutrition policies and strategies, including Food and Nutrition
Policy [12], the National Nutrition Program (NNP) I [13],and NNP II [14] and National Strategy for Newborn andChild survival [15]. However, the country is still experiencing a range of challenges related to child survival
contributing to the existing childhood anemia, undernutrition, and under-five mortality [8].
Data source

The study used nationally representative data from thethree rounds of Ethiopian Demographic and Health Survey (EDHS), collected between 2005 and 2016. The datasets were obtained from the DHS website (http://​dhspr​
ogram.​com) with secured online permission. The analysis focused on children’s files which include information
on socioeconomic, and demographic characteristics forunder-five children, their parents, and their households.
The pooled data from these three rounds of EDHS comprised 29,831 under-five children. Outcome and exposure variables were extracted from this combined dataset
for analysis.
Study variables
Outcome variables

The study focused on three outcome variables: childhood undernutrition, childhood anemia, and underfive mortality. Childhood anemia was categorized into
dummy variable where a child is considered to be anemic and assigned value of 1 if the child had severe, moderate or mild anemia level, and 0 if child is not anemic.
Childhood undernutrition status was categorized asundernourished and coded as 1 if child had any form ofanthropometric failure, and as nourished with assigned
value of 0 if the child had no anthropometric failure.
Anthropometric failure in this study refers to inadequate
physical growth and body composition, assessed throughindicators such as weight, height, and age. It includes
wasting (low weight-for-height), stunting (low heightfor-age), and underweight (low weight-for-age), which
are critical measures of malnutrition and essential forassessing child survival and health outcomes [5]. Underfive mortality was coded as 1 if the child died between 0
and 59 months and 0 if the child was alive at least until
age of 59 months [7].
Exposure variables

The framing and selection of the exposure variables
were based on the well-known child survival conceptual

Page 3 of 11

framework developed by Mosley and Chen [24] andreview of the related literature [1–3, 25, 26]. For analysis purposes, all exposure variables were coded dichotomously [28]. Accordingly, the eleven old administrative
regions of Ethiopia were regrouped into two regional
categories: emerging regions (Afar, Somali, Benishangul-Gumuz, and Gambella), and established regions(Amhara, Oromia, Harari, SNNPR and Tigray, including
Addis Ababa and Dire Dawa City Administrations) [4]. In
Ethiopia, an “emerging region” is an underdeveloped areawith limited infrastructure, lower economic activity, andhigher poverty rates compared to established regions,which have better infrastructure, diversified economies,
and better-quality services. Assessing the gap between
these regional categories is crucial for child survival anddevelopment.
The household asset-based wealth index’s five quintiles were regrouped into two categories: poor (poorest/poorer) and non-poor (medium, richer and richest).
Parental education (maternal/paternal) status was recategorized as (no formal education vs. primary+).
Additionally, the following exposure variables were
included: place of residence (rural vs. urban), household
size (< 5 vs. 5+), household head (male vs. female), sourceof drinking water (improved vs. unimproved), type of
toilet facility (improved vs. unimproved), type of cooking fuel (cleaned vs. solid), maternal employment status (employed vs. unemployed), maternal anemia status(anemic vs. non-anemic), total children ever born (< 3vs. 3+), antenatal visit (< 4 vs. 4+), place of birth (homevs. health facility), child sex (female vs. male), durationof breastfeeding (< 6 months vs. 6 + months), and childsize at birth (small vs. average and above) were used as
predictor variables for the present study. The study also
included maternal age at child birth (< 18 vs. 18+), considering that a maternal age < 18 has strong correlation
with increased risks of low birth weight, and child mortality [36].
Statistical analysis

We used STATA 17 for data management and analysis.
Children with missing and unknown responses for sourceof drinking water, type of sanitation facility, type of cooking fuel, place of delivery, duration of breastfeeding,anthropometric measures (height/length/weight), andsize at birth (accounting for less than 3% of the sample)
were excluded from the statistical analysis. Descriptive
analysis was performed to describe the background characteristics of the study participants, with data weighted
for descriptive analysis per DHS recommendation. Multicollinearity effect of the predictor variables was tested
using Variance Inflation Factor(VIF) with cutoff value ofTessema and Geda ﻿BMC Pediatrics(2024) 24:560

2.5 [21]. The VIF values for each variable were less than
2.5, with mean VIF value of 1.33, 134 and 1.34 for childhood anemia, undernutrition, and under-five mortality,
respectively, indicating no multicollinearity effect amongpredictor variables.
Bivariate logistic regression was employed to select the
exposure variables with a p-value < 0.2. Multiple logistic regression analyses were conducted to explore theassociation between selected predictor variables and thethree outcome variables. The Hosmer-Lemeshow test
was used to test the goodness of fit for our models [19]. A
p-value < 0.05 was considered statistically significant.
Population Attributable Fractions (PAFs) were calculated to estimate the proportion of associated risk factors
for childhood anemia, undernutrition, and under-five
mortality at the population level using STATA user command “punaf ” [27] after running logistic regression models. “punaf ” implements the method for estimating PAFs
for cross-sectional studies recommended by Greenlandand Drescher [17], suitable for large scale surveys withmultiple risk factors as suggested by Ruckinger et al. [28].
Following the procedural steps provided by Ruckinger
et al. [28] we:
1. Coded the risk factor as a binary variable. We
assumed that all individuals were not exposed to therisk factor, regardless of their actual status.
2. Used the following formula to estimate the predictedprobabilities for each-individual using the modified
dataset:1ppi =1 + exp(−(α + β ′ xi ))where α is the intercept, β is the vector of coefficientsfor the covariates, and xi is the vector of observations
for the covariates, with the risk factor set to zero for
all individuals.
3. Summed up all the predicted probabilities to get theadjusted number of cases of child survival that would
occur if the risk factor was absent in the population.
4. Finally “punaf ” Stata user command has been used toenable normalization and variance-stabilizing transformation with the following formula [27]:log(PUF ) = log(1 − PAF )where PUF stands for Population Unattributable Fraction, and PAF refers to Population Attributable fraction.

Results
Descriptive analysis results

Table 1 describes the background characteristics of
29,831 participants. Notably, a substantial portion ofPage 4 of 11

the population resides in rural areas (83.97%), where
access to improved drinking water (39.34%) and sanitation facilities (14.12%) remains limited. The prevalenceof maternal and paternal education levels below primaryeducation (70.78% and 53.82%, respectively) underscores potential challenges in health literacy and accessto resources. Household poverty (49.31%) and large family sizes (73.45% with 5 + members) highlight economic
vulnerabilities that may impact child health outcomes.
Maternal anemia rates (30.49%) and high rates of homebirths (81.99%) suggest potential health risks related tomaternal and neonatal health.
Bivariate logistic analysis results

Table 2 presents the results of bivariate logistic regression
analysis involving 18 predictors selected for the study
based on a literature review. Maternal age at childbirthand child sex did not show significant associations withchildhood anemia. Similarly, region and sex of household
head did not demonstrate associations with childhood
undernutrition at a significance level of p < 0.2. Additionally, sex of household head and maternal employment
status did not exhibit significant associations with underfive mortality at the p < 0.2 threshold. Due to their p-values exceeding 0.2, these variables were not included in
the subsequent multivariable logistic regression analysis
as detailed in Table 2.
Multivariate logistic regression analyses results

Table 3 presents the results of multivariable logistic
regression analyses examining the associations of various factors with childhood anemia, undernutrition, andunder-five mortality in Ethiopia. Significant associations were found for several predictors across the threeoutcomes. For childhood anemia, administrative region
showed notable disparities, with children in emerging regions having 1.571 times higher odds of anemia
compared to those in established regions (OR = 1.571,
p < 0.001). Additionally, children from poor households
were 1.391 times more likely to be anemic than thosefrom non-poor households (OR = 1.391, p < 0.001).
Maternal anemia significantly increased the odds ofchildhood anemia (OR = 1.808, p < 0.001), as did maternal
unemployment (OR = 1.417, p < 0.001).
Regarding childhood undernutrition, significant associations were observed with place of residence, household size, type of toilet facility, type of cooking fuel,paternal and maternal education, maternal age at childbirth, maternal employment status, number of children
ever born, child sex, place of birth, child size at birth,and breastfeeding duration (all p < 0.05). For example,
children in rural areas had 1.276 times higher odds ofTessema and Geda ﻿BMC Pediatrics(2024) 24:560

Page 5 of 11

Table 1 Background characteristics of study participants,
N = 29,831
Background Characteristics

Table 1 (continued)
Background Characteristics

N

%

N

%

Small

8,579

28.76

Emerging

11,403

38.23

Average and above

21,252

71.24

Established

18,42861.77

Duration of breastfeeding< 6 months

14,312

47.98

Rural

25,05083.97

6 months and above

15,519

52.02

Urban

4,781

16.03

29,831100

Administrative regions

Place of residenceChild size at birth

TotalHousehold asset-based status
Poor

14,71049.31

Non-Poor

15.121

50.69

Household size
<5

7,919

26.55

5+

21,91273.45

Household head
Female

4,893

16.40

Male

24,93883.60

Source of drinking water
Improved

11,737

39.34

Unimproved

18,09460.66

Type of toilet facility
Improved

4,211

14.12

Unimproved

25,62085.88

Type of cooking fuelCleaned

1,179

3.95

Solid

28,652

96.05

Paternal Education
No formal education

16,055

53.82

Primary and above

13,776

46.18

Maternal education
No formal education

21,11470.78

Primary and above

8,717

29.22

< 18 years

1,828

6.13

18 years and above

28.003

93.87

Maternal age at child birthMaternal anemia status
Anemic

7,284

30.49

Non-anemic

16,608

69.51

<3

8,701

29.17

3+

21,13070.83

Total children ever born

Maternal employment
Unemployed

18,421

61.75

Employed

11,41038.25

Child sex
Female15,294

51.27

Male

14,537

48.73

Place of birthHome

24,457

81.99

Health facility

5,374

18.01

undernutrition compared to urban children (OR = 1.276,
p < 0.001), and those from households using solid fuel forcooking had 1.696 times higher odds of undernutrition
than those using clean fuel (OR = 1.696, p < 0.001).
In terms of under-five mortality, significant predictors
included type of toilet facility, maternal age at childbirth,and number of children ever born (all p < 0.001). Children
from households using unimproved toilet facilities had
1.318 times higher odds of mortality compared to thoseusing improved facilities (OR = 1.318, p < 0.001), whilechildren born to mothers under 18 years old had 1.690
times higher odds of mortality compared to those born toolder mothers (OR = 1.690, p < 0.001). Moreover, having
three or more children ever born significantly increased
the odds of under-five mortality (OR = 3.036, p < 0.001).
These findings underscore the critical influence ofsocio-economic, environmental, and maternal health factors on childhood health outcomes in the country, highlighting the need for targeted interventions to mitigate
these disparities and improve child survival and wellbeing across the country.
Population attributable fraction analysis results

Table 4 provides population attributable fractions (PAFs)
for risk factors associated with childhood anemia, undernutrition, and under-five mortality in Ethiopia. These
PAFs estimate the proportion of each outcome that canbe attributed to specific risk factors.
For childhood anemia, significant risk factors contributing to PAFs include residence in emerging regions(7.1%), rural residence (4.7%), belonging to poor households (7.2%), having an anemic mother (7.2%), andmaternal unemployment (8.6%). These factors collectively contribute to 51.9% of childhood anemia cases.
Regarding childhood undernutrition, risk factors suchas rural residence (9.4%), belonging to poor households(4.8%), using unimproved toilet facilities (11.2%), maternal education (6.5%), maternal age at childbirth under
18 years (0.7%), and breastfeeding for less than 6 months(5.3%) contribute to a combined PAF of 73.9%.

Tessema and Geda ﻿BMC Pediatrics(2024) 24:560

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Table 2 Bivariate logistic regression of predictors by childhood anemia, undernutrition and mortality in Ethiopia
Predictors

Anemia (N=19,731)

Undernutrition (N=20,777)

Under-five mortality (N=29,831)

Odds ratio

95%Cl

p-value

Odds ratio

95%Cl

p-value

Odds ratio

95%Cl

p-value

1.8211

1.72-1.93

0.000

1.023

0.97-1.08

0.412

1.118

1.03-1.22

0.011

1.358

1.56-1,47

0.000

2.336

2.17-2.52

0.000

1.582

1.39-1.81

0.000

1.616

1.52-1.71

0.000

1.676

1.59-1.77

0.000

1.193

1.09-1.30

0.000

1.104

1.03-1.18

0.003

1.176

1.11-1.25

0.519

0.47-0.57

0.000

1.121

1.04-1.21

0.002

0.97

0.91-1.04

0.386

0.978

0.87-1.09

0.692

1.184

1.12-1.26

0.000

1.356

1.28-1.43

0.000

1.194

1.09-1.31

0.000

1.198

1.10-1.30

0.000

2.168

2.01-2.34

0.000

1.672

1.45-1.93

0.000

1.577

1.34-1.85

0.000

3.979

3.36-4.72

0.000

2.312

1.70-3.14

0.000

1.314

1.24-1.39

0.000

1.633

1.55-1.72

0.000

1.344

1.23-1.48

0.000

1.297

1.22-1.38

0.000

1.777

1.67-1.88

0.000

1.391

1.26-1.54

0.000

0.975

0.86-1.10

0.678

1.251

1.11-1.40

0.000

1.686

1.46-1.95

0.000

1.978

1.85-2.11

0.000

1.092

1.03-1.16

0.003

1.254

1.13-1.39

0.000

1.125

1.06-198

0.000

1.354

1.27-1.43

0.000

1.348

1.22-1.49

0.000

1.606

1.52-1.70

0.000

0.949

0.89-1.00

0.057

0.982

0.90-1.07

0.685

1.034

0.97-1.09

0.247

1.144

1.09-1.21

0.000

1.288

1.18-1.40

0.000

1.103

1.03-1.19

0.008

2.206

2.06-2.36

0.000

1.566

1.38-1.78

0.000

Regions
Emerging
Established(ref )Place of residence
Rural
Urban (ref )Household status
Poor
Non-Poor(ref )Household size
< 5(ref )5+

   0.000

Household head
FemaleMale(ref )Drinking water
Unimproved
Improved(ref )Type of toilet facility
Unimproved
Improved(ref )Type of cooking fuelCleaned (ref )SolidPaternal Education
No formal education
Primary+(ref )Maternal education
No formal education
Primary+(ref )Maternal age at birth< 18 years
18+ years (ref )Maternal anemia
Anemic
Non-anemic(ref )Children ever born
< 3(ref )3+
Maternal employment
Unemployed
Employed(ref )Child sex
FemaleMale(ref )Place of birth
Home
Health facility(ref )Tessema and Geda ﻿BMC Pediatrics(2024) 24:560

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Table 2 (continued)
Predictors

Anemia (N=19,731)

Undernutrition (N=20,777)

Under-five mortality (N=29,831)

Odds ratio

95%Cl

p-value

Odds ratio

95%Cl

p-value

Odds ratio

95%Cl

p-value

1.240

1.16-1.32

0.000

1.541

1.45-1.63

0.000

0,989

0.89-1.09

0.821

1.692

1.59-1.79

0.000

1.696

1.60-1.80

0.000

0,848

0.78-0.93

0.000Child size at birth
Small
Average & above(ref )Breastfeeding duration< 6 months
6+ months(ref )For under-five mortality, key risk factors include residence in areas with unimproved toilet facilities (27.4%),
maternal age at childbirth under 18 years (3.3%), having
an anemic mother (4.4%), and having three or more children ever born (51.3%), leading to a total PAF of 72.5%.
These findings underscore the significant impact ofsocio-economic, environmental, and maternal health factors on childhood health outcomes in Ethiopia, emphasizing the need for targeted interventions to mitigate
these risks and improve child survival rates across thecountry.

Discussion
This study represents a pioneering effort in examining
the population-level impacts of key risk factors associated with childhood undernutrition, anemia, and underfive mortality in Ethiopia. Our findings from multivariate
regression analyses underscored several significant risk
factors contributing to these health outcomes. For childhood anemia, factors such as administrative region(emerging regions), rural residence, household poverty
status, maternal anemia, maternal history of three ormore children, and shorter breastfeeding duration (< 6months) emerged as critical risk factors. Similarly, multivariable logistic regression highlighted rural residence,
household poverty, use of unimproved toilet facilities,
reliance on solid cooking fuels, lack of parental education, home deliveries, and smaller child size at birth assignificant risk factors for childhood undernutrition.
Additionally, our analysis revealed that children residing in households with unimproved sanitation facilities,
born to younger mothers (< 18 years old), and motherswith three or more children faced increased likelihoodsof under-five mortality compared to their counterparts.
This underscores the modifiability of risk factors suchas toilet facility quality, maternal age at childbirth, andnumber of children ever born in mitigating under-five
mortality rates.
Furthermore, Population Attributable Fraction (PAF)
analyses identified that 38.5% of childhood anemia cases

could be attributed to factors such as larger household
size (5+), household poverty, maternal anemia, maternal
unemployment, and inadequate breastfeeding duration.
Addressing these modifiable risk factors could potentially prevent a significant portion of childhood anemia
burden at the population level. Similarly, PAF analysis
indicated that approximately 45.6% of childhood undernutrition cases were linked to factors like unimproved
sanitation facilities, solid cooking fuels, and home deliveries, highlighting substantial opportunities for intervention. Notably, controlling these factors could potentiallyreduce childhood undernutrition by nearly half (46%)
in the target population. Moreover, our findings suggestthat addressing factors such as maternal age at childbirth and number of children ever born could potentially avert 51.3% of reported under-five mortality cases,
illustrating the critical role of maternal and child healthinterventions.
It is essential to note that our study did not directlycompare PAF findings with previous studies due tovarying prevalence rates of risk factors across different
contexts [28].
Our findings underscore the significant impact ofmodifiable risk factors on child health outcomes, particularly highlighting the pervasive influence of poverty andinadequate maternal support. Key determinants such as
educational attainment, access to maternal healthcare,anemia management, family planning services, and cultural factors like early marriage and childbearing wereidentified as critical contributors to child health disparities, consistent with existing literature.
However, our study acknowledges the need for further
exploration within the broader socio-economic context.
Future research should delve deeper into these wellestablished root causes to develop comprehensive strategies aimed at improving maternal health, reducing childmortality, and promoting gender equality. By enhancing our understanding of these complex relationships,
researchers can design more effective interventions that
address the fundamental issues identified in our study,

Tessema and Geda ﻿BMC Pediatrics(2024) 24:560

Page 8 of 11

Table 3 Multiple logistic regression of predictors by childhood anemia, undernutrition and mortality, EthiopiaN=29,831
Predictors

Anemia (N=19,731)

Undernutrition (N=20,777)

Under-five mortality (N=29,831)

Odds ratio

Odds ratio

95%Cl

p-value

1.059

0.95-1,18

0.298

Odds ratio

95%Cl

p-value

95%Cl

p-value

1.571

1.47-1.68

0.000

1.146

1.02-1.29

0.022

1.276

1.15-1.42

0.000

1.148

0.93-1.41

0.192

1.391

1.30-1.49

0.000

1.213

1.14-1.29

0.000

0.968

0.86-1.09

0.591

1.102

1.01-1.21

0.039

0.974

0.89-1.06

0.539

0.278

0.24-0.32

0.102

1.044

0.96-1.14

0.318

0.972

0.91-1.14

0.437

0.927

0.86-0.99

0.024

1.032

0.92-1.16

0.607

0.968

0.87-1.07

0.540

1.291

1.17-1.43

0.000

1.318

1.08-1.60

0.006

1.045

0.86-1.27

0.653

1.696

1.38-2.08

0.000

1.290

0.83-2.00

0.256

1.069

0.99-1.15

0.056

1.199

1.12-1.28

0.000

1.116

0.99-1.25

0.061

1.033

0.95-1.12

0.413

1.214

1.13-1.31

0.000

1.043

0.91-1.19

0.538

1.332

1.16-1.53

0.000

1.690

1.38-2.07

0.000

Regions
Emerging
Established(ref )Place of residence
Rural
Urban(ref )Household status
Poor
Non-poor(ref )Household size
< 5 (ref )5+Household head
FemaleMale (ref )Source of drinking water
Unimproved
Improved(ref )Type of toilet facility
Unimproved
Improved(ref )Type of cooking fuelCleaned (ref )SolidPaternal Education
No formal education
Primary and above(ref )Maternal education
No formal education
Primary+(ref )Maternal age at birth< 18 years
18+ years (ref )Maternal anemia
Anemic1.808

1.68-1.93

0.000

0.983

0.92-1.05

0.603

1.130

1.01-1.26

0.030

1.054

0.96-1.15

0.264

1.092

1.00-1.19

0.048

3.036

2.54-3.62

0.000

1.417

1.33-1.51

0.000

0.871

0.82-0.92

0.000

1.185

1.12-1.26

0.000

1.278

1.15-1.42

0.201

1.384

1.26-1.51

0.000

1.147

0.97-1.36

0.118

Non-anemic(ref )Children ever born
< 3(ref )3+
Maternal employment
Unemployed
Employed(ref )Child sex
FemaleMale(ref )Place of birth
Home
Health facility(ref )0.838

0.76-0.92

0.000

Tessema and Geda ﻿BMC Pediatrics(2024) 24:560

Page 9 of 11

Table 3 (continued)
Predictors

Anemia (N=19,731)

Undernutrition (N=20,777)

Under-five mortality (N=29,831)
Odds ratio

95%Cl

p-value

Odds ratio

95%Cl

p-value

Odds ratio

95%Cl

p-value

1.146

1.07-1.23

0.000

1.486

1.39-1.58

0.000

1.888

1.77-2.01

0.000

0.791

0.75-0.84

0.000

0.717

0.64-0.79

0.000

0.392

0.33-0.47

0.000

0.210

0.17-0.25

0.000

0.036

0.02-0.46

0.000

chi2 = 0.64

p=0.887chi2 =4.09

p=0.252

chi2 =6.16

Child size at birth
Small
Average and above(ref )Breastfeeding duration< 6 months
6 months and above(ref )Constant
Hosmer-Lemeshow test

p=0.104

Table 4 Population attributable fractions for risk factors of childhood anemia, undernutrition and mortality, Ethiopia
Risk factors

Anemia (N = 19731)
OR [95%, CI]

PAF (%)

Undernutrition(N = 20,777)

Under-five mortality(N = 29,831)

OR [95%, CI]

OR [95%, CI]

PAF (%)

PAF (%)

Emerging regions

1.58[1.47,1.68]

7.1%

-

-

-

-

Rural residence

1.14[1.03,1.27]

4.7%

1.26[1.13,1.40]

9.4%

-

-

Poor households

1.40[1.31,1.50]

7.2%

1.21[1.14,1,29]

4.8%

-

-

Household size (5+)

1.14[1.06,1.23]

4.3%

-

-

-

-

Unimproved toilet facility--

1.32[1,19,1,44]

11.2%

1.47[1.22,1.77]

27.4%

Solid fuel use for cooking--

1.62[1,34,1.97]

22.2%

-

-

Parental education (no education)--

1.19[1.12,1,27]

4.5%

-

Maternal education (no education)--

1.21[1,12,1.30]

6.5%

-

Mothers age at birth (< 18yrs)--

1.33[1.16,1.51]

0.7%

1.72[1.41,2.11]

3.3%

Anemic mothers

1.82[1.69,1.95]

7.2%

-

-

1.16[1.04,1.29]

4.4%

Number of children ever born (3+)--

1.07[0.99,1.15]

2.4%

3.19[2.68,3.79]

51.3%

Maternal employment(unemployed)

1.42[1.33,1.52]

8.6%

-

-

-

-

Home delivery--

1.36[1.24,1.48]

12.2%

-

-

Child size at birth (small)

1.15[1.08,1,23]

1.6%

1.48[1.39,1.57]

5.3%

-

-

Breastfeeding for < 6 months

1.88[1.77,2.01]

11.2%

0.79[0.75,0.84]-5.3%

0.71[0.64,0.79]-13.9%

Total for all significant variables

51.9%

thereby enhancing their relevance and impact in diverse
settings.
Strengths and limitations

The present study has examined the proportion of reduction on childhood anemia, undernutrition and under-five
mortality by removing modifiable risk factors at population level in Ethiopia. The findings will undeniably contribute to the existing literature gap and could be used as
evidence for child survival program implementers andinterventions, policy makers and future researches as far
as we used nationally representative surveys. However, thisstudy is not free from limitations. Firstly, as we used crosssectional data collected at a time specific point in time, it isdifficult to draw causal relationship between the predictors

73.9%

72.5%

and outcome variables. Secondly, while the survey year
identified the DHS wave, the sensitivity analyses did notaccount for the impact of different survey years. This may
affect the accuracy and reliability of our findings. Thirdly,
the study acknowledges a limitation regarding the potential
interactions between maternal age and the number of children ever born, which could impact the findings. Fourthly,some of the risk factors which were not included (especiallymacro level factors) in the multivariable regression models
might have affected the estimated level of PAFs.

Conclusion
This study focused on key modifiable risk factors associated with childhood anemia, undernutrition and underfive mortality in Ethiopia. The estimated attributable

Tessema and Geda ﻿BMC Pediatrics(2024) 24:560

burdens of these modifiable risk factors were found tobe substantial at population level. Therefore, the present
study suggests that substantial reduction in the prevalence of childhood anemia, undernutrition and underfive mortality in the country is attainable if policies andchild survival focused program interventions target low
socioeconomic mothers and households and those whohave low awareness on child health care, including breast
feeding practice and use of safe sanitation facilities.
Abbreviations
CSA	Central Statistical Agency
DHS	Demographic and Health Survey
EDHS	Ethiopia Demographic and Health Survey
EPHI	Ethiopian Public Health Institute
FDRE	Federal Democratic Republic of Ethiopia
FMOH	Federal Ministry of Health
MOH	Ministry of Health
NNP	National Nutrition Program
PAFs	Population Attributable Fractions
SDG	Sustainable Development Goal
SNNPR	Southern Nations, Nationalities, and Peoples’ Region
UNDP	United Nations Development Program
UNICEF	United Nations Children’s Fund
USAID	United States Agency for International Development
VIP	Variance Inflation Factor
WB	World Bank
WHO	World Health Organization

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12887-​024-​05019-z.
Supplementary Material 1.
Acknowledgements
We are thankful to the MEASURE DHS program for providing free access to theoriginal data.
Authors’ contributions
Conception and design of the study: NST & NRG; analysis and/or interpretation of data; drafting the manuscript: NST; revising the manuscript critically forimportant intellectual content: NRG. All authors have read and approved thefinal manuscript.
Funding
The study did not receive any funding.
Availability of data and materials
The dataset can be accessed at https://​dhspr​ogram.​com/​data/​avail​able-​datas​
ets.​cfm.

Declarations
Ethics approval and consent to participate
For the original conduct of the four rounds of Ethiopia Demographic and
Health Surveys (EDHSs), ethical approval was obtained from the ethical committee of the ICF. The enumerators obtained informed consent and authorization to anonymously use the data from all survey participants. In our study,
we obtained permission to use the data from the DHS program. Furthermore,
local ethical approval was obtained from Addis Ababa University, College ofDevelopment Studies IRB, and the present study solely involved secondary
data analysis of publicly available data that does not contain any identifiable
information that links to the actual survey participants. The authors also confirm that all methods were carried out by relevant guidelines and regulations.

Page 10 of 11

Consent for publication
Not Applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Center for Population Studies, College of Development Studies, Addis Ababa
University, Addis Ababa, Ethiopia. 2 College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, Canada.
Received: 29 November 2023 Accepted: 16 August 2024

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